Prior consumer satisfaction and alliance encounter satisfaction attributions
Bibliographic record
Abstract
Purpose Built upon brand attitude literature, particularly the cognitive dissonance theory and contrast theory, the authors' conceptual framework aims to explain how prior consumer satisfaction with each alliance partner affects consumer attributions (i.e. credit or blame) directed toward each partner for both highly satisfying and less‐than‐highly satisfying alliance experiences. Design/methodology/approach This paper extends the cognitive dissonance theory and contrast theory to the brand alliance context. Survey responses from 1,510 consumers, each having had purchase experiences with one of 18 brand alliances, were used to test hypotheses. Findings The authors identify which of the two theories provides greater explanatory power under varying conditions. Further, they find an intriguing host effect. That is, consumers tend to hold host partners more responsible for both highly satisfying and less‐than‐highly satisfying alliance encounters. Practical implications The authors' findings help firms better understand how consumers respond to alliance encounters. Practical insights include distinct advice for host versus guest partners in partner selection and resource commitments to alliance platforms. Originality/value This paper is among the first to investigate consumer reactions to actual alliance encounters, especially in market rather than experimental conditions. Further, whereas the literature has focused on positive consumer experiences with brand alliances, the authors' research includes both highly satisfying and less‐than‐highly satisfying alliance experiences and thus they uniquely report on the full range of alliance encounter outcomes.
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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 | 0.019 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".