A Factor Analysis Approach to Determine Extrinsic Determinants Influencing Apparel Purchase: A Study based in a Metropolitan City in India
Bibliographic record
Abstract
Abstract Understanding consumer purchase behaviour is vital in today’s competitive retail environment, particularly in the fast-growing apparel sector of India. The current study investigates the impact of external factors on the buying behaviour within the organized sector of Kolkata, an eastern metropolis in India, utilising the Stimulus–Organism–Response (SOR) model. The study uses Exploratory and Confirmatory factor analyses to extract factors and validate them. Initially, 24 external attributes were identified from past literature, out of which 20 of them were confirmed through a pilot survey. The main survey collected 550 responses, out of which 362 responses were found to be complete and valid for the study. Exploratory factor analysis (EFA) was performed on 253 responses and Confirmatory factor analysis (CFA) on the remaining 109 responses. Seven factors namely, Product Positioning, Shop Locale, Merchandising, Fabric Quality, Aesthetics, Elegance and Durability were extracted and confirmed. Independent t-tests conducted to examine gender-based differences showed disparities across four factors while chi-square tests show multiple statistically significant associations among the factors. The results validate the relevance of the SOR framework in the emerging apparel market scenario of the metropolis, showing a distinct relationship between external triggers, internal assessments and consumer buying intentions. The research provides practical guidance for the apparel marketers, recommending strategic retail approaches. Limitations and future research avenues have been addressed, highlighting the need for multi-city studies and integration of internal factors also.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".