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Selected recent publications in the top management and economics journals

Seizing the Commuting Moment: Contextual Targeting Based on Mobile Transportation Apps

( Ghose, Anindya | Kwon, Hyeokkoo Eric | Lee, Dongwon | Oh, Wonseok )

INFORMATION SYSTEMS RESEARCH2019-03

Abstract

Despite the average daily commuting time of commuters increasing by the day, the way marketers can benefit from our commuting behaviors has not yet been examined. In collaboration with one of the largest global mobile telecom providers, this study investigates how contextual targeting with commuting impacts user redemptions of mobile coupons. The analysis is based on a rich field study in which 14,741 mobile coupons were sent to 9,928 public transit app users consisting of commuters and noncommuters. The key findings indicate that commuters are about 3x as likely to redeem their mobile coupon compared with noncommuters. However, a multiple-coupon distribution strategy is more effective in increasing redemption among noncommuters than commuters. Moreover, the redemption rate of commuters is higher for coupons with shorter expiration periods, whereas that of noncommuters is higher for coupons with longer expiration periods. On the basis of theories from psychology and physiology, we argue that stress, which is exacerbated by commuting, increases commuters' coupon redemption rate. We provide empirical support for this argument and show that marketers can increase response rates by focusing on specific periods of the day when commuting stress is relatively high (e. g., rush hours). By carefully exploiting commuting, which is easily identifiable and occurs throughout the world, managers may improve their mobile marketing effectiveness.

(Rescaled) Multi-Attempt Approximation of Choice Model and Its Application to Assortment Optimization

( Chung, Hakjin | Ahn, Hyun-Soo | Jasin, Stefanus )

PRODUCTION AND OPERATIONS MANAGEMENT2019-02

Abstract

When designing an assortment, a fundamental trade-off exists between estimating consumer behavior more precisely and solving the corresponding optimization problem efficiently. For instance, a mixture of logit model can closely approximate any random-utility choice model; however, it is not suitable for optimization when the number of customer segments and the number of products considered are large. As many companies are using big data and marketing analytics toward microsegmenting consumers, a choice model that is amenable in the problem size becomes necessary for assortment design. In this work, we provide a new approach to approximate any random-utility choice model by characterizing the estimation errors in a classical exogenous demand model and significantly improving its performance with a rescaling method. We show that the resulting approximation is exact for Multinomial Logit (MNL). If, however, the underlying true choice model is not MNL, we show numerically that the approximation under our so-called rescaled two-attempt model outperforms the widely used MNL approximation, and provides performance close to the Markov chain approximation (in some cases, it performs better than the Markov chain approximation). Our proposed approximation can be used to solve a general assortment optimization problem with a variety of (linear) real-world constraints. In contrast to the more direct Mixed Integer Optimization (MIO) approach that utilizes Latent Class Multinomial Logit (LC-MNL), whose running time increases exponentially in the number of mixtures, our approximation yields an alternative MIO formulation whose empirical running time is independent of the number of mixtures.

Factor GARCH-Ito models for high-frequency data with application to large volatility matrix prediction

( Kim, Donggyu | Fan, Jianqing )

JOURNAL OF ECONOMETRICS2019-02

Abstract

Several novel large volatility matrix estimation methods have been developed based on the high-frequency financial data. They often employ the approximate factor model that leads to a low-rank plus sparse structure for the integrated volatility matrix and facilitates estimation of large volatility matrices. However, for predicting future volatility matrices, these nonparametric estimators do not have a dynamic structure to implement. In this paper, we introduce a novel Ito diffusion process based on the approximate factor models and call it a factor GARCH-Ito model. We then investigate its properties and propose a quasi-maximum likelihood estimation method for the parameter of the factor GARCH-Ito model. We also apply it to estimating conditional expected large volatility matrices and establish their asymptotic properties. Simulation studies are conducted to validate the finite sample performance of the proposed estimation methods. The proposed method is also illustrated by using data from the constituents of the S&P 500 index and an application to constructing the minimum variance portfolio with gross exposure constraints. (C) 2018 Elsevier B.V. All rights reserved.

Performance Feedback in Hierarchical Business Groups: The Cross-Level Effects of Cognitive Accessibility on R&D Search Behavior

( Rhee, Luke | Ocasio, William | Kim, Tae-Hyun )

ORGANIZATION SCIENCE2019-01

Abstract

This study examines the cross-level effect of group-level managers on member firms' problemistic search in hierarchical business groups. Usingmultilevel data from Korean business groups, we propose that the effects of failure to meet an aspiration level on R&D search intensity increase when member firm performance and R&D investments are more cognitively accessible to group-level managers. Specifically, we find, first, that when underperforming firms are widespread in a business group, a focal member firm intensifies R&D search in response to performance below an aspiration level because member firm performance, as a group-level problem, becomes cognitively accessible to group-level managers. Second, asmember firms operating in R&D intensive industries aremore prevalent in a business group, R&D investments, as a search solution, becomemore cognitively accessible to group-level managers. Thus, a focal member firm reinforces R&D search in response to the performance shortfall. We discuss the implications of these findings for research on the behavioral theory of the firm and performance feedback.

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