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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%.

Over the last decade, researchers and practitioners have become increasingly interested in how firms can leverage managerial responses (MRs) to capitalize on positive reviews and mitigate the potential harm caused by negative reviews. Intuitively, companies should respond to negative reviews (MR-N) to mitigate the damage they cause, while offering more general responses to positive reviews (MR-P). However, the proportion of MR-P on TripAdvisor has consistently increased since 2006 and has surpassed the percentage of MR-N since 2013. Two key questions remain: Given the constraints of limited resources, should managers prioritize soothing unsatisfied customers or pleasing satisfied ones? What are the effects of MRs on customer review ratings and financial outcomes?

Peer-to-peer (P2P) sharing marketplaces enable sharing of idle resources. When a renter requests an owner’s resource, the owner needs to decide whether to accept the request: accepting it helps the owner fill up the idle periods of the resource and generate a payoff but reduces the flexibility to serve a future request for a longer duration. This paper develops a framework to uncover the tradeoffs faced by owners on these platforms when making acceptance decisions, which can be used by owners to optimize their decisions and by platforms to improve their operations. The model explicitly accommodates two types of owners: some are attentive to the availability states of their cars and forward-looking, whereas others myopically make the acceptance decisions. Applying the model to unique data from a leading peer-to-peer car sharing platform in China, we obtain similar sizes of both types of owners and find that female, experienced, and younger owners are more likely to be strategic. The results also reveal the differentiated preferences of the two types of owners toward their renters. Building on model estimates, we calibrate the option value of each day in the future (i.e., the value of having the day available) for strategic owners and find it to first increase, then decrease. Two counterfactual analyses are conducted. The first analysis shows that if the platform imposes a minimum rental duration, strategic owners may become more reluctant to accept requests, even if the current availability state entails a higher expected payoff. The second analysis shows that with better understanding of its owners, the platform can greatly improve the matching efficiency by optimal (re)allocation of rental requests, a move that benefits almost all participants in the business.