A Framework for Farmers’ Acceptance of Smart Mobile Learning System for Pesticide Management Training: A Conceptual Review

  • Aminu Hamza
  • Yulita Hanum P. Iskandar

Abstract

Farmers lack of knowledge on how to responsibly handle pesticides has been causing pesticide poisoning in Nigeria. Smart Mobile Learning System (SMLS) is capable of addressing that ugly trend. SMLS can provide farmers with access to learning resources anytime they need them, regardless of their geographical location. SMLS will facilitate farmers' learning and training on pesticide management as it modifies their learning process, in order to curtail the issue of pesticide poisoning. Despite the fact that farmers want stress-free, easily accessible learning and training, it is clear that research on the acceptance intention of SMLS in the agricultural sector as it affects farmers is still lacking. As a result, a research to investigate the situation is required, especially from the perspective of farmers in a developing country like Nigeria. Some of the most widely utilized technology acceptance models are examined in this study. To investigate the acceptance intention of SMLS, this study adapted and harmonized the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with the Technology Acceptance Model 2 (TAM2). The proposed harmonized theoretical framework considers acceptance of SMLS from the context of farmers, by exploring the influence of Perceived Ubiquitous Value, Social Norm, Perceived usefulness, Perceived ease of use, Habit, Price Value, Hedonic Motivation, and facilitating conditions. The moderating effect of Trust on the dependent variables will also be examined. Subsequently, the proposed theoretical framework will be tested in order to facilitate future research on farmer acceptance of SMLS.

References

Açıkgül, K. & Sad, S. N. (2021). High school students’ acceptance and use of mobile technology in learning mathematics, Education and Information Technologies, 26, 4181–4201. https://doi.org/10.1007/s10639-021-10466-7
Adegoke, S. A., Oladokun, T. T., Ayodele, T. O., Agbato, S. E. & Jinadu, A. A. (2021). Analysing the criteria for measuring the determinants of virtual reality technology adoption in real estate agency practice in Lagos: a DEMATEL method, Property Management, 0263-7472. DOI 10.1108/PM-05-2021-0035
Adekunle, C. P., Akinbode, S. O., Akerele, D. 1., Oyekale, T. O. & Koyi, O. V. (2017). Effects of agricultural pesticide utilization on farmers’ health in egbeda local government area, oyo state, Nigeria, Nigerian Journal of Agricultural Economics, 7(1), 73-88
Ajzen, I. & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychol. Bull. 82 (2), 261.
Alghazi, S. S., Kamsin, A., Almaiah, M. A., Wong, S. Y. & Shuib, L. (2021). For Sustainable Application of Mobile Learning: An Extended UTAUT Model to Examine the Effect of Technical Factors on the Usage of Mobile Devices as a Learning Tool, Sustainability, 13, 1856. https://doi.org/10.3390/su13041856
Al-Azawei, A, & Alowayr, A. (2020). Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries, Technology in Society, 62, 101325. https://doi.org/10.1016/j.techsoc.2020.101325
Al-Emran, M. A., Mezhuyev, V., & Kamaludin, A. (2020). Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance, Technology in Society, 61,101247. https://doi.org/10.1016/j.techsoc.2020.101247
Alghazi, S. S., Kamsin, A., Almaiah, M. A., Wong, S. Y. & Shuib, L. (2021). For Sustainable Application of Mobile Learning: An Extended UTAUT Model to Examine the Effect of Technical Factors on the Usage of Mobile Devices as a Learning Tool. Sustainability, 13, 1856. https://doi.org/10.3390/su13041856
Alowayr, A. & Al-Azawei, A, (2021). Predicting mobile learning acceptance: An integrated model and empirical study based on the perceptions of higher education students. Australian Journal of Educational Technology, 37(3).
Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., & Al-Adwan, A. S. (2021). Exploring the Factors Affecting Mobile Learning for Sustainability in Higher Education. Sustainability, 13, 7893. https://doi.org/10.3390/Su13147893
Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U. et al. (2022). Acceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education, Educational Information Technology. https://doi.org/10.1007/s10639-022-10934-8
Alsahafi, R. A. A. (2020). Predicting Students' Intention to Use Gamified Mobile Learning in Higher Education [Master’s thesis, The University of Melbourne. Australia]. Minerva access. https://minerva-access.unimelb.edu.au/items/e69f691e-1e15-5b8e-a7d6-e19f27306b62
Alturki, U., & Aldraiweesh, A. (2022). Students’ Perceptions of the Actual Use of Mobile Learning during COVID-19 Pandemic in Higher Education, Sustainability, 14, 1125. https://doi.org/10.3390/su14031125
Alzaidi, M. S. & Shehawy, Y. M. (2022). Cross-national differences in mobile learning adoption during COVID-19, Education & Training, Vol. ahead-of-print No. ahead-of-print. Emerald. https://doi.org/10.1108/ET-05-2021-0179
Arain, A. A., Hussain Z., Rizvi, W. H., & Vighio, M. S. (2019). Extending UTAUT2 towards acceptance of mobile learning in the context of higher education, Universal Access in the Information Society, 18(3), 659-673. https://doi.org/10.1007/s10209-019-00685-8.
Ateş, H., & Garzón, J. (2022). Drivers of teachers’ intentions to use mobile applications to teach science, Education and Information Technologies, 27, 2521–2542. https://doi.org/10.1007/s10639-021-10671-4
Batani, J., Musungwini, S., & Rebanowako, T. G. (2019). An Assessment of the Use of mobile phones as sources of Agricultural information by tobacco Smallholder farmers in Zimbabwe, Journal of Systems Integration.
Caffaro, F., Cremasco, M. M., Roccato, M. & Cavallo, E. (2020). Drivers of farmers’ intention to adopt technological innovations in Italy: The role of information sources, perceived usefulness, and perceived ease of use, Journal of Rural Studies, 76, 264–271
Chao, C-M (2019). Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Front. Psychol. doi: 10.3389/fpsyg.2019.0165
Che, F. N., Strang, K. D., & Vajjhala, N. R. (2020). Voice of farmers in the agriculture crisis in North-East Nigeria: Focus group insights from extension workers, International Journal of Development Issues, 19(1), 43-61. DOI 10.1108/IJDI-08-2019-0136
Cheak, A. P. C., Chong, C. W., & Yuen, Y. Y. (2020). The role of quality perceptions and perceived ubiquity in adoption intention of mobile knowledge management systems (MKMS) in semiconductor industry, VINE Journal of Information and Knowledge Management Systems, 52(2), 243-269. Emerald. DOI 10.1108/VJIKMS-07-2020-0140
Cheng, G., & Shao, Y. (2022). Influencing Factors of Accounting Practitioners’ Acceptance of Mobile Learning, International Journal of Emerging Technologies in Learning, 17(01), 90–101. https://doi.org/10.3991/ijet.v17i01.28465
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13(3), 319–340.
DW. (2021, October, 9). Banned pesticides poison hundreds in Nigeria: Why is there still such a demand? [Video file]. YouTube. https://m.youtube.com/watch?v=IlpTvVq3-PA
Falaju, J. (2021, March 21). FG commences training of 1,110 extension agents. Guardian NG. https://guardian.ng/features/fg-commences-training-of-1110-extension-agents/
Fan, M., Ndavi, J. W., Qalati, S. A., Huang, L. & Pu, Z. (2022). Applying the time continuum model of motivation to explain how major factors affect mobile learning motivation: a comparison of SEM and fsQCA, Online Information Review, Vol. ahead-of-print No. ahead-of-print. Emerald. https://doi.org/10.1108/OIR-04-2021-0226
García, M. V., López, M. F. B., & Castillo, M. A. S. (2019). Determinants of the acceptance of mobile learning as an element of human capital training in organisations, Technological Forecasting & Social Change, 149, 119783. https://doi.org/10.1016/j.techfore.2019.119783
Gómez-Ramirez, I., Valencia-Arias, A., & Duque, L. (2019). Approach to M-learning Acceptance Among University Students: An Integrated Model of TPB and TAM, International Review of Research in Open and Distributed Learning, 20(3), DOI: https://doi.org/10.19173/irrodl.v20i4.4061
Hsu, H.-T., & Lin, C.-C. (2022). Extending the technology acceptance model of college learners' mobile-assisted language learning by incorporating psychological constructs, British Journal of Educational Technology, 53(2), 286-306. 10.1111/bjet.13165
Huang, D.-H., & Chueh, H.-E, (2022). Behavioral intention to continuously use learning apps: A comparative study from Taiwan universities, Technological Forecasting and Social Change, 177, 10.1016/j.techfore.2022.121531
Izuagbe, R., Ifijeh, G., Izuagbe-Roland, E. I., Olawoyin, O. R. &, L. O. (2019). Determinants of perceived usefulness of social media in university libraries: Subjective norm, image and voluntariness as indicators, The Journal of Academic Librarianship, 45(4), 394-405
Kaur, S. & Arora, S. (2021). Role of perceived risk in online banking and its impact on behavioral intention: trust as a moderator, Journal of Asia Business Studies, 15(1), 1-30, Emerald
Kim, S. (2021). How a company’s gamification strategy influences corporate learning: A study based on gamified MSLP (Mobile social learning platform), Telematics and Informatics, 57, 101505. https://doi.org/10.1016/j.tele.2020.101505
Khoa, B. T., Ha, N. M., Nguyen, T. V. H. & Bich, N. H. (2020). Lecturer’s adoption to use the online Learning Management System (LMS): Empirical evidence from TAM2 model for Vietnam. HO Chi Minh City Open University Journal of Science, 10(1), 3-7
Kumar, J. A., Bervell, B., Annamalai, N. & Osman, S. (2020). Behavioral Intention to Use Mobile Learning: Evaluating the Role of Self-Efficacy, Subjective Norm, and WhatsApp Use Habit, IEEEAccess, 8, 208058-208074. doi: 10.1109/ACCESS.2020.3037925
Ladd, D. A., Datta, A., Sarker, S. & Yu, Y. (2010). Trends in Mobile Computing within the IS Discipline: A Ten-Year Retrospective Communications of the Association for Information Systems, 27, 285-306.
Lai. Y., Saab, N., & Admiraal, W. (2022). University students’ use of mobile technology in self-directed language learning: Using the integrative model of behavior prediction, Computers and Education, 179. 10.1016/j.compedu.2021.104413
Landmann, D., Lagerkvist, C-J., & Otter, V. (2020). Determinants of Small‑Scale Farmers’ Intention to Use Smartphones for Generating Agricultural Knowledge in Developing Countries: Evidence from Rural India, The European Journal of Development Research, https://doi.org/10.1057/s41287-020-00284-x
Li, M. & Liu, L. (2022). Students' perceptions of augmented reality integrated into a mobile learning environment, Library Hi Tech, Vol. ahead-of-print No. ahead-of-print. Emerald. https://doi.org/10.1108/LHT-10-2021-0345
Lin, S. H., Lee, H-C, Chang, C-T., & Fu, C. J. (2020). Behavioral intention towards mobile learning in Taiwan, China, Indonesia and Vietnam, Technology in Society, 63, 101387
Livari, M., & Kauppinen, T. (2021, November 18). Accelerating the deployment of blockchain technology requires trust building and introduction of success example cases. https://www.oulu.fi/en/joy/news/accelerating-deployment-blockchain-technology-requires-trust-building-and-introduction-successful.
Matta, S., Rogova, N., & Luna-Cort´es, G. (2022). Investigating tolerance of uncertainty, COVID-19 concern, and compliance with recommended behavior in four countries: The moderating role of mindfulness, trust in scientists, and power distance, Personality and Individual Differences, 186, 111352
McKnight, D. H., (2005). Trust in Information Technology, in Davis, G.B. (Ed.), The Blackwell Encyclopedia of Management, Management Information Systems, Malden, MA: Blackwell, 7, 329-331.
Mensah, I. K., & Mwakapesa, D. S. (2022). The Impact of Context Awareness and Ubiquity on Mobile Government Service Adoption. Mobile Information System, https://doi.org/10.1155/2022/5918826
Mittal, N., & Alavi, S. (2020). Construction and psychometric analysis of teachers mobile learning acceptance questionnaire, Interactive Technology and Smart Education, 17(2), 171-196. Emerald. DOI 10.1108/ITSE-07-2019-0039
Moore, G. C. & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation, Information System Resources, 2(3), 192–222.
Moorthy, K., Yee, T. T., T'ing, L. C., & Kumaran, V. V. (2019). Habit and hedonic motivation are the strongest influences in mobile learning behaviours among higher education students in Malaysia, Australasian Journal of Educational Technology, 35(4), 174-191. https://doi.org/10.14742/ajet.4432.
Mussa, I. H., Sazalli, N. A. H., & Hassan, Z. (2022). Mobile learning by English literature students: the role of user satisfaction, Bulletin of Electrical Engineering and Informatics, 11(1), 550 – 557. DOI: 10.11591/eei.v11i1.3277
Mutambara, D., & Bayaga, A. (2021). Determinants of mobile learning acceptance for STEM education in rural areas, Computers & Education,160, 104010. https://doi.org/10.1016/j.compedu.2020.104010.
Nawaz, S. S. & Mohamed, R. (2020). Acceptance of Mobile Learning by Higher Educational Institutions in Sri Lanka: An UTAUT2 Approach. Journal of Critical Reviews, 7(12).
Nezamdoust, S., Abdekhoda, M., & Rahmani, A. (2022). Determinant factors in adopting mobile health application in healthcare by nurses, BMC Medical Informatics and Decision Making, 22, 47. https://doi.org/10.1186/s12911-022-01784-y
Ng, K. Y. N. (2020), The moderating role of trust and the theory of reasoned action, Journal of Knowledge Management, 24(6), 1221-1240, Emerald. DOI 10.1108/JKM-01-2020-0071
Nie, J., Zheng, C., Zeng, P., Zhou, B. & Lei, L., Wang, P. (2020). Using the theory of planned behavior and the role of social image to understand mobile English learning check-in behavior, Computers & Education, 156, 103942
Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet, Computers and Education Open, 2, 100041
Ntow, W. J., Gijzen, H. J., Kelderman, P. & Drechsel, P. (2006). Farmer perceptions and pesticide use practices in vegetable production in Ghana, Pest Management Science, 62, 356-365. PubMed.
Okoroji, V., Lees, N. J., & Lucock, X. (2021). Factors affecting the adoption of mobile applications by farmers: An empirical investigation, African Journal of Agricultural Research, 17(1), 19-29. DOI: 10.5897/AJAR2020.14909
Oesterlund, A. H., Thomsen, J. F., Sekimpi, D. K., Maziina, J., Racheal, A. & Jors, E. (2014). Pesticide Knowledge, practice and attitude and how it affects the health of small-scale farmers in Uganda: across-sectional study, African Health Science, 14(2), 420-433. Doi: 10.4314/ahs.v14i2.19
Panagiotopoulos, I., & Dimitrakopoulos, G. (2018). An empirical investigation on consumers’ intentions towards autonomous driving, Transport. Res. C Emerg. Technol. 95, 773–784.
Park, M. J., Choi, H., Kim, S. K. & Rho, J. J. (2015). Trust in government’s social media service and citizen’s patronage behavior, Telematics and Informatics, 32 (4), 629-641
Park, J., Hong, E. & Le, H.T. (2021). Adopting autonomous vehicles: The moderating effects of demographic variables. Journal of Retailing and Consumer Services, 63, 102687
Sabran, S. H., & Abas, A. (2021). Knowledge and Awareness on the Risks of Pesticide Use Among Farmers at Pulau Pinang, Malaysia, Sage Open, 1-13. https://doi.org/10.1177/21582440211064894
Samhale, K. (2022). The impact of trust in the internet of things for health on user engagement, Digital Business, 2, 100021. http://dx.doi.org/10.1016/j.digbus.2022.100021
Sarrab, M. (2015). M-learning in education: Omani Undergraduate students perspective, Procedia-Social and Behavioral Sciences, 176, 834-839.
Sayibu, M., Jianxun, C., Akintunde, T. Y., Hafeez, R. O., Koroma, J., Amosun, T. S., & Shahani, R. (2021). Nexus between students’ attitude towards self-learning, Tencent APP usability, mobile-learning, and innovative performance, Social Sciences & Humanities Open, 4, 100217. https://doi.org/10.1016/j.ssaho.2021.100217
Sekiyama, M., Tanaka, M., Gunawan, B., Abdoellah, O. & Watanabe, C. (2007). Pesticide usage and its association with health symptoms among farmers in rural villages in West Java, Indonesia, Environmental Science, 14, 23-33. PubMed.
Sennuga, S. O. (2019). Use of ICT among smallholder farmers and extension workers and its relevance to sustainable agricultural practices in Nigeria [Doctoral thesis, Coventry University. England]. Lanchester Library. https://pureportal.coventry.ac.uk/files/30430186/Sennuga_PhD_Pure.pdf
Sophea, D., Sungsuwan, T., & Viriyasuebphong, P. (2022). Factors influencing students’ behavioral intention on using mobile learning (M-learning) in tourism and Hospitality major in Phnom Penh, Cambodia, Current Applied Science and Technology, 22(2).
Tabowei, A. E. (2021). Technology Enhanced Learning: A Case Study of the Potentials of Mobile Technologies in Nigerian College of Education, [Unpublished doctoral thesis]. Faculty of Environment and Technology, University of the West of England Bristol.
Tamilmani, K., Rana, N., Dwivedi, Y., Sahu, G. P., & Roderick, S. (2018). Exploring the Role of ‘Price Value’ for Understanding Consumer Adoption of Technology: A Review and Meta-analysis of UTAUT2 based Empirical Studies, PACIS 2018 Proceedings, 64. https://aisel.aisnet.org/pacis2018/64
Thar, S. P., Ramilan, T., Farquharson, R. J., Pang, A., & Chen, D. (2020). An empirical analysis of the use of agricultural mobile applications among smallholder farmers in Myanmar, Electronic journal of information system development countries, DOI: 10.1002/isd2.12159
Triandis, H. C. (1980). Values, attitudes, and interpersonal behavior. In: Howe, H.E., Page, M.M. (Eds.), Nebraska Symposium on Motivation. University of Nebraska Press, Lincoln, NE, 195–259.
Tu, Y.-F., Hwang, G.-J., Chen, J. C.-C. & Lai, C. (2021). University students’ attitudes towards ubiquitous library-supported learning: an empirical investigation in the context of the Line@Library, The Electronic Library, 39(1), 186-207. https://doi.org/10.1108/EL-03-2020-0076
Venkatesh, V., & Davis, F. D., (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies, Management Science, 46(2), 186-204. http://dx.doi.org/10.1287/mnsc.46.2.186.11926.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis., F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-479.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology, MIS Quarterly, 36(1), 157-178
Wang, G., Tan, G. W., Yuan, Y., Ooi, K-B. & Dwivedi, Y. K. (2021). Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis, Technological Forecasting & Social Change, https://doi.org/10.1016/j.techfore.2021.121345
Wu, D., Lowry, P. B., Zhang, D., & Parks, R. F. (2021a). Patients’ compliance behavior in a personalized mobile patient education system (PMPES) setting: Rational, social, or personal choices? International Journal of Medical Informatics, 145, 104295. https://doi.org/10.1016/j.ijmedinf.2020.104295
Yan, M. Filieri, R. & Gordon, M. (2021). Continuance intention of online technologies: A Systematic Literature Review, International Journal of Information Management, 58, 102315. https://doi.org/10.1016/j.ijinfomgt.2021.102315
Zaidi, S. F. H., Osmanaj, V., Ali, O. & Zaidi, S. A. H. (2021). Adoption of mobile technology for mobile learning by university students during COVID-19, International Journal of Information and Learning Technology, 38(4), 329-343. https://doi.org/10.1108/IJILT-02-2021-0033
Published
2023-09-01
How to Cite
HAMZA, Aminu; P. ISKANDAR, Yulita Hanum. A Framework for Farmers’ Acceptance of Smart Mobile Learning System for Pesticide Management Training: A Conceptual Review. International Journal of Business and Technology Management, [S.l.], v. 5, n. 3, p. 13-28, sep. 2023. ISSN 2682-7646. Available at: <https://myjms.mohe.gov.my/index.php/ijbtm/article/view/23733>. Date accessed: 13 june 2024.
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