Intellectual capital, entrepreneurial orientation, and technical innovation in small and medium‐sized enterprises
Bibliographic record
Abstract
The purpose of this paper is to empirically investigate the effects of intellectual capital (IC) on technical innovation (TI) and entrepreneurial orientation (EO) in small and medium‐sized enterprises (SMEs). Hypotheses were tested using a survey data set of 464 questionnaires collected from senior, middle, and functional managers, in addition to employees working in 63 SMEs operating in Jordan. The findings show that all IC dimensions have positive significant effects on both TI as well as EO. More specifically, human capital and relational capital emerged as having the strongest effects on TI, whereas relational capital and human competence had the strongest effects on EO. Interestingly, relational capital was the only IC dimension that had a particularly strong and positive significant effect on both EO and TI. Moreover, all EO dimensions had positive significant effects on TI. As for the mediating effect of EO, it was found to have quite a strong and partial mediating effect on the relationship between each IC dimension (relational, structural, human, and human competence) and TI. It was also noted that EO had a particularly strong partial mediating effect on the relationship between structural capital and TI, as well as human competence and TI. Given the unique context within which SMEs are established and developed, in terms of heavily investing in IC, as well as their supposedly entrepreneurial and innovative nature, this study provides an original contribution to the TI literature.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".