Due to the rapid development of Internet communication technologies (ICTs), an increasing number of social media platforms exist where consumers can exchange comments online about products and services that businesses offer. The existing literature has demonstrated that online user-generated content can significantly influence consumer behavior and increase sales. However, its impact on organizational operations has been primarily focused on marketing, with other areas understudied. Hence, there is a pressing need to design a research framework that explores the impact of online user-generated content on important organizational operations such as product innovation, customer relationship management, and operations management. Research efforts in this dissertation center on exploring the co-creation value of online consumer reviews, where consumers' demands influence firms' decision-making. The dissertation is composed of three studies. The first study finds empirical evidence that quality signals in online product reviews are predictors of the timing of firms' incremental innovation. Guided by the product differentiation theory, the second study examines how companies' innovation and marketing differentiation strategies influence app performance. The last study proposes a novel text analytics framework to discover different information types from user reviews. The research contributes theoretical and practical insights to consumer-centric innovation and social media analytics literature. / PHD / The IT industry, and especially the mobile application (app) market, is intensively competitive and propelled by rapid innovation. The number of apps downloaded worldwide is 102,062 million, generating $88.3 billion in revenue, and projections suggest this will rise to $189 billion in 2020. Hence, there is an impetus to examine competition strategies of app makers to better understand how this important market functions. The app update is an important competitive strategy. The first study investigates what types of public information from both customers and app makers can be used to predict app makers’ updating decisions. The findings indicate customer provided information impacts app makers’ updating decisions. Hence, the study provides insights into the importance of customer-centric strategy to market players. In the second study, it explores the impacts of product differentiation strategies on app product performance in the mobile app marketplace. The results indicate that product updates, which the first study showed are influenced by consumer feedback, are a vertical product differentiation strategy that impacts app performance. Therefore, the results from the two studies illustrate the importance of integrating online customer feedback into companies’ technology strategy. Finally, the third study proposes a novel framework that applies a domain-adapted deep learning approach to categorizing and summarizing two types of innovation opportunities (i.e., feature requests) embedded in app reviews. The results show that the proposed classification approach outperforms traditional algorithms.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/95983 |
| Date | 20 June 2018 |
| Creators | Qiao, Zhilei |
| Contributors | Management, Wang, Gang Alan, James, Tabitha L., Fan, Weiguo, Abrahams, Alan Samuel, Rees, Loren P., Shen, Wenqi, Zobel, Christopher W. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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