Technological advancements are transforming agriculture, yet adoption rates among agricultural extension officers, especially in regions like West Java, remain modest due to several challenges. This study applies the Technology Acceptance Model (TAM) to investigate factors influencing the adoption of agricultural technologies by agricultural extension officers in West Java. Specifically, we explore the role of socialization, training, access to technology, cost, perceived ease of use, and perceived usefulness in shaping behavioral intention and actual adoption. Data were collected from 295 agricultural extension officers via structured surveys and analyzed using SmartPLS 4 software. The findings indicate that socialization and training collectively enhance both perceived ease of use and perceived usefulness, while Technology Investment Worth specifically enhances perceived usefulness by emphasizing the value of the investment. Access to technology also plays a critical role in increasing ease of use perceptions. Both perceived ease of use and usefulness positively influence behavioral intention, which in turn is a strong predictor of actual adoption. The results provide valuable insights for policymakers aiming to increase technology uptake among agricultural extension officers, promoting sustainable agricultural practices through improved access, support, and cost reduction initiatives.

Al-Mamary, Y. H., and Shamsuddin, A. (2015). The Impact of Top Management Support, Training, and Perceived Usefulness on Technology Acceptance. Mediterranean Journal of Social Sciences, 6(6 S4), Article 6 S4

Areal, F. J., and Pede, V. O. (2023). Editorial: Evaluating the adoption and impacts of agricultural technologies. Frontiers in Sustainable Food Systems, 7, 1340035. https://doi.org/10.3389/fsufs.2023.1340035

Brown, I. T. J. (2002). Individual and Technological Factors Affecting Perceived Ease of Use of Web‐based Learning Technologies in a Developing Country. THE ELECTRONIC JOURNAL OF INFORMATION SYSTEMS IN DEVELOPING COUNTRIES, 9(1), 1–15. https://doi.org/10.1002/j.1681-4835.2002.tb00055.x

Caffaro, F., Micheletti Cremasco, M., Roccato, M., and 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. https://doi.org/10.1016/j.jrurstud.2020.04.028

Cao, T., Cook, W. D., and Kristal, M. M. (2022). Has the technological investment been worth it? Assessing the aggregate efficiency of non-homogeneous bank holding companies in the digital age. Technological Forecasting and Social Change, 178, 121576. https://doi.org/10.1016/j.techfore.2022.121576

Chakraborty, I., Ilavarasan, P. V., and Edirippulige, S. (2021). Health-tech startups in healthcare service delivery: A scoping review. Social Science & Medicine, 278, 113949. https://doi.org/10.1016/j.socscimed.2021.113949

Choruma, D. J., Dirwai, T. L., Mutenje, M. J., Mustafa, M., Chimonyo, V. G. P., Jacobs-Mata, I., and Mabhaudhi, T. (2024). Digitalisation in agriculture: A scoping review of technologies in practice, challenges, and opportunities for smallholder farmers in sub-saharan africa. Journal of Agriculture and Food Research, 18, 101286. https://doi.org/10.1016/j.jafr.2024.101286

Cui, L., and Wang, W. (2023). Factors Affecting the Adoption of Digital Technology by Farmers in China: A Systematic Literature Review. Sustainability, 15(20), 14824. https://doi.org/10.3390/su152014824

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008

Dewayani, E. K. U. (2024). Assessing Village Development Institutions’ Role in Responsible Tourism: A Case Study of Rewarangga, Indonesia. Global Review of Tourism and Social Sciences, 1(1), Article 1

Dewi, D. E., Cahyani, P. N. A., and Megawati, L. R. (2023). Increasing Adoption of the Internet of Things in Indonesian Agriculture Based on a Review of Everett Rogers’ Diffusion Theory of Innovation. In S. Jahroh, K. Kamilah, A. Abdullah, R. D. Indrawan, & Sulistyo (Eds.), Proceedings of the Business Innovation and Engineering Conference (BIEC 2022) (Vol. 236, pp. 303–309). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-144-9_29

Dibbern, T., Romani, L. A. S., and Massruhá, S. M. F. S. (2024). Main drivers and barriers to the adoption of Digital Agriculture technologies. Smart Agricultural Technology, 8, 100459. https://doi.org/10.1016/j.atech.2024.100459

Duc, L. D. T., and Mujahida, S. (2024). Determinants of Consumer Preference for Local Brands: A Comprehensive Review of Recent Literature. Global Review of Tourism and Social Sciences, 1(1), Article 1

Ferrer, A. J. G., Thanh, L. H., Chuong, P. H., Kiet, N. T., Trang, V. T., Duc, T. C., Hopanda, J. C., Carmelita, B. M., and Bernardo, E. B. (2023). Farming household adoption of climate-smart agricultural technologies: Evidence from North-Central Vietnam. Asia-Pacific Journal of Regional Science, 7(2), 641–663. https://doi.org/10.1007/s41685-023-00296-5

Geng, W., Liu, L., Zhao, J., Kang, X., and Wang, W. (2024). Digital Technologies Adoption and Economic Benefits in Agriculture: A Mixed-Methods Approach. Sustainability, 16(11), 4431. https://doi.org/10.3390/su16114431

Hassan, A., Bhatti, S. H., Shujaat, S., and Hwang, Y. (2022). To adopt or not to adopt? The determinants of cloud computing adoption in information technology sector. Decision Analytics Journal, 5, 100138. https://doi.org/10.1016/j.dajour.2022.100138

Jimenez, I. A. C., García, L. C. C., Marcolin, F., Violante, M. G., and Vezzetti, E. (2021). Validation of a TAM Extension in Agriculture: Exploring the Determinants of Acceptance of an e-Learning Platform. Applied Sciences, 11(10), 4672. https://doi.org/10.3390/app11104672

Johan, D., Maarif, M. S., Zulbainarni, N., and Yulianto, B. (2024). Agricultural Digitalization In Indonesia: Challenges And Opportunities For Sustainable Development. Educational Administration: Theory and Practice. https://doi.org/10.53555/kuey.v30i7.6599

Kazeem, A., Dare, A., Olalekan, O., Abiodun, S., and Komolafe, T. (2017). Attitudes of farmers to extension trainings in Nigeria: Implications for adoption of improved agricultural technologies in Ogun state southwest region. Journal of Agricultural Sciences, Belgrade, 62(4), 423–443. https://doi.org/10.2298/JAS1704423K

Kelly, A. E., and Palaniappan, S. (2023). Using a technology acceptance model to determine factors influencing continued usage of mobile money service transactions in Ghana. Journal of Innovation and Entrepreneurship, 12(1), 34. https://doi.org/10.1186/s13731-023-00301-3

Khan, R. P., Gupta, S., Daum, T., Birner, R., and Ringler, C. (2024). Levelling the field: A review of the ICT revolution and agricultural extension in the Global South. Journal of International Development, jid.3949. https://doi.org/10.1002/jid.3949

King, W. R., and He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003

Ma, Q., and Liu, L. (2005). The Technology Acceptance Model: A Meta-Analysis of Empirical Findings. In M. A. Mahmood (Ed.), Advances in End User Computing (pp. 112–128). IGI Global. https://doi.org/10.4018/978-1-59140-474-3.ch006

Masimba, F., and Zuva, T. (2022). A Model for the Adoption and Acceptance of Mobile Farming Platforms (MFPs) by Smallholder Farmers in Zimbabwe. In R. Silhavy (Ed.), Software Engineering Perspectives in Systems (Vol. 501, pp. 710–725). Springer International Publishing. https://doi.org/10.1007/978-3-031-09070-7_59

McCormack, M., Buckley, C., and Kelly, E. (2021). Using a Technology Acceptance Model to investigate what factors influence farmer adoption of a nutrient management plan. Irish Journal of Agricultural and Food Research, 60(1). https://doi.org/10.15212/ijafr-2020-0134

Meera, S. N., Jhamtani, A., and Rao, D. (2004). INFORMATION AND COMMUNICATION TECHNOLOGY IN AGRICULTURAL DEVELOPMENT: A COMPARATIVE ANALYSIS OF THREE PROJECTS FROM INDIA. https://www.semanticscholar.org/paper/INFORMATION-AND-COMMUNICATION-TECHNOLOGY-IN-%3A-A-OF-Meera-Jhamtani/e71e6e32f00d8dd8de2774d629fa758b9dd9f459

Méndez-Zambrano, P. V., Tierra Pérez, L. P., Ureta Valdez, R. E., and Flores Orozco, Á. P. (2023). Technological Innovations for Agricultural Production from an Environmental Perspective: A Review. Sustainability, 15(22), 16100. https://doi.org/10.3390/su152216100

Mgendi, B. G., Mao, S., and Qiao, F. (2022). Does agricultural training and demonstration matter in technology adoption? The empirical evidence from small rice farmers in Tanzania. Technology in Society, 70, 102024. https://doi.org/10.1016/j.techsoc.2022.102024

Mobarak, A. M., and Saldanha, N. A. (2022). Remove barriers to technology adoption for people in poverty. Nature Human Behaviour, 6(4), 480–482. https://doi.org/10.1038/s41562-022-01323-9

Mujahida, S., Fatmasari, F., and Azizurrohman, M. (2024). Cultural Sensitivity and Ambiance in Indonesian Restaurants: The Mediating Role of Customer Satisfaction on Retention in Taiwan. Asia Pacific Management and Business Application, 13(1), 73–86. https://doi.org/10.21776/ub.apmba.2024.013.01.5

Mujahida, S., Remmang, H., and Azizurrohman, M. (2024). Cultural Familiarity and Its Impact on Customer Satisfaction and Brand Awareness: A Study of Indonesian Restaurants in Taiwan. Journal of Marketing Innovation (JMI), 4(2). https://doi.org/10.35313/jmi.v4i2.182

Nuryakin, N., Rakotoarizaka, N. L. P., and Musa, H. G. (2023). The Effect of Perceived Usefulness and Perceived Easy to Use on Student Satisfaction The Mediating Role of Attitude to Use Online Learning. Asia Pacific Management and Business Application, 011(03), 323–336. https://doi.org/10.21776/ub.apmba.2023.011.03.5

Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning. Journal of Educational Technology and Society, 12(3), 150–162

Parmaksiz, O., and Cinar, G. (2023). Technology Acceptance among Farmers: Examples of Agricultural Unmanned Aerial Vehicles. Agronomy, 13(8), 2077. https://doi.org/10.3390/agronomy13082077

Pedersen, S. M., Erekalo, K. T., Christensen, T., Denver, S., Gemtou, M., Fountas, S., Isakhanyan, G., Rosemarin, A., Ekane, N., Puggaard, L., Nertinger, M., Brinks, H., Puško, D., and Adrián, J. B. (2024). Drivers and barriers to climate-smart agricultural practices and technologies adoption: Insights from stakeholders of five European food supply chains. Smart Agricultural Technology, 8, 100478. https://doi.org/10.1016/j.atech.2024.100478

Rakholia, R., Tailor, J., Prajapati, M., Shah, M., and Saini, J. R. (2024). Emerging technology adoption for sustainable agriculture in India– a pilot study. Journal of Agriculture and Food Research, 17, 101238. https://doi.org/10.1016/j.jafr.2024.101238

Roger, E. (2003). Diffusion of Innovations, 5th Edition. Free Press. https://www.books.com.tw/products/F010215678

Shah, D. (2022). Role of Information and Communication Technology in Agricultural Development of India. Review of Market Integration, 14(2–3), 113–132. https://doi.org/10.1177/09749292221119286

Supiandi, S. (2024). The Impact of Water Infrastructure, Home Ownership, and Educational Facilities on Economic Growth in East Java. Global Review of Tourism and Social Sciences, 1(1), Article 1

Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008

Thomas, R. J., O’Hare, G., and Coyle, D. (2023). Understanding technology acceptance in smart agriculture: A systematic review of empirical research in crop production. Technological Forecasting and Social Change, 189, 122374. https://doi.org/10.1016/j.techfore.2023.122374

Thong, J. Y. L., Hong, S.-J., and Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799–810. https://doi.org/10.1016/j.ijhcs.2006.05.001

Venkatesh, V., and Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x

Wang, T.-L., and Oscar, W. (2024). How Supportive and Competitive Work Environments Influence Job Attitudes and Performance in French Sales Roles. Global Review of Tourism and Social Sciences, 1(1), Article 1

Wang, W., Huang, Z., Fu, Z., Jia, L., Li, Q., and Song, J. (2024). Impact of digital technology adoption on technological innovation in grain production. Journal of Innovation & Knowledge, 9(3), 100520. https://doi.org/10.1016/j.jik.2024.100520

Widiar, G., Yuniarinto, A., and Yulianti, I. (2023). Perceived Ease of Use’s Effects on Behavioral Intention Mediated by Perceived Usefulness and Trust. Interdisciplinary Social Studies, 2(4), 1829–1844. https://doi.org/10.55324/iss.v2i4.397

Yuli, S. B. C. (2024). Understanding the Dynamics of Tourist Experience through a Qualitative Lens: A Case Study Approach in Indonesia. Global Review of Tourism and Social Sciences, 1(1), Article 1

Zawacki-Richter, O., Marín, V. I., Bond, M., and Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

Zegeye, M. B. (2021). Adoption and Ex-post Impact of Agricultural Technologies on Rural Poverty: Evidence from Amhara Region, Ethiopia. Cogent Economics & Finance, 9(1), 1969759. https://doi.org/10.1080/23322039.2021.1969759

Zhang, B. S., Ali, K., and Kanesan, T. (2022). A model of extended technology acceptance for behavioral intention toward EVs with gender as a moderator. Frontiers in Psychology, 13, 1080414. https://doi.org/10.3389/fpsyg.2022.1080414