Successors’ Characteristics, Preparation, Innovation, and Firm Performance: Taiwan and Japan
Bibliographic record
Abstract
Recent research has revisited business succession. Although important issues have been discussed from time to time, no consensus on any particular pattern of succession has emerged. Lacking a body of empirical findings to draw upon, discussions of business succession are often based on limited qualitative case studies and findings vary widely among researchers. In this research, we focus on both theoretical explanations and data promotion, and draw the following conclusions.First, there are some differences in the corporate culture and business philosophy between Taiwanese and Japanese enterprises, and the training of successors is also different. Second, the number of female successors has been increasing in both Taiwan and Japan, while in Taiwan female successors have become even more open and innovative than their male counterparts. Third, successors in Taiwan and Japan make various kinds of preparations. The successors are expected to start from the front-line to win the loyalty of employees and managers, to keep in touch with front-line employees, to learn the enterprise’s operating mode, and to think about a future development strategy. Fourth, the grown-up environment of the successor also plays an important role and has a great influence on the successor. Last, but not least, many well-known disputes have occurred in the enterprise succession process both in Taiwan and Japan, and the support provided by the previous generation and the “veterans” will make the succession process go smoothly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".