2015 Informs Annual Meeting

SC23

INFORMS Philadelphia – 2015

SC24 24-Room 401, Marriott Fashion, Innovation and Technology Sponsor: Artificial Intelligence Sponsored Session Chair: Heng Xu, Associate Professor, Pennsylvania State University, 316H IST Building, University Park, PA, 16802, United States of America, hxu@ist.psu.edu 1 - Fashion-Telling: Predicting the Future of Digital Innovations Ping Wang, Associate Professor, University of Maryland, College Park, 4105 Hornbake Bldg., College of Information Studies, College Park, MD, 20742, United States of America, pwang@umd.edu The fusion of digital technologies and fashion makes innovation trajectories notoriously fickle and hard to predict. This study combines organizational ecology and network theories to conceptualize digital innovation ecosystems and proposes to use the ecology and structure of these ecosystems to predict the trajectories of digital technologies. The utility of this approach is illustrated in a case study, with implications to Information Systems strategy and management articulated. 2 - Making Sense of Materiality: The Case of 3d Printed Fashion Ning Su, Assistant Professor, Ivey Business School, Western University, 1255 Western Road, London, ON, N6G 0N1, Canada, nsu@ivey.uwo.ca 3D printing is increasingly embraced by fashion designers. Due to the nascent and evolving nature of 3D printing, however, there is significant uncertainty around its affordances and constraints. Drawing on qualitative data, this exploratory study shows that fashion designers seek to make sense of this emerging technology by pursuing a collective design process with multiple stakeholders. The ongoing interaction among diverse actors gives rise to innovative perceptions, practices, and products. 3 - Fashion and Text Analytics Heng Xu, Associate Professor, Pennsylvania State University, 316H IST Building, University Park, PA, 16802, United States of America, hxu@ist.psu.edu, Yilu Zhou The Fashion Industry is extremely competitive in terms of adapting to fast evolving fashion trends and consumer demand. Building upon various fashion theories, we empirically examine fashion designer’s evolution by studying a variety of data including fashion articles, magazines, blogs, and social media. By using text analytics, we envision the color, print, and style for upcoming trends and help retailers to stay on top of the latest fashion. 4 - Mining Social Media and Press Release for Competitive Intelligence: A Case Study on IBM Yuan Xue, The George Washington University, 2201 G Street, NW, Suite 515, Washington, DC, United States of America, xueyuan@gwmail.gwu.edu, Subhasish Dasgupta, Yilu Zhou Competitive intelligence (CI) refers to the study of competitors and competitive environment in support of decision-making. CI is usually generated by CI professionals. The recent CI trend is to collect competitor information online. We believe that social media analysis can help us to identify both direct and indirect competitors, rank competitors based on their market commonality and learn the strength and weakness of focal company.

3 - Optimal Online Selection of a Monotone Subsequence: A Central Limit Theorem Vinh V. Nguyen, PhD Student, The Fuqua School of Business, Duke University, 100 Fuqua Drive, Durham, NC, 27708, United States of America, vn26@duke.edu, Alessandro Arlotto, J. Michael Steele Consider a sequence of n independent random variables with common continuous distribution and consider the task of choosing an increasing subsequence where the observations are revealed sequentially and must be accepted or rejected when they are first revealed. There is a unique selection policy that maximizes the expected number of selected observations. We prove a central limit theorem for this number of optimally selected observations and characterize its mean and variance for large n. 4 - A Bayesian Decision-theoretic Model of Sequential Clinical Trials with Delayed Responses Stephen Chick, Professor, INSEAD, Technology Management Area, Boulevard de Constance, Fontainebleau, 77300, France, stephen.chick@insead.edu, Martin Forster, Paolo Pertile Clinical trials are necessary for evaluating the benefit of health technologies but are quite costly, but most analysis do not account for the effect of delayed observations on optimal trial design. We take a Bayesian sequential learning approach to the economics of clinical trial design with dynamic programming. We provide structural results and apply optimal stopping time solutions to data from recent clinical trials. SC23 23-Franklin 13, Marriott Solutions for Large Markov Chains Sponsor: Applied Probability Sponsored Session Chair: Mor Harchol-Balter, Professor, Carnegie Mellon University, Computer Science Dept., 5000 Forbes Ave., Pittsburgh, PA, United States of America, harchol@cs.cmu.edu 1 - Clearing Analysis on Phases: Distribution of Skip-free, Unidirectional, Quasi-birth-death Processes Sherwin Doroudi, PhD Candidate, Carnegie Mellon University, sdoroudi@andrew.cmu.edu, Mor Harchol-Balter, Brian Fralix A wide variety of problems in manufacturing, service, and computer systems can be modeled by infinite repeating quasi-birth-death process (QBD) Markov chains with infinitely many levels and finitely many phases. Many such chains have transitions that are skip-free in level and unidirectional in phase. We present the Clearing Analysis on Phases (CAP) method, which yields the exact limiting probabilities of the states in such chains as a linear combinations of scalar bases raised to powers. 2 - M/M/C Queue with Two Priority Classes Opher Baron, University of Toronto, 105 St. George St, Toronto, ON, Canada, opher.baron@rotman.utoronto.ca, Jianfu Wang, Alan Scheller-wolf We provide the first exact analysis of a preemptive M/M/C queue with two priority classes with different service rates. We introduce a technique to reduce the 2-dimensionally Infinite Markov Chain (IMC), representing this problem, into a 1-dimensionally IMC. We derive the law for the number of low-priority jobs. Numerical examples demonstrate the accuracy of our algorithm and generate new insights. We demonstrate how our methodology solves other problems. 3 - Product-Form Solutions for a Class of Structured Multi- Dimensional Markov Processes Jori Selen, PhD Candidate, Eindhoven University of Technology, De Zaale, Eindhoven, Netherlands, j.selen@tue.nl, Johan Van Leeuwaarden, Ivo Adan Motivated by queueing systems with heterogeneous parallel servers, we consider a class of structured multi-dimensional Markov processes whose state space can be partitioned into two parts: a finite set of boundary states and a structured multi-dimensional set of states, exactly one dimension of which is infinite. Using separation of variables, we show that the equilibrium distribution, typically of the queue length, can be represented as a linear combination of product forms.

SC25 25-Room 402, Marriott E-Commerce and Digital Marketing Sponsor: Information Systems Sponsored Session

Chair: Dokyun Lee, Carnegie Mellon University, United States of America, leedokyun@gmail.com 1 - Measuring Display Advertising Effects on Online Search Behavior Vilma Todri, PhD Candidate In Information Systems, NYU, 44 W 4th St, KMC Room 8-181, New York, Ne, 10012, United States of America, vtodri@stern.nyu.edu, Anindya Ghose The increasing availability of individual-level data has raised the standards for measurability and accountability in online advertising. Using a novel data set with granular measurements of advertising exposures, we examine a wide range of consumer behaviors and capture the effectiveness of display advertising across channels as well as the dynamics of these effects across the purchasing funnel path of consumers. We discover rich findings with important managerial implications.

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