Understanding Farmers’ Continuance Intention To Use The Electronic National Agriculture Market (E-NAM): A Theory Of Planned Behaviour Perspective
DOI:
https://doi.org/10.69980/sa797h11Keywords:
Electronic National Agriculture Market (e-NAM), Theory of Planned Behaviour, SmartPLS-SEM, Digital Agriculture Marketing, Continuance IntentionAbstract
The Electronic National Agriculture Market (e-NAM) was introduced to enhance transparency, efficiency, and market integration in agricultural marketing in India; however, sustained participation by registered farmers remains a critical concern. This study examines the factors influencing farmers’ continuance intention to use e-NAM by applying the Theory of Planned Behaviour (TPB) in a post-adoption context. Using a quantitative research design, primary data were collected through a structured questionnaire from 120 registered e-NAM farmers across four regulated market committees in Odisha. The data were analysed using Partial Least Squares–Structural Equation Modelling (PLS-SEM) with Smart-PLS, following a two-stage evaluation of the measurement and structural models. The results indicate that attitude toward e-NAM and perceived behavioural control have significant positive effects on farmers’ continuance intention, whereas subjective norm does not exert a statistically significant influence. These findings suggest that farmers’ continued engagement with e-NAM is driven primarily by favourable evaluations of the platform and their perceived ability to use it effectively, rather than by social pressure. The study extends the application of TPB to digital agricultural marketing in a continuance setting and provides empirical evidence from a rural, developing-economy context. From a practical perspective, the findings highlight the need for policymakers and implementing agencies to move beyond registration-focused metrics and prioritise user experience, digital capability enhancement, and infrastructural support to ensure the long-term sustainability of e-NAM and similar digital agriculture initiatives.
References
1.Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action Control (pp. 11–39). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-69746-3_2
2.Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, Theories of Cognitive Self-Regulation, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
3.Ali K. A., A., & Subramanian, R. (2024). Continuance intention to use smartphone-based payment services: The role of pre-adoption expectancies, usage experience, and conventional inhibitions. Journal of Financial Services Marketing, 29(3), 888–903. https://doi.org/10.1057/s41264-023-00240-w
4.Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta‐analytic review. British Journal of Social Psychology, 40(4), 471–499. https://doi.org/10.1348/014466601164939
5.Ashalatha, D. D., Rao, A. S., & Yamuna, M. (2022). Assessment of e-NAM (electronic national agriculture market) on jaggery producers in Visakhapatnam, Andhra Pradesh. International Journal of Multidisciplinary Education Research, 11(7). http://ijmer.in.doi./2022/11.07.94
6.Bhattacherjee, A. (2000). Acceptance of e-commerce services: The case of electronic brokerages. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 30(4), 411–420. https://doi.org/10.1109/3468.852435
7.Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
8.Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229–254. https://doi.org/10.2307/25148634
9.Chaudhary, S., & Suri, P. K. (2021a). Framework for agricultural e-trading platform adoption using neural networks. International Journal of Information Technology, 13(2), 501–510. https://doi.org/10.1007/s41870-020-00603-9
10.Chaudhary, S., & Suri, P. K. (2021b). Ranking the factors influencing e-trading usage in agricultural marketing. Global Journal of Flexible Systems Management, 22(3), 233–249. https://doi.org/10.1007/s40171-021-00276-8
11.Chaudhary, S., & Suri, P. K. (2022). Modelling the enablers of e-trading adoption in agricultural marketing: A TISM-based analysis of eNAM. Vision: The Journal of Business Perspective, 26(1), 65–79. https://doi.org/10.1177/0972262920977979
12.Chen, X., Zhang, X., & Chen, J. (2024). TAM-based study of farmers’ live streaming e-commerce adoption intentions. Agriculture, 14(4), 518. https://doi.org/10.3390/agriculture14040518
13.Chidanand Patil, D. P. S., & Meena, S. S. (2021). Impact of online agriculture marketing policy—E-NAM (electronic national agriculture market) on prices and arrivals of agricultural commodities in Punjab, India. International Journal of Current Microbiology and Applied Sciences, 10(2), 1573–1582. https://doi.org/10.20546/ijcmas.2021.1002.187
14.Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Modern Methods for Business Research. Psychology Press.
15.Cohen, J. (1988). Set correlation and contingency tables. Applied Psychological Measurement, 12(4), 425–434. https://doi.org/10.1177/014662168801200410
16.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
17.Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
18.Fishbein, M., & Ajzen, I. (2011). Predicting and changing behavior: The reasoned action approach. Psychology Press. https://doi.org/10.4324/9780203838020
19.Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
20.Gangwal, N., & Bansal, V. (2016). Application of decomposed theory of planned behavior for m-commerce adoption in India. Proceedings of the 18th International Conference on Enterprise Information Systems, 357–367. https://doi.org/10.5220/0005627503570367
21.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
22.Hair, J. J. F., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modelling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128
23.Hajli, N., Shanmugam, M., Powell, P., & Love, P. E. D. (2015). A study on the continuance participation in on-line communities with social commerce perspective. Technological Forecasting and Social Change, 96, 232–241. https://doi.org/10.1016/j.techfore.2015.03.014
24.Hsieh, H.-L., Lai, J.-M., Chuang, B.-K., & Tsai, C.-H. (2022). Determinants of telehealth continuance intention: A multi-perspective framework. Healthcare, 10(10), 2038. https://doi.org/10.3390/healthcare10102038
25.Ivanova, N., Ovchinnikov, M., Lata, M., & Korabelnikov, I. (2020). Digital agriculture: Opportunities for the development of small agribusinesses and the respective problems. In T. Kolmykova & E. V. Kharchenko (Eds.), Digital Future: Economic Growth, Social Adaptation, and Technological Perspectives (Vol. 111, pp. 593–600). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-39797-5_56
26.Karmakar, A., Giri, A., & Majee, A. (2023). E-nam (Electronic National Marketing): Direct link between farmers and consumers. Emerging Trends in Agricultural Economics and Extension.
27.Kazembe, C. (2021). The gap between technology awareness and adoption in Sub-Saharan Africa: A literature review for the DeSIRA project. International Food Policy Research Institute. https://doi.org/10.2499/p15738coll2.134301
28.Kumar, R., Jhajharia, A. K., Rohila, A. K., Rajpurohit, T. S., Shubham, Kumar, S., & Choudhary, N. (2024). Awareness and challenges faced by farmers in marketing agricultural produce through the e-national agriculture market (e-nam). Asian Journal of Agricultural Extension, Economics & Sociology, 42(6), 276–283. https://doi.org/10.9734/ajaees/2024/v42i62490
29.Lee, C., & Wan, G. (2010). Including subjective norm and technology trust in the technology acceptance model: A case of e-ticketing in China. SIGMIS Database, 41(4), 40–51. https://doi.org/10.1145/1899639.1899642
30.Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
31.Li, L., Wang, Q., & Li, J. (2022). Examining continuance intention of online learning during COVID-19 pandemic: Incorporating the theory of planned behavior into the expectation–confirmation model. Frontiers in Psychology, 13, 1046407. https://doi.org/10.3389/fpsyg.2022.1046407
32.Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737. https://doi.org/10.2307/25148817
33.McKnight, D. H., & Chervany, N. L. (2001). What trust means in e-commerce customer relationships: An interdisciplinary conceptual typology. International Journal of Electronic Commerce, 6(2), 35–59. https://doi.org/10.1080/10864415.2001.11044235
34.Mehta, P., Thakur, R., Raina, K. K., Thakur, P., & Mehta, R. (2019). Farmers’ perception towards electronic-national agriculture market (e-NAM) systems adopted by APMC market, Solan, Himachal Pradesh. AgricINTERNATIONAL, 6(1), 51. https://doi.org/10.5958/2454-8634.2019.00010.X
35.Rezaei, R., Safa, L., Damalas, C. A., & Ganjkhanloo, M. M. (2019). Drivers of farmers’ intention to use integrated pest management: Integrating theory of planned behavior and norm activation model. Journal of Environmental Management, 236, 328–339. https://doi.org/10.1016/j.jenvman.2019.01.097
36.Saini, S., Jirli, B., & Padhan, S. R. (2023a). Analysis of factors promoting the usage of electronic national agriculture market in Rajasthan, India. Current Science, 125(6), 643. https://doi.org/10.18520/cs/v125/i6/643-648
37.Saini, S., Jirli, B., & Padhan, S. R. (2023b). Awareness mapping of national agriculture market (e-nam) provisions in Rajasthan. Journal of Community Mobilization and Sustainable Development, 18(2), 431–439.
38.Shih, Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study internet banking in Taiwan. Internet Research, 14(3), 213–223. https://doi.org/10.1108/10662240410542643
39.Singh, N. K., V, P., & Alagawadi, M. (2021). Impact of e-nam (electronic-national agriculture market) in doubling farmers’ income. Proceedings of the International Conference on Advances in Management Practices. ICAMP 2021. https://doi.org/10.2139/ssrn.3992422
40.Suri, P. K. (2018). Towards an effective agricultural e-trading system in India. In J. Connell, R. Agarwal, Sushil, & S. Dhir (Eds.), Global Value Chains, Flexibility and Sustainability (pp. 187–203). Springer Singapore. https://doi.org/10.1007/978-981-10-8929-9_13
41.Taylor, S., & Todd, P. A. (1995). Understanding Information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144
42.The Hindu BusinessLine. (2019, July 10). Just 14% of farmers registered on eNAM platform. BusinessLine. https://www.thehindubusinessline.com/economy/agri-business/just-14-of-farmers-registered-on-enam-platform/article28363454.ece
43.Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
44.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
45.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 technology1. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412




