Journal article
Measuring Customer Agility from Online Reviews Using Big Data Text Analytics
Journal of management information systems, Vol.35(2), pp.510-539
04/03/2018
DOI: 10.1080/07421222.2018.1451956
Abstract
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.
Details
- Title: Subtitle
- Measuring Customer Agility from Online Reviews Using Big Data Text Analytics
- Creators
- Shihao ZhouZhilei QiaoQianzhou DuG. Alan WangWeiguo FanXiangbin Yan
- Resource Type
- Journal article
- Publication Details
- Journal of management information systems, Vol.35(2), pp.510-539
- Publisher
- Routledge
- DOI
- 10.1080/07421222.2018.1451956
- ISSN
- 0742-1222
- eISSN
- 1557-928X
- Grant note
- Chinese National Science Foundation
- Language
- English
- Date published
- 04/03/2018
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083261902771
Metrics
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