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CONCEPTUALIZING TOURISM RELATIONSHIP MANAGEMENT (TRM) FRAMEWORK AND ASSESSING ITS IMPACT ON TOURISM SERVICE QUALITY, TOURIST SATISFACTION AND DESTINATION LOYALTY

Published: | DOI: 10.5614/ajht.2014.13.1.03 | Vol 13, Issue 1 (2014)

Authors

Abstract

This study attempts to develop a tourist relationship management (TRM) framework by assimilating assorted dimensions. The basic foundation of the TRM framework will be rested on customer relationship management (CRM) model with justified addition of dimensions compatible to tourism dynamics. Further to this the study examines the intervening effects of modified relationship dimensions on service quality perception-tourist satisfaction-destination loyalty link. The study confirmed convergence of dimensions to justify TRM framework with adequate internal reliability and validity of the scale and further revealed that perceived tourist service quality; tourist satisfaction and destination loyalty can be colinked under the intervening effects of TRM dimensional performance. The default model also holds good to lend support to the theoretical findings.

Keywords

Tourism; Loyalty; Service quality; Marketing; Business; Perception; Service (business); Quality (philosophy); Reliability (semiconductor); Scale (ratio); Customer satisfaction; Psychology; Geography

INTRODUCTION

The quantitative development of tourism is accompanied by multi-aspects including qualitative and structural transformations. The recent geopolitical changes made in different regions of the world have had a great influence on the scale and structure of tourism. Majority of the researchers are unanimous regarding the expansion and direction on the transformations of demands, so much so that there is even an accepted concept known as ―Hard and Soft Tourism‖. It is based on the observational changes within the sphere of former and actual clients in travel agencies and set the standard characteristics of two opposite kinds of tourism: the traditional tourist and the modern tourist. These are presented in table 2. With the increase in the significance of 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. 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 tourist-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 touristservice-providers. But, identifying the perceived tourist service quality becomes imperative as it was empirically tested to be antecedent to tourist satisfaction (short-term effects) and destination loyalty (long-term effects). From the late 1990s the hospitality

2The University of Burdwan, West Bengal , India 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 tourism sector with an apprehension that it will help maintain a linear relationship between perceived service quality-tourist satisfaction and destination loyalty. But in most of the cases it was found that the conventional CRM dimensions failed to facilitate the relationship. This provoked a deeper research focus to identify the compatible dimensions of relationship management for tourism.

The inbound tourism in India registered 6.31 million (5.78 million in 2010) 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 highspending 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 2011 in the area of development perspective of eco and rural tourism indicated that it registered highest employment and investment ratio. Presently, in India, the healthcare sector has an estimated revenue of around $30 billion constituting 5% of GDP and offering employment to around 4 million people [Source: Confederation of Indian Industry (CII)]. As per a recent CII-McKinsey report, the growth of this sector can contribute to 6-7% of GDP and increase employment by at least 2.5 million by 2012. According to the study conducted by the CII and McKinsey consultants, in 2005, 150,000 medical tourists visited India with the number rising by 30 percent a year. They also have predicted that the industry will grow to earn additional revenue of $2.3 billion by 2012 and 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, and Magh Mela draw huge influx of domestic as well as international tourist. Santiniketan has been a major hub of educational tourism with Visva Bharati at the focal point. Every year, on an average, 5000-7500 (source: Visva Bharati)

students all over the globe make an attempt to register themselves in this university and alongwith them comes their relatives and other associates to enjoy the se renity of Santiniketan. The adjoining areas of Santiniketan namely Tarapith, Nalhati, Fullara, Bakreshwar etc. has emerged as pilgrimage-tourism centres. Tarapith, alone, caters to almost 5000 pilgrim visitors on a daily average. Santiniketan has gradually changed from a rural-based visitors' spot to a semi-urban tourist centre with technology-aided modern facilities namely online booking of hotels, ATM and kiosk banking facilities, syndicated tour-operations, traditional and fusion-based cuisine serving restaurants, hotels with adequate facilities and amenities, departmental stores etc. One of the significant attractions of Santiniketan is the gamut of handicraft items. Seeing Santiniketan's emergence as a major tourist hub, it is quite imperative that the assorted tourism service providers should have access to a well constructed tourist relationship management framework to set the dyadic relationship in a symbiotic and dynamic mode.

The objectives of this study were: (a) to justify the dimensions of Tourist Relationship Management (TRM) by modifying the existing dimensions of CRM and introducing new dimensions in the context of tourism industry, (c) to examine the possible impact of TRM dimensions on the link between tourist service quality, tourist satisfaction and destination loyalty and (d) to test the robustness of the proposed research model.

LITERATURE REVIEW

The relationship philosophy relies on co-operation and a trusting relationship with other stakeholders and network partners. While the approach has been widely used in the corporate sector, the potential application of this emerging body of knowledge for managing the complex and dynamic tourism domain has not been clearly addressed (Saxena, 2000). Hence, there is a need to examine its application to create partnerships with recognition that tourism plays an important role in revitalising rural communities and economies, historically dependent on natural resource-based commodities. Moller (2000) identified seven dimensions of this networking or collaboration: (i) basic goals, (ii) disciplinary background, (iii) key concepts (iv) methodological orientation, (v) ontological basis, (vi) level of focus & unit of analysis and (vii) focus on structure versus process. Relationship marketing was channelized to strike dyadic network with customers. Customer relationship management (CRM), an offshoot and spin -off to relationship marketing, has been observed as a continuous paradigmatic shift in managing relationship with customers by identifying the changing notions of customer attitudes, perceptions and behavioural manifestations in the context of their apprehension and expectation to be served as (Peppers and Rogers, 2004). Conceptually, CRM evolved from three basic foundations of marketing management: (a) customer orientation, (b) relationship marketing and (c) database marketing (Yim et al, 2004). Adoption, practice and implementation of CRM gained momentum among academicians and corporate houses (Gruen et al, 2000; Rigby and Ledingham, 2004; Srivastava et al, 1999; Thomas et al, 2004). 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). Inspite of its wide application, CRM, lacked a cohesive definition and identification of its dimensions. Yim (2002) provided some 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 key focal 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 customerinformation 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 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. Literature, however, hinted that destination competitiveness can be one of the critical components of a modified relationship management framework which would be compatible to the tourism industry and may be nomenclated as Tourist Relationship Management (TRM). The gradual transition of ‗hard tourism' into futuristic ‗soft tourism' (Table-1) has catapulted the relationship management integration with the basic operational aspects.

Table-1: Transition of tourist demand pattern: Hard tourism to soft-tourism

Hard tourismSoft tourism
Package toursIndividual touring
Short-term residencesLong-term residences
Model of one big travel during holidaysModel of two shorter travels during a year
Predetermined tour scheduleCustomized tour schedule
Comfort and passivityActivity and effort
Travels and attractionsNew experiences and higher quality
Sense of superiority, demonstration effectRespect and relationship
Lack of knowledge about attractions, cultureKnowledge about destination
and tradition about destination
imported life style and behaviourLifestyle following to an example of local
Population
PurchasesGifts
Freely available mass souvenirsIndividual & customized souvenirs
Lack of interest in language of destinationStudying local language
Fast transport and frequent movesLess importance of moving speed
CuriosityTact
Expectation of comfortComfort is not essential
Hard tourismSoft tourism
A distance between client and touristA good relationship with tourist
staffstaff

Source: Ostrowski S., Krippendorfa, J. (1983)

Studies observed that formulation of relationship strategies followed an analytical planning and destination competitiveness which will allow firms to stay ahead of its competitors and to ensure destination sustainability (Ritchie and Crouch, 2000a & 2000b; Mihalic, 2000; Buhalis, 2000; Flagestad and Hope, 2001; Kozak, 2001; Heath and Wall, 1992; Bordas, 1994; Pearce, 1997). Poon (1993) observed that tourist satisfaction can be achieved with proper strategic initiatives to build destination image and that destination competitiveness can be ensured by virtue of organized form of interactions with tourists. Destination has been apprehended to be pivotal in nurturing relationship between tourism service providers and tourists as Buhalis (2000) listed six major components of tourism attractions towards evaluating tourism destination:

  • a. Attraction natural, man-made, artificial, purpose-built, heritage, special events
  • b. Accessibility transportation system, terminals & vehicles
  • c. Amenities accommodations, catering facilities, retailing
  • d. Available packages prearranged packages by intermediaries and principals
  • e. Activities activities related to tourism products
  • f. Ancillary services banks, telecommunications, hospitals etc.

In addition to destination, ‗purpose' is expected to play an important role in the relationship between the tourism service providers and tourists. In addition to destination, ‗purpose' is expected to play an important role in the relationship between the tourism service providers and tourists. Literature revealed a number of issues pertaining to travel purpose and linked it with motivation for travelling sighting satisfying need hierarchy (Maslow, 1954, 1970; Burns and Holden, 1995; Hudson, 1999). Leisure and vacation have been identified as two major purposes of travelling and were linked to satisfying self actualization needs (Miller and Morrison, 2002) and self esteem needs (Pearce, 1993). Pearce (1993) identified five levels of travel purpose hierarchy: relaxation, stimulation, relationship, development and fulfillment. Brown (2010) also identified purpose of travelling as a major deterministic factor in strategizing tourism packages while analyisng volunteer tourism facets. Dann (1977) and later supported by Crompton (1979) identified seven ‗Push&Pull' factors driving travelling: (i) escape from perceived mundane environment, (ii) exploration and evaluation of self, (iii) relaxation, (iv) prestige, (v) regression, (vi) enhancement of kinship relationships and (vii) novelty and education. Purpose of travelling allows the service providers to understand the psychogenic profiles of the visitors thereby assist them to prepare the blueprint to establish a dyadic relationship.

Relationship management banks on service quality and the prospect of a long -run customer dividend is high (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). Baker and Crompton (2000) observed that the literature related to quality in the area tourism and allied area dates back to the early 1960's. Most of the contemporary research works involving service quality in tourism focused on the perceptual framework of tourists towards service quality (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). Pawitra and Tan (2003) used SERVQUAL in order to analyse the destination image of Singapore from the perspective of tourists from Indonesia and noted that the use of SERVQUAL in measuring a destination image requires that it be modified in order to ensure that the data reflect the unique attributes provided by the destination. Atilgan et al. (2003) suggest that cultural characteristics have an effect on perceptions of service quality in tourism and found that different cultural groups can have different levels of expectations and perceptions in terms of service quality dimensions.

Tourist satisfaction can be obtained by assessing the gap between predicted and perceived service. Service quality has been recognized as an antecedent to tourist satisfaction (Suki, 2013, Canny and Hidayat, 2012). Dmitrovic et al (2009), in a study observed that tourist satisfaction as a result of sequential interrelated consequences starting with destination image through perceived service quality and value. Oliver (1981) claimed that tourist satisfaction can be seen as a tourists' post-purchase evaluation of the destination. In many studies, satisfaction was distinguished as an antecedent of loyalty (Kozak, 2001; Jang & Feng, 2006). Although Oppermann (2000) stated that studies on tourist satisfaction and destination loyalty have not been thoroughly investigated, Chi and Qu (2008, p. 624) claimed tourist satisfaction as critical to profitability. several studies have been conducted to examine the influence of customer satisfaction on loyalty (Gummesson, 1993; Anderson and Fornell, 1994; Um et al., 2006; Hui et al., 2007). Gotlieb et al. (1994) asserted that positive satisfaction had positive influence on tourists' repurchase intention. Similarly, Baker & Crompton (2000); Petrick et al. (2001), and Jang & Feng (2006) highlighted that satisfaction is the primary antecedent of revisit intention.

In tourist destination researches (e.g. Oliver, 1997; Yoon & Uysal 2005), tourist satisfaction has been measured by different items such as overall satisfaction, performance, expectation, and positive recommendation. Notably, Chi and Qu (2008) maintained loyalty to be a better predictor of actual behavior compared to satisfactio n. In this respect, Chen and Tsai (2007) conclude that a key effect of tourist satisfaction that influences tourism intentions for revisit both in short and long term is loyalty to the destination. Importantly, there is an agreement among several scholars that satisfaction provided a ground for revisit and positive word of mouth recommendations which are the indicators of loyalty (e.g. Kozak & Rimmington, 2000; Yoon & Uysal 2005; Chi and Qu, 2008). Kozak (2001) pointed that level of satisfaction as one of the most dominant variables in explaining revisit intention. Accordingly, in tourism destination's researches, it has been widely underlined that tourist satisfaction, loyalty and revisit intention have strong relationship (eg. Yoon & Uysal, 2005; Awadzi &

Panda, 2007), while a few studies disapproved the positive relationship between tourist satisfaction and revisit intention (e.g. Um et al., 2006).

Researchers have verified the relationship between the tourist satisfaction and destination loyalty (Chi and Qu, 2008; Yoon and Uysal, 2005) not in terms of revisit intention but also through advocacy (Bigne et al, 2009; Murray and Howat, 2002; Yoon and Uysal, 2005). Destination loyalty has been highlighted as one of the most importan t subjects in tourism researches. In many studies, revisit intention and positive word of mouth recommendation are noted as indicators of loyalty (e.g. Yoon & Uysal 2005; Chi and Qu, 2008). Several studies have attempted to identify major antecedents of revisit intention including satisfaction (Petrick et al., 2001; Kozak 2001), novelty seeking (Jang & Feng, 2007), image (Chi & QU, 2008), motivation and satisfaction (Yoon & Usal, 2005), safety (Chen & Gursoy, 2001), overall satisfaction (Campo- Martinez et al. 2010), cultural difference ( Chen & Gursoy, 2001), perceived value ( Petrick et al.,2001), past vacation experience( Kozak ,2001), and the like. In this regard, notably, Jang and Feng (2007) asserted that even though the extent of research finding is well focused on determinants of repeat visit intention, it can be contested that understanding tourists' revisit intention and their behavior remains limited. Revisit intention has also been focused as an important issue from economic perspective in tourism studies (e.g. Darnell & Johnson, 2001). Hsu et al. (2008) observed preserving loyalty of established customer as a crucial contributor to the achievement and profitability of business. Accordingly, the main reason why researchers should consider revisit intention is the fact that ―globalization of markets, competitive pressure, brand multiplication and, above all, the ever-changing lifestyles and consumer behavior have forced companies to develop strategies to keep their clients and create consumer loyalty programs‖ (Flambard-Ruaud, 2005), particularly in tourism industry.

Research gap identified

Literature remained inconclusive regarding the compatibility of relationship management dimensions in tourism perspective. Even, if Tourist Relationship Management framework has been worked upon; its impact as antecedent and moderating variable has not been tested on critical variables namely service quality, satisfaction and loyalty.

Formulation of hypotheses

Based on the review of literature this paper attempts empirically to explore possible linkages between perceived tourist service quality (PTSQ), tourist satisfaction (TS) and destination loyalty (DL) with probable moderating impact of TRM dimensions (TRMD) on PTSQ, TS and DL link.

Accordingly it is hypothesized that,

H1: TRM dimensions share relationship with PTSQ, TS and DL.

H2: Better performance of TRM dimensions (TRMD) will have stronger impact of perceived tourist service quality (PTSQ) on tourist satisfaction (TS) and vice versa.

H3: Better performance of TRM dimensions (TRMD) will have stronger tourist satisfaction (TS) on destination loyalty (DL) and vice versa.

H4: Destination loyalty (DL) behaviour will be strengthened under the impact of TRM dimensions (TRMD), if perceived tourist service quality (PTSQ) and tourist satisfaction (TS) are high.

Proposed research model

Based on the literature reviewed and hypotheses formulated, the researchers would like to test the following research model (Fig.1) for robustness.

Fig.1: Research Model

(Legends description: PTSQ-Perceived tourist service quality, TS-Tourist satisfaction, DL-Destination loyalty, TRMD-TRM dimensions)

RESEARCH METHOD

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). 20 respondents representing tourists of assorted demography and academicians 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 four sections. Section-I was targeted for tourists and it asked questions about tourists' expectation and perception of service quality offered by the service providers at Santiniketan, section-II was designed to generate response from the tourists with regard to their level of satisfaction derived out of the services they were offered and allied elements, section-III targeted tourist response in context of destination loyalty, section-IV was intended for the service providers whereby they were asked about the relationship management practices they have deployed in integration with their service offerings and section-V attempted to collect the demographic profile of the tourists. A 7 point Likert scale (Alkibisi and Lind, 2011) was used to generate response. The second phase of the study used convenience sampling technique as the tourists visiting Santiniketan and its adjoining areas were dispersed geographically. A total number of 1974 usable responses were generated out of 2500 questionnaires used for the tourists, with a response rate of 78.96%. For the section-IV of the questionnaire, service employees of the rank of managers, relationship executives etc. were interviewed. As many as 546 personnel associated with assorted tourism services in Santiniketan and its adjoining tourist areas were interviewed.

Factor constructs measurement

SERVQUAL, developed by Zeithaml, Parasuraman and Malhotra (2005), was used to develop a measure for perception of service quality with adequate modification to suit

responses with regard to tourist services. Respondents' perception of satisfaction was measured using the items developed by Weiermair and Fuchs, 1999 and Fuchs and Weiermair, 2003. Revisit intention and tourist referrals (advocacy) made up the destination loyalty indices (Taylor, 1998; Oh and Parks, 1997; Oh, 2000). The TRM dimensions were scaled on 20 items developed by Yim et al (2004) (to dimensionalize CRM) which were adequately modified to suit tourism platform. The additional constructs to make relationship management compatible with tourism imperatives on the basis of destination and purpose of visit were created with 5 and 6 items respectively and were tested for internal reliability and validity.

Reliability and validity

To examine the internal reliability and validity of the constructs, exploratory factor analysis (EFA) was deployed using principal axis factoring procedure with orthogonal rotation through VARIMAX process. 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 composite reliability (CR) and average variance extracted (AVE). Higher CR and 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 8.80 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-2:

Table-2: Demographic data of the respondents

Demographic VariablesFactorsFrequency%
Male139770.77%
GenderFemale57729.23%
≤ 21 years643.27%
22-32 years34617.52%
Age33-43 years63432.11%
44-54 years59830.29%
≥ 55 years33216.81%
≤ Rs. 14999.00613.10%
Rs. 15000-Rs. 24999.0038919.70%
IncomeRs. 25000-Rs. 44999.00124963.27%
≥ Rs. 45000.0027513.93%
Demographic VariablesFactorsFrequency%
Service [govt./prv]104652.98%
Self employed46723.65%
OccupationProfessionals19810.03%
Student422.12%
Housewives1809.11%
Others [retd., VRS etc]412.11%
High school291.48%
Educational qualificationGraduate138670.21%
Postgraduate30515.45%
Doctorate & others (CA, fellow etc)25412.86%

The results of the EFA were displayed in Table-3. The Cronbach's Coefficient alpha 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). Further to this the average variance extracted (AVE) surpassed minimum requirement of .5 (Haier et al., 1998). The KMO measure of sample adequacy (0.893) indicated a high shared variance and a relatively low uniqueness in variance (Kaiser and Cerny, 1979). Barlett's sphericity test (Chi-square=1972.0438, df= 287, p<0.001) indicated that the distribution is ellipsoid and amenable to data reduction (Cooper and Schindler, 1998).

Table-3: Measurement of reliability and validity of the variables

ItemsFLtαAVE
Perceived Tourist Service Quality (PTSQ)
Physical infrastructures of tourism service providers at0.62322.761.9230.896
Santiniketan are updated. (PTSQ1)
Physical facilities of tourism service providers at Santiniketan
are visually appealing. (PTSQ2)		0.63427.028.9230.896
The service employees representing the tourism service
providers are smart in their appearance. (PTSQ3)		0.60118.312.9230.896
The tourism service providers at Santiniketan operate at0.67128.098.9230.896
convenient hours. (PTSQ4)
The tourism service providers at Santiniketan are easy to access.0.64327.906.9230.896
(PTSQ5)
The service employees representing the tourism service068128.921.9230.896
providers pay individual attention to tourists. (PTSQ6)
Services are provided to the tourists when committed by the0.67828.661.9230.896
tourism service providers. (PTSQ7)
The tourism service providers at Santiniketan are conveniently
located. (PTSQ8)0.69119.672.9230.896
Physical ambience of the premise of the tourism service0.68317.265.9230.896
providers touches heart. (PTSQ9)
Value proposition of the services are adequate to justify the0.70218.487.9230.896
benefit versus the sacrifices made. (PTSQ10)
ItemsFLtαAVE
The tourism service providers at Santiniketan are providing the
first time service right. (PTSQ11)		0.71923.921.9230.896
The ambience of the tourist venues is rich in aesthetics, culture
and ethnicity. (PTSQ12)		0.68817.731.9230.896
The tourist spots are rich in greenery and have minimum level of
pollution. (PTSQ13)		0.72726.001.9230.896
A number of well distinguished tourist spots are identifiable and
accessible (PTSQ14)		0.71924.911.9230.896
The cultural and ethnic events provide opportunity to absorb the
warmth of destination. (PTSQ15)		0.69119.672.9230.896
Santiniketan, as a tourist destination, is free from undesirable
disturbances. (PTSQ16)		0.68217.264.9230.896
Local administration of Santiniketan takes well care of problems
if reported. (PTSQ17)		0.72525.812.9230.896
Local people of Santiniketan are quite amicable and are ready to
help if required. (PTSQ18)		0.71323.091.9230.896
Tourist satisfaction (TS)
I am satisfied with the lodging facilities provided by my service
provider at Santiniketan. (TS1).		0.76125.501.9040.879
I am satisfied with
the dining facilities, covering variety of
cuisine,
quality, price, cleanliness, presentation, taste and
convenience. (TS2).		0.74226.113.9040.879
I am satisfied with the shopping opportunity of the tourist
destinations in Santiniketan. (TS3)		0.78927.815.9040.879
I am satisfied with the destination attractions namely cultural,
ethnic, scenic, and historical attractions. (TS4)		0.81732.298.9040.879
I am satisfied with the activities and events of Santiniketan
namely Pous Mela, Basantotsav, Baitalik,
and local cultural
programmes. (TS5)		0.80229.656.9040.879
I am satisfied with the general environment of Santiniketan
pertaining to safety & security, cleanliness, peaceful atmosphere
etc. (TS6)		0.79929.003.9040.879
I am satisfied with the accessibility of services at my tourist
destination in terms of transportation, hospitality, logistics,
parking, banking etc. (TS7)		0.77126.382.9040.879
Destination loyalty (DL)
I shall definitely revisit Santiniketan. (DL1).0.71826.442.9220.893
I shall promote Santiniketan as an excellent tourist destination
amongst my friends, colleagues, relatives and other associates
(DL2)		0.70124.052.9220.893
Tourist Relationship Management dimensions (TRMD)
Our organization establishes and monitors customer-centric
performance standards at all tourist touch points (TRMD1)		0.69922.981.9140.874
Our organization has established clear business goals related to
tourist acquisition, development, retention and reactivation.
(TRMD2)		0.68721.087.9140.874
Our organization has the sales and marketing expertise and
resources to succeed in TRM (TRMD3)		0.67119.001.9140.874
Our employee training programme has been designed to develop
the
skills
required
for
acquiring
and
deepening
tourist
relationships. (TRMD4)		0.71825.671.9140.874
ItemsFLtαAVE
Employee performance is measured and rewarded based on
meeting tourist needs and on successfully serving the tourist.
(TRMD5)		0.67918.762.9140.874
Our organizational structure has been designed to foster tourist
centricity. (TRMD6)		0.68119.002.9140.874
Our organization commits time and resources to manage tourist
relationships. (TRMD7)		0.66917.401.9140.874
Our organization has apt softwares to serve our tourists.
(TRMD8)		0.65215.204.9140.874
Our organization has
required hardwares to serve our tourists.
(TRMD9)		0.67218.110.9140.874
Our organization has the proper technical personnel to provide
technical support to our relationship management executives.
(TRMD10)		0.69120.028.9140.874
Our organization maintains a comprehensive database of our
tourists. (TRMD11)		0.70122.918.9140.874
Individual tourist information is available at every point of
contact (TRMD12)		0.68419.278.9140.874
Our organization provides customized services to our valued and
key tourists. (TRMD13)		0.66417.217.9140.874
Our organization communicates with key tourists to customize
our offerings on demand. (TRMD14)		0.63114.283.9140.874
Our organization makes an effort to find out what the key tourist
requirements are (TRMD15)		0.67919.005.9140.874
Our employees make coordinated efforts to deliver customize
service once a tourist places a demand for such service
(TRMD16)		0.70220.098.9140.874
Each and every employee of our organization treats tourists with
great care. (TRMD17)		0.61714.562.9140.874
Our organization provides channels to enable ongoing two-way
communication between our key tourists and us. (TRMD18)		0.62915.672.9140.874
Our tourists can expect exactly when services will be performed
(TRMD19)		0.71825.091.9140.874
Our organization fully understands the requirements of our key
tourists and us. (TRMD20)		0.66318.782.9140.874
Our organization maintains the database of major destination
attractions for our key tourists. (TRMD21)		0.68719.871.9140.874
Our organization facilitates tourists in accessing the major
destination attractions. (TRMD22)		0.62415.214.9140.874
Our organization provides requisite amenities to ensure safe visit
for tourists to destinations. (TRMD23)		0.60914.009.9140.874
Our organization provides adequate packages that cover smooth
and hassle-free destination visits. (TRMD24)		0.67218.918.9140.874
Our organization arranges activity supports for tourists as per
destination requirements. (TRMD25)		0.71123.091.9140.874
Our organization has networked to provide ancillary services to
tourists. (TRMD26)		0.70922.738.9140.874
Our organization understands the purpose of visit of tourists and
provides services accordingly. (TRMD27)		0.72426.198.9140.874
Our organization has resources to match the purpose of tourist
visits. (TRMD28)		0.69420.018.9140.874
ItemsFLtαAVE
Our
organization
has necessary tie-ups and networks to
synchronize with the purpose of visit of tourists. (TRMD29)		0.64816.552.9140.874
KMO0.893
2
Chi-square (χ
)		1972.0438
Barlett's Test of sphericitydf287.000
Sig..000

** FL: factor loadings, t: t-value, α: Cronbach's α, AVE: average variance extracted

The dimensions of perceived tourist service quality (PTSQ) and CRM have been nomenclated as per the component-wise factor loadings in Table-4.

Table-4: Dimensions of PTSQ and CRM

Items as per factor loadings postDimension name
EFA
PTSQ1, PTSQ2, PTSQ3, PTSQ9Servicescape
PerceivedPTSQ4, PTSQ5, PTSQ8Accessibility
TouristPTSQ6,
PTSQ7,
PTSQ10,
ServicePTSQ11Reliability
QualityPTSQ12,
PTSQ13,
PTSQ14,
(PTSQ)PTSQ15Ethnicity
PTSQ16, PTSQ17, PTSQ18Hospitality
TRMD1 –
TRMD7		Organizing around TRM (TRMO)
Integrating
TRM
technology
Tourist(TRMT)
RelationshipTRMD13 –
TRMD17		Key tourist focus (KFT)
ManagementTRMD18 –
TRMD20		Managing tourist knowledge (TKM)
TRMD 21-
TRMD26		Destination denomination (DD)
TRMD 27 –
TRMD 29		Purpose denomination (PD)
VariableTRMD8 –
TRMD12

The path-analysis using LISREL-9.1 (Fig.2) confirms the convergence of the observed variables (TRMO, TRMT, KFT, TKM, DD & PD) and the latent variable [(Tourist Relationship Management (TRMD)] confirming the fact that the identified dimensions of tourist relationship management are adequate to justify the reliability and validity of the same.

1

Fig.2: Path analysis depicting observed and latent variables

To test correlationship between perceived tourist service quality (PTSQ), tourist satisfaction (TS) destination loyalty (DL) and tourist relationship management (TRM), bivariate correlation was deployed. The mean response score was obtained for each of the variable across the items loaded in EFA for each individual tourist and later on summated and averaged to obtain the composite mean score for each variable. The results of the bivariate correlation analysis were displayed in Table-5. The results confirmed that TRM dimensional output shared strong and positive correlation with PTSQ (r=.162**, p<.001), moderately positive relationship with TS (r=.098*, p<.005) and strong and positive correlationship with DL (r=.101**, p<.001). TS and DL shared strong and positive correlation with each other (r=.136**, p<.001) while PTSQ shared significant correlation with DL (r=.205**, p<.001) and TS (r=.369**, p<.001). H1 is supported.

Table-5: Bivariate correlation between perceived tourist service quality (PTSQ), tourist satisfaction (TS) and destination loyalty (DL)

TRMDPTSQTSDL
Pearson Correlation1162**.098*101**
Sig. (2-tailed).000.001.000
TRMDSum
of Squares and Cross-products		49.61844.56138.76336.987
Covariance.7831.763.609.599
N1974197419741974
Pearson Correlation162**1.369**.205**
Sig. (2-tailed).000.000.000
PTSQSum of Squares and Cross-products44.56157.66248.63443.817
Covariance1.763.824.695.626
N1974197419741974
Pearson Correlation.098*.369**1.136**
Sig. (2-tailed).001.000.000
TSSum of Squares and Cross-products38.76348.63483.43753.718
Covariance.609.6951.192.767
N1974197419741974
Pearson Correlation101**.205**.136**1
Sig. (2-tailed).000.000.000
DLSum of Squares and Cross-products36.98743.81753.71883.859
Covariance.599.626.7671.198
N1974197419741974

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

Hierarchical regression analysis was deployed by considering the average (mean) values of the variables (across the items) to understand the direct and the mediating effects of TRMD on PTSQ-TS link and TS-DL link. For providing empirical evidence to our hypotheses, we proposed an ordinary least square (OLS) regression for our dependent variables TS and DL. The following models were constructed:

  • (i) TS = β0 + β1*PTSQ + β2*TRMD + β3*PTSQ*TRMD+ εi
  • (ii) DL = β0 + β1*TS + β2*TRMD + β3*TS*TRMD+ εi
  • (iii) DL = β0 + β1*TS + β2*PTSQ+ β3*TRMD + β4*TS*PTSQ+ β5*PTSQ*TRMD + β6* TS*TRMD + β7* PTSQ*TS*TRMD + εi

The regression models were displayed in Table-6. Three models were generated. Model 1 depicted the direct effects, model 2 represented the binary interaction and model 3 portrayed the ternary interaction between variables. 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). Results of Model-1 revealed that PTSQ is significantly predictive for TS (β = .652, t=6.009, p<0.01) while the direct effect of TRMD on TS was also found to be significant (β = .498, t=4.664, p<0.01). Model-1 further revealed that TS can significantly be associated with DL and that TS has the predicting capacity to predict DL ((β = .642, t=6.959, p<0.01). TRMD was also found to be predictive of DL (β = .589, t=5.876, p<0.01) and so was PTSQ (β = .354, t=2.873, p≤0.05). Results of Model-1 reinforced support to H1. The binary interaction between TRMD and PTSQ (Model-2) indicated that with the increase in TRMD performance the impact of PTSQ on TS increases significantly (β = .284, t=3.107, p<0.05) while the binary interaction between TRMD and TS assured that better performance of TRMDs will augment the impact of TS on DL (β = .553, t=6.252, p<0.01). Model-2 also revealed that an increased PTSQ will register a profound effect of TS on DL (β = .312, t=3.981, p<0.01). Results of Model-2 supported to H2 and H3. Model 3 revealed the ternary interaction whereby it was established that DL behaviour will be strengthened under moderating effects of TRMD if PTSQ and TS are perceived to be high (β = .491, t=4.871, p<0.01). Model-4 reassured H2 and H3 and supported H4.

Table-6: Hierarchical regression results

Dependent variable-TS, Independent variable-PTSQ, Moderating variable
IndependentTRMD
VariablesModel-1Model-2Model-3
β /t /Sig.β/t/Sig.β/t/Sig.VIF
PTSQ.652/6.009/.0001.541
TRMD.498/4.664/.0001.457
Binary interaction effects
PTSQ*TRMD.334/5.107/.0001.481
2
R		.492.555
Adjusted R2.484.542
F-value66.72542.374
Sig..000.000
Dependent variable-DL, Independent variable: TS, Moderating variable-TRMD
TS.642/6.959/.0001.339
TRMD.589/5.876/.0001.421
Binary interaction effects
TS*TRMD.553/6.252/.0001.879
2
R		.412.627
Adjusted R2.404.616
F-value48.43057.121
Sig..000.000
Dependent variable-DL, Independent variable: PTSQ & TS, Moderating variable-TRMD
PTSQ.354/2.873/.0051.401
TS.642/6.959/.0001.225
TRMD.739/9.115/.0001.398
Binary interaction effects
PTSQ*TS.312/3.981/.0011.562
PTSQ*TRMD.284/3.107/.0031.671
TS*TRMD.553/6.252/.0001.879
Ternary interaction effects
PTSQ*TS*TRMD.491/4.871/.0011.273
2
R		.412.476.664
Adjusted R2.404.461.649
F-value48.43030.89044.121
Sig..000.000.000

a. Dependent variable: TS, DL b. Independent variable: PTSQ, TS c. Moderating variable: TRMD

Confirmatory factor analysis (CFA) was applied to assess the convergence, discriminant validity and dimensionality for each construct to determine whether all the 56 items (Table-3) 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 fitstatistics were obtained (Table-7) for the default (proposed) model. The comparative fit indices namely CFI (0.981), NFI (0.991) and TLI (0.973) were found significant enough to accept the fitness of the default (proposed) model (Schreiber et al, 2006). The Parsimonious fit indices (PNFI=0.697, PCFI=0.781, PGFI=0.713) also confirmed robustness of the model and indicated an absolute fit (Schreiber et al, 2006). The GFI (0.979) and AGFI (0.974) 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 (0.981) for all the constructs were obtained as > .900 which indicated an acceptable fit to the data (Bentler, 1992). The expected crossvalidation index was found to be small enough (ECVI=0.0026) to confirm the superiority of the default model to the saturated and independence model. The RMSEA value obtained (0.049) is < 0.08 for an adequate model fit (Hu and Bentler, 1999). The RMR value (0.003) is small enough (close to 0.00) to assure a robust-fit of the model. The 2006, Anglim, 2007). The probability value of Chi-square (χ2=176.16, df=89, 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 (1.97) suggesting its usefulness to justify the default model as the nested model.

Absolute predictive fitComparative fitParsimonious fitOthers
2
χ		DfPECVINFITLICFIPNFIPCFIPGFIGFIAGFIRMRSRMRRMSEA
176.16890.020.00260.9910.9730.9810.6970.7810.7130.9790.9740.0030.03020.049

Table-7: Fit indices for the default model

To construct the nomological network structural equation modeling (SEM) was used to test the nomological validity of the proposed research model. Composite PTSQ, TS, DL and CRMD 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 17 paths (including direct and indirect effects) and 3 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). The direct and indirect effects of the constructs were calculated and tabulated in Table-8. Since there was an absence of indirect non-causal effect, model respecification was not required (Hair et al, 2010)

Table-8: Direct, indirect and total effects of independent variables on dependent variables

Effects
RelationshipDirect (causal)Indirect (causal)Indirect (non
causal)		Total
PTSQ
TS		0.960.96
TS
DL		0.910.91
PTSQ
DL		0.940.89
PTSQ
TS
DL		0.87 (0.96*0.91)0.87
1

Fig.2: Structural model showing the path analysis indicates moderating effects

IMPLICATIONS FOR THEORIES AND PRACTICE

The present study shall expand the research domain pertaining to relationship management and its implications and shall add up to the extant literature by providing the foundation of tourism relationship management framework (TRM), an offshoot to customer relationship management model, with validated dimensions like destination denomination and purpose denomination. The intervening effects of TRM while linking perceived tourist service quality, tourist satisfaction and destination loyalty were also found to be significant. Further to this, the study reinforces the applicability and integration of TRM dimensions (Yim et al, 2004) with the functional and behavioural modalities of tourism industry. The moderating effects of TRM on PTSQ and TS, TS and DL and on the link PTSQ-TS-DL opens up new research frontiers whereby additional exploration to the dimensional impact of TRM on sectoral tourist behaviours can be analysed. Existing literature emphasized the role of tourist service quality towards influencing tourist satisfaction by using the conventional dimensions of SERVQUAL. The service quality dimensions identified in this study may be tested for its robustness, but it definitely provides researchers with scope to identify ethnicity, hospitality and servicescape as significant and critical quality dimensions for indigenous and aborigin tourism.

The tourism phenomenon in Santiniketan is not new, but it has changed its dynamics with the rapid change in demographic, psychographic, cultural and ethnic factors. With the communication system to the destination improving by leaps and bounds the influx of tourist has also increased. The increased flow of assorted tourist from both domestic and foreign origin forced a complete metamorphosis of the hospitality and tourism map of Santiniketan. The hotels, restaurants, tour-arrangers and other down-the-line service providers underwent a serious make-over as they updated themselves to meet the specific demand and quality perception of both domestic and foreign tourist. The tourism service providers in Santiniketan are well aware about the tourist behaviour based on the destination dynamics and purpose of their visit. Technology has played a pivotal role towards allowing the tourists to avail services on virtual platform. The results ensured that the tourism services provided by the hoteliers, restauranters, logistic -service providers, tour-arrangers etc. at Santiniketan were well absorbed by the tourist and they were satisfied. It was revealed that the perceived tourist service quality was instrumental in assuring tourist satisfaction which subsequently was found to have a positive effect on destination loyalty. The hospitality industry as a whole in Santiniketan was found acceptable by the tourists who were visiting on the occasion of cultural and ethnic events like Pous Mela, Basantotsav etc. in terms of service quality and they have expressed their intention of revisiting the destination and promoting the destination to other tourists.

The study had managerial implication as the changing rural psychogeodemographic pattern (multi-attribute stratification process also known as geoclustering leading to compressed segmentation models namely PRIZM-Potential Index Rating by Zip Markets) of Santiniketan may pose challenges to the managers of tourism service providers to analyse tourist demand and personalize tourism products accordingly. TRM framework is likely to provide tourism managers with analytics to segregate tourists on the basis of identified dimensions particularly the destination denomination and purpose denomination which will enable them to strategise their approach towards satisfying the tourists.

The study had geographical limitations as it has been restricted to Santiniketan in West Bengal, which in future, can be widened to obtain a more generalized conclusion. Future extrapolations of the study can be done by considering other service variables into consideration namely impact of servicescape, perceived service recovery etc.

Research Intelligence

Data from OpenAlex ↗

Metrics

0.00
FWCIfield-weighted
40th
Percentilevs same year + field
Article
Work type
DIAMOND
Open Access

Topics

Customer Service Quality and Loyalty Primary
1.00
Organizational Behavior and Human Resource Management › Business, Management and Accounting › Social Sciences
Digital Marketing and Social Media
1.00
Diverse Aspects of Tourism Research
1.00

Related Research

Semantic Profile AI-classified research signals

Core Domains
Tourism 0.82
level 2
Loyalty 0.74
level 2
Marketing 0.66
level 1
Secondary Topics
Business 0.61 Perception 0.49 Service (business) 0.49 Quality (philosophy) 0.49 Scale (ratio) 0.44 Customer satisfaction 0.42 Psychology 0.33

Institution Network

References

  1. Ahmed, P.K. and Rafiq, M. (2003). Internal marketing issues and challenges. European Journal of Marketing, 37(9), 1177-1186 DOI: 10.1108/03090560310498813
  2. Aiken, L. S., and West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park: Sage.
  3. Anderson, E. W., and Fornell, C., (1994). A customer satisfaction research prospectus. In R. T. Rust, Oliver, R. (Ed.), Service quality: New directions in theory and practice (pp. 241-268). CA: Sage Publications, Thousand Oaks. DOI: 10.4135/9781452229102.n11
  4. Anglim, J. (2007). Structural equation modeling. Retrieved from http://www.jeromyanglim.googlepapers.com (retrieved on 12-07-2013).
  5. Armstrong, G. and Kotler, P. (2003). Marketing: An introduction, 6th ed., Upper Saddle River, NJ: Prentice Hall.
  6. Atilgan, E, Akinsy, S. and Aksoy, S., (2003). Mapping service quality in the tourism industry. Managing Service Quality, 13(5), 412-422
  7. Avkiran, N. K., (1994). Developing an Instrument to Measure Customer Service Quality in Branch Banking. International Journal of Bank Marketing, 12(6), 10-18. DOI: 10.1108/02652329410063223
  8. Awadzi, W., and Panda, D. (2007). Relationship Marketing in the tourism Industry: Towards an integrated model for research. Consortium Journal of Hospitality and Tourism, 12(1), 47-56.
  9. Babakus, E. and Boller, G.W. (1992). An empirical assessment of the SERVQUAL scale. Journal of Business Research, 24, 253-268.
  10. Baker, D.A. and Crompton, J.L. (2000). Quality, satisfaction and behavioral intentions. Annals of Tourism Research, 27(3), 785-804.
  11. Baumgartner, H. and Homburg, C., (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13, 139-161. DOI: 10.1016/0167-8116(95)00038-0
  12. Bentler P.M. (1992). On the fit of models to covariances and methodology to the Bulletin. Psychological Bulletin, 112(3), 400-404.
  13. Bigne, J. E., Sanchez, I., and Andreu, L. (2009). The role of variety seeking in short and long run revisit intentions in holiday destinations. International Journal of Culture, Tourism and Hospitality Research, 3(2), 103-115. DOI: 10.1108/17506180910962113
  14. Bolton, R. N. and Drew, J. H. (1991a). A multi-stage model of customers" assessment of service quality and value. Journal of Consumer Research, 17, 375-384.
  15. Bordas, E. (1994). Competitiveness of tourist destinations in long distance markets. The Tourist Review, 3, 3-9. DOI: 10.1108/eb058158
  16. Buhalis, D. (2000). Marketing the Competitive Destination of the Future. Tourism Management, 21(1), 97-116. DOI: 10.1016/s0261-5177(99)00095-3
  17. Buttle, F., (1996). SERVQUAL: Review, critique, research agenda. European Journal of Marketing, 30(1), 8-32. DOI: 10.1108/03090569610105762
  18. Butler, S. (2000). Changing the game: CRM in the E-world. Journal of Business Strategy, 21(2), 13-14.
  19. Brown, S.A. (2000). Customer relationship management: A strategic imperative in the world of e-business. Canada, John-Wiley & Sons.
  20. Campo-Martinez, S., VGarau-Vadell, J, and Martinez-Ruiz, M.P. (2010). Factors influencing repeat visits to a destination: The influence of group composition. Tourism Management, 31, 862-870.
  21. Canny, I. and Hidayat, N. (2012). The influence of service quality and tourist satisfaction on future behavioural intentions: The case of Borobudur temple as a UNESCO world culture heritage destination, International Proceedings of Economics Development and Research, 50(19), 89-97.
  22. Carman, J. M. (1990). Consumers" perceptions of service quality: an assessment of the SERVQUAL dimensions. Journal of Retailing, 66(1), 33-55.
  23. Chadee, D.D. and Mattsson, J., (1996). An empirical assessment of customer satisfaction in tourism. Service Industries Journal, 16(3), 305-320 DOI: 10.1080/02642069600000030
  24. Chen, J.S., and Gursoy, D. (2001). An investigation of tourist"s destination loyalty and preferences. International Journal of Contemporary Hospitality Management, 13(2), 79-85. DOI: 10.1108/09596110110381870
  25. Chen, C., and Tsai, D. (2007). How do destination image and evaluative factors affect behavioural intentions? Tourism Management, 28, 1115-1122.
  26. Chi, C. G.-Q., and Qu, H. (2008). Examining the structural relationships of destination image, tourist satisfaction and destination loyalty: An integrated approach. Tourism Management, 29, 624-636.
  27. Choi, K. S., Cho, W. H., Lee, S. H., Lee, H. J., and Kim, C. K. (2004). The Relationships among Quality, Value, Satisfaction and Behavioral Intention in Health Care Provider Choice: A South Korean Study. Journal of Business Research, 57(8), 913-921.
  28. Coyles, S. and Gokey, T. C. (2002). Customer Retention is not Enough. The McKinsey Quarterly, 2, 80-89
  29. Cronin, J., and Taylor, S.A. (1992). Measuring service quality: A re-examination and extension. Journal of Marketing, 56(July), 55-67. DOI: 10.1177/002224299205600304
  30. Cronin, J. Joseph, Jr. and Steven A. Taylor (1994). SERVPERF Versus SERVQUAL: Reconciling Performance-Based and Perceptions-Minus-Expectations Measurement of Service Quality. Journal of Marketing, 58, (January), 125-131. DOI: 10.1177/002224299405800110
  31. Crosby, L.A. and Johnson, S.L. (2001). High performance marketing in the CRM era. Marketing Management, Sept.-Oct, 10-11
  32. Crosby L A and Stephens N (1987). Effects of Relationship Marketing on Satisfaction, Retention, and Prices in the Life Insurance Industry. Journal of Marketing Research, 14(November), 404-411. DOI: 10.1177/002224378702400408
  33. Darnell, A. C., and Johnson, P. S. (2001). Repeat visits to attractions: a preliminary economic analysis. Tourism Management, 22(2), 119-126. DOI: 10.1016/s0261-5177(00)00036-4
  34. Day, G.S. (2003). Creating a superior customer-retailing capability. MIT Sloan Management Review, 44(3), 77-82
  35. DmitroviA, T., Cvelbar, L.J., Kolar, T., Brencic, M.M., Ograjenaek, I. and A1/2abkar, V., (2009). Conceptualizing Tourist Satisfaction at The Destination Level. International Journal of Culture, Tourism and Hospitality Research, 3(2), 116-126.
  36. Draper, N.R. and Smith, H. (1998). Applied Regression Analysis. Wiley-Interscience.
  37. Flagestad, A. & Hope, C. (2001). Strategic success in winter sports destinations: a sustainable value creation perspective. Tourism Management, 22, 445-461. DOI: 10.1016/s0261-5177(01)00010-3
  38. Flambard-Ruaud, S. (2005). Relationship marketing in emerging economies: some lessons for the future. Vikalpa, 30(3), 53. DOI: 10.1177/0256090920050305
  39. Fox, T. and Stead, S. (2001). Customer relationship management: Delivering the benefits. White paper, CRM (UK) Ltd., and SECOR Consulting Ltd.,
  40. Freeland, J. (2003). HBR case commentary: What can Barry do to save the project? Harvard Business Review, 81(12), 38.
  41. Frochot, I. (2004). An investigation into the influence of the benefits sought by visitors on their quality evaluation of historic houses DOI: 10.1177/135676670401000303
  42. Gotlieb, J. B., Grewal, D., and Brown, S. W. (1994). Consumer satisfaction and perceived quality: Complementary or divergent constructs? Journal of Applied Psychology, 79(6), 875-885.
  43. Gummesson, E. (1993). Quality Management in service organizations: An interpretation of the service quality phenomenon and a synthesis of international research. Karlstad, International Service Quality Association, Sweden.
  44. Grnroos, C. (1982). Strategic Management and Marketing in the Services Sector. Marketing Science, Cambridge, MA.
  45. Grnroos, C. (1984). A service quality model and its marketing implications. European Journal of Marketing, 18(4), 36-44 DOI: 10.1108/eum0000000004784
  46. Gruen, T.W., Summers, J.O. and Acito, F. (2000). Relationship marketing activities, commitment and membership behaviours in professional associations. Journal of Marketing, 64(3), 34-49
  47. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998). Multivariate data analysis. 5th edition, Prentice Hall, Upper Saddle, New Jersey.
  48. Hair, J.F., Black, W.C., Babin, B. and Anderson, R.E. (2010). Multivariate data analysis. 7th edition, Prentice Hall, Upper Saddle, New Jersey.
  49. Heath, E., G. Wall (1992). Marketing Tourism Destinations. New York, John Wiley & Sons, Inc.
  50. Homburg, C., Workman, J.P. and Jensen, O. (2000). Fundamental changes in marketing organization: The movement towards a customer-focused organizational structure. Journal of the Academy of Marketing Science, 28(4), 459-478 DOI: 10.1177/0092070300284001
  51. Holmes-Smith, P. (2002). Applied Structural Equation Modeling. Canberra.
  52. Hox, J.J. and Bechger, T.M. (1998). An introduction to structural equation modeling. Family Science Review, 11, 354-373
  53. Hu, L. and Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55 DOI: 10.1080/10705519909540118
  54. Hudson, S., Hudson, P. and Miller, G. A. (2004). A measurement of service quality in the tour operating sector: A methodological comparison. Journal of Travel Research, 42, 305-312. DOI: 10.1177/0047287503258839
  55. Hui, T. K., Wan D. and Ho Alvin (2007). Tourists
  56. Hulland, J., Chow, Y.H. and Lam, S. (1996). Use of causal models in marketing research: A review. International Journal of Research in Management, 13(2), 181-197 DOI: 10.1016/0167-8116(96)00002-x
  57. Irwin, J. R. and McClelland, G.H. (2001). Misleading Heuristics and Moderated Multiple Regression Models. Journal of Marketing Research, 38 (1), 100-109. DOI: 10.1509/jmkr.38.1.100.18835
  58. Jang, S. and Feng, R. (2006). Temporal destination revisit intention: The effects of Novelty seeking and satisfaction. Tourism Management, 28, 580-590. DOI: 10.1016/j.tourman.2006.04.024
  59. Jang, S. and Feng, R. (2007). Temporal destination revisit intention: The effects of novelty seeking and satisfaction. Tourism Management, 28(2), 580-590
  60. Jain, R. and Jain, S. (2006). Towards Relational Exchange in Service Marketing: Insights from Hospitality Industry. Journal of Services Research, 5(2), 139-149
  61. Jain, D. and Singh, S.S. (2002). Customer lifetime value research in marketing: A review and future directions. Journal of Interactive Marketing, 16(2), 34-46. DOI: 10.1002/dir.10032
  62. Julian, C. C. and B. Ramaseshan (1994). The role of customer-contact personnel in the marketing of a retail bank DOI: 10.1108/09590559410067316
  63. Kearns, D. and D. Nadler. (1992). Prophets in the Dark: How Xerox Reinvented Itself and Beat Back Japanese. New York: Harper Collins Publishers.
  64. Kline, R. B. (1998). Principles And Practice Of Structural Equation Modeling, New York, Guilford Press.
  65. Kozak, M. (2001). Repeaters
  66. Kozak, M. and Rimmington, M. (2000). Tourist satisfaction with Mallorca, Spain, as an Off-season holiday destination. Journal of Travel Research, 38, 260-269. DOI: 10.1177/004728750003800308
  67. Lewis, B.R. (1989). Quality in Service Sector - A Review. International Journal of Brand Marketing, 7(5), 4-12. DOI: 10.1108/02652328910134590
  68. Matthias Fuchs, Klaus Weiermair, (2003). New perspectives of satisfaction research in tourism destinations. Tourism Review, 58(3), 6 - 14.
  69. McIntosh, R.W. and Goeldner, C.R., (1984). Tourism principles, practices, philosophies. Wiley, New York
  70. McKinsey & Company and the Confederation of Indian Industry, cited in Laura Moser, "•The Medical Tourist,"- Slate, December 6, 2005, and Bruce Stokes, "•Bedside India,"- National Journal, May 5, (2007).
  71. Mihalic, T. (2000). Environmental Management of a Tourist Destination: A Factor of Tourism Competitiveness. Tourism Management, 21(1), 65-78. DOI: 10.1016/s0261-5177(99)00096-5
  72. Murray, D. and Howat, G. (2002). The relationships among service quality, value, satisfaction, and future intentions of customers at an Australian sports and leisure, NJ: Prentice-Hall. DOI: 10.1016/s1441-3523(02)70060-0
  73. Ojo, O. (2010). The relationship between service quality and customer satisfaction in the telecommunication industry: Evidence from Nigeria, Broad Research in Accounting, Negotiation, and Distribution, 1(1), 88-100.
  74. Oliver, R. L. (1981). Measurement and evaluation of satisfaction process in retail setting. Journal of Retailing, 57(3), 25-48.
  75. Oliver, R. L. (1997). Satisfaction: A Behavioural Perspective on the Consumer. New York: McGraw Hill.
  76. Oppermann, M. (2000). Tourism destination loyalty. Journal of Travel Research, 39 (August), 78-84. DOI: 10.1177/004728750003900110
  77. Ostrowski, S. and Krippendorfa, J. (1983). Cry of a new world political site. Problems of tourism, Institute of Warsaw, 146.
  78. Parasuraman, A., Berry, L. L. and Zeithaml, V. A. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing (pre-1986), 49, 41-50. DOI: 10.2307/1251430
  79. Parasuraman, A., Zeithaml, V. A. and Berry, L. L. (1988). A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64 (Spring), 12-37. DOI: 10.5281/zenodo.14579729
  80. Parasuraman, A., Berry, L. L. and Zeithaml, V. A. (1991). Refinement and reassessment of the SERVQUAL scale. Journal of Retailing, 67(4), 420-450.
  81. Pawitra, T. A. and Tan, K. C. (2003). Tourist satisfaction in Singagpore - a perspective from Indonesian tourists. Managing Service Quality, 13(5), 399-411.
  82. Pepperd, J. (2000). Customer relationship management (CRM) in financial services. European Management Journal, 18(3), 312-327. DOI: 10.1016/s0263-2373(00)00013-x
  83. Pearce, D.G. (1997). Competitive Destination Analysis in Southeast Asia. Journal of Travel Research, 35(4), 16-25 DOI: 10.1177/004728759703500403
  84. Plessis, M. and Boon, J.A. (2004). Knowledge management in ebusiness and customer relationship management: South African case study findings. International Journal of Information Management, 24, 73-86. DOI: 10.1016/j.ijinfomgt.2003.10.002
  85. Peppers, D. and Rogers, M. (2004). Managing customer relationships. Hoboken, NJ: John Wiley & Sons.
  86. Petrick, J., Morais, D. and Norman, W. (2001). An examination of determinants of entertainment vacationer
  87. Ritchie, J.R.B. and G.I. Crouch (2000a). The Competitive Destination: A Sustainability Perspective. Tourism Management, 21(1), 1-7.
  88. Ritchie, J.R.B. and G.I. Crouch (2000b). Are Destination Stars Born or Made: Must a Competitive Destination Have Star Genes? in Proceedings of the 31st Annual Travel and Tourism Research Association Conference, Norma P. Nickerson, R. Neil Moisey and Kathleen L. Andereck (eds.), June 11-14, 2000, Burbank, California, 306-315.
  89. Rigby, D. and Ledingham, D. (2004). CRM done right. Harvard Business Review, 80(2), 101-109
  90. Rust, R.T. and A.J. Zahorik (1993). Customer Satisfaction, Customer Retention and Market Share. Journal of Retailing, 69(2), 193-215.
  91. Ryals, L. and Knox, S. (2001). Cross-functional issues in the implementation of relationship marketing through customer relationship management. European Management Journal, 19(5), 534-542 DOI: 10.1016/s0263-2373(01)00067-6
  92. Ryan, C. and Cliff, A. (1997). Do travel agencies measure up to customer expectations? An empirical investigation of travel agencies service quality as measured bySERVQUAL. Journal of Travel and Tourism Marketing, 6(2), 1-32.
  93. Saxena, G. (2000) Case for Relationship Marketing: Issues in Relating the Approach to Tourism Partnerships. Proceedings of Trends 2000, 5th Outdoor Recreation and Tourism Trends Symposium, Michigan State University.
  94. Schmid, J. and Weber, A. (1998). Desktop database marketing. IL: NIC Business Books.
  95. Sheth, J.N., Sisodia, R.S. and Sharma, A. (2000). The antecedents and consequences of customer centric marketing. Journal of the Academy of Marketing Science, 28(1), 55-66. DOI: 10.1177/0092070300281006
  96. Suh, S. H., Lee, Y. H., Park, Y. and Shin, G. C. (1997). The impact of consumer involvement on the consumer
  97. Suki, N.M. (2013). Examining the correlations of hotel service quality with tourists
  98. Srinivasan, S.S., Anderson, R.E. and Ponnavolu, K. (2002). Customer loyalty in e-commerce: An exploration of its antecedents and consequences. Journal of Retailing, 78(1), 41-50 DOI: 10.1016/s0022-4359(01)00065-3
  99. Srivastava, R.K., Tasadduq, A.S. and Fahey, L. (1999). Marketing, business processes and shareholder value: An organizationally embedded view of marketing activities and the discipline of marketing. Journal of Marketing, 63(4), 168-179
  100. Stefanou, C., Sarmaniotis, C. and Stafyla, A. (2003). CRM and customer centric knowledge management: An empirical research. Business Process Management Journal, 9(5), 617-634. DOI: 10.1108/14637150310496721
  101. Stringfellow, A., Nie, W. and Bowen, D.E. (2004). Profiting from understanding customer needs. Business Horizons, 47(5), 45-52. DOI: 10.1016/j.bushor.2004.07.008
  102. Thomas, J.S., Blattberg, R.C. and Fox, E.J. (2004). Recapturing lost customers. Journal of Marketing Research, 41(1), 31-45 DOI: 10.1509/jmkr.41.1.31.25086
  103. Tian-Cole, S. and Cromption, J. L. (2003). A conceptualization of the relationships between service quality and visitor satisfaction, and their links to destination selection. Leisure Studies, 22, 65-80. DOI: 10.1080/02614360306572
  104. Um, S., Chon, K. and Ro, Y. (2006). Antecedents of revisit intention. Annals of Tourism Research, 33(4), 1141-1158. DOI: 10.1016/j.annals.2006.06.003
  105. Vandermerwe, S. (2004). Achieving deep customer focus. MIT Sloan Management Review, 45(3), 26-34. DOI: 10.1109/emr.2004.25108
  106. Vijayadurai, J. (2008). Service Quality, Customer Satisfaction and Behavioral Intention in Hotel Industry. Journal of Marketing & Communication, 3, 14-26.
  107. Vogt, C. and Fesenmaier, D. R. (1995). Tourists and retailers DOI: 10.1016/0160-7383(95)00039-9
  108. Vrechopoulos, A.P. (2004). Mass customization challenges in internet retailing through information management. International Journal of Information Management, 24, 59-71.
  109. Widmier, S, M., Jackson, D.W. and Mccabe, D.B. (2002). Infusing technology into personal selling. Journal of Personal Selling & Sales Management, 22(3), 189-198.
  110. Weirmair, K. and Fuchs, M. (1999). Measuring tourist judgment on service quality. Annals of Tourism Research, 26(4), 1004-1021. DOI: 10.1016/s0160-7383(99)00037-7
  111. Yavas, U., Z. Bilgin and D.J. Shemwell (1997). Service quality in the banking sector in an emerging economy: A consumer survey. International Journal of Bank Marketing, 15(6), 217-223 DOI: 10.1108/02652329710184442
  112. Yim, F.H.K. (2002). CRM orientation: Conceptualization and scale development. Masters Thesis, Chinese University of Hong Kong.
  113. Yim, F.H.K., Anderson, R.E. and Swaminathan, S. (2004). Customer relationship management: Its dimensions and effect on customer outcomes. Journal of Personal Selling & Sales Management, 25(4), 265-280. DOI: 10.1080/08853134.2004.10749037
  114. Yoon, Y., & Uysal, M. (2005). An examination of the effects of motivation and satisfaction on destination loyalty: a structural model. Tourism Management, 26(1), 45-56.