Jul 2026· Journal of Enterprise Information Management· pp. 1-25· 0 citations· 67 references
Abstract
This study aims to examine the role of artificial intelligence (AI)-enabled integrating capabilities (AIEIC) and environmental dynamism (ED) in enhancing supply chain agility (SCa) and customer integration (CI) for superior supply chain performance (SCp). The study further addresses the mediating roles of SCa and CI, adopting a human-centric perspective in which these mechanisms represent adaptive and relational responsiveness to improve SCp.
The primary data were collected from 373 valid respondents and analysed the linear relationship primarily through “structural equation modelling (SEM)”; and “fuzzy set qualitative comparative analysis (fsQCA)” was used for non-linear configurational insights into high performance outcomes. This study draws on the dynamic capability view and contingency theory to provide theoretical support for the analyses and evidence from India.
The study reveals that AIEIC and ED have a positive impact on SCp. SCa and CI were treated as human-centric responses, where SCa captures human-centric capabilities, and CI captures human-centric coordination to improve SCp. Additionally, the necessary conditions for fsQCA and the combined effect of all variables on solution consistency were examined, indicating that a high-performance outcome might be achieved in a dynamic environment even in the absence of AIEIC, highlighting the strong influence of human-centric responses.
Given the current results, managers should prioritise human-centric mechanisms, such as job security for employees and customer responsiveness. Note that, in resource-constrained organisations where managers cannot afford AI investments, they should focus on human-centric solutions.
The contribution of this study shifted from a technology-oriented solution to a human-centric approach, indicating that AIEIC performs better when integrated with SCa and CI in dynamic environments to improve SCp. By using both SEM and fsQCA, this study assesses the robustness of the interconnections among the study variables through multiple pathways.
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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Unknown authors· Journal of Business Insight...· 0 citations
Despite growing investments in artificial intelligence (AI), limited understanding exists regarding the organizational capabilities required to integrate AI into sustainable supply chain quality management. This study develops and validates a novel construct, AI-enabled Sustainable Supply Chain Quality Capability (AISSQC), and examines its role in enhancing supply chain resilience (SCR) and sustainable performance (SP). A sequential mixed-method research design was employed. The construct was developed through an integrative literature review, semi-structured interviews with industry experts, a modified Delphi study, and cognitive pretesting. Quantitative validation was subsequently conducted using two independent samples from Canadian manufacturing firms. Exploratory factor analysis established the dimensional structure of the scale, while Partial Least Squares Structural Equation Modeling (PLS-SEM) using a separate sample validated the reflective–formative higher-order construct and tested the proposed structural relationships. The results confirm that AISSQC is a multidimensional organizational capability comprising five complementary dimensions: AI-enabled Quality Intelligence, Sustainable Quality Integration, Collaborative Quality Orchestration, Adaptive Quality Improvement, and Responsible Quality Governance. AI Capability is positively associated with AISSQC, which in turn is associated with higher SCR and SP. SCR further mediates the relationship between AISSQC and SP, demonstrating that AI creates organizational value primarily through the development of complementary organizational capabilities rather than through technology adoption alone. This study introduces and empirically validates AISSQC as a novel higher-order organizational capability that integrates AI, quality management, sustainability, and supply chain management within a unified capability framework. By extending the Resource-Based View and Dynamic Capabilities Theory, the study explains how organizations transform AI resources into resilient and sustainable operational capabilities. The validated measurement scale provides researchers with a robust instrument for future empirical studies while offering managers a practical framework for guiding AI-enabled quality transformation in sustainable supply chains.
Keywords: Artificial Intelligence, AI-Enabled Sustainable Supply Chain Quality Capability, Scale Development, Dynamic Capabilities, Supply Chain Resilience, Sustainable Performance.
S. Yaghoubi, Shiva Yaghoubi· Radiant Journal of Business...· 0 citations
Aim: This study aimed to develop a framework for AI-enabled Resilient Sales Order Management (AI-RSOM), examine the potential of AI-enabled ESOM to enhance supply chain resilience, explain the mechanisms through which AI-enabled ESOM may influence resilience, and identify the conditions under which higher levels of automation may increase operational fragility.
Methods: The study employed a critical integrative review methodology and conceptual theory synthesis. A total of 41 verified scholarly sources, including journal articles, books, and relevant documents published between January 2020 and June 2026, together with selected seminal earlier works, were reviewed. The sources were classified according to lifecycle phase, AI function, decision rights, resilience mechanisms, outcomes, boundary conditions, and implementation risks.
Results: The synthesis identified five AI-RSOM capability stages namely integrate and sense, interpret, orchestrate, execute and learn, and govern. The findings suggest that these capabilities may influence supply chain resilience through improved order visibility, decision velocity, response flexibility, and organizational learning. The framework further outlines six theoretical propositions for future empirical testing, a process-level measurement architecture, and an evidence-gated implementation pathway. The findings are conceptual and theoretical therefore should not be interpreted as empirical estimates of AI effects on supply chain resilience.
Conclusion: The study concludes that AI-enabled ESOM has the potential to strengthen supply chain resilience when AI capabilities are integrated across the sales-order lifecycle and supported by appropriate decision rights, governance mechanisms, and organizational learning processes.
Recommendation: Evidence-gated implementation should be used to progressively expand automation only where performance and governance conditions are adequately demonstrated.
Unknown authors· American Journal of Technolo...· 0 citations
The traditional supply chain model can adapt to changes in environmental regulations in real time through the adoption of artificial intelligence. However, empirical evidence on how GAI‐enabled gradual, multistage capabilities can be built within the framework of green supply chain management (GSCM) by extending the theoretical concept of dynamic capability theory (DCT) remains limited in the existing literature. Three hundred thirty‐eight survey data were collected using a structured questionnaire and a 5‐point Likert scale to quantify responses, which were analysed using SEM. The results have revealed direct correlations among the main study constructs, which show different layers of capabilities in a green supply chain, that is, flexible information system (FIS), responsiveness, risk mitigation competency (RMC) and the final output, GSCM performance. The study findings confirmed a significant enhancement of GSCM performance by FIS through direct and indirect serial mediation effects of responsiveness and RMC, which are consistent with the fundamental concepts of DCT. Additionally, the study has evaluated the moderating effect of GAI convenience (GAIC) and risk avoidance (RA) to establish the pivotal role of GAI in GSCM. The findings of the moderation analysis depict that higher values of GAI convenience and risk avoidance amplify the positive correlation between supply chain capabilities across multiple stages and GSCM performance. The study has significant managerial and theoretical implications that contribute to the existing literature and provide guidelines for managers to achieve high levels of GSCM performance. Additionally, the present study provides in‐depth insights into how GAI can capture environmental and market changes in real time and make quick decisions to adjust the system as early as possible.
Dipanwita Chakrabarty, S. Mangla, Arunangshu Giri et al.· Business Strategy and the En...· 0 citations
Artificial intelligence (AI) is increasingly embedded in supply chain decision-making, yet its contribution to resilience depends on organizations’ ability to combine AI-generated intelligence with human expertise and adaptive organizational capabilities. This study examines how AI-enabled decision intelligence (AIDI) enhances supply chain resilience through human–AI collaboration and dynamic capabilities. Drawing on dynamic capabilities theory, a research model is developed in which AIDI strengthens human–AI collaboration and dynamic capabilities, while dynamic capabilities enable organizations to anticipate, respond to, and recover from supply chain disruptions. The model further proposes that human–AI collaboration and dynamic capabilities sequentially mediate the relationship between AIDI and supply chain resilience. Using survey data from 294 managers and professionals involved in AI-supported supply chain decision-making in Iranian organizations, the proposed relationships were tested using partial least squares structural equation modeling (PLS-SEM). The results support all hypothesized relationships, showing that AIDI positively influences human–AI collaboration, dynamic capabilities, and supply chain resilience. Dynamic capabilities significantly enhance supply chain resilience and mediate the AIDI–resilience relationship, while human–AI collaboration and dynamic capabilities sequentially mediate this relationship. These findings provide a capability-based explanation of how technological intelligence, human expertise, and organizational adaptability jointly contribute to resilient supply chain outcomes.
Keywords: AI-Enabled Decision Intelligence, Human–AI Collaboration, Dynamic Capabilities, Supply Chain Resilience, Artificial Intelligence, Supply Chain Management.
Farshad Naderpour, Ehsan Abdollahian· Radiant Journal of Business...· 0 citations
This study aims to examine the relationships between human–machine collaboration (HMC), operational efficiency (OE), resource efficiency (RE), workforce capability (WC), and sustainable logistics and supply chain performance (SLSCP) within the context of Industry 5.0. The main objective is to explain the mechanisms through which these factors jointly contribute to sustainability-oriented logistics and supply chain performance.
A structural research model was developed and tested using the partial least squares structural equation modelling method. The empirical analysis is based on survey data collected from 118 participants, including both academics and industry professionals operating in Italy, providing both theoretical and practice-oriented perspectives.
The results indicate that HMC primarily influences sustainable performance through OE, which acts as a central mediating mechanism linking collaboration practices to both RE and WC. RE emerges as the strongest determinant of SLSCP, particularly through energy savings, waste reduction and circular economy practices. In contrast, WC does not exhibit a significant direct effect on SLSCP, suggesting that its contribution is realised through its integration with operational and technological capabilities.
The findings are based on a limited sample size and focus on a single national context. Future studies may expand the model by incorporating cross-country comparisons or longitudinal data to further validate the proposed relationships.
The findings suggest that firms should align HMC, process improvement and resource-efficient practices to strengthen SLSCP.
This study contributes to the emerging Industry 5.0 literature by proposing and empirically testing an integrated structural model linking HMC, OE, RE, WC and SLSCP. Unlike previous studies that have primarily examined these dimensions in isolation, the proposed model demonstrates how HMC contributes to sustainable performance through interconnected operational and RE mechanisms, providing a more integrated empirical perspective.
Unknown authors· Logistics Research· 0 citations
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