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AI big-data capabilities and supply-chain agility are linked to better supply-chain performance alongside ethical leadership and strong management practices; however, integration alone did not translate into performance and AI’s moderating effects were negligible (model explains ~64.7% of variance).

The Impact of AI Big Data Capabilities and Supply Chain Agility on Supply Chain Performance: The Mediating Roles of Supply Chain Integration and Capabilities with Moderated Effects
Sadaqat Ullah, Hammad Zafar, Sarah Anjum · September 02, 2026 · Journal of Business Insight and Innovation
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A cross-sectional survey of 380 supply-chain professionals finds that AI big-data capabilities, supply-chain agility, ethical leadership, management practices, and firm-level supply-chain capabilities are positively associated with supply-chain performance, while supply-chain integration did not mediate and the hypothesized moderating effects of AIBDA and SCA were insignificant.

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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. 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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). 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Summary

Main Finding

AI big-data capabilities (AIBDA), supply chain agility (SCA), supply chain ethical leadership (SCEL), supply chain management practices (SCMP), and developed supply chain capabilities (SCCap) each have positive, significant direct effects on supply chain performance (SCP). Supply chain capabilities (SCCap) mediate several upstream effects on SCP, whereas supply chain integration (SCI) does not act as a significant mediator. Proposed moderating roles for AIBDA and SCA on the SCI→SCP and SCCap→SCP links were not supported. The structural model explains 64.7% of variance in SCP.

Key Points

  • Sample and outcome: Survey of 380 supply-chain professionals; model R2 for SCP = 0.647.
  • Significant direct predictors of SCP (standardized betas reported):
    • SCCap: β = 0.243 (largest direct effect)
    • SCMP: β = 0.194
    • SCEL: β = 0.187
    • SCA: β = 0.154
    • AIBDA: β = 0.125
  • Mediation:
    • SCCap functions as a mediator linking antecedents (e.g., SCC, SCEL, SCMP) to SCP (mediation hypotheses supported).
    • SCI did not mediate relationships between antecedents and SCP (mediation hypotheses for SCI not supported).
  • Moderation:
    • AIBDA as moderator: β = 0.023 (insignificant)
    • SCA as moderator: β = 0.065 (insignificant)
    • Overall, hypothesized amplification effects of AI big-data capabilities and agility on the value of integration/capabilities were not observed.
  • Theoretical framing: Resource-Based View (RBV), Dynamic Capabilities, and Stakeholder/Ethical Leadership theories underpin hypotheses and interpretation.
  • Limitations acknowledged by authors: cross-sectional design (limits causal claims) and convenience/snowball sampling (limits generalizability).

Data & Methods

  • Design: Deductive, cross-sectional quantitative study.
  • Respondents: 380 supply chain professionals across manufacturing, logistics, retail, and services.
  • Sampling: Convenience sampling supplemented with snowball sampling (non-probability).
  • Instrument: Structured self-administered questionnaire with established scales for constructs (AIBDA, SCA, SCC, SCEL, SCMP, SCI, SCCap, SCP).
  • Analysis: Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4; direct, mediating, and moderating effects tested.
  • Reporting: Standardized path coefficients (β) and variance explained reported; specific p-values and confidence intervals not provided in the abstract excerpt.

Implications for AI Economics

  • Microeconomic firm strategy:
    • Investments in AI big-data capabilities yield positive returns to supply chain performance, but their largest payoff appears indirect—through capability-building—rather than by simply amplifying integration effects.
    • Organizational capabilities (SCCap) are the highest-return lever in this study; AI investments should be paired with active capability development and management practices to realize performance gains.
    • Ethical leadership and robust management practices materially contribute to performance and to capability development—non-technical governance and organizational factors remain economically important complements to AI.
  • Complementarities and re-assessment of "AI as multiplier" view:
    • The lack of supportive moderation effects suggests AI does not automatically magnify the payoff of all organizational assets (integration or capabilities) in every context. Economists should model AI returns as conditional on organizational deployment and absorptive capacity, not as uniform multipliers.
  • Policy and investment appraisal:
    • Public or firm-level subsidies for AI adoption should be coupled with support for building managerial capabilities, training, and governance to secure social returns on AI investments.
    • Cost–benefit analyses of AI in supply chains must include investments in organizational change, ethical governance, and capability formation.
  • Research agenda for AI economics:
    • Causal identification: use longitudinal, experimental, or quasi-experimental designs to estimate causal returns to AI and interactions with organizational capabilities.
    • Heterogeneity: quantify cross-industry, firm-size, and geographic heterogeneity in AI returns and complementarities with supply chain practices.
    • Measurement and valuation: develop standardized economic measures of AIBDA and SCCap (capitalized vs. flow-like), and model depreciation/obsolescence of AI assets.
    • Distributional and labor effects: assess how AI-enabled productivity gains in supply chains affect wages, employment composition, and bargaining across tiers of suppliers.
    • Macro linkages: study how AI-driven supply chain performance affects market structure, entry/exit dynamics, prices, and consumer welfare.
  • Practical takeaway for economists advising firms: prioritize integrated investments—AI tools + managerial processes + capability building + ethical leadership—over isolated technology purchases when seeking measurable supply chain performance improvements.

Assessment

Paper Typecorrelational Evidence Strengthlow — Cross-sectional self-report survey with convenience/snowball sampling and PLS-SEM; associations are reported but there is no credible causal identification (no randomization, no exogenous variation, no longitudinal ordering), and results may be affected by common-method bias and selection bias. Methods Rigormedium — Sample size (n=380) is reasonable and PLS-SEM is a common technique for testing complex latent-variable models, but the study relies on non-probability sampling, cross-sectional self-reports, limited information on measurement validation in the supplied text, and no design elements to address endogeneity or common-method variance; these limitations reduce rigor. SampleCross-sectional, self-administered structured questionnaire completed by 380 supply-chain professionals across manufacturing, logistics, retail and service sectors; respondents recruited via convenience sampling supplemented with snowball sampling; analysis conducted with PLS-SEM (SmartPLS 4). Themesproductivity adoption org_design GeneralizabilityConvenience and snowball sampling limit representativeness and external validity, Likely single-country / single-region sample (authors based in Pakistan) — not demonstrated to be internationally representative, Self-reported organizational-level constructs increase risk of common-method bias, Cross-sectional design prevents causal inference and temporal ordering, Heterogeneous industries in sample (manufacturing, logistics, retail, services) may mask sector-specific effects

Claims (9)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Artificial Intelligence Big Data Capabilities (AIBDA) significantly and positively enhance Supply Chain Performance (SCP). Organizational Efficiency positive Supply chain performance, including timely delivery, acceptable quality, and acceptable cost
Reading fidelity high
Study strength medium
n=380
β=0.125
0.3
Supply Chain Agility (SCA) significantly and positively enhances Supply Chain Performance (SCP). Organizational Efficiency positive Supply chain performance
Reading fidelity high
Study strength medium
n=380
β=0.154
0.3
Supply Chain Ethical Leadership (SCEL) significantly and positively enhances Supply Chain Performance (SCP). Organizational Efficiency positive Supply chain performance
Reading fidelity high
Study strength medium
n=380
β=0.187
0.3
Supply Chain Management Practices (SCMP) significantly and positively enhance Supply Chain Performance (SCP). Organizational Efficiency positive Supply chain performance
Reading fidelity high
Study strength medium
n=380
β=0.194
0.3
Supply Chain Capabilities (SCCap) significantly and positively enhance Supply Chain Performance (SCP). Organizational Efficiency positive Supply chain performance
Reading fidelity high
Study strength medium
n=380
β=0.243
0.3
Supply Chain Capabilities (SCCap) mediate the relationships between the antecedent supply-chain constructs and Supply Chain Performance, whereas Supply Chain Integration (SCI) does not mediate those relationships. Organizational Efficiency mixed Supply chain performance through mediation by supply chain capabilities or supply chain integration
Reading fidelity high
Study strength medium
n=380
0.3
AIBDA does not significantly moderate the relationships between supply-chain integration or capabilities and Supply Chain Performance. Organizational Efficiency null_result Supply chain performance
Reading fidelity high
Study strength medium
n=380
β=0.023, insignificant
0.3
Supply Chain Agility does not significantly moderate the relationships between supply-chain integration or capabilities and Supply Chain Performance. Organizational Efficiency null_result Supply chain performance
Reading fidelity high
Study strength medium
n=380
β=0.065, insignificant
0.3
The study's structural model explains 64.7% of the variance in Supply Chain Performance. Organizational Efficiency positive Explained variance in supply chain performance
Reading fidelity high
Study strength medium
n=380
64.7% of the variance
0.3

Notes