Paper Number
ICIS2025-1080
Paper Type
Complete
Abstract
Generative artificial intelligence (GenAI) is increasingly integrated into organizational workflows, promising productivity and creativity gains while simultaneously introducing new user challenges. Drawing on the technostress trifecta (Tarafdar et al., 2019), this study explores how marketing and sales professionals experience GenAI-induced technostress. Based on 20 qualitative semi-structured interviews, we inductively develop an extended framework that captures both techno-distress and techno-eustress appraisals. We identify novel subdimensions such as techno-intransparency in distress and techno-integration in eustress, as well as establishing and reframing previously identified sub-dimensions (e.g., techno-uncertainty). Furthermore, we show how individuals both experience challenge and threat stressors depending on individual circumstances, task type, and organizational support. This study extends technostress theory to the context of GenAI and contributes a nuanced understanding of how individuals perceive, manage, and adapt to this emergent class of IS. Moreover, we offer theoretical and practical implications for supporting GenAI implementation in cognitively demanding, high-performance domains.
Recommended Citation
Tronnier, Frédéric; Pomrehn, Larissa; and Navakumaran, Rinusan, "Motivated or Overwhelmed? A Qualitative Study on Technostress in Generative Artificial Intelligence Use" (2025). ICIS 2025 Proceedings. 1.
https://aisel.aisnet.org/icis2025/is_transformwork/is_transformwork/1
Motivated or Overwhelmed? A Qualitative Study on Technostress in Generative Artificial Intelligence Use
Generative artificial intelligence (GenAI) is increasingly integrated into organizational workflows, promising productivity and creativity gains while simultaneously introducing new user challenges. Drawing on the technostress trifecta (Tarafdar et al., 2019), this study explores how marketing and sales professionals experience GenAI-induced technostress. Based on 20 qualitative semi-structured interviews, we inductively develop an extended framework that captures both techno-distress and techno-eustress appraisals. We identify novel subdimensions such as techno-intransparency in distress and techno-integration in eustress, as well as establishing and reframing previously identified sub-dimensions (e.g., techno-uncertainty). Furthermore, we show how individuals both experience challenge and threat stressors depending on individual circumstances, task type, and organizational support. This study extends technostress theory to the context of GenAI and contributes a nuanced understanding of how individuals perceive, manage, and adapt to this emergent class of IS. Moreover, we offer theoretical and practical implications for supporting GenAI implementation in cognitively demanding, high-performance domains.
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