Description
Too often, in previous marketing, consumer behavior, and IS research, satisfaction and dissatisfaction are treated as two ends of a bipolar continuum. The researchers of this study argue that satisfaction and dissatisfaction are two distinct dimensions and thus have different determinants. Online reviews, as one type of user-generated contents (UGC), can impact consumer purchase decision and IS user adoption decision. Online reviews are also valuable sources for researchers and practitioners to better understand consumers and users. The researchers of this study extract and analyze online user reviews in the App Store. Sentiment analysis is applied to model user satisfaction and dissatisfaction. Significant determinants, as well as their weights are identified. By using the text mining techniques, the current study demonstrates the separability of satisfaction and dissatisfaction and reveals different influencing factors. The research findings can provide insights into extant IS user satisfaction literature.
Recommended Citation
Yu, Yixiu (Ashley) and Davis, Fred D., "The Determinants of IS User Satisfaction and Dissatisfaction: A Text Mining Approach" (2017). AMCIS 2017 Proceedings. 12.
https://aisel.aisnet.org/amcis2017/DataScience/Presentations/12
The Determinants of IS User Satisfaction and Dissatisfaction: A Text Mining Approach
Too often, in previous marketing, consumer behavior, and IS research, satisfaction and dissatisfaction are treated as two ends of a bipolar continuum. The researchers of this study argue that satisfaction and dissatisfaction are two distinct dimensions and thus have different determinants. Online reviews, as one type of user-generated contents (UGC), can impact consumer purchase decision and IS user adoption decision. Online reviews are also valuable sources for researchers and practitioners to better understand consumers and users. The researchers of this study extract and analyze online user reviews in the App Store. Sentiment analysis is applied to model user satisfaction and dissatisfaction. Significant determinants, as well as their weights are identified. By using the text mining techniques, the current study demonstrates the separability of satisfaction and dissatisfaction and reveals different influencing factors. The research findings can provide insights into extant IS user satisfaction literature.