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The Impact of Farmers' Characteristics on Technology Adoption: A Meta Evaluation
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| Presenter(s):
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| Guy Blaise Nkamleu, African Development Bank, b.nkamleu@afdb.org
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| Abstract:
An abundant number of studies in recent years have been devoted to farmers' adoption of agricultural innovations. Most of the studies are conducted to investigate farmers' characteristics affecting their adoption decision. However, Results from different studies are often contradictory regarding the impact of any given variable on adoption decisions.
This paper examines these often conflicting results. A meta-analysis is conducted on 186 peer-reviewed adoption analyses from the recent literature on agricultural technology adoption. Meta-regressions method is used to evaluate the variations in the outcome of farmers' specific characteristics as significant determinants of their adoption decisions. The results generally show that differences across studies, in terms of study design processes pertaining either to methodological issues, spatiotemporal context and technology characteristics are important drivers of the adoption study results. We therefore conclude that the conflicting research results may, in many cases, be simply the results of differing study-specific design and technology characteristics rather than empirical facts.
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Achieving Knowledge Translation for Technology Transfer: Implications for Evaluation
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| Presenter(s):
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| Vathsala Stone, University at Buffalo - State University of New York, vstone@buffalo.edu
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| Abstract:
Research programs implementing public policies through information or product innovations are held accountable for evidence of societal impact. In response, evaluation tools such as PART and the logic model have been used for program reviews and program planning. Program managers in areas such as healthcare are seeking to demonstrate research impact through knowledge translation. This paper presents an evaluation framework for the special case of knowledge translation for technology transfer (KT4TT), to guide research project managers to plan for successful innovations. While logic models help research programs to plan for needed results, individual projects funded under them must provide credible and relevant data by framing research questions based on explicit connections between planned impacts and user needs. We propose integrating the CIPP model into the logic model so relevance is proactively ensured. By ensuring quality through formative and summative evaluations, the CIPP model is a fitting complement to the logic model.
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