ADOPTION READINESS OF ASSISTIVE SURGICAL ROBOTIC TECHNOLOGIES IN INDONESIAN NEUROSURGERY: AN EMPIRICAL ASSESSMENT USING THE READINESS INDEX
Published:
2026-09-14Downloads
Abstract
Abstract. The global expansion of assistive surgical robotic technologies creates an important opportunity for emerging healthcare markets, yet the adoption readiness of specialist users is still not well understood. This study assesses the readiness of neurosurgeons in Indonesia toward assistive robotic technologies and identifies practical pathways for accelerating adoption. A mixed-method exploratory design was used, combining Likert-scale survey data and qualitative thematic analysis from 41 practicing neurosurgeons across Indonesia. Rogers' Diffusion of Innovation framework guided the study. A Readiness Index developed for this study, calculated as the ratio of mean enabler scores to mean barrier scores multiplied by 100, was used to profile readiness and adopter segments. The findings show high awareness (mean 4.199) and conditionally favorable perceptions, with perceived enablers (mean 4.138) slightly outweighing barriers (mean 3.920). Financial constraints were the dominant barriers, while government support and structured training were the strongest enablers. The Readiness Index produced a mean of 108.5, with 26.8% High Readiness, 48.8% Medium, and 24.4% Low. The Early Majority segment was only 4.9%, indicating that mainstream diffusion conditions have not yet formed. Proposed pathways include a neuro-first strategy, capability-building programs, hybrid financing, and multi-stakeholder ecosystem coordination.
Keywords:
adoption readiness assistive robotic technology neurosurgery diffusion of innovation and IndonesiaReferences
Ball, T., González-Martínez, J., Zemmur, A., Sweid, A., Chandra, S., VanSickle, D., Neimat, J. S., Jabbour, P., & Wu, C. (2021). Robotic applications in cranial neurosurgery: Current and future. Operative Neurosurgery, 21(3), 371–379.
Chong, A. Y.-L., Blut, M., & Zheng, S. (2022). Factors influencing the acceptance of healthcare information technologies: A meta-analysis. Information & Management, 59(5), Article 103604.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82.
Hoeckelmann, M., Rudas, I. J., Fiorini, P., Kirchner, F., & Haidegger, T. (2015). Current capabilities and development potential in surgical robotics. International Journal of Advanced Robotic Systems, 12(1), 1–14.
Kato, Y., Liew, B. S., Sufianov, A. A., Rasulic, L., Chaurasia, B., Faruque, S., & Sharma, A. (2020). Review of global neurosurgery education: Horizon of neurosurgery in the developing countries. Chinese Neurosurgical Journal, 6, Article 19.
Lawrie, L., Gillies, K., Duncan, E., Davies, L., Beard, D., & Campbell, M. K. (2022). Barriers and enablers to the effective implementation of robotic assisted surgery. PLoS ONE, 17(8), e0273696.
Maynou, L., Fu, X., & Xu, B. (2022). Investment and cost-effectiveness of robotic surgery: Global evidence. Health Economics Review, 12(1), 1–12.
Ministry of Health Republic of Indonesia. (2023). Health Technology Transformation Report. Kementerian Kesehatan Republik Indonesia.
OECD. (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD Publishing.
Patton, M. Q. (2015). Qualitative research and evaluation methods (4th ed.). SAGE Publications.
Rivero-Moreno, M., Sanders, R., & Lloyd, O. (2023). Emerging trends in robotic digital microscopy and neurosurgical visualization. Neurosurgical Review, 46(1), 1–10.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Rudiman, R., Mirbagheri, A., & Candrawinata, V. S. (2023). Assessment of robotic telesurgery system among surgeons: A single-center study. Journal of Robotic Surgery, 18, Article 10.
Shah, P. (2021). Strategic analysis of surgical robotics adoption in developing markets. Health Systems Management Journal, 14(2), 88–101.
Singh, A., Kumar, P., & Gupta, H. (2022). Surgeons' readiness for robotic-assisted procedures: A global survey. Surgical Endoscopy, 36(5), 3241–3250.
Sobhanian, S., & Bate, A. (2022). Evaluating readiness for healthcare automation: A systematic framework. Health Informatics Journal, 28(2), 1–14.
Stumpo, V., Raffa, G., Germano, A., & Visocchi, M. (2021). Robotic neurosurgery: Current state and future perspectives. Journal of Neurosurgical Sciences, 65(4), 359–367.
Tustumi, F., Sallum, R. A. A., Szor, D. J., Ribeiro-Júnior, U., & Cecconello, I. (2025). Clinical outcomes of robotic minimally invasive surgery: A meta-analysis. Surgical Endoscopy, 39(1), 1–12.
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.
WHO. (2023). Global strategy on digital health and medical technology adoption. World Health Organization.
Wicaksono, A. S., Tamba, D. A., Sudiharto, P., Basuki, E., Pramusinto, H., Hartanto, R. A., Ekong, C., & Manusubroto, W. (2020). Neurosurgery residency program in Yogyakarta, Indonesia: Improving neurosurgical care distribution to reduce inequality. Neurosurgical Focus, 48(3), E5.
License
Copyright (c) 2026 Abdul Mutholib
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
