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This empirical investigation examines the complex dynamics between Artificial Intelligence (AI), Potential Development (PD), Training Initiatives (TI), and High-Performance Work Systems (HPWS) within manufacturing firms to gain valuable insights into how AI technologies influence high-performance work systems through employee development and training. Using a purposive sampling technique, around two hundred employees from twenty-four manufacturing firms in the textile, automotive, steel, and pharmaceutical sectors participated in the self-administered survey. The empirical analysis of the data sets was conducted using the PLS-SEM approach. This result demonstrated positive associations between AI, PD, and HPWS, emphasizing the key role of AI in supporting employee development and improving high-performance work systems. Furthermore, training’s amplification effect on the relation between artificial intelligence and professional development highlighted the significance of employees’ upskilling for AI integration. Conversely, the mediating role of PD between AI adoption and HPWS effectiveness highlighted the significant role of employee professional development in achieving HPWS through AI integration within the systems. The study offered insight into the mediation of PD between AI and HPWS effectiveness, emphasizing its centrality in translating AI-driven advances into tangible organizational outcomes. The study findings have significant ramifications for both theory and practice. Theoretically, this research adds to an evolving dialogue surrounding AI’s effects on HR practices and organizational outcomes; practically speaking, organizations can utilize this research’s insights in strategically integrating AI technologies, designing tailored training programs for their employees, and creating an environment conducive to ongoing employee development.
This empirical investigation examines the complex dynamics between Artificial Intelligence (AI), Potential Development (PD), Training Initiatives (TI), and High-Performance Work Systems (HPWS) within manufacturing firms to gain valuable insights into how AI technologies influence high-performance work systems through employee development and training. Using a purposive sampling technique, around two hundred employees from twenty-four manufacturing firms in the textile, automotive, steel, and pharmaceutical sectors participated in the self-administered survey. The empirical analysis of the data sets was conducted using the PLS-SEM approach. This result demonstrated positive associations between AI, PD, and HPWS, emphasizing the key role of AI in supporting employee development and improving high-performance work systems. Furthermore, training’s amplification effect on the relation between artificial intelligence and professional development highlighted the significance of employees’ upskilling for AI integration. Conversely, the mediating role of PD between AI adoption and HPWS effectiveness highlighted the significant role of employee professional development in achieving HPWS through AI integration within the systems. The study offered insight into the mediation of PD between AI and HPWS effectiveness, emphasizing its centrality in translating AI-driven advances into tangible organizational outcomes. The study findings have significant ramifications for both theory and practice. Theoretically, this research adds to an evolving dialogue surrounding AI’s effects on HR practices and organizational outcomes; practically speaking, organizations can utilize this research’s insights in strategically integrating AI technologies, designing tailored training programs for their employees, and creating an environment conducive to ongoing employee development.
The Fourth Industrial Revolution (Industry 4.0) has profoundly transformed industries worldwide through the integration of advanced digital technologies, including artificial intelligence, digital twins, building information modeling (BIM), and the Internet of Things (IoT). The Architecture, Construction, and Engineering (ACE) sectors are increasingly adopting these innovations to meet the evolving demands of the global market. Within this dynamic context, Saudi Arabia has emerged as a front-runner and significant investor in this sector, as evidenced by the launch of ambitious mega-projects such as NEOM and The Line. These developments prompt valuable discussions about the readiness of graduates to adapt to rapid technological advancements and meet the current demands of the Saudi market. Although numerous studies have explored this issue, the Saudi context presents unique challenges and opportunities due to the accelerated pace of change within the ACE sectors, driven by the goals of Vision 2030. For this reason, this paper aims to address this gap by exploring the readiness of architectural programs in the context of Saudi Arabia to meet the demands of Industry 4.0. To achieve this, a comprehensive literature review was conducted, developing an analytical framework. Subsequently, a multiple-cases approach was employed, with an overall top-level discussion on the undergraduate architecture program subjects available in the five regions in Saudi Arabia. A combination of field observations, domain expertise, and evidence-based coding methods was employed to develop the SWOT analysis. The SWOT framework was utilized to identify key strengths, weaknesses, opportunities, and threats within the current academic programs. The findings were then analyzed in a comprehensive discussion, highlighting necessary transformations in existing programs. The methodology employed in our study involves prolonged engagement and persistent observation to enhance the quality and credibility of the discussion. This paper serves as a roadmap for guiding future educational reforms and aligning architectural education with emerging industry demands and technological advancements in the field. Four key themes are essential for aligning architectural education with Industry 4.0: sustainability in the built environment, innovation and creativity, digital applications in the built environment, and entrepreneurship and leadership in venture engineering. It also strongly emphasized sustainability courses and noted notable deficiencies in preparing students for a digitally driven professional landscape. For example, the average program comprises 162 credit hours and 58 courses, with only six related to Industry 4.0. The top five institutions offering Industry 4.0 courses ranked from highest to lowest are ARCH-U11, ARCH-U8, ARCH-U3, ARCH-U4, and ARCH-U15. ARCH-U11 offers the most Industry 4.0 courses, totaling 15, which account for 26.8% of its courses and 15% of its credit hours, in contrast to ARCH-U20, which offers no courses. The novelty of this research lies in its comprehensive analysis of the readiness of architecture program curricula from 20 Saudi universities to meet the requirements of Industry 4.0. Importantly, these findings support previous studies that established guidelines that mandate the inclusion of sustainability, innovation, and digital skills in architectural education programs. Contribution to the knowledge and findings is valuable for educational institutions, policymakers, and industry leaders, offering insights into evolving architectural education to meet future industry demands and foster technological innovation and sustainable development. Moreover, it provides actionable recommendations for curriculum development in alignment with Vision 2030. Contrary to expectations, findings show that lower-ranked universities offer more Industry 4.0-related courses than higher-ranked ones, emphasizing the need to align university evaluation standards with labor market demands.
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