Empathetic Leadership: Motivating Organizational Citizenship Behavior and Strengthen Leader-Member Exchange Relationships
Bibliographic record
Abstract
Empathetic leadership can motivate employees to become more productive and improve job satisfaction. Motivation is a self-initiated behavior that influences organizational citizenship behavior. However, empathy remains a vague psychological construct that requires research into different forms of empathy. This study applied illocutionary (empathetic) speech to determine the ability to predict organizational citizenship behavior in the leader-member exchange relationship. Additionally, locutionary (meaning-making) and perlocutionary (direction-giving) speech was introduced to establish factors that may strengthen that relationship. The study consisted of three hundred nine full-time employees and revealed that illocutionary (empathetic) speech significantly predicted organizational citizenship behavior. Locutionary (meaning-making) and perlocutionary (direction-giving) speech strengthened that relationship and are consistent with felicity conditions. Therefore, leaders, managers, and supervisors should attend workshops or executive coaching to develop communication strategies based on empathetic, direction-giving, and meaning-making speech to motivate employee organizational citizenship in leader-member exchange relationships.
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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 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".