A causal model of human capital antecedents and consequents in the financial services industry
Bibliographic record
Abstract
Purpose Causal models have been used in recent intellectual capital research studies to better understand the various outcomes of antecedent configurations of intangible asset components. These studies have been conducted in various industry sectors including insurance, healthcare, banks, and others. The purpose of this study is to replicate and extend prior research results within a new financial services sub‐sector. Design/methodology/approach A survey instrument based on prior research was administered to 396 employees from ten credit unions across Canada. Findings The results show that the pattern and value of causal paths change slightly from one context to another. Research limitations/implications Six research implications are offered which summarize the key academic findings of the study related to how the interdependencies of the constructs alter from one context to another. Practical implications The empirical results presented here should lead analysts to recognize that measuring and strategically managing intellectual capital may in fact become the most important managerial activity for driving organizational performance. Originality/value The study provides a unique opportunity to test the generalizability and contextual implications of administering a similar survey instrument across various contexts.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".