Cognitive Consistency in Purchase Behaviour: Theoretical & Empirical Analyses
Bibliographic record
Abstract
The fundamental thrust of consistency theories is to enforce equilibrium among one’s cognitions. Man seeks haemostatic states among his cognitive elements or avoids conflicting stimuli. He loves the familiar; the unfamiliar is always discomforting and disturbing though common knowledge tells us that actual behaviour turns what seems novel at the pre-decision stage into familiar following series of learning and experience. The Heider’s balance theory, Osgood’s congruity model and Festinger’s cognitive dissonance theory are the three popular schools of thought that provide the foundational theories of cognitive consistency This paper critically analyzed and synthesized the major theoretical and empirical body of knowledge of these schools with a view to proffering a tripartite approach (involving the consumers, the organizations and the governments) to solving inconsistency among cognitive elements (e.g.; values, beliefs, knowledge and attitudes). These schools were specifically looked into and assessed in terms of their individual real world application and/or empirical fertility. Each represents an improvement upon the other with Festinger’s theory providing the most elaborate perspective of emphasizing on psychological tension and means of achieving consistency within and between the cognitive system and ultimately overt and covert behaviours.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".