Developing and validating a model for predicting and explaining consumer acceptance of autonomous technologies

Abstract

Autonomous technologies (ATs) are no longer a science fiction. Numerous studies have been devoted to investigating individuals’ adoption of ATs but various limitations and contradictions have been found. Four main gaps are identified in extant AT acceptance literature: deficiency of a consolidated understanding of factors influencing AT acceptance; limitation of existing technology acceptance theories in predicting AT acceptance; obsolescence of construct measurements used in extant literature; and, bias towards focusing on autonomous vehicle (AV) acceptance while significantly overlooking other ATs. Motivated by the advent of the autonomous era and these research gaps, this thesis adopts a four-study mixed-methods research design to investigate factors influencing individuals’ acceptance of ATs. Study 1 is a meta-analysis which provides the most comprehensive view of important factors determining AV adoption that have been empirically examined in extant literature. Study 2 is a qualitative exploration that uses interviews to discover factors affecting AT adoption. The integration of the meta-analytic and qualitative findings reveals that existing technology acceptance theories, despite still being predictive of AT adoption to some degree, require significant refinements. Study 3 focuses on updating measurements of popular constructs within existing literature that demonstrate obsolesce and develops measurements for new constructs identified in study 2. Based on the preceding studies, study 4 develops a model for predicting AT acceptance and validates the model under two distinct AT settings. The model is robust to different AT contexts – it can explain 80% variance of behavioural intention in the autonomous driving context and 76.3% for autonomous shopping technology. In addition, study 4 also pioneeringly examines the mediation role of trust and the moderating effect of task attribute-related factors. This thesis expands the current body of knowledge on AT acceptance and strengthens the relevance and applicability of existing technology acceptance theories in light of rapid technological advancements and societal changes in the autonomous era.

Date of Award	4 Feb 2025
Original language	English
Awarding Institution	University of Bristol
Supervisor	Emma Slade (Supervisor), Xiaojun Wang (Supervisor) & Davit Marikyan (Supervisor)