Decision, Operations & Information Technologies

Short Descriptions of Current DO&IT Research Projects

Revenue Management

Professors Ball, Elmaghraby, Chen, Karaesmen and Kumar

How should airlines control seat inventory given limited information?  Airlines face uncertainty in the number and sequence of seat requests, and on whether or not a passenger will actually show up for a flight.  How should the airline make overbooking decisions and control seat inventory?

Retail sales are great for the shopper, but tend to be a nightmare for the retailer.  This research is developing analytic models to determine when and how much to discount merchandise for sales.  The approach is to use a game-theoretic model to study the ramifications of a policy that allows customers to reserve goods before the markdown or sale period.

Outsourcing and Offshoring

Professors Agarwal, Gao, Mithas and Gopal

The first part of this research is focused on the type of service to be outsourced or sent offshore.  The outsourcing of information technology services has received the most attention, but there is a range of services that can be outsourced, from low-end activities like a call center to high-end knowledge work.  The research also looks at the characteristics of an outsourcing arrangement, for example, by comparing the vendor and client capabilities, experience and skills.  What is the performance of outsourcing vendors?  What kind of contracts are most effective in outsourcing?  How should the vendor and client manage and coordinate services?

Data Mining and Statistics

Professors Alt, Jank, Kumar, Shmueli

Data mining is the non-trivial extraction of implicit, previously unknown and potentially useful information from data.  The world is collecting data at a furious pace, with far too much data for manual analysis.  AT&T handles billions of calls per day.  Europe had a project with 16 telescopes that collected 1 gigabit/second of data over a 25-day observation session.  Data mining helps Google refine its search algorithm and AT&T detect fraud.

Other research examines prediction markets which are speculative markets created for the purpose of making predictions.  An asset’s final cash value is tied to an event, such as who will be the next president.  The Iowa prediction market is famous for estimating the results of political races, but there are many other markets, including some used in business to predict product sales and success.

Business Value of IT

Professors Agarwal, Bailey, Gao, Lucas, and Mithas

Over 50% of capital spending in the U.S. is for information technology.  How do organizations know they are getting a return from this investment?  Business value looks at individuals, organizations and industries to estimate a return from investments in IT.  One objective of this research is to understand how to manage IT to maximize its contribution to the organization.  An example of this kind of research is a project that looks at the relationship between product innovations and value compared to IT investments.

Other research on business value has examined how IT spending affects firm performance and how systems affect customer satisfaction.

Transformational IT

Professors Lucas, Viswanathan, Gao, Bailey and Mithas

Technology is enabling the transformation of organizations, markets and industries.  This stream of research seeks to understand the nature of these transformations to offer guidance on how to know that one is coming.  It has resulted in a book and a documentary The Transformation Age, which has appeared on over 200 public televisions stations in the U.S.

A specific example of research on transformation is a project looking at the impact of the Internet on newspapers in the U.S. and on where and how Internet users are getting their news. 

Open Source

Professor Stewart

Open source is a major force in technology; it refers to software and applications that are distributed freely on the Internet and that are developed largely by volunteers.  This research looks at how one measures and evaluates success in open source projects, especially smaller projects rather than well known open source products like Linux.

Auctions

Professors Ball, Dellarocas, Elmaghraby, Jank, Raghavan, Shmueli and Viswanathan

The faculty in DO&IT approaches auctions from several different perspectives.  Those with interests in statistics use empirical tools to explore and model auction dynamics and characteristics, such as bidder and seller behavior.  The management science approach is to model an auction as a non-trivial optimization problem where the challenge is to design a practical and computationally efficient auction.  The economic approach looks at an auction through the lens of game theory:  bidders report their costs/valuations and the auctioneer determines the allocation of payments as a function of the reported information.

Allocating Airport Access Rights

Professor Ball

This research uses market-based mechanisms for reduce airport congestion and flight delays.  The research is sponsored by the Federal Aviation Agency and involves a consortium of faculty from different Universities including Maryland, George Mason, Harvard, MIT and Berkeley.  The FAA used the results of this research as a part of its efforts to allocate landing slots at LaGuardia and JFK airports.

Supply Chain Management

Professors Ball, Chen, Elmaghraby, Fu, Goyal , Karaesmen, Souza

Supply chains include suppliers who provide material, organizations that transform material into output, distribution of the output to retailers, and finally sales to customers.  Research on supply chain looks at how to manage material and information flows.  Supply chain errors and malfunctions have a serious negative effect on firm performance.

One study looks at order promising and fulfillment in an environment in which a manufacturer assembles products based on orders.  Another project is concerned with integrated production and distribution operations as well as schedule conflict and cooperation in a supply chain.  Faculty are also studying closed-loop supply chains where there are flows of products (for example defective units) back to the manufacturer.

How to Save Healthcare

Professors Agarwal, Gao, Golden, Prasad, Raschid, Shmueli, Souza

Healthcare is in a crisis state in the U.S.; costs are rising faster than inflation and even faster than college tuition.  At the same time the quality of healthcare in the U.S. is uneven at best.  Research on healthcare focuses on how to use information technology to improve both the efficiency and quality of health services.  How can we design technology for these purposes that various workers in the industry will adopt?  Research focuses on electronic medical records: their adoption, diffusion and use.

One project in healthcare is to encourage lean management in the industry, which is all about reducing waste.  Research on patient flow is designed to help hospitals manage their resources more effectively, for example, to obtain greater utilization of operating rooms.

An ongoing stream of research is concerned with biosurveillance or the early detection of disease outbreaks.  Are there ways to use new sources of data to alert medical authorities to the outbreak of a disease faster than traditional approaches which use lab and physician reports?  For example, could one use school absenteeism data to spot the outbreak of a disease?

Optimization and Applications

Professors Ball, Chen, Fu, Golden, Jank, Karaesmen, Raghavan

The goal of this research is to create models for decision making through optimization and applied probability and simulation techniques.  The application areas include transportation, manufacturing, supply chain management, financial engineering and telecommunications.  For example, one study looks at the time-sharing of jet aircraft for on-demand air travel. Another project is concerned with scheduling a series of orders to minimize cost or maximize profit or service.  Efficient vehicle routing is another application.

Online Information, Communities and Search

Professors Dellarocas, Gao, Goyal, Raghavan, Viswanathan and Wang

This research studies some of the new phenomenon created around the Internet like social networks.  There are many new business models, information intermediaries and new business processes.  The Internet encouraged the development of search engines, which have evolved into powerful advertising vehicles with sponsored search. The technology has resulted in new, online information intermediaries such as those found in auto-retailing, financial services, real estate, insurance and healthcare.  Research projects seek to understand the impact of these intermediaries and their role in different industries. 

The Internet has made online reviews a fixture for retailing.  Research examines how these reviews influence and predict sales.  Other projects look at reputation mechanisms and how the degree of anonymity provided for a review influences one’s willingness to participate honestly.

Research Impact

Zhi-Long Chen

I have been doing research in the following application areas of Operations Management and Management Science: (i) Scheduling; (ii) Logistics and Transportation; (iii) Supply Chain Operations; (iv) Pricing. These areas cover a wide variety of operational, tactical and strategic managerial decision making problems faced by most manufacturing and service enterprises. Optimal or at least near-optimal solutions to such problems are of paramount importance because often times 1% of improvement over an existing solution to such problems means millions of dollars of cost savings to companies. My research mainly develops mathematical models and solution algorithms for finding optimal or near optimal solutions to these problems using optimization techniques such as dynamic programming, integer programming, and stochastic programming. I also design fast heuristics for NP-hard problems and analyze performance of heuristics in terms of worst-case and/or asymptotic behavior. Applications of my work to date in each of these areas are summarized in the following.

(i) Scheduling
Scheduling problems are commonly encountered in manufacturing and service industries. They are concerned about efficient allocation of resources (machines, manpower, utility) to tasks (jobs, customer orders) to achieve a desired performance of the tasks subject to resource availability. Optimal or near optimal solutions to these problems often play a critical role in achieving low cost and high resource utilization. One class of problems that I have worked on are production scheduling problems that arise in just-in-time manufacturing. In a JIT environment, both earliness and tardiness are not encouraged, and hence it is desired that a job be completed processing at a time as close as possible to its due date. A typical scheduling problem in such an environment is to schedule a set of jobs with a minimum total (weighted) earliness and (weighted) tardiness.

(ii) Logistics and Transportation 
I have worked on a number of real-world logistics and transportation problems. One of the problems involves optimal dispatching of trucks commonly encountered by logistics service providers in practice. Issues such as DOT rules, multiple time windows, crossdocking, and dynamic order arrivals are addressed in conjunction with classical vehicle routing decisions. Large-scale optimization techniques (column generation coupled with fast heuristics) are developed. Other practical problems that I have worked on involve food distribution, container vessel operations, and truck loading operations.

(iii) Supply Chain Operation 
I have worked on a class of so-called supply chain scheduling problems which address scheduling issues that cross multiple stages or multiple functional areas of a supply chain. One set of the problems that I studied involve integrating production and distribution operations at the tactical and scheduling levels. Such problems often occur in supply chains for time-sensitive, perishable, or make-to-order products. Examples include PC manufacturing and distribution, food preparation and delivery, newspaper printing and delivery, and concrete paste mixing and delivery, etc. In such applications, production operations are closely linked with outbound distribution operations without much finished product inventory in the middle. Hence it is critical to consider production scheduling and delivery routing and scheduling jointly. I have developed a number of models and solution algorithms.

(iv) Pricing 
I have been working with companies for several years on some practical markdown pricing problems involving multiple retail stores. Large retail chains frequently use markdown schemes to sell their products and it is important to develop a good markdown scheme by fully utilizing available data and analytical tools. In the markdown pricing literature, almost all existing papers consider problems with a single store. In the problem we studied, there are many stores (50 to 100) and the pricing decisions for different stores are coupled by a number of business rules. We developed an optimization based approach to generate a near-optimal inventory allocation across the stores and a markdown scheme for each store. Our solution outperforms all commonly used techniques in practice.

Wolfgang Jank

My research focuses on the application of data mining and statistics to business problems. In recent years, it has become easier and easier to collect and record data and many businesses now face the question "What to do with all that data?" Data mining tries to uncover hidden knowledge from large amounts of complex data. Data mining is successfully being employed by modern businesses such as AT&T who uses data mining to uncover fraud, or Google whose search algorithm relies on very sophisticated data mining techniques. The success of data mining and statistics has been documented in many recent reports, and e.g. the New York Times quotes Google's chief economist for saying that "statistics will be the sexy job in the next 10 years." My research focuses particularly on data mining in online markets. In that context, I am interested in understanding the dynamics of markets and competition between markets or between market participants. For instance, my research on online auctions shows that the incorporation of price dynamics into forecasting models leads to much more accurate predictions of the outcome of an auction. Moreover, automated bidding agents lead to a higher expected consumer surplus when equipped with models that not only take into account the auction of interest, but also all other, simultaneous auctions that compete for the same bidder. Building models that can incorporate dynamics and competition require new tools from data mining and statistics. Such tools include functional models, spatial or temporal methods or agent-based models. Agent-based models are new and exciting tools for business research since they allow us to study the effect of micro-level decisions (e.g. consumer's word-of-mouth) at the macro-level outcome (e.g. company revenues or market behavior). We study many of these complex phenomena in our new Center of Complexity in Business at the Smith School. I have studied a variety of different online markets, including auctions such as on eBay and prediction markets. Prediction markets are different from classical markets in that their main objective is not the creation of wealth, but the forecast of future business outcomes. To that end, prediction markets use traditional market mechanisms and incentives to collect the "wisdom of crowds" and transform it into an actionable number. My current research also focuses on B2B markets and understanding the mental models that drive decision making of a company's salesforce. While B2C markets have seen an explosion (and successful implementation) of automated, data-driven revenue management models, the same is not true for the B2B market since pricing decisions cannot be automated and are always influenced by the individual sales personnel. My research investigates how objective pricing models collide with subjective sales person decisions and how this "collision" can be controlled by management. My research has been published in many international journals and conferences, and the book "Statistical Challenges in eCommerce Research" summarizes many of the current efforts in this field.

Hank Lucas

Computer and communications technologies have transformed businesses ranging from recorded music to securities markets. How did managers in the firms that have been dramatically impacted by technology transformations miss what was happening? From the stories of Merrill Lynch's first response to new entrants in the brokerage business offering online trade execution, it is clear that there was a wide-spread lack of awareness about transformational IT at the very highest levels of the organization. One of the many CEOS of Kodak in the 1990s, George Fisher, described in an interview how he was unable to change middle management to a digital mindset, instead of a focus on chemistry and film. Executives in firms that produce and distribute video content are struggling with the threats and opportunities of Internet delivery, just as music industry managers have been searching for more than five years for a business model that can co-exist with peer-to-peer file sharing technology.

My research is focused on IT-enabled transformations and on how we can meet their challenges and opportunities. We have looked at organizations as diverse as the New York Stock Exchange and Kodak, and are currently studying the impact of the Web on the newspaper industry and on where we learn about news. In all of these cases, managers have been unprepared for dramatic changes in their organizations.

Figure 1 describes the process by through which technology potentially transforms an organization.

figure 1
Figure 1

The first task for managers is to recognize that an enabling technology will have an impact on her business. The manager can choose to adopt the technology voluntarily, or it can be forced on her by competitors. In either case, the end result requires massive changes in the organization and in the cognitions of managers in that organization. Our studies of the NYSE and Kodak found that these changes were very difficult to make, and it ended up taking traumatic shocks to bring about change. Figure 1 suggests that there is a high probability of failure in adopting transformational technologies because of these organizational and managerial challenges.

The bottom line is that managers have to recognize a transformational technology and then they have to manage the adoption process, a process that is likely to require major organizational and managerial change. My book Inside the Future: Surviving the Technology Revolution, which accompanies a PBS documentary co-produced by the Smith School and Maryland Public Television (www.transfromationage.org) offers advice for managers and individuals on how to cope with the significant changes technology enables in government, industry, organizations and our daily lives.

Louiqa Raschid

Mining the Annotated Biomedical Web
Louiqa Raschid
Funded by the NSF

The biomedical research enterprise has created a rich, publicly accessible Web of hyperlinked and curated data. In parallel, the healthcare enterprise (hospital systems, physician offices, insurers), the NIH, and individuals, are creating personal health information (PHI), and specialized portals (dbGaP, eMERGE) are emerging, to provide restricted access to de-identified data. In order to improve interoperability, these communities have created a number of ontologies such as GO, MeSH, SNOMED-CT and UMLS. Data entries (records) in these resources are typically annotated with concepts or controlled vocabulary (CV) terms from one or more of these ontologies. The data entries are often hyperlinked to entries in other repositories, creating a richly curated Web of semantic knowledge comprising this annotated and hyperlinked data. We are developing a set of tools exploiting techniques from data management, information retrieval, optimization approaches and approximation algorithms, data mining and visualization to help the scientist better understand and explore this wealth of knowledge. Results of this research may lead scientists to formulate interesting hypotheses relating genes, diseases, individuals and their response to treatments. This can lead to personalized treatments and can empower an individual to contribute personal knowledge.

GeoNets: A Semantic Dataspace for Humanitarian Assistance
Louiqa Raschid
Funded by the NSF

Access to up-to-date and quality information can have a significant impact on the humanitarian relief community as they coordinate relief efforts. In addition to data that is created and curated by experts, there is a vast volunteer community who are empowered by the social Web to blog and generate community curated content. Our research will explore the following challenges in setting up the GeoNets semantic dataspace for humanitarian assistance: GeoNets Semantic Dataspace: GoNets will leverage methodologies for event detection, document clustering, query answering, ranking and personalization to create a GeoNets semantic dataspace. A front end intuitive user language will be defined for users to specify their profiles and express their queries. A combination of techniques from query answering, ranking and optimization will be developed to provide relevant and important answers efficiently. GeoNets Quality Assessment:
We will develop a methodology to involve users in evaluating the quality of results to their queries. Quality criteria may include timeliness, accuracy, popularity and relevance. The feedback may be explicitly obtained from users or it may be implicit, e.g., popular content. User profiles and ranking will also be used to assess and improve the quality of the retrieved answers.

Monitoring, Sensing and Effective Retrieval from the Social Web
Louiqa Raschid

The social Web as captured by blogs and tweets represents a digital slice of thoughts and actions of Netizens. While the preponderance of this data is only of interest to the creator and a small social network, the social Web has the potential to track the emergence of information about disasters and diseases, to follow social trends or commodity price fluctuations, to serve as a vast database for validation of queries, etc. Research challenges include methods to answer the following questions: When did topic A emerge? Who is most likely to blog about topic A or who is most likely to follow topic A? Has the conversation about topic A reached a critical mass and then did that occur? The computational challenges include document similarity and clustering, maintaining social networks and blogger profiles, personalized ranking, and optimal monitoring strategies.

PhD Course Descriptions (OM/MS)

Operations Management / Management Science (OM/MS)

PhD students in OM/MS are required to take at least 14 courses, as outlined in the requirements. Below is a list of courses offered by the DO&IT department in the OM/MS area: 

BMGT 830: Operations Research: Linear Programming
BMGT 831: Operations Research: Extension of Linear Programming and Network Analysis
BMGT 832: Operations Research: Optimization and Nonlinear Programming
BMGT 833: Operations Research: Integer Programming
BMGT 834: Operations Research: Probabilistic Models
BMGT 835: Simulation of Discrete-Event Systems
BMGT 882: Applied Multivariate Analysis I
BMGT 883: Applied Multivariate Analysis II
BMGT 898: Seminar in Operations Management


Courses

BMGT808I Information Systems Research
Offered every Fall semester
This is an introductory seminar in information systems research for doctoral students. Its objective is to introduce participants to some major streams of research in information systems and to help seminar participants understand the role of research in an academic community and the methods of social science research. Sample Syllabus [PDF]

BMGT808I Research Methods in Information Technology
Offered every Spring semester
The purpose of this seminar is to introduce students to the broad range of research methods used by Information Technology researchers. The course makes frequent use of guest lecturers to lead discussions on areas of their research expertise. An emphasis is placed on applying research methods in the development of each student's own individual research interests. For doctoral students with an Information Systems major the culminating project in this course serves as the basis for their first year summer project. Sample syllabus [DOC]

BMGT808L Technology Artifact in Information Systems Research
Offered in the Fall of odd-numbered years (e.g., 2003, 2005, 2007)
Sample Syllabus [PDF]

BMGT808 Current Topics in IS research
Survey of literature in selected research areas in information research. Topics change every semester.

BMGT 808 Research in Decision, Operations, & Information Technologies
One credit. The course includes attendance at a series of seminars on topics related to  research in Decision, Operations, & Information Technologies.

BMGT808D Strategic Management of Information Technology
Offered in the Spring of even-numbered years (e.g., 2004, 2006, 2008)
The goal of this seminar is to provide an understanding of the issues related to the adoption and use of information technologies in organizations, the leverage of value from information technologies, and the management of information technologies in organizations. Students will read and discuss various theories, conceptual issues, and empirical papers pertaining to research on these topics of inquiry.
Sample Syllabus [PDF]

BMGT808D Information Systems Economics
Offered in the Fall of even-numbered years (e.g., 2004, 2006, 2008)
This is a research-oriented doctoral seminar on IS Economics. Its primary objective is to familiarize seminar participants with the applications of microeconomic theories and modeling techniques to IS research problems. The seminar is also intended to motivate participants to explore the use of mathematical models to analyze a research question in their domain of interest. Seminar participants are expected to have adequate familiarity with calculus and simple optimization techniques.
Sample Syllabus [PDF]

BMGT808A E-Commerce and Supply Chain Management
Offered in the Spring of odd-numbered years (e.g., 2003, 2005, 2007)
Sample Syllabus [PDF]

BMGT808X Applied Regression
Offered every Fall semester
The main course objectives are 1. To learn about a wide variety of regression techniques; to understand when to use what technique; to understand the limitations of a particular technique; 2. To generate a basic understanding of the methodological principles underlying these regression techniques in order to become a critical user; 3. To learn the powerful statistical software R; and to implement these regression techniques using this software. Sample Syllabus [PDF]

BMGT882 Applied Multivariate Analysis I
Offered every Fall semester
Multivariate statistical methods and their use in empirical research. Topics include summarization and visualization of multivariate data, multivariate paired comparisons and repeated-measures designs, multivariate analysis of variance, discriminant analysis, and canonical correlation. An important component of the course is analysis of data using contemporary software. Each student will complete a project that applies at least two of the methods covered in the course to a data set of his/her choice.
Sample Syllabus [PDF]

BMGT883 Applied Multivariate Analysis II
Offered every Spring semester
A continuation of BMGT 882. Topics include generalized least squares, seemingly unrelated regressions, simultaneous-equations models, principal components, factor analysis, structural-equations models with latent variables (covariance structure analysis), and specification testing. Sample Syllabus [PDF]

BMGT 830 Operations Research: Linear Programming (3)
Prerequisites: MATH 240 or equivalent; or permission of department.
Concepts and applications of linear programming models, theoretical development of the simplex algorithm, and primal-dual problems and theory.

BMGT 831 Operations Research: Extension of Linear Programming and Network Analysis (3)
Prerequisite: BMGT 830 or equivalent; or permission of department.
Concepts and applications of network and graph theory in linear and combinatorial models with emphasis on computational algorithms.

BMGT 832 Operations Research: Optimization and Nonlinear Programming (3)
Prerequisites: {BMGT 830; and MATH 241; or equivalent}; or permission of department.
Theory and applications of algorithmic approaches to solving unconstrained and constrained non-linear optimization problems. The Kuhn Tucker conditions, Lagrangian and Duality Theory, types of convexity, and convergence criteria. Feasible direction procedures, penalty and barrier techniques, and cutting plane procedures.

BMGT 833 Operations Research: Integer Programming (3)
Prerequisites: {BMGT 830; and MATH 241 or equivalent}; or permission of department.
Theory, applications, and computational methods of integer optimization. Zero-one implicit enumeration, branch and bound methods, and cutting plane methods.

BMGT 834 Operations Research: Probabilistic Models (3)
Prerequisites: {MATH 241; and STAT 400 or equivalent} or permission of department. Theoretical foundations for the construction, optimization, and applications of probabilistic models. Queuing theory, inventory theory, Markov processes, renewal theory, and stochastic linear programming.

BMGT 835 Simulation of Discrete-Event Systems (3)
Prerequisites: Knowledge of Fortran, Basic, C, or Pascal; and BMGT 630 or equivalent. Simulation modeling and analysis of stochastic discrete-event systems such as manufacturing systems, inventory control systems, and computer/ communications networks.

BMGT 898 Seminar in Operations Management (3)
This seminar reviews recent research in operations management. Examples of topics include supply chain management, revenue management, operations strategy, production planning, new product development.

Operations Management/Management Science Major

OM/MS PhD Program Coordinator: Wedad Elmaghraby

Overview

The requirements for the PhD program in OM/MS can be divided into the following categories (details about each below):

  • Coursework: four courses in research methodology, 6 courses in the major, and 4 courses in a minor to be chosen by the student. 
  • Additional requirements:  Four one-credit seminars in research in DO&IT.. Further, students entering the program without an MBA or BS in business administration have an additional business breadth course requirement.
  • Qualifying exam: This exam is taken at the end of the first year in the program.
  • Comprehensive exam: This exam is taken at the end of the second year in the program.
  • Teaching: A funded student is required to TA for BMGT 332 (or similar course) once during the program, typically in his/her second year, and to teach one section of BMGT 332, typically in the third year.
  • Dissertation proposal defense: An oral defense of the dissertation proposal, with a significant portion of the dissertation (at least 40%) already completed.
  • Dissertation completion and defense.

Research Methodology Courses (4 courses)

Specific course numbers can change between semesters.  The most recent designation for each course is shown below:

  • BMGT 830
    Operations Research: Linear Programming (Fall 1st year)
  • BMGT 834
    Operations Research: Probabilistic Models (Fall 1st year)
  • BMGT 808G
    Doctoral Seminar: Applied Microeconomics, or equivalent (e.g., ECON 603) (Fall 1st year)
  • BMGT 808X
  • Doctoral Seminar: Applied Regression Analysis or equivalent (Spring 1st year)
    If a student chooses to take a course different than BMGT 808G, BMGT 808X or ECON 603, then the student needs approval from the PhD coordinator.  For more information about these and other courses, see department website.

Major Specification (6 courses)

There are two major concentrations: Operations Management (OM) and Management Science (MS). Courses are as follows:   

  • BMGT 808F: Seminar in Operations Management (Required; Spring 1st year)

Plus five additional courses.  The choice of courses is open; however, the student needs approval from the PhD coordinator when choosing a course sequence.     

Minor Specification (4 courses)

Four courses in an area. The choice of area is open; examples are shown below:

  • Logistics/Supply Chain Management
  • Management and Organization
  • Marketing
  • Finance
  • Information Systems
  • Statistics (courses outside of major area)
  • Management Science (courses outside of major area)
  • Applied Mathematics
  • Computer Science

Additional Requirements

  1. All students need to be enrolled, during their first and second years, in BMGT 8xx:  Research in Decision, Operations, & Information Technologies (1 credit).  
    This is a one-credit course, which basically requires attendance to the DO&IT research seminar series. The student will take this seminar every semester during his/her first two years in the program (total = 4 credits) 
  2. Business breadth courses: Students who enter the PhD program without an undergraduate (BSBA) or graduate degree (MBA, MS) in business administration are required to take two business breadth courses (2 or 3 credits each) at the MBA or doctoral level. Each one of these two courses should be in a different functional area than OM/MS: finance, accounting, management & organization, marketing, or information systems.  Example: Consider a student with a masters and undergraduate degrees in IE, and with a minor in marketing. Given the marketing minor, the student only needs to take one additional business breadth course (examples: MBA core Finance class, a doctoral seminar in organizational behavior, etc).  These courses can be taken anytime during the doctoral program. Additional questions about this requirement should be directed to the OM/MS PhD coordinator.

Qualifying Exam

This exam is taken during the summer of the first year (typically, last week of July), and comprises four 2-hour parts.  Parts 1, 2, and 3 will cover BMGT 830 (Linear Programming), BMGT 834 (Stochastic Processes), and BMGT 808F (Seminar in Operations Management), respectively.  The Part 4 subject area can be chosen by the student to cover the content of one other course taken by the student.  The course could be chosen from among the remaining required courses (Economics or Statistics) or could be a course taken by the student as part of his/her major concentration courses.  The precise format of each exam part will be determined by the faculty member designated to prepare that part, e.g., each part could be open or closed book.  However, questions are not expected to be a mere “repeat” of the final exam in the respective course, but rather can be more unstructured and attempt to test research potential.  If the student does not pass the first trial, the student shall be given an opportunity to repeat the exam in the winter (six months later).  Only two trials are allowed.  A student who fails the qualifying exam twice will not be allowed to proceed further in the Ph.D.  Program.

Comprehensive Exam

Prior to taking the exam, each student must designate a three-person examination committee comprised of DO&IT faculty.  The committee must be approved by the DO&IT PhD Coordinator by email. This exam is taken during the summer of the second year, at the time requested by the student and agreed upon by the committee.  The student has two choices:

The student can submit a research paper co-authored by the student and other faculty members (but not with another student).  The research paper is expected to be of such scope that it can be submitted to a refereed journal, i.e., it has to present an original contribution and it has to be complete, with introduction, literature review, analysis (model and/or data analysis) and conclusions.  Any faculty member(s) who are co-author(s) of the student are required to supply a statement to the PhD Coordinator indicating that the student did a significant portion of the intellectual work and writing of the paper.  The student needs to prepare and deliver a one-hour presentation of the paper to that student’s examination committee and the presentation will be open to the University Community.  During and after the presentation, the examination committee may question the student on the research paper and on topics in his/her major concentration area as they relate to the research paper.

The student is given three papers.  The set of three papers assigned to a student will be taken from that student’s major concentration area.  The student will be given two weeks to read the papers, and submit two deliverables:  a written document of at least 10 pages (12 pt. font, single spaced), explaining how the papers relate to each other, and offering suggestions for future research.  The student must also prepare and deliver a one-hour presentation on his/her conclusions to that student’s examination committee.  During and after the presentation, the examination committee may question the student on the assigned papers and on topics in his/her major concentration area as they relate to the papers.  The presentation will be open to all members of the University community.

Each student’s examination committee will provide informal feedback to the student immediately following the oral presentation part of the comprehensive exam.  However, a final grade will be given later after a meeting of the OM/MS PhD Comprehensive Examination Committee.  The OM/MS PhD Comprehensive Examination Committee will consist of the combination of the individual student examination committees together with the PhD Coordinator.  That committee will assign grades to the comprehensive exam.  It is anticipated that the merits of each student can be openly debated and that the meeting will also serve the purpose of providing guidance to those admitted students on how they should proceed in the program, e.g., they might be given guidance on research areas, possible thesis supervisors, etc.  Students will be allowed to take Part II only once and the decision on admission to candidacy will be final.  However, students will be allowed to petition to retake the comprehensive exam if they fail the exam.  Flexible MS degree options will be given to students who are not advanced after the qualifying or comprehensive exam.

Admission to Candidacy

  1. Completion of, and satisfactory grades in, all required courses in the Department:
    • BMGT 808F: Seminar in Operations Management
    • BMGT 830: Operations Research: Linear Programming
    • BMGT 834: Operations Research: Probabilistic Models
    • BMGT 808G: Doctoral Seminar: Applied Microeconomics, or equivalent
    • BMGT 808X: Doctoral Seminar: Applied Regression Analysis or equivalent
    • Plus seven electives in the major and/or minor, for a total of 12 courses. The remaining 2 courses (total = 14 courses required for BMGT PhD degree) can be taken in the student’s third year in the program, after advancing to candidacy.   
  2. A passing grade on the Department's Comprehensive Exam (summer of 2nd year)
  3. A passing grade on Qualifying Exam (summer of 1st year)

Information Systems Major

PhD students in IS are required to complete at least 46 credits of coursework, as outlined below. Students generally complete their major coursework within their first two years in the program. During the summer after the first year, students work on a summer research project. A paper based on that project is submitted and presented to the faculty during the Fall of the 2nd year. After completing all relevant coursework, students take a comprehensive exam at the beginning of the third year. Following successful completion of the comprehensive exam, students commence work on their dissertation research. The dissertation is an independent research project conducted by the student under supervision of a dissertation committee, assembled by the student. Research interests of the current faculty include technical, behavioral, organizational, and social issues related to information systems. Students may, in their dissertations, choose to pursue any of these avenues.

Required Courses in the Major (18 credits):

Specific course numbers may change between semesters. The most recent designation for each course is shown below.

BMGT 808I

Social and Behavioral Research in Information Systems

BMGT 808I

Research Methods in Information Technology

BMGT 808D

Strategic Management of Information Technology

BMGT 808D

Information Systems Economics

BMGT 808A

Current Topics in IS research (taken twice, in the Fall of 1st and 2nd years)

Research Methodology (12 credits):

BMGT 808G

Applied Microeconomics

BMGT 808X

Applied Regression

BMGT 882

Applied Multivariate Analysis I

BMGT 883

Applied Multivariate Analysis II

Students should consult with their advisor to determine other methods training needed for their research. Students may substitute courses on a case-by-case basis when approved by the IS PhD program director, in consultation with the student’s advisor.

DO&IT Seminar (4 credits):

BMGT 808X

Research in Decision, Operations, & Information Technologies (1 credit, taken 4 times)

Minor (12 credits):

4 courses in the minor, determined in consultation with the student’s advisor and the IS PhD program director.

Other Requirements:

Incoming students will attend MathCamp before the start of their first year. This requirement can be waived if the student demonstrates sufficient math skills.

Any student admitted without a sufficient technology background will be required to take at least two technology-related classes, generally BUDT 620 and one other MBA-level course. These courses will be determined in consultation with the IS PhD program director.

Admission to Candidacy:

To be admitted to candidacy students must successfully complete all coursework in the major (including methods courses and 4 credits of the DO&IT Seminar), the first year summer paper, and the comprehensive exam.

Recommended Schedule and Milestones for the Information Systems PhD

Year and Semester

Milestones

Year 1, Semester 1

Complete BMGT808I Information Systems Research
Complete BMGT808G Microeconomics
Complete either BMGT808L Technology Artifact in Information Systems Research (o) or BMGT808A E-Commerce and Supply Chain Management (e),
Work on research assistantship with faculty
Plan to extend research on seminar papers

Year 1, Semester 2

Complete BMGT808I Research Methods in Information Technology
Complete BMGT808X Applied Regression
Complete either BMGT808D Strategic Management of Information Technology (e) or BMGT808D Information Systems Economics (o)
Work on at least one publication for submission to a conference

Year 1, summer

Conduct independent research with faculty advisor and write paper for presentation in year 2

Year 2, Semester 1

Complete either BMGT808L Technology Artifact in Information Systems Research (o), or BMGT808D Information Systems Economics (e)
Complete BMGT882 Applied Multivariate Analysis I
Complete one course in the minor
Begin work on a paper for submission to a journal

Year 2, Semester 2

Complete either BMGT808D Strategic Management of Information Technology (e) or BMGT808D Information Systems Economics (o)
Complete BMGT883 Applied Multivariate Analysis II
Complete one course in the minor
Prepare to submit journal article by the end of summer

Year 2, summer

Prepare for comprehensive examinations
Develop preliminary ideas for a dissertation topic

Year 3, Semester 1

Complete one course in the minor
Conduct research for dissertation proposal
Begin work on second journal article
Teach one undergraduate course here or next semester

Year 3, Semester 2

Complete one course in the minor
Defend proposal

Year 3, summer

Conduct dissertation research
Submit second journal article

Year 4, Semester 1

Complete enough of dissertation to be able to interview at the International Conference on Information Systems (ICIS) in December

Year 4, Semester 2

Complete enough of dissertation to be able to give a job talk by January

Year 4, Summer

Finish and defend dissertation; prepare articles from dissertation

Contact

For academic issues, contact:
Dr. Katherine Stewart, Associate Professor of Information Systems
Phone: 301-405-0576
E-mail: kstewart@rhsmith.umd.edu

For admission issues, application status, or other questions, please email bphd@rhsmith.umd.edu or call 301-405-2214.

Information On Choosing a Doctoral Program

How should a potential applicant choose among the large number of PhD programs in information systems and related fields? You can find considerable information about doctoral programs on ISWorld.

At universities like Maryland, the PhD program concentrates on research, and it is important for you to be excited by the prospects of a career as a researcher when considering doctoral studies.

In evaluating schools, the first question is what kind of research does the IS faculty conduct? Are faculty members prominent in the field, are they currently involved in leading-edge research and are they publishing their results? You can learn a great deal from looking at faculty research pages on different schools' websites. (We are pleased that a recent editorial in MISQ (Sept. 2001) rated Maryland's information systems group as one of the top in the country.) Schools have different emphases in their programs, for example, one school may focus on looking at information systems from an economics perspective while another might focus on technology. Maryland has a diverse group of IS faculty with interests in managerial, economics and technical areas.

You also might want to consider the size of the IS faculty; a larger, more diverse faculty can support research in many different areas, giving you a wide choice in dissertation topics.

Location is another consideration, especially if you are interested in field research. A school in a major metropolitan area offers greater access to businesses and potential research sites.

A good way to learn more about a school is to send email to faculty members with questions about the school and their research. You can also gain an interesting perspective by sending email to doctoral students who are currently enrolled in the PhD program.

Professor Kate Stewart
IS Doctoral Coordinator

Smith Business Close-Up: Improving Digital Intelligence

Thursday, August 2, 2012, 7:30 p.m.; Sunday, August 5, 2012, 7:30 a.m.

Digital intelligence, the ability to understand and make use of the power of information technology to one’s advantage, is becoming a critical skill for survival and success in today’s economy. Information technology (IT) can make or break any type of organization – large or small. Most companies are just beginning to figure out how to integrate IT, and they’re hiring CEOs and top executives based on ability to navigate the digital future facing their industries.

Smith Experts Available to Comment on Latest Kodak Moves to Exit Bankruptcy

COLLEGE PARK, Md. – Faculty experts in the University of Maryland’s Robert H. Smith School of Business are available to discuss, and give historical perspective on, the recently announced Kodak-management changes and job cuts as the company maneuvers to emerge from bankruptcy protection.

The Smith School has an in-house facility for live or taped interviews via fiber-optic line for television or multimedia content.

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