Longitudinal Analysis of Online Consumer Reviews

• Main Authors: Ping-Yu Liu, 柳秉佑
• Other Authors: Chun-Yao Huang
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/g8984a

博士 === 國立臺灣大學 === 商學研究所 === 107 === Consumers rely heavily upon online reviews and ratings to seek out opinions and experiences on the Internet from people they might be unfamiliar with or even have never met before. This is expressly true for experience goods with uncertain quality, because they are more difficult to evaluate. Although there are abundant studies on online reviews in the existing literature, there are scant studies exploring the issue of how online reviews of a specific product or service dynamic change over time. While most studies in the literature use experimental methods when analyzing the issue of service satisfaction change, this present research executes computerized content analysis of service satisfaction changes with an established semantic dictionary. We investigate if and how online ratings change over time on specific online review platforms. As it is very important for service management researchers and practitioners, our study aims to address these understudied but managerially relevant issues. In our empirical analysis we utilize sentiment analysis and the General Inquirer dictionary to identify and tag sentiment categories of words in the textual content after collecting actual online consumer reviews of hotels. The results reveal that when the score of emotionally or non-emotionally positive sentiment increases over time, a hotel’s review rating will also increase. Conversely, when the score of emotionally negative sentiment increases over time, the review rating of hotel will decline. Our study offers managers in the service industry with a new lesson to learn about reading customer minds through online review textual content. We show that managers can analyze online review in order to understand “what customers are saying”. Overall, by adopting a systematic computerized content analysis approach and utilizing an established semantic dictionary, our results demonstrate that information extracted from review textual content is able to substantially explain such differences. We thus offer a new research method and opportunities for marketing scholars and practitioners to explore changes in service satisfaction.