Does Availability Bias Have Influence on FMCG Investors? An Empirical Study on Cognitive Dissonance, Rational Behaviour and Mental Accounting Bias
Bibliographic record
Abstract
Investors usually rely on the first-hand information to take their investment decisions. Investors are prone to many types of heuristics and bias which can lead to indecisive situations or irrational behavior and affect their performance. Indecisive state of mind of investors is attributed due to cognitive dissonance as it differs from their belief and the facts they come in reality. Mental accounting has its own advantages of meeting the investor’s goal on time and allocating resources effectively to achieve each of their investment objectives, on the contrary it can also lead to poor portfolio management and performance. The current study is conducted in order to understand the influence of availability heuristic influence on cognitive dissonance and rational behavior through mental accounting bias as a mediating factor exclusively on Fast Moving Consumer Goods (FMCG) investor’s decision making in stock market. Data was collected by purposive sampling method using self-administered questionnaire from 614 FMCG investors through 15 stock broking firms registered in Hyderabad city, tools like correlation, analysis of variance, Cronbach Alpha test, linear regression and mediation are used for data analysis. The results reveal that availability bias, cognitive dissonance and rational behavior are highly correlated to one another. Availability bias influence on cognitive dissonance and rational behavior of FMCG investors through mental accounting bias is negligible. Though many studies have accepted the major role played by mental accounting bias and few against it in our study it is proved to be negligible in case of defensive sectors of FMCG investors.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".