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<p>This study investigates tourism demand behaviour in Thailand during and after the pandemic by applying a set of advanced quantitative modelling techniques designed to address decision-making under uncertainty. Specifically, the analysis integrates quantum walk distribution analysis, Bayesian inference, and Bayesian logistic regression to model how destination attractiveness and gastronomy-related attributes influence domestic tourism demand within a sustainability-oriented framework. The empirical analysis is based on 526 online survey responses collected during the pandemic recovery phase, capturing heterogeneous perceptions and mixed information environments. Model comparison and validation using the Deviance Information Criterion (DIC) indicate that specifications incorporating both destination-based characteristics and gastronomy-related perceptions provide superior explanatory and predictive performance. The results demonstrate that latent attitudinal factors, particularly those associated with sustainable practices, culturally embedded food experiences, and perceived responsibility in destination management, play a statistically significant role in shaping revisiting intentions and demand resilience. Beyond its empirical findings, the study contributes methodologically by illustrating how probabilistic and uncertainty-sensitive models can be combined to improve the measurement and interpretation of complex social behaviours. The proposed framework offers a transferable approach for analysing tourism demand and other social phenomena characterised by perceptual ambiguity, supporting more rigorous indicator construction and empirical inference in applied social science research.</p> Graphical abstract <p></p>

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A quantum-walk and Bayesian logistic approach to perceptual uncertainty: modelling domestic tourism demand during and after the COVID-19 pandemic in Thailand

  • 期刊论文

  • 发表日期：

    2026年4月2日

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  • Chukiat Chaiboonsri,
  • Nattapan Kongbuamai,
  • Warattaya Chinnakum,
  • Banjaponn Thongkaw,
  • Soumya Chatter

摘要

This study investigates tourism demand behaviour in Thailand during and after the pandemic by applying a set of advanced quantitative modelling techniques designed to address decision-making under uncertainty. Specifically, the analysis integrates quantum walk distribution analysis, Bayesian inference, and Bayesian logistic regression to model how destination attractiveness and gastronomy-related attributes influence domestic tourism demand within a sustainability-oriented framework. The empirical analysis is based on 526 online survey responses collected during the pandemic recovery phase, capturing heterogeneous perceptions and mixed information environments. Model comparison and validation using the Deviance Information Criterion (DIC) indicate that specifications incorporating both destination-based characteristics and gastronomy-related perceptions provide superior explanatory and predictive performance. The results demonstrate that latent attitudinal factors, particularly those associated with sustainable practices, culturally embedded food experiences, and perceived responsibility in destination management, play a statistically significant role in shaping revisiting intentions and demand resilience. Beyond its empirical findings, the study contributes methodologically by illustrating how probabilistic and uncertainty-sensitive models can be combined to improve the measurement and interpretation of complex social behaviours. The proposed framework offers a transferable approach for analysing tourism demand and other social phenomena characterised by perceptual ambiguity, supporting more rigorous indicator construction and empirical inference in applied social science research.

Graphical abstract