Modelling strategic actor relationships for risk management in organizations undergoing business process reengineering due to information systems adoption
Bibliographic record
Abstract
Purpose Because of the competitive economy, organizations today seek to rationalize, innovate and adapt to changing environments and circumstances as part of business process reengineering (BPR) efforts. Irrespective of the process reengineering program selected and the technique used to model it, BPR brings with it the issues of organizational and process changes. Thus, BPR initiatives involve risk taking. Effective management of risks and their prediction and estimation should help in minimizing failures from BPR efforts. Risk management is non‐trivial due to the large uncertainty involved with business success with BPR efforts. Though some attempt has been made to model risk management in enterprise information systems using conventional conceptual modelling techniques, the previous works have analyzed and modeled the same just by addressing “what” a process is like, but do not address “why” the process is the way it is. Design/methodology/approach The approach presents a new technique for analyzing and modelling early‐phase requirements of organizational risk management that provides the motivations, intents, and rationales behind the entities and activities. Findings A case study has been considered to illustrate this approach. Originality/value The approach is novel in the sense that there is no similar intentional modeling approach for risk management to the best of one's knowledge. The approach is expected to be valuable because by using this approach one can reason about the risks associated with BPR and can incorporate prominently the issues related to risk in the process of systems analysis and design.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".