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Record W2886549621 · doi:10.5539/ibr.v11n9p1

Successors’ Characteristics, Preparation, Innovation, and Firm Performance: Taiwan and Japan

2018· article· en· W2886549621 on OpenAlexvenueno aff
Jeng Liu

Bibliographic record

VenueInternational Business Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsnot available
Fundersnot available
KeywordsSuccessor cardinalBusinessLoyaltyPromotion (chess)MarketingEcological successionProcess (computing)Public relationsManagementPolitical scienceEconomicsComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.341
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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