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Understanding technology acceptance in smart agriculture: A systematic review of empirical research in crop production

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  • Thomas, Rosemary J.
  • O'Hare, Gregory
  • Coyle, David

Abstract

Smart agriculture offers the potential to analyse agricultural data at a scale not previously possible. Researchers argue that the combination of rich data and intelligent decision support has the potential to improve productivity and profitability in agriculture, whilst also improving sustainability. We argue that achieving this potential requires not just on technological advancement, it also requires a detailed understanding of factors that impact technology acceptance in smart agriculture. Acceptance is necessary if technical advances are to translate into real-world impact. However, technology acceptance is complex and often poorly understood. This systematic review focuses on technology acceptance in prediction and decision support systems in crop production. Major databases were searched to identify papers that formally address technology acceptance and include detailed data. 16 papers met the inclusion criteria and were included in the final analysis. Common facilitators and barriers are identified, and papers are mapping against the Theoretical Framework of Acceptability. This analysis showed that constructs including perceived effectiveness are addressed frequently, but others such as opportunity costs and burden have received less attention. The findings suggest the necessity for greater application of formal methods and the need for standardized, domain-specific methods to support this assessment.

Suggested Citation

  • Thomas, Rosemary J. & O'Hare, Gregory & Coyle, David, 2023. "Understanding technology acceptance in smart agriculture: A systematic review of empirical research in crop production," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:tefoso:v:189:y:2023:i:c:s0040162523000598
    DOI: 10.1016/j.techfore.2023.122374
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    References listed on IDEAS

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    1. Shabir Ahmad Mir & T. Padma, 2020. "Integrated Technology Acceptance Model for the Evaluation of Agricultural Decision Support Systems," Journal of Global Information Technology Management, Taylor & Francis Journals, vol. 23(2), pages 138-164, April.
    2. Daniel H. Jarvis & Mark P. Wachowiak & Dan F. Walters & John M. Kovacs, 2017. "Adoption of Web-Based Spatial Tools by Agricultural Producers: Conversations with Seven Northeastern Ontario Farmers Using the GeoVisage Decision Support System," Agriculture, MDPI, vol. 7(8), pages 1-22, August.
    3. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    4. Jakku, E. & Thorburn, P.J., 2010. "A conceptual framework for guiding the participatory development of agricultural decision support systems," Agricultural Systems, Elsevier, vol. 103(9), pages 675-682, November.
    5. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
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    Cited by:

    1. Yeboah, Samuel, 2023. "Unlocking the Potential of Technological Innovations for Sustainable Agriculture in Developing Countries: Enhancing Resource Efficiency and Environmental Sustainability," MPRA Paper 118215, University Library of Munich, Germany, revised 26 Jul 2023.
    2. Yeboah, Samuel, 2023. "Unlocking the Potential of Technological Innovations for Sustainable Agriculture in Developing Countries: Enhancing Resource Efficiency and Environmental Sustainability," MPRA Paper 118216, University Library of Munich, Germany, revised 04 Aug 2023.
    3. Fikriye Yazar & Arzu Secer, 2023. "Why Farmers Prefer to Use Warehouse Receipt System in Turkey: An Integrated Model Approach," Sustainability, MDPI, vol. 15(21), pages 1-12, October.

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