INFORMATION AND COMMUNICATION TECHNOLOGIES CONTRIBUTING TO TOURISTS’ SATISFACTION AND DESTINATION LOYALTY

 

 

ELENI GKIKA

Special Research & Education Personnel, Athens University of Applied Sciences, Department of Business Administration

 

 

ABSTRACT

The current study aims to shed light at factors that contribute to tourists’ satisfaction and specifically we are interested in the contribution of ICTs in tourists’ satisfaction and in consequent destination loyalty. The study took place at a Greek island. 139 tourists participated at the study. Cluster analysis revealed three groups of tourists: the first group consists of tourists who are “satisfied-independent” the second group are the “satisfied-organized” and in the last one are tourists who are “not satisfied”. The three groups differ in their satisfaction level and propensity to revisit the destination and recommend it to others. Quantitative data were analyzed in structural equation modelling (SEM). The results support the hypotheses: 1.Tourists’ preferences on travelling organized or independent affect their destination loyalty 2.Tourists’ booking preferences affect their satisfaction 3.Destination image positively influence Attribute satisfaction 4.Destination image positively influence Overall satisfaction and 5.Overall satisfaction positively influences Destination loyalty. These findings offer important implications to destination marketers and to local authorities in designing successful marketing strategies.

Key Words: ICT, Destination image, Destination Loyalty, Satisfaction, Structural Equation Modelling (SEM)

 

 

 

INTRODUCTION

Leisure is considered to be a necessity for the contemporary person and tourism contributes to the satisfaction of the need. Islands with sandy beaches are among the top priorities in tourist’s preferences and are one of the most important motives to visit an island. Greece is a country with 15.021 Km of coastline and almost 10.000 islands and islets. Special features have placed Greece among the firsts’ preferences of tourists who exceeded 30 million of non resident visitors in Greece during 2016 (SETE Authority). The internet has enabled tourism enterprises to distribute products through direct distribution and through a network of channels. The widespread of internet technologies enable consumers to communicate directly with tourist service providers or intermediaries to request information or purchase products. As intermediaries operate online travel agencies and search engines, providing static and dynamic information about availability or prices. Prospective tourists may find in Internet a great variety of offers and decide the options that best suits their requirements. Consumers may communicate instantly, inexpensively, interactively, regardless of the physical boarders or time zones.

Customers and tourist service providers increasingly depend on ITs and anyone who fails to participate in the electronic market place will confront competitive disadvantages. Tourists’ enterprises may cooperate and exchange customers’ information in order to facilitate the creation of total tourist product or in order to undertake joint marketing campaigns. Tourism organizations have enhanced their performance by reducing costs through the application of advanced marketing and management practices in using ITs. The ultimate aim of the marketing strategies is to satisfy customers and further to build bonds between consumers and organizations. These bonds imply loyalty to the tourist destination and to the tourist product.

In order to have a successful online marketing strategy one should continuously adapt it to the needs of customers based on measurements of their satisfaction, their experiences and the characteristics of their behaviour (Crnojevac et al., 2010).

The current study aims to shed light at factors that contribute to tourists’ satisfaction and specifically to the contribution of ICTs in tourists’ satisfaction and in destination loyalty. Studies developed so far have not examined destination loyalty and its’ relation to ICT’s usage. The article is organized as follows: The following section is devoted to a review of literature pertain to destination image, tourist loyalty and the contribution of ICT in tourists’ satisfaction. Findings of the relationship between them are articulated to substantiate the formation of hypotheses. In the subsequent section a detailed presentation of the procedure is presented. In the final section the findings of the study and their implications are presented. The findings should contribute to existing literature on destination loyalty and tourists’ satisfaction and also to provide guidelines for local authorities and to tourists’ service providers to formulate targeted marketing strategies and maximize the effective use of their resources.

 

 

LITERATURE REVIEW

Nowadays consumers have become more experienced in using ITs, they are more sophisticated and demanding in seeking exact information on destinations, suppliers and on the experiences they want to live. By using Information Technologies consumers enjoy more choices, customize products or services according to their needs and wishes and they save time away of bureaucratic procedures. The application of new technologies in tourism sector has created new era with computerized reservation systems, e-business and advanced marketing practices. According to Buhalis (1998) services provided at the tourism sector are intangible and are purchased before the time or away of the place of consumption. The relevant and accurate information delivered on time to customers’ needs ensure satisfaction on tourists demands, including high quality products and value for the money they spend. Internet applications have become very popular so many travel organizations (hotels, travel agencies, airlines, boat rental companies) have introduced internet technologies as part of their marketing strategy. Cohen (1972) classified tourists in four groups: organized mass tourists, which are least adventurous and follow travel agents’ pre-arranged plans, individual mass tourists, which use travel agents but have control over their time and decisions, explorers which make their own travel arrangements and finally drifters who seek novelty and want to incorporate easily with local culture. Contemporary tourists who are technology friendly are looking for reliable and accurate information and the opportunity to make reservations in less time, with fewer expenses and less inconvenience than conventional methods require. They are looking for “value for money” and “value for time” spent. They are interested in satisfying their own timetable and their own priorities. They are more independent and sophisticated with a wide range of tools to plan their travel. Internet provides them with reservation systems, online travel agencies, Internet search engines, information sharing through social networks, portals, sites for comparing prices and individual pages of suppliers and intermediaries.

H1: Tourists’ preferences on travelling organized or independent affect their destination loyalty.

Online booking uses information having characteristics as intangibility, heterogeneity and geographic fixation. The number of intermediaries between the hotel room and the tourist may reach to five making the distribution of the product complex and expensive. As Crnojevac et al., (2010) found most hotels prefer booking through their own website and that saves them money from agency commissions but makes difficult the access of tourists. Although internet is a helpful tool in information searching and in purchasing products and services there are still other channels that customers prefer in their decision making. Some customers prefer to search through internet but prefer to purchase offline through personal contact. The same applies for tourists. While the majority of them seek information online they use many different ways of purchasing travel products (Crnojevac et al., 2010).

H2: Tourists’ booking preferences affect their satisfaction.

Tourists with different characteristics evaluate differently tourist products or tourists’ destinations. The process of understanding how internet is used by different marketing segments offers the opportunity to increase the possibility of presenting the suitable product to the correct customer.

The image a destination has is important at tourists’ decision making and their subsequent behaviour (Zhang et al., 2014).  Definitions of destination image indicate the sum of beliefs, ideas and impressions a person has of a destination (Crompton, 1979), one’s mental representation of knowledge, feelings and global impressions (Baloglou & McCleary, 1999) and expectations toward a place over time (Kim & Richardson, 2003). Destination image has three components: the cognitive (beliefs and knowledge the tourists keep of the destination attributes), the affective (the feelings and emotional responses of features a destination have) and the conative one (consumptive behaviours at the destination). Tourists’ holistic impression of a destination creates the overall image of the destination, including tourists’ feelings and also the concrete attributes of the destination. Important role in visiting a place has the congruence between self-image of a tourist and the image of the destination. The closer match between the two images increases the likelihood of pre-visit preferences and post visit intentions (Kastenholz, 2004). The main elements considered by tourists at a destination are natural and scenic resources, cultural resources, night life, sight-seeing, accessibility, security, and quality/price ratio (Ramseook-Munhurrun et al., 2015). According to Perussia (1986) tourists tend to choose first destination they choose to visit or the kind of travel they want to make; and then they seek for the specific accommodation in the area. Hotel attributes are services and facilities that hotels offer and affect tourists in their choice among different options (Lewis, 1983). These features directly affect tourist’ decision making and stand out of other choices offered. Tourists’ perceptions on accommodation attributes can be defined as the degree of importance that tourists attach to a variety of services and facilities in meeting their needs and desires (Wuest, et al., 1996). Tourists when they choose a hotel, they also select location, price of accommodation or value for money, quality of service, cleanliness, security, hotel's physical attractiveness and hotel’s reputation as important attributes (Ananth et al.,1992), (Atkinson, 1988), (LeBlanc et al., 1996),  (Rivers, 1991), (Wilensky, 1988). Leisure tourists often prefer safety and personal contact during vacation. Personal contact and cleanliness are attributes that can easily be answered if experienced while price can easily be presented to any potential visitor through internet. When tourists have positive image deriving from positive travelling experiences then would result in positive evaluation of the destination. More favourable image would result in higher likelihood of revisiting the destination (Chi, Qu, 2008).

H3: Destination image positively influence Attribute satisfaction.

H4: Destination image positively influence Overall satisfaction.

According to Oliver (1980) Satisfaction or pleasure is a condition occurring when basic human needs are satisfied. Satisfaction refers to the variation between prior expectations and perceived performance after consumption. Customer expectations on a product or a service express one’s anticipated performance on that product or service. When performance and perceptions differ dissatisfaction occurs (Fu Chen, Shian Chen, 2010). Judgments of satisfaction are personal and depend upon the comparison of circumstances with what was thought to be appropriate as a standard (Diener et al., 1985).

Satisfaction resulting from tourism experiences contributes significantly to someone’s life satisfaction and well being (Bosque and Martin, 2008). Tourists consider that the connection between local cuisine, shopping opportunities, environment and safety as well as tourists’ attractions are significant dimensions of tourists’ satisfaction. (Arasli & Baradarani, 2014). Overall satisfaction and attribute satisfaction are distinct constructs but closely related (Oliver, 1993). Attribute satisfaction has significant positive and direct effects on overall satisfaction (Chi, Qu, 2008). Destinations having more positive image will more likely affect tourists’ behavioural intentions. Satisfaction with various components of the destination leads to overall satisfaction (Kozak & Rimmington, 2000). Satisfaction in the travelling experiences contributes to destination loyalty (Alexandris et al., 2006).

H5: Overall satisfaction positively influences destination loyalty.

Tourists’ loyalty refers to tourists’ intentions to revisit the destination and recommend it to friends and family (Chen & Tsai, 2007). Findings suggest that destinations’ special characteristics are determinants not only to tourists’ satisfaction and their positive word-of-mouth but also of their revisit intentions. Research on the loyalty field has confirmed significant positive relationships between customer satisfaction and loyalty (Chi & Qu, 2008). If tourists are satisfied with the products and services offered they are more likely to continue to purchase and they will be more willing to advertise the destination.

H6: Attribute satisfaction positively influences destination loyalty.

 

METHODOLOGY

The aim of the study is to seek possible differences in tourists using ICT and possible differences in their satisfaction and in their loyalty to the destination visited. Current research took place at a Greek island which is a popular tourists’ destination. Exploratory factor analysis (EFA) was employed to derive the underline dimensions of destination image and tourists satisfaction. Confirmatory factor analysis (CFA) and Structural equation modelling (SEM) were used to test the conceptual model that examined the antecedents of destination loyalty. Also cluster analysis was applied to classify respondents into groups with similar dimensions. In addition One Way Analysis of Variance (ANOVA) was applied to distinguish the differences among demographic groups and factors influencing satisfaction.

A questionnaire was developed and was distributed to tourists at the destination. Likert scales (1–5), with anchors ranging from “strongly disagree” to “strongly agree” were used for all perception items to ensure statistical variability among survey responses for all items measured. The items of the questionnaire were self-built based on previous literature and content analysis of tourism literature. The self administered survey consisted of two sections: the first section comprised of demographic variables to determine visitors’ demographics such age, marital status, annual income, country of origin, etc. The second section of the questionnaire was designed to determine the vacation preferences of the tourists including travel arrangements, cost of travel, motivation. To determine tourists’ satisfaction the survey was conducted to visitors at the island during June 2016. In order to maintain the technical and conceptual equivalence of instruments, a translation and back-translation strategy was applied. At first the structure and the content of the questionnaire were tested in a pilot study and a factor analysis was performed on the data collected. The results were satisfactory, resulting in six factors. All factors’ Cronbach’s alpha values were well above the commonly accepted threshold value of 0.70. In order to figure the factors that affect tourists’ satisfaction we randomly selected a sample of 150 visitors, of whom 11 did not complete the questionnaire. Raw data were encoded, imported and analyzed using the Microsoft Office Excel and they were analysed using the Statistical Package for Social Sciences (SPSS) version 22.

 

STATISTICAL DATA AND RESULTS

The demographic profiles of the survey respondents’ are presented at Table 1.

Table 1. Profile of Survey Respondents

Variable	%	Variable	%
Gender		Age
Male	46.8	21-30	34.5
Female	53.2	31-45	28.8
		46-65	25.2
Annual Income (in €)		>65	11.5
<20.000	22.3
20.001-50.000	50.4	Education
50.001-80.000	19.4	Elementary	8.6
80.001-120.000	5.0	Senior High sc.	11.5
>120.000	2.9	Vocational  ed.	44.6
		University Grad. or higher	35.2
Marital status
Married	51.8	Country of origin
Not married	37.4	Greece	30.9
Other	10.8	Germany	30.9
		Great Britain	10.1
Ways of booking		Scandinavian c.	17.3
Travel agency	61.6	Cyprus	2.9
Internet	29.7	Australia	2.9
Personal contact	6.5	France	2.2
At the arrival	2.2	USA	1.4
		Italy	1.2
Times Visiting		Travel
First time	63.3	Independent	62
Many times	36.7	Organized	77
Respondents’ decision to travel to the island was based on recommendations (28%), on tourist agents’ suggestions (50%) and on personal internet research (13%).		27% of tourists traveled alone, 60% travel with family and 11.5% traveled with friends.
62% of tourists booked their trip through a travel agent, 30% booked it on internet, 7% preferred personal contact and 2% booked after their arrival at the island.		45% of respondents traveled independently while 55% had their travel organized by a tourists’ agent.

Figure 1-4: Tourists’ profile according to travel preferences

In order to perform a factor analysis the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) was examined, an indication that the variables are able to group to smaller set of underling factors. The Barlett’s Test of Sphericity is an indicator that there are relationships between the variables since its value is significant. Principal Component Analysis and orthogonal Rotation with Varimax method was applied to increase the explanatory ability of the model. Varimax method, attempts to minimize the number of variables that have high loadings on each factor. Each variable should load strongly on only one component, and each component is represented by a number of strongly loading variables (Hair et al., 1998). In order to determine the number of factors extracted, the Kaiser’s criterion was applied, where the eigenvalue of a factor represents the amount of the total variance explained by that factor and eigenvalue should be greater than one. Other criteria examined were scree plot, percentage of variance, item communalities and factor loadings (Hair et al., 2010). Items were eliminated when they had loadings less than 0.4 and also items with loadings higher than 0.4 on more than one factor.

According to the findings the factor loadings of the variables ranged from 0.478 to 0.901 above the suggested threshold of 0.30 for practical and statistical significance (Hair et al., 2010). The Crombach’s alpha for the six factors varied from 0.609 to 0.897 just at the generally agreed upon lower limit of 0.60 for research at exploratory stage (Nunnally & Bernstein, 1994) indicating internal consistency among the variables within each factor. The factor analysis resulted at Kaiser Meyer Olkin Measure of sampling adequacy KMO=0,803. According to Pallant (2006) this measure is acceptable since Pallant gives KMO test equal or greater than 0.60. Also the Barlett’s Test of Sphericity, is statistically significant (χ² =1595.701; p<0.001). Therefore the factor analysis is feasible. The analysis reveals six factors with eigenvalues greater than 1. After elimination of items with low factor loadings and significant cross loadings a clean factor structure emerges explaining a satisfactory 67.432% of total variance. The first factor explains 29.87% of variance, second factor explains 13.8%, the third factor explains 8.67%, the fourth factor explains 5.93%, the fifth factor explains 4.7% and the last factor explains 4.47% of variance.

Determinants (18) are grouped into six factors affecting tourists’ satisfaction (See Table 1). These factors are: Infrastructure, Service quality, Entertainment, Travel environment, Cost of Staying, Safety & Reputation.

Table 2. Results of Factor analysis

Factors	Factor loadings	Eigenvalue	Variance Explained (%)	Crombach’s Alpha
Infrastructure (I)		6.87	29.87	.876
Organized services	.736
Cleanliness of the island	.678
Friendly and helpful local people	.671
Quality of local cuisine	.645
Accommodation value for money	.577
Variety of shops	.607
Service quality (SQ)		3.17	13.8	.859
Accommodation Services	.800
Cleanliness of Accommodation	.752
Accommodation facilities	.709
Accommodation food &             Beverages	.675
Accommodation value for money	.577
Accommodation location	.584
Entertainment (E)		1.99	8.67	.753
Wide variety of entertainment	.786
Nightlife	.775
Travel environment (TE)		1.36	5.93	.580
Good bargain shopping	.721
Pleasant weather	.710
Good value for money	.652
Cost of Staying (CS)		1.08	4.7	.753
General cost	.833
Cost of accommodation	.897
Safety & Reputation (SR)		1.03	4.47	.609
Safety and security	.619
Reputation	.761
Quality of Food	.686

Note: Each item is measured at a five point Likert Scale. Coefficient alphas for all dimensions exceed 0.60. Total scale reliability is 0.837.

 

Structural equation modelling techniques were applied to test the destination loyalty model in which 6 hypothesis was developed based on the literature (Figure 5).

The sample data was checked for Positive Definiteness, where the determinant of the correlation matrix was (4.45E-006) not equal το zero. We checked the multivariate normality by estimating the Mahalanobis’ distance and we exclude five outliers from the sample data and we checked for Multicollinearity. The tolerances of the coefficients were higher than .01 and VIF were less than 10. So the assumption of collinearity of the data is not violated (Kutner et al., 2004).

In the overall model fit, χ² value (χ² =722.384 with 225 degrees of freedom) has a significance level of 0.00. This statistic failed to support that the differences of the predicted and actual models were non-significant. Comparative Fit Index (CFI) and Normed Fit Index (NFI) were 0.656 and 0.575 respectively (Byrne, 1994). These measures are not above the recommended level of 0.90 indicating small support for the proposed model. But when samples are small, the fit is often underestimated (Ullman, 2001). The Root Mean Square Error of Approximation (RMSEA) provides a measure of fit that adjusts for parsimony by assessing the discrepancy per degree of freedom in the model. According to Browne & Cudeck, (1993) RMSEA value should be less than 0.8. The RMSEA value was a marginal 0.12. Also, Chi-square/ degrees of freedom is (CMIN/DF) =3.211 where according to Kline, (1998) and Ullman, (2001) it should be less than 2 or 3 and finally Goodness of fit index is (GFI) = 0.661 and it should exceed 0.90. According to these measures the overall model does not fit well to the original model proposed.

According to findings (Table 3) the hypothesis “H3: Destination image positively influence Attribute satisfaction” is supported. The hypothesis “H4: Destination image positively influence Overall satisfaction” is also supported. And the hypothesis “H5: Overall satisfaction positively influences Destination loyalty” is supported. Finally, the hypothesis “H6: Attribute satisfaction positively influences Destination Loyalty” is not supported.

 

Table 3: AMOS Results for Structural Model

Factors			Loadings
Attribute Satisfaction	<---	Destination image	.742
Overall Satisfaction	<---	Destination image	.768
Overall Satisfaction	<---	Attribute Satisfaction	.171 (ns)
Destination Loyalty	<---	Overall Satisfaction	.1.056
Destination Loyalty	<---	Attribute Satisfaction	.-.079 (ns)

 

Figure 5. Structural Equation Model Overall Satisfaction predictor of Destination Loyalty

In order to identify patterns in respondents relative to their information and communication technologies usage we classified respondents into groups. We performed two step cluster analysis (Punj & Stewart, 1983) based on mean scores for the dimension of information and communication technologies usage. Three clusters were identified with cases not equally distributed across them, differing in respondents’ satisfaction and in their booking preferences. These clusters are:

The 1st cluster, named “satisfied-independent” (N=57, 41.3%) consists of tourists who are very satisfied willing to recommend (mean 4.53) and revisit the destination (mean 4.14), who booked their vacation through internet or personal contact. These tourists prefer to travel independently[1].

The 2nd cluster named “not satisfied” (N=17, 12.3%) consists of tourists who are very unsatisfied and unwilling to recommend (mean 2.47) and also unwilling to revisit the destination (mean 2.00). they booked their vacation through travel agency (76.5%). These tourists prefer to travel organized (59%).

The 3th cluster named “satisfied-organized” (N=64, 46.4%) which is the largest group, consists of tourists who booked their vacation through travel agent, they are very satisfied, willing to revisit (mean 4.20) and willing to recommend the destination to others (mean 4.67). They prefer their vacations to be organized.

In order to reveal the impact on tourists’ destination loyalty while using information and communication technologies we used inferential statistics (ANOVA tests of Statistics). According to these tests:

Intention to revisit is positively correlated to tourists’ perception of satisfaction. Tourists who are satisfied are more willing to revisit the destination. Equal variances assumed and F (2, 135) = 54,580 (p=0.000<0.05) indicates differences among the means of tourists’ intentions, belonging in different clusters, to revisit the destination. As satisfaction increases, increase their intention to revisit. Tourists that belong to the cluster of “satisfied independent” have different intentions (mean 4.1404) to revisit the destination than tourists belonging to the cluster of “not satisfied” (mean 2.000). Also tourists that belong to the cluster “satisfied-organized” (mean 4.2031), have different intentions (mean 4.2031) to revisit the destination than tourists belonging to the cluster of “not satisfied” (mean 2.000). Tourists that prefer to take vacations organized have higher intentions to revisit the destination among other tourists.

Intention to recommend the destination is positively correlated to tourists’ satisfaction. Tourists that are satisfied are more willing to recommend the destination to others. Equal variances assumed F (2, 135) = 58,806 (p=0.000<0.05) indicates differences among the means of tourists’ intentions, belonging in different clusters, to recommend the destination to others. As satisfaction increases, so increase the intention of tourists to recommend the destination. Tourists that belong to the cluster of “satisfied independent” have different intentions (mean 4.5263) to revisit the destination than tourists belonging to the cluster of “not satisfied” (mean 2.4706). Also tourists that belong to the cluster of “satisfied-organized” have different intentions (4.6719), to revisit the destination than tourists belonging to the cluster of “not satisfied” (mean 2.4706). Tourists that prefer to take vacations organized have higher intentions to recommend the destination among other tourists.

So the hypothesis “H1: Tourists’ preferences on travelling organized or independent affect their destination loyalty” is supported.

The booking preferences are correlated to tourists’ satisfaction. Equal variances assumed F (2, 135) = 70.646 (p=0.000<0.05) indicates differences among the means of groups of tourists with different booking preferences. Tourists that belong to the group of “satisfied independent” have different booking preferences (mean 2.9) than tourists belonging to the group of “satisfied-organized” (mean 1.0), comparing to the group of “not satisfied” tourists (mean 1.35). Tourists that prefer to take vacations that are not organized have higher intentions to recommend the destination among all tourists[2]. So the hypothesis “H2: tourists’ booking preferences affect their tourists’ satisfaction” is supported.

 

CONCLUSIONS

The aim of the study is to identify factors influencing tourists’ satisfaction and destination loyalty according to their preferences on using information and communication technologies.

The SEM analysis offered support to the statistically significant relationships between destination image and overall satisfaction (H4), destination image and attribute satisfaction (H3), overall satisfaction and destination loyalty (H5). Also there are three clusters of tourists that emerged from cluster analysis, the cluster of “satisfied independent”, the cluster of “satisfied-organized” and the cluster of “not satisfied” tourists. These three clusters differ in their perceptions of recommending the tourists’ destination or differ in their intentions to revisit the destination. ANOVA analysis offered support to the statistically significant relationships between clusters and to different preferences of tourists, as to travel organized or independent (H1) or to their booking preferences (H2).

Organized infrastructures of the destination together with a decent travel environment where someone feels secured, enjoying good quality of services and having fun without paying too much are the factors that lead to tourists’ satisfaction.

Since destination image has positive effect on tourists’ satisfaction it is essential to understand factors influencing tourists’ loyalty to a destination. That could provide tourism and hospitality managers and marketers with useful tools in creating successful marketing strategies which will lead to positive post purchase tourists’ behaviours. Results indicate that tourists that are not satisfied are less willing to recommend the destination to others or revisit. The group of “not satisfied” tourists in the cluster analysis consists mainly of tourists that have made travel arrangements through a travel agent. Managers and authorities should seek the sources of this dissatisfaction and make efforts to come up to tourists’ expectations. Since attribute satisfaction and overall satisfaction are influencing destination loyalty, attention should be paid to these features that increase tourists satisfaction so as to ensure their destination loyalty.

Future research should check the possibility that tourists may move to multiple clusters.

 

 

REFERENCES

Ananth, M. DeMicco, F.J., Moreo, P.J., Howey R.M., (1992), ‘Marketplace lodging needs of mature travelers’. The Cornell Hotel and Restaurant Administration Quarterly, 33(4), pp. 12-24,

Atkinson, A., (1988), ‘Answering the eternal question: What does the customer want?’ The Cornell Hotel and Restaurant Administration Quarterly, 29(2), pp. 12-14,

Chen C.-F., Tsai D., (2007), ‘How destination image and evaluative factors affect behavioral intentions?’ Tourism management, 28, pp. 1115-1122.

Chi C.G.-Q., Qu H., (2008), ‘Examining the structural relationships of destination image, tourist’ satisfaction and destination loyalty: An integrated approach’. Tourism management, 29, pp. 624-636.

Crnojevac I., Gugić J., Karlovčan S., (2010), ‘E-Tourism: A comparison of Online and Offline Bookings and the Importance of Hotel Attributes’, JIOS, 34(1), pp. 41-54.

Dibb, S; Simkin, L., (1991), ‘Targeting segments and positioning’. International Journal of Retail and Distribution Management, 19(3), pp. 4–10.

Hair J.F Jr., Black W., Babin B., Anderson R., 2010. Multivariate Data analysis (7th ed.) Prentice Hall College.

Hosany S., Prayag G., (2013), ‘Patterns of tourists’ emotional responses, satisfaction and intention to recommend’. Journal of Business research, 66, pp. 730-737.

Kozak M., Rimmington M., (2000), ‘Tourist satisfaction with Majorca Spain, as an off-season holiday destination’. Journal of travel research, 38(1), pp. 260-269.

LeBlanc, G., Nguyen, N., (1996), ‘An examination of the factors that signal hotel image to travelers’. Journal of Vacation Marketing, 3(1), pp. 32-42,

Lewis, R. C., (1983), ‘Getting the most from marketing research’ [Part I]. The Cornell Hotel and Restaurant Administration Quarterly, 24(3), pp.25-35.

Punj G., Stewart D.W., (1983), ‘Cluster analysis in marketing research: a review and suggestions for applications’. Journal of marketing research, 20(2), pp. 134-148.

Ramseook-Munhurrun P., Seebaluck V.N., Naidoo P., (2015), ‘Examining the structural relationships of destination image, perceived value, tourists’ satisfaction and loyalty the case of Mauritius’, Procedia- Social and Behavioral Sciences, 175, pp. 252-259.

Rivers, M.J., Toh, R.S., Alaoui, M., (1991), ‘Frequent-stayer programs: The demographic, behavioral, and attitudinal characteristics of hotel steady sleepers’. Journal of Travel Research, 30(2), pp. 41-45,

Werthner, H., Klein, S., (1999), Information technology and tourism: A challenging relationship. Springer, Vienna

Wilensky, L; Buttle, F., (1988), ‘A multivariate analysis of hotel benefit bundles and choice trade-offs’. International Journal of Hospitality Management, 7(1), pp. 29-41,

Wuest, B.E.S., Tas, R.F., Emenheiser , D.A., (1996), ‘What do mature travelers perceive as important hotel/motel customer service?’ Hospitality Research Journal, 20(2), pp.77-93.

Zhang H., Fu X., Cai L., Lu L., (2014), ‘Destination image and tourists’ loyalty: a meta-analysis’. Tourism Management, 40, pp. 213-223.

SETE http://sete.gr/en  date accessed: 17/5/2017.

Hellenic Statistical Authority (ELSTAT): http://www.gnto.gov.gr/sites/default/files/files_basic_pages/ELSTAT2015.pdf  date accessed: 17/5/2017