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How to SHIFT Consumer Behaviors to be More Sustainable: A Literature Review and Guiding Framework
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Highlighting the important role of marketing in encouraging sustainable consumption, the current research presents a review of the academic literature from marketing and behavioral science that examines the most effective ways to shift consumer behaviors to be more sustainable. In the process of the review, the authors develop a comprehensive framework for conceptualizing and encouraging sustainable consumer behavior change. The framework is represented by the acronym SHIFT, and it proposes that consumers are more inclined to engage in pro-environmental behaviors when the message or context leverages the following psychological factors: Social influence, Habit formation, Individual self, Feelings and cognition, and Tangibility. The authors also identify five broad challenges to encouraging sustainable behaviors and use these to develop novel theoretical propositions and directions for future research. Finally, the authors outline how practitioners aiming to encourage sustainable consumer behaviors can use this framework.
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“The majority of them only focuses on consumer behaviours (Fogt Jacobsen et al., 2022; White et al., 2019), plastic waste management (Chae and An, 2018; Sarkar et al., 2022), recycling techniques (Franz and Welle, 2022), and supply chain management (Silva and Pålsson, 2022).”
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Metadata
- Authors
- White, K., Habib, R., & Hardisty, D. J.
- Venue
- Journal of Marketing
- Publisher
- SAGE Publications
- Published
- 2019-02-14
- Volume
- 83
- Issue
- 3
- Pages
- 22-49
- ISSN
- 0022-2429 (ISSN-L)
- 1547-7185
- OpenAlex
- W2912633426
- Cited by
- 2,386
- References
- 360
- FWCI
- 186.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
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.