HUMAN - AI COLLABORATION AND CUSTOMER INTENTION TO USE AUTONOMOUS DELIVERY ROBOTS IN LAST-MILE LOGISTICS: THE ROLE OF CUSTOMER TRUST
DOI:
https://doi.org/10.62985/j.huit_ojs.vol26.no3E.501Keywords:
Autonomous delivery robots, human–AI collaboration, AI transparency, perceived safety, customer trust, behavioral intention, PLS-SEM.Abstract
The growing adoption of artificial intelligence and robotic systems in logistics has brought autonomous delivery robots (ADRs) to the forefront of last-mile distribution innovation. This paper investigates the extent to which three consumer-level perceptions, human–AI collaboration (HAC), AI transparency (AIT), and perceived safety (PS) shape customer trust (CT) and subsequent behavioral intention (BI) toward ADR services. Using Trust Theory and the Technology Acceptance Model as theoretical anchors, ten research hypotheses were constructed and validated through Partial Least Squares Structural Equation Modeling (PLS-SEM) on survey responses from 750 Vietnamese consumers with exposure to autonomous delivery robots (ADRs)-related services. The findings revealed that all proposed relationships received statistical support that human–AI collaboration (HAC) emerged as a dominant predictor of both customer trust (CT) and behavioral intention (BI) toward autonomous delivery robots (ADRs) services, with perceived safety (PS) and AI transparency (AIT) also yielding significant coefficients; moreover, customer trust (CT) predicted BI (β = 0.197) and served as a partial mediator linking each antecedent to adoption intention, as confirmed by bootstrapped 95% BCa intervals that did not include zero. The reliance on self-reported, single-country data constrains causal interpretation and geographic generalizability, pointing to the need for longitudinal and multi-cultural research designs that incorporate real behavioral outcomes. By simultaneously examining human–AI collaboration (HAC), AI transparency (AIT), and perceived safety (PS) within one coherent framework, this study offers a novel theoretical contribution to autonomous delivery robots (ADRs) adoption scholarship and delivers concrete recommendations for logistics practitioners, technology designers, and regulatory bodies aiming to foster trustworthy autonomous delivery ecosystems.
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