The Impact of CEO Characteristics on Investment Efficiency in Jordan: The Moderating Role of Political Connections
Bibliographic record
Abstract
This study investigates the impact of CEO characteristics—specifically CEO age, founder status, and family membership—on investment efficiency in Jordanian non-financial companies, with a focus on the moderating role of political connections. Drawing on the existing literature, we identify conflicting views regarding how these characteristics influence investment decisions. Some studies suggest that younger CEOs may adopt more aggressive investment strategies, while older CEOs tend to be conservative, leading to balanced resource allocation. Similarly, CEOs with founder status and family membership are thought to have an emotional attachment to the company, theoretically resulting in cautious investment behavior. However, empirical evidence remains mixed. By using data from 62 non-financial firms listed on the Amman Stock Exchange (ASE) from 2019 to 2023, this study employs regression analysis to explore these relationships. The findings reveal that CEO age contributes to investment efficiency by mitigating both over- and under-investment. Contrary to expectations, CEO founder status shows no significant effect on investment efficiency. Additionally, family-member CEOs exhibit a tendency toward under-investment, driven by a desire to preserve family wealth. Political connections further complicate these dynamics, encouraging riskier investment strategies while diluting the positive effects of CEO characteristics. These results provide new insights into the intricate interplay between CEO traits and political networks, contributing to the discourse on corporate governance in emerging markets. The study concludes with practical implications for policymakers and company boards, emphasizing the need for balanced leadership selection strategies to optimize investment efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".