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.
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.
Multichannel Advertising: Budget Allocation in the Presence of Spillover and Carryover Effects05
( Chen, Huijun | Chen, Ying-Ju | Park, Sung-Hyuk | Shin, Dongwook )M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT2025-05
Abstract
Problem definition: This paper explores budget allocation strategies for a multichannel ad campaign, where a marketing agency strives to maximize the total conversions by dynamically adjusting budget allocation over marketing channels. A salient feature of the problem is the interplay of spillover and carryover effects; namely, customers are exposed to ads through multiple channels, and thus ads from one channel affect the effectiveness of the subsequent ads from other channels. Methodology/results: We construct a simple model that captures the essential features of this problem. Our theoretical analysis yields two main insights. First, motivated by common practice based on the last-click attribution method, we examine a class of budget allocation policies that are oblivious to the spillover and carryover effects. If the agency decreases the budget on a channel based on past low conversions while neglecting to account for the fact that the ads from that channel induced conversions through other channels, then the conversions from that channel will decrease. Consequently, the agency will further decrease the budget on the channel. This pattern repeats, eventually leading to suboptimal performance in the long run. Second, we derive a fluid approximation to consumer dynamics across multiple channels, which lends itself to characterizing structural properties of optimal dynamic budget allocation policies that internalize the cross-channel interactions. To enable practical implementation, we propose a static budget allocation policy that is both tractable in practice and near optimal for long campaigns. Managerial implications: Our theoretical results provide normative guidance for budget allocation in multichannel ad campaigns. We illustrate the efficacy of our proposed method through a numerical study based on data from an online multichannel ad campaign.
Targeted Advertising as Implicit Recommendation: Strategic Mistargeting and Personal Data Opt-out
( Ning, Z. Eddie | Shin, Jiwoong | YU, Jungju )MARKETING SCIENCE2025-04
Abstract
We study an advertiser's targeting strategy and its effects on consumer data privacy choices, both of which determine the advertiser's targeting accuracy. Targeted ads, serving as implicit recommendations when consumer preferences are uncertain, not only influence the consumer's beliefs and purchasing decisions, but also amplify the advertiser's temptation toward strategic mistargeting: sending ads to poorly matched consumers. Our analysis reveals that advertisers may, paradoxically, choose less precise targeting as accuracy improves. Even if prediction is perfect, the advertiser still targets the wrong consumers, leading to strategic mistargeting. Nevertheless, consumer surplus can remain positive because of improved identification of well-matched consumers, thereby reducing the incentive for consumers to withhold information. However, the scenario shifts with endogenous pricing; better prediction leads to more precise targeting although mistargeting persists. To exploit the recommendation effect of advertising, the advertiser raises prices instead of diluting recommendation power, lowering consumer welfare and prompting consumers to opt out of data collection. Furthermore, we investigate the impact of consumer data opt-out decisions under varying privacy policy defaults (opt in versus opt out). These decisions significantly affect equilibrium outcomes, influencing both the advertiser's targeting strategies and consumer welfare. Our findings highlight the complex relationship between targeting accuracy, privacy choices, and advertisers' incentives.
