Publications

When consumers avoid taking algorithmic advice, it can prove costly to both marketers (whose algorithmic product offerings go unused) and to themselves (who fail to reap the benefits that algorithmic predictions often provide). In a departure from previous research focusing on when algorithm aversion proves more or less likely, we sought to identify and remedy one reason why it occurs in the first place. In seven pre-registered studies, we find that consumers tend to avoid algorithmic advice on the often faulty assumption that those algorithms, unlike their human counterparts, cannot learn from mistakes, in turn offering an inroad by which to reduce algorithm aversion: highlighting their ability to learn. Process evidence, through both mediation and moderation, examines why consumers fail to trust algorithms that err across a variety of prediction domains and how different theory-driven interventions can solve the practical problem of enhancing trust and consequential choice in algorithms.

The seller of an asset has the option to buy hard information about the value of the asset from an intermediary. The seller can then disclose this information before selling the asset in a competitive market. We study how the intermediary designs and sells hard information to robustly maximize the intermediary's revenue across all equilibria. Even though the intermediary could use an accurate test that reveals the asset’s value, we show that robust revenue maximization leads to a noisy test with a continuum of possible scores. In addition, the intermediary always charges the seller for disclosing the test score to the market, but not necessarily for running the test. This enables the intermediary to robustly appropriate a significant share of the surplus resulting from the asset sale.

Mask usage remains low across many parts of the world during the COVID-19 pandemic, and strategies to increase mask-wearing remain untested. Our objectives were to identify strategies that can persistently increase mask-wearing and assess the impact of increasing mask-wearing on symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections.

We analyze a firm’s optimal pricing of a new service when consumers interact in a network and impose positive externality on one another. The firm initially provides its service for free, leveraging network externality to promote rapid service consumption growth. The firm raises the price and starts earning revenue only when a sufficient level of consumer interactions is established. Incorporating the local network effects in a nonstationary dynamic model, we study the impact of network structure on the firm’s revenue and optimal pricing decision. We find that the firm delays the timing of service monetization when it faces a more strongly connected network despite the fact that such a network allows the firm to monetize the service sooner by resulting in faster consumption growth. We also find that the firm benefits from network imbalance; that is, the firm prefers a network of consumers with varying degrees of connections to that with similar degrees of connections. We also study the value of knowing the network structure and discuss how such knowledge impacts the firm’s profitability. Our analyses rely on the techniques from algebraic graph theory, which enable us to solve the firm’s high-dimensional dynamic pricing problem by relating it to the network’s spectral characteristics.

We analyze nearly 7,000 shareholder activist campaigns across 56 countries and show that shareholder activism is now a global phenomenon. Our analyses provide evidence on factors that explain the spread of activism and two related questions. First, we measure the extent to which country-level governance regulations facilitate shareholder engagement, a necessary condition for activism, and show that our measure of shareholder-empowering governance regulation explains cross-country variation in the emergence of activism. Second, we show that changes in these regulations also affect outcomes for firms that face a high threat of activism but that are not targeted by activists (i.e., there are spillovers)—including increased profitability, higher payouts, and reduced investment. These effects are most pronounced in countries with weak minority shareholder rights, where activism had previously been relatively unimportant.

The pursuit of desirable outcomes is often hindered by the threat of failure. While extant research largely characterizes self-threatening outcomes as eliciting an avoidance motivation, the current work demonstrates a novel intervention that can shift people towards an approach motivation: ambivalence towards the outcome. Within professional and personal domains, we show in seven experiments that considering both the pros and cons, rather than just the pros, of a self-threatening outcome encourages people to pursue it. We find that this heightened approach motivation occurs because ambivalence reduces an outcome’s desirability, in turn reducing self-threat, serially mediating the relationship between ambivalence and likelihood of pursuing the outcome. Further, we show that people do not intuit this effect and are likely not taking advantage of it. We conclude by discussing the managerial and theoretical implications of ambivalence in the face of self-threat.

In our “post-truth” era, misinformation spreads not only because people believe falsehoods, but also because people sometimes give dishonesty a moral pass. The present research examines how the moral judgments that people form about dishonesty depend not only on what they know to be true, but also on what they imagine might become true. In six studies (N = 3,607), people judged a falsehood as less unethical to tell in the present when we randomly assigned them to entertain prefactual thoughts about how it might become true in the future. This effect emerged with participants from 59 nations judging falsehoods about consumer products, professional skills, and controversial political issues—and the effect was particularly pronounced when participants were inclined to accept that the falsehood might become true. Moreover, thinking prefactually about how a falsehood might become true made people more inclined to share the falsehood on social media. We theorized that, even when people recognize a falsehood as factually incorrect, these prefactual thoughts reduce how unethical the falsehood seems by making the broader meaning that the statement communicates, its gist, seem truer. Mediational evidence was consistent with this theorizing. We argue that prefactual thinking offers people a degree of freedom they can use to excuse lies, and we discuss implications for theories of mental simulation and moral judgment.

Job crafting refers to the myriad ways employees customize their jobs, such as by altering their tasks and social interaction at work. Numerous scholars over the past 20 years have remarked on the overall need to better understand the role of time in job crafting. However, the literature has not considered how employees think about time, or, relatedly, how they use and manage it—and why this might matter for job crafting. To address these unresolved issues, the current paper develops a conceptual model of individual-level, time-related characteristics that shape employees’ engagement in job crafting and the effects of job crafting efforts on their well-being. We first review the prevailing understanding of time in job crafting research: merely operating as a medium for change, in the background. We then introduce our new conceptualization of time as central to job crafting—as temporal characteristics of the job crafter

We propose a theory of gig economies in which workers participate in a shared labor pool utilized by multiple firms. Firms face a trade-off in setting pay rates; high pay rates are necessary to maintain a large worker pool and thus reduce the likelihood of lost demand, but they also lower profit margins. Larger firms pay more than smaller firms in the resulting pay equilibrium, leading to a free-riding effect. Specifically, firms smaller than a critical size pay the minimal rate possible (the workers' reservation wage), and all firms larger than the critical size earn the same total profit regardless of size. Together, these results show that gig firms experience strong diseconomies of scale in labor cost. We also show formation of a gig economy requires the existence of a firm large enough to support a worker pool on its own. We then examine the implications of this wage equilibrium on the demand side of the market. When firms share workers but sell in independent product markets, it is a dominant strategy for firms to price to maximize the surplus they generate. This maximum surplus provides an endogenous measure of firm size. When firms are perfectly competitive in the same product market, market collapse (zero output) is the only equilibrium, which may lead to tacit collusion among the firms to avoid this outcome. Our findings are consistent with stylized facts about the evolution of gig markets such as ride sharing.

This paper is an analysis of important considerations for policymakers seeking to establish a market support program (MSP). Our main purpose is to assist policymakers who have already made the decision to use an MSP in designing the most effective program possible. Our insights derive from 23 case studies the Yale Program on Financial Stability produced and existing literature on the topic.

By the onset of the Global Financial Crisis (GFC), market-based finance and traditional banking systems were significantly intertwined, and the panic in market-based finance threatened to spread quickly to both traditional banks and the real economy. In response, authorities in several advanced economies rolled out MSPs to ease the stress in wholesale-funding markets. In an earlier study, we surveyed 19 of these GFC-era programs along with two pre-GFC programs of similar design.

The GFC programs were just a prelude, however, as central banks established or revived a variety of MSPs during the early months of the COVID-19 crisis. Many countries in both advanced and emerging markets acted quickly to improve liquidity and restore confidence in critical funding markets, as well as restart the flow of credit to households and businesses.

In our review of these cases, we identified four major themes: (1) speed is important in the acute phase; (2) clear communication is a valuable policy tool in the acute phase, as announcement effects can be powerful; (3) combining MSPs with backup fiscal support can be a valuable tool; and (4) the use of a special purpose vehicle to house and manage the assets obtained through the program from the markets can be beneficial.

Disclosure is of fundamental interest to accounting research. When the sign/magnitude of disclosed news is unclear, the information in disclosure events is inferred using the ratio of return volatilities during event and non-event windows (Beaver 1968). We show that return noise due to microstructure frictions and mispricing affects this ratio, and that effect is comparable to or exceeds that of information content. We use the SEC's Tick Size Pilot program to confirm the causal effect of return noise on the ratio, and to evaluate alternative ways to control for it. The most promising approach is to use the difference between, rather than the ratio of, return volatilities during event and non-event windows. We illustrate its benefits by showing how it alters prior inferences regarding time-series and cross-sectional variation in information content as well as changes in the information content of earnings announcements around the 2004 amendments to Form 8-K filings