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Process Mining Adoption

A Technology Continuity Versus Discontinuity Perspective

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Business Process Management Forum (BPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 392))

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Abstract

Process mining is proffered to bring substantial benefits to adopting organisations. Nevertheless, the uptake of process mining in organisations has not been as extensive as predicted. In-depth analysis of how organisations can successfully adopt process mining is seldom explored, yet much needed. We report our findings on an exploratory case study of the early stages of the adoption of process mining at a large pension fund in the Netherlands. Through inductive analysis of interview data, we identified that successful adoption of process mining requires overcoming tensions arising from discontinuing old practices while putting actions into place to promote continuity of new practices. Without targeted strategies implemented to transition users away from old practices, data quality is jeopardised, decision-making is impeded, and the adoption of process mining is ultimately hampered.

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Notes

  1. 1.

    See https://www.apg.nl/en.

  2. 2.

    See https://www.celonis.com/.

References

  1. van der Aalst, W.M.P.: Process Mining - Data Science in Action, 2nd edn. Springer, Cham (2016). https://doi.org/10.1007/978-3-662-49851-4

    Book  Google Scholar 

  2. van der Aalst, W.M.P., Pesic, M., Song, M.: Beyond process mining: from the past to present and future. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 38–52. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13094-6_5

    Chapter  Google Scholar 

  3. Anderson, P., Tushman, M.L.: Technological discontinuities and dominant designs: a cyclical model of technological change. Adm. Sci. Q. 35(4), 604–633 (1990)

    Article  Google Scholar 

  4. Azemi, E., Bala, S.: Exploring BPM adoption and strategic alignment of processes at Raiffeisen Bank Kosovo. In: BPM Forum, vol. 2428, pp. 37–48 (2019)

    Google Scholar 

  5. Bazeley, P., Jackson, K.: Qualitative Data Analysis with NVivo. SAGE Publications Limited, Thousand Oaks (2013)

    Google Scholar 

  6. Bostrom, R.P., Olfman, L., Sein, M.K.: The importance of learning style in end-user training. MIS Q. 14(1), 101–119 (1990)

    Article  Google Scholar 

  7. Brynjolfsson, E.: The productivity paradox of information technology. Commun. ACM 36(12), 66–77 (1993)

    Article  Google Scholar 

  8. Brynjolfsson, E., Hitt, L.M.: Beyond the productivity paradox. Commun. ACM 41(8), 49–55 (1998)

    Article  Google Scholar 

  9. Brynjolfsson, E., Rock, D., Syverson, C.: Artificial intelligence and the modern productivity paradox: a clash of expectations and statistics. Technical report, National Bureau of Economic Research (2017)

    Google Scholar 

  10. Buijs, J.C.A.M., Bergmans, R.F.M., Hasnaoui, R.E.: Customer journey analysis at a financial services provider using self service and data hub concepts. In: BPM, vol. 2428, pp. 25–36 (2019)

    Google Scholar 

  11. Caldeira, J., e Abreu, F.B., Reis, J., Cardoso, J.: Assessing software development teams’ efficiency using process mining. In: ICPM, pp. 65–72. IEEE (2019)

    Google Scholar 

  12. Canjels, K.F., Imkamp, M.S.V., Boymans, T.A.E.J., Vanwersch, R.J.B.: Unraveling and improving the interorganizational arthrosis care process at Maastricht UMC+: an illustration of an innovative, combined application of data and process mining. In: BPM Industry Forum, vol. 2428, pp. 178–189 (2019)

    Google Scholar 

  13. Christensen, C.M., Overdorf, M.: Meeting the challenge of disruptive change. Harv. Bus. Rev. 78(2), 66–77 (2000)

    Google Scholar 

  14. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)

    Article  Google Scholar 

  15. Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management, 2nd edn. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-56509-4

    Book  Google Scholar 

  16. Eisenhardt, K.M.: Building theories from case study research. Acad. Manag. Rev. 14(4), 532–550 (1989)

    Article  Google Scholar 

  17. Fernández, W.D., et al.: The grounded theory method and case study data in IS research: issues and design. In: ISFW: CC, vol. 1, pp. 43–59 (2004)

    Google Scholar 

  18. Flick, U.: An Introduction to Qualitative Research. Sage Publications Limited, Thousand Oaks (2018)

    Google Scholar 

  19. Glaser, B.: Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Sociology Press, Mill Valley (1978)

    Google Scholar 

  20. Glaser, B.: Doing Grounded Theory: Issues and Discussions. Sociology Press, Mill Valley (1998)

    Google Scholar 

  21. Glaser, B.G., Strauss, A.L.: Discovery of Grounded Theory: Strategies for Qualitative Research. Routledge, London (2017)

    Book  Google Scholar 

  22. Gorla, N., Somers, T.M., Wong, B.: Organizational impact of system quality, information quality, and service quality. SIS 19(3), 207–228 (2010)

    Google Scholar 

  23. International, D.: The DAMA Guide to the Data Management Body of Knowledge - DAMA-DMBOK. Technics Publications, LLC, Denville (2009)

    Google Scholar 

  24. Kerremans, M.: Market guide for process mining. white paper (2019). https://www.gartner.com/en/documents/3939836/market-guide-for-process-mining

  25. Tushman, M.L., Murmann, J.P.: Dominant designs, technology cycles, and organization outcomes. Acad. Manag. Proc. 1998(1), A1–A33 (1998). https://doi.org/10.5465/apbpp.1998.27643428

    Article  Google Scholar 

  26. Lee, S.M., Kim, Y.R., Lee, J.: An empirical study of the relationships among end-user information systems acceptance, training, and effectiveness. MIS 12(2), 189–202 (1995)

    Google Scholar 

  27. Macris, A., Papakonstantinou, D., Malamateniou, F., Vassilacopoulos, G.: Using ontology-based knowledge networks for user training in managing healthcare processes. JTM 47(1–3), 5–21 (2009)

    Google Scholar 

  28. Mans, R.S., van der Aalst, W.M.P., Vanwersch, R.J.B., Moleman, A.J.: Process mining in healthcare: data challenges when answering frequently posed questions. In: Lenz, R., Miksch, S., Peleg, M., Reichert, M., Riaño, D., ten Teije, A. (eds.) KR4HC/ProHealth -2012. LNCS (LNAI), vol. 7738, pp. 140–153. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36438-9_10

    Chapter  Google Scholar 

  29. Nicolaou, A.I., McKnight, D.H.: Perceived information quality in data exchanges: effects on risk, trust, and intention to use. ISR 17(4), 332–351 (2006)

    Article  Google Scholar 

  30. Orlikowski, W.J.: Using technology and constituting structures: a practice lens for studying technology in organizations. Organ. Sci. 11(4), 404–428 (2000)

    Article  Google Scholar 

  31. Reinkemeyer, L.: Process Mining in Action: Principles Use Cases and Outlook. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40172-6

    Book  Google Scholar 

  32. Rojas, E., Munoz-Gama, J., Sepúlveda, M., Capurro, D.: Process mining in healthcare: a literature review. J. Biomed. Inform. 61, 224–236 (2016)

    Article  Google Scholar 

  33. Saldaña, J.: The Coding Manual for Qualitative Researchers. Sage, Thousand Oaks (2015)

    Google Scholar 

  34. Tushman, M.L., Anderson, P.: Technological discontinuities and organizational environments. Adm. Sci. Q. 31(3), 439–465 (1986)

    Article  Google Scholar 

  35. Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. MS 46(2), 186–204 (2000)

    Google Scholar 

  36. Venkatesh, V., Thong, J.Y., Xu, X.: Unified theory of acceptance and use of technology: a synthesis and the road ahead. AIS 17(5), 328–376 (2016)

    Google Scholar 

  37. Wiesche, M., Jurisch, M.C., Yetton, P.W., Krcmar, H.: Grounded theory methodology in information systems research. MIS Q. 41(3), 685–701 (2017)

    Article  Google Scholar 

  38. Wynn, M.T., et al.: Grounding process data analytics in domain knowledge: a mixed-method approach to identifying best practice. In: BPM, pp. 163–179 (2019)

    Google Scholar 

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Syed, R., Leemans, S.J.J., Eden, R., Buijs, J.A.C.M. (2020). Process Mining Adoption. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds) Business Process Management Forum. BPM 2020. Lecture Notes in Business Information Processing, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-030-58638-6_14

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  • DOI: https://doi.org/10.1007/978-3-030-58638-6_14

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