The Role of ChatGPT in Enhancing Tourist Decision-Making: A Study Based on Accessibility–Diagnosticity Theory: This study examines the influence of ChatGPT on tourist decision-making, focusing on the accessibility and diagnostic quality of its travel recommendations. By leveraging the Accessibility–Diagnosticity Theory (ADT), the analysis highlights ChatGPT’s ability to generate personalized, context-specific content for tourists. The findings reveal ChatGPT’s multidimensional advisory approach, centering on themes like ‘Tailored Engagement and Accessibility,’ ‘Diagnosticity of Information,’ and ‘Contextual Criteria Prioritization.’ These aspects underscore ChatGPT’s potential to significantly enhance travel planning by offering relevant, user-focused guidance tailored to individual needs.
The study further contributes to the theoretical understanding of AI in tourism by illustrating how ChatGPT’s content aligns with ADT, thereby supporting informed decision-making. The researchers developed a model to depict ChatGPT’s advisory dynamics, showing how it integrates personalization, diagnostic relevance, and contextual adaptation. This model is a blueprint for the tourism industry to harness AI to improve travel experience. Ultimately, the study addresses gaps in the literature on AI’s practical and theoretical applications, especially in enhancing decision-making processes within the tourism sector.
ChatGPT and AI in Tourism: Enhancing Tourist Decision-Making through ADT: Post-COVID-19, the tourism sector has adopted AI technologies to remain competitive, utilizing tools like predictive analytics and chatbots for personalized recommendations. ChatGPT, with its advanced language capabilities, has emerged as a powerful tool, enhancing customer experiences through tailored travel insights. By providing accessible and diagnostically relevant information, ChatGPT helps tourists make informed decisions. The ADT framework, focusing on ease of access and information usefulness, particularly applies to understanding how ChatGPT enhances the decision-making process in the complex, data-rich environment of tourism planning.
Methods:
For this study, ChatGPT-3.5 was used to explore its role in tourist decision-making during the pre-trip phase. Data was collected by prompting ChatGPT with queries on choosing ideal travel destinations across 30 contexts, such as wine or safari tourism. A thematic analysis of the 18,016-word responses was conducted to identify patterns in decision-making criteria. Both inductive and deductive approaches were used, integrating accessibility and relevance of information based on theoretical models. Content analysis was performed to examine the frequency and order of criteria in ChatGPT’s outputs, revealing an implicit prioritization of information delivery.
Key Findings on ChatGPT’s Travel Advisory Process:
The study revealed a consistent structure in ChatGPT’s travel advice, which begins with a personalized introduction and ends with tailored conclusions. Three major themes emerged from the analysis: “Tailored Engagement and Accessibility,†“Diagnosticity of Information,†and “Contextual Variation in Criteria Prioritization.†ChatGPT customizes its recommendations based on user preferences, prioritizing relevant decision-making criteria. These themes intersect to enhance the relevance and accessibility of the information provided, ultimately creating a comprehensive, user-centered advisory process that adapts dynamically to different travel contexts.
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Conclusions:
This study explores the role of ChatGPT in tourist decision-making, addressing the need for research on AI’s impact in this field. Through the lens of ADT, the research reveals how ChatGPT provides personalized, contextually adaptive advice by balancing user preferences, information utility, and criteria prioritization. A new model, termed “Holistic User-Centric Guidance,†highlights how tailored recommendations enhance relevance and decision-making. The study’s findings offer both theoretical and practical contributions, providing tourism practitioners with strategies for integrating AI-driven personalization while encouraging researchers to further explore AI’s role in tourism decisions.
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