School of Economic and Business Sciences 
 

Marketing Department 
 

University of the Witwatersrand 
 

 

 

 

 

 

 

 

 

 

 

 

 

Privacy concerns about data-usage for South African 

retail loyalty programs 

 

by 
 

Alexandra Cameron Dearden 
 

 

Submitted as part of the requirements for the degree: 
 

 

MASTER OF COMMERCE (RESEARCH) 
 

 

In the subject area of 
 

 

Marketing 
 

 

Under the supervision of: 
 

Dr Melissa Zulu 
 

& 

Professor Thomas Dorson Anning



ii 
 

ACKNOWLEDGEMENTS 

 

I would first like to express my deepest appreciation to my supervisors Dr Melissa Zulu and 

professor Thomas Dorson Anning for all their suggestions, assistance, support and patience 

throughout the year. Your guidance, inspiration and encouragement through each stage of the 

process has been of considerable help for me. I would also like to thank the University of 

Witwatersrand and Postgraduate funding, for freeing me of any potential financial burden, 

through the scholarship opportunity provided as a Postgraduate Merit student. 

 

I am also immensely grateful to my family and friends for their support throughout my Master's 

journey. A special thank you to my mom, Cathy, and my dad, John, for always believing in me 

and being there when I needed it most. Unexpected events and stresses throughout the year 

were made easier with their constant support and reassurance. Thanks to their help, I was able 

to reach a new milestone for our family. I would also like to thank my boyfriend, Tim van 

Kesteren, as well as my close friends, both new and old, for your emotional support and being 

there for me every step of the way. Having a solid support system of loved ones around me has 

helped in more ways than I know; and something I will forever cherish. 

 

It was a stressful year for me with lots of struggles, but I’m so grateful to everyone for helping 

me push through and make it this far. I know I’ll look back with fond sentiments towards my 

Master’s journey. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



iii 
 

ABSTRACT 

 

Data usage has arisen as a growing topic for researchers and practitioners in South Africa. This 

is particularly true for Loyalty Programs, where increased personalisation and consumer data 

usage are being used to fuel business decisions. However, the consumer perspective on data-

related privacy concerns provides room to be explored, alongside the investigation of culture, 

trust, personalisation and the privacy paradox. In this investigation into privacy concerns, the 

relevance of culture comes into place for its influence on such South African privacy related 

behaviour and perceptions. This study, therefore, explores privacy concerns and data usage in 

retail Loyalty programs, to better understand South African consumers' perceptions of data 

privacy, and their intention to engage with the program. This was done through a multi-

theoretical analysis, utilizing the Privacy Calculus Theory (PCT), the Theory of Planned 

Behaviour (TPB), and the Customer Relationship Management Theory (CRM). 

 

This study achieved its objectives through a quantitative research method utilising surveys 

distributed online. South African consumers above the age for 18 were used as the sample for 

the research for data collection, of which a non-probability sampling technique was used, and 

a total of 277 valid questionnaires were collected. Structural Equation Modelling (SEM) was 

utiliseutilised to analyse the data through partial least squares regression (Smart-PLS) to 

evaluate the relationship between the constructs. Here, the results indicated that privacy 

concerns  exist and are positively influenced by Awareness, Data Breach and Attitude. 

Furthermore, these concerns were negatively influenced by Intention, Trust, and Culture; and, 

therefore, a privacy paradox was not confirmed. Furthermore, Personalisation was shown to 

have no bearing on Privacy Concerns. However, Personalisation had a negative relationship 

with Attitude, while Trust had a positive one. Perceived Risk was also found to have a negative 

relationship with Intention. Moreover, this study revealed the existence and South African 

consumer perspective of privacy concerns and provides benefits for marketers and scholars in 

the retail industry, such as fueling trust through the recognition of consumer privacy.  

 

 

 

Keywords: Privacy Concerns, South Africa, Loyalty Programs, Privacy Calculus Theory, 

Theory of planned behaviour, Customer Relationship Management, Privacy Paradox, 

Hofstede’s dimensions 

 

 



iv 
 

 

Student number: 1452711 
 

DECLARATION 

 

I, Alexandra Cameron Dearden, hereby declare that this thesis, titled “Privacy concerns 

towards data-usage for South African loyalty programs”, is my own work. It is submitted for 

the degree Master of Commerce (Research), in the field of Marketing, at the school of 

Economic and Business Sciences at the University of Witwatersrand, Johannesburg in South 

Africa. It has never been submitted before for any degree or examination at this or any other 

university. 

 

 

 

 

 

 

 

_________________________ 
 

Alexandra Cameron Dearden 
 

 

 

Signed at Johannesburg on the…….. 31
st

……. day of…. March…. 2023 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



v 
 

 

 

TABLE OF CONTENTS 

ACKNOWLEDGEMENTS ................................................................................................... ii 

ABSTRACT ........................................................................................................................... iii 

DECLARATION ..................................................................................................................... iv 

TABLE OF CONTENTS ......................................................................................................... v 

List of Figures ......................................................................................................................... ix 

List of Tables ........................................................................................................................... ix 

Chapter 1 ................................................................................................................................ 10 

1.1 Introduction and Background......................................................................................... 10 

1.2 Problem statement and Research Gap .............................................................................. 5 

1.3 Research objectives .......................................................................................................... 8 

1.3.1 Aims and objectives ...................................................................................................... 8 

1.3.2 Research question ......................................................................................................... 9 

1.4 Ethical considerations ...................................................................................................... 9 

1.5 Chapter outlines ............................................................................................................... 9 

Chapter 2 - Industry and Background ................................................................................ 11 

2.1 Introduction .................................................................................................................... 11 

2.2 The Retail Industry ......................................................................................................... 11 

2.2.1 Loyalty Programs in Retail ......................................................................................... 12 

2.3 The Global state of the Retail industry .......................................................................... 12 

2.3.1 Global market performance ........................................................................................ 12 

2.3.2 Global market challenges ............................................................................................ 15 

2.4 The African state of the retail industry performance ..................................................... 16 

2.4.1 African market performances ...................................................................................... 16 

2.4.2 African market challenges ........................................................................................... 18 

2.5 The South African retail industry ................................................................................... 19 

2.5.1 Performance of the industry ........................................................................................ 19 

2.5.2 Growth factors of the industry .................................................................................... 22 

2.5.3 Challenges of the retail industry in South Africa ........................................................ 22 

2.5.4 The South African population – size and potential ..................................................... 23 

2.5.5 Privacy concerns and South African Loyalty Programs ............................................. 24 

Conclusion ........................................................................................................................... 26 



vi 
 

Chapter 3 - Theoretical Background ................................................................................... 27 

3. 1 Introduction and Background........................................................................................ 27 

3.2 Theoretical Grounding ................................................................................................... 27 

3.2.1 Historical context and overview for the chosen theories ............................................ 29 

3.2.2 Privacy Calculus Theory ............................................................................................. 30 

3.2.3 The Theory of Planned Behaviour .............................................................................. 36 

3.2.4 Discussion of Customer Relationship Management Theory ....................................... 40 

3.2.5 The Integrated model .................................................................................................. 44 

3.3 Construct conceptualization in literature ....................................................................... 47 

3.3.1 Trust ............................................................................................................................ 48 

3.3.2 Culture ......................................................................................................................... 48 

3.3.3 Awareness .................................................................................................................... 51 

3.3.4 Intention to use Loyalty Program................................................................................ 51 

3.3.5 Attitude ........................................................................................................................ 52 

3.3.6 Personalization ............................................................................................................ 53 

3.3.7 Perceived Risk ............................................................................................................ 54 

3.3.8 Data Breach ................................................................................................................. 55 

3.4. Hypothesis development ............................................................................................... 55 

3.4.1 Trust and Privacy Concerns ........................................................................................ 56 

3.4.2 Culture and Privacy Concern ...................................................................................... 57 

3.4.3 Awareness and Privacy Concern ................................................................................. 58 

3.4.4 Intention to use and Privacy Concern ......................................................................... 59 

3.4.5 Attitude and Privacy Concern ..................................................................................... 60 

3.4.6 Personalisation and Trust, Attitude and Privacy Concern ........................................... 61 

3.4.7 Perceived Risk and Intention to use ............................................................................ 62 

3.4.8 Data Breach and Privacy Concern .............................................................................. 63 

3.4.9 The Conceptual Model ................................................................................................ 65 

Conclusion ........................................................................................................................... 65 

Chapter 4 – Research Methodology ..................................................................................... 66 

4.1 Introduction .................................................................................................................... 66 

4.2 Research Philosophy and Approach ............................................................................... 67 

4.2.1 Positivist research philosophy ..................................................................................... 69 

4.2.2 Research strategy and methodological approach ........................................................ 71 

4.3 The marketing research process adopted in the study .................................................... 73 

4.3.1 Problem definition ...................................................................................................... 74 

4.3.2 Critical literature review ............................................................................................. 74 



vii 
 

4.3.3 Generation of hypotheses ............................................................................................ 75 

4.4 Research design ............................................................................................................. 76 

4.4.1 The purpose of the study ............................................................................................. 76 

4.4.2 Research strategy ........................................................................................................ 76 

4.4.3 Measurement and measures ........................................................................................ 76 

4.4.4 Development and pre-testing of the questionnaire ..................................................... 78 

4.4.5 Sampling design .......................................................................................................... 79 

4.4.6 Data collection process ............................................................................................... 85 

Conclusion ........................................................................................................................... 98 

5.1 Introduction .................................................................................................................... 99 

5.2 Descriptive Statistics ...................................................................................................... 99 

5.2.1 Demographic profile of respondents ........................................................................... 99 

5.2.2. Construct central tendency measures ....................................................................... 102 

5.2.3 Open-ended Questions .............................................................................................. 120 

5.3 Confirmatory Factor Analysis (CFA) ........................................................................... 121 

5.3.1 Assessment of normality ........................................................................................... 121 

5.3.2 Reliability and validity of construct results .............................................................. 122 

5.3.3. Multicollinearity Assessment ................................................................................... 129 

5.3.4 Validating Higher Order Construct Measurement Model ......................................... 130 

5.3.5 Model fit .................................................................................................................... 131 

Structural Model and Hypotheses Testing ......................................................................... 135 

5.4.1 Predictive capabilities and relevance ........................................................................ 135 

5.4.2 Hypothesis Results (Direct Relationships) ............................................................... 137 

5.4.3 Mediation analysis .................................................................................................... 140 

Conclusion ......................................................................................................................... 142 

Chapter 6 – Discussions of Findings .................................................................................. 143 

6.1 Introduction .................................................................................................................. 143 

6.2 Objective Achievement and Hypothesis Discussion .................................................... 143 

6.2.1 Recap of the empirical objectives ............................................................................. 143 

6.2.1.3 Objective 3: To investigate the relationship between Privacy Concerns and the 

intention to participate in South African retail Loyalty programs ..................................... 147 

6.2.1.4 Objective 4: To investigate the relationship between Perceived Risk and Intention to 

participate in South African retail Loyalty Programs ........................................................ 148 

6.3 Sample characteristics and observations ...................................................................... 151 

Conclusion ......................................................................................................................... 151 

Chapter 7: Conclusions and Recommendation ................................................................. 152 

7.1 Introduction .................................................................................................................. 152 



viii 
 

7.2 Overview of the study and Key Findings .................................................................... 152 

7.3 Recommendations ........................................................................................................ 153 

7.3.1 Effect of Trust on Privacy Concerns ......................................................................... 154 

7.3.2 Effect of Culture on Privacy Concerns ..................................................................... 154 

7.3.3 Effect of Awareness on Privacy Concerns ................................................................ 154 

7.3.4 Impact of Privacy Concerns on Intention to use Loyalty Programs ......................... 155 

7.3.5 Effect of Privacy Concerns on Attitude .................................................................... 156 

7.3.6 Influence of Personalisation, Trust and Attitude ....................................................... 156 

7.3.6 Effect of Data Breach on Privacy Concerns ............................................................. 157 

7.3.7 Impact of Perceived Risk on Intention to use Loyalty Programs ............................. 157 

7.4 Contributions ................................................................................................................ 158 

7.4.1 Theoretical contributions .......................................................................................... 158 

7.4.2 Empirical contributions ............................................................................................. 159 

7.4.3 Contextual contributions ........................................................................................... 159 

7.4.4 Practical contributions .............................................................................................. 160 

7.5 Limitations of the study ............................................................................................... 162 

7.6 Suggestions for Future research ................................................................................... 162 

Conclusion ......................................................................................................................... 163 

Reference List ....................................................................................................................... 164 

Appendix A ........................................................................................................................... 190 

Appendix B ........................................................................................................................... 208 

Appendix C ........................................................................................................................... 211 

Appendix D ........................................................................................................................... 214 

Appendix E ........................................................................................................................... 225 

Appendix F ........................................................................................................................... 228 

 

 

  



ix 
 

List of Figures 

 

Figure 1. Retail Market Segmentation (ExpertMarketResearch, 2022) .................................. 12 
 

Figure 2. Formal and Informal grocery retail in South Africa (based on rand sales) (Masojada, 

2021). ....................................................................................................................................... 17 

Figure 3. Top 10 retail Loyalty Programs in South Africa 2019 and 2021 (BusinessTech, 

2021) ........................................................................................................................................ 18 

Figure 4. Privacy Calculus Theory .......................................................................................... 28 

Figure 5. Theory of planned behaviour 

.................................................................................... 

34 

Figure 6. Customer Relationship Management Lifecycle ....................................................... 38 

Figure 7. Saunders et al., (2019) proposed Research Onion 

.................................................... 

62 

Figure 8. Sample, Population and Target Population derived from Saunders et al., (2019) ... 76 

Figure 9. Research Design process (researchers own design) ................................................. 82 

Figure 10. Data analysis (researchers own design) 

.................................................................. 

83 

Figure 11. Different types of HOC’s (taken from Sarstedt, Hair Jr, Cheah, Becker and Ringle, 

2019) 

........................................................................................................................................ 

88 

Figure 12. Culture as a Higher Order Construct (researchers own design) 

............................. 

89 

Figure 13. Predictor, Mediator and Outcome variables (researchers own design) 

.................. 

91 

Figure 14. Original Model (researchers own design) ............................................................ 125 

Figure 15. Adjusted Model (researchers own design) ........................................................... 126 

Figure 16. Gender demographics 

........................................................................................... 

203 

Figure 17. Age demographics 

................................................................................................ 

203 

Figure 18. Highest Education Level demographics 

............................................................... 

204 

Figure 19. Race demographics 

............................................................................................... 

204 

Figure 20. Marital Status demographics ................................................................................ 205 

Figure 21. Household Income demographics ........................................................................ 205 
 

 

 

List of Tables 

 

Table 1. Summary of findings within privacy research 42 
 

Table 2. Research philosophies 63 

Table 3. Quantitative and Qualitative, adapted by (Saundeers et. al., 2019; Stockemer, 2019; 

Trochim, 2006) 68 

Table 4. Study’s Constructs and their respective Scales 72 

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Table 5. Probability and non-probability sampling technique 78 

Table 6. Model fit Indices, taken from Cho, Hwang, Sarstedt and Ringle, (2020) and Yakubu 

and Dasuki, (2018). 86 

Table 7. Model fit Indices, taken from (SmartPLS Model Fit, 2023) 86 

Table 8. Demographic profile of respondents 95 

Table 9. Summary of construct central tendency measures. 96 

Table 10. Awareness Central Tendency Measures 97 

Table 11. Trust Central Tendency Measures 98 

Table 12. Intention to use Loyalty Programs Central Tendency Measures 100 

Table 13. Perceived Risk Central tendency Measures 101 

Table 14. Perceived Vulnerability (Data Breach) Central tendency measures 102 

Table 15. Privacy Concerns Central tendency Measures 103 

Table 16. Personalisation Central tendency Measures 105 

Table 17. Attitude Central tendency Measures 106 

Table 18. Masculinity Central tendency Measures 107 

Table 19. Individualism Central tendency Measures 108 

Table 20. Power Distance Central tendency Measures 109 

Table 21. Uncertainty Avoidance Central tendency Measures 110 

Table 22. Long-term Orientation Central tendency Measures 111 

Table 23. Indulgence Central tendency Measures 112 
 

Table 24. Nonnormality values 115 

Table 25. Initial reliability results (taken from Smart-PLS) 116 

Table 26. Final Reliability table 119 

Table 27. Heterotrait-monotrait (HTMT) matrix 121 

Table 28. Multicollinearity results (VIF) 122 

Table 29. HOC validation 123 

Table 30. Model fit indices Component-based approach 124 

Table 31. R-square values 127 

Table 32. Q- square predictive values 127 

Table 33. Hypothesis testing and results 128 

Table 34. Indirect Model Effects 131 

Table 35. Competitive and Complementary mediation relationships 132 

Table 36. Pilot test results - Cronbach alpha 200 

Table 37. Codebook 200 

Table 38. Abbreviated Construct List/ Guide 201 

Table 39. Open ended Responses – Loyalty Program Improvement 205 

Table 40. Open ended Responses - Data Breach 209 

Table 41. Kurtosis and Skewness values 214 

Table 42. Fornell and Lacker matrix - Discriminant Validity 216 

Table 43. F-squared values 217 

Table 44. Predictive validity (using Q-squared predictive values) 217 

Chapter 1 

 

1.1 Introduction and Background 

 



2 
 

Datausage and technology have become the building blocks on which a business can thrive 

(Mikalef, Pappas, Krogstie, and Pavlou, 2020). This study puts a focus on consumer data 

specifically, entailing the raw data companies collect through consumer behaviour, e.g., 

personal information, website visits, and purchasing history. Raw data is unprocessed data that 

can then be used to provide useful information and be sorted to make decisions (Ot, 2023; 

Wright, 2021). As technology has progressed, consumers have created vast amounts of 

consumer data through multiple sources accessed daily, i.e., social media, transactions, e-

commerce, mobile app usage, website usage, etc. (Ali, Septyanto, Chaudhary, Al Hamadi, 

Alzoubi and Khan, 2022; Belarbi, Tajmouati, Bennis, and Tirari, 2016). Businesses can, 

thereafter, use this data generated from customers to benefit themselves, such as to support 

their decision-making, identify specific customer buying behaviour, offer targeted services, 

increase engagement, and identify what consumers need and value from the business 

(Balakrishnan, Chui, Hall and Henke, 2020; Rejeb, Rejeb and Keogh, 2020). This data usage 

has also grown considerably in line with the COVID-19 outbreak, in which consumers' online 

presence increased and businesses moved towards hybrid models utilising e-commerce 

(Omoruyi, Dakora and Oluwagbemi, 2022). Additionally, digitalisation in retail was spedup as 

lockdown added pressure through limited in-person shopping for consumers and businesses 

alike (Njomane and Telukdarie, 2022). 

 

Moreover, the use of data to assist in decision support systems has been ongoing and beneficial 

for many industries, including the retail industry (Aluri, Price, and McIntyre, 2019; Kliestik, 

Zvarikova and Lăzăroiu, 2022). Some examples of additional industries that have benefited 

from data usage include the healthcare industry, the finance industry, and the entertainment 

industry. Here, datausage allows these industries to adjust business decisions and manage their 

customer relationships for a better outcome, i.e., the finance industry monitoring data for 

security and risk analysis; the healthcare industry providing quick insights into patient health; 

and the entertainment industry creating better content and monitoring performance on different 

platforms using data collected (Aceto, Persico and Pescapé, 2020; Bleier, Goldfarb and Tucker, 

2020; Lee, Li, Yu and Zhao, 2021; Raunch, 2023; SuccessiveCloud, 2023; van Es, 2023). 

These industries obtain similar data-usage benefits through observing and monitoring trends 

or consumer behaviour, improving the user experience and creating personalised content for 

users. The brand Netflix is a well-known example of  this, making recommendations to users 

based on watch-history, as well as using data analytics to pitch ideas for future shows using 



3 
 

“touch-points” that can make a show popular (Mixson, 2021). Of the aforementioned 

industries, the retail industry provides the focus for this study. 

 

Globally, the retail environment comprises one of the largest industries and continues to grow 

(Statista, 2022). In South Africa alone, the retail sector makes up approximately 20% of the 

country’s GDP (Retail Sector Report, 2022). Moreover, customer transactions within the retail 

industry can reach over 2.5 petabytes (PB) of data collected every hour, which the retailers can 

then use to better target their consumers (Chauhan, 2020). Extant research on the benefits of 

using such data has emerged through aspects such as engagement, value creation and improved 

experience (Calder, Malthouse, and Maslowska, 2016; Rejeb, Rejeb and Keogh, 2020; Zeng, 

and Glaister, 2018). These benefits have received the attention of retailers, and the popular 

South African grocers “Woolworths South Africa, Pick n Pay and Shoprite”, have now invested 

heavily to incorporate data-driven decision-making into their strategies (Global Powers of 

Retailing, 2021; Hartzenberg, 2019; Woolworths Holdings, 2022; Pick n Pay, 2022; Shoprite, 

2022). 

 

Furthermore, the African market has forecasted a growth of 28% every year until 2025 for data 

analytics (AfricanBusiness, 2020). In South Africa, mass data usage has been observed as an 

emerging but slow phenomenon due to the still-developing infrastructure, policies, standards, 

and training concerns in data analytics applications (Masenya and Ngulube, 2019; Mhlanga 

and Moloi, 2020). Regardless of this, many businesses, as mentioned, have implemented the 

various tools and techniques of managing data in their business strategies (Le Roux, 2019). 

One way in which retailers’ access and accumulate consumer data is with loyalty programs. 

 

Loyalty programs allow a company to build a relationship with their consumers through 

rewards systems (sometimes tiered), provide targeted support, and directly engage them 

(Lakshman, and Faiz, 2021; Sukmaningsih, Meyliana, Prabowo and Nizar Hidayanto, 2019). 

For example, consumers can be encouraged through a loyalty program to engage and make 

purchases to earn points for prizes, discounts, or special promotions. Additionally, retailers 

may design and adapt their loyalty programs based on the consumer data acquired, which 

allows them to understand how different consumers perceive and react to the loyalty program 

features and rewards. This in turn could help brands standout amongst competitors and improve 

their relationship and engagement with consumers (Hallikainen, Luongo, Dhir and Laukkanen, 

2022; Seetharaman, Niranjan, Tandon, and Saravanan, 2016). Loyalty programs in South 

Africa differ slightly, as none of the retailers require the consumer to pay to participate (Le 



4 
 

Roux, 2019). Although the usage of data has, therefore, been shown to be beneficial for a 

business, understanding the consumer’s direct response, awareness and attitude towards this 

data usage provides another avenue to be explored further (Yartey Ajayi, Omojola, Amodu and 

Ndubueze, 2020). One important topic that arises alongside this is the subject of privacy. 

 

Data-sharing behaviour and personalised advertising from firms can lead to consumers feeling 

concerned or apprehensive about their data being used (Shomakers, Lidynia and Ziefle, 2020). 

These concerns over the storage, control and usage of this data have been shown to have grown 

significantly in recent years (Gerber, Gerber and Volkamer, 2020). It has, therefore, become a 

necessity for businesses to safeguard and acknowledge their consumers' privacy to retain their 

loyalty and trust (Bandara, Fernando and Akter 2020). Within retail promotions, various 

privacy policies and requirements have been put in place, such as ensuring the consumer’s 

consent is received prior to their involvement (Bandara, Fernando and Akter, 2020). However, 

these privacy policies can be overlooked or even neglected by consumers (Gerber, Gerber and 

Volkamer, 2020). For example, a consumer may skim over the terms and conditions about 

datausage or simply ignore them. This interestingly leads to the concept of a privacy paradox, 

where consumers have been shown to value their data privacy and show concerns for it, but 

still provide their data willingly (Acquisti, Brandimarte and Loewenstein, 2020). 

 

Researchers have made attempts to understand the paradox; however, there is still a present 

need to explore it further (Dienlin and Trepte, 2015; Gerber, Gerber and Volkamer, 2020). 

Moreover, the privacy paradox can provide an extra layer into understanding consumers’ actual 

behaviour, which can then assist marketers in assessing how they should tackle any privacy 

concerns held (Gerber, Gerber and Volkamer, 2020). As a result, it is essential to consider the 

underlying variables that influence South African consumers' concerns about data privacy and, 

therefore, their intention to disclose their data to retailers. Although investigating privacy 

concerns has been highlighted as a necessity in literature (see Martin and Murphy, 2016; 

Gerber, Gerber and Volkamer, 2020), there are limited privacy related studies that have 

focused on the South African and Loyalty Program contexts. Studies have focused primarily 

on online privacy concerns through websites and social media (Bandara, Fernando and Akter 

2020; Bevan-Dye and Akpojivi, 2016; Shomakers, Lidynia and Ziefle, 2020; Vu, Law and Li, 

2018), investigating privacy concerns and data security in politics and South African 

governments (Maduku, 2020; Manda and Backhouse, 2016) and data concerns within bank 



5 
 

policies (Keshav, 2010). An extension of privacy research into South African Loyalty 

programs, therefore, helps with closing this knowledge gap. 

 

Moreover, South African consumers attitudes and comprehension of their privacy and data-

usage may differ from the findings of previous studies due to the culture, country-level factors, 

and diverse consumer needs that require a country-specific analysis (Fam, Brito, Gadekar, 

Richard, Jargal, and Liu, 2019). A consumer’s culture plays an interdisciplinary role when it 

comes to privacy concerns; through its relation to consumer behaviour and attitude 

(Schumacher, Eggers, Verhoef, and Maas, 2023). This provides for an interesting investigation 

in research on privacy-related behaviour, which this study then explored. Privacy is also 

contextual, so research within a specific field or country provides optimal results in 

understanding consumer behaviour and preferences (Bandara, Fernando and Akter, 2020). This 

study, therefore, looked to investigate privacy concerns and attitudes among South African 

consumers as well as identify whether the privacy paradox exists. In this search, this study used 

existing South African grocery retailers' to contextualise retail Loyalty Programs and data 

privacy. Additionally, this study provided an investigation into culture and privacy-related 

behaviour for a South African culture analysis and an understanding of South African 

consumers perceptions towards privacy.  

1.2 Problem statement and Research Gap 

 

Within retail, it has been noted that data privacy and awareness are lacking in existing firms, 

with only 20% of retailers globally integrating privacy policies into their overall strategies 

(Sides, Marsh, Goldberg and Mangold, 2019). Moreover, businesses may not be as mindful of 

customers' potential ignorance or naivety towards their data being used, i.e., consumers 

skimming over or ignoring the terms and conditions (Yartey Ajayi, Omojola, Amodu, and 

Ndubueze, 2020). Moreover, consumers may make use of the privacy paradox, where their 

actions contradict their beliefs and attitudes, causing a misunderstanding of what businesses 

interpret to drive consumers (Acquisti, Brandimarte and Loewenstein, 2020). This presents 

itself as an issue from the standpoint of businesses needing to practice good governance and a 

lack of understanding from a consumer perspective. This study, therefore, proposed the 

examination of how different elements affect privacy concerns and, thereafter, the intention to 

use loyalty programs. By understanding the consumer perspective and relative behaviour, 

businesses and policymakers can overcome any existing issues surrounding privacy concerns 

and specifically understand and identify how and why the different theoretical constructs 



6 
 

influenced this concern. A justification for these firms to then integrate privacy elements or 

additional policies into their overall strategy can be developed. 

 

Additionally, data usage has arisen as a growing topic for researchers and practitioners in South 

Africa (Le Roux, 2019). Regardless of this, there is a paucity of research on South African 

customers' privacy awareness and standpoint on their data being used to market to them 

(Akpojivi, 2013; Le Roux, 2019). As South Africa is a developing country with different 

cultural, legal, and socioeconomic factors, a country-specific analysis provides an addition to 

the focused problem in order to develop the desired understanding (Gielens and Gijsbrechts, 

2018). Previous literature exploring privacy concerns using culture has done this through 

demographics, proximity, ethics, organisation, and specific cultures or cultures in developed 

countries (Chai, 2020; D’Acunto, Volo and Filieri, 2021; Fleming, Bayliss, Edwards and 

Seger, 2021), though these do not apply to the South African Loyalty Program contexts and 

necessary understanding of this study, presenting a research gap. Hofstede’s culture model is 

a rational, well-tested strategy that has been used globally, has evolved to respond to world 

developments, and therefore provides a useful way to measure culture in South Africa (Chun, 

Zhang, Cohen, Florea, and Genc, 2021; Ibanez and Sisodia, 2020). Moreover, South African 

businesses have a lawful requirement to practice good governance and have privacy and 

security measures in place relative to consumer data (Western Cape Government, 2022; 

Sutherland, 2021). Furthermore, they need to receive customers' agreement to use their data in 

accordance with Acts such as the Cybercrimes Act 19 and POPI Act (Cybercrimes Act, 2021; 

POPIA, 2019; Western Cape Government, 2022; Sutherland, 2021; Thaldar, Townsend, 

Donnelly, Botes, Gooden, van Harmelen and Shozi, 2022).  

 

However, there has been some criticism from scholars stating that active privacy policies may 

also need to be incorporated by businesses to soothe consumers’ potential concerns and ensure 

they are actively protected (POPIA, 2019; Hoffmann, Lutz, and Ranzini, 2016; Jones, 2022; 

Townsend, Gooden, Botes and Thaldar, 2023). Townsend and Botes (2023) furthered this by 

stating that, although obligated by acts such as POPIA, the protection of consumers' privacy is 

left “up to the discretion of each responsible party”. Although acts like the POPIA have shifted 

the way businesses conduct themselves, data protection in the country remains imperative, with 

South Africa presenting among the top 3 highest African countries facing cybersecurity threats 

(Darwin and Nkongolo, 2023; INTERPOL’s 2022 Africa Cyberthreat Assessment, 2022).  

 



7 
 

Consumers’ varying preferences and perspectives towards privacy and data usage have become 

an emerging research avenue for researchers and practitioners alike (Narang and Shankar, 

2019). More specific to privacy, there have been few studies highlighting the privacy paradox 

in marketing (Bandara, Fernando and Akter, 2020). Moreover, there are few studies that have 

explored personalisation alongside privacy concerns and the privacy paradox, even though 

research into privacy and privacy issues has accelerated in recent years (Ameen, Hosany and 

Paul, 2022; Cho, Ko and Lee, 2018; Ozturk, Nusair, Okumus and Singh, 2017). Personalisation 

is a reoccurring and core theme when analysing data usage in Loyalty programs, as it provides 

a way to gratify and effectively target consumers; making it a useful avenue to explore further 

to benefit practitioners and scholars alike (Ioannou, Tussyadiah and Lu, 2020; Pingo, 2020; 

Volchek, Yu, Neuhofer, Egger and Rainoldi, 2021). Researchers have also emphasised the 

importance of researching loyalty programs through data, as the information accessed through 

the programs is otherwise untraceable and provides value for the business (Breugelmans and 

Liu-Thompkins, 2017; Lakshman and Faiz, 2021). Nobre and Rodrigues (2018) noted the 

importance of understanding the impact of loyalty programs on different aspects of consumer 

behaviour, which this study explored through data usage privacy concerns and, thereafter, how 

this affects the intention to participate in the loyalty program. Moreover, there are limited 

studies focusing on retail and data analytics, providing a knowledge gap for this research paper 

(Belarbi, Tajmouati, Bennis and Tirari, 2016). Research on data-usage in the retail industry has 

also been reflected as a crucial area of study for researchers and practitioners due to the 

magnitude of the industry, the growth of the middle class and technological improvements 

(Seetharaman, Niranjan, Tandon and Saravanan, 2016). 

 

From a theoretical perspective, this study provides a multifaceted model of privacy concerns 

and intention by integrating the Privacy Calculus Theory with elements of the Theory of 

Planned Behaviour and Customer Relationship Management Theory, i.e., culture, trust, and 

personalisation, which have been reflected as necessary for future research development 

(Ameen, Hosany and Paul, 2022; Ioannou, Tussyadiah and Lu, 2020). Therefore, the usage of 

these elements and the specific research context provide for a unique contribution through the 

knowledge gap filled. Additional research exploring privacy concerns has made use of the TPB 

alone as well as the Technology Acceptance Model in their investigations (Adebiyi and 

Olayemi, 2022; Bayaga and Ophoff, 2019; Cho, Ko and Lee, 2018; Dutot, Bhatiasevi and 

Bellallahom, 2019; Guhr, Werth, Blacha, 2020; Ismail, Hamzah, Hussin, Affandy and Ahmad, 

2023; Li, 2020). However, the Privacy Calculus Theory allows for a specific insight into 



8 
 

consumer perspectives towards privacy, while the integration of the TPB and CRM elements 

provides a contextualised and comprehensive exploration, which this study looks to explore 

(Trepte, Scharkow, and Dienlin, 2020).  

 

This study will allow these research gaps to be shortened, provide a theoretical contribution, 

and also provide guidance and knowledge for South African retailers and policymakers to 

recognise. 

1.3 Research objectives 

1.3.1 Aims and objectives 

 

The aim of this study is to explore the variables surrounding privacy concerns and data-usage 

in Loyalty programs, to better understand South African consumers' perception of data privacy, 

and, thereafter, their intention to engage with the program. 

1.3.1.1 Objectives 
 

1. To assess the influence of trust, culture and privacy awareness as key antecedents for 

privacy concerns in South African retail Loyalty programs 

 

2. To investigate the relationship between Privacy Concerns and Privacy Attitude for 

South African consumers in South African retail Loyalty programs 

 

3. To investigate the relationship between Privacy Concerns and the intention to 

participate in South African retail Loyalty programs 

 

4. To investigate the relationship between Perceived Risk and Intention to participate in 

South African retail Loyalty Programs 

 

5. To investigate the relationship between Personalisation on one hand and Trust, 

Privacy Attitude and Privacy Concerns on the other hand 

 

6. To investigate whether the Privacy Paradox exists within South African consumers, 

based on the found relationship between Privacy Concerns and their intention to 

participate in Loyalty Programs. 

 



9 
 

1.3.2 Research question 

 

Do South African consumers show privacy concerns for their data-usage in South Africa? 
 

1.4 Ethical considerations 

 

Ethical considerations include the researcher's personal dignity and the respect, permission, 

and confidentiality that the researcher extends to the study and individuals involved (Connelly, 

2014). Therefore, gender, age, race, education level, socioeconomic circumstances, and 

disability arise as ethical considerations and have no influence on participation in the study. 

Before participating in the study, individuals were informed of the study's aim, duration, and 

the option to leave the study ahead of time should they not wish to participate. Respondents 

were required to agree to participate in the study prior to their involvement. Respondents were 

also provided with contact details should they have any concerns or comments about the study. 

All demographic information that is regarded as sensitive was confidential, with the option for 

respondents to “prefer not to say”. Moreover, the collection of data only commenced post-

ethics clearance from the University of Witwatersrand. 

1.5 Chapter outlines 

 

Chapter 1 
 

This chapter provides the brief background and context of the study as well as the research 

problem, to introduce the reader to the topic. Thereafter, the research objectives and question 

were highlighted to provide the direction for the study before the review of literature and 

theoretical aspects of the study are touched on. Finally, a brief outline of the methodology of 

the study was reviewed. 

 

Chapter 2 
 

This chapter gives a detailed review on the industry surrounding the topic, namely: The Retail 

Industry. Here, the contextualisation of the topic, the African, Global and South African 

markets are observed; to provide an understanding of the research context. Here, the topic of 

privacy and data-usage within this context will also be observed. 

 

Chapter 3 
 



10 
 

This chapter provides the theoretical framework used for the study as well as an in-depth 

investigation into the constructs utilised and the different literature contexts that surround them. 

Here, previous literature will be explored and discussed relative to the topic to form the basis 

of what is already known about the topic, and its importance. The different models used in 

previous studies will also be explored, as well as the proposed model and constructs for this 

study, before; finally, presenting the hypothesis formation for the topic as well as the 

conceptual model. 

 

Chapter 4 
 

This chapter will provide the proposed research methodology for the study by exploring the 

different layers of the research onion, following the quantitative method of research. Here, the 

researcher will highlight the different strategies, techniques, target audience and data analysis 

that will be appropriate for this study. 

 
Chapter 5 
 

This Chapter will provide the results of the study, based on the data collected in the survey. 

Here, SEM techniques using SPSS and Smart-PLS will be used to analyse the data. Graphs and 

tables will be used in this Chapter to assist in visualizing the data. Lastly, previous literature 

will be explored relative to the results of the study, for comparison and review. 

 

Chapter 6 
 

This Chapter and will provide the findings from Chapter 5. Here a summary and the 

conclusions of the study will be presented, based the objectives of the study. 

 

Chapter 7 
 

This is the last Chapter for the study and will provide the Recommendations and contributions 

of the study. Moreover, the practical implications, limitations and recommendations for future 

research will be discussed, based on the findings of the study. 

 

 

 

 



11 
 

Chapter 2 - Industry and Background 

 

2.1 Introduction 

 

This chapter provides insight into the Global, African, and South African retail industry 

contexts, and more specifically, the grocery retail industry, for a detailed description of privacy 

concerns for data usage in Loyalty Programs. Here, the retail industry will be discussed, 

providing a deeper understanding of grocery retailers, data privacy and a brief look at the 

literature that has explored this focus and corresponding consumer viewpoints. Insight into 

South African grocery retailers' Loyalty Programs will also be provided, as well as their 

approach to data privacy and privacy concerns, to provide a contextualised understanding of 

the study. 

2.2 The Retail Industry 

 

The retail industry is among the largest in the world and continues to grow (Seetharaman, 

Niranjan, Tandon and Saravanan, 2016). The retail grocery industry provides general goods 

that are essential to consumers' day-to-day lives, making it one of the most popular and highest-

grossing industries worldwide (Johnson and Iyamu, 2019). Statistically, the retail sector has 

forecasted global sales of over 30 trillion US dollars by the year 2024 (Sabanoglu, 2022). 

Retailers can be divided into informal Spaza shops, Microbusinesses, Small businesses, and 

formal larger convenience stores and supermarkets, to name a few (Masojada, 2021). 

Moreover, numerous retailers have adopted an online marketing strategy, alongside or as a 

replacement for their retail sales in-store; to target consumers in other avenues that they access 

daily, such as social media and website pop-ups (Grewal, Hulland, Kopalle and Karahanna, 

2020; Vieira, de Almeida, Agnihotri, da Silva and Arunachalam, 2019). This could partly be 

due to the shift in internet access and consumer movement online, the technology dependency 

that is being seen amongst consumers, as well as the visual and engagement benefits that a 

digital strategy holds (Heinze, Fletcher, Rashid and Cruz, 2016; Seetharaman et al., 2016; 

Valentini, Romenti, Murtarelli and Pizzetti, 2018). The emergence of data usage in recent years 

has also been optimised by these higher-earning retailers as a strategic asset to then provide a 

competitive advantage, make use of valuable consumer information, and, thereafter, achieve 

their objectives (Santoro, Fiano, Bertoldi and Ciampi, 2018). 



12 
 

2.2.1 Loyalty Programs in Retail 

 

In efforts to meet increasing consumer demand and achieve a competitive advantage, retailers 

have begun implementing techniques and strategies through information and communication 

technologies (Johnson and Iyamu, 2019; Lakshman and Faiz, 2021). Here, methods such as e-

commerce and Loyalty Programs, are then used to engage with consumers and increase sales. 

These typically involve reward-based strategies such as reward cards, points, support, price 

savings, gifts, and tiers of service (Lakshman and Faiz, 2021). Loyalty Programs in retail have 

been noted as a crucial marketing technique to retain and activate customers, influence long-

term customer behaviour or repeated patronage, and fuel a customer-company relationship 

(Alshurideh, Gasaymeh, Ahmed, Alzoubi and Kurd, 2020; Mackay and Major, 2017). 

However, within the retail sector, literature has reflected dissatisfaction and disappointment 

amongst consumers, with over 70% noting frustration with their experiences and data privacy 

(Martin and Palmatier, 2020). Moreover, the necessary shift to account for these privacy 

concerns can cause retailers to allocate more time and resources to new strategies that would 

otherwise be used to build customer relationships and improve acquisition and retention 

strategies. Thus, it becomes imperative to understand the degree of concern and the cause of 

this concern to effectively target it. 

 

Retail, Loyalty Programs and Privacy concerns can further be examined through a Global lens. 

2.3 The Global state of the Retail industry 

2.3.1 Global market performance 

 

The global retail industry has grown consistently over the years, with the rise of the middle 

class, emerging markets, and technology development around the world (Mjongile, 2020). 

Globally, retailers have also reflected a shift towards online shopping, with consumers' 

purchasing behaviour changing around the world to make more and more purchases online, as 

well as retailers evolving to make use of technologies such as Artificial Intelligence, 

Augmented Reality, Cloud Computing and the usage of Big Data (Dwivedi, Ismagilova, 

Hughes, Carlson, Filieri, Jacobson, Jain, Karjaluoto, Kefi, Krishen and Kumar, 2021; Har, 

Rashid, Te Chuan, Sen and Xia, 2022). Moreover, with the rise of the COVID-19 pandemic, 

developing countries have seen a shift towards an increase in consumer purchasing behaviour 

online and the usage of these technologies in retail; however, these are still somewhat limited 



13 
 

(Gu, Ślusarczyk, Hajizada, Kovalyova and Sakhbieva, 2021; Rossolov, Aloshynskyi and 

Lobashov, 2022). This is further discussed in the South African context later in the chapter. 

Furthermore, the global e-commerce market has seen an increase in online sales of a significant 

19% during the pandemic (UNCTAD, 2021). Extant literature corroborates this through studies 

ranging from Portugal, the United Kingdom, Nigeria, South Africa, Belgium and India, to 

name a few (Beckers, Weekx, Beutels and Verhetsel, 2021; East, 2022; Gao, Shi, Guo and Liu, 

2020; Gomes and Lopes, 2022; Nivethitha, Manjula and Mallika, 2020; Okpara, 2021; 

Paramannand, 2021). 

 

Although this online performance is clear, globally, in-store or brick-and-mortar retailing 

remains the dominant method for sales (Camarena, 2020; Sabanoglu, 2022). Particularly in 

more developing countries, where the internet and online shopping are relatively new, 

shopping in-store remains a priority focus for retail strategies (Mwamba and Qutieshat, 2021). 

Thus, many retailers have an omni-channel model integrating their online and offline strategies 

(Staflund and Kersmark, 2015). A prominent global brand that can be used to exemplify this 

is Walmart Inc, which has utiliseutilised technology and data usage through its digital strategy 

alongside in-store retailing (Jindal, Gauri, Li and Ma, 2021). The performance of the different 

retail channels can further be seen in Figure 1. below, which visualises the current market 

segmentation for retail, focusing on supermarkets and e-commerce retailing 

(ExpertMarketResearch, 2022). The integration of an omni-channel strategy sees retailers 

globally focusing on sales in-store and promotional or delivery strategies online, using an 

online marketing strategy (Staflund and Kersmark, 2015). For example, online promotional 

tools through Loyalty Programs, apps, and AR-usage alongside in-store sales.  

 

 

 

 

 

 

 

 

 

 

 



14 
 

Figure 1. Retail Market Segmentation (ExpertMarketResearch, 2022) 

 

 
 

Moreover, the retail space globally is highly competitive, with brands entering and expanding 

into different markets and providing additional options to consumers (Gawankar, Kamble and 

Raut, 2016; Görçün, Zolfani and Çanakçıoğlu, 2022). Walmart is currently the top global 

retailer, successfully expanding into markets such as Brazil and Japan (Pandey, Dillip, Jayant, 

Vashishth, Nikhil, Qi, Kee, Mei, Xin and Qhi, 2021). This has brought about avenues of 

competing through price, service, quality, delivery, technology advancement, convenience, 

efficiency, and relationship building for retailers to stand out amongst competitors globally 

(Martin and Murphy, 2017; Mustikowati, Sarwoko, Arief and Nurfarida, 2021; Saha, 

Banaszak, Bocewicz and Nielsen, 2022). When examining the key leading retailers around the 

world, brands originating from the United States, such as Walmart Inc, Amazon.com Inc and 

Costco Wholesale Corporation, emerge as the top 3 industry players based on revenue reports, 

with brands from Germany and China following suit (Statista, 2020). Moreover, when 

exploring the current global retail space, Har et al., (2022) referred to North America and 

European economies as being more prepared in their usage, implementation, and adoption of 

data-usage technologies, while more developing areas in Sub-Saharan Africa were then 

described as unprepared due to the limitation of universal internet access and necessary skill 

development. Globally, retailers moving and emerging into different markets that are more 

saturated will also give suppliers within the areas an advantage, as they can use price and 

quality competition to control the market and forge agreements with retailers (David and 

Adida, 2015; Littlechild, 2018; Wang, Zhang and Fan, 2020). Moreover, a trend of network 

internationalisation amongst global retailers has been seen, with flexibility and utilising local 



15 
 

supplier relationships, granted such a relationship is suitable and beneficial for the business 

(Kalchschmidt, Birolini, Cattaneo, Malighetti and Paleari, 2020). 

 

In terms of retail consumers within the global space, research within western areas has reflected 

more consumers growing up surrounded by technology, and so they may have also had their 

thoughts and behaviours shaped by it (Tshiani and Tanner, 2018). As a result, these consumers 

have different technological competencies and tendencies compared to consumers in 

developing or less technologically advanced societies (Tshiani and Tanner, 2018). Moreover, 

from a data-privacy perspective, culture variances across countries reflect differences in how 

consumers in these spaces will respond to Loyalty Programs making use of their data (Li, 2022; 

Okazaki, Eisend, Plangger, de Ruyter and Grewal, 2020; Tshiani and Tanner, 2018). However, 

the same may be true for retail consumers who have more experience with technology therefore 

having been exposed to more threats and databreaches, which may then affect their data and 

privacy perceptions in retail Loyalty Programs. This brings the focus to the challenges in the 

global retail marketing landscape. 

2.3.2 Global market challenges 

 

Globally, various privacy policies and regulations have been implemented to protect 

consumers' privacy and their data usage. Regulations such as Europe’s General Data Protection 

Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the Protection of 

Personal Information Act (POPI Act) are some of the few to mention (Manda and Backhouse, 

2016; Tshiani and Tanner, 2018). Moreover, retailers themselves can implement various 

privacy policies and strategies alongside these regulations to ensure consumers are properly 

protected and aware of what they agree to. These regulations present challenges for retailers in 

a global market, as strategy adjustments and alterations to policies will need to be adhered to 

(Vincent, 2020; Yang and Gu, 2021). Improper enforcement of such adjustments will result in 

monetary and even legal repercussions for retailers (Mignacca, 2022; Theys, Ruhode and 

Harpur, 2021). Therefore, retailers on a global scale will need to be thorough, knowledgeable, 

and responsible in their international movements. Moreover, when looking at the challenges 

retailers face globally, the threat of new entrants and substitutes can be observed. 

 

As mentioned, the retail space globally is highly competitive, providing challenges for retailers, 

particularly smaller retailers. When entering new markets and needing to deal with loyalty, 

favouritism, and saturation from consumers and suppliers in the market towards a particular 



16 
 

retailer already existing in the market and a stable CRM (Crișan-Mitra, Stanca and Dabija, 

2020; Ramprabha, Gnanasundari and Kesavan, 2019). When addressing these challenges, 

entrance obstacles, buyer/supplier negotiating power, competitive pressure, and scale 

economies should all be considered (Danemo, 2018; Rodrigues, 2020). Furthermore, when 

entering certain markets, retailers will face the challenge of adjusting to skill levels, 

technological developments, and management differences that vary from their home market; 

e.g., countries such as the United Kingdom have retailers with self-service kiosks and 24-hour 

windows as their competitive point. However, other stores throughout the world lack the 

resources and equipment to take similar steps (Galdolage, 2021; Har et al., 2022). 

 

Moreover, when looking at the extant literature on Loyalty Programs in retail globally, a large 

portion of research puts a focus on Customer Relationship Management (CRM) and 

investigating the retention and acquisition of consumers (Chen, Mandler and Meyer-Waarden, 

2021; Kampani and Jhamb, 2020; Sota, Chaudhry, Chamaria and Chauhan, 2018). However, 

literature exploring the same aspect from a big data and privacy perspective, especially in a 

South African context, is still developing (Belarbi, Tajmouati, Bennis and Tirari, 2016; Le 

Roux, 2019). This provides an academic challenge to be overcome with studies such as this 

one. Moreover, research has noted the importance of measuring the different metrics that 

influence customer engagement and interaction with Loyalty Programs (Breugelmans et al., 

2015), something this study will inadvertently accomplish. 

2.4 The African state of the retail industry performance 

2.4.1 African market performances 

 

The African retail market has developed considerably in recent years, with the growth of malls, 

technology startups, and international businesses around the continent (Amungo, 2022). 

Moreover, this performance is expected to only grow further, with countries such as Nigeria, 

South Africa and Egypt being noted as the largest consumer markets for the year 2030 

(Mwamba and Qutieshat, 2021; Signé, 2018). Furthermore, the African Development Bank 

predicted a large $2.1 trillion of African consumer spending by 2025, an exciting prospect for 

retailers (Signé, 2018). However, this has been somewhat halted due to the rise of the COVID-

19 pandemic, where the African continent saw a decline in demand in retail as movements and 

engagement became restricted for consumers in lockdown (Mwamba and Qutieshat, 2021; 

Signé, 2018). Moreover, major operators such as Shoprite, TFG and Pep exited certain African 



17 
 

markets during this period (AfricanRetailReport, 2020). This, however, also allowed room for 

some growth with e-commerce and increased demand post-lockdown from consumers. 

 

Nigeria is one of the few African countries that saw the rise of e-commerce during the COVID-

19 pandemic, with a growth rate of 42% in e-commerce in the year 2020 (AfricanRetailReport, 

2020). Popular brands, such as Jumia and Sokowatch, also aided in encouraging this e-

commerce engagement with consumers, as well as bridging the gap between informal retailers 

and suppliers through convenience and pricing options made accessible (Deganis, Tagashira 

and Yang, 2021; Mwamba and Qutieshat, 2021; Ibam, Boyinbode and Afolabi, 2018). 

However, this performance has been somewhat limited due to boundaries in delivery structure 

and necessary safety regarding payments, some of the key challenges for e-commerce in Africa 

(Ibam, Boyinbode and Afolabi, 2018; Ndonga, 2012). This can be further examined by 

observing the challenges in the African retail industry. 

 

Furthermore, the African retail space leans largely towards informal retail, with room for more 

formal shopping centres and international retailers to enter the market (Mwamba and 

Qutieshat, 2021; Tawii, 2022). From a consumer perspective, African consumers, in general, 

have been found to buy and trust brands they recognise, something that has assisted 

international competitors in successfully establishing themselves in the market (Chinomona, 

2016; Haefner, Rosenbloom and Haefner, 2016; Michoma, 2019). Moreover, African countries 

have been reflected as those that have a young, globalised, cost-conscious, and sophisticated 

consumer base; surely to evolve and grow within the next 10 years (AfricanRetailReport, 

2020). It’s further predicted that consumer demand within retail will be fueled by elements 

such as increases in discretionary household spending and formal consumption (Signé, 2018). 

Some examples of the larger global and domestic retailers in Africa include Shoprite, 

Massmart, Choppies, Tuskys, Persians Group, Uchumi and the Savola Group, to name a few 

(Nandonde and Stanton, 2022). The past two decades has reflected an influx of these external 

companies into the African retailer space, with examples such as the Savola group. which is a 

Saudi Arabian company (source). This can also be seen with the European brand Spar, which 

holds a successful presence in numerous African countries, such as Botswana, Namibia, and 

Zimbabwe (Spar, 2023). Moreover, looking at the African retail market, a common theme of 

brands entering and exiting the market in recent years is also visible. For example, Shoprite 

exited Madagascar, Uganda, and Kenya in 2021; Mr Price exited Nigeria in 2021, and even 

the local Kenyan retailer Nakumatt closed in 2019; due to financial struggles (Madubela, 

2021). Of the brands across the continent, Shoprite is the largest retailer across 11 different 



18 
 

countries in Africa and reached the top 100 in Deloitte’s top 250 Global Powers of Retailing, 

although their success is founded in South Africa (Deloitte Global Powers of Retailing survey, 

2022; Shoprite, 2023). Moreover, of the affluent brands that are noted for success in Africa, a 

large majority of them are found in South Africa, such as Shoprite and Pick n Pay (African 

Powers of Retailing, 2015). Additionally, as South Africa holds a more saturated retail market, 

it would be difficult for other African retailers to enter; as evident by Choppies which needed 

to sell their subsidiary after having trouble with their expansion into the South African market 

in 2019 (Crotty and Shevel, 2023). Taking the lens away from South Africa, Choppies is noted 

as the top leading retailer in southern Africa, as they operate across 4 different African markets, 

are expanding into Namibia and had a 4% increase in gross profit in 2022 (Choppies, 2023). 

Regardless, traditional retailing still predominates in the retail sector of most African nations, 

and even though these nations are beginning to embrace e-commerce, sales are still 

concentrated in modest traditional stores like kiosks (Ivers, Niavas, Mitchell, Sqalli and Frikha, 

2022). 

2.4.2 African market challenges 

 

Challenges within retail across the African continent have arisen surrounding informal 

business, skill gaps, technology, and resources (Cooke, Wood and Horwitz, 2015). As 

mentioned, the African continent has many countries with an informal retail majority, 

predominantly for food and clothing (Tawii, 2022). This is particularly true for areas in sub-

Saharan Africa such as Kenya and Nigeria, where informal retailers make up 67% of total 

retail, e.g., through market stalls and vendors alike (Siele, 2022; Skinner, 2019; Tawii, 2022). 

This poses a challenge within the African retail market regarding issues surrounding morality 

and rights for workers within these businesses, as arrangements and services are less monitored 

(Boubakri, El Ghoul, Guedhami and Wang 2021; Mwamba and Qutieshat, 2021). This, 

therefore, could pose potential ethical and safety risks for employees. Mwamba and Qutieshat 

(2021) even go as far as to describe informality as hindering the efforts to promote equitable 

development and goodwill or lawfulness within the retail business. 

 

Moreover, African countries like Angola and Zambia are good examples of challenges 

surrounding the continent's resources, specifically for power supply, where retailers must 

manage inconsistencies with power through excessive generator reliance (Blimpo and 

Cosgrove-Davies, 2019; Hugo, 2016). This extends to shortfalls in infrastructure, corruption, 

skills development, and general macroeconomic environment volatility (Malikane, 2015; 



19 
 

Meyer and Meyer, 2017). Moreover, currency devaluations and the rise of inflation over recent 

years pose challenges for local and international businesses alike (Mwamba and Qutieshat, 

2021). In terms of technology, various emerging markets in Sub-Saharan African countries are 

still in the embryonic stage of implementation, with retailers struggling to overcome its 

challenges, i.e., shifting towards an omnichannel strategy, digitalizing expectations, lack of 

data and security risks (allAfrica, 2021; Choi, Dutz and Usman, 2020; Ibam, Boyinbode and 

Afolabi, 2018). Moreover, retailers that have implemented various digital marketing strategies 

in Africa still need to account for the threat of personal data usage that can occur (Findlay and 

Remolina, 2022; Mage, 2020; Wang, 2021). This is particularly true for banking and 

pharmaceutical retailers that store valuable personal data. 

 

However, with these challenges considered, the growth and technological potential that exists 

within the different African countries are clear, making it an exciting region to watch over the 

next few years (Choi, Dutz and Usman, 2020). This shift is already somewhat evident through 

major marketplace brands such as Shoprite utilising data-technology usage to analyse 

customers' behaviour and provide key market insights, an innovation that has advanced in the 

South African market in recent years (Dakora, and Rambe, 2022). 

2.5 The South African retail industry 

2.5.1 Performance of the industry 

 

In South Africa alone, the retail sector makes up approximately R135 984 billion of the 

country’s GDP (Statssa, 2018). Moreover, retail sales were shown to have increased by 1,7% 

from 2021 to 2022 within the country (Statssa, 2022). Performance-wise, the country stands 

out as the most developed formal retail industry on the continent, with relatively better 

infrastructure and policies than other sub-Saharan areas (Mwamba and Qutieshat, 2021). The 

formal/informal divide can further be visualised in Figure 2 below. Moreover, the industry is 

highly concentrated, with big retailers like Woolworths, Pick ‘n Pay and Spar dominating the 

market and expanding into other African markets (Malgas, Khatle and Mason, 2017). In terms 

of e-commerce in South African retail, revenue of $4, 062 million was recorded in 2020, of 

which is only expected to grow (van Rooyen and Amoah, 2021). This can be linked with the 

influence of the COVID-19 pandemic, which saw many retailers using delivery systems and 

online shopping due to concerns and limitations in lockdown (Njomane and Telukdarie, 2022; 

Reardon, Heiman, Lu, Nuthalapati, Vos and Zilberman, 2021). For example, this hybrid 



20 
 

approach saw three of the most popular brands launch on-demand grocery systems: Dash 

(Woolworths), Sixty60 (Checkers) and ASAP! Bottles (Pick n Pay), of which these can then 

be linked with the brand’s rewards program (Dakora. and Rambe, 2022; Njomane and 

Telukdarie, 2022). Additionally, a comparison done by Njomane and Telukdarie (2022) on the 

three retailers showed the resilience of the brands as they each reflected a growth in turnover 

during the pandemic period.  

 

Figure 2. Formal and Informal grocery retail in South Africa (based on rand sales) (Masojada, 
2021). 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Extant research on the benefits of using data has also emerged through aspects such as 

engagement, value creation, and improved experience (Calder, Malthouse and Maslowska, 

2016; Rejeb, Rejeb and Keogh, 2020; Zeng and Glaister, 2018). These benefits have received 

the attention of retailers, and the popular South African grocers “Woolworths South Africa, 

Pick n Pay and Shoprite”, have now invested heavily to incorporate data-driven decision-

making into their strategies (Global Powers of Retailing, 2021, Hartzenberg, 2019, 

Woolworths Holdings, 2022, Pick n Pay, 2022, Shoprite, 2022). These supermarkets have a 

broad product focus ranging from groceries and household goods to clothes (Beneke, 

Chamberlain, Chohan and Neethling, 2015). Therefore, these retailers will be mentioned and 

referenced in this study as a guide for the status of retailers in South Africa; as well as to assist 

participants in understanding the area of study. In terms of loyalty programs, these retailers are 

all among the key players to have implemented the promotional tool in South Africa, with 

Shoprite being one of the most recent additions (BusinessTech, 2021; Perumal, 2021). 

 



21 
 

Furthermore, when observing the performance of retailers in the country, Loyalty Programs 

stand out as a key aspect, with South Africa having one of the highest incidences of loyalty 

card usage globally, with the average South African being a member of at least nine different 

loyalty programs (Nielson, 2018; Thukwana, 2021). This is possibly due to the nature of the 

majority of retail Loyalty Programs being free in the country; therefore, removing price 

factoring that might deter consumers (Moneytoday, 2022). However, as consumers' account 

information and shopping behaviour are almost always utilised in these instances, concerns for 

privacy and the inconvenience of creating an account can still play factors in consumers not 

engaging with respective retailer Loyalty Programs (Awotunde, Jimoh, Folorunso, Adeniyi, 

Abiodun and Banjo, 2021; Chen, Sun and Liu, 2022). The top 10 most-used retail Loyalty 

Programs from 2019 to 2021 can be further visualized in Figure 3. below. 

 

Figure 3. Highest retail Loyalty Programs in South Africa in 2019 and 2021 (BusinessTech, 
2021) 
 

 

 
 

 

 

 



22 
 

2.5.2 Growth factors of the industry 

 

Within the South African retail market, one of the most notable growth elements has been the 

development and implementation of e-commerce in recent years (Malgas, Khatle and Mason, 

2017). It will be interesting to see how this changes further in the coming years as growth in 

internet access continues across the country. Moreover, this area provides an opportunity for 

expansion, as smaller retailers and producers can also access external and internal markets that 

previously may have been unattainable (Goga, Paelo and Nyamwena, 2019; Johnson and 

Iyamu, 2019; Malgas, Khatle and Mason, 2017). Moreover, the retail industry for grocery and 

supermarket retailers has been described as having the highest potential for growth (van 

Rooyen and Amoah, 2021). 

 

From a consumer perspective, growth has also been highlighted in the focus of more retailers 

on customer relationship management (CRM) techniques as retailers shift towards acquisition 

and retention strategies. This is central to current retailing efforts as brands struggle to deal 

with consumer switching and need to find competitive strategies to build relationships and 

meet consumer preferences, often through Loyalty program rewards and differences to 

persuade consumers and combat consumer attrition (Mackay and Major, 2017). This brings the 

focus to the challenges within the South African industry. 

2.5.3 Challenges of the retail industry in South Africa 

 

As the retail market has shifted in recent years, so has consumer behaviour. South African 

consumers have become more knowledgeable and selective about the types of service and 

quality that they receive (Mackay and Major, 2017). Moreover, the increasing demands of 

consumers create additional challenges for brands to meet preferences while considering the 

highly competitive nature of the industry and frequent consumer switching behaviour (Ernst 

and Young 2017; Mackay and Major, 2017; Malgas, Khatle and Mason, 2017). This extends 

to the challenge for retailers to keep up with trends in the market as consumer behaviour shifts 

towards different buying patterns and product options. Checkers is a good example of a retailer 

adapting themselves according to consumer needs, as they released the “Simple Truth” line for 

healthier and more natural products that meet the needs of their vegetarian and eco-friendly 

consumers (Shopriteholdings; 2018). Additionally, the South African retailer market poses a 

particularly large challenge for new entrants due to the oversaturation of retailers in the country 

(Charman, Petersen and Piper, 2012; Reinert, 2016). 

 



23 
 

From a technological standpoint, certain wealth and technological gaps exist within the 

country, as many consumers are only recently accessing and utilising the internet (Malgas and 

Zondi, 2020). This presents challenges for retailers engaging this consumer base and 

behavioural constraints that may exist for actively using Loyalty Programs (Malgas and Zondi, 

2020; Teuteberg, 2020). Literature on data privacy has discovered that, while South Africa has 

adopted various steps to address existing privacy issues, inadequate implementation, 

integration, management, and compliance, as well as contradictions in policy and regulation, 

all serve as countermeasures to these efforts (Manda and Backhouse, 2016). 

 

2.5.4 The South African population – size and potential 

 

The South African population is made up of around 60,6 million people, with a growth rate, 

despite the terrible impact of the COVID-19 pandemic (Statitsa, 2022). Moreover, Gauteng is 

reflected as having the largest share of the population, with roughly 26.6% of youth and adults 

in the province. Furthermore, the country is widely diverse, with numerous cultures, religions, 

and races (Stiehler, 2017). From a national cultural perspective, the country has been defined 

as individualistic, masculine, more risk-averse and indulgent (HofstedeInsights, 2016). These 

findings, however, are somewhat outdated and will be explored more recently in this study. 

Moreover, this study specifically looks at South Africans in general, with no particular focus 

on one group and a majority focus on South African consumers in Gauteng for convenience 

purposes. 

 

In targeting the South African population, major retailers in the industry have evolved towards a 

diversified format that targets the different income segments in South Africa through their product 

offerings and geography (Masojada, 2021). Retailers can also acknowledge various celebrations 

and traditions in their product and service offerings, provided they have proper research and 

understanding of these events, for example, through specific cuisine offerings to meet a certain 

consumer base. However, as per South African regulations, retailers will not be able to segregate 

or observe specific consumer groups with product offerings, rewards, and their data usage in 

Loyalty Programs (Consumer Protection Act, 2008: 8:1(g)). From an ethical research standpoint, 

observing a particular group over another would also require a specific research benefit for that 

group, of which the retail industry is nation-focused, not group-focused; providing reasoning for 

exploring South Africans as a whole (Wynn, Mason and Everett, 2008). Moreover, in exploring 

privacy concerns amongst South Africans, this study will investigate general consumer opinion 



24 
 

and not only Loyalty card users, to include potential consumers who aren’t engaging in Loyalty 

Programs due to this concern. This brings the focus towards Privacy for data usage in South 

Africa and existing trends in literature. 

 

2.5.5 Privacy concerns and South African Loyalty Programs 

 

Within the context of South Africa, various regulations exist to protect South African 

consumers' data and their privacy (Manda and Backhouse, 2016). These are highlighted below: 

 

• The Protection of Personal Information (POPI) Act (2013) 

• The Electronic Communication and Transactions Act (2002) 

• The Regulation of Interception of Communications and Provision of Communication-

Related Information Act (2002) 

• The Public Service Act (1994) 

• The State Information Technology Agency (SITA) Act (1998) 

• The Consumer Protection Act (2008) 

• Cybercrimes and Cybersecurity bill (2015) 

However, even with these regulations in place, the strategies and measures established have 

been found in research to be compromised and ineffective and require the implementation of 

‘micro-policies’ (Hoffmann, Lutz, and Ranzini, 2016; Wright, Gutwirth, Friedewald, De Hert, 

Langheinrich and Moscibroda, 2009). Moreover, a large portion of these existing regulations 

make mention of requiring permission and consent when using consumer data, of which 

retailers can acquire using ‘terms and conditions’ agreements, which are often overlooked or 

ignored by consumers (Gerber, Gerber and Volkamer, 2020). A consumer survey investigating 

app usage and privacy in America revealed that  77% of consumers admitted they don’t read 

privacy policy terms and conditions (TheClearingHouse, 2021). The report further emphasised 

the lengthiness of the policies as well as the lack of confidence that consumers felt when 

reading the terms, which may contribute to them not reading the terms (TheClearingHouse, 

2021). Moreover, a study by Griggio, Nouwens and Klokmose, (2022) had participants state 

that terms and conditions were “frustrating or inaccessible to read”. This highlights an 

important issue where consumers don’t come away knowledgeable after putting the effort into 

reading the terms and conditions. A study by Obar and Oeldorf-Hirsch, (2020) attributes this 

to consumers focusing on the end goal without being obstructed or held back by the privacy 

policy and regarding the policy as “a nuisance”. For example, a consumer looking to access a 



25 
 

website simply accepts the terms without reading them, to not be denied access or to continue 

with their objective. Obar and Oeldorf-Hirsch (2020) demonstrated this through their findings, 

where 74% of their participants skipped the policy, and of those that accepted, 98% did not 

read the terms properly. Customers' failure to read the fine print may also be attributed to the 

policies' length, ambiguity, or complexity; as they frequently contain jargon that may be 

intimidating (Jilka, Simblett, Odoi, van Bilsen, Wieczorek, Erturk, Wilson, Mutepua and 

Wykes, 2021).  

 

This brings about an ethical issue regarding consumers' awareness of what they’re agreeing to; 

where retailers should consider whether consumers are aware of the privacy of their data. 

Delays in updates to privacy policies and regulations can also lead to a disregard for privacy 

protection measures and a lack of harmonisation for new laws with a focus on technology as 

the country evolves, and new regulatory requirements arise (Manda and Backhouse, 2016). 

 

Moreover, from a consumer perspective, variances in consumer opinion towards data 

sensitivity and privacy exist depending on the culture, philosophies, and societal environment 

of the person, which this study will explore specifically for the focus of South African 

consumers (Matemba and Li, 2018; Tshiani and Tanner, 2018). Furthermore, African countries 

have been described as collectivist, holding philosophies such as ‘Ubuntu’ with a focus on 

values and initiatives that benefit the community; however, governments and individuals are 

now pushed towards reconsidering privacy for the individual as the e-commerce market grows 

and more individuals’ data is exposed and used by companies (Tshiani and Tanner, 2018). 

Additionally, concerns and uneasiness surrounding how much companies know about 

consumers have arisen in recent years, with consumers showing concerns for aspects such as 

personalised marketing (Ameen, Hosany and Paul, 2022; Lee and Cranage, 2011; Riegger, 

Klein, Merfeld and Henkel, 2021). One example of this from a global perspective is when the 

brand Target received backlash after they knew a teenage girl was pregnant before her family 

using her data, where they then sent her marketing promotional tools along with their other 

pregnant consumer base (Mathur, 2019). Privacy concerns have also been described as 

disadvantageous to retailers, as they provide barriers to success within the market (Joubert, 

Murawski and Bick, 2021). These are further reflected in concerns for data usage from a risk 

perspective through previous or potential experiences with data breaches, unauthorised access, 

third-party data-selling and general uncertainty regarding confidentiality (Chakraborty, Lee, 



26 
 

Bagchi-Sen, Upadhyaya and Rao, 2016; Di Minin, Fink, Hausmann, Kremer and Kulkarni, 

2021; Gupta and Dubey, 2016). 

 

However, as the market shifts towards an industry fused with technology, marketing efforts 

and understanding consumer behaviour necessitate the use of consumer data, particularly to 

compete in the highly competitive and oversaturated South African retail market. Therefore, it 

becomes important for retailers to adapt to the market while still recognising the degree to 

which various privacy concerns that consumers may hold exist and how to target them. 

Conclusion 

 

This chapter reflected the growth and potential of the retail industry, specifically observing the 

global, African, and South African markets; alongside the exploration of Loyalty Programs, 

data-usage and the consumers within these markets. Moreover, considering the COVID-19 

pandemic, clear directions towards e-commerce and changes to customer preferences are 

found, with a further increase in the data usage for an improved understanding of market 

consumers. Alongside this, various regulations in these markets have also been highlighted, 

with a need for exploring privacy concerns and, thereafter, the adaption of ‘micro-policies’ and 

input from retailers to safeguard consumers and incorporate strategies to approach data privacy 

concerns. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



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Chapter 3 - Theoretical Background 

3. 1 Introduction and Background 

 

This study observes the data-usage side of Loyalty Programs through privacy and the 

perspectives and corresponding concerns consumers may have towards it. Therefore, theories 

specifically surrounding privacy concerns and data usage will be utiliseutilised for the study. 

This chapter will touch on the theoretical grounding for the study by going into depth and 

providing justification for each of the chosen theories, the constructs and, thereafter, presenting 

the conceptual model and hypotheses for the study. This, along with an investigation into the 

literature for each of the topics, will provide an understanding of the study concepts and the 

development and support for the study’s model. 

3.2 Theoretical Grounding 

 

Observing chosen theories in literature allows researchers to logically express relationships 

between concepts to better understand a certain topic, which are then connected and developed 

along with other theories and constructs to ground research (Varpio, Paradis, Uijtdehaage and 

Young, 2020). Theories themselves are not solely the data, diagrams, hypotheses, or variables but 

rather the understanding, the ‘why’, and the connections that are made surrounding the topic 

(Sutton and Staw, 1995). Furthermore, a strong theory is one that explains, predicts, and delights, 

to have an impact and inform the literature (Weick, 1995). This study looks to observe the data-

usage side of Loyalty Programs with a focus on privacy, and the perspectives and corresponding 

concerns consumers may have towards it. Therefore, chosen theories specifically surrounding 

privacy concerns and data usage, from a consumer behaviour perspective, will be utiliseutilised for 

the study. 

 

Over the past two decades, research on consumer perspectives towards data usage has been 

scarce, with a major focus being on the data itself and how companies can use it to better 

understand their different consumer drives and consumer targetability (De Battista, Curmi, and 

Said, 2021; Fam, Brito, Gadekar, Richard, Jargal and Liu, 2019; Sukmaningsih et al., 2019). 

For example, as technology has progressed in developing countries such as South Africa, 

literature has taken a focus on how companies make use of this data, how it builds brand loyalty 

and preference, as well as the readiness of consumers for big data elements and drivers in 



28 
 

marketing (Dong and Yang, 2020). In an age of personalisation and ever-increasing targeted 

ads, however, an avenue for understanding how consumers respond to their data privacy has 

arisen. Existing literature on information privacy and, in particular, privacy concerns, has a 

current focus on major theories such as the Privacy Calculus Theory (PCT), Theory of Planned 

Behaviour (TPB), Theory of Reasoned action (TRA), General Deterrence Theory (GDT), 

Cognitive Dissonance Theory (CDT), Protection Motivation Theory (PMT) and the 

Technology Acceptance Model (TAM) (Fam et al., 2019; Hagger, 2019; Rawlings, 2020). 

 

Upon further investigation of these theories and their context in literature, the TAM, TPB, and 

PCT have been used in contexts that align and justify this study’s aim of understanding privacy 

concerns rather than providing insight into e-commerce, m-commerce, technology, and 

branding variables (Bayaga and Ophoff, 2019; Dhagarra, Goswami and Kumar, 2020; 

Gutierrez, O'Leary, Rana, Dwivedi and Calle, 2019; Kang and Namkung, 2019). However, 

certain limitations and the overuse of the TAM provide a research avenue for this study to 

utiliseutilise the other theories to reach the objective of understanding privacy concerns 

amongst South African consumers (Dutot, Bhatiasevi and Bellallahom, 2019; Malatji, Eck, and 

Zuva, 2020). Furthermore, this study brings in elements of consumer relationships, examining 

purchase intention and Loyalty Programs alongside privacy concerns, providing room to 

integrate the Customer Relationship Management (CRM) theory. Some honourable mentions 

include the Quantum model, Cognitive theories, and Trust, which have also been explored in 

literature researching information privacy or have been adapted alongside other theoretical 

foundations (Kokolakis, 2017; Lăzăroiu, Neguriţă, Grecu, Grecu and Mitran, 2020; Smith, 

Dinev and Xu, 2011). 

 

Upon investigation of what the study looks to achieve and the possible theories to ground the 

study, additional constructs of trust, perceived risk, culture, and personalisation are also 

explored alongside the theories to create an adapted model to better understand privacy 

concerns and subsequent purchase intention towards Loyalty Programs. Investigating privacy 

concerns, attitude and intention provides a way for researchers to investigate how consumers 

feel about their data being used (Gerber, Gerber and Volkamer, 2020; Kokolakis, 2017; von 

Kalckreuth and Feufel, 2021). Moreover, by exploring the antecedents of privacy concerns, a 

deeper analysis can be provided using past experiences and personal consumer characteristics 

to allow for a more detailed and context-specific observation. These will all be discussed 

further below. 



29 
 

3.2.1 Historical context and overview for the chosen theories 

 

Privacy-related behaviour through information systems is something that has been explored in 

literature since the 1990’s and continues to be of relevance as more consumers and companies 

move online and online privacy becomes more prevalent (Awad and Krishnan, 2006; Bartsch 

and Dienlin, 2016.; Boerman and Smit, 2022; Kitkowska, Shulman, Martucci and Wästlund, 

2020; Kokolakis, 2017; Malhotra, Kim and Agarwal, 2004). Here, companies making use of 

data allows for marketing and engagement opportunities; however, this can be considered 

‘intrusive’ or an invasion of privacy for some consumers. Particularly in more developed 

countries, where data is more frequently provided and accessed, literature has begun to 

understand the necessity of consumers' privacy and safety (Mohammed and Tejay, 2017; Till 

and Densmore, 2019). This, alongside past events, and issues regarding data privacy, such as 

location-tracking potential, data breaches, and leakages that have occurred over the years 

(Martin, Kim, Palmatier, Steinhoff, Stewart, Walker, Wang and Weaven, 2020; Nair and 

Tyagi, 2021). Some well-known ones in recent years include the data-breach of Facebook in 

2021, which acquired 533 million user details around the world; the Aadhaar leak in 2018, 

where every registered Indian citizen’s data stored in the biometric database was exposed and 

later sold for less than 6 pounds; the Syniverse breach in 2021, where over 500 million user 

data was exposed within the many companies that are connected with the telecommunications 

company, and unseen ongoing leakages were found (Komnenic, 2022). These companies have 

since taken the necessary action to combat such breaches and strengthen their systems; 

however, the damage to consumers' data and perspectives cannot be easily remedied. Over the 

past two decades, research on consumer perspectives towards data usage has been scarce, with 

a major focus being on the data itself and how companies can use it to better understand their 

different consumer drives and consumer targetability (De Battista, Curmi and Said, 2021; Fam, 

Brito, Gadekar, Richard, Jargal and Liu, 2019; Sukmaningsih et al., 2019). Furthermore, in the 

exploration of understanding such perspectives surrounding privacy and potential concerns, 

the Privacy Calculus Theory arises. Moreover, of the three theories that are chosen and 

combined for this study, the most observed theory when exploring privacy concerns in 

literature is the Privacy Calculus Theory. 

 

 

 



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3.2.2 Privacy Calculus Theory 

 

The privacy calculus theory (PCT) was first developed by Laufer and Wolfe (1977) 

surrounding behaviours and decisions associated with privacy and corresponding concerns. 

Here, the decision typically observes consumers associating a benefit and a risk with their 

responsive action, based on their perspective of data privacy, i.e., a trade-off that will then lead 

to an outcome, such as engaging or not engaging with a Loyalty Program (Trepte, Scharkow, 

and Dienlin, 2020). Privacy in this case relates to information disclosure, e.g., secondary use 

or unauthorised access (Alashoor, Al-Maidani and Al-Jabri, 2018; Osatuyi, 2015). Moreover, 

when exploring Loyalty Programs and the advancement of technology, such a trade-off is often 

done automatically for the consumer through elements of personalization, in which machine-

based learning techniques make use of respective profiling and recommendations for 

consumers to provide an objective benefit or predicted utility, i.e., location-services, ease, 

relevance and reduced risk through conflict (Knijnenburg, Raybourn, Cherry, Wilkinson, 

Sivakumar and Sloan, 2017). This trade-off was noted by Culnan and Armstrong (1999), and 

extended to reflect that consumers willingly provide private information when the benefit 

outweighs the risk. Moreover, the privacy calculus theory was used to understand consumer 

behaviour by building on the Behaviour Calculus Theory; where the outcome of sharing 

information indicated a greater benefit perspective and not sharing indicated a greater risk 

perspective, alongside the consumer’s respective privacy concerns (Laufer and Wolfe, 1977). 

 

Due to the nature of the PCT, it is also often seen alongside studies exploring the privacy 

paradox (Kokolakis, 2017; Lutz, Hoffmann, Bucher and Fieseler, 2018; Smith, Dinev and Xu, 

2011). Dinev et al. (2015) furthered this by noting the paradoxical nature of privacy behaviour 

and the fact that researchers cannot make assumptions about how consumers will respond or 

behave. Furthermore, when examining the PCT, an imbalance is often reflected between the 

attitudes and behaviours observed, as consumers can still be influenced by external elements 

and determinants where their attitudes and behaviours don’t match up; a parallel to the privacy 

paradox (Knijnenburg et al., 2017.). Moreover, Laufer and Wolfe, (1977) further noted the 

contextual dependency of the theory, as consumers cannot provide the same results due to 

variances in their privacy concerns and perspectives (Alashoor et al., 2018). Thus, research has 

reflected the importance of investigating additional factors alongside the PCT, as well as 

exploring privacy concerns in respective contexts. 

 

Over the years, the Privacy Calculus Theory has also been adapted to incorporate theories to 

provide added depth. Among these being: the Prospect theory, to manage and understand 



31 
 

consumer risk-aversion; Elaboration Likelihood theory, to explore the cognitive and peripheral 

influences of a consumer; Hofstede’s Cultural Dimensions, to explore the cultural influences 

on behaviour; Trust t