The Impact of Loyalty Program on Customer Retention: Empirical Evidence from Bangladesh

  • AHSAN, Syed Md. Hasib (Department of Business Administration, International Islamic University Chittagong) ;
  • ALAUDDIN, Md. (Department of Marketing, City University of Hong Kong, Department of Business Administration, International Islamic University Chittagong) ;
  • ALAM, Mohammad Manjur (Department of Computer Science and Engineering, International Islamic University Chittagong) ;
  • NAZIA, Adiba (Faculty of Business Studies, Premier University Chittagong) ;
  • ISLAM, Tasnim (Department of Business Administration, Southern University Bangladesh)
  • Received : 2022.03.10
  • Accepted : 2022.05.30
  • Published : 2022.06.30


The purpose of this paper is to examine how loyalty programs designed by retail stores affect customer retention. The present study is one of the few empirical studies finding the role of different loyalty programs that help to build customer retention. The study also seeks to explore the moderation role of customer perceived value between intrinsic motivation - customer retention and extrinsic motivation - customer retention. The researchers employed a quantitative research design to collect data from the 350 respondents who purchased goods from the retail chain/departmental stores in Bangladesh. The collected data are then analyzed using structural equation modeling. The result of their findings indicated that the size of the organizational reward has a positive effect on intrinsic and extrinsic motivation; further extrinsic motivation has a significant relation to customer retention. Surprisingly, no support was found for the positive impact of intrinsic motivation on customer retention. Customer perceived value (CPV) is critical as a moderator in building client retention, which improves the positive association between extrinsic motivation and customer retention. CPV, on the other hand, was found to mitigate the negative link between intrinsic motivation and client retention.


1. Introduction

The retail chain industry is considered a profitable business around the world, and its success is mainly dependent on the organization’s ability to retain customers so that they may become loyal to the organization. Loyalty programs play a crucial role in ensuring customer retention. Managers have realized the importance of loyalty programs because of the increase in a firm’s sales volume and market share (Zeithaml et al., 1996; Fook & Dastane, 2021). As a result, most companies in today’s world encourage loyalty programs, and they are gaining acceptance worldwide (Yi & Jeon., 2003; Anderson & Srinivasan, 2003). The retail chain industry is one of the most popular industries in Bangladesh. Nationwide there are many retail chain stores, and they are encouraging their clients to purchase from them by providing a variety of loyalty programs. Customer loyalty programs help any retail organization to get success. Besides, it also helps develop a strong relationship between buyers and sellers. Different authors have defined loyalty in diverse ways. The definition of (Knox & Denison, 2004) is very well known to the academicians and practitioners, and he stated: “the consumer’s inclination to patronize a given store or chain of stores over time” (p. 34). Customer loyalty programs are critical strategies for any company seeking to build customer loyalty (Lewis, 2004, p. 281) defined “loyalty programs that base rewards on cumulative chasing are an explicit attempt to enhance retention that encourages repeat buying and thereby improves retention rates by providing incentives for customers to chase more frequently and in larger volumes.”

Most retail organizations cannot imagine using loyalty programs to entice their customers because of their ability to attract a large amount of attention (Bloemer & de Ruyter, 1998; Frenzen & Nakamoto, 1993). The programs are based on cumulative purchasing to reward the clients. By offering various incentives to buy more frequently and in larger quantities, loyalty programs help to inspire repeat purchases and advance the rate of retention (Lewis, 2004). Intense competition in the retail sector leads most companies to create loyal customers. These clients enable organizations to generate more revenues over time by reducing operating costs and frequently buying (Zakaria et al., 2014; Dholakia, 2006). Recent research revealed that the recent economic crisis had augmented the competition level for the retailing industry. Consequently, many organizations are now trying to give more attention to their current loyalty programs to dissuade customers from decamping to their competitors.

Researchers’ growing interest in the retail business (Meyer-Waarden, 2007, 2008; Yi & Jeon, 2003; Lin & Bowman, 2022; Gao & Huang, 2021) motivate the current researchers’ plan to investigate the effect of LPs on customer retention in this sector. Specifically, the researchers seek to evaluate the role of CLP on customer loyalty and how customer perceived value can create a linkage between customer retention and loyalty program. Previously, Omar et al. (2007) investigated the relationship between LPs and customer loyalty among superstore customers, while Demoulin and Zidda (2008) investigated the impact of LP satisfaction on store loyalty in the food industry. In Bangladesh, very few studies have been conducted on CLP (Siddiqi, 2011; Islam et al., 2012). Limited research has been done to learn about the effect of extrinsic & intrinsic motivation and the reward size on customer retention in the retail industry, and we want to explore this gap. Further, we want to see the role of CPV as a moderator on the link between intrinsic motivation-extrinsic motivation and customer retention.

Based on the exchange theory, customer retention, and customer loyalty program literature, this paper aims to find out the effect of CLP on customer retention. It will help marketers and entrepreneurs in the retail industry to develop better strategies that persuade customers to buy more. The findings also help retailers recognize the role of CLP and CPV and understand how they contribute to increasing customer retention. In practical terms, this study offers several management suggestions for the service industry, particularly for the retail sector, which seeks to build customer retention by developing CLP.

2. Conceptual Framework and Hypothesis Development

This study explains multiple marketing theories based on LP, which are customer-centered, and managers of firms have developed their emphasis on building better relationships with clients for customer retention.

2.1. Loyalty Program

LP is a promotional strategy driven by companies to attract buyers. According to (Rayer, 1996), “LP is a mechanism for identifying and rewarding loyal customers, ” generally by giving them points for the quantity they consume (p. 8). It is seen in numerous cases; for example, buyers are given some specific points based on their purchase or some extra points if they have a membership card which is usually offered at the point of sale (Wright & Sparks, 1999). Loyalty programs play a vital role in encouraging customers to repeat purchases (Rizan et al., 2020). Companies offer diverse types of loyalty programs in different situations to attract many buyers and meet competition. For instance, companies also offer attractive incentives to maintain and retain a large number of buyers (Yi & Jeon, 2003). Every retail company tries to attract buyers by using this marketing tool, which rewards only loyal and recurring customers rather than any other customer who agrees to buy on specific promotions.

Researchers differentiate the rewards offered by loyalty programs as ‘hard’ and ‘soft’ benefits (Capizzi & Ferguson, 2005; Kimura, 2021; Kumar & Reinartz, 2006; Leenheer et al., 2007). Hard rewards of loyalty programs are generally physical components such as gifts and discounts; however, soft rewards are distinct communications and superior treatment offered by the retail shop. Soft rewards have an emotional appeal that gives clients a sense of appreciation (Harris, 2000). Barlow (1995) examined their comparisons and found that “soft benefits generally provide a much stronger loyalty-building impact than hard benefits” (p. 16). According to Peterson (1995, p. 33), “monetary savings develop from cash-back offers and coupons that participants accumulate while regularly buying the same brand or shopping with the same retailer.” Hence, saving money is one of the prime motivations for customers to join the loyalty programs offered by retail organizations.

2.2. Reward Size and Intrinsic- Extrinsic Motivation

Loyalty programs are a unified arrangement of marketing activities that help make more customers and to build a better interaction with them (Yi & Jeon, 2003). These programs are beneficial and, thus, encourage customers to buy more. Different marketing tools can be considered loyalty programs, such as tier service levels, reward cards, gifts, or others. According to Henderson et al. (2011), customer loyalty programs are mainly designed to build a strong bridge between buyers and companies. Moreover, customer relationship management systems have become progressively enlightened by applying retail LPs. These systems help retailers know more about customer information and track the purchasing behavior of individual customers (Smith, 2008). Before purchasing, customers primarily focus on the reward size, and there is a connotation between reward size and intrinsic-extrinsic motivation. Customers always expect better reward systems from companies, and they try to engage based on the reward size. It helps them choose a new product from the retailers. Thus, reward size is a drive of extrinsic and intrinsic motivational programs. Intrinsic motivation means “doing something because it is inherently interesting or enjoyable, ” whereas scholars define extrinsic motivation as “doing something because it leads to a separable outcome” (Ryan & Deci, 2000, p. 55). Previous research revealed that rewards escalate the behavioral gratification internally, and thus the internal motivation becomes a reason for maintaining it. Based on the reward size, customers make decisions, particularly in the retail sector, where they want to enjoy buying and save money. Based on the literature, we can propose that,

H1: Reward size positively influences intrinsic motivation.

H2: Reward size positively influences extrinsic motivation.

2.3. Intrinsic- Extrinsic Motivation and Customer Retention

Customer retention is the capacity to maintain standing customers by forming profitable and friendly relationships while providing superior quality services. Oliver (1997, p. 392) views it as a “deeply held commitment to rebuy or patronize a preferred product or service consistently in the future, despite situational influences and marketing efforts having the potential to cause switching behavior.” Companies try to satisfy their clients by building long-term relationships so that they can repeat purchases. Retail stores adopt different strategies to retain customers, and loyalty programs help achieve the objective. This paper explains intrinsic and extrinsic motivation programs to retain customers. Intrinsic motivation means “doing something because it is inherently interesting or enjoyable.” On the contrary, “extrinsic motivation means doing something because it leads to a separable outcome” (Ryan & Deci, 2000, p. 55).

Intrinsic motivation arises when people involve themselves in an activity due to internal reward, only for themselves; however, extrinsic motivation becomes apparent when people engage in activities for external rewards in exchange for their preferred behavior. Although external rewards help consumers get economic benefits, intrinsic rewards help in many other ways (Deci et al., 1999). For example, it provokes people to purchase to get the value that matches their individual buying goals such as enjoyment, fun, and altruism. Extrinsic incentives encourage them to obtain a particular benefit from their purchase target (Ryan & Deci, 2000). Kendrick (1998), in his paper, found that external rewards such as rebates, gifts, or discounts motivate consumers to become more loyal to the store than a general courtesy note ‘Thank you. Besides, exclusive rewards made consumers loyal to a retail front than a comparable value discount. Hence, customer loyalty programs are positively related to customer retention in the retail sector (Zakaria et al., 2014).

Retailers offer a variety of reward items associated with loyalty programs to meet the needs and demands of customers (Dao, 2017). Consumers expect positive things from the service providers if they positively observe LPs’ excellence while returning to the same (Susanti et al., 2019). Customers’ loyalty turns into assets for firms if the existing buyers are happy and recommend other, such as friends, family members, neighbors, etc., to buy from them (Kaynak & Hartley, 2008). Uncles et al. (2003, p. 304) argued that “when the program is attractive, customers may come to build a relationship with the program rather than the brand.” So, binding customers to an LP with tangible benefits might improve customers’ repurchase intention. Based on the literature, we can propose that,

H3: Intrinsic motivation is positively related to customer retention.

H4: Extrinsic motivation is positively associated with customer retention.

2.4. The Moderating Role of Customer Perceived Value (CPV)

CPV is perceived by customers as a valuable asset for any organization that plays a critical role in developing loyalty through the loyalty program (O’Brien & Jones, 1995). Zeithaml (1988, p. 14) conceptualized customer perceived value as “the consumer’s overall assessment of the utility of a product based on perceptions of what is received and what is given.” CPV is a vital antecedent of customer retention that helps firms retain their customers and increase sales. Further, customer intention is the probability that buyers will continue to purchase in the future (Seiders et al., 2005). Zeithaml et al. (1996) defined intention as the prospect that a customer will work for the firm voluntarily by recommending and saying positive things about the company from which he purchased. Numerous studies have provided an experiential indication of a link between CPV and customer retention (Brodie et al., 2009; Parasuraman & Grewal, 2000; Chiu et al., 2005; Bolton et al., 2004). Indeed, preceding studies have revealed that CPV is the core forerunner leading to customer repurchase intention (Baker et al., 2002; Zeithaml, 1988). Perceived value reduces a consumer’s need to seek alternate service organizations. Low perceived value motivates customers to shift to divergent businesses to increase perceived value, thus paying for a decline in loyalty. Even satisfied customers are unlikely to support a company if they believe it cannot provide great value for their money. Instead, consumers frequently look for alternative service providers to get superior value (Chang, 2006). Based on the discussion (Figure 1), we can propose that,

H5: CPV moderates the link between intrinsic motivation and customer retention.

H6: CPV moderates the link between extrinsic motivation and customer retention.

Figure 1: Conceptual Framework

3. Methodology

3.1. Data Collection and Procedure

The researchers used a convenience sample strategy to collect data from respondents in Bangladeshi retail chains and department stores. Those clients were our target respondents. Initially, three academicians validated the questionnaire and pre-test on 50 respondents to know the factor structure. The data was collected from 400 respondents in a three-month data collection period from January to March 2021, and 350 respondents finished the survey. After data screening, the researchers eliminated 50 cases due to incomplete responses. According to Chawla and Sondhi (2011), the sample size must be 4 or 5 times greater than the total number of attributes. The present study has 17 items of 5 variables collected from 350 respondents, therefore satisfying the condition.

There were 350 respondents; 45% were females and 55% of males. Depending on the level of education, most respondents (40%) have a bachelor’s degree, followed by higher secondary (30%), postgraduate degree or higher (25%), and only 5% of respondents have passed the secondary school. The majority of the customers’ ages lie between 31 and 40 years old, and their percentage is 37. 30% of the respondents’ age is between 41 and 50 years, 16% of the respondents’ age is between 20 and 30 years, and the remaining age is above 50 years old. The data illustrates that most of the respondents are engaged in the private sector, which accounts for 35 percent of the total. Self-employment is the second-highest occupation level, and their percentage is 30%, while only 5% of the respondents are students.

3.2. Measurement Scales

The researchers developed different constructs for measuring the role of LP in building customer retention. A scale consisting of three (3) items adapted from Ryu and Feick (2007) was applied to evaluate reward size. The intrinsic and extrinsic motivation scales were adapted from Fagan et al. (2008) and Mimouni-Chaabane and Volle (2010). Customer perceived value included a four-item scale adapted from Puncheva-Michelotti and Michelotti (2010). Finally, the four items measuring customer retention were adapted from Chaudhuri and Holbrook (2001). Five-point Likert scales fixed at 1= “strongly disagree” and 5 = “strongly agree” were used to measure the constructs.

3.3. Analysis Method

Analysis of collected data used in this research was done with structural equation modeling (SEM) by SPSS AMOS 24, and the moderating effect was tested by using PROCESS Macro for IBM SPSS v24. We followed a two-step analytical technique (Hair et al., 2006), where the measurement model was first assessed for reliability, validity, and fit of the model. Hypothesized path models, structural models, and moderating effects were conducted in the second step.

4. Results

4.1. Measurement Model

Confirmatory factor analyses (CFA) were done to validate the suggested structure statistically (Marsh & Hocevar, 1988; Milfont & Duckitt, 2004). SPSS AMOS 24 was conducted to determine the construct validity, dimensionality, and reliability. All the variables were sufficiently normally distributed within the range of –3.00 to +3.00 (Pallant, 2010; Rayner, 1996; Razali & Wah, 2011). The proposed model claims to have a good fit because the various fit indices of the CFA model are within the recommended ranges. In particular, the χ2 value of 1.506 is lower than the suggested value of 3.00 (Bagozzi et al., 1998; Kline, 1998), which indicates a well fit. The goodness-of-fit indices show acceptable model fit (CMIN/DF = 1.308 (p < 0.001), AGFI = 0.937, NFI = 0.947, CFI = 0.987, GFI = 0.955, RMSEA = 0.030) that approves the uni-dimensionality of the measurement model (Hair et al., 2010).

The CFA and measurement model fit findings were summarized in Table 1 and Figure 2. The factor loadings of all the constructs presented in Table 1 were larger than the lowest threshold value (0.7). The construct reliability value for each construct is above 0.7, and the average variance extracted (AVE) of all constructs is above 0.5. Additionally, composite reliability (CR) and AVE of all measurement items were found below the acceptable levels of 0.6 and 0.5, respectively (Bagozzi & Yi, 1988; Fornell & Larcker, 1981). The Cronbach’s α coefficient for each construct measurement was above 0.7 (Nunnally & Bernstein, 1994; Hair et al., 2010).

Table 1: Summary of the Measurement Model

Note: FL: factor loadings, AVE: average variance extracted, CR: composite reliability, and CA: Cronbach’s alpha.

Table 2 represents the mean, standard deviation (SD), and correlation coefficients for each construct. Analysis of the study shows that all the constructs are significantly correlated with each other. However, there is no multicollinearity exists because all the constructs’ correlations are less than 0.9 (Tabachnick & Fidell, 2012), and 72.44% of the total variance of the dependent variable was explained by the model. Results in Table 2 also show that all the values within brackets were above the inter-construct correlation values (Park, 2015) and the condition for discriminant validity was satisfied.

Table 2: Summary Statistics and Correlation Matrix

Note: *, p-value < 0.05. The value within brackets specifies the square root of the AVE values.

Figure 2: Initial Measurement Model

4.2. Structural Model

Testing the structural model and corresponding theoretical relationships is the subsequent step in data analysis. Two standard tools SEM and path analysis, are used to determine the strength of associations between numerous constructs, particularly those that help to deal with latent constructs (Hair et al., 2006; Kline, 2010). χ2 and key fit indices, including the CFI, NFI, GFI, IFI, RMSEA, and the Tucker-Lewis index (TLI) were used to evaluate the goodness-of-fit of the model (Lee & Jeong, 2014). Result of the test of the overall structural model, indicate a good model fit with (CMIN/DF = 1.405 (p < 0.001), CFI = 0.987, GFI = 0.964, AGFI = 0.946, SRMR = 0.06). Acceptable model fit is reflected by CFI > 0.95, GFI > 0.90, IFI > 0.90, NFI > 0.9, TLI > 0.90, and RMSEA < 0.06 (Hair et al., 2010).

Further examination of the structural path coefficients from Table 3 and Figure 3 shows that reward size has the highest significant influence on extrinsic motivation (β = 0.243, p < 0.001) and it also has a significant effect on intrinsic motivation (β = 0.200, p < 0.005). So, H1 and H2 are accepted. Moreover, extrinsic motivation significantly influencing effect on customer retention (β = 0.337, p < 0.001) and H3 also accepted. However, an insignificant relationship exists between intrinsic motivation and customer retention (β = 0.07, p < 0.001) so it can be said that hypothesis H4 is not accepted.

Table 3: Results of Hypotheses Test Hypotheses Path Relationship

***p < 0.001, **p < 0.01, *p < 0.05.

Figure 3: Structural Model

4.3. Moderation Analysis

This section investigates the moderating effect of customer perceived value on intrinsic motivation- customer retention and the extrinsic motivation- customer retention and whether or not such an effect is significant in explained variables. Analysis results are summarized in Tables 5 and 6, and the interaction effect is further plotted in Figures 4 and 5, respectively. The table and figure show the impacts of testing moderating hypotheses H5 and H6. The findings of the moderation analysis displayed in Table 4 showed that the moderate effect of CPV on intrinsic motivation and customer retention is insignificant. Therefore, H5 is rejected.

Table 4: Model Summary

Table 5: Model

Figure 4: Moderation Effect of Customer Perceived Value Between IM & CR

Figure 5 also shows that CPV is negatively associated with intrinsic motivation and customer retention. In particular, the effect of customer perceived value does not fully contribute to the intrinsic motivation in customer retention.

Figure 5: Moderation Effect of Customer Perceived Value Between EM & CR

Further, we continued to study moderating effect of CPV between EM & CR. The findings of this study suggest that CPV does not moderate the effect (Table 5 and Figure 5) of extrinsic motivation on customer retention. Therefore, H6 is also rejected. Although the hypothesis has been rejected, it can be concluded that CPV encourages customer retention by acting as a partial moderator in the loyalty program based on the model summaries of Tables 5 and 6.

Table 6: Model Summary

Figure 5 further shows that the slope of high CPV is steeper than that of low CPV. This suggested that CPV strengthens the insignificant positive relationship between extrinsic motivation and customer retention.

5. Discussion

This research paper specifically examined the role of reward size on intrinsic and extrinsic motivation and then examined their effects on customer retention. The analysis of the results reveals that organizational reward size has a significant impact on extrinsic and intrinsic motivation. Further, extrinsic motivation is associated positively with customer retention. However, intrinsic motivation has no significant effect on customer retention. The investigation of our results supports the conceptualized model. This research paper contributes to current knowledge about loyalty programs and their relation to customer intention. We propose a new framework in this paper about the significance of LP in the retail chain industry. Our results create a linkage between LP and customer retention, supported by (Meyer-Waarden et al., 2013; Soliha et al., 2021; Zaid & Patwayati, 2021). The findings show that extrinsic motivation is more important than intrinsic rewards in the retailing industry in Bangladesh, and these findings are supported by (Lewis, 2004). Moderation analysis reveals that CPV has an insignificant effect on customer retention’s intrinsic and extrinsic motivation. Furthermore, while the moderating variable CPV strengthens the positive relationship between extrinsic motivation and customer retention, it negatively dampens the relationship between intrinsic motivation and customer retention.

5.1. Theoretical Contribution

This study investigates the effects of LP on customer retention grounded on the exchange theory and motivational theory. Using data collected from clients of retail chains in Bangladesh, we find that reward size positively impacts customer retention. This study contributes to the effects of reward size on extrinsic and intrinsic motivation in the retail chain sector in Bangladesh. This research also contributes to extrinsic and intrinsic motivations’ effect on customer retention. Based on the exchange and motivational theory, this research explains the relationship between loyalty programs and customer retention. In addition, this research enriches the current body of literature on the role of customer perceived value as a moderator on the link between intrinsic-extrinsic motivation and customer retention. Finally, this research demonstrates how the clients of retail chains become loyal through reward programs or loyalty programs in the retail sector in Bangladesh.

Table 7: Results of Moderating Analysis of Access Convenience

5.2. Managerial Implications

This study provides some necessary guidelines for the companies. Firstly, the managers will understand the significance of a loyalty program on customer retention from the findings of the results and how it contributes to retaining existing customers. Secondly, the managers can realize which LP between intrinsic and extrinsic motivation plays a vital role in retaining customers in the retail chain sectors, and they can adopt new strategies based on that. From the results of the analysis, managers should concentrate more on extrinsic motivation than intrinsic motivation. Thirdly, companies should offer more value to clients to obtain more perceived value than costs.

6. Conclusion and Limitations

The study findings must be deduced in the context of its limitations. First, this study refers to loyalty programs such as intrinsic-extrinsic motivation and customer retention in the retail chain sector, but the model might also be useful in some other contexts. For instance, in the case of the tourism sector, airlines sector, and financial sector, how loyalty programs affect customer retention. Secondly, the sample size is a problem in this study; we have conducted only 350 respondents, which is not so large to get a good scenario for research. Moreover, the sample only covered the clients of retail chains in Bangladesh. A future research study should be based on diverse countries to enhance the logic between LP and customer retention. In conclusion, this research uses the survey items, and we collected primary data for this study. So, to avoid the risk of common method bias, researchers must use a good combination of both primary and secondary data.


  1. Anderson, R. E., & Srinivasan, S. S. (2003). E-satisfaction and e-loyalty: A contingency framework. Psychology & Marketing, 20(2), 123-138.
  2. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
  3. Baker, J., Parasuraman, A., Grewal, D., & Voss, G. B. (2002). The influence of multiple store environment cues on perceived merchandise value and patronage intentions. Journal of Marketing, 66(2), 120-141.
  4. Barlow, R. G. (1995). Five mistakes of frequency marketing. Direct Marketing-Garden City-, 57, 16-16.
  5. Bloemer, J., & De Ruyter, K. (1998). On the relationship between store image, store satisfaction, and store loyalty. European Journal of Marketing, 23(5), 499-513.
  6. Bolton, R. N., Lemon, K. N., & Verhoef, P. C. (2004). The theoretical underpinnings of customer asset management: A framework and propositions for future research. Journal of the Academy of Marketing Science, 32(3), 271-292.
  7. Brodie, R. J., Whittome, J. R., & Brush, G. J. (2009). Investigating the service brand: A customer value perspective. Journal of Business Research, 62(3), 345-355.
  8. Capizzi, M. T., & Ferguson, R. (2005). Loyalty trends for the twenty-first century. Journal of Consumer Marketing, 22(2), 72-80.
  9. Chang, J. C. (2006). Customer satisfaction with tour leaders' performance: A study of Taiwan's package tours. Asia Pacific Journal of Tourism Research, 11(1), 97-116.
  10. Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty. Journal of Marketing, 65(2), 81-93.
  11. Chawla, D., & Sondhi, N. (2011). Assessing work-life balance among Indian women professionals. Indian Journal of Industrial Relations, 47(2), 341-352.
  12. Chiu, H. C., Hsieh, Y. C., & Kao, C. Y. (2005). Website quality and customer's behavioral intention: an exploratory study of the role of information asymmetry. Total Quality Management and Business Excellence, 16(2), 185-197.
  13. Dao, T. Q. (2017). Effects of brand association, brand loyalty, brand awareness, and brand image on brand equity in training industry via the social media: The case of summit capital [Doctoral dissertation, International University-HCMC]. KEEP Digital Repository.
  14. Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627.
  15. Demoulin, N. T., & Zidda, P. (2008). On the impact of loyalty cards on store loyalty: Does the customers' satisfaction with the reward scheme matter? Journal of Retailing and Consumer Services, 15(5), 386-398.
  16. Dholakia, U. M. (2006). How customer self-determination influences relational marketing outcomes: evidence from longitudinal field studies. Journal of Marketing Research, 43(1), 109-120.
  17. Fagan, M. H., Neill, S., & Wooldridge, B. R. (2008). Exploring the intention to use computers: An empirical investigation of the role of intrinsic motivation, extrinsic motivation, and perceived ease of use. Journal of Computer Information Systems, 48(3), 31-37.
  18. Fook, A. C. W., & Dastane, O. (2021). Effectiveness of Loyalty Programs in Customer Retention: A Multiple Mediation Analysis. Jindal Journal of Business Research, 10(1), 7-32.
  19. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  20. Frenzen, J., & Nakamoto, K. (1993). Structure, cooperation, and the flow of market information. Journal of Consumer Research, 20(3), 360-375.
  21. Gao, M., & Huang, L. (2021). Quality of channel integration and customer loyalty in omnichannel retailing: The mediating role of customer engagement and relationship program receptiveness. Journal of Retailing and Consumer Services, 63, 102688.
  22. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. (2006). Multivariate data analysis. Upper Saddle River, NJ: Prentice-Hall.
  23. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Canonical correlation: A supplement to multivariate data analysis. Multivariate data analysis: A global perspective (7th ed). Upper Saddle River, NJ, USA: Pearson Prentice Hall Publishing.
  24. Harris, E. (2000). Recognize, reward, and reap the benefits. Sales & Marketing Management, 152(9), 109-109.
  25. Henderson, C. M., Beck, J. T., & Palmatier, R. W. (2011). Review of the theoretical underpinnings of loyalty programs. Journal of Consumer Psychology, 21(3), 256-276.
  26. Islam, M. A., Khadem, M., & Sayem, A. (2012). Service quality, customer satisfaction and customer loyalty analysis in Bangladesh apparel fashion retail: an empirical study. International Journal of Fashion Design, Technology, and Education, 5(3), 213-224.
  27. Kaynak, H., & Hartley, J. L. (2008). A replication and extension of quality management into the supply chain. Journal of Operations Management, 26(4), 468-489.
  28. Kendrick, A. (1998). Promotional products vs price promotion in fostering customer loyalty: a report of two controlled field experiments. Journal of Services Marketing, 12(4),312-326.
  29. Kimura, M. (2021). Customer segment transition through the customer loyalty program. Asia Pacific Journal of Marketing and Logistics, 34(3), 611-626.
  30. Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford.
  31. Knox, S. D., & Denison, T. J. (2000). Store loyalty: Its impact on retail revenue. An empirical study of purchasing behavior in the UK. Journal of Retailing and Consumer Services, 7(1), 33-45.
  32. Kumar, V., & Reinartz, W. J. (2006). Customer relationship management: A databased approach. New York: Wiley.
  33. Lee, S. A., & Jeong, M. (2014). Enhancing online brand experiences: An application of congruity theory. International Journal of Hospitality Management, 40, 49-58.
  34. Leenheer, J., Van Heerde, H. J., Bijmolt, T. H., & Smidts, A. (2007). Do loyalty programs really enhance behavioral loyalty? An empirical analysis accounting for self-selecting members. International Journal of Research in Marketing, 24(1), 31-47.
  35. Lewis, M. (2004). The influence of loyalty programs and short-term promotions on customer retention. Journal of Marketing Research, 41(3), 281-292.
  36. Lin, C., & Bowman, D. (2022). The impact of introducing a customer loyalty program on category sales and profitability. Journal of Retailing and Consumer Services, 64, 102769.
  37. Marsh, H. W., & Hocevar, D. (1988). A new, more powerful approach to multitrait-multimethod analyses: Application of second-order confirmatory factor analysis. Journal of Applied Psychology, 73(1), 107-117.
  38. Meyer-Waarden, L. (2007). The effects of loyalty programs on customer lifetime duration and share of wallet. Journal of Retailing, 83(2), 223-236.
  39. Meyer-Waarden, L. (2008). The influence of loyalty program membership on customer purchase behavior. European Journal of Marketing, 42(1), 87-114.
  40. Meyer-Waarden, L., Benavent, C., & Casteran, H. (2013). The effects of purchase orientations on perceived loyalty programs' benefits and loyalty. International Journal of Retail & Distribution Management.
  41. Milfont, T. L., & Duckitt, J. (2004). The structure of environmental attitudes: A first-and second-order confirmatory factor analysis. Journal of Environmental Psychology, 24(3), 289-303.
  42. Mimouni-Chaabane, A., & Volle, P. (2010). Perceived benefits of loyalty programs: Scale development and implications for relational strategies. Journal of Business Research, 63(1), 32-37.
  43. Nunnally, J.C., & Bernstein, I. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.
  44. O'Brien, L., & Jones, C. (1995). Do rewards really create loyalty? Long Range Planning, 28(4), 130-130.
  45. Oliver, R. L. (1997). Satisfaction: A behavioral perspective on the consumer. New York NY: Irwin-McGraw-Hill.
  46. Omar, N. A., Musa, R., & Nazri, M. A. (2007). Program perceived value and program satisfaction influence store loyalty. Gadjah Mada International Journal of Business, 9(3),345-368.
  47. Parasuraman, A., & Grewal, D. (2000). The impact of technology on the quality-value-loyalty chain: a research agenda. Journal of the Academy of Marketing Science, 28(1), 168-174.
  48. Park, H. M. (2015). Univariate analysis and normality test using SAS, Stata, and SPSS. Bloomington, Indiana: Indiana University.
  49. Pallant, J. (2020). SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS. London, UK: Routledge.
  50. Peterson, R. A. (1995). Relationship marketing and the consumer. Journal of the Academy Of Marketing Science, 23(4), 278-281.
  51. Puncheva-Michelotti, P., & Michelotti, M. (2010). The role of the stakeholder perspective in measuring corporate reputation. Marketing Intelligence & Planning, 28(3): 249-274.
  52. Rayner, S. (1996). Customer loyalty schemes: Effective implementation and management. London: FT Retail & Consumer Publishing.
  53. Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Villiers, and Anderson-darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21-33.
  54. Rizan, M., Febrilia, I., Wibowo, A., & Pratiwi, R. D. R. (2020). Antecedents of customer loyalty: a study from Indonesia's largest e-commerce. The Journal of Asian Finance, Economics, and Business, 7(10), 283-293.
  55. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67.
  56. Ryu, G., & Feick, L. (2007). A penny for your thoughts: Referral reward programs and referral likelihood. Journal of Marketing, 71(1), 84-94.
  57. Seiders, K., Voss, G. B., Grewal, D., & Godfrey, A. L. (2005). Do satisfied customers buy more? Examining moderating influences in a retailing context. Journal of Marketing, 69(4), 26-43.
  58. Siddiqi, K. O. (2011). Interrelations between service quality attributes, customer satisfaction, and customer loyalty in the retail banking sector in Bangladesh. International Journal of Business and Management, 6(3), 12-36.
  59. Smith, A. D. (2008). Customer loyalty card programs and their interaction with support technology in the retail industry. International Journal of Management and Enterprise Development, 5(2), 157-195.
  60. Soliha, E., Maskur, A., Widyasari, S., & Ariyani, E. (2021). The Effect of Trust and Customer Value on Loyalty: An empirical study in Indonesia. The Journal of Asian Finance, Economics, and Business, 8(5), 1001-1010.
  61. Susanti, V., Sumarwan, U., Simanjuntak, M., & Yusuf, E. Z. (2019). How to manage customer satisfaction through brand association and perceived value strategy. GATR Journal of Management and Marketing, 4(3), 184-193.
  62. Tabachnick, B.G., & Fidell, L.S. (2012). Using multivariate statistics (6th ed.). New York, NY, U.S.A: Pearson Education, Inc.
  63. Uncles, M. D., Dowling, G. R., & Hammond, K. (2003). Customer loyalty and customer loyalty programs. Journal of Consumer Marketing, 20(4), 294-316.
  64. Wright, C., & Sparks, L. (1999). Loyalty saturation in retailing: exploring the end of retail loyalty cards? International Journal of Retail & Distribution Management, 27(10), 429-440.
  65. Yang, Z., & Peterson, R. T. (2004). Customer perceived value, satisfaction, and loyalty: The role of switching costs. Psychology & Marketing, 21(10), 799-822.
  66. Yi, Y., & Jeon, H. (2003). Effects of loyalty programs on value perception, program loyalty, and brand loyalty. Journal of the Academy of Marketing Science, 31(3), 229-240.
  67. Zaid, S., & Patwayati, P. (2021). Impact of customer experience and customer engagement on satisfaction and loyalty: A case study in Indonesia. The Journal of Asian Finance, Economics, and Business, 8(4), 983-992.
  68. Zakaria, I., Rahman, B. A., Othman, A. K., Yunus, N. A. M., Dzulkipli, M. R., & Osman, M. A. F. (2014). The relationship between loyalty program, customer satisfaction and customer loyalty in retail industry: A case study. Procedia-Social and Behavioral Sciences, 129, 23-30.
  69. Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22.
  70. Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46.