Exploring collaborative decision-making: a quasi-experimental study of human and generative AI interaction

Hao, Xinyue, Demir, Emrah ORCID: https://orcid.org/0000-0002-4726-2556 and Eyers, Daniel ORCID: https://orcid.org/0000-0001-5499-0116 2024. Exploring collaborative decision-making: a quasi-experimental study of human and generative AI interaction. Technology in Society 78 , 102662. 10.1016/j.techsoc.2024.102662

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Abstract

This paper explores the effects of integrating Generative Artificial Intelligence (GAI) into decision-making processes within organizations, employing a quasi-experimental pretest-posttest design. The study examines the synergistic interaction between Human Intelligence (HI) and GAI across four group decision-making scenarios within three global organizations renowned for their cutting-edge operational techniques. The research progresses through several phases: identifying research problems, collecting baseline data on decision-making, implementing AI interventions, and evaluating the outcomes post-intervention to identify shifts in performance. The results demonstrate that GAI effectively reduces human cognitive burdens and mitigates heuristic biases by offering data-driven support and predictive analytics, grounded in System 2 reasoning. This is particularly valuable in complex situations characterized by unfamiliarity and information overload, where intuitive, System 1 thinking is less effective. However, the study also uncovers challenges related to GAI integration, such as potential over-reliance on technology, intrinsic biases particularly ‘out-of-the-box’ thinking without contextual creativity. To address these issues, this paper proposes an innovative strategic framework for HI-GAI collaboration that emphasizes transparency, accountability, and inclusiveness.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Publisher:	Elsevier
ISSN:	0160-791X
Date of First Compliant Deposit:	15 July 2024
Date of Acceptance:	14 July 2024
Last Modified:	06 Aug 2024 13:55
URI:	https://orca.cardiff.ac.uk/id/eprint/170599

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