An Integrated RBV-KBV Conceptual Framework for Generative AI Adoption in Small Manufacturing Enterprises
DOI:
https://doi.org/10.29207/resti.v10i4.7622Keywords:
conceptual framework, generative artificial intelligence, knowledge-based view, manufacturing information systems, resource-based viewAbstract
Small manufacturing enterprises remain economically important but continue to face recurring operational constraints in planning, scheduling, quality control, maintenance, and process-data use. Generative artificial intelligence offers increasingly accessible support for these bounded operational tasks, yet adoption remains uneven because many firms lack a coherent basis for linking digital opportunity to internal resources, organizational knowledge, and measurable operational improvement. This study develops a conceptual framework that integrates the resource-based view and the knowledge-based view to explain generative artificial intelligence adoption in small manufacturing enterprises. Using an evidence-grounded theory-development approach, the study builds a staged framework that separates foundational conditions, perceived operational AI opportunity, organizational translation mechanisms, and performance outcomes. The framework theorizes internal resources and knowledge assets as foundational antecedents, perceived generative artificial intelligence potential in operational functions as the adoption bridge, knowledge integration and dynamic capability as organizing mechanisms, and performance improvement as the downstream consequence. It further explains how conceptual clarity can support later empirical reduction without losing the richer logic needed for practical implementation. The study also clarifies how the framework can guide applied information-system design through data-readiness assessment, bounded decision-support use cases, human-in-the-loop verification, and operational KPI monitoring. The resulting architecture strengthens theoretical explanation and operational design logic for generative artificial intelligence adoption in constrained manufacturing environments, while preserving clear boundaries for subsequent validation and applied deployment.
Downloads
References
L. Volf et al., “Navigating the Fourth Industrial Revolution: SBRI - A Comprehensive Digital Maturity Assessment Tool and Road to Industry 4.0 for Small Manufacturing Enterprises,” Manufacturing Technology, vol. 24, no. 4, 2024, doi: 10.21062/mft.2024.074.
P. Burggräf et al., “Machine learning implementation in small and medium-sized enterprises: insights and recommendations from a quantitative study,” Production Engineering, vol. 18, pp. 751–764, 2024, doi: 10.1007/s11740-024-01274-2.
G. Thomas et al., “Exploring the Determinants of Artificial Intelligence Adoption Intention in the SMEs of United Arab Emirates,” SAGE Open, 2025, doi: 10.1177/21582440251395561.
P. K. Dey, S. Chowdhury, A. Abadie, E. Yaroson, and S. Sarkar, “Artificial intelligence-driven supply chain resilience in Vietnamese manufacturing small- and medium-sized enterprises,” International Journal of Production Research, vol. 62, pp. 5417–5456, 2024, doi: 10.1080/00207543.2023.2179859.
S. Chatterjee, R. Chaudhuri, M. H. Shah, and P. Maheshwari, “Big data driven innovation for sustaining SME supply chain operation in post COVID-19 scenario: Moderating role of SME technology leadership,” Computers & Industrial Engineering, Art. no. 108058, 2022, doi: 10.1016/j.cie.2022.108058.
M. Kamariotou, F. Kitsios, C. Charatsari, E. D. Lioutas, and M. A. Talias, “Digital strategy decision support systems: Agrifood supply chain management in SMEs,” Sensors, vol. 22, no. 1, Art. no. 274, 2022, doi: 10.3390/s22010274.
M. Kim, J. Lee, C. Lee, and J. Jeong, “Framework of 2D KDE and LSTM-Based Forecasting for Cost-Effective Inventory Management in Smart Manufacturing,” Applied Sciences, vol. 12, no. 5, Art. no. 2380, 2022, doi: 10.3390/app12052380.
H. Sun, W.-T. Teo, K. Wong, B. Dong, J. Polzer, and X. Xu, “Automating Quality Control on a Shoestring, a Case Study,” Machines, vol. 12, no. 12, Art. no. 904, 2024, doi: 10.3390/machines12120904.
T. Raamets, K. Karjust, J. Majak, and A. Hermaste, “Implementing an AI-Based Digital Twin Analysis System for Real-Time Decision Support in a Custom-Made Sportswear SME,” Applied Sciences, vol. 15, no. 14, Art. no. 7952, 2025, doi: 10.3390/app15147952.
H. Lu and M. S. Shaharudin, “Role of digital transformation for sustainable competitive advantage of SMEs: a systematic literature review,” Cogent Business & Management, vol. 11, no. 1, Art. no. 2419489, 2024, doi: 10.1080/23311975.2024.2419489.
A. Islam, M. A. Islam, F. Dal Mas, J. Fijałkowska, M. Rahman, and M. Massaro, “Configuring AI-guided sustainable competitive advantage for SMEs through business model innovation: A systematic literature review approach,” Journal of Engineering and Technology Management, Art. no. 101921, 2025, doi: 10.1016/j.jengtecman.2025.101921.
M. Peñarroya-Farell et al., “A Complex Leadership Perspective on Generative AI Adoption in SMEs: The Interplay of TAM, TMT, and RBV,” Administrative Sciences, vol. 15, Art. no. 494, 2025. doi: 10.3390/admsci15120494
M. Saeedikiya, S. Salunke, and M. Kowalkiewicz, “Toward a dynamic capability perspective of digital transformation in SMEs: A study of the mobility sector,” Journal of Cleaner Production, Art. no. 140718, 2024, doi: 10.1016/j.jclepro.2024.140718.
M. X. Yao, D. Wang, and S. Freeman, “Digital barriers and capabilities in aspirant emerging-market SMEs: A resource-adaptive dynamic capabilities model of transformation phases,” Technovation, Art. no. 103506, 2026, doi: 10.1016/j.technovation.2026.103506.
N. A. da Silva, J. L. Abreu, C. Klingenberg, J. A. V. Antunes, and D. P. Lacerda, “Industry 4.0 and micro and small enterprises: Systematic literature review and analysis,” Production & Manufacturing Research, 2022, doi: 10.1080/21693277.2022.2124466.
S. Njah, C. Danjou, F. Armellini, C. Beaudry, and E. Mosconi, “A digital maturity model for assessing SMEs in the manufacturing sector,” Digital Engineering, Art. no. 100084, 2026, doi: 10.1016/j.dte.2025.100084.
S. Jung, D. Kim, and N. Shin, “Success Factors of the Adoption of Smart Factory Transformation: An Examination of Korean Manufacturing SMEs,” IEEE Access, 2023, doi: 10.1109/ACCESS.2022.3233811.
J. H. Ávila-Bohórquez and R. J. Gil-Herrera, “Proposal and Validation of an Industry 4.0 Maturity Model for SMEs,” Journal of Industrial Engineering and Management, 2022, doi: 10.3926/jiem.3673.
Q. Chen, W. Zhang, N. Jin, X. Wang, and P. Dai, “Digital Transformation Evaluation for Small- and Medium-Sized Manufacturing Enterprises Using the Fuzzy Synthetic Method DEMATEL-ANP,” Sustainability, vol. 14, no. 20, Art. no. 13038, 2022, doi: 10.3390/su142013038.
S. Jayashree et al., “Testing an adoption model for Industry 4.0 and sustainability: A Malaysian scenario,” Sustainable Production and Consumption, vol. 31, pp. 313–330, 2022. doi: 10.1016/j.spc.2022.02.015
N. T. P. Giang et al., “Determinants of E-commerce adoption and its effect on marketing performance among Vietnamese SMEs: A PLS-SEM approach using the TOE framework,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, Art. no. 100670, 2025. doi: /10.1016/j.joitmc.2025.100670
Y. Prakasa and Z. A. Jumani, “Linking digital capability to small business performance: the mediating role of digital business transformation,” Cogent Business & Management, vol. 11, no. 1, Art. no. 2342486, 2024, doi: 10.1080/23311975.2024.2342486.
C. Mariani and M. Mancini, “Harnessing AI for value: examining the impact of AI capabilities and the mediating role of organizational agility on project value proposition,” International Journal of Managing Projects in Business, vol. 18, no. 8, 2025. doi: 10.1108/IJMPB-03-2025-0068
M. Atalay, U. Murat, B. Oksuz, A. M. Parlaktuna, E. Pisirir, and M. C. Testik, “Digital twins in manufacturing: Systematic literature review for physical-digital layer categorization and future research directions,” International Journal of Computer Integrated Manufacturing, vol. 35, no. 7, pp. 679–705, 2022, doi: 10.1080/0951192X.2021.2022762.
M. Metzger et al., “Generative artificial intelligence augmenting SME financial management,” Technovation, vol. 147, Art. no. 103313, 2025. doi: 10.1016/j.technovation.2025.103313
S. Alharthi, “Harnessing Knowledge: The Robust Role of Knowledge Management Practices and Business Intelligence Systems in Developing Entrepreneurial Leadership and Organizational Sustainability in SMEs,” Sustainability, vol. 17, Art. no. 6264, 2025. doi: 10.3390/su17146264
Q. M. Alshammakhi and R. A. Sheikh, “Driving Sustainable Entrepreneurship Through AI and Knowledge Management: Evidence from SMEs in Emerging Economies,” Sustainability, vol. 17, Art. no. 10928, 2025. doi: 10.3390/su172410928
N. Drydakis, “Artificial Intelligence and Reduced SMEs' Business Risks: A Dynamic Capabilities Analysis During the COVID-19 Pandemic,” Information Systems Frontiers, vol. 24, pp. 1223–1247, 2022, doi: 10.1007/s10796-022-10249-6.
D. T. Siahaan and C. S. L. Tan, “What Drives the Adaptive Capability of Indonesian SMEs during the Covid-19 Pandemic: The Interplay between Perceived Institutional Environment, Entrepreneurial Orientation, and Digital Capability,” Asian Journal of Business Research, vol. 12, no. 2, 2022, doi: 10.14707/ajbr.220125.
R. B. Soomro et al., “A SEM-ANN analysis to examine impact of artificial intelligence technologies on sustainable performance of SMEs,” Scientific Reports, 2025, doi: 10.1038/s41598-025-86464-3.
S. K. Sahoo, S. S. Goswami, S. Sarkar, and S. Mitra, “A Review of Digital Transformation and Industry 4.0 in Supply Chain Management for Small and Medium-sized Enterprises,” Science and Technology for Energy Transition, vol. 79, pp. 58–70, 2024, doi: 10.2516/stet/2024003.
J. A. Piat, S. M. D. M. B. Moraes, K. M. Rodal, and A. R. Lucena, “A guideline to implement a CPS architecture in an SME,” Production & Manufacturing Research, vol. 11, no. 1, pp. 165–179, 2023, doi: 10.1080/21693277.2023.2218910.
A. Haug, K. A. Wickstrom, J. Stentoft, and K. Philipsen, “Adoption of additive manufacturing: A survey of the role of knowledge networks and maturity in small and medium-sized Danish production firms,” International Journal of Production Economics, vol. 255, Art. no. 108714, 2023, doi: 10.1016/j.ijpe.2022.108714.
A. H. Lassen and M. S. S. Larsen, “Manufacturing innovation for Industry 4.0: an innovation capability perspective,” Journal of Manufacturing Technology Management, vol. 36, no. 9, pp. 19–44, 2025, doi: 10.1108/JMTM-09-2023-0414.
B. Ietto, C. Ancillai, A. Sabatini, E. G. Carayannis, and G. L. Gregori, “The Role of External Actors in SMEs' Human-Centered Industry 4.0 Adoption: An Empirical Perspective on Italian Competence Centers,” IEEE Transactions on Engineering Management, vol. 71, pp. 1057–1072, 2024, doi: 10.1109/TEM.2022.3144881.
M. Pedota, “Big data and dynamic capabilities in the digital revolution: The hidden role of source variety,” Research Policy, vol. 52, no. 7, Art. no. 104812, 2023, doi: 10.1016/j.respol.2023.104812.
A. Vuorio and L. Torkkeli, “Dynamic managerial capability portfolios in early internationalising firms,” International Business Review, vol. 32, no. 1, Art. no. 102049, 2023, doi: 10.1016/j.ibusrev.2022.102049.
K. Tao and C. C. Tan, “Linking Psychological Safety Climate to Dual Innovation Through AI-Enabled Dynamic Capabilities,” Emerging Science Journal, vol. 9, no. 6, 2025, doi: 10.28991/ESJ-2025-09-06-022.
J. Yan and F. Ahmad, “Bridging Institutional Voids in a Volatile Emerging Economy: Role of Regulatory Cultural Stewardship as a Dynamic Capability for Sustainable AI-Enabled Digital Transformation in SMEs,” Sustainability, vol. 17, no. 22, Art. no. 10397, 2025, doi: 10.3390/su172210397.
H. M. J. C. B. Heenkenda, F. Xu, K. M. M. C. B. Kulathunga, and W. A. R. Senevirathne, “The Role of Innovation Capability in Enhancing Sustainability in SMEs: An Emerging Economy Perspective,” Sustainability, vol. 14, no. 17, Art. no. 10832, 2022, doi: 10.3390/su141710832.
X. Ma, L. Chen, and X. Yu, “Failure Analysis and SME Growth: The Role of Dynamic Capabilities and Environmental Dynamism,” Systems, vol. 13, no. 8, Art. no. 690, 2025, doi: 10.3390/systems13080690.
R. R. Panigrahi et al., “AI Chatbot Adoption in SMEs for Sustainable Manufacturing Supply Chain Performance: A Mediational Research in an Emerging Country,” Sustainability, vol. 15, no. 18, Art. no. 13743, 2023.
D. Añón Higón and J. Torrent-Sellens, “AI-twin and AI-triple firm's transition: Unveiling the predictors from European SMEs,” Journal of Cleaner Production, vol. 521, Art. no. 146134, 2025, doi: 10.1016/j.jclepro.2025.146134.
E. Sánchez, R. Calderón, and F. Herrera, “Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges,” Applied Sciences, vol. 15, no. 12, Art. no. 6465, 2025, doi: 10.3390/app15126465.
A. Shore, M. Tiwari, P. Tandon, and C. Foropon, “Building Entrepreneurial Resilience During Crisis Using Generative AI: An Empirical Study on SMEs,” Technovation, vol. 145, Art. no. 103063, 2025, doi: 10.1016/j.technovation.2024.103063.
F. ul Haq, N. M. Suki, M. Setini, A. Masood, and T. A. Khan, “Adopting green AI for SME sustainability: mediating role of green investment and moderation by green servant leadership,” Sustainable Futures, vol. 10, Art. no. 101002, 2025, doi: 10.1016/j.sftr.2025.101002.
S. N. Shaikh, J. M. Sohu, S. Shaikh, F. Z. Kherazi, and S. Soomro, “Artificial Intelligence Adoption, Green HRM, and Employee Behavior: Driving Environmental Sustainability in SMEs with Organizational Culture as a Moderator,” SAGE Open, vol. 15, no. 4, pp. 1–18, 2025, doi: 10.1177/21582440251392673.
C. M. Doktoralina, M. A. F. Abdullah, W. D. Febrian, and D. Maulana, “Empowering Coastal SMEs: A Digital Transformation Model to Enhance Competitiveness and Sustainability,” Quality - Access to Success, vol. 26, no. 206, pp. 422–427, 2025, doi: 10.47750/QAS/26.206.41.
M. A. Frikha and M. Mrad, “AI-Enabled Demand Forecasting, Technological Capability, and Supply Chain Performance: Empirical Evidence from the Global Logistics Sector,” International Journal of Advanced Computer Science and Applications, vol. 16, no. 9, pp. 167–174, 2025, doi: 10.14569/IJACSA.2025.0160917.
I. S. Al Koliby, A. K. Al-Swidi, M. A. Al-Hakimi, and S. A. G. Farhan, “How green knowledge-oriented leadership drives green innovation in SMEs: the mediating role of environmental strategy and the moderating role of green AI capability,” Cogent Business & Management, vol. 12, no. 1, Art. no. 2520914, 2025, doi: 10.1080/23311975.2025.2520914.
R. Alexandro, “Strategic Human Resource Management in the Digital Economy Era: An Empirical Study of Challenges and Opportunities Among MSMEs and Startups in Indonesia,” Cogent Business & Management, vol. 12, no. 1, Art. no. 2528436, 2025, doi: 10.1080/23311975.2025.2528436.
A. Szenftner, S. Stumpp, and T. Knopf, “Exploring the Potential of AI to Increase Productivity in Small Marketing Teams,” in Proc. 19th Eur. Conf. Innovation and Entrepreneurship (ECIE), 2024, pp. 970–977, doi: 10.34190/ecie.19.1.2468.
Downloads
Published
Issue
Section
License
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
