Generative-AI–enabled information systems raise firms' green supply-chain performance both directly and via a chain of improved responsiveness and risk-mitigation capabilities; gains are amplified when AI tools are user-friendly and organizations prioritize risk avoidance.

Generative AI as a Driver of Green Supply Chain Performance: Role of Flexibility, Responsiveness and Risk Mitigation

Dipanwita Chakrabarty, Sachin Kumar Mangla, Arunangshu Giri, Meghna Goel, Keng‐Boon Ooi · August 31, 2026 · Business Strategy and the Environment

openalex correlational low evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

Structured author observations

Linked only from stored provider relations; the raw author line above is never matched by name.

OpenAlex

Latest observation: September 3, 2026

Dipanwita Chakrabarty provider ID
Sachin Kumar Mangla provider ID
Arunangshu Giri provider ID
Meghna Goel provider ID
Keng‐Boon Ooi provider ID

Semantic Scholar

Latest observation: September 3, 2026

Dipanwita Chakrabarty unresolved corpus identity
Sachin Kumar Mangla unresolved corpus identity
Arunangshu Giri unresolved corpus identity
Meghna Goel unresolved corpus identity
Keng-Boon Ooi unresolved corpus identity

Survey-based SEM evidence indicates that generative-AI–enabled flexible information systems improve green supply-chain management directly and indirectly through increased responsiveness and risk-mitigation competency, with stronger effects when AI is perceived as convenient and stakeholders emphasize risk avoidance.

Citation observations

Cumulative provider counts captured on specific dates; providers are never combined.

0 cumulative citations

OpenAlex · Observed September 3, 2026

View corpus context

0 cumulative citations

Semantic Scholar · Observed September 3, 2026

View corpus context

ABSTRACT 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.

Summary

Main Finding

Flexible information systems (FIS) enabled by generative AI (GAI) significantly improve green supply chain management (GSCM) performance. This effect occurs both directly and indirectly through a serial chain of capabilities—FIS → responsiveness → risk mitigation competency (RMC) → GSCM performance—consistent with Dynamic Capability Theory (DCT). The positive links between these multistage capabilities and GSCM performance are strengthened when GAI is perceived as convenient (GAI convenience, GAIC) and when stakeholders emphasize risk avoidance (RA).

Key Points

Theoretical framing: extends Dynamic Capability Theory to show how gradual, multistage capabilities enabled by GAI operate inside green supply chains.
Constructs studied: Flexible Information System (FIS), Responsiveness, Risk Mitigation Competency (RMC), GSCM Performance; moderators: GAI Convenience (GAIC) and Risk Avoidance (RA).
Main empirical result: FIS → responsiveness → RMC → GSCM performance forms a significant serial mediation chain; FIS also has a direct positive effect on GSCM performance.
Moderation: higher perceived GAI convenience and stronger risk-avoidance orientations amplify the positive relationships among supply chain capabilities and final GSCM outcomes.
Managerial implication highlighted: GAI can capture environmental and market changes in real time and support quicker decision-making to adapt supply chains to regulatory and market shifts.

Data & Methods

Sample: n = 338 survey responses collected with a structured questionnaire.
Measurement: 5-point Likert scale for constructs (self-reported).
Analysis: Structural Equation Modeling (SEM) to test direct, indirect (serial mediation), and moderation effects.
Limitations implicit in method: cross-sectional survey data (limits causal claims), self-reported measures, sample/generalizability not described in detail in abstract.

Implications for AI Economics

Investment value: Results support economic cases for investing in AI-enabled information infrastructures (FIS) as they generate direct productivity/welfare gains in GSCM and indirect gains via enhanced responsiveness and risk mitigation.
Complementarities and capability building: Gains from GAI are not standalone; they depend on building sequential capabilities (responsiveness, RMC). Policymakers and firms should treat AI as part of a complementary bundle of organizational investments (processes, training, governance).
Adoption heterogeneity: Economic benefits of GAI depend on perceived convenience and risk perceptions. Reducing friction (better UX, integration) and addressing perceived/actual risks (controls, transparency) can magnify returns on AI investments.
Regulatory and environmental externalities: Faster, AI-enabled real-time adjustments reduce compliance costs and environmental harms, potentially lowering the social cost of regulation and increasing firms’ competitiveness in green markets.
Labor and skill implications: Achieving these dynamic capabilities requires workforce upskilling and new organizational roles (data governance, model validation), with distributional effects on labor demand and wages in supply-chain-related occupations.
Research and policy priorities: Need for longitudinal and objective-outcome studies (e.g., emissions, compliance costs, financial performance), sectoral analyses, and cost–benefit evaluations to quantify macroeconomic impacts of scaling GAI in green supply chains.

Practical recommendations (managerial/policy): - Prioritize investment in interoperable, user-friendly AI-enabled information systems to maximize GAIC. - Build processes that translate AI signals into rapid operational responsiveness and robust RMC. - Implement governance and risk-control measures to reduce perceived RA and unlock stronger performance gains. - Support workforce training and change management to capture complementarities between AI tools and human decision-making.

Limitations and next steps: validate findings with longitudinal, objective performance data; test across industries/countries; quantify welfare/financial returns from GAI-enabled GSCM adoption.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings come from a single cross-sectional, self-reported survey (n=338) analyzed with SEM; serial mediation and moderation are statistical associations compatible with the theory but do not establish causality due to lack of temporal ordering, experimental or quasi-experimental variation, objective outcome measures, and potential common-method bias. Methods Rigormedium — The paper uses established techniques (SEM) and a modest sample (n=338), and tests mediation and moderation consistent with the theoretical framework, but relies entirely on self-reported Likert measures in cross-section with limited information on sample frame, control variables, measurement validation, or checks for common-method bias and endogeneity. Samplen = 338 survey responses collected via a structured questionnaire; constructs measured via 5-point Likert scales and self-report; sample frame, respondent roles, industries, countries, and sampling/recruitment procedures not specified in provided text. Themesproductivity org_design IdentificationCross-sectional survey analyzed with structural equation modeling (SEM) testing hypothesized direct, serial-mediation (FIS → responsiveness → RMC → GSCM) and moderation effects; identification rests on theoretical ordering and covariance structure rather than exogenous variation, temporal ordering, or experimental manipulation. GeneralizabilityCross-sectional, self-reported data limits causal generalizability to real-world outcomes (e.g., emissions, costs, financial performance)., Unknown sampling frame (industries, geographies, firm sizes, respondent roles) restricts external validity to broader populations., Common-method bias and social desirability could inflate relationships between constructs measured in the same survey., Findings pertain to perceptions and self-reported capabilities rather than objective operational or economic metrics.

Claims (5)

Claim	Direction	Outcome	Confidence & Evidence	Details
Flexible information systems enabled by generative AI significantly improve green supply chain management performance. Organizational Efficiency	positive	Green supply chain management performance	Reading fidelity high Study strength medium	n=338 0.3
The relationship between flexible information systems and green supply chain management performance is partially transmitted through a significant serial mediation chain consisting of responsiveness followed by risk mitigation competency. Organizational Efficiency	positive	Green supply chain management performance through responsiveness and risk mitigation competency	Reading fidelity high Study strength medium	n=338 0.3
Flexible information systems also have a direct positive effect on green supply chain management performance, beyond their indirect effect through responsiveness and risk mitigation competency. Organizational Efficiency	positive	Green supply chain management performance	Reading fidelity high Study strength medium	n=338 0.3
Higher perceived generative-AI convenience strengthens the positive relationships among supply-chain capabilities and green supply chain management performance. Organizational Efficiency	positive	Green supply chain management performance and capability relationships	Reading fidelity high Study strength medium	n=338 0.3
Stronger risk-avoidance orientations strengthen the positive relationships among supply-chain capabilities and green supply chain management performance. Organizational Efficiency	positive	Green supply chain management performance and capability relationships	Reading fidelity high Study strength medium	n=338 0.3