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Conjoint Analysis as an Instrument of Market Research Practice

In: Conjoint Measurement

Author

Listed:
  • Anders Gustafsson

    (Karlstad University
    Linköping University)

  • Andreas Herrmann

    (University of St. Gallen)

  • Frank Huber

    (University of Mainz)

Abstract

The essay by the psychologist Luce and the statistician Tukey (1964) can be viewed as the origin of conjoint analysis (Green and Srinivasan 1978; Carroll and Green 1995). Since its introduction into marketing literature by Green and Rao (1971) as well as by Johnson (1974) in the beginning of the 1970s, conjoint analysis has developed into a method of preference studies that receives much attention from both theoreticians and those who carry out field studies. For example, Cattin and Wittink (1982) report 698 conjoint projects that were carried out by 17 companies in their survey of the period from 1971 to 1980. For the period from 1981 to 1985, Wittink and Cattin (1989) found 66 companies in the United States that were in charge of a total of 1062 conjoint projects. Wittink, Vriens, and Burhenne counted a total of 956 projects in Europe carried out by 59 companies in the period from 1986 to 1991 (Wittink, Vriens, and Burhenne 1994; Baier and Gaul 1999). Based on a 2004 Sawtooth Software customer survey, the leading company in Conjoint Software, between 5,000 and 8,000 conjoint analysis projects were conducted by Sawtooth Software users during 2003. The validation of the conjoint method can be measured not only by the companies today that utilize conjoint methods for decision-making, but also by the 989,000 hits on www.google.com. The increasing acceptance of conjoint applications in market research relates to the many possible uses of this method in various fields of application such as the following: new product planning for determining the preference effect of innovations (for example Bauer, Huber, and Keller 1997; DeSarbo, Huff, Rolandelli, and Choi 1994; Green and Krieger 1987; 1992; 1993; Herrmann, Huber, and Braunstein 1997; Johnson, Herrmann, and Huber 1998; Kohli and Sukumar 1990; Page and Rosenbaum 1987; Sands and Warwick 1981; Yoo and Ohta 1995; Zufryden 1988) or to improve existing achievements (Green and Wind 1975; Green and Srinivasan 1978; Dellaert et al., 1995), the method can also be applied in the field of pricing policies (Bauer, Huber, and Adam 1998; Currim, Weinberg, and Wittink 1981; DeSarbo, Ramaswamy, and Cohen 1995; Goldberg, Green, and Wind 1984; Green and Krieger 1990; Kohli and Mahajan 1991; Mahajan, Green, and Goldberg 1982; Moore, Gray-Lee, and Louviere 1994; Pinnell 1994; Simon 1992; Wuebker and Mahajan 1998; Wyner, Benedetti, and Trapp 1984), advertising (Bekmeier 1989; Levy, Webster, and Kerin 1983; Darmon 1979; Louviere 1984; Perreault and Russ 1977; Stanton and Reese 1983; Neale and Bath 1997; Tscheulin and Helmig 1998; Huber and Fischer 1999), and distribution (Green and Savitz 1994; Herrmann and Huber 1997; Oppewal and Timmermans 1991; Oppewal 1995; Verhallen and DeNooij 1982).

Suggested Citation

  • Anders Gustafsson & Andreas Herrmann & Frank Huber, 2007. "Conjoint Analysis as an Instrument of Market Research Practice," Springer Books, in: Anders Gustafsson & Andreas Herrmann & Frank Huber (ed.), Conjoint Measurement, edition 0, chapter 1, pages 3-30, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-71404-0_1
    DOI: 10.1007/978-3-540-71404-0_1
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    Citations

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

    1. Abdul Hamid Mar Iman & Fu Yek Pieng & Christopher Gan, 2012. "A Conjoint Analysis of Buyers' Preferences for Residential Property," International Real Estate Review, Global Social Science Institute, vol. 15(1), pages 73-105.
    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. Andreas Falke & Harald Hruschka, 2017. "A Monte Carlo study of design-generating algorithms for the latent class mixed logit model," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(4), pages 1035-1053, October.
    4. Moser, Nataliya, 2009. "Untersuchung Der Präferenzen Russischer Fachbesucher Für Ausgewählte Messeleistungen," IAMO Discussion Papers 91768, Institute of Agricultural Development in Transition Economies (IAMO).
    5. repec:zbw:iamodp:91768 is not listed on IDEAS
    6. Möser, Nataliya, 2009. "Untersuchung der Präferenzen russischer Fachbesucher für ausgewählte Messeleistungen [Analysis of preferences of Russian trade visitors for selected exhibit attributes]," IAMO Discussion Papers 124, Leibniz Institute of Agricultural Development in Transition Economies (IAMO).
    7. Kick, Markus & Littich, Martina, 2015. "Brand and Reputation as Quality Signals on Regulated Markets," EconStor Preprints 182503, ZBW - Leibniz Information Centre for Economics.
    8. Charles Cunningham & Ken Deal & Heather Rimas & Heather Campbell & Ann Russell & Jennifer Henderson & Anne Matheson & Blake Melnick, 2008. "Using Conjoint Analysis to Model the Preferences of Different Patient Segments for Attributes of Patient-Centered Care," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 1(4), pages 317-330, October.
    9. Cornelia Schön & Pratibha Saini, 2018. "Market-Oriented Service Network Design When Demand is Sensitive to Congestion," Transportation Science, INFORMS, vol. 52(5), pages 1253-1275, October.
    10. Jan R. Landwehr & Andreas Herrmann & Mark Heitmann, 2009. "To be different or to be average? [Die Attraktivität des Durchschnittsprodukts]," Schmalenbach Journal of Business Research, Springer, vol. 61(3), pages 226-250, May.

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