Measuring the Impact of Customer Loyalty Programs and ...

Measuring the Impact of Customer Loyalty Programs and Mediating

Factors on Customer Loyalty: The Beauty & Health Retailing Stores in

Hong Kong

by

Mr. Mohammed Sardaran Khan

Student No. 3118692

A thesis submitted to the

Faculty of Business and Law

In Partial Fulfillment of the Requirements for the degree of

Doctorate of Business Administration

Newcastle Business School

The University of Newcastle

Australia

2019 September

Declaration

I hereby certify that the content of this dissertation is the result of original research

and has not been submitted for a higher degree to any other universities or tertiary

institutions.

Candidate Signature:

Date: 2019 September 1

iii

Acknowledgments

I wish to express my deep appreciation to all those who have supported and

encourage me to finish this doctoral journey.

First, I would like to express my sincere gratitude to my supervisor and lecturers

from the University of Newcastle, Australia, for their teaching and advice

throughout the programme of the Doctorate of Business Administration.

I am also thankful for the support and understanding of my classmates, colleagues,

friends.

Finally, I would like to express sincere appreciation to all members of my family

where a lot of “together time” was scarified. Without the support and

encouragement from all of them, I do not believe I could have completed this DBA

journey.

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Table of contents Declaration ..............................................................................................................ii Acknowledgments................................................................................................. iii List of Tables .......................................................................................................... ix

List of Figures ........................................................................................................ xi Abstract …… ........................................................................................................xii 1. INTRODUCTION ................................................................................ 1

1.1. Research background .................................................................... 3

1.1.1. Hong Kong Special Administrative Region ............................ 4

1.1.2. Culture in Hong Kong ............................................................. 5

1.1.3. The context of beauty & healthcare Products ......................... 8

1.1.4. Distribution channels in Hong Kong .................................... 10

1.1.5. The shifting market focus amongst major Hong Kong retailers ............................................................................................. 12

1.2. Mechanism of customer loyalty programs .................................. 14

1.2.1. Importance of customer loyalty programs in Hong Kong .... 14

1.2.2. Price sensitivity in the Chinese society ................................. 16

1.3. Research justification .................................................................. 18

1.4. Research questions ...................................................................... 19

1.5. Research methodology ................................................................ 20

2. LITERATURE REVIEW .................................................................... 22

2.1. Store loyalty ................................................................................ 24

2.1.1. Behavioural action ................................................................ 31

2.1.2. Word-of-mouth...................................................................... 34

2.1.3. Commitment ......................................................................... 38

2.2. Customer loyalty programmes .................................................... 42

2.2.1. Evolution of CLP .................................................................. 42

2.2.2. Hard attributes ....................................................................... 47

2.2.3. Soft attributes ........................................................................ 50

2.2.4. Relationships between loyalty program and store loyalty .... 52

2.3. Price sensitivity ........................................................................... 59

2.3.1. Research related to price sensitivity ..................................... 59

2.3.2. The moderation effect of price sensitivity factor .................. 61

2.4. Communications with customers ................................................ 64

2.4.1. Research related to communication with customers ............. 65

2.4.2. Relationship between communication and store loyalty ...... 66

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2.5. Visual merchandising .................................................................. 68

2.5.1. Research related to visual merchandising ............................. 68

2.5.2. Relationship between communication and visual merchandising ..................................................................... 70

2.6. Price image .................................................................................. 72

2.6.1. Research related to price image ............................................ 72

2.6.2. Relationship between price image and store loyalty............. 74

2.7. Location ...................................................................................... 75

2.7.1. Research related to location .................................................. 75

2.7.2. Relationship between location and price image.................... 77

2.8. The development of research questions ...................................... 79

2.9. The development of hypotheses .................................................. 80

2.10. The development of research model ........................................... 91

2.10.1. The parental frameworks ...................................................... 92

2.10.2. The proposed model .............................................................. 93

3. RESEARCH METHODOLOGY ........................................................ 96

3.1. Introduction ................................................................................. 96

3.2. Research paradigm ...................................................................... 96

3.3. Research design .......................................................................... 98

3.3.1. Cross-sectional research design ............................................ 98

3.3.2. Population ........................................................................... 100

3.3.3. Sample................................................................................. 101

3.3.4. Sampling technique ............................................................. 101

3.3.5. Research site ....................................................................... 103

3.3.6. Sample size ......................................................................... 104

3.4. Data collection method ............................................................. 105

3.5. Design of the questionnaire items ............................................. 107

3.5.1. Development of research instrument .................................. 109

3.5.2. Demographic questions ....................................................... 112

3.5.3. Measurements for store loyalty ........................................... 115

3.5.4. Measurements for customer loyalty .................................... 117

3.5.5. Measurements for price sensitivity ..................................... 119

3.5.6. Measurements for communication with customer .............. 120

3.5.7. Measurements for visual merchandising ............................ 121

3.5.8. Measurements for price image scale ................................... 121

3.5.9. Measurements for location scale ......................................... 122

3.6. Common method variance and treatment ................................. 123

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3.7. Statistical data analysis ............................................................. 124

3.7.1. Data screening and data coding .......................................... 124

3.7.2. Descriptive analysis ............................................................ 125

3.7.3. Measurement assessment – reliability test .......................... 126

3.7.4. Measurement assessment – validity tests ............................ 127

3.8. Structural equation modelling and applications ........................ 129

3.8.1. The measurement model ..................................................... 130

3.8.2. The structural model ........................................................... 130

3.8.3. The fit indices in structural equation modelling ................. 131

3.8.4. The test on mediation variables .......................................... 132

3.8.5. The test of the moderation construct ................................... 133

3.9. Limitation of methodology used ............................................... 134

3.10. Ethical considerations ............................................................... 135

3.11. Significance and limitation of current work ............................. 136

4. DATA ANALYSIS ............................................................................ 137

4.1. Introduction ............................................................................... 137

4.2. Naming of variables and constructs .......................................... 137

4.3. Data screening ........................................................................... 138

4.4. Descriptive analysis .................................................................. 139

4.4.1. Descriptive analysis of demographic data .......................... 140

4.4.2. Descriptive analysis for behavioural action ........................ 145

4.4.3. Descriptive analysis for word-of-mouth ............................. 145

4.4.4. Descriptive analysis for commitment ................................. 146

4.4.5. Descriptive analysis for hard attributes............................... 147

4.4.6. Descriptive analysis for soft attributes ................................ 148

4.4.7. Descriptive analysis for price sensitivity ............................ 148

4.4.8. Descriptive analysis for communication with customers ... 149

4.4.9. Descriptive analysis for visual merchandising ................... 150

4.4.10. Descriptive analysis for price image ................................... 150

4.4.11. Descriptive analysis for location ......................................... 151

4.5. Measurement assessment .......................................................... 152

4.5.1. Kaiser-Meyer-Olkin and Bartlett’s Test of Sphericity ........ 153

4.5.2. Exploratory factor analysis ................................................. 154

4.5.3. Communalities .................................................................... 154

4.5.4. Total variance and eigen values .......................................... 155

4.5.5. Content validity ................................................................... 156

4.5.6. Convergent validity ............................................................. 157

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4.5.7. Confirmatory factor analysis............................................... 159

4.5.8. Reliability statistics ............................................................. 164

4.5.9. Discriminant validity .......................................................... 166

4.5.10. Common method variance .................................................. 166

4.6. Structural equation modelling ................................................... 167

4.7. Testing of moderation construct ................................................ 172

4.7.1. Moderation effect of PS on HA and BA ............................. 176

4.7.2. Moderation effect of PS on HA and WOM ......................... 176

4.7.3. Moderation effect of PS on HA and COM .......................... 176

4.7.4. Moderation effect of PS on SA and BA .............................. 177

4.7.5. Moderation effect of PS on SA and WOM ......................... 177

4.7.6. Moderation effect of PS on SA and COM .......................... 178

4.7.7. Testing results summary ..................................................... 178

4.8. Testing of the mediation constructs .......................................... 179

4.8.1. Mediation effect of CWC on VM and BA .......................... 180

4.8.2. Mediation effect of CWC on VM and WOM ..................... 180

4.8.3. Mediation effect of CWC on VM and COM ...................... 181

4.8.4. Mediation effect of PI on LOC and BA .............................. 182

4.8.5. Mediation effect of PI on LOC and WOM ......................... 183

4.8.6. Mediation effect of PI on LOC and COM .......................... 184

4.8.7. Testing results summary ..................................................... 185

4.9. Summary ................................................................................... 186

5. DISCUSSION AND CONCLUSION ............................................... 189

5.1. Introduction ............................................................................... 189

5.2. Discussion of analysis results ................................................... 189

5.2.1. Findings from the structural model assessment .................. 190

5.2.2. The answer to research question 1 ...................................... 192




5.2.6. The rejected hypotheses ...................................................... 198

5.3. Integrating the findings from existing literature ....................... 204

5.3.1. Implications of the findings ................................................ 204

5.3.2. Implications for the researchers .......................................... 205

5.4. Theoretical implications ............................................................ 205

5.5. Implications for managerial practice ........................................ 207

5.6. Limitations ................................................................................ 209

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5.6.1. Quantitative research method ............................................. 210

5.6.2. Cross-sectional research design .......................................... 210

5.6.3. Data collection method ....................................................... 211

5.6.4. Measurement instrument ..................................................... 212

5.7. Recommendations for future research ...................................... 213

5.8. Summary and conclusion .......................................................... 213

REFERENCES ................................................................................................... 215

Appendix 3-01 Invitation Letter (English) ......................................................... 231

Appendix 3-02 Invitation Letter (Traditional Chinese) ...................................... 232

Appendix 3-03 Information Statement (English) ............................................... 233

Appendix 3-04 Information Statement (Traditional Chinese) ............................ 235

Appendix 3-05 Questionnaire (English) ............................................................. 237

Appendix 3-06 Questionnaire (Traditional Chinese) .......................................... 245

Appendix 4-01 Naming Convention of the Questionnaire Items ....................... 252

Appendix 4-02: Descriptive analysis of all constructs’ items ............................. 258

Appendix 4-03: Communalities .......................................................................... 262

Appendix 4-04: Total variance explained ........................................................... 263

Appendix 4-05: Pattern matrix table for all 42 items ......................................... 264

Appendix 4-06: Fit indices of the measurement model ...................................... 265

Appendix 4-07: AMOS - Modification indices of the measurement model ....... 267

Appendix 4-08: Fit indices of the enhanced measurement model ...................... 274

Appendix 4-09: Comparison between constrained and unconstrained models .. 276

Appendix 4-10: Structural model fit indices ....................................................... 278

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List of Tables

Table 1-1: Hong Kong culture ................................................................................ 7

Table 2-1: Dick and Basu (1994) loyalty model ................................................... 28

Table 2-2: Effectiveness of word-of-mouth from Villanueva et al. (2008)........... 35

Table 3-1: Mapping of research constructs and questions .................................. 112

Table 3-2: Demographic questions ..................................................................... 114

Table 3-3: Measuring items for store loyalty ...................................................... 116

Table 3-4: Measuring items for customer loyalty program ................................ 118

Table 3-5: Measuring items for price sensitivity ................................................ 120

Table 3-6: Measuring items for communication with customer ......................... 120

Table 3-7: Measuring items for visual merchandising ........................................ 121

Table 3-8: Measuring items for price image ....................................................... 122

Table 3-9: Measuring items for location ............................................................. 122

Table 3-10: Cronbach's alpha measurement ....................................................... 127

Table 3-11: Kaiser-Meyer-Olkin (KMO) measurement ...................................... 129

Table 3-12: Measure of sampling adequacy ....................................................... 129

Table 3-13: The fit indices in structural equation modelling .............................. 132

Table 3-14: The conditions for different types of mediators ............................... 133

Table 4-1: The precondition questions ................................................................ 140

Table 4-2: Respondent demographics details ..................................................... 142

Table 4-3: Statistics of the favourite beauty & healthcare products retailers ..... 143

Table 4-4: Frequency of visit and memberships ................................................. 144

Table 4-5: Kaiser-Meyer-Olkin and Bartlett’s Test of Sphericity ....................... 153

Table 4-6: Factor loading in relation to sample size ........................................... 154

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Table 4-7: Factor & Items Mapping .................................................................... 158

Table 4-8: Measurement model fit indices and acceptance levels summary ...... 163

Table 4-9: Reliability test result of all customer loyalty constructs .................... 165

Table 4-10: The convergent validity and discriminant validity results ............... 166

Table 4-11: Structural model’s model fit indices and the acceptance values ...... 170

Table 4-12: Regression weights summary .......................................................... 171

Table 4-13: The regression weights of the model with moderator(s) added ....... 175

Table 4-14: Moderating interactive test results summary ................................... 178

Table 4-15: The requirement for a mediation relationship ................................. 179

Table 4-16: The mediation effect of CWC on VM and BA ................................ 180

Table 4-17: The mediation effect of CWC on VM and WOM ........................... 181

Table 4-18: The mediation effect of CWC on VM and COM ............................ 182

Table 4-19: The mediation effect of PI on LOC and BA .................................... 183

Table 4-20: The mediation effect of PI on LOC and WOM ............................... 184

Table 4-21: The mediation effect of PI on LOC and COM ................................ 185

Table 5-1: Hypothesis result of the relationships among the variables .............. 190

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List of Figures Figure 1-1: Distribution channel of healthcare & beauty product in Hong Kong 11

Figure 2-1: Store loyalty construct ....................................................................... 29

Figure 2-2: Store loyalty construct ....................................................................... 30

Figure 2-3: Customer loyalty program constructs ................................................ 46

Figure 2-4: Framework model .............................................................................. 92

Figure 2-5: Framework model .............................................................................. 93

Figure 2-6: Research framework used in current study ........................................ 94

Figure 2-7: The research framework with hypothesis items ................................. 95

Figure 4-1: The components of measurement model.......................................... 159

Figure 4-2: Path diagram of the measurement model ......................................... 160

Figure 4-3: The coefficient of the enhanced measurement model ...................... 162

Figure 4-4: The research framework of the proposed model .............................. 168

Figure 4-5: The proposed structural model ......................................................... 169

Figure 4-6: The components of structural model and the legend for AMOS ..... 169

Figure 4-7: The overall model with moderation constructs ................................ 173

Figure 4-8: The plots two-way interaction effects for the variables ................... 177

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Abstract

Customer loyalty program, associated with physical rewards, intangible awards,

communication with customer, visual merchandising, location, price sensitivity

and price image, was an important concept in the beauty and healthcare product

retail industry in Hong Kong as it was known to help retailers to improve customer

acquisition and retaining cost while improving both market share and revenue.

Studying the acceptance of customer loyalty from the perspective of Hong Kong’s

local customers was the objective of this research, which focused on the beauty

and healthcare product retail industry and intended to help industry practitioners to

better understand the needs of customers and to strengthen their strategies in

devising or improving plans to promote their products and services.

The customer loyalty framework developed by Bridson et al. (2008) and Leong

(2013) was a widely recognized model for studying customer loyalty behaviour

and perceptions of customers concerning the retailers’ products. Based on the

context of Hong Kong, this research adopted Bridson et al. (2008) and Leong

(2013)’s framework and added a few key elements in the framework. Firstly, a

construct named visual merchandising was added. Secondly, price sensitivity was

set as moderator construct in between customer loyalty program and customer

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loyalty. Thirdly, two constructs namely communication with customers and price

image were put into the framework to verify their mediation effects on the

relationship between customer loyalty program and customer loyalty.

Ten constructs and 25 hypotheses were developed, with 53 questionnaire items in

the proposed model. Quantitative methodology and cross-sectional research

were adopted, with a self-administered questionnaire for data collection. There

were 220 samples drawn from the population in the beauty and healthcare product

industry. Descriptive analysis, confirmatory factor analysis and structural

equation modelling were performed using SPSS and AMOS statistical packages,

with positive and supported results for the proposed model.

The research findings showed that Hong Kong beauty and healthcare product

customers generally support customer loyalty, with consideration of price

sensitivity, price image, and communication with customer. The findings indicated

that the soft attribute of customer loyalty program was negatively affecting the

store loyalty in the local beauty and healthcare retailing industry with moderating

effect from the price sensitivity.

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1. INTRODUCTION

Crossover shopping, sharing of products to friends through social media, upgraded

mall shopping experience, local shoppers’ propensity to consume more on online

shop, China’s economic downturn and the long-lasting China’s fight against

corruption environment are uncertainties affecting Hong Kong beauty and

healthcare product retailers and distributors. They are under the pressure to shift

the focus of their sales strategies from the Chinese mainland shoppers to the Hong

Kong local shoppers to protect their business (HKGCC, 2017). In order to generate

a more diversified revenue streams and build solid portfolio of Hong Kong local

shoppers on the less expensive product range from the renowned beauty and

healthcare product brands in Hong Kong, most of the distributors of beauty and

healthcare products encouraged their sales to look for paths leading to stable

business growth based on customer relationship management.

Relationship marketing, meeting customers’ needs through supplier-customer

interaction and exceeding customers’ expectations are part of customer relationship

management that go through a wide range of customer retaining campaigns

(Ahmad & Buttle, 2001; Philippus Brink, & van Rensburg, 2017). A review of

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customer loyalty literature reveals that there are several factors that influence

customer loyalty. Satisfaction, rewards, quality, visual merchandising, trust and

word-of-mouth are amongst the most cited factors that are seen to play a major role

in determining customer loyalty (Agrawal, Gaur, & Narayanan, 2012; Philippus

Brink, & van Rensburg, 2017). While a mix of factors suiting a specific context

has been generally studied when determining customer loyalty program, no

comprehensive framework comprising key variables such as price sensitivity,

communication with customer, price image, visual merchandising, location,

rewards, word-of-mouth, commitment and actions is currently being studied in the

Hong Kong beauty and healthcare products retail industry.

With the driving forces of customer loyalty in the Hong Kong beauty and

healthcare product retail market, this paper explores the connection between the

customer loyalty program and the store loyalty - especially for the moderation

effect of price sensitivity, the mediation effect of communication with customers,

and the mediation effect of price image such that the findings of the results could

provide an understanding for the practitioners in the field to recognize their target

local customer groups as well as to benefit the local customers by having their

expectations fulfilled via the services provided by retailers.

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This chapter is divided into five sections. Section 1.1 covers the background of

customer loyalty program in Hong Kong. Section 1.2 describes the mechanism of

customer loyalty programs that affect store loyalty. Section 1.3 provides the

justification of this research. Section 1.4 describes the research questions, and

Section 1.5 provides the research methodology.

1.1. Research background

The beauty and healthcare products retail market in Hong Kong is facing an

intensified competition as it has a wide range of parties involved in the sales

channels. Most of the distributors in the industry are traders who act as agents for

international cosmetics brands looking to sell to the Chinese mainland, Macau and

Southeast Asia markets and many of these distributors are experienced and well

versed in regional markets and regulations (HKTDC, 2018). Also, most of the

distributors are dedicated for the function of marketing and sales (Australian Trade

Commission, 2014) as Hong Kong has only a small cosmetics and toiletries

manufacturing sector (HKTDC, 2018). The lack of local manufacturers heated the

competition due to the similarity of functions (Australian Trade Commission,

2014). To stand out, many of the distributors encouraged their sales to provide

4

customer retaining campaigns (Oliaee, et al., 2016). The following subsections

summarized the development of the Hong Kong beauty and healthcare products

retailing industry into five unique aspects namely, Hong Kong Special

Administrative Region, Hong Kong’s culture, the context of beauty and healthcare

products, the shifting market focus amongst some Hong Kong retailers and the

distribution channels in Hong Kong respectively.

1.1.1. Hong Kong Special Administrative Region

Hong Kong Special Administrative Region (‘Hong Kong’) is an area located in

Southern China (Floyd, 1998) with 1,104- square-kilometre and living with over

7.4 million people of diverse nationalities (Hong Kong Fact Sheet, 2015).

Therefore, Hong Kong was ranked the 4th densely populated area on earth

(Wikipedia, 2018). Hong Kong and the mainland of China (the Mainland) have

maintained a close trading relationship with each other for many years as Hong

Kong has played an important role in the external trade of the Mainland (CSD,

2018). In 2014, Hong Kong hosted 60 million tourists, of which an estimated 47

million came from China (ITA, 2016). Chinese was the majority ethnic group in

Hong Kong.

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1.1.2. Culture in Hong Kong

In Kroeber and Parsons (1958, p.583), the word culture was described as

“……Transmitted and created content and patterns of values, ideas, and other

symbolic-meaningful systems as factors in the shaping of human behaviour and the

artifacts produced through behaviour.” Frey (2005, p.1) described the effect of

culture values as “Learned, relatively enduring, emotionally charged,

epistemologically grounded and represented moral conceptualizations that assist

us in making judgments and in preparing us to act. In other words, the priorities

we set and the choices we make are significantly based on the (cultural) values we

hold.”

Hong Kong was also an attractive location for foreign branded beauty products such

as Europe, the US and Japan (Australian Trade Commission, 2014). Many Hong Kong

people strive to stay young and beautiful amidst societal expectations (Fan, 2016;

Hongkongbusiness, 2018). For every 1165 female, there was 1000 male (Hong Kong

Fact Sheet, 2015). South Korean beauty and healthcare product sales’ rise is found

being attributed to the increasing popularity of Korean-pop culture among young Hong

Kong women (ITA, 2016) and men (HKTDC, 2017). Some of the success factors of

South Korean cosmetic are attributed to its fair-coloured image of these skin care

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products to the Asian skin type and skin colour (ITA, 2016), the seeing of well-

groomed South Korean actors in television dramas and movies, and the use of online

influencers, social selling and user generated content (Deloitte, 2017).

Furthermore, Hong Kong had fallen as a colony of Great Britain and the western

believes of individualism impacted the Confucianism culture and thinking, and the

living style of the Hong Kong people (De Mooij, 2005). Traditional Chinese culture

or confucianism dominated Hong Kong before mid-19th century till the invasion of

Great Britain (Siu, et al., 2003). Chinese values emphasised the importance of family

unity over individualism (Abelmann, 1997). For example, studies on the content

analyses of Hong Kong media such as Hong Kong home-made television dramas,

commercials and print advertisements have found that women were frequently

portrayed in stereotyped roles (i.e. predominantly in family and home-oriented,

decorative and non-functioning entities) and men were more likely to be authoritative

and as central-figure (Chan & Cheng, 2012). Both gender advocate Chinese values

associated with personal courtesy and “face-giving” (Siu, et al., 2003) which may

interpret as “dignity” but not aptly covering all face-giving’s detail nuances.

7

Hong Kong citizen followed Confucianism in most of the area and the detail is

depicted in below table.

Table 1-1: Hong Kong culture

Sources: Hofstede (1998) and Hofstede & Hofstede (1997)

Confucianism emphasises on “Wulun”, which defines that father is the leader of a

family and father inherits the right to make the final decision for the family (Siu,

et al., 2003). In addition, Hong Kong is a masculinity society with a large power

distance (Hofstede & Hofstede, 1997) and the society has also the characteristics

of collectivism because Chinese people emphasised the importance of family unity

over individualism (Abelmann, 1997; De Mooij, 2005; Hofstede, 1980).

Confucianism also advocates that hard working to achieve significant future

rewards is a virtue (Siu, et al., 2003).

8

As a result of the aformentioned diverse cultural background and confucianism

belief, Hong Kong people like to seek new experience including trying new

products from different countries and to sacrifice short-term interests so as to

achieve long-term rewards (Siu, et al., 2003; Hofstede & Hofstede, 1997). Given

this cultural background and the tendency to try new products from various

retailers from time to time, the store loyalty of Hong Kong people on beauty and

healthcare products was one of the important issues to the beauty and healthcare

products retailers especially those retailers who offered one or more customer

loyalty programmes in Hong Kong.

1.1.3. The context of beauty & healthcare Products

Beauty products shared the similar meaning with cosmetics, skin-care and toiletry

products, while it was defined as the externally applied preparations for skin, nails,

hair, lips, and eyes, etc. so as to change or modify the appearance and personal

hygiene in enhancing the overall beauty (The Columbia Encyclopaedia, 2013;

Barel, et al., 2001).

9

On the other hand, healthcare products include prescription, over-the-counter,

medical product diagnosis or restoration, improvement or modification of the

organism's physiological functions with the properties of prevention or treatment

of diseases (Hys, 2018).

In a recent consumer health consumption research written by PWCHK (2017),

beauty and healthcare product consumers were more engaged than ever in their

healthcare, fitness and wellness. In the Hong Kong market, beauty and healthcare

products such as whitening and anti-ageing products remained popular and they

were targeting the women segment (Parry, 2005; Wood, 2010, HKTDC, 2018).

Furthermore, the demand for men’s grooming and skincare products was found

increased in recent years (Fan, 2016; HKTDC, 2018). As the types and choices

available for selection has increased so much that would give customers an

alternative of beauty and healthcare products, it is therefore of critical importance

for the industry players to outsmart their competitors through a variety of

relationship marketing strategies and tactics. One such tactic would be the speeding

up of ‘loyalty program’ such that companies have the ways and means to prevent

the customer from switching over to the competing beauty and healthcare products

and services.

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1.1.4. Distribution channels in Hong Kong

In Hong Kong, the beauty, healthcare, cosmetics and toiletries manufacturing

sector were small as it had only a few number of companies producing mid-priced

beauty and healthcare products under their own brands such as Choi Fung Hong

and Joseristine, for instance, were made in Hong Kong (HKTDC 2018). Lately, a

few Hong Kong beauty and healthcare product brands which emphasised their

products were “made in Hong Kong”, such as JaneClare, The Happiest

Things and iSUM. However, international brands were still playing the dominant

role in Hong Kong market (Nielsen, 2018).

Hong Kong had been hosting most of the renowned beauty and healthcare product

brands in the world and these products dominated the most expensive and

prominent areas in the popular shopping malls, shopping districts and stores

(Hongkongbusiness, 2018). The distribution channel of beauty and healthcare

products had been developed in local market and was summarised in Figure 1-1.

http://economists-pick-research.hktdc.com/business-news/article/Success-Stories/Building-a-Hong-Kong-Skincare-Brand/rp/en/1/1X330ZNG/1X0ACU8A.htm

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Figure 1-1: Distribution channel of healthcare & beauty product in Hong Kong

A wide range of parties involved in the sales channels: department and specialty

stores, chains, supermarkets, etc. (Australian Trade Commission, 2014) and these

parties were selling similar foreign branded products, given the situation of a lack

of local manufacturers, product differentiation was less useful to attract customers

(Australian Trade Commission, 2014; Oliaee, et al., 2016, HKTDC, 2018).

From the related statistics, the sales of high-end and international branded products

were concentrated in the department stores, while speciality stores focused on

dedicated brands (Australia Trade Commission, 2014). Additionally, middle to

low-end products could be found in chain stores, chain pharmacies and

supermarkets (Australian Trade Commission, 2014). Foreign branded

homogeneities offered in department stores and speciality stores largely reduced

12

the switching costs of customers (Australian Trade Commission, 2014; Oliaee, et

al., 2016).

Due to the complicated processes from the suppliers to the end-consumers as well

as the scratted consumption in nature (Harris, 2000; Johnson, 1999), this structure

of distribution channel, as illustrated in Figure 1-1, was common in the relationship

among department stores and speciality stores (Hoch, et al., 1999; Zboja &

Voorhees, 2006; Mitchell, 2008).

1.1.5. The shifting market focus amongst major Hong Kong retailers

In the past decade, Chinese mainland shoppers and tourists accounted for almost

one third of the luxury spending of goods in Hong Kong and created high-volume

sales for many traditional beauty and healthcare product retailers (HKTDC, 2018;

Hongkongbusiness, 2018). However, several incidents including the financial

crisis, the anti-corruption in the Mainland including the clampdown in gift giving

China’s three-year anti-corruption campaign (Cendrowski, 2015), the aggressive

Mainland firms that courted high-end Mainland shoppers with online beauty and

healthcare retail websites and applications stocked with beauty and healthcare

13

products which they previously could only purchase by travelling to Hong Kong

or other countries. Although, the market of the Mainland shoppers slumped in the

past few years (Hongkongbusiness, 2018), this provided an opportunity to

traditional retailers to lower their rental cost on shop as the rent on major street

shops in Hong Kong was dropped significantly.

In addition, a recent study by KPMG found that Hong Kong consumers had plan

to increase the number of online purchases in the coming years (KPMG, 2016) and

their target products to be purchased were less-expensive products.

(Hongkongbusiness, 2018). In addition, studies found that consumers’ tendency to

use digital devices for shopping-related activities before or during their most recent

trip to the store is increasing (Gao & Su, 2016; Kim, Park, & Lee, 2017) and Hong

Kong local shoppers’ propensity to consume via their mobile phones than they had

previously is also expanding (Brennan, 2016).

Given the push by the Hong Kong economic downturn in the last few years, and

the pull by the awareness of digital disruption, many major beauty and healthcare

products retailers had shifted their sales focus to local Hong Kong people who had

becoming a strong segment for the retail industry and these retailers executed

14

customer loyalty programmes and marketing plans to meet both the online and

offline demands of new and current customers in order to sustain their business.

1.2. Mechanism of customer loyalty programs

Gaining the understanding about the effect of customer loyalty in the retention of

the loyalty was valuable to the operators of the related companies. Additionally,

due to the switching of the target shoppers from Chinese mainland shoppers to

Hong Kong local shoppers for the sellers of the local beauty and healthcare

products (Hongkongbusiness, 2018), the repurchasing was one of the key issues to

the practitioners, while it also highlighted the importance of the study for the

customer loyalty program as a way to extend their reach in different markets.

1.2.1. Importance of customer loyalty programs in Hong Kong

Customer loyalty could be interpreted as a consistent re-patronize behaviour from

a customer in the same store or to the same brand disregarding competitors

marketing to effort to attract customer to switch to other brand and location on the

one hand and on the others that customer was willing to recommend the brand and

location among friends and colleagues (Oliver, 1999; McIlroy & Barnett, 2000).

Customer loyalty stimulated consistent re-patronize behaviour because loyal

15

customers were less vulnerable to negative word-of-mouth to their loyal brand and

store location and they were hard to be attracted by other alternatives (Ou, et al.,

2011).

Nonetheless, the growth of the beauty and healthcare market was highly affected

by the economic factors (Viola, et al., 2013). Therefore, the improved customer

loyalty was an essential way for the companies in retaining the clients so as to keep

their market share (Oliver, 1999; Bhattacharya & Sen, 2003). Given the highly

competitive distribution channels of retailing industry, beauty and healthcare

products were without exception, while they should maintain the local customers

so as to stay their market occupancy (HKTDC, 2018, Hongkongbusiness, 2018).

Product differentiation was less useful to attract customers when foreign branded

homogeneities offered by distributors largely reduced the switching costs of

customers (Australian Trade Commission, 2014; Oliaee, et al., 2016); therefore,

most of the distributors encouraged sales with a wide range of customer retaining

campaigns and marketing techniques such as: customer loyalty program, visual

merchandising and selection of suitable location (Australian Trade Commission,

2014; Oliaee, et al., 2016) to improve customer loyalty to store (Bhattacharya &

Sen, 2003; Oliver, 1999).

16

One of the tactics adopted was providing reward or discount to the returned

customers (Barlow, 1996; Capizzi & Furguson, 2005), while the special treatment

for the repurchasing consumers could be observed as well (Barlow, 1996; Harris,

2000). The aim was to keep the customers and their consumption of the retailers’

products. Although quality and price may be two of the keys, these two factors

were hard to be alternated due to the high competition. As a result, the customer

retention programs and marketing techniques seem to be the last resort for the

owners in beauty and healthcare products retailing industry to compete with the

store loyalty among the customer base.

1.2.2. Price sensitivity in the Chinese society

Price was a cue/indicator to measure quality of products and services in different

societies (Dawar & Parker, 1994; Probert & Lasserre, 1997; McGowan &

Sternquist, 1998), but the degree to which buyers depend on price to measure

quality was different (Le Claire, 1992; Tan & McCullough’s, 1985) as well as the

value for quality (Kim, et al., 2002). In the examination of the relationship between

product quality and patronise decision in apparel retailing, Chinese customers

preferred products with more functions compare to South Korean customers (Kim,

17

et al., 2002). Kim et al. (2002) attributed the result to the difference in culture.

Chinese customers had less individualistic values than those of South Korean

customers; therefore, the extrinsic value was more important to attract customer in

the Chinese society.

Given that collective culture relied heavily on word-of-mouth feedbacks from

interpersonal networks to valuate a product, higher extrinsic values (products of a

lower price with the same function or more functions) rasied the chances of

positive word-of-mouth in Chinese society that gradually translated to higher

degree of acceptance to buy the product (Schutte & Ciarlante, 1998).

Moreover, in the study of Le Claire (1992) and Tan & McCullough’s (1985), a

society like Hong Kong which shared deeper Chinese beliefs was found more price

oriented in making patronise decision. As a result, price sensitivity can be viewed

as a part of the mechanism of customer loyalty programs that affect store loyalty

of a brand.

18

1.3. Research justification

Understanding the effect of customer loyalty in the retention of the loyalty is

valuable to the retailers. In Hong Kong, the shifting of beauty and healthcare

products market from Chinese mainland shoppers to local Hong Kong people

pushed retailers to go deeper to explain many facets of the market. Additionally,

due to the switching of the target consumers for the sellers of the local beauty

products, the repurchasing has become one of the key issues to the practitioners in

the industry.

Although a review on relevant customer loyalty programme and loyalty literatures

revealed the direct influence of customer loyalty programme on customer loyalty,

it also revealed that there was a dearth of studies on the mediating roles played by

communication with customers and price image on the relationship between

customer loyalty programme and store loyalty in Hong Kong. For example, Chang

and Chieng’s study (2006) revealed that brand image mediate the relationship

between experience and consumer relationship at coffee chain stores in Shanghai

and Taiwan only. Also, Gao, Zhang and Mittal’s study (2017) found that sacrifice

mindset mediates the relationship between a consumer’s local identity and price

sensitivity in the food product industry from over 142 countries. There findings

19

reviewed that there was a general lack of studies on the moderating roles played

by price sensitivity on the relationship between customer loyalty programme and

store loyalty in the beauty and healthcare products industry in Hong Kong.

Being an important source market for local consumers, Hong Kong’s beauty and

healthcare product market awaits more academic attention. Thus, the current study

was conducted using the customer loyalty program constructs from Bridson et al.

(2008) and Leong (2013) to fill the knowledge gaps. It aimed at exploring the

factors leading to the store loyalty behavioural actions by Hong Kong local beauty

and healthcare product customers, and helping the industry players to strengthen

their strategies to improve the customer loyalty programmes to promote

repurchasing.

1.4. Research questions

The primary objective of this research is to identify factors leading to customer

loyalty in Hong Kong’s beauty and healthcare products industry. The key

outcome of the research will provide statistically reliable and significant factors on

strategy forming for beauty and healthcare product industry retailers and

20

distributors to optimize their investments to meet customers’ expectations. Hence,

the main focus of the research question (RQ) is:

“What are the mediating roles of two drivers of customer loyalty in the

customer loyalty programs—store loyalty relationship and the moderating role of

price sensitivity on the linkage between customer loyalty programs and store

loyalty?”

1.5. Research methodology

The current study began with a review of the customer loyalty program and store

loyalty literature so as to sort out suitable constructs for building a model to explore

the relationship between customer retention program and the store loyalty (Nguyen,

et al., 2017). The primary study powered by the questionnaire-based was conducted

so as to verify the model with the help of the statistical tools (Oliaee, et al., 2016;

Resnick, et al., 2016; Strategic Direction, 2012). Quantitative methodology and

cross-sectional research design were adopted in this research. Self-administrated

online questionnaires were developed for the collection of the responses. There

were pre-condition questions in the survey for the participants’ selection, according

to which non-local beauty and healthcare products consumers were by passed in

the systems. Invitation scripts were sent to potential participants via Internet social

21

networks through Facebook messenger and Whatsapp. The data collection period

was run from 7 September 2017 to 14 November 2017 and a total 220

questionnaires was received. SPSS was used to run analysis with reliability and

validity test performed to check if collected data was suitable for structural

equation modelling (Braganza, et al., 2017; Fu, 2016). Finally, with the evidence

from the primary research (Tsai, et al., 2016; Virgona, Helene, 2012), the findings

and related suggestions would be valuable.

22

2. LITERATURE REVIEW

Following the introduction chapter that described the aims, scope and approach

taken in the study, this chapter provided an exhaustive literature review of previous

customer loyalty program and store loyalty scholarly journals to support the

research study.

A customer loyalty program refers to set of actions devised to keep up with target

customers so as to minimise the rate of lost customers, and increase the amount of

repurchases. These programs often consist in issuing loyalty memberships. Prior

literature has extensively study on the customers’ relation to suppliers. For instance,

many researchers associated customer loyalty with some variables such as

consumption behaviour (Brown, 2001; Cunningham, 1961), attitude (Dunkovic &

Petkovic, 2015; Jacoby & Olson 1977; Jacoby 1971), mixed behaviours and

attitudes (Šapić, Kocić, & Radaković, 2018; Jacoby & Olson, 1971), satisfaction

(Oliver 1999; Barnes, 1997; Oliver 1997), dependence (Barnes, 1997), trust

(Philippus Brink, & van Rensburg, 2017; Agrawal, Gaur, & Narayanan, 2012;

Hennig-Thurau & Klee 1997), commitment (Marshall, 2010; Bansal, et al., 2004;

Fullerton, 2003; Pritchard, et al., 1999) or even relation to the brand (Šapić, Kocić,

23

& Radaković, 2018; Fournier and Yao 1997). Yet the researcher found that on the

empirical level, few studies emphasised the moderation role of price sensitivity,

the mediation role of communications with customer, and the mediation role of

price image. The purpose of this literature review was to establish a theoretical

basis for the research model and associated hypotheses, to identify gaps from

existing literature especially on the relationship among the ten constructs (i.e. price

image, price sensitivity, soft attributes, hard attributes, commitment, behavioural

activities, word-of-mouth, location, communication with customer, and visual

merchandising), two mediators and moderator.

This chapter was divided into the following sections. Section 2.1 began with a

review on store loyalty literature. Section 2.2 examined the customer loyalty

programmes and its impact on store loyalty. Section 2.3 elaborated the price

sensitivity variable and reviewed its moderation interactive effect on some

variables. Section 2.4 described the communication-with-customers variable and

its mediating interactive effect. Section 2.5 described the visual merchandising and

its connection with other variables. Section 2.6 elaborated the price image variable

and its linkage with store loyalty. Section 2.7 described the location and its

relationship with price image. Section 2.8 explained the development of research

24

questions. Section 2.9 elaborated the development of hypotheses. Section 2.10

explained the development of research model and the last section summarized the

chapter.

2.1. Store loyalty

Cunningham (1961) referred the brand loyalty as the degree of behavioural loyalty

towards a choice brand. In Cunningham's studies on brand loyalty, he measured the

share of purchase devoted to the physical store to accounts for the biggest, second

biggest, third biggest, etc. According to Cunningham (1961), brand loyalty had

very little relationship with prices and the proportion of purchase devoted by a

customer to the main brand among a category was used to classify the customers

as loyal customers or non-loyal customers. Customers with a high proportion of

purchase above a percentage were regarded as loyal customers and those with a

relatively low proportion of purchase were regarded as un-loyal customers. Brand

loyalty was different from customer loyalty as brand loyalty measured everything

that due with how customers perceive the store's brand (McIlroy & Barnett, 2000).

Whereas customer loyalty was mostly related to the overall spending capacity of

the customer and was measured by the re-patronize pattern of the customers and

the emotional attachment of customer toward the brand (Day, 1969). In this paper,

25

the term “store loyalty” was not only related to those customers that keep coming

back to the brand including both physical store and online store, but also interpreted

as a consistent re-patronize behaviour from a customer in the same brand, which

had products available in either physical retail store or product images being

displayed in online store, disregarding competitors marketing to effort to allure

he/she to switch to other brand and location on the one hand and on the others that

customer was willing to recommend the brand and location among friends and

colleagues (Oliver, 1999; McIlroy & Barnett, 2000).

The re-patronize pattern was considered as customer’s loyal action by Oliver (1999)

“…. a deeply held commitment to re-buy or re-patronize a preferred

product/service consistently in the future, thereby causing repetitive same-brand

or same brand-set purchasing….”. Oliver (1999) defined the development of

customer loyalty from the degree of loyalty:

1) Cognitive loyalty was the most surface level of loyalty when the decision was

made on the cost and benefit of the offerings but not the brand of the company;

therefore, customers would switch to other providers when a more cost-effective

offering was available (Olive, 1999).

26

2) Affective loyalty was the second level of loyalty when the customers attained

satisfactory experienced with the company (Olive, 1999). Satisfaction was an

emotional factor that was found harder to be replicated by providers; however, in

the study of Reichheld, Markey and Hopton (2000), a significant proportion of

loyal customers swapped to other brand was found being satisfied with their

previous products and services provider; therefore, both cognitive and affective

loyalty could be viewed as vulnerable to competition (Olive, 1999).

3) Conative loyalty was the third level of loyalty (Olive, 1999). Customers

intended to plan for re-patronise from the same company. It could be achieved

when frequent positive experiences translated into a positive attachment towards a

company; however, negative experiences like continuous product failure, would

lead to customers’ avoidance to the brand or defection to other companies (Olive,

1999).

4) Action loyalty was the deepest level of loyalty (Olive, 1999). Customers were

satisfied with previous experiences and positively attached to the company;

therefore, customers were acting more consciously to approach the companies by

themselves even with a high approaching cost (Olive, 1999).

27

Oliver (1999) elaborated the expression to loyalty as a purchase behaviror. The

attitudinal/emotional attachment could only be translated into repeated purchasing

behaviour and a longer time frame of this behaviour; Oliver (1999) contributed to

a clear demonstration of loyalty development.

In Dick and Basu’s (1994) study, customer loyalty to the company was measured

by the degree of repeat patronage and relative attitude. Their study referred the

trust loyalty to a company was a result of the combination of high degree re-

patronize behaviour and relatively high degree of positive view towards the

company compare to others (Dick & Basu, 1994). Repeat patronage was simply

the degree of repeat purchase to the same store (Dick & Basu, 1994; Oliver, 1999;

McIlroy & Barnett, 2000). The relative attitude was the attitudinal judgement

between different companies; a relatively high positive attitude towards one

company over the others could be viewed as an emotional attachment towards that

the most favoured store (Dick & Basu, 1994). Furthermore, Dick and Basu (1994)

identified spurious loyalty, which referred to the behaviour of customers

repurchased frequently not from the favoured store and latent loyalty, which was

related to the customer behaviour when repurchased less frequent from their most

28

favoured store. The loyal model matrix of the relationships was depicted in Table

2-1 in below.

Table 2-1: Dick and Basu (1994) loyalty model

The interest in relative attitude and repeat patronage within the retail research

community has not been far from new. Dwyer et al. (1987) and Fornell (1992)

identified that word-of-mouth behaviour was an expression of relative attitude in

the customer loyalty research and their research established that a positive word-

of-mouth behaviour was commonly found on loyal customers. Furthermore, loyal

customers were less price sensitive and more willing to promote the companies by

sharing their experiences within social networks (Fornell, 1992). Moreover, large

proportion of loyal customers re-patronise frequently (Dwyer, et al., 1987; Fornell,

1992). Besides, Khan (2009) described word-of-mouth behaviour as a clear

expression of what the customer felt of the company which should be categorised

as attitudinal loyalty. Meanwhile, behavioural loyalty was an expression of what

29

customers did and therefore, re-purchase decisions were considered as a

measurement of behavioural action (Khan, 2009). Khan’s (2009) store loyalty

model is depicted in Figure 2-1 in below.

Sources: Khan (2009)

Figure 2-1: Store loyalty construct

Last but not least, the most advisable situation for a company was a solid long-term

positive word-of-mouth behaviour and re-patronise behaviour carried out by the

customers and the company required to build a solid relationship to achieve the

combination of solid re-buy pattern and positive word-of-mouth (Dwyer, et al.,

1987; Marshall, 2010). This type of behavioural ties was related to commitment,

which symbolised the relationship of the on-going combination of the behavioural

and attitudinal actions, carried out by customers and followers of store (Dwyer, et

al., 1987; Marshall, 2010). The committed parties preferred a long-term

30

relationship for the sake to reduce full costs which included the cost of swapping

to other location, uncertainty on quality of new products and an anticipated high

switching costs in long-run (Dwyer, et al., 1987; Achrol, 1991; Marshall, 2010).

Moreover, customer loyalty stimulated consistent re-patronize behaviour because

loyal customers were less vulnerable to negative word-of-mouth to their loyal

brand and store location. Therefore, loyal customers were hard to be allured by

other alternatives (Ou, et al., 2011). In view of the above, the store loyalty construct

of this study would be measured by three variables including behavioural action,

word-of-mouth and commitment (Bridson et al, 2008; Leong, 2013). The

relationship among these three variables was depicted in Figure 2-2.

Sources: Bridson et al. ((2008)

Figure 2-2: Store loyalty construct

31

From the brief review of the store loyalty literature in the retail industry, it could

be seen that wide range of contents was involved. Nonetheless, for the purpose of

yielding controllable and manageable scope for the discussion and the development

of the related factors, the concept of store loyalty was limited to the three aspects,

namely, behavioural action, word-of-mouth and commitment, while they were

illustrated in the following subsections with the corresponding subtitles.

2.1.1. Behavioural action

First, the behavioural action or behavioural loyalty was defined by the behavioural

conducts of the loyalty customers of a store (Kaur & Soch, 2013). According to the

findings by Kaur and Soch (2013), they identified that a loyal customer would like

to maintain a positive interaction with the firm, while they would like to re-

patronize consistently in the same company. Besides, some of the researchers,

including Bowen and Chen (2001), utilised a number of re-purchasing behaviour

as a measure of the intensity of customer loyalty to a brand. Forasmuch as aa

behavioural loyalty referred to the purchasing behaviour of the consumer, while

particular preference would be dedicated to the favoured brand.

32

Although the number of returning to the store yielded the benefits of simplicity, the

criticism for the oversimplification can be observed from the research field. For

example, Dick & Basu (1994) disagreed to treat re-patronization as a sole indicator

of customer loyalty due to the ignorance to the situational constraints of customers.

Different reasons can alternate the continuous decision of purchasing for a

customer, such as, the common stock level for certain product in the retailing

location, while the low re-patronization may be contributed by the different

constraints, i.e. the specific usage of certain product, the inadequacy of the product

stock, etc. (Back, 2001; Bass, 1974; Hoyer, 1984; Jacoby & Chestnut, 1978).,

instead of lack of support to the company or the brand loyalty.

Moreover, repeated purchases could be an outcome of high switching costs and

customers’ inertia (Jacoby and Kyner, 1973; Reichheld, 2003). Kuusik (2007)

partitioned behavioural actions by the faithfulness of the customers and conditions.

Customers were forced to be loyal when they patronized certain products or brands

unwillingly; it was conditioned to either product was sold by monopoly, tough exit

constraints which forced customers to stay loyal and a budget constraint that kept

customers away from alternatives (Kuuisk, 2007). Therefore, customers were

forced to be inertia and resulted in loyalty to store.

33

However, customers were willing to be inertia when they found convenience to re

patronize from the same location and the products valued less important to them

(Kuuisk, 2007). Given that the customers could easily patronize from the same

location in the first hand, customers were unwilling to spend time to seek for

alternatives when the value of the product and service were low (Kuuisk, 2007).

This kind of customers’ inertia was in line with the values of cognitive loyalty:

Cognition could be built by earlier or current experiences which was an ongoing

standard behaviour like garbage pick-up (Oliver, 1999; Kuuisk, 2007). It was

simply an execution that had no relationship with satisfaction and loyalty (Oliver,

1999).

Lastly, functional loyalty was a subjective loyalty to certain values: like brand,

price, quality, distribution channels, convenience of usage and loyalty program

(Wernerfelt, 1991); however, these values were easily replicable and therefore, the

functional effect could be neutralized by replicas when time went on (Kuuisk,

2007). Therefore, it could be observed that the behavioural action may be a simple

indicator of the loyalty, but a comprehensive indicator should include diversified

34

components in the measuring scale so as to yield the better characterisation to the

related phenomenon.

2.1.2. Word-of-mouth

Apart from the behavioural action, the attitudinal loyalty referring to the emotional

awareness of the favour or preference of the brand might be another dimension in

showing the loyalty to a brand instead of behavioural conduct, such as,

repurchasing, while it could be commonly observed as word-of-mouth promotion

to certain products. The customers would normally promote and persuade their

relatives and friends in using the related products if they found attitudinal loyal to

the products. Therefore, word-of-mouth might be a good indicator of the attitudinal

loyalty (Lumpkin & McConkey, 1984).

Katz & Lazarsfeld (1955) discovered that receivers were more easily affected by

informal advice from the personal network rather than from the traditional formal

mass media advertising like television advertising, etc. and this personal network

could be utilised as a promotion tool to sell consumer products (Brooks, 1957).

Later, this kind of informal advice-message created, received and delivered from

personal network which aimed at the promotion of products, consumption, and

35

store loyalty were categorised as word-of-mouth effect (Keller, 2007). According

to Keller’s (2007) study, there were three and a half billion word-of-mouth

conversations per day; meanwhile, brands were discussed by Americans more than

2 billion times per day. Hence, word-of-mouth effect could be treated as a common

phenomenon in a society and word-of-mouth was a common behaviour among

people (Gildin, 2003). Villanueva et al. (2008) summarized the effectiveness of

word-of-mouth on customer acquirement was depicted in Table 2-2 in below.

Table 2-2: Effectiveness of word-of-mouth from Villanueva et al. (2008)

The flow of word-of-mouth communication was best described by Dwyer (2007;

64); he defined word-of-mouth communication as “……a network phenomenon:

People create ties to other people with the exchange of units of discourse (that is,

36

messages) that link to create an information network while the people create a

social network.”. When people exchanged messages, they were creating a social

network, the messages could positively or negatively affect brand or product image

of the receivers within this information network and at the same time, the

exchanged messages would be seen as an informal advice on the decision-making

process by receivers that affect the receivers’ purchasing patterns and decisions

(Brooks, 1957; Dwyer, 2007; Helm, 2000; Katz & Lazarsfeld, 1955). Positive

word-of-mouth could be a result of satisfactory experiences from store or being

affected by positive word-of-mouth form others and vice versa (Brooks, 1957;

Dwyer, 2007; Helm, 2000; Katz & Lazarsfeld, 1955).

Positive word-of-mouth happened when people exchanged positive messages and

it was treasured by companies because positive word-of-mouth was a very effective

tool to promote brand, sales and customer life time value compare to traditional

marketing (please see table 2.2). Conversely, negative word-of-mouth resulted in

a spread of negative image and gradually, encouraged customer disloyal to brand

and deteriorating life time value (Cowley, 2014; Donthu & Carl, 2013; Li, et. al.,

2010).

37

Arndt (1967) discovered that the impact of positive word-of-mouth on patronise

decision of a particular brand was 50% weaker than that of negative word-of-mouth.

Other scholars found similar results in their research (Skowronski & Carlston, 1989;

Assael, 2004; Kroloff, 1988; Fiske, 1980). The results could be attributed to the

perceived value of message receivers. Positive message was commonly found in

different communication channels and therefore, it was perceived as a normal, a

more frequent and a lot more casual message to the audiences while negative

messages were found less in public. As a result, negative messages drove more

alert to audiences and lastly, it was perceived as more important to the

consideration of choice (Skowronski & Carlston, 1989; Assael, 2004; Kroloff,

1988; Fiske, 1980).

However, East et. al., (2008) empirically supported that the negative impact created

by negative word-of-mouth was less harmful to loyal customers because loyal

customers refused to accept and resisted negative word-of-mouth on their favour

brands. Therefore, the perceived brand loyalty could be viewed as a pre-posited

factor that affected the effectiveness of positive and negative messages to the

receivers. To sum up, word-of-mouth was considered as the behaviour in

38

promoting the service or product of the company, while the advantage or the

preference of the products may be conveyed to the consumers.

2.1.3. Commitment

Finally, the commitment was the composite dimension symbolised the relationship

of the on-going combination of the behavioural and attitudinal actions carried out

by customers and followers of the store, while it was the most advisable situation

for the company or the retailer (Dwyer, et al., 1987; Marshall, 2010).

The committed parties preferred a longer-term relationship for the sake to reduce

full costs (Marshall, 2010). Full costs included the cost of a swap of location,

uncertainties of the quality of new products and an anticipated high switching costs

of repeated purchases (Dwyer, et al., 1987; Achrol, 1991; Marshall, 2010).

Moreover, the committed parties preferred the feeling of being valued by the

company and other committed parties within his/her own interpersonal networks

(Pritchard, et al., 1999; Fullerton, 2003; Bansal, et al., 2004; Marshall, 2010).

Therefore, commitment could be divided into two types, namely, calculative

commitment and affective commitment. The calculative commitment was based

on the rational determination of switching costs and benefits, while the affective

39

commitment was based on the emotional attachment to committed parties (Dwyer,

et al., 1987; Achrol, 1991; Pritchard, et al., 1999; Fullerton, 2003; Bansal, et al.,

2004; Marshall, 2010).

The calculative commitment could be viewed as the willingness to commit long-

term behavioural and attitudinal actions under the considerations of long-term

nominal net benefits (Dwyer, et al., 1987; Achrol, 1991; Marshall, 2010). More

committed parties could be found in monopoly and oligopoly markets when no

alternatives were found or the costs involved in a change were too high that they

could not offset by the benefits from a swap (Evanschitzky, et al., 2006; Marshall,

2010). As a result, the market structure was a prepositioned factor in building up

calculative commitment. Therefore, no significant relationship was found between

calculative commitment to both behavioural and attitudinal loyalty in the study of

Marshall (2010); however, in the study of Fullerton (2003), a weaker positive effect

of the calculative commitment on customer retention/ behavioural loyalty was

found. To sum up, no evidence showed that the calculative committed customers

were willing to commit to long-term attitudinal actions to store (Fullerton, 2003;

Marshall, 2010).

40

The affective commitment could be viewed as the willingness to commit long-term

behavioural and attitudinal actions under the consideration of how depth was the

relationship between the company and its’ followers (Dick & Basu, 1994; Marshall,

2010). The most favourable condition was the followers considered the retailer as

a close partner that required frequent promising responses. Frequent promising

responds were frequent re patronise and positive word-of-mouth behaviour (Dick

& Basu, 1994; Evanschitzky, et al., 2006; Marshall, 2010; Garbarino & Johnson,

1999; Morgan & Hunt, 1994). On the one hand, the affective commitment could

be pre-positioned by the customers when customers’ choice to be loyal on their

wishes (Dick & Basu, 1994; Marshall, 2010). On the other hand, the affective

commitment could be translated as an emotional attachment beyond the

considerations of facts (Dick & Basu, 1994; Marshall, 2010).

Positive and significant effect of affective commitment to both behavioural and

attitudinal loyalty were found in the study of Marshall (2010); unlike calculative

commitment, the value of emotion benefit helped to build up partnership relations

and gradually, strengthened the intangible status and image of a brand in the view

of the customers and at the same time, attracted more frequent patronise behaviour.

Therefore, the tighter the partnership, the more promising actions like positive

41

word-of-mouth and re-patronise behaviour guaranteed by customers (Evanschitzky,

et al., 2006; Garbarino & Johnson, 1998; Marshall, 2010). As a result, the

affective commitment was emotional based and affected attitudinal loyalty on one

hand and the value of partnership creation could be viewed as “extra” benefit

placed on top of tangible quality that gradually translated in to an “extra” gave up

if the affective committed customers swapped to other shops (Dick & Basu, 1994;

Morgan & Hunt, 1994; Garbarino & Johnson, 1998; Evanschitzky, et al., 2006;

Marshall, 2010).

In conclusion, the commitment was the most advisable situation for the retailer

because the related consumer would not only pay for the product with their pocket

but also influence others in adopting the product (Kaur & Soch, 2013; Gronroos,

1995). In the most extreme situation, the consumer would commit to the related

brand so that they would exclusively adopt the product from the particular

company (Gounaris & Stathakopoulos, 2004; Kaur & Soch, 2013). More

importantly, the users would affect the persons in the social circle in adopting the

related products (Gounaris & Stathakopoulos, 2004; Kaur & Soch, 2013).

Therefore, the commitment concerns about the influence and pressure of others

42

within the social circle, while the friends and relatives of the consumer might be

affected in adopted the service and product eventually.

2.2. Customer loyalty programmes

This section would describe the function of the customer loyalty programmes and

campaigns in enhancing the loyalty variables. This current section was divided into

five subsections, namely, history of customer loyalty programme, hard attributes,

soft attributes, and impacts of customer loyalty programmes (CLP) on store loyalty

in order to provide the general classification of the programmes with the support

of the development of it. Additionally, the impact of the programmes on the store

loyalty was included so as to provide the support for the connection between them

and the necessity for the investigation of them.

2.2.1. Evolution of CLP

From the 80s, a wide range of investment could be observed from the firms

dedicated to the development of customer loyalty program (Lacey & Sneath, 2006)

because an attractive loyalty program was able to keep customer repeatedly

patronise in the same store, that was customer loyalty to the store or store loyalty

(Reichheld & Teal, 1996). However, in the following study, the related topic had

43

been considered as axillary factors under marketing and consumer behaviour. Till

the study of Leong (2013), the related topic has been considered formally in the

research topic, while he defined customer loyalty program as a marketing tool that

often carries rewards to customers in order to build a better relationship between

firm and customer.

In conducting of the related program, the prolong lifetime of loyalty to the firm

could result (Leong, 2013). In the latter development, the program has been

escalated to the topic related to customer retention (Lacey & Sneath, 2006;

O’Malley, 1998). Under the related definition, some of the scholars shared the

similar point of views, while they considered customer loyalty program was a

marketing practice tailored to tighten the relationship between companies and

customers by the provision of the rewards in motivating the customers in

purchasing so as to yield the improved revenue in covering the expense devolved

in the program (Lacey & Sneath, 2006; O’Malley, 1998). Rather than attracting

new customers, the scholars would have the repeated patronization to the same

store so as to yield the reduction to the expense in promotion (Lacey & Sneath,

2006; Yi & Jeon, 2003; O’Malley, 1998) as well as the marketing efforts

(Narasimham, 1984; Shapiro & Varian, 1998).

44

In the empirical study conducted by Demoulin & Zidda (2009), the customer

retention was achieved with the launching of the customer loyalty program, while

the stimulation to the sales could be observed. Therefore, the function of the loyalty

program in the customer retention was confirmed though the effects might vary in

different industries. In fact, similar observation could be seen in the studies related

to the customer retention by the conducting the customer loyalty program (Meyer-

Waarden, 2004; Demoulin & Zidda, 2008; Dowling & Uncles, 1997; Gronroos,

1995; Sharp & Sharp, 1997; Yi & Jeon, 2003).

More than 90% of population in America, Britain and Canada participated actively

in single and multiple customer loyalty programs (Berman, 2006). Customer

loyalty programs prolonged a one-time decision-making process to multiple

considerations of the same store by strengthening competitive values through hard

and soft rewards (Hanover Research, 2011; Yi & Jeon, 2003). The rewards were

made effective to strengthened loyalty when the rewards fulfilled the expectation

of the program holders (Hanover Research, 2011; Yi & Jeon, 2003).

45

The hard rewards were easily and actively replicable as the value of economic

benefits could be objectively ascertained by quality, face value and functions of

rewards; therefore, the effect on customer retention could be neutralised by replica

from competitors easily (Hanover Research, 2011; Yi & Jeon, 2003). Conversely,

the soft rewards were intangible and less likely to be ascertained objectively by

holders as the value towards communication quality, service precisions and value

of interpersonal relationship varied among different individuals; therefore, soft

rewards were less likely to be replicated entirely. It could be attributed to the

variables like manners and communication skills of employees, precision of given

rewards and preference of holders were less replicable (Bridson, et al., 2008; Leong,

2013; Yi & Jeon, 2003). Moreover, quality of employees and customer data base

were different between companies (Hanover Research, 2011; Yi & Jeon, 2003).

Soft rewards were useful because providing personal service was a chance to

interact and gain deeper understanding of the holders on the surface level. The

deeper level was to gain trust through frequent satisfactory interaction between

companies and holders (Hanover Research, 2011; Yi & Jeon, 2003). However, the

soft rewards were often neglected as a vital part of customer loyalty program. In

this study, customer loyalty program construct would be measured by Hard and

46

Soft rewards (Bridson, et al., 2008; Leong, 2013). The relationship among

customer loyalty program, hard attributes and soft attributes were depicted in

Figure 2-3.

Sources: Bidson, et al. (2008) and Leong (2013)

Figure 2-3: Customer loyalty program constructs

Although complicated and mixed concepts for the hard and soft attributes of the

customer loyalty program, the simplified explanation of the two kinds of attributes

could be observed from the following subsections, namely, Hard attributes and Soft

attributes, correspondingly (Bridson, et al., 2008; Leong, 2013). To conclude,

rewards to customer could be described or translated into physical/economic

rewards and psychological/emotional rewards; customers receive

physical/economic rewards in terms of discounts, awarded based on purchase

47

volume or value, gifts which could be calculated in dollars while psychological /

emotional rewards were delivered as special recognition and services (Bridson, et

al., 2008; Leong, 2013; Yi & Jeon, 2003).

2.2.2. Hard attributes

The hard attributes of a customer loyalty program referred to the inclusion of the

physical and substantial reward to the consumer, while it referred to the economic

benefits commonly (Capizzi & Furguson, 2005; Bridson, et al., 2008). Economic

rewards were given on patronising frequency of customers or in the other words,

economic rewards were given at specific times of purchases, the common examples

of the related rewards were rebates, prizes, gifts, discount, and buy one get two, etc

(Berman, 2006; Charania, 2011; Hoogenberg, 2010; Zeidler, 2009; Analysys

Mason, 2011). In the case of mobile phone service, loyalty program members were

given free minutes when they recharged the plan for a certain amount or they would

receive bonus internet package or a brand-new mobile phone when they upgraded

their plan (Furinto, et al., 2009).

48

Moreover, tangible rewards were commonly found in the market (Arantola, 2003).

Competitors used to offer similar rebates, prizes, gifts, discount to their loyalty

members so as to neutralise the effect from tangible rewards loyalty programs

launched by competitors (Whyte, 2004; Yi & Jeon, 2003); therefore, scholars

stressed on correct mix of hard and soft rewards in a customer loyalty program

(Rosenbaum et al. 2005; Capizzai & Ferguson, 2005; Liu 2007). One of it is Roehm

et al. (2002), and they found that hard reward itself attract the customer, but it could

not stimulate the loyalty of that customer to the brand after the program ended. As

a result, tangible rewards/hard attributes became a discount but failed to create

sustainable loyalty to the store (Roehm, et al., 2002).

Arantola (2013) stressed that concentration on shopping was a presupposition of

the effectiveness of tangible rewards offered by customer loyalty program.

Tangible rewards offered by customer loyalty program became attractive when

consumers were highly concentrated on shopping. Furthermore, Arantola (2003)

questioned the perceptions of consumers towards tangible rewards and found that

the perceptions of tangible rewards were different between consumers, given a

similar background or the same age group. Tangible rewards were made useful

when the consumer believed that the rewards offered by the customer loyalty

49

program were important to them, then they would follow that customer loyalty

program. Customers refused to enter loyalty programs if they did not find the

tangible rewards important (Arantola, 2003). As a result, stimulating customer

concentration on shopping and picking correct tangible rewards were two factors

that made hard attributes succeed.

In addition, Melancon et al. (2010) investigated the effect of rewards had on

customers’ commitment to the rewards suppliers. They discovered that perceived

economic rewards lead to continuous re-purchased behaviour of customers, but the

relationship was not intrinsic, once the rewards suppliers changed, cut-off and

reduced the amount of economical rewards, the customers would swap to others

because they were loyal to the rewards but not to the brand, the store, or the

company (Melancon, et al., 2010). Therefore, tangible rewards/hard attributes

could not create emotional binding between customers and store, so tangible

rewards/hard attributes enhanced calculative commitment only, not affective

commitment (Marshall, 2010).

50

2.2.3. Soft attributes

On the other hand, the soft attributes of a customer loyalty program referred to the

special treatment to the consumer, while it connected to the intangible rewards to

the consumers commonly (Keh & Lee, 2006; Meyer-Waarden & Benavent, 2008;

O’Malley & Tynan, 2000; O’Malley, 1998). Lacey, Suh & Morgan (2007)

described intangible rewards as prestige rewards, the rewards were not valued for

money or having practical functions which tangibly benefits the receivers. Furinto

et al. (2009) simply described soft attributes or privileges programs as a special

treatment; members were given special rewards like invitations and access to

special events (Analysys Mason, 2011).

These special rewards/soft attributes were more effective in building longer term

binding relationship with customer loyalty program holders because hard attributes

were excessively offered by different companies in different sectors; instead of

rewarding customers on the base of their purchase amount and discount, companies

were suggested to build the feeling of having relationship with loyalty program

holders (Arantola, 2003; Berry, 1995). It could be attributed to the sense of

recognition, which was a social reward (Arantola 2003, 126-127). The social

reward was a recognition of being identical and being remembered by the loyalty

51

program providers (Arantola, 2003; Berry, 1995). The effectiveness of recognition

was remarkable in high volume and low switching cost industry like supermarket

(Bhattachraya & Sen, 2003; Leong, 2013). Moreover, in the study of Melancon et

al. (2010), recognition improved customers’ commitment to store, unlike

economical rewards which the effect on commitment was uncertain, customers

could commit to the company, to the economical rewards or to both.

Normally, loyalty programs providers held a comprehensive database of the

holders’ demographic and patronise data in order to provide the specific messages,

communications and rewards to the target loyalty program holders (Berman, 2006).

The common instances for the related rewards were a qualification of very

important people (VIP), special rewards triggered their emotions, feeling of

acknowledgement, feeling of superiority, feeling of exceptional, feeling of

different from other customers, etc (Analysys Mason, 2011; Chandon, Wansink &

Laurent, 2000; Feinberg, Krishna & Zhang, 2002; Furinto, et al., 2009; Kivetz &

Simonson, 2002) and among these rewards, generally, loyalty program holders

were more satisfaction with services related to improved services and saving time;

differentiate return policies for loyalty members were also valued by loyalty

program holders in retailing industry (Arantola, 2003).

52

To conclude, soft rewards or soft attributes were said to be intangible; however, it

could arouse similar feelings of having tangible or monetary rewards but less

practical and more emotional; when loyalty holders engage in word-of-mouth

behaviour on intangible or soft rewards, the discussion would be more emotional

as well because they were expressing their feelings on a more subjective

perspective; discussion of monetary and tangible rewards would be less emotion

in most of the case because tangible rewards were valued more objectively by their

price and intrinsic values (Arantola, 2003).

2.2.4. Relationships between loyalty program and store loyalty

The study conducted by Bloemer & de Ruyter (1998) provided an empirical

demonstration of the connection between customer loyalty program and store

loyalty in the configuration of a department store, while the positive significant

effect of the program was recorded, customer loyalty programmes improved store

loyalty. Taylor and Neslin (2005) investigated the twenty-four months patronise

data of household in the USA and they discovered positive impacts of customer

loyalty programs to store loyalty in both short and long term respectively. Instant

impact was found when customers raised patronise volume in order to gain points

53

for exchanging rewards. Gradually, more repeated purchased were found after

rewards were given. Seeking for points to exchange rewards motivated instant

purchased and rewards itself attracted commitment for delay purchased (Blattberg

& Neslin, 1990; Rothchild & Gaidis, 1981).

However, Dekimpe & Hanssens (1999) argued that customers were unlikely to

commit to re-patronising after rewards were given to them because customers were

unlikely to spend more for the same reward. Therefore, the rewards earned by

points accumulation could be viewed as discount on regular price. As result,

customers ‘commitment to re-buy on regular price was uncertain. But Cortiñas et

al. (2008) found that program holders were less reluctant to regular price products

on one hand and they bought more variety of items and contributed more on sales

to compare to non-program holders. Investigation on share-of-wallet and customer

lifetime could further explain the relation between rewards and store loyalty

(Leenheer et al., 2007; Meyer-Waarden., 2007).

Leenheer et al. (2007) investigated the twenty-four months’ grocery patronise data

of nineteen hundred families in Netherlands and they discovered a weak positive

impact of customer loyalty program on share-of-wallet. Loyalty programs

54

improved the spending proportion of the customer to the retailer. Meyer-Waarden

(2007) found a positive effect of loyalty programs had on share-of-wallet and

consumer lifetime. The study was taken in France. Customers who engaged in a

single loyalty program or holding a single loyalty card of the company were found

more loyal compare to non-program and non-card holder; however, the degree of

loyalty deteriorated with the number of loyalty programs engaged and loyalty cards

held by the customers. The more the programs and cards held by the same holder,

the shorter the duration of usage of each program (Meyer-Waarden, 2007).

Therefore, offering similar loyalty cards and programs to competitors’ loyal

customers were useful to neutralise their loyalty to competitors (Meyer-Waarden.,

2007). Moreover, given that loyalty program holders were more willing to re-

patronise than non-loyalty program holders, the peak of re-buy behaviour

happened in the beginning of participation in a loyalty program. Re-buy started to

fall in the sixth-ninth months of participation (Meyer-Waarden, 2007).

Also, the study conducted by Leong (2013) showed a similar result with the

mediator of customer satisfaction in the local retailing industry dedicated for a

supermarket. Customer loyalty program improved customer satisfaction to store

and gradually, customer satisfaction to store improved customer loyalty to store

55

(Leong, 2013). Demoulin & Zidda (2008) found that instead of customer

satisfaction to store, customer satisfaction to the rewards offered by customer

loyalty programs was vital to the contribution to store loyalty. In their study of

grocery retailer in Belgium, satisfied program holders were more affected by

instant rewards and more committed to longer term loyalty to the company.

Moreover, all program holders were less reluctant to regular price products offered

by the company and the aggregate sold items and sales volume contributed by

program holders were found more than that of non-program holders (Cortiñas, et

al., 2008). An attractive loyalty program could keep customer repeatedly patronise

in the same store, that was customer loyalty to the store or store loyalty (Reichheld

& Teal, 1996). When the program stopped customers from swapping to other stores,

that was differentiation loyalty; consequently, the retailer could charge a higher

price for loyalty customers, and more revenue could be achieved by more cross-

selling and upselling because loyalty customers were not switching to other

competitors (Reichheld & Teal, 1996). To conclud, the conduction of loyalty

program yielded two significant advantages namely, attracting old customers and

the reduction of possibility in joint other program provided the other companies

(Meyer-Waarden & Benavent, 2008).

56

In contrast to the above study, Sharp and Sharp (1997) discovered that there was

no difference in the degree of loyalty between loyalty card holder and non-holder

in the study of grocery retailing in Australia. Moreover, Mägi (2003) discovered a

mixed result in Sweden, loyalty program positively impact loyalty on chain level

but not particular store; these results could be attributed to excess supply of loyalty

programs (Leong, 2013). Competition on loyalty programs raised more channels

for customers to gain points to exchange for rewards on one hand and on the other

hand, similar rewards or even homogeneous rewards would be given to customers

from different companies; therefore, the positive effect of the program was

neutralised (Mägi, 2003; Meyer-Waarden., 2007; Leong, 2013).

Furthermore, Dowling and Uncles (1997) argued that keen competition of

marketing strategies between companies forced all companies to replicate the best

offered in the market in order to maintain their positions and gradually, the yielding

of program dropped while cost of it remained the same for all companies (Uncle,

1994). As a result, loyalty program was effective to raise customer’s spending, to

prolong duration of shopping and to attract more revisit to store when customer

57

held no other program from its’ competitors (Mägi, 2003; Meyer-Waarden., 2007;

Leong, 2013).

However, Wright and Sparks (1999) argued that excess supply of loyalty programs

caused no neutralisation effect on store loyalty because coexistence of attractive

programs was commonly found in the highly competitive market but it resulted in

identification problem. Customers failed to store all loyalty cards in the wallet and

gradually, customers forgot those programs when the cards were not in the wallet

(Wright & Sparks, 1999). But Leong (2013) disagreed with it. Leong (2013)

discovered that loyal card was replaced by computer record of customer’s cell

phone numbers, therefore, no card was required reduce the cost to hold a program

which became a merit to attract more customers to join the program. Moreover,

demand and competition on rewards (both physical and non-physical) changed

from time to time, either factor changed could impact on the defection of customers

and store loyalty (Leong, 2013). As a result, neutralisation effect did happen

between loyalty program but the degree of effect changed frequently because the

demand and supply for rewards changed from time to time (Leong, 2013).

58

Furthermore, earlier studies discovered that companies did not pay attention to the

arrangement and design of the loyalty program (Wirtz & Chew, 2002). Therefore,

the loyalty program could not separate itself from another program when

competitors offered similar rewards in their program (Keh & Lee, 2006; Kim, et

al., 2013; Meyer-Waarden & Benavent, 2008; O’Malley & Tynan, 2000; O’Malley,

1998). Sophisticated programs raised difficulties for customers to understand it and

to apply it (Dowling & Uncles, 1997). As a result, customers were reluctant to join

and to use sophisticated programs unless extra efforts could generate excess

fulfilment/enjoyment through experiencing the program (Dowling & Uncles, 1997;

Keh & Lee, 2006).

Last but not least, Mauri (2003) found that large proportion of loyalty card holders

was not loyal to the supermarket in Italy and in Benavent and Crie’s (1998, 2000),

the growth of loyalty cards offered did not generate excessive yielding on sales and

earnings, more customers’ revisit and a bigger basket size. Loyalty cards were

found useless to motivate customers to revisit the store (Mägi, 2003; Liebermann,

1999; Passingham, 1998). As a result, loyalty card itself could not generate store

loyalty.

59

To sum up, regarding on the mixed results found by different scholars, the study

posited that the customer loyalty program influenced store satisfaction directly and

linearly because of the substantial evidence collected from different industry

(Blattberg & Neslin, 1990; Rothchild & Gaidis, 1981; Reichheld & Teal, 1996;

Taylor & Neslin, 2005; Demoulin & Zidda, 2008; Bridson, et al., 2008; Leong,

2013). Hence, the customer loyalty program would affect the store satisfaction of

the consumer, while the proportional association could be observed. It should be

no exception to the industry related to the beauty and healthcare product retailing

(Bloemer & de Ruyter, 1998; Leong, 2013).

2.3. Price sensitivity

Then, the price sensitivity as a moderator between the customer loyalty

programmes and store loyalty was investigated, while the corresponding

illustration was started with a general definition so as to provide the discussion for

the moderation effect of price sensitivity among customer in affecting the store

loyalty under the influence of customer loyalty programmes.

2.3.1. Research related to price sensitivity

60

In the customer loyalty program, the rewards to the customer were illustrated as

the translation of benefits and advisable behaviour of the customers to the firm (Yi

& Jeon, 2003). However, the resistance or assistance of the customers in

conducting the advisable behaviour should be different after receiving the related

rewards (Narasimham, 1984; Shapiro & Varian, 1998). Therefore, the

consideration of the price sensitivity should be included so as to investigate the

difference between price sensitive customers and less/non-price sensitive

customers (Narasimham, 1984; Shapiro & Varian, 1998).

Price sensitivity could be viewed as the reaction of customers to a change of price

and to regular price (Goldsmith & Newell, 1997). Generally, given the same quality

of products, the lower the regular price, the more discount offered, the more

attractive to customers (Gabor, 1988). Price was a cue/implication on quality

when customers faced imperfect/asymmetric information on quality of product:

branding, building materials, location of store and manufacturers, etc. and new

products: lack of past experiences and competitions to do comparison (Nagle &

Holden, 2003). Therefore, customers were more willing to accept regular price

(less/non-price sensitive) and to purchase more on regular price (Suri &

Manchanda, 2001; Nagle & Holden, 2003).

61

However, in a competitive market which information on the quality of products

could be easily attained, high price sensitive customers would like to repurchase

on discount through a loyalty program (Gabor, 1988; Suri & Manchanda, 2001;

Nagle & Holden, 2003). On the other hand, the customers with relatively lower

price sensitivity would consider the related rewards could be spotted in the future,

so they would be unlikely in committing for the repurchasing due to the provision

of the discount offered (Narasimham, 1984; Shapiro & Varian, 1998; Shugan,1995).

Nonetheless, it should be noted that the price sensitivity only affected the decision

of purchasing under the alternation of the price. Therefore, the company who

offered the rewards to the customers might not yield the same effect to the price

discount. As a result, the marketers were advised to provide the mix marketing

strategy so as to suit the related needs for the customers (Narasimham, 1984;

Shapiro & Varian, 1998).

2.3.2. The moderation effect of price sensitivity factor

The researchers also found that the price sensitive people would like to shop after

receiving a discount, while the price-insensitive people would like to purchase after

getting rewards instead of price reduction (Leong, 2013). Price sensitive customers

62

were more likely to repeatedly purchase when the firm offered discount through

loyalty program and price-insensitive customers or customers who treasured

rewards other than discount would not be affected by the discount scheme offered

by the loyalty program; therefore, marketer could change their marketing mix and

offerings to cope with the needs of different kind of customers: physical/economic

rewards on price sensitive customers and more psychological/emotional rewards

on price-insensitive customers (Narasimham, 1984; Shapiro & Varian, 1998). As a

result, there was a possibility that it was true that price sensitivity had a strong

moderation effect to the store loyalty after a customer received the discount from

a customer loyalty program (Leong, 2013; Narasimham, 1984; Shapiro & Varian,

1998).

In the study of Cortiñas et al. (2008) and Demoulin & Zidda (2008) on the effect

of a loyalty program, they discovered a significant difference in price sensitivity

between program holders and non-holders. Holders who satisfied with rewards

achieved from loyalty program were found price insensitive to regular price, and

they also patronised bigger basket size and more varieties (Cortiñas, et al., 2008;

Demoulin & Zidda, 2008). This result could be attributed to customer satisfaction

to rewards, customers received higher level of satisfaction on rewards in turn

63

generated extra values which gradually converted to an acceptance of a high price.

Similarly, inverse relationship between customer satisfaction and price sensitivity

was found in the study of Stock (2005) and Blackwell et al. (2006) while positive

relationship between price acceptance level and patronise behaviour was found in

Herrmann et al. (2001) and Homburg et al. (2005). Moreover, holders who were

not satisfied or less satisfied with the rewards were found a relatively lower price

sensitivity than non-holders (Cortiñas, et al., 2008; Demoulin & Zidda, 2008). To

conclude, customer loyalty program holders were low in price sensitivity and they

were loyal to store (Cortiñas, et al., 2008; Demoulin & Zidda, 2008).

Moreover, price sensitivity was different across culture. In the study of examining

the relationship between product quality and patronise decision in apparel retailing,

Chinese preferred products with more functions compare to South Korean (Kim,

et al., 2002). Kim et al. (2002) attributed the result to the difference in culture.

Chinese were less individualistic compare to South Korean; therefore, higher

extrinsic values (products of a lower price with the same function or more functions)

rasied the chances of positive word-of-mouth in Chinese society that gradually

translated to higher degree of acceptance to buy the product (Schutte & Ciarlante,

64

1998). As a result, the extrinsic value was more important to attract customer in

Chinese society.

Furthermore, in the study of Le Claire (1992) and Tan & McCullough’s (1985), a

society like Hong Kong which shared deeper Chinese beliefs was found more price

oriented in making patronise decision. Although the related results were not

conducted for the beauty and healthcare product industry, the similar effect should

be observed in the current study.

Additionally, the diversity for the construct of the loyalty program also yielded

different effects, while the outcomes were unpredictable and unexplored. For

instance, it could be seen that accumulation of reward points or stamps might yield

the repeated consuming in short burst, while the membership program might have

prolonged but gradual effects to the loyalty of customers (Shugan, 2005).

2.4. Communications with customers

The communication with customers was related to store loyalty as positive

communication would allow retailers to understand their customer better and

enhance the retailers’ brand (Kuan-Yin, et al., 2010). The improvement of the store

65

loyalty resulted from the enhanced communication was assessed and described in

the current section. For the sake of having a comprehensive analysis of the related

dynamics, the current section started from a review of literature on communication

especially for the communication with customers, while the connection and linkage

with store loyalty were included in the later subsection of the current section.

2.4.1. Research related to communication with customers

Further to the illustration to the price sensitivity, the mediation effect of

communication with customers was investigated, while the related factor should

take an important role in the persuasion of the customer in engaging the

consumption of the product. In essence, the factor was defined as the message that

would like to share with the customers, while the companies would commonly use

advertisement and propagator in convoy the related message (Kuan-Yin, et al.,

2010; Lalos & Cestre, 2009; Carpenter & Fairhurst, 2003) so as to yield the desired

results, such as, improving the brand image, encouraging the consumption,

introducing the new products to the market, etc. (Boulding, et al., 1993). As a result,

it was the flow of information or messages from the company to the customers

(Bridson, et al., 2008; Leong, 2013) while van Staden et al. (2002) expanded it as

66

a process that companies could deliver a message and customer would gradually

respond by feedbacks.

Moreover, van Staden et al. (2002) stated successful communication could result

in building up a better relationship with customers, raising the probability of

impulsive buying and reducing information costs for solutions. In the case of the

products related to healthcare and beauty, the factor in corresponding to the

communication with customers would outweigh importance particularly as the

related medicines and items highly relied on the trustworthiness of the retailers;

therefore, the personal consultancy service promoted by some companies were

welcomed as it encouraged the communication between the store and the

customers (Mannings, 2015).

2.4.2. Relationship between communication and store loyalty

From the definition mentioned above, it could be seen that the factor of

communication with customers measured the connection between the store and the

clients in the delivery of products and service. Therefore, it should be with a strong

association with the store loyalty (Keh & Lee, 2006; Kim, et al., 2013; Meyer-

Waarden & Benavent, 2008; O’Malley & Tynan, 2000; O’Malley, 1998).

67

Essentially, the improved communication would trigger the good perception

among the clients, while the communication also enabled the users to release the

true potential and efficiency of the purchased products because of the availability

of the information and knowledge obtained from the store (Keh & Lee, 2006; Kim,

et al., 2013; Meyer-Waarden & Benavent, 2008; O’Malley & Tynan, 2000;

O’Malley, 1998).

Moreover, Duncan (2002) highlighted communication was part of relationship

marketing that could eventually translate into perceived value of customers that

gradually strengthened commitment to store (Rensburg & Cant, 2003) if the

messages were being received by customers successfully (Rouse & Rouse, 2002).

Surface level of a message was the contend like price and variety of products which

could only strengthen calculative commitment (Dwyer, et al, 1987; Marshall, 2010);

while in-depth level message was the implied information like the sense of

superiority and uniqueness by matching receivers with the brand and the products

which gradually, raised affected commitment to store (Dick & Basu, 1994;

Marshall, 2010; Rouse & Rouse, 2002).

68

To conclude, communication with customers enhanced the user experience and the

perceived quality assurance for the brands from the surface level and raised

emotional attachment from the in-dept level, while they were the stepping stone of

the store loyalty. As a result, the communication with customers was selected as a

mediator linking visual merchandising and store loyalty.

2.5. Visual merchandising

Visual merchandising had been considered as one of the essential elements in

contributing the customer loyalty and the communication with customers

(Strategic Direction, 2012; Virgona, Helene, 2012). Therefore, the related

association and the content of the related factor was explained in the following

subsections.

2.5.1. Research related to visual merchandising

From the general definition, visual merchandising could be considered an

aesthetics of science, while it laid the foundation for the retail industry (Law, et al.,

2012) and it was without exception to the retailers of beauty and healthcare product

(Wu, et al., 2013). The approach adopted the hidden techniques to channel the

products to the market (Morgan, 2010) so that the number of employee and the

69

areas for the selling would be reduced along with the maximisation of the

attractiveness of the products (Strategic Direction, 2012; Virgona, Helene, 2012).

In common application, these activities were referring to the development of the

unified floor plan and the window displays so as to attract eye-browsing from

window shoppers and customers (Sebastian, 2008; Rowe, 2014). Visual

merchandise displays attempted to grab their customer attention and to alter their

customer perceptions to store by the design of floor plan and windows display

(Sebastian, 2008; Rowe, 2014). If customers were satisfied, they would be

gradually being attracted to shop in store; therefore, visual merchandising

optimised sales at last (Rowe, 2014).

Building attractive windows and sales floor display required excellent presentation

skills and extra efforts to rebalance the window displays and shopping floor which

resulted in higher costs for layouts on one hand and on the other hand, extra

spending on skilled visual merchandisers (Bustos, 2004a). But Dawes (2008)

argued that given strong competition from localization, visual merchandise

displays were vital to attracting attentions from customers in similar location.

Moreover, it was an instant message of the first sight of visitors which impact the

positioning of store in customers’ mind directly (Maier, 2009). With the

70

highlighting of the features and benefits of the goods and service, the visual

merchandising was able to attract the engagement of the consumer along with the

motivation for the purchasing behaviour (Ha & Lennon, 2010). Therefore, it could

be seen that the close connection could be expected to the consumer experience in

purchasing (Trigoni, 2016), and it was worthy of the inspection of the related

impact on the customer satisfaction and loyalty to the store (Bashford, 2011;

Cervellon & Coudriet, 2013).

2.5.2. Relationship between communication and visual merchandising

Apart from the strong suspicious in the connection between the visual

merchandising and the store loyalty, the visual merchandising, like other promotion

and advertisement in other media, should have a message to deliver to the

customers (Kuan-Yin, et al., 2010; Lalos & Cestre, 2009; Carpenter & Fairhurst,

2003). Therefore, visual merchandising should have a function in facilitating the

communication with customers so as to yield the improvement to the store loyalty

as well as to enable the returned customers (Garvey, 2010). Garvey (2010) defined

the visual merchandising as an attractive tailored message delivered to receivers in

an attempt to guide receivers’ feel as what the marketers tailored for. Moreover,

Storms (2006) discovered that the role of visual themes and first sight on visual

71

themes had a large impact on patronise decision and behaviours in the study of

shop decoration. Passive visual images travelled through first sight on the themes

approached to receivers and the feeling retained in the memory of receivers for a

longer period of time compare to active messages through verbal communications

(Storm, 2006). Therefore, it could be observed that a mediating role of

communication with customers act between visual merchandising and store loyalty.

To conclude, visual merchandising delivered in-depth level message in the first

glance of receivers that possibly altered perception on positioning of store and

gradually, raised commitment to store (Dick & Basu, 1994; Marshall, 2010; Rouse

& Rouse, 2002; Storm, 2006). As a result, in the current study, the exploration of

the mediation effect of communication customers between the visual

merchandising and store loyalty was included and conducted so as to construct the

model in explaining the interaction between the customer's loyalty program and

store loyalty.

Furthermore, communication with customer has been found moderating the

relationship between visual merchandising and behavioural action, the relationship

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between visual merchandising and word-of-mouth, and the relationship between visual

merchandising and commitment.

2.6. Price image

Similarly, the illustration to the price image was included in the presentation for

the content of visual merchandising, while the brief history and the development

of the concept and understanding of price image among customers were contained

so as to yield the common ground for the development of the current study along

with the linkage to the store loyalty.

2.6.1. Research related to price image

In essence, price image was defined as the perceived value of a product and it could

be viewed as casual conviction of costs to deal with specific retailer (Alba, et al.,

1994; Gewal & Marmorstein, 1994; Bell & Lattin, 1998; Buyukkurt, 1986) as

while it was affected by diversified contributors, such as, the quality of the product

(Hoch, et al., 1999; Lumpkin & McConkey, 1984; van Herpen & Pieters, 2002),

received customer service in purchasing the related products (Brown, 2001; King

& Ring, 1980; Lumpkin & McConkey, 1984), the brand of the product (Bowen &

Chen, 2001; Gounaris & Stathakopoulos, 2004), the location of purchasing

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(Strategic Direction, 2012; Virgona, Helene, 2012), etc. It was not a precise

calculation in terms of dollar, but a scale measurement made by comparison of

perceived expensiveness on offered products and perceived expenditure to a

retailer (Alba, et al., 1994; Gewal & Marmorstein, 1994; Bell & Lattin, 1998;

Buyukkurt, 1986; Srivastava & Lurie, 2001; 2004). Therefore, a wide range of

factors would affect the resultant perception, while the commonly agreed approach

was to measure the dimension from the customers directly (Marketing Science

Institute, 2017). Thus, the function and influence of price image on the customers

in the related factors were illustrated.

In the study conducted by Miranda et al. (2005), the price image was confirmed as

one of the essential elements for the store satisfaction, while the factor would

interfere the customers in selection the store of purchasing and the customer choice

of the products. On the other hand, due to the persistent behaviour of the consumers,

the store would provide a response to the related desire, and it would eventually

alter the physical location or decoration of the branches (Cox & Cox, 1990; Desai

& Talukdar, 2003). For instance, if a customer would like to enjoy the low-priced

products, they would like to shop in the branch with simple decoration instead of

the heavily decorated shop (Baltas & Papastathopoulou, 2003). Hence, in suiting

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the identity of the store, the operators of the store would change the decoration and

physical feature according so as to fulfil the expectation of the customers.

Consequently, in the retailing industry of beauty and healthcare products, the

customers would like to shop in discount stores and department stores rather than

specialty stores (Paulins & Geistfeld, 2003) so as to enjoy the discount and the low

price.

2.6.2. Relationship between price image and store loyalty

As aforementioned the price image construct was an essential component of store

loyalty, while the mediation effect of it had been confirmed from the perceived

worthiness, quality and services (Bolton, 1998; Fornell, et al., 1996; Lemon &

Lemon, 1999). Under the contexts related to retailing industry, the price image also

contributed to the returned purchasing (Fornell, et al., 1996) and the positive image

of the shop (Singh & Sirdeshmukh, 2000; Bauer, et al., 2002; Flavian, et al., 2006),

while it was a prerequisite for the cultivation of loyalty. Imperfect/asymmetric

information on price of certain items offered from different retailers resulted in

loyalty behaviour from customers based on casual conviction on price to retailer.

As a result, price image overruled the actual price strategies of different retailers

but it was not applicable to individual who could cheaply and easily attain

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information on actual price and to those who had strong desire to seek for the

lowest actual price (Grewal & Marmorstein, 1994; Bell & Lattin, 1998; Byukkurt,

1986).

Moreover, scholars discovered that customers with less spending power acted

loyalty to stores with perceived low-price images; meanwhile, loyalty towards

price image would deteriorate when similar perceived value was found in

competitors (Alba, et al., 1994; Burton, et al., 1994; Singh, et al., 2006). Therefore,

it could be supported that the price image was closely related to the store loyalty,

while the mediation effect of it may exist in different parameters.

2.7. Location

Finally, after the illustration to the role of the price image in affecting the store

loyalty, the meaning and the concept of location were explored, while the related

contents were included in the subsequent subsections so that the related value and

the linkage with price image were explained in detail in the current section.

2.7.1. Research related to location

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Location of a branch was an important consideration to the purchasing intention

along with the subsequent loyalty to the store. Nonetheless, the mechanism of the

effect to the loyalty from the geographical difference had not been established

clearly, especially for the local situation related to the beauty and healthcare

products (Strategic Direction, 2012; Virgona, Helene, 2012). The studies of

Christaller (1935) and Losch (1954) provided the observation to the predictable

sales volume related to location, while it was investigated related to the transaction

costs of the customers. For example, the customers would like to shop nearby so

as to minimize the traffic cost and time so that the loyalty could be cultivated in

the repeated purchasing. The related consideration might contribute partially to the

impact of the store loyalty, but it could not fully explain the diversity of sales with

the population of the related regions (Christaller, 1935; Losch. 1954).

In the observation of Losch (1954), it could be seen that the areas with the

population of relatively low income would yield lower sales, while some of the

customers were willing to spend in a remote location to them, such as, downtown

or cosmopolitan, etc. Therefore, it could be revealed the absolute location of a

branch instead of the relative location of the customers might have a certain value,

while it would contribute to the culmination of loyalty even the place was not

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convenience for them (Oliver, 1997). As a result, the mediation effect of price

image was suspected, while it would be investigated in the current study.

2.7.2. Relationship between location and price image

Scholars discovered that customers with less spending power acted loyalty to stores

with perceived low-price images; meanwhile, loyalty towards price image would

deteriorate when similar perceived value was found in competitors (Alba, et al.,

1994; Burton, et al., 1994; Singh, et al., 2006). In fact, the connection between the

price image and the value of location were spotted by Leong (2013) along with the

observation to the mega stores established in the blooming regions of a city, while

the positive price image to the related products or brands were transferred to the

store loyalty ultimately. In addition, the study conducted by Leong (2013) also

statistically and empirically confirm the positive effect to the price image and store

satisfaction from the selection of the particular place of store establishment.

Moreover, Mägi (1995) studied the relationship between travel distance to store

and store loyalty in Sweden and found that sixty percent of customers viewed their

favourite store was not located near their residential area but eighty-six percent of

customers repeated patronise in the store near their home. As a result, location

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affected behavioural loyalty and calculative commitment but not attitudinal loyalty

to store (Dwyer, et al., 1987; Achrol, 1991; Marshall, 2010).

Furthermore, Bell et al. (1998) examined the relationship between location and

different cost incurred to shop in a location and they discovered that customers

were calculative, customers were willing to travel longer distances to shop in low

price image location for the sake of cost benefit unless no dollar surplus could

generate from patronising in low price image location. Therefore, low price image

reduced the awareness of traveling cost. As a result, price image could have

mediating effect between location and attitudinal loyalty to store and calculative

commitment to store (Dwyer, et al., 1987; Achrol, 1991; Marshall, 2010).

Nonetheless, due to the geographical similarity of Hong Kong in different regions,

the related difference among the shops in dissimilar places could not be observed,

even though the related study was dedicated for the local supermarket. The related

effect should happen in the study of beauty and healthcare products due to the

labeling effects. For instance, Hong Kong local people would purchase computers

and notebooks in Sham Shui Po, while aquatic lovers would like to go to Goldfish

Market, etc. (Hong Kong Tourism Board, 2017). Hence, for the high-end beauty

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and healthcare products, the consumers would like to go the places with relatively

high price image.

2.8. The development of research questions

In spite of the plethora of studies on customer loyalty program and store loyalty

models, there was a dearth of studies examining the interrelationships among the

ten variables. Hence, the following research questions were posed:

RQ1: What are the moderating effects of price sensitivity with respect to the

relationship between hard attribute and behavioural action, the

relationship between hard attribute and word-of-mouth and the

relationship between hard attribute and commitment in the local beauty

and healthcare retailing industry?


relationship between soft attribute and behavioural action, the

relationship between soft attribute and word-of-mouth and the

relationship between soft attribute and commitment in the local beauty


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RQ3: What are the mediating effects of communication with customer with

respect to the relationship between virtual merchandising and

behavioural action, the relationship between virtual merchandising and

word-of-mouth and the relationship between virtual merchandising and

commitment in the local beauty and healthcare retailing industry?


respect to the relationship between location and behavioural action, the

relationship between location and word-of-mouth and the relationship

between location and commitment in the local beauty and healthcare

retailing industry?

2.9. The development of hypotheses

Along with the development and the illustration for the building blocks of current

study, the hypotheses to be verified and reviewed in the current work were

developed in the above sections in order to support the invention of the research

instrument and the related methods of the analysis.

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Bloemer & de Ruyter (1998) provided an empirical demonstration of the

connection between customer loyalty program and store loyalty in the department

store, while the positive significant effect of the program was recorded, customer

loyalty programmes improved store loyalty. Taylor and Neslin (2005) discovered

positive short term and long term impact of customer loyalty programs to store

loyalty; customers were found to raise their patronise volume to gain points for

rewards which was viewed as the instant impact and gradually, more repeated

purchased were found after rewards were given; it could be viewed as seeking for

rewards motivated instant purchased and rewards itself attracted commitment of

delay purchased (Blattberg & Neslin, 1990; Rothchild & Gaidis, 1981). Moreover,

Cortiñas et al. (2008) and Demoulin & Zidda (2008) found that program holders

were less reluctant to regular price products and they bought more variety of items

and contributed more on total sales to company to non-program holders. Hence,

the customer loyalty program would affect the store satisfaction of the consumer,

while the proportional association could be observed. It should be no exception to

the industry related to the beauty and healthcare product retailing (Bloemer & de

Ruyter, 1998; Leong, 2013).

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Therefore, the study posited that the customer loyalty program influenced store

loyalty directly and linearly with moderating effect from price sensitivity because

of the substantial evidence collected from different industries (Blattberg & Neslin,

1990; Rothchild & Gaidis, 1981; Reichheld & Teal, 1996; Taylor & Neslin, 2005;

Demoulin & Zidda, 2008; Bridson, et al., 2008; Leong, 2013; Cortiñas, et al., 2008;

Demoulin & Zidda, 2008) and so H1 was drawn as: Customer loyalty program is

directly and positive contributing to the store loyalty in the local beauty and

healthcare retailing industry with moderating effect from the price sensitivity. With

reference to Bridson et al. (2008)’s study and Leong (2013)’s, the hypotheses for

the theoretical framework to examine the customer loyalty in Hong Kong are as

follows:

H1a: Hard attribute is positively associated with price sensitivity in the local beauty

and healthcare retailing industry.

H1b: Hard attribute is positively associated with behavioural action in the local

beauty and healthcare retailing industry.

H1c: Hard attribute is positively associated with word-of-mouth in the local beauty


H1d: Hard attribute is positively associated with commitment in the local beauty and

healthcare retailing industry.

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H1a, H1b, H1c and H1d were related to the direct influence of hard attribute on

price sensitivity, behavioural action, word-of-mouth and commitment, indicating

the salience of hard attribute to retail store sustainability specifically associate with

customer loyalty concepts that are relevant to local beauty and healthcare retailing

industry.

Visual merchandising, like other promotion and advertisement in other media,

should have a message to deliver to the customers (Kuan-Yin, et al., 2010; Lalos

& Cestre, 2009; Carpenter & Fairhurst, 2003). Therefore, visual merchandising

should have a function in facilitating the communication with customers so as to

yield the improvement to the store loyalty as well as to enable the returned

customers (Garvey, 2010). Storms (2006) discovered that the role of visual themes

and first sight on visual themes had a large impact on patronise decision and

behaviours. Passive visual images travelled through the sights and retained in the

memory of receivers for a longer period of time compare to active messages

through verbal communications (Storm, 2006); therefore, it could be observed that

a mediating role of communication with customers act between visual

merchandising and store loyalty; visual merchandising delivered in-depth level

message in the first glance of receivers and possibly altered the store positioning

84

and gradually raised commitment to store (Dick & Basu, 1994; Marshall, 2010;

Rouse & Rouse, 2002; Storm, 2006). As a result, the exploration of the mediation

effect of communication customers between the visual merchandising and store

loyalty was included in this study. Therefore, H2 was drawn as: The relationship

between visual merchandising and store loyalty is affected by the mediation of

communication with customers. The hypotheses listed below were developed from

the adapted Bridson et al. (2008) and Leong (2013).

H2a: Soft attribute is positively associated with price sensitivity in the local beauty


H2b: Soft attribute is positively associated with behavioural action in the local


H2c: Soft attribute is positively associated with word-of-mouth in the local beauty


H2d: Soft attribute is positively associated with commitment in the local beauty and


H2a, H2b, H2c and H2d relate to the direct influence of soft attribute on price

sensitivity, behavioural action, word-of-mouth and commitment, indicating the

salience of soft attribute to retail store sustainability specifically associate with

85


industry.

Scholars discovered that customers with less spending power acted loyalty to stores

with perceived low-price images; loyalty towards price image would deteriorate

when similar perceived value was found in competitors (Alba, et al., 1994; Burton,

et al., 1994; Singh, et al., 2006). In fact, the connection between the price image

and the value of location were spotted by Leong (2013) along with the observation

to the mega stores established in the blooming regions of a city, while the positive

price image to the related products or brands were transferred to the store loyalty

ultimately.

In addition, the study conducted by Leong (2013) also statistically and empirically

confirmed the positive effect to the price image and store satisfaction from the

selection of the particular place of store establishment. Bell et al. (1998) examined

the relationship between location and different cost incurred to shop in a location

and discovered that customers were calculative, they were willing to travel longer

distances to shop in low price image location for the sake of cost benefit, unless no

dollar surplus could generate from patronising in low price image location, low

86

price image reduced the awareness of traveling cost; therefore, price image could

have mediating effect between location and attitudinal loyalty to store and

calculative commitment to store (Dwyer, et al., 1987; Achrol, 1991; Marshall,

2010). Therefore, H3 is drawn as: The relationship between location and store

loyalty is affected by the mediation of price image. The hypotheses listed below

were developed from the adapted Bridson et al. (2008) and Leong (2013).

H3a: Visual merchandising is positively associated with behavioural action in the

local beauty and healthcare retailing industry.

H3b: Visual merchandising is positively associated with word-of-mouth in the local


H3c: Visual merchandising is positively associated with commitment in the local


H3d: Visual merchandising is positively associated with communication with

customer in the local beauty and healthcare retailing industry.

H3a, H3b, H3c and H3d relate to the direct influence of visual merchandising on

behavioural action, word-of-mouth and commitment, indicating the salience of

visual merchandising to retail store sustainability specifically associate with


industry.

87

With reference to Bridson et al. (2008)’s study and Leong (2013)’s study, the

hypotheses listed below were developed from the adapted Bridson et al. (2008) and

Leong (2013).

H4a: Location is positively associated with behavioural action in the local beauty


H4b: Location is positively associated with word-of-mouth in the local beauty and


H4c: Location is positively associated with commitment in the local beauty and


H4d: Location is positively associated with price image in the local beauty and


H4a, H4b, H4c and H4d relate to the direct influence of price image on behavioural

action, word-of-mouth and commitment, indicating the salience of price image to

retail store sustainability specifically associate with customer loyalty concepts that

are relevant to local beauty and healthcare retailing industry.

H5a: Price sensitivity is positively associated with behavioural action in the local


H5b: Price sensitivity is positively associated with word-of-mouth in the local

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H5c: Price sensitivity is positively associated with commitment in the local beauty


H5a, H5b, and H5c relate to the direct influence of price sensitivity on behavioural

action, word-of-mouth and commitment, indicating the salience of price sensitivity

to retail store sustainability specifically associate with customer loyalty concepts

that are relevant to local beauty and healthcare retailing industry.

H6a: Communication with customer is positively associated with behavioural

action in the local beauty and healthcare retailing industry.

H6b: Communication with customer is positively associated with word-of-mouth

in the local beauty and healthcare retailing industry.

H6c: Communication with customer is positively associated with commitment in

the local beauty and healthcare retailing industry.

H6a, H6b, and H6c relate to the direct influence of communication with customer

on behavioural action, word-of-mouth and commitment, indicating the salience of

communication with customer to retail store sustainability specifically associate

with customer loyalty concepts that are relevant to local beauty and healthcare

retailing industry.

H7a: Price image is positively associated with behavioural action in the local beauty

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H7b: Price image is positively associated with word-of-mouth in the local beauty


H7c: Price image is positively associated with commitment in the local beauty and


H7a, H7b and H7c relate to the direct influence of price image on behavioural

action, word-of-mouth and commitment, indicating the salience of price image to

retail store sustainability specifically associate with customer loyalty concepts that

are relevant to local beauty and healthcare retailing industry.

H8a: Hard attribute is positively associated with behavioural action in the local

beauty and healthcare retailing industry with moderating effect from the price

sensitivity.

H8b: Hard attribute is positively associated with word-of-mouth in the local


sensitivity.

H8c: Hard attribute is positively associated with commitment in the local beauty

and healthcare retailing industry with moderating effect from the price

sensitivity.

H9a: Soft attribute is positively associated with behavioural action in the local

90


sensitivity.

H9b: Soft attribute is positively associated with word-of-mouth in the local beauty


sensitivity.

H9c: Soft attribute is positively associated with commitment in the local beauty


sensitivity.

H10a: Visual merchandising is positively associated with behavioural action in the

local beauty and healthcare retailing industry with mediating effect from the

communication with customer.

H10b: Visual merchandising is positively associated with word-of-mouth in the

local beauty and healthcare retailing industry with mediating effect from the


H10c: Visual merchandising is positively associated with commitment in the local

beauty and healthcare retailing industry with mediating effect from the


H11a: Location is positively associated with behavioural action in the local beauty

and healthcare retailing industry with mediating effect from the price image.

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H11b: Location is positively associated with word-of-mouth in the local beauty

and healthcare retailing industry with mediating effect from the price image.

H11c: Location is positively associated with commitment in the local beauty and

healthcare retailing industry with mediating effect from the price image.

These hypotheses are used to formulate a questionnaire for the research, as

described in later chapter.

2.10. The development of research model

After the illustration to the hypotheses studied in the current study, the summary

of them was included so as to ease the referring. Along with the comparison with

the models used in the previous studies related to the store loyalty in the retail

industry, the highlight and contrasting of the model adopted in the current work

could be achieved. In order to specify the contribution and uniqueness of the model

proposed, the current section was developed into two subsections, namely, The

parental frameworks and The proposed model , so as to start the development of

framework with a review of the existing models and to end with the newly

proposed model in suiting the local context of the beauty and healthcare retailing

industry.

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2.10.1. The parental frameworks

In the original model proposed by Bridson et al. (2008) in explaining the dynamic

between the customer loyalty program and store loyalty, the mediation effect of the

customer loyalty program and store loyalty had been inspected, while it found that

the connection between the customer loyalty program and the store loyalty could

be mediated by the store satisfaction. Therefore, the improved store satisfaction

was beneficial to the loyalty to the store, and it provided empirical support for the

devolvement of cultivating the store satisfaction. Essentially, the related model was

illustrated by the following Figure 2-4.

Sources: Bridson et al. (2008)

Figure 2-4: Framework model

Moreover, after the development Bridson et al. (2008), the effect of the location

had been spotted by Leong (2013), while the research study pointed out that the

93

store satisfaction would be affected by the location of the branches. Therefore, the

related study confirmed that the effect of the store satisfaction was localised around

the different branch. The related model, as shown in Figure 2-5, added the

implication of location in the understanding of the dynamic between customer

loyalty program and store loyalty.

Sources: Leong (2013)

Figure 2-5: Framework model

2.10.2. The proposed model

With reference to the development and illustration of the parental model and the

related hypotheses, the research framework adopted in the current study was

visualised in Figure 2-6. From the depiction, it could be observed that customer

loyalty program, visual merchandising, and location would contribute to the store

94

loyalty. Nonetheless, the relation between customer loyalty program and store

loyalty was subjected to the moderation effect of price sensitivity, while the

associations of visual merchandising and location with store loyalty were found to

be mediated by communication with customer and price image, correspondingly.

Therefore, one of the major objectives of the current study was to establish and to

verify the proposed connection with the data from the primary study.

Figure 2-6: Research framework used in current study

The research framework with hypothesis items marked was duplicated in Figure 2-

7 which was shown in below.

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Figure 2-7: The research framework with hypothesis items

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3. RESEARCH METHODOLOGY

3.1. Introduction

In the last chapter, the main literatures relevant to customer loyalty program, store

loyalty, price sensitivity, communication with customer and price image were

reviewed and highlighted. Based upon the discussion of customer loyalty

knowledge, the research questions were developed and the research framework

was established with corresponding hypotheses. This chapter focused on the

rationale and the choices of research design and research methodology and

describes the sampling frame, sample, research instruments and measurement, data

collection method and data analysis. The limitation in relation to research design

and ethical issues were explained together in the final part of the chapter.

3.2. Research paradigm

This study followed a research paradigm that guided the way to identify the

required factors and proposed model for characterising the interaction between the

customer loyalty program and store loyalty. A research paradigm provided a set of

common beliefs, values and guides on research methods which focused on analysis

and surveys development (Brand, 2009; Bryman, 2003; Cavana, Delahaye, &

97

Sekaran, 2001). According to Brand (2009), there were many types of research

paradigms and the two major types of research orientation were ontological and

epistemological.

The ontological orientation was a term referred to the study of the fundamental

nature of existence where the social world or the way people viewed topics from

their perceptions, whereas the epistemological orientation was referred to the ways

of knowing the discipline of the social world and the questions on whether or not

a study on the social world should be following the principles (Bryman, & Bell,

2015). The epistemological framework encompassed two predominant research

paradigms, namely interpretivism and positivism (Cohen, & Manion, 1994).

The interpretive paradigm applied scientific methods to interpret inductively the

social phenomena and construed the human behaviour and social theory (Cohen,

& Manion, 1994). As information on the phenomena was collected from the

research participants and was verified before generating the theories, the

interpretive paradigm was to suit the study of qualitative analysis. Differently,

the positivism paradigm referred to a deterministic philosophy that tests a theory

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or describes a personal experience from observation or measurement which would

have no interfering with the phenomena being studied (Levin, 1988).

As the ground theory this study was based on the identified customer loyalty

models, applying the positivism paradigm was suitable to this study and for testing

the theory objectively. Therefore, the proposed modified model would be used to

explore the relationships amongst different customer loyalty constructs and

customer store loyalty behaviours.

3.3. Research design

3.3.1. Cross-sectional research design

According to Bryman and Bell (2015), cross-sectional and longitudinal research

designs were the two common research designs for observational social and

business studies. A cross-sectional design did not provide definite facts about the

cause-and-effect associations and did not consider the changes that happened

before or after a snapshot in time (Bryman & Bell, 2015). However, a longitudinal

design would provide definite information related to the cause-and-effect

relationships as the changes that happened at two or more different snapshots

would be collected for analysis (Bryman & Bell, 2015). The current study aimed

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at identifying the behaviours of beauty and healthcare product consumers and their

relationships with different store loyalty factors in Hong Kong, i.e., a research that

studies the relationships between facts and outcomes. As prior literatures such as

Praharsi, Wee, Sukwadi, & Padilan (2014)'s study on loyalty program in

Indonesian as well as Izogo (2015)’s study on attitudinal loyalty in Nigerian

telecom service sector had adopted cross-sectional research, therefore cross-

sectional research design was adopted in this research to yield the impact of the

responses from research participants (Hunt, 2002) at one snapshot in time.

In addition, a single cross-sectional design was adopted as the population was the

residents of Hong Kong and only one set of samples was drawn from the population.

The collected information would be scrutinized and brought forward the results of

the effect of different attributes (e.g. soft attributes and hard attributes) on customer

loyalty behaviour which would relate to the comparison of opinions expressed

from the viewpoint of research participants in the theoretical framework, it was

beneficial to employ a single cross-sectional research design to compare all the

variables at the same time.

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3.3.2. Population

Hong Kong, ranking the 4th densely populated area on earth, had 7.24 million

residents living in less than 1100 sq km (Hong Kong Fact Sheet, 2015). In April

2018, it was expected that Hong Kong would have hosted more than 60 million

tourists and Chinese was the majority ethnic group in Hong Kong (SCMP, 2018).

As healthcare and beauty products can be imported duty free from Hong Kong into

the mainland China, a significant portion of healthcare and beauty products

imported into Hong Kong were repackaged and processed for the Chinese

mainland market (ITA, 2016). Therefore, Hong Kong was an attractive location for

foreign branded healthcare and beauty products import and was one of the

primarily important launch pads for marketing healthcare and beauty products into

the Chinese mainland.

The target population of this study was the local Hong Kong residents over the age

of 18 who had experience in purchasing beauty and healthcare products in Hong

Kong. To keep the population focused, only those adults who were capable to read

and understand either Chinese or English were considered.

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3.3.3. Sample

According to Sekaran and Bougie (2013), a sample would be a manageable subset

of a population and the sample in this research was Hong Kong residents who were

over the age of 18, had capability to read either Chinese or English, and had

experience in purchasing beauty and healthcare products in Hong Kong.

3.3.4. Sampling technique

In behavioural science studies, non-probability sampling techniques which include

judgmental, quota sampling, snowballing and convenience, are commonly adopted

sampling processes that do not give all the individual in the population equal

chances of being selected (Hair Jr et al., 2010). The next few paragraphs provided

a brief description on each of these sampling techniques and the approach adopted

in this research.

Hair Jr et al. (2010) provided description on the judgmental sampling method,

which was a sampling technique that a researcher was required to select sample

units based on the researcher’s professional judgment and knowledge of the people,

and the quota sampling method, which was a sampling skill that a researcher was

required to identify representative individuals out of a specific subgroup such as a

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sample of 50 males, or 50 individuals with monthly income of HK$ 15,000 or more.

Both of these two sampling methods were purposive sampling and researchers

might not be able to avoid conscious and unconscious personal bias going into the

data collection process (Cavana, Delahaye, Sekaran, 2001). Hence, the researcher

did not consider these methods as the needs to weighing several pieces of evidence

of selection and making statements about how sure the researcher that it was right

or how accurate the researcher was about somethings at the same time was too

complex and not as well developed to simple formulas.

For the convenience sampling technique, where the research participants were

selected because of their convenient accessibility to the researcher, and the

snowballing sampling technique, where the researcher encouraged the researcher

participants to recruit other research participants from among their acquaintances

across different social media, they were non-probability sampling techniques as the

research subjects were not selected by chance (Nardi, 2013; Hair Jr et al., 2010;

Morgan, 2008; Goodman, 1961).

In this study, both convenience sampling and snowballing techniques were

employed as the sample was drawn from the researcher’s social network and the

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research participants would invite other research participants from among their

acquaintances. The rationale behind choosing these two techniques was the easy

access to research participants and the simplicity to carry out with few rules

governing how the research sample should be collected from the population. In

addition, the snowball chain referral process allowed the researcher to reach

population that might not be easy to sample when using other sampling methods.

For example, some people do not like to be asked about their age in the street. It

was not cost-efficient to stand in a street corner to pick up pedestrians to ask them

if they were adult and whether they had purchased healthcare and beauty products

in Hong Kong. With snowball sampling, each research participants will

recommend others who were adult and might be interested in taking part in the

survey. However, there was a disadvantage to adopt this approach as it might not

be possible to determine the sampling error about a population based on the

obtained samples.

3.3.5. Research site

Research sample was come from the researcher personal network and the social

media network. The questionnaire of this study was built using the Qualtrics and

invitations were sent to potential participants via internet social networks

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(Facebook and Whatsapp) through Facebook messenger and Whatsapp (Neuman,

1997; Rundle-Thiele, 2005). Samples were gathered from more than one source.

As the research participants were anonymous while filling the survey, the

researcher could not run comparison on the difference from the two sources. Hence,

some research participants might receive invitations from three sources; one email

invitation from researcher, one invitation from the post in Facebook, and one

invitation from Whatsapp groups.

3.3.6. Sample size

To determine the sample size of this research, this study was based on Sekaran and

Bougie’s (2013) study which suggested that a multivariate research such as a

multiple regression analysis should have a sample size several times (preferably

ten times or more) as large as the number of variables in the study. As this study

had ten major constructs including behavioural action, word-of-mouth,

commitment, hard attributes, soft attributes, price sensitivity, communication with

customer, visual merchandising, price image, and location, a minimum of ten

responses per questionnaire item in a complex construct is recommended (Hair et

al., 2010). Therefore, the estimated minimum number of research participants

needed for the sample should be greater than 100. Having considered the available

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resources such as cost and time and the need to have the representative sample to

be generalizable to make estimation and inferences about the population (Bryman

& Bell, 2011), this study was designed to have a sample size of 200 or more to

ensure the sample statistics would be acceptable to estimate and reflect the

population parameters as closely as possible (Sekaran & Bougie, 2013).

3.4. Data collection method

Self-administrated online questionnaires were developed for the collection of the

responses due to the benefits of online data collection and the benefits include ease

of data gathering, minimal cost and increase in response rate (Bryman & Bell,

2015). The online questionnaire of this research was available between 7

September 2017 and 14 November 2017 inclusively and it was powered by

Qualtrics. The English version of the invitation script was attached in Appendix 3-

01 and the Traditional Chinese version of the invitation script was attached in

Appendix 3-01. Both of these invitation scripts were sent to potential participants

via Internet social networks through Facebook messenger and Whatsapp (Neuman,

1997; Rundle-Thiele, 2005).

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Potential participants were presented a copy of the information statement which

was attached on the survey explaining the scope of the study and the rights of the

participants. The English version of the information statement was attached in

Appendix 3-03 and the Traditional Chinese version of the information statement

was attached in Appendix 3-04 in this thesis. The potential participants were asked

if they were willing to participate voluntarily in this research by completing an

online questionnaire. The completed questionnaires would be considered as the

implied consent from research participants. The English version of the

questionnaire was attached in Appendix 3-05 and the Traditional Chinese version

of the questionnaire was attached in Appendix 3-06 in this thesis. As both Chinese

and English were the official languages adopted in Hong Kong, the invitation letter,

information statement and questionnaire, were available in both Traditional

Chinese and English language. It would take approximately 10 minutes to complete.

Participation was voluntary and participants could exit the questionnaire at any

time prior to submission.

The questionnaire did not ask for any personal information from the research

participants and this was to preserve anonymity. All information was exported from

Qualtrics and stored within the computers of the supervisor and the student

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researcher for scientific analysis. All information from Qualtrics and the computer

was removed at the completion of analysis and one single copy would be securely

stored on the researcher’s own Cloud secure for 5 years.

Once the research participants completed the online questionnaire, the participants

were given a chance to invite their friends to participate in the same questionnaire

based interview so as to achieve the expected number of the responses of 200 in

suiting the population of 7.2 million in Hong Kong (Hong Kong Census and

Statistic Department, 2014). In essence, snowball sampling (Morgan, 2008, p. 816

– 817; Goodman, 1961) was adopted in the conduction of current study.

3.5. Design of the questionnaire items

From a practical point of view, the respondents were invited to give their responses

in relation to the purchasing behaviour of the beauty related items. Therefore, the

questions were designed to study the response on the factors that reflect the

customer behaviour. In particular, the demographic information and their perceived

importance of the components in a customer loyalty program were accessed with

the questions which were based on validated literature and research articles.

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Multiple choice questions were adopted in the collection of data related to the

personal information of the participants, while they were invited to state their level

of acceptance to the corresponding statements in corresponding to the importance

of different components of the loyalty program.

A seven-point Likert scale (Likert, 1932; Likert, 1961; Wuensch, 2009) was

employed in facilitating the expression of the opinion. Furthermore, store

satisfaction, location, visual merchandising, store loyalty, and price sensitivity

were accessed with similar configuration, but the research participants were invited

to answer the related questions with reference to the most favour or the most

frequently purchased retailer.

The questionnaire was designed to have nine sections. Part 1 was the pre-condition

questions. Part 2 was a list of demographic questions. Part 3 was a list of store

loyalty questions. Part 4 was a list of customer loyalty program questions. Part 5

was a list of price sensitivity questions. Part 6 was a list of communication with

customer questions. Part 7 was a list of visual merchandising questions. Part 8 was

a list of price image questions. Part 9 was a list of location related questions. In

order to give the justification and evidence for the development of the research

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instrument, details of the research instrument were explained in the next few

sections.

3.5.1. Development of research instrument

Four pre-conditions questions were asked in the beginning to identify if the

participants were Hong Kong residents over the age of 18 who had experience in

purchasing beauty and healthcare products in Hong Kong. With reference to the

research instrument developed by Leong (2013) and Bridson, et al. (2008) as well

as the customization in suiting the purpose of the current study, the research

instrument was defined. As there was a total of 53 questions in the survey, the

questions were put into several categories to ease the collection of responses from

the participants.

The first eleven questions were dedicated in clarifying the demographic

background of the respondents, i.e. gender, age, marital status, monthly income,

received education along with the research participants’ favourite stores, the

frequency of visiting the store (in-person and online) and the participation to the

loyalty program and membership. The questions along with the related options

were provided so that the participants can opt the most proper answers out of the

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options. Then, from the question number 12 and onwards, the question items in

measuring the related research constructs were included and all of these questions

were encapsulated into positively coded statements. Thus, the participants could

express their level of agreeing to the statements with 7-point Likert scale (Likert,

1932; Likert, 1961; Wuensch, 2009) with the related options of “Strongly

Disagree”, “Disagree”, “Slightly Disagree”, “Neural”, “Slightly Agree”, “Agree”,

and “Strongly Agree”. Furthermore, the related arrangement also facilitated the

analysis of the related results, as the related opinion could be quantized by the

assignment of numbers ranging from 1 to 7 respectively.

In the second part of accessing the factors involved in the current study, the

questions related to store loyalty were asked, while behavioural action, word-of-

mouth, and commitment were considered as the components of it, and 16

statements were assigned in accessing the research participants’ point of view

related to store loyalty of them. Then, in the following part, the questions of

customer loyalty program containing the hard and soft attributes were included,

while 14 statements were dedicated to accessing the related item. Finally, price

sensitivity, communication with the customer, visual merchandising, price image,

and location were contained in the remaining parts of the questionnaire, while three

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questions each were assigned to price sensitivity and location and two questions

each were allocated to communication with the customer, visual merchandising

and price image.

The detailed mapping between the measures of each construct and question could

be seen from below table, while it summarised the corresponding for the questions

and the intended measures.

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Table 3-1: Mapping of research constructs and questions

3.5.2. Demographic questions

First, prior to the accessing to the opinion related to the customer loyalty program

and the satisfaction and loyalty of the customers along with the additional factors,

the demographic information were collected so as to support the investigation to

the segmentation of responses in regarding to the demographic classification

(Loker-Murphy, 1996; Brown, 2001; Thyne, et al., 2004). Therefore, the related

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seven pieces of information, namely, gender, age, marital status, monthly income,

received education, the frequency of visiting the favour store, and participation in

loyalty program was accessed with the corresponding questions shown in below

table.

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Table 3-2: Demographic questions

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After the review of different customer loyalty models and the related theories, the

model developed by Leong (2013) had been selected due to its simplicity and

generalizability (Gephart, 1999; Leong, 2013; Lincoln & Guba, 2000), while the

related research instrument had been referenced in the definition of the

questionnaire used in this study.

3.5.3. Measurements for store loyalty

Apart from the questions related to the demographic information, the measurement

of store loyalty had been designed on a 7-point semantic differential scale between

number 1 (strongly disagree) to point 7 (strongly agree) (Leong, 2013). In essence,

16 statements had been defined in accessing the related information from the

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respondents, and they were composed to access the behavioural aspect, word-of-

mouth, and commitment, respectively. The Table 3-3 in below indicated the

measures and their sources.

Table 3-3: Measuring items for store loyalty

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3.5.4. Measurements for customer loyalty

The measurement of important items for hard and soft rewards had been designed

on a Likert scale ranging from number 1 (not at all important) to number 7 (critical).

In below table, there were 14-scale questions in this part (Leong, 2013), while five

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of them related to hard attributes and nine of them related to soft attributes,

correspondingly.

Table 3-4: Measuring items for customer loyalty program

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3.5.5. Measurements for price sensitivity

The moderator between the customer loyalty program and store loyalty, namely,

price sensitivity was accessed with the defined three statements shown in below

table, while the respondents were invited to give the responses based on their

acceptance to the statements related to their point of view in purchasing a product.

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Table 3-5: Measuring items for price sensitivity

3.5.6. Measurements for communication with customer

The communication with the customer was one of the essential mediators for the

research framework, and it commonly existed in the forms of catalogues and

advertisements (McGoldrick & Ho, 1992; Walters & Knee, 1989). Therefore, the

perceived information provided by the shop and the utilisation of the loyalty

program (Leong, 2013) were accessed with the following two questions

summarised in below table.

Table 3-6: Measuring items for communication with customer

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3.5.7. Measurements for visual merchandising

In the same sense, the self-deviated parameter in influencing the store satisfaction

was accessed with the corresponding statement along with the 7-point Likert scale,

while two statements had been dedicated to facilitating the respondents in giving

their opinion to the related issues. The related statements and the references to the

statements were shown in table below.

Table 3-7: Measuring items for visual merchandising

3.5.8. Measurements for price image scale

As one of the most important dimensions in evaluating customer satisfaction

suggested by Bridson et al. (2008), price image referred to the perceived pricing of

the customers, while it was considered as a mediator in bridging the location and

store loyalty. Additionally, it was subdivided into the sub-dimension of the

suitability of the strategy and the frequent of promotion. As a result, the related

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sub-factors were accessed by the corresponding two statements shown in below

table.

Table 3-8: Measuring items for price image

3.5.9. Measurements for location scale

Finally, the independent variable of location was accessed with the corresponding

three statements along with the scale dedicated for the level of agreeing to them.

The related statements adopted in the current study had been summarised in the

table below.

Table 3-9: Measuring items for location

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3.6. Common method variance and treatment

According to the study by MacKenzie and Podsakoff (2012) and Podsakoff,

MacKenzie, Lee, and Podsakoff (2003), self-administered questionnaires might be

subject to common method variance (CMV) which included biases from a common

source, a questionnaire item characteristic, a questionnaire item context or

measurement context. For example, a research participant might easily provide

convenience answers to a survey question by referring to their choices in previous

questions (MacKenzie & Podsakoff, 2012). With the implementation of online self-

administered questionnaire, the survey application was designed to prevent

research participants from rolling back to consult previous answers with a remedy

feature which disabled the button for rolling back to the previous questions. The

online survey tool Qualtrics prepared questionnaire that prohibited research

participants from rolling back to previous pages for previous answers.

In addition, CMV might exist when research participants intended to provide

consistent answers all the way throughout the questionnaire. The Harman’s single

factor test was employed and it was a technique to inspect the existence of common

method variance (MacKenzie & Podsakoff, 2012), to determine if there was a

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single factor emerged that would account for the majority of the covariance among

the measures. The result of Harman single factor test was illustrated in the ‘Total

variance explained’ table in Appendix 4-04 and there was no single factor account

for the majority of the variance.

3.7. Statistical data analysis

After the completion of the data collection, the raw data was exported into

Microsoft Excel file and then converted into Statistical Package for the Social

Sciences (SPSS) data file. Researcher ran data analysis by five stages data screening,

data coding, descriptive analysis, validity and reliability measurement assessments,

confirmatory factor analysis and structural equation model. The next few sections

explained the process.

3.7.1. Data screening and data coding

The raw data was coded with unique identification number and screened using SPSS

to check for data accuracy, data completeness, response range and outliers. In the

online survey tool Qualtrics, all questions were made mandatory, where none of

the questions could be skipped. Hence, missing value cases would not occur. In

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addition, when a research participant closed the survey site before completing the

questionnaire, the records of incomplete data were not accepted for analysis.

3.7.2. Descriptive analysis

Descriptive statistic provided a quantitative description of the raw data and this

description included the frequency, percentage, mean, skewness value, kurtosis

value and standard deviation. Furthermore, it provided an overall picture of the

normality of data (Hair et al, 2010) and the distribution might depend on the

variable type, ordinal scale and nominal scale.

Both skewness and kurtosis measurements had been used to assess the normality

of data distribution. For Likert scale measurements, a positive skewness value

indicated that more research respondents “strongly disagree” or “disagree” with

the implication of statement in the question, whereas a negative skewness value

implied that more research respondents “strongly agree” or “agree” (Hair Jr et al.,

2010). For nominal scale measurement, kurtosis was employed to measure the

thickness of the tails of a distribution. Positive kurtosis value indicated a

relatively peaked distribution of responses, whereas negative value indicated a

relatively flat distribution.

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3.7.3. Measurement assessment – reliability test

Data validity tests were conducted to assess the diversity of the different

demographic group and to verify the causal relationship along with the moderation

effect. In this thesis, Cronbach’s alpha (α) was conducted to perform the reliability

test.

Reliability test measured the consistency of a measuring instrument for a concept

or perceived value of a statement or item, and validity was the measurement of

how well the instrument could measure that particular concept which was intended

to measure (Sekaran & Bougie, 2013). Reliability tests had been used to evaluate

the consistency of research participants’ understanding of the questionnaire items.

Measuring stability and consistency were concerned with Cronbach’s alpha test

reliability.

To compute the inter-correlations among the questionnaire items for the proposed

model, SPSS was employed. The closer Cronbach’s alpha was to 1.0, the higher

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was the internal consistency reliability. Cronbach’s alpha as a reliability

measurement was tabulated in below table below ( Sekaran & Bougie, 2013).

Table 3-10: Cronbach's alpha measurement

3.7.4. Measurement assessment – validity tests

Validity tests was adopted to check whether a measurement used in a study was

measuring what it purports to measure (Bryman & Bell, 2011).

According to Cavana et al.’s (2001) study, there were three types of validity tests

including content validity, convergent validity and discriminant validity. Content

validity referred to the use of relevant literatures to support the theoretical

underpinning of the scales and questionnaire items selected, was assured in this thesis

(Bryman & Bell, 2011). The convergent validity and discriminant validity had been

tested by an exploratory factor analysis (EFA) and a confirmatory factor analysis.

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As EFA explored the dataset and provided information on the number of factors needed

to represent the data, this thesis employed an EFA to find out whether items within a

construct were correlated and homogeneous (Hair et al., 2010). Both EFA and CFA

techniques had used SPSS to run the analysis before conducting the structural

equation modelling (SEM) analysis (Sekaran & Bougie, 2013).

There were three basic assumptions on EFA. First, the measurements of Kaiser-

Meyer-Olkin (KMO) would reflect the sampling adequacy at different values. The

Table 3-11 in below listed the KMO measurement. Second, a Bartlett’s Test of

Sphericity, which tested the null hypothesis that the original correlation matrix was

an identity matrix, had been used to test the inter-correlation among the variables.

This statistical test would identify if the significant p-value (used to determine the

significance of hypothesis tests) was < 0.05 or not. When p-value < 0.05, it

indicated that sufficient correlation existed among the variables to proceed. Third,

a measure of sampling adequacy (MSA) index had been used to interpret the

appropriateness of factor analysis. The MSA was commonly adopted to quantify

the degree of inter-correlation amongst the different variables. The MSA value

would increase when the following conditions occurred (1) increased sample size

(2) increased average correlation between variables, (3) increased number of

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variables increases, or (4) decreased number of factors. The Table 3-12 in below

provided the interpretation of the MSA index.

Table 3-11: Kaiser-Meyer-Olkin (KMO) measurement

Table 3-12: Measure of sampling adequacy

3.8. Structural equation modelling and applications

Structural Equation Modelling (SEM) was a procedure of examining the causal

processes presented by a series of structural equations which could be graphically

presented in a model and its analysis included measurement errors, multiple

indicators and multiple-group comparisons (Byrne, 2010; Hair et al., 2010). Thus,

SEM would provide an integration tool for regression analysis and confirmatory

factor analysis to ascertain factual (CFA) assumption of surveys.

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The conceptual framework of this research was modelled into the SEM statistical

software package (i.e., AMOS) in order to ensure that the exogenous and

endogenous variables were all distinct. In SEM, any changes in the values of

exogenous variables would not be explained by the model, but they were

influenced by other factors external to the model. For the endogenous variables,

any fluctuation in the values had been explained by the model since all the latent

variables were embedded in the model (Byrne, 2010)

3.8.1. The measurement model

The measurement model in SEM had been evaluated through CFA which assumed

that all constructs had interrelationships with each other, and CFA would also be

used to determine and strengthen the validity of constructs and data respectively

(Hair Jr et al., 2010; Kline, 2010).

3.8.2. The structural model

The structural model had been composed of theoretical networks of relationships

among constructs (Hair Jr et al., 2010; Kline, 2010), based on the modified

Customer Loyalty model in this research. As the researcher had defined the

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hypotheses between constructs in the proposed model, the structural model would

specify a fewer number of relationships among constructs when compared with the

measurement model. The AMOS was employed to formulate the relationships

among hypothesized constructs.

3.8.3. The fit indices in structural equation modelling

The fit indices of SEM were used to determine the level of acceptance of the

measurement model and the structural model and these fit indices had been taken

referenced on the Afthanorhan’s study (2013) which summarized different

measurement indices from different scholars and grouped the indices into three

main categories, namely absolute fit, incremental fit and parsimonious fit. The

Table in below listed the descriptions and level of acceptance of these indices.

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Table 3-13: The fit indices in structural equation modelling

3.8.4. The test on mediation variables

Tests would be run to identify the mediation effects among a dependent variable, a

mediator and an independent variable and this analysis would be based on the p-

value of the regression coefficients among the variables. In below Table 3-14, the

regression coefficients among the variables were denoted by β1 (i.e. between X

and Y), β2 (i.e. between Z and Y) and β3 (i.e. between X and Z) respectively. The

two mediators, communication with customer and price image, in the structural

equation modelling would be tested.

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Table 3-14: The conditions for different types of mediators

3.8.5. The test of the moderation construct

There was a test on the moderation interactive effect, which was the joint effects

of two predictor variables in addition to the individual main effects and looked for

a form of relationship changes between two constructs which depended on the

value of the 'assumed' moderator variable. First, the variables including, for

example, the price sensitivity construct (PS), hard attribute construct (HA) and soft

attribute construct (SA) were standardized. Then the multiplicated effect of HA and

PS was computed in AMOS and formed a new construct ‘HA_x_PS'. This

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construct had been put into the structural model as indicated for running regression

test in AMOS. Finally, from the AMOS output, the structural model's regression

weights among the constructs and the p-values had been listed in a table. The

construct relationships would be evaluated based on whether the p-values are

significant (<0.05) or not (>0.05).

The explanation could be a positive-to-negative sign change or negative-to-

positive sign change or stronger sign change or weaker sign change.

3.9. Limitation of methodology used

First, in this research, the theoretical framework was based on customer loyalty

program model, which was mainly based on the perceptions of customers.

However, their perceptions and past experience on purchasing different types of

healthcare and beauty products might have relevant to their perceptions.

Second, in a self-administered questionnaire, there was no direct way to prove that

a research participant was really a customer of a healthcare and beauty products. It

was assumed all these research participants were real customers.

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3.10. Ethical considerations

The study was complied with the Universities Australia/National Health and

Medical Research Council (UA/NHMRC) Australian Code for the Responsible

Conduct of Research, which has been accepted by the University of Newcastle.

The student researcher had ensured that all collected data was kept strictly

confidential. All information collected would be used only for the purpose of this

research.

The collected data was stored securely in a password-protected device and was

accessible only by the research team. Data would be retained for a minimum of

five years and destroyed in accordance with guidelines of the University of

Newcastle.

All the responses made by research participants contributed to the survey results,

and might be published as part of the researcher’s DBA dissertation or as part of

research papers in academic journals or conference proceedings. The data was non-

identifiable and would be shared with other parties to encourage scientific scrutiny,

and to contribute to further research and public knowledge, or as required by law.

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3.11. Significance and limitation of current work

Last but not least, the current study would provide an updated access to the local

retailing market related to beauty and healthcare product, while the suggestion on

the devolvement of the customer loyalty program and the resulted effectiveness

could be revealed so as to yield a practical precaution for the practitioners in the

related industry.

Although the necessary measures in preventing the failure of the conclusion and

the conduction of the current work, certain limitations of the current study were

unavoidable. First, the current study was designed as a cross-sectional base, but the

causal relation between the program and the store loyalty. Second, the direct and

positivism were assumed, so the separated effects from the hard and soft attributes

cannot be resolved. Third, there was a miss on the analysis to the probability

improvement brought by the program, while the recommendation may not be

sufficient enough for the decision making. Finally, the holistic overview of the

dynamics between the factors may not be covered, while the hidden factors may

alternate the casual relation.

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4. DATA ANALYSIS

4.1. Introduction

This chapter described the characteristics of the samples collected from the

questionnaire-based study, tested the measurement assessment and ran the

structural equation modelling (SEM) and followed by the significance test of the

assumed path relationship in the proposed model. SPSS was used to analysis

descriptive data and conduct factor analysis. To test the measurement model, the

structural model and the mediation and moderator constructs, AMOS was

employed.

4.2. Naming of variables and constructs

In the SPSS package, there were certain rules applying to the naming of variables

to facilitate the loading and testing of data file (IBM Knowledge Center, 2015). A

naming convention was adopted to assign a set of characters to the source code of

the constructs, questionnaire items and demographic data. The naming convention

was listed in Appendix 4-01.

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4.3. Data screening

A total of 220 completed records was collected from a web-based survey software

named Qualtrics and the data record was downloaded into Microsoft Excel file

format.

A data cleansing process was performed on the downloaded data as it was an

important process before analysis (Salkind, 2010) and the data was screened for

extracting both missing values and outliers by using Microsoft Excel. As the

Qualtrics survey tool would prompt the research participants when there were

missing values in any questionnaire item, the completed questionnaires would have

no missing value. The collected data was checked and was found having no missing

values and no outliers. From eMarketer (2014)’s study, the response rate of a survey

using snowball sampling technique would be approximately equal to or less than

20% and prior literature found similar result for survey using online and email for

data collection (Han, Fang, Ye, Chen, Wan, & Qian, 2019). As the response

representativeness was more essential than the response rate in any organisation

survey research (Cook, Heath, & Thompson, 2000) and there was a snowball

sampling technique involved, the study therefore did not take into account the

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actual return rate of the consent form in the data analysis as it is not relevant in this

study.

4.4. Descriptive analysis

Descriptive statistic was used to provide a quantitative description of the samples

and an overview of the characteristics of the key variables (Mann, 2013). This

research had both nominal and ordinal types of measurement to collect the

respondents’ demographic data and their perception on the values of their favourite

stores’ customer loyalty program. Also, there were four precondition questions, 11

demographic questions and 42 customer loyalty questions.

The precondition questions were set up in a way to allow the survey tool to evaluate

the research participants’ precondition. If any one of the preconditions was not

fulfilled or responded with a ‘No’, then the application would stop generating the

remaining questions and informed the research participants not to proceed the

filling of questionnaire. If the preconditions were fulfilled, then the tool would

generate the questions in the pages following. The precondition questions allowed

the researcher to collect data only from adult research participants who permanent

residents were at Hong Kong and had the experience in purchasing beauty and

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healthcare products. The table in below listed the precondition required for each

question.

Table 4-1: The precondition questions

For the 11 demographic questions, a frequency statistic was used to describe the

demographics of the research participants. The following sections described the

demographic analysis result.

4.4.1. Descriptive analysis of demographic data

From 220 respondents, the statistics showed that there were more women (55.9%)

than men (44.1%) participating in the survey and there were 86 research

participants (39.1%) below 30 years old and 102 research participants aged

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between 31 and 40 years old. In addition, the findings indicated that 35.9% and

24.1% of the research participants have monthly income ranged from $15,001-

$25,000 and $25,001-$35,000 respectively. This meant that more than half of the

research participants had monthly income over $15,000. Furthermore, most of the

research participants had a bachelor degree as there were 137 of them (62.3% of

the total number of participants). The second highest education level obtained

among the research participants was diploma/high diploma/associate degree and

there were 43 research participants. The statistics of the demographics were

extracted into Table 4-2.

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Table 4-2: Respondent demographics details

Furthermore, the top six beauty and healthcare products retailers being marked as

participants’ favourite stores were Aster (7.7%), SaSa (7.3%), Angel (6.8%),

Wellcome (6.8%), Manning’s (5.9%) and City Super/Logon (5.9%). The statistics

of the findings were extracted into Table 4-3 in below.

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Table 4-3: Statistics of the favourite beauty & healthcare products retailers

Furthermore, the statistics in Table 4-4, as shown in below, indicated that 137 out

of 220 research participants (62.3%) had visited their favourite stores at least once

a week and 164 out of 220 research participants (74.5%) had bought products from

the online platform of their favourite stores at least once per month. Lastly, there

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were 191 out of 220 (or 86.8%) research participants participating in a beauty and

healthcare products retailer’s loyalty program in Hong Kong.

Table 4-4: Frequency of visit and memberships

The perception on the research participants on customer loyalty questions were

measured by Likert scales rating from 1 to 7. The means, standard deviations,

skewness and kurtosis were employed to run the analysis. According to Hair et al.

(2010), the mean was the average response of the participants, the standard

deviation provided the broadness of the dataset, the skewness outlined the

inclination of the data and the kurtosis described the tailedness of the distribution

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of the response. The next few sections provided descriptive analysis of the

customer loyalty constructs.

4.4.2. Descriptive analysis for behavioural action

The results of descriptive analysis for the behaviour construct, which was defined

as the behavioural conducts of the loyalty customers of a store (Wulf & Odekerken-

Schröder, 2001) was tabulated in Appendix 4-02. The average of all five items’

mean was approximately 6.04, which indicated that the research participants were

strongly in agreement with their decisions to purchase beauty and healthcare

related product from their favourite retailers. The standard deviations were high in

value which meant a high dispersion of the responses around the mean. The

skewness values lied within ±2.0, which indicated that there were no abnormalities,

and the responses were normally distributed (Zikmund et al., 2013) and the

distribution of responses was at the right of the mean. The kurtosis values were

positive which indicated the distribution of responses had heavier tails and a

sharper peak than the normal distribution.

4.4.3. Descriptive analysis for word-of-mouth

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The descriptive analysis for the word-of-mouth construct, which was defined as

the behavioural conducts of the loyalty customers of a store when the customers

exchanged message that could affect brand or product image (Dwyer et al., 2007)

was tabulated in Appendix 4-02. The average of all five items’ mean was

approximately 4.74, which indicated that there was a general amount of WOM

among respondents. The standard deviations were mostly less than 1 except

WOM2’s standard deviation and these standard deviations meant that a low

dispersion of the responses around the mean. The skewness values lied within ±2.0,

which indicated that there were no abnormalities, and the responses were normally

distributed (Zikmund et al., 2013). The kurtosis was mixed with both positive and

negative values which indicated the direction of nonnormality in the participants’

responses.

4.4.4. Descriptive analysis for commitment

The results of descriptive analysis for the commitment construct, which was

defined as the relationship of on-going combination of behavioural and attitudinal

actions (Dwyer et al., 1987; Achrol, 1991), was shown in Appendix 4-02. The

average of all six items’ mean was approximately 5.36, which indicated that there

was a moderate amount of commitment among respondents. The standard

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deviations were slightly higher than 1.0 except COM1’s standard deviation and

these standard deviations meant that a low dispersion of the responses around the

mean. The skewness values were slightly larger than zero and these values

indicated that the distribution of responses was at the left of the mean and the

distribution’s right tail is longer than the left tail. The kurtosis were all negative

values which indicated the distribution of responses had lighter tails and a flatter

peak than the normal distribution.

4.4.5. Descriptive analysis for hard attributes

The descriptive analysis for the hard attributes construct, which was defined as the

inclusion of the physical and substantial reward to a customer (Capizzi & Furguson,

2005; Bridson et al., 2008), was listed in Appendix 4-02. The average of all five

items’ mean was approximately 3.17, which indicated that there was a low amount

of hard attributes among respondents. The standard deviations were approximately

equal to 1.0 and these meant that a low dispersion of the responses around the mean.

The negative skewness values indicated that the distribution of responses was at

the right of the mean. The kurtosis values were all negative which indicated the

distribution of responses had lighter tails and a flatter peak than the normal

distribution.

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4.4.6. Descriptive analysis for soft attributes

The descriptive analysis of the soft attributes construct, which was defined as the

special treatment for the consumer and the treatment was connected to the

intangible rewards to the consumers (Meyer-Waarden & Benavent, 2008;

O’Malley, 1998), was shown in Appendix 4-02. The average of all nine items’

mean was approximately 5.65, which indicated that indicated that there was a

moderate amount of soft attributes among respondents. The standard deviations

were approximately equal to 1.0 and these values meant that a low dispersion of

the responses around the mean. The negative skewness values indicated that the

distribution of responses was at the right of the mean. The kurtosis values were

positive which indicated the distribution of responses had heavier tails and a

sharper peak than the normal distribution.

4.4.7. Descriptive analysis for price sensitivity

The descriptive analysis for the price sensitivity construct, which was the degree

to which customers behaviours were affected by the prices of the beauty and

healthcare products, was shown in Appendix 4-02. The average of all three items’

mean was approximately 4.14, which indicated that there was a moderate amount

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of price sensitivity among respondents. The standard deviations were all slightly

larger than 1.0 and these values meant that a low dispersion of the responses around

the mean. The negative skewness values indicated that the distribution of responses

was at the right of the mean. The kurtosis values were positive which indicated the

distribution of responses had heavier tails and a sharper peak than the normal

distribution.

4.4.8. Descriptive analysis for communication with customers

The descriptive analysis for the communication with customers construct, which

was defined as the level of satisfaction about the flow of advertising information

or messages from the participants favourite retailers to the participants (Leong,

2013; Bridson et al., 2008), was shown in Appendix 4-02. The average of all two

items’ mean was approximately 3.46, which indicated that there was a low amount

of communication with customers among respondents. The standard deviations

were both close to 0.9 and these values meant that a low dispersion of the responses

around the mean. The negative skewness values indicated that the distribution of

responses was mostly at the right of the mean. The kurtosis are all negative in

values which indicated the distribution of responses had lighter tails and a flatter

peak than the normal distribution.

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4.4.9. Descriptive analysis for visual merchandising

The descriptive analysis for the visual merchandising construct, which was defined

as the level of agreement about the retailers’ floor plan and the window displays

that attract eye-browsing from window shoppers (Sebastian, 2008; Rowe, 2014),

was shown in Appendix 4-02. The average of all two items’ mean was

approximately 4.6, which indicated that there was a moderate amount of visual

merchandising among respondents. The standard deviations were close to 0.9 and

these values meant that a low dispersion of the responses around the mean. The

negative skewness values indicated that the distribution of responses was at the

right of the mean. The kurtosis values were positive which indicated the


distribution.

4.4.10. Descriptive analysis for price image

The descriptive analysis for the price image construct, which was defined as the

perceived value of a product (Alba, et al., 1994; Gewal & Marmorstein, 1994; Bell

& Lattin, 1998; Buyukkurt, 1986), was depicted in Appendix 4-02. The average of

two items was approximately 4.47, which indicated that there was a moderate

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amount of price image among respondents. The standard deviations were both over

1.2 and these values meant that a normal dispersion of the responses around the

mean. The negative skewness values indicated that the distribution of responses

was at the right of the mean. The kurtosis values were mixed with both positive

and negative values which indicated the direction of nonnormality in the

participants’ responses.

4.4.11. Descriptive analysis for location

The descriptive analysis for the location construct, which was defined as the value

of the branch location that the beauty and healthcare products retailers provided

(Oliver, 1997; Losch, 1954), was shown in Appendix 4-02. The average of all three

items’ mean was approximately 5.65, which indicated that there was a moderate

amount of branch location value among respondents. The standard deviations were

close to 1.0 and these values meant that a low dispersion of the responses around

the mean. The negative skewness values indicated that the distribution of responses

was at the right of the mean. The kurtosis values were positive which indicated the


distribution.

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4.5. Measurement assessment

Most of the measurement scales used in this research paper had not been used in

the Hong Kong beauty and healthcare products industry, therefore all questionnaire

items were tested for validity and reliability even though the measurement scales

were taken from well-established literatures. The assessment of the collected data

was based on reliability tests, KMO sample adequacy and exploratory factory

analysis (EFA) and then verified by the confirmatory factor analysis (CFA).

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4.5.1. Kaiser-Meyer-Olkin and Bartlett’s Test of Sphericity

Kaiser-Meyer-Olkin (KMO) measured the sampling adequacy of the collected data

and Bartlett’s Test of Sphericity compared the observed correlation matrix to the

identity matrix. According to Hair et al. (2010), the KMO measure of sampling

adequacy value was better to be greater than 0.6 and p-value would better be

statistically significant (p< 0.05) to indicate that sufficient correlation existed

among the variables to proceed. From the table listed in below, the KMO measure

of sampling adequacy value was 0.816 (>0.6) and the p-value of Bartlett’s Test

values was significant (p=0.000). These results showed that the collected data had

achieved the required sampling adequacy and sufficient correlation existed among

the variables.

Table 4-5: Kaiser-Meyer-Olkin and Bartlett’s Test of Sphericity

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .816

Bartlett's Test of Sphericity

Approx. Chi-Square 7127.773

df 861

Sig. .000

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4.5.2. Exploratory factor analysis

After the reliability tests, KMO and the Bartlett’s Test of Sphericity were passed,

the EFA was used to determine the correlation and assess the factor structure of the

items. This research adopted the maximum likelihood approach and selected

promax rotation and suppressed loadings below 0.5. The table in below showed the

significant factor loading 0.40 was acceptable when the sample size was above 200

(Hair et al., 2010).

Table 4-6: Factor loading in relation to sample size

Factor Loading value Sample size

0.30 350 0.35 250 0.40 200 0.50 150

The p-value is significant at 0.05 (adapted from Hair et al. (2010))

4.5.3. Communalities

The communalities which indicated the total amount of variance an original

variable shared with all other variables in an analysis was attached in Appendix 4-

03. The initial communalities, which were the estimates of the variance in each

variable accounted for by all factors, and the extraction communalities, which were

the estimates of the variance in each variable accounted for by the factors in the

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factor solution, were both checked (Hair et al., 2010). All extraction communalities

were above 0.4 except HA5 (0.352) and the results indicated that each variable

could load significantly on a factor except the HA5 which might be difficult to load

significantly on a factor.

4.5.4. Total variance and eigen values

While conducting the factor analysis, each factor or questionnaire item in this

survey explained a certain amount of total variance percentage. The total variance

table, as attached in Appendix 4-04, indicated that there were four columns namely

factor, initial eigenvalues, extraction sums of squared loadings and rotation sums

of squared loadings.

The ‘Factor’ column listed out all the 42 variables used in the analysis. The ‘Initial

Eigenvalues’ (i.e., the variance of the factors) column listed out the eigenvalue for

a given factor and it measured the variance in all the variables which was accounted

for by that factor. From this column, there were 10 factors which had eigenvalues

greater than 1.0 and this result matched the expected number of factors in the

proposed model. The ‘Extraction Sums of Squared Loadings’ column listed out ten

rows in each sub-column and these ten rows correspond to the number of factors

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retained. The values in this column were lower than the values in the ‘Initial

Eigenvalues’ column because common variance was used for this column whereas

total variance was used for the initial eigenvalues. The ‘Rotation Sums of

Squared Loadings’ was the column representing the distribution of the variance

after the ‘promax’ rotation. ‘Promax’ rotation attempted to maximize the

variance of each of the factors such that the total amount of variance was

redistributed over the ten extracted factors. From the Appendix 4-04, the

“Extraction Sums of Squared Loadings” column indicated that the first factor

represented approximately 4.859% of the total variance explained, and the

cumulative variance for the first ten factors was approximately 70% which met the

criteria (above 60%).

4.5.5. Content validity

For content validity, this study had covered extensive literature on the measuring scale

and the scales had been reviewed in Chapter Two, therefore content validity was also

affirmed.

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4.5.6. Convergent validity

The results of Rotated Factor Matrix, extracted from factor analysis, was shown in

Appendix 4-05. It had the rotated factor loadings which represented not only how

the 42 items were weighted under the factors but also the correlation between the

items and the factor. The matrix indicated that the loading values were ranging

from -1 to +1. Based on Hair et al. (2010), the minimum factor loading value was

0.4 for a sample size of 200. To ensure clear loading between items, all coefficient

values that were less than 0.50 were suppressed, on the assumption that each item

should load highly (>0.50) on one factor for meaningful for interpretation.

From the loading pattern in Appendix 4-05, it indicated that all 42 items of the

questionnaire were distributed into ten factors accordingly and no cross-loading of

an item to more than one factor was found. This result indicated that convergent

validity was achieved as the items/variables within a single factor were highly

correlated. For example, WOM1, WOM2, WOM3, WOM4 and WOM5 had

loading values 0.836, 0.811, 0.802, 0.692 and 0.770 respectively and these loadings

were all greater than 0.5.

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Based on the results from the pattern matrix table in Appendix 4-05, the factors

and their corresponding items were mapped and listed in Table 4-08. This meant

that Factor 1 was the soft attribute construct, Factor 2 was the communication

construct, Factor 3 was the behavioural action construct, Factor 4 was word-of-

mouth construct, Factor 5 was the hard attribute construct, Factor 6 was the price

sensitivity construct, Factor 7 was the location construct, Factor 8 was the price

image construct, Factor 9 was the communication with customer construct and

Factor 10 was the visual merchandizing construct.

Table 4-7: Factor & Items Mapping

As mentioned in Chapter 3, discriminant validity refers to the extent to which

factors are distinct and uncorrelated. This means that the variables should relate

much stronger to their own factor than to another factor. The results in the pattern

matrix indicated that the collected data has achieved adequate discriminant

validity as each variable loads high in one single factor only.

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4.5.7. Confirmatory factor analysis

After the EFA, a confirmatory factor analysis (CFA) was performed to determine

the factor structure based on the data collected and to analyse the measurement

model in Structural Equation Modelling (SEM). The components of SEM were

defined in below Figure 4-1.

Figure 4-1: The components of measurement model

In CFA, all ten constructs were assumed having interrelationships with one another

and had ridden on the interrelationships to determine the validity of the data in all

the constructs. A measurement model was built using AMOS and it was created

based on the ‘Pattern Matrix’ (i.e., Appendix 4-05) output from SPSS where the 42

items were categorized by their corresponding factors and the relationships among

the latent variables, observed variables and their co-variances. Figure 4-2 in below

indicated the path diagram.

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Figure 4-2: Path diagram of the measurement model

From AMOS, the model fit indices of the above measurement model had Chi-

square = 1220.721, root mean square error approximation (RMSEA) = 0.051,

comparative fit index (CFI) = 0.934, Tucker Lewis Index (TLI) = 0.927, and Chi-

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square/degree of freedom = 1.577. The full model fit indices table of the

measurement model was attached in Appendix 4-06. Next, the AMOS was

employed to generate modification indices which were summarized in a covariance

table listing out the covariance among all the measurement error terms. Based on

the modification indices table, which was attached in Appendix 4-07, the

measurement model was improved by defining the covariance (i.e. manual addition

of a two-way arrow) between measurement error terms within a construct until no

further covariance could be defined among the measurement error terms. The

enhanced measurement model was shown in Figure 4-3 in below.

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Figure 4-3: The coefficient of the enhanced measurement model

Based on the above enhanced measurement model, a list ‘Model Fit’ indices list

was generated from AMOS. The measurement model’s model fit indices together

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with the model fit acceptance values suggested by Kline (2011) and Awang, Ahmad,

and Zin (2010) were compared with result listed in the table below.

Table 4-8: Measurement model fit indices and acceptance levels summary

Sources: Kline (2011) and Awang, Ahmad, and Zin (2010),

The full model fit indices table for the above enhanced model was attached in

Appendix 4-08. As mentioned in Chapter 3, Awang, Ahmad, and Zin (2010)

recommended to classify the fitness indices into three categories namely absolute

fit, incremental fit and parsimonious fit. Kline (2011) recommended employing at

least three fit indices by including at least one index from each of the fitness

categories. From the results in the above table, only the Chi-square, GFI, AGVI

and NFI did not pass the good fit criteria and the rest of the indices passed the good

fit criteria. Therefore, the data fitted for the confirmatory factor analysis of the

model.

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4.5.8. Reliability statistics

A Cronbach’s alpha test was performed on each construct and the detail was listed

in below table. Each construct had a Cronbach’s alpha value of over 0.84 and this

result indicated that all these constructs were a good measurement of the reliability

of the collected data (Punch, 2013; Sekaran & Bougie, 2013).

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Table 4-9: Reliability test result of all customer loyalty constructs

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4.5.9. Discriminant validity

As mentioned in Chapter 3, the measures to check if the factors demonstrating

sufficient convergent validity and discriminant validity were Average Variance

Extracted (AVE) and Maximum Shared Variance (MSV) (Hair et al., 2010). The

AVE and MSV of the factors were listed in below table. The results indicated that

all AVE were greater than 0.5 and MSV were all less than AVE. Therefore, the

collected data had established adequate discriminant validity.

Table 4-10: The convergent validity and discriminant validity results

4.5.10. Common method variance

As mentioned in Chapter 3, a common method variance, such as an online survey,

might influence the responses provided by the research participants and the

responses might be inflated or attenuated (Richardson, Simmering, & Sturman,

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2009) and researcher should check and minimize it effect (Podsakoff, MacKenzie,

Lee, & Podsakoff, 2003). To check for common method variance, a common latent

factor was added to capture the potentially inflated variance among all 42 observed

variables in both the unconstrained measurement model and constrained

measurement model and ran AMOS to generate their chi-square and degree of

freedom values and analysed using chi-square difference test on their p-values. The

results indicated that there was a significant difference in the chi-square values as

the p-value was 0.000 (<0.05). The full constrained measurement model fit indices

table and the full unconstrained measurement model fit indices table were attached

in Appendix 4-09. From the above findings, the common latent factor was retained

in the measurement model to handle the common method variance.

4.6. Structural equation modelling

According to Hair et al. (2010) and Kline (2010), a structural equation modelling

included diversified sets of mathematical models, path relationships analysis,

confirmatory factor analysis, and statistical methods that fit networks of constructs

to the collected data. The research framework of this study and its hypothesis items,

as stated in Chapter 2, was illustrated in Figure 4-4 below for easy reference.

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Figure 4-4: The research framework of the proposed model

As the aforementioned measurement model’s factors had been used for the

structural equation modelling, the AMOS data imputation, where the model

parameters were generated based on the maximum likelihood, was set up to

generate the estimates and the base linear regression was used to concurrently

predict the unobserved values for each construct similar to a linear combination of

the observed values (Arbuckle, 2010). Ten factors’ scores for all the variables in

the measurement model were formed and these factors’ scores had all account for

the share variance (Coakes, 2011). The imputed factors were used to form the

constructs of a structural model which was shown in Figure 4-5 in below.

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Figure 4-5: The proposed structural model

In the above structural model diagram, additional components corresponding to the

structural relationship had been added. Below Figure 4-6 listed the structural

model’s components.

Figure 4-6: The components of structural model and the legend for AMOS

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In structural equation modelling, the structural model’s fit indices were generated

from AMOS and attached in Appendix 4-05. The table in below compared the

structural model fit indices with the model good fit critical values. The results

indicated that only the adjusted goodness of fit (GFI) pass the good fit criteria and

the rest of the indices did not pass the good fit criteria. The result indicated there is

only a path model and the path model analysis for the proposed model was not

supported.

Table 4-11: Structural model’s model fit indices and the acceptance values

From the AMOS output, the model’s unstandardized regression weights among the

constructs and the p-values were listed in below table. The construct relationships

with significant p-values (<0.05) were highlighted in grey colour under the ‘p-

value’ column. In AMOS, when p-value < 0.001, it was indicated by symbol ***.

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Table 4-12: Regression weights summary

The results from above table indicated that the following relationships were

statistically significant:

• H1a: Hard attribute is significantly associated with price sensitivity

• H1c: Hard attribute is significantly associated with word-of-mouth

• H1d: Hard attribute is significantly associated with commitment

• H2a: Soft attribute is significantly associated with price sensitivity

• H2b: Soft attribute is significantly associated with behavioural action

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• H2c: Soft attribute is significantly associated with word-of-mouth

• H4a: Location is significantly associated with behaviour action

• H4b: Location is significantly associated with word-of-mouth

• H4d: Location is significantly associated with price image

• H5b: Price sensitivity is significantly associated with word-of-mouth

• H6b: Communication with customer is significantly associated with word-of-

mouth

• H6c: Communication with customer is significantly associated with

commitment

• H7b: Price image is significantly associated with word-of-mouth

• H7c: Price image is significantly associated with commitment

4.7. Testing of moderation construct

As described in Chapter 3, the moderation effect was the joint effects of two

predictor variables in addition to the individual main effects. A form of relationship

changes between two constructs which depended on the value of the ‘assumed’

moderator variable was what this study was looking for. For instance, if price

sensitivity was a moderator between soft attribute and word-of-mouth, the study

looked for more precise explanation of causal effects by providing a mathematical

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method for explaining not only how soft attribute affects word-of-mouth, but also

under what circumstances the effect of soft attributes changed depending on the

moderating variable of price sensitivity. The explanation could be a positive-to-

negative sign change or negative-to-positive sign change or stronger sign change

or weaker sign change. The test results of the moderator, price sensitivity, on the

constructs were described in the next few sections. As the overall model does not

fit as indicated in the previous section, it is important to note that moderation results

providing adequate fit using this model may not be trusted. The Figure 4-8 in below

illustrated the overall model with the multiplication effect of the moderator.

Figure 4-7: The overall model with moderation constructs

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To test the moderation interactive effect, the variables including price sensitivity,

hard attribute and soft attribute were standardized first, then the multiplicated effect

of hard attribute and price sensitivity was computed and a new construct

‘HA_x_PS’ was formed. Also, the multiplicated effect of soft attribute and price

sensitivity was also computed and a new construct ‘SA_x_PS’ was formed. The

two constructs namely ‘HA_x_PS’ and ‘SA_x_PS’ were put into the structural

model as indicated in Figure 4-7 and regression was run in AMOS. From the

AMOS output, the structural model’s regression weights among the constructs and

the p-values were listed in below table and the construct relationships with

significant p-values (<0.05) were highlighted in grey colour under the ‘p-value’

column. In AMOS, when p-value < 0.001, it was indicated by symbol ***.

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Table 4-13: The regression weights of the model with moderator(s) added

From Table 4-13, the path relationship between word-of-mouth (WOM) and the

multiply effect of soft attribute and price sensitivity (SA_x_PS) was significant as

the p-value < 0.05. This meant that price sensitivity did have an interactive

moderation effect on the relationship between WOM and soft attribute. However,

price sensitivity (PS) did not have interactive moderation effect on all the other

path relationships including the relationship between BA and HA_x_PS, the

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relationship between WOM and HA_x_PS, the relationship between COM and

HA_x_PS, the relationship between BA and SA_x_PS, and the relationship

between COM and SA_x_PS. Detail finding were listed in the following sections.

4.7.1. Moderation effect of PS on HA and BA

From the Table 4-13, the path relationship between behavioural attitude (BA) and

the multiply effect of hard attribute and price sensitivity (HA_x_PS) was

insignificant as the p-value was 0.503 (>0.05).

4.7.2. Moderation effect of PS on HA and WOM

From the Table 4-13, the path relationship between word-of-mouth (WOM) and



4.7.3. Moderation effect of PS on HA and COM

From the Table 4-13 above, the path relationship between commitment (COM) and



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4.7.4. Moderation effect of PS on SA and BA

From the Table 4-13, the path relationship between behavioural attitude (BA) and

the multiply effect of soft attribute and price sensitivity (SA_x_PS) was


4.7.5. Moderation effect of PS on SA and WOM

From the Table 4-13, the path relationship between word-of-mouth (WOM) and

the multiply effect of soft attribute and price sensitivity (SA_x_PS) was significant

as the p-value was <0.05. From the Figure 4-8 in below, it showed the plots two-

way interaction effects for the variables. The results indicated that price sensitivity

dampened the negative relationship between soft attributes and word-of-mouth.

Figure 4-8: The plots two-way interaction effects for the variables

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4.7.6. Moderation effect of PS on SA and COM

From the Table 4-13, the path relationship between commitment (COM) and the

multiply effect of soft attribute and price sensitivity (SA_x_PS) was insignificant

as the p-value was 0.533 (>0.05).

4.7.7. Testing results summary

A summary of the interactive effect of the mediators namely communication with

customer and price image was listed in below table.

Table 4-14: Moderating interactive test results summary

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4.8. Testing of the mediation constructs

As described in Chapter 3, the mediation effects among a dependent variable, a

mediator and an independent variable had been tested based on the p-value of the

regression coefficients among the variables. In below table, the regression

coefficients among the variables were denoted by β1 (i.e. between X and Y), β2

(i.e. between Z and Y) and β3 (i.e. between X and Z) respectively. The test result

of the two mediators, communication with customer and price image, in the

structural equation modelling were described in the next few paragraphs.

Table 4-15: The requirement for a mediation relationship

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4.8.1. Mediation effect of CWC on VM and BA

The table in below summarized the regression relationships and the significant

level of the regression coefficients of the variables in the testing of the mediation

effect of ‘communication with customer’ on the relationship between ‘visual

merchandizing’ and ‘behavioural action’ constructs.

Table 4-16: The mediation effect of CWC on VM and BA

The above table indicates that the p-values of β1, β2 and β3 were all not significant.

Therefore, the ‘communication with customer’ construct did not have a mediation

effect on the relationships between ‘visual merchandizing’ and ‘behavioural action’

constructs.

4.8.2. Mediation effect of CWC on VM and WOM

The tablet in below summarized the regression relationships and the significant


181


merchandizing’ and ‘word-of-mouth’ constructs.

Table 4-17: The mediation effect of CWC on VM and WOM

The above table indicated that the p-values of both β1 and β3 were all not

significant. Therefore, the ‘communication with customer’ construct did not have

a mediation effect on the relationships between ‘visual merchandizing’ and ‘word-

of-mouth’ constructs.

4.8.3. Mediation effect of CWC on VM and COM




merchandizing’ and ‘commitment’ constructs.

182

Table 4-18: The mediation effect of CWC on VM and COM

The above table indicated that the p-values of β1 and β3 were all not significant.

Therefore, the ‘communication with customer’ construct did not have a mediation

effect on the relationships between ‘visual merchandizing’ and ‘commitment’

constructs.

4.8.4. Mediation effect of PI on LOC and BA


level of the regression coefficients in the testing of the mediation effect of ‘price

image’ on the relationship between ‘location’ and ‘behavioural action’ constructs.

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Table 4-19: The mediation effect of PI on LOC and BA

The above table indicated that the p-value of β2 was not significant. Therefore, the

‘price image’ construct did not have a mediation effect on the relationships between

‘location’ and ‘behavioural action’ constructs.

4.8.5. Mediation effect of PI on LOC and WOM



effect of ‘price image’ on the relationship between ‘location’ and ‘word-of-mouth’

constructs.

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Table 4-20: The mediation effect of PI on LOC and WOM

From the above table, the p-values of β1, β2 and β3 were all significant. The

product of the coefficient β2 and β3 was -0.017136. As the coefficient β1 (-0.174)

was less than the product of β2 and β3 (-0.017136), the ‘price image’ construct did

have a partial mediation effect on the relationships between ‘location’ and ‘word-

of-mouth’ constructs.

4.8.6. Mediation effect of PI on LOC and COM



effect of ‘price image’ on the relationship between ‘location’ and ‘commitment’

constructs.

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Table 4-21: The mediation effect of PI on LOC and COM

From the above table, the p-value of β1 was not significant and the p-values of both

β2 and β3 were all significant. Therefore, the ‘price image’ construct did have a

full mediation effect on the relationship between ‘location’ and ‘commitment’

constructs.

4.8.7. Testing results summary

A summary of the interactive effects of the mediators namely communication with

customer and price image were listed in below table.

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Table 4-22: Summary of the mediating interactive test results

4.9. Summary

In this study, a total of 220 questionnaires were collected from an online survey

tool and most of the research participants were had monthly income more than

HK$ 15,000 and own a bachelor degree. The assessment of the collected data

indicated the data passed the required criteria for reliability test, KMO test, and the

Barlett’s test of sphericity test.

The result of confirmatory factor analysis confirmed the loading of the

questionnaire items were appropriate as no cross-loading of factors was observed.

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Furthermore, model fit analysis of the proposed structural model passed the

acceptance criteria of model fit.

The structural equation modelling had results of the regression weights and

significant levels of the hypotheses and the results indicated that H1a, H1c, H1d,

H2a, H2b, H2c, H4a, H4b, H4d, H5b, H6b, H6c, H7b and H7c were all having

significant p-values and they were all statistically supported. However, the

hypotheses H1b, H2d, H3a, H3b, H3c, H3d, H4c, H5a, H5c, H6a, and H7a were

not statistically supported as all their p-values were insignificant.

In addition, the mediating test results found that the assumed mediator

‘communication with customer’ was not having any mediation effect on the

relationships between the ‘visual merchandizing’ and the three store loyalty

constructs namely behaviour action, word-of-mouth and commitment. However,

the mediator ‘price image’ was identified as having a partial mediating effect on

the relationships between ‘location’ and ‘word-of-mouth’ constructs. Furthermore,

the ‘price image’ construct was found having a full mediation effect on the

relationship between ‘locations’ and ‘commitment’ constructs.

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Moreover, the interactive moderation test identified that the path relationship

between word-of-mouth and the multiply effect of soft attribute and price

sensitivity (SA_x_PS) was significant. The result indicated that price sensitivity

dampened the negative relationship between soft attributes and word-of-mouth.

The detail results were discussed in Chapter 5 and this chapter provided a

conclusion to the study and included limitations and contributions of the study with

suggestions for future related research.

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5. DISCUSSION AND CONCLUSION

5.1. Introduction

This chapter presented the implication of this study on the association between

customer loyalty constructs, namely hard attributes and soft attributes, and the store

loyalty constructs including behavioural action, word-of-mouth and commitment.

This chapter began with a brief outline of the research findings on the hypotheses,

the mediation effect and moderation effect of the constructs. Then a brief review

on both the customer loyalty and store loyalty models was integrated with the

findings from the literature on the measurement assessment. Next, the implication

of this study was discussed in both the theoretical and practical aspect. The final

section would cover the limitation of this study and the future direction for related

research.

5.2. Discussion of analysis results

This thesis adopted a modified extended customer loyalty model and store loyalty

model and associated hypotheses as the main research framework. SPSS and

AMOS were employed to run statistical analysis on the 220 completed

questionnaires. The structural equation modelling was run and the model did not

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render a good-fit model. Though a few interactive relationships existed in between

constructs but these overall results may not be trusted due to the not good-fit model.

5.2.1. Findings from the structural model assessment

The path model analysis for the proposed research model had model fit indices

including Chi-square index, RMSEA, and Chi-square/degree of freedom that all not

passed the tests. In addition, most of the hypotheses results, as listed in below table,

which were based on the not-good-fit model were not significant.

Table 5-1: Hypothesis result of the relationships among the variables

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These results indicated that the path analysis for the model was not supported and

three categories of results were generated:

Category (1) – Hypotheses having statistically significant results and the hypotheses

were supported are listed in below.

i.e. H1a, H2a, H2b, H4d, H5b, and H6c

Category (2) - Hypotheses having statistically significant results but the hypotheses

were not supported (i.e. results showed an unexpected direction) are listed in below.

i.e. H1c, H1d, H2c, H4a, H4b, H6b, H7b, and H7c,

Category (3) - Hypotheses were statistically insignificant and the statistical results

were not supported are listed in below.

i.e. H1b, H2d, H3a, H3b, H3c, H3d, H4c, H5a, H5c, H6a, and H7a

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Though the path model analysis was supported by the model fit indices, there were

only six hypotheses out of 25 hypotheses found being supported. Some

relationships are in the opposite direction than hypothesised. This is potentially

quite interesting. For example, the negative association between hard attributes and

word-of-mouth may be the result of different types of hard attributes had different

impacts on the evaluation of word-of-mouth. This could happen in some studies,

where the potential buyers may get discount or rewards if they would refer their

friends and relatives toward a store/brand; however, the potential buyers might

have an impression that the store/brand has enticed friends into profiting from their

relationship and this higher social costs associated with money offset the hard

attribute (benefit) gained further render hard attribute an inferior incentive (Jin &

Huang, 2014).

5.2.2. The answer to research question 1


relationship between hard attribute and behavioural action, the

relationship between hard attribute and word-of-mouth and the

relationship between hard attribute and commitment in the local beauty


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The moderator effect test results indicated the followings:

1. Price sensitivity was not a moderator with respect to the relationship

between hard attribute and behavioural action as there was no significant

association identified.


between hard attribute and word-of-mouth as there was no significant



between hard attribute and commitment as there was no significant

association

From above results, the hard attribute was not associated with the store loyalty

construct as the hypotheses related to hard attribute were all rejected. The findings

supported Roehm et al.’s (2002) findings that hard reward or hard attribute itself

could not stimulate the loyalty of customer to the brand after a promotion program

ended. Therefore, the study had found that price sensitivity was not having any

moderating effect in the relationship between hard attribute and customer loyalty.

Also, when price sensitivity was inserted as a moderator in the model, store loyalty

194

program was found not directly positively contributing to the store loyalty in the

local beauty and healthcare retailing industry.



relationship between soft attribute and behavioural action, the

relationship between soft attribute and word-of-mouth and the

relationship between soft attribute and commitment in the local beauty


The moderator effect test results indicated the followings:


between soft attribute and behavioural action as there was no significant


2. Price sensitivity was a moderator with respect to the relationship between

soft attribute and word-of-mouth as there was a significant association

identified.


between soft attribute and commitment as there was no significant

association

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From above results, two out of three of the relationships between soft attribute and

the store loyalty factors were not significant as the hypotheses related to soft

attribute were not all supported. Firstly, price sensitivity was only found as a

moderator with respect to the relationship between soft attribute and word-of-

mouth. This finding supported prior literature (Arantola, 2003) as loyalty program

customers did engage in word-of-mouth behaviour on tangible award or soft

attribute. This could be explained that loyalty program customers expressed their

feelings through word-of-mouth in a subjective perspective with a much more

emotional indication than the feelings they had when discussing about monetary

rewards which would be less emotional in most of the cases because tangible

rewards were usually valued objectively. (Arantola, 2003). Secondly, price

sensitivity was found dampening the negative relationship between soft attribute

and word-of-mouth. Therefore, the findings from this result indicated that the soft

attribute of customer loyalty program was only negatively affecting the store

loyalty in the local beauty and healthcare retailing industry with moderating effect

from the price sensitivity.


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respect to the relationship between virtual merchandising and

behavioural action, the relationship between virtual merchandising and

word-of-mouth and the relationship between virtual merchandising and

commitment in the local beauty and healthcare retailing industry?

The mediating effect test results indicated the followings:

1. Communication with customer was not a mediator with respect to the

relationship between virtual merchandising and behavioural action as there

was no significant association identified.


relationship between virtual merchandising and word-of-mouth as there

was a significant association identified.


relationship between virtual merchandising and commitment as there was

no significant association

From the above results, all hypotheses were not supported. There were three

findings from these results. Firstly, this study identified that the relationship

between visual merchandising and store loyalty was not affected by the mediation

effect of communication with customers. Though the communication with

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customer was not a mediator, however, this study identified that it was positively

and significantly associated with commitment to store and this result matched with

Duncan’s (2002) and Rensburg and Cant’s (2003) study. Secondly, the

communication with customers was not significantly associated with word-of-

mouth. This finding contradicted with the prior studies (Bridson et al., 2008 and

Leong, 2013) as their studies indicated that communication with customer could

enhance customer experience and the quality assurance perceived for the brands

would increase emotional attachment to brands and expressed through word-of-

mouth. Thirdly, the communication with customers was not significantly

associated with behavioural action. This finding indicated that the communication

with customer might be able to enhance customer experience and the perceived

quality of the brand but it could not form a stepping stone of purchase or

behavioural action.



respect to the relationship between location and behavioural action, the

relationship between location and word-of-mouth and the relationship

198

between location and commitment in the local beauty and healthcare

retailing industry?

The mediating effect test results indicated the followings:

1. Price image was not a mediator with respect to the relationship between

location and behavioural action as there was no significant association

identified.

2. Price image was a partial mediator with respect to the relationship between

location and word-of-mouth as there was a significant association identified.

3. Price image was a full mediator with respect to the relationship between

location and commitment as there was no significant association

From above results, only two of the path relationships between location and loyalty

store construct were significant. As not all of the path relationships between

location and loyalty store construct were significant, this study identified that the

relationship between visual location and store loyalty was only partially affected

by the mediation effect of price image.

5.2.6. The rejected hypotheses

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As there were nineteen hypotheses, namely H1b, H1c, H1d, H2c, H2d, H3a, H3b,

H3c, H3d, H4a, H4b, H4c, H5a, H5c, H6a, H6b, H7a, H7b and H7s rejected, the

researcher would like to emphasize that this could be the result of the small sample

size (220) in the survey. Though the researcher could not find significant results in

some of the hypotheses, the results did not suggest the relationship among the

constructs did not exist, but rather the researcher could not find them from the data

collected.

The rejected hypotheses H1b, H1c and H1d were all related to the hard attribute

construct. Hard attribute was one of the customer loyalty rewards and it was

originally defined in the trade literature as things like free gifts, discount and free

vouchers (Barlow, 1996). According to Barlow (1996), free discount was a tangible

element of reward which had positive effect on customers’ purchasing behaviour

(i.e. behavioural action) as customers could purchase more products at a lower

price. Also, customers receiving retail stores’ discount offer might spread the words

to friends (i.e. Word-of-mouth) in their social group and might plan to purchase the

goods in the offer promotional period (i.e. Committed to purchase). Therefore, hard

attribute was perceived as positively related to and behavioural action word-of-

mouth. The rejection of these hypothesis testing results seemed to imply two things.

200

First, the hard attributes offered by beauty and healthcare retail stores were not

attractive enough to trigger positive association with customers’ behaviour action.

Second, the hard attributes offer by beauty and healthcare retail stores were not

perceived positively and could only had negative effect on word-of-mouth and

commitment.

The rejected hypotheses H2c and H2d were related to soft attributes. As explained

in Chapter 2, soft attribute was one of the customer loyalty intangible rewards and

it was originally defined in the trade literature as a sense of recognition and

preferential treatment of merchandising service which did not carry economic

value (Barlow, 1996). As soft attribute did not provide direct cost saving value to

customers, it was perceived that the positive impact of soft attribute on customer

purchasing behaviour, word-of-mouth and commitment to purchase would be less

effective than hard attribute but still. The rejection of these hypothesis testing

results seemed to imply two things. First, the soft attributes offered by beauty and

healthcare retail stores were not attractive enough to trigger positive association

with customers’ word-of-mouth, only a significantly negatively word-of-mouth

was affected. Second, the soft attributes offer by beauty and healthcare retail stores

had no effect customers’ long-term commitment to purchase.

201

Hypotheses H3a, H3b, H3c and H3d, regarding visual merchandising, were all

rejected. The visual merchandising construct, which was theorized from

merchandising literature, was concerned with the attractiveness of the visual

merchandise displays that result in grabbing customers attention and altering the

customers’ perception to the beauty and healthcare store (Strategic Direction, 2012;

Virgona, 2012). The result of the rejection of these hypothesis testing results seemed

to imply two things. First, the visual merchandise displays designed by stores were

not attractive enough to trigger strong attention from customers with the motivation

for the purchasing behaviour and the spread of words to friends (Sebastian, 2008;

Rowe, 2014). Second, there were studies identified that visual merchandising would

be able to catch customers’ engagement when the features and benefits of the goods

and services provided by the retail stores were highlighted (Ha & Lennon, 2010).

The other rejected hypotheses namely H4a, H4b, H4c, were related to location. For

cost saving purpose, customers would have preference on goods available in a

nearby store and this might contribute partially to store loyalty (Christaller, 1935).

The result of the rejection of these hypotheses seemed to imply three things. First,

the location of retail stores were not attractive enough to trigger strong attention

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from customers with the motivation for the purchasing behaviour and the spread

of words to friends (Sebastian, 2008; Rowe, 2014). Second, Hong Kong was a city

with a high population density and most of the people lived in cosmopolitan area where

locations of beauty and healthcare stores might be within acceptable distance. Third,

the transaction cost saving for customers to purchase goods from a nearby stores or to

purchase them from an online stores might not have significant difference. If the stores’

good and services provided sufficient contribution to trigger the culmination of store

loyalty behaviour, then the store locations would not have any significant convenience

for these customers to alter their store loyalty behaviours (Oliver, 1997).

The rejected hypotheses H5a and H5c were related to price sensitivity. The price

sensitivity construct was referring to the resistance or assistance of the customers

in conducting a behaviour after receiving a related reward (Shapiro & Varian, 1998).

These rejected hypotheses might imply that price sensitivity was not related to

customer purchasing behaviours. Therefore, people with different price sensitivity

might need different kinds of rewards to suit their needs.

Hypotheses H6a and H6b regarding communication with customers, were rejected.

The communication with customers construct, originally theorized from the

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adaptation level theory, was being applied in customer store loyalty behaviour

study to investigate the effect of communication strategies on the customers’

products evaluations (Gotlieb & Sarel, 1991). The communication strategies

included letters, emails, design elements of a store environment, and promotional

marketing displays, etc. These strategies were part of stores’ brand presentation

aiming at influencing the customers’ perception of the stores. The result of these

hypotheses indicated that communication with customers had no influence on

customers’ patronise behaviour and communication with customers might be

dependence on the customers’ prior experience on the stores’ brand. Also, as

aforementioned this study did not identify any association between visual

merchandising displays and store loyalty (Garvey, 2010). Therefore, it was unclear

any way to have communication with customers influence customer purchase

intentions. Store managers should find better way to yield the improvement on

customers’ store loyalties.

The last set of rejected hypotheses H7a, H7b and H7c were related to price image.

The price image was defined as the perceived value of a product (Bell & Lattin,

1998) and the value was not in precise calculation of dollars but it was essential

elements of store satisfaction. These hypothesis results contradicted with the

204

studies by Miranda et al. (2005) as the price image was found interfere with

customers’ behaviour in the selection of products and services. This meant that

customers who like to buy low-priced products might like to shop in stores which

had simple low-cost decoration. As the hypotheses result were not supported, this

implied that customers would buy products disregard the price image and the

identity of stores.

5.3. Integrating the findings from existing literature

5.3.1. Implications of the findings

This paper empirically examined the extent to which loyalty program attributes

explained variations in customers store loyalty behaviours. A two-dimension of

loyalty program attributes were produced to include hard attributes and soft

attributes. Store loyalty was examined in terms of the purchasing behaviours,

word-of-mouth and commitment. The results of this study showed that

emphasising too much on hard and soft attributes to enhance customer loyalty or

satisfaction of the store might not be successful to attract and retain customers.

Retailers of beauty and healthcare products who would like to strengthen their store

brands should also look into the store location as store location was found

significantly associated with price image which was branded with the store image.

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In addition, retailers should also identify correct communication with customers

attribute as this was found significantly related to the customers’ commitment.

5.3.2. Implications for the researchers

In an increasingly competitive environment, beauty and healthcare retailers seldom

used one influence strategy to guide the preferable behaviours of their customers

(Lal & Matutes, 1994). As both hard attributes and soft attributes had significant

influence on price sensitivity and the direction of influence were both positive,

little was known about what constituted an appropriate reward nor had their effect

on consumers been thoroughly examined. This unknown area should be further

studied.

This study examined the customer loyalty program attributes and the store loyalty

attributes in the beauty and healthcare industries in Hong Kong. Hence, identifying

factors of customers perception on loyalty program and store loyalty, which is a

gap that differentiates this study from past research projects.

5.4. Theoretical implications

206

According to the literature review, prior research had found positive relationship

between hard attributes and behavioural action, word-of-mouth and commitment

respectively. Furthermore, prior study also found a positive relationship between

soft attributes and behavioural action, word-of-mouth and commitment

respectively. However, this research had some findings giving conflicting results.

For instance, this research empirically found that hard attributes were not

significantly associated with behavioural action but soft attributes were. One of the

interpretation of this result could be that the potential buyers might have an

impression that the store/brand has enticed friends into profiting from their

relationship and this higher social costs associated with money offset the hard

attribute (benefit) gained (Jin & Huang, 2014). Therefore, the theoretical

implications of the result is that marketer of the band loyalty have to be aware of

the impression of the “implied” message associated with the hard attributes

delivered when applying hard attributes on customer loyalty program.

In the business world, retailers seldom use only one influence strategy to guide the

preferable behaviour of their customers. They might employ influence strategies

which they consider effective targeting their customers. Therefore, this research

207

could be extended by testing the combined effect of hard attributes and soft

attributes on different segment of customers.

In addition, there were other customer loyalty attributes, such as visual

merchandising and word-of-mouth were found having significant but opposite

direction relationships with store loyalty attributes. Also, price sensitivity was a

moderator with respect to the relationship between soft attribute and word-of-

mouth as there was a significant association identified. Furthermore, other factors

such as satisfaction, trust and many contextual factors could also be used for further

examination. Overall, this study shed light on the effective use of customer loyalty

program attributes in different situations in the beauty and healthcare products

industry in Hong Kong.

5.5. Implications for managerial practice

From practitioners’ point of view, the finding of this research enables retailers to

understand the effective use of hard attributes and soft attributes under different

store loyalty strategies. In the beauty and healthcare products industry, hard

attribute, location and communication with customers are vital and any sudden

changes in the customer rewards, discount offers and communication with

208

customer may lead to negative impact to the customer store loyalty. Therefore,

selecting correct hard attributes and soft attributes to influence the customers is

crucial in order to act quickly to the dynamic change and to market the beauty and

healthcare products to the target segment customers faster than the competitors

(Lieberman, 2005). Failure to manage the customer loyalty programs and store

loyalty relationship properly will inexorably cause detrimental consequences

leading to healthcare products quickly becoming obsolete in the market.

This study was dedicated to investigating the beauty and healthcare products, and

it is assumed that the findings can be applicable to similar contexts. In the beauty

and healthcare products industry, online and offline shopping channels change their

strategies quickly. The retailers are expected to use different customer loyalty

program strategies. The implication of the evidence that hard attributes has more

significant impact than soft attributers on price sensitive customers would be very

important for retailers and brand managers because they are highly interested in

classifying consumers and understanding the way that different market segments

perceive, evaluate, and ultimately purchase their store products.

209

According to the study conducted by Miranda et al. (2005), the price image was

one of the essential elements for the store satisfaction, while the factor would

interfere the customers in selection the store of purchasing and the customer choice

of the products. This study identified that price image was a significant full

mediator on the relationship between location and commitment. In the current

digital age, where the price images are predominant and most powerful, the

knowledge of how to use price images to manage store customers’ perceptions are

important. This will be particular true for the retail store managers and offline or

online retailers who wish to launch, develop, or even rebranding their store

identifies in the luxury beauty and healthcare product markets.

Although the findings of this study are mixed in comparison to prior customer

loyalty program studies in similar research subjects, this study gives profound

implications for store retailers on customer loyalty programs and insightful

practicable implications.

5.6. Limitations

With constraints in this research design, resources and the tight schedule of this

doctorate degree, a few limitations were inevitable. These limitations would be

210

identified in five sections namely: quantitative research method, cross-sectional

research design, data collection method, common method variance, and lack of

generalizability. Therefore, when evaluating the contributions of this research, these

limitations must be examined.

5.6.1. Quantitative research method

. Quantitative methods are known to be less comprehensive than qualitative methods

and are likely to discard any anticipated answers from the research participants

(Burns, 1997). The researcher might have overlooked the concerns that the research

participants have and these might be their subjective’ values, healthcare product

purchasing behaviour pattern as well as belief system. Qualitative methods thus

provided a broader perspective for the researcher to find a deeper context of the

research participants.

5.6.2. Cross-sectional research design

As a cross-sectional research does not explore and collect the change or development

before and after the investigation that is held at a single point of time (Cavana et al.,

2001), the researcher may not be able to investigate and record the changes of the

211

characteristics in the samples and generate observations either as a group or

individually. Thus, this kind of research design limit the researcher to find the cause-

and-effect relationships among the factors.

Owing to the constraints of time and resources, this research was a cross-sectional one.

It was difficult to infer the cause-effect relationships between hard attributes and store

loyalty behaviours as well as the cause-effect relationships between soft attributes and

price sensitivity (Cavana et al., 2001). However, as digital technology are changing the

way we live and work, these customer loyalty program and store loyalty variables are

sporadic in nature and changed over time. Although the research questions should

determine the research design, cross-sectional research incorporating a longitudinal

study could provide a better picture of the observations. Therefore, the researcher

suggests to ride on the result of a cross-sectional study first to establish the correlations

between different variables and undertake a longitudinal study to examine the cause-

and-effect relationship afterwards.

5.6.3. Data collection method

This was the first study aiming to identify a suitable, reliable, and valid model to predict

the relationships among customer loyalty program and store loyalty variables in Hong

212

Kong. This research adopted e-questionnaires to gather data only from the perspective

of beauty and healthcare customers and this might have given rise to a few limitations.

For example, when the respondents had queries about the contents of the questions, it

was hard for them to ask for explanations of the true meaning of the questions.

Furthermore, with such a methodological strategy, it is difficult to rule out respondent

bias interpretations of the findings.

In addition, with snowball sampling, each research participants will recommend others

who are adult and might be interested in taking part in the survey. However, it might

not be possible to determine the sampling error about a population based on the

obtained samples.

5.6.4. Measurement instrument

In this research, price image and price sensitivity were measured using research

participants’ perceptions of uncertainty regarding the external environment. Therefore,

future research can include more dimensional characteristics of uncertainty in the

research to evaluate the relationship more objectively. For example, different types of

beauty product may have different frequency of store visiting needs.

213

5.7. Recommendations for future research

If the sample size can be enlarged, flexible methods can be adopted to measure the

relationships among testable variables and researcher might be able to obtain better

reliable analysis to warrant future research. A mixed method which combines both

quantitative and qualitative methods can elicit a wider and deeper understanding

of the target populations. Also, employing different instrument measurements can

highlight the specific characteristics of the ten constructs adopted in this study.

5.8. Summary and conclusion

This research used a positivist approach and a quantitative research method to

explore the relationship among ten customer loyalty program attributes and store

loyalty attributes. The findings from this research had mixed results and revealed

that hard attributes had more significant impact than soft attributes on price

sensitivity. Price sensitivity was identified as a moderator on the relationship

between soft attribute and word-of-mouth. Also, price image was found having

mediating effect on the relationship between location and commitment. The

research findings have provided the answers to the four research questions

developed in Chapter 2 although the conceptual model and the hypotheses were

only partially supported.

214

In the future research, a better research design can allow the researchers to overcome

the limitations mentioned previously. In conclusion, this research had made a

contribution to the significant theoretical and managerial implications regarding the

relationship between customer loyalty program attributes and store loyalty attributes

and the moderating role and mediating roles played by price sensitivity, price image

and communication with customers in the beauty and healthcare industry in Hong

Kong.

215

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231

Appendix 3-01 Invitation Letter (English)

Hi,

I am writing to invite you to participate in an anonymous, voluntary and brief online

questionnaire conducted by Mr. Mohammed Sardaran KHAN, a research student from the

Faculty of Business and Law at the University of Newcastle.

The purpose of the questionnaire is to examine the Mechanism Between Customer Loyalty

Programs and Store Loyalty of Beauty and Health retailers in Hong Kong.

We would greatly appreciate your help with this. The questionnaire will take no more than

10 minutes to complete and is approved by the University’s Human Research Ethics

Committee (H-2017-0180). The questionnaire is available here:

[The link to the questionnaire will be placed right here.]

Your help with this will be very much appreciated: our aim is to inform marketers on factors

affecting customer loyalty so that they could make a better decision on loyalty projects.

If you would like further information, please contact the Chief Investigator, Dr Stacey

BAXTER, on [email protected], or +61 (4) 4921 6279.

Many thanks,

Mr Mohammed Sardaran KHAN

Research Student of University of Newcastle

Tel: +852 6419 1922

Email: [email protected]

mailto:[email protected]


232

Appendix 3-02 Invitation Letter (Traditional Chinese)

你好，

我想邀請你參加一個匿名的，自願的，簡短的網上調查問卷，由紐卡斯爾大學商業與法律學

院的 Mohammed Sardaran KHAN 先生發起。

調查問卷的目的是在香港美容保健零售業內，研究客戶忠誠度計畫和商店忠誠度之間的機制。

我們將非常感謝您的幫助。完成問卷的時間將不會超過 10 分鐘，該問卷已獲得大學人類研

究倫理委員會的許可（h-2017-0180）。

調查問卷可在這裡找到： [調查問卷的鏈接將放在這裏。]

我們將非常感謝您的幫助：我們的目標是告知行銷人員影響顧客忠誠度的因素，從而使他們

能在忠誠度專案中做出更好的決定。

如果您需要進一步的資訊，請與首席調查員聯繫 , Dr Stacey BAXTER，聯繫方式：

[email protected],或 +61 (4) 4921 6279.

萬分感謝，

Mohammed Sardaran KHAN 先生

紐卡斯爾大學研究生

電話： +852 6419 1922

電子郵箱： [email protected]



233

Appendix 3-03 Information Statement (English)

235

Appendix 3-04 Information Statement (Traditional Chinese)

237

Appendix 3-05 Questionnaire (English)

Questionnaire title: A Study Of Measuring The Impact Of Customer Loyalty Programs And Mediating Factors On Store Loyalty In The Beauty & Healthcare Products Retailing Stores In Hong Kong Instruction: Please fill in the appropriate box for each question. Part 1: Pre-condition questions A) Are you 18 years old or above? □ Yes □ No (Adult only, please do not proceed if your choice is “No”.) B) Are you a permanent resident of Hong Kong? □ Yes □ No (Hong Kong permanent resident only, please do not proceed if your choice is “No”.) C) Do you live in Hong Kong? □ Yes □ No (This research is tailored for Hong Kong, please do not proceed if your choice is “No”.) D) Do you have experience in the purchase of Beauty and Healthcare Products in Hong Kong? □ Yes □ No (This research is tailored for a participant who has experience in purchasing Beauty and Healthcare Products, please do not proceed if your choice is “No”.) Part 2: Personal Information Of the Participant

240

Part 3: Accessing the Measures of Store Loyalty All of the following questions are specific to the loyalty action with your favourite beauty and healthcare products retailer. Please rate in between 'strongly disagree' (1) and 'strongly agree' (7).

241

Part 4: Accessing the Measures of Customer Loyalty Program All of the following questions are specific to the attribution of loyalty rewards to you.

242

How important are the following rewards to you? Please rate between 'not at all important' (1) to 'extremely important' (7).

Part 5: Accessing the Measures of Price Sensitivity All of the following questions are specific to price sensitivity. Please rate in between 'strongly disagree' (1) and 'strongly agree' (7).

243

Part 6: Accessing the Measures of Communication with Customers All of the following questions are specific to the communication between you and your favourite beauty and healthcare products retailer. What is your level of satisfaction? Please rate in between 'not at all satisfied' (1) and 'extremely satisfied' (7).

Part 7: Accessing the Measures of Visual Merchandising All of the following questions are specific to the visual merchandising of your favourite beauty and healthcare products retailer. Please rate in between 'strongly disagree' (1) and 'strongly agree' (7).

Part 8: Accessing the Measures of Price Image All of the following questions are specific to the price image of your favourite beauty and healthcare products retailer. What is your level of satisfaction? Please rate in between 'not at all satisfied' (1) and 'extremely satisfied' (7).

244

Part 9: Accessing the Measures of Location All of the following questions are specific to the location of beauty and health retailer. Please rate in between 'strongly disagree' (1) and 'strongly agree' (7).

This is the End. Thank you very much.

245

Appendix 3-06 Questionnaire (Traditional Chinese) 香港美容保健產品零售業的顧客忠誠計畫和仲介因素對商店忠誠度影響的

研究調查

說明：請勾選合適的方格來回答每一個問題。

第1部分：前提問題

A) 你是否年滿18歲？

□ 是 □ 否（僅成年人可參與，如果您選擇“否”，請不要繼續。） B) 你是否香港永久居民？ □ 是 □ 否（僅香港永久居民可參與，如果您選擇“否”，請不要繼續。） C)你是否住在香港？ □ 是 □ 否（這份調查是針對在香港居住的人，如果您選擇“否”，請不要繼續。） D) 你是否有在香港購買美容和保健產品的經驗？ □ 是 □ 否 (這份調查針對的是購買過美容保健產品的參與者, 如果您選擇“否”，請

不要繼續。）

第 2部分：個人資料

247

第 3 部分：調查商店忠誠度的影響

下列所有問題都與你對最喜愛的美容保健產品零售商的忠誠度行為有關。

請根據自己的感受選擇，“強烈反對”（1）和“非常同意”（7）。

248

第 4部分：調查顧客忠誠計畫的影響下列所有問題都與你的忠誠度回饋有關。下列回饋對你有多重要？

249

請根據自己的感受選擇，“根本不重要”（1）和“非常重要”（7）。

第 5 部分：調查價格敏感度的影響下列所有問題都與價格敏感度有關。請根據自己的感受選擇，“強烈反對”（1）和“非常同意”（7）。

250

第 6 部分：調查客戶溝通的影響下列所有問題都與你和你最喜愛的美容保健產品零售商之間的溝通有關。你的滿意度如何？請根據自己的感受選擇，“根本不滿意”（1）和“非常滿意”（7）。

第 7 部分：調查商品展示設計的影響

下列所有問題都與你最喜愛的美容保健產品零售商的商品展示設計有關。請根據自己的感受選擇，“強烈反對”（1）和“非常同意”（7）。

第 8 部分: 調查價格形象的影響以下所有問題都是針對你最喜歡的美容保健產品零售商的價格形象。

你的滿意度如何？請根據自己的感受選擇，“根本不滿意”（1）和“非常滿意”（7）。

251

第 9 部分：調查位置的影響以下所有問題都是針對你最喜歡的美容保健產品零售商的位置。

請根據自己的感受選擇，“強烈反對”（1）和“非常同意”（7）。

問卷結束，謝謝參與。

252

Appendix 4-01 Naming Convention of the Questionnaire Items

Construct

names

Questionnaire number and question Naming

convention

variable

code

Behavioural

Action

12. I would buy the beauty and

healthcare related product from the

retailer even if another retailer has a

sale.

BA1

13. I would shop at the favoured

retailer regardless the offers provided

by the competitors.

BA2

14. I would buy from the retailer even

if they are hard to reach.

BA3

15. If the particular branch of the

retailer is closed, it is difficult to find

the substitution.

BA4

253

16. I have never considered switching

to another retailer.

BA5

Word-of-mouth 17. I would tell the persons in my

social network about the positive

experience of shopping in the retailer.

WOM1

18. I would convince the persons in my

social network to change their retailer

because of my positive experience

from my favour retailer.

WOM2

19. I would say positive side about the

retailer to others.

WOM3

20. I will make a recommendation

about the retailer if a person is seeking

for my advice.

WOM4

21. I would encourage others to

purchase in the retailer.

WOM5

Commitment 22. I would consider myself as a

regular customer of the retailer.

COM1

254

23. I am loyal to the retailer. COM2

24. I would consider the retailer as the

first choice in the selection of the

beauty products.

COM3

25. I would like to purchase more in

the future.

COM4

26. I would consistently and

continuously purchase beauty and

healthcare related products from the

retailer.

COM5

27. I make most of the purchases in the

retailer.

COM6

Hard attributes 28. I think the discount provided is

necessary for a customer loyalty

program.

HA1

29. I think the free item in

accompanying to the purchasing is

HA2

255

important for a customer loyalty

program.

30. I think the receiving of the free

coupon is necessary for a customer

loyalty program.

HA3

31. I think the provision of the gift

voucher is important for a customer

loyalty program.

HA4

32. I think the enjoyment of point

collection and redemption is important

for a customer loyalty program.

HA5

Soft attributes 33. I think the better service offered by

the stores to the program members is


program.

SA1

34. I think paying more effort to the

program member rather than non-

SA2

256

regular consumers is important for a

customer loyalty program.

35. I think the personal

communication between the program

members and the store is important for

a customer loyalty program.

SA3

36. I think the personal welfare of the

program members is important for a


SA4

37. I think being recognised by the

business is important for a customer

loyalty program.

SA5

38. I think cultivation of the sense of

belonging is important for a customer

loyalty program.

SA6

39. I think trust among the program

members and the store is important for

a customer loyalty program.

SA7

257

40. I think being considered as unique

by the business is important for a


SA8

41. I think the closeness between the

program members and the retailer is


program.

SA9

Price sensitivity 42. I would shop based on the price of

the product.

PS1

43. I would shop for the special offers. PS2

44. I can accept lower quality if the

price is low.

PS3

Communication

with customer

45. The advertisement of the retailer

for providing information of the

provision of offers

CWC1

46. The communication channels

established by the store in facilitating

information flow

CWC2

258

Visual

merchandising

47. The store layouts of the retailer are

properly designed.

VM1

48. The design of the display of the

store is aligning to the theme properly.

VM2

Price image 49. The pricing strategy of the retailer PI1

50. The frequency of promotional

offers given by the stores

PI2

Location 51. I find the location of the branches

is well planned.

LOC1

52. I find convenient to do shopping in

the nearby stores of my favourite

brand.

LOC2

53. I find the retailers pick good

locations to establish the branches.

LOC3

Appendix 4-02: Descriptive analysis of all constructs’ items Items Mean Std.

Deviation

Skewness Kurtosis

Statistic

Statistic

Statistic

Std. Error

Statistic

Std. Error

259

BA1 6.16 1.519 -2.047

.164

3.130 .327

BA2 6.19 1.549 -2.116

.164

3.351 .327

BA3 6.02 1.471 -1.838

.164

2.456 .327

BA4 6.02 1.428 -1.605

.164

1.663 .327

BA5 5.80 1.339 -1.653

.164

3.036 .327

WOM1

4.73 .934 -.220 .164

-.544 .327

WOM2

4.78 1.012 -.796 .164

.815 .327

WOM3

4.72 .961 -.479 .164

-.406 .327

WOM4

4.79 .942 -.529 .164

.463 .327

WOM5

4.67 .861 -.395 .164

-.420 .327

COM1

5.43 .960 .040 .164

-.944 .327

COM2

5.34 1.037 .221 .164

-1.105

.327

COM3

5.44 1.065 .144 .164

-1.206

.327

COM4

5.28 1.047 .364 .164

-1.040

.327

COM5

5.29 1.058 .083 .164

-.939 .327

COM6

5.40 1.013 .169 .164

-1.057

.327

HA1 3.14 1.008 -.260 .164

-.512 .327

HA2 3.30 .906 -.322 .164

-.391 .327

260

HA3 3.25 .910 -.186 .164

-.242 .327

HA4 3.10 .979 -.173 .164

-.293 .327

HA5 3.07 .986 -.233 .164

-.570 .327

SA1 5.67 .990 -2.044

.164

5.608 .327

SA2 5.59 .949 -1.850

.164

5.484 .327

SA3 5.78 .850 -2.306

.164

8.666 .327

SA4 5.63 1.024 -2.269

.164

5.823 .327

SA5 5.49 1.157 -1.780

.164

3.504 .327

SA6 5.70 .912 -2.211

.164

6.403 .327

SA7 5.61 .918 -1.901

.164

5.788 .327

SA8 5.77 .774 -1.959

.164

8.435 .327

SA9 5.59 .915 -1.359

.164

3.871 .327

PS1 4.13 1.237 -.750 .164

.441 .327

PS2 4.15 1.165 -.706 .164

.617 .327

PS3 4.13 1.079 -.595 .164

.339 .327

CWC1

3.49 .963 -.333 .164

-.309 .327

CWC2

3.43 .891 -.417 .164

-.161 .327

VM1 4.61 .871 -1.169

.164

1.671 .327

261

VM2 4.59 .925 -.777 .164

.684 .327

PI1 4.42 1.340 -.880 .164

-.144 .327

PI2 4.52 1.248 -.942 .164

.204 .327

LOC1

5.62 .997 -.608 .164

.460 .327

LOC2

5.67 .999 -.707 .164

.572 .327

LOC3

5.65 1.042 -.516 .164

.111 .327

262

Appendix 4-03: Communalities

Extraction Method: Maximum Likelihood.

263

Appendix 4-04: Total variance explained

Extraction Method: Maximum Likelihood.

a. When factors are correlated, sums of squared loadings cannot be added to obtain a total

variance.

264

Appendix 4-05: Pattern matrix table for all 42 items

265

Appendix 4-06: Fit indices of the measurement model

267

Appendix 4-07: AMOS - Modification indices of the measurement model

274

Appendix 4-08: Fit indices of the enhanced measurement model

276

Appendix 4-09: Comparison between constrained and unconstrained models

278

Appendix 4-10: Structural model fit indices

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