http://orcid.org/0000-0002-5026-881X
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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.
Acknowledgments
The authors would like to thank the editor and referees for their helpful comments and suggestions, which improved the quality of this article significantly. They also thank seminar participants at the China Summer Workshop on Information Management in Dalian for their helpful comments.
The first and second authors contributed equally to this work.
Additional information
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Notes on contributors
Shihao Zhou
Shihao Zhou ([email protected]) is an assistant professor in the Department of Marketing and Electronic Business, School of Business at the Nanjing University. He received his Ph.D. in management from Virginia Tech. His research interests focus on technological innovation, information technology strategy, and interfirm competition and collaboration. He has published in the Journal of Knowledge Management and presented his research at the annual meetings of the Academy of Management and the Strategic Management Society.
Zhilei Qiao
Zhilei Qiao ([email protected]) is a Ph.D. candidate in the Department of Business Information Technology at Virginia Tech. His research interests include social media analytics, product innovation, and text mining. He has published in conference proceedings of the Academy of Management, Americas Conference on Information Systems, Hawaii International Conference on System Sciences, and others.
Qianzhou Du
Qianzhou Du ([email protected]) is a Ph.D. student in the Department of Business Information Technology at Virginia Tech. His research interests include business intelligence, text analytics, social media analytics, crowd sourcing, and business analytics for the tourism and hospitality industries. He has published in Tourism Management and in conference proceedings, including Americas Conference on Information Systems and Hawaii International Conference on System Sciences.
G. Alan Wang
G. Alan Wang ([email protected]; corresponding author) is an associate professor in the Department of Business Information Technology at Virginia Tech. He received his Ph.D. in management information systems from the University of Arizona. His research interests include text mining, data mining, web and social media analytics, service computing, and quality engineering. He has published 35 refereed journal articles in Productions and Operations Management, Journal of Business Ethics, Communications of the ACM, and IEEE Transactions of Systems, Man and Cybernetics (Part A), and others.
Weiguo Fan
Weiguo Fan ([email protected]) is R. B. Pamplin Professor of Accounting and Information Systems and Full Professor of Computer Science (courtesy) at Virginia Polytechnic Institute and State University (Virginia Tech). He received his Ph.D. in business administration from the Ross School of Business, University of Michigan. His research interests focus on the design and development of novel information technologies—information retrieval, data mining, text analytics, social media analytics, business intelligence techniques—to support better business information management and decision making. He has published more than 200 referred journals and conference papers. His research has appeared in the premier journals such as Information Systems Research, Journal of Management Information Systems, Productions and Operations Management, IEEE Transactions on Knowledge and Data Engineering, and others.
Xiangbin Yan
Xiangbin Yan ([email protected]) is a professor of management science and engineering in the School of Economics and Management at the University of Science & Technology Beijing, China. He received a Ph.D. from the Department of Management Science & Engineering at Harbin Institute of Technology. His research interests include electronic commerce, social media analytics, social network analysis, and business intelligence. His work has appeared in Journal of Informetrics, Scientometrics, Computers in Human Behavior, International Journal of Medical Informatics, and others.