Performance expectancy and change agency: the imperative drivers of AI adoption in emerging economy technical organizations

Abstract

The rapid integration of artificial intelligence (AI) into organizational processes is reshaping strategic decision-making across technical environments. Although AI offers significant transformative potential, limited research examines the factors influencing its adoption among engineering managers in emerging economies. This study addresses this gap by investigating how organizational pressures, managerial role perceptions, and performance expectations shape the intention to adopt AI within technical organizations. A survey of 186 senior engineering managers from Pakistani technical firms was analyzed using a structural equation modeling approach. The hypotheses were developed using the Unified Theory of Acceptance and Use of Technology and Ulrich’s Human Resource (HR) roles framework. The structural model results revealed that performance expectancy was the strongest predictor of AI adoption intention among engineering managers (beta = 0.753,
p
 < 0.001), followed by the change agent role (beta = 0.136,
p
 < 0.001). Competitive pressure also had a positive but weak significant effect on behavioral intention to adopt AI (beta = 0.095,
p
 = 0.043). However, top management support (beta = 0.102,
p
 = 0.092), administrative expert role (beta = 0.003,
p
 = 0.926), and strategic partner role (beta = 0.009,
p
 = 0.789) did not significantly influence AI adoption intention. Moreover, these results indicate that engineering managers will promote the adoption of AI if they can see a direct correlation between adopting AI and achieving improved performance. They also identify with being the leader of the change process. Therefore, enhancing organizational readiness for AI requires redefining managerial roles and building change-oriented capabilities rather than relying solely on competitive forces or hierarchical directives.