Permanent record · RIR–3002
Optimizing Horizon Scanning Inputs to Enhance Predictive Value in National Innovation Policy Scenarios
This research evaluates the predictive performance of horizon scanning compared to conventional forecasting methods in government scenarios. It suggests that refining input data and broadening field scope may further improve the predictive accuracy of future scenarios.
To what extent does the diversification of input data sources improve the predictive value of horizon scanning scenarios?
Knowledge gap
What remains worth asking
It remains useful to test whether specific adjustments to input data layers and field breadth consistently correlate with higher predictive accuracy.
Potential contribution
Why it may matter
Refining these methodologies can lead to more reliable foresight tools for national innovation systems.
Academic placement
OECD fields and topic tags
Scope: National innovation system scenario planning · Method signals: Survey Research, Comparative Analysis
Possible study pathways
One question, different levels
Innovation strategy and policy evaluation
Methodological validation of foresight techniques
Quantitative assessment of predictive foresight models
Qualification signal
88/100
- Builds directly on the authors' implications for improving predictive value.
- Open-access scholarly source and DOI metadata verified
Provenance
Research Idea Registry curation
- DOI and bibliographic metadata independently resolved
- Open-access status verified
- The research direction is transparently marked as AI-inferred
APA 7 source
Washida, Y., & Yahata, A. (2020). Predictive value of horizon scanning for future scenarios. foresight, 23(1), 17-32. https://doi.org/10.1108/fs-05-2020-0047
Paper abstract and discussion context; AI-inferred direction
Open source ↗