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Conceptualizing a Capability-Based View of Artificial Intelligence Adoption in a BPM Context

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Business Process Management Workshops (BPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 397))

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Abstract

Advances in Artificial Intelligence (AI) technologies are creating new opportunities for organizations to improve their performance; however, as with other technologies, many of them have difficulties leveraging AI technologies and realizing performance gains. Research on the business value of information technology (IT) suggests that the adoption of AI should improve organizational performance, though indirectly, through improved business processes and other mediators, but research so far has not extensively empirically investigated the way AI creates business value. The paper proposes a capability-based view of AI adoption based on the conception that, with the adoption of AI, an organization develops AI-enabled capabilities – abilities to mobilize AI resources to effectively exploit, create, extend, or modify its resource base. This leads to higher organizational performance through cognitive process automation, innovation, and organizational learning. The first step in this research is to clarify the AI adoption construct. The goal of the paper is thus to provide a conceptual definition, and deeper insights into the components of the AI adoption construct at the organizational level.

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Notes

  1. 1.

    https://harver.com/.

  2. 2.

    https://www.cytoreason.com/.

  3. 3.

    https://www.stitchfix.com/.

  4. 4.

    https://www.valuer.ai/blog/best-agtech-startups-in-europe#top_EU_startups.

References

  1. Bawack, R.E., Fosso Wamba, S., Carillo, K.: Artificial Intelligence in Practice: Implications for IS Research (2019)

    Google Scholar 

  2. Russel, S., Norvig, P.: Artificial intelligence: a modern approach. Pearson Education Limited (2016)

    Google Scholar 

  3. Cockburn, I.M., Henderson, R., Stern, S.: The impact of artificial intelligence on innovation. National bureau of economic research (2018)

    Google Scholar 

  4. Bresnahan, T.F., Trajtenberg, M.: General purpose technologies ‘Engines of growth’? J. Econ. 65, 83–108 (1995)

    Article  Google Scholar 

  5. Davenport, T.H., Ronanki, R.: Artificial intelligence for the real world. Harvard Bus. Rev. 96, 108–116 (2018)

    Google Scholar 

  6. Mishra, A.N., Pani, A.K.: Business value appropriation roadmap for artificial intelligence. VINE Journal of Information and Knowledge Management Systems (2020)

    Google Scholar 

  7. Agrawal, A., Gans, J., Goldfarb, A.: What to expect from artificial intelligence. MIT Sloan Management Review (2017)

    Google Scholar 

  8. Mikalef, P., Fjørtoft, S.O., Torvatn, H.Y.: Developing an artificial intelligence capability: a theoretical framework for business value. In: Abramowicz, W., Corchuelo, R. (eds.) BIS 2019. LNBIP, vol. 373, pp. 409–416. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36691-9_34

    Chapter  Google Scholar 

  9. Chui, M.: Artificial intelligence the next digital frontier? McKinsey and Company Global Institute 47, (2017)

    Google Scholar 

  10. Santhanam, R., Hartono, E.: Issues in linking information technology capability to firm performance. MIS Q. 27, 125–153 (2003)

    Article  Google Scholar 

  11. Mikalef, P., Krogstie, J., Pappas, I.O., Pavlou, P.: Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Inf. Manage. 57, 103169 (2020)

    Article  Google Scholar 

  12. Shanks, G., Bekmamedova, N.: Achieving benefits with business analytics systems: an evolutionary process perspective. J. Decis. Syst. 21, 231–244 (2012)

    Article  Google Scholar 

  13. Krishnamoorthi, S., Mathew, S.K.: Business analytics and business value: A comparative case study. Inf. Manag. 55, 643–666 (2018)

    Article  Google Scholar 

  14. Wamba-Taguimdje, S.-L., Wamba, S.F., Kamdjoug, J.R.K., Wanko, C.E.T.: Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Bus. Process Manag. J. (2020)

    Google Scholar 

  15. Marie Burvill, S., Jones-Evans, D., Rowlands, H.: Reconceptualising the principles of Penrose’s (1959) theory and the resource based view of the firm: The generation of a new conceptual framework. J. Small Bus. Enterprise Develop. 25, (2018)

    Google Scholar 

  16. Bhatt, G.D., Grover, V.: Types of information technology capabilities and their role in competitive advantage: An empirical study. J. Manag. Inf. Syst. 22, 253–277 (2005)

    Article  Google Scholar 

  17. Kim, G., Shin, B., Kim, K.K., Lee, H.G.: IT capabilities, process-oriented dynamic capabilities, and firm financial performance. J. Assoc. Inf. Syst. 12, 1 (2011)

    Google Scholar 

  18. Melville, N., Kraemer, K., Gurbaxani, V.: Review: information technology and organizational performance: an integrative model of it business value. MIS Q. 28, 283–322 (2004)

    Article  Google Scholar 

  19. Aydiner, A.S., Tatoglu, E., Bayraktar, E., Zaim, S.: Information system capabilities and firm performance: Opening the black box through decision-making performance and business-process performance. Int. J. Inf. Manage. 47, 168–182 (2019)

    Article  Google Scholar 

  20. Liao, S.-H., Wu, C.-c.: System perspective of knowledge management, organizational learning, and organizational innovation. Expert Syst. Appl. 37, 1096–1103 (2010)

    Google Scholar 

  21. Jiménez-Jiménez, D., Sanz-Valle, R.: Innovation, organizational learning, and performance. J. Bus. Res. 64, 408–417 (2011)

    Article  Google Scholar 

  22. Zasada, A.: How Cognitive Processes Make Us Smarter (2019)

    Google Scholar 

  23. Frohm, J.: Levels of Automation in production systems. Chalmers University of Technology Göteborg (2008)

    Google Scholar 

  24. Hull, R., Motahari Nezhad, H.R.: Rethinking BPM in a cognitive world: transforming how we learn and perform business processes. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 3–19. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_1

    Chapter  Google Scholar 

  25. Bohanec, M., Robnik-Šikonja, M., Borštnar, M.K.: Organizational learning supported by machine learning models coupled with general explanation methods: A Case of B2B sales forecasting. Organizacija 50, 217–233 (2017)

    Article  Google Scholar 

  26. Samek, W., Wiegand, T., Müller, K.-R.: Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296 (2017)

  27. Banasiewicz, A.D.: Organizational Learning in the Age of Data (2019)

    Google Scholar 

  28. Podsakoff, P.M., MacKenzie, S.B., Podsakoff, N.P.: Recommendations for creating better concept definitions in the organizational, behavioral, and social sciences. Organ. Res. Methods 19, 159–203 (2016)

    Article  Google Scholar 

  29. MacKenzie, S.B., Podsakoff, P.M., Podsakoff, N.P.: Construct measurement and validation procedures in mis and behavioral research: integrating new and existing techniques. MIS Q. 35, 293–334 (2011)

    Article  Google Scholar 

  30. Alsheibani, S., Cheung, Y., Messom, C.: Artificial intelligence adoption: ai-readiness at firm-level. Artif. Intell. 6, 26–2018 (2018)

    Google Scholar 

  31. Chen, H.: Success Factors Impacting Artificial Intelligence Adoption—Perspective From the Telecom Industry in China (2019)

    Google Scholar 

  32. OECD: Artificial Intelligence in Society (2019)

    Google Scholar 

  33. Sonenshein, S., DeCelles, K.A., Dutton, J.E.: It’s not easy being green: The role of self-evaluations in explaining support of environmental issues. Acad. Manage. J. 57, 7–37 (2014)

    Article  Google Scholar 

  34. Jarvis, C.B., MacKenzie, S.B., Podsakoff, P.M.: A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J. Cons. Res. 30, 199–218 (2003)

    Article  Google Scholar 

  35. Appelbaum, D., Kogan, A., Vasarhelyi, M., Yan, Z.: Impact of business analytics and enterprise systems on managerial accounting. Int. J. Account. Inf. Syst. 25, 29–44 (2017)

    Article  Google Scholar 

  36. Burgess, A.: AI capabilities framework. The Executive Guide to Artificial Intelligence, pp. 29–54. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-63820-1_3

    Chapter  Google Scholar 

  37. Bawack, R.E., Wamba, S.F.: Where Information Systems Research Meets Artificial Intelligence Practice: Towards the Development of an AI Capability Framework (2019)

    Google Scholar 

  38. Roeglinger, M., Seyfried, J., Stelzl, S., Muehlen, M.: Cognitive computing: what’s in for business process management? an exploration of use case ideas. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 419–428. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_32

    Chapter  Google Scholar 

  39. Kelly, J.E.: Computing, cognition and the future of knowing. Whitepaper, IBM Reseach 2, (2015)

    Google Scholar 

  40. Mele, C., Spena, T.R., Peschiera, S.: Value creation and cognitive technologies: opportunities and challenges. J. Creat. Value 4, 182–195 (2018)

    Article  Google Scholar 

  41. Phillips-Wren, G.: Ai tools in decision making support systems: a review. Int. J. Artif. Intell. Tools 21(02), 1240005 (2012)

    Google Scholar 

  42. Sam Ransbotham, S.K., Ronny, F., Burt, L., David, K.: Winning With AI. MIT Sloan Management Review (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Economics and Business, University of Ljubljana, Kardeljeva ploščad 17, 1000, Ljubljana, Slovenia

    Aleš Zebec

  2. School of Economics and Business, Academic Unit for Business Informatics and Logistics, University of Ljubljana, Kardeljeva ploščad 17, 1000, Ljubljana, Slovenia

    Mojca Indihar Štemberger

Authors
  1. Aleš Zebec
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  2. Mojca Indihar Štemberger
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Correspondence to Aleš Zebec .

Editor information

Editors and Affiliations

  1. Universidad de Sevilla, Seville, Spain

    Adela Del Río Ortega

  2. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

  3. Universidade do Estado do Rio de Janeiro – UERJ, Rio de Janeiro, Brazil

    Flávia Maria Santoro

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Zebec, A., Indihar Štemberger, M. (2020). Conceptualizing a Capability-Based View of Artificial Intelligence Adoption in a BPM Context. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds) Business Process Management Workshops. BPM 2020. Lecture Notes in Business Information Processing, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-030-66498-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-66498-5_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66497-8

  • Online ISBN: 978-3-030-66498-5

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