Insider vs. Outsider: Choosing Local Market Knowledge Source in the Emerging Market
Bibliographic record
Abstract
Since multinational corporations (MNCs) in host countries frequently face location-based disadvantages, local market knowledge acquisition has become an increasingly important topic in the knowledge management literature. Within this research stream, the process of local market knowledge transfer has been widely discussed. However, the issues of the source (especially the internal and external sources) for MNCs to obtain local market knowledge have not been well studied, especially in the emerging market context. Also, the consequence of choosing a local market knowledge source, which interests practitioners more, rarely appears in the literature. This study aims to be the first conceptual paper discussing both antecedents and outcomes of choosing either inside or outside local market knowledge source in the emerging market context. The framework we propose, using MNCs as the unit of analysis, includes both micro-environmental factors (organizational culture and the home market performance of MNCs) and macro-environmental factor (the openness of the economic system in the emerging market) that impact the local market knowledge source choice (insider or outsider) of MNCs. In addition, we discuss how the extent of the environmental turbulence (especially market turbulence) in the emerging market moderates the relationship between either inside or outside local market knowledge source and the performance of MNCs in the emerging market. We believe our framework is a parsimonious and systematically clear way of explaining what is actually a complicated phenomenon and we hope that this paper provides a conceptual framework, which stimulates subsequent empirical studies.
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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 | 0.004 | 0.013 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".