Famers’ Intention to Use Precision Farming Technologies, Application of the Extended Technology Acceptance Model: A Case in Ardabil Province

Document Type : Original Research

Authors
Department of Water Engineering and Agricultural Management, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Islamic Republic of Iran.
Abstract
Precision agriculture promises to enhance economic benefits while maintaining more environmentally friendly farming practices. Despite the efforts to facilitate the adoption of Precision Farming Technologies (PFTs), the adoption remains low. Using an extended version of the Technology Acceptance Model (TAM) with two external constructs of Personal Innovativeness (PI) and Compatibility (COM), this study investigated the pioneer farmers’ Intention (INT) to use PFTs. In this survey research, a questionnaire was used for data collection from a sample of 295 farmers (N= 295). The results showed that the extended model could promote the explanatory power of the TAM and explain 72.6% of the variation in farmers’ INT to use PFTs. Respondents were relatively innovative (Mean= 3.25), had positive Attitudes (ATT) (Mean= 3.53), and had relatively positive INT to use PFTs (Mean= 3.24). In contrast, they perceived that PFTs were challenging to use (Mean= 2.7), relatively useful (mean=2.93), and lowly compatible with their small-scale farming systems (Mean= 2.66). COM was the most critical factor affecting INT, followed by Perceived Ease of Use (PEU), Perceived Usefulness (PU), PI, and ATT. At the same time, PEU had no significant effect on ATT, indicating that when farmers assess PFTs, ease of use is not a problem, but PEU is essential when they intend to use these technologies. Considering the high initial investment requirement and knowledge-intensive nature of PFTs, policy, and educational interventions are required to facilitate farmers' utilization of these technologies. To achieve the best results, one should begin with pioneer farmers.

Keywords

Subjects


Adnan, N., Nordin, S.M., bin Abu Bakar, Z., 2017. Understanding and facilitating sustainable agricultural practice: a comprehensive analysis of adoption behavior among Malaysian paddy farmers, Land Use Policy, 68, 372–382. https://doi.org/10.1016/j.landusepol.2017.07.046
Adrian, A.M., Norwood S.H., Mask, P.L., 2005. Producers’ perceptions and attitudes toward precision agriculture technologies, Computers and Electronics in Agriculture, 48, 256–271. https://doi.org/10.1016/j.compag.2005.04.004
Agarwal, R., Prasad, J., 1998. A c Information Systems Research, onceptual and operational definition of personal innovativeness in the domain of information technology, Inf. Syst. Res., 9(2), 204–215.
Ahmadi, K., Ebadzadeh, H., Abdshah, H., Kazemian, A., Rafiei, M., 2017. Agricultural statistics for the crop year 2015-2016. The first volume: Crops. Ministry of Jihad and Agriculture, Planning and Economic Deputy, Information and Communication Technology Center Tehran, Iran.
Austin, E. J., Willock, J., Deary, I. J., Gibson, G. J., Dent, J. B., Edwards-Jones, G., et al., 1998. Empirical models of farmer behavior using psychological, social, and economic variables. Part I: Linear modelling. Agric. Syst. 58, 203–224. https://doi.org/10.1016/S0308-521X(98)00066-3
Bagheri, A., Bondori, A., Allahyari, M. S., Damalas, C.A., 2019. Modeling farmers’ intention to use
pesticides: an expanded version of the theory of planned behavior. J Environ Manage. 248, 109291. https://doi.org/10.1016/j.jenvman.2019.109291
Bamberg, S., Moser, G., 2007. Twenty years after Hines, Hungerford, and Tomera: a new meta-analysis of psycho-social determinants of pro-environmental behavior, J. Environ. Psychol. 27, 14–25. https://doi.org/10.1016/j.jenvp.2006.12.002
Barnes, A.P., Soto, I., Eory, V., Beck, B., Balafoutis, A., Sánchez, B., Vangeyte, J., Fountas, S., van der Wal, T., Gómez-Barbero, M., 2019. Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers, LAND USE POLICY, 80, 163–174. https://doi.org/10.1016/j.landusepol.2018.10.00
Bijttebier, J., Ruysschaert, G., Hijbeek, R., Werner, M., Pronk, A.A., Zavattaro, L., Bechini, L., Grignani, C., ten Berge, H., Marchand, F., Wauters, E., 2018. Adoption of noninversion tillage across Europe: use of a behavioural approach in understanding decision making of farmers, LAND USE POLICY, 78, 460–471. https://doi.org/10.1016/j.landusepol.2018.05.044
Blasch, J., van der Kroon, B., van Beukering, P., Munster, R., Fabiani, S., Nino, P., Vanino, S., 2022. Farmer preferences for adopting precision farming technologies: a case study from Italy, Eur. Rev. Agric. Econ. 49 (1), 33–81 https://doi.org/10.1093/erae/jbaa031
Ciftci, O., Berezina, K., Kang, M., 2021. Effect of personal innovativeness on technology adoption in hospitality and tourism: Meta-analysis, In: W. Wörndl et al. (Eds.): Information and Communication Technologies in Tourism, 162–174, https://doi.org/10.1007/978-3-030-65785-7_14
Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology, Manag. Inf. Syst. Quart. 319–340. DOI:10.2307/249008
Davis, F.D., Bagozzi, R.P., Warshaw, P.R., 1989. User acceptance of computer technology: a comparison of two theoretical models, Manage. Sci. 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Daxini, A., Ryan, M., Donoghue, C.O., Barnes, A.P., 2019. Understanding farmers’ intentions to follow a nutrient management plan using the theory of planned behavior. LAND USE POLICY, 85, 428–437. https://doi.org/10.1016/j.landusepol.2019.04.002
DeLay, N.D., Thompson, N.M., Mintert, J.R., 2022. Precision agriculture technology adoption and technical efficiency, J. Agric. Econ. 73, 195–219. https://doi.org/10.1111/1477-9552.12440
Department of Environment., 2022. Available online at: https://ardebil.doe.ir/portal/home/?261436
Feola, G., Lerner, A.M., Jain, M., Montefrio, M.J.F., Nicholas, K.A., 2015. Researching farmers’ behavior in climate change adaptation and sustainable agriculture: lessons learned from five case studies. J Rural Stud. 39, 74–84. https://doi.org/10.1016/j.jrurstud.2015.03.009
Flett, R., Alpass, F., Humphries, S., Massey, C., Morriss, S., Long, N., 2004. The technology acceptance model and use of technology in New Zealand dairy farming. Agric. Syst., 80, 199–211. https://doi.org/10.1016/j.agsy.2003.08.002
Fornell, C., Larcker, D.F., 1981. Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. https://doi.org/10.1177/002224378101800313
Gandorfer, M., Sebastian Schleicher, S., Klaus Erdle, K., 2018. Barriers to adoption of smart farming technologies in Germany. Proceedings of the 14th International Conference on Precision Agriculture June 24 – June 27, 2018, Montreal, Quebec, Canada.
Gebbers, R., Adamchiuk, V., 2010. Precision Agriculture and Food Security. Science, 327, 828–831. DOI: 10.1126/science.1183899
Hair, J.R., Joseph, F., Black, W.C., Anderson, R.E., 2006. Multivariate Data Analysis, 7th Ed. Available at. 〈http://www.Mediafire.Com/Mkrzmjmmonn〉(1 May 2020).
Hansson, H., Ferguson, R., Olofsson, C., 2012. Psychological constructs underlying farmers’ decisions to diversify or specialize their businesses – an application of theory of planned behaviour, J. Agric. Econ. 63, 465–482. https://doi.org/10.1111/j.1477-9552.2012.00344.x
Hess, T.J., McNab, A.L., Basoglu, K.A., 2014. Reliability generalization of perceived ease of use, perceived usefulness, and behavioral intentions, MIS Quarterly, 2014, 38(1), 1–28. doi:10.25300/ MISQ/2014/38.1.01
Jongeneel, R.A., Polman, N.B.P., Slangen, L.H.G., 2008. Why are Dutch farmers going multifunctional? LAND USE POLICY, 25, 81–94. https://doi.org/10.1016/j.landusepol.2007.03.001
Karahanna, E., Agarwal, R., Angst, C.M., 2006. Reconceptualizing Compatibility Beliefs in Technology Acceptance Research, Manag. Inf. Syst. Quart. 30, (4), 781-804. https://doi.org/10.2307/25148754
Kolady, D.E., Van der Sluis, E., Mahi Uddin, M., Deutz, A.P., 2020. Determinants of adoption and adoption intensity of precision agriculture technologies: evidence from South Dakota. Precis. Agric. https://doi.org/10.1007/s11119-020-09750-2
McCarthy, M., O’Reilly, S., O’Sullivan, A., Guerin, P., 2007. An investigation into the determinants of commitment to organic farming in Ireland. Journal of Farm Management, 13, 135–152.
Naspetti, S., Mandolesi, S., Buysse, J., Latvala, T., Nicholas, P., Padel, S., et al., 2017. Determinants of the acceptance of sustainable production strategies among dairy farmers: Development and testing of a modified technology acceptance model. sustain., 9, 1805–1821. https://doi.org/10.3390/su9101805
Natarajan, T., Balasubramanian, S.A., Kasilingam, D.L., 2017. Understanding the intention to use mobile shopping applications and its influence on price sensitivity. J. Retail. Consum. Serv. 37, 8–22. https://doi.org/10.1016/j.jretconser.2017.02.010
Niles, M.T., Brown, M., Dynes, R., 2016. Farmer’s intended and actual adoption of climate change mitigation and adaptation strategies. CLIMATE CHANGE. 135, 277–295. https://doi.org/10.1007/s10584-015-1558-0
Okumus, B., Ali, F., Bilgihan, A., Ozturk, A.B., 2018. Psychological factors influencing customers' acceptance of smartphone diet apps when ordering food at restaurants, Int. J. Hosp. Manag. 72, 67-77. DOI:10.1016/j.ijhm.2018.01.001
Pathak, H.S., Brown, P., Best, T., 2019. A systematic literature review of the factors affecting the precision agriculture adoption process, Precis. Agric. https://doi.org/10.1007/s11119-019-09653-x
Paustian, M., Theuvsen, L., 2016. Adoption of precision agriculture technologies by German crop farmers. Precis. Agric. 18(5): 701-716. https://doi.org/10.1007/s11119-016-9482-5
Rogers, E.M., 1995. Diffusion of Innovations; The Free Press: New York, NY, USA.
San Martín, H., Herrero, A., 2012. Influence of the user’s psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework, Tour. Manag. 33(2), 341-350. https://doi.org/10.1016/j.tourman.2011.04.003
Senger, I., Borges, J.A.R., Machado, J.A.D., 2017. Using the theory of planned behavior to understand the intention of small farmers in diversifying their agricultural production, J Rural Stud. 49, 32–40. https://doi.org/10.1016/j.jrurstud.2016.10.006
Silva, A.G., Canavari, M., Sidali, K.L., 2018. A Technology Acceptance Model of common bean growers’ intention to adopt Integrated Production in the Brazilian Central Region. Die Bodenkultur. Journal of Land Management, Food and Environment, 68(3), 131–143. DOI:10.1515/boku-2017-0012
Takagi, C., Purnomo, S.H., Kim, M.K., 2020. Adopting Smart Agriculture among organic farmers in Taiwan, Asian J. Technol. Innovation, DOI:10.1080/19761597.2020.1797514
Tohidyan Far, S., Rezaei-Moghaddam, K., 2015. Determinants of Iranian agricultural consultants’ intentions toward precision agriculture: Integrating innovativeness to the technology acceptance model, J. Saudi Soc. Agric. Sci. http://dx.doi.org/10.1016/j.jssas.2015.09.003
Van den Ban, A.W., 1957. Some characteristics of progressive farmers in the Netherlands. J Rural Stud. 22, 205-212.
Vecchio, V., Agnusdei, G.P., Miglietta, P.P., Capitanio, F., 2020. Adoption of Precision Farming Tools: The Case of Italian Farmers, Int. J. Environ. Res. Public Health. 17, 869. ttps://doi.org/10.3390/ijerph17030869
Venkatesh, V. Davis, F.D., 2000. A theoretical extension of the technology acceptance model: four longitudinal field studies, Manage. Sci. 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M.G., Davis, F.D., Davis, G.B., 2003. User acceptance of information technology: toward a unified view. Manag. Inf. Syst. Quart.27(3), 425–478. https://doi.org/10.2307/30036540
Zeweld, W., Van Huylenbroeck, G., Tesfay, G., Speelman, S., 2017. Smallholder farmers’ behavioural intentions towards sustainable agricultural practices. J Environ Manage. 187, 71–81. https://doi.org/10.1016/j.jenvman.2016.11.014