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Paper Number
ICIS2025-1344
Paper Type
Complete
Abstract
Actual usage of GenAI tools appears to fall behind expectations within organizations. Prior studies emphasize enablers of GenAI adoption but overlook factors that inhibit adoption. Hence, innovation resistance theory (IRT) is applied to decompose inhibitors into distinct resistance barriers. Specifically, we examine why software developers resist adoption of GenAI despite its promising productivity benefits through 15 semi-structured interviews. We further refine our analysis by considering task complexity and developer experience. Our results reveal that developers embrace GenAI for medium-complex tasks yet often reject it for both low- and high‐complexity tasks. While this pattern holds for juniors and seniors alike, the underlying reasons for their resistance differ by experience level. By highlighting how the open-ended nature of GenAI models increasingly complicates adoption, our study advances theoretical understanding of resistance to GenAI. Practically, we recommend organizations to identify measures that support employees navigating GenAI in augmenting tasks of mid-level complexity.
Recommended Citation
Mueller, Leon and Bruhin, Olivia, "Developer Resistance to Generative AI Adoption: Identifying Barriers in Software Development" (2025). ICIS 2025 Proceedings. 3.
https://aisel.aisnet.org/icis2025/impl_adopt/impl_adopt/3
Developer Resistance to Generative AI Adoption: Identifying Barriers in Software Development
Actual usage of GenAI tools appears to fall behind expectations within organizations. Prior studies emphasize enablers of GenAI adoption but overlook factors that inhibit adoption. Hence, innovation resistance theory (IRT) is applied to decompose inhibitors into distinct resistance barriers. Specifically, we examine why software developers resist adoption of GenAI despite its promising productivity benefits through 15 semi-structured interviews. We further refine our analysis by considering task complexity and developer experience. Our results reveal that developers embrace GenAI for medium-complex tasks yet often reject it for both low- and high‐complexity tasks. While this pattern holds for juniors and seniors alike, the underlying reasons for their resistance differ by experience level. By highlighting how the open-ended nature of GenAI models increasingly complicates adoption, our study advances theoretical understanding of resistance to GenAI. Practically, we recommend organizations to identify measures that support employees navigating GenAI in augmenting tasks of mid-level complexity.
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14-Implementation