An Open Access, Peer Reviewed, Refereed, Online and Print International Research Journal 1

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1 A STUDY ON CUSTOMER SERVICE VARIABLES WHICH INFLUENCE RELATIONSHIP QUALITY OF E-CRM BY THE BANK Dr. ANITHA.G Associate professor, Trinity Institute of professional studies, Dwarka, Delhi. ABSTRACT: The research examines and measures the service variables which influence relationship quality of E- CRM by the bank. Because most E_CRM implementation cannot be directly seen or recognized by customers. The banking industry were used to develop a new construct called Customer based service attributes to measure service variable which influence relationship quality of E-CRM from customer s perspectives. In the emerging competitive driven banking era, banks have to strive hard for set their customer base INTRODUCTION: The service industry, especially financial institutions grapples with increasing competition currently. In such environment, differentiation is necessary and banks begin to realize that no bank can offer all products and be the best/ leading bank for all customers. When a bank begins to migrate from humanintensive bank to one that emphasizes multiple electronic contact points such as phone, fax, and the web, the ability to develop, manage and measure customer relationships increases dramatically. Creating long-term relationships with valued customers is usually viewed as the key profitability in a increasingly dynamic market. It has become the major paradigm of relationship marketing in the e-world NEED FOR THE STUDY: The e-crm approach in relationship management and it gives great benefits to its stake holder including employees, customers, suppliers and channel partners. Further the e-crm gives more benefits viz., creating long term relationship with customer with minimum cost, reduce customer defection rate, increase the profitability from low profit customers and focus on high value of customers. The present study concentrates on the customer service variables which influence relationship quality of E-CRM by the bank. (Chen & Chen, 2004) METHODOLOGY OF THE STUDY: The present study is Qualitative in approach. The instrument has been created by including 13 statements pertaining to the various dimensions. SAMPLE SELECTION: The researcher wanted to examine various e-crm banking benefits derived by the customers by considering various e-crm activities. The researcher has selected 6 customers from 1.Nationalized Banks 2. Old Private Sector Banks and 5.Both. Thus the by combining all these banks a total of 50 respondents were included in the study and the researcher has adopted snow ball sampling technique to collect and record the opinions of the customers. 1

2 STATISTICAL TOOL: The researcher has applied factor analysis for analyzing and drawing meaningful inferences from the opinion/ benefits derived from e-crm. SPSS version 14 has been applied to analyze the data. Table No: 1 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy..794 Approx. Chi-Square Bartlett's Test of Sphericity Df 78 Sig..000 Table No: 2 Communalities Initial Extraction Lack of Trust Decreased Customer Satisfaction Lack of commitment by the banker on defaults Lack of Communication Way for Conflict Opportunism for fault Lack of Co operational Understanding Customer power is not properly used Coordination between the banker and customer is limited Bonds of association doesn t happen Goal congruence is delayed Adaptation of e-crm initiatives takes lot of training In Table Bartlett s test of sphericity and Kaiser Meyer Olkin measures of sample adequacy were used to test the appropriateness of the factor model. Bartlett s test was used to test the null hypothesis that the variables of this study are not correlated. Since the approximate chi-square satisfaction is which are significant at 1% level, the test leads to the rejection of the null hypothesis. The value of KMO statistics (0.794) was also large and it revealed that factor analysis might be considered as an appropriate technique for analysing the correlation matrix. The communality table showed the initial and extraction values. 2

3 Table No: 3 Total Variance Explained Component Total Initial Eigen Values % of Variance Cumulative % Total Extraction Sums of Squared Loadings % of Variance Cumulative % Rotation Sums of Squared Loadings Total % of Variance Cumulative % From the table it was observed that the labelled Initial Eigen Values gives the EIGEN values. The EIGEN Value for a factor indicates the Total Variance attributed to the factor. From the extraction sum of squared loadings, it was learnt that the I factor accounted for the variance of which was %, the II factor accounted for the variance of which was %, the III factor accounted for the variance of which was 9.010%. The three components extracted accounted for the total cumulative variance of % Determination of factors based on Eigen Values In this approach only factors with Eigen values greater than 1.00 are retained and the other factors are not included in this model. The three components possessing the Eigen values which were greater than 1.0 were taken as the components extracted. 3

4 Table No: 4 Component Matrix a Component Lack of co operational.815 Bonds of association doesn t happen.810 Decreased customer satisfaction.807 Lack of communication.792 Opportunism for fault.787 Customer power is not properly used.755 Goal congruence is delayed.752 Lack of trust.744 Way for conflict.733 Lack of commitment by the banker on defaults.729 Coordination between the banker and customer is limited.567 Understanding.734 Adaptation of e-crm initiatives takes lot of training a. 3 components extracted. Table No: 5 Rotated Component Matrix a Component Way for Conflict Adaptation of e-crm initiatives takes lot of training Decreased Customer Satisfaction Customer power is not properly used Coordination between the banker and customer is limited Understanding Lack of Co operational Bonds of association doesn t happen Lack of commitment by the banker on defaults Lack of Communication Goal congruence is delayed Lack of Trust Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations. The rotated component matrix shown in Table is a result of VARIMAX procedure of factor rotation. Interpretation is facilitated by identifying the variables that have large loadings on the same factor. Hence, those factors with high factor loadings in each component were selected. The selected factors were shown in the table. 4

5 Factor I (27.828) Customer Satisfaction II (52.338) Understanding Table No: 6 Clustering of inducing variables into factors Rotated Inducing Variable factor loadings Way for Conflict X Adaptation of e-crm initiatives takes lot of training X Decreased Customer Satisfaction X Customer power is not properly used X Coordination between the banker and customer is limited X Understanding X Lack of Co operational X Bonds of association doesn t happen X III(72.384) Delayed Congruence Lack of commitment by the banker on defaults X Lack of Communication X Goal congruence is delayed X Lack of Trust X In this table three factors were identified as being maximum percentage variance accounted. The variable X5, X9, X2 and X13 constitutes factor I and it accounts for per cent of the total variance. The variable X10, X8, X7 and X11 constitutes factor II and it accounts for per cent of the total variance. The variable X3, X4, X12 and X1 constitutes factor III and it accounts for per cent of the total variance. CUSTOMER SATISFACTION The customer gets dissatisfied when they get into conflicts, hence the bankers have to provide enormous training over e-crm initiatives, and hence customer power can be properly used to reap better profits. UNDERSTANDING The banker s now-a-days implement customer relationship program to create bonds of association, if it is not made the customer and the banker hails with the lack of understanding and cooperation. DELAYED CONGRUENCE The bankers at times fail to keep the commitment hence defaults appear, by which communication, trust gets lacked. CONCLUSION: The study has identified three concrete factors which induce the customer service variables which influence relationship quality of E-CRM in the different sector of the banks. The different dimensions include 1.Customer Satisfaction 2. Understanding 3. Delayed Congruence. 5

6 REFERENCES: 1. Chen, Q., & Chen,H.M.,(2004) Exploring the success factors of E-CRM in practice. Database Marketing & Customer Strategy Management, 11(4), Zineldin,M. (1996), Bank strategic positioning and some determinants of bank selection. International Journal of Bank Marketing,14(6), Gruen,T.W., Summers, J.O., & Acito,F. (2000). Relationship Marketing Activities, commitment, and member behaviour in professional associations. Journal of Marketing, 64(3), Payne,A.,&Frow.P (2005). A strategic framework for customer relationship management. Journal of Marketing, 69(4), Rigby, D.K. & Ledingham, D. (2004) CRM done right, Harvard Business Review, 82(11), PP