Examining the Business Performance Model Based on the Intellectual Capital Approach in an Iranian Industrial Town
Bibliographic record
Abstract
Purpose - The aim of this study is to investigate the impact of intellectual capital dimensions including human capital, relational capital, and structural capital on the business performance. Design/Methodology/Approach - In this paper, it is tried to identify the effect of intangible assets within the business environment on the business performance. The statistical population was located in an Iranian industrial town. The data collection tool was Bontis questionnaire, which was applied originally in Malaysia, Canada, and Jordan. Moreover, the data analysis was carried out using Visual PLC software. Findings - The results verified that the used structural equation model is a strong theoretical model for examining the impact of intellectual capital on the organization’s business performance. The findings demonstrated that while customer capital can be changed and improved directly by changing the human capital, changing structural capital through human capital is impossible.Furthermore, the results of the analysis confirmed that all aspects of intellectual capital, except human capital and structural capital, have positive and significant relationship to each other. Finally, it was perceived that the structural capital is of a significant relationship to each other. Finally, it was perceived that the structural capital is of a significant positive effect on the business performance. On the other hand, it was comprehended that human capital has a positive and significant relationship with structural capital. Practical Implications - This paper is a very useful source of information for investigators as well as practitioners with regard to intellectual capital in Iranian firms. Originality/Value - This paper is pioneering the analysis of intangible assets in Iranian industrial organizations, and has attempted to determine some of the most relevant intangible assets used by those organizations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".