Faculty Perceptions of the Research-Teaching Nexus in Oman Business School
Bibliographic record
Abstract
The research–teaching nexus (RTN) is a core accreditation requirement of the Oman Authority for Academic Accreditation and Quality Assurance of Education (OAAAQA). Embedding research into teaching enhances educational quality, bridges the gap between theory and practice, and develops students’ critical and analytical skills. This study investigates faculty perceptions and practices of RTN within a business college in Oman. A quantitative research design was employed, using a structured survey administered to academic staff and analyzed through descriptive statistics and a one-way multivariate analysis of variance (MANOVA). Results reveal strong engagement with RTN: 76% of faculty integrate their research, 83% embed external research, 80% involve students in projects, and 79% supervise and provide feedback on theses. Moreover, 92% reported that research keeps them updated on emerging trends, 87% indicated it introduces new insights that enrich teaching, and another 87% affirmed it stimulates pedagogical innovation. Significantly, 97% agreed that RTN directly enhances students’ competencies and employability skills. The MANOVA results indicate a statistically significant multivariate effect on respondents’ perceptions of RTN practices, educational quality, and student skill development. In contrast, neither gender nor teaching experience produced statistically significant differences, suggesting that these demographic variables exert limited influence on how respondents evaluate institutional RTN practices or their associated outcomes. Despite these positive practices, challenges remain, including heavy teaching loads, limited time, and restricted research funding. The study concludes that effective implementation of RTN requires institutional support, workload alignment, and faculty development initiatives. The findings provide practical implications for academic leaders and quality assurance bodies seeking to advance innovation and educational quality in higher education.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".