Modelling Job-related and Personality Predictors of Intention to Pursue Accounting Careers among Undergraduate Students in Ghana
Bibliographic record
Abstract
This study principally investigates job-related and personality factors that determine Ghanaian accounting students’intentions to pursue careers in accounting. It draws on a rich body of existing literature to develop a research model.Primary data were collected from a cross-sectional survey of 516 final year accounting students in a Ghanaian publicuniversity. Data were analysed using SmartPLS 2.0 to conduct Partial Least Squares Structural Equation Modelling(PLS-SEM). The results show that five factors are key determinants of accounting students’ intentions to pursueaccounting careers. Among the significant predictors, feelings about accounting profession made the greatestinfluence on career intentions, followed by accountants’ reputation, job requirements, job outcomes and self-efficacy.Two factors, negative perception of ethical behaviour of accountants and accounting knowledge did not contributesignificantly to predicting students’ career intentions in the research context. Finally, the results show that strongerintention to pursue accounting career influences accounting students’ recommendation of accounting careers toothers. This study contributes to filling the dearth of empirical research in developing countries in Sub-SaharanAfrica (SSA) on career-choice predictors of accounting students’ career intentions and its behavioural consequence.Theoretical, managerial and educational policy implications of this study are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".