Examining the sociotechnical determinants of revolution in military affairs on electronic warfare capability development: Evidence from a military organization unit in Indonesia

Emanuel Ario Bimo; Amarulla Octavian; Suyono Thamrin; Rudy Laksmono

doi:10.31893/multirev.2025230

Keywords:

personnel competence

doctrine

technology

Abstract

Electronic warfare capability is an important area in the revolution of military affairs (RMA), which needs be understood holistically through a sociotechnical lens. This study aims to examine the influence of personnel competence, doctrine, and materiel as three sociotechnical determinants of electronic warfare capability development in a military organization unit by employing quantitative research design. Partial Least Square-Structural Equation Modeling (PLS-SEM) analysis was conducted on 82 personnel in an organization unit within the Indonesian Armed Forces (TNI). The results show that materiel has a significant and positive influence on electronic warfare capability development in the studied object, whereas personnel competence and doctrine have a positive but insignificant influence. These findings provide evidence that strongly support the technology-driven RMA discourse while also emphasizing the importance of aligning the social aspects of personnel competence and doctrine with the technological aspect of developing electronic warfare capability as a part of RMA undertaking. The implication of these findings warrants a call for broader policy development to strengthen and integrate national research and development (R&D) and industrial bases to provide state-of-the-art technology for electronic warfare capability development.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Bimo, E. A., Octavian, A., Thamrin, S., & Laksmono, R. (2025). Examining the sociotechnical determinants of revolution in military affairs on electronic warfare capability development: Evidence from a military organization unit in Indonesia. Multidisciplinary Reviews, 8(8), 2025230. https://doi.org/10.31893/multirev.2025230

[1] 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

[2] Aref, M. A., Jayaweera, S. K., & Yepez, E. (2020). Survey on cognitive anti‐jamming communications. IET Communications, 14(18), 3110-3127. https://doi.org/10.1049/iet-com.2020.0024

[3] Baltes, S., & Ralph, P. (2022). Sampling in software engineering research: A critical review and guidelines. Empirical Software Engineering, 27(4), 1-31. https://doi.org/10.1007/s10664-021-10072-8

[4] Bērziņš, J. (2020). The theory and practice of new generation warfare: The case of Ukraine and Syria. The Journal of Slavic Military Studies, 33(3), 355-380. https://doi.org/10.1080/13518046.2020.1824109

[5] Borchert, H., Schütz, T., & Verbovszky, J. (2024). The Very Long Game: 25 Case Studies on the Global State of Defense AI. Springer.

[6] Bratianu, C., Hadad, S., & Bejinaru, R. (2020). Paradigm shift in business education: A competence-based approach. Sustainability, 12(4), 1348. https://doi.org/10.3390/su12041348

[7] Burmaoglu, S., & Sarıtas, O. (2017). Changing characteristics of warfare and the future of military R&D. Technological Forecasting and Social Change, 116, 151-161. https://doi.org/10.1016/j.techfore.2016.10.062

[8] Chin, W. W. (2010). How to Write Up and Report PLS Analyses. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares (pp. 655-690). Springer.

[9] Choi, S., Kwon, O. J., Oh, H., & Shin, D. (2020). Method for effectiveness assessment of electronic warfare systems in cyberspace. Symmetry, 12(12), 2107-2122. https://doi.org/10.3390/sym12122107

[10] Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092-121102. https://doi.org/10.1016/j.techfore.2021.121092

[11] Dos Santos, T. P., & Freire, M. E. L. S. (2023). Beyond the buzzwords: Some thoughts on interoperability and military change challenges in Brazil. Defense Studies, 23(3), 497-526. https://doi.org/10.1080/14702436.2023.2213635

[12] Edwards, J. S. (2005). Business Processes and Knowledge Management. In M. Khosrow-Pour (Ed.), Encyclopedia of Information Science and Technology (pp. 350-355). IGI Global.

[13] Eizaguirre, A., García-Feijoo, M., & Laka, J. P. (2019). Defining sustainability core competencies in business and management studies based on multinational stakeholders’ perceptions. Sustainability, 11(8), 2303-2323. https://doi.org/10.3390/su11082303

[14] Gat, A. (2012). The ‘Revolution in Military Affairs’ (RMA) as an Analytical Tool for the Interpretation of Military History. In D. Adamsky & K. I. Bjerga (Eds.), Contemporary Military Innovation: Between Anticipation and Adaptation (pp. 6-19). Routledge.

[15] Gemke, P., Den Hengst, M., Rosmalen, F. V., & Boer, A. D. (2021). Toward a maturity model for intelligence-led policing: A case study research on the investigation of drugs crime and on football and safety in the Dutch Police. Police Practice and Research, 22(1), 190-207. https://doi.org/10.1080/15614263.2019.1689135

[16] Haigh, K., & Andrusenko, J. (2021). Cognitive Electronic Warfare: An Artificial Intelligence Approach. Artech House.

[17] Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling. Sage Publications.

[18] Hämäläinen, R., Nissinen, K., Mannonen, J., Lämsä, J., Leino, K., & Taajamo, M. (2021). Understanding teaching professionals' digital competence: What do PIAAC and TALIS reveal about technology-related skills, attitudes, and knowledge?. Computers in Human Behavior, 117, 106672-106686. https://doi.org/10.1016/j.chb.2020.106672

[19] 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

[20] Kayikci, Y., Subramanian, N., Dora, M., & Bhatia, M. S. (2022). Food supply chain in the era of Industry 4.0: Blockchain technology implementation opportunities and impediments from the perspective of people, process, performance, and technology. Production Planning & Control, 33(2-3), 301-321. https://doi.org/10.1080/09537287.2020.1810757

[21] Krelina, M. (2021). Quantum technology for military applications. EPJ Quantum Technology, 8, 396-448. https://doi.org/10.1140/epjqt/s40507-021-00113-y

[22] Le, T. (2018). Japan and the revolution in military affairs. Journal of Asian Security and International Affairs, 5(2), 172-196. https://doi.org/10.1177/2347797018783112

[23] LeDoux, R. J., Lee, C. P., Ghoshal, S., & Haselkorn, M. (2024). Concept of operations as epistemic object: The sociotechnical design roles of a systems engineering document. In Proceedings of the ACM on Human-Computer Interaction (p. 34). https://doi.org/10.1145/3637311

[24] Legate, A. E., Hair Jr, J. F., Chretien, J. L., & Risher, J. J. (2023). PLS‐SEM: Prediction‐oriented solutions for HRD researchers. Human Resource Development Quarterly, 34(1), 91-109. https://doi.org/10.1002/hrdq.21466

[25] McCrory, D. (2020). Russian electronic warfare, cyber and information operations in Ukraine: Implications for NATO and security in the Baltic States. The RUSI Journal, 165(7), 34-44. https://doi.org/10.1080/03071847.2021.1888654

[26] Mohammed, U., Abdullah, S. S., & Ahmad, S. (2024). Narratives of total quality management, green management, risk-taking on organizational performance: The moderating role of IT infrastructure. Multidisciplinary Science Journal, 6(11), 2024230-2024230. https://doi.org/10.31893/multiscience.2024230

[27] Monge, M. A. S., & Vidal, J. M. (2021). Conceptualization and cases of study on cyber operations against the sustainability of the tactical edge. Future Generation Computer Systems, 125, 869-890. https://doi.org/10.1016/j.future.2021.07.016

[28] Oh, S. J., Cho, S. K., & Seo, Y. (2024). Harnessing ICT-enabled warfare: A comprehensive review on South Korea’s military meta power. IEEE Access, 12, 46379-46400. https://doi.org/10.1109/ACCESS.2024.3378735

[29] Omair, B., & Alturki, A. (2020). A systematic literature review of fraud detection metrics in business processes. IEEE Access, 8, 26893-26903. https://doi.org/10.1109/ACCESS.2020.2971604

[30] Rachman, A., Octavian, A., Irdham, A., Ali, Y., Putra, I., & Susilo, A. (2023). Revolution in military affairs (RMA) by Indonesian Armed Forces toward competitive advantage. Decision Science Letters, 12(2), 413-430. http://dx.doi.org/10.5267/j.dsl.2022.12.002

[31] Raska, M. (2021). The sixth RMA wave: Disruption in military affairs?. Journal of Strategic Studies, 44(4), 456-479. https://doi.org/10.1080/01402390.2020.1848818

[32] Saarnisaari, H., Djapic, R., Tuukkanen, T., Low, W., Chuberre, N., & Saha, S. (2023). DOTMFLPI analysis of 5G for several military use cases. In 2023 IEEE Military Communications Conference (MILCOM) (p. 71). New York, NY. https://doi.org/10.1109/MILCOM58377.2023.10356271

[33] Sangwan, D., & Raj, P. (2021). The philosophy of be, know, and do in forming the 21st-century military war-front competencies: A systematic review. Defense Studies, 21(3), 375-424. https://doi.org/10.1080/14702436.2021.1937135

[34] Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial Least Squares Structural Equation Modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of Market Research (pp. 587-632). Springer International Publishing.

[35] Shao, G., Chen, Y., & Wei, Y. (2020). Convolutional neural network-based radar jamming signal classification with sufficient and limited samples. IEEE Access, 8, 80588-80598. https://doi.org/10.1109/ACCESS.2020.2990629

[36] Sharma, P., Sarma, K. K., & Mastorakis, N. E. (2020). Artificial intelligence aided electronic warfare systems-recent trends and evolving applications. IEEE Access, 8, 224761-224780. https://doi.org/10.1109/ACCESS.2020.3044453

[37] Sony, M., & Naik, S. (2020). Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model. Technology in Society, 61, 1-11. https://doi.org/10.1016/j.techsoc.2020.101248

[38] Swallow, R. C. (2023). Considering the cost of cyber warfare: advancing cyber warfare analytics to better assess tradeoffs in system destruction warfare. The Journal of Defense Modeling and Simulation, 20(1), 3-37. https://doi.org/10.1177/15485129221114354

[39] Uren, V., & Edwards, J. S. (2023). Technology readiness and the organizational journey toward AI adoption: An empirical study. International Journal of Information Management, 68, 102588-102599. https://doi.org/10.1016/j.ijinfomgt.2022.102588

[40] Vitello, S., Greatorex, J., & Shaw, S. (2021). What Is Competence? A Shared Interpretation of Competence to Support Teaching, Learning and Assessment. Cambridge: Cambridge University Press & Assessment.

[41] You, S., Diao, M., & Gao, L. (2019). Deep reinforcement learning for target searching in cognitive electronic warfare. IEEE Access, 7, 37432-37447. https://doi.org/10.1109/ACCESS.2019.2905649

[42] Yusif, S., Hafeez-Baig, A., Soar, J., & Teik, D. O. L. (2020). PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model. Health and Technology, 10(6), 1497-1513. https://doi.org/10.1007/s12553-020-00484-9

Abstract

References

How to cite