The Influence of Job Alienation on Financial Performance, the Mediating Effect of Governance: A Study on the Lebanese Banking Sector
Bibliographic record
Abstract
Job alienation decreases employee engagement and productivity and negatively affects financial performance. This problem is critical in the Lebanese banking sector, where governance ensures competitiveness and sustainability. This research explores job alienation and its impact on governance and the financial performance of Lebanese banking institutions. Governance evades financial losses due to employee disengagement. Inspired by Karl Marx’s work, the framework of this research explores the role that governance plays when it is a mediating variable in transmitting the effect of job alienation on financial performance. This research utilizes a quantitative approach based on a survey conducted on employees and managers of banks operating in Lebanon. The sample includes 300 respondents from different departments. The researchers constructed a questionnaire based on a deductive approach to measure variables. They assessed the relationship between the variables using structural equation modeling (SEM). The research rendered worthwhile results, mainly that job alienation has a direct influence on financial performance. Governance mediates and alleviates the relationship between constructs. Banks with effective governance can overcome the impacts of job alienation, resulting in stable financial performance. This research bridges a gap in the literature relevant to the relationship between variables in the banking sector. Banks should implement recognition policies to monitor and reduce job alienation. This research equips bank managers with actionable insights to promote ethical and transparent practices, enhancing employee trust.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".