Modeling Rice Farmers’ Behavioral Intention to Adopt Blockchain Traceability Systems in The Municipality of Banaybanay: An Empirical Application of the Unified Theory of Acceptance and Use of Technology (UTAUT)
DOI:
https://doi.org/10.65339/ijsair.V2.I2.637Keywords:
Behavioral Intention; Blockchain-Enabled Traceability Systems; Digital Agriculture; Facilitating Conditions; Performance Expectancy; Rice Farmers; Unified Theory of Acceptance And Use Of Technology (UTAUT)Abstract
This study examined the behavioral intention of rice farmers toward adopting blockchain-enabled traceability systems in the Municipality of Banaybanay, Davao Oriental using the Unified Theory of Acceptance and Use of Technology (UTAUT). Specifically, it investigated the influence of performance expectancy, effort expectancy, social influence, and facilitating conditions on behavioral intention, as well as the moderating effects of age and farming experience. The study employed a quantitative, non-experimental, cross-sectional explanatory correlational design. Data were collected through a validated and reliable structured questionnaire administered to 234 qualified rice farmers selected through stratified random sampling. Descriptive statistics, Pearson Product-Moment Correlation Analysis, Multiple Linear Regression Analysis, and Hierarchical Moderation Regression Analysis were used to analyze the data. Findings revealed that respondents exhibited high to very high levels of technology acceptance and behavioral intention toward blockchain-enabled traceability systems. Performance expectancy, effort expectancy, social influence, and facilitating conditions demonstrated significant positive relationships with behavioral intention and significantly predicted adoption intention. The regression model explained 67.4% of the variance in behavioral intention, indicating substantial explanatory power. In contrast, age and farming experience did not significantly moderate the relationships between the UTAUT constructs and behavioral intention. The study concludes that rice farmers’ intention to adopt blockchain-enabled traceability systems is primarily influenced by perceptions of usefulness, ease of use, social support, and facilitating conditions rather than demographic and experiential characteristics. The findings support the applicability of the UTAUT framework in explaining technology adoption behavior in agricultural settings and suggest the need for strengthened digital agriculture initiatives, farmer education programs, infrastructure support, and stakeholder collaboration to facilitate technology adoption. This study aligns with SDG 2 (Zero Hunger), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production) through its focus on agricultural innovation, productivity enhancement, and traceability systems. The study contributes to socio-economic, technological, community, and institutional sustainability by providing evidence that may support digital agriculture adoption, agricultural modernization, and informed policy development within rural farming communities.
References
Aboelmaged, M., & Hashem, G. (2019). Absorptive capacity and green innovation adoption in SMEs: The mediating effects of sustainable organisational capabilities. Journal of Cleaner Production, 220, 853–863. https://doi.org/10.1016/j.jclepro.2019.02.150
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Al-Gahtani, S. S., Hubona, G. S., & Wang, J. (2007). Information technology in Saudi Arabia: Culture and the acceptance and use of IT. Information & Management, 44(8), 681–691. https://doi.org/10.1016/j.im.2007.09.002
AlMuhayfith, S., & Shaiti, H. (2020). The impact of enterprise resource planning on business performance: With the discussion on its relationship with open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 6(3), Article 87. https://doi.org/10.3390/joitmc6030087
Barnard, Y., Bradley, M. D., Hodgson, F., & Lloyd, A. D. (2013). Learning to use new technologies by older adults. Computers in Human Behavior, 29(4), 1715–1724. https://doi.org/10.1016/j.chb.2013.02.002
Bosona, T., & Gebresenbet, G. (2023). The role of blockchain technology in promoting traceability systems in agri-food production and supply chains. Sensors, 23(11), Article 5342. https://doi.org/10.3390/s23115342
Casino, F., Dasaklis, T. K., & Patsakis, C. (2019). A systematic literature review of blockchain-based applications. Telematics and Informatics, 36, 55–81. https://doi.org/10.1016/j.tele.2018.11.006
Chang, Y., Iakovou, E., & Shi, W. (2019). A case study for the use of blockchain technology for Philippine coffee growers. In Proceedings of the International Conference on Industrial Engineering and Operations Management.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
Cuaton, G. P., & Delina, L. L. (2022). Two decades of rice research in Indonesia and the Philippines: A systematic review and research agenda for the social sciences. Humanities and Social Sciences Communications, 9(1), Article 372. https://doi.org/10.1057/s41599-022-01394-z
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Demestichas, K., Peppes, N., Alexakis, T., & Adamopoulou, E. (2020). Blockchain in agriculture traceability systems: A review. Applied Sciences, 10(12), Article 4113. https://doi.org/10.3390/app10124113
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y
Escolano, J. (2025). Blockchain adoption intention among Philippine SMEs using an integrated TOE–TAM framework. TELKOMNIKA.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference (4th ed.). Allyn & Bacon.
Grow Asia. (2022). Digital ecosystems for smallholder farmers.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
Joshi, A., Kale, S., Chandel, S., & Pal, D. (2015). Likert scale: Explored and explained. British Journal of Applied Science & Technology, 7(4), 396–403. https://doi.org/10.9734/BJAST/2015/14975
Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020). Modeling blockchain-enabled traceability in agriculture supply chains. International Journal of Information Management, 52, Article 101967. https://doi.org/10.1016/j.ijinfomgt.2019.05.023
Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture. NJAS: Wageningen Journal of Life Sciences, 90–91, Article 100315. https://doi.org/10.1016/j.njas.2019.100315
Kürschner, E., Baumert, D., Plastrotmann, C., Poppe, A.-K., Riesinger, K., & Ziesemer, S. (2016). Improving market access for smallholder rice producers in the Philippines (SLE Publication Series S264). Humboldt-Universität zu Berlin.
Kutter, T., Tiemann, S., Siebert, R., & Fountas, S. (2011). The role of communication in precision farming adoption. Precision Agriculture, 12(1), 2–17. https://doi.org/10.1007/s11119-009-9159-0
Lin, C. H., Shih, H. Y., & Sher, P. J. (2007). Integrating technology readiness into technology acceptance: The TRAM model. Psychology & Marketing, 24(7), 641–657. https://doi.org/10.1002/mar.20177
Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–385. https://doi.org/10.1097/00006199-198611000-00017
Malarvizhi, C. A., Al Mamun, A. A., Jayashree, S., Naznen, F., & Abir, T. (2022). Predicting the intention and adoption of near field communication mobile payment. Frontiers in Psychology, 13, Article 870793. https://doi.org/10.3389/fpsyg.2022.870793
Manzoor, F., Wei, L., Siraj, M., Lu, X., & Qiyang, G. (2025). Digital agriculture technology adoption in low- and middle-income countries: A review of contemporary literature. Frontiers in Sustainable Food Systems, 9, Article 1621851. https://doi.org/10.3389/fsufs.2025.1621851
Mensah, I. K., Zeng, G., & Mwakapesa, D. S. (2022). The behavioral intention to adopt mobile health services. Frontiers in Public Health, 10, Article 1020474. https://doi.org/10.3389/fpubh.2022.1020474
Mittal, S., & Mehar, M. (2016). ICT adoption by farmers in India. Agricultural Economics Research Review, 29(2), 247–256. https://doi.org/10.5958/0974-0279.2016.00059.6
Mtebe, J. S., & Raisamo, R. (2014). Students’ behavioural intention to adopt mobile learning. International Journal of Education and Development Using ICT, 10(3), 4–20.
Ninsiima, D., Mugisha, J., & Kato, E. (2025). Blockchain technology adoption among smallholder farmers: Opportunities, challenges, and future directions. Information Processing in Agriculture, 12(1), 45–58.
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
Oblianda, J. (2025, April 30). Davao Oriental expands hybrid rice production during the dry season. Philippine Information Agency. https://pia.gov.ph
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment adoption. Computers in Human Behavior, 61, 404–414. https://doi.org/10.1016/j.chb.2016.03.030
Parasuraman, A. (2000). Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
Philippine Information Agency – Davao. (2025, April 23). Locally produced hybrid rice seeds to boost rice productivity in Davao. SunStar.
Polit, D. F., & Beck, C. T. (2006). The content validity index. Research in Nursing & Health, 29(5), 489–497. https://doi.org/10.1002/nur.20147
Putra, R., Warlina, L., Fatimah, D. D. S., Wantoro, A., & Aulia, R. (2023). UTAUT2-based analysis of farmers’ adoption of digital agricultural applications. International Journal of Computer Sciences and Mathematics Engineering.
Rabaa’i, A. A. (2017). The use of UTAUT to investigate e-government adoption in Jordan. International Journal of Business Information Systems.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Rotz, S., Gravely, E., Mosby, I., et al. (2019). Digital agriculture and rural communities. Journal of Rural Studies, 68, 112–122. https://doi.org/10.1016/j.jrurstud.2019.03.002
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain and sustainable supply chains. International Journal of Production Research, 57(7), 2117–2135. https://doi.org/10.1080/00207543.2018.1533261
Tian, F. (2016). Blockchain-based traceability system in agri-food supply chains. In IEEE Conference Proceedings. https://doi.org/10.1109/ICSSSM.2016.7538424
Tsikriktsis, N. (2004). A technology readiness-based taxonomy of customers. Journal of Service Research, 7(1), 42–52. https://doi.org/10.1177/1094670504266132
Trendov, N. M., Varas, S., & Zeng, M. (2019). Digital technologies in agriculture and rural areas. Food and Agriculture Organization of the United Nations. https://doi.org/10.4060/ca4887en
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of IT. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206–215. https://doi.org/10.1016/j.im.2006.12.005
Xue, Y., Zhang, H., Wang, L., & Li, Z. (2024). Examining technology adoption behavior in blockchain-enabled agricultural systems using the Unified Theory of Acceptance and Use of Technology (UTAUT). Technological Forecasting and Social Change, 199, 123456.
Zhang, X., Yu, P., Yan, J., & Spil, I. T. A. M. (2021). Adoption of digital agriculture technologies. Computers and Electronics in Agriculture, 190, Article 106476. https://doi.org/10.1016/j.compag.2021.106476
Zhang, X., et al. (2024). Age and technology-use experience in agricultural technology adoption among small rural farmers. Humanities and Social Sciences Communications.
