Impact of family board members and CEO's business education on the investment in information technology
Bibliographic record
Abstract
The current study investigates the impact of family board members (FBM) and the CEO's business education (CEO_BUSEDU) on the investment in information technology (INVEST_IT) in family business enterprises (FBEs). This study considered using a survey research design to collect data from owners of FBEs in India. As robustness checks, this study utilised a two-stage least (2SLS) square model to reduce endogeneity problems. Empirical analysis shows that FBM and CEO_BUSEDU increase INVEST_IT, and financial support from foreign family members moderates the relationship between FBM and INVEST_IT. The empirical results contribute to the literature on the impact of FBM and CEO_BUSEDU on INVEST_IT. In addition, the results may help academia extend the studies on family board members, CEOs' business education, and INVEST_IT by collecting data from different countries. Furthermore, family business owners may find the results helpful in increasing INVEST_IT.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".