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Identifying Sources of Heterogeneity for Empirically Deriving Strategic Types: A Constrained Finite-Mixture Structural-Equation Methodology

Author

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  • Wayne S. DeSarbo

    (Smeal College of Business Administration, Pennsylvania State University, University Park, Pennsylvania 16802)

  • C. Anthony Di Benedetto

    (Fox School of Business Administration, Temple University, Philadelphia, Pennsylvania 19122)

  • Kamel Jedidi

    (Graduate School of Business, Columbia University, 3022 Broadway, New York, New York 10027)

  • Michael Song

    (Henry W. Bloch School of Business and Public Administration, University of Missouri-Kansas City, Kansas City, Missouri 64110, and Eindhoven Centre for Innovation Studies, Eindhoven University of Technology, Eindhoven, The Netherlands)

Abstract

The resource-based view (RBV) of the firm suggests that strategic deployment of capabilities allows strategic business units (SBUs) to exploit distinctive competencies and create sustainable competitive advantage. Following the RBV, we propose a new predictive methodology for deriving typologies of SBUs that accommodates heterogeneity among SBUs with respect to their strategic capabilities, how effectively they are employed, and performance. Statistically, we devise a constrained finite-mixture structural-equation procedure that simultaneously accounts for firm capabilities, performance outcomes, and the relationships between them. The procedure allows for a comprehensive modeling and grouping of entities, and simultaneously provides a diagnosis of the sources of heterogeneity via the flexibility of estimating a series of nested models. Managerially, our proposed methodology is grounded in the strategic type and RBV literature and can capture the effects of environmental and industry-specific factors. Using data obtained from 216 SBUs in the United States for illustration, the results show that our derived four mixed-type solution dominates the four-group, Prospectors-Analyzers-Defenders-Reactors classification as well as a number of other nested model solutions in terms of objective statistical fit criteria for this data set, suggesting a more contingency-driven strategic stance adopted by these SBUs. We conclude with a discussion of the theoretical and managerial benefits of an improved methodology for empirically deriving strategic typologies.

Suggested Citation

  • Wayne S. DeSarbo & C. Anthony Di Benedetto & Kamel Jedidi & Michael Song, 2006. "Identifying Sources of Heterogeneity for Empirically Deriving Strategic Types: A Constrained Finite-Mixture Structural-Equation Methodology," Management Science, INFORMS, vol. 52(6), pages 909-924, June.
  • Handle: RePEc:inm:ormnsc:v:52:y:2006:i:6:p:909-924
    DOI: 10.1287/mnsc.1060.0529
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    References listed on IDEAS

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    1. Sarstedt, Marko & Radomir, Lăcrămioara & Moisescu, Ovidiu Ioan & Ringle, Christian M., 2022. "Latent class analysis in PLS-SEM: A review and recommendations for future applications," Journal of Business Research, Elsevier, vol. 138(C), pages 398-407.
    2. Laurette Dubé & Antoine Bechara & Ulf Böckenholt & Asim Ansari & Alain Dagher & Mark Daniel & Wayne DeSarbo & Lesley Fellows & Ross Hammond & Terry Huang & Scott Huettel & Yan Kestens & Bärbel Knäuper, 2009. "Towards a brain-to-society systems model of individual choice," Marketing Letters, Springer, vol. 20(1), pages 105-106, March.
    3. Ralf van der Lans & Bram Van den Bergh & Evelien Dieleman, 2014. "Partner Selection in Brand Alliances: An Empirical Investigation of the Drivers of Brand Fit," Marketing Science, INFORMS, vol. 33(4), pages 551-566, July.
    4. Radomska Joanna, 2016. "Approach to strategy and enterprise size," Management, Sciendo, vol. 20(2), pages 118-128, December.
    5. Hao, Shengbin & Song, Michael, 2016. "Technology-driven strategy and firm performance: Are strategic capabilities missing links?," Journal of Business Research, Elsevier, vol. 69(2), pages 751-759.
    6. Ralf van der Lans & Joseph A. Cote & Catherine A. Cole & Siew Meng Leong & Ale Smidts & Pamela W. Henderson & Christian Bluemelhuber & Paul A. Bottomley & John R. Doyle & Alexander Fedorikhin & Janaki, 2009. "Cross-National Logo Evaluation Analysis: An Individual-Level Approach," Marketing Science, INFORMS, vol. 28(5), pages 968-985, 09-10.
      • van der Lans, R.J.A. & Cote, J.A. & Cole, C.A. & Leong, S.M. & Smidts, A. & Henderson, P.W. & Bluemelhuber, C. & Bottomley, P.A. & Doyle, J.R. & Fedorikhin, A.S. & Janakiraman, M. & Ramaseshan, B. & S, 2008. "Cross-National Logo Evaluation Analysis: An Individual Level Approach," ERIM Report Series Research in Management ERS-2008-055-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Matias Bronnenmayer & Bernd Wirtz & Vincent Göttel, 2016. "Success factors of management consulting," Review of Managerial Science, Springer, vol. 10(1), pages 1-34, January.
    8. Zrelli, Imen & Demnati, Haykel & Ben Yedder, Moez, 2019. "The effect of the interaction between tariff modulation and transparency on the customer's dissatisfaction: The case of Tunisia," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 1-10.
    9. Lin, Chinho & Tsai, Hua-Ling & Wu, Ju-Chuan, 2014. "Collaboration strategy decision-making using the Miles and Snow typology," Journal of Business Research, Elsevier, vol. 67(9), pages 1979-1990.
    10. Ahmadi, Hormoz & O'Cass, Aron & Miles, Morgan P., 2014. "Product resource–capability complementarity, integration mechanisms, and first product advantage," Journal of Business Research, Elsevier, vol. 67(5), pages 704-709.

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