Vicarious Learning And Inferential Accuracy in Adoption Processes
构建了一个推断性替代学习模型，解释企业如何通过观察其他组织的采纳、放弃等行为，推断一项实践的价值是否因组织而异，并据此选择参考对象。
We develop a model of inferential vicarious learning that explicates how firms can learn whether to adopt a practice whose value varies across organizations. We first theorize how firms can infer this variation from samples of adopters, nonadopters, abandoners, and nonabandoners. Surprisingly, incomplete samples may allow more accurate inferences than complete ones. We then propose how firms, equipped with an understanding of the variation, can select referent organizations that differ from those identified in previous research.