Effects in Consumer Behaviours due to the Increasing Availability of Counterfeit Products on E-Commerce Websites and Social Media Platforms in India
Bibliographic record
Abstract
The study assesses how the rise in popularity of social media applications like TikTok and Instagram, and the increasing availability of counterfeit products on e-commerce websites such as DHgate, impacts the purchase behaviour of Indian consumers. The research considers the ease with which counterfeit goods can be marketed and purchased through these platforms as a determinant factor motivating consumers’ behaviour. Fourteen Indian participants aged 18-35 were recruited to participate in semi-structured interviews. Their responses were examined through Trickle Down theory, Theory of Leisure Class and Planned Behaviour Theory frameworks and against the extant literature. The analysis uncovered that while e-commerce is not as prevalent in fostering consumption of counterfeit goods in India, social media does play a key role in affecting purchase behaviour. This research fills the gap in scholarly literature on the consumption of counterfeit goods in general, as studies on this topic are scarce.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".