Previous research findings have indicated that service quality is a key factor affecting tourist satisfactions and future consumption behaviors in the Travel and Hospitality industry, However, to date no instrument with acceptable measurement properti...
Previous research findings have indicated that service quality is a key factor affecting tourist satisfactions and future consumption behaviors in the Travel and Hospitality industry, However, to date no instrument with acceptable measurement properties to assess service quality of cultural and heritage destinations has been found in the literature. This study was designed to develop the service quality assessment scale to evaluate tourist perception and satisfaction toward service in heritage sites. The scale development process was carried out in four stages: (1)the formula6on of the preliminary scale, (2)a pilot study, (3)the initial test administration and exploratory factor analysis, and (4)confirmatory factor analysis.
In step one, a preliminary scale with 44 items was generated. A pilot study was then carried out by administrating the scale to tourist (N-132) of Pulkuk-sa temple. Using exploratory factor analysis, the scale was revised to contain 34 items. The revised scale was administrated to tourists of Pulkuk-sa and Haein-sa temples. A total of 987 questionnaires were completed and used for data analysis. Exploratory factor analysis(EEA) was conducted on sample using alpha extraction and promax rotation methods. Six factors emerged with a total variance of 65.48% The alpha reliability coefficients for the six factors (i.e., facilities, interpretation, attractiveness, responsiveness, education, consumables) were .9196, .8462, .7913, .8183, .8524, and .7847, respectively.
In next step, confirmatory factor analysis (CFA) was conducted. It was suggested from the CFA that the scale made up of six factors was proposed. Using Windows LISREL 8.50 computer program, the six-factor model (34 items) was analyzed based on the NT estimation method. The chi-square statistics of the model was significant (i.e.,x²=909.648, p<.01). The goodness-of-fit indexes of the model were within tolerable ranges (e.g., GFI-.856, CFI-.924). In order to improve the model, items with the lowest lambda values were eliminated.
Eventually, a six-factor model with 28 items was generated : facilities(6 items), interpretation (6 items), education (4 items), responsiveness (4 items), consumables (4 items), attractiveness (4 items). The fit indexes suggested that the final version of the model provided an adequate fit to the data (e.g., SRMR-.043, GEl-.911, CF1-.974, and ECVl-1,792).
The model was found to have a significantly (p<.05) better fit than other six nested models. This further suggested that the model was robust and had sound psychometric property.