Pengaruh Satellite Monitoring terhadap Efektivitas Pemantauan Lahan dengan Data-Driven Decision Making sebagai Variabel Mediasi pada Agritech PT XYZ
DOI:
https://doi.org/10.61132/lokawati.v4i5.2768Keywords:
Agritech, Agronomist, Data-Driven Decision Making, Land Monitoring Effectiveness, Satellite MonitoringAbstract
Farming on a large scale at Agritech PT XYZ calls for satellite monitoring so that supervision actually works, yet a heavy schedule of field visits has not translated into effective monitoring on its own. Earlier work largely stops at the direct link between monitoring technology and farming performance, leaving the pathway joining the two thinly and inconsistently evidenced, especially across the Indonesian agritech landscape. This study therefore weighs how four dimensions of satellite monitoring implementation, covering information availability, information accuracy, monitoring speed, and operational utilisation, shape land monitoring effectiveness, while asking whether data-driven decision making carries that influence. The design is quantitative and causal, drawing on 120 field agronomists enumerated as a census, with estimation through partial least squares path modelling. Every dimension lifted both land monitoring effectiveness and data-driven decision making at a meaningful level; the latter in turn raised effectiveness and conveyed the influence of all four. Each dimension travels a different route, since information availability moves largely along the direct path whereas information accuracy contributes almost wholly through decision making, with monitoring speed standing out overall. Investment therefore needs sorting by mechanism rather than treating remote sensing as one uniform construct.
Downloads
References
Atzberger, C. (2013). Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. International Journal of Applied Earth Observation and Geoinformation, 23, 2–15. https://doi.org/10.1016/j.jag.2012.12.011 https://doi.org/10.3390/rs5020949
Balderas, D., Chen, Y., Huang, W., Wang, L., & Li, J. (2025). Data-driven crop management policies in smart agriculture. Smart Agricultural Technology.
Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decision making affect firm performance? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1819486
Cameron, K. S. (1978). Measuring organizational effectiveness in institutions of higher education. Administrative Science Quarterly, 23(4), 604–632. https://doi.org/10.2307/2392582
Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing (5th ed.). Guilford Press.
Cole, S., Harigaya, T., Killeen, G., & Krishna, A. (2025). Using satellites and phones to evaluate and promote agricultural technology adoption: Evidence from smallholder farms in India. Journal of Development Economics, 176, 103463. https://doi.org/10.1016/j.jdeveco.2025.103463
Davenport, T. H. (2013). Decision making in the age of big data. MIT Sloan Management Review, 54(2), 1–10.
Hair, J., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications. https://doi.org/10.1007/978-3-030-80519-7
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
Jin, Z., & Azzari, G. (2020). Smallholder maize area and yield mapping using satellite data. Nature Food, 1(11), 720–728. https://doi.org/10.1038/s43016-020-00167-7
Kurniawan, K., Saiful, S., & Midiastuti, P. P. (2021). Pengaruh kemampuan sumber daya manusia dan pemanfaatan teknologi informasi terhadap kualitas laporan barang milik negara. Jurnal Fairness, 6(2), 125–134. https://doi.org/10.33369/fairness.v6i2.15131
Kurniawan, W., & Sanapiah, A. A. (2021). How BPS-Statistics Indonesia to handle the effectiveness of information system of public budgeting management? Jurnal Ilmiah Administrasi Publik, 7(1), 31–39. https://doi.org/10.21776/ub.jiap.2021.007.01.4
Laudon, K. C., & Laudon, J. P. (2020). Management information systems: Managing the digital firm (16th ed.). Pearson.
Li, Z., Liu, C., Zhang, Y., & Wang, P. (2022). Satellite earth observation for agricultural monitoring: A review. Agricultural Systems, 190, 103117. https://doi.org/10.1016/j.agsy.2021.103117
Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote sensing and image interpretation (7th ed.). Wiley.
Lobell, D. B. (2013). The use of satellite data for crop yield estimation and forecasting. Annual Review of Environment and Resources, 38, 1–24. https://doi.org/10.1146/annurev-environ-102012-021535
Long, L., & Cui, J. (2025). Analysis of agricultural production efficiency improvement and economic sustainability based on multi-source remote sensing data. Frontiers in Environmental Science, 13, 1546643. https://doi.org/10.3389/fenvs.2025.1546643
Mikram, M., Moujahdi, C., & Rhanoui, M. (2025). Deep learning and machine learning approaches for data-driven risk management and decision support in precision agriculture. International Journal of Sustainable Agricultural Management and Informatics, 11(2), 226–247. https://doi.org/10.1504/IJSAMI.2025.145317
Munawar, Z., Herdiana, Y., Putri, N. I., & Rustiyana. (2021). Dampak intelijen bisnis pada kualitas pengambilan keputusan. Infotronik: Jurnal Teknologi Informasi dan Elektronika, 6(1), 32–41. https://doi.org/10.32897/infotronik.2021.6.1.661
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
Putri, L. A., & Nurbaiti, B. (2024). Pengaruh kualitas informasi, dukungan manajemen, dan teknologi informasi terhadap efektivitas pengambilan keputusan eksekutif. Dinasti Information and Technology, 2(2). https://doi.org/10.38035/dit.v2i2.1364
Satria, D., Maghraby, W., & Setyanti, A. M. (2025). Digital agricultural technology for smallholder farmers: Barriers and opportunities in Indonesia. SOCA: Jurnal Sosial Ekonomi Pertanian, 18(3). https://doi.org/10.24843/SOCA.2024.v18.i03.p01
Singh, N., & Kapoor, S. (2024). Agtech platforms: Complementors and value propositions. Technology Analysis & Strategic Management. https://doi.org/10.1080/09537325.2024.2306636
Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136. https://doi.org/10.3390/rs12193136
Steers, R. M. (1975). Problems in the measurement of organizational effectiveness. Administrative Science Quarterly, 20(4), 546–558. https://doi.org/10.2307/2391827
Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159–205. https://doi.org/10.1016/j.csda.2004.03.005
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
Wahyudi, D., Sahertian, P., & Sarwoko, E. (2021). Peran sistem informasi pada peningkatan efektifitas kerja pegawai. Management and Business Review, 5(1), 1–13. https://doi.org/10.21067/mbr.v5i1.5544
Zhang, X., Wang, J., Li, Y., & Chen, H. (2021). Satellite-based monitoring of crop conditions using NDVI time series. Remote Sensing of Environment, 256, 112124. https://doi.org/10.1016/j.rse.2021.112124
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Lokawati : Jurnal Penelitian Manajemen dan Inovasi Riset
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
