Retrospective evaluations of playful experiences
Bibliographic record
Abstract
Purpose This paper aims to examine the impact of key affective moments of a playful experience on consumers’ overall retrospective evaluations. Design/methodology/approach The authors build on past literature on hedonic psychology and sequential preferences and link it to specific characteristics of playful experiences to derive their hypotheses. The hypotheses are tested through two field experiments conducted at a videogame arcade. Findings Results demonstrated that consumers’ overall evaluations are better aligned with the affective intensity at the final or end moment of a playful experience. Findings also revealed the complexity of understanding playful experiences, for it is the meaningfulness of end moments rather than simply their recent position in the experience that underlies overall evaluations. When end moments cease to be meaningful, the trough or least affective intense moment impacts overall evaluations. Practical implications This research has implications for marketers who are deciding on which point of a playful experience to concentrate their resources for optimizing evaluations. Originality/value This research contributes to literature on playful consumption by illuminating how consumers rely on affective moments of a playful experience to construct overall evaluations. Additionally, it highlights the important role of meaningfulness of end moments, a relatively underexplored process, which extends literature on key moments and retrospective evaluations.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".