A STUDY ON A CONTEMPORARY MARKETING STRATEGY OF SOME SELECTED BANKS IN KOLKATA
Sarajit Sardar
Department of Commerce, Vidyasagar Evening College, West Bengal, India
Corresponding Author’s Email: sarani3@rediffmail.com
ABSTRACT
Relationship Marketing Strategy (RMS) which “strives to get the firm close to the customers in order to enable it to accurately and adequately discern and satisfy their needs” is one of the most innovative and dynamic marketing strategy that is being practiced by the banks of modern India in the present business environment marked by intense competition and complexity set in due to the liberalization policies of the successive Governments. The banks which have predominantly promoted the transactional trait of its operation, of late are focusing on the relational aspect of marketing as is evident in the following promotional efforts. The slogans of the banks viz. United Bank of India - The Bank that begins with U, United Commercial Bank-Honours your trust, Allahabad Bank- A tradition of trust, speaks about the effort being put in by the banks to not only attract customers but to retain them. To understand RMS thoroughly a study has been conducted on the cash/credit account holders of three Kolkata based banks viz. United Bank of India, Allahabad Bank and United Commercial Bank. A questionnaire was devised from previous studies and relevant literature was completed by 312 bank customers selected among 5508 customers via convenience sampling. Appropriate statistical tool was used to analyze the responses and study the impact on RMS of two key constructs (reciprocity and empathy). It was found out that the two variables have a significant impact and predict a considerable proportion of the variance in RMS.
Keywords: Relationship Marketing Strategy, Reciprocity, Empathy, Bank, Statistical Tool
INTRODUCTION
The advent of globalization has forced the business houses to adopt innovative and competitive marketing strategies. Indian business scenario is not an exception. The Indian banks which play a dominant role in the Indian financial environment have had to bring in a sea change over the last three decades in their attitude and approach induced if not forced by the liberalization policies of the successive governments. The complex and competitive environment has also resulted in a shift in the customers’ attitude as they have become highly demanding in respect to the banking products and services. And once dissatisfied with the banks, customers are not thinking twice to switch their allegiance according to their own sweet will. The high customer turnover today is attributed to the fact that they have wider choice and easy access to multiple options owing largely to the technological revolution. Hence it has become necessary for the banks to devise comprehensive and all-inclusive marketing strategies from ‘reaching out to retaining’ of customers with a special focus on the latter. The banks which have predominantly promoted the transactional trait of its operation, of late are focusing on the relational aspect of marketing as is evident in the promotional efforts. The promotional slogans of the banks like United Bank of India-The Bank that begins
with U, United Commercial Bank-Honours your trust, Allahabad Bank-A tradition of trust are evidence of the statements. Furthermore most importantly the banks of today, be it private or public, inevitably have a personal banking division headed by a Relationship Manager, offers redeemable bonus points for increased usage, provides facility of priority banking, has been opening touch points (e.g. SBI e-corner), are introducing digital application based mobile services (apps like Buddy), all these with a single motive, to establish, maintain, enhance and most importantly retain a long-lasting and mutually beneficial relational bond. Keeping these in mind an effort has been made to find out the extent to which some selected Kolkata based Indian banks are practicing Relationship Marketing Strategy (RMS) as a tool to ‘reaching out to retaining’ of customers.
LITERATURE REVIEW
The earliest known reference to influence of relationship on marketing could be traced to an educator and economist of business administration Edmund Mcgarry’s (1951, 1953) writings on ‘Contactual Function’ of marketing. Mcgarry was of the view that ‘contactual’ information formed the basis of cooperation/collaboration between buyer and seller. He opined that long-term, continuous relationship between buyer and seller help to develop a bond of mutual interest, besides, mutual confidence and respect can bring down the marketing cost by 10-20 percent. He further advocated that long-term cooperation/collaboration between the seller and buyer is a mechanism to increase marketing efficiency from the seller’s point of view.
In 1975, Richard Bagozzi, Professor of Behavioral Science in Management, Ross School of Business, University of Michigan, put forward the idea that exchange relationship can fall into three types of exchanges: restricted, generalized and complex. Restricted exchanges are about reciprocal relationships between two parties like customers and company or company representatives, generalized exchanges are about univocal reciprocal relationships between at least three actors like the middleman, the company and its customers, complex exchanges are mutual relationships between at least three parties and are the closest to the concept of relationship Marketing.
The origins of modern relationship marketing can be traced back to a passage by Schneider (1980) in which he observes: "What is surprising is that researchers and businessmen have concentrated far more on how to attract customers to products and services than on how to retain customers". The initial research was done by Gronroos (1982) at the Swedish School of Economics who introduced what he called "Interactive Marketing". In 1989, Gronroos wrote, marketing is a mutual exchange and fulfillment of promises and it is through making promises and keeping them that trust develops and out of trust long-term relationships grow.
Berry (1983) a distinguished professor of marketing at Texas A&M University and a former President of American Marketing Association, coined the term "Relationship Marketing" (RM). Berry emphasized the importance of maintaining and enhancing the relationships with existing customers in addition to creating new customers. He suggested five types of Relationship Marketing Strategies which are core service, relationship customization, service augmentation, relationship pricing and internal marketing.
First generation marketing theorist Levitt (1983) at Harvard wanted to broaden the scope of marketing beyond individual transactions. In practice, RM originated in industrial and B-2-B markets where long-term contracts have been quite common for many years.
Academics like Jackson (1985) at Harvard re-examined these industrial marketing practices and applied those to marketing proper.
Besides different scholars and academicians have listed and theorized key relationship drivers/constructs/variables like trust (Moorman, Deshpande & Zaltman 1993; Morgan & Hunt, 1994; Ndubisi, 2004), Commitment (Moorman, Deshpande & Zaltman 1993; Morgan and Hunt, 1994; Ndubisi, 2004), satisfaction (Sheith, Gardner, & Garrett, 1988), Conflict Handling (Dwyer, Schurr, & Oh, 1987), empathy (Ndubisi, 2004) and many others through various marketing literatures.
The relationship drivers/constructs/variables chosen for the current study are Reciprocity and Empathy based on the following facts:
The mid-level bank officials whom the researcher interviewed and who act as a link between the rank & file supplying ground level information to the policy makers stressed on the traits.
The customers spoken to by the researcher also corroborated the fact that they give importance to the variables chosen by the researcher.
Before deliberating on the issue, it is thus pertinent to have some discussion on the independent variables Empathy and Reciprocity.
Empathy is the ability to see a situation from another person’s perspective (Wang, 2007) or it is the ‘capacity to share and understand another’s ‘state of mind’ or emotion. It is defined as seeking to understand somebody else’s desires and goals. It involves the ability of individual parties to view the situation from the other party’s perspective in a truly cognitive sense (Chattananon & Trimetsoontorn, 2009). Empathy should not be equated with sympathy; marketers can be empathetic while still driving a hard bargain with customers (Murphy et al., 2007). In the networking literature, empathy has been considered as an independent variable in explaining franchisor–franchisee working relationships (Sin et al., 2002). It is often characterized as the ability to ‘put oneself into another shoes’, or in some way experience the outlook or emotions of another being within oneself. As mentioned by Parasuraman et al., (1985, 1988) empathy is one of the important elements to measure the service quality in service industries. Thus, it is so important to each bank manager to recruit staff with social skills that will assist the development of long-standing relationship with the customers.
Empathy not only increases the level of quality of the relationship between customers and the organization, but also empowers the relationship to deliver superior value, which results in customer’s repeat purchase, customer retention, and sustained loyalty.
As per the present study empathy towards the customers can be expressed by giving them individual attention, offering them convenient working hours, and providing attention to the needs and interests of the customers.
Reciprocity on the other hand is the dimension of a business relationship that causes either party to provide favors or make allowances for the other in return for similar favors or allowances to be received later (Callaghan, McPhail & Yau, 1995) which was also of the view of Chattananon & Trimetsoontorn (2009).
It covers the bilateral contingency, interdependence for mutual benefit and equality of exchanged values aspects of social action between two individuals (Lebra, 1976) and can be regarded as ‘social dualism’ and ‘mutual legal obligations of repaying’ (Malinowski, 1959). Houston, Gassenheimer & Maskulka, (1992), reinforced by Ellis et al., (1993) and acknowledged by Smith & Jhonson (1993) has indicated links of reciprocity and empathy to relationship marketing and exchange. Reciprocity and bonding are linked in that a reciprocal arrangement is indicative of cooperation.
The rule of reciprocity focuses on a recipient’s behavior by the social norm expressed as “if you have received a drop of beneficence from other people, you should return to them a fountain of beneficence” (Wang, 2007).
The links of reciprocity to relationship marketing have been considered as a basis for the interface between exchange transactions and marketing activities. In fact, relationship marketing is characterized by “….interactions, reciprocities, and long-term commitments” (Sin et al., 2002).
Reciprocity is promoted by the banks in the present study by taking timely action, communicating with customers properly and the bank managers giving personal attention to the customers.
Objective of the study
The objective of this study is to investigate the influence of selected independent variables (Reciprocity, Empathy) on the dependent variable (relationship marketing strategy) of the selected banking branches in Kolkata Metropolitan area.
To give effect to the problem statement several null hypotheses have been formulated, stating that no relationship exists, as depicted in the conceptual figure 1. Alternative hypotheses have been formulated stating that relationship exists, as depicted in conceptual diagram.
Specifically, the null and alternative hypotheses are:
Ho1: There is no relationship between perceived Reciprocity and RMS in the selected banking branches in Kolkata Metropolitan area.
Ha1: There exists a relationship between perceived Reciprocity and RMS in selected banking branches in Kolkata Metropolitan area.
Ho2: There is no relationship between perceived Empathy and RMS in selected banking branches in Kolkata Metropolitan area.
Ha2: There exists a relationship between perceived Empathy and RMS in selected banking branches in Kolkata Metropolitan area.
Hence in the current study the influence of each of the independent variable (empathy and reciprocity) on the dependent variable i.e. RMS will be empirically tested. The researcher has further made an attempt to find out the impact of the sub-variables [4 sub-variables of Reciprocity viz Timely Action, Availability of Managers, Communication1 and Communication2, and 4 sub-variables of Empathy viz. Attention, Convenient Working Hours, Needs of Customers and Interest of Customers] of the independent variables (Reciprocity and Empathy) on the sub-variables of dependent variable Relationship Marketing Strategy [5 sub-variables viz. Satisfaction, Relationship Benefits, Cooperation, Bond, Pleasant Experience]. In other words, the practicability of the conceptual framework of figure 1 has been tested.
Figure 1: Conceptual Framework
RESEARCH METHODOLOGY
A structured questionnaire was developed including some selected items from previous studies. In addition, interviews were conducted with officers of the banks selected for the study to identify important aspects of Relationship Marketing to assist development of questionnaire items.
The questionnaire has two components- Part A and Part B.
The first part (i.e., Part A) contains five items to gather some relevant biographical information about the respondents viz., gender, age, qualification, annual income, and number of years of association with the bank.
The second part (i.e., part B) contains 13 items to gather the responses (i.e., the perceptions) of respondents on a five point scale (the response alternatives being, strongly disagree/disagree/neither agree nor disagree/agree/strongly agree) with respect to three aspects viz., Reciprocity [4 sub-variables viz Timely Action, Availability of Managers, Communication1 and Communication2], Empathy [4 sub-variables viz. Attention, Convenient Working Hours, Needs of Customers and Interest of Customers] and Relationship Marketing Strategy [5 sub-variables viz. Satisfaction, Relationship Benefits, Cooperation, Bond, Pleasant Experience].
The primary data collected have been intelligently collated, analyzed and tabulated using appropriate statistical techniques with the help of a statistical software package, viz., SPSS (version 20).
The researcher has chosen the public sector banks designated as scheduled commercial banks headquartered at Kolkata viz. United Commercial Bank, United Bank of India and Allahabad Bank. The reason being:
The study is based on banks in Kolkata.
It helped the researcher design the questionnaire after talking to the officials who are involved in devising marketing strategies and policies.
It is basically a perception study. Hence it was essential to identify the customers interacting frequently with the bank. Speaking with the officials helped the researcher to identify such customers.
There are 351 branches of UCO (79), UBI (North + South =118) and Allahabad (154) Bank in Kolkata. 35 branches were chosen (representing 10% of the number of branches) from among the 351 branches in the following manner. The branches were selected based on the premise of Convenience Sampling.
United Commercial Bank - 8 branches
United Bank of India - 12 branches
Allahabad Bank - 15 branches
Total 35 Branches
Information relating to 5508 Cash/Credit account holders was collected. Out of them contact details [telephone numbers (landline and cellular), e-mail i/ds] of 550 (i.e. 10% of 5508) account holders could be gathered and contacted to 384 respondents (i.e. 70% of 550) responded out of which 312 (57% of 550) completed questionnaires were selected.
Crosstabs and Chi-square -The researcher performed the Chi Square test for independence of attributes between two categorical variables which uses cross classification table to examine the nature of relationship between the variables (independent, and dependent).
Factor Analysis- Factor analysis is a statistical tool used to group variables with similar characteristics together. It helps to reduce large number of variables to a smaller number of manageable variables. The reduced number of factors is utilized to explain the observed variance in the large number of variables.
Correlation and Regression- The word “correlation” is used to denote the degree of association between the variables. In the current research the statistical tool has been used to find out the relationship between the individual independent variables (Reciprocity, Empathy) and the dependent variable (Relationship Marketing Strategy)
“Regression” is used to denote estimation or prediction of the average value of one variable for a specified value of another variable.
Pilot Survey:
The researcher conducted a pilot survey to verify the strength of the measuring instrument viz., the questionnaire. A total of 50 participants were chosen for the pilot test and reliability test was performed using Cronbach’s Alpha.
Reliability of a measure is an established tool for testing both consistency and stability. Consistency indicates closeness of the items measuring a concept. Cronbach’s Alpha is a reliability coefficient that indicates how well the items in a set are positively correlated to one another. The coefficient can range between 0 and 1. Closer the value of item to 1 greater is its reliability/consistency. In the present (pilot) study the three Key Constructs Reciprocity (4 items), Empathy (4 items) and Relationship Marketing Strategy (5 items) have coefficient scores of 0.748, 0.741, and 0.748 which indicates that the items within the Key Constructs are closely related, hence are consistent/reliable.
Empirical Survey and its Findings
The researcher then proceeded with the analysis of the data of the main survey. The Reliability test was performed using Cronbach’s Alpha on the responses of the 312 customers. The coefficient scores of 0.724, 0.713 and 0.722 respectively of the independent variables (Reciprocity and Empathy) and dependent variable (Relationship Marketing Strategy) shows that the responses are consistent and reliable.
Cross tabulation and Chi Square test
Cross tabulation and Chi Square test were then performed in two stages to find out whether and how the sub variables (referred to earlier) of independent variables (Reciprocity and Empathy) and the sub variables (referred to earlier) of dependent variable (relationship marketing strategy) are related to each other. The null hypothesis being that, there is no statistically significant relationship between the sub variables of independent variables and the sub variables of dependent variable.
In the first instance the low p-values (p<0.001) obtained gave the strength to reject the null hypothesis and accept the alternative hypothesis that there is relationship between the sub variables of the independent variable (Reciprocity) and the sub-variables of the dependent variable (Relationship Marketing Strategy).
In the second instance the null hypothesis that there is no statistically significant relationship between the sub-variables of the independent variable empathy and the sub-variables of the dependent variable relationship marketing strategy is also rejected based on the low p-values (p<0.001) obtained.
Factor Analysis
Factor Analysis was then performed in three stages to reduce the number of variables into a manageable limit.
The tables showing the results are given below:
Table1: Factor Analysis – Reciprocity
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy
0.744
Bartlett's Test of Sphericity
Approx. Chi-Square
242.654
Df
6
p value
<0.001
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
The value of KMO measure of Sampling Adequacy as shown in the table 1 above for the variable Reciprocity is 0.744 which is acceptable hence factor Analysis is appropriate for this data.
The Bartlett’s test of Sphericity tests the null hypothesis that original matrix is an identity matrix. The p-value for the variable Reciprocity is highly significant (p<0.001) thus the null hypothesis is rejected. In other words, there are relationships between the variables.
Table 2: Total Variance Explained – Reciprocity
Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
2.198
54.961
54.961
2.198
54.961
54.961
2
0.715
17.875
72.836
3
0.615
15.382
88.218
4
0.471
11.782
100.000
Extraction Method: Principal Component Analysis.
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
The above table 2 shows the eigen values associated with each factor which represents the variance explained by that linear component. SPSS was used to extract factors having eigen values more than 1 which left us with one factor having actual eigen value of 2.198. The variance explaining capacity of that factor is 54.961% which is satisfactory.
Table 3: Component Matrix: Reciprocity
Component Matrixa
Component
1
RECIPROCITY: TIMELY ACTION: The bank responds to my requests in time
0.670
RECIPROCITY: AVAILABILITY OF MANAGERS: The senior managers of the bank are always available for appointment
0.819
RECIPROCITY: COMMUNICATION1: The employees of the bank communicate with me whenever I ask for
0.735
RECIPROCITY: COMMUNICATION2: The bank always communicates the new and important banking information to me
0.734
Extraction Method: Principal Component Analysis.
1 components extracted.
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
The Component matrix above in table 3 shows factor loadings against component 1 extracted earlier. All the loadings are significant as the values are more than 0.4. The order in which the customers considered the factors to be important is Availability of Managers, Communication1, Communication2, and Timely Action.
Table 4: Factor Analysis – Empathy
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
0.737
Bartlett's Test of Sphericity
Approx. Chi-Square
237.727
Df
6
p value
<0.001
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
The value of KMO measure of Sampling Adequacy as shown in the table 4 above for the variable Empathy is 0.737 which is good hence factor Analysis is appropriate for this data.
The Bartlett’s test of Sphericity tests the null hypothesis that original matrix is an identity matrix. The p-value for the variable Empathy is highly significant (p<0.001) thus the null hypothesis is rejected. In other words, there are relationships between the variables.
Table 5: Total Variance Explained – Empathy
Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative
%
1
2.165
54.122
54.122
2.165
54.122
54.122
2
0.792
19.812
73.934
3
0.564
14.104
88.038
4
0.478
11.962
100.000
Extraction Method: Principal Component Analysis.
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
The above table 5 shows the eigenvalues associated with each factor which represents the variance explained by that linear component. SPSS was used to extract factors having eigenvalues more than 1 which left us with one factor having actual eigenvalue of 2.165. The variance explaining capacity of that factor is 54.122 % which is satisfactory.
Table 6: Component Matrix – Empathy
Component Matrixa
Component
1
EMPATHY: ATTENTION: The bank gives me individual attention
0.799
EMPATHY: CONVENIENT WORKING HOURS: The operating hour of the bank is convenient
0.788
EMPATHY: INTEREST OF CUSTOMERS: The bank looks after my interest
0.576
EMPATHY: NEED OF CUSTOMERS: The bank understands my specific needs
0.758
Extraction Method: Principal Component Analysis.
1 components extracted.
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
The Component matrix above in table 6 shows factor loadings against component 1 extracted earlier. All the loadings are significant as the values are more than 0.4. The order in which the customers considered the factors to be important is Attention, Convenient working hours, Need of customers and Interest of customers.
Table 7: Factor Analysis - Relationship Marketing Strategy
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
0.773
Bartlett's Test of Sphericity
Approx. Chi-Square
283.513
Df
10
p value
<0.001
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
The value of KMO measure of Sampling Adequacy as shown in the table 7 above for the variable Relationship Marketing Strategy is 0.773 which is good hence factor Analysis is appropriate for this data.
The Bartlett’s test of Sphericity tests the null hypothesis that original matrix is an identity matrix. The p-value for the variable Relationship Marketing Strategy is highly significant (p<0.001) thus the null hypothesis is rejected. In other words, there are relationships between the variables.
Table 8: Total Variance Explained – Relationship Marketing Strategy
Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
2.393
47.869
47.869
2.393
47.869
47.869
2
0.837
16.743
64.612
3
0.697
13.944
78.555
4
0.579
11.583
90.138
5
0.493
9.862
100.000
Extraction Method: Principal Component Analysis.
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
The above table 8 shows the eigenvalues associated with each factor which represents the variance explained by that linear component. SPSS was used to extract factors having eigenvalues more than 1 which left us with one factor having actual eigenvalue of 2.393. The variance explaining capacity of that factor is 47.869% which is satisfactory.
Table 9: Component Matrix: Relationship Marketing Strategy
Component Matrixa
Component
1
Relationship Marketing Strategy: SATISFACTION: I am satisfied with the overall relationship that I have with my bank
0.753
Relationship Marketing Strategy: PLEASANT EXPERIENCE: I have had a pleasant experience of working with my bank
0.539
Relationship Marketing Strategy: RELATIONSHIP BENEFITS: I receive benefits due to my relationship with bank
0.741
Relationship Marketing Strategy: BOND: I feel that I have a strong bond with my bank
0.680
Relationship Marketing Strategy: COOPERATION: I always receive cooperation from the bank due to my relationship with the bank
0.723
Extraction Method: Principal Component Analysis.
a. 1 components extracted.
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
The Component matrix above in table 9 shows factor loadings against component 1 extracted earlier. All the loadings are significant as the values are more than 0.4. The order in which the customers considered the factors to be important is Satisfaction, Relationship Benefits, Cooperation, Bond and Pleasant Experience.
Correlation and Regression
The table 10 below represents the result of Correlations between independent variables and dependent variable. The outcome (correlation and p-value) reveals that the strength and significance of the relationship between individual Independent Variables (Reciprocity, Empathy) and the Dependent Variable (Relationship Marketing Strategy). In other words, the independent variables have a statistically significant relationship with the dependent variable individually as shown by the sig. value.
Table 10: Correlations between Independent and Dependent Variables
Correlations
Relationship Marketing Strategy
Reciprocity
Pearson Correlation
0.443
p value
<0.001
Empathy
Pearson Correlation
0.590
p value
<0.001
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
The researcher motivated by the above findings tried to find out the regression equation between the independent and the dependent variable. The result of the Regression analysis is given below:
Table 11: Regression Analysis
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
0.600a
0.360
0.356
0.726
a. Predictors: (Constant), Reciprocity, Empathy
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
In the first table R2, also called the Coefficient of Determination measures the proportion of total variation of the Dependent Variable (RMS) explained by the Independent Variables (Trust and Commitment). In the current research the Independent Variables explain 35.6% of the total variation of Dependent Variable which can be considered as satisfactory.
Table 12: ANOVA
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
91.777
2
45.888
87.056
<0.001b
Residual
162.877
309
0.527
Total
254.654
311
a. Dependent Variable: Relationship Marketing Strategy
b. Predictors: (Constant), Reciprocity, Empathy
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
In the table 12 the focus is on F-statistic. The tool tests the null hypothesis that none of the Independent Variables (Reciprocity and Empathy) help explain the variation in Dependent Variable (RMS). The p value (p<0.001) indicates that the F-statistic is large enough to reject the null hypothesis and accept the alternative hypothesis that the Independent Variables (Reciprocity and Empathy) help explain the variation in Dependent Variable (RMS).
Table 13: Relationship between Independent and Dependent Variables
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
T
Sig.
B
Std. Error
Beta
1
(Constant)
1.195
0.168
7.098
0.000
Reciprocity
0.142
0.057
0.140
2.467
>0.001
Empathy
0.476
0.054
0.506
8.894
<0.001
a. Dependent Variable: Relationship Marketing Strategy
Source: Worked out by using the SPSS (version 20) based on responses to questionnaire
The table 13 helps us determine whether the Independent Variables (Reciprocity and Empathy) together have a statistically significant relationship with the Dependent Variable (RMS) and the direction and strength of the relationship.
It is found that the Independent Variables Reciprocity and Empathy are positively correlated with the Dependent Variable Relationship Marketing Strategy. The regression equation is as follows:
RMS=1.195 + 0.142 Reciprocity+ 0.476 Empathy
Further the following Hypothesis were tested
Relationship between independent variables and dependent variable (RMS)
Ho1: There is no relationship between perceived Reciprocity and RMS in the selected banking branches in Kolkata Metropolitan area.
Ha1: There exists a relationship between perceived Reciprocity and RMS in selected banking branches in Kolkata Metropolitan area.
In case of the relationship between Reciprocity and Relationship Marketing Strategy the p value (p>0.001) indicates that the Ha1 is rejected. In other words, original Hypothesis Ho1 that, there exists no relationship between perceived Reciprocity and RMS in selected banking branches in Kolkata Metropolitan area is accepted.
Ho2: There is no relationship between perceived Empathy and RMS in selected banking branches in Kolkata Metropolitan area.
Ha2: There exists a relationship between perceived Empathy and RMS in selected banking branches in Kolkata Metropolitan area.
In case of the relationship between Commitment and Relationship Marketing Strategy the p value (p<0.001) indicates that the original hypothesis Ho2 is rejected. In other words, the alternative hypothesis (Ha2) that there is relationship between perceived Empathy and RMS in selected banking branches in Kolkata Metropolitan area is accepted.
CONCLUSION
The order of importance of the sub-factors of Reciprocity, as per the customers of the selected banking branches of the Kolkata based banks are Availability of managers, Communication1, Communication2, and Timely Action.
The order of importance of the sub-factors of Empathy, as per the customers of the selected banking branches of the Kolkata based banks are Attention, Convenient working hours, Need of customers and Interest of customers.
The order of importance of the sub-factors of Relationship Marketing Strategy, as per the customers of the selected banking branches of the Kolkata based banks are Satisfaction, Relationship Benefits, Co-operation, Bond, Pleasant experience.
The independent variables (i.e. Reciprocity and Empathy) have strong individual correlation with the dependent variable Relationship Marketing Strategy.
The researcher offers the following suggestions based on the empirical research carried out on the responses of the customers of selected banking branches of United Commercial Bank, United Bank of India and Allahabad Bank:
Banks may concentrate more on Empathy while designing Relationship Marketing Strategies.
Banks wishing to retain customers by developing reciprocity may focus on factors like Availability of managers, Communication1, Communication2, and Timely Action.
Banks wishing to retain customers by developing empathy may focus on factors like, Attention, Convenient working hours, Need of customers and Interest of customers.
REFERENCES
Bagozzi, R.P. (1975). Marketing as Exchange. Journal of Marketing, 39(4), pp 32-39.
Berry, L.L. (1983). Relationship Marketing. In: Berry, L.L., Shostack, G.L. and Upah, G.D. (Eds.), Emerging Perspectives on Services Marketing, American Marketing Association, Chicago, IL, pp 25-80.
Callaghan, M., McPhail, J. & Yau, O.H.M. (1995). Dimensions of a Relationship Marketing Orientation: An Empirical Exposition'. Proceedings of the Seventh Biannual World Marketing Congress, 7(2), pp 10-65.
Chattananon, A. & Trimetsoontorn, J. (2009). Relationship Marketing: A Thai Case.
International Journal of Emerging Markets, 4(3), pp 252-274.
Dwyer, F.R., Schurr, P.H. & Oh, S. (1987). Developing Buyer-Seller Relationships, Journal of Marketing, 51(2), pp 11-27.
Ellis, K.L., Lee, J. & Beatty, S.E. (1993). Relationships in Consumer Marketing: Directions for Future Research. AMA Educators' Proceedings: Enhancing Knowledge Development in Marketing, 4, AMA, Chicago, IL, pp 225-230.
Grönroos, C. (1982). An Applied Service Marketing Theory. European Journal of Marketing, 16(7), pp 30–41.
Grönroos, C. (1989). Defining Marketing: A Market-Oriented Approach. European Journal of Marketing, 23(1), pp 52-60.
Houston, F.S., Gassenheimer, J.B. & Maskulka, J.M. (1992). Marketing Exchange, Transactions and Relationships. Quorum Books. US.
Jackson, B.B. (1985). Build Customer Relationships that Last. Harvard Business Review, Nov-Dec, pp 120-128.
Lebra, T.S. (1976). Japanese Patterns of Behavior, University of Hawaii Press, Honolulu, HI. Levitt, T. (1983). After the Sale is Over. Harvard Business Review, 61(5), pp 87-93.
Malinowski, B. (1959). Crime and Custom in Savage Society. Rowman & Littlefield Publishers. US.
McGarry, E.D. (1951). The Contactual Function in Marketing. The Journal of Business of the University of Chicago, 24(2), pp 96-113.
McGarry, E.D. (1953). Some New Viewpoints in Marketing. The Journal of Marketing. 18(1), pp 33-40.
Moorman, C., Deshpande, R. and Zaltman, G. (1993). Relationships Between Providers and Users of Market Research: The Role of Personal Trust. Marketing Science Institute, pp 93- 111, Cambridge, MA.
Morgan, R.M. & Hunt, S.D. (1994). The Commitment-Trust Theory of Relationship Marketing. Journal of Marketing, 58(3), pp 20-38.
Murphy, P.E., Laczniak, G.R & Wood, G. (2007). An Ethical Basis for Relationship Marketing: A Virtue Ethics Perspective. European Journal of Marketing, 41 (1/2), pp 37-57.
Ndubisi, N.O. (2004). Understanding the Salience of Cultural Dimensions on Relationship Marketing, its Underpinnings and Aftermaths. Cross Cultural Management, 11(3), pp 70-89.
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), pp 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), pp 12- 40.
Schneider, B. (1980). The Service Organization: Climate is Critical. Organizational Dynamics, 9(2), pp 52-65.
Sheith, J.N., Gardner, D.M. & Garrett. D.E. (1988). Marketing Theory: Evolution and Evaluation. John Wiley & Sons. New York.
Sin, L.Y.M., Tse, A.C.B., Yau, O.H.M., Lee, J.S.Y. & Chow, R. (2002). The Effect of Relationship Marketing Orientation on Business Performance in a Service-Oriented Economy. Journal of Services Marketing, 16(7), pp 656-676.
Smith, T.F., & Johnson, R.L. (1993). Facilitating the Practice of Relationship Marketing, 1993 AMA Educators. Proceedings: Enhancing Knowledge Development in Marketing, 4, AMA, Chicago, IL, pp 232-3.
Wang, C.L. (2007). Guanxi vs. Relationship Marketing: Exploring Underlying Differences.
Industrial Marketing Management, 36(1), pp 81-86.