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Examining the Linkages of Technology Adoption Enablers in Context of Dairy Farming Using ISM-MICMAC Approach

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  • Kaushik, Hans
  • Rajwanshi, Rohit

Abstract

In the context of agribusiness, technology and innovation have led to major transformations in many countries. Precision dairy farming technologies enable cost optimization, quality control, waste reduction, achieving economies of scale, efficiency in dairy resource utilization, improvement in productivity, standardized processes, enhanced decision support system and overall farm management. Despite being an overall production-wise rich country, India’s dairy sector lacks in terms of yield per cattle, overall dairy farm output, effective herd management and lack of effective technology acceptance and implementation. With the help of NGT based outcome, this research is an attempt to showcase the enablers of technology adoption in dairy farming and how these enablers interact with each other in a hierarchical form using ISM methodology. Experience in the dairy business, competitive pressure and digital literacy were found as the most crucial and driving enablers. However, agreeableness and managerial interest were found as the most dependent enablers of technology adoption. The interpretations drawn from the model can help the decision makers, policy makers and farmers not only in India but can serve as the base for other nations dependent upon agriculture to understand the inter dependency among enablers and suggestions to plan and channel technology adoption by focusing upon critical ones.

Suggested Citation

  • Kaushik, Hans & Rajwanshi, Rohit, . "Examining the Linkages of Technology Adoption Enablers in Context of Dairy Farming Using ISM-MICMAC Approach," Research on World Agricultural Economy, Nan Yang Academy of Sciences Pte Ltd (NASS), vol. 4(4).
    • Handle: RePEc:ags:reowae:338876
    DOI: 10.22004/ag.econ.338876
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    References listed on IDEAS

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    Cited by:

    1. Vrinda Tikkha & Rohit Rajwanshi & Prachi Agarwal & Sanjeev Swami, 2025. "Modelling the key drivers of agro-based startups: an ISM-based study," Journal of Global Entrepreneurship Research, Springer;UNESCO Chair in Entrepreneurship, vol. 15(1), pages 1-17, December.
    2. Reitano, Matilde & Segovia, Michelle S. & Nayga, Rodolfo M., 2025. "A systematic review on the impact of Artificial Intelligence in the agri-food supply chain," Food Policy, Elsevier, vol. 137(C).

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