Information and the spread of innovation adoption

AbstractWithin 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.A.J. Fischer, A.J. Arnold, and M. Gibb