IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i4p1879-d496460.html
   My bibliography  Save this article

A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy

Author

Listed:
  • Maurizio Canavari

    (Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy)

  • Marco Medici

    (Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy)

  • Rungsaran Wongprawmas

    (Department of Food and Drug, University of Parma, 43121 Parma, Italy)

  • Vilma Xhakollari

    (Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy)

  • Silvia Russo

    (Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy)

Abstract

Irrigated agriculture determines large blue water withdrawals, and it is considered a key intervention area to reach sustainable development objectives. Precision agriculture technologies have the potential to mitigate water resource depletion that often characterises conventional agricultural approaches. This study investigates the factors influencing farmers’ intentions to adopt variable rate irrigation (VRI) technology. The Technology Acceptance Model 3 (TAM-3) was employed as a theoretical framework to design a survey to identify the factors influencing farmers’ decision-making process when adopting VRI. Data were gathered through quantitative face-to-face interviews with a sample of 138 fruit and grapevine producers from the Northeast of Italy (Veneto, Emilia-Romagna, Trentino-Alto Adige, Friuli-Venezia Giulia). Data were analysed using partial least squares path modelling (PLS-PM). The results highlight that personal attitudes, such as perceived usefulness and subjective norm, positively influence the intention to adopt VRI. Additionally, the perceived ease of use positively affects intention, but it is moderated by subject experience.

Suggested Citation

  • Maurizio Canavari & Marco Medici & Rungsaran Wongprawmas & Vilma Xhakollari & Silvia Russo, 2021. "A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy," Sustainability, MDPI, vol. 13(4), pages 1-12, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:1879-:d:496460
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/4/1879/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/4/1879/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    2. 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.
    3. Athanasios T. Balafoutis & Stefanos Koundouras & Evangelos Anastasiou & Spyros Fountas & Konstantinos Arvanitis, 2017. "Life Cycle Assessment of Two Vineyards after the Application of Precision Viticulture Techniques: A Case Study," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    4. Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
    5. J. S. Famiglietti, 2014. "The global groundwater crisis," Nature Climate Change, Nature, vol. 4(11), pages 945-948, November.
    6. Louis Sears & Joseph Caparelli & Clouse Lee & Devon Pan & Gillian Strandberg & Linh Vuu & C. -Y. Cynthia Lin Lawell, 2018. "Jevons’ Paradox and Efficient Irrigation Technology," Sustainability, MDPI, vol. 10(5), pages 1-12, May.
    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. Veronika Hannus & Johannes Sauer, 2021. "Understanding Farmers’ Intention to Use a Sustainability Standard: The Role of Economic Rewards, Knowledge, and Ease of Use," Sustainability, MDPI, vol. 13(19), pages 1-21, September.
    2. Osrof, Hazem Yusuf & Tan, Cheng Ling & Angappa, Gunasekaran & Yeo, Sook Fern & Tan, Kim Hua, 2023. "Adoption of smart farming technologies in field operations: A systematic review and future research agenda," Technology in Society, Elsevier, vol. 75(C).

    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. Debora Bettiga & Lucio Lamberti & Emanuele Lettieri, 2020. "Individuals’ adoption of smart technologies for preventive health care: a structural equation modeling approach," Health Care Management Science, Springer, vol. 23(2), pages 203-214, June.
    2. John D'Ambra & Concepción S. Wilson & Shahriar Akter, 2013. "Application of the task-technology fit model to structure and evaluate the adoption of E-books by Academics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 48-64, January.
    3. Daniel Belanche & Marta Flavián & Alfredo Pérez-Rueda, 2020. "Mobile Apps Use and WOM in the Food Delivery Sector: The Role of Planned Behavior, Perceived Security and Customer Lifestyle Compatibility," Sustainability, MDPI, vol. 12(10), pages 1-21, May.
    4. Wang, Yu-Yin & Wang, Yi-Shun & Lin, Tung-Ching, 2018. "Developing and validating a technology upgrade model," International Journal of Information Management, Elsevier, vol. 38(1), pages 7-26.
    5. Yan, Min & Filieri, Raffaele & Raguseo, Elisabetta & Gorton, Matthew, 2021. "Mobile apps for healthy living: Factors influencing continuance intention for health apps," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    6. Schikofsky, Jan & Dannewald, Till & Kowald, Matthias, 2020. "Exploring motivational mechanisms behind the intention to adopt mobility as a service (MaaS): Insights from Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 296-312.
    7. Normalini Md Kassim & T. Ramayah, 2015. "Perceived Risk Factors Influence on Intention to Continue Using Internet Banking among Malaysians," Global Business Review, International Management Institute, vol. 16(3), pages 393-414, June.
    8. Claudio Vitari & Elisabetta Raguseo, 2016. "Big data value and financial performance: an empirical investigation [Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data]," Post-Print halshs-01923271, HAL.
    9. Yu Wang & Shanyong Wang & Jing Wang & Jiuchang Wei & Chenglin Wang, 2020. "An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model," Transportation, Springer, vol. 47(1), pages 397-415, February.
    10. Paul Juinn Bing Tan, 2013. "Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan," SAGE Open, , vol. 3(4), pages 21582440135, October.
    11. Peter Mantello & Manh-Tung Ho & Minh-Hoang Nguyen & Quan-Hoang Vuong, 2023. "Machines that feel: behavioral determinants of attitude towards affect recognition technology—upgrading technology acceptance theory with the mindsponge model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    12. Fox, Stephen & Groesser, Stefan N., 2016. "Reframing the relevance of research to practice," European Management Journal, Elsevier, vol. 34(5), pages 457-465.
    13. Mäntymäki, Matti & Salo, Jari, 2013. "Purchasing behavior in social virtual worlds: An examination of Habbo Hotel," International Journal of Information Management, Elsevier, vol. 33(2), pages 282-290.
    14. Fatima Zahra Barrane & Gahima Egide Karuranga & Diane Poulin, 2018. "Technology Adoption and Diffusion: A New Application of the UTAUT Model," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1-19, December.
    15. Chen, Shih-Chih & Hung, Chung-Wen, 2016. "Elucidating the factors influencing the acceptance of green products: An extension of theory of planned behavior," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 155-163.
    16. Joan Torrent-Sellens & Cristian Salazar-Concha & Pilar Ficapal-Cusí & Francesc Saigí-Rubió, 2021. "Using Digital Platforms to Promote Blood Donation: Motivational and Preliminary Evidence from Latin America and Spain," IJERPH, MDPI, vol. 18(8), pages 1-17, April.
    17. Garima Malik & A. Sajeevan Rao, 2019. "Extended expectation-confirmation model to predict continued usage of ODR/ride hailing apps: role of perceived value and self-efficacy," Information Technology & Tourism, Springer, vol. 21(4), pages 461-482, December.
    18. Wang, Guoqiang & Tan, Garry Wei-Han & Yuan, Yunpeng & Ooi, Keng-Boon & Dwivedi, Yogesh K., 2022. "Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    19. Riffat Ara Zannat Tama & Md Mahmudul Hoque & Ying Liu & Mohammad Jahangir Alam & Mark Yu, 2023. "An Application of Partial Least Squares Structural Equation Modeling (PLS-SEM) to Examining Farmers’ Behavioral Attitude and Intention towards Conservation Agriculture in Bangladesh," Agriculture, MDPI, vol. 13(2), pages 1-22, February.
    20. Scott, Stephanie & Hughes, Paul & Hodgkinson, Ian & Kraus, Sascha, 2019. "Technology adoption factors in the digitization of popular culture: Analyzing the online gambling market," Technological Forecasting and Social Change, Elsevier, vol. 148(C).

    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:gam:jsusta:v:13:y:2021:i:4:p:1879-:d:496460. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.