An actor‐dependency technique for analyzing and modeling early‐phase requirements of organizational change management 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, which involves managing organizational changes (also called “change management”). Change management is non‐trivial, as organizational changes are difficult to accomplish. Though some attempt has been made to model change management in enterprise information systems using conventional conceptual modeling techniques, they have just addressed “what” a change process is like, and they do not address “why” the process is the way it is. Design/methodology/approach The approach presents an actor‐dependency‐based technique for analyzing and modeling early‐phase requirements of organizational change management that provides the motivations, intents, and rationales behind the entities and activities. Findings A case study illustrates this approach. Originality/value This approach is novel in the sense that there is no similar intentional modeling approach for change management to the best of our knowledge. The approach is expected to be valuable because using this approach one can reason about the opportunities and changes that are associated with BPR and can incorporate prominently the issues related to change in the process of system 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".