期刊名称：Journal of Economics and Sustainable Development
印刷版ISSN：2222-2855
电子版ISSN：2222-2855
出版年度：2016
卷号：7
期号：9
页码：38-50
语种：English
出版社：The International Institute for Science, Technology and Education (IISTE)
摘要：Background and methodology: The objective of the study was to identify consumer’s sensory preference of quality protein maize and conventional maize traditional dishes and then try to estimate the willingness to pay for quality protein maize grain in Jimma Zone. It used modified home-use test sensory evaluation technique and Becker-De Groote- Marschak experimental auction mechanism. The data was collected from 210 mothers and children aged 6-23 months. All mothers participated on modified home-use test was participated on experimental auction. SPSS-20 was used for for descriptive statistics and ordinal logistic regression and Stata 12.1 was used for random effect model to explore factors related to willingness to pay. Result: The result from modified home-use test explored quality protein maize genfo was appreciated by all sensory attributes than conventional maize genfo particularly high appreciation for the yellow quality protein maize genfo. The overall score of children also realized that quality protein maize genfo was significantly appreciated than the conventional counterpart. The experimental auction result revealed that sample respondents were willing to pay more for quality protein maize grain and the main driving factor was its sensory quality. The result also shows information has boosted bids of white and yellow quality protein maize grain. Recommendation: The study finally recommends concerning bodies to use sensory superiority and market potential of quality protein maize for its adoption, dissemination, processing in food industries, marketing and then consumption among farmers in rural areas as quality protein maize is recognized as a tool to tackle protein malnutrition.
关键词：Willingness to pay; modified home-use test; Becker-De Groote- Marschak mechanism; ordinal logistic regression; random effect model.
