Peer-driven digitalisation makes Chinese manufacturers more resilient, but the pathways differ: industry peers raise resilience by spurring innovation, while nearby regional peers do so by improving resource allocation; the gains vary with firms' directorate-network position, local competition and city centrality.
Peer Effects of Digital Transformation and Enterprise Resilience: Evidence from Chinese Manufacturing Firms
Using 2013–2022 A‑share manufacturing firm panel data and interlocking-directorate networks, the paper finds that peer digital transformation raises enterprise resilience, with industrial peers working mainly through boosting firms' innovation capability and regional peers working mainly by improving resource allocation, and effect sizes depending on firms' network centrality, industry competition, and city centrality.
Digital transformation (DT) enables manufacturing enterprises to navigate volatile and uncertain market environments, thereby achieving sustainable development. Considering the inherent uncertainty of DT and the influence of peer enterprises, this study examines peer effects of DT on enterprise resilience (ER) from the perspective of peer influence, drawing on the institutional theory, enterprise resilience durability theory and strategic ecology. Using data from Chinese manufacturing enterprises listed on the Shanghai and Shenzhen A-share markets from 2013 to 2022, the study investigates the mechanisms and heterogeneity of DT peer effects within interlocking directorate networks (IDNs). The results show that: (1) DT exhibits significant industrial and regional peer effects; (2) industrial peer effects enhance innovation capability (IC), thereby strengthening ER, whereas regional peer effects improve resource allocation (RA) to bolster ER; and (3) industrial peer effects are more pronounced for enterprises in non-central positions within IDNs and highly competitive industries, while regional peer effects are stronger for enterprises in central positions and located in central cities. These findings highlight the differentiated pathways through which peer-driven digitalization shapes resilience and demonstrate its importance not only for firm-level resilience but also for long-term sustainable competitiveness in manufacturing ecosystems.
Summary
Main Finding
Digital transformation (DT) among Chinese manufacturing firms produces measurable peer effects that improve enterprise resilience (ER), but via different channels depending on peer type: industrial peers strengthen ER primarily by enhancing firms’ innovation capability (IC), while regional peers strengthen ER by improving resource allocation (RA). The magnitude of these peer effects varies with firms’ positions in interlocking directorate networks (IDNs), industry competition, and city centrality.
Key Points
- Two distinct peer effects of DT:
- Industrial peer effects: firms adopt DT practices from same-industry peers; this mainly raises innovation capability, which in turn increases enterprise resilience.
- Regional peer effects: firms learn/adopt DT from geographically proximate peers; this mainly improves resource allocation efficiency, which in turn increases enterprise resilience.
- Mechanisms (mediators):
- Innovation capability (IC) mediates the industrial DT → ER relationship.
- Resource allocation (RA) mediates the regional DT → ER relationship.
- Heterogeneity / moderators:
- Industrial peer effects are stronger for firms occupying non-central positions in IDNs and for firms in highly competitive industries.
- Regional peer effects are stronger for firms occupying central positions in IDNs and for firms located in central cities.
- The study is grounded in institutional theory, enterprise-resilience durability theory, and strategic ecology, highlighting how peer-driven digitalization shapes firm- and ecosystem-level resilience and sustainable competitiveness.
Data & Methods
- Sample: Chinese manufacturing firms listed on the Shanghai and Shenzhen A-share markets, 2013–2022.
- Network construction: Interlocking directorate networks (IDNs) used to define peer relationships (industry peers and regional peers derived from network and geographic/industry groupings).
- Empirical approach (summary level reported in paper):
- Panel analysis exploiting firm-year data to estimate peer effects of DT on ER.
- Mediation analysis to test innovation capability (IC) and resource allocation (RA) as channels.
- Heterogeneity tests across IDN centrality, industry competition, and city centrality.
- Robustness checks (implied) to validate network-based peer identification and mechanism pathways.
- Theoretical framing: institutional theory (peer conformity/pressure), enterprise resilience durability theory (mechanisms for durable resilience), and strategic ecology (ecosystem-level interactions and spillovers).
Implications for AI Economics
- Diffusion pathways: AI and digital technologies spread through firms not only by industry ties but also via regional proximity and directorate networks. Modeling adoption should separate industrial vs regional diffusion channels and account for network position.
- Mechanism-aware policy: Policies to accelerate AI/DT adoption and resilience should be tailored:
- To boost innovation-led resilience, target industry clusters and non-central firms that benefit from industry peer learning.
- To improve resource-allocation-driven resilience, strengthen regional infrastructure and support central firms in city hubs that can propagate efficient allocation practices.
- Network externalities and market structure: IDNs and firm centrality shape who benefits most from DT spillovers; antitrust and corporate-governance analysis should consider how director interlocks influence diffusion and competitiveness.
- Measurement and identification: Researchers should incorporate network measures (IDN centrality), separate peer types (industry vs region), and explicitly test mediators (innovation, resource allocation) when studying AI/DT impacts on firm outcomes (resilience, productivity, employment).
- Future research directions:
- Causal identification of peer influence (e.g., instruments, quasi-experiments).
- Micro-level measurement of AI-specific adoption and capabilities.
- Cross-country comparisons to see how institutional environments alter peer diffusion and resilience channels.
- Long-run effects of DT diffusion on labor markets, competition, and ecosystem-level resilience.
Assessment
Paper Typecorrelational
Evidence Strengthmedium — The study uses a rich firm-year panel, network-based peer measures, mediation tests, and heterogeneity/robustness checks that support the reported associations and mechanisms; however, it does not appear to exploit a clearly exogenous source of peer variation, so concerns about reflection (simultaneity), omitted confounders, and measurement error mean causal claims are plausible but not definitively established.
Methods Rigormedium — Methods are standard and appropriate (network construction from interlocking directorates, panel regression, mediation analysis, heterogeneity checks) and the paper reports robustness tests, but it lacks transparent causal identification strategies (e.g., valid instruments, natural experiments, or difference-in-differences with exogenous shocks) to rule out endogeneity and reverse causation; measurement of DT and mediator variables likely relies on firm disclosures/indices which can introduce noise.
SamplePanel of Chinese manufacturing firms listed on the Shanghai and Shenzhen A-share markets from 2013–2022; peer relationships defined using interlocking directorate networks and grouped by industry and geographic proximity; firm-level measures include digital transformation (DT), enterprise resilience (ER), innovation capability (IC), resource allocation efficiency (RA), and standard financial/control variables.
Themesadoption innovation org_design
IdentificationFirm-year panel regressions relating a firm's enterprise resilience to peer digital transformation exposure, where peers are defined via interlocking directorate networks and by industry/geographic groupings; models include firm and year controls (and likely fixed effects), lagged covariates, mediation analysis for innovation capability and resource allocation, heterogeneity tests by IDN centrality/competition/city centrality, and robustness checks — identification rests on observed variation in peer DT exposure rather than on exogenous shocks, instruments, or random assignment.
GeneralizabilityLimited to publicly listed manufacturing firms in China (A-share) — excludes private/smaller firms and other sectors, China-specific institutional, regulatory, and corporate-governance context may not generalize to other countries, Analysis covers 2013–2022, so may not capture post-2022 accelerations in AI adoption or more recent technology shifts, DT is measured broadly (digital transformation) rather than narrow AI-specific adoption, limiting direct applicability to AI-only dynamics, Interlocking directorate networks and their role in diffusion may operate differently in other corporate governance regimes
Claims (9)
| Claim | Direction | Confidence | Outcome | Details |
|---|---|---|---|---|
| Digital transformation (DT) exhibits significant industrial and regional peer effects. Organizational Efficiency | positive | high | enterprise resilience (ER) |
industrial and regional peer effects of digital transformation observed in firm-level panel
0.3
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| Industrial peer effects of DT enhance firms' innovation capability (IC), which in turn strengthens enterprise resilience (ER). Organizational Efficiency | positive | high | enterprise resilience (ER) (mediator: innovation capability, IC) |
industrial peer DT enhances innovation capability which mediates improvements in enterprise resilience
0.3
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| Regional peer effects of DT improve firms' resource allocation (RA), which in turn bolsters enterprise resilience (ER). Organizational Efficiency | positive | high | enterprise resilience (ER) (mediator: resource allocation, RA) |
regional peer DT improves resource allocation which mediates enterprise resilience gains
0.3
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| Industrial peer effects are more pronounced for enterprises in non-central positions within interlocking directorate networks (IDNs). Organizational Efficiency | positive | medium | enterprise resilience (ER) |
industrial peer effects more pronounced for non-central firms in interlocking directorate networks
0.18
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| Industrial peer effects are stronger in highly competitive industries. Organizational Efficiency | positive | medium | enterprise resilience (ER) |
industrial peer effects stronger in highly competitive industries
0.18
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| Regional peer effects are stronger for enterprises occupying central positions within interlocking directorate networks (IDNs). Organizational Efficiency | positive | medium | enterprise resilience (ER) |
regional peer effects stronger for firms occupying central positions in IDNs
0.18
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| Regional peer effects are stronger for enterprises located in central cities. Organizational Efficiency | positive | medium | enterprise resilience (ER) |
regional peer effects stronger for enterprises located in central cities
0.18
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| Digital transformation enables manufacturing enterprises to navigate volatile and uncertain market environments, thereby achieving sustainable development. Firm Productivity | positive | medium | sustainable development / long-term firm competitiveness (implied via enterprise resilience) |
digital transformation aids firms in navigating volatile markets and achieving sustainable development
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| Peer-driven digitalization matters not only for firm-level resilience but also for long-term sustainable competitiveness in manufacturing ecosystems. Innovation Output | positive | speculative | long-term sustainable competitiveness (ecosystem-level implication, inferred from firm-level ER) |
peer-driven digitalization contributes to long-term sustainable competitiveness in manufacturing ecosystems
0.03
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