Intellectual capital and performance measurement systems in Iran
Bibliographic record
Abstract
Purpose The purpose of this paper is to empirically explore how the effect of intellectual capital (IC) on organizational performance is indirect and mediated through performance measurement (PM) systems. Design/methodology/approach Data were collected from a survey of 128 chief financial officers of Iranian publicly listed companies. Hypotheses were tested using partial least squares regression, a structural modeling technique which is appropriate for highly complex predictive models. Findings Results from the structural model indicate that, in general, companies with a higher level of IC place a premium on the balanced use of PM systems in a diagnostic and interactive style. Furthermore, the results provide some evidence that IC is indirectly associated with organizational performance through the intervening variable of the balanced use of interactive and diagnostic PM systems. Practical implications This study sheds light on the issue of how senior management should use PM systems to take full advantage of intellectual assets which could lead to improved organizational performance. Originality/value This is the first study of its kind to synthesize a model which examines IC, PM systems, and organizational performance. Although the effect of different types of intangible assets on performance has been substantially examined in the literature, less effort has been devoted to understanding the role of PM systems in leveraging an organization’s IC.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".