Bibliographic record
Abstract
Purpose The purpose of this paper is to present a discussion of the ways that personality tests have been used in accounting research. Design/methodology/approach The paper is structured as a literature review of the personality testing area, with particular emphasis on its application in accounting research. Findings The idea of personality impacting accounting has received some attention in recent years. However, it is an understudied area and the research to date is somewhat inconclusive. The findings are that over the last decade personality psychologists have made significant advances in personality theory and measurement. This paper summarizes: the theory of personality; the two most common personality typologies (i.e. the Jungian psychology‐based Myers‐Briggs Type Indicator (MBTI) and the Five Factor Model (FFM); and discusses the application of personality in accounting research. Research limitations/implications It is somewhat problematical to draw precise boundaries that include all relevant studies, and yet exclude appropriately distant ones, as there are a number of constructs that may, or may not, be considered to be “personality”. Another limitation is that the research studies published so far do not all agree one with another. Practical implications The conclusion reached is that, while there is a role for personality/accounting research using both MBTI and FFM, research using the FFM is particularly important for analytical and predictive research in this area and to triangulate previous MBTI studies. Originality/value As a literature review, there is little that is intrinsically new here, but the juxtaposition of different approaches and findings will be informative to researchers in the area and, to a lesser extent, practitioners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".