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Human and machine

Organizations increasingly rely on AI systems to support decision-making. Yet the quality of these decisions is not solely determined by technology or individual preferences. Kees Maton, a PhD candidate in the Human Performance Management group at IE&IS, demonstrates that the social relationships of the decision-maker with colleagues that are stakeholders of these decisions play a critical role.

Relationships matter

In his award-winning paper, Maton connects workplace relationship data to 18,000 decision logs, which include both AI and stakeholder advice, the actual decision and the outcome. The findings are striking: strong relationships with less competent colleagues tend to result in poorer decisions, likely due to overreliance on socially engaging but inaccurate advice. At the same time, valuable insights from competent colleagues are often overlooked when the relationship lacks energy.

International recognition

Maton received the Most Innovative Student Paper Award from the Academy of Management, one of the world鈥檚 most prestigious management conferences.. The award honors student-led papers that creatively build or test theory, investigate novel organizational phenomena, or use groundbreaking research designs. Maton鈥檚 work meets all three criteria: it extends affective decision-making theory to interpersonal dynamics, explores human-AI collaboration, and uses a unique real-world dataset.

Looking ahead

Although his PhD contract ends soon, Maton will continue his academic journey as a lecturer within IE&IS, where he will teach AI and innovation related courses. Meanwhile, he plans to further develop his research line on social influences in human-AI decision-making, supported by an NWO grant that enables the hiring of a postdoc. He also works on interventions to improve human-AI collaboration and explores how generative AI, such as ChatGPT, can enhance creativity and performance.

Further reading

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