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Advancing Decision-Making through AI-Human Collaboration: A Systematic Review and Conceptual Framework

Han Li & Feng Tian

Group Decision and Negotiation2026https://doi.org/10.1007/s10726-026-09980-1article
AJG 2ABDC A
Weight
0.50

Abstract

The interplay between humans and artificial intelligence (AI) in decision-making has become increasingly intricate and significant. Despite rapid advancements, the literature remains fragmented, with limited integrative frameworks to explain how AI-human dynamics and decision-making typologies shape outcomes. This study addresses this critical gap by conducting a systematic review and bibliometric analysis of 627 articles, culminating in a novel conceptual framework. The framework identifies two critical dimensions, AI-human dynamics and decision typologies, that shape decision outcomes and introduces four distinct paradigms of AI-human collaborative decision-making: adaptive intuitive decision, programmed algorithmic decision, interpretive analytical decision and integrative hybrid decision. By synthesizing these paradigms, this research advances the theoretical understanding of hybrid decision-making systems and provides actionable insights for organizations navigating complex and AI-driven environments. By elucidating the mechanisms and trade-offs inherent in AI-human collaboration, this work lays a robust foundation for future research on adaptive decision systems in an era marked by accelerating technological change.

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https://doi.org/https://doi.org/10.1007/s10726-026-09980-1

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@article{han2026,
  title        = {{Advancing Decision-Making through AI-Human Collaboration: A Systematic Review and Conceptual Framework}},
  author       = {Han Li & Feng Tian},
  journal      = {Group Decision and Negotiation},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s10726-026-09980-1},
}

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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

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