The Role of the Social Network in Adopting New Turfgrass Varieties: An Analysis of Twitter Data

Abstract

This study examines the effect of social learning on new turfgrass variety adoption decisions using data from 231 turfgrass professionals’ Twitter accounts between 1 Jun 2018 and 31 Dec 2019. To determine the social learning effect, we decompose networking effects into social learning, individual-level and group-level similarities, herd behavior, and clustering effects. Our study estimates a spatial autoregressive probit model that directly incorporates the social network structure to account for unobservable networking effects and potential reflection problem. A Bayesian estimation procedure is used to alleviate the convergence problem caused by the complexity of model specification. Empirical results show that the social learning effect positively influences the new technology adoption and was greater than herd behavior effect. The results also suggest that turf professionals rely more on suggestions and information from online social networking among themselves than recommendations from advisors.

Keywords

• new technology adoption
• social network analysis
• turfgrass
• social media
• twitter
• bayesian estimation
• spatial autoregressive probit model