Vol. 14 No. 4 (2025)
Full Research Articles

Farmers’ intention to use Agriculture 4.0 in marginal and non-marginal conditions

Maria Sabbagh
Department of Agricultural Sciences, University of Sassari, Sassari, Italy
Luciano Gutierrez
Department of Agricultural Sciences, University of Sassari, Sassari, Italy
Bio

Published 2025-06-26

Keywords

  • Agriculture 4.0,
  • Technology Adoption,
  • Marginal areas,
  • UTAUT2

How to Cite

Sabbagh, M., & Gutierrez, L. (2025). Farmers’ intention to use Agriculture 4.0 in marginal and non-marginal conditions. Bio-Based and Applied Economics, 14(4), 45–66. https://doi.org/10.36253/bae-17229

Abstract

Agriculture 4.0 enhances efficiency, sustainability, and yields while supporting climate change mitigation and adaptation. This study explores the adoption of Agriculture 4.0 among 131 durum wheat farmers in Sardinia, focusing on differences between marginal and non-marginal areas. Using an extended Unified Theory of Acceptance and Use of Technology (UTAUT2) framework, which includes perceived performance risk, the study identifies key factors influencing adoption. Facilitating conditions positively impact the adoption intentions, and perceived performance risk has a negative impact. However, performance expectancy, effort expectancy, social influence and price value don’t significantly affect adoption intentions. Policy recommendations include financial support, technical advice access, training programs, and awareness campaigns to promote adoption. These interventions aim to address barriers and foster equitable integration of Agriculture 4.0 technologies across diverse farming contexts.

References

  1. Abikari, M. (2024). Emotions, perceived risk and intentions to adopt emerging e-banking technology amongst educated young consumers. International Journal of Bank Marketing.
  2. Abiri, R., Rizan, N., Balasundram, S. K., Shahbazi, A. B., & Abdul-Hamid, H. (2023). Application of digital technologies for ensuring agricultural productivity. Heliyon.
  3. Abrol, D., & Ramani, S. (2014). Pro-poor innovation making, knowledge production, and technology implementation for rural areas. Cambridge University Press.
  4. Adrian, A. M., Norwood, S. H., & Mask, P. L. (2005). Producers’ perceptions and attitudes toward precision agriculture technologies. Computers and electronics in agriculture, 48(3), 256-271.
  5. AgendaDigitale. (2023). Agricoltura 4.0 in Italia: a che punto siamo? Retrieved 20 March 2024 from https://www.agendadigitale.eu/industry-4-0/agricoltura-4-0-in-italia-a-che-punto-siamo/
  6. Ahmadzai, H., Tutundjian, S., Dale, D., Brathwaite, R., Lidderr, P., Selvaraju, R., Malhotra, A., Boerger, V., & Elouafi, I. (2022). Marginal lands: potential for agricultural development, food security and poverty reduction.
  7. Ahmadzai, H., Tutundjian, S., & Elouafi, I. (2021). Policies for sustainable agriculture and livelihood in marginal lands: a review. Sustainability, 13(16), 8692.
  8. Ahmed, H., & Ahmed, M. (2023). Influencing factors on adoption of modern agricultural technology in developing economy countries. Developing Country Studies, 13(2), 1-15.
  9. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
  10. Akter, A., Mwalupaso, G. E., Wang, S., Jahan, M. S., & Geng, X. (2023). Towards climate action at farm-level: Distinguishing complements and substitutes among climate-smart agricultural practices (CSAPs) in flood prone areas. Climate Risk Management, 40, 100491.
  11. Alghatrifi, I., & Khalid, H. (2019). A systematic review of UTAUT and UTAUT2 as a baseline framework of information system research in adopting new technology: a case study of IPV6 adoption. 2019 6th International Conference on Research and Innovation in Information Systems (ICRIIS),
  12. Alhajj Ali, S., Tallou, A., Vivaldi, G. A., Camposeo, S., Ferrara, G., & Sanesi, G. (2024). Revitalization Potential of Marginal Areas for Sustainable Rural Development in the Puglia Region, Southern Italy: Part I: A Review. Agronomy, 14(3), 431. https://www.mdpi.com/2073-4395/14/3/431
  13. An, L., Han, Y., & Tong, L. (2016). Study on the factors of online shopping intention for fresh agricultural products based on UTAUT2. The 2nd Information technology and mechatronics engineering conference (ITOEC 2016),
  14. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
  15. Appelbaum, S. H., & Hare, A. (1996). Self‐efficacy as a mediator of goal setting and performance: some human resource applications. Journal of Managerial Psychology, 11(3), 33-47.
  16. Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019). Extending UTAUT2 toward acceptance of mobile learning in the context of higher education. Universal Access in the Information Society, 18, 659-673.
  17. Arata, L., & Menozzi, D. (2023). Farmers’ motivations and behaviour regarding the adoption of more sustainable agricultural practices and activities. Bio-based and Applied Economics, 12(1), 3-4.
  18. Araújo, S. O., Peres, R. S., Barata, J., Lidon, F., & Ramalho, J. C. (2021). Characterising the agriculture 4.0 landscape – emerging trends, challenges and opportunities. Agronomy, 11(4), 667.
  19. Arenas Gaitán, J., Peral Peral, B., & Ramón Jerónimo, M. (2015). Elderly and internet banking: An application of UTAUT2. Journal of Internet Banking and Commerce, 20 (1), 1-23.
  20. Balyan, S., Jangir, H., Tripathi, S. N., Tripathi, A., Jhang, T., & Pandey, P. (2024). Seeding a Sustainable Future: Navigating the Digital Horizon of Smart Agriculture. Sustainability, 16(2), 475.
  21. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986(23-28).
  22. Benfica, R., Chambers, J., Koo, J., Nin-Pratt, A., Falck-Zepeda, J., Stads, G.-J., & Arndt, C. (2023). Food system innovations and digital technologies to Foster productivity growth and rural transformation. Science and innovations for food systems transformation, 421.
  23. Benos, L., Makaritis, N., & Kolorizos, V. (2022). From precision agriculture to Agriculture 4.0: integrating ICT in farming. In Information and Communication Technologies for Agriculture – Theme III: Decision (pp. 79-93). Springer.
  24. Bollen, K. A., & Liang, J. (1988). Some properties of Hoelter’s CN. Sociological Methods & Research, 16(4), 492-503.
  25. Brick, K., & Visser, M. (2015). Risk preferences, technology adoption and insurance uptake: A framed experiment. In Journal of Economic Behavior & Organization (Vol. 118, pp. 383-396).
  26. Budhi, G., & Aminah, M. (2010). Swasembada kedelai: antara harapan dan kenyataan. Forum Penelitian Agro Ekonomi. https://epublikasi.pertanian.go.id/berkala/fae/article/view/1848
  27. Budhi, H., Santosa, T., & Setiyawati, S. (2022). Effect Of Perceived Risk on The Intention to Use Internet Banking by Implementing the Technology Acceptance Model. International Journal of Economics and Business Issues, 1(1), 61-71.
  28. Burland, A., & von Cossel, M. (2023). Towards Managing Biodiversity of European Marginal Agricultural Land for Biodiversity-Friendly Biomass Production. Agronomy, 13(6), 1651.
  29. Caffaro, F., & Cavallo, E. (2019). The effects of individual variables, farming system characteristics and perceived barriers on actual use of smart farming technologies: Evidence from the Piedmont region, northwestern Italy. Agriculture, 9(5), 111.
  30. Caffaro, F., Cremasco, M. M., Roccato, M., & Cavallo, E. (2020). Drivers of farmers’ intention to adopt technological innovations in Italy: The role of information sources, perceived usefulness, and perceived ease of use. Journal of rural studies, 76, 264-271.
  31. Caffaro, F., Roccato, M., Micheletti Cremasco, M., & Cavallo, E. (2019). An ergonomic approach to sustainable development: The role of information environment and social‐psychological variables in the adoption of agri‐environmental innovations. Sustainable Development, 27(6), 1049-1062.
  32. Chang, A. (2012). UTAUT and UTAUT 2: A review and agenda for future research. The Winners, 13(2), 10-114.
  33. Cialdini, R. B. (2009). Influence: Science and practice (Vol. 4). Pearson education Boston.
  34. Cogato, A., Meggio, F., De Antoni Migliorati, M., & Marinello, F. (2019). Extreme weather events in agriculture: A systematic review. Sustainability, 11(9), 2547.
  35. Cohen, J. (2013). Statistical power analysis for the behavioral sciences. routledge.
  36. Confagricoltura. (2024). Agricoltura 4.0: il credito d’imposta per investimenti nel 2024. Confagricoltura Rovigo. Retrieved 27 March 2024 from https://www.confagricolturaro.it/confagricoltura-informa/fiscale/redditi-imposte-e-tasse/agricoltura-40-il-credito-dimposta-per-investimenti-nel-2024/
  37. Cook, S., Jackson, E. L., Fisher, M. J., Baker, D., & Diepeveen, D. (2022). Embedding digital agriculture into sustainable Australian food systems: pathways and pitfalls to value creation. International Journal of Agricultural Sustainability, 20(3), 346-367.
  38. Coxhead, I., Shively, G., & Shuai, X. (2002). Development policies, resource constraints, and agricultural expansion on the Philippine land frontier. Environment and Development Economics, 7(2), 341-363.
  39. Cramb, R. A. (2000). Processes influencing the successful adoption of new technologies by smallholders.
  40. Csikós, N., & Tóth, G. (2023). Concepts of agricultural marginal lands and their utilisation: A review. Agricultural systems, 204, 103560.
  41. Da Silveira, F., Da Silva, S. L. C., Machado, F. M., Barbedo, J. G. A., & Amaral, F. G. (2023). Farmers’ perception of the barriers that hinder the implementation of agriculture 4.0. Agricultural systems, 208, 103656.
  42. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
  43. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. In Journal of applied social psychology (Vol. 22, pp. 1111-1132).
  44. De Rosa, M., & Chiappini, S. (2012). The adoption of agricultural extension policies in the Italian farms.
  45. Deißler, L., Mausch, K., Karanja, A., McMullin, S., & Grote, U. (2022). A complex web of interactions: Personality traits and aspirations in the context of smallholder agriculture. Bio-based and Applied Economics Journal, 12(1), 53-67.
  46. Deng, Z., Hong, Z., Ren, C., Zhang, W., & Xiang, F. (2018). What predicts patients’ adoption intention toward mHealth services in China: empirical study. JMIR mHealth and uHealth, 6(8), e9316.
  47. Dibbern, T., Romani, L. A. S., & Massruhá, S. M. F. S. (2024). Main drivers and barriers to the adoption of Digital Agriculture technologies. Smart Agricultural Technology, 8, 100459.
  48. Diekmann, M., & Theuvsen, L. (2019). Non-participants interest in CSA–Insights from Germany. Journal of rural studies, 69, 1-10.
  49. Douthwaite, B., Keatinge, J., & Park, J. (2001). Why promising technologies fail: the neglected role of user innovation during adoption. Research policy, 30(5), 819-836.
  50. Duong, T. T., Brewer, T., Luck, J., & Zander, K. (2019). A global review of farmers’ perceptions of agricultural risks and risk management strategies. Agriculture, 9(1), 10.
  51. Elsawah, S., Filatova, T., Jakeman, A. J., Kettner, A. J., Zellner, M. L., Athanasiadis, I. N., Hamilton, S. H., Axtell, R. L., Brown, D. G., & Gilligan, J. M. (2020). Eight grand challenges in socio-environmental systems modeling. Socio-Environmental Systems Modelling, 2, 16226.
  52. Ena, G. W. W., & Siewa, A. L. S. (2022). Factors Influencing the Behavioural Intention for Smart Farming in Sarawak, Malaysia. Journal of Agribusiness, 9(1), 37-56.
  53. ESG360. (2023). Agricoltura 4.0: cos’è, incentivi e tecnologie abilitanti. Retrieved 30 March 2024 from https://www.esg360.it/agrifood/agricoltura-4-0-cose-incentivi-e-tecnologie-abilitanti/
  54. Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. In International journal of human-computer studies (Vol. 59, pp. 451-474).
  55. Fischer, E. M., Sedláček, J., Hawkins, E., & Knutti, R. (2014). Models agree on forced response pattern of precipitation and temperature extremes. Geophysical Research Letters, 41(23), 8554-8562.
  56. Food, & Nations, A. O. o. t. U. (2017). The future of food and agriculture: Trends and challenges. Fao.
  57. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. In Journal of marketing research (Vol. 18, pp. 39-50).
  58. Fragomeli, R., Annunziata, A., & Punzo, G. (2024). Promoting the Transition towards Agriculture 4.0: A Systematic Literature Review on Drivers and Barriers. Sustainability, 16(6), 2425.
  59. Fraser-Baxter, S. (2024). Climate change key driver of extreme drought in water scarce Sicily and Sardinia. https://coilink.org/20.500.12592/1yrjrxf
  60. Fuentes-Peñailillo, F., Gutter, K., Vega, R., & Silva, G. C. (2024). Transformative technologies in digital agriculture: Leveraging Internet of Things, remote sensing, and artificial intelligence for smart crop management. Journal of Sensor and Actuator Networks, 13(4), 39.
  61. Gardezi, M., & Bronson, K. (2020). Examining the social and biophysical determinants of US Midwestern corn farmers’ adoption of precision agriculture. Precision Agriculture, 21(3), 549-568.
  62. Garson, G. (2015). Structural Equation Modeling (blue book series). In: Asheboro: Statistical Associates Publishing.
  63. Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the association for information systems, 4(1), 7.
  64. Girma, Y., Kuma, B., & Bedemo, A. (2023). Risk aversion and perception of farmers about endogenous risks: An empirical study for maize producers in Awi Zone, Amhara Region of Ethiopia. Journal of risk and financial management, 16(2), 87.
  65. Giua, C., Materia, V. C., & Camanzi, L. (2022). Smart farming technologies adoption: Which factors play a role in the digital transition? Technology in Society, 68, 101869.
  66. Güven, B., Baz, İ., Kocaoğlu, B., Toprak, E., Erol Barkana, D., & Soğutmaz Özdemir, B. (2023). Smart farming technologies for sustainable agriculture: From food to energy. In A sustainable green future: Perspectives on energy, economy, industry, cities and environment (pp. 481-506). Springer.
  67. Multivariate data analysis 6th Edition, New Jersey: Prentice Hall (2006).
  68. Harisudin, M., Riptanti, E. W., Setyowati, N., & Khomah, I. (2023). Determinants of the Internet of Things adoption by millennial farmers. AIMS Agriculture & Food, 8(2).
  69. Hasselwander, M., & Weiss, D. (2024). Super App Adoption Intention Based on Utaut2 with Perceived Risk. Available at SSRN 4784554.
  70. Heinzl, A., Buxmann, P., Wendt, O., & Weitzel, T. (2011). Theory-guided modeling and empiricism in information systems research. Springer Science & Business Media.
  71. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43, 115-135.
  72. Hidayat, B. A., Supartoyo, Y. H., Setiawan, S., Ragimun, R., & Salim, Z. (2024). Government infrastructure investment stimulation through booming natural resources: Evidence from a lower-middle-income country. PloS one, 19(5), e0301710.
  73. Hines, J. M., Hungerford, H. R., & Tomera, A. N. (1987). Analysis and synthesis of research on responsible environmental behavior: A meta-analysis. The Journal of environmental education, 18(2), 1-8.
  74. Hoelter, J. W. (1983). The analysis of covariance structures: Goodness-of-fit indices. Sociological Methods & Research, 11(3), 325-344.
  75. Hurlbert, M., Krishnaswamy, J., Johnson, F. X., Rodríguez-Morales, J. E., & Zommers, Z. (2019). Risk management and decision making in relation to sustainable development.
  76. Iacobucci, D. (2010). Structural equations modeling: Fit indices, sample size, and advanced topics. In Journal of consumer psychology (Vol. 20, pp. 90-98).
  77. Im, I., Kim, Y., & Han, H.-J. (2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information & management, 45, 1-9.
  78. Jacobs, C., Van Minnen, J., Junttila, V., Pirttioja, N., Smets, B., & Bonte, K. (2022). Climate Change Impacts on Biomass Production:(national Case Studies). European Topic Centre on Climate change adaptation and LULUCF (ETC-CA).
  79. Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks, 3, 150-164.
  80. Jouanjean, M.-A. (2013). Targeting infrastructure development to foster agricultural trade and market integration in developing countries: an analytical review. London: Overseas Development Institute, 1-26.
  81. Jussila, H., Leimgruber, W., & Majoral, R. (2019). Perceptions of marginality: Theoretical issues and regional perceptions of marginality in geographical space. Routledge.
  82. Kapoor, M., & Singh, H. (2023). Information Needs and Dissemination Among Farmers: A Step Towards Sustainability. International Management Review, 19, 71-199.
  83. Kendall, H., Clark, B., Li, W., Jin, S., Jones, G. D., Chen, J., Taylor, J., Li, Z., & Frewer, L. J. (2022). Precision agriculture technology adoption: a qualitative study of small-scale commercial “family farms” located in the North China Plain. Precision Agriculture, 1-33.
  84. Khan, F. U., Nouman, M., Negrut, L., Abban, J., Cismas, L. M., & Siddiqi, M. F. (2024). Constraints to agricultural finance in underdeveloped and developing countries: a systematic literature review. International Journal of Agricultural Sustainability, 22(1), 2329388.
  85. Kimhi, A., & Nachlieli, N. (2001). Intergenerational succession on Israeli family farms. Journal of Agricultural Economics, 52(2), 42-58.
  86. Kirk, N. A., & Cradock-Henry, N. A. (2022). Land Management Change as Adaptation to Climate and Other Stressors: A Systematic Review of Decision Contexts Using Values-Rules-Knowledge. Land, 11(6), 791. https://www.mdpi.com/2073-445X/11/6/791
  87. Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS-Wageningen journal of life sciences, 90, 100315.
  88. Kolady, D. E., Van der Sluis, E., Uddin, M. M., & Deutz, A. P. (2021). Determinants of adoption and adoption intensity of precision agriculture technologies: evidence from South Dakota. Precision Agriculture, 22, 689-710.
  89. Kutter, T., Tiemann, S., Siebert, R., & Fountas, S. (2011). The role of communication and co-operation in the adoption of precision farming. Precision Agriculture, 12, 2-17.
  90. Lal, R. (2004). Soil carbon sequestration impacts on global climate change and food security. Science, 304(5677), 1623-1627.
  91. LEAP. (2023). Educational poverty in Italy - Our First LEAP Policy Brief. Laboratory for Effective Anti-poverty Policies (LEAP), Bocconi University. Retrieved 4 April 2024 from https://leap.unibocconi.eu/newsevents/educational-poverty-italy-our-first-leap-policy-brief
  92. Li, L., Min, X., Guo, J., & Wu, F. (2024). The influence mechanism analysis on the farmers’ intention to adopt Internet of Things based on UTAUT-TOE model. Scientific reports, 14(1), 15016.
  93. Lobley, M., Baker, J. R., & Whitehead, I. (2010). Farm succession and retirement: some international comparisons. Journal of Agriculture, Food Systems, and Community Development, 1(1), 49-64.
  94. Long, T. B., Blok, V., & Coninx, I. (2016). Barriers to the adoption and diffusion of technological innovations for climate-smart agriculture in Europe: evidence from the Netherlands, France, Switzerland and Italy. Journal of Cleaner Production, 112, 9-21.
  95. Loo, M. K., Ramachandran, S., & Raja Yusof, R. N. (2023). Unleashing the potential: Enhancing technology adoption and innovation for micro, small and medium-sized enterprises (MSMEs). Cogent Economics & Finance, 11(2), 2267748.
  96. Macedo, I. M. (2017). Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Computers in Human Behavior, 75, 935-948.
  97. Maffezzoli, F., Ardolino, M., Bacchetti, A., Perona, M., & Renga, F. (2022). Agriculture 4.0: A systematic literature review on the paradigm, technologies and benefits. Futures, 142, 102998.
  98. Mana, A., Allouhi, A., Hamrani, A., Rahman, S., el Jamaoui, I., & Jayachandran, K. (2024). Sustainable AI-Based Production Agriculture: Exploring AI Applications and Implications in Agricultural Practices. Smart Agricultural Technology, 100416.
  99. Marra, M., Pannell, D. J., & Ghadim, A. A. (2003). The economics of risk, uncertainty and learning in the adoption of new agricultural technologies: where are we on the learning curve? In Agricultural Systems (Vol. 75, pp. 215-234).
  100. Masi, M., Di Pasquale, J., Vecchio, Y., & Capitanio, F. (2023). Precision farming: Barriers of variable rate technology adoption in Italy. Land, 12(5), 1084.
  101. Mazzucato, M., & Willetts, D. (2019). A mission-oriented UK industrial strategy.
  102. McCormick. (2023). Agricoltura 4.0: quali sono strumenti e vantaggi. McCormick Trattori (Argo Tractors S.p.A.). Retrieved 22 March 2024 from https://www.mccormick.it/agricoltura-4-0-quali-sono-strumenti-e-vantaggi/
  103. Medeiros, M., Ozturk, A., Hancer, M., Weinland, J., & Okumus, B. (2022). Understanding travel tracking mobile application usage: An integration of self determination theory and UTAUT2. Tourism Management Perspectives, 42, 100949.
  104. Mefleh, M., Conte, P., Fadda, C., Giunta, F., Piga, A., Hassoun, G., & Motzo, R. (2019). From ancient to old and modern durum wheat varieties: Interaction among cultivar traits, management, and technological quality. Journal of the Science of Food and Agriculture, 99(5), 2059-2067.
  105. Menozzi, D., Fioravanzi, M., & Donati, M. (2015). Farmer’s motivation to adopt sustainable agricultural practices. Bio-based and Applied Economics, 4(2), 125-147.
  106. Mercure, J.-F., Sharpe, S., Vinuales, J. E., Ives, M., Grubb, M., Lam, A., Drummond, P., Pollitt, H., Knobloch, F., & Nijsse, F. J. (2021). Risk-opportunity analysis for transformative policy design and appraisal. Global Environmental Change, 70, 102359.
  107. Mereu, V. (2010). Climate change impact on durum wheat in Sardinia.
  108. Mills, E. (2007). Synergisms between climate change mitigation and adaptation: an insurance perspective. Mitigation and Adaptation Strategies for Global Change, 12, 809-842.
  109. Moorthy, K., Chun T’ing, L., Ming, K. S., Ping, C. C., Ping, L. Y., Joe, L. Q., & Jie, W. Y. (2019). Behavioral intention to adopt digital library by the undergraduates. International Information & Library Review, 51(2), 128-144.
  110. Moriuchi, E. (2021). An empirical study on anthropomorphism and engagement with disembodied AIs and consumers’ re‐use behavior. Psychology & Marketing, 38(1), 21-42.
  111. Nhuong, B. H., & Truong, D. D. (2024). Factors affecting the adoption of high technology in vegetable production in Hanoi, Vietnam. Frontiers in Sustainable Food Systems, 8, 1345598.
  112. Osorio, C. P., Leucci, F., & Porrini, D. (2024). Analyzing the relationship between agricultural AI adoption and government-subsidized insurance. Agriculture, 14(10), 1804.
  113. Osservatori.net, O. D. I. (2023). Agricoltura 4.0: cos’è, vantaggi, tecnologie. Osservatori.net. Retrieved 24 March 2024 from https://blog.osservatori.net/agricoltura-4-0-cose-vantaggi-tecnologie
  114. Palau-Saumell, R., Forgas-Coll, S., Sánchez-García, J., & Robres, E. (2019). User acceptance of mobile apps for restaurants: An expanded and extended UTAUT-2. Sustainability, 11(4), 1210.
  115. Pannell, D. J., Marshall, G. R., Barr, N., Curtis, A., Vanclay, F., & Wilkinson, R. (2006). Understanding and promoting adoption of conservation practices by rural landholders. Australian journal of experimental agriculture, 46(11), 1407-1424.
  116. Paustian, M., & Theuvsen, L. (2017). Adoption of precision agriculture technologies by German crop farmers. Precision Agriculture, 18, 701-716.
  117. Pechlivani, E. M., Gkogkos, G., Giakoumoglou, N., Hadjigeorgiou, I., & Tzovaras, D. (2023). Towards Sustainable Farming: A Robust Decision Support System’s Architecture for Agriculture 4.0. 2023 24th International Conference on Digital Signal Processing (DSP),
  118. Peter, B. G., Messina, J. P., & Snapp, S. S. (2018). A multiscalar approach to mapping marginal agricultural land: smallholder agriculture in Malawi. Annals of the American Association of Geographers, 108(4), 989-1005.
  119. Raj, M., Gupta, S., Chamola, V., Elhence, A., Garg, T., Atiquzzaman, M., & Niyato, D. (2021). A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0. Journal of Network and Computer Applications, 187, 103107.
  120. Raji, E., Ijomah, T. I., & Eyieyien, O. G. (2024). Improving agricultural practices and productivity through extension services and innovative training programs. International Journal of Applied Research in Social Sciences, 6(7), 1297-1309.
  121. Rizzo, G., Migliore, G., Schifani, G., & Vecchio, R. (2024). Key factors influencing farmers’ adoption of sustainable innovations: a systematic literature review and research agenda. Organic Agriculture, 14(1), 57-84.
  122. Rogers, E. (2003). Diffusion of Innovations fifth Ed Free Press. New York. Rezvani, Z., Jansson. J. & Bodin.
  123. Rogers, E. M. (1962). Diffusion of innovations. In (Third Edit ed.): New York, Free Press of Glencoe [1962].
  124. Rondinelli, D. A. (1992). Location analysis and regional development: summing up and moving on. International Regional Science Review, 15(3), 325-340.
  125. Rose, D. C., & Chilvers, J. (2018). Agriculture 4.0: Broadening responsible innovation in an era of smart farming. Frontiers in Sustainable Food Systems, 2, 87.
  126. Ruddle, K. (1991). Integrated farming systems and future directions for Asian farming systems research and extension. Journal of the Asian Farming Systems Association, 1(1), 91-99.
  127. Ruzzante, S., Labarta, R., & Bilton, A. (2021). Adoption of agricultural technology in the developing world: A meta-analysis of the empirical literature. World development, 146, 105599.
  128. Sabbagh, M., & Gutierrez, L. (2022). Micro-irrigation technology adoption in the Bekaa Valley of Lebanon: A behavioural model. Sustainability, 14(13), 7685.
  129. Sabbagh, M., & Gutierrez, L. (2023). Farmers’ acceptance of a micro-irrigation system: A focus group study. Bio-based and Applied Economics.
  130. Saidakhmedovich, G. S., Uralovich, M. D., Saidakhmedovich, G. S., & Tishabayevna, R. M. (2024). Application of Digital Technologies for Ensuring Agricultural Productivity. British Journal of Global Ecology and Sustainable Development, 25, 6-20.
  131. Sallustio, L., Pettenella, D., Merlini, P., Romano, R., Salvati, L., Marchetti, M., & Corona, P. (2018). Assessing the economic marginality of agricultural lands in Italy to support land use planning. Land Use Policy, 76, 526-534.
  132. Schukat, S., & Heise, H. (2021). Towards an understanding of the behavioral intentions and actual use of smart products among German farmers. Sustainability, 13(12), 6666.
  133. Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2007). The constructive, destructive, and reconstructive power of social norms. Psychological science, 18(5), 429-434.
  134. Scoones, I., Thompson, J., & Cambers, J. (2009). Farmer first revisited: Innovation for agricultural research and development. Practical Action Pub.
  135. Shafi, U., Mumtaz, R., García-Nieto, J., Hassan, S. A., Zaidi, S. A. R., & Iqbal, N. (2019). Precision agriculture techniques and practices: From considerations to applications. Sensors, 19(17), 3796.
  136. Soddu, A., Deidda, R., Marrocu, M., Meloni, R., Paniconi, C., Ludwig, R., Sodde, M., Mascaro, G., & Perra, E. (2013). Climate variability and durum wheat adaptation using the AquaCrop model in southern Sardinia. Procedia Environmental Sciences, 19, 830-835.
  137. Sohn, S. (2024). Consumer perceived risk of using autonomous retail technology. Journal of Business Research, 171, 114389.
  138. Solaw, F. (2011). The state of the world’s land and water resources for food and agriculture. Rome, Italy.
  139. Stern, P. C., & Dietz, T. (2002). New tools for environmental protection: Education, information, and voluntary measures. National Academies Press.
  140. Straub, D., Boudreau, M.-C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the association for information systems, 13, 24.
  141. Stupina, A., Rozhkova, A., Olentsova, J., & Rozhkov, S. (2021). Digital technologies as a tool for improving the efficiency of the agricultural sector. IOP Conference Series: Earth and Environmental Science,
  142. Šumak, B., & Šorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre-and post-adopters. Computers in Human Behavior, 64, 602-620.
  143. Sureth, M., Kalkuhl, M., Edenhofer, O., & Rockström, J. (2023). A welfare economic approach to planetary boundaries. Jahrbücher für Nationalökonomie und Statistik, 243(5), 477-542.
  144. Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. International journal of information management, 57, 102269.
  145. Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. In MIS quarterly (pp. 561-570).
  146. Tey, Y. S., & Brindal, M. (2012). Factors influencing the adoption of precision agricultural technologies: a review for policy implications. Precision Agriculture, 13, 713-730.
  147. Thaler, R., & Sunstein, C. (2008). Nudge: Improving decisions about health, wealth and happiness. Amsterdam Law Forum; HeinOnline: Online,
  148. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS quarterly, 125-143.
  149. Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108(50), 20260-20264.
  150. Toral, S., Martínez-Torres, M., & Gonzalez-Rodriguez, M. (2018). Identification of the unique attributes of tourist destinations from online reviews. Journal of Travel Research, 57(7), 908-919.
  151. Trivelli, L., Apicella, A., Chiarello, F., Rana, R., Fantoni, G., & Tarabella, A. (2019). From precision agriculture to Industry 4.0: Unveiling technological connections in the agrifood sector. British food journal, 121(8), 1730-1743.
  152. Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and electronics in agriculture, 177, 105709.
  153. van Valkengoed, A. M., Abrahamse, W., & Steg, L. (2022). To select effective interventions for pro-environmental behaviour change, we need to consider determinants of behaviour. Nature human behaviour, 6(11), 1482-1492.
  154. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. In MIS quarterly (pp. 425-478).
  155. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
  156. Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the association for Information Systems, 17(5), 328-376.
  157. Wheaton, E., & Kulshreshtha, S. (2017). Environmental sustainability of agriculture stressed by changing extremes of drought and excess moisture: A conceptual review. Sustainability, 9(6), 970.
  158. Widodo, M., Irawan, M. I., & Sukmono, R. A. (2019). Extending UTAUT2 to explore digital wallet adoption in Indonesia. 2019 International Conference on Information and Communications Technology (ICOIACT),
  159. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big data in smart farming–a review. Agricultural Systems, 153, 69-80.
  160. Wreford, A., Ignaciuk, A., & Gruère, G. (2017). Overcoming barriers to the adoption of climate-friendly practices in agriculture.
  161. Wu, H.-X., Yan, S., Yu, L.-S., & Yan, G. (2023). Uncertainty aversion and farmers’ innovative seed adoption: Evidence from a field experiment in rural China. Journal of Integrative Agriculture, 22(6), 1928-1944.
  162. Yap, C. K., & Al-Mutairi, K. A. (2024). A Conceptual Model Relationship between Industry 4.0 – Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps. Foods, 13(1), 150.
  163. Yigezu, Y. A., Mugera, A., El-Shater, T., Aw-Hassan, A., Piggin, C., Haddad, A., Khalil, Y., & Loss, S. (2018). Enhancing adoption of agricultural technologies requiring high initial investment among smallholders. Technological Forecasting and Social Change, 134, 199-206.
  164. Yu, C.-S. (2012). Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of electronic commerce research, 13(2), 104.
  165. Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and electronics in agriculture, 170, 105256.