团队

Many important platforms, particularly in healthcare, hospitality, and content streaming, depend on a small number of strategic participants on the “seller” side. As a result, individual-level bargaining and participant-specific network effects are central to platform growth and profitability. This paper introduces a novel modeling framework that integrates bargaining outcomes with heterogeneous direct network and cross-network effects to capture platform evolution. We estimate participant-level direct network and cross-network effects based on the attributes of strategic sellers and assess how these shape bargaining outcomes. By modeling both time-varying, participant-specific network effects, which influence market growth, and their impact on bargaining outcomes, which affect profitability, our framework enables platforms to evaluate growth strategies. We apply the model to data from a major Chinese online healthcare platform connecting hospitals and consumers for health checkups. We find substantial heterogeneity in hospitals’ network effects, which drive variations in bargaining outcomes. Hospitals with stronger network effects negotiate lower commission rates, whereas the platform secures higher commission rates in markets where it holds a larger market share. Through policy simulations, we explore strategies including seeding, targeting sequence, and market entry. Our findings highlight key trade-offs between growing market size and maximizing profitability, offering insights for platforms built on negotiated partnerships.

Managerial responses (MRs) have gained increasing attention as an important intervention strategy for addressing online customer reviews. This study seeks to answer the question of how a firm should prioritize responding to customers’ positive reviews (MR-P) and negative reviews (MR-N). We examine the differential effect of the MR-P ratio and the MR-N ratio on subsequent customer review ratings and a firm’s financial performance. Our findings show that while the MR-P ratio leads to an increase in subsequent customer review ratings and revenue, the MR-N ratio results in a decrease in customer review ratings and revenue in the short run, but contributes to improvements in these metrics in the long run. Furthermore, we find that the influence of MR-P and MR-N on subsequent review ratings diminishes among firms whose MRs contain highly similar content and firms whose competitors actively create MRs. This research not only advances our understanding of the managerial response literature but also provides valuable guidance for firms seeking to maximize the effectiveness of their MR campaigns.

Workers often possess characteristics such as soft skills that are important for teamwork but unobserved by managers. In this paper, we develop a teamwork model based on the econometric teamwork framework in Bonhomme [Bonhomme S (2021) Teams: Heterogeneity, sorting, and complementarity. Becker Friedman Institute for Economics Working Paper No. 2021-15, University of Chicago, Chicago] and stochastic blockmodels for binary outcomes (e.g., Bickel et al. [Bickel P, Choi D, Chang X, Zhang H (2013) Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels. Ann. Statist. 41(4):1922–1943]) when only team-level outputs are observed. Our model does not impose any functional form restrictions on the complementarity between workers with unobserved characteristics, which are modeled as latent types. We apply our model to a data set from a leading Chinese real estate company; the data contain the complete history of team assignments, team performances, and property details. We find that complementarities between different agent types are heterogeneous and cannot be captured by commonly used production functions. More specifically, workers with intermediate solo performance complement all other workers the most, whereas those with the best solo performance are not the best team players. Our results suggest that firms can boost productivity by redesigning teams without incurring additional hiring costs. Leveraging our complementarity estimates, our counterfactual experiments demonstrate that reorganizing teams could enhance overall team output by up to 26.6%.

消费者之间（P2P）的共享市场有助于实现社会闲置资源的共享。当租客对物主的资源发出共享请求时，物主则需要决定是否接受该请求：接受请求可以马上填补资源的闲置期，给物主带来即刻回报，但却降低了物主满足未来对资源有更长时间的请求（更高回报）的灵活性。本文建立了一个模型，来揭示这些共享平台上物主在决定接受租客请求时面临的权衡。该模型可以优化物主的决策，并改善平台的运营。该模型明确容纳了两种类型的物主：一类物主具有前瞻性，会考虑其资源可供租用的状态，而另一类物主则只考虑眼前的请求，短视地做出接受决定。我们将该模型应用于中国一家领先的共享汽车租赁平台的数据集上。结果显示，数据中前瞻型和短视型车主人数各半；女性、经验丰富及年轻的车主更具备前瞻性。结果还显示两类车主对租客有不同的偏好。根据模型的估计结果，我们计算了未来每一天对前瞻型车主的期权价值（即资源一天可用的价值），发现期权价值随时间的推移先增后减。我们进行了两个反事实推论分析。第一项分析表明，如果平台规定最短租期，即便当前的租用条件可带来更高的预期回报，前瞻型物主仍会倾向拒绝请求。第二项分析则表明，随着平台对物主更深入的了解，平台可以通过对租赁请求的优化分配或再分配，大幅提高匹配效率，此举可令绝大部分平台参与者收益。