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Exploring Design Principles Promoting Organizational Knowledge Creation via Robo-Advisory: The Case of Collaborative Group Decision-Making in the After Sales Management

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Design Science Research for a Resilient Future (DESRIST 2024)

Abstract

Along with recent advances of team-AI collaboration, we observe the emergence of adaptive, collaborative, and explainable AI technologies that spur the creation of organizational knowledge for group decision-making. This is substantiated by the explicit and tacit knowledge that decision-makers can create with AI and by the procedural support that AI can provide for the organizational knowledge conversion processes among decision-makers. However, research on AI design for effective organizational knowledge creation is in a nascent state. This is problematic because this leaves organizations without guidance for the implementation and assessment of AI that enables effective knowledge creation. We see potential in robo-advisors, which represent a form of AI, to facilitate such organizational knowledge creation for decision-making in economic contexts. We aim to realize this potential and apply an action design research approach to identify meta-requirements and design principles for a robo-advisor prototype. The robo-advisor is contextualized in the after-sales domain of a German car manufacturer, the Dr. Ing. h.c. F. Porsche AG, where complex decision problems are informed and solved by expert groups. The robo-advisor prototype contributes to collaborative knowledge creation that informs the group’s decision-making on field measures in the event of product quality issues aimed at ensuring product safety and customer satisfaction.

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References

  1. Alavi, M., Leidner, D.E.: Review: knowledge management and knowledge management systems. MIS Q. 25, 107–136 (2001)

    Article  Google Scholar 

  2. Grant, R.M.: Toward a knowledge-based theory of the firm. Strat. Mgmt. J. 17, 109–122 (1996)

    Article  Google Scholar 

  3. Nonaka, I., von Krogh, G.: Tacit knowledge and knowledge conversion. Organ. Sci. 20, 635–652 (2009)

    Article  Google Scholar 

  4. Nonaka, I.: A dynamic theory of organizational knowledge creation. Organ. Sci. 5, 14–37 (1994)

    Article  Google Scholar 

  5. Argote, L., Lee, S., Park, J.: Organizational learning processes and outcomes: major findings and future research directions. Manag. Sci. 67, 5399–5429 (2021)

    Article  Google Scholar 

  6. Huber, G.P.: Organizational learning: the contributing processes and the literatures. Organ. Sci. 2, 88–115 (1991)

    Article  Google Scholar 

  7. Kogut, B., Zander, U.: What firms do? Coordination, identity, and learning. Organ. Sci. 7, 502–518 (1996)

    Article  Google Scholar 

  8. March, J.G.: Exploration and exploitation in organizational learning. Organ. Sci. 2, 71–87 (1991)

    Article  Google Scholar 

  9. Zercher, D., Jussupow, E., Heinzl, A.: When AI joins the team: a literature review on intragroup processes and their effect on team performance in team-AI collaboration. In: ECIS 2023 Proceedings (2023)

    Google Scholar 

  10. Seeber, I., et al.: Machines as teammates: a collaboration research agenda. In: HICCS, vol. 57 (2018)

    Google Scholar 

  11. Sturm, T., et al.: Coordinating human and machine learning for effective organization learning. MIS Q. 45, 1581–1602 (2021)

    Article  Google Scholar 

  12. Anthony, C., Bechky, B.A., Fayard, A.-L.: “Collaborating” with AI: taking a system view to explore the future of work. Organ. Sci. 34, 1672–1694 (2023)

    Article  Google Scholar 

  13. Cabitza, F., Campagner, A., Simone, C.: The need to move away from agential-AI. Int. J. Hum. Comput. 155, 102696 (2021)

    Article  Google Scholar 

  14. Brynjolfsson, E., Mitchell, T.: What can machine learning do? Workforce implications. Science 358, 1530–1534 (2017)

    Article  Google Scholar 

  15. Argote, L., Miron-Spektor, E.: Organizational learning: from experience to knowledge. Organ. Sci. 22, 1123–1137 (2011)

    Article  Google Scholar 

  16. Tertilt, M., Scholz, P.: To advise, or not to advise— how robo-advisors evaluate the risk preferences of private investors. JWM 21, 70–84 (2018)

    Article  Google Scholar 

  17. Sironi, P.: FinTech Innovation. Wiley, West Sussex (2016)

    Book  Google Scholar 

  18. Jung, D., Erdfelder, E., Glaser, F.: Nudged to win: designing robo-advisory to overcome decision inertia. In: ICIS 2018 Proceedings (2018)

    Google Scholar 

  19. Namyslo, N.M., Jung, D.: Towards designing robo-advisory to promote consensus-efficient group decision-making in new types of economic scenarios. In: ECIS 2023 Proceedings (2023)

    Google Scholar 

  20. Cichocki, A., Kuleshov, A.P.: Future trends for human-AI collaboration. Comput. Intell. Neurosci. 1–21 (2021)

    Google Scholar 

  21. Rai, A., Constantinides, P., Sarker, S.: Next-generation digital platforms: toward human–AI hybrids. MIS Q. 43, iii–ix (2019)

    Google Scholar 

  22. Sein, M.K., Henfridsson, O., Purao, S., Rossi, M., Lindgren, R.: Action design research. MIS Q. 35, 37–56 (2011)

    Article  Google Scholar 

  23. Lesjak, D., Natek, S.: Knowledge management systems and tacit knowledge. IJIL 29, 166 (2021)

    Article  Google Scholar 

  24. Phang, C., Kankanhalli, A., Sabherwal, R.: Usability and sociability in online communities. JAIS 10, 721–747 (2009)

    Article  Google Scholar 

  25. Yi, J.: Externalization of tacit knowledge in online environments. IJEL 5 (2006)

    Google Scholar 

  26. Fügener, A., Grahl, J., Gupta, A., Ketter, W.: Will humans-in-the-loop become borgs? Merits and pitfalls of working with AI. MIS Q. 45, 1527–1556 (2021)

    Article  Google Scholar 

  27. Lyons, J.B., Sycara, K., Lewis, M., Capiola, A.: Human-autonomy teaming: definitions, debates, and directions. Front. Psychol. 12 (2021)

    Google Scholar 

  28. McNeese, N.J., Demir, M., Cooke, N.J., Myers, C.: Teaming with a synthetic teammate. Hum. Factors 60, 262–273 (2018)

    Article  Google Scholar 

  29. Agrawal, A., Gans, J., Goldfarb, A.: How AI will change the way we make decisions. HBR 26, 1–5 (2017)

    Google Scholar 

  30. Bansal, G., Nushi, B., Kamar, E., Weld, D.S., Lasecki, W.S., Horvitz, E.: Updates in human-AI teams: understanding and addressing the performance/compatibility tradeoff. In: AAAI, vol. 33, pp. 2429–2437 (2019)

    Google Scholar 

  31. Metcalf, L., Askay, D.A., Rosenberg, L.B.: Keeping humans in the loop: pooling knowledge through artificial swarm intelligence to improve business decision making. Calif. Manage. Rev. 61, 84–109 (2019)

    Article  Google Scholar 

  32. Fügener, A., Grahl, J., Gupta, A., Ketter, W.: Cognitive challenges in human-artificial intelligence collaboration. ISR 33, 678–696 (2019)

    Article  Google Scholar 

  33. Jussupow, E., Spohrer, K., Heinzl, A., Gawlitza, J.: Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence. ISR 32, 713–735 (2021)

    Article  Google Scholar 

  34. Wang, J.F.: An Affordance Perspective of RAs 2.0: Theorizing the New Generation of Recommendation Agents (2021)

    Google Scholar 

  35. Cao, G., Duan, Y., Edwards, J.S., Dwivedi, Y.K.: Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation 106 (2021)

    Google Scholar 

  36. Jung, D., Dorner, V.: Decision inertia and arousal: using NeuroIS to analyze bio-physiological correlates of decision inertia in a dual-choice paradigm. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, P.-M., Randolph, A.B. (eds.) Information Systems and Neuroscience. LNISO, vol. 25, pp. 159–166. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67431-5_18

    Chapter  Google Scholar 

  37. Kobets, V., Petrov, O., Koval, S.: Sustainable robo-advisor bot and investment advice-taking behavior. In: Maślankowski, J., Marcinkowski, B., Rupino da Cunha, P. (eds.) Digital Transformation, pp. 15–35. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-23012-7_2

  38. Torno, A., Bähnsch, S., Dreyer, M.: Taming the next wolf of wall street–design principles for ethical robo-advice. In: PACIS 2022 Proceedings (2022)

    Google Scholar 

  39. Rico-PĂ©rez, H., Arenas-Parra, M., Quiroga-GarcĂ­a, R.: Scientific Development of Robo-Advisor: A Bibliometric Analysis (2022)

    Google Scholar 

  40. Namyslo, N.M., Jung, D., Rieker, T.: The use of robo-advisory and AI in reliability analysis for field measure decision-making. In: VDI, pp. 257–270 (2023)

    Google Scholar 

  41. Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature review. MIS Q. 26, xiii–xxiii (2002)

    Google Scholar 

  42. Paetsch, F., Eberlein, A., Maurer, F.: Requirements engineering and agile software development. In: Proceedings of the IEEE WETICE (2003)

    Google Scholar 

  43. Shirley, G., Kruse, C.L., Stefan, S.: Research perspectives: the anatomy of a design principle. AIS 21, 1622–1652 (2020)

    Google Scholar 

  44. Kanawattanachai, Y.: The impact of knowledge coordination on virtual team performance over time. MIS Q. 31, 783 (2007)

    Article  Google Scholar 

  45. Faraj, S., Sproull, L.: Coordinating expertise in software development teams. Manage. Sci. 46, 1554–1568 (2000)

    Article  Google Scholar 

  46. Wu, M., Gao, Q.: Understanding the acceptance of robo-advisors. In: HCI, pp. 262–277 (2021)

    Google Scholar 

  47. Levin, D.Z., Cross, R.: The strength of weak ties you can trust. Manag. Sci. 50, 1477–1490 (2004)

    Article  Google Scholar 

  48. Nourallah, M.: One size does not fit all: young retail investors’ initial trust in financial robo-advisors. J. Bus. Res. 156, 113470 (2023)

    Article  Google Scholar 

  49. Ruf, C., Back, A., Weidenfeld, H.A.: Designing Tablet Banking Apps for High-Net-Worth Individuals (2015)

    Google Scholar 

  50. Hildebrand, C., Bergner, A.: Conversational robo advisors as surrogates of trust. J. Acad. Mark. Sci. 49, 659–676 (2021)

    Article  Google Scholar 

  51. Deng, B., Chau, M.: Anthropomorphized financial robo-advisors and investment advice-taking behavior. In: Proceedings of the AMCIS, vol. 4 (2021)

    Google Scholar 

  52. Zhang, G., Chong, L., Kotovsky, K., Cagan, J.: Trust in an AI versus a human teammate. CHB 139, 107536 (2023)

    Google Scholar 

  53. Harris-Watson, A.M., Larson, L.E., Lauharatanahirun, N., DeChurch, L.A., Contractor, N.S.: Social perception in human-AI teams. CHB 145, 107765 (2023)

    Google Scholar 

  54. Sabir, A.A., Ahmad, I., Ahmad, H., Rafiq, M., Khan, M.A., Noreen, N.: Consumer acceptance and adoption of AI robo-advisors in fintech industry. Mathematics 11, 1311 (2023)

    Article  Google Scholar 

  55. Ruf, C., Back, A., Bergmann, R., Schlegel, M.: Elicitation of requirements for the design of mobile financial advisory services. In: HICCS (2015)

    Google Scholar 

  56. Gomez, C., Unberath, M., Huang, C.-M.: Mitigating knowledge imbalance in AI-advised decision-making through collaborative user involvement. Int. J. Hum. Comput. 172 (2023)

    Google Scholar 

  57. Nussbaumer, P., Matter, I., Schwabe, G.: “Enforced” vs. “casual” transparency. ACM Trans. Manage. Inf. Syst. 3, 1–19 (2012)

    Article  Google Scholar 

  58. Westphal, M., Vössing, M., Satzger, G., Yom-Tov, G.B., Rafaeli, A.: Decision control and explanations in human-AI collaboration. CHB 144 (2023)

    Google Scholar 

  59. Heinrich, P., Schwabe, G.: Facilitating informed decision-making in financial service encounters. Bus. Inf. Syst. Eng. 60, 317–329 (2018)

    Article  Google Scholar 

  60. Nonaka, I.: The knowledge-creating company. HRB 85(7/8), 162–171 (2007)

    Google Scholar 

  61. Huang, K.-Y., GĂĽney, S.: Toward a framework of web 2.0-driven organizational learning. CAIS 31 (2012)

    Google Scholar 

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Namyslo, N., Jung, D., Sturm, T. (2024). Exploring Design Principles Promoting Organizational Knowledge Creation via Robo-Advisory: The Case of Collaborative Group Decision-Making in the After Sales Management. In: Mandviwalla, M., Söllner, M., Tuunanen, T. (eds) Design Science Research for a Resilient Future. DESRIST 2024. Lecture Notes in Computer Science, vol 14621. Springer, Cham. https://doi.org/10.1007/978-3-031-61175-9_21

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  • DOI: https://doi.org/10.1007/978-3-031-61175-9_21

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