2015 Informs Annual Meeting

TB24

INFORMS Philadelphia – 2015

TB24 24-Room 401, Marriott Urban Data Analytics and Mining Sponsor: Artificial Intelligence Sponsored Session Chair: Xun Zhou, Assistant Professor, University of Iowa, S210 Papajohn Business Building, 21 E Market Street, Iowa City, IA, 52242, United States of America, xun-zhou@uiowa.edu 1 - A Data Mining Approach to the Discovery of Emerging Hotspot Patterns in Urban Data Emerging hotspots can be observed in urban data, e.g., cellular service or traffic congestions due to non-periodic events (e.g., sport games, accidents). Efficiently identifying these patterns help city planners and service provides response early to the congestions. Previous hotspot detection techniques focused on patterns with fixed footprints. We propose an efficient data mining approach to detect emerging congestion patterns with dynamic footprints and validate our method on real urban data. 2 - Exploiting Geographic Dependencies for Real Estate Ranking Yanjie Fu, Rutgers University, 504 N 5th St, Harrison, NJ, 07029, United States of America, yanjie.fu@rutgers.edu, Hui Xiong, Hui Xiong We propose a geographic method, named ClusRanking, for estate evaluation by leveraging the mutual enforcement of ranking and clustering power model the geographic dependencies of estates for enhancing estate ranking. Indeed, the geographic dependencies of the investment value of an estate can be from the characteristics of its own neighborhood (individual dependency), the values of its nearby estates (peer dependency), and the prosperity of the affiliated latent business area (zone dependency). 3 - A General Geographical Probabilistic Factor Model for Point of Interest Recommendation Bin Liu, Rutgers University, 900 Davidson Road, 47 Nichols Apartment, Piscataway, NJ, 08854, United States of America, binben.liu@rutgers.edu The problem of point of interest recommendation is to provide personalized places. The decision process for a user to choose a POI can be influenced by numerous factors, such as personal preferences, geographical considerations, and user mobility behaviors. We propose a general geographical probabilistic factor model framework which takes various factors into consideration. Extensive experimental results show promise of the proposed methods. TB25 25-Room 402, Marriott Software-Driven Innovation and Business Strategies Sponsor: Information Systems Sponsored Session Chair: Narayan Ramasubbu, University of Pittsburgh, 354 Mervis Hall, Pittsburgh, PA, 15228, United States of America, nramasubbu@katz.pitt.edu 1 - Business Value of the Mobile Enterprise: An Empirical Study of Mobile Sales Force in Banking Ajit Sharma, Ross School of Business, 701 Tappan Street, Ann Arbor, MI, United States of America, asharmaz@umich.edu The press and research on mobility has remained focused on the customer-firm interface. While there is sufficient evidence of gains from mobile marketing in better targeting and lift, the benefits of mobile-centric enterprise processes remain under-studied. In this paper, we empirically assess the reduction in process time and error rates by shifting from a traditional “sales person in the field-computer in the office” sales process to a “sales person in the field with a tablet” sales process. 2 - Lost in Cyberspace: An Investigation of Digital Borders, Location Recognition, and Experience Attribution Brian Dunn, Assistant Professor, University of Oklahoma, 307 West Brooks Ste. 307D, Norman, OK, 73072, United States of America, bkdunn@ou.edu, Narayan Ramasubbu, Dennis Galletta, Paul Lowry Do website users know where they are? Given that they may visit multiple sites in the same session, they may not, which has important implications for the owners of those sites. However, past research has yet to account for this possibility. To understand when users recognize where they are online and how they attribute credit to the sites that are helpful to them, we introduce the Amin Vahedian Khezerlou, University of Iowa, S283 Pappajohn Business Building, The University of Iowa, Iowa City, IA, 52242-1994, United States of America, amin-vahediankhezerlou@uiowa.edu, Xun Zhou

concepts of ‘digital borders’ and ‘border strength’ and use them in an experimental investigation. 3 - Design Control in Open Innovation: An Examination of Open Source Software Production Shivendu Pratap Singh, University of Pittsburgh, Room 229, Mervis Hall, Pittsburgh, 15260, United States of America, shs161@pitt.edu Firms are opting for co-creating software, by attracting developers on platforms like GitHub.This shared model of development requires flexible software design controls to influence community engagement, which could result in proliferation of design options. Flexible design control policy could have side effects such as accumulation of technical debt and need to be judiciously managed. This paper examines the antecedents and consequences of design control policies in social software production. 4 - Time-dependent Pricing for Mobile Data: Analysis, Systems, and Trial Soumya Sen, ssen@umn.edu, Carlee Joe-wong, Mung Chiang, Sangtae Ha Dynamic pricing of mobile data traffic can alleviate network congestion by creating temporally-varying price discounts. But realizing it requires developing analytical models for price point computation, systems design, and field experiments to study user behavior. In this paper, we present the architecture, implementation, and a user trial of a day-ahead time-dependent pricing. Chair: Kathy Stecke, UT Dallas, SM30 JSOM, 800 W Campbell Rd, Richardson, TX, 75080, United States of America, kstecke@utdallas.edu Co-Chair: Xuying Zhao, University of Notre Dame, Notre Dame, IN, Xuying.Zhao.29@nd.edu 1 - Optimal Price Trajectories and Inventory Allocation for Inventory Dependent Demand Stephen Smith, Professor, Santa Clara University, 500 El Camino Real, Lucas Hall 216H, Santa Clara, CA, 95053-0382, United States of America, ssmith@scu.edu, Narendra Agrawal Retail demand is often inventory dependent because larger inventories create more attractive displays and low inventories can create broken assortments. This research jointly optimizes the price trajectory and the allocation of a given amount of inventory across a set of non-identical stores with inventory dependent demands. 2 - The Effect of Reward Purchase on Dynamic Pricing Hakjin Chung, Stephen M. Ross School of Business, University of Michigan, Ann Arbor, MI, United States of America, hakjin@umich.edu, So Yeon Chun, Hyun-soo Ahn In many loyalty programs, consumers are provided with an option to acquire products by redeeming loyalty points instead of cash. We characterize when consumers use points or cash depending on their willingness-to-pay in cash as well as in points. Then, we incorporate this consumer choice model into the seller’s dynamic pricing model, where the revenues from both posted price and reimbursement for reward sales are embedded in each period. 3 - An Off-price Retailer with Two Ordering Opportunities Moutaz Khouja, Professor, UNC Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, United States of America, mjkhouja@uncc.edu, Jing Zhou We develop a model of an off-price retailer who has two procurement opportunities for next season. The first opportunity occurs after the end of the current season where she buys excess inventory from retailers and manufacturers and store them until next season. The second opportunity occurs before the selling season begins again. The product quantity available in the first opportunity is limited while the price in the second opportunity is a random variable that depends on consumer demand. 4 - Multi-product Price Promotions with Reference Price Effects Kevin Li, UC Berkeley, IEOR Department, Berkeley, CA, 94720, United States of America, kbl4ew@berkeley.edu, Candace Yano We consider a retailer’s problem of setting prices, including promotional prices, over a multi-period horizon for multiple products with correlated demands, considering customer stockpiling and the effect of reference prices on customers’ buying behavior. These factors limit the efficacy of deep discounts and frequent promotions. We present structural results and numerical examples that provide insight into the nature of optimal policies and the impact of various parameters. TB26 26-Room 403, Marriott Retailer Pricing Cluster: Operations/Marketing Interface Invited Session

292