Drivers of Farmers’ Intention to Adopt Digital Platforms for Natural Farming Inputs in Andhra Pradesh
0
Authors
-
Aravind Kumar Saride
Indian Institute of Technology, Kharagpur
-
Mrigank Sharad
Keywords:
ICT adoption , Natural Farming, Behavioural intention, Unified theory of acceptance and use of technology, Structural equation modelling, Artificial neural networkAbstract
The study examined the drivers of farmers' intention to adopt a Natural Farming digital input platform. The Unified Theory of Acceptance and Use of Technology was extended by incorporating Personal Innovativeness, Perceived Cost and Perceived Risk alongside Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions. Primary data were collected between July and November 2024 from 795 Natural Farming farmers across Andhra Pradesh, employing a structured questionnaire through direct interviews and supported by Rythu Sadhikara Samstha, the state nodal agency for Natural Farming. An integrated methodology combining a two-stage Partial Least Squares Structural Equation Modelling with an Artificial Neural Network approach, complemented by Importance Performance Map Analysis, was employed. Results showed a positive and significant influence of Personal Innovativeness, Performance Expectancy, Effort Expectancy and Facilitating Conditions on behavioural intention, explaining 51.8% of its variance, while Social Influence, Perceived Cost and Perceived Risk were statistically non-significant. The Artificial Neural Network sensitivity analysis confirmed Personal Innovativeness as the most important predictor. Strengthening rural digital infrastructure and designing intuitive regional-language interfaces could accelerate inclusive adoption intention among small and marginal farmers.
References
Ahn, M., Kang, J., & Hustvedt, G. (2016). A model of sustainable household technology acceptance. International Journal of Consumer Studies, 40(1), 83–91. https://doi.org/10.1111/ijcs.12217
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
Alharbi, A., & Sohaib, O. (2021). Technology readiness and cryptocurrency adoption: PLS-SEM and deep learning neural network analysis. IEEE Access, 9, 21388–21394. https://doi.org/10.1109/ACCESS.2021.3055785
Alhumaid, K., Habes, M., & Salloum, S. A. (2021). Examining the factors influencing the mobile learning usage during COVID-19 pandemic: An integrated SEM-ANN method. IEEE Access, 9, 102567–102578. https://doi.org/10.1109/ACCESS.2021.3097753
Al-Saedi, K., Al-Emran, M., Ramayah, T., & Abusham, E. (2020). Developing a general extended UTAUT model for M-payment adoption. Technology in Society, 62, 101293. https://doi.org/10.1016/j.techsoc.2020.101293
Balla, J., & Goswami, K. (2022). Understanding the constraints and reasons to adopt natural farming – a study on rice growing farmers of Andhra Pradesh, India. International Journal of Agricultural Sustainability, 20(6), 1209–1224. https://doi.org/10.1080/14735903.2022.2070340
Bharucha, Z. P., Mitjans, S. B., & Pretty, J. (2020). Towards redesign at scale through zero budget natural farming in Andhra Pradesh, India. International Journal of Agricultural Sustainability, 18(1), 1–20. https://doi.org/10.1080/14735903.2019.1694465
Centre for Economic and Social Studies. (2019). Impact assessment of zero budget natural farming in Andhra Pradesh: 2018–2019 report. https://apcnf.in/wp-content/uploads/2021/09/CESS-2018-2019-Report.pdf
Chin, K. Y., Lee, K. F., & Chen, Y. L. (2015). Impact on student motivation by using a QR-based u-learning material production system to create authentic learning experiences. IEEE Transactions on Learning Technologies, 8(4), 367–382. https://doi.org/10.1109/TLT.2015.2416717
Cimino, A., Coniglio, I. M., Corvello, V., Longo, F., Sagawa, J. K., & Solina, V. (2024). Exploring small farmers’ behavioral intention to adopt digital platforms for sustainable and successful agricultural ecosystems. Technological Forecasting and Social Change, 204, 123436. https://doi.org/10.1016/j.techfore.2024.123436
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R. Springer. https://doi.org/10.1007/978-3-030-80519-7
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hew, T. S., & Kadir, S. L. S. A. (2016). Predicting instructional effectiveness of cloud-based virtual learning environment. Industrial Management & Data Systems, 116(8), 1557–1584. https://doi.org/10.1108/IMDS-11-2015-0475
Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry, 11(3), 340. https://doi.org/10.3390/sym11030340
Jain, M., Soni, G., Mangla, S. K., Verma, D., Toshniwal, V. P., & Ramtiyal, B. (2025). Mediating and moderating role of socioeconomic and technological factors in assessing farmers’ attitude towards adoption of Industry 4.0 technology. British Food Journal, 127(5), 1810–1830. https://doi.org/10.1108/BFJ-12-2023-1139
Koner, N., & Laha, A. (2021). Economics of alternative models of organic farming: Empirical evidences from zero budget natural farming and scientific organic farming in West Bengal, India. International Journal of Agricultural Sustainability, 19(3–4), 255–268. https://doi.org/10.1080/14735903.2021.1905346
Kumar S., Nain M. S., Sangeetha V. & Satyapriya (2023). Determinants and constraints for adoption of zero budget natural farming (ZBNF) practices in farmer field school, Indian Journal of Extension Education, 59(4), 135-140. https://doi.org/10.48165/IJEE.2023.59427
Kumar S., Nain M S., Sangeetha V., Satyapriya, Das S., Anbukkani P. & Khan S. A. (2025). Measuring sustainability of zero-budget natural farming practices. Journal of Community Mobilization and Sustainable Development, 20(2), 613-618 https://doi.org/10.5958/2231-6736.2025.00141.4
Kumar, S. (2025). Determining the knowledge level and accessibility of major ICT tools by farmers in Madhya Pradesh. Indian Journal of Extension Education, 61(4), 101–105. https://doi.org/10.48165/IJEE.2025.61417
Larasati, N., Putri, A. A., Soemodinoto, A. S., Alyssa, N., & Shoofiyani, O. S. (2024). Unified theory of acceptance and use of technology model to understand farmers’ readiness: Implementation of precision agriculture based on digital IoT monitoring apps in West Java, Indonesia. Asian Journal of Agriculture and Rural Development, 14(4), 176–183. https://doi.org/10.55493/5005.v14i4.5258
Lee, C. T., Pan, L. Y., & Hsieh, S. H. (2022). Artificial intelligent chatbots as brand promoters: A two-stage structural equation modeling–artificial neural network approach. Internet Research, 32(4), 1329–1356. https://doi.org/10.1108/INTR-01-2021-0030
Leong, L. Y., Hew, T. S., Ooi, K. B., & Wei, J. (2020). Predicting mobile wallet resistance: A two-staged structural equation modeling–artificial neural network approach. International Journal of Information Management, 51, 102047. https://doi.org/10.1016/j.ijinfomgt.2019.102047
Li, L., Min, X., Guo, J., & Wu, F. (2024). The influence mechanism analysis on the farmers’ intention to adopt Internet of Things based on UTAUT-TOE model. Scientific Reports, 14, 15016. https://doi.org/10.1038/s41598-024-65415-4
Lin, D., Fu, B., Xie, K., Zheng, W., Chang, L., & Lin, J. (2023). Research on the improvement of digital literacy for moderately scaled tea farmers under the background of digital intelligence empowerment. Agriculture, 13(10), 1859. https://doi.org/10.3390/agriculture13101859
Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873–891. https://doi.org/10.1016/j.chb.2004.03.003
Mangudhla, T., Zhao, X., Tong, J. J. J., Ahakwa, I., Bram, A. K., & Boadi, E. B. (2026). Examining adaptive capacities of tobacco farmers in Zimbabwe: The role of climate information and livelihood diversification in climate change adaptation – a PLS-SEM, ANN and fsQCA approach. Sustainable Development, 1–24. https://doi.org/10.1002/sd.70853
Mastiholi, A. B., Sowmya, B., Maheswarappa, H. P., Gondi, S. P., Shantappa, T., Rudresh, D. L., & Gopali, J. B. (2023). Organic and natural farming improve microbial diversity and dehydrogenase activity in cluster bean–tomato cropping sequence. Archives of Agronomy and Soil Science, 69(15), 3705–3716. https://doi.org/10.1080/03650340.2023.2273899
Morepje, M. T., Sithole, M. Z., Msweli, N. S., & Agholor, A. I. (2024). The influence of e-commerce platforms on sustainable agriculture practices among smallholder farmers in Sub-Saharan Africa. Sustainability, 16(15), 6496. https://doi.org/10.3390/su16156496
NITI Aayog. (2025a, January 3). Implementation progress. Retrieved January 3, 2025, from https://naturalfarming.dac.gov.in/NaturalFarming/ImplementationProcess
NITI Aayog. (2025b, April 17). Natural farming. Retrieved April 17, 2025, from https://naturalfarming.niti.gov.in/
Pragnya, P. N., Vardhan, P. N. H., Kumar, A., Nihal, R., Mounika, T., & Pulletikurthi, V. (2026). Determinants of farmers’ knowledge and safe pesticide utilisation behaviour: Artificial neural network approach. Indian Journal of Extension Education, 62(3), 141–147. https://doi.org/10.48165/IJEE.2026.62322
Press Information Bureau. (2024). Universal connectivity and Digital India initiatives reaching to all areas, including tier-2/3 cities and villages [Press release]. https://pib.gov.in/PressReleaseIframePage.aspx?PRID=2040566
Sabbir, M. M., Islam, M., & Das, S. (2020). Understanding the determinants of online pharmacy adoption: A two-staged SEM–neural network analysis approach. Journal of Science and Technology Policy Management, 12(4), 666–687. https://doi.org/10.1108/JSTPM-07-2020-0108
Saride, A. K., & Sharad, M. (2024). Augmentation of farmers’ income through non-pesticidal management (NPM) input production: An economic analysis. In Proceedings of the 2024 9th International Conference on Energy Efficiency and Agricultural Engineering (EEAE). IEEE. https://doi.org/10.1109/EEAE60309.2024.10600518
Shanmuka A., Kumbhare N.V. , Nain M.S., Muralikrishnan L., Sinha K. , Prasad S. , Kumar P. & Meena S. L.(2025). Economics of chemical and natural farming with and without cattle integration in rice-jowar cropping system. Journal of Community Mobilization and Sustainable Development, 20(1), 136-142 https://doi.org/10.5958/2231-6736.2025.00021.4
Sharma, A., Mohan, A., Johri, A., & Asif, M. (2024). Determinants of fintech adoption in agrarian economy: Study of UTAUT extension model in reference to developing economies. Journal of Open Innovation: Technology, Market, and Complexity, 10(2), 100273. https://doi.org/10.1016/j.joitmc.2024.100273
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, 14(11), 6640. https://doi.org/10.3390/su14116640
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189
Singh, S., Kumar, S., Doharey, R. K., Singh, S., & Sahoo, S. (2026). Understanding entrepreneurial behaviour of rural youth in agriculture and allied sectors. Indian Journal of Extension Education, 62(2), 10–14. https://doi.org/10.48165/IJEE.2026.62202
Sonu, K., & Jha, K. K. (2025). Understanding entrepreneurial behaviour of Makhana growers in Bihar using SEM-PLS approach. Indian Journal of Extension Education, 61(2), 62–66. https://doi.org/10.48165/IJEE.2025.61212
Soomro, R. B., Al-Rahmi, W. M., Dahri, N. A., Almuqren, L., Al-mogren, A. S., & Aldaijy, A. (2025). A SEM–ANN analysis to examine impact of artificial intelligence technologies on sustainable performance of SMEs. Scientific Reports, 15(1), 5438. https://doi.org/10.1038/s41598-025-86464-3
Timilsina, R. R., Khosla, S., Rahut, D. B., Jena, P. R., & Sonobe, T. (2025). A comprehensive review of the soil health status for enhancing agricultural sustainability. Frontiers in Environmental Science, 13, 1548095. https://doi.org/10.3389/fenvs.2025.1548095
Upadhyay, C. K., & Kaur, S. (2026). Trust and risk for technology adoption: Extending UTAUT for digital crowd farming in rural India. Journal of Agribusiness in Developing and Emerging Economies. Advance online publication. https://doi.org/10.1108/JADEE-10-2025-0486
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), 425–478. https://doi.org/10.2307/30036540
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. https://doi.org/10.2307/41410412
Wang, H., Qi, W., & Guo, W. (2026). Does ICT adoption influence agricultural efficiency of Chinese farmers? Mechanism analysis based on agricultural insurance and credit. Digital Economy and Sustainable Development, 4(1), 1.
Yang, Q., Al Mamun, A., Naznen, F., & Masud, M. M. (2024). Adoption of conservative agricultural practices among rural Chinese farmers. Humanities and Social Sciences Communications, 11, Article 450. https://doi.org/10.1057/s41599-024-02956-z
Yu, Z., Zhao, Y., Shi, G., & Yu, X. (2026). Structured neural network model reveals how resettler perceptions influence agricultural effectiveness. Discover Agriculture, 4(1), 134. https://doi.org/10.1007/s44279-026-00605-4
Yuan, Y. P., Tan, G. W. H., & Ooi, K. B. (2024). Does COVID-19 pandemic motivate privacy self-disclosure in mobile fintech transactions? A privacy-calculus-based dual-stage SEM-ANN analysis. IEEE Transactions on Engineering Management, 71, 2986–3000. https://doi.org/10.1109/TEM.2022.3204285
Zeng, Y., Cao, S., & Li, L. (2016). Exploring the factors affecting consumers’ online agricultural products purchase behavior based on the UTAUT model. In Proceedings of the International Conference on Electronic Business (ICEB 2016). https://aisel.aisnet.org/iceb2016/73
Zhou, W., Li, M., & Achal, V. (2025). A comprehensive review on environmental and human health impacts of chemical pesticide usage. Emerging Contaminants, 11(1), 100410. https://doi.org/10.1016/j.emcon.2024.100410
Submitted
Published
Data Availability Statement
The data supporting the findings of this study are available from the corresponding author upon request.
Issue
Section
License
Copyright (c) 2026 Indian Society of Extension Education, Division of Agricultural ExtensionICAR- IARI, New Delhi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
- The manuscripts once accepted and published in the Indian Journal of Extension Education will automatically become the property of the Indian Society of Extension Education, New Delhi. The Chief Editor on behalf of the Indian Journal of Extension Education holds the copyright.
How to Cite
