Strategic foresight, knowledge management, and open innovation: Drivers of new product development success
Bibliographic record
Abstract
To remain competitive and make effective decisions in increasingly challenging markets, firms must integrate internal and external knowledge by embedding knowledge management strategies and technologies into their operations. This study aims to examine the roles of strategic foresight and knowledge management in promoting open innovation and driving new product development. Grounded in the knowledge-based view (KBV) of the firm, it investigates how strategic foresight influences open innovation processes and how knowledge management catalyzes innovation success. Using structural equation modelling (SEM) on data collected from 298 technology-based firms located in Lithuania ( n = 142) and Slovakia ( n = 156), the study demonstrates that strategic foresight directly impacts open innovation and significantly improves new product development through open innovation; in addition, knowledge exploration and exploitation are shown to play important roles in open innovation, with balanced effects on new product development outcomes. The study identifies open innovation as a critical mechanism that links strategic foresight and knowledge management to improve new product development, extending the KBV of the firm by highlighting the integration of external knowledge with internal processes, particularly in smaller, emerging economies. Practically, managers are recommended to prioritize foresight and balanced knowledge management practices while leveraging strategic alliances and networks to improve new product development outcomes. This integrated approach highlights the importance of collaborative innovation and external knowledge in achieving competitive advantage in dynamic business environments.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".