Exploring Enablers and Inhibitors of AI-Enabled Drones for Manufacturing Process Audits: A Mixed-Method Approach

Abstract

The objective of this study is to explore the enablers and inhibitors of AI-enabled drone adoption for manufacturing process audit using a mixed-method design. A qualitative study was performed to explore the enablers and inhibitors. Further, based on the findings of the qualitative studies, a framework was proposed, and proposed hypotheses were examined using a survey-based study. The results indicated that function, environmental, and epistemic values are major enablers, whereas vulnerability and sunk cost barriers are major inhibitors to adoption intention. The initial trust and inertia were crucial mediators, and organizations' technological innovativeness played a crucial moderating role. This study enriches the literature on technological adoption for sustainability and helps audit service providers design strategies to enhance AI-enabled drone adoption for process audits.

Keywords: AI-enabled drone, Industry 4.0, innovation resistance theory, mixed-method approach, process audit, theory of consumption values