Intellectual capital and firm performance: the mediating role of innovation speed and quality
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.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".