Adoption of Emerging Technologies Under Output Uncertainty
构建了一个在信息不完全和产出不确定性下可分割技术采纳的模型，并用德克萨斯州奶牛场对牛生长激素的采纳数据估计了影响采纳决策和强度的因素，发现传统模型会高估采纳率。
Abstract A model of divisible technology adoption under incomplete information dissemination and output uncertainty is developed. We identify economic and subjective factors affecting technology adoption and its intensity. Empirical estimation employs a mixed dichotomous‐continuous framework with nonrandom sample selection. Producers' adoption intensity is conditional on their knowing about and deciding to adopt the new technology. Using survey data on bST (bovine somatotropin) adoption among Texas dairy producers, we find that larger and more educated operators are likely to adopt more intensively. Traditional dichotomous adoption models without sample selection significantly overestimate the adoption rate.