Measuring Customer Agility from Online Reviews Using Big Data Text Analytics

Shihao Zhou, Zhilei Qiao, Qianzhou Du, G. Alan Wang, Weiguo Fan, Xiangbin Yan

Research output: Contribution to journalReview articlepeer-review


Large volumes of product reviews generated by online users have important strategic value for product development. Prior studies often focus on the influence of reviews on customers‘ purchasing decisions through the word-of-mouth effect. However, little is known about how product developers respond to these reviews. This study adopts a big data analytical approach to investigate the impact of online customer reviews on customer agility and subsequently product performance. We develop a singular value decomposition-based semantic keyword similarity method to quantify customer agility using large-scale customer review texts and product release notes. Using a mobile app data set with over 3 million online reviews, our empirical study finds that review volume has a curvilinear relationship with customer agility. Furthermore, customer agility has a curvilinear relationship with product performance. Our study contributes to innovation literature by demonstrating the influence of firms‘ capability of utilizing online customer reviews and its impact on product performance. It also helps reconcile inconsistencies found in literature regarding the relationships among the three constructs.

Original languageEnglish (US)
Pages (from-to)510-539
Number of pages30
JournalJournal of Management Information Systems
Issue number2
StatePublished - Apr 3 2018
Externally publishedYes

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science Applications
  • Management Science and Operations Research
  • Information Systems and Management


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