This article explores the application of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework in the context of integrating self-driving tractors into agricultural practices. With a focus on understanding the factors influencing the acceptance and adoption of this transformative technology, we delve into the implications for farmers, industry stakeholders, and the future of sustainable agriculture and rural tourism.

Abidine, A. Z., BC, H., SK, U., & DJ, H. (2022). Autoguidance system operated at high speed causes almost no tomato damage (Hungarian). Semantic Scholar.

Abraham., H., Reimer.B., Fitzgerald.C., et al. (2018). Consumer Interist in Automation: Change over One Year. Available online: https://www.researchgate.net/publication/322400007_Consumer_Interest_in_Automation_Change_over_One_Year), pp.50-67 (accessed on 2 June 2023).

Andorkó, I. (2016). Legal sc-fi or the potential impact of technological developments in motoring on jurisprudence (Hungarian). Available online: http://jogaszvilag.hu/rovatok/szakma/jogi-sci-fi-avagy-az-utozas-technologiaifejlodesenek-lehetseges-hatasai-a-jogtudom (accessed on 2 June 2023).

Bagozzi, R. (2007). The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift. Journal of the Association for Information Systems, 8(4), 244–254. https://doi.org/10.17705/1jais.00122

Kaur, B., Mansi, Dimri, S., et al. (2023). Insights into the harvesting tools and equipment’s for horticultural crops: From then to now. Journal of Agriculture and Food Research, 14, 100814. https://doi.org/10.1016/j.jafr.2023.100814

BCG. (2019). Boston Consulting Group, Survey Industry 4.0. Available online: https://www.bcgperspectives.com/content/articles/engineered_products_project_business_i ndustry_40_future_productivity_growth_manufacturing_industries/ (accessed on 2 June 2023).

Bell, A. (2021). Quotes (Hungarian). Available online: https://www.citatum.hu/szerzo/Alice_Bell (accessed on 2 June 2023).

Berecz, A. (2018). Proposal for a classification of e-learning models (Hungarian). Journal of Applied Multimedia, XII(4), 55-75.

Bernardi, A., & Inamasau.R.Y. (2014). Adoption of precision agriculture in Brazil. Adoption factors (Portuguese). Available online: https://www.alice.cnptia.embrapa.br/alice/handle/doc/1003682 (accessed on 2 June 2023).

Bitkom V, & Zvei. (2015). Industry 4.0 implementation strategy—Industry 4.0 platform results report (German). Available online: https://www.plattform-i40.de/IP/Redaktion/DE/Downloads/Publikation/umsetzungsstrategie-2015.html (accessed on 2 June 2023).

Blanchet, M.-R. T. (2018). The Industry 4.0 Transition Ouantified-How the fourth industrial revolution is reshuffling the economic, social and industrial model, Roland Berger GmbH, Munich, Germany. Available online: https://www.rolandberger.com/en/Publications/ The-Industrie-4.0-transition-quantified.html (accessed on 2 June 2023).

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

Bussiness. (2017). Bussiness Intelligence and Strategy Research. Available online: https://bisresearch.com/industry-report/precision-irrigation-market.html (accessed on 2 June 2023).

Dhanaraju, M., Chenniappan, P., Ramalingam, K., et al. (2022). Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agriculture, 12(10), 1745. https://doi.org/10.3390/agriculture12101745

Digitaleurope. (2016). Digitaleurope and the EC’s skills strategy 2016. Available online: http://www.digitaleurope.org/Bring2mind/DMX/Download.aspx?Command=Core_Downl oad&EntryId=1089&language=en-US&PortalId=0&TabId=35 (accessed on 2 June 2023).

Dixon, G., Hart, P. S., Clarke, C., et al. (2018). What drives support for self-driving car technology in the United States? Journal of Risk Research, 23(3), 275–287. https://doi.org/10.1080/13669877.2018.1517384

Dwivedi, Y. K., Rana, N. P., Janssen, M., et al. (2017). An empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34(2), 211–230. https://doi.org/10.1016/j.giq.2017.03.001

Fountas, S., FK, V. E., Gaitain-Chremaschi, & C., K. (2018). Can presicion agriculture increase the profitability and sustainability of the production of potatoes and olives. Available online: https://www.mdpi.com/2071-1050/9/10/1863 (accessed on 2 June 2023).

Gál T., Nagy L., Dávid L., et al. (2013). Technology planning system as a decision support tool for dairy farms in Hungary. Acta Polytechnica Hungarica, 10(8), 231-244. Available online: http://acta.uni-obuda.hu//Gal_Nagy_David_Vasa_Balogh_46.pdf (accessed on 2 June 2023).

GÉPmax. (2021). Robotic machines and autonomous devices in agriculture (Hungarian). Robotic machines and autonomous devices in agriculture. Available online: https://gepmax.hu/lapozhato/2021-es-kiadvanyaink/ (accessed on 30 December 2023).

Ghobadpour, A., Monsalve, G., Cardenas, A., & Mousazadeh, H. (2022). Off-Road Electric Vehicles and Autonomous Robots in Agricultural Sector: Trends, Challenges, and Opportunities. Vehicles, 4(3), 843–864. https://doi.org/10.3390/vehicles4030047

Golbabaei, F., Yigitcanlar, T., Paz, A., & Bunker, J. (2020). Individual Predictors of Autonomous Vehicle Public Acceptance and Intention to Use: A Systematic Review of the Literature. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 106. https://doi.org/10.3390/joitmc6040106

GTAI. (2018). Industrie 4.0 Germany Market Report and Outlook. Available online: https://www.gtai.de/resource/blob/829986/e5c70ab44e4a3752251386bc32205cc2/FDI_Report_2018_GTAI.pdf (accessed on 2 June 2023).

INTA. (2018). Competitiveness and efficiency-Tecnologia precisa INTA reports Ano XII#135 (Spanish). Available online: https://www.argentina.gob.ar/inta (accessed on 2 June 2023).

Kagermann, H., W., W., & HelbringJ. (2013a). Securing the Future of German Manufacturing Industry: Recommendations for Implementing the Strategic Initiative Industrie 4.0. Final report of the Industrie 4.0 Forschungsunion im Stifterverband für die Deutsche Wirtschaft e.V., Berlin.

Kagermann, H., Wolf-Dieter, & Lukas. (2011b). Industry 4.0: On the way to the 4th Industrial Revolution with the Internet of Things (German). VDI Nachrichten, 13, 2.

Karunathilake, E. M. B. M., Le, A. T., Heo, S., et al. (2023). The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture. Agriculture, 13(8), 1593. https://doi.org/10.3390/agriculture13081593

Kemény, G., Lámfalusi, I., & Molnár, A. (2017). Comparative study of precision arable crop production=Comporative study of precision arable crop production (Hungarian). Agrárgazdasági könyvek Agrárgazdasági Kutató Intézet, Budapest.

Lukovics, M., Udvari, B., Zuti, B., & Kézy, B. (2018). Self-driving cars and responsible innovation (Hungarian). Közgazdasági Szemle, 65(9), 949–974. https://doi.org/10.18414/ksz.2018.9.949

Kohli, P., & Chadha, A. (2019). Enabling Pedestrian Safety Using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash. Advances in Information and Communication, 261–279. https://doi.org/10.1007/978-3-030-12388-8_19

Koleva, N. (2019). An Empirical Study on Human Resoureces’ Attitude Towards Manufacturing Digitalization. In: Proceedings of the 2019 International Conference on Creative Business for Smart and Sustainable Growth (CREBUS).

Kuhn, T. (1984). The structure of scientific revolutions (Hungarian). Gondolat kiadó.

Lagnelöv, O., Larsson, G., Larsolle, A., & Hansson, P.-A. (2021). Life Cycle Assessment of Autonomous Electric Field Tractors in Swedish Agriculture. Sustainability, 13(20), 11285. https://doi.org/10.3390/su132011285

Lowenberg‐DeBoer, J., Behrendt, K., Ehlers, M., et al. (2021). Lessons to be learned in adoption of autonomous equipment for field crops. Applied Economic Perspectives and Policy, 44(2), 848–864. https://doi.org/10.1002/aepp.13177

Magda, S., Marsalek, S., & Magda, R. (2017). Skills of the agricultural workforce and future needs (Hungarian). Available online: http://real-j.mtak.hu/20493/3/gazd_2019_63_3_.pdf (accessed on 2 June 2023).

Mérő, L. (2016). Human mathematics (Hungarian). Tericium Kiadó.

Mohd, J., Abi, H., Ibrahim, H. K., & Rajiv, S. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15–30. https://doi.org/10.1016/j.aac.2022.10.001

Mohd, J., Abid, H., Ravi, P. S., & Rajiv, S. (2022). Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks, 3, 150–164. https://doi.org/10.1016/j.ijin.2022.09.004

Nábelek, F. (2017b). Potential labour market effects of automation in Hungary, 2012-2016 (Hungarian). MKIK Institute of Economic and Business Research GVI.

Nábelek, F., Sturcz, A., & Tóth, I. J. (2016a). The labour market effects of automation. Estimating the exposure of municipal labour markets to automation (Hungarian). MKIK Gazdaság-és Vállalkozáskutató Intézet GVI.

Nastjuk, I., Herrenkind, B., Marrone, M., et al. (2020). What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user’s perspective. Technological Forecasting and Social Change, 161, 120319. https://doi.org/10.1016/j.techfore.2020.120319

Nath, D. (2023). Smart Farming: Automation and Robotics in Agriculture. In: Recent Trends in Agriculture. Integrated Publications.

Niculescu, A. I., Dix, A., & Yeo, K. H. (2017). Are You Ready for a Drive? Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. https://doi.org/10.1145/3027063.3053182

Nordhoff, S., Louw, T., Innamaa, S., et al. (2020). Using the UTAUT2 model to explain public acceptance of conditionally automated (L3) cars: A questionnaire study among 9,118 car drivers from eight European countries. Transportation Research Part F: Traffic Psychology and Behaviour, 74, 280–297. https://doi.org/10.1016/j.trf.2020.07.015

Nordhoff, S., Madigan, R., Van Arem, B., et al. (2020). Interrelationships among predictors of automated vehicle acceptance: a structural equation modelling approach. Theoretical Issues in Ergonomics Science, 1–26. https://doi.org/10.1080/1463922x.2020.1814446

NTP. (2017). Industry 4.0 National Technology Platform (Hungarian). Available online: https://www.i40platform.hu/sites/Industrie%204.0_Definition.pdf (accessed on 2 June 2023).

Nyírő, N. I. (2011). Adoption and diffusion of media technology innovations (Hungarian) [PhD thesis]. Corvinus University of Budapest Doctoral School of Business Administration.

Obermayer, N., Csizmadia, T. H., & Kigyós, T. A. (2021). Analysis of managerial motivations and barriers to the implementation of Industry 4.0 based on the opinions of domestic CEOs. Available online: https://www.researchgate.net/publication/349160381_Az_Ipar_40_implementacioval_kapcsolatos_vezetoi_motivaciok_es_akadalyozo_tenyezok_elemzese_hazai_vallalatvezetok_velemenye_alapjan (accessed on 2 June 2023).

OECD. (2019). AI Principles Overview. Available online: https://oecd.ai/en/ai-principles (accessed on 20 March 2024)

Paunov, C., & Satorra, S. (2019). How are digital technologies changing innovation? (2019). OECD Science, Technology and Industry Policy Papers. Available online: https://doi.org/10.1787/67bbcafe-en (accessed on 2 June 2023).

Popp, J. E. (2018). Outlook of Precision Farming in Hungary. International Journal of Engineering and Management Sciences, 3(1), 133–147. https://doi.org/10.21791/ijems.2018.1.15

Quentin, A., Carmon, Z., Wertenbroch, K., C. A., et al. (2018). Consumer Choice and Autonomy, in the Age of Artificial Intelligence and Big Data. https://link.springer.com/article/10.1007/s40547-017-0085-8

Ravis, T., & Notkin, B. (2020). Urban Bites and Agrarian Bytes: Digital Agriculture and Extended Urbanization. Berkeley Planning Journal.

Rowe, R. (2015). Self driving cars, timeline TopSpeed. Available online: http://www.topspeed.com/cars/car-news/self-driving-cars-timeline-ar169802.html (accessed on 2 June 2023).

SAE. (2018). SAE International Releases Updated Visual Chart for Its “Levels of Driving Automation” Standard for Self-Driving Vehicles. Available online: https://www.sae.org/publications/journals (accessed on 2 June 2023).

Sara, O., Pere., R., José, R., et al. (2023). Machine Learning Applications in Agriculture: Current Trends Challenges, and Future Perspectives. Agronomy, 13, 2976.

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. https://doi.org/10.3390/su13126666

Schwab, K. (2016a). The Fourth Industrial Revolution WEF Geneva., Klaus Schwab. Világgazdasági Fórum.

Schwab, K. (2016b). The Human Capital Report 2016. Available online: https://www.weforum.org/publications/the-human-capital-report-2016 (accessed on 2 June 2023).

Shi, Y., Siddik, A. B., Masukujjaman, M., et al. (2022). The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory. Sustainability, 14(11), 6640. https://doi.org/10.3390/su14116640

Szalavetz, A. (2016a). The economic impact of Industry 4.0 technologies: Questions from a start-up research (Hungarian). Foreign Economy, 60(7-8), 27-50.

Szalavetz, A. (2016b). Chronicle of an announced revolution in Hungary: industry 4.0 technologies and domestic manufacturing subsidiaries (Hungarian). Foreign Economy, 60(9-10), 28-48.

Szőke, V., & Kovács, L. (2021). Technologies of Agriculture 4.0. Gazdálkodás, 65(1), 64–85.

Technavio. (2017). Global precision agriculture market 2015-2019. Available online: https://www.technavio.com/report/precision-agriculture-market-industry-analysis (accessed on 2 June 2023).

Tennant, C., Stares, S., & Howard, S. (2019). Public discomfort at the prospect of autonomous vehicles: Building on previous surveys to measure attitudes in 11 countries. Transportation Research Part F: Traffic Psychology and Behaviour, 64, 98–118. https://doi.org/10.1016/j.trf.2019.04.017

Thompson, R., Compeau, D., Higgins, C., & Lupton, N. (2008). Intentions to Use Information Technologies. End User Computing Challenges and Technologies, 79–101. https://doi.org/10.4018/978-1-59904-295-4.ch006

Tilesch, & Hatamleh. (2021). Artificial Intelligence-Taking control of our destiny in the age of AI (Hungarian). Budapest: Libri.

VDMA. (2015). Industry 4.0 Readines (German). VDMA’s Impulse Foundation Aachen Cologne. Available online: https://www.egsautomation.de/?gclid=Cj0KCQiA3uGqBhDdARIsAFeJ5r0VOVi1KkkL-sjVdCVqdJRw-2OQ5CJhMkiwnPsRe2L0oKs_6dYf004aAhlfEALw_wcB (accessed on 2 June 2023).

Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540

Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273–315. Portico. https://doi.org/10.1111/j.1540-5915.2008.00192.x

Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412

Venkatesh, Viswanath., & Thong., Y. J. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157-178.

Vértesy, L. (2023). A change of mindset in crop production: Circularity and sustainability: Workshop paper Circular economy (Hungarian). MATE Press.

Younger, P. (2004). Using the internet to conduct a literature search. Nursing Standard, 19(6), 45–53. https://doi.org/10.7748/ns.19.6.45.s63