Communicating the Value of Location-Based Social Networks (LBSN)

Master of Business Administration Project

MBA Project

Communicating the Value of

Location-Based Social Networks (LBSN)

Artur S. Uroda10020031

August 2013

Supervisor: David Stevenson

Artur Uroda, Communicating the Value of Location-Based Social Networks (LBSN)

DeclarationMBA

I declare that the work undertaken for this MBA Project has been undertaken by myself and the final Project produced by me. The work has not been submitted in part or in whole in regard to any other academic qualification.

Title of Project: Communicating the Value of Location-Based Social Networks (LBSN)

Name (Print): Artur S. Uroda

Signature: _____________________________________________

Date: 15.08.2013

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For Memory of My FatherJerzy Uroda1953-2013

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Table of Contents

ABSTRACT.....................................................................................................................................................................6

INTRODUCTION...........................................................................................................................................................7

Aims and objectives..................................................................................................................................................11

CHAPTER ONE............................................................................................................................................................13

LITERATURE REVIEW..............................................................................................................................................13

Theoretical framework .............................................................................................................................................13

Perception of value....................................................................................................................................................15

The uni-dimensional approach..................................................................................................................................15

The multi-dimensional approach...............................................................................................................................16

Research framework..................................................................................................................................................20

CHAPTER TWO...........................................................................................................................................................25

RESEARCH METHODOLOGY..................................................................................................................................25

Background of the LBSN case..................................................................................................................................25

Privacy issues............................................................................................................................................................27

Research design.........................................................................................................................................................28

Development of the measurement scale....................................................................................................................29

Data collection..........................................................................................................................................................30

CHAPTER THREE.......................................................................................................................................................35

FINDINGS AND DISSCUSIONS................................................................................................................................35

Factor Analysis..........................................................................................................................................................35

Regression Analysis..................................................................................................................................................38

Interpretation. Antecedent effect of Conditional Value............................................................................................39

Interpretation. Effect of Convenience, Emotional, Monetary, Social and Epistemic values on BI..........................41

CHAPTER FOUR..........................................................................................................................................................43

CONCLUSIONS AND RECOMMENDATIONS........................................................................................................43

Managerial Implications ...........................................................................................................................................44

Limitations and future research direction ................................................................................................................47

CHAPTER FIVE...........................................................................................................................................................48

BIBLIOGRAPHY AND REFERENCES......................................................................................................................48

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List of Figures

Figure 1. Communication Channel …................................................................................................9

Figure 2: The Five Values of Consumption (Sheth et al., 1991) …..................................................17

Figure 3. Researched Model ….......................................................................................................24

Figure 4. Demographic Data (age, children, income, education) …................................................34

List of Tables

Table 1. Gender ….........................................................................................................................32

Table 2. Which mobile service do you use the most often to 'check in'? …...................................33

Table 3. Which mobile service do you use the most often to 'check in'? …...................................33

Table 4. Measurement Model …....................................................................................................37

Table 5. Hypothesis Test …...........................................................................................................38

Table 6. Hypotheses Test …..........................................................................................................39

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ABSTRACT

Purpose

The purpose of this paper was to examine the customer perceived value (CPV) of Location-Based

Social Networks (LBSN) to determine how active users perceived the value of LBSN and how the

value dimension can be structured. Research has examined up to date major theories of value,

models and authorities’ opinions and has concluded a comprehensive and systematic overview of

the research on perceived value. Discussed nature and dimensions of value have pointed out which

values approach is the most sufficient to examine the LBSN industry.

Design

In the primary area, positivist philosophy and a deductive approach were adopted. During the

literature review a major theory was selected and a set of hypotheses was set out and tested.

Qualitative methods were used, including a conduct questionnaire survey that was focused on active

users of LBSN.

Findings

Generally, the study found empirical support for the role of conditional value as antecedents that

intensify the need to use a service in certain situations and, consequently, enhance emotional, social,

monetary, epistemic and convenience values were derived from the use of LBSN. Further analysis

showed that a set of predictor variables significantly predicted the level of Behavioural Intention (BI).

Analysis reported that two of the values (Convenience and Emotional) had a very strong influence,

while Monetary and Social Values were less so. The Epistemic value had a negative impact on

Behavioural Intention.

Practical implications

This study has researched only valuable active users who check-in more than three times in week.

This group happened to be predominantly young (18-24 year old) and young-middle aged (25-34

years old) females. Knowing how this segment perceives the value, any company in the LBSN sector

can focus on truly important values and try to increase their perception, which will positively influence

BI to use LBSN.

Keywords: Value, Consumer Perceived Value (CPV), Behavioural Intention (BI), Theory of

Consumption Value, Location-Based Services (LBS), Location-Based Social Networks (LBSN),

Mobile Marketing

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INTRODUCTION

According to Kotler (2008) the marketer’s job is to, ‘create, communicate and deliver value to the

target market for profit.’ Additionally, Holbook (1994) believes that the concept of value is, ‘the

fundamental basis for all marketing activity’. Indeed, most marketing experts understand the

importance of promoting the idea of value as a marketing strategy. The aim of the research

outlined in this paper is to examine the concept of value and the consumer’s perception of value

in relation to the use of location-based social networks (LBSN).

By analysing previous research undertaken over the past two decades it is possible to gain a

deeper understanding of how value is perceived by customers, how the marketing industry has

learned what motivates customers, and what triggers the impulse to buy. The end aim of the

research is to suggest ways in which the direction of LBSN marketing might be driven in the

future, and to also examine how a marketing strategy could be designed in order to match user

expectations of value.

Portable mobile communications devices, such as smartphones and tablets, have now become

a part of daily life for many of us. A smartphone has become as important as a wallet. Sales

through mobile channel are growing rapidly. In 2012 EBay reported that PayPal processed more

than $14 billion in mobile payments. That’s a 250 percent increase over 2011’s $4 billion in

mobile volume. What’s more, that $14 billion represents a significant increase over the

company’s initial projection of $10 billion in mobile payment volume for 2012. EBay is now

projecting that mobile payment volume will increase to $20 billion in 2013 (2012, PayPal

website). In a relatively short period of time mobile communications have evolved from the basic

mobile phone format into sophisticated technological equipment that can perform different

functions. This continually developing new technology gives users the opportunity to interact with

their devices in new ways, and then become active participants in the communications process.

Consumers now have greater choice, and communications firms are constantly trying to attract

potential customers to use certain services and devices. These firms attempt to attract, offer

communications, and then deliver on their promises in real time through the Location-Based

Services (LBS) they offer.

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LBS have the potential to be able to reach a customer’s mobile phone and communicate

marketing content associated with a particular location or place. The two main types of

positioning devices used are the geo-positioning system known as GPS (with accuracy to the

distance of approximately 5m) and the nearest cell phone tower (with accuracy to the distance of

approximately 500m). It is also possible to locate a mobile phone from a wireless access point or

a Bluetooth hot spot. Dushinski (2012) explains that, now, mobile phone users are also able to

initiate communication with others by using LB apps or a check-in function.

It is clear that there are substantial marketing opportunities offered by this new form of mobile

communication. The potential to be able to customise and target marketing campaigns to suit

precise customer needs has already been spotted by firms such as Google, Apple, Microsoft

and Nokia, all of whom are competing to gain a bigger market share. According to a research

report by Berg Insight, the total value of global real-time marketing will grow from $192 million in

2011, at a compound annual growth rate (CAGR) of 91%, to $4.9 billion by 2016 (2012,

Business Wire).

The ability to be able to identify a customer’s location at a certain time of day is one of the most

exciting and lucrative marketing opportunities offered by mobile commerce. Barnes (2003)

explains that new locating techniques can help service providers create entirely new services, or

add value to current ones by taking usage context into account. The uniqueness of mobile

marketing, especially of LBS marketing, is that campaigns can be personalised and can target

individual consumers according to their own preferences or habits. For example, a personalised

offer can be sent to consumers when they are entering a certain shopping location or town.

However, further research into understanding why and how consumers prefer to interact with

LBS will help to develop more successful mobile marketing strategies.

LBSN are very good in terms of driving customers into shops. Companies like Foursquare or

Yelp are able to measure how effective they are. They can suggest customers go to particular

places and within hours they can measure if the customer actually went there and how much

time and money they spent there. The ability to drive and measure additional foot traffic creates

unique relationships with many merchants especially those within chain operations groups.

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Figure 1. Communication Channel

The figure above shows in a schematic way the new-born business that can be delivered to

customers’ communication channels. Local businesses are able to place a profile of their

business through LBSNs like Foursquare, Yelp, Google Places and Facebook Nearby. An

interested party can create a profile themselves or users can create it. LBSN are trying to

engage potential customers in service development. Such committed users are highly valuable

and are often rewarded for extra activity by receiving extra points and badges. In an ideal

situation active users create content and service owners provide a system which allows users,

through their portable devices, to use services at the right time and place. Communication takes

place in both directions. Small or chain businesses can place extra offers to attract more users

who can be considered as potential customers. Because offers can reach customers at the right

time and place it can be perceived as a more customised service (Pura, 2005). In the opposite

direction customers/users can write reviews or tips, add photo, or share their location with

friends. All of these interactions contribute to the development of services.

This research aims to examine active users in the contest of CPV of LBSN. Other research has

already shown that the downloading of information using smartphones and other devices has

significant current and future potential as a method of mass marketing products and services.

Digital technology continues to improve, and this will open up new opportunities for firms.

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However, the selling technology already available is also an exciting and challenging avenue for

further exploration, and one that, if approached correctly, can reap substantial rewards for firms.

Concepts of value are used in many subjects and academic areas like social science,

economics, marketing and social psychology. Taking into account so many areas where the

concept is used, it is easy to misunderstand the definitions. Perceived and personal values are

not the same concept (Woodruff, 1997). Value is an outcome of an evaluative judgement,

whereas the term values refer to the standards, rules criteria, norms, goals, or ideas that serve

as the basis for such an evaluative judgement (Holbrook, 1994). Consumers interact with service

providers and their experiences shape further interaction and influence the evaluation of other

alternative services. In relation to the above value, this is something that is perceived by the

consumer/customer rather than objectively determined by the seller (Zeithaml, 1988). Perceived

value has been a subject of much research and, over the last few decades, academic studies

have developed two approaches: one-dimensional and two-dimensional.

Uni-dimensional approaches to perceived value represent the origin and earlier stage of the

study of the concept. In this approach perceived value is essentially conceived from a utilitarian

perspective, whereby economic and cognitive reasoning is used to assess the relevant benefits

and costs (Sanchez-Fernandez and Iniesta-Bonilo, 2007). The second approach consists of

several interrelated attributes or dimensions that form a holistic representation of a complex

phenomenon (Holbrook, 1994; Sheth, 1991; Sweeney and Soutar, 2001).

In this study an overall assessment of value and the key determinants of BI to use LBSN have

been undertaken. The research examined already available theories and models relating to

consumer behaviour, adoption models, and the perception of value, in order to develop a set of

hypotheses upon which future marketing strategy and research can be based. This has been

undertaken using the perception of value as an important dimension of strategy.

The aim of this research is to enhance academic understanding about the values that drive

potential users to engage with LBSN using smartphones and other devices, and to examine the

future role of marketing strategy in relation to the use of these devices. To do this, it is important

to look at the users’ perception of value in the context of location-based social networks. The

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research aims to answer the following question: What are the key value components that drive

behavioural intention to use LBSN?

The research aims to deliver useful guidelines for both marketing and sales managers, in order

to help them better understand customer perceptions of value in an LBSN context. In the future,

these guidelines will be a useful tool for corporations to use when they are looking to develop

their digital marketing strategy, especially with a view to developing LBSN marketing to meet the

expectations of potential customers.

Aims and objectives

a) To critically examine academic theories and models that concern how consumers perceive

value.

b) To identify the most workable theory to explain CPV.

c) To examine the LBSN sector.

d) To identify the active users’ segment.

e) To conduct a quantitative survey among active LBSN users and test a set of hypotheses

based on an adopted model.

f) To identify the most significant values that influence intention of use of LBSN for the

segment group.

i. Segmenting users in terms of perception of value.

- Determining dominant values.

- Constructing a hierarchy of values.

- Classifying differences between users.

g) To look at how value-based segmentation can be applied in marketing strategy in the

context of other LBSN.

h) To generate a consumer’s ‘perception of value’ model to be used when designing a

marketing campaign for location-based mobile communications services.

The research will aim to answer the following questions:

a) Does the perceived value of LBSN positively affect an individual’s BI to use them?

b) What are the key value components that drive the adoption of LBSN?

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The research focuses on services that are able to match the location of a user with the location

of a commercial enterprise, and then communicate this using map technology. The research

analyses gathered data on when users interact only with this type of LBSN.

The objectives of the research have been developed in such a way as to support a logical flow

as the research progresses. The literature is used to help identify major value theories and as a

tool for creating a set of hypotheses. The research then move onto the next stage, which will be

to undertake a quantitative survey. This approach ensures that the research focuses on key

perceived values that influence consumer behaviour.

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CHAPTER ONE

LITERATURE REVIEW

Theoretical framework

Marketing experts and psychologists have been studying factors that influence consumer

behaviour for many years. Many have designed models and theories relating to consumer

behaviour which have assisted marketers in their strategy.

From one side, the technology-related stream of research is mainly focused on explaining the

initial adoption of technology. The commonly known and popular Diffusion of Innovations Theory

seeks to explain how, why, and at what rate new ideas and technology spread through cultures.

Diffusion is the process by which innovation is communicated through certain channels over time

among the members of a social system (Rogers, 2003). Diffusion of an innovation occurs

through a five-step process. This process is a type of decision-making. It occurs through a series

of communication channels over a period of time among the members of a similar social system

(Rogers, 1962). Rogers’ five stages (steps): awareness, interest, evaluation, trial, and adoption

are integral to this theory. In later editions of the Diffusion of Innovations Theory Rogers changes

the terminology of the five stages to: knowledge, persuasion, decision, implementation, and

confirmation. However, the descriptions of the categories have remained similar throughout the

editions.

Another important example of a technology-related view is Davis’s The Technology Acceptance

Model (TAM), which is an information systems theory that models how users come to accept

and use a technology. The model suggests that when users are presented with a new

technology, a number of factors influence their decision about how and when they will use it,

notably: its perceived usefulness (the degree to which a person believes that using a particular

system would enhance his or her job performance) and perceived ease-of-use (the degree to

which a person believes that using a particular system would be free from effort) (Davis, 1989).

Formulated by Fisbein and Azjen in 1975, The Theory of Reasoned Action (TRA), is a model for

the prediction of behavioural intention, spanning predictions of attitude and behaviour. The TRA

suggests that a person’s behavioural intention depends on their attitude on behaviour and

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subjective norms (BI = A + SN). If a person intends to undertake a particular behaviour then it is

likely that the person will actually do it.

Later, Azjen, inspired by TRA, developed The Theory of Planned Behaviour (TPB), which

explains dependence and the link between beliefs and behaviour. The concept was proposed by

Icek Ajzen to improve on the predictive power of TRA by including perceived behavioural control.

The theory states that attitudes toward behaviour, subjective norms and perceived behavioural

control together shape an individual’s behavioural intentions and behaviours (Ajzen, 1991).

According to the theory, if people evaluate the suggested behaviour as positive (attitude), and if

they think that their significant others want them to perform the behaviour (subjective norm), this

results in a higher intention (motivations) and they are more likely to do so. A high correlation of

attitudes and subjective norms to behavioural intention, and subsequently to behaviour, has

been confirmed in many studies (Sheppard, 1988).

However, these theories, together, cannot clearly explain user acceptance and interaction with

LBSN. Theories for assessing customer behaviour in electronic environments, e.g. Theory of

Reasoned Action (Fisbein and Azjen 1975), Theory of Planned Behaviour (Ajzen, 1991),

Technology Acceptance Model (Davis, 1989), and Diffusion of Innovations Theory (Rogers,

2003) were created especially for assessing technology adoption in organisations, and are not

considered suitable for assessing services for personal use, on the move (Nysveen et al., 2005).

In mobile self-services the importance of time and location for a customer’s value perception

should be emphasised (Heinonen, 2004). This approach has received criticism for ignoring

additional factors that affect the behaviour of the user, such as perceived needs and values,

emotions, image, social influence, and perceived enjoyment. Furthermore, technology is only an

enabler of new and innovative LBSN. Customers’ evaluation of the usage experience is not

based on technology but mainly on how valuable the location-based information can be in a

certain context (Pura, 2005).

There is a need for in depth understanding of what influences customer behaviour and attitudes

not only to technology, but also more importantly to the content and service provider offering the

services. Bettman et al. (1998) suggest that CVP steers purchase behaviour, whilst Turel (2007)

believes that any specific information system is determined by BI. Further, Turel believes that BI

is determined by users’ perceptions of any given system. Perceived value theories provide a

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good context for studying engagement with mobile phone marketing and how a customer

perceives the value of the content. For the purpose of this study perceived value theories are

much more suitable then technology related ones.

Perception of value

The concept of ‘value’ has been discussed in many streams of marketing literature, and has

become one of the most overused - with terms such us ‘judgement value’, ‘consumer value’,

‘customer value’, ‘perceived value’ or ‘shopping value’. Moreover, the term ‘value’ has often

been poorly differentiated from other related constructs, such us ‘values’, ‘utility’, ‘price’, ‘quality’

and ‘satisfaction’ (Sanchez and Iniesta-Bonilo, 2007).

The term ‘value’ refers to a preferential judgement of either a single transaction or an ultimate

end-state, whereby ‘values’ are the determinants of any social behaviour including attitude,

ideology, beliefs and justification. In other words, values, standards, rules, criteria, norms or

ideals serve as a basis for any preferential judgement (Booksberg, 2011).

Researchers examining perception of value theories traditionally begin by analysing previously

available models and theories. Previous research undertaken in connection with perceived value

is usually divided into two different approaches: uni-dimensional and multi-dimensional.

The uni-dimensional approach

Uni-dimensional theory embraces a utilitarian approach, whereby economic and cognitive

reasoning is used to assess relevant benefits and costs (Sanchez and Iniesta-Bonilo, 2007). The

utilitarian perspective of perceived value is derived from a psychological construct describing the

common intuition that “... any increase in wealth, no matter how significant, will always result in

an increase in utility which is inversely proportionate to the quantity of goods already possessed”

(BemoulIi, 1967).

Starting from Monroe’s (2003) pioneering conceptualisation, it has finally been transformed into

a model of perceived value with four components: acquisition value (the benefit relative to

monetary costs that consumers believe they are getting by acquiring a service), transaction

value (the pleasure obtained from taking advantage of a good price deal), in-use value (the utility

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derived from using or consuming the service) and redemption value (the residual benefit at the

time of trade-in or termination) (Parasuraman and Grewal, 2000).

According to this perspective, although value is formally defined in terms of the quality-price

relationship, external cues (such as price, brand name, and store name) influence perceptions of

product quality and value and, as such, the price has a negative effect on a product’s value but a

positive effect on perceived product quality (Dodds, 1991).

Another influential model was proposed by Zeithaml. Means-End Theory (1988) provides a

theoretical and conceptual structure which connects a CPV with BI. The theory suggests that

decision-making processes regarding consumption are influenced by links among product

attributes, perceived consequences of consumption, and the personal values of consumers. The

central hypothesis of the means-end theory is that individuals are goal-directed and they use

product or service attributes as a means of achieving desired end states (Sanchez and Iniesta-

Bonilo, 2007).

Zeithaml (1988) described four different definitions of value: (I) value as low price; (II) value as

whatever the consumer wants in a product; (Ill) value as the quality obtained for the price paid;

and (IV) value as what the consumer gets for what he or she gives. He defines value as a bi-

directional trade-off between “giving” and “getting”, and believes that people evaluate products

on the basis of their perception of price quality and value, rather than on the basis of their

objective attributes. This infers that an idea of value is based on the individualistic or the

personal, rather than the actual quality of the item. Zeithaml suggests that perceived value is

constructed at a higher level of thought than that which perceives quality, because quality can be

sacrificed in the buying process to meet an end desire. Also, he suggests that situational or

contextual factors can affect the formation of value perception, and that perceived value is

subject to the influence of a consumer’s frame of reference.

The multi-dimensional approach

Based on their studies of the uni-dimensional approach, Woodruff and Gardial (1996) decided to

look at the concept of value from a broader perspective. In doing so they formulated a model

which proposes that consumers perceive value by considering product attributes, purchase

consequences, desires, and states. The latter (states) is achieved by means of a compromise

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between desired and received value. Woodruff and Gardial argue that a customer’s learned

perception, preferences, evaluation, and value judgments are shaped by a specific use situation,

and this may change over the time. Also, they note that when a customer is considering buying a

product, their evaluation of product attributes, performance, and the consequences arising from

use, can work to facilitate, or block, the customer’s perception of the achievement of certain

goals and purposes, and therefore the sale (Woodruff, 1997).

Previous studies have mostly ignored the customer’s emotional engagement with shopping, and

have concentrated more on the rational processes of shopping. However, Babin (1994) explored

the emotional aspect of consumer decision-making, and he proposed a scale of values

positioned between the utilitarian and the hedonistic. Babin took inspiration from logician and

philosopher Robert S. Hartman (1967) who pioneered an axiological theory of value, which

identifies three value dimensions: extrinsic (emotional engagement), intrinsic (utilitarian or

instrumental engagement) and epistemic (rational, logical and systematic engagement).

Sheth et al. (1991) developed a theory of consumption which aims to explain how consumers

make choices in the marketplace. They suggest that consumer choice behaviour is influenced by

many factors, but they identify five main consumption values. During the course of most

purchases consumers choose between a few alternatives, and products are evaluated using

multiple value choices. These values are: functional, social, emotional, epistemic and

conditional.

Figure 2: The Five Values of Consumption (Sheth et al., 1991)

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Functional value. The perceived utility acquired from an alternative’s capacity for functional,

utilitarian or physical performance. An alternative acquires functional value through the

possession of salient functional, utilitarian or physical attributes. Traditionally, functional value is

presumed to be the primary driver of consumer choice and an alternative’s functional value may

be derived from its characteristics or attributes such as reliability, durability and price.

Social value. The perceived utility acquired from alternative association with one or more specific

social groups. An alternative acquires social value through the association with positively or

negatively stereotyped demographic, socioeconomic and cultural-ethic groups. Choices

involving highly visible products and goods or services to be shared with others are often driven

by social value.

Emotional value. The perceived utility acquired from an alternative’s capacity to arouse feelings

or affective states. There is an alternative-acquired emotional value when associated with

specific feelings or when precipitating those feelings or affective states. This is often driven by

non-cognitive and unconscious motives.

Epistemic value. The perceived utility through the arousal of curiosity, the provision of novelty

and/or the satisfaction of a desire for knowledge. This is an entirely new experience that

provides epistemic value. However, an alternative that provides a simple change of pace can

also be imbued with epistemic value.

Conditional value. The perceived utility through the presence of antecedent physical or social

contingencies in a specific situation.

Sheth et al. (1991) propose that, when purchasing a product, customers prioritise functional

value, and this sits at the top of the hierarchy of value choices because the most important

customer consideration is that a product is fit for the purpose it was intended. Therefore, product

attributes such as reliability, durability, price, and performance will be considered in any product

comparison process. Additionally, social value is classed as one of the higher values. Products

are often compared by potential purchasers according to their perceived values of social

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acceptability, and purchases can be related to ‘the willingness to please’ and ‘social acceptance’

(Sanchez and Iniesta-Bonilo, 2007).

Often products or services are capable of arousing positive (or negative) feelings. Emotional

value may be perceived in relation to an alternative if it is associated with a specific feeling

(which can be negative too) or when the product is able to precipitate or perpetuate these

feelings. These states are often driven by non-cognitive or unconscious motives. Therefore, from

a consumer perspective, novelty can arouse curiosity and/or satisfy a desire for knowledge, and

this can be perceived as a value with epistemic character. Alternatives products that inspire

novelty are chosen mainly because the purchaser wants to try something new, or their curiosity

is aroused.

A specific situation or set or circumstances can arouse perceptions of value too. This type of

value is called conditional value and can often influence, reinforce or complement the perception

of other values such as physical and social. For the purposes of this research conditional value

will be defined as a value existing in a specific context. Pura (2005) defines this as information

which characterises a situation, and is related to interaction with human applications and the

surrounding environment, or customised information relating to the current location of the

customer.

Reflecting on the behavioural nature of perceived value, Holbrook (1994) proposed a typology of

consumer value with three dimensions, as follows:

Extrinsic/intrinsic – the consumer perceives value in using or owning a product or service as a

means to an end versus an end in itself.

Self-/Other-oriented – the consumer perceives value for their own benefit versus for the benefit

of others.

Active/reactive – the consumer perceives value through direct use of an object versus

apprehending, appreciating or otherwise responding to an object.

Hedonistic use of services in a social context and communicating with peers for socialising

purposes are gaining importance. Current mobile services such as a friend finder, dating ad chat

services offer new hedonistic and social service experiences and these possibilities should be

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taken into consideration also in reference to perceived value theory development (Pihlstrom,

2008).

In terms of its suitability for this research, the framework used by Sheth et al. (1991) is deemed

the most applicable to use as a basis for creating a set of testable hypotheses. This framework

takes into account context dependency, which is a characteristic feature of LBSN, and it

facilitates the consideration of utilitarian and hedonistic values, as well as functional and

emotional values.

Uni-dimensional and multi-dimensional methods have been used to measure perceived value.

Early efforts to measure perceived value by using a uni-dimensional scale have been criticised

for lacking validity (Woodruff and Gardial, 1998). Generally, measuring perceived value as a

trade-off between price as the sacrifice and quality as the benefit is too simplistic since

consumers may be able to identify fifty or more different attributes that shape their perceptions of

value prior to, during and after consumption (Gale, 1994). Therefore, perceived value has been

dominantly operationalised using multiple item scales for better measurement.

Recognition of hedonistic values as a component of perceived value dimension affects the

redefinitions of perceived value. Earlier approaches defined value as a trade of between benefits

and sacrifice (Zeinthaml, 1988; Monroe 1990) and have been subject to criticism. A utilitarian

approach seems too shallow to test mobile service facility. The need to take the hedonistic

approach into account affects the change in the perceived value approach toward a benefit-

oriented value judgement. In this case, the customer assessment of the benefits of using a

service is based on perceptions and experiences of use that facilitate achieving the customer’s

purposes in a specific use situation compared to other alternatives (Pihlstrom, 2008).

Research framework

It was proven earlier that the multidimensional view of perceiving the value is the best approach

to examine LBSN.

For the purpose of this study the original five consumption value dimensions (functional, social,

emotional, epistemic, conditional) developed by Sheth (1991a) has been modified. Taking into

account LBSN and its strictly mobile nature, the research accepted Sweeney and Soutar’s

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(2001) suggestion that functional value has sub-dimensions that should be measured separately

and so functional value can be divided into monetary value and expected performance of the

product, which in this case we call convenience value. Earlier research reported that

convenience has as an important role for mobile services users (Anckar and D’Incau, 2002).

Sheth’s theory assumes that the consumption values are independent. The researcher had to

abandon this axiomatic principle and apply suggestions made by Sweeney and Soutar (2001).

Researchers encouraged the idea that, in the future, the role of conditional value should be

tested as a different order dimension than the other value dimensions. In the context of LBSN for

the purposes of this search, conditional value was pulled out from the row of values. Research

especially highlighted the mobile field where perceived value should include the time and

location in which the service process occurs (Heinonen, 2004). Sheth defined conditional value

as a perceived utility acquired by alternative as a result of the specific situation or set of

circumstances facing the choice maker. He argued that an alternative acquires conditional value

in the presence of antecedent physical or social contingencies that enhance its functional or

social value. In the context-related environment, many factors such as time (lack of time),

location (unfamiliar location), access (no alternative access) and uncertain conditions (urgency)

can shape conditional value, which influence other values.

Conditional value denotes the value derived from the independence of time and place and is

experienced only in certain contexts or situations (Holbrook, 1994; Sheth et al., 1991). It is

generated as a result of a specific situation or set of circumstances under certain conditions

depending on time, location, and the social and technological environment. Therefore, we

suggest an antecedent effect of conditional value which positively influences monetary,

convenience, emotional, social value and epistemic conditions.

Further researchers (Pihlstrom and Brush, 2008) accepted this recommendation and

successfully readopted Sheth’s model, which suggested the important role of conditional value in

influencing other values, in particular monetary, convenience and emotional worth. However,

authors went further and divided Sheth’s values dimensions into two groups: context-related and

content-related value perceptions. Conditional value was rightly placed in the first group.

Moreover, researchers recognised the ascendant effect of epistemic value on monetary,

emotional and social values.

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Although Pihlstrom and Brush tests proved an insignificant influence of epistemic value on

emotional and social value, the researcher decided to recognise the direct influence of epistemic

value on consumer choice behaviour. In this case, epistemic value plays an important role that

stands independently with other values. This uniqueness results from factors influencing their

nature such as curiosity, novelty and knowledge-seeking, which have been suggested to

activate product search, trial, and switching behaviours (Howard and Sheth, 1969). It must be

considered that discussed value occurs regardless of social and emotional value but can have

an insignificant impact on them. More importantly, the initial period can trigger behaviour so it is

a major factor in consumer behaviour before the rest of the value is fully defined.

Hypothesis

Monetary value denotes perceptions of good value for money or low price compared with

alternatives (Sheth et al., 1991). All researched LBSN are free of charge. By using the

researched services, the customer does not pay for content but also has the opportunity to

select the offer with an additional discount. Taking into account his location he is able to pick up

the closest discount offer and cash it almost immediately. Because electronic self-services are

often perceived to save time and money (Meuter, Ostrom, Roundtree and Bitner, 2000),

monetary value is expected to have a positive effect on the use of all mobile services.

H1. There is a positive relationship between monetary value and intention to use LBSN.

And

H2. There is a positive relationship between conditional value and monetary value.

Convenience value represents the ease and speed of achieving a task effectively and efficiently,

thus saving time and effort (Anderson and Srinivasan, 2003). Convenience has been considered

a general motivation to use self-services (Anckar and D’Incau, 2002). Additionally, convenience

value can be influence by conditional value under a specific context like, for example, location in

an unfamiliar place, the ability to position mobile services in this place and access to a database

with local services. Is expected that convenience value has a positive effect on the intention to

use LBS and that conditional value positively affects convenience value.

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H3. There is a positive relationship between convenience value and intentions to use LBSN

And

H4. There is a positive relationship between conditional value and convenience value.

Emotional value arises through fun or enjoyable service experiences (Holbrook, 1994). Service

acquires emotional value when it becomes associated with specific feelings or affective states

(Sheth, 1991a). It can arouse positive or negative feelings or reduce those feelings. Like for

example customer who is lost in an unfamiliar place and uses his service can find a way to

reduce stress. Emotional value is expected to influence the use of mobile information content

and conditional value positively affects emotional value.

H5. There is a positive relationship between emotional value and intentions to use LBSN.

And

H6. There is a positive relationship between conditional value and emotional value.

Social value is defined as the social approval or enhanced social self-concept generated by

service use (Sweeney and Soutar, 2001). Using the service once through the tested LBS user

can feel associated with a specific social group. Moreover, he or she can create a stronger bond

with their peers by sharing their locations and actual place between members of his group.

H7. There is a positive relationship between social value and intention to use LBSN.

And

H8. There is a positive relationship between conditional value and social value.

Epistemic value relates to experienced curiosity, novelty or knowledge gained by using products,

services or technology (Pihlstrom, 2008) As was mentioned earlier, curiosity can be a primary

trigger of purchase (Sheth et al., 1991a) but it has to be remembered that novelty value

gradually vanishes after a trial, and the service may not be used in the long-term if the primary

motivation is curiosity or novelty-seeking (Pura and Gummerus, 2007). Moreover, uniqueness of

LBSN comes from location ability. Users can download applications and instantly check their

location on the map, check places of interest, and find their friends and places where they were

in the last couple of hours. It can be easily assumed that location ability drives curiosity at the

beginning stage of use.

23


H9. There is a positive relationship between epistemic value and intention to use LBSN.

And

H10. There is a positive relationship between conditional value and epistemic value.

The value dimensions have each been conceptualised as having a direct influence on

consumption behaviour (Sheth et al., 1991). The conceptual research model proposed the direct

effect of monetary, convenience, emotional, social and epistemic values on Behavioural

Intentions to use LBSN. The figure below shows the conceptualised model.

Figure 3. Researched model.

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CHAPTER TWO

RESEARCH METHODOLOGY

Background of the LBSN case

Location-Based Social Networks are a part of the quickly developing industry of Location Based

Services (LBS), which are used in a variety of contexts such a place annotation, social activities,

places recommendation, tracking an object, tracking people, helping tourists and travellers,

indoor object search, venues search, work and personal life.

For the purpose of this research services that mainly focused on place recommendation were

chosen. They are Foursquare, Yelp, Google+ Local and Facebook Nearby.

Foursquare is a geographical location-based social network that incorporates gaming elements.

Users share their location with friends by “checking in” via a smartphone app. Their location is

based on GPS hardware in the mobile device or network location provided by the application.

Points are awarded for checking in at various venues. Users can connect their Foursquare

accounts to their Twitter and Facebook accounts, which can update when a check-in is

registered. By checking in a certain number of times, or in different locations, users can collect

virtual badges. In addition, users who have checked in the most times at a certain venue will be

crowned “Mayor” until someone surpasses their number. Various venues have embraced

Foursquare, and offer special deals to users who are “mayors”. This system is able to give the

user personalised recommendations and deals based on where he/she, his/her friends, and

people with similar tastes have been. The Foursquare community has over 30 million unique

users worldwide and has over 3 billion check-ins already, with millions more made every day

(https://foursquare.com/about, 2013).

Yelp is a Location-Based Social Service and review site with social networking features which

provide online bias in relation to local search capabilities for its visitors. A typical search includes

what the user is seeking and the location from which the search is to be performed. Each

business listing result contains a filtered 5-point rating, filtered reviews from other site visitors,

and the business details. Yelp combines local reviews and social networking functionality to

create a local online community. Adding social web functionality to user reviews creates a

reputation system, whereby site visitors can see which contributing users are the most popular,

25


respected and prolific, how long each has been a member, and which have interests similar to

theirs. Peer feedback mechanisms, and placement of popular reviews on the site and in local

market Yelp newsletters helps to motivate contributors. Business owners can also communicate

with contributors who post reviews on their page via messages or public comments. Yelp also

has a “First to Review” reward system to create competition among contributing members,

further motivating the creation of reviews and adding to the site’s business coverage. Yelp

requires reviewers to register an account, and encourages them to use their real name and

photo on their profile. This body of social reviews creates a participatory culture where users

share their personal insight and suggestions, thus creating an evolving body of “collective

intelligence” on local businesses. Essentially, this form of intelligence allows people to actively

participate and share their knowledge with other users.

The company strengthens the online community through off-line events at nightclubs, bars,

restaurants and cultural venues in various cities for its most prolific and loyal contributors, who

are named “Elite” members on the site. These members must provide a photo and their real

name, be at least of legal drinking age, and not own a local business. In return, these members

receive a special badge on their personalised page for every year they author a specific number

of reviews or contribute to the improvement of the online community. The concept is meant to

indicate that the user is a trusted author of business reviews. To gain Elite status, it is often

helpful to be nominated by other Elite users but recognition is bestowed when one writes useful,

funny or cool reviews so members can vote on those reviews. Yelp.com had more than 100

million monthly unique visitors as of January 2013.

Google+ Local is a location-based mobile service, part of the Google+ platform. It gives users

information about different restaurants, stores, venues and businesses in their local area of

interest. G+ Local allows a mobile phone user to search in their area and ‘check-in’ there.

Moreover, the system allows certain people to view their current location. Via their own Google

Account, the user’s cell phone location is mapped on Google Maps. Users can write their own

reviews and upload pictures to Local, and can see their friends’ reviews (if their friends are on

Google+, that is). As friends report their reviews, opinions and pictures, that input is integrated

into the overall score, which other people can see. The service started on 30 May 2012,

replacing an earlier version, Google Places. Google announced in October 2012 that its social

26


networking site was home to 400 million members with more than 100 million active monthly

users.

Facebook, the social-networking giant, entered the check-in world on August 2010. Facebook

Places, which was similar to services such as Foursquare and Yelp, allowed people to use the

GPS on their mobile phones to let their friends know exactly where they are. A year later

Facebook Places was reported to have been discontinued. However, in 2012, Facebook

launched its mobile “Nearby” feature. Thanks to this users are allowed to find local spots nearby

which have been recommended by their friends or simply ‘check-in’ and post their status on their

Facebook wall. The ranking of the places close to a location is based on a number of criteria

such as the number of likes, recommendations and star ratings, mostly those by your friends but

when your friends have not been at a specific place it will show ‘general’ results. In May 2013,

only for iOS users, the company renamed “Nearby” as “Local Search”.

Privacy issues

Every time a consumer uses LBSN, there is a risk that the company offering the service may be

collecting and retaining detailed records of who she/he is, where she/he goes, and what she/he

does. Once collected, outdated privacy laws and varying corporate practices can leave this

sensitive information vulnerable to access by the government and third parties. One implication

of this technology is that data about a subscriber’s location and historical movements is owned

and controlled by the network operators, including mobile carriers and mobile content providers.

A legal framework for data protection that may be applied for location-based services is provided

by the European Union. Privacy issues are regulated by several European directives such as:

(1) Personal data: Directive 95/46/EC); (2) Personal data in electronic communications: Directive

2002/58/EC; (3) Data Retention: Directive 2006/24/EC. However, the applicability of the legal

provisions on varying forms of LBS and processing location data is very complex and unclear.

With European Directives that partially overlap, using definitions of personal data, traffic data,

and location data that are not mutually exclusive, it is difficult to determine which legal provisions

apply when LBS providers process location data (Cuijpers, 2007).

The role of an operator or ISP as the controller of personal data can be questioned in the

provision of value-added services. It could be argued that in this kind of situation it is actually the

27


user of the mobile terminal who is the controller of the data, utilising such data for his/her own

purposes.

Another issue is that there is a distinctive difference between asking someone to check-in versus

taping it to natural behaviour and enabling the technology to just check-in. There are potential

privacy issues perceived by users if their phone just automatically checks in for them. Yet only

Google+ Places providing in their service to automatically check-in. During the check-in process

a user is able to tick-in and next time the system will check-in him automatically. As LBSNs

continue to develop, automatic check-in soon will become a reality so it is critical to establish

legal, technological and social mechanisms in order to protect user privacy.

Research design

To conduct the research below a positivist philosophy was adopted. Positivist philosophers

share fundamental beliefs that the social and natural world conforms to certain fixed and

unalterable laws in an endless chain of causation (Malhotra and Birks, 2006). By using

experimental processes, positivist researchers aim to establish causality in order to explain

phenomena and predict the recurrence of what has been observed. Positivist philosophers

believe that knowledge confirmed by the senses can genuinely be classed as knowledge

(Bryman and Bell, 2011). One important element of positivist research design is to generalise

findings to a target population, and develop the profile of a target market.

The adoption of a deductive approach, which takes into account the relationship between theory

and research, will meet the needs of this research. Bryman and Bell (2011) suggest that theory

and the hypotheses deducted from this theory drive the process of gathering data. However, for

this research an inductive approach has been rejected. The reason for this is because there is

not enough time and resources available to carry out the research using an inductive approach.

Additionally, part of this research will involve undertaking a service usage study, and for this a

deductive approach is more suitable in order to analyse consumer perceptions and behavioural

patterns.

Although quantitative investigation has existed since people first began to record events or

objects that had been counted, the modern idea of quantitative processes have their roots in

Auguste Comte’s positivist framework. Positivism emphasised the use of the scientific method

28


through observation to empirically test hypotheses explaining and predicting what, where, why,

how, and when phenomena occurred (Schunk, 2008). Positivist scholars like Comte believed

that only scientific methods rather than previous spiritual explanations for human behaviour

could advance understanding.

The research methodology demands a two-step approach. Firstly, it is necessary to develop a

scale of measurement by means of a comprehensive review of secondary research and existing

literature. Next, a qualitative content analysis is conducted in order to generate the pool of items

necessary for developing the measurement scale. The literature review provides insight into

consumer perceptions of value, which is broadly researched and tested. In order to test

assumptions about LBSN, an applicable testing model is created by means of adopting existing

theory or combining already existing theories.

Quantitative research involves the collection of data, which is analysed to deduce a series of

conclusions. Bryman and Bell (2011) explain that quantitative research is deductive, uses a

natural science approach (positivism in particular), and is objective in its conception of social

reality. It seeks conclusive evidence based on representative samples, and typically it involves

some form of statistical analysis. The process of measurement is central to quantitative research

because it provides the fundamental connection between empirical observation and the

mathematical expression of quantitative relationships. The researcher analyses the data with the

help of statistics and is hoping that the numbers will yield an unbiased result that can be

generalised to some larger populations (Given, 2008).

Development of the measurement scale

The role of measurement in quantitative research is often regarded as being a means by which

observations are expressed numerically in order to investigate causal relations or associations.

To develop the multi-dimensional scale, existing value scales and associate items were obtained

by drawing from secondary literature research of mobile services and perceived value. Suitable

items were adopted one-to-one, whilst others were slightly adapted in terms of wording in order

to be more applicable to the context of LBS. Eventually, a measurement scale with a total of 34

items divided into six value dimensions and one variable, behavioural intention could be

developed.

29


Convenience value. This dimension was measured by using a six-item scale, was adapted from

Anderson and Srinivasan (2003) and Mathwick et al. (2001) and was then modified to LBSN

purposes.

Monetary value. The four-item scale is a modified version developed by Chen and Dubinsky

(2003), Dodds and Monroe (1991) and Sweeney and Soutar (2001).

Social value. The seven-item scale employed represents a modified version of the scale

developed by Soutar and Sweeney (2003) and Sweeney and Soutar (2001).

Emotional value. The four-item scale operationalised by Soutar and Sweeney (2003) and

Sweeney and Soutar (2001) has been employed.

Epistemic value. The four-item scale has been adapted from Donthu and Garcia (1999).

Conditional value. The six-item scale was adapted from Pura (2005).

Behavioural intentions. The three-item scale was adapted from Gremler and Gwinner (2000),

Taylor and Baker (1994) and Zeithaml et al. (1996).

Data collection

For this research a structured approach to the collection of data was taken. The target group

sample was limited to LBSN active users only, and the sample data was collected by means of a

self-selection online survey. The wording of questions was pre-determined, and those taking part

in the questionnaire were asked to choose from a range of responses presented as multiple

choice. Respondents were asked to evaluate a pool of items in order to assess value

dimensions. Responses were measured using a seven-point Likert-scale model (Saunders et al.,

2007) by asking for the respondent’s extent of agreement or disagreement, ranging from 1

(strongly disagree) to 7 (strongly agree). An online questionnaire snowball strategy was adopted.

A group of approximately 700 LBSN active users were recruited and asked to participate in the

questionnaire. Users of applications such as Google Local+ (excluded during the survey), Yelp,

Foursquare, and Facebook Nearby (excluded during the survey) took part. All of these services

30


utilise a “check-in” function, which allows potential customers to be located through the user’s

smartphone; their location position is then calculated and determined on a map.

The LBSN ‘check-in’ system works when the locations of local facilities are stored on a database

and then compared with a user’s locational position. Facilities within the shortest distance are

then listed on the user’s smartphone screen. A user can then choose a particular location and

‘check-in’. These ‘check-in’ locations can be positioned in many and varied commercial business

premises, such as coffee shops or retail outlets. Some retailers offer various ‘check-in’

incentives to encourage users to ‘check-in’ at their premises.

Only participants above 18 years old were selected and all of them were invited to participate on

a voluntary basis. The invitations were sent out online and no one was coerced into participating

in the research. The prospective research participants were informed about the procedures and

purpose of the research. The information gathered has been kept confidential, and all

participants remained anonymous.

An empirical investigation of current LBSN users was conducted in May 2013 and ran for at least

3 weeks. The questionnaire consisted of two parts. The first solicited demographic information

such as age, sex and utilised mobile services.

The second presented questions pertaining to the proposed model. The questions for the

perceived value were adapted from various academic authors. To measure actual LBSN usage,

users were asked to self-report their frequency of use ‘check-in’ function. To the purpose of this

study, only respondents who have been using a service longer then three months and ‘checking-

in’ more than three times per week were taken into account. Participants that used services

randomly or for a short period of time were considered as non-active users and screened out. To

address face validity, a pilot questionnaire ware conducted. Based on their feedback, several

items were changed to reflect the purpose better. This pre-test examination provided us with

reasonable surety of the validity of the scale items.

The target population was defined as “young (18-24 years old) to middle aged (25-34 years old)

female active users” The responses were collected online for users around the world from

31


Australia, Singapore, Japan, Europe but mostly from the US. All LBSNs are more developed in

the US so users are more conscious of all their functions.

The Yelp service has a friendly design and allows users to communicate with each other. Over

300 personalised letters were sent to users of Yelp with a request to participate in the survey.

To the Foursquare users over 250 letters was sent. Users of this service are not able to

randomly communicate. This service allows users to find Facebook profiles and send messages

via the Facebook platform. For example, if Foursquare’s active users were pre-identified, usually

by the number of check-ins, the researcher visited their Facebook profile and sent a request

letter where he explained the purpose of the research and asked about participation. This

method influenced the number of responses so to achieve a similar number of participants so,

similar to Yelp, more request letters were sent.

During the collection of the data, Facebook was excluded from the questionnaire survey. Despite

the fact that Facebook allows users to check-in, the platform is still recognisable as a purely

social service. Because only location-based social networks are included in this study to avoid

bias Facebook was excluded from the research.

Thus, 162 usable responses were obtained from a non-random population of young and

middle age adults. 87% of the respondents were female (Table 1). A comprehensive study

of this market segment had previously shown that there were existing and significant

differences between perceiving value in the female and male cases. With this in mind, the

target group was limited to females only and all male responses were delayed.

Frequenc

y

Percent Valid

Percent

Cumulative

PercentValid Female 141 87 87.0 87.0

Male 21 13.0 13.0 100.0Total 162 100.0 100.0

Table 1. Gender.

Further analysis showed that responses from Yelp and Foursquare users dominated the

survey. Together they represented 85.8% of all respondents. Facebook ‘check-in’ users

and Google Local managed to collect only 15 and 8 confirmed questionnaires (Table 2).

32


There is no doubt that populations of active users of the above services exist but in this

research a benchmark of active using was set up. Facebook users use their service

generally as a social platform, and for them the check-in function is just an additional

feature; if they use it they do so very randomly. Users of Google Locals represent only 4.9

% of all active users. The researcher was not able to reach a wider base of users.

Although there are several fan groups of ‘latitude’ and the ‘check-in’ function on the

Google+ platform they nave not managed to persuade a larger number of users to

actively participate in the study. Additionally, the Google platform provides several

services which are able to localise smartphones, for example Google Places, and this

service enables users to use the ‘check-in’ function and let the system know where the

user is. Google Latitude allows selected users to share their location in real time. Google

Places has similar principles of operation to Yelp and Foursquare but is not as popular

among the users. Due to the complexity of Google services and a small number of

collected questionnaires, that data was excluded from the survey.

Frequenc

y

Percent Valid

Percent

Cumulative Percent

ValidGoogle

Local/Latitude8 4.9 4.9 4.9

Yelp 78 48.1 48.1 53.1Foursquare 61 37.7 37.7 90.7Facebook check-in 15 9.3 9.3 100Total 162 100.0 100.0

Table 2. Which mobile service do you use the most often to ‘check-in’?

Data collected from Google Local and Facebook Nearby was removed. Moreover, users

who are using their services less then three times per week were removed too. Adopted

definitions of active users were users who are ‘checking-in’ fort a minimum of three times

a week. After analysis, the remaining group members numbered 133 users, which

included 72 active Yelpers and 61 users of Foursquare (Table 3).

Frequenc

y

Percent Valid

Percent

Cumulative Percent

Valid Yelp 72 54.1 54.1 54.1Foursquar

e61 45.9 45.9 100.0

33


Total 133 100.0 100.0Table 3. Which mobile service do you use the most often to ‘check-in’?

The final characteristics of the tested group accepted further examination and showed the

segment population of young (18-24) and young middle-aged (25-34) females, with small group

of middle age (34-43) and mature (+44) females, who are predominantly childless and well

educated. Small differences can be observed between Yelp and Foursquare users in age level.

Foursquare users seem to be of a somewhat younger population with slightly lower income. Yelp

users, according to the collected data, are better educated. Future research could compare

those two LBSNs.

Figure 4. Demographic data (age, children, income, education)

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CHAPTER THREE

FINDINGS AND DISSCUSIONS

A two-step modelling approach was used. The measurement model evaluation included factor

and regression analyses in order to purify and test the resulting measures.

Factor Analysis

Factor analysis is used to uncover the latent structure (dimensions) of a set of variables. It

reduces attribute space from a larger number of variables to a smaller number of factors and as

such is a ‘non-dependent’ procedure (that is, it does not assume a dependent variable is

specified). Factor analysis could be used to create a set of factors to be treated as uncorrelated

variables as one approach to handling multicollinearity in procedures such as multiple regression

(Garson, 2013).

Factor analysis is one of a few methods which is able to discover the structure of researched

subjects and is often used to estimate the structure and the relationship phenomenon. The

Varimax method was used because correlation between the factors was hard to establish. In this

context, the above solution is the best, as it maximises variation between dimensions of the

chosen tool. Cronbach’s alpha reliability analysis is the best and easiest method that can be

used to determine the accuracy of the measurement indicators. In the context of the research it

is the generally accepted method to verify accuracy.

To determine whether the theoretically established tool dimensions overlap in the research

sample the principal components analysis was performed. Analysis of the Kayser-Mayer-Olkin

test .68 indicates that input data is suitable and can be used to further analysis. In the analysis,

the isolation of five factors was imposed. The total explained variance analysis showed that five

factors explained approximately 66.6% of the variability of results.

In order to investigate the reliability of the scale, the Cronbach’s alpha scaling test was used.

The analysis showed that the Conditional Value reliability index was accurate a = .72. The items

making up the Convenience Value factor were very reliable, whereby the measure of alpha for

35


these items amounted to a value of 0.8. The dimension the Monetary Value of Cronbach’s Alpha

was high enough to be considered an accurate measure of a = 0.72. The Social Value indicator

as well as other scales achieved a reliability rate of a = 0.81. The Emotional Value scale has

proved to be a very accurate measure of the selected features (reliability) and Cronbach’s alpha

was a = .75. The Epistemic Value dimension measured by the research tool was characterised

by a high degree of accuracy and achieved a = .74. An overall measurement accuracy for the

Behavioral Intention alpha index was a = .72. and was thus characterised by a very satisfactory

accuracy of the measurement. Results accuracy, reliability after removing the item and

discriminatory powers are presented in the following table.

36


37

Factor α Indicator Load α

Convenience .800 I v alue the ease of using this mobile serv ice. .688 .730

I v alue the opportunity to use this serv ice instantly v ia my mobile dev ice. .459 .786

I v alue the inf ormation/content prov ided by the serv ice. .618 .751

I value the functions that allow me to locate myself on the map. .681 .733

I v alue the f unctions that help me to locate places and businesses on the map. .519 .777

I sav e time when I check f or inf ormation using the serv ice. .394 .804

Monetary .720 I sav e money when I check f or inf ormation using the serv ice. .499 .671

I v alue the 'check in' of f ers I can get, such as discounts, coupons and v ouchers .619 .620

I v alue the loy alty points I can collect when I use the serv ice. .708 .534

I v alue the 'badges' I can collect when I use the serv ice. .295 .791

Social .810 When I use this mobile serv ice I f eel more accepted by my peers. .741 .741

When I use this mobile serv ice I f eel that it giv es other people a good impression of me. .746 .737

I f eel that I gain greater social approv al when I use this mobile serv ice. .724 .741

Rev iews of places and serv ices posted by my f riends are more important to me than others .361 .806

I like to share content and rev iews with my f riends. .292 .817

I like to share my location with my friends. .624 .765

Using this kind of mobile serv ice helps me to meet interesting people. .334 .824

Emotional .750 Using this kind of mobile serv ice makes me f eel good. .601 .692

I like it when people respond to my posting activ ity when I use the serv ice. .700 .680

Using this kind of mobile serv ice is interesting .667 .705

Using this kind of mobile serv ice is enjoy able .534 .783

Epistemic .740 I use this mobile serv ice to experiment with new way s of doing things. .450 .724

I use this mobile serv ice to try out and engage with new location-based technology . .782 .516

I have started using this service because of unique location-based features. .526 .683

I use this mobile serv ice out of curiosity . .396 .754

Conditional .720 I v alue the inf ormation, with the help of which I get what I need in a certain situation. .539 .655

I v alue the independence of place and time of f ered by the use of this mobile serv ice. .535 .655

I v alue inf ormation that has been customised according to my location .439 .686

I v alue receiv ing customised inf ormation about my location. .120 .762

I v alue how the serv ice can tell me about the location of my f riends. .534 .653

I v alue how the serv ice can tell my f riends about my location. .568 .639

Behavioral .720 I intend to continue using this serv ice in the f uture. .607 .557

Intention Next time, when I need this ty pe of inf ormation, I will use this serv ice. .518 .666

I will use location based mobile serv ices more f requently in the f uture. .530 .674

Table 4. Measurement Model.


Regression Analysis

Multiple regression methods can establish whether a set of independent variables explains a

proportion of the variance in a dependent variable at a significant level (through a significance

test of R2), and can establish the relative predictive importance of the independent variables by

comparing beta weights (Garson, 2012).

Regression analysis was chosen because the hypothesis was clearly set up and had theoretical

support. This type of analysis in this context gives the best quality output in terms of the effect on

the dependent variables and the relationships between the independent variables.

An antecedent effect of Conditional Value on Convenience, Monetary, Social, Emotional and

Epistemic Values

In order to verify the research problem, a series of regression analyses was conducted. The

analysis showed a significant effect of Conditional Value on the Convenience Value F (1,131) =

5.23, p = 0.024, on Monetary F (1,131) = 42.71, p <.001, on Social F (1,131) = 103.73, p <0.001

for Emotional F (1,131) = 84.39, p <0.001 and Epistemic F (1,131) = 50.73, p <0.001.

The analysis showed that the Conditional Value has the largest influence on the level of Social B

= 0.797, a little less on the level of Epistemic B = 0.742, less Emotional B = 0.735, less on

Monetary level B = 0.619, and the lowest on Convenience B = 0.215. Results of these analyses

are presented in the table below.

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Model t P-value Sup.B Std. Error Beta

H2 Conditional → Monetary .619 .095 .496 6.535 .000 Yes

H4 Conditional → Convenience .215 .094 .196 2.287 .024 Yes

H6 Conditional → Emotional .735 .080 .626 9.187 .000 Yes

H8 Conditional → Social .797 .078 .665 10.185 .000 Yes

H10 Conditional → Epistemic .742 .104 .528 7.123 .000 Yes

Table 5. Hypothesis Test

Unstandardized Coefficients

Standardized Coefficients


Influence the Convenience, Monetary, Social, Emotional and Epistemic Values on the

Behavioural Intention

In order to verify the hypothesis a Multiple Regression Analysis was performed. The analysis

showed that a set of predictor variables significantly predicted levels of Behavioural Intention F

(1,127) = 16.42, p <.001.

Independent variables were associated with the level of Behavioural Intention by strong

relationship R = 0.63. Analysis of the adjusted R-square showed that all the variables in the

model explains about 37% of the variability Behavioural Intention.

Analysis of the factors showed that the Convenience Value B = 1.04, p <0.001 has the greatest

impact on the growth of the Behavioural Intention, is less influenced by the Emotional dimension

B = .839, p <.001, next by Social B = 0.263, p = 0.052 and Monetary B = 0.194 p = 0.043. The

Epistemic value had negative impact on Behavioural Intention. B = -0.222, p = 0.019. The results

are shown in Table 6.

Interpretation. Antecedent effect of Conditional Value.

This study contributes to the value literature by testing the antecedent effects of conditional

value on other value dimensions. It was proved that conditions under which services are used

through the mobile channel are essential factors in deriving perceived value. In the researched

sample of young to middle age females (YMAF) and the context of Location Based Social

Networks, the analysis showed that Conditional Value was the predecessor of all of the five

values. The greatest impact was seen on the level of Social Value. To active users others are

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Model t p-value SupB Std. Error Beta

H1 Convenience → Behavioural Intention 1.044 .201 .383 5.184 .000 YesH3 Monetary → Behavioural Intention .194 .202 .139 2.474 .043 YesH5 Emotional → Behavioural Intention .839 .247 .329 3.403 .001 YesH7 Social → Behavioural Intention .263 .248 .105 0.053 .052 YesH9 Epistemic → Behavioural Intention -.222 .176 -.121 -1.872 .019 No

Table 6. Hypotheses Test

Unstandardized Coefficients

Standardized Coefficients


considered as their social group members. The simplest way to prove a ‘membership’ in a group

is to actively participate in the portal by making comments, writing reviews and checking-in. All of

those activities, especially check-in, are used to indicate that a user is an active member of

group but to check in an user has to be in the specific situation, in a place, or have faced a set of

circumstances because the system does not allow a user to check-in from outside the place. Re-

assuming in the context of Location Based Social Networks’ Conditional Value strongly

influences Social Value and, moreover, in many situations, users will not perceive social value if

they first do not perceive conditional value.

Similarly, this type of context occurs in the cases of other values. Epistemic value occurs when

services arouse curiosity, provide novelty or satisfy desire for knowledge. In the case of LBSN,

novelty can be experienced during the check-in process and this can only occur in a specific

situation. Emotional value occurs if a service, compared to other alternatives, is able to arouse

positive feelings or affective states. This is more likely to happen if, in a certain place, we are

able to localise ourselves or check-in to our favourite place and then inform our peers where we

are.

Active use of LBSN gives users opportunity to save money. By checking-in to some places users

are reviving discounts or coupons. This is only possible if they use services in specific situation

in certain places. Analysis showed that conditional value influences convenience value but not

as much as one would expect. Generally, the study found empirical support for the role of

conditional value as antecedents that intensify the need to use a service in certain situations and

consequently this enhances emotional, social, monetary, epistemic and convenience values that

are derived from the use of LBSN.

Conditional value is transient and has little worth to the consumer until they are faced with a

specific set of circumstances that give rise to specific behaviour. Holbrook (1994) postulates that

conditional value depends on the context in which the value judgment occurs and exists only

within a specific situation. A marketing offer through mobile devices could enhance perception of

value if it brings context-specific value for the customer. In order to deliver contextual value to

customers, marketing communication needs to be highly personalised. To make a personalised

offer, marketers may need a lot of user information inclusive of the user’s profile (sex, age,

personal anniversary, favourites etc.) and context information such as location-type, time-base

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and mode of spending time. LBSN is an ideal platform to collect this data, tailor and personalise

it and deliver it to users.

Interpretation. Effect of Convenience, Emotional, Monetary, Social and Epistemic values on BI.

The results of the second part of the model are especially interesting. Five values were

examined in the context of influence the Behavioural Intention of use in regard to LBSN. The

analysis showed that a set of predictor variables significantly predicted levels of Behavioural

Intention. The analysis reported that two of the values (Convenience and Emotional) had a very

strong influence, but Monetary and Social values had a reduced effect. The Epistemic value had

a negative impact on Behavioural Intention.

It should not be a surprise that Convenience value influences Behavioural Intention in the

strongest way. Both services are designed in a clear way, whereby users are able to find

relevant functions quickly. Check-in buttons are displayed in a visible place. Platforms

automatically suggest a list of places nearby from which you can check-in. A list of the

surrounding attractions loads quickly and can be display on the map clearly. Profiles of the

places are design in a transparent way, which allows users to browse photos, check directions

and read reviews. Friends’ check-ins are displayed on the list or can be seen on the map.

Convenience is a major factor as to why these two services are gaining users and surviving in

this highly competitive market. Therefore, usability of LBSN should be a major element. Usable

and useful services on phones could give the user key, summarised information. Also, highly

adaptive interfaces will be necessary because of the limited screen display. Platform developers

need to be fully aware of the importance of usability issues.

For the sampling group, young to middle aged females’ emotional value plays a particular role

when generating the intention to use a service. Emotional value is acquired when the service

arouses feelings or effects. Using a service often gives users an opportunity to express

themselves, since check-ins are tools to tell others how active the user is, how interesting the

places that he/she visits are, the standard of restaurants he/she eats in and so on. Writing

reviews can be a way to show how significant a person is, to elucidate what kind of expertise

they possess in this area. All activity can be posted on other social platforms like a Facebook

Wall and amplify positive emotions. Intersections with other users can increase positive

emotions or if they are valued enough can be a sign of belonging to a social group. Two LBSN to

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be examined are Foursquare and Yelp. Use of both can cause a positive emotional state but

Yelp is more pro-social. Users of Yelp can become a part of an Elite group. For those who value

this highly such an incentive can be a real motivator to actively participate in development of a

service. Rewards such as an Elite badge can allow one to be a member of a specific social

group with associations such as being active, dynamic and familiar with local attractiveness.

However, Foursquare doesn’t provide many consciously planned social incentives, as users who

were not members of a social group before can join together with their peers who were

previously unable to express social value using the Foursquare platform. Analysis reports

influence social value on Behavioural Intention but at a slow level. This may be caused by

Foursquare’s marketing strategy, which does not put as much attention on perceived social

value as its competitor Yelp.

Both platforms can be downloaded free of charge. Moreover, users are able to save money by

actively checking-in to places which offer discounts or coupons. For a sampling group this is a

predictor of the behavioural intention to use a service but is not as strong as one would expect. It

should be stressed that such discounts, special offers and savings may not be attractive enough

for young to middle age females. Offers could increase their attractiveness if they are published

on other social platforms.

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CHAPTER FOUR

CONCLUSIONS AND RECOMMENDATIONS

After critically examining a broad range of academic perspectives as they apply to mobile

services, traditional technology-oriented theories such as the Diffusion of Innovations Theory

(Rogers, 2003), the Technology Acceptance Model (Davis, 1989), The Theory of Planned

Behaviour (Ajzen, 1991) and The Theory of Reasoned Action (Fisbein and Azjen 1975) were

rejected. It was admitted that research on LSBNs demands a more user-centric perspective.

Literature, concepts, models and theories of user perception of value were researched. From the

beginning, a clear division between two approaches could be observed. Price-based

approaches, the trade off between benefits and sacrifices, relations between utility - dis-utility,

giving - and the getting approach: all of these are characterised as being uni-dimensional. These

approaches were considered to be too simple and will not provide answers for all research

questions. Moreover, the uni-dimensional approach ignores a range of hedonistic values, which

is important when researching mobile services that are mostly free of charge. In a mobile service

context, a multidimensional view of the I value is necessary. Sheth’s Theory of Consumption

Value was adopted. This theory was used in similar research and confirmed its utility in the

mobile services industry. It is worth noting the definition of perceived value by Pihlstrom (2008)

in a mobile content service context:

“Customer assessment of the benefits of using a service based on perceptions and experiences

of use facilitates achieving the customer’s purposes in a specific use situation compared to other

alternatives.”

The assessment of benefits of mobile services is based on customer perceptions and

experiences when using this service. It follows a very important assumption: to perceive value of

a mobile service a user must have experienced it or a similar mobile service. Because of that,

the researcher assumed that only active users are able to consciously perceive the value of

Location-Based Social Networks. Another assumption noted in the above definition is that user

assessment occurs in a specific use situation.

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Managerial Implications

With the advent of social media platforms and mobile applications, geo-location is now

everywhere. With more smartphones sold daily than people being born, check-ins have become

the norm. This phenomenon is quite new and has become possible thanks to the capacity of the

smartphones to localise people using GPS integration or via Wi-Fi hotspots. With the prospect of

sharing information, geo-location is experiencing a boom. A lot of start-ups and companies have

understood the benefits and the opportunities this technology can provide.

Giants like Facebook and Google work hard to transform their social platforms into location-

based social networks. Facebook started as a free social database for Harvard University and

realised that users were becoming mobile. Adding a check-in feature to their service was a

natural move. Facebook has gained an enormous database of users, over one billion people.

Most of them are active on their Facebook wall, which is not yet very pro check-in.

Google has got extremely accurate maps but their database of users is half the size of

Facebook’s. Companies who want to be able to recognise users need to find a place between

these two giants. Some of them have entered the market with a better understanding of mobile

users’ behaviour and how to add value to their platforms. Yelp and Foursquare are a new

generation of social platforms. From the beginning these were designed to be location-based,

which is a huge advantage.

Giants want to transform their static platforms. Analysis of Facebook’s acquisitions suggests a

shift form traditional social platforms to location-based social networks. In 2012 Facebook

acquired Spool (a mobile bookmarking and sharing content app), and in the same year attained

Glancee (a social discovery platform), in 2011 bought Gowalla (a location-based service), and in

purchased 2010 Hot Potato (check-ins/status updates app).

To be called an LBSN, a company must have an updated map of places, and this will lead to

updated reviews. This indicates that users are actively participating in developing the service. In

the last couple of years Facebook has been using a Google raw map without places on it. As

such, businesses add users or business owners who spot on opportunity to promote their

business. Google is in a different situation. They have accurate maps through their reviews

system, but users seem to be very passive. They are not treating Google+ as a social

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networking platform. To resolve this problem, in 2011 Google decided to buy Zagat, a restaurant

review platform. Zagat’s reviews and ratings became tightly integrated into Google’s services.

Google had initially planned to acquire Yelp, a competing site, but they decided to stay

independent and soon announced a business partnership with Microsoft’s Bing. According to the

agreement, Yelp provides review snippets, photos, and other business attributes that Bing will

feature prominently on their Bing Local pages. Although the Bing Local search pages will begin

to be “Powered by Yelp”, they are competing directly with the new Google+ Local. However,

they believe that that the Yelp data will help them to capture a market share at Google’s

expense.

Geo-location brings another dimension. It’s not only about saying where you are, it is also about

contributing to something bigger: collective intelligence. Indeed, it brings an added value to the

information. Mobile devices are an excellent medium to deliver contextually valuable messages

to users. The average LBSN user may not fully understand how much value they can get

through their device until they receive a marketing offer that is relevant to their needs through

their mobile device.

Value conceptualisation encourages service providers to know in depth the different dimensions

that generate consumer value before designing an appropriate offering. This should enable

marketers to improve service design, develop more sophisticated positioning strategies, conduct

more precise market-segmentation analyses, target communications more effectively, and - in

general - refine the various supply chain activities that culminate in the value offering that is

ultimately delivered to consumers. Further, organisations can and should interpret the results of

the present research as an invitation to identify new value creation opportunities and, therefore,

as one justification for expenditures on the design, the communication and delivery of strategies

that create the kind of consumer value that plays an important role in developing long-term

relationships with users (Wang, 2004).

Companies should choose the customer segments they wish to attract and keep. Research

showed that different segments perceive values differently. For YMAF, the intention to use LBSN

is mostly triggered by perceiving high convenience, emotional values and less so by engaging

with social, monetary values. It should be expected that different segments would perceive

values differently and some factors will be more important than others. Through understanding

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how targeted groups perceive the value of LBSN, a marketer can create tailored marketing

strategies for certain segments and plan marketing communications that address the critical

motivations driving continuous service use. Companies like Foursquare and Yelp implemented

marketing strategies that encouraged users to develop their platform. Others like Google Places

and Facebook Nearby should examine their marketing strategies and thus evaluate how their

users perceive their values.

Moreover, companies can also attract new customers by emphasising the perceived value

dimensions that are considered important by current customers, and thereby promote self-

selection of the service provider based on similar customer-perceived value. For YMAF those

are convenience and emotional value, and less so in terms of social and monetary values.

To increase the perception of convenience value, the platform/app/system in use must be more

user friendly than other competitors on the market. Active users frequently make assessments of

the usability of a service based on their own perception, which is often affected by emotions, and

experiences with similar services, and then compare this with other alternatives. This is work for

engineers, developers and graphic designers to undertake.

Good knowledge on the value perception of each customer segment is crucial when tailoring an

effective marketing strategy. Value-based approach segmentation gives a good foundation for

segmenting and planning marketing strategies that are differentiated to customer segments that

use the mobile service for the same reason. Communicating these benefits to potential

customers who share similar value perception is essential (Pura, 2005).

This study has researched valuable active users who check-in more than three times in week.

This group happened to be young to middle aged females. Knowing their needs and perception

of value hierarchy, a company can focus on truly important values and try to increase their

perception, which will increase their intention to use LBSN. To do this, portals should increase

chances for interaction between users, between users and businesses and between users and

other platforms. A good solution is to actively stimulate and promote such interactions by

delegating a special person to care about this interaction. This action seems to influence

perception of social value but all users desire interaction and this arouses positive feelings.

Users assess positive situations, compared with other alternatives, which can be quiet platforms,

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and can begin to perceive that this service is of high emotional value. It lets them talk and

rewards them for activity but users have to be able to share their reward with others.

Limitations and future research direction

The adopted model has not confirmed the direct influence of epistemic value on BI. Taking this

into account it should be considered that Pihlstrom’s original model is more suitable to research

LBSN. Here, the model placed epistemic value together with condition value as context-related

values and recognised the antecedent effect of other remaining values. Future research could

use Pihlstrom’s model to investigate CPV of other LBSN.

I have to remember that some LBSNs from the beginning were developed as purely social

services with location-based function as a core of its service (Yelp, Foursquare). From other

point “old” social networks like Facebook or Google+ are slowly evolving from being based on

PC devices into Location Based Social Networks that can be used on mobile devices. Mixing

these two types of services could cause considerable distortion in the test results. It should be

assumed that the CPV of Facebook will be different from the CPV of LBSNs. For many users

Facebook is still perceived as only a social network.

Moreover, the survey collected data from Yelp and Foursquare active users. Demographic

analysis has singled out a clear segment population of young to middle-aged females, who are

predominantly childless and well educated. Future research could compare those two LBSNs in

terms of how individual users perceive their value, what the differences between Foursquare and

Yelp active users are in the context of perceiving value, and which value is dominant for each

LBSN. Similar research could take into account Facebook Nearby active users and compare the

results with proper LBSNs such as Yelp or Foursquare.

ACKNOWLEDGEMENTS

Artur Uroda would like to thank all of those anonymous active users of Yelp and Foursquare who

participated in this research and answered the questionnaire. The author also extends his

thanks to Mr David Stevenson for his valuable comments and professional support.

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Communicating the Value of Location-Based Social Networks (LBSN)

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