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Measuring Ethical Values with AI for Better Teamwork

Author

Listed:
  • Erkin Altuntas

    (Cologne Institute for Information Systems, University of Cologne Pohligstrasse 1, 50969 Cologne, Germany)

  • Peter A. Gloor

    (MIT Center for Collective Intelligence, 245 First Street, Cambridge, MA 02142, USA)

  • Pascal Budner

    (Cologne Institute for Information Systems, University of Cologne Pohligstrasse 1, 50969 Cologne, Germany)

Abstract

Do employees with high ethical and moral values perform better? Comparing personality characteristics, moral values, and risk-taking behavior with individual and team performance has long been researched. Until now, these determinants of individual personality have been measured through surveys. However, individuals are notoriously bad at self-assessment. Combining machine learning (ML) with social network analysis (SNA) and natural language processing (NLP), this research draws on email conversations to predict the personal values of individuals. These values are then compared with the individual and team performance of employees. This prediction builds on a two-layered ML model. Building on features of social network structure, network dynamics, and network content derived from email conversations, we predict personality characteristics, moral values, and the risk-taking behavior of employees. In turn, we use these values to predict individual and team performance. Our results indicate that more conscientious and less extroverted team members increase the performance of their teams. Willingness to take social risks decreases the performance of innovation teams in a healthcare environment. Similarly, a focus on values such as power and self-enhancement increases the team performance of a global services provider. In sum, the contributions of this paper are twofold: it first introduces a novel approach to measuring personal values based on “honest signals” in emails. Second, these values are then used to build better teams by identifying ideal personality characteristics for a chosen task.

Suggested Citation

  • Erkin Altuntas & Peter A. Gloor & Pascal Budner, 2022. "Measuring Ethical Values with AI for Better Teamwork," Future Internet, MDPI, vol. 14(5), pages 1-28, April.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:5:p:133-:d:803426
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    References listed on IDEAS

    as
    1. Ronald S. Burt & Robin M. Hogarth & Claude Michaud, 2000. "The Social Capital of French and American Managers," Organization Science, INFORMS, vol. 11(2), pages 123-147, April.
    2. Alex (Sandy) Pentland, 2008. "Honest Signals: How They Shape Our World," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262162563, December.
    3. Susan R Fisk & Jon Overton, 2020. "Bold or reckless? The impact of workplace risk-taking on attributions and expected outcomes," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-19, March.
    4. Gloor, Peter A. & Fronzetti Colladon, Andrea & Grippa, Francesca, 2020. "The digital footprint of innovators: Using email to detect the most creative people in your organization," Journal of Business Research, Elsevier, vol. 114(C), pages 254-264.
    5. Qingyuan Zhao & Trevor Hastie, 2021. "Causal Interpretations of Black-Box Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 272-281, January.
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