Big data analytics-Artificial Intelligence, ambidexterity, and green supply chain management: Implications on responsible economy
Main Article Content
Abstract
In the current dynamic market businesses have recognized the pivotal role of data and sustainability technologies in attaining competitive advantage. Big Data Analytics-Artificial Intelligence and Green Supply Chain Management are significant sustainability promotion strategies. The research collected data from 220 employees in the Taiwanese manufacturing sector with the help of a survey methodology. The findings revealed significant impacts of Big Data Analytics-Artificial Intelligence on both green supply chain management and supply chain ambidexterity. Moreover, supply chain ambidexterity significantly influences green supply chain management. Lastly, supply chain ambidexterity was also found to mediate the relationship between Big Data Analytics-Artificial Intelligence, and green supply chain management. This study provides several implications for fostering a responsible economy. It elucidates how leveraging Big Data Analytics-Artificial Intelligence enhances supply chain ambidexterity, reinforcing sustainable practices without detectable alterations.
Downloads
Metrics
Article Details
A RAE compromete-se a contribuir com a proteção dos direitos intelectuais do autor. Nesse sentido:
- adota a licença Creative Commoms BY (CC-BY) em todos os textos que publica, exceto quando houver indicação de específicos detentores dos direitos autorais e patrimoniais;
- adota software de detecção de similaridades;
- adota ações de combate ao plagio e má conduta ética, alinhada às diretrizes do Committee on Publication Ethics (COPE)
References
Albergaria, M., & Jabbour, C. J. C. (2020). The role of big data analytics capabilities (BDAC) in understanding the challenges of service information and operations management in the sharing economy: Evidence of peer effects in libraries. International Journal of Information Management, 51, 102023. https://doi.org/10.1016/j.ijinfomgt.2019.10.008
Aljumah, A. I., Nuseir, M. T., & Alam, M. M. (2021). Organizational performance and capabilities to analyze big data: Do the ambidexterity and business value of big data analytics matter? Business Process Management Journal, 27(4), 1088-1107. https://doi.org/10.1108/BPMJ-07-2020-0335
Alves, A. P. F., & Nascimento, L. F. (2014). Green supply chain: Protagonista ou coadjuvante no Brasil? RAE-Revista de Administração de Empresas, 54, 510-520. https://doi.org/10.1590/S0034-759020140505
Ara, A., & Mifa, A. F. (2024). Integrating artificial intelligence and big data in mobile health: A systematic review of innovations and challenges in healthcare systems. Global Mainstream Journal of Business, Economics, Development & Project Management, 3(1), 1-16. https://doi.org/10.62304/jbedpm.v3i01.70
Aydiner, A. S., Tatoglu, E., Bayraktar, E., Zaim, S., & Delen, D. (2019). Business analytics and firm performance: The mediating role of business process performance. Journal of Business Research, 96, 228-237. https://doi.org/10.1016/j.jbusres.2018.11.028
Bag, S., Dhamija, P., Bryde, D. J., & Singh, R. K. (2022). Effect of eco-innovation on green supply chain management, circular economy capability, and performance of small and medium enterprises. Journal of Business Research, 141, 60-72. https://doi.org/10.1016/j.jbusres.2021.12.011
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. https://doi.org/10.1016/j.techfore.2020.120420
Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change, 165, 120557. https://doi.org/10.1016/j.techfore.2020.120557
Bharadiya, J. P. (2023). A comparative study of business intelligence and artificial intelligence with big data analytics. American Journal of Artificial Intelligence, 7(1), 24. https://doi.org/10.11648/j.ajai.20230701.14
Birkinshaw, J., & Gibson, C. B. (2004). Building an ambidextrous organisation (Research Paper n. 3). Advanced Institute of Management. https://dx.doi.org/10.2139/ssrn.1306922
Borzillo, S., Schmitt, A., & Antino, M. (2012). Communities of practice: Keeping the company agile. Journal of Business Strategy, 33(6), 22-30. https://doi.org/10.1108/02756661211282765
Brei, V. A. (2018). Qual o papel do consumo na sociedade contemporânea capitalista? RAE-Revista de Administração de Empresas, 58(2), 212-213. https://doi.org/10.1590/s0034-759020180207
Bui, T.-D., Tsai, F. M., Tseng, M.-L., Tan, R. R., Yu, K. D. S., & Lim, M. K. (2020). Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis. Sustainable Production and Consumption, 22, 209-223. https://doi.org/10.1016/j.spc.2020.04.005
Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86-97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004
Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., & Childe, S. J. (2018). Supply chain agility, adaptability and alignment: Empirical evidence from the Indian auto components industry. International Journal of Operations & Production Management, 38(1), 129-148. https://doi.org/10.1108/IJOPM-04-2016-0173
Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226, 107599. https://doi.org/10.1016/j.ijpe.2020.107599
Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2016). The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufacturing Technology, 84, 631-645. https://doi.org/10.1007/s00170-015-7674-1
Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S. J., Shibin, K., & Wamba, S. F. (2017). Sustainable supply chain management: Framework and further research directions. Journal of Cleaner Production, 142, 1119-1130. https://doi.org/10.1016/j.jclepro.2016.03.117
Elgendy, N., & Elragal, A. (2014, July 16-20). Big data analytics: A literature review paper. Advances in Data Mining. Applications and Theoretical Aspects: Proceedings of 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia. https://doi.org/10.1007/978-3-319-08976-8_16
Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National Science Review, 1(2), 293-314. https://doi.org/10.1093/nsr/nwt032
Ficapal-Cusí, P., Torrent-Sellens, J., Palos-Sanchez, P., & González-González, I. (2023). The telework performance dilemma: Exploring the role of trust, social isolation and fatigue. International Journal of Manpower, 44(3), 345-360. https://doi.org/10.1108/IJM-02-2022-0041
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
Francisco, E. d. R., Kugler, J. L., Kang, S. M., Silva, R., & Whigham, P. A. (2020). Beyond technology: Management challenges in the Big Data era. RAE-Revista de Administração de Empresas, 59, 375-378. https://doi.org/10.1590/S0034-759020200406
Gallo, H., Khadem, A., & Alzubi, A. (2023). The relationship between big data analytic-artificial intelligence and environmental performance: A moderated mediated model of Green Supply Chain Collaboration (GSCC) and Top Management Commitment (TMC). Discrete Dynamics in Nature and Society, 2023, 4531457. https://doi.org/10.1155/2023/4531457
Gamoura, S. C. (2019). A cloud-based approach for cross-management of disaster plans: Managing risk in networked enterprises. In Emergency and disaster management: Concepts, methodologies, tools, and applications (pp. 857-881). IGI Global. https://doi.org/10.4018/978-1-5225-7368-5.ch041
Geng, R., Mansouri, S. A., & Aktas, E. (2017). The relationship between green supply chain management and performance: A meta-analysis of empirical evidences in Asian emerging economies. International Journal of Production Economics, 183, 245-258. https://doi.org/10.1016/j.ijpe.2016.10.008
Ghasemaghaei, M., & Calic, G. (2019). Does big data enhance firm innovation competency? The mediating role of data-driven insights. Journal of Business Research, 104, 69-84. https://doi.org/10.1016/j.jbusres.2019.06.023
Giannetti, B. F., Lopez, F. J. D., Liu, G., Agostinho, F., Sevegnani, F., & Almeida, C. M. (2023). A resilient and sustainable world: Contributions from cleaner production, circular economy, eco-innovation, responsible consumption, and cleaner waste systems. Journal of Cleaner Production, 384, 135465. https://doi.org/10.1016/j.jclepro.2022.135465
Golicic, S. L., & Smith, C. D. (2013). A meta‐analysis of environmentally sustainable supply chain management practices and firm performance. Journal of supply Chain Management, 49(2), 78-95. https://doi.org/10.1111/jscm.12006
Hair, J. F., Jr., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
Hair, J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part I–method. European Business Review, 28(1), 63-76. https://doi.org/10.1108/EBR-09-2015-0094
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195-204. https://doi.org/10.1002/(SICI)1097-0266(199902)20:2%3C195::AID-SMJ13%3E3.0.CO;2-7
Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829-846. https://doi.org/10.1080/00207543.2018.1488086
Junni, P., Sarala, R. M., Taras, V., & Tarba, S. Y. (2013). Organizational ambidexterity and performance: A meta-analysis. Academy of Management Perspectives, 27(4), 299-312. https://doi.org/10.5465/amp.2012.0015
Khan, A., Chen, C.-C., Lu, K.-H., Wibowo, A., Chen, S.-C., & Ruangkanjanases, A. (2021). Supply Chain Ambidexterity and Green SCM: Moderating role of network capabilities. Sustainability, 13(11), 5974. https://doi.org/10.3390/su13115974
Khan, A., Chen, C.-C., Suanpong, K., Ruangkanjanases, A., Kittikowit, S., & Chen, S.-C. (2021). The impact of CSR on sustainable innovation ambidexterity: The mediating role of sustainable supply chain management and second-order social capital. Sustainability, 13(21), 12160. https://doi.org/10.3390/su132112160
Khan, N. H., & Adnan, A. (2018). Urdu optical character recognition systems: Present contributions and future directions. IEEE Access, 6, 46019-46046. https://doi.org/10.1109/ACCESS.2018.2865188
Kofod-Petersen, A., & Cassens, J. (2005). Using activity theory to model context awareness. International Workshop on Modeling and Retrieval of Context, 1-17. https://doi.org/10.1007/11531227_7
Kortmann, S. (2015). The mediating role of strategic orientations on the relationship between ambidexterity‐oriented decisions and innovative ambidexterity. Journal of Product Innovation Management, 32(5), 666-684. https://doi.org/10.1111/jpim.12219
Kozjek, D., Rihtaršič, B., & Butala, P. (2018). Big data analytics for operations management in engineer-to-order manufacturing. Procedia CIRP, 72, 209-214. https://doi.org/10.1016/j.procir.2018.03.113
Kristal, M. M., Huang, X., & Roth, A. V. (2010). The effect of an ambidextrous supply chain strategy on combinative competitive capabilities and business performance. Journal of Operations Management, 28(5), 415-429. https://doi.org/10.1016/j.jom.2010.01.002
Kumar, S., & Putnam, V. (2008). Cradle to cradle: Reverse logistics strategies and opportunities across three industry sectors. International Journal of Production Economics, 115(2), 305-315. https://doi.org/10.1016/j.ijpe.2007.11.015
Kumar, V., Holt, D., Ghobadian, A., & Garza-Reyes, J. A. (2015). Developing green supply chain management taxonomy-based decision support system. International Journal of Production Research, 53(21), 6372-6389. https://doi.org/10.1080/00207543.2014.993773
Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation. The International Journal of Logistics Management, 29(2), 676-703. https://doi.org/10.1108/IJLM-06-2017-0153
Lee, I., & Mangalaraj, G. (2022). Big data analytics in supply chain management: A systematic literature review and research directions. Big Data and Cognitive Computing, 6(1), 17. https://doi.org/10.3390/bdcc6010017
Lee, S. M., & Rha, J. S. (2016). Ambidextrous supply chain as a dynamic capability: Building a resilient supply chain. Management Decision, 54(1), 2-23. https://doi.org/10.1108/MD-12-2016-0939
Levinthal, D. A., & March, J. G. (1993). The myopia of learning. Strategic Management Journal, 14(S2), 95-112. https://doi.org/10.1002/smj.4250141009
Lima, G. d. M. R., & Júnior, T. W. (2014). O impacto social da pesquisa em administração de empresas e da administração pública. RAE-Revista de Administração de Empresas, 54(4), 458-463. https://doi.org/10.1590/S0034-759020140407
Liu, J., Chen, M., & Liu, H. (2020). The role of big data analytics in enabling green supply chain management: A literature review. Journal of Data, Information and Management, 2, 75-83. https://doi.org/10.1007/s42488-020-00028-7
Lutfi, A., Alrawad, M., Alsyouf, A., Almaiah, M. A., Al-Khasawneh, A., Al-Khasawneh, A. L., Alshira'h, A. F., Alshirah, M. H., & Ibrahim, N. (2023). Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling. Journal of Retailing and Consumer Services, 70, 103129. https://doi.org/10.1016/j.jretconser.2022.103129
Lyu, T., Guo, Y., & Lin, H. (2022). Understanding green supply chain information integration on supply chain process ambidexterity: The mediator of dynamic ability and the moderator of leaders’ networking ability. Frontiers in Psychology, 13, 1088077. https://doi.org/10.3389/fpsyg.2022.1088077
March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71-87. https://doi.org/10.1287/orsc.2.1.71
Mugoni, E., Nyagadza, B., & Hove, P. K. (2023). Green reverse logistics technology impact on agricultural entrepreneurial marketing firms’ operational efficiency and sustainable competitive advantage. Sustainable Technology and Entrepreneurship, 2(2), 100034. https://doi.org/10.1016/j.stae.2023.100034
Munir, M. A., Hussain, A., Farooq, M., Habib, M. S., & Shahzad, M. F. (2023). Data-driven transformation: The role of ambidexterity and analytics capability in building dynamic and sustainable supply chains. Sustainability, 15(14), 10896. https://doi.org/10.3390/su151410896
Ojha, D., Acharya, C., & Cooper, D. (2018). Transformational leadership and supply chain ambidexterity: Mediating role of supply chain organizational learning and moderating role of uncertainty. International Journal of Production Economics, 197, 215-231. https://doi.org/10.1016/j.ijpe.2017.12.002
Oliveira, U. R. de, Espindola, L. S., da Silva, I. R., da Silva, I. N., & Rocha, H. M. (2018). A systematic literature review on green supply chain management: Research implications and future perspectives. Journal of Cleaner Production, 187, 537-561. https://doi.org/10.1016/j.jclepro.2018.03.083
Partanen, J., Kohtamäki, M., Patel, P. C., & Parida, V. (2020). Supply chain ambidexterity and manufacturing SME performance: The moderating roles of network capability and strategic information flow. International Journal of Production Economics, 221, 107470. https://doi.org/10.1016/j.ijpe.2019.08.017
Pertheban, T., & Arokiasamy, L. (2019). The relationship between supply chain resilience elements and organisational performance: The mediating role of supply chain ambidexterity. Global Business & Management Research, 11(1), 1-17. https://doi.org/10.1108/JBMR-11-2018-0184
Rao, P., & Holt, D. (2005). Do green supply chains lead to competitiveness and economic performance? International Journal of Operations & Production Management, 25(9), 898-916. https://doi.org/10.1108/01443570510613956
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. https://doi.org/10.1016/j.jclepro.2019.03.181
Ringle, C., Silva, D. Da, & Bido, D. (2015). Structural equation modeling with the SmartPLS. Brazilian Journal of Marketing, 14(3), 56-73. https://doi.org/10.1108/JBM-03-2015-0017
Sharma, M., & Singh, N. K. (2023). Framework for environmental and socially responsible economic growth. Journal of Law and Sustainable Development, 11(6), e1187-e1187. https://doi.org/10.1108/JLSD-05-2023-0021
Singh, S. K., & El-Kassar, A.-N. (2019). Role of big data analytics in developing sustainable capabilities. Journal of Cleaner Production, 213, 1264-1273. https://doi.org/10.1016/j.jclepro.2018.12.199
Song, C., Wu, L., Xie, Y., He, J., Chen, X., Wang, T., Lin, Y., Jin. T., Wang, A., Liu, Y., Dai, Q., Liu, B., Wang, Y., Mao, H. (2017). Air pollution in China: Status and spatiotemporal variations. Environmental Pollution, 227, 334-347. https://doi.org/10.1016/j.envpol.2017.04.075
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296. https://doi.org/10.1007/s11165-016-9602-2
Tortorella, G. L., Vergara, A. M. C., Garza-Reyes, J. A., & Sawhney, R. (2020). Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers. International Journal of Production Economics, 219, 284-294. https://doi.org/10.1016/j.ijpe.2019.06.023
Vieira, L. M., Barcellos, M. D. d., Araujo, G. P. d., Eriksson, M., Dora, M., & Matzembacher, D. E. (2021). Food waste: Challenges and opportunities in sustainable operations. RAE-Revista de Administração de Empresas, 61, e0000-0019. https://doi.org/10.1590/S0034-759020210002
Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2020). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journal of Production Economics, 222, 107498. https://doi.org/10.1016/j.ijpe.2019.09.019
Weerakkody, V., Irani, Z., Kapoor, K., Sivarajah, U., & Dwivedi, Y. K. (2017). Open data and its usability: An empirical view from the Citizen’s perspective. Information Systems Frontiers, 19(2), 285-300. https://doi.org/10.1007/s10796-016-9679-3
Wu, Z., & Pagell, M. (2011). Balancing priorities: Decision-making in sustainable supply chain management. Journal of Operations Management, 29(6), 577-590. https://doi.org/10.1016/j.jom.2010.10.001
Wu, Z., Shen, L., Ann, T., & Zhang, X. (2016). A comparative analysis of waste management requirements between five green building rating systems for new residential buildings. Journal of Cleaner Production, 112, 895-902. https://doi.org/10.1016/j.jclepro.2015.06.025
Younis, H. (2016). The impact of the dimensions of green supply chain management practices on corporate performance. Journal of Operations and Supply Chain Management, 9(1), 73-84. https://doi.org/10.12660/joscmv9n1p73-84
Zhang, X., Zhang, X., Yang, B., Hui, J., Liu, M., Chi, Z., Liu, S., Xu, J., & Wei, Y. (2014). A novel method for preparing AIE dye based cross-linked fluorescent polymeric nanoparticles for cell imaging. Polymer Chemistry, 5(3), 683-688. https://doi.org/10.1039/C3PY01060K
Zhu, Q., Sarkis, J., & Geng, Y. (2005). Green supply chain management in China: Pressures, practices and performance. International Journal of Operations & Production Management, 25(5), 449-468. https://doi.org/10.1108/01443570510593148