Discovering new business models for knowledge intensive organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Assists senior managers with generating new business models by mapping the competitive space occupied by knowledge intensive organizations and outlining strategic positioning options. Design/methodology/approach Provides a conceptual paper based on studies of knowledge intensive organizations. Findings Based on four strategic positioning characteristics, the authors identify three types of knowledge intensive organizations; diagnosis, search, and design shops. All knowledge intensive organizations are either pure types or combinations of these types. Practical implications While mapping the competitive space lets managers of knowledge intensive organizations pinpoint where they are relative to their rivals, strategy involves finding unique, profitable business models. To help managers detect potential opportunities, the paper outlines a full menu of competitive positioning options. Generating new business models in this manner should allow managers to enter existing, profitable niches or establish new, potentially profitable niches. Originality/value Few studies delineate the competitive terrain occupied by knowledge intensive organizations and then outline competitive positioning options for knowledge intensive organizations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it