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Specifying Formatively-measured Constructs In Endogenous Positions In Structural Equation Models: Caveats and Guidelines For Researchers


  • Dirk Temme

    () (University of Wuppertal)

  • Adamantios Diamantopoulos

    () (University of Vienna)

  • Vanessa Pfegfeidel

    () (University of Wuppertal)


Formatively-measured constructs (FMCs) are increasingly used in marketing research as well as in other disciplines. Although constructs operationalized by means of formative indicators have mostly been placed in exogenous positions in structural equation models, they also frequently occupy structurally endogenous positions. The vast majority of studies specifying endogenously positioned FMCs have followed the common practice of modeling the impact of antecedent (predictor) constructs directly on the focal FMC without specifying indirect effects via the formative indicators. However, while widespread even in top journals, this practice is highly problematic as it can lead to biased parameter estimates, erroneous total effects, and questionable conclusions. As a result both theory development and empirically-based managerial recommendations are likely to suffer. Against this background, the authors offer appropriate modeling guidelines to ensure that a conceptually sound and statistically correct model specification is obtained when a FMC occupies an endogenous position. The proposed guidelines are illustrated using both covariance structure analysis (CSA) and partial least squares (PLS) methods and are applied to a real-life empirical example. Implications for researchers are considered and ‘good practice’ recommendations offered.

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  • Dirk Temme & Adamantios Diamantopoulos & Vanessa Pfegfeidel, 2014. "Specifying Formatively-measured Constructs In Endogenous Positions In Structural Equation Models: Caveats and Guidelines For Researchers," Schumpeter Discussion Papers SDP14005, Universitätsbibliothek Wuppertal, University Library.
  • Handle: RePEc:bwu:schdps:sdp14005

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    References listed on IDEAS

    1. Cadogan, John W. & Lee, Nick, 2013. "Improper use of endogenous formative variables," Journal of Business Research, Elsevier, vol. 66(2), pages 233-241.
    2. Diamantopoulos, Adamantios & Riefler, Petra & Roth, Katharina P., 2008. "Advancing formative measurement models," Journal of Business Research, Elsevier, vol. 61(12), pages 1203-1218, December.
    3. Jörg Henseler & Marko Sarstedt, 2013. "Goodness-of-fit indices for partial least squares path modeling," Computational Statistics, Springer, vol. 28(2), pages 565-580, April.
    4. Franke, George R. & Preacher, Kristopher J. & Rigdon, Edward E., 2008. "Proportional structural effects of formative indicators," Journal of Business Research, Elsevier, vol. 61(12), pages 1229-1237, December.
    5. Jarvis, Cheryl Burke & MacKenzie, Scott B & Podsakoff, Philip M, 2003. " A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research," Journal of Consumer Research, Oxford University Press, vol. 30(2), pages 199-218, September.
    6. Dirk Temme & Lutz Hildebrandt, 2006. "Formative Measurement Models in Covariance Structure Analysis: Specification and Identification," SFB 649 Discussion Papers SFB649DP2006-083, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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    Cited by:

    1. Sarstedt, Marko & Hair, Joseph F. & Ringle, Christian M. & Thiele, Kai O. & Gudergan, Siegfried P., 2016. "Estimation issues with PLS and CBSEM: Where the bias lies!," Journal of Business Research, Elsevier, vol. 69(10), pages 3998-4010.
    2. Tomczyk, Przemysław & Doligalski, Tymoteusz & Zaborek, Piotr, 2016. "Does customer analysis affect firm performance? Quantitative evidence from the Polish insurance market," Journal of Business Research, Elsevier, vol. 69(9), pages 3652-3658.
    3. Chang, Woojung & Franke, George R. & Lee, Nick, 2016. "Comparing reflective and formative measures: New insights from relevant simulations," Journal of Business Research, Elsevier, vol. 69(8), pages 3177-3185.

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    formatively-measured constructs; endogenous formative indicators; covariance structure analysis; partial least squares;

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