Data analytics by management accountants
Bibliographic record
Abstract
Purpose This paper aims to understand how the tasks of management accountants (MA) are affected by data analytics (DA). Design/methodology/approach A qualitative methodology was deemed most appropriate given the exploratory nature of the research questions (RQ). In total, 10 open-ended interview questions were used to gather the evidence. The case study design was inductive, yielding rich data from 29 respondents representing 20 different organizations. Findings Answers were provided to three interrelated RQs about the use of DA by MA, namely, what are their responsibilities? How does this work support inference, prediction and assurance? And how can they ensure insights from DA can be turned into decisions that add value? The findings also indicate that MA have not taken charge of the data analytic opportunities and at present, their activities remain largely focused on descriptive and financial data analysis rather than more complex activities using external data, operational data and modeling. Research limitations/implications The limitation of this research is that it is based on a relatively small, geographically restricted sample (20 organizations in south-central Canada) as well by interviews that were only 60 min in duration. Practical implications Provides a base for the existing practice of management accounting with DA. Social implications Explains the social relationship between DA and management accounting. Originality/value Documented and explained the extent of actual DA use by MA.
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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 | 0.033 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".