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Measurement of tourist satisfaction with restaurant services: A segment-based approach Atila Yüksel and Fisun Yüksel Journal of Vacation Marketing 2003; 9; 52 DOI: 10.1177/135676670200900104 The online version of this article can be found at: http://jvm.sagepub.com/cgi/content/abstract/9/1/52

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Journal of Vacation Marketing

Volume 9 Number 1

Measurement of tourist satisfaction with restaurant services: A segment-based approach Atila Yu¨ ksel and Fisun Yu¨ ksel Received (in revised form): 4th September, 2002 Anonymously refereed paper Turizm Isletmeciligi ve Otelcilik Yuksekokulu, Adnan Menderes Universitesi, Candan Tarhan Bulvari, No. 6 Kusadasi Aydin, 09400 Turkey Tel: +90 256 612 5503; Fax: +90 256 612 9842; E-mail: [email protected]

Atila Yu¨ksel is a lecturer at the School of Tourism Administration and Hotel Management, Adnan Menderes University, Turkey. Fisun Yu¨ksel is a PhD candidate at Sheffield Hallam University, UK.

ABSTRACT KEYWORDS: satisfaction, measurement, segmentation, dining experience

Journal of Vacation Marketing Vol. 9 No. 1, 2002, pp. 52–68, & Henry Stewart Publications, 1356-7667

It is widely recognised that the tourist market is not homogeneous. Yet segment-specific satisfaction analysis has received inadequate attention both from researchers and managers. Focusing on tourists’ dining experiences, this study investigated whether tourists could be grouped into distinct segments; whether the composition of variables determining customer satisfaction differed among the identified segments; and whether market segmentation strategy could contribute to more parsimonious satisfaction prediction models. Factor analysis was performed to determine dimensions that are likely to influence tourist restaurant selection and evaluations, whereas cluster analysis was employed to identify homogeneous groups of respondents. Multiple regression analyses were then employed to examine the relative importance of service dimensions in determining satisfaction judgments of each segment. Based on the analyses, five distinct dining segments were identified. Different sets of service dimensions appeared to affect satis-

faction judgments. Greater variation in satisfaction was explained when analysis was undertaken at market segment level compared to aggregate market level. Management and marketing implications of the study are discussed.

INTRODUCTION Customer satisfaction is an important topic for both researchers and managers, because a high level of customer satisfaction leads to an increase in repeat patronage among current customers and aids customer recruitment by enhancing an organisation’s market reputation. Successfully being able to judge customers’ satisfaction levels and to apply that knowledge are critical starting points to establishing and maintaining long-term customer retention and long-term competitiveness.1 Conducting customer satisfaction research (CSR) is imperative not only because it provides critical managerial information, but also it enables communication with customers.2 Proper CSR is likely to produce information on service attributes that are considered important by customers, the relative importance of the attributes in customer decision making and how well an organisation is currently meeting its customer needs. It will also demonstrate an organisational interest in communication with customers, which gives a sense of importance and recognition.3

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Given the vital role of customer satisfaction, one should not be surprised that a great deal of research has been devoted to investigating the process by which customers form judgments about a service experience. As a result, customer satisfaction measurement has become one of the most frequent applications of market research in the 1990s. Despite the noticeable progress achieved in the application of customer satisfaction information within service industries, this area of research is still replete with many conceptual and practical difficulties and under-examined research issues. CSR has often been either oversimplified or too complicated, and has generally lacked managerial focus. In most cases, it has not produced actionable information that identifies relative organisational strengths and weaknesses. Researchers seem to have been largely concerned with the conceptual antecedents of customer satisfaction. In contrast, little attention has been paid to the development of informative and straightforward models that help managers understand what customers regard as the components of a satisfactory service experience. How these elements can be better managed to improve satisfaction and repeat business has received inadequate attention. There is still an absence of consensus on how best to conceptualise customer satisfaction. Satisfaction remains an elusive, indistinct and ambiguous construct.4 Another continuing debate is the question of how one can and ought to measure customer satisfaction.5 The existence of several satisfaction measurement frameworks is causing confusion among practitioners, as no consensus has been reached on which framework is best suited to assess customer satisfaction. There also remains an uncertainty as to the reliability and validity of these proposed satisfaction measurement frameworks, particularly of the expectancy-disconfirmation paradigm, in determining customer satisfaction.6 The importance of the reliability and validity of the measurement frameworks can hardly be debated, as the success of satisfaction improvement programmes relies heavily on reliable and valid information. The majority of satisfaction theories con-

cur that satisfaction is a relative concept, always judged in relation to a standard. The selection of an appropriate standard of comparison that ought to be used in a research, however, represents a dilemma for both managers and researchers. This is partly because there is not sufficient research evidence available to answer precisely what comparison standard consumers use in different situations.7 While different forms of standards have been proposed in the marketing and consumer behaviour literature, with the exception of predictive expectations other standards have received little empirical research in tourism and hospitality literature.8 There is also limited understanding of whether the use of different comparison standards yields different results in terms of satisfaction. Several marketing studies have demonstrated that the tourist market is not homogeneous.9 Surprisingly, however, examination of segment-based satisfaction has attracted only limited attention from researchers.10 The majority of past satisfaction studies have explored tourist satisfaction at an aggregate market level. The subjects are regarded in a single aggregate market. The structure of predictor variables is assumed to be generic. These studies have made a substantial contribution to the understanding of the customer satisfaction concept. However, a critical question remains — would distinct segments differ in their service evaluation? If yes, what are the implications of this? It is probable that satisfaction drivers of different segments may not be identical. Additionally, different customer segments may base their behavioural intention judgments on different service attributes. Segment-based satisfaction analysis may offer a number of benefits to managers. It would enable development of more focused and successful marketing efforts. It would allow managers to investigate the differential influence of specific service variables across segments. For example, while the healthy food dimension may not be found to be important in determining the satisfaction judgments for the entire sample, it is entirely possible for this dimension to be important

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for some untapped subset of the population. Limited studies undertaken on segmentbased satisfaction reported that the structure of predictor variables was indeed different across segments.11 The differences will require different marketing strategies in appealing to viable market segments. Marketing efforts which are directed primarily persuading new visitors to visit a destination might be entirely inappropriate for encouraging previous visitors to return.12 Knowing what different segments consider when making selection decisions, and how the satisfaction judgments of each segment evolve during a given service experience, is the ultimate key to accessing new or growing markets and to maintaining repeat business. Segmenting fast-food customers based on their expectations, Oh and Jeong13 reported that segment-focused satisfaction analyses provided a clearer understanding of the market and a robust prediction of customer satisfaction. They called for future studies that use different measurement methods in different settings in order to compare their results. Building on their proposition, this study aimed to explore whether: — tourists visiting independent restaurants could be grouped into distinct subsegments — the structure of predictor variables differed across segments — market segmentation strategy could contribute to more parsimonious satisfaction prediction models. In the following section, previous research on customer dining experiences and market segmentation is reviewed to provide a conceptual background for the paper. The next section presents the method used in data collection and the techniques used in the analysis. The study findings and their managerial implications are discussed and recommendations for future research are provided. DINING EXPERIENCE Restaurants are generally assumed to be in the business of selling food only. Restaurants

are, however, primarily retailers of ‘foodservice experiences’. The food plays a key but by no means the only part.14 Previous studies reported that restaurant services were a blend of tangible and intangible components. They are subjectively experienced processes where production and consumption activities take place simultaneously.15 A series of moments of truth — the time and place when and where the service provider has the opportunity to demonstrate to the customer the quality of its services — occur between the customer and the service provider. Thus, what happens in these interactions will obviously have a substantial impact on consumer evaluations of a service organisation.16 Not only what the consumer receives (technical outcome of the process) is important to her evaluation of the quality of service, but also the way in which the technical outcome is delivered to her functionally.17 Studies showed that expressive performance (functional quality) might be a more necessary condition for satisfaction, provided that the technical quality of the product was of acceptable standard.18 The food may be outstanding, the comfort of the restaurant is unequivocal but pleasures afforded by the event are tested when the waiter is unexpectedly insouciant or service slow.19 Service environment holds a central role in shaping the nature of customers’ behaviour, their reactions to places and their social interactions. Customers are likely to spend their time and money in service environments that prompt a feeling of pleasure, whereas they will avoid unpleasant environments.20 The conditions in the service environment (eg seating arrangements, size and flexibility) are found to affect the nature of social interaction in terms of duration of interaction and the actual progression of events.21 In addition to attracting and deterring entry, the service environment may also affect consumers’ cognitive, emotional and physiological responses, which in turn influence their evaluations and behaviours.22 The perceived service environment may prompt cognitive responses, affecting prospective customers’ beliefs about the people and product found in that place, and this can be

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regarded as a method of non-verbal communication.23 It may also elicit an emotional response.24 Waiting time is another significant factor in the consumer’s evaluation of restaurant services. Customer satisfaction tends to decrease as the perception of waiting time increases.25 Waiting time and experience are central to the customer experience because they are an identifiable and memorable part of the total experience.26 Waiting entails both economic and psychological costs. The consumer may experience a considerable amount of stress because of the uncertainty of how long s/he must wait.27 Depending on the circumstances, customers would like to have faster or slower services. Therefore service time would be a prime determinant of perceived service quality in the same way as monetary cost would.28 If a customer is given a slow service s/he will be unhappy, but the same is true if a customer is rushed through the meal.29 The majority of customers tend to frequent restaurants not only because of good food, quality service and a pleasant service environment, but also because they feel the price they are paying represents value for money.30 Value may have a different meaning to different individuals. It might be regarded as low price, or whatever the consumer wants in a product, or the quality the consumer gets for the price and/or value is what the consumer gets for what they give.31 Previous studies have contributed to understanding of customer satisfaction with restaurant services and of its measurement. They showed that customer evaluation of foodservice experiences is a complex process in which multitude of factors are processed. By identifying dimensions contributing to customer satisfaction in restaurants, and the attributes that make up these specific dimensions of satisfaction, these studies have provided practical knowledge for management to take effective actions to improve service quality and customer satisfaction. The majority of previous satisfaction studies, however, treated their subjects as if they were a single aggregate market. The assumption that variables identified in a research (eg food

quality, cleanliness, value, price, convenience, speed of service, courtesy, menu variety and atmosphere) would have an identical influence on satisfaction judgments of all customers is debatable. No two individuals are alike. Different customers may have different perceptions about what contributes to a satisfactory experience (eg business traveller versus pleasure traveller). Tourists from different cultural backgrounds may perceive destination attributes differently or base their evaluation on different aspects of the destination. In a recent study, Yu¨ ksel 32 reported that different segments of visitors (in this case repeat and first-time visitors) to Turkey developed their satisfaction judgments based on different aspects of the holiday destination. Oh and Mount33 reported a similar finding in research on repeat purchase behaviour within the hospitality industry. The results of these studies suggest that transferring the focus of satisfaction research from aggregate market level to market segment level is needed. This would increase prediction power of satisfaction models and the contribution of CSR to management. MARKET SEGMENTATION The underlying logic and the possible rewards that market segmentation can offer are well established in the travel and hospitality industry.34 Customer needs are diverse, and it is obvious that they can no longer be satisfied through a mass marketing and management approach.35 The diversity in customer needs requires hospitality and tourism managers to identify groups of customers with homogeneous characteristics and behaviours, and try to adjust their product offer as much as possible to the unique needs and desires of the target market(s).36 In so doing, they will be likely to reach target market(s) in the most effective and efficient way, allocate resources more effectively and satisfy their customers’ needs more successfully.37 When carried out properly, segmentation can actually enhance sales and profits, as it will allow the organisation to target segments that are much more likely to patronise the organisation’s services and facilities.38

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In recent years, benefit segmentation has emerged as an effective approach to market segmentation through which it is possible to identify market segments by causal rather than descriptive factors. The belief underlying this strategy is that the benefits which people are seeking in consuming a given product are the basic reasons for the existence of true market segments, and are better determinants of behaviour than other approaches.39 One of the major benefits of this approach is to enable a service provider to implement different marketing strategies for different segments by offering the unique benefits sought by each segment.40 It is reported that benefits predict behaviour better than personality and lifestyle, volumetric, demographic or geographic measures, which merely describe behaviour without explaining it.41 A review of previous segmentation studies clearly demonstrates that there are distinct customer groups within the aggregate market. Managers can enhance sales volume and profits by developing market-specific strategies.42 The majority of previous studies have focused on the identification of factors that may differentiate segments. The scrutiny of satisfaction and repeat business judgments of different segments, however, seems to have received inadequate attention from researchers (with a few exceptions).43 Identification of segments and attracting them could be one thing, but to secure satisfaction and repeat business is another.44 Hence, it is imperative to understand what brings or discourages satisfaction and repeat business in relation to specific markets.

METHODOLOGY Research instrument and data collection A questionnaire was developed for the research. It comprised over 110 items and was grouped into four major areas: general information about the respondent and dining occasion, 42 restaurant selection items, 44 performance evaluation items and ratings on overall dining satisfaction and behavioural

intentions. The items were derived from interviews and previous studies, and their adequacy was checked through a pilot test. The respondents were first required to indicate the importance of 42 items when selecting a restaurant while on a summer holiday. A seven-point labelled Likert-type scale was used. Specifically, respondents were required to give a rating between 1 ¼ not important and 7 ¼ very important for each of the attribute variables included in the questionnaire. The respondents were then required to assess the performance of restaurant services on seven-point semantic differential scales. Literature suggests that both Likert-type and semantic differential scales can be employed for the purpose of evaluating tourist experiences with services because they are effective in measuring consumer attitudes and are easy to construct and manage. The study adopted the use of a single (delight-terrible) overall measure of tourist satisfaction.45 Respondents were also given ample space to make any further written comments (compliments and complaints). The questionnaire was written in English and German. Two bilingual translators were employed in the translation of the research instrument. The actual survey was carried out with 500 tourists who had dined in independent non-fast-food restaurants. This type of restaurant was selected because sit-down table service operations provide more opportunities to examine interaction between customers and employees than a fast-food or cafeteria-type operation. The research was conducted with tourists departing from an international airport during an 18-day period in May–June 1998, in Turkey. The airport was chosen due to permission-related difficulties that the researchers encountered in implementing the surveys in restaurants. In order to eliminate any possible effect of memory lapse, participants were requested to evaluate their most recent dining experience within a non-fast-food restaurant outside their accommodation. A pre-test was run in order to understand whether this approach posed any memory problem. None of the respondents reported any problem about

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recalling and evaluating their most recent dining experiences. Assessment of customers’ last experiences with a product or a service is found in other studies.46 Thirty-one tourists refused to participate due to their boarding times and 20 returned incomplete questionnaires. More than half of the respondents were female (56 per cent) and more than half (54 per cent) had eaten out in the restaurant more than once. The locations of restaurants are as follows: 51 per cent were in Marmaris, 5 per cent in Fethiye and 44 per cent in other resorts, including Bodrum, Turgutreis and Hisaronu. Eightythree per cent of the respondents stated that their reason for dining was casual. The remaining stated that it was for a special occasion. Given the flight destinations at the time of survey implementation, together with the growing number of British tourists, the majority of respondents were British (64 per cent), followed by German (11 per cent), Scandinavian (11 per cent) and others (including Italian, French, Russian and Benelux).

Data analysis Respondents’ ratings on the importance and perception items included in the research were subjected to principal factor analyses with Varimax rotations to reduce potential multicollinearity among the items and improve reliability in market segmentation and customer satisfaction prediction.47 The coefficient alpha of reliability was computed for each factor to see each dimension’s internal consistency. Consistent with the suggestions,48 customer restaurant selection dimensions, identified by the factor analysis, provided the data for cluster analysis instead of the original ratings on variables. The identified cluster structure was then subjected to discriminant analysis to doublecheck in part the classification reliability. Customers’ overall satisfaction was predicted by regressing the subjects’ satisfaction scores on the factor scores of performance perception. This regression analysis was repeated for each cluster.

Figure 1 exhibits the statistical techniques used to meet the study objectives. Step one: Factor analysis The 42 importance items could be reduced to nine orthogonal factor dimensions, which explained 61.2 per cent of the overall variance. The communality of each variable was relatively high, ranging from 0.42 to 0.76. This indicates that the variance of the original values was captured fairly well by these nine factors. Each factor name was based on the characteristics of its composing variables (Table 1). The first factor was labelled as ‘Service quality and staff attitude’, as this factor was formed by the variables of high standard of service, efficient service, attentive service, competent staff, helpful staff and staff appearance. This factor explained 29 per cent of the total variance and had an eigenvalue of 12.29 (Table 1). The second factor was labelled as ‘Product quality and hygiene’, as this factor was markedly composed of food quality and hygiene-related variables. This factor explained 8 per cent of the total variance and had an eigenvalue of 3.33. Other factors were labelled in accordance with their composite characteristics. Step two: Cluster analysis Given the sample size, consisting of several hundreds,49 a non-hierarchical clustering approach (K-means), in which the individuals are iteratively moved between clusters so as to minimise the variability within clusters and maximise the variability between clusters, was adopted.50 Because the cluster analysis is known to be sensitive to the outliers, the data were first examined for outlying observations.51 On examination, the outlying observations (n ¼ 25) were deleted so as to make cluster analysis safe for the entire data.52 Consistent with Hair et al. and Alzua et al.’s suggestion,53 a hierarchical technique (complete linkage with squared Euclidean distance) was conducted on a randomly selected subsample to obtain some idea about the number of clusters. In so doing, the advantages of this hierarchical method were

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Measurement of tourist satisfaction with restaurant services

Figure 1

Statistical techniques utilised

Factor analysis

Factor analysis was used to extract factors from the 42 attributes of restaurant selection.

Reliability analysis

Reliability analysis was utilised to estimate the reliability coefficients for each factor.

Hierarchical cluster analysis

Factor scores were then used in a hierarchical cluster analysis to obtain some idea about the number of homogeneous groups represented by the data.

K-means cluster

Quick cluster analysis was run on the total number of respondents.

Multiple regression

Multiple regression analysis was then employed to analyse the formation of satisfaction within each group.

complemented by the non-hierarchical method to ‘fine tune’ the results.54 Based on the guidelines for the identification of clusters in the literature,55 a visual inspection was carried out of the horizontal icicle dendogram on the computer printout and the sudden jumps in the algorithm schedule.56 This inspection suggested that a three-, fourand five-cluster solution might be appropriate. Subsequently, consistent with Woo’s57 approach, K-means cluster analysis was performed on the three different cluster solutions (n ¼ 3, 4 and 5). Comparing the results obtained from these solutions, the five-cluster solution was selected for further analysis because it provided the greatest difference between clusters and it yielded the most interpretable results.58

Step three: Discriminant analysis In order to test whether significant differences in restaurant selection criteria exist across segments, a multivariate analysis of variance (MANOVA) was conducted using the segments as the independent variable and the nine factors as dependent variables. Wilks’ lambda was 0.06 and significant at 0.000 level, which indicated overall differences between clusters. It is important to note that a fundamental assumption of MANOVA was satisfied, as a test of equality of group covariance matrices using Box’s M (Box’s M ¼ 3449:778, F ¼ 1:81 with 180, 117504 df, p ¼ 0:000) indicated that the covariance was equal. Then, a univariate F test was used to investigate the sources of these group differences. The results revealed

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Table 1: Restaurant selection factors Factor name

Loadings

Mean

Factor 1. Service quality and staff attitude Service standard Service efficiency Attentive service Helpful staff Competent staff Staff appearance Prices shown clearly

0.534 0.566 0.576 0.772 0.752 0.533 0.739

5.82 5.61 5.70 6.17 6.02 5.48 6.06

Factor 2. Product quality and hygiene Food preparation consistency Food tastiness High-quality food Fresh ingredients Hygienic food preparation Staff cleanliness

0.660 0.589 0.681 0.718 0.765 0.625

5.98 6.01 5.98 6.21 6.40 6.55

Factor 3. Adventurous menu Adventurous menu Availability of local dishes Availability of interesting food A place frequented by locals

0.687 0.773 0.770 0.689

4.88 5.38 5.04 4.50

Factor 4. Price and value Reasonable food prices Food value for money Hearty portions

0.632 0.695 0.569

5.76 5.90 5.20

Factor 5. Atmosphere and activity Restaurant atmosphere Activity and entertainment

0.772 0.695

5.32 3.74

Factor 6. Healthy food Availability of healthy food Nutritious food

0.759 0.788

4.90 4.82

Factor 7. Location and appearance Impression from the road Convenient location

0.713 0.684

5.10 4.67

Factor 8. Smoke Availability of non-smoking area

0.723

4.49

Factor 9. Visibility Visibility of food preparation area

0.617

4.58

that clusters were significantly different on all determinant restaurant selection factors. The classification results for the use of the analysis indicated that the discriminant analysis model could correctly classify 95.7 per cent of the individuals into groups.

Variance Eigenvalues (%)

Reliability

12.29

29.3

0.88

3.33

7.9

0.86

2.45

5.9

0.79

1.64

3.9

0.65

1.36

3.3

0.63

1.2

2.9

0.81

1.17

2.8

0.54

1.12

2.7



1.09

2.6



Cluster one: Value seekers As in the factor analysis, each cluster was labelled in accordance with the characteristics of its composites. The first cluster contained 19 per cent of the total cluster sample (58 out of 304). Mean scores (Table 2) sug-

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1.10189 0.15976 0.50339 0.37907 0.11434 F ¼ 20.057 p , 0.000 0.61848 0.17885 0.96049 0.42266 0.54155 F ¼ 38.428 p , 0.000

0.11793 0.02219 0.74047 0.35181 0.12983 F ¼ 25.392 p , 0.000

0.18646 0.48819 0.30311 0.28568 0.09315 F ¼ 10.065 p , 0.000

gest that tourists in this cluster select restaurants which provide food value for money. They attach a great deal of importance to food quality and hygiene when making a selection decision. This segment has the lowest mean score for Factor 7 (1.10), followed by Factor 6 and Factor 3. This indicates that they do not take location of the restaurant into consideration in their selection; they are not overly concerned about the availability of healthy and nutritious food choices; and they do not prefer restaurants which offer local or adventurous food. In addition, atmosphere and service quality are not particularly important in their restaurant selection. Cluster two: Service seekers This cluster attaches the highest importance to the availability of quality service when selecting a restaurant. The value of food, the availability of healthy choices and the restaurant’s location are among the other important criteria taken into account when selecting a restaurant. In contrast to cluster five and cluster one, apparently this cluster does not take food quality into consideration when selecting a restaurant. In contrast to clusters three and four, the availability of local and interesting food does not appear to play a significant role in this segment’s selection decision-making process.

Number of respondents a

0.52866 0.28207 0.59885 0.46662 0.77846 F ¼ 9.082 p , 0.000 0.23853 0.71444 0.62119 0.42359 0.03498 F ¼ 34.934 p , 0.000 Cluster 1 [58]a Cluster 2 [50] Cluster 3 [45] Cluster 4 [92] Cluster 5 [59] Results of ANOVA

0.08785 0.34055 0.05908 0.15151 0.28657 F ¼ 4.031 p , 0.003

0.29073 0.93254 0.03919 0.08121 0.55615 F ¼ 58.718 p , 0.000

0.15460 0.10756 0.14155 0.60355 0.62475 F ¼ 3.324 p , 0.008

Factor 7 [Location] Factor 4 [Value] Factor 3 [Menu] Factor 2 [Product] Factor 1 [Service]

Table 2: Segment mean scores

Factor 5 [Atmosphere]

Factor 6 [Healthy]

Factor 8 [Smoke]

Factor 9 [Visibility]

Measurement of tourist satisfaction with restaurant services

Cluster three: Adventurous food seekers Tourists in this cluster attach the highest importance to the availability of local, new and interesting food, followed by the location of a restaurant. The availability of healthy and nutritious food choices is not particularly important to this cluster. In contrast to value seekers, tourists in this cluster do not seem to attach any importance to prices. Contrary to the fourth cluster, atmosphere does not play significant role in their selection decision-making process. Apparently, the availability of non-smoking areas and the visibility of the food preparation area

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do not have any influence on this cluster’s restaurant selection decision. Cluster four: Atmosphere seekers In contrast to other clusters, this cluster does not have negative mean scores on any of the selection factors, which suggests that all of the factors might be taken into account in selection. Contrary to the value seekers, service seekers and healthy food seekers, this cluster has the highest mean score for Factor 5, which indicates they search for a restaurant that is capable of offering a convivial dining atmosphere and a good time. They are moderately concerned about prices when selecting a restaurant. The availability of local and interesting food and the availability of nutritious and healthy food are the next two most important requirements. The restaurant’s location is an important factor in influencing the selection. The remaining factors also appear to have a moderate importance in restaurant selection. Cluster five: Healthy food seekers Contrary to the value seekers, this cluster has one of the highest mean scores for Factor 6, which indicates that they search for restaurants which offer healthy food choices. Food quality and hygiene are another highly important requirement of this group when selecting a restaurant. Price does not appear to be an important consideration for this group in selecting a restaurant. They do not take atmosphere into consideration when making selection decisions. Adventurous food is not particularly important in their restaurant selection.

Step four: Segment-specific satisfaction In order to use respondents’ performance ratings as the predictors of satisfaction, the perceived performance items were factor analysed first. From the orthogonal (Varimax) rotated factor matrix, ten factors with 37 variables, with factor loadings equal to

0.50 or above, were identified (Table 3). Ten factors explained 64.5 per cent of the overall variance. Each factor was given a label, which was based on the common characteristics of the variables it included (Table 3). Regression analysis A series of stepwise regression analyses were then performed to predict satisfaction by the ten performance factors in aggregate and in each market segment. The results of aggregate and segment-level predictions are presented in Table 4. The subjects’ scores on the overall satisfaction scale were regressed on their factor scores in order to understand the structure of predictor variables and their relative importance in determining dining satisfaction. When all participants were regarded to be in a single aggregate market, nine factors emerged as significant (sig. t , 0:05) predictor variables in the regression model. The regression equation characteristics of overall dining satisfaction indicated an R2 of 0.58. Following this, each segment’s satisfaction scores were regressed on the factor scores of performance perception to examine whether the structure of predictor variables changes and whether the predictive power of the model increases when satisfaction is examined at segment level. The resulting R2 ranged from 45 per cent for the atmosphere seekers segment to 86 per cent for the service seekers segment. The structures of predictor variables within each model were different compared to that of the aggregate market model. DISCUSSION The majority of previous studies have explored customer satisfaction at aggregate market level. These studies generally assumed the structure of factors affecting customer satisfaction to be generic. Satisfaction drivers of different subsegments, however, could be different. This study examined whether tourists could be grouped into distinct subsegments based on similarities and differences in benefits that they seek from

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Table 3: Foodservice evaluation factors Factor name

Factor Loadings

Mean

Factor 1. Service quality Service standards Consistent service quality Service courtesy Friendly restaurant managers/staff Knowledge of the staff Willingness to help Communication with the staff Competent restaurant staff Attentive restaurant staff

0.54335 0.51983 0.62275 0.63951 0.66044 0.72324 0.60341 0.74190 0.78413

5.66 5.28 6.21 6.20 5.75 6.20 5.33 5.92 5.98

Factor 2. Product quality Food quality Food portions Food tastiness Food temperature Food presentation Food preparation consistency Non-greasy food

0.73233 0.62586 0.75729 0.67403 0.71101 0.71193 0.52362

5.76 5.34 5.77 5.75 5.99 5.77 5.30

Factor 3. Menu diversity Menu variety Availability of menu items Availability of dishes liked Availability of local dishes Availability of beverages liked Healthy food choice

0.62499 0.69244 0.73487 0.63429 0.61575 0.54050

5.27 5.89 5.90 5.67 6.02 5.34

Factor 4. Hygiene Cleanliness of restaurant staff Cleanliness of the restaurant/utensils Tidiness of the restaurant Appearance of staff

0.60983 0.54619 0.61739 0.60821

6.15 5.83 5.79 5.90

Factor 5. Convenience and location Location of the restaurant Crowd level in the restaurant Operating hours

0.61164 0.66140 0.52870

5.73 5.28 6.07

Factor 6. Noise Quietness of restaurant surroundings Quietness of restaurant

0.72182 0.78276

4.57 4.77

Factor 7. Service speed Waiting time for dishes Efficiency of service

0.80539 0.54044

4.57 5.76

Factor 8. Price and value Food prices Value of food for the price charged

0.81528 0.67035

5.45 4.75

Factor 9. Facilities Children facilities

0.78242

5.58

Factor 10. Atmosphere The atmosphere in the restaurant

0.77200

4.75

Eigenvalues

Variance (%)

Reliability

15.06

34.2

0.9063

2.35

5.4

0.9042

2.23

5.1

0.8088

1.77

4.0

0.8136

1.41

3.2

0.6490

1.19

2.7

0.6853

1.16

2.6

0.6592

1.12

2.5

0.7149

1.04

2.4



1.01

2.3



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Table 4: Determinants of dining satisfaction B

S. Error

Beta

0.646 0.413 0.381 0.296 0.280 0.207 0.188 0.206 0.200 5.60

0.071 0.067 0.068 0.065 0.065 0.062 0.058 0.071 0.070 0.065

0.474 0.342 0.295 0.244 0.226 0.174 0.167 0.157 0.154

8.98 6.12 2.58 4.51 4.27 3.30 3.216 2.89 2.86 86.14

0.000 0.000 0.000 0.000 0.000 0.001 0.001 0.004 0.004 0.0000

0.58 (24.40)

0.961 0.844 0.646 0.366 5.329

0.172 0.182 0.220 0.172 0.190

0.762 0.574 0.391 0.259

5.598 4.623 2.985 2.130 28.025

0.000 0.000 0.008 0.045 0.000

0.68 (11.78)

0.672 0.467 0.748 0.307 6.458

0.123 0.129 0.208 0.122 0.121

0.507 0.375 0.330 0.256

5.484 3.629 3.598 2.511 5.331

0.000 0.002 0.002 0.022 0.000

0.86 (27.07)

Adventurous Service quality food seekers Convenience location Constant

1.065 0.566 5.271

0.272 0.220 0.274

0.739 0.484

3.917 2.566 19.235

0.002 0.025 0.000

0.60 (9.00)

Atmosphere seekers

Product quality Price and value Constant

0.780 0.369 5.873

0.143 0.104 0.107

0.577 0.376

5.468 3.564 54.876

0.000 0.001 0.000

0.45 (20.45)

Healthy food Service quality seekers Product quality Facilities Menu diversity Constant

0.619 0.654 0.329 0.310 5.574

0.092 0.122 0.089 0.087 0.098

0.697 0.543 0.372 0.375

6.702 5.366 3.691 3.543 56.908

0.000 0.000 0.002 0.003 0.000

0.83 (21.27)

Aggregate market

Service quality Product quality Hygiene Menu variety Price/value Convenience/location Service efficiency Atmosphere Amenities Constant

Value seekers Product quality Service quality Menu diversity Noise Constant Service seekers

Product quality Service quality Menu diversity Service speed Constant

restaurants. It investigated whether drivers of dining satisfaction differed across segments and whether segment-specific satisfaction analysis increased the predictive power of the model. The study results provided a strong degree of support for the necessity and efficiency of predicting customer satisfaction at market segment level. This finding is consistent with that of Oh and Jeong.59 When the analysis was run on the aggregate market, nine perception factors were included in the model and explained 58 per cent of the variation in customer satisfaction.

T

Sig T

R2 (F Value)

However, all models undertaken with each segment needed fewer perception factors to explain an increased amount of variation in customer satisfaction, with the exception of the model in atmosphere seekers. The results indicated that there were different segments seeking different sets of benefits. Five dining segments were identified within the aggregate market. The first segment (value seekers) was found to be more concerned with product value when making their restaurant selection decision. The second segment (service seekers) appeared to

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attach greater importance to quality service when selecting a restaurant. The third segment (adventurous food seekers) was found to take the opportunity of tasting new, interesting and local dishes more into account in their selection decisions. This segment was found not to be particularly concerned with the nutritiousness and healthiness of food. The fourth segment (atmosphere seekers) sought restaurants capable of offering a pleasant atmosphere and good time. The final segment (healthy food seekers) was found to be more concerned with the availability of healthy food when selecting a restaurant. The results further showed that segments differed in their evaluation of services. DETERMINANTS OF SATISFACTION The tourists’ evaluation of dining experiences appears to be a complex process in which several factors are processed. Nine factors were significant at aggregate-marketlevel analysis (Table 4). Service quality had the most significant effect on dining satisfaction at aggregate market level. This may suggest that no matter how brilliant the marketing plan, it all comes down to nothing if there is a breakdown at the most critical interface: the point at which the customer comes in contact with the employee. Service quality was followed by product quality, hygiene, menu diversity, price-value and convenience. The multidimensional nature of factors affecting tourist dining satisfaction is consistent with the results of other studies which reported that physically consumable service elements (eg the food), the manner in which the product is delivered to the customer (eg the behaviour), the service environment where material products are produced and consumed (eg the ambience), the other customers simultaneously consuming the same or similar services (social temperature) and the product cost and value would determine satisfaction. The structure of predictor variables and their relative importance, however, changed when the analysis was undertaken separately with identified segments (Table 4). In contrast to nine factors, only four factors were

relatively more influential in the determination of dining satisfaction judgments of the value seekers segment. The first factor with the greatest influence on dining satisfaction of this segment was product quality, followed by service quality, menu diversity and noise. All coefficients carried a positive sign, indicating that there was a positive relationship between these four perception factors and overall satisfaction. Any attempt to increase performance on these dimensions should increase satisfaction proportionately. In the case of service seekers, the predictor variables were different from both those of the value seekers segment and those of the aggregate market. In this case, the first factor with the greatest effect on dining satisfaction was service quality, followed by product quality, menu diversity and speed of service. It is important to note that apart from menu diversity, all factors had positive signs. There was a negative relationship between menu diversity and overall satisfaction. The more diverse the menu, the less satisfaction it may create. Different from these two segments, only two predictor variables emerged as significant in the formation of dining satisfaction judgments of adventurous food seekers. The most significant factor affecting their dining satisfaction was service quality, followed by convenience and location. In the case of atmosphere seekers, product quality and price-value were the significant predictor variables of satisfaction. The most significant determinants of dining satisfaction judgments of healthy food seekers included service quality, followed by product quality, facilities and menu diversity. Different segments appeared to base their satisfaction judgments on different service attributes. This has management implications. Managers aiming to build a long-term relationship with customers in the value seekers segment should deliver a quality product and service, a diversified menu and an acceptable noise level. To retain the service seekers segment, managers should take necessary steps to provide services that meet this segment’s requirements. Additionally, the divergence between attributes considered

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in selection and evaluation decisions suggests that different strategies might be needed to attract and retain the target markets. To attract customers is one thing, but keeping them is another. No matter how attractive their advertising might be, managers are likely to have difficulties in the area of building and maintaining relationships with customers unless they develop a sound understanding of the decision processes that each market segment uses in their service evaluations. CONCLUSION The proposition of this study concerned an effort to expand CSR beyond scrutiny of predictor variables at aggregate market level; the most applied mode of satisfaction research. Identification of segments and understanding their service evaluation processes are essential to develop effective marketing messages that would appeal to the target market(s).60 Understanding variations across segments is managerially important, as they will require considerably different levels of fulfilment. Cross-segment satisfaction analysis may be useful for optimising the use of resources, identifying performance shortfalls, deciding on making service improvements and presenting a product in the most favourable form. While this study adds to understanding of cross-segment satisfaction analysis, certain limitations of the present study should be noted. The generalisability of the findings is limited due to sample and methodological considerations. The majority of the sample in this study were British tourists. This study should be replicated with different nationalities. It would be useful to have a larger sample. Note that the investigation was conducted during a specific period in the summer season. It could be argued that the study results might have been different had the survey been conducted in different seasons. This study used a recall-based approach. This may have limitations due to probable memory lapses. Additionally, this study used attribute importance as the basis for segmentation. It may, however, have limitations.

Different interpretations of the importance may have important implications for research findings. Consumers may not know how important a particular feature is in their decision until they actually experience the feature. The need for a careful choice of segmentation variables can hardly be debated, as follow-up management decisions will rely on the information based on the selected variables. As there is no one correct way to segment the market, future studies may employ different suitable bases, including personal values. Finally, this study did not attempt to understand the expected benefits of tourists from different countries. However, culture may determine attributeseeking patterns of customers. More empirical studies utilising different segmentation bases are needed to explore what specific segments there are; what service elements they consider most in their post-purchase evaluations; and how segment-specific information can be put into practice effectively.

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