Language Analytics as a Driver of Competitive Intelligence Effectiveness: A Strategic Decision-Making Perspective
DOI:
https://doi.org/10.37497/eagleSustainable.v17i.522Keywords:
Language analytics, Natural language processing, Text mining, Competitive intelligence, Systematic literature review, AI-driven decision making, Organizational effectivenessAbstract
Purpose: The goal of the research is to investigate the role of language analytics as a source of competitive intelligence (CI) efficacy in businesses and give an in-depth account of the international scientific output in 2013-2026 and discover the ways in which textual data in its unstructured form can be converted into a strategic advantage.
Methodology/approach: A systematic literature review (SLR) was carried out according to PRISMA 2020 guidelines. Scopus, Web of Science, IEEE Xplore and Google Scholar were searched with a combination of keywords such as language analytics, natural language processing, text mining, sentiment analysis, topic modeling and competitive intelligence. Having screened 487 initial records and evaluated 142 full-text articles, 62 high-quality studies were incorporated in the final synthesis.
Originality/Relevance: Language analytics, including NLP, text mining, and large language models (LLMs), can provide organizations with an effective solution to transform raw text into timely, accurate, and predictive information.
Key Findings: The findings indicate that there are four main mechanisms by which language analytics can be used to improve the effectiveness of CI: real-time monitoring and speed, semantic depth and accuracy, predictive and prescriptive capabilities, and multilingual/cross-platform scalability. The study positions language analytics as a strategic facilitator of intelligence-driven decision-making, aiding organisations in augmenting competitive advantage.
Theoretical/Methodological Contributions: This article provides a theoretical contribution by developing the traditional SCIP CI cycle model with a specific language analytics layer and by bringing dynamic capabilities and knowledge-based visions into the AI-driven intelligence. It has a methodological contribution to provide a reproducible SLR protocol and a conceptual framework summarizing existing knowledge.
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