2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - Free Riders Versus Social Capital: An Empirical Analysis Of An
Exogenous Shock On Online Reviews
Zaiyan Wei, Purdue University, West Lafayette, IN, United States,
, Paulo B Goes, Yang Wang, Dajun Daniel Zeng
We study the effects of network sizes on individuals’ contributions to online
product reviews. Individuals have conflicting incentives of free riding and
maximizing social benefits when producing online reviews. We leverage a
“natural experiment,” an exogenous expansion in the users population on a
major third-party platform, to better understand the tradeoffs between the
conflicting incentives. We find that a larger population of users caused individuals
to post more and longer reviews. In addition, the larger population of audience
led individuals to assign higher and more diverse ratings in their reviews.
However, the helpfulness or “quality” of reviews is not affected.
3 - Enterprise Systems And Merger And Acquisition Activities
Chengxin Cao, University of Minnesota, 321 Nineteenth Avenue
South, Minneapolis, MN, United States, ,
Gautam Ray, Alok Gupta, Mani Subramani
This paper examines the relationship between Enterprise Resource Planning
(ERP) and Customer Relationship Management (CRM) systems and upstream and
downstream mergers and acquisitions (M&A). We also investigate how any such
relationship is contingent on the characteristics of the focal firms’ industry
environment. Using a sample of 491 Fortune 1000 firms that made 4543 M&A
deals from 2006 to 2012 the empirical analysis suggests that ERP (CRM) systems
are negatively associated with upstream (downstream) M&A. However, if the
upstream (downstream) industry is concentrated (dynamic), ERP (CRM) systems
are associated with more vertical M&A.
4 - The Influences And Biases Of Social Network In Referral Hiring:
Empirical Study
Kyungsun Rhee, University of Washington, 4725 24th Avenue NE,
# 405, Seattle, WA, 98105, United States, ,
Elina Hwang, Param Vir Singh
It is well known that importance of social networks in labor market has been
growing rapidly. However, there have been rigorous researches on characteristics
of job seekers who are likely to achieve better results in job market, but not many
on the referrer behavior. Using data from social referral platform, this paper
constructs an empirical model to capture the influences and biases of referrers’
social capital on their actual referring behavior in the IT labor market.
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Mockingbird 2- Omni
Data Analytics for Quality and Reliability Assurance
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Mingyang Li, Tampa, FL, United States,
1 - A Data-driven Heterogeneity Quantification Approach For
Chloride Ingress Profiles Of Aging Marine Infrastructures
Suiyao Chen, University of South Florida,
4202 E. Fowler Avenue ENB302, Tampa, FL, 33620, United States,
, Lu Lu, Yisha Xiang, Alberto A Sagüés,
Mingyang Li
Chloride ingress is the leading cause to corrosion failures of aging infrastructures
in marine environments. Existing studies on chloride ingress mainly assumed
homogeneous populations and were constrained by the simplified physical
assumptions and availability of chloride ingress profiles. In this work, a data-
driven approach is presented to comprehensively explore, quantify and analyze
the heterogeneous chloride ingress profiles collected from a field survey on
marine infrastructures. A real-world case study is provided to illustrate the
proposed work and demonstrates its validity and performance.
2 - Reliability Meets Big Data: Opportunities And Challenges
Yili Hong, Virginia Tech,
In this talk, I will review some applications where field reliability data are used
and explore some of the opportunities to use modern reliability data to provide
stronger statistical methods to operate and predict the performance of systems in
the field. I will also provide some examples of recent technical developments
designed to be used in such applications and outline remaining challenges.
3 - Heterogeneous Recurrence Representation And Quantification Of
Dynamic Transitions In Continuous Nonlinear Processes
Hui Yang, Penn State,
Many real-world systems are evolving over time and exhibit dynamical behaviors.
In order to cope with system complexity, sensing devices are commonly deployed
to monitor system dynamics. Online sensing brings the proliferation of big data
that are nonlinear and nonstationary. Although there is rich information on
nonlinear dynamics, significant challenges remain in realizing the full potential of
sensing data for system control. This paper presents a new approach of
heterogeneous recurrence analysis for online monitoring and anomaly detection
in nonlinear dynamic processes.
4 - Latent Dirichlet Allocation (lda) Based Analytic Framework For
Topic Modeling Of Cfpb Consumer Complaints
Kaveh Bastani, Recovery Decision Science, Cincinnati, OH,
United States, , Hamed Namavari, Jeffrey Shaffer
We propose a text mining analytic framework based on latent Dirichlet allocation
(LDA) to analyze Consumer Financial Protection Bureau (CFPB) consumer
complaints. The proposed analytic framework aims to extract latent topics/clusters
in CFPB complaint narratives, and explores their associated trends over time. The
time trends will then be used to evaluate the quality of industry regulations and
expectations on financial institutions in creating a consumer oriented culture that
takes into account consumer protection in their decision making processes.
TB67
Mockingbird 3- Omni
IIE Transactions Invited Session
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Jianjun Shi, Georgia Institute of Technology, Atlanta, GA,
United States,
1 - A Random Effect Autologistic Regression Model With Application
To The Characterization Of Multiple Microstructure Samples
Qingyu Yang, Wayne State University,
The microstructure of the material can strongly affect material properties which in
turn plays an important role of the product quality produced by these materials.
The existingstudies on material microstructure mainly focus on a single
microstructure sample’s characteristics, while the variation among different
microstructure samples is ignored. In this paper, we propose a novel random
effect autologistic regression model to characterize the microstructure variation of
different samples for the two phase materials. A simulation study is conducted to
verify the proposed methodology. A real world example of a dual-phase high
strength steel is used to illustrate the developed methods.
2 - A Bayesian Variable Selection Method For Joint Diagnosis Of
Manufacturing Process And Sensor Faults
Yong Chen, University of Iowa, Iowa City, IA, 52242,
United States,
This paper presents a Bayesian variable selection based diagnosis approach to
identify both process mean shift faults and sensor mean shift faults
simultaneously in manufacturing processes. Important concepts are introduced to
understand the diagnosability of the proposed method. A conditional maximum
likelihood method is proposed as an alternative method to provide robustness to
selection of some key model parameters. Systematic simulation studies are used
to provide insights on the relation between the success of the diagnosis method
and related system structure characteristics. And a real assembly example is used
to demonstrate the effectiveness of the proposed diagnosis method.
3 - A Preposterior Analysis To Predict Identifiability In The
Experimental Calibration Of Computer Models
Daniel Apley, Northwestern University,
When calibrating computer simulation models using physical experimental data,
it is usually very difficult to identify unknown physical parameters and
distinguish their effects from the discrepancy function that represents the
difference between the simulation model and reality. We develop a preposterior
analysis to predict (prior to conducting physical experiments but after conducting
simulations) the identifiability that will result for any candidate physical
experimental design. This can be used as a criterion for designing physical
experiments to achieve better identifiability of the physical calibration parameters.
TB66