Information and the Speed of Innovation Adoption
构建了一个贝叶斯随机效应模型，研究南澳大利亚农民采用新小麦品种的过程，发现信息获取速度比以往模型慢得多，并能解释滞后采用和部分采用现象，对农民和推广机构有实际意义。
Abstract Within a Bayesian framework, a random‐effects model is developed and applied to adoption of new wheat varieties in South Australia. In this model, not all pieces of information add equally to knowledge about the innovation. The model shows the acquisition of information to be much slower than has been suggested by previous Bayesian models and can also explain laggards and partial adoption. The results have important practical implications for farmers and support agencies. The paper's theoretical contributions are to highlight the structure of information, and to demonstrate how qualitative results can be obtained where the posterior Bayesian distribution is intractable.