Article metadata

"Innovation adoption by forward-looking social learners"


 
Dublin Core PKP Metadata Items Metadata for this document
 
1. Title Title of document Innovation adoption by forward-looking social learners
 
2. Creator Author's name, affiliation Mira Frick; Department of Economics, Princeton University
 
2. Creator Author's name, affiliation Yuhta Ishii; Department of Economics, Pennsylvania State University
 
 
3. Subject Subject(s) Innovation adoption, social learning, informational free-riding, strategic experimentation, exponential bandits
 
3. Subject Subject classification D80, D83, O33
 
4. Description Abstract We build a model studying the effect of an economy's potential for social learning on the adoption of innovations of uncertain quality. Assuming consumers are forward-looking (i.e.,\ recognize the value of waiting for information), we analyze how qualitative and quantitative features of the learning environment affect equilibrium adoption dynamics, welfare, and the speed of learning. Based on this, we show how differences in the learning environment translate into observable differences in adoption dynamics, suggesting a purely informational channel for two commonly documented adoption patterns---S-shaped and concave curves. We also identify environments that are subject to a saturation effect: Increased opportunities for social learning can slow down adoption and learning and do not increase consumer welfare, possibly even being harmful.
 
5. Publisher Organizing agency, location Econometric Society
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 2024-11-14
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format pdf
 
10. Identifier Universal Resource Indicator https://econtheory.org/ojs/index.php/te/article/view/20241505
 
11. Source Journal/conference title; vol., no. (year) Theoretical Economics; Volume 19, Number 4 (November 2024)
 
12. Language English=en en
 
15. Rights Copyright and permissions Authors who publish in Theoretical Economics will release their articles under the Creative Commons Attribution-NonCommercial license. This license allows anyone to copy and distribute the article for non-commercial purposes provided that appropriate attribution is given.