Constructing a definition for intangibles using the resource based view of the firm
Bibliographic record
Abstract
Purpose The purpose of this paper is to construct and propose a definition for intangibles derived from the resource‐based view (RBV) of the firm for use in academic research and practical applications. Design/methodology/approach Intangibles are defined as a subset of corporate resources. In this paper, various definitions for intangibles are tested against the RBV framework. Findings The majority of definitions in the extant literature are (implicitly or explicitly) in synchronization with the RBV. Thus, it is possible to find and propose a common definition for intangibles. Research limitations/implications Some researchers argue that the field is still in its embryonic stages and thus the concepts might still be too fresh in order to find a stable common definition. Practical implications The paper offers a conceptual lens through which one can clearly link intangibles to strategy and offers a proposed definition of intangibles that incorporates a meta‐review of the literature. Originality/value The paper shows that it is in fact possible to accommodate various definitions of intangibles under one common framework and propose a unified characterization.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".