Firms seeking agile supply chains must not only upgrade data-management processes and technology platforms but also cultivate executives’ understanding of AI model logic, bias detection, and results application—enabling rapid sensing, swift decision-making, and timely execution in volatile environments.
Abstract
This study investigates how big data analytics capabilities and managerial AI literacy jointly shape supply‐chain decision agility in multinational firms. Using 511 valid questionnaires from senior executives of high-tech electronics multinationals headquartered in Taiwan, we find that both managerial and technical aspects of big data analytics significantly boost decision agility. All three dimensions of AI literacy likewise show positive effects, with descriptive and diagnostic analytics exerting the most pronounced influence. Interaction tests further demonstrate that higher AI literacy magnifies the positive impact of analytics capabilities on agility. The findings suggest that firms seeking agile supply chains must not only upgrade data-management processes and technology platforms but also cultivate executives’ understanding of AI model logic, bias detection, and results application—enabling rapid sensing, swift decision-making, and timely execution in volatile environments. The study extends the integrated lens of dynamic capabilities and technology acceptance theory and provides a practical organisational-cognitive framework for multinational digital transformation.
This study investigates how big data analytics capability appears to support competitive strategy development in the Palestinian dairy industry, focusing on the role of data utilization in developing organizational capabilities, creating value and positioning the firm strategically.
This study adopts an exploratory multiple-case study approach supported by descriptive survey evidence and Visualization-Based Pattern Analysis. Data were collected through unstructured interviews with managers and employees from leading Palestinian dairy companies, alongside descriptive questionnaire-based evidence. The qualitative findings were analyzed using thematic and VRIO-based analysis to identify patterns related to big data practices, capability development, organizational maturity and competitive strategy dimensions.
The findings indicate that participating firms recognize the value of data for customer understanding, product development and operational decision-making. However, analytics practices remain fragmented and rely largely on conventional or manual processes. Limited system integration, analytical expertise, data governance and real-time processing restrict the incorporation of analytics into organizational routines and strategic decision-making. These conditions reflect a gap between awareness of data's potential value and the development of integrated analytics capabilities.
Managers should prioritize the gradual integration of customer, operational and market data, supported by appropriate infrastructure, analytical skills and governance procedures. These measures may improve the consistency of data-informed decision-making, operational coordination, customer responsiveness and market positioning.
This study provides context-specific empirical evidence on data utilization and analytics capability development within the Palestinian dairy industry, an underexplored resource-constrained setting. Its primary contribution is contextual rather than the development of a new theoretical construct. The study uses the term data maturity gap as an interpretive lens to organize evidence on the disconnect between firms' recognition of data value and their ability to integrate analytical, technological and organizational capabilities. The findings illustrate how mechanisms already identified by the Resource-Based View, Dynamic Capabilities Theory, absorptive capacity and analytics maturity research operate within this particular industrial context.
Ibrahim M. Awad, A. Awad, M. El-Jafari et al.· Journal of Enterprise Inform...· 0 citations
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.
Md Luman Jamali, R. I. Rezvi, Mir Protik et al.· Journal of business and mana...· 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
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
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.
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Sadaqat Ullah, Hammad Zafar, Sarah Anjum· Journal of Business Insight...· 0 citations
Inefficiencies and low productivity persist in the construction industry due to limited digital integration and weak data use in decision-making. This study examines how internal data analytics, such as the systematic use of organisational data-like cost reports, safety logs, and project schedules, can enhance decision-making and organisational capability in New Zealand’s small- and medium-sized construction enterprises (SMEs). A comprehensive systematic literature review following PRISMA guidelines analysed 76 peer-reviewed empirical and theoretical studies (2015–2025). A thematic synthesis was conducted using NVivo 12 Plus and VOSviewer to identify patterns grounded in Evidence-Based Management, the Knowledge-Based View, and Bounded Rationality theories. The research highlights that analytics tools, including Building Information Modelling, Decision Support Systems, and Internet of Things platforms, enable real-time visibility, predictive forecasting, and coordination, thereby transforming operational data into strategic intelligence. However, adoption barriers persist, with technical interoperability issues, organisational resistance, low data literacy, and weak governance structures, significantly impacting resource-constrained SMEs. The study proposes a strategic framework that addresses four critical domains: robust data governance, leadership commitment and training, alignment with maturity models, and integration of emerging technologies. These domains demonstrate potential for standardisation and capacity building within SMEs, which also have implications for SMEs in New Zealand. Overall, the research provides a socio-technical framework which positions analytics as a transformative enabler of organisational learning, governance transparency, and sustainable performance and could support the development of an evidence-based construction sector.
James O. B. Rotimi, Upuli Rasanjani Kaluarachchi Kaluarachchillage· Buildings· 0 citations
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