Dysfunctional Behavior in Organizations: Insights from the Management Control Literature
Bibliographic record
Abstract
SUMMARY As the scope of the audit continues to broaden (Cohen, Krishnamoorthy, and Wright 2017), research questions in management control and internal control are beginning to overlap. Even so, there is little overlap between these fields in terms of published research to date. The purpose of this paper is to take a step in bridging the gap between the management control and the internal control literatures. We survey relevant findings from the extant management control literature published between 2003 and 2016 on dysfunctional behavior and the ways in which it might be mitigated. We then use the fraud triangle as an organizing framework to consider how the management control literature might help to address audit risk factors identified in SAS 99/AU SEC 316 (AICPA 2002). The outcome of our analysis is meant to identify and classify the extant management control literature of relevance to research on internal control in a manner that researchers new to the management control literature will find accessible. We conclude with a set of future research opportunities that can help to broaden the scope of current research in internal control.
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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".