Factors Influencing Lecturers' Organizational Commitment in Higher Education: A Systematic Literature Review
Bibliographic record
Abstract
This paper analyzes 30 research articles published between 2018 and 2024 through a systematic literature review methodology to explore the multidimensional factors that influence organizational commitment among higher education lecturers. The research focused on individual subjective factors such as gender, age, emotional intelligence, personal values and beliefs, self-efficacy, career satisfaction, mental health status, and external environmental factors, including leadership style, managerial effectiveness, organizational support, and work environment. In addition, the article provides an in-depth examination of socio-cultural factors, such as societal values towards education, family support, and social support, and how these factors combine to contribute to lecturers' organizational commitment. The article reveals the complex interactions between these factors and highlights the joint importance of personal and external factors in shaping lecturers' organizational commitment. The findings of this study not only provide practical strategies for educational administrators to enhance lecturers' organizational commitment and point to the need for future research to consider wider cultural and geographical differences to contribute to the overall improvement of educational quality and teaching effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".