Catch-up and Leapfrogging of Latecomers: Samsung vs. Huawei
Bibliographic record
Abstract
This case compares two global giants, Samsung and Huawei, which operate in the information and communications technology (ICT) field. Both have faced similar situations as targets of trade wars between their home country and another developed country. However, they both remained robust despite external hardships. They also grew quickly from latecomers from less developed countries to key players in the global market. The case looks back on how Samsung overtook Sony. The external economic shock to the Japanese economy brought by the Plaza Accord radically shifted Sony's focus from hardware to software and entertainment content, bringing great opportunities for Samsung. Samsung survived the Asian Financial Crisis and expanded its international business. To catch the new technological wave, Samsung chose to be a fast-follower, while Sony attempted to be a rule-maker in the digital age. Huawei took a similar path to that of Samsung in its global rise. Huawei benefited profoundly from China's economic boom. Spiking demand in rural areas helped it to survive. Then, Huawei began to learn from Western incumbents and imitate their practices. It also invested heavily in R&D and took the lead in 5G through open innovation. The fourth-generation industrial revolution—encompassing the Internet of Things (IoT), AI, and Big Data—created a great window of opportunity for industry upheaval. The comparison between the pair leads to some natural questions: Will Huawei overtake Samsung? Or will Huawei lag behind for the foreseeable future? What will determine their relative positions in the global market?
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".