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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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    1. Wolfert, Sjaak & Ge, Lan & Verdouw, Cor & Bogaardt, Marc-Jeroen, 2017. "Big Data in Smart Farming – A review," Agricultural Systems, Elsevier, vol. 153(C), pages 69-80.
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    4. Pivoto, Diesson & Barham, Bradford & Dabdab, Paulo & Zhang, Debin & Talamin, Edson, 2019. "Factors influencing the adoption of smart farming by Brazilian grain farmers," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 22(4), April.
    5. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
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    1. Piancharoenwong, Assanee & Badir, Yuosre F., 2024. "IoT smart farming adoption intention under climate change: The gain and loss perspective," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Julián F. Becerra-Encinales & Paloma Bernal-Hernandez & Jorge A. Beltrán-Giraldo & Alexandre P. Cooman & Luis H. Reyes & Juan C. Cruz, 2024. "Agricultural Extension for Adopting Technological Practices in Developing Countries: A Scoping Review of Barriers and Dimensions," Sustainability, MDPI, vol. 16(9), pages 1-20, April.
    3. Idowu, Abiodun Rasheed & Wachenheim, Cheryl & Hanson, Erik & Sickler, Alexandra, 2023. "The disposition of data from precision agricultural technologies: What do young agriculturalists think?," Technology in Society, Elsevier, vol. 75(C).

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