Aug 2026· Journal of business and management studies· Vol 8, pp. 75-80· 0 citations
TL;DR
The results show that BDAC enhances the visibility of supply chain, predictive decisions, and responsiveness of an organization and outlines the significance of digital transformation strategies, data infrastructure advancement, and analytics-based capabilities with regard to supply chain managers aiming to increase agility and resilience.
Abstract
This paper analyses how Big Data Analytics Capability (BDAC) can enhance supply chain resilience and agility in more challenging and uncertain business contexts. The study aims at the application of the data-driven decision-making to the operational responsiveness and to improve the supply chain performance. The research approach will be qualitative research design with thematic analysis of scholarly articles based on peer-reviewed journals, academic books, and conference papers on the topic of big data analytics and the supply chain management. The results show that BDAC enhances the visibility of supply chain, predictive decisions, and responsiveness of an organization. The analytics features allow companies to predict disruptions, minimize the response time, and enhance the supply chain operation flexibility. Practical Implications: The study outlines the significance of digital transformation strategies, data infrastructure advancement, and analytics-based capabilities with regard to supply chain managers aiming to increase agility and resilience. The research paper has value in the supply chain literature since it combines the lenses of dynamic capability and information processing theories. Future research can be done to examine industry-specific applications and empirical support on BDAC effects.
Big data analytics (BDA) can transform operations and supply chain management (OSCM), yet firms often struggle to convert analytics investments into consistent value. This review examines how BDA capabilities shape OSCM outcomes and identifies the conditions under which value is realized or constrained.
The study combines bibliometric mapping of 508 Scopus-indexed publications from 2015 to 2026 with qualitative synthesis of 145 studies. Keyword co-occurrence and co-citation analyses are triangulated with thematic coding to identify the field’s intellectual structure, dominant themes and blind spots.
Six knowledge clusters structure the field: analytics capability, supply chain visibility, Industry 4.0 transformation, resilience, sustainability and governance. BDA can enhance decision-making, operational performance, resilience and sustainability, but value depends on data-resource orchestration, governance maturity, analytical capability and organizational readiness. Persistent constraints include poor data quality, fragmented systems, cybersecurity exposure, weak absorptive capacity and resistance to change. The review develops a contingent capability framework explaining how BDA creates, limits or fails to create OSCM value.
Managers should strengthen data governance, analytical capabilities and decision-process alignment before scaling advanced analytics. The proposed framework supports assessment of BDA readiness, analytics maturity and governance risk.
By framing BDA value creation as a contingent capability pathway rather than a direct technology–performance relationship, this review explains why similar analytics investments produce uneven returns and advances a more critical theoretical foundation for future BDA–OSCM research.
Godfried B. Adaba, R. Addo-Tenkorang, F. Frimpong et al.· Benchmarking : An Internatio...· 0 citations
This study aims to examine how the implementation of big data analytics (BDA) influences operational performance and supply chain resilience in China's manufacturing sector. It aims to explain the mechanisms through which BDA contributes to both immediate efficiency gains and long-term adaptability, drawing on the Dynamic Capabilities Theory (DCT).
A mixed-method approach was adopted. Quantitative analysis was conducted using survey data from 304 manufacturing firms, analyzed through structural equation modelling. To complement the statistical findings, two in-depth case studies were carried out to provide contextual insights into how BDA initiatives are implemented and managed in practice.
The results show that BDA implementation significantly enhances operational performance and supply chain resilience. Dynamic capabilities partially mediate the relationship between BDA adoption and supply chain resilience, indicating that BDA not only drives short-term operational improvements but also fosters long-term strategic adaptability. Case study evidence further demonstrates that firms' motivations, process focus, and implementation strategies shape the performance outcomes of BDA adoption.
The study focuses on manufacturing firms in China, which may limit the generalisability of the findings to other sectors or regions. Future research could explore cross-country comparisons and longitudinal designs to capture the evolving role of BDA and dynamic capabilities in global supply chain resilience.
This study advances understanding of BDA by showing that its strategic value extends beyond process efficiency to the development of dynamic capabilities that enhance long-term agility and adaptability. It demonstrates that performance outcomes depend on how BDA is strategically aligned with organisational priorities, while strategic integration drives sustained competitive advantage. The research reframes BDA as a capability enabler rather than a technological tool, emphasizing the need for learning environments, predictive analysis, collaborative practices, and data-driven decision-making to unlock its transformative potential for supply chain resilience and competitiveness.
Ying Xie, Yahui Chen, Teng Teng et al.· International Journal of Log...· 0 citations
Digital transformation has increased organizational reliance on big data analytics (BDA) to support strategic decisions and improve business performance. This study synthesizes evidence on how BDA contributes to strategic decision-making, organizational performance, and innovation through a systematic literature review. The review followed the PRISMA 2020 framework and searched Scopus, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language journal and conference publications from 2021 to 2026. After identification, screening, and full-text eligibility assessment, 33 studies were included and examined using thematic analysis. The findings show that BDA strengthens decision quality and speed by combining analytics capability, predictive modeling, artificial intelligence, and data-driven insights. BDA is also associated with operational efficiency, project success, organizational agility, customer personalization, competitive advantage, sustainability, and innovation capability. The dominant themes were strategic decision-making, business performance, sustainability and innovation, and artificial intelligence with predictive analytics. However, the literature provides limited evidence on explainable and ethical artificial intelligence, human-AI collaboration, real-time analytics, and BDA adoption among small and medium-sized enterprises and organizations in developing economies. The review contributes an integrated view of BDA as a socio-technical and strategic capability and recommends transparent, scalable, and human-centered analytics governance.
Amelia Contesa, Ilzi Adrolis, Wenni Syafitri et al.· Business System & Innova...· 0 citations
Green supply chain management has emerged as a key strategy for manufacturing firms seeking to balance environmental responsibility and economic performance, particularly in emerging economies. This study examines how green supply chain management practices relate to perceived environmental and financial performance in Mexican manufacturing firms, considering the mediating roles of supply chain agility and resilience and the moderating role of big data analytics capabilities. Data were collected through a survey of 270 manufacturing firms in Mexico and analyzed using partial least squares structural equation modeling. Findings indicate that green supply chain management is positively associated with both performance dimensions. Supply chain agility and resilience partially mediate these relationships, while big data analytics capabilities strengthen the association with environmental performance but not with financial performance. The results clarify how sustainability initiatives operate under enabling organizational capabilities in an emerging economy context.
Jorge Alberto Esponda Pérez, Julia María Marroquín Figueroa, Francisco Javier Rocha Leyva et al.· Acta Universitaria· 0 citations
Purpose: The growing complexity of global supply chains, driven by digital transformation and increasing competition, requires a better understanding of the factors influencing Supply Chain Performance (SCP). Grounded in the Resource-Based View (RBV) and Dynamic Capabilities perspectives, this study examines the direct and indirect effects of Artificial Intelligence Big Data Capabilities (AIBDA), Supply Chain Agility (SCA), Supply Chain Collaboration (SCC), Supply Chain Ethical Leadership (SCEL), and Supply Chain Management Practices (SCMP) on SCP.
Design/Methodology/Approach. Using a cross-sectional design, data were collected from 380 supply chain professionals through a structured questionnaire and analyzed using PLS-SEM in SmartPLS 4. Supply Chain Integration (SCI) and Supply Chain Capabilities (SCCap) were examined as mediators, while AIBDA and SCA were tested as moderators.
Findings. Results indicate that AIBDA (β=0.125), SCA (β=0.154), SCEL (β=0.187), SCMP (β=0.194), and SCCap (β=0.243) significantly enhance SCP. The mediating roles of SCCap are supported, whereas SCI does not mediate the relationships. The moderating effects of AIBDA (β=0.023) and SCA (β=0.065) are insignificant. The model explains 64.7% of the variance in SCP, providing theoretical and practical implications for digitally enabled supply chains.
Research Limitations. The cross-sectional design limits causal inferences, and convenience sampling restricts generalizability. Future studies should employ longitudinal designs and probability sampling.
Practical Implications. The findings guide managers in prioritizing investments in AI capabilities, agility, ethical leadership, and management practices to enhance supply chain performance, while recognizing that integration alone may not directly translate to performance without capability development.
Originality/Value. This study offers an integrated framework examining AIBDA and SCA alongside traditional supply chain constructs, providing empirical evidence on their direct, mediating, and moderating effects on SCP.
References
Akhavan, P., & Philsoophian, M. (2023). Improving of supply chain collaboration and performance by using blockchain technology as a mediating role and resilience as a moderating variable. Journal of the Knowledge Economy, 14(4), 4561–4582. https://doi.org/10.1007/s13132-022-01085-9
Alabdullah, T. T. Y., & AL-Qallaf, A. J. M. (2023). The impact of ethical leadership on firm performance in Bahrain: Organizational culture as a mediator. CASHFLOW: Current Advanced Research on Sharia Finance and Economic Worldwide, 2(4), 482–498. https://doi.org/10.55047/cashflow.v2i4.736
Al-Doori, J. A. (2019). The impact of supply chain collaboration on performance in automotive industry: Empirical evidence. Journal of Industrial Engineering and Management, 12(2), 241–253. https://doi.org/10.3926/jiem.2835
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
Anderson, J., & Thomas, R. (2024). Supply chain integration: Enhancing collaboration for competitive advantage. EasyChair Preprints. https://easychair.org/publications/preprint_download/GtvR
Arawati, A. (2011). Supply chain management, supply chain flexibility and business performance. Journal of Global Strategic Management, 9(1), 134–145. https://www.acarindex.com/dosyalar/makale/acarindex-1423906231.pdf
Asamoah, D., Agyei-Owusu, B., Andoh-Baidoo, F. K., & Ayaburi, E. (2021). Inter-organizational systems use and supply chain performance: Mediating role of supply chain management capabilities. International Journal of Information Management, 58, 102195. https://doi.org/10.1016/j.ijinfomgt.2020.102195
Aslam, H., Blome, C., Roscoe, S., & Azhar, T. M. (2018). Dynamic supply chain capabilities: How market sensing, supply chain agility and adaptability affect supply chain ambidexterity. International Journal of Operations & Production Management, 38(12), 2266–2285. https://doi.org/10.1108/IJOPM-09-2017-0555
Baah, C., Acquah, I. S. K., & Ofori, D. (2022). Exploring the influence of supply chain collaboration on supply chain visibility, stakeholder trust, environmental and financial performances: A partial least square approach. Benchmarking: An International Journal, 29(1), 172–193. https://doi.org/10.1108/BIJ-10-2020-0519
Bag, S., & Rahman, M. S. (2023). The role of capabilities in shaping sustainable supply chain flexibility and enhancing circular economy-target performance. Supply Chain Management: An International Journal, 28(1), 162–178. https://doi.org/10.1108/SCM-05-2021-0246
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Bhatti, A. A., Bhatti, S. H., & Saif, S. (2022). A moderated-mediation analysis of supply chain efficiency, flexibility, integration, and risk management. Pakistan Business Review, 24(3). https://doi.org/10.22555/pbr.v24i3.770
Cahyono, Y., Purwoko, D., Koho, I., Setiani, A., Supendi, S., Setyoko, P., Sosiady, M., & Wijoyo, H. (2023). The role of supply chain management practices on competitive advantage and performance of halal agroindustry SMEs. Uncertain Supply Chain Management, 11(1), 153–160. http://dx.doi.org/10.5267/j.uscm.2022.10.012
Cao, M., & Zhang, Q. (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of Operations Management, 29(3), 163–180. https://doi.org/10.1016/j.jom.2010.12.008
Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: Moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360–387. https://doi.org/10.1108/09600030810882816
Cheng, Y., Farooq, S., & Jajja, M. S. S. (2021). Does plant role moderate relationship between internal manufacturing network integration, external supply chain integration, operational performance in manufacturing network? Journal of Manufacturing Technology Management, 32(6), 1267–1289. https://doi.org/10.1108/JMTM-06-2019-0237
Chopra, S., & Meindl, P. (2022). Supply chain management: Strategy, planning, and operation (7th ed.). Pearson Education.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Cui, L., Wu, H., Wu, L., Kumar, A., & Tan, K. H. (2023). Investigating the relationship between digital technologies, supply chain integration and firm resilience in the context of COVID-19. Annals of Operations Research, 327(2), 825–853. https://doi.org/10.1007/s10479-022-04735-y
Dash, A., Samantray, B. S., Reddy, K. H. K., Amin, F., & Mishra, S. K. (2026). A secure framework for smart waste management using IoT, machine learning, and blockchain. In 2026 1st International Conference on Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT) (pp. 1-7). IEEE. https://doi.org/10.1109/ETAACT69135.2026.11541497
Delic, M., & Eyers, D. R. (2020). The effect of additive manufacturing adoption on supply chain flexibility and performance: An empirical analysis from the automotive industry. International Journal of Production Economics, 228, 107689. https://doi.org/10.1016/j.ijpe.2020.107689
Deshpande, A. (2012). Supply chain management dimensions, supply chain performance and organizational performance: An integrated framework. International Journal of Business and Management, 7(8), 2–19. http://dx.doi.org/10.5539/ijbm.v7n8p2
Dubey, R., Bryde, D. J., Dwivedi, Y. K., Graham, G., Foropon, C., & Papadopoulos, T. (2023). Dynamic digital capabilities and supply chain resilience: The role of government effectiveness. International Journal of Production Economics, 258, 108790. https://doi.org/10.1016/j.ijpe.2023.108790
Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Wamba, S. F., Giannakis, M., & Foropon, C. (2022). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 251, 108535. https://doi.org/10.1016/j.ijpe.2022.108535
Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58–71. https://doi.org/10.1016/j.jom.2009.06.001
Fosso Wamba, S., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2020). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
Freeman, R. E. (1984). Strategic management: A stakeholder approach. Pitman.
Friday, D., Savage, D. A., Melnyk, S. A., Harrison, N., Ryan, S., & Wechtler, H. (2021). A collaborative approach to maintaining optimal inventory and mitigating stockout risks during a pandemic. Journal of Humanitarian Logistics and Supply Chain Management, 11(2), 248–271. https://doi.org/10.1108/JHLSCM-07-2020-0061
Ghaleb, M. M. S., & Alshiha, F. A. (2023). Effects of supply chain management practices on organizational supply chain performance: A mediated model. International Journal of Construction Supply Chain Management, 13(2), 1–22. https://doi.org/10.14424/ijcscm2023230101
Gligor, D., Feizabadi, J., Russo, I., Win, M. A., & Whiteside, M. (2019). An examination of logistics versus supply chain management foundations. International Journal of Logistics Management, 30(4), 1142–1167. https://doi.org/10.1108/IJLM-06-2018-0155
Gosling, J., Jia, F., Gong, Y., & Brown, S. (2016). The role of supply chain leadership in the learning of sustainable practice: Toward an integrated framework. Journal of Cleaner Production, 137, 1458–1469. https://d
Sadaqat Ullah, Hammad Zafar, Sarah Anjum· Journal of Business Insight...· 0 citations
The circular supply chain plays a critical role in minimizing operational costs and enhancing eco-efficiency by strategically aligning diverse organizational processes. To effectively generate these circular supply chains, it is vital to comprehend the dynamic capabilities shaped by big data analytics within a comprehensive framework. In this context, the capabilities driven by big data analytics are analyzed by using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, which assists in identifying intricate cause-and-effect relationships among the various factors affecting the supply chain. This method enables a nuanced understanding of how different elements interact and influence one another. Moreover, based on their level of influence, the Interpretive Structural Modeling (ISM) method is employed to organize these capabilities hierarchically. The resulting hierarchical model categorizes the factors into four distinct levels. The results reveal a four-level hierarchy in which the Sensing DC (BDDC3) and Seizing DC (BDDC13, BDDC11) at Level IV act as core capabilities for the entire system. The findings specifically demonstrate that prioritizing, sensing, and other capabilities are the primary drivers of operational cost optimization in successful resource reconfiguration and circular operations. Organizations can better navigate the complexities of circular supply chains by establishing this structured approach. It ultimately leads to improved sustainability outcomes and enhanced economic performance.
Metin Uyar· Istanbul Business Research· 0 citations
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