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The main objective of this study is to determine the factors that have the greatest impact on travelers' opinions of airports.

11,656 customer reviews for 649 airports around the world were gathered following the COVID-19 outbreak from the website that rates airport quality. The dataset was examined using hierarchical regression, PLS-SEM, and the unsupervised Bayesian algorithm-based PSEM in order to verify the hypothesis.

The results showed that people’s intentions to recommend airports are significantly influenced by their opinions of how well the servicescape, staff, and services are.

By encouraging air travelers to have positive intentions toward recommending the airports, this research offers airport managers decision-support implications for how to improve airport service quality. This will increase the likelihood of retaining more passengers.

This study also suggests a quick-to-implement visual decision-making mechanism based on PSEM that is simple to understand.

This study examines how structured procedures and psychological safety can enhance six sigma processes’ effectiveness and foster individual creativity within an organization.

This study employs a novel methodological approach, combining structural equation modeling (SEM) and multi-value qualitative comparative analysis (mvQCA).This combination gave us a unique lens through which to view the complex connections between the constructs that were the subject of the investigation.

The results substantiate that a well-structured six sigma approach, paired with a psychologically safe environment, fosters knowledge exploration and exploitation as well as triggering conducive learning behaviors. These elements, in turn, augment individual creativity, which is found to be instrumental in boosting six sigma performance.

This study is one of the first to combine mvQCA and SEM to investigate the complex interplay between structured methods, psychological safety and individual creativity in enhancing six sigma performance. This research highlights the importance of fostering creativity in strategic management processes and the unrealized potential of combining these methods.

This study aims to investigate the effects of smart tourism technology experience (STTE) on tourist satisfaction and happiness in Bordeaux, with a focus on understanding the mediating role of self-gratification. By examining these relationships, the study seeks to provide insights into how smart tourism technologies can enhance tourist experiences.

The study uses partial least squares-structural equation modeling (PLS-SEM) to analyze data collected from 380 tourists who visited Bordeaux. The measurement model assesses reliability and validity, while the structural model evaluates the proposed hypotheses and the mediation effects of self-gratification.

The results confirm that STTE positively impacts tourist satisfaction, with accessibility, informativeness and personalization significantly enhancing tourist satisfaction, while interactivity does not. Tourist satisfaction, in turn, positively affects tourist happiness. Furthermore, self-gratification partially mediates the relationship between tourist satisfaction and happiness, highlighting its importance in the smart tourism context.

This research extends the understanding of STTE by demonstrating its effects on tourist satisfaction and happiness. It introduces the mediating role of self-gratification, providing a novel perspective on how personalized smart tourism experiences contribute to overall tourist happiness.

This paper aims to systematically review the application of artificial intelligence (AI) in the tourism industry. By integrating human–computer interaction, machine learning, big data and other relevant technologies, the study establishes a comprehensive research framework that explores the systematic connections between AI and various facets of tourism.

This paper conducts a keyword co-occurrence analysis of 4,048 articles related to AI in tourism. The analysis identifies and classifies dominant topics, which are further refined through thematic literature review and manual coding for detailed discussion.

The analysis reveals five main topics: AI’s impact on tourist experience, AI in tourism marketing and prediction, AI in destination management, AI’s role in tourism enterprises and AI integration in strategic and regulatory framework. Each topic is reviewed to construct an integrated discussion that maps the current landscape and suggests directions for future research.

This paper transcends the fragmented discourse commonly found in the literature by establishing a unified framework that not only enhances understanding of the existing methodologies, theories and applications of AI in tourism but also identifies critical areas for breakthroughs, aiming to inspire a more humane and sustainable integration of AI in the tourism industry.

本文旨在系统回顾人工智能（AI）在旅游业中的应用。通过整合人机交互、机器学习、大数据和其他相关技术, 本研究建立了一个全面的研究框架, 探索人工智能与旅游业各方面之间的系统联系。

本文对4048篇与旅游业人工智能相关的文章进行了关键词共现分析。分析确定了主要议题并对其进行了分类, 然后通过主题文献梳理和手动编码对其进行了进一步完善, 以便进行详细讨论。

分析揭示了五个主要主题：人工智能与旅游体验、人工智能与旅游营销和预测、人工智能与目的地管理、人工智能与旅游企业, 以及人工智能在战略和监管框架中的整合。每个主题都进行了回顾, 以构建一个综合讨论, 勾勒出当前的研究格局, 并提出了未来的研究方向。

研究力图突破目前关于旅游与人工智能的碎片化讨论, 建立了一个统一的框架, 旨在加强对旅游业中人工智能现有方法、理论和应用的理解, 还点明了需要突破的关键领域, 以助力旅游业与人工智能共同创造更加人性化和可持续发展的前景。

Este artículo pretende revisar sistemáticamente la aplicación de la inteligencia artificial (IA) en el sector turístico. Mediante la integración de la interacción humano-ordenador, el aprendizaje automático, big data y otras tecnologías relevantes, el estudio establece un marco de investigación exhaustivo que explora las conexiones sistemáticas entre la IA y diversas facetas del turismo.

Este trabajo realiza un análisis de co-ocurrencia de palabras clave de 4.048 artículos relacionados con la IA en el turismo. El análisis identifica y clasifica los temas dominantes, sobre los que se profundiza mediante una revisión temática de la literatura y una codificación manual para su discusión detallada.

El análisis presenta cinco temas principales: El impacto de la IA en la experiencia turística, la IA en el marketing y la predicción turística, la IA en la gestión de destinos, el papel de la IA en las empresas turísticas y la integración de la IA en el marco estratégico y normativo. Cada tema se revisa para construir un debate integrado que trace el panorama actual y sugiera direcciones para futuras investigaciones.

Este artículo expande el análisis fragmentado que suele encontrarse en la bibliografía al establecer un marco unificado que no sólo mejora la comprensión de las metodologías, teorías y aplicaciones existentes de la IA en el turismo, sino que también identifica las áreas críticas para los avances, con el objetivo de inspirar una integración más humana y sostenible de la IA en la industria turística.

This study aims to unravel the complex coopetitive interactions in supply chains. It delves into the paradoxical relationship between cooperative and competitive interactions among supply chain entities, offering a comprehensive exploration of coopetition’s manifestations and management across various supply chain types.

The study uses a three-phase methodology, beginning with a scoping review to establish a theoretical framework, followed by a systematic literature review yielding 130 papers and concluding with correspondence analysis using similarity indexes. This approach facilitates a deep dive into the diverse aspects of coopetition, including its drivers, practices, outcomes and associated risks.

The research identifies three distinct types of coopetition in supply chains: technology-based, socially based and channel-based. These models are underscored by specific drivers and outcomes, with technology-based coopetition focusing on market competitiveness and operational capacity, socially based on trust and power dynamics and channel based on product characteristics. The study introduces five propositions for further investigation and provides a comprehensive typology of coopetition within supply chains.

The study’s findings are limited by the scope of the existing literature and the chosen academic databases. Future research should empirically validate the proposed coopetition configurations and propositions, exploring their applicability in other, less studied supply chains.

The study offers practitioners a valuable typology and framework to understand and manage coopetition in their respective supply chains. This typology serves as a decision-making tool for identifying suitable coopetition strategies and maximizing their benefits while mitigating associated risks.

This study stands out for its unique approach to categorizing coopetition in supply chains, offering a novel typology that goes beyond the manufacturer’s perspective. It fills a significant gap in the literature by providing a broad view of coopetition, considering various supply chain types and their respective coopetitive dynamics.

Recently, mHealth apps for COVID-19 have emerged as a new research area due to the diverse efforts to control the COVID-19 epidemic. Although there are many bibliometric studies on mHealth and its applications, no bibliometric study sheds light on mHealth apps for COVID-19 as a new research area. To address the above-mentioned research gap, the current study conducts a bibliometric analysis of research in mHealth apps for COVID-19. It aims to provide a comprehensive overview of the new area and its directions.

The study uses a bibliometric approach to provide an analysis of the overall status of research in mHealth apps for COVID-19. The Scopus database provided by Elsevier was used to extract the analyzed data in this study. SciVal was used to perform the analyses, while VOSviewer was used for scientific mapping.

A total of 457 publications were published between 2020 and 2021 (until Tuesday, June 1) and cited 3,559 times. Publications were written by 2,375 authors, with an average of 5.20 authors per publication. Articles play a pivotal role in the literature on mHealth apps for COVID-19 in terms of production and impact. The research area of mHealth apps for COVID-19 is multidisciplinary. The United States made the largest contribution to this area, while the UK was the most influential. This study reveals the most productive and influential sources, institutions and authors. It also reveals the research hotspots and major thematic clusters in mHealth apps for COVID-19, highly cited publications and the international collaboration network.

mHealth apps for COVID-19 are gaining more and more importance due to their influential role in controlling the COVID-19 epidemic. Using bibliometric analysis, the study contributes to defining the knowledge structure of global research in mHealth apps for COVID-19 as a new, interdisciplinary area of research that has not previously been studied. Therefore, the study results and the comprehensive picture obtained about research in mHealth apps for COVID-19, especially at the level of Internet of Things (IoT) and artificial intelligence applications, make it an effective supplement to the expert evaluation in the field.

The significant role of universities in generating and diffusing new knowledge in the interest of society has positioned faculty as knowledge creators. The present study contributes to the domain of positive psychology by heeding the happiness call in academia. The research intends to develop and validate a scale for measuring happiness at work (HAW) for knowledge creators.

The study is systematically designed across a series of four independent studies: (1) Dimensionality and item analysis, (2) scale purification, (3) scale refinement and nomological validation and (4) generalizability. Additionally, common method bias (CMB) was checked utilizing the marker variable technique.

HAW has been established as a second-order reflective-reflective construct with six factors, namely work satisfaction, self-directedness, self-love, positive thinking, positive social relationships and work-family balance. The nomological validity and generalizability of the scale have also been established.

The study is an attempt to address an important topic of HAW among knowledge creators. By conceptualizing HAW as a combination of intraindividual and organizational factors, this study offers a comprehensive measure of HAW that was previously absent in the literature. The results of the study will assist management in making strategic decisions to ensure the HAW of knowledge creators.

Knowledge creators’ happiness is a major concern in academia and has received little attention till date. The primary contribution of this study is the conceptualization and development of a validated scale for measuring knowledge creators’ HAW. A valid and reliable scale for measuring HAW would enable researchers to gain fresh perspectives on the essence, attributes and quantification of this particularly noteworthy construct.

Despite global advancements, the Indian construction industry lags in adopting technologies like robots, artificial intelligence, drones, unmanned ground vehicles and Internet of Things sensors due to various barriers. This reliance on traditional practices leads to persistent issues such as labour shortages, low productivity, safety risks, cost overruns and delays. Identifying and addressing these barriers is crucial for adopting advanced technologies. Hence, this study aims to identify and assess the influential barriers to construction automation and robotics (AaR) in India using a systematic approach.

An extensive literature review identified key barriers, including technological, financial, regulatory, environmental and organisational. A questionnaire survey was conducted among industry professionals, and the Fuzzy Decision-Making Trial and Evaluation Laboratory technique was used to evaluate the interrelationships and relative significance of these barriers.

The results of the study reveal the most critical cause barriers as “high initial cost”, “rigidity in organisational processes and procedures”, “interoperability”, “lack of competency” and “lack of standardisation”. These barriers subsequently influence the top two effect barriers such as “market uncertainty” and “ethical concerns”.

This research offers a systematic and quantitative assessment, enabling stakeholders to make informed decisions and develop strategies to overcome barriers to AaR adoption, unlocking the transformative potential of AaR in India’s construction sector.

Despite potential benefits, the adoption of AaR in Indian construction remains limited due to various barriers. This study provides novel insights with the first comprehensive assessment of these barriers and their interrelationships.

In today’s business landscape, drawing upon the critical role of environmental sustainability, this study investigates the intricate relationship between green human resource management practices (GHRMP), big data analytics capability (BDAC), green competitive advantage (GCA) and environmental performance (EP), further moderated by managerial environmental concern (MEC).

This study employs a quantitative approach using the latest version of SmartPLS 4 version 4.0.9.6 on a data sample of 467 participants representing a diverse range of manufacturing SMEs. Data were collected from managers and directors using a structured questionnaire and analyzed using structural equation modeling (SEM). This study contributes to the existing knowledge by integrating GHRMP and BDAC within the GCA framework, providing a comprehensive understanding of how these practices enhance SME`s sustainability.

The findings provide valuable insights into the manufacturing sector, aiming to enhance SMEs' green competitive advantage. Implementing GHRMP fosters environmental awareness within the workforce, and building BDAC allows for effectively translating that GHRMP into actionable insights, maximizing the potential for achieving GCA. Furthermore, recognizing MEC’s moderating role strengthens positive environmental outcomes associated with GCA. The findings confirm that GHRMP and BDAC are valuable resources and key drivers contributing to competitive advantage in sustainability of enterprises.

For SMEs, our findings suggest that strategically integrating GHRMP with BDAC not only boosts environmental stewardship but also improves operational efficiency and market positioning. This research outlines actionable steps for SMEs aiming to achieve sustainability targets while enhancing profitability. This research provides actionable insights for SMEs in strategic decision-making and policy formulation, aiding SMEs in navigating the complexities of sustainable development effectively.

This study contributes to the existing knowledge by integrating GHRMP and BDAC within the GCA framework, providing a robust theoretical explanation of how HRM practices and BDAC help SMEs gain green competitiveness. The implication of this study reveals that SMEs implementing and integrating green HRM practices with advanced data analytics are more likely to gain competitive advantage. This study draws theoretical support from the resource-based view (RBV) theory, positing that a firm’s sustainable competitive advantage stems from its unique and valuable resources and capabilities that are difficult for competitors to imitate or substitute.

This study aims to examine the direct effects of Green Human Resource Management (GHRM) practices on employee green behavior (EGB) in the university setting in Bangladesh and to find the indirect effects of how GHRM promotes EGB through sequentially mediating employee environmental knowledge management (EEKM) (environmental knowledge and knowledge sharing) and green self-efficacy (GSE).

For the empirical study, the researcher uses partial least squares structural equation modeling to test the proposed conceptual model built on existing literature for greening workplaces in the university in Bangladesh. The study has collected data from 288 Bangladeshi university employees using convenient sampling.

The findings that GHRM practices positively and significantly promote EGB, which captures the employee's tendencies to exercise green behavior in daily routine activities such as turning off lights, air conditioning, computers and equipment after working hours, printing on both sides, recycling (reducing, repair, reuse), disseminating good green ideas, concepts, digital skills and knowledge to peers and champion green initiatives at work. Moreover, the findings also revealed the sequential mediation of EEKM (environmental knowledge and knowledge sharing) and GSE of employees between the link GHRM and EGB. At last, the findings suggested that HR managers can implement the GHRM practices to promote green behaviors among the academic and non-academic staff of the university.

This study contributes to the field by extending knowledge of Social Cognition Theory and Social Learning Theory for greening workplaces in Bangladesh, particularly universities. Specifically, this empirical study is unique to the best of our knowledge and highlights the role of EEKM and GSE as mediation between GHRM and EGB association.