News Update on Technology Adoption : Nov 2021

Barriers to Technology Adoption and Development

We propose a theory of economic development in which technology adoption and barriers to such adoptions are the focus. The size of these barriers differs across countries and time. The larger these barriers, the greater the investment a firm must make to adopt a more advanced technology. The model is calibrated to the U.S. balanced growth observations and the postwar Japanese development miracle. For this calibrated structure we find that the disparity in technology adoption barriers needed to account for the huge observed income disparity across countries is not implausibly large.[1]

Microeconomics of Technology Adoption

Differences in technology levels across countries account for a large component of the differences in wages and per-capita GDP across countries worldwide. This article reviews micro studies of the adoption of new technologies and the use of inputs complementary with new technologies to shed light on the barriers to technology diffusion in low-income countries. Among the factors examined affecting decisions pertaining to technology choice and input allocations are the financial and nonfinancial returns to adoption, one’s own learning and social learning, technological externalities, scale economies, schooling, credit constraints, risk and incomplete insurance, and departures from behavioral rules implied by simple models of rationality.[2]

Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs

The process of information technology adoption and use is critical to deriving the benefits of information technology. Yet from a conceptual stand-point, few empirical studies have made a distinction between individuals’ pre-adoption and post-adoption (continued use) beliefs and attitudes. This distinction is crucial in understanding and managing this process over time. The current study combines innovation diffusion and attitude theories in a theoretical framework to examine differences in pre-adoption and post-adoption beliefs and attitudes. The examination of Windows technology in a single organization indicates that users and potential adopters of information technology differ on their determinants of behavioral intention, attitude, and subjective norm. Potential adopter intention to adopt is solely determined by normative pressures, whereas user intention is solely determined by attitude. In addition, potential adopters base their attitude on a richer set of innovation characteristics than users. Whereas pre-adoption attitude is based on perceptions of usefulness, ease-of-use, result demonstrability, visibility, and trialability, post-adoption attitude is only based on instrumentality beliefs of usefulness and perceptions of image enhancements.[3]

Determinants of Climate Smart Agriculture Technology Adoption in the Drought Prone Districts of Malawi using a Multivariate Probit Analysis

Climate variability is one of the limiting factors to increasing per capita food production for most smallholder farmers in Africa. The adoption and diffusion of climate smart agriculture technologies, as a way to tackle this barrier, has become an important issue in the development policy agenda for sub-Saharan Africa. This paper examines the adoption decisions for climate smart agriculture technologies using cross sectional household data, collected in 2014 from 619 farm households, in 2 districts of southern Malawi. In contrast to other studies that analyse technology adoption decisions separately, we analyse all four adoption decisions simultaneously using the multivariate probit method. This not only improves the precision of the estimation results and provides consistent standard errors of the estimates, but also enables us to analyse the interrelations between the four adoption decisions. This study shows how the estimation results, and particularly the estimated correlation coefficients, can be utilized to gain a deep insight into the interrelations between the different adoption decisions. The study reveals that gender, age, location, farmer type, level of education, livelihood status/ off-farm participation, land size and source/ownership, household income, household expenditure, anticipated weather pattern, climate variability knowledge/signs, access to credit, all influence the adoption decision of Climate Smart Technologies either positively or negatively.[4]

Technology Adoption in Broiler Farming-A Methodical Study among the Broiler Farmers of Sonitpur District of Assam

The study undertaken is an attempt to investigate and analyse the level of technology adoption across different size groups of broiler farms in Sonitpur district of Assam during 2011-12. The study was conducted with a sample of 100 numbers of broiler farms using specially designed pre-tested schedules and questionnaires through personal interview with the respondent farmers. Sampling design followed for the study was stratified random sampling design. The results of the study reveal that the entire sample followed scientific rearing and management practices right from housing and feeding to utilization of equipments to medication and vaccination, with some deviation from the recommendations. However, large sized farms were more technology oriented than the small farms. In terms of adoption of recommended stocking density, vaccination, utilization of equipments etc. the level of adoption is satisfactory for the entire sample, while in regard to housing, utilization of litter and lime, feeding and nutrition, large sized farms were close to recommendation than the smaller farms. Financial self-sufficiency, education, exposure to the outer world, decision making capacity etc. were the factors that determined the level of adoption of recommended technologies by the broiler farmers. Lack of proper training and awareness along with poor financial condition stood as a hinder for the small sized farms in adopting scientific rearing practices while large sized farms with sound financial condition and good awareness adopted scientific rearing practices as per recommendations.[5]


[1] Parente, S.L. and Prescott, E.C., 1994. Barriers to technology adoption and development. Journal of political Economy, 102(2), pp.298-321.

[2] Foster, A.D. and Rosenzweig, M.R., 2010. Microeconomics of technology adoption. Annu. Rev. Econ., 2(1), pp.395-424.

[3] Karahanna, E., Straub, D.W. and Chervany, N.L., 1999. Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS quarterly, pp.183-213.

[4] Maguza-Tembo, F., Edriss, A.K. and Mangisoni, J., 2017. Determinants of climate smart agriculture technology adoption in the drought prone districts of Malawi using a multivariate probit analysis. Asian Journal of Agricultural Extension, Economics & Sociology, pp.1-12.

[5] Borah, M. and Halim, R.A., 2017. Technology Adoption in Broiler Farming-A Methodical Study among the Broiler Farmers of Sonitpur District of Assam. Asian Journal of Agricultural Extension, Economics & Sociology, pp.1-8.

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