9월 2주차

Information asymmetry is a persistent challenge in online labor markets because employers cannot directly observe worker quality. Platforms attempt to mitigate this problem by introducing signaling mechanisms. We examine how employers respond to a novel signaling device: a voluntary “worker-offered guarantee,” in which workers deposit their own money alongside job bids. Unlike traditional money-back guarantees, forfeited deposits are donated to charity rather than compensating the employer, making the signal non-compensatory and selectively deployed. Leveraging detailed bid-level data from a leading freelance platform, we find that medium-reputation workers are more likely to adopt the guarantee than both high- and low-reputation workers. Contrary to the intended positive effect, employers systematically penalize workers who offer guarantees. This negative response is attenuated for high-reputation workers, complex projects, and experienced employers. Overall, rather than ignoring the signal, employers engage in counter-screening, actively discounting its use in hiring decisions. More strikingly, this skepticism appears economically justified, as workers offering guarantees perform worse on average than those who do not. Our findings advance signaling theories by empirically documenting receiver-side sophistication in response to a newly introduced signal in a mature market with strong preexisting reputation mechanisms, where employers receive no immediate or reliable feedback about signal validity. From a practical perspective, our study highlights the complexity of user reactions to platform and mechanism design innovations.

Membership plans, which are ubiquitous in many industries, have recently emerged on platforms operating under the sharing economy model. Unlike traditional firms with stable, captive supply and fixed marginal costs, sharing economy platforms rely on independent service providers who self-schedule their participation. These platforms dynamically adjust consumer prices and provider wages in response to real-time demand and supply conditions, resulting in variable marginal costs. The introduction of a membership plan further alters marginal costs and revenues. We examine a platform’s strategic decision to offer a membership plan on the consumer side, the provider side, or both. Our analysis reveals a sharp contrast in the conditions under which the platform benefits from offering membership on one side versus the other. Specifically, a consumer-side-only (provider-side-only) membership plan is optimal when the expected supply (demand) potential is greater than a threshold. The primary source of profit gain from a consumer-side (provider-side) membership plan is the membership fee revenue (operational profits). The platform prefers the both-sides membership plan over no plan only when neither the expected demand potential nor the expected supply potential is too large. Introducing a membership plan on only one side harms participants on that side while benefiting those on the opposite side. Under both-sides membership plan, one side benefits at the expense of the other. Notably, both members and non-members on a given side maybeworse offfollowing the introduction of a membershipplan. Fromasocialwelfareperspective, a consumer-side-only membership plan consistently improves welfare, whereas provider-side-only and both-sides membership plans do not necessarily do so. Our findings highlight the platform’s exposure to risks arising from supply and demand fluctuations and the role of self-scheduling, by consumers and providers, in a platform’s membership plan decision.

Causal forests estimate how treatment effects vary across individuals, guiding personalized interventions in areas like marketing, operations, and public policy. A standard practice is honest estimation—dividing the data into two samples, one to define subgroups and another to estimate treatment effects within them. This is intended to reduce overfitting and is the default in many software packages. But is it the right choice? We show that honest estimation can reduce the accuracy of estimates of individual treatment effects—especially when effect heterogeneity is substantial and datasets are large enough to detect it. The reason is a bias-variance trade-off: honesty lowers the risk of overfitting but increases the risk of underfitting by limiting the data available to detect and model heterogeneity. Across more than 7,000 benchmark datasets, we find that the cost of using honesty by default can be as high as requiring 27% more data to match the performance of models trained without it. Honesty is best understood as a form of regularization. Whether to adopt it should depend on the goals of the application and its empirical performance, not on reflexive default use.

Rating inflation is prevalent in reputation systems and can reduce their informational value. Through an exploratory mixed‐method study, we examine how and why rating inflation behavior evolves over time as reviewers socialize within an online reputation system platform community. We show that some reviewers are more prone to inflation while others avoid it. We draw on the theory of reciprocity and distinguish among three archetypical phases of reviewers: (1) the Newcomer Phase, (2) the Inflator Phase, and (3) the Veteran Phase. Newcomers who are not yet socialized in the platform community are less likely to inflate their ratings. Inflators exhibit the social norm of direct reviewer reciprocity and inflate their ratings. Veterans, reflecting the social norm of generalized reviewer reciprocity, are less likely to inflate ratings. Instead, they are more candid because they are driven by a commitment to help the platform community. These archetypical phases correspond to reviewers’ increasing socialization within the platform community and exposure to different digital platform affordances. Together, these processes shape which forms of reciprocity become salient over time—whether lacking, direct, or generalized—which consequently drive distinct reviewer behaviors. While most research on rating inflation emphasizes direct reciprocity, we highlight how reviewers can shift toward less inflated behaviors over time, aligning with generalized reciprocity towards the platform community. Thus, this exploratory work suggests an evolutionary process of rating behavior whereby rating inflation follows an inverted U-shape over the reviewer lifecycle.

This research examines how externally hired senior technology executives reshape product innovation in startup complementors operating within software platform ecosystems. We distinguish two innovation types tied to the platform’s layered modular architecture: layer-expansion innovation that extends products into previously unused technical layers, and within-layer innovation that deepens the use of layers already embedded in the complementor’s products. Using longitudinal data from startup complementors in the Hadoop ecosystem, we find that externally hired senior technology executives are positively associated with layer-expansion innovation but negatively associated with within-layer innovation, indicating a reallocation of innovation efforts across platform layers. Drawing on the cognitive and technical dimensions of tacit knowledge, we theorize that externally hired senior technology executives influence innovation by introducing knowledge from their prior experience in technology and product development. Multiple mechanism analyses provide evidence consistent with this knowledge-based explanation. The innovation effects are stronger when executives are hired into newly created positions and when their prior expertise extends beyond the startup’s existing technical layers. We also find that these effects are more pronounced among growth-stage startups and startups without senior technical founders. This research contributes to the software platform ecosystem, executive human capital, and entrepreneurial innovation literature by investigating the impact of externally hired senior technology executives on startup innovation relative to the platform’s layered architecture.

For a collaboration between humans and artificial intelligence (AI) to be fruitful, tasks should be allocated based on their complementary capabilities. Prior research shows that when humans are responsible for allocating tasks between themselves and an AI through delegation, they often delegate too infrequently or delegate the wrong tasks, preventing complementary performance gains.We study how different types of AI system information affect both delegation frequency and delegation effectiveness, which capture the extent to which humans can leverage existing complementarities with AI. Specifically, we study ex-ante AI certainty (the AI’s estimated likelihood of being correct) and ex-post AI outcome information (whether the AI was actually correct on a given task). We show experimentally that presenting either AI certainty before or the AI’s outcome after a delegation decision has no or even negative effects on combined human-AI performance. However, providing both AI certainty and AI outcome information leads to increased delegation frequency as well as more effective delegation, ultimately leading to beneficial performance. We find that ex-ante certainty information calibrates users’ expectations about AI performance on the task-instance level, while ex-post outcome information confirms or disconfirms these expectations. This complementary use of AI system information supports more accurate mental models of the AI’s capabilities, reduces unwarranted algorithm aversion and improves appropriate task allocation. Overall, our results show that the effects of AI system information should not be assessed in isolation. While each signal on its own can be uninformative or even harmful, combining them can reverse the potentially harmful individual effects and facilitate effective human-AI collaboration. Our findings have implications for the design of AI systems in collaborative delegation settings, suggesting that carefully designed system information can help users better leverage complementarities with AI.

Recent regulatory scrutiny has highlighted concerns regarding Amazon’s use of market data in competing with its sellers through its private-label operations, prompting the introduction of new data governance policies across multiple jurisdictions. Motivated by this development, this paper examines the effectiveness of regulations governing platform data use. We study two policies: Policy S, which prohibits the retailer’s use of market data while granting exclusive access to sellers, and Policy RS, which allows shared data access between the retailer and sellers, reflecting emerging industry practices. These policies are evaluated against a baseline case with no data restrictions. Our analysis shows that while Policy S effectively restricts platform data use and protects sellers, it also generates strong incentives for the retailer to engage in strategic responses, including contractual adjustments and policy circumvention, as it represents the least favorable outcome for the platform. Policy RS, in contrast, emerges as a practical compromise that better aligns platform and seller incentives. We further show that the effectiveness of data regulations depends on the contractual environment and the platform’s ability to adjust its organizational structure. A key insight is that the retailer can undermine regulatory intent through subtle but systematic strategies, such as steering sellers across contract forms and exploiting regulatory ambiguities. These effects become more pronounced under the wholesale contract. Our findings highlight that recent regulations may be insufficient if they do not account for strategic platform behavior. We find that recent data regulations face important challenges, as they may not prevent the retailer from exploiting contractual flexibility and regulatory loopholes. We therefore provide guidance for designing more robust regulatory frameworks with clearer conditions and enforcement mechanisms that explicitly incorporate contract structure and limit opportunities for regulatory circumvention, thereby strengthening protection against unfair competitive practices by dominant platforms.

Participants on tokenized platforms (i.e., platforms with blockchain implementation) can simultaneously take multiple roles, such as user, investor, and laborer, and draw income from the last two roles. Unlike traditional markets that typically prioritize one means of profitable participation, participants on such platforms need to allocate their efforts on the platform to increase revenue. We developed a decision framework for determining participants’ strategic participation on tokenized platforms to maximize earnings from investment and labor. Individual participants were distinguished from the platform-average participant, and decision-making is cast into two subproblems: (1) ignoring individual actions’ impact on platform state, we constructed strategies based on metrics that characterized model projections of future platform development and derived the metrics from Monte Carlo ensembles; (2) considering individuals’ actions as explicitly influencing the platform state, we formulated the control problem as a Markov decision process and solved it via reinforcement learning (RL). The framework addresses parameter uncertainty from model estimation, system uncertainty in model projection, and input uncertainty during participant-platform interaction. We compared metric-based and RL strategies from the two solution approaches using historical token price series; the results suggest good performance of our decision framework.

Racial discrimination in crowdfunding is a significant barrier to equitable access to capital, as racial minorities face greater challenges in achieving their fundraising goals. While prior research has documented discriminatory patterns in crowdfunding outcomes, the underlying mechanisms driving this discrimination remain unclear. This is a critical gap that must be addressed to develop effective interventions. Drawing on economic theories of taste-based and statistical discrimination, we examine how discrimination mechanisms vary across crowdfunding types. We posit that donation crowdfunding primarily exhibits taste-based discrimination, while investment crowdfunding manifests statistical discrimination. We tested these predictions through three preregistered randomized experiments. Confirming our prediction, the first experiment revealed taste-based discrimination against Black fundraisers in donation crowdfunding. The second experiment demonstrated statistical discrimination in investment crowdfunding. However, contrary to prior research showing negative discrimination against minorities, our second experiment revealed positive statistical discrimination toward Asian fundraisers. We posit that this positive discrimination is due to the decision frame participants adopt, and this explanation is supported by Experiment 3: The direction of discrimination is reversed when task instructions are modified to elicit a negative decision frame. Our research advances both the crowdfunding and the racial discrimination literatures while providing insights for platform design and debiasing interventions.