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This research aims to use meta-analytical structural equation modeling to look into how hospitality employees use technology at work. It further investigates if the relationship between the constructs of the technology acceptance model (TAM) is moderated by job level (supervisory versus non-supervisory) and different cultures (eastern versus western).

In total, 140 relationships from 30 empirical studies (N = 6,728) were used in this study’s data analysis in accordance with the preferred reporting items for systematic reviews and meta-analysis.

The findings demonstrated that perceived usefulness had a greater influence on “user attitudes” and “acceptance intention” than perceived ease of use. This study also identified that the effect sizes of relationships among TAM constructs appeared to be greater for supervisory employees or in eastern cultures than for those in non-supervisory roles or western cultures.

The findings provide valuable information for practitioners to increase the adoption of employee technology. Practitioners need to focus on the identification of hospitality employee attitudes, social norms and perceived ease of use. Moreover, hospitality practitioners should be cautious when promoting the adoption of new technologies to employees, as those at different levels may respond differently.

This is the very first empirical investigation to meta-analyze the predictive power of the TAM in the context of hospitality staff technology adoption at the workplace. The findings also demonstrated differences in the predictive power of TAM constructs according to job level and cultural differences.

The technology acceptance model (TAM) is a widely used framework explaining why users accept new technologies. Still, its relevance is questioned because of evolving consumer behavior, demographics and technology. Contrary to a research paper or systematic literature review, the purpose of this critical reflection paper is to discuss TAM's relevance and limitations in hospitality and tourism research.

This paper uses a critical reflective approach, enabling a comprehensive review and synthesis of recent academic literature on TAM. The critical evaluation encompasses its historical trajectory, evolutionary growth, identified limitations and, more specifically, its relevance in the context of hospitality and tourism research.

TAM's limitations within the hospitality and tourism context revolve around its individual-centric perspective, limited scope, static nature, cultural applicability and reliance on self-reported measures.

To optimize TAM's efficacy, the authors propose several strategic recommendations. These include embedding TAM within the specific context of the industry, delving into TAM-driven artificial intelligence adoption, integrating industry-specific factors, acknowledging cultural nuances and using comprehensive research methods, such as mixed methods approach. It is imperative for researchers to critically assess TAM's suitability for their studies and be open to exploring alternative models or methods that can adeptly navigate the distinctive dynamics of the industry.

This critical reflection paper prompts a profound exploration of technology adoption within the dynamic hospitality and tourism sector, makes insightful inquiries into TAM's future potential and presents recommendations.

The technology readiness and acceptance model were mostly employed as theoretical bases to analyze tourist behaviors of adoption of innovative technologies that are part of the smart tourism offer. This study attention now turns to the field of smart tourism human resource management and tries to establish a model to explain the relationship between general dimensions of personality with systematic specific dimensions toward ICT acceptance.

The authors adopted the TRAM model as a way to better understand and evaluate the causal relationships between the personality dimensions, factors of perception, attitudes and behavior of users regarding the acceptance and use of innovative technologies. Based on the empirical data of 478 employees operating in tourist organizations, travel agencies and hotels, the authors employed regression analysis as the basic method for examining the research hypotheses and causal relationships between variables in the presented model.

The analysis revealed that employees in the tourism sector are inclined to adopt new technology, and that technological readiness has an impact on attitudes and intentions toward the use of technologies. Moreover, the results show that the path between the dimensions of technological readiness and intention to use technologies is to a certain extent mediated through perceived ease of use and perceived usefulness.

This research is among the first empirical studies that examine technology readiness and adoption in the context of smart tourism development. Besides the findings extend the literature on the mediating effect of perceived usefulness and perceived ease of use between technology readiness index and technology acceptance behavior.

The use of smartphone applications is the primary means by which modern technology improves the experiences of tourists in the tourism industry by bringing novel and superior features. The purpose of this study is to look into and examine the factors that affect tourists’ intention to use technology when looking for information in areas of tourism.

This study quantitatively uses online surveys. A total number of 315 questionnaires were collected from the tourism industry in Saudi Arabia. Partial least squares-structural equations model is used to analyze the data to evaluate the measurement and structural models.

Results of this study reveal that attitude, perceived usefulness, perceived ease of use and perceived enjoyment significantly and positively influence the intention of tourists, while customer relationship management and personal innovativeness in technology are insignificant. Furthermore, customer relationship management and personal innovativeness in technology indirectly impact tourists’ intentions.

This study offers novel insights by comprehensively analyzing the factors that influence tourists' intention to adopt technology in the context of destination information searches. Finding new avenues, the research emphasizes the distinct functions of customer relationship management, personal innovativeness in technology and enjoyment, offering insightful viewpoints for improving technology integration in the tourism industry.

The Metaverse is rapidly reshaping the understanding of tourism, yet the public perception of this new domain remains largely uncharted empirically. This paper aims to build on the technology acceptance model (TAM) and diffusion of innovations theory (DIT) to fill this gap, offering crucial insights that could inform scholars and practitioners in both the tourism and technology sectors.

Using a big-data approach, the authors applied machine learning to scrape comments made by social media users on recent popular posts or videos related to tourism in the Metaverse from three prominent social media platforms. The cleaning process narrowed down 15,461 comments to 2,650, which were then analysed using thematic, emotion and sentiment analysis techniques.

The thematic analysis revealed that virtual tourism evokes a complex range of public beliefs. While many express awe and excitement toward its immersive capabilities, others remain sceptical about authenticity compared to physical travel. Additional themes show people draw comparisons to real-world tourism, discuss technology’s role and note educational value and novelty. However, some comments raise concerns about potential societal harms, exploitation and mental health impacts. Sentiment analysis found over half of the comments positive, though some were negative. Emotion analysis showed contentment, happiness and excitement as most frequent, though sadness, worry and loneliness also featured. Overall, perceptions of Metaverse tourism encompass enthusiasm yet substantial ambivalence.

To the best of the authors’ knowledge, this study is one of the first to comprehensively analyse public discussions on Metaverse tourism. It takes TAM and Rogers’s DIT a step further and provides fresh insights into how these theories can be employed in the emerging field of Metaverse tourism. The themes revealed new conceptual insights into multidimensional factors shaping public beliefs about Metaverse tourism and thus informing scholarly research on virtual interaction and technology acceptance regarding Metaverse tourism. In addition, the results can help tourism providers, platforms and marketers address salient public beliefs and sentiments/attitudes in developing marketing offerings, experiences and communications. Over time, this analysis methodology can be used to track the evolving public perceptions of Metaverse tourism.

A wealth of studies have identified numerous antecedents to online self-disclosure. However, the number of competing theoretical perspectives and inconsistent findings have hampered efforts to obtain a clear understanding of what truly influences online self-disclosure. To address this gap, this study draws on the antecedent-privacy concern-outcome (APCO) framework in a one-stage meta-analytical structural equation modeling (one-stage MASEM) study to test a nomological online self-disclosure model that assesses the factors affecting online self-disclosure.

Using the one-stage MASEM technique, this study conducts a meta-analysis of online self-disclosure literature that comprises 130 independent samples extracted from 110 articles reported by 53,024 individuals.

The results reveal that trust, privacy concern, privacy risk and privacy benefit are the important antecedents of online self-disclosure. Privacy concern can be influenced by general privacy concern, privacy experience and privacy control. Furthermore, moderator analysis indicates that technology type has moderating effects on the links between online self-disclosure and some of its drivers.

First, with the guidance of the APCO framework, this study provides a comprehensive framework that connects the most relevant antecedents underlying online self-disclosure using one-stage MASEM. Second, this study identifies the contextual factors that influence the effectiveness of the antecedents of online self-disclosure.

Adopting an interdisciplinary perspective, the purpose of this paper was to posit an industry-wide technological intervention for hotel housekeeper safety and health through the advancement of wearable technology.

Using the task-technology fit (TTF) model and examples of successful safety and health applications of wearable technologies in the health-care and construction management industries, interventions and future research directions are presented to address workplace hazards experienced by hotel housekeepers.

The fit between a variety of hotel housekeeper user requirements, task demands and wearable functions are explored with justification for the use of wearable devices to improve safety and health-related outcomes.

A research agenda is proposed for the adoption and use of wearables in the hospitality industry with the intention to generate meaningful interventions beyond corporate wellness, and the mitigation of employee privacy concerns to enhance wearable adoption.

Given the importance of consumer safety and health assurance in a post-pandemic business environment, hospitality and tourism organizations should place greater emphasis on protecting front line employees who will be essential in regaining economic viability.

Theoretical and practical foci should move beyond a simplistic view of hospitality and tourism worker safety and health that generally centers on wellness initiatives and other baseline strategies, toward a more holistic view benefitting the hospitality industry.

Extant concerns about hotel housekeeper safety and health, in addition to new concerns and threats in a post-pandemic work environment, are largely understudied and worthy of investigation.

The aim of this mixed methods study sought to investigate the preferences and challenges towards fourth industrial revolution (FIR) technologies adoption at hotels in Malaysia.

The study adopted partially-mixed concurrent quantitative-dominant mixed methods. The quantitative part of the study examined the preferred FIR technologies for hotels; whiles the qualitative part explored the challenges towards their adoption.

The results revealed that Internet of things, big data, cloud computing, artificial intelligence and cyber physical systems were the preferred FIR technologies for hotels. The findings revealed significant relationships between respondents' socio-demographic characteristics (age, gender, working experience, etc.) and preferred FIR technologies. Penultimately, challenges that affect the adoption of FIR technologies including investment cost, migration cost, trust, etc., were discussed.

In effect, findings from this study provide valuable information for policy makers (i.e. hotel owners, management, technology providers and future researchers). Most especially, the study unearths specific FIR technologies preferred by the hospitality and tourism industry. Also, the significant interplay of these preferences vis a vis respondents' socio-demographic characteristics, as well as challenges influencing their adoption also have major implications for policy makers. Another valuable contribution of this study lies in the research method utilised, since most studies on technology adoption either use quantitative or qualitative. These contributions for theory and practice have been thoroughly discussed.

Amidst ongoing digital transformation, the current paper provides a 360-degree overview of technology-adoption in Tourism and Hospitality. By combining and consolidating a wide range of sources, mainly in the tourism literature, the analysis depicts how the complex technological ecosystem often enhances or hinders the successful adoption, integration and interoperability of different technologies.

The critical review method was used to assess, analyse and synthesise existing literature in the area of digitisation in tourism and hospitality. The critical review process included a thematic analysis of the literature, where recurring themes, patterns and trends were identified towards addressing the study’s research questions.

The analysis identifies current trends, opportunities, challenges and strategies for technology adoption in tourism and hospitality, the implications for theory, practicable executive directions and avenues for further research.

The paper’s main contribution lies in its comprehensive identification, consideration and incorporation of all primary contemporary technological elements, and the ensuing development of a corresponding conceptual charting framework, which illustrates a multifaceted process with practical implications for various stakeholders, including businesses, authorities, consumers and employees.

The use of artificial intelligence (AI) in the workplace is on the rise. To help advance research in this area, the authors synthesise the academic research and develop research propositions on the antecedents and consequences of AI adoption and application in the workplace to guide future research. The authors also present AI research in the socio-technical system context to provide a springboard for new research to fill the knowledge gap of the adoption and application of AI in the workplace.

This paper summarises the existing literature and builds a theoretically grounded conceptual framework on the socio-technical system theory that captures the essence of the impact of AI in the workplace.

The antecedents of AI adoption and application include personnel subsystem, technical subsystem, organisational structure subsystem and environmental factors. The consequences of AI adoption and application include individual, organisational and employment-related outcomes.

A research agenda is provided to identify and discuss future research that comprises not only insightful theoretical contributions but also practical implications. A greater understanding of AI adoption from socio-technical system perspective will enable managers and practitioners to develop effective AI adoption strategies, enhance employees' work experience and achieve competitive advantage for organisations.

Drawing on the socio-technical system theory, the proposed conceptual framework provides a comprehensive understanding of the antecedents and consequences of AI adoption and application in the work environment. The authors discuss the main contributions to theory and practice, along with potential future research directions of AI in the workplace related to three key themes at the individual, organisational and employment level.