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Antecedents of smart farming adoption to mitigate the digital divide – extended innovation diffusion model

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  • Dixit, Krishna
  • Aashish, Kumar
  • Kumar Dwivedi, Amit

Abstract

This study fulfils the need by identifying the factors affecting the adoption of smart farming to understand the cause of low adoption despite having continuous support from the different emerging economies. The extensive study of the past literature helped us to build a framework of the factors that affect the intention to use smart farming. Data from 597 respondents was analysed with Partial least square (PLS) - structural equation modelling (SEM) technique. The findings suggests that , Relative Advantage, Compatibility, Ease of use, Attitude, Agriculture Extention Communication effectiveness, and Government support positively influence the adoption of smart farming. At the same time, as we hypothesized, perceived risk negatively influences smart farming adoption. While Visibility and Result demonstrability do not influence smart farming adoption. In the case of Attitude, Relative advantage, Compatibility, Ease of Use, and Result demonstrability have a positive influence, and Compatibility and Visibility found to have no impact on Attitude. In addition, the independent variables explain the variance of 72.9%in the adoption of smart farming.

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  • Dixit, Krishna & Aashish, Kumar & Kumar Dwivedi, Amit, 2023. "Antecedents of smart farming adoption to mitigate the digital divide – extended innovation diffusion model," Technology in Society, Elsevier, vol. 75(C).
    • Handle: RePEc:eee:teinso:v:75:y:2023:i:c:s0160791x23001537
    DOI: 10.1016/j.techsoc.2023.102348
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    References listed on IDEAS

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