Effects of leader-member exchange and organizational culture on work engagement and employee performance
Bibliographic record
Abstract
The objectives of this study are: (1) To determine whether the Leader-Member Exchange (LMX) and organizational culture can improve employee performance, (2) To conduct further research on employee performance by elaborating and analyzing variables that can affect work engagement, among others: members of the leadership and organizational culture. This research was conducted at a Telecommunication Company in Makassar, South Sulawesi with a sample size of 93 people. The analysis model used to determine the influence between variables was a structural model with the Partial Least Square (PLS) approach. In this study it was found that 1. LMX had no significant effect on job involvement. 2. LMX had no significant effect on worker performance. 3. Organizational culture had a significant effect on work engagement. 4. Organizational culture had a significant effect on employee performance, 5. Work management had no significant effect on employee performance. Leaders need to build high-level LMX relationships, equip workers with skills, increase employee professionalism and provide opportunities for employees, help solve the difficulties they face related to assigned tasks and make employees as friends so that they can increase their engagement and performance.
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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 | 0.012 |
| 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 | 0.001 | 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".