The effects of self-reflection on individual intellectual capital
Bibliographic record
Abstract
Purpose The aim of this paper is to develop a tool measuring individual intellectual capital (IIC) and investigate the relationship between self-reflection and IIC. Design/methodology/approach This study developed a theoretical model based on social cognitive theory and the literature of self-reflection and intellectual capital (IC). This research collected responses from 502 dyads of employees and their direct supervisors in 150 firms in China, and the study tested the research model using structural equation modeling (SEM). Findings The results indicate that three components of self-reflection, namely, need for self-reflection, engagement in self-reflection and insight, significantly contribute to all the three components of IIC, such as individual human capital, individual structural capital and individual relational capital. The findings suggest that need for self-reflection is the weakest component to impact individual human capital and individual relationship capital, while insight is the one that mostly enhances individual structural capital. Practical implications This paper suggests that managers can enhance employees' IIC by facilitating their self-reflection. Managers can develop appropriate strategies based on findings of this study, to achieve their specific goals. Originality/value First, this study develops a tool for measuring IIC. Second, this study provides an enriched theoretical explanation on the relationship between self-reflection and IIC – by showing that the three subdimensions of self-reflection, such as need, engagement and insight, influence the three subdimensions of IIC, such as individual human capital, individual structural capital and individual relational capital.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".