The Effect of Transformational Leadership on Organizational Culture Change: A Case Study of the Manufacturing Industry
Bibliographic record
Abstract
This study aims to analyze the influence of transformational leadership on organizational culture changes in the manufacturing industry. The phenomenon behind this research is the challenges faced by the manufacturing industry in adapting to the dynamics of the business environment, such as technological developments, global competition, and demands for increased innovation and efficiency. Many manufacturing companies have difficulty adapting due to rigid organizational cultures and employee resistance to change. Transformational leadership is seen as an approach that is able to overcome these obstacles by encouraging adaptive, innovative, and performance-oriented cultural change through empowerment, motivation, and effective communication. This study uses a quantitative method with survey techniques as a data collection tool. A total of 121 respondents, consisting of employees in the manufacturing sector, were selected using the purposive sampling technique. The research instrument is in the form of a closed questionnaire with a five-point Likert scale. The data obtained were analyzed using SPSS version 25 software to conduct validity, reliability, and multiple linear regression tests to test the relationship and influence between transformational leadership and organizational culture change. The results of the study show that transformational leadership has a significant influence in creating a more adaptive, innovative, and performance-oriented organizational culture. Factors such as effective communication, employee engagement, and an appreciation for creativity were found to be important elements in supporting such change. These findings suggest that the application of transformational leadership is particularly relevant in the dynamic manufacturing industry, especially in the face of market challenges and changing business environments. Leaders in this industry are expected to adopt transformational leadership to build a more responsive and productive organizational culture.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".