On Measuring the Criticality of Various Variables and Processes in Organization Information Systems: Proposed Methodological Procedure
Bibliographic record
Abstract
This paper proposes methodological procedures to be used by the accounting, organizational and managerial researchers and executives to ascertain the criticality of the variables and the processes in the measurement of management control system. We have restricted the validation of proposed methods to the extraction of critical success factors (CSF) in this study. We have also provided a numerical illustration and tested our methodological procedures using a dataset of an empirical study conducted for the purpose of ascertaining the CSFs. The proposed methods can be used by the researchers in accounting, organizational information systems, economics, and business and also in other relevant disciplines of organizational sciences. The main contribution of this paper is the extension of Rockart’s work [33] on critical success factors. We have extended the theory of CSF beyond the initially suggested domain of information into management control system decision making. The methodological procedures developed by us are expected to enrich the literature of analytical and empirical studies in accounting and organizational areas where it can prove helpful in understanding the criticality of individual variables, processes, methods or success factors.
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 | 0.091 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".