Looking forward: anticipation enhances service experiences
Bibliographic record
Abstract
Purpose This paper aims to explore the nature and relationships among the dimensions that constitute expectations, anticipation and post-experience assessment of cultural events, before and after an aesthetic experience, namely, a live Norwegian opera or ballet performance. Design/methodology/approach A triangulation approach is used to combine qualitative and quantitative analyses. Quantitative data collection was conducted at the site before and after a performance experience. Findings Expectations, anticipation and post-experience assessment are found to be multi-dimensional. Expectations and anticipation are identified as distinct constructs. Three dimensions of expectations of quality are extrinsic cues: building and functional attributes, available services and level of employee service. In addition, two dimensions of pre-experience anticipation are identified: anticipation of information gathering activities and anticipation of the event. Post-experience assessment has two dimensions: satisfaction and pride in the building. Two post-experience associations are enthusiasm and inclusiveness. Anticipation of the event and enthusiasm, not expectations, are found to be predictors of satisfaction. Research limitations/implications An understanding of the role of anticipation in consumer engagement and satisfaction with aesthetic experiences could be broadened and enriched by studies that include other service or arts disciplines and within a more complex model of consumer engagement. Originality/value Anticipation is a significant pre-experience phenomenon. Enthusiasm is identified as a post-experience association.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".