Competitive-Intelligence Enabling Infrastructure, Psychological Adaptability, and Organizational Performance: Evidence from Chinese Manufacturing Firms

Abstract

Purpose: This study examines whether competitive-intelligence enabling infrastructure (CIEI) is associated with a cognitive-horizon proxy of psychological adaptability and subsequent organizational performance in Chinese manufacturing firms. It addresses the distinction between digital and analytical infrastructure and a complete competitive-intelligence system comprising intelligence planning, collection, analysis, dissemination, utilization, feedback, ethics, and governance. Methodology/Approach: The study analyzes 19,035 firm-year observations from 3,169 listed Chinese manufacturing firms during 2014–2023. CIEI is modeled as a formative archival index comprising big-data capability, digital-intelligence investment, and analytical capability. Low managerial myopia is used as a limited cognitive-horizon proxy of psychological adaptability, while organizational performance combines Tobin’s Q, gross profitability, and asset turnover. The analytical hierarchy includes descriptive diffusion analysis, pooled models, firm and year fixed effects, one-year-lagged specifications, component-level and principal-component composition analyses, a 1,000-replication firm-cluster bootstrap, and dynamic-panel GMM. China’s 2024 World Bank Enterprise Survey is used only as a separate contextual source because consistently defined 2024–2025 firm-level observations are unavailable for the primary panel. Originality/Relevance: The study does not equate digital infrastructure with competitive intelligence. Instead, it identifies an empirically observable enabling layer while explicitly separating it from unmeasured intelligence-cycle processes, including governance, dissemination, utilization, feedback, and accountable decision use. This distinction provides a more defensible basis for using archival corporate data in competitive-intelligence research. Key Findings: CIEI diffused substantially across the observed firms; 76.3% of the 772 firms in the ten-year balanced cohort moved to a higher infrastructure quartile. Prior-year CIEI was positively associated with subsequent organizational performance (b = 0.054, p = .019), while low managerial myopia showed a smaller positive association (b = 0.016, p = .048). Prior CIEI was not significantly associated with the adaptability proxy, and the cluster-bootstrap indirect-effect interval included zero. Component-level estimates were heterogeneous, and GMM results varied with instrument classification; consequently, the findings do not establish causality or validate a complete competitive-intelligence system. Theoretical/Methodological Contributions: The study reconnects archival analytics research with classical competitive-intelligence process, maturity, governance, and decision-use perspectives. Methodologically, it establishes explicit construct boundaries, treats CIEI as a formative enabling index rather than a reflective CI scale, separates primary from sensitivity specifications, and combines temporal ordering, firm fixed effects, clustered inference, bootstrap analysis, and dynamic-panel diagnostics. The results show that analytical infrastructure can be monitored through public data, but its conversion into competitive intelligence requires organizational processes that cannot be inferred from technology indicators alone.