2015 Informs Annual Meeting

MC27

INFORMS Philadelphia – 2015

2 - How Our Networks Shape Our Privacy Yotam Shmargad, University of Arizona, 1515 E. First St., Tucson, United States of America, yotam@email.arizona.edu In this study, I relate characteristics of people’s networks to the level of privacy they experience in their social environments. I analyze over half a million users with nearly 40 million connections on a social network site, and show that characteristics of users’ networks can be used to predict various behaviors on the site – including decisions to share and consume information. In particular, users with networks containing several distinct social groups are more active on the site. 3 - Inter-firm Managerial Social Ties, IT Supplier Selection and IT Standardization Oliver Yao, George N. Beckwith, Professor, Lehigh University, 621 Taylor Street, Bethlehem, PA, 18015, United States of America, yuy3@lehigh.edu, Ling Xue, Ke Yang We empirically test links between inter-firm managerial social ties (IMST) and IT supplier selection and IT standardization. We find that: (1) A firm is more likely to use an IT vendor if the firm has more IMST with the IT vendor. (2) A firm with more IMST with its potential IT vendors uses more IT vendors. (3) More IT vendors is associated with lower IT standardization for the firm, and such relationship is strengthened when the firm has a greater number of IMST with its IT vendors. 4 - Latent Space Inference of Internet-Scale Networks Junming Yin, University of Arizona, Department of MIS, Tucson, AZ, 85721, United States of America, junmingy@email.arizona.edu, Qirong Ho, Eric Xing The rise of internet-scale networks with hundreds of millions to billions of nodes, presents new scientific opportunities, such as overlapping community detection to discover the structure of the internet. However, many existing models are difficult or impossible to deploy at these massive scales. We propose a scalable approach for overlapping community detection in internet-scale networks, and we demonstrate our method on real networks with up to 100 million nodes and 1000 communities. MC25 25-Room 402, Marriott Data-Driven Research on Economics of Digitization Sponsor: Information Systems Sponsored Session Chair: Hossein Ghasemkhani, Assistant Professor, Purdue University, 425 W. State Street, West Lafayette, IN, 47907, United States of America, hossein@purdue.edu 1 - Predicting Buying Opportunity in Retail Market with Machine Learning Warut Khern-Am-Nuai, Purdue University, 403 W. State Street, West Lafayette, IN, 47907, United States of America, wkhernam@purdue.edu, Karthik Kannan, Hossein Ghasemkhani Previous literature has shown that many machine learning techniques are effective in predicting stock price. However, it is not clear if those practices can be applied to a non-financial context or not. This paper employs three machine learning algorithms: ANNs, SVMs, and MARS to predict buying opportunities of products in a retail market. The preliminary results suggest that machine learning could be one potential avenue to help managers in optimizing buying decisions. 2 - Dynamic Estimation of Peer Effects and Product Engagement Daniel Rock, Doctoral Candidate, MIT Sloan School of Management, 30 Memorial Drive, Office 341, Cambridge, MA, 02142, United States of America, drock@mit.edu, Sinan Aral, Sean Taylor After product adoption, consumers make decisions about continued use. These choices can be influenced by peer decisions in networks, but identifying causal peer influence effects is challenging. Using engagement data for Yahoo Go, a mobile application, we apply a dynamic version of the Bramoullé et al. (Journal of Econometrics 2009) identification strategy to estimate usage peer effects. We compare the performance of a variety of prediction models for the instrumental variables “first stage”. 3 - Information Technology and the Rise of the Power Law Economy Guillaume Saint-jacques, PhD Candidate, MIT Sloan School of Management, 100 Main St, E62-459, Cambridge, MA, 02142, United States of America, gsaintja@mit.edu, Erik Brynjolfsson We show that the dramatically increasing share of income going to top earners can be explained by the rise of the “power law economy” and argue this reflects increased digitization and networks. Specifically, tax data (1960-2008) show that more individual incomes are drawn from a power law, as opposed to the long- established log-normal distribution. We present a simple theoretical model to argue that the increased role of power laws is consistent with the growth of information technology.

4 - The Value of Live Chat in Online Purchase Xue Tan, University of Washington, Seattle, WA, United States of America, Youwei Wang, Yong Tan In today’s competitive online marketplace, adopting a live chat tool is widely considered by merchants as a way to conduct one-to-one selling like in physical store. By allowing customer representatives to talk to potential buyers, e-tailer can answer consumers’ questions and decrease the level of information asymmetry. This paper empirically examine the role of live chat in terms of purchase conversion.

MC26 26-Room 403, Marriott

Academic Job Search Panel Cluster: INFORMS Career Center Invited Session Chair: Beril Toktay, Georgia Tech, Atlanta, GA, United States of America, beril.toktay@scheller.gatech.edu 1 - Academic Job Search Panel Moderator: Beril Toktay, beril.toktay@scheller.gatech.edu, Panelists: Kris Johnson Ferreira, H. Edwin Romeijn, Wedad Elmaghraby, Gad Allon The panel will discuss the academic interview process and do’s and don’ts associated with the job search. In addition to comments by current and former search chairs, time will be provided for questions and answers.

MC27 27-Room 404, Marriott

Multi-objective Design Problems Sponsor: Multiple Criteria Decision Making Sponsored Session

Chair: Diclehan Tezcaner Ozturk, Dr., TED University, Industrial Engineering, Ankara, Turkey, diclehan.ozturk@tedu.edu.tr 1 - A Control Chart Recommendation System Sidika Tunc, Research Assistant, Middle East Technical University, Cankaya, Ankara, Turkey, tsidika@metu.edu.tr, Gulser Koksal An approach is developed to recommend the most appropriate control chart to a novice decision maker in statistical process control. The chart selection problem is formulated as an MCDM problem. Overall desirability of each chart is determined. Expert knowledge is utilized. The system is tested and calibrated by statistical experiments. 2 - Estimating Non-additive Value Functions with Active Learning in the Ordinal Classification Setting Levent Eriskin, Middle East Technical University, Industrial Preference modeling is used to represent Decision Maker’s subjective preference structure. Preference structure having some kind of interaction among criteria is hard to model. In this study, we present results of analyses conducted for estimating non-additive value functions having interaction structure by utilizing active learning techniques in the ordinal classification setting. 3 - Interactive Mean-variance-covariance Optimization for Two Responses Melis Ozates, Research Assistant, Middle East Technical University, Universiteler Neighborhood, Dumlupinar Avenue No.1, Ankara, 06800, Turkey, mozates@metu.edu.tr, Gulser Koksal, Murat Koksalan We develop an interactive approach for the two-response product and process design optimization problem, explicitly considering decision maker preferences and allowing for correlated responses. We use several performance measures to represent the objectives that facilitate effective communication with the decision maker. Engineering Department, Ankara, Turkey, levent.eriskin@gmail.com, Gulser Koksal

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