2015 Informs Annual Meeting

MD39

INFORMS Philadelphia – 2015

MD39 39-Room 100, CC Innovative Strategies in the Interface of Operations and Marketing Cluster: Operations/Marketing Interface Invited Session Chair: Tingliang Huang, Assistant Professor, Boston College, Carroll School of Management, 140 Commonwealth Avenue, Chestnut Hill, MA, 02467, United States of America, tingliang.huang@bc.edu 1 - Dynamic Management of Opaque Selling with Boundedly Rational Customers Tingliang Huang, Assistant Professor, Boston College, Carroll School of Management, 140 Commonwealth Avenue, Chestnut Hill, MA, 02467, United States of America, tingliang.huang@bc.edu, Zhe Yin We study a practically important problem, where a firm sells an opaque product to boundedly rational customers and has to dynamically determine its selling strategies. We characterize the optimal selling policies depending on the market environment. 2 - Optimal Staffing under Endogenous Arrivals with Heterogeneous Customer Time-of-service Preferences Yang Li, Rotman School of Management, University of Toronto, 105 St. George Street, Toronto, ON, M5S3E6, Canada, Yang.Li10@Rotman.Utoronto.Ca, Philipp Afeche The service operations literature usually treats arrivals as exogenous processes. However, arrival processes may be endogenous in many settings. That is, customers may account for system congestion in choosing their time of service. We propose an equilibrium model that captures how rational customers with heterogeneous preferences decide their time-of-service. We also study the optimal staffing policies, taking into account customers’ time-of-service choices. 3 - Money-back Guarantees in a Distribution Channel: Bargaining Power and Downstream Competition Yufei Huang, PhD Student, University College London, Gower Street, London, United Kingdom, yufei.huang.10@ucl.ac.uk, Tingliang Huang, Ying-ju Chen Although existing literature emphasizes the usefulness of Money-back Guarantees (MBG), little is known about why retailers may adopt different MBG choices in practice. To understand this, we examine two competing retailers’ MBG decisions, who also simultaneously bargain for wholesale prices with a wholesaler in a distribution channel. We show that, retailers’ asymmetric bargaining power may lead to asymmetric MBG choices. We provide economic rationales for all possible MBG outcomes. 4 - Effects of Channel Decentralization on Optimal Product Quality under Uncertainty Hongyan Shi, Assistant Professor, Nanyang Business School, Nanyang Technological University, BLK S3-B1A-32, 50 Nanyang Ave, Singapore, 639798, Singapore, hyshi@ntu.edu.sg, Qin Geng, Nicholas Petruzzi, Yan Liu We provide insights on how a manufacturer’s optimal quality decision depends on its channel structure when its market is defined by consumer heterogeneity and market size uncertainty. We find that, in contrast to the quality-decreasing effect of decentralization by consumer homogenization, market size uncertainty fuels an opposing quality-increasing effect of decentralization. Moreover, we find that the effect by market size uncertainty by and large prevails over that by consumer homogenization. MD40 40- Room 101, CC Gender, Leadership, and Governance Sponsor: Organization Science Sponsored Session Chair: Susan Perkins, Northwestern University, Evanston, IL, s-perkins@kellogg.northwestern.edu 1 - Can Female Leaders Mitigate the Negative Effects of Racial Diversity? National Leaders and Structural Shifts Susan Perkins, Northwestern University, Evanston, IL, United States of America, s-perkins@kellogg.northwestern.edu, Jaee Cho, Katherine W. Phillips, Negin R. Toosi Using a multi-method research design we study the effects of inequality and exclusion on productivity. We find that female leaders are expected improve economic outcomes more than male leaders; are associated with greater tolerance for diversity and self-expression, and have mitigating effects on diversity that

resulted in positive economic growth. The results imply that leadership in more diverse environments is key to making positive structural shifts around inequality and participation. 2 - Hierarchy Maintenance and Whites’ Decreased Support for High-identity White Politicians Sora Jun, Stanford University, Stanford, CA, sorajun@stanford.edu, Lucia Guillory, Brian Lowery We hypothesized and found that Whites withhold their political support for high racial identity White candidates (Experiments 1-3) because of concerns that such candidates will provoke minority discontent with the racial hierarchy (Experiment 1). In Experiments 2 (hypothetical candidates) and 3 (real politicians) this effect was eliminated when the social hierarchy was described as stable, suggesting that this reduced support for high-identity candidates is a hierarchy maintenance strategy. 3 - Naming Your Daughter Jack: The Effect of Gender in the Evaluation Process Tristan L. Botelho, Massachusetts Institute of Technology, Cambridge, MA, tbotelho@mit.edu, Mabel Abraham Double standards, or the belief that women are held to unfairly higher standards, is commonly invoked as an explanation for observed gender differences in evaluative outcomes. Despite lab-based evidence supporting this argument, it remains unclear whether double standards affect evaluations in organizational and market contexts, where competitive pressures create a disincentive to discriminate. Thus, we lack a complete understanding of when and how double standards penalize women in the evaluation process. Drawing on broader status theory, we identify the conditions under which this status-based source of discrimination is most prevalent. Using data from a financial market setting, comprised of investment professionals, we find that double-standards disadvantaging women are most likely when evaluators are faced with higher levels of uncertainty stemming from search costs and variation in the availability of pertinent information. We also rule out that systematic gender differences in the behavior, or characteristics, of these investment professionals are driving these results. 4 - Leadership and the Single Woman Penalty: A Role Expectations Account of Promotion Penalties toward Female Professionals Damon Phillips, Columbia University, New York, NY, dp2588@columbia.edu, Jennifer Merluzzi We advance scholarship on workplace gender inequality by drawing attention to professional single women. We contend that single non-mother status is inconsistent with the role expectations of leadership associated with both men (agentic) and women (communal). We test our thesis on the early careers of business professionals using a multi-method approach. The result support a discrimination-based penalty where the status and role of professional single womanhood conflicts with that of leadership.

MD41 41-Room 102A, CC High-dimensional Data Models for Cost-effective Healthcare Sponsor: Manufacturing & Service Oper Mgmt/Healthcare Operations Sponsored Session

Chair: Mohsen Bayati, Assistant Professor, Stanford Graduate School of Business, 655 Knight Way, Stanford, CA, United States of America, bayati@stanford.edu 1 - Accurate Emergency Department Wait Time Prediction

Sara Kwasnick, Stanford Graduate School of Business, 655 Knight Way, Stanford, CA, United States of America, kwasnick@stanford.edu, Mohsen Bayati, Erica Plambeck

We develop a new method for predicting Emergency Department wait times. The method combines fluid model estimators and statistical learning, and is much more accurate than existing approaches to ED wait time prediction. We validate the method on historical and post-implementation data from four hospitals and discuss lessons learned from the implementation. 2 - Online Decision-making with High-dimensional Covariates Hamsa Bastani, Graduate Student, Stanford University, Stanford, CA, United States of America, hsridhar@stanford.edu, Mohsen Bayati Big data has enabled decision-makers to tailor choices at the individual-level. However, this involves learning a model of decision rewards conditional on individual-specific covariates, which are often high-dimensional. We present an efficient method to solve this problem in an online setting and a corresponding regret analysis. Unlike previous methods whose regret scales with the cube of the covariate’s dimension, our method’s regret scales linearly with the number of sparse features.

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