Dynamic Enterprise Architecture Capabilities: Conceptualization and Validation

  • Rogier van de WeteringEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 354)


The notion of enterprise architecture (EA) and EA-based capabilities in IS literature has emerged as an important research domain. However, the conceptualizations of EA-based capabilities remain ambiguous, largely not validated and still lack a firm base in theory. This study, therefore, aims to rigorously conceptualize EA-based capabilities grounded in theory and puts forward the notion of dynamic enterprise architecture capabilities. These capabilities highlight the core areas in which organizations should infuse EA. The purpose of this study is to develop a reliable and valid measurement scale. This scale is validated using item-sorting analyses, expert reviews and an empirical study of 299 CIOs and enterprise architects. The outcomes support the validity and reliability of the scale. The dynamic enterprise architecture capabilities scale developed in this research contributes to theory development and the EA knowledge base. The scale may be used as an assessment or benchmarking tool in practice.


Enterprise architecture Enterprise architecture capabilities Dynamic enterprise architecture capabilities Dynamic capabilities view Scale development and validation 



I want to thank Tom Hendrickx, Kevin Billen and Salo Langer for their contributions in the data collection and for sharing their perspectives in numerous discussions.


  1. 1.
    Hazen, B.T., et al.: Enterprise architecture: a competence-based approach to achieving agility and firm performance. Management 193, 566–577 (2017)Google Scholar
  2. 2.
    Ross, J.W., Weill, P., Robertson, D.: Enterprise Architecture as Strategy: Creating a Foundation for Business Execution. Harvard Business Press, Boston (2006)Google Scholar
  3. 3.
    Shanks, G., et al.: Achieving benefits with enterprise architecture. J. Strateg. Inf. Syst. 27(2), 139–156 (2018)CrossRefGoogle Scholar
  4. 4.
    Tamm, T., et al.: How does enterprise architecture add value to organisations. Commun. Assoc. Inf. Syst. 28(1), 141–168 (2011)Google Scholar
  5. 5.
    Bernard, S.A.: An Introduction to Enterprise Architecture, 3rd edn. AuthorHouse, Bloomington (2012)Google Scholar
  6. 6.
    Janssen, M.: Framing enterprise architecture: a metaframework for analyzing architectural efforts in organizations. In: Coherency Management: Architecting the Enterprise for Alignment, Agility and Assurance. Authorhouse (2009)Google Scholar
  7. 7.
    Kotusev, S.: Enterprise architecture and enterprise architecture artifacts: questioning the old concept in light of new findings. J. Inf. Technol. p. 0268396218816273 (2019)Google Scholar
  8. 8.
    Lange, M., Mendling, J., Recker, J.: An empirical analysis of the factors and measures of Enterprise Architecture Management success. Eur. J. Inf. Syst. 25(5), 411–431 (2016)CrossRefGoogle Scholar
  9. 9.
    Ahlemann, F., et al.: Strategic Enterprise Architecture Management: Challenges, Best Practices, and Future Developments. Springer, Heidelberg (2012). Scholar
  10. 10.
    Foorthuis, R., et al.: A theory building study of enterprise architecture practices and benefits. Inf. Syst. Front. 18(3), 541–564 (2016)CrossRefGoogle Scholar
  11. 11.
    Korhonen, J.J., Molnar, W.A.: Enterprise architecture as capability: strategic application of competencies to govern enterprise transformation. In: 2014 IEEE 16th Conference on Business Informatics (CBI). IEEE (2014)Google Scholar
  12. 12.
    Lankhorst, M.: Enterprise Architecture at Work: Modelling, Communication and Analysis. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    van de Wetering, R., Bos, R.: A meta-framework for efficacious adaptive enterprise architectures. In: Abramowicz, W., Alt, R., Franczyk, B. (eds.) BIS 2016. LNBIP, vol. 263, pp. 273–288. Springer, Cham (2017). Scholar
  14. 14.
    Doucet, G., et al.: Coherency management: using enterprise architecture for alignment, agility, and assurance. J. Enterp. Architecture 4(2), 1–12 (2009)Google Scholar
  15. 15.
    Greefhorst, D., Koning, H., Van Vliet, H.: The many faces of architectural descriptions. Inf. Syst. Front. 8(2), 103–113 (2006)CrossRefGoogle Scholar
  16. 16.
    Wilkinson, M.: Designing an ‘adaptive’enterprise architecture. BT Technol. J. 24(4), 81–92 (2006)CrossRefGoogle Scholar
  17. 17.
    Mikalef, P., Pateli, A., van de Wetering, R.: IT flexibility and competitive performance: the mediating role of IT-enabled dynamic capabilities. In: 24th European Conference on Information Systems (ECIS) (2016)Google Scholar
  18. 18.
    Pavlou, P.A., El Sawy, O.A.: Understanding the elusive black box of dynamic capabilities. Decis. Sci. 42(1), 239–273 (2011)CrossRefGoogle Scholar
  19. 19.
    Wheeler, B.C.: NEBIC: a dynamic capabilities theory for assessing net-enablement. Inf. Syst. Res. 13(2), 125–146 (2002)CrossRefGoogle Scholar
  20. 20.
    Toppenberg, G., Henningsson, S., Shanks, G.: How Cisco Systems used enterprise architecture capability to sustain acquisition-based growth. MIS Q. Executive 14(4), 151–168 (2015)Google Scholar
  21. 21.
    Abraham, R., Aier, S., Winter, R.: Two speeds of EAM—a dynamic capabilities perspective. In: Aier, S., Ekstedt, M., Matthes, F., Proper, E., Sanz, J.L. (eds.) PRET/TEAR -2012. LNBIP, vol. 131, pp. 111–128. Springer, Heidelberg (2012). Scholar
  22. 22.
    Labusch, N., Aier, S., Winter, R.: Beyond Enterprise Architecture Modeling-What are the Essentials to Support Enterprise Transformations? (2013)Google Scholar
  23. 23.
    Winter, R., Fischer, R.: Essential layers, artifacts, and dependencies of enterprise architecture. In: 10th IEEE International Enterprise Distributed Object Computing Conference Workshops. IEEE (2006)Google Scholar
  24. 24.
    Brosius, M., et al.: Enterprise Architecture Assimilation: An Institutional Perspective. Association for Information Systems (2018)Google Scholar
  25. 25.
    Schmidt, C., Buxmann, P.: Outcomes and success factors of enterprise IT architecture management: empirical insight from the international financial services industry. Eur. J. Inf. Syst. 20(2), 168–185 (2011)CrossRefGoogle Scholar
  26. 26.
    Hinkelmann, K., et al.: A new paradigm for the continuous alignment of business and IT: combining enterprise architecture modelling and enterprise ontology. Comput. Ind. 79, 77–86 (2016)CrossRefGoogle Scholar
  27. 27.
    Teece, D.J., Pisano, G., Shuen, A.: Dynamic capabilities and strategic management. Strateg. Manag. J. 18(7), 509–533 (1997)CrossRefGoogle Scholar
  28. 28.
    Overby, E., Bharadwaj, A., Sambamurthy, V.: Enterprise agility and the enabling role of information technology. Eur. J. Inf. Syst. 15(2), 120–131 (2006)CrossRefGoogle Scholar
  29. 29.
    Sambamurthy, V., Bharadwaj, A., Grover, V.: Shaping agility through digital options: reconceptualizing the role of information technology in contemporary firms. MIS Q. 27(2), 237–263 (2003)CrossRefGoogle Scholar
  30. 30.
    Pavlou, P.A., El Sawy, O.A.: From IT leveraging competence to competitive advantage in turbulent environments: the case of new product development. Inf. Syst. Res. 17(3), 198–227 (2006)CrossRefGoogle Scholar
  31. 31.
    Drnevich, P.L., Kriauciunas, A.P.: Clarifying the conditions and limits of the contributions of ordinary and dynamic capabilities to relative firm performance. Strateg. Manag. J. 32(3), 254–279 (2011)CrossRefGoogle Scholar
  32. 32.
    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(2), 293–334 (2011)CrossRefGoogle Scholar
  33. 33.
    Wilden, R., et al.: Dynamic capabilities and performance: strategy, structure and environment. Long Range Plan. 46(1–2), 72–96 (2013)CrossRefGoogle Scholar
  34. 34.
    Nahm, A.Y., et al.: The Q-sort method: assessing reliability and construct validity of questionnaire items at a pre-testing stage. J. Mod. Appl. Stat. Meth. 1(1), 15 (2002)CrossRefGoogle Scholar
  35. 35.
    Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2(3), 192–222 (1991)CrossRefGoogle Scholar
  36. 36.
    Presser, S., et al.: Methods for testing and evaluating survey questions. Public Opin. Q. 68(1), 109–130 (2004)CrossRefGoogle Scholar
  37. 37.
    Coltman, T., et al.: Formative versus reflective measurement models: two applications of formative measurement. J. Bus. Res. 61(12), 1250–1262 (2008)CrossRefGoogle Scholar
  38. 38.
    Wetzels, M., Odekerken-Schröder, G., Van Oppen, C.: Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Q. 33(1), 177–195 (2009)CrossRefGoogle Scholar
  39. 39.
    Petter, S., Straub, D., Rai, A.: Specifying formative constructs in information systems research. MIS Q. 623–656 (2007)CrossRefGoogle Scholar
  40. 40.
    Becker, J.-M., Klein, K., Wetzels, M.: Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models. Long Range Plan. 45(5–6), 359–394 (2012)CrossRefGoogle Scholar
  41. 41.
    Jarvis, C., MacKenzie, S., Podsakoff, P.: A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J. Consum. Res. 30(2), 199–218 (2003)CrossRefGoogle Scholar
  42. 42.
    Teece, D., Peteraf, M., Leih, S.: Dynamic capabilities and organizational agility: risk, uncertainty, and strategy in the innovation economy. Calif. Manag. Rev. 58(4), 13–35 (2016)CrossRefGoogle Scholar
  43. 43.
    Kim, G., et al.: IT capabilities, process-oriented dynamic capabilities, and firm financial performance. J. Assoc. Inf. Syst. 12(7), 487 (2011)Google Scholar
  44. 44.
    Fischer, T., et al.: Exploitation or exploration in service business development? Insights from a dynamic capabilities perspective. J. Serv. Manag. 21(5), 591–624 (2010)CrossRefGoogle Scholar
  45. 45.
    Protogerou, A., Caloghirou, Y., Lioukas, S.: Dynamic capabilities and their indirect impact on firm performance. Ind. Corp. Change 21(3), 615–647 (2012)CrossRefGoogle Scholar
  46. 46.
    van Oosterhout, M., Waarts, E., van Hillegersberg, J.: Change factors requiring agility and implications for IT. Eur. J. Inf. Syst. 15(2), 132–145 (2006)CrossRefGoogle Scholar
  47. 47.
    Warkentin, M., Johnston, A.C., Shropshire, J.: The influence of the informal social learning environment on information privacy policy compliance efficacy and intention. Eur. J. Inf. Syst. 20(3), 267–284 (2011)CrossRefGoogle Scholar
  48. 48.
    Podsakoff, P.M., et al.: Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88(5), 879 (2003)CrossRefGoogle Scholar
  49. 49.
    Ringle, C.M., Wende, S., Becker, J.-M.: SmartPLS 3. Boenningstedt: SmartPLS GmbH (2015).
  50. 50.
    Hair Jr., J.F., et al.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications, Thousand Oaks (2016)Google Scholar
  51. 51.
    Ringle, C.M., Sarstedt, M., Straub, D.: A critical look at the use of PLS-SEM in MIS Quarterly. MIS Q. 36(1) (March 2012) Google Scholar
  52. 52.
    Hair, J.F., Ringle, C.M., Sarstedt, M.: PLS-SEM: indeed a silver bullet. J. Mark. Theory Pract. 19(2), 139–152 (2011)CrossRefGoogle Scholar
  53. 53.
    Hair Jr., J.F., et al.: Advanced Issues in Partial Least Squares Structural Equation Modeling. SAGE Publications, Thousand Oaks (2017)Google Scholar
  54. 54.
    Nunnally, J., Bernstein, I.: Psychometric Theory. McGraw-Hill, New York (1994)Google Scholar
  55. 55.
    Fornell, C., Larcker, D.: Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 18(1), 39–50 (1981)CrossRefGoogle Scholar
  56. 56.
    Farrell, A.M.: Insufficient discriminant validity: a comment on Bove, Pervan, Beatty, and Shiu (2009). J. Bus. Res. 63(3), 324–327 (2010)CrossRefGoogle Scholar
  57. 57.
    Henseler, J., Ringle, C.M., Sarstedt, M.: A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43(1), 115–135 (2015)CrossRefGoogle Scholar
  58. 58.
    Kock, N., Lynn, G.: Lateral collinearity and misleading results in variance-based SEM: an illustration and recommendations (2012)CrossRefGoogle Scholar
  59. 59.
    Rai, A., Tang, X.: Leveraging IT capabilities and competitive process capabilities for the management of interorganizational relationship portfolios. Inf. Syst. Res. 21(3), 516–542 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.The Open University of the NetherlandsHeerlenthe Netherlands

Personalised recommendations