Exploring bus drivers' intentions to collaborate with level 4 autonomous buses: Integrating the technology acceptance model and assemblage theory

Jyun-Kai Liang, Yu-Kai Huang and Chung-Cheng Lu

Research in Transportation Economics, 2025, vol. 111, issue C

Abstract: As AI proliferates, human-AI collaboration has become necessary in many domains, not least in public transportation, where highly automated, if not fully driverless buses, require human-AI cooperation. However, existing technology acceptance models lack insight into the unique factors that influence acceptance in collaborative human-AI contexts. This study integrates the Technology Acceptance Model (TAM) with Assemblage Theory to provide a comprehensive framework that does explicate key mechanisms underlying bus drivers' behavioral intentions toward Level 4 autonomous buses. Drawing upon Assemblage Theory, we conceptualize the driver and the autonomous bus as a human-machine collaborative assemblage. Perceived usefulness and perceived ease of use from TAM are modeled as antecedents, with compatibility and trust from Assemblage Theory as mediators, predicting attitude and behavioral intention. The theoretical model is examined using structural equation modeling on data collected from 719 bus drivers of four major transit companies in Taipei. Results robustly support all hypotheses, with perceived usefulness exhibiting stronger positive effects on trust and compatibility than perceived ease of use. Trust and compatibility positively influenced attitude, which strongly predicted behavioral intention to cooperate with Level 4 autonomous bus introduction. The empirical findings show TAM is enriched by the integration of Assemblage Theory concepts, extending both theories' ability to facilitate autonomous mobility human-AI collaboration.

Keywords: Autonomous bus; Human-AI collaboration; Technology acceptance model; Assemblage theory; Public transportation (search for similar items in EconPapers)
JEL-codes: R41 R42 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.retrec.2025.101555

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