Bibliographic record
Abstract
Purpose Drawing largely upon resource orchestration theory, this study aims to contribute to the intellectual capital (IC) literature by testing a model where intrapreneurship mobilizes resources to trigger firm performance. More specifically, this study investigates how intrapreneurship mediates the relationship between IC and financial performance. Design/methodology/approach Data was collected using a structured questionnaire administered to a target sample of publicly-listed Iranian companies across a variety of sectors. Archival data supplemented the survey findings to capture financial performance. A structural equation modelling (SEM) approach, using LISREL, was used to assess the measurement and structural models. Findings The results supported the hypothesized associations among IC, intrapreneurship, and financial performance. Furthermore, the findings provided some evidence that IC is indirectly related to financial performance through the mediating role of intrapreneurship. Research limitations/implications The focus on Iranian publicly listed companies limits the generalizability of results. Practical implications Managers need to align the company's strategic resources with other competencies such as intrapreneurial initiatives. The synthesis of knowledge resources and intrapreneurship can help organization to better organize, synchronize and support – i.e. “orchestrate” – their human and structural capital, improving the firm's social and innovation capital and eventually enhancing overall performance. Originality/value To our knowledge, this is the first study ever to explore the mediating role of intrapreneurship in the relationship between IC and financial performance from the resource orchestration lens.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".