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This paper aims to cover mental health research related to tourism and hospitality, starting in 1984, and track its development until 2020. Relevant research published between 2020 and 2023 during the COVID-19 pandemic is also reviewed to determine how this research stream is evolving.

A detailed search of Scopus and Google Scholar yielded 4,790 mental health studies in tourism and hospitality; 102 were ultimately retained for systematic review. VOSviewer was used to visualize cluster analysis results.

Research on mental health in the context of tourism and hospitality is limited and can be classified into four themes. The most prominent involves mental health in relation to COVID-19. Thematic differences between studies published before and after the onset of the pandemic are also specified. The findings inform a critical reflection on the conceptual framework linking tourism and mental health, as well as potential research avenues, covering research populations, topics, methods, data sources and outcome measures.

This in-depth analysis of the extant literature provides a foundation for stakeholders to better understand, address and promote mental health in tourism and hospitality. Such insights can steer future research and enlighten industry practitioners, thus contributing to sustainable industry development.

This paper represents a pioneering effort to systematically review mental health studies in tourism and hospitality. It offers a holistic perspective and unique insights, bridging substantial knowledge gaps. This paper is also meant to prompt academics and practitioners to contemplate mental health-related research and practice.

本文系统性地回顾了自1984年以来与旅游和酒店业相关的心理健康研究, 以追踪该领域在2020年之前的研究进展。本研究还审视了在2020年至2023年COVID-19大流行期间发表的相关文章, 以洞察这一研究流派的演变趋势。

通过对Scopus和Google Scholar数据库进行详尽的搜索, 我们发现了4,790篇有关旅游和酒店业的心理健康研究。经过严格筛选, 我们最终保留了102篇文章进行系统性综述。为了更清晰地呈现聚类分析的结果, 我们采用了VOSviewer对相关研究主题进行可视化。

目前有关旅游与酒店业心理健康的研究十分有限, 主要分为四个主题。其中, 与COVID-19相关的心理健康研究是最突出的主题。此外, 本文还详细比较了大流行病爆发前后发表的相关研究之间存在的主题差异。我们创造性地提出了连接旅游和心理健康的概念模型, 并对未来研究方向进行了批判性反思, 涉及研究人群、主题、方法、数据来源和测量指标等方面。

本文对现有文献的深入分析为关键利益相关者更好地理解、应对和促进旅游和酒店业中的心理健康提供了基础。这些见解可以指导未来的学术研究, 并启示业界从业者, 从而促进行业的可持续发展。

本文是旅游和酒店业心理健康研究的一项开创性工作。它通过系统的文献回顾提供了全面的视角和独特的见解, 填补了这一领域的重要知识空白。本文还旨在促使学术界和从业者思考与心理健康相关的研究和实践。

Esta revisión sistemática abarca la investigación en salud mental relacionada con el turismo y la hostelería, a partir de 1984, para seguir su evolución hasta 2020. También se revisan las investigaciones pertinentes publicadas entre 2020 y 2023, durante la pandemia de COVID-19, para determinar cómo evoluciona esta corriente de investigación.

Una búsqueda detallada en Scopus y Google Scholar arrojó 4.790 estudios sobre salud mental en turismo y hostelería; finalmente se seleccionaron 102 para la revisión sistemática. Se utilizó VOSviewer para visualizar los resultados del análisis de conglomerados.

La investigación sobre la salud mental en el contexto del turismo y la hostelería es limitada y puede clasificarse en cuatro temas. El más destacado se refiere a la salud mental en relación con COVID-19. También se especifican las diferencias temáticas entre los estudios publicados antes y después del inicio de la pandemia. Los resultados aportan una reflexión crítica sobre el marco conceptual que vincula turismo y salud mental, así como posibles vías de investigación, que abarcan poblaciones de investigación, temas, métodos, fuentes de datos y medidas de resultados.

Este análisis en profundidad de la bibliografía existente sienta las bases para que las partes interesadas comprendan, aborden y promuevan mejor la salud mental en el turismo y la hostelería. Estos conocimientos pueden orientar futuras investigaciones e ilustrar a los profesionales del sector, contribuyendo así al desarrollo sostenible de la industria.

Este artículo representa un esfuerzo pionero por revisar sistemáticamente los estudios sobre salud mental en el turismo y la hostelería. Ofrece una perspectiva holística y una visión única, cubriendo importantes lagunas de conocimiento. Este artículo también pretende incitar a académicos y profesionales a contemplar la investigación y la práctica relacionadas con la salud mental.

In light of the shift in focus from information communication technology (ICT) access (access divide) and skills (skills divide) to the tangible impacts of ICT use (impact divide), a growing number of scholars have called for further investigation into the inter-territorial and multi-dimensional aspects of the digital divide in China. This study aims to address these gaps by examining the disparities across 31 provinces, particularly emphasizing the transition from the traditional access and skills divides to the impact divide.

Multivariate regression analysis extensively investigates the transition from the access and skills divides to the impact divide across 31 provinces. Additionally, ArcGIS software is used to analyze spatial agglomeration and the auto-correlation (Moran-i) and predict mapping patterns in the data corresponding to all three levels of the digital divide.

According to the study's findings, poverty is a significant factor in the digital divide between different regions in China. The research shows that provinces with advanced administrative systems, such as Guangdong, Shanghai, Beijing, Jiangsu, Shandon and Zhejiang, have high scores on the digital development index (DDI). However, regions with poverty-ridden and rural areas, primarily located in southwest, central and western China, tend to have lower DDI scores.

This study offers a novel contribution to the literature by presenting an innovative conceptual framework that explores the impact divide within China's provinces. The authors also address this lacuna in the literature by developing and testing two dimensions to examine the relationships statistically under a wide range of socioeconomic and ICT indicators.

Marketing Technology (Martech) is the cornerstone of creating digital experiences and interactive marketing, providing consumers with high experiential value. Drawing on the mindfulness theory, this study aims to explore how to achieve close psychological distance and experiential value in Martech servicescape (such as augmented reality [AR]).

We employed mixed methods research to clarify the research question. In Study 1, we conducted a systematic literature review of psychological closeness (PC) using a bibliographic coupling approach, identifying gaps in the research stream and discussing the research implications for the interactive marketing field. In Study 2, we used a task-based laboratory assessment to empirically verify our hypotheses and research framework. Two virtual try-on environments, AR and non-AR (e.g. traditional webpage browsing), were applied in a virtual fitting context. The two e-shopping environments were directly compared in terms of their moderating effects on the relationships among the mindfulness-oriented MarTech servicescape, PC and experiential value.

This study elucidates the antecedent of close psychological distance formation, indicating that the features of the mindfulness-oriented Martech servicescape – vivid sensory experience, consumer-focused shopping information and autonomous navigation, then result in creating experiential value. Moreover, this study also revealed that compared to a non-AR e-shopping environment, AR makes the better effect of the mindfulness-oriented Martech servicescape driving experiential marketing.

This study extends the research stream on mindfulness-oriented service to the Martech servicescape (e.g. AR try-on). In this way, this study’s findings will contribute to clarifying the interactive elements and design principles of mindfulness-oriented service in the Martech servicescape. By establishing the association between these three theoretical perspectives—mindfulness-oriented service research stream, construal level theory and experience economy paradigm—the study provides valuable insights into how Martech can enhance experiential marketing. Such research insights can help digital marketing managers shape appropriate Martech servicescape for effective experiential marketing.

Given the contradictory findings of standardization/adaptation of marketing strategy in explaining export performance in the extant research, this study aims to examine the contingent effects of managerial ties and born global orientation in the standardized advertising-export performance conceptualization.

The study used two-respondent method in the survey research by a sample of 155 exporting firms operating in the industrial marketing based in Australia and New Zealand and applied hierarchical regression analysis to test the hypotheses.

The findings demonstrate that standardized advertising has a significant effect on export performance and this relationship is positively moderated by business ties. Such effect is particularly enhanced for born global firms (than nonborn global firms). However, political ties negatively influence the impact of standardized advertising on performance and such effect is stronger for born global firms.

A broader perspective of contingent variables should be included to examine the underlying relationship between standardized advertising and export performance in capturing the dynamism in international marketing contexts, such as institutional frameworks or sociocultural environments in host countries.

Standardized advertising is critical for born global firms’ export performance as it can increase efficiency and speed up internationalization processes. Such positive impact of standardized advertising on export performance is further enhanced if born global firms allocate resources to develop strong business ties with host country partners instead of building political ties with host country governments, because smooth business networking can facilitate standardized advertising on industrial marketing, yet justifiable political relations require intricate negotiations that often prolong internationalization progress.

This study incorporates managerial ties and born global orientation as contingent factors in fixing the theoretic interlock between standardization advertising strategy and export firm performance.

This paper aims to utilize an extended involvement-commitment and trust commitment model to examine post-consumption decisions related to food delivery app use.

A self-administered online survey was used to collect data from food delivery app users in the USA.

Findings validate a favorable role of perceived app security and menu description on trust in app recommendations. Trust was found to be positively related to involvement, commitment and willingness to provide feedback. The positive moderating role of perceived convenience and rewards and incentives was also confirmed in relation to consumers’ trust in app recommendations, and involvement and commitment

A key contribution of this study includes the development of a comprehensive model to understand postconsumption decisions related to the usage of food delivery apps. To the best of the authors’ knowledge, this study is also the first to unveil the antecedent and moderating factors related to food delivery app users’ willingness to provide feedback, share personal data and to pay more.

Computer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.

The developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.

Three DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.

This study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

This study empirically investigated the different attributes (i.e. convenience, online reviews, online ratings, visual appeal and various food choices) of food delivery applications (FDAs) that impacted customers' perceived benefits, satisfaction and loyalty. Price consciousness was positioned as the moderator while customer satisfaction was positioned as the mediator.

Utilizing the purposive sampling technique, 360 useable respondents were gathered from existing FDAs' customers in China. Partial least squares structural equation modelling (PLS-SEM) was employed to verify the interrelationships among variables.

The results confirmed the significant impact of four FDA attributes (i.e. convenience, online ratings, visual appeal and various food choices) on perceived benefits. Customer satisfaction positively influenced loyalty. Moreover, the mediating role of customer satisfaction and the moderating role of price consciousness were confirmed.

FDA operators should engage in providing more benefits to stimulate satisfaction and loyalty among customers. Promotion discounts remain a key factor in enhancing the satisfaction and loyalty of price-conscious customers.

This study extends the applicability of the stimulus–organism–response (SOR) framework and enriches FDA literature by assessing the external stimuli, organisms and moderating factors that trigger customer loyalty.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

This study aims to explore the spatial impact of an increase in the minimum wage on the labor productivity of star-rated hotels in China.

The impact is analyzed by using the dynamic spatial Durbin model.

The authors find a U-shaped link between the increase in minimum wage and labor productivity of star-rated hotels. The long-term impact of a minimum wage increase has a greater influence on labor productivity than its short-term effects. While there is no notable spatial spillover impact observed in the sample of 31 provinces in China, the authors do identify a spatial spillover effect of the minimum wage rises on the labor productivity of star-rated hotels in the central area. Furthermore, they observe heterogeneity across China. The eastern and western regions exhibit a U-shaped relationship, whereas the central region exhibits an inverted U-shaped relationship.

The findings of this study allow government agencies to get a more comprehensive comprehension of the actual consequences of minimum wage hikes on the tourism and hospitality sector, thereby establishing a solid basis for them to develop appropriate policies. Moreover, it offers a variety of suggestions aimed at enhancing the quality and efficiency of hotel management.

Research on the effects of minimum wage standards is scant in the hospitality industry. Based on human capital investment theory, this study examines the effect of the minimum wage standard hikes on labor productivity of star-rated hotels from the spatial perspective, filling the existing research gap.