Online markets pose a difficulty for evaluating products, particularly experience goods, such as used cars, that can not be easily described online. This exacerbates product uncertainty, the buyer's difficulty in evaluating product characteristics, and predicting how a product will perform in the future. However, the IS literature has focused on seller uncertainty and ignored product uncertainty. To address this void, this study conceptualizes product uncertainty and examines its effects and antecedents in online markets for used cars(eBay Motors).Extending the information asymmetry literature from the seller to the product, we first theorize the nature and dimensions (description and performance) of product uncertainty. Second, we propose product uncertainty to be distinct from, yet shaped by, seller uncertainty. Third, we conjecture product uncertainty to negatively affect price premiums in online markets beyond seller uncertainty. Fourth, based on the information signaling literature, we describe how information signals (diagnostic product descriptions and third-party product assurances) reduce product uncertainty.The structural model is validated by a unique dataset comprised of secondary transaction data from used carson eBay Motors matched with primary data from 331 buyers who bid on these used cars. The results distinguish between product and seller uncertainty, show that product uncertainty has a stronger effect on price premiums than seller uncertainty, and identify the most influential information signals that reduce product uncertainty.The study's implications for the emerging role of product uncertainty in online markets are discussed
This article discusses the role of commonly used neurophysiological tools such as psychophysiological tools (e.g., EKG, eye tracking) and neuroimaging tools (e.g., fMRI, EEG) in Information Systems research. There is heated interest now in the social sciences in capturing presumably objective data directly from the human body, and this interest in neurophysiological tools has also been gaining momentum in IS research (termed NeuroIS). This article first reviews commonly used neurophysiological tools with regard to their major strengths and weaknesses. It then discusses several promising application areas and research questions where IS researchers can benefit from the use of neurophysiological data. The proposed research topics are presented within three thematic areas: (1) development and use of systems, (2) IS strategy and business outcomes, and (3) group work and decision support. The article concludes with recommendations on how to use neurophysiological tools in IS research along with a set of practical suggestions for developing a research agenda for NeuroIS and establishing NeuroIS as a viable subfield in the IS literature.
This research essay outlines a set of guidelines for conducting functional Magnetic Resonance Imaging (fMRI) studies in social science research in general and also, accordingly, in Information Systems research. Given the increased interest in using neuroimaging tools across the social sciences, this study aims at specifying the key steps needed to conduct an fMRI study while ensuring that enough detail is provided to evaluate the methods and results. The outline of an fMRI study consists of four key steps: (1) formulating the research question, (2) designing the fMRI protocol, (3) analyzing fMRI data, and (4) interpreting and reporting fMRI results. These steps are described with an illustrative example of a published fMRI study on trust and distrust in this journal (Dimoka 2010). The paper contributes to the methodological literature by (1) providing a set of guidelines for designing and conducting fMRI studies, (2) specifying methodological details that should be included in fMRI studies in academic venues, and (3) illustrating these practices with an exemplar fMRI study. Future directions for conducting high-quality fMRI studies in the social sciences are discussed.
This paper introduces the idea of drawing upon the cognitive neuroscience literature to inform IS research (herein termed "NeuroIS"). Recent advances in cognitive neuroscience are uncovering the neural bases of cognitive, emotional, and social processes, and they offer new insights into the complex interplay between IT and information processing, decision making, and behavior among people, organizations, and markets. The paper reviews the emerging cognitive neuroscience literature to propose a set of seven opportunities that IS researchers can use to inform IS phenomena, namely (1) localizing the neural correlates of IS constructs, (2) capturing hidden mental processes, (3) complementing existing sources of IS data with brain data, (4) identifying antecedents of IS constructs, (5) testing consequences of IS constructs, (6) inferring the temporal ordering among IS constructs, and (7) challenging assumptions and enhancing IS theories. The paper proposes a framework for exploring the potential of cognitive neuroscience for IS research and offers examples of potentially fertile intersections of cognitive neuroscience and IS research in the domains of design science and human-computer interaction. This is followed by an example NeuroIS study in the context of e-commerce adoption using fMRI, which spawns interesting new insights. The challenges of using functional neuroimaging tools are also discussed. The paper concludes that there is considerable potential for using cognitive neuroscience theories and functional brain imaging tools in IS research to enhance IS theories.
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 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.
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.