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Künstliche Intelligenz in Familienunternehmen

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Herausforderungen im Management von Familienunternehmen

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Zusammenfassung

In den letzten Jahren wurden die Tools der künstlichen Intelligenz (KI) zunehmend in Familienunternehmen eingesetzt. Dies ist auf die zahlreichen Vorteile zurückzuführen, die der Einsatz KI-gestützter Systeme mit sich bringt, wie z. B. eine verbesserte Effizienz durch die Automatisierung von Aufgaben, eine höhere Produktivität, ein besseres Kund*innenerlebnis und eine bessere Entscheidungsfindung. Empirische Untersuchungen haben jedoch gezeigt, dass bestimmte Hindernisse, darunter hohe Anschaffungskosten, ethische Fragestellungen und die Angst vor Datenschutzverletzungen, einer maximalen Integration von KI-Tools in Familienunternehmen noch im Wege stehen. Dieser Beitrag beleuchtet den Zusammenhang zwischen den Innovationsansätzen und der Implementierung von KI-Tools in Familienunternehmen, wirft einen kritischen Blick auf die Vor- und Nachteile des KI-Einsatzes und bietet einen Ausblick auf zukünftige Entwicklungen der KI in Familienunternehmen an.

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Literatur

  • Alberti, F. G., & Pizzurno, E. (2013). Technology, innovation and performance in family firms. International Journal of Entrepreneurship and Innovation Management, 17(1/2/3), 142–161.

    Article  Google Scholar 

  • Amato, S., Patuelli, A., Basco, R., & Lattanzi, N. (2023). Family firms amidst the global financial crisis: A territorial embeddedness perspective on downsizing. Journal of Business Ethics, 183(4), 213–236.

    Article  Google Scholar 

  • Amore, M. D., Pelucco, V., & Quarato F. (2022). Family ownership during the Covid-19 pandemic. Journal of Banking & Finance, 135, 106385 https://doi.org/10.1016/j.jbankfin.2021.106385.

    Article  Google Scholar 

  • Anderson, R. C., & Reeb, D. M. (2003) Founding-family ownership and firm performance: Evidence from the S&P 500. Journal of Finance, 58(3), 1301–1327.

    Article  Google Scholar 

  • Astrachan, J. H., & Zellweger, T. (2008) Performance of family firms: A literature review and guidance for future research. Zeitschrift für KMU und Entrepreneurship, 56(2), 83–108.

    Article  Google Scholar 

  • Bellandi, M., Santini, E., Vecciolini, C., & De Propris, L. (2020). Industry 4.0: Transforming local productive systems in the Tuscany region. In L. De Propris & D. Bailey (Hrsg.), Industry 4.0 and Regional Transformations (S. 84–96). London: Routledge.

    Chapter  Google Scholar 

  • Berrone, P., Cruz, C., & Gómez-Mejía, L. R. (2012). Socioemotional wealth in family firms: Theoretical dimensions, Assessment approaches, and agenda for future research. Family Business Review, 25(3), 258–279.

    Article  Google Scholar 

  • Block, J. H. (2012). R&D Investments in family and founder firms: An agency perspective. Journal of Business Venturing, 27(2), 248–265.

    Article  Google Scholar 

  • Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The role of big data and predictive analytics in retailing. Journal of Retailing, 93(1), 79–95.

    Article  Google Scholar 

  • Bresciani, S., Thrassou, A, & Vrontis, D. (2013). Change through innovation in family businesses: Evidence from an Italian sample. World Review of Entrepreneurship Management and Sustainable Development, 9(2), 195–215.

    Article  Google Scholar 

  • Brigham, K. H., & Payne, G. T. (2019). Socioemotional wealth (SEW): Questions on construct validity. Family Business Review, 32(4), 326–329.

    Article  Google Scholar 

  • Buntak, K., Kovačić, M., & Mutavdžija, M. (2021). Application of Artificial Intelligence in the business. International Journal for Quality Research, 15(2), 403–416.

    Article  Google Scholar 

  • Calabrò, A, Vecchiarini, M, Gast, J., Campopiano, G., De Massis, A., & Kraus, S. (2019). Innovation in family firms: A systematic literature review and guidance for future research. International Journal of Management Reviews, 21(3), 317–355.

    Article  Google Scholar 

  • Campopiano, G., & De Massis, A. (2015). Corporate social responsibility reporting: a content analysis in family and non-family firms. Journal of Business Ethics, 129(3), 511–534.

    Article  Google Scholar 

  • Canhoto, A. I., & Clear, F. (2020). Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential. Business Horizons, 63(2), 183–193.

    Article  Google Scholar 

  • Cassia L., De Massis A., & Pizzurno E. (2012) Strategic innovation and new product development in family firms: An empirically grounded theoretical framework. International Journal of Entrepreneurial Behaviour & Research, 18(2), 198–232.

    Article  Google Scholar 

  • Chaudhuri, R., Chatterjee, S., Kraus, S., & Vrontis, D. (2023). Assessing the AI-CRM technology capability for sustaining family businesses in times of crisis: the moderating role of strategic intent. Journal of Family Business Management, 13(1), 46–67.

    Article  Google Scholar 

  • Chua, J.H., Chrisman, J.J., & Sharma, P. (1999) Defining the Family Business by Behavior. Entrepreneurship Theory and Practice, 23(4), 19–39.

    Article  Google Scholar 

  • Coles, L. S. (1977). The application of artificial intelligence to medicine. Futures, 9(4), 315–323.

    Article  Google Scholar 

  • Cucculelli, M., Le Breton-Miller, I., & Miller, D. (2016). Product innovation, firm renewal and family governance, Journal of Family Business Strategy, 7(2), 90–104.

    Article  Google Scholar 

  • Cyert, R., & March, J. (1963). A behavioral theory of the firm. Prentice-Hall, Englewood Cliffs.

    Google Scholar 

  • De Massis, A., Frattini, F., Pizzurno, E., & Cassia, L. (2015). Product innovation in family versus nonfamily firms: An exploratory analysis. Journal of Small Business Management, 53(1), 1–36.

    Article  Google Scholar 

  • Decker, C., & Günther, C. (2017). The impact of family ownership on innovation: Evidence from the German machine tool industry. Small Business Economics, 48(1), 199–212.

    Article  Google Scholar 

  • Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121(C), 283–314.

    Google Scholar 

  • Dukino, C., Friedrich, M., Ganz, W., Hämmerle, M., Kötter, F., Meiren, T., Neuhüttler, J., Renner, T., Schuler, S., & Zaiser, H. (2020). Künstliche Intelligenz in der Unternehmespraxis: Studie zu Auswirkungen auf Dienstleistungen und Produktion. Stuttgart: Fraunhofer Verlag.

    Google Scholar 

  • Duran, P, Kammerlander, N., van Essen, M., & Zellweger, T. (2016). Doing More with less: Innovation input and output in family firms. Academy of Management Journal, 59(4), 1224–1264.

    Article  Google Scholar 

  • Felden, B., Schirrmeister, M., & Schönmoser (2018). Familien-Unternehmen-Zukunft: Einfluss gesellschaftlicher Entwicklungen auf größere Familienunternehmen in Deutschland. (Schriftenreihe des EMF-Instituts der HWR Berlin, Band 10). Dollerup: Flying Kiwi Media GmbH.

    Google Scholar 

  • Feuerriegel, S., Shrestha, Y.R., von Krogh, G., & Zhang, C. (2022). Bringing artificial intelligence to business management. Nature Machine Intelligence, 4(7), 611–613.

    Article  Google Scholar 

  • Garbellano, S., & Da Veiga, M. D. R. (2019). Dynamic capabilities in Italian leading SMEs adopting industry 4.0. Measuring Business Excellence, 23(4), 472–483.

    Article  Google Scholar 

  • Gersick, K., Davis, J. A., Hampton, M. M., & Lansberg, I. (1997). Generation to Generation: Life Cycles of the Family Business. Boston: Harvard Business School Press.

    Google Scholar 

  • Gläß, R. (2018). Künstliche Intelligenz im Handel 2 – Anwendungen. Effizienz erhöhen und Kunden gewinnen. Wiesbaden: Springer.

    Google Scholar 

  • Gnecco, G.; Amato, S.; Patuelli, A., & Lattanzi, N. (2020). Machine learning application to family business status classification. 6th International Conference on Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science, 12565 (S. 25–36). Cham: Springer.

    Google Scholar 

  • Gómez-Mejía, L. R., Haynes, K. T., Nunez-Nickel, M., Jacobson, K. J. L., & Moyano-Fuentes, J. (2007). Socioemotional wealth and business risk in family-controlled firms: Evidence from Spanish olive oil mills. Administrative Science Quarterly, 52(1), 106–137.

    Article  Google Scholar 

  • Götz, M., & Jankowska, B. (2017). Clusters and Industry 4.0 – do they fit together?, European Planning Studies, 25(9), 1633–1653.

    Article  Google Scholar 

  • Ingram, A. E., Lewis, M. W., Barton, S., & Gartner, W. B. (2016). Paradoxes and innovation in family firms: The role of paradoxical thinking. Entrepreneurship Theory and Practice, 40(1), 161–176.

    Article  Google Scholar 

  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.

    Article  Google Scholar 

  • Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., Kim, B. H., & Noh, S. D. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), 111–128.

    Article  Google Scholar 

  • Kraus, S., Pohjola, M., & Koponen, A. (2012). Innovation in family firms: an empirical analysis linking organizational and managerial innovation to corporate success. Review of Managerial Science, 6(3), 265–286.

    Article  Google Scholar 

  • Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155.

    Article  Google Scholar 

  • Lee, J., Davari, H., Singh, J., & Pandhare, V. (2018). Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 18(1), 20–23.

    Article  Google Scholar 

  • Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging technology and business model innovation: the case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44.

    Article  Google Scholar 

  • Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129(C), 911–926.

    Google Scholar 

  • Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434.

    Article  Google Scholar 

  • Nieto M. J., Santamaría L., & Fernández Z. (2015). Understanding the innovation behavior of family firms. Journal of Small Business Management, 53(2), 382–399.

    Article  Google Scholar 

  • Oosthuizen, K., Botha, E., Robertson, J., & Montecchi, M. (2021). Artificial intelligence in retail: The AI-enabled value chain. Australasian Marketing Journal, 29(3), 264–273.

    Article  Google Scholar 

  • Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2023). Toward AI governance: Identifying best practices and potential barriers and outcomes. Information Systems Frontiers, 25(1), 123–141.

    Article  Google Scholar 

  • Patuelli, A., Carungu, J., & Lattanzi, N. (2022). Drivers and nuances of sustainable development goals: Transcending corporate social responsibility in family firms. Journal of Cleaner Production, 373, 133723.

    Article  Google Scholar 

  • Peltonen, J. (2018). Can supervised machine learning be used to identify family firms using a sophisticated definition? Academy of Management Proceedings, 16425. https://doi.org/10.5465/AMBPP.2018.154.

  • Poza, E. J. (2013). Family Business. Mason, OH: Cengage Learning.

    Google Scholar 

  • Prestifilippo, G. (2020). Auswirkungen von Industrie 4.0 auf Warehouse-, Transport- und Supply-Chain-Management-Systeme. In M. ten Hompel, T. Bauernhansl & B. Vogel-Heuser (Hrsg.), Handbuch Industrie 4.0 (S 347–360). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Rainsberger, L. (2021). KI – die neue Intelligenz im Vertrieb: Tools, Einsatzmöglichkeiten und Potenziale von Artificial Intelligence. Wiesbaden: Springer.

    Book  Google Scholar 

  • Rizomyliotis, I., Kastanakis, M. N., Giovanis, A., Konstantoulaki, K., & Kostopoulos, K. (2022). “How mAy I help you today?” The use of AI chatbots in small family businesses and the moderating role of customer affective commitment. Journal of Business Research, 153, 329–340.

    Article  Google Scholar 

  • Roed, I. (2016). Disentangling the family firm’s innovation process: A systematic review. Journal of Family Business Strategy, 7(3), 185–201.

    Article  Google Scholar 

  • Ruiz-Real, J. L., Uribe-Toril, J., Torres, J. A., & De Pablo, J. (2021). Artificial intelligence in business and economics research: Trends and future. Journal of Business Economics and Management, 22(1), 98–117.

    Article  Google Scholar 

  • Schwab, K. (2017). The Fourth Industrial Revolution. New York: Crown Publishing Group.

    Google Scholar 

  • Sharma, P., & Nordqvist, M. (2008). A classification scheme for family firms: From family values to effective governance to firm performance. In J. Tàpies & J. L. Ward (Hrsg.), Family Values and Value Creation. A Family Business Publication (S. 71–101). London: Palgrave Macmillan.

    Chapter  Google Scholar 

  • Simon, F. B., Wimmer, R., & Groth, T. (2012). Mehr-Generationen-Familienunternehmen – Erfolgsgeheimnisse von Oetker, Merck, Haniel u. a. (2. Auflage). Heidelberg: Carl Auer.

    Google Scholar 

  • Simon, H.A. (2000). Bounded rationality in social science: Today and tomorrow. Mind & Society 1(1), 25–39.

    Article  Google Scholar 

  • Sipior, J. C., Lombardi, D. R., & Gabryelczyk, R. (2021). AI recruiting tools at ShipIt2Me. com. Communications of the Association for Information Systems, 48(1), 433–455.

    Article  Google Scholar 

  • Soluk, J., Miroshnychenko, I., & Nambisan, S. (2022). Adoption of artificial intelligence: A mixed methods study on network ties and family influence. Academy of Management Annual Meeting Proceedings, 2022(1), 13303. https://doi.org/10.5465/AMBPP.2022.13303abstract.

    Article  Google Scholar 

  • Stoilova, E. (2021). AI chatbots as a customer service and support tool. ROBONOMICS: The Journal of the Automated Economy, 2, 21–26.

    Google Scholar 

  • Stover, R. Jr, & McKibbin, P. (2022). How family offices can boost efficiency with artificial intelligence. Ernst & Young, https://www.ey.com/en_us/family-enterprise/how-family-offices-can-boost-efficiency-with-ai. Zugegriffen: 25. April 2023.

  • Swab R. G., Sherlock C., Markin E., & Dibrell C. (2020). “SEW” what do we know and where do we go? A review of socioemotional wealth and a way forward. Family Business Review, 33(4), 424–445.

    Article  Google Scholar 

  • Tagiuri, R., & Davis, J. (1996). Bivalent attributes of the family firms. Family Business Review, 9(2), 199-208.

    Article  Google Scholar 

  • Ulrich, P. S. (2021). Artificial intelligence in SMES and family firms – a plea for more research based on socioemotional wealth. International Conference on Information Systems (ICIS) TREO (Technology, Research, Education, and Opinion), 34. https://aisel.aisnet.org/treos_icis2021/34.

  • Ulrich, P., Frank, V., & Buettner, R. (2023). Artificial intelligence in small and medium-sized family firms: An empirical study on the impact of family influence. Corporate Governance and Organizational Behavior Review, 7(1), 72–80.

    Article  Google Scholar 

  • Upadhyay, N., Upadhyay, S., Al-Debei, M.M., Baabdullah, A.M., & Dwivedi, Y.K. (2023). The influence of digital entrepreneurship and entrepreneurial orientation on intention of family businesses to adopt artificial intelligence: examining the mediating role of business innovativeness. International Journal of Entrepreneurial Behavior & Research, 29(1), 80–115.

    Article  Google Scholar 

  • Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924.

    Article  Google Scholar 

  • Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1–37.

    Article  Google Scholar 

  • Zapata-Cantu, L., Sanguino, R., Barroso, A., & Nicola-Gavrilă, L. (2022). Family business adapting a new digital-based economy: Opportunities and challenges for future research. Journal of the Knowledge Economy, 1–18.

    Google Scholar 

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Authors and Affiliations

  1. Northumbria University Amsterdam Campus, Amsterdam, Niederlande

    Alessia Patuelli

  2. Max Planck Institut für Intelligente Systeme, Stuttgart, Deutschland

    Ksenia Keplinger

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  1. Alessia Patuelli
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Correspondence to Ksenia Keplinger .

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Editors and Affiliations

  1. Institut für Angewandte Statistik, Johannes Kepler Universität Linz, Linz, Österreich

    Christine Duller

  2. Lehrstuhl für BWL, insbesondere Management Accounting and Control, Universität Siegen, Siegen, Deutschland

    Martin R. W. Hiebl

  3. Accounting & Financial Management am Department für Business & Tourism, Fachhochschule Salzburg, Puch/Salzburg, Österreich

    Michael Kuttner

  4. Institut für Controlling & Consulting, Johannes Kepler Universität Linz, Linz, Österreich

    Stefan Mayr

  5. Accounting & Financial Management am Department für Business & Tourism, Fachhochschule Salzburg, Puch/Salzburg, Österreich

    Christine Mitter

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Patuelli, A., Keplinger, K. (2023). Künstliche Intelligenz in Familienunternehmen. In: Duller, C., R. W. Hiebl, M., Kuttner, M., Mayr, S., Mitter, C. (eds) Herausforderungen im Management von Familienunternehmen . Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-41978-3_11

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