Information Systems Control: A Review and Framework for Emerging Information Systems Processes
Bibliographic record
Abstract
A major stream of information systems (IS) research examines the topic of control, which focuses on attempts to affect employee behavior as a means to achieve organizational objectives. Despite a rich history of IS control research, approximately 90 percent of the publications focus on only three IS processes: managing information systems development, managing IS outsourcing, and managing security. However, the emergence of new IS processes and technologies with distinct control challenges, such as managing enterprise architecture and managing innovation, highlights a need to consider the wider applicability of past control insights. In this paper, we first integrate existing IS control constructs and relationships into a comprehensive IS control model. Second, we apply this model to emerging IS processes to guide future research and practice. We review 65 influential IS control-related journal papers and identify five control dimensions. We then consolidate these dimensions into a single, integrated model to apply past IS control findings to the challenges of emerging information systems by posing a series of related propositions. With this paper, we position current IS control research to be increasingly applicable and relevant to tomorrow’s emerging IS opportunities and challenges.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".