FACTORS INFLUENCING YOUNGSTERSâ BEHAVIOR TOWARDS ONLINE SHOPPING IN VELLORE DISTRICT OF TAMILNADU, INDIA
Bibliographic record
Abstract
Digital universe in India is doubling in size every two years and will multiply nine-fold between 2014 and 2020. As per ASSOCHAM, the value of Indian e-commerce market in 2012 was $8.5 billion and $16 billion in 2013 and it is estimated to be $56 billion in 2023. Sources of e-commerce depend on the effective shopping, prompt delivery and increased use of online payment mechanism. Online shopping has changed the face of marketing globally. It has helped in easier, simpler and faster business transactions. Today each and every household is using online shopping. India being a highly populated country is positively transforming towards online shopping. Therefore there is a huge scope for both business and teenagers in India for online selling and buying household goods. As the Indian population is adding more educated and expert in internet technology, online shopping is moving drastically. In this background, the present descriptive study is an attempt to investigate the important factors influencing teenager’s behaviour, attitude and perception towards online shopping in Vellore district of Tamil Nadu in India.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".