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The study aims to investigate the integration of human and machine intelligence in Industry 4.0 (I4.0), particularly in the convergence of industrial revolutions 4.0 (IR4.0) and 5.0. It seeks to identify employee competencies aligned with industry 5.0 (I5.0) and propose a framework for deep multi-level cooperation to improve human integration within the intelligence system.

This study uses bibliometric analysis to review 296 research papers retrieved from the Scopus database between 2002 and 2022. The prominence of the research is evaluated by analyzing the publication trend, sample statistics, theoretical foundation, commonly used keywords, thematic evolution, country-based contributions and top-cited documents.

The study observed that research in I5.0 has been limited in the past but has gained momentum since 2015. An analysis of research papers from 2002 to 2022 reveals a gradual shift toward human-centric practices. The literature on I4.0, the internet of things (IoT), artificial intelligence (AI), cloud manufacturing, blockchain and big data analysis has been increasingly highlighting the growing importance of digitalization in the future. An increase in the number of countries contributing to the field of study has also been observed.

This analysis offers valuable insights for managers, policymakers, information technology (IT) developers and stakeholders in understanding and implementing human-centric practices in I5.0. It emphasizes staying current with trends, embracing workforce empowerment through reskilling and upskilling, and prioritizing data privacy and security in adaptable systems. These strategies contribute to developing effective, inclusive and ethically sound approaches aligned with the principles of I5.0.

This paper aims to investigate the continuing experience of the employer and employees in terms of work–life balance (WLB) and productivity from the employees engaged in knowledge work working from home. Based on the findings, this study expands the key performance indicators (KPIs) of remote work and future policy decision including reorienting training practices that can be implemented to manage the workforce in a hybrid model.

Focus group interviews with the corporate executives and human resources (HR) managers of information technology (IT) sectors were conducted to pool critical success and challenging variables in various phases as in the initial and matured phase of work from home (WFH) followed by administering a structured questionnaire to identify employee perceptions toward work from office, home and hybrid mode among knowledge work employees.

WLB and lower stress were the highlights of WFH, and women employees benefit from flexibility of work as the major highlighted success factor. But there were challenges ahead; teams have become more siloed with issues of ineffective communication resulting in team coherence issues, increased health risks and also can result in attrition. Productivity increased throughout, but in the extended phase of WFH, it has affected the WLB of a major portion of technology-driven employees in terms of health risks, burnout and job attrition.

Only the current employees working at IT companies in Kerala were surveyed, and this limited scope may not be consistent with other types of industries. The companies can approach the next phase of work by embracing the positives and learning from the challenges of WFH. The employers need to identify what exactly the employees feel through their interpersonal relationship through connect and trust.

A model with listed KPIs which will address the strategy enhancement for the future of work to uphold the success of work from anywhere at any time is suggested guaranteeing the best talent and productivity to progress forward. To foster the positive experience of WFH and hybrid mode of work, human resources (HR) interventions by reorienting effective training among employees are investigated and suggested as best practices to manage WLB and drive in flexibility to bring out the productivity potential among employees.

Coworking spaces can be arranged for those employees who have space and location issues and also taking into consideration, the nature of work. Out of five to six working days, three days of work can be from office desk based on employee preference as quality of deliverables and performance seem to be the established striking factor of traditional mode of work.

A model with listed KPIs and investigating the role of training which will address the strategy enhancement for the future of work to uphold the success of work from anywhere at any time is recommended guaranteeing the best talent and productivity to progress forward.

This paper aims to discover the effectiveness of Farsi-English query using ontology.

The present study is quasi-experimental. The sample consisted of 60 students and graduate and doctoral staff from Shiraz University and the Regional Center for Science and Technology. A researcher-made questionnaire was used to assess the level of English language proficiency of users, background knowledge and their level of satisfaction with search results before and after using ontology. Each user also evaluated the relevance of the top ten results on the Google search engine results page before and after using ontology.

The findings showed that the level of complexity of the task, the use of ontology, the interactive effect of the level of complexity of the task with the domain knowledge of the users, and the interactive effect of the level of complexity of the task with ontology, influence the effectiveness of retrieval results from the users' point of view. The results of the present study also showed that the level of complexity of the task, the use of ontology, and the interactive effect of the level of complexity of the task and the use of ontology, affect the level of user satisfaction.

The results of this research are significant in both theoretical and practical aspects. Theoretically, given the lack of research in which the interactive effect of the use of ontology has examined the level of complexity of tasks and domain knowledge of users, the present study can be considered as an attempt to improve information retrieval systems. From a practical point of view, the results of this research will help researchers and designers of information retrieval systems to understand that the use of ontologies can be used to retrieve information and improve the query and assess the needs of users and their satisfaction in this field, and ultimately, making the information retrieval process more effective.

This study profiles and segments potential tourists on the basis of their various attitudes toward artificial intelligence (AI) services. Furthermore, this study distinguishes descriptors among the different clusters, such as preference for using diverse AI services, overall image of AI services, willingness to use AI services (WUAI), willingness to pay more for AI services (WPAI) in tourism and hospitality, and characteristics of respondents.

An online survey was conducted in South Korea. Data on 758 potential tourists were used for K-means cluster analysis.

This study identified three distinct tourist segments with differentiated attitudes toward AI services: the group aspiring to use or fantasizing about AI services (Cluster 1), the group being knowledgeable and supportive of AI services (Cluster 2), and the group having low interest about AI services (Cluster 3).

Members of Cluster 2 were the most marketable as this segment exhibited the greatest knowledge of and support for AI services, while Cluster 1 would be an ideal segment to launch and test novel AI services.

This study extends the authors’ knowledge of AI scholarship by unpacking the existing market segments, which could be tapped to sustain AI penetration in the tourism industry. Hence, this study contributes to existing debates on AI scholarship, which is predominated by conceptual reflections and issues of AI services in the tourism and hospitality field.