Technological intensity as a moderating variable for the intellectual capital–performance relationship
Bibliographic record
Abstract
Abstract The purpose of this study is to provide empirical evidence of the moderating influence of technology intensity on the relationship between intellectual capital (IC) and corporate performance in Italian small‐ and medium‐sized enterprises (SMEs). An empirical analysis was developed for the period 2012–2016 and included 62,849 Italian SMEs. Data were collected from the AIDA database (Bureau Van Dijk—A Moody's Analytics Company), and the sample was composed of high‐tech, medium‐high‐tech, medium‐low‐tech, and low‐technology manufacturing firms, according to the “Classification of Manufacturing Industries by Technological Intensity,” as defined by the OECD. The empirical results highlight that profitability is significantly and positively affected by financial and physical capital efficiency and by human capital efficiency (HCE), but the effect of HCE is weak, and the structural capital efficiency has a negative effect on corporate performance. The time variables positively affect corporate performance, with the highest coefficient in 2016. Additionally, technology intensity reinforces the positive effect of HCE on firm performance: the higher the technological intensity, the higher the positive impact of HCE on corporate performance. The managerial implications are relevant; in fact, tangible, financial, and current assets (employed capital) represent the principal lever of performance for managers in technology sectors. The negative effect of structural capital could be caused by inefficient use of this resource, or the employed variable could not be adequate to effectively measure this IC component. It is necessary for managers to appreciate technological intensity as a contingency variable affecting the IC–performance relationship.
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.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".