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

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

Technological Forecasting and Social Change, 2023, vol. 189, issue C

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.

Keywords: Technology acceptance; Systematic review; Smart agriculture; Crop farming; Prediction; Decision support (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:189:y:2023:i:c:s0040162523000598

DOI: 10.1016/j.techfore.2023.122374

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