IDEAS home Printed from https://ideas.repec.org/a/spr/busres/v12y2019i1d10.1007_s40685-018-0072-4.html
   My bibliography  Save this article

Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice

Author

Listed:
  • Joseph F. Hair

    () (University of South Alabama)

  • Christian M. Ringle

    () (Hamburg University of Technology (TUHH)
    University of Waikato)

  • Siegfried P. Gudergan

    () (University of Waikato)

  • Andreas Fischer

    () (Hamburg University of Technology (TUHH))

  • Christian Nitzl

    () (University of the German Federal Armed Forces Munich)

  • Con Menictas

    () (Strategic Precision Pty Ltd)

Abstract

Commonly used discrete choice model analyses (e.g., probit, logit and multinomial logit models) draw on the estimation of importance weights that apply to different attribute levels. But directly estimating the importance weights of the attribute as a whole, rather than of distinct attribute levels, is challenging. This article substantiates the usefulness of partial least squares structural equation modeling (PLS-SEM) for the analysis of stated preference data generated through choice experiments in discrete choice modeling. This ability of PLS-SEM to directly estimate the importance weights for attributes as a whole, rather than for the attribute’s levels, and to compute determinant respondent-specific latent variable scores applicable to attributes, can more effectively model and distinguish between rational (i.e., optimizing) decisions and pragmatic (i.e., heuristic) ones, when parameter estimations for attributes as a whole are crucial to understanding choice decisions.

Suggested Citation

  • Joseph F. Hair & Christian M. Ringle & Siegfried P. Gudergan & Andreas Fischer & Christian Nitzl & Con Menictas, 2019. "Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 115-142, April.
  • Handle: RePEc:spr:busres:v:12:y:2019:i:1:d:10.1007_s40685-018-0072-4
    DOI: 10.1007/s40685-018-0072-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40685-018-0072-4
    File Function: Abstract
    Download Restriction: no

    References listed on IDEAS

    as
    1. Louviere, Jordan J. & Islam, Towhidul, 2008. "A comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best-worst scaling," Journal of Business Research, Elsevier, vol. 61(9), pages 903-911, September.
    2. Lu, Hui & Hess, Stephane & Daly, Andrew & Rohr, Charlene, 2017. "Measuring the impact of alcohol multi-buy promotions on consumers' purchase behaviour," Journal of choice modelling, Elsevier, vol. 24(C), pages 75-95.
    3. Rosenthal, Robert W, 1989. "A Bounded-Rationality Approach to the Study of Noncooperative Games," International Journal of Game Theory, Springer;Game Theory Society, vol. 18(3), pages 273-291.
    4. Amaro, Suzanne & Duarte, Paulo, 2015. "An integrative model of consumers' intentions to purchase travel online," Tourism Management, Elsevier, vol. 46(C), pages 64-79.
    5. Lee, Lorraine & Petter, Stacie & Fayard, Dutch & Robinson, Shani, 2011. "On the use of partial least squares path modeling in accounting research," International Journal of Accounting Information Systems, Elsevier, vol. 12(4), pages 305-328.
    6. Peter J Buckley & Timothy M Devinney & Jordan J Louviere, 2007. "Do managers behave the way theory suggests? A choice-theoretic examination of foreign direct investment location decision-making," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 38(7), pages 1069-1094, December.
    7. Avi Goldfarb & Catherine Tucker, 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing Science, INFORMS, vol. 30(3), pages 389-404, 05-06.
    8. Avi Goldfarb & Catherine E. Tucker, 2011. "Privacy Regulation and Online Advertising," Management Science, INFORMS, vol. 57(1), pages 57-71, January.
    9. 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.
    10. Nitzl, Christian, 2016. "The use of partial least squares structural equation modelling (PLS-SEM) in management accounting research: Directions for future theory development," Journal of Accounting Literature, Elsevier, vol. 37(C), pages 19-35.
    11. Louviere, Jordan & Lings, Ian & Islam, Towhidul & Gudergan, Siegfried & Flynn, Terry, 2013. "An introduction to the application of (case 1) best–worst scaling in marketing research," International Journal of Research in Marketing, Elsevier, vol. 30(3), pages 292-303.
    12. Carsten Hahn & Michael D. Johnson & Andreas Herrmann & Frank Huber, 2002. "Capturing Customer Heterogeneity Using A Finite Mixture Pls Approach," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 54(3), pages 243-269, July.
    13. Avi Goldfarb & Catherine Tucker, 2011. "Rejoinder--Implications of "Online Display Advertising: Targeting and Obtrusiveness"," Marketing Science, INFORMS, vol. 30(3), pages 413-415, 05-06.
    14. Evermann, Joerg & Tate, Mary, 2016. "Assessing the predictive performance of structural equation model estimators," Journal of Business Research, Elsevier, vol. 69(10), pages 4565-4582.
    15. Horrace, William C. & Oaxaca, Ronald L., 2006. "Results on the bias and inconsistency of ordinary least squares for the linear probability model," Economics Letters, Elsevier, vol. 90(3), pages 321-327, March.
    16. James J. Heckman & James M. Snyder, Jr., 1996. "Linear Probability Models of the Demand for Attributes with an Empirical Application to Estimating the Preferences of Legislators," NBER Working Papers 5785, National Bureau of Economic Research, Inc.
    17. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, April.
    18. Hall, Jane & Viney, Rosalie & Haas, Marion & Louviere, Jordan, 2004. "Using stated preference discrete choice modeling to evaluate health care programs," Journal of Business Research, Elsevier, vol. 57(9), pages 1026-1032, September.
    19. Ayeh, Julian K. & Au, Norman & Law, Rob, 2013. "Predicting the intention to use consumer-generated media for travel planning," Tourism Management, Elsevier, vol. 35(C), pages 132-143.
    20. Sarstedt, Marko & Ringle, Christian M. & Smith, Donna & Reams, Russell & Hair, Joseph F., 2014. "Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers," Journal of Family Business Strategy, Elsevier, vol. 5(1), pages 105-115.
    21. Gudergan, Siegfried P. & Devinney, Timothy M. & Susan Ellis, R., 2016. "Cooperation and compliance in non-equity alliances," Journal of Business Research, Elsevier, vol. 69(5), pages 1759-1764.
    22. Kahneman, Daniel & Tversky, Amos, 1979. "Prospect Theory: An Analysis of Decision under Risk," Econometrica, Econometric Society, vol. 47(2), pages 263-291, March.
    23. Hazel Bateman & Christine Eckert & Fedor Iskhakov & Jordan Louviere & Stephen Satchell & Susan Thorp, 2017. "Default and naive diversification heuristics in annuity choice," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 32-57, February.
    24. Zhang, Jing & Reed Johnson, F. & Mohamed, Ateesha F. & Hauber, A. Brett, 2015. "Too many attributes: A test of the validity of combining discrete-choice and best–worst scaling data," Journal of choice modelling, Elsevier, vol. 15(C), pages 1-13.
    25. Jakobowicz, Emmanuel & Derquenne, Christian, 2007. "A modified PLS path modeling algorithm handling reflective categorical variables and a new model building strategy," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3666-3678, May.
    26. Lancsar, Emily & Louviere, Jordan & Flynn, Terry, 2007. "Several methods to investigate relative attribute impact in stated preference experiments," Social Science & Medicine, Elsevier, vol. 64(8), pages 1738-1753, April.
    27. Carlo Giacomo Prato & Shlomo Bekhor & Cristina Pronello, 2012. "Latent variables and route choice behavior," Post-Print halshs-00733464, HAL.
    28. Carlo Prato & Shlomo Bekhor & Cristina Pronello, 2012. "Latent variables and route choice behavior," Transportation, Springer, vol. 39(2), pages 299-319, March.
    29. Andrew Daly & Stephane Hess & Bhanu Patruni & Dimitris Potoglou & Charlene Rohr, 2012. "Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour," Transportation, Springer, vol. 39(2), pages 267-297, March.
    30. Rungie, Cam M. & Coote, Leonard V. & Louviere, Jordan J., 2012. "Latent variables in discrete choice experiments," Journal of choice modelling, Elsevier, vol. 5(3), pages 145-156.
    31. Maria Kamargianni & Moshe Ben-Akiva & Amalia Polydoropoulou, 2014. "Incorporating social interaction into hybrid choice models," Transportation, Springer, vol. 41(6), pages 1263-1285, November.
    Full references (including those not matched with items on IDEAS)

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:busres:v:12:y:2019:i:1:d:10.1007_s40685-018-0072-4. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Springer Nature Abstracting and Indexing). General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.