Competitor intelligence and analysis (CIA) model and online reviews: integrating big data text mining with network analysis for strategic analysis

Abstract

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.

Exploring the attributes of hotel service quality in Florianópolis-SC, Brazil: An analysis of tripAdvisor reviews

“…Previous studies have also identified a significant occurrence of the attribute "breakfast" in reviews (Köseoglu et al, 2019;Xu & Li, 2016). As in this study, the "breakfast" attribute was linked to positive reviews (B.…”

“…This attribute was also identified as an important subject by several studies (B. Kim et al, 2016;Köseoglu et al, 2019;Padma & Ahn, 2020;Sann & Lai, 2020a;Xu & Li, 2016;Ying et al, 2020) and, as in this paper, its occurrence was mostly associated with positive reviews (Alrawadieh & Law, 2019;Brochado & Brochado, 2019;Wu et al, 2017).…”

“…Many of these studies identified this attribute as a positive comment generator (Alrawadieh & Law, 2019;Lee et al, 2020;Sann & Lai, 2020a), and linked it to customer satisfaction (Herjanto et al, 2017;Liu et al, 2017;Padma & Ahn, 2020;Wu et al, 2017). On the other hand, there were also studies that corroborated the results of this research, by observing the attribute "room" as a negative comment generator (Hu et al, 2019;Köseoglu et al, 2019).…”

Heritage hotels and customer experience: a text mining analysis of online reviews

“…This study aims to identify the satisfied and dissatisfied factors of heritage hotels by analyzing online reviews written by customers on the third-party website. Hotels use online reviews to apply the business intelligence techniques to understand the customers’ preferences, opinions on service offerings (Köseoglu et al , 2020; Köseoglu et al , 2019) and to gain the competitive advantage by offering innovative services and products (Kandampully and Solnet, 2019; Köseoglu et al , 2019; Leung, 2019). We chose TripAdvisor as a data source for this study because of its popularity in the hospitality industry (Filieri et al , 2015; Xie et al , 2014).…”

“…Customers express their experiences, recommendations and opinions about the product/service in reviews on webpages, blogs, forums, mobile applications and social network sites (Yoo and Gretzel, 2011). Online reviews are a part of eWOM (Köseoglu et al , 2020; Liu et al , 2019; Xu and Li, 2016). Online reviews create an opinion, provide user information and help in the decision-making process for potential buyers (Helversen et al , 2018).…”