Psychic distance is not a national constant but a firm–market information gap: reframing it as a dyadic, multilevel and dynamic construct explains inconsistent findings and guides better measurement for predictive models and policy interventions.

Main Finding

The paper argues that inconsistent results in psychic distance research stem from a "locus problem": researchers have drifted away from the original definition (barriers to information flow between firm and market) and instead equated psychic distance with country-level cultural/institutional differences or with isolated individual perceptions. Re-centering psychic distance on the firm–market information flow, and specifying macro-, meso-, and micro-level antecedents, explains prior mixed findings and shows that psychic distance is context-specific, asymmetric, and varying across firms and markets.

Key Points

• Locus problem: psychic distance has been mis-specified by many studies; the correct locus is the firm–market information interface (barriers to information flow), not merely national attributes or individual perceptions.
• Multilevel framework: psychic distance is shaped jointly by
  • Macro-level: country-level cultural, institutional, and infrastructural differences that influence information availability and interpretation.
  • Meso-level (firm-level): firm resources, routines, networks, and market engagement strategies that affect the firm's ability to acquire, interpret, and act on market information.
  • Micro-level (individual): managers' perceptions, social cognition, and interactions that mediate firm–market information processing.
• Operationalization problems: Many empirical measures conflate national distance with firm–market distance or treat individual perceptions as equivalent to firm-level constructs, producing inconsistent results.
• Properties of psychic distance:
  • It is neither symmetrical (firm A to market B may differ from firm B to market A) nor temporally stable.
  • It varies across firms within the same country and across different markets within the same host country.
• The paper provides conceptual clarity and guidance for better measurement and theory development by focusing on alignment/misalignment of information barriers between a specific firm and a specific market.

Data & Methods

• The paper is conceptual and theoretical, not empirical:
  • Method: conceptual analysis grounded in Johanson and Wiedersheim-Paul (1975) definition of psychic distance.
  • Approach: synthesis of literature across macro, meso, and micro perspectives to build an integrative multilevel framework tracing antecedents of firm–market information flow.
  • No new primary data or empirical tests are presented; the contribution is theoretical clarification and a framework for future empirical operationalization.

Implications for AI Economics

• Measurement & modeling for empirical AI work:
  • Avoid using country-level proxies alone when training models to predict market-entry outcomes or internationalization strategies; include firm–market dyadic features (firm routines, prior engagement, communication channels).
  • Use multilevel/hierarchical modeling that nests firms within countries and markets, and explicitly model interaction effects between firm capabilities and market features.
• Data needs for computational studies:
  • Collect firm–market dyadic data (transaction logs, communication frequency, prior exports, partner networks), manager-level surveys tied to firm actions, and market-level information flow indicators (digital connectivity, media coverage, regulatory clarity).
  • Longitudinal data are important because psychic distance is dynamic.
• AI systems and platforms:
  • AI tools intended to reduce "distance" (e.g., market intelligence, translation, recommendation systems) should be evaluated on their effect on firm–market information alignment, not just country similarity metrics.
  • Algorithmic market-entry support should incorporate firm heterogeneity (resources, routines) to avoid over-generalized recommendations based solely on national indicators.
• Causal inference and policy evaluation:
  • When estimating the effect of interventions (e.g., trade promotion, digital platforms) on international engagement, control for firm-level absorptive capacity and manager perceptions that mediate information flow.
• New research directions at the intersection of AI and international business economics:
  • Develop operational definitions of firm–market psychic distance suitable for machine learning (features capturing misalignment in available vs. actionable information).
  • Evaluate whether AI-mediated signals (natural language market reports, social media analytics) reduce psychic distance and for which types of firms or markets.
  • Use network and text-as-data methods to measure information barriers (e.g., topic alignment between firm communications and local market discourse).
• Practical takeaway for AI economists and practitioners:
  • Models and interventions should treat psychic distance as a dyadic, multilevel construct. Simple country-distance features are insufficient and can mislead both predictive models and policy prescriptions.