From Catching Up to Convergence of the Latecomer Firms: Comparing Behavior and Innovation Systems of Firms in Korea and the US
Bibliographic record
Abstract
ABSTRACT: This paper addresses the issue of catching and convergence at the level of firms, and investigates whether Korean firms tend to converge toward mature firms represented by the US firms in terms of their behavior and performance as well as firm-level innovations systems. It conducts regression analyses of several behavior and performance variables, using the data of the Korean and US firms during the 1990s, the 2000s, and 2010s. It finds some evidence of convergence, such that Korean firms become more profitability- rather than growth-oriented, borrowing and investing less, and thus being less indebted. However, they have not changed much in terms of their behavior toward firm values and dividend tendencies. Further analyses, using the patent-derived, innovation system variables, also confirm some aspects of convergence, compared with the early results, for which self-citations become significant and positive for firm values; furthermore, the variable of cycle time of technology is no longer significant for profitability, which is consistent with the results from the US firms. Meanwhile, changes in corporate governance associated with the rise of foreign shareholder are also shown to have resulted in higher profitability but insignificant change in firm values. An emerging conclusion is an ongoing but partially completed process of convergence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".