Dean Geoffrey Garrett
Diversity, Equity and Inclusion
Teaching + Innovation
Experiential Learning Center
Open Expression Statement
BS Business Administration (BUAD)
BS Accounting (ACCT)
World Bachelor in Business (WBB)
BS Business of Cinematic Arts (BCA)
BS Artificial Intelligence for Business (BUAI)
Full-Time MBA (FTMBA)
Executive MBA (EMBA)
Part-Time MBA (MBA.PM)
International MBA (IBEAR)
Online MBA (OMBA)
MS Business Administration (MSBUSAD)
MS Business Analytics (MSBA)
MS Entrepreneurship + Innovation (MSEI)
MS Finance (MSF)
MS Global Supply Chain Management (MSGSCM)
MS Marketing (MSMKT)
MS Social Entrepreneurship (MSSE)
Master of Business for Veterans (MBV)
Master of Management Studies (MMS)
Master of Accounting (MAcc)
Master of Business Taxation (MBT)
Master of Business Taxation for Working Professionals (MBT.WP)
Data Sciences + Operations
Management + Organization
GC in Business Analytics
GC in Financial Analysis + Valuation
GC in Management Studies
GC in Marketing
GC in Optimization + Supply Chain Management
GC in Strategy + Management Consulting
GC in Sustainability + Business
GC in Technology Commercialization
GC in Library and Information Management – Online
Business Communication (BUCO)
Data Sciences and Operations (DSO)
Finance + Business Economics (FBE)
Leventhal School of Accounting (ACCT)
Lloyd Greif Center for Entrepreneurial Studies (BAEP)
Management and Organization (MOR)
Institutes + Centers
Randall R. Kendrick Global Supply Chain Institute
Peter Arkley Institute for Risk Management
VanEck Digital Assets Initiative
Institute for Outlier Research in Business
Lloyd Greif Center for Entrepreneurial Studies
USC Marshall Venture Fund
Brittingham Social Enterprise Lab
Neely Center for Ethical Leadership and Decision Making
Center for Effective Organizations
Center for Global Innovation
Center for Investment Studies
Initiative on Digital Competition
Giving + Support
Alumni Engagement + Resources
iORB is a new type of business research institute. Our plan is nothing less than to revolutionize research that is performed at Marshall and other business schools, with the goal of encouraging work that has the potential for a transformational impact on society.
The institute was in part formed based on a belief by many leading scholars that decades of status quo thinking has created a focus on incremental research, and that bold changes are needed to re-incentivize society’s best researchers to work on issues that more profoundly impact business and society at large. Specifically, iORB provides resources for researchers, managers and policy makers to encourage, fund, and reward outlier research through entrepreneurial programs and initiatives.
We are also working to recruit the most impactful academics, who have already demonstrated the ability to change the way business operates. Currently iORB has three main programs: Outlier Research Funding, a Distinguished Visiting Fellows Program, and Conference Funding.
Gerard Hoberg's research is primarily in the area of empirical corporate finance with a focus on innovation, mergers, IPOs, disclosure, informational environments, and industrial organization. His work often focuses on issues relating to important theories that have proved difficult to test, and to issues that are of importance to regulatory agencies and financial stability. He is known for methodological contributions that bring technologies from computational linguistics into finance.
Paat Rusmevichientong is the Justin Dart Professor of Operations Management and Professor of Data Sciences and Operations in the Marshall School of Business at the University of Southern California. Prior to joining the Marshall School, he was a faculty in the School of Operations Research and Information Engineering at Cornell University. His research interests focus on revenue management, choice modeling, pricing, assortment optimization, and large-scale dynamic programming. From 2003 through 2004, he worked in the data mining and personalization group at Amazon.com. He received BA (1997) in Mathematics from University of California, Berkeley, and MS (1999) and PhD (2003) in Operations Research from Stanford University. He is a member of INFORMS.
Gerard J Tellis (PhD Michigan) is Neely Chaired Professor of American Enterprise, Director of the Institute for Outlier Research in Marketing, Director of the Center for Global Innovation, at the USC Marshall School of Business. Dr. Tellis is an expert in innovation, advertising, social media, new product growth, and global market entry. He has published 7 books and over 200 papers (http://www.gtellis.net ) that have won over 30,000 citations in Google Scholar. His publications have won over 25 awards. He is Past-President ISMS and was an Associate Editor of Marketing Science and Journal of Marketing Research.
Dan's research and teaching examines how entrepreneurial processes drive socio-economic change. He has published in leading journals in management (AMJ, SMJ, SEJ, JMS) and business history (BHR, BH, E&S), and is co-editor of Organizations in Time: History, Theory, Methods (Oxford University Press, 2014). Dan is former chair of the AoM Management History Division and currently president-elect of the Business History Conference, the leading business history association in North America. He has received research and teaching awards, most recently the Williamson Prize which is awarded every 2-3 years to a mid-career scholar "who has made significant contributions to business history."
Cheryl Wakslak’s research focuses on how people interact with others across various forms of distance. Much of her recent research looks at communication, exploring when people use more big-picture, abstract language versus more specific language, and how this influences the way they are perceived by others. Projects here focus especially on the role of gender, power, diversity, and perceptions of leadership and future potential. Cheryl is further interested in how these dynamics -- and other communication signals -- play out in the entrepreneurial space. She explores these interests using a variety of approaches, including experiments, field studies, and archival data analysis.
Distinguished Visiting Fellows Program
The primary goal of the program is to bring scholars to USC Marshall to inspire cutting edge research, network with faculty and doctoral students, provide feedback on ongoing research, and stimulate joint research with faculty and doctoral students. A secondary goal is to increase the visibility of the school and educate the visitor about the research environment at USC Marshall or potentially interest him or her to join USC Marshall. This year, iORB invites proposals for two categories of visitors: i) Distinguished Fellow and ii) Research Fellow.
Donald C. Hambrick, Spring 2019
Professor, Evan Pugh University Professor, and Smeal Chaired Professor of Management
Penn State Smeal College of Business
Professor Hambrick is an internationally acclaimed scholar in the field of strategy. He has authored numerous articles and books on strategy formulation, strategy implementation, executive psychology, executive staffing and incentives, the composition and processes of top management teams, and corporate governance. Professor Hambrick’s book, Strategic Leadership: Theory and Research on Executives, Top Management Teams, and Boards, is one of the most cited works in the field and is extensively used by scholars of executive leadership. His book with David Nadler, Navigating Change: How CEOs, Top Teams, and Boards Steer Transformation, presents leading-edge thinking for executives who are embarking on corporate change initiatives.
Dan Ariely, Spring 2019
James B. Duke Professor of Psychology and Behavioral Economics
Founder, Center for Advanced Hindsight
Duke Fuqua School of Business
He gave a specialty talk to the marketing department on his research program in behavioral decision making. He gave a general talk to the USC Marshall community on the Dishonesty of Honest People. In addition, he met with faculty and PhD students to mentor them and advise on their research.
Dan Ariely is a behavioral economist and the James B. Duke Professor of Psychology and Behavioral Economics at Duke University. He has published widely in top peer reviewed academic journals. In addition, he has published in popular newspapers and magazines. He authors a weekly advice column in the Wall Street Journal titled “Ask Ariely” on common decision problems and animas that lay people face.
Kathleen Eisenhardt, Spring 2018
Stanford W. Ascherman, MD Professor in the School of Engineering
Professor Eisenhardt is the author of over 100 articles published in research and business journals. She has made fundamental contribution to strategy and organization research on technology-based companies and high-velocity industries. For her contributions, she has received the Career Scholarly Contribution Award from the Academy of Management, the Global Award for Entrepreneurship Research, the Irwin Award for
contributions in strategy, the Distinguished Scholar Awards from the Organization Theory and Management (OMT), and Technology and Innovation Management (TIM) divisions of the Academy of Management, the Administrative Science Quarterly Scholarly Contribution Award for the most influential paper five years after publication, as well as the Strategic Management Society’s Schendel Best Paper Prize. She was recently noted as the most cited research author in strategy and organization research
for the past 25 years.
George Loewenstein, Spring 2018
Herbert A. Simon University Professor of Economics and Psychology
PhD in Economics
Carnegie Mellon University
Professor Loewenstein is co-director of the Center for Behavioral Decision Research at Carnegie Melon University and the Director of Behavioral Economics at the Center for Health Incentives at the Leonard Davis Institute of the University of Pennsylvania. He is one of the foremost thinkers in the field of behavioral economics and is a fellow of the American Academy of Arts and Sciences. He has published more than 200 journal articles in fields as diverse as economics, psychology, law, medicine and neuroeconomics.
Sheridan Titman, Fall 2017
Department Chair, Finance; Director, Energy Management and Innovation Center; Professor
University of Texas at Auston MCombs School of Business
Professor Titman has made important research contributions on a broad set of topics in Finance, Real Estate, and Energy Economics. His most important work in corporate finance concerns that nature of the indirect costs of financial distress that encourage firms to limit their use of debt financing. His most impactful work on the pricing of financial assets concerns the identification of momentum trends in security prices, i.e. the tendency of financial assets that have recently appreciated in value to continue to appreciate. Professor Titman’s evidence on momentum effects almost certainly constitutes the most convincing large-sample challenge to the Efficient Markets Hypothesis, which has been the dominant capital-markets paradigm in academic finance since the 1960s.
Andrei Shleifer, Spring 2017
John L. Loeb Professor of Economics
Professor Shleifer is a distinguished economist who is the most cited economist in the world with over 225,000 Google Scholar citations. He is an Editor of the Quarterly Journal of Economics, and a fellow of the Econometric Society, the American Academy of Arts and Sciences, and the American Finance Association. In 1999, Professor Shleifer won the John Bates Clark medal of the American Economic Association that is awarded annually to an American economist under the age of forty who is judged to have made the most significant contribution to economic thought and knowledge. He is known for designing conceptual frameworks that lend structure to new perspectives in comparative corporate governance, law and finance, behavioral finance, as well as institutional economics. He has published six books and more than 100 articles.
Jianqing Fan, Spring 2017
Professor of Statistics; Frederick L. Moore '18 Professor of Finance
Jianqing Fan is a distinguished statistician, financial econometrician, and data scientist. He is Frederick L. Moore '18 Professor of Finance, Professor of Statistics, and Professor of Operations Research and Financial Engineering at Princeton University, where he chaired the department from 2012 to 2015. He is a leading scholar on statistical theory and methods in data science, finance, economics, machine learning, computational biology, and biostatistics. Professor Fan is Co-Editor of the Journal of Econometrics and associate editor of the Journal of the American Statistical Association. He has also previously served as Co-Editor(-in-Chief) of the Annals of Statistics, Co-Editor of the Econometrics Journal, and Editor of Probability Theory and Related Fields. He was President of the Institute of Mathematical Statistics (2006-2009) and President of the International Chinese Statistical Association (2008-2010). Professor Fan has been consistently ranked as a top 10 highly-cited mathematical scientist since the existence of such a ranking.
CONFERENCES + WORKSHOPS
iORB provides workshop and conference funding with the goal of stimulating the exchange of ideas that lead to outlier research. Examples of the use of such funding include: staff to organize or run the conference, food and drinks for participants, location rental, or support for highly visible speakers. A call for proposals is sent out annually as part of a competitive submission process and the iORB executive board reviews the proposals and makes final funding decisions.
2023 AIM Conference
3rd annual Artificial Intelligence in Management Workshop and Conference at USC Marshall will be held March 16-17, 2023.
OUTLIER RESEARCH FUNDING
iORB’s core mission is to nurture and grow outlier research.
Consistent with this mission, iORB provides funding to support ambitious research projects that require additional resources but have significant potential impact. iORB aims to annually fund several outstanding proposals that will positively impact business and society.
A call for proposals is sent out each Fall as part of a competitive submission process and respected business scholars review the proposals. Based on the reviews, the iORB executive board makes final funding decisions.
The proposals for funding are evaluated according to the following main criteria:
Funded Outlier Research Proposals
The Evolution of Gender (in)Equality in the Workforce: Evidence from the American Legal Sector, 1870-1962
Joe Raffie, Department of Management and Organization
Nan Jia, Department of Management and Organization
This project will study the evolution and emergence of gender equality (inequality) in the American legal services industry. To do so, we will build and examine a novel database that we are constructing from historical law firm directories. This database spans a near century of time,starting in the year 1870, a time when women in the USA did not have the right to vote and when virtually no women were licensed attorneys, and ending in the year 1962, at which time significant changes and advancements had been made (albeit not enough). The historical nature of this data and the time span it covers (92 years), provides an unusual and unique opportunity to examine the factors which contributed to (or detracted from) changes in gender equality, starting at a point in time when female labor force participation in this industry was effectively zero. Our goal is to learn from history in a way which can be applied to the plethora of equality issues we currently face and to generate knowledge which can be used to promote and enhance gender equality, diversity, and inclusion, within modern firms.
The Long Run Effects of Financial Sector Competition
Rodney Ramcharan, Department of Finance and Business Economics
The motivation for the data collection begins with the fact that influential theories in macroeconomics and finance predict that the structure of financial intermediation and the balance sheet of intermediaries can have a powerful effect on economic fluctuations and asset prices (Brunnermeier and Sannikov (2014), (He & Krishnamurthy, 2013). These effects can however be ambiguous. Greater competition in credit markets can generate more efficient intermediation, reduce borrowing costs and relax credit constraints for marginalized borrowers. This can lead to faster economic growth in the long run—the “credit is good for growth” hypothesis. But more competition in the financial system can also erode the profitability of incumbent financial institutions, leading to riskier lending and more unstable banking. In the most extreme case, increased competition in credit markets can foster an ex-post misallocation of credit to riskier borrowers, producing asset price booms and busts, as well as profound and longlasting shifts in financial regulation (Mian and Sufi (2009), (Favara & Imbs, 2015), and (Raghuram Rajan & Ramcharan, 2015)).
Foundations of Neural Networks
Jason Lee, Department of Data Sciences and Operations
In the past decade, neural networks have made a remarkable impact on application domains including computer vision, robotics, and natural language understanding. Specific advances from Deep Learning include AlphaGo, a superhuman Go-playing AI, strategy game playing, and realistic chat-bots. At their core, these applications all involve learning the parameters of a neural network via the stochastic gradient descent algorithm. This proposal lays out a program to study provable learning methods in neural networks by separating the study into two interrelated problems:
(i) Optimization of Neural Networks. The loss function of neural networks is highly non-convex, yet standard SGD and its variants attain near global minima, as evidenced by zero training error. What properties (e.g. overparametrization) allow for gradient methods to converge to local or even global minima? Can we design the landscape by modifying the architecture and loss to allow for efficient training?
(ii) Generalization and Regularization in Neural Networks. Since commonly used neural networks are overparametrized, the model is able to perfectly interpolate the training data. In fact, there are innately many global minima that perfectly interpolate the training data. Common statistical wisdom suggests that most of these models will incur high generalization error; however, empirically SGD nds (near) global minima that generalize. Why does the stochastic gradient algorithm nd global minima that do not overt? Does the stochastic gradient algorithm induce an implicit regularizer? Can we isolate the regularizer and use it as an explicit regularizer to further improve generalization?
A Road to Efficiency Through Communication and Commitment
Joao Ramos, Department of Finance and Business Economics
Ala Avoyan, Indiana University
Economic situations often require agents to coordinate their actions, and coordination failures leading to under performance are pervasive in society. For instance, firms have to make investment decisions with uncertain returns that depend on the amount invested by other firms; thus, coordination failure may limit economic activity, with firms choosing suboptimal investment levels (see,for instance, Rosenstein-Rodan (1943)). Focusing inside the firm, consider a team that has to hand in a joint report before a deadline. Each team member is responsible for a section, and the report is complete only when all sections are delivered. A team member would be willing to put extra effort to hand in her part on time, but only if she was certain all others would do it as well.
Coordination failure may occur because, although group members have common preferences over outcomes, to achieve the best outcome requires taking an action they are unwilling to take, unless others do it as well. This makes strategic uncertainty a relentless feature: although superior outcomes (e.g. all putting extra effort) can be reached, given the uncertainty about others’ strategy, the risk of mis-coordination makes those outcomes unattainable.
Although the benefits of overcoming coordination failures are obvious, it is not clear how institutions can mitigate strategic uncertainty. Given that institutions are rarely random, this is a natural question to be answered from an experimental perspective. Recent experimental research focuses on communication institutions—comparing players sending public to private messages before choosing actions, or one-round of messages to many rounds—with mixed results for efficiency. Given the lack of theoretical implications for many of the institutions studied, it is unclear how a particular institution could help agents coordinate on higher outcomes. Furthermore, even if an institution improves coordination in the lab, it is unclear which institutional feature is responsible for the success.
Opening up the Black Box of Auditing
Clive Lennox, Leventhal School of Accounting
Due to a lack of publicly available data, there is relatively little evidence on how partners are incentivized and what audit partners do during an audit. In this project we will use three unique and proprietary data sources to peer into this black box:
1) the equity ownership stakes of audit partners2) the identities of review partners and engagement partners, and3) the audit adjustments that are made to reported earnings during the audit.
We expect to generate two papers using these data. First, we will examine how the ownership stakes of audit partners affect audit quality. Second, we will examine why internal control audits can have adverse consequences for financial reporting quality. As explained below, these two research questions are very important for regulators and practitioners, as well as the academic community.
Valid Inference for High-dimensional Statistical Models
Adel Javanmard, Department of Data Sciences and Operations
As we often hear, we are living in the era of data deluge. `Big data' technologies have allowed the acquisition of vast amount of fine-grained data and their accumulation into large scale databases, at an unprecedented speed. Powerful hardware and software systems have also been developed to crunch these data and extract information from them. Due to all these developments, the data-driven approach has become de rigueur nowadays in almost every field. Given the dataset, one of the off-the-shelf software packages is used to fit a statistical model which is then used for prediction, discovering new associations between variables (e.g. a specific demographic variable with the future income), clustering, policy design, decision making, and so on. However, this trend is a double-edge sword; The increasing complexity of these data and of the algorithms used has made statistical models significantly less interpretable. Employing the derived models without a proper understanding of their validity can lead to large number of false discoveries, wrong predictions and massive costs. Consider a concrete example where the medical records of patients are used to develop a model for providing personalized risk score for a chronic disease. A high risk score can trigger an intervention, such as incentive for healthy behavior, additional tests, and medical follow-ups which are all costly. Now, the question is how certain are we from the predictions made by this statistical model? What is their limit of validity? How biased is the resulting model? Closely related is the concern of reproducibility of the results. Researchers would like to know if their findings in a study can be successfully replicated in another study under the same conditions, not exactly but up to statistical error. Answering these questions for modern high-dimensional data has created a need for novel foundational perspectives on inferential thinking.
Data plays a central role in the progress of research, the discovery of new phenomena, and the testing and advance of theories.
iORB longitudinal data initiative serves this mission.
The goals of this initiative:
2020-2021 Funded Longitudinal Research
Intelligent Systems Index: Robotic Process Analysis
Daniel O'leary, Leventhal School of Accounting
Monitoring Healthcare Discussions in Social Media
Dinesh Puranam, Department of Marketing
Vrinda Kadiyali, Professor of Management, Cornell University
Nicholas H. Noyes, Professor of Marketing, Cornell University
Entrepreneurial Culture, Innovation, and Stock Market Returns: A Longitudinal Study of Entry, Disruption, and Survival in the US
Gerry Tellis, Department of Marketing
Understanding the Commercialization of Digital Entertainment via Investor-Startup Networks
Yanhao “Max” Wei, Department of Marketing
Tianshu Sun, Department of Data Sciences and Operations
Sha Yang, Department of Marketing
Nielson Television and Streaming Data: A Resource to Study Media Consumption and Competition
Milan Miric, Department of Data Sciences and Operations
The Role of Digital Platforms in Increasing Access to Affordable Medication
Shantanu Dutta, Department of Marketing
Gourab Mukherjee, Department of Data Sciences and Operations
Dinesh Puranam, Department of Marketing
Reetabrata Mookherjee, Head of Data, GoodRx, Santa Monica
Institute for Outlier Research in BusinessUSC Marshall School of Business3670 Trousdale ParkwayBridge Hall, Room 101Los Angeles, CA 90089-0802EMAIL Phone: (213) 821‑6579
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