Determinants of Farmers’ Willingness to Pay for IoT Adoption in Rice Farming: Evidence from Malaysia
DOI:
https://doi.org/10.32890/jict2026.25.2.1Keywords:
Internet of Things, willingness to pay, rice farming, technology adoption, digital agricultureAbstract
Rice farming faces persistent challenges that constrain productivity and sustainability, underscoring the urgent need for digital innovations such as the Internet of Things (IoT). While IoT technologies offer the potential for efficiency and resilience, their adoption depends on farmers’ willingness to pay (WTP), which remains poorly understood in resource-constrained contexts. This study aimed to identify the determinants of WTP for IoT adoption by integrating farmer-related, technological, and institutional factors. A structured survey was conducted among 229 rice farmers in the Integrated Agricultural Development Area, Barat Laut Selangor, Malaysia. The findings revealed that most farmers demonstrated moderate IoT awareness, with the awareness index highlighting gaps in technical knowledge and training. The IoT readiness index recorded relatively high scores for shared infrastructure (71.8) and Internet connectivity (71.6) but lower levels for technical support (60.0) and energy supply (54.4), reflecting uneven preparedness. Logistic regression results indicated that higher-income farmers were significantly more likely to invest, with middle- and high-income groups showing 20.13% and 22.45% higher probabilities, respectively. Secure land tenure increased WTP by 3.18%, while each additional year of age reduced WTP by 0.61%. IoT infrastructural readiness raised WTP by 10.53%, and perceived ease of use increased WTP by 8.27%. However, perceived usefulness exhibited a negative marginal effect, suggesting that recognising IoT’s benefits does not necessarily translate into WTP when financial or institutional barriers persist. The findings indicate that financial support, reliable IoT infrastructure, and practical training are crucial to fostering inclusive IoT adoption and enhancing resilience in rice farming.
References
Adam, B., & Abdulai, A. (2023). Minimum tillage as climate-smart agriculture practice and its impact on food and nutrition security. PLoS ONE, 18(12), e0287441. https://doi.org/10.1371/journal. pone.0287441
Alfred, R., Obit, J. H., Chin, C. P. Y., Haviluddin, H., & Lim, Y. (2021). Towards paddy rice smart farming: A review on big data, machine learning, and rice production tasks. In IEEE Access (Vol. 9, pp. 50358–50380). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2021.3069449
Alhassan, M. (2012). Estimating farmers' willingness to pay for improved irrigation: An economic study of the Bontanga Irrigation Scheme in Northern Ghana [Unpublished master's thesis]. Colorado State University.
Ali, A., Hussain, T., & Zahid, A. (2025). Smart irrigation technologies and prospects for enhancing water use efficiency for sustainable agriculture. AgriEngineering, 7(4), 106. https://doi.org/10.3390/agriengineering7040106
Anugwa, I. Q., Onwubuya, E. A., Chah, J. M., Abonyi, C. C., & Nduka, E. K. (2022). Farmers’ preferences and willingness to pay for climate-smart agricultural technologies on rice production in Nigeria. Climate Policy, 22(1), 112–131. https://doi.org/10.1080/14693062. 2021.1953435
Athirah, R. N., Norasma, C. Y. N., & Ismail, M. R. (2020). Development of an Android application for smart farming in crop management. IOP Conference Series: Earth and Environmental Science, 540(1). https://doi.org/10.1088/1755-1315/540/1/012074
Bamurigire, P., Vodacek, A., Valko, A., & Ngoga, S. R. (2020). Simulation of internet of things water management for efficient rice irrigation in Rwanda. Agriculture (Switzerland), 10(10), 1–12. https://doi.org/10.3390/agriculture10100431
Blasch et al. (2022). Farmer preferences for adopting precision farming technologies: A case study from Italy. European Review of Agricultural Economics, 49(1), 33–81. https://doi.org/10.1093/ erae/jbaa031
Bontsa, N. V., Mushunje, A., & Ngarava, S. (2023). Factors influencing the perceptions of smallholder farmers towards adoption of digital technologies in Eastern Cape Province, South Africa. Agriculture (Switzerland), 13(8). https://doi.org/10.3390/agriculture13081471
Boone Jr, H. N., & Boone, D. A. (2012). Analysing Likert data. The Journal of Extension, 50(2), 48.
Carolan, M. (2020). Automated agrifood futures: Robotics, labor and the distributive politics of digital agriculture. Journal of Peasant Studies, 47(1), 184–207. https://doi.org/10.1080/03066150. 2019.1584189
Chanchaichujit, J., Balasubramanian, S., & Shukla, V. (2024). Barriers to Industry 4.0 technology adoption in agricultural supply chains: A Fuzzy Delphi-ISM approach. International Journal of Quality & Reliability Management, 41(7), 1942-1978. https://doi.org/10.1108/IJQRM-07-2023-0222
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 et al. (2025). Agricultural extension services from transfer of technology to facilitation for innovation key messages. Global food policy report 2025: Food policy: Lessons and priorities for a changing world, 195-220. https://hdl.handle.net/10568/174108
Diallo, A., & Ronald Dossou-Yovo, E. (2024). A gendered analysis of farmers’ access to and willingness to pay for climate information services: Evidence from rice farmers in Mali. Climate Services, 35. https://doi.org/10.1016/j.cliser.2024.100507
Dorairaj, D., & Govender, N. T. (2023). Rice and paddy industry in Malaysia: Governance and policies, research trends, technology adoption and resilience. Frontiers in Sustainable Food Systems, 7, 1093605. https://doi.org/10.3389/fsufs.2023.1093605
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. https://doi.org/10.1016/j.inpa.2018.06.
Fahad et al. (2018). Rice responses and tolerance to high temperature. In Advances in Rice Research for Abiotic Stress Tolerance (pp. 201–224). Elsevier. https://doi.org/10.1016/B978-0-12-814332-2.00010-
Feder, G., Just, R. E., & Zilberman, D. (1985). Adoption of agricultural innovations in developing countries: A survey. Economic Development and Cultural Change, 33(2), 255-298. https://www.jstor.org/stable/1153228
Ghozali, M. T., Satibi, & Fortwengel, G. (2025). Adoption intentions toward AI-based clinical decision support tools: A TAM study on hospital pharmacists. Journal of Information and Communication Technology, 24(3), 44–69. https://doi.org/10.32890/jict2025.24.3.
Greene, W. (2012). Econometric analysis 7th ed. Prentice Hall.
Gulzar, Y., Reegu, F. A., Soomro, A. B., Mir, M. S., Zahir, A., & Onn, C. W. (2025). Exploring the economic implications of IoT adoption in agriculture: A cost-benefit study in Jazan, Saudi Arabia. Sarhad Journal of Agriculture, 41(1), 51–65. https://doi.org/10.17582/journal.sja/ 2025/41.1.51.65
Gutiérrez Cano, L. F., Zartha Sossa, J. W., Orozco Mendoza, G. L., Suárez Guzmán, L. M., Agudelo Tapasco, D. A., & Quintero Saavedra, J. I. (2023). Agricultural innovation system: Analysis from the subsystems of R&D, training, extension, and sustainability. Frontiers in Sustainable Food Systems, 7. https://doi.org/10.3389/fsufs.2023.1176366
Hanemann, W. M. (1991). Willingness to pay and willingness to accept: How much can they differ?. The American Economic Review, 81(3), 635-647. https://www.jstor.org/stable/2006525
Hidrobo, M., Palloni, G., Gilligan, D. O., Aker, J. C., & Ledlie, N. (2022). Paying for digital information: Assessing farmers’ willingness to pay for a digital agriculture and nutrition service in Ghana. Economic Development and Cultural Change, 70(4), 1367–1402. https://doi.org/10.1086/713974
Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. John Wiley & Sons.
Hu, C., Bo, N., & Zhao, Q. (2023). A study on the impact of rural finance on high-quality agricultural development in China—a test based on intermediation, threshold and spillover effects. Frontiers in Environmental Science, 11. https://doi.org/10.3389/fenvs.2023.1217422
Huang, Y., Zhao, J., & Yin, S. (2023). Does digital inclusive finance promote the integration of rural industries? Based on the mediating role of financial availability and agricultural digitization. PLoS ONE, 18(10). https://doi.org/10.1371/journal.pone.0291296
Idris, N. A. M., & Zulkifli, Z. (2024). Knowledge, attitude and practices of smart farming technology for small scale farmers in paddy production: A case study in Melaka. IOP Conference Series: Earth and Environmental Science, 1397(1). https://doi.org/10.1088/1755-1315/1397/1/012032
Islam, M. H., Anam, Md. Z., Hoque, M. R., Nishat, M., & Bari, A. B. M. M. (2024). Agriculture 4.0 adoption challenges in the emerging economies: Implications for smart farming and sustainability. Journal of Economy and Technology, 2, 278–295. https://doi.org/10.1016/j. ject.2024.09.
Jabbari, A., Humayed, A., Reegu, F. A., Uddin, M., Gulzar, Y., & Majid, M. (2023). Smart farming revolution: Farmers’ perception and adoption of smart IoT technologies for crop health monitoring and yield prediction in Jizan, Saudi Arabia. Sustainability (Switzerland), 15(19). https://doi.org/10.3390/su151914541
Jayashankar, P., Nilakanta, S., Johnston, W. J., Gill, P., & Burres, R. (2018). IoT adoption in agriculture: the role of trust, perceived value and risk. Journal of Business and Industrial Marketing, 33(6), 804–821. https://doi.org/10.1108/JBIM-01-2018-0023
Kahwai, J. N., Mburu, J. I., Oulu, M. O., & Hutchinson, M. J. (2021). Willingness to pay for hexanal technology among banana farmers in Meru County, Kenya. International Journal of Food Science, 2021. https://doi.org/10.1155/2021/6676148
Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS-Wageningen Journal of Life Sciences, 90, 100315. https://doi.org/10.1016/j.njas.2019.100315
Klerkx, L., & Rose, D. (2020). Dealing with the game-changing technologies of Agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways? Global Food Security, 24. https://doi.org/10.1016/j.gfs.2019.100347
Knowler, D., & Bradshaw, B. (2007). Farmers’ adoption of conservation agriculture: A review and synthesis of recent research. Food Policy, 32(1), 25–48. https://doi.org/10.1016/j.foodpol. 2006.01.
Kumar, V., Sharma, K. V., Kedam, N., Patel, A., Kate, T. R., & Rathnayake, U. (2024). A comprehensive review on smart and sustainable agriculture using IoT technologies. Smart Agricultural Technology, 8. https://doi.org/10.1016/j.atech.2024.100487
Li, J., Liu, G., Chen, Y., & Li, R. (2023). Study on the influence mechanism of adoption of smart agriculture technology behavior. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-35091-x
Long, J. Scott., & Freese, Jeremy. (2001). Regression models for categorical dependent variables using Stata. Stata Press.
Luangchosiri, N., Chaichana, C., Yalangkan, P., Dumlao, S. M. G., Okumura, H., & Ishihara, K. N. (2025). Willingness to pay for renewably sourced irrigation with Solar Water Pumping (SWP) systems in drought-prone areas of Thailand. Water (Switzerland), 17(6). https://doi.org/10.3390/w17060858
Mangole, C. D., Mulungu, K., Kaghoma, C. K., Tschopp, M., & Kassie, M. (2026). Smallholder farmers’ willingness to pay for the farmbetter mobile app-based agricultural extension: Evidence from a BDM experiment in Tanzania and Burkina Faso. International Journal of Agricultural Sustainability, 24(1). https://doi.org/10.1080/14735903.2025.2609433
Masoomi, B., Sahebi, I. G., Gholian-Jouybari, F., Mejia-Argueta, C., & Hajiaghaei-Keshteli, M. (2024). The role of Internet of Things adoption on the sustainability performance of the renewable energy supply chain: A conceptual framework. Renewable and Sustainable Energy Reviews, 202. https://doi.org/10.1016/j.rser.2024.114610
Masud, M. M., Junsheng, H., Akhtar, R., Al-Amin, A. Q., & Kari, F. B. (2015). Estimating farmers’ willingness to pay for climate change adaptation: The case of the Malaysian agricultural sector. Environmental Monitoring and Assessment, 187(2). https://doi.org/10.1007/s10661-014-4254-z
McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.). Frontiers in Econometrics.
Mgale, Y. J., Timothy, S., & Dimoso, P. (2022). Measuring rice price volatility and its determinants in Tanzania: An implication for price stabilization policies. Theoretical Economics Letters, 12(02), 546–563. https://doi.org/10.4236/tel.2022.122031
Minh, D. D., Tai, N. D., Quang, L. N. L., & Quan, T. T. (2022). Estimated the willingness to pay levels for the adoption of the Internet of Things-IoTs technology: An empirical study in swiftlet farming in Binh Thuan Province, Vietnam. Asian Social Science, 18(11), 5. https://doi.org/10.5539/ass.v18n11p
Mohsin et al. (2025). Prediction of rice yields in a changing climate using the mobile rice yield prediction application. Journal of Information and Communication Technology, 24(2), 1–18. https://doi.org/10.32890/jict2025.24.2.
Munasser, H., & Golshan, B. (2024). Understanding the adoption challenges of IoT among small to medium-size enterprises (SMEs) study in Sweden. https://www.diva-portal.org/smash/get/ diva2:1918587/FULLTEXT01.pdf
Munthali, M. G., Nankwenya, B., Chiwaula, L., & Muyanga, M. (2025). Agricultural diversification and commercialization of smallholder farming in Malawi: Extent, drivers, impacts and policy options. https://ageconsearch.umn.edu/record/350157/files/WP25_01%20Diversification% paperfinal.pdf
Najib, F., & Mustika, I. W. (2022). Weather forecasting using artificial neural network for rice farming in Delanggu Village. IOP Conference Series: Earth and Environmental Science, 1030(1). https://doi.org/10.1088/1755-1315/1030/1/012002
Navarro, E., Costa, N., & Pereira, A. (2020). A systematic review of IoT solutions for smart farming. Sensors, 20(15), 4231. https://doi.org/10.3390/s20154231
Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625–632. https://doi.org/10.1007/s10459-010-9222-y
Omar, S. C., Shaharudin, A., & Tumin, S. A. (2019). The status of the paddy and rice industry in Malaysia. Khazanah Research Institute. Kuala Lumpur.
Omar et al. (2025). Smart farming technologies: Exploring the factors of adoption among smallholder farmers in Peninsular Malaysia. Journal of Advanced Research in Applied Sciences and Engineering Technology, 62(1), 90–97. https://doi.org/10.37934/araset.62.1.9097
Onugo, T. N., & Onyeneke, R. U. (2022). Farmers’ preference and willingness to pay for climate-smart rice varieties in Uzo-Uwani Local Government Area of Enugu State, Nigeria. Ekologia Bratislava, 41(3), 262–271. https://doi.org/10.2478/eko-2022-0027
Osrof, H. Y., Tan, C. L., Yeo, S. F., & Tan, K. K. (2025). Unveiling the truth: Are farmers ready for smart agriculture? Insights from a hybrid PLS-SEM-ANN approach. Journal of Modelling in Management, 21(1), 357–391. https://doi.org/10.1108/JM2-03-2025-0094
Paltasingh, K. R. (2018). Land tenure security and adoption of modern rice technology in Odisha, Eastern India: Revisiting Besley’s hypothesis. Land Use Policy, 78, 236–244. https://doi.org/10.1016/j.landusepol.2018.06.031
Patil, K. A., & Kale, N. R. (2016). A model for smart agriculture using IoT. ICGTSPICC 2016 Proceedings-International Conference on Global Trends in Signal Processing, Information Computing and Communication, 22-24 December 2016, Jalgaon, India. IEEE. https://doi.org/10.1109/ICGTSPICC.2016.7955360
Paudel, G. P., KC, D. B., Rahut, D. B., Khanal, N. P., Justice, S. E., & McDonald, A. J. (2019). Smallholder farmers’ willingness to pay for scale-appropriate farm mechanization: Evidence from the mid-hills of Nepal. Technology in Society, 59. https://doi.org/10.1016/j.techsoc. 2019.101196
Phupattanasilp, P., & Tong, S. R. (2019). Augmented reality in the integrative internet of things (AR-IoT): Application for precision farming. Sustainability (Switzerland), 11(9). https://doi.org/10.3390/su11092658
Prinsloo, R., & Smuts, H. (2025). Internet of Things technologies’ contribution to sustainable agricultural practices in developing countries. In B. Shukla, M. Jaiswal, N. Hasteer, R. Sindhwani, & S. Khorana (Eds.), Intelligent IT Solutions to Promote Indigenous Innovations (pp. 25–35). Springer Nature Singapore.
Ren, Z., & Zhong, K. (2022). Driving mechanism of subjective cognition on farmers’ adoption behavior of straw returning technology: Evidence from rice and wheat producing provinces in China. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.922889
Rezaei-Moghaddam, K., & Salehi, S. (2010). Agricultural specialists’ intention toward precision agriculture technologies: Integrating innovation characteristics to technology acceptance model. Article in African Journal of Agricultural Research, 5(11). https://www.researchgate. net/publication/228652680
Rogers, E. M. (1995). Diffusion of innovations. Free Press.
Rose, D. C., & Chilvers, J. (2018). Agriculture 4.0: Broadening responsible innovation in an era of smart farming. Frontiers in Sustainable Food Systems, 2. https://doi.org/10.3389/fsufs.2018. 00087
Ruslan, N. A., Kamaruddin, R., & Samah, R. (2024). Determinants of IoT technology adoption in rice farming: An empirical analysis. Pakistan Journal of Life and Social Sciences (PJLSS), 22(2). https://doi.org/10.57239/PJLSS-2024-22.2.001335
Ruzzante, S., Labarta, R., & Bilton, A. (2021). Adoption of agricultural technology in the developing world: A meta-analysis of the empirical literature. World Development, 146. https://doi.org/10. 1016/j.worlddev.2021.105599
Saad, M., Hasliza, N., & Peng, W. W. (2024). A model of adoption determinants of IoT within the TOE framework in the Malaysian agriculture sector. Global Business & Management Research, 16.
Saili et al. (2024). Smart farming: Challenges and adoption factors among paddy farmers in IADA Barat Laut, Selangor. Information Management and Business Review, 16(4), 394-400.
Sambas, A., Gundara, G., Refiadi, G., Sri Mulyati, N., & Mohammed Sulaiman, I. (2023). Development of smart farming technology on ginger plants in Padamulya Ciamis Village, West Java, Indonesia. International Journal of Research in Community Service, 4(3), 93–99.
Shamshiri et al. (2024). Digitalization of agriculture for sustainable crop production: A use-case review. Frontiers in Environmental Science, 12, 1375193. https://doi.org/10.3389/fenvs.2024.1375193
Shee, A., Azzarri, C., & Haile, B. (2020). Farmers’ willingness to pay for improved agricultural technologies: Evidence from a field experiment in Tanzania. Sustainability (Switzerland), 12(1). https://doi.org/10.3390/SU12010216
Shepherd, M., Turner, J. A., Small, B., & Wheeler, D. (2020). Priorities for science to overcome hurdles thwarting the full promise of the ‘digital agriculture’ revolution. Journal of the Science of Food and Agriculture, 100(14), 5083–5092. https://doi.org/10.1002/jsfa.9346
Shi, Y., Siddik, A. B., Masukujjaman, M., Zheng, G., Hamayun, M., & Ibrahim, A. M. (2022). The antecedents of willingness to adopt and pay for the IoT in the agricultural industry: An application of the UTAUT 2 theory. Sustainability (Switzerland), 14(11). https://doi.org/10.3390/su14116640
Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2015). Security, privacy and trust in Internet of Things: The road ahead. Computer Networks, 76, 146-164. https://doi.org/10.1016/j. comnet.2014.11.
Tao, H., Xiong, H., You, L., & Li, F. (2023). Farmers’ willingness to pay for smart farming technologies: Evidence from a smart drip irrigation technology in North China. China Agricultural Economic Review. https://doi.org/10.1108/CAER-03-2023-0050
Tripicchio, P., Unetti, M., Giordani, N., Avizzano, C. A., & Satler, M. (2014). A lightweight SLAM algorithm for indoor autonomous navigation. In Proc. Australas. Conf. Robot. Automat.(ACRA) (pp. 2-4).
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 157-178.
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural Systems, 153, 69-80.
Zhang, L., Dabipi, I. K., & Brown Jr, W. L. (2025). Internet of Things applications for agriculture. Internet of Things A to Z: Technologies and Applications, 327-343. https://doi.org/10.1002/ 9781394280490.ch
Published
Issue
Section
License
Copyright (c) 2026 Journal of Information and Communication Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Research impact
Harvested 2026-09-06Counts differ between services because each indexes a different body of literature. None of them is the whole picture.
- OpenAlex 0 View →
- Semantic Scholar 0 View →
- OpenCitations 0 View →
- Crossref 0 View →
- Scopus 0 View →
- Google Scholar no free count Search →
- Dimensions no free count Search →
2002 - 2020
