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.

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Determining whom to trust and whom to distrust is a major decision in impersonal IT-enabled exchanges. Despite the potential role of both trust and distrust in impersonal exchanges, the information systems literature has primarily focused on trust, alas paying relatively little attention to distrust. Given the importance of studying both trust and distrust, this study aims to shed light on the nature, dimensionality, distinction, and relationship, and relative effects of trust and distrust on economic outcomes in the context of impersonal IT-enabled exchanges between buyers and sellers in online marketplaces. This study uses functional neuroimaging (fMRI) tools to complement psychometric measures of trust and distrust by observing the location, timing, and level of brain activity that underlies trust and distrust and their underlying dimensions. The neural correlates of trust and distrust are identified when subjects interact with four experimentally manipulated seller 1 David Gefen was the accepting senior editor for this paper. Dennis Galletta served as the associate editor. The figures and appendices for this paper are located on the "Online Supplements" section of the MIS Quarterly's website (http://www.misq.org). profiles that differ on their level of trust and distrust. The results show that trust and distrust activate different brain areas and have different effects, helping explain why trust and distrust are distinct constructs associated with different neurological processes. Implications for the nature, distinction and relationship, dimensionality, and effects of trust and distrust are discussed.

In the past decade, there has been a tremendous increase in the use of neurophysiological methods to better understand marketing phenomena among academics and practitioners. However, the value of these methods in predicting advertising success remains underresearched. Using a unique experimental protocol to assess responses to 30-second television ads, the authors capture many measures of advertising effectiveness across six commonly used methods (traditional self-reports, implicit measures, eye tracking, biometrics, electroencephalography, and functional magnetic resonance imaging). These measures have been shown to reliably tap into higher-level constructs commonly used in advertising research: attention, affect, memory, and desirability. Using time-series data on sales and gross rating points, the authors attempt to relate individual-level response to television ads in the lab to the ads’ aggregate, market-level elasticities. The authors show that functional magnetic resonance imaging measures explain the most variance in advertising elasticities beyond the baseline traditional measures. Notably, activity in the ventral striatum is the strongest predictor of real-world, market-level response to advertising. The authors discuss the findings and their significant implications for theory, research, and practice.

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