Informs Annual Meeting 2017

SC19

INFORMS Houston – 2017

SC19

2 - Motivating Participation and Effort in Innovation Contests Konstantinos Stouras, Postdoctoral Research Fellow, The Darden School, University of Virginia, 100 Darden Boulevard, Charlottesville, VA, 22903, United States, kostas@virginia.edu, Jeremy Hutchison-Krupat, Raul Chao An innovation contest is a business process through which a firm crowdsources innovation to a large pool of solvers. Solvers are ex ante heterogeneous and decide whether to participate and how much effort to exert to enhance their performance. We characterize how firm’s choice of reward allocation affect solver voluntary participation and effort behavior in equilibrium. We show that multiple awards are beneficial to balance the tension between how many solvers to attract and of what type, and how much effort they would exert towards maximizing the top performance of the participating solvers. 3 - Simultaneous vs. Sequential Crowd Sourcing Contests Lu Wang, University of Toronto, Rotman School of Management, 105 St George Street, Toronto, ON, M5S.3E6, Canada, lu.wang12@rotman.utoronto.ca, Ming Hu In a crowdsourcing contest, innovation is outsourced to an open crowd. We consider two alternative mechanisms for an innovative project involving multiple attributes. One is a simultaneous contest, where the best solution is chosen from the aggregate solutions simultaneously submitted by all contestants. The other is multiple sequential sub-contests, with each dedicated to one attribute and the contestants asked to build upon the best work in progress from previous sub- contests. While both mechanisms have their own advantages, either could win over depending on situations. 4 - Designing Internal Innovation Contests Lakshminarayana Nittala, University of California-San Diego, Rady School of Management, 9500 Gilman Drive, La Jolla, CA, 92093, United States, lnittala@ucsd.edu, Sanjiv Erat, Viswanathan Krishnan Firms can use contests to engage employees in finding solutions to its problems related to innovation and product development. We present an analytical model inspired by field work to highlight the unique aspects associated with such internal contests and discuss design recommendations. 342C Data Analytics Models for Healthcare Invited: InvitedHealthcare Systems and Informatics Invited Session Chair: Kai Yang, Wayne State University, Detroit, MI, 48201, United States, kai.yang@wayne.edu 1 - Integration of Multiple Health Information Systems for Quality Improvement of Radiologic Care: Jing Li, Ph.D, Arizona State University, AZ, United States, jing.li.8@asu.edu In radiology, several electronic Health Information Systems (HISs) are used: EHR, RIS, and PACS. Each HIS records partial and complementary information about the radiologic care process. We developed a novel technology called Department Data Depot (DDD) that integrates multiple HISs in radiology. We propose nine Quality of Care (QoC) metrics defined upon the data from DDD that measure various dimensions of care quality such as timeliness, efficiency, patient satisfaction, and workload distribution. To demonstrate the clinical utility of DDD, we developed and deployed a web application system, the Radiology Quality Dashboard (RQD), at Mayo Clinic in Arizona (MCA). Use cases will be presented. 2 - Mining Major Patterns of Disease Progression in Patients with Multiple Chronic Conditions Approximately one in four Americans and 75% of citizens aged 65+ years are burdened with MCC. Treatment for people living with MCC currently accounts for an estimated 66% of the Nation’s health care costs; a number that is only expected to grow. However, it is still not known precisely how MCC emerged among individuals or in the general population. Using a dataset of more than half million patients being monitored over 10 years, this study investigates major patterns of MCC progression in a diverse population of patients and identifies the risk factors affecting the patterns. SC21 Adel Alaeddini, Assistant Professor, University of Texas at San Antonio (UTSA), San Antonio, TX, United States, Adel.Alaeddini@utsa.edu

342A Fintech: The Interface of Technology and Finance Sponsored: Manufacturing & Service Oper Mgmt, iFORM Sponsored Session Chair: Jun Li, Ross School of Business, University of Michigan, Ross School of Business, University of Michigan, Ann, MI, 48109, United States, junwli@umich.edu 1 - The Freak Out Factor: Retail Investor Behavior in Response to the Global Financial Crisis Andrew W. Lo, Massachusetts Institute of Technology, Sloan School of Management, Charles E. & Susan T. Harris Professor, Cambridge, MA, 02142, United States, alo@mit.edu This paper represents the first look at a novel and comprehensive historical brokerage trading dataset that is unique in its breadth of coverage, with over 700k U.S. accounts, and longevity, containing monthly snapshots spanning January 2005 to December 2015. By synthesizing this data with daily asset price series, we are able to observe the dynamics of retail households’ investment portfolios at daily frequency before, during, and after the 2008 Global Financial Crisis. We identify canonical empirical behaviors such as the propensity to sell securities and/or portfolios when experiencing extreme positive or negative returns in the recent past, known in the psychology literature as the “disposition” and “loss aversion” effects. We use modern machine learning methods to develop performant predictive models of trading and liquidation, and assess the sensitivity of these models to their key factors including past returns, past trading, balances, allocations, and demographic characteristics. We also document liquidation behavior during the crisis, defined as the reallocation of holdings in risky assets to cash for prolonged periods of time, both for investors facing different drawdowns in their portfolios and those from different demographic segments. We find that investors with the worst historical experiences during the crisis were more likely to liquidate, maintained less aggressive allocations in risky assets for longer periods of time, and experienced worse risk-adjusted returns during the post- crisis recovery period. 1 - Text Based Positioning of Firms Along the Supply Chain Dimitry Slavin, Ross School of Business, University of Michigan, Ann Arbor, MI, United States, dslavin@umich.edu An objective method of identifying a firm’s economic position based on the content of its annual financial disclosures is proposed. The method’s key components include (i) the creation of a consumption-risk related lexicon, (ii) several natural language processing techniques (keyword-extraction, part-of- speech tagging, named-entity recognition, etc.), and (iii) an iterative clustering procedure. The proposed method reveals previously-unknown interdependencies between firms, and is expected to serve as a new, unbiased measure of a firm’s supply-chain-related risk. 2 - R&D Spending: Dynamic or Persistent? Christophe Pennetier, INSEAD, 6 Marina Boulevard, # 27-15, Singapore, 018985, Singapore, christophe.pennetier@insead.edu, Karan Girotra, Jurgen Mihm We study how a given amount of R&D spending is best allocated over time to optimize R&D performance. Using a sample of US public companies, we estimate the interaction between R&D investment and types of innovation strategy. & Entrepreneurship Sponsored Session Chair: Lakshminarayana Nittala, Rady School of Management, lakshmi.nittala@rady.ucsd.edu 1 - Innovation Tournaments with Multiple Contributors Ersin Korpeoglu, University College London, Flat 27 Kingfisher Heights Waterside Way, London, N17 9GL, United Kingdom, e.korpeoglu@ucl.ac.uk, Laurence Ales, Soo-Haeng Cho In an innovation tournament, an organizer seeks innovative solutions from multiple agents. Agents exert efforts to improve solutions, whose performance is uncertain. We analyze a general model of uncertainty and consider a case in which the organizer seeks multiple solutions. We show that contrary to existing theories, increased competition in a tournament can increase agents’ efforts when agents expect good outcomes with high likelihood, and a free-entry open tournament should be encouraged only when agent uncertainty is high or the organizer seeks many diverse solutions. SC20 342B Sourcing Innovation Using Contests Sponsored: Technology, Innovation Management

74