Keywords

Banking Industry, Customer Loyalty, Customer Satisfaction, Brand Preferences, Switching Cost.

Introduction

The market environment of banking industry changes rapidly. Increased customer diversity, the development of information technology and government regulation creates tremendous challenges for the banking industry in Hong Kong. To enhance sustainability and improve profitability in the future, it is necessary for banks to review the current business practices in order to develop the effective strategies which cater to meet customers’ needs today. Kuusik (2007) indicates that merely optimizing product price and ensuring customer service quality are insufficient to bring success to business, instead, building a long-term and mutual beneficial customer relationship is the key. To cultivate this relationship, banks should enhance customers’ overall satisfaction by providing reliable and acceptable services so as to increase customer loyalty, which is an essential factor affecting profitability (Aldas-Manzano et al., 2011). Moreover, built upon the emotion-as-social information perspective, Wang et al. (2017) demonstrated that employees’ positive affective displays boosted customer loyalty, indicating that a satisfactory relationship with customer can also bring success to the company. Recently, a meta-analysis study aggregated 195 individual studies provided robust support to the association between customer satisfaction and customer loyalty with a large effect size where r = 0.536 (Liu et al., 2018). For all marketing effort, customer loyalty is the most valuable outcome (Bagdonien? & Jakštait?, 2007). Developing customer loyalty becomes an important focus in marketing strategies. In view of the significance of customer loyalty on profitability, this research examines factors that may lead to higher level of customer loyalty in Hong Kong’s banking industry.

Weir, the regional senior partner of KPMG Hong Kong, and the member of Financial Services Development Council (FSDC), points out that “Hong Kong is a major international financial centre with a large network of banks and other advantages, in order for Hong Kong to maintain its important position as a major international financial centre, new developments are required” (Hong Kong Banking Survey 2017, 2017). Artificial Intelligence, cognitive computing and robotics provide the solution for financial institutions on increasingly complex environment and fast-evolving consumer behaviour, while it potentially alters the ways for banks in Hong Kong to interact with their customers. These new technologies can operate different tasks with less costs and higher efficiency. As banking industry is starting to adopt cognitive computing to improve her products and services, this research provides references regarding consumers’ behaviour and preferences in the current banking industry. Specifically, the relationship between customer satisfaction, switching costs, brand preference and customer loyalty which have shown effects in profitability will be studied.

Literature Review

Customer Satisfaction and its Consequences

In general terms, customer satisfaction is an overview of judgment reflecting the service quality after consumption. Many different models are proposed to explain the concept of customer satisfaction. For instance, Kotler defined customer satisfaction as “personal feeling of pleasure resulting from comparing a product’s perceived performance in relation to his/her expectations” (2002, p.36). Similar to Kotler’s definition, Churchill & Surprenant (1982) suggested that customer satisfaction is an outcome generated by customers when they make comparison of their expected performance, actual performance and the incurred cost. Customer’s evaluation to the service quality is the main determining factor which leads to customer satisfaction (Kim et al., 2004). Breivik & Thorbjornsen (2008) indicated that customers would be satisfied with the services when their expectations are met or exceeded. On the contrary, failure of meeting the expectation leads to dissatisfaction and affects the post-purchase behaviour, including the poor attitude towards the chosen brand. Several studies also reported that customer satisfaction has a great impact on the repurchase behaviour, the higher the satisfaction level of customers toward a service experience, the more likely they repurchase (Kotler, 1977; Keith 1960; Leavitt, 1960). Therefore, customer satisfaction is expected to have effects on customers’ attitude towards brand and repurchasing behaviour.

Customer satisfaction can also impact switching behaviours, preventing customers from switching to the competitors (Chowdhury, 2011). The satisfaction customers received from a company acted as a force to increase their resistance to switch to other companies, it is probably because they might regard the potential drop in satisfaction as a critical cost elicited in the switching behaviour. Nonetheless, mixed findings have been documented in the literature about the relationship between customer satisfaction and switching behaviour. For instance, Chuang (2011) found that switching behaviours in mobile communication services industry were not resulted from the perceived switching cost in the customer dissatisfaction, but instead they switched because of the more attractive packages from competitors. These mixed results suggested that there is a need to re-examine the association between customer satisfaction and switching costs. In the present study, we targeted to examine this link in the banking industry. Although there are conflicting results in the literature, the most recent findings more inclined to imply the positive association between customer satisfaction and switching costs. For instance, Liang et al. (2018) found that both transaction-based and experience-based satisfaction negatively predicted the switching intention as well as positively predicted the repurchase intention in the context of Airbnb. Liu et al. (2016) followed cognitive dissonance theory and found that customer satisfaction decreased the switching intention, which in turn affect the actual switching behaviours in the context of social network game industry. As a result, we expected that customer satisfaction will also have a positive prediction on switching costs in the banking industry.

The Antecedent of Customer Loyalty: Brand Preference

Several studies showed that the relationship of satisfaction and loyalty is positive, such that a brand’s profitability can be increased if the customer loyalty towards the brand is high (Awara & Anyadighibe, 2014; Bagram & Khan, 2012). Oliver (1997) defined customer loyalty as “a deeply held commitment to re-buy or re-patronize a preferred product offering consistently in the future, despite situational influences and marketing efforts having the potential to cause switching behavior”. That is, a loyal customer pays for a product or service repeatedly and the likelihood of brand switching becomes low. Draker et al. (1998) suggested that the relationship between customer satisfaction and customer loyalty can be explained through analysing the three perspectives of customer loyalty, namely, behavioural, cognitive and affective perspectives. Behavioural loyalty can be reflected by the purchase behaviours of the customers; cognitive loyalty implies the future plans of behaviour shown by customers; affective loyalty indicates the attitude of a customer toward a firm. Customer loyalty can be categorized either behavioural or attitudinal (Zeithaml, 2000). Behavioural approach signifies a repurchasing behaviour in which the customer consistently buy and use a product and service whilst attitudinal approach is a sense of emotional commitment to a brand (Zeithaml et al., 1996). Attitudinal loyalty is a psychological process which the deposition with regard to preferences resulting in brand commitment (Jacoby & Chestnut, 1978).

Having a positive attitude of a brand results the continuation to the brand instead of switching to other brand, thus, it is vital for marketers to understand brand switching as they need to prevent the loss of customers caused by switching bank brand, maintain the current customers and encourage customers to switch from other competitors. Brand is an important asset for a bank which links customer and the bank to establishment of customer loyalty. A brand can be perceived by the customers as the total accumulation of all his/her experiences, which differentiate the products in a company from the products in other companies, signifying a symbol that motivates people to sustain their consuming behaviours. Rahi et al. (2017) demonstrated that customer-perceived brand image positively predicted their loyalty to the company, supporting the beneficial effect of brand preference. Besides, Amoako et al. (2017) revealed that brand preference mediated the effect of advertisement effectiveness of a company on customer loyalty, demonstrating the robust prediction from brand preference to customer loyalty. Given that customer satisfaction directly influences customers’ attitude towards a brand, and the affective loyalty and disposition of brand preference affects the customer loyalty, this study hypothesized that customer satisfaction affects customer loyalty through brand preference, forming a mediational relationship.

The Antecedent of Customer Loyalty: Switching Cost

Switching cost is the penalty for customers to switch brand from one to another. This cost is not limited to money for breaking the contracts but may also involve time spending on switching brand and psychological factors, such as the uncertainty of the new services from other brands (Bloemer et al., 1998; Porter, 1998; Patterson & Sharma, 2000; Sharma, 2003; Hawkins et al., 2007). These can be considered as the consequences for customers being disloyal by switching to other rival brands. Due to its nature, switching cost is considered an important factor with direct impacts that encourages customers for being loyal to the brand, by exerting negative consequences, and can be served as a short term solution for businesses to keep their customers switching from their products or services before the improvements of their quality of services or other factors. Studies showed that switching cost can retain the current customer base as well as gaining advantages against other competitors (Klemperer, 1987a, 1995; Farrell & Shapiro, 1988). Burnham et al. (2003) stated that the marketing activities of current companies focus on controlling switching cost. A switching cost example where it may apply is when a customer closing an account from one bank and switch to a rival bank. Klemperer (1987b) also provided an example where switching cost can apply when switching long-distance calling service. Fornell (1992) stated that switching cost affects customer loyalty level by reducing price sensitivity of customers and their satisfaction level. Klemperer (1987c) also pointed out that, under the effects of switching cost, customers become aware of other brands providing similar products and services such that making comparison between brands. Furthermore, other studies showed that switching cost affects price sensitivity of customers which influences customer loyalty (Bloemer et al., 1998; Eber, 1999; Feick et al., 2001; Jones et al., 2002; Burnham et al., 2003).

More recently, Ngo & Pavelková (2017) mentioned that although switching cost is usually regarded as a moderator in the association between customer satisfaction and customer loyalty, it has been posited that customer satisfaction cannot affect loyalty if it cannot be concretely transformed to be the commitment and willingness to invest in relationship with the company. They found that switching cost played a mediating role between customer satisfaction and customer loyalty, facilitating the transformation of customer satisfaction in to commitment and willingness. Thus, aligned with this study, we expected that switching costs will also mediate the effect of customer satisfaction on customer loyalty in the banking industry.

Hypothesis

According to the above literature, this study aims at examining the effect of customer satisfaction, brand preference and switching cost towards customer loyalty in banking industry. There are nine hypotheses proposed in total:

Hypothesis 1: Customer satisfaction has positive effect on customer loyalty.

Hypothesis 2: Customer satisfaction has positive effect on brand preference.

Hypothesis 3: Brand preference has positive effect on customer loyalty.

Hypothesis 4: Customer satisfaction has positive effect on switching cost.

Hypothesis 5: Switching cost has positive effect on customer loyalty.

Hypothesis 6: Switching cost has positive effect on brand reference.

Hypothesis 7: Brand preference mediates the effect of customer satisfaction on customer loyalty.

Hypothesis 8: Switching cost mediates the effect of customer satisfaction on customer loyalty.

Hypothesis 9: Brand preference mediates the effect of switching cost on customer loyalty.

Methodology

Participants

A total of 376 respondents participated in this study. Majority of respondents aged 18 to 23 (84%), while around 10% of them aged 24 or above. In terms of the use of banking services, more than half of the respondents (61%) reported less than 3 years of the use of their current banking services, while 21% of them reported more than 5 years.

Measures

Customer satisfaction

The 3-item scale of customer satisfaction (Hellier et al., 2003) was adopted in the current study. Responses of each statement were anchored on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with an alpha of 0.90 in the current study.

Brand preference

The 3-item scale of brand preference (Hellier et al., 2003) was adopted in the current study. Responses of each statement were anchored on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with an alpha of 0.76 in the current study.

Customer loyalty

The 4-item scale of customer loyalty (Nguyen & Leblanc, 2001) was adopted in the current study. Responses of each statement were anchored on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with an alpha of 0.88 in the current study.

Findings

Table 1 presented the descriptive statistics, including means, standard deviations, and correlation coefficients among all the measures in this study. Generally speaking, measurement error is inherent in nearly all of the psychological constructs. Structural relations among psychological variables are normally estimated with bias if measurement errors in the constructs are not properly handled. Hence, to provide unbiased estimates in this study, structural equation modeling (SEM) was employed to examine the proposed mediation model, handling constructs via latent variable approach (Iacobucci et al., 2007). Assessment of model fit was based on multiple criteria, including absolute misfit and incremental fit indices. A model with Root-Mean- Square Errors of Approximation (RMSEA) <0.08, Standardized Root Mean Squared Residual (SRMR) <0.08 and Comparative Fit Index (CFI) >0.90 was considered as having acceptable fit to the data (Hoyle, 1995). All the analyses were conducted using Mplus 7.0 (Muthén & Muthén, 2011) with maximum likelihood estimation.

Table 1: Means, Standard Deviations, And Intercorrelations For The Measures
	M	SD	BP	SC	CL
1.CS	3.54	0.71	0.48***	0.22***	0.69***
2.BP	3.26	0.71	-	0.29***	0.38***
3.SC	3.34	0.68	-	-	0.28***
4.CL	3.47	0.72	-	-	-

Overall Model Assessment

the mediation model fitted the data well, χ² (95) = 342.89, χ² /df = 3.61, p < 0.001, CFI = 0.91, RMSEA = 0.08, SRMR = 0.07 (Table 2). Although the significant p-value and relatively high χ²/df ratio might indicate inadequate fit of the current fitted model, the Chi-square index has been known to be over-sensitive to sample size, resulting inadequate indication to the model fit. Generally, the larger the sample size is, the higher the chance that the model will be rejected no matter it is true or false (Bagozzi & Yi, 1988). Thus, merely based on these two Chisquare indices alone might result rejection of a well-fitted model. As a remedy, alternative overall fit indices were used in this study, namely CFI, SRMR, and RMSEA. As been shown in Table 2, all fit indices were found to be in an acceptable range. Overall speaking, the latent mediation model was considered to fit the data reasonably well.

Table 2: Goodness-Of-Fit Of The Latent Mediation Model
Fit Indices	Values	Desired Levels*
χ2	342.89	--
Df	95	--
p-value	<0.001	> 0.05
Comparative fit index (CFI)	0.91	> 0.90
Standardized-Root-Mean-Square-Residual (SRMR)	0.07	< 0.08
Root-Mean-Square Errors of Approximation (RMSEA)	0.08	< 0.08

Measurement Model Assessment

Prior to the investigation of structural relations among different variables, reliability and convergent validity of the constructs were first assessed to ensure the psychometric property of the variables.

Reliability

The Cronbach’s Alpha which ranges between 0 and 1, capturing internal consistency of a set of items, has been commonly used to measure the instrument reliability. In general, the closer it is to one, the higher is the reliability of the instrument. A rule-of-thumb towards instrument reliability is that an alpha larger than 0.7 might indicate acceptable reliability. In preliminary analyses, alpha coefficients of reliability were estimated by using SPSS version 13.0 for scales from each instrument (Table 3). Reliability coefficients of the four key variables ranged from 0.719 to 0.896, indicating that the measuring instruments used in this study yielded at least moderate to high reliability.

Table 3: Reliability Of The Four Constructs
	No. of items	Cronbach’s Alpha
Customer Satisfaction	3	0.896
Brand Preference	3	0.755
Switching Costs	3	0.719
Customer Loyalty	4	0.875

Convergent validity

Reliability of a set of items for the measuring instruments, as measured by alpha coefficient, provides an overall evaluation of a scale in terms of convergent validity. To offer additional evidence on convergent validity, examination of factor loadings can be a good candidate as an item-based evaluation. Generally speaking, the significantly moderate to high factors loadings of indicators in the measurement model can already provide additional evidence of convergent validity of the constructs (Anderson & Gerbing, 1988; Dabholker et al., 1996). As shown in Table 4, all factor loadings were statistically significant (ps < 0.001) with moderate to high magnitudes (from 0.55 to 0.90), demonstrating acceptable convergent validity.

Structural model assessment

Table 4: Results Of Factor Loadings In Four Latent Constructs
Items	Factor loadings
Customer Satisfaction (CS)
CS1 My decision to use banking services from my current bank was a wise one.	0.82
CS2 I feel good about my decision to use banking services from my current bank.	0.90
CS3 I am pleased that I used banking services from my current bank.	0.86
Brand Preference (BP)
BP1 My current bank meets my banking service requirements better than other banks.	0.58
BP2 I am not interested in trying banking services from another bank.	0.82
BP3 I do not intend, in the near future, to switch to another bank for my banking services.	0.80
Switching Costs (SC)
SC1 I am concerned about not being able to keep my banking products when changing banking service provider.	0.55
SC2 Changing banking service provider is costly.	0.77
SC3 Changing banking service provider requires a lot of effort.	0.74
Customer Loyalty (CL)
CL1 If I had needed banking service now, my current bank would be my first choice.	0.76
CL2 I will continue to do business with my current bank.	0.73
CL3 I would recommend my current bank as the best banking service provider.	0.86
CL4 I would encourage friends and relatives to do business with my current bank.	0.85

Consistent with the hypothesis 2 (Table 5), customer satisfaction positively predicted brand preference, β = 0.42, p < 0.001, 95% CIs [0.30, 0.53], which in turn positively predicted customer loyalty, β = 0.48, p < 0.001, 95% CIs [0.38, 0.58] (hypothesis 3). After accounting the effect of brand preference on customer loyalty, customer satisfaction still yielded a significant direct effect on customer loyalty, β = 0.50, p < 0.001, 95% CIs [0.41, 0.59] (hypothesis 1). Taken together, brand preference partially mediated the effect of customer satisfaction on customer loyalty, yielding a significant indirect effect, estimate = 0.20, p < 0.001, 95% Bias-corrected Bootstrap CIs [0.05, 0.35] (hypothesis 7).

Table 5: Testing Of The Hypotheses
	Construct relationship	Standardized coefficients	t-value	Hypothesis supported
H1	CS ?CL	0.50	8.698	Yes
H2	CS ?BP	0.42	5.242	Yes
H3	BP ? CL	0.48	7.508	Yes
H4	CS ?SC	0.23	3.287	Yes
H5	SC ? CL	0.04	0.865	No
H6	SC ?BP	0.27	3.859	Yes
H7	CS ? BP ? CL	0.20	5.000	Yes
H8	CS? SC? CL	0.01	0.853	No
H9	SC?BP ?CL	0.13	3.611	Yes

Note. CS = Customer Satisfaction; BP = Brand Preference; SC = Switching Costs; CL = Customer Loyalty.

Dashed lines represent non-significant relationships (ps > 0.05). **p < 0.01. ***p < 0.001.

As indicated in Figure 1, though switching costs did not predict customer loyalty, it was found to significantly predict brand preference, β = 0.27, p < 0.001, 95% CIs [0.15, 0.39] (hypothesis 6). As qualified by a significant indirect effect, estimate = 0.13, p <0.001, 95% Biascorrected Bootstrap CIs [0.04, 0.21], it is evident that brand preference can mediate the effect of switching costs on customer loyalty (hypothesis 9). Overall, the predictors explained 32% of total variance in brand preference, 10% of total variance in switching costs and 77% of total variance in customer loyalty.

Discussion

The present research attempted to examine a multiple-mediator model linking four factors of customer satisfaction, switching cost, and brand preference and customer loyalty in the banking industry. Among nine hypotheses, seven of them were supported. First, it finds that brand preference is able to mediate the effect from customer satisfaction to customer loyalty, which is consistent with the proposal in previous literature (Awara & Anyadighibe, 2014; Bagram & Khan, 2012). Second, it is surprising that switching cost is unable to channel the influence of customer satisfaction on customer loyalty, mainly due to the non-significant direct effect from switching cost to customer loyalty. Integrated with the significant indirect effect found between switching cost to customer loyalty through brand preference, one of the possible reasons about the non-significant direct effect from switching cost to customer loyalty is that brand preference plays a particular important role in explaining all the effects from switching cost.

Conclusion

The present study investigates the dynamics among customer satisfaction, brand preference, switching costs and customer loyalty. With the development of technology adopted by the banking industry to improve her products and services and customers have been shown to exhibit a different consuming behavioural pattern, it makes the current research important in filling the gap in the literature. Furthermore, the complex dynamic among the four key constructs have only been investigated in the healthcare insurance industry. Compared the results of the present study with the previous study placed in the healthcare insurance industry, similar findings were found except that switching cost has positive effect on customer loyalty and switching cost mediates the effect of customer satisfaction on customer loyalty in the healthcare insurance industry. It brings the unanswered questions in the linkages among the four constructs in the banking industry. As a whole, the current study attempts to extend previous findings found in various samples and industries to the banking industry. Thus, the current study helps to enrich our understanding in this area and provide valuable data for commercial management when making decisions on the marketing strategies to enhance sustainability and profitability. Further study should be conducted to examine why customer loyalty could be primarily driven by brand preference, leaving relatively less important role of switching cost in Hong Kong banking contexts.

References

1. Aldas-Manzano, J., Ruiz-Mafe, C., Sanz-Blas, S., & Lassala-Navarre, C. (2011). Internet banking loyalty: Evaluating the role of trust, satisfaction, perceived risk and frequency of use. The Service Industries Journal, 31(7), 1165-1190.
2. Amoako, G.K., Anabila, P., Asare Effah, E., & Kumi, D.K. (2017). Mediation role of brand preference on bank advertising and customer loyalty: A developing country perspective.International Journal of Bank Marketing,35(6), 983-996
3. Anderson, J.C., & Gerbing, D.W. (1988). Structural equation modeling in practice: A review and recommended two-step approach.Psychological bulletin,103(3), 411.
4. Awara, N.F., & Anyadighibe, J.A. (2014). The relationship between customer satisfaction and loyalty: A study of selected eateries in calabar, cross river state. Interdisciplinary Journal of Contemporary Research in Business, 5, 110- 125.
5. Bagozzi, R.P., & Yi, Y. (1988). On the evaluation of structural equation models.Journal of the academy of marketing science,16(1), 74-94.
6. Bagram, M.M.M., & Khan, S. (2012). Attaining customer loyalty: The role of consumer attitude and consumer behavior. International Review of Management and Business Research, 1, 1-8.
7. Bagdoniene, L., & Jakstaite, R. (2007). Estimation of loyalty programmes from customers' point of view: Cases of three retail store chains. Inzinerine Ekonomikaa-Engineering Economics, (5), 51-58.
8. Bloemer, J., Ruyter, K., & Wetzels, M. (1998). On the relationship between perceived service quality, service loyalty and switching costs. International Journal of Industry Management, 9(5), 436-453.
9. Breivik, E., & Thorbjørnsen, H. (2008). Consumer brand relationships: An investigation of two alternative models. Journal of the Academy of Marketing Science, 36(4), 443-472.
10. Burnham, T., Frels, A., & Mahajan, J. (2003). Consumer switching costs: A typology, antecedents, and consequences. Journal of the Academy of Marketing Science, 31(2), 109-126.
11. Chuang, Y.F. (2011). Pull-and-suck effects in Taiwan mobile phone subscribers switching intentions.Telecommunications Policy,35(2), 128-140.
12. Churchill, G., & Surprenant, C. (1982). An investigation into the determinants of customer satisfaction. Journal of Marketing Research, 19(4), 491-504.
13. Dabholkar, P.A., Thorpe, D.I., & Rentz, J.O. (1996). A measure of service quality for retail stores: scale development and validation.Journal of the Academy of marketing Science,24(1), 3.
14. Eber, N. (1999). Switching costs and implicit contracts. Journal of Economics, 69(2), 159-171.
15. Feick, L., Lee, J., & Lee, J. (2001). The impact of switching costs on the customer satisfaction-loyalty link: Mobile phone service in France. Journal of Services Marketing, 15(1), 35-48.
16. Fornell, C. (1992). A national customer satisfaction barometer: The Swedish Expe. Journal of Marketing, 56(1), 6.
17. Farrell, J., & Shapiro, C. (1988). Dynamic Competition with Switching Costs. The RAND Journal of Economics, 19(1), 123-137.
18. Hellier, P.K., Geursen, G.M., Carr, R.A., & Rickard, J.A. (2003). Customer repurchase intention: A general structural equation model.European journal of marketing,37(11/12), 1762-1800.
19. Hong Kong Banking Survey 2017.(2017). Hong Kong: KPMG. Retrieved from: https://assets.kpmg.com/content/dam/kpmg/cn/pdf/en/2017/06/hong-kong-banking-survey-2017.pdf
20. Chowdhury, H.M. (2011). Ethical issues as competitive advantage for bank management.Humanomics,27(2), 109-120.
21. Hoyle, R.H. (1995).SEM: Concepts, issues, & applications. Sage.
22. Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: Evidence that structural equations models perform better than regressions.Journal of Consumer Psychology,17(2), 139-153.
23. Jones, M.A., Beatty, S.E., & Mothersbaugh, D.V. (2002). Why customers stay: Measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes. Journal of Business Research, 55(6), 441-450.
24. Jacoby, J., & Chestnut, R. (1978). Brand Loyalty: Measurement and Management. New York: John Wiley & Sons.
25. Kuusik, A. (2007). Affecting customer loyalty: Do different factors have various influences in different loyalty levels? IDEAS Working Paper Series from RePEc, IDEAS Working Paper Series from RePEc, 2007.
26. Kim, P., & Jeong. (2004). The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services. Telecommunications Policy, 28(2), 145-159.
27. Kotler, P. (1977). From sales obsession to marketing effectiveness. Harvard Business Review, 67-75.
28. Keith, R.J. (1960). The marketing revolution. Journal of Marketing, 24, 35-38.
29. Kotler, P. (2002). Principles of Marketing (Sixth Edition). New Jersey: Prentice-Hall, Inc.
30. Klemperer, P. (1987a). Entry deterrence in markets with consumer switching costs. The Economic Journal, 97, 99-117.
31. Klemperer, P. (1987b). The competitiveness of markets with switching costs. The RAND Journal of Economics, 18(1), 138-150.
32. Klemperer, P. (1987c). Markets with consumer switching costs. The Quarterly Journal of Economics, 102(2), 376-394.
33. Klemperer, P. (1995). Competition when consumers have switching costs: An overview with applications to industrial organization, macroeconomics, and international trade. The Review of Economic Studies, 62(4), 515-539.
34. Leavitt, T. (1960). Marketing myopia. Harvard Business Review, 37, 45-56.
35. Liang, L.J., Choi, H.C., & Joppe, M. (2018). Exploring the relationship between satisfaction, trust and switching intention, repurchase intention in the context of Airbnb.International Journal of Hospitality Management,69, 41-48.
36. Liu, H., Chen, Y., Zha, Y., Ling, L., & Wang, D. (2018). The effect of satisfaction on loyalty in consumption and service industry based on meta-analysis and it’s algorithm.Wireless Personal Communications, 1-20.
37. Liu, Y., Li, H., Xu, X., Kostakos, V., & Heikkilä, J. (2016). Modeling consumer switching behaviour in social network games by exploring consumer cognitive dissonance and change experience.Industrial Management & Data Systems,116(4), 801-820.
38. Muthén, L. K., & Muthén, B. O. (2011).Mplus: Statistical analysis with latent variables: User's guide(pp. 1998-2007). Los Angeles: Muthén & Muthén.
39. Ngo, V.M., & Pavelková, D. (2017). Moderating and mediating effects of switching costs on the relationship between service value, customer satisfaction and customer loyalty: Investigation of retail banking in Vietnam.Journal of International Studies,10(1), 9-33.
40. Nguyen, N., & Leblanc, G. (2001). Corporate image and corporate reputation in customers’ retention decisions in services.Journal of retailing and Consumer Services,8(4), 227-236.
41. Oliver, R.L. (1997). Satisfaction: A Behavioral Perspective on the Consumer. New York: McGraw-Hill.
42. Parasuraman, A., Zeithaml, V., & Berry, L. (1988). SERVQUAL: Multiple-item scale for marketing consumer perceptions of service. Journal of Retailing, 64(1), 12-40.
43. Rahi, S., Yasin, N.M., & Alnaser, F.M. (2017). Measuring the role of website design, assurance, customer service and brand image towards customer loyalty and intention to adopt internet banking. The Journal of Internet Banking and Commerce, 22(S8).
44. Ranaweera, C., & Prabhu, J. (2003). The influence of satisfaction, trust and switching barriers on customer retention in a continuous purchasing setting.International Journal of Service Industry Management,14(4), 374-395.
45. Urumsah, D. (2015). Factors influencing consumers to use E-services in Indonesian airline companies. Advances in Business Marketing and Purchasing, 23B, 5-254.
46. Wang, Z., Singh, S.N., Li, Y.J., Mishra, S., Ambrose, M., & Biernat, M. (2017). Effects of employees’ positive affective displays on customer loyalty intentions: An emotions-as-social-information perspective. Academy of Management Journal, 60(1), 109-129.
47. Zeithaml, V., Berry, L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46.
48. Zeithaml, V.A. (2000). Service quality, profitability, and the economic worth of customers: What we know and what we need to learn. Journal of the Academy of Marketing Science, 67-85.