Abstract
Academics and practitioners have made various claims regarding the benefits that Enterprise Architecture (EA) delivers for both individual projects and the organization as a whole. At the same time, there is a lack of explanatory theory regarding how EA delivers these benefits. Moreover, EA practices and benefits have not been extensively investigated by empirical research, with especially quantitative studies on the topic being few and far between. This paper therefore presents the statistical findings of a theory-building survey study (n = 293). The resulting PLS model is a synthesis of current implicit and fragmented theory, and shows how EA practices and intermediate benefits jointly work to help the organization reap benefits for both the organization and its projects. The model shows that EA and EA practices do not deliver benefits directly, but operate through intermediate results, most notably compliance with EA and architectural insight. Furthermore, the research identifies the EA practices that have a major impact on these results, the most important being compliance assessments, management propagation of EA, and different types of knowledge exchange. The results also demonstrate that projects play an important role in obtaining benefits from EA, but that they generally benefit less than the organization as a whole.
Similar content being viewed by others
Notes
Tamm et al. (2011) have made a worthwhile effort to render some of these implicit views explicit.
References
Adelman, I., & Lohmöller, J. (1994). Institutions and development in the 19th century: a latent variable regression model. Structural Change and Economic Dynamics, 5(2), 329–359.
Andersson, M., Lindgren, R., & Henfridsson, O. (2008). Architectural knowledge in inter-organizational IT innovation. The Journal of Strategic Information Systems, 17(1), 19–38.
Andreev, P., Heart, T., Maoz, H., & Pliskin, N. (2009). Validating formative partial least squares (PLS) models: Methodological review and empirical illustration. In Proceedings of ICIS 2009.
Aral, S., & Weill, P. (2007). IT assets, organizational capabilities, and firm performance: how resource allocations and organizational differences explain performance variation. Organization Science, 18(5), 763–780.
Armour, F. J., Kaisler, S. H., & Liu, S. Y. (1999). A big-picture look at enterprise architectures. IT Professional, 1(1), 35–42.
Babyak, M. A. (2004). What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosomatic Medicine, 66, 411–421.
Bagozzi, R. P. (2011). Measurement and meaning in information systems and organizational research: methodological and philosophical foundations. MIS Quarterly, 35(2), 261–292.
Bandara, W., Indulska, M., Chong, S., & Sadiq, S. (2007). Major issues in business process management: An expert perspective. In Proceedings of the 15th European Conference on Information Systems (ECIS 2007).
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.
Bélanger, F., Ceferatti, M., Carte, T., & Markham, S. E. (2014). Multilevel research in information systems: concepts, strategies, problems, and pitfalls. Journal of the Association for Information Systems, 15(9), 614–650.
Bernard, S. A. (2012). An introduction to enterprise architecture (3rd ed.). Bloomington: AuthorHouse.
Bernus, P. (2003). Enterprise models for enterprise architecture and ISO9000:2000. Annual Reviews in Control, 27(2), 211–220.
Bidan, M., Rowe, F., & Truex, D. (2012). An empirical study of IS architectures in French SMEs: integration approaches. European Journal of Information Systems, 21, 287–302.
Boh, W. F., & Yellin, D. (2007). Using enterprise architecture standards in managing information technology. Journal of Management Information Systems, 23(3), 163–207.
Bollen, K. A. (2007). Interpretational confounding is due to misspecification, not to type of indicator: comment on Howell, Breivik, and Wilcox (2007). Psychological Methods, 12(2), 219–228.
Bollen, K. A. (2011). Evaluating effect, composite, and causal indicators in structural equation models. MIS Quarterly, 35(2), 359–372.
Boucharas, V., Van Steenbergen, M., Jansen, S., & Brinkkemper, S. (2010). The contribution of enterprise architecture to the achievement of organizational goals: A review of the evidence. In Proceedings of TEAR 2010, LNBIP 70.
Bradley, R. V., Pratt, R. M. E., Byrd, T. A., Outlay, C. N., & Wynn, D. E. (2011). Enterprise architecture, IT effectiveness and the mediating role of IT alignment in US hospitals. Information Systems Journal, 22(2), 97–127.
Bucher, T., Fisher, R., Kurpjuweit, S., & Winter, R. (2006). Enterprise architecture analysis and application. An exploratory study. In Proceedings of TEAR 2006, EDOC Workshop. URL: http://tear2006.telin.nl
Capgemini (2007). Enterprise, business and IT architecture and the integrated architecture framework. URL: http://www.au.capgemini.com/m/en/tl/tl_Enterprise__Business_and_IT_Architecture_and_the_Integrated_Architecture_Framework.pdf
Carver, C. S. (1989). How should multifaceted personality constructs be tested? Issues illustrated by self-monitoring, attributional style, and hardiness. Journal of Personality and Social Psychology, 56(4), 577–585.
Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly, 33(4), 689–707.
Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 655–690). New York: Springer.
Ciborra, C., & Osei-Joehene, D. (2003). ICT corporate infrastructures and risk: A dual perspective. In Proceedings of ECIS 2003 (pp. 473–479).
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Erlbaum.
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9–30.
Dess, G. G., & Robinson, R. B. (1984). Measuring organizational performance in the absence of objective measures: the case of the privately-held firm and conglomerate business unit. Strategic Management Journal, 5(3), 265–273.
Drolet, A. L., & Morrison, D. G. (2001a). Do we really need multiple-item measures in service research? Journal of Service Research, 3(3), 196–204.
Drolet, A. L., & Morrison, D. G. (2001b). Rejoinder to Grapentine. Journal of Service Research, 4(2), 159–160.
Edwards, J. R. (2011). The fallacy of formative measurement. Organizational Research Methods, 14(2), 370–388.
Esposito Vinzi, V., Trinchera, L., & Amato, S. (2010). PLS path modeling: From foundations to recent developments and open issues for model assessment and improvement. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 47–82). New York: Springer.
Feelders, A. J. (2002). Data mining in economic science. In J. Meij (Ed.), Dealing with the data flood (pp. 166–175). The Hague: SST/Beweton.
Firestein, S. (2012). Ignorance: How it drives science (1st ed.). New York: Oxford University Press, Inc.
Foorthuis, R. M. (2012). Project compliance with enterprise architecture. Doctoral dissertation (PhD thesis). Utrecht University, Department of Information and Computing Sciences, ISBN: 978-90-393-5834-4.
Foorthuis, R. M., & Brinkkemper, S. (2008). Best practices for business and systems analysis in projects conforming to enterprise architecture. Enterprise Modelling and Information Systems Architectures, 3(1), 36–47.
Foorthuis, R. M., Hofman, F., Brinkkemper, S., & Bos, R. (2012). Compliance assessments of projects adhering to enterprise architecture. Journal of Database Management, 23(2), 44–71.
Gefen, D., Straub, D. W., & Boudreau, M. (2000). Structural equation modeling techniques and regression: guidelines for research practice. Communications of the AIS, 7(7), 1–78.
Glass, D. J. (2010). A critique of the hypothesis, and a defense of the question, as a framework for experimentation. Clinical Chemistry, 56(7), 1080–1085.
Goodhue, D. L., Kirsch, L. J., Quillard, J. A., & Wybo, M. D. (1992). Strategic data planning: lessons from the field. MIS Quarterly, 16(1), 11–34.
Greenwald, A. G., Pratkanis, A. R., Leippe, M. R., & Baumgardner, M. H. (1986). Under what conditions does theory obstruct research progress? Psychological Review, 93(2), 216–229.
Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly, 30(3), 611–642.
Gregor, S., Hart, D., & Martin, N. (2007). Enterprise architectures: enablers of business strategy and IS/IT alignment in government. Information Technology & People, 20(2), 96–120.
Grisot, M., Hanseth, O., & Thorseng, A. A. (2014). Innovation of, in, on infrastructures: articulating the role of architecture in information infrastructure evolution. Journal of the Association for Information Systems, 15(4), 197–219.
Haenlein, M., Kaplan, A. M. (2004). A beginner’s guide to partial least squares analysis. Understanding Statistics, 3(4), 283–297.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River: Pearson Prentice Hall.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–151.
Hanseth, O., Jacucci, E., Grisot, M., & Aanestad, M. (2006). Reflexive standardization: side effects and complexity in standard making. MIS Quarterly, 30(SI), 563–581.
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Jr., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common beliefs and reality about PLS: comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182–209.
Howell, R. D., Breivik, E., & Wilcox, J. B. (2007). Reconsidering formative measurement. Psychological Methods, 12(2), 205–218.
Hox, J. (2002). Multilevel analysis: Techniques and applications. London: Lawrence Erlbaum Associates, Inc.
Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30, 199–218.
Jewett, D. L. (2005). What’s wrong with single hypotheses? Why it is time for Strong-Inference-PLUS. Scientist, 19(21), 10–11.
Jonkers, H., Lankhorst, M. M., ter Doest, H. W. L., Arbab, F., Bosma, H., & Wieringa, R. J. (2006). Enterprise architecture: management tool and blueprint for the organisation. Information Systems Frontiers, 8(2), 63–66.
Kaisler, S. H., Armour, F., & Valivullah, M. (2005). Enterprise architecting: Critical problems. In Proceedings of the 38th Hawaii International Conference on System Sciences.
Kappelman, L., Pettite, A., McGinnis, T., & Sidorova, A. (2008). Enterprise architecture: Charting the territory for academic research. In Proceedings of AMCIS 2008, Americas Conference on Information Systems.
Kim, S. (2007). IT compliance of industrial information systems: technology management and industrial engineering perspective. Journal of Systems and Software, 80(10), 1590–1593.
Kim, H. M., Fox, M. S., & Sengupta, A. (2007). How to build enterprise data models to achieve compliance to standards or regulatory requirements (and share data). Journal of the Association for Information Systems, 8(2), 105–128.
Kim, G., Shin, B., & Grover, V. (2010). Investigating two contradictory views of formative measurement in information systems research. MIS Quarterly, 34(2), 345–365.
Ko, M., & Osei-Bryson, K. (2008). Reexamining the impact of information technology investment on productivity using regression tree and multivariate adaptive regression splines (MARS). Information Technology & Management, 9(4), 285–299.
Kock, N. (2011). WarpPLS’ treatment of formative latent variables: PLS regression is more conservative and stable. URL: http://warppls.blogspot.nl/2011/06/warppls-treatment-of-formative-latent.html
Kock, N. (2012). WarpPLS 3.0. user manual. Laredo: ScriptWarp Systems.
Kock, N. (2014). Advanced mediating effects tests, multi-group analyses, and measurement model assessments in PLS-based SEM. International Journal of e-Collaboration, 10(1), 1–13.
Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: an illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580.
Lange, M., Mendling, J., & Recker, J. (2012a). Measuring the realization of benefits from enterprise architecture management. Journal of Enterprise Architecture, 8(2), 30–44.
Lange, M., Mendling, J., & Recker, J. (2012b). A comprehensive EA benefit realization model: An exploratory study. In Proceedings of the 45th Hawaii International Conference on System Sciences.
Lankhorst, M., Iacob, M. E., Jonkers, H., et al. (2005). Enterprise architecture at work: Modelling, communication and analysis. Berlin: Springer.
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114–121.
Lux, J., Riempp, G., Urbach, N. (2010). Understanding the performance impact of enterprise architecture management. In AMCIS 2010 Proceedings.
Malloy, T. F. (2003). Regulation, compliance and the firm. Temple Law Review, 76(3), 451–531.
Morganwalp, J. M., & Sage, A. P. (2004). Enterprise architecture measures of effectiveness. International Journal of Technology, Policy and Management, 4(1), 81–94.
Mulholland, A., & Macaulay, A. L. (2006). Architecture and the integrated architecture framework. URL: http://architectes.capgemini.com/communauteDesArchitectes/laMethodologieIAF/b_Architecture_and_the_Integrated_Architecture_Framework.pdf
Niemi, E. (2006). Enterprise architecture benefits: Perceptions from literature and practice. In Proceedings of the 7th IBIMA Conference on Internet & Information Systems in the Digital Age. Brescia, Italy.
Norušis, M. J. (2008). SPSS statistics 17.0 guide to data analysis. Upper Saddle River: Prentice Hall.
Norušis, M. J. (2009). SPSS 17.0 advanced statistical procedures companion. Upper Saddle River: Prentice Hall.
Obitz, T., & Babu K. M. (2009). Enterprise architecture expands its role in strategic business transformation. Infosys Enterprise Architecture Survey 2008/2009.
Padmanabhan, B., Zheng, Z., & Kimbrough, S. O. (2006). An empirical analysis of the value of complete information for eCRM models. MIS Quarterly, 30(2), 247–267.
Persson, A., & Stirna, J. (2001). An exploratory study into the influence of business goals on the practical use of enterprise modelling methods and tools. In Proceedings of ISD2001.
Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(4), 623–656.
Pulkkinen, M., & Hirvonen, A. (2005). EA planning, development and management process for agile enterprise development. In Proceedings of the 38th Hawaii International Conference on System Sciences, (pp. 223.3).
Radeke, F. (2010). Awaiting explanation in the field of enterprise architecture management. In Proceedings of AMCIS 2010.
Richardson, G. L., Jackson, B. M., & Dickson, G. W. (1990). A principles-based enterprise architecture: lessons from Texaco and Star Enterprise. MIS Quarterly, 14(4), 385–403.
Rigdon, E. E. (2012). Rethinking partial least squares path modeling: in praise of simple methods. Long Range Planning, 45(5–6), 341–358.
Roberts, N., & Thatcher, J. B. (2009). Conceptualizing and testing formative constructs: tutorial and annotated example. The Data Base for Advances in Information Systems, 40(3), 9–39.
Ross, J. W., Weill, P., & Robertson, D. (2006). Enterprise architecture as strategy: Creating a foundation for business execution. Boston: Harvard Business School Press.
Sarstedt, M., Ringle, C. M., & Henseler, J. (2014). On the emancipation of PLS-SEM. A commentary on Rigdon. Long Range Planning, 47(3), 154–160.
Schmidt, C., & Buxmann, P. (2011). Outcomes and success factors of enterprise IT architecture management: empirical insight from the international financial services industry. European Journal of Information Systems, 20, 168–184.
Schryen, G. (2013). Revisiting IS business value research: what we already know, what we still need to know, and how we can get there. European Journal of Information Systems, 22, 139–169.
Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310.
Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572.
Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychological Methods, 7(4), 422–445.
Slot, R., Dedene, G., & Maes, R. (2009). Business value of solution architecture. In E. Proper, F. Harmsen, 7 J. L. G. Dietz (Eds.), Advances in enterprise engineering II, LNBIP (28), (pp. 84–108). Berlin: Springer.
Tamm, T., Seddon, P. B., Shanks, G., & Reynolds, P. (2011). How does enterprise architecture add value to organizations? Communications of the AIS, 28(1), Article 10.
The Open Group (2009). TOGAF Version 9: The Open Group Architecture Framework. TOG.
Tiwana, A., & Konsynski, B. (2010). Complementarities between organizational IT architecture and governance structure. Information Systems Research, 21(2), 288–304.
Urbach, N., Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40.
van der Raadt, B., Soetendal, J., Perdeck, M., & van Vliet, H. (2004). Polyphony in architecture. In Proceedings of the 26th International Conference on Software Engineering (ICSE’04).
Van Steenbergen, M., (2011). Maturity and effectiveness of enterprise architecture. PhD Thesis, Utrecht University.
Van Steenbergen, M., Schipper, J., Bos, R., & Brinkkemper, S. (2010). The dynamic architecture maturity matrix: Instrument analysis and refinement. In A. Dan, F. Gittler, & F. Toumani (Eds.), ICSOC/ServiceWave 2009, LNCS 6275 (pp. 48–61). Berlin: Springer.
Venkatraman, N., & Ramanujam, V. (1987). Measurement of business economic performance: an examination of method convergence. Journal of Management, 13(1), 109–122.
Versteeg, G., & Bouwman, H. (2006). Business architecture: a new paradigm to relate business strategy to ICT. Information Systems Frontiers, 8(2), 91–102.
Wade, M., & Hulland, J. (2004). The resource-based view and information systems research: review, extension, and suggestions for future research. MIS Quarterly, 28(1), 107–142.
Wagter, R., van den Berg, M., Luijpers, J., & van Steenbergen, M. (2005). Dynamic enterprise architecture: How to make it work. Hoboken: John Wiley & Sons.
Wall, T. D., Michie, J., Patterson, M., Wood, S. J., Sheehan, M., Glegg, C. W., & West, M. (2004). On the validity of subjective measures of company performance. Personnel Psychology, 57(1), 95–118.
Weinstein, J. A. (2010). Applying social statistics: An introduction to quantitative reasoning in sociology. Plymouth: Rowman & Littlefield Publishers, Inc.
Wetzels, M., Odekerken-Schröder, G., & Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195.
Wold, H. (1980). Model construction and evaluation when theoretical knowledge is scarce. Theory and application of partial least squares. In J. Kmenta & J. B. Ramsey (Eds.), Evaluation of econometric models (pp. 47–74). New York: Academic.
Wooldridge, J. M. (2012). Introductory econometrics (5th ed.). Mason: South-Western.
Ylimäki, T., Niemi, E., & Hämäläinen, N. (2007). Enterprise architecture compliance: The viewpoint of evaluation. In: Proceedings of The European Conference on Information Management and Evaluation (ECIME 2007).
Acknowledgments
The authors wish to thank Rik Bos, Jurriaan van Reijsen, Verena Dräbing, Nico Brand, Anne-Francoise Rutkowski, Joe Nandhakumar and the reviewers for their valuable remarks.
Author information
Authors and Affiliations
Corresponding author
Additional information
January 10th 2015. Final version. Accepted for publication in Information Systems Frontiers.
Appendices
Appendix 1: Questionnaire items and examples
1.1 Key questionnaire items
Regarding the architecture approach…
-
T1. The EA is formally approved (i.e. by line management).
-
T2. The choices made in the EA are explicitly linked to the business goals of the enterprise as a whole.
-
T3. Management propagates the importance of EA.
-
T4. Projects are being explicitly assessed on their degree of compliance with EA. [Note: this concerns the number of projects being judged on compliance (not the number of times one project is being assessed).]
-
T5. There is an organized knowledge exchange between different types of architects (for example enterprise, domain, project, software and infrastructure architects).
-
T6. There is an organized knowledge exchange between architects and other employees participating in projects that have to conform to EA (for example project managers, functional designers, developers and testers).
-
T7. Assistance is being offered in order to stimulate conformance to EA. (For example enterprise architects or change managers who help projects to make new designs conform to EA.)
-
T8. Projects make use of a PSA (Project Start Architecture).
-
T9. Document templates are being used to stimulate conformance to EA. (For example templates that focus attention on the EA by means of guiding texts and by requiring filling in relevant information.)
-
T10. Financial rewards and disincentives are being used in order to stimulate conformance to EA. (For example by covering the IT-expenses of a project if the solution is designed and built conform EA, or by imposing a fine for non-conformance.)
EA turns out to be a good instrument to…
-
B1. …accomplish enterprise-wide goals, instead of (possibly conflicting) local optimizations.
-
B2. …achieve an optimal fit between IT and the business processes it supports.
-
B3. …provide an insight into the complexity of the organization.
-
B4. …control the complexity of the organization.
-
B5. …integrate, standardize and/or deduplicate related processes and systems.
-
B6. …control costs.
-
B7. …enable the organization to respond to changes in the outside world in an agile fashion.
-
B8. …co-operate with other organizations effectively and efficiently.
-
B9. …depict a clear image of the desired future situation.
-
B10. EA turns out to be a good frame of reference to enable different stakeholders to communicate with each other effectively.
-
B11. EA, in general, turns out to be a good instrument.
Projects that have to conform to EA turn out to…
-
B12. …exceed their budgets less often than projects that do not have to conform to EA.
-
B13. …exceed their deadlines less often than projects that do not have to conform to EA.
-
B15. …deliver the desired quality more often than projects that do not have to conform to EA.
-
B16. …deliver the desired functionality more often than projects that do not have to conform to EA.
-
B14. …be better equipped to deal with risks than projects that do not have to conform to EA.
-
B17. …be better equipped to deal with complexity (of the project and/or its immediate environment) than projects that do not have to conform to EA.
-
B18. …get initialized faster than projects that do not have to conform to EA.
-
O1. Projects that are required to conform to EA turn out to actually conform to the architectural principles, models and other prescriptions.
-
O2. Principles, models and other architectural prescriptions turn out to be open to multiple interpretations.
1.2 Example of a slightly non-linear relationship (between EA approach and architectural insight)
Appendix 2: Overview of individual contributions
Formative constructs allow summarizing the individual indicators in order to focus on the substantive theoretical relationships at a higher level of abstraction. However, since each formative indicator represents a different aspect, it is advisable to also drill down and study the relationships at the indicator level (Carver 1989). The discussion of individual practices and benefits in Sections 4.3.3 and 4.3.4 is informed by the tables below. These tables provide insight into which individual formative indicators of independent constructs contribute to one or more indicators of dependent constructs. This gives further support for the causal effects in our study and to the composition of constructs. To the best of our knowledge, no methodological instructions exist for this at the time of our research (note that the construct weights cannot be used in our study). We therefore took a critical and conservative approach for verifying the individual contributions: each table column represents a separate PLS model with one dependent variable, with the partialized coefficients of the independent variables in the rows competing with each other (as opposed to mere correlations, which have a higher likelihood of being statistically significant). Therefore, the more rows a column has, the higher the probability of non-significant coefficients. Also note that these columns are not ‘final’ models, since we did not drop statistically insignificant variables. Rather, they are aimed at providing insight into all variables.
*p < 0.1 **p < 0.05 ***p < 0.01 ****p < 0.001
Rights and permissions
About this article
Cite this article
Foorthuis, R., van Steenbergen, M., Brinkkemper, S. et al. A theory building study of enterprise architecture practices and benefits. Inf Syst Front 18, 541–564 (2016). https://doi.org/10.1007/s10796-014-9542-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10796-014-9542-1