Surprise, Anticipation, and Sequence Effects in the Design of Experiential Services
Bibliographic record
Abstract
The most salient or peak aspect of a service experience often defines customer perceptions of the service. Across two studies, using the same novel form of a scenario‐based experiment, we investigate the design of peak events in a service sequence by testing how anticipated and surprised peaks influence customer perceptions. Study 1 captures the immediate reactions of participants and Study 2 surveys participants a week later. In both studies, we find a main effect for the temporal peak placement, confirming the positive influence of a strong peak ending. When assessing the peak design strategies of surprise and anticipation, we find in Study 1 that surprise and anticipation moderate the temporal peak placement (e.g., early peak vs. late peak) on overall customer perceptions, with the surprise peak at the end of an experience yielding the strongest effect. In Study 2 we see that the remembered experience of a surprise peak positively affects customer perceptions compared to an anticipated peak regardless of the temporal placement of the peak. We also find that the infusion of a surprise peak ending has a lasting effect that amplifies the peak‐end effect of remembered experiences. Drawing on these findings, we discuss the role of surprise, anticipation, and sequence effects in experience design strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".