Digital tools helped Chinese tourism firms weather COVID: listed firms with more digitalization—notably AI and big data—suffered smaller stock-price losses and secured finance more easily, driven by diversified supply chains and greater investor attention.

Main Finding

Digitalization materially increased the organizational resilience of China’s A‑share listed tourism firms during COVID‑19: firms with higher digitalization suffered smaller stock‑price declines after pandemic shocks. The resilience effect is concentrated in regions with strong government attention to tourism and higher digital‑payment penetration, is stronger for non‑SOEs, and is driven specifically by adoption of artificial intelligence and big‑data analytics. Mechanisms include reduced supply‑chain concentration, greater investor attention, and improved access to syndicated bank loans.

Key Points

• Context: Contact‑intensive tourism firms were highly vulnerable under China’s strict pandemic prevention policies; the study examines whether firm digitalization mitigated that vulnerability.
• Outcome: Digitalized tourism firms experienced less stock‑price damage in response to COVID‑19 shocks (measured using high‑frequency market data).
• Heterogeneity:
  • Stronger effects in regions with high government focus on tourism and higher digital‑payment usage.
  • Stronger effects for non‑state‑owned enterprises (non‑SOEs) than for SOEs.
• Technology heterogeneity: Among digital technologies, only AI and big‑data analytics show significant positive links to resilience; other digital investments did not.
• Mechanisms: Evidence points to three channels — (1) reduced supply‑chain concentration (more diversified or resilient supplier networks), (2) increased investor attention (improved market recognition/signaling), and (3) easier access to syndicated bank loans (reduced information frictions for lenders).

Data & Methods

• Sample: China’s A‑share listed tourism enterprises observed over an almost 3‑year window covering the period when strict COVID‑19 prevention policies were in effect.
• Outcomes: High‑frequency stock market data used to quantify firm‑level stock‑price damage following COVID‑19 shocks (event/reaction measured at high temporal resolution).
• Key regressors: Firm‑level digitalization indicators (with disaggregation to technologies such as AI and big data).
• Empirical strategy: Comparative analysis of stock responses across firms with varying digitalization levels; heterogeneity tests by region, ownership, and payment environment; mediation/ mechanism analyses testing supply‑chain concentration, investor attention, and syndicated lending as channels.
• Limitations (as reported or implied): Study focuses on listed tourism firms in China during a specific policy regime; identification relies on observational variation in digitalization and stock reactions, so unobserved confounders and generalizability should be considered.

Implications for AI Economics

• Risk‑mitigation value of AI and big‑data: Beyond productivity gains, AI and analytics provide tangible downside protection for contact‑intensive firms during systemic shocks—reflected in market valuation and financing outcomes.
• Capital allocation and finance: Digitalization reduces information frictions and appears to improve access to syndicated credit, implying that lenders and capital markets reward AI/data adoption — this affects credit pricing, lender behavior, and financial intermediation models.
• Investor behavior and signaling: Adoption of AI/big data increases investor attention and likely alters how markets price operational resilience; models of firm value should incorporate digitalization as a state variable affecting downside risk.
• Policy: Public support for digital infrastructure (including digital payments) and promotion of AI/data capabilities in vulnerable sectors can increase economic resilience. Targeted policies may be especially effective in non‑SOE‑dominated regions and where government tourism support is active.
• Research directions: Broaden analysis beyond Chinese tourism to other sectors and countries; improve causal identification of digitalization effects; disaggregate AI/big‑data mechanisms (forecasting, demand sensing, supply‑chain optimization) and quantify welfare implications of public interventions to accelerate digital adoption.