Internet Exchanges for Used Goods: An Empirical Analysis of Trade Patterns and Adverse Selection

Abstract

The past few years have witnessed the increasing ubiquity of user-generated content on seller reputation and product condition in Internet based used-good markets. Recent theoretical models of trading and sorting in used-good markets provide testable predictions to use to examine the presence of adverse selection and trade patterns in such dynamic markets. A key aspect of such empirical analyses is to distinguish between product-level uncertainty and seller-level uncertainty, an aspect the extant literature has largely ignored. Based on a unique, 5-month panel dataset of user-generated content on used good quality and seller reputation feedback collected from Amazon, this paper examines trade patterns in online used-good markets across four product categories (PDAs, digital cameras, audio players, and laptops). Drawing on two different empirical tests and using content analysis to mine the textual feedback of seller reputations, the paper provides evidence that adverse selection continues to exist in online markets. First, it is shown that after controlling for price and other product and seller-related factors, higher quality goods take a longer time to sell compared to lower quality goods. Second, this result also holds when the relationship between sellers' reputation scores and time to sell is examined. Third, it is shown that price declines are larger for more unreliable products, and that products with higher levels of intrinsic unreliability exhibit a more negative relationship between price decline and volume of used good trade. Together, our findings suggest that despite the presence of signaling mechanisms such as reputation feedback and product condition disclosures, the information asymmetry problem between buyers and sellers persists in online markets due to both productbased and seller-based information uncertainty. No consistent evidence of substitution or complementarity effects between product-based and seller-level uncertainty are found. Implications for research and practice are discussed.

Research Commentary—Too Big to Fail: Large Samples and the p-Value Problem

“…Authors of several of the papers conducted robustness/sensitivity analysis, modifying the independent measures (Forman et al 2008), or the variable structure (Brynjolfsson et al 2009, Ghose 2009). Others include new variables (Ghose and Yang 2009), or rerun the model on random subsets of the data (Yao et al 2009).…”

“…In such cases the interpretation of effects must take into account the additional coefficients (e.g., X 2 or X 1 * X 2 ). Moreover, with a large sample, interactions that involve a categorical variable can be studied by splitting the data into the separate categories and fitting separate models (Asvanund et al 2004, Forman et al 2008, Gefen and Carmel 2008, Ghose 2009, Gordon et al 2010, Li and Hitt 2008, Mithas and Lucas 2010, Overby and Jap 2009, Yao et al 2009). In general, a large sample often provides sufficient data for conducting analyses on subsamples of interest while maintaining sufficient power in each subsample.…”

“…A large sample also enables the researcher to incorporate many control variables into the model without worrying about power loss (Forman et al 2008, Ghose 2009, Mithas and Lucas 2010, thereby reducing concerns for alternative explanations and strengthen the main arguments if results remain consistent.…”

The Theory of the Lemon Markets in IS Research

“…The basic construct of the LMT, information asymmetry is actually well researched. This was shown on Internet exchanges for used goods like PDAs, digital cameras, audio players and laptops (Ghose 2009). Information asymmetry between buyer and seller creates the possibility of igniting a lemon market but is certainly not a sufficient condition.…”

Latent Semantic Analysis: five methodological recommendations

“…Content analysis is defined as a systematic, replicable technique for reducing a large body of text into content categories based on explicit rules of coding (Weber 1990). Content analysis offers a bridge between textual data and quantitative analysis and has been employed in IS research extensively, especially in the analysis of interviews (Dam and Kaufmann 2008), open-ended surveys (Couger andO'Callagher 1994, Panteli et al 1999) or customer feedback (Ghose 2009). Traditional content analysis is cited as having the potential to contaminate coded output by the theoretical prejudices of the researcher who compiles the coding protocol (Franzosi 2004, p. 60).…”