IDEAS home Printed from https://ideas.repec.org/a/spr/sjobre/v53y2001i8d10.1007_bf03372669.html
   My bibliography  Save this article

Nutzenermittlung in wahlbasierter Conjoint-Analyse: Ein Vergleich von Latent-Class- und hierarchischem Bayes-Verfahren

Author

Listed:
  • Thorsten Teichert

    (Universität Bern)

Abstract

Summary Two different concepts of disentangling noise from systematic deviations in Choice-Based Conjoint evaluations are compared: The Latent Class Technique and the Hierarchical Bayes procedure. In addition, a probabilistic interpretation of LC estimates is presented as an interims model. Conceptual differences between these models are discussed and hypotheses on resulting differences in estimates are derived. These are tested in a large-scale empirical study. The relative performance is evaluated in two distinct application areas: segment/level and individual/level estimates. The expected patterns are confirmed only partly by empirical evidence. It is shown that the structure of the underlying heterogeneity concept influences the achievable outcomes. Contrary to expectations it is shown that the segment-level estimates are highly stable across methods. While individual Hierarchical Bayes estimates are often of questionable quality, they are to be preferred against the Latent Class estimates, because they detect outliers reasonably well and provide more flexibility in the data evaluation.

Suggested Citation

  • Thorsten Teichert, 2001. "Nutzenermittlung in wahlbasierter Conjoint-Analyse: Ein Vergleich von Latent-Class- und hierarchischem Bayes-Verfahren," Schmalenbach Journal of Business Research, Springer, vol. 53(8), pages 798-822, December.
    • Handle: RePEc:spr:sjobre:v:53:y:2001:i:8:d:10.1007_bf03372669
    DOI: 10.1007/BF03372669
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/BF03372669
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1007/BF03372669?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
    ---><---

    References listed on IDEAS

    as
    1. Neeraj Arora & Greg M. Allenby & James L. Ginter, 1998. "A Hierarchical Bayes Model of Primary and Secondary Demand," Marketing Science, INFORMS, vol. 17(1), pages 29-44.
    2. Louviere, Jordan J., 1991. "Experimental choice analysis: Introduction and overview," Journal of Business Research, Elsevier, vol. 23(4), pages 291-297, December.
    3. Venkatram Ramaswamy & Wayne S. Desarbo & David J. Reibstein & William T. Robinson, 1993. "An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data," Marketing Science, INFORMS, vol. 12(1), pages 103-124.
    4. 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)

    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. Charles Cunningham & Ken Deal & Yvonne Chen, 2010. "Adaptive Choice-Based Conjoint Analysis," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 3(4), pages 257-273, December.
    2. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2011. "Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 378-388.
    3. Theodoros Evgeniou & Constantinos Boussios & Giorgos Zacharia, 2005. "Generalized Robust Conjoint Estimation," Marketing Science, INFORMS, vol. 24(3), pages 415-429, May.
    4. Olivier Toubia & Duncan I. Simester & John R. Hauser & Ely Dahan, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Marketing Science, INFORMS, vol. 22(3), pages 273-303.
    5. 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.
    6. Braun, Alexander & Schmeiser, Hato & Schreiber, Florian, 2016. "On consumer preferences and the willingness to pay for term life insurance," European Journal of Operational Research, Elsevier, vol. 253(3), pages 761-776.
    7. Crabbe, M. & Vandebroek, M., 2012. "Improving the efficiency of individualized designs for the mixed logit choice model by including covariates," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2059-2072.
    8. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
    9. Natter, Martin & Feurstein, Markus, 2002. "Real world performance of choice-based conjoint models," European Journal of Operational Research, Elsevier, vol. 137(2), pages 448-458, March.
    10. Lieven, Theo, 2015. "Policy measures to promote electric mobility – A global perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 78-93.
    11. Pradeep K. Chintagunta & Harikesh S. Nair, 2011. "Structural Workshop Paper --Discrete-Choice Models of Consumer Demand in Marketing," Marketing Science, INFORMS, vol. 30(6), pages 977-996, November.
    12. Marco Guerra & Francesca Bassi & José G. Dias, 2020. "A Multiple-Indicator Latent Growth Mixture Model to Track Courses with Low-Quality Teaching," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 361-381, January.
    13. Ioana Gutu & Daniela Tatiana Agheorghiesei & Alexandru Tugui, 2023. "Assessment of a Workforce Sustainability Tool through Leadership and Digitalization," IJERPH, MDPI, vol. 20(2), pages 1-30, January.
    14. Robert Zeithammer & Peter Lenk, 2006. "Bayesian estimation of multivariate-normal models when dimensions are absent," Quantitative Marketing and Economics (QME), Springer, vol. 4(3), pages 241-265, September.
    15. Jaehwan Kim & Greg M. Allenby & Peter E. Rossi, 2002. "Modeling Consumer Demand for Variety," Marketing Science, INFORMS, vol. 21(3), pages 229-250, December.
    16. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    17. Ana Oliveira-Brochado & F. Vitorino Martins, 2006. "Examining the segment retention problem for the “Group Satellite” case," FEP Working Papers 220, Universidade do Porto, Faculdade de Economia do Porto.
    18. Pettenella, Davide & Thiene, Mara & Scarpa, Riccardo & Masiero, Mauro & Mattea, Stefania & Franceschinis, Cristiano, 2016. "First economic assessment of ecosystem services from Natura 2000 network in Lombardy (Northern Italy)," 2016 Fifth AIEAA Congress, June 16-17, 2016, Bologna, Italy 242326, Italian Association of Agricultural and Applied Economics (AIEAA).
    19. Schreier, Alayna & Stenersen, Madeline R. & Strambler, Michael J. & Marshall, Tim & Bracey, Jeana & Kaufman, Joy S., 2023. "Needs of caregivers of youth enrolled in a statewide system of care: A latent class analysis," Children and Youth Services Review, Elsevier, vol. 147(C).
    20. John Liechty & Duncan Fong & Eelko Huizingh & Arnaud Bruyn, 2008. "Hierarchical Bayesian conjoint models incorporating measurement uncertainty," Marketing Letters, Springer, vol. 19(2), pages 141-155, June.

    More about this item

    Keywords

    M31; C81;

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

    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:sjobre:v:53:y:2001:i:8:d:10.1007_bf03372669. 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.