Researcher insights on theoretical contribution building in qualitative management accounting research
Bibliographic record
Abstract
While the management accounting literature has utilized and discussed theory, little has been written on the processes, thoughts and logics of management accounting researchers as they build theory. This paper presents interviews with leading researchers who have offered theoretical contributions over the past two decades or so to the management accounting change framework of Burns & Scapens (2000) - which is used as an exemplar here. Questioning probed how theoretical contributions were developed, teasing out key processes and actors in the journey to publication. The findings reveal several influencing “levers” which are used by researchers as they develop a theoretical contribution. Two potential routes researchers take towards theoretical contributions are also identified, as are some factors which may influence the processes of developing a contribution. Based on these insights, this paper develops implications for fellow qualitative management accounting researchers that may increase their chances of creating theoretical contributions.
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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 | 0.149 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".