2016 INFORMS Annual Meeting Program

MB67

INFORMS Nashville – 2016

4 - A Preference-Based Evolutionary Algorithm For Bi-Objective UAV Route Planning In Continuous Space Erdi Da demir, Hacettepe University, Ankara, Turkey, dasdemir.erdi@metu.edu.tr, Murat Köksalan, Diclehan Tezcaner Öztürk We address the bi-objective route planning problem for unmanned air vehicles that move in a continuous terrain. We develop a preference-based evolutionary algorithm that converge the desired regions of the Pareto front using the route planner’s preferences. The algorithm determines both the visiting order of the targets and the specific trajectory between targets. Experiments show that the algorithm works well. MB65 Mockingbird 1- Omni Field Experiment Research on Mobile and Social Applications Sponsored: Information Systems Sponsored Session Chair: Tianshu Sun, University of Southern California, 3670 Trousdale Pkwy, Los Angeles, CA, 90089, United States, tianshu.sun@gmail.com 1 - Evaluating Consumer m-Health Services For User Engagement And Health Promotion Mobile apps have great potential to deliver promising health-related interventions to engage consumers and change their behaviors such as healthy eating. This study proposes and evaluates three mobile-enabled interventions to address these challenges: a mobile-based visual diary, image-based dietitian support, and peer engagement. We examined their effects on user engagement and food choices via a 4-month randomized field experiment and show a positive impact of the mobile diary and dietitian support on improving customer engagement. Specifically, the mobile-based visual diary and dietitian support each increases the log-odds ratio of user engagement by 43.8% and 50.7%, respectively. 2 - The Effect Of Product Placement On Shopping Behavior At The Point Of Purchase: Evidence From A Randomized Experiment Using Video Tracking In A Physical Bookstore Pedro Ferreira, Carnegie Mellon University, pedrof@cmu.edu, Qiwei Han, João Paulo Costeira Physical retailers are increasingly trying to understand in-store shopping behav- ior in order to increase sales. However, measuring and analyzing shopper behav- ior at the point of purchase in physical retailing remains challenging. In this paper, we implement an in-vivo randomized field experiment in a physical book- store. We leverage video tracking technologies to monitor how shoppers respond to random book placement, which induces random search costs. More specifical- ly, we randomize the position of newly released books on the top of a large table with several rows and columns such that each book’s search cost becomes inde- pendent of the book’s characteristics. We use advanced 3D cameras and vision- understanding algorithms that can track human motions in real-time to over- come the large costs associated to large-scale video data. This way we are able to significantly reduce the cost of encoding shopper activities by more than 80%. Our experimental results show that on an average day books placed at the edge of the table are both picked and taken more often by consumers than books placed in the center of the table. However, the likelihood of taking a book that was picked is on average similar for books placed at the edge and at the center of the table, that is, books at the edge of the table sell more only because they are, on average, picked more often. Armed with this knowledge, the bookstore man- ager may maximize profit by placing books with higher margins at the edge of the table. 3 - Stimulating UGC Contribution Via Performance Feedback: A Randomized Mobile Field Experiment Yili Hong, Arizona State University, 832 W Wagner Dr, Gilbert, AZ, 85233, United States, hong@asu.edu, Bin Gu, Chen Liang, Gordon Burtch, Nina Huang This study analyzes the effect of performance feedback on user content generation through a randomized mobile experiment. We find heterogeneous treatment effects that depend upon a subject’s gender and the framing of the feedback supplied (altruistic vs. competitive). Specifically, we found that female users are more responsive to altruism-framed performance feedback, whereas male users are more responsive to competitively-framed performance feedback. Vibhanshu Abhishek, Carnegie Mellon University, vibs@andrew.cmu.edu, Rema Padman, Yi-Chin Lin

4 - Motivating Mobile App Adoption: Evidence From A Large-scale Field Experiment Tianshu Sun, University of Southern California, Los Angeles, CA, United States, tianshu.sun@gmail.com, Lanfei Shi, Siva Viswanathan, Elena Zheleva Using a randomized field experiment involving 250,000 customers, we investigate 1) whether and how a platform can motivate customers’ app adoption and 2) the causal effect of induced mobile app adoptions on customer engagement. We find that 1) providing information or incentives can both significantly increase customers’ app adoption; 2) the effect of app adoption varies greatly depending on how customers are motivated. Providing incentives increases adoption but not engagement. In contrast, providing information leads to effective mobile app adoptions that sustainably increase customers’ engagement. We further look into multi-channel browsing data to understand the effect of app adoption.

MB66 Mockingbird 2- Omni Data Visualization Sponsored: Quality, Statistics and Reliability Sponsored Session

Chair: Fadel Mounir Megahed, Auburn University, 3301 L Shelby Center Auburn University, Auburn, AL, 36849, United States, fmegahed@auburn.edu 1 - Stock Market Exploration And Prediction Through Visualization Xing Wang, Auburn University, Auburn, AL, 36849, United States, xzw0005@auburn.edu, Bin Weng, Fadel Mounir Megahed Stock Market prediction has attracted much attention from both business and academia. To explore the insight from the stock market history data could help investors to make their decision more efficiently. The purpose of this study is to develop a tool for visualizing and predicting the stock market through data mining methods. In this study, the tool is developed using Shiny R, which gives users useful information via visualizing related data from disparate data sources to assist investors to make decisions. By introducing our application, we focus on two things, one is the data visualization system design, and the other is reactive Fadel Megahed, Miami University, fmegahed@miamioh.edu Theyab Alhwiti, Mohammad Alamdar Yazdi, Maria Weese, L. Allison Jones-Farmer The size and scope of the literature on statistics can be overwhelming, which makes it difficult to identify emerging trends and see the relationships between different developments. Visualization techniques, coupled with statistical and data mining methods, have been found effective in achieving these goals in a number of application domains including healthcare and manufacturing research. In this paper, we apply these concepts to the field of statistical sciences. Our dataset is based on bibliographic information, including: authors, keywords, abstracts, citations, and funding information, extracted from 10,030 papers published in the 17 ASA journals in the period of 1991-2015. MB67 Mockingbird 3- Omni Advances in Degradation Modeling and Operations Management Sponsored: Quality, Statistics and Reliability Sponsored Session Chair: Xiao Liu, IBM T.J. Watson Research Center, 1101 Kitchwan Road, Yorktown Heights, NY, 10598, United States, liuxiao@us.ibm.com 1 - Kalman Filter Based Logistic Regression For Degradation Analysis Erotokritos Skordilis, University of Miami, Miami, FL, United States, sge12@miami.edu, Ramin Moghaddass We propose a new stochastic approach for degradation analysis using a combination of Bayesian Filtering and Binary Classification that can transform real-time condition monitoring signals to actionable insights. Analytical results for important reliability measures (e.g. RUL) will be given and a closed-form solution for the marginal log-likelihood function will be developed. Finally, a dynamic cost-effective predictive maintenance policy based on the proposed degradation structure will be introduced and its benefit over time-based preventive maintenance and corrective maintenance policies will be presented with a set of numerical experiments. programming, both are emphasized in our development process. 2 - What We Learned From Visualizing 25 Years Of Statistics Research?

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