Does Rich Content Make Online Reviews Better?: An Empirical Study Using a Text Analysis

Rapidly increasing in number and availability, online consumer reviews have become a reliable source of information about products and services that consumers consult prior to making their purchase decisions. However, although each online review has the potential to influence consumers’ purchase decision, all reviews are not equally influential. This study examines the impact that the textual content of online reviews has on their perceived helpfulness in the context of the service industry. Using online review data collected from Yelp, we quantify richness of online reviews by measuring meaningfulness and diversity via a text analysis process. Our empirical results show that the review richness significantly influences their perceived helpfulness. Overall, our findings shed light on how the textual content of online reviews influences their perceived helpfulness. This paper also offers significant practical insights and guidelines for online review providers in identifying helpful reviews as well as, for online reviewers, in providing more helpful reviews to others.

[1]  P. Nelson.  Information and Consumer Behavior , 1970, Journal of Political Economy.

[2]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[3]  Anindya Ghose,et al.  Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets , 2008, Inf. Syst. Res..

[4]  Lara Khansa,et al.  Creating a Taxonomy for Mobile Commerce Innovations Using Social Network and Cluster Analyses , 2012, Int. J. Electron. Commer..

[5]  Panagiotis G. Ipeirotis,et al.  Designing Ranking Systems for Consumer Reviews : The Impact of Review Subjectivity on Product Sales and Review Quality , 2006 .

[6]  Uzi Vishkin,et al.  On Finding Lowest Common Ancestors: Simplification and Parallelization , 1988, AWOC.

[7]  Chrysanthos Dellarocas,et al.  The Digitization of Word-of-Mouth: Promise and Challenges of Online Feedback Mechanisms , 2003, Manag. Sci..

[8]  Martin Wetzels,et al.  More than Words: The Influence of Affective Content and Linguistic Style Matches in Online Reviews on Conversion Rates , 2013 .

[9]  Charles H. Schwenk.  Information, Cognitive Biases, and Commitment to a Course of Action , 1986 .

[10]  Eric K. Clemons,et al.  When Online Reviews Meet Hyperdifferentiation: A Study of Craft Beer Industry , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[11]  Qing Cao,et al.  Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach , 2011, Decis. Support Syst..

[12]  Han Zhang,et al.  Anxious or Angry? Effects of Discrete Emotions on the Perceived Helpfulness of Online Reviews , 2014, MIS Q..

[13]  Erik Wästlund,et al.  Using Heuristics to Revisit Consumer Choice Processes through the Eyes of the Consumer , 2013 .

[14]  RacherlaPradeep,et al.  Perceived 'usefulness' of online consumer reviews , 2012 .

[15]  Karl Rihaczek,et al.  1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.

[16]  H. Bansal,et al.  Word-of-Mouth Processes within a Services Purchase Decision Context , 2000 .

[17]  E. Clemons,et al.  When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry , 2006 .

[18]  David Schuff,et al.  What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com , 2010 .

[19]  Gilad Ravid,et al.  Information overload and the message dynamics of online interaction spaces: a theoretical model and empirical exploration , 2004, IEEE Engineering Management Review.

[20]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[21]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[22]  Panagiotis G. Ipeirotis,et al.  Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics , 2010, IEEE Transactions on Knowledge and Data Engineering.

[23]  Michael Luca.  Reviews, Reputation, and Revenue: The Case of Yelp.Com , 2016 .

[24]  P. A. Dabholkar.  Factors Influencing Consumer Choice of a "Rating Web Site": An Experimental Investigation of an Online Interactive Decision Aid , 2006 .

[25]  Chrysanthos Dellarocas.  The Digitization of Word-of-Mouth: Promise and Challenges of Online Feedback Mechanisms , 2003 .

[26]  Michael D. Smith,et al.  All Reviews are Not Created Equal: The Disaggregate Impact of Reviews and Reviewers at Amazon.Com , 2008 .

[27]  Pradeep Racherla,et al.  Perceived 'usefulness' of online consumer reviews: An exploratory investigation across three services categories , 2012, Electron. Commer. Res. Appl..

[28]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[29]  Rand W. Ressler,et al.  Advertising and information: an empirical study of search, experience and credence goods , 1995 .