The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Partitioning default effects: Why people choose not to choose.
Open accessAbstract
Default options exert an influence in areas as varied as retirement program design, organ donation policy, and consumer choice. Past research has offered potential reasons why no-action defaults matter: (a) effort, (b) implied endorsement, and (c) reference dependence. The first two of these explanations have been experimentally demonstrated, but the latter has received far less attention. In three experiments we produce default effects and demonstrate that reference dependence can play a major role in their effectiveness. We find that the queries formulated by defaults can produce differences in constructed preferences and further that manipulating queries can also mitigate default effects. The experimental context involves two environmentally consequential alternatives: cheap, inefficient incandescent light bulbs, and expensive, efficient compact fluorescent bulbs. Within this context we also measure the impact of each potential rationale for a default effect.
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Cites
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When and why defaults influence decisions: a meta-analysis of default effects
JACHIMOWICZ, J. M., DUNCAN, S., WEBER, E. U., & JOHNSON, E. J. (2019) · Behavioural Public Policy · doi:10.1017/bpp.2018.43 · Cited by 481
Self-citationAssessing the Energy-Efficiency Gap
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Metadata
- Authors
- Dinner, I., Johnson, E. J., Goldstein, D. G., & Liu, K.
- Venue
- Journal of Experimental Psychology: Applied
- Published
- 2011
- Volume
- 17
- Issue
- 4
- Pages
- 332-341
- ISSN
- 1076-898X (ISSN-L)
- 1939-2192
- OpenAlex
- W2121354923
- Cited by
- 340
- References
- 28
- FWCI
- 7.24
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
