Does trust matter? Unpacking creative workers’ deep use of generative AI in human-AI interaction

Wang, Yun; Wang, Chunyue; Chen, Xiaoyu

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https://hdl.handle.net/2142/126215 Copy

Description

Title

Does trust matter? Unpacking creative workers’ deep use of generative AI in human-AI interaction

Author(s)

• Wang, Yun
• Wang, Chunyue
• Chen, Xiaoyu

Issue Date

2025-03-11

Keyword(s)

• Generative AI (GAI)
• Human-AI Interaction
• Trust, Deep Technology Use
• Creative Work

Abstract

Trust is important for creative workers’ deep structure use of generative AI (GAI) technologies like ChatGPT. This poster investigates the role of trust in facilitating creative workers’ deep engagement with GAI technologies by drawing upon Self-Determination Theory (SDT) and the lens of human-AI interaction. The authors examined how both human factors—autonomy, competence, and relatedness—and AI system attributes—accountability, transparency, and interpretability—shaped trust in GAI. A survey conducted among 300 creative professionals revealed that autonomy and relatedness were key predictors of benevolence-based trust, while accountability and transparency significantly influenced credibility. These findings suggested that trust in GAI played a crucial role in motivating users to explore advanced features, leading to more intensive and sophisticated use. The study offers theoretical contributions by highlighting the nuanced relationship between trust and technology adoption, particularly in creative industries, and provides practical recommendations for the design of GAI systems that prioritize user-centric and transparent functionalities.

Publisher

iSchools

Series/Report Name or Number

iConference 2025 Proceedings

Type of Resource

Other

Genre of Resource

Conference Poster

Language

eng

Handle URL

https://hdl.handle.net/2142/126215

Copyright and License Information

Copyright 2025 is held by Yun Wang, Chunyue Wang, and Xiaoyu Chen. Copyright permissions, when appropriate, must be obtained directly from the author.

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