Service Bureau–Enabled Additive Manufacturing Adoption among Indonesian Contract Manufacturing SMEs: A Structural Model Approach

Authors

  • Beny Institut Teknologi dan Bisnis Sabda Setia

DOI:

https://doi.org/10.37888/bjrm.v9i2.621

Keywords:

Attractiveness, Key Opinion Leader (KOL), Minat Beli

Abstract

This study examines the factors influencing service bureau–enabled additive manufacturing (AM) adoption among Indonesian contract manufacturing small and medium-sized enterprises (SMEs). Drawing on the Technology–Organisation–Environment (TOE) framework, a structural model was developed in which cost transparency and lead time reliability influence perceived adoption effectiveness and adoption continuity intention, with perceived adoption effectiveness serving as a mediating variable. A quantitative, cross-sectional survey was administered to managerial-level respondents from Indonesian manufacturing SMEs engaged in contract-based production, and data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The measurement model demonstrated satisfactory reliability and validity, and all five hypothesised relationships were statistically supported. Cost transparency and lead time reliability both exerted positive and significant effects on perceived adoption effectiveness and continuity intention, with cost transparency showing a marginally stronger effect on effectiveness. Perceived adoption effectiveness was the strongest predictor of continuity intention, confirming its mediating role between service bureau characteristics and sustained adoption. The model explained 52% of the variance in perceived adoption effectiveness and 61% of the variance in continuity intention. These findings extend the TOE framework to service-based manufacturing contexts by showing that external service provider characteristics, not only internal organisational readiness, shape SME adoption decisions. The study offers practical guidance for SMEs, AM service bureaus, and policymakers on the importance of pricing clarity and delivery consistency, contributing new empirical evidence from an Indonesian contract manufacturing setting.

References

Agostini, L., & Nosella, A. (2020). The adoption of Industry 4.0 technologies in SMEs: results of an international study. Management Decision, 58(4), 625–643. https://doi.org/10.1108/MD-09-2018-0973

Al-Hattami, H. M., & Almaqtari, F. A. (2023). What determines digital accounting systems' continuance intention? An empirical investigation in SMEs. Humanities and Social Sciences Communications, 10, Article 814. https://doi.org/10.1057/s41599-023-02332-3

AL-Shboul, M. A. (2022). An investigation of transportation logistics strategy on manufacturing supply chain responsiveness in developing countries: The mediating role of delivery reliability and delivery speed. Heliyon, 8(11), Article e11283. https://doi.org/10.1016/j.heliyon.2022.e11283

Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics, 229, 107776. https://doi.org/10.1016/j.ijpe.2020.107776

Bain & Company. (2023). The new ROI: Defy uncertainty by boosting return on innovation. https://www.bain.com/insights/new-roi-defy-uncertainty-by-boosting-return-on-innovation/

Baker, J. (2012). The Technology–Organization–Environment framework. In Y. K. Dwivedi, M. R. Wade, & S. L. Schneberger (Eds.), Information Systems Theory (pp. 231–245). Springer. https://doi.org/10.1007/978-1-4419-6108-2_12

Baumers, M., Dickens, P., Tuck, C., & Hague, R. (2016). The cost of additive manufacturing: Machine productivity, economies of scale and technology-push. Technological Forecasting and Social Change, 102, 193–201. https://doi.org/10.1016/j.techfore.2015.02.015

Bell, E., Bryman, A., & Harley, B. (2022). Business research methods (6th ed.). Oxford University Press. https://doi.org/10.1093/hebz/9780198869443.001.0001

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370.

Cao, M., & Zhang, Q. (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of Operations Management, 29(3), 163–180. https://doi.org/10.1016/j.jom.2010.12.008

Chen, I. J., Paulraj, A., & Lado, A. A. (2004). Strategic purchasing, supply management, and firm performance. Journal of Operations Management, 22(5), 505–523. https://doi.org/10.1016/j.jom.2004.06.002

Coreynen, W., Matthyssens, P., Vanderstraeten, J., & van Witteloostuijn, A. (2020). Unravelling the internal and external drivers of digital servitization: A dynamic capabilities and contingency perspective on firm strategy. Industrial Marketing Management, 89, 265–277. https://doi.org/10.1016/j.indmarman.2020.02.014

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.

Culot, G., Nassimbeni, G., Orzes, G., & Sartor, M. (2020). Behind the definition of Industry 4.0: Analysis and open questions. International Journal of Production Economics, 226, 107617. https://doi.org/10.1016/j.ijpe.2020.107617

Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383–394. https://doi.org/10.1016/j.ijpe.2018.08.019

Dubey, R., Gunasekaran, A., & Childe, S. J. (2019). Big data analytics capability in supply chain agility: The moderating effect of organizational flexibility. Management Decision, 57(8), 2092–2112. https://doi.org/10.1108/MD-01-2018-0119

Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), 110–128. https://doi.org/10.1080/00207543.2019.1582820

Durach, C. F., Kurpjuweit, S., & Wagner, S. M. (2017). The impact of additive manufacturing on supply chains. International Journal of Physical Distribution and Logistics Management, 47(10), 954–971. https://doi.org/10.1108/IJPDLM-11-2016-0332

Faiz, F., Le, V., & Masli, E. K. (2024). Determinants of digital technology adoption in innovative SMEs. Journal of Innovation & Knowledge, 9(4), Article 100610. https://doi.org/10.1016/j.jik.2024.100610

Ferraris, A., Santoro, G., & Luca, S. (2017). How MNC's subsidiaries may improve their innovative performance? The role of external sources and knowledge management capabilities. Journal of Knowledge Management, 21(3), 540–552. https://doi.org/10.1108/JKM-09-2016-0411

Ford, S., & Despeisse, M. (2016). Additive manufacturing and sustainability: An exploratory study of the advantages and challenges. Journal of Cleaner Production, 137, 1573–1587. https://doi.org/10.1016/j.jclepro.2016.04.150

Ghuge, S., & Akarte, M. (2024). Additive manufacturing service bureau selection: A Bayesian network integrated framework. International Journal of Production Economics, 276, Article 109348. https://doi.org/10.1016/j.ijpe.2024.109348

Gibson, I., Rosen, D., Stucker, B., & Khorasani, M. (2020). Additive manufacturing technologies (3rd ed.). Springer. https://doi.org/10.1007/978-3-030-56127-7

Grant, A., Haider, Z., & Levy, A. (2021). How global companies can manage geopolitical risk | McKinsey. https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/how-global-companies-can-manage-geopolitical-risk

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), Article 100027. https://doi.org/10.1016/j.rmal.2022.100027

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

Horváth, D., & Szabó, R. Z. (2019). Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities? Technological Forecasting and Social Change, 146, 119–132. https://doi.org/10.1016/j.techfore.2019.05.021

Hossain, M. I., Kumar, J., Islam, M. T., & Valeri, M. (2023). The interplay among paradoxical leadership, industry 4.0 technologies, organisational ambidexterity, strategic flexibility and corporate sustainable performance in manufacturing SMEs of Malaysia. European Business Review, 35(6), 943–964. https://doi.org/10.1108/EBR-04-2023-0109

ISO. (2021). Additive manufacturing — General principles (ISO/ASTM 52900:2021). https://www.iso.org/obp/ui/#iso:std:iso-astm:52900:ed-2:v1:en

Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727

Kamble, S. S., Gunasekaran, A., Ghadge, A., & Raut, R. (2020). A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs — A review and empirical investigation. International Journal of Production Economics, 229, Article 107853. https://doi.org/10.1016/j.ijpe.2020.107853

Kumar, R., Singh, R. K., & Dwivedi, Y. K. (2020). Application of industry 4.0 technologies in SMEs for ethical and sustainable operations: Analysis of challenges. Journal of Cleaner Production, 275, Article 124063. https://doi.org/10.1016/j.jclepro.2020.124063

Kumar, V., & Reinartz, W. (2016). Creating enduring customer value. Journal of Marketing, 80(6), 36–68. https://doi.org/10.1509/jm.15.0414

Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2020). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of Manufacturing Systems, 49, 194–214. https://doi.org/10.1016/j.jmsy.2018.10.005

Paramitha, R. W. L., Santosa, W., & SD, T. (2023). The effect of supply chain responsiveness, flexibility, & quality on customer development. Journal of International Trade, Logistics and Law, 9(1), 77–87.

Putro, A. K., & Takahashi, Y. (2024). Entrepreneurs' creativity, information technology adoption, and continuance intention: Mediation effects of perceived usefulness and ease of use and the moderation effect of entrepreneurial orientation. Heliyon, 10(3), Article e25479. https://doi.org/10.1016/j.heliyon.2024.e25479

Rayna, T., & Striukova, L. (2016). From rapid prototyping to home fabrication: How 3D printing is changing business model innovation. Technological Forecasting and Social Change, 102, 214–224. https://doi.org/10.1016/j.techfore.2015.07.023

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

Wohlers Associates. (2023). Wohlers report 2023: 3D printing and additive manufacturing state of the industry. ASTM International.

Zhou, L., Chong, A. Y. L., & Ngai, E. W. T. (2016). Supply chain management in the era of the internet of things. International Journal of Production Economics, 159, 1–3. https://doi.org/10.1016/j.ijpe.2014.11.014

Downloads

Published

2026-07-20

How to Cite

Beny. (2026). Service Bureau–Enabled Additive Manufacturing Adoption among Indonesian Contract Manufacturing SMEs: A Structural Model Approach. BJRM (Bongaya Journal of Research in Management), 9(2), 577–590. https://doi.org/10.37888/bjrm.v9i2.621