Social network factors affecting adoption of Mobile app by farmers

VINAYAK NIKAM; SHIV KUMAR; KINGSLY I M

doi:10.56093/ijas.v91i2.111574

Authors

VINAYAK NIKAM ICAR-National Institute of Agriculture Economics and Policy Research, New Delhi 110 012, India
SHIV KUMAR ICAR-National Institute of Agriculture Economics and Policy Research, New Delhi 110 012, India
KINGSLY I M ICAR-National Institute of Agriculture Economics and Policy Research, New Delhi 110 012, India

https://doi.org/10.56093/ijas.v91i2.111574

Keywords:

Adoption, Mobile app, Social network characteristics, Social network behavior

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.

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References

Ali J. 2011. Adoption of mass media information for decision-making among vegetable growers in Uttar Pradesh. Indian Journal of Agricultural Economics 66(2): 241–54.

Bandiera O and Rasul I. 2006. Social networks and technology adoption in northern Mozambique. Economic Journal 116(514): 869–902. DOI: https://doi.org/10.1111/j.1468-0297.2006.01115.x

Carlsson B and Stankiewicz R. 1991. On the nature, function and composition of technological systems. Journal of Evolutionary Economics 1: 93–118. DOI: https://doi.org/10.1007/BF01224915

Case A. 1992. Neighborhood influence and technological change. Regional Science and Urban Economics 22: 491– 508. DOI: https://doi.org/10.1016/0166-0462(92)90041-X

Centola D. 2010. The spread of behavior in an online social network experiment. Science 329: 1194–97. DOI: https://doi.org/10.1126/science.1185231

Conley T and Udry C. 2010. Learning about a new technology: Pineapple in Ghana, American Economic Review 100: 35– 69. DOI: https://doi.org/10.1257/aer.100.1.35

Das B. 2014. ICTs adoption for accessing agricultural information: evidence from Indian agriculture. Agricultural Economics Research Review 27(2): 199–208. DOI: 10.5958/0974-0279.2014.00024.X DOI: https://doi.org/10.5958/0974-0279.2014.00024.X

Doss C R. 2006. Analyzing technology adoption using microstudies: limitations, challenges, and opportunities for improvement. Agricultural Economics 34(3): 207–19. DOI: https://doi.org/10.1111/j.1574-0864.2006.00119.x

Granovetter M S. 1973. The strength of weak ties. American Journal of Sociology 1360–80. DOI: https://doi.org/10.1086/225469

Maertens A and Barrett C B. 2011. Measuring social networks’ effects on agricultural technology adoption. American Journal of Agricultural Economics Papers and Proceedings, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2024678 or http:// dx.doi.org/10.2139/ssrn.2024678. DOI: https://doi.org/10.2139/ssrn.2024678

Magnan N, Spielman D J, Lybbert T J and Gulati K. 2013. Leveling with friends: social networks and Indian farmers’ demand for agricultural custom hire services.” IFPRI Discussion Paper 01302 (Washington, DC: IFPRI). DOI: https://doi.org/10.2139/ssrn.2373213

Matuschke I and Matin Q. 2009. The impact of social networks on hybrid seed adoption in India. Agricultural Economics 40: 493–505. DOI: https://doi.org/10.1111/j.1574-0862.2009.00393.x

Mittal S and Mehar M. 2015. Socio-economic factors affecting adoption of modern information and communication technology by farmers in India: analysis using Multivariate Probit Model, The Journal of Agricultural Education and Extension, DOI: 10.1080/1389224X.2014.997255. DOI: https://doi.org/10.1080/1389224X.2014.997255

Muange E and Schwarze S. 2014. Social networks and the adoption of agricultural innovations: The case of improved cereal cultivars in Central Tanzania, Socioeconomics Discussion Paper Series Number 18. [Socioeconomics Discussion Paper Series]

Ramirez A. 2013. Influence of social network on agriculture technology adoption. 9th conference on social network on agriculture technology. Procedia-social and behavioral science 79: 101–16. DOI: https://doi.org/10.1016/j.sbspro.2013.05.059

Rogers E M. 1995. Diffusion of Innovations. Free Press.

Todo Y, Matous P and Mojo D. 2015. Effects of social network structure on the diffusion and adoption of agricultural technology: Evidence from rural Ethiopia. Waseda Institute of Political Economy Waseda University Tokyo, Japan. WINPEC Working Paper Series No. E1505. DOI: https://doi.org/10.2139/ssrn.2447208

Ward P S and Pede V O. 2014. Capturing social network effects in technology adoption: the spatial diffusion of hybrid rice in Bangladesh. Australian Journal of Agricultural and Resource Economics 59(2): 225–41. DOI.org/10.1111/1467-8489.12058. DOI: https://doi.org/10.1111/1467-8489.12058