E-commerce et vente de vin en ligne: lâÂÂapproche stratégique dâÂÂunepetite entreprise(Strategic Analysis Of A Small Wine E-business Company)
Bibliographic record
Abstract
Wine and spirit e-business is currently one of the most growing and competitive industries in France. In this study, we analyze the competitive model of wine e-business, through Porter’s competitive forces. We propose to highlight on strategic groups that interact within this industry in order to explain the strength and the aggressiveness of the competition within online wine’s business. We aim hereby to understand if and how small businesses take market shares in this particular business. Resource based view theory will be widely used to analyze key resources and competencies that are mandatory for a small company to get success in the wine industry. Based on a wine cellar case study, and its online website, this study demonstrates how important competencies valuation and value creation to customers are as part of a wine ebusiness strategy (build online catalog, design the web interface with value added for users/visitors, choose the most appropriate web infrastructure, redirect traffic to the website and convert visitors into customers, manage customers relationship and loyalty, select an appropriate supply chain infrastructure…). By using Resource Based View theory we demonstrate that value offer is a core component of the wine e-business model which is continuously moving alongside with the company and its website’s life cycle.
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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 | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".