We ask how competition influences the prescribing practices of physicians. Law changes granting nurse practitioners (NPs) the ability to prescribe controlled substances without physician collaboration or oversight generate exogenous variation in competition. In response, we find that general practice physicians (GPs) significantly increase their prescribing of controlled substances such as opioids and controlled anti-anxiety medications. GPs also increase their co-prescribing of opioids and benzodiazepines, a practice that goes against prescribing guidelines. These effects are more pronounced in areas with more NPs per GP at baseline and are concentrated in physician specialties that compete most directly with NPs. Our findings are consistent with a simple model of physician behavior in which competition for patients leads physicians to move toward the preferences of marginal patients. These results demonstrate that more competition will not always lead to improvements in patient care and can instead lead to excessive service provision.

Discrete-choice models such as the multinomial-logit (MNL) model are increasingly being used to model customer purchase behaviour in hotels, airlines, fashion retail and e-commerce. Their estimation however has proved difficult in practice. One reason for this difficulty is well-known: most firms in retail do not, or unable to, record customers who were interested in purchasing but did not buy the product (``no-purchasers"). Estimating demand even with the simplest discrete-choice model such as the MNL becomes challenging then as we do not know the fraction that have chosen an outside option (not purchased). Indeed, the parameters of the MNL model may not be identifiable with such data. Some previous approaches have proposed using ``market-share" to pin down the parameter associated with the outside option. However, in many industries, market-share data is difficult to obtain, and for some, such as fashion products, has little meaning. In this paper we point out an additional difficulty that arises in practice: Many firms constantly monitor sales and optimize their prices and assortments based on partially observed demand. This leads to an optimization-induced endogeneity as the input used for estimation has been processed by optimization that takes both past data as well as future demand trends in setting controls. As we demonstrate, methods that work well on randomly generated assortments may do badly on optimized assortment data. In this paper we propose a robust method when the firm cannot observe no-purchases, has no market-share information, and the optimization-induced endogeneity exists in the data. The method is a two-step GMM (Generalized Method of Moments) procedure for which we show the estimates are consistent, and we give intuition for its robustness. In Monte-Carlo simulations the performance of our method matches existing methods on randomly generated controls, and is superior in accuracy and robustness when optimization-induced endogeneity is present. On a large real-world data set from the fashion industry -- subject to markdowns as well as stock-outs and unknown management controls -- our method provides very reasonable and robust estimates compared to existing methods.

Very little attention has been paid to the impact of sub-national heterogeneity on multinationals' entry mode choices and/or their subsidiaries' performance. Accordingly, this study seeks to identify the effects of sub-national heterogeneity on entry mode selection, and to detect the factors that contribute to mode performance across different sub-national regions. The literature identifies several sub-national political, economic and cultural factors that might have an impact on entry mode choices. With regard to the mode-performance relationship, three hypotheses are posited: (1) joint venture (JV) mode outperforms whollyowned subsidiary (WOS) mode in less-developed sub-national regions; (2) WOS mode performs better than JV mode in developed regions; and (3) the difference in performance value is smaller in less-developed regions than in developed regions. To test these hypotheses, 113 secondary observations of foreign subsidiaries located in two provinces of China were collected. Shandong Province and Gansu Province, from the eastern and western region of China respectively, were used as the sampling fields, as they exhibit the critical differences between the eastern and western regions with regard to political, economic, social, and environmental attributes. Hierarchical Regression Analysis and T-test statistical methods were used to analyse the data. The results indicate that the JV mode outperforms the WOS mode in the less-developed subnational region, whereas the WOS mode outperforms the JV mode in the developed region. The difference in performance value between WOSs and JVs is, however, smaller in the developed region than in the less-developed region. This study confirms that entry mode performance varies across sub-national regions, and the influence of mode choices on performance also varies across regions. It concludes with a discussion of possible explanations of these findings and presents recommendations for future research, management practice, and policy reform.

We compare the influence of entry mode choice on subsidiary performance in two developmentally-differentiated regions of a developing host country. Analysis of 113 subsidiaries located in two provinces of China indicates that wholly owned subsidiaries outperform joint ventures in the developed region, whereas joint ventures outperform wholly owned subsidiaries in the less developed region. However, the smaller performance gap between wholly owned subsidiaries and joint ventures in the developed region indicates that the magnitude of influence of entry mode choices on performance varies across subnational regions. Firms must therefore be more discriminating in formulating entry strategies to regionally heterogeneous countries.Full paper available at doi:10.1017/jmo.2017.59.

This thesis handles fundamental problems faced by retailers everyday: how do consumers make choices from an enormous variety of products? How to design a product portfolio to maximize the expected profit given consumers’ choice behavior? How to frame products if consumers’ choices are influenced by the display location? We solve those problems by first, constructing mathematical models to describe consumers’ choice behavior from a given offer set, i.e., consumer choice models; second, by designing efficient algorithms to optimally select the product portfolio to maximize the expected profit, i.e., assortment optimization. This thesis consists of three main parts: the first part solves assortment optimization problem under a consideration set based choice model proposed by Manzini and Mariotti (2014) [Manzini, Paola, Marco Mariotti. 2014. Stochastic choice and consideration sets. Econometrica 82(3) 1153-1176.]; the second part proposes an approximation algorithm to jointly optimize products’ selection and display; the third part works on optimally designing a product line under the Logit family choice models when a product’s utility depends on attribute-level configurations.

Team members can experience a range of emotions and to varying degrees. We examine the effects of team emotion diversity on information sharing among team members and, consequently, team performance. By integrating the categorization-elaboration model and self-categorization theory, we argue that social class diversity moderates the relationship between team emotion diversity and information sharing. Specifically, we propose that team emotion diversity elicits information sharing when social class homogeneity is high but hinders information sharing when social class homogeneity is low. In addition, we hypothesize that the relationship between information sharing and team performance will be stronger for teams with more, rather than less, social class diversity. To test our propositions, we studied 75 teams enrolled in an Indian master of business administration program, collecting measures of positive and negative affect, information sharing, and team performance at multiple times. Our results mostly support our predictions. We discuss the implications of these findings for team diversity research and for managing diverse teams. Full paper available at https://doi.org/10.1037/gdn0000083.

Tech companies, bearing money, jobs and innovations assume state-level responsibilities and take on projects that address urban issues. Uber rethinks public transit; Google Sidewalk Labs designs Toronto's waterfront with integrated mobility system; Elon Musk's Boring Company privately funds a rapid transit line from O'Hare Airport to downtown; Amazon provides CIA data and security services for sensitive information. Willingly or reluctantly, governments are handing over civic infrastructure to these companies. While reshaping the public realm at an unprecedented pace, tech companies also put public spaces at risk. In negotiation of mega-corporation tenancy, as seen in Amazon's RFP for its second headquarters, space becomes a bargaining chip between misaligned agendas of companies and governments. This thesis investigates the unusual architectural opportunities this risk could bring to the city, the corporation, and the citizens while acknowledging that machines, supported by automation, are progressively reorganizing our environment and we are surrendering our control in gradual but consequential ways. Amazon Here is our case study in the urban context of Chicago, an abandoned historic building retrofitted into a densely-packed, hyper-efficient machine that weaves together business and public-oriented spaces. This hypothetical development pushes for a model in which corporate-sponsored public architecture operates as the fourth industrial revolution's version of a company town, balancing Amazon's interests with those of the community. In this experimental model, public space is a negotiation for corporate and civic interest; hefty logistical infrastructure normally hidden behind closed doors now becomes part of the new urban experience, transforming the industrial warehouse into a spectacle for all.