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Choosing PLS path modeling as analytical method in European management research: A realist perspective

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  • Rigdon, Edward E.

Abstract

Today, there is heightened controversy about the value of partial least squares (PLS) path modeling as a quantitative research method, including within the domain of European management research. Critical lines of argument within the management and psychology literature assert that there is no reason to use PLS path modeling at all. At the same time, authors using PLS path modeling continue to advance fallacious arguments to justify their choice of method. This paper identifies flaws on both sides—invalid arguments in favor of using PLS path modeling and invalid arguments opposing its use—within the context of a unifying framework and a realist philosophy of science.

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  • Rigdon, Edward E., 2016. "Choosing PLS path modeling as analytical method in European management research: A realist perspective," European Management Journal, Elsevier, vol. 34(6), pages 598-605.
    • Handle: RePEc:eee:eurman:v:34:y:2016:i:6:p:598-605
    DOI: 10.1016/j.emj.2016.05.006
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    References listed on IDEAS

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    1. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    2. Edward E. Rigdon & Jan-Michael Becker & Arun Rai & Christian M. Ringle & Adamantios Diamantopoulos & Elena Karahanna & Detmar W. Straub & Theo K. Dijkstra, 2014. "Conflating Antecedents and Formative Indicators: A Comment on Aguirre-Urreta and Marakas," Information Systems Research, INFORMS, vol. 25(4), pages 780-784, December.
    3. Gigerenzer, Gerd, 2004. "Mindless statistics," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 33(5), pages 587-606, November.
    4. Niels Waller & Jeff Jones, 2010. "Correlation Weights in Multiple Regression," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 58-69, March.
    5. John Antonakis & Samuel Bendahan & Philippe Jacquart & Rafael Lalive, 2010. "On making causal claims : A review and recommendations," Post-Print hal-02313119, HAL.
    6. Saunders, Mark N.K. & Bezzina, Frank, 2015. "Reflections on conceptions of research methodology among management academics," European Management Journal, Elsevier, vol. 33(5), pages 297-304.
    7. Wickens, Michael R, 1972. "A Note on the Use of Proxy Variables," Econometrica, Econometric Society, vol. 40(4), pages 759-761, July.
    8. P. Bentler, 1986. "Structural modeling and psychometrika: An historical perspective on growth and achievements," Psychometrika, Springer;The Psychometric Society, vol. 51(1), pages 35-51, March.
    9. Ronald L. Wasserstein & Nicole A. Lazar, 2016. "The ASA's Statement on p -Values: Context, Process, and Purpose," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 129-133, May.
    10. James Steiger, 1979. "The relationship between external variables and common factors," Psychometrika, Springer;The Psychometric Society, vol. 44(1), pages 93-97, March.
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