Iranian Agricultural Extension and Education Journal

Iranian Agricultural Extension and Education Journal

Smart Agriculture as a Sustainable Solution: Investigating Key Factors in the Adoption of New Technologies

Document Type : Original Article

Authors
Maryam Mola Ghalghachi 1
Hossein Norouzi 1
Bagher Asgarnezhad Nouri 2
Ebrahim Asgharpour 2
1 Department of Business Management, Faculty of Management, Kharazmi University, Tehran, Iran
2 Department of Business Management, Faculty of Economics and Management, Urmia University, Urmia, Iran
10.22034/iaeej.2026.548518.1880
Abstract
Smart agriculture, as an innovative approach, offers great potential for enhancing sustainability, increasing productivity, and improving the quality of agricultural products. However, the effective and widespread adoption of new technologies in this field requires a comprehensive understanding of the factors influencing farmers’ decisions to implement these innovations. Accordingly, this study focuses on the importance of smart agriculture as a sustainable solution and examines the key factors influencing farmers’ adoption of new technologies. The statistical population of the study consisted of managers, experts, and specialists in the field of smart agriculture in Tehran and Alborz provinces. One method for determining the minimum sample size is based on the ratio of the number of sample members to the number of questions in the research model or theory (N), for which the minimum ratio was proposed by Jackson. Based on the 44 questionnaire items, the required sample size for this study was estimated to be 440 participants. The questionnaire was used as the main instrument for data collection in this study. The research model was tested using the Partial Least Squares (PLS) method with SmartPLS 3 software. The results of the data analysis indicated that the proposed research model confirmed the influence of psychological, technological, and infrastructural factors on the intention to adopt smart agriculture technologies, considering the mediating roles of perceived ease of use, perceived usefulness, and attitude toward adopting these technologies. According to the findings, infrastructural factors had the greatest impact on perceived ease of use and perceived usefulness, while perceived ease of use had the strongest effect on the attitude toward adopting smart agriculture technologies. The study reveals that psychological, technological, and infrastructural factors exert positive and significant influences on the intention to adopt smart agriculture technologies through perceived ease of use, perceived usefulness, and attitude toward adoption as mediating variables.
Keywords
Infrastructure factors
Perceived usefulness
Smart agricultural
Technology adoption
Theory of reasoned action
سازمان تحقیقات، آموزش و ترویج کشاورزی. (1404). گزارش هوشمندسازی آبیاری کلید عبور از بحران کم آبی در کشاورزی ایران. تهران. قابل دسترس در آدرس اینترنتی: <https://areeo.ac.ir/fa/news/7039>
سازمان جهاد کشاورزی استان البرز. (1404). گزارش تولید 5/2 میلیون تن محصولات کشاورزی و دامی در البرز. البرز. قابل دسترس در آدرس اینترنتی: <https://www.isna.ir/news/1404081408463>
سازمان جهاد کشاورزی استان البرز. (1404). گزارش سازمان جهاد کشاورزی استان البرز پیشگام در اجرای سامانه الکترونیکی توزیع بذر. البرز. قابل دسترس در آدرس اینترنتی: <https://alborz.maj.ir/XSKk>
سازمان جهاد کشاورزی استان تهران. (1403). گزارش ایرنا؛ تولید پسته در استان تهران به بیش از ۱۰ هزار و ۵۰۰ تن رسید. تهران. قابل دسترس در اینترنتی: <https://www.irna.ir/amp/85649367/>
 سازمان جهاد کشاورزی استان تهران. (1404). گزارش ایسنا؛ تهران با وجود 2 درصد خاک کشاورزی، 5 درصد تولید محصول دارد. تهران. قابل دسترس در اینترنتی: <https://www.isna.ir/news/97010701335/>
 سازمان جهاد کشاورزی پاکدشت. (1404). گزارش کشاورزی هوشمند با پهپاد در پاکدشت. پاکدشت. قابل دسترس در اینترنتی: <http://seda.ir/sh/?5956797>
 شرکت سوواران. (1404). گزارش سیستم آبیاری هوشمند، سنسور رطوبت خاک و آبیاری اتوماتیک. قابل دسترس در اینترنتی: <https://www.soovaran.com>
طاهری، ز.، عباسی، عنایت.، بیژنی، م.، و قاسمی، ج. (1401). راهکارهای بهبود فرآیند مدیریت دانش در نظام نوین ترویج کشاورزی (مورد مطالعه: استان زنجان). علوم ترویج و آموزش کشاورزی ایران، دوره 18، صص 71-61.
عسگرنژاد نوری، ب.، و مولا قلقاچی، م. (1403). بررسی مدل جامع عوامل مؤثر بر پذیرش فناوری اطلاعات سبز (مورد مطالعه: دانشگاه ارومیه). فصلنامه علمی آموزش محیط زیست و توسعه پایدار، دوره 13، شماره 2، صص 97-115. https://doi.org/10.30473/ee.2024.68654.2660
عسگرنژاد نوری، ب.، ابراهیم‌پور، ح.، نامی، ب.، و حمیدزاده اربابی، ع. (1401). تأثیر مدیریت دانش بر توسعه حرفه‌ای کارکنان: نقش واسطه‌ای قابلیت‌های کارآفرینی. زیست‌بوم نوآوری، دوره 2، شماره 1، صص 45-25. https://doi.org/10.22111/innoeco.2022.41876.1031
کریمی، ح.، و عطائی، پ. (1402). زیست‌پذیری در شرایط خشکسالی و ریسک‌های اجتماعی در استفاده کشاورزان از پساب شهری. علوم ترویج و آموزش کشاورزی ایران، دوره 19، شماره 1، صص 119-105. https://dor.isc.ac/dor/20.1001.1.20081758.1402.19.1.7.7
لطفوی، م.، صالحی، ل.، و منوری فرد، ف. (1402). رفتار مسئولانه محیط‌زیستی گردشگران پارک جنگلی طاق‌بستان: جایگاه گونه‌های ارزشی. علوم ترویج و آموزش کشاورزی ایران، دوره 19، شماره 2، 175-157. https://doi.org/10.22034/iaeej.2024.414858.1754
مولا قلقاچی، م.، و بشیر خداپرستی، ر. (1401). واکاوی ارتباط شست‌وشوی سبز بر اعتماد سبز و قصد خرید سبز با میانجی‌گری سردرگمی سبز و خطر درک‌شده سبز. تحقیقات بازاریابی نوین، دوره 12، شماره 4، صص 194-177. https://doi.org/10.22108/nmrj.2023.136059.2819
نوروزی، ح.، اصانلو، ب.، و صالح گوهری، ع. (1403). بررسی تأثیر عوامل شناختی و عاطفی بر پاسخ رفتاری مصرف‌کنندگان در تبلیغات رفتاری آنلاین. مدیریت بازرگانی، دوره 16، شماره 1، صص 33-1. https://doi.org/10.22059/jibm.2023.352929.4511
یزدانجو، ش.، ملک سعیدی قصرالدشتی، ح.، و معمارباشی، پ. (1403). ادراک، نگرش و رفتار سبزی‌کاران بخش مرکزی شهرستان سنندج نسبت به مدیریت ایمنی و سلامت غذا در مزرعه. علوم ترویج و آموزش کشاورزی ایران، دوره 20، صص 75-59. https://doi.org/10.22034/iaeej.2025.226309
 
Adnan, N., Rehman, H. M., and Alam, M. N. (2025). Exploring agricultural innovation: an empirical investigation of factors influencing the adoption and non-adoption of smart fertilizer technology among farmers in developing countries. Agriculture & Food Security, 14(1), 11.‏ https://doi.org/10.1186/s40066-025-00529-0
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.‏ https://doi.org/10.1016/0749-5978(91)90020-T
An, S., Eck, T., and Yim, H. (2023). Understanding consumers’ acceptance intention to use mobile food delivery applications through an extended technology acceptance model. Sustainability, 15(1), 832.‏ https://doi.org/10.3390/su15010832
Asadi, S., Hussin, A. R. C., and Dahlan, H. M. (2018). Toward green IT adoption: From managerial perspective. International Journal of Business Information Systems, 29(1), 106-125.‏https://doi.org/10.1504/IJBIS.2018.094002
Aslan, M. F., Durdu, A., Sabanci, K., Ropelewska, E., and Gültekin, S. S. (2022). A comprehensive survey of the recent studies with UAV for precision agriculture in open fields and greenhouses. Applied Sciences, 12(3), 1047.‏ https://doi.org/10.3390/app12031047
Azadi, H., Moghaddam, S. M., Burkart, S., Mahmoudi, H., Van Passel, S., Kurban, A., and Lopez-Carr, D. (2021). Rethinking resilient agriculture: From climate-smart agriculture to vulnerable-smart agriculture. Journal of Cleaner Production, 319, 128602.‏ https://doi.org/10.1016/j.jclepro.2021.128602
Balafoutis, A. T., Evert, F. K. V., and Fountas, S. (2020). Smart farming technology trends: economic and environmental effects, labor impact, and adoption readiness. Agronomy, 10(5), 743.‏ https://doi.org/10.3390/agronomy10050743
Binaei, S., Fami, H. S., Kalantari, K., and Barati, A. A. (2024). Investigation of dDeveloping smart agriculture in greenhouses of tehran province. Journal of Agricultural Economics & Development (2008-4722), 37(4). https://doi.org/10.22067/jead.2024.85975.1237
Block, J. B., Michels, M., Mußhoff, O., and Hermann, D. (2024). How to reduce the carbon footprint of the agricultural sector? Factors influencing farmers’ decision to participate in carbon sequestration programs. Journal of Environmental Management, 359, 121019.‏ https://doi.org/10.1016/j.jenvman.2024.121019
Chen, L., and Aklikokou, A. K. (2020). Determinants of E-government adoption: testing the mediating effects of perceived usefulness and perceived ease of use. International Journal of Public Administration, 43(10), 850-865.‏ https://doi.org/10.1080/01900692.2019.1660989
Chen, Z., Li, X., and Xia, X. (2022). Socioeconomic status, ambidextrous learning, and farmers' adoption of biological control technology: Evidence from 650 kiwifruit growers in China. Pest Management Science, 78(2), 475-487.‏ https://doi.org/10.1002/ps.6642
Chen, Z., Li, X., and Xia, X. (2024). The impact of internet use and cognitive conflict on farmer IPM technology adoption: evidence from China. Pest Management Science, 80(10), 5050-5063.‏ https://doi.org/10.1002/ps.8233
Coffie, I. S., Tweneboah-Koduah, E. Y., Ocloo, E. C., Nkukpornu, A., and Kastner, A. N. A. (2024). Improving local rice consumption in Sub-Saharan Africa through social marketing: Evidence from Ghana. International Review on Public and Nonprofit Marketing, 21(1), 177-196.‏ https://doi.org/10.1007/s12208-023-00370-w.
Dai, Q., and Cheng, K. (2022). What drives the adoption of agricultural green production technologies? An extension of TAM in agriculture. Sustainability, 14(21), 14457.‏ https://doi.org/10.3390/su142114457
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology, 22(14), 1111-1132.‏ https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
Douglas, B. D., Holley, K., Isenberg, N., Kennedy, K. R., and Brauer, M. (2024). Social sanctions in response to injunctive norm violations. Current Opinion in Psychology, 59, 101850.‏ https://doi.org/10.1016/j.copsyc.2024.101850
Erdem, M., and Ağır, H. B. (2024). Enhancing dairy farm welfare: A holistic examination of technology adoption and economic performance in Kahramanmaraş Province, Turkey. Sustainability, 16(7), 2989.‏ https://doi.org/10.3390/su16072989
Erekalo, K. T., Gemtou, M., Kornelis, M., Pedersen, S. M., Christensen, T., and Denver, S. (2025). Understanding the behavioral factors influencing farmers' future adoption of climate-smart agriculture: A multi-group analysis. Journal of Cleaner Production, 145632. https://doi.org/10.1016/j.jclepro.2025.145632.
Feng, X., and Zailani, S. (2025). The antecedents of the willingness to adopt and pay for climate-smart agricultural technology among cooperatives in China. Sustainability, 17(1), 19.‏ https://doi.org/10.3390/su17010019
Fishbein, M., and Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Available at: <https://philpapers.org/rec/FISBAI?all_versions=1>
Gao, Y., Zhao, D., Yu, L., and Yang, H. (2020). Influence of a new agricultural technology extension mode on farmers' technology adoption behavior in China. Journal of Rural Studies, 76, 173-183.‏ https://doi.org/10.1016/j.jrurstud.2020.04.016
Gerli, P., Clement, J., Esposito, G., Mora, L., and Crutzen, N. (2022). The hidden power of emotions: How psychological factors influence skill development in smart technology adoption. Technological Forecasting and Social Change, 180, 121721. https://doi.org/10.1016/j.techfore.2022.121721
Giua, C., Materia, V. C., and Camanzi, L. (2022). Smart farming technologies adoption: Which factors play a role in the digital transition?. Technology in Society, 68, 101869.‏ http://dx.doi.org/10.1016/j.techsoc.2022.101869
Granić, A. (2023). Technology acceptance and adoption in education. In Handbook of open, distance and digital education (pp. 183-197). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-2080-6_11
Han, X., and Gooi, L. M. (2025). Multi-Level determinants of sustainable blockchain technology adoption in SCM: individual, organisational, and societal perspectives. Sustainability, 17(6), 2621.‏ https://ideas.repec.org/a/gam/jsusta/v17y2025i6p2621-d1613552.html
Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Hussien, Z. A., Abdulmalik, H. A., Hussain, M. A., Nyangaresi, V. O., Ma, J., Abduljabbar, Z. A., and Abduljaleel, I. Q. (2023). Lightweight integrity preserving scheme for secure data exchange in cloud-based IoT systems. Applied Sciences, 13(2), 691.‏ http://dx.doi.org/10.3390/app13020691
Hüttel, S., Leuchten, M. T., and Leyer, M. (2022). The importance of social norm on adopting sustainable digital fertilisation methods. Organization & Environment, 35(1), 79-102.‏ https://doi.org/10.1177/1086026620929074
Jackson, D. L. (2003). Revisiting sample size and number of parameter estimates: Some support for the N: q hypothesis. Structural Equation Modeling, 10(1), 128-141.‏ http://dx.doi.org/10.1207/S15328007SEM1001_6
Kai, L. I., Yu, J. I. N., and Jie-hong, Z. H. O. U. (2023). Are vulnerable farmers more easily influenced? Heterogeneous effects of Internet use on the adoption of integrated pest management. Journal of Integrative Agriculture, 22(10), 3220-3233.‏ https://doi.org/10.1016/j.jia.2023.08.005
Khaspuria, G., Aarushi, K., Mahima, A., Mayank, B., Rupesh, Y., and Abhishek, Y. (2024). Adoption of precision agriculture technologies among farmers: A comprehensive review. Journal of Scientific Research and Reports, 30(7), 86-671. Available at: <http://archive.sdpublishers.com/2705/1/Khaspuria3072024JSRR118640.pdf>.
Kirungi, D., Senyange, B., Wesana, J., Sseguya, H., Gellynck, X., and De Steur, H. (2023). Entrepreneurial and attitudinal determinants for adoption of Climate-smart Agriculture technologies in Uganda. Cogent Food & Agriculture, 9(2), 2282236.‏ https://doi.org/10.1080/23311932.2023.2282236
Li, J., Liu, G., Chen, Y., and Li, R. (2023). Study on the influence mechanism of adoption of smart agriculture technology behavior. Scientific Reports, 13(1), 8554.‏ http://dx.doi.org/10.1038/s41598-023-35091-x.
Li, L., and Cheng, K. (2021). Research on the adoption willingness of internet of things technology of Chinese large and specialized vegetable farmers based on integrated UTAUT model and TOE framework.‏ Available at: <https://ideas.repec.org/p/ags/asae21/329381.html>
Liao, Q., Wang, X., and Yang, R. (2025). Complements or substitutes? The impact of social interactions and Internet use on farmers’ green production technology adoption behavior. Journal of Cleaner Production, 518, 145964. https://doi.org/10.1016/j.jclepro.2025.145964
Lieder, S., and Schröter-Schlaack, C. (2021). Smart farming technologies in arable farming: Towards a holistic assessment of opportunities and risks. Sustainability, 13(12), 6783.‏ https://doi.org/10.3390/su13126783
Luh, Y. H., Tsai, M. H., and Chang, Y. C. (2023). Actions speak louder than words: Adoption of sustainable farming practices based on an integrated model. Journal of Cleaner Production, 416, 137856.‏ https://doi.org/10.1016/j.jclepro.2023.137856
Ma, R., Zou, J., Han, Z., Yu, K., Wu, S., Li, Z., Liu, S., Niu, S., Horwath, W. R., and Zhu‐Barker, X. (2021). Global soil‐derived ammonia emissions from agricultural nitrogen fertilizer application: A refinement based on regional and crop‐specific emission factors. Global Change Biology, 27(4), 855-867.‏ http://dx.doi.org/10.1111/gcb.15437
Ma, W., Owusu-Sekyere, E., Zheng, H., and Owusu, V. (2025). Factors influencing smartphone usage of rural farmers: Empirical analysis of five selected provinces in China. Information Development, 41(1), 61-74.‏ https://doi.org/10.1177/02666669231201828
Mana, A. A., Allouhi, A., Hamrani, A., Rahman, S., el Jamaoui, I., and Jayachandran, K. (2024). Sustainable AI-based production agriculture: Exploring AI applications and implications in agricultural practices. Smart Agricultural Technology, 7, 100416.‏ http://dx.doi.org/10.1016/j.atech.2024.100416
Mohr, S., and Kühl, R. (2021). Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 22(6), 1816-1844. https://doi.org/10.1007/s11119-021-09814-x
Ninsiima, R., Mshenga, P., and Okello, D. (2025). Influence of social norms on blockchain technology adoption: A structural equation modelling approach among smallholder barley farmers in Uganda. Discover Agriculture, 3(1), 89.‏ https://doi.org/10.1007/s44279-025-00254-z
Omigie, N. O., Zo, H., and Rho, J. J. (2015). User acceptance of mobile broadband in Nigeria. In Information and Knowledge Management (Vol. 5, No. 7, pp. 62-78).‏ http://dx.doi.org/10.13140/RG.2.1.2812.0806
Otieno, M. (2023). An extensive survey of smart agriculture technologies: Current security posture. World Journal of Advanced Research and Reviews, 18(3), 1207-1231.‏ http://dx.doi.org/10.30574/wjarr.2023.18.3.1241
Pillai, R., and Sivathanu, B. (2020). Adoption of internet of things (IoT) in the agriculture industry deploying the BRT framework. Benchmarking: An International Journal, 27(4), 1341-1368.‏ https://doi.org/10.1108/BIJ-08-2019-0361
Pooja, P., Chikhale, M. M., and Dhir, S. (2025). Uncovering the strategic potential of blockchain technology adoption: A systematic literature review. Strategic Change, 34(2), 151-180.‏ https://doi.org/10.1002/jsc.2620
Raj, V. A., Jasrotia, S. S., and Rai, S. S. (2025). Role of perceived risks and perceived benefits on consumers behavioural intention to use Buy-Now, Pay-Later (BNPL) services. Journal of Facilities Management, 23(2), 330-351.‏ https://doi.org/10.1108/JFM-01-2023-0004
Ramadan, E., Abdalla, S., Al Ahbabi, A., Gibreel, T., and Al Hosani, N. (2025). Toward sustainable urban agriculture in the arid GCC states: Drivers of technology adoption among small-scale farmers. City and Environment Interactions, 100222.‏ https://doi.org/10.1016/j.cacint.2025.100222
Raven, P. H., and Wagner, D. L. (2021). Agricultural intensification and climate change are rapidly decreasing insect biodiversity. Proceedings of the National Academy of Sciences, 118(2), e2002548117.‏ https://doi.org/10.1073/pnas.2002548117
Rezaei, R., and Ghofranfarid, M. (2018). Rural households' renewable energy usage intention in Iran: Extending the unified theory of acceptance and use of technology. Renewable Energy, 122, 382-391.‏ https://doi.org/10.1016/j.renene.2018.02.011
Sambas, A., Mujiarto, M., Gundara, G., Refiadi, G., Mulyati, N. S., and Sulaiman, I. M. (2023). Development of smart farming technology on ginger plants in padamulya Ciamis Village, West Java, Indonesia. International Journal of Research in Community Services, 4(3), 93-99.‏ http://dx.doi.org/10.46336/ijrcs.v4i3.483
‏Shang, L., Heckelei, T., Gerullis, M. K., Börner, J., and Rasch, S. (2021). Adoption and diffusion of digital farming technologies-integrating farm-level evidence and system interaction. Agricultural Systems, 190, 103074. https://doi.org/10.1016/j.agsy.2021.103074
‏Sharma, A., Mohan, A., Johri, A., and Asif, M. (2025). Determinants of financial technology (FinTech) adoption by the farmers in agrarian economy. Social Sciences & Humanities Open, 11, 101370. https://doi.org/10.1016/j.ssaho.2025.101370
Sivathanu, B. (2018). Adoption of internet of things (IOT) based wearables for healthcare of older adults–a behavioural reasoning theory (BRT) approach. Journal of Enabling Technologies, 12(4), 169-185.
Tu, M. (2018). An exploratory study of internet of things (IoT) adoption intention in logistics and supply chain management: A mixed research approach. The International Journal of Logistics Management, 29(1), 131-151.‏ https://doi.org/10.1108/IJLM-11-2016-0274
‏Verbeke, W., Diallo, M. A., van Dooremalen, C., Schoonman, M., Williams, J. H., Van Espen, M., D’Haese, M., and de Graaf, D. C. (2024). European beekeepers’ interest in digital monitoring technology adoption for improved beehive management. Computers and Electronics in Agriculture, 227, 109556. https://doi.org/10.1016/j.compag.2024.109556
Wang, C., Zhang, J., Cao, J., Hu, H., and Yu, P. (2019). The influence of environmental background on tourists' environmentally responsible behaviour. Journal of Environmental Management, 231, 804-810. https://doi.org/10.1016/j.jenvman.2018.10.089
Yoon, C., Lim, D., and Park, C. (2020). Factors affecting adoption of smart farms: The case of Korea. Computers in Human Behavior, 108, 106309.‏ https://doi.org/10.1016/j.chb.2020.106309
Zhong, W., Chen, Y., and Xie, L. (2023). How does internet use promote joint adoption of sustainable agricultural practices? Evidence from rice farmers in China. International Journal of Agricultural Sustainability, 21(1), 2270244. https://doi.org/10.1080/14735903.2023.2270244
Zhou, W., Qing, C., Deng, X., Song, J., and Xu, D. (2023). How does Internet use affect farmers’ low-carbon agricultural technologies in southern China?. Environmental Science and Pollution Research, 30(6), 16476-16487.‏ https://doi.org/10.1007/s11356-022-23380-3
 
Iranian Agricultural Extension and Education Journal
Volume 22, Issue 1
September 2026
Pages 155-185