A STUDY ON DEVELOPING A DESTINATION LOYALTY MODEL
CHAPTER ONE: INTRODUCTION
Background of the Problem
Developing Destination Loyalty
The link between customer satisfaction and company success has historically been a matter of faith, and numerous satisfaction studies have also supported the case (Hill and Alexander, 2000). Customer satisfaction has always been considered an essential business goal because it was assumed that satisfied customers would buy more. However, many companies have started to notice a high customer defection despite high satisfaction ratings (Taylor, 1998; Oliver, 1999). This phenomenon has prompted a number of scholars (e. g., Jones and Sasser, 1995; Reichheld, 1996; Oliver 1999) to criticize the mere satisfaction studies and call for a paradigm shift to the quest of loyalty as a strategic business goal.
As a result, satisfaction measurement has recently been displaced by the concept of customer loyalty, primarily because loyalty is seen as a better predictor of actual behavior. Two of the three measures making up most Customer Loyalty Indices (CLIs) are behavior-based, such as "likelihood to repurchase the product or service" and "likelihood to recommend a product or service to others". The third element of a CLI is usually "overall satisfaction" itself (Taylor, 1998).
The move to measure loyalty is based on a desire to better understand retention, which has a direct link to a company's bottom line. Studies have documented that a 5% increase in customer retention can generate a profit growth of 25 – 95% across a range of industries (Reichheld, 1996; Reichheld and Sasser, 1990). In addition, retaining existing customers usually has a much lower associated costs than winning new ones (Fornell and Wernerfelt, 1987), so a larger proportion of the gross profit counts towards the bottom line. Furthermore, loyal customers are more likely to act as free word-of-mouth (WOM) advertising agents that informally bring networks of friends, relatives and other potential consumers to a product/service (Shoemaker and Lewis, 1999). In fact, WOM referrals account for up to 60% of sales to new customers (Reicheld and Sasser, 1990). With such exceptional returns, loyalty becomes a fundamental strategic component for organizations.
However, in the context of travel and tourism, a review of literature reveals an abundance of studies on tourist satisfaction; and destination loyalty has not been thoroughly investigated (Oppermann, 2000). Therefore, it is time for practitioners and academics to conduct more studies of loyalty in order to have greater knowledge of this concept, to understand the role of customer satisfaction in developing loyalty, other non- satisfaction determinants of customer loyalty, and their interrelationships.
Understanding the determinants of customer loyalty will allow management to concentrate on the major influencing factors that lead to customer retention. A number of studies have examined the antecedents or causes of repeat purchase intensions (Backman and Crompton, 1991; Cronin, Brady, and Hult, 2000; Petrick and Backman, 2001). Results of this body of research have shown that satisfaction, quality/performance and
different other variables are good predictors of customer intended loyalty. The more satisfied the customers are, the more likely they are to repurchase the product/service and to encourage others to become customers. In order to retain customers, organizations must seek to satisfy them, but a further objective must be to establish customer loyalty.
In a tourism context, satisfaction with travel experiences contributes to destination loyalty (Bramwell 1998; Oppermann 2000; Pritchard and Howard 1997). The degree of tourists’ loyalty to a destination is reflected in their intentions to revisit the destination and in their willingness to recommend it (Oppermann 2000). Tourists’ positive experiences of service, products, and other resources provided by tourism destinations could produce repeat visits as well as positive word-of-mouth effects to friends and/or relatives. Recommendations by previous visits can be taken as the most reliable information sources for potential tourists. Recommendations to other people (word-of- mouth) are also one of the most often sought types of information for people interested in traveling.
Given the vital role of customer satisfaction, one should not be surprised that a great deal of research has been devoted to investigating the antecedents of satisfaction. Previous satisfaction research has focused predominantly on the following antecedents to consumer satisfaction: expectations (e. g., Oliver and DeSarbo, 1988), disconfirmation of expectations (e. g., Oliver 1980), performance (e. g., Churcuill and Suprenant, 1982), affect (e. g., Mano and Oliver, 1993), and equity (e. g., Tse and Wilton, 1988). Customer satisfaction / dissatisfaction (CS/D) appears to be influenced independently or in combination by these antecedents.
Most early research work focused on satisfaction at the global level (e. g., Oliver 1980). Until recently, there emerges an attribute-level conceptualization of the antecedents of satisfaction (e. g., Oliver 1993). Under an attribute-level approach, overall satisfaction is a function of attribute-level evaluations. These evaluations typically capture a significant amount of variation in overall satisfaction (e. g., Bolton and Drew 1991; Oliver 1993).
It is important in tourism to distinguish overall satisfaction from satisfaction with individual attributes. The particular characteristics of tourism have a noTable effect on tourist satisfaction (Seaton and Bennett, 1996). Beyond the generic characteristics that distinguish services from goods, such as intangibility, inseparability, heterogeneity and perishability (Zeithaml, Parasuraman and Berry, 1985), there are some further differences between tourism and other services. For example, Middleton and Clarke (2001) highlighted interdependence - sub-sector inter-linkage of tourism products. Tourists experience a medley of services such as hotels, restaurants, shops, attractions, etc.; and they may evaluate each service element separately. Satisfaction with various components of the destination leads to overall satisfaction (Kozak and Rimmington 2000). Therefore, overall satisfaction and attribute satisfaction are distinct, though related, constructs (Oliver 1993). This study focused on overall evaluation, attributes satisfaction, and the relationship between the two.
Furthermore, it has been widely acknowledged that destination image affects tourists’ subjective perception, consequent behavior and destination choice (e. g., Chon 1990, 1992; Echtner and Ritchie, 1991; Baloglu and McCleary, 1999a; Milman and Pizan, 1995; Bigne, Sanchez, and Sanchez, 2001). Tourists’ behavior is expected to be
partly conditioned by the image that they have of destinations. Image will influence tourists in the process of choosing a destination, the subsequent evaluation of the trip and in their future intentions. Destination image exercises a positive influence on perceived quality and satisfaction. A positive image deriving from positive travel experiences would result in a positive evaluation of a destination. Tourist satisfaction would improve if the destination has a positive image. Destination image also affects tourists’ behavioral intentions. More favorable image will lead to higher likelihood to return to the same destination.
To sum up, the following sequence could be established: destination image O tourist satisfaction O destination loyalty. Destination image is an antecedent of satisfaction. Satisfaction in turn has a positive influence on destination loyalty. In an increasingly saturated marketplace, the success of marketing destinations should be guided by a thorough analysis of destination loyalty and its interplay with tourist satisfaction and destination image. Nevertheless, the tourism studies to date have addressed and examined the constructs of image, satisfaction and loyalty independently (Bigne et al. 2001); lacking are studies discussing the causal relationships among destination image, tourist satisfaction, and destination loyalty.
To bridge the gap in the destination loyalty literature, one of the main purposes of this study was to offer an integrated approach to understanding destination loyalty and examines the theoretical and empirical evidence on the causal relationships among destination image, tourist satisfaction, and destination loyalty. A research model was proposed and tested. The model investigated the relevant relationships among the constructs by using a structural equation modeling (SEM) approach. The primary aim of
SEM is to explain the pattern of a series of interrelated dependence relationships simultaneously between a set of latent (unobserved) constructs, each measured by one or more manifest (observed) variables (Reisinger and Turner, 1999).
Segmenting Destination Loyalty
In recent years, hospitality and tourism scholars has shown increasing interests in different market segments based on tourists’ demographic profiles and travel characteristics (Sonmez and Graefe, 1999; Oppermann, 2000; Mykletum, Crotts, and Mykletun, 2001; Kim, Wei, and Ruys, 2003; Hsu, 2000, 2003). The purposes are to help destination managers develop better understanding of the specific groups of consumers in order to accommodate their distinct needs and wants, and establish efficient and effective marketing and promotion strategies. It has been widely acknowledged that there is a need for market segmentation in order to plan a consumer-oriented marketing strategy and cope with the large diversity of vacation behavior (Veen and Verhallen, 1986). Segmentation is often based on social-demographics, psychographics, behavioral characteristics, trip characteristics, or other variables of interests. One of the most common approaches is to first assign consumers to groups by using demographic and trip characteristics; and then the similarities and differences between the matching groups are analyzed.
Since many attractions and tourist destinations rely heavily on the repeat visitor segment, researchers and practitioners find it meaningful to examine the differences between first-time and repeat visitors, and the impact of previous visitation experience on tourists’ image perception and future behavior. For example, Milman and Pizam (1995) empirically tested the impact of previous visitations on consumer’s destination image.
They found that higher number of visits with a destination result in more positive image of the destination, and higher interests and likelihood to revisit it. A number of empirical works revealed that the number of visits to and the length of stay at a destination influence the perceived image (Echtner and Ritchie, 1993; Baloglu and Mangaloglu, 2001; Chon, 1991; Hu and Ritchie, 1993).
Previous studies also indicated a close relationship between past experiences and consumer satisfaction and loyalty. Past experiences of visiting a destination have increased tourists’ intention to travel there again. For instance, Petrick and Sirakaya’s empirical study (2004) suggested that repeat visitors are more satisfied with their travel experiences, and are more likely to return and spread positive WOM. Juaneda (1996) and Gyte and Phelps (1989) confirmed that repeat tourists are more likely than first-timers to return to the same destination. Oppermann (2000) found a significant relationship between previous experience and future tourist visitation behavior. Sonmez and Graefe (1998) showed that past travel experiences have a powerful influence on behavioral intentions. Chen (1998) stated that past trip experiences often influence tourists' choice behaviors directly and/or indirectly.
A few empirical studies (e. g., Gyte and Phelps, 1989; Juaneda, 1996; Kozak and Rimmington, 2000; Mazursky, 1989) investigated the influences of satisfaction and previous visits on the revisit probability - both previous visits and satisfaction were found to be determinants of the revisit intentions, although Kozak (2001) found that future intentions were influenced more by satisfaction than by past experience. Other researchers (McAlexander, Kim, and Roberts, 2003; Garbarino and Johnson, 1999) found that customer satisfaction affected customer loyalty depending on consumption
experience. Satisfaction had a significant influence on loyalty for less experienced group, but its effect in the more experienced group was not significant, and other determinants replaced satisfaction as drivers of loyalty. They concluded that satisfaction was most effective for developing loyalty among less experienced customers.
Demographics-based research has also drawn increasing attention in the tourism and travel literature. A number of studies have been conducted to investigate the effects of tourists’ demographics on their image perceptions and destination choices; and mixed results were generated from these studies. For example, empirical studies explored relationship between the perceived image and tourists’ demographic characteristics such as gender, age, education, occupation, income, marital status, and country of origin (Stern and Krakover, 1993; Baloglu and McCleary, 1999a; Beerli and Martin, 2004). As for the findings, some researchers identified tourists’ personal characteristics such as age and education as one of the key forces that affect destination image; while others found no relationship between tourists’ demographics and their image perceptions.
Similarly, prior research showed mixed results in terms of the relationship between satisfaction / loyalty and demographics (Snyder 1991). Some studies found little difference in demographics between customers who are loyal and those who are not (Exter, 1986), for example, people’s loyalty towards a brand does not vary based on their income level. Other studies found that age may influence consumer loyalty (Schiffman and Kanuk, 1997). Older customers (> 50 years old) tend to show higher satisfaction and loyalty than the younger group (< 50 years old) (Pritchard and Howard, 1997; Hsu, 2000).
Oh, Parks and DeMicco (2002) studied the age- and gender-based effects on tourist satisfaction and behavioral intentions via SEM. Their findings suggested that senior travelers (> 55 years old) tend to develop higher satisfaction and behavioral intentions than their younger counterparts; while male and female travelers show comparable satisfaction levels and behavioral intentions. They also found that despite the mean differences in the latent constructs, the decision-making process in the structural model remains similar across age and gender groups.
Mykletun et al. (2001) tried to predict visitors’ perception of a destination and revisit probability by using a number of demographic variables including age, household income, and education. They found that 1) only age is an important predictor of visitor satisfaction - senior tourists (> 60 years old) hold the most positive evaluations of a destination compared with the younger visitor segment; and 2) age, education and income are not related to visitors’ revisit probability.
Taken together, most of these previous studies are somewhat descriptive in nature by conducting only univariate or multivariate comparisons. Few researchers have investigated these tourist segments in a systematic framework. To fill the void in the travel literature, one of the main objectives of the study was to examine if various tourist groups differed in the systematic relationships depicted in the destination loyalty model,
i. e., if different tourist segments formed loyalty in different ways. The focus here was on the comparison of an entire process rather than on attribute- or factor-level description.
Tourist groups based on previous visitation(s), age, gender, education and income were investigated because marketing literature has indicated that these types of variables should be included in consumer behavior research in order to segment the markets
(Gitelson and Crompton, 1984). The findings would contribute to advances in theoretical understanding of the effects of previous visitation(s) and demographics on the destination loyalty formation process.
Assessing Service Quality of a Historic Destination
The empirical data for the study was collected in a major tourism destination in the state of Arkansas – Eureka Springs. Eureka Springs is a unique city with old-world charm and European flavor. Known as "the Little Switzerland of the Ozarks", this historic city is nestled in the hills of the Ozark Mountains, encircled by two beautiful lakes and two scenic rivers. Eureka Springs began as a legend of healing among the native tribes of the region via tales of the "Medicine Spring" flowing from the hillside. Reports of the marvels of the restorative springs brought in tourists as well as people to live here. Today, the history lives on - in 2001, Eureka Springs was named one of 12 Distinctive Destinations by the National Trust for Historic Preservation. For over 100 years the city has been attracting people of all ages, from all around the country. With its Victorian Architecture, narrow winding streets and the entire downtown area being listed on the National Register of Historic Places, Eureka Springs remains to be a popular resort area, though it has never conducted any type of tourist survey.
Cities blessed with history and heritages have advantages when attempting to develop their tourism products. However, in order to turn the advantages into sustainable success, historic cities need to think like a business and take a proactive role in their stewardship of the cities’ tourism development. Therefore, one other main purpose of the study was to help Eureka Springs gain a better understanding of its visitors’ traveling behavior, demographic profiles, visitors’ aspirations, their attitudes towards traveling to
Eureka Springs, their opinions on the image of Eureka Springs as a travel destination and their perceptions of the service quality of the city’s hospitality businesses. The information obtained would help the city 1) identify and preserve those assets that establish their unique identity and distinguish them from the surrounding areas; 2) pinpoint those areas that require further improvement and promotional efforts; and 3) design a more comprehensive marketing plan, and further expand the market.
The significant role of service quality in business success has been well acknowledged. High levels of service quality can help organizations achieve a competitive edge and position themselves more effectively in the marketplace (Lewis, 1993). Unfortunately, the evaluation of quality has always remained more difficult for services than for products due to the complex nature of services: heterogeneity, intangibility, and inseparability of production and consumption (Zeithaml et al., 1985). A strong body of literature has provided guidance for exploring service quality, and different instruments have been proposed for assessing this relatively elusive and abstract construct. Among them, SERVQUAL, developed by Parasuraman, Zeithaml, and Berry (1985, 1988), and importance-performance analysis (IPA), introduced by Martilla and James (1977) have gained the most recognition in various service contexts.
In spite of extensive usage of the SERVQUAL scale, a number of studies have questioned the efficacy of the instrument, on both empirical and theoretical grounds (e. g., Babakus and Boller, 1992; Cronin and Taylor, 1992, 1994; Brown, Churchill, and Peter, 1993; Teas, 1993, 1994). These studies pointed to the unstable nature of SERVQUAL's purported five-factor structure, the inadequacies of the expectations and
perceptions gap model that underlies the SERVQUAL, and the problems in the interpretation and operationalization of expectations.
Because of the problems of using SERVQUAL, importance-performance analysis (IPA) has earned popularity in a variety of fields for measuring service quality. IPA excludes the controversial ‘expectations’ from the analysis, and instead examines the ‘importance’ customers place on any given product/service attribute. It is a simple and flexible technique for analyzing consumers’ attitudes towards salient product/service attributes. IPA has been used to design marketing strategies for businesses, to guide planning decisions for governments, and to evaluate the organization and management of events and programs.
Research Objectives and Hypotheses
Objective 1: Developing and Testing Destination Loyalty Model
The first objective of the study was to develop a theoretical model of destination loyalty by examining the interrelationships among destination image, tourist satisfaction and destination loyalty. All the relationships were tested jointly using a structural equation model. Two types of conclusions could be drawn. From a destination management perspective, the importance of improving the image and tourist satisfaction could be confirmed. From the research point of view, the systematic examination of causal relationships among the constructs could facilitate a clearer understanding of the concept of destination loyalty. It was hoped that the results derived from the model would serve as the basis for the development of destination marketing strategies. In order to provide a theoretical background for the proposed model, in chapter two the author
conducted a comprehensive review of literature regarding destination image, consumer satisfaction and consumer loyalty.
Figure 1 depicts the hypothetical causal model that examined the structural, causal relationships among destination image, satisfaction, and destination loyalty. Hypothetically destination image influenced tourists’ satisfaction with traveling experiences, which then affected destination loyalty. Each component of the model was selected based on a comprehensive literature review. The theoretical underpinning of this model was discussed in the following section.
Figure 1 Hypothetical Model for Developing Destination Loyalty
Destination Image
H1
Overall Satisfaction
H4
Destination Loyalty
H2
H3
Attribute Satisfaction
The following hypotheses were drawn in this study:
H1: Destination image positively influenced tourists’ overall satisfaction. H2: Destination image positively influenced tourists’ attribute satisfaction.
H3: Attribute satisfaction partially mediated the relationship between destination image and overall satisfaction.
H4: Attribute satisfaction positively influenced overall satisfaction. H5: Overall satisfaction positively influenced destination loyalty.
H6: Overall satisfaction fully mediated the relationship between destination image and destination loyalty.
H7: Overall satisfaction fully mediated the relationship between attribute satisfaction and destination loyalty.
Objective 2: Comparing Destination Loyalty Model across Groups
The second objective was to investigate if the destination loyalty model varied among different tourist groups based on previous traveling experiences, age, gender, education, and income level. The findings could facilitate destination managers to carry out market segmentation, which is an essential marketing tool in today’s increasingly competitive business world and has become part of the everyday thinking of tourism managers in their efforts to improve planning and productivity. By dividing the broad categories of the market into more specific component parts, managers are able to gain strategic marketing insights. This in turn allows them to direct their marketing efforts to attract and satisfy tourists more efficiently.
Previous studies found that destination image, tourist satisfaction, and destination loyalty as separate constructs were affected by tourists’ personal characteristics and travel characteristics (Beerli and Martin, 2004; Baloglu and McCleary, 1999a; Kozak and Rimmington, 2000; Oppermann, 2000). However, few studies have looked into the potential differences in the systematic relationships among these constructs for various tourist groups. For market segmentation purpose, it would be of prime interest for the destination managers to see how various tourist groups develop loyalty in different ways. Therefore, in addition to examining differences across groups in levels of key constructs (latent means) in the destination loyalty model, this study also focused on differences in relationships among these constructs (structural paths) across groups. The following hypotheses were proposed:
H8: The structural paths in the destination loyalty model differed based on tourists’ previous experience with a destination.
H9: the means of the latent constructs in the destination loyalty model differed based on tourists’ previous experience with a destination.
H10: the structural paths in the destination loyalty model differed based on tourists’ gender.
H11: the means of the latent constructs in the destination loyalty model differed based on tourists’ gender.
H12: the structural paths in the destination loyalty model differed based on tourists’ age.
H13: the means of the latent constructs in the destination loyalty model differed based on tourists’ age.
H14: the structural paths in the destination loyalty model differed based on the tourists’ education level.
H15: the means of the latent constructs in the destination loyalty model differed based on the tourists’ education level.
H16: the structural paths in the destination loyalty model differed based on the tourists’ income level.
H17: the means of the latent constructs in the destination loyalty model differed based on the tourists’ income level.
Objective 3: Assessing Service Quality
The third objective of the study was to measure the quality of the services provided by the hospitality and tourism industry in Eureka Springs. Service quality has
received enormous attention in the literature for the critical role it plays in distinguishing services/products and building competitive advantages. One of the most well-known and most commonly-used instruments for service quality assessment is SERVQUAL (Parasuraman et al., 1985, 1988). However, SERVQUAL has drawn wide criticisms in marketing literature, mainly for the psychometric properties of the instrument, its inferior predictive validity, and its use of difference scores: SERVQUAL = f (performance – expectation) (e. g., Carman, 1990; Cronin and Taylor, 1992, 1994; Brown, Churchill, and Peter, 1993). As an alternative, another technique for quality assessment has garnered recognition in a plethora of service settings for its simplicity and ease of application – importance-performance analysis (IPA), introduced by Martilla and James in 1977. IPA plots customers’ ratings of the importance of and their satisfaction with salient service attributes in a two-dimensional grid. The IPA grid analyzes how well an organization meets customers’ concerns over important service/product attributes; and the results can be used to prioritize attributes for improvement and provide guidelines for the organization’s future resource allocation decisions.
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