Digital competency adoption and maritime workforce transformation: a mixed-methods PLS-SEM validation of an integrated technology-organization-environment model in indonesian shipping
Jurnal Riset Tindakan Indonesia October 17, 2026 Volume 11, Number 2 4 authors
Abstract
The maritime sector’s digital transformation requires not only technology adoption but also workforce capability development. While the Technology–Organization–Environment (TOE) framework explains organizational technology adoption and TAM/UTAUT explain individual technology use, neither adequately explains how contextual conditions translate into workforce capabilities. Addressing this gap, this study develops and tests a Maritime Human-Capital Transformation Model for Society 5.0, emphasizing human capability and resilience. An exploratory sequential mixed-methods design was employed, comprising in-depth interviews with 15 maritime stakeholders from four stakeholder groups, followed by a survey of 250 maritime professionals analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrated satisfactory reliability and validity across all constructs. Qualitative findings identified three competency dimensions: hard digital skills, socio-technical skills, and psychological readiness. Quantitative results showed that Technological Readiness, Organizational Support, and Environmental Pressure significantly influenced Digital Competency Adoption and Human Capital Transformation Effectiveness. Technological Readiness had the strongest effect on Digital Competency Adoption (β = 0.586, p < 0.001), while Digital Competency Adoption positively influenced transformation effectiveness (β = 0.364, p < 0.001) and partially mediated all three TOE relationships. The model explained 51.5% of the variance in Digital Competency Adoption and 50.9% in Human Capital Transformation Effectiveness. These findings extend the TOE framework by positioning digital competency adoption as a key mechanism linking contextual conditions to human-capital transformation in the maritime sector, advancing a human-centered approach to digitalization aligned with Society 5.0.
Authors
Jose Beno
Politeknik Ilmu Pelayaran Semarang
Awel Suryadi
Politeknik Ilmu Pelayaran Semarang
Yuliani Yuliani
Politeknik Ilmu Pelayaran Semarang
Widya Putri Idayatma
Politeknik Ilmu Pelayaran Semarang
Keywords
Maritime human capitalSociety 5.0Digital competencyTechnology-organization-environmentMixed methods
References
Attride-Stirling, J. (2001). Thematic networks: An analytic tool for qualitative research. Qualitative Research, 1(3), 385-405. https://doi.org/10.1177/146879410100100307
Baker, J. (2012). The technology-organization-environment framework. In Y. K. Dwivedi, M. R. Wade, & S. L. Schneberger (Eds.), Information systems theory: Explaining and predicting our digital society (Vol. 1, pp. 231-245). Springer. https://doi.org/10.1007/978-1-4419-6108-2_12
Baxter, G., & Sommerville, I. (2011). Socio-technical systems: From design methods to systems engineering. Interacting with Computers, 23(1), 4-17. https://doi.org/10.1016/j.intcom.2010.07.003
Belabyad, M., Kontovas, C., Pyne, R., Shi, W., Li, N., Szwed, P., & Chang, C.-H. (2025). The human element in autonomous shipping: A study on skills and competency requirements. WMU Journal of Maritime Affairs, 24, 575-605. https://doi.org/10.1007/s13437-025-00366-9
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46. https://doi.org/10.1177/001316446002000104
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Erstad, E., Hopcraft, R., Vineetha Harish, A., & Tam, K. (2023). A human-centred design approach for the development and conducting of maritime cyber resilience training. WMU Journal of Maritime Affairs, 22, 241-266. https://doi.org/10.1007/s13437-023-00304-7
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149-1160. https://doi.org/10.3758/BRM.41.4.1149
Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs Principles and practices. Health Services Research, 48(6 Pt 2), 2134-2156. https://doi.org/10.1111/1475-6773.12117
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Fruth, M., & Teuteberg, F. (2017). Digitization in maritime logistics What is there and what is missing? Cogent Business & Management, 4(1), 1411066. https://doi.org/10.1080/23311975.2017.1411066
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. https://doi.org/10.1177/1525822X05279903
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
Hennink, M., & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social Science & Medicine, 292, 114523. https://doi.org/10.1016/j.socscimed.2021.114523
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
Ivankova, N. V., Creswell, J. W., & Stick, S. L. (2006). Using mixed-methods sequential explanatory design: From theory to practice. Field Methods, 18(1), 3-20. https://doi.org/10.1177/1525822X05282260
Jović, M., Tijan, E., Vidmar, D., & Pucihar, A. (2022). Factors of digital transformation in the maritime transport sector. Sustainability, 14(15), 9776. https://doi.org/10.3390/su14159776
Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227-261. https://doi.org/10.1111/isj.12131
Mallam, S. C., Nazir, S., & Sharma, A. (2020). The human element in future maritime operations Perceived impact of autonomous shipping. Ergonomics, 63(3), 334-345. https://doi.org/10.1080/00140139.2019.1659995
Malterud, K., Siersma, V. D., & Guassora, A. D. (2016). Sample size in qualitative interview studies: Guided by information power. Qualitative Health Research, 26(13), 1753-1760. https://doi.org/10.1177/1049732315617444
Moen, I., Oruc, A., Amro, A., Gkioulos, V., & Kavallieratos, G. (2024). Survey-based analysis of cybersecurity awareness of Turkish seafarers. International Journal of Information Security, 23, 3153-3178. https://doi.org/10.1007/s10207-024-00884-2
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022). A literature review of the challenges and opportunities of the transition from Industry 4.0 to Society 5.0. Energies, 15(17), 6276. https://doi.org/10.3390/en15176276
Munim, Z. H., Dushenko, M., Jaramillo Jimenez, V., Shakil, M. H., & Imset, M. (2020). Big data and artificial intelligence in the maritime industry: A bibliometric review and future research directions. Maritime Policy & Management, 47(5), 577-597. https://doi.org/10.1080/03088839.2020.1788731
Nahavandi, S. (2019). Industry 5.0 A human-centric solution. Sustainability, 11(16), 4371. https://doi.org/10.3390/su11164371
Narayanan, S. C., Emad, G. R., & Fei, J. (2023). Theorizing seafarers’ participation and learning in an evolving maritime workplace: An activity theory perspective. WMU Journal of Maritime Affairs, 22, 165-180. https://doi.org/10.1007/s13437-023-00311-8
Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 116(9), 1849-1864. https://doi.org/10.1108/IMDS-07-2015-0302
O’Connor, C., & Joffe, H. (2020). Intercoder reliability in qualitative research: Debates and practical guidelines. International Journal of Qualitative Methods, 19. https://doi.org/10.1177/1609406919899220
Oliveira, T., & Martins, M. F. (2010). Understanding e-business adoption across industries in European countries. Industrial Management & Data Systems, 110(9), 1337-1354. https://doi.org/10.1108/02635571011087428
Oruc, A., Chowdhury, N., & Gkioulos, V. (2024). A modular cyber security training programme for the maritime domain. International Journal of Information Security, 23, 1477-1512. https://doi.org/10.1007/s10207-023-00799-4
Ployhart, R. E., & Moliterno, T. P. (2011). Emergence of the human capital resource: A multilevel model. Academy of Management Review, 36(1), 127-150. https://doi.org/10.5465/amr.2009.0318
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
Raza, Z., Woxenius, J., Vural, C. A., & Lind, M. (2023). Digital transformation of maritime logistics: Exploring trends in the liner shipping segment. Computers in Industry, 145, 103811. https://doi.org/10.1016/j.compind.2022.103811
Sanchez-Gonzalez, P.-L., Díaz-Gutiérrez, D., Leo, T. J., & Núñez-Rivas, L. R. (2019). Toward digitalization of maritime transport? Sensors, 19(4), 926. https://doi.org/10.3390/s19040926
Shahbakhsh, M., Emad, G. R., & Cahoon, S. (2022). Industrial revolutions and transition of the maritime industry: The case of seafarer’s role in autonomous shipping. The Asian Journal of Shipping and Logistics, 38(1), 10-18. https://doi.org/10.1016/j.ajsl.2021.11.004
Sharma, A., & Kim, T. (2022). Exploring technical and non-technical competencies of navigators for autonomous shipping. Maritime Policy & Management, 49(6), 831-849. https://doi.org/10.1080/03088839.2021.1914874
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347. https://doi.org/10.1108/EJM-02-2019-0189
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319-1350. https://doi.org/10.1002/smj.640
Theotokas, I. N., Lagoudis, I. N., & Raftopoulou, K. (2024). Challenges of maritime human resource management for the transition to shipping digitalization. Journal of Shipping and Trade, 9, 6. https://doi.org/10.1186/s41072-024-00165-0
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
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889-901. https://doi.org/10.1016/j.jbusres.2019.09.022
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118-144. https://doi.org/10.1016/j.jsis.2019.01.003
Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326-349. https://doi.org/10.1016/j.lrp.2018.12.001
Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0 Inception, conception and perception. Journal of Manufacturing Systems, 61, 530-535. https://doi.org/10.1016/j.jmsy.2021.10.006
Zhao, X., Lynch, J. G., Jr., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197-206. https://doi.org/10.1086/651257
Zhu, K., & Kraemer, K. L. (2005). Post-adoption variations in usage and value of e-business by organizations: Cross-country evidence from the retail industry. Information Systems Research, 16(1), 61-84. https://doi.org/10.1287/isre.1050.0045
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Jose Beno; Awel Suryadi; Yuliani Yuliani; Widya Putri Idayatma (2026). Digital competency adoption and maritime workforce transformation: a mixed-methods PLS-SEM validation of an integrated technology-organization-environment model in indonesian shipping. Jurnal Riset Tindakan Indonesia, 11(2), 362-374. https://doi.org/10.29210/30037016000
Jose Beno; Awel Suryadi; Yuliani Yuliani; Widya Putri Idayatma. "Digital competency adoption and maritime workforce transformation: a mixed-methods PLS-SEM validation of an integrated technology-organization-environment model in indonesian shipping." Jurnal Riset Tindakan Indonesia 11, no. 2 (2026): 362-374. https://doi.org/10.29210/30037016000
Jose Beno; Awel Suryadi; Yuliani Yuliani; Widya Putri Idayatma (2026) 'Digital competency adoption and maritime workforce transformation: a mixed-methods PLS-SEM validation of an integrated technology-organization-environment model in indonesian shipping', Jurnal Riset Tindakan Indonesia, 11(2), pp. 362-374. https://doi.org/10.29210/30037016000
Jose Beno; Awel Suryadi; Yuliani Yuliani; Widya Putri Idayatma. "Digital competency adoption and maritime workforce transformation: a mixed-methods PLS-SEM validation of an integrated technology-organization-environment model in indonesian shipping." Jurnal Riset Tindakan Indonesia, vol. 11, no. 2, 2026, pp. 362-374. https://doi.org/10.29210/30037016000