An assessment of forerunners for customer loyalty in the selected financial sector by SEM approach toward their effect on business

The purpose of this paper is to identify and describe the relationship between the precursors and consequences of customer loyalty (CL) in the Indian financial sector, specially banking and insurance context, taking a sample of individual customers as respondents from the Indian State of Punjab.,The collected data have been analyzed using univariate, bivariate and multivariate analysis techniques. Specifically, descriptive statistics have been assessed to examine the basic characteristics of the sample data. Confirmatory factor analysis with maximum likelihood criteria has been adopted for the measurement and validation of various constructs. Independent samples t-test has been used to compare the CL of public and private firms, banks and insurance firms, and for some of the demographic variables like gender, marital status, etc. One-way ANOVA has been used to compare the CL for variables having more than two groups. Structural equation modeling (SEM) has been used to measure the impact of CL on the BP of financial services firms.,The result shows that BP is a higher-order construct measured in terms of word-of-mouth, repurchase intention, price premium and share of wallet. Though each of the four measures of CL is special and unique in nature, yet a high level of positive correlation has been seen among these dimensions. The study reveals that CL is not significantly different for the banking firms and insurance firms in Punjab.,The authors consider this work as one of the foundational elements that will enable further advances toward the governance of multi-layer business impact modeling systems. Extensive usability tests would enable to further confirm the findings of the paper. This study contributes to the customer relationship management and services marketing literature by providing empirical support for CL and BP relationship in the Indian context.,The approach described here should improve the maintainability, reuse and clarity of business process models and in extension improve data for CL in large banking and insurance organizations. The approaches described here should improve the maintainability, reuse and clarity of loyalty and relationship of the customer with that of organizations. This can improve data for customer relationship and loyalty in banking and insurance sector.,This paper fulfills an identified gap to enabling SEM enabled models for data regarding customer relationship and loyalty. Loyalty revolves around the concept of relationship. CL is not a new concept, but recent years have demonstrated a developing interest to fabricate CL because of customer-oriented techniques or strategies. Over the previous era, CL has been broadly inspected inside marketing, trades and transactions. It can be concluded that the CL significantly influences BP.

[1]  M. A. Darzi,et al.  Customer relationship management: An approach to competitive advantage in the banking sector by exploring the mediational role of loyalty , 2016 .

[2]  Ellen Day,et al.  Clients' Selection and Retention Criteria , 1988 .

[3]  Elizabeth Chang,et al.  A methodology to map customer complaints and measure customer satisfaction and loyalty , 2014, Service Oriented Computing and Applications.

[4]  Iguácel Melero-Polo,et al.  Can complaint-handling efforts promote customer engagement? , 2016 .

[5]  Waseem Bahadur,et al.  Effect of employee empathy on customer satisfaction and loyalty during employee–customer interactions: The mediating role of customer affective commitment and perceived service quality , 2018 .

[6]  R. Bagozzi Marketing as Exchange , 1975 .

[7]  E. Anderson Customer Satisfaction and Word of Mouth , 1998 .

[8]  Paul A. Pavlou,et al.  Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior , 2002, MIS Q..

[9]  Ivana Adamson,et al.  Relationship marketing: customer commitment and trust as a strategy for the smaller Hong Kong corporate banking sector , 2003 .

[10]  R. Kazemi,et al.  Relationship Marketing and Word-of-Mouth Communications: Examining the Mediating Role of Customer Loyalty , 2016 .

[11]  B. Ehigie Correlates of customer loyalty to their bank: a case study in Nigeria , 2006 .

[12]  F. Miranda,et al.  Customer delight: perception of hotel spa consumers , 2016 .

[13]  Amjad D. Al-Nasser,et al.  Evaluating functional relationship between image, customer satisfaction and customer loyalty using general maximum entropy , 2000 .

[14]  Jing Yang,et al.  Social network fatigue affecting continuance intention of social networking services , 2019, Data Technol. Appl..

[15]  Richard P. Bagozzi,et al.  Assessing Construct Validity in Organizational Research , 1991 .

[16]  M. A. Darzi,et al.  Antecedents of Customer Loyalty in Banking Sector: A Mediational Study , 2018, Vikalpa: The Journal for Decision Makers.

[17]  D. Aaker MEASURING BRAND EQUITY ACROSS PRODUCTS AND MARKETS , 1996 .

[18]  B. Narteh,et al.  Factors influencing consumer loyalty towards 3G mobile data service providers: evidence from Ghana , 2018 .

[19]  Mark S. Johnson,et al.  The Different Roles of Satisfaction, Trust, and Commitment in Customer Relationships , 1999 .

[20]  D. Campbell,et al.  Convergent and discriminant validation by the multitrait-multimethod matrix. , 1959, Psychological bulletin.

[21]  John P. Meyer,et al.  The measurement and antecedents of affective, continuance and normative commitment to the organization , 1990 .

[22]  Donghong Ding,et al.  M-banking barriers in Pakistan: a customer perspective of adoption and continuity intention , 2019, Data Technol. Appl..

[23]  Alexander E. Ellinger,et al.  Customer‐based brand equity, equity drivers, and customer loyalty in the supermarket industry , 2011 .