NNT : 2017SACLE022

THESE DE DOCTORAT DE

L’UNIVERSITE PARIS-SACLAY

PREPAREE A

“UNIVERSITE D’EVRY-VAL-D’ESSONE”

ECOLE DOCTORALE N° 578

Sciences de l’homme et de la société

Spécialité de doctorat : Sciences de gestion

Par

Madame Galina Kondrateva

Mobile application use in the tourism and restaurant industries:

comparative study between France and Russia

Thèse présentée et soutenue à « Paris », le « 13/10/2017 » :

Composition du Jury : Mme Clergeau Cécile, Professeure des Universités, IAE Université de Nantes, Présidente du Jury

M. Bidan Marc, Professeur des Universités, Ecole Polytechnique de l’Université de Nantes, Rapporteur

M. Trinquecoste Jean-François, Professeur des Universités, IAE, Université de Bordeaux, Rapporteur

M. Godelier Éric, Professeur des Universités, Ecole Polytechnique de l’Université Paris-Saclay, Examinateur

Mme Ammi Chantal, Professeure, Telecom Ecole de Management, Directrice de thèse

Acknowledgement

First of all I offer my sincerest, deep gratefulness to my supervisor, Professor Chantal Ammi, for

her valuable and appreciable guidance and advises, her extraordinary engagement and

enthusiasm, her fully involvement in my research. Her ability to inspire helped me a lot to

accomplish the research. I am very glad, that I could have Professor Chantal Ammi as my

supervisor and I really enjoyed doing research under her direction.

I would like to send my thanks to the members of the jury. My great thankfulness addresses to

the reporters, Professor Marc Bidan and Professor Jean-François Trinquecoste, for their detailed

comments on my research. I also address my acknowledgment to the examiners, Professor Cécile

Clergeau and Professor Éric Godelier, for their kindness and support.

I am very grateful to Dr. Wilsonn Labossiere for practical advises concerning the methodology

and especially to Dr. Patricia Baudier for help in many questions, for her time and support. They

really made me feel as a part of the research team.

I would like to thank CRM director of LaFourchette, Amélie Naudin, and general manager of

Resto, Daria Galkina, for their time to answer my questions.

I would like to express my thanks to all my friends in Russia and France, who were involved in

collecting data, as well as for their support and belief in me.

And finally, I send my thanks to my family and my partner for their support and understanding.

Without all help and support I had during these three years it would be difficult or impossible to

complete my thesis.

1

SOMMAIRE

LIST OF TABLES ............................................................................................................................................................ 5

LIST OF FIGURES .......................................................................................................................................................... 8

ABBREVIATIONS ........................................................................................................................................................ 10

GLOSSARY ................................................................................................................................................................. 12

GENERAL INTRODUCTION ......................................................................................................................................... 16

PART I: THEORETICAL ASPECTS .................................................................................................................................. 22

CHAPTER I: CONTEXT OF THE RESEARCH ................................................................................................................... 23

Introduction ............................................................................................................................................................. 24 I. Tourism and restaurant industry .......................................................................................................................... 24 I.1.1. Definition of Tourism ...................................................................................................................................... 25 I.1.2. Development of tourism. ................................................................................................................................ 25 I.1.3. Components of the tourism industry .............................................................................................................. 27 I.1.4. Food service and restaurant industry ............................................................................................................. 28 I. 3. Conclusion ......................................................................................................................................................... 30 II. Modern technologies. .......................................................................................................................................... 30 II.1. Internet ............................................................................................................................................................. 31 II.2. Networks services used in tourism .................................................................................................................... 31 II.2.1. Travel agent, tour operator, transportation, attraction and accommodation .............................................. 32 II.2.2. Tourist information and guiding services ...................................................................................................... 34 II.3. Big data in tourism ........................................................................................................................................... 34 II.4. Mobile technologies.......................................................................................................................................... 36 II.4.1. Mobile application ......................................................................................................................................... 36 II.4.2. Mobile technologies in tourism ..................................................................................................................... 38 II.5. Mobile technologies in restaurant industry ...................................................................................................... 40 II.6. Conclusion ......................................................................................................................................................... 41 III. The choice of the countries ................................................................................................................................. 42 III. 1. Russian Federation.......................................................................................................................................... 43 III.1.1. General Data to the country ......................................................................................................................... 43 III.1.2. Demography ................................................................................................................................................. 44 III.1.3. Economic crises ............................................................................................................................................. 45 III. 1.3.1. Structure of Russian GDP and the place of tourism industry. ................................................................... 47 III. 1. 4. Tourism industry ......................................................................................................................................... 48 III.1.4.1. Strategy of Tourism Development in the Russian Federation .................................................................... 49 III.1.4.2. Crises 2014 of outgoing tourism in Russia ................................................................................................. 50 III.1.5. Restaurant industry ...................................................................................................................................... 50 III.1.6. Mobile applications in Russia ....................................................................................................................... 51 III.1.7. Moscow ........................................................................................................................................................ 53 III.1.8. Conclusion ..................................................................................................................................................... 54 III. 2. France ............................................................................................................................................................. 55 III.2.1. General information about the country ........................................................................................................ 55 III.2.2. Demography ................................................................................................................................................. 56 III.2.3. Economy of France ....................................................................................................................................... 57 III. 2.3.1. Structural Crisis of French economy .......................................................................................................... 58 III. 2.3.2. Structure of GDP and place of tourism ..................................................................................................... 59 III.2.4. Tourism in France ......................................................................................................................................... 59 III.2.5. Restaurants France ....................................................................................................................................... 61 III.2.6. Mobile applications in France ....................................................................................................................... 63 III.2.7. Paris .............................................................................................................................................................. 65 III.3. Conclusion for countries .................................................................................................................................. 66 III.4. Choice of mobile applications in compared countries ..................................................................................... 68 III.4.1. Review of catering mobile applications in Russia. ........................................................................................ 69 III.4.2. Review of applications for table booking in France. ..................................................................................... 70 III.4.3. Chosen mobile applications for the research ................................................................................................ 71

2

III.4.3.1 Resto.ru ....................................................................................................................................................... 71 General information ................................................................................................................................................ 71 III.4.3.2. LaFourchette .............................................................................................................................................. 73 General information ................................................................................................................................................ 73

CHAPTER II: LITERATURE REVIEW .............................................................................................................................. 78

Introduction ............................................................................................................................................................. 79 I. The theories of technology adoption and usage................................................................................................... 79 I.1. Diffusion of innovation, Everett Rogers. ............................................................................................................ 80 I.1.1. Stages in the innovation-decision process. ..................................................................................................... 81 I.1.2. Types of adopters and the innovation-decision process ................................................................................. 84 I.1.3. Limitation of the theory diffusion of innovation. ............................................................................................ 85 I.2. Theory of reasoned action (TRA) by Martin Fishbein and Acek Ajzen (1975). ................................................... 87 I.2.1. The theory of planned behavior (TPB), Icek Ajzen (1985) ............................................................................... 89 I.2.2. Limitations of TBP ........................................................................................................................................... 91 I.3. The technology acceptance model by Fred D. Davis (1986) .............................................................................. 93 I.3.1. Comparison of TAM and TPB .......................................................................................................................... 95 I.3.2. Comparison of diffusion theory and TAM ....................................................................................................... 97 I.3.3. Theoretical extension of the technology acceptance model, TAM 2 .............................................................. 98 I.4. Unified Theory of Acceptance and Use of Technology (UTAUT), Venkatesh et al. (2003). .............................. 102 I.5. UTAUT2, Venkatesh et al. (2012). .................................................................................................................... 106 I.6. Mobile application usability, H. Hoehle and V. Venkatesh (2015). .................................................................. 108 I.7. Conclusion ........................................................................................................................................................ 116 II. Relationship marketing ...................................................................................................................................... 119 II.1. Definition ........................................................................................................................................................ 122 II.2. The Commitment-Trust theory of RM ............................................................................................................ 123 II.3. Interfirm Relationship Marketing Theory ...................................................................................................... 130 II.4. Interpersonal Relationship Marketing Theory ................................................................................................ 134 II.5. Multilevel relationships. ................................................................................................................................. 136 II.6. Information technology and RM ..................................................................................................................... 138 II.7. Conclusion. ...................................................................................................................................................... 139 III. Cultural theories. .............................................................................................................................................. 141 III.1. Cultural values concept in researched countries. .......................................................................................... 142 III.1.1. Orthodoxy, Autocracy and Nationality of Russia ........................................................................................ 142 III.1.2. Culture values of France ............................................................................................................................. 143 III.1.3. Conclusion ................................................................................................................................................... 145 III.2. Cultural dimensions theory by Gert Jan Hofstede (2001) .............................................................................. 145 III.3. Adoption of communication technologies and national culture, TAM and Hofstede’s cultural dimensions . 150 III.4. The role of culture in RM ............................................................................................................................... 152 III.5. Conclusion ...................................................................................................................................................... 158 IV. Final choice of constructs for the research model. ........................................................................................... 159 IV.1. Technology use constructs. ............................................................................................................................ 162 IV.2. Relationship marketing constructs. ............................................................................................................... 162 IV.3. Cultural dimensions of Hofstede.................................................................................................................... 163

CHAPTER III: CONCEPTUAL MODEL AND RESEARCH HYPOTHESIS ............................................................................ 165

Introduction ........................................................................................................................................................... 166 I.1. Conceptual model of the research ................................................................................................................... 166 I.2. Constructs and measuring items ..................................................................................................................... 167 I.2.1. Technology use ............................................................................................................................................. 168 I.2.2. Relationship marketing ................................................................................................................................. 171 I.2.3. Cultural dimensions. ..................................................................................................................................... 172 I.2.4. Moderating constructs ................................................................................................................................. 172 II. Hypotheses of the research. .............................................................................................................................. 174 II.1. Factors influencing the intention of mobile application use. .......................................................................... 175 II.1.1. Price value.................................................................................................................................................... 175 II.1.2. Facilitating conditions .................................................................................................................................. 175 II.1.3. Trust ............................................................................................................................................................. 176 II.1.4. Indulgence ................................................................................................................................................... 177

3

II.1.5 Mobile application usability.......................................................................................................................... 178 II.2. Factors influencing the use of mobile application. ......................................................................................... 179 II.2.1. Intention to use ............................................................................................................................................ 179 II.2.2. Mobile application usability ........................................................................................................................ 179 II.3. Outcome factors ............................................................................................................................................. 180 II.3.1. Mobile application usability ........................................................................................................................ 180 II.3.2. Continued intention to use .......................................................................................................................... 180 II.4. Moderation effects ......................................................................................................................................... 181 III. Conclusion ......................................................................................................................................................... 181

PART II: EXPERIMENTAL RESEARCH ......................................................................................................................... 183

CHAPTER IV: EPISTEMOLOGY AND METHODOLOGY OF THE RESEARCH .................................................................. 184

Introduction ........................................................................................................................................................... 185 I. Epistemological position and methodological approach. ................................................................................... 185 I.1. Epistemology ................................................................................................................................................... 185 I.2.1. Positivism. ..................................................................................................................................................... 186 I.2.2. Constructivism. ............................................................................................................................................. 187 I.2.3. Pragmatism. ................................................................................................................................................. 188 I.3. Choice and justification of epistemology ......................................................................................................... 189 II. Design of the research ....................................................................................................................................... 191 II.1 Hypothetico-deductive research process ......................................................................................................... 192 II.2. Comparative design ........................................................................................................................................ 197 III. Methodological approach................................................................................................................................. 198 III.1. Quantitative and qualitative research strategy ............................................................................................. 198 III.2. Quantitative research approach. ................................................................................................................... 200 III.3. Concepts and measurement instruments ...................................................................................................... 200 III.4 Survey ............................................................................................................................................................ 201 III.5. Online survey ................................................................................................................................................. 203 III.6. Sampling ........................................................................................................................................................ 204 III.7. Error in survey research ................................................................................................................................. 206 III.8.Conclusion ....................................................................................................................................................... 206 IV. Methodology to develop and validate variables and construct – Churchill paradigm ..................................... 207 IV.1. First phase - Specify domain of the construct ................................................................................................ 209 IV.2. Exploratory phase .......................................................................................................................................... 210 IV.3. Confirmatory phase ....................................................................................................................................... 218 V. Methodology to verify the research model - Structural equation modeling ..................................................... 219 V.1.Choice and justification of software. ............................................................................................................... 223 V.2. PLS-SEM approach and software choice. ....................................................................................................... 226 V.3. Conclusion ...................................................................................................................................................... 231

CHAPTER V: VALIDATION OF THE MODEL ................................................................................................................ 233

Introduction ........................................................................................................................................................... 234 I. Exploratory phase ............................................................................................................................................... 234 II. Pre-test of survey, statistical validation by SmartPLS ........................................................................................ 237 III.Conclusions ........................................................................................................................................................ 242

CHAPTER VI: RESEARCH RESULTS AND DISCUSSION. ............................................................................................... 243

Introduction ........................................................................................................................................................... 244 I. General model evaluation .................................................................................................................................. 244 I.1. Sampling characteristics .................................................................................................................................. 244 I.2. Validation of the measurement model ............................................................................................................ 250 I.2.1. Indulgence .................................................................................................................................................... 252 I.2.2. Continued intention to use ........................................................................................................................... 253 I.2.3. Use of mobile application ............................................................................................................................. 254 I.2.4. Price value..................................................................................................................................................... 255 I.2.5. Facilitating conditions ................................................................................................................................... 255 I.2.6. Trust .............................................................................................................................................................. 256 I.2.7. Intention to use ............................................................................................................................................. 257

4

I.2.8. Mobile application usability.......................................................................................................................... 257 I.2.9. Mobile application loyalty ............................................................................................................................ 258 I.3. Discriminant validity ........................................................................................................................................ 259 I.4.1. Percent of variance explained (R²) ................................................................................................................ 260 I.4.2. Size effect f² .................................................................................................................................................. 261 I.4.3. Model’s capability to predict (Q² and q²) ...................................................................................................... 263 I.4.4. Size effect of capability to predict q² ............................................................................................................ 264 I.4.5. Evaluation of parameters (Path coefficients and T-value)............................................................................ 265 I.4.6. Quality of the model - Goodness of fit .......................................................................................................... 266 I.4.7. Summary of results ....................................................................................................................................... 267 I.5. Moderation effects .......................................................................................................................................... 270 I.6. Validation of hypotheses ................................................................................................................................. 273 I.6.1 Factors influencing intention to use .............................................................................................................. 274 I.6.2. Factors influencing mobile application use .................................................................................................. 274 I.6.3. Outcome factors ........................................................................................................................................... 275 I.6.4. Moderation effects ....................................................................................................................................... 275 I.6.5. Conclusion about validation of the hypotheses. ........................................................................................... 276 II. Comparative study between France and Russia ................................................................................................ 277 II.1. Sub group France ............................................................................................................................................ 277 II.2. Sub group Russia............................................................................................................................................. 278 III. Discussion of the results ................................................................................................................................... 282 III.1. General model ............................................................................................................................................... 282 III.1.1. Factors influencing the intention to use the mobile application ................................................................ 282 III.1.2. Factors influencing the use of mobile application ...................................................................................... 285 III.1.3. Factors influencing outcomes ..................................................................................................................... 286 III.2. Comparison of French and Russian Models ................................................................................................... 286 III.2.1. Factors influencing the intention to use the mobile application ................................................................ 287 III.2.2. Factors influencing the use of mobile application ...................................................................................... 288 III.2.3. Factors influencing outcomes ..................................................................................................................... 288 IV. Conclusion ........................................................................................................................................................ 288

GENERAL CONCLUSION ........................................................................................................................................... 292

I. Contributions ...................................................................................................................................................... 293 I.1.Theoretical contributions .................................................................................................................................. 294 I.2.Managerial contributions ................................................................................................................................. 296 I.3. Professional contributions ............................................................................................................................... 297 II.Limitations .......................................................................................................................................................... 297 II.1. Limitations related to the context of the research ......................................................................................... 297 II.2. Limitations related to the conceptual model .................................................................................................. 298 II.3. Limitations related to the sampling ................................................................................................................ 298 II.4. Limitations related to the questionnaire ........................................................................................................ 298 II.5. Limitations related to the software application ............................................................................................. 299 III. Perspectives ...................................................................................................................................................... 299

REFERENCES ............................................................................................................................................................ 301

Sites consulted ....................................................................................................................................................... 331

ANNEXES ................................................................................................................................................................. 333

5

List of Tables

Table1: Population of Russia, million person

Table2: Russia mobile phones market value, 2011-2015

Table3: Russia mobile phones market segmentation: million handsets, 2015

Table4: Russia mobile phones market value forecast, 2015-2020

Table5: Population of Metropolitan France, million person

Table6: France: Selected Economic Indicators 2013–16

Table7: France mobile phones market value, 2011-2015

Table8: France mobile phones market segmentation: million handsets, 2015

Table9: France mobile phones market value forecast, 2015-2020

Table10: Reasons to visit Paris (leisure tourists), %

Table11: Top 10 Greater Paris cultural venues – 2014-2015 figures

Table12: Comparison of two countries

Table13: Conditions for cooperation with the restaurants (Russia)

Table14: Provided services for the restaurants

Table15: Adaptation of the TRA constructs for the mobile application’s use

Table16: Adaptation of the TPB constructs for the mobile application’s use

Table17: Selected constructs of TPB for the mobile application’s use

Table18: Selected constructs of TAM for the mobile application’s use

Table19: Comparison of constructs of TAM and TPB with examples of the

mobile application’s use

Table20: Comparison of constructs of TAM and diffusion theory with examples of the mobile

application’s use

Table21: Selected constructs of TAM2 for the mobile application’s use

Table22: Selected constructs of UTAUT for the mobile application’s use

Table23: Comparison between lafourchete and resto usability

Table24: Selected constructs of the technology’s use theories

Table25: Adaptation of the Commitment-Trust theory of RM for the mobile

application’s use

Table26: Adaptation of Interfirm Relationship Marketing theory for

the mobile application’s use

Table27: Adaptation of Interpersonal Relationship Marketing theory

for the mobile application’s use

Table28: Selected constructs of the RM theories

6

Table29: Hofstede’s dimensions

Table30: Hofstede’s dimensions and mobile application use

Table31: Selected constructs of the cultural theories

Table32: Final choice of the constructs

Table33: Key differences between indulgent and restrained societies

Table34: Measuring items for technology use aspects

Table35: Measuring items of relationship marketing aspect

Table36: Measuring items of cultural aspect

Table37: Measuring items of the moderating constructs

Table38: Synthesis of hypotheses

Table39: Four Worldviews Source

Table40: Positivism, Constructivism, Pragmatism in business research

Table41: Induction, Deduction, Combination

Table42: Fundamental differences between quantitative and qualitative research strategies

Table43: Example of measuring items development – mobile application loyalty

Table44: Experience survey

Table45: Modifications after the experts’ validation

Table46: Comparison CB-SEM and PLS-SEM approaches (adapted from Hair et al. 2011)

Table47: Model evaluation using SmartPLS 3 Pro

Table48: Loadings of the pretest

Table49: Loadings after deleted items

Table50: Reliability and validity

Table51: Cross-loadings

Table52: Root of AVE

Table53: Professional occupation and age of the responders

Table54: Frequency of visits

Table55: Duration of use

Table56: Geolocation function

Table57: Influence of promotions for the change of location

Table58: Loadings

Table59: Loadings after deleted items

Table60: Characteristics of the measurement items of indulgence

Table61: Characteristics of the measurement items of continued intention to use

Table62: Characteristics of the measurement items of use

Table63: Characteristics of the measurement items of price value

7

Table64: Characteristics of the measurement items of facilitating conditions

Table65: Characteristics of the measurement items of trust

Table66: Characteristics of the measurement items of intention to use

Table67: Characteristics of the measurement items of mobile application usability

Table68: Characteristics of the measurement items of mobile application loyalty

Table69: Cross loadings

Table70: Root square of AVE

Table71: R square

Table72: Size effect f square

Table73: Model’s capability to predict Q square

Table74: Size effect q square

Table75: Evaluation of parameters (Path coefficients and T-value)

Table76: Calculation of Goodness of fit

Table77: Duration of use

Table78: Frequency of visits

Table79: Place of use

Table80: Summary of the analysis results of the structural model

Table81: Summary of the hypotheses validation

Table82: Results of sub group France compared with general model

Table83: Results of sub group Russia compared with general model

Table84: Comparison of the results for general, French and Russian models

Table85: Comparison of hypotheses validation

8

List of figures

Figure1: Change in the international arrivals in the period from 1950 till 2012

Figure2: Out of home food market’s components

Figure3: Categories of tourism mobile applications

Figure4: The structure of gross value added in 2014

Figure5: Model of stages in the innovation-decision process

Figure6: Types of adopters

Figure7: Model of reasoned action by Martin Fishbein and Icek Ajzen

Figure8: Model of planned behavior by Icek Ajzen

Figure9: Technology acceptance model (TAM)

Figure10: Proposed TAM2 – extension of the technology acceptance model

Figure11: Unified Theory of Acceptance and Use of Technology (UTAUT),

Venkatesh et al. (2003).

Figure12: Unified Theory of Acceptance and Use of Technology 2,

Venkatesh et al. (2012)

Figure13: Steps for development of measurement instruments

of mobile application usability

Figure14: Structural model of the mobile application usability,

Venkatesh and Hoehle (2015).

Figure15: Relationship <holder – restaurant – user> in use of the mobile application

Figure16: The KMV Model of RM (Morgan and Hunt, 1994)

Figure17: Model of Interfirm Relationship Marketing, Palmatier

Figure18: Model of Interpersonal Relationship Marketing

Figure19: Multi-level exchange relationships

Figure20: Cultural dimension Hofstede & Minkov

Figure21: Hofstede’s dimension. Russia in comparison with France

Figure22: International RM framework.

Stephen A. Samaha, Joshua T. Beck, & Robert W. Palmatier (2012)

Figure23: Use of mobile application in restaurant industry: conceptual model of the research

Figure24: The research process by Sekaran and Bougie

Figure25: Dependent and independent variables

Figure26: Moderating variable

9

Figure27: Mediating variable

Figure28: Survey structure (Bryman and Bell, 2014)

Figure29: Process of web-based questionnaire diffusion

Figure30: Suggested procedure for developing better measures

(Gilbert A. Churchill, 1979)

Figure31:Scale validation

Figure32: Global model

Figure33: Reflective and formative relationships

Figure34: Collected data by country

Figure35: Results of research model with SmartPLS

10

Abbreviations

ATM – Automated Teller Machine

CAGR - Compound Annual Growth Rate

CARC - Compound Annual Rate of Change

CCI – Chambres de Commerce et d’industrie

CEN - European Committee for Standardization

CRM - Customer Relationship Management

CRS -Computer Reservations System or Central Reservation System

CWI - Centrum voor Wiskunde en Informatica (Center for Mathematics and Computer Science)

DNS – Domain Name System

FNC - Federal Commission for Networks

FTP – File Transfer Protocol

GDP – Growth Domestic Product

GDS - Global Distribution System

GIS - Geographic Information System

GNI - Groupement National des Indépendants de l’Hôtellerie Restauration

GPS - The Global Positioning System

HRI - Hotel Restaurants Institutions

HTML - Hypertext Markup Language

IDT - Innovation Diffusion Theory

IP - Internet Protocol

LBS - Location Based Services

LTE - Long-Term Evolution

C-TAM-TPB - Model Combining the Technology Acceptance Model and Theory of Planned

Behavior

MPCU - Model of PC Utilization

MM - Motivational Model

OECD – Organization of Economic Cooperation and Development

PLS-SEM – Partial Least Square Structural Equation Modeling

RBC - RossBusinessConsulting

RM - Relationship Marketing

SCT - Social Cognitive Theory

TCP / IP - Transmission Control Protocol / Internet Protocol

TDI -Theory of Diffusion of Innovation

TAM - Technology Acceptance Model

11

TPB - Theory of Planned Behavior

TRA - Theory of Reasoned Action

UTAUT - Unified Theory of Acceptance and Use of Technology

UNWTO – United Nations World Tourism Organization

VK – V Kontakte

VSM - Values Survey Module

WFTA - World Food Travel Association

Wi-Fi - Wireless fidelity

WOM - Word of Mouth

WTTC - World Travel and Tourism Council

12

Glossary

Accommodation is defined by European Committee for Standardization as “the

provision of at least sleeping and sanitary facilities”

Attraction and

Entertainment

include historic sites, heritage homes, museums, halls of fame,

art galleries, botanical gardens, aquariums, zoos, water parks,

amusement parks, casinos and cultural attractions

Backpacker in tourism: a person who travels with backpack

Budget rule (in Russia) the measure of Russian government to prevent budget deficit,

especially by low oil-prices.

Chain restaurants are restaurant establishments that are franchised in the large

number.

Commercial foodservice includes table service restaurants, self-service restaurants, quick

service restaurants, hotel establishments, transport foodservice

Communitainment

combination of two words “communication” and

“entertainment”, used by S.Khalaf (Flurry annual mobile

application study, 2016), to describe the phenomenon of huge

growth of social and messaging mobile applications’ use.

Consumerism desire to buy and own things

Culinary tourism the intentional, exploratory participation in the foodways of another –

participation including the consumption, preparation, and

presentation of the food item, cuisine, meal system, or eating style

considered to belong to a culinary system not one’s own.

Delivering catering business of providing food by delivery in consumer’s place.

Domestic tourism tourism inside home country

13

Fast Casual restaurants do not offer full table service with higher quality food than in

fast food restaurants

Fast food cheap, often hot food, quickly prepared and served in the restaurant

establishment

Fine-dining way of food consumption that usually takes places in expensive

restaurants, where good food is served in the formal way

Food tourism is an experiential trip to a gastronomic region, for recreational or

entertainment purposes, which includes visits to primary and

secondary producers of food, gastronomic festivals, food fairs,

events, farmers’ markets, cooking shows and demonstrations, tastings

of quality food products or any tourism activity related to food

Foodservice and

restaurant industry

is broad concept. It can include food sold to consumers for

preparation and consumption at home as well as the final

preparation of food for consumption away from home

Franchising is the practice of the right to use a firm's business model and

brand for a prescribed period of time, very known in restaurant

industry.

Gastronomic restaurants expensive restaurant establishment with gastronomic food,

prepared in the special way, usually by famous Chef

Horizontalization the process in French society of reducing the traditional

hierarchy

International tourism tourism across the countries

Leisure park is an amusement park

Orthodoxy one of the values of Russian society included communalism,

14

equality of people, spirituality

Ruble russian currency

Singularity one of the values in French society. It is manifested in the need

of admiration, critical evaluation, and wish of differentiation

Special way the concept in Russian philosophy, describing the special

position of Russian culture compared to Western cultures

Statehood one of the values of Russian society which includes the

“fetishizing” of state power

Street food food, cooked and sold in public places mostly outdoors for

immediate consumption

Technologization the dominated role of technology on modern society

The Triad "Orthodoxy,

Autocracy and

Nationality”

(Russian: Правосла́вие,

самодержа́вие,

наро́дность, Pravoslaviye,

Samoderzhaviye,

Narodnost

the corn concept of Russian culture proposed by Minister of

Education Sergey Uvarov in XIX century

Tour operators are companies providing package tours, which usually include

transportation, accommodation, catering and entertainment

Tourism

comprises the activities of persons traveling to and staying in

places outside their usual environment for not more than one

consecutive year for leisure, business and other purposes.

(“Recommendations on Tourism Statistics” by UNWTO and

15

UNSTAT, 1994)

Tourist traveler who travel to foreign countries by curiosity and

idleness, which are a kind of tour of the usually visited by their

fellow country

Transportation

is a service of transports: planes, trains, cars, boats, cruise ships

and others, used by tourists for travelling

Travel agents or travel

agencies

are companies engaged in selling and arranging transportation,

accommodations, tours, or trips for travelers.

Zapping the process in modern society which means desire the quick

change of everything.

16

General Introduction

Managerial background of the research

Smartphones, through their application stores, have created a new base of customers who constantly

surf on those platforms searching for new offers, but who also use the mobile versions of their

favorite websites, chat, share information and obtain new knowledge via smartphone.

Year over year the usage of mobile applications increases, becoming the source for data analytics.

The time spent on mobile applications increased in 2016 by 69% (Flurry Analytics annual mobile

applications study, 2016). Time spent in social and messaging applications grew by

394 percent over the past year. This phenomenon the analytics call “Communitainment” (Simon

Khalaf, senior vice president of Yahoo).

Tourism industry produces a big amount of data every day: tickets, hotel booking systems,

entertainments, maps and guiding systems, social networks, and restaurants bookings.

Restaurant industry has special position, on the one side it is regarded as a component of tourism

together with accommodation; on the other side it is the part of lifestyle and shopping.

The relationship between restaurant establishment and a client is often regarded as personal. Word

of mouth is the most powerful marketing tool for the restaurant management to win new clients, to

make client loyal, and to keep client loyal. Today the restaurant management should participate in

the “communitainment” to be in trend with new technologies. One of the way to organize this

communication process is for the restaurant management to participate in the digital mobile

restaurants guides, where the users can find information about the restaurant including menus and

contacts, where the users have tools of social communication like reviews, comments, ratings,

evaluations, where the users can do theirs choices without personal contacts to the restaurants. Such

mobile applications develop various tools to influence the choice of the consumer, in fact they can

change the consumers behavior, for example, instead of going out in usual place the consumer can

decide to try new restaurant establishment receiving interesting proposal from the mobile

application, or even get a table in the restaurant establishment automatically by being registered in a

specific program of the mobile application instead of calling and loosing time to find free table.

Mobile restaurant guide as an application gives the continuous access to the information. Even

when the restaurant establishment is closed, is not open yet, the users can check menus, pictures,

reviews and do their choices without waiting till the restaurant personal can answer the questions.

With discounts for the specific hours and days the restaurant can win clients in “dead” times, and

this information might be also available on the application. Mapping function allows finding place

17

next to the user and online booking allows booking the table immediately and fast. This easy

availability and economizing of time should increase the restaurants occupancy in general, but how

does it work in fact?

To clarify the degree of influence of the mobile application use in restaurant industry on the

consumer behavior this thesis proposes the research problem: Does the mobile application as

technology impact on the development of the restaurant industry?

We see that the mobile technology erases more and more boundaries between countries; we live in

the global information space. But still we speak about cultural/national differences, which include

different economical conditions, different traditions, and different behaviors.

The development of mobile technology as well as of restaurant industry is influenced firstly by the

purchasing power of population (how many people can effort to buy smartphone, how often they

can go out to eat in the restaurant). Eating out can be also the part of the culture: in some cultures

the trying the “cuisine” is important experience of leisure time, in others the place by itself is more

important (fancy, expensive place etc.). For the management of the mobile restaurants guides is

critically important to know, whether the behavior of their users in different cultures might be

different or not.

In this research we are aiming to compare two countries in the use of mobile applications in

restaurant industry.

France is known as a touristic goal number one in the world. Restaurant business occupies the

special place. Well developed and famous it attracts visitors from all around a world. The data

collected in the sectors gives big opportunity for analysis the consumer behavior.

Russia is the largest country according to its territory, from the economical side it is still regarded

as developing economy according to the International Monetary Fund's World Economic Outlook

Report (2016). The Russian tourism and restaurant industries have been experiencing a crisis since

2014.

The comparison of two countries with different levels of tourism and restaurant industries can give

us the possibility to see how the usage of mobile application can change the behavior of consumer,

what cultural and economical characteristics are by the importance, or whether the use of mobile

application erase the differences in the consumer behavior.

To clarify the problem of the research we investigated theories and models of various researchers in

the area of technology use, relationship marketing and culture.

18

Theoretical background of the research

Theoretically the research is based on three main theories: i) use of the technologies, ii) relationship

marketing theory, and iii) cultural theories. All the theories were chosen for building the research

model and taking in the account three categories of users of an application: the holder of the

application or provider, the restaurants, which use the application for selling and advertising their

services, and finally the consumers, the actual users of the application, who downloads, opens the

mobile applications and choose the restaurant establishment within a mobile application.

Firstly, we reviewed the theories of technology use. Mobile application is a part of modern

technology provided by mobile devices. Because the mobile application is the main object of our

research it is important to examine existing theories and models of the adoption and use of the

technologies.

We started with the Diffusion of Innovation (Rogers, 1962). Mobile application as technology has

been already around ten years in the market, but still in some industries the adoption and diffusion

of this technology is much slower than in others. As initial mobile technologies were implemented

for the communication reasons and still communication is the most used area of mobile technology.

Restaurant industry as part of “real” and not digital reality should have found the points of applying

of the mobile technology, so the mobile application went through the stage of being innovative in

this industry. Rogers wrote that “implementation (of the innovation) involves overt behavior

change” (Rogers, 1995, p. 173), which is conscious action to put innovation into practice. Outgoing

from the standpoint that implementation of the mobile application is a conscious reasoned action of

a restaurant establishment or an individual we regarded Theory of Reasoned Action (1975, 1980)

and its extension the Theory of Planed Behavior (Ajzen in 1985). With growing technology usage

predicting system use became an area of interest for research. Fred D. Davis proposed the

Technology Acceptance Model, which was based on principles adopted from Fishbein and Ajzen’s

Theory of Reasoned Action. Since the use of the mobile application is not just action or behavior,

but it is behavior performing the use of technology, we reviewed TAM (Davis, 1986) and its

extensions TAM2 (Venkatesh and Davis, 2000).

The most important for our research are Unified Theory of Acceptance and Use of Technology

(UTAUT) (Venkatesh et al. 2003) and its extension UTAUT2 (Venkatesh et al. 2012). Venkatesh et

al. (2003) developed UTAUT as a comprehensive synthesis of previous technology acceptance

research. The eight original models and theories of individual acceptance include the Theory of

Reasoned Action (TRA), Technology Acceptance Model (TAM), Motivational Model (MM),

Theory of Planned Behavior (TPB), Model Combining the Technology Acceptance Model and

Theory of Planned Behavior (C-TAM-TPB), Model of PC Utilization (MPCU), Innovation

Diffusion Theory (IDT), and Social Cognitive Theory (SCT).

19

UTAUT 2 as extension has two the most important for our research constructs hedonic motivation,

because the use of the mobile application in restaurant industry is the private use of the technology,

and price, because the mobile applications provide the bonuses and loyalty programs which add

value to use of them.

The last reviewed theory of technology use allowed us accomplishing our model with Mobile

application usability (Venkatesh and Hoehle, 2015). The preference of one about another mobile

application might be in the functionality, structure and design of each. Moreover this theory permits

comparing the use of two different mobile applications used in each country.

According to the reviewed technology use theories we pose the first research question:

How the mobile application’ use can standardize the consumer’s behavior by choice of

restaurants?

The usage of the mobile application gives the companies, in our case the restaurants and mobile

application providers, a huge data base of consumers, what makes important to build stable

relationship between restaurants and consumers. From that point of view the main theory of

relationship marketing is included in the theoretical part of the work.

The Commitment-Trust Theory of Relationship Marketing by Morgan and Hunt (1994) is one of

the most influential in Relationship Marketing. The commitment between mobile application holder

and restaurant might be easy purchasing of the advertising services; the commitment between

mobile application holder and final user might be the trustworthy information about all restaurant

establishments. And finally the commitment between restaurant and the user of mobile application

consists not only in expected services (food and beverage), but also adequacy of this services to the

information received in the mobile application. For our research trust is of high importance. The

complexity of trust is added through technology. The user should not only trust the company like

restaurant, he/she should also trust the technology.

The usage of mobile application, which contains the services of many restaurants and is used by

final customer, involves three actors. Firstly the application holder or provider builds up the

relationship with the restaurants or B to B relationship, where the exchange of mutually profitable

services takes place (Interfirm RM, Palmatier , 2007). The restaurant’s management tries to win final

users of the mobile application as a loyal client that means appearance of the Individual-to-

Individual relationship (Interpersonal RM, Palmatier, 2007). Moreover, the application holder obtains

the control over the data base of all final users of application, and also develops the Individual-to-

firm and the Individual-to-Individual relationships (Multi-level exchange Relationships in RM,

Palmatier , 2007).

20

According to reviewed Relationship Marketing Theory we pose the second research question:

How the mobile application as technology changes the relationship between restaurant and

client?

The comparison of two countries suggests that the cultural differences might have influence on the

use of the mobile application. The concept of cultural value impacts the behavior of the individuals,

so it can predict also the use of technology, implementation, adoption or rejection of it. The use of

any mobile application within one culture can be the same, for example all the users use mobile

application for gaming, or social networking, or searching for information. The cultural differences

start to be significant inside of the particular industry, in our case in the restaurant industry.

Many authors predict the equation of societies in connection with the development of new

technologies. We regarded cultural dimensions of Hofstede (Hofstede, Minkov, 2010) for

evaluating the differences between nations and cultures. This theory is based on the idea that the

cultural value may be distributed over six cultural dimensions. High technology is a major driving

force behind cultural change that leads to a similarity inside of different societies, but there is not

the evidence that it blurs the distinction by other dimensions. Moreover, it could lead to further

divergence during societies modernize technical experience, on the basis of already existing values.

We also reviewed Hofstede’s culture dimension theory by its implication in use of technology and

in relationship marketing.

According to reviewed theory of cultural dimensions we pose the third question of the research:

How cultural differences influence on the use of mobile application in restaurant industry?

Can the mobile application reduce or erase this difference?

Research design

This thesis is structured in two parts. The first part consists of three chapters: i) context of the

research, ii) theoretical background and literature review, iii) conceptual model and hypotheses of

the research.

First chapter presents the general context of the research. Firstly, we provide overlook of tourism

and restaurant industries and components of both. Secondly, the development of mobile technology

is regarded, with the particular view on the technologies important for the tourism and restaurant

industries. Thirdly, we present two countries starting with general information and economical

situations in both, and then characterizing the level of development of tourism and restaurant

industries and finally the development of mobile application market. In the end of this chapter we

present two mobile applications one for each country.

21

Second chapter focuses on the theoretical background, where all theories are reviewed

chronologically inside of three parts: i) technology use theories, ii) relationship marketing theory,

iii) cultural theories.

In the end of the first part of the research, in the third chapter we present our conceptual research

model with latent variables, measuring items, and moderating variables. We provide ten hypotheses

and four hypotheses of moderation.

Second part of the thesis consists of further three chapters: iv) epistemology and methodology of

the research, v) validation of the model, quantitative pretest vi) research results and discussions.

Fourth chapter concerns epistemology and methodology used in the research. Our research applies

epistemological position of positivism and hypothetico-deductive approach. For methodology we

employ quantitative approach, online questionnaire and convenience sampling methods. To develop

and validate variables Churchill paradigm is considered as methodology and structural equation

modeling particularly PLS-SEM is taken as the method to verify conceptual model and hypotheses.

In the fifth chapter we present exploratory phase with two interviews of the representatives of the

mobile application providers and the statistical validation of the conceptual model and measuring

items effectuated by pretest with SmartPLS application.

And in the last chapter the research results and discussion are presented, which involves sample

analysis, model and hypotheses validation, and for better understanding of the cultural differences

we analyze the countries’ subgroups and compare the results with the general model. The chapter

ends with discussion of findings in general sample and in both countries.

Finally, we present the contributions of the research, its limitations and perspectives in general

conclusion of the research.

22

Part I: Theoretical aspects

23

Chapter I: Context of the research

24

Introduction

The first chapter approaches the objectives of the research from three standpoints: tourism and

restaurant industry, development of information technologies implemented in these industries, and

cross-country analysis of Russia and France, including the situation in considered industries.

The chapter consists of three sections. In the first section the tourism and restaurant industries are

characterized. The second section reviews the information technologies. And the third section

observes two compared countries.

Tourism as a sector of economic activity contains many components, wherein the use of

technologies is well developed and it is producing a big amount of data interesting for researchers.

Anyway, the restaurant industry is often regarded as a part of tourism, as well as independent

business included in trade on the level of statistics.

Information technology is presented in this chapter from the historical point of view and point of

view of typology, in other words what kind of technologies are of interest for the research, and what

kind of technologies are used today in the tourism and restaurant industries.

The overlook at the economical, demographical, and political situation of the two compared

countries proposes the understanding of the backgrounds of development in both sectors: tourism

and technologies. It is apparent that the conditions are different in France and Russia; the countries

have not the same living standards, and belong to different economical categories according to the

International Monetary Fund (IMF Economic Review, 2015) and classification of economics of

World Bank (World Development Report, 2015). This might have influence on the use of

technologies as well as on the development of the researched domain – tourism and restaurant

industries.

And finally, the two different mobile applications are introduced, that are used in the presented

countries in the restaurant sector. Both mobile applications have the same goal to propose the users

the restaurant services.

In this way the first chapter answers the objectives of the research, forming the overture of the

mobile application use’ comparison in France and Russia

I. Tourism and restaurant industry

The first section of this chapter has a purpose to describe the tourism starting with definitions and

development of tourism worldwide.

Tourism today is a global phenomenon with a massive infrastructure. Its importance as a branch is

evident in its influences on society, politics, culture, and the economy. In the last decade consumer

behavior of the tourists changed under the development of information technologies. Tourism is one

25

of the sectors, where it is possible to collect the big data. The implementing of the mobile

applications is widely spread in tourism.

Restaurant industry is regarded as part of tourism on the one hand, but on the other hand it is part of

the big industry of foodservice. The smartphones’ usage influenced also on the behavior of the

restaurants’ clients, the information about restaurant’s establishments became easy to find.

I.1.1. Definition of Tourism

There are several terms used by describing the tourism, which are important to define first: travel,

displacement, departure, arrival, trip, tour etc.

Tourism is "the sum of the phenomena and relationships arising from the interaction of tourists,

business suppliers, host governments and host communities in the process of attracting and hosting

these tourists and other visitors" (Macintosh and Goeldner, 1986, 1995, Goeldner 2003).

In order to prevent the disaccords to define "Tourism", United Nations World Tourism Organization

(UNWTO) worked out following definition of tourism:

"Tourism comprises the activities of persons traveling to and staying in places outside their usual

environment for not more than one consecutive year for leisure, business and other purposes."

(“Recommendations on Tourism Statistics” by UNWTO and UNSTAT, 1994).

Tourism can be domestic or international, and international tourism has both incoming and

outgoing implications on a country's balance of payments. Today, tourism is a major source of

income for many countries, and affects the economy of both: the source and host countries.

I.1.2. Development of tourism.

Tourism as a mass social phenomenon started to develop after the Second World War, although the

roots of tourism go back to the deep past. Four stages can be remarked in the history of the

development of tourism. (Gyr, 2010).

The first stage: from antiquity to the beginning of the XIX century. In this period the main reason

for traveling were trade, pilgrimage, cure, and education.

The second stage: from the beginning of the XIX century to the beginning of the XX century. This

period is known as elite tourism, because only elite class could travel with the reason of pleasure

not for need. The first enterprise appeared specialized in manufacturing of tourist services. The

most important role in this stage had the development of transport.

The third stage: from the beginning of the XX century until World War II. The First World War,

economic depression 30s, and World War II had a negative impact on the development of tourism.

However, during this period there were elements of mass tourism, the beginning of the formation of

social tourism.

26

The fourth stage: after the Second World War to the present day. This stage is called the period of

mass tourism (See Figure 1).

Figure 1: Change in the international arrivals in the period from 1950 till 2012 (in millions)

Source: statistical charts and graphs on areppim.com

During this period tourism becomes widespread. From a luxury it becomes a necessity for the

majority of the population of industrialized countries. The industry of leisure and entertainment was

formed with its institutions, products, production cycle, methods of organization and management.

Following factors led to the development of tourism, especially mass tourism (Gyr, 2010):

• Growth of social wealth and income had a significant impact on the structure of consumer

spending. In many countries of Western Europe the growth of the gross annual income per

capita was accompanied by a decrease in the share of spending on food and goods.

However, the share of tourism in the structure of consumer expenditures of citizens grew

up.

• Reduction of working time and the growth of free time. Working time in the Western

countries declined from 2 350 hours per year in 1950 to 1716 hours in 1987. The average

vacation increased from 12 days in 1950 to 31 days in 1988.

• Progress in the development of road transports, aviation, communications and information

technologies was the major stimulus for the development of tourism and caused an

increase in the mobility of society. The prerequisites for this success were the

transportation construction, the development of air traffic and the reduce of the cost for

airline tickets, car boom and the availability of cars for the average consumer.

27

• Urbanization as a factor of tourism development led to the need of an individual to go out of

stressful city and spend vacation in relaxed places. Many researchers and sociologists say

that urban life style is distinguished by a stressful situation, an accelerated pace of life,

lack of contact with people. Therefore, tourism is for many people the ability to escape

from the stressful life in the city.

• Changing of cultural values of society. Tourism needs of an individual were changing.

Firstly the individuals went on holidays for having rest only, now the goals are complex.

This change is connected with the change in the theoretical concept of free time. First, in

50-ies., free time was used for restoring physical shape for work continuation. Then, in the

60-70-ies., free time was spent for the consumption of material goods, provided a growing

wealth of society. Finally, in the 80th it becomes apparent trend towards the use of free

time in order to obtain pleasure, to get as much as possible of new impression from life.

UNWTO published latest data for tourism number 2016 in press release # 17003 of the 17th of

January 2017. 2016 was the seventh year of sustainable growth following the 2009 global economic

and financial crisis. International tourist arrivals reached 1,235 million in 2016, a 3.9 % increase

over the previous year, according to the UNWTO World Tourism Barometer. 46 million more

tourists travelled internationally in 2016 compared to 2015.

Europe (+2%), the most visited region with over half of the world’s international tourists, was

affected by safety and security challenges. Northern Europe (+6%) and Central Europe (+4%) were

the most visited, while Southern and Mediterranean Europe arrivals grew only by 1 % (compared to

2015), Western Europe (0%) stagnated after three years of strong growth.

Except international tourism each country faces domestic tourism. Domestic tourism has positive

influence on the development of the general infrastructure in the country, on the development of the

hotels and restaurants services, transport modes and roads. Later we will see characteristics of the

both international and domestic tourism in compared countries.

I.1.3. Components of the tourism industry

As a mass industry the tourism has impact on many economical aspects, which are good to see in its

components. Tourism industry includes following components (OECD1 Tourism Trends and

Policies 2015):

- Travel agents or travel agencies are companies engaged in selling and arranging

transportation, accommodations, tours, or trips for travelers. Travel agencies can sell

1 OECD - The Organization for Economic Co-operation and Development. www.oecd.org

28

services from different tour operators, also arrange single trips for travelers, or provide

separately such services as transportation, accommodation, entertainment etc.

- Transportation is a service of transports: planes, trains, cars, boats, cruise ships and

others, used by tourists for traveling.

- Attraction and Entertainment include historic sites, heritage homes, museums, halls

of fame, art galleries, botanical gardens, aquariums, zoos, water parks, amusement parks,

casinos and cultural attractions. Many attractions are educational in nature, others are solely

for entertainment.

- Accommodation is defined by European Committee for Standardization as “the

provision of at least sleeping and sanitary facilities” (A dictionary of travel and tourism

terminology, 2005).

- Foodservice and restaurant industry

- Tourist information and guiding services include such components as touristic

offices, touristic guides, maps, excursions, audio guides, internet guides etc.

- Tour operators are companies providing package tours, which usually include

transportation, accommodation, catering and entertainment. Tour operators sell their

services directly or through travel agencies.

These components are whether the direct derivatives of the tourism (like travel agencies or tour

operators) or related branches which enjoyed significant influence of the tourism (like

transportation and restaurant industry).

In our research we will put attention on the restaurant industry, because the goal of this thesis is the

investigation of the mobile applications’ use in the restaurant business.

I.1.4. Food service and restaurant industry

One of the components of tourism industry is the foodservice and restaurant industry. Foodservice

is broad concept. It can include food sold to consumers for preparation and consumption at home as

well as the final preparation of food for consumption away from home. GIRA foodservice

suggested following components of the out of home food market (See Figure 2).

Commercial foodservice includes table service restaurants, self-service restaurants, quick service

restaurants, hotel establishments, transport foodservice. Other establishments like bars, night life,

vending etc belong also to commercial foodservices, but preparing and serving food are not their

priorities.

29

Figure 2: Out of home food market’s components

Source: GIRA foodservice, 2010

The restaurants and cafes segment was the industry's most lucrative in 2014, with total sales of

$1,213.1bn, equivalent to 44.3% of the industry's overall value. The fast food segment contributed

sales of $848.1bn in 2014, equating to 31% of the industry's aggregate value. The performance of

the industry is forecast to accelerate, with an anticipated CAGR2 of 6.8% for the five-year period

2014 - 2019, which is expected to drive the industry to a value of $3,805.8bn by the end of 2019.

Comparatively, the AsiaPacific and US industries will grow with CAGRs of 8.5% and 4.3%

respectively, over the same period, to reach respective values of $1,927.5bn and $842.1bn in 2019

(MarketLine, Industry Profile: Global Restaurants, 2015).

Two main trends impact the increase rate of restaurants are tourism and urbanization.

Food demand is rising with the growth of global population. Increasing urbanization and a general

migration from rural areas to urban areas is causing a change in lifestyle trends, and particularly

eating habits.

With increasing numbers of people working in office environments and growing family households

with two parents in the workforce, time to prepare food at home is limited. Consumers under time

constraints are opting to eat outside in cafés and restaurants, with price-conscious consumers often

turning to fast-food options such as mobile trucks and street stalls.

Specific impact on the restaurant industry has also such type of tourism as culinary tourism.

Culinary tourism as an academic concept appeared in the end of 20th century and developed in the

first decade of the 21st. Lucy Long (1998) was the first author, who published on the topic. She gave

her definition of culinary tourism as:

“The intentional, exploratory participation in the foodways of another – participation including the

consumption, preparation, and presentation of the food item, cuisine, meal system, or eating style

considered to belong to a culinary system not one’s own.”

2 CAGR - the compound annual growth rate

30

The World Food Travel Association (WFTA, 2015) refused the word “culinary” in the term

“culinary tourism” and substituted it with “food” and defines food tourism as:

“The pursuit and enjoyment of unique and memorable food and drink experiences, both far and near.”

One of the most utilized definition of food tourism used in the literature is that proposed by Hall

and Sharples (2003), according to which “food tourism is an experiential trip to a gastronomic region,

for recreational or entertainment purposes, which includes visits to primary and secondary producers of

food, gastronomic festivals, food fairs, events, farmers’ markets, cooking shows and demonstrations,

tastings of quality food products or any tourism activity related to food.”

Historically the two countries most associated with culinary tourism are Italy and France (Everett,

2016). These countries have developed and respected cuisines have native populations that are

knowledgeable and willing to travel within their own countries for food experiences. France boasts

historical and contemporary culture of wine consumption, strong family traditions of vineyards and

vintners. French cuisine has been declared a “world intangible heritage” by UNESCO (2010). Wine

tourism is one of the most dynamic industries.

Anyway, general trend to discover local cuisine of travel destination brought additional attention to

local restaurants in many countries. Thus, the development of the restaurant industry led to

development of marketing strategies of the management and above all to usage of modern

technologies.

I. 3. Conclusion

Started with providing primary services of the travelers’ needs, both, tourism and restaurant

industry changed first to luxury branches for reach and noble people in the end of 19th beginning of

the 20th centuries and then to the mass sectors.

Changes in social life such as reduction of working hours and higher salaries developed tourism and

restaurant industry to leisure. Phenomenon of quick serving restaurants appeared due to the

urbanization and to changes in the modern family’s structure. We can divide restaurant’s goers into

two groups: tourists, who discover the local food, and city’s residents, who is economizing time.

Culinary tourism became one of the popular types of tourism. Du to these facts the number of the

restaurant establishments increased rapidly. In our research we are going to see, how the restaurants

adapt themselves to the new time of modern technologies.

II. Modern technologies.

The development of the tourism industry is closely linked with the development of technologies.

The first stage of the evolution of mass tourism is associated with the development of transport, in

particular civil aviation. The mover of the 21st century’s tourism is clearly the Internet. The

31

appearance of a global network and online payment did information revolution in the tourism

industry.

In opposite restaurant industry is more conservative in its relationships with the clients. Only

several types of restaurant establishments really won in the global “Internetlization”, like quick

service restaurants, or take away services. In other restaurants types the relationships with a client

are difficult to replace with the technology. Nevertheless, the use of modern technologies became

important for the marketing strategy of the restaurant’s management.

This section is designed to introduce modern technologies, which have the biggest impact on the

development of the tourism and restaurant industry.

II.1. Internet

The history of Internet started in the middle of 60th in the USA, and in the early 90th the World

Wide Web is almost completely replaces the concept of the Internet (Leiner et al, 2009).

October 24, 1995 the Federal Commission for networks (FNC) adopted a resolution defining the

term "Internet". This definition was developed in consultation with members of the Internet

community and the owners of intellectual property rights. RESOLUTION: Federal Commission for

the networks unanimously agreed that the term "Internet" reveals the following definition. The term

"Internet" means the global information system that (i) is logically connected via a globally unique

address space based on the Internet Protocol (IP) or its subsequent extensions / add-ons; (ii) to

communicate with the protocol stack using Transmission Control Protocol / Internet Protocol (TCP

/ IP) or its subsequent extensions / add-ons and / or other IP-compatible protocols; and (iii) provide,

using or making accessible to the public or private level, the upper level services, built on the basis

of communication and related infrastructure, which is described herein.

II.2. Networks services used in tourism

Today there is a wide variety of services of Internet to ensure work with all types of resources. The

most interesting and popular among them are: Email, World Wide Web, Blogs, Web Forums, Wiki,

Online auctions and shops, Social networking and dating sites, Teleconferences and newsgroups

(Usenet), FTP service, Telnet service, DNS Service, IRC service etc.

Development of the Internet has given rise to a number of systems, without which today it is

difficult to imagine the tourism industry.

The usage of modern technologies in tourism due to the main features:

- the subject of sales in the tourism is a service,

- a touristic service is a complex of several services,

32

- touristic services provide a lot of information. The tourism industry is suited for the

implementation of modern information technologies because a touristic service at the time of

sale is just information.

Today it is possible to identify several key technologies that are already being used in tourism

industry. These technologies depend on the components of tourism.

II.2.1. Travel agent, tour operator, transportation, attraction and accommodation

Such components of tourism as travel agent, tour operator, transportation, attraction and

accommodation are direct users of a computer reservations system or central reservation system

(CRS). CRS is a computerized system used to store and retrieve information and conduct

transactions related to air travel, hotels, car rental, or activities. Originally designed and operated by

airlines, CRSes were later extended for the use of travel agencies. Major CRS operations that book

and sell tickets for multiple airlines are known as Global Distribution System (GDS).

Global Distribution System is global reservation system, based on the global integration of internal

databases and accounting systems (Schulz, 1996). These systems are an association of hotels that

provide information about themselves, show prices and availability with the help of back-office

established in hotels, or via the Internet. Thereby hotels are accessible to any user of the Internet,

what increases their sales. By booking the order goes directly to the service provider.

Characteristics of the main GDS:

SABRE established in 1960 by the airline «American Airlines», headquartered in SOUTHLAKE,

TX, USA, personnel - 6,500 people in 60 countries, resource - 420 airlines, 58,000 hotels, 53 car

rental companies, cruise 9 and 33 rail roads, 232 tour operators, users - 60,000 travel agencies.

AMADEUS founded in 1987 by three major European airlines - «Air France», «Iberia»,

«Lufthansa». Headquarter is located in Madrid, Spain (host - Erding, Germany), the staff - 4250

people., Resource - 470 airlines, 59,600 hotels, 48 car rental companies, all major cruises, railways,

ferries, insurance companies and tour operators hundreds users - more than 70,000 travel agencies

and airline counters.

GALILEO founded in 1987, owners - Air Lingus, Air Canada, Alitalia, Austrian Airlines, British

Airways, KLM, Olympic Airways, Swissair, TAP Air Portugal, United Airlines and US Airways,

headquartered in Parsippany, New Jersey, US, personnel - 3,000 people in 116 countries around the

world, a resource - 500 airlines, 51,000 hotels, 31 car rental company, all the major cruise lines,

tour operators 430 users - 47,000 travel agencies.

Another important role in tourism plays Internet technology to build customer relationships or

Customer Relationship Management (CRM). Collected and processed information about the clients

33

(the story of their travels, the average budget, needs and preferences) is used in order to specify for

clients their proposals, which are more likely to be accepted by them.

CRM-systems obtain important for functionalities for users, such as (Perna, Baraldi, 2014).:

- Contact management (customer information, history of contacts);

- Communication control (communication, its safety, etc.);

- Forecasting (sales prospects, forecast, marketing);

- Management of motivating factors to attract customers;

- Order management;

- Document management, development and implementation of standards, customizable

reports, information, and advertising materials;

- Analysis of sales;

- Product configuration;

- Encyclopedia of Marketing provides updated information on travel services, prices,

promotions, results of various studies and competitor information.

The evolution of electronic payments has been of great importance for tourism. American banks,

again for the first time in the world, were beginning to introduce electronic methods of money

transfer. In 1975, there were the first automatic teller machines for obtaining cash – ATMs in the

USA. Really revolutionary in the history of electronic money was the year of 1993, when Dr. David

Chaum, the head of cryptography in CWI3, the Dutch national research center, developed a software

solution – eCash technology for working with digital cash. ECash concept puts into practice the

concept of e-currency to pay for goods or services via computer networks, which had already spread

significantly by that time.

These days, electronic payment is a very common method for commercial transactions in the

tourism sector.

So the Internet provides tourists with many advantages when it comes to carrying out their

purchases:

• Speed and ease of access to tourism products and services;

• Price comparison ;

• Availability of reviews and comments from other visitors;

• Possibility of making a purchase 24 hours a day /365 days a year.

All these advantages explain the current increase in electronic sales in the tourism sector. Parallel to

this growth, the electronic payment market provides an opportunity for innovation in the ways in

which the tourism companies can obtain their income.

3 Centrum voor Wiskunde en Informatica (Center for Mathematics and Computer Science), Amsterdam, Holland

34

Hotels, flights, tours, car rentals and even restaurant reservations anywhere in the world can be

booked and paid via sites reservation systems. Online travel market worldwide is estimated today at

$ 300 billion. The main share falls on the United States, where more than 60% of all tourists prefer

independent booking and purchase tickets and hotels (Statistics and facts on the online travel

market, 2015).

II.2.2. Tourist information and guiding services

Internet provides many information resources for tourists, including websites of touristic offices,

video tours and guides, social networks (e. g. tripadvisor), blogs, official websites of cities and

destinations, online schedules for transports etc. Special service is provided by geographic

information system. A GIS4 is a computerized data management system used for encoding, storing,

processing, maintaining, analyzing and presenting data in association with their geophysical

location. It is a "geo-referenced" data analysis and presentation system. A GIS offers the possibility

of a structured data management, data access as well as efficient analysis procedures applied to the

data. GIS is used to compose information of a digital map base for printed maps, digital files for

Internet mapping, and digital files for mobile mapping, attractions map, and website with

interactive mapping.

II.3. Big data in tourism

In modern world, information is a strategic resource; because of this Big Data has great value.

Big Data is massive amounts of unstructured data from a variety of sources. These data amounts are

difficult to process by using traditional tools to work with databases and software. Key

characteristics of Big Data received the code name 5V - Volume, Variety, Velocity, Veracity, and

Value (Hilbert, 2013):

- Volume. According to forecasts of the consulting company IDC, by 2020 the world will

accumulate at least 40 trillion gigabytes of data.

- Variety. The number of information sources has increased with the progress, therefore, a

variety of data types grew also, that require different tools for collecting, storing and

processing.

- Velocity. The velocity of reading data from the media, their transmission over the Internet

and other communication channels, the level of computing power have to be as fast as

information flow is.

- Veracity. With an increasing amount of data it is important sorting information for the

selection of reliable data.

4 GIS – geographic information system

35

- Value. The value of information is a key parameter for business to evaluate the effectiveness

of investments in its processing.

Big Data appeared in the tourism industry thanks to the CRM system when service providers began

to collect data to improve the quality of services and increase sales.

Airlines and hotels have used information on purchases, buying preferences, data from social

networks to monetize their activities and to offer a more personalized service. Thus, Big data in

tourism today is used more often for: 1) improving the quality of service, 2) marketing and sales.

The list of big data sources is limitless, but they can be divided into three main groups (Mayer-

Schönberg, Cukier, 2013):

- Data from social networks. These include all the information obtained by monitoring customer

activity on social networks, pages viewed and comments on forums and so on.

- Data on transactions. Sources of information in this case are the invoices, payment orders,

receipts, statements and other financial documentation.

- The machine data. It consists of the sensors, cameras, technical equipment, web machines and

track user behavior online.

Data can also be divided into current and historical, to obtained from public and private sources, to

structured and unstructured.

The company InsightExpress did a study in 2013 on behalf of Cisco Systems5 in 18 countries

among the companies that collect, record, and analyze data. According to this study 60% of

respondents admit that Big Data solutions can improve decision-making processes and increase

competitiveness, but only 28% said that already receive real strategic advantages of the

accumulated information. Traditional methods of data processing are often useless when working

with Big Data. It was spent about $ 34 billion in 2013 for technologies of Big Data processing and

this sector created already 4, 4 million of jobs. Tools and algorithms for Big Data processing are

extremely diverse. Among them are the traditional methods of statistics and computer science, as

well as specially designed for Big Data software. This is a technique to identify the relationships

that allow predicting the behavior of consumers in a particular market segment, analysis tools for

social networks, machine learning algorithms, methods of analysis of spatial data, simulation

models, visualization tools, and many others. The most famous solution today is Hadoop, the

project of Foundation Apache Software Foundation. At present, almost all modern means of

analyzing Big Data provide integration with Hadoop.

5 Cisco Systems, Inc. (known as Cisco) is an American multinational technology conglomerate headquartered in San José, California, in the center

of Silicon Valley, that develops, manufactures, and sells networking hardware, telecommunications equipment, and other high-technology services and products. www.cisco.com

36

II.4. Mobile technologies

At the beginning of the XXI century progress of mobile devices started. The market consists of

laptops, cell phones, smartphones, tablets, in other words all the devices which can be moved or/and

taken to serve at any possible point and moment. In our research we will focused on the

smartphones as advanced cell phones that serve as computing devices in addition to being mobile

handsets and that run on an advanced operating systems.

Today's smartphones are systems for communication, with their choices of several types of wireless

connections. One of these is the Wi-Fi, used everywhere for wireless access to the Internet, building

local networks, etc. The standard Wi-FI was approved in 2009 and allowed to transmit data at

speeds up to 54 MBits / s (www.wi-fi.org ). First, the device operates with a data transfer protocol

for Wi-FI were located a few meters away from each other, but the distance to units grew quickly of

several hundred meters. At the moment there are data transmission technologies of Wi-Fi at a

distance of 100 km or more.

A huge impact on the usage of mobile devices and especially smartphones has had a development

of the mobile Internet (Hamill, Lasen, 2005).

The development of the mobile Internet went through four stages so called 4 Generations (G) and

resulted in 4G. Today, thanks to an ultra-modern technology LTE, the download speed in Internet

4G networks can be up to 150 Mbit/s. Besides higher speed, the fourth generation network is more

stable and has better signal quality. For customers this means a perfect quality of communication,

instant of loading of even fairly large files, the ability to enjoy streaming video in HD quality and

modern games in real time (Dahlman, Parkvall, Scold, 2013).

To meet the demand of consumer and business for wireless services, many providers are beginning

to deploy technology 4G. Service providers in many emerging markets are creating new mobile

infrastructure, and in some developed regions 4G technology supplement or replace solutions for

2G and 3G. In 2014, each 4G-compound generated in an average of 2.2 GB of mobile data traffic

per month. According to the annual study of Cisco Systems Ink, this figure will reach 5.6 GB by

2019, which is 5.4 times higher than the average volume of traffic generated by the connection, do

not use technology 4G (www.cisco.com ).

II.4.1. Mobile application

A mobile application is software products developed specifically for mobile devices, smartphones,

tablets or other mobile devices. Mobile applications are distributed through shops applications:

Apple App Store, Google Play, Windows Phone Store, BlackBerry App World and others. Mobile

applications help to solve various tasks starting from mobile mapping and receiving e-mail to

highly specialized functions. They are designed to make life easier for users of mobile devices. The

37

market for mobile applications was born in 2008. The first company, launched an innovative model

of distributing applications, has been Apple. Later Google came on this market, which created

serious competition for Apple.

Mobile application is not the same as a mobile website which is similar to any other website in that

it consists of browser-based HTML pages. The obvious characteristic that distinguishes a mobile

website from a standard site is in the design.

By contrast, mobile applications are actual applications that are downloaded and installed on mobile

device, rather than being rendered within a browser. Users visit device-specific stores such as

Apple’s App Store, Android Market or Blackberry App World to find and download application for

a given operating system. The application may pull content and data from the Internet, similar to a

website. Or it may download the content so it can be accessed without an Internet connection

(Holzer, Ondrus, 2011).

Mobile applications are a global phenomenon with mass consumer appeal. For example, Apple’s

App Store has reached 100 billion downloads in June 2015(Apple press info, 2015).

From games, social media and video to news, stock market and mind maps, the number of mobile

applications available in applications stores is constantly growing.

Across the globe, the mobile channel is growing fast. People in every country are buying more and

more advanced mobile devices, companies are launching smartphone applications by the thousands,

and businesses and consumers alike are using mobile phones for everyday activities (i.e., checking

the weather, taking advantage of discounts, shopping, or sending and receiving financial

information).

The success of smartphones and tablets and their applications is driven by several factors (Hamill,

Lasen, 2005):

• Increasing impact of mobile social networking,

• Emergence of new mobile platform Windows 8,

• Development of mobile payment systems and mobile banking,

• Changing consumption patterns, so called global mobilization. Mobile devices are becoming

more user-friendly for everyday tasks than computers.

• Reducing the cost of communication through mobile devices,

• Function of Geolocation,

• Penetration of high speed internet connection and its impact on the market of mobile

applications,

• E-commerce or mobile commerce.

38

Experts of J'son & Partners Consulting6 (2013) divided applications market in the following

segments:

✓ Content applications.

Today activities such as listening to music, watching a variety of films, videos and photos, as well

as reading digital books are the most accessible and convenient for users of mobile devices, so the

demand for this segment of mobile applications rises.

✓ Business Applications.

Business applications have become an essential tool for many users that will help them simplify

their job.

✓ Mobile Games.

Mobile games are the most demanded in the market of mobile applications.

✓ Mobile Social Networking.

Our research reviews the mobile applications used in the restaurant industry, on the one hand it is

content mobile applications, which provide the users necessary information, on the other hand such

mobile applications can be business tool for the restaurants.

II.4.2. Mobile technologies in tourism

Mobile technologies in tourism are used on the analogy of computer usage. Such computer’s

functions as calendar, notebook, e-books reader, internet access etc are available on tablets and

smartphones. Mobile applications have become one of the main trends in the development of

mobile technologies. The choice to use a mobile application or internet website is connected with a

tourist’s purposes.

Each category of the mobile applications has its level of engagement and customers’ loyalty. The

understanding of the audience for which a mobile application is designed influences the

implementation strategy and marketing.

Regarding frequency of use and loyalty (engagement), all applications can be divided into 4 groups

(J'son & Partners Consulting, 2013).

Group I includes the mobile applications that are used frequently and constantly. News and

communications - are the two categories included in this group.

Group II includes the applications which are used frequently, but in a limited period of time. They

are used “discretely”. Streaming music, dating and social games are typical categories of mobile

applications in this group.

6 J'son & Partners Consulting covers more than 30 market segment across Telecommunications, IT and Media. Consultancy

projects, analytical reports and data matrixes. http://json.ru/en/about/

39

Group III contains the applications that are rarely used and have a high churn rate. These mobile

applications perform the task “performed and removed”. A consumer uses such application for

example to change a screen saver or to choose a subject of the operating system.

Group IV consists of the applications that are used infrequently, but have a high value for a long

period. Even if they are used only occasionally, these applications can remain in the consumer's

smart phone almost indefinitely. While the application may remain unused for a long time, its value

increases dramatically due to the corresponding need. All mobile applications designed for the

travelers: applications of airlines, hotels, restaurants, maps and guides belong to this group.

The usage in tourism industry the mobile applications depend directly on demand of tourists. It is

fair to speak about travel-related applications, which are used by travelers, but such applications are

also used in everyday life, and specific travel applications, such as guides or hotels-booking.

All applications, used by travelers can be divided in 5 categories: Navigation, Social networks,

Entertainment, Information and e-commerce (Figure 3). This division is conditional and based on

application usage by travelers. Also it should be noted that some applications are more than one

category, and can include guides, maps, social networking and booking opportunities. The

categories of information and e-commerce include applications specific for tourism with small

exceptions, while navigation, social networks and entertainment groups present increasing the usage

during the travel, but are not used specific by occasion of travel.

Figure 3: Categories of tourism mobile applications.

Source: composited by author of the thesis based on the references

Location Based Services (LBS) are services for mobile users that take the current position of the

user into account. Simply put, we can define LBS as a service that determines where a mobile

device and its user are geographically located and also acts as an information gateway for the user

by providing various kinds of services. The geographic location of the device is determined by

using a positioning service, nowadays most commonly involving the use of the GPS. In case of

tourism and travel, tourist generally needs information about the location when they don’t know it

very well. The main functions of LBS for tourism are usually regarded as being the positioning of

Mobile Applications in Tourism

Navigation

(geolocation, maps,

subway maps)

Social networks

(including

messengers)

Entertainment

(games, movies, e-

readers)

Information

(guides, weather,

currency converter,

translators)

E-commerce

(ticketing, hotels and

restaurants booking)

40

places, persons, and objects, routing between them, search for objects in nearby places such as

restaurants, shops, hotels, or points of interest, and information about traveling conditions, such as

traffic-related data. The application areas of LBS for tourism include mapping, tracking, routing,

traffic information, destination information, recreation spots, and for restaurant business it includes

functions “around me, next to me, where to eat, closest restaurants etc.” (GNSS market report, issue

4, 2015).

Social networks used by occasion of traveling can be divided in travel related social networks such

as tripadvisor, WAYN (Where Are You Now), and others. After social networks started the

geolocation service, it became popular to check in places, also when traveling. 47% of online

travelers visit social networking sites to influence destination selection (Google travel report, 2014).

E-commerce. According to the Travel report of company Criteo7, published in October 2016, the

frequency of travel bookings from mobile devices grew by 12 % in 2016, within 33% of all

bookings are dome by smartphones and 16 % by tablets. The popularity of mobile applications for

travel increased, so booking through applications or “in-app” represent 54% of all bookings from

mobile devices. Usage of mobile devices by hotels bookings has been depended on the period of

booking, by 58% of bookings made within last 24 hours the mobile devices were used, but by

booking in advance (more than 12 weeks) users prefer still PC, 18 % contra versa 82%. Globally

the leader of the mobile devices booking is Japan (38% of all bookings), while the highest over the

year growth showed Australia (36% more bookings done by mobile devices) (Croteo travel report,

2016).

Information services include guides, weather, currency converter, translators. Some services are

multi functional. Application can track of all itineraries for user. This includes hotels, flights, trains,

and any other travel documents needed to keep in order. Currency applications offer real time

exchange rates and take the guess work out of getting cash out of the ATM, bills dining out, and

converting foreign currencies back into the currency of the home country. And translators

applications have ability to use till 100 languages and immediately translation of the sings, names

and pointers.

Entertainment. According the tripadvisor8 report 38% of mobile phone users plays games on their

devices during the traveling. Except games e-books and videos belong to entertainment applications

(TripBarometer, 2016).

II.5. Mobile technologies in restaurant industry

A growing number of people are comparing restaurant menu items and their benefits on the

Internet. Also increasing number of people are making restaurant choices based on online reviews.

7 Company Criteo is one of the world leaders in technology for digital-advertising platforms. www.criteo.com 8 Tripadvisor: TripBarometer 2016, www.tripadvisor.com/TripAdvisorInsights

41

Restaurateurs' are using the Internet and social networking platforms to compete more effectively in

the restaurant marketplace. Internet and social networking sites help restaurants to communicate the

range and quality of food items served and thereby to reach out to a larger section of customers.

According to the survey of Deloitte Development LLC9 customers of restaurants are using not

really gladly the mobile applications. Less than one-fifth (18.9 percent, 2014) of survey respondents

have actually downloaded at least one restaurant application into their smartphones or tablets.

Concerns about security and “spam” may explain some customers’ reluctance to participate in

loyalty programs while others may see obstacles in the requirement to download mobile application

and manage user accounts. Deloitte Consulting LLP’s Travel, Hospitality, and Leisure practice

surveyed more than 4,000 restaurant customers in the U.S. who visited a fast service restaurant (fast

casual) at least four times, or a casual dining restaurant at least two times, in the prior 30 days. The

survey focused on dining behavior and attitudes, including customer engagement preferences,

restaurant choice behavior, and the effectiveness of loyalty programs.

The survey did not show particular evidence that the use of a restaurant application drives brand

loyalty. For example, only 37 percent of those who have downloaded a restaurant application said

they would serve as brand ambassadors for their most-frequented restaurant, compared to 38

percent of those who have not. Some 36 percent of those who have downloaded a restaurant

application say they have built a relationship with their most-frequented restaurant, compared to 30

percent who have not.

Despite these findings, restaurant organizations see great potential in the integration of traditional

loyalty programs into a mobile platform. Survey responses indicate that customers generally do not

want to engage in a dialogue with restaurants about their dining experiences and preferences.

Nevertheless the restaurant applications, which can provide the booking service in many

restaurants, not only in one, have become a recent trend. More than 14 million of bookings in the

world are done every day: 95% of them are made through a phone call, and only 5% - through the

Internet. The world's largest table-operator might be a service Opentable10.

II.6. Conclusion

Private mobile devices, smartphones and tablets, with stable Internet connection have change the

consumer behavior in many ways. The users of smartphones have habit to access immediately to

any kind of information. They can purchase goods and services at any moment they need or want.

Thus, companies adopt their marketing strategies to these changes to win loyalty of the customers.

Tourism is closely connected with development of technologies; mobile technologies cheapen the

9 Building consumer loyalty in the fast service and casual dining restaurant sector. Consumer survey findings, 2014 www.deloitte.com 10 OpenTable is an online restaurant-reservation service company founded by Chuck Templeton in 2 July 1998 and is based in San

Francisco, California. The service operates in 19 countries, including 6 European countries . www.opentable.com

42

expenses of the tourists and contribute the mass tourism. Through all private mobile devices and

mobile application huge amount of personal information is collected. Big Data is becoming the

important topic for scientists and business.

In our research we will focus on the usage of the mobile applications in the restaurant industry,

where the implementation of the mobile services depends on many factors. On the one hand the

personal contact and close relationship between restaurant and client are the most important; on the

other hand, restaurant management should adopt the strategy according to the user’s demand to

access information immediately.

III. The choice of the countries

For the research we have chosen two countries: Russia and France. I am from Russia and my

primary interest is to compare this country with France in the area of mobile application use in

tourism and restaurant industries. France is known for its cuisine and restaurants all over the world.

It’s one the most famous touristic destination, and French gastronomy was added by the UNESCO

to its list of the world’s intangible cultural heritage (2010).

The history of Russian restaurant industry was interrupted during the time of Soviet Union and

became a real business branch only in the beginning of the nineties of 20th century. Even if Russia is

attractive touristic destination for foreign tourist, short season because of difficult climate,

underdeveloped infrastructure, and visa issues make the access to the country limited.

Despite this apparent difference of two markets, the comparison of these two countries might be

interesting for many reasons:

- rush development of the touristic and restaurant industry in Russia lead in quick evolving of

consumption of these services;

- mobile technologies arrived in both countries at the same time in the 2008;

- impact of mobile technologies on the consumption of restaurant services is ambiguous:

which side is adopting technologies faster;

- how strong is cultural aspect in the restaurant industry, talking about mobile technologies.

The goal of this section is to give main characteristics of two countries, Russia and France,

underlining the economical development, and presenting tourism and restaurant industries in

details. The first look at both states will provide us the background information, which might be

used for analyzing the role of the tourism and restaurant industry in the economies.

For the comparison we will focus on two capitals, Moscow and Paris, and two mobile applications:

Resto and LaFourchette. This choice has been made based on comparability. Thus, Moscow and

Paris are capitals, and have similar characteristics, such as tourist flows and touristic attractions,

43

number of restaurant establishments, and from the point of view of countries’ structure in both

cases the power is centralized in the capitals, what simplify comparison of the standards of living.

More detailed information about both capitals will be presented in further sections of this chapter.

As for applications we have chosen two local brands, firstly because there is no restaurant mobile

application presented equal in both capitals, and secondly we have the intention to analyze culture

specification, which is better to see in local brands.

To indicate the comparability of chosen countries we describe each country by general information

including geopolitical structure and demography, economy and place of tourism and restaurant

industries in it. This information will allow us to specify the level of comparability, for example, the

size and population of Russia are much bigger than of France, but the development of tourism and

restaurant industries are higher in France than in Russia.

III. 1. Russian Federation

III.1.1. General Data to the country

Russia (Russian Federation) is the state in Eastern Europe and North Asia. It borders with North

Korea, China, Kazakhstan, Mongolia, Azerbaijan, Georgia, Ukraine, Belarus, Poland, Lithuania,

Latvia, Estonia, Finland and Northway. Russia is one of the biggest countries in the world with the

area of 17,075,400 km2.

Administrative-territorial structure of Russia has different types and levels. At present, the country

consists of 85 subjects of the Russian Federation (Russian regions), which are combined into nine

federal districts. In accordance with the Constitution Russian Federation consists of 22 republics, 9

territories, 46 regions, 3 federal cities (Moscow, St. Petersburg, since March 2014 -Sevastopol), 1

autonomous region, 4 autonomous districts.

Despite the fact, that Russia is divided in regions and each region has its own governmental units

and its own budget, the power is well centralized in Russia. This has impact on the development of

the regions; the most important regions for the country, mostly rich on natural resources enjoy the

attention of the central power, from the other side, very often all the revenues go directly to the

Moscow. On the level of the management all programs, plans, and directions of the regional

development are agreed firstly with the central government.

44

Picture 1: Map of Russian Federation,

source Wikipedia

According to the Constitution of Russia, the country is a federation and semi-presidential republic,

wherein the President is the head of state and the Prime Minister is the head of government. The

Russian Federation is fundamentally structured as a multi-party representative democracy, with the

federal government composed of three branches:

- Legislative: The bicameral Federal Assembly of Russia, made up of the 450-member State

Duma and the 166-member Federation Council, adopts federal law, declares war, approves treaties,

has the power of the purse and the power of impeachment of the President.

- Executive: The President is the Supreme Commander-in-Chief of the Armed Forces, can

veto legislative bills before they become law, and appoints the Government of Russia (Cabinet) and

other officers, who administer and enforce federal laws and policies. The president is elected by

universal suffrage for a six-year term.

- Judiciary: The Constitutional Court, Supreme Court and lower federal courts, whose judges are

appointed by the Federation Council on the recommendation of the President, interpret laws and

can overturn laws they deem unconstitutional.

III.1.2. Demography

According to the Federal State Statistics Service (Rosstat), the number of inhabitants of the Russian

Federation on the basis of the population census was 142 million 857 thousand people (population

census 2010). At the end of 2015 it increased to 146, 2 million. (See table 1). Russia occupies the

8th place in the world in terms of population.

45

The correlation of city’s and countryside residents is in Russia 74% and 26% respectively. There

are 2 386 cities and towns and 134 000 villages, 12 cities are of more than 1 million people large.

The population is concentrated mostly in The Central district (38.4 million people) and the least in

the Far East Federal District (6.3 million).

The most numerous nations are Russian (111 million people). Altogether there are more than 180

different national and ethnic groups in Russia.

Table 1: Population of Russia, million person

Year Total Men women

2004

144334 67072 77262

2005 143801 66696 77105

2006 143236 66302 76934

2007 142863 66052 76811

2008 142748 65976 76772

2009 142737 65961 76776

2010 142833 66015 76818

2011 142865 66050 76815

2012 143056 66176 76880

2013 143347 66353 76994

2014 143667 66547 77120

2015 146267 67771 78495

Source of information - The Demographic Yearbook of Russia, Federal State statistics service, 2016

Federal State Statistics Service (Rosstat) publishes the data for the level of education based on

population census 2010. So secondary education (high schools) in Russia has 18.2% of the

population, professional education (professional schools) – 37.8%, the higher education

(universities, higher schools) – 23.4%, and 0.5% of people have no education.

III.1.3. Economic crises

Russia is industrial-agrarian country, whose territory is divided into 11 economic regions (North,

Northwest, Central, Volga-Vyatka, Central Black Earth, Volga, North Caucasus, Urals, Western

Siberia, Eastern Siberia, and Far East).

Economy of Russian Federation has changed significantly after the collapse of the Soviet Union.

Over the past 25 years it has gone from being an isolated and centralized economy to a free market

economic system, which is integrated into the world economy. During the economic reforms of the

1990s most state-owned enterprises were privatized. Meanwhile, the protection of property rights in

Russia is still weak and not well developed, and the state intervenes easily in the private sector.

The first economic growth after the collapse of the Soviet Union in Russia took place only in 1997.

However, in the same year the Asian financial crisis began, which had a negative impact on the

Russian economy. This led to the fact that in 1998 the Government of Russia has failed to pay its

debts, which caused a sharp depreciation of the ruble and as a consequence a significant reduction

46

in the standard of living for citizens. Thus, the year 1998 went down in history as the year of the

crisis and a large outflow of capital from the country.

The economy started to recover in 1999. The main stimulus for economic growth was the low rate

of the ruble against the major world currencies, which had a positive impact on domestic production

and exports. From this point on began the period of sustained economic growth, what was possible

because of high oil prices.

Russian industry has been focused on the extraction of mineral resources from the very beginning

and the whole economic system was in direct dependence on the export of gas and oil. This

dependence has made Russia weak in the global economic crisis.

The crisis of 2008-2009 in Russia has affected the banking and financial sector, the oil sector, the

political situation. The main reasons were: 1) mortgage crisis in the US, 2) drop in oil prices, 3)

deterioration of the investment climate due to the military conflict with Georgia.

Starting with a collapse of three major Russian banks the crisis quickly spread to the real economy.

Capitalization of Russian companies decreased in September-November 2008 by three-quarters;

foreign exchange reserves fell by 25%. The financial crisis has reduced public confidence in banks

and led to an outflow of deposits. Companies began to file for bankruptcy. The negative process

began such as firing workers, sending them on forced administrative leave, reducing salaries. The

crisis had V-shaped character: rush fall down of revenues till the bottom of the crisis in December

2009 and two years of growing up afterwards.

After the world economic crisis 2008-2009, the government worked out the budget rule. Since

2013, this rule officially began to operate. The budget rule determines the maximum level of

expenditure budget, based on oil prices. The declared goal of the rule is to reduce Russian fiscal

policy’s dependence on cycles in the world economy. According to the official position of the

Russian Ministry of Finance, the budget rule reduces the dependence of the federal budget on the

state of world markets, and also provides a "safety cushion" in other words the insurance fund.

The 2014–15 financial crisis of the Russian economy showed the weakness of the budget rule, in

the first way, because of lack of time to do real savings. The new crisis is the result of two factors:

1) international economic sanctions imposed on Russia following Russia's annexation of Crimea

and presumed military presence in Ukraine; 2) the fall in the world price of oil in 2014. Russia's

GDP at the end of November 2014 went into minus the first time since 2009.

The economic crises of 1998 and 2008-2009 had direct impact on all branches of economy, but

particularly on tourism and restaurant industries, which are strongly depended on the purchasing

power of the population. In 1998 the inflation in the country arrived at more than 84,4% within 4

months. As consequences most of small and medium companies in the tourism and restaurant

industries went bankrupt, standard of living felt down. In the crises 10 years after (2008-2009) the

47

crises influenced deeper big industries and enterprises, while the small and middle companies, like

restaurants and touristic agencies adapted their supply to changes in the demand of the population:

in this period fast food and street food establishments grew up compared to fine-dining and

gastronomic restaurants. In outgoing tourism the agencies opened cheap destinations of low-cost

and all-included formats.

The recent crises of 2014-2015 had stronger impact on tourism and restaurant industries compared

to the crises of 2008-2009. The Ruble devaluation against the U.S. Dollar and Euro is one of the

factors influencing the industries. In 2014, the Russian ruble depreciated 70 percent to the U.S.

dollar. The weak ruble presents decrease of household spending, thus the prices for traveling and

outdoor eating became more expensive, pushing down key indicators of the industries. According to

the Russian Federal Statistics Service (Rosstat), consumer price inflation in 2014 rose by 11.4

percent. At the same time international political position of Russian government and caused by it

food embargo reduced interest of international investment and tourism of EU and other western

countries.

III. 1.3.1. Structure of Russian GDP and the place of tourism industry.

Nominal volume of Russia's GDP in 2016 amounted to 1433,36 billion of Euros (Rosstat), of which

gross value added - 86%, net taxes on products - 14%. GDP decrease was 0.2%. Structure of gross

value added by industries Russia can show the contribution of the most important industries

including catering and tourism and related branches (Figure 4).

Figure 4: The structure of gross value added in 2014.

Source: Rosstat, 2014

Agriculture, hunting and forestry - 4.0%

Fishing, fish farming - 0.2%

Mining and quarrying - 10.3%

Manufacturing - 15.6%

Production and distribution of electricity, gas and water - 3.4%

Construction - 6.5%

48

Wholesale and retail trade - 17.3%

Hotels and restaurants - 1.0%

Transport and communications - 8.7%

Financial activities - 5.3%

Transactions with real estate, renting and business activities - 12.2%

Public Administration - 6.5%

Education - 3.0%

Health care and social services - 4.1%

Other community, social and personal services - 1.8%

According to WTTC (World Travel and Tourism Council) the direct contribution of Travel and

Tourism in Russia to GDP was 14,78 billion of Euro (1.26% of total GDP) in 2016, and after the

fall by 5.2% in 2015 compared to 2014, the forecast shows first stability, so the prediction for 2017

stays by 0% of growth. Russian statics gives number of 3% and 6.5 % including the related

branches, such as hotels and restaurants (1.0%), transport and communications (8.7%), other

community, social and personal services (1.8%). After the comparing all dates, it is possible to

conclude, that there is no experience in Russian economy to count and to regard the tourism as an

independent branch of economy.

III. 1. 4. Tourism industry

Tourism in Russia is developing industry. The history of the development of tourism in Russia has

its own specialty. In Soviet times, it has been a problem for foreigners to get in the USSR ("Iron

Curtain"), particularly for tourism purposes, and at the same time domestic tourism was developed

as a strong tourist movement, focused mostly on the "wild" tourism (backpackers). In addition, the

Soviet resorts for the most part remained in the territory of modern Ukraine, Georgia, Abkhazia,

Armenia, Azerbaijan, Latvia, Estonia and Lithuania, and Russia lost this part of the tourism

infrastructure. For these reasons, and also because of the crisis 90s, there was almost no tourism at

the beginning of the XXI century. According to the preliminary data of Rosstat11, for 2015 Russia

took about 26,8 million international visitors. In 2015 the leadership was still by neighborhood

countries of former Soviet Union. Within other countries the biggest amount of tourists was from

Poland (1725000), Finland (1416000) and China (1122000).

Russian Federation Federal Law "About bases of tourist activity in the Russian Federation" defines

the tourism industry as "a complex of hotels and other accommodation, transport, catering, objects

of cognitive, business, health, sports and other purposes, organizations engaged in tourism and

travel agency activities, as well as organizations providing travel services and guide service.”

The tourist industry includes the following components(1996):

- organizers of tourism: touristic and travel agencies,

11 Federal State Statistic Service of Russian Federation, www.gks.ru

49

-companies providing accommodation services (hotels, motels, recreation, etc.),

- catering (restaurants, cafes, bars, etc.),

- transport companies (vehicle and aviation enterprises, railway, river and sea transport, etc.),

- tourist offices,

- production enterprises (production of tourist souvenirs, hotel furniture, camping equipment),

- trade enterprises (shops of tourist equipment and souvenirs),

- entertainment enterprises (theme parks, concert halls, clubs, halls of slot machines and other.),

- backpackers (hiking, climbing, cycling),

- tourism authorities (government agencies, public tourist organizations),

- educational, scientific, design agencies.

III.1.4.1. Strategy of Tourism Development in the Russian Federation

Tourism is regarded by the government as a source of financial income of the budget system of the

Russian Federation, as a way of increasing employment and quality of life, to maintain the health of

citizens, as a basis for the development of social and cultural environment and education, as well as

a powerful tool for education and moral formation of the platform of civil society.

In accordance with the list of orders of the President of the Russian Federation on the development

of inbound and domestic tourism in the Russian Federation dated July 30, 2013 Strategy of Tourism

Development in the Russian Federation was worked out for the period up to 2020 (hereinafter - the

Strategy).

The main objective of the development of tourism in the Russian Federation for the period up to

2020 is a comprehensive development of domestic and incoming tourism for economic, social and

cultural progress in all regions of the Russian Federation.

Effectuation of this objective requires the solution of following tasks:

- the formation of the information environment;

- comprehensive security in the area of tourism and sustainable development of the sphere of

tourist services;

- promotion of tourist products of the Russian Federation in the domestic and international

tourism markets;

- integration of tourist services provided by the Republic of Crimea and Sevastopol12, in tourism

management and professional tourist community of the Russian Federation.

12Sevastopol is a city located in the southwestern region of the Crimean Peninsula on the Black Sea. As a result of the 2014 Russian

annexation, the city is administered as a federal city of the Russian Federation, though Ukraine and most of the UN member

countries continue to regard Sevastopol as a city with special status within Ukraine.

50

The main problems of the tourism industry today include the high domestic prices and undeveloped

touristic infrastructure. In many ways, the high cost of travel in Russia is associated with the high

cost of transport services. The infrastructural problems cover all components of tourism: lack of

touristic offices in the cities, lack of accommodation and catering facilities.

Strategy of Tourism Development in the Russian Federation was developed to solve indicated

above problems. It provides the cluster approach, which is an advanced and effective mechanism to

focus both public authorities and private business initiatives for the creation of tourist infrastructure.

It started to create an information bank of tourist resources of the Russian Federation and to make it

available to potential tourists, to create tourist information centers in each region.

III.1.4.2. Crises 2014 of outgoing tourism in Russia

The year 2014 changed the point of view of tourism industry in Russia. The crisis of outgoing

tourism led to setting new priorities for business.

The collapse of the Russian tourist market began with a large company "Neva" (Saint-Petersburg).

Number of clients decreased during last several years, mostly because of rush development Internet

services, but the political and economical crises 2014 has put an end to the history of the company.

Up to 7000 clients of this travel company remained abroad without tickets and hotels. About 20000

customers count on the return of their funds for canceled trips.

"Wind Rose World" was the next company after "Neva", which failed to meet its obligations to

partners and customers. Then the collapse came. "Expo Tour", "Labyrinth", "Ideal Tour" and

many other large travel companies declared bankruptcy.

The crisis in the touristic industry was caused by ruble’s exchange rate, increasing of

independent traveling. Travel companies underestimated the economical risks.

Today since the fall in the industry in 2015 the tourism in Russia is stagnated and doesn’t show

the growth or decrease in numbers.

III.1.5. Restaurant industry

According to the Russian Federal Statistics Service (Rosstat), turnover of restaurants and cafes in

Russia fell by 5,5 percent to 396 billion of rubles (6.6 billion of Euros) in 2015. Thus, the year 2015

turned out to be a crisis for the Russian restaurant market.

In Russia, there are about 88,000 cafes, restaurants and other catering establishments, while the total

number of chain restaurants is about 11,000. According to research by RBC13 (Russian food market,

2015), about 29% of the Russian restaurant market present "fast food" segment, more than 25% of

13 RBC is a large Russian media group headquartered in Moscow. The company holds an informational agency

RosBusinessConsulting. RBC Research is a largest provider of business researches. www.research.rbc.ru

51

the market is taken by "street food", to restaurants are counted 19%, coffee houses and

confectionery - 14%, about 7% - takeaway food, 6% - Fast Casual restaurants. Chained restaurant

from lower-cost foodservice segments have successfully developed in recent years making

franchising one of the key expansion methods for foodservice chains in Russia. The total number of

franchises in Russian foodservice industry totaled 182 in 2013 and jumped to 203 in 2014 - up 11.5

percent. Currently there are 6,500 foodservice franchise outlets in Russia and its share is 8 percent

of total Russian restaurant market.

Russian food service industry faced several problems since the middle of 2014.

According to the new federal low smoking was prohibited in restaurants, cafes and bars on June 1,

2014. This had a negative impact on Russia’s growing coffee culture. Shortly afterwards, in August

2014, Russia banned a long list of food and agricultural products from Australia, Canada, Norway,

the United States and the European Union in response to the application of economic sanctions. The

immediate trade restrictions created a $9.5 billion gap in Russia’s food market. The foodservice

industry had to find new suppliers from non-affected countries. Creation of new channels led in

difficulties and growth of the final price to consumers. Russian restaurants depended on imported

food products ingredients from affected countries ranging from 15 percent to 80 percent. Moreover

the purchasing power of the people decreased because of weak ruble and low oil-prices, what led to

a decrease of spending for leisure like tourism, restaurants, and entertainments. Market analysts

showed that from 10 to 20 percent of all restaurants were closed in the first six months of 2015 due

to high food prices, to weak Russian ruble and to more careful spending by Russian consumers.

III.1.6. Mobile applications in Russia

The Russian mobile phones market experienced volume growth in recent years and had total

revenues of 5,1 million of Euro in 2015 (see Table 2).

The performance of the Russian market has been impacted by the country’s economic crisis.

Growth is expected to return in 2017, which should drive growth in the mobile market.

Table 2: Russia mobile phones market value, 2011-2015

Year RUB million EUR million % growth

2011 342,468 5,044.4 -

2012 374,026 5,509.2 9,2

2013 397,107 5,849.2 6,2

2014 358,989.2 5,287.2 9,6

2015 346,505.6 5,105.1 3,5

CAGR:2011-2015 0,3

Source: Marketline, 2016

While the mobile market in Russia is greatly inferior to the market size of the United States, Asia

and Europe, many experts share the view that it has significant growth potential. According to a

52

study J'son & Partners Consulting in 2015 the Russian market for mobile applications was

estimated at $ 440 million.

And analysts expect it to continue to grow. Research shows that more than 50% of users of tablets

and smartphones at least once a day to go online and start video applications every day and play

mobile games. It is also reported that 40% of mobile Internet users would rather lose the TV than a

smartphone.

Table 3: Russia mobile phones market segmentation: million handsets, 2015

Category 2015 %

Smartphone 29,051.8 87,9

Feature phone 4,017 12,1

Total 33,068.8 100

Source: Marketline, 2016

The increase will ensure factors such as the growing number of mobile devices, the rapid

development of 4G networks and a general development of consumer’s habits in the direction of

mobility (for example, mobile shopping, mobile banking and mobile payment systems, mobile

social networking and mobile instant messengers, geo-location services etc.).

Table 4: Russia mobile phones market value forecast, 2015-2020

Year RUB million EUR million % growth

2015 346,585.6 5,105.1 (3,5)

2016 344,552.5 5,075.1 (0,6)

2017 377,681.9 5,563.1 9,6

2018 393,144.1 5,790.8 4,1

2019 382,461.3 5,633.5 (2,7)

2020 688,622.8 5,724.2 1,6

CAGR:2015-2020 2,8

Source: Marketline, 2016

The company WapStart14 studied what applications use Russian tourists and travelers on their

mobile devices. In total 896 people participated in the study. 74, 7% of users use applications on

vacation and while traveling. The applications are used more often by women (56.4%) than by men

(43.6%). The most active age group of users is people aged 25 to 35 years (32.6%). The most active

social group is representatives of working professions (30.9%). In the categories of applications the

14 WapStart - owner of the largest mobile advertising network in Russia “Plus1 WapStart” and the catalog of mobile

websites and applications “Top WapStart”.

53

leading position take maps applications (59,5%). On the second place are games (58,9%). The most

useless applications during one traveling are business applications. They are used by only 5.7% of

respondents. This study shows the mobile application activity of Russian people during the

vacation. In our research we will investigate the behavior of Russian users in every-day life.

III.1.7. Moscow

Moscow is the capital of the Russian Federation with the territory of 1081 km². It has a population

of 12, 3 million of residents (official data of 2016), although a great number of both permanent and

temporary illegal migrants.

Already the most populous city in Europe, Moscow continues to grow. This is largely due to its

20% per annum economic growth, which attracts workers from other parts of Russia and

neighborhood countries of Ex-Soviet Union. As well as being a thriving economic center (boasting

more billionaires than any other city in the world), Moscow is also a center of the culture and

tourism with 17 million touristic arrivals (2015) announced by the department of national policies,

regional relations and tourism.

The main attraction of Moscow still remains the ensemble of the Moscow Kremlin and Red

Square — a unique complex of architectural monuments. The Moscow Kremlin is included into the

list of UNESCO Heritage Sites (1990). There is a number of excursions held in the Kremlin which

familiarize tourists with the architectural ensemble as well as the treasures of the Armory Chamber,

old Kremlin cathedrals, historic necropolis, collections of ancient icons and so forth.

In total, there are 450 museums in the city. Fine art lovers will be attracted by the State Pushkin

Fine Arts Museum, the State Tretyakov Gallery, Tretyakov Gallery at Krymsky Val and etc. Those

who are interested in natural sciences, take an opportunity to visit the State Darwin Museum, the

State Timiryazev Biological Museum and Orlov Paleontological Museum. Each museum offers

excursion programs for both for adults and children.

In addition to indoor museums, Moscow possesses unique architectural, artistic and landscape

cultural and educational facilities in the open air — Tsarytsyno and Kolomenskoe. Tsaritsyno is

former Royal estate built 1775; today it is the park and museum with the Grand Palace, the number

of pavilions and bridges around the main structure. Kolomenskoye is also the former Royal

residence. It is situated on the Southern part of Moscow’s shore on the Moskva River. Originally, it

was first mentioned in 1339 and became the favorite place of Russian Tsars. In 1539 the Ascension

church was built on the grounds of the Kolomeskoye village to mark the birth of one of Russia’s

most famous Tsar – Ivan the Terrible. It was the first stone church in Russia and was covered by

a tented roof.

54

Every day, 43 theatres open their doors to theatre-goers in Moscow. The best-known are the

Bolshoi Theatre, the Lenkom, the Chekhov Artistic Theatre, the Moscow Operetta Theatre and

many others.

According to Moscow government statistics, on the 1 January of 2015, there were 11,087 registered

foodservice establishments in the nation's capital, of which 7,651 were publicly accessible, and

3,436 were canteens at institutions and organizations, 2700 are regarded as restaurants (according to

Russian certification service GOST15 31985-2013 the restaurant is defined as “business that

provides food and leisure or non-leisure services to the consumer, with a wide range of complex

menu, including specialties; alcoholic, soft, hot and other kinds of beverages, confectionery and

bakery products, and other goods, including tobacco products”) . By the end of 2014 the total

Moscow foodservice decreased by 8 percent. One of the main reasons for this phenomenon is a

weak consumer purchasing power of the population. With a falling economy and depreciation ruble,

consumers ate out less to economize their spending. Moscow restaurateurs noticed a drop of

average check by 20-25 percent. Mid-range restaurants with average check up to 2,000 rubles

(27EUR) are the ones having the biggest drop in clientele while the lower cost (fast food) style

restaurants are reporting good sales.

III.1.8. Conclusion

With three serious economic crises (1998, 2008-2009, 2014-2015) since the collapse of Soviet

Union tourism and restaurant industries have been developed wide range of suggestions for all kind

of clients: from expensive exclusive services for oligarchs till cheap formats like all-included

packages, fast and street food. The most sensitive to crises clients group was always middle class,

so the number of casual dining restaurants decreased or changed after each crisis.

In the tourism industry the recent crisis was caused by ruble’s exchange rate, and by increasing

of independent traveling due to digitalization of the life.

Food embargo and weak consumer purchasing power of the population led to closure 10 to 20

percent of all restaurants in Russia in 2015. New market conditions pushed the restaurateurs to

search for local product suppliers and to change their price policy. New trend started to be the

Russian cuisine with old recipes for fine-dining restaurants, and simple well-known local specialties

for fast-food and street café and kiosk. Digitalization had less impact on restaurant industry

compared to tourism. But mobile applications could also play important role for the modern and

15 GOST refers to a set of technical standards maintained by the Euro-Asian Council for Standardization, Metrology and

Certification (EASC), a regional standards organization operating under the auspices of the Commonwealth of Independent States

(CIS).

55

future state of restaurant industry, if the restaurants are likely to adopt the new payment systems,

simplify services and build loyalty programs.

III. 2. France

III.2.1. General information about the country

France (The French Republic) is the state in the western part of the European continent. With the

area of 632 834 km2 (551 695 km2 of which are covered by Metropolitan France) France is the

largest state in EU. It shares its borders with Belgium and Luxembourg in the north, Germany,

Switzerland and Italy in the east, and with Spain in the south. The metropolitan territory has over 5

500 km of coastline stretching from the North Sea and along the Channel to the Atlantic Ocean in

the west, and along the Mediterranean in the south.

Picture 2: Map of the Metropolitan France

Source: geocurrent.info

For administrative purposes France is divided into 17 regions (régions) and Corsica. 12 regions and

Corsica are situated on mainland France (Metropolitan France) whereas the remaining five

comprise the oversea regions of Guadeloupe (Caribbean), French Guiana (South America),

Martinique (Caribbean), Mayotte, and Réunion (Indian Ocean).

France is further subdivided into districts (arrondissements) and cantons. The cantons serve an

electoral function at department levels as each and every canton elects one representative to the

Conseil Général. At the lowest administrative level, France comprises nearly communes. The

communes are governed by the mayor (maire, indirectly elected) and the municipal council (conseil

municipal, directly elected for a six-years term).

56

In addition to the five oversea regions, four additional oversea territories have the status of oversea

communities (collectivité d'outre-mer). These are French Polynesia, Nouvelle Caledonia (Pacific

Ocean), Saint-Pierre and Miquelon (Atlantic Ocean) and Wallis and Futuna (Pacific Ocean).

According to the constitution France is a republic. At this moment the Fifth Republic functions.

The Fifth Republic was established in 1958, and was largely the work of General de Gaulle - its

first president, and Michel Debré his prime minister. It has been amended 17 times. Though the

French constitution is parliamentary, it gave relatively extensive powers to the executive (President

and Ministers) compared to other western democracies.

Executive branch: The head of the state and head of the executive is the President, elected by

universal suffrage. Originally, a president of the Fifth Republic was elected for a 7-year term (le

septennat), renewable any number of times. Since 2002 the President has been elected for a 5-year

term (le quinquennat). Since the passing of the 2008 Constitutional reform, the maximum number

of terms a president can serve has been limited to two. The President appoints a prime minister,

who forms a government.

Legislative branch: The French parliament is made up of two chambers. The lower and principal

chamber of parliament is the Assemblée nationale, or national assembly; the second chamber is the

Sénat or Senate. Members of Parliament, called Députés, are elected by universal suffrage, in

general elections (élections législatives) that take place every five years. Senators are elected by

"grand electors", who are mostly other local elected representatives.

Judiciary branch: While the Minister of Justice, le Garde des Sceaux, has powers over the running

of the justice system and public prosecutors, the judiciary is strongly independent of the executive

and legislative branches. The official handbook of French civil law is the Code Civil. The

Constitutional Council, le Conseil constitutionnel, exists to determine the constitutionality of new

legislation or decrees. It has powers to strike down a bill before it passes into law, if it is deemed

unconstitutional, or to demand the withdrawal of decrees even after promulgation. The Council is

made up of nine members, appointed (three each) by the President of the Republic, the leader of the

National Assembly, and the leader of the Senate, plus all surviving former heads of state.

III.2.2. Demography

According to the National Institute of Statistics and Economic Studies (INSEE)16, with 714 683 000

inhabitants in 2016 (Table 5) France occupies the 20th place in the world in terms of population.

The urbanization of France reaches 85% and it increases by 1% per year. The three largest French

communes - Paris, Marseille and Lyon are divided into 45 municipal or urban districts: Paris has

20, Marseille has 16 and Lyon has 9 districts.

16 Institut national de la statistique et des études éconoliques. www.insee.fr

57

Table 5: Population of Metropolitan France, million person

Men women Total

2004 407 444 389 470 796 914

2005 409 516 390 707 800 223

2006 418 923 399 007 817 930

2007 411 208 392 561 803 769

2008 413 940 396 331 810 271

2009 412 089 392 723 804 812

2010 413 467 395 630 809 097

2011 402 592 386 436 789 028

2012 398 387 379 276 777 663

2013 387 686 369 946 757 632

2014 385 188 369 526 754 714

2015 373 941 356 886 730 827

2016 366 239 348 444 714 683

Source of information - insee.fr (Institut national de la statistique et des études économiques), 2016

The country's average life expectancy is 84, 82 years for woman and 78,45 for man (data for 2013).

III.2.3. Economy of France

The French economy is playing a leading role in the world economy. In terms of GDP, France

consistently ranked 5th in the world.

France has come over the global economic crisis 2008-2009 better than most other major

economies of the EU due to the relative stability of domestic consumer spending, a social politic,

and less dependency on falling demand for exports than in other countries.

A significant part of GDP amounts industrial production - 20%, it provides 24% of employment,

40% of investments and 80% of exports. France has significant reserves of minerals. This creates a

base for mining and heavy industry. Main industries: machine building (2.6% of world production),

chemical (fourth place in world exports), aerospace (France plays a leading role in the European

Space Agency), automotive (third in the world for the production of cars), food (by volume of

exports the second highest in the world after the United States), electronic, computer, shipbuilding,

electrical.

France is the largest agricultural producers in Western Europe. The share of agriculture accounted

for 1, 9 % of GDP (data 2013) it gave 25% of the production in the EU. France takes 1st place in

Western Europe by the volume of produced products and the 3rd place in the world after the United

States and Canada. It is the largest European producer of wheat, butter, beef, cheese (over 400

varieties). More than 50% of the products give livestock (cattle). Traditionally, there are a high

58

proportion of wine exports. French farmers are the main opponents of genetically modified products

in Europe, so French products traditionally highly valued because of the quality.

New technologies hold an important place in the developing sectors and are mostly supported by

large enterprises. France is also renowned for its luxury and tourism sector. It is the leading tourist

destination in the world.

III. 2.3.1. Structural Crisis of French economy

Despite a temporary improvement in 2010, France has not completely recovered from the global

financial crisis 2008. Despite the short-term measures, which were implemented by government,

France started to experience a structural crisis. This resulted in significant pessimism among the

population.

The business survey of INSEE («La reprise différée», Note de Conjoncture, 2014) demonstrated

factors that might explain the economic situation. First of all, the GDP was stagnating in real terms

together with steadily expanding of population this lead to decrease of GDP per capita. This

resulted in a declining income, but higher taxes to try to reduce various deficits, whether the budget

deficit, the social security deficit, or the foreign trade deficit. Furthermore, the variations of public

expenditure, especially social spending, are determined by both the increase in unemployment and

the changing household income. These various factors lead to rising public debt (as a proportion of

GDP) and budget deficit. In addition, changes in domestic consumption act as an obstacle on

growth but also on investment. The different phenomena work in conjunction to form the features

of an economy in a long-term crisis.

The issue of unemployment statistics is a central issue in the structural crises. In France, it is the

extent of unemployment that feeds a polemic. Unemployment rate dropped to 9.9 percent in the

June quarter of 2016 from 10.2 percent in the first quarter. It was the lowest jobless rate since the

September quarter 2012. Meanwhile, in metropolitan France only, the unemployment rate decreased

to 9.6 percent, as the number of unemployed declined by 74,000 to 2.8 million people unemployed.

It decreased across all age groups, particularly among youths.

It can therefore be seen that the crisis of the French economy was not just a short-term crisis but a

structural one, or, more precisely, a crisis that stems from the contradiction between its structures

(including demographics) and the framework imposed by the Euro Zone.

If the ingoing tourism is in France stable high, the impact of crises on the restaurant industry is the

consequence of decreasing purchasing power because of instability in the job market and

unemployment. That is good to see in the sensitivity of French users of mobile applications to

loyalty programs with discounts, collecting bonuses etc.

59

III. 2.3.2. Structure of GDP and place of tourism

The Gross Domestic Product (GDP) in France was worth EUR 2308 billion in 2016, reported by the

World Bank Group. The GDP value of France represents 4.56 percent of the world economy.

The direct contribution of Travel & Tourism to GDP was EUR80.4bn (3.7% of total GDP) in 2015,

and is forecast to rise by 2.9% in 2016, and to rise by 2.7%, from 2016-2026, to EUR107.6bn (4.2%

of total GDP) in 2026.

Table 6: France: Selected Economic Indicators 2013–16

2013 2014 2015 2016

(Proj.) (Proj.)

Real economy (change in percent) Real GDP 0.7 0.2 1.2 1.5

Nominal GDP (billions of euros) 2117 2132 2174 2224 Gross national savings (percent of GDP) 20.9 21.2 21.3 21.1

Gross domestic investment (percent of GDP) 22.3 22.2 21.7 21.6 Public finance (percent of GDP)

General government balance -4.1 -4.0 -3.8 -3.4 General government gross debt 92.3 95.6 97.3 98.2

Labor market (change in percent) Employment -0.2 0.2 0.5 0.7

Unemployment rate (in percent) 10.3 10.3 10.2 9.9 Balance of payments (in percent of GDP)

Exports of goods 20.7 20.6 23.8 24.5 Imports of goods -22.7 -22.2 -25.0 -25.9

Trade balance -2.0 -1.7 -1.2 -1.3

Source: Monetary Fund, 2015

The French economy is diversified across all sectors. The government has partially or fully

privatized many large companies, including Air France, France Telecom, Renault, and Thales.

However, the government maintains a strong presence in some sectors, particularly power, public

transport, and defense industries.

With at least 82 million foreign tourists per year, France is the most visited country in the world and

maintains the third largest income in the world from tourism. France's leaders remain committed to

a capitalism in which they maintain social equity by means of laws, tax policies, and social

spending that mitigate economic inequality. Despite stagnant growth and fiscal challenges, France's

borrowing costs have declined in recent years because investors remain attracted to the liquidity of

France’s bonds.

III.2.4. Tourism in France

Tourism is a fundamental sector in the French economy taking into account its contribution to the national

economy as well as the relative national employment rate.

60

The history of tourism in France began around 1760 in the south of France in Hyères, a winter

resort renowned by English people through its relatively mild Mediterranean climate. And the

conception of "gastronomic tourism" was born under Louis XIV (Voyage de Chapelle et de

Bachaumont, 1663).

But the tourism as we know it truly started in the 18th century, the term originally was taken from

the English word "tourist" or "round trip". In French, the term "tourist" (1803) was used to

designate "travelers who travel to foreign countries by curiosity and idleness, which are a kind of

tour of the usually visited by their fellow country" and "says especially English travelers in France,

Switzerland and Italy "(Littre). In 1838, the publication of Memoirs of a Tourist Stendhal

popularized this word (Adenis: Steps of a tourist in France). After 1815, travel was booming.

Finally, in 1841, appears the word "tourism", the same year that Thomas Cook opened in England

the first travel agency. In 1875 was opened "Comité des Promenades" in Gerardmer in the Vosges

and it was the first tourist office in France. Thereafter, many French tourist organizations appeared.

New transport vehicles made possible different forms of travel: the therapeutic tourism, the

discovery of the mountain, sea bathing, sports tourism. 1900 was published the first Michelin guide,

an innovative promotional tool: a guide to restaurants and useful addresses for motorists. The 20th

century confirmed the tourism trend, which went back to the law on paid holidays in 1936 and

which lasted in the development of mass tourism during the postwar boom.

With 84, 5 million visitors France kept its position as number one worldwide tourism destination in

2015 ahead of Spain and the United States, with a market share of 16 % in Europe. The total

spending of French and foreign tourists represented € 42,7 billion, representing 7,4% of France's

GDP, which makes the country rank third in the world in terms of revenue from tourism (Data of

Ministere de l’Economie, de l’Industire et du Numerique, 2015).

In 2009, "Atout France", Tourism Development Agency of France, was created under the

framework of the law of development and modernization of tourist services. This agency is a result

of the merger of ODIT France, tourism engineering agency established as a public interest group,

and Maison de la France, promotion agency of France abroad. Since then, the Agency contributes to

the implementation of public policies in favor of tourism, including the promotion of tourism in

France.

Tourism is one of the first surpluses in the balance of payments. It plays a major role in the national

economy. Being the first destination in the world cannot protect from competition or provide a

tourism policy. In its Communication to the Council of Ministers of 11 July 2012, the Minister for

Tourism presented the main directions of tourism policy, including the creation of a genuine

industry of tourism, capable of uniting all actors, as well as improving the quality of their offering.

61

The French touristic industry proposes wide choice of services: cultural heritage tourism, religious

tourism, health and wellness tourism, coastal tourism, adventure tourism, business and

congress/convention travel (MICE), sailing / sea cruising, wine and food tourism.

France has 37 sites inscribed in UNESCO's World Heritage List. It features cities of high cultural

interest (Paris,Toulouse, Strasbourg, Bordeaux, Lyon, and others), beaches and seaside resorts, ski

resorts, and rural regions (green tourism). Small and picturesque French villages of quality heritage

(such as Collonges-la-Rouge or Locronan) are promoted through the association Les Plus Beaux

Villages de France (litt. "The Most Beautiful Villages of France"). The "Remarkable Gardens" label

is a list of the over two hundred gardens classified by the French Ministry of Culture. This label is

intended to protect and promote remarkable gardens and parks.

III.2.5. Restaurants France

The restaurant market in France emerged in 2015 from the red zone after three years of recession

and a cumulative loss of 350 million customers and reached value of 70 billion of Euros.

Affected by the decline linked to the terrorist attacks, the French restaurant showed the loss in

turnover between 3% and 4.5%. Paris and the Côte d'Azur are the most affected areas. The GNI17

reported in quarterly economic report a decrease in turnover of restaurateurs of around 4.5% in

2016. Many professionals agree that the activity of the sector could tend towards stabilization at the

beginning of 2017.

The average meal expense decreased in 2014 to $10.76 (8.82 Euros), while the number of meals

served continued to increase. This is explained by the shift of consumers to lower-cost meals and

snack alternatives, such as fast food outlets, coffee shops, food trucks and carry-out chains. It also

reflects changes in snacking habits and a preference for cheap, fast but healthy meals. This is

increasing pressure on traditional and independent restaurants and cafes that are losing price-

conscious consumers.

The food industry is the leading industry in France. As a result of ongoing economic difficulties,

consumers continued to reduce spending in 2014, including expenditures in the food service sector.

According to the food consultant Gira Conseil, the global market for food consumption outside

home decreased by 0.3 percent in 2014, the first decrease in ten years. In addition to the economic

crisis, consumer expectations are changing, and the food service market is being forced to adapt.

Many traditional restaurant outlets are lagging behind in respect of investment, introducing new

concepts, service, and the quality/price ratio. While traditional restaurants, fast food outlets, and

hotels/resorts experienced sales declines in 2014, alternative food outlets increased their sales by

17 GNI - Groupement National des Indépendants de l’Hôtellerie Restauration

62

two percent. All types of food service outlets increased their prices to compensate, even though this

could potentially push customer visits down even further. Innovation in the market is dynamic, with

some outlets introducing new concepts, others seeking to boost wine consumption, some

highlighting their culinary specialties, and efforts to provide customers with fast and high-quality

services. Counter sales continue on a growth path. There are growing numbers of ethnic and exotic

cuisines, development of sweet snacks, creative work around bread outlets, and the arrival of

foreign outlets and other ready-to-eat foods.

The French Institute for Economic Statistics (INSEE) estimates the percentage of consumers eating

outside the home will reach 20 percent by 2020. Overall sales in the hotel, restaurant and institution

(HRI) sector have grown steadily over the past five years.

The commercial food service sector consists of:

- “Traditional restaurants”. This includes individual owner restaurants, multi-restaurant

companies, and large corporations, which represent nearly 57 percent of the commercial

foodservice sector. A large number of restaurants in and around Paris, and in other major

French cities, are medium/high end restaurants serving a wide range of traditional food,

although an increasing number specialize in cuisine from Asia, Africa, India, and the United

States. In 2013 and 2014, the increase in unemployment and decrease in purchasing power

pushed French consumers made budget cuts.

- “Cafeterias, Cafes and Brasseries” are operated by individuals, companies, and large

corporations. After over thirty years of decline, cafes and bars diversified to attract new

consumers. In rural areas, cafes are often the only businesses remaining and become a multi-

service point. In urban areas, they developed new activities and pay more attention to

decoration, cleanliness, and innovations.

- “Fast food outlets”, including street vendors: this segment represents the most dynamic and

promising sector (54 percent of sales of the sector) with an estimated four percent growth.

The sector operated by companies and large corporations has been able to better resist the

economic slowdown by combining price. For long, the term “fast food” has not had a very

good connotation in France. It recalled fat and “junk” hamburger food. Conscious of the

consumer demand for healthy/dietetic/organic products and balanced meals, the fast food

chains have re-invented the composition of their menus.

- “Delivering catering”, including ethnic (e.g., sushi): A competitive sector operated by

companies and large corporations, this growing segment of commercial catering is

comprised generally of delivery meals (mainly to companies) and pizza delivery (mainly to

households).

63

- “Hotels and resorts with restaurants”. These establishments are operated by individual

owners, companies or large corporations and account for about six percent of the

commercial catering market with over 18,500 restaurants.

- “Leisure parks”. France has about 90 leisure parks, which are worldwide known (f.e.

Disneyland Paris, Parc Asterix and Futuroscope).

Traditional ways of foodservice consumption based on the local culture, along with the economy

may lie behind them. Tourism continues to have a significant impact on consumer foodservice in

France, especially in popular tourist destinations, such as Paris and the Côte d’Azur. Overall, tourist

expenditure has been steadily increasing.

III.2.6. Mobile applications in France

The French mobile phones market value has been growing at a steady, strong rate over the period

2011-2015. A shift towards more expensive smartphones over the past few years, and the

subsequent movement away from cheaper feature phones, has driven up the average selling price of

mobile phones, allowing for the market to continue growing in value despite significant overall

volume declines.

The French mobile phones market grew by 4.4 per cent in 2015 and reached a value of 5,728.9

millions of Euro, representing a compound annual growth rate (CAGR)18 of 7.5 % between 2011

and 2015.

Table 7: France mobile phones market value, 2011-2015

Year EUR million % growth

2011 4,287.5 -

2012 4,574.5 6,7

2013 5,001.4 9,3

2014 5,489.0 9,7

2015 5,728.9 4,4

CAGR:2010-2014 7,5

Source: Marketline, 2016

The market volume increased with a CAGR of 2,9% between 2011 and 2015, to reach a total of

22,8 million handsets in 2015.

Smartphone sales had the highest volume in the French mobile phones market in 2015, with total

sales of 19,6 million handsets, equivalent to 85,9% of the market's overall volume. In comparison,

sales of feature phones had a volume of 3,2 million units in 2015, equating to 14,1% of the market

18 Compound Annual Growth Rate (CAGR) - business and investing specific term for the geometric progression ratio

that provides a constant rate of return over the time period.

64

total. While the majority of the market is accounted for by smartphones in France, it is worth noting

that the segment’s share of the total volume remains lower than in other mature Western European

markets, such as Germany and the UK.

Table 8: France mobile phones market segmentation: million handsets, 2015

Category 2015 %

Smartphone 19,6 85,9

Feature phone 3,2 14,1

Total 22,8 100

Source: Marketline, 2016

The performance of the market is forecast to decline, with an anticipated compound annual rate of

change (CARC) of 1,5% for the five-year period 2015 - 2020, which is expected to drive the market

to a value of 6,164.5 million of Euro by the end of 2020.

Table 9: France mobile phones market value forecast, 2015-2020

Year EUR million % growth

2015 5,728.9 4,4

2016 5,845.4 (2)

2017 5,960.8 (2)

2018 6,070.5 (1,8)

2019 6,136.8 (1,1)

2020 6,164.5 (0,5)

CAGR:2015-2020 (1,5)

Source: Marketline, 2016

According to market research of the French Mobile Marketing Association 53.8% of the French

population are mobile device users. This massive user base provides a huge market opportunity for

software applications publishers. However, while mobile device use is still growing, the demand for

mobile applications is slowing down. In France, June 2014 data showed that the majority of mobile

users download no more than two applications each and only 7% downloaded eight or more apps.

There is also a gap between downloading and actually using an application. According to the web

survey by Médiamétrie, only a quarter out of 50 downloaded applications is actually used.

In the tourism industry social networking and communications are dominating in the smartphone

activities in Europe, there are differences with regards to smartphone usage and internet penetration.

eMarketer notes the internet penetration in France with 74%. Email was most popular with around

65

81%, French consumers aged between 15 and 60. Search was also very important, 72% of French

consumers see that as an important online activity (eMarketer, 2014).

III.2.7. Paris

Paris is the capital of France with the territory of 762 km² (le Grand Paris). Statistically we often

can see information about Paris by itself and about Greater Paris (le Grand Paris), also when we

speak about tourism in Paris the data can include the whole region Ile-de-France. In this way Paris

has population of 2, 25 million of people (official data for 2013), Greater Paris has 6,8 million of

people and 18, 8% of country’s population (or 11, 96 million) have residence in Ile-de-France. In

our case we will use statistics concerning tourism and restaurant industry mostly in Greater Paris,

and sometimes the whole region of Ile-de-France.

Paris is the most popular destination in the world with 22,2 million visitors in 2015 and 46,7 million

in Ile-de-France (Office du Tourisme et des Congrиs de Paris, 2015). Tourists tax revenues brought

to the city budget 65,7 millions of Euros. The main attractions for tourists are world know museums

and monuments. Visiting the museums and discovering the city are the most popular reason to go to

Paris among leisure tourists (Table 10).

Table 10: Reasons to visit Paris (leisure tourists), %

Activity Discovering

Paris

Visiting

museums and

monuments

Shopping Gastronomy Parks and

gardens

Events and

shows

French 37,1 25,8 16,9 3,4 6,9 14,8

Foreigners 65,9 59,5 14,2 13,6 11,2 5,9

Total 54,9 46,3 15,2 9,6 9,5 9,4

Source: L’Office du Tourisme et des Congrès de Paris, www.parisinfo.com, 2015

In total there are 140 museums in Paris, including 3 of the world’s most visited museums: the Louvre,

Pompidou Centre, and Orsay Museum. The region possesses of 4,000 historical monuments, 402

cinemas, 355 theatres, 5 opera houses, 55 foreign cultural institutes.

In addition to indoor museums and historical places, Paris is surrounded by unique architectural and

cultural facilities, parks, castles, and outdoor monuments.

Paris is location for many important events, accepting business travelers and heads of governments.

All together 1004 congresses, 91 exhibitions and 407 salons have taken place in the French capital

in 2015.

66

Table 11: Top 10 Greater Paris cultural venues – 2014-2015 figures

2014 2015

1. Cathédrale Notre-Dame de

Paris*

14 300 000 13 600 000

2. Basilique du Sacré-Coeur de

Montmartre*

11 000 000 10 000 000

3. Musée du Louvre 9 134 612 8 422 000

4. Tour Eiffel 7 097 302 6 917 000

5. Musée d’Orsay 3 480 609 3 439 832

6. Centre Pompidou 3 450 000 3 060 000

7. Cité des sciences et de

l’industrie de la Villette

2 675 383 2 013 046

8. Chapelle Notre-Dame de la

Médaille Miraculeuse

2 000 000 2 000 000

9. Muséum national d’histoire

naturelle - Jardin des Plantes

2 077 718 1 886 919

10. Arc de Triomphe 1 751 046 1 760 694

Source: L’Office du Tourisme et des Congrès de Paris, www.parisinfo.com, 2015

Regarding the spending, leisure tourism generate 80,2% of Travel and Tourism GDP. Average

daily expenditure per tourist is 137 Euros, 33 (or 24%) of it is spent for food.

Rungis International Market, the world’s leading fresh produce market “Saveurs Paris Île-de-

France” is a regional food brand created to highlight close to 600 products and 70 kinds of fruit and

vegetables: Brie from Meaux, beer from the Vexin, honey and saffron from the Gâtinais, asparagus

from Argenteuil, Belle de Fontenay potatoes, etc.

According to CCI of Ile-de-France over 4,852 restaurants are working in the region, 109 among

them are starred restaurants, including 40% of France's 3-star restaurants.

III.3. Conclusion for countries

The crisis of outbound tourism in Russia contributes to the development of domestic tourism which

should lead to the development of internal infrastructure, which in turn will lead to an increase in

international tourists. But the tourism infrastructure needs the stable investments from two sides,

government and business. According to official data of Rosstat, the share of tourism in the overall

structure of paid services to the population currently stands at about 2%. It is almost not possible to

find data about investment in the tourism industry in Russia in official documents. Such information

is not available also in the federal statistical observation of the activities of organizations that

provide tourism services. Analyzing the official data, it should be noted that the largest share of

investments directed to the hotel industry as the most capital-intensive touristic area. Investments in

tourism enterprises and institutions tend to rise. However, their absolute value on the scale of

67

investment in the economy of the country is very small. Investment support must be long-term

objective of the development of tourism in Russia. It is the basis for the creation, preservation and

reproduction of tourist resources, as well as for the development of tourism infrastructure and

tourism industry.

France stays the number one destination for international tourist also with well developed domestic

tourism. With its combination of rich history and attractions, ski resorts and coastlines, France

ranks high in cultural and natural resources. These are complemented by its emphasis on

environmental sustainability, strongly enforced environmental regulation and a sustainable

approach to developing the travel and tourism industry. France is well connected, with developed

infrastructure for air transport, ground transport and tourism services facilities. Further development

of the sector would require improving the business environment, where taxation is relatively high.

In addition, safety and security according to European Standard is emerging as a sensitive issue that

needs to be addressed.

Taking into account the population and tourists in Moscow and Paris, we can see the almost the

same size of market (12,3 millions of residents and 17 millions of visitors in Moscow, and 6,8

million of residents and 22 millions of visitors in Greater Paris), but the quality of the market is

different. Prevailing role of the tourists in Paris (76% contra local population) facilitates the

development of restaurant services. More over the French gastronomy as shown in Table 10 is a

part of touristic attraction in French capital. In Moscow not only the number of tourist is lower

(56% contra local population), but also the purchasing power of residents decreased significantly in

the last two years.

It is important to notice, that the number of restaurant establishments is difficult to estimate in

Russian capital, official data count 11000 of foodservice companies, but this includes all kind of

facilities, which can sell prepared food, also supermarkets and cooking shops. For comparison we

will take into account the data of Russian catalog 2GIS19, which published the number of 2700

working restaurants different types (full-service, fast food, cafes, and bars).

Smartphone penetration in compared counties has the difference of 8,4% in favor of France.

The key figures, interesting for our research are given in the Table 12:

19 2GIS – Russian catalog of companies, www.2gis.ru

68

Table 12: Comparison of two countries.

Indicator France Russia

Territory, km2 632 834 17 075 400

Population, millions of people, 2015 66,3 146,2

GDP, billions of Euros, 2016 2308 1433

GDP per capita, 2016 42,384 26,109

Contribution of tourism to GDP,

billions of Euros, 2015

80,4 (3,7) 14,78 (1,28)

Ingoing tourism, foreigners, millions

of people

84,5 26,8

Restaurants industry revenues,

billions of Euros, 2015

70 6,6

Capital Paris Moscow

Population in capital (in million of

people)

6,8 (le Grand Paris) 13,5

Number of ingoing tourists in capital

(in millions of people), 2015

22,2 17

Number of restaurant establishment in

capital

4852 (Ile-de-France) 2700

Number of smartphones, millions of

handsets, 2015

19,6 (85,9%) 29,05 (87,9%)

Source: author of the thesis based on data above.

III.4. Choice of mobile applications in compared countries

The choice of the mobile applications in restaurant industry by country is directly influenced by the

following parameters:

a) Availability of various services.

An application "pizza delivery" will only be used when the user needs to order a pizza. But

in the case an application covers all services associated with restaurants (reservations, maps,

description, menus, delivery orders etc), the consumer will use the same application each

time some restaurant service is needed, because of that we will choose the mobile

applications with various services.

b) Updates, speed of downloading, usability.

Any outdated data in a source of information leads to the users churn. Because of a high

competition between different applications, the user will quickly find a replacement, rather

than to use an inconvenient, unreliable and out of date application.

c) Loyalty programs, bonuses and discounts.

69

The mobile application with only features such as search and contacts might be less

interesting for users, than that, which has additional services making users loyal. One of the

attractive functions might be discounts and bonus-systems, suggested by mobile application.

d) Internet connection availability in order to use an application

There is a set of services, which depends on the availability of Internet, such as online

booking, maps. However, a large number of services can be used without an Internet

connection that is especially convenient for the application for the reference data such as

contact information or menu.

In the food and restaurant industry are some of the most common categories of the applications to

find:

- Guidebooks for travelers, covering all necessary services, from booking tickets to restaurant

reservations. Typically, such applications have a geolocation function, the possibility to

leave reviews, addresses and phone numbers of the restaurants, pictures.

- Various maps and guides for certain cities, where the main function is to find the right place,

followed by the information about nearby cultural public places and monuments, as well

as the public catering locations.

- Specialized restaurants guides.

- Applications of the individual restaurants and chain restaurants, usually with electronic

menu, table reservation services, take-away ordering or delivery.

- Cookbooks, recipe collections often from the chefs of famous restaurants or restaurant

critics.

- Magazines with articles about cuisine and restaurants.

For our research the most interesting is to analyze specialized restaurants guides with loyalty

programs for clients. Firstly, we will focus on two capitals Moscow and Paris. Secondly, we will

take into account the residents of both cities. And thirdly, we need to investigate the mobile

application with various services to fulfill the tasks of the research.

III.4.1. Review of catering mobile applications in Russia.

The Russian catering mobile applications market is saturated with its diversity in the two major

cities (Moscow and St. Petersburg) compared to the rest of the country. Usually other big cities are

a part of the country’s guide application, often with incomplete information and lack of desired

functionality, such as table reservation, geolocation and other important services for users.

Together with worldwide services the local mobile applications are developing in Russia. The most

used are allcafe, resto.ru and stoliki.

70

- AllCafe. An application that will help to choose a restaurant, cafe or bar with a view of eating

habits, financial opportunities, locations, and many other parameters. In addition to searching the

restaurant on the map, it gives an opportunity to find a catering establishment by name, see the

reviews of people who have visited the institution previously, and leave a comment. Application

operates in 20 cities of Russia.

- RESTO.RU is promoted as national search engine for gourmands. Resto group suggests for the

users several services: website, guide-book, discount card and booking, working in 19 Russian

cities. According to the company’s data in total 17 millions of users utilize different services, the

booking service has around 2000 orders per day. Its application is an interactive restaurant guide

with navigation and updated content on a daily basis. It has about 5000+ downloads.

- Stoliki. Service specialized for table reservation in restaurants and cafes. Technologically highly

reliable and eliminates user disinformation. Customers are offered to choose the place, and fill out

an application and enter the verification code, and the information about booking is delivered by

SMS. Currently, the service has more than 15,000 catering establishments in 154 cities of Russia.

III.4.2. Review of applications for table booking in France.

French restaurants and food application market is high developed and saturated with suggestions.

For the review the three are regarded, Lafourchette, Michelin and OpenTable.

- Lafourchette operates with 12 000 restaurants (including the prestigious Alain Ducasse, Paul

Bocuse, Yannick Alleno, Anne-Sophie Pic etc.) and has more than 2 million Internet users who

have already booked more 20 million times. In May 2014, Lafourchette Group joined the

TripAdvisor, to build the world's leading tourism on the Internet. According to the company’s

statistics 1,3 mil. of users downloaded the mobile application and 25% of them are using the

application for booking the restaurants.

- Michelin has suggested its mobile offering. Now the app is free, and thanks to a partnership with

Bookatable.co.uk, you can browse and reserve from inside the application. So far, around a fifth of

Michelin’s restaurant recommendations are bookable in-application.

- When it comes to statistics, OpenTable free application still leads the way. It lists more than

30,000 restaurants worldwide, including France. Users can filter restaurants by cuisine type,

location or price range, and menus are usually available to browse. Recent OpenTable innovations

include Hot Tables, a service that automatically alerts to coveted late cancellations.

71

III.4.3. Chosen mobile applications for the research

The choice of the catering mobile application is done not according to the rating of the mobile

application in stores, and even not according to the functionality. The basic reason why we have

chosen the below presented mobile application is the availability of data and readiness of the

management to collaborate for this research.

III.4.3.1 Resto.ru

General information

The mobile application Resto.ru belongs to Resto Group, which exists on the Russian market of

Internet services since 1995. Head-office is located in Moscow.

Main specialization of the Group is creation and management of online projects in the restaurant

business, beauty industry, and in the market of banking products.

Resto.ru is the project of the Group, which includes website, mobile application, call-center and

table booking system. Resto.ru is an interactive restaurant guide with convenient navigation and

daily updated content. The last version of the website is designed as restaurant social network

(Annex 1).

Easy access, varied and detailed information, as well as advanced online capabilities make it

popular both among regular users of the Internet, and at key players in the market restaurant.

The business model consists from three main components: call-center, website and table booking

service collaborated with many others catering searching engines like tripadvisor, yandex, 2GIS etc.

(Annex 2, 3, 4). ZON is special program which is constructed for online booking. For now except

restaurants this booking system is working in many other services, like beauty salons, haircuts,

resorts etc.

Call-center was the first project, its success allowed the management to develop the service of

searching and booking afterwards in more recent technologies. In 1998 the company bought the

telephone number by the famous restaurateur in Moscow, that phone number united several popular

restaurants firstly. The target audience at that time referred to the rich Russians (nouvaux riches).

After the quick increase of the clients’ number the company started to sign the contracts to join the

other restaurants. Almost at the same time the Internet took its important place in the people’s life,

in this way the website as a guide in restaurant life was developed. The latest project is connected

with the development of the mobile devices and also the growth of the Internet in general. Table

booking online became demanded as contactless function focused on the easiness of use: user could

avoid of calling the restaurant but at the same time the bottom of booking could motivate him/her to

book table without any efforts. The bottom of table booking is presented not only on the original

website of the company but also on partners-websites and applications.

72

Mobile application functionality

The mobile application Resto.ru appeared in 2013 with simple design and minimum functions as an

additional service for the clients of the company.

First of all the user can or have to choose manually the city (the mobile application have

information about 19 cities in Russia). According to the city the client will have access to the list of

the restaurants, which he/she can choose by cuisine, average bill, location, or others (Annex 5, 6).

The user can create personal account, or if he/she has already one on the website Resto.ru subscribe

with his/her previous profile name and password. The mobile application can be used also without

personalization. The function to register with the facebook or VK20 profile is not available.

Geolocation is presented in two ways, first as “around me” function, second with location of the

“next metro”, what is very popular in Moscow for mobile applications of any kind. The mobile

application allows to book table with bottom “book the table”. The user can create the list of

favorite restaurants with a function “add to favorites”.

It is possible also to leave or to read reviews of the restaurants. Unfortunately many other functions,

which are available on the website, are not designed in the mobile application.

Loyalty program

Resto.ru doesn’t provide discounts in the restaurants, even if it publishes the existing discounts and

bonus available on restaurants (according to the partnership package see Table 12 below).

Nevertheless, resto.ru provides bonus points for bookings, done though its system ZON. This

system is working in the way, that the managing team possesses the information about each client

spending in the booked restaurant, so for each 100 rubles of the bill the client receives 10 bonus

points. The collected bonus points the client can exchange for gifts, provided by Resto.ru (not by

restaurants). It can be free dinner or free drink in the partner-restaurant, invitation for the party, or

some valuable gifts (bottle of wine, even household appliances).

Another part of the loyalty program aims the involving of the clients into active exchange of their

restaurant experiences through Resto.ru. Each client can get the specific status according to the

number of the restaurants booking done in the last 90 days: new visitor (3 booking for last 90 days),

amateur (3-5 bookings), profi (more than 5 bookings). According to the status the reviews of the

visitors are shown.

For promotion of the mobile application usage, company also provides specific gifts for booking

done specifically with mobile application (Annex 7).

20 VK – Russian social network, www.vk.com

73

Suggestions for restaurants (business)

The restaurants are included in the guide in different ways. For free all restaurants are included with

names and address, sometimes phone numbers, online booking is not possible and in this way the

client cannot collect the points of loyalty. Advanced type of cooperation can be bought in different

volumes (Table 13).

Table 13: Conditions for cooperation with the restaurants

Mini Standard Start* for new restaurants

Description Ok Ok Ok

Pictures of the interior Ok Ok Ok

Pictures of the dishes - 5 pictures 5 pictures

Menu Ok Ok Ok

Panoramic view Ok Ok Ok

News Ok Ok Ok

Searching priority Low Medium High

Gastronomic dictionary 2 words 5 words 5 words

Banner in the news ticker - 100000 displays 200000 displays

Contextual advertising - 500 clicks 2000 clicks

Special publishing Ok Ok Ok

Picture of the chef - Ok Ok

Receipt of the chef - Ok Ok

Group “new restaurants” - - Ok

Invitations cards for clients - 12 cards 4 cards in the first month

Bottom “I want to read

news of the restaurant”

- Ok Ok

Rubric “Discounts and

bonuses”

- - Ok

Export of the news to

facebook and VK

- Ok Ok

Price XXX XXX XXX

Source: resto.ru, 2017

III.4.3.2. LaFourchette

General information

LaFourchette belongs to Tripadvisor Group since 2014. The platform is majority owned since 2009

by Otium Capital. According to this investment holding, the site generated directly in the order of €

250 million in revenue for its restaurant partners and employs 190 people mainly in Paris and

Barcelona. Early 2014, it surpassed the 700,000 bookings per month.

Head-office is located in Paris.

74

Based in Paris and founded in 2007, La Fourchette is present in Spain with the brand "eltenedor"

and "thefork" in England. In total, the range has more than 36,000 restaurant partners in 11

countries (Annex 8).

Mobile application functionality

The mobile application Lafourchette appeared in 2013 with similar design and functions as on its

website.

First of all, the user can use mobile application registered or not registered. Nonregistered use is

limited in the way, that a user cannot collect the loyalty points, and by booking a restaurant

establishment the nonregistered user still have to do pre registration.

Second, the mobile application responds well the function of geolocation, usually suggesting as first

choice the restaurants next to the user, or in automatically in the city. Anyway the user can choose

the city manually, to check restaurants, for example, in the other city which is included in the data

base of lafourchette. Also, it is possible to do searching for the restaurant by cuisine, average bill,

location, or others.

The user can create personal account, or if he/she has already one on the website lafourchette

subscribe with his/her previous profile name and password. The function to register with the

facebook is well available too.

Geolocation, accept the above mentioned case, is presented as “around me” function. The mobile

application allows to book table with bottom “book the table”. The user can create the list of

favorite restaurants with a function “add to favorites”, and also see the history of his/her searching,

and of course history bookings (Annex 9-11).

It is possible also to leave or to read reviews of the restaurants. After the company was bought by

Tripadvisor, the user of the application can see the review and rating of the restaurant on

tripadvisor.

Loyalty program

There are two types of programs to motivate users to user regularly Lafourchette.

Firstly, restaurants can provide special offer.The user can get discount till 50% for the meal, if the

user books the restaurant by Lafourchette (Annex 12-14).

And secondly, collecting loyalty points, so called Yums. Every reservation made on the mobile

application (or website) bring the user 100 points (yums), than in different countries there are

different ways to spend the points, in France, each 1000 Yums =10 € discount.

Suggestions for restaurants (business)

The company proposes not only advertizing tool for the owner and managers of the restaurants

establishments, but also simple software for managing reservations. The usage of this software is

possible from any devise including tablet or smartphone. Software has functions of CRM and

75

booking. So on the one hand the team of the restaurant can see the occupancy rate of the

establishment, as well lead the booking history of the particular client. At the same time all

marketing tools are also available for the management. Provided by Lafourchette services are

presented in the Table 14.

Table 14: Provided services for the restaurants

FREE PRO PRO+

YOUR RESTAURANT PUBLISHED ON THEFORK + + +

YOUR RESERVATION BUTTON ON TRIPADVISOR + + +

YOUR RESERVATION BUTTON ON YOUR OWN WEBSITE + + +

YOUR OWN TURNKEY WEBSITE. + + +

INTELLIGENT RESERVATION MANAGEMENT SOFTWARE – THEFORK MANAGER STANDART STANDART ADVANCED

ELECTRONIC RESERVATION DIARY + + +

CENTRALIZE ALL YOUR BOOKINGS (ONLINE, PHONE, E-MAIL, WALK-

INS…)

+ + +

TRACK THE PROGRESS OF YOUR SERVICES IN REAL TIME + + +

MANAGE YOUR AVAILABILITY ONLINE + + +

MANAGE YOUR THEFORK WEB PAGE (MENUS, SPECIAL OFFERS, ETC.) + + +

MULTI-USER CONFIGURATION - + +

MONITOR AND CONTROL THE ACTIONS OF ALL USERS - + +

MANAGE RESERVATIONS AND SERVICES USING A PERSONALIZED

DINING ROOM PLAN

- + +

CUSTOMIZE THE TOOL TO THE REQUIREMENTS OF YOUR RESTAURANT - + +

STATISTICS AND PROGRESS REPORTS - + +

SEND SMS AND/ OR PERSONALIZED E-MAILS TO CONFIRM YOUR

RESERVATIONS

- + +

MANAGEMENT AND CUSTOMER LOYALTY TOOL – THEFORK MANAGER

AUTOMATICALLY SEND SATISFACTION SURVEYS TO YOUR

CUSTOMERS

+ + +

RESPOND TO CUSTOMER REVIEWS + + +

CUSTOMIZE YOUR CUSTOMER DATABASE AND THE INFORMATION

COLLECTED

- + +

EXPORT, DUPLICATE, AND IMPORT DATABASES - - +

SEGMENT YOUR DATABASE - - +

EASILY CREATE AND SEND E-MAIL CAMPAIGNS - - +

EASILY CREATE AND SEND SMS CAMPAIGNS - - +

Source: https://www.theforkmanager.com/fr/tarifs/

The restaurant can choose among three packages: free, pro and pro+ and use as well mobile

application, Lafourchette Manager (Annex 15).

Conclusion

Both companies (Resto and Lafourchette) represent the modern type of the restaurants’ catalog

using the new technologies and provide their services in the similar business model: free services

for users financed in two ways: restaurants subscriptions and electronic advertising on their

platforms. Both companies cover the restaurant establishments in capitals, as well both propose

their services in others cities, and in case of Lafourchette even in several countries.

76

The developing of the mobile devises such as smartphones and tablets necessitated both companies

to create the mobile applications together with the smartphone’s versions of webpage to reach users,

who doesn’t want to download the mobile application but still uses smartphone for bookings.

The design and functionality of Russian mobile application is developed in the very simple way.

Compared to the webpage and its smartphone’s version it lost network approach and availability to

reviews. The main idea of mobile application is to provide the possibility to find and book the table

in the restaurant establishment. For that the basic functions such as geolocation are working in the

manual and automatically ways.

Lafourchette kept the design and functionally of the webpage for the mobile application. And the

geolocation function became the most important in the mobile application. It included as well

possibility of the registration with facebook account to facilitate the access for users.

Both companies have well-developed CRM departments. The work inside of such departments is

divided by types of clients: users and restaurants. There are such ways of communications with

users: news-letters for subscribers, social networks, direct calling by phone (very well works in

Russia). The restaurants are in addition visited at least twice a year for renewing information about

menu and pictures of the establishment on the website and mobile application. The websites are

also designed as some kind of intranet for users and restaurateurs with personal profiles. The

difference between them is in the functionality. While the user has a set of functions personalized

for further search and booking, loyalty program statement and history of bookings, the restaurateur

can use the system for business goals, like analyze advertising effects, see the occupation of the

tables and also save the contacts of the clients, who has booked the table with the website or mobile

application.

The differences of the basic design of the mobile applications might result out of decision made by

the companies’ management regarding the real need and the way of use by different users.

The users of smartphones have habit to access immediately to any kind of information and the

restaurateurs supposed to adapt to this fact. In our research we will focus on the usage of the mobile

applications in the restaurant industry, where the implementation of the mobile services depends on

many factors. Some of these factors can be based on the economic situation in the country, some

factors depend on industry development, and some are connected with the cultural difference. And

what it is equally important, the new technology but itself impacts the process of adoption and

implementation. In the next chapter we will try to find the theoretical background of all factors,

which can have impact on the usage of mobile application in the restaurant industry.

77

78

Chapter II: Literature review

79

Introduction

The goal of this chapter is to discuss the theoretical basement for adoption of new technologies in

restaurant industry regarding to national, cultural differences.

The chapter is divided in three sections, each section concentrates on the theories and researches,

which are used for the developing of the research model:

- Use of technology,

- Relationship marketing,

- Cultural theories.

For understanding and explanation the usage of mobile application in the restaurant business it is

firstly important to examine existing theories and models of the technology adoption and usage.

Mobile application is made as a tool, which can replace the usual understanding of relationships

between restaurant’s establishment and customer, or final user of the applications where the

application’s holder plays the role of mediator. From that point of view, the theory of relationship

marketing is also included in the theoretical part of this thesis enunciating how the relationships

change between company and customers while using the technology.

The comparison of two countries suggests that the cultural differences might have influences on the

usage of the mobile application or frequency of eating outside. The cultural theories comprise

philosophical concepts of cultures which are different in both countries, as well as universal

cultures dimensions developed by Hofstede.

Those theoretical backgrounds answer the purpose to develop the hypotheses and the problem of

our research.

I. The theories of technology adoption and usage.

The mobile applications relate to information technology (IT) with several theories form the

conception of technology diffusion, acceptance, adoption, usage and after usage adoption/rejection.

Decision making process to use a new technology or innovation is complex and includes several

elements, which are to find in the different models of the researches during the last decades. Many

researches concern themselves with the phenomena of the technology adoption. To understand the

important variables for use of a mobile application as a technology, following models are regarded

in this thesis:

- Diffusion of innovation by E. Rogers (1962).

- Theory of reasoned action (TRA) by M. Fishbein and A. Ajzen (1975, 1980).

- Technology acceptance model by F. D. Davis (1986).

80

- Unified Theory of Acceptance and Use of Technology (UTAUT), V. Venkatesh et al. (2003)

and extended Unified Theory of Acceptance and Use of Technology (UTAUT2), V. Venkatesh

(2012).

- Mobile application Usability: Conceptualization and instrument development, H. Hoehle and V.

Venkatesh (2015)

This section presents the models chronically.

I.1. Diffusion of innovation, Everett Rogers.

Sociologist Everett Rogers in 1962 wrote a book in which he tried to explain the concept of

"diffusion of innovations", the spread of new ideas and technologies. The theory shows ways of

appearance of the product on the market that will be adopted and will spread in society. The key

concepts are:

Innovation includes ideas, processes and technology that the people find as new ones. It may be

something completely new, a fresh approach to an existing idea or the entrance of the idea the new

market. In our case it is the mobile application used in the restaurant industry.

Adopters are those who accept innovation. Rogers distinguishes five types of adopters: innovators,

early adopters, early majority, late majority and laggards. We have two categories of adopters:

professionals or restaurants, and nonprofessionals or final users. Obviously, the mobile application

should be adopted by both of the adopter’s categories.

The innovation-decision process is a five-step process, leading to the adoption / rejection of an idea

or technology. Probably the process of mobile application adoption/ rejection might be different

according to our types of adopters.

For Rogers, adoption is a decision of “full use of an innovation as the best course of action

available” and rejection is a decision “not to adopt an innovation”. Rogers defines diffusion as “the

process in which an innovation is communicated thorough certain channels over time among the

members of a social system”.

The spread of mobile applications within the restaurant industry presumes that inside of well known

relationships <restaurant-customer> the diffusion of innovation happened to be. This innovation in

our case is the use of the mobile application which provides several services, or replaces previous

tools for the same services for the restaurants clients, like online-booking instead of phone calling,

reviews and rating instead of word of mouth etc. But also the restaurants are the professional users

of the mobile application; with help of it the mobile application’s holder provides the restaurants its

services and tools, like advertising, CRM systems, call-centre etc, which also replace the previous

tools.

81

On the one side, we are interested to see, if the adoption/rejection of the innovation is different by

professional users and nonprofessional users of the same application. On the other side, our

research includes also cultural differences of two countries.

I.1.1. Stages in the innovation-decision process.

Diffusion scholars have long recognized that individual’s decision about an innovation is not an

instantaneous act. Rather, it is a process that occurs over time (Rogers, 1962). The model of the

innovation-decision process includes five stages (Figure 5):

Figure 5: Model of stages in the innovation-decision process

Source : Rogers, 1962

(1)Knowledge occurs when an individual is exposed to an innovation’s existence and gains some

understanding of how it functions. An individual can play a passive or active role in being exposed

to awareness-knowledge about an innovation. So a restaurant manager can learn about the mobile

application during the presentation by the salesperson of the mobile application holder, also he or

she can expose the awareness to every innovation concerning the restaurant business. The final user

of the mobile application can download it together with all others tools on the smartphone, and at

the same time he or she can have specific interest in the mobile applications about the restaurants.

According to Rogers there are three types of knowledge:

- Awareness-knowledge is information that an innovation exists. Today people use

smartphones and different mobile applications, what means, the people know that also the

mobile applications about the restaurants can exist.

- How-to knowledge consists of information necessary to use an innovation property. The

adopters understand how to use the mobile applications in general. If the particular mobile

82

application answers this understanding the mobile application tends rather to be adopted in

the future.

- Principles-knowledge consists of information dealing with the functioning principles

underlying how innovation works. In other words, the understanding how useful is the

innovation. The restaurant manager will rather adopt the mobile application for his/her

business, if he/she understands how this application can influence the wished results. Also

the final user who knows the functionality of the mobile application, such as table-booking,

discounts etc. will rather adopt it than not.

Knowing about the innovation is different from using it. Attitudes toward an innovation influence

the decision making about adoption of the innovation. The individual should not only know about

the innovation, he/she should regard the function of it as relevant to his/her particular situation.

(2) Persuasion occurs when an individual form a favorable or unfavorable attitude towards the

innovation. Persuasion has perceived characteristics of innovation

People do not just get used to something new. They make a conscious decision. This is something

deliberate. The theory of diffusion of innovation identifies five characteristics of the innovation:

- relative advantage, which determines how much better is the product/innovation compared with

the previous generation, or an analog. These changes may include improved services, improved

interface, reduced impact on the environment, saving money, saving of time etc. Mobile application

in restaurant industry is multifunctional. On the one hand, it replaces the direct contact with the

restaurant: online-booking instead of use of telephone. On the other hand it changes marketing

tools, such as for example word of mouth (WOM), which is important for the marketing in the

restaurants. It competes with analogs such as book-guides for restaurants, and general guides for

tourists (f.e. lonely planet, Michelin, tripadvisor).

- compatibility is an indicator of how innovation is assimilated in people's lives. Potential buyers

or users should be aware that new product or service is "compatible" with them. If new product or

service requires a lot of changes in their way of life, it is fated to failure. Innovations are successful

only when people are taking them in their lives imperceptible for themselves. The implementation

of the new technology in the restaurant business requires several decisions and changes in the

management, including the implementation of new software and new instructions and trainings of

the personal. But the final users of the mobile application are normally the users of any applications

that mean such kind of innovations is already assimilated in their lives.

- complexity and simplicity are the important things how people learn to use new product.

Complex innovations bring difficulties in their acceptance into life of people. Potential buyers or

users do not usually have enough time to deal with all the subtleties. The more intuitive is the new

product or service, the better it will go to the masses. Complexity and simplicity are important for

83

the mobile application use in any cases. The spread of the mobile application depends on the fact,

how easy is the use of it. For the restaurant client it is preferable to have confidence, that he/she can

have booked table at the right time, or that he/she can get the promised discount etc., in case of

technological failure, the clients can get strong negative emotions, which will push them to reject

the innovation.

- trialability is characteristic, showing how well people can explore new product or service before

purchasing it. This is important in making innovation. A user needs to know what represents new

product or service and even to make a "test drive." This is the main point of the demo versions and

beta releases. Potential buyers or users need to understand what their life will be after the adoption

of the new product or service. Speaking about the mobile applications we should notice, that the

final user can try the application only after he/she downloads it, but he/she can read the reviews, can

see instructions and pictures, how to use it before downloading. It is important to underline, that the

investigated mobile applications are free for downloading that means, that the person can easily

delete it, if he/she is not satisfied, without having any expenses. It is absolutely different for the

professional users, or restaurants, because they should buy for expensive price the services of the

mobile application holder. And as said before, the process requires additional software, and

trainings for the personal. So the holder of the application provides usually demo versions, free

trainings, free technical support in the beginning or complete solution for business.

- observability is the degree of visibility of the results of using the new product or service. Not all

users immediately accept innovations. Absolutely all types of users need to see the benefits of

adoption and use of innovation. The advantages and benefits of use of the mobile application in

restaurant industry are more observable for final users: saving time, easy access from smartphone,

discounts and clients program etc. For the professional use the benefits become observable only

with time, and are influenced usually by many other factors, which are not connected with the

mobile application at all. The task of the mobile application’ holder is to provide information for the

restaurants, which can be interpreted and used to make the decision of the technology’s

implementation.

(3) Decision occurs when an individual engages in activities that lead to a choice to adopt or reject

the innovation.

Adoption is a decision to make full use of an innovation as the best course of action available.

Rejection is a decision not to adopt an innovation.

(4) Implementation occurs when an individual puts an innovation into use. “Until the

implementation the innovation-decision process has been mental exercise” (Roger, Diffusion of

innovation, p.172).

84

But the implementation involves behavior change. Problems of implementation are much more

serious when the adopter is an organization rather than an individual. In the restaurant the

implementation of the mobile application leads in big changes for several departments: financial,

service, marketing. For the individuals it is just a new program on the smartphone, which will be

used occasionally

(5) Con firmation occurs when an individual seeks reinforcement of an innovation-decision already

made or reverses a previous decision to adopt or reject the innovation if exposed to conflicting

messages about the innovation.

The innovation-decision process is deferent for an organization and for an individual. All stages of

this process presented in the Roger model take more time and involve more complex decision when

the innovation is adopting by the organization. The investigation of these differences is important

for our research. Moreover both processes are closely related: the success of the adoption of the

mobile application in the restaurants depends on the numbers of the final users of the mobile

application what means on the adoption of this application by them. Moreover the final users can

adopt the mobile application only after the certain amount of the restaurants has already adopted

and implemented it as well as on the constant diffusion of the mobile application inside of the

catering, because the mobile application can be successful only if it provides information about a

big quantity of the establishments, creating the big choice for the final users. Regarding that, the

construct decision with both adoption and rejection of Rogers’s model might be interesting for the

future research model.

I.1.2. Types of adopters and the innovation-decision process

Another finding of Rogers’s theory affects the interest of marketing research. Rogers describes in

his study five types of users or adopters. In general, each category determinates when innovation

will be adopted (Figure 6).

Figure 6: Types of adopters

Source : Rogers, 1962

85

- Innovators. Typically, such people have a good income, interested in the science behind

innovation, and have a high status in their social group. Researchers say that 2.5% of the population

of people is innovators. Sometimes they set the direction of development, but the rest of the people

do not base on their opinion in the choice of technologies.

- Early adopters see the need for innovations or changes. They know that they need a specific

solution to a specific problem. Researchers notice that 13.5% of users belong to this category. As

well as innovators, they have a high income, more often they are well educated and can boast of

high social status.

- Early majority. It takes time before they want to adopt innovation. This will happen only when it

has already been tested by others. Early majority has less income; they do not so much affect the

society as innovators or early adopters, and, above all, less willing to take risks. 34% of all users are

early majority.

- Late majority. These people are skeptical to innovations and do not think to take them until the

early adopters and early majority do not prove the success of these products. It is believed that "late

majority" make up 34% of the total number of users. They have a small income, social status is

below average, and they have no impact on others.

- Laggards. They adopt innovation the last. Normally they should be almost forced to do it.

To identify, who belongs to which group, is very important for innovation-makers and for

marketing researcher. This will affect marketing and how to explore the different categories of

people. The diffusion of the mobile applications might involve restaurant’s clients in varies ways:

do loyal customers act as Innovators or mostly the foreign tourist who has already adopted another

similar application, and then the loyal clients suppose to be the early majority? Moreover, the

adopters of the technology within one culture are influenced by adopters of another cultures,

therefore not all type of adopters proposed by Rogers might be important or also presented equal in

selected countries.

I.1.3. Limitation of the theory diffusion of innovation.

This theory has been used successfully in many fields including communication, agriculture, public

health, criminal justice, social work, and marketing. There are several limitations of Diffusion of

Innovation Theory.

Firstly, the innovation-decision process explains how an innovation becomes adopted, rejected, or

abandoned. It does not, however, explain why one technology may be adopted over another.

Rogers’s diffusion of innovations proposes five factors that shape the rate and likelihood of

adoption. Some factors are inherent to the innovation, while others concern the adopters themselves

and their usage of the innovation. Secondly, the theory doesn’t count users, who stops to use an

86

innovation after the adoption (Bhattacherjee, 2001), and thirdly, also the types of adopters are under

the critics (Bardini, 1996). According to Bardini (1996) the major problematic issue is the method

of adopter categorization, or how to standardize categories of adopters through the time of adoption

(innovators, early adopters, early majority, late majority and laggards). Rogers does so by

employing a behaviorist attribute of adopters that he calls innovativeness but he admits that “such

classification is a simplification that aids the understanding of human behavior, although it loses

some information as a result of grouping individuals” (Rogers, 1995, p. 261). On the contrary,

Bardini argues that these categories of adopters constitute ‘ideal types,’ “conceptualizations based

on empirical observations and aiming to effectuate comparisons,” which are “supposedly explained

in terms of allegedly independent variables covering the socio-economic traits, the personality and

the communicational behavior of the studied individuals” (Bardini, 1996, p. 130). The problem with

categorizations is that they do not permit to conceive the possibility that the adopter may change her

or his mind. In fact, the adopter can decide even to reject an innovation at any time and not only

during decision-making. Thus, Boullier (1989) accuses Rogers of propagating a false and ‘positivist

vision of technology,’ according to which diffusion becomes effective only when innovation is

completed and it is restricted inside the process of being adopted, a vision which apprehends users

as passive subjects who either accept or reject innovations.

For our research, the diffusion of the mobile applications in the restaurant sector is the first step to

study the trial relationships <application’s holder – restaurants - customers> in two countries. It is

necessary to analyze national or cultural characteristic of adopters, if they act differently concerning

similar innovations in the restaurant sector. The Rogers’s model gives the standpoints: 1) the

adopters of the mobile applications in our case are the restaurants, using the service to attract

clients, to win new clients, to develop loyalty and others; 2) the adopters of the mobile applications

are also the final users, customers, who adopt/reject the usage of studied mobile applications; 3)

whether the mobile application is adopted or not, there are to explain, how to predict the adoption

by applications holder, should the applications holder of both countries use the same instruments.

Despite the mentioned limitations the Roger’s theory of diffusion of innovation gives us the

possibility to discuss such important issues as:

- the role of the mobile application’s holder in the spread of innovation;

- categorizing of professional adopters: what restaurants establishments adopted the mobile

applications first, and what was the role of the application’s holder to make them aware

about the innovation,

- categorizing of nonprofessional adopters, and how they become aware about the innovation.

Listed issues can lose of importance after the analysis of further theories and models, but we will

keep attention on them.

87

I.2. Theory of reasoned action (TRA) by Martin Fishbein and Acek Ajzen (1975).

Rogers wrote that “implementation (of the innovation) involves overt behavior change” (Rogers,

1995), which is conscious action to put innovation into practice. Outgoing from the standpoint that

implementation of the mobile application is a conscious reasoned action of a restaurant

establishment or an individual; it is necessarily to look at the Theory of reasoned action.

The theory of reasoned action, developed by Martin Fishbein and Acek Ajzen (1975, 1980),

proposes that the behavior depends on the intention of the person to perform or not to perform the

behavior.

Figure 7: Model of reasoned action by Martin Fishbein and Icek Ajzen

Source: Fishbein and Ajzen, 1975

Behavioral intention is determined, first of all, by positive or negative attitude toward the behavior.

The second determinant of behavioral intention is the subjective norm. The attitude towards an act

or a behavior is the individual’s positive or negative feeling about performing a behavior,

determined through an assessment of one’s beliefs. Subjective norm is defined as an individual’s

perception of whether people important to the individual think the behaviors should be performed.

The model of Reasoned Action separates behavioral intention from behavior and discusses the

factors that limit the influence of attitudes (or behavioral intention) on behavior. The model uses

two elements, attitude and norm, to predict behavioral intent.

Whenever the individual’s attitude leads the person to do an action but the norm suggests the

individual should do something else, both factors influence behavioral intent. Specifically, the

model of Reasoned Action predicts that behavioral intent is created or caused by these two

88

elements. Attitudes are expressed in the outcome evaluation, positive or negative judgments about

features of the behavior, and behavioral beliefs, beliefs about consequences of the behavior.

Subjective norms consist in normative beliefs, belief about whether referents approve or disapprove

of the behavior, and motivation to comply, motivation to do what salient referents think an

individual should do. The TRA is based on the assumption that an individual performs the behavior

consciously. It is designed to explain the behavior in the specific context.

Appropriate example in the context of mobile application in the restaurant sector would be:

a) behavior of the restaurant manager:

If the restaurant manager wants to attract new clients, he/she intends to use the mobile application

(Intention). Usage of mobile application is useful/not useful at all (Attitude). Most of the colleagues

are thinking that usage of the mobile application can be useful (Subjective Norm). Usage of mobile

application attracts new clients (Behavioral beliefs). Attraction of new clients can be useful/not

useful at all (Evaluation of behavioral outcomes). The owner of the restaurant thinks the restaurant

manager should use the mobile application (Normative beliefs). Concerning the restaurant he/she

wants to do what the owner thinks he/she should do (Motivation to complain).

b) behavior of the restaurant customer:

If he/she wants to choose the restaurant, he/she intends to use the mobile application (Intention).

Usage of mobile application is useful/not useful at all (Attitude). Most of his/her friends are

thinking that usage of the mobile application can be useful (Subjective Norm). Usage of mobile

application reduces his/her time to choose the restaurant (Behavioral beliefs). Reduction of the time

to choose a restaurant can be useful/not useful at all (Evaluation of behavioral outcomes). His/her

best friend/wife/husband thinks he/she should use the mobile application (Normative beliefs).

Concerning the restaurants he/she wants to do what his/her best friend/wife/husband thinks he/she

should do (Motivation to complain).

These examples can illustrate how the individual might make a decision about usage/not usage of

the mobile application, but because of its limits the model can work well for the business behavior,

where the intention and final performance have higher level of consciousness than the behavior of

an individual in personal affairs.

Table 15: Adaptation of the TRA constructs for the mobile application’s use

Construct Definition Example of Mobile application’s

use

Behavioral intention The individual’s decision to perform or

not the behavior

a) By choosing of the restaurant

individual is in the situation when

he/she can use mobile application

instead of other tools.

89

b) Because the mobile application is

more and more common, the

restaurant manager is in the situation

when he/she can use this application

as a marketing tool.

Attitude The individual’s positive or negative

feeling about performing a behavior

For both, the user and the restaurant

manager the use of mobile application

can be useful or not useful.

Subjective norm An individual’s perception of whether

people important to the individual think

the behaviors should be performed

Important referent’s opinion is more

significant for the restaurant manager,

because the use of mobile application

impacts the business.

Behavioral beliefs Beliefs about consequences of the

behavior

As consequences the advantages and

disadvantages can be regarded.

Advantages might be:

for the user: saving time, collecting

discounts etc.

for the restaurant manager: marketing

tools, collecting data, increase of the

occupancy rate etc.

Evaluation of behavioral outcomes Positive or negative judgments about

features of the behavior

Evaluation of given above advantages

and disadvantages.

Normative beliefs Belief about whether referents approve or

disapprove of the behavior

Same as for subjective norm, more

significant for the restaurant manager.

Motivation to complain Motivation to do what salient referents

think an individual should do

More significant for the restaurant

manager.

Source: completed by author of the thesis based on the references

I.2.1. The theory of planned behavior (TPB), Icek Ajzen (1985)

The theory of reasoned action was revised and extended by I. Ajzen in 1985. The main difference

between the models is that TRA does not consider perceived behavioral control. It predicts behavior

from attitudes and subjective norms, and is predictive in those situations where there are no

significant barriers to behavioral performance (Fishbein and Ajzen, 1975).

Perceived behavioral control influences the behavior when individual has the intention to perform a

behavior but the actual acting is stopped by lack of confidence or control over behavior.

This was a result of the opening of succession, according to which the behavior is not entirely

voluntary and controlled process, what led to the concept of "subjective control over behavior."

According to the TPB, the person commits the action, guided by three main factors:

- Behavioral beliefs. The persuasion concerning the possible consequences of certain behaviors.

- Normative beliefs, beliefs about the normative expectations of others.

90

- Control beliefs, beliefs about the presence of factors that may contribute to or hinder certain

behavior.

Figure 8: Model of planned behavior by Icek Ajzen

Source: Ajzen, 1985

These three factors of Ajzen are crucial in circumstances / projects / programs that require

behavioral change. Taken together, behavioral beliefs lead to the approval or disapproval of certain

behavior, normative beliefs - to subjective social pressure or subjective norm, and the control

beliefs give rise to subjective control over behavior. Combined with each other, the attitude toward

the behavior, subjective norm, and a perceived control over the behavior form behavioral intentions.

Generally, the positive attitude and subjective norms are stronger than the subjective control and

intention of the person to commit actions.

We can add Perceived behavioral control in the above given examples:

a) behavior of the restaurant manager:

Usage of mobile application will be difficult to put in the general operation system of the restaurant

(Perceived behavioral control). If he/she wants to, he/she can easily to implement the mobile

application in the general operation system of the restaurant (Control beliefs). How much control

does he/she have over the mobile application data? Little control/complete control (Perceived

power).

b) behavior of the restaurant customer:

Usage of mobile application is difficult/absolutely not difficult for him/her (Perceived behavioral

control). If he/she wants to, he/she can easily use the mobile application to choose the restaurant

(Control beliefs). How much control does he/she have over the usage the mobile application? Little

control/complete control (Perceived power).

91

Table 16: Adaptation of the TPB constructs for the mobile application’s use

(Since TPB is the extension of the TRA, only three additional constructs are given)

Construct Definition Example of Mobile application’s

use

Perceived behavioral control The intention to perform a behavior

but the actual acting is stopped by

lack of confidence or control over

behavior

a) User can hesitate to use the mobile

application if he/she believes that it is

too complicated compared with

relative advantages.

b) The restaurant manager can hesitate

to implement the application, because

of difficulties of integration it into the

financial system etc.

Control beliefs Beliefs that presence of factors that

may facilitate or impede performance

of a behavior.

If the user believes that he/she can use

the mobile application despite the

difficulties, he/ she will use it. The

same for the restaurant manager, who

can implement the new system

nevertheless the complexity.

Perceived power A person's perceived behavioral

control over each of given above

factors.

How much the user can control the

use of the mobile application, after

having the lack of confidence in the

system?

Source: completed by author of the thesis based on the references

I.2.2. Limitations of TBP

Regarding these examples we can do the same conclusion as before, the more consciously is the

behavior the better the model works. However, TRA and TPB are pragmatic theories. Both models

were used in several studies of usage of information technologies and innovations. TBP has

following limitations on explaining all mechanisms of the actual use of an innovation:

• It does not account for irrational variables in behavioral intention and motivation,

such as fear, threat, mood, or past experience. Irrational variables might be important for the

individuals using the mobile application.

• While it does consider normative influences, it still does not take into account

environmental or economic factors that may influence a person's intention to perform a

behavior. Economic factors influence the decision of the restaurants manager to implement

or not the mobile application as well as decision of the consumer to eat outside or not.

92

• It assumes that behavior is the result of a linear decision-making process, and does

not consider that it can change over time.

• While the added construct of perceived behavioral control was an important addition

to the theory, it doesn't say anything about actual control over behavior.

• The time frame between "intent" and "behavioral action" is not addressed by the

theory.

Because of its limits the TBP corresponds partly the goal of the research; the variables of attitude,

intention and behavior are of interest for this study.

So the implementation of the mobile application by the restaurant (behavior) depends on the aim

(intention) to change the business model resulted out the positive or negative evaluation (attitude)

of this business model. The use of the mobile application by individual (behavior) depends on the

desire (intention) to enjoy the advantages provided by this technology resulted out the positive or

negative evaluation (attitude) of this advantages. Behavioral control might be interesting in the

investigated case for restaurant use of mobile application, because the implementation of

technology brings certain difficulties in the work of the restaurant, and also there are several

decision makers with different level of influence. But it has very weak significance for the

individual use of the mobile application. For the research we need the construct which have impact

more or less on both types of users, therefore we will not take into account behavioral control by

now.

Table 17: Selected constructs of TPB for the mobile application’s use

Construct Definition Example of Mobile

application’s use

Limits for the mobile

application’s use

Intention The individual’s decision to

perform or not the behavior

Before the use the final user

and the restaurant manager are

in the situation to decide

whether the mobile application

should be used or not,

influenced by different factors.

This period can be decisive.

It is more convenient for the

professional use, when the

period before use (intention)

can be long and therefore

easy to analyze. The

individuals take decisions

concerning the use of mobile

application fast.

Attitude The individual’s positive or

negative feeling about performing

a behavior

Depending on the relative

advantages or perceived

difficulties to use the mobile

application, the final user as

well as restaurant manager can

develop the positive or negative

attitude about mobile

By now this construct is

very interesting.

93

application use. That will lead

in adoption or rejection of

mobile application, what is

important for our research.

Behavior Individual’s action The use of the mobile

application by itself.

-

Behavioral control The intention to perform a

behavior but the actual acting

is stopped by lack of

confidence or control over

behavior

All the factors which can

influence the intention to use

the mobile application:

complexity, efforts and others,

nevertheless the advantages of

the use.

Behavioral control is an

important, but it doesn't say

anything about actual

control over behavior.

Anyway it is more

significant for professional

use of the mobile

application.

Source: completed by author of the thesis based on the references

I.3. The technology acceptance model by Fred D. Davis (1986)

With growing technology usage predicting system use became an area of interest for many

researchers. Fred D. Davis proposed the technology acceptance model, which was based on

principles adopted from Fishbein and Ajzen’s theory of reasoned action. TAM replaced TRA’s

attitude measures with the two technology acceptance measures— perceived ease of use,

and perceived usefulness.

Figure 9: Technology acceptance model (TAM)

Source : Davis, 1986

Adopted from TRA the technology acceptance model explains the behavior of an individual

concerning the decision making about usage of the technology, in our case the mobile application. It

creates the basement for research of this phenomenon. TAM includes behavioral intention to use

94

directly influenced by perceived usefulness (Davis, Bagozzi and Warshaw, 1989), also perceived

ease of use has a small but significant influence on perceived usefulness and the main finding in the

research showed that both perceived usefulness and perceived ease of use have direct effect on

behavioral intention, thus eliminated the need for the attitude construct in TRA. Intention and

behavior are kept from the TRA, so it is no need to discuss them. More important is to regard the

new constructs and to try them for the use of the mobile application.

Perceived ease of use is defined as “the degree to which an individual believes that using a

particular system would be free of physical and mental effort” (Davis, 1989; Goodwin, 1987; Gould

et al., 1991; Hill, Smith & Mann, 1987). Perceived ease of use has direct influence on perceived

usefulness, but it is not mutual influence so perceived usefulness is not hypothesized to have an

impact on perceived ease of use. This construct determinates well the use of the technology,

because normally before the person starts to use any innovation, he or she estimates what

difficulties this use might bring with it. We already have discussed that the implementation of the

mobile application is connected with changes in the business model of the restaurant. From the

point of view of final user the mobile application with intuitive interface is likely to use than one

with complicated guides and instructions. So, perceived ease of use is important variable for our

research.

Perceived usefulness is defined as “the degree to which an individual believes that using a particular

system would improve his/her job performance” (Hellriegel et al., 1992). This variable is limited

by its definition. On the one hand the use of the mobile application in business improves the job

performance, but in our case it is not the performance of one particular person, and the decision

about use is made usually by owner of the business to develop relationships with the clients. On the

other hand for the final users the use of the mobile application has nothing in common with his/her

professional activity. So this mobile application is not relevant for the improvement of the job

performance. Therefore we will not take this variable.

The use of the mobile application is not just action or behavior; it is behavior performing the use of

technology. Since the constructs intention and behavior are adopted in TAM from TRA we keep

regarding them, however we also add the variable of perceived ease of use to our range of research,

because it describes the use of mobile application from the technological point of view.

Table 18: Selected constructs of TAM for the mobile application’s use

Construct Definition Example of Mobile application’s

use

Intention The individual’s decision to perform or

not the behavior

TAM is adopted for the technology’s

use, so is more convenient for our

95

research. Anyway, intention to use

mobile application as a construct is

useful for the professional use.

Behavior The individual’s action/ use of technology The use of mobile application by two

types of users: professionals and

nonprofessionals.

Perceived ease of use The degree to which an individual

believes that using a particular system

would be free of physical and mental

effort

The complexity of the mobile

application is not the same for the

final user and the restaurant manager,

because in the restaurant several

departments and systems can be

involved to serve the mobile

application.

Source: completed by author of the thesis based on the references

I.3.1. Comparison of TAM and TPB

Mathieson, (1991) did comparative study of these two theories: technology acceptance model and

the theory of planned behavior. According to this study, there are three main differences between

TAM and TPB (Mathieson, 1991, Peacock, Chin, Mathieson, 2001).

Degree of generality. TAM supposes that beliefs about usefulness and ease of use are always the

primary determinants of use decision. In TPB beliefs are specific to each situation. This difference

between models brings up following concerns. Firstly, here could be variables besides ease of use

and usefulness that predict intention. Secondly TAM’s constructs are measured in the same way in

every situation. TPB requires a pilot study to identify relevant outcomes. And thirdly, TAM does

not require the identification of a specific comparison behavior, but TPB items do.

Social variables. TAM does not explicitly include any social variables. Davis et al. (1989, 1998,

2007) pointed out that social norms are not independent of outcomes. The social norm in TPB may

still capture unique variance in intention.

Behavioral control. TAM and TPB treat differently behavioral control, referring to the skills,

opportunities, and resources needed to use the system. External control issues are not considered in

TAM in any obvious way. TPB taps the important control variables for each situation

independently, and is more likely to capture such situation-specific factors.

Mathieson concluded, that both the technology acceptance model and the theory of planned

behavior explain intention well. Although TAM explained more variance than TPB, the difference

is not large enough to conclude that one model is better than the other. However, TAM explains

attitude towards using technology much better than TPB. TAM supplies very general information

about ease of use and usefulness. TPB delivers more specific information, measuring the system’s

performance on various outcomes. The information TPB furnishes is probably more useful during

96

development and post-implementation evaluation than information TAM provides. Speaking about

the cost of using the models, TAM is easier to use than TPB; Davis has developed standard

instruments for TAM, while measures of TPB’s beliefs need to be developed for each context.

Using TAM as the starting point and comparing this model with the theory of reasoned action and

the theory of planned behavior V. Venkatesh and F. Davis extended the model to TAM2.

Table 19: Comparison of constructs of TAM and TPB with examples of the mobile application’s use

Findings Constructs

involved

Differences or similarities Mobile application’s

use TAM TPB

Degree of

generality

Usefulness, ease

of use, perceived

behavioral

control

-Constructs are

measured in the same

way in every situation.

-Only two outcomes

are important.

-Model does not

require the

identification of a

specific comparison

behavior.

Beliefs are specific to

each situation.

Model requires a pilot

study to identify relevant

outcomes.

Theory requires the

identification of a

specific comparison

behavior

Since mobile

application is a

technology, TAM

answers better the

need for our analysis.

Social variables Subjective

norm, normative

beliefs,

motivation to

complain

No explicit social

variables in TAM

The social norm may

capture unique variance

in intention.

Social variables of

TPB describe well the

professional use of

mobile application,

when the important

referents are really

involved in the process

especially of decision

making. Very weak in

nonprofessional use.

Behavioral control Behavioral

control, control

beliefs, perceived

power

No explicit variables of

behavioral control in

TAM

Theory describes the

important control

variables for each

situation independently,

and is more likely to

capture such situation-

specific factors.

Variables of

behavioral control

describe well the

professional use of

mobile application.

Intention Intention Both explain well intention Important construct for

our research by now.

Attitude Attitude No explicit variable of Ease of use and TAM gives better

97

attitude in TAM, but

the model explains

attitude towards using

technology much better

than TPB through ease

of use and usefulness.

usefulness are not

presented, theory needs

specific information,

measuring the system’s

performance on various

outcomes.

explanation of the use

of mobile application,

because it focuses on

the technological

aspects.

Cost - Easy to use because of

universal instruments.

Need to be adopted for

each context.

For our research the

universal instruments

for measurement are

better, because of

complex structure:

provider, restaurant,

final user in two

countries.

Source: completed by author of the thesis based on the references

I.3.2. Comparison of diffusion theory and TAM

The difference between these two theories, theory of diffusion of innovation and technology

acceptance model is based on the objectives of the models. Whereas TAM explains the usage of the

informational system, or technology, the theory of diffusion of innovation is focused on the process

of adoption an innovation. TDI helps to explain the behavior of adopters before the using the

technology, and moreover the theory doesn’t study the rejection of innovation after first adoption.

Besides TAM was originally designed specific for explanation of phenomenon of informational

system, while TDI was designed for the area of health and agriculture.

For our research TAM answers better the goal, because the mobile application is a part of

informational system, but it does not explain the spread of the innovation, what might be interesting

to look at. We are investigating the behavior of users in a well developed sector of catering, with

some traditional relationship between the restaurant establishment and restaurant clients. So, by

now we keep constructs from both theories.

Table 20: Comparison of constructs of TAM and diffusion theory with examples of the mobile application’s use

Findings Constructs involved Differences or similarities Mobile application’s

use TAM Diffusion theory

Objectives Usage of technology,

process of adoption an

innovation.

Model focuses on

the usage of the

technology.

Theory focuses on

the adoption an

innovation.

Since the mobile

applications we are

researching about, are

not the every first

mobile applications, we

98

cannot regard them as

real innovation.

Behavior Behavior Model explains

the usage by

itself.

Theory explains the

behavior before the

adoption/rejection of

the technology.

The goal of our

research is to

investigate the mobile

application use not the

decision making

process.

Area of the

researchers

- Technology, IT Agriculture, health Mobile application is

the technology, so the

TAM is better for our

research.

Source: completed by author of the thesis based on the references

I.3.3. Theoretical extension of the technology acceptance model, TAM 2

One of the important extensions brought to TAM is by Venkatesh and Davis, who proposed TAM

2. It was found out that TAM had limitations to explain the reasons for which an individual

perceives a technology useful. Venkatesh and Davis (Venkatesh, Davis, 2000) proposed additional

theoretical constructs explaining social influence processes and cognitive instrumental processes

(Figure 10).

Figure 10: Proposed TAM2 – extension of the technology acceptance model (TAM)

Source: Venkatesh, Davis, 2000

The basic Technology Acceptance Model was preserved with constructs of intention to use, usage

behavioral, perceived usefulness and perceived ease of use. Since these variables were discussed in

this thesis above, we will focus attention on new added constructs only.

TAM2 includes three social variables influencing the person in the decision to adopt/reject the

technology:

99

- subjective norm, which is adopted from TRA and defined as “person's perception that most

people who are important to him think he should or should not perform the behavior in question”

(Fishbein and Ajzen, 1975). Subjective norm is believed that people may use to choose to perform

the behavior under the opinion of the important referent (like the owner of the restaurant might have

that influence on the manager in the decision of usage the mobile application or not).

- image, defined as ‘the degree to which use of an innovation is perceived to enhance one's status

in one's social system” (Moor and Benbasat 1991). TAM 2 theorizes that subjective norm will

positively influence image, the important members of the person’s environment believe that he or

she should perform a behavior. The use of mobile application shows that the person is using the up-

to-date technologies, what surely strengthen his/her status in the modern social system, and this is

fair for both: professional use and nonprofessional use of the mobile application;

- voluntariness, is a moderating variable, defined as “the extent to which potential adopters

perceive the adoption decision to be non-mandatory” (Agarwal ans Prasad 1997, Hartwick and

Barki 1994, Moore and Benbasat 1991). An individual downloads the mobile application absolutely

voluntary. The restaurant manager can make the decision to implement the mobile application two-

way: non-mandatory, because he/she thinks, that it is good for the business, but also mandatory, in

case it is the decision of the owner, or of the executive group of managers.

Unless the social influence processes Venkatesh and Davis (2000) added four cognitive

instrumental determinants:

- job relevance, which is “individual's perception regarding the degree to which the target system

is relevant to his or her job” (Venkatesh and Davis, 2000). This is regarded as cognitive judgment

that has direct effect on perceived usefulness. This construct is important for the professional

adoption of the mobile application, but has no relevance for the individual use in our case;

- output quality, as the “degree to which an individual believes that the system performs his or her

job tasks well” (Venkatesh and Davis, 2000). In other words, the decision of usage is influenced by

the belief of positive or negative effect. Again the definition limits the use of this construct for our

research, because the mobile applications are used mostly for leisure by individuals. And from the

point of view of the professional use the case is not connected with job performance of one

particular person;

- result demonstrability, which means “tangibility of the results of using the innovation” (Moore

and Benbasat, 1991). Rather if the technology can give relevant results, but provides it in not clear

way, users of the technology are unlikely to understand how useful such a technology really is (e. g.

how to implement the received data base from the mobile application to the operation system of the

restaurant, or how much discounts the person can collect using the mobile application).

100

- perceived ease of use is taken from TAM as a direct determinant of perceived usefulness (Davis

et al., 1989). The easier is the technology to use the more increases the using of it. And as

mentioned before this variable is relevant for both users, professional and nonprofessional, and is of

interest for our research.

Venkatesh and Davis also added experience as a moderator variable into TAM2. Experience

measures the user’s experience of the technology, which will influence the perceived usefulness and

the intention to use the system trough subjective norm. Over the time the influence of subjective

norm will loose of importance because of the increasing experience. At the same time the

instrumental cognitive influence has no correlation with the experience, so users will continue to

confirm their job goals and consequences of the technology’s usage (job relevance). Thus, the

influence of important referent reduces over the time, as well as its impact on the intention to use

technology and perceived usefulness. We regarded the example, when the implementation of the

mobile application in the restaurant was influenced by the owner of the restaurant as an important

referent. Over the time this impact can decline, because the owner is not involved in the

management process, or because the restaurant manager faces difficulties to implement the

technology into the financial system, or because the technology works well and needs no more

positive reinforcement.

Since the TAM2 has a basement of TAM, we preserve still the constructs intention, behavior and

perceived ease of use, because they answer our interest. But not all added variables are relevant for

the use of the mobile application in the restaurant business. So, subjective norm might have

relevance for the professional use on the stage of decision making about the implementation of the

mobile application. But this relevance depends on the management structure of the establishment,

and it has weak influence for the final users. Image describes better the motivation to use the mobile

application for both categories of the users, because the mobile application is a part of the modern

social and professional lives. Voluntariness plays not big role in the use of the mobile application.

Inside of the instrumental determinants job relevance and output quality are limited by their

definitions, so they have restricted impact on the use of the mobile applications in our case. On the

contrary result demonstrability might be decisive for both cases of use of the mobile application,

because it contributes information about benefits and advantages of technology usage.

So, we keep regarding image, subjective norm and result demonstrability, as well as perceived

usefulness, because the chosen variables impact this construct directly.

101

Table 21: Selected constructs of TAM2 for the mobile application’s use

Construct Definition Example of Mobile application’s

use

Intention The individual’s decision to perform or

not the behavior.

We reserved this construct from TPB

and TAM. Anyway, intention to use

mobile application is more useful for

the professional use than for

nonprofessional.

Behavior The individual’s action/ use of

technology.

We reserved this construct from TPB

and TAM. We are regarding under the

behavior the use of mobile application

in the restaurant business.

Perceived ease of use The degree to which an individual

believes that using a particular system

would be free of physical and mental

effort.

We reserved this construct TAM. The

complexity of the mobile application

is not the same for the final user and

the restaurant manager, because in the

restaurant several departments and

systems can be involved to serve the

mobile application.

Image The degree to which use of an

innovation is perceived to enhance

one's status in one's social system.

The use of mobile application shows

that the person is using the up-to-date

technologies, what surely strengthen

his/her status in the modern social

system, and this is fair for both:

professional use and nonprofessional

use of the mobile application. It can

be interesting for our research.

Subjective norm Person's perception that most people

who are important to him think he

should or should not perform the

behavior in question.

We discussed similar variables in

TPB and TRA; the important

referents are more influential in the

professional use of mobile

application.

Result demonstrability Tangibility of the results of using the

innovation.

This construct has links with the

concept of advantages, like how to

implement the received data base

from the mobile application to the

operation system of the restaurant, or

how much discounts the person can

collect using the mobile application.

Therefore can be interesting for our

102

research.

Perceived usefulness The degree to which an individual

believes that using a particular system

would improve his/her job

performance.

Limited by its definition, but can be

interesting in general meaning of

relative advantages, which are

provided by using of the mobile

application, and which are not

connected with job for the final user,

like discounts, saving time etc.

Source: completed by author of the thesis based on the references

I.4. Unified Theory of Acceptance and Use of Technology (UTAUT), Venkatesh et al. (2003).

Venkatesh et al. (2003) developed UTAUT as a comprehensive synthesis of previous technology

acceptance research. The eight original models and theories of individual acceptance include the

Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Motivational Model

(MM), Theory of Planned Behavior (TPB), Model Combining the Technology Acceptance Model

and Theory of Planned Behavior (C-TAM-TPB), Model of PC Utilization (MPCU), Innovation

Diffusion Theory (IDT), and Social Cognitive Theory (SCT).

Figure 11: Unified Theory of Acceptance and Use of Technology (UTAUT).

Source: Venkatesh et al. (2003)

UTAUT continues to regard the constructs of behavioral intention and behavior, as an action of the

technology’s use.

103

However, in UTAUT model seven constructs appeared to determine the intention of information

technology usage. Four constructs influence significant on the users acceptance and usage behavior

(performance expectancy, effort expectancy, social influence, facilitating conditions), and three

constructs are theorized not to be the direct determinants of behavioral intention (attitude towards

using technology, self-efficacy, and anxiety). The model operates also by four key moderators

(gender, age, voluntariness, and experience).

Performance expectancy is defined as the degree to which an individual believes that using a

particular system would improve his or her job performance. We discussed previously in the model

TAM2 the construct of perceived usefulness, which has the close definition. According to the

Venkatesh et al, performance expectancy is moderated by age and gender; particularly effect is

stronger by the younger men. For the usage of the mobile application in restaurant business the

construct of the performance expectancy has weak influence in general, because this kind of

application is used for leisure not for job. Even when we speak about the implementation of the

mobile application in the restaurant in the first way we mean adoption of the mobile application as a

marketing tool. The personal of the restaurant do not use the mobile application by itself, they use

the other system, like intranet etc, or they use nothing, if the services (like call-center, measurement

systems etc) are provided by the mobile application holder. Thus, the performance expectancy plays

no role, or very limited role for use of the mobile application in catering.

Effort expectancy is defined as the degree of simplicity associated with the use of a particular

system. Similar construct – perceived ease of use- we regarded in TAM and TAM2. This construct

is significant in both voluntary and mandatory usage contexts (the clients of the restaurant, and the

restaurant management). However it is significant only in the first period of use and is moderated

by experience. This period is different for the restaurant clients as mobile application user and for

the restaurants as establishment. Normally a person needs several minutes to download the mobile

application and to learn quickly how to use it. If the mobile application is too complicated, it will be

not used. Actually the user has already experiences with other mobile applications. But if this one is

a very first mobile application the user uses, the period will be longer. For the management of the

restaurant the decision is by itself complicated. But also the implementation of the system needs

changes in the inner systems of the restaurant. So it depends often on the services-packages

provided by application holder how long the restaurant will adopt the new system.

According Venkatesh et al. effort expectancy is moderated also by gender and age, so more silent

by women and older users.

Social influence is defined as the degree to which an individual perceives that others believe he or

she should use a particular system. Social influence as a direct determinant of behavioral intention

is represented as subjective norm in above discussed TRA, TPB, and TAM2. The individual’s

104

behavior is influenced by the way in which they believe others will view them as a result of having

used the technology (Venkatesh et al, 2003). According to Venkatesh et al, social influence is

significant always in not voluntary contexts (as in our case it is more important for the restaurant

than for an individual). The construct is moderated also by age and gender, so the effect is stronger

for older women particularly in mandatory setting in the starting period of usage.

For our research the concept of image used in TAM 2 is more significant than social influence. On

the one hand the use of any mobile application is regarded today as a normal behavior, but the

opinion of the important referent could be decisive for first decision making to use or not to use the

mobile application. On the other hand, the mobile application is regarded as a part of the modern

culture or way of life, the absence of the mobile application can influence negatively the image,

especially the image of the restaurant.

Facilitating conditions are defined as the degree to which an individual believes that an

organizational and technical infrastructure exists to support the use of a particular system. The

construct includes aspects of the technological and/or organizational environment that are designed

to remote barriers to use. In contradistinction to other mentioned constructs, facilitating conditions

have no significant influence on the behavioral intention, but direct impact on the usage, moderated

by experience and age. Thus, the effect will be stronger for older workers with increasing

experience.

This construct is very important for our research because it is connected with trust of clients over

the mobile application, which will replace traditional relationship between restaurant and client. So,

in case of problems with some services provided by the mobile application, the client prefers to

have support like for example call-center, or personal account in the mobile application to give

feedback, to receive improvements of the system and other help. The same situation is with

restaurant management, who prefers to have supporting organization providing services about

system. In this context the mobile application holder plays a very important role.

UTAUT does not include attitude, self-efficacy and anxiety as direct determinants.

Self-efficacy is defined as the degree to which an individual judges his or her ability to use a

particular system to accomplish a particular job or task.

Anxiety is defined as the degree of anxious or emotional reactions associated with the use of a

particular system.

Both are conceptually and empirically distinct from effort expectancy (perceived ease of use) and

have no direct effect on intention above and beyond effort expectancy (Venkatesh et al, 2003).

Attitude toward using technology is defined as an individual’s overall affective reaction to using a

system. This construct has similar definition as attitude toward behavior, which we regarded in

TRA and TPB. Empirically affective reactions may operate trough effort expectancy (Venkatesh

105

2000). The non-significance of the attitude has been reported in the presence of such constructs as

effort and performance expectancy. Given that Venkatesh et al excluded attitude, because the effort

and performance expectancy showed strong relationships with the behavioral intention.

In our research, effort and performance expectance are not significant, thus, we will not argue the

attitude to be significant as well.

All constructs in UTAUT are moderated by age, gender, experience, and voluntariness. We already

defined experience, and voluntariness in TAM2, and it is no need to give definition of age and

gender. For use of mobile application we keep the moderator of age, because this technology can be

related with the age of the user. Gender, experience and voluntariness are less important, moreover

experience and voluntariness play no significant role for the mobile application use.

We conclude that the construct of facilitating conditions is important to keep for the future research

model. The constructs of UTAUT assume professional usage of the technology. In the usage of

mobile application in the restaurant sector the variables are needed, which can predict the usage of

the technology from both standpoints, personal and professional usage of the mobile application. It

is for this reason that the model of the UTAUT 2 is more convenient for our research.

Table 22: Selected constructs of UTAUT for the mobile application’s use

Construct Definition Example of Mobile application’s

use

Behavioral intention The individual’s decision to perform or

not the behavior

The construct is adopted from TRA,

TPB, TAM, TAM2.

Behavior use The individual’s action/ use of technology The construct is adopted from TRA,

TPB, TAM, TAM2.

Facilitating conditions the degree to which an individual

believes that an organizational and

technical infrastructure exists to

support the use of a particular system

New construct in the model, has

important influence on the use

behavior. It is connected with trust of

clients over the mobile application,

which will replace traditional

relationship between restaurant and

client.

Age Variable of the moderation It can be used as moderator, because

the final users of the mobile

application have different level of

confidence in use of mobile

application and that can be connected

with age, but we need other

influential moderator and will keep

searching for it.

Source: completed by author of the thesis based on the references

106

I.5. UTAUT2, Venkatesh et al. (2012).

This version of the UTAUT is an extension of the basic model developed by Venkatesh et al.

(2012) to study the acceptance and use of technology in the context of individual consumption.

First, UTAUT 2 maintained four direct determinants (performance expectancy, effort expectancy,

social influence, and facilitating conditions) and three of key moderators (gender, age, and

experience). We will not define them again.

UTAUT2 is paying the particular attention to the consumer use context. This is very important for

our research, because the mobile applications in catering are used by the users for leisure mostly.

The professional usage is also presented in UTAUT2 through the constructs it kept from previous

model.

Figure 12: Unified Theory of Acceptance and Use of Technology 2, Venkatesh et al. (2012).

Source: Venkatesh et al. (2012)

The extended model integrated three additional key constructs, which actually help to develop

consumer use context – hedonic motivation, price value, and habit.

Hedonic motivation is defined as the fun or pleasure derived from using a technology, and it has

been shown to play an important role in determining technology acceptance and use (Brown and

Venkatesh, 2005; Venkatesh, Thong, Xu, 2012). In information systems research, such hedonic

motivation (conceptualized as perceived enjoyment) has been found to influence technology

107

acceptance and use directly (e.g., van der Heijden, 2004; Thong et al., 2006, Venkatesh, Thong, Xu,

2012). In the consumer context, hedonic motivation has also been found to be an important

determinant of technology acceptance and use (e.g., Childers et al., 2001, Brown and Venkatesh,

2005, O’Brien, 2010). The mobile applications which we are investigating contain not only the lists

of the restaurants, but also other possibilities like to leave reviews, to rate the best restaurants, to

check in the restaurant etc. Such kind of the functions are the part of the entertainment, so as more

similar services has the application, as stronger hedonic motivation of the users is.

Price Value. An important difference between a consumer use setting and the organizational use

setting, where UTAUT was developed, is that consumers usually bear the monetary cost of such use

whereas employees do not. The cost and pricing structure may have a significant impact on

consumers’ technology use. In marketing research, the monetary cost/price is usually

conceptualized together with the quality of products or services to determine the perceived value of

products or services (Zeithaml, 1988, Lai and Chen, 2011). The price value is positive when the

benefits of using a technology are perceived to be greater than the monetary cost and such price

value has a positive impact on intention. The researched mobile applications are free for

downloading. The importance of the price value can be regarded here as aggregated utility: benefits

collected by application’s use (discounts, loyalty programs, client’s points, gifts etc).

Habit has been defined as the extent to which people tend to perform behaviors automatically

because of learning (Limayem et al., 2007), while Kim et al. (2005) equate habit with automaticity.

Habit is important for the information technology use (Lally, Van Jaarsveld, Potts, Wardle, 2010)

and especially for mobile application use. On the one side, people use today the mobile applications

for everything: shopping, information, gaming, communication. On the other hand, restaurant sector

in general is pretty traditional sector; there is no particular need to use mobile application to go into

the restaurant (if it is not about delivery order). So modern generation makes it (uses applications)

also as a general habit to use any mobile application. It is regarded often as routine action and habit.

UTAUT2 correspond well the research goal to investigate the usage of the mobile application in the

restaurant sector. From the position of restaurant manager the usage of mobile application forces

performance expectancy, effort expectancy, price value and social influence, at the same time

hedonic motivation, social influence and habit explain the personal usage of the same mobile

application, and facilitating conditions influence well both of usage context.

The most important constructs for our research are facilitating condition, prices and habit,

moderated by age.

108

I.6. Mobile application usability, H. Hoehle and V. Venkatesh (2015).

As it presented in the first chapter there is a various range of the mobile applications in markets,

French and Russian, but not all mobile applications are used and demanded in the same way by

users. The market research suggested that the lack of usability has been identified as the important

factor influencing consumers’ decision to reject mobile applications (Deloitte, 2012). Based on this

the detailed consideration of the impact of the usability on the intention of use and use of the mobile

application is needed. H.Hoehle and V.Venkatesh conceptualized and developed the instruments of

measurement the usability of the mobile application. For that they followed the guidelines presented

by MacKenzie et al. (2011), which consist of 10 steps (Figure 13).

Figure 13: Steps for development of measurement instruments of mobile application usability

Source: MacKenzie et al. (2011)

To develop the construct concept Apple’s user experience guidelines was reviewed and analyzed.

The authors posed the questions about main usability criteria and keywords which are describing it

and are associated with it. On the basement of the open codes of the Apple’s guidelines the

constructs were defined and chosen. Since the interest of this thesis is in the analyses of the user’s

attitude and behavior about mobile application usability, we are going to present the final structural

model of the usability concept, and will not focus on all the steps of the measurement development

proposed by Venkatesh and Hoehle (2015).

The structural model includes first-order constructs and second-order constructs of usability and

shows the impact of the usability on the continued intention use and mobile application loyalty

(Figure 10). We suggest to define all constructs and to see their presence in the investigated mobile

applications as much as possible so far.

Mobile application usability is defined as the extent to which a mobile application can be used by

specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a

specified context of use (Venkatesh and Ramesh, 2006). In our research we regard the users of the

mobile application “Lafourchette” in Paris and users of the mobile application “Resto” in Moscow

(specified users), who are using the mobile applications for searching for the restaurant and for

109

booking tables in the restaurants (specified goals). In this way effectiveness refers to instruments

the mobile application has for searching and booking, efficiency refers to ability of the mobile

application to provide not only information but also for example bonuses, and satisfaction refers to

the user’s satisfaction with the functions of the mobile application. We investigate the leisure

context, when the mobile application is used for the having a meal in the restaurant. These mobile

applications can be used also in professional context, but in this case the managers of the restaurants

not really use the mobile application by itself, they use professional version of software. We also

research private use of the mobile application, not the corporative use (e. g. booking of the

restaurant for the business meeting etc.). Mobile application usability is not the same as mobile

device usability.

Figure 14: Structural model of the mobile application usability, Venkatesh and Hoehle (2015).

Source: Venkatesh and Hoehle (2015).

110

According to the research of Venkatesh and Hoehle (2015) mobile application usability is formed

by the second-order constructs like application design, applications utility etc, which consists in

their turn from first-order constructs also modeled using four reflective indicators. Recent ones are

not included in the structural model, but just shown as presented in blank quadrates (see the Figure

10).

First-order constructs.

Branding is the degree to which a user perceives that the mobile application integrates branding

appropriately, generally speaking whether the mobile application uses the brand’s colors or images.

In both mobile applications branding is presented well (see the pictures 3 and 4, 5 and 6) in the

meaning that the colors and firm-logo fit the colors and firm-logo used in the website of the

companies.

Picture 3: resto.ru – mobile aplication Picture 4: resto.ru- website

111

Picture 5: La fourchette –

mobile application

Picture 6: La fourchette – website

Data preservation is the degree to which a user perceives that the mobile application preserves data

automatically. Both applications have functions like your last booking, your last search etc, also in

the settings it’s possible to register and save the personal data (Picture 7 and 8). “Lafourchette” has

also a function to connect with facebook, what allows the user to save time by the registering, it is

also possible to skip this step and start to look for the restaurant establishment, but in this case the

data will be not conserved for further use.

Picture 7: Personal settings on the mobile application Resto

112

Picture 8: Personal settings on the mobile application Lafourchette

Instant start is the degree to which a user perceives that the mobile application starts instantly after

switching on it. Both applications need less than one second to start. But as we noticed before that

mobile application usability is not the same as mobile device usability the instant start can depend

on the model, inter alia, year of the devise and its operating system.

Orientation is the degree to which a user perceives that the mobile application displays information

well independent of whether the device is held horizontally or vertically. Both mobile applications

have ability to change orientation depending on the horizontally or vertically position. But

“Lafourchette” shows this ability only when the user regards the particular restaurant, on the main

page with the list of the restaurants this function is not available.

Collaboration is the degree to which a user perceives that the mobile application enables users to

connect with other individuals. Even if the website of resto.ru is designed as a social networks the

company hasn’t preserve this ability in the mobile application. “Lafourchette” has for example

function of leaving the reviews, what can be regarded as collaboration ability. In any cases as a part

of the tripadviser company, all the reviews the restaurant has on the tripadviser are shown on the

“Lafourchette” application.

Content relevance is the degree to which a user perceives that the mobile application focuses on the

most relevant content. On the assumption of the use context (searching and booking tables) both

applications focus only on contents connected with the restaurant issues.

Search is the degree to which a user perceives that the mobile application helps users to search for

information. In the mobile application “Resto.ru” the search function takes the priority place, also

the start page of the application consists only lines for searching. “Lafourchette” application uses

more advanced technology and functionality like bottom “around me”, what allows automatically

find the restaurant next door.

Aesthetic graphics is the degree to which a user perceives that the mobile application makes use of

aesthetic graphics. The first look at the both application gives the impression, that the mobile

113

application “Resto.ru” is very poor on the aesthetic graphics compared with “Lafourchette”.

“Resto.ru” has pictures and graphics only on the restarants’ pages, while “Lafourchette” uses more

advanced graphics through the whole application.

Realism is the degree to which a user perceives that the mobile application incorporates realistic

icons or pictures. Realistic pictures of the restaurants, dishes are presented in both mobile

applications.

Subtle animation is the degree to which a user perceives that the mobile application uses subtle

animation effectively. The mobile application “Resto.ru” has no animation. “Lafourchette” uses

videos, for example.

Control obviousness is the degree to which a user perceives that the mobile application deploys

controls that are immediately obvious. Unfortunately in the mobile application “Resto.ru” controls

are not obvious, even if the design and structure of the mobile application make impression of the

easy access to any information, to find out how to choose for example city of search is not obvious.

“Lafourchette” deploys obvious controls.

De-emphasis of user settings is the degree to which a user perceives that the mobile application de-

emphasizes user settings. “Resto.ru” has the same difficulty as for control obviousness, on the one

hand the mobile application is easy to use without personalization but at the same time the options

don’t provide primary functionality. “Lafourchette” is possible to use without personalized data (see

above).

Effort minimization is the degree to which a user perceives that the mobile application minimizes

the effort to input data. In general both applications minimize the efforts to input data.

Finger-size controls is the degree to which a user perceives that the mobile application deploys

finger-size controls. Both applications deploy finger-size controls.

Concise language is the degree to which a user perceives that the mobile application makes use of

concise language. Both mobile applications use the concise language well.

Standardized user-interface element is the degree to which a user perceives that the mobile

application deploys standardized user-interfaces that are commonly used by other mobile

application. Both mobile applications use the standardized user-interface elements, like search,

maps, booking.

User-centric terminology is the degree to which a user perceives that the mobile application deploys

user-centric terminology. No technical jargons are used in both mobile applications.

Logical path is the degree to which a user perceives that the mobile application presents

information logically and predictably. Mostly the information is presented logical and easy for users

to predict in both applications.

114

Top-to-bottom structure is the degree to which a user perceives that the mobile application displays

frequently used information on the top of the application. In the both mobile application the search

function is on the top position, what completely responds the task and use context.

This superficial analysis showed us availability of the different constructs in the mobile

applications, thus is important for the next step to choose the constructs which we can use

afterwards for our research model. The overall results of the comparison are indicated in the Table

9, where each of the first-order construct are estimated as “well presented”, “presented”, “poor

presented” for each mobile application.

Table 23: Comparison between lafourchete and resto usability

La Fourchette Resto.ru

well

presented

Presented poor

presented

Well

presented

presented poor

presented

Branding + +

Data preservation + +

Instant start + +

Orientation + +

Collaboration + +

Content relevance + +

Search + +

Aesthetic graphics + +

Realism + +

Subtle animation + +

Control obviousness + +

De-emphasis of user

settings

+ +

Effort minimization + +

Finger-size controls + +

Concise language + +

Standardized user-

interface element

+ +

User-centric terminology + +

Logical path + +

Top-to-bottom structure + +

Source: completed by author of the thesis based on the references

115

Second-order constructs:

Application design is the degree to which a user perceives that a mobile application is generally

designed well. Data preservation, instant start, orientation, and branding are proposed as first-order

constructs forming the design of the mobile application. And all of them are well presented or

presented in both mobile applications (see Table 10 above).

Application utility is defined as the degree to which a user perceives a mobile application generally

serves its purpose well. Content relevance, search, and collaboration are proposed as first-order

construct forming application utility of mobile applications. Search and collaboration are better

presented in “Lafourchette” application, than in “Resto” application.

User interface graphics is defined as the degree to which a user perceives a mobile application’s

user interface graphics to be effectively designed. Subtle animation, realism, and aesthetic graphics

are proposed as first-order constructs forming user interface graphics. “Resto” application has no

subtle animation and poor aesthetic graphics compared to “Lafourchette” application.

User interface input is the degree to which a user perceives that a mobile application allows easy

input of data. Finger-size controls, control obviousness, effort minimization, and de-emphasis of

user settings are proposed as first-order constructs forming user interface input. Except finger-size

controls the constructs are less presented in “Resto” application than in “Lafourchette”.

User interface output is the degree to which a user perceives that a mobile application presents

content effectively. User-centric terminology, concise language, short icon labeling, and

standardized user interface elements are proposed as first-order constructs forming user interface

output. All elements are well presented in both mobile applications.

User interface structure is the degree to which a user perceives that a mobile application is

structured effectively. Top-to-bottom structure ad logical path are proposed as first-order constructs

forming user interface structure. All elements are well presented in both mobile applications.

The structural model results showed 47 percent of the variance in continued intention to use mobile

application loyalty according to Venkatesh and Hoehle (2015). All second-order are significant and

three of them application design, application utility and user interface graphics displayed being the

strongest predictors. We already noticed that the interface graphics are poor presented in the mobile

application “Resto.ru”, thus to do fair comparison we need to keep equal elements of the usability.

In our case it will be application design and user interface structure to verify the mobile application

usability. We keep for the research model the constructs of the mobile application usability,

continued intention to use and mobile application loyalty, because they respond well the research

problem of this thesis.

116

I.7. Conclusion

In the Table 24 all the constructs are presented which are regarded as interesting for the future

model.

Table 24: Selected constructs of the technology’s use theories.

Construct Definition Theories Positive aspects Negative aspects Conclusion

1 Adopters Those who accept

innovation

Diffusion of

innovation

(Rogers)

The categorizing of

adopters gives the

marketer the possibility to

place products and

services.

In our case we can use

this concept, but not the

types of adopters. We

have professional and non

professional adopters.

The types of adopters

by Rogers are difficult

to predict, especially

in the mass market.

F.e. the user of the

mobile application are

eventually all users of

smartphone, who are

the early adopters of

one particular

application is not to

define.

Maybe

2 Decision

(adoption/

rejection)

A choice to adopt or reject

the innovation. Adoption is

a decision to make full use

of an innovation as the best

course of action available.

Rejection is a decision not

to adopt an innovation.

Diffusion of

innovation

(Rogers)

Rogers described decision

as one of the 5 steps in

technology adoption. The

decision is not the final

step, and after that the

person can also change

his/her decision.

In other theories this

step was replaced with

the behavioral

intention.

Not keeping

3 Attitude The individual’s positive or

negative feeling about

performing a behavior

TRA (M.

Fishbein, I.

Ajzen), TPB (I.

Ajzen),

TAM (Davis)

Attitude is expressing the

emotional aspect about

behavior (negative or

positive evaluation).

In later models has not

direct influence on the

intention or behavior,

is expressed through

others variables.

Not keeping

4 Intention The individual’s decision

about the use behavior

TRA (M.

Fishbein, I.

Ajzen), TPB (I.

Ajzen),

TAM (Davis),

TAM 2

(Venkatesh,

Davis), UTAUT

(Venkatesh et

al.), UTAUT2

(Venkatesh et

al.)

This construct is

presented in all models.

The two most

important construct for

our research have

direct impact on both

– intention and

behavior, so this

construct can be

removed in the future.

Maybe

5 Behavior,

or use

behavior

There is no exact definition

of behavior, or use

behavior: whether it is the

adoption, or

implementation, or usage.

TRA (M.

Fishbein, I.

Ajzen), TPB (I.

Ajzen),

TAM (Davis),

TAM 2

(Venkatesh,

Davis), UTAUT

(Venkatesh et

al.), UTAUT2

(Venkatesh et

al.)

This construct is

presented in all models.

Yes

6 Perceived

ease of use

The degree to which an

individual believes that

using a particular system

would be free of physical

and mental effort

TAM (Davis),

TAM 2

(Venkatesh,

Davis)

Important for the adoption

of the new technology.

More significant for the

professional use in our

case.

In general people have

habit to use mobile

applications, and

normally the

applications have

similar design, so this

variable is not

Not keeping

117

significant for our

research.

7 Image The degree to which use of

an innovation is perceived

to enhance one's status in

one's social system

TAM 2

(Venkatesh,

Davis)

Important for status in the

modern society – you use

the smartphone, you use

the mobile application,

you behavior in the

modern way.

The technology is not

new, so the image is

playing less significant

role for already known

technologies, than for

new.

Not keeping

8 Subjective

norm

An individual’s perception

of whether people important

to the individual think the

behaviors should be

performed

TRA (M.

Fishbein, I.

Ajzen), TAM 2

(Venkatesh,

Davis)

Important for professional

use

We will contact the

users and restaurants

who are already use

the mobile application.

And this construct is

important for the

period of

implementation.

Not keeping

9 Result

demonstra-

bility

Tangibility of the results of

using the innovation

TAM 2

(Venkatesh,

Davis)

Significant for the

professional use

In researched case

difficult to predict.

Not keeping

10 Perceived

usefulness

The degree to which an

individual believes that

using a particular system

would improve his/her job

performance

TAM 2

(Venkatesh,

Davis)

Significant for

professional use

The mobile

application in

restaurant sector is

used for leisure, so the

definition of this

construct makes it not

relevant for us.

Not keeping

11 Facilitating

conditions

The degree to which an

individual believes that an

organizational and technical

infrastructure exists to

support the use of a

particular system

UTAUT

(Venkatesh et

al.), UTAUT2

(Venkatesh et

al.)

Significant for both :

restaurant use and clients

use. The implementation

of the mobile application

replaces the traditional

relationships between

client and establishment.

The holder plays

significant role. Trust

becomes relevant.

In the models

(UTAUT and UTAUT

2) this construct is

moderated by

experience, meaning

that with time the

variable loses its

importance.

Yes

12 Hedonic

motivation

The fun or pleasure derived

from using a technology

UTAUT2

(Venkatesh et

al.)

Gives big push for the

application’s holder to

provide different

functions in the mobile

application, like sharing

pictures, leaving reviews

etc, so the user could have

fun, and come back to

application oftener.

Not relevant for

professional use, and

limited trough goals of

the individuals, f.e. the

goal is just to reserve

table, so hedonic

motivation is not

significant.

Maybe

13 Price value The monetary cost of

technology.

UTAUT2

(Venkatesh et

al.)

Decisive for

professionals. For

individual’s has no direct

meaning, can be

calculated as aggregated

benefits of discounts etc.

Researched mobile

applications are free

for download. But the

restaurants pay the

application’s holder to

have promotional

services.

Maybe

14 Habit The extent to which people

tend to perform behaviors

automatically because of

learning

UTAUT2

(Venkatesh et

al.)

The use of the mobile

application today is often

routine and habit. It is

important to see how this

construct influence on the

behavior concerning the

restaurants. E. g. do the

people use more such

kind of applications when

they travel or are in the

unknown place?

Yes

15 Mobile

application

usability

The extent to which a

mobile application can be

used by specified users to

achieve specified goals with

effectiveness, efficiency,

and satisfaction in a

Mobile

application

usability

(Venkatesh and

Ramesh, 2006).

Mobile application

usability plays important

role in the user’s decision

to continue the use of the

mobile application or not.

It also predicts the loyalty

As shown above the

mobile application we

are going to

investigate are not

equal in all elements

of the mobile

Yes

118

specified context of use of the users toward the

mobile application.

application usability.

16 Mobile

application

loyalty

Degree to which a user has

a deeply held commitment

to rebuy or repatronize a

mobile application.

Mobile

application

usability

(Venkatesh and

Hoehle, 2015

adapted from

Johnson et al.

2006)

Mobile application

loyalty can be the wished

outcome for the providers

of the mobile

applications, in this way

this construct plays

important role for

prediction of the user’s

behavior.

Yes

17 Continued

intention to

use

Degree to which a user feels

he/she will keep using a

mobile application.

Mobile

application

usability

(Venkatesh and

Hoehle, 2015

adapted from

Bhattacherjee

2001)

Continued intention to use

is the outcome, which can

explain the continued use

of the mobile application.

Yes

Source: completed by author of the thesis based on the references

Firstly, the concept of the adopters in Roger’s theory of diffusion was included in the list despite

the limitations of his definition and categorizing of adopters. We have two kinds of adopters –

professional and non-professional. This division is critical in our case, because not all of constructs

will work in the same way depending on who is adopting the mobile application. The Rogers

classification of adopters is not working in our model clearly, because the mobile application is

spread and adopted/rejected already.

The construct decision with both adoption and rejection of Roger’s model of stages in the

innovation-decision process were also kept for the future research model, but actually the construct

of intention of later models has replaced it. So we can exclude it out of our list.

Then the variables of attitude, intention and behavior were taken from the TRA and TPB. So the

implementation of the mobile application by the restaurant (behavior) depends on the aim

(intention) to change the business model resulted out the positive or negative evaluation (attitude)

of this business model. The use of the mobile application by individual (behavior) depends on the

desire (intention) to enjoy the advantages provided by this technology resulted out the positive or

negative evaluation (attitude) of this advantages. Attitude showed no significant influence in late

models and was replaced by more convenient constructs, so we will not keep it in our list.

In the following the models which are better related to the technologies, were reviewed. The use of

the mobile application is not just action or behavior; it is behavior performing the use of

technology. Variable of TAM perceived ease of use was added to our range of constructs, because

it describes the use of mobile application from the technological point of view. Since the TAM2 has

a basement of TAM, we preserved perceived ease of use and found of interest also image,

subjective norm and result demonstrability, as well as perceived usefulness. In ensuing researches

this construct were modified or lost their importance. So, image, subjective norm or social influence

119

(UTAUT/UTAUT2) are moderated by experience, but in case of the mobile application’s use are

not significant, because we admit, that the investigated mobile application is not the very first

mobile application used by the users, so the users feel confident to use such technology in general.

The effort and performance expectancy are typical for the professional use, but less important for

the individual use in the mobile application context. Facilitating conditions and habit moderated

by age might be significant for the mobile application use, so the younger people have stronger

habit to use mobile application in any context. Normally the user before they have habit to use

technology should trust this technology, and for that they should believe that some organization is

behind to support them.

All at all we keep by now three technological constructs for sure: use behavior, facilitating

conditions, and habit (see Table 11), but some of the constructs are still under the question, like

adopters, hedonic motivation, price value.

And finally the structural model of the mobile application usability was regarded. Mobile

application usability is considered as a predictor of the mobile application loyalty and continued

intention to use, the both can be viewed as wished outcomes for the mobile application provider and

explain the behavior of the users. We keep mobile application usability, mobile application loyalty,

and continued intention to use for the research model. In the next chapter the concept of loyalty

will be presented as an important part of the Relationship marketing.

II. Relationship marketing

The usage of mobile application, which contains the services of many restaurants and is used by

final customer, involves three actors. Firstly the application holder or provider builds up the

relationship with the restaurants or b2b relationship, where the exchange of mutually profitable

services takes place. The restaurants benefits in this case are connected however with the customers,

or the final users of the mobile application, that means appearance of the Individual-to-firm

relationship. Moreover, the application holder obtains the control over the data base of all final

users of application, and also develops the Individual-to-firm relationships. The use of technology,

mobile application, seems also to be not fully voluntary for both, but settled and ruled by provider.

120

Figure 15: Relationship <holder – restaurant – user> in use of the mobile application

Source: completed by author of the thesis based on the references

Traditional relationship has direct relation between customer and restaurant with mutual goals:

a) Customer wants to get service of providing food and beverage,

b) Restaurant wants to be chosen by customer for providing this service. We can describe

traditional steps of building relationship:

- Choice. Customer chooses the restaurant establishment often with help of WOM (word of

mouth), in other words with help of recommendation of important referents: friends, family

members; or advertising, when the restaurant establishment uses marketing tools to attract

customers.

- Taking in contact. In general customer can book the table in advance or not, he/she can call

in the restaurant to get know more details about menu, payments, happy hours or not etc.

- Service by itself.

- Feedback. After visiting the restaurant establishment customer participates more or less in

further marketing of the restaurant, so he/she can recommend/criticize the establishment to

his/her friends, family members, colleagues and other producing WOM; he/she can become

the loyal client thank to the loyalty program of the restaurant establishment etc.

In traditional relationship the marketing strategy of the restaurant establishment is of high

importance in all steps and demands efforts and financial investments. And sometime there are

difficulties to control the results of marketing activities for example on the step of feedback, when

the management of the restaurant can learn about non satisfaction of the client only occasionally or

many weeks and months after.

The technology of the interactive mobile application changed traditional relationship with

advantages and disadvantages for both parties. And what is important also, it brought third party in

the relationship – the holder of the mobile application, who plays the role of intermediary between

restaurant and client.

If we go back to the steps, we can see the changes (we assume that the customer is the user of

mobile application):

121

- Choice. Even if the customer learns about the restaurant establishment from his/her

important referents, the user of mobile application is checking the information on the mobile

application: reviews, rating, discounts, menus etc. The given information can change his/her

opinion to go into this or that restaurant or not.

- Taking contact. This step is eliminated completely (except some specific cases), because the

user can book table, get all kind of necessary information, and even pay with help of mobile

application.

- Service by itself. This step is the only one which stays not changeable in the relationship.

- Feedback. The mobile application gives the user possibility to leave reviews, pictures, share

information in social networks, check in (to show other users the visiting of the restaurant

establishment). On the one side, this possibility makes the user more active about his/her

experience with the restaurant (to share good impression or to criticize), on the other hand, it

gives the management of the restaurant control over the feedback – to get better rating, to

apologize in the bad situations and change the opinion of the client, to make changes in the

menu, service staff etc.

The interest of the restaurant is to develop the loyalty of the customer to its particular

establishment; the task of the provider is to develop the loyalty of each establishment included in

the mobile application and also to develop loyalty of the customer to its particular mobile

application. Consequently both are facing a new paradigm, which is the best way to explain with

the concept of relationships marketing.

Relationship Marketing is particularly relevant in a services context. Services are based on

interactive marketing, and, therefore, direct contact is necessary (Bitner, 1995, Gronroos, 2004,

2011, Wilson, Zeithaml, Bitner, Gremler, 2012). In fact, services marketing was the discipline that

first introduced Relationship Marketing in the 1970s after debates that the marketing mix was

insufficient for services (Möller and Halinen, 2000, Möller, 2013) requiring more interactive

processes. Customers view their perceptions of quality on the basis of satisfaction with their

relationship (Berry and Parasuram, 1993, Möller and Halinen, 2000, Ryu, Lee, Gon Kim, 2012,

Möller, 2013), again underlining the importance of Relationship Marketing. In services, customers

are active participants in the interaction, and trust and commitment are required constructs (Morgan

and Hunt, 1994, Aurier, N’Goala, 2010, Vivek, Beatty, Morgan, 2012).

The researched relationships between provider, restaurant and user of mobile application present the

interaction in which the key-products are services, but the use of technology eliminates the direct

contact entirely or partly. Not only technological aspect impacts the examined case, also culture

affects the relationships. Following issues of the Relationship Marketing Theory are discussed to

be used in the research model:

122

- Definition and roots of RM

- The Commitment-Trust theory of RM

- Interfirm RM

- Interpersonal RM

- Multi-level exchange Relationships in RM

- RM and information technology.

Technology changes the relationship between client and seller. On the plus side it simplifies the

access to products and services, as well as it simplifies for the seller attracting more clients; on the

minus side it eliminates the personal contact between seller and client, what can lead in the lack of

trust and loyalty as consequences. This influence we are going to investigate in our research.

II.1. Definition

Relationships marketing emerged as marketing discipline during the 1990s. The aspect of long-term

relationships with customers became of importance in mainstream marketing management and was

pointed out by several researchers (Christopher et al 1991, Ford, 1990, Gronroos, 1994, Parvatyar and

Shath 1997, Gronroos, 2011, Hall, Timothy, Duval, 2012 etc).

The domain that deals with “relationships,” termed relationship marketing and often attributed to

Berry (1983), has been defined in many different ways by researchers from various research

perspectives.

However three key aspects appear in all most significant definitions (e.g., Gronroos 1997, 2011;

Sheth and Parvatiyar, 2000).

The first aspect deals with stages of the relationship lifecycle. Relationships are regarded as dynamic

processes that develop over time through typical stages (Dwyer and Oh 1987, Wilson 1995, Park,

Kim, Kim, 2011).

The second key aspect deals with the target or the field of relationship marketing. Relationships can

be evaluated between individuals (person-to-person, interpersonal), between an individual and a

firm or group of people (person-to-firm, firm-to-person), and between firms (firm-to-firm,

interfirm).

The third aspect describes the benefits derived from relationship marketing activities. In other

words, does the success of relationship marketing efforts depend only on the perspective of the

implementer (e.g., seller), or must both parties’ outcomes be evaluated? In practice, relationship mar-

keting needs to generate benefits for both parties, even if one party’s benefit is limited to social

rewards, to achieve the implementers’ long-term performance objectives.

Integrating these three aspects results in the following definition:

123

Relationship marketing (RM) is the process of identifying, developing, maintaining, and terminating

relational exchanges with the purpose of enhancing performance. (Palmatier, 2008,).

Researchers from different disciplines have studied the impact of relationship on the individual’s

behavior. Many of these disciplines take central positions in the development of relationship

marketing.

The roots of marketing as well as relationship marketing are to find firstly in trade relationship.

Moller and Halinen (Moller and Halinen, 2000, Moller, 2013) proposed to regard the current

discussion of relationship marketing in four traditions: business marketing, marketing channels,

services marketing, database marketing, and direct marketing.

The database marketing and direct marketing tradition is best characterized as a practice. The

organization-customer relationship perspective is limited. Relationships are seen as long-term, but

efforts to develop their dynamism are restricted. The focus is on keeping customer loyal and

profitable in an efficient way (Moller and Halinen, 2000, Moller, 2013).

The service marketing tradition regards the relationship between individual consumer and the

service company personal. The service marketing is focused on service encounters and service

quality. Relationships are viewed primary from the point of management. The tradition paid little

attention to the context of relationship.

The marketing channels tradition centralizes on business relationships, and on economic exchange.

And finally, network tradition of business markets aims firstly at understanding and explaining

interaction behavior in a network context, secondly it attempts to explain the development of nets

relationships between actors, and thirdly it researches the development of markets from the

networks perspective. The analysis of the roots of relationship marketing makes clear two general

tractions, consumer-related and organizations-related one.

II.2. The Commitment-Trust theory of RM

The Commitment-Trust Theory of Relationship Marketing by Morgan and Hunt (1994) is one of

the most influential in Relationship Marketing. They theorized that “presence of relationship

commitment and trust is central to successful relationship marketing” (Morgan and Hunt, 1994).

This central role of commitment and trust is caused by the behavior of the marketer. Commitment

and trust encourage the marketer:

a) to work at preserving relationship investments by cooperating with exchange partner;

b) to resist attractive short-term alternatives in favor of the expected long-term benefits of

staying with existing partners;

124

c) to view potentially high risk actions as being prudent because of the belief that their partners

will not act opportunistically.

Therefore when both commitment and trust are presented they produce outcomes that promote

efficiency, productivity, and effectiveness.

Morgan and Hunt construed their framework as the key mediating variable (KMV) model of

relationship marketing (Figure 16).

Because relationship commitment and trust are the key constructs, they play the role of the

mediating variables between five important antecedents (relationship termination cost, relationship

benefits, shared values, communication, and opportunistic behavior) and five outcomes

(acquiescence, propensity to leave, cooperation, functional conflict, and decision-making

uncertainty).

Figure 16: The KMV Model of RM

Source: Morgan and Hunt, 1994

Relationship Commitment is defined as “an exchange partner believing that an ongoing relationship

with another is so important as to warrant maximum efforts at maintaining it” (Morgan and Hunt,

1994). In other words, the committed parties believe that the relationship is worth working on to

ensure that it endures indefinitely.

So, the mobile application holder believes that relationships with the user and restaurant are

important; therefore, for example, such services can be provided for free like technical support or

call center, or loyalty cards for users etc. At the same time the restaurant prefers to implement the

new system (mobile application) to be competitive and to win new clients.

Morgan and Hunt proposed that relationship commitment is central to relationship marketing.

Commitment has been long central in the social literature. Commitment also is viewed as critical in

the literatures of organizational and buyer behavior. Specific and important for this thesis is also the

concept of commitment regarded in the services relationship marketing, because the investigated

125

technology is implemented in the restaurant business, where the services play the most important

role. So, Berry and Parasuraman (1991) underlined that “relationships are built on the foundation

of mutual commitment”.

The commitment between mobile application holder and restaurant might be easy purchasing of the

advertising services; the commitment between mobile application holder and final user might be the

trustworthy information about all restaurant establishments. And finally the commitment between

restaurant and the user of mobile application consists not only in expected services (food and

beverage), but also adequacy of this services to the information received in the mobile application.

Trust. The second key variable of the Commitment-Trust theory is defined as “confidence in an

exchange partner’s reliability and integrity” (Morgan and Hunt, 1994, Chenet, Dagger, O'Sullivan,

2010, Gummesson, 2011, Vivek, Beatty, Morgan, 2012).

Like commitment, trust also has been studied in the social exchange literature. For example in

organizational behavior the study of norms of trust is considered a characteristic distinguishing

management theory from organizational economics. In services marketing Berry and Parasuraman

(1991) found that “customer-company relationships require trust. Effective service marketing

depends on the management of trust because the customer typically must buy a service before

experiencing it”.

For our research trust is of high importance. First, developed trust about the mobile application

expends also to the restaurants included in the mobile application (the user trust that the reviews are

fair, the rating is independent). And conversely the user can lose trust about the restaurant in case of

false information on the mobile application, or he/she can lose trust about the application in case of

false services received in the restaurant (f.e. the mobile application promises the discount for the

visit of the restaurant, but the restaurant refuses to give this discount; or the mobile application can

contain fake restaurant establishments, wrong addresses etc.). The complexity of trust is added

through technology. The person should not only trust the company like restaurant, it should also

trust the technology. As we discussed previously UTAUT2 model has the construct, facilitating

conditions, which says that the individuals might tend to believe in the technology if he/ she is sure

that the real company or person is behind this technology. In our case the mobile application holder

provides technical support for both: restaurant establishment, as well as for the final users.

So, trust is the first important variables of Relationship Marketing, which is to use in our future

model.

Trust impacts the relationship commitment directly, that is the committed parties with high level of

trust to each other will also commit themselves to such relationship. So Morgan and Hunt (1994)

theorized that trust is the main determinant of relationship commitment.

126

Morgan and Hunt (1994) indentified five antecedents of commitment and trust. They theorized that

a) relationship termination costs and relationship benefits directly influence the relationship

commitment, b) shared values impact both commitment and trust, c) communication and

opportunistic behavior directly influence trust and through trust indirectly influence on the

commitment ( because trust is the main determinant of the commitment).

Relationship termination costs. An assumption in the relationship marketing, that a terminated party

will seek an alternative relationship and have “switching cost”, which lead to dependence. For

example, the restaurant decided to use mobile application should to implement additional counting

system, or modify its own, or to do training for the personal etc; at the moment the restaurant

management decides to change the service provider, or to refuse the further use of the mobile

application it will have switching costs, for example for new software, new trainings etc. Even if the

management decided not to use another application, the spending which were already paid, would

be lost. On the contrary the user of the mobile application has no dependence on the mobile

application obviously. He/she can lose discount points, but it cannot be decisive.

Relationship benefits. In global market place the competition requires that firms often add

additional services, products, technologies, so it could add value to their own offerings. In other

words, the partners that deliver superior benefits will be highly valued; firms will commit

themselves to establishing, developing, and maintaining relationships with those partners. So the

restaurant establishment, which uses the mobile application, provides its client additional services

like online-booking, or online- payment etc. Probably the lack of such services is critically for some

of the users.

Shared value is “the extent to which partners have beliefs in common about what behaviors, goals,

and policies are important or unimportant, appropriate or inappropriate, and right or wrong”

(Morgan and Hunt, 1994). This concept is the only one that is consolidated to have direct impact on

both – commitment and trust (Porter, Hill, Pfitzer, Patscheke, Hawkins, 2011). The restaurant

manager will choose the mobile application holder that shares the same values to build long-term

relationship. Also the user will choose between several similar applications the one which

represents the values relevant for the user. For example, the traveler will choose probably rather the

application with advises from other travelers, the gourmet will choose mobile application with

reviews done by the restaurant critiques etc.

Communication. The main antecedent of trust is the communication, defined as “the formal as well

as informal sharing of meaningful and timely information between firms” (Anderson and Narus,

1990), of course not only between two firms, but between all participants of the relationship (Light,

McNaughton, 2014). Frequent and of high quality communication will result in the great trust.

Seldom and weak communication will result respectively in the lack of trust. In our case, the

127

traditional communication between restaurant establishments and customers is replaced by the

mobile application and communication leads in the updated information, perfectly organized

communication technologies, like online profile messages, SMS, or other way of information

exchanges.

Opportunistic behavior is defined as “self-seeking with guile” in the transaction cost analysis

literature (Williamson, 1975, Moeller, 2010). So, Morgan and Hunt theorized that believing that a

partner engages in opportunistic behavior will lead to decrease of trust. This variable influences also

the relation commitment but not directly, trust decreases because of opportunistic behavior of one

partner and after that the relationship commitment decreases as well. Using the mobile application

in the restaurant, we can imagine such situation, that the client of the restaurant, who is the user of

the mobile application, can ask for the discount, promoted in the application, but the restaurant

refuses that discount with any reason, in this case a) customer will not trust and probably stop to use

the application, if he/she just started to use it; 2) he/she will not trust this restaurant, if he/she used

the application many times and this service worked well before.

We will not use the antecedents in our model, but they are important for understanding of the

concept of trust and commitment.

Morgan and Hunt (1994) have chosen five qualitative outcomes, which promote the relationship

marketing success. They theorized that acquiescence and propensity to leave directly go out of

relationship commitment, functional conflict uncertainty are direct result of trust, and cooperation

arises directly from both relationship commitment and trust.

Acquiescence is “the degree to which a partner accepts or adheres to another’s specific requests or

policies (Morgan and Hunt, 1994). Acquiescence is influenced directly by relationship commitment

and through that by trust.

If we take the relationship commitment between restaurant and user: the restaurant is implementing

the mobile application to give the client the possibility to use its services etc. In this relationship

commitment, the restaurant makes more effort to build the relationship, than the client, so the

restaurant is acquiescent to the requests of the client. In the relationship between restaurant and

application holder the more acquiescent is the application holder, who is interested in the restaurant

as in a client.

Propensity to leave is the perceived likelihood that a partner will terminate the relationship in the

(reasonably) near future (Bluedorn, 1982, Bande, Fernández-Ferrín, Varela, Jaramillo, 2015).

Strong relationship commitment will minimize the risk that the partner will leave. In our case the

mobile application holder cannot control over the mobile application user, they can easily download

application, but also easily delete it from their smart phones. This outcome is fair only for B2B

relationship.

128

Cooperation refers to situation in which parties work together to achieve mutual goals (Anderson

and Narus, 1990, Marwell, Schmitt, 2013). Cooperation is the only one outcome influenced directly

by both relationship commitment and trust. A partner committed to the relationship will cooperate

with another member because of a desire to make the relationships work. After trust is established

in the relationships, both parties make efforts to succeed in the work together. Mobile application

holder build the relationship with the restaurant management, both parties have the goal to have as

much as possible of the final user of application: the restaurant will improve in this way the client’s

loyalty, for example, the holder will increase the number of downloads.

Functional conflict is relational disagreement which can be resolved amicable. Such conflicts

prevent stagnation, stimulate interest and curiosity. Functional conflicts may increase productivity

in relationship marketing (Gummesson, 2011). For the mobile application holder all kind of

feedback from the users can be functional conflict, because it will stimulate the technical

development of the application.

Decision-making uncertainty refers to the extent to which a partner (Edwards, 1954, Morgan and

Hunt, 1994, Hastie, Dawes, 2010, Polasky, Carpenter, Folke, Keeler2011):

a) has enough information to make key decision (f.e. to choose the restaurant inside of the

mobile application, the user has the prices, the addresses, the information about cuisine,

chef, discounts etc),

b) can predict the consequences of those decision (if the user has chosen the Chinese

restaurant he/she will not expect the pizzeria)

c) has confidence in those decisions (the user is sure that the application provides the correct

information, so he/she trusts the mobile application).

Trust decreases a partner’s decision-making uncertainty. So it is not necessary for the user of the

mobile application to check all the information he/she found about this or that restaurant on the

mobile application, after he/she confirmed his/her trust to this technology.

The described outcomes are important for this thesis because they show us significance of the trust

and commitment in the Relationship Marketing.

The Commitment-Trust theory of Relationship Marketing gives us the very important constructs for

our future model trust and commitment. The concept of trust has connection with technological

construct of facilitating condition which was chosen for our future research model. To understand

the role of both (trust and commitment) in the relationship between mobile application holder, user

and restaurant the regarding of interfirm and interpersonal relationship marketing theories is

needed.

129

Table 25: Adaptation of the Commitment-Trust theory of RM for the mobile application’s use.

Construct Antecedents Mobile application’s use Outcomes Mobile

application’s use

Relationship

commitment

Relationship

termination

costs

Important for professional

use.

Acquiescence The restaurant is

acquiescent to the request

of the client normally in

higher degree than vice

versa.

Relationship

benefits

The sum of all relative

advantages received from

the use of mobile

application.

Propensity to

leave

Important for the b2b

relationship, in our case

between provider and

restaurant. But actually,

also if the client stops the

use of mobile application,

he/she can stay the client of

the restaurant.

Relationship

commitment and Trust

Shared values For the user influence the

choice of the mobile

application, according to

the point of view

(traveling, discounts, and

critics).

Cooperation Provider and restaurant

manager work together to

attract clients; that is their

mutual goal.

Trust Communication The communication

between the restaurant and

client is replaced by the

mobile application, what

can influence significant

the trust between them.

Functional

conflict

The mobile application has

wide range of tools to

collect feedback from the

final users, as well as from

the restaurants.

Opportunistic

behavior

This antecedent influences

the trust to the mobile

application as a

technology also, the user

can ask, whether such

technology can provide the

real information and real

services in case of

opportunistic behavior f.e.

of restaurant personal. .

Uncertainty Uncertainty has significant

influence on the trust to the

mobile application.

Source: completed by author of the thesis based on the references

130

II.3. Interfirm Relationship Marketing Theory

Theory of interfirm relationship marketing investigates multiple interactions among people, or

network of relationships. Thus are in the area of network-based relationship marketing. The

network perspective introduces the fact, that not only relationship quality (trust and commitment)

influences on the exchange performance but also, relationship breadth (network density) and

relationship composition (network diversity/attractiveness). Integrating the network theory develops

an interfirm-specific RM framework (Palmatier, 2008).

Figure 17: Model of Interfirm Relationship Marketing,

Source : Palmatier, 2008

Seller’s RM activities consist of three principal drivers of RM effectiveness: relationship breadth,

quality and composition. Each of these drivers expresses a different and important aspect of

interfirm relationships and has a positive impact on the seller’s performance outcomes.

Relationship quality equalizes the concepts of the strength in the network theory. According to prior

research (Crosby, Evans, and Cowles 1990, Kumar, Sheer, and Steenkamp 1995, Rauyruen, Miller,

2007, Gummesson, 2011), the construct of relationship quality contains the interaction

characteristics required to create a strong relational bond, such as commitment, trust, reciprocity

norms, and exchange efficiency. So, commitment represents exchange partner’s desire to keep

valued relationships and thus their relational motivation toward partners. Trust generates confidence

in the partner’s future actions and supports cooperation. Reciprocity norms have pervasive impacts

on exchange behavior. Exchange efficiency enhances exchange performance. Basically, relationship

quality affects positively relationships performance.

In the researched case the interfirm relationship is created between restaurant establishment and

mobile application holder. In general the restaurant establishment pays for the services of mobile

application such as subscribed program of advertising, call-center or online-service of table-booking

(commitment), so the restaurant management expects high quality service, which can mean updated

information, true ratings etc (trust), and mobile application holder expects regular renewal of the

131

contract in case of satisfaction (reciprocity norms). And both are interested in the big amount of

mobile application users (exchange efficiency).

Relationship breadth represents the number of relational bonds with an exchange partner.

Interorganisational relationships with many interpersonal connections can uncover key information;

find profit – increasing opportunities, and resist destruction of individuals bond (like

reorganizations). Additionally, relationship breadth reflects the network concepts of network

density and degree centrality (Houston et al. 2004, Mende, Bolton, Bitner, 2013). Networks

interconnections positively affect cooperation, knowledge transfer, and communication efficiency.

In other words, a seller and customer with more interpersonal connections (i.e. breadth) enjoy better

access to information and sales opportunities and less disruption when contact personnel turnover,

which then results in increased exchange performance (Palmatier, 2008, Samaha, Palmatier, Dant,

2011).

Normally the exchange between restaurant establishment and mobile application holder takes place

through sales personal of the mobile application company and restaurant management. Often the

restaurant establishment has no marketing manager; these functions are in the responsibility of the

general manager, or sometimes the owner of the restaurant. In big restaurants or in restaurants

chains there are also specialists in marketing. So the mobile application company should adopt its

work with different kind of people, and try to have many different contacts to build strong network.

For example, the restaurant manager can change the establishment, so the new relationship with

another restaurant can be created through this person. Or the owner of one restaurant can open

another one and stay loyal to this mobile application company.

Relationship Composition refers to the decision making capability of relational contacts (Beck,

Palmatier, 2012). A portfolio with different and authoritative contacts increases a seller’s ability to

effect change in customer organization. This driver has similar meaning that relationship breadth.

So for the mobile application company is important to know all key people in the restaurant, or

restaurant chain.

Relationship breadth and composition may correlate positively, since is all else is equal, sellers with

more contacts have diverse contacts. These constructs may diverge if seller has many homogeneous

contacts or only few different (Beck, Palmatier, 2012).

Each of these three relational drivers represents different aspects of interferm relationships. All

together they reinforce one another and promote optimum relationships value.

Relationship strength identifies the interaction between relationship quality and relationship breadth.

The ability of interorganizational relationships to confront stress and conflict, as well as multiple

high quality rational bonds result in strong, resilient relationships (Palmatier, 2008, Samaha,

Palmatier, Dant, 2011). The interdependence between relationship quality and breadth works in the

132

way that many depthless contacts (greater breadth, low quality) protect weakly against stress of a

service failure. For examples, the mobile application company can have in its data base all the

contacts of the restaurant establishment, but the key people don’t trust the services of its particular

company or the technology in general, so in case of any technical problem (like false table-booking)

the restaurant establishment can close the contract.

Similarly, a single high-quality contact (high quality, low breadth) will not support the seller and

cannot influence a decision making group. The restaurant manager can be loyal to the mobile

application company, but if he/she has weak influence on the decision (sometimes the owner decide

about all financial investments), the company cannot create long term relationship till it has contact

with another person.

In contrast multiple high quality contacts (greater breadth and high quality) experience both rational

motivation (commitment, norms of reciprocity) and confidence (trust), and therefore support the

seller. The goal of the sales personal of the mobile application holder is to find for each restaurant

establishments the multiple contacts and build high quality relationships with them.

Relationship efficacy or interaction of relationship quality and composition captures an

interorganizational relation’s ability to achieve desired objectives. The seller can accomplish well

their selling strategies having high quality bonds and well structured contact composition.

Combination of high composition (a seller possess good contacts portfolio with key decision

makers) and low quality (weak interpersonal bonds) can result in the lack of information and

reciprocity debts. Relationship composition reflects the ability of seller’s contacts to establish

change; only high quality relationships can turn this potential into reality and enable the seller to

achieve its goals (Anderson and Narus 1991, Morgan and Hunt 1994, Palmatier, 2008, Samaha,

Palmatier, Dant, 2011, Beck, Palmatier, 2012). So, having all key people of the restaurant

establishment in data base only is not enough, if no one of these contacts is loyal to the propose of

the mobile application company.

Contrariwise, high quality contacts restricted to one functional area cannot allow the seller to

promote customer change (low relationships composition, high relationship quality). As said

before, sometimes the restaurant manager is not only one person who can decide about the

marketing budget, so the proposal of the mobile application company can stay without response.

Relationships efficacy positively affects seller performance outcomes.

The model clarifies that not only relationship quality but also two other drivers impact the interfirm

relationships on performance. It also shows the increased effect of the interaction of these drivers.

This model describes well the relationship between restaurant establishment and mobile application

holder, but it useless regarding the mobile application user. In general these relationships are

“simple” relationships between two companies about advertising/marketing services for the

133

restaurant. Nevertheless it is important for our research to see the place and role of trust and

commitment inside of this relationship.

Table 26: Adaptation of Interfirm Relationship Marketing theory for the mobile application’s use.

Construct Definition Mobile application’s use

Relationship quality Relationship quality contains the interaction

characteristics required to create a strong

relational bond, such as commitment, trust,

reciprocity norms, and exchange efficiency.

In general the restaurant establishment

pays for the services of mobile

application such as subscribed program

of advertising, call-center or online-

service of table-booking (commitment),

so the restaurant management expect

high quality service, which can mean

updated information, true ratings etc

(trust), and mobile application holder

expect regular renewal of the contract

in case of satisfaction (reciprocity

norms). And both are interested in the

big amount of mobile application users

(exchange efficiency).

Relationship breadth The number of relational bonds with an

exchange partner.

Normally the exchange between

restaurant establishment and mobile

application holder takes place through

sales personal of the mobile application

company and restaurant management.

Relationship Composition The decision making capability of relational

contacts.

For the mobile application company is

important to know all key people in the

restaurant, or restaurant chain.

Relationship strength The interaction between relationship quality

and relationship breadth.

The goal of the sales personal of the

mobile application holder is to find for

each restaurant establishments the

multiple contacts and build high quality

relationships with them.

Relationship efficacy Interaction of relationship quality and

composition.

Having all key people of the restaurant

establishment in data base only is not

enough, if no one of these contacts is

loyal to the propose of the mobile

application company.

Source: completed by author of the thesis based on the references

134

II.4. Interpersonal Relationship Marketing Theory

Most theoretical and empirical RM research relies on models of interfirm relationships, which then

extend to interpersonal or consumer research to suggest that the effects of RM on performance

depend on combination of trust and commitment (Grosby, Evans, and Cowles 1990, De Wulf,

Odekerken-Schröder, and Iacobucci 2001, Garbarino and Jonson 1999, Sirderschmukh. Singh, and

Sabol 2002, Palmatier, 2008, Samaha, Palmatier, Dant, 2011, Beck, Palmatier, 2012).

Interpersonal trust and commitment mediate interpersonal relationships as they do interfirm

relationships, but the explanation of interpersonal RM effectiveness must include other constructs

namely gratitude and norms of reciprocity (Palmatier 2008). At the same time relationship breadth

and composition become less important in interpersonal relationships (Figure 13).

We have already discussed in details the concepts of trust and commitment, so other constructs of

the model of Interpersonal Relationship Marketing are regarded to see their significance for our

research.

Figure 18: Model of Interpersonal Relationship Marketing

Source: Palmatier, 2008

Consumer gratitude. Retailing research say, consumers satisfy their obligation to sales personal by

purchasing (Dahl, Honea, and Manchnda 2005), which means the seller investment in RM makes

consumers feel grateful, which then stimulates them to participate in behaviors that improve sellers

performance (Palmatier, 2008, Beck, Palmatier, 2012). The constructs of commitment and trust

cannot be separated from the concept of gratitude, which positively influences judgments of trust.

Consumer gratitude increases short-term consumer purchasing behavior and also assists consumer

trust and reciprocity norms with long-term effects (Palmatier, 2008, Thomas, Skinner, 2010,

Samaha, Palmatier, Dant, 2011, Beck, Palmatier, 2012). In other words, gratitude represents

“starting mechanism” that influences social behavior as long as emotion lasts and then extends to

135

longer-term effects because it “builds the relationship” (Bartlett and De Steno, 2006) by stimulating

norms of reciprocity in consumer’s mind. Consumer’s gratitude increases RM performance in three

main ways:

(1) consumers purchase to satisfy their feeling of obligation, responding to RM –actuated feelings

of gratitude (Halinen, 2012).

The loyal client will visit the same restaurant especially if he/she has built already personal contact

with waiter, bartender, chef-cook or owner of the establishment, or with all. Feeling of obligation

will not allow him/her to change this establishment till the relationship stay stable.

(2) due to gratitude, the consumer trust and commitment increase and in this way relational

performance outcomes enhance (Raggio et al., 2014). Client of the restaurant can return to the

restaurant and become the loyal client if the personal of the restaurant establishment treat the clients

very welcomed, giving him/her the loyalty card with discount or free meal after several visits or

other propose etc.

(3) gratitude assists the development of norms of reciprocity over longer term and starts a

reciprocation cycle, which has long-term effects on consumer behaviors (Halinen, 2012). Loyal

client will not only return to the restaurant again and again, he/she will recommend it to all his/her

friends, family members, colleagues or random people occasionally. In return of his loyalty the

personal of the restaurant will try to propose a little bit more than just good food, like to keep the

favorite table for the client, to give small snacks/drinks for free, to inform about new menu etc.

Consumer norms of reciprocity. Various studies in different contexts support the importance of

reciprocity norms in decision process (Samaha, Palmatier, Dant, 2011, Halinen, 2012, Beck,

Palmatier, 2012). Norms of reciprocity are effective in RM context because consumer gratitude

increases responding to RM triggers, and thus it enforces the influence of norms on purchasing

decision. Consumers norm of reciprocity have long-term, positive effect on consumer purchasing

behavior that makes them important for success of RM activities (Beck, Palmatier, 2012).

As given in the example above, client’s gratitude develops the norm of reciprocity. What is

important to underline the client of the restaurant will expect some return from the restaurant, like if

he/she is visiting the establishment regular so he/she will expect to be recognized, for example, or

to get loyal card, etc. At the same time the breakdown of these norms from the side of the restaurant

leads probably in the rejection to visit the restaurant in the future.

The model of interpersonal RM integrates gratitude from psychology and norms of reciprocity from

sociology to present a framework in which two other relational drivers other than trust and

commitment influence performance (Palmatier, 2008, Halinen, 2012). Consumers’ gratitude and

norms of reciprocity take central role in interpersonal RM and help to explain the strong empirical

and managerial support for the impact of interpersonal relationships on the decision making.

136

The Model of Interpersonal Relationship Marketing theory is useful only for the relationship

between restaurant establishment and client and loses importance in the context of mobile

application use. Anyway it shows the role of trust and commitment and their influence on this

relationship.

Table 27: Adaptation of Interpersonal Relationship Marketing theory for the mobile application’s use.

Construct Definition Mobile application’s use

Consumer Commitment

and Trust

Consumer commitment is belief that an

ongoing relationship with seller is enough

important to participate in behaviors that

improve sellers performance. Consumer

trust is confidence in a seller’s reliability and

integrity.

Commitment of the restaurant’s client

makes him/her leave reviews about the

restaurant on the mobile application, or

grade it because client believes that it

can improve the relationship.

Restaurant’s client trusts the mobile

application as technology.

Consumer gratitude Obligation of consumer to sales personal by

purchasing.

Since the mobile application replaces

the personal contact on the first step in

the relationship between restaurant and

client, such tools as discounts or happy

hours can create the consumer

gratitude, but the long-term relationship

needs to have personal contact

according to the interpersonal RM

theory.

Consumer norms of

reciprocity

The concept in the sociology saying that one

should help those who have helped him/her

in the past and retaliate against those who

have been detrimental to his/her interests.

Such performances of client as leaving

reviews, or grading on the mobile

application represent the consumer

norms of reciprocity.

Source: completed by author of the thesis based on the references

II.5. Multilevel relationships.

The model of interfirm RM is described through relational quality (trust, commitment), breadth, and

composition. But each interfirm connection represents an interpersonal match, so the model of

interfirm relationship could be extend by integrating gratitude and reciprocity into a multilevel RM

model.

Relationships often develop and operate at multiple levels within one exchange (Figure 19).

137

Figure 19: Multi-level exchange relationships

Source: Palmatier, 2008

For example, firm 1 is a provider of mobile application, firm 2 is a restaurant. The two firms firstly

have interfirm relationships, the provider firm can develop specific relationships with the owner or

manager of the restaurant, having the person in the data base of loyal clients no matter if the

establishment by its self works or the manager changes the work-place (boundary spanner 1), in this

way the individual-to-firm relationship takes place. Also the owner or executive director of the

provider firm might have originally personal relationship with owner of the restaurant (boundary

spanner 2 and N). All this relationships can exist at the same time and actually will influence the

consumer purchasing behavior and outcomes.

Depending on the relationship level or target (individual, group), different processes influence the

RM according the social judgment theory (Hamilton and Sherman 1996, Lickel, Hamilton, and

Sherman, 2001, Crump, Hamilton, Sherman, Lickel, Thakkar, 2010, Bitektine, 2011). Consumers

judgments about individual salesperson form differently than their about the firm in general.

Consumer judges the individual’s action using all available or perceived information beginning

with the first meeting. In contrast the judgments about a firm episodic and demand less information.

Thus relationships with individuals (salesperson) have a greater direct effect on relational

behaviors, and also indirect impact on outcomes.

Another important difference between individual- and firm-level relationships involves the concept

of loyalty (Boora, Singh, 2011, Sabir, 2015). Individual-level relationships have a stronger impact

on behaviors and outcomes, but they also more sensitive to disruption as a result of individuals

138

employee turnover. The customer loyalty can be regarded as salesperson-owned loyalty, when the

consumer develops personal relationship with a specific person; seller-owned loyalty, loyalty to the

seller specifically, independent of the salesperson; and synergistic loyalty, loyalty arose by the

benefits which customer can have (Halinen, 2012, Beck, Palmatier, 2012)

Relationship between restaurant and client can be interpersonal (between client and

waiter//bartender/chef/owner) or individual-to-firm (when client is loyal to the place by itself), and

very seldom interfirm (in case when the particular company is the client of the restaurant: official

events, business dinner, special reception etc.). However the loyalty is indispensable aspect for the

relationship between restaurant and client, and because of the decisive role of individual (client)

such relationship are sensitive to disruption.

Relationship between client/user and mobile application holder is always individual-to-firm.

Moreover it is the relationship of the user with the technology, when the user doesn’t know any

person staying behind the technology. That means, that the user doesn’t develop personal

relationship, and as consequence his/her loyalty is very weak and based only on the benefits, which

the mobile application can provide (like comfortable interface, discounts, saving time etc.).

If we go back to the communication steps described in the beginning of this chapter, we will see

that three of them (choice, taking contact and feedback) are replaced in the traditional relationship

by mobile application. Thus, the mobile application takes important role in the Relationship

Marketing of the restaurant, and therefore the development of its relationship with the mobile

application holder will impact on its marketing program inside of the application. In other words, to

create high quality relationship with the client is possible with help of mobile application, if the

restaurant establishment can develop high quality relationship with the mobile application holder.

II.6. Information technology and RM

The use of IT in RM is increasingly important for this research, because mobile application is in the

heart of the relationships between restaurant management and clients. In contrast with interfirm,

individuals-to-firm or interpersonal relationships, where the IT is regarded as added service, in the

studied case the use of IT is actual service.

It is of significance for organizations to consider customer satisfaction with IT because this will

contribute toward satisfaction with the relationship, commitment to the relationship and trust or the

relationship – all key constructs in Relationship Marketing. Use of IT impacts on relationships,

particularly in terms of trust and commitment. Furthermore, if something went wrong with IT

services, customers require human beings to assist (Bitner, Ostrom & Meuter, 2002). Interaction is

necessary in relationship marketing, regardless of whether this interaction is face-to-face or not. As

we concluded in the section about the technology use, the construct of facilitating conditions

139

moderates use behavior of users. So, during the use of mobile application for the restaurant the user

should be sure, that in any failure cases there are the personal to support him/her. That is connected

with the concept of Self-Service Technology, and the mobile application is one of it. So if the

technology in our case the mobile application fails, the customer is often dissatisfied with both the

failure and resultant action by the firm; the way in which these problems are solved can have large

implications for customer evaluation of a firm as well as impacting on the overall perception the

customer has about the firm. As already mentioned above, a role of moderator in the relationship

between restaurant and client plays the mobile application holder, so this fact eliminates direct

impact on the relation. In other words, if in case of technology’s failure the responsibility takes the

mobile application holder and not the restaurant.

Trust and the resulting evaluation of the firm by a customer are fundamental in reducing perceived

risk in the service. Solving that problem in the technology use must be effective to build trust and

commitment in the relationship (Morgan and Hunt, 1994, Chenet, Dagger, O'Sullivan, 2010,

Gummesson, 2011, Vivek, Beatty, Morgan, 2012). Therefore, where there are problems with

technology, it is essential for marketers to deal with these issues effectively so as to maintain a

positive relationship. Moreover, dissatisfaction with technology use impacts on the customer’s

perception of the service delivery. This means that a complete understanding of technology

requirements is necessary in order to implement effective Relationship Marketing.

II.7. Conclusion.

Table 28 assumes the variables of RM, which are of interest for the use of mobile application in the

restaurant business.

In general only two of constructs so far are interesting for present research: trust and commitment.

Both are necessary variables of Relationship Marketing and therefore are presented in all RM-

strategies, also in ones provided by technology. Without trust the RM outcomes cannot be

developed, without commitment there is no object of RM. Two other constructs included in the

table 2 are taken from the Interpersonal model of RM because they enforce the variables of trust

and commitment and influence the performance, but they lose their significance in case of

technology’s use, so are probably not appropriate in the future model. We will keep them to see in

further research, if they become more important or not.

We already discussed the mobile application loyalty in the previous part. In Relationship Marketing

the loyalty is indispensable aspect for the relationship between restaurant and client, and because of the

decisive role of individual (client) such relationship are sensitive to disruption. In our case, when the

relationship are built through the mobile application the concept of loyalty is more important as loyalty

140

towards the technology, because we need to investigate the role which the mobile application can play

in the catering sector.

Table 28: Selected constructs of the RM theories.

Construct Definition Theories Positive aspects Negative

aspects

Conclu-

sion

1 Trust Confidence in an

exchange partner’s

reliability and integrity

The Commitment-

Trust Theory of

Relationship

Marketing. Morgan

and Hunt (1994),

Interfirm and

Interpersonal

Relationship

Marketing theories

(Palmatier, 2008,

Halinen, 2012, Beck,

Palmatier, 2012)

This construct is

important for all

kind of

technology’s use:

professional or

non professional.

Also is has

connection with

construct of

facilitating

conditions of

UTAUT 2.

- yes

2 Relation-

ship

commit-

ment

An exchange partner

believing that an

ongoing relationship

with another is so

important as to warrant

maximum efforts at

maintaining it.

An enduring desire to

maintain a valued

relationship

The Commitment-

Trust Theory of

Relationship

Marketing. Morgan

and Hunt (1994),

Interfirm and

Interpersonal

Relationship

Marketing theories

(Palmatier, 2008,

Halinen, 2012, Beck,

Palmatier, 2012)

The construct is

key one in the

RM. Especially

important for

services, which is

actually the use of

mobile

application.

This construct

plays more

significant role

in the

professional

relationship,

like interfirm. In

individual-to-

firm relationship

commitment is

often onsides.

Maybe

3 Consumer

Gratitude

Obligation of consumer

to sales personal by

purchasing

Interpersonal

Relationship

Marketing theory

(Palmatier, 2008,

Halinen, 2012, Beck,

Palmatier, 2012)

Influence

positively the

trust in long-term

relationship

Very seldom to

develop when

we speak about

use of mobile

application in

the restaurant

business

No

4 Consumer

Reciprocity

norms

The concept in the

sociology saying that

one should help those

who have helped

him/her in the past and

retaliate against those

who have been

detrimental to his/her

interests.

The Norm of

reciprocity: a

preliminary

statement, (Alvin W.

Gouldner, 1960,

Thomas, Skinner,

2010, Samaha,

Palmatier, Dant,

2011),

Interpersonal

Relationship

Marketing theory

(Palmatier, 2008,

Halinen, 2012, Beck,

Palmatier, 2012)

In the

interpersonal

relationship

influence rather

than trust and

commitment on

performance.

This concept is

limited in case

of technology

use.

No

Source: completed by author of the thesis based on the references

141

III. Cultural theories.

The cultural concept is necessary for our research to analyze whether the technology usage of

mobile application and RM is related to national or cultural specificity of the researched countries

or not. The main goal is to see firstly if there are critical differences, and secondly to choose which

differences might have influence on the consumer behavior.

There are many definitions of the culture. "Culture is the collective programming of the mind that

distinguishes the members of one group or category of people from others". (Hofstede, 2001). The

most common the term culture is used to refer to the culture of tribes or ethnic groups (in

anthropology), the nations (in political science, sociology and management) and organizations (in

sociology and management). Less researched is the culture of professional groups (engineers,

compared with accountants or representatives of various academic disciplines). The term also

applies to gender, age or social group.

The culture contains selected list of values, specific for the group. The classic concept of values in

anthropology was introduced by Kluckhohn and Strodtbeck (1961). According to them values

answer basic existential questions, helping to provide the meaning in the people’s lives. Economists

regarded values as quality of the objectives used in social exchange (Stingler, 1950, Spash, 2000,

Throsby, 2001, Davis, 2013, Von Wieser, 2013). So objectives have value but people have

preferences, which establish hierarchies of goods. Sociologists distinguished a different conception

of values. For them values are believed to help people ease the conflict between individual and

collective interests. Values are important because they guide people’s behavior within one culture

(Parsons and Shils, 1951, Hitlin, Piliavin, 2004, Schwartz, 2012). Despite the fact that the cultures

might be different, the researches worked out the universal values presented in all cultures to a

varying degree.

The concept of cultural value impacts the behavior of the individuals, so it can predict also the use

of technology, implementation, adoption or rejection of it. The use of any mobile application within

one culture can be the same, for example all the users use mobile application for gaming, or social

networking, or searching for information. The cultural differences start to be significant inside of

the sectors, in our case in the restaurant sector. Of course there might be differences in the culture

rites and rituals to visit the restaurant in general, e. g. according the occasions, or day-time, or

restaurant-types. For the present research the most important is the influence of the culture on the

use of technology, and RM.

To see the importance of the culture influence we are going to regard:

- The concept of the modern Russian values;

- The concept of the modern French values;

- Cultural dimension of Hofstede.

142

- Impact of Hofstede’s cultural dimension on technology use and on relationship marketing.

III.1. Cultural values concept in researched countries.

Firstly is it important to regard the basement of cultural values in both countries. During decades

the sociologists, philosophers and anthropologists developed the theories of cultural values. That

values influence the general processes inside of the country and they determinate as well the life of

individuals.

III.1.1. Orthodoxy, Autocracy and Nationality of Russia

In the XIX century the theory of official nationality of Russian empire appeared which determined

Russian cultural values for long time. "The Triad" Orthodoxy, Autocracy and Nationality

(Russian: Правосла́вие, самодержа́вие, наро́дность, Pravoslaviye, Samoderzhaviye, Narodnost′)

was originally proposed by Minister of Education Sergey Uvarov in his letter to subordinate

educators in April 2, 1833 (Miller, 2012) as the antithesis of the national motto of The French

Revolution "liberty, equality, fraternity" (фр. Liberté, Égalité, Fraternité). The motto gained wide

public recognition, and was supported by intellectuals like Mikhail Pogodin, Fyodor

Tyutchev and Nikolai Gogol.

The values of modern Russian society are a combination of values started in XIX century and soviet

values. V. Kotlarova (2010) distinguishes following cultural values of modern Russian society:

- Orthodoxy. For Orthodoxy such characteristic are typical: communalism, equality of people,

spirituality. Russian people perceive themselves more as a spiritual and religious community, and

not as ethnic. Communalism means a moral community, subordinated the interests of the church,

religion. In the bright meaning this values denies the materialistic activeness of the person.

- Nationalism as a love for people and for country. In Russian culture, nationality logically

combined with patriotism.

- Patriotism is understood as a sense of national pride, as a means of national identity, as a value,

which determines the life. For Russian culture patriotism has always been an indicator of their

value-ideological spirituality. Russian people used to love their country not for financial reward, but

because it was given to them by God, and because they were born here (V. Kotlarova, 2010).

- Triarchy of values: family, friendship and love. These values are promoted strongly by

government, through social programs of financial help for young families.

-Freedom. It should be noted that freedom of Russians is recognized as an asset due to existing

traditions and peculiarities of Russian mentality; not as a political or legal value (freedom of

thought or freedom of speech). Freedom as a key concept of the European mentality is not just a

choice for yourself, but also respect of the same rights of others, what means freedom for all,

143

protection of the freedom of others like your own. In the culture of the Russian people the main idea

of freedom includes in the first place a freedom concerning the one person and disinterest to other

people's freedom, disinterest in the wide meaning from indifference to repression.

- Statehood. For the culture of the Russian people the “fetishizing” of state power is typical.

Russian people lived many years with the idea of "people's king" (tsar), who is able to suffer for the

interests of the nation, to organize resistance to external enemies and protect people from arbitrary

action by bureaucrats. Therefore it is logical that the greatest value is the strength and stability of

the Russian state, the stability of society and the state as a strong guarantor of confidence in the

future.

Berdyaev (1992) noted that Russia is located in the middle of the two streams of world history - the

east and west; therefore Russian society is often characterized by a combination of the values of

Western and Eastern cultures in the wide meaning.

At the same time the philosophy of “special way” became famous between intellectuals in Russia.

An important impetus for that was the victory over Napoleon in the war of 1812. It became

obviously that despite the fact that Russian troops entered Paris, autocratic feudal Russia still could

not compete with Europe (Belyaev, 2012). The need for ideological and cultural compensation

increased, which is reflected in the slavophilistic idea of a "special way", highlights of which were

the glorification of Russia's past, its grand messianic vision of the future, revealing vices of

European civilization compared to Orthodox Russia.

The idea of a "special way" is understood as the implementation mechanism of the emerging

national idea, as a phenomenon, which replaced the perception of backwardness of the country

(Belyaev, 2010, Miller, 2012). The idea of “special way” became important again after the Soviet

Union collapse to protect the feeling of national proud.

The listed values show in the fist way that the Russian society tends to be traditional with strong

collectivistic characteristics. This finding proves dual impact on the penetration of the new

technologies in the wide meaning: on the one side the strong government control makes it easier to

spread and develop the technologies, but mostly thus which answer the needs of the state, on the

other side the individual use of the technology, what is the case of mobile application use, can be

much slower, because the personal achievements play limited role.

III.1.2. Culture values of France

According to G. Mermet (1996) the French society can be characterized by five main values:

community, authority, singularity, action, and pleasure.

Community. This value has historical roots, and it is characterized by a sense of belonging, a wish

to own, and the collective virtues.

144

Authority. This value is typical for older generation, and means the following instruction and rules

in professional life. It is characterized by the need in order, which decline in two modes: the

spiritual order and social order.

Singularity. This value is spread widely among younger generation and urbanites. It is manifested

in the need of admiration, critical evaluation, and wish of differentiation.

Action. This value means pragmatism, wish of power, and wish of conflict or confrontation. It is

more typical for urbanites, also pragmatism is peculiar more for older generation while wish of

power is peculiar more for young people, confrontation is more typical for men than for women.

Pleasure. This includes searching for impression, idealistic dream, cultural elevation.

G. Mermet (1996) considers also that for 30 years in France happened 7 revolutions:

individualization, feminization, globalization, technologization, consumerism, " horizontalization"

and "zapping".

Individualization refers to the increasing prevalence of I above We.

Feminization means not only the rule of women in the society, but also qualities and values,

which are traditionally considered as feminine (intuition, pacifism, humility, practicality).

Technologization influenced all sides of life. It freed and enslaved people at the same time.

Despite the fact that French people have negative attitude to globalization, this process affected

deeply the society.

Consumerism appeared together with high standards of life. Development of mass media, high

technologies, and ease of life had as consequences the desire to buy and own things.

For a long time, French society was upright, pyramidal and with a clear hierarchy. And while

authority and power distance are still very strong in the French society, the process of

horizontalization takes place little by little.

Professional, family, social and private lives now consist of rapid changes. People change easily

jobs, places to live, partners, habits and hobbies. A new word appeared “le zap”. “Zapping” of the

modern society means the quick change of everything.

Sociologist J.-L. Excousseau (2000) proposed to classify the French people in four generations. At

each generation corresponded different sensitivity:

- Generation of heritage. They were born before 1941. They experienced the Roaring Twenties,

the 1929 crash, wars, the appearance of "miraculous" the consumer society. This explains that this

generation is more interested in the possession - for "having" - by introspection. Hence they have

difficulty in transmitting the power.

- Generation of nature. Born between 1942 and 1967, they are individualistic and protesters. They

made May 68 against the consumer society. This is the generation of sexual freedom, feminism, and

145

the consolidation between the sexes. Having preserved their adventurous mindset, they get excited

today to the Exchange and the great capitalist thrill.

-Generation of networks. Born between 1968 and 1976, they are much less confidence in the ego.

Increasingly turned to the other, they organize networks to fight against certain failings of society.

Pragmatic and responsible, they are committed to ecology, against epidemics, poverty and

exclusion.

- Mosaic generation. Born after 1977, they live in a world where music and audiovisual media are

omnipresent. They were born in social changes and easily adapt to it. To feel comfortable in this

unstable world, they multiply their memberships in groups (music, skating club, Internet discussion

groups). They are a generation of miscegenation, open and infinite tolerance.

Listed values of French society are easy to correlate with variables of UTAUT2, such value as

pleasure is correlated with hedonic motivation, consumerism and pragmatism - with price value,

community - with social influence as well as singularity - with performance expectancy.

III.1.3. Conclusion

The overview of cultural values based on the philosophical and sociological points of view doesn’t

provide us useful construct for the researched model. Anyway the main findings are important for

the understanding of cultural dimension theory of Hofstede.

Firstly, we can conclude that this overview shows that two cultures have different basement of

modern values despite the process of globalization and spread of modern technologies.

Secondly, one of the purpose of this research to see whether the use of technology eliminate the

cultural differences or not. Therefore the comparison of two countries with different cultures is

more interesting.

And thirdly, the use of mobile application in the restaurant sector is a part of individual everyday

life, so the general cultural values can lose their meaning in everyday life, as for example, Russian

society is regarded as collectivistic, but it doesn’t mean that the people don’t have private property.

Anyway to research the impact on the use of technology and Relationship Marketing in the

restaurant business we should choose universal system of values or cultural characteristics,

therefore the cultural dimensions of Hofstede are more convenient for maintaining the experimental

integrity.

III.2. Cultural dimensions theory by Gert Jan Hofstede (2001)

Hofstede and Minkov (Hofstede, 2001, Hofstede and Minkov, 2010) developed the cultural

dimensions theory providing a systematic basis for evaluating the differences between nations and

cultures. The theory is based on the idea that the value may be distributed over six cultural

146

dimensions. Most of the information about the world's cultural values Hofstede received from a

survey conducted by IBM, the US technology and consulting corporation. Both countries Russia

and France are included in his survey, and the data can be used for our case. He suggested rating

system on a scale of 1 to 120.

Figure 20: Cultural dimension

Source: Hofstede & Minkov (2010).

These dimensions include:

Individualism vs. Collectivism: “the degree to which people in a society are integrated into groups”

(Hofstede, 2010). This dimension is not related to politics and concerns more groups than

individuals. Culture with individualism tends to greater importance of the achievement of personal

goals. In societies which are characterized by collectivism, social welfare is placed above personal

interests (Brewer, Venaik, 2011).

With a lower score of 39 Russia (Figure 21) has collectivistic culture. Family, friends and the

neighborhood are extremely important to get along with everyday life’s challenges. Relationships

are crucial in obtaining information, getting introduced or successful negotiations. Russians need to

be personal, authentic and trustful before one can focus on tasks and build on a careful to the

recipient, rather implicit communication style.

France, with a score of 71, is shown to be an individualist society. The French combination of a

high score on Power Distance (see below) and a high score on Individualism is rather unique. This

combination is not only unique, but it also creates a contradiction. For examples in the area of

customer services the French culture is poor in the eyes of all others individualistic cultures who

believe that the customer is king. The French are self-motivated to be the best in their trade. They,

therefore, expect respect for what they do, after which they are very much willing to serve you well.

147

Figure 21: Hofstede’s dimension. Russia in comparison with France.

Source http://geert-hofstede.com/

Power distance. According to Hofstede (2010) “power distance is the extent to which the less

powerful members of organizations and institutions (like the family) accept and expect that power

is distributed unequally". This dimension does not characterize the level of the distribution of power

in a given culture, but rather, it examines the perception of society. Low power distance index

means that the culture expects and accepts democratic relations with the power and all members of

the society are treated as equal. A high power distance index means that less power vested members

of the society take their place and realize the existence of formal hierarchical structures (Rinne,

Steel, Fairweather, 2012).

Russia, scoring 93, is a nation where power holders are very distant in society. The huge

discrepancy between the less and the more powerful people leads to a great importance of status

symbols.

France, with a score of 68, has high Power Distance in comparison with other countries in Europe.

Power like in Russia is centralized in the capital – Paris. Many comparative studies have shown that

French companies have normally one or two hierarchical levels more than comparable companies in

Germany and the UK.

Uncertainty avoidance. “Uncertainty Avoidance is not the same as risk avoidance; it deals with a

society's tolerance for ambiguity” (Hofstede, 2010). This dimension describes the public reaction to

unfamiliar situations, unexpected events, and the pressure for change. Culture, for which the index

is high, is less tolerant to changes and seeks to avoid the anxiety that brings uncertainty, by setting

strict rules, regulations and / or laws. The low index shows that the culture is more open to change,

and uses less of rules and laws (Myers, Tan, 2003, Litvin, Crotts, Hefner, 2004, Im, Hong, Kang,

2011).

Scoring 95 Russians feel very much threatened by ambiguous situations, as well as they have

established one of the most complex bureaucracies in the world. Detailed planning and briefing is

148

very common in negotiations. Russians prefer to have context and background information. As long

as Russians interact with people considered to be strangers they appear very formal and distant. At

the same time formality is used as a sign of respect.

At 86 French culture scores high on Uncertainty Avoidance too. As a consequence, the French are

good in developing complex technologies and systems in a stable environment, such as in the case

of nuclear power plants, rapid trains and the aviation industry. There is a strong need for laws, rules

and regulations to structure life. This, however, doesn’t mean that most French will try to follow all

these rules because of high score in individualism.

Masculinity vs. femininity. “Masculinity as a societal, not as an individual characteristic, refers to

the distribution of values between the genders which is another fundamental issue for any society,

to which a range of solutions can be found” (Hofstede, 2010). This dimension characterizes the

level of importance of traditional male values such as assertiveness, ambition, lust for power and

materialism, and traditional female values such as human relations, culture (De Mooij, Hofstede,

2011).

Russia’s relatively low score of 36 may surprise with regard to its preference for status symbols, but

these are in Russia related to the high Power Distance. Russians understate at workplace as well as

when meeting a stranger their personal achievements, contributions or capacities.

With a score of 43, France has also more Feminine culture. At face value this may be indicated by

its famous welfare system.

Long-term orientation vs. short-term orientation. This dimension describes the time horizon of

society. Culture, focused on short-term, value traditional methods, to devote much time to

developing relations and considering the whole time as a vicious circle. This means that the future

and the past are linked to each other, and that cannot be done today can be done tomorrow. The

opposite of this is a long-term orientation towards the future, at which time is considered as a

vector, and people tend to look to the future more than the present or interested in remembering the

past. Such a society is focused on achieving the goals and appreciates the results (Hofstede and

Bond, 1988, Hofstede, Minkov, 2011, Cannon, Doney, Mullen, Petersen2010).

With a very high score of 81, Russia is definitely a country with a pragmatic mindset. France

scores 63 in this dimension, making it pragmatic also.

Indulgence vs. restraint. This dimension describes the ability of culture to meet the immediate

needs and personal desires of society. In societies where restraint is a value prevail strict social rules

and norms, within which self-gratification constrained and are not encouraged (Hofstede, Minkov,

2010).

The Restrained nature of Russian culture is easily visible through its very low score of 20 on this

dimension. Societies with a low score in this dimension have a tendency to cynicism and

149

pessimism. Also, in contrast to Indulgent societies, restrained societies do not put much emphasis

on leisure time and control the gratification of their desires.

France scores in the middle 48, but 2, 5 more than Russia, but still lower than normally is expected.

This, in combination with a high score on Uncertainty Avoidance, implies that the French are less

relaxed and enjoy life less often than it is commonly assumed.

The Hofstede’s dimensions confirm some of our finding in previous section, like collectivistic

nature of culture, strong role of the state and government (power distance) in Russia, or singularity

(individualism) and authority (power distance) in France. Moreover the analyze of culture from the

point of view of Hofstede’s dimensions shows us also unexpected similarities in both countries like

almost the same score of uncertainty avoidance and domination of feminine characteristics.

Many authors (e. g. Martin, 1996, Leidner, Kayworth, 2006, Castells, 2011, Lyon, 2013, Webster,

2014) predict the equation of societies in connection with the development of new technologies.

High technology is a major driving force behind cultural change that leads to a similarity inside of

different societies, but there is not the evidence that it blurs the distinction by other dimensions.

Moreover, it could lead to further divergence during societies modernize technical experience, on

the basis of already existing values.

To choose significant constructs in Hofstede’s culture dimension theory we need to regard its

implication in use of technology and in relationship marketing.

Table 29: Hofstede’s dimensions.

Dimension Definition Score Conclusion

France Russia

Individualism vs.

Collectivism

The degree to which people

in a society are integrated

into groups.

71 39 Compared with Russia France is

individualistic culture, this

difference can be interesting for

our comparison.

Power distance Power distance is the extent

to which the less powerful

members of organizations

and institutions (like the

family) accept and expect

that power is distributed

unequally.

68 93 Stronger in Russian culture than

in French one, where it is still

pretty high compared with other

countries of EU. Visiting

restaurant is the personal issue,

so this dimension will probably

have weak significance for our

comparison.

Uncertainty

avoidance

Uncertainty Avoidance is not

the same as risk avoidance; it

deals with a society's

tolerance for ambiguity.

86 95 Both countries score high. This

similarity can be interesting.

Masculinity vs. Masculinity as a societal, not 43 36 Both countries are regarded as

150

femininity as an individual

characteristic, refers to the

distribution of values

between the genders which is

another fundamental issue for

any society, to which a range

of solutions can be found.

more feminine cultures than

masculine. Since this dimension

is related with concepts of

achievement it is better to

explain the professional

relationship, so it can be useful.

Long-term orientation

vs. short-term

orientation

This dimension describes the

time horizon of society.

63 81 Both countries are long-term

oriented, what is connected with

achievement of present time

according Hofstede.

Indulgence versus

restraint

This dimension describes the

ability of culture to meet the

immediate needs and

personal desires of society.

48 20 France scores almost two times

higher than Russia. This

dimension can be interesting in

the way how it is connected

with society’s norms and rules

(also the relation between

leisure time and working time).

Source: completed by author of the thesis based on the http://geert-hofstede.com/

III.3. Adoption of communication technologies and national culture, TAM and Hofstede’s cultural dimensions

Hofstede investigated the question what attributes of countries influence the differential speed at

which they adopt new communication technologies (Hofstede, 2001). The five dimension of his

cultural dimensions theory were examined according to their impact on the spread of

communication technologies. He hypothesized that dimensions would have following impact:

a) individualism and power distance are associated with the spread of all communication

technologies. This relation is moderated by GNP per capita;

b) individualism and power distance will still be predictors of penetration of communication

technologies, when corrected GNP per capita;

c) uncertainty avoidance will correlate negatively with the spread of new technologies;

d) masculinity will not correlate significantly with the spread of communication technologies;

e) long-term orientation will correlate significantly with the spread of newspapers.

The new technologies expand through the countries differently, and not only because of differences

in wealth. National culture plays an important role. Hofstede’s strongest statements about the role

of national culture on technology adoption relate to uncertainty avoidance. This dimension of

national culture predicts the speed of adoption a new technology, the higher a country scores on

uncertainty avoidance, the slower will it be in adopting a new technology. In our interest is to

151

analyze the speed of adoption of the mobile application in one particular sector, restaurant sector.

From the standpoint that the technology by itself has been already adopted by users in both

countries, its adoption can be different depending on the sector. Restaurant business requires

personal relationship between restaurant establishment and client, so together with dimension of

uncertainty avoidance this relationship will slow down the adoption of the mobile application. Both

counties score high in the dimension of the uncertainly avoidance, what can mean that both cultures

prefer to build traditional relationship between seller and client, or prefer traditional instruments,

before they adopt new ones.

The level of penetration of the communication technologies Hofstede (2001) explains with

dimension of individualism/collectivism. In individualistic cultures this level is stronger than in

collectivistic ones. In our case this finding means, that once the mobile application was adopted by

the users, it can be used in all sectors easily in France with high score of individualism, and it can

be used with restrictions or occasionally in Russia with low score of individualism.

Power distance plays weak role. Both countries score relative high. The restaurant sector is regarded

as personal issue as well as use of mobile application, so this dimension is not important for us.

Wealth, individualism and small power distance are strongly correlated, but high individualism

predicts penetration of established communication technologies and Internet host even after

correcting for GNP/cap (Hofstede, 2001).

Masculinity correlates positively with the spread of devices for one-way communication (faxes),

and negatively with that of devices for two way communication (mobile phones). France and Russia

are feminine culture, and mobile application is the two way communication technology, so this

finding is not important for our research.

Long-term orientation was analyzed by Hofstede (2001) in his research with correlation with the

spread of newspapers, these findings are not important for our research.

Scoring 95 and 86 in uncertainty avoidance Russia and France have both slow speed of adoption a

new technology. In the dimension of individualism Russia scores very low (39 out of 120), that

means that it has weak level of penetration of the communication technology, conversely France is

with a score of 71, is shown to be an individualist society and has strong level of the technology

penetration. Thus, two dimensions are of importance for our research: uncertainty avoidance and

individualism/collectivism according to their impact on technology use.

Important finding presented in this research of Hofstede is that his findings are relevant even after

correcting for GNP/cap, because investigated countries have different level of economies (see 1

chapter).

152

Table 30: Hofstede’s dimensions and mobile application use.

Dimension Impact on technology use Mobile application use

Individualism/Collectivism The level of penetration of the

communication technologies is higher in

individualistic cultures.

France represents individualistic

culture while Russia is collectivistic

one. The penetration of the mobile

application in both countries is

different. We concluded that speed of

adoption can be relative the same, but

from the point of view of this

dimension, ones the technology is

adopted in France it can be used

everywhere with no difficulties, while

in Russian culture adopted technology

can be used only occasionally or in

restricted sectors.

Power distance Power distance plays weak role. Both countries score relative high. The

restaurant sector is regarded as personal

issue as well as use of mobile

application, so this dimension is not

important for us.

Uncertainty avoidance The higher a country scores on uncertainty

avoidance, the slower will it be in adopting a

new technology.

Russia and France score high,

concerning the mobile application use

we can conclude, that at the moment

this technology by itself has been

already adopted by both countries, but

it can mean, that the speed of mobile

application adoption will be slow in

one particular sector, in our case in the

restaurant sector.

Masculinity/Femininity Masculinity correlates positively with the

spread of devices for one-way

communication (faxes), and negatively with

that of devices for two way communication

(mobile phones).

Both cultures are feminine, and mobile

application is the two way

communication technology, so this

finding is not important for our

research.

Long-term orientation

/Short-term orientation

Correlates significantly with the spread of

newspapers.

News papers are not important for our

research.

Source: completed by author of the thesis based on the references

III.4. The role of culture in RM

In researched case the additional variables are the cultural dimensions, because the same type of

relationships can be different in two researched countries, for example we know already that

153

Russian culture as collectivistic one has weak level of penetration of the communication technology

(mobile application in our case) compared to France, but we are interested to analyze how for

example this dimension proceeds the Relationship Marketing, whether the aggregate impact of the

culture dimensions on both, technology and RM, will enforce difference between two counties or

eliminate it.

The comparison of two countries demands to analyze the role of culture in relationships marketing

as well. The research by Samaha, Beck, and Palmatier (2012) concerning The Role of Culture in

International Relationship Marketing is regarded to accomplish the analysis. For our research the

finding about cultural influence are of interest, rather than the international relationships. To

investigate the role of culture the researcher used the cultural dimension of Hofstede, what also is

lying behind the interest of our study.

A country’s culture shapes people’s perceptions, dispositions, and behaviors. Because RM

interactions are social exchanges, culture influences the norms, roles, and expectations of these

relationships. Culture also influences the types of socially engaging and disengaging emotional

processes that people experience (Kitayama, Mesquita, and Karasawa 2006.)

In the conceptual model (Figure 18) the moderating effects of each construct were tested in the

linkage with trust and commitment, or relational mediators. The Model has five relational

antecedents and two outcomes. The five antecedents are relationship investments, communication,

dependence on seller, seller expertise, and relationship duration.

Relationship investments capture the “seller’s time, effort, spending, and resources focused on

building a stronger relationship,” and communication refers to the “amount, frequency, and quality

of information shared between exchange partners” (Palmatier et al. 2006). Seller investments often

generate gratitude and cycles of reciprocation that increase trust and social bonds. Communication

increases trust and commitment (Rauyruen, Miller, 2007, Thomas, Skinner, 2010, Halinen, 2012,

Beck, Palmatier, 2012).

Dependence on seller refers to the “customer’s evaluation of the value of seller-provided resources

for which few alternatives are available from other sellers” (Palmatier et al. 2006, Beck, Palmatier,

2012).

Seller expertise is the “knowledge, experience, and overall competency of the seller” (Palmatier et

al. 2006, Beck, Palmatier, 2012). Expertise enhances relational mediators by increasing the

perceived credibility of seller claims and establishing the superiority of a seller in a market (Crosby,

Evans, and Cowles 1990, Gummesson, 2011).

154

Figure 22: International RM framework.

Source: Stephen A. Samaha, Joshua T. Beck, & Robert W. Palmatier (2012)

Relationship duration, which is the “length of time that the relationship between the exchange

partners has existed” (Palmatier et al. 2006), enhances relationships by providing exchange partners

with opportunities to learn about seller capabilities and motives, thereby confirming expectations

and reducing risk in the relationship (Doney and Cannon 1997, Gummesson, 2011, Beck, Palmatier,

2012).

The model presents two performance outcomes: WOM and performance.

Word of mouth is the “likelihood of a customer positively referring the seller to another potential

customer” (Palmatier et al. 2006). Relational mediators increase WOM because customers develop

a desire to promote preferred sellers over competitors, communicate access to relational resources

(e.g., preferred treatment, insider knowledge), and demonstrate their opinion leadership to others

(Hennig-Thurau et al. 2004).

Performance refers to improvements in outcomes such as sales, share of wallet, profit performance,

and other positive changes to the seller’s business. Relational mediators influence positively on

performance because the reciprocity norms that govern the relationship increase customers’ desire

to reward relational partners with repeat business and higher margins (Palmatier et al. 2009).

In regard to use of mobile application restaurant improves its relationship with the user of mobile

application through activities on the application (relational antecedents):

155

- service package (relationship investment), so the restaurant can subscribe by the mobile

application company different services starting with basic package (just general information

about the restaurant) and till the advanced subscription (includes advertising, call-center,

online-booking etc);

- communication activities are depended on the restaurant management, some of the managers

control the communication inside of the application, answer the question of the users,

apologize in failure cases, provide additional information, some of the managers don’t

participate at all in the communication;

- restaurant manager can provide competitive information to win the users (dependence on

seller), for example special menu for children, additional discounts, happy hours etc.

- rating (seller expertise) is not direct the expertise provided by restaurant of course, but the

high rating inside of its group (e. g. best pizzerias) makes the restaurant expert in this area;

- how much the restaurant management respect the user’s history (relationship duration) on

the mobile application can give the restaurant advantages, for example whether the user is

active on the application, how many reviews he/she has left etc, whether he/she is registered

in the data base of the restaurant after visiting it etc.

As regarded outcomes the examples are:

- WOM: published reviews and grading of the restaurant by user after the visiting. The

management of the restaurant can promote this aspect giving small “gifts” for leaving

review or grading;

- Performance: the best performance for the restaurant would be when the user returns after

the first visiting of the restaurant, but also the “checking in” on the social networks, sharing

information, publishing pictures of meals etc are the desired activities, because they

positively/negatively change the business of the restaurant.

Moderating Role of Individualism–Collectivism in RM

Collectivist cultures may respond more positively to RM efforts compared with individualist

cultures, because they are more sensitive and responsive to RM norms. In collectivist cultures,

reciprocity norms and mutual interdependence manage relationships (Morishima and Minami 1983,

Hofstede, Minkov 2010, Brewer, Venaik, 2011).

Collectivists are more concerned with the collective wellbeing of their entire group, and members

rely on and work with one another to achieve mutually beneficial outcomes. Collectivists also are

more receptive to social bonding (the process of forming attachments with others) than members of

individualist cultures and value long-term group ties, similar to the ties binding extended families

(Triandis 1995, Hofstede, Minkov 2010, Brewer, Venaik, 2011). Thus, collectivism emphasizes

156

long-term social bonding and dependence, manifested as familiarity, friendship, long-term ties, and

close personal relationships (Williams, Han, and Qualls 1998).

In contrast, individualists prefer less close relationships that is preserved for self-serving (as

opposed to mutually beneficial) reasons (Steensma et al. 2000). Because individualists value

individual goals over group goals, they build and maintain relationships only to the extent that

doing so is instrumental to their individual goal achievement (Triandis 1989, 1995, Hofstede,

Minkov 2010, Brewer, Venaik, 2011). Relationships based on long-term social bonding and

dependence becomes more difficult to form, and the benefits associated with having strong

relationships decline.

Because long-term social bonding and interdependence are central to collectivist cultures, the RM

strategies and outcomes linked to long-term social bonding and dependence grow stronger

(positively moderated) in collectivist cultures but weaker (negatively moderated) in individualist

cultures. Five constructs in the model related to long-term social bonding and dependence are

affected by this cultural dimension: (1) communication, (2) dependence, (3) relationship duration,

(4) WOM, and (5) performance (Gupta, Harris, 2010).

Russia scores high in collectivisms, so the clients respond more positively RM efforts. In traditional

relationships the clients of the restaurant in Russia expect particular attention, if he/she has personal

contacts in the restaurant establishment; such situation is often to experience, when clients present

themselves booking table and underline their social connections. But in return they develop high

loyalty to the places. At the same time high score of collectivism means weak level of technology

penetration. All together that leads in weak trust to technology replacing traditional relationship.

For example, if the client books the table with helps of mobile application he/she can call in the

restaurant to check his/her booking.

France is individualistic country according the Hofstede’s dimension. The RM outcomes are weaker

here; the relationships are built to achieve personal goals. And at the same time the level of

technology penetration is strong. As result, the restaurant management can easily use RM activities

provided by mobile application enhancing the ones which give the personal benefits for the clients,

like discounts, “gifts”, promotions etc.

Moderating Role of Power Distance

Because customers are more opened to and accept status differences, when power distance

increases, status-based RM strategies should be more effective in high power distance cultures. In

cultures with higher power distance, status-based relationships become easier to form, and the

beneficial effects of relationships on status-based outcomes are stronger (Hofstede, Minkov 2010,

Rinne, Steel, Fairweather, 2012).

157

Power distance may moderate three relational constructs related to status in the model: seller

expertise, WOM, and performance.

Both countries score high in power distance, but Russia a little bit higher. Such tools of mobile

application like rating (seller expertise), reviews (WOM), or sharing information in social networks

(performance) should be respected and used by the restaurant management.

Power distance plays weak role in technology penetration and adoption.

Moderating Role of Uncertainty Avoidance

Because risk management has a central role in high uncertainty avoidance cultures, the RM

strategies linked to uncertainty reductions are more effective at building and maintaining

relationships in high uncertainty avoidance cultures and less effective in low uncertainty avoidance

cultures. In cultures with higher uncertainty avoidance, activities that reduce uncertainty make

relationships easier to form (Litvin, Crotts, Hefner, 2004, Hofstede, Minkov 2010).

The two relational constructs related to uncertainty reduction in the model that may be moderated

by uncertainty avoidance are seller expertise and relationship duration.

Both countries score high in this dimension. For the restaurant management using the mobile

application is important to participate in ratings, grading and pay attention on the user’s history in

the profiles. Moreover this dimension shows that the adoption of new technology is very slow.

Theoretically in both countries clients prefer to create traditional relationship with the restaurant

establishment.

Moderating Role of Masculinity–Femininity

The values of feminine cultures tend to band better with key relational processes, such as

reciprocation and mutuality. So when femininity increases, decisions should be more affected by

relationships factors. In cultures with higher masculinity, the beneficial effects of relationships on

outcomes are weaker (De Mooij, Hofstede, 2011).

Strong relationships drive outcomes (e.g., WOM, performance) are more effectively in feminine

than masculine cultures. Customers in feminine cultures likely reciprocate relational benefits

received from a seller, more purchases, or payments of higher prices. Alternatively, in more

masculine cultures, competitiveness, aggressiveness, and a lack of reciprocity undermine the

connection between relational bonds and positive outcome behaviors because customers are less

likely to reward sellers who provide relational benefits. Thus, customers in masculine cultures

spend little time or effort to provide WOM or other benefits to relationally linked sellers. Overall,

the positive effects of relationships on WOM and performance should decrease in masculine

cultures, which devalue reciprocating exchange partners for any relational benefits received (Gupta,

Harris, 2010).

158

Russia and France scores in the middle, but are more feminine culture than masculine.

Relationships outcomes are effective. Clients of the restaurants might leave likely the reviews and

share impression of visiting this or that restaurant establishment. So the management should control

the communication, and react on the positive/negative feedback.

This dimension is not correlating with the penetration or adoption of the technology.

In sum, the most significant difference between two countries is to find by now in the impact of

individualism/collectivism dimension on both: technology and RM activities. The combination of

high score in uncertainty avoidance and high level of collectivism makes the use of mobile

application in RM of the restaurant in Russia complicated. In contrast the individualistic culture of

France restricts the RM strategy in general even if it correlates positively with technology

penetration.

Moreover it seems to be important to add to selected constructs of RM two other constructs, WOM

and performance, as the resulting outcomes of mobile application use by user.

III.5. Conclusion

In the Table 31 the constructs of third section are listed. We included only the dimension of

Hofstede theory, because the first regarded philosophical and sociological cultural values were to

find in this dimension, and the Hofstede’s theory is universal for all countries, what gives the

possibility for comparison. Out of six dimensions the most significant are two: uncertainty

avoidance and individualism, because they have impact on both interesting for us aspects: use of

technology and RM.

The dimension of Indulgence is interesting to investigate in our thesis, even if it wasn’t regarded by

the reviewed authors, because the going out for having meal belongs to the leisure time. The

countries have different scores firstly, and secondary the food is an important part of the French

culture, when in Russia it is seen more as social aspect than the cultural.

Table 31: Selected constructs of the cultural theories.

Construct Definition Theories Positive aspects Negative aspects Conс-

lusion

1 Individualism the degree to which

people in a society are

integrated into groups

Cultural dimension.

Hofstede (2001,

2010, 2011)

Brewer, Venaik,

(2011).

Correlates with

both: technology

and RM

- Maybe

2 Power

Distance

the extent to which the

less powerful members

of organizations and

institutions (like the

family) accept and

expect that power is

distributed unequally

Cultural dimension.

Hofstede (2001,

2010, 2011)

Rinne, Steel,

Fairweather, (2012)

- High score of both

countries limit the

RM. No significance

for technology use

No

3 Uncertainty

avoidance

not the same as risk

avoidance; it deals

with a society's

Cultural dimension.

Hofstede (2001,

2010, 2011)

Correlates with

both: technology

and RM

- Maybe

159

tolerance for

ambiguity. The public

reaction to unfamiliar

situations, unexpected

events, and the

pressure for change

Litvin, Crotts,

Hefner, (2004)

4 Long (short)-term

orientation

time horizon of society Cultural dimension.

Hofstede (2001,

2010, 2011)

Cannon, Doney,

Mullen, Petersen,

(2010)

- This dimension is not

included in both

researches:

technology or RM

No

5 Masculinity as a societal, not as an

individual

characteristic, refers to

the distribution of

values between the

genders which is

another fundamental

issue for any society,

to which a range of

solutions can be found.

Cultural dimension.

Hofstede (2001,

2010, 2011)

De Mooij,

Hofstede, (2011)

Low masculinity of

both countries

impacts positively

the RM outcomes,

like WOM and

performance.

No significance for

technology use

No

6 Indulgence the ability of culture to

meet the immediate

needs and personal

desires of society.

Cultural dimension.

(Hofstede, Minkov,

2010, 2011)

DeMooij, Hofstede,

(2011)

This dimension

describes the

relation between

leisure time and

working time, and

going out in the

restaurants is a part

of the leisure time.

This dimension is not

included in both

researches:

technology or RM

Yes

Source: completed by author of the thesis based on the references

IV. Final choice of constructs for the research model.

In the beginning of this chapter in the section of technology use we distinguished between

professional and nonprofessional use of technology concerning of mobile application use. In RM

section we concluded that mobile application plays no role in relationship between mobile

application holder and restaurant, because it is just a product or new service to implement in the

restaurant business. From this standpoint the relationship between mobile application holder and

restaurant produces not big changes because of use of technology; it is just about new product and

is out of our interest.

In contrast the mobile application changes the relationship between client and restaurant

management, because the mobile application is a tool to interact with the clients who are the final

users of mobile application. The mobile application holder is playing the role of mediator in this

relationship.

Combining all the findings Table 19 presents all constructs selected for the research model of

mobile application use in the restaurant’s relationship marketing influenced by culture.

160

Table 32: Final choice of the constructs

Construct Definition Theories Positive aspects Negative aspects Conс-

Lusio

n

Technology’s use theories

1 Behavior, or

use behavior

In our research we will

understand under use behavior

the use of mobile application by

user

TRA (M. Fishbein, I.

Ajzen, 1975), TPB (I.

Ajzen, 1985),

TAM (Davis, 1986),

TAM 2 (Venkatesh,

Davis, 2000), UTAUT

(Venkatesh et al.,

2003), UTAUT2

(Venkatesh et al.,

2012)

This construct is

presented in all

models. The

culture dimensions

have direct impact

on this construct,

and it has central

position for the

RM.

There is no exact

definition of

behavior, or use

behavior. Is this

the adoption, or

implementation,

or usage.

Yes

2 Facilitating

conditions

the degree to which an individual

believes that an organizational

and technical infrastructure

exists to support the use of a

particular system

UTAUT (Venkatesh

et al., 2003),

UTAUT2 (Venkatesh

et al., 2012)

Significant for the

use of mobile

application and

correlate with the

trust concept of

RM.

In the models

(UTAUT and

UTAUT 2) this

construct is

moderated by

experience,

meaning that with

time the variable

loses its

importance. In

our case we will

try to do

correlation with

habit

Yes

3 Habit the extent to which people tend

to perform behaviors

automatically because of

learning

UTAUT2 (Venkatesh

et al., 2012) The use of the

mobile application

today is often

routine habit. It is

important to see

how this construct

influence on the

behavior

concerning the

restaurants. F.e. do

the people use

more such kind of

applications when

they travel or are in

the unknown

place? For us this

construct can play

the mediating role.

The concept of

habit is also

presented in the

construct of

facilitating

conditions,

therefore is not

necessary.

No

4 Price value the monetary cost of technology. UTAUT2 (Venkatesh

et al., 2012) Decisive for

professionals. For

individual’s has no

direct meaning, can

be calculated as

aggregated benefits

of discounts etc.

Researched

mobile

applications are

free for download.

But the

restaurants pay

the application’s

holder to have

promotional

services.

Yes

5 Mobile

application

usability

the extent to which a mobile

application can be used by

specified users to achieve

specified goals with

effectiveness, efficiency, and

satisfaction in a specified context

of use

Mobile application

usability (Venkatesh

and Ramesh, 2006,

Venkatesh and

Hoehle, 2015).

Mobile application

usability plays

important role in

the user’s decision

to continue the use

of the mobile

application or not.

It also predicts the

loyalty of the users

toward the mobile

application.

As shown above

the mobile

application we are

going to

investigate are not

equal in all

elements of the

mobile

application

usability.

Yes

161

6 Mobile

application

loyalty

Degree to which a user has a

deeply held commitment to

rebuy or repatronize a mobile

application.

Mobile application

usability

(Venkatesh and

Hoehle, 2015 adapted

from Johnson et al.

2006)

Mobile application

loyalty can be the

wished outcome

for the providers of

the mobile

applications, in this

way this construct

plays important

role for prediction

of the user’s

behavior.

Yes

7 Continued

intention to

use

Degree to which a user feels

he/she will keep using a mobile

application.

Mobile application

usability (Venkatesh

and Hoehle, 2015

adapted from

Bhattacherjee 2001)

Continued

intention to use is

the outcome, which

can explain the

continued use of

the mobile

application.

Yes

RM Consctucts

8 Trust Confidence in an exchange

partner’s reliability and integrity

The Commitment-

Trust Theory of

Relationship

Marketing. Morgan

and Hunt (1994)

This construct is

important for all

kind of

technology’s use:

professional or non

professional. Also

is has connection

with construct of

facilitating

conditions of

UTAUT 2.

- Yes

9 Relationship

commitment

An exchange partner believing

that an ongoing relationship with

another is so important as to

warrant maximum efforts at

maintaining it.

An enduring desire to maintain a

valued relationship

The Commitment-

Trust Theory of

Relationship

Marketing. Morgan

and Hunt (1994)

The construct is

key one in the RM.

Especially

important for

services, which is

actually the use of

mobile application.

This construct

plays more

significant role in

the professional

relationship, like

interfirm. In

individual-to-firm

relationship

commitment is

often onsides.

No

10 WOM “likelihood of a customer

positively referring the seller to

another potential customer”

Relationship

Marketing theory

(Palmatier et al. 2006)

One of the most

important RM

outcomes in the

mobile application,

created through

reviews and

grading/rating.

No

11 Performance refers to improvements in

outcomes such as sales, share of

wallet, profit performance, and

other positive changes to the

seller’s business.

Relationship

Marketing theory

(Palmatier et al. 2009)

Expressed in

continued

intention to use

No

Selected cultural dimensions of Hofstede’s theory

12 Individualism the degree to which people in a

society are integrated into groups

Cultural dimension.

Hofstede (2001)

Correlates with

both: technology

and RM

Not connected

with research

context

maybe

13 Uncertainty

avoidance

The public reaction to unfamiliar

situations, unexpected events,

and the pressure for change

Cultural dimension.

Hofstede (2001)

Correlates with

both: technology

and RM

Not connected

with research

context

maybe

Indulgence the ability of culture to meet the

immediate needs and personal

desires of society.

Cultural dimension.

(Hofstede & Minkov,

2010)

This dimension

describes the

relation between

leisure time and

working time, and

going out in the

restaurants is a part

of the leisure time.

This dimension is

not included in

both researches:

technology or RM

Yes

Source: completed by author of the thesis based on the references

162

IV.1. Technology use constructs.

Despite the fact that many of technology use constructs were defined and analyzed in the first

section of the chapter, we decided to keep only seven of them: four from the UTAUT2 and three

from the structural model of the mobile application usability.

Firstly, we keep the variable of use behavior as a key construct of research model. The mobile

application replaced the three out of four steps in the relationship between client and restaurant

establishment, so the use behavior of the mobile application user is key variables for our research.

We will define it as use of mobile application. We describe use of mobile application with

technology’s adoption and technology’s penetration. So the adoption characterizes the use from the

point of view of downloading of application, while penetration characterizes the actual use. In other

words the user can have on his/her smart phone the mobile application, but doesn’t use it because of

lack of trust. As a second variable we will include the intention to use, this variable precedes the

actual use. Many elements and aspects influence the user before he/she starts actual use.

Third variable of technology’s use we have chosen is the facilitating conditions, which correlates

with the concept of trust. Fourth variable to include is price value, we know, that both mobile

applications are free for downloading, because of that we will understand under this variable the

bonuses, financial or other benefits like gifts and loyalty cards. So, as much mobile applications are

used by the users, as confident they can feel using additional one. Otherwise stated, developed use

habit reduces the impact of facilitating conditions. In this way we eliminated the moderation of age,

finalizing that the duration of smartphone’s use is prior (because it develops habit to use mobile

application).

Three next variables are to keep from the structural model of mobile application usability. Mobile

application usability predicts continued intention to use and mobile application loyalty as two

wished outcomes of the use of the mobile application.

IV.2. Relationship marketing constructs.

The use of mobile application in the restaurant business context replaces the traditional relationship

between restaurant establishment and client. So, the restaurant management can use the mobile

application as a marketing tool and develop new relationships as well as reinforce relationship with

the loyal clients. From that point of view two of constructs of RM create the basement for the

successful relationship – trust and commitment. Both are influenced and changes by technology.

The mobile application holder plays here also significant role, because it is the mediator between

restaurant and client.

To make clear the importance of the technology in the relationship between restaurant

establishment and user we might include two relationship marketing outcomes. Word of mouth

163

(WOM) characterizes well the activity of mobile application user and is easy to experiment because

of such functions as reviews and grading inside of investigated mobile applications. Performance is

more complicated to define in our case: should it be any activities on the mobile application which

can positively/negatively change business of restaurant establishment, or number of loyal clients

perceived with help of mobile application. Anyway we already kept the mobile application loyalty

as an important construct for the research model. Even if this consctruct is taken out the

technological theories, it has connection with RM. To conclude we keep trust for the research

model and will add measurement items to the mobile application loyalty to describe the concept of

loyalty also from the point of view of relationship marketing.

IV.3. Cultural dimensions of Hofstede.

Two of six cultural dimensions can be included in our final research model, because both of them

have significant impact on technology’s use and on the relationship marketing, uncertainty

avoidance and individualism/collectivism. France and Russia score closely in uncertainty

avoidance and opposite in individualism/collectivism, because of that the combinations of scores

and their influence on technology’s use and relationship marketing provide us the possibility to

analyze differences in the mobile application use.

High uncertainty avoidance restricts the adoption of the technology, and at the same time makes

important such relational antecedents as seller expertise (rating) and relationship duration (user

history). Thus, probably the mobile application users will check the reviews, rating and grading

before to choose the restaurant, but still will prefer traditional WOM, or traditional way to take

contact. It can also result in longer process of adoption the mobile application.

High individualism impacts positively the penetration of technology, but reduces the significance of

the RM-antecedents and as result leads in weak RM outcomes. Combination of high individualism

and high uncertainty avoidance force from the restaurant management the appropriative activities

inside of the mobile application, so, obviously that one the user has adopted the mobile application

he/she will need positive reinforcement to continue the use, like discounts, promotions etc.

High collectivism has negative impact on the technology penetration. So the combination of both,

high uncertainty avoidance and high collectivism demands high involvement of the restaurant

management in the mobile application use, like to create the account, to control the reviews, to

control the comments etc.

Nevertheless we decided to choose another one variable of the cultural dimensions namely

Indulgence. On the one hand this variable wasn’t presented in researches neither in the connection

with the technology use nor in relationship marketing, but exactly this dimension shows well the

attitude of the users towards the leisure time, what is important for our context use – restaurant

164

industry. In this way the influence of the variable of Indulgence can predict the intention to use the

mobile application in the researched context. According to Hofstede’s countries comparison tool

France scored with 48 points and Russia – 20. That demonstrates cultural difference in leisure time

perception and self gratification. Key differences between indulgent and restrained cultures are

shown in the Table 33.

Table 33: Key differences between indulgent and restrained societies

Indulgent Restained

Higher percentage of very happy people Lower percentages of very happy people

A perception of personal life control A perception of helplessness; what happens to me is not

my own doing

Higher importance of leisure Lower importance of leisure

High importance of having friends Lower importance of having friends

Thrift is not very important Thrift is important

Loose society Tight society

More likely to remember positive emotions Less likely to remember positive emotions

Less moral discipline Moral Discipline

Positive attitude Cynicism

Higher percentage of people who feel healthy Lower percentage of people who feel healthy

Higher optimism More pessimism

More satisfying family life Less satisfying family life

Household tasks should be shared between partners Unequal sharing of household tasks is no problem

Loosely prescribed gender roles Strictly prescribed gender

In wealthy countries, less strict sexual norms In wealthy countries, stricter sexual norms roles

Smiling as a norm Smiling as suspect

Freedom of speech is viewed as relatively important Freedom of speech is not a primary concern

Maintaining order in the nation is not given a high

priority

Maintaining order in the nation is considered a high

priority

Lower numbers of police officers per 100.000 Higher numbers of police officers per 100.000

Population

Source: Hofstede et al. 2010

We selected the differences, which are influential in the restaurant context: percentage of very

happy people, importance of leisure, remembering of positive emotions, optimism/pessimism. We

assume that in culture with higher percentage of very happy people, higher importance of leisure,

higher ability to remember positive emotions and higher optimism people go more often out for

pleasure, therefore should use more often the mobile application. Moreover these values can

influence the intention to use, because of difference in expectations (for example, pessimistic point

of view can limit the intention to use, because of lack of trust etc.).

All together selected construct answer the purpose and problem of this thesis.

165

Chapter III: Conceptual model and research hypothesis

166

Introduction

Based on theoretical background and knowledge on use of technology, relationship marketing and

culture dimensions the conceptual model is proposed in this chapter of the thesis. The model

includes constructs from UTAUT 2 (Venkatesh et al., 2012), mobile application usability

(Venkatesh and Hoehle, 2015), Commitment-Trust theory of relationship marketing (Morgan and

Hunt, 1994) and cultural dimensions theory (Hofstede, 2010), as well the moderating constructs,

which are important for the use of mobile application in catering context. The hypotheses are

developed to clarify the relationships between the chosen constructs.

I.1. Conceptual model of the research

The conceptual model is developed based on following principals: (i) observation; (ii) desk-study

concerning theoretical and empirical researches on use of technology, relationship marketing and

cultural dimensions; (iii) consultation of experts and practitioners in marketing; and (iv) pre-test

with the mobile application users in France and Russia.

The use of mobile application is placed in the central position of the model, and based on this

position the factors influencing the use are regarded as well as results or outcomes of the use, like

loyalty and continued intention to use.

Figure 23: Use of mobile application in restaurant industry: conceptual model of the research.

The model is constituted by the following variables:

(i) Four variables are taken from the UTAUT 2 (Venkatesh et al., 2012), which shows the

adoption, intention to use and use of the mobile application in voluntary context. Thus the

167

central concept of the model is the use of the mobile application as a technology in catering

context used by users, or clients of the restaurants.

(ii) Three variables are the variables of the structural model of the mobile application usability

(Venkatesh and Hoehle, 2015): usability by its selves, which also includes latently two

constructs, application design and user interface structure. And two outcomes of this model

are included in the research model, mobile application loyalty, which has a connection with

the relationship marketing, and continued intention to use.

(iii) One cultural dimension presents the cultural theory of dimensions (Hofstede, 2010).

Indulgence as influential factors of voluntary leisure context is considered as the most

important construct of cultural dimensions.

(iv) One variable is conserved from the Commitment-Trust theory (Morgan and Hunt, 1994).

Technology can eliminate trust between client and seller, so the influence of this is regarded

as important and is included in the research model.

(v) Moderator variables comprise of three elements critical for the type of the technology –

mobile application. Duration of use the mobile phone and mobile application helps to

precise the relation between the intention to use and use, as well as it can enhance or weaken

trust, needed for the start to use of technology. Place of the use is connected with the

geolocation function, which is very important for the users of smartphones. And finally

frequency of visits or in other words, how often the user goes out for having a meal, is

critical for prediction his/her continued intention to use the mobile application in catering

context.

All the constructs serve the purpose of the thesis to answer the research problem, whether the

mobile applications can permit the development of the catering sector.

I.2. Constructs and measuring items

The constructs developed in the research model involve three main aspects:

- use of technology,

- relationship marketing,

- cultural dimensions.

The central constructs describe the technology’ use, because the objective of the research is to

investigate the changes in consumer’s behavior in catering sector under the influence of modern

technology particularly mobile application, what belongs to everyday life today.

For all constructs measuring items are designed, based on three types of scales: (i) Five-points

Likert agreement scale (strongly agree-strongly disagree), (ii) Five-points Likert frequency scale

(never-several times per week), and (iii) Five-points Likert likehood scale (very likely – very

168

unlikely) (Mathieson, Doane, 2005). The survey is developed firstly in English, and then translated

into French and Russian languages.

I.2.1. Technology use

The aspect of technology use consists of two main subgroups: i) use of technology in voluntary

context (Unified Theory of Acceptance and Use of Technology 2, Venkatesh et al. 2012) and ii)

mobile application usability (Mobile Application Usability: Conceptualization and Instrument

Development, Venkatesh and Hoehle, 2015).

First construct, use of mobile application, is measured by Choice-question, where is needed to

precise the frequency of use according to the five points scale never-many times per week. The user

has to choose between use of the investigated mobile application (“Resto.ru” in Russia and

“Lafourchetee” in France), the website of the same company, the other similar mobile application,

and the other similar website. The question is adapted by Venkatesh, Thong, Xin Xu (2012).

Second construct, intention to use, is defined as individual’s decision to use mobile application, the

measuring item is adapted by the same authors as first one. There are three questions, contenting the

statements with five-point agreement scale “strongly agree- strongly disagree”. These statements

refer to investigate the plans of the user towards the mobile application.

Third construct, facilitating conditions refers to consumers’ perceptions of the resources and

support available to perform a behavior. Three of the questions disclose the technical difficulties of

the user, which he/she can face using the mobile application, and one question describes the

construct of habit, which was not included in the model, but was considered as important inside of

facilitating conditions. All questions are designed as statements, adapted by Venkatesh, Thong, Xin

Xu (2012) and estimated with five-point agreement scale.

Fourth construct, price value, is defined as the monetary cost of mobile application. Three

measuring items are designed to disclose the monetary value of mobile application for the user. In

our case the mobile application by its self’s doesn’t have a cost, but contra versa can provide

financial or nonfinancial benefits. From that point of view the questions are constituted to measure

the user’s attitude towards this benefits. All questions are adapted by Venkatesh, Thong, Xin Xu

(2012) and use the five-point agreement scale “strongly agree – strongly disagree”.

Next three constructs compose the mobile application usability aspect of the technology use.

Mobile application usability is defines as the extent to which a mobile application can be used by

specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a

specified context of use. It consists of two elements: application design and user interface structure.

The measuring items of these two elements supposed to disclose the attitude of the user toward the

169

mobile application usability. Each of the elements has two questions, adapted by Venkatesh and

Hoehle (2015) and estimated with five-point agreement statements.

Next construct, continued intention to use, is defined as degree to which a user feels he or she will

keep using a mobile application. The measuring items consist of three statements, estimated by

five-point agreement scale. The questions are adapted by Bhattacherjee (2001), Venkatech and

Goyal (2007), and Hoehle and Venkatesh (2015).

And last construct of the technology use theories is the mobile application loyalty, which is defined

as degree to which a user has a deeply held commitment to rebuy or repatronize a mobile

application. Three questions are aimed to disclose the loyalty of the user toward the mobile

application, adapted by Johnson et al., and Hoehle and Venkatesh (2015). The five point agreement

scale is used for estimation. The aspect of loyalty will be measured also from the point of view of

relationship marketing.

All the measuring items are presented in the Table 34.

Table 34: Measuring items for technology use aspects.

Construct Definition Question/Item Theoretical

justification

Use of technology

1 Use of mobile

application

Use of the mobile

application

Please choose your usage frequency for

each of the following by choosing the

restaurant:

a) mobile application N21

b) website of N22

c) other website

d) other mobile application

Frequency ranged from “never” to

“many times per week.”23

Wiswanath

Venkatesh

James Y.L.Thong

Xin Xu (2012)

2 Intention to use The individual’s

decision to use IS.

1. I intend to continue using

mobile application in the future.

2. I will always try to use mobile

application in my daily life.

3. I plan to continue to use mobile

application frequently.

Wiswanath

Venkatesh

James Y.L.Thong

Xin Xu (2012)

3 Facilitating

conditions

Refers to

consumers’

perceptions of the

resources and

support

available to

perform a behavior

1. I have resources (IOS, Android,

Wi-Fi) necessary to use mobile

application.

2. I have the knowledge necessary

to use the mobile application

3. I can get help from mobile

application provider when I

Wiswanath

Venkatesh

James Y.L.Thong

Xin Xu (2012)

21 Mobile application N will be replaced in the final questions by “lafourchette” for France and “resto” for Russia

22

Website N will be replaced in the final questions by “lafourchette” for France and “resto” for Russia

23 The range is designed as a table

170

have difficulties using mobile

application.

4. The use of mobile applications

has become a habit for me.

4 Price value The monetary cost

of technology.

1. The mobile application N is free

for downloading.

2. The bonuses provided by

mobile application N are

reasonable.

3. I use mobile application N to

enjoy promotions

Wiswanath

Venkatesh

James Y.L.Thong

Xin Xu (2012)

Usability

6 Mobile

application

Usability

Mobile application

usability is defined

as the extent to

which a mobile

application can be

used by specified

users to achieve

specified goals

with effectiveness,

efficiency, and

satisfaction in a

specified context

of use

(Venkatesh and

Ramesh, 2006)

Application design:

1. Overall, I think the mobile

application N is designed well

2. I am very satisfied with the

overall design of the mobile

application N.

Hoehle&Venkatesh

(2015)

User interface structure:

1. Overall, I think the mobile

application N structures

information effectively

2. I am very satisfied with the way

the mobile application N is

structured

Hoehle&Venkatesh

(2015)

7 Continued

intention o use

Degree to which a

user feels he or she

will keep using a

mobile application

1. I intend to continue using

mobile application rather than

discontinue its use.

2. My intentions are to continue

using mobile application than

use any alternative means.

3. If I could, I would like to

discontinue my use of mobile

application (reverse coded).

Bhattacherjee,

(2001)

Venkatech and

Goyal, (2007)

Hoehle&Venkatesh

(2015)

8 Mobile

application

loyalty

Degree to which a

user has a deeply

held commitment

to rebuy or

repatronize a

mobile application

1. I encourage friends and

relatives to be the customers of

the mobile application N

2. I will use more services offered

by the mobile application N in

the next few months/years

3. I consider the mobile

application N to be my first

choice

Johnson et al.,

(2006)

Hoehle&Venkatesh

(2015)

Source: completed by author of the thesis based on the references

171

I.2.2. Relationship marketing

Relationship marketing is the second aspect of the developed model. The use of mobile application,

which contains the services of many restaurants and is used by final customer, involves three actors.

Moreover the traditional relationship between client and restaurant is replaced or supplemented with

new technology of mobile application.

First construct of RM is trust, defined as confidence in an exchange partner’s reliability and

integrity. In our case it is the confidence in technology and technology provider. The measuring

items are adapted by Palmatier (2008) and designed as three statements, which the user should

answer with five point agreement scale. The purpose of the questions is to investigate how much the

users trust the mobile application and information given by the mobile application. Trust will

directly impact the intention to use of the mobile application.

The second construct belongs to mobile application loyalty, which we discussed in the

technological aspects of the researched model. Nevertheless the concept of loyalty can be predicted

not only because of the mobile application usability, what is shown above, but also be influenced by

other aspects, therefore we added two further measuring items adapted by Sirdeshmukh, Singh, and

Sabol (2001). These items are the only two which are using the five-point likehood scale.

The measuring items of relationship marketing aspect are presented in the Table 35.

Table 35: Measuring items of relationship marketing aspect

Construct Definition Items Theories

RM Constructs

1 Trust Confidence in an

exchange partner’s

reliability and

integrity

1. Mobile application N gives me

a feeling of trust.

2. The information provided by

mobile application N is always

honest.

3. Mobile application N is

trustworthy.

Palmatier (2008)

2 Loyalty

(Relationship

marketing)

Consumer’s desire

and intention to

continue a

relationship with

the company,

recommending it

and choosing it

from the market

Five-point scale very unlikely/very

likely

How likely are you to:

1. make more than in 50 % of

reservation in restaurants using

mobile application N?

2. use mobile application N the very

next time you choose the

restaurant?

Deepak

Sirdeshmukh,

Tagdip Singh and

Barry Sabol (2002)

Gremler, Brown,

(1996); Lovelock,

Wirtz, (1996)

Source: completed by author of the thesis based on the references

172

I.2.3. Cultural dimensions.

In the cultural aspect we have finally reserved only one dimension, which hasn’t shown on the one

hand any relations with the technology or relationship marketing according to the reviewed authors.

On the other hand we decided to take it into consideration, because it shows well the cultural

deference in the spending of leisure time and attitude toward it. Restaurant industry is a part of

leisure or tourism, since we are going to pose the question to the inhabitants of the cities and not to

the tourists, the leisure aspect becomes more important (Parr, 1999, Werthner, Ricci, 2004).

Indulgence as cultural dimension is defined as ability of culture to meet the immediate needs and

personal desires of society. Three questions inspired and adapted by Value Survey Model (Hofstede

and Minkov, 2013) are designing the measurement of indulgence. We will use the five-point

agreement scale “strongly agree – strongly disagree”.

Table 36: Measuring items of cultural aspect.

Construct Definition Items Theories

Cultural factors

1 Indulgence Ability of culture

to meet the

immediate needs

and personal

desires of society.

1. I keep time free for fun

2. Other people or circumstances

cannot change my intention

3. I like my city <Moscow or

Paris>

Hofstede and

Minkov (2013)

Source: completed by author of the thesis based on the references

I.2.4. Moderating constructs

While all of the constructs described above are independent or dependent variables that are

measured to observe the use of mobile application in restaurant industry taking into account also

cultural aspect, there are also moderating variables selected to discover whether they modify the

relations between nine chosen constructs.

Traditionally such moderators are used in the technology’s use models: age, professional

occupation and gender. These moderates require high level in the diversity of the sampling to see

real influence on the relationships in the model and they are not specified according to industry.

We decided to look at specific moderators for restaurant industry, where the segmentation of the

clients if often done according to RFM (recency, frequency, monetary) segmentation (Wansbeek,

1995, Blattberg et al., 2008, Birant, 2011). On the other hand we included as well moderator related

to GIS-technology, because this mobile application function can impact on the decision of the users

to use the mobile application.

If before age was regarded as important variable which moderated the use of technology (Venkatesh

et al.2012), in the use of mobile application the period of time the user uses the smartphone

173

becomes more relevant. As longer the user has the smartphone as quicker he/she can adopt new

mobile application for different purposes. The measuring items to estimate the duration are design

for two aspects: duration of the use of smartphone and duration of the use of mobile applications

and particularly investigated mobile application. The estimation requires the answers in periods.

That moderator we adapted and modified from the notion of recency of RFM segmentation.

Moderating construct, frequency of visits, which we define as frequency of going out with purpose

to have a meal, aims to disclose the effect on the relation between loyalty and continued intention to

use. As oftener the user goes out, as oftener he/she can to use the mobile application, or be loyal to

it, and finally to show stable intention to continue the use of the mobile application. In other case, if

the person goes out once a year, for example, it is no need for him/her to use specific mobile

application for choosing a restaurant. This moderator fully expresses the notion of frequency of

RFM segmentation.

Performance of complex geographic queries in geographic context (Lin, Kao, Lam, Tsai, 2014)

describes the location function of the technology and used actively in the tourism industry. Such

function became important on all applications connected with the searching of places. For

researched mobile applications use the place of use can be decisive moderator. Firstly, mobile

applications are used on the mobile devises, what often means not in the place with personal

computer thereby the search of information is possible only with mobile devise and supposed to

easier on the specialized application. Secondly, modern mobile applications suggest often the

function “next to me, around me” using the geoposition of the user. In this case the place of the use

can impact the relation between intention to use and use of the mobile application, if the mobile

application f.e. can propose promotions and happy hours. Regarding this points, the measuring

items are aimed to investigate, whether the user choose restaurant according to his/her geoposition,

or/and according to the promotions. To estimate the moderation effects we use four questions, made

as statement and required the five-point agreement scale answers.

Moderating constructs with their measuring items are presented in the Table 37.

Table 37: Measuring items of the moderating constructs.

Construct Definition Question/ Items Author

1 Duration of

use

Time period of use

of the smartphone

and the technology

of the mobile

application

1. How long are You using smart

phone (in years)?

2. How long are You using the

mobile application N?

3. How long are You using any

mobile application for choosing a

restaurant?

RFM segmentation

(Wansbeek, 1995,

Blattberg et al., 2008,

Birant, 2011)

174

2 Place of use The location of the

user before and

during the use of

the mobile

application

1. I use geolocation on mobile

application N when reserving the

restaurant

2. I check the promotions on mobile

application N in the restaurant

before entering it.

3. I prefer to go to the restaurant next

to me even there are no

promotions

4. I prefer to go to the long-run

restaurant because of promotions

on the mobile application

Lin, Kao, Lam, Tsai,

2014

3 Frequency of

visits

Frequency of going

out for having a

meal

1. How often You go out for

lunch/dinner

2. How often You go out for

lunch/dinner because of promotion

on the mobile application N

RFM segmentation

(Wansbeek, 1995,

Blattberg et al., 2008,

Birant, 2011)

II. Hypotheses of the research.

The hypotheses are statements expressing the relation between two or more measurable constructs

that have been developed in the above sections. They are considered as working instrument of

theory and tentative explanations that account for a set of facts and can be tested by further

investigation. The hypotheses are alternative, directional, and quantitative used to relate and

describe the variables of the research model.

The hypothesis of this research derive in deductive manners, what means, that they were formed by

selecting theories firstly, then presented in the form of statements leading to deductions and

accompanied by an argument.

Developing in deductive manners, the hypotheses of this thesis concentrate on the explanation of

mobile application use in catering context, taking into account aspects of relationship marketing and

cultural aspects.

175

II.1. Factors influencing the intention of mobile application use.

Before of actual use of mobile application the user experiences the situation to decide whether to

choose using of this application, or another one, or better to use website, or better to call a friend

and ask for advice. This situation is considered as intention to use. Different factors can influence

the user and change his/her intention in enhancing it or weaken it. The context of the mobile

application use is the catering, which we classified as leisure time. In this context the user is not

under the stress and has time to estimate all possibilities.

II.1.1. Price value

An important difference between a consumer use setting and the organizational use setting, where

UTAUT2 was developed, is that consumers usually bear the monetary cost of such use whereas

employees do not. The cost and pricing structure may have a significant impact on consumers’

technology use. In marketing research, the monetary cost/ price is usually conceptualized together

with the quality of products or services to determine the perceived value of products or services

(Zeithaml 1988). The price value is positive when the benefits of using a technology are perceived

to be greater than the monetary cost and such price value has a positive impact on intention. The

researched mobile applications are free for downloading. The importance of the price value can be

regarded here as aggregated utility: benefits collected by application’s use (discounts, loyalty

programs, client’s points, gifts etc).

Anyway, in case of free mobile applications price value can be strong predictor for the intention of

use the mobile application, if the mobile application provides the benefits and discounts. So in the

situation of choosing one of the search and booking means the decisive factor compared with others

would be the price value, therefore this study is supposed to test the relationship between price

value and intention to use the mobile application. It adapts the hypothesis of Venkatesh et al.

H1: Price value increases intention to use the mobile application. Users who believe to receive

financial or other profit using the mobile application will intent to use this mobile application.

II.1.2. Facilitating conditions

Facilitating conditions include aspects of the technological and/or organizational environment that

are designed to remote barriers to use. In contradistinction to other mentioned constructs,

facilitating conditions have no significant influence on the behavioral intention, but direct impact on

176

the usage, moderated by experience and age. Thus, the effect will be stronger for older workers with

increasing experience.

This construct is very important for our research because it is connected with trust of clients over

the mobile application, which will replace traditional relationship between restaurant and client. So,

in case of problems with some services provided by the mobile application, the client prefers to

have support like f.e. call-center, or personal account in the mobile application to give feedback, to

receive improvements of the system and other help. The same situation is with restaurant

management, who prefers to have supporting organization providing services about system. In this

context the mobile application holder plays a very important role.

We also put forward a hypothesis that facilitating conditions will impact not the use directly, as it

was discussed in UTAUT by Venkatesh et al, but in case of mobile application this construct will

impact firstly the intention to use the mobile application. As it was shown in the previous sections,

each of investigated mobile applications were developed from the website or other projects of the

companies known in the sector, thus will lead to increasing of the trust toward the mobile

application as a part of the company. In the situation of choosing the means to book table or to find

the information, the user will tend to select the already known company. And this belief to have

supporting organization will increase his/her intention to use investigated mobile application.

H2: Facilitating conditions impact positively trust. The mobile application users who believe

that supporting organization stays behind the mobile application develop trust quicker then

who don’t.

H3: Facilitating conditions impact positively the intention to use the mobile application. The

mobile application users who believe that supporting organization stays behind the mobile

application will rather to use it than not.

II.1.3. Trust

For our research trust is of high importance. First, developed trust about the mobile application

expends also to the restaurants included in the mobile application (the user trust that the reviews are

fair, the rating is independent). And conversely the user can lose trust about the restaurant in case of

177

false information on the mobile application, or he/she can lose trust about the application in case of

false services received in the restaurant (f.e. the mobile application promises the discount for the

visit of the restaurant, but the restaurant refuses to give this discount; or the mobile application can

contain fake restaurant establishments, wrong addresses etc.). The complexity of trust is added

through technology. The person should not only trust the company like restaurant, it should also

trust the technology. As we discussed UTAUT2 model has the construct, facilitating conditions,

which says that the individuals might tend to believe in the technology if he/ she is sure that the real

company or person is behind this technology. In our case the mobile application holder provides

technical support for both: restaurant establishment, as well as for the final users.

Trust impacts the intention of use, obviously the user will choose the mean he/she is confident in.

H4: Trust impacts positively the behavioral intention to use the mobile application. The

mobile application users, who have developed trust to mobile application’s provider, or

technology, intent to use mobile application more likely, than who have not.

II.1.4. Indulgence

The dimension of Indulgence connects the mobile application with cultural aspects of our research.

Catering context and mobile application use in this context belong to leisure time of the user. The

dimension of the indulgence describes the attitude of the culture toward the leisure time. As

consequence it can explain the differences in going out traditions, for example. The investigated

countries have different scores in this dimension according to Hofstede. In this way we can

hypothesize that the culture with higher score in Indulgence will tend to use more often the mobile

application to choose the restaurant, because generally this culture tends to spend more time for

leisure. The indulgence will impact the intention to use the mobile application, because in the

situation when the user has less time for leisure he/she will tend to use other mean to choose the

restaurant, like WOM, or choose all the time the same restaurant to be sure in quality of the

establishment. And in other way, if the user spends more time for leisure he/she will tend to try new

establishment, using also mobile application. The users from the cultures with the high score in

indulgence tend to use more often the mobile application to book a table in the restaurant. On the

countries’s level France scored more than two times higher (48) in this dimension than Russia (20).

We assume, that French users tend to use mobile application in restaurant context more likely than

Russians.

178

H5: High indulgence as cultural factor impacts positively the intention to use of the mobile

application.

II.1.5 Mobile application usability

In our research we regard the users of the mobile application “Lafourchette” in Paris and users of

the mobile application “Resto” in Moscow (specified users), who is using the mobile applications

for searching for the restaurant and for booking tables in the restaurants (specified goals). In this

way effectiveness refers to instruments the mobile application has for searching and booking,

efficiency refers to ability of the mobile application to provide not only information but also for

example bonuses, and satisfaction refers to the user’s satisfaction with the functions of the mobile

application. We investigate the leisure context, when the mobile application is used for the having a

meal in the restaurant. Users intent to use mobile application if they are satisfied with design and

information’s structure of the mobile application, as the elements of mobile application usability.

The positive impression of the mobile application usability impacts the preference of the users the

one application compared others, or mobile applications compared to other means.

The elements forming the mobile application design are branding, data preservation, instant start

and orientation, all of them can be estimated in two first second of using the mobile application.

Longer usage is needed to estimate the information’s structure, but anyway in the situation of

choosing the restaurant mobile application design will predict the intention to use the mobile

application strongly.

H6: Mobile application usability increases the behavioral intention to use the mobile

application.

179

II.2. Factors influencing the use of mobile application.

II.2.1. Intention to use

H7: Strong behavioral intention leads to the use of the mobile application.

After the user made the decision to use the mobile application, he/she is still under the factors

which can lead to rejection or continuation of mobile application usage. If the user developed the

strong behavioral intention under already discussed factors, he/she would use the mobile application

for the purpose to search for the restaurant establishment or to book a table in the restaurant. Strong

intention can be developed by attractive financial and nonfinancial benefits (price value), by using

the known company as provider of mobile application (trust), by clear information what to do in the

fail-cases (facilitating conditions), and by the feeling of having enough time for leisure so also for

the going out (indulgence).

All this relations lead to strong intention to use mobile application and as result in the actual use of

the mobile application.

II.2.2. Mobile application usability

Mobile application usability is formed from many elements. If the design of the mobile application

will impact the intention to use the mobile application, so the information structure will impact the

actual use of mobile application.

In the free mobile applications the mobile application usability should provide the information

needed by the users in the first place, else it will be easy to stop using the application and even to

delete it. Logical path and top-to-bottom structure are the elements forming the user interface

structure and answering the goal of the user to find necessary information easy and quickly. In this

way mobile application usability lead to the use of the mobile application.

H8: Mobile application usability leads to the use of the mobile application.

180

II.3. Outcome factors

The expected or desired outcomes of the use of mobile application are the mobile application

loyalty and continued intention to use of the mobile application.

II.3.1. Mobile application usability

User, who is satisfied with design and information’s structure of the mobile application, has a deep

commitment to repatronize a mobile application.

H9: Mobile application usability increases the mobile application loyalty.

Mobile application usability is a strong predictor of the mobile application loyalty. Known brand,

possible data preservation, instant start, and orientation not only impact the intention to use the

mobile application, but also preserve the wish to use it again. For example, after the user registered

him/herself on the mobile application, he/she start to be involved in the loyalty programs of the

mobile application, collecting benefits points. Preserved data of previous search allows the user to

get needed information quickly, so he/she will tend to use the mobile application again, developing

loyalty.

II.3.2. Continued intention to use

Mobile application loyalty leads to continued intention to use the mobile application. After the user

accepted and adopted the mobile application, he/she will choose all the time the chosen mobile

application in the similar situation of the decision making process. In this case the factors we

regarded before will loose their importance because of already developed loyalty. Such aspects as

trust, facilitating conditions, price value, or indulgence will be no anymore relevant in the decision

making.

H10: Mobile application loyalty moderated by frequency of visits leads to continued intention

to use the mobile application.

181

II.4. Moderation effects

The hypotheses of moderating effects are developed based on the context of the mobile application

use, namely leisure time and catering that incorporate the effects of the duration of the use, place of

the use and frequency of visits. Meanwhile, four hypotheses related to the effects of moderating

variables are formulated.

Hm1: Duration of the use moderates the relation between trust and intention of use

Hm2: Duration of the use moderates the relation between intention of use and use of the

mobile application.

Hm3: Place of use moderates the relation between intention of use and use of the mobile

application.

Hm4: Frequency of visits moderates relation between mobile application loyalty and

continued intention to use

III. Conclusion

Based on the theories and empirical researches on technology use, relationship marketing, and

cultural dimensions, the research model is developed with nine main variables and three moderator

variables. Nine main variables include price value, facilitation conditions, trust, indulgence,

intention to use, mobile application use, mobile application usability, mobile application loyalty,

and continued intention to use. Meanwhile duration of the use, place of the use, and frequency of

visits are the three moderating variables.

Ten hypotheses are developed concerning the mobile application use in the catering context. All the

hypotheses are synthesized in the Table 38.

182

Table 38: Synthesis of hypotheses.

Hypotheses

Factors influencing intention to use

H1 Price value increases intention to use the mobile application. Users who believe to receive financial or

other profit using the mobile application will intent to use this mobile application.

H2 Facilitating conditions impact positively trust. The mobile application users who believe that

supporting organization stays behind the mobile application develop trust quicker then who don’t.

H3 Facilitating conditions impact positively the intention to use the mobile application. The mobile

application users who believe that supporting organization stays behind the mobile application will

rather to use it than not.

H4 Trust impacts positively the behavioral intention to use the mobile application. The mobile application

users, who have developed trust to mobile application’s provider, or technology, intent to use mobile

application more likely, than who have not.

H5 Indulgence as cultural factor impacts positively the intention to use of the mobile application. The

users from the cultures with the high score in indulgence tend to use more often the mobile application

to book a table in the restaurant.

H6 Mobile application usability increases the behavioral intention to use the mobile application. Users,

who are satisfied with design and information’s structure of the mobile application, intent to use it.

Factors influencing the mobile application use

H7 Strong behavioral intention leads to the use of the mobile application.

H8 Mobile application usability leads to the use of the mobile application.

Outcomes factors

H9 Mobile application usability increases the mobile application loyalty. User who is satisfied with design

and information’s structure of the mobile application, has a deep commitment to repatronize a mobile

application.

H10 Mobile application loyalty moderated by frequency of visits leads to continued intention to use the

mobile application.

Moderation effects

Hm1 Duration of the use moderates the relation between trust and intention of use

Hm2 Duration of the use moderates the relation between intention of use and use of the mobile application.

Hm3 Place of use moderates the relation between intention of use and use of the mobile application.

Hm4 Frequency of visits moderates relation between mobile application loyalty and continued intention to

use

183

Part II: Experimental research

184

Chapter IV: Epistemology and methodology of the research

185

Introduction

This chapter discusses the research epistemology and methodology. Epistemological position of the

researcher not only defines his/her philosophical point of view towards the notion of knowledge,

but also determines the choice of methods to extract and to develop theories, to deal with the

knowledge. In this chapter we present our epistemological position of positivism and verify this

position through important issues of research design.

As methodological approach the quantitative approach is used in this thesis. We developed and

validated measuring items, construct and variables regarding the Churchill paradigm as

methodology. The verification of the conceptual model and hypotheses is based on the

methodology of structural equation modeling.

I. Epistemological position and methodological approach.

I.1. Epistemology

Epistemology aims determining the nature of knowledge and learning, learning opportunities, and

its connection with reality, the study of knowledge prerequisites, including the conditions for its

validity and truth (Creswell, 2012). The fundamental thesis of the relationship between the material

world and the knowledge of its material or subjective conditionality is the basis of the philosophical

understanding of epistemology, determining the context of a scientific approach to the study of

paradigms.

Epistemological position of the research helps to deal with the interaction of knowledge with truth

and belief by using the rules to describe reality.

Creswell (2012) suggests four different paradigms of science relating to ontology, epistemology and

methodology (Table 39).

Table 39: Four Worldviews Source

Positivism Constructivism

• Determination

• Reductionism

• Empirical observation and

measurement

• Theory verification

• Understanding

• Multiple participant meaning

• Social and historical construction

• Theory generation

Advocacy/Participatory Pragmatism

• Political

• Empowerment issue-oriented

• Consequences of actions

• Problem-centered

186

• Collaborative

• Change-oriented

• Pluralistic

• Real- world practice oriented

Source: Creswell, 2012

Positivism, constructivism and pragmatism are applied in management science.

David (1999) refers to the work of many researchers who have dealt with the epistemological and

methodological approaches specific to management sciences: Lewin (1951), Checkland (1984),

Argyris, Putnam and Smith (1985), Hatchuel and Molet (1986), Roy (1992), and Koenig (1997).

Different authors concluded and gave various opinions about the possible research classifications in

context to variety of methodologies used in the subject of management sciences. Cecez-

Kecmanovic (2007) concluded that there are the numerous methods of empirical research, which

encompasses the positivistic, interpretive and pragmatic paradigms. Chua (1986) identified the three

categories of research classifications i.e. positivistic, interpretive and pragmatic perspectives. In the

domain of management sciences the purpose of positivistic approach is to investigate and test the

theories and causal realities that predict the phenomenon which impact an organization. This

methodology relies on hypotheses/ hypothesis testing on the basis upon population sample (Myers,

2004). In the interpretative approach, researchers combined the results of their own subjective

opinion, considering the reality as a social product that cannot be understood independently of

society including researchers (Klein & Myers, 1999). Cecez-Kecmanovic (2007) believed that

instead of formulating conclusions as the established facts, interpretative research provides

interpretational analysis on the subject.

I.2.1. Positivism.

The positivist assumptions have represented the traditional form of research, and these assumptions

hold true more for quantitative research than qualitative one (Creswell, 2012). Positivists hold a

deterministic philosophy in which causes determine effects or outcomes. Thus, the problems

studied by positivists reflect the need to identify and assess the causes that influence outcomes, such

as found in experiments.

The knowledge that develops through a positivist lens is based on careful observation and

measurement of the objective reality that exists. This knowledge is objective, value free, or neutral

(Breen and Darlaston-John, 2008). There are laws and theories that govern the world, and these

need to be tested or verified so that we can understand the world. Thus, in the scientific method the

researcher begins with the theory, collects data, which either support or refuse the theory, and then

the researcher makes necessary revisions before additional tests are made.

Key assumptions of the positivist position (Phillips and Burbules, 2000, Breen and Darlaston-John,

187

2008, Creswell, 2012):

- Knowledge is conjectural, absolute truth can never be found. Thus, evidence established in

research is always imperfect and fallible.

- Research is the process of making claims and then refining or abandoning some of them for

other claims more strongly warranted.

- Data, evidence, and rational consideration shape knowledge. In practice, the researcher collects

information on instruments based on measures completed by the participants or by observations

recorded by the researcher.

- Research seeks to develop relevant, true statements, one that can serve to explain the situation

of concern or that describe the causal relationships of interest.

- Being objective is an essential aspect of competent inquiry; researcher must examine methods

and conclusions for bias.

Positivists assume that natural and social sciences measure independent facts about a single

apprehensible reality composed of discrete elements whose nature can be known and categorized

(Tsoukas, 1989, Guba and Lincoln, 1994, Patton, 2002, Breen and Darlaston-John, 2008). The

objectives of the research inquiry often include the measurement and analysis of causal

relationships between variables that are consistent across time and context. The primary data

collection techniques include controlled experiments and sample surveys, and outcomes orient and

assume natural laws and mechanisms, with the primary mode of the research inquiry being theory-

testing or deduction (Serakan, 2003, Serakan and Bougie, 2010, 2013). Data is usually collected in

a structured manner with the researcher not intervening in the phenomenon of interest and seeking

for theory testing in value-free generalizations. In other words, the data and its analysis are value-

free and data does not change because they are being observed.

Positivism as epistemological position in business research applies to the methods of the natural

science. Thus, the observed phenomenon is tested by developing of hypothesis, which can be

confirmed or rejected. The knowledge about the phenomenon is arrived by gathering facts,

reviewing theories.

I.2.2. Constructivism.

Constructivists hold assumptions that individuals seek understanding of the world in which they

live and work. Individuals develop subjective meaning of their experiences – meanings directed

toward certain objects or things. These meanings are varied and multiple, leading the researcher to

look for the complexity of views rather than narrowing meanings into a few categories or ideas

188

(Creswell, 2012). Watzlawick (1984), Von Glasersfeld (1988), and Jonassen (1991) share the

opinion that the observer creates reality by giving the meaning to what he/she observes. Dickerson

and Zimmerman (1996) postulate that all interpretations are equally valid ad long as it is

conditioned in context. The goal of the research is to rely as much as possible on the participant’s

views of the situation being studied.

The social effect was firstly excluded in constructivism (Held, 1990), the role of social and cultural

factors in the creation of individuals meaning was developed later, when the authors recognized,

that epistemology shows claims and opinions of individuals under the influences of theirs living

culture and societies (Owen, 1992).

Key assumptions of constructivists (Cambell, 1998, Crotty, 1998, Creswell, 2012):

- Meanings are constructed by human beings as they engage with the world they are interpreting.

- Humans engage with their world and make sense of it based on their historical and social

perspectives. Thus, qualitative researchers seek to understand the context or setting of the

participants through visiting this context and gathering information personally. They also

interpret what they find, an interpretation shaped by the researcher’s own experiences and

background.

- The basic generation of meaning is always social, arising in and out of interaction with human

community. The process of qualitative research is largely inductive, with the inquirer generating

meaning from the data collected in the field.

From a constructivist's perspective, truth is a construction which refers to a particular belief system

held in a particular context. Realities appear as multiple realities which are socially and

experientially based intangible mental constructions of individual persons. Meaning has more value

than measurement, for perception itself is the most important reality. Constructivism enquires about

the ideologies and values which lie behind a finding. Researching this created knowledge depends

on the interaction between interviewer and respondent, that is, the researcher has to be a ‘passionate

participant’' during his or her field work (Guba and Lincoln, 1994).

Constructivism is an alternative to the positivism epistemological paradigm that is influential in

business research. It is based on the point of view of the strategy, when the differences between

people and the objects are important to the research.

I.2.3. Pragmatism.

Pragmatism arises out of actions, situations, and consequences rather than antecedent conditions

(Creswell, 2012). Instead of focusing on methods, researchers emphasize the research problem and

use al approaches available to understand the problem.

189

Key assumptions (Cherryholmes, 1992, Morgan, 2007, Creswell, 2012):

- Pragmatism is not committed to any one system of philosophy and reality. This applies to mixed

method research in that inquirers draw liberally from both quantitative and qualitative

assumptions when they engage in their research.

- Individual researchers have a freedom of choice. In this way, researchers are free to choose the

methods, techniques, and procedures of research that best meet their needs and purposes.

- Truth is what works as the time. It is not based in a duality between reality independent of the

mind or within the mind.

- Pragmatists agree that research always occurs in social, historical, political, and other contexts.

- Pragmatists have believed in an external world independent of the mind as well as that lodged in

the mind.

- For the mixed method researcher, pragmatism opens the door to multiple methods, different

worldviews, and different assumptions, as well as different form of data collection and analysis.

Pragmatism as epistemological paradigm applies to the mixed-method in business research and is

used often in management science.

Table 40: Positivism, Constructivism, Pragmatism in business research:

Positivism Constructivism Pragmatism

Knowledge is conjectural, absolute

truth can never be found.

Meanings are constructed by

human beings, they are interpreting

the knowledge.

Truth is what works as the time.

Data, evidence, and rational

consideration shape knowledge.

Understanding the context through

visiting this context and gathering

information personally.

Different form of data collection

and analysis depending the

situation and need.

Examination of methods and

conclusions for bias.

Generation of meaning is always

social, arising in and out of

interaction with human

community.

Pragmatism opens the door to

multiple methods.

Quantitative method Qualitative method Mixed method

Source: completed by the author of thesis, based on different references above.

I.3. Choice and justification of epistemology

In our research positivism is considered as scientific paradigm. This choice is based on three

important points, including the object of the research, the nature of the data, and scientific traditions

in the previous researches:

1) A prime focus of the research is the behavior of consumers in the particular industry

(restaurant) influenced by information technology. Nevertheless, information technology (in

our case it is a mobile application) is the important object of the research. The phenomenon

190

of information technology forces the researcher to work with big amount of data, and to

treat this data statistically. In our research we have observed the fact of using the mobile

applications in the restaurant industry by consumers as part of existed reality. Mobile

technology is a part of the modern reality. As we presented in the context of the research,

the number of smartphones increases each year producing big data, and people use mobile

applications in their everyday life, also the restaurants as part of the reality and everyday life

tend to adapt and accept the mobile technology.

2) The nature of the data in the researched context is the second factor of the choice of the

positivist paradigm. Digital data is rather to measure with quantitative method than

qualitative.

3) The theoretical tradition is based in our research on three parts of theories:

- Technology’s use theory

- Culture dimensions theory, and

- Relationship marketing theory

❖ Technology’s use theory

The positivist vision is strong on the unit of the technology’s use theories.

Started with Rogers’s (1962) theory of technology diffusion the method of sampling and

statistically treatment of the results are used. On the step of observation the hypothesis and theories

are developed, which are confirmed or rejected on the empirical stage. In our researched following

theories are regarded:

➢ Theory of technology diffusion by Rogers(1962);

➢ Theory of Reasoned Action by Ajzen and Fishbein (1975);

➢ Theory of planned behavior by Ajzen (1985);

➢ Technology Acceptance Model by Devis (1986) and TAM2 by Devis and

Venkatesh(2000);

➢ United Theory of Acceptance and Use of Thechnology(UTAUT) and UTAUT2 by

Venkatesh et al.(2003,2012);

➢ Mobile Application Usability by Venkatesh and Hoehle (2015).

In all listed concepts sampling, statistical measurement and treatments using software are the

researching strategies. All theories have positivism scientific paradigm, with developing hypothesis

at the first step and empirical analyze based on the quantitative method.

❖ Cultural dimensions

Hofstede has as well positivistic point of view in his theory of cultural dimensions. His research

is based on 116000 survey responses in IBM units in approximately 60 countries (Hofstede,

2001, Hofstede, Minkov 2010).

191

❖ Relationship marketing

Theory of relationship marketing is the part of theoretical background of our research, which

includes both: quantitative and qualitative method, depending on the problem of the research.

Pragmatic scientific paradigm allows choosing corresponding means of collecting and interpreting

data. Methodological orientation includes differences in schools and directions, thus, the tradition

can emphasize on hypothetical-deductive reasoning, or understanding through qualitative research.

In our research we regarded:

➢ Commitment-Trust theory by Morgan and Hunt (1994) - hypothetical-deductive

reasoning;

➢ Interfirm Relationship marketing by Palmatier (2008) – hypothesis testing by

multivariate analysis;

➢ Interpersonal Relationship marketing by Palmatier (2008) – qualitative research;

➢ Multi-level Exchange Relationships in Relationship marketing by Palmatier (2008) -

both methods;

➢ The role of culture in Relationship Marketing by Samaha, Beck, and Palmatier

(2012) – hypothetical-deductive reasoning.

In this way we can conclude that positivism as scientific paradigm is our primary choice and has

been confirmed by object of the research, nature of data, and regarded theories.

II. Design of the research

Research design provides a framework for the collection and analysis of data. The choice of

research design shows the most important consideration given to a range of dimensions of the

research process.

Research design is an important tool of planning, substantiation, and practical guidance of the

research procedures. It allows identifying four main issues: 1) research questions or problem, 2)

theoretical corpus, 3) relevant data to collect, 4) ways to analyze and interpret data (Thiétart, 2007).

According to De Vaus (2001) research design is “a logical task, undertaken to ensure that the

evidence collected enables to answer questions or to test theories as unambiguously as possible”.

The researchers not only select a quantitative, qualitative or mixed methods, they also decide the

type of research inside of the selected methods.

The choice of the research design can be found either by the process of deduction or the process of

induction, or by a combination of the two. Induction is formation of the generalization derived from

examination of a set of particulars, while deduction is the identification of an unknown particular,

drawn from its resemblance to a set of known facts (Rotchild, 2006).

192

In other words, deduction is the process by which the researcher arrives at a reasoned conclusion by

logical generalization of a known fact (Sekaran, 2003). For example, we know that all users of the

website LaFourchette tend to download its mobile application. So, if a person is using the website

LaFourchette, we can say that he/she will very probably download this mobile application sooner or

later. Induction, on the other hand, is a process where the researcher observes certain phenomena

and on this basis arrives at conclusions (Sekaran, 2003). For example, we see that the participating

in bonus program of LaFourchette is the prime motivation for users to download its mobile

application. Therefore we can conclude that users download the mobile application LaFourchette to

participate in bonus program.

Table 41: Induction, Deduction, Combination

Induction Deduction Combination of both

Observation is prior; the researcher

observes certain phenomena and

on this basis arrives at conclusions.

Knowledge is prior; the researcher

arrives at a reasoned conclusion by

logical generalization of a known

fact.

Nothing is prior; the researcher

combines two processes depending

on the researched phenomena.

Source: completed by the author of thesis, based on different references above.

II.1 Hypothetico-deductive research process

When research is designed to test some specific hypothesized outcomes, the researcher begins with

reviewing the corresponded theories. The hypothesis is then generated. Based on this a research

project is designed to test the hypothesis. The results of the study help the researcher to deduce or

conclude if his/her hypothesis are confirmed or rejected. This method of starting with a theoretical

framework, formulating hypotheses, and logically deducing from the results of the study is known

as the hypothetico-deductive method (Serakan, 2003).

Hypothetico-deductive research process proposed by Sekaran and Bougie (2013) is presented on the

Figure 24:

193

Figure 24: The research process

Source: Sekaran and Bougie, 2013

Adapted the presented frame of research process by Serakan and Bougie we are designing our

research according to hypothetico-deductive process respecting following steps:

- Observation, or identifying a broad area of research,

- Preliminary information gathering (literature review),

- Theory formulation,

- Hypothesizing,

- Data collection,

- Data analysis,

- Data interpretation,

- Presentation of results (for example in the form of an article or the thesis and further

defense of the thesis).

Observation is the first stage, when the researcher notices that some changes are occurring and the

observed phenomena are seen to have potentially important consequences. In our case, the observed

phenomena occur when the behavior of the consumer changes influenced by developed mobile

technologies. Restaurants face the change in the obtaining of information, being reviewed

immediately, checking in with social networks, and online booking. The observation of phenomena

is presented in details in the Research Context, chapter I of this thesis. Firstly, we showed the

overlook of the tourism and restaurant industries, giving necessary definitions, historical references,

194

and modern state. Then, we looked into the development of the technology, that has the most

significant influence on these industries, and finally, we presented the context of two countries

Russia and France, underlining the development stage of both: technology and industries.

Preliminary information gathering involves the search for information to explain the observed

phenomenon, including the study of previous researches. Based on our research problem we

decided to focus on three theoretical backgrounds.

❖ Technology’s use theories, because the key-object of our research is the mobile application,

as a modern tool for both participants of the restaurant market: restaurant establishments and

restaurant clients. We reviewed:

- Diffusion of innovation, Everett Rogers (1962).

- Theory of reasoned action (TRA) by Martin Fishbein and Acek Ajzen (1975, 1980).

- The technology acceptance model by Fred. D. Davis (1986).

- Unified Theory of Acceptance and Use of Technology (UTAUT), Venkatesh et al. (2003).

- UTAUT2, Venkatesh et al. (2012).

- Mobile application usability, Venkatesh and Hoehle (2015)

We adopted and included in our model seven constructs of technology’s use theories.

❖ Relationship Marketing, because the interaction of restaurant establishment and its client

involves developing of relationship with such constructs as trust, commitment, end loyalty.

Here we reviewed:

- The Commitment-Trust theory of RM by Morgan and Hunt( 1994)

- Interfirm RM by Palmatier (2008)

- Interpersonal RM by Palmatier (2008)

- Multi-level exchange Relationships in RM by Palmatier (2008)

- The role of culture in Relationship Marketing by Stephen A. Samaha, Joshua T. Beck, & Robert

W. Palmatier (2012).

We adopted and included in our model two constructs of relationship marketing theory.

❖ Cultural dimensions theory, because we are going to compare the use of mobile applications

in two countries, Russia and France. Here we reviewed:

- Cultural dimensions theory by Gert Jan Hofstede (2001, 2010);

- Adoption of communication technologies and national culture, TAM and Hofstede’s (2001);

- The concept of the modern Russian values (Belyaev, 2010, Kotlarova, 2010);

- The concept of the modern French values (Mermet, 1996, Excousseau, 2000);

195

- Cultural dimension of Hofstede (2001, 2010);

- Impact of Hofstede’s cultural dimension on technology use and on relationship marketing

(2012).

We adopted and included in our model one construct of culture dimensions theory.

Theory formulation is the step when the information is integrated in the logical way, so the factors

which are important for the problem can be conceptualized and tested. The theoretical framework

presents chosen variables. According to Zikmund, Babin, Carr, and Griffin (2009) there are

variables in the research among which the researcher study relationships:

❖ Independent variable is the predictor variable which is supposed to be cause of change in

the dependent variable. In our research model independent variables are indulgence, price

value, facilitating conditions, and usability.

❖ Dependent variables in our research model are use, loyalty, continued intention to use.

Figure 25: Dependent and independent variables

❖ Moderating variable is one that has a strong contingent effect on the relationship between

independent and dependent variable (Sekaran, Bougie, 2010). In our research model

moderating variables are duration and place of the use, frequency of visits.

Figure 26: Moderating variable

❖ Mediating variable or intervening variable is one that provides a causal link between

independent and dependent variables (Serakan, Bougie, 2010). In our research model

mediating variables are trust, intention to use, loyalty.

196

Figure 27: Mediating variable

Hypothesizing is the next step, when the researcher based on the theoretical framework hypnotizes

the relationships among the chosen variables. The hypothesis fulfills three conditions (Cooper and

Schindler, 2006):

1) It is adequate for its purpose

2) It is testable

3) It is better than its rivals

In our research we have ten hypotheses and four hypotheses of moderation. In correspondence with

these three conditions the model and hypotheses are reviewed by experts, and after that they are

checked during the pretest inside of smaller sampling, enough for treatment with the software

SmartPLS3.

Further scientific data collection occurs when hypothesis are developed and pretested. Empirical

data for this study was collected in cooperation with two companies: Lafourchette in Paris and

Resto in Moscow. In this way the most important feature of the sampling is that all the responders

are registered as user one of the mobile application, depending on the city. The companies included

the survey in their news-letter and sent to all users in the areas of Paris in France and Moscow in

Russia.

In Data analysis step the data gathered are statistically analyzed to see of the hypothesis that were

developed have been supported. Hypotheses are tested through appropriate statistic software in our

case SmartPLS which is discussed in the section 4 of this chapter.

Interpretation is the process of arriving to conclusions by interpreting the meaning of the results.

Based on the deduction the researcher would make recommendations on problems’ solution.

Presentation depends on the type and goal of the research and can be done in form of an article,

thesis, or conference presentation.

197

II.2. Comparative design

Second important framework of our research is the comparison of two countries Russia and France;

therefore it is important to point out the features of comparative research design. Comparative

design involves the study using identical methods on two or more cases. It comprises the logic of

comparison so the researched phenomena are regarded in two or more different situations (Bryman

and Bell, 2015). Thus, the mobile application use can be observed and compared in different

situations of usage, like for example usage when online-shopping and usage of messengers, or two

different mobile applications can be observed and compared in the same usage situation. In any

case, the most important issue for researcher is to work out the criteria of comparison and preserve

the identical method of data collecting and interpreting.

One of the forms of comparative design is cross-cultural or cross-national research. Such research

occurs when researcher examines particular phenomena in two or more countries with intention

comparing his/her findings using the same research methods and instruments (Hantrais, 1996,

Harkness, Van de Vijver, & Mohler, 2003, Delva, Allen-Meares, & Momper, 2010). The goal

might be to explain similarities and differences or to strengthen awareness and understanding of the

phenomena in different national contexts. Cross-national research is the form of comparative design

which we are using in our study. The researched phenomenon is the mobile application use in the

restaurant industries of two countries - Russia and France. To accomplish the comparison the

national context is presented in three parts: general situation, industry’s development, and position

of the mobile technology today in each country (see more in the chapter I). Further two mobile

applications used in the restaurant sector are described in details. And cooperation with the

management of these applications is created.

Cross-cultural research regards culture as major explanatory variable that affects consumer

behavior. Nevertheless such research should not be treated as only concerned comparisons between

nations. The logic of comparison can be applied to a variety of situations to inform a number of

levels of analysis. In our researched model the important role of culture is added by theory of

cultural dimensions of Hofstede. In this way not only differences and similarities of use by different

groups of people can be regarded but also the cultural aspect is respected.

Important issues of cross-cultural comparison are connected often with data. Researcher should be

aware about that data are comparable in terms of categories and data collection methods. The need

to translate data collections instruments in the proper way. Even with correct translations there is

always the possibility of potential insensitivity to specific national or cultural contexts. In our case

after the development of the research model, the survey was worked out, and translated into both

198

languages in French and in Russian; the surveys have identical questions for both cases. The

sampling and data collecting methods are also identical in the way that the surveys are suggested in

the first line to the clients of the companies, in other words loyal or tending to be loyal users of

applications. Strict abidance of this requirement causes some difficulties in collecting data and

increases the length of the research. So, for example, we are limited in sample size because of low

activity of Russian responders.

Another issue in comparative research is achieving equivalence between the samples, variables, and

methods (McDonald, 2000). For example, in many cases nationality is used as a substitute of

culture, but differences in researched phenomena can apply to cultural attribute as well as to

national situation. For example what we could notice, the tradition to eat out of home is stronger in

France than in Russia, therefore the habit to search for the restaurant supposed to be less developed

in Russia. On the other hand the mobile technology came into the life of the people of both

countries about the same time. Other significant reason of different consumer behavior lies in the

category of income. The purchasing power in Russia is lower and it can also explain for example,

why people go out less in Russia than in France, but it does not influence the use of mobile

application in the situation of the restaurant choice.

Therefore the use of comparative design is in its ability to allow distinguishing characteristics of

two or more cases what is leading in theoretical reflections about contrasting findings.

III. Methodological approach

This section proposes presentation of methodological approach and the choice of methods for the

research.

III.1. Quantitative and qualitative research strategy

Scientific methodology distinguishes between quantitative and qualitative methodological

approach. The choice one above another determines the different methods of the research. The

quantitative/qualitative distinction is needed because it classifies means of the research:

measurements, data collecting and interpreting.

Creswell (2012) defines quantitative approach as “ one, in which the investigatory primarily uses

postpositive claims for developing knowledge…..employs strategies of inquiry such as experiments

and surveys, and collect data on predetermined instrument that yield statistic data”. Thus,

quantitative research is prerogative of the positivists, with linking variables, testing theories and

hypotheses, making predictions.

Qualitative approach focuses on exploring; categories are isolated and defined during research

process. Significant phenomena are supposed to be interpreted historically and culturally.

199

According to Creswell (2012) qualitative approach is “one in which the inquirer often makes

knowledge claims based primarily on constructivists perspectives…..It also sues strategies of

inquiry such as narratives, phenomenology, ethnography, grounded theory studies, or case studies.

The researcher collects open-ended, emerging data with the primary intent of developing them from

the data”.

Generally saying the difference between both approaches lies in the fact, that in quantitative

research the researchers use measurement and in qualitative one they do not, but from the

epistemological point of view these two research strategies are fundamentally different (Table 42).

Table 42: Fundamental differences between quantitative and qualitative research strategies.

Quantitative Qualitative

Role of theory in the research Deductive; testing of theory Inductive; generation of theory

Epistemology Natural science model, positivism Interpretivism

Ontology Objectivism Constructivism

Strength Precision because of quantitative

and reliable measurements

High control level because of

sampling and design

Ability to produce causal

statements in the use of controlled

experiments

Statistical techniques and

sophisticated analysis

Replicable

Have an insider view on the field

because of close involvement of the

researcher

Suggest possible relationships,

causes, effects, and dynamic

processes

Have new insight of knowledge

because of descriptive and narrative

style

Limitations Assumptions about the facts are

generalized to all cases

Result interpretation are subjective

Weak control over all variables

Problem in proves of validity and

reliability

Not replicable

No generalizations

Not anonymous

Source: adapted by Bryman and ball, 2015, Burns, 2000

Thus, quantitative research is a research strategy that emphasizes quantification in the collection

and analysis of data (Bryman and Bell, 2015). That means:

• A deductive approach to the relationship between theory and research, in which the theory is

testing.

• Practices and norms are adopted from natural scientific model. Positivism is the dominated

scientific paradigm.

• Reality is regarded as an external, objective one.

In opposite, qualitative research is a research strategy that usually emphasizes words rather that

200

quantification in the collection and analysis of data (Bryman and Bell, 2015). That means:

• An inductive approach to the relationship between theory and research, in which theory is

generating.

• Researchers interpret the reality. Interpretivism/constuctivism is the scientific paradigm.

• Reality is a part of individual’s creation.

Quantitative and qualitative researches represent different research strategies, in terms of the role of

theory, epistemological paradigm, and ontological concerns. However, the distinction can be not

strict and in this way the mixed method appears as research strategy. Anyway but by contrasting of

two approaches the researcher can easier make a decision of implementation one or another method

according to his/her research objectives.

III.2. Quantitative research approach.

Quantitative research was driven by investigators with the need to quantify data. A quantitative

research method involves a numeric or statistical approach to research design. Leedy and Ormrod

(2001) alleged that quantitative research is specific in its surveying and experimentation, as it builds

upon existing theories. The methodology of a quantitative research maintains the assumption of an

empiricist paradigm (Creswell, 2009). Data is used to objectively measure reality.

Quantitative research can be used in response to relational questions of variables within the

research. It begins with a problem statement and involves the formation of a hypothesis, through

literature review. Creswell (2009) states, quantitative research “employ strategies of inquiry such as

experimental and surveys, and collect data on predetermined instruments that yield statistical data”.

The findings from quantitative research can be predictive, explanatory, and confirming.

Quantitative research involves the collection of data so that information can be quantified and

subjected to statistical treatment in order to support or refute “alternate knowledge claims”

(Creswell, 2009). The researcher uses mathematical models as the methodology of data analysis.

Three historical trends pertaining to quantitative research include research design, test and

measurement procedures, and statistical analysis. Quantitative research also involves data collection

that is typically numeric and the researcher tends to use mathematical models as the methodology of

data analysis. Additionally, the researcher uses the inquiry methods to ensure alignment with

statistical data collection methodology.

III.3. Concepts and measurement instruments

Concepts are categories for organization of ideas and observations (Bulmer, 1984, Bryman and

Bell, 2015). If a concept is to be employed in quantitative research, it will be needed to measure it.

201

Once the concept is measured, it can be in the form of independent or dependent variables. In other

words, when conducting quantitative research, a researcher should have a clear understanding about

the concepts of research units and variables and how to measure them. Concepts provide the

explanation of an aspect of reality; they stay for phenomenon the researcher wants to explain. For

example, a concept of culture can be used as a possible explanation of mobile technology use in

cross-cultural research, or mobile technology use can be explained by the concept of mobile

application usability. When we start to investigate these issues we are likely to formulate hypothesis

connecting different concepts, but also we need to measure variables.

There are three main reasons for developing measurement instruments in quantitative research:

• Measurement allows delineating fine differences between people in terms of the

characteristic questions. So, we can find out people who are using the mobile applications

in their everyday life and distinguish them from the people who have never used or use

seldom the mobile applications.

• Measurement gives a consistent device for making such distinctions. A measurement device

provides a consistent instrument of evaluation.

• Measurement provides the basis for more precise estimates of the degree of relationship

between concepts.

In order to provide a measure of a variable, it is necessary to have measuring items that will stand

for the concept. There are a number of ways in which the measuring items can be devised:

• Through a question (or series of questions) that is part of structured interview schedule or

self-completion questionnaire.

• Trough the recording of individual’s behavior using structured observation schedule.

• Trough an examination of mass media content.

To confirm one concept or variable the researcher should examine it with variety of measuring

items to be sure that concept can be sufficient. In this case researcher uses multiple-item measures.

So, it is possible that single measuring item can incorrectly classify many individuals. For example,

due of wording of the question the misunderstanding of question can appear. Single measuring item

can be also too general, in this case another question is needed to precise the concept and to

decrease the level of generalization. Multiple-items help make the distinctions.

III.4 Survey

Survey is the way to collect data to validate the research model. According to Bryman and Bell

(2015) survey can be accomplished as structured interview or self-completion (self administrated)

questionnaire (see Figure 28).

202

Figure 28: Survey structure

Source: Bryman and Bell, 2014

The most significant difference between both lays in the presence or absence of interviewer during

the time, when the responder fulfills the questions.

In our research we use the self-completion questionnaire.

Self-completion questionnaires

Self-completion or self-administrated questionnaire is the instrument of survey, when responders

answer questions by completing questionnaire by themselves. It can have several forms:

- Supervised: in this case the questionnaire is completed on responder’s own but under the

supervision, for example in the class, or in the enterprise during the meeting. Supervisor does not

actively participate; nevertheless his/her presence can influence somehow the responder. Very often

in this type of self-completion questionnaire the participants have no or little choice to answer or

not, and as consequence supervised questionnaires have high response rate.

-Postal: a questionnaire is sent through the post to the responders and completed forms is usually

asked to be sent back, for this purpose the envelope with the address is included in the package. In

other cases the completed questionnaire can be deposit to the box in the office, super-market etc.

- Internet. Galan and Vernette (2000), Gueguen and Yami (2007) distinguish between two types of

Internet investigation: statistic and dynamic. In the statistic Internet investigation the researchers

use email to diffuse the questionnaire. Usually the questions are designed inside of the email text or

in the attached file. Kittleson (1995) assumes that email questionnaire has risk to be deleted because

of poor attraction power of responders, or it can be blocked by antivirus and antispam software. In

the dynamic investigation Internet but itself is used for diffusion of the questionnaire, which is

203

posted on a server, and then its address of uniform resource locator (URL) is sent to targeting

audience in different ways. The responders can open the questionnaire in the way they use Internet

(browser or mobile). Results are collected automatically (Alvarez and Van Beselaeree, 2003). This

kind of survey is very interactive and rich (Stanton, 1998).

In our research we use dynamic online questionnaire, particularly Google survey.

III.5. Online survey

Internet-based studies are roughly systematized in four categories (Reips, 2006): Internet-based

experiments, web surveys and questionnaires, Internet-based assessment, and nonreactive data

collection on the Internet. In a wider sense, studies about human activities on the Internet can also

be defined as Internet-based studies. The first Internet-based studies appeared in the middle of 1990

(Musch & Reips, 2000; Reips, 2006). Conducting studies via the Internet is considered a second

revolution in behavioral research, after the computer revolution in the late 1960s and early 1970s

that brought about many advantages over widely used paper-and-pencil procedures (e.g., automated

processes, heightened precision). Internet added the dimension of interactivity in research.

Although Internet-based studies have some inherent limitations due to a lack of control and

observation of conditions, they also have a number of advantages (Birnbaum, 2004; Kraut et al.,

2004; Reips, 2002; Schmidt, 1997). Some of the main advantages are that (1) researchers can

recruit and study large numbers of participants very quickly; (2) it is possible to collect and access

large data sets, and (3) the method is more cost-effective in time and space.

Many authors agree that the Internet is a viable option for conducting studies in the behavioral and

social sciences (Birnbaum, 2004; Joinson, McKenna, Postmes, & Reips, 2007; Kraut et al., 2004;

Musch & Reips, 2000; Reips, 2002, 2006). Care should be taken to properly implement an adequate

methodology suited for Internet-based studies (Reips, 2002) and consider potentially biasing effects

of technology (Schmidt, 2007). Two important issues for much of human interaction on and with

the Internet are: real and perceived privacy as well as trust (Buchanan, Joinson, Paine, & Reips,

2007; Eysenbach et al., 2004). Threats to privacy on the Internet reduce the willingness to look for

information online. Benefits like wider reach, better quality of data from truly voluntary

respondents, and a reduced tendency for socially desirable responding, reduced costs, and validated

tools for Internet-based research increase the use of the Internet-based survey.

Google Survey in Google forms application provides a fast way to create an online survey, with

responses collected in an online spreadsheet. As a tool for online survey Google forms has such

advantages as: voluntariness and anonymity of responding, what positively impacts the trust

towards the survey; easy to spread the link by publishing it or sending via email; free of charge,

204

Google forms is available for free. Nevertheless, the most important limitation of this tool is the

lack of control over the process of data collecting. In this way the researcher should be aware and

count the length of the time, he/she can need to accomplish the survey.

Figure 29: Process of web-based questionnaire diffusion

Source: adapted by Gueguen and Yami (2007)

III.6. Sampling

Consideration about sampling is very important in quantitative research. Generally speaking the

researcher should decide where, how and by whom he/she should collect the data for the analysis.

The collected data should allow the researcher generalize or not the findings. In order of

generalization of findings from the sample to the population, the sample must be representative

(Bryman and Bell, 2014). If we are researching the mobile application use, so surely we should

collect data from users of mobile applications, our research is in the restaurant industry, so the users

should not only use any of mobile applications, but actually the specific mobile applications, and on

the other hand they should be not only users of technology, they should also visit more or less often

the restaurants. And finally, one of the specificity of our research is the cultural comparison, so we

are obligated to collect data from different countries, else we will be not able to compare our

findings.

There are two main strategies of sampling: probability and non-probability.

Probability sample

a) Simple random sample is the most basic form of probability sample. With random sampling

each unit of population has an equal probability of inclusion in the sample. This is known as

sampling fraction and is expressed as

n/N

where n is the sample size and N is the population size. The key steps in the simple random

sample are:

205

- definition of population ( for example, users of mobile application Lafourchette living in

Paris region, French speaking)

- selection a comprehensive sampling frame (it is possible that our survey will responded by

people never used mobile application, or living in other cities in France, but these

responders are out of our frame)

- decision about sample size

- listing all corresponding population from 1 to N

- using the random numbers or a computer software generate random numbers, select n

different numbers that lie between 1 and N

- responders to which the n random numbers refer constitute the sample.

b) Systematic sample is a variation on the simple random sample. With this kind of sample

units are directly chosen from the sampling frame without reasoning to a table of random

numbers.

c) Stratified random sampling means stratifying the population by a criterion and selecting

either a simple random sample or a systematic sample from each of the resulting strata.

The advantage of the stratified sampling is that the resulting sample will be distributed in the

same way as population in terms of the stratifying criterion. This sampling is only feasible when

data are available that allow the ready identification of the members of the population.

d) Multi-stage cluster sampling occurs when the widely dispersed population is involved in the

survey. With cluster sampling the primary sampling unit is not the units of population to be

sampled but groupings of those units.

Cluster sampling is always a multi-stage approach. The advantage of multi-stage cluster

sampling lies in the possibility to interview geographically wide spread population.

Non-probability sampling

There are two main types of non-probability sample: the convenience and the quota samples.

a) A convenience sample is the one which is easy to access for the researcher. For example, to

complete our research we are cooperating with two companies – one in France and one in

Russia. These companies have access to their mobile application users directly, so that can

simplify our access to data base and declines non-response rate. According to Bryman and

Bell (2015), that kind of sample can cause a problem in generalizations of findings.

Nevertheless convenience sample become the norm in some research fields including

researches on consumer behavior. For our research where we need to investigate particular

206

situation of mobile application use the convenience sample provides a considerable

possibility for access to data base.

b) Quota sampling is used intensively in commercial research like market research and political

opinion polling. The goal of quota sampling is to produce a sample that reflects a population

in terms of the relative proportions of people in different categories. Good example of quota

sampling is focus groups in television or in marketing.

III.7. Error in survey research

The term “error” is employed on the number of occasions made of four main factors (Bryman and

Bell, 2015):

- Sampling error is the difference between a sample and the population from which it is selected. In

fact this error arises when the responders are not really representative sample. For example, if we

collect data for our research by people who are playing mobile games, on the one hand they are

users of the mobile applications, but it is not sure that they are going out in restaurants and use

appropriate mobile applications. And in opposite not all people who are going out frequently in

restaurants are users of mobile applications. Our task is to find representatives of both.

- Sampling-related error concern not only error in choosing of responders who are appropriate for

the questionnaire, but also inaccurate sampling and non-response.

- Data collection error is connected with implementation of the research process. It includes such

errors as poor wording in the questionnaire, mistakes in the administration of survey.

- Data processing error appears when the management of data is organized on the low level, or there

are mistakes in data coding.

- Non-response: a source of non-sampling error that is particularly likely to happen when

individuals are being sampled. It occurs whenever some members of the sample refuse to cooperate

cannot be contacted or for some reason cannot supply the required data

III.8.Conclusion

For our research we are using a convenience sample as strategy to collect data. Firstly, we cooperate

with companies which are providers of particular mobile applications in restaurant industry to

collect data with help of their data base where all users are registered. We consider that if the people

use specific mobile application for booking restaurants, they also are the clients of the restaurants.

Secondly, we use social networks and emailing to collect data, but also we note in the begging that

that survey is aimed for people who are using the mobile applications to search for or to book table

in the restaurants. The process is organized equally in both countries.

207

We are aware of the limits in generalization which can arise by using convenience sample (Bryman,

1989, Hooghe. 2010, Bryman and Bell, 2015). The findings from our study have applicability for

companies which are providers of mobile application in restaurant industry in two countries, and

this finding will be not applicable for mobile applications in others industries or countries.

IV. Methodology to develop and validate variables and construct – Churchill paradigm

In this section the methodology to develop and validate variables and constructs of research is

presented. The marketing researchers are often facing a problem how to measure, assess and

validate the variables. This problem is caused by the definition of measurement process, which

involves according to Nunnally (1967) « rules for assigning numbers to objects to represent

quantities of attributes». As Churchill (1979) pointed out in his article the attributes of objects that

are measured are not the objects by themselves, and there is no specifying of the rules by which the

numbers are assigned.

In an example construct C, such as “mobile application loyalty” in our research model, we can say

that every user can have at any time some “true” level of loyalty Xt. Each measurement we make

will produce an observed score Xₒ, equal to the object’s true score Xt. In the desired by researcher

situation differences between objects with respect to their Xₒ scores would be completely referable

to true differences. But in fact Xₒ score reflect also other differences (Selltiz, 1976):

- true differences on other stable characteristics ( person’s willingness to express his or her

true feelings, for example in the surveys where the person is responding anonymously the

level of honesty might be higher)

- differences due to situational factors ( where and how the interview is taken, in our survey

which is made online, we have no control above the circumstances of responding, and also

we can here technical aspects, whether the user answers with smartphone or PC, whether

she/he has stable Internet access etc.)

- differences due to variations in administration ( who is the interviewer, because we are using

convenience sample, we collaborate with the management of mobile applications, so the

understanding of user whether she/he is interviewed by the mobile application by itself or by

some random person in Internet can definitely affect the answer)

- differences due to sampling of items (e. g. our questionnaire is translated into French and

Russian, it is very possible that affect will be different because of choice of words in both

languages; also specific technical words can complicate the understanding of items)

- differences due to lack of clarity of measuring instruments (ambiguous questions, e.g. in the

first edition of our questionnaire we used the question “How long are you using mobile

208

application N” without giving the time periods, that caused the difficulty for responders to

count the years or months, as well as interpreting afterwards the results; thus, we added the

periods so the user can choose the closest)

- differences due to mechanical factors (e.g. incorrectly coding).

We described these factors with the possible examples concerning our case. Not all of them should

necessarily appear in every measurement we produced. Nevertheless, these factors distort the

observed scores away from the true score Xt. According to Churchill the relationship can be

expressed as:

Xₒ= Xt + Xs + XR

where:

Xs = systematic errors

XR = random errors

A measure is valid when the differences in observed scores reflect true differences on the

characteristic Xₒ= Xt. A measure is reliable to the extent independent but comparable measures of

the same construct of a given object agree. Reliability depends on how much of the variation in

scores is attributable to random or chance errors. The fundamental objective in measurement is to

produce Xₒ scores which approximate Xt scores as closely as possible.

The Churchill paradigm is applicable in marketing researches as our research is. It is also applicable

to multi-item measures, what is also respected in our research.

Churchill paradigm provides a framework to organize measures of reliability and validity of

variables and constructs, so the researcher can do decision about one or another.

Figure 26 shows the suggested procedure, which consists of 8 steps that should be followed and a

list of some calculations that should be performed in developing measures of constructs.

These eight steps correspond three phases:

- definition of conceptual domain of constructs,

- exploratory and

- confirmatory phases.

In the first phase there is only one step, actual formulating of the domain; steps two, three and four

explain exploratory phase; and steps 5-8 coincide the confirmatory phase. There are two steps to

collect data in the exploratory and confirmatory phases.

209

IV.1. First phase - Specify domain of the construct

On the first step of the Churchill paradigm researcher specifies the domain of the construct. This

should be done with help of literature in the researched domain. To illustrate importance of this step

we can use example construct of our research model mobile application loyalty.

In Relationship Marketing the concept of loyalty is indispensable aspect for the relationship

between client and company, it is defined as consumer’s desire and intention to continue a

relationship with the company, recommending it and choosing it from the market (Gremler, Brown,

1996; Lovelock, Wirtz, 1996), it is a degree of personal relationship caused by level of consumer’s

gratitude and norms of reciprocity. In the restaurant industry this concept concerns the personal of

the restaurant establishment (the chef, the administrator of the establishment, or the director) and

the client and is expressed in times the client returns to the establishment or/and to how many

people he/she will recommend the restaurant establishment.

In our case the technology, mobile application, changes the relationship between restaurant and the

client, proposing benefits, discounts and others. In this way we are more interested in the

measurement of the construct of loyalty regarding this concept as attitude of the user towards the

mobile application. Venkatesh and Hoehle define the mobile application loyalty as degree to which

a user has a deeply held commitment to rebuy or repatronize a mobile application. In other words,

how often the user opens the mobile application to book a table, or if the user tends to delete the

mobile application or keep it, etc.

210

Figure 30: Suggested procedure for developing better measures

Source: Gilbert A. Churchill, 1979

Regarding this example the domain of the construct “loyalty” consist of two constituting elements:

relationship marketing and technology use. The specifying of that necessitates us to measure this

construct with items of both.

IV.2. Exploratory phase

Generate sample of items

On this step the items are generated regarding the specified domain. This step is based on such

techniques as literature searches, experience surveys, insight-stimulating examples, and focus

groups. Each construct included in the research model should be measured through the items, which

are in the first place to find in the previous researches. That includes the exact definition of the

variables, dimensions and elements the variables have. Davis (1986) suggested using the conceptual

definitions of existing variables in the literature as a guide for development of measurement

instruments. The literature review preserves the validity of the measurement items (Straub et al.

2004).

211

The number of measurement items for each construct should be respected, classical test theory

recommends to develop 4 items pro construct (Devellis, 2003). More recent authors argue the

number of measurement items (Diamantopoulos, Sarstedt, Fuchs, Wilczynski, Kaiser, 2012; Yong,

Pearce, 2013). As a general guide, variables that have two or fewer items should be interpreted with

caution. Such variables is only considered reliable when the items are highly correlated with each

another (r > .70) but fairly uncorrelated with others.

The literature should indicate first how the variables are defined and how many dimensions or

components they have. On this step the first edition of questionnaire of our research was created.

Thus, in the example of mobile application loyalty we have extracted the measurement items of

previous researches and adapted them for our case, as it shown in the Table 43:

Table 43: Example of measuring items development – mobile application loyalty

Source: author of the thesis

On the first stage, the experts confirmed the model, hypotheses and questionnaire (Table 44).

Table 44: Experience survey

N Country Function Domain

1 France Researcher Information systems

2 France Researcher Information systems

3 France Researcher Marketing

Construct Measurement items Authors Final measurement items

Mobile

application

loyalty

1)I encourage friends and relatives

to be the customers of the mobile

application N

2)I will use more services offered by

the mobile application N in the next

few years

3)I consider the mobile application

N to be my first choice

Johnson et al.

2006

Hoehle&Ven

katesh, 2015

1)I encourage friends and relatives to

use mobile application (lafourchette,

resto)

2) I will use advantages of the mobile

application (laoruschette, resto) in the

next months.

3) I consider the mobile application

(lafourchette, resto) to be my first

choice when choosing the restourant

Loyalty

(Relationship

marketing)

How likely are you to

1) spend more than 50% of

your purchasing in the

shop?

2) Use this shop the very next

time you go for shopping?

Deepak

Sirdeshmukh,

Tagdip Singh

and Barry

Sabol (2002)

1. I make more than in 50 % of

reservation in restaurants using mobile

application (lafourchette, resto)

2. I use mobile application

(lafourchette, resto) the very next time

you choose the restaurant

212

4 France Researcher Marketing

5 France Researcher Information systems and Marketing

6 France Researcher Relationship Marketing, Tourism

7 France Marketing director of Lafourchette

France, Paris

Marketing practitioner on the domain

restaurant industry, mobile

application developer

8 Russia General manager of Resto group,

Moscow

Marketing practitioner on the domain

restaurant industry, mobile

application developer

9 Russia Marketing director of Philip Morris

International, Moscow

International marketing, consumer

behavior

10 Russia General manager of restaurant agency,

Vladivostok

Marketing practitioner on the domain

restaurant industry

11 Russia Marketing responsible in the online

journal “Far Post”, Vladivostok

Marketing practitioner on the domain

of relationship marketing

12 Russia Accountant in Best logistics group,

Moscow

consumer

13 Russia Commercial director Best logistics group,

Moscow

Consumer

14 Russia Student of medical university, Moscow Consumer

15 Russia Sport trainer of children, Moscow Consumer

16 Russia Housewife, Moscow Consumer

17 France Student of Business school, Paris Consumer

18 France Responsible of sales in Europe, Enerdis

company, Ile-de-France

Consumer

19 France Responsible of sales in France, Enerdis

company, Ile-de-France

Consumer

20 France Notary, Paris Consumer

21 France Dentist, Ile-de-France Consumer

22 France Lawyer, Paris Consumer

Source: author of the thesis

The composition of the experts was following: we included two responsible persons of the both

companies, Resto and Lafourchette, three other marketing practitioners, 6 researchers, and 11

consumers (5 from Russia and 6 from France).

The objectives of the expert evaluations are to check:

• Adequacy of the measurement items of the constructs

• Formulation of the questions (measurement items)

• Redundancy of the questions

213

This is important issues for the researcher, because the errors in the interpretation of the questions

can cause the errors in the data, if the formulation of the question does not meet the norms of

conversations (Krosnick, Li and Lehman, 1990).

After the expertise we did following changes:

1) According to the advice of researcher expert we added one moderating construct (place of

use), because the importance of geolocation’s function in the mobile application has became

significant, we should also check its effect on the intention to use the mobile applications in

restaurant industry. That was also confirmed by the representatives of Resto and

Lafourchtte.

2) Initial survey had 42 questions and two questions of social state of the responders (age and

profession). That number was found redundant by experts, especially marketing

practitioners and consumers. The repetitive questions we deleted.

For example, the constructs intention to use and continued intention to use have questions with

very close and repetitive meaning:

Intention to use The individual’s

decision to use IS.

4. I intend to continue using

mobile application in the future.

5. I will always try to use mobile

application in my daily life.

6. I plan to continue to use mobile

application frequently.

Wiswanath

Venkatesh

James Y.L.Thong

Xin Xu (2012)

Continued

intention o use

Degree to which a

user feels he or she

will keep using a

mobile application

4. I intend to continue using

mobile application rather than

discontinue its use.

5. My intentions are to continue

using mobile application than

use any alternative means.

6. If I could, I would like to

discontinue my use of mobile

application (reverse coded).

Bhattacherjee

(2001)

Venkatech and

Goyal, (2010)

Hoehle&Venkates,

(2015)

Thus, we deleted the question 3 and question 1 respectively from the final survey, but respecting the

minimum required number of measurement items for formative variables.

3) To better design we combined questions under the common point like in the example below.

Picture 9: Example of modifications of the questions.

214

4) Four questions were designed as questions with open, free answers:

For example,

3 How long are You using the mobile application N in years? (If

your answer on the previous question a) was other than never)

4 How long are You using any mobile application for choosing a

restaurant in years? (If your answer on the previous question d)

was other than never)

According to the advice of the experts we modified these questions and suggested choice incuded

several periods of time for better process of data treatment.

The experts suggested us to do modifications in the items, concerning the clear understanding of the

items, choice of words, quantity of items pro construct. After all items were carefully corrected, we

could start the collecting of actual data. The final questionnaires in both languages and with a

translation into English are in the Annex 16.

The summary of the changes is presented in the Table 45:

Table 45: Modifications after the experts’ validation

Initial model After the experts’ validation Changes

Structural model 9 latent variables: indulgence,

price value, facilitating

conditions, trust, intention to

use, use of mobile application,

mobile application usability,

mobile application loyalty,

continued intention to use and

two moderators: duration of

the use and frequency of

visits.

9 latent variables: indulgence, price

value, facilitating conditions, trust,

intention to use, use of mobile

application, mobile application

usability, mobile application loyalty,

continued intention to use and three

moderators: duration of the use,

frequency of visits, and place of use.

Moderator of place

of use

Survey 42 questions 34 questions Deleted repetitive

215

questions, questions

about time length

combined in one, not

clear questions

deleted.

Three open questions Modified to one question with

multiple choice

Picture 9

Not correct formulations, like

mobile application is free

Language’s modifications: f.e.:

mobile application is free for

downloading

Annex 16

For the composition of the questionnaire the researcher can use several types of the scale, Likert

scale, differential semantics, icons etc (Chandon and Bartikowski, 2004).

According to Churchill and Peter (1984) the accuracy of the scale depends on the number of

possible answers. In marketing the most often is used five-point and seven-point scales to give the

responders a possibility for answering the most honest and close to his/her experience (Vernette,

2011).

In our survey we used five-point Likert scale (Likert, 1932). There are two types of the suggested

semantic sentences: most of the questions require agreement-disagreement (strongly disagree,

disagree, neither agree not disagree, agree, strongly agree) weighted respectively from 1 till 5, and

several questions require to note frequency of use (never, less than once a month, several times per

month, once a week, several times per week) also weighted from 1(never) till 5 (several times per

week). One question in our survey is reverse (If I could, I would stop using of the mobile

application), weighted from 5 (strongly disagree) till 1(strongly agree). We added also in the

questionnaire two questions of social position (age and profession).

All questions we use are closed questions, because closed questions allow collecting standardized

data and avoiding bias of entering data (Evrard et all, 2009). In the very beginning we suggested 4

questions of free answers (How often do you use smartphone? How often do you go out for dinner?

etc.), inviting people to give their own answers, but the experts proposed us to change them for

scale (once a week, several times per week etc.), so the treatment of the data can be standardized.

216

Figure 31: Scale validation

Source : Adapted by Jolibert and Jourdan, 2011

Collect data

In the end of exploratory phase the first set of data is collected to purify the measurement items.

The way of the data collection can be chosen according the research method. The objective of the

researcher is to check reliability and validity of measurement items. This is made in statistical

analysis of reliability (Loadings, Cronbach’s alpha, composite reliability), as well as convergent and

discriminate validity.

We diffused the online survey, designed with google forms application, with help of Email and

publishing of the link on social networks. Before the treatment of the data with SmartPLS, we

coded the responses according to growth of agreement (Martin, 2009):

Strongly disagree 1

Disagree 2

Neither agree nor disagree 3

Agree 4

Strongly agree 5

The reverse questions were reverse coded:

Strongly disagree 5

Disagree 4

Neither agree nor disagree 3

Agree 2

Strongly agree 1

Purify the measure

The process of purifying a measure is focused on estimation of the score in case if all items in the

domain were used. In fact researcher does not use all items of the domain, but the sample of items.

In our research we do not use all measurement items suggested in previous works for the construct

217

loyalty for example, we adapted and specified the items for mobile application loyalty. According to

that the primary source of measurement errors might be the inadequate sampling of the domain

relevant items, and that should be purified on this step.

Coefficient Alpha, or Cronbach’s alpha

To verify the internal consistency of the items sample the researchers use coefficient alpha, known

in social sciences as Cronbach’s alpha (Cronbach, 1957). This coefficient should be the first

measure to assess the quality of the instruments. The square root of coefficient alpha is the

estimated correlation of the k-item test with errorless true scores (Nunnally, 1967).

Cronbach's alpha will generally increase as the correlations among test items increase, and is thus

known as an internal consistency estimate of reliability of test scores. Because correlations among

test items are maximized when all items measure the same construct, Cronbach's alpha is widely

believed to indirectly indicate the degree to which a set of items measures a single one-dimensional

latent construct. It is easy to show, however, that tests with the same test length and variance, but

different underlying factorial structures can result in the same values of Cronbach's alpha. Indeed,

several investigators have shown that alpha can take on quite high values even when the set of items

measures several unrelated latent construct (Nunnally, Berstein, 1967). As a result, alpha is most

appropriately used when the items measure different substantive areas within a single construct.

Alpha treats any covariance among items as true-score variance, even if items co varies for spurious

reasons. For example, alpha can be artificially inflated by making scales which consist of superficial

changes to the wording within a set of items or by analyzing speeded tests.

Factor Analysis

The main applications of factor analytic techniques are: (1) to reduce the number of variables and

(2) to detect structure in the relationships between variables, that is to classify variables. Therefore,

factor analysis is applied as a data reduction or structure detection method (the term factor analysis

was first introduced by Thurstone, 1931).

Confirmatory factor analysis. Structural Equation Modeling (SEM) allows you to test specific

hypotheses about the factor structure for a set of variables, in one or several samples (e.g., you can

compare factor structures across samples).

Correspondence analysis. Correspondence analysis is a descriptive/exploratory technique designed

to analyze two-way and multi-way tables containing some measure of correspondence between the

rows and columns. The results provide information which is similar in nature to those produced by

factor analysis techniques, and they allow you to explore the structure of categorical variables

included in the table. For more information regarding these methods, refer to Correspondence

Analysis.

218

IV.3. Confirmatory phase

Collect data

If during the first treatment of data the coefficient alpha is acceptable or better, the measures are

ready for new sample of data, but if not the researcher should take decisions to purify the

measurement instruments, in this case the constructs of the domain can be eliminated, changed, or

the new measurements items could be found etc. it can be also possible that sampling size or

sampling quality was not corresponding the requirements of the domain.

Assess reliability with new data

In the confirmatory phase, the researcher estimates the measurement instruments and develops the

norms.

Reliability refers to consistency of a measure of a construct. By the internal reliability the researcher

tries to find whether or not the items that compose the scale are consistent, in other words, whether

or not the scores on one item tend to be related to their scores on the other items.

Coefficient alpha is the basic statistic for determination of the reliability based on internal

consistency. Coefficient alpha does not adequately estimate errors caused by factors external to the

instruments. It is recommended thus to collect additional data to rule out the possibility that the

previous findings are due to chance, or to re-test, if the external factors such as different situations

over the time has less impact on responses.

Assess construct validity

Construct validity is most directly related to the question of what the instrument is measuring. The

preceding steps should produce an internally consistent or internally homogeneous set of items. To

establish the construct validity of a measure the researcher must determine the extent to which the

measure correlates with other measures designed to measure the same thing, and whether the

measure behaves as expected. As we have shown in our example with construct mobile application

loyalty.

Correlation with other measures.

According to fundamental principle in science any particular construct should be measured by two

or more different methods. Evidence of the convergent validity of measure is provided by the extent

to which it correlates highly with other methods designed to measure the same construct.

Except convergent validity the measures should have also discriminant validity, which is extent to

which the measure is novel and not simply a reflection of some other variable. One of the ways of

assessing the convergent and discriminant validity of a measure is through the multitrait-

multimethos matrix, which is a matrix of zero order correlations between different traits when each

of the traits is measured by different methods (Campbell and Fiske, 1959).

Expected or not behavior of the measures.

219

Internal consistency is a necessary but insufficient condition for construct validity. The final step is

to show that the measure behaves as expected in relation to other constructs. Does for example

mobile application loyalty really predict continued intention to use this mobile application? It is

expected that the loyal user tend to continue the usage of the same mobile application, but if not one

of the reasons of this unexpected construct behavior might lay in the incorrect or not sufficient

measurement items. The task of the researcher is to check the relationships among constructs, or

among measurement items, before conclusions.

Develop norms

As Churchill (1979) noticed in his paradigm the measuring instrument is not informative about the

position of a given object on the characteristic being measured. In other words it is not enough just

to measure constructs producing some scores; it is needed to develop some standards, with what it

will be possible to compare the scores. Technically it is a process of developing norms.

The norms can be developed when researcher compares scores of many cases, in this way the

quantity of the cases will show the stability and definition of the norm quality.

On our study we use structural equation modeling approach, thus, the developing norms is not a

constraint of our research.

V. Methodology to verify the research model - Structural equation modeling

The positivist paradigm of scientific research requires strong power techniques to test the

conceptual models and theories.

Structural Equation Modeling (SEM) is the second-generation multivariative data analysis method

that is often used in marketing and IS researches because this method provides the possibility to test

theoretically supported linear and additive causal models (Chin, 1998, Haenlein and Kaplan, 2004,

Statsoft, 2014). The use of SEM is increased because of developed software based on covariance

(e.g. LISREL) and on components (e.g. Smart PLS).

As Wynne W. Chin (1998) claims, SEM provides the researcher with the flexibility on:

- model relationships among multiple predictor and criterion variables;

- construct unobservable latent variables;

- model errors in measurements for observed variables;

- statistically test a priori substantive/theoretical and measurement assumptions against

empirical data.

Major applications of SEM include:

220

- causal modeling, or path analysis, which hypothesizes causal relationships among variables

and test the causal models with a linear equation system. Causal models can involve either

manifest variables, latent variables or both;

- confirmatory factor analysis, an extension of factor analysis in which specific hypotheses

about the structure of the factor loadings and correlations are tested;

- second order factor analysis, a variation of factor analysis in which specific the correlation

matrix of the common factors is itself factor analyzed to provide second order factors;

- regression models, an extension of linear regression analysis in which regression weights

may be constrained to be equal to each other, or to specified numerical values;

- covariance structure models, which are hypothesize that a covariance matrix has a particular

form;

- correlation structure models, which hypothesize that a correlation matrix has a particular

form.

Basic idea of SEM is in the testing whether variables are interrelated through a set of linear

relationships by examining the variances and covariances of the variables.

Graphical presentation of SEM

All variables are placed in the equation system in the path diagram, latent variables in ovals and

observed variables in squares. Each equation is represented on the diagram as follows: independent

variables have arrows pointing to the dependent variables; the weighting coefficient is placed above

the arrow. On the Figure 32 global model of SEM is presented:

221

Figure 32: Global model

Source: Joreskog(1970), Wold (1973)

Global model is composed of two submodels: inner or structural model which specifies

relationships among the independent and dependent latent variables, and outer or measurement

model, which specifies relationships among latent variables and their observed indicators/variables.

Our structural model consist of 9 latent variables, each of them has between two and five observed

variables, or measurement items.

To effectuate SEM applications the researcher needs to follow a five-step process (Chin, Peterson,

Brown, 2008):

- model specification;

- model identification;

- model estimation;

- model evaluation;

- model respecification.

On the model specification step two aspects are important: firstly the researcher should justify not

only indicated structural relationships but also not indicated (Boomsma, 2000). Our research model

was developed on basis on UTAUT2 model, which included such variable as age. We excluded this

variable of our research model, but still we kept the question about the age in the questionnaire. In

this way we can see later, if the variable of age has impact on mobile application use in the

restaurant sector or not. In other words the relationship between age and mobile application use is

not indicated.

222

Another aspect about which the researcher should concern is to justify whether relationships among

constructs are formative or reflective.

In a formative relationships the observed variables cause the construct, in reflective – constructs

cause the observed variables.

Figure 33: Reflective and formative relationships

Measurement item is classified as formative when is construed as

a) defining characteristics of the latent construct;

b) causing changes in the construct;

c) not sharing a common theme among themselves;

d) being fundamental to the definition of the construct;

e) not necessarily covarying;

f) not having the same antecedents and consequences.

Example in our model could be application design, utility, user interface graphics, input, output and

structure as formative measurements for mobile application usability construct, which are not

included in our model.

On the step of model identification the researcher verifies measurement and structural portions of

the model; both can cause problems with identification.

Normally three measurement items per construct are required for identification if the construct is

reflective. Contrary for formative construct it is possible to have less items, if reliability index is

higher than 0,7 (Diamantopoulos, Sarstedt, Fuchs, Wilczynski, Kaiser, 2012; Yong, Pearce, 2013).

When there are only two items per construct and each item is connected only to its construct,

identification can be achieved if there are structural connections among all the constructs. There are

four constructs in our model measured with only two items: trust, intention to use, mobile

application usability, and continue intention to use.

223

Next step of model estimation involves statistical methods to estimate model parameters. A variety

of methods is available for researcher: maximum likelihood (ML), generalized least squares (GLS),

weighted least squares (WLS), arbitrary distribution free (ADF), ordinary least squares (OLS), path

least squares (PLS).

After estimates are obtained the researcher evaluate model with indices. Many indices are

influenced by sample size such as the goodness-of-fit index (GFI) and adjusted goodness-of-fit

index (AGFI). Following indices are recommended (Garver and Mentzer, 1999) to use:

• Root mean square error of approximation (RMSEA) is an informative criterion in evaluating

model fit. It measures the divergence between observed and estimated covariance per degree

of freedom in terms of population, but not the sample. The range of the index lays between 0

and 1.

• Comparative fit index (CFI) is a noncentrality parameter-based index to overcome the

limitation of sample size effects. The value of the index ranges from 0 to 1, with 0.90

acceptable.

• Nonnormed fit index (NNFI), also known as Tucker-Lewis index compares a proposed

model’s fit to a nested baseline or zero-model. It measures degree of freedom from the

proposed model to the degree of freedom of the zero-model. The acceptable threshold of

NNFI is 0.90 or greater.

• Chi-square (χ²) is the most common methods of goodness-of-fit evaluating. Low χ² value,

indicating non significance, would point to good fit, because chi-square test is used to assess

actual and predicted matrixes. In this way low significance shows low differences between

actual and predicted matrixes. χ² test is sensitive to sample size.

More information about used indexes to estimate model is presented in the section PLS-approach of

this chapter.

On the step of model respecification the conceptual model can be modified according to the

evaluation of indices. Relatively few of the initially estimated structural equation models in

marketing studies remain intact (Chin, Peterson, Brown, 2008).

Modification of the model can concern adding or deleting paths, or both, or modification of indices.

Model modifications capitalize on knowledge of path values and chance characteristics in the data.

Anyway, modifying an initially estimated structural equation model reduces its generality and

requires that it should be validated with an independent sample.

V.1.Choice and justification of software.

There are several approaches to SEM and developed software to complete these approaches. The

first approach is covariance-based SEM (CB-SEM), to which apply such software packages as

224

AMOS, LISREL, EQS and MPLus. The second approach presents Partial Least Squares (PLS). PLS

focuses on the analysis of variance and can be effectuate by use of PLS-Graph, VusualPLS, Smart

PLS, and WarpPLS. The third approach is component-based SEM or Generalized Structured

Component Analysis (GSCA). This approach is conducted by VisualGSCA or a web-based

application GeSCA. Another way to perform SEM is use of Nonlinear Universal Structural

Relational Modeling (NEUSREL) which is processed by NEUSREL’s Causal Analytics software.

CB-SEM versus PLS-SEM

For many researchers SEM was long time equivalent to conducting covariance-based SEM analyses

using appropriating software; but SEM also should be regarded as another useful approach – partial

least square. PLS-SEM is causal modeling approach aimed at maximizing the explained variance of

the dependent latent variables (Hair et al, 2011). This is contrasting the CB-SEM’s objective of

reproducing the theoretical covariance matrix, without focusing on explained variance. PLS-SEM

has been increasingly applied in marketing and other business disciplines.

The distinction between CB-SEM and PLS-SEM concerns several important points of the research

(see Table 46):

• CB-SEM is appropriative method in case when research’s goals are theory testing and

confirmation, while PLS-SEM approach is better for prediction of key target constructs, or

exploratory goal. In our research the research goal concerns the prediction of variables:

mobile application use, continued intention to use and mobile application loyalty.

• PLS-SEM approach gives possibility to work efficient with wider range of sample size and

complex models. It has less restrictions about data, thus, it can address a broader range of

problems than CB-SEM approach.

• Measurements are less restrictive in PLS-SEM method, constructs with fewer items can be

used, than CB-SEM requires.

• PLS-SEM is useful when latent variables are used in subsequent analyses, which enable to

prioritize activities for potential improvements in managerial decisions.

• CB-SEM and PLS-SEM differ from statistical point of view, but PLS-SEM estimates can be

good proxies of CB-SEM results. PLS-SEM has higher statistical power compared to CB-

SEM.

Table 46: Comparison CB-SEM and PLS-SEM approaches (adapted from Hair et al. 2011)

CB-SEM PLS-SEM Our research model

Research goals

Theory testing, theory Prediction of key target constructs, Prediction of constructs: mobile

225

confirmation, comparison of

alternative theories

identifying of key construct application use, continued

intention of use. Identifying of key

construct impacting on mobile

application use.

Exploratory goal, or extension or existing

structural theory

Extension of UTAUT

Measurement model specification

Formative measures can be used,

but accounting for complex and

limiting specifications rules are

required

Formative constructs are part of the

structural model

Reflective

Additional specification is

required, such as covariation

Structural model

Complex structural model (many

constructs and indicators)

Nine constructs plus three

construct of moderation, twenty

one measurement items plus nine

measurement items of moderation

constructs.

Model is nonrecursive

Data characteristics and Algorithm

Data should meet the CB-SEM

assumption exactly

PLS-SEM is good approximation of CB-

SEM results

Normal data conditions Data are to some extent no normal

Sample size

Large data set Relatively low sample size

Minimum sample size should be equal to

the larger of the following: ten times the

largest number of formative indicators

used to measure one construct or ten

times the largest number of structural

path directed at a particular latent

construct in the structural model

Minimum sample size 50

responders

Model Evaluation

Latent variables in subsequent analysis

Global goodness-of-fit criterion

Measurement model invariance

Source: Hair et al, 2011

CB-SEM is widely applied in the field of social science and the preferred data analysis method in

cases when the sample size is large and the model is correctly specified. However, it is often

difficult for researcher to collect sufficient volume of data to meet the requirements of CB-SEM

226

approach and applicable software packages. The other problem which the researcher can face lays

in the exploratory objectives of the research when there is a lack of knowledge about the

relationships that exist among variables, and in this case researcher can not apply to CB-SEM and

prefer to effectuate the PLS approach.

For our research we have chosen the PLS-SEM approach, because of following important issues:

research goal to predict variables, it is possible to use small sample size, and to analyze sub groups,

and to test complex model. Also on the step of model evaluation, our intention is to use latent

variables in subsequent analysis. The objective of our research is to compare the cultural aspect of

the mobile application use. To reach this goal the data are collected in two countries. PLS approach

among mentioned before issues can allow effectuating the analyses in sub groups. In this way the

collected data can be analyzed in generalized way, but also according to national groups, age groups

etc.

V.2. PLS-SEM approach and software choice.

The basic PLS-SEM algorithm follows a two-stage approach. In the first stage, the latent variables

are estimated by scores of observed variables, proxies for structural model relationships among the

latent variables, scores of latent variables, coefficients in the measurement model. The second stage

calculates the final estimation of the outer weights and loadings as well as the structural model’s

path coefficient (Hair et al, 2011).

The path modeling procedure is called partial because the iterative PLS-SEM algorithm estimates

the coefficients for the partial ordinary least squares regression models in the measurement models

and the structural model.

PLS-SEM approach was developed in the 1960-s, but the first generation of software was

implemented in 1980-s with LVPLS 1.8. In 2000s the PLS-SEM approach became more and more

used in the researchers, what led to developing of more professional and useful software like PLS-

Graph, Visual PLS, SmartPLS.

In our research we are using SmartPLS 3 Professional developed by Ringle, C. M., Wende, S., and

Becker, J.-M. in 2015. SmartPLS software fits for researcher without strong computer science

background, what is one of the main advantages of the application.

Model evaluation in PLS-SEM approach

PLS-SEM evaluation follows two-step process, when the measurement models and structural model

are assessed separately.

227

Measurement model (outer model)

On the first step the measure’s reliability and validity are examined by certain criteria. On this step

the researcher should prove that the measures are adequate. This step is indicated as pretest of the

model, normally 30 percent of the original sample is recommended to run for the pretest, but not

less than required by used software (the pretest of our model if to find in the next chapter).

Reflective measurement models such as ours should be evaluated to their reliability and validity.

Cronbach’s alpha

Assessment of construct reliability includes the evaluation of construct internal consistence,

examined by Cronbach’s alpha, which is already presented in previous sections.

In marketing research Cronbach’s alpha is used most often to estimate reliability of constructs.

Its formula is:

α = (1 - )

k – number of measuring items;

σᵢ - standard deviation of the item;

- the variance of the observed total item’s scores.

Cronbach’s alpha vary between 0 (independent item) and 1 (completely correlated item), as closer

is this indicator to 1 as better is internal consistence. According to Nummaly and Bernstein (1994)

the minimum weight of Cronbach’s alpha should be 0.7. According to Peter (1979) the weight

between 0.5 and 0.6 is acceptable for exploratory researches.

Loadings

Each indicator’s reliability needs to be taken into account; each indicator’s absolute standardized

loading should be higher than 0.70, indicators between 0.40 and 0.70 should be removed from the

scale and thus leads to an increase on composite reliability above the suggested threshold value.

Rho

Rho of Joreskog and Rho of Dillon-Goldstein (composite reliability) are also the indexes used for

the evaluation of reliability of inner model. Rho of Joreskog is used in the algorithm of LISREL,

while Rho of Dillon-Goldstein in PLS. In both cases this index should be greater than 0.70

(Tenrnhaus et al, 2005).

The formula for the Rho is:

with >0

228

= ж + = ж +

Var (ж) = 1

- loading of indicator (item j)

- Error in the measure of the indicator j

Some authors (Devellis,2003, Jakobwicz, 2007) consider that Rho are to privilege compared to

Cronbach’s alpha, because their high sensibility towards the number of measurement items, what

can provide the instrument and methods of calculation, which might set the terms of errors.

Anyway, these two criteria are estimated after Cronbach’s alpha. The weight of 0.7 is considered as

reliable for the item, 0.6 for the exploratory research (Fornell and Larcker, 1981).

Reflective measurement models evaluation of validity focuses on convergent validity and

discriminant validity.

Average Variance Extracted (AVE)

For convergent validity researchers need to examine the average variance extracted (AVE). An

AVE value of 0.50 and higher indicates a sufficient degree of convergent validity (Fornell end

Larcker, 1981), meaning that the latent variable explains more than half of its indicator’s variance.

The formula is:

AVE =

лᵢ: standardized coefficient of the measure n: number of observed variables i

εᵢ: error in the measure of the indicator

Fornell-Larcker criterion and cross loadings

For the assessment of discriminant validity two indicators are to regard: the Fornell-Larcker

criterion and cross loadings. The Fornell-Larcker criterion (Fornell and Lacker, 1981) postulates

that a latent construct shares more variance with its assigned indicators than with another latent

variable in the structural model. In other words, the AVE of each latent constructs should be greater

than the latent construct’s highest squares correlation with any other latent construct. The cross

loadings mean that an indicator’s loading with its associated latent construct should be higher than

its loading with all remaining constructs.

229

Structural model (inner model)

According to Ringle et al. (2012) the structural model should be evaluated by researcher with R²,

Q², p-value, t-value, f square.

R²

The primary evaluation criteria for the structural model are R² measures and the level and

significance of the path coefficients.

In the prediction-oriented approach, the key target construct’s level of R² should be high. The

judgment of the level of R² depends on the research discipline. So R² results of 0.20 are considered

high in consumer behavior, R² values of 0.75 would be perceived high in success driver studies. In

marketing research R² values of 0.75, 0.50, or 0.25 in the structural model can be described as

substantial, moderate, or weak respectively (Hair et al, 2011). According to Chin (1998) R² weight

of 0.67 explains 67 % of construct’s variance and is substantial, 0.33 - 33% is moderate and 0.19 –

19 % is weak.

Path coefficients

The path coefficients of PLS structural model can be interpreted as standardized beta coefficients of

ordinary least squares regressions. Each path coefficient’s significance can be evaluated by means

of a bootstrapping procedure. Nonsignificant paths do not support a prior hypothesis; significant

paths support the proposed causal relationship.

f square

The impact of an observed variable on the explanation of the latent variable (R²) may be more or

less important to measure it. Researchers can calculate the size effect f² using the following

formula:

fІ =

and are the R² obtained according to whether the observed variable is used or

not.

According to Cohen (1988) f² with the weight 0.02 indicates a reliable effect, 0.15 – moderate

effect, and 0.35 - high effect.

Q² (Cross-validated redundancy)

Another assessment of structural model involves the model’s capability to predict. One of measure

of predictive relevance is the Stone-Geisser’s Q² (Geisser, 1974, Stone, 1974). This indicator

230

postulates that the model should be able to predict each endogenous construct’s indicators. The Q²

value is obtained by using a blindfolding procedure. This procedure is only applied to endogenous

latent constructs that have a reflective measurement model specification. The technique is the

omission of dth data point part and reuse of resulting assessments to predict omitted part. The

omission distance d must be chosen so that number of valid observation divided by d is not an

integer. d values between 5 and 10 are advantageous (Hair et al, 2011).

q²

Like for f² in order to determine in the model the part of the observed variable on the prediction of

another variable (Q²), the q² it is used, which is calculated:

qІ =

and are the Q² obtained according to whether the observed variable is used or

not. According to Cohen (1988) the q² with the weight 0.02 indicates a reliable effect, 0.15 –

moderate effect, and 0.35 – high effect.

t-value

Once the model is evaluated by the percentage of explained variance (R²), we must verify the

stability of the estimates using the calculation of the statistics t obtained by the resampling

procedure by bootstrapping (Hair et al, 2011). It is used the resampling to 5000 (De Souza Bido,

2012). The number of cases corresponds the size of the sampling. T-values should be greater than

1.96. After that we precede the estimation of the parameters, which should be minimum by 0.2 or

better by 0.3 (Chin, 1998).

p-value

The t-test can be used to measure the significance of the model: p-value. The minimum weight for

this value is fixed on 0.05; lower weights mean that the hypothesis zero can be rejected.

Particularly speaking, by estimation of parameters and significance t-value should be higher than

1.96 with p<0.05.

Quality of the model: Goodness-of-fit (GOF)

PLS does not estimate directly the global quality index Goodness-of-fit. Esposito (2010) proposes

following procedure for evaluation of the model global quality:

GOF =

231

According to Latan and Ghozali (2012) GOF within SmartPLS with the weight of 010 means

reliable quality, 0.25 – moderate quality, and 0.36 – high quality.

Another important aspect of structural model evaluation is heterogeneity of observation, which can

be threat to the PLS-SEM results validity. Different population parameters occur for different

subpopulations such as segments of consumers, firms, or countries. In our research we plan to use

the analysis of subgroups of consumers regarding the countries (Russia and France).

V.3. Conclusion

This research provides theoretical background in technology use theory, relationship management,

and cultural dimensions theory. It applies to positivism as epistemological paradigm and uses the

quantitative methods of research.

As sampling strategy this research implements the non-probability convenience sample, managing

in two ways: in cooperation with mobile application providers and publishing in social networks in

specific groups of mobile application users. The survey is designed as self-administrated online

questionnaire.

We have chosen PLS-SEM approach and appropriate SmartPLS software, because of three factors:

possibility to treat data of small sample size, sub group analysis, and complex structural model. We

are aware about possible limitation of findings generalization because of convenience sampling, and

will take it into account by doing practical implications.

Table 47 summaries the model evaluation that concerns our choice of approach and software.

Table 47: Model evaluation using SmartPLS 3 Pro

Indicator Meaning Results

Measurement models

Cronbach’s Alpha Internal consistency reliability 0.70 and higher

Rho Internal consistency reliability Higher than 0.70 (in exploratory

research 0.60 to 0.70 is acceptable)

Indicator “loadings” Reliability higher than 0.70

The average variance

extracted (AVE)

Convergent validity higher than 0.50

Square root of AVE Discriminant validity Square root of AVE of variables

should be higher than correlation of

that variables with others variables

Cross loadings Discriminant validity loading with its associated latent

construct should be higher than its

232

loading with all remaining

constructs

Structural model

R² values significance of the path

coefficients

0.67 – substantial, 0.35 – moderate,

0.19 – weak

Q² the model’s capability to

predict

Q² should be higher than 0. d values

- between 5 and 10

f² Effect of sampling size 0.02 – weak effect

0.15 – moderate effect

0.35 – high effect

Path coefficient t-value and p-

value

Estimation of parameters and

significance

t-value should be higher than 1.96

with p<0.5

q² Effect of sampling size 0.02 – weak effect

0.15 – moderate effect

0.35 – high effect

Goodness-of-fit (GOF)

Quality of the model 0.10 – weak quality

0.25 – moderate quality

0.36 – high quality

Adapted by Patricia Baudier

233

Chapter V: Validation of the model

234

Introduction

This chapter presents exploratory phase and statistical validation of the research and consists of two

sections. First section introduces analysis of two interviews with representatives of French and

Russian companies. Second section deals with statistical validation of the measurement model.

I. Exploratory phase

We accomplished two interviews with representatives of French and Russian companies to check

our research model and understand point of view of the companies-providers of the mobile

application use. Interview in France was organized personally in English language, interview in

Russia was organized par skype in Russian language.

We posed question to constructs of our model.

a) Factors influencing the intention to use

We started with technical questions, to see how the work inside of the company is organized. In our

model two constructs are connected with technical side of the application: facilitating conditions

and mobile application usability.

Concerning mobile application usability we asked:

“Do you have your own designers / web and mobile application developers?

How did you determine which functions should work? Did you do a survey before

development? Who first of all proposes changes: departments of CRM and marketing or the

developers themselves?”

LaFourchette: “yes we have a team dedicated to the mobile app and all developments are made by

this team. There are also product owners…. They prioritize which feature we want to perform the

app…… In fact the organization today of lafourchtte is … we have marketing team within we can

find an acquisition team, CRM team, brand team and strategic marketing team. The product team is

in charge of all the products and all data and is responsible for the development of the website and

mobile app so this is not in the marketing team”

Resto: “Yes, we have our own employees in the office and also specialists on outsourcing.

Normally decisions on implementation are taken after general discussion based on cost and

expediency. But the idea can bring anyone, including (and often) our customers”

From these answers we can conclude that French company has stronger technical department and

stronger hierarchy when it deals with the technical development. Russian company is more flexible

and depends on the cost of the technical development. Wenn we presented mobile application

usability, we already concluded that Russian mobile application has only critical necessary

235

functions to correspond the purposes of the users, while French mobile application is updated

according to the recent innovations in the mobile application development.

Concerning facilitating conditions we posed the question:

“How do you support clients in difficult situations? (with restaurants, who did not do booking

etc.)”

Lafourchette: “There is a plan that is defied for one year, now we collect what we call the irritating

factors so when the customer leaves review on the site the second page is a lot of questions, about

the restaurant experiences: did the restaurant have your booking when you arrived in the

restaurants, was the discount applied if not did you understand why, or not, did the restaurant meet

your expectations, what you saw on the fork etc etc. We collect that factors and thank to this data

we have dashboard, that is send to the what we call account manager because we have in the b2b

team lot of account managers that are responsible for X restaurants and so we send this report to

this account managers and they have to call each restaurant that has irritating factors their goal is

to decrease the number of irritating ”.

Resto: “We try not to allow such situations. In case of emergence, we have the opportunity to

immediately contact the restaurant management and resolve the situation”.

Russian company shows strong involvement in relationship marketing and tries to build personal

communications with all parts of the process, the priority is by the users. French company has also

well developed communications strategy, collecting constantly feedback from the users.

Price value is expressed in the loyalty programs in both companies. Due to these programs the users

can make positive decision, whether they want to use mobile application or not.

“What factors are decisive for users from your experience: loyalty program, information in

itself, some specific functions that are not available on other similar services?”

Lafourchette: “In fact discounts are totally part of product of our because it’s the way lafourchette

has been build ……. the idea was when the founder launched the fork (la fourchette) the idea was to

do some gift management with the restaurants so ………. when they (restaurants) know that their

service or their day service will be not full because they have fix fees, like the rent the room, the

location….. with discounts they have no empty room, this is the idea of lafourchette, the discounts,

after that we we launched the yums, the loyalty program ….. yums is one of this ways if we better

guide them etc etc all the service that we can provide to the customers should be useful but the

yums are one of this ways to make customers loyal because if when you book on the fork you win

yums and when you book using your phone you win nothing we think that is ”

236

Resto: “I think, first of all, the level of client support, that is, the confidence in the reliability of

booking, is important. In addition, we have special preferences in many restaurants,

This gives us the opportunity to book tables for our customers in those restaurants and those rush

hours that are not available to other services. Today our loyalty program consists as well in

assigning points to customers who booked and visited the restaurant. Points are summed up and

accumulated in the client's personal account. To exchange points it is possible on various gifts from

a box of sweets to a mobile phone”.

There are two different points of view on the value of the mobile application use in two companies,

connected probably with history of their development. French company was launched as discount

guide for restaurants, while Russian company made a prior task to support clients of the restaurants

and give them privileges.

b) Use of mobile application

To understand the use of mobile application we asked the question about competitive tools:

“What tools are from your point of view the main competitors for your mobile application?”

LaFourchette: “….what we currently see is that our main competitor is the phone, people reserve

restaurants when they go to restaurants but they don’t have the fork as reflex and they most of the

time they use their phone to reserve the restaurant….”

Resto: “The main competitor of booking is the very lack of booking. That is, the guest expects that

there is a table for him and there is nothing to book in advance…”

From the point of view of Russian company the lack of booking and low occupancy rate in

restaurants impacts also the use of mobile application. That is probably connected with the recent

economical crises we presented in the context of our research. The income of middle class in Russia

decreased and influenced the occupancy of the restaurants. In France the mobile application

competes primary with other tool, a phone, which is sometimes easier for the people, than to

effectuate the mobile applications booking.

c) Outcomes

Mobile application loyalty and continued intention to use

“How do you assess the loyalty of the user? By the amount of time he / she uses mobile

application for a month, a week, a year? By activity, such as reviews, ratings, and so on.”

Lafourchette: “we look at the bookings, the number of bookings the number and the frequency and

recency of the booking …….. so we call our loyal most loyal customers the lovers and the exact

definition is did at least booking in last month and more than three ……..so first one is starters

those, who did their first booking this month; after that we have the flirters this are those who did

a booking last month but didn’t book since three month before so they come, they go away etc. the

237

lovers the most loyal I think it’s more than three bookings during the three last months more one

booking a month and a booking last month and after that we have the decrease of the activity so we

have the cheaters, the breakers, the ex, etc etc etc so more the more time has spent since the last

booking more they will be dead, in fact. And we act on this on each segment differently so we have

some … for the starters we have welcoming program to welcome them we have yums to boost the

second booking , on the flirters we will more speak about mobile app for those who didn’t book on

mobile app because we know that the customers that use mobile app are more loyal so this is the

main ax of the communication and action on the flirters; after when we speak about cheaters,

breakers those who didn’t book since more than four –five months we have plan of kind of

reactivation plan who will reexplain them the advantages of the fork and at the end ask them do you

still want to receive our communication etc etc because I prefer to delete them rather than keep

them in my database ”

Resto: “We have a core audience; this is approximately 150,000 regular customers. We evaluate

their activity in bookings and reviews. On average, a loyal customer uses resto 3-5 times a week,

once a week he reviews.”

We assume that the big difference in estimations of who is loyal client is connected with the size of

the companies and restaurant market. It is smaller in Russia, so the clients enjoy personal attitude to

them from the side of the company and therefore use the services of the “resto” very often. In

opposite, French market is well developed and it is not possible to build the personal relationships

with them, therefore many clients use mobile application occasionally to get discount, so the loyal

clients are defined differently.

These interviews allowed us to understand better how the connection with users is structured inside

of the companies. We can see that Russian company pays more attention on the personal

relationships, while French company works very well with technical capabilities of the technology.

Some of the differences are connected with the histories of the companies.

II. Pre-test of survey, statistical validation by SmartPLS

This section presents the statistical validation of the measurement model of our research realized by

statistical pretest with SmartPLS application. As recommended by authors (Churchill, 1979, Chin,

1998, Serakan and Bougie, 2013, Hair at al., 2012) before to run statistical analysis of the

researched model, the measurement items should be purified and tested with the first sampling. The

running of pretest is aimed to evaluate the measurement items of the model. This is made to ensure

consistency, reliability and validity of the items. We have chosen convenience sample, one of the

238

important required criterion is for us, that responders should be real users of the researched mobile

application. Normally 30 percent of the original sample is recommended to run for the pretest, but

not less than required by used software. SmartPLS approach requires collecting the minimum

sample ten times bigger than the maximum measurement items directed to one latent variable

(Chin, 1998, Marcoulides and Saunders, 2006, Hair et al, 2011). Our biggest number of

measurement items is five (latent variable mobile application loyalty). To accomplish the pretest

with SmartPLS we collected 52 responders in France.

The final survey consists out of 30 questions and two question of social state of responders. This

survey was published online with help of google forms application

https://goo.gl/forms/Wow8SxBfSxMG8yVh2

Loadings

The first analysis allow us indentifying the measurement items with lower loadings (lower than

0.7), which should be deleted out of scale as no representative.

The abbreviations in the model are deciphered as follows: CITU – continued intention to use, FC –

facilitating conditions, INDUL – indulgence, ITU – intention to use, MAL – mobile application

loyalty, MAU – mobile application usability, PV – price value, TRUST – trust, UMA – use of

mobile application.

Table 48: Loadings of the pretest

CITU FC ITU PV TRUST INDUL LOYALTY USABILITY USE

CITU1 0,935

CITU2 0,775

FC1 0,900

FC2 0,894

FC3 0,305

INDUL1 0,769

INDUL2 0,271

INDUL3 0,884

ITU1 0,875

ITU2 0,948

MAL1 0,876

MAL2 0,791

MAL3 0,899

MAL4 0,842

MAL5 0,873

MAU1 0,959

MAU2 0,945

PV1 0,847

PV2 0,795

PV3 0,896

TRUST1 0,896

TRUST2 0,953

UMA1 0,953

UMA2 0,663

UMA3 0,672

239

Three constructs have poor measurement items: facilitating conditions (FC3), indulgence

(INDUL2) and use of mobile application (UMA2 and UMA3). To increase the composite weight

we should delete items with weight lower than 0.7. Our first concern is about the use of mobile

application, because this construct has three measurement items and two of them are weak.

According the rule we cannot delete both, so we decided to delete UMA3 only.

Table 49: Loadings after deleted items

CITU FC ITU PV TRUST INDUL LOYALTY USABILITY USE

CITU1 0,935

CITU2 0,775

FC1 0,904

FC2 0,960

INDUL1 0,767

INDUL3 0,886

ITU1 0,875

ITU2 0,948

MAL1 0,876

MAL2 0,791

MAL3 0,899

MAL4 0,842

MAL5 0,873

MAU1 0,960

MAU2 0,944

PV1 0,847

PV2 0,795

PV3 0,896

TRUST1 0,881

TRUST2 0,962

UMA1 0,975

UMA2 0,665

Still after modifications the measurement items of construct use of mobile application show poor

meanings. But this construct is one of central in our model. We regarded close into the answers and

found out that we can explain weakness of the measurement items by the sampling characteristics.

Our questionnaire is aimed for the user of the mobile application Lafourchette (French part of the

research), but for the pretest the participants were selected randomly, so we have many answers

“tres rarement” for the use of the mobile application and in this way this construct of the model

couldn’t be validated. That means that by collecting of the further data we should do strong

invitation to answer the questionnaire only if responders really use the selected application. And

before running the model we should also delete answers, which are negative for the use of mobile

application.

For other measurement items the loadings are higher than 0.7, we can conclude, that our

measurement items are reliable.

Reliability, consistency and validity

240

To verify the reliability, internal consistency and validity we looked at Cronbach’s alpha, Rho,

composite reliability and average variance extracted (AVE).

Table 50: Reliability and validity

Assessment of construct reliability includes the evaluation of construct internal consistence,

examined by Cronbach’s alpha. Three constructs showed weights lower than required meaning of

0.7: continued intention to use (CITU), indulgence and use of the mobile application.

Use of the mobile application

We already concluded that use of mobile application is central of the model and we should check it

again with convenient sample. Anyway the other indexes have sufficient weight. Rho of use is 1.4

(the minimum required is 0.7). Some authors (Devellis, 2003, Jakobwicz, 2007) consider that Rho

is to privilege compared to Cronbach’s alpha, because of high sensibility towards the number of

measurement items. The convergent validity examined by AVE of use is 0.697, what is higher than

required minimum (0.5). And composite reliability is 0.816, also higher than required 0.7.

Nevertheless, obviously the construct has problem with internal consistency, what we already

explained before.

Continued intention to use

Cronbach’s alpha by 0.667 of continued intention to use makes concerns about the internal

consistency, but because of the sufficient weights of Rho (0.822), composite reliability (0.848) and

AVE (0.737), we tend to explain it again with the sampling characteristics of the pretest. It is very

possible that responders who answered “tres rarement” for the use of mobile application are thought

to answer negatively for continued intention to use of the mobile application. Thus, we will not

delete it by now out of research model, but we will be aware for the collecting further data.

Indulgence

Cronbach's

Alpha rho_A

Composite

Reliability

Average

Variance

Extracted

(AVE)

CITU 0,667 0,822 0,848 0,737

FC 0,856 0,971 0,930 0,870

ITU 0,806 0,911 0,908 0,832

PV 0,814 0,887 0,884 0,718

TRUST 0,836 1,018 0,919 0,851

INDUL 0,553 0,590 0,813 0,687

LOYALTY 0,909 0,915 0,932 0,734

USABILITY 0,898 0,915 0,951 0,907

USE 0,652 1,406 0,816 0,697

241

Unfortunately we collected data for the pretest mostly among French responders, in this way it is

expected that the cultural construct will show low reliability. According Hofstede cultural

dimensions should not be used with a sample of respondents from one country. But essential to the

use of the cultural dimensions’ scores is that comparisons should be based on matched samples of

respondents: people similar on all criteria other than nationality that could systematically affect the

answers (Hofstede, Minkov, 2010).

We would like to run the model included this construct for the general analysis to see of it has

impact or not.

Discriminant validity

Table 51: Cross-loadings

CITU FC ITU LOYALTY USABILITY PV TRUST USE

CITU1 0,935 0,439 0,828 0,849 0,462 0,428 0,553 0,522

CITU2 0,775 0,151 0,469 0,477 0,536 0,433 0,600 0,365

FC1 0,358 0,904 0,285 0,217 0,107 0,158 0,189 0,142

FC2 0,360 0,960 0,493 0,404 0,138 0,131 0,149 0,276

ITU1 0,655 0,361 0,873 0,786 0,278 0,314 0,238 0,324

ITU2 0,782 0,430 0,949 0,784 0,450 0,370 0,420 0,621

MAL1 0,677 0,304 0,769 0,876 0,518 0,392 0,468 0,405

MAL2 0,627 0,334 0,652 0,791 0,425 0,405 0,450 0,419

MAL3 0,796 0,293 0,764 0,899 0,498 0,399 0,595 0,512

MAL4 0,644 0,280 0,655 0,842 0,395 0,375 0,338 0,619

MAL5 0,732 0,304 0,805 0,873 0,433 0,382 0,399 0,512

MAU1 0,581 0,147 0,431 0,540 0,960 0,274 0,630 0,248

MAU2 0,472 0,106 0,356 0,467 0,944 0,231 0,530 0,209

PV1 0,403 0,164 0,319 0,319 0,268 0,848 0,413 0,397

PV2 0,332 0,039 0,171 0,239 0,144 0,795 0,264 0,231

PV3 0,472 0,142 0,396 0,514 0,235 0,896 0,366 0,372

TRUST1 0,487 0,129 0,235 0,390 0,589 0,399 0,881 0,185

TRUST2 0,674 0,184 0,422 0,555 0,561 0,391 0,962 0,349

UMA1 0,556 0,305 0,591 0,592 0,282 0,368 0,306 0,975

UMA2 0,223 -0,087 0,178 0,271 0,003 0,388 0,191 0,665

As we can see continued intention to use shares more variance with indicators of another latent

variable not its own: the weight CITU – ITU2 and CITU – MAL3 is higher than CITU- CITU2. In

this way we should have real concern about this construct in our research model.

242

Table 52: Root of AVE

CITU FC ITU PV TRUST LOYALTY USABILITY USE

CITU 0,859

FC 0,383 0,933

ITU 0,796 0,438 0,912

PV 0,489 0,151 0,378 0,847

TRUST 0,650 0,176 0,379 0,423 0,922

loyalty 0,815 0,352 0,854 0,455 0,531 0,857

usability 0,557 0,134 0,416 0,267 0,613 0,532 0,952

use 0,531 0,238 0,549 0,412 0,310 0,574 0,241 0,835

Root of AVE of each latent constructs should be greater than the latent construct’s highest squares

correlation with any other latent construct, that rule is respected in our model.

III.Conclusions

The run of pretest allowed us to do modifications and raised some concerns about the research

model:

• Use of mobile application as the central construct has not sufficient measurement items,

because of that we should collect further data more carefully.

• Continued intention to use as construct showed weak significance in Cronbach’s alpha, and

cross loadings. Nevertheless, we decided to keep it for analysis. This decision is to explain by

the type of our sampling strategy: to accept responses only of truly users. Also we would like to

see the role of this construct in second country.

• Cultural aspects are included in our model by the construct of indulgence. Because the

collected data for pretest are predominantly from France we concluded that surely we cannot

use this construct in monoculture analysis, but we will try it for the whole set of data.

After the pretest we run the survey in both countries to collect data for further analysis.

243

Chapter VI: Research results and discussion.

244

Introduction

This chapter presents the results of our research. After the pretest we fulfilled the statistical analysis

of our research model. This analysis we effectuated in two phases according to researchers’

recommendations (Henseler et al, 2009). On the first phase we evaluated the reliability and validity

of the measurement model with the purpose to ensure the stability of psychometric model quality.

On the second phase we treated statistically our hypotheses of structural model.

The chapter consists out of three sections. First section presents the sampling characteristics. We

described the results of social state of responders, particularly age and professional occupation. We

also gave statistical information of mobile application use process, like duration of the use,

frequency of the restaurant visits. Another important statistical parameter we used describes the

situation of the mobile application use and the place of use.

Second section presents the results for general model, where the collected data of two countries

were analyzed together. All together we observed 244 answers.

The third section presents comparison between France and Russia. In the end we introduce the

comparison of the results: general model and French model and Russian model.

In the end we present the discussion of the results.

I. General model evaluation

The first section presents the sampling and mobile application use characteristics, and results of

general model.

I.1. Sampling characteristics

Collection of the data for our model was done in cooperation with two companies: Lafourchette in

France and Resto Group in Russia. The covered areas were Paris and Moscow. The used means

were: newsletters of both companies, groups of users on facebook and VK (Russian social

network). Despite this cooperation we collected not as much data as we hoped, because of low

users’ activity in Russia. We spent three months to finish the gathering of the data, in our case the

data in France were ready in three weeks, but the same process in Russia took three months. All

together we had for analysis 244 responds: 123 from Paris (France) and 121 from Moscow (Russia).

The average time the responder needed to answer the survey was between two and three minutes.

In our survey we used two parameters of social characteristics of the responders: age and

professional occupation (Table 58). The majority of responders (39.3%) are in the age 25-34 years.

We expected that this age group will be the most active in usage of mobile applications. Compared

to younger age groups in the age of 25-34 users have financial stability to go out for eating more

245

often, in this way they use mobile application more often. In the same time compared to older age

users have stronger habit to use technology including mobile applications.

Figure 34: Collected data by country:

Source: composed by author of the thesis according to the data

Professional occupation gives us the possibility to estimate social status and possible income of the

responders, because as we have clarified in the context (chapter I) the expenses for restaurant

belong to leisure and not to major household expenses. Thus, the representative of middle and

upper-middle classes will go out for lunch/dinner more often than others, therefore the use of

specialized mobile application will be more demanded.

Table 53: Professional occupation and age of the responders

Social

characteristics

Type France Russia Total %

Age >18 0 2 2 0.8

18-24 20 19 39 15.98

25-34 46 50 96 39.3

35-49 28 48 76 31.1

50-69 27 2 29 11.88

+70 2 0 2 0.8

Occupation manager/

entrepreneur

21 69 90 36.88

employee 67 14 81 33.2

student 21 23 44 18

No activity 2 6 8 3.28

Other 10 11 21 8.6

Source: composed by author of the thesis according to the data

246

The majority has marked their activity as manager/entrepreneur (90%) and employee (81%), what

confirmed the assumption about the importance of social status. According to the countries’

distribution the majority of Russian responders indicated their activity as manager/entrepreneur

while the majority of French responders chose employee. We can explain it with the decrease of

purchasing power in Russia due to current economical crises. We can conclude that income of the

Russian middle class decreased and had impact on the structure of personal expenses. As for age

there is also noticeable difference possible connected with the major occupation of Russians, the

responders between 35-49 years in Russia are more active than in France, and in both countries the

most active group is between 25-34 years. As mentioned above this group age corresponds to basic

conditions of usage of mobile applications in restaurant context: users have sufficient financial

resources to go out and developed habit to use mobile technologies.

Frequency of visits and duration of use

According to RFM segmentation (Wansbeek, 1995, Blattberg et al., 2008, Birant, 2011) we

included in our survey two questions to describe frequency as a number of visits in restaurants and

one question of recency, which is described as duration of use, the last component of RFM

segmentation, monetary, is less important because the mobile applications are free for download

and its meaning is included in our construct of price value.

First question of frequency describes the actual frequency of going out by responders; the second

question clarifies the frequency of visits by the mobile applications (Table 54).

Table 54: Frequency of visits

How often do you go out for

dinner/lunch?

France Russia Total %

Several times per week 18 30 48 19.7

One time per week 28 33 61 25

Several times per month 55 42 97 39.7

Less than one time in a month 21 16 37 15.1

Very seldom 1 0 1 0.4

How often do you go out because

of promotions on the mobile

application?

France Russia Total %

Several times per week 3 2 5 2.0

One time per week 8 4 12 4.9

Several times per month 23 34 59 24.1

Less than one time in a month 37 37 74 30.3

Very seldom 52 44 96 39.3

Source: composed by author of the thesis according to the data

247

The majority of responders indicated their frequency to eat out of home as several times per month

(39.7%) and 25% answered one time per week, what is factually the same as several times per

month. There is no important difference by country, but what is interesting to underline, that more

Russians go out several times per week than French.

Probably it refers to the conclusions we did about professional occupation, when the majority of

Russian users are managers and entrepreneurs, they need probably to go out more often, also for

business goals. We did not analyze inside of present research the reasons to go out for dinner/lunch.

For duration of use we composed one question divided in three parts: duration of smartphone use as

technical tool for using the mobile applications, duration of any mobile application use concerning

the search for restaurants or table booking, and duration of researched mobile applications’ use.

79.9 % of the responders use the smartphone long time (more than 5 years), the majority (30.3%)

uses any restaurants’ mobile applications during 3-4 years, and longest duration of researched

mobile applications’ use is 2-3 years (25.8%). This data correspond the described in context

(chapter I) statistics about the smartphone and mobile application use by country.

Table 55: Duration of use

How long are you using the following means?

Mean duration France Russia Total %

Smartphone 5 years and more 89 106 195 79.9

3-4 years 10 12 22 9.01

2-3 years 8 3 11 4.5

1 year and less 8 0 8 3.3

I don’t use 8 0 8 3.3

Any mobile

application for

booking the restaurant

5 years and more 21 9 30 12.3

3-4 years 27 47 74 30.3

2-3 years 34 29 63 25.8

1 year and less 14 10 24 9.8

I don’t use 27 26 53 21.7

Lafourchtte/Resto 5 years and more 19 23 42 17.2

3-4 years 21 30 51 20.9

2-3 years 40 23 63 25.8

1 year and less 19 22 41 16.8

I don’t use 24 23 47 19.26

Source: composed by author of the thesis according to the data

248

GIS – technology, Place of use

In regards to geolocation function, the mobile application uses a geographic search to perform

complex geographic queries in geographic context (Lin, Kao, Lam, Tsai, 2014). In researched

mobile applications this function is presented as button “(restaurants) around me”. It allows to find

the restaurant establishment according attributes, like object type, object name with geographic

criterions like nearness, distance, location.

We composed four questions (Table 56) to see how the users use this function when they search for

restaurants inside of the researched mobile applications. First question is direct whether the

responders use or not the geolocation function and 46.3% do use. It is important to mention that

Russians use this function two times more than French.

Table 56: Geolocation function

I use geolocation function on the

mobile application

France Russia Total %

Totally agree 37 76 113 46.3

Agree 27 20 47 19.2

Neither agree nor disagree 26 11 37 15.1

Disagree 16 6 22 9.01

Totally disagree 17 8 25 10.2

Source: composed by author of the thesis according to the data

Three other questions describe how strong the mobile application can impact the choice of the user

with promotions and depending on the location of the user. The majority (34.4%) of the responders

do not check the promotions on the mobile application before they go out and this number is three

times higher in Russia than in France. This difference refers to the mobile applications’ images.

LaFourchette in Paris was made as discount mobile application and many users perceive it in the

first way as discount tool. Russian Resto was made as restaurant guide in the beginning, even if

they provide now days information about discounts, users do not perceive it as discount tool.

Also, the majority (37.3% agree and 19.6% totally agree) prefer to go to the next restaurant without

promotions rather than to go to the faraway restaurant because of promotions (31.1% totally

disagree, 17.6 % disagree).

In both cases the results are influenced by the dominant numbers of Russian responders, for

example 63 Russian responders do not check promotions compared to 21 of French. More or less

equal opinion responders have about restaurants “next door”: in both cities (Paris and Moscow)

responders prefer to go to close located places. And significant difference we can see in the

intention to change location according to the suggested by mobile application promotions: this

249

intention is higher among French responders than Russians. The attitude of the responders to the

loyalty programs suggested by the mobile applications we will see in the stage of the model

evaluation. This question aimed to study the attitude of the responders to the idea of changing

place/location caused by mobile application. We can assume that Russian responders as we showed

it are representative of upper-middle class mostly and are probably less motivated to change their

location.

Table 57: Influence of promotions for the change of location

I check promotions in the restaurant on the

mobile application before entering

France Russia Total %

Totally agree 17 8 25 10.2

Agree 30 30 60 24.59

Neither agree nor disagree 33 16 49 20

Disagree 22 4 26 10.6

Totally disagree 21 63 84 34.4

I prefer to go to the restaurant next to me even

if there are no promotions

France Russia Total %

Totally agree 24 24 48 19.67

Agree 37 54 91 37.3

Neither agree nor disagree 32 19 51 20.9

Disagree 23 19 42 17.2

Totally disagree 7 5 12 4.9

I prefer to go to faraway restaurant because of

the promotions

France Russia Total %

Totally agree 3 4 7 2.8

Agree 41 25 66 27

Neither agree nor disagree 28 24 52 21.3

Disagree 29 14 43 17.6

Totally disagree 22 54 76 31.1

Source: composed by author of the thesis according to the data

Location of the place and the function of “mapping” on the mobile application is important to the

users in both cities and it can be connected as well with the size of the city, traffic, public transports

and parking possibilities in the city center. In this way discount or promotion in the restaurant can

loose its importance compared to possible difficulties, but discounts and promotions can be

important by changing the choice if other conditions are more or less equal (e.g. to choose between

several restaurant establishments located equal faraway or equal close to the user).

250

We conclude that according to the sample characteristics the biggest difference appeared because of

the professional occupation of responders. In the further sections we will analyze the moderating

effect of the regarded parameters on the mobile applications’ use.

I.2. Validation of the measurement model

The abbreviations in the model are deciphered as follows: CITU – continued intention to use, FC –

facilitating conditions, INDUL – indulgence, ITU – intention to use, LOYALTY – mobile

application loyalty, USABILITY – mobile application usability, PRICE – price value, TRUST –

trust, USE – use of mobile application.

Table 58: Loadings

CITU FC INDUL ITU LOYALTY PRICE TRUST USABILITY USE

CITU1 0,864

CITU2 0,683

FC1 0,628

FC2 0,725

FC3 0,787

INDUL1 0,673

INDUL2 0,806

INDUL3 0,183

ITU1 0,918

ITU2 0,900

LOYALTY1 0,853

LOYALTY2 0,798

LOYALTY3 0,881

LOYALTY4 0,859

LOYALTY5 0,889

PRICE1 0,793

PRICE2 0,808

PRICE3 0,647

TRUST1 0,939

TRUST2 0,942

USABILITY1 0,960

USABILITY2 0,948

USE1 0,977

USE2 0,615

USE3 0,282

Source: SmartPLS report

251

Several loadings have lower values than required 0.70, so to continue we need to delete not reliable

items and see, if that improves the results (Roussel and Wacheux, 2005). CITU has only two

measurement items, so we will keep construct for the moment, to see other indexes.

We removed FC3, PRICE3 that increased the reliability of other items. The removal of the USE3

and INDUL3 did not only increase the values of other items, but even decreased the value of second

weak item. Thus, it is obvious that we need to delete the constructs of indulgence, use and

continued intention to use out of the researched model.

Table 59: Loadings after deleted items

CITU FC INDUL ITU LOYALTY PRICE TRUST USABILITY USE

CITU1 0,864

CITU2 0,683

FC1 0,851

FC2 0,945

INDUL1 0,666

INDUL2 0,821

ITU1 0,916

ITU2 0,902

LOYALTY1 0,853

LOYALTY2 0,798

LOYALTY3 0,881

LOYALTY4 0,859

LOYALTY5 0,889

PRICE1 0,884

PRICE2 0,746

TRUST1 0,934

TRUST2 0,947

USABILITY1 0,960

USABILITY2 0,948

USE1 0,978

USE2 0,611

Source: SmartPLS report

Before we do it according to the framework of our research we will effectuate the analysis of

psychometric characteristics of the measurement model construct by construct, after that we can

take final decisions. We start with this three constructs, which are under the question.

252

I.2.1. Indulgence

We have chosen the construct of indulgence from all cultural dimensions because this construct is

connected with self-gratification and relation between leisure time and work (Hofstede, 2011).

We were aware about the weakness of the measurement items of this latent variable after the

pretest; nevertheless we wanted to check its reliability and validity in the final sample with data

from two countries. Table 60 shows us the characteristics of the indulgence measurement items.

The construct indulgence is measured by three items. Internal consistency reliability is not strong

enough, the obtained values are much less than acceptable of 0,7: Cronbach’s alpha – 0,213 and

Rho 0,242. Composite reliability is 0.597, the required threshold is 0,7. Convergent validity

expressed in AVE is also much lower (0.379) than required minimum of 0.5. T-values for

measurement items are significant for INDUL1 and INDUL2, and not acceptable for INDUL3. As

mentioned before the removal of the weakest item INDUL3 did not increase significance of other

items.

Table 60: Characteristics of the measurement items of indulgence

Items Loadings T-value

INDUL1 0,673 2,452

INDUL2 0,806 2,947

INDUL3 0,183 0,548

Cronbach’s alpha 0,213

Rho 0,242

Composite

reliability

0,597

AVE 0,379

Source: SmartPLS and SPSS reports

Low weights of reliability and validity mean that the measurement items do not reflect the meaning

of the concept, in other words the construct which is measured by these items cannot be interpreted

inside of the researched model or will have poor significance for prediction.

The Values Survey Module manual (Hofstede, 2013) gives explanation of survey reliability,

underlining that answers on questions used to measure a country-level dimension do not necessarily

correlate across individuals. A reliability test like Cronbach’s alpha should in this case not be based

on individual scores but on country mean scores. Obviously this presupposes data from a sufficient

number of countries, in practice at least ten (Hofstede, Minkov, 2013). For comparison across fewer

countries the reliability of the VSM at the country level has to be taken for granted; it can indirectly

be shown through the validity of the scores in predicting dependent variables.

253

Taken into account this precision we compared the samplings of the responders and calculated

scores within the samplings. Thus, French sampling’s scored at 94 and Russian one at 85. These

scores are extremely high compared to national scores provided by Hofstede’s contries comparison

tool (France – 48, Russia – 20). Moreover according to calculated scores we cannot see cultural

difference in the dimension of indulgence. One of the reason might be in the sampling

characteristics, we showed above. Income and social status expressed in professional occupations of

the responders eliminate the cultural differences referred to concepts of leisure time and self

gratification. Also, we should admit, that the measurement items were not used in full that could

provoke the limitations in calculations. Anyway, the construct of indulgence could be useful to

evaluate the difference in attitude towards “going out”, but in our case we are more interested to

analyze the attitude towards the use of technology in the context of “going out”.

Thus, we have to delete the construct of indulgence out of our research model and continue the data

analysis without it.

I.2.2. Continued intention to use

Construct continued intention to use is measured only with two items CITU1 and CITU2, what is

possible in SmartPLS application (Ringle, Wende, Becker, 2015).

Loadings of the item CITU2 are lower than 0.7. Thus, according to the rule we should delete the

construct out of the researched model. But we decided to check indexes of reliability and validity

(Table 61).

Normally Cronbach’s alpha is used to measure internal consistency reliability, some researchers

suggest using composite reliability as replacement (Bagozzi and Yi, 1988, Hair et al., 2012). In this

case as we see the value of composite reliability is higher than required 0.7, by low weights of

Cronbach’s alpha (0.364) and Rho (0.395). AVE of the construct is higher than 0.50, thus the

convergent validity is sufficient.

T-values are higher than 9.0 what is much higher than 1.96 showing the significance of the items’

path coefficients.

Table 61: Characteristics of the measurement items of continued intention to use

Items Loadings T-value

CITU1 0,864 21,505

CITU2 0,683 9,624

Cronbach’s alpha 0,364

Rho 0,395

Composite reliability 0,753

AVE 0,607

Source: SmartPLS reports

254

The construct of continued intention to use is the outcome of the construct of mobile application

loyalty and has no connection or impact on other variables of the model, so we can keep it for the

moment to verify the impact of loyalty on it.

I.2.3. Use of mobile application

Latent variable use of mobile application is key-construct of our research model. It is measured

with three items: USE1, USE2, and USE3. First we deleted USE3 as the item with lowest loadings,

but its removal did not change the score of the second weak item USE2. Nevertheless, we checked

the validity and reliability of the measurement items (Table 62).

Table 62: Characteristics of the measurement items of use

Items Loadings T-value

USE1 0,977 25,671

USE2 0,615 5,236

USE3 0,282 1,758

Deleted item USE3

Cronbach’s alpha 0,605

Rho 1,452

Composite reliability 0,790

AVE 0,665

Source: SmartPLS reports

By lower Cronbach’s alpha (0,605) other weights are sufficient (composite reliability 0,790 and

AVE 0,665) or superior (Rho 1,452). T-values are highly significant (USE1=25,671, USE2=5,236).

Low level of reliability of the measurement items is to explain with the context of mobile

application use. This use is directly connected with times the responders go out to have food in the

restaurants, because only in this case they are in the context of use. At the same time the mobile

application use is competing here with other means like websites, Google, phone, and location of

the users (when the person chooses the restaurant establishment by seeing one next to him/her).

These two factors might influence on the answers and lead to lower weights of reliability.

Nevertheless, the construct even with low reliability can produce estimates of the relationships

among constructs in the confirmatory analysis (Little, Lindenberger, Nesselroad, 1999); our

research is an extension of the UTAUT2, where the use is main construct. Together with AVE

higher than 0,5, we can keep the construct of use of mobile application to analyze its relationships

with other variables.

255

I.2.4. Price value

Latent variable price value is measured with three items: PRICE1, PRICE 2, and PRICE3.

Loadings of PRICE3 are less than required (0,647). After the removal of this item the score of

PRICE1 and PRICE2 correspond the threshold of 0,7 (Table 63).

By lower Cronbach’s alpha (0,517) and Rho (0,558) other weights of composite reliability 0,790

and AVE 0,665 are sufficient. T-values are highly significant (PRICE1=11,481, PRICE2=12,376).

As mentioned before concerning Cronbach’s alpha and Rho we can still keep construct even with

lower scores of this coefficients, if composite reliability and AVE are higher than required

threshold.

Table 63: Characteristics of the measurement items of price value

Items Loadings T-value

PRICE1 0,793 11,481

PRICE2 0,808 12,376

PRICE3 0,647 6,717

Deleted item PRICE3

Cronbach’s alpha 0,517

Rho 0,558

Composite reliability 0,790

AVE 0,669

Source: SmartPLS reports

I.2.5. Facilitating conditions

The latent variable facilitating conditions had one weak measurement item – FC1. But after we

removed this item, the score of measurement item FC2 decreased till 0,648, what actually mean that

the construct should be deleted out of the model. Than we checked the scores removing each item in

rotation and found out, that by removing the item FC3 (initial score 0,787), loadings of the FC1 and

FC2 are well improved (Table 64). That gave us possibility to keep the latent variable of facilitating

conditions.

To understand why the removal of FC3 improves the weights of other measurement items we

analyzed the answers of the responders. In fact, most of the answers (104) on the question

corresponding item FC3 are neutral (neither agree nor disagree), while the answers on the other two

questions are mostly positive (agree or totally agree). Thus, the removal of measurement item with

mostly neutral/undecided responses increases the positive meaning of the other items.

256

Table 64: Characteristics of the measurement items of facilitating conditions

Items loadings Loadings after

removal

T-value

FC1 0,628 0,851 6,624

FC2 0,725 0,945 10,475

FC3 0,787 14,732

Deleted item FC3

Cronbach’s alpha 0,775

Rho 0,902

Composite reliability 0,894

AVE 0,809

Source: SmartPLS reports

T-values of FC1 and FC2 are much higher than required minimum of 1,96 (6,624 and 10,475).

Cronbach’s alpha is higher than 0,7, when Rho (0,902) and composite reliability(0,894) are much

stronger than thresholds. The convergent validity of the latent variable facilitating conditions

measured by AVE is 0,809, what is significantly higher than minimum of 0,5. The measurement

items are valid restoring 80% of variance.

I.2.6. Trust

We composed two items to measure the latent variable trust. Loadings of both items are much

higher than threshold of 0,7: TRUST1=0,939 and TRUST2 = 0, 942. T-values are higher than 64,

compared to minimum of 1,96 the weights are very significant (Table 65).

Table 65: Characteristics of the measurement items of trust

Items Loadings T-value

TRUST1 0,939 64,795

TRUST2 0,942 74,412

Deleted item Non

Cronbach’s alpha 0,870

Rho 0,870

Composite reliability 0,939

AVE 0,885

Source: SmartPLS reports

Cronbach’s alpha and Rho indexes scored the same 0,870, and composite reliability exceeded the

minimum of 0,7 with big advantage (0,939). The convergent validity of the latent variable trust

measured by AVE is 0,885, what is significantly higher than minimum of 0,5. The measurement

items are valid restoring 88% of variance.

257

I.2.7. Intention to use

There are two items measuring the latent variable intention to use: ITU1 and ITU2.

Table 66: Characteristics of the measurement items of intention to use

Items Loadings T-value

ITU1 0,918 72,267

ITU2 0,900 42,314

Deleted item Non

Cronbach’s alpha 0,790

Rho 0,795

Composite reliability 0,905

AVE 0,826

Source: SmartPLS reports

Loadings of both items are higher than threshold of 0,7: ITU1=0,918 and ITU2 = 0, 900. T-values

are higher than 42, compared to minimum of 1,96 the weights are very significant(Table 66).

Internal consistency reliability of intention to use is confirmed by Cronbach’s alpha (0,790) and

Rho (0,795) indexes. Composite reliability with the value of 0,905 exceeded the weight of 0,7. The

convergent validity of the latent variable intention to use measured by AVE is 0,826, what is

significantly higher than minimum of 0,5. The measurement items are valid restoring 82% of

variance.

I.2.8. Mobile application usability

We measured the latent variable mobile application usability with items USABILITY1 and

USABILITY2 (Table 67).

Table 67: Characteristics of the measurement items of mobile application usability

Items Loadings T-value

USABILITY1 0,960 140,098

USABILITY2 0,948 77,857

Deleted item Non

Cronbach’s alpha 0,902

Rho 0,912

Composite reliability 0,953

AVE 0,910

Source: SmartPLS reports

Loadings of both items are significantly higher than threshold of 0,7: USABILITY1= 0,960 and

USABILITY2=0,953. T-values are higher than 77 and 140, compared to minimum of 1,96 the

weights are extraordinary. Internal consistency reliability of mobile application usability is fully

confirmed by Cronbach’s alpha (0,902) and Rho (0,912) indexes. Composite reliability with the

258

value of 0,953 exceeded the minimum weight of 0,7. The convergent validity of the latent variable

mobile application usability measured by AVE is 0,910, what is significantly higher than minimum

of 0,5. The measurement items are valid restoring 91% of variance.

I.2.9. Mobile application loyalty

Mobile application loyalty is the only one construct of the model measured by five measurement

items. We estimate loyalty from the two points of view, loyalty to the mobile application as

technology (compared foe example with the phone call) and loyalty to mobile application as a

known in the restaurant sector company. Loadings of all items are higher than 0,7:

LOYALTY1=0,853, LOYALTY2=0,798, LOYALTY3=0,881, LOYALTY4=0,859,

LOYALTY5=0,889 (Table 68).

T-values are higher than 23, compared to minimum of 1,96 the weights are very significant. Internal

consistency reliability of mobile application loyalty is fully confirmed by Cronbach’s alpha (0,909)

and Rho (0,919) indexes. Composite reliability with the value of 0,932 exceeded the minimum

weight of 0,7. The convergent validity of the latent variable mobile application loyalty measured by

AVE is 0,734, what is higher than minimum of 0,5. The measurement items are valid restoring 73%

of variance.

Table 68: Characteristics of the measurement items of mobile application loyalty

Items Loadings T-value

LOYALTY1 0,853 49,215

LOYALTY2 0,798 23,805

LOYALTY3 0,881 47,598

LOYALTY4 0,859 44,615

LOYALTY5 0,889 50,030

Deleted item Non

Cronbach’s alpha 0,909

Rho 0,919

Composite reliability 0,932

AVE 0,734

Source: SmartPLS reports

Conclusion

We removed latent variable indulgence out of our research model, because of very low indexes’

values of reliability and validity.

Composite reliability is higher than the required minimum of 0,7 by all other variables, even if

continued intention of use, use of mobile application and price value have lower weights of

259

Cronbach’s alpha or/and Rho they obtain good scores in the composite reliability. Sufficient scores

in composite reliability allow us to conclude that our measurement model is reliable.

AVE higher of 0,5 indicates good convergent validity of latent variables in our measurement model.

Measurement items of each particular variable are good connected with among themselves. Mobile

application usability explains 90% of the variance of its items, what is the strongest result, while

continued intention to use explains only 60 % of variance and is the weakest.

I.3. Discriminant validity

Discriminant validity studies the correlation and verifies whether this correlation is representative

or not. The observed variable should correlate with other observed variables of its associated latent

variable better than with observed variables of other latent variables.

For evaluation of discriminant validity we used cross loadings (Table 69) and root square of AVE

(Table 70).

The cross loadings mean that an indicator’s loading with its associated latent construct should be

higher than its loading with all remaining constructs.

Table 69: Cross loadings

CITU FC ITU LOYALTY PRICE TRUST USABILITY USE

CITU1 0,864 0,245 0,575 0,558 0,322 0,405 0,497 0,556

CITU2 0,683 0,201 0,387 0,384 0,314 0,397 0,402 0,215

FC1 0,178 0,851 0,218 0,246 0,154 0,174 0,195 0,153

FC2 0,313 0,945 0,318 0,368 0,195 0,317 0,311 0,219

ITU1 0,580 0,245 0,915 0,744 0,352 0,532 0,469 0,465

ITU2 0,566 0,317 0,903 0,581 0,359 0,447 0,453 0,429

LOYALTY1 0,613 0,332 0,626 0,853 0,428 0,577 0,573 0,523

LOYALTY2 0,410 0,307 0,558 0,798 0,354 0,541 0,454 0,397

LOYALTY3 0,561 0,306 0,670 0,881 0,453 0,630 0,510 0,517

LOYALTY4 0,458 0,239 0,593 0,859 0,474 0,522 0,451 0,571

LOYALTY5 0,559 0,319 0,673 0,889 0,475 0,605 0,558 0,537

PRICE1 0,356 0,171 0,369 0,431 0,884 0,422 0,385 0,379

PRICE2 0,301 0,152 0,259 0,412 0,746 0,371 0,349 0,228

TRUST1 0,421 0,280 0,468 0,600 0,437 0,934 0,604 0,382

TRUST2 0,529 0,264 0,544 0,664 0,473 0,947 0,716 0,411

USABILITY1 0,596 0,277 0,507 0,599 0,426 0,667 0,960 0,423

USABILITY2 0,505 0,283 0,458 0,543 0,428 0,679 0,948 0,341

USE1 0,545 0,233 0,514 0,628 0,425 0,464 0,448 0,978

USE2 0,211 0,036 0,176 0,196 0,043 0,066 0,042 0,611

Source: SmartPLS reports

260

All loadings are higher for observed variables of associated with them latent variable than of all

remaining variables. Even two lower loadings of CITU and USE are correlated better with second

observed variable of its latent variable, than with others.

Table 70: Root square of AVE

CITU FC ITU LOYALTY PRICE TRUST USABILITY USE

CITU 0,779

FC 0,287 0,899

ITU 0,631 0,308 0,909

LOYALTY 0,616 0,353 0,732 0,857

PRICE 0,403 0,198 0,391 0,511 0,818

TRUST 0,508 0,289 0,540 0,673 0,485 0,940

USABILITY 0,580 0,293 0,507 0,600 0,448 0,705 0,954

USE 0,527 0,213 0,492 0,596 0,383 0,422 0,403 0,816

Source: SmartPLS reports

The root square of AVE of each latent constructs is greater than the latent construct’s highest

squares correlation with any other latent construct.

We can conclude that measurement model demonstrates good discriminant validity of the latent

variables’ concepts

Conclusion

Our measurement model is completely reflective and as required has been evaluated to their

reliability and validity. First evaluation we proceeded with loadings, than we estimated construct

internal consistence with Cronbach’s alpha and Rho.

For convergent validity we examined the average variance extracted (AVE). And finally we

regarded cross loadings and root square of AVE for the assessment of discriminant validity.

Evaluation of outer model confirmed sufficient reliability and validity of our measurement items

and associated with them latent variables. We conclude that the measurement model is reliable and

valid.

I.4. Structural model

In this section we assess the structural or outer model and relationships between latent variables.

First we check percent of variance explained (R² and f²) and model’s capability to predict (Q² and

q²). Than we evaluate parameters (path coefficients, T-value and p-value). And finally we calculate

the quality of the model (Goodness of fit).

I.4.1. Percent of variance explained (R²)

R² evaluates percent of variance of endogenous latent variables. Obtained results of ITU, CITU and

LOYALTY are equal or higher than 35 percent: 0,354, 0,379 and 0,360 respectively. That indicates

261

that our research model explains 35% of these latent variables and according to Chin (1998) these

effects are moderate. The model explains only 27% of the endogenous variable USE, what

approximates moderate effect, but still is weak. TRUST is explained with less than 1% that is very

weak. In average the research model explains moderate the latent variables and relationships among

them (Table 71):

Table 72: R²

R Square

PRICE exogenous 0

FC exogenous 0

USABILITY exogenous 0

CITU endogenous 0,379 moderate

ITU endogenous 0,354 moderate

LOYALTY Endogenous 0,360 moderate

TRUST Endogenous 0,083 -weak

USE Endogenous 0,274 +weak

Source: SmartPLS reports

Since R2 is the percentage of response variable variation that is explained by its relationship with

one or more predictor variables, the obtained results show that facilitating conditions as a single

predictor of trust does not really explain it (1%), or in other words the collected data do not fit that

relationship. So, there is no real dependence between both: even if the user knows good how to use

mobile application it does not affect whether he/she trust this mobile application or not. Thus, trust

can be developed without knowledge about the mobile application technology. As for use it has two

predictor variables intention to use and usability. The main concern is nevertheless that even if the

users have the mobile application on their smartphones, it is not always the case that they use it, so

probably this two predictors are not enough to explain the use of mobile application, and it would

be reasonable to add other variables into the model in the future researches.

Anyway, even when R-squared is low, low P values still indicate a real relationship between the

significant predictors and the response variable (Frost, 2014). We will analyze P-values later in this

chapter.

I.4.2. Size effect f²

In order to determine in the model the part of the observed variable we calculated size effect f²

(Table 72).

262

Table 72: Size effect f²

Variables f² Effect

Intention to use Price value 0,020 weak

Facilitating conditions 0,027 weak-

Trust 0,063 weak+

Usability 0,030 weak

Use Intention to use 0,153 moderate

Usability 0,044 weak

Trust Facilitating conditions 0,091 weak+

Loyalty Usability 0,563 high

Continued intention to use Loyalty 0,611 high

Source: SmartPLS reports

a) Size effect f² of intention to use.

All the variables have weak size effect according to Cohen (1988) on the latent variable

intention to use. The greatest size effect has trust (0,063), the weakest – price value (0.020).

Other variables obtained following results: facilitating conditions (0,027) has size effect

approximating the minimum of 0,02, indicating very weak effect; usability obtained the result

0,30 and showed also weak effect. Thus, we can conclude that the most important size effect on

the latent variable intention to use has trust.

The low size of the effect of the variables facilitating conditions, trust, price value and

usability on the variable intention to use can be interpreted by the context of mobile application

use: choice and booking of the restaurants with mobile application. We have chosen these

variables after the literature review, but the strength of relationships between them and

intention to use above some other variables which we did not include into the model was not

obvious. Anyway, we will analyze later the path significance, because low size effect does not

mean that relationships are not significant.

b) Size effect f² of use.

There are two variables explaining use: intention to use and mobile application usability. The

greatest size effect between them has intention to use =0,153, this effect is moderate (Cohen,

1988).

Mobile application usability has weak size effect (0,044). Explanatory power expressed in size

effect shows the low dependence of use on functionality of mobile application; probably it is

connected with standardization in the mobile applications’ development today: usability could

263

have noticeable effect by innovations in the product development, or negative effect by outdated

usability.

c) Size effect f² of trust.

Latent variable trust is measured only by one latent variable: facilitating conditions which has

weak+ size effect f² 0,091.

As mentioned above, technical side of mobile application expressed in facilitating conditions

does not predict trust, so the size of this relationship is low.

d) Size effect f² of mobile application loyalty.

Latent variable mobile application loyalty is measured only by one latent variable: mobile

application usability which has high size effect f² 0,563.

e) Size effect f² of continued intention to use.

Latent variable continued intention to use is measured only by one latent variable: mobile

application loyalty which has high size effect f² 0,611.

I.4.3. Model’s capability to predict (Q² and q²)

The measure of predictive relevance is the Stone-Geisser’s Q² (Geisser, 1974, Stone, 1974). This

indicator postulates that the model should be able to predict each endogenous construct’s indicators.

This procedure is only applied to endogenous latent constructs that have a reflective measurement

model specification as we have.

Q² should be higher than 0 and tend to 1, as we can see in the Table 73 the highest capability to

predict has ITU (intention to use) with the value of 0,269 and the weakest capability to predict has

trust with the value of 0,073. Loyalty (0,260) and CITU (0,225) showed stronger capability than

use (0,149). All latent variables have scored higher than 0 showing that they are capable to predict.

Table 73: Model’s capability to predict Q²

Q²

CITU 0,225

ITU 0,269

LOYALTY 0,260

TRUST 0,073

USE 0,149

Source: SmartPLS reports

264

I.4.4. Size effect of capability to predict q²

To determine in the model the part of the observed variable on the prediction of another variable

(Q²), the q² it is used. The procedure of the obtaining the values of size effect q² requires removing

the paths of the model one by one and evaluation the changes of the values during that process. In

our model we could not follow this procedure for all variables, because endogenous latent variables

mobile application loyalty and continued intention to use have only one path connected to the

model, the removing of that path destroys the model.

Table 74: size effect q²

Variables Q² q² Effect

Intention to use Q² included 0,269

Q² without PRICE 0.263 0.008 No effect

Q² without FC 0.269 0 No effect

Q² without

TRUST

0.237 0.04 weak

Q² without

USABILITY

0.257 0.01 weak -

Use Q² included 0,149

Q² without ITU 0.081 0.07 weak

Q² without

usability

0.149 0 No effect

trust Q² included 0,073 0 No effect

Q² without FC

Source: SmartPLS reports

a) Size effect q² of intention to use

Latent variables trust and mobile application usability have weak size effect q² on the capability to

predict of latent variable intention to use: 0,04 and 0,01 respectively. Latent variables price value

and facilitating conditions showed no size effect q².

b) Size effect q² of use

Latent variable intention to use has weak size effect q² (0,07) on the capability to predict of latent

variable intention to use. Latent variable mobile application usability showed no size effect q².

c) Size effect q² of trust

Latent variable facilitating conditions showed no size effect q² on the endogenous latent variable

trust.

Conclusion for capability to predict

265

The model’s capability to predict is low with greatest value of 0,269. But even if we can conclude

that scores above zero show that its explanatory variables provide predictive relevance, the size

effects of latent variables demonstrated very weak or no size effects.

Price value does not have predictive power for intention to use, so benefits of mobile application

use do not influence on the decision to use or not to use the mobile application. There is no effect of

the size of relationship between facilitating conditions and intention to use, so users do not use

more actively mobile application because they possess technical knowledge about it. Weak size

effect we can see in the relationship between trust and intention to use, so mobile application as the

source of information does not develop trust by the users to enforce their intention to use. Usability

in the relationship with intention to use has no effect, so well developed functionality of the mobile

application is not the primer condition for intention to use it.

Concerning the use, the size of the effect of the intention to use on the use is weak, so intention to

use does not have predictive power over the use: even if the users have mobile application they do

not use it. And with no effect of size in the relationship between usability and use we can see that

good functionality of the mobile application do not predict the use of it.

There is no effect size in the relationship between facilitating conditions and trust, so the

knowledge above the technical side of the mobile application does not predict that the trust towards

the mobile application will be developed.

We can conclude that our model is explanatory and not predictive, we can explain the use of mobile

application with help of chosen variables, but to predict the use we would need to change variables

in the model.

I.4.5. Evaluation of parameters (Path coefficients and T-value).

After we evaluated the model by the percentage of explained variance (R²), we must verify the

stability of the estimates using the calculation of the statistics T obtained by the resampling

procedure by bootstrapping (Hair et al, 2011). By estimation of parameters and significance t-value

should be higher than 1.96 with p<0.05. The graphical presentation of the inner model is to find in

the Annex 17 and results are summarized in the Table 75.

Table 75: Evaluation of parameters (Path coefficients and T-value)

Path coefficient T Statistics P Values

FC -> ITU 0,138 2,042 0,042

FC -> TRUST 0,289 5,504 0,000

ITU -> USE 0,387 6,359 0,000

LOYALTY -> CITU 0,616 14,496 0,000

PRICE -> ITU 0,131 1,925 0,055

TRUST -> ITU 0,296 4,004 0,000

266

USABILITY -> ITU 0,199 2,319 0,021

USABILITY ->

LOYALTY 0,600 14,606 0,000

USABILITY -> USE 0,207 2,663 0,008

Source: SmartPLS reports

Intention to use

Trust explains strongly the intention to use the mobile application (path coefficient=0,296, T-

value=4,004; p=0,000) while mobile application usability has less but still significant impact on

intention to use with path coefficient=0,199 approximating to recommended 0,2 (Chin, 1998), T-

value=2,319; p=0,021. Path coefficient of facilitating conditions is 0,138 lower than 0,2, but T-

value =2,042 and p=0,0042 are still higher than thresholds, so the impact is weak, but still

acceptable. On the other hand price value has not significant impact on intention to use the mobile

application of the users (path coefficient=0,131, T-value=1,925; p=0,055).

Trust

The single latent variable facilitating conditions explains well the trust (path coefficient=0,289, T-

value=5,504; p=0,000).

Use of mobile application

The impact of the intention to use on use of mobile application is very strong (path

coefficient=0,387, T-value=6,359; p=0,000) while mobile application usability explains this

variable less strong but still significant (path coefficient=0,207, T-value=2,663; p=0,008).

Mobile application loyalty

Mobile application usability is the single latent variable predicting the mobile application loyalty in

our research model and with path coefficient = 0,600, T-value = 14,606 and p=0,000 has greatest

impact on this variable.

Continued intention to use

In turn mobile application loyalty explains significantly strong the variable continued intention to

use(path coefficient=0,616, T-value=14,496; p=0,000).

I.4.6. Quality of the model - Goodness of fit

To ensure the quality of our research model we calculated index of quality “Goodness of fit”.

For calculation we used data of R² and AVE obtained in SmartPLS (Table 76):

Table 76: Calculation of Goodness of fit

R ² AVE

PRICE exogenous 0 0,669

FC exogenous 0 0,809

USABILITY exogenous 0 0,910

CITU endogenous 0,379 0,607

267

ITU endogenous 0,354 0,826

LOYALTY endogenous 0,360 0,734

TRUST endogenous 0,083 0,884

USE endogenous 0,274 0,665

Average

0.29

0.763

Source: SmartPLS reports

According to Latan and Ghozali (2012) GOF within SmartPLS with the weight of 0,10 means

reliable quality, 0,25 – moderate quality, and 0,36 – high quality. Thus, we can conclude that our

research model has high quality with the weight of GOD equal 0,470.

Average of R² Average of AVE Goodness of fit

0.29 0.763 0.470- high

I.4.7. Summary of results

The analysis of structural model provided us relevant results of indicators: percent of variance

explained, model’s capability to predict, evaluation of parameters and model quality. The graphical

view of the results is presented on the Figure 35:

Figure 35: Results of research model with SmartPLS

Out of the Figure 35 we can conclude about three different effects among latent variables. We also

showed the removed variable of indulgence, the effect of this variable we did not evaluated,

because its reliability and validity were too weak to effectuate the further evaluation process.

268

1) No effect

Price value has low effect and explains weakly the intention to use (path coefficient=0,131, T-

value=1,925, p=0,055). Initial meaning of price value construct is connected with the price of the

technology. Mobile applications we have chosen are free to download and price value consists of

the benefits the use can receive by using the mobile application. In both mobile applications the use

can collect points and exchange them for gifts or discounts. We can conclude that these benefits are

not of high value for the users, so this construct does not explain the intention to use of mobile

application.

2) Weak effects:

• Construct facilitating conditions impacts weak on intention to use the mobile

application (path coefficient =0,138, T-value = 2,042, p=0,042). Facilitating

conditions as construct has a meaning of possession of technical knowledge about

mobile application as technology. Most of the users answered that mobile

technology became a part of their everyday life. We do not have in the data variance

of the answers to see, whether the users with developed knowledge use easier or

more active, or that this knowledge makes them tend to use more often these

particular mobile application. We can only conclude that facilitating conditions in

our case has weak impact on the intention to use mobile application.

• Mobile application usability explains weakly intention to use the mobile application

(path coefficient =0,199, T-value=2,319, p=0,021). Mobile application usability is

connected with functionally, design and structure of mobile application. There are

some standards in these components in all similar mobile applications, so probably

users do not pay attention on it if the mobile application fits typical functionally

design and structure. The impact could be seen in the innovative usability, or

negative effect could be seen in outdated usability.

3) Moderate and high effects:

• Facilitating conditions has upper moderate impact on trust (path coefficient = 0,298,

T-value=5,504, p=0,000). In previous section we obtained weak results for the

facilitating conditions’ capability to explain and predict trust. But the results in path-

coefficient and T-value show the stability of this relationship. Even if the facilitating

conditions as predictor is not relevant the developed knowledge about technology of

mobile application positively affect on the trust. In other case the lack of this

knowledge could lead to lack of trust in the mobile application.

269

• Trust has as well upper moderate impact on intention to use (path coefficient=0,296,

T-value=4,004, p=0,000). The relationship is stable; users with developed trust

towards the mobile application tend to use it.

• Intention to use explains strong the use of mobile application its impact is high (path

coefficient = 0,387, T-value = 6,359, p= 0,000). In previous section we noticed the

moderate explanatory and weak predictive effects of intention to use on the use of

mobile application. Path analysis showed the stability of this relationship. As

stronger is the intention to use the mobile application as more possible it will lead to

actual use of it.

• Mobile application usability has moderate impact on the use of mobile application

(path coefficient= 0,207, T-value =2,663, p=0,008). Explanatory and predictive

power in this relationship was also estimated as weak, but with path analysis this

relationship can be interpret as stable. That means: even if the users do not pay

attention on the usability as such, still outdated or in opposite very innovative

functionally of the mobile application can change their attitude towards the mobile

application.

• Mobile application usability has significantly strong effect on the mobile application

loyalty (path coefficient = 0,600, T-value = 14,606, p= 0,000). This relationship has

moderate explanatory and predictive effect, and very high stability. Even if the

mobile application usability is not relevant on the stage of decision about its use, it is

important for developing loyalty towards it. Poor usability can discourage the user

and he/she can easily change it for another one. Probably the high competing

conditions (mobile applications compete with each others, with phone, websites etc)

makes the mobile application usability important for being loyal.

• Mobile application loyalty impacts in the strongly significant way on the continued

intention to use (path coefficient = 0,616, T-value = 14,496, p = 0,000). Loyalty

leads to reuse of mobile application, in other words in continued intention to use.

In the next paragraph we will see the effect of moderation on the research model and evaluate

whether the moderator duration of use, frequency of visits and place of use effect the presented

relationships in the positive way or not.

270

I.5. Moderation effects

Our model has three moderator and four moderation relationships. In order to confirm the direction

of the moderating effect our sample was divided into three subsamples representing respondents

with high frequency of visits, long duration of use and significant place of use. These three

subsamples were analyzed independently and the results compared with initial data. While we put

forward the particular hypotheses concerning the moderating effect, we accepted also the

moderating effects on other weak relationships to see if the results can be improved, particularly on

the relationship between price value and intention to use.

We include two moderators adapted by RFM (Recency Frequency Monetary) segmentation

(Wansbeek, 1995, Blattberg et al., 2008, Birant, 2011): frequency as a number of visits in

restaurants and duration of use as component of recency. As evaluation we decided to use high

results: more often the users go out more significant should be effect on the model; as longer the

users use the mobile application as greater should be effect on the model.

Moderation effects of the duration of use

We assumed that duration of use should have positive impact on the relationships between trust and

intention to use, intention to use and use of mobile application (Table 77).

Trust -> intention to use

T-value and path coefficient increased a little bit under the moderation effect of duration of use (T-

value: before 4,004, after 4,015; path coefficient: before 0,296 after 0,326), but still this relationship

we can describe as upper moderate, not high. Thus, we conclude that the moderating effect of

duration of use on the relationship between trust and intention to use is positive, but not significant.

Table 77: Duration of use

Without duration of use With duration of use

Path

coefficient T Statistics Path coefficient T Statistics p-value

FC -> ITU 0,138 2,042 0,088 1,212 0,226

FC -> TRUST 0,289 5,504 0,280 4,460 0,000

ITU -> USE 0,387 6,359 0,403 6,446 0,000

LOYALTY -> CITU 0,616 14,496 0,606 13,785 0,000

PRICE -> ITU 0,131 1,925 0,139 1,843 0,066

TRUST -> ITU 0,296 4,004 0,326 4,015 0,000

USABILITY -> ITU 0,199 2,319 0,165 1,794 0,073

USABILITY ->

LOYALTY 0,600 14,606

0,585 11,986 0,000

USABILITY -> USE 0,207 2,663 0,203 2,643 0,008

Source: SmartPLS reports

271

Intention to use -> use of the mobile application

The moderation of the duration of use impacts positively but not strong the relationship between

intention to use and use of mobile application. T-value increased from 6,359 to 6,446, path

coefficient increased from 0,387 to 0,403. Thus, the relationship between intention to use and use of

mobile application are positively influenced by duration of use.

Other effects

There are not other positive effects of duration of use on the research model.

Moderation effects of the frequency of visits

We assumed that frequency of visits should have positive impact on the relationships between

mobile application loyalty and continued intention to use (Table 78).

Table 78: Frequency of visits

without frequency of visits With frequency of visits

Path

coefficient T Statistics Path coefficient T Statistics p-value

FC -> ITU 0,138 2,042 0,287 2,935 0,003

FC -> TRUST 0,289 5,504 0,276 2,815 0,005

ITU -> USE 0,387 6,359 0,551 6,856 0,000

LOYALTY -> CITU 0,616 14,496 0,799 26,412 0,000

PRICE -> ITU 0,131 1,925 0,080 0,777 0,438

TRUST -> ITU 0,296 4,004 0,275 2,385 0,017

USABILITY -> ITU 0,199 2,319 0,187 1,291 0,197

USABILITY ->

LOYALTY 0,600 14,606

0,608 8,794 0,000

USABILITY -> USE 0,207 2,663 0,115 1,064 0,288

Source: SmartPLS reports

Mobile application loyalty -> continued intention to use

Moderation effect of frequency of visits has great impact on the relationship between mobile

application loyalty and continued intention to use. T-value increased significantly from 14,496 to

26,412, path coefficient increased from 0,616 to 0,799. Thus, frequency of visits moderates the

relationship between mobile application loyalty and continued intention to use in the relevant way

and improves their correlation.

Other effects

Other significant moderation of frequency of visits we can notice in the impact on the relationship

between intention to use and use (T-value: before 6,359, after 6,856; path coefficient: before 0,387

272

after 0,551). This relationship was already described as strong, but moderation effect also improved

it.

Much relevant is the moderation effect of frequency of visits on the relationship between facilitating

conditions and intention to use, before we presented this relationship as weak with T-value 2,042

and path coefficient less than threshold 0,138. As moderator frequency of visits improved the

relationship: T-value 2,935 and path coefficient 0,283. So this relationship we can describe as

moderate.

Gis-technology

Geolocation function is a part of functionality in other word mobile application usability. Today it

is necessary function of any application which is providing the information of geographical location

in our case of restaurant establishments. The mobile application uses a geographic search to

perform complex geographic queries in geographic context (Lin, Kao, Lam, Tsai, 2014).

We included the moderator place of use to see how this function and geographical location are

important to the users. We estimate moderation effect of its significance: as higher are obtained

results as more relevant is the effect on the model.

Moderation effects of place of use

We assumed that place of use should have positive impact on the relationships between intention to

use and use of mobile application (Table 79).

Intention to use -> use of the mobile application

Place of use has small negative effect on the relationship between intention to use and use of mobile

application. T-value decreased from 6,359 to 6,317 and path coefficient decreased from 0,387 to

0,386. The moderation of place of use is not significant.

Table 79: Place of use

without place of use With place of use

Path

coefficient T Statistics Path coefficient T Statistics p-value

FC -> ITU 0,138 2,042 0,153 1,923 0,055

FC -> TRUST 0,289 5,504 0,309 5,164 0,000

ITU -> USE 0,387 6,359 0,386 6,317 0,000

LOYALTY -> CITU 0,616 14,496 0,602 13,748 0,000

PRICE -> ITU 0,131 1,925 0,152 2,097 0,037

TRUST -> ITU 0,296 4,004 0,267 3,179 0,002

USABILITY -> ITU 0,199 2,319 0,162 1,933 0,054

USABILITY ->

LOYALTY 0,600 14,606

0,574 11,969 0,000

USABILITY -> USE 0,207 2,663 0,186 2,328 0,020

Source: SmartPLS reports

Other effects

273

Significant effect appeared in the relationship between price value and intention to use by

moderation of place of use. T-value increased from 1,925, what is below the threshold of 1,96, to

2,097 what is acceptable(p=0,037). On the other hand path coefficient (0,152) is still less than

required minimum of 0,2. Thus, place of use impacts positively but not strong enough on the

relationship between price value and intention to use, this relationship are still not significant and

weak.

I.6. Validation of hypotheses

The research model was evaluated. In the first instance we studied the percentage of variance

explained (R²) and size effect (f²) of each endogenous latent variable as well as we analyzed

model’s capability to predict (Q²) and size effect (q²). In the second turn we made the evaluation of

parameters (path coefficient) and T-value as well as their significance to determinate the

relationships among latent variables. Finally, we calculated the index of model’s quality (Goodness

of fit). Then we verified the moderation effects of three moderating variables: duration of use,

frequency of visits and place of use.

We intent to confirm or to reject the hypotheses based on the obtained analysis results of our

structural model (Table 80):

Table 80: Summary of the analysis results of the structural model

Construct Predictive

construct

R² f² Path

coef

T

value

P

value

Q² q² H

Intention to

use

Indulgence Removed construct R

Intention to

use

0,379 0,269

Price value 0,020 0,131 1,925 0,055 0,008 O

Facilitating

conditions

0,027

0,138 2,042 0,042

0 V

Trust 0,063 0,296 4,004 0,000 0,04 V

Usability 0,030 0,199 2,319 0,021 0,01 V

Use 0,274 0,149

Intention to use 0,153 0,387 6,359 0,000 0,07 V

Usability 0,044 0,207 2,663 0,008 0 V

Trust Facilitating

conditions

0,083 0,091

0,289 5,504 0,000

0,073 0 V

Loyalty Usability 0,360 0,563 0,600 14,606 0,000 0,260 - V

Continued

intention to

use

Loyalty 0,379 0,611

0,616 14,496 0,000

0,225 - V

V-validated, R-rejected, O - not significant effect. Source: SmartPLS reports

274

I.6.1 Factors influencing intention to use

The impact on the intention to use mobile application is estimated according to following

hypotheses:

Factors influencing intention to use

H1 Price value increases intention to use the mobile application. Users who believe to receive financial

or other profit using the mobile application will intent to use this mobile application.

H2 Facilitating conditions impact positively trust. The mobile application users who believe that

supporting organization stays behind the mobile application develop trust quicker then who don’t.

H3 Facilitating conditions impact positively the intention to use the mobile application. The mobile

application users who believe that supporting organization stays behind the mobile application will

rather to use it than not.

H4 Trust impacts positively the behavioral intention to use the mobile application. The mobile

application users, who have developed trust to mobile application’s provider, or technology, intent to

use mobile application more likely, than who have not.

H5 Indulgence as cultural factor impacts positively the intention to use of the mobile application. The

users from the cultures with the high score in indulgence tend to use more often the mobile

application to book a table in the restaurant.

H6 Mobile application usability increases the behavioral intention to use the mobile application. Users,

who are satisfied with design and information’s structure of the mobile application, intent to use it.

Out of 6 hypotheses concerning the impact on the intention to use mobile application, four are

validated, one is rejected and one is to discuss.

• Facilitating conditions has upper moderate effect on the trust. Hypothesis H2 is validated.

• Facilitating conditions has weak impact on the intention to use which can be positively

moderated by the place of use and improve its significance. Thus, hypothesis H3 is

validated.

• Trust impacts very positively on the intention to use mobile application and increases its

effect with moderation of duration of use. Hypothesis H4 is validated.

• Mobile application usability has weak impact on the intention to use, but still this effect is

acceptable. Hypothesis H6 is validated.

• Price value showed not significant impact on the intention to use, but its significance

increased with the moderation by place of use. Still the obtained results are not high enough

to confirm the hypothesis, but it can be discussed. Hypothesis H1 is not validated but can be

discussed.

• We removed the construct indulgence out of our research model, because its measurement

constructs cannot measure it in the correct way. So, we did not evaluate the impact of

indulgence on the intention to use. Thus, hypothesis H5 is rejected.

I.6.2. Factors influencing mobile application use

The impact on the use of mobile application is estimated with following hypothesis:

275

Factors influencing the mobile application use

H7 Strong behavioral intention leads to the use of the mobile application.

H8 Mobile application usability leads to the use of the mobile application.

Both hypotheses H7 and H8 are validated.

• Intention to use has high impact on the use of mobile application and is increasing strongly

with moderating of the duration of use; on the other hand the moderating effect of pace of

use is negative: intention to use looses the significance for use of mobile application.

• Mobile application usability has moderate effect on use of mobile application which is also

negatively influenced by moderating of place of use.

I.6.3. Outcome factors

We have two outcomes in our research model: mobile application loyalty and continued intention to

use, which are evaluated by following hypotheses:

Outcomes factors

H9 Mobile application usability increases the mobile application loyalty. User, who is

satisfied with design and information’s structure of the mobile application, has a

deep commitment to repatronize a mobile application.

H10 Mobile application loyalty moderated by frequency of visits leads to continued

intention to use the mobile application.

Both hypotheses H9 and H10 are validated.

• The impact of mobile application usability on mobile application loyalty is highly

significant. The relationship is a little bit negative impacted by moderation of place of use

and duration of use.

• Mobile application loyalty has strong impact on continued intention to use, which increases

significantly by moderation of frequency of visits. Place of use and duration of use moderate

this relationship negatively, but not significant.

I.6.4. Moderation effects

We tested the moderation effects of duration of use, place of use and frequency of visits on the

particular relationships, but also we estimated their impacts on other relationships in the model with

intention to regard positive effects on the not significant or weak relationships. The moderating

effects are expressed in following hypotheses:

276

Moderation effects

Hm1 Duration of the use moderates the relation between trust and intention of use

Hm2 Duration of the use moderates the relation between intention of use and use of the

mobile application.

Hm3 Place of use moderates the relation between intention of use and use of the mobile

application.

Hm4 Frequency of visits moderates relation between mobile application loyalty and

continued intention to use

• Duration of use has small positive impacts on the relationship between trust and intention to

use and on the relationship between intention to use and mobile application use. These

effects are not strong enough to confirm the hypotheses Hm1 and Hm2, but it can be

discussed.

• Place of use has small negative effect on the relationship between intention to use and use of

mobile application, but this effect is too weak to confirm the hypothesis Hm3, so we

rejected it. On the other hand, positive effect appeared in the relationship between price

value and intention to use by moderation of place of use.

• Frequency of visits has very strong positive effect on the relationship between mobile

application loyalty and continued intention to use. We validate the hypothesis Hm4.

Significant negative effect had this moderator on the relationship between mobile

application usability and use of mobile application, decreasing this relationship till the

unacceptable threshold.

I.6.5. Conclusion about validation of the hypotheses.

The test of hypotheses of our research is effectuated with help of SmartPLS 3 application and SEM-

PLS approach. We succeed in evaluation, validation and acceptance of the structural model and

validated the hypotheses of the research. Out of fourteen hypotheses, nine are validated, three are

under the discussion and two are rejected. In the Table 81 the summary of the validation of

hypotheses of our research is presented. In the next section we will compare the results of general

model with models of France and Russia.

277

Table 81: Summary of the hypotheses validation

Hypotheses

Factors influencing intention to use

H1 Price value increases intention to use the mobile application. Users who believe to

receive financial or other profit using the mobile application will intent to use this

mobile application.

O

H2 Facilitating conditions impact positively trust. The mobile application users who

believe that supporting organization stays behind the mobile application develop

trust quicker then who don’t.

V

H3 Facilitating conditions impact positively the intention to use the mobile application.

The mobile application users who believe that supporting organization stays behind

the mobile application will rather to use it than not.

V

H4 Trust impacts positively the behavioral intention to use the mobile application. The

mobile application users, who have developed trust to mobile application’s provider,

or technology, intent to use mobile application more likely, than who have not.

V

H5 Indulgence as cultural factor impacts positively the intention to use of the mobile

application. The users from the cultures with the high score in indulgence tend to

use more often the mobile application to book a table in the restaurant.

R

H6 Mobile application usability increases the behavioral intention to use the mobile

application. Users, who are satisfied with design and information’s structure of the

mobile application, intent to use it.

V

Factors influencing the mobile application use

H7 Strong behavioral intention leads to the use of the mobile application. V

H8 Mobile application usability leads to the use of the mobile application. V

Outcomes factors

H9 Mobile application usability increases the mobile application loyalty. User, who is

satisfied with design and information’s structure of the mobile application, has a

deep commitment to repatronize a mobile application.

V

H10 Mobile application loyalty moderated by frequency of visits leads to continued

intention to use the mobile application.

V

Moderation effects

Hm1 Duration of the use moderates the relation between trust and intention of use To discuss

Hm2 Duration of the use moderates the relation between intention of use and use of the

mobile application.

To discuss

Hm3 Place of use moderates the relation between intention of use and use of the mobile

application.

R

Hm4 Frequency of visits moderates relation between mobile application loyalty and

continued intention to use

V

V-validated, R-rejected, O - not significant effect. Source: author of the thesis

II. Comparative study between France and Russia

We effectuated the analysis o the research model according two national sub groups: France and

Russia, with the purpose to compare the mobile application use in restaurant context. We had 123

responders in France and 121 in Russia, we realized our survey only in the capitals: in Paris and in

Moscow. This section presents the results.

II.1. Sub group France

To accomplish the comparison we calculated with SmrtPLS the weights of path coefficient, T-

Value and p. In the Table 82 we can see the results in comparison with general model:

278

Table 82: Results of sub group France compared with general model

General model French Model

Path

coefficient T Statistics Path coefficient T Statistics p-value

FC -> ITU 0,138 2,042 0,240 2,835 0,005

FC -> TRUST 0,289 5,504 0,229 3,392 0,001

ITU -> USE 0,387 6,359 0,541 8,138 0,000

LOYALTY -> CITU 0,616 14,496 0,817 30,380 0,000

PRICE -> ITU 0,131 1,925 0,216 2,434 0,015

TRUST -> ITU 0,296 4,004 0,137 1,501 0,134

USABILITY -> ITU 0,199 2,319 0,275 2,767 0,006

USABILITY ->

LOYALTY 0,600 14,606

0,544 7,492 0,000

USABILITY -> USE 0,207 2,663 -0,049 0,453 0,651

Source: SmartPLS reports

There are positive and negative significant effects in French model compared to general model:

Positive effects:

1) Price value has not significant impact on intention to use in general model, but the results of

French model are moderate (path coefficient=0,216, T-value=2,343, p=0,015).

2) Impact of facilitating conditions on intention to use has stronger effect in French model

(path coefficient=0,240, T-value=2,835, p=0,005)

3) The mobile application loyalty impacts the continued intention to use also much stronger in

French model than in general model (path coefficient=0,817, T-value=30,380, p=0,000).

Negative effects:

1) Trust lost its significant for intention to use completely in French model (path

coefficient=0,137, T-value=1,501, p=0,134).

2) Mobile application usability has no impact on the use of mobile application (path

coefficient=-0,049, T-value=0,453, p=0,651)

Thus, compared to general model the price value significance can allow us to confirm hypothesis of

its impact on the intention to use for French model, on the other hand we need to reject the

hypothesis of impact of mobile application usability on use of mobile application and impact of

trust on intention to use. Other effects are interesting but they do not validate or reject the

hypotheses differently that in general model.

II.2. Sub group Russia

There are positive and negative significant effects in Russian model compared to general model

(Table 83):

279

Table 83: Results of sub group Russia compared with general model

General model Russian Model

Path

coefficient T Statistics Path coefficient T Statistics p-value

FC -> ITU 0,138 2,042 -0,001 0,010 0,992

FC -> TRUST 0,289 5,504 0,675 9,857 0,000

ITU -> USE 0,387 6,359 0,192 2,045 0,041

LOYALTY -> CITU 0,616 14,496 0,514 7,796 0,000

PRICE -> ITU 0,131 1,925 0,033 0,329 0,743

TRUST -> ITU 0,296 4,004 0,630 5,558 0,000

USABILITY -> ITU 0,199 2,319 0,036 0,276 0,782

USABILITY ->

LOYALTY 0,600 14,606

0,642 10,497 0,000

USABILITY -> USE 0,207 2,663 0,461 5,728 0,000

Source: SmartPLS reports

Positive effects:

1) Facilitating conditions has strong impact on trust in Russian model (path coefficient=0,675,

T-value=9,857, p=0,000).

2) Mobile application usability has greater effect on use of mobile application (path

coefficient=0,461, T-value=5,728, p=0,000)

Negative effects:

1) Facilitating conditions lost their impact on intention to use in Russian model (path

coefficient=-0,001, T-value=0,010, p=0,992).

2) Intention to use impacts weakly the use of mobile application (path coefficient=0,192, T-

value=2,045, p=0,041).

3) Price value has no impact on intention to use (path coefficient=0,033, T-value=0,329,

p=0,743).

4) Mobile application usability has no effect on intention to use (path coefficient=0,036, T-

value=0,276, p=0,782).

In general, negative effects are strong in Russian model; according to the obtained results we can

reject three hypotheses for Russian model, which are valid for general and French models. Before

we discussed the hypotheses, we compared the results of three models (Table 84):

280

Table 84: Comparison of the results for general, French and Russian models.

General model Russian Model French Model

Path

coefficient T Statistics

Path

coefficient

T

Statistics

p-

value

Path

coefficient

T

Statistics p-value

FC -> ITU 0,138 2,042 -0,001 0,010 0,992 0,240 2,835 0,005

FC -> TRUST 0,289 5,504 0,675 9,857 0,000 0,229 3,392 0,001

ITU -> USE 0,387 6,359 0,192 2,045 0,041 0,541 8,138 0,000

LOYALTY ->

CITU 0,616 14,496

0,514 7,796 0,000

0,817 30,380 0,000

PRICE -> ITU 0,131 1,925 0,033 0,329 0,743 0,216 2,434 0,015

TRUST -> ITU 0,296 4,004 0,630 5,558 0,000 0,137 1,501 0,134

USABILITY ->

ITU 0,199 2,319 0,036 0,276 0,782 0,275 2,767 0,006

USABILITY ->

LOYALTY 0,600 14,606

0,642 10,497 0,000

0,544 7,492 0,000

USABILITY ->

USE 0,207 2,663

0,461 5,728 0,000

-0,049 0,453 0,651

There are only three hypotheses validated in all three models according to data in Table 85:

1) Facilitating conditions impact positively the trust; even if the weights are significantly

different in Russian and French models in both cases the obtained results are strong enough

to validate the hypothesis H2.

2) Mobile application loyalty impacts positively on continued intention to use the mobile

application. The difference in weights is almost twice higher in France than in Russia, but

still all values are correct to validate the hypothesis H10 in all models.

3) Mobile application usability impacts great the mobile application loyalty in both models. In

both models the effect is strong. Hypothesis H9 is validated in all models.

4) Intention to use has greater impact on use of mobile application in French model than in

Russian one, and even if the relationships are weak in Russian model, we can validate this

hypothesis H7 for both models.

Hypotheses validated in France and rejected in Russia:

1) Price value has strong impact on intention to use the mobile in French model and absolutely

no effect in Russian one. The hypothesis H1 is validated in France and rejected in Russia.

2) Facilitating conditions has moderate impact on intention to use the mobile application in

French model and no impact in Russian model. The hypothesis H3 is validated in France

and rejected in Russia.

3) Mobile application usability has moderate impact on intention to use the mobile application

in French model and no impact in Russian model. The hypothesis H6 is validated in France

and rejected in Russia.

Hypotheses validated in Russia and rejected in France:

281

1) Trust impacts very strong intention to use the mobile application in Russian model, but has

no effect in French model. The hypothesis H4 is validated in Russia and rejected in France.

2) Mobile application usability has great impact on the use of the mobile application in Russian

model and no effect in French model. The hypothesis H8 is validated in Russia and rejected

in France.

Thus, we can conclude that seven hypotheses are validated in French model and five are validated

in Russian model, the hypothesis H7 is under discussion in Russian model (Table 85).

Table 85: Comparison of hypotheses validation

Hypotheses

Factors influencing intention to use general fr ru

H1 Price value increases intention to use the mobile application.

Users who believe to receive financial or other profit using the

mobile application will intent to use this mobile application.

O V R

H2 Facilitating conditions impact positively trust. The mobile

application users who believe that supporting organization stays

behind the mobile application develop trust quicker then who

don’t.

V V V

H3 Facilitating conditions impact positively the intention to use the

mobile application. The mobile application users who believe

that supporting organization stays behind the mobile application

will rather to use it than not.

V V R

H4 Trust impacts positively the behavioral intention to use the

mobile application. The mobile application users, who have

developed trust to mobile application’s provider, or technology,

intent to use mobile application more likely, than who have not.

V R V

H5 Indulgence as cultural factor impacts positively the intention to

use of the mobile application. The users from the cultures with

the high score in indulgence tend to use more often the mobile

application to book a table in the restaurant.

R R R

H6 Mobile application usability increases the behavioral intention to

use the mobile application. Users, who are satisfied with design

and information’s structure of the mobile application, intent to

use it.

V V R

Factors influencing the mobile application use

H7 Strong behavioral intention leads to the use of the mobile

application.

V V O

H8 Mobile application usability leads to the use of the mobile

application.

V R V

Outcomes factors

H9 Mobile application usability increases the mobile application

loyalty. User, who is satisfied with design and information’s

structure of the mobile application, has a deep commitment to

repatronize a mobile application.

V V V

H10 Mobile application loyalty moderated by frequency of visits

leads to continued intention to use the mobile application.

V V V

V-validated, R-rejected, O - not significant effect. Source: author of the thesis

282

III. Discussion of the results

The purpose of the research is to study the mobile application use in the restaurant industry and

compare this use in two countries France and Russia regarding the research problem: Does the

mobile application as technology impact on the development of the restaurant industry?

We analyzed the mobile application use from three theoretical points of view: theories of

technology’s use, relationship marketing and cultural dimensions. Accordingly, we selected

indulgence, price value, facilitating conditions, mobile application usability and trust as factors

influencing the intention to use the mobile applications; intention to use and mobile application

usability as factors influencing the use of mobile applications and mobile application loyalty and

continued intention to use as outcomes.

We evaluated the general model with collected data in France and in Russia, then we estimated

French model and Russian model separately and after that we compared the results.

III.1. General model

III.1.1. Factors influencing the intention to use the mobile application

Intention to use precedes the actual use of mobile application. Many factors can influence the user

before he/she starts to use the mobile application. We have chosen factors from three parts of

reviewed theories: technology’s factors (UTAUT2, Venkatesh et al., 2012, Mobile application

usability, Venkatesh and Hoehle, 2015); relationship marketing’s factors (the Commitment-Trust

theory, Morgan and Hunt, 1994), and cultural factors (cultural dimension, Hofstede, Minkov, 2010).

Technology’s factors

In the initial model of UTAUT2 there are seven constructs which predict the intention to use:

performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic

motivation, price value and habit (Venkatesh et al., 2012). Performance expectancy and effort

expectancy are related to professional use of technology only, therefore we excluded them out of

our analysis; social influence refers to the public use of technology (for example, users can be

influenced by their social groups in the choice of their smartphones, but much less influenced by the

choice of mobile applications they download, if this applications are for private use only, like

researched applications); hedonic motivation is limited, because of specific purpose to use the

mobile application (normally design could add pleasure into the use, and this aspect is expressed in

the construct of usability); habit is expressed in the construct of facilitating conditions, so is also

excluded out of the model. Finally, we decided to test two of the constructs for the use of mobile

application in the restaurant context: facilitating conditions and price value.

283

The researched mobile applications play the mediating role between final clients and restaurant

establishments, therefore the facilitating conditions is significant, so we put forward the hypothesis,

that belief in support and developed knowledge about the technology can enforce the intention of

users to use the mobile application.

Both mobile applications are free for downloading, we defined price value as possible benefits

suggested by the mobile applications. Thus, we assumed that belief of users in possible profit from

use of mobile application will enforce the intention to use the mobile application.

Our results showed that facilitating conditions has weak effect on the intention to use mobile

application, but it is increasing with moderation of frequency of visits. More often the users visit the

restaurants more often they use the mobile application as stronger they develop the knowledge

about the mobile application and its provider and the belief in the mobile application provider’s

capability to support them.

Price value has no significant impact on the intention to use the mobile application. This

relationship is increasing a little bit by moderation of place of use. Users are not sensitive to

promotions and bonuses during the choice of the restaurant, but they can check the propositions

before they enter the selected restaurant.

We added the mobile application usability as factor, which can impact on the intention to use the

mobile application. We assumed that users develop stronger intention to use the mobile application

if they are satisfied with its usability. Our results showed that actually mobile application usability

affects weak on the users’ intention to use the mobile application. The effect is weak but acceptable.

The mobile applications today have standardized choice of functions and design, on the stage of

decision about the use of one mobile application above another usability could have relevance in

two cases: first, if the mobile application had innovative functionality, users would pay attention on

it; and secondly, in opposite, if the mobile application had outdated usability not corresponding the

goal of the use, the users would reject it on the stage of the decision making.

The relationship between usability and intention to use mobile application deteriorates by the

moderation of frequency of visits. Users with high frequency of visits can be already familiar with

the application and do not pay attention on its usability.

Relationship marketing’s factors

The use of mobile application in the restaurant context replaces the traditional relationship between

restaurant establishment and client. So, the restaurant management can use the mobile application

as a marketing tool and develop new relationships as well as reinforce relationship with the loyal

clients. From that point of view two of constructs of RM create the basement for the successful

relationship – trust and commitment (Morgan and Hunt, 1994, Palmatier, 2008). Commitment

plays more significant role in the professional relationship, so our context commimant would be

284

important in the relationship between restaurant management and the mobile application’s

management, and less or not important for the users of the mobile application, so we decided to

choose trust as relationship marketing construct.

Trust expresses confidence in the relationship between partners, their reliability and integrity

(Morgan and Hunt, 1994, Gummesson, 2011, Vivek, Beatty, Morgan, 2012). In researched context

there are three objects towards which the user can develop trust: towards the mobile application as

technology, towards the mobile application’s provider as company and towards the restaurant

establishments listed in the mobile application. We tested the trust towards the mobile application

as technology and accordingly towards the provider; which provides that application. We assumed

that trust impacts on the intention to use the mobile application. After the test we verified this

hypothesis. If the user developed trust in technology and in the provider of this technology, he/she

intents to use it. Duration of use did not enforce that relationship as strong as we expected.

Additionally we connected facilitating conditions and trust, assumed that belief in the supporting

organization (provider of the mobile application) impacts positively on trust. Our results

demonstrated upper moderate effect of facilitating conditions on trust. Thus, knowledge about the

technology and belief in the supporting organization contribute to trust.

Cultural factors

Indulgence.

We assumed that indulgence impacts the intention to use the mobile application. French and

Russian users supposed to have different attitude towards the going out for dinner/lunch and in this

way their intention to use can be impacted by this cultural dimension.

On the stage of measurement model analysis the measurement items had low scores in reliability

and validity, so according to the recommendations of the authors (Chin, 1999, Chin, Peterson,

Brown, 2008, Hair et all, 2012) we had to delete this construct and not to take it into account. To

understand the situation we did calculations of the construct according to The Values Survey

Module manual (Hofstede, 2013). Obtained results demonstrated us extremely high scores

compared to national scores provided by Hofstede’s countries comparison tool (France – 94

compared to 48, Russia –84 compared to 20). In general in the dimension of indulgence both

samplings showed resemblance what we did not expected. One of the reasons might be in the

sampling characteristics, like income and social status expressed in professional occupations of the

responders. For comparative study the equivalence of the sampling is important (McDonald, 2000).

Initial condition for us was that all responders should be actual users of the researched mobile

applications. On the level of professional occupation Russian responders belong to upper-middle

class, what is according to income more a less could correspond to the French middle class

285

(depending on the branches), but the social status would be different. That erased the cultural

difference which was expressed in indulgence construct. Another reason can be in the measurement

items’ error (so the questions did not express fully the meaning of this construct in the restaurant

context).

As said before we did not find in the literature connection of indulgence and technology use, so we

adapted measurement items (questions) for our research, but our results showed that these items

cannot measure the indulgence in the correct way. Thus, we deleted the construct out of the model

on the phase of the evaluation of measurement model. The hypothesis was rejected.

III.1.2. Factors influencing the use of mobile application

We defined use behavior of UTAUT2 as use of mobile application. We described use of mobile

application with technology’s adoption and technology’s penetration. So the adoption characterizes

the use from the point of view of downloading of application, while penetration characterizes the

actual use. In other words the user can have on his/her smartphone the mobile application, but does

not use it because of any reason.

There are only two factors which we assumed to have an impact on the use of mobile application:

intention to use and mobile application usability.

The impact of intention to use on the use is confirmed by many researchers (Fishbein, Ajzen, 1975,

1980, Davis, 1986, Venkatesh, Davis, 2000, Venkatesh et al., 2003, 2012). However, we decided to

test it in the context of use of mobile application for particular purpose – searching for and booking

of the restaurants.

We assumed that intention to use will lead in the actual use of mobile application. Our results

validated completely the hypothesis, showing strong effect of intention to use on use of mobile

application. In addition, duration of use increased the relationship between intention to use and use.

Users who use long time the mobile application decide quicker to use it, so the transition between

intention to use and use is shorter. On the other hand, place of use had no effect on impact of

intention to use on actual use. The absence of the effects of place of use is to explain by the

questions, we included in the survey. We wanted to test, how the mobile application can motivate

the users to change their location. As we can conclude this impact is not confirmed. But still

according to our survey 46,3% of responders are using geolocation function of the mobile

application (see sampling characteristics section of this chapter).

Because we do research of mobile application use of two different mobile applications, we decided

to add mobile application usability as a factor influencing on the use of mobile application. Under

the mobile application usability we understand the extent to which a mobile application can be used

286

by specified (Russian and French) users to achieve specified goals (searching for restaurants and

booking tables) with effectiveness, efficiency, and satisfaction in a specified context of use

(restaurant context) (Venkatesh and Ramesh, 2006).

We assumed that mobile application usability impacts positively on the use of mobile application.

Our results confirmed that impact between them is moderate. However, frequency of visits

moderates this impact negatively. We can conclude that users who use mobile application often and

go out often do not pay attention on the mobile application usability.

III.1.3. Factors influencing outcomes

According to Venkatesh and Hohle (2015) mobile application usability can have direct effect on

mobile application loyalty and mobile application loyalty in its turn has impact on continued

intention to use. We added two question of relationship marketing (Sirdeshmukh, Singh and Sabol,

2002) to expand the construct of loyalty.

We assumed that satisfaction in mobile application usability increases mobile application loyalty.

Our results confirmed this hypothesis. The effect of usability on loyalty is very strong and

significant. Weak and moderate effects of mobile application usability on intention to use and use

are connected with expected usability, as we mentioned before, mobile applications are developed

by standards accepted for the type of the mobile application. But the strong impact of mobile

application usability on the mobile application loyalty shows the satisfaction of the users with the

mobile application. In other words, the users did not find “bugs” or mistakes in the usage, as well as

they did find all expected functions (like maps, pictures, online booking etc.), so they will prefer

this mobile application above another and develop loyalty.

As an outcome of mobile application loyalty the continued intention to use is developed when the

users have strong loyalty toward the mobile application and reuse it frequently. We assumed that

mobile application loyalty positively impacts on the continued intention to use.

The obtained results confirmed that hypothesis and showed significantly strong effect. Moreover

frequency of visits increased impact of loyalty on continued intention to use strongly. Users, who

often go out, reuse the mobile application also more often.

III.2. Comparison of French and Russian Models

Even if we deleted the cultural dimension out of our model we still can see cultural impact on the

mobile application use by the comparison of the research models in sub groups with SmartPLS

application.

287

III.2.1. Factors influencing the intention to use the mobile application

Technology’s factors

We have four hypotheses to evaluate technology’s factors influencing intention to use mobile

application: one of them is validated in both models, and three rejected in Russia and validated in

France. We would like to start with hypothesis valid in both models.

Even if we included the hypothesis of impact of facilitating conditions on trust in technology’s

factors, it concerns both: the technology by itself and the mobile application provider. Anyway,

there is no significant difference in results of two models: French users as well as Russian users

develop trust towards the mobile application if they have knowledge about the technology and

provider and if they have belief in support of mobile application provider.

Other technology’s factors have very weak or no impact in Russian model.

Price value has strong impact on intention to use the mobile application in French model and

absolutely no effect in Russian one. We can conclude that French users are more sensitive to

benefits provided by researched mobile application. But also we should admit that the history of

both mobile applications is deferent. So, French website and application are known firstly as

discount tool for restaurants, as the representative of Lafourchette mentioned, while Russian

website and application are known mostly as restaurant guide and only loyal clients could enjoy the

benefits in the past. Another explanation can be in sampling characteristic of Russian users,

between them 57% of responders noticed their occupation as manager/entrepreneur, the social

group which is less sensitive to benefits in Russia.

Even if impact of facilitating conditions on trust was validated in Russian model, facilitating

conditions has no impact on intention to use in this model compared to moderate effect in French

model. So, Russian users can develop trust towards the mobile application provider, but facilitating

conditions does not impact their intention to use mobile application.

Mobile application usability has moderate impact on intention to use the mobile application in

French model and no impact in Russian model. Russian users do not pay attention on the usability

of the mobile application at least on the stage of the decision about the use of mobile application.

Relationship marketing’s factors

Second hypothesis concerning trust found the validation in Russian model, trust impacts very strong

intention to use the mobile application in Russian model, in turn it has no effect in French model.

French users are not sensitive to relationship marketing’s factor and more sensitive to technological

functionality. In opposite, Russian users make the decision about use of the mobile application if

they trust technology and provider.

288

III.2.2. Factors influencing the use of mobile application

In both models intention to use leads to the actual use of the mobile application, the effect is

stronger in French model, but also valid in Russian model.

Mobile application usability has great impact on the use of the mobile application in Russian model

and no effect in French model. It is very interesting finding because in opposite usability has impact

on intention to use in France and no effect in Russia. Thus, Russian users are not influenced by

usability when they take decision about use of mobile application and strongly influenced during

the actual use. French users are strongly influenced by usability when they take the decision to use

but they stop to pay attention on it during the actual use.

III.2.3. Factors influencing outcomes

Both outcomes were validated in French and in Russian models.

Mobile application usability impacts great on the mobile application loyalty in both models. And

even if the impact of mobile application loyalty on continued intention to use is much stronger in

French model, the effect was also validated for Russian model.

IV. Conclusion

This chapter presented the results of our research. Firstly, we provided descriptive analysis of the

sampling characteristics according to professional occupation, age, FRM segmentation and situation

of use. The study involved 244 users from two countries: France and Russia.

Secondly, we evaluated general model in two phases: on the first phase the assessment of

measurement model was provided of measurement items in validity and reliability; on the second

phase we estimated structural model and obtained the results of correlation between latent variables.

In the end of this phase we confirmed or rejected hypotheses of our research. Out of fourteen

hypotheses, nine are validated, three are under the discussion and two are rejected.

Thirdly, we compared sub groups of France and Russia between each other and with general model.

We founded similarities and differences in the use of mobile application and evaluated hypotheses

for both models. Seven hypotheses are validated in French model and five are validated in Russian

model, one hypothesis is under the discussion in Russian model.

In the modern society mobile application as technology is strong marketing tool for business. The

comparison of two countries allowed us to resume findings and to clarify the research problem:

does the mobile application as technology impact on the development of restaurant industry:

✓ Mobile application should provide clear, easy and profitable benefits program to enforce the

mobile application use and motivate users to go out more often.

289

✓ Technical support is less important than personalization of the relationships. Today users

have developed knowledge about technology; use of mobile applications became a habit, but

personal attitude towards the clients and personal support increase trust towards the mobile

application.

✓ Users expect standardized design of mobile application. Innovative design can increase the

use of mobile applications, as well as errors can decrease it.

✓ Mobile application loyalty consists of two aspects: loyalty towards the mobile application

and towards the provider of this mobile application. In the mobile application use loyalty is

strongly connected with the technical characteristics of application.

✓ Continued intention to use the same mobile application refers to satisfaction in technical

characteristics of application and is direct outcome of mobile application loyalty.

Regarded listed findings we can answer the first research question: How the mobile application’

use can standardize the consumer’s behavior by choice of restaurants?

Technology deals always with standards, according to the type of mobile application there are also

technical standards expected by the users. In our findings we can see that mobile application loyalty

and continued intention to use are strongly impacted by technical characteristics of the mobile

application. Nevertheless, personalization is relevant in the relationships when we speak about

individuals-to-firm relationship (Palmatier, 2008). The use of mobile application can standardize

the consumer’s behavior in two ways: 1) with technical tools (all users use maps, online booking

etc.), 2) with profitable benefit program (information about actual promotions, or about availability

of the free tables etc.). In these two cases the behavior of users can be expected, or standard. The

personalization of the relationship is well developed in Russian case, what we could see in the

results concerning trust. But as we discussed with representative of French company, it is also the

primary goal today in French case. To conclude, the standardization is possible with technical tools

of mobile application, but is not relevant for development of business today.

Concerning the second question of our research: How the mobile application as technology

changes the relationship between restaurant and client?, the obtained results and findings did

not show us direct answers. Most of the restaurants do not use only one mobile application as

marketing tool: some have their own mobile applications, some use advertizing on searching

engines and presented on different restaurants guides. Anyway, our findings demonstrate the

relevance of the technical characteristics expressed in mobile application usability, loyalty and

continued intention to use. In this way the mobile application use can result in the more convenient

choices for the users, so the restaurant can get new client thanks to it, or loose clients because of it.

290

But the development of the relationships with clients who in fact visited the particular restaurant is

still the task of the restaurant management, and mobile application has relatively weak impact on it.

Comparison of two models in sub groups gives us the possibility to answer third question of the

research: How cultural differences influence on the use of mobile application in restaurant

industry? Can the mobile application reduce or erase this difference?

Cross-national comparison involves the primer analysis of the countries’ context. On this stage we

already found out that purchasing power is the most important aspect for the restaurant industry.

Recent economical crisis in Russia decreased personal income of population and as result people go

out less. It resulted in the sampling characteristics of the Russian users, who answered our survey;

most of them are representatives of upper-middle class that influenced probably the results of

Russian model.

We compared two different mobile applications with the same type of structure and with more or

less same functionality. The companies, providers of mobile applications, have different history of

business, developed with respect of local users’ demand, but these differences could also impact on

the obtained results.

Nevertheless, we can conclude, that use of mobile application in restaurant industry is definitely not

the same in France and Russia. The biggest distinction we noticed concerns the stage of decision

making (intention to use). On this stage technical side is important in France and not important in

Russia, and in opposite relationship marketing is important in Russia and not important in France.

As a result we can explain, why the Russian company prefers to build the relationship with users on

the personal level, and French company does not. But the use of mobile application can reduce

cultural differences on the later stage, as we could see the mobile application usability impacts on

mobile application loyalty which leads to reuse of mobile application (continued intention to use) in

both countries.

To resume, mobile application can impact on the development of the restaurant industry as a

marketing tool. It does not change in general the relationships between restaurants and clients, but it

can strengthen or decrease the restaurants’ business performance due to technical tools, which

economize users’ time, spending, efforts. The most significant cultural difference is on the decision

making stage, but with help of mobile application usability the mobile application can reduce this

difference on the actual use stage.

Next section will present general conclusion.

291

292

General conclusion

293

In this research we analyzed the use of mobile application in tourism and restaurant industries and

did cross-cultural comparison between Russia and France. This research has multidisciplinary

character and involves addresses the issues of marketing (relationship marketing), information

systems and culture.

To clarify the research problem we firstly presented research context, in which we regarded tourism

and restaurant industries, mobile technologies and countries’ characteristics.

Secondly, we reviewed theoretical aspects (technology’s use, relationship marketing and cultural

theories), what allowed us to develop conceptual research model and to include variables from three

parts of theory, and to form questionnaire. In the part of technology’s use theory we studied:

diffusion of innovation, theory of reasoned action (TRA), technology acceptance model (TAM and

TAM2), unified theory of acceptance and use of technology (UTAUT), and its extension

(UTAUT2), mobile application usability. In the relationship marketing we analyzed: the

commitment-trust theory, interfirm RM, interpersonal RM, multi-level exchange relationships. In

the part of cultural theory we examined: the concept of the modern Russian values, the concept of

the modern French values, and cultural dimensions. These theories were completed by the

interdisciplinary researches involving the adaption of cultural dimensions and relationship

marketing, cultural dimensions and technology.

On the next stage we accomplished two interviews with representatives of the two mobile

application providers, one in Paris and one in Moscow. After that we validated our model and

questionnaire with help of experts and effectuated the quantitative pretest of measurement model to

verify measurement items.

On the last stage we realized quantitative analysis with SmartPLS application, we completed

analysis of general model and two models of the researched countries and we compared the

obtained results. In the end we discussed our findings and answered on the research questions.

This section aims to give general conclusion of the research, where we present theoretical and

managerial contributions, limitations of the research and perspectives for future research in the field

of mobile application use in tourism and restaurant industries.

I. Contributions

This research is useful for both theory and practice. Theoretical contributions concern the

information systems, relationship marketing and cultural theory. Managerial contributions bring

practical knowledge for the companies, which provide the mobile applications in tourism and

restaurant industries.

294

I.1.Theoretical contributions

Our theoretical contributions address the use of mobile application in the tourism and restaurant

industry and cross-cultural comparison. We enlist support by the important researches done on the

field of technology’s use, relationship marketing and cultural theory. We united these three aspects

in one conceptual model and tested it.

Central object of our research is technology, particularly mobile application. The context of the

mobile application use is connected with services (restaurant), and involves the relationship

marketing to personalize the individual-to-technology relationships.

We modified the UTAUT2 model, excluded constructs performance expectancy, effort expectancy,

social influence, hedonic motivation, and habit as not relevant for our use context and added trust

from relationship marketing and mobile application usability. As outcomes we regarded mobile

application loyalty and continued intention to use.

According to our findings the contributions affect: 1) intention to use, 2) outcomes, 3) moderation

effect.

Intention to use

Between price value, facilitating conditions, trust and mobile application usability the strongest

effect on the intention to use has trust. Trust is the construct of relationship marketing. Thus, in the

context of mobile application use when the final service relates to individual-to-firm relationship

the priority is still in the relationship’s construct and not technology’s one. In the restaurant industry

where the competitiveness is high, personal relationship to the client is still high developed.

Another new construct we added as predictor of the intention to use is mobile application usability.

We validated the hypothesis, that usability impacts positively the intention to use, but the results

were lower than we expected. As conclusion we can resume that today the users have developed

good knowledge about mobile technology use and on the stage of decision making technical

characteristics play less important role than before, especially in the private use. That concerns the

use of already know mobile applications and typical ones. In case of innovative usability or new

mobile application, technical constructs should be tested first.

Outcomes

In opposite of intention to use as first stage of mobile application use, the outcomes are directly

connected with technical characteristics, what we confirmed with high results of the impact of

mobile application usability on mobile application loyalty and continued intention to use. Mobile

application market is highly competitive and usability can be very important for mobile application.

295

Usually it is free to download new application, then test it and it is easy to remove the application

from smartphone.

Moderating effects

We use two moderator of FRM segmentation, what is the part of the service marketing, and one

moderator related to the technology. Even if we collected information about age and occupation we

used data to characterize the samplings and not used them as moderator.

Duration of use expresses the recency in our model. Our strongest finding is in the relationship

between intention to use and use of mobile application, which are positively influenced by duration

of use.

Moderation effect of frequency of visits has great impact on the relationship between mobile

application loyalty and continued intention to use. And unexpected finding is the moderation effect

of frequency of visits on the relationship between facilitating conditions and intention to use,

because we could assume that as more often users go out as less support they need, but actually, as

more often they go out, as more often they use mobile application and in this way they develop

better knowledge about the technology.

Place of use presents Gis-technology and is a part of functionality of the mobile application. Place

of use has small negative effect on the relationship between intention to use and use of mobile

application, what is opposite of what we expected.

To resume, we suggest that moderation constructs which are important for use in the particular

industry are preferable than standard ones, in case if the sampling is relatively small and has small

variety, like in our case.

Culture

We did not mentioned before construct of cultural dimension, because we deleted it out of model.

Nevertheless our negative findings can be useful for further researcher.

As construct of cultural dimensions we have chosen indulgence, because this construct express the

attitude towards the leisure time and concept of self gratification, what we though can be important

for restaurant context. On the national level according to the national comparison tool of Hofstede,

the cultural difference between France and Russia is pretty significant (48 for France and 20 for

Russia). But in fact, the sampling characteristic showed us that users who participated in the survey

scored on the same level in this dimension, so this dimension has no relevant impact on the model.

Moreover we could not confirm reliability and validity of the measurement items, which probably

did not correspond to the model.

296

Cultural differences we found when we compared two models as sub groups. And our finding

concerns mostly the stage of the intention to use: Russian users are more sensitive to relationship

marketing and French users are more sensitive to technical constructs. That finding helped us to

contribute to the theory, that good balance between technical constructs and relationship marketing

on the stage of decision making (intention to use) can lead to the actual use of the mobile

application, particularly in the restaurant industry.

I.2.Managerial contributions

The use of mobile application was not so much researched in the context of restaurant industry;

normally it is regarded only from technical side and not from the side of relationship marketing. In

this way cultural differences are not always respected and standardized approach is preferred.

For companies, who is working on the international level, it is important to change point of view on

this subject and pay more attention on relationship marketing traditions for successful

performances. There are not so many researches with comparison of mobile application use

between Russia and France. Analysis of two countries allowed us to see in more details where the

management of companies in both cases could improve their marketing strategy.

In our research we have chosen factors of technology, relationship marketing and culture.

Following findings can be useful for the management:

• On the stage of decision making (intention to use) technical characteristics of the mobile

application loose their importance. It can be still important for the new clients, who just are

choosing the appropriate mobile application. Because we analyzed sampling of actual users,

who has already experienced the use. In the process of decision making which tools to use,

the technical support and usability are less relevant than usually is expected. Price value

which could be the decisive factor for intention had no importance. Trust on the other hand

demonstrated strong effect in intention to use. For the marketing strategy it could useful to

develop relationship marketing tools which are not connected directly with technical issues.

In fact technology by itself gives wide possibilities to involve users in process of use.

• In the stage of actual use mobile application usability in opposite becomes more important

for users and ends up in mobile application loyalty and continued intention to use. The

provider should respect this stage and renew information, design and functionally according

to the expectation of the users and branch’s needs.

• On the international level management of the companies should respect the cultural

traditions in the individual-to-firm relationships. In our comparison French users answered

well the benefits program and technical characteristics, while Russian users preferred

297

developed personal relationship and trust. We compared companies which are not working

in the compared countries, but the similar issues they can face in the regions where they are

presented as a brand.

• For French company we would recommend to pay more attention on the stage of intention

to use and enforce relationship marketing strategy. That can help to compete with other

similar tools for choice and booking of restaurants also on the international level.

• For Russian company we would recommend to develop stronger the mobile application

usability, not to loose clients on the stage of actual use and be able to compete with similar

mobile applications.

I.3. Professional contributions

On the level of professional use of our findings we address them to following deparments:

CRM department: the main task of the CRM is to develop tools contributing to personalization

and relationship marketing on the stage of decision making to use the mobile application.

Development department: this department should update mobile applications usability according

to the branch’s needs and users expectations.

Technical support: with development of the mobile application technology this department is less

and less important and deals only with possible “bugs”.

Restaurant management:

Indirectly our research involves restaurants as provider of actual service. For the restaurants the

researched mobile applications can be a marketing tool and attracts additional clients, but also can

be used as CRM instrument to strengthen the relationships with loyal clients. In the competitive

market such mobile applications can be also the reason to loose the loyal clients. It is important for

the restaurant management to be able to maintain the relationship with customers in the modern

world, where the mobile applications became the essential part of the people’s lives.

II.Limitations

As all researches our thesis faced the limitations. The limitations are related to the context of the

research, conceptual model, sampling, questionnaire, and software application.

II.1. Limitations related to the context of the research

There is one contextual limitation in our research. We have chosen as a field of the investigation

restaurant industry, even if we presented in details the tourism industry. By the choice of restaurant

industry we faced the limit that there are no one and the same mobile application equal developed in

298

both countries in Russia and France. Because of that we decided to compare two different mobile

applications, and added mobile application usability as factor which can reduce this limit.

II.2. Limitations related to the conceptual model

Our conceptual model was developed based on the multidisciplinary literature review including

technology’s use, relationship marketing and cultural dimensions.

Regarding the problem of the research we excluded several constructs which are usually presented

in the researches about technology’s use: performance expectancy, effort expectancy, social

influence, hedonic motivation, and habit and replaced the usual moderators with moderator related

to our subject. We also could not use in full all six cultural dimensions of Hofstede, because limits

in time. Ideal would be to analyze sampling groups with all dimensions using the VSM (Values

Survey Module) and then to add most relevant cultural dimensions into the model.

II.3. Limitations related to the sampling

Sampling size was limited by the Russian part. Dispite the fact that contacted people are using the

mobile application; it was difficult to obtain the responces and to collect data.

Russian users have no habit to participate in the scientific surveys and they demonstrated negative

attitude to the number of question (too much), in the future it’s better to organize such survey not

online, but with help of other methods, like “face to face”.

Characteristic of the sampling is related to the restaurant context; people who often go out and

therefore use more the mobile application are representatives on middle and upper-middle classes in

both countries that we could see by the age and professional occupation. For our research it was not

important to involve population of different social types, because we understand the specificity of

the usage context.

II.4. Limitations related to the questionnaire

The limitation related to our questionnaire concerns the length. Because of that we reduced in the

beginning the number of questions, what lead to the problem with measurement items for our

measurement model: we have three constructs with only two measurement items.

Another limitation of the questionnaire was the fact, that we analized two difirent applications; it

was also difficult to adapt questions in the way they would fit in both cases, and still be similar.

Some of the results are to explain because of this difference in mobile applications.

299

II.5. Limitations related to the software application

To test the model we used the SmartPLS 3 Professional. This software application is more and more

used in the researches and allows doing analysis with small sampling size and limited measurement

items. Also it is possible to examine sub groups, what was important for our comparison.

III. Perspectives

Our research opens the perspectives for future researches of the mobile application use in tourism

and restaurant industries in several aspects: contextual, theoretic, and methodological. It can be

extended to other geographical zones and involve comparison of many countries.

- Contextual aspect.

Mobile application use is well developed in the tourism, especially for booking tickets and hotels. It

would be interesting to test our research model in different components of tourism industry, only

for restaurant one, and compare the results between each others, what has more impact on the use

for example in the hotel industry compared to restaurant industry etc. The findings could be useful

for management in the tourism oriented companies. On the other hand we think it would be possible

to continue research in the restaurant industry and find for the same comparison between Russia and

France one mobile application, which is aimed to be presented in both countries.

- Theoretical aspect.

The longtime research could permit us to do analysis of the mobile application users of particular

mobile application in cultural dimensions using full Value Survey Model. That would permit to

include in the model cultural dimensions and return to our first conceptual model before we deleted

the dimension of indulgence.

We also would like to test other constructs of relationship marketing in their impact on the intention

to use mobile applications and investigate relationship marketing outcomes for final companies; in

our research it would restaurants.

We think that we can find better moderators according to the context of use. Many factors can

moderate the relationships inside of our model. Like for example for restaurant context it could be

time of use, because it would depend on time of the day, the day of the week, or even season etc.

-Methodological aspect.

Our model can be tested with another algorithm like LISREL for example or SPSS to complete the

research and verify its stability. To realize this we need to recheck our measurement model and

collect much more data.

- Geographical extension.

300

Our research interest stays by investigation of mobile application use in Russia and comparison of

this country with other countries. But also we are interested to accomplish an analysis of several

countries where one mobile application is presented, like Lafourchette in Europe. These results

would be interesting to compare with results obtained in France and Russia. In the future we could

extend our research to the Asian region, where the mobile application users are most active. We are

interested particularly in China, Japan and South Korea.

301

References

302

Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of

information technology: a replication. MIS quarterly, 227-247.

Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and

beliefs about information technology usage. MIS quarterly, 665-694.

Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness

in the acceptance of information technologies. Decision sciences, 28(3), 557-582.

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision

processes, 50(2), 179-211.

Ajzen, I. (2015). The theory of planned behaviour is alive and well, and not ready to retire: a

commentary on Sniehotta, Presseau, and Araújo-Soares. Health Psychology Review, 9(2), 131-137.

Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of

empirical research. Psychological bulletin, 84(5), 888.

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social

behavior. Englewood Cliffs, NJ: Prentice-Hall.

Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. The handbook of

attitudes, 173, 221.

Akter, M. S., Upal, M., & Hani, U. (2008). Service quality perception and satisfaction: a study over

sub-urban public hospitals in Bangladesh. Journal of Services Research, 125.

Albarracin, D., Johnson, B. T., Fishbein, M., & Muellerleile, P. A. (2001). Theories of reasoned

action and planned behavior as models of condom use: a meta-analysis. Psychological bulletin,

127(1), 142.

Alekseeva D. A. (2016). Condition and tendencies of public catering development in Russia //

Scientific and methodical electronic magazine "Concept" 2016. – Т. 6. – С. 151–155. – URL:

http://e-koncept.ru/2016/56066.htm

Allen, Gary J., Albala, Ken (2007). The Business of Food: Encyclopedia of the Food and Drink

Industries. Greenwood.

Alsnih, R. (2006). Characteristics of web based surveys and applications in travel research. In

Travel survey methods: Quality and future directions (pp. 569-592). Emerald Group Publishing

Limited.

Ammi, C. (Ed.). (2009). Innovative Technology and Globalization. Cambridge Scholars

Publishing.

Ammi, C. (Ed.). (2013). Global consumer behavior. John Wiley & Sons.

Ammi, C. Culture and Diversity. Global Consumer Behavior, 53-66.

Ampt, E. S. (2003). Respondent burden. In Transport survey quality and innovation (pp. 507-521).

Emerald group publishing limited.

303

Anandarajan, M., Igbaria, M., & Anakwe, U. P. (2002). IT acceptance in a less-developed country:

a motivational factor perspective. International Journal of Information Management, 22(1), 47-65.

Anderson, J. C., & Gerbing, D. W. (1982). Some methods for respecifying measurement models to

obtain unidimensional construct measurement. Journal of marketing research, 453-460.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and

recommended two-step approach. Psychological bulletin, 103(3), 411.

Anderson, J. C., & Narus, J. A. (1990). A model of distributor firm and manufacturer firm working

partnerships. the Journal of Marketing, 42-58.

Andrews, D., Nonnecke, B., & Preece, J. (2003). Electronic survey methodology: A case study in

reaching hard-to-involve Internet users. International journal of human-computer interaction,

16(2), 185-210.

Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta analytic

review. British journal of social psychology, 40(4), 471-499.

Aurier, P., & N’Goala, G. (2010). The differing and mediating roles of trust and relationship

commitment in service relationship maintenance and development. Journal of the Academy of

Marketing Science, 38(3), 303-325.

Avdimiotis, S., & Christou, E. (2004). GIS Applications In Tourism Planning “A Tool For

Sustainable Development Involving Local Communities”. Journal of Environmental Protection &

Ecology, 5(2), 457-468.

Baabdullah, A. M., & Williams, Y. K. D. M. D. (2013). Evaluating the unified theory of acceptance

and use of technology (UTAUT2) in the Saudi Arabian context. NASCENT CONNECTIONS 2013,

8.

Bachmann, D. P., Elfrink, J., & Vazzana, G. (1999). E-mail and snail mail face off in rematch.

Marketing Research, 11(4), 10.

Bagozzi, R. P. (1980). Causal models in marketing. New York: Wiley.

Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a

paradigm shift. Journal of the association for information systems, 8(4), 3.

Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation

models. Journal of the academy of marketing science, 40(1), 8-34.

Bande, B., Fernández-Ferrín, P., Varela, J. A., & Jaramillo, F. (2015). Emotions and salesperson

propensity to leave: The effects of emotional intelligence and resilience. Industrial Marketing

Management, 44, 142-153.

Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of

acceptance and use of technology combined with cultural moderators. Computers in Human

Behavior, 50, 418-430.

Bardini, T. (1996). Changement et réseaux socio-techniques: de l'inscription à l'affordance.

Réseaux, 14(76), 125-155.

304

Barki, H., & Hartwick, J. (1994). Measuring user participation, user involvement, and user attitude.

MIS quarterly, 59-82.

Barki, H., Titah, R., & Boffo, C. (2007). Information system use–related activity: an expanded

behavioral conceptualization of individual-level information system use. Information Systems

Research, 18(2), 173-192.

Bartlett, M. Y., & DeSteno, D. (2006). Gratitude and prosocial behavior: Helping when it costs

you. Psychological science, 17(4), 319-325.

Barua, P. (2013). The moderating role of perceived behavioral control: The literature criticism and

methodological considerations. International Journal of Business and Social Science, 4(10).

Baskerville, R. F. (2003). Hofstede never studied culture. Accounting, organizations and society,

28(1), 1-14.

Bazeley, P. (2008). Mixed methods in management research. Dictionary of qualitative management

research, 133-136.

Beaver, A. (2005). A dictionary of travel and tourism terminology. CABI.

Beck, J. T., & Palmatier, R. W. (2012). 16 Relationship marketing. Handbook on Business to

Business Marketing, 293.

Beck, J. T., & Palmatier, R. W. (2012). 16 Relationship marketing. Handbook on Business to

Business Marketing, 293.

Beck, J. T., Chapman, K., & Palmatier, R. W. (2015). Understanding relationship marketing and

loyalty program effectiveness in global markets. Journal of International Marketing, 23(3), 1-21.

Becker, J. M., Klein, K., & Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM:

guidelines for using reflective-formative type models. Long Range Planning, 45(5), 359-394.

Belyaev, A. (2012). The problem of Russia's “special way”. Gramota.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological bulletin, 107(2),

238.

Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of

covariance structures. Psychological bulletin, 88(3), 588.

Bentler, P. M., & Chou, C. P. (1987). Practical issues in structural modeling. Sociological methods

& research, 16(1), 78-117.

Berdyaev, N., & Bamford, C. (1992). The Russian Idea. SteinerBooks.

Berry, L. L. (1983). Relationship marketing. American Marketing Association.

Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic

brokerages. IEEE Transactions on systems, man, and cybernetics-Part A: Systems and

humans, 30(4), 411-420.

305

Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward

information technology usage: A theoretical model and longitudinal test. MIS quarterly, 229-254.

Bidan, M., & Trinquecoste, J. F. (2010). Gouvernance et innovation à l'épreuve des technologies de

l'information. Management & Avenir, (4), 125-127.

Bidan, M., Rowe, F., & Truex, D. (2012). An empirical study of IS architectures in French SMEs:

integration approaches. European journal of information systems, 21(3), 287-302.

Birant, D. (2011). Data mining using rfm analysis. INTECH Open access publisher.

Bitektine, A. (2011). Toward a theory of social judgments of organizations: The case of legitimacy,

reputation, and status. Academy of management review, 36(1), 151-179.

Bitner, M. J. (1995). Building service relationships: it's all about promises. Journal of the Academy

of marketing science, 23(4), 246-251.

Bitner, M. J., Ostrom, A. L., & Meuter, M. L. (2002). Implementing successful self-service

technologies. The academy of management executive, 16(4), 96-108.

Blattberg, R. C., Kim, B. D., Neslin, S. A., Blattberg, R. C., Kim, B. D., & Neslin, S. A. (2008).

Multiple Campaign Management. Database marketing: analyzing and managing customers, 743-

780.

Bokhari, R. H. (2005). The relationship between system usage and user satisfaction: a meta-

analysis. Journal of enterprise information management, 18(2), 211-234.

Boomsma, A. (2000). Reporting analyses of covariance structures. Structural equation

modeling, 7(3), 461-483.

Boora, K. K., & Singh, H. (2011). Customer loyalty and its antecedents: A conceptual framework

understanding e-marketing–optimization of resources. Asia Pacific Journal of Research in Business

Management, 2(1), 151-164.

Bosnjak, M., Tuten, T. L., & Wittmann, W. W. (2005). Unit (non) response in web‐based access

panel surveys: An extended planned‐behavior approach. Psychology & Marketing, 22(6), 489-505.

Boullier, D. (1989). Du bon usage d'une critique du modèle diffusionniste: discussion-prétexte des

concepts de Everett M. Rogers. Réseaux, 7(36), 31-51.

Brewer, P., & Venaik, S. (2011). Individualism–collectivism in Hofstede and GLOBE. Journal of

International Business Studies, 42(3), 436-445.

Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline

model test and extension incorporating household life cycle. MIS quarterly, 399-426.

Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010). Predicting collaboration technology use:

Integrating technology adoption and collaboration research. Journal of Management Information

Systems, 27(2), 9-54.

306

Brown, S. A., Venkatesh, V., & Goyal, S. (2007). Expectation confirmation in technology adoption:

An examination of six competing theoretical models. In Under Review, available from: http://misrc.

umn. edu/workshops/2007/spring/Susan. pdf.

Brown, S. A., Venkatesh, V., & Goyal, S. (2012). Expectation confirmation in technology

use. Information Systems Research, 23(2), 474-487.

Brown, S. K. (2006). Structural assimilation revisited: Mexican-origin nativity and cross-ethnic

primary ties. Soc. F., 85, 75.

Bryman, A., & Bell, E. (2015). Business research methods. Oxford University Press, USA.

Bulmer, M. (1984). Facts, concepts, theories and problems. In Sociological Research Methods (pp.

37-50). Macmillan Education UK.

Bult, J. R., & Wansbeek, T. (1995). Optimal selection for direct mail. Marketing Science, 14(4),

378-394.

Callegaro, M., Manfreda, K. L., & Vehovar, V. (2015). Web survey methodology. Sage.

Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-

multimethod matrix. Psychological bulletin, 56(2), 81.

Candela, G., & Figini, P. (2012). The economics of tourism destinations. In The Economics of

Tourism Destinations (pp. 73-130). Springer Berlin Heidelberg.

Cannon, J. P., Doney, P. M., Mullen, M. R., & Petersen, K. J. (2010). Building long-term

orientation in buyer–supplier relationships: The moderating role of culture. Journal of Operations

Management, 28(6), 506-521.

Cardon, P. W., & Marshall, B. A. (2008). National culture and technology acceptance: The impact

of uncertainty avoidance. Issues in Information Systems, 9(2), 103-110.

Carlsson, C., Carlsson, J., Hyvonen, K., Puhakainen, J., & Walden, P. (2006, January). Adoption of

mobile devices/services—searching for answers with the UTAUT. In System Sciences, 2006.

HICSS'06. Proceedings of the 39th Annual Hawaii International Conference on (Vol. 6, pp. 132a-

132a). IEEE.

Castells, M. (2011). The rise of the network society: The information age: Economy, society, and

culture (Vol. 1). John Wiley & Sons.

Celhay, F., & Trinquecoste, J. F. (2015). Package graphic design: investigating the variables that

moderate consumer response to atypical designs. Journal of Product Innovation Management,

32(6), 1014-1032.

Chang, M. K. (1998). Predicting unethical behavior: a comparison of the theory of reasoned action

and the theory of planned behavior. Journal of business ethics, 17(16), 1825-1834.

Chang, M. K. (1998). Predicting unethical behavior: A comparison of the theory of reasoned action

and the theory of planned behavior. Journal of business ethics, 17(16), 1825-1834.

Chau, P. Y. (1996). An empirical assessment of a modified technology acceptance model. Journal

of management information systems, 13(2), 185-204.

307

Chen, S. C., Shing-Han, L., & Chien-Yi, L. (2011). Recent related research in technology

acceptance model: A literature review. Australian Journal of Business and Management Research,

1(9), 124.

Chenet, P., Dagger, T. S., & O'Sullivan, D. (2010). Service quality, trust, commitment and service

differentiation in business relationships. Journal of Services Marketing, 24(5), 336-346.

Cherryholmes, C. H. (1992). Notes on pragmatism and scientific realism. Educational

researcher, 21(6), 13-17.

Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2002). Hedonic and utilitarian motivations for

online retail shopping behavior. Journal of retailing, 77(4), 511-535.

Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2002). Hedonic and utilitarian motivations for

online retail shopping behavior. Journal of retailing, 77(4), 511-535.

Chin, W. (2000, December). Partial least squares for IS researchers: an overview and presentation

of recent advances using the PLS approach. In ICIS (Vol. 2000, pp. 741-742).

Chin, W. W. (1995). Partial least squares is to LISREL as principal components analysis is to

common factor analysis. Technology Studies, 2(2), 315-319.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern

methods for business research, 295(2), 295-336.

Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples

using partial least squares. Statistical strategies for small sample research, 1(1), 307-341.

Chin, W. W., Peterson, R. A., & Brown, S. P. (2008). Structural equation modeling in marketing:

Some practical reminders. Journal of marketing theory and practice, 16(4), 287-298.

Chismar, W. G., & Wiley-Patton, S. (2003, January). Does the extended technology acceptance

model apply to physicians. In System Sciences, 2003. Proceedings of the 36th Annual Hawaii

International Conference on (pp. 8-pp). IEEE.

Christopher, M., Payne, A., & Ballantyne, D. (1991). Relationship marketing: bringing quality

customer service and marketing together. Heinemann, London

Churchill Jr, G. A. (1979). A paradigm for developing better measures of marketing

constructs. Journal of marketing research, 64-73.

Churchill Jr, G. A., & Peter, J. P. (1984). Research design effects on the reliability of rating scales:

A meta-analysis. Journal of marketing research, 360-375.

Churchill, G. A., & Iacobucci, D. (2006). Marketing research: methodological foundations. New

York: Dryden Press.

Chuttur, M. Y. (2009). Overview of the technology acceptance model: Origins, developments and

future directions. Working Papers on Information Systems, 9(37), 9-37.

308

Clergeau, C., & Violier, P. (2012). Le concept de cluster est-il soluble dans le tourisme?. Téoros.

Revue de recherche en tourisme, 31(31-2), 60-71.

Cobanoglu, C., Warde, B., & Moreo, P. J. (2001). A comparison of mail, fax and web-based survey

methods. International journal of market research, 43(4), 441.

Cohen, J. (1977). Statistical power analysis for the behavioural sciences (Rev. ed.). New York:

Academic.Consumer Research, 15, 325-343.

Cooper, D. R., & Schindler, P. S. (2006). Marketing research (p. 261). New York: McGraw-

Hill/Irwin.

Cooper, R. B., & Zmud, R. W. (1990). Information technology implementation research: a

technological diffusion approach. Management science, 36(2), 123-139.

Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications.

Journal of applied psychology, 78(1), 98.

Couper, M. P. (2000). Review: Web surveys: A review of issues and approaches. The Public

Opinion Quarterly, 64(4), 464-494.

Couper, M. P., Traugott, M. W., & Lamias, M. J. (2001). Web survey design and administration.

Public opinion quarterly, 65(2), 230-253.

Coursaris, C., & Kim, D. (2006). A qualitative review of empirical mobile usability studies. AMCIS

2006 Proceedings, 352.

Creswell, J. W. (2009). Editorial: Mapping the field of mixed methods research. Journal of mixed

methods research, volume 3, number 2, 95-108

Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches.

Sage publications.

Creswell, J. W., & Zhang, W. (2009). The application of mixed methods designs to trauma

research. Journal of traumatic stress, 22(6), 612-621.

Criteo, Rapport d’activité sur le commerce mobile. 1er semestre 2016 France

Criteo, Travel Flash Report. October 2016 edition.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3),

297-334.

Cronbach, L. J. (1984). A research worker's treasure chest. Multivariate behavioral research, 19(2-

3), 223-240.

Cronbach, L. J., & Thorndike, R. L. (1971). Educational measurement. Test validation, 443-507.

Cronin, J. J., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value, and

customer satisfaction on consumer behavioral intentions in service environments. Journal of

retailing, 76(2), 193-218.

309

Crosby, L. A., Evans, K. R., & Cowles, D. (1990). Relationship quality in services selling: an

interpersonal influence perspective. The Journal of marketing, 68-81.

Crotty, M. (1998). The foundations of social research: Meaning and perspective in the research

process. Sage.

Crump, S. A., Hamilton, D. L., Sherman, S. J., Lickel, B., & Thakkar, V. (2010). Group entitativity

and similarity: Their differing patterns in perceptions of groups. European Journal of Social

Psychology, 40(7), 1212-1230.

Cui, W. (2007). Comparison of methods of collecting data for research: Conventional methods and

electronic methods. Decision Sciences Institute, Southwest Region.

D. Raggio, R., M. Walz, A., Bose Godbole, M., & Anne Garretson Folse, J. (2014). Gratitude in

relationship marketing: theoretical development and directions for future research. European

Journal of Marketing, 48(1/2), 2-24.

Dahlman, E., Parkvall, S., & Skold, J. (2013). 4G: LTE/LTE-advanced for mobile broadband.

Academic press.

Davis Jr, F. D. (1986). A technology acceptance model for empirically testing new end-user

information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of

Technology).

Davis, F.D. (1993). User acceptance of information technology: system characteristics, user

perception and behavioral impacts. Academic press limited.

Davis, J. B. (2013). The theory of the individual in economics: Identity and value. Routledge.

De Leeuw, D. (2005). To mix or not to mix data collection modes in surveys. Journal of official

statistics, 21(2), 233.

De Mooij, M., & Hofstede, G. (2011). Cross-cultural consumer behavior: A review of research

findings. Journal of International Consumer Marketing, 23(3-4), 181-192.

Debats, D. L., & Bartelds, B. F. (1996). The structure of human values: a principal components

analysis of the Rokeach Value Survey (RVS). Website: http://www. dissertations. ub. rug.

nl/FILES/faculties/ppsw/1996/dlhm debats/c5. pdf (Erişim: 05.12. 2011).

DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent

variable. Information systems research, 3(1), 60-95.

Delva, J., Allen-Meares, P., & Momper, S. L. (2010). Cross-cultural research. Oxford University

Press, USA.

Demchenko, Y., De Laat, C., & Membrey, P. (2014, May). Defining architecture components of the

Big Data Ecosystem. In Collaboration Technologies and Systems (CTS), 2014 International

Conference on (pp. 104-112). IEEE.

Depreciation. Report Number: RFATO029, 15.12.2015

310

Des Horts, C. H. B., Isaac, H., & Leclercq, A. (2006). Adoption and appropriation: towards a new

theoretical framework. An exploratory research on mobile technologies in French companies (No.

hal-00664064).

Devis, F.D., Bagozzi, R.P and Warshaw, P.R. (1989) User acceptance of computer technology: a

comparison of two theoretical models. Management science.

Diamantopoulos, A. (2011). Incorporating formative measures into covariance-based structural

equation models. Mis Quarterly, 335-358.

Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for

choosing between multi-item and single-item scales for construct measurement: a predictive

validity perspective. Journal of the academy of marketing science, 40(3), 434-449.

Dillman, D. A., Tortora, R. D., & Bowker, D. (1998, August). Principles for constructing web

surveys. In Joint meetings of the american statistical association.

Donner, J. (2008). Research approaches to mobile use in the developing world: A review of the

literature. The information society, 24(3), 140-159.

Dornas, K. B., de Mesquita, J. M. C., & Patrocinio, R. (2014). The Relationship Between Trust,

Value and Loyalty in the Internet Era. Journal of business and economics, 5(5), 802-812.

Dowell, D., Morrison, M., & Heffernan, T. (2015). The changing importance of affective trust and

cognitive trust across the relationship lifecycle: A study of business-to-business

relationships. Industrial marketing management, 44, 119-130.

Dwyer, F. R., & Oh, S. (1987). Output sector munificence effects on the internal political economy

of marketing channels. Journal of marketing research, 347-358.

Edwards, W. (1954). The theory of decision making. Psychological bulletin, 51(4), 380.

Everett, S. (2016). Food and drink tourism: Principles and practice. Sage.

Excellence, 17(3), 355-372.

Excousseau, J. (2000). La mosaïque des générations. Editions d’Organisation.

Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods

designs—principles and practices. Health services research, 48(6pt2), 2134-2156.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior. Reading, MA: Addison-

Wesley.

Fisk, R. P., Brown, S. W., & Bitner, M. J. (1993). Tracking the evolution of the services marketing

literature. Journal of retailing, 69(1), 61-103.

Flurry Analytics annual mobile applications study, 2016

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable

variables and measurement error. Journal of marketing research, 39-50.

311

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and

measurement error: Algebra and statistics. Journal of marketing research, 382-388.

Frary, R. B. (1996). Hints for designing effective questionnaires (pp. 28-30). ERIC clearinghouse

on assessment & evaluation, the catholic university of America.

Frost, J. (2014). How to interpret a regression model with low R-squared and low p values. Minitab

Inc.(ed) getting started with minitab, 17.

Gaddis, S. E. (1998). How to design online surveys. Training & Development, 52(6), 67-72.

Garbarino, E., & Johnson, M. S. (1999). The different roles of satisfaction, trust, and commitment

in customer relationships. the Journal of Marketing, 70-87.

Garver, M. S., & Mentzer, J. T. (1999). Logistics research methods: employing structural equation

modeling to test for construct validity. Journal of business logistics, 20(1), 33.

Gefen, D., & Straub, D. W. (2004). Consumer trust in B2C e-Commerce and the importance of

social presence: experiments in e-Products and e-Services. Omega, 32(6), 407-424.

Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 101-107.

GNSS market report, issue 4, 2015, European global navigation satellite systems agency (GSA)

Godelier, É. (2005). Les élites managériales entre logiques nationales endogènes et globalisation

exogène. Entreprises et histoire, (4), 6-14.

Godin, G., & Kok, G. (1996). The theory of planned behavior: a review of its applications to health-

related behaviors. American journal of health promotion, 11(2), 87-98.

Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have advantages for small sample

size or non-normal data?. Mis Quarterly, 36(3), 891-1001.

Goodwin, N. C. (1987). Functionality and usability. Communications of the ACM, 30(3), 229-233.

Google travel report, 2014

Gorokhov, A. (2012). The idea of unity of Russian culture in the 19th century. Vestnik.

GOST 31985-2013 « Услуги общественного питания. Термины и определения » 2015

Gould, J. D., Boies, S. J., & Lewis, C. (1991). Making usable, useful, productivity-enhancing

computer applications. Communications of the ACM, 34(1), 74-85.

Gouldner, A. W. (1960). The norm of reciprocity: A preliminary statement. American sociological

review, 161-178.

Green, R. T., & White, P. D. (1976). Methodological considerations in cross-national consumer

research. Journal of international business studies, 81-87.

312

Gremler, D. D., & Brown, S. W. (1996). Service loyalty: its nature, importance, and

implications. Advancing service quality: A global perspective, 5, 171-181.

Grönroos, C. (1994). From marketing mix to relationship marketing: towards a paradigm shift in

marketing. Management decision, 32(2), 4-20.

Grönroos, C. (1997). Value‐driven relational marketing: from products to resources and

competencies. Journal of marketing management, 13(5), 407-419.

Grönroos, C. (2004). The relationship marketing process: communication, interaction, dialogue,

value. Journal of business & industrial marketing, 19(2), 99-113.

Grönroos, C. (2011). A service perspective on business relationships: The value creation,

interaction and marketing interface. Industrial marketing management, 40(2), 240-247.

Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. Handbook of

qualitative research, 2(163-194), 105.

Guerin, A.-J. (2004). The French initiative for innovation in tourism: how to rejuvenate supply and

increase the productivity of the tourism sector. Paris: Organization for Economic Cooperation and

Development.

Gummesson, E. (2011). Total relationship marketing. Routledge.

Gunn, H. (2002). Web-based surveys: Changing the survey process. First Monday, 7(12).

Gupta, P., & Harris, J. (2010). How e-WOM recommendations influence product consideration and

quality of choice: A motivation to process information perspective. Journal of Business

Research, 63(9), 1041-1049.

Gyr, U. (2010). The history of tourism: Structures on the path to modernity.EGO european history

online, URL: http://ieg-ego.eu/en/threads/europe-on-the-road/the-history-of-tourism

Haenlein, M., & Kaplan, A. M. (2004). A beginner's guide to partial least squares

analysis. Understanding statistics, 3(4), 283-297.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of

marketing theory and practice, 19(2), 139-152.

Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The use of partial least squares

structural equation modeling in strategic management research: a review of past practices and

recommendations for future applications. Long range planning, 45(5), 320-340.

Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial

least squares structural equation modeling in marketing research. Journal of the academy of

marketing science, 40(3), 414-433.

Hale, J. L., Householder, B. J., & Greene, K. L. (2002). The theory of reasoned action. The

persuasion handbook: Developments in theory and practice, 259286.

313

Halinen, A. (2012). Relationship marketing in professional services: a study of agency-client

dynamics in the advertising sector. Routledge.

Hall, C. M., Sharples, L., & Smith, A. (2003). The experience of consumption or the consumption

of experiences? Challenges and issues in food tourism. Food tourism around the world:

Development, management and markets, 314-331.

Hall, C. M., Timothy, D. J., & Duval, D. T. (2012). Safety and security in tourism: relationships,

management, and marketing. Routledge.

Hamill, L., & Lasen, A. (2005). Mobile world: Past, present and future. Springer Science &

Business Media.

Hamilton, D. L., & Sherman, S. J. (1996). Perceiving persons and groups. Psychological

review, 103(2), 336.

Han, S. (2003). Individual adoption of information systems in organizations: A literature review of

technology acceptance model. Turku Centre for Computer Science (TUCS).

Hantrais, L. (1999). Contextualization in cross-national comparative research. International Journal

of Social Research Methodology, 2(2), 93-108.

Hantrais, L., & Mangen, S. P. (Eds.). (1996). Cross national research methods. A&C Black.

Harkness, J. A., Van de Vijver, F. J., & Mohler, P. P. (2003). Cross-cultural survey methods (Vol.

325). Hoboken, NJ: Wiley-Interscience.

Harwell, M. R. (2011). ReseaRch Design in Qualitative/Quantitative. The Sage handbook for

research in education: Pursuing ideas as the keystone of exemplary inquiry, 147.

Hastie, R., & Dawes, R. M. (2010). Rational choice in an uncertain world: The psychology of

judgment and decision making. Sage.

Hellriegel, D. & J. W. Slocum (2003). Organizational Behavior. Mason, OH: South-Western

College Publishers.

Hennig-Thurau, T. (2004). Customer orientation of service employees: Its impact on customer

satisfaction, commitment, and retention. International Journal of Service Industry

Management, 15(5), 460-478.

Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth

via consumer-opinion platforms: what motivates consumers to articulate themselves on the

internet?. Journal of interactive marketing, 18(1), 38-52.

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path

modeling in international marketing. In New challenges to international marketing (pp. 277-319).

Emerald Group Publishing Limited.

Hilbert, M. (2013). Big Data for Development: A Systematic Review of Promises and

Challenges. United Nations Economic Commission for Latin America and the Caribbean (UN

ECLAC), 1(1), 1-36.

314

Hill, T., Smith, N. D., & Mann, M. F. (1987). Role of efficacy expectations in predicting the

decision to use advanced technologies: The case of computers. Journal of applied psychology,

72(2), 307-313.

Hills, M. D. (2002). Kluckhohn and Strodtbeck's values orientation theory. Online readings in

psychology and culture, 4(4), 3.

Hitlin, S., & Piliavin, J. A. (2004). Values: Reviving a dormant concept. Annu. Rev. Sociol., 30,

359-393.

Hoe, S. L. (2008). Issues and procedures in adopting structural equation modeling technique.

Journal of applied quantitative methods, 3(1), 76-83.

Hoehle, H., & Venkatesh, V. (2015). Mobile Application Usability: Conceptualization and

Instrument Development. Mis Quarterly, 39(2), 435-472.

Hofstede, G. (1995). Multilevel research of human systems: Flowers, bouquets and gardens. Human

Systems Management, 14(3), 207-217.

Hofstede, G. (2010). The GLOBE debate: Back to relevance. Journal of International Business

Studies, 41(8), 1339-1346.

Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online readings in

psychology and culture, 2(1), 8.

Hofstede, G. J. (2001). Adoption of communication technologies and national culture. Systèmes

d’information et management, 6(3), 55-74.

Hofstede, G., & Bond, M. H. (1984). Hofstede's culture dimensions an independent validation using

Rokeach's value survey. Journal of cross-cultural psychology, 15(4), 417-433.

Hofstede, G., & Bond, M. H. (1988). The Confucius connection: From cultural roots to economic

growth. Organizational dynamics, 16(4), 5-21.

Hofstede, G., & Minkov, M. (2010). Long-versus short-term orientation: new perspectives. Asia

Pacific Business Review, 16(4), 493-504.

Hojman, P., Ortuzar, J. D. D., & Rizzi, L. (2004). Internet-based surveys to elicit the value of risk

reductions. In 7th International Conference on Survey Methods in Transport, Costa Rica.

Holzer, A., & Ondrus, J. (2011). Mobile application market: A developer’s perspective. Telematics

and informatics, 28(1), 22-31.

Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika,

30(2), 179-185.

Hox, J. J., & Bechger, T. M. (1998). An introduction to structural equation modeling. Family

Science Review, 11, 354-373.

Hsu, S. H., Chen, W. H., & Hsieh, M. J. (2006). Robustness testing of PLS, LISREL, EQS and

ANN-based SEM for measuring customer satisfaction. Total Quality Management & Business

315

Hwang, G. J., & Tsai, C. C. (2011). Research trends in mobile and ubiquitous learning: A review of

publications in selected journals from 2001 to 2010. British Journal of Educational Technology,

42(4).

Igbaria, M. (1990). End-user computing effectiveness: A structural equation model. Omega, 18(6),

637-652.

Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption:

Testing the UTAUT model. Information & management, 48(1), 1-8.

IMF Economic Review, 2015

IMP Group. (1990). Understanding business markets: Interaction, relationships and networks. D.

Ford (Ed.). London: Academic Press.

INSEE (2014) La reprise différée, Note de Conjoncture (Business Survey), Paris.

James, L. R., & Brett, J. M. (1984). Mediators, moderators, and tests for mediation. Journal of

applied psychology, 69(2), 307.

Jarvenpaa, S. L., Tractinsky, N., & Saarinen, L. (1999). Consumer trust in an internet store: a cross

cultural validation. Journal of computer mediated communication, 5(2), 0-0.

Jick, T.D. (1979). Mixing qualitative and quantative methods: triangulation in action.

Administrative science quarterly.

Johnson, M. S., & Garbarino, E. (2001). Customers of performing arts organisations: are

subscribers different from nonsubscribers?. International Journal of Nonprofit and Voluntary

Sector Marketing, 6(1), 61-77.

Jöreskog, K. G. (1970). A general method for estimating a linear structural equation system. ETS

Research Report Series, 1970(2).

Jöreskog, K. G. (1970). Estimation and testing of simplex models. ETS Research Report Series,

1970(2).

Jöreskog, K. G., & Sörbom, D. (1982). Recent developments in structural equation modeling.

Journal of marketing research, 404-416.

Joshi, K. (1992). A causal path model of the overall user attitudes toward the MIS function: the case

of user information satisfaction. Information & Management, 22(2), 77-88.

Jovanović, V., & Njeguš, A. (2008). The application of GIS and its components in

tourism. Yugoslav Journal of Operations Research, 18(2), 261-272.

Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and

psychological measurement, 20(1), 141-151.

Kang, M., Liew, B. Y. T., Lim, H., Jang, J., & Lee, S. (2015). Investigating the determinants of

mobile learning acceptance in Korea using UTAUT2. In Emerging issues in smart learning (pp.

209-216). Springer, Berlin, Heidelberg.

316

Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across

time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS quarterly, 183-

213.

Kelley, S. W., & Davis, M. A. (1994). Antecedents to customer expectations for service

recovery. Journal of the Academy of Marketing Science, 22(1), 52-61.

Kemmis, S., & Wilkinson, M. (1998). Participatory action research and the study of practice. Action

research in practice: Partnerships for social justice in education, 1, 21-36.

Khalid, K., Hilman, H., & Kumar, D. (2012). Get along with quantitative research process.

International Journal of Research in Management, 2(2), 15-29.

Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative

view of four mechanisms underlying postadoption phenomena. Management science, 51(5), 741-

755.

Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Research note—two competing perspectives

on automatic use: A theoretical and empirical comparison. Information Systems Research, 16(4),

418-432.

Kim, Y., Kim, D., & Wachter, K. (2012). Smartphones: User engagement motivations effect on

their value, satisfaction, and future engagement intention. AIS electronic library, URL:

http://aisel.aisnet.org/amcis2012/proceedings/HCIStudies/21/

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information &

management, 43(6), 740-755.

Kippax, S., & Crawford, J. (1993). Flaws in the theory of reasoned action. The theory of reasoned

action: Its application to AIDS-preventive behavior, 253-269.

Kitayama, S., Mesquita, B., & Karasawa, M. (2006). Cultural affordances and emotional

experience: socially engaging and disengaging emotions in Japan and the United States. Journal of

personality and social psychology, 91(5), 890.

Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.

Kluckhohn, F. R., & Strodtbeck, F. L. (1961). Variations in value orientations. Evantson, IL: Row,

Peterson.

Kollat, D. T., Engel, J. F., & Blackwell, R. D. (1970). Current problems in consumer behavior

research. Journal of Marketing Research, 327-332.

Kotlarova, V. (2010). Traditional values in contemporary culture. Gramota.

Kroeber, A. L., & Kluckhohn, C. (1952). Culture: A critical review of concepts and definitions.

Papers. Peabody Museum of Archaeology & Ethnology, Harvard University.

Kulta, H. P., & Karjaluoto, H. (2016, October). Conceptualizing engagement in the mobile context:

a systematic literature review. In Proceedings of the 20th International Academic Mindtrek

Conference (pp. 169-176). ACM.

317

Kumar, N., Scheer, L. K., & Steenkamp, J. B. E. (1995). The effects of supplier fairness on

vulnerable resellers. Journal of marketing research, 54-65.

Lai, W. T., & Chen, C. F. (2011). Behavioral intentions of public transit passengers—The roles of

service quality, perceived value, satisfaction and involvement. Transport Policy, 18(2), 318-325.

Lally, P., Van Jaarsveld, C. H., Potts, H. W., & Wardle, J. (2010). How are habits formed:

Modelling habit formation in the real world. European journal of social psychology, 40(6), 998-

1009.

Lamb, C. W., Hair, J. F., & McDaniel, C. (2011). Essentials of marketing. Cengage Learning.

Le Tourisme à Paris Chiffres clés, 2015

Lee, A. S. (1991). Integrating positivist and interpretive approaches to organizational research.

Organization science, 2(4), 342-365.

Lee, D., Moon, J., Kim, Y. J., & Mun, Y. Y. (2015). Antecedents and consequences of mobile

phone usability: Linking simplicity and interactivity to satisfaction, trust, and brand loyalty.

Information & Management, 52(3), 295-304.

Lee, S. G., Trimi, S., & Kim, C. (2013). The impact of cultural differences on technology adoption.

Journal of world business, 48(1), 20-29.

Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and

future. Communications of the Association for information systems, 12(1), 50.

Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and

future. Communications of the Association for information systems, 12(1), 50.

Leedy, P. D., & Ormrod, J. E. (2001). Practical research: Planning and design. New Jersey, Pearson

Merill Prentice hall.

Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A

critical review of the technology acceptance model. Information & management, 40(3), 191-204.

Legris, P., Ingham, J., Collerette, P. (2003) Why do people use information technology? A critical

review of the technology acceptance model. Information&Management.

Leidner, D. E., & Kayworth, T. (2006). Review: a review of culture in information systems

research: toward a theory of information technology culture conflict. MIS quarterly, 30(2), 357-399.

Leigh, J. H., & Gabel, T. G. (1992). Symbolic interactionism: its effects on consumer behavior and

implications for marketing strategy. Journal of Services Marketing, 6(3), 5-16.

Leiner, B. M., Cerf, V. G., Clark, D. D., Kahn, R. E., Kleinrock, L., Lynch, D. C., ... & Wolff, S.

(2009). A brief history of the Internet. ACM SIGCOMM Computer Communication Review, 39(5),

22-31.

Li, J. P., & Kishore, R. (2006, April). How robust is the UTAUT instrument?: a multigroup

invariance analysis in the context of acceptance and use of online community weblog systems. In

Proceedings of the 2006 ACM SIGMIS CPR conference on computer personnel research: Forty

318

four years of computer personnel research: achievements, challenges & the future (pp. 183-189).

ACM.

Liao, C., Chen, J.-L., Yen, D.C. (2007) Theory of planning behavior (TPB) and customer

satisfaction in the continued use of e-service: an integrated model. Computers in human behavior.

Lickel, B., Hamilton, D. L., & Sherman, S. J. (2001). Elements of a lay theory of groups: Types of

groups, relational styles, and the perception of group entitativity. Personality and Social Psychology

Review, 5(2), 129-140.

Light, J., & McNaughton, D. (2014). Communicative competence for individuals who require

augmentative and alternative communication: A new definition for a new era of communication?

Augmentative and alternative communication.

Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of

intention: The case of information systems continuance. MIS quarterly, 705-737.

Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of

intention: The case of information systems continuance. MIS quarterly, 705-737.

Lin, H. H., & Wang, Y. S. (2006). An examination of the determinants of customer loyalty in

mobile commerce contexts. Information & management, 43(3), 271-282.

Lin, P. J., Kao, C. C., Lam, K. H., & Tsai, I. C. (2014). Design and implementation of a tourism

system using mobile augmented reality and gis technologies. In Proceedings of the 2nd

International Conference on Intelligent Technologies and Engineering Systems (ICITES2013) (pp.

1093-1099). Springer International Publishing.

Lincoln, Y. S., & Guba, E. G. (1994). RSVP: We are pleased to accept your invitation. Evaluation

Practice, 15(2), 179-192.

Little, T. D., Lindenberger, U., & Nesselroade, J. R. (1999). On selecting indicators for multivariate

measurement and modeling with latent variables: When" good" indicators are bad and" bad"

indicators are good. Psychological Methods, 4(2), 192.

Litvin, S. W., Crotts, J. C., & Hefner, F. L. (2004). Cross‐cultural tourist behaviour: a replication

and extension involving Hofstede's uncertainty avoidance dimension. International Journal of

Tourism Research, 6(1), 29-37.

Lohr, S. (2012). The age of big data. New York Times, 11(2012).

Long, L. M. (Ed.). (2004). Culinary tourism. Lexington: University Press of Kentucky.

Lovelock, C. H., & Wirtz, J. (2001). Services marketing: people, technology, strategy. Pearson

Prentice Hall.

Lyon, D. (2013). The information society: Issues and illusions. John Wiley & Sons.

MacKenzie, I. S., & Soukoreff, R. W. (2002). Text entry for mobile computing: Models and

methods, theory and practice. Human–Computer Interaction, 17(2-3), 147-198.

319

MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and

validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS

quarterly, 35(2), 293-334.

Makarenko S.N., Saak A. E. Tourism history. URL:

http://tourlib.net/books_history/makarenko5.htm

Malinowski, B. (2001). A scientific theory of culture and other essays (Vol. 9). Psychology Press.

Marangunic, N., & Granic, A. (2015). Technology acceptance model: a literature review from 1986

to 2013. Universal Access in the Information Society, 14(1), 81.

Marcus, C. (1998). A practical yet meaningful approach to customer segmentation. Journal of

consumer marketing, 15(5), 494-504.

MarketLine Industry Profile Mobile Phones in Russia, 2015

MarketLine Industry Profile : Mobile Phones in France, 2015

MarketLine, Industry Profile: Global Restaurants, 2015

Martin, W. J. (1996). Global information society. Ashgate Publishing Company.

Marwell, G., & Schmitt, D. R. (2013). Cooperation: An experimental analysis. Academic Press.

Mastorakis, G., Trihas, N., Perakakis, E., & Kopanakis, I. (2015). E-CRM in tourism exploiting

emerging information and communication technologies. Anatolia, 26(1), 32-44.

Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with

the theory of planned behavior. Information systems research, 2(3), 173-191.

Mathieson, K., & Doane, D. (2005). Using Fine-Grained Likert Scales in Web Surveys. Alliance

Journal of Business Research, 1(1), 27-34.

Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model:

the influence of perceived user resources. ACM SigMIS Database, 32(3), 86-112.

Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we

live, work, and think. Houghton Mifflin Harcourt.

McDonald, G. (2000). Cross-cultural methodological issues in ethical research. In Business

challenging business ethics: New instruments for coping with diversity in international

business (pp. 89-104). Springer Netherlands.

McIntosh, R. W., Goeldner, C. R., & Ritchie, J. R. B. (1986). Tourism. Principles, Practises,

Philosophies. New York: John Willey & Sons.

McIntosh, R. W., Goeldner, C. R., & Ritchie, J. R. B. (1995). Pleasure travel motivation. Tourism:

principles, practices, philosophies., (Ed. 7), 167-190.

320

Mende, M., Bolton, R. N., & Bitner, M. J. (2013). Decoding customer–firm relationships: how

attachment styles help explain customers' preferences for closeness, repurchase intentions, and

changes in relationship breadth. Journal of Marketing Research, 50(1), 125-142.

Mermet, G. (1996). Francoscopie 1997: comment vivent les Français?: faits, analyses, tendances,

comparaisons, 10,000 chiffres. Larousse.

Mermet, G. (2010). Francoscopie: tout sur les Français. Paris: Larousse.

Miller, A, I, (2012). History of the concept of a nation in Russia. Миллер, А. И. (2012). История

понятия нация в России. Отечественные записки, (1), 162-186.

Mingers, J. (2003). A classification of the philosophical assumptions of management science

methods. Journal of the operational research society, 54(6), 559-570.

Minkov, M., & Hofstede, G. (2011). The evolution of Hofstede's doctrine. Cross cultural

management: an international journal, 18(1), 10-20.

Mittal, V., Kumar, P., Tsiros, M. (1999). Attribute-level performance, satisfaction, and behavioral

intention over time: a consumption-system approach. Journal of marketing.

Moeller, K. (2010). Partner selection, partner behavior, and business network performance: An

empirical study on German business networks. Journal of accounting & organizational

change, 6(1), 27-51.

Möller, K. (2013). Theory map of business marketing: Relationships and networks

perspectives. Industrial marketing management, 42(3), 324-335.

Möller, K., & Halinen, A. (2000). Relationship marketing theory: its roots and direction. Journal of

marketing management, 16(1-3), 29-54.

Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of

adopting an information technology innovation. Information systems research, 2(3), 192-222.

Morgan, D. L. (2007). Paradigms lost and pragmatism regained methodological implications of

combining qualitative and quantitative methods. Journal of mixed methods research, 1(1), 48-76.

Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. The

journal of marketing, 20-38.

Morishim, M., & Minami, T. (1983). Task Interdependence and Internal Motivation: Application of

Job Characteristic Model to" Collectivist" Cultures. 哲學, 77, 133-147.

Morosan, C., & DeFranco, A. (2016). It's about time: Revisiting UTAUT2 to examine consumers’

intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management,

53, 17-29.

Morrel-Samuels, P. (2003). Web surveys’ hidden hazards. Harvard Business Review, 81(7), 16-17.

Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions:

Implications for a changing work force. Personnel psychology, 53(2), 375-403.

321

Morse, J.M., Niehaus, L. (2009). Mixed methods design: Principles and procedures. Routledge.

Myers, M. D., & Tan, F. B. (2003). Beyond models of national culture in information systems

research. Advanced topics in global information management, 2, 14-29.

Nair, P. K., Ali, F., & Leong, L. C. (2015). Factors affecting acceptance & use of ReWIND:

Validating the extended unified theory of acceptance and use of technology. Interactive Technology

and Smart Education, 12(3), 183-201.

Nakata, C. (Ed.). (2009). Beyond Hofstede: Culture frameworks for global marketing and

management. Springer.

Nistor, N. (2014). When technology acceptance models won’t work: Non-significant intention-

behavior effects. Computers in human behavior, (34), 299-300.

Nistor, N., Lerche, T., Weinberger, A., Ceobanu, C., & Heymann, O. (2014). Towards the

integration of culture into the Unified Theory of Acceptance and Use of Technology. British

journal of educational technology, 45(1), 36-55.

Nunnally, J. C. (1967). Psychometric theory. NY: McGraw-Hill.

O’Brien, H. L. (2010). The influence of hedonic and utilitarian motivations on user engagement:

The case of online shopping experiences. Interacting with Computers, 22(5), 344-352.

OECD Tourism Trends and Policies 2015

Oechslein, O., Fleischmann, M., & Hess, T. (2014, January). An application of UTAUT2 on social

recommender systems: Incorporating social information for performance expectancy. In System

Sciences (HICSS), 2014 47th Hawaii International Conference on (pp. 3297-3306). IEEE.

Ovcharov, A. O., Vasiljeva, M. V., & Shirin, S. S. (2015). The Russian tourist industry: Structure,

trends, competitiveness at the world market. Review of European Studies, 7(9), 151.

Palmatier, R. W. (2008). Relationship marketing (pp. 1-140). Cambridge, MA: Marketing Science

Institute.

Palmatier, R. W., Dant, R. P., Grewal, D., & Evans, K. R. (2006). Factors influencing the

effectiveness of relationship marketing: a meta-analysis. Journal of marketing, 70(4), 136-153.

Palmatier, R. W., Gopalakrishna, S., & Houston, M. B. (2006). Returns on business-to-business

relationship marketing investments: Strategies for leveraging profits. Marketing Science, 25(5),

477-493.

Palmatier, R. W., Houston, M. B., Dant, R. P., & Grewal, D. (2013). Relationship velocity: toward

a theory of relationship dynamics. Journal of Marketing, 77(1), 13-30.

Palmatier, R. W., Jarvis, C. B., Bechkoff, J. R., & Kardes, F. R. (2009). The role of customer

gratitude in relationship marketing. Journal of marketing, 73(5), 1-18.

Parasuraman, A., Berry, L. L., & Zeithaml, V. A. (1991). Understanding customer expectations of

service. MIT Sloan Management Review, 32(3), 39.

322

Paris Region Key Figures 2016

Park, J. H., Kim, D. Y., & Kim, S. H. (2011). The Effects of Structural Factors of Administered

Channels on the Retailer's Trust in the Supplier and Long-Term Orientation: Focusing on the

Moderating Effect of Relationship Lifecycle. Journal of distribution research, 16.

Parker, L. (1992). Collecting data the e-mail way. Training & Development, 46(7), 52-55.

Parr, M.G. (1999). Leisure theory and practice: a critical approach. Canadian association of leisure

studies.

Parsons, T., & Shils, E. A. (1951). Values, motives, and systems of action. Toward a general theory

of action. Glencoe, Ill., Free P. 33, 247-275.

Parvatiyar, A., & Sheth, J. N. (2000). The domain and conceptual foundations of relationship

marketing. Handbook of relationship marketing, 1, 3-38.

Perna, A., & Baraldi, E. (2014). CRM, Its Roots in Management Studies and Recent Research

Trends. In CRM Systems in industrial companies (pp. 57-76). Palgrave Macmillan UK.

Perna, A., & Baraldi, E. (2014). CRM Systems in industrial companies: Intra-and inter-

organizational effects. Springer.

Peter, J. P. (1981). Construct validity: A review of basic issues and marketing practices. Journal of

marketing research, 133-145.

Phillips, D. C., & Burbules, N. C. (2000). Postpositivism and educational research. Rowman &

Littlefield.

Philosophies. New York: John Willey & Sons.

Polasky, S., Carpenter, S. R., Folke, C., & Keeler, B. (2011). Decision-making under great

uncertainty: environmental management in an era of global change. Trends in ecology &

evolution, 26(8), 398-404.

Polites, G. L., & Karahanna, E. (2012). Shackled to the status quo: The inhibiting effects of

incumbent system habit, switching costs, and inertia on new system acceptance. MIS

quarterly, 36(1), 21-42.

Porter, M. E., Hills, G., Pfitzer, M., Patscheke, S., & Hawkins, E. (2011). Measuring shared

value. How to Unlock Value by Linking Social and Business Results, 10-11.

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and

comparing indirect effects in multiple mediator models. Behavior research methods, 40(3), 879-

891.

Premkumar, G., & Bhattacherjee, A. (2008). Explaining information technology usage: A test of

competing models. Omega, 36(1), 64-75.

Raaij, W. (1978). Cross-cultural research methodology as a case of construct validity. NA-Advances

in Consumer Research Volume 05.

323

Rahimi, R., Nadda, V. K., & Wang, H. (2015). CRM in Tourism: Customer Relationship

Management (CRM). In Emerging Innovative Marketing Strategies in the Tourism Industry (pp. 16-

43). IGI Global.

Rauyruen, P., & Miller, K. E. (2007). Relationship quality as a predictor of B2B customer

loyalty. Journal of business research, 60(1), 21-31.

Ravald, A., & Grönroos, C. (1996). The value concept and relationship marketing. European

journal of marketing, 30(2), 19-30.

Read, W., Robertson, N., & McQuilken, L. (2011). A novel romance: The Technology Acceptance

Model with emotional attachment. Australasian Marketing Journal (AMJ), 19(4), 223-229.

Recommendations on Tourism Statistics by UNWTO and UNSTAT in 1994.

Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of

covariance-based and variance-based SEM. International Journal of research in Marketing, 26(4),

332-344.

Reisinger, Y., & Turner, L. W. (2003). Cross-cultural behaviour in tourism: Concepts and analysis.

Elsevier.

Ringle, C. M., Sarstedt, M., & Straub, D. (2012). A critical look at the use of PLS-SEM. MIS

quarterly.

Ringle, C. M., Wende, S., & Will, A. (2010). Finite mixture partial least squares analysis:

Methodology and numerical examples. Handbook of partial least squares, 195-218.

Rinne, T., Steel, G. D., & Fairweather, J. (2012). Hofstede and Shane revisited: the role of power

distance and individualism in national-level innovation success. Cross-cultural research, 46(2), 91-

108.

Rogers, E. M. (1976). New product adoption and diffusion. Journal of consumer Research, 2(4),

290-301.

Rogers, E. M. (1994). History of communication study. New York: Free Press.

Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.

Rogers, E.M. (2001). New product adoption and diffusion. The journal of consumer research.

Rogers, E.M., Mahler, A. (1999). The diffusion of interactive communication innovations and the

critical mass: the adoption of telecommunications services by German banks. Elsevier science Ltd.

Rokeach, M. (1973). Values list. The nature of human values. Free press.

Rondan-Cataluña, F. J., Arenas-Gaitán, J., & Ramírez-Correa, P. E. (2015). A comparison of the

different versions of popular technology acceptance models: A non-linear perspective. Kybernetes,

44(5), 788-805.

324

Roster, C. A., Rogers, R. D., Albaum, G., & Klein, D. (2004). A comparison of response

characteristics from web and telephone surveys. International journal of marketing research, 46,

359-374.

Rothchild, I. (2006). Induction, deduction, and the scientific method. Soc. study Reprod.

Rudy, I. A. (1996). A critical review of research on electronic mail. European Journal of

Information Systems, 4(4), 198-213.

Ruengaramrut, V., Ribiere, V., & Ammi, C. (2015, November). A component diagram presenting a

gamified environment supporting customer engagement in a service innovation process. In

International Conference on Intellectual Capital and Knowledge Management and Organisational

Learning (p. 401). Academic Conferences International Limited.

RUSSIA` 2015, Statistical pocketbook.

Russian food market, 2015 RBC

Ryu, K., Lee, H. R., & Gon Kim, W. (2012). The influence of the quality of the physical

environment, food, and service on restaurant image, customer perceived value, customer

satisfaction, and behavioral intentions. International Journal of Contemporary Hospitality

Management, 24(2), 200-223.

Sabir, L. B. (2015). Customer Loyalty: Concept, Context and Character. Al-Barkaat Journal of

Finance & Management, 7(2), 103-104.

Sackmary, B. (1998, January). Internet survey research: Practices, problems, and prospects. In

American marketing association. Conference proceedings (Vol. 9, p. 41). American Marketing

Association.

Salovaara, A., & Tamminen, S. (2009). Acceptance or appropriation? A design-oriented critique of

technology acceptance models. In Future interaction design II (pp. 157-173). Springer London.

Salovaara, A., & Tamminen, S. (2009). Acceptance or appropriation? A design-oriented critique of

technology acceptance models. In Future interaction design II (pp. 157-173). Springer London.

Samaha, S. A., Beck, J. T., & Palmatier, R. W. (2014). The role of culture in international

relationship marketing. Journal of Marketing, 78(5), 78-98.

Samaha, S. A., Palmatier, R. W., & Dant, R. P. (2011). Poisoning relationships: Perceived

unfairness in channels of distribution. Journal of Marketing, 75(3), 99-117.

Sanakulov, N., & Karjaluoto, H. (2015). Consumer adoption of mobile technologies: a literature

review. International Journal of Mobile Communications, 13(3), 244-275.

Satorra, A., & Bentler, P. (2011). Scaling corrections for statistics in covariance structure analysis.

Department of Statistics, UCLA.

Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model:

Investigating subjective norm and moderation effects. Information & management, 44(1), 90-103.

325

Schillewaert, N., & Meulemeester, P. (2005). Comparing response distributions of offline and

online data collection methods. International journal of market research, 47(2), 163-178.

Schneider, B. (2001, August). GIS functionality in multimedia atlases: spatial analysis for everyone.

In Proc. of the 20th Int. cartographic conference, Beijing.

Schuldt, B. A., & Totten, J. W. (1994). Electronic mail vs. mail survey response rates. Marketing

Research, 6(1), 3-7.

Schulz, A. (1996). The role of global computer reservation systems in the travel industry today and

in the future. Newsletter competence center electronic markets, 6(2).

Schwartz, S. H. (2012). An overview of the Schwartz theory of basic values. Online readings in

Psychology and Culture, 2(1), 11.

Segars, A. H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: A

confirmatory factor analysis. MIS quarterly, 517-525.

Sekaran, U. (2006). Research methods for business: A skill building approach. John Wiley & Sons.

Sekaran, U., & Bougie, R. (2013). Research methods for business: A skill-building approach .[e-

book].

Selltiz, C., Wrightsman, L. S., Cook, S. W., Balch, G. I., Hofstetter, R., & Bickman, L. (1976).

Research methods in social relations. New York : Holt, Rinehart and Winston

Sezgin, E. (Ed.). (2016). e-Consumers in the Era of New Tourism. Springer.

Shaiq, H. M. A., Khalid, H. M. S., Akram, A., & Ali, B. (2011). Why not everybody loves

Hofstede? What are the alternative approaches to study of culture. European Journal of Business

and Management, 3(6), 101-111.

Shao, X., & Siponen, M. (2011, January). Consumer acceptance and use of information technology:

Adding consumption theory to UTAUT2. In Proceedings> Proceedings of SIGSVC Workshop.

Sprouts: Working Papers on Information Systems (Vol. 11, No. 157, pp. 11-157).

Sheehan, K. B. (2001). E-mail survey response rates: A review. Journal of computer mediated

communication, 6(2), 0-0.

Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-

analysis of past research with recommendations for modifications and future research. Journal of

consumer research, 15(3), 325-343.

Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-

analysis of past research with recommendations for modifications and future research. Journal of

consumer research, 15(3), 325-343.

Shin, C., Hong, J. H., & Dey, A. K. (2012, September). Understanding and prediction of mobile

application usage for smart phones. In Proceedings of the 2012 ACM Conference on ubiquitous

computing (pp. 173-182). ACM.

326

Sirdeshmukh, D., Singh, J., & Sabol, B. (2002). Consumer trust, value, and loyalty in relational

exchanges. Journal of marketing, 66(1), 15-37.

Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015). Modeling consumers’

adoption intentions of remote mobile payments in the United Kingdom: extending UTAUT with

innovativeness, risk, and trust. Psychology & Marketing, 32(8), 860-873.

Slade, E. L., Williams, M. D., & Dwivedi, Y. (2013, March). Extending UTAUT2 To Explore

Consumer Adoption Of Mobile Payments. In UKAIS (p. 36).

Sniehotta, F. F., Presseau, J., & Araújo-Soares, V. (2014). Time to retire the theory of planned

behaviour.

Spang, R. (2000). The Invention of the Restaurant: Paris and Modern Gastronomic Culture.

Cambridge: Harvard University Press.

Spash, C. L. (2000). Multiple value expression in contingent valuation: economics and ethics.

Environ. Sci. Technol. 2000, 34, 1433-1438

Stanton, J. M. (1998). An empirical assessment of data collection using the Internet. Personnel

psychology, 51(3), 709-725.

Steenkamp, J. B. E. (2001). The role of national culture in international marketing research.

International marketing review, 18(1), 30-44.

Steenkamp, J. B. E., & Baumgartner, H. (2000). On the use of structural equation models for

marketing modeling. International journal of research in marketing, 17(2), 195-202.

Steenkamp, J. B. E., & Van Trijp, H. C. (1991). The use of LISREL in validating marketing

constructs. International journal of research in marketing, 8(4), 283-299.

Steensma, H. K., Marino, L., Weaver, K. M., & Dickson, P. H. (2000). The influence of national

culture on the formation of technology alliances by entrepreneurial firms. Academy of management

journal, 43(5), 951-973.

STINGLER, G. J. (1951). The division of labor is Iimited by the extend of the market. Journal of

Political Economy, (59).

Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the

royal statistical society. Series B (Methodological), 111-147.

Subhani, M. I., Hasan, S. A., & Osman, A. (2011). Research methodologies for management

sciences & interdisciplinary research in contemporary world. European Journal of Scientific

Research, 63(4), 543-547.

Suhr, D. (2006). The basics of structural equation modeling. Presented: Irvine, CA, SAS User group

of the western region of the United States (WUSS).

Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance.

International journal of human-computer studies, 64(2), 53-78.

327

Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management

science, 42(1), 85-92.

Taiwo, A. A., & DOWNE, A. G. (2013). The theory of user acceptance and use of technology

(UTAUT): A meta-analytic review of empirical findings. Journal of Theoretical & Applied

Information Technology, 49(1).

Takhar-Lail, A., Chorbani, A. (2014). Market research methodologies : multi-method and

qualitative approaches. ISI Global.

Tanaka, J. S. (1993). Multifaceted conceptions of fit in structural equation models. Sage focus

editions, 154, 10-10.

Tang, D., & Chen, L. (2011, May). A review of the evolution of research on information

Technology Acceptance Model. In Business Management and Electronic Information (BMEI), 2011

International Conference on (Vol. 2, pp. 588-591). IEEE.

Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling.

Computational statistics & data analysis, 48(1), 159-205.

The Demographic Yearbook of Russia, Federal State statistics service.

Thomas, R., & Skinner, L. (2010). Total trust and trust asymmetry: Does trust need to be equally

distributed in interfirm relationships?. Journal of relationship marketing, 9(1), 43-53.

Thompson, L. F., Surface, E. A., Martin, D. L., & Sanders, M. G. (2003). From paper to pixels:

Moving personnel surveys to the Web. Personnel psychology, 56(1), 197-227.

Thong, J. Y., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the

expectation-confirmation model for information technology continuance. International journal of

human-computer studies, 64(9), 799-810.

Throsby, D. (2001). Economics and culture. Cambridge university press.

Tolstrup, M. (2015). Indoor Radio Planning: A Practical Guide for 2G, 3G and 4G. John Wiley &

Sons.

Tourangeau, R., Conrad, F. G., Couper, M., P., (2013). The science of web survey.

Tourism: normative legal acts of Russia: Collection of acts / Comp. NI Voloshin

Triandis, H. C. (1989). The self and social behavior in differing cultural contexts. Psychological

review, 96(3), 506.

Triandis, H. C. (1995). Individualism & collectivism. Westview press.

Trinquecoste, J. F. (2005). Marketing, stratégie et rhétorique. Décisions marketing, 77-80.

Trinquecoste, J. F., & Bidan, M. (2011). Regards croisés sur le processus d'appropriation des

Technologies de l'Information et de la Communication. Management & Avenir, (5), 175-178.

Tsoukas, H. (1989). The validity of idiographic research explanations. Academy of management

review, 14(4), 551-561.

328

Turk, T., & Gumusay, M. U. (2004, July). GIS design and application for tourism. In XXth ISPRS

Congress (pp. 12-23).

Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010). Does the technology

acceptance model predict actual use? A systematic literature review. Information and Software

Technology, 52(5), 463-479.

USDA Foreign Agricultural Service, GAIN Report: Food Service - Hotel Restaurant, Institutional,

Russian Federation. HRI Sector Adapting to Slowing Economy and Ruble. USDA GAIN Reports.

USDA Foreign Agricultural Service, GAIN Report: Food Service - Hotel Restaurant, Institutional,

France. Annual. Report Number: FR9186, 31.12.2015

USDA Foreign Agricultural Service, GAIN Report: Franchise Restaurants Doing Well Despite

Slowing Economy, Russian Federation, Report number: RSATO13, 18.05.2015

USDA Foreign Agricultural Service, GAIN Report: Russian Federation. Report number:

RFATO031, 22.01.2016

van Biljon, J., & Renaud, K. (2008). A qualitative study of the applicability of technology

acceptance models to senior mobile phone users. Advances in conceptual modeling–Challenges and

opportunities, 228-237.

Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS quarterly, 695-

704.

Vaus, D. de, (2014). Surveys in Social Research

Venkatesh, V. (2000). Determinants of perceived easy of use: integrating control, intrinsic

motivation, and emotion into the technology acceptance model. Information systems research.

Venkatesh, V. (2014). Technology acceptance model and the unified theory of acceptance and use

of technology. Wiley Encyclopedia of Management.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on

interventions. Decision sciences, 39(2), 273-315.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model:

Four longitudinal field studies. Management science, 46(2), 186-204.

Venkatesh, V., & Ramesh, V. (2006). Web and wireless site usability: understanding differences

and modeling use. MIS quarterly, 181-206.

Venkatesh, V., Davis, F. D., & Morris, M. G. (2007). Dead or alive? The development, trajectory

and future of technology adoption research. Journal of the association for information

systems, 8(4), 267.

Venkatesh, V., Goyal, S. (2010). Expectation disconfirmation and technology adoption: polynomial

modeling and response surface analysis. MIS quarterly.

329

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information

technology: Toward a unified view. MIS quarterly, 425-478.

Venkatesh, V., Ramesh, V. (2006). Web and wireless site usability: understanding differences and

modeling use. MIS quarterly.

Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the

two‐stage information systems continuance model: Incorporating UTAUT predictors and the role of

context. Information Systems Journal, 21(6), 527-555.

Venkatesh, Viswanath and Thong, James Y.L. and Xu, Xin, (2012). Consumer Acceptance and Use

of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology

MIS Quarterly, Vol. 36, No. 1, pp. 157-178, 2012. Available at

SSRN: https://ssrn.com/abstract=2002388

Vilares, M. J., Almeida, M. H., & Coelho, P. S. (2010). Comparison of likelihood and PLS

estimators for structural equation modeling: a simulation with customer satisfaction data. In

Handbook of partial least squares (pp. 289-305). Springer Berlin Heidelberg.

Vinzi, V. E., Chin, W. W., Henseler, J., & Wang, H. (Eds.). (2010). Handbook of partial least

squares: Concepts, methods and applications. Springer science & Business media.

Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer engagement: Exploring customer

relationships beyond purchase. Journal of marketing theory and practice, 20(2), 122-146.

Von Wieser, F. (2013). Social economics. Routledge.

Wall, G. and Mathieson, A. (2006). Tourism. Change, impacts and opportunities. Pearson.

Wang, L., Law, R., Hung, K., & Guillet, B. D. (2014). Consumer trust in tourism and hospitality: A

review of the literature. Journal of Hospitality and Tourism Management, 21, 1-9.

Wang, Y. S., Li, H. T., Li, C. R., & Zhang, D. Z. (2016). Factors affecting hotels' adoption of

mobile reservation systems: A technology-organization-environment framework. Tourism

management, 53, 163-172.

Webster, F. (2014). Theories of the information society. Routledge.

Werthner, H., Ricci, F. (2004). E-commerce and tourism. Communications of the ACM.

Williams, C. (2011). Research methods. Journal of business & Economics research (JBER), 5(3).

Williams, J. D., Han, S. L., & Qualls, W. J. (1998). A conceptual model and study of cross-cultural

business relationships. Journal of business research, 42(2), 135-143.

Williamson, O. E. (1975). Markets and hierarchies. New York, 26-30.

Williamson, O. E. (1996). Economic organization: The case for candor. Academy of management

review, 21(1), 48-57.

330

Wilson, A., Zeithaml, V. A., Bitner, M. J., & Gremler, D. D. (2012). Services marketing:

Integrating customer focus across the firm. McGraw Hill.

Wilson, D. T. (1995). An integrated model of buyer-seller relationships. Journal of the academy of

marketing science, 23(4), 335-345.

Wirtz, J., & Chew, P. (2002). The effects of incentives, deal proneness, satisfaction and tie strength

on word-of-mouth behaviour. International journal of service industry management, 13(2), 141-

162.

Wold, H. (1975). Path models with latent variables: The NIPALS approach. Acad. Press, (pp. 307-

357).

Wold, H. (1985). Partial least squares. Encyclopedia of statistical sciences.

Wong, K. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques

using SmartPLS. Marketing bulletin, 24(1), 1-32.

World Development Report, 2015

World Economic Outlook Report (2016)

World Travel and Tourism Council. Travel and Tourism, economic impact 2015, France

Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce?: An empirical evaluation of the

revised technology acceptance model. Information & management, 42(5), 719-729.

Wulf, K. D., Odekerken-Schröder, G., & Iacobucci, D. (2001). Investments in consumer

relationships: A cross-country and cross-industry exploration. Journal of marketing, 65(4), 33-50.

Yong, A. G., & Pearce, S. (2013). A beginner’s guide to factor analysis: Focusing on exploratory

factor analysis. Tutorials in Quantitative methods for psychology, 9(2), 79-94.

Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta-analysis of

the TAM: Part 2. Journal of Modelling in Management, 2(3), 281-304.

Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: a means-end model and

synthesis of evidence. The journal of marketing, 2-22.

Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1993). The nature and determinants of customer

expectations of service. Journal of the academy of marketing science, 21(1), 1-12.

Zhang, D., & Adipat, B. (2005). Challenges, methodologies, and issues in the usability testing of

mobile applications. International journal of human-computer interaction, 18(3), 293-308.

Zikmund, W. G., & Babin, B. J., Carr, JC, & Griffin, M.(2009). Business research methods.

331

Sites consulted

http://geert-hofstede.com/

http://habrahabr.ru/

http://innospace.ru/mobilnyie-marketingovyie-kommunikatsi/

http://worldfoodtravel.org

http://www.distimo.com/about

http://www.economie.gouv.fr/

http://www.euromonitor.com/

http://www.flurry.com/

http://www.gartner.com/technology/home.jsp

http://www.gks.ru/

http://www.jiwire.com/news-updates/press-releases/

http://www.russiatourism.ru/

http://www.softpressrelease.com/

http://www.worldbank.org/

http://www.wttc.org/

http://www2.unwto.org/

https://www.cen.eu/

http://www.imf.org/

http://www.girafoodservice.com/

http://worldfoodtravel.org/

http://web.json.ru/en/json_partners/

http://www.criteo.com/

https://www2.deloitte.com/

https://www.opentable.com/

http://en.unesco.org/

https://wapstart.ru/en/

https://www.mos.ru/en/

https://www.insee.fr/

http://www.oecd.org/

http://www.economie.gouv.fr/les-ministeres/directions-ministere-economie-industrie-et-

numerique

http://atout-france.fr/

http://www.parisinfo.com/

http://www.cci-paris-idf.fr/

332

https://2gis.ru/

https://www.tripadvisor.com/

http://allcafe.ru/

https://msk.resto.ru/

http://stoliki.ru/

https://www.lafourchette.com/

https://restaurant.michelin.fr/

http://statistica.io/resources/

http://www.rostourunion.ru

https://www.apple.com/pr/

333

Annexes

334

Annex 1: Homepage of the website resto.ru

Annex 2-4: Examples of cooperation with tripadvisor

List of the restaurants on tripadvisor The application form for booking Logo of ZON, booking project of

Resto.ru

335

Annex 5: Choice of the city

Annex 6: Choice of the restaurant: by name, by location

Annex 7: Bottle of champagne as a gift for booking

336

Annex 8: Homepage of the website Lafourchette

Annex 9-11: Mobile application’s functionality

Choice of the city Search for the restaurant Tripadvisor rating

337

Annex 12-14: Loyalty program

Yums score Special offer by restaurant

establishment

Annex 15: Mobile application for the restaurants’ managers

338

Annex 16: The first version of survey before the experts’ validation.

Please mark one answer in each line Several

times per

week

Once a

week

Several

times per

month

Less than

once a month

Never

1 How often do You go out for lunch/dinner?

2 Please choose your usage frequency for

each of the following by choosing the

restaurant:

a) Mobile application N

b) Website of N

c) other website

d) other mobile application

3 How long are You using the mobile

application N in years? (If your answer on

the previous question a) was other than

never)

4 How long are You using any mobile

application for choosing a restaurant in

years? (If your answer on the previous

question d) was other than never)

5 How long are You using smart phone (in

years)?

Please mark one answer in each line

across

Strongly

Agree

Agree Neither

agree nor

disagree

disagree Strongly

disagree

6 I have resources (IOS, Android, Wi-Fi)

necessary to use mobile application.

7 I have the knowledge necessary to use the

mobile application

8 The use of mobile applications has become

a habit for me.

9 The mobile application N is free for

downloading.

10 Application design:

Overall, I think the mobile application N is

designed well

11 I am very satisfied with the overall design

of the mobile application N.

12 User interface structure:

Overall, I think the mobile application N

structures information effectively

13 I am very satisfied with the way the mobile

application N is structured

14 I use geolocation on mobile application N

when reserving the restaurant

15 I can get help from mobile application

provider when I have difficulties using

mobile application N.

16 Mobile application N gives me a feeling of

trust.

339

17 The information provided by mobile

application N is always honest.

18 Mobile application N is trustworthy

19 I intend to continue using mobile

application in the future.

20 I will always try to use mobile application

in my daily life.

21 I plan to continue to use mobile application

frequently.

22 I intend to continue using mobile

application rather than discontinue its use.

23 My intentions are to continue using mobile

application than use any alternative means

24 If I could, I would like to discontinue my

use of mobile application.

25 I encourage friends and relatives to be the

customers of the mobile application N

26 I will use more services offered by the

mobile application N in the next few

months/years

27 I consider the mobile application N to be

my first choice

Please mark one answer in each line very likely likely undecided unlikely very

unlikely

28

How likely are you to:

make more than in 50 % of reservation in

restaurants using mobile application N?

29 use mobile application N the very next time

you choose the restaurant?

Please mark one answer in each line Several

times per

week

Once a

week

Several

times per

month

Less than

once a month

Never

30 How often You go out for lunch/dinner

because of promotion on the mobile

application N

Please circle one answer in each line Strongly

Agree

Agree Neither

agree nor

disagree

disagree Strongly

disagree

31 The bonuses and promotions provided by

mobile application N are reasonable.

32 I use mobile application N to enjoy

promotions

33 I check the promotions on mobile

application N in the restaurant before

entering it

34 I prefer to go to the restaurant next to me

even there are no promotions

35 I prefer to go to the long-run restaurant

because of promotions on the mobile

application

36 I keep time free for leisure

37 Other people or circumstances can not

change my intention

340

38 I like to live in Moscow / Paris

39 Your year of birth?

40 Your gender?

41 Your professional occupation?

Titre : Utilisation des applications mobiles dans les secteurs du tourisme et de la restauration: étude

comparative entre la France et la Russie

Mots clés : marketing mobile, application mobile, technologie, comportement des consommateurs,

tourisme, restaurants

Résumé : Les smartphones avec leurs

différentes applications ont créé une nouvelle

base de clients. Les données de l'utilisateur des

smartphones, obtenues en grande quantité,

permettent aux entreprises de développer leurs

stratégies marketing et d'augmenter également

le nombre de biens et de services achetés. Les

marchés du tourisme et de la restauration offrent

de nouvelles possibilités grâce à l'utilisation

d'applications mobiles dans le but d'améliorer la

qualité des services offerts.

L'objectif de cette recherche est d'étudier

l'utilisation des applications mobiles dans les

industries du tourisme et de la restauration et

d'effectuer une comparaison interculturelle entre

la Russie et la France. Pour atteindre cet objectif

tant théorique qu’empirique, nous avons

appliqué une approche hypothético-déductive.

Dans notre recherche, deux applications

mobiles sont analysées: Lafourchette en France

et Resto en Russie. L'utilisation de l'application

mobile est examinée à partir de trois points de

vue théorique: l'utilisation de la technologie, le

marketing relationnel et la théorie culturelle. Le

modèle TUAUT2 est le modèle central de notre

recherche; modèle qui a fait l'objet de

nombreuses études. Nous avons considéré ce

modèle en relation avec le marketing relationnel

et les dimensions culturelles.

Les résultats de notre étude sur l'utilisation des

applications mobiles ont confirmé nos

hypothèses. La comparaison interculturelle a

démontré des différences liées aux problèmes

économiques, culturels et technologiques.

Nos résultats sont discutés à partir des

perspectives théoriques et managériales.

Title : Mobile application use in the tourism and restaurant industries : comparative study between

France and Russia

Keywords: mobile marketing, mobile application, technology, consumer behavior, tourism,

restaurant

Abstract: The smartphones with application

stores have created a new customer base. The

smartphone user's data, obtained in huge

amount every second, enables companies to

build their marketing strategies, and to increase

the number of goods and services purchased.

The tourism and restaurant markets offer new

possibilities with the use of mobile applications

in order to improve the quality of the provided

services.

The objective of this research is to study

mobile application use in the tourism and

restaurant industries and to accomplish cross-

cultural comparison between Russia and

France. Applying a hypothetico-deductive

approach both theoretical and empirical

investigations are serving this objective.

In the research two mobile applications are

analyzed: Lafourchette in France and Resto in

Russia. The mobile application use is regarded

from three theoretical points of view:

technology’s use, relationship marketing and

cultural theory. UTAUT2 model is the central

model of the research; this model has been a

subject of numerous studies. We regarded this

model in relation to relationship marketing and

cultural dimensions.

The results of the survey about mobile

application use confirmed the hypotheses.

Cross-cultural comparison demonstrated

differences related to the economical, cultural

and technology’s issues.

Our findings are discussed from theoretical and

managerial perspectives.