CEO Attributes and Corporate Performance in Frontier Markets: The Case of Jordan
Bibliographic record
Abstract
The objective of this study is to examine the impact of Chief Executive Officer (CEO) attributes on corporate performance in Jordan, a representative frontier market. The analysis focuses on four key CEO attributes, comprising two socio-demographic variables—age and educational—and two corporate governance-related ones—tenure and origin. Return on assets (ROA) and return on equity (ROE) are used as proxies for firm performance. Using a sample of 416 firm-year observations from companies listed on the Amman Stock Exchange (ASE) during 2015–2023, the study employs the system GMM methodology to estimate dynamic panel data models, addressing potential endogeneity and capturing the dynamic nature of firm performance. The results show that CEO age has a positive but insignificant effect, whereas CEO education and tenure significantly enhance firm performance. Conversely, CEO origin has a statistically negative impact on firm performance, reflecting the value of insider CEOs. The significant effects of CEO education, tenure, and origin—observed within the models that also incorporated firm- and country-level controls—reflect their incremental contribution to firm performance in frontier markets. Robustness checks, including controls for the COVID-19 pandemic and industry effects, confirm these findings. The study contributes to the literature by demonstrating the applicability of established theories—namely Upper Echelons, Stewardship, Resource Dependence, and Human Capital Theories—while identifying the CEO traits that drive success in frontier markets. It also offers practical guidance for shareholders, board directors, and policymakers in designing effective leadership and governance strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".