Chenhui Guo

Online labor platforms often introduce new features to help employers identify reliable workers. This study examines one such feature: a worker-offered guarantee, in which freelancers voluntarily deposit their own money with a bid, with forfeited funds donated to charity rather than paid to the employer. Although such guarantees are intended to signal confidence and commitment, our analysis of bid-level data from a major freelance platform shows that employers interpret them differently. Workers with medium reputations are most likely to offer guarantees, and employers are less likely to hire workers who use them. This skepticism appears economically justified: guarantee-offering workers receive lower post-contract ratings on average. The findings suggest that well-intentioned platform tools can backfire when users see them as signs of weakness or desperation rather than quality. For platform managers and policymakers interested in digital labor markets, the study highlights the need to design signals with credible costs, clear enforcement, and alignment with existing reputation systems. New marketplace features should be evaluated not only by whether users adopt them, but also by how other market participants interpret and act on them.

Open-source software (OSS) platforms have become important infrastructure for scientific knowledge production, particularly in code-intensive fields where executable code artifacts are central to replication and reuse. We study attributable code sharing, defined as complete papercode linkage in which a paper provides a code link and the associated GitHub repository explicitly identifies the paper. Using 14,193 papers in computer science, mathematics, and physics published between 2013 and 2019, we examine how attributable code sharing relates to two dimensions of scientific impact: citation impact and disruptive impact. To isolate the incremental role of attribution, we restrict the analysis to papers that already provide a public code link and compare complete with incomplete linkage, using inverse probability of treatment weighting to balance observable differences. We document a systematic citation-disruption tradeoff. Attributable code sharing is positively associated with citation impact, consistent with what we term an audience formation effect, whereby complete linkage lowers the discovery, attribution, and implementation frictions facing downstream researchers. It is simultaneously negatively associated with disruptive impact, consistent with what we term a knowledge-lineage formation effect, whereby code-linked papers become more deeply embedded in the prior methods and benchmarks their repositories inherit. The citation association is stronger in code-intensive fields, and both associations are stronger for high-status author teams. OSS-mediated openness thus appears related not only to how much attention research receives, but also to the form that follow-on knowledge production takes.

Word-of-mouth (WOM) metrics are commonly treated as comparable across digital platforms, even when platforms organize information around different purposes. We introduce platformconditioned diagnosticity, the proposition that the same WOM characteristic can have different predictive implications depending on the platform's functional orientation and information environment. Using 734,367 WOM entries from a quality-focused complaint platform and a comprehensive automobile platform, matched with monthly sales for 50 Chinese manufacturers over 2018-2022, we examine their joint dynamics. WOM volume positively predicts subsequent sales on both platforms, whereas negative sentiment intensity predicts lower sales on both. Topic focus shows the sharpest divergence: it predicts lower sales on the quality-focused platform but higher sales on the comprehensive platform. WOM volume is the most persistent characteristic, and quality-focused volume gains relative predictive importance at longer horizons. These findings show why identical WOM metrics should not be interpreted independently of the platform environments that produce them.

As cybersecurity threats intensify, firm financial risk continues to rise despite growing cybersecurity investments, raising doubts about their effectiveness. We address this paradox by developing a signaling-based theoretical framework, integrating signaling theory with homophily, to explain how cybersecurity investments may relate to firm financial risk through the lens of corporate governance. Leveraging a comprehensive panel dataset from 1,071 firms over 10 years, we provide empirical evidence of a striking inverted U-shaped relationship: initial cybersecurity investments are associated with higher financial risk, but risk declines at higher levels of investment, underscoring the importance of investment scale and maturity. Board experience heterogeneity flattens this curve, dampening the rise in risk at lower levels of investment while slowing the decline at higher levels. These findings reconcile competing views of cybersecurity investment, highlight the strategic importance of board composition, and underscore the need to integrate cybersecurity investments into enterprise risk management.

Online labor markets routinely employ auction mechanisms to assist the project providers (i.e., buyers or employers) in recruiting eligible participants (i.e., sellers or workers) who have placed bids in either sealed or open auctions. In a sealed auction, participants know only the number of competitors, but not their profiles and bidding amounts. Conversely, in an open auction, all this information is public. To understand which auction format results in more optimal allocation (i.e., effectively matching buyers and sellers) and greater time efficiency, we build a theoretical model to generate a series of hypotheses, then test these hypotheses using data from an abrupt change of auction format in an online labor market. Our findings indicate that in sealed auctions, where sellers cannot observe their competitors' bids, they tend to place lower bids and bid more quickly than sellers in open auctions. Additionally, the effect of auction format switching is less pronounced for experienced sellers compared to new sellers. The empirical results align with our theoretical explanation: In open-bid auctions, sellers have strong incentives to delay bidding or place initial bids higher than their valuation to learn from competitors' bids and adjust accordingly (if there is a chance). This strategic bidding behavior can lead to misallocation and time inefficiency. Specifically, while open auctions attract more participants, buyers take longer to make hiring decisions, and the likelihood of hiring decreases. Furthermore, sealed auctions result in better project completion and fewer seller exits.

Signaling mechanisms are prevalent in two-sided markets because they typically help reduce information asymmetry and improve efficiency. While a growing number of studies focus on the actions of signal-senders, there has been little empirical research on how signal-receivers may react to the signals, despite the importance of this question. A well-intentioned signal could backfire, or "boomerang," if the receiver does not react the way that its sender or designer intends. The introduction of a novel project-specific, voluntary guarantee-deposit ("guarantee" for short) in an online labor market provides an excellent opportunity to answer this question. Leveraging detailed transaction data, we study how employers (signal-receivers) react to the presence guarantees in the bids placed by workers (signal-senders). We find strong evidence that, despite the platform's advocacy for the benefits of providing a guarantee, employers are less likely to award contracts to workers who use this signaling mechanism than those who do not. This is particularly prominent when uncertainty about the project or worker is high. Only workers who already possess high customer ratings improve their chances of winning contracts by offering such a guarantee. These results indicate that employers do not just ignore this novel signal, but use it to "counter-screen" workers. In other words, this well-intentioned "signal" boomerangs (or backfires) for most workers who need them the most. Remarkably, such employers' reactions seem economically justified: Workers who offer guarantees are less likely to perform well on the job compared to those who do not. The level of sophistication the employers demonstrate regarding the signaling mechanism in online labor markets is striking. Our findings provide fresh nuances to the signaling literature in two-sided markets and have implications for platform designs.

UGC (User-generated content) websites routinely deploy incentive hierarchies, where users achieve increasingly higher status in the community after achieving in