The Effect of Cognitive Dissonance on External Information Search and Consumer Complaint Responses
Bibliographic record
Abstract
The cost of influencing a new customer rapidly increases and exceeds the cost of retaining existing customer. Thus, companies tend to be more concerned with customer retention. Keeping their current market share is one of the important tasks for companies. Understanding why customers complain and switch from one company to another help companies to retain their customers. One of the reasons consumers engage in negative responses can result from dissonance experienced after purchase. The concept cognitive dissonance has been studied widely in the literature of consumer behavior. However, there are few studies discussing the relation between cognitive dissonance (its dimensions) and consumers’ complaint responses. This study adopts the 22-item scale of Sweeney et al. (2000) in order to evaluate consumers’ level of cognitive dissonance after purchasing a smartphone. This study offers three dimensions of cognitive dissonance –emotion, wisdom of purchase and concern over deal- as the predictors of external information search and consumer complaint responses. This study tests whether cognitive dissonance has significant effects on consumers’ search for external information, and in turn, on consumers’ complaint and switching intention. The empirical analysis was carried out based on the data gathered by 400 smartphone users, living in Ankara, the capital city of Turkey. The survey result was analyzed by using Partial Least Squares (PLS-PM) analysis method. The results reveal that when consumers feel emotional and rational inconsistencies after smartphone purchasing, they need information from external sources (such as asking friends, relatives, other stores), and this information search behavior leads to negative consumer responses as complaint and switching intention.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".