Human-centered artificial intelligence: a field experiment
Open accessAbstract
Humans and artificial intelligence (AI) algorithms increasingly interact on unstructured managerial tasks. We propose that tailoring this human-AI interaction to align with individuals’ cognitive preferences is essential for enhancing performance. This hypothesis is examined through a field experiment in a multinational pharmaceutical firm. In the experiment, we manipulated four contextual parameters of human-AI interaction—work procedures, decision-making authority, training, and incentives—to align with sales experts’ cognitive styles, categorized as either adaptors or innovators. Our results show that tailored interaction significantly improves sales performance, whereas untailored interaction results in negative treatment effects compared with both the tailored and control conditions. Qualitative evidence suggests that this negative outcome arises from role conflicts and ambiguities in untailored interaction. Exploring the mechanisms underlying these outcomes further, a mediation analysis of AI login data reveals that human-AI interaction tailoring leads sales experts to adjust their AI utilization, which contributes to the observed performance outcomes. These findings support a human-centered approach to AI that prioritizes individuals’ information-processing needs and tailors their interaction with AI accordingly. This paper was accepted by Catherine Tucker, Special Issue on the Human-Algorithm Connection. Funding: This work was supported by Erling Persson Family Foundation; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Grants 100013M 204670, 181364, 185164]; Jan Wallanders och Tom Hedelius Stiftelse samt Tore Browaldhs Stiftelse [Grant W20-0036]; Marianne and Marcus Wallenberg Foundation [Grants 2021.0074, 2021.0133]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03849 .
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The proposed human-centered AI conceptual framework could support and empower nurses, particularly in under-resourced areas, by prioritizing individual patient needs, clinician-patient relationships, and care coordination services.
“We propose a human-centered AI conceptual framework that could support and empower nurses, particularly in under-resourced areas, by prioritizing individual patient needs, clinician–patient relationships, and care coordination services.8”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [8]
The Introduction and §6.3 (page unavailable) describe human-centered AI that supports people by tailoring their interactions with AI. The study concerns pharmaceutical sales experts, however; it does not address nurses in under-resourced areas or prioritizing patient needs, clinician–patient relationships, and care coordination. The citing authors’ proposed nursing framework is their own application of the general approach. (LLM, conf=0.85)
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Smet, W. D., Dam, L. V., & Janssens, P. (2026) · Annals of Organizational Culture Leadership and External Engagement Journal · doi:10.51847/zyogeeulsn
More Agency, Fewer Futures: Generative AI and the Paradox of Developing EntrepreneurialismPreprint
Pal, B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7388018
How they collaborate matters: collaboration modes and task fit in human-AI recommendations
Shao, B., Chen, Y., Teng, H., & He, X. (2026) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2026.105090
Andersen, T., Nielsen, L., & Sørensen, M. (2025) · Journal of Digital Business and Management Studies · doi:10.68159/t371404869
「上書きできる」は、医療AIを統制できている証明ではない: Hilleら(2026)とPuranam(2021)を読む
森元, 大. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.22667803
Meaning in the age of AI: Experiencing less, needing more
Mead, N. L., Heynicke, M., Williams, L. E., & Heitmann, M. (2026) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2026.102395
Louadi, M. E., & Kallel, M. (2026) · AI and Ethics · doi:10.1007/s43681-026-01341-7
The Founder Attention Wall: When Selective Oversight Scales in AI-augmented Solo VenturesPreprint
Yang, M. M.-H., & Hunt, R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7519055
Kannan, P. K., Nam, H., Kim, C., & Kulkarni, G. (2026) · Journal of Service Research · doi:10.1177/10946705261474262
Nguyen, A., Kromah, M., Nguyen, G., & Pham, V. K. (2026) · International Journal of Educational Management · doi:10.1108/ijem-05-2026-0728
DEVELOPMENT OF A MANAGEMENT SYSTEM IN THE CONTEXT OF HUMAN–ALGORITHMIC INTERACTION
Shalomova, A. (2026) · Socio-economic relations in the digital society · doi:10.55643/ser.3.61.2026.686
AI-Driven Decision Support for Systemic Innovation
Qin, Y., Luo, Y., & Wang, X. (2026) · Applied System Innovation · doi:10.3390/asi9100211
- Authors
- Krakowski, S., Haftor, D., Luger, J., Pashkevich, N., & Raisch, S.
- Affiliations
- Stockholm School of Economics (SE)
- Uppsala University (SE)
- University of Zurich (CH)
- Södertörn University (SE)
- University of Geneva (CH)
- Venue
- Management Science
- Published
- 2026-01
- Volume
- 72
- Issue
- 1
- Pages
- 57-72
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W4411147502
- Semantic Scholar
- 1167da92852ead1a96368088129c064f6149e7e6
- Cited by
- 53
- References
- 71
- Citations by year
- 2026: 47 · 2025: 6
- FWCI
- 42.44
- Type
- journal article
- Language
- en
- Open access
- green
- urn.kb.se
- Licence
- other-oa
- Locations
- Management Science (publishedVersion)
- Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
- Funders
- Jan Wallanders och Tom Hedelius Stiftelse samt Tore Browaldhs Stiftelse
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (185164, 181364, 204670)
- Marcus och Amalia Wallenbergs minnesfond (2021.0133)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- human-AI interaction, human-centered AI, cognitive style, sales performance, drug sales, decision-making power, role conflict, AI utilization, management decision-making
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