Do People Really Experience Information Overload While Reading Online Reviews?

Abstract

Online consumer reviews have become a substantial component of e-commerce and provide online shoppers with abundant information about products. However, previous studies provided mixed results about whether consumers experience information overload from such a vast volume of reviews. Thus, this study investigates how users perceive products depending on various numbers of reviews (from 0 to 3,000 reviews) and different review valences (generally positive, generally negative, and divided). Two crowdsourced studies with 1,783 participants were conducted. The study found no clear evidence to suggest that information overload increases as the number of reviews increases. Instead, the participants relied on a very limited number of reviews in making purchase decisions. In addition, it was observed that the review valence affected how the participants used different information sources from the interface. Based on the results, this article provides a set of interesting implications and design guidelines.

ACKNOWLEDGMENTS

We thank Nag Varun Chunduru and Zhihua Dong for their help in website design and literature review throughout this project. We also thank Sukwon Lee, who provided constructive feedback on this study.

Additional information

Notes on contributors

Bum Chul Kwon

Bum Chul Kwon is a postdoctoral researcher in the Department of Computer Science and Information Science at University of Konstanz, Germany. His main research area is human factors and computational supports in information visualization and visual analytics. He received his PhD in industrial engineering from Purdue University in 2013.

Sung-Hee Kim

Sung-Hee Kim is a postdoctoral researcher in the Computer Science Department at University of British Columbia. Her main focus of her research is applying information visualization techniques to improve individuals’ decision-making processes. She received her PhD at the School of Industrial Engineering at Purdue University in 2014.

Timothy Duket

Timothy Duket received both his bachelor and master of science in industrial engineering from Purdue University. His research interests range across many aspects of industrial engineering, most recently focusing on production systems and optimization. He is currently working as an engineering consultant at Bastian Solutions.

Adrián Catalán

Adrián Catalán is an educator, tinkerer, software developer, Innovation Lab at GalileoU lead, and Android Google Developer Expert. He has worked as researcher in human–computer interaction and crowd–computer interaction areas. He has taught undergraduate and graduate master’s courses at several universities in Guatemala.

Ji Soo Yi

Ji Soo Yi is an associate professor at the School of Industrial Engineering at Purdue University. His research interests are in applying information visualization to decision making and developing relevant theories. He received his PhD in industrial and systems engineering from Georgia Institute of Technology in 2008.