IMPACT OF CHATBOT USER’S DRIVING POWER AND CHATBOT DESIGN ON CUSTOMER EXPERIENCE: THE CASE OF TOURISM SERVICE BUSINESSES IN BANGKOK METROPOLITAN AREA

แรงผลักดันของผู้ใช้บริการแชทบอทและการออกแบบแชทบอทต่อประสบการณ์การใช้บริการแชทบอทของลูกค้า: กรณีธุรกิจบริการท่องเที่ยวในเขตกรุงเทพมหานครและปริมณฑล

Abstract

This study aims to investigate the factors influencing chatbot users' driving power and the design of chatbots that impact customer experience, as well as the moderating effect of interaction quality between these elements. A sample of 400 customers with experience using chatbot services in tourism businesses within the Bangkok metropolitan area was surveyed using a questionnaire as the data collection tool. The data were analyzed using descriptive and inferential statistics, including mediation analysis and multiple regression analysis. The study found that, within the overall tourism service industry, the design factors of chatbots positively influence the quality of interactions and customer experience, with a statistically significant p-value of less than 0.001. The regression coefficients for chatbot design affecting interaction quality and customer experience were 0.670 and 0.588, respectively. Furthermore, the quality of interactions—specifically, attitudes towards chatbots and their problem-solving capabilities—also positively impacts customer experience, again with a statistically significant p-value of less than 0.001. The regression coefficients for attitudes towards chatbots and their problem-solving capabilities were 0.326 and 0.193, respectively. In the hotel and accommodation sector, it was determined that the user’s driving forces behind chatbot usage and design positively affect interaction quality, with a statistically significant p-value of less than 0.001. The regression coefficients for these user’s driving forces were 0.036 and 0.731, respectively. Additionally, the design factors of chatbots positively influence customer experience, with a regression coefficient of 0.641 and a statistically significant p-value of less than 0.001. Moreover, the quality of interaction—considering attitudes towards chatbots, the expertise of chatbot interactions, and their problem-solving capabilities—also positively impacts customer experience, with a statistically significant p-value of less than 0.001. The regression coefficients for attitudes towards chatbots, expertise in chatbot interactions, and problem-solving capabilities were 0.309, 0.067, and 0.213, respectively. Finally, in the context of other types of tourism services, the design factors of chatbots were found to positively affect both the quality of interaction and customer experience, with a statistically significant p-value of less than 0.004. The regression coefficients for chatbot design affecting interaction quality and customer experience were 0.583 and 0.555, respectively. Notably, only the quality of interaction in terms of attitudes towards chatbots significantly and positively affects customer experience, with a p-value of less than 0.001 and a regression coefficient of 0.334. In the analysis of mediating variables, it was determined that the quality of interaction serves as a mediating variable between chatbot design and customer experience, with a statistically significant p-value of 0.05 across all business groups. The findings of this research underscore the importance of both chatbot design and the quality of interaction in shaping attitudes towards chatbots, which in turn influence customer experience in services provided by chatbots within the tourism sector. Additionally, hotel and accommodation businesses should prioritize understanding the motivations of chatbot users, alongside focusing on chatbot design and the quality of interaction, to enhance attitudes towards chatbots.