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An investigation of factors influencing user information adoption behavior in human–AI interaction contexts: a hybrid SEM and fsQCA approach

Gan Tang et al.

Aslib Journal of Information Management2026https://doi.org/10.1108/ajim-04-2025-0196article
AJG 1
Weight
0.50

What the paper says

Purpose To ensure the effective utilization of information resources by users in the era of artificial intelligence, it is crucial to explore the factors influencing user information adoption behavior and its configurational pathways within human–AI interaction contexts, which is the aim of this study. Design/methodology/approach This study focuses on users of AIGC platforms and employs the Elaboration Likelihood Model (ELM) as a theoretical foundation. Data analysis is conducted using Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). Findings The SEM results indicate that, with the exception of technological characteristics, all other factors positively influence user information adoption behavior. The fsQCA identifies four distinct configurations that contribute to information adoption behavior. Originality/value The findings suggest that AIGC platforms should enhance user information adoption by optimizing interaction systems, ensuring information quality, simplifying operational processes, and integrating emotional design.

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https://doi.org/https://doi.org/10.1108/ajim-04-2025-0196

Or copy a formatted citation

@article{gan2026,
  title        = {{An investigation of factors influencing user information adoption behavior in human–AI interaction contexts: a hybrid SEM and fsQCA approach}},
  author       = {Gan Tang et al.},
  journal      = {Aslib Journal of Information Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/ajim-04-2025-0196},
}

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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.