Peak–End rule versus average utility: How utility aggregation affects evaluations of experiences

Cojuharenco, Irina; Ryvkin, Dmitry

doi:10.1016/j.jmp.2008.05.004

Cited by 35 publications

(30 citation statements)

References 26 publications

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“…There is an ongoing debate regarding the theoretical explanations and the cognitive processes underlying the peak-end rule (e.g. Cojuharenco & Ryvkin, 2008;Geng et al, 2013), but many psychological explanations refer to memory-related processes. For example, in support of a peak effect, extreme values might be better remembered (Ludvig, Madan, & Spetch, 2014;Tsetsos, Chater, & Usher, 2012).…”

Section: Are Negative Experiences Over Weighted?mentioning

confidence: 99%

How does the peak-end rule smell? Tracing hedonic experience with odours

Scheibehenne

Coppin

2019

Cognition and Emotion

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The peak-end rule predicts that retrospective evaluations of affective events heavily depend on their most intense and last moment and imply duration neglect. It was originally proposed for negative experiences such as painful medical procedures. It is unclear, however, to what degree it also applies to positive experiences. Previously, rigorous comparisons between the two domains were limited due to the use of qualitatively different stimuli. Hence, it is not clear if the peak-end rule holds for short positive and negative experiences alike. To address these questions in a genuinely emotional domain, we conducted two experiments (n = 48 each) in which we used odours as stimuli. Participants repeatedly evaluated continuous odour sequences delivered into their noses via an olfactometer. The sequences differed in valence (positive vs. negative), length (36 vs. 72 s), and trajectory (increasing, decreasing, U-shaped, and inverse U-shaped). Results provide evidence for the peak-end rule for both positive and negative experiences alike. Results further show an overweighting of intense negative experiences for sequences that contain both pleasant and unpleasant episodes but provide little evidence for an effect of the trajectory manipulation.

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Section: Are Negative Experiences Over Weighted?mentioning

confidence: 99%

How does the peak-end rule smell? Tracing hedonic experience with odours

Scheibehenne

Coppin

2019

Cognition and Emotion

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“…According to this theory, the peak and end values determine a patient’s satisfaction with the healthcare service provided. That is to say, if the patient felt satisfied with the peak and final experiences of the healthcare service, they were more likely to feel satisfied with the entire service process; therefore, the most extreme (peak) and final (end) impressions are often better predictors of overall evaluations of experience than average impressions [ 15 ]. According to Kahneman, the overall satisfaction value of a single sample, b i ′ , can be determined by Eq.…”

Section: Methodsmentioning

confidence: 99%

Key nodes affecting patient satisfaction in a cross-regional referral service process: an empirical analysis study in Sichuan

Zhang

Wang

et al. 2018

BMC Health Serv Res

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BackgroundThe referral service is a significant component of healthcare reform in China, and the measurement of patient satisfaction with the referral service process will help to improve the quality of referral medical delivery. Furthermore, the referral service in China includes inter-institutional collaborations between hospitals at different levels and multi-nodes throughout the referral process. It is therefore necessary to identify the key nodes that affect patient satisfaction during the referral service process.MethodsThis study conducted a questionnaire survey of 110 patients to collect data regarding patient satisfaction at the following healthcare nodes: primary-level hospital, referral appointment registration, claim of appointment number in the outpatient department, examination service, admission service, and overall satisfaction during the referral service process. Correlation analysis and logistic regression methods were used to establish a mathematical model of patient satisfaction between five nodes and overall satisfaction. Additionally, a peak-end model was formed to identify the peak node impacting overall patient satisfaction during the referral service based on the sample data.ResultsOver 80% of referral patients rated the overall referral service as ‘good’. The correlation analysis revealed that there was a significant correlation between the satisfaction of each node and the overall satisfaction (P < 0.05). The results of the regression model showed that the satisfaction of five nodes determined the overall satisfaction and that “admission service at the higher-level hospital” exerted the greatest impact on overall satisfaction (β = 0.312), while “referral appointment registration” had the lowest influence on overall satisfaction (β = 0.177). The peak-end model also revealed that “admission service at the higher-level hospital” had a greater effect on overall satisfaction.ConclusionOur study showed that the key nodes affecting patient satisfaction were “transferring service at the primary-level hospital” and “admission service at the higher-level hospital”. Furthermore, the efficacy of the referral services is determined by the gatekeepers’ management of the referral system at the primary-level hospital and the allocation and management of bed resources at the higher-level hospital. These findings can serve as a science-based guidance for them to improve their performance in inter-regional healthcare collaborations in the referral service process.

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“…According to this rule the final outcome (end) and the most intense outcome (peak) are averaged. The rule is thus a special case of a general averaging rule which has been contrasted to an additive rule (e.g., Cojuharenco and Ryvkin, 2008 ; Seta et al, 2008a , b ). Below we propose a weighted averaging rule.…”

Section: Recalled Mood Impactsmentioning

confidence: 99%

Current Mood vs. Recalled Impacts of Current Moods after Exposures to Sequences of Uncertain Monetary Outcomes

et al. 2017

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Events in a sequence may each be evaluated as good or bad. We propose that such good-bad evaluations evoke emotional responses that change current mood. A model of recurrent updating of current mood is developed and compared to a model of how a sequence of events evoking emotional responses is evaluated retrospectively. In Experiment 1, 149 undergraduates are presented sequences of lottery outcomes with a fixed probability of losing or winning different amounts of money. Ratings of current mood are made after the sequence. Retrospective evaluations are either made after the ratings of current mood or, in a control condition, when no ratings of current mood are made. The results show an expected effect on current mood of the valence of the end of the sequence. The results are less clear in showing an expected beginning effect on the retrospective evaluations. An expected beginning effect on retrospective evaluations is found in Experiment 2 in which 41 undergraduates are first asked to remember the different amounts of money, then to evaluate the sequence as lottery outcomes.

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Peak–End rule versus average utility: How utility aggregation affects evaluations of experiences

Cited by 35 publications

References 26 publications

How does the peak-end rule smell? Tracing hedonic experience with odours

How does the peak-end rule smell? Tracing hedonic experience with odours

Key nodes affecting patient satisfaction in a cross-regional referral service process: an empirical analysis study in Sichuan

Current Mood vs. Recalled Impacts of Current Moods after Exposures to Sequences of Uncertain Monetary Outcomes

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