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Abstract
Online reviews have become extremely valuable sources of information about products and their customers as electronic commerce continues to proliferate rapidly. Previous research has shown that reviews of a product change and evolve over its life. Identifying and understanding patterns of change in reviews and the forces that shape them is an underexplored topic with substantial potential for predicting and improving the market performance of products. In this study, we analyze review text of nearly 50 products over the course of their lives. Our longitudinal analysis of reviews reveals changes in certain personality-related characteristics of buyers in ways that are consistent with the predictions of product adoption and diffusion theories. The main findings and conclusions still hold when we replicate the same procedure on reviews of a different product category. Accordingly, based on online user-generated content in the form of online reviews, this research introduces a novel empirical method for identifying the product adoption and diffusion stage. Implications of the study for theory, methodology, and practice are discussed.
Editor: Prof. Dov Te’eni.
Associate Editor: Dr. Michael J Gallivan.
Editor: Prof. Dov Te’eni.
Associate Editor: Dr. Michael J Gallivan.
Additional information
Notes on contributors
Roozmehr Safi
Roozmehr Safi is an assistant professor of Management Information Systems at the University of Missouri-Kansas City’s Henry W. Bloch School of Management. He earned his Ph.D. in Information Systems with a minor in Business Statistics from Texas Tech University in 2016. He also holds an M.B.A. degree and a B.S. in Computer Engineering. Roozmehr’s research interests include Big Data, information security, and Neuro Information Systems (NeuroIS). His work has appeared in Information Systems and Neuroscience and in proceedings of several conferences.
Yang Yu
Yang Yu is an assistant professor of Management Information Systems at Rochester Institute of Technology. In 2013, he received his Ph.D. degree in Management Information Systems from Texas Tech University. He also earned another Ph.D. Degree in Management Science from Beijing University of Aeronautics and Astronautics, China. Yang’s research interests include Big Data Analytics (unstructured data), Business Intelligence (BI) and E-Commerce. He has published papers in academic journals such as Decision Support Systems, Communications of the ACM, and International Journal of Production Research, etc.