The role of cultural differences on customer satisfaction: Analysis of online reviews in tourism sector

The use of internet with developing technology has become one of the most frequently used resources in accessing information and has begun to facilitate the purchasing process of consumers. Potential customers, particularly in the sectors that include experiential products and services, such as the tourism sector, resort to persuasive and explanatory information sources to avoid risk in the procurement decisions process. While these sources are known in the past as family, friends and neighborhoods, they are now online platforms such as internet sites, forums, blogs and social media and the online reviews with the star ratings on these platforms. Consumer reviews on online platforms can affect potential customers either positively or negatively. Tourism sector policiy makers need to have a good reputation and get the satisfaction of their customers in order to win potential customers. As a result of the online evaluations of the customers who are satisfied with the service they receive, the reputation of the enterprises increases and the potential customers are positively affected. Hotels with customers from many different countries need to go beyond the stereotypes of its services. Hotel businesses that make a difference in their services by considering cultural differences can satisfy their customers from any culture and have a good reputation for potential customers. In this context, the main aim of the study was to find the effect of cultural differences on the perception of satisfaction. In this study, online reviews of 5 different cultures belonging to a hotel in Antalya were analyzed. As a result, the effects of cultural differences on satisfaction perception in tourism sector have been revealed. According to the study purpose, it is considered as quantitative research. In terms of the time it covers, it is an instant study and reveals the current situation. Accordingly, the study was carried out by content analysis with text mining method.