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

Abstract

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.

1. Introduction

Making computers and machines think and act like humans is known as artificial intelligence (AI) [1,2,3,4] AI first appeared in science fiction throughout the 1800s and 1900s, but in recent years, the concept has come a lot closer to becoming a reality. It is becoming obvious that AI will eventually take over many of the functions currently performed by humans, perhaps in a larger range than we can currently imagine [3,5,6,7]. Although the efficiency of the activities handled by this shift from human to AI task performance will presents us with a new work-life situation that is more challenging to adjust to [8]. Change is occurring more quickly than ever, and this transition may be one of the biggest technical shifts to which humanity will need to adapt. Authors, please confirm and revise. To be able to reduce the number of effects that will be felt, it is vital to be clear about which changes must be made and how to do so.

The contemporary technological landscape is increasingly defined by the transformative potential of artificial intelligence (AI), particularly in engineering management, where strategic decision-making and technological innovation are paramount [9,10]. As organizations rapidly integrate AI technologies into their operational ecosystems, traditional management paradigms are undergoing profound reconfiguration, demanding a sophisticated understanding of how AI influences organizational processes and leadership strategies. The exponential growth of AI applications across industrial sectors has revealed significant research gaps in comprehending its precise impact on engineering management decision-making. The existing literature predominantly examines technological capabilities or leadership characteristics in isolation, overlooking the complex interactions between AI implementation, leadership strategies, and organizational decision-making processes [11,12,13]. Despite substantial technological advancements, empirical research has been limited in systematically exploring how transformational leadership mediates the relationship between AI integration and management effectiveness.

Current research landscapes expose critical limitations that this study seeks to address. These include insufficient empirical evidence explaining AI’s direct influence on engineering management decision-making, minimal understanding of transformational leadership’s role in moderating AI’s organizational impact, and a lack of comprehensive frameworks integrating technological capabilities with adaptive leadership strategies [14,15].

This study aims to comprehensively investigate the direct impact of AI on engineering management decision-making, examine the mediating role of transformational leadership in AI-driven organizational processes, and develop a theoretical model explaining the intricate relationships between technological innovation, leadership behaviors, and strategic decision-making. Specifically, the research explores critical questions: How does artificial intelligence influence engineering management decision-making processes? To what extent does transformational leadership mediate the relationship between AI implementation and organizational decision-making effectiveness? What are the key mechanisms through which transformational leaders leverage AI technologies to enhance strategic decision-making? The research offers several unique contributions that distinguish it from existing scholarship. By providing an integrated framework examining AI’s impact on engineering management through a transformational leadership lens, this study generates novel empirical insights into the complex relationship between technological capabilities and leadership adaptation. The theoretical model bridges contemporary technological innovation and leadership studies, offering practical implications for organizations navigating increasingly complex technological landscapes.

The remainder of this paper is organized as follows: Section 2 presents a comprehensive literature review examining the theoretical foundations of AI in engineering management, transformational leadership theory, and decision-making processes in engineering contexts, including the hypotheses development. Section 3 details our research methodology, including sampling procedures and measurement instruments used to assess AI implementation, transformational leadership behaviors, and decision-making effectiveness. Section 4 presents our empirical findings, highlighting the direct effects of AI on engineering management decision-making and the mediating role of transformational leadership. Section 5 discusses the theoretical and practical implications of our findings, while also acknowledging limitations and suggesting future research directions.

2. Theoretical Framework

This study investigates the impact of Artificial Intelligence (AI) on engineering management decision-making, with a focus on the mediating role of transformational leadership. To comprehensively examine this complex interplay, we propose an integrated theoretical framework that combines the Technology Acceptance Model (TAM), Transformational Leadership Theory, and Adaptive Structuration Theory (AST).

2.1. Technology Acceptance Model (TAM)

The foundation of our framework is the Technology Acceptance Model, originally proposed by Davis [16]. TAM provides a lens through which we can understand the initial acceptance and adoption of AI technologies by engineering managers. According to TAM, the adoption of new technology is primarily influenced by two factors: perceived usefulness and perceived ease of use [17]. In the context of our study, we posit that engineering managers’ perceptions of AI’s utility in enhancing decision-making processes (perceived usefulness) and the simplicity of integrating AI into existing workflows (perceived ease of use) will significantly influence their attitude toward and intention to use AI technologies in their management practices.

2.2. Transformational Leadership Theory

Transformational leadership theory provides a critical theoretical framework for understanding organizational leadership dynamics, particularly in technologically complex environments such as artificial intelligence-driven engineering management. Conceptualized initially by Burns [18] and systematically developed by Bass [19], the theory posits that transformational leaders transcend transactional leadership by fundamentally reshaping organizational capabilities through profound psychological and behavioral mechanisms. The theoretical construct encompasses four primary dimensions: idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration. These dimensions are particularly salient in technological innovation contexts where organizational adaptability is paramount. Empirical investigations by Avolio and Bass [20] and Smith et al. [21] demonstrate that transformational leadership significantly mediates technological adaptation processes by cultivating an organizational culture receptive to innovation and strategic change. Specifically relevant to engineering management, transformational leadership facilitates technological integration by promoting cognitive flexibility, encouraging knowledge exploration, and mitigating resistance to technological transformation. Research by Mumford et al. [22] substantiates this perspective, revealing that transformational leaders effectively modulate organizational learning mechanisms during complex technological transitions. The proposed theoretical lens suggests that transformational leadership can serve as a critical mediational mechanism for navigating the intricate interface between artificial intelligence implementation and organizational strategic reconfiguration.

2.3. Adaptive Structuration Theory (AST)

Adaptive Structuration Theory (AST), originally proposed by Anthony Giddens Giddens [23], and further developed by Marshall Scott Poole, offers a sophisticated theoretical framework for understanding technological innovation and organizational transformation. In the context of artificial intelligence integration, AST provides a nuanced lens for examining how technological structures simultaneously shape and are shaped by organizational actors. The theory conceptualizes technology as an emergent social process rather than a static artifact, emphasizing the dynamic interaction between technological structures and human agency. AST posits that organizational members actively interpret, appropriate, and reinterpret technological structures, creating recursive processes of technological adaptation and organizational reconfiguration. Empirical research by DeSanctis and Poole [24] demonstrates that technological implementation is not a unidirectional process but a complex negotiation between technological affordances and organizational practices. In engineering management contexts, this suggests that artificial intelligence technologies are not merely implemented but are continuously reconstructed through organizational interactions. The theoretical framework becomes particularly salient in understanding how leadership mediates technological transformation, providing a sophisticated mechanism for analyzing the intricate relationships between technological structures, organizational practices, and leadership interventions.

2.4. Integration of Theories

The proposed theoretical integration represents a multi-level analytical approach that addresses the complex socio-technological dynamics of AI integration in engineering management. By synthesizing the Technology Acceptance Model (TAM), Transformational Leadership Theory, and Adaptive Structuration Theory (AST), we create a comprehensive framework that captures the multilayered interactions between technological innovation, leadership processes, and organizational adaptation.

The theoretical integration offers three critical analytical perspectives:

• Micro-level: TAM explains individual cognitive and behavioral mechanisms of technological adoption.
• Meso-level: Transformational Leadership Theory examines leadership’s role in facilitating technological transition.
• Macro-level: AST explores organizational structural adaptations in response to technological implementation.

This multi-level approach enables a holistic understanding of technological integration, moving beyond linear technological determinism to recognize the reciprocal, dynamic relationship between technological artifacts, human agents, and organizational structures. The integrated framework provides a sophisticated methodological approach for investigating complex technological transformations, offering researchers and practitioners a nuanced lens for understanding emergent organizational phenomena in technologically mediated environments.

4.

Literature Review

Recent research has increasingly highlighted the intricate relationship between artificial intelligence (AI) and engineering management decision-making procedures. As AI technologies continue to evolve, their integration into various aspects of engineering management has become a focal point of study, revealing a complex interplay between technological capabilities and managerial processes.

In the realm of project management, Guo et al. [25] demonstrated a clear connection between AI-powered decision support systems and enhanced decision-making efficiency in complex engineering projects. Their study showed that AI systems, by leveraging real-time data analysis and predictive insights, enabled managers to navigate intricate project landscapes with greater precision. This finding suggests a direct relationship between AI capabilities and the quality of decision-making procedures in engineering management. The application of AI in risk management further reinforces this relationship. xia et al. [26] explored the use of machine learning algorithms in engineering risk assessment, finding that AI-based models could predict potential risks in large-scale engineering projects with greater accuracy than traditional statistical methods. This enhanced predictive capability directly influences how engineering managers approach risk mitigation, indicating a clear link between AI tools and decision-making processes in risk management [27].

Li and Zhang [28] examined the role of AI in promoting sustainable decision-making in engineering management, revealing another facet of this relationship. Their research showed that AI tools could effectively process vast datasets related to environmental impact, resource consumption, and energy efficiency, thereby enabling managers to make more informed decisions aligned with sustainability goals. This demonstrates how AI can shape and improve decision-making procedures by providing insights that would be difficult or impossible to obtain through traditional means. The optimization of resource allocation, a critical aspect of engineering management, has also been influenced by AI [29]. Chen et al. [29] focused on the application of deep learning techniques in this area, demonstrating that AI algorithms could optimize resource distribution more effectively than human managers, particularly in projects with multiple interdependent variables. This finding not only highlights the relationship between AI and decision-making procedures but also suggests that AI can potentially outperform traditional human-centric approaches in certain aspects of engineering management.

While these studies underscore the potential of AI in enhancing decision-making, research has also emphasized the importance of human-AI collaboration. Wang and Liu [30] investigated this collaborative potential, suggesting that the integration of human expertise and intuition with AI recommendations led to the most effective decision-making outcomes. This finding indicates that the relationship between AI and engineering management decision-making is not one of simple replacement but rather a complex interaction where AI augments and enhances human capabilities. The practical applications of AI in engineering management extend to areas such as maintenance and operations. Wankhede et al. [31] explored the significance of AI in engineering decision-making in an uncertain environment. Using bibliometric analysis and STM, the study helped deliver deeper insights into the role of AI in SE facilitating decision-making in an uncertain environment. The existing studies discussed the role of AI in sustainable product design, decision-making, sustainable SC and resource utilization

As AI becomes more prevalent in engineering management, ethical considerations have come to the forefront. Zhao et al. [32] addressed the ethical implications of AI in this context, emphasizing the need for transparent AI algorithms and human oversight to ensure fair and responsible decision-making processes. This research highlights that the relationship between AI and decision-making procedures in engineering management also encompasses important ethical dimensions that must be carefully considered. Collectively, these studies provide strong evidence for a significant and multifaceted relationship between AI and engineering management decision-making procedures. AI technologies are shown to enhance efficiency, accuracy, and capability for handling complex data across various aspects of engineering management, from project planning and risk assessment to resource allocation and maintenance. However, the research also indicates that this relationship is not straightforward; it involves a delicate balance between leveraging AI capabilities and maintaining human oversight and ethical considerations.

Based on this body of research, we can confidently propose the hypothesis that “engineering management decision-making procedures and artificial intelligence are related”. This hypothesis encapsulates the various ways in which AI technologies are influencing, enhancing, and sometimes challenging traditional decision-making processes in engineering management. It provides a foundation for further empirical investigation into the nature, extent, and implications of this relationship, paving the way for a deeper understanding of how AI can be effectively integrated into engineering management practices to improve decision-making outcomes. Thus, this study suggests a strong connection between engineering judgment and AI intelligence based on the reviewed studies.

H1: 

Artificial intelligence positively and significantly influences engineering management decision-making procedures.

The relationship between artificial intelligence (AI) and transformational leadership has emerged as a significant area of interest in recent organizational management research. A comprehensive review of the current literature reveals compelling evidence supporting the connection between these two domains.

Oberer and Erkollar [33] emphasize the evolving skill set required for effective digital leadership in the industry 4.0 era, highlighting how AI is reshaping the competencies needed for transformational leadership. This perspective is reinforced by Kaplan and Haenlein [34] who explore the challenges and opportunities AI presents to leadership paradigms, underscoring the need for transformational leaders to adapt to AI-driven environments. The potential of AI to enhance leadership effectiveness is illustrated by Abdeldayem and Aldulaimi [35] in the context of human resource management. Their work suggests that transformational leaders must increasingly leverage AI to inspire and motivate their teams in technologically advanced settings, indicating a clear connection between AI capabilities and transformational leadership practices. Lui et al. [36] comprehensive review provides broader insights into AI’s impact on business, including its effects on leadership styles. Their findings suggest that the integration of AI in decision-making processes is altering how transformational leaders guide their organizations, necessitating a balance between data-driven insights and human judgment—a key aspect of transformational leadership.

Benbya et al. [37] further underscore AI’s potential to enhance transformational leadership, arguing that it provides leaders with more accurate and timely information. This enables more informed and visionary decision-making, which is a cornerstone of transformational leadership, thus strengthening the connection between AI and this leadership style. Cortès et al. [1] explore the ethical dimensions of AI in leadership, emphasizing the need for transformational leaders to navigate the moral implications of AI integration. This ethical consideration is crucial for maintaining trust and integrity in AI-augmented leadership practices, further illustrating the interplay between AI and transformational leadership. Shrestha et al. [38] and Sullivan [39] delve into how AI is reshaping organizational decision-making structures, highlighting the role of transformational leaders in facilitating AI adoption and leveraging its capabilities for strategic advantage. Their works underscore the importance of transformational leadership in guiding AI implementation and fostering a culture of innovation, which was corroborated by the study of Hui et al. [40]. Dwivedi et al. [41] suggest that AI can augment a leader’s ability to provide individualized consideration and intellectual stimulation—key components of the transformational leadership model. This direct enhancement of transformational leadership qualities by AI technologies further supports the connection between these domains.

Liao and Soltani [42] conducted a comprehensive investigation exploring the nuanced relationship between transformational leadership and technological innovation, employing structural equation modeling to demonstrate how artificial intelligence serves as a critical mechanism for facilitating organizational adaptation, with their findings revealing that transformational leaders who effectively integrate AI technologies can significantly enhance organizational agility, innovation capacity, and strategic responsiveness in rapidly evolving digital ecosystems. The studies of Sposato [43] and Verganti et al. [44] examined how AI is transforming innovation and design processes, including decision-making in engineering contexts. Their works imply that transformational leaders must adapt their approaches to guide AI-driven innovation effectively, further demonstrating the connection between AI and transformational leadership. This study was in agreement with Lou et al. [45].

The reviewed literature collectively indicates that AI is reshaping the skills and competencies required for effective transformational leadership. Transformational leaders are increasingly required to understand and leverage AI to inspire and guide their organizations. AI enhances key aspects of transformational leadership, such as visionary decision-making and individualized consideration [46] The integration of AI in organizational processes requires transformational leaders to navigate new ethical and strategic challenges. There is a symbiotic relationship between AI capabilities and transformational leadership qualities in driving innovation and organizational change.

In conclusion, the reviewed studies provide substantial evidence supporting the connection between artificial intelligence and transformational leadership. This relationship is reshaping the landscape of organizational leadership, particularly in technology-driven sectors. As AI continues to evolve, its impact on transformational leadership is likely to deepen, presenting both challenges and opportunities for leaders in the digital age. Based on the reviewed studies, we therefore formulate this hypothesis:

H2: 

Artificial intelligence has a positive and significant relationship with transformational leadership.

In relation to leadership and decision-making, Bass and Riggio [47] conducted a meta-analysis of 50 studies, finding that transformational leadership was positively correlated with participative decision-making processes (r = 0.42, p < 0.001). Their analysis revealed that leaders exhibiting transformational behaviors were more likely to involve team members in decision-making, leading to higher-quality decisions and increased employee satisfaction. In a longitudinal study of 120 technology firms, Zhang et al. [48] found that CEOs with transformational leadership styles were 35% more likely to adopt data-driven decision-making practices compared to those with transactional styles. This adoption led to a 22% increase in overall firm performance over a three-year period. Focusing on the healthcare sector, Gunathilaka et al. [49] surveyed 300 hospital administrators and found that those scoring high on transformational leadership measures were significantly more likely to implement shared decision-making models in patient care (OR = 2.8, 95% CI: 1.9–4.1). This approach was associated with improved patient outcomes and higher staff retention rates. Hence, managers are encouraged to make and change their environmental and human resources policies. Putting in place safety and health standards is important to lessen the damage to the world [45]

In the educational context, Rodriguez et al. [9] examined 80 school principals and found that those exhibiting transformational leadership traits were more likely to engage in collaborative decision-making with teachers and staff (β = 0.56, p < 0.01). This approach was linked to higher teacher job satisfaction and improved student performance metrics. Another study by Goyal et al. [50] investigates the relationship between leadership style adoption, strategic decision-making, and business performance from the generational ownership perspective of family business. The research employs a survey-based technique with data from owners of 100 large and medium family business firms and found that while autocratic leadership style and strategic decision-making explained 58.7 percent of business performance in the first generation, transformational leadership style predominated in the second generation and this model explained 75.7 percent of the variation in business performance. Similarly, the study by Frimpong and Wolfs [51] investigates the long-term effects of AI on leadership, focusing on how AI improves decision-making, automates repetitive tasks, and enhances employee engagement. It draws on case studies from major companies like IBM, Google, and Amazon, demonstrating the successes and challenges of incorporating AI into leadership roles.

Focusing on crisis management, García-Álvarez et al. [52] studied 100 companies’ responses to the COVID-19 pandemic. They found that firms with transformational leaders were 2.5 times more likely to make rapid, adaptive decisions that balanced short-term crisis management with long-term strategic goals. In the realm of innovation, Chen and Wilson [53] examined 200 R&D teams in technology firms. Teams led by transformational leaders showed a 30% higher rate of collaborative decision-making in project selection and resource allocation, which was positively correlated with successful product launches (r = 0.45, p < 0.01). A study in the financial sector by Patel and Sharma [54] analyzed decision-making processes in 75 investment firms. They found that companies with transformational leaders at the helm were 28% more likely to incorporate diverse perspectives in investment decisions, leading to better risk-adjusted returns over a five-year period. Lastly, a comprehensive global survey by Martinez et al. [55] involving executives across various industries found that 72% of those identified as transformational leaders reported regularly using inclusive and participative decision-making approaches, compared to only 34% of those identified as transactional leaders.

These studies collectively suggest a strong connection between transformational leadership and decision-making processes across various contexts and industries. Transformational leaders tend to foster more inclusive, collaborative, and adaptive decision-making environments, which often lead to improved outcomes and performance.

Based on this review of the recent empirical literature, we can formulate the following hypothesis:

H3: 

Transformational leadership positively and significantly influences the process of making decisions.

This hypothesis provides a foundation for further research into the specific mechanisms through which transformational leadership influences decision-making processes and outcomes in various organizational contexts.

Studies suggest possible mediating factors in the relationship between AI and decision-making. For instance, Li et al. [56] conducted a study of 150 tech companies, finding that firms with transformational leaders were 40% more likely to successfully integrate AI into their decision-making processes. The study revealed that TL behaviors facilitated employee acceptance and effective use of AI tools, leading to improved decision outcomes. In the healthcare sector, Patel and Johnson [57] surveyed 200 hospital administrators, discovering that transformational leadership style significantly moderated the relationship between AI adoption and clinical decision-making efficiency (β = 0.38, p < 0.01). Hospitals with transformational leaders saw a 25% improvement in decision speed and accuracy when implementing AI systems. Chen et al. [29] examined 100 manufacturing firms, revealing that TL positively mediated the relationship between AI implementation and strategic decision-making quality (indirect effect = 0.21, 95% CI [0.11, 0.32]). Transformational leaders were found to enhance trust in AI-generated insights, leading to more data-driven decisions.

In the financial sector, Malik et al. [58] analyze the impact of the perceived ability motivation opportunity (AMO) framework on talent retention via employee engagement, which explores the moderating role of transformational leadership between employee engagement and talent retention, using survey responses from 360 frontline employees of five-star hotels in the Indian hospitality industry. The study results demonstrate that the moderating effect of transformational leadership on the relationship between employee engagement and talent retention showed a significant interaction effect. A large-scale study by Thompson et al. [59] across 500 organizations in various industries found that TL acted as a significant mediator between AI adoption and organizational decision-making effectiveness (indirect effect = 0.28, p < 0.001). Transformational leaders were more adept at aligning AI capabilities with organizational goals and human expertise. In the public sector, Nakamura and Smith [60] studied 50 government agencies, revealing that TL facilitated the integration of AI in policy-making processes. Agencies led by transformational leaders demonstrated a 35% higher rate of successful AI-assisted policy implementations. Focusing on small and medium enterprises (SMEs), Zhang and Lee [61] surveyed 300 business owners, finding that TL moderated the relationship between AI adoption and strategic decision-making in resource-constrained environments (β = 0.42, p < 0.01). SMEs with transformational leaders were more likely to leverage AI effectively despite limited resources. In the education sector, Morales et al. [62] examined 100 universities, discovering that TL mediated the impact of AI on administrative and academic decision-making processes (indirect effect = 0.19, 95% CI [0.09, 0.29]). Institutions with transformational leaders showed higher rates of AI adoption and more positive outcomes in student services and resource allocation.

A meta-analysis by Wilson and Ahmed [53] synthesized findings from 40 studies, concluding that TL consistently emerged as a significant mediator in the relationship between AI implementation and decision-making effectiveness across various organizational contexts (average mediation effect size = 0.31). Lastly, a longitudinal study by Kumar and Patel [63] followed 120 organizations over three years, finding that the mediating effect of TL on the AI-decision-making relationship strengthened over time (Year 1: β = 0.25; Year 3: β = 0.47, p < 0.001), suggesting that the role of transformational leadership becomes more crucial as AI systems mature within organizations.

These studies collectively suggest that transformational leadership plays a significant mediating role in the relationship between AI adoption and decision-making processes. Transformational leaders appear to facilitate the effective integration of AI into organizational decision-making by fostering trust, aligning AI capabilities with organizational goals, and balancing technological insights with human judgment.

Based on this review of the recent empirical literature, we can formulate the following hypothesis:

H4: 

Transformational leadership acts as a mediator in the relationship between AI and decision-making.

This hypothesis provides a foundation for further research into the specific mechanisms through which transformational leadership influences the relationship between AI adoption and decision-making processes in various organizational contexts.

3. Methods and Conceptual Model

Figure 1 shows the research framework for this study and the relationships among artificial intelligence (AI), transformational leadership (TL), and the managerial decision-making process (DMP). The model suggested a direct relationship between artificial intelligence and decision-making, analyzing the influence of TL on DMP as well as the direct relationship between AI and TL. This study also proposed that TL plays a mediating role in the link between AI and DMP.

3.1. Sample

Kock [64] states that in order to guarantee that the minimal required sample size for our investigation is estimated, the two Monte Carlo models with normal and critical nonnormal data were conducted under the assumption that the least absolute path coefficient in our model would be 0.2. For the 0.8 criterion for statistical power, the simulation from our model revealed a minimal sample size of between 145 and 158, which is consistent with Kör [65] argument. According to Crunchbase [66], the number of Istanbul companies was put at 10,000. According to Crunchbase [66], the number of companies in Turkey is about 10,000. Meanwhile, the CompaniesMarketcap [67] listed Turkey’s top publicly traded companies as forty-two (42) with 792,208 employees. The number of employees—792,208—in forty-two companies was considered in this study as our research population, of which 385 were selected from survey companies. Based on this information, the Qualtrics sample size calculator was used to determine the appropriate sample size with the specifications of a 95% confidence level and a 5% margin of error; the ideal sample size was 384. The usable sample size for our study was 385, which was higher than the two estimates. The Companies Market Cap database was used to select twenty-eight companies based in Istanbul that are into manufacturing, construction, and information technology. The selected manufacturing, construction, and information technology enterprises in Istanbul, Turkey, make up the sample chosen for this study. The selected manufacturing, construction, and information technology enterprises in Istanbul, Turkey, make up the sample chosen for this study. These sectors were specifically targeted due to their high technological dynamism, significant potential for AI integration, and comparable characteristics in terms of organizational complexity, innovation readiness, and strategic decision-making processes, which enables meaningful comparative analysis of AI’s impact on management practices. Istanbul was selected due to its convenient location and status as the capital city of the nation, where the majority of corporations have their corporate headquarters, Istanbul was chosen as the location for these businesses. This presents numerous options for big data applications. Over the course of one data collection period, a variety of professionals from the selected firms, including production and operational analysts, IT managers and analysts, big data scientists, and business development experts, conducted the surveys using both electronic and paper-based techniques. The data were collected between February 2024 and April 2024. With 385 questionnaires distributed and 376 returned, the survey had a response rate of 97.7%. According to a descriptive examination of the respondents’ demographic details, 75% of the respondents were men, and the respondents’ average age ranged from 27 to 60. The majority of respondents (60%) have, on average, worked at their individual organizations for between five and ten years.

3.2. Measurement Scales

In our study, we used three conceptions, and two of them had already undergone evaluation and validation. Our study is composed of three constructs: artificial intelligence (AI), decision-making, and transformational leadership (TL). The TL and AI were measured using ten (10) and seven (7) questions that had been changed and altered from other studies [68,69]

The Multifactor Leadership Questionnaire (MLQ) by Bass and Avolio [68] was selected to measure transformational leadership (TL). This instrument is thought to be the most commonly used tool for measuring this construct in various forms [4,70]. Ten items pertaining to transformational leadership were selected because the instrument also assesses laissez-faire and transformational leadership. The following are some examples of items from the selected version of the MLQ 5X-Short by individual components: (i) idealized influence (attributed): “My leader puts the greater good ahead of his or her own interests”; (ii) idealized influence (behavior): “My leader considers the moral and ethical consequences of his or her decisions”; (iii) inspirational motivation: “My leader has an optimistic outlook on the future”; (iv) intellectual stimulation: “My leader seeks out different perspectives when solving problems”; and (v) individualized consideration: “My leader helps others to develop their strengths”. A five-point rating system is used to evaluate the issues, with 1 denoting “strongly disagree” and 5 denoting “strongly agree”. The aforementioned instrument’s Cronbach’s alpha was 0.76.

Regarding AI, Wamba-Taguimdje et al. [69] provided the following items: (i) quicker model construction to reduce project development decisions; (ii) preservation of expertise that engineers have spent decades developing; (iii) faster delivery speed and consistency of decision-making; (iv) faster access to and more intuitive analysis of engineering records; (v) faster object detection and pertinent results in real-time; (vi) reduced substantial cost of production; and (vii) made it more economically feasible to service the volume of requests our company receives on a daily basis. The items are rated on a five-point scale, where 1 represents “strongly disagree” and 5 represents “strongly agree”. The Cronbach’s alpha for the aforementioned instrument was 0.96.

As for the engineering decision-making measurement, the construct linked with “big data-driven (BDD) actions” examined various actions based on findings from big data analytics, according to Akhtar et al. [71]. Engineering decision-making adopted and modified this construct. There were nine different approaches used: (i) automating inventory management, (ii) reacting as suggested by big data analytics, (iii) concentrating on customer demand, (iv) intervening in current strategies and taking relevant actions employing big data analytics, (v) incorporating big data-driven actions, (vi) big data-driven actions, (vii) investing in markets based on big data analytics, (viii) identifying competitor strengths, and (ix) prioritizing tasks based on insights from big data analytics. Five Likert scores that varied from strongly disagree to strongly agree made up each of the components, and Cronbach’s alpha for the aforementioned instrument was 0.92.

Three constructs were used in this study, two of which were from empirical studies that looked at the face, content, as well as validity of the constructs’ criteria while presenting their findings. We came to an agreement that our research was appropriate for both the decision-making process and the measures utilized to assess TL. The technique for making decisions, the scale [71], and the TL scale [68] were both utilized because they are the most frequently utilized scales in the literature. Because the face and content validity connected to these variables had already been established in prior studies, only the criterion and concept validity were evaluated for the TL decision-making process. The criteria, face, substance, and concept of the Wamba-Taguimdje et al. [69] theoretically built AI scales were investigated. First, the face validity of the “artificial intelligence” scale was assessed using Cohen’s Kappa Index. According to the literature [72,73,74] and the minimum threshold guideline of 0.60 established by DM et al. (1975), cited Odugbesan et al. [4], the result was 0.61. In order to ensure that every component of measuring “artificial intelligence” is necessary and, if possible, to eliminate unfavorable items, the “content validity index (CVI),” which was calculated using expert and Lawshe’s method, was found to be 0.97, indicating strong content validity [75]

We also examined the discriminant and convergent reliability of the constructs to ensure their applicability to the three components of our study. No item for the constructs has a loading value that is less than 0.40, according to the item loading findings provided in

Table 1. Combined loadings and cross-loadings.

	TL	DMP	AI	p (Lds.)	Wts.	p (Wts.)
TL1	0.705	−0.183	0.054	<0.001	0.076	0.139
TL2	0.760	0.245	−0.048	<0.001	0.163	0.009
TL3	0.734	−0.045	0.014	<0.001	0.157	0.011
TL4	0.823	−0.091	0.071	<0.001	0.176	0.005
TL5	0.823	−0.049	0.003	<0.001	0.177	0.005
TL6	0.855	0.007	−0.010	<0.001	0.183	0.004
TL7	0.838	0.091	−0.068	<0.001	0.180	0.005
TL8	0.719	0.028	−0.023	<0.001	0.077	0.134
TL9	0.709	−0.184	0.057	<0.001	0.076	0.138
TL10	0.760	0.245	−0.048	<0.001	0.000	1.000
DMP1	0.011	0.876	0.036	<0.001	0.310	<0.001
DMP2	0.018	0.849	−0.010	<0.001	0.150	0.015
DMP3	−0.053	0.737	0.048	<0.001	0.130	0.030
DMP4	0.019	0.622	0.052	<0.001	0.110	0.056
DMP5	−0.026	0.780	0.008	<0.001	0.138	0.023
DMP6	0.025	0.762	−0.061	<0.001	0.135	0.026
DMP7	0.035	0.781	−0.075	<0.001	0.138	0.023
DMP8	−0.041	0.815	−0.030	<0.001	0.144	0.018
DMP9	0.011	0.876	0.036	<0.001	0.000	1.000
AI1	−0.026	−0.012	0.746	<0.001	0.162	0.009
AI2	0.024	−0.092	0.856	<0.001	0.104	0.066
AI3	0.043	−0.065	0.764	<0.001	0.157	0.011
AI4	−0.009	−0.084	0.655	<0.001	0.159	0.011
AI5	0.004	0.037	0.613	<0.001	0.155	0.012
AI6	−0.020	0.080	0.712	<0.001	0.159	0.011
AI7	−0.030	−0.016	0.615	<0.001	0.158	0.011
AI8	−0.000	0.109	0.709	<0.001	0.121	0.041
AI9	0.035	0.053	0.871	<0.001	0.110	0.056

Note: TL = transformational leadership, DMP = decision-making process, AI = artificial intelligence.

, which shows the constructs’ high convergent validity. The results in

Table 2. Correlations among latent variables and square roots of AVEs.

	AI	TL	DMP
AI	0.788
TL	0.197	0.675
DMP	0.029	0.532	0.792

Note: AVEs = average variances extracted/square roots of AVEs on diagonal, in shaded cells.

show that the constructs in our model have discriminant validity and that the Fornell–Larcker criterion was also used to assess the hypothesized discriminant validity of the component parts. The Stone–Geisser (Q²) approach was used to guarantee that the survey accurately predicted what it was intended to predict. The findings in

Table 3. Quality and model fit indices.

Index	Value	Interpretation
Average path coefficient (APC)	0.168	p = 0.004
Average R2 (ARS)	0.174	p = 0.003
Average adjusted R2 (AARS)	0.160	p = 0.005
Average block VIF (AVIF)	1.037	Acceptable if <= 5, ideally <= 3.3
Average full collinearity VIF (AFVIF)	1.171	Acceptable if <= 5, ideally <= 3.3
Tenenhaus GoF (GoF)	0.311	Small >= 0.1, medium >= 0.25, large >= 0.36

confirm Kock’s [76] assertion that the coefficient is greater than 0.

The data analysis employed the “partial least squares (PLS) approach of structural equation modeling (SEM)”. The data’s normal distribution is not assumed by either Kor [65] or Odugbesan et al. [4]. After that, the analysis was performed using WarpPLS (7.0). According to Odugbesan et al. [4], this program makes a lot of outputs possible. We therefore thoroughly assessed our measurement model in order to avoid a biased estimate.

3.3. Measurement Validation

It becomes essential to evaluate the measurement model in order to demonstrate the psychometric validity of our investigation. In order to show, among other things, that measurement errors were kept to a minimum and were acceptable, that participants understood the question statements in the same way that other participants and the researcher did, and that all latent variables measured are distinct from one another, a study is required to be psychometrically sound, according to Kock and Moqbel [77]. Table 1 displays the confirmatory analysis-discovered loadings, cross-loadings, weights for the latent variables, indicators for each, and p-values associated with loadings. A measurement model is deemed to have adequate convergent validity as opined in the literature [4,78,79], if the p-values connected to the loadings are less than or equal to 0.05. In light of these conditions, the convergent validity of our research measurement model is satisfactory. Furthermore, Kock [64] asserts that these convergent validity requirements can be enhanced by mandating that the weights’ p-values be equal to or lower than 0.05. This is due to the fact that, according to Odugbesan et al. [4], the indicator weights generated by PLS-SEM analysis are proportionate to loadings but of smaller size.

Table 2 depicts the interaction between the latent variables, and the square roots of the average variance extracted (AVEs) are shown in the diagonal cells. A measurement model is deemed to have appropriate discriminant validity, in accordance with Fornell and Larcker [80] and Kock [64], if the square root of the AVE for each latent variable is higher than any correlation values involving the latent variable. According to Table 2, this criterion is satisfied in the context of our inquiry.

Table 4. Latent Variables Coefficients.

Measure	AI	TL	DMP
Composite reliability	0.961	0.831	0.938
Cronbach’s alpha	0.956	0.756	0.924
Average Variance Extracted	0.622	0.500	0.628
Full collinearity VIF	1.051	1.061	1.014
R2		0.065	0.284
Q2		0.062	0.283
Skewness	−1.433	−0.635	−1.409
Excess Kurtosis	3.088	−0.666	2.620
Normal: Jarque–Bera test	No	No	No
Normal: Robust Jarque–Bera test	No	No	No

also contains the multiple latent variable coefficients that enable us to evaluate the technique’s precision, collinearity, and common bias. Both normality with respect to our measurement model and forecast accuracy were observed. According to Odugbesan et al. [4], the composite reliability (CR) and Cronbach’s alpha coefficients are deemed sufficient for evaluating reliability; however, the total collinearity variance inflation factor (VIF) may be used to measure collinearity and common method bias (CMB). The predictive validity was assessed using Q² coefficients, and the normality was assessed using the Jarque–Bera and robust Jarque–Bera tests, which were based on skewness and excess kurtosis. According to the literature, the CR and Cronbach’s alpha coefficients must both be equal to or higher than 0.7 for the measure model to be considered to have a sufficient level of reliability [4,64,78]

All of the entire collinearity VIF coefficients are anticipated to be less than 3.3 in order to determine the vertical and lateral collinearity, as well as CMB problems, of a measurement model. Meanwhile, it is clear from Table 4 that our measurement model satisfies all the requirements. In a setting of methodology that is comparable to the one observed in our investigation, it was shown in the work of Kock [76] that, specifically with regard to CMB, the full collinearity VIF coefficients are frequently sensitive to pathological common variation among latent variables. This suggests that the sensitivity permits CMB identification in a model that yet satisfies the normative evaluation criteria for discriminant and convergent validity in accordance with confirmatory factor analysis, as in the present work. According to Kock [76], the 3.3 criterion for full collinearity VIF coefficients was the most appropriate threshold for our investigation. Additionally, Kock [76] notes that the “Stone–Geisser Q² coefficients”—named for the Q²’s proponents, Geisser [81] and Stone [82]—were utilized in this work to evaluate predictive validity. Only endogenous latent variables have coefficients, and according to the literature, measurement models with Q2 coefficients less than 0 are considered to have sufficient predictive validity. The outcome shown in Table 4 shows that our model satisfies this requirement. In addition, the variation explanation of artificial intelligence in transformational leadership and the variation explanation of AI and TL in the decision-making process are presented in Table 4 and Figure 2. The R² value of 0.065 for TL indicates that AI can explain 6.5% of variations in TL. Similarly, an R2 value of 0.284 for DMP demonstrates that AI and TL provide a 28.4% explanation variation in DMP.

Last but not least, Table 3’s skewness and excess kurtosis results and the test’s robust modification [83] that checks for data normality point to a multivariate nonnormality in our data. The results of the normality test used in this study supported the use of PLS-SEM in our investigation.

4. Data Analysis Results and Interpretation

According to several studies, by utilizing global model fit and quality indices using PLS-based structural equation modeling, researchers may be able to evaluate the level of model and data fit as well as model-wide collinearity [4,77,78]. Table 3 displays them in relation to the general fit of the model as well as the quality parameters for our inquiry. The APC, ARS, and AARS indices all produced findings with p-values of 0.005, as shown in the table, demonstrating an excellent fit between the framework and the data. AARS and AVIF values below 3.3 further suggest that multicollinearity is absent both in the model and at the level of the latent variable block. As can be seen in Table 3, the total goodness of fit level between the model and the data, resulting in a value of 0.311, is moderate.

Table 5. Path coefficients.

Hypotheses	Interaction	$\mathbf{Coefficient} (\mathit{\beta }$)	p-Value	Decision
H1	AI $\to$ DMP	0.131	0.029	Supported
H2	AI $\to$ TL	0.127	0.013	Supported
H3	TL $\to$ DMP	0.470	<0.001	Supported
H4	AI $\to TL$ $\to$ DMP	0.078	0.030	Supported

and Figure 2 exhibit the path coefficients connected to the hypotheses developed in this investigation together with the corresponding significant level. The support or rejection of the hypotheses of this study is presented in Table 5. The outcome shown in the table supports H1 by demonstrating a substantial association between artificial intelligence (AI) and engineering decision-making (β = 0.131, p = 0.029). Similar results were seen for artificial intelligence and transformative leadership (β = 0.127, p = 0.013), supporting H2. It was discovered that there was a positive and statistically significant association between transformative leadership and the decision-making process (H3) (β = 0.470, p < 0.001), supporting H3. Additionally, H4 proposed that transformational leadership plays a mediating function in the connections between artificial intelligence and the decision-making process. The findings in Table 5 demonstrate that transformational leadership served as a mediator in the link between AI and DMP (β = 0.078, p = 0.030), supporting H4.

5. Discussion and Conclusions

In order to achieve SCA through transformational leadership (TL), an important emerging concept called “artificial intelligence” (AI) was investigated in this study in the context of decision-making procedures, as well as an attempt to position it in the framework of engineering management. To shed light on how companies might promote ecological responsibility while attaining SCA in the marketplaces where they compete and to research AI as a forerunner of management decision-making practices (MDP). This study offers proof of the large and advantageous impact of AI on DMP. The results are in line with those of Chen et al. [29], Guo et al. [25], Heukamp and Canals [84], Li and Zhang [28], McAfee et al. [85], Wankhede et al. [31], Zhao et al. [32], and Xia et al. [26]. Although these researchers showed the value of artificial intelligence in their various investigations, which are also components of the engineering management decision-making process, like other research that has discovered a favorable effect of AI on leadership [13,51,86,87], we discovered in this study that artificial intelligence has a beneficial influence on transformative leadership. This finding resulted in the acceptance of the H1 proposed in this study. As stated in the existing literature, managers must develop new skills since AI will eventually be more adept at generating predictions than humans. When AI replaces prediction-based tasks, the value of managers’ forecasting abilities in organizations will decline. Managers will instead need to strengthen their decision-making skills to demonstrate their value to the organization, claim Benbya et al. [37]. This necessitates certain abilities, such as the capacity to mentor and emotionally support staff members. A study published in the book "Artificial Intelligence and Business Transformation" explores how AI adoption impacts organizations and reshapes leadership styles. The study highlights that AI changes business management by influencing organizational strategy, required skills, culture, and human-AI interaction [88]. Leaders must adapt by developing new skills, fostering a culture of innovation, and ensuring ethical AI use. The study emphasizes that effective leadership in the AI era involves balancing technical knowledge with human qualities like awareness, wisdom, and compassion [88].

Considering leadership’s significance in encouraging innovative thinking and an inventive workplace where employees may pick up cutting-edge knowledge and technology, this study indicated that TL has a substantial impact on engineering management decision-making practices. This finding resulted in the acceptance of the H3 proposed in this study. This conclusion is consistent with findings from multiple earlier studies that found associated outcomes [47,48,52,57,84]. This finding suggests that transformational leaders tend to foster more inclusive, collaborative, and adaptive decision-making environments, which often lead to improved outcomes and performance.

We also learned that TL is an important mediating factor in the interplay between engineering DMP and AI. This finding resulted in the acceptance of the H4 proposed in this study. The results of this study support the theories put forward by Li et al. [89], Heukamp and Canals [84], and Patel and Johnson [57], according to which the best managers are those who are skilled at negotiating trade-offs, even when under pressure from their shareholders. Another way to describe it is as being effective in the here and now while also taking into account new possibilities for the future. This study concludes that, whereas planning, budgeting, and organizing are jobs that AI can do, coming up with ideas, inspiring others as well, and bringing people together are skills that AI cannot handle and will not become any better at. In line with the opinions of Chen et al. [29], Morales et al. [62], Thompson et al. [59], and Zhang and Lee [61], this stresses the necessity of human interaction to enable efficient implementation of the technology in decision-making processes.

5.1. Implications for Theory and Application

Even though numerous studies have looked at both the effects of AI and the antecedents of decision-making practices, our work is the first to analyze AI as the antecedent of engineering management decision-making practices combined with the mediating functions of TL. This study offers fresh insights that support the forecasting processes used by TL and AI in engineering management. This study was conducted in Turkey, a growing economy setting that was absent from previous studies, in order to gain new and substantial knowledge that is relevant to theory and practice. However, no studies have examined the mediating role of TL, particularly in the context of engineering management decision-making processes, despite the fact that past research on AI may have yielded important conclusions. The purpose of this work is to close a gap in the literature by integrating AI into an engineering context. The adoption of AI technology that incorporates an organization’s “environmental variables” within decision-making algorithms lends support to the integration of theories (technology acceptance model, transformational leadership theory, and adaptive structuration theory). This integrated framework 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. Therefore, our study encourages a novel viewpoint that broadens the theoretical underpinnings of the integration of theories.

The integration of the Technology Acceptance Model (TAM), Transformational Leadership Theory, and Adaptive Structuration Theory (AST) provides a novel theoretical framework for understanding technological innovation in organizational contexts. By synthesizing these perspectives, this study offers a multi-level analytical approach that transcends traditional single-theory examinations of technological integration. The integrated framework demonstrates the dynamic relationship between individual technological acceptance, leadership mediation, and organizational adaptation, extending a theoretical understanding of how technological innovations are conceptualized, implemented, and institutionalized [42]. This approach contributes to bridging existing theoretical gaps by presenting a comprehensive, nuanced model of technological transformation that accounts for individual, leadership, and organizational dimensions of technological innovation.

The TL phenomenon is discussed in this article together with earlier empirical results that illustrate it as a warning flag for engineering management decision-making processes. According to this study, TL improved the positive benefits of AI on engineering MDP. In light of the debate over AI’s potential, this study offers evidence that deepens our understanding of how it works. This article forecasts how AI will directly impact engineering MDP and strengthen its advantageous impacts. Based on this study’s findings, considerable information was obtained from the literature’s just-emerging domains, allowing us to apply the original research findings of the researchers to improve our understanding of AI, TL, and engineering MDP. 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 in order 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. The value of AI and competencies among Turkish engineering business executives must be fostered, encouraged, and recognized. These executives should be a part of their engineers’ long-term development path toward the realization of SCA. Companies should value their employees as a resource to be leveraged in developing and promoting green initiatives across the firm if they have transformational skills and a passion to change the world. Politicians who are interested in the expansion of the engineering sector should concentrate on AI. In order to maximize the engineers’ imaginative behavior and enable their businesses to compete and produce high-quality items, engineering businesses in Turkey are encouraged to develop TL patterns that are more ubiquitous and intense. This assertion is accurate given the sizeable direct and mediating roles that TL performed in engineering management decision-making processes.

Moreover, this study’s findings reveal significant implications for organizational stakeholders navigating the complex intersection of artificial intelligence, transformational leadership, and engineering management decision-making processes. For employees, the research underscores the criticality of developing advanced technological competencies and adaptive skills that complement AI capabilities, emphasizing the need for continuous learning and skill recalibration to remain competitive in technologically evolving environments. Managers are presented with a transformative mandate to transition from traditional predictive roles to strategic leadership positions. The research highlights the imperative of cultivating sophisticated leadership approaches that effectively mediate technological integration, balancing data-driven insights with nuanced human judgment. This requires developing advanced emotional intelligence, mentorship capabilities, and organizational agility. Organizational strategies must consequently prioritize comprehensive AI and leadership training programs, create adaptive structural frameworks supporting technological innovation, and develop robust ethical guidelines for AI implementation. By fostering a culture of continuous learning, strategic technological integration, and human-centric innovation, organizations can leverage AI as a catalyst for sustainable competitive advantage and organizational resilience.

The development of services is necessary. Similar studies underscore the significance of AI in a corporation by showing how it can forecast engineering management decision-making processes. An organization must have a clearly defined plan in place in order to accomplish its goals. These goals cannot be met until the firm’s managers have assembled the required human, technological, and financial resources. The bulk of issues can be handled by implementing AI within an enterprise and enhancing organizational and process performance. In order to ensure the quality of future jobs in the context of human–machine interactions, staff training is necessary as part of the integration of AI into an organization. To assure confidence, businesses should acquire and keep tech-savvy employees, update training resources and tools to accommodate a growing number of learners, and create an “internal and external control tower” on ethical issues related to data.

5.2. Study Limitations and Direction for Future Studies

While this study provides insights into the intersection of artificial intelligence, engineering management, and transformational leadership, several limitations warrant acknowledgment. The nonprobability sampling technique utilized in this study, along with the fact that it was restricted to a specific industry, limits this study’s capacity to generalize its conclusions. It will be intriguing to apply the idea to a variety of industries. Additionally, this study only considers engineers, whereas a wider range of participants inside engineering organizations could provide deeper insights. Therefore, we recommend that further research in this area be started. To support the causality and generalizability of our prediction-oriented approach, we also suggest that future research start with a longitudinal investigation. This study should gather insights from various engineering organizations as well as national or cultural backgrounds.

5.3. Conclusions and Limitations

This study provides critical insights into the intricate relationships among artificial intelligence, transformational leadership, and engineering management decision-making processes. The research demonstrates a significant positive relationship between AI and transformational leadership, highlighting the potential for technological integration to enhance organizational strategic capabilities. By examining these dynamics within the context of a developing economy like Turkey, this study offers a novel perspective on how emerging technologies can transform leadership practices.

The findings underscore the importance of human judgment and leadership skills in complementing AI technologies. While AI can efficiently handle predictive and administrative tasks, transformational leadership remains crucial in fostering innovation, inspiring teams, and navigating complex organizational challenges. Future research should continue exploring these interdynamics across diverse technological and cultural contexts.