From Intention to Integration: How Adoption Cost and Technical Complexity Constrain Artificial Intelligence in Aviation Supply Chains

Abstract

Artificial intelligence (AI) can improve forecasting, inventory control, maintenance planning, logistics visibility, and decision support, yet organizations often struggle to move from pilots to embedded operational use. This study examines how two salient implementation barriers—cost of adoption and technical complexity—shape AI integration in aviation supply chains and whether behavioral intention mediates these relationships. The empirical setting is Pakistan’s aviation industry, covering airlines, airport authorities, maintenance, repair and overhaul organizations, parts suppliers, ground-handling providers, and aviation logistics firms. A structured survey of supply chain, operations, procurement, logistics, and technology professionals was analyzed using partial least squares structural equation modeling. The measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity. Cost of adoption and technical complexity both reduced AI integration directly and also reduced behavioral intention. Behavioral intention, in turn, increased AI integration. The indirect effects of both cost and technical complexity through behavioral intention were significant, indicating complementary partial mediation. The structural model explained 14.6% of the variance in behavioral intention and 25.2% of the variance in AI integration. The findings show that AI adoption in safety-critical supply chains is simultaneously an economic, technical, and behavioral problem: organizations must make the investment case credible, reduce integration friction, and build user willingness in parallel. The study extends technology-adoption reasoning by distinguishing direct implementation constraints from intention-based mechanisms and offers actionable guidance for staged AI deployment in aviation supply chains.