2015 Informs Annual Meeting

TA07

INFORMS Philadelphia – 2015

TA07 07-Room 307, Marriott Pricing and Risk Modeling in Financial Engineering,

2 - “If at First You Don’t Succeed”: Understanding Serial Entrepreneurs on Kickstarter Hallie Cho, INSEAD, 1 Ayer Rajah Avenue, Singapore, Singapore, hallie.cho@insead.edu, David Clough From the crowdfunding platform Kickstarter, we have data on 27,399 technology and design projects created by 6960 entrepreneurs—1376 of whom are serial entrepreneurs. We examine characteristics of the projects and the entrepreneurs to understand what distinguishes a serial entrepreneur from a one timer. For 779 of the serial entrepreneurs, their first projects were failures. We investigate how serial entrepreneurs respond to setbacks and how their resource gathering strategy changes over time. 3 - Wisdom or Madness? Comparing Crowds with Expert Evaluation in Funding the Arts Ethan Mollick, Assistant Professor, U. Penn, 2000 Steinberg Hall- Dietrich Hall, 3620 Locust Walk, Philadelphia, PA, 19004, United States of America, emollick@wharton.upenn.edu, Ramana Nanda Drawing on a panel of experts and data from the largest crowdfunding site, we examine funding decisions for proposed theater projects. We find significant agreement between the funding decisions of crowds and experts. Our findings suggest that crowdfunding can play a role in complementing expert decisions by allowing projects the option to receive multiple evaluations and thereby lowering the incidence of false negatives. 4 - Crowdsourcing Exploration Yiangos Papanastasiou, Haas School of Business, UC Berkeley, Berkeley, CA, 94720, United States of America, yiangos@haas.berkeley.edu, Nicos Savva, Kostas Bimpikis In an online review platform, information on the quality of alternative service providers is both generated and utilized by the consumer population. Inefficiencies arise from the fact that information is generated as a byproduct of self-interested consumer choices, rather than with the benefit of future consumers in mind. Within a multi-armed bandit framework, we study how such inefficiencies relate to alternative policies of information-disclosure to the platform’s users. TA09 09-Room 309, Marriott Using Big Data Analytics for Technology Intelligence: Methods and Cases to Gather Intelligence on Technological Innovations Sponsor: Technology, Innovation Management & Entrepreneurship Sponsored Session Chair: Tugrul Daim, Professor, Portland State University, P.O. Box 751, Portland, OR, 97201, United States of America, ji2td@pdx.edu 1 - Business Partner Recommendation Based on Machine Learning of Customer-Supplier Relationships Yuya Kajikawa, Tokyo Institute of Technology, 3-3-6 Shibaura, Minato-ku, Tokyo, Japan, kajikawa@mot.titech.ac.jp, Naoko Matsuda, Yi Zuo Business partnership is vital not only for business development but also information sharing and collaboration for innovation. In this work, we modeled customer-supplier relationships among firms using statistical learning model by support vector machine to support firms to find plausible business partners. The result showed prediction accuracy over 80% in average, but a variance was found between different sizes of firms. We discuss the mechanism determining the relationships. 2 - The Circle of Innovation There is a high-level feedback between technological innovation and social change. Innovation brings about new products and services, and new ways of using them. These in turn lead to new ways to interact and organize. The new structures generate new unfilled needs, which are opportunities for still more innovation. This changes how we classify innovations, how we should analyze statistics, and our views of technology assessment, market segmentation, and product development for sustainability. 3 - Technology Assessment: Case of Robotics for Power Applications Tugrul Daim, Professor, Portland State University, P.O. Box 751, Portland, OR, 97201, United States of America, ji2td@pdx.edu, Judith Estep This paper presents an integration of data analytics methods and expert judgment quantification to evaluate multiple robotics technologies for the power utilities. Fred Phillips, Distinguished Professor, Yuan Ze University, R60401, Building 6, No.135, Yuan-Tung Rd, Taoyuan, Taiwan - ROC, fred.phillips@stonybrook.edu

Operations Research Cluster: Risk Management Invited Session

Chair: Hongzhong Zhang, Assistant Professor, Columbia University, 1255 Amsterdam Ave, New York, NY, 10027, United States of America, hz2244@columbia.edu 1 - Counterparty Risk in a Heterogenous Random Network Model Stephan Sturm, Worchester Polytechnic Institute, ssturm@wpi.edu, Eric Schaanning We discuss the consequences of the central clearing mandate for OTC derivatives. Analysing the expected total and potential future counterparty exposure in a heterogeneous random graph network allows us to analyse the consequences of central clearing in a realistic model calibrated to actual market data. 2 - On Minimizing Drawdown Risks of Lifetime Investments Bin Li, University of Waterloo, 200 University Avenue West, M3 Building, Waterloo, Canada, b226li@uwaterloo.ca, David Landriault, Dongchen Li, Xinfu Chen We study a lifetime investment problem to minimize the risk of occurrence of significant drawdowns. We examine two financial market models and closed-form optimal strategies are obtained. Our results show that it is optimal to minimize the portfolio variance when the fund value is at its historic high-water mark. When the fund value drops, the fund manager should increase the proportion invested in the asset with a higher instantaneous rate of return. 3 - Beating the Omega Clock: An Optimal Stopping Problem with Random Time-horizon Hongzhong Zhang, Assistant Professor, Columbia University, 1255 Amsterdam Ave, New York, NY, 10027, United States of America, hz2244@columbia.edu, Neofytos Rodosthenous We study the optimal stopping of a perpetual call option in a random time- horizon under exponential spectrally negative Levy models. The time-horizon is modeled as the so-called Omega default clock, which is the first time the occupation time of the underlying process below a level exceeds an independent exponential random variable. We show that the shape of the value function varies qualitatively with model parameters. In particular, we show the possibility of two disjoint continuation regions. 4 - Impact of Bayesian Learning and Externalities on Strategic Investment Wenxin Xu, University of Illinois, Urbana IL 61801, United States of America, wxu9@illinois.edu We investigate the interplay between learning effects and externalities in the problem of competitive investments with uncertain returns. We find a region of a war of attrition between the two firms in which the interplay between externalities and learning gives rise to counterintuitive effects on investment strategies and payoffs.

TA08 08-Room 308, Marriott e-Business Models Cluster: Business Model Innovation Invited Session

Chair: Simone Marinesi, Wharton, 562 Jon M. Huntsman Hall, 3730 Walnut St, Philadelphia, PA, 19104, United States of America,

marinesi@wharton.upenn.edu 1 - Online Grocery Retailing

Elena Belavina, University of Chicago Booth School of Business, 5807 S Woodlawn Ave, Chicago, United States of America, elena.belavina@chicagobooth.edu Grocery delivery is a market that many try to conquer. Appropriate pricing is key for success. There is little consensus among different players (at times even within one firm operating in different locations) on what is the best pricing scheme. For example, Amazon Fresh in Seattle is using per order pricing while in San Francisco - subscription fee. We provide recommendation for the preferred pricing scheme based on various characteristics (delivery logistics, demand variability etc.).

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