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An Empirical Assessment of Stimulus Presentation Mode Bias in Conjoint Analysis

Author

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  • Debi Mishra

    (Binghamton University [SUNY] - SUNY - State University of New York)

  • Junhong Min
  • M. Deniz Dalman

Abstract

"Conjoint analysis, which aims to uncover the optimal combination of attributes influencing customer choice, is widely used by marketers to predict the success of new product and service introductions. In recent years, researchers have incorporated considerable mathematical sophistication into conjoint models and extended its domain to diverse areas such as pricing, market share, profitability, product positioning, distribution channels, and advertising. Despite these advances, the predictive power of conjoint applications is often compromised by response biases and measurement errors. The purpose of this research is to isolate and investigate the impact of one such bias that arises from the manner in which stimuli are presented to respondents. Based upon an appraisal of over four decades of conjoint studies in the major marketing journals, the authors make a case for the possible existence of two types of biases, i.e.: (1) stimulus joint presentation bias, when concept cards are shown simultaneously (side by side) to respondents, and (2) stimulus separate presentation bias, where cards are presented separately (one at a time). Two conjoint experiments were designed to investigate the effects of these biases on respondent choices. Results indicate that bias manifests itself in conjoint designs when there is a mismatch between presentation mode and respondents' cognitive (evaluable) burden. Left unaddressed, stimulus presentation mode bias may: (1) have a deleterious effect on respondents' choice behavior; and (2) compromize the predictive accuracy of conjoint models. The authors discuss several approaches that can account for and mitigate the negative impact of presentation mode biases on conjoint outcomes."

Suggested Citation

  • Debi Mishra & Junhong Min & M. Deniz Dalman, 2012. "An Empirical Assessment of Stimulus Presentation Mode Bias in Conjoint Analysis," Post-Print hal-04325786, HAL.
  • Handle: RePEc:hal:journl:hal-04325786
    Note: View the original document on HAL open archive server: https://hal.science/hal-04325786
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    References listed on IDEAS

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    1. Catherine W. M. Yeung & Dilip Soman, 2005. "Attribute Evaluability and the Range Effect," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 32(3), pages 363-369, December.
    2. Mehta, Raj & Moore, William L & Pavia, Teresa M, 1992. "An Examination of the Use of Unacceptable Levels in Conjoint Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(3), pages 470-476, December.
    3. Sethuraman, Raj & Kerin, Roger A. & Cron, William L., 2005. "A field study comparing online and offline data collection methods for identifying product attribute preferences using conjoint analysis," Journal of Business Research, Elsevier, vol. 58(5), pages 602-610, May.
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