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Department of Management (Tourism and Hospitality), Bharath Institute of Science and Technology, Chennai, Tamil Nadu, India
Associate Professor & Head, Department of Tourism and Hospitality, Bharath Institute of Science and Technology, Chennai, Tamil Nadu, India
In recent times, tourism organizations have been requiring personalized promotional plans based on the preferences, behavior, and psychology of tourists. Traditional methods of promotion often target larger customer segments, which may not always suit individual tourism contexts. In such scenarios, this study will try to bridge the gap by considering the aspects of psychology that are relevant to tourism promotions and coming up with an AI-assisted tourism promotional decision-making framework. The study made use of a quantitative cross-sectional research design with the use of a questionnaire distributed among 183 respondents who had involvement with tourism or travel activities at selected places in India. Four psychological aspects – Celebrating Success (CS), Feeling Loneliness (FL), Extreme Happiness (EH), and Feeling Badly or Sadly (FBS) were considered together with other demographic and behavioral characteristics. Both frequency and percentage analyses were used in profiling respondents' purpose, preferences, and behavior. Respondents were made up of 50.8% males and 49.2% females. Activities such as culture, sport, religion, and recreation accounted for the highest proportion of businesses (29.5%). Leisure was the most popular travel purpose (35.5%), followed by shopping/purchase related travel (24.6%). Respondents preferred traveling during April (20.2%). Occasional travel was the most common travel behavior (44.8%). Semi-packages were the most favored form of travel (31.1%), followed by packages (30.1%). Personal vehicle was the preferred mode of transport (15.8%), followed by intermittent halt travel (13.1%) and train travel (12.6%). The results show that there is a lot of diversity in terms of tourism preferences and travel behaviors, and therefore there should be more context-specific promotion efforts. The proposed framework makes use of psychological feature representation, promotion response prediction, tourist segmentation, recommendation, and feedback-based learning. As a conceptual framework, the AI aspects of the model need to be empirically trained and validated through future research.
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