Latest Research Publications

Customer referencing is a strategy that firms can use to disclose their connections with reputable customers as a means of enhancing their own reputations. We study the capital market benefits of naming reputable nonmajor customers in firms' financial reports to provide empirical evidence on whether this form of customer referencing has important practical implications. We predict and find that firms enjoy a lower cost of equity when they engage in customer referencing in their financial reports, consistent with the argument that this form of voluntary disclosure increases investor attention and customer certification. In cross-sectional analyses, we predict and find that the benefits of customer referencing are more pronounced for firms that (1) lack major customers or reputable major customers, (2) name customers whose reputations exceed their own, and (3) face higher competition. Overall, our study provides evidence that communicating certain interorganizational connections can generate capital market benefits for disclosing firms.

While venture capital firms are increasingly relying on recommendation models in investment decisions, existing startup recommendation models fail to consider the uniqueness of venture capital context, including two-sided matching between investing and investee firms and a lack of information disclosure requirements on startups. Following the design science research paradigm and guided by the proximity principle from social psychology, we develop a novel framework called SocioLink by depicting and analyzing various relations in a knowledge graph via machine learning. Our experimental results show that SocioLink significantly outperforms state-of-the-art startup recommendation methods in both accuracy and quality. This improvement is driven by not only the inclusion of social relations but also the superiority of modelling relations via knowledge graph. We also develop a web-based prototype to demonstrate explainable artificial intelligence. This work contributes to the FinTech literature by adding an innovative design artifact—SocioLink—for decision support in the investment context.

We present a novel explanation of why organizations tend to lose their agility over time despite their efforts to foster worker initiative in adapting to local information. Worker initiative ensures efficiency but requires strong incentives. When incentives are relational and the firm faces shocks to its credibility, it may adopt standardized work processes that ignore local information but yield satisfactory (though suboptimal) performance. The adoption of such standardized processes helps the firm survive the current shock but inflicts inefficiencies in the future. Although the firm may recover, it becomes more vulnerable to future shocks, and consequently, more reliant on the standardized work procedures.

Informative online ratings enable digital platforms to reduce the search cost for buyers to find good sellers. However, rating inflation, a phenomenon in which average rating increases and rating variance across listings decreases, threatens the informativeness of ratings. We empirically identify the consequences of rating inflation by conducting a quasi-experiment with a digital platform that exogenously changed its rating display rule in a treated neighborhood, which resulted in rating inflation. Using a differences-in-differences approach, we find that platforms benefit from one aspect of rating inflation: user purchases and seller sales increase because of the increased average rating. However, they also face negative consequences: rating inflation causes a decrease in user trial and a greater concentration of sales among popular restaurants. Overall, our results illustrate the potential consequences of rating inflation that platforms need to consider when designing and managing their rating system.

Manufacturers of consumer-packaged goods invest heavily in trade promotions (i.e., temporary wholesale price discounts), but retailer stockpiling often yields trade promotions unprofitable. In this paper, we investigate how a manufacturer should respond to the retailer’s and consumers’ stockpiling ability by contracting with the retailer. Specifically, we examine when the manufacturer should restrict the retailer’s stockpiling ability and when it should issue trade promotions. Our analysis suggests the following. First, the manufacturer should restrict the retailer’s stockpiling ability when the storage cost is low; such restriction also benefits the retailer, resulting in a win-win outcome. Second, the manufacturer should offer trade promotions when the retailer cannot stockpile products and the storage cost is low but raise the wholesale price when the retailer can stockpile products. Third, stockpiling improves channel coordination and increases the manufacturer’s profit; therefore, the manufacturer should design products to be more storable.

Websites commonly use visual formats to display numerical product ratings. Highlighting the overlooked notion of the “aesthetics” of product ratings, the current research examines how the shape of basic visual rating units (rectangular vs. non-rectangular) influences product preference. Seven experiments (and 23 supplementary experiments; N = 17,994) demonstrate a visual rounding effect. Specifically, compared to the rectangular rating format (e.g., bar ratings), the non-rectangular rating format (e.g., star ratings) increases product preference when product ratings (e.g., 3.7, 3.8, 3.9) are below the nearest integer. In contrast, the non-rectangular rating format decreases product preference when product ratings (e.g., 4.1, 4.2, 4.3) are above the nearest integer. Occurring for both the overall rating and by-attribute ratings of a product, the visual rounding effect results from a visual completeness restoration process, wherein consumers perceive non-rectangular rating units to be incomplete after vertical cutting. This research contributes to the product rating and visual marketing literatures and provides actionable implications by demonstrating what visual rating format should be adopted based on rating distribution, how the visual rounding effect can be prevented if needed, and who are even more susceptible to the visual rounding effect.

This paper studies whether and how environmental, social, and governance (ESG) disclosure regulations imposed on banks generate transmission effects along the lending channel. I use a setting of U.S. firms borrowing from non-U.S. banks and exploit the staggered adoption of ESG disclosure regulations in banks’ home countries. I find that exposed borrowers of affected banks improve their environmental and social (E&S) performance following the disclosure mandate. Consistent with banks enhancing both their engagement and selection activities, affected banks impose more environmental action covenants in loan contracts, and they are more likely to terminate a borrower with bad E&S records following the regulation. Further evidence shows that the transmission effects are stronger when a disclosure regulation is well-enforced (as indicated by a greater increase in banks’ disclosure) and among borrowers with greater switching costs. Collectively, the findings document the role of lending relationships in transmitting the real effect of ESG disclosure regulations from banks to borrowing firms.

Empirical researchers are increasingly faced with rich data sets containing many controls or instrumental variables, making it essential to choose an appropriate approach to variable selection. In this paper, we provide results for valid inference after post- or orthogonal L2-boosting is used for variable selection. We consider treatment effects after selecting among many control variables and instrumental variable models with potentially many instruments. To achieve this, we establish new results for the rate of convergence of iterated post-L2-boosting and orthogonal L2-boosting in a high-dimensional setting similar to Lasso, i.e., under approximate sparsity without assuming the beta-min condition. These results are extended to the 2SLS framework and valid inference is provided for treatment effect analysis. We give extensive simulation results for the proposed methods and compare them with Lasso. In an empirical application, we construct efficient IVs with our proposed methods to estimate the effect of pre-merger overlap of bank branch networks in the US on the post-merger stock returns of the acquirer bank.

Employee treatment is an important but challenging element of corporate environmental, social, and governance policies. Satisfying employee needs can increase corporate productivity, but is also costly to shareholders. Using unique data of Chinese publicly listed firms, we show that having satisfied employees is valuable to the firm. Specifically, firms with higher employee satisfaction scores withstand COVID-19 better, in terms of stock market performance. Such an effect is more pronounced for firms with more intangible assets and in knowledge-based industries. Moreover, higher employee satisfaction scores predict better operating performance. While not fully revealed in tranquil times, the effect of employee satisfaction is materialized when the firms experience negative shocks, such as COVID-19. Our findings suggest that firms can do well in crisis periods by doing good in normal times.