AN AHP BASED EXPERT STUDY OF INTRINSIC PSYCHOLOGICAL DETERMINANTS OF APPAREL PURCHASES IN AN INDIAN METROPOLITAN CITY
Bibliographic record
Abstract
In today’s competitive market, understanding the drivers of consumer purchase decisions is critical for the retailers. This study uses an expert based Analytic Hierarchy Process (AHP) to prioritize six intrinsic psychological factors namely motivation, attitude, perception, learning, personality and self-esteem believed to influence apparel purchase decisions in Kolkata’s organized retail sector. Three domain experts selected on the basis of their experience provided pairwise comparisons. Group judgments were aggregated using the geometric mean method. Individual and aggregated values of maximum eigenvalue (λ max), consistency index (CI) and consistency ratio (CR) were computed. All CR values were below 0.10, indicating acceptable consistency of expert judgments. Final AHP results revealed motivation (44.72%) as the most influential factor, followed by attitude (24.30%), perception (14.42%), self-esteem (7.43%), learning (7.05%), and personality (3.45%). Results of the study suggest marketers to concentrate on motivational stimuli, attitudinal interventions, perceptual cues and communication strategies to influence customers for purchase. However, small expert panel, lack of direct consumer feedback and confinement to one city are limitations of the study. Future studies with more experts and consumer feedback across different cities can validate the findings. Multi-criteria decision approach for pair wise comparison and ranking of internal factors adds to the literature in the area of consumer psychology. The study provides practical recommendation to the marketers to shape consumer perception and their purchase behaviour.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".