`` sit journal of management vol.2. no. 2. december 2012 ... · `` sit journal of management vol.2....

`` SIT Journal of Management

Vol.2. No. 2. December 2012. Pp.90-121

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Process and Dimensional Measurement and Impact of Customer Relationship Management

on Service Quality using Structural Equation Modeling: An Empirical Investigation

Arup Kumar Baksi* & Bivraj Bhusan Parida**

ABSTRACT

Customer relationship management (CRM), as a business process, has transformed the eco-system of firm-

customer relationship from a one-time transactional format to a long-term relationship environment.

Academicians, researchers, practitioners and business analysts pondered over the varied dimensionalities

and process pattern of the CRM system and came out with various propositions, though, CRM performance

(process and dimension driven) as a potential enhancer of service quality, remained as a grey area. This

paper empirically investigates the process and dimensional measurement and possible impact of CRM on

the perceived service quality of customers in the context of banking industry. The paper articulates on

multivariate statistical applications on primary data to test the postulated hypotheses formulated from

review of literatures. The researchers have also used structural equation modeling approach to test the

proposed model involving the three major constructs namely CRM process, CRM dimensions and service

quality. The results revealed a positive and significant impact relationship between CRM process and

perceived service quality with further confirmation of significant moderating effects of CRM dimensions

on CRM process-service quality outcome.

Key words: Customer Relationship Management, Process, Dimensions, Impact, Service Quality,

Bank

*Dr. Arup Kumar Baksi, Asst Professor , Department of Management Science, Bengal Institute

of Technology & Management, Santiniketan, e-mail: [email protected],

[email protected] Mobile No.: +91(0)9434155575.

** Prof. (Dr.) Bivraj Bhusan Parida, Professor, Department of Tourism Management, University

of Burdwan, e-Mail : [email protected], Mobile No.: +91(0)9153212456, +91(0)9438081781.

mailto:[email protected]

mailto:[email protected]



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1. Introduction

The emergence of relationship marketing has modified the transactional interface between the

buyer and seller and has evolved as a potential strategic imperative to analyze, interpret, augment

and retain valued customers particularly by the intangibility and heterogeneity dominated service

sector. The application of relationship marketing is valid both for internal as well as for external

customers whereby information exchange and sharing becomes responsive and flexible

(Abdullateef et al., 2010). Customer relationship management (CRM) explicitly recognizes the

long-run value of potential and current customers, and seeks to increase revenues, profits, and

shareholder value through targeted marketing activities directed toward developing, maintaining,

and enhancing successful company-customer relationships. CRM has been conceptualized as a

strategic approach concerned with creation of improved shareholder value through the

development of appropriate relationships with key customers and customer segments (Payne and

Frow, 2005). CRM synchronizes and unites the potential of relationship marketing strategies and

IT [information technology] to create profitable, long-term relationships with customers and other

key stakeholders. CRM provides enhanced opportunities to use data and information to both

understand customers and co-create value with them. This requires a high-degree cross-functional

integration of processes, people, operations and marketing capabilities that is enabled through

information, technology and applications.

Over the last decade or so, CRM has been the key business philosophy with the service sector with

a focal objective to strengthen both external and internal marketing and to detangibilize the service

offer for better quality perception. Barnabas and Mekoth, (2010) were of the opinion that the

modern market-orientation of service firms were more of failure-prevention in nature which they

eventually referred to as hygiene factor. Some of the research findings from hospitality sector

clearly indicate that disposition and behavior of the service provider are crucial in the final

determination of consumer‟s evaluation of the service quality delivered as well as the level of



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satisfaction (Reisinger, 2000; Collie and Sparks, 1999). For financial services sector especially for

the banks the application of CRM has been justified (Sobti, 2003). In heterogeneity dominated

service market CRM started providing the firms with competitive edge by ensuring high degree of

customization of services with supportive and meaningful human interactions. CRM, as a business

process, has started gaining strategic attention specifically by the service firms as they considered

the process to have a positive impact on augmentation of perceived service quality.

Although researchers have identified different defining constructs in the area of CRM pertaining

to CRM process and its dimensions (Wang et al., 2004; Sin et al., 2005; Ndubisi et al., 2007;

Chahal and Kumari, 2010; Lo et al., 2010; Basar et al., 2011; Mamoun et al., 2011), there is still

much debate over what exactly constitutes CRM when it comes to dimensionality and process

function (Nevin, 1995; Parvatiyar and Sheth, 2001; Sin et al., 2005; Agariya & Singh, 2012a).

This paper empirically attempts to investigate the nature of relationship between CRM-process

and perceived service quality and the moderating effects of CRM dimensions on CRM process-

service quality outcome in the context of banking industry in India. CRM is gradually picking up

its applications and deployment in Indian banks and is definitely considered as a viable

proposition in providing improved service quality to their customers (Gupta and Shukla, 2002).

Although

2. Review of Literature

Customer relationship management (CRM) marked the end of transaction-based marketing

dominated by marketing mix elements. According to Reinartz et al. (2004), CRM refers to 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



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address issues that are potentially inhibitors or enhancers of profitability (Yueh et al., 2010; Aihie

and Bennani, 2007; Adam and Michael, 2005; Gummesson, 2004; Sin et al., 2005). CRM systems

on the other hand, provide the infrastructure that facilitates long-term relationship building with

customers (Hendricks et al. 2007). CRM performance considers delivery of superior customer

value towards building and sustaining competitive advantage (Wang et al., 2004; Ahmad &

Hashim, 2010; Sadeghi & Farokhian, 2010) and facilitates developing relationships with

differentiated customers via interdependent collaboration with those of highest value to the

company (Lowe, 2008; Sadeghi & Farokhian, 2010). Reinartz, Krafft and Hoyer (2004) were of

the opinion that CRM process embodies a systematic and proactive management of relationships

across various touch-points and interfaces related to customer contact channels. Academicians and

researchers, over the years, have focused on developing CRM measurement frameworks (Jain

et.al, 2003, Lindgreen et.al, 2006). While some research works have focused more on IT-related

factors (Avlonitis & Panagopoulos, 2005; Roh, Ahn, & Han, 2005; Wilson, Daniel, &McDonald,

2002), 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 & Frow, 2004; Reinartz, Krafft, & Hoyer, 2004). Studies were also made to link

CRM components and their performance output, namely, linking customer satisfaction with

business performance (Kamakura et al. 2002), the linking customer loyalty with 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 &

Schiemann, 2003; Jain, Jain, & Dhar, 2003; Kim, Suh, & Hwang, 2003; Lindgreen et al., 2006;

Zablah, Bellenger, & Johnston, 2004). Lindgreen et.al, 2006, proposed a CRM assessment tool

comprising of three categorical elements namely strategic elements (customer and brand strategy),

infrastructural elements (culture and people) and process elements (relationship-management



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process). While researchers continued to focus on tangible and quantitative key performance

indicators (KPI) such as revenue generated, customer acquisition, retention and defection rates,

time to execute services including service recovery process, cost optimization etc., to explain the

success/failure of a CRM system, Jain et.al (2003) explored into the behavioural dimensions of

CRM that considered the „people‟ element as a pivotal factor. The behavioural dimensions thus

identified were attitude to serve, quality perceptions, understanding the expectations of customers,

reaction time, situation handling capability etc. 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.

Ghafari, Karjalian and Mashayekhnia (2011) identified five dimensions of CRM namely

information sharing, customer involvement, long-term partnership, joint problem solving and

technology-based CRM to explore a possible linkage with innovation capabilities of a bank.

Successful implementation of CRM requires synergistic synchronization between four identified

dimensions namely focusing on key customers, organizing around CRM, managing knowledge,

and incorporating CRM-based technology (Yim, Anderson and Swaminathan, 2004). The

dimensions of CRM are supposed to influence the CRM process which focuses on value creation

resulting in manifested behavioural intention of the customers. Reinartz, Krafft, & Hoyer, 2004,

concluded that successful implementation of CRM process is associated with enhanced firm

performance. Other researchers have also explored CRM process frameworks from diversified

point of views namely service profit chain (Heskett et al., 1994), return on quality (Rust, Zahorik

and Keiningham, 1995), customer asset management (Berger et al., 2002), customer equity

(Blattberg, Getz and Thomas, 2001; Rust, Lemon and Zeithaml, 2001). Review of literature also

revealed that there are different methods and categorizations of CRM performance namely

financial versus non-financial, single-dimensional versus multi-criteria, tangible versus intangible

(Chi et al., 2004, Kim et al., 2003, Llamas and Sule, 2004, Payne and Frow, 2005).



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Community and retail banking system has evolved as a major domain of CRM application. As

banks automated back-office functions with mainframes, and the number of products and services,

particularly related to cross-selling and up-selling activities, grew, banks found it increasingly

necessary to introduce and replace its branch-based filing cards with a Central Information file

(CIF). Panda and Parida (2005) have identified the key drivers of CRM in retail banking which

has been categorized under two factors: (i) Internal factors (ii) External factors. The drivers are

presented in Table-1 below:

Table 1: CRM drivers for banks

CRM drivers for banks

Internal factors External factors

1. Improving customer satisfaction and cross-

selling/up-selling initiatives

1. Reduced competitive barriers

2. Increasing share of customer spend 2. Reduced scope for differentiation

3. Operational performance 3. Customer demand

4. Competitive pressure 4. Relationship banking

5. Realization of Customer Lifetime value 5. Increased risk and their intermediation

6. Multi-Channel Integration 6. Advances in technology

7. Automated Business processes 7. Affordable data-storage for the retention

The pursuance of CRM by firms, particularly in the service sector, has been strongly focused on

augmentation in the perception of service quality leading to favourable behavioural intention

namely customer retention, attitudinal loyalty and repatronization (Swift, 2001). Service quality

has been recognised as a critical prerequisite and determinant of competitiveness for establishing

and sustaining long-term satisfying relationships with customers (Wang & Wang, 2006) which

inevitably reinforces the philosophy of CRM. A number of studies were targeted towards

revealing the global attributes of services that significantly contribute to quality assessments in

conventional service environment (Gronroos, 1982, 1984; Parasuraman et al., 1985, 1988). Over

the years, exploration to enhancement of service quality has remained as the focal research object



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(Yavas et.al., 1997, Rust and Zahorik, 1993; Cronin and Taylor, 1992, 1994; Buttle, 1996; Crosby

and Stephens, 1987; Parasuraman et.al. 1988; Kearns and Nadler, 1992; Avkiran, 1994; Julian and

Ramaseshan, 1994; Lewis, 1989, 1993; Llosa et.al., 1998). Grönroos (1982) described service

quality as a customer‟s perception of difference between the expected service and the perceived

service. The study of service quality was pioneered by Parasuraman, Zeithaml and Berry (PZB),

who developed the gaps framework in 1985 and its related SERVQUAL instrument in 1988

(Parasuraman, Zeithaml and Berry [PZB] 1985, 1988, 1991). Numerous researchers have also

highlighted the independent effect of perceptions on service quality evaluations and have

questioned the use of disconfirmation paradigm as the basis for the assessment of service quality

(Carman, 1990; Bolton & Drew 1991a, Babakus & Boller, 1992; Cronin & Taylor, 1992. A

number of scholars were of the opinion that service quality can be represented by a dual-

dimension process (Grönroos, 1984; Lehtinen and Lehtinen, 1982). The first dimension deals with

what the service actually delivers and is referred to by PZB (1985) as “outcome quality” and by

Grönroos (1984) as “technical quality”. The second dimension deals with the delivery process of

the service. PZB (1985) described it as “process quality” while Grönroos (1984) termed it as

“functional quality”. Parasuraman, Zeithaml and Berry (1991) defined service quality as “the

degree and direction of discrepancy between customers‟ service perceptions and expectations”.

One of the results of the studies initiated by Parasuraman, Zeithaml and Berry (1985) was the

identification of ten determinants of service process quality namely responsiveness, competence,

access, courtesy, communication, credibility, security, knowing the customers and tangibles.

Review of literature confirmed that although studies were made to identify the dimensionality,

components and process of CRM, there is an absolute dearth in research to link CRM dimensions

and process with service quality, particularly with respect to intangibility & heterogeneity

dominated service sector. This paper, therefore, empirically explores the link between CRM

process and dimensions with perceived service quality. Our approach to identify and quantify the

CRM process and dimensions supplements the CRM dimensional frameworks reviewed and



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CR

M P

roce

ss

CRM initiation

CRM maintenance

CRM termination

Tangibles

Reliability

Assurance

Responsiveness

Empathy

Serv

ice Qu

ality

Dim

ensio

ns

Customer orientation

CRM dimensions

justifies our objective to explore the linkage with perceived service quality as an output measure to

CRM deployment.

Following „introduction‟ the layout of the paper follows: „review of literature, model construct and

hypothesis formulation, methodology, data analysis and conclusion including future research and

limitations.

3. Model construct and hypothesis formulation

Appropriate to the literature reviewed, we propose the following model (Fig.-1); depicting the

probable impact of CRM dimensions and process on perceived service quality. We have used

„CRM initiation‟, „CRM maintenance‟ and „CRM termination‟ as the CRM process elements as

was successfully used by Reinartz, Krafft, & Hoyer, (2004); „customer orientation‟, „CRM

organization‟, „knowledge management‟ and „CRM technology‟ as the CRM dimensions

(Abdullateef, Mokhtar and Yousoff, 2010); and service quality dimensions as identified by

Parasuraman , Zeithaml and Berry (1985,1988,1991).



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Fig.1: Proposed model based on review of literature

The researchers intend to explore the possible link between CRM process and service quality

owing to the dearth of research focus in this area. Accepting retention as the thematic philosophy

of CRM, the researchers further expect a significant and positive associationship between CRM

process and service quality as satisfied and retained customers are a result of superior service

quality (Varki and Colgate, 2001; Ranaweera and Neely, 2003;) and service quality is a potent

antecedent to customer satisfaction (Cronin et al., 2000). Accordingly it is hypothesized that:

H1: Superior perceived service quality (PSQ) is associated with enhanced implementation of CRM

process at its various stages (initiation, maintenance and termination)



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Customer orientation has been considered to be a pivotal dimension of CRM (Yueh et al., 2010;

Sin et al, 2005; Yim et al, 2005) and so was CRM organization or organizational alignment of

CRM process been considered to be a important dimensions by the researchers particularly when

studies were undertaken to explore the linkage between CRM organizational alignment and

organizational performance and profitability based on delivery of superior service quality (Black

and Boal, 1994; Miller, 1996). Harnessing and managing knowledge about customers have proved

to be a significant dimensional input for enhanced service quality and long-term customer

relationship (Roland and Werner, 2005; Dean, 2007; Antonio et al., 2005). The success of CRM

process has also been accounted to the degree to which a firm has made justified technology

integration. Several existing literatures have argued in support of the positive impact that CRM

technology had on perceived service quality (McNally, 2007; Wang et al, 2006; Ravipa and Mark,

2004; fox and Stead, 2001). In the following study the researchers intend to explore the

moderating effects of CRM dimensions on perceived service quality which led to hypothesize that:

H2: Superior customer orientation will lead to stronger and positive relation between CRM

process and perceived service quality (PSQ).

H3: Greater level of CRM organizational alignment will ensure stronger and positive link between

CRM process and perceived service quality (PSQ).

H4: Greater degree and efficiency of managing customer knowledge will ensure stronger and

positive link between CRM process and perceived service quality (PSQ).

H5: Greater degree and efficiency of technology integration will ensure stronger and positive link

between CRM process and perceived service quality (PSQ).

4. Methodology

The objectives of the study were (a) to explore the impact relationship between CRM process

elements and service quality dimensions (b) to assess the moderating effects of CRM dimensions



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on CRM process-service quality outcome and (c) to test the proposed model (Fig.1) 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, refinement of research protocol and confirmation of scale

reliability was given special emphasis (Teijlingen and Hundley, 2001). FGI was administered

among 20 respondents representing assorted demography and originated from different

geographical locations. The structured questionnaire thus obtained after refinement contained four

sections. Section-I asked the external respondents (customers) about their perception of service

quality as offered by their bank (SBI), section-II was intended to generate response from the

internal customers (employees) with regard to CRM process initiated by their bank (SBI), section-

III was designed to generate response across the CRM dimensions from the internal customers

(employees) and section-IV focused to collect demographic data of the respondents. The

SERVQUAL scale developed by Parasuraman, Zeithaml and Berry (1985, 1988) was used with

adequate modification to generate response about customers‟ perception of service quality. Three

CRM process elements were used namely CRM initiation (15 items), CRM maintenance (20

items) and CRM termination (4 items) (Reinartz, Krafft, & Hoyer, 2004). Four CRM dimensions

were identified for the study namely customer orientation (4 items), CRM organization (5 items),

knowledge management (5 items) and CRM technology (5 items) following a number of

literatures reviewed. A 7 point Likert scale (Alkibisi and Lind, 2011) was used to generate

response. 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 1558 usable responses with a response

rate of 78.00% (approximately). A total number of 70 bankers were also interviewed for section-II



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and section-III of the questionnaire. Exploratory factor analysis (EFA) was employed using

principal axis factoring procedure with orthogonal rotation through VARIMAX process with an

objective to understand the factor loadings/cross loadings across components across SERVQUAL

response. Cronbach‟s α was obtained to test the reliability of the data, Kaiser-Meyer-Olkin (KMO)

was done for sample adequacy and Barlett‟s sphericity test was conducted. Structural equation

modeling approach using Lisrel 8.80 was used to test the research model.

5. Data analysis and interpretation

The demographic data obtained were tabulated in Table-2:

Table-2: Demographic data of the respondents (external customers)

Demographic Variables Factors Freq.

envy %

Gender Male 934 59.95%

Female 624 40.05%

Age

≤ 21 years 12 0.77%

22-32 years 579 37.16%

33-43 years 678 43.52%

44-54 years 199 12.77%

≥ 55 years 90 5.78%

Income

≤ Rs. 14999.00 21 1.35%

Rs. 15000-Rs. 24999.00 641 41.14%

Rs. 25000-Rs. 44999.00 567 36.39%

≥ Rs. 45000.00 329 21.12%

Occupation

Service [govt./prv] 829 53.21%

Self employed 429 27.54%

Professionals 131 8.41%

Student 44 2.82%

Housewives 61 3.92%

Others [retd., VRS etc] 64 4.11%



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Educational qualification

High school 21 1.35%

Graduate 939 60.27%

Postgraduate 476 30.55%

Doctorate & others (CA, fellow etc) 122 7.83%

Exploratory factor analysis (EFA) was used to test reliability of the SERVQUAL instrument. The

Cronbach; s Coefficient alpha (.926) was found significant enough across the five dimensions and

therefore it are reasonable to conclude that the internal consistency of the SERVQUAL instrument

used was adequate. The KMO measure of sample adequacy (0.901) indicated a high-shared

variance and a relatively low uniqueness in variance (Kaiser and Cerny, 1979). Barlett‟s sphericity

test (Chi-square=1987.127, p<0.001) indicated that the distribution is ellipsoid and amenable to

data reduction (Cooper and Schindler, 1998).

The exploratory factor analysis with principal component analysis and varimax rotation didn‟t

justify the „Assurance‟ dimension due to very low factor loadings/cross loadings (<0.500) and

poor reliability (Cronbach‟s‟ s alpha 0.266). Therefore the assurance dimension was discarded for

the study. The modified SERVQUAL had the items related to technology usage in the banks, the

augmented processes concerning service delivery, cross-selling and up-selling integration with the

core baking service which scored considerably and consistently high in terms of factor loadings

and cross loadings was also found to be significant i.e. >0.700 (significant as per

recommendations of Nunnally, 1978). These items loaded in a specific component across all the

four metros were nomenclated as „Convenience‟. The five dimensions of service quality thus

retained to obtain the perception score from the respondent were: (i) Tangibles (4 items), (ii)

Reliability (5 items), (iii) Convenience (6 items), Empathy (4 items) and Responsiveness (3 items)

(Table-3). Application of exploratory factor analysis, has therefore, reduced the 33 item scale into

a 22-item scale.

Table 3: Rotated Component Matrix following EFA

Rotated Component Matrix on respondents' Expectation Variables (Kolkata)



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Component Dimension

naming 1 2 3 4 5

Modern Looking Equipments .627

Tangibility Professional Appearance of Employees .878

Visible display of Materials .888

Physical Comfort Level of Customers .903

Convenient Business Hour .933

Convenience

Convenient location .676

Electronic Network [i-banking & mobile

banking] .881

Networking of Branches .610

ATM Services [Network] .725

Cross-selling/Up-selling products/services

integration .752

Error free Records .692

Reliability

Exact Information Provided .590

Safety of Transactions .631

Knowledge of Employees .639

Confidentiality of Records and Information

of Customer .745

Prompt Service to the Customers .826

Empathy Willingness to Help .753

Deals Public Situation with Care .601

Understands Customers' Specific Needs .731

Service Commitment .757

Responsivene

ss

Involvement & Interest to solve customers'

problems .750

Instill Confidence in Customers .716

Initial Eigenvalues 4.262 3.644 2.497 2.308 1.979

% of Variance 26.391 11.322 9.338 7.031 6.920

Cumulative % 26.391 37.713 47.051 54.082 61.002

Cronbach’s α .926

KMO .901

Barlett’s sphericity Chi-square=1987.127, p<0.001

Extraction Method: Principal Component Analysis, Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 13 iterations.



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Index construction for CRM process components was done based on formative indicators

(Diamantopoulos and Winklhofer, 2001) as our constructs represented composite individual

indicators across different components. The critical issues identified by Diamantopoulos and

Winklhofer, 2001, with regard to successful construction of indexes with formative indicators

namely (a) content specification, (b) indicator specification, (c) indicator collinearity, (d) external

validity and (e) nomological validity were considered and found to be justified with the construct.

Bivariate correlation was obtained to understand the relationship between service quality

dimensions and CRM process elements and the results (Table-4) confirmed a strong and positive

relationship between the same: tangibles with CRM initiation (r=.618**, p<.001), CRM

maintenance (r=.726**, p<.001) and CRM termination (r=.452**, p<.001); reliability with CRM

initiation (r=.349**, p<.001), CRM maintenance (r=.052*, p<.005) and CRM termination

(r=.848**, p<.001); convenience with CRM initiation (r=.163**, p<.001), CRM maintenance

(r=.104**, p<.001) and CRM termination (r=.112**, p<.001); responsiveness with CRM initiation

(r=.056*, p<.005), CRM maintenance (r=.084**, p≤.001) and CRM termination (r=.100**,

p<.001) and empathy with CRM initiation (r=.332**, p<.001), CRM maintenance (r=.527**,

p<.001) and CRM termination (r=.198**, p<.001).

Table: 4 Bivariate correlation between service quality dimensions and CRM process

elements

Tangibl

es

Reliabilit

y

Convenie

nce

Respon

sivenes

s

Empat

hy

CRM

In

CRM

Mn CRMtr

Tangibles

Pearson Correlation 1.000 .086** .120** .026 .547** .618** .726** .452**

Sig. (2-tailed) .001 .000 .314 .000 .000 .000 .000

N 1558 1558 1558 1558 1558 1558 1558 1558

Reliability Pearson Correlation .086** 1.000 -.060* -.116** .296** .349** .052* .848**



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Sig. (2-tailed) .001 .018 .000 .000 .000 .039 .000

N 1558 1558 1558 1558 1558 1558 1558 1558

Convenience

Pearson Correlation .120** -.060* 1.000 .233** .209** .163** .104** .112**

Sig. (2-tailed) .000 .018 .000 .000 .000 .000 .000

N 1558 1558 1558 1558 1558 1558 1558 1558

Responsiven

ess

Pearson Correlation .026 -.116** .233** 1.000 .269** .056* .084** .100**

Sig. (2-tailed) .314 .000 .000 .000 .027 .001 .000

N 1558 1558 1558 1558 1558 1558 1558 1558

Empathy

Pearson Correlation .547** .296** .209** .269** 1.000 .332** .527** .198**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000

N 1558 1558 1558 1558 1558 1558 1558 1558

CRM In

Pearson Correlation .618** .349** .163** .056* .332** 1.000 .030 .094**

Sig. (2-tailed) .000 .000 .000 .027 .000 .230 .000

N 1558 1558 1558 1558 1558 1558 1558 1558

CRMMn

Pearson Correlation .726** .052* .104** .084** .527** .030 1.000 -.004

Sig. (2-tailed) .000 .039 .000 .001 .000 .230 .882

N 1558 1558 1558 1558 1558 1558 1558 1558

CRMtr

Pearson Correlation .452** .848** .112** .100** .198** .094** -.004 1.000

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .882

N 1558 1558 1558 1558 1558 1558 1558 1558

To conduct the regression analysis, we constructed the CRM initiation, CRM maintenance and

CRM termination by weighted multiplication of the individual indicators with the standardized

PLS weights as was successfully used by Reinartz, Krafft, & Hoyer, 2004.

The model specification is depicted as a equation below with the variables grouped into major

effects (βs), moderation effects (γs) and control variables (δ).

Perceived Service Quality (PSQ) = α + β1 CRM initiation + β2 CRM maintenance + β3 CRM termination

+ β4 Customer orientation + β5 CRM organization + β6 Knowledge management + β7 CRM technology + γ1

(CRM initiation x Customer orientation) + γ2 (CRM maintenance x Customer orientation) + γ3 (CRM



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termination x Customer orientation) + γ4 (CRM initiation x Customer organization) + γ5 (CRM

maintenance x Customer organization) + γ6 (CRM termination x Customer organization) + γ7 (CRM

initiation x Knowledge management) + γ8 (CRM maintenance x Knowledge management) + γ9 (CRM

termination x Knowledge management) + γ10 (CRM initiation x CRM technology) + γ11 (CRM

maintenance x CRM technology)+ γ12 (CRM termination x CRM technology) + δ1 Industry1**

** only 1industry has been chosen

Where, PSQ (expectation) = multi-item measure (modified SERVQUAL) (Parasuraman, Zeithaml & Berry,

1985, 1988, 1991),

PSQ (perception) = multi-item measure (modified SERVQUAL) (Parasuraman, Zeithaml & Berry, 1985,

1988, 1991),

CRM process elements and CRM dimensions = multi-item formative measures adopted from (Reinartz,

Krafft, & Hoyer, 2004, Yueh et al., 2010; Sin et al, 2005; Yim et al, 2005, Abdullateef, Mokhtar and

Yousoff, 2010),

Industry1 = banking services

Two models were estimated for PSQ (perception) and PSQ (expectation):

Model 1: PSQ (expectation) = f(covariates)sample1

Model 2: PSQ (perception) = f(covariates)sample1

The results of the estimation were summarized in Table-5.

Table: 5 Results of Models

Dependent

variable Description

Coeffi

cients

PSQ

(expectation)

Model 1

PSQ (perception

Model 2

Estima

te

Std.

Error

Estima

te

Std.

Error

Intercept α 21.96 .61 19.87 .69

Main effects

CRM initiation β1 .231** .17 .110** .09

CRM maintenance β2 .697** .28 .579** .25

CRM termination β3 .017 .06 .091* .08



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Customer orientation β4 .452** .19 .521** .21

CRM organization β5 .101* .11 .441** .22

Knowledge management β6 .041 .09 .088* .12

CRM technology β7 .587** .21 .397** .18

Moderation

effects

CRM initiation x Customer orientation γ1 .289** .19 .257** .17

CRM maintenance x Customer orientation γ2 .366** .20 .299** .19

CRM termination x Customer orientation γ3 .093* .11 .104* .16

CRM initiation x CRM organization γ4 .333** .29 .431** .27

CRM maintenance x CRM organization γ5 .698** .21 .321** .17

CRM termination x CRM organization γ6 .197** .15 .221** .18

CRM initiation x Knowledge management γ7 .045* .08 .216** .12

CRM maintenance x Knowledge management γ8 .099** .14 .109** .12

CRM termination x Knowledge management γ9 .003 .04 .071* .11

CRM initiation x CRM technology γ10 .601** .19 .412** .14

CRM maintenance x CRM technology γ11 .471** .25 .201** .17

CRM termination x CRM technology γ12 .119** .09 .216** .13

Control variable Industry 1 δ1 1.12** .89 .870** .31

N 1558 1558

R2 .39 .52

Dependent variable: PSQ (expectation) [Model-1], PSQ (perception) [Model-2]**p≤ .01, * p≤ .05

We hypothesized (H1) that perceived service quality is influenced by CRM process elements and

that the superiority of service quality can be linked to enhanced degree of CRM process

implementation. Results as tabulated in Table-5 confirmed that for PSQ (expectation) CRM

initiation (β1=.231**, p≤ .01) and CRM maintenance (β1=.697**, p≤ .01) exhibited strong and

positive significance whereby PSQ (perception) has been supported by all the three process

elements: CRM initiation (β1=.110**, p≤ .01), CRM maintenance (β1=.579**, p≤ .01), CRM

termination (β1=.091*, p≤ .05). Our hypothesis-2 (H2) considered that customer orientation would

have a moderating effect on CRM process-PSQ outcome. H2 was strongly and positively



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supported for initiation and maintenance process both in case of PSQ (expectation): initiation

(γ1=.289**, p≤ .01), maintenance (γ1=.366**, p≤ .01), PSQ (perception): initiation (γ1=.257**, p≤

.01), maintenance (γ1=.299**, p≤ .01), and marginally for termination process, PSQ (expectation):

termination (γ1=.093*, p≤ .05) and PSQ (perception): termination (γ1=.104*, p≤ .05). Our

hypothesis-3 (H3) considered that CRM organization would have a moderating effect on CRM

process-PSQ outcome. H3 was strongly and positively supported both in case of PSQ

(expectation): initiation (γ1=.333**, p≤ .01), maintenance (γ1=.698**, p≤ .01) & termination

(γ1=.197**, p≤ .01) and PSQ (perception): initiation (γ1=.431**, p≤ .01), maintenance (γ1=.321**,

p≤ .01) & termination (γ1=.221**, p≤ .01). Our 4th hypothesis (H4) considered that knowledge

management would have a positive moderating effect on CRM process-PSQ outcome. H4 has been

supported strongly and positively for maintenance process (γ1=.099**, p≤ .01), marginally for

initiation (γ1=.045*, p≤ .05) and not supported at all by termination process in case of PSQ

(expectation). H4 received strong to marginal support for PSQ (perception) in case of initiation

(γ1=.216**, p≤ .01), maintenance (γ1=.109**, p≤ .01) & termination (γ1=.071**, p≤ .05)

respectively. Our final hypothesis (H5) assumed that CRM-technology would exhibit a positive

and moderating effect on CRM process-PSQ outcome. H5 was strongly and positively supported

both in case of PSQ (expectation): initiation (γ1=.601**, p≤ .01), maintenance (γ1=.471**, p≤ .01)

& termination (γ1=.119**, p≤ .01) and PSQ (perception): initiation (γ1=.412**, p≤ .01),

maintenance (γ1=.201**, p≤ .01) & termination (γ1=.216**, p≤ .01). The R2 value for Model-2

(PSQ-perception) measured .52 indicating that CRM-process elements & CRM dimensions

(independent variables) measures 52.00% of the variation in perceived service quality (perception)

which was considered to be significant enough for predictability of the model. Thus hypotheses H1

to H5 were confirmed and accepted.

Confirmatory factor analysis was used to understand the dimensionality, convergence and

discriminant validity for each construct to determine whether all the 80 items (including

SERVQUAL, CRM-process elements and CRM dimensions) measure the construct adequately as



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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 (Table-6) were obtained. The GFI, AGFI and NFI 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 (Baumgartner and Homburg, 1996; Hair et al, 1998, 2006; Hulland, Chow and

Lam, 1996; Kline, 1998; Holmes-Smith, 2002, Byrne, 2001). The CFI value for all the constructs

were obtained as > .900 which indicated an acceptable fit to the data (Bentler, 1992). The RMSEA

values obtained are < 0.08 for an adequate model fit (Hu and Bentler, 1999). The probability value

of Chi-square is more than the conventional 0.05 level (P=0.20) indicating an absolute fit of the

models to the data. The Cronbach‟s α values were consistently >.700 and hence the scale is

reliable (Nunnally and Bernstein, 1994). The factor loadings considered for the model is >.600 and

items having <.600 factor loadings were eventually discarded.

Table-6: Summary representation of Confirmatory Factor Analysis (CFA)

Factor indicators χ2

df P-

value GFI AGF

I CFI NFI RMSEA Factor

loading

s

α –

value

CRM initiation 9.817 7 0.089 0.951 0.954 0.971 0.967 0.059 0.982

CI 1 0.844

CI 2 0.809

CI 5 0.776

CI 9 0.764

CI 10 0.871

CI 12 0.791

CI 14 0.809

CRM maintenance 8.889 15 0.077 0.924 0.918 0.954 0.941 0.067 0.959

CM 3 0.809

CM 4 0.771

CM 7 0.742



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CM 8 0.801

CM 12 0.701

CM 15 0.696

CM 18 0.751

CM 19 0.702

CM 20 0.689

CRM termination 9.661 3 0.029 0.901 0.902 0.919 0.921 0.031 0.921

CT 1 0.822

CT 2 0.831

Customer orientation 8.919 5 0.059 0.929 0.918 0.968 0.959 0.064 0.877

CO 1 0.831

CO 3 0.781

CRM organization 8.197 3 0.116 0.980 0.974 0.951 0.952 0.020 0.891

COR 2 0.861

COR 3 0.865

COR 5 0.708

Knowledge mgmt. 9.076 4 0.028 0.911 0.917 0.931 0.941 0.059 0.921

KM 2 0.803

KM 5 0.792

CRM technology 9.219 4 0.031 0.919 0.917 0.921 0.923 0.073 0.929

CT 1 0.881

CT 2 0.781

CT 4 0.817

CT 5 0.811

Tangibles 8.886 7 0.089 0.971 0.987 0.978 0.941 0.049 0.948

TAN 1 0.791

TAN 2 0.715

TAN 3 0.807

TAN 4 0.799

Reliability 7.129 13 0.035 0.951 0.936 0.954 0.931 0.071 0.937

REL1 0.798

REL 2 0.698

REL 3 0.667

REL 4 0.729

REL 5 0.781

Convenience 7.098 14 0.061 0.971 0.963 0.970 0.961 0.064 0.891

CON 1 0.866



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CON 2 0.837

CON 3 0.833

CON 4 0.801

CON 5 0.799

CON 6 0.822

Empathy 8.752 3 0.069 0.955 0.943 0.959 0.967 0.049 0.978

EM 1 0.792

EM 2 0.811

EM 3 0.781

EM 4

Responsiveness 9.693 4 0.091 0.967 0.981 0.991 0.987 0.051 0.997

RES 1 0.873

RES 2 0.859

RES 3 0.786

Structural Equation Modeling (SEM) was used to test the relationship among the constructs. A

number of fit-indices (Table-7) namely Chi-square/df = 459.98/79, GFI = 0.981, AGFI = 0.957,

CFI = 0.955, NFI=0.961, RMSEA=0.042, expected cross validation index (ECVI)=0.921 were

found to be significant. All the 55 paths drawn were found to be significant at p<0.05. The

research model 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 Є1, Є2, Є3, etc. The regression weights are represented by λ. The

co-variances are represented by β. To provide the latent factors an interpretable scale; one factor

loading is fixed to 1 (Hox & Bechger).

Table-7: Goodness of fit indices

Goodness of Fit Indices Value

Chi-square 459.98

Degrees of freedom (df) 79

Root Mean Square Error of Approximation RMSEA 0.042

Goodness of Fit Index (GFI) 0.981

Adjusted Goodness of Fit Index (AGFI) 0.957



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Normed Fitness Index (NFI) 0.961

Comparative Fit Index (CFI) 0.955

Standardized Root Mean Square Residual (SRMR) 0.37

Expected Cross-Validation Index (ECVI) 2.82

Minimum Fit Function Value 1.61

P-value for test of Cross-fit (RMSEA <0.05) 0.10

90% confidence interval for RMSEA 0.0; 0.77

CRM process

PSQ

CRM maintenance

CM3 CM4 CM7 CM8 CM12 CM15 CM18 CM19 CM20

CRM termination

CT1 CT2

CRM initiation

CI1

CI2

CI5

CI9

CI10

CI12

Tangibles

TAN1

TAN2

TAN3

TAN4

Reliability

REL1

REL2

REL3

REL4

REL5

CON1

Є1

Є2

Є3

Є4

Є5

Є6

1.58

1.43

1.41

1.27

1.33

1.20

λ1=1.00

λ2=0.93

λ2=0.97

λ4=0.89

λ5=0.91

λ6=0.94

λ7=0.90

Є8 Є9 Є10 Є11 Є12 Є13 Є14 Є15 Є16

1.44 1.31 1.29 1.51 1.61 1.30 1.21 1.17 1.26

λ8=1.00 λ9=0.98

λ10=.88

λ11=0.91 λ12=0.8

7 λ13=0.8

4

λ14=0.9

5 λ15=0.93 λ16=0.9

2

Є17 Є18

λ17=0.92 λ18=0.92

Є30

Є31

Є32

Є33

Є35

Є34

Є36

Є37

Є38

Є39

1.19

1.23

1.31

1.26

1.27

1.09

1.18

1.26

1.11

0.99

λ34=0.93

λ35=0.91

λ37=0.9

1

λ36=0.89

λ38=0.91

λ39=0.88

λ42=0.81

λ41=0.91

λ40=0.94

λ43=0.91

λ55=0.9

7

λ56=0.92

λ57=0.95



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1.09

Fig.1: Structural model showing the path

analysis



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6. Conclusion

The objectives of the study was threefold (a) to explore the impact relationship between CRM

process elements and service quality dimensions (b) to assess the moderating effects of CRM

dimensions on CRM process-service quality outcomes and (c) to test the proposed model

involving the three major variables using structural equation model.

The data analysis provided support to conceptualize that the CRM process (comprising of three

elements) has been instrumental in influencing the perceived service quality of the customers.

Therefore adoption and subsequent implementation of CRM philosophy by State Bank of India

(SBI) was justified as the bank can expect greater degree of customer loyalty and profitability

owing to such pro-customer strategy. The information gathered from the bankers with respect to

CRM process and CRM dimensions revealed that SBI is poised to ensure strategic gains from

CRM deployment and the CRM framework has been adopted rather well. The analysis further

revealed that the CRM dimensions, particularly CRM organization and CRM technology has

strong and positive effect on CRM process-service quality outcome confirming and justifying the

upgradation and automation of SBI in technological and behavioural front. Thus CRM process-

service quality link is more likely to produce positive-synergistic results if SBI continues to

emphasize on differentiated training programme for their employees, motivate pro-customer

attitude in their employees and maintaining profitable customer touch-points. Investment made in

CRM technology (specifically the automation done through the implementation of CBS in SBI

and allied IT infrastructure) was also justified as it assured a strong and positive impact on CRM-

process – service quality relationship. Customer orientation of SBI also revealed a positive impact

on perceived service quality. The study suggested an improvement in the knowledge management

aspects of the bank. Lastly, the study also confirmed the significant constructs of CRM process,

perceived service quality (PSQ) and CRM dimensions on the structural model. The researchers



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believe the model can be used by the bankers for continuous upgradation of CRM process and

CRM dimensions, with addition/deletion/modification of components to ensure a higher degree of

perception of customers towards service quality. The model can also be used to identify possible

reasons for customer defection also.

The study had geographical limitations as it has been restricted to some restricted places of West

Bengal, which in future, can be widened to obtain a more generalized conclusion. Further

extrapolations can be made by considering the behavioural impact of CRM-process-service quality

interaction. The study can be taken up for other service sectors also, particularly hospitality and

tourism industry which thrives on CRM. The study was cross-sectional in nature; therefore

longitudinal research may be taken up also to realize the gradual changes of CRM process-CRM

dimensions and perception of service quality over time.

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