Hitting Purchase: The Influence of Social and Demographic Variables on Fast Fashion Consumers
Bibliographic record
Abstract
With an average annual growth rate of around 11.68%, the fast fashion industry is expanding immensely. Increasing sales of affordable yet trendy clothes are driven by the rising youth population, boosting the fast fashion market. Previous research on influences of the life cycle of fashion and consumer behavior theories sparked this research study’s goal: for fast fashion marketers to understand consumer behavior in terms of social and demographic variables. To assess the most prominent themes that influenced fast fashion consumer behavior in Southern California, two procedures were implemented: a survey on consumers’ shopping behaviors and short interviews with a range of demographics and genders for both qualitative and quantitative analysis. In this study, five occurring themes of (1) Trendiness of Apparel, (2) Broad Range of Apparel, (3) Age and Gender, (4) Affordability, and (5) Follower-Leader Relationships were found to be the largest influences to draw consumers. Three core themes were found to influence consumer behavior the most: (1) Age and Gender, (2) Affordability, and (3) Follower-Leader Relationships. This study’s findings may improve future marketing tactics to expand a fast fashion business’s popularity and sales. It was concluded that while fast fashion companies should focus on expanding their trendiness and range of clothing, companies should target females in the 11-20 age group using social media influences to involve more potential consumers. It was further concluded that attraction of the business will proliferate through word-of-mouth recommendations by customers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".