4.5 Article

Team Formation for Human-Artificial Intelligence Collaboration in the Workplace: A Goal Programming Model to Foster Organizational Change

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEM.2021.3077195

Keywords

Artificial intelligence; Tools; Context modeling; Indexes; Scheduling; Resource management; Psychology; Artificial intelligence (AI); goal programming (GP); multiple criteria decision-making (MCDM); team formation; technology acceptance; organizational change; innovation drivers

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The preparation for digital transformation is a hot topic now, and AI has the potential to lower costs, increase efficiency, so organizations need to form decision-making teams that can accept AI. A multi-criteria model has been proposed to address human resource costs and human-machine trust, helping teams reduce resistance to adopting machine decisions.
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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