The Critical Role of Knowledge Management in Achieving and Sustaining Organisational Competitive Advantage
Bibliographic record
Abstract
The critical role of knowledge management in achieving and sustaining competitive advantage has been strongly emphasised in the extant literature. However, most previous studies were conceptually grounded and empirically examined in advanced, developed and newly industrialised countries. In addition, research to date has predominantly explored findings from a large company view while little attempt has been made to address the relative importance of different factors constituting the organisational KM capability in the context of Asian emerging, less developed countries such as Vietnam where a socialist market economy, a Confucian culture and a majority of small and medium sized enterprises currently exist. By adopting a resource-based theory of the firm with an extension of a knowledge-based perspective, this paper aims at developing and empirically validating a conceptual model of the relationships between KM capability components and their impacts on a firm’s competitive advantage in Vietnam. The results of 148 surveyed respondents in the construction industry reconfirmed a general agreement found in the literature that a combined social and technological approach is ideal to take advantage of their significant positive correlations in improving organisational competitive advantage. Moreover, in the Vietnamese-specific context, the study indicated that while culture is the most important issue affecting knowledge management, integration with information technology may assist to overcome cultural barriers and provide a stronger contribution to competitive advantage.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".