Leadership Impact on Lecturer Retention at University Preparation Programs in Malaysia
Bibliographic record
Abstract
Many higher education institutions (HEIs) offer pre-university/foundation programs that help students transition from secondary school to universities, in Malaysia, or around the world. To be globally competitive and meet the needs of students looking to enter world class universities, HEIs in Malaysia often need to employ expatriate lecturers (ELs) (Trembath, 2016). Many ELs choose to depart their institution at the end of their first contract. This can have a negative impact on programs and adds additional recruitment costs to program budgets (Theron et al., 2014). This study’s purpose was to understand the importance of leadership and organizational climate on ELs’ decision to either renew their contract or depart. The objective was to provide the leaders of such programs with insights to help them mitigate the challenges expatriates face and develop a supportive environment that encourages longer-term commitment of lecturers beyond an initial contract.
 A convergent, parallel mixed methods research design was used for this study in which 63 participants completed an online questionnaire. In addition, 31 participants also completed a semi-structured interview. The research population for this study included current and former ELs who have worked at university preparation programs at Malaysian HEIs.
 Five themes that affect EL retention surfaced from the analysis of the qualitative and quantitative data: (a) professional growth and fulfilment, (b) the direct influence of the leader, (c) institutional factors, (d) cultural adjustment factors (Froese, 2012), and (e) country-specific factors. These key factors have influenced the decision making around contract renewal for ELs at Malaysian HEIs.
 Based on the results of this study, The Leadership Model for Expatriate Lecturer Satisfaction and Fulfillment was developed in order to guide program directors in developing a system to foster the conditions that encourage EL retention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".