All’s well that ends (and peaks) well? A meta-analysis of the peak-end rule and duration neglect
•The peak-end rule is an efficient heuristic for evaluating past experiences.•The meta-analytic peak-end effect is large and robust across moderators examined.•The peak-end effect is stronger than the duration, beginning, and trough effects.•The peak-end effect is stronger than the trend and variabi...
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| Published in | Organizational behavior and human decision processes Vol. 170; p. 104149 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Inc
01.05.2022
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| Abstract | •The peak-end rule is an efficient heuristic for evaluating past experiences.•The meta-analytic peak-end effect is large and robust across moderators examined.•The peak-end effect is stronger than the duration, beginning, and trough effects.•The peak-end effect is stronger than the trend and variability effects.•The peak-end effect is comparable to the overall average effect.
The peak-end rule (Fredrickson & Kahneman, 1993) asserts that, when people retrospectively evaluate an experience (e.g., the previous workday), they rely more heavily on the episode with peak intensity and on the final (end) episode than on other episodes in the experience. We meta-analyzed 174 effect sizes and found strong support for the peak-end rule. The peak-end effect on retrospective summary evaluations was: (1) large (r = 0.581, 95% Confidence Interval = 0.487–0.661), (2) robust across boundary conditions, (3) comparable to the effect of the overall average (mean) score and stronger than the effects of the trend and variability across all episodes in the experience, (4) stronger than the effects of the first (beginning) and lowest intensity (trough) episodes, and (5) stronger than the effect of the duration of the experience (which was essentially nil, thereby supporting the idea of duration neglect; Fredrickson & Kahneman, 1993). We provide a future research agenda and practical implications. |
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| AbstractList | •The peak-end rule is an efficient heuristic for evaluating past experiences.•The meta-analytic peak-end effect is large and robust across moderators examined.•The peak-end effect is stronger than the duration, beginning, and trough effects.•The peak-end effect is stronger than the trend and variability effects.•The peak-end effect is comparable to the overall average effect.
The peak-end rule (Fredrickson & Kahneman, 1993) asserts that, when people retrospectively evaluate an experience (e.g., the previous workday), they rely more heavily on the episode with peak intensity and on the final (end) episode than on other episodes in the experience. We meta-analyzed 174 effect sizes and found strong support for the peak-end rule. The peak-end effect on retrospective summary evaluations was: (1) large (r = 0.581, 95% Confidence Interval = 0.487–0.661), (2) robust across boundary conditions, (3) comparable to the effect of the overall average (mean) score and stronger than the effects of the trend and variability across all episodes in the experience, (4) stronger than the effects of the first (beginning) and lowest intensity (trough) episodes, and (5) stronger than the effect of the duration of the experience (which was essentially nil, thereby supporting the idea of duration neglect; Fredrickson & Kahneman, 1993). We provide a future research agenda and practical implications. |
| ArticleNumber | 104149 |
| Author | Baines, Julia I. Alaybek, Balca Dalal, Reeshad S. Fyffe, Shea Aitken, John A. Zhou, You Roman, Alexis Qu, Xiao |
| Author_xml | – sequence: 1 givenname: Balca surname: Alaybek fullname: Alaybek, Balca email: balaybek@mitre.org organization: George Mason University, United States – sequence: 2 givenname: Reeshad S. surname: Dalal fullname: Dalal, Reeshad S. organization: George Mason University, United States – sequence: 3 givenname: Shea surname: Fyffe fullname: Fyffe, Shea organization: George Mason University, United States – sequence: 4 givenname: John A. surname: Aitken fullname: Aitken, John A. organization: George Mason University, United States – sequence: 5 givenname: You surname: Zhou fullname: Zhou, You organization: George Mason University, United States – sequence: 6 givenname: Xiao surname: Qu fullname: Qu, Xiao organization: George Mason University, United States – sequence: 7 givenname: Alexis surname: Roman fullname: Roman, Alexis organization: George Mason University, United States – sequence: 8 givenname: Julia I. surname: Baines fullname: Baines, Julia I. organization: George Mason University, United States |
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| Keywords | Trough Heuristics Duration neglect Peak End Gestalt characteristics Retrospective evaluations |
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| Title | All’s well that ends (and peaks) well? A meta-analysis of the peak-end rule and duration neglect |
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