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Marketing Mix Modeling Using PLS-SEM, Bootstrapping the Model Coefficients

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  • Mariano Méndez-Suárez

    (Department of Market Research and Quantitative Methods, ESIC Business & Marketing School, Pozuelo de Alarcón, 28223 Madrid, Spain)

Abstract

Partial least squares structural equations modeling (PLS-SEM) uses sampling bootstrapping to calculate the significance of the model parameter estimates (e.g., path coefficients and outer loadings). However, when data are time series, as in marketing mix modeling, sampling bootstrapping shows inconsistencies that arise because the series has an autocorrelation structure and contains seasonal events, such as Christmas or Black Friday, especially in multichannel retailing, making the significance analysis of the PLS-SEM model unreliable. The alternative proposed in this research uses maximum entropy bootstrapping (meboot), a technique specifically designed for time series, which maintains the autocorrelation structure and preserves the occurrence over time of seasonal events or structural changes that occurred in the original series in the bootstrapped series. The results showed that meboot had superior performance than sampling bootstrapping in terms of the coherence of the bootstrapped data and the quality of the significance analysis.

Suggested Citation

  • Mariano Méndez-Suárez, 2021. "Marketing Mix Modeling Using PLS-SEM, Bootstrapping the Model Coefficients," Mathematics, MDPI, vol. 9(15), pages 1-12, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:15:p:1832-:d:607727
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    References listed on IDEAS

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    1. Florian Schuberth & Jörg Henseler & Theo K. Dijkstra, 2018. "Partial least squares path modeling using ordinal categorical indicators," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 9-35, January.
    2. Hrishikesh D. Vinod, 2013. "Maximum Entropy Bootstrap Algorithm Enhancements," Fordham Economics Discussion Paper Series dp2013-04, Fordham University, Department of Economics.
    3. Mariano Méndez-Suárez & Abel Monfort, 2021. "Marketing Attribution in Omnichannel Retailing," Springer Proceedings in Business and Economics, in: Francisco J. Martínez-López & Juan Carlos Gázquez-Abad (ed.), Advances in National Brand and Private Label Marketing, pages 114-120, Springer.
    4. Jörg Henseler, 2018. "Partial least squares path modeling: Quo vadis?," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 1-8, January.
    5. Richter, Nicole Franziska & Hauff, Sven & Schlaegel, Christopher & Gudergan, Siegfried & Ringle, Christian M. & Gunkel, Marjaana, 2016. "Using Cultural Archetypes in Cross-cultural Management Studies," Journal of International Management, Elsevier, vol. 22(1), pages 63-83.
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    7. Vinod, Hrishikesh D. & Lopez-de-Lacalle, Javier, 2009. "Maximum Entropy Bootstrap for Time Series: The meboot R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i05).
    8. Streukens, Sandra & Leroi-Werelds, Sara, 2016. "Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results," European Management Journal, Elsevier, vol. 34(6), pages 618-632.
    9. Méndez-Suárez, Mariano & Monfort, Abel, 2020. "The amplifying effect of branded queries on advertising in multi-channel retailing," Journal of Business Research, Elsevier, vol. 112(C), pages 254-260.
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