Factors Influencing the Students’ Choice of Accounting as a Major: The Case of X University in United Arab Emirates
Bibliographic record
Abstract
This study tried to examine the factors that influence students’ choice of accounting at X Private University. A questionnaire survey was used to collect data for the five hypotheses which were tested in this study related to the factors of reputation of the university or college, personal interests, job prospect, family members and peers, and media. The findings of the study revealed that personal interests, personality, job prospect, reputation of the university and media did not have a significant influence on the students’ choice of accounting as a major. On the other hand, data analysis shows thatfamily members and peers, significantly related to the students’ choice of accounting as a major. In other words, hypotheses H1, H2, H3, and H5 are rejected, whereas hypothesis H4 is accepted. This research contributes to literature by identifying the relationship between the independent variables (reputation of the university, personal interests, job prospect, family members and peers, and media) and the dependent variable (student’s choice of accounting as a major), and the results of the study give valuable information to university's management to know how they can influence families to increase the number of students enrolling in such university, especially accounting students. This research also tested a new variable, which is reputation of the university – a variable that is expected to influence students’ choices.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".