Adaptive Workflow Design Based on Blockchain

  • Daniel Narh TrekuEmail author
  • Jun Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11491)


Increasingly, organizational processes have become more complex. There is a need for the design of workflows to focus on how organizations adapt to emergent processes while balancing the need for decentralization and centralization goal. The advancement in new technologies especially blockchain provides organizations with the opportunity to achieve the goal. Using blockchain technology (i.e. smart contract and blocks of specified consensus for deferred action), we leverage the theory of deferred action and a coordination framework to conceptually design a workflow management system that addresses organizational emergence (e-WfMS). Our artifact helps managers to predict and store the impact of deferred actions. We evaluated the effectiveness of our system against a complex adaptive system for utility assessment.


Workflow Emergent organization Coordination Blockchain Smart contract 


  1. 1.
    Williams, C.K., Karahanna, E.: Causal explanation in the coordinating process: a critical realist case of federated IT governance structures. MIS Q. 37, 933–964 (2013)CrossRefGoogle Scholar
  2. 2.
    Basu, A., Kumar, A.: Research commentary: workflow management issues in e-business. Inf. Syst. Res. 13, 1–14 (2002). Scholar
  3. 3.
    Lei, Y., Singh, M.P.: A comparison of workflow metamodels. In: ER-97 Workshop on Behavioral Modeling and Design Transformations: Issues and Opportunities in Conceptual Modeling, Los Angeles (1997)Google Scholar
  4. 4.
    Sambamurthy, V., Zmud, R.W.: Research commentary: the organizing logic of an enterprise’s IT activities in digital era—a prognosis of practice and call for research. Inf. Syst. Res. 11, 105–114 (2000)CrossRefGoogle Scholar
  5. 5.
    Joosten, S., Brinkkemper, S.: Fundamental concepts for workflow automation in practice. In: Proceedings of the International Conference on Information Systems (ICIS 1995). AIS, Amsterdam (1995)Google Scholar
  6. 6.
    Hevner, A.R., Chatterjee, S.: Design Research in Information Systems: Theory and Practice. Springer, New York (2010). Scholar
  7. 7.
    Kuechler, W., Vaishnavi, V.: A framework for theory development in design science research: multiple perspectives. J. Assoc. Inf. Syst. 13, 395–423 (2012)Google Scholar
  8. 8.
    Patel, N.V.: Theory of deferred action. Organization and Systems Design, pp. 83–107. Palgrave Macmillan, London (2006)CrossRefGoogle Scholar
  9. 9.
    Patel, N.V., Eldabi, T., Khan, T.M.: Theory of deferred action: agent-based simulation model for designing complex adaptive systems. Organ. Syst. Des. 23, 521–537 (2010). Scholar
  10. 10.
    Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research. J. Manag. Inf. Syst. 24, 45–77 (2007)CrossRefGoogle Scholar
  11. 11.
    Alter, S.: Same words, different meanings: are basic IS/IT concepts our self-imposed tower of babel? Commun. AIS. 3, 1–89 (2000)Google Scholar
  12. 12.
    Reijers, H., Vanderfeesten, I., van der Aalst, W.M.P.: The effectiveness of workflow management systems: a longitudinal study. Int. J. Inf. Manag. 36, 126–141 (2016)CrossRefGoogle Scholar
  13. 13.
    Cooper, R.B.: Information technology development creativity: a case study of attempted radical change. MIS Q. 24, 245–276 (2000)CrossRefGoogle Scholar
  14. 14.
    Basu, A., Blanning, R.W.: A formal approach to workflow analysis. Inf. Syst. Res. 11, 17–36 (2000)CrossRefGoogle Scholar
  15. 15.
    van der Aalst, W.M.P., Berens, P.J.S.: Beyond workflow management: product-driven case handling. In: International ACM SIGGROUP Conference on Supporting Group Work (GROUP 2001), pp. 42–51, New York (2001)Google Scholar
  16. 16.
  17. 17.
    Kokina, J., Mancha, R., Pachamanova, D.: Blockchain: emergent industry adoption and implications for accounting. J. Emerg. Technol. Account. 14, 91–100 (2017). Scholar
  18. 18.
    Fridgen, G., Radszuwill, S., Urbach, N., Utz, L.: Cross-organizational workflow management using blockchain technology - towards applicability, auditability, and automation. Presented at the Hawaii International Conference on System Sciences (2018)Google Scholar
  19. 19.
    Beck, R., Muller-Bloch, C.: Blockchain as radical innovation: a framework for engaging with distributed ledgers as incumbent organization. Presented at the 50th Hawaii International Conference on System Sciences, Waikoloa, Hawaii (2017)Google Scholar
  20. 20.
    Nofer, M., Gomber, P., Hinz, O., Schiereck, D.: Blockchain. Bus. Inf. Syst. Eng. 59, 183–187 (2017)CrossRefGoogle Scholar
  21. 21.
    Korpela, K., Hallikas, J., Dahlberg, T.: Digital supply chain transformation toward blockchain integration. In: 50th Hawaii International Conference on System Sciences, Waikoloa, Hawaii (2017)Google Scholar
  22. 22.
    Dorri, A., Kanhere, S.S., Jurdak, R., Gauravaram, P.: Blockchain for IOT security and privacy: the case study of a smart home. In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops, Kona, Big Island, Hawaii (2017)Google Scholar
  23. 23.
    Dwivedi, Y.K., Wade, M.R., Schneberger, S.L.: Information Systems Theory: Explaining and Predicting Our Digital Society. Springer, New York (2012). Scholar
  24. 24.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28, 75–105 (2004). Scholar
  25. 25.
    Gregory, R.W., Muntermann, J.: Research note—heuristic theorizing: proactively generating design theories. Inf. Syst. Res. 25, 639–653 (2014)CrossRefGoogle Scholar
  26. 26.
    Tremblay, M.C., Hevner, A.R., Berndt, D.J.: Focus group for artifact refinement and evaluation in design research. Des. Res. Inf. Syst. 26, 599–618 (2010). Scholar
  27. 27.
    Pesaran, M.H.: A simple panel unit root test in the presence of cross-section dependence. J. Appl. Econom. 22, 265–312 (2007)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Dickey, D., Fuller, W.A.: Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74, 427–431 (1979)MathSciNetzbMATHGoogle Scholar
  29. 29.
    Adams, M., Edmond, D.: The Application of Activity Theory to Dynamic Workflow Adaptation Issues, p. 17 (2003)Google Scholar
  30. 30.
    Narendra, N.C.: Design considerations for incorporating flexible workflow and multi-agent interactions in agent societies. J. Assoc. Inf. Syst. 3, 77–113 (2002). Scholar
  31. 31.
    Romanow, D., Rai, A., Keil, M.: CPOE-Enabled coordination: appropriation for deep structure use and impacts on patient outcomes. MIS Q. 42, 189–212 (2018). Scholar
  32. 32.
    Alaa, G.: Derivation of factors facilitating organizational emergence based on complex adaptive systems and social autopoiesis theories. Emerg. Complex. Organ. 11(1), 1–19 (2009)MathSciNetGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Information Systems Department, VCOBEUniversity of Texas Rio Grande ValleyEdinburgUSA

Personalised recommendations