Intellectual capital and firm performance: the mediating role of innovation speed and quality
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
The purpose of this study is to explore the influence of intellectual capital (IC) on firm performance, considering the mediating role of innovation speed and quality. We develop a research model based on the IC perspective and innovation literature. We test the model by using structural equation modeling to analyze data collected from 328 high-technology firms in China. The results show that the three components of IC, namely human capital, structural capital, and relational capital, are positively related to innovation speed and quality, which in turn facilitate the operational and financial performance of a firm. The impacts of human and structural capital on financial performance are fully mediated by innovation speed and quality, whereas the impact of relational capital on financial performance is partially mediated. Innovation speed and quality partially mediate the effect of IC on operational performance. As one of the first studies to investigate how IC may influence firm performance through the mediating effects of innovation speed and quality, this study not only contributes to HRM literature on IC and innovation, but also offers managers with insights on how to align their HRM strategies and practices to develop IC when pursuing innovation and performance outcomes.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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