Managerial Ownership and Firm Performance under South Africa's Black Economic Empowerment Policy
Bibliographic record
Abstract
Purpose: This study examines the impact of managerial ownership on firm performance and the implementation of demographic diversity of ownership and control of listed firms on the JSE market. Method: This study employs a dynamic panel model with generalized method of moments (GMM) estimation and a probit model. Results: The study finds that managers holding both managerial and ownership positions enhance the performance of listed firms during the implementation phase of demographic diversity in management, ownership, and control in South Africa. However, such dual managerial ownership and control roles do not optimally promote the Black Economic Empowerment (BEE) program in support of corporate governance practices. Novelty: This research not only examines the relationship between managerial ownership and company performance, but also introduces the unique transformative policy context of the South African BEE. Contribution: This study provides corporate boards and policymakers with evidence-based strategies to balance performance incentives with Black Economic Empowerment compliance Abstrak : Kepemilikan Manajerial dan Kinerja Perusahaan berdasarkan Kebijakan Pemberdayaan Ekonomi Kulit Hitam Afrika Selatan Tujuan: Studi ini mengkaji dampak kepemilikan manajerial terhadap kinerja perusahaan dan penerapan keragaman demografis kepemilikan dan kontrol perusahaan tercatat di pasar JSE. Metode: Penelitian ini menggunakan model panel dinamis dengan estimasi metode momen umum (GMM) dan model probit. Hasil: Studi ini menemukan bahwa manajer yang memegang posisi manajerial dan kepemilikan meningkatkan kinerja perusahaan terdaftar selama fase implementasi keragaman demografis dalam manajemen, kepemilikan, dan kontrol di Afrika Selatan. Namun, peran kepemilikan dan kontrol manajerial ganda tersebut tidak secara optimal mempromosikan program Black Economic Empowerment (BEE) untuk mendukung praktik tata kelola perusahaan. Kebaruan: Penelitian ini tidak hanya menguji hubungan antara kepemilikan manajerial dan kinerja perusahaan, tetapi memperkenalkan konteks kebijakan transformatif unik Afrika Selatan BEE. Kontribusi: Studi ini memberi dewan perusahaan dan pembuat kebijakan strategi berbasis bukti untuk menyeimbangkan insentif kinerja dengan kepatuhan Pemberdayaan Ekonomi Hitam.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".