31 02 / 23 PERSONALquarterly SUMMARY Research question: Do factors at the person, task, technical, and organizational level influence the acceptance of AI-based systems in personnel planning? Methodology: In addition to current research, we describe a study in which the desired degree of AI support (no AI, AI suggestions, AI autonomy) was asked depending on personal and situational factors. Practical implications: Functional AI systems for personnel planning should offer varying levels of support that can be adapted to the situation as well as to individuals. PROF. DR. THOMAS ELLWART Professur an der Fakultät für Wirtschaftspsychologie Universität Trier E-Mail: ellwart@uni-trier.de www.uni-trier.de LITERATURVERZEICHNIS Burton, J. W./Stein, M./Jensen, T. B. (2020): A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making, 33(2), 220–239. https://doi.org/10.1002/bdm.2155 Glikson, E./Woolley, A. W. (2020): Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi. org/10.5465/annals.2018.0057 Hodson, H. (2014): The AI boss that deploys Hong Kong’s subway engineers. New Scientist. https://www.newscientist.com/article/mg22329764-000-the-ai-bossthat-deploys-hong-kongs-subway-engineers/ Howard, F. M./Gao, C. A./Sankey, C. (2020): Implementation of an automated scheduling tool improves schedule quality and resident satisfaction. PLOS ONE, 15(8), e0236952. https://doi.org/10.1371/journal.pone.0236952 Jarrahi, M. H./Newlands, G./Lee, M. K./Wolf, C. T./Kinder, E./Sutherland, W. (2021): Algorithmic management in a work context. Big Data & Society, 8(2), 1–15. https://doi.org/10.1177/20539517211020332 Jöhnk, J./Weißert, M./Wyrtki, K. (2021): Ready or Not, AI Comes—An Interview Study of Organizational AI Readiness Factors. Business & Information Systems Engineering, 63(1), 5–20. https://doi.org/10.1007/s12599-020-00676-7 Jussupow, E./Benbasat, I./Heinzl, A. (2020): Why are we averse to algorithms? A comprehensive literature review on algorithm aversion. ECIS 2020 Proceedings, 1–16. https://www.researchgate.net/publication/344401293 Langer, M./Landers, R. N. (2021): The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers. Computers in Human Behavior, 123, 106878. https://doi.org/10.1016/j.chb.2021.106878 Lee, M. K./Kusbit, D./Metsky, E./Dabbish, L. (2015): Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 1603–1612. https://doi.org/10.1145/2702123.2702548 Marler, J. H./Boudreau, J. W. (2017): An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3–26. https:// doi.org/10.1080/09585192.2016.1244699 Mathew, D./Bergmann, R./Weyers, B./Ellwart, T./Bohrmann, D./Hölzchen, E. (2022): FlexiTeam: Flexible Team and Work Organization using Process-Oriented Case-Based Reasoning. ICCBR POCBR’22: Workshop on Process-Oriented Casebased Reasoning at ICCBR-2022, September, 2022, Nancy, France. O’Neill, T./McNeese, N./Barron, A./Schelble, B. (2022): Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature. Human Factors: The Journal of the Human Factors and Ergonomics Society, 64(5), 904–938. https://doi. org/10.1177/0018720820960865 Sharma, A. (2018, August 16): Article: How AI reinvented hiring practice at L’Oréal—People Matters. https://www.peoplematters.in/article/technology/ how-the-worlds-largest-cosmetic-company-transformed-its-hiring-practice-with-ai19006?media_type=article&subcat=techhr-2018&title=how-the-worldslargest-cosmetic-company-transformed-its-hiring-practicewith-ai&id=19006 Shrestha, Y. R./Ben-Menahem, S. M./von Krogh, G. (2019): Organizational Decision-Making Structures in the Age of Artificial Intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257 Weyers, B./Bergmann, R./Timm, I./Ellwart, T. (2021-2023): FlexiTeams – Methoden der Künstlichen Intelligenz zur Unterstützung der flexiblen Reorganisation von Arbeitsteams, Universität Trier, gefördert durch das Land Rheinland-Pfalz im Rahmen des Sonderprogramm Stärkung der Digitalisierung an den Hochschulen. THOMAS SCHILLING, PHD Wissenschaftlicher Mitarbeiter an der Fakultät für Wirtschaftspsychologie Universität Trier E-Mail: thomas.schilling@uni-trier.de www.uni-trier.de DANKSAGUNG: Wir danken Simone Kauffeld für die Einladung und kritisches Feedback zu unserem Artikel und bedanken uns bei Rosemarie Kirmse und Julia Lellmann für die Unterstützung bei der Literaturrecherche sowie für Kommentare. Ebenfalls bedanken wir uns bei Benedikt Graf, Rebecca Müller, Mona Rynek und Henrike Peiffer für hilfreiche Kommentare. Darüber hinaus danken wir dem Land Rheinland-Palz für die Förderung des Projekts im Rahmen des Sonderprogramms zur Stärkung der Digitalisierung an den Hochschulen.
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