Mobile App Technology Adoption in Indonesia’s Agricultural Sector. An Analysis of Empirical View From Public R&D Agency

Rendi Febrianda

doi:10.14203/STIPM.2021.302

Mobile App Technology Adoption in Indonesia’s Agricultural Sector. An Analysis of Empirical View From Public R&D Agency

Rendi Febrianda

Abstract

Adoption of digital technologies are expected to transform current agricultural system towards sustainability. Mobile application (app) designed to assist farmers decision making has started to revolutionize the agriculture sector of Indonesia. The app offer solutions to farmers by providing information of season prediction, cropping pattern, recommended fertilizers and varieties and so on. This paper aims to review a framework related to diffusion and adoption of digital farming technologies that integrating farm level evidence then compare it with local data based on the empirical view of public agricultural R&D agency. The result indicates that the framework could be the basis for application to foster the adoption of digital technologies where the highlight provides important aspects of the adoption process. The findings also provide what matters more or less. However, limitations remain and future research is needed to improve the understanding.

Keywords: Digital technologies, mobile application, adoption, agriculture sector

Full Text:

PDF

References

Alexander, P., Moran, D., Rounsevell, M. D. A., & Smith, P. (2013). Modelling the perennial energy crop market: the role of spatial diffusion. Journal of The Royal Society Interface, 10(88), 20130656. https://doi.org/10.1098/rsif.2013.0656

Aubert, B. A., Schroeder, A., & Grimaudo, J. (2012). IT as enabler of sustainable farming: An empirical analysis of farmers’ adoption decision of precision agriculture technology. Decision Support Systems, 54(1), 510–520. https://doi.org/https://doi.org/10.1016/j.dss.2012.07.002

Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., & Ruggeri, M. (2019). The Digitisation of Agriculture: a Survey of Research Activities on Smart Farming. Array, 3–4, 100009. https://doi.org/https://doi.org/10.1016/j.array.2019.100009

Beretta, E., Fontana, M., Guerzoni, M., & Jordan, A. (2018). Cultural dissimilarity: Boon or bane for technology diffusion? Technological Forecasting and Social Change, 133, 95–103. https://doi.org/https://doi.org/10.1016/j.techfore.2018.03.008

Caffaro, F., & Cavallo, E. (2019). The Effects of Individual Variables, Farming System Characteristics and Perceived Barriers on Actual Use of Smart Farming Technologies: Evidence from the Piedmont Region, Northwestern Italy. Agriculture . https://doi.org/10.3390/agriculture9050111

Chuang, J.-H., Wang, J.-H., & Liou, Y.-C. (2020). Farmers’ Knowledge, Attitude, and Adoption of Smart Agriculture Technology in Taiwan. International Journal of Environmental Research and Public Health, 17(19), 7236. https://doi.org/10.3390/ijerph17197236

Cole, M. B., Augustin, M. A., Robertson, M. J., & Manners, J. M. (2018). The science of food security. Npj Science of Food, 2(1), 14. https://doi.org/10.1038/s41538-018-0021-9

Dearing, J. W., & Kreuter, M. W. (2010). Designing for diffusion: How can we increase uptake of cancer communication innovations? Patient Education and Counseling, 81, S100–S110. https://doi.org/https://doi.org/10.1016/j.pec.2010.10.013

Drewry, J. L., Shutske, J. M., Trechter, D., Luck, B. D., & Pitman, L. (2019). Assessment of digital technology adoption and access barriers among crop, dairy and livestock producers in Wisconsin. Computers and Electronics in Agriculture, 165, 104960. https://doi.org/https://doi.org/10.1016/j.compag.2019.104960

Febrianda, R., Ririh, K. R., Romadona, M. R., Laili, N., Maludin, S., & Manalu, R. (2020). Tren Produk Pengembangan Teknologi Industri 4.0 di Lembaga Litbang Indonesia [Trend of Industry 4.0 Products in Indonesia Research And Development Institutes]. Research Report. Research Center for Science Technology and Innovation Policy and Management. Indonesian Institute of Sciences, December. Jakarta, Indonesia.

Finger, R., Swinton, S. M., El Benni, N., & Walter, A. (2019). Precision Farming at the Nexus of Agricultural Production and the Environment. Annual Review of Resource Economics, 11(1), 313–335. https://doi.org/10.1146/annurev-resource-100518-093929

Girard, P., & Payrat, T. Du. (2017). An inventory of new technologies in fisheries. Issue Paper OECD, Greening T.

Indraningsih, K. S. (2018). Strategi Diseminasi Inovasi Pertanian Dalam Mendukung Pembangunan Pertanian [Agricultural Innovation Dissemination Strategy in Supporting Agricultural Development]. Forum Penelitian Agro Ekonomi, 35(2), 107–123. Retrieved from http://dx.doi.org/10.21082/fae.v35n2.2017.107-123

Jamal, E., Mardiharini, M., & Sarwani, M. (2008). Proses Diseminasi Pengelolaan Tanaman Dan Sumberdaya Terpadu (PTT) Padi : Suatu Pembelajaran Dan Perspektif Ke Depan. [Dissemination Process for Integrated Rice Crop and Resource Management (PTT): A Lesson learned and future perspective] Analisis Kebijakan Pertanian, 6(3), 272–285.

Katalin, T. G., Ibolya, L., András, M., Dénes, S., Márta, G., Zsuzsanna, K. H., … Andrea, K. (2018). Precision agriculture in Hungary : assessment of perceptions and accounting records of FADN arable farms. Studies in Agricultural Economics, 120, 47–54. https://doi.org/http://dx.doi.org/10.22004/ag.econ.273117

Kutter, T., Tiemann, S., Siebert, R., & Fountas, S. (2011). The role of communication and co-operation in the adoption of precision farming. Precision Agriculture, 12(1), 2–17. https://doi.org/10.1007/s11119-009-9150-0

Laliene, R., & Liepe, Z. (2015). R&D Planning System Approach at Organizational Level. Procedia - Social and Behavioral Sciences, 213, 812–816. https://doi.org/10.1016/j.sbspro.2015.11.482

Magsamen-Conrad, K., & Dillon, J. M. (2020). Mobile technology adoption across the lifespan: A mixed methods investigation to clarify adoption stages, and the influence of diffusion attributes. Computers in Human Behavior, 112, 106456. https://doi.org/https://doi.org/10.1016/j.chb.2020.106456

Michels, M., Fecke, W., Feil, J.-H., Musshoff, O., Pigisch, J., & Krone, S. (2020). Smartphone adoption and use in agriculture: empirical evidence from Germany. Precision Agriculture, 21(2), 403–425. https://doi.org/10.1007/s11119-019-09675-5

OECD,. (2019). Digital Opportunities for Better Agricultural Policies. https://doi.org/https://doi.org/https://doi.org/10.1787/571a0812-en

Pivoto, D., Barham, B., Dabdab, P., & Rogério, C. (2019). Factors influencing the adoption of smart farming by Brazilian grain farmers. International Food and Agribusiness Management Review, 22(4), 571–588. https://doi.org/10.22434/IFAMR2018.0086

Rashid, Y., Rashid, A., & Warraich, M. A. (2019). Case Study Method : A Step-by-Step Guide for Business Researchers. International Journal of Qualitative Method, 18, 1–13. https://doi.org/10.1177/1609406919862424

Reichardt, M., & Jürgens, C. (2009). Adoption and future perspective of precision farming in Germany: results of several surveys among different agricultural target groups. Precision Agriculture, 10(1), 73–94. https://doi.org/10.1007/s11119-008-9101-1

Rice, R. E. (2017). Intermediality and the diffusion of innovations. Human Communication Research, 43(4), 531–544.

Robertson, M. J., Llewellyn, R. S., Mandel, R., Lawes, R., Bramley, R. G. V, Swift, L., … O’Callaghan, C. (2012). Adoption of variable rate fertiliser application in the Australian grains industry: status, issues and prospects. Precision Agriculture, 13(2), 181–199. https://doi.org/10.1007/s11119-011-9236-3

Rogers, E. (2003). Diffusion of Innovations, 5th Edition. Simon and Schuster. Retrieved from http://www.citeulike.org/group/596/article/352228

Salimi, M., Pourdarbani, R., & Nouri, B. A. (2020). Factors Affecting the Adoption of Agricultural Automation Using Davis’s Acceptance Model (Case Study: Ardabil). Acta Technologica Agriculturae, 23(1), 30–39. https://doi.org/doi:10.2478/ata-2020-0006

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, https://doi.org/https://doi.org/10.1016/j.agsy.2021.103074

Shiau, S. J. H., Huang, C.-Y., Yang, C.-L., & Juang, J.-N. (2018). A Derivation of Factors Influencing the Innovation Diffusion of the OpenStreetMap in STEM Education. Sustainability, 10(10), https://doi.org/10.3390/su10103447

Statistics Indonesia. (2019). Indikator Pertanian [Agricultural Indicators]. Jakarta, Indonesia. Retrieved 10 January 2021 from https://www.bps.go.id/publication/2020/11/30/a5f6025eb90c86561ce449e0/indikator-pertanian-2019.html

Tamirat, T. W., Pedersen, S. M., & Lind, K. M. (2018). Farm and operator characteristics affecting adoption of precision agriculture in Denmark and Germany. Acta Agriculturae Scandinavica, Section B — Soil & Plant Science, 68(4), 349–357. https://doi.org/10.1080/09064710.2017.1402949

Thar, S. P., Ramilan, T., Farquharson, R. J., Pang, A., & Chen, D. (2021). An empirical analysis of the use of agricultural mobile applications among smallholder farmers in Myanmar. The Electronic Journal of Information Systems in Developing Countries, 87(2), e12159. https://doi.org/https://doi.org/10.1002/isd2.12159

Walter, A., Finger, R., Huber, R., & Buchmann, N. (2017). Smart farming is key to developing sustainable agriculture. Proceedings of The National Academy of Sciences, 114(24), 6148–6150. https://doi.org/10.1073/pnas.1707462114

Xu, Q., Huet, S., Perret, E., & Deffuant, G. (2020). Do Farm Characteristics or Social Dynamics Explain the Conversion to Organic Farming by Dairy Farmers? An Agent-Based Model of Dairy Farming in 27 French Cantons. Journal of Artificial Societies and Social Simulation, 23(2), 4. https://doi.org/10.18564/jasss.4204

Zheng, S., Wang, Z., & Wachenheim, C. J. (2019). Technology adoption among farmers in Jilin Province, China. China Agricultural Economic Review, 11(1), 206–216. https://doi.org/10.1108/CAER-11-2017-0216

DOI: http://dx.doi.org/10.14203/STIPM.2021.302