PurposeAs Artificial Intelligence (AI) continues to reshape organizational structures and processes, understanding its integration with human capital factors becomes crucial. This study examines the associations between Applied Artificial Intelligence (AAI), Digital Literacy (DL), Job Autonomy (JA), Employee Empowerment (EE), and the perceived effectiveness of High-Performance Work Systems (HPWS) in Pakistan's manufacturing sector. It further investigates EE as a moderator of the relationships between AAI, DL, JA, and HPWS effectiveness.Design/methodology/approachA cross-sectional quantitative study design was employed to achieve the study objectives. Data were collected from employees in AI-adopting manufacturing firms via purposive sampling. Partial Least Squares Structural Equation Modelling (PLS-SEM) was conducted to test the validity, reliability, and construct validity of the study's measurement and structural models, as well as the significance and strength of the relationships.FindingsThe findings indicate that the integration of AI, employees' digital competencies, and job autonomy significantly enhances employees' perceptions of HPWS effectiveness. Psychological empowerment plays a pivotal role, exerting a direct positive effect on the effectiveness of HPWS while also strengthening the positive influence of AI use, digital literacy, and job autonomy. These results suggest that technological adoption alone is insufficient; organizations must simultaneously cultivate empowered, digitally capable, and autonomous workforces to fully realize the benefits of AI-enabled work systems.Practical implicationsThe findings offer practical implications for managers, emphasizing the need to complement AI adoption with empowerment-oriented HR practices to maximize the effectiveness of AI-driven work systems.Originality/valueThis study provides one of the early empirical examinations from a resource-constrained emerging economy, highlighting that the effectiveness of AI-enabled HPWS depends on the alignment between technological capabilities and human-centered enablers. By adopting a socio-technical perspective, it demonstrates that AI-driven transformation yields stronger outcomes when supported by digitally competent, autonomous, and empowered employees. The findings offer practical implications for managers, emphasizing the need to complement AI adoption with empowerment-oriented HR practices to maximize the effectiveness of AI-driven work systems.
The study examines the time-frequency connectedness between oil and stock prices in the expanded BRICS countries (EBRICS). Using the time-varying parameter vector autoregression (TVP-VAR) frequency-based connectedness approach of Chatziantoniou et al. (2021), we account for multiple dimensions of uncertainty, including economic policy uncertainty (EP), geopolitical risk (GP), their interaction (EGP), and broader indices of financial (FU), macroeconomic (MU), and real uncertainty (RU) in the oil-stock connectedness. Findings reveal heterogeneous but systematic spillover patterns. In the EBRICS bloc, oil, China, and Saudi Arabia are persistent net recipients of shocks, while Brazil, Russia, and South Africa are key transmitters. The UAE shows a mixed role, transmitting in the short-term but receiving in the long-term. We date-stamp high levels of connectedness between oil and stock markets in periods coinciding with critical global phenomena such as the GFC, the energy crisis, and the pandemic, for both the total connectedness indexes (TCIs) returns and volatility. Accommodating baseline uncertainty (EP, GP, EGP), total connectedness is generally dominated by short-term spillovers, while under alternative measures (FU, MU, RU), spillovers become more persistent and long-term driven. Comparisons with the original BRICS show that excluding new members yields tighter, short-lived spillovers, while the inclusion of Egypt, Saudi Arabia, and the UAE shifts the connectedness towards long-term persistence under uncertainty. Uncertainty indicators reshape the oil-stock connectedness and affect the magnitude of the TCIs. The study informs practical implications.
The current study aimed to develop and validate the Executive Function-Based Learning Difficulties Scale for AI-Driven Educational Environments (EFB-LDS-AI). In this study, EF-BLD refers to AI-mediated executive-control learning difficulties, operationalized as self-reported maladaptive reliance behaviors and perceived reductions in effortful monitoring/verification during AI-supported learning, rather than a diagnosis of stable executive-function deficits. Accordingly, two phases—qualitative and quantitative—were conducted. In the qualitative phase, face-to-face interviews with 18 cognitive science specialists, teachers, and students, along with a literature review, were conducted to generate items and dimensions associated with Executive Function-Based Learning Difficulties (EF-BLD) in AI-based educational settings. The analysis identified six key dimensions reflecting how AI-supported learning may weaken independent cognitive engagement, including overreliance on AI for thinking, superficial understanding, reduced verification of AI-generated information, weaker memory for learned material, diminished cognitive effort, and reduced critical evaluation. Psychometric properties, such as validity and reliability, were assessed during the quantitative phase. These factors captured distinct yet related patterns of AI-mediated learning difficulties: cognitive offloading, illusion of understanding, AI-induced informational bias, memory erosion, AI-induced cognitive laziness, and decline in critical thinking. This structure reflected six theoretically grounded dimensions: cognitive offloading, illusion of understanding, AI-induced informational bias, memory erosion, AI-induced cognitive laziness, and decline of critical thinking. Confirmatory Factor Analysis (CFA) confirmed the scale’s fit, with all items showing factor loadings above 0.4 (p < .001). The scale demonstrated excellent internal consistency, with Cronbach’s alpha values ranging from 0.893 to 0.958 across the six factors. Measurement invariance analysis revealed no significant gender differences. Exploratory Graph Analysis (EGA) further validated the six-factor structure. The results show that the EFB-LDS-AI is a valid, reliable, and robust tool for assessing learning difficulties related to AI interactions in educational environments. In conclusion, this scale offers a practical tool for identifying students’ AI-related patterns of cognitive offloading, weak verification, and reduced independent engagement, thereby supporting the design of AI-literacy instruction, metacognitive scaffolding, and future longitudinal research on learning in AI-integrated educational environments.
This study investigates the role of social media influencers (SMIs) in crisis communication during the 2023 floods in Derna, Libya, caused by Hurricane Daniel. Using the NodeXL Pro plugin and social network analysis (SNA) from the X platform, the paper identifies key actors and communication clusters within the crisis-related discussion and how SMIs disseminated information, mobilised support, and interacted within different groups during the crisis. The results reveal that SMIs were crucial in providing real-time updates, raising awareness, and coordinating relief efforts. Nine key influencer groups were identified, highlighting the multifaceted nature of social media's influence on disaster response. These actors often functioned as intermediaries between institutional sources, journalists, and online audiences. We introduced the concept of networked legitimacy, suggesting that authority in digital crisis environments may emerge from actors' structural positions within communication networks rather than solely from institutional authority. Positive posts were associated with greater engagement, aid distribution, and volunteer mobilisation. The findings contribute to the transformative potential of social networks in crisis communication and call for enhanced support and training for emergency influencers.
This study examines the integration of expressive arts in Jordanian kindergarten classrooms, offering insights into teacher attitudes, classroom practices, and opportunities for professional development. Using a two-phase cross-sectional design, we developed and validated two instruments: The Expressive Arts Attitudes and Beliefs Scale (EAABS) and the Expressive Arts Activity Frequency Scale (EAAFS), which provide reliable measures of teachers’ perceptions and the prevalence of arts-based activities. Data from 320 kindergarten teachers revealed that movement-based activities, reflective discussions, and music are central to classroom practice, while visual, digital, and nature-based activities are underutilized. Network analysis highlighted the interconnections among practices, with music facilitation, family-involved art events, and guided movement emerging as core nodes that amplify learning outcomes across cognitive, social, and emotional domains. These findings suggest that strategically focusing on high-leverage activities can maximize the developmental impact of expressive arts in early childhood education. The study underscores the gap between teachers’ recognition of expressive arts benefits and their practical implementation, pointing to the need for targeted professional development, culturally responsive pedagogy, and resource allocation. By leveraging central activities and gradually expanding the diversity of arts modalities, educators can enhance children’s creativity, self-expression, and engagement. These findings carry implications for classroom practice, curriculum design, and policy, providing a roadmap for integrating expressive arts in ways that are both culturally relevant and developmentally impactful. Ultimately, the study demonstrates that when expressive arts are thoughtfully embedded in early childhood education, they become more than supplementary activities—they are foundational to fostering holistic development, lifelong creativity, and engagement in learning.
