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Factors influencing the behaviour of extension agents towards the adoption of digital technologies in agricultural extension: A Theory of Planned Behaviour Perspective
Abstract
The study examined the agricultural extension agents’ behaviour towards adopting digital technologies such as mobile applications and the factors influencing their adoption behaviour. The Theory of Planned Behaviour was used as the theoretical framework to evaluate the adoption intention and behaviour of the extension agents. A descriptive survey design was employed to sample 125 extension agents in four administrative regions in Ghana. Data were collected using a questionnaire and analysed using Statistical Package for Social Sciences (SPSS) v27 and SmartPLS software v 4.0. Frequencies, percentages, means, standard deviations, and partial least squares structural equation modelling (PLS-SEM) were used for data analysis. The results indicated that male (91.2%) extension agents dominate their female counterparts with a mean age of 35.67±7.00 years and 8.06±6.53 years of experience. The results of the PLS-SEM also showed that intention and perceived behavioural control (PBC) predicted 62% of the variations in behaviour. In comparison, attitudes and subjective norms (SN) were determinants of 58% of the intention to adopt digital technologies. Extension agents showed positive intention and behaviour regarding adopting digital technologies in discharging agricultural extension advisory services. However, they perceived the use of these tools as complex or challenging. The results of this study highlight the necessity of customized interventions and capacity building programmes that support extension agents in successfully using digital technology.
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Abdulai, A. R., Quarshie, P. T., Duncan, E., and Fraser, E. 2023. Is agricultural digitization a reality among smallholder farmers in Africa? Unpacking farmers’ lived realities of engagement with digital tools and services in rural Northern Ghana. Agriculture & Food Security, 12(11): 1–14.
Ahsan, M. B., Leifeng, G., Azam, F. M. S., Xu, B., Rayhan, S. J., Kaium, A. and Wensheng, W. 2023. Barriers, challenges, and requirements for ICT usage among Sub-assistant agricultural officers in Bangladesh: Toward sustainability in agriculture. Sustainability (Switzerland), 15(782): 1–27.
Ajzen, I. 1991. The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
Aliabadi, V., Gholamrezai, S. and Ataei, P. 2020. Rural people’s intention to adopt sustainable water management by rainwater harvesting practices: application of TPB and HBM models. Water Supply, 20(5): 1847–1861.
Annor-Frempong, F. and Akaba, S. 2020. Socio-economic impact and acceptance study on drone-applied pesticides on maize in Ghana. Technical Report ISBN: 978-92-9081-673-7
Annor-Frempong, F., Kwarteng, J., Agunga, R. and Zinnah, M. M. 2006. Challenges and prospects of infusing Information Communication Technologies (ICTs) in extension for agricultural and rural development in Ghana. AIAEE 22nd Annual Conference Proceedings, 36–46.
Asante, I. K., Inkoom, E. W., Ocran, J. K., Kyeremateng, E., Sabari, G. and Odamtten, F. T. 2023. Intention of smallholder maize farmers to adopt integrated pest management practices for fall armyworm control in the Upper East region of Ghana. International Journal of Pest Management, 1–18.
Asante, I. K., Ocran, J. K., & Inkoom, E. W. (2023). Modeling pesticide use behavior among farmers in the Upper East Region of Ghana: An empirical application of the Theory of Planned Behavior. Environmental Protection Research, 3(1): 130–149.
Atengdem, P. B., Fiafor, B., Tah, C. and Perkins, K. 2022. Agricultural e-extension services in Ghana: Strategy and plan (2022-2030). Farm Radio International. https://farmradio.org/publications/e-extension-services-in-ghana-strategy-and-plan/
Bagheri, A., Emami, N. and Damalas, C. A. 2021. Farmers’ behavior towards safe pesticide handling: An analysis with the theory of planned behavior. Science of the Total Environment, 751(141709): 1–9.
Bagozzi, R. P., Yi, Y. and Phillips, L. W. 1991. Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3): 421–458.
Baydur, H., Eser, E., Sen Gundogan, N. E., Ayhan, E., Eser, S., Dede, B., Hazneci, E., Öztekin, Y. B., Ekuklu, G., Cevizci, S. and Van den Broucke, S. 2023. Psychological determinants of Turkish farmers’ health and safety behaviors: An application of the Extended Theory of Planned Behavior. Agriculture (Switzerland), 13(5).
Bishop, P. A. and Herron, R. L. 2019. Use and misuse of the Likert item responses and other ordinal measures. Journal of Chemical Information and Modeling, 53(9): 1689–1699.
Bosompem, M. 2021. Potential challenges to precision agriculture technologies development in Ghana: scientists’ and cocoa extension agents’ perspectives. Precision Agriculture, 22(5): 1578–1600
Carfora, V., Cavallo, C., Caso, D., Del Giudice, T., De Devitiis, B., Viscecchia, R., Nardone, G. and Cicia, G. 2019. Explaining consumer purchase behavior for organic milk: Including trust and green self-identity within the theory of planned behavior. Food Quality and Preference, 76: 1–9.
Chen, H., & Kuo, H. (2022). Green energy and water resource management: A case study of fishery and solar power symbiosis in Taiwan. Water, 14(1299), 1–15
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates, Publishers.
Damalas, C. A. 2021. Farmers’ intention to reduce pesticide use: the role of perceived risk of loss in the model of the planned behavior theory. Environmental Science and Pollution Research, 28(26): 1–8.
Danso-Abbeam, G., Ehiakpor, D. S. and Aidoo, R. 2018. Agricultural extension and its effects on farm productivity and income: Insight from Northern Ghana. Agriculture and Food Security, 7(1): 1–10.
Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3): 319–339
Daxini, A., Ryan, M., O’Donoghue, C. and Barnes, A. P. 2019. Understanding farmers’ intentions to follow a nutrient management plan using the theory of planned behaviour. Land Use Policy, 85: 428–437
Dibaba, B. and Biazin, H. 2022. Agricultural Extension Service in Ethiopia: The role of social system and farmers’ - agricultural development agents relationship. American Journal of IT and Applied Sciences Research, 1(5): 1–15.
Dong, H., Wang, H., & Han, J. (2022). Understanding ecological agricultural technology adoption in China using an Integrated Technology Acceptance Model—Theory of Planned Behavior Model. Frontiers in Environmental Science, 10: 1–11.
El Bilali, H., & Allahyari, M. S. (2018). Transition towards sustainability in agriculture and food systems: Role of information and communication technologies. Information Processing in Agriculture, 5(4): 456–464
Fishbein, M. and Ajzen, I. 1975. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley. https://people.umass.edu/aizen/f&a1975.html
Fishbein, M. and Ajzen, I. 2010. Predicting and changing behavior: The reasoned action approach. Psychology Press under Taylor & Francis Group.
Gefen, D., Straub, D. and Boudreau, M.-C. 2000. Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7): 1–78.
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1): 101–107.
Ghali-zinoubi, Z. 2022. Examining drivers of environmentally conscious consumer behavior: Theory of planned behavior extended with cultural factors. Sustainability, 14(8072): 1–17.
Gholamrezai, S., Aliabadi, V. and Ataei, P. 2021. Understanding the pro‑environmental behavior among green poultry farmers: Application of behavioral theories. Environment, Development and Sustainability, 23: 16100–16118.
Govindharaj, G., Gowda, B., Sendhil, R., Adak, T., Raghu, S., Patil, N., Mahendiran, A., Chandra, P., Kumar, G. A. K. and Damalas, C. A. 2021. Determinants of rice farmers’ intention to use pesticides in eastern India: Application of an extended version of the planned behavior theory. Sustainable Production and Consumption, 26: 814–823.
Hair, J. F., Hult, G. T. M., Ringle, C., Sarstedt, M., Danks, N., and Ray, S. 2021. Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer Nature Switzerland.
Hair, J. F., Ringle, C. M. and Sarstedt, M. 2011. PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2): 139–152.
Hair, J. F., Risher, J. J., Sarstedt, M. and Ringle, C. M. 2019. When to use and how to report the results of PLS-SEM. Psychometrika, 31(1): 2–24.
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management and Data Systems, 117(3), 442–458.
Hair Jr, J., Hult, G. T., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE Publications Inc.
Hall, J. 2011. Cross-sectional survey design. In P. J. Lavrakas (Ed.), Encyclopedia of Survey Research Methods (pp. 173–178). Sage Publications, Inc.
Hayes, A. F. and Coutts, J. J. 2020. Use Omega rather than Cronbach’s alpha for estimating reliability. But…. Communication Methods and Measures, 00:1–24.
Henseler, J., Ringle, C. M. and Sarstedt, M. 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1): 115–135.
Hulland, J. 1999. Use of partial least squares (PLS) in strategic management research: A review of four resent studies. Strategic Management Journal, 20(2): 195–204.
Hur, G., Barry, D. M., Alford, K., Jagger, C. B. and Roberts, T. G. 2024. Why pursue a career in teaching agriculture?: Application of Self-Determination Theory and the Theory of Planned Behavior. Journal of Agricultural Education, 65(2): 15–34.
Ibrahim, F. M., Osikabor, B., Olatunji, B. T. and Ogunwale, G. O. 2022. Understanding forest land conversion for agriculture in a developing country context : An application of the theory of planned behaviour among a cohort of Nigerian farmers. Folia Forestalia Polonica, Series A – Forestry, 64(3): 117–130
Jakku, E., Fielke, S., Fleming, A. and Stitzlein, C. 2022. Reflecting on opportunities and challenges regarding implementation of responsible digital agri-technology innovation. Sociologia Ruralis, 62(2): 363–388.
Karapandzin, J., Rodić, V. and Caracciolo, F. 2019. Factors affecting farmers’ adoption of integrated pest management in Serbia: An application of the theory of planned behavior. Journal of Cleaner Production, 228: 1–27.
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607–610.
Lai, P. (2017). The literature review of technology adoption models and theories for the novelty technology. Journal of Information Systems and Technology Management, 14(1): 21–38.
Lee, C. S., Chen, Y. C., Tsui, P. L. and Chiang, M. C. 2023. Using the Theory of Planned Behavior to examine the sustainable extension of rural food preparation techniques. Agriculture (Switzerland), 13(5). h
Loha, K. M., Klous, G., Lamoree, M. and Boer, J. De. 2022. Pesticide use and practice of local farmers in the Central Rift Valley (CRV) of Ethiopia: implications for the environment and health hazards. International Journal of Pest Management, 0(0), 1–14.
Markets and Markets. 2024. Digital agriculture market offering, technology (peripheral, core), operation (farming & feeding, monitoring &scouting, marketing & demand generation) type (hardware, software, services), region – Global forecast to 2028: Report Code AGI 8180. https://www.marketsandmarkets.com/Market-Reports/digital-agriculture-market-235909745.html
McEachan, R. R. C., Conner, M., Taylor, N. J. and Lawton, R. J. 2011. Prospective prediction of health-related behaviours with the theory of planned behaviour: A meta-analysis. Health Psychology Review, 5(2): 97–144.
Memon, M. A., Ramayah, T., Cheah, J.-H., Ting, H., Chuah, F. and Cham, T.-H. 2023. Addressing common method bias, operationalization, sampling, and data collection issues in quantitative research: Review and recommendations. Journal of Applied Structural Equation Modeling, 7(2): i–xiv.
Ministry of Local Government and Rural Development. 2023. Background of regions in Ghana. Ghana Districts. http://www.ghanadistricts.com/
MoFA-DAES. 2021. Directorate of agricultural extension services 2021 report.
MoFA-Directorate of Agricultural Extension Services. 2003. Agricultural extension approaches being implemented in Ghana. mofa.gov.gh/site/wp-%0Acontent/uploads/2011/03/Extension-approaches-in-Ghana-.pdf
Moon, M. A., Mohel, S. H. and Farooq, A. 2019. I green, you green, we all green: Testing the extended environmental theory of planned behavior among the university students of Pakistan. Social Science Journal, 58(3): 1–12.
Nunnally, J. C. 1978. Psychometric theory. McGraw-Hill.
Nyarko, D. A. and Kozári, J. 2021. Information and communication technologies (ICTs) usage among agricultural extension officers and its impact on extension delivery in Ghana. Journal of the Saudi Society of Agricultural Sciences, 20(3): 164–172.
Ocran, J. K., Asante, I. K. and Ametepey, K. T. E. 2024. Benefits, barriers, challenges and requirements for the application of digital technologies in agricultural extension in selected regions in Ghana: Perspectives from extension agents. Journal of Agricultural Extension and Rural Development, 16(2): 88–105.
Omega, S., Annor-Frempong, F., Akaba, S., Ghartey, W., Ocran, J. K. and Asante, I. K. 2020. Willingness to pay for drone technology in the application of pesticides for control of FallArmy Worm. Paper Presented at the 1st African Conference on Precision Agriculture, 353–356.
Ortiz-Crespo, B., Steinke, J., Quirós, C. F., van de Gevel, J., Daudi, H., Gaspar Mgimiloko, M. and van Etten, J. 2021. User-centred design of a digital advisory service: enhancing public agricultural extension for sustainable intensification in Tanzania. International Journal of Agricultural Sustainability, 19(5–6): 566–582.
Prince, M. and Das-Munshi, J. 2020. Cross-sectional surveys. In J. Das-Munshi, T. Ford, M. Hotopf, M. Prince, & R. Stewart (Eds.), Practical Psychiatric Epidemiology (Online, pp. 127–144). Oxford University Press.
Quintal, V. A., Lee, J. A. and Soutar, G. N. 2010. Risk, uncertainty and the theory of planned behavior: A tourism example. Tourism Management, 31(6): 797–805.
Rajeh, M. T. 2022. Modeling the theory of planned behavior to predict adults’ intentions to improve oral health behaviors. BMC Public Health, 22(1391), 1–9.
Rezaei, R., Seidi, M. and Karbasioun, M. 2019. Pesticide exposure reduction: Extending the theory of planned behavior to understand Iranian farmers’ intention to apply personal protective equipment. Safety Science, 120: 527–537.
Ringle, C. M., Wende, S. and Becker, J. M. 2022. SmartPLS 4 (4.0; pp. 1–225). SmartPLS GmbH. www.smartpls.com
Rogers, E. M. 2003. Diffusion of innovations (5th ed.). Free Press. https://www.worldcat.org/title/diffusion-of-innovations/oclc/52030797
Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020). Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses! International Journal of Market Research, 62(3): 288–299.
Sarstedt, M., Ringle, C. M. and Hair, J. F. 2021. Handbook of market research. In C. Homburg, M. Klarmann, & A. E. Vomberg (Eds.), Handbook of Market Research (pp. 1–47). Springer, Cham.
Shiau, W. L., Sarstedt, M. and Hair, J. F. 2019. Internet research using partial least squares structural equation modeling (PLS-SEM). Internet Research, 29(3): 398–406.
Shirahada, K. and Zhang, Y. 2022. Counterproductive knowledge behavior in volunteer work: perspectives from the theory of planned behavior and well-being theory. Journal of Knowledge Management, 26(11): 22–41.
Shmueli, G. and Koppius, O. R. 2011. Predictive analytics in information systems research. MIS Quarterly: Management Information Systems, 35(3): 553–572.
Shmueli, G., Ray, S., Velasquez Estrada, J. M. and Chatla, S. B. 2016. The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10): 4552–4564.
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S. and Ringle, C. M. 2019. Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11): 2322–2347
Şimşek, G. G. and Noyan, F. 2013. McDonald’s ω t , Cronbach’s α, and Generalized θ for Composite Reliability of Common Factors Structures . Communications in Statistics - Simulation and Computation, 42(9): 2008–2025.
Sniehotta, F. F., Presseau, J. and Araújo-Soares, V. 2014. Time to retire the theory of planned behaviour. Health Psychology Review, 8(1): 1–7.
Stone, M. 1974. Cross - validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological ), 36(2): 111–133
Tian, X., Xu, P., Liu, X. and Xu, X. 2023. The impact of digital information treatment on the evaluation of service performance of agricultural extension agents. Information Development, 0: 1–16.
Tsan, M., Totapally, S., Hailu, M. and Addom, B. K. 2019. The digitalisation of African Agriculture Report (2018-2019) (J. Lichtenstein, M. Schnapf, & B. Beks (eds.); 1st ed.). The Technical Centre for Agricultural and Rural Cooperation (CTA). https://cgspace.cgiar.org/bitstream/handle/10568/101498/CTA-Digitalisation-report.pdf
Uzun, V., Shagaida, N. and Lerman, Z. 2019. Russian agriculture: Growth and institutional challenges. Land Use Policy, 83: 475–487.
VanderStoep, S. W. and Johnston, D. D. 2009. Research Methods for everyday life: Blending Qualitative and Quantitative Approaches (1st ed.). Jossey-Bass of John Wiley & Sons, Inc. www.josseybass.com
Venkatesh, V. and Bala, H. 2008. Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2): 273–315.
Venkatesh, V. and Davis, F. D. 2000. Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2): 186–204.
Vonglao, P. 2017. Application of fuzzy logic to improve the Likert scale to measure latent variables. Kasetsart Journal of Social Sciences, 38(3): 337–344.
Wastutiningsih, S. P. and Aulia, D. 2023. Influence sustainable consumption campaigns on intention to perform food waste reduction behavior of young consumers in Yogyakarta. In book: Proceedings of the International Conference On Multidisciplinary Studies (ICOMSI 2022) (pp.335-343)
Xu, Y., Lyu, J., Xue, Y. and Liu, H. 2022. Intentions of farmers to renew productive agricultural service contracts using the Theory of Planned Behavior: An empirical study in Northeastern China. Agriculture (Switzerland), 12(9).
Yuriev, A., Dahmen, M., Paillé, P., Boiral, O. and Guillaumie, L. 2020. Resources, conservation & recycling pro-environmental behaviors through the lens of the theory of planned behavior: A scoping review. Resources, Conservation & Recycling, 155(104660): 1–12.
Zaremohzzabieh, Z., Krauss, S. E., D’Silva, J. L., Tiraieyari, N., Ismail, I. A. and Dahalan, D. 2022. Towards agriculture as career: predicting students’ participation in the agricultural sector using an extended model of the theory of planned behavior. Journal of Agricultural Education and Extension, 28(1): 67–92.
DOI: 10.33687/ijae.012.003.5340
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