Paper Type
Complete
Paper Number
PACIS2025-1450
Description
Generative AI has significantly impacted creative professionals’ work, yet little is known about how they adapt their skills in response to GenAI technostress and its influence on performance and use patterns, which is crucial for comprehending the dynamics of human-GenAI interaction. Building on PMT theory and SSO framework, this study investigates the role of GenAI technostress in shaping job performance and usage patterns through skill adaptation. Empirical evidence from a study of 494 creative professionals supports our hypothesis. The results show that GenAI technostress (eustress vs. distress) positively predicted multiskilling and reskilling, which in turn positively predicted innovative and routine performance, respectively, while negatively predicting GenAI avoidance. In contrast, GenAI technostress negatively predicted deskilling, which positively predicted GenAI avoidance and dependence. The moderating role of AI literacy is also examined. This study enhances our understanding of creative professionals’ adaptation to GenAI and offers insights into human-GenAI interaction in creative domains.
Recommended Citation
jia, mingxia; Zhao, Yuxiang Chris; and Zhang, Xiaoyu, "The Role of Skill Adaptation in Navigating Technostress and Job Performance among Creative Professionals" (2025). PACIS 2025 Proceedings. 2.
https://aisel.aisnet.org/pacis2025/hci/hci/2
The Role of Skill Adaptation in Navigating Technostress and Job Performance among Creative Professionals
Generative AI has significantly impacted creative professionals’ work, yet little is known about how they adapt their skills in response to GenAI technostress and its influence on performance and use patterns, which is crucial for comprehending the dynamics of human-GenAI interaction. Building on PMT theory and SSO framework, this study investigates the role of GenAI technostress in shaping job performance and usage patterns through skill adaptation. Empirical evidence from a study of 494 creative professionals supports our hypothesis. The results show that GenAI technostress (eustress vs. distress) positively predicted multiskilling and reskilling, which in turn positively predicted innovative and routine performance, respectively, while negatively predicting GenAI avoidance. In contrast, GenAI technostress negatively predicted deskilling, which positively predicted GenAI avoidance and dependence. The moderating role of AI literacy is also examined. This study enhances our understanding of creative professionals’ adaptation to GenAI and offers insights into human-GenAI interaction in creative domains.
Comments
HCI