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Logistics firms in China that build AI, big-data and information‑collaboration capabilities report stronger innovation, and that innovation in turn boosts strategic adaptability and organizational resilience; the study implies returns to AI accrue through capability-building rather than isolated tool adoption.

Enhancing logistics organizational resilience via digital competencies: A dynamic capability perspective
Xi Pei, Xin Li, Senyu Xu, Lingchao Deng, Jie Lou · September 01, 2026 · Asia Pacific Management Review
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:

  1. Xi Pei provider ID
  2. Xin Li provider ID
  3. Senyu Xu provider ID
  4. Lingchao Deng provider ID
  5. Jie Lou provider ID

Semantic Scholar

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  1. Xi Pei unresolved corpus identity
  2. Xin Li unresolved corpus identity
  3. Sen-Yu Xu unresolved corpus identity
  4. Ling-Chao Deng unresolved corpus identity
  5. Jie Lou unresolved corpus identity
Survey evidence from 450 Chinese logistics professionals finds that AI, big data, and information-collaboration capabilities are positively associated with firms' innovation capability, which mediates their relationships with strategic adaptability and organizational resilience.

Citation observations

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

This study examines the impact of digital competencies—artificial intelligence, big data, and information collaboration—on innovation capability, subsequently improving strategic adaptability and organizational resilience. It proposes and empirically tests a theoretical model using survey data from 450 logistics professionals in China's logistics sector. Structural equation modeling demonstrates that all three digital competencies significantly contribute to innovation capability, serving as a critical link between digital resources and both strategic and operational outcomes. Additionally, innovation capability directly impacts strategic adaptability and resilience, with strategic adaptability also serving as a strong predictor of resilience. The findings highlight the importance of integrating digital resources as cohesive capabilities rather than treating them as separate tools, enabling innovation and maintaining competitive advantage in volatile environments. By conceptualizing innovation capability as a functional dynamic capability, this study offers an integrative framework that explains how logistics organizations can systematically transform digital resources into adaptive and resilient capabilities, thereby strengthening competitiveness in volatile and disruption-prone environments.

Summary

Main Finding

Digital competencies—artificial intelligence, big data, and information collaboration—each significantly enhance firms’ innovation capability in China’s logistics sector. Innovation capability functions as a mediating dynamic capability that channels digital resources into greater strategic adaptability and organizational resilience; strategic adaptability further strengthens resilience.

Key Points

  • Three digital competencies examined: artificial intelligence (AI), big data, and information collaboration.
  • All three competencies significantly and positively influence innovation capability.
  • Innovation capability mediates the relationship between digital competencies and (a) strategic adaptability and (b) organizational resilience.
  • Innovation capability also directly improves both strategic adaptability and resilience.
  • Strategic adaptability is a strong predictor of organizational resilience.
  • Conceptual contribution: treating digital resources cohesively as capabilities (a functional dynamic capability) rather than separate tools explains how organizations systematically transform digital resources into adaptive and resilient outcomes.

Data & Methods

  • Sample: survey data from 450 logistics professionals in China’s logistics sector.
  • Method: structural equation modeling (SEM) to test hypothesized relationships and mediation paths.
  • Key constructs measured: AI capability, big data capability, information collaboration capability, innovation capability, strategic adaptability, organizational resilience.
  • Main empirical findings: statistically significant direct effects from digital competencies to innovation capability; mediation of innovation capability between digital competencies and adaptability/resilience; direct paths from innovation capability to adaptability and resilience; positive path from adaptability to resilience.

Implications for AI Economics

  • Valuation and ROI: Economic models should account for complementarities between AI, big data, and information collaboration—returns accrue via strengthened innovation capability rather than from isolated tool deployment.
  • Investment strategy: Firms (and policymakers) should prioritize bundled investments in digital competencies and capabilities-building (skills, processes, governance) that convert tech resources into dynamic capabilities.
  • Labor and skill implications: Demand for roles that combine digital fluency with innovation management and cross-functional coordination will rise; human capital policies should target these skill mixes.
  • Competition and resilience: AI-driven competitiveness is mediated by organizational innovation and strategic flexibility—market analyses should account for firms’ dynamic capability endowments when forecasting disruption resilience.
  • Measurement for macro/sectoral models: Incorporate metrics for capability formation (e.g., process integration, innovation output, cross-functional information flows) rather than binary tech adoption indicators to better predict productivity and robustness under shocks.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on cross-sectional, self-reported survey data and SEM, which identify associations and mediation under strong modeling assumptions but cannot establish causal effects; common-method bias, potential omitted variables, and unclear sample representativeness further weaken causal interpretation. Methods Rigormedium — Use of SEM and a sample of 450 is standard for testing latent-variable relationships and mediation, but the design lacks exogenous variation or temporal ordering, relies on self-reported constructs (raising common-method and measurement concerns), and the description lacks detail on sampling, control variables, robustness checks, or measurement validation. SampleCross-sectional survey of 450 professionals in China's logistics sector reporting on firm-level capabilities (AI capability, big data capability, information collaboration), innovation capability, strategic adaptability, and organizational resilience; details on sampling frame, response rate, firm size distribution, or geographic coverage are not provided. Themesinnovation org_design human_ai_collab IdentificationCross-sectional survey of 450 logistics professionals in China analyzed with structural equation modeling (SEM) to estimate associations and mediation paths; no experimental or quasi-experimental source of exogenous variation, instruments, or longitudinal identification—identification rests on SEM model specification and assumptions (e.g., no unmeasured confounding, correct measurement and directionality). GeneralizabilityLimited to China's logistics sector and professional respondents—may not generalize to other industries or countries, Self-reported, perceptual measures of capabilities and outcomes may not reflect objective firm performance, Cross-sectional design prevents causal generalization to longitudinal effects or responses to shocks, Unknown representativeness across firm sizes, ownership types, and technology adoption levels

Claims (8)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Artificial intelligence capability significantly and positively enhances innovation capability in China's logistics sector. Innovation Output positive Firm innovation capability
Reading fidelity high
Study strength medium
n=450
0.3
Big data capability significantly and positively enhances innovation capability in China's logistics sector. Innovation Output positive Firm innovation capability
Reading fidelity high
Study strength medium
n=450
0.3
Information collaboration capability significantly and positively enhances innovation capability in China's logistics sector. Innovation Output positive Firm innovation capability
Reading fidelity high
Study strength medium
n=450
0.3
Innovation capability mediates the relationships between digital competencies and strategic adaptability. Organizational Efficiency positive Strategic adaptability
Reading fidelity high
Study strength medium
n=450
0.3
Innovation capability mediates the relationships between digital competencies and organizational resilience. Organizational Efficiency positive Organizational resilience
Reading fidelity high
Study strength medium
n=450
0.3
Innovation capability directly improves both strategic adaptability and organizational resilience. Organizational Efficiency positive Strategic adaptability and organizational resilience
Reading fidelity high
Study strength medium
n=450
0.3
Strategic adaptability positively predicts organizational resilience in China's logistics sector. Organizational Efficiency positive Organizational resilience
Reading fidelity high
Study strength medium
n=450
0.3
The study conceptualizes innovation capability as a dynamic capability that converts digital resources into strategic adaptability and organizational resilience. Organizational Efficiency positive Strategic adaptability and organizational resilience
Reading fidelity high
Study strength medium
n=450
0.3

Notes