Board Characteristics and Foreign Direct Investment in Public Listed Property Companies: A Malaysian Evidence
Bibliographic record
Abstract
The Malaysian government has encouraged the corporate sector to be seriously committed in supporting sustainable development. One of the elements in sustainable development is the inclusiveness of foreign direct investment (FDI) in the corporate sector. FDI plays an important role in the economy as it generates an economic growth by increasing the domestic capital formation and hence, promoting sustainability. This study examines the relationship of corporate governance characteristics on foreign direct investment among property public listed companies in Malaysia. Specifically, this study examines the effect of board of directors’ characteristic namely board size, board meeting, board independence and CEO-Chairman duality role on foreign direct investment of the property public listed companies. This study relies on content analysis on the annual reports of 50 public listed property companies in in Malaysia for year 2007-2016. The results show that board meetings and board independency have a significant positive relationship on foreign direct investment of the public listed property companies. However, the results show that board size and CEO-Chairman duality do not have a significant relationship on foreign direct investment of the public listed property companies. The findings in this study implicate the importance of the board of directors’ involvement in ensuring effective and efficient decisions related to foreign direct investment.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".