Igniting social commerce: using Instagram for mobile retail shopping
Bibliographic record
Abstract
Social media can act as an invaluable tool that businesses can use as a means of reaching out and engaging with current and potential customers. Instagram, a Social Network Service known for its predominance as a photo-and-video focused sharing platform, is often used and even presented by the company as a tool to drive awareness about a business and pique interest in the products or services that they offer to its over 700 million users. However, this particular platform is being employed as more than just an advertising and marketing agent outside of Canada and the USA. In particular, Instagram in South Korea has transformed into an even more multifaceted experience, from being used as a product catalogue for retail startups to operating as a mobile online marketplace where direct, transactional exchange occurs. While social media platforms are continually being modified to suit the behaviours and attitudes of this technologically advancing world, Instagram has evolved into a more dynamic online forum for commercial exchange, further expanding the capacities of Social Commerce. This major research paper engages in qualitative observations on how Instagram is being utilized in South Korea for the purposes of marketing, advertising and mobile commerce. Furthermore, several best practices are outlined on how Instagram can be organized for businesses, particularly startup companies, through the use of case studies on current South Korean company.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".