Precision agriculture; Structural equation modelling; Technology acceptance model (TAM); Wireless sensor networks; Cost effective; Environmentally-friendly technology; Farm management; Farm production; Network diffusions; Precision Agriculture; Structural equation models; Technology acceptance model; Wireless sensor network applications; Business and International Management; Applied Psychology; Management of Technology and Innovation
Abstract :
[en] Background: Wireless Sensor Networks (WSNs) are environmentally friendly technology supporting more timely and cost-effective farm management and production. Noting that the adoption rate of WSNs is particularly low in emerging and developing countries, agricultural professionals can play a key role in facilitating WSN adoption through dedicated training and extension activities. Objective: This study examines the determinants of the agricultural professionals' intention towards WSN diffusion using an extended version of the Technology Acceptance Model (TAM). Methodology: Data was collected from 109 professionals in Khuzestan province, Southwest Iran. The data was analysed using a Structural Equation Modelling (SEM) approach in SPSS Amos. Results: The key constructs of an original TAM significantly affected the intention expressed by the professions and explained 40 % of its variance. By adding additional constructs of knowledge, confidence, social influence and facilitating conditions, the extended TAM significantly improved the explanatory power of the original model to 54 %. By adding control variables, the inclusive TAM explained 60 % of the variation in the professionals' intention. Conclusion: The findings of this study provide a clearer understanding of the decisive factors affecting the promotion of WSNs and can provide a valuable frame for policy and decision-makers to develop WSN policy and practical plans.
Disciplines :
Agriculture & agronomy
Author, co-author :
Taheri, Fatemeh; Department of Agricultural Economics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
D'Haese, Marijke; Department of Agricultural Economics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
Fiems, Dieter; Department of Telecommunications and Information Processing, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium
Azadi, Hossein ; Université de Liège - ULiège > TERRA Research Centre > Modélisation et développement ; Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic ; Faculty of Environmental Science and Engineering, Babeș-Bolyai University, Cluj-Napoca, Romania
Language :
English
Title :
The intentions of agricultural professionals towards diffusing wireless sensor networks: Application of technology acceptance model in Southwest Iran
Abawajy, J.H., Hassan, M.M., Federated internet of things and cloud computing pervasive patient health monitoring system. IEEE Commun. Mag. 55:1 (2017), 48–53, 10.1109/MCOM.2017.1600374CM.
Abayomi-Alli, O., Odusami, M., Ojinaka, D., Shobayo, O., Misra, S., Damasevicius, R., Maskeliunas, R., Smart-Solar Irrigation System (SMIS) for sustainable agriculture. Florez, H., Diaz, C., Chavarriaga, J., (eds.) Applied Informatics. ICAI 2018 Communications in Computer and Information Science, 942, 2018, Springer, Cham, 10.1007/978-3-030-01535-0_15.
Abayomi-Alli, A.A., Arogundade, O., Misra, S., Akala, M.O., Ikotun, A.M., Ojokoh, B.A., An ontology-based information extraction system for organic farming. Int. J. Semant. Web Inf. Syst. 17:2 (2021), 79–99, 10.4018/IJSWIS.2021040105.
Abbad, M.M., Morris, D., de Nahlik, C., Looking under the bonnet: factors affecting student adoption of E-learning systems in Jordan. Int. Rev. Res. Open Dist. Learn. 10 (2009), 1–25, 10.19173/irrodl.v10i2.596.
Abdollahzadeh, G., Damalas, C.A., Sharifzadeh, M.S., Ahmadi-Gorgi, H., Attitude towards and intention to use biological control among citrus farmers in Iran. Crop Protect. 108 (2018), 95–101, 10.1016/j.cropro.2018.02.016.
Agudo-Peregrina, A.F., Hernandez-García, A., Pascual-Miguel, F.J., Behavioral intention, use behavior and the acceptance of electronic learning systems: differences between higher education and lifelong learning. Comput. Hum. Behav. 34 (2014), 301–314, 10.1016/j.chb.2013.10.035.
Ahmadi, K., Ebadzadeh, H., Abdshah, H., Kazemian, A., Rafie, M., Agricultural Census of 2017–2018. 2018, Ministry of Agriculture Jihad, Deputy of Planning and Economics, Information and Communication Technology Center, Tehran, Iran.
Ajzen, I., The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50 (1991), 179–211, 10.1016/0749-5978(91)90020-T.
Al-Emran, M., Mezhuyev, V., Kamaludin, A., Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance. Technol. Soc., 61, 2020, 101247, 10.1016/j.techsoc.2020.101247.
Al-Gahtani, S.S., Empirical investigation of e-learning acceptance and assimilation: a structural equation model. Appl. Comput. Inform. 12 (2016), 27–50, 10.1016/j.aci.2014.09.001.
Allahyari, M.S., Damalas, C.A., Ebadattalab, M., Determinants of integrated pest management adoption for olive fruit fly (Bactrocera oleae) in Roudbar, Iran. Crop Protect. 84 (2016), 113–120, 10.1016/j.cropro.2016.03.002.
Anderson, J.C., Gerbing, D.W., Structural equation modeling in practice: a review and recommended two-step approach. Psychol. Bull. 103 (1988), 411–423, 10.1037/0033-2909.103.3.411.
Arogundade, O., Qudus, R., Abayomi-Alli, A., Misra, S., Agbaegbu, J., Akinwale, A., Ahuja, R., A Mobile-based farm machinery hiring system. Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, 2021, Springer, Singapore, 2021, 10.1007/978-981-16-0733-2_15.
Arpaci, I., Antecedents and consequences of cloud computing adoption in education to achieve knowledge management. Comput. Hum. Behav. 20 (2017), 382–390, 10.1016/j.chb.2017.01.024.
Aubert, B.A., Schroeder, A., Grimaudo, J., IT as enabler of sustainable farming: an empirical analysis of farmers’ adoption decision of precision agriculture technology. Decis. Support Syst. 54 (2012), 510–520, 10.1016/j.dss.2012.07.002.
Bagherpour, H., Minaei, S., Abdolahian Noghbi, M., KhorasaniFardavani, M.E., Development of an exterior-mount real time sugar beet yield monitoring system for a sugar beet harvester. Cercetari Agronomice in Moldova 1:161 (2015), 17–24, 10.1515/cerce-2015-0013.
Bakhtiyari, Z., Yazdanpanah, M., Forouzani, M., Kazemi, N., Intention of agricultural professionals toward biofuels in Iran: implications for energy security, society, and policy. Renew. Sust. Energ. Rev. 69 (2017), 341–349, 10.1016/j.rser.2016.11.165.
Baptista, G., Oliveira, T., Understanding mobile banking: the unified theory of acceptance and use of technology combined with cultural moderators. Comput. Hum. Behav. 50 (2015), 418–430, 10.1016/j.chb.2015.04.024.
Bonn, M.A., Kim, W.G., Kang, S., Cho, M., Purchasing wine online: the effects of social influence, perceived usefulness, perceived ease of use, and wine involvement. J. Hosp. Mark. Manag., 1–29, 2015, 10.1080/19368623.2016.1115382.
Bosompem, M., Predictors of ex-ante adoption of precision agriculture technologies by cocoa farmers in Ghana. J. Sustain. Dev. Afr. 21:4 (2019), 89–110 https://jsd-africa.com/Jsda/2019%20V21%20No4%20Winter/PDF/Predictors%20of%20Ex-Ante%20Adoption%20of%20Precision_Martin%20%20Proofread_April_2020.pdf.
Cavite, H.J., Mankeb, P., Kerdsriserm, C., et al. Do behavioral and socio-demographic factors determine consumers’ purchase intention towards traceable organic rice? Evidence from Thailand. Org. Agric. 12 (2022), 243–258, 10.1007/s13165-022-00387-1.
Chen, F., Sensitivity of goodness of fit indexes to lack of measurement invariance. Struct. Equ. Model. 14 (2007), 464–504, 10.1080/10705510701301834.
Conner, M., Armitage, C.J., Extending the theory of planned behavior: a review and avenues for further research. J. Appl. Soc. Psychol. 28:15 (1998), 1429–1464, 10.1111/j.1559-1816.1998.tb01685.x.
Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. Manag. Inf. Syst. Q. 13 (1989), 319–340, 10.2307/249008.
Davis, F.D., User acceptance of information technology: system characteristics, user perceptions and behavioural impacts. Int. J. Man-Mach. Stud. 38 (1993), 475–487, 10.1006/imms.1993.1022.
Ducey, A.J., Coovert, M.D., Predicting tablet computer use: an extended technology acceptance model for physicians. Health Policy Technol. 5 (2016), 268–284, 10.1016/j.hlpt.2016.03.010.
Fishbein, M., Ajzen, I., Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. 1975, Addison-Wesley, Reading, MA.
Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18 (1981), 39–50, 10.1177/002224378101800104.
Forward, S.E., The theory of planned behaviour: the role of descriptive norms and past behaviour in the prediction of drivers’ intentions to violate. Transport. Res. F: Traffic Psychol. Behav. 12:3 (2009), 198–207, 10.1016/j.trf.2008.12.002.
Fourati, M.A., Chebbi, W., Kamoun, A., Development of a web-based weather station for irrigation scheduling. Information Science and Technology (CIST), 2014 Third IEEE International Colloquium, 2014, IEEE, 37–42, 10.1109/CIST.2014.7016591.
García-Sánchez, E., García-Morales, V.J., Bolívar-Ramos, M.T., The influence of top management support for ICTs on organisational performance through knowledge acquisition, transfer, and utilisation. Rev. Manag. Sci. 11 (2017), 19–51, 10.1007/s11846-015-0179-3.
Gautam, Y.B., Pelkonen, P., Halder, P., Perceptions of bioenergy among Nepalese foresters – survey results and policy implications. Renew. Energy 57 (2013), 533–538, 10.1016/j.renene.2013.02.017.
Ghasemi, S., Karami, E., Azadi, H., Knowledge, attitudes and behavioral intentions of agricultural professionals toward genetically modified (GM) foods: a case study in Southwest Iran. Sci. Eng. Ethics 19:3 (2013), 1201–1227, 10.1007/s11948-012-9383-6.
Glenna, L.L., Jussaume, R.A., Dawson, J.C., How farmers matter in shaping agricultural technologies: social and structural characteristics of wheat growers and wheat varieties. Agric. Hum. Values 28 (2011), 213–224, 10.1007/s10460-010-9275-9.
Gupta, A., Arora, N., Understanding determinants and barriers of mobile shopping adoption using behavioral reasoning theory. J. Retail. Consum. Serv. 36 (2017), 1–7, 10.1016/j.jretconser.2016.12.012.
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Multivariate Data Analysis. 2010, Pearson Prentice Hall Publisher, New Jersey.
Hojati, S., Use of spatial statistics to identify hotspots of lead and copper in selected soils from north of Khuzestan Province, southwestern Iran. Arch. Agron. Soil Sci. 65:5 (2019), 654–669, 10.1080/03650340.2018.1520977.
Im, I., Hong, S., Kang, M.S., An international comparison of technology adoption testing the UTAUT model. Int. J. Inf. Manag. 4 (2011), 1–8, 10.1016/j.im.2010.09.001.
Irancell. (2022). Information technology. Retrieved from Khabar Online: https://www.khabaronline.ir/news/1650738/۷%-از-شی-بیاجراDB%B0–5تیساG–سالانیدر-شهر-اهواز-تا-پا.
Iskandar, Y.H.P., Subramaniam, G., Majid, M.I.A., Ariff, A.M., Rao, G.K.L., Predicting healthcare professionals’ intention to use poison information system in a Malaysian public hospital. Health Inf. Sci. Syst 8:6 (2020), 1–15, 10.1007/s13755-019-0094-0.
Jamaluddin, N., Adoption of e-commerce practices among the Indian farmers, a survey of Trichy District in the State of Tamilnadu, India. Procedia Econ. Financ. 7 (2013), 140–149, 10.1016/S2212-5671(13)00228-1.
Jawad, M.H., Nordin, R., Gharghan, S., Mahmood Jawad, A., Ismail, M., Energy-efficient wireless sensor networks for precision agriculture: a review. Sensors, 17, 2017, 1781, 10.3390/s17081781.
Jayaraman, P.P., Yavari, A., Georgakopoulos, D., Morshed, A., Zaslavsky, A., Internet of things platform for smart farming: experiences and lessons learnt. Sensors 16:11 (2016), 1–17, 10.3390/s16111884.
Kabbiri, R., Dora, M., Kumar, V., Elepu, G., Gellynck, X., Mobile phone adoption in agri-food sector: are farmers in Sub-Saharan Africa connected?. Technol. Forecast. Soc. Chang. 131 (2018), 253–261, 10.1016/j.techfore.2017.12.010.
Kamal, S.A., Shafiq, M., Kakria, P., Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technol. Soc., 60, 2020, 101212, 10.1016/j.techsoc.2019.101212.
Kamara, L.I., Dorward, P., Lalani, B., Wauters, E., Unpacking the drivers behind the use of the Agricultural Innovation Systems (AIS) approach: the case of rice research and extension professionals in Sierra Leone. Agric. Syst., 176, 2019, 102673, 10.1016/j.agsy.2019.102673.
Karimi, N., Arabhosseini, A., Karimi, M., Kianmehr, M.H., Web-based monitoring system using Wireless Sensor Networks for traditional vineyards and grape drying buildings. Comput. Electron. Agric. 144 (2018), 269–283, 10.1016/j.compag.2017.12.018.
Khorasani Fardavani, M.E., Alimardani, R., Omid, M., Development and laboratory evalution of a noise reducing technique as based on a free mass load cell for sugarcane yield monitoring scale platform. Iran. J. Biosyst. Eng. 40:1 (2009), 52–63 Corpus ID: 112994772 https://www.sid.ir/en/Journal/ViewPaper.aspx?ID=172275.
Kim, S., Factors affecting the use of social software: TAM perspectives. Electron. Libr. 30:5 (2012), 690–706, 10.1108/02640471211275729.
Kim, Y.G., Woo, E., Consumer acceptance of a quick response (QR) code for the food traceability system: application of an extended technology acceptance model (TAM). Food Res. Int. 85 (2016), 266–272, 10.1016/j.foodres.2016.05.002.
Kohnke, A., Cole, M., Bush, R., Incorporating UTAUT predictors for understanding home care patients’ and clinician's acceptance of healthcare telemedicine equipment. J. Technol. Manag. Innov. 9:2 (2014), 1–10 https://www.jotmi.org/index.php/GT/article/view/1523.
Kolady, D.E., Van der Sluis, E., Uddin, M.M., Deutz, A.P., Determinants of adoption and adoption intensity of precision agriculture technologies: evidence from South Dakota. Precis. Agric. 22 (2021), 689–710, 10.1007/s11119-020-09750-2.
Leeuwis, C., van den Ban, A., Communication for Rural Innovation: Rethinking Agricultural Extension. third ed., 2004, Blackwell Science Ltd, Netherlands.
Li, S.L., Han, Y., Li, G., Zhang, M., Zhang, L., Ma, Q., Design and implementation of agricultural greenhouse environmental monitoring system based on Internet of Things. Appl. Mech. Mater. 121 (2012), 2624–2629, 10.4028/www.scientific.net/AMM.121-126.2624.
Liébana-Cabanillas, F., Sánchez-Fernández, J., Muñoz-Leiva, F., Antecedents of the adoption of the new mobile payment systems: the moderating effect of age. Comput. Hum. Behav. 35 (2014), 464–478, 10.1016/j.chb.2014.03.022.
Liu, Q., Jin, D., Shen, J., Fu, Z., Linge, N., A wsn-based prediction model of microclimate in a greenhouse using extreme learning approaches. 18th International Conference on Advanced Communication Technology (ICACT), 2016, IEEE, Pyeongchang, South Korea, 730–735, 10.1109/ICACT.2016.7423609.
López Riquelme, J., Soto, F., Suardíaz, J., Sánchez, P., Iborraa, A., Vera, J., Wireless sensor networks for precision horticulture in Southern Spain. Comput. Electron. Agric. 68 (2009), 25–35.
Lu, J., Yao, J.E., Yu, C., Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. J. Strateg. Inf. Syst. 14:3 (2005), 245–268, 10.1016/j.jsis.2005.07.003.
Lubua, E.W., Social Patterns Influencing the Adoption of Mobile Phones in the Farming Community. A thesis submitted to the Department of Information Systems, University of Cape Town http://hdl.handle.net/11427/25059, 2017.
Lubua, E.W., Kyobe, M.E., The influence of socioeconomic factors to the use of Mobile phones in the agricultural sector of Tanzania. African Journal of Information Systems 11:4 (2019), 352–366 https://digitalcommons.kennesaw.edu/ajis/vol11/iss4/5.
Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A., Bhansali, S., Review—machine learning techniques in wireless sensor network based precision agriculture. J. Electrochem. Soc., 167, 2020, 037522, 10.1149/2.0222003JES.
Michels, M., Bonke, V., Musshoff, O., Understanding the adoption of smartphone apps in dairy herd management. J. Dairy Sci. 102:10 (2019), 9422–9434, 10.3168/jds.2019-16489.
Migdadi, M.M., Abu Zaid, M.K.S., Al-Hujran, O.S., Aloudat, A.M., An empirical assessment of the antecedents of electronic-business implementation and the resulting organizational performance. Internet Res. 26:3 (2016), 661–688, 10.1108/IntR-08-2014-0203.
Mirzaei, A., Saghafian, B., Mirchi, A., Madani, K., The groundwater-energy-food nexus in Iran's agricultural sector: implications for water security. Water, 11, 2019, 1835, 10.3390/w11091835.
Misra, S., A step by step guide for choosing project topics and writing research papers in ICT related disciplines. Communications in Computer and Information Science, 1350, 2021, Springer International Publishing, 727–744 https://link.springer.com/chapter/10.1007/978-3-030-69143-1_55.
Mohammad Zamani, D., Taghavi, A., Gholami Pareshkoohi, M., Massah, J., Design, implementation and evaluation of a potato yield monitoring system. J. Agric. Mach. 40:1 (2014), 50–56 https://agris.fao.org/agris-search/search.do?recordID=IR2016800059.
Mondal, P., Basu, M., Adoption of precision agriculture technologies in India and in some developing countries: scope, present status and strategies. Prog. Nat. Sci. 19 (2009), 659–666, 10.1016/j.pnsc.2008.07.020.
Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., Nillaor, P., IoT and agriculture data analysis for smart farm. Comput. Electron. Agric. 156 (2019), 467–474, 10.1016/j.compag.2018.12.011.
Nejadrezaei, N., Khara, H., Allahyari, M.S., Sadeghzadeh, M., Gharra, K., Effective factors on adoption technology among trout fish farms in Guilan Province. Iran. J. Fish. Sci. 24:3 (2015), 107–124 http://hdl.handle.net/1834/10864.
Nejadrezaei, N., Allahyari, M.S., Sadeghzadeh, M., Michailidis, A., El Bilali, H., Factors affecting adoption of pressurized irrigation technology among olive farmers in Northern Iran. Appl Water Sci, 8, 2018, 190, 10.1007/s13201-018-0819-2.
Nyamba, S., Malongo, M., Factors influencing the use of Mobile phones in communicating agricultural information: a case of Kilolo District, Iringa, Tanzania. Int. J. Inf. Commun. Technol. Res. 27:2 (2012), 558–563 Corpus ID: 9688603 http://www.suaire.suanet.ac.tz:8080/xmlui/handle/123456789/1786.
Pang, Z., Chen, Q., Han, W., Zheng, L., Value-centric design of the internet-of things solution for food supply chain: value creation, sensor portfolio and information fusion. Inf. Syst. Front. 17 (2015), 289–319, 10.1007/s10796-012-9374-9.
Pappa, I.C., Iliopoulos, C., Massouras, T., What determines the acceptance and use of electronic traceability systems in agri-food supply chains?. J. Rural. Stud. 58 (2018), 123–135, 10.1016/j.jrurstud.2018.01.001.
Pathak, H.S., Brown, P., Best, T., A systematic literature review of the factors affecting the precision agriculture adoption process. Precis. Agric. 20 (2019), 1292–1316, 10.1007/s11119-019-09653-x.
Paustian, M., Theuvsen, L., Adoption of precision agriculture technologies by German crop farmers. Precis. Agric. 18:5 (2017), 701–716, 10.1007/s11119-016-9482-5.
Purnomo, S.H., Lee, Y., E-learning adoption in the banking workplace in Indonesia: an empirical study. Inf. Dev. 29 (2012), 138–153, 10.1177/0266666912448258.
Qiang, W., Research on data transmission model of agricultural wireless sensor network based on game theory. Acta Agric. Scand. - B Soil Plant Sci. 72:1 (2022), 67–80, 10.1080/09064710.2021.1990389.
Renny, S., Siringoringo, H., Perceived usefulness, ease of use, and attitude towards online shopping usefulness towards online airlines ticket purchase. Procedia Soc. Behav. Sci. 81 (2013), 212–216, 10.1016/j.sbspro.2013.06.415.
Rezaei, R., Ghofranfarid, M., Rural househo'ds’ renewable energy usage intention in Iran: extending the unified theory of acceptance and use of technology. Renew. Energy 122 (2018), 382–391, 10.1016/j.renene.2018.02.011.
Rezaei, R., Safa, L., Damalas, C.A., Ganjkhanloo, M.M., Drivers of farm'rs’ intention to use integrated pest management: integrating theory of planned behavior and norm activation model. J. Environ. Manag. 236 (2019), 328–339, 10.1016/j.jenvman.2019.01.097.
Rezaei, R., Safa, L., Ganjkhanloo, M.M., Understanding farmers’ ecological conservation behavior regarding the use of integrated pest management- an application of the technology acceptance model. Glob. Ecol. Conserv., 22, 2020, e00941, 10.1016/j.gecco.2020.e00941.
Sadeghi, S., Soltanmohammadlou, N., Nasirzadeh, F., Applications of wireless sensor networks to improve occupational safety and health in underground mines. J. Saf. Res., 2022, 10.1016/j.jsr.2022.07.016.
Saengavut, V., Jirasatthumb, N., Smallholder decision-making process in technology adoption intention: implications for Dipterocarpus alatus in Northeastern Thailandv. Heliyon, 7(4), 2021, 06633, 10.1016/j.heliyon.2021.e06633v.
Santos, U.J.L., Pessin, G., Costa, C.A., Righi, R.D.R., AgriPrediction: a proactive internet of things model to anticipate problems and improve production in agricultural crops. Comput. Electron. Agric. 161 (2019), 202–213, 10.1016/j.compag.2018.10.010.
Sarker, J., Hossen, S., Ahmmed, R., Kabir, A., Hossain, T., Ali, H., Wireless sensor network based sustainable cattle farm feed management and monitoring system using internet of things. 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 2022, 2022, 1–4, 10.1109/CONECCT55679.2022.9865698.
Scherer, R., Siddiq, F., Tondeur, J., The technology acceptance model (TAM): a meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Comput. Educ. 128 (2019), 13–35, 10.1016/j.compedu.2018.09.009.
Senger, I., Borges, I.A.R., Machado, J.A.D., Using the theory of planned behavior to nderstand the intention of small farmers in diversifying their agricultural production. J. Rural. Stud. 49 (2017), 32–40, 10.1016/j.jrurstud.2016.10.006.
Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., Borozan, V., Environmental parameters monitoring in precision agriculture using wireless sensor networks. J. Clean. Prod. 2015:88 (2015), 297–307, 10.1016/j.jclepro.2014.04.036.
Svendsen, G.B., Johnsen, J.-A.K., Almås-Sørensen, L., Vittersø, J., Personality and technology acceptance: the influence of personality factors on the core constructs of the Technology Acceptance Model. Behav. Inf. Technol. 32:4 (2013), 323–334, 10.1080/0144929X.2011.553740.
Terzis, V., Economides, A.A., The acceptance and use of computer based assessment. Comput. Educ. 56:4 (2011), 1032–1044, 10.1016/j.compedu.2010.11.017.
Ulhaq, I., Pham, N.T.A., Le, V., Pham, H., Le, T.C., Factors influencing intention to adopt ICT among intensive shrimp farmers. Aquaculture, 547, 2022, 737407, 10.1016/j.aquaculture.2021.737407.
Vahdat, A., Alizadeh, A., Quach, S., Hamelin, N., Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australas. Mark. J., 1-10, 2020, 10.1016/j.ausmj.2020.01.002.
Venkatesh, V., Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf. Syst. Res. 11 (2000), 342–365, 10.1287/isre.11.4.342.11872.
Venkatesh, V., Davis, F.D., A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46 (2000), 186–204, 10.1287/mnsc.46.2.186.11926.
Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., User acceptance of information technology: toward a unified view. MIS Q. 27 (2003), 425–478, 10.2307/30036540.
Verma, P., Sinha, N., Integrating perceived economic wellbeing to technology acceptance model: the case of mobile based agricultural extension service. Technol. Forecast. Soc. Chang. 126 (2018), 207–216, 10.1016/j.techfore.2017.08.013.
Villa-Henriksen, A., Edwards, G.T.C., Pesonen, L.A., Green, O., Sørensen, C.A.G., Internet of Things in arable farming: implementation, applications, challenges and potential. Biosyst. Eng. 191 (2020), 60–84, 10.1016/j.biosystemseng.2019.12.013.
Wang, B., Chen, T., Understanding the continuous usage in wireless sensor networks of wisdom agriculture. Int. J. Mob. Commun. 17:4 (2019), 1741–5217, 10.1504/IJMC.2019.100502.
Wang, Y.S., Shin, Y.W., Why do people use information kiosks? A validation of the unified theory of acceptance and use technology. Gov. Inf. Q. 26 (2009), 158–165, 10.1016/j.giq.2008.07.001.
Wheeler, S.A., What influences agricultural profession'ls’ views towards organic agriculture?. Ecol. Econ. 65 (2008), 145–154, 10.1016/j.ecolecon.2007.05.014.
Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.J., Big data in smart farming – a review. Agric. Syst. 153 (2017), 69–80, 10.1016/j.agsy.2017.01.023.
World Bank, Iran, Islamic Rep. Accessed May 6, 2020. https://data.worldbank.org/country/iran-islamic-rep, 2020.
Yaghoubi, J., Yazdanpanah, M., Komendantova, N., Iranian agriculture advis'rs’ perception and intention toward biofuel: Green way toward energy security, rural development and climate change mitigation. Renew. Energy 130 (2019), 452–459, 10.1016/j.renene.2018.06.081.
Yi, M.Y., Jackson, J.D., Park, J.S., Probst, J.C., Understanding information technology acceptance by individual professionals: toward an integrative view. Inf. Manag. 43 (2006), 350–363, 10.1016/j.im.2005.08.006.
Yu, C.S., Why do people use information kiosks? A validation of the unified theory of acceptance and use technology. Electron. Commer. Res. 26 (2012), 158–165, 10.1016/j.giq.2008.07.001.
Zarafshani, K., Solaymani, A., D'Itri, M., Helms, M.H., Sanjabi, S., Evaluating technology acceptance in agricultural education in Iran: a study of vocational agriculture teachers. Soc. Sci. Humanit. Open, 2(1), 2020, 100041, 10.1016/j.ssaho.2020.100041.
Zaremohzzabieh, Z., Abu Samah, B., Muhammad, M., Omar, S.Z., Bolong, J., Hassan, M.S., Shaffril, H.A.M., A test of the technology acceptance model for understanding the ICT adoption behavior of rural young entrepreneurs. Int. J. Bus. Manag. 10 (2015), 158–169, 10.5539/ijbm.v10n2p158.
Zhang, T., Tao, D., Qu, X., Zhang, X., Zeng, J., Zhu, H., Zhu, H., Automated vehicle acceptance in China: social influence and initial trust are key determinants. Transp. Res. Part C Emerg. Technol. 112 (2020), 220–233, 10.1016/j.trc.2020.01.027.