Jamel Choukir

As higher education increasingly relies on data-driven decision-making, the use of artificial intelligence (AI) to predict student success and dropout rates is gaining further importance. The primary aim of this study is to understand the evolutionary research trends of data-driven student success management. A PRISMA-based bibliometric analysis of 457 articles is implemented using the R software version 4.5.3 and the bibliometrix package (Version 5.0). Published articles are retrieved from Web of Science Core Collection indexes (SSCI, AHCI, ESCI), covering a three-decade period (1996-2025). The rigorous inclusion/exclusion criteria restricted the dataset to English-language, and peer-reviewed journal articles. Furthermore, co-word analysis and conceptual structure mapping, utilizing the Louvain clustering algorithm, were applied to extract the thematic architecture of the domain. Performance analysis reveals China and U.S. universities as the most influential affiliations shaping the structural volume of this research field, with Applied Sciences emerging as the dominant journal. Additionally, the network analysis indicates an international collaboration rate of 25%. The thematic map reveals that while predictive modeling-based Early Warning Systems (EWS) have matured into Basic Themes, driving Motor Themes included generative artificial intelligence and psychological constructs. Furthermore, Emerging Themes show that explainable artificial intelligence (XAI) is drawing a transformative pathway to the field. Higher education managers should integrate XAI while addressing ethical AI governance. Future higher education data-driven management should focus on building transparent systems leading to the empowerment of human decision-makers.

Higher education faces social and technological challenges driven by the democratization of the generative artificial intelligence (GenAI). The generative shockwave has marked a major shift in traditional learning from centralized and controlled Intelligent Tutoring Systems (ITS) to decentralized and unmanaged Shadow IT. To rigorously map higher education institution transition during the generative shockwave, the current study used a pre-defined protocol for an exploratory PRISMA-based bibliometric analysis of 459 academic documents retrieved from the Web of Science Core Collection. Its main findings highlight the magnitude of this shift, as 82% of the considered articles were published in 2024 and 2025, confirming the generative AI shockwave. Thematic mapping shows that while the key problems with artificial intelligence in education are universally recognized, higher education institutions lack adequately adapted policies to manage them. Furthermore, important psychological factors that influence technology acceptance, such as students' self-efficacy and mental strain, require special academic interest. By considering the dual theoretical lenses, using a scalar analysis approach, of the micro-level Technology Acceptance Model (TAM) and the macro-level Institutional Theory, the current study highlights the relationship between AI-based learning evolution and the inefficient management of AI risks through top-down IT systems. Instead, a collaborative approach that integrates stakeholders must be built. By working with localized institutional entrepreneurs and providing appropriate socio-technical support, universities can bridge gaps, meet the expectations of modern students, sustain their legitimacy, and adapt to the inevitable challenges posed by artificial intelligence, especially in an adaptive personalized learning ecosystem.

The aim of the study is to investigate the contribution of ethical leadership and job demands to the development of managerial decision-making effectiveness in the aviation sector, based on Job Demands-Resources (JD-R) model. Although research has been conducted in relation to leadership and stress independently, there is still a need for research to investigate the integration of the two concepts and their resulting influence on managerial decision-making effectiveness in the aviation industry. Job resources are conceptualized as ethical skilled leadership to improve the quality of decisions made, and job demands are defined as stressors that poor decision-making abilities of managers. Emotional exhaustion is found to be the mediate procedure on that such dynamics are operated. The structured survey was conducted among aviation managers and supervisors (N = 232), and the analysis conducted with the help of Partial Least Squares Structural Equation Modeling (PLS-SEM) using software SmartPLS version 4. The findings shows that ethical leadership has a great and indirect effect on the effectiveness in decision making among managers by reducing the emotional exhaustion. The latter when compared to job demand affect the effectiveness of decision making in the opposite and negative way, and emotional exhaustion is the significant moderator which can explain the effects of stress on performance. These results provide empirical support for the JD-R framework in a safety-sensitive industry and for the two ways in which organizational resources and stressors may affect managerial results. This study extends previous literature by combining ethical leadership and job demands as the key factors explaining the role of psychological resources and stressors in effective decision making in aviation management. Policy implications include leadership development, workload control, and well-being initiatives to protect effective decision-making in aviation organizations.

The objective of this study is to investigate perceptions among HR managers in Saudi Arabia and compare these perceptions across demographic characteristics. Furthermore, the study examines the influence of AI knowledge and frequency of use on perceptions. An online survey was administered to a purposive sample of 420 HR managers working in the chemical industry in Saudi Arabia, and 234 complete responses were received. Data were analyzed using descriptive statistics, one-way ANOVA, and structural equation modeling. Findings show that AI was perceived positively, particularly in salary management, recruitment, performance evaluation, and training, but there were concerns about the loss of jobs and privacy. HR managers with higher education had a higher positive perception towards recruitment, selection, training, and performance appraisals. Knowledge and frequency of AI use had a positive influence on performance appraisal, recruitment and selection, and training, but had no influence on compensation and rewards. This study contributes to the literature by investigating perceptions of HR managers in the Saudi Arabia context. This is especially relevant in the context of technological advancement and Vision 2030 ambitions. Specifically, AI has the potential to create a skilled workforce eager for green innovation.

This research investigated the mediating role of attitudes and perceptions between working from home (WFH) and employees’ job performance. It also explored the role of gender, education level, and job position in the relationship between specifications and facilities when working from home, as well as attitudes and perceptions. This study is exploratory and capitalizes on novel findings from a questionnaire. Data were collected from 399 employees employed by the principal Saudi businesses. The current study uses structural equation modeling to test the research hypotheses and examines the direct and indirect relationship between working from home and employees’ job performance. The results confirmed the significant direct linkage between WFH and employees’ job performance through the mediating roles of WFH employees’ attitudes and perceptions. Our findings also confirm the significant relationship between WFH employees’ attributes and their job performance, and the significant association between WFH and job performance. However, our results identified the fact that perceptions have an inverse impact on job performance. This study also provides significant theoretical and practical insights for managers who are adopting WFH. It contributes empirically to the literature by informing managers of the factors driving job performance in WFH, helping organizations to cope with the many issues related to a workforce who are working from home. Our research findings also ascertained that WFH seems likely to become a permanent managerial practice in terms of human resources, rather than a simple circumstantial measure. Moreover, this study can be considered as one of the first studies that assess the effect of WFH on employees’ job performance via a mediation role of employee attributes, in the context of Saudi firms.