Bibliographic record
Abstract
Purpose Firms are increasingly confronted with a complex and dynamic competitive environment. The purpose of this paper is to shed some light on the way firms can cope with – and succeed in – such an environment. Design/methodology/approach The article examines major factors that are driving a global structural shift toward increased global competition. After identifying the difficulties behind the management of value creation, it focuses on the specific role of intangibles with a view to building a responsive firm. Findings It is found that intangibles are the key to sustaining value creation in a complex and dynamic environment. Following this, some consideration is given to the elements that help build responsive firms. The paper concludes by proposing actionable ways for managers to leverage intangible‐based practices. Originality/value The case for leveraging intangibles is advanced through a mix between an international business perspective and the combined role of knowledge and cluster‐like environments. Numerous real‐world examples help substantiate the analysis.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".