Effects of Big Data Capacity on Sustainable Manufacturing and Circular Economy in Brazilian Industries
PDF

Keywords

Resource-based theory
big data capability
sustainable manufacturing
circular economy

How to Cite

Klein, L., & Sano Guilhem, A. P. (2024). Effects of Big Data Capacity on Sustainable Manufacturing and Circular Economy in Brazilian Industries. Review of Business Management, 26(01). https://doi.org/10.7819/rbgn.v26i01.4250

Abstract

Purpose – The objective is to analyze the relationship between big data analytics (BDA) capability and the development of sustainable manufacturing and circular economy (CE) in Brazilian industries.

Theoretical framework – The construction of BDA capability, according to the resource-based theory, is established through the implementation, integration and processing of big data resources. It is argued that BDA capability can contribute to the sustainable development of industries based on the collected data, as well as influencing the development of CE.

Design/methodology/approach – The research was descriptive and quantitative, and was conducted using a survey of employees in Brazilian industries that use big data. The hypotheses were tested using structural equation modeling.

Practical & social implications of research – BDA capability has a positive and significant relationship with sustainable manufacturing and CE. Sustainable manufacturing is a complementary mediator between BDA capability and CE.

Originality/value – The study provides knowledge on the interaction between BDA and the development of sustainable and circular practices in Brazilian industries, providing incentives for changes in manufacturing companies that can successfully reduce social pressures due to resource scarcity, sustainable production and supply chain uncertainty.

Keywords: Resource-based theory, big data capability, sustainable manufacturing, circular economy

https://doi.org/10.7819/rbgn.v26i01.4250
PDF

References

Acerbi, F., Forterre, D. A., & Taisch, M. (2021). Role of artificial intelligence in circular manufacturing: A systematic literature review. IFAC-PapersOnLine, 54(1), 367-372. http://dx.doi.org/10.1016/j.ifacol.2021.08.040.

Agarwal, R., & Dhar, V. (2014). Big data, data science, and analytics: The opportunity and challenge for IS research. Information Systems Research, 25(3), 443-448. http://dx.doi.org/10.1287/isre.2014.0546.

Alaerts, L., Van Acker, K., Rousseau, S., De Jaeger, S., Moraga, G., Dewulf, J., De Meester, S., Van Passel, S., Compernolle, T., Bachus, K., Vrancken, K., & Eyckmans, J. (2019). Towards a more direct policy feedback in circular economy monitoring via a societal needs perspective. Resources, Conservation and Recycling, 149, 363-371. http:// dx.doi.org/10.1016/j.resconrec.2019.06.004.

Ang, K. L., Saw, E. T., He, W., Dong, X., & Ramakrishna, S. (2021). Sustainability framework for pharmaceutical manufacturing (PM): A review of research landscape and implementation barriers for circular economy transition. Journal of Cleaner Production, 280, 124264. http://dx.doi.org/10.1016/j.jclepro.2020.124264.

Ardolino, M., Rapaccini, M., Saccani, N., Gaiardelli, P., Crespi, G., & Ruggeri, C. (2018). The role of digital technologies for the service transformation of industrial companies. International Journal of Production Research, 56(6), 2116-2132. http://dx.doi.org/10.1080/0020754 3.2017.1324224.

Awan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. N. (2021). Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance. Technological Forecasting and Social Change, 168, 120766. http://dx.doi.org/10.1016/j. techfore.2021.120766.

Azeem, M., Haleem, A., Bahl, S., Javaid, M., Suman, R., & Nandan, D. (2022). Big data applications to take up major challenges across manufacturing industries: A brief review. Materials Today: Proceedings, 49, 339-348. http:// dx.doi.org/10.1016/j.matpr.2021.02.147.

Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, 120420. http://dx.doi.org/10.1016/j.techfore.2020.120420.

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

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. http:// dx.doi.org/10.1177/014920639101700108.

Cabrera-Sánchez, J. P., & Villarejo-Ramos, A. F. (2019). Fatores que afetam a adoção de análises de Big Data em empresas. Revista de Administração de Empresas, 59(6), 415-429.

Carvalho, A. C. P., Carvalho, A. P. P., & Carvalho, N. G. P. (2020). Industry 4.0 technologies: What is your potential for environmental management? In J. Hamilton Ortiz (Ed.), Industry 4.0: Current status and future trends. London: IntechOpen.

Colorado, H. A., Velásquez, E. I. G., & Monteiro, S. N. (2020). Sustainability of additive manufacturing: The circular economy of materials and environmental perspectives. Journal of Materials Research and Technology, 9(4), 8221- 8234. http://dx.doi.org/10.1016/j.jmrt.2020.04.062.

Cui, Y., Kara, S., & Chan, K. C. (2020). Manufacturing big data ecosystem: A systematic literature review. Robotics and Computer-integrated Manufacturing, 62, 101861. http://dx.doi.org/10.1016/j.rcim.2019.101861.

Desing, H., Brunner, D., Takacs, F., Nahrath, S., Frankenberger, K., & Hischier, R. (2020). A circular economy within the planetary boundaries: Towards a resource-based, systemic approach. Resources, Conservation and Recycling, 155, 104673. http://dx.doi.org/10.1016/j.resconrec.2019.104673.

Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., Wamba, S. F., & Roubaud, D. (2019a). Can big data and predictive analytics improve social and environmental sustainability? Technological Forecasting and Social Change, 144, 534-545. http://dx.doi.org/10.1016/j. techfore.2017.06.020.

Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019b). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource‐based view and big data culture. British Journal of Management, 30(2), 341-361. http://dx.doi.org/10.1111/1467-8551.12355.

Elkington, J. (2019). Green swans: The coming boom in regenerative capitalism. Austin: Greenleaf Book Group. Ellen MacArthur Foundation. (2022). Conceito. https:// www.ellenmacarthurfoundation.org/

Enyoghasi, C., & Badurdeen, F. (2021). Industry 4.0 for sustainable manufacturing: Opportunities at the product, process, and system levels. Resources, Conservation and Recycling, 166, 105362. http://dx.doi.org/10.1016/j.resconrec.2020.105362.

Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G* Power 3.1: Tests forcorrelation and regression analyses. Behavior Research Methods, 41(4), 1149-1160. http://dx.doi.org/10.3758/ BRM.41.4.1149. PMid:19897823.

Geissdoerfer, M., Savaget, P., Bocken, N. M., & Hultink, E. J. (2017). The Circular Economy: A new sustainability paradigm? Journal of Cleaner Production, 143, 757-768. http://dx.doi.org/10.1016/j.jclepro.2016.12.048.

Grant, R. M. (1991). The resource-based theory of competitive advantage: Implications for strategy formulation. California Management Review, 33(3), 114-135. http:// dx.doi.org/10.2307/41166664.

Grant, R. M. (1996). Prospering in dynamically-competitive environments: Organizational capability as knowledge integration. Organization Science, 7(4), 375-387. http:// dx.doi.org/10.1287/orsc.7.4.375.

Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064. http://dx.doi. org/10.1016/j.im.2016.07.004.

Gupta, S., Chen, H., Hazen, B. T., Kaur, S., & Santibañez Gonzalez, E. D. (2019). Circular economy and big data analytics: A stakeholder perspective. Technological Forecasting and Social Change, 144, 466-474. http:// dx.doi.org/10.1016/j.techfore.2018.06.030.

Gupta, H., Kumar, A., & Wasan, P. (2021). Industry 4.0, cleaner production and circular economy: An integrative framework for evaluating ethical and sustainable business performance of manufacturing organizations. Journal of Cleaner Production, 295, 126253. http://dx.doi. org/10.1016/j.jclepro.2021.126253.

Hair Jr., J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Cham: Springer. Hapuwatte, B. M., & Jawahir, I. S. (2019). A total life cycle approach for developing predictive design methodologies to optimize product performance. Procedia Manufacturing, 33, 11-18. http://dx.doi.org/10.1016/j. promfg.2019.04.003.

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. http:// dx.doi.org/10.1007/s11747-014-0403-8.

Ibarra, D., Ganzarain, J., & Igartua, J. I. (2018). Business model innovation through Industry 4.0: A review. Procedia Manufacturing, 22, 4-10. http://dx.doi.org/10.1016/j. promfg.2018.03.002.

International Organization for Standardization – ISO. (2018). ISO/TC 323: Circular economy. Genebra: ISO. Jabbour, A. B. L. S., Luiz, J. V. R., Luiz, O. R., Jabbour, C. J. C., Ndubisi, N. O., Oliveira, J. H. C., & Horneaux, F., Jr. (2018). Circular economy business models and operations management. Journal of Cleaner Production, 235, 1525- 1539. http://dx.doi.org/10.1016/j.jclepro.2019.06.349.

Jabbour, C. J. C., Jabbour, A. B. L. S., Sarkis, J., & Godinho Fo., M. (2019). Unlocking the circular economy through new business models based on large-scale data: An integrative framework and research agenda. Technological Forecasting and Social Change, 144, 546-552. http://dx.doi.org/10.1016/j.techfore.2017.09.010.

Jabbour, C. J. C., Fiorini, P. D. C., Ndubisi, N. O., Queiroz, M. M., & Piato, É. L. (2020a). Digitallyenabled sustainable supply chains in the 21st century: A review and a research agenda. The Science of the Total Environment, 725, 138177. http://dx.doi.org/10.1016/j. scitotenv.2020.138177. PMid:32302825.

Jabbour, C. J. C., Fiorini, P. D. C., Wong, C. W., Jugend, D., Jabbour, A. B. L. S., Seles, B. M. R. P., Pinheiro, M. A. P., & Silva, H. M. R. (2020b). First-mover firms in the transition towards the sharing economy in metallic natural resource-intensive industries: Implications for the circular economy and emerging industry 4.0 technologies. Resources Policy, 66, 101596. http://dx.doi.org/10.1016/j. resourpol.2020.101596.

Kamal, M. M., Sivarajah, U., Bigdeli, A. Z., Missi, F., & Koliousis, Y. (2020). Servitization implementation in the manufacturing organisations: Classification of strategies, definitions, benefits and challenges. International Journal of Information Management, 55, 102206. http://dx.doi.org/10.1016/j.ijinfomgt.2020.102206.

Kleindorfer, P. R., Singhal, K., & van Wassenhove, L. N. (2005). Sustainable operations management. Production and Operations Management, 14(4), 482-492. http://dx.doi. org/10.1111/j.1937-5956.2005.tb00235.x.

Kristoffersen, E., Blomsma, F., Mikalef, P., & Li, J. (2020). The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies. Journal of Business Research, 120, 241-261. http://dx.doi. org/10.1016/j.jbusres.2020.07.044.

Kristoffersen, E., Mikalef, P., Blomsma, F., & Li, J. (2021). Towards a business analytics capability for the circular economy. Technological Forecasting and Social Change, 171, 120957. http://dx.doi.org/10.1016/j. techfore.2021.120957.

MacArthur, E. (2013). Towards the circular economy. Journal of Industrial Ecology, 2, 23-44. Majeed, A., Zhang, Y., Ren, S., Lv, J., Peng, T., Waqar, S., & Yin, E. (2021). A big data-driven framework for sustainable and smart additive manufacturing. Robotics and Computer-integrated Manufacturing, 67, 102026. http://dx.doi.org/10.1016/j.rcim.2020.102026.

Malek, J., & Desai, T. N. (2019). Interpretive structural modelling based analysis of sustainable manufacturing enablers. Journal of Cleaner Production, 238, 117996. http://dx.doi.org/10.1016/j.jclepro.2019.117996.

Malek, J., & Desai, T. N. (2021). A framework for prioritizing the solutions to overcome sustainable manufacturing barriers. Cleaner Logistics and Supply Chain, 1, 100004. http://dx.doi.org/10.1016/j.clscn.2021.100004.

Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578. http://dx.doi. org/10.1007/s10257-017-0362-y.

Moreno, M., Turner, C., Tiwari, A., Hutabarat, W., Charnley, F., Widjaja, D., & Mondini, L. (2017). Redistributed manufacturing to achieve a Circular Economy: A case study utilizing IDEF0 modeling. Procedia CIRP, 63, 686-691. http://dx.doi.org/10.1016/j.procir.2017.03.322.

Nagorny, K., Lima-Monteiro, P., Barata, J., & Colombo, A. W. (2017). Big data analysis in smart manufacturing: A review. International Journal of Communications, Network and Systems Sciences, 10(3), 31-58. http://dx.doi. org/10.4236/ijcns.2017.103003.

Navare, K., Muys, B., Vrancken, K. C., & Van Acker, K. (2021). Circular economy monitoring: How to make it apt for biological cycles? Resources, Conservation and Recycling, 170, 105563. http://dx.doi.org/10.1016/j. resconrec.2021.105563.

Nobre, G. C., & Tavares, E. (2017). Scientific literature analysis on big data and internet of things applications on circular economy: A bibliometric study. Scientometrics, 111(1), 463-492. http://dx.doi.org/10.1007/s11192- 017-2281-6.

Okorie, O., Salonitis, K., Charnley, F., Moreno, M., Turner, C., & Tiwari, A. (2018). Digitisation and the circular economy: A review of current research and future trends. Energies, 11(11), 3009. http://dx.doi.org/10.3390/ en11113009.

Peng, D. X., Schroeder, R. G., & Shah, R. (2008). Linking routines to operations capabilities: A new perspective. Journal of Operations Management, 26(6), 730-748. http://dx.doi.org/10.1016/j.jom.2007.11.001.

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. The Journal of Applied Psychology, 88(5), 879- 903. http://dx.doi.org/10.1037/0021-9010.88.5.879. PMid:14516251.

Queiroz, M. M., & Pereira, S. C. F. (2019). Intention to adopt big data in supply chain management: A Brazilian perspective. RAE, 59(6), 389-401. http://dx.doi.org/10.1590/ s0034-759020190605.

Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. E. (2019). Linking big data analytics and operational sustainability practices for sustainable business management. Journal of Cleaner Production, 224, 10-24. http://dx.doi.org/10.1016/j. jclepro.2019.03.181.

Ren, S., Zhang, Y., Liu, Y., Sakao, T., Huisingh, D., & Almeida, C. M. (2019). A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions. Journal of Cleaner Production, 210, 1343-1365. http://dx.doi.org/10.1016/j. jclepro.2018.11.025.

Reslan, M., Last, N., Mathur, N., Morris, K. C., & Ferrero, V. (2022). Circular economy: A product life cycle perspective on engineering and manufacturing practices. Procedia CIRP, 105, 851-858. http://dx.doi. org/10.1016/j.procir.2022.02.141.

Schoenherr, T., & Speier‐Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120-132. http://dx.doi. org/10.1111/jbl.12082.

Tayal, A., Solanki, A., & Singh, S. P. (2020). Integrated frame work for identifying sustainable manufacturing layouts based on big data, machine learning, meta-heuristic and data envelopment analysis. Sustainable Cities and Society, 62, 102383. http://dx.doi.org/10.1016/j.scs.2020.102383.

Tseng, M. L., Tan, R. R., Chiu, A. S., Chien, C. F., & Kuo, T. C. (2018). Circular economy meets industry 4.0: Can big data drive industrial symbiosis? Resources, Conservation and Recycling, 131, 146-147. http://dx.doi. org/10.1016/j.resconrec.2017.12.028.

United Nations. (2015). General Assembly Resolution A/ RES/70/1. Transforming Our World, the 2030 Agenda for Sustainable Development. http://www.un.org/ga/search/ view_doc.asp?symbol=A/RES/70/1&Lang=E

Wang, C. H., Ali, M. H., Chen, K. S., Negash, Y. T., Tseng, M. L., & Tan, R. R. (2021). Data driven supplier selection as a circular economy enabler: A Taguchi capability index for manufactured products with asymmetric tolerances. Advanced Engineering Informatics, 47, 101249. http:// dx.doi.org/10.1016/j.aei.2021.101249.

Yu, W., Chavez, R., Jacobs, M. A., & Feng, M. (2018). Data-driven supply chain capabilities and performance: A resource-based view. Transportation Research Part E, Logistics and Transportation Review, 114, 371-385. http:// dx.doi.org/10.1016/j.tre.2017.04.002.

Zeng, H., Chen, X., Xiao, X., & Zhou, Z. (2017). Institutional pressures, sustainable supply chain management, and circular economy capability: Empirical evidence from Chinese eco-industrial park firms. Journal of Cleaner Production, 155, 54-65. http://dx.doi.org/10.1016/j. jclepro.2016.10.093.

Zhang, Y., Ren, S., Liu, Y., & Si, S. (2017). A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. Journal of Cleaner Production, 142, 626-641. http://dx.doi. org/10.1016/j.jclepro.2016.07.123.

Zhang, D., Pan, S. L., Yu, J., & Liu, W. (2022). Orchestrating big data analytics capability for sustainability: A study of air pollution management in China. Information & Management, 59(5), 103231. http://dx.doi.org/10.1016/j. im.2019.103231.

If a paper is approved for publication, its copyright has to be transferred by the author(s) to the Review of Business Management – RBGN.

Accordingly, authors are REQUIRED to send RBGN a duly completed and signed Copyright Transfer Form. Please refer to the following template: [Copyright Transfer]

The conditions set out by the Copyright Transfer Form state that the Review of Business Management – RBGN owns, free of charge and permanently, the copyright of the papers it publishes. Although the authors are required to sign the Copyright Transfer Form, RBGN allows authors to hold and use their own copyright without restrictions.

The texts published by RBGN are the sole responsibility of their authors.

The review has adopted the CC-BY Creative Commons Attribution 4.0 allowing redistribution and reuse of papers on condition that the authorship is properly credited.