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How do generative AI applications impact employees’ exploitative innovation behavior? An explanation based on the technology-organization-environment framework

Zhangxiang Zhu et al.

Journal of Systems and Information Technology2026https://doi.org/10.1108/jsit-05-2025-0221article
AJG 1ABDC B
Weight
0.50

What the paper says

Purpose We aim to adopt a technology-organization-environment framework to explain how Generative Artificial Intelligence (GenAI) applications influence exploitative innovation behavior among employees. Design/methodology/approach Based on the technology-organization-environment framework, we take perceived task-technology fit as the mediator to explore the mechanism of GenAI application on employees’ exploitative innovation behavior from technology, organization, and environmental aspects, and verifies the structural equation model with data from 374 questionnaires. Findings The personalization, information quality, organizational readiness, digital leadership, and competitive pressure significantly affect perceived task-technology fit, which in turn affects employees’ exploitative innovation behavior. Furthermore, AI awareness moderates the relationship between perceived task-technology fit and exploitative innovation behavior. Specifically, the higher the AI awareness, the stronger the positive influence of perceived task-technology fit on attitude, and the stronger the mediating effect of user attitude on the relationship between perceived task-technology fit and exploitative innovation behavior. Originality/value We identified the underlying mechanisms linking GenAI adoption to exploitative innovation behavior, thus offering the theoretical guidance for enterprises to apply GenAI further and improve the practice of employee innovation management.

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https://doi.org/https://doi.org/10.1108/jsit-05-2025-0221

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@article{zhangxiang2026,
  title        = {{How do generative AI applications impact employees’ exploitative innovation behavior? An explanation based on the technology-organization-environment framework}},
  author       = {Zhangxiang Zhu et al.},
  journal      = {Journal of Systems and Information Technology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/jsit-05-2025-0221},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.