Latest Research Publications

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.

Portfolio optimization aims at constructing a realistic portfolio with significant out-of-sample performance, which is typically measured by the out-of-sample Sharpe ratio. However, due to in-sample optimism, it is inappropriate to use the in-sample estimated covariance to evaluate the out-of-sample Sharpe, especially in the high dimensional settings. In this article, we propose a novel method to estimate the out-of-sample Sharpe ratio using only in-sample data, based on random matrix theory. Furthermore, portfolio managers can use the estimated out-of-sample Sharpe as a criterion to decide the best tuning for constructing their portfolios. Specifically, we consider the classical framework of Markowits mean-variance portfolio optimization under high dimensional regime of p/n→c∈(0,∞), where p is the portfolio dimension and n is the number of samples or time points. We propose to correct the sample covariance by a regularization matrix and provide a consistent estimator of its Sharpe ratio. The new estimator works well under either of the following conditions: (a) bounded covariance spectrum, (b) arbitrary number of diverging spikes when c<1, and (c) fixed number of diverging spikes with weak requirement on their diverging speed when c≥1. We can also extend the results to construct global minimum variance portfolio and correct out-of-sample efficient frontier. We demonstrate the effectiveness of our approach through comprehensive simulations and real data experiments. Our results highlight the potential of this methodology as a useful tool for portfolio optimization in high dimensional settings. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

The remarkable success in personalized recommendations on digital platforms has sparked interest in extending this advancement to physical spaces. In response, our study introduces a generalized recommendation problem, named point-of-interest (POI) recommendations in physical spaces with pedestrian movement (P3M). A critical yet under-investigated impediment in addressing P3M is exposure bias: When the exposure likelihood of items to users is unevenly distributed, indiscriminately treating all unobserved user–item interactions as negative feedback introduces bias to the learning of recommender systems. Unlike existing debiasing literature on digital platforms, we focus on the unique source of uneven exposure in physical spaces, arising from the dynamic interaction between pedestrian movement and spatial layout. To address this issue, we propose a novel recommendation method, unbiased movement-aware pairwise ranking (UMPR), which considers dynamic pedestrian movement to achieve unbiased POI recommendations. Specifically, we formulate an unbiased pairwise learning framework, propose a movement-aware recommendation model, and devise an alternating learning algorithm to optimize model parameters. Using real-world mall data, we demonstrate that our method outperforms state-of-the-art benchmarks in delivering store recommendations for pedestrian shoppers. Further investigations confirm that the improved recommendation performance translates into added monetary value while maintaining humanistic fairness across customers and store tenants. Overall, this study underscores the significance of addressing exposure bias through adequate spatial movement modeling, paving the way for effective recommendations in the physical landscape.

This study examines the impact of discrete emotional expression (i.e., expression of anxiety, sadness, anger, disgust, love, joy, surprise, and anticipation) on the differential diffusion of online content in social media networks. We conducted an analysis on a random sample of 387,486 online articles and their corresponding diffusion cascades, involving more than six million unique individuals, on a major online social networking platform. Our investigation focused on the relationships between discrete emotional expression and the diffusion of online articles, specifically the structural properties of diffusion cascades, such as size, depth, maximum breadth, and structural virality. We employed various econometric model specifications, and our results robustly demonstrate that articles expressing higher levels of anxiety, love, and surprise reach a larger number of individuals and diffuse more deeply, broadly, and virally. In contrast, expression of anger, sadness, and joy exhibit the opposite effect. Additionally, we find that articles with different emotional expression tend to spread differently based on individual characteristics and social ties. Our findings offer valuable insights into the diffusion and regulation of online content from the perspectives of emotional expression and social networks.

Latent variable models are popularly used to measure latent embedding factors from large-scale assessment data. Beyond understanding these latent factors, the covariate effect on responses controlling for latent factors is also of great scientific interest and has wide applications, such as evaluating the fairness of educational testing, where the covariate effect reflects whether a test question is biased toward certain individual characteristics (e.g., gender and race), taking into account their latent abilities. However, the large sample sizes and high-dimensional responses pose challenges to developing efficient methods and drawing valid inferences. Moreover, to accommodate the commonly encountered discrete responses, generalized latent factor models are often assumed, adding further complexity. To address these challenges, we consider a covariate-adjusted generalized factor model and develop novel and interpretable conditions to address the identifiability issue. Based on the identifiability conditions, we propose a joint maximum likelihood estimation method and establish estimation consistency and asymptotic normality results for the covariate effects. Furthermore, we derive estimation and inference results for latent factors and the factor loadings. We illustrate the finite sample performance of the proposed method through extensive numerical studies and an educational assessment dataset from the Programme for International Student Assessment (PISA).

In the competitive landscape of modern business, tacit knowledge (TK) sharing has emerged as a critical enabler of innovation and transformation, particularly within the realm of operations management. In this context, the efficient flow of TK is essential for optimizing processes, promoting technology development, and sustaining competitive advantage. While the role of workplace friendship in facilitating TK sharing has been well-documented, there is a common misconception that close personal relationships alone suffice for effective knowledge transfer. However, in the complex dynamics of operational environments, these relationships can be unexpectedly fragile and prone to disruption. Even employees with strong workplace friendships may encounter inevitable challenges such as competition over limited resources, conflicts of interest, and diminished incremental benefits of knowledge exchange among functionally overlapping colleagues. Grounded in social information processing (SIP) theory, this study proposes a moderated mediation model and explores the negative moderating effects of competitive climate and functional similarity on the relationship between workplace friendship and TK sharing and how these factors subsequently impact innovation performance. Through a mixed-method design, including a main field study and a supplementary experiment involving 1,809 participants across various industries, our findings underscore that the positive association between workplace friendship and TK sharing will be diminished or even dissolved when employees perceive higher levels of competitive climate and functional similarity. By adopting a SIP perspective, this research elucidates how contextual factors in operational environments can overshadow the influence of workplace friendship on TK sharing. It also reveals the intricate interplay (i.e., mutual reinforcement) between the attitudes of TK holders and recipients, which is crucial for ensuring the seamless exchange of knowledge that drives operational excellence in highly competitive workplaces. These insights are vital for operations managers aiming to cultivate workforce diversity, regulate peer competition, inspire employee enthusiasm and engagement, and initiate cross-functional technology development projects. Such strategies foster effective and sustainable TK sharing, ultimately contributing to superior innovation performance in operational settings.

Research suggests that firms participating in stock market liberalization programs are exposed to global investors who can exert cross-border influence on management decisions. Accordingly, as global investors increasingly adopt environmental, social, and governance (ESG) principles, firms in these programs may enhance their corporate responsibility and their commitment to addressing grand challenges. We challenge this literature by explaining why this effect of stock market liberalization programs should not be taken for granted, especially in emerging markets and contribute to the field by showing that institutional factors moderate this relationship. Using China's stock market liberalization programs as natural experiments that quasi-exogenously connect emerging-market firms to global investors, we find that emerging-market firms in stock liberalization programs reduce their disclosure of supplier identity information, an important step in tackling environmental grand challenges. However, when emerging-market firms have certain characteristics, such as a large proportion of committed foreign ownership, global environmental certifications, and top leadership with overseas experience, the negative effect is diminished and even reversed as the balance between the long-term upsides and short-term downsides of voluntary supplier disclosure shifts.

We document that firms prefer counties with higher ethnic diversity in locating their interstate investments, especially for those pursuing innovation, seeking to establish service centers, or capable of managing a diverse workforce. We also find some evidence that interstate investment in high ethnic diversity locations results in increased patent applications, sales growth, positive media coverage, and overall operating performance. Taken together, we show that firms prefer to invest in ethnically diverse locations as they recognize the potential benefits of leveraging a diverse labor supply such as enhancing problem-solving, innovation, and performance.

Do real assets protect against inflation? Stocks’ core inflation betas are negative, while their energy betas are positive. Currencies, commodities, and real estate mostly hedge against energy inflation, but not core inflation. These hedging properties are reflected in the prices of inflation risks: only core inflation carries a negative risk premium, and its magnitude is consistent within and across asset classes, uniquely among macroeconomic risk factors. Energy inflation has become more procyclical and volatile since the 1990s, which helps explain the time-varying correlation between stock and bond returns. A two-sector New Keynesian asset pricing model accounts for these facts quantitatively.