Traditional banks have faced the threat of bankruptcy because of the trend towards free trade in an increasing number of financial institutions. Numerous branches of foreign banks have set up operations in Taiwan since it became part of the World Trade Organization in 2001. Additionally, rapid advances in information technology are accelerating a revolution in the financial industry. Internet banking, which has revolutionized the banking industry worldwide, is a product of this innovation (Malhotra & Singh, 2010). A Nielsen online survey (2007) revealed that as more consumers around the world begin to use Internet-based banks, a bank’s brand image and customer opinions shift from the street to the network. Of Internet users worldwide 32% use Internet banking services two to three times per week and of these, 14% report using online banking services every day. Among Taiwanese Internet users, bank services used at least once a week were automated teller machine (ATM) banking services (48%), Internet banking (26%), and branch services (17%). Although 26% of Taiwanese respondents use Internet banking at least once a week by accessing the bank’s online network, approximately 47% of respondents had never used Internet banking services (the seventh highest such group of respondents in the world). There are many development opportunities in Taiwan within the spectrum of global Internet banking. Domestic and foreign banks that are currently in this electronic financial territory can increase their added value and attract more customers through Internet banking. Since the advent of the e-commerce era most Taiwanese companies have remained either small or medium in size. Thus, it is important to create a friendly e-commerce environment.

Internet applications provide customers with timely service. Furthermore, banks can use relationship marketing to improve customer loyalty. High relationship quality indicates that online users trust Internet banks, are satisfied with the banking services, and perceive Internet banking to be secure.

Additionally, a cooperative relationship can be created by establishing good interactions between banks and customers. The best ways to retain customers are satisfying customer needs and cultivating customer trust and loyalty.

Numerous researchers have attempted to identify the various influences on customer retention. The key to business survival and success is building and maintaining strong customer relationships. Crosby, Evans, and Cowles (1990) noted that services are intangible and complicated, and that customers have little knowledge of their nature. Message delivery can take a long time owing to high customer uncertainty regarding services. In order to build and retain a good sales relationship with customers, businesses must establish good quality relationships. A good relationship can satisfy customers, cultivate trust, and consequently reduce service uncertainty. We used Crosby et al.’s relationship quality model in our exploration of the correlation between relationship quality and customer loyalty in the Internet banking industry.

Internet banks provide services that include high interest rates, low service fees and no time or place restrictions. The use of Internet banks enables customers to save money and enjoy greater convenience. Because Taiwan could become one of the major developers of Internet banking services it is important to set up timely and appropriate strategies for approaching customers and earning their loyalty.

Service Quality, Relationship Quality, and Customer Loyalty

Regan (1963) divided service characteristics into four categories: intangibility, inseparability, heterogeneity, and perishability. Service quality is the key to the survival of service businesses, and the measurement of service quality is relatively important. Parasuraman, Zeithaml, and Berry (1985) developed a conceptual model of service quality (PZB) emphasizing the main prerequisites for delivering the level of service quality expected by customers. The model is also used to identify five gaps that lead to unsuccessful service delivery (Parasuraman et al., 1985). These authors developed a list of the major determinants of service quality, and found that consumers apply similar criteria in consumption regardless of service type, namely, access, communication, competence, courtesy, credibility, reliability, responsiveness, security, tangibility, and understanding the customer.

Cyber Dialogue (1999) performed a website survey from June 1998 to June 1999 and found that while customers using the Internet to trade marketable securities increased 53%, and customers using the Internet for insurance transactions increased 47%, users of Internet banking had increased only 2%. Furthermore, although there were 3.2 million new Internet banking accounts, some 3.1 million users had stopped using the service. Of these users, some found Internet banking to be complicated and unsatisfying. Kettinger and Lee (1994) applied SERVQUAL questionnaires (Parasuraman, Zeithaml, & Berry, 1988) to investigate satisfaction with information technology services on college campuses and found that tangible criteria were unnecessary. In a similar study Jensen and Markland (1996) found a correlation between responsiveness and assurance in the five quality dimensions of SERVQUAL of 0.974, thus permitting these dimensions to be merged. Findings of some researchers investigating electronic delivery channels (including internet banking, ATMs, and telephone banking) confirm that banking success and profitability are dependent on service quality (Al-Hawari & Ward, 2006; Herington & Weaven, 2009). On the basis of these findings, we have extended the PZB model to apply 11 dimensions for measuring Internet banking service quality: (1) reliability, (2) responsiveness, (3) competence, (4) access, (5) courtesy, (6) communication, (7) credibility, (8) security, (9) understanding, (10) web design, and (11) legality.

Relationship marketing is mainly designed to increase customer coherence. The relationship between buyers and sellers is the key to successful business operations and is crucial to maintaining a competitive advantage. The development of global logistics systems increases the profit of traditional banks as Internet technologies are combined with information applications. In the virtual context of the Internet, customer loyalty is strongly related to his/her trust and satisfaction with an Internet bank. The relationship quality model of Crosby et al. (1990) is applied in this and other studies to describe customer perceptions of relationship quality and to examine the correlation between Internet banking service quality and customer loyalty.

In the service industry, and in particular in service industries that require more personal interaction and customized service, the interaction between customers and salespeople determines the ability to make deals (Dwyer, Schurr, & Oh, 1987). Trust means that behavior is predictable and reduces the risk of uncertainty. Mutual familiarity can break down psychological barriers. Consequently, trust serves as a barometer of interpersonal relationship quality. According to social exchange theory, the benefit is the award (or profit) gained from the interpersonal relationships minus the cost of the commodity. This benefit determines the satisfaction obtained from the relationship.

Relationship quality is focused more on long-term customer relationships rather than on short-term transactions. Previous researchers have shown that a high quality relationship can earn life-long customer commitment and is valuable for a business. Such a relationship reduces uncertainty for customers and helps maintain a good business relationship. Crosby et al. (1990) suggested that relationship quality comprises two parts, trust and satisfaction, which are each considered an “emotional state that occurs in response to an evaluation of these interaction experiences” (Westbrook, 1981, p. 68).

According to the 80/20 rule, 80% of business profit comes from 20% of its customers. If a business can increase its number of customers by 5%, it may be able to increase profitability between 25% and 85%, depending on the nature of industry (Reichheld & Sasser, 1990). Lowenstein (1997) found that a 2% retention increase is equivalent to a 10% cost reduction in the development of new customers. Thus, retaining loyal customers is crucial. It has been noted in previous studies that loyalty impacts positively on buying behavior (McMullan & Gilmore, 2003; Reichheld, Markey, & Hopton, 2000a). Loyal customers exhibit buying behavior through proactive support and purposeful buying rather than by the passive acceptance of products or services. Smith (2001) has shown for both Internet-based and traditional customers that e-loyalty is essentially the same as traditional loyalty. Whether loyalty can be successfully transformed into e-loyalty depends on how relationships are built between digital tools and consumers. Consequently, e-loyalty is “loyalty in relationships with customers of services provided via Internet” (Reichheld et al., 2000b). The rules used in the physical world are equally applicable to the networking world. E-loyalty is defined in this study in the same way as traditional loyalty.

Interactions must also be measured because loyalty measurements via physical world interactions are inaccurate, as loyal customers may not interact with salespersons and also because adding interactions enables the separation of brand loyalty from network loyalty. Consumer identity is hidden in Internet transactions, which also have different interfaces from physical world interactions. More precisely, consumers are now proactively participating in purchasing through networking activities and are passively accepting offers presented by salespeople in the physical world. Therefore, customers who are loyal to a particular website may have more networking activities, such as using email to make inquiries, participating in product focused interest groups, or even calling website service personnel. In contrast, customers with strong brand loyalty do not necessarily require interactions with the website. In short, adding a behavioral measure of interaction increases the measurement integrity of e-loyalty. Smith (2001) studied the measurement methods of these behaviors and found that the database of customers registered with a website can be analyzed as e-loyalty. However, real e-loyalty depends on the revisit count and the degree of interactions. Smith used five questions to measure e-loyalty: “How often do visitors return to the website?”, “How much time do visitors spend on the website?”, “What is the ratio of returning visitors versus first-time visitors?”, “Do visitors respond or make transactions?”, and “Do visitors refer the website to others?”.

Service quality is the most significant influence on relationship quality and we reasoned that the variables of the relationship quality model developed by Crosby and colleagues in 1990 are trust and satisfaction. The hypotheses tested here are listed below:
H1: The service quality of Internet banks will directly and positively correlate with relationship quality.
H2: Relationship quality will directly and positively correlate with customer loyalty.
H3: The service quality of Internet banks will directly and positively correlate with customer loyalty.

Method

Questionnaire and Data Analysis Design

The questionnaire was designed according to theories and antecedent studies: we performed a questionnaire pretest to make sure that all respondents fully understood the wording. The questionnaire was composed of three parts: (1) basic information, (2) the service quality of Internet banks, and (3) customer trust, satisfaction, and loyalty. Following the pretest, the questionnaire was revised and ambiguous sentences were clarified. The measure was based on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) and the data were gathered via convenience sampling using face-to-face surveys and the posting of the questionnaire on various free homepages.

The questionnaires included the following research questions: “How does customer loyalty differ between customer groups?”, “What correlation, if any, exists between service quality and relationship quality in Internet banks?”, “What correlation, if any, exists between relationship quality and customer loyalty in Internet banks?”, “What correlation, if any, exists between service quality and customer loyalty in Internet banks?”, and “Can data analysis provide direction to Internet banks on how to build and maintain customer loyalty?”

The methods of data analysis used included (1) Reliability, based on Cronbach’s α (low reliability when α < 0.35, high reliability when α > 0.7, and acceptable reliability when 0.35 < α < 0.7, Guilford’s (1965) model); (2) Construct and content validity, supplied to verify the data accuracy; (3) Factor analysis, or service factors extracted using the principal components method which were rotated through variables, and exploratory factor analysis used to derive the dimensions of service quality; (4) The Pearson method was used to test the correlation significance, and (5) Regression analysis in relation to variables of service quality based on relationship quality and customer loyalty.

The service quality variables were based on the PZB model (1985), with adjustments made for the characteristics of Internet banking. The relationship quality variables were based on the method of Crosby et al. (1990). The adjustment made for the characteristics of Internet banking with customer loyalty is denoted as a dependent variable. Consequently, the content of this research is valid. Pearson’s correlation analysis was applied to test the relationships between service quality and relationship quality, relationship quality and customer loyalty, and service quality and customer loyalty.

An independent samples t test was applied to determine the influence of gender on customer loyalty and analysis of variance (ANOVA) was used to test whether customer loyalty is influenced by age, education level, and income level. Further analysis using the least significant difference (LSD) method was applied to significant results from the previous t test.

Participants

The data were collected in 2003. Of the 444 returned questionnaires, 23 respondents were from the display of the questionnaire on free websites, the rest were our relatives and friends, and people in bank branch lobbies. The average response rate was 10%. Respondents comprised 244 (55%) males and 200 (45%) females. The majority were between 20 and 30 years old (probably reflecting the fact that the computer-using population is still mainly composed of young adults), with the age distribution (20-30, 341, 77.48%; 31-40, 89, 20.05%; 41 and over, 14, 2.25%) analyzed in terms of age groups and the number of respondents in each age group. Respondents were mostly college graduates, with the levels of education being: lower education, 10.14%; vocational school, 18.47%; college, 56.76%; master’s and above, 14.64%. Owing to the high concentration of young adults in the sample, 65.76% had a monthly income level of NT$40,000 (US$1,143) or lower, representing an average income for young adults in Taiwan.

Results

In Table 1 a significant difference (p = .02) is revealed between gender and customer loyalty. Regarding demographic variables, monthly income does not have a significant effect on loyalty (F = 1.094, p = .34). Both age (F = 12.45, p = .00) and education (F = 8.289, p = .00) have a significant effect on customer loyalty (see Table 2). Tables 3 and 4 contain the results of the further analysis of age and education attributes using LSD. Significant differences in customer loyalty are revealed in the age groups (see Table 3). The 31-40 age group shows higher customer loyalty than the 20-30 age group, and the 41 and over age group displays the lowest loyalty. As for education characteristics, a significant difference exists between college graduates and high school graduates. Moreover, college graduates have higher loyalty than high school graduates (see Table 4).

The results of reliability analysis are shown in Table 5. The Cronbach’s alphas are all above .80. Principal components analysis (PCA) was carried out to explore the underlying factors associated with the 26 items. Construct validity was tested using Bartlett’s test of sphericity and the Kaiser-Meyer-Olkin (KMO)measure of sampling adequacy, which were first computed to determine the suitability of employing a factor analysis. The value of KMO varies between 0 and 1, and the KMO overall value should be .60 or higher to perform a factor analysis. As the results from Bartlett’s test of sphericity and KMO revealed that the factors were highly significant we concluded that these variables were suitable for a factor analysis (see Table 6). To determine the minimum loading necessary to include an item in its respective construct, Hair et al. (2006) suggested that variables with loadings greater than .40 are more important. In our study all variables met this condition. The total explained variance for the six factors was 60.59%. Multiple exploratory factor analysis was used to derive the six dimensions: competence, offering information actively, information delivery, security, crisis handling, and relationship retention (see Table 7).

Table 8 contains the analytical results of the regression between service quality and relationship quality. Regression analysis demonstrates that, as predicted, the relationship between service quality and relationship quality is significant (F = 110.91, p < .05). Service quality can explain 60% of variance in relationship quality (R² = 0.60). The order or significance ranking for the relationship between service quality and customer relationship is as follows: competence (β = .62), security (β = .32), offering information actively (β = .25), information delivery (β = .20), relationship retention (β = .13), and crisis handling (β = -.01). Table 9 contains the analytical results of the regression between relationship quality and customer loyalty. Relationship quality is positively correlated with customer loyalty with significance (p = .000 < α = 0.001), meaning that customer loyalty increases when relationship quality increases. Regression analysis indicates that the predicted effect between relationship quality and customer loyalty is significant (F = 496.94, p < .01). Relationship quality can explain 53% of variance in customer loyalty (R² = 0.53). The relationship between relationship quality and customer relationship is positive (β = .74). Table 10 contains the analytical results of the regression between service quality and customer loyalty. Regression analysis demonstrates that, as predicted, the relationship between service quality and customer loyalty is significant (F = 88.69, p < .05). Service quality can explain 55% of variance in relationship quality (R² = 0.55). The order or significance ranking for the relationship between service quality and customer relationship is as follows: competence (β = .49), offering information actively (β = .40), security (β = .33), information delivery (β = .16), relationship retention (β = .12), and crisis handling (β = .05).

Conclusion

Internet banking, classified within electronic commerce as belonging to the field of cash flow, is a new trend in Internet finance. If Internet users who are unfamiliar with Internet banking face an uncertain and unsafe transaction model, the main determinant of customer loyalty for Internet banking providers is the creation of beneficial interactive relationships in order to gain user trust. The following conclusions have been developed in this study:
(1) Internet banks can enhance service quality by focusing on six dimensions: competence, offering information actively, information delivery, security, crisis handling, and relationship retention. This finding confirms previous research findings on service quality (Parasuraman, Zeithaml, & Berry, 1994; Zeithaml, Berry, & Parasuraman, 1996).
(2) Internet banks can establish good relationships via the enhancement of service quality. If banks improve service quality, they can earn the trust and satisfaction of online users. This encourages online users to patronize a bank, thereby establishing a good relationship (Ellis-Chadwick, McHardy, & Wiesehofer, 2002).
(3) Internet banks can increase customer loyalty by enhancing relationship quality. Good customer relationships are the primary determinant of customer loyalty (Mathwick, 2002).

References

ACNielsen. (2007). Online survey. http://tw.cn.nielsen.com/site/news20070427.htm

Al-Hawari, M., & Ward, T. (2006). The effect of automated service quality on Australian banks’ financial performance and the mediating role of customer satisfaction. Marketing Intelligence & Planning, 24(2), 127-147.

Crosby, L. A., Evans, K. R., & Cowles D. (1990). Relationship quality in services selling: An interpersonal influence perspective. Journal of Marketing, 54(3), 68-81.

Cyber Dialogue. (1999, August 18). Online banking growth stagnates due to user churn. The Free Library. Retrieved June 23, 2011, from http://www.thefreelibrary.com/Online+Banking+Growth+Stagnates+Due+to+User+Churn.-a055488389

Dwyer, F. R., Schurr, P. H., & Oh, S. (1987). Developing buyer-seller relationships. Journal of Marketing, 51(2), 11-27.

Ellis-Chadwick, F., McHardy, P., & Wiesehofer, H. (2002). Online customer relationships in the European financial services sector: A cross-country investigation. Journal of Financial Services Marketing, 6(4), 333-345.

Guilford, J. P. (1965). Fundamental statistics in psychology and education (4th ed.). New York: McGraw-Hill.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). London: Prentice-Hall.

Herington, C., & Weaven, S. (2009). E-retailing by banks: E-service quality and its importance to customer satisfaction. European Journal of Marketing, 43(9/10), 1220-1231.

Jensen, J. B., & Markland, R. E. (1996). Improving the application of quality conformance tools in service firms. Journal of Services Marketing, 10(1), 35-55.

Kettinger, W. J., & Lee, C. C. (1997). Pragmatic perspectives on the measurement of information system service quality. MIS Quarterly, 21(2), 223-240.

Lowenstein, M. W. (1997). The customer loyalty pyramid. Westport, CT: Greenwood.

Malhotra, P., & Singh, B. (2010). An analysis of Internet banking offerings and its determinants in India. Internet Research, 20(1), 87-106.

Mathwick, C. (2002). Understanding the online consumer: A typology of online relational norms and behavior. Journal of Interactive Marketing, 16(1), 40-55.

McMullan, R., & Gilmore, A. (2003). The conceptual development of customer loyalty measurement: A proposed scale. Journal of Targeting Measurement and Analysis for Marketing, 11(3), 230-243.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41-50.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1994). Reassessment of expectations as a comparison standard in measuring service quality: Implications for further research. Journal of Marketing, 58(1), 111-124.

Regan W. J. (1963). The service revolution. Journal of Marketing, 27(3), 57-62.

Reichheld, F. F., Markey, R. G., Jr., & Hopton, C. (2000a). The loyalty effect: The relationship between loyalty and profits. European Business Journal, 12(3), 134-139.

Reichheld, F. F., Markey, R. G., Jr., & Hopton, C. (2000b). E-customer loyalty: Applying the traditional rules of business for online success. European Business Journal, 12(4), 173-179.

Reichheld, F. F., & Sasser, W. E., Jr. (1990). Zero defections: Quality comes to services. Harvard Business Review, 68, 105-111.

Smith, E. R. (2001). Seven steps to building e-loyalty. Medical Marketing and Media, 36(3), 94-102. Westbrook, R. A. (1981). Sources of consumer satisfaction with retail outlets. Journal of Retailing, 57(3), 68-85.

Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46.

ACNielsen. (2007). Online survey. http://tw.cn.nielsen.com/site/news20070427.htm

Al-Hawari, M., & Ward, T. (2006). The effect of automated service quality on Australian banks’ financial performance and the mediating role of customer satisfaction. Marketing Intelligence & Planning, 24(2), 127-147.

Crosby, L. A., Evans, K. R., & Cowles D. (1990). Relationship quality in services selling: An interpersonal influence perspective. Journal of Marketing, 54(3), 68-81.

Cyber Dialogue. (1999, August 18). Online banking growth stagnates due to user churn. The Free Library. Retrieved June 23, 2011, from http://www.thefreelibrary.com/Online+Banking+Growth+Stagnates+Due+to+User+Churn.-a055488389

Dwyer, F. R., Schurr, P. H., & Oh, S. (1987). Developing buyer-seller relationships. Journal of Marketing, 51(2), 11-27.

Ellis-Chadwick, F., McHardy, P., & Wiesehofer, H. (2002). Online customer relationships in the European financial services sector: A cross-country investigation. Journal of Financial Services Marketing, 6(4), 333-345.

Guilford, J. P. (1965). Fundamental statistics in psychology and education (4th ed.). New York: McGraw-Hill.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). London: Prentice-Hall.

Herington, C., & Weaven, S. (2009). E-retailing by banks: E-service quality and its importance to customer satisfaction. European Journal of Marketing, 43(9/10), 1220-1231.

Jensen, J. B., & Markland, R. E. (1996). Improving the application of quality conformance tools in service firms. Journal of Services Marketing, 10(1), 35-55.

Kettinger, W. J., & Lee, C. C. (1997). Pragmatic perspectives on the measurement of information system service quality. MIS Quarterly, 21(2), 223-240.

Lowenstein, M. W. (1997). The customer loyalty pyramid. Westport, CT: Greenwood.

Malhotra, P., & Singh, B. (2010). An analysis of Internet banking offerings and its determinants in India. Internet Research, 20(1), 87-106.

Mathwick, C. (2002). Understanding the online consumer: A typology of online relational norms and behavior. Journal of Interactive Marketing, 16(1), 40-55.

McMullan, R., & Gilmore, A. (2003). The conceptual development of customer loyalty measurement: A proposed scale. Journal of Targeting Measurement and Analysis for Marketing, 11(3), 230-243.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41-50.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1994). Reassessment of expectations as a comparison standard in measuring service quality: Implications for further research. Journal of Marketing, 58(1), 111-124.

Regan W. J. (1963). The service revolution. Journal of Marketing, 27(3), 57-62.

Reichheld, F. F., Markey, R. G., Jr., & Hopton, C. (2000a). The loyalty effect: The relationship between loyalty and profits. European Business Journal, 12(3), 134-139.

Reichheld, F. F., Markey, R. G., Jr., & Hopton, C. (2000b). E-customer loyalty: Applying the traditional rules of business for online success. European Business Journal, 12(4), 173-179.

Reichheld, F. F., & Sasser, W. E., Jr. (1990). Zero defections: Quality comes to services. Harvard Business Review, 68, 105-111.

Smith, E. R. (2001). Seven steps to building e-loyalty. Medical Marketing and Media, 36(3), 94-102. Westbrook, R. A. (1981). Sources of consumer satisfaction with retail outlets. Journal of Retailing, 57(3), 68-85.

Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46.

The authors wish to thank Miss Yang

Kai Rong

Miss Wu

Ya Ting

Mr Liao

Yi Shen

Miss Zhuang

Wan Ru

Mr Liu

Wei Yu

Miss Zhang

Ya Ling

and Mr Wang

Yu Cheng

students in the Department of Business Administration at Chung Hua University

Hsinchu

Taiwan

ROC

for their assistance in collecting the questionnaires and materials.
Appreciation is due to anonymous reviewers.

Yu Chao, Department of Business Administration, Chung Hua University, 707, Sec. 2, WuFu Road, Hsinchu, Taiwan 300, ROC. Email: [email protected]