Using Demographics and Leisure Activities to Predict Satisfaction with Tourism Services in Greece

Using Demographics and Leisure Activities to Predict Satisfaction with Tourism Services in Greece Rodoula Tsiotsou Eleytheria Vasioti ABSTRACT. The purpose of the study was to predict the level of satisfaction with tourism services in Greece. In particular, the study investigated the degree to which demographic variables and leisure activities could discriminate consumers who are highly satisfied from those who are less satisfied with tourism services. A self-administered anonymous questionnaire was given to 170 subjects who participated in a three-day trip organized by travel agencies to the mountainous region of Hepeiros, Greece, from November 2003 to February 2004. The questionnaire was given the last day of the trip and was answered by 115 individuals (68% response rate). Classification with discriminant analysis was used to identify consumers with different satisfaction levels (high and low). The hypothesis that demographics and leisure activities will classify consumers highly satisfied from consumers less satisfied was partially confirmed. Education, age and leisure activities could discriminate between the two groups of consumers (high satisfied and low satisfied). However, the findings of the study provide theoretical and practical implica- Rodoula Tsiotsou, PhD, is Assistant Professor, Department of Commerce and Advertising, ATEI of Crete, Greece. Eleytheria Vasioti, PhD, is Administrative Assistant, Region of Hepeiros. Address correspondence to: Rodoula Tsiotsou, N. Plastira 57 TL 14123, Lykovrisi, Athens, Greece (E-mail: rtsiotsou@yahoo.gr). Journal of Hospitality & Leisure Marketing, Vol. 14(2) 2006 Available online at http://www.haworthpress.com/web/jhlm 2006 by The Haworth Press, Inc. All rights reserved. doi:10.1300/j150v14n02_05 69

70 JOURNAL OF HOSPITALITY & LEISURE MARKETING tions in predicting satisfaction level and increasing tourism marketing effectiveness. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: <docdelivery@haworthpress.com> Website: <http://www.haworthpress.com> 2006 by The Haworth Press, Inc. All rights reserved.] KEYWORDS. Consumer satisfaction, tourism services, leisure activities, tourist satisfaction INTRODUCTION Consumer satisfaction has been extensively studied in marketing the last few decades. However, marketing scholars have not yet, agreed upon a generally accepted definition of satisfaction. Giese and Cote (2000) after conducting a review of literature and consumer interviews defined satisfaction as a summary affective response of varying intensity with a time specific point of determination and limited duration directed toward focal aspects of product acquisition and/or consumption. Consumer satisfaction has been considered one of the most important construct (Morgan, Attaway, & Griffin, 1996; McQuitty, Finn, & Willey, 2000) and one of the main goals in marketing (Erevelles & Leavitt, 1992). Satisfaction plays a central role in marketing because it is a good predictor of purchase behavior (repurchase, purchase intentions, brand choice and switching behavior) (McQuitty, Finn, & Willey, 2000). Due to its importance, various theories and models have been developed in an effort to define the construct and explain satisfaction in different products/services and consumption stages. The expectancy-disconfirmation paradigm (Oliver, 1980), the perceived performance model (Churchill & Suprenant, 1982) as well as attribution models (Folkes, 1984), affective models (Westbrook, 1987) and equity models (Oliver & DeSarbo, 1988) are only some of the main theoretical bases developed to explain consumer satisfaction. The above approaches have raised several issues and debates among marketing scholars. Some of the questions refer to (1) the application of each model; which of the models are best applicable in different consumption situations and for different products (Erevelles & Leavitt, 1992); (2) the measurement of satisfaction; different measurement instruments should be used for different products and services; (3) the definition of satisfaction; should it be defined with focus on the response (construct) or on the process (model) (Giese &

Rodoula Tsiotsou and Eleytheria Vasioti 71 Cote, 2000)? Several studies have been conducted on consumer satisfaction whilst several data exists on satisfaction in tourism services. Satisfaction in tourism has become a major research area in the last three decades (Fallon & Schofield, 2003; Kozak, Bigne, & Andreu, 2003). Tourist satisfaction has been studied from different perspectives and in different destination types. Swan, Martin and Trawick (2003) studied compensatory satisfaction in a birding field trip. Their findings confirmed the expectancy-disconfirmation paradigm. Similarly, it has been suggested that tourist (visitor) satisfaction is determined by the extent to which desired outcomes or benefits are realized (Tian-Cole & Cromption, 2003). Webb and Hassall measured visitor satisfaction with Western Australia s conservation estate on a two year period. They found that the type of location and the number of facilities were the strongest indicators of satisfaction (2002). Satisfaction and enjoyment demonstrated significant differences among segments in tourism market while demographic variables displayed little difference in a study conducted by Andereck and Caldwell (1994). Sung, Morrison and O Leary (2000) studied the adventure travel market and suggested that activity sets are associated with distinct groups of customers who have varying demographic and travel characteristics (p. 17). They further argued that activities should be taken into account when studying adventure traveler segments because they are associated with consumer preferences. Moreover, it has been proposed that the role of personnel is important in explaining tourist satisfaction and for this reason it should be studied for gaining better understanding (Noe and Uysal, 2003). Tourist satisfaction has been the topic of interest in cross-cultural studies that attempted to identify differences among countries (Kozak, Bigne, & Andreu, 2003). Several scholars developed models in an effort to explain satisfaction with tourism services. Knutson et al. (2003) have proposed the American Consumer Satisfaction Index (ACSI) model as a measurement instrument of service quality in the hotel industry. In this model, customer expectations, perceived quality and perceived value are the antecedents of satisfaction whereas customer complaints and customer loyalty are the consequences. Balonglu et al. (2003) proposed and tested a model where satisfaction plays a mediating role between destination performance and behavioral intentions (return intention and recommendation). Lee, Graefe and Burns (2004) found that service quality is an antecedent of satisfaction and satisfaction is a mediator between service quality and behavioral intentions.

72 JOURNAL OF HOSPITALITY & LEISURE MARKETING Another aspect of tourism satisfaction being studied is the role of the length of a trip. Neal (2003) studied the differences in satisfaction between short-term visitors (those who stayed from one to six nights) and long-term visitors (those who stayed seven or more nights). She found significant differences between the two groups of visitors in terms of the perceived satisfaction with the service quality. Short-term visitors were less satisfied with perceived service quality and perceived reasonableness of the cost of their travel destination than long-term visitors. Huh and Uysal (2003) also found that overall satisfaction varies according to the length of stay, gender and decision horizon. Other studies have focused on measurement issues of tourist satisfaction. Fallon and Schofield (2003) compared the expectation-performance, the importance-performance and performance-only measures of satisfaction. Their findings confirmed previous studies (Cronin and Taylor, 1992) and showed that the performance-only model was the best predictor of overall satisfaction. Ekinci (2003) compared service quality and tourist satisfaction. He found that only satisfaction was a significant predictor of purchase intentions. Furthermore, he stated that multiple comparison standards are used by consumers when determining their satisfaction. Contradictory findings have been presented by Lee, Graefe and Burns (2004). The found that service quality is a predictor of behavioral loyalty, thus, the effect of service quality on behavioral intentions is as important as that of satisfaction. Tourist satisfaction has been proposed to be taken into account when assessing the strengths and weaknesses of a tourism organization. Moreover, it should be considered when forecasting demand for developing the marketing strategy. Tourist satisfaction is central to marketing and should feed into the strategic and operational planning of tourism organizations (Satish & Menezes, 2001). Though there is data on tourist satisfaction, there is not much data in satisfaction with tourism services in Greece either from foreign or Greek tourists. Greece is a tourism destination and tourism is one of the main sectors that contribute significantly to the economic development of the country. However, no much data exists on satisfaction with tourism services of Greek tourists. Internal tourism (Greeks who take their vacation in Greece) is developing rapidly in Greece throughout the year. According to Eurostat (the statistics office of the European Union), 56% of the Greek population prefers Greece as the place for its vacation. Moreover, Greek tourists spend more during their vacation than foreign tourists (Glynia, Lytras, & Maras, 2004). Thus, it is imperative to study if Greek tourists are satisfied with the tourism services being offered to them.

Rodoula Tsiotsou and Eleytheria Vasioti 73 PURPOSE OF THE STUDY The purpose of this study was to measure satisfaction with tourism services in Greece. Specifically, the intention was to investigate the degree to which demographics such as education, age, employment and family status along with leisure activities discriminate individuals highly satisfied from those who are less satisfied in an effort to predict satisfaction level of consumers in tourism services. METHODOLOGY The study utilized the survey research method to study consumer satisfaction with tourism services. Specifically, en route survey methodology was used due to its cost effectiveness and the reduction of response errors (memory bias) (Hurst, 1994). An anonymous questionnaire was given to 170 subjects who participated in a three-day trip ( short-term visitors ) organized by travel agencies to the mountainous region of Hepeiros, Greece, from November 2003 to February 2004. The questionnaire was given the last day of the trip before their departure and was answered by 115 individuals (68% response rate). Based on the review of literature, the questionnaire of the study was developed to gather data. The questionnaire consisted of three parts. Part I measured satisfaction, Part II gathered data on leisure activities and Part III gathered demographic data. Part I consisted of 32 items that had to be rated on a 5-point Likert scale (1 = Not at all satisfied, 5 = Completely satisfied). The satisfaction items were developed because of the unique aspects of the specific tourist destination (leisure activities, cultural activities, traditional food testing and sight seeing were included in the three-day trip). We believe that another instrument (developed and used in other studies) could not capture these unique aspects. Thus, we developed a set of items that measured satisfaction with the different aspects of the specific trip. It had been recommended that different satisfaction survey instruments should be developed, tailored to different types of customers and research questions (Fallon & Schofield, 2003; Giese & Cote, 2000). A factor analysis was used to identify the underlying structure of the 32 variables reflecting various aspects of satisfaction. The variables were reduced to 22 during the analysis (10 items dropped because either they were highly correlated with others or not correlated at all). The Maximum Likelihood procedure was used to determine the dimensionality

74 JOURNAL OF HOSPITALITY & LEISURE MARKETING of the factor model. Sampling tests were used to determine the appropriateness of the sample being used for the factor analysis. A Kaiser-Meyer- Olkin test measuring the adequacy of sampling was conducted (produced a p-value of.792) and provided evidence that the sample used for the study was adequate. Based on Kaiser s rule of selection (eigenvalues larger to 1), six factors were extracted; they accounted for 74.6% of the total variance. However, a six-factor solution indicated a lack of model fit (p-value for fit test =.186 and 64 (21%) residuals). Thus, a five-factor solution was examined and seemed that it was the best approach (explained variance 70.3%; p-value for fit test =.038; Chi-square statistic = 199.682, 59 residuals (21%) (Table 1). An oblique rotation (delta = 0) was chosen because of the theoretical expectation that the resulting factors would in reality be correlated. Based on the items of the factor loadings, the factors were labeled: personnel satisfaction (factor 1), food satisfaction (factor 2), excursion satisfaction (factor 3), socialization satisfaction (factor 4) and landscape satisfaction (factor 5). The factors Cronbach s coefficient alpha ranged from.847 to.957 and the correlations between the factors ranged from.033 to.481 (Table 2). Sample Demographics RESULTS The majority of the respondents participating in the study were males (64%) whereas only 36% were females (Table 3). From these 66% were single, 8% were married with no children and 25% were married with children (1% declared that had another family status). Regarding the age group were they belonged, 52% of the sample was between 26-35 years old, 22% was up to 25 years, 22% was between 36-45 years and 4% was between 46-55 years old. Regarding the level of education, most of the respondents had a university degree (50%), 20% had a high school diploma, 18% had a graduate degree and 12% graduated from a vocational education institution. Finally, most of the people in the study were independent professionals (32%), several also worked in the private sector (25%) whereas only 22% were employees in the public sector, some were students (17%) or unemployed (4%). In terms of leisure activities, the respondents could participate in activities such as horseback riding, rafting, walking and others. The

Rodoula Tsiotsou and Eleytheria Vasioti 75 TABLE 1. Factor Loadings for Satisfaction (N =115) Factor 1: Personnel Satisfaction Expert drivers/escorts Consistent drivers/escorts Helpful drivers/escorts Useful guides Friendly personnel Well organized trip Factor 2: Food Satisfaction Tasteful local food Good food Opportunities for trying new food Factor 3: Excursions Satisfaction Fascinating excursions Opportunities for excursions Opportunities for tours Factor 4: Socialization Satisfaction Opportunities for shopping Socialization with others Good quality for the money spent Interesting cultural choices Opportunities for socializing with new people Factor 5: Landscape Satisfaction Fascinating view Beautiful environment Opportunity for multiple paths Enough free time Friendly people Factor 1 Factor 2 Factor 3 Factor 4 Factor 5.842.809.779.739.657.413.967.924.838.878.731.559.699.603.541.538.441.836.664.466.406.305 Eigenvalue 9.273 2.932 1.877 1.498 1.288 Variance (%) 38.6 12.2 7.8 6.2 5.4 Cumulative Variance (%) 38.6 50.9 58.7 64.9 70.3 Cronbach s Alpha.941.957.877.853.847 Number of Items (total = 22) 6 3 3 5 5 TABLE 2. Factor Correlations (N = 115) Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 1 1.000 Factor 2.222 1.000 Factor 3.242.033 1.000 Factor 4.481.322.242 1.000 Factor 5.453.112.356.299 1.000

76 JOURNAL OF HOSPITALITY & LEISURE MARKETING TABLE 3. Sample Demographics (Frequencies and Proportions), N = 115 Gender Age Marital Status Males 73 (64%) Up to 25 25 (22.3%) Single 74 (66.1%) Females 40 (36%) 26-35 58 (51.8%) Married no children 9 (8%) 36-45 25 (22.3%) Married with children 28 (25%) 46-55 4 (3.6%) Other 1 (.9%) Education Employment Status Leisure Activities High school 22 (20.2%) Unemployed 5 (4.4%) Horseback riding 11 (9.7%) IEK 13 (11.9%) Student 19 (16.8%) Rafting 61 (54%) University 54 (49.5%) Public sector 25 (22.1%) Walking 39 (34.5%) Graduate degree 19 (17.4%) Private sector 28 (24.8%) Other 2 (1.8%) Other 1 (.9%) Free agent 36 (31.9%) study showed that the most preferred activity was rafting (54%), second most selected was walking (34.5%), third most preferred was horseback riding (9.7%) whereas only 1.8% preferred another activity. Classification with Discriminant Analysis The basic analysis of the study was classification with discriminant analysis. Classification with discriminant analysis involves classifying subjects into the one of several groups on the basis of a set of measurements. Subjects with an overall satisfaction score of 84 (mean satisfaction score) and less were categorized as low (y = 0) and those with an overall satisfaction score of larger than 84 as high (y = 1). The total sample was randomly split into a development sample of 52 subjects and a cross validation sample of 63 subjects to assess the classification accuracy of the discriminant variates. The classification function was computed first on the development sample and then checked its hit rate on the cross validation sample. The classification variables were age, educational level, employment, family status and leisure activities. Descriptive statistics of the variables are presented on Table 4. In a preliminary analysis of the data, a case analysis was conducted to identify possible outliers and violations of the assumptions of independence, multivariate normality and the homogeneity of variance/covariance matrices. No serious violations of the assumptions were identified. The homogeneity of variance/covariance test (Box s M) indicated that the data did not violate the assumption (fail to reject at the.05 level; F =.922, p =.538).

Rodoula Tsiotsou and Eleytheria Vasioti 77 TABLE 4. Descriptive Statistics (N = 115) Variable Means Standard Deviations y = 0* y = 1** y = 0* y = 1** Employment status 3.80 3.63 1.08 1.33 Education 2.12 2.88.97.93 Family status 1.80 1.48.96.80 Age 2.36 1.88.91.64 Leisure activities 2.56 2.11.51.64 * Subjects with satisfaction scores less than 84 (y = 0). ** Subject with satisfaction scores higher than 84 (y = 1). The overall multivariate relationship (MANOVA) was statistically significant at the.05 (chi square = 17.470; Wilk s L =.692; p =.0037) indicating that the two satisfaction groups were significantly different. Thus, the variables used were able to classify the subjects into the two satisfaction level groups. Moreover, three of the univariate F-tests were significant as shown on Table 5. The classification was based on the Bayesian probability of group membership, assuming group priors equal to the relative group sizes (.48 for likely to have law satisfaction and.52 for likely to have high satisfaction). The Bayesian cutoff point was 0.1. To accomplish this classification the Fisher s Linear Discriminant Functions were used (Table 6). The analysis continued with the evaluation of the performance of the classification procedure. Table 7 shows the hit rate for both the development and the cross validation sample. The results for the development sample indicated a 71.15% correct classification rate. There were 5 misclassified cases. The hit rate for the cross validation sample decreased to 63.64%. There were 6 subjects misclassified. The precision of correct classification was fairly satisfactory and for this reason the use of the procedure for classification of future subjects is recommended with caution only. DISCUSSION/IMPLICATIONS The overall results of the study are very interesting and important in understanding tourist satisfaction. They also provide useful information to tourism managers for improving their services in Greece. The five factor solution used to measure satisfaction seems to explain much of the variance of the construct (70.3%) and do better than other instruments developed to measure tourist satisfaction. For example, Shanka and Taylor (2003) extracted a three-factor solution (three important

78 JOURNAL OF HOSPITALITY & LEISURE MARKETING TABLE 5. Univariate F-tests for Education, Employment Status and Leisure Activities (N = 115) Variable Wilks s F Significance Education.855 8.469.005 Employment status.995.254.617 Family status.967 1.699.198 Age.914 4.734.034 Leisure activities.865 7.772.007 TABLE 6. Fisher s Linear Discriminant Functions (N = 115) Variable Low satisfaction High satisfaction Employment status 3.528 3.280 Education 2.508 3.383 Family status 1.670 1.466 Age 1.584.815 Leisure activities 10.192 8.922 Constant 26.514 22.768 attributes: physical facilities, service experience and services provided) that explained only 60.6% of tourist satisfaction. Fallon and Schofield s study resulted in five factors ( performance only : facilitators, secondary attractions, tertiary attractions, core attractions and transport) that explained 56.54% of the total variance of satisfaction (2003). Thus, the satisfaction instrument developed in this study seems to do better job in predicting tourist satisfaction than other ones. Of course improvements could be made to increase predictability and explain more of the variance of satisfaction. However, it could be used for future studies of short-term visitors in similar tourism settings and it could be tested for long-term visitors. The demographics of the sample showed that the majority were men, 26-35 years old, single with a university degree, working as free agents. Their most preferred leisure activity was rafting. The classification with discriminant analysis was used to identify consumers who would be highly satisfied from those being less satisfied in a three-day trip to the mountainous region of Hepeiros. Demographic variables such as education, age, employment and family status along with leisure activities being selected were the variables used to predict

Rodoula Tsiotsou and Eleytheria Vasioti 79 TABLE 7. Classification Accuracy (N = 115) Results for the development sample*: Actual group No of cases Classification Low satisfaction High satisfaction Low satisfaction 25 18 (72.0%) 7 (28%) High satisfaction 27 8 (29.6%) 19 (70.4%) Total correct classification = 71.15% * There were five ungrouped cases Results for the validation sample**: Actual group No of cases Classification Low satisfaction High satisfaction Low satisfaction 19 8 (42.1%) 11 (57.9%) High satisfaction 25 5 (20.0%) 20 (80.0%) Total correct classification = 63.64% ** There were six ungrouped cases levels of satisfaction. The hypothesis that those variables will classify consumers highly satisfied from consumers less satisfied was partially confirmed. Only employment and family status could not be used to predict satisfaction though the two satisfaction groups (high and low) were statistically significant different. The results of the study are significant for theoretical and practical reasons. The analysis has given some very important insight into the role of education, age, employment, and family status and leisure activities on consumer satisfaction with travel services. Employment and family status seems to have no effect on satisfaction whereas education, age and activities could be used to classify consumers in terms of their satisfaction level. The group means of the last three variables show that less educated people are less satisfied with the travel services whereas more educated people are more satisfied. Moreover, younger people are less satisfied than older people. Regarding leisure activities, the highest the activity level (rafting, horse riding) being preferred, the lowest the satisfaction is and the lowest the activity level (walking) being preferred, the higher the satisfaction is. Thus, educated younger people, who prefer during their vacation low intensity leisure activities such as walking, are more likely to be satisfied than less educated older people (still young) who prefer higher intensity activities (rafting, horseback riding). The implications of the study are several. Marketers could predict the level of satisfaction of prospective consumers if education, age and lei-

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