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Measuring Preferences for Really New Products
Open accessAbstract
The goal of this research is to improve preference measurement for really new products. An initial assumption validated in the first study is that consumers have greater uncertainty when estimating the usefulness of really new products than they have with incremental new products. Consumers cope with this uncertainty by using certain inferential techniques that are not well captured by standard preference measurement techniques, such as conjoint. This research examines techniques for incorporating both mental simulation and analogies into an existing preference measurement technique and shows that some methods enhance and other methods hinder predictive accuracy.
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Cites
The Roles of Similarity in Transfer: Separating Retrievability From Inferential Soundness
Gentner, D., Rattermann, M. J., & Forbus, K. D. (1993) · Cognitive Psychology · doi:10.1006/cogp.1993.1013 · Cited by 640
Cited by
Predictive Analytics in Information Systems Research1
Shmueli, G., & Koppius, O. R. (2011) · MIS Quarterly · doi:10.2307/23042796 · Cited by 1,328
Predictive Analytics in Information Systems Research
Shmueli, G., & Koppius, O. (2010) · doi:10.2139/ssrn.1606674 · Cited by 676
Decision difficulty in the age of consumer empowerment
Broniarczyk, S. M., & Griffin, J. G. (2014) · Journal of Consumer Psychology · doi:10.1016/j.jcps.2014.05.003 · Cited by 256
The roles of users in electric, shared and automated mobility transitions
Axsen, J., & Sovacool, B. K. (2019) · Transportation Research Part D: Transport and Environment · doi:10.1016/j.trd.2019.02.012 · Cited by 183
Stock, R. M. (2011) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-010-0215-4 · Cited by 122
Metadata
- Authors
- Hoeffler, S.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2003-11
- Volume
- 40
- Issue
- 4
- Pages
- 406-420
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W2014638744
- Cited by
- 551
- References
- 1
- FWCI
- 19.09
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
