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Cited by 256

The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.

The Challenge of Staying Happier: Testing the Hedonic Adaptation Prevention Model

Open access
Sheldon, Kennon M., Lyubomirsky, Sonja·2012·Personality and Social Psychology Bulletin

Abstract

The happiness that comes from a particular success or change in fortune abates with time. The Hedonic Adaptation Prevention (HAP) model specifies two routes by which the well-being gains derived from a positive life change are eroded—the first involving bottom-up processes (i.e., declining positive emotions generated by the positive change) and the second involving top-down processes (i.e., increased aspirations for even more positivity). The model also specifies two moderators that can forestall these processes—continued appreciation of the original life change and continued variety in change-related experiences. The authors formally tested the predictions of the HAP model in a 3-month three-wave longitudinal study of 481 students. Temporal path analyses and moderated regression analyses provided good support for the model. Implications for the stability of well-being, the feasibility of “the pursuit of happiness,” and the appeal of overconsumption are discussed.

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Metadata

Authors
Sheldon, K. M., & Lyubomirsky, S.
Venue
Personality and Social Psychology Bulletin
Published
2012-02-23
Volume
38
Issue
5
Pages
670-680
ISSN
  • 0146-1672 (ISSN-L)
  • 1552-7433
OpenAlex
W2143725060
Cited by
256
References
60
FWCI
23.45
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: The Challenge of Staying Happier: Testing the Hedonic Adaptation Prevention Model. https://app.cochat.ai/papers/10.1177/0146167212436400
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Sheldon, K. M., & Lyubomirsky, S. (2012). The Challenge of Staying Happier: Testing the Hedonic Adaptation Prevention Model. Personality and Social Psychology Bulletin. https://doi.org/10.1177/0146167212436400

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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.