The DOI matches its Crossref record, with a small difference in title or year; 60 of the 60 references it cites were found in a registry or an index.
Team Formation for Human-Artificial Intelligence Collaboration in the Workplace: A Goal Programming Model to Foster Organizational Change
Abstract
The need for preparing for digital transformation is a recurrent theme in the recent public and academic debate. Artificial Intelligence (AI) has the potential to reduce operational costs, increase efficiency, and improve customer experience. Thus, it is crucial to forming project teams in an organization, in such a way that they will welcome AI in the decision-making process. The current technological revolution is demanding a rapid pace of change to companies and has increased the attention to the role of teams in fostering innovation adoption. We propose an innovative multicriteria model based on the goal programming approach for solving the optimal allocation of individuals to different groups. The model copes with human resources’ cost and human–machine trust. Indeed, we propose an aggregated measure of the attitude towards AI tools to be employed to support tasks in an organization: more precisely our index is based on three dimensions: technology acceptance, technology self-efficacy, and source credibility. By incorporating this index in a team formation model, each team can be guaranteed to have less resistance to change in adopting machine-based decisions, a scenario that will characterize the years to come. The proposed index can also be integrated into more complex and comprehensive models to support business transformation.
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Cited by
Sorted by citations
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023) · Human Resource Management Review · doi:10.1016/j.hrmr.2022.100899 · Cited by 927
Gama, F., & Magistretti, S. (2025) · Journal of Product Innovation Management · doi:10.1111/jpim.12698 · Cited by 388
Brem, A., Giones, F., & Werle, M. (2021) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2021.3109983 · Cited by 249
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Alenezi, M., & Akour, M. (2025) · Applied Sciences · doi:10.3390/app15031344 · Cited by 121
Metadata
- Authors
- La Torre, D., Colapinto, C., Durosini, I., & Triberti, S.
- Affiliations
- SKEMA Business School, Université Côte d'Azur - Sophia Antipolis Campus, SOPHIA ANTIPOLIS CEDEX, France
- Ca' Foscari University of Venice (IT)
- European Institute of Oncology (IT)
- University of Milan (IT)
- Venue
- IEEE Transactions on Engineering Management
- Published
- 2023-05
- Volume
- 70
- Issue
- 5
- Pages
- 1966-1976
- ISSN
- 0018-9391 (ISSN-L)
- 1558-0040
- OpenAlex
- W3172537176
- Cited by
- 110
- References
- 60
- Citations by year
- 2026: 23 · 2025: 29 · 2024: 29 · 2023: 15
- FWCI
- 14.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Engineering Management (publishedVersion)
- ARCA (Università Ca' Foscari Venezia) (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
- Social Sciences › Psychology › Social Psychology › Team Dynamics and Performance
- Keywords
- team formation, human-AI collaboration, goal programming, multi-criteria decision making, organizational change, digital transformation, human-machine trust, technology acceptance, technology self-efficacy, source credibility, resistance to change, artificial intelligence adoption, human resource allocation, project team formation
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