IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v308y2022i1d10.1007_s10479-020-03922-z.html
   My bibliography  Save this article

Disruptive technologies in agricultural operations: a systematic review of AI-driven AgriTech research

Author

Listed:
  • Konstantina Spanaki

    (Loughborough University)

  • Uthayasankar Sivarajah

    (University of Bradford)

  • Masoud Fakhimi

    (University of Surrey)

  • Stella Despoudi

    (University of Western Macedonia
    Aston University)

  • Zahir Irani

    (University of Bradford)

Abstract

The evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of agricultural technology (AgriTech) with applications of artificial intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations.

Suggested Citation

  • Konstantina Spanaki & Uthayasankar Sivarajah & Masoud Fakhimi & Stella Despoudi & Zahir Irani, 2022. "Disruptive technologies in agricultural operations: a systematic review of AI-driven AgriTech research," Annals of Operations Research, Springer, vol. 308(1), pages 491-524, January.
    • Handle: RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03922-z
    DOI: 10.1007/s10479-020-03922-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-020-03922-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-020-03922-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    2. Mikalef, Patrick & Pateli, Adamantia, 2017. "Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA," Journal of Business Research, Elsevier, vol. 70(C), pages 1-16.
    3. Elias G. Carayannis & Stelios Rozakis & Evangelos Grigoroudis, 2018. "Agri-science to agri-business: the technology transfer dimension," The Journal of Technology Transfer, Springer, vol. 43(4), pages 837-843, August.
    4. Konstantina Spanaki & Zeynep Gürgüç & Richard Adams & Catherine Mulligan, 2018. "Data supply chain (DSC): research synthesis and future directions," International Journal of Production Research, Taylor & Francis Journals, vol. 56(13), pages 4447-4466, July.
    5. Ilias O. Pappas & Patrick Mikalef & Michail N. Giannakos & John Krogstie & George Lekakos, 2018. "Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies," Information Systems and e-Business Management, Springer, vol. 16(3), pages 479-491, August.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Issa, Helmi & Jabbouri, Rachid & Palmer, Mark, 2022. "An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    2. Tarun Jain & Jishnu Hazra & T. C. E. Cheng, 2023. "Analysis of upstream pricing regulation and contract structure in an agriculture supply chain," Annals of Operations Research, Springer, vol. 320(1), pages 85-122, January.
    3. Shengxing Yang, 2022. "A systematic literature review on the disruptions of artificial intelligence within the business world: in terms of the evolution of competences [Une revue systématique de la littérature sur les bo," Post-Print hal-03694170, HAL.
    4. Sushil Gupta & Hossein Rikhtehgar Berenji & Manish Shukla & Nagesh N. Murthy, 2023. "Opportunities in farming research from an operations management perspective," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1577-1596, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Damminda Alahakoon & Rashmika Nawaratne & Yan Xu & Daswin Silva & Uthayasankar Sivarajah & Bhumika Gupta, 2023. "Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities," Information Systems Frontiers, Springer, vol. 25(1), pages 221-240, February.
    2. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    3. Mark Ryan & Josephina Antoniou & Laurence Brooks & Tilimbe Jiya & Kevin Macnish & Bernd Stahl, 2020. "The Ethical Balance of Using Smart Information Systems for Promoting the United Nations’ Sustainable Development Goals," Sustainability, MDPI, vol. 12(12), pages 1-22, June.
    4. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    5. Rajesh Chidananda Reddy & Biplab Bhattacharjee & Debasisha Mishra & Anandadeep Mandal, 2022. "A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy," Information Systems and e-Business Management, Springer, vol. 20(1), pages 223-255, March.
    6. Sheng, Margaret L. & Saide, Saide, 2021. "Supply chain survivability in crisis times through a viable system perspective: Big data, knowledge ambidexterity, and the mediating role of virtual enterprise," Journal of Business Research, Elsevier, vol. 137(C), pages 567-578.
    7. Ahmad Ibrahim Aljumah & Mohammed T. Nuseir & Md. Mahmudul Alam, 2021. "Traditional marketing analytics, big data analytics and big data system quality and the success of new product development," Post-Print hal-03538161, HAL.
    8. Li, Lei & Lin, Jiabao & Ouyang, Ye & Luo, Xin (Robert), 2022. "Evaluating the impact of big data analytics usage on the decision-making quality of organizations," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    9. Sebastiano Cupertino & Gianluca Vitale & Angelo Riccaboni, 2018. "L?impatto dei Big Data sulle attivit? di pianificazione & controllo aziendali: In caso di studio di una PMI agricola Italiana," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(3), pages 59-86.
    10. Meadows, Maureen & Merendino, Alessandro & Dibb, Sally & Garcia-Perez, Alexeis & Hinton, Matthew & Papagiannidis, Savvas & Pappas, Ilias & Wang, Huamao, 2022. "Tension in the data environment: How organisations can meet the challenge," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    11. H. Kava & K. Spanaki & T. Papadopoulos & S. Despoudi & O. Rodriguez Espindola & M. Fakhimi, 2024. "Data analytics diffusion in the UK renewable energy sector: an innovation perspective," Post-Print hal-04478933, HAL.
    12. Kazancoglu, Yigit & Sagnak, Muhittin & Mangla, Sachin Kumar & Sezer, Muruvvet Deniz & Pala, Melisa Ozbiltekin, 2021. "A fuzzy based hybrid decision framework to circularity in dairy supply chains through big data solutions," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    13. Ciampi, Francesco & Demi, Stefano & Magrini, Alessandro & Marzi, Giacomo & Papa, Armando, 2021. "Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation," Journal of Business Research, Elsevier, vol. 123(C), pages 1-13.
    14. Aljumah, Ahmad Ibrahim & Nuseir, Mohammed T. & Alam, Md. Mahmudul, 2021. "Organizational Performance and Capabilities to Analyze Big Data: Do the Ambidexterity and Business Value of Big Data Analytics Matter?," OSF Preprints an8er, Center for Open Science.
    15. Mikalef, Patrick & Boura, Maria & Lekakos, George & Krogstie, John, 2019. "Big data analytics and firm performance: Findings from a mixed-method approach," Journal of Business Research, Elsevier, vol. 98(C), pages 261-276.
    16. Wamba, Samuel Fosso & Dubey, Rameshwar & Gunasekaran, Angappa & Akter, Shahriar, 2020. "The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism," International Journal of Production Economics, Elsevier, vol. 222(C).
    17. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    18. Al-Baraa Abdulrahman Al-Mekhlafi & Ahmad Shahrul Nizam Isha & Nicholas Chileshe & Mohammed Abdulrab & Anwar Ameen Hezam Saeed & Ahmed Farouk Kineber, 2021. "Modelling the Relationship between the Nature of Work Factors and Driving Performance Mediating by Role of Fatigue," IJERPH, MDPI, vol. 18(13), pages 1-17, June.
    19. Hrosul, Viktoriia & Kruhlova, Olena & Kolesnyk, Alina, 2023. "Digitalization of the agricultural sector: the impact of ICT on the development of enterprises in Ukraine," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 9(4), December.
    20. FeCheng Ma & Farhan Khan & Kashif Ullah Khan & Si XiangYun, 2021. "Investigating the Impact of Information Technology, Absorptive Capacity, and Dynamic Capabilities on Firm Performance: An Empirical Study," SAGE Open, , vol. 11(4), pages 21582440211, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03922-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.