Selected Publications
Inter-Unit Executive Redeployment in Multiunit Firms: Evidence from Korean Business Groups
( Chang, Sea-Jin | Kim, Young-Choon | Park, Sangchan )ORGANIZATION SCIENCEforthcoming
Abstract
Building on the literature on resource reconfiguration theory, we formulate a new theoretical framework that explains how executive redeployment within a diversified firm transfers different types of human capital embodied in executives to different units facing specific business challenges. In the empirical context of Korean business groups, we find that executives with unit-specific human capital, like turnaround experience, competitive experience, and international expansion experience, are redeployed to units with corresponding business challenges like financial difficulties, intensifying competition, and early-stage international expansion, respectively. We also show that executives with unit-generic human capital, like corporate management practices and interunit coordination experiences, are redeployed to younger units seeking to establish corporate-level policies and practices. Additional analyses also show that the value of firm-specific human capital in driving the redeployment of executives is contingent on their functional orientation and seniority.
Managers' Inventory Holding Decisions in Response to Natural Disasters
( Cho, Seunghyun | Jung, Boo Chun | Silva, Felipe B. G. | Yoo, Choong-Yuel )ACCOUNTING REVIEW2026-03
Abstract
We study how firms' inventory holdings are affected by natural disasters. Building on the premise that managers often make decisions in line with the availability heuristic, we investigate whether managers increase inventory holdings in response to heightened disaster risk perceptions and the need to hedge against inventory shortages. Through a battery of tests, we show that the occurrence of disasters in neighboring counties triggers inventory stockpiling, an effect that is unlikely to be driven by the real disaster disruptions. Our results also indicate that inventory stockpiling is likely inconsistent with a rational expectations equilibrium. Collectively, our results highlight another undesirable consequence of natural disasters and warn about supply chain implications due to increased climate ambiguity.
Auction design with ambiguity: Optimality of the first-price auction
( Hwang, Sung-Ha | Koh, Youngwoo | Baik, Sosung )GAMES AND ECONOMIC BEHAVIOR2026-03
Abstract
We study the optimal auction design problem when bidders are ambiguity averse and follow the max-min expected utility model. Each bidder's set of priors consists of beliefs that are close to the seller's belief, where "closeness" is defined by a divergence. For a given allocation rule, we show that optimal transfers belong to a specific class of transfers, termed win-lose dependent transfers, in which bidders' transfers upon winning and losing depend only on their own types but not on their opponents' type reports. This result effectively reduces the infinite-dimensional problem of identifying an optimal transfer function into a two-dimensional problem of determining two constants-one for winning and another for losing. Solving this reduced problem, we show that among efficient mechanisms without transfers to losing bidders, the first-price auction is optimal, thereby outperforming other auction formats such as the second-price auction. We also discuss how the structure of the set of priors is related to the revenue ranking between the first-and second-price auctions.
To Split or to Merge? How Partitioning Affects Consumption and Engagement with Digital Content
( Lee, Heeseung Andrew | Choi, Angela Aerry | Oh, Wonseok | Sun, Tianshu )INFORMATION SYSTEMS RESEARCH2025-12
Abstract
Despite the rising popularity of serialized digital content on online platforms, authors and publishers currently lack a comprehensive understanding of the economic implications associated with content partitioning. This research investigated how content partitioning affects the consumption patterns, engagement activities, and subsequent economic behavior of consumers in the context of serialized e-books. Identical e-book titles were partitioned into two formats: small partitioning (SP), where extended narratives are split into numerous short episodes per installment, and large partitioning (LP), where stories are divided into a limited number of episodes, each delivered through more extensive storytelling. Drawing on the literature on resource partitioning and cognitive processing, we formulated hypotheses exploring how these partitioning structures influence consumption quantity (i.e., the total number of words read) and progression rate (i.e., how far a consumer progresses into an entire serialized book). We then assessed how content characteristics moderate the relationship between partitioning structures and those consumption patterns. Finally, attention was directed toward how partitioning structures influence engagement activities, such as consumption intensity (i.e., the use of textual annotations and highlights), review characteristics (i.e., submission, length, informativeness, and valence), and subsequent purchase behavior. For empirical validations, we collaborated with a partner company to develop a consumption-tracing scheme, which keeps track of individual users' consumption of and engagement with serialized content. The findings revealed that SP structures more effectively increase consumption quantity (measured by the number of words read) compared with LP formats. However, LP outcompetes SP in elevating progression rate. Notably, LP is more effective than SP in inducing higher levels of engagement as well as a predisposition to submit high-quality book reviews and make subsequent purchases. Furthermore, the positive effects of LP over SP reinforce as book popularity and quality increase. This research offers both scholarly and practical implications for how the partitioning of serialized content influences consumption and engagement patterns. These insights are invaluable for stakeholders seeking to ensure the sustained growth and viability of digital content platforms.
Selection of the most probable best
( Kim, Taeho | Kim, Kyoung-Kuk | Song, Eunhye )OPERATIONS RESEARCH2025-11
Abstract
We consider an expected-value ranking and selection (R&S) problem where all k solutions' simulation outputs depend on a common parameter whose uncertainty can be modeled by a distribution. We define the most probable best (MPB) to be the solution that has the largest probability of being optimal with respect to the distribution and design an efficient sequential sampling algorithm to learn the MPB when the parameter has a finite support. We derive the large deviations rate of the probability of falsely selecting the MPB and formulate an optimal computing budget allocation problem to find the rate-maximizing static sampling ratios. The problem is then relaxed to obtain a set of optimality conditions that are interpretable and computationally efficient to verify. We devise a series of algorithms that replace the unknown means in the optimality conditions with their estimates and prove the algorithms' sampling ratios achieve the conditions as the simulation budget increases. Furthermore, we show that the empirical performances of the algorithms can be significantly improved by adopting the kernel ridge regression for mean estimation while achieving the same asymptotic convergence results. The algorithms are benchmarked against a state-of-the-art contextual R&S algorithm and demonstrated to have superior empirical performances.
