Leveraging cubism principles to foster innovation in manufacturing systems: an AI-enabled mixed-methods analysis
DOI:
https://doi.org/10.1108/INMR-03-2025-0058Keywords:
Cubist principles, Innovation, Manufacturing systems, Modularity, Abstraction, Multiple perspectives, Mixed-methods analysis, Machine learningAbstract
PurposeThis study examines how Cubist principles – modularity, abstraction and multiple perspectives – relate to innovation in manufacturing systems. It identifies factors associated with innovation, organizational challenges and approaches for strengthening innovation capabilities.
Design/methodology/approachA mixed-methods design combined quantitative and qualitative analyses with machine-learning techniques. Data were collected through a structured questionnaire comprising closed- and open-ended questions from employees across manufacturing roles. Quantitative analysis included demographic analysis, confirmatory factor analysis (CFA), structural equation modeling (SEM) and machine-learning models, including neural networks and random forests. Qualitative analysis involved text preprocessing, word-frequency analysis, sentiment analysis and latent dirichlet allocation (LDA) topic modeling. The results were integrated to provide a comprehensive understanding of innovation in manufacturing systems.
FindingsModularity, abstraction and multiple perspectives were significantly associated with innovation, with multiple perspectives showing the strongest association (path coefficient = 0.52). Random forest and neural network models demonstrated strong predictive performance (R2 > 0.80). Qualitative findings highlighted resistance to change and resource constraints as key challenges, while advanced tools and cross-departmental collaboration emerged as potential solutions. Sentiment analysis showed a predominantly positive outlook (56.25% positive responses).
Research limitations/implicationsManufacturing organizations may strengthen innovation by promoting modular design, investing in AI and 3D modeling, fostering cross-departmental collaboration, addressing resource constraints and resistance to change and providing employee training.
Originality/valueThis study contributes by integrating Cubist principles with manufacturing innovation through a mixed-methods approach that combines statistical rigor, machine-learning prediction and qualitative insights.
Downloads
References
Abualrob, A. A. (2024). An analytical comparison between cubism and purism in modern plastic art. An-Najah University Journal for Research - B (Humanities), 39(4), 231–242. doi: https://doi.org/10.35552/0247.39.4.2332.
Acheampong, A., & Berg, P. (2015). Sculptural cubism in product design: Using design history as a creative tool. In International Conference on Engineering and Product Design Education, 3 & 4 September 2015 Loughborough, UK: Loughborough University, Design School.
Agote-Garrido, A., Martín-Gómez, A. M., & Lama-Ruiz, J. R. (2023). Manufacturing system design in Industry 5.0: Incorporating sociotechnical systems and social metabolism for human-centered, sustainable, and resilient production. Systems, 11(11), 537. doi: https://doi.org/10.3390/systems11110537.
Andreassen, T. W. (2024). Commentary: Perceiving innovation: Unveiling the impact on market performance. Journal of Service Research, 27(4), 56–72. doi: https://doi.org/10.1177/10946705241253993.
Antliff, M., & Leighten, P. (2001). Cubism and culture. London: Thames & Hudson.
Birsel, Z., Marques, L., & Loots, E. (2022). Daring to disentangle: Towards a framework for art-science-technology collaborations. Interdisciplinary Science Reviews, 48(1), 109–128. doi: https://doi.org/10.1080/03080188.2022.2134539.
Brauer, K., Ranger, J., & Ziegler, M. (2023). Confirmatory factor analyses in psychological test adaptation and development. Psychological Test Adaptation and Development, 4(1), 4–12.
Burns, T., Cosgrove, J., & Doyle, F. (2019). A review of interoperability standards for Industry 4.0. Procedia Manufacturing, 38, 646–653. doi: https://doi.org/10.1016/j.promfg.2020.01.083.
Cajulis, R. C. J., Tuazon, J. A., & Cabauatan, R. R. (2025). Artificial intelligence in digital art: A comparative analysis on impacts to artists. International Journal of Arts, Recreation and Sports, 4(1), 1–27. doi: https://doi.org/10.47941/ijars.2444.
Callebaut, W., Rasskin-Gutman, D., & Simon, H. A. (2005). Modularity: Understanding the development and evolution of natural complex systems. MIT Press. Available from: Link to the website
Casellas, A. (2009). Barcelona’s urban landscape: The historical making of a tourist product. Journal of Urban History, 35(6), 815–832. doi: https://doi.org/10.1177/0096144209339557.
Cicerone, G., Crociata, A., & Mantegazzi, D. (2021). Cultural and creative industries and regional diversification: Does size matter? Papers in Regional Science, 100(3), 671–688. doi: https://doi.org/10.1111/pirs.12585.
Clifford, R. (1990). Abstract expressionism: Creators and critics: An anthology. Harry N Abrams.
Cottington, D. (1998). Cubism in the shadow of war: The avant-garde and politics in Paris 1905-1914. New Haven: Yale University Press.
Davenport, T. H., & Miller, S. M. (2022). Working with AI: Real stories of human-machine collaboration. MIT Press.
Ehrlich, E. G. (2012). Analytical and synthetic cubism: Picasso and Braque. School of Doctoral Studies (European Union) Journal, 163–170.
Fry, E. (1966). Cubism. London: Thames & Hudson.
Gallagher, M. W., & Brown, T. A. (2013). Introduction to confirmatory factor analysis and structural equation modeling. In Handbook of quantitative methods for educational research (pp. 289–314). Rotterdam: SensePublishers.
Golding, J. (1988). Cubism: A history and an analysis, 1907-1914. Harvard University Press.
Gómez, J. C. (2006). Introducción: La modularidad a debate. Infancia y Aprendizaje, 29(3), 245–248. doi: https://doi.org/10.1174/021037006778148042.
González-Zamar, M. -D., & Abad-Segura, E. (2020). Implications of virtual reality in arts education: Research analysis in the context of higher education. Education Sciences, 10(9), 225. doi: https://doi.org/10.3390/educsci10090225.
Hitt, M., & Tyler, B. (1991). Strategic decision models: Integrating different perspectives. Strategic Management Journal, 12(5), 327–351. doi: https://doi.org/10.1002/smj.4250120502.
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. doi: https://doi.org/10.1080/00207543.2020.1750727.
Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry, final report of the Industrie 4.0 working group, Forschungsunion.
Karmel, P. (2003). Picasso and the invention of cubism. New Haven: Yale University Press.
Keshvarparast, A., Berti, N., Chand, S., Guidolin, M., Lu, Y., Battaia, O., … Battini, D. (2024). Ergonomic design of human-robot collaborative workstation in the era of Industry 5.0. Computers & Industrial Engineering, 198, 110729. doi: https://doi.org/10.1016/j.cie.2024.110729.
Kumar, A., Gajrani, B. K., & Kishor, X. Y. (2023). Challenges in achieving sustainability during manufacturing. In N. Khanna, K. Kishoor, K. G. Gajrani, & J. P. Davim (Eds.), Sustainable materials and manufacturing technologies (pp. 17). CRC Press.
Kyriazos, T., & Poga, M. (2024). Application of machine learning models in social sciences: Managing nonlinear relationships. Encyclopedia, 4(4), 1790–1805. doi: https://doi.org/10.3390/encyclopedia4040118.
Lettl, C. (2007). User involvement competence for radical innovation. Journal of Engineering and Technology Management, 24(1-2), 53–75. doi: https://doi.org/10.1016/j.jengtecman.2007.01.004.
Liu, L., Schoen, A. J., Henrichs, C., Li, J., Mutlu, B., Zhang, Y., & Radwin, R. G. (2024). Human robot collaboration for enhancing work activities. Human Factors, 66(1), 158–179. doi: https://doi.org/10.1177/00187208221077722. PubMed
Ma, Z., Sun, Y., Ma, M., & Li, L. (2024). The innovation and influence of cubism in architectural education and design. In 2024 5th International Conference on Urban Construction and Management Engineering (ICUCME 2024) (pp. 510–517). Atlantis Press.
Mallen, E. (2022). The embodiment of artistic objects in Pablo picasso’s cubism. Arts, 11(1), 32. doi: https://doi.org/10.3390/arts11010032.
Marquez, E. J. (2009). A general framework for inferring the developmental causes of modularity of morphological variation with applications to the craniomandibular complex in rodents.
Martinez Martin, D., de Leeuw, T., & Haefliger, S. (2024). Two dimensions of product modularity and innovation: The case of R&D teams. Industrial and Corporate Change, dtae037.
McCarthy, J., & Wright, P. (2004). Technology as experience. Cambridge, MA: MIT Press.
Montini, E., Cutrona, V., Dell’Oca, S., Bettoni, A., Landolfi, G., Rocco, P., & Carpanzano, E. (2024). An industrial human–robot collaboration case study for flexible assembly. Procedia CIRP, 128, 350–355. doi: https://doi.org/10.1016/j.procir.2023.12.058.
More, L. S., & Kumar, B. (2024). Understanding and applying machine learning models. In The pioneering applications of Generative AI (pp. 274–309). IGI Global.
Mukherjee, A., Divya, A., Sivvani, M., & Surjya Kanta Pal, S. (2024). Cognitive intelligence in industrial robots and manufacturing. Computers and Industrial Engineering, 191(C), 110106. doi: https://doi.org/10.1016/j.cie.2024.110106.
Nye, C. D. (2023). Reviewer resources: Confirmatory factor analysis. Organizational Research Methods, 26(4), 608–628. doi: https://doi.org/10.1177/10944281221120541.
Okten, B., Dilaveroglu, B., Kudumovic, L., Salbacak, S., & Yılmaz, N. (2022). Abstraction as an approach to the learning of basic design. Technics Technologies Education Management, 17(2). Available from: Link to the website
Pisano, G. P. (2019). The hard truth about innovative cultures. Harvard Business Review, 97(1), 62–71.
Quaiser, R. M., & Pandey, S. K. (2023). Design thinking enabling innovation: A literature review. Innovation: The European Journal of Social Science Research, 36(4), 579–601. doi: https://doi.org/10.1080/13511610.2023.2238910.
Rashid, S. (2024). Exploring virtual reality in arts education. Research Invention Journal of Research in Education, 4(1), 26–30. doi: https://doi.org/10.59298/rijre/2024/412630.
Santilli, T., Ceccacci, S., Mengoni, M., & Giaconi, C. (2025). Virtual vs traditional learning in higher education: A systematic review of comparative studies. Computers & Education, 227, 105214. doi: https://doi.org/10.1016/j.compedu.2024.105214.
Sarkale, S. L., Bhinde, H. N., Tatia, A., Mahajan, Y., & Sharma, V. (2024). Integration of structural equation modeling and machine learning in supply chain management. In Digital transformation for improved industry and supply chain performance (pp. 93–107). IGI Global.
Sgourev, S. (2013a). How Paris gave rise to cubism (and Picasso): Ambiguity and fragmentation in radical innovation. Organization Science, 24(6), 1601–1617. doi: https://doi.org/10.1287/orsc.1120.0819.
Sgourev, S. V. (2013b). The dynamics of risk in innovation: A primer of Picasso’s success. American Sociological Review, 78(5), 944–967. doi: https://doi.org/10.1177/0003122413505192.
Spee, B. T., Arato, J., Mikuni, J., Tran, U. S., Pelowski, M., & Leder, H. (2024). The dynamics of experiencing Gestalt and Aha in cubist art: Pupil responses and art evaluations show a complex interplay of task, stimuli content, and time course. Frontiers in Psychology, 15, 1192565. doi: https://doi.org/10.3389/fpsyg.2024.1192565. PubMed
Stavraki, G., & Anninou, I. (2023). Arts-based methods in business education: A reflection on a photo-elicitation project. Management Learning, 54(4), 531–555. doi: https://doi.org/10.1177/13505076221075046.
Stoffels, M., Torre, D. M., Sturgis, P., Koster, A. S., Westein, M. P., & Kusurkar, R. A. (2023). Steps and decisions involved when conducting structural equation modeling (SEM) analysis. Medical Teacher, 45(12), 1343–1345. doi: https://doi.org/10.1080/0142159x.2023.2263233. PubMed
Strothotte, T., & Schlechtweg, S. (2002). Non-photorealistic computer graphics: Modeling, rendering, and animation. Morgan Kaufmann.
Taylor, S. S., & Ladkin, D. (2009). Understanding arts-based methods in managerial development. Academy of Management Learning & Education, 8(1), 55–69. doi: https://doi.org/10.5465/amle.2009.37012179.
Tekaat, J. L., Anacker, H., & Dumitrescu, R. (2021). The paradigm of design thinking and systems engineering in the design of cyber-physical systems: A systematic literature review. In 2021 IEEE International Symposium on Systems Engineering (ISSE) (pp. 1–8) IEEE.
Vaccaro, M., Almaatouq, A., & Malone, T. (2024). When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behaviour, 8(12), 2293–2303. doi: https://doi.org/10.1038/s41562-024-02024-1. PubMed
Varela, L., Putnik, G., & Romero, F. (2022). The concept of collaborative engineering: A systematic literature review. Production & Manufacturing Research, 10(1), 784–839. doi: https://doi.org/10.1080/21693277.2022.2133856.
Vecchio, Y., De Rosa, M., Pauselli, G., Masi, M., & Adinolfi, F. (2022). The leading role of perception: The FACOPA model to comprehend innovation adoption. Agricultural and Food Economics, 10(1), 1–19. doi: https://doi.org/10.1186/s40100-022-00211-0. PubMed
Vegesack, A. (1997). Czech cubism: Architecture, furniture, and decorative arts 1910-1925 ( (4th ed.) ). Princeton Architectural Press.
Vetterli, C. (2015). Embedded design thinking in organizations: A literature review.
White, J. C. (2022). Innovation in the arts: Concepts, theories, and practices ( (1st ed.) ). Routledge.
Yang, Y., Deb, S., He, M., & Kobir, M. H. (2023). The use of virtual reality in manufacturing education: State-of-the-art and future directions. Manufacturing Letters, 35, 1214–1221. doi: https://doi.org/10.1016/j.mfglet.2023.07.023.
Further reading
Berthon, P., Pitt, L., & Campbell, C. (2009). Does brand meaning exist in similarity or difference? Journal of Business Research, 62(3), 356–361. doi: https://doi.org/10.1016/j.jbusres.2008.05.015.
Cottington, D. (1997). Maison cubiste and the meaning of modernism in pre-1914 France. In Blau, E., & Nancy, T.J. (Eds.), Architecture and cubism. Cambridge, Mass, London: MIT.
Fox, R. A., McManus, I. C., & Winder, B. C. (2001). The shortened study process questionnaire: An investigation of its structure and longitudinal stability using confirmatory factor analysis. British Journal of Educational Psychology, 71(4), 511–530. doi: https://doi.org/10.1348/000709901158659. PubMed
Hoyle, R. H. (2000). Confirmatory factor analysis. In Handbook of applied multivariate statistics and mathematical modeling (pp. 465–497). Academic Press.
Jin, J., Yang, M., Hu, H., Guo, X., Luo, J., & Liu, Y. (2025). Empowering design innovation using AI-generated content. Journal of Engineering Design, 36(1), 1–18. doi: https://doi.org/10.1080/09544828.2024.2401751.
Lee, G., & Widrig, D. (2020). Making a case for modularity. Cubic Journal, 3, 74–103. doi: https://doi.org/10.31182/cubic.2020.3.025.
Manzini, E. (2015). Design, when everybody designs: An introduction to design for social innovation. MIT Press.
Mesa, J., Pierce, J., Zuñiga, J., Esparragoza, I., & Maury, H. (2021). Sustainable manufacture of scalable product families based on modularity. CIRP Journal of Manufacturing Science and Technology, 35, 80–95. doi: https://doi.org/10.1016/j.cirpj.2021.05.011.
Nabergoj, A. S., & Uršič, T. (2024). Designing for creativity: Harnessing visual thinking and collaboration in hybrid workspaces. In 2024 28th International Conference Information Visualisation (IV) (pp. 302–307). IEEE.
O’Reilly, C. A., & Tushman, M. L. (2013). Organizational ambidexterity: Past, present, and future. Academy of Management Perspectives, 27(4), 324–338. doi: https://doi.org/10.5465/amp.2013.0025.
Richardson, J. (1996). A life of Picasso: The cubist rebel, 1907-1916. New York: Knopf.
Tan, Q., & Li, H. (2024). Application of computer aided design in product innovation and development: Practical examination on taking the industrial design process. IEEE Access, 12, 85622–85634. doi: https://doi.org/10.1109/access.2024.3404963.
Tan, T., Mills, G., Papadonikolaki, E., Li, B., & Huang, J. (2023). Digital-enabled design for manufacture and assembly (DfMA) in offsite construction: A modularity perspective for the product and process integration. Architectural Engineering and Design Management, 19(3), 267–282. doi: https://doi.org/10.1080/17452007.2022.2104208.
Wang, Y., & Pang, N. (2024). Industrial product design and case analysis combining artificial intelligence and cubism. In 2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon) (pp. 1–5). IEEE.
Warfield, J. N. (2006). An introduction to systems science. World Scientific.
Welling, H. (2007). Four mental operations in creative cognition: The importance of abstraction. Creativity Research Journal, 19(2-3), 163–177. doi: https://doi.org/10.1080/10400410701397214.
Wooldredge, J. (2023). Structural equation modeling. In Oxford research encyclopedia of criminology and criminal justice.
Zhang, Y. (2024). Cultivating a culture of innovation: The impact of leadership style on employee well-being and organizational creativity. International Journal of Global Economics and Management, 2(1), 202–210. doi: https://doi.org/10.62051/ijgem.v2n1.26.
Published
Issue
Section
License
Copyright (c) 2026 Saeed Jahanyan, Fatemeh Shaker
This work is licensed under a Creative Commons Attribution 4.0 International License.
• O(s) autor(es) autoriza(m) a publicação do artigo na revista;
• O(s) autor(es) garante(m) que a contribuição é original e inédita e que não está em processo de avaliação em outra(s) revista(s);
• A revista não se responsabiliza pelas opiniões, ideias e conceitos emitidos nos textos, por serem de inteira responsabilidade de seu(s) autor(es);
• É reservado aos editores o direito de proceder ajustes textuais e de adequação do artigos às normas da publicação.
