Weinmann, M. (Markus)

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    When the stars shine too bright: the influence of multidimensional ratings on online consumer ratings
    (Informs, 2022-10-22) Weinmann, M. (Markus); Schneider, C. (Christoph); Mohr, P.N.C. (Peter N.C.); Brocke, J. (Jan) vom
    Scholars generally assume that consumer ratings reflect consumer satisfaction, but ratings can be influenced by the design of the rating system. We examine two rating designs—single-dimensional rating systems, which elicit overall ratings only, and multidimensional (MD) rating systems, which elicit both dimensional and overall ratings—and how they impact overall ratings. Drawing on the accessibility–diagnosticity framework, we argue that dimensional ratings in MD systems influence overall ratings based on how the dimensions have been rated. We support this explanation with seven experiments. Our results suggest that across various experimental settings, rating objects, dimensions, and numbers of dimensions, overall ratings are systematically influenced by the design of the rating system.
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    The path of the righteous: Using trace data to understand fraud decisions in real time
    (Management Information Systems Research Center, 2022-12) Valacich, J.S. (Joseph S.); Weinmann, M. (Markus); Schneider, C. (Christoph); Hibbeln, M. (Martin); Jenkins, J.L. (Jeffrey L.)
    Trace data—users’ digital records when interacting with technology—can reveal their cognitive dynamics when making decisions on websites in real time. Here, we present a trace ­data method, analyzing movements captured via a computer mouse, to assess potential fraud when filling out an online form. In contrast to existing fraud ­detection methods, which analyze information after submission, mouse ­movement traces can capture the cognitive deliberations as possible indicators of fraud as it is happening. We report two controlled studies using different tasks, where participants could freely commit fraud to benefit themselves financially. As they performed the tasks, we captured mouse ­cursor movement data and found that participants who entered fraudulent responses moved their mouse significantly more slowly and with greater deviation. We show that the extent of fraud matters such that more extensive fraud increases movement deviation and decreases movement speed. These results demonstrate the efficacy of analyzing mouse ­movement traces to detect fraud during online transactions in real time, enabling organizations to confront fraud proactively as it is happening at internet scale. Our method of analyzing actual user behaviors in real time can complement other behavioral methods in the context of fraud and a variety of other contexts and settings.