This study investigates the effect of organizational artificial intelligence (AI) absorptive capacity on employees’ perceptions of the smart work environment and how these perceptions influence their digital innovative behavior in the telecommunications industry, with Digital Transformational Leadership as a moderating variable. In this study, two important theoretical perspectives were utilized: Absorptive Capacity Theory, which highlights how organizations acquire, assimilate, and apply their knowledge related to AI, and Socio-Technical Systems Theory, which addresses the connection between technology and the social aspect of the working environment. A quantitative research design was used through partial least squares structural equation modeling (PLS-SEM). The data were gathered from 279 respondents working in the telecommunication industry in Jordan. These respondents worked for internet service providers, network operators, and mobile network operators. The findings show that organizational AI absorptive capacity has a strong positive effect on Smart Work Environment Perception (β = 0.742), which in turn enhances Digital Innovative Behavior (β = 0.681). Smart Work Environment Perception partially mediates the relationship between AI absorptive capacity and Digital Innovative Behavior (indirect effect β = 0.506), while Digital Transformational Leadership positively moderates the relationship between AI absorptive capacity and the smart work environment (β = 0.276) and between the smart work environment and Digital Innovative Behavior (β = 0.241). The current research adds value to the literature surrounding AI adoption within organizations by examining how technical capabilities and organizational leadership help foster Digital Innovative Behavior in the telecommunications industry.
To build trust among the parties involved in the supply chain, in addition to technological readiness, organizational readiness is also needed to maintain halal integrity in the applications of blockchain in halal logistics. This study developed and tested a blockchain readiness model for halal logistics by integrating the Technology-Organization-Environment (TOE) framework with halal readiness dimensions. 230 respondents from logistics service providers and halal warehousing and distribution managers in Indonesia, especially in Jakarta, completed a quantitative survey. The analysis used Partial Least Squares Structural Equation Modeling (PLS-SEM), and the data were processed in SmartPLS4. The result showed that technological readiness and organizational readiness significantly affected halal integrity. Meanwhile, technological readiness, environmental readiness and halal readiness significantly influence trust. In the next stage, halal integrity and trust were found to significant effect blockchain readiness. Trust significantly affect blockchain readiness (β = 0.322; p = 0.001). Findings in the assessment of blockchain readiness in halal logistics are based on technical and organizational readiness, environmental support, capabilities, the ability to integrate elements into logistics activities, and stakeholder trust. An analysis and processing model can expand the TOE framework by adding halal-aspect readiness, with halal integrity and trust outcomes as blockchain readiness factors in halal logistics.
Digital competency frameworks play a pivotal role in shaping modern higher education (HE), particularly in rapidly evolving technological contexts. In Iraq, the integration of robust digital competency frameworks tailored to the specific needs of higher education institutions (HEI) is crucial. However, among numerous available frameworks, selecting the most suitable one poses a significant challenge due to differences in scope, cultural values, practices, available resources, and educational systems. Previous studies or literature in the field have generally overlooked the comparison of competency frameworks to identify the most appropriate one for a given context. This research aims to address this challenge by employing multi-criteria decision-making (MCDM) techniques, including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to systematically evaluate and choose the optimal digital competency framework for Iraq's HE among the worldwide frameworks. Evaluating these frameworks involves examining their alignment with institutional goals, adaptability to diverse educational contexts, scalability, and relevance to specific learning objectives. The research findings can inform policymakers, educational leaders, and stakeholders about adopting digital competency frameworks. This could enhance the academic experience, providing learners and educators with essential digital skills needed for the changing global context.
This study benchmarks geometric and deep learning architectures for vision-based assessment of behavioral risk factors associated with Computer Vision Syndrome (CVS). Eye blinking, head pose deviation, and body twist were the three behaviors assessed. With accuracies of 0.9034 for blink detection and 0.9037 for head pose estimation, the results demonstrate that Dlib offered the most dependable performance for facial analysis. For head pose analysis, the highest F1-score was 0.7896 at a yaw threshold of 11°, with stable performance across the 9°–11° range. MediaPipe showed greater practical suitability for real-time body twist detection, with processing speeds of 29.98–32.79 FPS, although its maximum accuracy was 0.4668. In contrast, the CNN-based model was constrained by low processing speed, ranging from 4.22 to 7.32 FPS, making it less suitable for immediate deployment. These findings indicate that effective CVS risk assessment requires a balance between detection reliability and computational efficiency. Dlib is more appropriate for accuracy-sensitive facial measurements, whereas MediaPipe offers better practical value for real-time posture and movement monitoring.
The accelerating evolution of digital infrastructures and cognitive technologies has fundamentally reshaped contemporary social fabrics; a complex matrix of systemic opportunities and ethical challenges. However, while the socio-economic dividends of automation abound, the swift and uncoordinated spread of these tools poses significant disruptions to traditional socio-cultural norms, ethical paradigms, and long-standing religious values. For highly advanced governmental and judicial technical frameworks in a rapidly growing economy like the United Arab Emirates, this digital transformation happens during a critical transition through legacy infrastructure disparities, often termed the digital divide, that exist in tandem with advanced governmental and judicial technical frameworks. While sophisticated solutions, including automation and artificial intelligence, such as intelligent legal informatics, predictive machine learning, and nuanced digital forensics, play a substantial role in optimizing security frameworks, compliance architectures, and compliance processes, they also introduce new sources of exploitation for automated bias, algorithmic manipulation, and the erasure of cryptographic evidence. At the same time, rampant deepfakes, organized disinformation schemes, and generative AI systems undermine trust, necessitating strict legal and operational scrutiny of content authenticity, platform accountability, and user cognitive health. This paper provides a systematic empirical analysis of next-generation Emirati IT professionals’ levels of awareness about these sociotechnical vectors. Using an extensive questionnaire-level survey given to about 200 upper-level computer information sciences students, our initial empirical results suggest a rather sophisticated awareness baseline. The participants express considerable critical awareness of the structural, ethical, and socio-psychological dangers of these new paradigms and show a strong cultural and occupational capability to imaginatively think, govern, and steer future technologies responsibly.
