Multiple roles of consumption emotions in post‐purchase satisfaction with extended service transactions
Bibliographic record
Abstract
The focus of this paper is on the emotional experience of consumption and its impact on satisfaction in the context of extended service transactions. Drawing on a multi‐component perspective of emotions, we present a series of research propositions to specify how both retrospective global judgments of consumption emotions as well as their in‐process experience (i.e. their experience during the sequence of episodes composing the transaction) determine post‐purchase satisfaction in multiple ways. At the retrospective level, we propose that while the expected direct relationship between satisfaction and positive emotions will emerge, the equally expected inverse relationship between negative emotions and satisfaction will be found only for negative emotions attributed to the service provider. Negative emotions attributed to causes other than the provider (i.e. to the situation or to oneself) may be positively associated with satisfaction. At the in‐process level, we propose that the experience of distinct emotions at a certain stage of the service may influence the consumer’s expectations for and perceptions of the provider performance, and/or the expressions and behaviors of the consumer himself and those of the provider in the subsequent episode. Such in‐process effects of emotions will then determine their relationship with post‐purchase satisfaction. Theoretical and managerial implications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".