IDEAS home Printed from https://ideas.repec.org/a/spr/joamsc/v45y2017i1d10.1007_s11747-016-0498-1.html
   My bibliography  Save this article

Phenomena, theory, application, data, and methods all have impact

Author

Listed:
  • John R. Hauser

    (Massachusetts Institute of Technology)

Abstract

No abstract is available for this item.

Suggested Citation

  • John R. Hauser, 2017. "Phenomena, theory, application, data, and methods all have impact," Journal of the Academy of Marketing Science, Springer, vol. 45(1), pages 7-9, January.
  • Handle: RePEc:spr:joamsc:v:45:y:2017:i:1:d:10.1007_s11747-016-0498-1
    DOI: 10.1007/s11747-016-0498-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11747-016-0498-1
    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/s11747-016-0498-1?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. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    2. 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.
    3. Pradeep Chintagunta & Dominique M. Hanssens & John R. Hauser, 2016. "Editorial—Marketing Science and Big Data," Marketing Science, INFORMS, vol. 35(3), pages 341-342, May.
    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. Jochen Wirtz & Valarie Zeithaml, 2018. "Cost-effective service excellence," Journal of the Academy of Marketing Science, Springer, vol. 46(1), pages 59-80, January.
    2. Elina Jaakkola & Stephen L. Vargo, 2021. "Assessing and enhancing the impact potential of marketing articles," AMS Review, Springer;Academy of Marketing Science, vol. 11(3), pages 407-415, December.
    3. Abhishek Borah & Xin (Shane) Wang & Jun Hyun (Joseph) Ryoo, 2018. "Understanding Influence of Marketing Thought on Practice: an Analysis of Business Journals Using Textual and Latent Dirichlet Allocation (LDA) Analysis," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 146-161, December.

    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. 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.
    2. 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.
    3. José Niño-Mora, 2020. "Fast Two-Stage Computation of an Index Policy for Multi-Armed Bandits with Setup Delays," Mathematics, MDPI, vol. 9(1), pages 1-36, December.
    4. 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.
    5. 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.
    6. Kick, Markus & Littich, Martina, 2015. "Brand and Reputation as Quality Signals on Regulated Markets," EconStor Preprints 182503, ZBW - Leibniz Information Centre for Economics.
    7. Theodoros Evgeniou & Constantinos Boussios & Giorgos Zacharia, 2005. "Generalized Robust Conjoint Estimation," Marketing Science, INFORMS, vol. 24(3), pages 415-429, May.
    8. Burbano, Vanessa & Padilla, Nicolas & Meier, Stephan, 2020. "Gender Differences in Preferences for Meaning at Work," IZA Discussion Papers 13053, Institute of Labor Economics (IZA).
    9. Gui Liberali & Alina Ferecatu, 2022. "Morphing for Consumer Dynamics: Bandits Meet Hidden Markov Models," Marketing Science, INFORMS, vol. 41(4), pages 769-794, July.
    10. Yu-Cheng Ku & Tsun-Feng Chiang & Sheng-Mao Chang, 2017. "Is what you choose what you want?—outlier detection in choice-based conjoint analysis," Marketing Letters, Springer, vol. 28(1), pages 29-42, March.
    11. YiChun Miriam Liu & Jeff D. Brazell & Greg M. Allenby, 2022. "Non-linear pricing effects in conjoint analysis," Quantitative Marketing and Economics (QME), Springer, vol. 20(4), pages 397-430, December.
    12. 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.
    13. Paul E. Green & Abba M. Krieger & Yoram Wind, 2001. "Thirty Years of Conjoint Analysis: Reflections and Prospects," Interfaces, INFORMS, vol. 31(3_supplem), pages 56-73, June.
    14. Robert Steiger & Eva Posch & Gottfried Tappeiner & Janette Walde, 2020. "Effects of climate change on tourism demand considering individual seasonal preferences," Working Papers 2020-08, Faculty of Economics and Statistics, University of Innsbruck.
    15. 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.
    16. Schlereth, Christian & Skiera, Bernd & Schulz, Fabian, 2018. "Why do consumers prefer static instead of dynamic pricing plans? An empirical study for a better understanding of the low preferences for time-variant pricing plans," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1165-1179.
    17. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2020. "How much do consumers know about the quality of products? Evidence from the diaper market," The Japanese Economic Review, Springer, vol. 71(4), pages 541-569, October.
    18. Adriane Hartmann & Henrik Sattler, 2004. "Wie robust sind Methoden zur Präferenzmessung?," Schmalenbach Journal of Business Research, Springer, vol. 56(1), pages 3-22, February.
    19. Xinfang (Jocelyn) Wang & Jeffrey D. Camm & David J. Curry, 2009. "A Branch-and-Price Approach to the Share-of-Choice Product Line Design Problem," Management Science, INFORMS, vol. 55(10), pages 1718-1728, October.
    20. Maximilian Schäfer & Geza Sapi & Szabolcs Lorincz, 2018. "The Effect of Big Data on Recommendation Quality: The Example of Internet Search," Discussion Papers of DIW Berlin 1730, DIW Berlin, German Institute for Economic Research.

    More about this item

    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:joamsc:v:45:y:2017:i:1:d:10.1007_s11747-016-0498-1. 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.