Implementation of Bayesian decision-making with Markov chain Monte Carlo on halal tourism governance

Abstract

Purpose This study develops a Bayesian decision-making model employing Markov chain Monte Carlo (MCMC) methods to enhance halal tourism governance in Pekalongan City, Indonesia. The model addresses the complexity of decision-making processes and supports data-driven policy formulation by local authorities. Design/methodology/approach This research uses both quantitative and qualitative data, including tourist preferences, economic conditions and regulatory frameworks. A Bayesian approach incorporates prior knowledge and stakeholder input, while MCMC computes posterior distributions of model parameters, ensuring a comprehensive analysis of governance challenges. Findings Combining Bayesian decision-making with MCMC improves halal tourism governance by providing insights into policy outcomes and risks, helping align strategies with religious and cultural needs, though satisfaction and cultural aspects need further study. Research limitations/implications This study’s limitations include small sample size, focus on one region, and limited exploration of external factors, such as political influences, which may affect the generalizability of findings. Practical implications The developed model provides policymakers with practical tools to make informed decisions by considering uncertainties and stakeholder perspectives. It contributes to improving governance strategies in the halal tourism sector, ensuring alignment with religious and cultural expectations. Social implications This study contributes to the social sustainability of halal tourism by promoting inclusive and culturally sensitive governance frameworks that cater to diverse Muslim travelers. By integrating Bayesian decision-making, policymakers can design adaptive tourism policies that balance economic growth, environmental responsibility and cultural preservation, ensuring that local communities benefit from sustainable tourism practices. Additionally, improving halal certification standardization and enhancing infrastructure for Muslim-friendly tourism fosters greater social acceptance and integration between local and international visitors, strengthening cross-cultural interactions and economic opportunities for local businesses. Originality/value This research is one of the first to apply Bayesian decision-making and MCMC methods in halal tourism governance, offering a novel, adaptive framework for local governments and stakeholders to optimize policy decisions.