Hitting a moving target: a process model of information systems control change
Bibliographic record
Abstract
Abstract Controls are widely regarded as a key factor in driving high performing organisational processes. However, because of ongoing changes within information systems (IS) processes, control modifications are commonly required in order to maintain performance levels. Although past research recognises the ongoing benefits derived from successful control changes, there is a limited understanding of the actual steps taken by organisations, particularly with regard to avoiding negative performance implications such as process delays or employee resistance. This research draws on empirical data from six case studies to propose a new process model that depicts the interconnected steps involved in control changes. Our findings suggest that the sources of IS control change may be more diverse than most past research suggests and that control changes within non‐project‐oriented processes (e.g. enterprise architecture) present additional challenges in comparison to project‐oriented processes (e.g. systems development). Insights from this research can aid practitioners in streamlining control changes as a means to improve effectiveness, whilst also contributing to research by uncovering an enhanced understanding of why and how control changes are made in IS processes.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".