EFFECTS OF RELATIONSHIP INERTIA AND SWITCHING COST ON … · (Ouellette and Wood, 1998) and become reluctant to find an alternative (Colgate and Danaher, 2000) – a phenomenon subsequently

EFFECTS OF RELATIONSHIP INERTIA AND SWITCHING COST ON CRM DIMENSIONAL

PERFORMANCE-SERVICE QUALITY-CUSTOMER SATISFACTION-RETENTION LINK:

EMPIRICAL EVIDENCE FROM HOSPITALITY MARKETS

Dr. Arup Kumar Baksi Assistant Professor

Department Of Management Science

Bengal Institute Of Technology & Management, Santiniketan, West Bengal

ABSTRACT

a)

b)

Keywords: customer relationship management, service quality, satisfaction, retention, inertia, switching cost,

hospitality

Introduction:

The advent of customer relationship management (CRM) induced a domain shift in the practice of

marketing and triggered a sweeping change in the area of hospitality and tourism. CRM, as a business

analytical process has been accepted as a business analytical process, has been accepted as a philosophy as

well as a system as it introduced technology as a major driver to communicate with customers and deliver

automated services. 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 Vlčková and Bednaříková (2007) suggested that customer retention over their lifetime

will significantly contribute to enhance company’s profitability.

With the increase in the significance of hospitality and tourism as a major contributing source to the

enhancement of nation’s GDP, the academic researchers too has started to get involved in identifying its

nature, dynamics, dimensions and effects. Hospitality and tourism has been observed as the aggregate of

interactions and relationships between tourists, business houses, host governments and administration and host

communities (McIntosh and Goeldner, 1984). As a service sector, tourism has its own criticalities which

assume significant proportion while perceiving quality associated with it. The intensive dyadic encounter

between a host of hospitality-service-providers and the tourists, often, does not allow the services to be

homogenized. These, rather heterogeneous, services create ambiguity in perceiving quality of services

received from specific hospitality-service-providers. But, identifying the perceived hospitality service quality

Although customer relationship management (CRM) as a business analytical process has been

integrated with the marketing transactions of the major service sectors, there has been a lacking of

CRM dimensional inputs in the area of hospitality and tourism. Empirical evidences supported the

impact of CRM on the enhancement of service quality and subsequent behavioural intentions of

customer in banking and insurance services. For the hospitality service providers too, the apparent

linear relationship between CRM dimensional performance-service quality-customer satisfaction-

customer retention is critical to assess. This paper empirically tests the moderating effect of

relationship inertia and switching costs on this link to understand the dimensional impact of CRM on

service quality of hospitality services and its consequences. The study was conducted in Santiniketan,

West Bengal, one of the most potent tourist destinations of the state. Multivariate data analysis was

applied, as appropriate; to assess the relationship between the constructs and structural equation

modeling was used to test the default research model. The results adequately supported the hypotheses

formulated.

becomes imperative as it was empirically tested to be antecedent to customer satisfaction (short-term effects)

and destination loyalty (long-term effects). From the late 1990s the hospitality and tourism sector started using

the philosophy of customer relationship management (CRM) as it proved to be a proactive business process to

understand the tourists (customers), segment the tourists on the basis of their psychographic determinants and

to design integrated communication with the same. CRM was adopted by the hospitality and tourism sector

with an apprehension that it will help maintain a linear relationship between perceived service quality-

customer satisfaction and customer retention. But in most of the cases it was found that the conventional CRM

dimensions failed to facilitate the relationship.

The hospitality and tourism sector in India registered 6.31 million (5.78 million in 2010) inbound

tourists visiting with an annual growth of 9.2% (India Tourism Statistics, 2011, Ministry of Tourism, Govt. of

India). This huge influx of tourists boosted the foreign exchange earnings to 77591 crores (in INR terms) with

an annual growth rate of 19.6% (India Tourism Statistics, 2011, Ministry of Tourism, Govt. of India). This

phenomenal growth rate has catapulted India’s share in international tourist arrivals (0.64%), India’s rank in

world tourist arrivals (38), India’s share in international tourism receipts (1.61%) and India’s rank in world

tourism receipts (as per RBI estimates—17) (India Tourism Statistics, 2011, Ministry of Tourism, Govt. of

India). The reason for this boom can be attributed to a number of factors namely burgeoning Indian middle

class, growth of high-spending foreign tourists, augmentation in communication system-both physical and

virtual, infrastructure & super structure and the initiatives taken up by the state governments to showcase their

individual states as tourist destinations, thereby building up the brands (Gujarat, Odissa, Kerala, Madhya

Pradesh etc. are some of the major branded tourism destinations). A study conducted by Federation of Indian

Chambers of Commerce and Industry (FICCI) in the area of development perspective of eco and rural tourism

indicated that it registered highest employment and investment ratio. Study conducted by McKinsey also

revealed that medical tourism has the potentiality to generate as much as 100 billion in INR by the end of

2012. India’s cultural and natural heritage is truly incredible. The brand title ‘Incredible India’ not only

projects India as a tourist destination but also promotes the nation as a potential export and investment hub.

‘Yatra Visawam Bhavati Ekanidam’ – where the whole world meets in one nest. Rabindranath

Tagore, India’s first Nobel laureate, wanted Santiniketan to be that spot, where the whole world would settle,

forgetting illusory geographical boundaries. Little wonder then that India’s nodal authority Archaeological

Survey of India (ASI) submitted Santiniketan as its official entry this year for UNESCO’s list on World

Heritage Sites. ASI has submitted the dossier on Santiniketan to UNESCO’s world heritage centre in Paris,

and has received a letter from the body, saying the dossier received is as per operational guidelines.

Santiniketan has emerged as a tourist destination with updated facilities and amenities with regard to

hospitality industry and allied services. The cultural events like Pous Mela, Basantotsav, Magh Mela draw

huge influx of domestic as well as international tourist. With the changing dynamics of quality perception of

services related to tourism, the expectation and zone of tolerance have also been modified.

Statement of problem

The major issues with hospitality and tourism markets lie in the enormous heterogeneity in service

transaction, as a result of which, standardisation of services is almost impossible. This phenomenon

aggravated the problem of perception of service quality and subsequent behavioural consequences. CRM has

also not been tested much to understand its effectiveness in resolving this issue in the context of hospitality

markets as a result of which relationship inertia and switching costs were often considered as potent reasons

for customer retention. But with the increasing competition and relentless effort by the hospitality service

provider to ensure value addition for customers, the effect of relationship inertia and switching costs seem to

erode as a possible reason for customer retention.

Objectives of the study

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

performance-service quality-customer satisfaction-customer retention (b) to assess the moderating effects of

relationship inertia and perceived switching costs on CRM dimensional performance-service quality-customer

satisfaction-customer retention link and (c) to test the proposed model framework (Fig.1) involving the

variables under study using structural equation modeling.

Review of literature

Customer relationship management (CRM) has reoriented customer attitudes, perceptions and

behavioral manifestations in the context of their apprehension and expectation to be served as (Peppers and

Rogers, 2004). Conceptually, CRM embarks upon three basic foundations of marketing management: (a)

customer orientation, (b) relationship marketing and (c) database marketing (Yim et al, 2004). CRM, as a

business analytical process gained momentum among academicians and corporate houses (Gruen et al, 2000;

Rigby and Ledingham, 2004; Srivastava et al, 1999; Thomas et al, 2004) in terms of empirical exploration of

its potentiality to be implemented. CRM has been widely used by the sales personnel in augmenting their

relationship with the customers (Widmier et al, 2002) to improve sales forecasting, lead management and

customization (Rigby and Ledingham, 2004). Yim (2002) provided conceptual clarity of CRM by synthesizing

the literatures (Crosby and Johnson, 2001; Fox and Stead, 2001; Ryals and Knox, 2001) pertaining to

marketing, technology and management and came out with four critical areas: (a) strategy, (b) people, (c)

processes and (d) technology. Day (2003) confirmed that the key focal factors identified by Yim (2002) can

create a synergistic relationship value when they work in unison (rather than in isolate), thereby conforming to

the objective and realm of CRM. Study of extant literatures revealed that implementation of CRM necessarily

involved four specific activities: (a) focusing on key customers (Schmid and Weber, 1998; Srivastava et al,

1999; Sheth et al, 2000; Ryals and Knox, 2001; Armstrong and Kotler, 2003; Vandermerwe, 2004; Srinivasan

et al, 2002, Jain and Singh, 2002) which encompassed the view of a customer-centric organizational structure

with dyadic interactive points targeted towards identification of key or valued customers through lifetime

value computations, (b) organizing around CRM (Brown, 2000; Homburg et al, 2000; Ahmed and Rafique,

2003) which emphasized on customer-centric organizational functions with an objective to ensure value

proposition to customers, (c) managing knowledge (Peppard, 2000; , Freeland, 2003; Stefanou et al, 2003;

Stringfellow et al, 2004, Yim et al, 2004; Plessis and Boon, 2004; Brohman et al, 2003) whereby customer-

information are effectively transformed into customer-knowledge and disseminated across the organizational

hierarchy which will equip salespeople with better understanding of customers’ requirements and (d) adopting

CRM-based technology (Butler, 2000; Pepperd, 2000; Vrechopoulos, 2004; Widmier et al, 2002) to optimize

communication with customers, accurate service delivery with back-up and supportive information, managing

customer-knowledge by data warehousing and data mining and providing customized services. However, there

has been a dearth of research in identifying these CRM dimensions in the context of hospitality and tourism

industry. CRM philosophy was adopted by the tourism sector as it allowed them to be more proactive in

predicting the changing line of customer demands and allowed them to realize the extent to which they can

customize their service offer with adequate differentiation. Jain and Jain (2006) delved into CRM practices of

hotels in central India to measure the effectiveness against factors like: value proposition, recognition,

customer orientation, reliability, relationship orientation, credibility, customization, personalization and

gestures. CRM has been proved to be an effective contributor to enhance perception of service quality.

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). 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.

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

establishing and sustaining long-term satisfying relationships with customers (Coyles and Gokey, 2002; Choi

et al, 2004, Ojo, 2010). 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 (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; Llosa et.al., 1998). 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. Baker and

Crompton (2000) observed that the literature related to service quality in the area of hospitality and tourism

and allied area dates back to the early 1960’s. Over the years researchers have made various attempts to make

sense of how tourists evaluate the quality of services they receive while touring to specific destinations having

tourist attraction (Atilgan, Akinci, & Aksoy, 2003; Baker & Crompton, 2000; Chadee & Mattsson, 1996;

Frochot, 2004; Hudson, Hudson, & Miller,2004; Vogt & Fesenmaier, 1995; Weirmair & Fuchs, 1999), tour

operator and travel agency quality (Ryan& Cliff, 1997), hotel and its hospitality quality (Suh, Lee, Park, &

Shin, 1997) etc. However, Frochot (2004) pointed out that given the nature of service, the evaluation of its

quality is quite complex. Vijayadurai (2008) identified service quality factors in hospitality industry and

assumed them to be critical in creating loyal visitors who will return to the destination and recommend it to

others (Tian-Cole & Cromption, 2003).

Service quality has been empirically tested to be effective antecedent to customer satisfaction and

CRM seems to reinforce the effect over the relationship (Lin, 2007, Joewono and Kubota, 2007). CRM as a

precursor to customer satisfaction has been well studied by Molina et al (2007) and Yuksel et al (2010) in the

context of banking services, Seeman and Ohara (2006) in the context of educational services and Sarlak and

Sanavi (2009) in the context of agricultural banking and found significant impact of CRM on customer

satisfaction. 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). Satisfied customers tend to struck a permanent bond with their service

providers, a phenomenon nomenclated as ‘inertia’ by the researchers.

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 & 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 tested to have moderating effects on CRM

performance-customer satisfaction-retention in the context of banking sector (Baksi and Parida, 2013) and was

also instrumental in mediating automated service quality-customer satisfaction-retention relationship in a

CRM ecosystem (Baksi and Parida, 2012).

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; Lee et

al, 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. Baksi and Parida (2013) observed mediating effects of relationship

inertia and switching costs on CRM performance-customer satisfaction-customer retention link in the context

of banking services.

Research gap identified

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 dimensional

performance-service quality-customer satisfaction-customer retention link in an integrated manner in the

context of hospitality markets. This paper, therefore, empirically explores the relationship between CRM

dimensional performance, service quality, 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 & model construct, methodology, data analysis and conclusion including future research and

limitations.

Formulation of hypotheses and model construct

Apropos to the literature reviewed the following hypotheses were formulated:

H1: CRM dimensional performance (CRMDP) has an impact on perceived service quality

(SQ)

H2: SQ has a positive impact on customer satisfaction (CS)

H3: CS has a positive effect on customer retention (CR)

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 on CRM dimensional performance-service quality-

satisfaction-retention link, although CRM dimensional performance happens to have a positive effect on

service quality, an antecedent to customer satisfaction-customer retention link

Hence we hypothesized that

H4: Higher degree of relationship inertia and switching cost will reduces impact of CRMDP on SQ.

H5: Higher degree of switching costs and relationship inertia, in combination, will reduce the impact

of CRMDP on SQ.

H6: Higher degree of relationship inertia and switching cost will reduces impact of CRMDP on CS.

H7: Higher degree of switching costs and relationship inertia, in combination, will reduce the impact

of CRMDP on CS.

H8: CRMD performance impact on SQ will affect CS under the mediating effect of RI & SC.

H9: Higher degree of relationship inertia and switching cost will reduces impact of CRMDP on CR.

H10: Higher degree of switching costs and relationship inertia, in combination, will reduce the impact

of CRMDP on CR.

H11: CRMD performance impact on SQ will have a progressive impact on CS to influence CR under

the mediating effect of RI & SC.

Based on the literature reviewed and hypotheses framed, the following model framework was proposed

(Fig.1):

Fig.1: Proposed model framework

(Legends description: CRMDP- CRM dimensional performance, SQ-service quality, CS-Customer

satisfaction, CR-Customer retention, RI- Relationship Inertia, SC-Switching cost)

: Direct effects : Moderating effects

Methodology

The study was conducted in two phases. A structured questionnaire was developed to obtain the

primary data. 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). 40 respondents representing hospitality services recipients of assorted

demography, academicians and researchers were included to conduct the pilot study through focus group

interview technique. Cronbach’s α coefficient (>0.7) established scale reliability (Nunnally and Bernstein,

1994). The refined survey instrument had five sections. Section-I was aimed at the hospitality service provider

to generate response with regard to the CRM deployment that they have initiated. Section-II was targeted for

CRM

DP

SQ

CR

CS

SC

customers/tourists and it asked questions about their expectation and perception of service quality offered by

the service providers; section-III was designed to generate response from the customers/tourists with regard to

their level of satisfaction derived out of the services they were offered and allied elements, section-IV targeted

customer/tourist response in context of their intention to remain associated with their hospitality service

provider in future and section-V attempted to collect the demographic profile of the customers/tourists. 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 a structured questionnaire which was distributed amongst 2000

tourists who visited Santiniketan on the eve of Pous Mela (December 23rd

to 26th

, 2012), Basantotsav (March,

8th

to March 10th

, 2012) and on other occasions in the year 2012. Systematic random sampling technique was

administered, from the list of customer/tourist-occupants in the hotels and resorts in Santiniketan, whereby

every 7th

customer/tourist from the list was approached to franchise their views. A total number of 1457 usable

responses were generated out of 2000 questionnaires used for the tourists, with a response rate of 72.85%

(approximately). For the section-I of the questionnaire, service employees of the rank of managers,

relationship executives etc. were interviewed. As many as 214 personnel associated with assorted hospitality

and tourism services in Santiniketan were interviewed.

Factor constructs measurement

To develop a measure for perception of service quality the SERVQUAL scale developed by

Zeithaml, Parasuraman and Malhotra (2005) was used with adequate modification to suit response with regard

to tourist services. To develop a measure for CRM performance three CRM process elements namely CRM

initiation, CRM maintenance, and CRM termination (Reinartz, Krafft, & 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.

Reliability and validity

Exploratory factor analysis (EFA) was deployed using principal axis factoring procedure with

orthogonal rotation through VARIMAX process with an objective to assess the internal reliability of all factor

constructs and to understand the factor loadings/cross loadings across components. 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. Cronbach’s α coefficient (>0.7) established scale reliability (Nunnally and

Bernstein, 1994). The scales used in this study were adapted from established existing measures that have

been applied and validated in numerous tourism studies. In addition, the validity of the measurement scales

was also assessed via the confirmatory factor analysis. The convergent validity of the scales were measured by

tests of average variance extracted (AVE). Higher AVE values indicate higher convergent reliability of the

measurement. The Discriminant validity is established when the AVE values exceed the square of the

correlations between each pair of latent constructs (Fornell and Larcker, 1981).

Finally, LISREL 9.90 programme was used to conduct the Structural Equation Modeling (SEM) and

Maximum Likelihood Estimation (MLE) was applied to estimate the CFA models.

Data analysis and interpretation

The demographic data obtained were tabulated in Table-1:

Table-1: Demographic data of the respondents

Demographic Variables Factors Frequency %

Gender Male 969 66.5

Female 488 33.5

Age

≤ 21 years 5 0.34

22-32 years 364 24.98

33-43 years 589 40.42

44-54 years 394 27.04

≥ 55 years 105 7.22

Income

≤ Rs. 14999.00 36 2.47

Rs. 15000-Rs. 24999.00 898 61.63

Rs. 25000-Rs. 44999.00 421 28.89

≥ Rs. 45000.00 102 7.01

Occupation

Service [govt./prv] 857 58.81

Self employed 311 21.34

Professionals 97 6.65

Student 15 1.02

Housewives 167 11.46

Others [retd., VRS etc] 10 0.72

Educational qualification

High school 7 0.48

Graduate 1147 78.72

Postgraduate 283 19.42

Doctorate & others (CA, fellow etc) 27 1.38

To assess the reliability and validity of the constructs, the exploratory factor analysis (EFA) was

applied using principal axis factoring procedure with orthogonal rotation through VARIMAX process. The

results of the EFA were displayed in Table-2. The Cronbach's Coefficient alpha (0.928) was found significant

enough, as it measure >.7 (Nunnally and Bernstein, 1994) for all constructs and therefore it is reasonable to

conclude that the internal consistency of the instruments used were adequate. Each accepted construct

displayed acceptable construct reliability with estimates well over .6 (Hair, Anderson, Tatham and William,

1998). The KMO measure of sample adequacy (0.879) indicated a high-shared variance and a relatively low

uniqueness in variance (Kaiser and Cerny, 1979). Barlett’s sphericity test (Chi-square=891.214, df=458,

p<0.001) indicated that the distribution is ellipsoid and amenable to data reduction (Cooper and Schindler,

1998). The CRMD was loaded on 20 items, SQ on 18 items, CS on 3 items, CR on two items, RI on 3 items

and SC on 3 items.

Table-2: Measurement of reliability and validity of the variables

Items FL** t-value α** AVE**

CRMD

Our organization establishes and monitors customer-centric

performance standards at all customer touch points (CRMD1) 0.634 16.23 0.926 0.762

Our organization has established clear business goals related

to customer acquisition, development, retention and

reactivation. (CRMD2)

0.619 12.28 0.926 0.762

Our organization has the sales and marketing expertise and

resources to succeed in CRM (CRMD3) 0.651 17.89 0.926 0.762

Our employee training programme has been designed to

develop the skills required for acquiring and deepening

customer relationships. (CRMD4)

0.701 23.09 0.926 0.762

Employee performance is measured and rewarded based on

meeting customer needs and on successfully serving the

customer. (CRMD5)

0.629 16.11 0.926 0.762

Our organizational structure has been designed to foster

customer centricity. (CRMD6) 0.616 10.45 0.926 0.762

Our organization commits time and resources to manage

customer relationships. (CRMD7) 0.671 18.62 0.926 0.762

Our organization has apt softwares to serve our customers.

(CRMD8) 0.723 28.39 0.926 0.762

Our organization has required hardwares to serve our 0.73 30.18 0.926 0.762

customers. (CRMD9)

Our organization has the proper technical personnel to provide

technical support to our CRM executives. (CRMD10) 0.648 17.56 0.926 0.762

Our organization maintains a comprehensive database of our

customers. (CRMD11) 0.659 18.09 0.926 0.762

Individual customer information is available at every point of

contact (CRMD12) 0.606 10.02 0.926 0.762

Our organization provides customized services to our key

customers. (CRMD13) 0.647 17.02 0.926 0.762

Our organization communicates with key customers to

customize our offerings on demand. (CRMD14) 0.618 10.48 0.926 0.762

Our organization makes an effort to find out what the key

customer requirements are (CRMD15) 0.602 9.76 0.926 0.762

Our employees make coordinated efforts to deliver customize

service once a customer places a demand for such service

(CRMD16)

0.717 24.55 0.926 0.762

Each and every employee of our organization treats customers

with great care. (CRMD17) 0.615 10.41 0.926 0.762

Our organization provides channels to enable ongoing two-

way communication between our key customers and us.

(CRMD18)

0.661 16.73 0.926 0.762

Our customers can expect exactly when services will be

performed (CRMD19) 0.624 15.1 0.926 0.762

Our organization fully understands the requirements of our

key customers and us. (CRMD20) 0.702 23.26 0.926 0.762

SQ

Physical infrastructures of hospitality service providers at

Santiniketan are updated. (SQ1) 0.621 14.31 0.901 0.754

Physical facilities of hospitality service providers at

Santiniketan are visually appealing. (SQ2) 0.613 12.09 0.901 0.754

The service employees representing the hospitality service

providers are smart in their appearance. (SQ3) 0.656 19.17 0.901 0.754

The hospitality service providers at Santiniketan operate at

convenient hours. (SQ4) 0.679 24.32 0.901 0.754

The hospitality service providers at Santiniketan are easy to

access. (SQ5) 0.702 28.91 0.901 0.754

The service employees representing the hospitality service

providers pay individual attention to tourists. (SQ6) 0.637 18.05 0.901 0.754

Services are provided to the customers/tourists when

committed by the hospitality service providers. (SQ7) 0.611 11.98 0.901 0.754

The hospitality service providers at Santiniketan are

conveniently located. (SQ8) 0.639 18.95 0.901 0.754

Physical ambience of the premise of the hospitality service

providers touches heart. (SQ9) 0.603 9.01 0.901 0.754

Value proposition of the services are adequate to justify the

benefit versus the sacrifices made. (SQ10) 0.648 22.28 0.901 0.754

The hospitality service providers at Santiniketan are providing

the first time service right. (SQ11) 0.616 13.65 0.901 0.754

The ambience of the tourist venues is rich in aesthetics, culture

and ethnicity. (SQ12) 0.716 31.27 0.901 0.754

The tourist spots are rich in greenery and have minimum level

of pollution. (SQ13) 0.722 33.17 0.901 0.754

A number of well distinguished tourist spots are identifiable

and accessible (SQ14) 0.673 23.91 0.901 0.754

The cultural and ethnic events provide opportunity to absorb

the warmth of destination. (SQ15) 0.719 31.76 0.901 0.754

Santiniketan, as a tourist destination, is free from undesirable

disturbances. (SQ16) 0.638 18.92 0.901 0.754

Local administration of Santiniketan takes well care of

problems if reported. (SQ17) 0.624 15.1 0.901 0.754

Local people of Santiniketan are quite amicable and are ready

to help if required. (SQ18) 0.659 22.33 0.901 0.754

CS

As a customer of SBI, I am satisfied with the services

provided by my hospitality service provider (CS1) 0.715 25.61 0.921 0.759

As a customer of SBI, I would positively recommend my

hospitality service provider to new prospects (CS2) 0.899 29.789 0.921 0.759

As a customer, I feel good about my decision to avail services

from my hospitality service provider (CS3) 0.908 33.016 0.921 0.759

CR

I intend to remain associated with my hospitality service

provider for the time being (CR1) 0.658 24.44 0.901 0.733

I intend to continue my relationship with my hospitality

service provider as a customer for the next five years (CR2) 0.681 30.401 0.901 0.733

RI

Unless other hospitality service provider provide me with

some distinct advantages, I am habituated in getting services

from my hospitality service provider (RI1

0.668 26.71 0.913 0.748

Unless I am extremely dissatisfied with my hospitality service

provider, switching to an alternative hospitality service

provider will be a bother (RI2)

0.623 23.081 0.913 0.748

Unless I am extremely dissatisfied with my hospitality service

provider, switching to an alternative hospitality service

provider will be inconvenient for me (RI2)

0.672 28.643 0.913 0.748

SC

For me the costs involved in searching, investing time and

money and accessing an alternative hospitality service

provider other than SBI is high (SC1)

0.847 - 0.903 0.699

It would take a lot of effort to change my hospitality service

provider (SBI) (SC2) 0.869 28.432 0.903 0.699

It would be a hassle to change my existing hospitality service

provider (SBI) (SC3) 0.891 30.712 0.903 0.699

Cronbach’s α 0.928

KMO measure 0.879

Barlett’s test of sphericity

apprx. Chi-square 891.214

df 458

sig. p<.001

**FL- Factor loadings, α - Cronbach’s α, AVE – Average variance extracted

The dimensions of CRM have been nomenclated as per the component wise factor loadings (shown by colour

grade in Table-2) in Table-3.

Table-3: Dimensions of PTSQ and CRM

Sl. No. Variable Items as per factor loadings post EFA Dimension name

6 Customer

Relationship

Management

CRMD1 – CRMD7 Organizing around CRM

7 CRMD8 – CRMD12 Technology integration

8 CRMD13 – CRMD17 Key customer focus

9 CRMD18 – CRMD20 Managing knowledge

Bivariate correlations were obtained to assess the relationship between the variables. The results were

displayed in Table-4. Correlation results revealed that CRMDP shared positive and significant relationship

with SQ (r=.167**, p<.001), CS (r=.201**, p<.001), SC (r=.163**, p<.001) and moderately significant with

CR (r=.091*, p<.005) and RI (r=.082*, p<.005).

Table-4: Bivariate correlation between the variables

Variables CRMDP SQ CS CR RI SC

CRMDP 1

SQ .167** 1

CS .201** 187** 1

CR .091* 101** 226** 1

Relationship Inertia .082* 0.028 .117** .332** 1

Switching cost 163** 0.051 0.043 .079* 1

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

To assess the strength of association ship between CRMD and SQ and to understand the predictive

capability of CRMD to predict SQ, regression analysis was used. The results of the regression analysis were

presented in Table-5a, 5b and 5c. The model summary revealed that the R2 and adjusted R

2 values are .487 and

.472 respectively which indicate that CRMD measures 48.70% of the variation in SQ, which is considered to

be significant enough for predictability of the model (Draper and Smith, 1998). The results of ANOVA

established that the variation showed by CRMD was significant at 1% level (f=71.725, p<.001). The

standardised regression coefficient results confirmed that the predictive capacity of CRMD to predict the

degree of SQ has statistical significance (β=.692, t=11.201, p<.001). The results of regression analysis lend

support to H1.

Table-5a: Model summary (Regression between CRMD and SQ)

Mo

del R

R

Squar

e

Adjusted R

Square

Std. Error of the

Estimate Change Statistics

Durbin-

Watson

R Square

Change

F

Chan

ge

df

1

df

2

Sig. F

Change

1 .69

8a

0.487 0.472 0.63427 0.487 71.72

5 1

6

7 0 1.769

Predictors: (Constant) CRMD

Dependent variable: SQ

Table-5b: ANOVA (Regression between CRMD and SQ)

Model Sum of Squares df Mean Square F Sig.

1

Regression 28.348 1 27.297 71.725 .000b

Residual 29.314 67 0.417

Total 57.662 70

Dependent variable: SQ

Predictors: (Constant) CRMD

Table-5c: Regression coefficients (Regression between CRMD and SQ)

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

1

(Constan

t) 0.517 0.147 3.526

0.00

1

TS 0.583 0.071 0.692 11.20

1 0 1 1

Dependent variable: SQ

The second regression was applied to test H2. The results displayed in Table-6a, 6b and 6c assured

that SQ measures 46.10% (R2=.461) of the variation in CS, which is considered to be significant enough for

predictability of the model (Draper and Smith, 1998) which was confirmed by the results of ANOVA at 1%

level (f=52.174, p<.001). The standardised regression coefficient results confirmed that SQ to be a significant

predictor of CS (β=.528, t=9.482, p<.001). The results of regression analysis lend support to H2.

Table-6a: Model summary (Regression between SQ and CS)

Model R R

Square

Adjusted

R

Square

Std. Error of

the Estimate

Change Statistics Durbin

-

Watson R Square

Change

F

Chang

e

df1 df

2

Sig. F

Change

1 .679a 0.461 0.449 0.84506 0.461 52.174 1 69 0 1.511

Predictors: (Constant) SQ

Dependent variable: CS

Table-6b: ANOVA (Regression between SQ and CS)

Model Sum of Squares df Mean Square F Sig.

1

Regression 34.585 1 34.585 52.17 .000b

Residual 49.274 69 0.714

Total 83.859 70

Dependent variable: CS

Predictors: (Constant) SQ

Table-6c: Regression coefficients (Regression between SQ and CS)

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig

.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

1

(Constant

) 0.749 0.19

3.93

8 0

TS 0.644 0.093 0.528 9.48

2 0 1 1

Dependent variable: CS

To examine the combined impact of CS on CR (H3), regression analysis was applied, the results of

which were displayed in Table-7a, 7b and 7c. The results confirmed that the impact of CS measures 62.8% of

variation in CR, considered to be adequate (Draper and Smith, 1998) and the results of ANOVA at 1% level

(f=67.237, p<.001) reinforces the same. The standardized regression coefficient results confirmed that CS to

be a significant combined-predictor of CR (β=.583, t=12.873, p˂.001). The results of regression analysis lend

support to H3.

Table-7a: Model summary (Regression between CS and CR)

Mo

del R

R

Squar

e

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

Durbin-

Watson R Square

Change

F

Chan

ge

df

1

df

2

Sig. F

Change

1 .79

3a

0.628 0.619 0.80384 0.628 67.23

7 2

6

7 0 1.652

Predictors: (Constant) CS

Dependent variable: CR

Table-7b: ANOVA (Regression between CS and CR)

Model Sum of Squares df Mean Square F Sig.

1

Regression 39.92 2 19.96 67.237 .000b

Residual 43.939 67 0.646

Total 83.859 70

Dependent variable: CR

Predictors: (Constant) CS

Table-7c: Regression coefficients (Regression between CS and CR)

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

1

(Constant

) 0.528 0.197 2.688

0.00

9

PTSQ 0.427 0.148 0.583 12.87

3

0.00

5 0.508 1.967

Dependent variable: CR

Hierarchical regression analysis was deployed by considering the average (mean) values of the items

for the factor constructs: (a) service quality (SQ) as the dependent variable, (b) 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 SQ, CS and CR. The following models were constructed:

Regression equation-1

SQ=β0+β1*CRMD+β2*RI+β3*SC+β4*CRMD*RI+β5*CRMD*SC+β4*CRMD*RI*SC + εi

Regression equation-2

CS = β0 + β1*CRMD + β2*SQ +β3*RI + β4*SC + β5*CRMD*RI + β6*CRMD*SC +

β7*CRMD*RI*SC + +β8*CRMD*SQ*RI*SC + εi

Regression equation-3

CR=β0+β1*CRMD+β2*SQ+β3*CS+β4*RI+β5*SC+β6*CRMD*RI+β7*CRMD*SC

+β8*CRMD*RI*SC+β9*CRMD*SQ*RI*SC + β10*CRMD*SQ*CS*RI*SC + εi

The regression models were displayed in Table-8 (for equation-1), Table-9 (for equation-2) and

Table-10 (for equation-3). For equation-1 & equation-2, four regression models whereas for equation-3, five

regression models were established. Model 1 depicted the direct effect of CRMD, RI and SC on SQ. 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 Multicollinearity between

interaction variables (Irwin 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-8 revealed that Model-1 reinforced support for H1, H2, and H3, as

CRMD was found to have a positive and significant effect on SQ (β = .573, t=9.871 p<0.01), RI exhibited

significant and positive impact on SQ (β = .281, t=8.442, p<0.01) and SC showed significant and positive

relationship with SQ (β = .209, t=6.719, p<0.01). The results of Model-2 revealed that the binary interaction

between CRMD and RI indicated that the relationship between CRMD and SQ can be significantly moderated

by RI (β = - .348,t=17.262 p<0.01). The negative interaction confirmed our prediction that with the increase in

RI the impact of CRMD performance on SQ will decrease indicating a habitual-trap-of-consumption for

customers. Model-3 revealed that the binary interaction between CRMD and SC indicated that SC had a

moderately intervening effect on CRMD-SQ link (β = - .098*,t=4.512 p<0.05) and the negative interaction

confirmed our prediction that with the increase in SC, the impact of CRMD performance on SQ will decrease.

Model-4 represented the ternary interaction and revealed that as SC increases, the negative mediating effect (β

= -.172, t=12.32, p<0.01) of RI on CRMD performance and SQ strengthens. The results supported H4 and H5.

Table-8: Regression models testing the interaction effects (equation-1)

Independent

Variables

Dependent variable: Service quality

Model-1 Model-2 Model-3 Model-4 VIF

β /t /Sig. β/t/Sig. β/t/Sig. β/t /Sig.

CRMD .573/9.871/.000 2.342

RI .281/8.442/.000 2.216

SC .209/6.719/.000 2.229

Binary interaction effects

CRMD*RI #DIV/0! 2.719

CRMD*SC -

5.429964539 2.091

Ternary interaction effects

CRMD*RI*SC #DIV/0! 1.916

Adjusted R2 0.493 0.516 0.489 0.507

F-value 201.36** 187.89** 175.56** 163.09**

The results of hierarchical regression-2 have been tabulated in Table-9. It revealed that Model-1

provided reinforced support for H1, H2, and H3, as CRMD displayed a positive and significant effect on CS (β

= .421, t=18.73, p<0.01), SQ exhibited significant and positive impact on CS (β = .222, 11.987, p<0.01), RI

showed significant and positive relationship with CS (β = .327, t=14.87, p<0.01) and SC exhibited significant

and positive relationship with CS (β = .117, t=9.034 p<0.01). Results of Model-2 revealed that the binary

interaction between CRMD and RI indicated that the relationship between CRMD and CS depends on the

level of RI (β = - .288, t=13.09, p<0.01) and the negative interaction confirmed our prediction that with the

increase in RI the impact of CRMD on CS will decrease.. Model-3 exhibited that the binary interaction

between CRMD and SC indicated that the relationship between CRMD and CS depends on the level of SC (β

= - .121, t=8.772, p<0.01). The negative interaction revealed our prediction that with the increase SC, the

impact of CRMD on CS will decrease. H6 is supported. Model-4 represented the ternary interaction and

revealed that as SC increases the negative mediating effect (β = -.113, t=6.276, p<0.01) of RI on CRMD and

CS strengthens. H7 is supported. The combined mediating impact of RI and SC on CRMDP is insignificant

towards SQ influencing CS. It seems that customer satisfaction on the basis of perceived SQ is independent of

RI and SC and their impact on CRMD. H8 is rejected.

Table-9: Regression models testing the interaction effects (equation-2)

Independent Variables

Dependent variable: Customer satisfaction

Model-1 Model-2 Model-3 Model-4 Model-1 VIF

β /t /Sig. β/t/Sig. β/t/Sig. β/t /Sig. β /t /Sig.

CRMD .421/18.73/.000 2.647

SQ .222/11.98/.000 2.327

RI .327/14.87/.000 2.111

SC .117/9.034/.000

Binary interaction effects

CRMD*RI #DIV/0! 2.018

CRMD*SC #DIV/0!

Ternary interaction effects

CRMD*RI*SC #DIV/0! 2.321

Quaternary interaction effects

CRMD*SQ*RI*SC .092/2.121/.039

Adjusted R2 0.477 0.487 0.499 0.472

F-value 151.33** 159.1913** 109.60** 112.62**

The results of hierarchical regression-3 have been tabulated in Table-10. It revealed that Model-1

provided reinforced support for H1, H2, and H3, as CRMD displayed a positive and significant effect on CR (β

= .261, t=11.87, p<0.01), SQ exhibited significant and positive impact on CR (β = .136, t=8.064, p<0.01), CS

revealed a significant and positive impact on CR (β = .381, t=14.92, p<0.01), RI showed moderately

significant and positive relationship with CR (β = .089, t=5.07, p<0.05) and SC exhibited significant and

positive relationship with CS (β = .109, t=8.117 p<0.01). Results of Model-2 revealed that the binary

interaction between CRMD and RI indicated that the relationship between CRMD and CR moderately

depends on the level of RI (β = - .091, t=3.68, p<0.05) and the negative interaction confirmed our prediction

that with the increase in RI the impact of CRMD on CR will decrease.. Model-3, did not exhibit significant

binary interaction between CRMD and SC which indicated that the relationship between CRMD and CR did

not depend on the level of SC (β = - .019, t=1.528, p<0.45). H9 is partially supported. Model-4 represented the

ternary interaction and revealed that as the negative mediating effect of RI and SC increases (β = -.201,

t=10.113, p<0.01), the relationship between CRMD and CR strengthens. H10 is supported. The combined

mediating impact of RI and SC on CRMDP is significant (β = .415, t=19.528, p<0.01), on the combined effect

of SQ and CS to affect CR. It seems that customer retention on the basis of perceived SQ and CS is strongly

dependent on RI and SC and their impact on CRMD. H11 is accepted.

Table-10: Regression models testing the interaction effects (equation-3)

Independent

Variables

Dependent variable: Customer retention

Model-1 Model-2 Model-3 Model-4 Model-1 VIF

β /t /Sig. β/t/Sig. β/t/Sig. β/t /Sig. β /t /Sig.

CRMD .261/11.87/.000 1.99

SQ .136/8.064/.000 2.09

CS .381/14.92/.000

RI .089/5.07/.002 2.33

SC .109/8.117/.000

Binary interaction effects

CRMD*RI -

.091/3.68,/.003 2.07

CRMD*SC -

0.027571075

Ternary interaction effects

CRMD*RI*SC #DIV/0! 2.2

Quaternary interaction effects

CRMD*CS*RI*SC .415/19.528/.000 1.879

Adjusted R2 0.491 0.522 0.627 0.509

F-value 122.11** 131.13** 97.60** 89.62**

Confirmatory factor analysis (CFA) was applied to assess the convergence, discriminant validity and

dimensionality for each construct to determine whether all the 49 items (Table-2) measure the construct

adequately as they had been assigned for. LISREL 9.90 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 (Table-11) for the default (proposed) model. The comparative

fit indices namely CFI (0.988), NFI (0.994) and TLI (0.979) were found significant enough to accept the

fitness of the default (proposed) model (Schreiber et al, 2006). The Parsimonious fit indices (PNFI=0.702,

PCFI=0.793, PGFI=0.747) also confirmed robustness of the model and indicated an absolute fit (Schreiber et

al, 2006). The GFI (0.971) and AGFI (0.969) 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; Baumgartner and Homburg, 1996;

Hulland et. al, 1996; Kline, 1998; Holmes-Smith, 2002, Byrne, 2001). The CFI value (0.984) for all the

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

cross-validation index was found to be small enough (ECVI=0.0021) to confirm the superiority of the default

model to the saturated and independence model. The RMSEA value obtained (0.051) is < 0.08 for an adequate

model fit (Hu and Bentler, 1999). The RMR value (0.002) is small enough (close to 0.00) to assure a robust-fit

06,

Anglim, 2007). The probability value of Chi-square (χ2=148.23, df=79, p=0.000) is more than the

conventional 0.05 level (P=0.02) indicating an absolute fit of the model to the data and the χ2/df value is ≤ 2

(1.87) suggesting its usefulness to justify the default model as the nested model.

Table-11: Fit indices for the default model

Absolute predictive

fit Comparative fit Parsimonious fit Others

χ2

D

f P

ECV

I NFI TLI CFI

PNF

I

PCF

I

PGF

I GFI

AGF

I

RM

R

SRM

R

RMSE

A

148.

2 79 0 0.002

0.9

9

0.9

8

0.9

9 0.7 0.79 0.75

0.9

7 0.97

0.00

2

0.029

4 0.051

To construct the nomological network structural equation modeling (SEM) was used to test the

nomological validity of the proposed research model. Composite CRMD, SQ, CS, CR, RI and SC scores

across individual items were obtained by summating the ratings on the scale provided in the survey instrument

items which were used as indicators of their latent version.

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

16 paths (including direct and indirect effects) and 10 paths (depicting moderating effects) drawn were found

to be significant at both p<0.01 and p<0.05 levels. The research model holds well (Fig.2) as the fit-indices

supported adequately the model fit to the data. The double-curved arrows indicated correlation between the

exogenous and endogenous observed variables which was found significant. The residual variables (error

variances) are indicated by Є1, Є2, Є3, etc. The regression weights are represented by λ. The relationship

between the exogenous variables was represented by β. One of the factor loading was fixed to ‘1’ to provide

the latent factors an interpretable scale (Hox & Bechger).

Fig.2: Structural model showing the path analysis

The direct and indirect effects of the constructs were calculated and tabulated in Table-12. Since there was an

absence of indirect non-causal effect, model respecification was not required (Hair et al, 2010)

Table-12: Direct, indirect and total effects of independent variables on dependent variables

Relationship

Effects

Direct

(causal

)

Indirect

(causal)

Indirect (non-

causal) Total

CRMD SQ 0.84 0.84

SQ CR 0.81 0.81

CRMD CS 0.89 0.89

SQ CS 0.78 0.74(0.84*0.89) 1.52

CS CR 0.85

0.52

(0.84*0.81*0.77

) 0.62

(0.81*0.77)

1.99

CRMD CR 0.77 0.77

CRMD SQ CR 0.68

(0.84*0.81) 0.68

CRMD CS CR 0.75

(0.89*0.85) 0.75

CRMD SQ CS CR 0.55(0.84*0.7

8*0.85) 0.55

Implications for theories and practice

The existing extant literature on CRM limits its dimensional performance measurement in relation to

service quality, customer satisfaction and customer retention in the context of hospitality and tourism industry.

Further to this, analysis of CRM dimensional performance under the dual mediating impact of relationship

inertia and switching costs to determine service quality and subsequent behavioural consequences in

hospitality markets was not undertaken at all. Therefore the study will add-up to the existing body of literature

and shall provide opportunity to the researchers to fine-tune the CRM scale for the hospitality and tourism

sector and come out with a more specific tourism relationship management (TRM) concept. Baksi and Parida

(2013, 2012) made two studies to identify the impact of relationship inertia and switching costs on CRM

performance, automated service quality, customer satisfaction and customer retention in banking context and

came out with observations that both relationship inertia and switching costs imparted negative impact on the

relationship confirming the theory of habitual trap dominating the intangible service sectors.

The study revealed that CRM performance has a strong and positive impact on service quality,

customer satisfaction and customer retention. Therefore, strategically it becomes significant for the hospitality

and tourism service providers to maintain high level of CRM dimensional performance and thereby ensuring

enhanced level of perceived service quality which is considered to be a critical element for repatronization

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 moderates the constructs under study and

diminishes the effect of CRM dimensional performance. The study also showed that the impact of customer

satisfaction on customer retention becomes irrelevant as perceived switching cost and relationship inertia

increases. Hospitality organisations 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). 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 tourist 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 elevated price 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 dimensional performance-service quality-

customer satisfaction-customer retention link over time.

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