Social network factors affecting adoption of Mobile app by farmers

Abstract

Effect of social network on adoption of Mobile app was studied in Nashik and Sangli districts of Maharashtra, India by interviewing 800 grape growers during 2016-17. Individual characteristics like income, landholding, caste, area under grapes and number of smartphones were significant factors determining the adoption of mobile app. In social network factors, village adoption rate, membership, education, landholding and frequency of communication were significant determinants of adoption. Village adoption rate of the technology resembles network behavior. Therefore, both social network characteristics and social network behavior influences the adoption decision of farmers. It shows that adoption is not just mimicking by the farmers, but it is a social learning process. The results emanating from the study provide insights to extension agents to devise strategy for introduction of any technology/ICT in agriculture. The way out to implement the strategy is to use extension methods that focus on social learning like Farmers Field Schools; target the farmers having more education, landholdings and who frequently interact with the large number of farmers to facilitate and enhance the adoption of new technology/ICT.

Unraveling heterogeneity in farmer's adoption of mobile phone technologies: A systematic review

“…(Hannes & Macaitis, 2012;Ng et al, 2014) was utilized to define the key components of this review. (Kabbiri et al, 2018;Kacharo, Mvena, & Sife, 2018;Landmann, Lagerkvist and Otter, 2020 ;Nabhani et al, 2016;Sikundla, Mushunje, & Akinyemi, 2018;Verma & Sinha, 2018; Okoroji, Lees & Lucock 2021) (Beza et al, 2018;Michels, Bonke, and Musshoff, 2020;Molina-Maturano et al, 2021;Mwalukasa et al, 2018;Narine, Harder, & Roberts, 2019;Omar et al, 2021) (Abdullahi, Oladele, & Yusuf, 2019;Bonke et al, 2018;Folitse et al, 2018;Kabirigi et al, 2022;Krell et al, 2020) (Abdullahi, Oladele & Akinyemi, 2021;Folitse et al, 2018;Hoang & Drysdale, 2021;Kabirigi et al, 2022;Kenny & Regan, 2021;Lubua & Kyobe, 2019;Otene, Ezihe, & Torgenga, 2017) (Bonke et al, 2018;Krell et al, 2020;Michels, Bonke, & Musshoff, 2019;Michels, Bonke, & Musshoff, 2020;Molina-Maturano et al, 2021;Nikam, Kumar, & Kingsly, 2021;Owusu, Yankson, & Frimpong, 2017;Thar et al, 2021) (Abdullahi, Oladele, & Yusuf, 2019;Aricat & Ling, 2017;Islam & Gronlund, 2011;Kaske et al, 2017;…”

“…Similarly, the effect of education on MPT adoption is mixed. Education positively influenced MPT adoption in some studies (e.g., Mwalukasa, Mlozi, & Sanga, 2018;Nikam, Kumar, & Kingsly, 2021) and had no significant influence in other studies (Filippini, Marescotti, Demartini, & Gaviglio, 2020;Narine, Harder, & Roberts, 2019). Besides the inconclusiveness of studies on the effect of various factors on technology adoption among farmers, they are also inconsistent with the indicators and measurement of adoption -thus presenting a methodological challenge.…”

“…Positive (Nikam, Kumar, & Kingsly, 2021) (Mwalukasa et al, 2018;Nikam, Kumar, & Kingsly, 2021) (Animashaun et al, 2014;Bonke et al, 2018;Kabirigi et al, 2022;Kassem et al, 2020;Thar et al, 2020) (Kabirigi et al, 2022) Note: Intention and attitude were reported as latent variables predicting MPT adoption and an outcome measure (dependent variable). Likewise, mobile phone ownership and constraints were both descriptive and dependent variables.…”