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This study aims to investigate the different effects of physical and social servicescapes on brand love for internet-famous restaurants, focusing on their pathways and strengths of influence.

Structural equation modeling was applied to analyze data from 387 online questionnaires in China.

Results indicate that social servicescape directly influences brand love for internet-famous restaurants, while physical servicescape does not. The effect of physical servicescape on brand love for internet-famous restaurants is mediated by perceived coolness and perceived enjoyment, whereas social servicescape’s influence is mediated solely by perceived enjoyment. Overall, physical servicescape has a stronger impact on brand love for internet-famous restaurants compared to social servicescape.

The findings help internet-famous restaurants create effective physical and social servicescapes to enhance brand love, underscoring that physical servicescape is more crucial than social servicescape for cultivating this love.

This study contributes to the literature by analyzing the heterogeneous pathways and strengths of physical and social servicescapes influencing brand love for internet-famous restaurants, while highlighting the mediating role of perceived coolness and expanding the application scope of cognitive appraisal theory.

Although artificial intelligence (AI) is an essential component of hospitality in the technological empowerment era, AI’s effectiveness as an attraction in this context remains unclear. Grounded in Herzberg’s motivation theory and complexity theory, this study aims to explore configurational paths whereby combinations of qualities lead to success for different types of AI-themed hotels.

This study innovatively blends topic modeling and fuzzy-set qualitative comparative analysis (fsQCA) to investigate configurational paths whereby combined qualities produce positive guest evaluations of 12 AI-themed hotels as evidenced by 7,431 customer reviews.

The results indicate that AI could serve as a “theme” to attract customers under certain circumstances. First, “attractive” and “must-be” qualities are first identified for different types of AI-themed hotels. Furthermore, 6, 15 and 15 configurational paths inspiring favorable guest evaluations of luxury-independent, budget-independent and chain AI-themed hotels, respectively. Technology-related qualities are found to be especially attractive for luxury-independent AI-themed hotels, whereas the role of technology is minimal for budget AI-themed hotels. The impact of technology is salient for chain AI-themed hotels when combined with other factors. In addition, the effect of price differs among the configurational paths for the three hotel types.

This study expands the understanding of AI applications within the hospitality context by exploring the role of AI in AI-themed hotels and comparing its effectiveness in attracting customers across various hotel types. It also provides operational strategies for adopting AI for different types of hotels and for other hospitality and tourism sectors.

This study represents an early attempt to integrate topic modeling and fsQCA to clarify customers’ perceptions of AI-themed hotels and the combined impacts of various qualities. The findings expand on Kano’s model by classifying technology-related qualities into attractive qualities within AI-themed hotels.

With an increasing array of new technologies and devices emerging as novel approaches to museum narratives, scholars are intrigued by the narrative effect they produce. Within this context, this study aims to conduct a quantitative case study on the influencing mechanisms among technology embodiment, technology attachment, museum narrative effect and perceived museum image based on embodied cognitive theory (ECT).

By introducing the Opium War Museum, Dongguan City, Guangdong Province, China, as an empirical case study, the authors collected data, through questionnaires, from 425 visitors who had experienced virtual reality (VR) technology in the museum. The data was then analyzed by using the maximum likelihood estimate for structural equation modeling.

Technology embodiment and technology attachment have significant positive correlations with the museum narrative effect. Technology attachment provides a partial mediating effect between technology embodiment and the museum narrative effect. The perceived museum image moderates such mediating effects of technology attachment.

This study brings the emerging idea of developing visitor experiences with VR through expanding the applications of ECT in the museum scenario. First, the authors identified the underlying interaction patterns between various factors that influence the museum narrative effect. Second, the authors verified that technology can optimize the museum narrative effect and help the creative transformation of cultural relics and culture. This study also provides practical recommendations to improve the technological service experience and strengthen the “audience-centric” management concept of museums

随着越来越多的新技术和设备作为博物馆新颖的叙事方法出现, 学者们对它们产生的叙事效果感到兴趣。在这一背景下, 我们基于具身认知理论对技术具身感、技术依恋、博物馆叙事效果和感知博物馆形象之间的影响机制进行了定量案例研究。

通过引入中国广东省东莞市鸦片战争博物馆作为实证案例研究, 我们从体验了博物馆VR技术的425名参观者中收集了问卷数据。然后, 我们使用极大似然法估计结构方程模型对数据进行了分析。

(1) 技术具身感和技术依恋与博物馆叙事效果存在显著正相关关系。 (2) 技术依恋在技术具身感与博物馆叙事效果之间起到了部分中介作用。 (3) 感知博物馆形象调节了技术依恋的上述中介效应。

本研究通过扩展具身认知理论在博物馆场景中的应用, 提出了利用VR优化访客体验的新兴理念。首先, 我们识别了影响博物馆叙事效果的各种因素之间的潜在互动模式。其次, 我们验证了技术可以优化博物馆叙事效果, 并有利于文物和文化的创造性转化。本研究还提出了实际建议, 以改善技术服务体验, 并加强博物馆“以观众为中心”的管理理念。

The study aims to provide a comprehensive understanding of the existing literature on women’s leadership in academia by identifying the existing challenges for their underrepresentation, and proposing a new-age leadership interventions to address the inherent systemic biases and develop foster an equitable academic climate.

The study employed bibliometric analysis to map the literature by investigating publication and geographical trends. Techniques like citation, co-citation, bibliographic coupling and co-word analysis identified seminal research and emerging themes, providing insights into research developments and facilitating identification of avenues for future research.

Our study highlights how social, organizational and individual barriers disadvantage women academic leaders. Existing enablers for women in leadership, like mentorship, leadership development and family friendly policies, focus on bringing change within the prevailing academic culture, reinforcing the notion “women need support”, overlooking the influence of systemic barriers. Such interventions are often ineffective in bringing sustainable change. We propose integrating AI/machine learning (ML) technologies in leadership selection to reduce bias arising from subjectivity.

This study contributes to the discourse on gender inequality in academic leadership by offering a robust understanding of the research topic and informing avenues for future research.

Policymakers and higher education institutions can use the findings of the study to aid the formulation of policies, initiatives and institutional procedures to mitigate the prevalent gender bias in academia and cultivate an inclusive culture for growth of women.

The paper analyses women’s under-representation as academic leaders and proposes a novel data-driven intervention using gamification, AI and ML, aiming to reshape gender dynamics in academic leadership.

Mobile edge computing (MEC) services have long been used by private enterprises in Saudi Arabia with considerable success; however, there has been a stark lack of insight into how these services can be used to improve mobile government (M-Government) services for KSA citizens. This study aims to bridge this gap by integrating MEC with an enhanced version of the technology acceptance model (TAM) and examining its effects on user behavior and acceptance.

A closed-ended survey was administered to 1,500 people, and the responses were analyzed using sophisticated advanced statistical techniques to test an expanded TAM, using a quantitative method that uses structural equation modeling to validate the proposed model and hypotheses.

This study reveals that MEC significantly influences users’ intentions about using M-Government services and their tolerance for new technology adoption. Specifically, service cost and social influence are positively linked with end users’ intention to adopt M-Government services.

The novelty and contribution of this paper to existing literature are in highlighting the pivotal role of MEC in transforming public sector service delivery through technology. This study not only supports the adoption of M-Government services to enhance social welfare but also demonstrates and concludes some practical and theoretical ramifications of MEC service adoption.

This study aims to explore the dynamic interplay of key resources (i.e. research and development (R&D), advertising and exports) in affecting the performance of USA manufacturing firms. Specifically, the authors examine the dynamic impact of joint resources and predict differential effect scales contingent on firm capabilities.

This study presents a combined multiple regression analysis (MRA)-multilayer perceptron (MLP) neural network modeling and investigates the complex interlinkage of capabilities, resources and performance. As an innovative approach, the MRA-MLP model investigates the effect of capabilities under the combinatory deployment of joint resources.

This study finds that the impact of joint resources and synergistic rents is not uniform but rather distinctive according to the combinatory conditions and that the pattern is further shaped by firm capabilities. Accordingly, besides signifying the contingent aspect of capabilities across a range of resource combinations, the result also shows that managerial sophistication in adaptive resource control is more than a managerial ethos.

The proposed analytic process provides scientific decision support tools with control mechanisms with respect to deploying multiple resources and setting actionable goals, thereby presenting pragmatic benchmarking options to industry managers.

Using the theoretical underpinnings of the resource-based view (RBV) and resource orchestration, this study advances knowledge about the complex interaction of key resources by presenting a salient analytic process. The empirical design, which portrays holistic interaction patterns, adds to the uniqueness of this study of the complex interlinkages between capabilities, resources and shareholder value.

After the changes in its privacy policies, WhatsApp has faced serious backlash from its users, with opinions and posts suggesting boycotts and a shift toward alternative applications becoming prominent. However, research on this reaction after the policy change might create in usage practices is quite limited. This study aims to investigate user decision-making in the dynamic landscape of mobile technologies, focusing on the factors influencing users’ choices between continuing with their current platform and migrating to alternatives.

Using migration theory and the push–pull mooring (PPM) model, the research identifies push factors (institutional privacy concerns), pull factors (relative privacy protection) and mooring factors (switching costs and exhaustion) as collectively shaping WhatsApp usage continuity.

Notably, the research highlights that institutional privacy concerns surprisingly do not significantly impact users’ intentions to persist with WhatsApp. Conversely, the relative privacy protection of an alternative platform emerges as a significant pull factor, influencing users’ intentions. Switching costs, particularly replacement and evaluation costs, exert a robust mooring effect, hindering migration and contributing to switching exhaustion. Results suggest that heightened exhaustion further strengthens the intention to persist with the current platform.

This research contributes to the literature by presenting findings that resonate with traditional migration approaches, offering a nuanced perspective on users’ decision-making processes. It delves into the impact of exhaustion on users’ intentions to continue using a platform, as well as the aftermath of policy changes, shedding light on switching dynamics and privacy concerns in reactive processes.

This study aims to investigate the work experience of women in hospitality with the job demands-resources (JD-R) theory. By focusing on job resources, job demands (emotional and physical), work engagement, emotional exhaustion and turnover intention, this study examines (1) female workers’ perceptions of these variables; (2) how race, age and job positions affect perceptions and (3) structural relationships among these variables.

The study collected 412 responses from American female hospitality workers via an online survey. Descriptive statistics and independent T-tests were conducted using SPSS to analyze professional experiences and differences based on individual characteristics. Path analysis was conducted using Amos 28 to assess the structural relationships among variables.

Female hospitality workers generally feel engaged, experience low emotional exhaustion and have low turnover intention. Younger or front-line women reported higher emotional exhaustion, lower engagement and greater likelihood to quit. Unexpectedly, emotional resources do not significantly affect work engagement, but physical resources increase it.

The study provides directions to establish specific well-being and organizational support initiatives to retain female hospitality workers.

This study offers fresh insights into the JD-R theory by examining the experiences of American female hospitality employees using a non-comparative lens. Although existing literature highlights women’s unfavorable positions relative to male counterparts, this study reveals rather positive perspectives. Additionally, it presents a dual psychological process of how job resources and demands affect women’s work experience and the varying impacts of job demands on work engagement.

This study aims to examine the causal and reciprocal relationships between participation in travel and recreational activities and depression alleviation among survivors of traumatic events. It seeks to uncover the transformative potential of these activities as nonpharmacological interventions and to provide insights that inform the therapeutic design of travel in rehabilitation contexts.

Using a longitudinal dataset, this study employs the random intercept cross-lagged panel model – a robust technique for evaluating causal relationships in nonexperimental longitudinal research – to assess the reciprocal effects between travel and recreation participation and depression severity over time.

Individuals with more severe depression are more likely to engage in travel as a coping mechanism. However, travel does not inherently alleviate depressive symptoms, likely because its physical and emotional demands often outweigh its benefits. This highlights the need to transform travel into structured interventions to maximize therapeutic potential. In contrast, recreational activities exhibit a stronger therapeutic effect, with early participation reducing depressive symptoms and lower depression levels, subsequently encouraging continued engagement in recreation over time.

By highlighting the interplay between social causation and social withdrawal theories, this research proposes transforming travel experiences into structured therapeutic interventions, pioneering a new perspective on integrating health and tourism studies. The study encourages researchers to adopt a holistic approach to understanding the health benefits of travel, considering both the influence of external factors on health and how individuals respond to their psychological state.

本研究旨在深入探讨创伤事件幸存者参与旅行与娱乐活动与抑郁症状缓解之间的因果和相互作用关系, 旨在揭示这些活动作为非药物干预的潜在疗效, 并为康复场景下旅行的治疗性设计提供理论依据和实践参考。

本研究利用纵向数据集, 采用随机截距交叉滞后面板模型（RI-CLPM）进行分析。该方法是一种适用于非实验性纵向研究的模型工具, 能够有效评估旅行与娱乐活动参与和抑郁严重程度之间的双向动态影响。

研究发现, 抑郁程度较高的个体更倾向于将旅行作为一种应对机制。然而, 由于旅行活动的身体与情感成本通常超过其潜在收益, 旅行本身并未显著缓解抑郁症状。这表明, 需要将旅行体验转化为结构化的治疗干预, 以充分发挥其潜在价值。相较而言, 娱乐活动表现出更显著的治疗作用。早期参与娱乐活动能够有效降低抑郁症状, 而抑郁水平的降低反过来又会激励个体持续参与娱乐活动, 从而形成良性循环。

本研究通过结合社会因果理论和社会退缩理论, 提出了有必要将旅行体验转化为结构化治疗干预的新视角, 为健康与旅游研究的整合提供了新的理论创新路径。同时, 本研究倡导从整体视角理解旅行的健康益处, 兼顾外部环境对健康的影响以及个体心理状态对行为的反馈作用, 为未来研究提供了重要启示。

Este estudio tiene como objetivo examinar las relaciones causales y recíprocas entre la participación en actividades de viaje y recreación y el alivio de la depresión entre los sobrevivientes de eventos traumáticos. Busca descubrir el potencial transformador de estas actividades como intervenciones no farmacológicas y proporcionar ideas que informen el diseño terapéutico de viajes en contextos de rehabilitación.

Utilizando un conjunto de datos longitudinales, este estudio emplea el Modelo de Panel Cruzado con Intercepto Aleatorio (RI-CLPM), una técnica robusta para evaluar relaciones causales en investigaciones longitudinales no experimentales, para analizar los efectos recíprocos entre la participación en viajes y recreación y la gravedad de la depresión a lo largo del tiempo.

Las personas con depresión más severa tienden a participar en viajes como mecanismo de afrontamiento. Sin embargo, el viaje en sí no alivia inherentemente los síntomas depresivos, probablemente porque sus demandas físicas y emocionales suelen superar sus beneficios. Esto resalta la necesidad de transformar los viajes en intervenciones terapéuticas estructuradas para maximizar su potencial. En contraste, las actividades recreativas muestran un efecto terapéutico más fuerte, con una participación temprana que reduce los síntomas depresivos y niveles más bajos de depresión que fomentan la participación continua en recreación a lo largo del tiempo.

Al destacar la interacción entre las teorías de causalidad social y retirada social, esta investigación propone transformar las experiencias de viaje en intervenciones terapéuticas estructuradas, abriendo una nueva perspectiva para integrar estudios de salud y turismo. El estudio fomenta que los investigadores adopten un enfoque holístico para comprender los beneficios de salud de los viajes, considerando tanto la influencia de factores externos en la salud como cómo las personas responden a su estado psicológico.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.