Advancing Circular Business Models through Big Data and Technological Integration: Pathways for Sustainable Value Creation
Keywords:
Circular business models, sustainable business, big data analyticsAbstract
The global transition toward sustainable development has intensified research on circular business models (CBMs) as mechanisms for economic, social, and environmental value creation. This study synthesizes existing literature on CBMs and explores the intersection of technology, big data analytics, and circular economy principles. Circular business models aim to decouple economic growth from resource consumption by promoting strategies such as product life extension, resource recovery, and service-based value delivery (Geissdoerfer, Vladimirova, & Evans, 2018; Frishammar & Parida, 2019). While numerous typologies and frameworks have been proposed, the integration of digital technologies remains underexplored in systematically advancing CBM implementation (Ellen MacArthur Foundation, 2019; Gupta et al., 2018). This research adopts a qualitative literature synthesis approach, drawing on 30 seminal and recent publications that address sustainable business models, circular economy tools, and technology-enabled business innovations. The study identifies the mechanisms through which big data, artificial intelligence, and cloud-based manufacturing systems enhance circularity by improving resource tracking, predictive maintenance, and lifecycle optimization (Grover et al., 2018; Fisher et al., 2018). Results suggest that CBMs benefit from a hybridized approach that combines traditional sustainability strategies with digital transformation, enabling firms to navigate complex supply chains, manage critical material scarcity, and foster stakeholder engagement (Gaustad et al., 2018; Hopkinson et al., 2018). The discussion elaborates on the theoretical implications of CBM digitalization, highlighting the role of data-driven decision-making in sustaining competitive advantage while addressing environmental imperatives. Limitations include the predominance of secondary data analysis and the need for empirical validation across industries and geographies. Future research directions involve the development of quantitative frameworks to measure circularity impact, longitudinal studies on CBM performance, and policy integration strategies that harmonize technological adoption with regulatory incentives (Wasserbaur, Sakao, & Milios, 2022; Kanther, 2025). This article contributes to the scholarship on sustainable business models by emphasizing the strategic integration of technology and circular economy principles, offering a roadmap for researchers, practitioners, and policymakers committed to sustainable industrial transformation.
References
Abdelmeguid, A., Afy-Shararah, M., & Salonitis, K. (2022). Investigating the challenges of applying the principles of the circular economy in the fashion industry: A systematic review. Sustainable Production and Consumption, 32, 505–518. https://doi.org/10.1016/j.spc.2022.05.009
Arribas-Ibar, M., Nylund, P. A., & Brem, A. (2022). Circular business models in the luxury fashion industry: Toward an ecosystemic dominant design? Current Opinion in Green and Sustainable Chemistry, 37, 100673. https://doi.org/10.1016/j.cogsc.2022.100673
Bocken, N., Strupeit, L., Whalen, K., & Nußholz, J. (2019). A review and evaluation of circular business model innovation tools. Sustainability, 11, 2210. https://doi.org/10.3390/su11082210
Dentchev, N., Rauter, R., Johannsdottir, L., Snihur, Y., Rosano, M., Baumgartner, R., Nyberg, T., Tang, X., van Hoof, B., & Jonker, J. (2018). Embracing the variety of sustainable business models: A prolific field of research and a future research agenda. Journal of Cleaner Production, 194, 695–703.
Ellen MacArthur Foundation. (2015). Delivering the circular economy - A toolkit for policymakers. Retrieved from https://www.ellenmacarthurfoundation.org/assets/downloads/publications/EllenMacArthurFoundation_PolicymakerToolkit.pdf
Ellen MacArthur Foundation. (2019). Artificial intelligence and the circular economy - AI as a tool to accelerate the transition. Retrieved from https://www.ellenmacarthurfoundation.org/assets/downloads/Artificial-intelligenceand-the-circular-economy.pdf
Favi, C., Marconi, M., Germani, M., & Mandolini, M. (2019). A design for disassembly tool oriented to mechatronic product de-manufacturing and recycling. Advanced Engineering Informatics, 39, 62–79.
Fisher, O., Watson, N., Porcu, L., Bacon, D., Rigley, M., & Gomes, R. L. (2018). Cloud manufacturing as a sustainable process manufacturing route. Journal of Manufacturing Systems, 47, 53–68.
Foss, N., & Saebi, T. (2017). Fifteen Years of Research on Business Model Innovation: How Far Have We Come, and Where Should We Go? Journal of Management, 43(1), 200–227.
Frishammar, J., & Parida, V. (2019). Circular Business Model Transformation: A Roadmap for Incumbent Firms. California Management Review, 61(2), 5–29.
Gaustad, G., Krystofik, M., Bustamante, M., & Badami, K. (2018). Circular economy strategies for mitigating critical material supply issues. Resources, Conservation & Recycling, 135, 24–33.
Geissdoerfer, M., Vladimirova, D., & Evans, S. (2018). Sustainable business model innovation: A review. Journal of Cleaner Production, 198, 401–416.
Grover, V., Chiang, R., Liang, T., & Zhang, D. (2018). Creating Strategic Business Value from Big Data Analytics: A Research Framework. Journal of Management Information Systems, 35(2), 388–423.
Kanther, S. D. (2025). Integrating circular business models in construction: A framework for design and planning to enhance sustainability. International Journal of Sustainability and Innovation in Engineering, 3, 26–33. https://doi.org/10.56830/IJSIE202502
Gupta, S., Chen, H., Hazen, B., Kaur, S., & Santibañez Gonzalez, E. (2018). Circular economy and big data analytics: A stakeholder perspective. Technological Forecasting and Social Change.
Günther, W., Rezazade Mehrizi, M., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems.
Hartmann, P. (2016). Capturing value from big data – a taxonomy of data-driven business models used
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