Artificial Intelligence-Driven Convergence and its Moderating Effect on Multi-Source Trust Transfer
Files
Date
2023-01-03
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
5208
Ending Page
Alternative Title
Abstract
AI-driven convergence describes how innovative products emerge from the interplay of embedded artificial intelligence (AI) in existing technologies. Trust transfer theory provides an excellent opportunity to deepen prevailing discussions about trust in such converged products. However, AI-driven convergence challenges existing theoretical assumptions. The context-specific interplay of multiple trust sources may affect users’ trust transfer and the predominance of trust sources. We contextualized AI-driven convergence and investigated its impact on multi-source trust transfer. We conducted semi-structured interviews with 25 participants in the context of autonomous vehicles. Our results indicate that users’ perceived trust source control, perceived trust source accessibility, and perceived trust source value creation share may moderate users’ trust transfer. We contribute to research by contextualizing convergence in AI, revealing the impact of AI-driven convergence on trust transfer and the importance of trust as a dynamic construct.
Description
Keywords
Advances in Trust Research: How Context and Digital Technologies Matter, ai-driven convergence, autonomous vehicles, convergence, trust in technology, trust transfer
Citation
Extent
10
Format
Geographic Location
Time Period
Related To
Proceedings of the 56th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.