IDEAS home Printed from https://ideas.repec.org/a/spr/jbecon/v90y2020i1d10.1007_s11573-019-00927-4.html
   My bibliography  Save this article

Analyzing the capabilities of the HB logit model for choice-based conjoint analysis: a simulation study

Author

Listed:
  • Maren Hein

    (Clausthal University of Technology)

  • Peter Kurz

    (bms Marketing Research + Strategy)

  • Winfried J. Steiner

    (Clausthal University of Technology)

Abstract

The authors conduct an extensive simulation study to examine the capabilities of the Hierarchical Bayes (HB) logit model for choice-based conjoint (CBC) studies. The statistical performance of HB is evaluated under experimentally varying factor level settings using criteria for goodness-of-fit, parameter recovery and predictive accuracy. The results provide guidance to market researchers who are confronted with the problem that clients desire to include more and more attributes while keeping the choice task manageable. The results show that for simple CBC settings HB estimation proves to be quite robust. One of the main findings for simple CBC settings is that holding other factors at convenient levels far more attributes than previously suggested can be used in CBC studies. Further, sample size and/or the number of choice tasks per respondent can be noticeably reduced. However, for more complex CBC settings with an already high number of parameters (part-worths) but rather little information available from respondents, the HB model is starting to collapse if more than one of those factors (attributes, sample size, choice tasks) is set to an extreme level.

Suggested Citation

  • Maren Hein & Peter Kurz & Winfried J. Steiner, 2020. "Analyzing the capabilities of the HB logit model for choice-based conjoint analysis: a simulation study," Journal of Business Economics, Springer, vol. 90(1), pages 1-36, February.
    • Handle: RePEc:spr:jbecon:v:90:y:2020:i:1:d:10.1007_s11573-019-00927-4
    DOI: 10.1007/s11573-019-00927-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11573-019-00927-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11573-019-00927-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dhar, Ravi, 1997. "Consumer Preference for a No-Choice Option," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(2), pages 215-231, September.
    2. Vithala R. Rao, 2014. "Applied Conjoint Analysis," Springer Books, Springer, edition 127, number 978-3-540-87753-0, October.
    3. Thomas Kneib & Bernhard Baumgartner & Winfried Steiner, 2007. "Semiparametric multinomial logit models for analysing consumer choice behaviour," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 91(3), pages 225-244, October.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    5. Wittink, Dick R & Krishnamurthi, Lakshman & Nutter, Julia B, 1982. "Comparing Derived Importance Weights Across Attributes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 8(4), pages 471-474, March.
    6. Chakraborty, Goutam & Ball, Dwayne & Gaeth, Gary J. & Jun, Sunkyu, 2002. "The ability of ratings and choice conjoint to predict market shares: a Monte Carlo simulation," Journal of Business Research, Elsevier, vol. 55(3), pages 237-249, March.
    7. Hensher, David A. & Stopher, Peter R. & Louviere, Jordan J., 2001. "An exploratory analysis of the effect of numbers of choice sets in designed choice experiments: an airline choice application," Journal of Air Transport Management, Elsevier, vol. 7(6), pages 373-379.
    8. Currim, Imran S & Weinberg, Charles B & Wittink, Dick R, 1981. "Design of Subscription Programs for a Performing Arts Series," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 8(1), pages 67-75, June.
    9. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    10. 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.
    11. Peter J. Lenk & Wayne S. DeSarbo & Paul E. Green & Martin R. Young, 1996. "Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs," Marketing Science, INFORMS, vol. 15(2), pages 173-191.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Plananska, Jana & Gamma, Karoline, 2022. "Product bundling for accelerating electric vehicle adoption: A mixed-method empirical analysis of Swiss customers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    2. Rilling, Benedikt & Kurz, Peter & Herbes, Carsten, 2024. "Renewable gases in the heating market: Identifying consumer preferences through a Discrete Choice Experiment," Energy Policy, Elsevier, vol. 184(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hein, Maren & Goeken, Nils & Kurz, Peter & Steiner, Winfried J., 2022. "Using Hierarchical Bayes draws for improving shares of choice predictions in conjoint simulations: A study based on conjoint choice data," European Journal of Operational Research, Elsevier, vol. 297(2), pages 630-651.
    2. Hein, Maren & Kurz, Peter & Steiner, Winfried J., 2019. "On the effect of HB covariance matrix prior settings: A simulation study," Journal of choice modelling, Elsevier, vol. 31(C), pages 51-72.
    3. Carsten Herbes & Johannes Dahlin & Peter Kurz, 2020. "Consumer Willingness To Pay for Proenvironmental Attributes of Biogas Digestate-Based Potting Soil," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    4. Steiger, Robert & Posch, Eva & Tappeiner, Gottfried & Walde, Janette, 2020. "The impact of climate change on demand of ski tourism - a simulation study based on stated preferences," Ecological Economics, Elsevier, vol. 170(C).
    5. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    6. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    7. Cho, Woohyun & Windle, Robert J. & Dresner, Martin E., 2017. "The impact of operational exposure and value-of-time on customer choice: Evidence from the airline industry," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 455-471.
    8. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    9. Domínguez-Torreiro, Marcos & Soliño, Mario, 2011. "Provided and perceived status quo in choice experiments: Implications for valuing the outputs of multifunctional rural areas," Ecological Economics, Elsevier, vol. 70(12), pages 2523-2531.
    10. Johanna Lena Dahlhausen & Cam Rungie & Jutta Roosen, 2018. "Value of labeling credence attributes—common structures and individual preferences," Agricultural Economics, International Association of Agricultural Economists, vol. 49(6), pages 741-751, November.
    11. Fraser, Iain & Balcombe, Kelvin & Williams, Louis & McSorley, Eugene, 2021. "Preference stability in discrete choice experiments. Some evidence using eye-tracking," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 94(C).
    12. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    13. Carlsson, Fredrik & Martinsson, Peter, 2008. "Does it matter when a power outage occurs? -- A choice experiment study on the willingness to pay to avoid power outages," Energy Economics, Elsevier, vol. 30(3), pages 1232-1245, May.
    14. Lynd Bacon & Peter Lenk, 2012. "Augmenting discrete-choice data to identify common preference scales for inter-subject analyses," Quantitative Marketing and Economics (QME), Springer, vol. 10(4), pages 453-474, December.
    15. von Haefen, Roger H. & Adamowicz, Wiktor L., 2003. "Not Playing The Game: Non-Particpation In Repeated Discrete Choice Models," 2003 Annual meeting, July 27-30, Montreal, Canada 22037, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    16. Max J. Pachali & Peter Kurz & Thomas Otter, 2020. "How to generalize from a hierarchical model?," Quantitative Marketing and Economics (QME), Springer, vol. 18(4), pages 343-380, December.
    17. De Ayala Bilbao, Amaya & Hoyos Ramos, David & Mariel Chladkova, Petr, 2012. "Landscape valuation through discrete choice experiments: Current practice and future research reflections," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    18. Boxall, Peter C. & Adamowicz, Wiktor L. & Moon, Amanda, 2009. "Complexity in choice experiments: choice of the status quo alternative and implications for welfare measurement," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 53(4), pages 1-17.
    19. Hu, Wuyang, 2006. "Effects of Endogenous Task Complexity and the Endowed Bundle on Stated Choice," 2006 Annual meeting, July 23-26, Long Beach, CA 21437, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    20. Huang, Youlin & Qian, Lixian & Tyfield, David & Soopramanien, Didier, 2021. "On the heterogeneity in consumer preferences for electric vehicles across generations and cities in China," Technological Forecasting and Social Change, Elsevier, vol. 167(C).

    More about this item

    Keywords

    Choice-based conjoint analysis; Hierarchical Bayes; Simulation; Sensitivity analysis;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

    Statistics

    Access and download statistics

    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:jbecon:v:90:y:2020:i:1:d:10.1007_s11573-019-00927-4. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    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 (email available below). General contact details of provider: http://www.springer.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.