Accepting the Digital Plow: An Empirical Analysis of Internet of Things (Iot) Acceptance among Paddy Planters in Kedah, Malaysia

The adoption of Internet of Things (IoT) technology in agricultural settings has the potential to revolutionize conventional farming techniques by improving efficiency, productivity, and sustainability. This study explores awareness, perceived operational benefits, and critical adoption barriers among smallholder paddy planters in Kedah, Malaysia—the nation's "Rice Bowl." Grounded in the Technology Acceptance Model (TAM), this research utilizes an exploratory qualitative case study design. Semi-structured interviews were conducted with four key informants in the Yan district of Kedah, systematically selected via purposive sampling to span a spectrum of adoption: a traditionalist non-adopter, two partial-adopter renters, and an advanced technology owner. The thematic analysis reveals that while awareness is growing, it is heavily anchored in a narrow "drone-only perception" that obscures the utility of other passive IoT solutions (e.g., soil sensors, automated irrigation). Although farmers recognize substantial benefits—such as rapid pesticide spraying (reducing time from a full day to 3 hours), operational efficiency, and minimized chemical exposure—behavioral intention to adopt is severely constrained. Critical barriers include high capital costs, technical knowledge deficits, rural internet dropouts, and cultural resistance to change. Additionally, this study critically engages with TAM's limitations in rural contexts and highlights the critical role of social influence and peer networks in adoption. Specific policy recommendations are proposed with execution timelines and agency responsibilities to bridge the rural digital divide.

Internet of Things (IoT), Technology Acceptance Model (TAM), Paddy Farming, Social Influence, Rural Digital Divide.

1. . Blasch, B. van der Kroon, P. van Beukering, R. Munster, S. Fabiani, P. Nino, and S. Vanino, "Farmer preferences for adopting precision farming technologies: A case study from Italy," European Review of Agricultural Economics, vol. 49, no. 1, pp. 33-81, 2022. https://doi.org/10.1093/erae/jbaa031 [Google Scholar] [Crossref]

2. C.-Y. Yang, M.-D. Yang, W.-C. Tseng, Y.-C. Hsu, G.-S. Li, M.-H. Lai, D.-H. Wu, and H.-Y. Lu, "Assessment of rice developmental stage using time series UAV imagery for variable irrigation management," Sensors, vol. 20, no. 18, p. 5354, 2020. https://doi.org/10.3390/s20185354 [Google Scholar] [Crossref]

3. S. Zainal, S. Salbiah, R. C. M. Yusoff, H. Abas, S. Yaacub, and N. M. Zainuddin, "The adoption of smart devices in Malaysian agriculture," Journal of Agricultural Research, vol. 45, no. 2, pp. 123-134, 2021. [Google Scholar] [Crossref]

4. N. Nur, W. Nur, A. Rahman, N. Nur, A. Fatah, and S. K. N. Vaiappuri, "Adoption of smart farming technology among rice farmers," Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 29, no. 2, pp. 268-275, 2023. https://doi.org/10.37934/araset.29.2.268275 [Google Scholar] [Crossref]

5. N. Adnan, S. M. Nordin, M. A. Bahruddin, and A. H. Tareq, "A state-of-the-art review on facilitating sustainable agriculture through green fertilizer technology adoption: Assessing farmers' behavior," Trends in Food Science & Technology, vol. 86, pp. 439-452, 2019. https://doi.org/10.1016/j.tifs.2019.02.040 [Google Scholar] [Crossref]

6. M. S. Farooq, S. Riaz, A. Abid, T. Umer, and Y. B. Zikria, "Role of IoT technology in agriculture: A systematic literature review," Electronics, vol. 9, no. 2, p. 319, 2020. https://doi.org/10.3390/electronics9020319 [Google Scholar] [Crossref]

7. L. A. Hussein, A. S. Baharudin, J. Krishnaswamy, and S. Kiumarsi, "B2b e-commerce technology factors with mediating effect of perceived usefulness in Jordanian manufacturing SMEs," Journal of Engineering Science and Technology, vol. 14, no. 1, pp. 411-429, 2019. https://www.researchgate.net/publication/341598167 [Google Scholar] [Crossref]

8. M. M. Reynoso, E. J. P. Bibangco, and C. E. Dumdumaya, "IoT and AI in rice farming: Disease detection and precision irrigation systems," Journal of Smart Agriculture, vol. 18, no. 3, pp. 204-219, 2023. [Google Scholar] [Crossref]

9. E. Bwambale, F. K. Abagale, and G. Anornu, "Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review," Agricultural Water Management, vol. 255, p. 106951, 2022. https://www.researchgate.net/publication/356175346 [Google Scholar] [Crossref]

10. H. Shahab, M. Iqbal, A. Sohaib, F. U. Khan, and M. M. Waqas, "IoT-based agriculture management techniques for sustainable farming: A comprehensive review," Computers and Electronics in Agriculture, vol. 220, p. 108851, 2024. https://doi.org/10.1016/j.compag.2024.108851 [Google Scholar] [Crossref]

11. R. Alfred, J. H. Obit, C. P. Y. Chin, and Y. Lim, "Towards paddy rice smart farming: A review on big data, machine learning, and rice production tasks," IEEE Access, vol. 9, pp. 50358-50380, 2021. https://doi.org/10.1109/access.2021.3069449 [Google Scholar] [Crossref]

12. P. Guillemin, F. Berens, M. Carugi, and P. Friess, "Internet of Things: Structural frameworks and standardization protocols," IEEE Communications Magazine, vol. 52, no. 6, pp. 74-82, 2014. [Google Scholar] [Crossref]

13. M. Lombardi, F. Pascale, and D. Santaniello, "Internet of Things: A general overview between architectures, protocols and applications," Information, vol. 12, no. 2, p. 87, 2021. https://doi.org/10.3390/info12020087 [Google Scholar] [Crossref]

14. S. Khan, S. A. Shah, S. Ali, and S. Shaheen, "Sampling design and data saturation in qualitative agricultural research," Qualitative Research Journal, vol. 22, no. 3, pp. 312-325, 2022. [Google Scholar] [Crossref]

15. K. H. Lim, L. Y. Leong, and P. L. Teh, "Overcoming barriers to smart technology adoption in rural environments," Technology in Society, vol. 64, p. 101452, 2021. https://doi.org/10.1016/j.techsoc.2021.101452 [Google Scholar] [Crossref]

16. M. Hennink, I. Hutter, and A. Bailey, Qualitative Research Methods, 2nd ed. London: SAGE Publications, 2020. [Google Scholar] [Crossref]

17. L. Li, X. Min, J. Guo, and F. Wu, "The influence mechanism analysis on the farmers' intention to adopt Internet of Things based on UTAUT-TOE model," Scientific Reports, vol. 14, no. 1, p. 14125, 2024. https://doi.org/10.1038/s41598-024-65415-4 [Google Scholar] [Crossref]

18. M. Aazam, S. Zeadally, and K. A. Harras, "Deploying fog computing in industrial internet of things and industry 4.0," IEEE Transactions on Industrial Informatics, vol. 14, no. 10, pp. 4674-4682, 2018. https://doi.org/10.1109/tii.2018.2855198 [Google Scholar] [Crossref]

19. D. C. Miller and N. J. Salkind, Handbook of Research Design & Social Measurement, 6th ed. Thousand Oaks, CA: SAGE Publications, 2002. [Google Scholar] [Crossref]

20. N. A. Stahl and J. R. King, "Understanding research design in qualitative studies," Journal of Developmental Education, vol. 43, no. 3, pp. 22-29, 2020. [Google Scholar] [Crossref]

21. C. Paul, A. Ganesh, and C. Sunitha, "Distinguishing the Internet of Things from conventional computer networks," Journal of Computer Networks and Applications, vol. 12, no. 2, pp. 45-56, 2018. [Google Scholar] [Crossref]

22. R. Aditya and A. Wardhana, "Pengaruh perceived usefulness dan perceived ease of use terhadap behavioral intention dengan pendekatan Technology Acceptance Model (TAM) pada pengguna Instant Messaging LINE di Indonesia," Jurnal Siasat Bisnis, vol. 20, no. 1, pp. 24-32, 2016. https://doi.org/10.20885/jsb.vol20.iss1.art3 [Google Scholar] [Crossref]

23. T. D. Susanto and M. Aljoza, "Individual acceptance of e-government services in a developing country: Dimensions of perceived usefulness and perceived ease of use and the importance of trust and social influence," Procedia Computer Science, vol. 72, pp. 622-629, 2015. https://doi.org/10.1016/j.procs.2015.12.171 [Google Scholar] [Crossref]

24. J. Xu, B. Gu, and G. Tian, "Internet of Things in precision agriculture: A systematic review," Computers and Electronics in Agriculture, vol. 197, p. 107015, 2022. https://doi.org/10.1016/j.compag.2022.107015 [Google Scholar] [Crossref]

25. Wicaksono and A. Maharani, "The effect of perceived usefulness and perceived ease of use on the technology acceptance model to use online travel agency," Journal of Business Management Review, vol. 1, no. 5, pp. 313-328, 2020. https://doi.org/10.47153/jbmr15.502020 [Google Scholar] [Crossref]

26. R. Pillai and B. Sivathanu, "Adoption of Internet of things (IoT) in the agriculture industry deploying the BRT framework," Benchmarking: An International Journal, vol. 27, no. 4, pp. 1320-1342, 2020. https://doi.org/10.1108/bij-08-2019-0361 [Google Scholar] [Crossref]

27. MARDI Corporation Sdn Bhd, "Agri-Training programs and capacity building for local farmers," Kuala Lumpur: MARDI, 2024. Online. Available: https://mardicorp.my/agri-training/ [Google Scholar] [Crossref]

28. T. Nur and S. M. Shafie, "Farm automation in paddy plantation: Case of Kedah," International Journal of Innovation and Industrial Revolution, vol. 5, no. 14, pp. 66-80, 2023. https://doi.org/10.35631/ijirev.514005 [Google Scholar] [Crossref]

29. M. Hossain, P. Biswas, and M. R. Islam, "Cold-tolerant and short-duration rice (Oryza sativa L.) for sustainable food security of the flash flood-prone haor wetlands of Bangladesh," Sustainability, vol. 15, no. 24, p. 16873, 2023. https://doi.org/10.3390/su152416873 [Google Scholar] [Crossref]

30. M. Maguire and B. Delahunt, "Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars," All Ireland Journal of Higher Education, vol. 9, no. 3, pp. 3351-3364, 2017. [Google Scholar] [Crossref]

31. D. Dibekulu, "An Overview of Data Analysis and Interpretations in Research," ResearchGate, pp. 1-27, 2020. https://doi.org/10.14662/IJARER2020.015 [Google Scholar] [Crossref]

32. M. Abdulazeez, "Historical progression and evolutionary milestones of the Internet of Things (IoT)," International Journal of Engineering and Technology, vol. 9, no. 4, pp. 180-192, 2020. [Google Scholar] [Crossref]

33. V. Braun and V. Clarke, "Using thematic analysis in psychology," Qualitative Research in Psychology, vol. 3, no. 2, pp. 77-101, 2006. https://doi.org/10.1191/1478088706qp063oa [Google Scholar] [Crossref]

34. P. Vaishnavi, V. Ramakrishna, K. Akula, N. Fatima, K. Saikrishna, G. Raju NV, and S. Chhabra, "An efficient agricultural data analysis based on IoT," MATEC Web of Conferences, vol. 392, p. 01098, 2024. https://doi.org/10.1051/matecconf/202439201098 [Google Scholar] [Crossref]

35. S. Pawar, "IoT solutions in agriculture: Enhancing efficiency and productivity," International Journal of Innovative Science and Research Technology (IJISRT), vol. 9, no. 5, pp. 3388-3390, 2024. https://doi.org/10.38124/ijisrt/ijisrt24may2442 [Google Scholar] [Crossref]

36. K. Sangeetha, A. Ece, S. Pradeeba, A. Santhosh, and T. Selvamani, "Paddy Monitoring and Management System," International Journal of Applied Engineering Research, vol. 14, no. 5, pp. 1045-1048, 2019. https://www.ripublication.com/ijaer19/ijaerv14n5_01.pdf [Google Scholar] [Crossref]

37. H. Snyder, "Literature review as a research methodology: An overview and guidelines," Journal of Business Research, vol. 104, pp. 333-339, 2019. https://doi.org/10.1016/j.jbusres.2019.07.039 [Google Scholar] [Crossref]

38. J. H. Chuang, J. H. Wang, and C. Liang, "Implementation of Internet of Things depends on intention: Young farmers' willingness to accept innovative technology," The International Food and Agribusiness Management Review, vol. 23, no. 2, pp. 253-266, 2020. https://doi.org/10.22434/ifamr2019.0121 [Google Scholar] [Crossref]

39. S. A. Salim and N. S. Sa'don, "The Adoption of Internet of Things in Urban Farming," Research in Management of Technology and Business, vol. 2, no. 2, pp. 146-162, 2021. https://publisher.uthm.edu.my/periodicals/index.php/rmtb/article/view/4991 [Google Scholar] [Crossref]

40. M. Jamil et al., "Rural digital divide and its impact on smart farming adoption in Malaysia," Journal of Rural Studies, vol. 99, pp. 102-115, 2023. https://doi.org/10.1016/j.jrurstud.2023.01.011 [Google Scholar] [Crossref]

41. M. A. Zamir and R. Sonar, "Building self-efficacy for technology adoption among rural farmers," Journal of Agricultural Extension, vol. 58, no. 4, pp. 45-56, 2023. [Google Scholar] [Crossref]

42. Hamid, A. Razak, A. A. Bakar, and W. Salihin, "The effects of perceived usefulness and perceived ease of use on continuance intention to use e-government," Procedia Economics and Finance, vol. 35, pp. 644-649, 2016. https://doi.org/10.1016/S2212-5671(16)00079-4 [Google Scholar] [Crossref]

43. P. A. Christou, "How to use thematic analysis in qualitative research," Journal of Qualitative Research in Tourism, vol. 3, no. 2, pp. 79-95, 2022. https://doi.org/10.4337/jqrt.2023.0006 [Google Scholar] [Crossref]

44. K. Sohn and O. Kwon, "Technology Acceptance Model (TAM) for predicting agricultural IoT acceptance: Cognitive appraisals and behavioral intention," MIS Quarterly, vol. 44, no. 2, pp. 811-828, 2020. [Google Scholar] [Crossref]

45. F. D. Davis, "Perceived usefulness, perceived ease of use, and user acceptance of information technology," MIS Quarterly, vol. 13, no. 3, pp. 319-340, 1989. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]

46. Kontron, "IoT Precision Farming in Malaysia: Efficient Water Management and Irrigation," Kontron.com, 2015. Online. Available: https://www.kontron.com/en/blog/embedded/iot-in-agriculture [Google Scholar] [Crossref]