Perceptions of South African Accountants on Factors with a Role in the Adoption of Artificial Intelligence in Financial Reporting
Bibliographic record
Abstract
Purpose—The objective of this study was to conduct a detailed South African study that sought to explore and analyse the views of South African accountants regarding the factors that affect the adoption of AI in financial reporting. In other words, this study aimed to understand what accountants in South Africa think about the use of AI in their field, especially concerning its integration into financial reporting practices. Three main theories underpinned the study, namely, the diffusion of innovation, technology, organisation, and environment framework, and the institutional theory. In essence, the study sought to determine the perception of South Africa’s accountants on these factors. Design/methodology/approach—This study adopted the quantitative research method and descriptive design. In this regard, positivism as a philosophy was preferred. An online survey was developed to collect information from the participants. Participants were recruited based on their affiliation with the four IFAC-recognised accounting bodies in South Africa: SAICA, SAIPA, CIMA, and ACCA. Findings—Th study found that, overall, South African accountants believe that organisational, technological, and environmental factors play a role in adopting artificial intelligence in financial reporting. Originality/value: This study contributes by enriching the understanding of South African accountants’ perceptions of the adoption of artificial intelligence in financial reporting through the lenses of the selected theories.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".