Book chapter
The Impact of Customer Reviews on Product Innovation: Empirical Evidence in Mobile Apps
Analytics and Data Science, pp.95-110
Annals of Information Systems, Springer International Publishing
10/07/2017
DOI: 10.1007/978-3-319-58097-5_8
Abstract
Product innovation is important for firms to gain competitive advantages in a dynamic business environment. Traditionally, customers are not very much involved in product innovation processes. With the technology of Web 2.0, online users are enabled and motivated to provide reviews and discussions about product features and use experiences. User generated product reviews have been found to have a word-of-mouth effect as a new element of marketing communication. However, their implication on improving product innovation cycles have not been studied before. Guided by a persuasion theory, we extracted the central and peripheral persuasion cues from user generated reviews and examined their impact on mobile app developers’ product innovation decisions. Using data collected from the Google App store, our empirical study shows that long and easy-to-read user reviews with mildly negative reviews can increase the likelihood of a future mobile app update. Our findings highlight the need for researchers to explore user generated reviews in the context of customer-centered product innovation.
Details
- Title: Subtitle
- The Impact of Customer Reviews on Product Innovation: Empirical Evidence in Mobile Apps
- Creators
- Zhilei Qiao - Department of Business Information Technology, Pamplin College of Business, Blacksburg, USAG. Alan Wang - Department of Business Information Technology, Pamplin College of Business, Blacksburg, USAMi Zhou - Department of Accounting and Information Systems, Pamplin College of Business, Blacksburg, USAWeiguo Fan - Department of Accounting and Information Systems, Pamplin College of Business, Blacksburg, USA
- Resource Type
- Book chapter
- Publication Details
- Analytics and Data Science, pp.95-110
- Publisher
- Springer International Publishing; Cham
- Series
- Annals of Information Systems
- DOI
- 10.1007/978-3-319-58097-5_8
- eISSN
- 1934-3213
- ISSN
- 1934-3221
- Language
- English
- Date published
- 10/07/2017
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083243602771
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