Bibliographic record
Abstract
The case describes the process of entering the China market undertaken by InMobi, an Indian Internet Company that provides a mobile advertising platform and value-added services for brands. InMobi was intentionally developed as an international venture by its founders. After opening offices in South Africa, Europe, and North America, the board had a long discussion and eventually decided to enter the China market. The case describes how, under the leadership of Jessie Yang, InMobi China successfully adapted to the unique ecosystem of China’s internet industry. In 2019 InMobi's China revenue exceeded $100 million for the first time, accounting for over one-quarter of its parent company's global revenue, and next only to the US market. The case ends by noting the expansion of the Beijing office by inviting InMobi technologists from India and also plans for spinning off InMobi China as an independent company. After achieving the aspiring milestone, Yang also faced the business growth challenges. How could she strengthen InMobi's first-mover advantage in the China mobile advertising market and maintain good relations with headquarters to gain autonomy and trust?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 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".