Select a year:
2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998
Modern organizations increasingly rely on teams to act as information processors—pooling and integrating various sources of information in order to solve complex problems and reach quality decisions. Traditional frameworks for the influence of diversity suggest that diversity can enhance decision making by adding to the backgrounds and perspectives that can be applied to a given task.
Technology has greatly impacted how economic agents interact in various markets, including transportation and online display advertising. This calls for a better understanding of some of the key features of these marketplaces and the development of fundamental insights for this class of problems. In this thesis, we study markets for which spatial and incentive considerations are crucial factors for their operational and economic success. In particular, we study pricing and staffing decisions for ride-hailing platforms.
This dissertation explores the dynamic nature of passion. To do so, I theoretically and empirically examine the pursuit, experience, and perception of passion. This dissertation took its initial shape when my review of the passion literature revealed two key gaps. First, there was a proliferating number of definitions of passion; many of them focused on different, but what I deemed to be essential, aspects of passion.
Incentives are fundamental and often powerful motivators of human behavior. Considerable research has focused on financial rewards as a tool to encourage “good” decisions. This dissertation examines the psychology and efficacy of monetary incentives—compared to multiple nonmonetary incentives—with respect to individuals’ choices, performance, and habits.
Negotiations are not solely an exchange of numbers. Rather, negotiators often surround their offers with explanations, accounts, and rationales that seek to justify, explain, and legitimize whatever terms they are proposing. However, surprisingly little scholarship has studied the role of these stories and the evidence that does exist seems inconclusive. In this dissertation, I examine how, why, and when the words we use in trying to explain and justify our positions work but also often fail to work in negotiations.
The last decade has witnessed a dramatic growth in passive investing via exchange-traded funds (ETFs). To the extent that the demand for stocks via ETF flows is not related to firm-specific fundamental values, large ETF flows may push the price of the underlying stocks away from their fundamentals-based value. In this study I provide evidence consistent with this conjecture. In particular, I first document a positive association between ETF flows and the price-to-fundamentals relation of underlying stocks.
Across three essays, I explore how modern statistical machine learning approaches can be used to glean novel marketing insights from data and to facilitate data-driven decision support in new domains. In particular, I draw on Bayesian nonparametrics, deep generative modeling, and modern Bayesian computational techniques to develop new methodologies that enhance standard marketing models, address modern challenges in data-driven marketing, and, as I show through applications to real world data, glean new, managerially relevant insights.
Financial integration is often perceived to lead to convergence of asset prices, as well as higher comovements across countries, with the idea that the dependence on world factors should increase as markets integrate. This dissertation focuses on analyzing how integration has changed over time in developed and, especially, emerging markets. In particular, the chapters tackle different aspects of how integration has changed over time and the relevance of particular global factors in pricing.
One of the most prominent and potentially transformative trends in society today is machines becoming more human-like, driven by progress in artificial intelligence. How this trend will impact individuals, private and public organizations, and society as a whole is still unknown, and depends largely on how individual consumers choose to adopt and use these technologies. This dissertation focuses on understanding how consumers perceive, adopt, and use technologies that blur the line between human and machine, with two primary goals.