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Exploring the moderating effects of relationship inertia and switching cost on CRM performance-customer satisfaction-retention link: Empirical evidence from Indian banking industry.

Introduction

In the era of relationship marketing, retention of customers has been factor critical for the service industries. Researchers have found empirical evidence that customer satisfaction is an important determinant to customer retention (Oliver, 1980; Fornell, 1992; Anderson and Sullivan, 1993, Terblanche, 2006, Hsu, 2008). Some researchers found that when customers were involved in satisfied transaction habit for a prolonged period with a specific firm, they would like to continue with the momentum of relationship (Ouellette and Wood, 1998) and become reluctant to find an alternative (Colgate and Danaher, 2000)-a phenomenon subsequently nomenclated as relationship inertia. Studies were also made to explain the defection behaviour of the customers on the basis of perceived switching costs. The switching costs were estimated not only on the basis of pure monetary value involved in the switching process from one service provider to another but also on the basis of the effort and time invested to search and access alternative service providers. For many a service provider this can be an important strategic paradigm whereby they can elevate the switching costs for their existing customers and create a high exit barrier for the same. Customer relationship management (CRM), as a business philosophy, has been one of the applied formats of relationship marketing which marked the end of transaction-based marketing dominated by marketing mix elements. CRM, as a business process focused on the management of maintaining relationship with the customer on the basis of symbiotic sharing of value and profit. The satisfaction-retention link has an obvious antecedent effect in the form of 'perceived service quality' and also has a desired output namely increase in profitability/market share. Studies conducted by Vlckova and Bednafikova (2007) suggested that customer retention over their lifetime will significantly contribute to enhance company's profitability.

The banking industry in India adopted CRM as a business process and tried to redefine the customer satisfaction-retention-loyalty sequence in the light of inertia and switching costs, where inertia has been considered as a potent habit of consumption and brand association while switching costs has been conceptualized as a barrier to defect which allowed the banks to assort and customize products/services for the customers.

This study concentrates on finding empirical evidence of the moderating effects of relationship inertia and switching costs on CRM performance-customer satisfaction-customer relationship link.

2. Review of Literature

Reinartz et al. (2004) conceptualized customer relationship management (CRM) as a systematic strategic process of managing initiation of customer relationship through customer acquisition process, maintenance of relationship on the basis of symbiotic sharing of value and profit, and termination of a potentially devalued relationship. Nguyen (2007) defined CRM as a information system that tracks customers' interaction with their firms and enable the firms to address issues that are potentially inhibitors or enhancers to profitability (Yueh et al., 2010; Aihie and Bennani, 2007; Adam and Michael, 2005; Gummesson, 2004; Sin et al., 2005) while CRM performance was considered to develop a sustainable competitive advantage by delivering superior customer value (Wang et al., 2004; Ahmad and Hashim, 2010; Sadeghi and Farokhian, 2010) and facilitates in developing relationship with assorted and differentiated customers via interdependent collaboration with those of highest perceived value to the company (Lowe, 2008; Sadeghi and Farokhian, 2010). A major part of the contemporary research concentrated in developing a comprehensive CRM performance measurement framework. Greenberg (2004) introduced the metrics model comprising of three key elements to measure CRM performance namely customer metrics, performance metrics and diagnostic metrics. Hyung Su and Young Gul (2007) mentioned the score-card approach while Hughes (2009) utilized the Balanced Score Card (BSC) and six sigma concept to evaluate the CRM performance. Researchers also focused on identifying the critical elements responsible for CRM performance manifestation and categorized them into CRM process elements and CRM dimensional components. While some research has focused more on IT-related factors (Avlonitis and Panagopoulos, 2005; Roh, Ahn, and Han, 2005), others have emphasized organizational factors like human resources, organizational structure, and reward systems (Rigby et al., 2002), or business process- related factors (Campbell, 2003; Payne and Frow, 2004). Studies were also made to link CRM components and their performance output, namely, link between customer satisfaction and business performance (Kamakura et al. 2002), the link between customer loyalty and firm profitability (Reinartz and Kumar 2000), heterogeneity in customer profitability as an output to CRM deployment (Niraj, Gupta, and Narasimhan 2001), and exhibition of customer loyalty as a behavioural function to CRM adoption (Verhoef 2003). Literatures revealed a few take on CRM performance measurement based on CRM process and dimensionality ((e.g., Brewton and Schiemann, 2003; Jain, Jain, and Dhar, 2003; Kim, Suh, and Hwang, 2003; Lindgreen et al., 2006; Zablah, Bellenger, and Johnston, 2004). Abdullateef, Mokhtar and Yousoff (2010) concentrated on four dimensions of CRM namely customer orientation, CRM organization, knowledge management and CRM technology to identify caller satisfaction in contact centers.

Customer satisfaction, reported to be a state of behavioural expression of customers as an output to perceived service quality where enhancement of satisfaction has been considered to be directly proportional to elevated perceptual level of service quality (Lin, 2007, Joewono and Kubota, 2007). Since 1990s researchers focused on customer satisfaction and customer retention link and pondered over its antecedent effects particularly in terms of long-term behavioural intention like customer loyalty and its financial impact on firms (profitability). Studies revealed that customer satisfaction has a positive impact on customer retention (Jones et al, 2000; Ranaweera and Prabhu, 2003; Ranaweera and Neely, 2003; Tsoukatos and Rand, 2006).

While analysing prolonged relationship of consumers with their firms, researchers adopted the term 'inertia' as a concept to explain this unchanged bond between the customers and their firms. Huang and Yu (1999), projected inertia as a condition where repurchasing behaviour occurs as a response to situational stimulus and it reflects a non-conscious process. Relationship inertia has also been conceptualized as a habitual process (Assael, 1998; Solomon, 1994) which does not manifest emotional outburst and is predominantly convenience driven (Gounaris and Stathakopoulos, 2004; Lee and Cunningham, 2001). According to White and Yanamandram (2004), relationship inertia is a behavioural complex reflected in inert customers who avoid making new purchase decisions and price comparisons (Pitta et al, 2006) and try to maintain status quo (Ye, 2005). Colgate and Danaher (2000) observed relationship inertia as a basic human nature that confirms human habits as an auto-behavioural-tendency responding to past developments (Limayem and Hirt, 2003; Verhoef, 2003). Researchers also pointed out to the fact that past behaviour of relationship continuum might represent inertia effect (Rust et al, 2000) and customer loyalty may be an output to prolonged relationship inertia (Anderson and Srinivasan, 2003; Beckett et al, 2000; Colgate and Lang, 2001, Odin et al, 2001, Yanamandram and White, 2006; Weiringa and Verhoef, 2007). Although major investigations were made to justify the effect of relationship inertia on satisfied customers, Anderson and Srinivasan (2003) found that relationship inertia can be a potent inhibitor for the dissatisfied customers even and restrict them from defection.

Relationship inertia has been attributed by the researchers to switching cost as they were of the opinion that perceived switching cost is directly proportional to relationship inertia or in other words, switching cost acts as a potential inhibitor of changing service providers (Ranaweera and Prabhu, 2003; Leeetal, 2001; Jones et al, 2000; Bansal and Taylor, 1999). Switching cost has been conceptualized as the cost of changing services in terms of time, monetary value and psychological factors (Dick and Basu, 1994) and was found to be comprised of search cost and transaction cost (Eckardt, 2008). Furthermore, review of literature revealed the impact of switching costs on customer retention (Jones, Mothersbaugh and Beatty, 2000; Lee, Lee and Feick, 2001; Ranaweera and Prabhu, 2003). Study conducted by Lai, Liu and Lin (2011) showed that inertia and switching costs weaken the impact of satisfaction on customer retention in the perspective of auto liability insurance industry. Cheng, Chiu, Hu and Chang (2011), in their study explored the impact of relationship inertia as a mediator on customer satisfaction-loyalty link and observed that relationship inertia has a strong mediating effect on the link.

Review of literature confirmed that although studies were made to identify the impact of inertia and switching costs on customer satisfaction and customer retention, there has been a dearth of research focusing the moderating effects of relationship inertia and perceived switching costs on CRM performance-customer satisfaction-customer retention link in an integrated manner in the context of banking industry. This paper, therefore, empirically explores the relationship between CRM performance, customer satisfaction and customer retention and further attempts to identify the moderating effects of relationship inertia and perceived switching costs on the relationship.

Following 'introduction' the layout of the paper follows: 'review of literature and formulation of hypothesis anc model construct, methodology, data analysis and conclusion including future research and limitations.

2.1 Formulation of hypotheses and model construct

Apropos to the literature reviewed the following hypotheses were formulated:

H1: CRM performance has a positive impact on customer satisfaction.

H2: Relationship inertia has a positive impact on customer satisfaction.

H3: Switching cost has a positive impact on customer satisfaction.

H4: Customer satisfaction has a positive effect on customer retention

H5: Higher degree of relationship inertia will ensure superior level of customer retention

H6: Higher degree of switching costs will ensure higher level of customer retention

While studying the moderating effect of relationship inertia on customer satisfaction-retention link, Anderson and Srinivasan, 2003, found that customers with higher level of relationship inertia had lesser impact of satisfaction on loyalty. In a similar kind of study conducted on auto-liability insurance services, Lai, Liu and Lin (2011) made the same observations. But, literature did not reveal any comprehensive study involving moderating effect of relationship inertia or CRM performance-satisfaction-retention link, although CRM performance happens to have a positive effect on service quality, an antecedent to customer satisfaction-customer retention link.

Hence the study hypothesized that:

H7: Higher degree of relationship inertia will reduce impact of CRM performance on customer satisfaction

H8: Higher level of perceived switching costs will reduce impact of CRM performance on customer satisfaction

H9: Higher level of relationship inertia decreases the impact of customer satisfaction on customer retention

H10: Higher level of perceived switching costs decreases the impact of customer satisfaction on customer retention

Literature showed dearth in study involving CRM performance-customer satisfaction-retention link under moderating effects of perceived switching costs, although evidences revealed that elevated switching costs will pacify the link between CRM performance, customer satisfaction and customer retention. Barnes et al (2004) talked of a behavioural lock-in effect whereby customers with high perceived switching costs willingly fall into an inertia-trap. Thus high switching costs elevates exit barriers for customers and reduces the effect of CRM performance on customer satisfaction and customer retention relationship.

Therefore it was hypothesized that:

H11: With the increase in perceived switching costs, the moderating effect of inertia on the relationship between CRM performance and customer satisfaction strengthens.

H12: With the increase in perceived switching costs, the moderating effect of inertia on the relationship between customer satisfaction and customer retention strengthens.

Based on the literature reviewed and hypotheses framed, the following model framework was proposed (Fig.1):

3. Methodology

The objectives of the study were (a) to explore the relationship between CRM performance-customer satisfaction-customer retention (b) to assess the moderating effects of relationship inertia and perceived switching costs on CRM performance-customer satisfaction-customer retention link and (c) to test the proposed model framework (Fig. 1) involving the variables under study using structural equation modeling. The study was carried out in the banking sector involving the largest public sector bank of India namely State Bank of India (SBI) across 5 cities in West Bengal (Asansol, Durgapur, Ranigunj, Andal and Bolpur) involving 14 branches. The study was comprised of two phases. Phase-I involved a pilot Study to refine the test instrument with rectification of question ambiguity, refinementof research protocol and confirmation of scale reliability was given special emphasis (Teijlingen and Hundley, 2001). FGI was administered. Cronbach's [alpha] coefficient (>0.7) established scale reliability (Nunnally and Bernstein, 1994). The structured questionnaire thus obtained after refinement contained four sections. Section-I asked the respondents (customers) about their perception of CRM performance as of their bank (SBI), section-ll was intended to generate response from the respondents with regard to their level of satisfaction with their bank, section-Ill was designed to understand the degree to which SBI was successful to retain their customers and the willingness of the customers to stay associate with their bank and section-IV focused on demographic data of the respondents. The second phase of the cross-sectional study was conducted by using the structured questionnaire. Systematic simple random sampling technique was administered as every fifth customer coming out of the bank premise was requested to fill-up the questionnaire. A total number of 2000 questionnaires was used which generated 1301 usable responses with a response rate of 65.05 percent (approximately).

3.1 Factor constructs measurement

To develop a measure for CRM performance three CRM process elements namely CRM initiation, CRM maintenance, and CRM termination (Reinartz, Krafft, and Hoyer, 2004) and four CRM dimensions namely customer orientation, CRM organization , knowledge management, and CRM technology (Abdullateef, Mokhtar and Yousoff, 2010) were identified for the study. The CRM performance items thus obtained were subsequently modified to suit the study. The customer satisfaction items were an adaptation from Hellier et al (2003) which emphasized the service provider's capability to meet the expectation and perception of customers adequately. The customer retention items were based on Morgan and Hunt (1994). The items that measured the relationship inertia were adopted from Cheng, Chiu, Hu and Chang (2011), Lai, Liu and Lin (2011), Huang and Yu (1994) and Anderson and Srinivasan (2003). To measure the perceived switching costs, we adopted the items from Chen and Hitt (2002) and Jones et al. (2000). A 7 point Likert scale (Alkibisi and Lind, 2011) was used to generate response.

4. Data Analysis and Interpretation

Bivariate correlations were obtained to assess the relationship between the variables. The results were displayed in Table-I. Correlation results revealed a positive and significant relationship between the variables.

Exploratory factor analysis (EFA) was employed using principal axis factoring procedure with orthogonal rotation through VARIMAX process with an objective to assess the reliability and validity of all five factor constructs (TableII). The Cronbach's Coefficient alpha was found significant enough. The KMO measure of sample adequacy (0.917) indicated a high-shared variance and a relatively low uniqueness in variance (Kaiser and Cerny, 1979). Barlett's sphericity test (Chi-square=1651.127, p<0.001) indicated that the distribution is ellipsoid and amenable to data reduction (Cooper and Schindler, 1998).

Items with very low factor loadings/cross loadings (< 0.500) and poor reliability (Cronbach's' alpha) were discarded. Thus CRM performance items were reduced from 58 to 34.

Regression analysis was deployed by considering the average (mean) values of the items for the factor constructs. A double regression was applied: (a) considering customer satisfaction (CS) as the dependent variable and (b) customer retention (CR) as the dependent variable. For providing empirical evidence to our hypotheses, we proposed an ordinary least square (OLS) regression for our dependent variables CS and CR. The following models were constructed:

Regression equation-1

CS = [beta]0 + [beta]1 * CRMP + [beta]2*RI + [beta]3 * SC + [beta]4 * CRMP * RI + [beta]5 * CRMP * SC + [beta]4 * CRMP * RI * SC + [epsilon]i

where, CS represented customer satisfaction, CRMP represented CRM performance, Rl represented relationship inertia and SC represented switching cost. CRMP*RI and CRMP*SC represented binary interaction between CRM performance and inertia and CRM performance and switching cost respectively. CRMP*RI*SC represented the ternary interaction between CRM performance, inertia and switching cost.

Regression equation-2

CR = [beta]0 + [beta]1 * CS + [beta]2 * RI + [beta]3 * SC + [beta]4 * CS * RI + [beta]5 * CS * SC + [beta]4 * CS * RI * SC + [epsilon]i

where, CR represented customer retention, CS represented customer satisfaction, Rl represented inertia and SC represented switching cost. CS*I and CS*SC represented binary interaction between customer satisfaction and inertia and customer satisfaction and switching cost respectively. CS*I*SC represented the ternary interaction between customer satisfaction, inertia and switching cost.

The regression models were displayed in Table-Ill (for equation-1) and Table-IV (for equation-2). For each equation, four regression models were established. Model 1 depicted the direct effect of CRM performance, customer satisfaction, customer retention inertia and switching costs, Model 2 and 3 revealed the binary interaction terms and Model 4 represented the ternary interaction. Standardization was applied to avoid interference with regression coefficients arising out of MulticolIinearity between interaction variables (Irwir and McClellan, 2001; Aiken and West, 1991). The VIF (variance inflation factor) corresponding to each independent variable is less than 5, indicating that VIF is well within acceptable limit of 10 (Ranaweera and Neely, 2003). Table-Ill revealed that Model-1 provided support for H1, H2, and H3, as CRM performance was found to have a positive and significant effect on customer satisfaction ([beta] = .439 **, p<0.01), relationship inertia exhibited significant and positive impact on customer satisfaction ([beta] = .265 **, p<0.01), perceived switching costs showed significant and positive relationship with customer satisfaction ([beta] = .209 **, p <0.01). Results of Model-2 supported H7. The binary interaction between CRM performance and inertia indicated that the relationship between CRM performance and customer satisfaction depends on the level of inertia ([beta] = -.301**, p<0.01). The negative interaction confirmed our prediction that with the increase in relationship inertia the impact of CRM performance on customer satisfaction will decrease indicating a habitual-trap-of-consumption for customers. Model-3 supported HS. It revealed that the binary interaction between CRM performance and perceived switching costs indicated that the relationship between CRM performance and customer satisfaction depends on the level of perceived switching costs ([beta] = -. 1 51 **, p<0.01). The negative interaction confirmed our prediction that with the increase in perceived switching costs, the impact of CRM performance on customer satisfaction will decrease. Model-4 represented the ternary interaction and revealed that as perceived switching costs increases the negative mediating effect ([beta] = -.1 78 **, p<0.01) of relationship inertia on CRM performance and customer satisfaction strengthens, thereby lending support to H11.

Table-IV revealed that Model-1 provided support for H4, H5, and H6, as customer satisfaction displayed a positive and significant effect on customer retention ([beta] = .421**, p<0.01), relationship inertia exhibited significant and positive impact on customer retention ([beta] = .241**, p<0.01), perceived switching costs showed significant and positive relationship with customer retention ([beta] = .189**, p<0.01). Results of Model-2 supported H9. The binary interaction between customer satisfaction and inertia indicated that the relationship between customer satisfaction and customer retention depends on the level of inertia ([beta] = -.288**, p<0.01). The negative interaction confirmed our prediction that with the increase in relationship inertia the impact of customer satisfaction on customer retention will decrease. Model-3 supported H10. It revealed that the binary interaction between customer satisfaction and perceived switching costs indicated that the relationship between customer satisfaction and customer retention depends on the level of perceived switching costs ([beta] = - .176**, p<0.01). The negative interaction revealed our prediction that with the increase in perceived switching costs, the impact of customer satisfaction on customer retention will decrease. Model-4 represented the ternary interaction and supported H12 and revealed that as perceived switching costs increases the negative mediating effect ([beta] = -.143**, p<0.01) of relationship inertia on customer satisfaction and customer retention strengthens.

Confirmatory factor analysis (CFA) was deployed to understand the convergence, discriminant validity and dimensionality for each construct to determine whether all the 34 items (Table-IV) measure the construct adequately as they had been assigned for. LISREL 8.80 programme was used to conduct the Structural Equation Modeling (SEM) and Maximum Likelihood Estimation (MLE) was applied to estimate the CFA models. A number of fit-statistics were obtained. The GFI (0.992) and AGFI (0.982) scores for all the constructs were found to be consistently >.900 indicating that a significant proportion of the variance in the sample variance-covariance matrix is accounted for by the model and a good fit has been achieved (Hair et al, 1998, 2006; Holmes-Smith, 2002, Byrne, 2001). The CFI value (0.987) for all the constructs were obtained as > .900 which indicated an acceptable fit to the data (Bentler, 1992). The RMSEA value obtained (0.049) is < 0.08 for an adequate model fit (Hu and Bentler, 1999). The probability value of Chi-square ([chi square] = 231.09, df=97, p = 0.000) is more than the conventional 0.05 level (P=0.20) indicating an absolute fit of the models to the data.

Structural Equation Modeling (SEM) was used to test the relationship among the constructs. All the 18 paths drawn were found to be significant at p<0.05. The research mode holds well (Fig.2) as the fit- indices supported adequately the model fit to the data. The double-curved arrows indicate co-variability of the latent variables. The residual variables (error variances) are indicated by 1 [euro], 2 [euro],3 [euro],etc. The regression weights are represented by [Lambda]. The co-variances are represented by [beta]. To provide the latent factors an interpretable scale; one factor loading is fixed to 1 (Hox and Bechger).

5. Conclusion

Customer satisfaction and subsequent retention of valued customers are the two pivotal strategic intents of customer relationship management (CRM) and it becomes more relevant in service industries as they are predominantly intangible and heterogeneous as a result of which perception of service quality is quite difficult. This study empirically investigates the relational impact of CRM performance on customer satisfaction and subsequent customer retention and further attempts to investigate the moderating effects of relational inertia and perceived switching costs on the said relation in the perspective of banking industry in India whereby the largest nationalized bank, the State Bank of India, was considered as a case. The study revealed that CRM performance has a strong and positive impact on customer satisfaction. A positive relationship also existed between customer satisfaction and customer retention. Therefore, strategically it becomes significant for the bankers to maintain high level of CRM performance and thereby ensuring enhanced level of perceived service quality which is considered to be a critical element for repurchasing decisions to create a sustained base of customers (Tsoukatos and Rand, 2006). In addition to this the study explained that perceived switching costs and relationship inertia create high exit barriers for the customers and prevents them from switching to alternative service providers. The study also showed that the impact of customer satisfaction on customer retention becomes irrelevant as perceived switching cost and relationship inertia increases. Higher perceived switching costs would not allow a customer to search for new alternatives and relationship inertia will create a habitual-trap for the customers to stay in a relationship with their existing firm and produce a behavioural lock-in effect. Similarly the relationship inertia strengthens CRM performance -customer satisfaction link as perceived switching costs become high. In other words, the barriers raised by switching costs to prevent customer defection, reinforces the habitual trap or the behavioural lock-in effect produced by relationship inertia and increase the level of customer retention. Therefore the bankers must try and ensure to maintain a high level of perceived switching costs for their valued customers by practicing proactive CRM and ensuring elevated level of customer satisfaction and beyond. Firms offering assorted and customized services are more likely to ensure the habitual-trap or behavioural lock-in for the customers as their perceived switching costs is raised to a higher level (Lai, Liu and Lin, 2011). This hinted towards regular analysis and updation of product/service portfolio offered by State Bank of India. Finally, the proposed model holds good depicting cause and effect relationship of the variables under study.

The study had geographical limitations as it has been restricted to specific places of West Bengal, which in future, can be widened to obtain a more generalized conclusion. Further extrapolations can be made by considering the impact of differentiated offerings of alternative firms at competitive price. In addition to this, specific investigation may be undertaken to investigate the exact behavioural attitude and intention of dissatisfied customers under the impact of higher perceived switching cost and relationship inertia. It would be also interesting for the researchers to study the impact of switching cost and inertia on satisfied customers facing better and technologically upgraded service offers at an elevated price. The study can be taken up for other service sectors also, particularly hospitality and tourism industry which thrives on CRM practices, customer retention and repatronization of the same. The study was cross-sectional in nature; therefore longitudinal research may be taken up also to realize the gradual changes in the perception and impact of switching costs and inertia on CRM performance-customer satisfaction-customer retention link over time.

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Arup Kumar Baksi

Assistant Professor, Bengal Institute of Technology and Management, Birbhum.

Bivraj Bhusan Parida

Professor, The University of Burdwan, West Bengal.


Table-I
Bivariate Correlation between the Variables

Variables               CRM performance     Customer     Customer
                                          satisfaction   Retention
CRM performance                1
Customer satisfaction      0.468 **            1
Customer Retention         0.219 **         0.327 **         1
Relationship Inertia        0.109 *         0.121 *      0.176 **
Switching cost             0.227 **         0.134 **      0.119 *

Variables               Relationship Inertia   Switching cost

CRM performance
Customer satisfaction
Customer Retention
Relationship Inertia             1
Switching cost                0.339 **               1

** Correlation significant at 0.01 level (2-tailed), * Correlation
significant at 0.05 level (2-tailed),

Table-II
Measurement of Reliability and Validity of the Variables

Items                                              FL **

CRM performance
SBI has a well documented system to acquire new    0.871
  customer (CRMINI11
SBI offers customized differentiated products      0.874
  to prospects (CRMINI2)
SBI communicates with prospects via assorted       0.837
  media channels (CRMINI3)
SBI maintains a system to interact with            0.845
  defected customers (CRMINI4)
SBI maintains a continuous relationship with its   0.882
  existing customers (CRMMAIN1)
SBI updates its customers about new                0.817
  products/services (CRMMAIN2)
SBI assists its customers to upgrade them to an    0.861
  enhanced level of services (CRMMAIN3)
SBI offers customized incentives for valued        0.812
  customers (CRMMAIN4)
SBI deals with customer complaints and problems    0.823
  promptly and efficiently (CRMMAIN5)
SBI uses satisfied customers as advocates          0.798
  (CRMMAIN6)
SBI passively dissociates itself from              0.791
  de-valued customers (SBITERM1)
SBI is committed to meeting customer's needs and   0.801
  expectations (C01)
SBI has installed system to update customer        0.789
  database  on a regular basis (C02)
SBI has well documented system to disseminate      0.842
  customer information (C03)
SBI maintains customer centric performance         0.837
  standards at all customer touch-points
  (CRM0RG1)
SBI has resources and expertise to succeed         0.866
  in CRM process (CRM0RG2)
SBI's employees are knowledgeable enough to        0.818
  deal with contingent situation (KM1)
SBI shares customer information across all         0.826
  points of contact through MCI (KM2)
SBI maintains that mining data intelligently is    0.776
  a source of competitive advantage (KM3)
SBI banks on updated CBS to establish long term    0.829
  customer relationships (CRMTECH1)
SBI uses IT to facilitate the management of        0.876
  customer relationships (CRMTECH2)
SBI uses CRM technology to create customized       0.885
  offerings to customers (CRMTECH3)
SBI provides customer information at every         0879
  technology interface (CRMTECH4)

                 Customer satisfaction

As a customer of SBI, I am satisfied with the      0.913
  services provided by SBI (CS1)
As a customer of SBI, I would positively           0.899
  recommend SBI to new prospects (CS2)
As a customer, I feel good about my decision       0.908
  to bank with SBI (CS3)

                  Customer retention

I intend to remain associated with SBI for         0.887
I intend to continue my relationship with SBI as   0.907
  a customer for the next five years (CR2)

                Relationship inertia

Unless other bank's provide me with some           0.817
  distinct advantages, I am habituated in
  getting services from SBI (Rl 1)
Unless 1 am extremely dissatisfied with SBI,       0.798
  switching to an alternative bank will
  be a bother (RI2)
Unless 1 am extremely dissatisfied with SBI,       0.812
  switching to an alternative bank will
  be inconvenient for me (RI2)

                    Switching costs

For me the costs involved in searching,            0.847
  investing time and money and accessing an
  alternative bank other than SBI is high (SCI)
It would take a lot of effort to change            0.869
  my bank (SBI) (SC2)
It would be a hassle to change my                  0.891
  existing bank (SBI) (SC3)
KMO                                                .917
Barlett's sphericity                               Chi-square =
                                                   1651.127, p<0.001

Items                                              t-value

CRM performance
SBI has a well documented system to acquire new    -
  customer (CRMINI11
SBI offers customized differentiated products      27.875
  to prospects (CRMINI2)
SBI communicates with prospects via assorted       24.356
  media channels (CRMINI3)
SBI maintains a system to interact with            26.332
  defected customers (CRMINI4)
SBI maintains a continuous relationship with its   28.319
  existing customers (CRMMAIN1)
SBI updates its customers about new                22.764
  products/services (CRMMAIN2)
SBI assists its customers to upgrade them to an    26.731
  enhanced level of services (CRMMAIN3)
SBI offers customized incentives for valued        22.098
  customers (CRMMAIN4)
SBI deals with customer complaints and problems    23.009
  promptly and efficiently (CRMMAIN5)
SBI uses satisfied customers as advocates          22.432
  (CRMMAIN6)
SBI passively dissociates itself from              22.216
  de-valued customers (SBITERM1)
SBI is committed to meeting customer's needs and   22.981
  expectations (C01)
SBI has installed system to update customer        22.117
  database  on a regular basis (C02)
SBI has well documented system to disseminate      26.118
  customer information (C03)
SBI maintains customer centric performance         26.097
  standards at all customer touch-points
  (CRM0RG1)
SBI has resources and expertise to succeed         26.912
  in CRM process (CRM0RG2)
SBI's employees are knowledgeable enough to        22.818
  deal with contingent situation (KM1)
SBI shares customer information across all         23.001
  points of contact through MCI (KM2)
SBI maintains that mining data intelligently is    22.009
  a source of competitive advantage (KM3)
SBI banks on updated CBS to establish long term    23.327
  customer relationships (CRMTECH1)
SBI uses IT to facilitate the management of        28.098
  customer relationships (CRMTECH2)
SBI uses CRM technology to create customized       29.235
  offerings to customers (CRMTECH3)
SBI provides customer information at every         28.106
  technology interface (CRMTECH4)

                  Customer satisfaction

As a customer of SBI, I am satisfied with the      -
  services provided by SBI (CS1)
As a customer of SBI, I would positively           29.789
  recommend SBI to new prospects (CS2)
As a customer, I feel good about my decision       33.016
  to bank with SBI (CS3)

                  Customer retention

I intend to remain associated with SBI for         -

I intend to continue my relationship with SBI as   32.401
  a customer for the next five years (CR2)

                 Relationship inertia

Unless other bank's provide me with some           *
  distinct advantages, I am habituated in
  getting services from SBI (Rl 1)
Unless 1 am extremely dissatisfied with SBI,       27.094
  switching to an alternative bank will
  be a bother (RI2)
Unless 1 am extremely dissatisfied with SBI,       28.643
  switching to an alternative bank will
  be inconvenient for me (RI2)

                  Switching costs

For me the costs involved in searching,            -
  investing time and money and accessing an
  alternative bank other than SBI is high (SCI)
It would take a lot of effort to change            28.432
  my bank (SBI) (SC2)
It would be a hassle to change my                  30.712
  existing bank (SBI) (SC3)
KMO
Barlett's sphericity

Items                                              [alpha] **   CR **

CRM performance
SBI has a well documented system to acquire new    0.901        0.901
  customer (CRMINI11
SBI offers customized differentiated products      0.901        0.901
  to prospects (CRMINI2)
SBI communicates with prospects via assorted       0.901        0.901
  media channels (CRMINI3)
SBI maintains a system to interact with            0.901        0.901
  defected customers (CRMINI4)
SBI maintains a continuous relationship with its   0.901        0.901
  existing customers (CRMMAIN1)
SBI updates its customers about new                0.901        0.901
  products/services (CRMMAIN2)
SBI assists its customers to upgrade them to an    0.901        0.901
  enhanced level of services (CRMMAIN3)
SBI offers customized incentives for valued        0.901        0.901
  customers (CRMMAIN4)
SBI deals with customer complaints and problems    0.901        0.901
  promptly and efficiently (CRMMAIN5)
SBI uses satisfied customers as advocates          0.901        0.901
  (CRMMAIN6)
SBI passively dissociates itself from              0.901        0.901
  de-valued customers (SBITERM1)
SBI is committed to meeting customer's needs and   0.901        0.901
  expectations (C01)
SBI has installed system to update customer        0.901        0.901
  database  on a regular basis (C02)
SBI has well documented system to disseminate      0.901        0.901
  customer information (C03)
SBI maintains customer centric performance         0.901        0.901
  standards at all customer touch-points
  (CRM0RG1)
SBI has resources and expertise to succeed         0.901        0.901
  in CRM process (CRM0RG2)
SBI's employees are knowledgeable enough to        0.901        0.901
  deal with contingent situation (KM1)
SBI shares customer information across all         0.901        0.901
  points of contact through MCI (KM2)
SBI maintains that mining data intelligently is    0.901        0.901
  a source of competitive advantage (KM3)
SBI banks on updated CBS to establish long term    0.901        0.901
  customer relationships (CRMTECH1)
SBI uses IT to facilitate the management of        0.901        0.901
  customer relationships (CRMTECH2)
SBI uses CRM technology to create customized       0.901        0.901
  offerings to customers (CRMTECH3)
SBI provides customer information at every         0.901        0.901
  technology interface (CRMTECH4)

                    Customer satisfaction

As a customer of SBI, I am satisfied with the      0.921        0.921
  services provided by SBI (CS1)
As a customer of SBI, I would positively           0.921        0.921
  recommend SBI to new prospects (CS2)
As a customer, I feel good about my decision       0.921        0.921
  to bank with SBI (CS3)

                    Customer retention

I intend to remain associated with SBI for         0.889        0889

I intend to continue my relationship with SBI as   0.889        0.889
  a customer for the next five years (CR2)

                    Relationship inertia

Unless other bank's provide me with some           0.907        0.907
  distinct advantages, I am habituated in
  getting services from SBI (Rl 1)
Unless 1 am extremely dissatisfied with SBI,       0.907        0.907
  switching to an alternative bank will
  be a bother (RI2)
Unless 1 am extremely dissatisfied with SBI,       0.907        0.907
  switching to an alternative bank will
  be inconvenient for me (RI2)

                       Switching costs

For me the costs involved in searching,            0.903        0.903
  investing time and money and accessing an
  alternative bank other than SBI is high (SCI)
It would take a lot of effort to change            0.903        0.903
  my bank (SBI) (SC2)
It would be a hassle to change my                  0.903        0.903
  existing bank (SBI) (SC3)
KMO
Barlett's sphericity

Items                                              AVE **

CRM performance
SBI has a well documented system to acquire new    0.789
  customer (CRMINI11
SBI offers customized differentiated products      0.789
  to prospects (CRMINI2)
SBI communicates with prospects via assorted       0.789
  media channels (CRMINI3)
SBI maintains a system to interact with            0.789
  defected customers (CRMINI4)
SBI maintains a continuous relationship with its   0.789
  existing customers (CRMMAIN1)
SBI updates its customers about new                0.789
  products/services (CRMMAIN2)
SBI assists its customers to upgrade them to an    0.789
  enhanced level of services (CRMMAIN3)
SBI offers customized incentives for valued        0.789
  customers (CRMMAIN4)
SBI deals with customer complaints and problems    0.789
  promptly and efficiently (CRMMAIN5)
SBI uses satisfied customers as advocates          0.789
  (CRMMAIN6)
SBI passively dissociates itself from              0.789
  de-valued customers (SBITERM1)
SBI is committed to meeting customer's needs and   0.789
  expectations (C01)
SBI has installed system to update customer        0.789
  database  on a regular basis (C02)
SBI has well documented system to disseminate      0.789
  customer information (C03)
SBI maintains customer centric performance         0.789
  standards at all customer touch-points
  (CRM0RG1)
SBI has resources and expertise to succeed         0.789
  in CRM process (CRM0RG2)
SBI's employees are knowledgeable enough to        0.789
  deal with contingent situation (KM1)
SBI shares customer information across all         0.789
  points of contact through MCI (KM2)
SBI maintains that mining data intelligently is    0.789
  a source of competitive advantage (KM3)
SBI banks on updated CBS to establish long term    0.789
  customer relationships (CRMTECH1)
SBI uses IT to facilitate the management of        0.789
  customer relationships (CRMTECH2)
SBI uses CRM technology to create customized       0.789
  offerings to customers (CRMTECH3)
SBI provides customer information at every         0.789
  technology interface (CRMTECH4)

                 Customer satisfaction

As a customer of SBI, I am satisfied with the      0.731
  services provided by SBI (CS1)
As a customer of SBI, I would positively           0.731
  recommend SBI to new prospects (CS2)
As a customer, I feel good about my decision       0.731
  to bank with SBI (CS3)

                 Customer retention

I intend to remain associated with SBI for         0.774

I intend to continue my relationship with SBI as   0.774
  a customer for the next five years (CR2)

                 Relationship inertia

Unless other bank's provide me with some           0.849
  distinct advantages, I am habituated in
  getting services from SBI (Rl 1)
Unless 1 am extremely dissatisfied with SBI,       0.849
  switching to an alternative bank will
  be a bother (RI2)
Unless 1 am extremely dissatisfied with SBI,       0.849
  switching to an alternative bank will
  be inconvenient for me (RI2)

                   Switching costs

For me the costs involved in searching,            0.699
  investing time and money and accessing an
  alternative bank other than SBI is high (SCI)
It would take a lot of effort to change            0.699
  my bank (SBI) (SC2)
It would be a hassle to change my                  0.699
  existing bank (SBI) (SC3)
KMO
Barlett's sphericity

** FL- Factor loadings, [alpha]--Cronbach's [alpha], CR--Composite
reliability, AVE--Average variance extracted

Table-III
Regression Models Testing the Interaction Effects (equation-1)

Independent Variables           Dependent variable: Customer
                                       satisfaction

                                    Model-1            Model-2
                                [beta] (t value)   [beta] (t value)

CRM performance (CRMP)              .439 **
Relationship inertia (I)            .265 **
Perceived switching costs (SC)      .209 **

                                Binary interaction effects

CRMP*I                                                 -.301 **
CRMP*SC

                                Ternary interaction effects

CRMP*I*SC
Adjusted R2                                              .482
F-value                                               197.36 **

Independent Variables            Dependent variable: Customer
                                         satisfaction

                                    Model-3            Mode 1-4
                                [beta] (t value)   [beta] (t value)

CRM performance (CRMP)
Relationship inertia (I)
Perceived switching costs (SC)

                                Binary interaction effects

CRMP*I
CRMP*SC                             -.151 **

                               Ternary interaction effects

CRMP*I*SC                                              -.178 **
Adjusted R2                           .493               .501
F-value                            142.29 **          119.17 **

Independent Variables           Dependent variable: Customer
                                       satisfaction

                                  VIF

CRM performance (CRMP)           2.791
Relationship inertia (I)         2.216
Perceived switching costs (SC)   3.019

                               Binary interaction effects

CRMP*I                           2.011
CRMP*SC                          2.521

                                Ternary interaction effects

CRMP*I*SC                        1.869
Adjusted R2                       .516
F-value                         97.09 **

Table-IV
Regression Models Testing the Interaction Effects (equation-2)

Independent Variables            Dependent variable: Customer
                                           retention

                                     Model-1            Model-2
                                 [beta] (t value)   [beta] (t value)

Customer satisfaction                .421 **
Relationship inertia (I)             .241 **
Perceived switching costs (SC)       .189 **

                                 Binary interaction effects

CS*I                                                    -.288 **
CS*SC

                                 Ternary interaction effects

CS*I*SC
Adjusted R2                                               .448
F-value                                                142.11 **

Independent Variables            Dependent variable: Customer
                                           retention

                                     Model-3            Model-4
                                 [beta] (t value)   [beta] (t value)

Customer satisfaction
Relationship inertia (I)
Perceived switching costs (SC)

                                 Binary interaction effects

CS*I
CS*SC                                -.176 **

                                 Ternary interaction effects

CS*I*SC                                                 -.143 **
Adjusted R2                            .462               .493
F-value                             121.13 **          109.60 **

Independent Variables            Dependent variable: Customer
                                          retention

                                   VIF

Customer satisfaction             1.619
Relationship inertia (I)          1.988
Perceived switching costs (SC)    2.179

                                 Binary interaction effects

CS*I                              1.697
CS*SC                             2.018

                                 Ternary interaction effects

CS*I*SC                           2.118
Adjusted R2                        .501
F-value                          89.62 **
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Author:Baksi, Arup Kumar; Parida, Bivraj Bhusan
Publication:Abhigyan
Date:Jan 1, 2013
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