Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate how consumers’ affective goal pursuit influences the relationship between their affect and satisfaction in services. In particular, it examines when affect can directly influence satisfaction and when such an impact is mediated by perceived service quality. Design/methodology/approach This research explores consumers’ consumption goals in three different service contexts, i.e., a primarily pleasure-seeking hedonic service context, a primarily arousal-seeking hedonic service context and a utilitarian (non-affect-seeking) service context. Findings Results from two studies show that the primary affective consumption goal determines which specific affect can directly influence satisfaction. Other desirable non-primary affect influences satisfaction through the mediation of perceived service quality. Research limitations/implications This research focuses on the service contexts in which consumers’ primary consumption goals vary. Further research may focus on the priority and strength of a consumer’s various consumption goals in different services and study how the priority and strength of different consumption goals determine how affect influences quality and satisfaction. Practical implications The study provides several insights for service providers and retailers to recognize that consumers’ primary consumption goals may vary in different service contexts, for different consumers, and even at different usage situations. Accordingly, marketers need to develop different strategies for consumer with different goal pursuit in services. Originality/value While the literature has documented that consumer affect influences consumer satisfaction in general, it is unclear how different consumption goals influence the impact of affect on satisfaction. This research contributes to the consumer goal literature by demonstrating the importance of primary consumption goals in the post-consumption evaluation of services.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".