From Explorer to Leader: The DSRES Index as a Framework for Design Science Research Maturity
DOI:
https://doi.org/10.14738/assrj.1302.20062Keywords:
Design Science Research, DSRES Index, Composite Index ConstructionAbstract
This article introduces the Design Science Research Effectiveness Index (DSRES Index), a methodological innovation for evaluating the maturity and impact of managerial design research. Building on the validated DSRES Scale, the Index integrates five dimensions—Rigor, Utility, Knowledge, Engagement, and Evaluation—into a composite measure of research effectiveness, with weights informed by confirmatory factor analysis and theoretical reasoning. In addition to measurement, the study advances a typology that distinguishes four organizational maturity profiles: Explorer, Developer, Integrator, and Leader. Each profile reflects specific characteristics and challenges, and outlines pathways for progression. A distinctive contribution of this work lies in the inclusion of managerial recommendations and interpretive insights, which transform the typology into a practical tool for guiding organizational development. By offering a valid and replicable assessment method, the DSRES Index enables comparative studies, strengthens design-oriented projects, and supports the integration of scientific evidence into managerial practice.
References
Alegre, J., Lapiedra, R., & Chiva, R. (2006). A measurement scale for product innovation performance. European Journal of Innovation Management, 9(4), 333–346.
Alturki, A., Gable, G. G., & Bandara, W. (2011). A design science research roadmap. In Proceedings of the 6th International Conference on Service-Oriented Perspectives in Design Science Research. Springer.
Baskerville, R., Pries-Heje, J., & Venable, J. (2009, May). Soft design science methodology. In Proceedings of the 4th international conference on design science research in information systems and technology (pp. 1-11).7
Baskerville, R., & Pries-Heje, J. (2019). Projectability in design science research. Journal of Information Technology Theory and Application (JITTA), 20(1), 3.
Bunge, M. (1980). Epistemologia. T. A. Queiroz/EDUSP
Calik, E., Calisir, F., & Cetinguc, B. (2017). A Scale Development for Innovation Capability Measurement. Journal of Advanced Management Science, 5(2), 69–76. https://doi.org/10.18178/joams.5.2.69-76
Churchill Jr, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of marketing research, 16(1), 64-73.
Clerino, P., Fargue-Lelièvre, A., Prost, L., Salembier, C., & Meynard, J. M. (2026). Comparison of two contrasted approaches of participatory design: lessons from designing tools for the sustainability assessment of urban agriculture projects. Research in Engineering Design, 37(1), 8.
DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications. Sage publications.
Page | 240 Scholar Publishing de Medeiros, M. S., & Martins, T. C. (2024). Design Science Research (DSR) como método de pesquisa em políticas públicas. Missões: Revista de Ciências Humanas e Sociais, 10(3).
Dewar, R. D., Whetten, D. A., & Boje, D. (1980). An Examination of the Reliability and Validity of the Aiken and Hage Scales of Centralization, Formalization, and Task Routineness. Administrative Science Quarterly, 25(1), 120–128. https://doi.org/10.2307/2392230
Dresch, A., Lacerda, D. P., & Junior, J. A. V. A. (2020). Design science research: método de pesquisa para avanço da ciência e tecnologia. Bookman Editora.
Eekels, J., & Roozenburg, N. F. M. (1991). A methodological comparison of the structures of scientific research and engineering design: Their similarities and differences. Design Studies, 12(4), 197–201. https://doi.org/10.1016/0142-694X(91)90025-U
Gioia, D. A., & Pitre, E. (1990). Multiparadigm Perspectives on Theory Building. Academy of Management Review, 15(4), 584–602. https://doi.org/10.5465/amr.1990.4310758
Goldmann, P., Schäfer, B., & Altendorfer, C. (2025). Development and validation of the digital mindset scale. Journal of Business Research, 199, 115554.
Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS quarterly, 337-355.
Gregor, S., & Hevner, A. R. (2011). Introduction to the special issue on design science. Information Systems and e-Business Management, 9(1), 1-9.
Gregor, S., & Zwikael, O. (2024). Design science research and the co-creation of management knowledge. International Journal of Management , 42(3), 102584.
Hair, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi.org/10.1504/IJMDA.2017.087624
Hair, J. F., Ringle ,Christian M., & and Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hammer, M., & Champy, J. (1993). Reengineering the Corporation: A Manifesto for Business Revolution. Business Horizons, 36(5), 90–91. https://doi.org/10.1016/S0007-6813(05)80064-3
Harrington, D. (2009). Confirmatory factor analysis. Oxford university press.
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., KetchenJr., D. J., 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. https://doi.org/10.1177/1094428114526928
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hevner, A. R., et al. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625
Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational research methods, 1(1), 104-121.
Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
Lacerda, D. P., et al. (2013). Design science research: A research method in production engineering. Gestão & Produção, 20(4), 741–761.
Scholar Publishing MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological methods, 4(1), 84.
Manson, N. J. (2006). Is operations research really research? ORION, 22(2), 155–180. https://doi.org/10.5784/22-2-16
March, S. T., & Smith, G. F. (1995). Design and natural science research on information technology. Decision support systems, 15(4), 251-266.
March, S. T., & Storey, V. C. (2008). Design science in the information systems discipline: An introduction to the special issue on design science research. MIS Quarterly, 32(4), 725–730.
Marxt, C., & and Hacklin, F. (2005). Design, product development, innovation: All the same in the end? A short discussion on terminology. Journal of Engineering Design, 16(4), 413–421.
Mason, C. H., & Perreault JR., W. D. (1991). Collinearity, Power, and Interpretation of Multiple Regression Analysis. Journal of Marketing Research, 28(3), 268–280.
Mazur, A. K., & Pisarski, A. (2015). Major project managers' internal and external stakeholder relationships: The development and validation of measurement scales. International Journal of Management , 33(8), 1680-1691.
Miles, M., Huberman, A. & Saldaña, J. (2014). Qualitative data analysis: a methods sourcebook. 3rd edition. Sage.
Myers, M. D. & Newman, M. (2007). The Qualitative Interview in IS Research: Examining the Craft. Information and Organization, 17, 2-26.
Narazaki, R. S., Silveira Chaves, M., & Drebes Pedron, C. (2020). A project knowledge management framework grounded in design science research. Knowledge and Process Management, 27(3), 197-210.
Nunamaker, J. E., Chen, M., & Purdin, T. D. M. (1991). Systems development in information systems research. Journal of Management Information Systems, 7(3), 89–106. https://doi.org/10.1080/07421222.1990.11517998
Peffers, K., et al. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/MIS0742-1222240302
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
Rauschnabel, P. A., Krey, N., Babin, B. J., & Ivens, B. S. (2016). Brand management in higher education: the university brand personality scale. Journal of Business Research, 69(8), 3077-3086.
Ringle, C. M., Wende, S., & Becker, J. M. (2022). SmartPLS 4. SmartPLS. http://www.smartpls.com
Romme, A. G. L. (2003). Making a difference: Organization as design. Organization science, 14(5), 558-573.
Roozenburg, N. F., & Eekels, J. (1995). Product design: fundamentals and methods. Wiley–Blackwell
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research Methods for Business Students. Prentice-Hall, 5th edition.
Simon, H. A. (1969). The sciences of the artificial. MIT Press Page | 242
Scholar Publishing Strauss, A., & Corbin, J. (1990). Basics of qualitative research. Sage.
Takeda, H., et al. (1990). Modeling design processes. AI Magazine, 11(4), 37–48. https://doi.org/10.1609/aimag.v11i4.854
Thomas, L. D., Romasanta, A. K. G., & Priego, L. P. (2026). Jagged competencies: Measuring the reliability of generative AI in academic research. Journal of Business Research, 203, 115804.
Vaishnavi, V., & Kuechler, W. (2004). Design research in information systems. [s.n.]. Retrieved from http://desrist.org (Accessed December 20, 2011).
Van Aken, J. E. (2004). Management research based on the paradigm of design science: The quest for field-tested and grounded technological rules. Journal of Management Studies, 41(2), 219–246. https://doi.org/10.1111/j.1467-6486.2004.00430.x
Winter, R. (2008). Design science research in Europe. European Journal of Information Systems, 17(5), 470-475.
Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and psychological measurement, 73(6), 913-934.
Yu, Y., Chan, H. L., & Cho, E. (2026). Enhancing ESG performance through digital transformation: Recent development, cases and relationships. Journal of Business Research, 202, 115763.
Liu, Y., & Zolghadri, M. (2026). What is happening in product obsolescence management? Three decades of review from 1993 to 2023. Research in Engineering Design, 37(2), 9.
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