Evaluating the Impact of Language Analytics on Competitive Intelligence Effectiveness
PDF (English)

Palabras clave

Language analytics capabilities
Competitive intelligence effectiveness
Decision-making effectiveness
Technological readiness
PLS-SEM
Resource-Based View
Dynamic Capability Theory

Cómo citar

Zheng, Z., Srisomthawin, B., & Amornwanitsak, S. (2026). Evaluating the Impact of Language Analytics on Competitive Intelligence Effectiveness. Journal of Sustainable Competitive Intelligence , 16, e0645. https://doi.org/10.37497/eagleSustainable.v16i.645

Resumen

Purpose: This study examines the effect of language analytics capabilities on competitive intelligence effectiveness, while also evaluating the mediating role of decision-making effectiveness and the moderating role of technological readiness.

Methodology/approach: The study employed an explanatory cross-sectional survey design based on responses from 312 professionals working in competitive intelligence, analytics, data science, and strategy-related functions. The hypotheses were tested using partial least squares structural equation modeling (PLS-SEM) with bootstrapping.

Originality/Relevance: The study conceptualizes language analytics as an organizational capability rather than merely a technical tool. By integrating the Resource-Based View and Dynamic Capability Theory, it explains how text-oriented analytics can generate strategic value in competitive intelligence processes.

Key findings: The results show that language analytics capabilities positively affect competitive intelligence effectiveness (β = 0.413, p < 0.001) and decision-making effectiveness (β = 0.524, p < 0.001). Decision-making effectiveness also positively affects competitive intelligence effectiveness (β = 0.318, p < 0.001) and partially mediates the focal relationship (indirect β = 0.167; VAF = 28.8%). In addition, technological readiness strengthens the relationship between language analytics capabilities and competitive intelligence effectiveness (β = 0.219, p < 0.001). The structural model explained 48.7% of the variance in competitive intelligence effectiveness.

 Theoretical/methodological contributions: This study contributes to the competitive intelligence literature by linking language analytics capabilities with intelligence effectiveness through both direct and indirect pathways. It also identifies technological readiness as an important boundary condition, thereby extending capability-based explanations of how organizations create value from text-oriented analytics in intelligence and decision-support environments.

https://doi.org/10.37497/eagleSustainable.v16i.645
PDF (English)

Citas

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