JD.com: leveraging the edge of e-business
Bibliographic record
Abstract
Subject area E-Business; Corporate Strategy; Strategic Management; Operation Management. Study level/applicability Senior undergraduate; MBA; EMBA. Case overview After development for 10 years, JD was now China’s second largest business-to-customer (B2C) e-retailer and the largest in self-operated sector. It was September 2015 when Liu Qiangdong was deciding whether to persist with JD’s self-operated model and the heavy investment in the self-built logistics system. JD’s business model had been functioning well. However, as JD grew bigger and bigger, it became too expensive to expand its logistics system. JD had not made a profit since it raised funds from investors. Liu had to come up with a good proposal before the next monthly meeting to convince them that JD would finally overtake its biggest rival, Alibaba which ran on a different business model. In addition, JD was exploiting the rural and the global markets, as well as a new business in internet finance. Facing challenges and dilemmas, should JD persist with its model? How could Liu align short-term profitability with long-run development? How could JD overcome attacks from Alibaba and other competitors? Expected learning outcomes This case is appropriate for courses in e-business and strategy, particularly those with a strong focus on doing e-business in emerging markets (e.g. China). After studying the case, students should be able to: understand the e-commerce market in China; understand business models and key strategies of e-retailers; identify and analyse the pros and cons of the self-operated business model and self-built logistics system in e-commerce; learn how to evaluate performance, strategies and business models of e-commerce companies; and extract key trends in the market and compare different strategies. Supplementary materials Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code: CSS 11: Strategy.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".