Exploring the relationship between the knowledge creation process and intellectual capital in the pharmaceutical industry
Bibliographic record
Abstract
Purpose – This paper aims to explore the relationship between knowledge creation and intellectual capital (IC) through an empirical study in the pharmaceutical industry. In the current economy, knowledge and IC are considered as the most important organizational assets and are the key resources in gaining competitive advantage. Design/methodology/approach – This paper adopts the socialization, externalization, combination and internalization (SECI) model to examine the format of knowledge creation processes (KCP) and uses a model to demonstrate the relationship between KCP and IC and its components in the pharmaceutical industry. A valid instrument was adopted to collect the required data on KCP and and IC dimensions. Structural equation modeling was used to assess the measurement model and to test the research hypotheses using the data collected from 470 completed questionnaires. Findings – The results supported the research model and revealed that KCP has significant influence on the accumulation of human capital. The performance of human capital manifests significant impact on structural capital and relational capital. Practical limitations/implications – Given the strong association between KCP and IC, managers should define their own robust operations for knowledge creation to improve their IC accumulation. Originality/value – This research departs from the earlier research on KCP–IC by adopting the SECI model and a research model that facilitates the exploration of the relationship between KCP and IC dimensions in the pharmaceutical industry. The research results provided strong support for the KCP–IC 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".