Crowd-based Platforms

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Recent Submissions

Now showing 1 - 5 of 9
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    Facts vs. Stories - Assessment and Conventional Signals as Predictors of Freelancers’ Performance in Online Labor Markets
    ( 2018-01-03) Holthaus, Christian ; Stock, Ruth Maria
    This paper investigates how freelancers’ use of signals predicts earnings in online labor markets. Extant literature has questioned the usefulness of some assessment signals to evaluate a freelancer’s quality. We find that conventional signals - signals based on non-verifiable information - can be predictors of higher revenue, when they are based on anecdotes of positive past events (self-promotion). However, mere kindness and flattery towards the customer (ingratiation) is negatively associated with a freelancers’ earnings in OLM. Moreover, we find evidence that the number of tests performed on the platform is significantly associated with higher earnings - with each test that is added to the profile a freelancer-˜s revenue increases by 4.1 %. We base our analysis on a sample of 1065 freelancers using objective financial earnings data, independent codings and survey data.
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    Home Bias in Online Employment
    ( 2018-01-03) Liang, Chen ; Hong, Yili (Kevin) ; Gu, Bin
    We study the nature of home bias in online employment, wherein the employer prefers workers from his/her own home country. Using a unique large-scale dataset from one of the major online labor platforms, we identify employers’ home bias in their online employment decisions. Moreover, we investigate the cause of employers’ home bias using a quasi-natural experiment wherein the platform introduces a monitoring system to facilitate employers to keep track of workers’ progress in time-based projects. After matching comparable fixed-price projects as a control group using propensity score matching, our difference-in-difference estimations show that the home bias does exist in online employment, and at least 54.0% of home bias is driven by statistical discrimination.
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    Exploring the Effect of Monetary Incentives on User Behavior in Online Sharing Platforms
    ( 2018-01-03) Lu, Yixin ; Ou, Carol ; Angelopoulos, Spyros
    We examine the impact of monetary incentives on user onboarding in online sharing platforms. Specifically, drawing upon the literature of monetary incentives, privacy, and consumer behavior, we conduct a randomized field experiment to explore users' initial engagement and interaction with an online car-sharing platform. Our empirical analyses show that monetary incentives are no better than simple email reminders in encouraging users' self-disclosure of private information nor their active engagement with the platform (i.e., actual booking via the platform). Our work sheds new light on the heated debate over the design and deployment of monetary incentives in digital platforms, and provides useful implications for both academia and the industry.
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    Temporally Networked Cournot Platform Markets
    ( 2018-01-03) Pang, John ; You, Pengcheng ; Chen, Minghua
    In networked markets, information can help firms make better decisions on which market (platform), and how much, to participate. However, these markets may be temporally separated, e.g., independent system operators in different geographical locations. We model these via networked Cournot markets, but instead consider the participation of one firm to either be with the realization (or full information) of a random market, or only with the statistics of the random market, modeled by an additive zero-mean random variable on the maximal price. We show that firms not knowing the realization of the random variable would participate in both markets in the same way as if the mean was realized. We then present global effects: we prove that profit is improved for every firm when one's information improves but social welfare may get better or worst with more information.
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    Investor’s Anticipation and Future Market Movement: Evidence of Self-Fulfilling Prophecy Effect from Chinese Stock Market
    ( 2018-01-03) Wan, Yun ; Yang, Xiaoguang
    We analyzed data collected from retail investors in Chinese stock market from a fin-tech mobile platform to find evidence of self-fulfilling prophecy effect. We found statistically significant correlation between the predicted and actual Shanghai Stock Exchange Composite Index (SSECI) as well as non-random deviation patterns. We also analyzed participating investor behaviors and discussed the implications and future research.