AI-Driven Decision Support Systems for Tour Operators

Abstract

Purpose: Industry 4.0 technologies are now causing a paradigm shift in the tourism industry. This article empirically examines how Artificial Intelligence-driven Decision Support Systems (AI-DSS) would foster the
performance of tour operators. Particularly, it studies the effect of the AI potential in demand forecasting, personalization, dynamic pricing, resource allocation, and risk management on the business performance. Design/Methodology/Approach: The quantitative research design was used. The questionnaire comprised a structured questionnaire data were collected using the structured questionnaire on 142 registered tour operators and travel agencies in Jaipur, India. Multiple regression analysis was used to test the conceptual model with the assistance of the Exploratory Factor Analysis (EFA) and reliability tests (Cronbachs Alpha)
in the SPSS. Findings: The findings show that, there is a large positive correlation between AI-DSS adoption and operational performance (R2 =
0.768). As the most important predictors, AIDriven Personalization and Dynamic Pricing Efficiency were the most important elements of operational success, which were then succeeded by Demand Forecasting Accuracy. Interestingly, although the Risk Management was important, it also had a poorer correlation with revenue generating factors. Originality/Value: Although the literature on the subject is concentrated around the concept of AI in a hotel or general tourism marketing environment, the present study is a gap-bridging study since it addresses the segment of the Indian heritage tourism destination (Jaipur) specifically, presenting a proven framework of introducing AI decision-making instruments to its workflow.