Knowledge Management and Innovation Strategy: The Challenge for Latecomers in Emerging Economies
Bibliographic record
Abstract
The success of latecomer firms from the emerging economies challenges the conventional wisdom on entry timing and resource-based competence. Building on research on institutions in emerging economies and the resource-based perspective in strategic management, we propose a model to explain how resource poor latecomer firms in emerging economies catch up with the multinational incumbents. We classify latecomers based on their strategic learning intent as either emulators or blind imitators. The strategic learning intent depends on a firm’s complementary assets and its absorptive capacity. Firms that choose emulation develop flexible routines, while firms that choose blind imitation end up with rigid routines. Over time, when there is a need for resource renewal, firms that have flexible routines are better positioned to respond. We take the Chinese mobile phone industry as an exemplar to illustrate the core issues in latecomer catching up of emerging economy firms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".