The Role of Corporate Social Responsibility and Emotional Intelligence Towards Effective Management: Empirical Evidence from Saudi Arabia
Bibliographic record
Abstract
In today’s fast-paced business world, companies are experiencing rapid changes and fierce competition, where Social and Environmental Responsibility needs to be prioritized for good management practices. Corporate Social Responsibility (CSR) and Emotional Intelligence (EI) have become increasingly important in management. However, there is a shortage of research that scientifically explores how these two elements interact, particularly within Saudi Arabia’s private corporate sector. To fill the gap, this research aims to examine the relationship between CSR, EI and effective management in the private corporate sector and its impact on organizational performance, on any possible mediating or moderating factors. An online survey with 200 respondents (employers and managers of the business sector) was conducted and analyzed using SPSS along with factor analysis to extract latent factors from the observed variables. The findings revealed that both EI and CSR have a significant positive impact on effective management. At the same time, CSR and EI emerged as stronger predictor of effective management. These results highlight the importance of fostering EI and integrating CSR initiatives to enhance managerial effectiveness in organizations, leading to long-term sustainability with a positive impact on the community.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".