The effects of executives’ overseas background on enterprise digital transformation: evidence from China
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the effects of executives’ overseas education and work experience on enterprise digital as executives’ overseas background is critical to the development of enterprises. It also explored the mediating role of enterprise digital transformation on the relationship between executives’ overseas background and enterprise growth. Design/methodology/approach Chinese A-share companies listed on the Shanghai and Shenzhen Stock Exchanges for the period 2018–2020 were analyzed using regression analysis and bootstrapping to verify hypothesized relationships. Findings Executives’ overseas study and work experience both enhanced enterprise digital transformation significantly, thus improving enterprise growth. The level of employee education moderated the mediating role proposed in the theoretical model. Moreover, the promoting effect of executives’ overseas background on enterprise digital transformation was more significant for non-state-owned enterprises and those in eastern China. Practical implications The findings provide reference for the formulation and optimization of companies’ human resource structure and have implications on the improvement of enterprise digital transformation and enterprise growth. Originality/value This study explored the factors influencing enterprise digital transformation at the microlevel of corporate human capital, thereby providing microlevel empirical evidence for research on the factors influencing enterprise digital transformation. Its findings shed light on the mechanism and context under which executives with overseas backgrounds may enhance enterprise digital transformation and growth.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".