Competing Through Knowledge and Information Systems Strategies: A Study of Small and Medium-Sized Firms
Bibliographic record
Abstract
In this study, we investigated strategies that small and medium-sized enterprises (SMEs) in Canada employ to create, transfer, and apply knowledge, and we evaluated the importance of supporting dynamic knowledge capabilities and information systems. To examine the empirical support for a model based on the resource-based view of the firm, we conducted a survey of SMEs operating in knowledge-intensive industries. We tested relationships among knowledge strategy, information systems strategy, dynamic knowledge capabilities, and firm performance. SME performance was measured by their physical and financial capital, as well as four intangible types of capital: structural, human, innovation, and relational. We observed that dynamic knowledge capabilities only partially mediate the link between knowledge strategy and performance in SMEs. However, dynamic knowledge capabilities fully mediate the link between information systems (IS) strategy and performance in the small and medium-sized firms studied. We observed that information systems only indirectly influence firm performance, but they directly support the knowledge and innovation capital of SMEs. Further, our results indicated that, in SMEs, knowledge strategies directly influence IS strategies, and that alignment between knowledge strategies and IS strategies positively impacts dynamic knowledge capabilities, and hence firm performance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".