Causes and consequences of emotions on consumer behaviour
Bibliographic record
Abstract
Purpose Consumption situations can be emotionally charged. Identifying the cause(s) of emotions has clear practical import to the understanding of consumer behaviour. Cognitive appraisal theory serves this purpose; however, a consensus has not yet emerged concerning terminology, number of relevant concepts and concomitant construct measurements, and theoretical linkages between constructs. This paper attempts to rectify this shortcoming. Design/methodology/approach This conceptual paper provides an extant review of emotions literature as it pertains to cognitive appraisals and consumption behaviours. Based on this review an integrative cognitive appraisal theory is advanced that is parsimonious and incorporates similarities across the various appraisal theory perspectives to date. Findings Four appraisals are proffered that appear capable of implicating specific emotions and their effects on consumer behaviour. The appraisals advanced are outcome desirability that encompasses pleasantness and goal consistency, agency which includes responsibility and controllability, fairness, and certainty. Sample propositions concerning how cognitive appraisals affect information processing extensiveness have also been provided. Originality/value First, the paper provides an extant review of cognitive appraisal theories of emotions, which makes transparent the looseness in terminology and differences in theoretical perspectives that currently exist. Second, based on this review the paper advances a unifying theory of consumption appraisals and explore their relevance to marketers. The theory proposed could explain inconsistent findings in the current literature. Third, directions for future research highlighting confounds that should be considered in study designs complete the paper.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".