Bibliographic record
Abstract
Purpose The purpose of this paper is to describe the dynamic development of technical controls in different companies and to interpret the observations using Van de Ven and Poole's typology of change process theories. Design/methodology/approach Case study data were obtained through semi‐structured interviews, observation and document analysis in three organisations (Company A, Company B and Company C). Findings The paper highlights the life‐cycle development of technical controls, where controls are implemented, improved and eventually removed. It highlights the fact that the progression through the life‐cycle can follow either a dialectical motor of change based on conflict or a teleological motor of change based on consensus. Research limitations/implications The findings of the paper enhance the theory of rules developed by March et al. , by providing insight into how change actually occurs, i.e. how inertia is broken. Practical implications The paper offers practitioners some guidelines for the management of their control systems to help them maintain more effective and efficient control systems. Originality/value The paper explains that under a teleological motor of change, inertia is broken more easily than under a dialectical one, because there is less tolerance for control obsolescence, hence improvement and removal of obsolete controls are more likely to occur. This is important for listed organisations having to implement more and more technical controls to comply with laws such as SOX. The paper also suggests that the life‐cycle is not a “motor” of change as suggested by Van de Ven and Poole, because it cannot explain how inertia is broken.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".