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The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.

The emergence of virtual reality (VR) has the potential to revolutionize various industries, including tourism, as it delivers a simulated environment that closely emulates real-life experiences. Therefore, this study aims to explore how the factors, i.e. enjoyment, emotional involvement, flow state, perceived privacy risk, physical risk and cost, influence the customers’ intention to use VR for tourism.

This study integrates the technology acceptance model, hedonic consumption theory with other factors, including cognitive response, authenticity, perceived privacy risk, perceived physical risk, perceived cost and perceived presence. Partial least squares structural equation modelling approach was used to test the proposed research model.

The finding based on the sample of 252 respondents revealed that authenticity is the most influential factor impacting behavior intention followed by perceived cost, attitude, cognitive response and enjoyment. Also, the study supported the moderating impact of personal innovativeness between attitude and behavioral intention to use VR for tourism.

The findings of the study offers practical implications for service providers, site managers, destination marketers, tourist organizations and policymaker to develop more effective strategies for offering VR services for tourism.

This study enriches the current understanding of VR adoption in context of tourism with empirical evidences.

虚拟现实的出现有可能彻底改变包括旅游在内的多个行业, 因为它提供了一个模拟环境, 密切模拟真实生活体验。因此, 本研究旨在探讨愉悦、情感投入、流体状态、感知隐私风险、身体风险和成本等因素如何影响顾客使用虚拟现实进行旅游的意愿。

本研究将TAM模型、享乐消费理论与其他因素相结合, 包括认知反应、真实性、感知隐私风险、感知身体风险、感知成本和感知存在。 PLS SEM 方法用于测试所提出的研究模型。

基于 252 名受访者样本的研究结果表明, 真实性是影响行为意图的最大因素, 其次是感知成本、态度、认知反应和享受。此外, 该研究还支持个人创新性对使用虚拟现实进行旅游的态度和行为意图之间的调节影响。

该研究的结果为服务提供商、站点管理者、目的地营销人员、旅游组织和政策制定者制定更有效的策略来为旅游业提供 VR 服务提供了实际意义。

本研究通过实证证据丰富了当前对旅游背景下虚拟现实采用的理解。

La aparición de la realidad virtual tiene el potencial de revolucionar diversas industrias, incluyendo el turismo, ya que proporciona un entorno simulado que emula de cerca las experiencias de la vida real. Por lo tanto, este estudio tiene como objetivo explorar cómo los factores, como la diversión, la participación emocional, el estado de flujo, el riesgo percibido de privacidad, el riesgo físico y el costo, influyen en la intención de los clientes de utilizar la realidad virtual para el turismo.

Este estudiointegra el modelo TAM, la teoría del consumohedónico con otrosfactores que incluyen la respuestacognitiva, la autenticidad, el riesgo de privacidadpercibido, el riesgofísicopercibido, el costopercibido y la presenciapercibida. Se utilizó el enfoque PLS SEM para probar el modelo de investigaciónpropuesto.

El hallazgobasadoen la muestra de 252 encuestadosreveló que la autenticidades el factor másinfluyente que afecta la intención de comportamiento, seguido del costopercibido, la actitud, la respuestacognitiva y el disfrute. Además, el estudioapoyó el impactomoderador de la innovación personal entre la actitud y la intención de comportamiento al utilizar la realidad virtual para el turismo.

Los hallazgos del estudioofrecenimplicacionesprácticas para que losproveedores de servicios, administradores de sitios, comercializadores de destinos, organizacionesturísticas y formuladores de políticasdesarrollenestrategiasmásefectivas para ofrecerservicios de realidad virtual para el turismo.

Este estudioenriquece la comprensión actual de la adopción de la realidad virtual en el contexto del turismo con evidenciasempíricas.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.