Corporate Governance Reform as Institutional Innovation: The Case of Japan
Bibliographic record
Abstract
To address the convergence-divergence debate in corporate governance, we conduct a multiple-case, multiple-level study to analyze the diffusion of governance innovation in Japan. We argue that Japanese systems of corporate governance neither fully converge to, nor completely diverge from, the Anglo-American model. Rather, Sony—the pioneer of corporate governance reforms—and its followers selectively adopted features from this model, decoupled them from the original context, and tailored them to fit to their own situations to generate governance innovation. However, we find that the spread of innovation across firms and institutional levels is far from linear and straightforward, and that other well-regarded firms raised strong opposition to the institutionalization of corporate governance reforms. Eventually, the Ministry of Justice revised the Commercial Code to legitimize different systems, which led to the emergence of diverse corporate governance practices. Based on the results of our study, we construct an analytical framework to examine innovation diffusion in light of conflicting institutional pressures for change and continuity. Our analysis adds complexity to the convergence-divergence debate by identifying the creation of hybrid corporate governance systems and the nonlinear evolution of such systems as a result of interactions across multiple levels. We show the various degrees of decoupling from the Anglo-American model and identify the antecedents. We then extend the conventional focus of innovation research on diffusion across firms to examine diffusion across institutional levels. We also contribute to institutional theory by offering insights into organizational field formation and the conceptualization of the state in shaping institutional change and continuity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".