A Literature Review on the Studies of Internet Retailing Management
Bibliographic record
Abstract
This paper reviewed the studies of Internet retailing management. It found that, in general, most of the pa-pers on the topic took focus on Internet retailing strategy and online merchandise management. Specifically, it drawn following conclusions: First, there were six major incentives for firms to adopt Internet retailing, including improving internal communication, improving operational efficiency, facing competition, enhanc-ing customer services, reaching out to a wider audience, and improving relations with suppliers. Second, cost of Internet trading and consumer preference were negatively, while status of Internet retailing strategy, tech-nology capability, Internet communication preference, Internet marketplace, and market development op-portunity were positively, affect firms to adopt Internet retailing. Third, market positioning, business model, estimation of the market size, Internet-based innovative application, and strong brand of website were the key factors for the success of an online retailing website. Fourth, cross-channel conflict might be reduced by reconstructing the business process or adjusting the pricing strategy, while Internet retailing inventory might be improved by firms’ zero inventory policy, in-stock inventory policy, and dynamic inventory policy. Fi-nally, a firm’s quality of logistics and delivery could be increased by high-value-package strategy and emer-gency transshipments. Implications of these conclusions were suggested。
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".