Competitor Intelligence and Analysis (CIA) Model and Online Reviews: Integrating Big Data Text Mining with Network Analysis for Strategic Analysis


Competitive intelligence; Competitor analysis; Competitor intelligence; Hotels; Network analysis; Online reviews; Text mining


Purpose: This study aims to propose a competitor intelligence and analysis (CIA) model that can be used for the analysis of a firm's competitors. Empirically, it investigates the application of the CIA model on online reviews. This proposed model clarifies the confusion between terms such as competitive intelligence, competitor intelligence and competitor analysis and provides a more efficient process for managers. Design/methodology/approach: The approach of the model integrates text mining techniques as a big data method with network analysis to form a competitor analysis. This study has considered two centrality metrics – degree centrality and betweenness centrality – to identify the functional associations among the resources elaborated by the customers of the hotels. Findings: Findings show online reviews may be used as a solid source of intelligence. The intelligence maps visualized through the text-net technique is an efficient representation of tourist satisfaction and dissatisfaction with a tourism company and its competitors. Practical implications: The proposed approach can be used in the hotel industry along with many others. The implications for scholars and managers and the possible directions for future research are also discussed in the study. Originality/value: This study develops a new approach for competitive intelligence practices in the hotel industry and tests a new method for competitor analysis as a part of the competitive intelligence and analysis approach developed in this study.

Publication Date


Original Citation

Köseoglu, M. A., Mehraliyev, F., Altin, M., & Okumus, F. (2021). Competitor intelligence and analysis (CIA) model and online reviews: integrating big data text mining with network analysis for strategic analysis. Tourism Review, 76(3), 529–552. https://doi.org/10.1108/TR-10-2019-0406

Document Type




Source Title

Tourism Review






Rosen College of Hospitality Management


Rosen College of Hospitality Management

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