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This paper aims to explore the dimensions of spiritual tourism development, examine its current status, effectiveness and scope and analyze the knowledge landscape in terms of theories, contexts and research methodologies. The study also seeks to guide future research on spiritual tourism development.

A systematic literature review (SLR) and bibliometric analysis were used using a framework-based approach. The theories, constructs, characteristics and methods (TCCM) framework guided the SLR, whereas VOS-Viewer facilitated comprehensive bibliometric analysis.

The study conducted a quantitative SLR, analyzing 80 research articles published between 2003 and 2023. Using the TCCM framework, the research identified crucial factors influencing the growth of spiritual tourist destinations, such as intrinsic motivation, destination physicality, tourist experience, spiritual activities and host community support.

This study contributes to theoretical advancement in spiritual tourism, provides insights into the current research landscape, offers practical guidance for stakeholders and serves as a roadmap for future research endeavors.

This research enhances knowledge by thoroughly assessing prior research, addressing gaps and offering practical managerial insights for spiritual tourism development. The managerial implications outlined in the study offer practical insights for destination planning and promotion in the context of spiritual tourism.

Outside of Europe and the United States, the knowledge on skills dynamics is scarce due to a lack of data. We therefore assess whether online data on vacancies and applications to a job board can help fill this gap. We propose a taxonomy with three broad categories – cognitive, socioemotional, and manual skills – and 14 commonly observed subcategories, which we define based on unique skills identified through keywords and expressions. The taxonomy is comprehensive but succinct, suitable for developing and emerging economies, and adapted for online data. We then develop a text-mining approach to implement the taxonomy. Based on Uruguayan data from the job board BuscoJobs, we find that our model is able to assign skills to 64% of applicants' employment spells and 94% of vacancies. While online data are usually skewed toward highly qualified work, we show that our data include meaningful numbers of vacancies and applicants of intermediate and even lower qualification levels. Our approach relies on data that are currently available in many countries, thereby allowing for country-specific analysis that does not assume that occupational skills are constant across countries. This is key as we find considerable differences between our findings and those using US O-NET data. Finally, we end with an illustration of how our approach can inform the analysis of skills dynamics. To our knowledge, we are the first to explore this approach in the context of emerging economies.

This study aims to investigate the relationship between Chief Executive Officer (CEO) characteristics and audit report lag (ARL) in Egypt, an emerging economy characterized by high power distance and a culture of secrecy. The study utilizes a theoretical framework that integrates agency theory, stewardship theory, and upper echelons theory as the foundation for examining this relationship.

The sample consists of 587 firm-year observations from non-financial firms listed on the EGX100, covering the period from 2012 to 2019. The primary variable of the study (ARL) is measured using different proxies. The analysis utilizes both Ordinary Least Squares (OLS) and logistic regression models, with additional analysis considering CEO power and using board gender diversity as a moderating variable.

The study finds that CEO characteristics significantly affect ARL, demonstrating a negative association between CEO ownership, founder status, family ties, duality and ARL. These findings remain robust after a series of tests using alternative measures. Additional analysis reveals that CEO power is negatively and significantly related to ARL. Interestingly, the negative association between CEO characteristics and ARL is more pronounced in boards without female members.

Although extensive research has been conducted on the factors determining ARL, few studies have examined the impact of CEO characteristics on ARL, particularly in emerging economies such as Egypt. The business environment in Egypt is characterized by high power distance and a secretive culture, providing a unique context for this study.

This study aims to present systematic analysis of research concerning the intersection of human resource management (HRM) and the integration of artificial intelligence (AI) technologies within a digitalized economy further analyzing the trends in research with specific emphasis on utilization of diverse AI technologies within HRM.

This research is based on bibliometric analyses and content analyses. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses review methodology is implemented, using the Scopus database as the primary source which gathered 1,414 articles between 1978 and 2024. This study investigates publishing trends, the most prolific countries, universities, journals, publications and authors in the field. Further, the research trends based on the use of AI in HRM were accomplished through scientific mapping using VOSviewer.

The outcomes demonstrate a rising inclination toward using various AI techniques in HRM which shows increasing influence and growing appeal of the subject. The research uncovers the deployment of diverse technologies, including emerging ones, within the HRM field. It accomplishes this by scrutinizing the connections among various keywords and unearths both contradictions and focal areas of interest within the domain.

The study contributes to the existing body of literature by ascertaining suggestions for further research in the field of HRM integrated with various AI technologies. The integration of these technologies in HR holds a promising and optimistic outlook for the managers, thereby enhancing employee productivity.

This study aims to uncover the constraints of automation and the affordances of augmentation related to implementing artificial intelligence (AI)-powered systems across different task types: mechanical, thinking and feeling.

Qualitative study involving 45 interviews with various stakeholders in artistic gymnastics, for which AI-powered systems for the judging process are currently developed and tested. Stakeholders include judges, gymnasts, coaches and a technology vendor.

We identify perceived constraints of automation, such as too much mechanization, preciseness and inability of the system to evaluate artistry or to provide human interaction. Moreover, we find that the complexity and impreciseness of the rules prevent automation. In addition, we identify affordances of augmentation such as speedier, fault-less, more accurate and objective evaluation. Moreover, augmentation affords to provide an explanation, which in turn may decrease the number of decision disputes.

While the unique context of our study is revealing, the generalizability of our specific findings still needs to be established. However, the approach of considering task types is readily applicable in other contexts.

Our research provides useful insights for organizations that consider implementing AI for evaluation in terms of possible constraints, risks and implications of automation for the organizational practices and human agents while suggesting augmented AI-human work as a more beneficial approach in the long term.

Our granular approach provides a novel point of view on AI implementation, as our findings challenge the notion of full automation of mechanical and partial automation of thinking tasks. Therefore, we put forward augmentation as the most viable AI implementation approach. In addition, we developed a rich understanding of the perception of various stakeholders with a similar institutional background, which responds to recent calls in socio-technical research.

This study aims to identify the most significant barriers and the stationary barrier to modular construction (MC) implementation and promote MC widespread use. By doing so, the construction industry can leverage the benefits of MC, such as faster construction times, improved quality control, reduced waste and increased sustainability.

This study uses a Gini’s mean analysis approach to identify the stationary barriers hindering the MC adoption in residential projects. The research focuses on the Egyptian context and uses a questionnaire survey to gather data from professionals in the construction industry.

According to the survey findings, the top five significant MC barriers are inability to modify the design; contractors asking for high bidding prices (higher initial cost); scepticism, conservation and resistance of clients to innovation and change; transportation restrictions; and lack of a one-size-fits-all tool for the design. In addition, Gini’s mean of dispersion demonstrated that the stationary barrier that faces MC adoption is the apprehension that architectural creativity will suffer because of MC.

The identified obstacles could be useful for decision makers in countries that have not yet adopted MC and may aid in the planning process to manage the risks associated with MC projects. The paper stresses the significance of devising techniques to overcome these barriers and proposes several methods to tackle these challenges.

This study fills the knowledge gap by identifying the stationary barrier and emphasising the potential risks associated with MC barriers. Furthermore, it suggests several strategies for overcoming and reducing these barriers in developing countries residential projects.