Bridging Strategy from Both Business Economics and Design Sciences
Bibliographic record
Abstract
Consensus on the impact of design on perfor-mance can be said to be evident at all three levels of decision-making in organizations: strategic, tac-tical, operational (Brunswicker et al. 2019; Gemser & Leenders 2001). This impact broadly assumes the following forms: Design impact for strategy in action and customer experience Design impact for business strategy, process, innovation, and performance Design impact for cultural change and organization transformation Despite these revelations, precious little guidance is found in the way of forming a holistic view of the why of design science, core capabilities, theo-ries, and methods in business economics and the ultimate pertinence of the design function in any given organization. Similarly, the how, which would outline the ways in which these capacities could be built and coordinated towards the support of stra-tegic design and forward-looking decision-making processes is at best assumed, yet very rarely articulated. This issue includes both the papers from academia and professionals we received through our Call, as well as the results of a complementary survey con-ducted by the editors with Chief Design Officers. Our editorial foreword uses the model (Figure 1) as the framework for a synthesis, linking strategy in design science and strategy in business science: Part I - The vertical axis of Strategy from Vision to Mission through Value: design strategy versus cor-porate strategy, and business economics in design-driven organizations.Part II - The horizontal axis of Strategic Manage-ment and the Strategic design decision path. From design leadership and strategic positioning to busi-ness strategy and design management to strategy in action 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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.031 | 0.036 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".