Impact of Brand Equity, Advertisement and Hedonic Consumption Tendencies on Cognitive Dissonance: A Mediation Study
Bibliographic record
Abstract
Current study puts light on the role of brand equity, hedonic consumption tendencies, and advertisement on cognitive dissonance. Impulse Buying plays the role of a mediator in the study. Purpose of this study is to examine how brand equity, hedonic consumptions, and advertisement affect cognitive dissonance of the customers. Analyses of a sample of 370 customers, drawn from different outlets of famous fashion clothes in Multan city, revealed attention-grabbing findings. A mediation analysis was conducted through regression analysis in the study. Hedonic consumption tendencies and advertisement were found significant in predicting cognitive dissonance while brand equity was found having an insignificant relation with cognitive dissonance where impulse buying found significant in predicting cognitive dissonance being having positive relation. Further to this, impact of brand equity, advertisement and hedonic consumption tendencies found directly related to impulse purchase. Product category wise examination of current model adds new directions and findings in the future. Prior studies on purchase intentions have mainly focused on simple models at any given time. However, researchers increasingly argue that a complex representation may give better understanding of customers’ purchase intentions. To the best of the authors’ knowledge, this study is one of the first empirical studies to address a complex structure of the proposed variables.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".