A Focus on ‘Control’: Reconciling Contemporary Transaction Cost Economics with Behavioural Contingency Accounting Perspectives
Bibliographic record
Abstract
Transaction cost economics and contingency research in managerial accounting currently are approached largely as two formally distinct fields of study. This brief review paper aims to reconcile the literature on both subjects in so far as possible, by examining broadly their underlying assumptions and reported conclusions with the view towards identifying differences and similarities. An important integration is achieved by showing that transaction cost economics and the ‘decision influencing’ role of management accounting concentrate essentially on different aspects to a shared concern with organisational control. However, it additionally is revealed that transaction cost economics does not account adequately for the existence of management accounting’s ‘decision facilitating’ function, and that in comparison to the former the latter’s relatively stronger empirical focus is more suited towards finding potential solutions to the actual control problems which confront real organisations. The paper concludes by observing that both paradigms presently to a large extent do not pay attention to a particular principal-agent situation in which control may need to be exercised.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.023 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".