The impacts of observational learning and word-of-mouth learning on farmers’ adoption decision:

does interpersonal trust play a role?

Yangmei Zenga,b,c, Feng Qiuc, Junbiao Zhanga,b a College of Economics & Management, Huazhong Agricultural University, 430070, b Hubei Rural Development Research Center, Wuhan Hubei 430070, China cFaculty of Agricultural, Life and Environmental Science, University of Alberta, Edmonton Alberta T6G 2H1 Canada

Selected Poster prepared for presentation at the 2020 Agricultural & Applied Economics Association Annual Meeting, Kansas City, MO July 26-28, 2020

Copyright 2020 by [Yangmei Zeng, Feng Qiu, Junbiao Zhang]. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. The impacts of observational learning and word-of-mouth learning on farmers’ adoption decision: does interpersonal trust play a role? Yangmei Zenga,b,c, Feng Qiuc, Junbiao Zhanga,b

a College of Economics & Management, Huazhong Agricultural University, Wuhan Hubei 430070, China b Hubei Rural Development Research Center, Wuhan Hubei 430070, China cFaculty of Agricultural, Life and Environmental Science, University of Alberta, Edmonton Alberta T6G 2H1 Canada Introduction Results & discussions Ø Residue-based biogas is seen as an attractive means to enhance energy security and to improve rural household livelihoods. Ø However, the use of residue-based biogas in rural areas around the world remains low. Ø Undeveloped transportation and the relatively close geographical environment in rural areas makes social learning by farmers prominent in the adoption of technology Ø To our knowledge, no research has examined the impacts of two typical social learning mechanisms simultaneously, namely observational learning and word of mouth learning, on farmers’ adoption of residue-based biogas. Ø Furthermore, research on the mediating role of trust in the impacts of social learning on farmers’ technology adoption is still lacking. Objectives Ø To examine the impacts of observational learning and word-of- mouth learning from relatives, neighbors, cadres, cooperative members and technical instructors on farmers’ adoption of residue-based biogas. Ø To investigate whether and to what extent interpersonal trust could mediate the influences of these social learnings. Theoretical framework Observational Interpersonal learning trust Adoption Positive word- of residue- of-mouth based learning biogas Negative word- of-mouth Interpersonal Ø Observational learning from technical instructors positively and learning trust significantly influences the adoption behavior. Ø Interpersonal trust significantly and positively influences the Methodology & Data impact of observational learning on farmers’ adoption decision. (1) Binary Logistic model Ø Interpersonal trust significantly and positively moderates the 1 exp( y * ) P r ( y )  F ( y * )   i influence of positive word-of-mouth learning on the adoption i i 1  exp(  y * ) 1  exp( y * ) i i decision. Whether the farmer i adopts or not has a Bernoulli distribution Pr(y ). i Ø Significantly negative moderating role of interpersonal trust is Pr(y ) is assumed to be determined by factors through the logistic i found in the relationship between negative word-of-mouth cumulative density function, F, that maps the unbounded index  learning and the adoption. yi   0  1 X i   2 Ii  3 X i Ii   4 Z i  i  into the bounded probability space [0,1]: P r ( y i )  F ( y i ) . Conclusions Xi is a vector of key explanatory variables including observational Ø Farmers’ observational learning from technical instructors’ learning, positive and negative word-of-mouth learnings. Ii denotes adoption can help promote the diffusion of residue-based biogas. interpersonal trust variables. Xi Ii is the interaction term. Zi is the Ø With farmers’ trust in relatives, the technology dissemination vector of control variables. {β1,...β4} denote the corresponding set of effort through observational learning from relatives may be parameters. β0 is the intercept. μi is the error term. effective. (2) Data sources Ø When Farmers trust in others, efforts to diffuse residue-based Ø Data are from a survey of rural households in city, biogas via positive word-of-mouth learning are likely to be Jiangxia in Wuhan City, in Wuhan City, effective. Yet when farmers trust in others, diffusing residue- City and City in Hubei, China. based biogas via negative word-of-mouth learning is likely to Ø A multistage systematic random sampling procedure was applied fail. to determine the households surveyed. Ø Future empirical analyses can benefit from the use of time series Ø 913 observations are suitable for this study. data and nationalwide data.