Member’s performance in human–AI hybrid teams: a perspective of adaptability theory

Purpose As human–AI hybrid teams become more common, it is essential for team members to interact effectively with artificial intelligence (AI) to complete tasks successfully. The integration of AI into the team environment alters the cooperative dynamics, prompting inquiry into how the design characteristics of AI impact the working mode and individual performance. Despite the significance of this issue, the effects of AI design on team dynamics and individual performance have yet to be fully explored. Design/methodology/approach Drawing upon coping theory, this study presents a research model aimed at elucidating how the characteristics of AI in human–AI interaction influence human members’ adaptive behavior, subsequently impacting individual performance. Through the creation of experiments that require human–AI collaboration to solve problems, we observe and measure various aspects of AI performance and human adaptation. Findings We observe that the explainability of AI enhances the behavioral adaptation of human team members, whereas the usability and intellectuality of AI improve their cognitive adaptation. Additionally, we find that human team members’ affective adaptation is negatively affected by the likability of AI. Our findings demonstrate that both behavioral and cognitive adaptations positively impact individual performance, whereas affective adaptation negatively impacts it. Practical implications Our research findings provide recommendations for building efficient human–AI hybrid teams and insights for the design and optimization of AI. Originality/value Overall, these results offer insights into the adaptive behavior of humans in human–AI interaction and provide recommendations for the establishment of effective human–AI hybrid teams. These findings pioneer an understanding of how design characteristics of AI impact team dynamics and individual performance, establishing a connection between AI attributes and human adaptive behavior.

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Publication Details

Journal
Information Technology and People
Published
2024-12-07
DOI
https://doi.org/10.1108/itp-05-2023-0513
Citations
3
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
1.04
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article

Member’s performance in human–AI hybrid teams: a perspective of adaptability theory

Aihui Chen, Yaobin Lu, Anran Lyu
3 citations
Information Technology and People
AI in Service Interactions
1.04
article

Member’s performance in human–AI hybrid teams: a perspective of adaptability theory

Aihui Chen, Yaobin Lu, Anran Lyu
article en
3 citations

Abstract

Purpose As human–AI hybrid teams become more common, it is essential for team members to interact effectively with artificial intelligence (AI) to complete tasks successfully. The integration of AI into the team environment alters the cooperative dynamics, prompting inquiry into how the design characteristics of AI impact the working mode and individual performance. Despite the significance of this issue, the effects of AI design on team dynamics and individual performance have yet to be fully explored. Design/methodology/approach Drawing upon coping theory, this study presents a research model aimed at elucidating how the characteristics of AI in human–AI interaction influence human members’ adaptive behavior, subsequently impacting individual performance. Through the creation of experiments that require human–AI collaboration to solve problems, we observe and measure various aspects of AI performance and human adaptation. Findings We observe that the explainability of AI enhances the behavioral adaptation of human team members, whereas the usability and intellectuality of AI improve their cognitive adaptation. Additionally, we find that human team members’ affective adaptation is negatively affected by the likability of AI. Our findings demonstrate that both behavioral and cognitive adaptations positively impact individual performance, whereas affective adaptation negatively impacts it. Practical implications Our research findings provide recommendations for building efficient human–AI hybrid teams and insights for the design and optimization of AI. Originality/value Overall, these results offer insights into the adaptive behavior of humans in human–AI interaction and provide recommendations for the establishment of effective human–AI hybrid teams. These findings pioneer an understanding of how design characteristics of AI impact team dynamics and individual performance, establishing a connection between AI attributes and human adaptive behavior.

Information Technology and PeopleVol. 39(1)
Tianjin University of Finance and Economics (CN), Systems Analytics (United States) (US), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 18%
AI in Service Interactions
1.04
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