Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
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.
Recommended Citation
Renner, Maximilian; Lins, Sebastian; Söllner, Matthias; Jarvenpaa, Sirkka; and Sunyaev, Ali, "Artificial Intelligence-Driven Convergence and its Moderating Effect on Multi-Source Trust Transfer" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 3.
https://aisel.aisnet.org/hicss-56/os/trust/3
Artificial Intelligence-Driven Convergence and its Moderating Effect on Multi-Source Trust Transfer
Online
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.
https://aisel.aisnet.org/hicss-56/os/trust/3