2011 · 26 citations · 89 references
Public AffairsInnovation ManagementScience EducationExperimental DesignImpact MeasurementInnovationData AnalysisExperimental EvaluationImpact EvaluationInnovation Impact AssessmentQuasi-experimental EvaluationTechnology AssessmentData Quality MetricsData Quality ModelingData Sources EvaluationInnovation Effects AnalysisObservational Data CollectionScientific Impact MeasurementScientific Innovation
The purpose of this guideline is to provide ideas and technical advice on how to measure the effectiveness of Science, Technology and Innovation Programs (STIP). The paper addresses the specific challenges of evaluating STIP, from the assessment of the intervention logic to the choice of the most appropriate method to solve the attribution problem. Much attention is devoted to the topic of data, discussing pros and cons of different data sources, data quality issues, and strategies for data collection. The paper analyzes in detail the potential application of experimental and quasi-experimental methods to STIP.
89
Credit Rationing in Markets with Imperfect Information
Joseph E. Stiglitz, Andrew Weiss · American Economic Review · 1981 · 12.9K citations · Full text
Innovation and Learning: The Two Faces of R & D
Wesley M. Cohen, Daniel A. Levinthal · The Economic Journal · 1989 · 7.5K citations