Journal Press India^®

Author Details ( * ) denotes Corresponding author

Data-driven marketing (DDM) practices are revolutionizing India’s coffee sector by improving market access, operational effectiveness, and sustainability. This research examines the barriers and enablers of DDM adoption among farmers, including financial limitations, digital literacy, infrastructure quality, and training availability. A cross-sectional design was employed, and primary data were examined using Structural Equation Modeling (SEM). Findings show that financial limitations restrict perceived ease of use, while microfinance enhances accessibility. Community influence and digital literacy drive perceptions of usefulness, and adoption is enhanced through customized training. Strong infrastructure, including internet connectivity, is an essential enabler. Perceived ease of use and usefulness are strong mediators of adoption. Subsidies, rural digital infrastructure investments, and farmer-oriented training are policy recommendations. Solving these challenges can spur DDM adoption, enhancing market links, profitability, and sustainability in India’s coffee industry.

Keywords

Data-driven marketing; Financial constraints; Digital literacy; Infrastructure quality; Training access

1. Abdullah, R., Najim, M. M. M. & Esham, M. (2024). Agriculture for sustainable development to empower smallholder farming communities. Journal of Agricultural Sciences–Sri Lanka, 19(3), 462–474. Retrieved from https://doi.org/10.4038/JAS.V19I3.1 0831
2. Abdul-Rahaman, A. & Abdulai, A. (2022). Mobile money adoption, input use, and farm output among smallholder rice farmers in Ghana. Agribusiness, 38(1), 236–255. Retrieved from https://doi.org/10.1002/AGR.21721
3. Addison, M., Bonuedi, I., Arhin, A. A., Wadei, B., Owusu-Addo, E., Fredua Antoh, E. & Mensah-Odum, N. (2024). Exploring the impact of agricultural digitalization on smallholder farmers’ livelihoods in Ghana. Heliyon, 10(6). Retrieved from https://doi.org/10.1016/j.heliyon.2024.e27541
4. Aker, J. C. & Ksoll, C. (2016). Can mobile phones improve agricultural outcomes? Evidence from a randomized experiment in Niger. Food Policy, 60, 44–51. Retrieved from https://doi.org/10.1016/J.FOODPOL.2015.03.006
5. Aker, J. C., Ksoll, C. & Lybbert, T. J. (2012). Can mobile phones improve learning? Evidence from a field experiment in Niger. American Economic Journal: Applied Economics, 4(4), 94–120. Retrieved from https://doi.org/10.1257/APP.4.4.94
6. Alemu, A. (2021). Determinants of participation in farmers training centre based extension training in Ethiopia. Journal of Agricultural Extension, 25(2), 86–95. Retrieved from https://doi.org/10.4314/JAE.V25I2.8
7. Asravor, R. K., Boakye, A. N. & Essuman, J. (2022). Adoption and intensity of use of mobile money among smallholder farmers in rural Ghana. Information Development, 38(2), 204–217. Retrieved from https://doi.org/10.1177/0266666921999089
8. Aziz, A. & Naima, U. (2021). Rethinking digital financial inclusion: Evidence from Bangladesh. Technology in Society, 64. Retrieved from https://doi.org/10.1016/J.TECH SOC.2020.101509
9. Beuermann, D. W., McKelvey, C. & Vakis, R. (2012). Mobile phones and economic development in Rural Peru. Journal of Development Studies, 48(11), 1617–1628. Retrieved from https://doi.org/10.1080/00220388.2012.709615
10. Bhat, S. A. & Huang, N. F. (2021). Big data and ai revolution in precision agriculture: Survey and challenges. IEEE Access, 9, 110209–110222. Retrieved from https://doi.org/10.1109/ACCESS.2021.3102227
11. Bolfe, É. L., Jorge, L. A. de C., Sanches, I. D., Júnior, A. L., Costa, C. C. da, Victoria, D. de C., Inamasu, R. Y., Grego, C. R., Ferreira, V. R. & Ramirez, A. R. (2020). Precision and digital agriculture: Adoption of technologies and perception of Brazilian farmers. Agriculture (Switzerland), 10(12), 1–16. Retrieved from https://doi.org/10.3390/AGRI CULTURE10120653
12. Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S. & Goudos, S. K. (2022). Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things (Netherlands), 18. Retrieved from https://doi.org/10.1016/J.IOT.2020.100187
13. Cayabyab, B. A. G., Quimbo, M. A. T., Serrano, E. P. & Calalo, F. C. (2024). Effectiveness of application of knowledge of agricultural training among farmer-scientist training participants in the Philippines. Journal of Agricultural Extension, 28(3), 111–123. Retrieved from https://doi.org/10.4314/JAE.V28I3.12
14. Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487. Retrieved from https://doi.org/10.1006/IMMS.1993.1022
15. Deng, X., Xu, D., Zeng, M. & Qi, Y. (2019). Does internet use help reduce rural cropland abandonment? Evidence from China. Land Use Policy, 89. Retrieved from https://doi.org/10.1016/j.landusepol.2019.104243
16. Dibra, M. (2015). Rogers theory on diffusion of innovation-the most appropriate theoretical model in the study of factors influencing the integration of sustainability in tourism businesses. Procedia - Social and Behavioral Sciences, 195, 1453–1462. Retrieved from https://doi.org/10.1016/j.sbspro.2015.06.443
17. Dixit, K., Aashish, K. & Kumar Dwivedi, A. (2023). Antecedents of smart farming adoption to mitigate the digital divide – extended innovation diffusion model. Technology in Society, 75. Retrieved from https://doi.org/10.1016/J.TECHSOC.2023.102348
18. Gabriel, A. & Gandorfer, M. (2023). Adoption of digital technologies in agriculture—an inventory in a european small-scale farming region. Precision Agriculture, 24(1), 68–91. Retrieved from https://doi.org/10.1007/S11119-022-09931-1
19. Johnson, D. (2024). Food security, the agriculture value chain, and digital transformation: The case of Jamaica’s agricultural business information system (ABIS). Technology in Society, 77. Retrieved from https://doi.org/10.1016/j.techsoc.2024.102523
20. Kamble, S. S., Gunasekaran, A. & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179–194.
21. Khatun, M. N., Sarker, M. N. I. & Mitra, S. (2024). Adoption of mobile banking to promote financial inclusion among rural farming community: Drivers and satisfaction level perspective. Journal of Agriculture and Food Research, 18, 101448. Retrieved from https://doi.org/10.1016/J.JAFR.2024.101448
22. Klerkx, L. & Rose, D. (2020). Dealing with the game-changing technologies of Agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways? Global Food Security, 24. Retrieved from https://doi.org/10.1016/J.GFS.2019. 100347
23. Kolady, D. E., Van der Sluis, E., Uddin, M. M. & Deutz, A. P. (2021). Determinants of adoption and adoption intensity of precision agriculture technologies: evidence from South Dakota. Precision Agriculture, 22(3), 689–710. Retrieved from https://doi.org/10.1007/ S11119-020-09750-2
24. Koutsos, T. & Menexes, G. (2019). Economic, agronomic, and environmental benefits from the adoption of precision agriculture technologies: A systematic review. International Journal of Agricultural and Environmental Information Systems, 10(1), 40–56. Retrieved from https://doi.org/10.4018/IJAEIS.2019010103
25. Kurtaliqi, F., Lancelot Miltgen, C., Viglia, G. & Pantin-Sohier, G. (2024). Using advanced mixed methods approaches: Combining PLS-SEM and qualitative studies. Journal of Business Research, 172. Retrieved from https://doi.org/10.1016/J.JBUSRES.2023.114464
26. Legris, P., Ingham, J. & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information and Management, 40(3), 191–204. Retrieved from https://doi.org/10.1016/S0378-7206(01)00143-4
27. Lio, M. & Liu, M. C. (2006). ICT and agricultural productivity: Evidence from cross-country data. Agricultural Economics, 34(3), 221–228. Retrieved from https://doi.org/10.1 111/J.1574-0864.2006.00120.X
28. Liu, W., Shao, X. F., Wu, C. H. & Qiao, P. (2021). A systematic literature review on applications of information and communication technologies and blockchain technologies for precision agriculture development. Journal of Cleaner Production, 298. Retrieved from https://doi.org/10.1016/j.jclepro.2021.126763
29. Mahmood, I., Shengze, Q., Chunping, X., Amar, R. & Arshed, B. (2024). Do e-credit and institutional support drive climate-smart, environmentally sustainable practices in Punjab’s agriculture? Polish Journal of Environmental Studies, 33(5), 5805-5817.
30. Miine, L. K., Akorsu, A. D., Boampong, O. & Bukari, S. (2023). Drivers and intensity of adoption of digital agricultural services by smallholder farmers in Ghana. Heliyon, 9(12), e23023. Retrieved from https://doi.org/10.1016/J.HELIYON.2023.E23023
31. Ogunyiola, A., Gardezi, M. & Vij, S. (2022). Smallholder farmers’ engagement with climate smart agriculture in Africa: role of local knowledge and upscaling. Climate Policy, 22(4), 411–426. Retrieved from https://par.nsf.gov/servlets/purl/10341111
32. Pando-Garcia, J., Periañez-Cañadillas, I. & Charterina, J. (2016). Business simulation games with and without supervision: An analysis based on the TAM model. Journal of Business Research, 69(5), 1731–1736. Retrieved from https://doi.org/10.1016/j.jbus res.2015.10.046
33. Quansah, E. M. (2024). Digital divide: Accessing digital technologies for firms in BOP countries. Online Information Review, 48(3), 476–490. Retrieved from https://doi.org/10. 1108/OIR-05-2023-0213
34. Quaye, W., Onumah, J. A., Boimah, M. & Mohammed, A. (2022). Gender dimension of technology adoption: The case of technologies transferred in Ghana. Development in Practice, 32(4), 434–447. Retrieved from https://doi.org/10.1080/09614524.2021.20005 88
35. Rajkhowa, P. & Qaim, M. (2021). Personalized digital extension services and agricultural performance: Evidence from smallholder farmers in India. PLOS ONE, 16(10), e0259319. Retrieved from https://doi.org/10.1371/journal.pone.0259319
36. Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). The Free Press.
37. Samadder, S., Pandya, S. P. & Lal, S. P. (2023). Bridging the digital divide in agriculture: An investigation to ICT adoption for sustainable farming practices in Banaskantha district of Gujarat, India. International Journal of Environment and Climate Change, 13(9), 1376–1384. Retrieved from https://doi.org/10.9734/IJECC/2023/V13I92367
38. Shang, L., Heckelei, T., Gerullis, M. K., Börner, J. & Rasch, S. (2021). Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction. Agricultural Systems, 190. Retrieved from https://doi.org/10.1016/j.agsy.20 21.103074
39. Solodovnik, A. I., Savkin, V. I. & Gulyaeva, T. I. (2023). Agro-digital ecosystems in agriculture 4.0 and FoodTech initiatives: Perspectives from Russia. Innovation, Technology and Knowledge Management, 17–23. Retrieved from https://doi.org/10.1007/ 978-3-031-13913-0_3
40. Suvattanadilok, M. (2024). Market variables influence customer behavior toward coffee business growth. Cogent Business and Management, 11(1). Retrieved from https://doi.org/ 10.1080/23311975.2024.2329242
41. Tanko, M., Muhammed, M. A. & Ismaila, S. (2023). Reshapping agriculture technology adoption thinking: Malthus, Borlaug and Ghana’s fail green revolution. Heliyon, 9(1). Retrieved from https://doi.org/10.1016/j.heliyon.2022.e12783
42. Tantalaki, N., Souravlas, S. & Roumeliotis, M. (2019). Data-driven decision making in precision agriculture: The rise of big data in agricultural systems. Journal of Agricultural & Food Information, 20(4), 344–380.
43. Uy, T. C., Limnirankul, B., Kramol, P., Hoang Gia, H. & Nguyen Thi, D. T. (2024). Digital technology adoption among smallholder farmers in Vietnam: Implications for digital agricultural extension strategies. Journal of International Development. Retrieved from https://doi.org/10.1002/JID.3904
44. Vecchio, Y., De Rosa, M., Adinolfi, F., Bartoli, L. & Masi, M. (2020). Adoption of precision farming tools: A context-related analysis. Land Use Policy, 94. Retrieved from https://doi.org/10.1016/j.landusepol.2020.104481
45. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. Retrieved from https://doi.org/10.1287/ISRE.11.4.342.11872
46. Venkatesh, V. & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. Retrieved from https://doi.org/ 10.1111/j.1540-5915.1996.tb01822.x
47. Venkatesh, V., Thong, J. Y. L. & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376.
48. Wang, X., Zhang, J., Ma, D. & Sun, H. (2023). Green agricultural products supply chain subsidy scheme with green traceability and data-driven marketing of the platform. International Journal of Environmental Research and Public Health, 20(4). Retrieved from https://doi.org/10.3390/ijerph20043056
49. Wu, F. (2022). Adoption and income effects of new agricultural technology on family farms in China. PLoS ONE, 17(4 April). Retrieved from https://doi.org/10.1371/JOURN AL.PONE.0267101
50. Xia, Y., Guo, Q., Sun, H., Li, K. & Mu, Z. (2022). Green R&D financing strategy in platform supply chain with data-driven marketing. Sustainability (Switzerland), 14(15). Retrieved from https://doi.org/10.3390/su14159172
51. Yadav, S., Kaushik, A., Sharma, M., & Sharma, S. (2022). Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis. AgriEngineering, 4(2), 424–460. Retrieved from https://doi.org/10.3390/AGRIENGINEERING4020029
52. Yuan, Y. & Sun, Y. (2024). Practices, challenges, and future of digital transformation in smallholder agriculture: Insights from a literature review. Agriculture, 14(12), 2193. Retrieved from https://www.google.com/search?q=https://doi.org/10.3390/agriculture141 22193
53. Zhang, Y. & Zhang, Y. (2024). The influence of digital literacy on the phenomenon of deviation between farmers’ e-commerce sales willingness and behavior: Evidence from Rural China. Sustainability (Switzerland), 16(7). Retrieved from https://doi.org/10.3390/ SU16073000
54. Zul Azlan, Z. H., Junaini, S. N., Bolhassan, N. A., Wahi, R. & Arip, M. A. (2024). Harvesting a sustainable future: An overview of smart agriculture’s role in social, economic, and environmental sustainability. Journal of Cleaner Production, 434. Retrieved from https://doi.org/10.1016/J.JCLEPRO.2023.140338