An Empirical Study on Factors Influencing Business Students’ Choice of Specialization with Reference to Nizwa College of Technology, Oman
Bibliographic record
Abstract
The academic specialization chosen by students is of crucial importance for their future career and therefore they should have access to appropriate information and guidance that would help facilitate a more optimal decision. This study aims to identify the variables that influence business students to choose their specialization. A sample of 163 business students from Nizwa College of Technology, Sultanate of Oman, is selected for the study. Factor analysis and multiple regression analysis are used for analysis. The most important variable that influences the students’ choice of specialization is the variable ‘Liking and preference of specialization’ (X2) with the highest mean. The factor analysis analysis output reveals the five sub-scales that influence students’ choice of specialization; Preference and dissonance post choice, Self and Peer influence, Nature of marketing specialization, Gender and specialization choice, Convenience and career. College orientation on specialisation (X5) is a significant factor that influences the students to advice their juniors to make the first choice of specialisation viz. HRM (63%), Accounting (32%) and Marketing (5%).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".