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Yes, AI Can: The Artificial Intelligence Gold Rush Between Optimistic HR Software Providers, Skeptical HR Managers, and Corporate Ethical Virtues

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AI for the Good

Part of the book series: Management for Professionals ((MANAGPROF))

Abstract

Artificial intelligence (AI) is THE future topic for companies. While corporate departments such as marketing, controlling, or logistics already use AI applications as a matter of course, human resources management (HRM) often lags behind. Against this background, this chapter explores the potential of AI in HRM. Based on the task technology fit theory, a conceptual framework for the effectiveness of HRM-related AI applications is first developed, followed by an outline of 11 task technology combinations along the human resource management systems. Based on a theory–practice comparison with the status quo in 118 German companies, the technology acceptance model is used to derive a framework conducive to AI-based HRM. The focus here is on data protection issues (GDPR) and ethical virtues (corporate ethic virtues model). The chapter is rounded off by the development of a competence profile for HR managers in the AI age. The chapter concludes with an outlook for future research and derives scientific implications.

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Notes

  1. 1.

    At the time of submission of this manuscript, the survey had not yet been completed, so that not all of the final generated data points could be considered in the evaluation of this chapter. The results presented here are therefore to be considered preliminary.

  2. 2.

    The query for the forecast of termination intentions was deliberately omitted, as this application is in a gray area both legally and ethically.

References

  • Arena, D., Tsolakis, A. C., Zikos, S., Krinidis, S., Ziogou, C., Ioannidis, D., Voutetakis, S., Tzovaras, D., & Kiritsis, D. (2018). Human resource optimisation through semantically enriched data. International Journal of Production Research, 56(8), 2855–2877.

    Article  Google Scholar 

  • Bauer, W., Ganz, W., Hämmerle, M., & Renner, T. (2020). Künstliche Intelligenz in der Unternehmenspraxis. Studie zu Auswirkungen auf Dienstleistungen und Produktion. Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO. URL: https://www.digital.iao.fraunhofer.de/de/publikationen/KIinderUnternehmenspraxis.html

  • Bernstein, E., & Waber, B. (2019). The truth about open offices. Harvard Business Review, 97(6), 82–91.

    Google Scholar 

  • Besimi, A., Dika, Z., Shehu, V., & Selimi, M. (2019). Applied text-mining algorithms for Stock Price prediction based on financial news articles. Managing Global Transitions: International Research Journal, 17(4), 335–351.

    Google Scholar 

  • Biemann, T., & Weckmüller, H. (2016). Mensch gegen Maschine: Wie gut sind Algorithmen im HR? Personal Quaterly, 4, 44–47.

    Google Scholar 

  • Chang, W.-J. A., Wang, Y.-S., & Huang, T.-C. (2013). Work design-related antecedents of turnover intention: A multilevel approach. Human Resource Management, 52(1), 1–26.

    Article  Google Scholar 

  • Chehouri, A., Younes, R., Khoder, J., Perron, J., & Ilinca, A. (2017). A selection process for genetic algorithm using clustering analysis. Algorithms, 10(4), 123–138.

    Article  Google Scholar 

  • Chu, S. (2016). Design factors affect user experience for different cultural populations. Journal of Educational Issues, 2(2), 307–319.

    Article  Google Scholar 

  • Cohen, T. (2019). How to leverage artificial intelligence to meet your diversity goals. Strategic HR Review, 1–6.

    Google Scholar 

  • Dahm, M., & Dregger, A. (2019). Der Einsatz von künstlicher Intelligenz im HR: Die Wirkung und Förderung der Akzeptanz von KI-basierten Recruiting-Tools bei potenziellen Nutzern. In: Hermeier, B., Heupel, T., & Fichtner-Rosada, S., Arbeitswelten der Zukunft. Springer Professional „Wirtschaft und Recht“.

    Google Scholar 

  • Davis, F., Bagozzi, P., & Warshaw, P. (1989). User acceptance of computer technology – a comparison of two theoretical models. Management Science, 35(8), 982–1003.

    Article  Google Scholar 

  • Dorozalla, F., & Jeddeloh, M. (2019). Hey Boss, ich brauch‘ mehr Geld. Personalmagazin, 6, 64–67.

    Google Scholar 

  • Drucker, P. F. (2007). Alles über Management. Heidelberg: Redline Wirtschaftsverlag.

    Google Scholar 

  • Ekawati, A. D. (2019). Predictive analytics in employee churn: A systematic literature review. Journal of Management Information and Decision Sciences, 4, 387–398.

    Google Scholar 

  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28, 689–707.

    Article  Google Scholar 

  • Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.

    Article  Google Scholar 

  • Groß, M., & Dorozalla, F. (2020). Das digitale Erwachen – HR-Reifegradmodell der digitalen Transformation. Personalführung, 3, 26–33.

    Google Scholar 

  • Groß, M., Dorozalla, F., & Rödiger, K. (2019). Auswahlinstrumente auf dem Prüfstand. Rekrutierung agiler Mitarbeiter. Personalführung, 6, 36–43.

    Google Scholar 

  • Grotenhermen, J.-G., Oldeweme, A., Bruckes, M., & Uhlending, L. (2020). Künstliche Intelligenz im Personalwesen: Studie zur Akzeptanz von Entscheidungen intelligenter Systeme. Zeitschrift Führung + Organisation, 1, 4–9.

    Google Scholar 

  • Hagstrom, S. L., & Maranzan, K. A. (2019). Bridging the gap between technological advance and professional psychology training: A way forward. Canadian Psychology, 60(4), 281–289.

    Article  Google Scholar 

  • Heinrich, C., & Stühler, G. (2018). Die Digitale Wertschöpfungskette: Künstliche Intelligenz im Einkauf und Supply Chain Management. In C. Gärtner & C. Heinrich (Eds.), Fallstudien zur Digitalen Transformation: Case Studies für die Lehre und praktische Anwendung (pp. 77–88). Wiesbaden: Springer Gabler.

    Chapter  Google Scholar 

  • Huff, J., & Götz, T. (2020). Was datengestütztes Personalmanagement kann und darf? Personalmagazin, 1, 48–52.

    Google Scholar 

  • Ivancevich, J. M. (1985). Predicting absenteeism from prior absence and work attitudes. Academy of Management Journal, 28(1), 219–228.

    Article  Google Scholar 

  • Kahraman, C., Kaya, I., & Çevikcan, E. (2011). Intelligence decision Systems in Enterprise Information Management. Journal of Enterprise Information Management, 24(4), 360–379.

    Article  Google Scholar 

  • Kangas, M., Muotka, J., Huhtala, M., Mäkikangas, A., & Feldt, T. (2017). Is the ethical culture of the organization associated with sickness absence? A multilevel analysis in a public sector organization. Journal of Business Ethics, 140(1), 131–145.

    Article  Google Scholar 

  • Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25.

    Article  Google Scholar 

  • Kaptein, M. (2008). Developing and testing a measure for the ethical culture of organizations: The corporate ethical virtues model. Journal of Organizational Behavior, 29(7), 923–947.

    Article  Google Scholar 

  • Kowald, C., & Bruns, B. (2019). New learning scenarios with Chatbots: Conversational learning with Jix: From digital tutors to serious interactive fiction games. International Journal of Advanced Corporate Learning, 12(2), 54–62.

    Google Scholar 

  • Kulkarni, S. B., & Che, X. (2019). Intelligent software tools for recruiting. Journal of International Technology & Information Management, 28(2), 2–16.

    Article  Google Scholar 

  • Leao, A. L. M., Barbosa-Branco, A., Turchi, M. D., Steenstra, I. A., & Cole, D. C. (2017). Sickness absence among municipal Workers in a Brazilian Municipality: A secondary data analysis. BMC Research Notes, 10, 1–9.

    Article  Google Scholar 

  • Lee, D., Kim, M., Na, I., & Hwang, S. O. (2018). Artificial intelligence based career matching. Journal of Intelligent & Fuzzy Systems, 35(6), 6061–6070.

    Article  Google Scholar 

  • Libuda, I., & Fleischmann, F. (2018). Personalplanung und people analytics. Simulation der zukünftigen Personalstruktur. Personalführung, 5, 29–35.

    Google Scholar 

  • Mainzer, K. (2016). Künstliche Intelligenz – Wann übernehmen die Maschinen? Technik im Fokus. Berlin, Heidelberg: Springer.

    Book  Google Scholar 

  • McCarthy, B., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence.

    Google Scholar 

  • Mustafa, M. K., Allen, T., & Appiah, K. (2019). A comparative review of dynamic neural networks and hidden Markov model methods for Mobile on-device speech recognition. Neural Computing & Applications, 31(2), 891–899.

    Article  Google Scholar 

  • Pappas, I. O., Mikalef, P., Giannakos, M. N., & Kourouthanassis, P. E. (2019). Explaining user experience in Mobile gaming applications: An fsQCA approach. Internet Research, 29(2), 293.

    Article  Google Scholar 

  • Pérez-Campdesuñer, R., De-Miguel-Guzmán, M., Sánchez-Rodríguez, A., García-Vidal, G., & Martínez-Vivar, R. (2018). Exploring neural networks in the analysis of variables that affect the employee turnover in the organization. International Journal of Engineering Business Management, 10, 1–12.

    Article  Google Scholar 

  • Pinkwart, N., & Rüdian, S. (2020). Die Vermessung des Lernens. Personalmagazin, 2, 30–35.

    Google Scholar 

  • Popovski, G., Seljak, B. K., & Eftimov, T. (2020). A survey of named-entity recognition methods for food information extraction. IEEE Access, 8, 31586–31594.

    Article  Google Scholar 

  • Ramella, F. (2017). The “Enterprise of Innovation” in hard times: Corporate culture and performance in Italian high-tech companies. European Planning Studies, 25(11), 1954–1975.

    Article  Google Scholar 

  • Randall, K., Isaacson, M., & Ciro, C. (2017). Validity and reliability of the Myers-Briggs personality type indicator: A systematic review and meta-analysis. Journal of Best Practices in Health Professions Diversity, 10(1), 1–27.

    Google Scholar 

  • Reichertz, J. (2003). Die Abduktion in der qualitativen Sozialforschung. Opladen: Leske & Budrich.

    Book  Google Scholar 

  • Rich, E. (1986). Artificial intelligence and the humanities. Computers and the Humanities, 19(2), 117–125.

    Article  Google Scholar 

  • Richter, A., Gačić, T., Kölmel, B., & Waidelich, L. (2019). Künstliche Intelligenz und potenzielle Anwendungsfelder im Marketing. In Deutscher Dialogmarketing Verband e.V (Ed.), Dialogmarketing Perspektiven 2018/2019. Wiesbaden: Springer Gabler.

    Google Scholar 

  • Roedenbeck, M. (2020). Die richtigen Fragen stellen: Wie künstliche Intelligenz die Personalarbeit verändern kann. Organisationsentwicklung, 1, 64–69.

    Google Scholar 

  • Schein, E. H. (1990). Organizational culture. American Psychologist, 45(2), 109–119.

    Article  Google Scholar 

  • Slattery, J., Selvarajan, T., Anderson, J., & Sardessai, R. (2010). Relationship between job characteristics and attitudes: A study of temporary employees. Journal of Applied Social Psychology, 49, 1539–1565.

    Article  Google Scholar 

  • Stock, R., Six, B., & Zacharias, N. (2013). Linking multiple layers of innovation-oriented corporate culture, product program innovativeness, and business performance: A contingency approach. Journal of the Academy of Marketing Science, 41(3), 283–299.

    Article  Google Scholar 

  • Stock-Homburg, R., & Groß, M. (2019). Personalmanagement. Theorien – Konzepte – Instrumente. Wiesbaden: Springer.

    Google Scholar 

  • Strohmeier, S. (2020). Künstliche Intelligenz in HR – eine Gefahr? Personalmagazin, 3, 38–39.

    Google Scholar 

  • Strohmeier, S., & Piazza, F. (2015). Artificial intelligence techniques in human resource management—a conceptual exploration. In C. Kahraman & S. Çevik Onar (Eds.), Intelligent techniques in engineering management. Intelligent systems reference library (Vol. 87). Cham: Springer.

    Google Scholar 

  • Tanwar, P., Prasad, T. V., & Aswal, M. S. (2010). Comparative study of three declarative knowledge representation techniques. International Journal of Computer Science and Engineering, 2(7), 2274–2281.

    Google Scholar 

  • Teece, D., Peteraf, M., & Leih, S. (2016). Dynamic capabilities and organizational agility. California Management Review, 58(4), 13–35.

    Article  Google Scholar 

  • Thurstone, L. L. (1999). The nature of intelligence. Hove: Psychology Press.

    Google Scholar 

  • Tolino, U., & Mariani, I. (2018). Do you think what I think? Strategic ways to design product-human conversation. Strategic Design Research Journal, 11(3), 254–262.

    Article  Google Scholar 

  • Tractica. (2018). Artificial intelligence for Enterprise applications: Deep learning, machine learning, natural language processing, computer vision, machine reasoning, and strong AI: Global market analysis and forecasts. URL: https://www.bmwi.de/Redaktion/DE/Publikationen/Studien/perspektiven-kuenstliche-intelligenz-fuer-einzelhandel.pdf?__blob=publicationFile&v=12

  • Tyler, T. R., & Blader, S. L. (2005). Can business effectively regulate employee conduct? The antecedents of rule following in work settings. Academy of Management Journal, 48(6), 1143–1158.

    Article  Google Scholar 

  • Ulrich, D. (1996). Human resource champion: The next agenda for adding value and delivering results. Boston: Harvard Business School Press.

    Google Scholar 

  • Upadhyay, A. K., & Khandelwal, K. (2019). Artificial intelligence-based training learning from application. Development and Learning in Organizations: An International Journal, 33(2), 20–23.

    Google Scholar 

  • Vieweg, S. (2020). Agiles Management als Wegbereiter gelebter Compliance und besserer Corporate Governance. In M. Pfannstiel & P. J. Steinhoff (Eds.), Transformationsvorhaben mit dem Enterprise Transformation Cycle meistern. Wiesbaden: Springer Gabler.

    Google Scholar 

  • Villarroel Ordenes, F., Ludwig, S., De Ruyter, K., Grewald, D., & Wetzels, M. (2017). Unveiling what is written in the stars: Analyzing explicit, implicit, and discourse patterns of sentiment in social media. Journal of Consumer Research, 43(6), 875–894.

    Google Scholar 

  • Weibel, A., Schafheitle, S., & Ebert, I. (2019). Goldgräberstimmung im Personalmanagement? Wie Datafizierungs-Technologien die Personalsteuerung verändern. (German). Organisationsentwicklung, 3, 23–29.

    Google Scholar 

  • Wilson, H. J., & Daughtery, P. R. (2018). Mensch und Maschine als Team. Harvard Business Manager, 10, 54–65.

    Google Scholar 

  • Wu, J., & Lederer, A. (2009). A meta-analysis of the role of environment-based voluntariness in information technology acceptance. MIS Quarterly, 33(2), 419-A-9.

    Article  Google Scholar 

  • Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., & Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge Information Systems, 14(1), 1–37.

    Article  Google Scholar 

  • Zhao, L., Chen, L., Liu, Q., Zhang, M., Copland, H., Yuan, X., & Elhoseny, M. (2019). Artificial intelligence-based platform for online teaching management systems. Journal of Intelligent & Fuzzy Systems, 37(1), 45–51.

    Article  Google Scholar 

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  1. Technische Hochschule Mittelhessen, Campus Giessen, Gießen, Germany

    Matthias Groß

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  1. Matthias Groß
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Correspondence to Matthias Groß .

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  1. Institute for Compliance and Corporate Governance, RFH - University of Applied Sciences Cologne, Cologne, Germany

    Stefan H. Vieweg

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Groß, M. (2021). Yes, AI Can: The Artificial Intelligence Gold Rush Between Optimistic HR Software Providers, Skeptical HR Managers, and Corporate Ethical Virtues. In: Vieweg, S.H. (eds) AI for the Good. Management for Professionals. Springer, Cham. https://doi.org/10.1007/978-3-030-66913-3_10

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