Bibliographic record
Abstract
This chapter examines Khanna et al .'s idea that emerging economies are primarily characterized by important institutional voids (i.e., a lack of both local intermediary firms and broader macro-level institutions such as contractenforcing governmental institutions), and that the primary challenge for MNEs operating in emerging economies is to understand and deal with these voids. According to these authors, an emerging economy's institutional voids are even more important than traditional metrics (e.g., GDP analysis). Building on their theory, the authors supply a list of institution-related questions that senior managers should ask in order to assess whether and how to penetrate an emerging economy. These ideas will be examined and then criticized using the framework presented in Chapter 1. Significance Emerging economies are playing an increasingly important role in both the world economy and MNE strategic activity. Since the early 1990s emerging economies have provided the world's fastest growing markets for most products and services. MNEs are attracted to these countries as they offer potential cost and innovation advantages, and represent new output markets. First, the availability of relatively inexpensive skilled labour and trained managers in emerging economies offers MNEs lower manufacturing and service costs. Second, these economies can also give MNEs access to a different genre of innovation than can be found in mature markets. The foundation of such innovation often resides in the creativity of individuals driven to find original solutions to meet basic needs of large but poor segments of the emerging economy's population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".