Harvesting Online Reviews to Identify the Competitor Set in a Service Business

Fei Ye, Qian Xia, Minhao Zhang, Yuanzhu Zhan, Yina Li

Research output: Contribution to journalArticle (Academic Journal)

Abstract

In today’s global service industry, online reviews posted by consumers offer critical information that influences subsequent consumers’ purchasing decisions and firms’ operations strategies. However, little research has been done on how the same information can be used to identify key competitors and improve services to increase competitiveness. In this paper, we propose an analytical framework based on an improved k-nearest neighbor (kNN) model and a Latent Dirichlet Allocation (LDA) model, for service managers to harvest online reviews to identify their key competitors and to evaluate the strengths and weaknesses of their businesses. With a sample comprising over 8 million customer reviews of 6409 hotels in 50 Chinese cities from Ctrip.com, we validate the effectiveness of the proposed approach in the analysis of a hotel’s service competitiveness and its key competitors. The findings indicate that the importance of particular attributes of a hotel varies in different segments according to hotel star ratings. This study extends the literature by bridging online reviews and competitor identification for service industries. It also contributes to practice by offering a systematic and effective way for managers to identify their key competitors, monitor market preferences, ensure service quality and formulate effective marketing strategies.
Original languageEnglish
JournalJournal of Service Research
Publication statusAccepted/In press - 24 Oct 2020

Keywords

  • Online reviews
  • competitor identification
  • k-nearest neighbor
  • Latent Dirichlet Allocation
  • hotel attributes

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