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Human–generative AI cooperation and knowledge collaboration in online knowledge communities: an analysis based on adaptive structuration theory for individuals

Hongyu Cui & Peng Yu

Information Technology & People2026https://doi.org/10.1108/itp-09-2024-1184article
AJG 3ABDC A
Weight
0.37

Abstract

Purpose Human-generative artificial intelligence (AI) cooperation (HGAIC) offers new opportunities for knowledge collaboration in online knowledge communities (OKCs). However, existing studies neglect to explore how different HGAIC strategies (exploitative and explorative) affect knowledge collaboration (knowledge acquisition and contribution) under the influence of GAI capabilities (generativity, autonomy and interactivity) and OKC factors (question variety and platform involvement). This means there is a lack of sufficient theoretical and practical guidelines on promoting flexible HGAIC to enhance knowledge collaboration in OKC. Design/methodology/approach The partial least squares structural equation modeling method was employed to analyze data from 355 OKC users, and the vignette experiment method was used to analyze data from 345 OKC users. Findings The findings indicate that both exploitative and explorative cooperation positively affect knowledge acquisition and contribution. GAI generativity, GAI autonomy, GAI interactivity and platform involvement positively affect both exploitative and explorative cooperation. In contrast, question variety only significantly enhances exploitative cooperation. Furthermore, the structured antecedents can enhance knowledge acquisition and contribution by facilitating exploitative and explorative cooperation. In addition, creative time pressure weakens the positive relationship between exploitative cooperation and knowledge contribution while enhancing the positive relationship between explorative cooperation and knowledge contribution. Originality/value First, this study classifies HGAIC into exploitative and explorative cooperation and reveals their crucial roles in knowledge collaboration in OKC. Second, this study supplements and examines the ASTI framework, identifying the antecedents and outcomes of exploitative and explorative cooperation. Finally, this study emphasizes the bidirectional moderating role of creative time pressure in the impact of exploitative and explorative cooperation on knowledge contribution.

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https://doi.org/https://doi.org/10.1108/itp-09-2024-1184

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@article{hongyu2026,
  title        = {{Human–generative AI cooperation and knowledge collaboration in online knowledge communities: an analysis based on adaptive structuration theory for individuals}},
  author       = {Hongyu Cui & Peng Yu},
  journal      = {Information Technology & People},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/itp-09-2024-1184},
}

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Evidence weight

0.37

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

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
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.