The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation

Information Systems Research Vol. 17, No. 4, December 2006, pp. 392–414

23 Pages Posted: 26 Apr 2014

See all articles by Paul A. Pavlou

Paul A. Pavlou

Temple University - Department of Management Information Systems; Temple University - Department of Strategic Management

Angelika Dimoka

Temple University - Department of Marketing and Supply Chain Management; Center for Neural Decision Making, Temple University

Date Written: September 15, 2005

Abstract

For online marketplaces to succeed and prevent a market of lemons, their feedback mechanism (reputation system) must differentiate among sellers and create price premiums for trustworthy sellers as returns to their reputation. However, the literature has solely focused on numerical (positive and negative) feedback ratings, alas ignoring the role of feedback text comments. These text comments are proposed to convey useful reputation information about a seller’s prior transactions that cannot be fully captured with crude numerical ratings. Building on the economics and trust literatures, this study examines the rich content of feedback text comments and their role in building a buyer’s trust in a seller’s benevolence and credibility. In turn, benevolence and credibility are proposed to differentiate among sellers by influencing the price premiums that a seller receives from buyers.

This paper utilizes content analysis to quantify over 10,000 publicly available feedback text comments of 420 sellers in eBay’s online auction marketplace, and to match them with primary data from 420 buyers that recently transacted with these 420 sellers. These dyadic data show that evidence of extraordinary past seller behavior contained in the sellers’ feedback text comments creates price premiums for reputable sellers by engendering buyer’s trust in the sellers’ benevolence and credibility (controlling for the impact of numerical ratings). The addition of text comments and benevolence helps explain a greater variance in price premiums (R2 = 50%) compared to the existing literature (R2 = 20%-30%). By showing the economic value of feedback text comments through trust in a seller’s benevolence and credibility, this study helps explain the success of online marketplaces that primarily rely on the text comments (versus crude numerical ratings) to differentiate among sellers and prevent a market of lemon sellers. By integrating the economics and trust literatures, the paper has theoretical and practical implications for better understanding the nature and role of feedback mechanisms, trust building, price premiums, and seller differentiation in online marketplaces.

Keywords: feedback, feedback mechanisms, feedback text comments, price premiums, seller differentiation, seller heterogeneity, trust, benevolence, credibility, numerical ratings, online marketplaces, auctions

Suggested Citation

Pavlou, Paul A. and Dimoka, Angelika, The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation (September 15, 2005). Information Systems Research Vol. 17, No. 4, December 2006, pp. 392–414. Available at SSRN: https://ssrn.com/abstract=2428900

Paul A. Pavlou

Temple University - Department of Management Information Systems ( email )

1810 N. 13th Street
Floor 2
Philadelphia, PA 19128
United States

Temple University - Department of Strategic Management ( email )

Fox School of Business and Management
Philadelphia, PA 19122
United States

Angelika Dimoka (Contact Author)

Temple University - Department of Marketing and Supply Chain Management ( email )

Philadelphia, PA 19122
United States

Center for Neural Decision Making, Temple University ( email )

Philadelphia, PA 19122
United States

HOME PAGE: http://www.fox.temple.edu/minisites/neural/index.html

Register to save articles to
your library

Register

Paper statistics

Downloads
167
Abstract Views
831
rank
176,217
PlumX Metrics