AI and sustainable digitalisation strengthen supply-chain resilience in Romanian logistics firms, but chiefly by creating competitive advantage; the resilience boost is largest when AI is embedded in green logistics practices and when firms have higher digital maturity.

The Impact of Artificial Intelligence and Sustainable Digitalisation on the Resilience of Logistics Chains in Romania

Elena Botezat, Alexandru Constangioara, Olimpia Ban, Diana Sabau-Popa, Diana Perticas, Veronika Fenyves · February 01, 2026 · Amfiteatru Economic

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A PLS-SEM analysis of Romanian logistics firms finds AI adoption and sustainable digitalisation are associated with stronger supply-chain resilience, but most of AI's positive effect operates indirectly by generating competitive advantage—especially when AI is integrated into green logistics and when organisational digital maturity is high.

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In the context of increasingly frequent disruptive phenomena, the adoption of artificial intelligence (AI) in supply chain logistics holds the promise of strengthening economic resilience.However, the existing literature has not sufficiently investigated the mechanisms through which this consolidation can be effectively achieved.Accordingly, the present study examines the complex relationships between factors associated with sustainable digital transformation and the economic resilience of supply chains, using SmartPLS software to analyse data collected from Romanian companies.The modelling is based on variance-based structural equation modelling (PLS-SEM), grounded in the Diffusion of Innovations theory, the Technology-Organisation-Environment (TOE) framework, and the Dynamic Capabilities Theory.The results highlight that the adoption of AI and sustainable digitalisation contribute to strengthening SCR, but its positive impact is mainly manifested through competitive advantage, identified as the main predictor.The originality of this study lies in the development and testing of an econometric model centred on AI adoption as a strategic instrument to gain competitive advantage, significantly amplified through the integration of AI techniques into green logistics practices and closely linked to organisational digital maturity.Thus, the study expands the understanding of the complex causal mechanisms that link the adoption of AI and sustainable digitalisation, based on green logistics practices, to the resilience of logistics chains.The results also provide strategic benchmarks

Summary

Main Finding

Adoption of AI and sustainable digitalisation strengthen supply-chain resilience (SCR) in Romanian firms, but most of AI’s positive effect operates indirectly: AI increases competitive advantage, and competitive advantage is the main predictor of SCR. Integration of AI into green logistics and higher organisational digital maturity amplify this pathway.

Key Points

Research question: How do AI adoption and sustainable digitalisation affect logistics/supply-chain resilience in Romania, and through which mechanisms?
Theoretical framing: Diffusion of Innovations (DOI), Technology–Organisation–Environment (TOE), and Dynamic Capabilities Theory.
Hypotheses tested:
- H1: Green logistics practices → SCR (positive).
- H2: AI adoption → Competitive advantage (positive).
- H3: Competitive advantage → SCR (positive).
- H4: AI adoption → SCR (direct and indirect via competitive advantage).
Core empirical result: AI adoption contributes to SCR primarily via strengthening competitive advantage; green logistics practices also have a direct positive effect on SCR. Digital maturity strengthens the strategic effect of AI.
Original contribution: an econometric PLS-SEM model that positions AI adoption as a strategic lever for competitive advantage, amplified by AI-enabled green logistics and organisational digital maturity, linking these to resilience outcomes in an emerging-European context.

Data & Methods

Sample: Convenience sample of 114 valid responses from mid-level managers in Romanian companies across sectors (data collected Mar–Aug 2025). Authors report a priori power analysis: N≥103 required for medium effect (f² = 0.15) at 90% power, so N=114 is sufficient.
Instrument: Online questionnaire with validated, adapted scales:
- AI adoption (4 items)
- Digital maturity (5 items)
- Competitive advantage (4 items)
- Green logistics practices (4 items)
- Supply-chain resilience (4 items)
Modeling: Variance-based structural equation modelling (PLS-SEM) using SmartPLS; bootstrapping with 5,000 iterations.
Measurement quality:
- Indicator loadings > 0.50
- Cronbach’s alpha: 0.825–0.975
- Composite reliability (ρc) > 0.85; AVE > 0.60
- VIF < 5 (no multicollinearity concerns)
- Discriminant validity assessed by Fornell–Larcker
Model fit: SRMR = 0.067, NFI = 0.943 (reported as adequate fit).
Limitations noted by authors: cross-sectional design, convenience sampling, self-reported measures, and context-specific focus (Romania).

Implications for AI Economics

For firm strategy and microeconomic analysis:
- AI investments yield resilience largely by improving competitive positioning (cost, differentiation, responsiveness). Evaluations of AI ROI should explicitly account for mediated benefits via competitive advantage, not only direct operational gains.
- Digital maturity is an important complement—policy or firm-level subsidies that raise digital capabilities can increase the marginal productivity of AI investments.
- Combining AI with green logistics produces compounded strategic value: environmental improvements can also be instruments of resilience and competition, so green investment externalities matter for firm-level returns.
For industry- and macro-level policy:
- Policymakers aiming to boost economic resilience should support both digital capabilities (training, infrastructure, data governance) and green-logistics transitions (incentives for low-emission fleets, renewable fuels, sustainable distribution).
- Regulation and data-governance frameworks that reduce implementation friction for AI (privacy, interoperability, standards) can accelerate resilience-enhancing adoption.
For theory and empirical work in AI economics:
- Supports a mediated view: AI → Competitive Advantage → Resilience. Models of technology diffusion and productivity should explicitly model intermediate strategic variables (e.g., competitive advantage, green practices).
- Encourages integrating environmental dimensions (green logistics) into models of technology-driven resilience and productivity.
Directions for future research:
- Use larger, stratified or representative samples and objective performance/resilience measures (operational/financial outcomes, disruption recovery times).
- Longitudinal or quasi-experimental designs to identify causal effects and dynamics (how AI adoption affects resilience over time, and whether effects persist).
- Sectoral analyses to capture heterogeneity (manufacturing vs. services vs. logistics providers).
- Economic evaluation of costs vs. resilience gains (cost–benefit, distributional impacts, spillovers).

If you want, I can (a) extract the questionnaire items (Annex 2) into a compact table, (b) sketch the PLS-SEM path coefficients and mediation magnitudes if you provide them, or (c) draft short policy recommendations tailored to Romanian policymakers or EU-level programs.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings come from a cross-sectional, self-reported survey analyzed with PLS-SEM, which establishes associations and tests theoretically specified mediation but cannot rule out reverse causality, omitted variable bias, or common-method bias; thus causal interpretation is weak despite coherent theory and model fit. Methods Rigormedium — The study uses established theoretical frameworks and an appropriate SEM tool (SmartPLS) for exploratory/complex latent-variable modeling, but reliance on single-source cross-sectional data, likely non-random sampling, and inherent PLS-SEM limitations (sensitivity to measurement/Specification, no causal identification checks such as instrumental variables, longitudinal tests, or robustness to endogeneity) reduce overall rigor. SampleFirm-level survey responses collected from Romanian companies operating in supply chain/logistics contexts (respondents likely managers/decision-makers); exact sample size, sampling frame, response rate, and sectoral breakdown are not provided in the prompt. Themesadoption org_design IdentificationTheory-driven cross-sectional survey analyzed with variance-based structural equation modelling (PLS-SEM) using SmartPLS; causal claims are supported by mediation paths (e.g., AI adoption -> competitive advantage -> supply-chain resilience) grounded in DOI/TOE/Dynamic Capabilities theory but there is no exogenous variation, experiment, or longitudinal design to credibly identify causation. GeneralizabilitySingle-country (Romania) context limits external validity to other institutional and market environments, Likely sector-limited to logistics/supply-chain firms, limiting applicability to other industries, Cross-sectional, self-reported data may over-represent digitally mature or survey-interested firms (selection bias), Cultural, regulatory and infrastructure differences mean findings may not generalize to large multinationals or emerging/advanced economies, Model results dependent on measurement items and survey design; different operationalizations could yield different patterns

Claims (8)

Claim	Direction	Outcome	Confidence & Evidence	Details
The adoption of AI and sustainable digitalisation contribute to strengthening supply chain resilience (SCR). Organizational Efficiency	positive	supply chain resilience (SCR)	Reading fidelity high Study strength medium	not reported 0.3
The positive impact of AI adoption on SCR is mainly manifested through competitive advantage, identified as the main predictor. Organizational Efficiency	positive	competitive advantage as predictor of supply chain resilience	Reading fidelity high Study strength medium	not reported 0.3
The integration of AI techniques into green logistics practices significantly amplifies the effect of AI adoption as a strategic instrument to gain competitive advantage. Organizational Efficiency	positive	amplification of AI's effect on competitive advantage (via integration with green logistics)	Reading fidelity high Study strength medium	not reported 0.3
Competitive advantage is the main predictor linking AI adoption and sustainable digitalisation to improved supply chain resilience. Organizational Efficiency	positive	role of competitive advantage in predicting SCR	Reading fidelity high Study strength medium	not reported 0.3
Organisational digital maturity is closely linked to the amplification of AI's strategic benefits when integrated into green logistics practices. Organizational Efficiency	positive	linkage between organisational digital maturity and AI-driven competitive advantage/amplification effects	Reading fidelity medium Study strength medium	not reported 0.18
This study developed and tested an econometric model centred on AI adoption as a strategic instrument to gain competitive advantage. Organizational Efficiency	positive	existence and testing of an econometric model focused on AI adoption	Reading fidelity high Study strength low	not reported 0.15
The study expands understanding of causal mechanisms linking AI adoption and sustainable digitalisation (via green logistics practices) to the resilience of logistics chains. Organizational Efficiency	positive	causal mechanisms linking AI/sustainable digitalisation to supply chain resilience	Reading fidelity high Study strength medium	not reported 0.3
The results provide strategic benchmarks (for decision-makers) regarding AI adoption, green logistics, and digital maturity to strengthen supply chain resilience. Organizational Efficiency	positive	availability of strategic benchmarks derived from study results	Reading fidelity high Study strength speculative	not reported 0.05