Influence of Artificial Intelligence on Engineering Management Decision-Making with Mediating Role of Transformational Leadership

• Published in: Systems (Basel) 2024-12, Vol.12 (12), p.570
• Main Authors: Abositta, Abdullah, Adedokun, Muri Wole, Berberoğlu, Ayşen
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Business competition; Decision making; engineering firms; Engineering management; High technology industry; Leadership; management decision-making; Management decisions; Managers; Mediation; Negotiation, mediation and arbitration; Subscribers; sustainable competitive advantage; Technology assessment; Transformational leadership; Turkey
• ISSN: 2079-8954; 2079-8954
• DOI: 10.3390/systems12120570

The relationship between AI and management decision-making has received increasing attention in the literature, but the impact of AI on managerial decision-making through transformational leadership has not yet been thoroughly examined. Thus, this study investigates the impact of artificial intelligence on engineering management decision-making through transformational leadership. The participants include 385 employees drawn from manufacturing, construction, and information technology firms in Turkey. The data were processed using WarpPLS (7.0), and the estimation was conducted with the use of “partial least squares structural equation modeling (PLS-SEM)”. A positive and significant direct influence of “artificial intelligence” and “transformational leadership” on engineering management decision-making practices was demonstrated in this study, while transformational leadership was also found to have a significant mediating role in the relationship between artificial intelligence and engineering management decision-making practices. This study concluded with theoretical and practical implications for policymakers in the engineering industry by providing an integrated framework that allows for a nuanced examination of how AI impacts engineering management decision-making. It accounts for individual perceptions, leadership influences, and organizational adaptations, providing a comprehensive lens through which to analyze the complex interplay between AI technology, leadership, and decision-making processes in engineering management contexts. In addition, the findings of our study have significant implications for engineers and for governments creating standards to help preserve engineering businesses. Leaders and practitioners should research the instillation of values inherent to AI for an organization like engineering businesses to ensure that AI is being used to enable effective decision-making towards ensuring the accomplishment of their sustainable competitive advantage.