Intellectual capital and performance within the banking sector of Luxembourg and Belgium
Bibliographic record
Abstract
Purpose Intellectual capital is widely acknowledged as the most critical resource of modern organizations. Nevertheless, empirical evidence on its actual contribution to the dynamics of the value creation process remains scarce, especially within certain sectors and geographic regions. The purpose of this paper is to address this gap by investigating the effects of intellectual capital and its components on business performance in banking institutions within Luxembourg and Belgium. Design/methodology/approach This empirical research is conducted using a dedicated survey instrument administered to over 200 banks. Data analysis is achieved through structural equation modeling. Findings Results indicate that human capital contributes both directly and indirectly to business performance in the banking sector. Structural and relational capital are positively related to business performance, though results are not statistically significant. Surprisingly, relational capital has been evidenced to negatively moderate the effect of structural capital on performance. Research limitations/implications Traditional limitations of a cross‐sectional study apply with respect to the attribution of causality and the time lag effects. Practical implications A set of reliable items to capture intellectual capital has been identified and represents actionable knowledge for implementing an intellectual capital dashboard in banks. The dominant role of human capital also provides insight to managers with respect to business performance levers. Originality/value Disentangling the effects of intellectual capital on business performance is of the utmost importance in service firms, as they are heavily reliant on intangible resources and capabilities. This research contributes to develop current understanding of these effects. Moreover, interaction effects between human, structural and relational capital have also been uncovered, thus extending prior knowledge on these complex relationships.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".