Antecedent Conditions for Leveraging Intellectual Capital: A Contingency Perspective
Bibliographic record
Abstract
In the new global economy, leveraging intellectual capital (IC), in a manner in which such competitive advantage would gained and sustained, has become a central issue. Drawing primarily on contingency theory, this paper aims to empirically explore the antecedent conditions necessary for an effective development of intellectual capital (IC). In this respect, some contextual factors, namely organizational culture, industry type, and firm size were investigated for their potential impact on IC. The paper reports the results of a study carried out in Iran through a questionnaire survey of Chief Financial Officers (CFOs) in 128 companies within Tehran Stock Exchange (TSE). Partial least squares (PLS) was employed for confirmatory factor analysis as well as hypothesis testing. The results of the survey reveal that organizational culture and size could provide some impacts on IC within Iranian public listed companies. While the influence of knowledge-related resources on measurable performances has been considerably examined in the IC literature, little is known concerning the antecedent conditions necessary to leverage IC more effectively. Hence, this paper extends the current IC literature and contributes to the field through scrutinizing the influence of a series of contextual factors on IC. From practical angle, addressing the antecedent conditions necessary for IC may highlight the importance of firm-specific variables and traits which must be taken into consideration by managers and organizations for a sustainable IC development. Such insight could support organizations to remedy the deficiency in managing and leveraging their knowledge-based assets as their most critical and strategic resources.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".