Self‐Reflection and Articulated Consumer Preferences

Abstract

Accurate measurement of consumer preferences reduces development costs and leads to successful products. Some product‐development teams use quantitative methods such as conjoint analysis or structured methods such as Casemap. Other product‐development teams rely on unstructured methods such as direct conversations with consumers, focus groups, or qualitative interviews. All methods assume that measured consumer preferences endure and are relevant for consumers' marketplace decisions. This article suggests that if consumers are not first given tasks to encourage preference self‐reflection, unstructured methods may not measure accurate and enduring preferences. This paper provides evidence that consumers learn their preferences as they make realistic decisions. Sufficiently challenging decision tasks encourage preference self‐reflection which, in turn, leads to more accurate and enduring measures. Evidence suggests further that if consumers are asked to articulate preferences before self‐reflection, then that articulation interferes with consumers' abilities to articulate preferences even after they have a chance to self‐reflect. The evidence that self‐reflection enhances accuracy is based on experiments in the automotive and mobile phone markets. Consumers completed three rotated incentive‐aligned preference measurement methods (revealed‐preference measures [as in conjoint analysis], a structured method [Casemap], and an unstructured preference‐articulation method). The stimuli were designed to be managerially relevant and realistic (53 aspects in automobiles, 22 aspects for mobile phones) so that consumers' decisions approximated in vivo decisions. One to three weeks later, consumers were asked which automobiles (or mobile phones) they would consider. Qualitative comments and response times are consistent with the implications of the measures of predictive ability.

Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning

“…Second, as a number of prior studies (e.g., Ding et al 2005;Ding 2007;Ding et al 2009;Dong et al 2010;Ding et al 2011;Hauser et al 2014) suggest that incentive alignment offers benefits such as greater respondent involvement, less boredom, and higher data quality, we incorporated incentive alignment in this application. At the beginning of the experiment, we told the participants that we would award a computer tablet device to one randomly selected participant from this study, plus cash representing difference between the price of the tablet device and $900.…”

“…We also told each participant that, if chosen as a winner, he would receive a computer tablet based on: 1) either his choice from one of the validation questions; or 2) his most preferred tablet among a list of 25 tablets, inferred from his answers to the preference elicitation questions. Following Ding et al (2011) and Hauser et al (2014), participants were told that this list was pre-determined by the researchers and it would be made public after the study. Therefore, the respondents have incentives to answer the questions carefully and truthfully.…”

“…The KL divergence measure is an information-theory-based measure of divergence (Kullback and Leibler 1951;Chaloner and Verdinelli 1995). Dzyabura and Hauser (2011), Ding et al (2011), and Hauser et al (2014 demonstrate that, for consideration data, the KL divergence measure provides an evaluation of predictive ability that is rigorous and discriminates well. We followed the formulae provided in Dzyabura and Hauser (2011) to calculate the KL divergence measures.…”

“…We included both initial and delayed validation tasks as in Ding et al (2011) and Hauser et al (2014).…”

“…Following Hauser et al (2014), the delayed validation questions were sent to the respondents by email one week after the preference measurement task. Additionally, we included both pre-and post-preference measurement validation questions as suggested by Netzer and Srinivasan (2011).…”