Knowledge Creation Capability and Innovation in Global Consulting Firms
Bibliographic record
Abstract
Knowledge creation has been recognized as a core competence for competitive advantage in knowledge intensive organizations. The purpose of this study is to identify the relationships between knowledge creation capability, intellectual capital and innovation in multinational consulting organizations. Survey data of 172 professional consultants from Korean subsidiaries of multinational management consulting firms was collected and empirically analyzed against our hypothesis. The key findings are as follows. Firstly, knowledge creation capability has a significant positive influence on subsidiaries’ innovation. Specifically, tacit knowledge creation has a significant influence on subsidiaries’ innovation, while explicit knowledge creation does not have substantial effects on innovation. Secondly, human capital as intellectual stock has negative moderating effects on how tacit knowledge creation influences innovation, but plays a positive moderating role on the relationship between explicit knowledge creation and innovation. This negative mediating effect of human capital appears stronger in the senior group of employees. On the other hand, social capital has no moderating effects between knowledge creation capability and innovation. This study confirms the importance of knowledge creation capability, particularly the effect of the creation of tacit knowledge creation on innovations by multinational subsidiaries’ in professional service firms. This study also suggests that human capital has adverse effects on the impacts of tacit knowledge creation capability on innovation. These results provide managerial implications for subsidiaries’ knowledge creating capabilities as a driver for successful, innovative change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".