Designing Business Models and Similar Strategic Objects: The Contribution of IS
Bibliographic record
Abstract
In this paper, we argue that information systems (IS) research has the potential to contribute to improving strategic planning, just like it has substantially contributed to improving decision making and its support in organizations in the past. Based on our work and experience in the field of business models, we outline how IS research can help strategic management researchers study the design of business models and other similar strategic notions. The paper suggests that the current research focus in strategic management could be improved and enlightened by some of the more conceptual and design-oriented research in IS. We highlight three areas in particular in which IS research has excelled that could inform research in strategic management. The first area concerns the identification, formalization, and visualization of the core constructs and models of interest related to the design and analysis of strategic business issues. The second area corresponds to the exploration of how design techniques and tools might contribute to improving the design of answers and alternatives to strategic business questions. The third area addresses the research in computer-aided design assisting the process of designing strategic management objects such as business models.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".