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 Knowledge‐intensive firms are growing in importance yet there are few tools to help managers to analyze and improve their performance, which this paper aims to describe. Design/methodology/approach This paper builds on Michael Porter's strategic frameworks for industrial firms. It outlines how his frameworks, in particular the five forces and value chain, need to be modified if they are to be effectively applied to knowledge‐intensive firms. Findings Managers of knowledge‐intensive firms need to use the old tools in new ways, if they are to improve their business models and ultimately increase their profitability. Practical implications The paper outlines ways for managers of knowledge‐intensive firms to improve their firm's performance. First, managers using a revised five forces can improve their value capture by reducing bargaining power of its experts, making outsourcing of expert services more attractive, or improving their reputational status. Second, the paper outlines a continuum of business models and suggests that the appropriate choice of business model depends on the firm's problem‐solving expertise, its target clients, desired risk level and aspirations. The paper elaborates on the business model by examining choices surrounding the scope of the firm's problem‐solving activities, suggesting that these allow the firm to find profitable niches. Originality/value This is one of the first attempts to develop strategic tools that managers of knowledge‐intensive firms can used to increase their firm's profitability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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