Download 1 Master of Science in Business Analytics Proposal Submitted by the

Master of Science in Business Analytics Proposal
Submitted by the Information Systems/Operations Management Department
Charles F. Dolan School of Business
Fairfield University
May 2014
1 Table of contents
Table of contents
2
Description, overview, and summary
3
Need
3
Rationale
4
Objectives
6
Impact
6
Program details
7
The Competition: Distinctiveness of a Fairfield Program
10
Market Demand Study
10
Administrative structure and governance
11
Resources
12
Program Evaluation
13
Projections for the future
14
Appendix A- Regional Competitors
15
Appendix B- Full- v. Part-Time Program Schedule
20
Appendix C- Course Syllabi
21
Appendix D- IS/OM Faculty and CVs
59
Appendix E- Proposed Budget: Part- v. Full-Time
89
Appendix F- Institutional Research Demand Study
92
Appendix G - Minutes from IS/OM Department, DSB Grad Curriculum,
DSB Full faculty, Educational Planning Committee (EPC)
96
2 Description, overview, and summary
Business analytics focuses on developing new insights and understanding of business
performance based on data and statistical methods. A variety of industries are in need of
individuals who can take on positions of responsibility for collecting, analyzing and interpreting
information in order to make sound strategic business decisions. Business analytics refers to the
skills, technologies, applications and practices for continuous iterative exploration and
investigation of past business performance to gain insight and drive business planning.
“Increasingly, top thinkers in academia and business believe that analytics, especially analytics
connected with big data, is going to be a driving force in our economy and society in the next 10
to 20 years.”1 To ensure continuing relevance for organizations in the future, a new MS program
in business analytics should be designed with special emphasis on database management and
business intelligence.
Need
According to McKinsey & Company in a 2011 report, the projected demand for deep business
analytical positions could exceed the supply produced with the current trend by 140,000 to
190,000 positions.2 Overall, the McKinsey Global Institute estimated that by 2018 there will be 4 million big data related positions in the U.S. that require quantitative and analytical skills. However, there will be a potential shortfall of 1.5 million data-­‐savvy managers and analysts to fill these positions. “As the demand for employees with quantitative and analytical skills increases you're seeing more quants (data professionals) being placed in key decision-­‐making roles,” said Linda Burtch, founder and managing director of Burtch Works, a U.S.-­‐based executive recruitment agency for quantitative business professionals (CNBC, June 3, 2014). The Master of Science in Business Analytics (MSBA) at Fairfield University’s Dolan School of
Business (DSB) seeks to fill the talent gap in the area and to prepare graduates for this fastgrowing field by developing students’ critical skills in data- and model-driven management
decision-making in the context of a firm’s strategic vision. This degree meets the demand of
corporations to make sense of the ever-burgeoning datasets available to them through social
media and other sources. The Dolan School of Business Advisory Council, a group of business
leaders, mostly alumni, who advise the dean on business trends, has unanimously supported the
need for a business analytics degree. It also meets the demand of students, who have asked our
faculty for more in-depth curriculum in this critical area. Current DSB faculty possess the
expertise required for this degree, and the motivation to foster its development (see Appendix D).
Our competition—regional business schools—has answered this demand with programs of their
own (Appendix A provides details on regional programs/competitors). We need to respond to
this existing demand and recent actions by our competitors.
1 Research Report by MIT Sloan Management Review and SAS Institute, “From Value to Vision: Reimagining the
Possible with Data Analytics,” Spring 2013. 2 Research Report by McKinsey & Company, “Big Data: The next frontier for innovation, competition, and
productivity,” May 2011.
3 Rationale
Central to the DSB mission statement is the Jesuit notion of educating the “whole” person to be a
socially responsible professional who has career-ready competencies and is prepared to serve
others. As indicated above, businesses are in need of individuals who can take on positions of
responsibility for collecting, analyzing and interpreting information in order to make sound
strategic decisions. Building on the strong reputation of our existing graduate programs, and
faculty expertise in these areas, the Dolan School is poised to create an educational opportunity
that will fill this void.
Further, the Dolan School’s mission guides us to develop “innovative curricula” that are “shaped
by involvement with…business leaders.” The proposal for a MS in Business Analytics speaks to
this aspect of our mission, as we attempt to meet the needs of the market, needs that are being
emphasized in the finance, information systems, health care, and marketing fields.
In developing this proposal, the Dean and faculty have spoken to a range of business leaders, on
the DSB Advisory Council and in other forums, to assess potential demand for an MSBA degree.
The Advisory Council, in its March, 2014 meeting, was strongly supportive of moving forward
with the degree, with high interest among executives from a variety of sectors, including finance,
accounting, real estate, government agencies, and marketing. The Council responded
unequivocally with their needs for individuals with “Big Data” management and analysis
skillsets. Anecdotal evidence from faculty interactions with business leaders in information
technology has been similarly supportive. Our market demand study (see Appendix F) provides
further indication of market demand.
Beyond the apparent demand in the marketplace for individuals who are educated in analysis and
management of big data, Fairfield’s location and connections with area corporations will
enhance the attractiveness of an MSBA. Local corporations (e.g., GE, United Technologies,
Bic/Energizer, Subway) provide excellent opportunities for well-educated individuals to analyze
and interpret these firms’ consumer and financial databases. ]
Tangible outcomes. The MSBA can provide a number of tangible outcomes for students who
are seeking a highly specialized graduate program focused on managing big data. These
outcomes cover not only the development of critical academic and professional skills but also
opportunities for employment in highly visible and needed sectors of the marketplace.
Business analytics makes extensive use of data, statistical and quantitative analysis, and
explanatory and predictive modeling to help make actionable decisions and to improve business
operations. We expect graduates from the MSBA program to be analytics competent and big data
savvy so that they can not only analyze past performance but also identify opportunities to
improve future performance. To that end, the students will be exposed to the following:
•
•
•
•
•
Communication and quantitative reasoning skills;
SQL and database management skills;
Data mining and data warehousing;
Statistics skills;
Data visualization;
4 •
•
•
Text mining;
NoSQL skills;
Emerging topics.
Companies across industries reap the benefit of using skills from business analytics to tackle
complex business challenges. Career opportunities can be found in commerce, government, forprofit and non-for-profit organizations, and the services or manufacturing sectors. Examples of
employment opportunities for MSBA graduates include:
•
•
•
•
•
•
•
•
•
Business Analytics Analyst/Consultant;
Big Data Analytics Specialist;
Financial Analytics;
Marketing Analytics;
Operations Analytics;
Logistics Analytics;
Risk Management;
Healthcare Analytics;
Information Analytics.
A recent report by Hanover Research for Fairfield University exploring the fastest growing
occupations in the regions surrounding the University indicated that “every CIP (Classification
of Instructional Programs) code associated with “Statisticians” has reported increasing
completions since 2009 which confirms that applied math/statistics represents a strong option for
expansion.3 Business analytics represents the fastest-growing area of applied statistics.4 As
indicated above, the MSBA program fits in well with the mission of Fairfield University,
because it enables us to provide an educational resource to a marketplace that is deeply in need
of professionals with advanced skillsets who can manage an overabundance of both customer
and industry data. In order for decision-making and strategic planning to be truly meaningful for
the firm, while also being beneficial for all stakeholders in a particular organization- i.e.,
employees, customers, general public, etc. management must be able to consider vast amounts of
information in their deliberations of “next steps” for the company. A well-informed management
team can hopefully make decisions that not only positively affect the bottom line but also
account for how the business impacts all involved—either directly or indirectly—with the firm.
The MSBA educates managers who are equipped to either provide the information or else make
the decisions based on such information.
3 “Advanced Degree Market Scan” Prepared for Fairfield University, August, 2014. Hanover Research, p. 20. 4 Cosentino, Tony, “Business Analytics in 2014: Trends and Possibilities,” Information Management, January 23, 2014. 5 Objectives
In the near-term, our objective will be to establish (and maintain) a level of demand of
approximately 10-12 students who are able to move through the proposed sequence of courses as
a cohort. As will be discussed below in the Program Detail section, and later in the Resources
section, students will be able to complete the program in either a full-time (one year) or part-time
(two years) format, though if necessary accepted students will have five years to complete the
degree requirements. At the outset, we anticipate current MBA students bolstering enrollments in
existing courses (OM 400, QA 400, IS 520, OM 525). Over the long-term, it is our intention to
bring in cohorts of individuals who can move through the program together (ideally 15-20
individuals at a time) in a one-year timeframe.
In order to evaluate program effectiveness and quality, we will closely monitor application and
admissions numbers each semester and academic year. We will be able to evaluate the quality of
the applicants based on the applications received (e.g., applicant’s work and academic
background, reasons for undertaking the program). The Director of DSB Graduate Programs will
work closely with the IS/OM department to understand the characteristics of the current students
(strengths/weaknesses), as well as the structure of the program (what is working, what needs to
be changed or enhanced). The MS program will fall under the School of Business’ existing
stringent assessment regimen, so that each of the School’s four over-arching learning goals
(Critical thinking and expression, Leading, teambuilding and presenting, Acting ethically,
responsibly and legally, and Acquiring discipline-specific knowledge and skills) will be assessed
on a regular basis. Key benchmarks for program success will include:
•
Graduation rate
•
Academic performance in specific courses
•
Attainment of program learning objectives via assessment process
•
Job placement/advancement opportunities
Impact
Impact on current DSB programs. This new degree will draw on five existing courses and
propose four new courses (see Program Details below). Such an approach helps to address the
University’s need for higher graduate program enrollments and revenue, as articulated by the
Fairfield 2020 strategic process, by maximizing existing resources while proposing new
directions for growth. Internally, this program would help drive enrollments to courses already
offered as part of the MBA program. Currently, the IS/OM department services graduate
students via the MBA program. In addition to providing two of the MBA core courses
(Operations and Supply Chain Management [OM 400] and Applied Business Statistics [QA
400], each running once a year) and one of the required breadth classes (IS 500, offered twice a
year), the department attempts to offer at least one IS/OM advanced elective each academic year.
Downward pressure on applications to the DSB part-time MBA program has meant declining
enrollments for IS/OM graduate courses. As a result, it has been difficult for IS/OM faculty to
6 offer upper-level courses, including those that would feature individual faculty members’
professional backgrounds and research expertise.
As will be discussed below, five of the nine required courses that are being proposed for the
MSBA program already exist and are offered in the MBA program. The department has recently
reviewed a number of these courses and has renamed and/or updated the course description and
content in order to keep the classes relevant with current trends in the industry. The four new
courses being proposed speak to topics that are essential for an analytics or “Big Data”
management professional. Further, existing IS/OM faculty members have the expertise to
develop and offer these courses in a rigorous and high-quality manner that the market expects
from a Fairfield graduate education. By adding these four new courses to the existing set of
courses currently offered by the IS/OM department, the Dolan School will be able to not only
offer a new and distinctly-packaged MS program but also increase the topical offerings in the
existing IS/OM concentration within the MBA program.
Impact on other Fairfield Programs. This program is not intended to replace any existing
programs at the University, but certainly represents an opportunity for interaction and crossfertilization with existing programs, such as Mathematics in CAS and the School of Engineering.
The DSB has initiated discussions with Mathematics, and proposes a graduate-level mathematics
course as an elective in this program (see Details below).
Program details
The MS in Business Analytics is designed as a 30-credit program that can be completed either
full-time over one calendar year or part-time over two academic years. This structure is identical
to the DSB’s existing MS in Finance program, a long-standing degree that has proven attractive
to both domestic and international students seeking to advance in their current finance fields. For
the sake of flexibility, students may take up to five years to complete the degree. If this is the
case, the student will be advised at the outset of the program with regard to the regularity of
course offerings.
The MSBA will benefit from the Dolan School's flexible graduate course offering schedule. In
addition to providing courses during the traditional fall and spring semester (typically one
evening each week, over 14 weeks), the courses can also be offered in 7-week formats during the
fall and spring. Further, the opportunity exists to provide either two- or four-week sessions
during the winter break and over the summer months. Finally, the IS/OM department will
actively pursue online and hybrid options for some of its courses. The intent is to enhance the
flexibility of the program to foster student accessibility while preserving the academic rigor and
high-interaction environment essential to a Fairfield program.
Appendix B provides an illustration of both a full- and part-time schedule.
The proposed curriculum includes:
9 required courses
1 elective course
7 Existing Courses (all required):
QA 400
OM 400
IS 500
IS 520
OM 525
Applied Business Statistics
Operations and Supply Chain Management
Information Systems and Database Management
Project Management
Process Improvement and Quality Management
New Courses (all required):
QA 500
OM 500
IS 540
IS 550
Business Forecasting and Predictive Analytics
Introduction to Business Analytics
Data Mining and Business Intelligence
Business Analytics and Big Data Management
Graduate Elective (Choose 1 from the following existing courses. A second course may be
chosen from this list and substituted for one of the above existing courses with permission of the
Director of Graduate Programs. For example, someone with a strong professional background
in project management may opt to take another course in lieu of IS 520):
MK 520 Marketing Research
OM 535 Global Logistics and Supply Chain Management
IS 585
Contemporary topics in Information Systems & Operations Management
Another Graduate-level Business course commensurate with one’s professional
background/goals
A Graduate-level Mathematics course that complements required coursework*
*The MSBA provides an exciting opportunity to create an interdisciplinary connection with the
Department of Mathematics. The expertise of various faculty members in mathematics has
enabled the department to construct its own master-level program. A number of the graduate
courses could be highly beneficial for analysts, including the following: MA 551- Applied
Statistical Models, MA 553- Statistical Forecasting, MA 555- Statistical Consulting. Recent
discussions among Dr. James He, Dr. Stephen Sawin (chair, Mathematics Department) and the
Director of Graduate Programs raised the future possibility of considering additional
mathematics courses in the MSBA curriculum beyond one elective.
The intention is to further diversify the MSBA program curriculum as enrollment grows via
individuals with diverse academic and professional backgrounds. While the four new courses
proposed above cover the various advanced topics essential for business analytics and as a result
will always be required, one’s professional experience may serve as a viable proxy for the
project management, operations and/or information systems classes. Those who come into the
program specifically with professional backgrounds in areas such as statistics/modeling,
marketing research, or financial services could substitute courses in other disciplines for these
foundational courses. Ultimately, the hope is to develop different tracks within the MSBA, such
that all students study the core concepts of data analytics but then have the opportunity to further
enhance their graduate work by choosing topics within profession-specific disciplines. For
example, while all students must complete (QA 400, OM 500, IS 540, IS 550), statisticians and
actuaries could opt for advanced mathematics classes in forecasting and modeling, marketing
8 researchers and brand managers would take research, branding and communication courses, and
those with financial services backgrounds could take in classes in mathematics and research
methodology in finance. Further, the hope is that other professions, such as organizational
psychology in GSEAP and healthcare management in the School of Nursing would find some
synergies with the analytics courses. Given the proliferation of data in almost every industry, the
need exists to have capable individuals who can make sense of this wealth of information about
the marketplace.
Appendix C contains the syllabi of all relevant IS/OM courses, existing and new.
Target markets. Regarding student profile, it is possible that Fairfield business, arts and
sciences (especially STEM), or engineering undergraduates may be interested in pursuing this
program. The MSBA is a logical “next step” for those who have majored or minored in any of
the undergraduate business disciplines because every division of the firm is faced with an
increasing amount of data and relatively few (if any) resources to make sense of such
information. However, the main focus will be to target business professionals who have some
work experience and can use the degree to leverage and better focus that experience. Thus, the
MSBA is designed expressly for:
•
Graduates from analytic disciplines who want to develop advanced skills to solve
complex business problems in light of Big Data challenges.
•
Graduates with degrees in quantitative areas such as business, economics, computer
science, engineering and statistics.
•
Experienced professionals seeking career advancement through specialized training.
For such individuals, it may be possible to also align specific non-credit certifications with
certain MS classes, including “six-sigma” and “project management.” In addition, we believe
that the mix of both quantitative skills and managerial decision-making that come from these
specific IS/OM classes will be extremely attractive to international students. This program
could be particularly attractive for international students with engineering backgrounds, as
the MSBA will provide a managerial perspective on business decisions.
With regard to growth potential and expanding on the initial market reach, this program is geared
for anyone with a strong analytic background and/or professionals in a position that involves a
good deal of data analysis and decision-making based on such analysis. As a result, individuals
with diverse backgrounds (business, economics, mathematics, natural sciences, etc.) could be
attracted to this program. Certainly, initial marketing/promotion efforts must focus on the
business sector and those firms where Fairfield’s reputation is already established (via MBA, MS
Accounting, and MS Finance alums/candidates). Ideally, as the number of students who enroll in
the MSBA increases, more effort should be spent on reaching out to non-traditional sectors (e.g.,
natural and social sciences).
9 The Competition: Distinctiveness of a Fairfield program
As shown in Appendix A, there are six programs in the region that would compete against the
proposed MSBA at Fairfield. While this is substantial competition, the proposed MSBA offers
several distinctions:
•
The content of the program represents a dynamic combination of quantitative skills
learning and higher-level managerial training. The expertise of the IS&OM faculty
includes abilities in analytical statistics along with project management, managerial
decision-making, and quality management. This integration of quantitative analysis and
people management skills is a differentiator in the marketplace.
•
It is compact; while competitive programs have total credits ranging from 30 to 36, the
MSBA would require 30-credits. The faculty believes that with focused content on
critical topics, this program can offer students the skills and knowledge they need to be
effective big data analysts. The flexible “packaging” of courses, as noted above, into 7week, winter intersession, and summer courses, also increases flexibility in comparison to
the competition.
•
With graduate programs, commuting distance to the school is a critical variable. Our
closest competitor, Quinnipiac University, offers only an online version of its MS in
Business Analytics degree. Our program would offer primarily on-ground, highinteraction teaching with the flexibility for students to supplement with online and hybrid
offerings. For highly quantitative coursework, interaction with expert faculty is essential
and will be an attraction for students.
Market demand study
The School of Business worked with the Office of Institutional Research in spring 2014 on a
market demand study for the MSBA. Because we foresee the potential target market for this
degree to include international students, we cast a wider net than has been typical to try to get a
sense of response to this proposed program. The following audiences received the demand
survey:
• Current undergraduate juniors and seniors (N = 1,770).
• Undergraduate alums from the past 15 years (N = 10,585).
• Graduate level Fairfield University alumni, past 10 years (N = 2,866)
• GMAT and GRE test-takers who identified Fairfield as a recipient of score reports
(domestic and international participants, N = 4.293).
Overall, 18,623 participants were invited (not including 868 e-mails that bounced back), and the
final responses were N = 1,040.
Preliminary data from this market demand study look supportive for this program (see Appendix
F). 19% of respondents (197) indicated that they are “definitely interested” in an MSBA, and of
those who intend to go to graduate school in the next 2 to 4 years, 30% indicated that they were
“very likely” to apply to a Fairfield MSBA. A caveat to these results is that this is a somewhat
10 different sampling strategy than in past Fairfield market research (i.e., including GMAT and
GRE test takers), and the overall response rate is low. However, given the very broad nature of
the sample (i.e., including all majors and schools for Fairfield alumni), combined with the very
specific program being presented, it is not surprising that interest in this particular degree would
represent a small portion of the sample. As it is, these results indicate that in order to obtain the
10-student initial enrollment we are seeking, we would only need 5% of the 197 who responded
that they are “definitely interested.” We find these market survey results convincing that there is
interest in Fairfield University offering this degree program. It is also worth noting that other
Business Analytics programs have qualified as STEM programs for the purposes of extending F1 visa allowances for international students working in the US. For STEM programs, Optional
Practical Training (OPT) periods for internships and full-time work may be extended beyond the
current 12-month period. This aspect could be very attractive to international students seeking
US work experience.
Administrative structure and governance
As has been the case with other specialized master-level programs in the DSB, the faculty
members who offer the curriculum are the primary “keepers” of the program. In this capacity,
the School of Business will rely on the IS/OM department (currently chaired by Dr. James He) to
ensure that the MSBA curriculum remains rigorous, relevant and competitive with other
institutions’ graduate offerings in the topical areas. Thus, all decisions pertaining to curriculum
begin at the department level.
The Dolan School has a Graduate Curriculum Committee (GCC), which has oversight for
curricular issues in all the graduate business programs. The IS/OM representative (currently Dr.
Vishnu Vinekar) is responsible for bringing any curriculum changes or proposals to the GCC, to
be vetted by its members (each academic discipline is represented; the current chair is Dr. Carl
Scheraga- Professor of Business Strategy and Technology Management). It is only after approval
has been granted by the GCC that curricular issues can be brought before the DSB faculty for
consideration.
The Associate Dean of the School of Business (Dr. Mark Ligas, Associate Professor of
Marketing) is also the Director of Graduate Business Programs. In this capacity, his office
oversees the entire admissions process for all graduate business programs. In addition, his office
works with various other parties on campus (e.g., Graduate Admissions, Institutional
Marketing/Communications, Office of International Students/Study Abroad) on tactics and
strategies for communicating graduate programs to the larger marketplace. Further, the Director
serves as the faculty advisor for each graduate student. In the case of each of the specialized
master-level programs, a faculty member within the discipline also serves as an academic
advisor. This model will be replicated for the MSBA.
The Dean of the School of Business (Dr. Don Gibson, Professor of Management) has the
responsibility of not only communicating the graduate programs to the larger marketplace but
also specifically to the alumni, recruiters, employers and friends of Fairfield University. In
addition, the Dean is the primary liaison between each graduate program (and its accompanying
faculty/department) and the DSB Advisory Council. Thus, the Dean is able to provide guidance
and input from the council members on matters concerning industry needs and trends.
11 Resources
The Information Systems/Operations Management department in the Dolan School of Business
has the expertise to create, maintain and regularly enhance the Master of Science in Business
Analytics. The individual research expertise of each faculty member, in combination with their
past experience teaching in existing graduate programs and courses, provide a strong set of skills
that ensure the development and maintenance of a unique and rigorous program of study.
Currently, the IS/OM department consists of five full-time, tenured faculty members:
J. He, Professor and Chair
C. Huntley, Associate Professor
P. Lee, Associate Professor
Y. Ozcelik, Associate Professor
V. Vinekar, Associate Professor
A sixth faculty member, Dr. Campbell, left the University June 30, 2014. The department is
conducting a search for a new tenure-track faculty member in fall 2014, in anticipation of a hire
for fall 2015. A new faculty member will be sought with expertise in areas to include
quantitative analysis, Big Data, and business intelligence. Dr. He, and Dr. Lee have expertise
mainly in quantitative analysis (QA) and operations management (OM) and are capable of
teaching the following two new courses in the proposed curriculum:
QA 500 – Business Forecasting and Predictive Analytics
OM 500 – Introduction to Business Analytics
Dr. Huntley, Dr. Ozcelik, and Dr. Vinekar have expertise mainly in information systems (IS) and
database management and are capable of teaching the other two new courses:
IS 540 – Data Mining and Business Intelligence
IS 550 – Business Analytics and Big Data Management
Given the vision for growth in the MSBA (particularly to the point of having a full-time cohort
program), we anticipate the need for an additional faculty line in IS/OM as the program
demonstrates growth in enrollments. Currently, the IS/OM department is at capacity with regard
to teaching obligations. Although the existing faculty can construct and successfully offer the
proposed new courses for the MSBA, with potential growth of the program this will only further
tax the departmental resources. As noted above, although the IS/OM concentration is down in the
MBA, the department still provides required MBA core and breadth classes. At the
undergraduate level, where the Business School continues to realize strong growth, the
department covers two required business core courses (IS 100 and OM 101) and has been asked
to offer a large number of sections in a given semester of each of these classes. In addition, the
IS major is beginning to grow at the undergraduate level, which necessitates offering more
upper-level electives. Given the requirements of our accrediting agency (AACSB) that at least
75% of our course credit hours must be taught by full time participating faculty, we will need to
address the potential future shortage in faculty resources with an additional faculty line.
Appendix D provides a summary of IS/OM faculty and their qualifications.
12 In addition to faculty resources, the greatest costs for the MSBA, especially at the outset, are
those associated with effectively communicating Fairfield’s program to the outside market.
Given the competitive alternatives that currently exist, as well as those that are being planned
based on demand from industry, it will be imperative to educate the marketplace on Fairfield’s
offering, with the hope that Fairfield’s reputation for high interaction-high quality and rigorous
education will enhance demand for the MSBA. With a number of competitive threats in
relatively close proximity, it is necessary to have a well-developed and separate
communications/advertising plan for the MSBA.
Appendix E provides illustrative budgets for a: full-time (one calendar year), part-time (two
academic years), and “mixed” program (part- and full-time students). The various costs and
revenues contained within these budgets are based on current numbers, with a conservative
estimation of increased communication costs in future years.
Program Evaluation
The Dean, Associate Dean, and members of the Information Systems/Operations Management
Department will undertake a number of activities over the first three years, to obtain feedback on
the growth of the new MSBA program. These initiatives will include closely tracking the
following criteria:
•
•
•
•
•
change in application and enrollment numbers from year to year;
number of program inquiries within the school and through Graduate Admissions;
professional placement opportunities as a result of graduating from the program;
whether the variety of professional backgrounds of the students increase;
whether/to what extent the international marketplace becomes interested in the program.
In addition, the Dolan School of Business learning goals will be applied to the MSBA
curriculum. As a result, the MSBA will be held to the same rigorous curriculum standards as are
the other degree-granting programs. This requires that student work pertaining to specific
learning goals (e.g., thinking and expression; leading, teambuilding, and presenting; acting
ethically, responsibly and legally) be assessed on a yearly basis. Further, goals specific to the MS
program, especially with regard to quantitative reasoning and analysis (an objective of the
thinking and expression learning goal) will be articulated and added into the curricular
assessment process, as a means of maintaining the rigor and relevance of the MSBA.
Over the long term (5 years), the DSB will formally assess the need and relevance of the MSBA.
To do this, a formal committee (Dean, Associate Dean, members of the DSB graduate
curriculum committee, IS/OM faculty) will review all statistics related to demand, including:
inquiry information, application and admission numbers, cohort sizes (if developed), student
academic and professional background, placement and job opportunities, and financial results of
the program. In addition, it will be of paramount importance to work with the DSB Advisory
Council to determine the continued relevance of the MSBA program and demand for its
specialized knowledge by firms/industry leaders. Given that the DSB Advisory Council has been
a strong supporter of business analytics education, they should be able to provide candid and
insightful commentary on where Fairfield's program stands in five years.
13 Projections for the future
As has been noted throughout, it is the intention of the business school to market the MSBA as a
highly specialized, high-demand degree that can be completed in a relatively short period of
time. The hope is that we can quickly get to a point of bringing in cohorts that will work through
the program on a yearly basis. As indicated in the budget estimates, with moderate growth
expected on a yearly basis and reasonable increases in advertising and communication costs, the
MSBA has the potential to begin generating revenues in excess of costs for the institution in the
first year for a fulltime cohort, and in the second year under a part-time model.
14 Appendix A
Regional Competitors
1. UConn – MS in Business Analytics and Project Management
Location: School of Business Graduate Learning Center in downtown Hartford, CT
About the Program: The mission of the Masters in Business Analytics and Project
Management (MSBAPM) is to deliver a program of excellence in the study of advanced
business analytics and project management. The program delivers a core set of
advanced courses in both business analytics and project management. MSBAPM
provides an integrated curriculum and a global perspective using evolving technology
platforms to facilitate and support the learning process. MSBAPM is structured to
provide businesses a pipeline of talented and energized professionals who will create
immediate value for their organization and the communities they serve.
The program requires 33 credit hours, including four 3-credit courses in Business
Analytics, four 3-credit courses in Project Management, and 9 credit hours in elective
courses.
Course Descriptions: Required courses are listed here. Detailed course descriptions
can be found at: http://msbapm.business.uconn.edu/academics/course/
Business Analytics (12 credits)
1.
2.
3.
4.
Business Process and Modeling and Data Management
Predictive Modeling
Business Decision Modeling
Data Mining and Business Intelligence
Project Management (12 credits)
5.
6.
7.
8.
Introduction to Project Management
Project Leadership and Communications
Project Risk and Cost Management
Advanced Management
2. Fordham – MS in Business Analytics
About the Program: Fordham's Master of Science in Business Analytics (MSBA)
program integrates analytic techniques, data management, IT, modeling, and statistics to
train students to become effective analysts and informed users of business data. MSBA
Students develop the skills required to succeed in data-driven industries such as banking,
consumer products, energy, government, health care, insurance, manufacturing and
pharmaceuticals.
This program consists of 30 credits over 3 semesters, and can be completed in 1 year
15 of full-time study or in flexible part-time study. The program starts in August and runs 12
months, full-time, until the following August. It is completed over three trimesters: Fall,
Spring, Summer.
Course Descriptions: Detailed program description can be found at:
http://www.bnet.fordham.edu/academics/ms_programs/ms_business_analytics/index.asp
Fall Term (12 Credits)
1.
2.
3.
4.
Database Management
Data Warehousing
Data Mining for Business
1 Elective
Spring Term (12 Credits)
5.
6.
7.
8.
Business Analytics for Managers
Text Analytics
Web Analytics
1 Elective
Summer Term (6 Credits)
9. Business Performance & Risk Management and Analytics
10. 1 Elective
3. Steven’s Institute of Technology – MS in Business Intelligence & Analytics
About the Program: The market has an increasing need for professionals with data
management knowledge, analytical capability and problem-solving skills. Stevens is one
of a select few universities worldwide to offer a master’s degree in this emerging field.
Currently, Stevens is the only university in the NYC area to offer a BI&A master’s
degree intended to train students to fill the growing demand for big data analysts.
The Business Intelligence & Analytics (BI&A) degree is a 36-credit graduate
program for students who have already completed an undergraduate degree in science,
mathematics, computer science, engineering or a related field. Stevens offers flexible
study options for both full- and part-time students interested in advancing their careers
within industry-specific analytical fields such as finance, information technology,
telecommunications and engineering.
Course Descriptions: Detailed course descriptions can be found at:
https://www.stevens.edu/howe/academics/graduate/business-intelligence-analytics-bims/msbi-overview
i. Organizational Context
16 • Financial Decision Making
ii. Data Management
• Strategic Data Management
• Data Warehousing and Business Intelligence
iii. Optimization & Risk Analysis
• Process Analytics and Optimization
• Financial Enterprise Risk Engineering
iv. Statistics
• Multivariate Data Analytics
• Experimental Design
v. Data Mining & Machine Learning
• Knowledge Discovery in Databases
• Statistical Learning & Analytics
vi. Social Network Analytics
• Social Network Analytics
• Web Analytics
vii. Industry Practicum (Select 1)
• Applied Analytics in the Life Sciences
• Algorithmic Trading Strategies
viii. Electives – Electives in additional departments are available for students who
waive one or more of the required courses (Financial Decision Making or
Strategic Data Management). To waive courses, students must have approval
from a faculty advisor.
1. Finance
a. Investment and Capital Markets
b. Many Financial Engineering electives are available
2. Information Systems
a. IT Strategy
b. Integrating IT Architecture
c. Marketing Online
4. New York University – MS in Business Analytics
About the Program: The Master of Science in Business Analytics Program is
designed with busy working professionals in mind. Participants live and work in their
home countries and attend five concentrated, rigorous modules in New York and
Shanghai. There is an optimization of classroom time, with usage of distance learning
between modules. The modules are time intensive so that all teaching is done in-person
only. Experienced managers who can benefit from unlocking the potential of big data.
Participants come from a broad range of sectors: financial services, communications,
consulting, health and pharmaceuticals, manufacturing, energy, nonprofit/NGO,
education, IT, etc.
This program is 1 year in length and starts in May of each year.
Course Descriptions: Detailed module descriptions can be found at:
17 http://www.stern.nyu.edu/programs-admissions/global-degrees/businessanalytics/academics/course-index/index.htm
The MS in Business Analytics modules are spread out over a period of 12 months.
Between modules, students complete approximately 20 hours of work per week on preand post-module tasks.
Module 1: New York
1.
2.
3.
4.
5.
6.
Digital Analytics and Strategy: An Introduction
Dealing with Big Data
Data Mining for Business Analytics
Decision Models
Probabilistic Models for Finance
Prediction
Module 2: New York
1. Data Driven Decision Making
2. Social Media and Digital Marketing Analytics
3. Managing for Quality
Module 3: Shanghai
1. Operations Analytics
2. Advanced Decision Models
3. Data Visualization
Module 4: Shanghai
1. Special Topics in Analytics: Revenue Management & Pricing
2. Strategy, Change, and Analytics
3. Market Modeling
Module 5: Closing – New York
1. Strategic Capstone
5. St. John’s University – MBA in Business Analytics
The Master of Business Administration with a concentration in Business Analytics
develops professionals with training in the emerging field of integrated statistical
analysis, data mining, predictive modeling, business intelligence and optimization
methodologies with state-of-the-art information technology tools to automate or support
decision-making activities in the ever-changing economy.
This program option provides students with a combination of technical and managerial
18 coursework needed for dealing with future challenges in the technology and data-driven
global environment.
Potential career options for graduates from this program include data scientist, health
care analyst, statistician, predictive modeler, quantitative analyst, project manager,
market research analyst, computer systems analyst and technical team leader.
Detailed module descriptions can be found at:
http://www.stjohns.edu/academics/graduate/tobin/academics/departments/cis/mbacis/mba
businessanalytics.stj
6. Quinnipiac University – MS in Business Analytics (online program)
About the Program: The Master of Science in Business Analytics Program is 33
credits in length and provides a strong quantitative foundation that is inclusive of
advanced statistics, data mining, text mining, tools for analysis and presentation and other
relevant courses. The mission of the program is to develop in working professionals the
skill sets needed to address the massive amount of data that has become universally
available in order to leverage this toward successful business and decision-making
applications. Numerous business functions and industries have noted the enormous need
for individuals who possess the quantitative, analytical and presentation skills required to
apply data to the solution of business problems, to create new business opportunities and
to support innovative practices. These skills are also critical to decision-making in the
nonprofit, governmental and educational industries as well as to entrepreneurship and
small business management. More than 50% of the courses are offered online.
Course Descriptions:
Core Courses
1.
2.
3.
4.
5.
6.
7.
BA 610 – Statistics and Probability*
BA 615 – Predictive Modeling*
CIS 620 Data Management
CIS 627 – Data Warehousing and Data Mining
CIS 628 – Business Intelligence and Data Mining
BA 620 – Text Mining*
BA 690 Business Analytics Capstone*
Four Electives
1.
2.
3.
4.
5.
6.
CIS 625 – ERP Design and Implementation
MG 603 – Project Management
CIS 690 - Managing Information Technology Projects
BA 680 - Statistical Quality Control*
BA 660 – Optimization*
BA 650 - Data Visualization*
19 Appendix B
Full- v. Part-Time Program Schedule
Full-Time
Fall
Semester
Winter
Semester
Spring
Semester
Summer
Semester
QA 400
IS 500
OM 400
IS 520
OM 525
New course
New course
Elective in either Fall or Spring Semester
New course
New course
Part-Time
Fall
Semester
Spring
Semester
Summer
Semester
Fall
Semester
QA 400
New course
OM 400
New course
IS 500
New course
IS 520
OM 525
New course
Elective in either Fall or
Spring Semester
20 Spring
Semester
Appendix C
Course Syllabi
Information Systems CRN 10814 Winter Session -­‐ 2014 Course Syllabus Class Schedule: January 2 – January 14 Meeting Days/Times: Weekdays / 6:00pm – 9:30pm & Saturdays / 9:00am – 4:00pm Class Location: Room TBA Online Discussions TBD Instructor: Arthur C. McAdams, III, Ph.D. Department: ISOM Office : NA Phone: 203.259.4740 E-­‐Mail: [email protected] Office Hours: By Appointment Books & Technology Required Books Cowen, T. (2011) Average is Over: Powering America Beyond the Age of the Great Stagnation. New York: Penguin Group. Drucker, P. (2002). Managing the Next Society. New York: St. Martin’s Press. Friedman, T. (2005) The World is Flat: A Brief History of the Twenty-­‐First Century. New York: Farrar, Straus, & Giroux. Peters, T. (2003). Re-­‐Imagine! London: Dorling Kindersley Limited. Recommended Books American Psychological Association. (2003). Publication Manual of the American Psychological Association (5th ed.). Washington: APA. ISBN 1-­‐55798-­‐791-­‐2 Required Technology You will need access to a computer, printer, Fairfield University systems, and the Internet. For presentations, please copy an electronic version to a USB drive and send yourself a backup version via e-­‐mail. You may also deliver the presentation from a laptop. 21 Course Description & Approach In this course, we will examine the interrelationship between information technology, organizations, and knowledge workers, as well as the relationship of these dynamics with industry, corporations, governments, and society. We will review the history of human and technological advancement that has built the foundation for the current digital/information age as a way to explore the challenges and methods for improving organizational performance in the future. Using a combination of required class material, books, scholarly journals, and industry publications, and contemporary sources, such as daily newspapers, students will perform research as a way to apply course lessons in their areas of interest. Through these exercises, students will gain an understanding of many management tenets, such as value, strategy, lifecycles, organization, information, innovation, quality management, change and project management, and technology. The textbooks in this course, and for that matter any document, should not be considered the final source of “absolute truths,” but as thought-­‐provoking instruments that both educate and challenge our understanding of organizations, talent, information, knowledge, technology, etc. Learning Outcomes
1. Gain an appreciation for the cause and effect of technology in different eras 2. Distinguish data, information, knowledge, and wisdom 3. Analyze and improve information/knowledge-­‐based organizational systems 4. Understand the basic attributes of IT 5. Empathize with the challenges associated with change management 6. Contemplate the attributes of leaders and managers in the new economy 7. Build a systematic mindset for solving problems 8. Understand the interrelated nature of talent, technique, and technology 9. Define the relationships between knowledge workers, organizations, and IT 10. Bridge the gap between theory and practice 11. Assess opportunities and emerging trends in the knowledge economy 12. Contemplate the ways IS may contribute to a better world Course Story Line 1. Society, business, and technology are indistinguishable 2. Technology is neutral -­‐ people are the differentiator 3. Applying technology appropriately, a primary tenet of management, has been a competitive advantage in previous eras 4. The new economy is driven by information/knowledge and the technology is IT 5. IS, an evolved form of management, is misunderstood and subsequently undervalued 6. Progress in the new economy is dependent upon people who can ethically, effectively, and efficiently employ the tenets of IS 7. Society and business need enlightened knowledge workers and IT professionals 22 Course Outline
Module 1 -­‐ The Knowledge Economy In this module, we will review the interaction of technology with business and society as presented by Peter Drucker in Managing the Next Society. In particular, we will focus on his description of knowledge work and the new economy. We will review the history of technological advancement with an emphasis on the somewhat recent evolution of our economy from a manufacturing model to an information-­‐based model. We will explore the interaction between people and technology in various social and economic environments with an emphasis on the knowledge workforce. In this module, we will establish definitions for technology, IT, data, information, knowledge, quality, strategy, processes, operations, leadership, management science, knowledge workers, systems, lifecycles, projects, and value. Module 2 – Information Technology & Systems As the “technology-­‐is-­‐not-­‐magic” module, we will review and discuss the impact of recent technological advancement as presented by Thomas Friedman. We will review the components of computers (hardware and software), and focus on the three attributes of information technology: electronic data, instructions, and connectivity. We will examine data management as well as the many forms of IT architecture (e.g. centralize versus decentralize). We will review the initial waves of data warehousing/mining and its more modern interpretations, such as organizational intelligence, analytics, and big data. We will also examine security and risk management. Module 3 – Organizational Systems & Intelligence In this module, we will integrate the lessons from the previous two books with the ideas proposed by Tom Peters. We will discuss strategic planning and explore the challenges associated with introducing technologically innovative (sustaining and disruptive) products, services, and processes. We will review formal methods that define, design, and deliver information systems and technology in today’s economy, which include traditional systems development life cycle (SDLC), joint/rapid application development (JAD/RAD), and agile. We will also study the science of project management and its primary tools, such as critical path analysis. We will evaluate the risks and rewards of radically redesigning systems and we will discuss change management and the “art” of successfully delivering innovative solutions. Module 4 – Talent Dynamics in the New Economy In this module, we will explore the dynamic relationship between technology and talent to better understand contemporary employment issues, as well as the factors identified by Cowen that may be influencing distribution of wealth, success, power, etc. Building from material in the other modules, we will review similar patterns in earlier eras as a way to better understand our current situation. In particular, we will explore the emerging opportunities and challenges that are enabled by IS in a real-­‐world context. We will discuss value streams and the integrated nature of knowledge workers, management science, and information technology during this module. As an extension of this, we will explore the evolving role of managers and specialists in the new economy. 23 Module 5 – Review & Reflection In this module, I will ask you to reflect on the ways IT may affect society, industry, companies, professions, hobbies, personal relationships, etc. In addition to the broad themes, students will be asked to evaluate the current state of, and to imagine the possibilities created by IT in their chosen industry and discipline. This industry environmental analysis should include factors, such as lifecycle, performance, and need or likelihood, of significant change. Course Requirements & Guidelines This course requires both oral and written assignments. Research is an important component of this class and it allows you the opportunity to integrate your interests with the course theories. You will be required to complete three informal oral reports on current events, three five-­‐page papers, two 20-­‐slide 10-­‐minute formal presentations, and three 12-­‐
slide 10-­‐minute team research round-­‐table presentations. All printed work is due at the beginning of class. Collected assignments require at least seven sources. An MBA degree is designed to prepare students for a career as a general manager (executive, CEO, entrepreneur, etc.). With this in mind, please try to think of all assignments as really important business opportunities. Imagine showing up for an important meeting without any preparation, or the proper paperwork, or delivering a presentation that is littered with spelling errors. For this class, please write e-­‐mails with at least semi-­‐
professional form and refrain from using slang or any poor writing style. Each written assignment should be typed and meet the minimum requirement for content and length. All assignments should be printed, stapled, paginated, and include a title, the student’s name and student number, the course name and section, and the type of assignment. All papers should use 12-­‐point font, be double-­‐spaced, and utilize a formal writing style. Presentations should include a final slide with references and be printed in gray scale that contains six slides per page. Each slide should have a heading. Try to stay consistent (a.k.a. parallelism) within each slide. In other words, if your first statement is a proper sentence, then keep using sentences for the remainder of the slide and use proper punctuation. If you use bullets and start with verbs, then continue to use verbs for the remainder of the slide. Please use proper punctuation regardless of style. Please keep in mind that communicating clearly and concisely is a valuable skill in today’s information-­‐based and time-­‐sensitive culture. These skills are also transferable to any discipline and vocation you choose to explore. Speaking proper English was the overriding theme in the 1964 musical My Fair Lady in which Professor Higgins sings; “this verbal class distinction by now should be antique.” This is not a communication class, and my English is far from perfect, but just as in most office settings, you will be required to write and present your thoughts to multiple audiences. I create these difficult course requirements because I want you to be prepared for post-­‐academic life (I’m really not trying to punish you with random onerous demands!). 24 Mark Twain once quipped, “I apologize for the length of this letter, if I had had more time it would have been much shorter.” With this in mind, you may want to build a theoretical or conceptual framework for your paper. This is fairly easy and is just for you -­‐ so keep it simple. Write a few sentences for each of the major sections and try to build your paper around these markers. This may help you stay focused on the subject. Occasionally papers, much like projects, suffer from “scope creep” and take on too many divergent topics. You may want to save the other topics for future papers. Most students enjoy hearing what other students have researched, which is one of the reasons I will often ask you to share your research with the class. Although public speaking still ranks as one of the most feared things in this world (along with death) it is a required in most vocations, and an academic setting is an ideal stage for building this important skill. When presenting, please do not read the slides verbatim. Your introduction should be really clear and objective. This is your only chance to gain the audience’s attention. A confusing message or a biased attack will almost always disengage or alienate your audience. Try to paraphrase your points and give the audience some insight into the important points in each slide. Please try to end your presentation by restating the primary problem and then stating your position, which may include your conclusion, a recommendation, a request for approval/funding, etc. Over the years I have noted a few “areas of opportunity” related to submitted work. First, don’t look past the really easy stuff. Please make sure you use the proper number of references and satisfy the required length in pages or slides. Don’t forget to run a “spell check” function on your work and check for homophones (e.g. to, too, two). Please follow the framework! I do not want a simple report that restates a collection of data. As future leaders you know there is an information hierarchy: data, information, knowledge, and wisdom. Critical thinking involves reflection and knowledge that reveals insight that builds on established theoretical ideals or offers new revelations. Knowing that the stock market crashed in 1987 is a pretty interesting fact; knowing how to think about the market and any of the possible cause-­‐and-­‐effect relationships that may materialize in the future is much more valuable. Please be sure to cite all references properly and it is very important that you comply with the Fairfield University’s position on academic honesty that may be found on the website and in the Student Handbook. Deliverables 1. Class Participation: Attendance at each class session is expected. A significant portion of your learning will accrue through the constructive and respectful exchange of ideas. Class lectures complement, but do not duplicate, textbook information. Students are expected to be on time and prepared for class. Please turn off and store all unnecessary technology before class. Please bring a relevant news article to class each week and to be prepared to lead a brief classroom conversation on your chosen topic. Students are also required to write a journal 25 about the class that highlights the student’s thoughts related to the key concepts. These assignments will not be collected. 2. Team Research Presentations: All students will participate in three team presentations. Each team will be assigned an article to evaluate in each module and the team will be required to lead a twenty-­‐minute roundtable discussion. You may use notes, such as the printed PowerPoint slides that I will collect, but you will not use electronic projection tools for this exercise. The presentations should include the following five sections: 1. Environmental Analysis – Explain the time, place, and historical context of the article 2. Biography – Describe the author’s background and any important factors that may have influenced/motivated his/her writing 3. Summary -­‐ Summarize the most important messages in the article 4. Lessons Learned -­‐ Identify and relate lessons to current events 5. Remaining Questions -­‐ Identify one question that you would like to have discussed in class 3 Individual Research Papers & Presentations: As detailed in the Course Requirements section you will be required to complete three formal papers and two formal presentations (one for each module) to successfully complete this course. You may choose any topic included in my PowerPoint slides or anything from the representative readings in each module as your subject for research. I may ask all of you to meet with me individually during each module at which time you will briefly summarize your paper, and respond to any questions I may have related to your assignment or anything related to that content in the respective module. Course Schedule Each module will require about 20% of the total class time, although the first module usually requires additional time. Time may vary depending on interests, questions, etc. Please read through the required books before the first day of class and prepare an informal document for each that highlights one area in which you feel the author has identified an important phenomenon and one area that you feel the author’s view may need revision. Grading
Inevitably, the question of grading criteria surfaces sometime during the semester. As you have probably noted, I do not administer easily quantifiable methods for evaluating your knowledge of the concepts in the course. I choose to use reports, presentations, and open-­‐
ended questions in classroom discussion as a way to measure your learning, which are more qualitative. This probably makes it a little harder for both of us, but I believe it is the best way to measure knowledge. I will use the following rubric and matrix for your papers and presentations: Argument 26 H: clear distinction between criteria and a concise persuasive conclusion L: no distinct choice between criteria: ambiguous framework, “book report” Compliance H – all requirements fully met L – more than two major deficiencies Creativity H: new or novel approach/concept to a subject L: familiar set of ideas and contrasts Grammar H: no errors or very few minor errors L: difficult to interpret content/message Organization H: clear outline, symmetrical, and aesthetically acceptable L: unprofessional, poor construction of content with little use of proper formatting Any paper that includes any form of plagiarism will be graded “50.” For the first offense, the student will have an opportunity to send me a three-­‐paragraph email that describes the specific plagiarism offense, a comment on the seriousness of academic integrity, and a promise from the student that he/she has now read the full description of plagiarism and commits to a plagiarism-­‐free academic experience. The initial “50” will then be revised favorably for the student; though not to exceed “80.” A second offense will result in an “F” for the course and a written comment in the student’s file. Please keep in mind that your final grade is cumulative so you will not be able to “make up” for missed classes and poor performance during the last week of the semester. If your attendance and punctuality are nearly perfect, your alertness and attention are proper, your participation is acceptable, and you comply with the guidelines for deliverables, you will probably earn a “B” for the course. I will happily award higher grades for engaged participation and work that exhibits exceptionally rigorous and insightful thinking as described in the rubric. Grading Breakdown Class Participation 20% Team Research Presentations 20% Individual Papers 20% Individual Presentations 20% Individual Reflection Paper 20% 100% Recommended Reading/Sources for Research Papers and Presentations 27 Baldrige National Quality Program (2007). National Institute of Standards and Technology. Retrieved on July 13, 2007 from http://baldrige.nist.gov/PDF_files/2007_Business_Nonprofit_Criteria.pdf Collins, J., & Porras, J. (1994). Built to Last. New York: HarperCollins Publishers Inc. Christensen, C. (1997). The Innovator’s Dilemma. Boston: Harvard Business School Press. Cooper, A. (2004). The Inmates are running the Asylum. Indianapolis, IN: Sams Publishing. Davenport, T., & Prusak, L. (2000). Working Knowledge: How Organizations Know What They Know. Cambridge, MA: Harvard Business School Press. Davis, S., & Meyer, C. (1998). Blur. Reading, MA: Addison-­‐Wesley. DeMarco, T. (1997). The Deadline: A Novel about Project Management. New York: Dorset House Publishing. ISBN 0-­‐932633-­‐39-­‐0 Dess, G., Lumpkin, G.T., & Eisner, A. (2010). Strategic Management: Creating Competitive Advantages (5th Edition). New York: McGraw-­‐Hill/Irwin. ISBN: 978-­‐0-­‐07-­‐724626-­‐6 Drucker, P. (2001). The Essential Drucker. New York: HarperCollins Publishers. ISBN 978-­‐0-­‐
06-­‐134501-­‐2 Drucker, P. (2002). Managing the Next Society. New York: St. Martin’s Press. Evans, J., & Dean, J. (2000). Total Quality – Management, Organization, and Strategy. Cincinnati: OH: South-­‐Western College Publishing. ISBN 0-­‐324-­‐01276-­‐4 Extreme Chaos. (2001). The Standish Group Report. Retrieved July 4, 2005, from http://www.standishgroup/com/sample_research/PDFpages/extreme_chaos.pdf Friedman, M. (1962). Capitalism and Freedom. Friedman, T. (2005). The World is Flat: A Brief History of the Twenty-­‐First Century. Farrar, Straus, & Giroux. Gladwell, M. (). Tipping Point or Blink. Hamel, G., & Prahalad, C. (1994). Competing for the Future. Boston: Harvard Business School Press. Hammer, M., & Champy, J. (2003). Reengineering the Corporation: A Manifesto for Business Revolution. New York: HarperCollins Publishers Inc. Hiltzik, M. (1999). Dealers of Lightening: Xerox PARC and the Dawn of the Information Age. New York: HarperCollins Publishers Inc. Holbeche, L. (2005). The High Performance Organization: Creating Dynamic Stability and Sustainable Success. Boston: Butterworth-­‐Heinemann. Hunger, D., & Wheelen, T. (1998). Strategic Management. Reading, MA: Addison-­‐Wesley. Johnson, S. (1998). Who moved my Cheese? Putnum. Kaplan, R., & Norton, D. (1996). The Balanced Scorecard. Boston: Harvard Business School Press. Kim, W., & Mauborgne, R. (2005). Blue Ocean Strategy: How to Create Uncontested Market Space and Make Competition Irrelevant. Boston: Harvard Business School Publishing Corporation. Knoke, W. (1996). Bold New World. New York: Kodansha International. Lewis, C.S. (1982). The Screwtape Letters. NY: Touchstone. Lowney, C. (2005). Heroic Leadership Machiavelli, N (1532). The Prince. Marx & Engels. (). The Communist Manifesto McElroy, M. (2001). Second-­‐Generation Knowledge Management. Macroinnovation Associates LLC. Retrieved on January 14, 2004, from www.macroinnovation.com Orwell, G. (). Animal Farm or 1984. 28 Palmer, D. (1994). Looking at Philosophy: The Unbearable Heaviness of Philosophy made Lighter (2nd ed.). Mountain View, CA: Mayfield Publishing Company. Peters, T. (2003). Re-­‐Imagine! London: Dorling Kindersley Limited. Plato (380BC). The Republic. Porter, M. (1998). Competitive Strategy. Free Press. Senge, P. (1990). The Fifth Discipline: The Art & Science of the Learning Organization. NY: Doubleday. Smith, A (1776). The Wealth of Nations. Sveiby, K. (1997). The New Organizational Wealth. San Francisco, CA: Berrett-­‐Koehler Publishers Inc. Swift, J. (). A Modest Proposal. Teich, A. (2002). Technology and the Future (9th ed.). Boston: Wadsworth Publishing. Tzu, S. (2006). The Art of War. Filiquarian Publishing LLC. Volti, R. (2001). Society and Technological Change (4th ed.). New York: Worth Publishers. Welch, J. (2001). Straight from the Gut. NY: Warner Books Inc. Womack, J., & Jones, D. (1996). Lean Thinking: Banish Waste and Create Wealth in Your Organization. NY: Simon & Schuster. Wooden, J., & Carty, J. (2009). Pyramid of Success. Gospel Light Publications. Wysocki, R., & McGary, R. (2004). Effective Project Management (3rd ed.). Indianapolis, IN: John Wiley & Sons Inc. Other Valuable Sources Business periodicals – Business Week, Forbes, Economist, Forbes, Financial Times, Fortune, Harvard Business Review, New York Times, Newsweek, Sloan Management Review, Wall Street Journal et al. 29 IS520 – Fall 2012 – Project Management Instructor: Gerard M. Campbell, Ph.D. [email protected] office: DSB 1121 phone: (203) 254-­‐4000 x-­‐3118 office hours: Monday 2:00 – 3:30 Thursday 2:00 – 3:30 & 5:30 – 6:30 Classroom Time DSB 107 Thursday 6:30 – 9:30 p.m. Overview: This course explores the process and practice of project management. Topics to be covered include project life-­‐cycle and organizations, teambuilding and productivity, task scheduling and resource allocation, and progress tracking and control. Cases are used to illustrate issues such as change management, managing stakeholders and considering risk. Student projects will enable the application of concepts learned in class, and software will be used to help formulate and communicate project plans. Prerequisites: IS500 or OM 400 Text: Contemporary Project Management, 2nd edition, by Timothy J. Kloppenborg.
ISBN 978-0-538-47702-4
Cases: Cases may be purchased online at Harvard Business Publishing
Course-­‐specific link: http://cb.hbsp.harvard.edu/cb/access/15491016
Supplements: PowerPoint slides and other materials will be made available through the Mentor course management system. Project Management Software: Early in the semester, an online project management software system will be selected for use throughout the course. Course Objectives:
1.
To teach the terminology, concepts and techniques associated with project management.
2. To provide an understanding of practical applications of the concepts and techniques.
3. To provide knowledge of and hands-on experience with project management software.
4. To provide exposure to research related to project management.
Course Grading and Assignments:
30 Component
•
•
•
•
•
•
Homework
Student Project
In-class software work
Research Article Presentation
Case presentation
Class participation
Weight
25 %
25 %
15 %
15 %
10 %
10 %
Homework:
Homework will be assigned each week and collected at the beginning of the following class.
Each student’s lowest homework grade will be dropped.
Student Project:
Each student will undertake planning for a project of their own choosing, which they will use to
illustrate the application of key concepts from the course. Assignments related to student
projects will be worked on in class as the semester progresses. Each student will present a
summary of their project work to the class at the end of the semester.
In-class software work:
During the semester, assignments will be worked on in-class using online project management
software.
Research Article Presentation:
In a 20 - 25 minute PowerPoint presentation, each student will present a research paper to the
class. The student will also provide the class with a 1 – 2 page summary of the article’s findings.
Articles will be provided by the instructor, with assignments and presentation dates established
early in the semester. Criteria for evaluating the research article presentations are shown on the
Evaluation Form attached to this syllabus.
Case Presentation:
Each case will be presented by a two-student team, who will lead class discussion of the case.
After presenting a summary of the major aspects of the case, they will solicit inputs from other
class members and then present their analysis of the case. Criteria for evaluating the case
presentations are shown on the Evaluation Form attached to this syllabus.
Class Participation:
During discussion of cases and at other times, student participation is encouraged. Class
participation grades will be based on quality of participation, not just attendance.
31 Communication: Email is probably the most effective way to communicate with the instructor.
The instructor may occasionally communicate with students using the email distribution list on
Mentor.
Course Schedule
Class # Textbook Reading Topics Article & Case Presentations 1 Sept. 6 Introduction to Project Management 2 Sept. 13 Project Selection & Prioritization 3 Sept. 20 Organization Capability Ch. 3 Project Portfolio Management 4 Sept. 27 Chartering Projects Ch. 4 Model of Project
Knowledge Management
Ch. 5 Case: TrustWeb Ch. 1 Ch. 2 Stakeholder Analysis & Communication Planning 5 Oct. 4 6 Oct. 11 Scope Planning Ch. 6 Project Knowledge Transfer 7 Oct. 18 Scheduling Projects Ch. 7 Case: Novo Nordisk 8 Oct. 25 Resourcing Projects Ch. 8 Risk Management Affecting Success 9 Nov. 1 Budgeting Projects Ch. 9 Success Criteria in Malaysia 10 Nov. 8 Project Risk Planning Ch.10 Case: Tetra Tech 11 Nov. 15 Ch. 11 eCollaboration Project Supply Chain Management Ch. 12 Green Project Management Week of Project Quality Planning & Project Kick-­‐off Nov. 22 THANKSGIVING 12 Nov. 29 13 Dec. 6 Leading and Managing Project Teams Ch. 13 Case: American Constructors 14 Dec. 13 Determining Progress; Finishing the Project Ch.14 & Ch. 15 Translation & Convergence 15 Dec. 20 Student Presentations 32 IS520 Presentation Evaluation Form Presentation title: ________________________ Today’s date: _____________ Concerning the Material: (replace) (keep) Should this material be used again for IS520? 1 2 3 4 5 To what extent did the Presenter: (not at all) (a great deal) -­‐-­‐ Raise meaningful issues 1 2 3 4 5 & stimulate the audience? (not at all) (a great deal) -­‐-­‐ facilitate class participation? 1 2 3 4 5 (not at all) (a great deal) -­‐-­‐ articulate well and convey enthusiasm? 1 2 3 4 5 (not at all) (a great deal) -­‐-­‐ exhibit good preparation? 1 2 3 4 5 (poor) (excellent) Overall, how would you rate the presenter’s performance? 1 2 3 4 5 Please provide written feedback in the space below. 33 Sample Course Syllabus
IS 540 – Data Mining & Business Intelligence
Course Description
This course will change the way you think about data and its role in business. Businesses,
governments, and individuals create massive collections of data as a byproduct of their activity.
Increasingly, decision-makers and systems rely on intelligent technology to analyze data
systematically to improve decision-making. In many cases automating analytical and decisionmaking processes are necessary because of the volume of data and the speed with which new
data are generated. We will examine how data analysis technologies can be used to improve
decision making. We will study the fundamental principles and techniques of data mining, and
we will examine real-world examples and cases to place data-mining techniques in context, to
develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a
science. In addition, we will work “hands-on” with data mining software. Prerequisite: IS 500.
Learning Outcomes
1. Approach business problems data-analytically. Think carefully & systematically about
whether & how data can improve business performance, to make better-informed decisions
for management, marketing, investment, etc.
2. Be able to interact competently on the topic of data mining for business intelligence. Know
the basics of data mining processes, algorithms, and systems well enough to interact with
CTOs, expert data miners, consultants, etc. Envision opportunities.
3. Have had hands-on experience mining data. Be prepared to follow up on ideas or
opportunities that present themselves, e.g., by performing pilot studies.
Text
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, by
Gordon S. Linoff and Michael J.A. Berry, Third Edition, South-Western, Wiley (2011), ISBN
978-0-470-65093-6.
Grading Policy
Participation & Class Contribution
Homework
Term Project
Final Exam
Total
10%
30%
30%
30%
100%
Participation
34 Students are expected to attend and participate in each class session. Participation means being
prepared, being punctual, being respectful, actively listening and responding to questions.
Homework
All homework assignments will be submitted through Blackboard.
Term Project
A term project report will be prepared by student teams. Student teams should comprise 3-4
students. You should decide on your teams by the end of the third class, and submit them to me.
Teams are encouraged to interact with the instructor electronically or face-to-face in developing
their project reports. You will submit a proposal for your project about half way through the
course. Each team will present its project at the end of the semester. We will discuss the project
requirements and presentations in class
Final Exam
The final exam will be a take-home one to be completed during the week following the last class.
The subject matter covered and the exact dates will be discussed in class
Outline of Course Content: Reading Case & Homework Module Topics
1 Introduction
What is Data Mining and Why Do It?
Ch 1 Info Sheet
Data Mining Applications
Decision Trees
The Data Mining Process
Descriptions and Predictions
Ch 2 Ch 7 Ch 3 Ch 5 HW#1 due Try HW#2
HW#2 due
Group Lists due
Nearest Neighbor Approaches--Memory
Based Reasoning and Collaborative
Filtering
What you should know about data
Using Classic Statistic Techniques
Ch 4 Ch 6 HW#3 due Project Proposal due
HW#4 due
2 3 Data Mining
Fundamentals:
Predictive Modeling
4 5 Data Mining
Fundamentals:
Descriptive Data Mining
Toward Analytical Engineering &
Possible Applications: Fraud Detection,
Targeted Marketing, Customer Retention
Application: Online Advertising Ethics of data mining and privacy
6 7 8 9
Data Mining in Action:
Cases, Applications,
and Practical Insight
Ch 9 Ch 8 Ch 2 Knowing When to Worry: Using Survival
Analysis to Understand Customers
Ch 10 Generic Algorithms and Swarm
Intelligence
Ch 11 35 HW#5 due
10
11
12
13
Tell Me Something New: Pattern
Discovery and Data Mining
HW#6 due
Wrap Up and Review
Project Report due
Project Presentation
Final Exam
FAIRFIELD UNIVERSITY Ch 12 36 Charles F. Dolan School of Business IS 550 BUSINESS ANALYTICS AND BIG DATA MANAGEMENT SAMPLE COURSE SYLLABUS REQUIRED TEXT:
[1] Data Science for Business: What You Need to Know about
Data Mining and Data-Analytic Thinking, by Foster Provost and
Tom Fawcett, O’Reilly Media, 2013, ISBN 978-1-44-936132-7.
[2] Big Data Analytics: From Strategic Planning to Enterprise
Integration with Tools, Techniques, NoSQL, and Graph, by David
Loshin, Morgan Kaufmann, 2013, ISBN 978-0-12-417319-4.
Note: The cases in the textbook will become part of the class assignments, and additional sources
may be used and distributed in class. Students are encouraged to bring their own laptop
computers to the class.
I. COURSE DESCRIPTION: This course will survey state-­‐of-­‐the-­‐art topics in Big Data, looking at data collection (smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting structured data from unstructured databases, systems issues (exploiting multicore, security), analytics (machine learning, data compression, efficient algorithms), visualization, and a range of applications. Each of the five modules will introduce broad concepts as well as provide the most recent developments in research. Prerequisite: QA 500 and OM 500. II. COURSE OBJECTIVES: Objectives of this course are: •
•
•
•
•
Distinguish what is Big Data (volume, velocity, variety), and will learn where it comes from, and what are the key challenges Determine how and where Big Data challenges arise in a number of domains, including social media, transportation, finance, and medicine Investigate multicore challenges and how to engineer around them Explore the relational model, SQL, and capabilities of new relational systems in terms of scalability and performance Understand the capabilities of NoSQL systems, their capabilities and pitfalls, and 37 •
•
•
•
III.
how the NewSQL movement addresses these issues Learn why building secure Big Data systems is so hard and survey recent techniques that help, including learning direct processing on encrypted data, information flow control, auditing, and replay Discover user interfaces for Big Data and what makes building them difficult Understand the benefits and challenges of open-­‐linked data Comprehend machine learning and algorithms for data analytics OUTLINE OF COURSE CONTENT: Reading Modules 1 Modules one – Introduction, Big Data Challenges, and Opportunities 2 Case & Homework [1] Ch 1 Ch 2 [2] Ch 1 Ch 2 Assignment 1 [2] Ch 3 Big Data Collection, Cleaning, and Integration [2] Ch 4 3 4 5 6 7 Big Data Storage: Modern Databases, Distributed Computing, NoSQL, and NewSQL 8 9
[2] Ch 5 Ch 6 [2] Ch 8 10
11
Big Data Analytics: Machine Learning, Text
Mining, Data Visualization
12
[2] Ch 9 [2] Ch 10 Assignment 3 [2] Ch 11 Big Data Systems and Applications
Assignment 2 [1] Ch 4
Ch 13 Assignment 4 [1] Ch 8
Ch 10
[1] Ch 11
Ch 12
Assignment 5 13
• Final Exam
38 Project Report Due 14
• Project Presentation
IV. GRADING POLICY: The final grade will be calculated as follows: Case Analyses Final Exam Term Project Homework Assignments Class Participation Total 20% 30% 20% 20% 10% 100% Case Analysis The class will be divided into two-­‐student groups for case analysis. Each group is responsible for all cases, including data analysis, statistical tests, potential applications, and class discussion. While a formal written case report is not required, students are expected to show their work in writing and to participate in class discussion. Term Project The “Stats Application” project has two components: a presentation and a paper. Students will choose their own project topic, based on their own interests and preferences. Students should discuss their project topic with the instructor as soon as possible to ensure it is appropriate. Presentations should last approximately 10 -­‐ 15 minutes. It would be helpful if presentations could involve the rest of the class somehow, e.g., by including a problem or problems that could serve as the basis for class participation. Written reports are due on the last day of class. Reports should include references to current research related to the project topic. At least two current references should be cited in the report. Class Participation Class participation includes participating in case analysis and class discussion, in addition to class attendance. OM400 Operations and Supply Chain Processes Spring 2014 39 Instructor: Patrick Lee, PhD Office Hrs.: M, R, 12:30-­‐1:30 p.m., and R, 5:00-­‐6:00 p.m. or by appointment Office: DSB 2120 Phone: x2846 Email: [email protected] Textbook Krajewski, Ritzman and Malhotra, Operations Management: processes and supply chains, 10th edition, Prentice-­‐Hall, 2013. ISBN 13: 978-­‐0-­‐13-­‐280739-­‐5. Course Description Production and Operations Management has evolved into one of the most important business disciplines over the last several decades. Although the discipline started in the 1900s with the manufacturing industries, its focus has shifted to the service sector as well. With today’s highly competitive environment, the need for continuous improvement of operations has never been greater. This course is intended to provide the basic concepts that led to the discipline based on process analysis that integrates goods and services from the perspective of the value chain that cuts across the broad scope of businesses. Our purpose is to investigate the important managerial issues and decisions of OM with technical tools and quantitative applications, emphasizing the relevance to the work environment and personal lives. Integrative Learning Goals Global Citizenship • International operations and global value chains • Information sharing and global co-­‐operations • Coordinated operations are win-­‐win propositions Quantitative Reasoning • Being able to clearly define problems on hand • Identifying the proper measurements and models for analysis • Using Excel spreadsheets to: perform numerical analysis, and/or construct diagrams • Solving models and being able to interpret the picture behind the numbers Rhetoric and Reflection • Compose and make an oral presentation on case study and project • Develop a reading/studying strategy to understand and to apply for the workplace • How to use reflective learning techniques to modify conclusions to meet the actual needs of business. Course Objectives 40 This course examines the basic but current issues of Operations Management and Supply Chain processes. It is intended to provide students with the following objectives: • Fundamentals of OM, and processes: productivity and quality • Integrating and designing the supply chain operations • Managing and controlling production and service operations Evaluations and Grading Policy Each student is evaluated based on the performance criteria described in the following: • Case presentation 35% • Final project—report and presentation 50% • Participation 15% Owing to some of the difficult concepts in the discipline, it is strongly recommended that students should prepare the readings ahead of the class as listed in the schedule. Class time will be devoted to the major concepts and case presentations. Case studies are used to expand the concepts and techniques outlined in the text. It is an extremely useful for business students to see the hows and whys of the scenarios presented. The final project is used as a tool to demonstrate the students’ proficiency of the subject matters in POM and supply chain management. It is also intended as a community cooperative effort to enhance the learning experience. This project will conclude with a formal presentation and an executive summary report not exceeding 20 pages. Tentative Schedule (To be amended) Week of Topics covered Chapters Assignments/Cases Jan. 16 Defining OSCM 1 Chad’s Creative Concepts (p. 29) Jan. 23 Process Strategy, Process Wyatt Earp Case Customer Molds, Inc. (p. Analysis 3, 4 115), Jose’s Authentic Mexican Restaurant (p. 154), The Lawn Care Company* Jan. 30 Manufacturing Designs, Supplement C, 7, Service Designs Ritz Carlton Case Feb. 6 Project Management 2 The Pert Mustang (p. 87) Feb. 13 Quality Management 5, Quality Crusaders Case Feb. 20 Supply Chain Design 10 Brunswick Distribution, Inc. (p. 381) Feb. 27 Supply Chain Integration 12 Wolf Motors (p. 439) Mar. 6 Capacity Planning, Case 6, Supplement A Fitness Plus,A (p. 223), Presentations Start Recoding For the Blind* Mar. 13 Forecasting 14 Yankee Fork and Hoe Company (p. 502) Mar. 20, 27 Mid-­‐term, Spring Break 41 Apr. 3 Lean Production, Inventory Control, Enterprise Systems (SCM, CRM) Apr. 10 Wrap-­‐up Apr. 17 Easter Break Apr. 24 Final Project Presentations May 1 Final Project Presentations May 8 Final Papers Due *Cases to be distributed! 8, 9, 16 Copper Kettle Catering (p. 304), Parts Emporium (p. 343) FAIRFIELD UNIVERSITY 42 Charles F. Dolan School of Business OM 500 INTRODUCTION TO BUSINESS ANALYTICS SAMPLE COURSE SYLLABUS REQUIRED TEXT:
Business Analytics: Methods, Models, and Decisions, by James R.
Evans, Pearson/Prentice-Hall, 2013, ISBN 978-0-13-295061-9.
Note: The cases in the textbook will become part of the class assignments, and additional sources
may be used and distributed in class. Students are encouraged to bring their own laptop
computers to the class.
I. COURSE DESCRIPTION: This course introduces basic skills necessary for business analytics such as data analysis using
basic statistics, data visualization and summarization, descriptive and inferential statistics,
spreadsheet modeling for prediction, linear regression, risk analysis using Monte-Carlo simulation,
linear and nonlinear optimization, and decision analysis. Microsoft Excel 2010 is used as the
platform for conducting analyses and performing statistical calculations. Prerequisite: QA 400.
II. COURSE OBJECTIVES: Objectives of this course are: •
•
•
•
•
IV.
•
Use Microsoft Excel to summarize, visualize, and analyze data in practical business
situations
Develop and analyze mathematical and spreadsheet-based models for practical business
decisions
Apply simple and multiple linear regression analysis
Develop and analyze spreadsheet models for risk analysis using Monte Carlo simulation
and Risk Solver Platform
Formulate and solve models for linear, integer, and nonlinear optimization, and interpret
the results provided by Excel Solver
Use decision analysis concepts and techniques to model and analyze decision strategies
OUTLINE OF COURSE CONTENT: 43 Session Topics 1 Reading • Introduction to Business Analytics 2 • Predictive Modeling and Analysis 3 • Regression Analysis 4 5 6 Ch 9 Assignment 2 Ch 11 Ch 12 Ch 13 Ch 14 • Applications of Linear Optimization Assignment 3 • Linear Optimization 10
Assignment 1 9
Ch 8 • Introduction to Data Mining 8 Ch 10 7 Ch 1 • Forecasting Techniques • Simulation and Risk Analysis Case & Homework Assignment 5 • Integer Optimization
Ch 15 • Nonlinear and Non-Smooth Optimization
Ch 16 Assignment 6 • Optimization Models with Uncertainty
Ch 17
• Decision Analysis
Ch 18
Assignment 7 11
12
13
• Final Exam
Project Report Due 14
• Project Presentation
IV. GRADING POLICY: The final grade will be calculated as follows: Case Analyses 44 10% Final Exam Term Project Homework Assignments Class Participation Total 30% 20% 28% 12% 100% Case Analysis The class will be divided into two-­‐student groups for case analysis. Each group is responsible for all cases, including data analysis, statistical tests, potential applications, and class discussion. While a formal written case report is not required, students are expected to show their work in writing and to participate in class discussion. Term Project The “Stats Application” project has two components: a presentation and a paper. Students will choose their own project topic, based on their own interests and preferences. Students should discuss their project topic with the instructor as soon as possible to ensure it is appropriate. Presentations should last approximately 10 -­‐ 15 minutes. It would be helpful if presentations could involve the rest of the class somehow, e.g., by including a problem or problems that could serve as the basis for class participation. Written reports are due on the last day of class. Reports should include references to current research related to the project topic. At least two current references should be cited in the report. Class Participation Class participation includes participating in Excel practice, case analysis and discussion, in addition to class attendance. Communication
Email is probably the most effective way to communicate with the instructor. The instructor
may occasionally communicate with students using class lists on StagWeb.
45 OM525 – Spring 2011 – Business Process Improvement Instructor: Gerard M. Campbell, Ph.D. [email protected] office: DSB 1121 phone: (203) 254-­‐4000 x-­‐3118 office hours: Tuesday: 8:45 – 9:20 a.m. and 12:20 – 1:00 p.m. Friday: 8:45 – 9:20 a.m. and 12:20 – 1:00 p.m. Wednesday: 5:30 – 6:30 p.m. Room Time DSB 107 Wednesday 6:30 – 9:30 p.m. Overview: This course addresses topics and methods related to the improvement of business processes along dimensions such as cost, quality, speed, and flexibility. Through the use of case studies, students learn to approach problems using methods that have proven effective for a variety of organizations. Topics include: financial justification of operational improvements; change management; six-­‐sigma process improvement methods and tools; business process reengineering; and lean production concepts applied in both manufacturing and service organizations. This course will also reinforce skills involved in communicating recommendations effectively. Students are expected to complete a significant research paper as a requirement of this course. Prerequisites: OM 400 and QA 400 Textbooks: Evans, J.R. & Lindsay, W.M. (2005) "An Introduction to Six Sigma &
Process Improvement," South-Western/Cengage Learning.
ISBN-10: 0-324-30075-1
Kelton, W.D., Smith, J.S., Sturrock, D.T. & Verbraeck, A. (2010) “Simio & Simulation –
Modeling, Analysis, Applications,” McGraw-Hill.
Print Version: ISBN-13: 978-0-07-340888-3, Approximate List Price: $94
E-book Version: ISBN-13: 978-1-12-111686-3, Approximate List Price $51
Order information: http://www.simio.com/publications/SASMAA/
Supplements: PowerPoint slides and other materials will be made available through Eidos/Mentor. 46 Simulation Software: “Simio is a unique multi-paradigm simulation software tool that provides a rapid and flexible
modeling capability without requiring programming.” (Simio.com)
The Academic Version of Simio has been installed on classroom computers in DSB 107. Student Software Students can use the Academic Version above that is installed on university computers, however many students prefer to have software installed on their own computers to use at their convenience. The Student Version has all the same functionality (no feature or size limits) as the Academic Version, except that it is licensed to individual students for one year. The Student Version is only for students taking a class and is available for a nominal fee (US $25 for 1 year unlimited access to a $10,000 product). Course Objectives:
1. To teach the principles of six sigma and their relevance to business process
improvement.
2. To provide an understanding of the DMAIC methodology.
3. To teach design for six sigma as a means for product development and process
improvement.
4. To provide knowledge of and hands-on experience with business process simulation.
5. To provide an opportunity for each student to perform a research project related to
business process improvement.
Course Grading and Assignments:
Component
•
•
•
•
•
•
Exam 1
Exam 2
Homework
Case presentations
In-class Simio work
Research Project
Weight
15 %
15 %
20 %
15 %
15 %
20 %
47 Exams:
Exams will cover material from both the Six Sigma and Simulation texts. Exams will be open
book / open notes.
Homework:
Homework will be assigned each week and collected at the beginning of the following class.
Each student’s lowest homework grade will be dropped.
Case Presentations:
Each student will prepare and present two PowerPoint presentations lasting about 15 minutes
each. These will be based on Case Studies included in the Six Sigma text (except for the last
two, which are TBD). As part of the case presentation, the presenter should lead discussion of
any end-of-chapter questions related to the case (e.g., for Xerox, questions 10-12 on p. 26 of the
Six Sigma text). Students may also include related material from other sources in their
presentations. Criteria for evaluating the presentations are shown on the Evaluation Form
attached to this syllabus.
In-class Simio work:
During the semester, assignments and case studies will be worked on in-class using the Simio
simulation software.
Research Project:
Each student will complete a research project related to Business Process Improvement. For
their projects, students are encouraged to use Simio to model and investigate improvements for
one or more processes. The project should include a literature review section that cites at least
five references from the literature. Project proposals are due March 16th, and project papers and
presentations are due May 4th.
Communication:
Email is probably the most effective way to communicate with the instructor. The instructor
may occasionally communicate with students using class lists on StagWeb.
48 Course Schedule
Six Sigma Simulation Case Presentation Reading Reading Class # Week of Topics 1 Principles of Quality Jan. 19 Management. Intro to Simio 2 Jan. 26 3 Feb. 2 4 5 Ch. 1 Principles of Six Sigma. Intro to Simulation Ch. 2 Ch. 1 Evolution of Quality at Xerox Project Definition. Basics of Queueing Theory Ch. 3 Ch. 2 Ford’s Drive to Six Sigma Quality Process Measurement. Feb. 9 Approaches to Simulation Ch. 4 Ch. 3 Fidelity Investments
Feb. 16 Process Analysis. Input Analysis Ch. 5 Ch. 4 Middletown Regional Hospital 6 Feb. 23 Process Improvement. First Simio Models Ch. 6 Ch. 5 GE Fanuc 7 Mar. 2 Exam 1 8 Process Control. Mar. 9 Intermediate Modeling with Simio Ch. 7 Ch. 6 Reduce Medical Errors 9 Design for Six Sigma 1. Mar. 16 Working with Model Data Ch. 8 Ch. 7 Control Chart in Receiving Process Mar. 23 No Class – Spring Break Ch. 9 Ch. 8 Pivot Initiative at Midwest Bank 11 Implementing Six Sigma. Apr. 6 Advanced Modeling with Simio. Ch.10 Ch. 9 DOE for Battery Life 12 Apr. 13 Customizing and Extending Simio. Ch.10 Six Sigma at Samsung 13 Apr. 20 Exam 2 14 Apr. 27 Project Management. Six Sigma Certification. Handouts Additional Case 1 Additional Case 2 15 May 4 Research Project Presentations 10 Design for Six Sigma 2. Mar. 30 Animation and Entity Movement. 49 OM525 Presentation Evaluation Form Presentation title: ________________________ Today’s date: _____________ Concerning the Material: (replace) (keep) Should this material be used again for OM525? 1 2 3 4 5 To what extent did the Presenter: (not at all) (a great deal) -­‐-­‐ Raise meaningful issues 1 2 3 4 5 & stimulate the audience? (not at all) (a great deal) -­‐-­‐ facilitate class participation? 1 2 3 4 5 (not at all) (a great deal) -­‐-­‐ articulate well and convey enthusiasm? 1 2 3 4 5 (not at all) (a great deal) -­‐-­‐ exhibit good preparation? 1 2 3 4 5 (poor) (excellent) Overall, how would you rate the presenter’s performance? 1 2 3 4 5 Please provide written feedback in the space below. 50 FAIRFIELD UNIVERSITY Charles F. Dolan School of Business QA 400 APPLIED BUSINESS STATISTICS COURSE SYLLABUS – FALL 2013 INSTRUCTOR: DEPARTMENT: OFFICE NUMBER: OFFICE HOURS: OFFICE PHONE: E-­‐MAIL: INTERNET: MEETING TIME: REQUIRED TEXT:
Dr. James He Information Systems & Operations Management Room 2115, Charles F. Dolan School of Business MR 11:00 a.m. – 12:30 p.m. or by appointment 203-­‐254-­‐4000 Ext. 2835 [email protected] faculty.fairfield.edu/xhe 6:30 – 9:30 p.m. Thursdays Room 107 DSB Essential of Modern Business Statistics with Microsoft
Office Excel, by Anderson, Sweeney, and Williams, South
Western, 5th Edition, 2012, ISBN 978-0-8400-6238-3.
Note: The cases in the textbook will become part of the class assignments, and additional sources
may be used and distributed in class. Students are encouraged to bring their own laptop
computers to the class.
I. COURSE DESCRIPTION: This course uses numerous case studies and examples from finance, marketing, operations
management, accounting, information systems, and other areas of business to illustrate the
important roles and applications of statistics in the business world. Topics include: data
presentation and communication, probability distributions, sampling and sampling distributions,
confidence intervals, hypothesis testing, regression analysis, and time series forecasting.
Statistical software, such as Excel, will be introduced to facilitate the data analysis and decision
making process.
II. COURSE OBJECTIVES: Objectives of this course are: 1. To gain factual knowledge in terms of terminology, classifications, methods, and trends with
respect to basic concept and applications
51 •
Analyzing and converting statistical data into meaningful information by means of
statistical software.
2. To learn to apply course material to improve critical thinking, problem solving, and decisionmaking
•
Introducing the tools of descriptive statistics and influential statistics for effective
communication and decision-making.
3. To develop specific skills, competencies, and points of view needed by professionals in the
field most closely related to this course
•
V.
Investigating potential applications of business statistics in various areas through real
world case analysis.
OUTLINE OF COURSE CONTENT: 52 Reading Session Topics 1 9/05 • Introduction to Business Statistics 2 9/12 • Descriptive Statistics: Presentations Graphical 3 9/19 • Descriptive Statistics: Numerical Measures • Introduction to Probability 4 9/26 • Discrete Probability Distributions • Continuous Probability Distributions 5 Case & Homework Ch 1 Excel Practice: COUNTIF, FREQUENCY, BAR CHART, p.38 -­‐ 43 Ch 2 Excel Practice: FREQUENCY – Using PivotTable-­‐ pp.49-­‐51 HISTOGRAM, PP.52-­‐54 Ch 3 Ch 4 Excel Practice: PivotTable, pp.69-­‐71 Scatter Diagram, pp.75-­‐77 Case 1: Pelican Stores (Ch2), p.90 Excel Practice: Descriptive Statistics, p.116 Covariance, pp.140-­‐142 Case 2: Pelican Stores (Ch3), p.157 Homework #1 Ch 5 Ch 6 Excel Practice: Normal Distribution, 267 10/03 • Sampling and Sampling Distributions 6 10/10 • Confidence Interval Estimation Ch 8 Excel Practice: Confidence Interval, 339 Case 3: Gulf Real Estate (Ch8), 361 7 10/17 • “Stats Application” Project Proposal Draft Proposal Due 8 10/24 • Hypothesis Tests 10/31
• Comparisons Involving Means
9
10
11/07
• Comparison Involving Proportions
11
11/14
• Review for Mid-Term Exam
• Mid-Term Exam (Take-Home)
11/21
• Simple Linear Regression
12
Ch 7 Ch 9 Ch 10 Ch 11 Draft Project Report Due Ch 12
11/28
No Class – Thanksgiving Recess
13
12/05
• Multiple Regression
14
Ch 13
Excel Practice: Simple Reg., 518 Case 6: Measuring Stock Market Risk (Ch12), 577 Excel Practice: Multiple Regression, 590 Homework #4 12/12
• Problem Solving Session
• Prepare for Final Project Report/Presentation
Excel Practice: Hypothesis Test, 383, 394 Case 4: Quality Associates (Ch9), 410 Homework #2 Excel Practice: Test of 2 Means, 423, 433 Analysis of Variance, 457 Case 5: Par, Inc. (Ch10), 469 Excel Practice: Test of 2 Proportions, 481 Homework #3 53 15
12/19
• “Stats Application” Project Presentation
Final Paper Due IV. GRADING POLICY: The final grade will be calculated as follows: Case Analyses and Presentations Mid-­‐term Exam “Stats Application” Project Homework Assignments Class Participation Total 10% 35% 25% 20% 10% 100% Case Analysis The class will be divided into two-­‐student groups for case analysis. Each group is responsible for all cases, including data analysis, statistical tests, potential applications, and class discussion. While a formal written case report is not required, students are expected to show their work in writing and to participate in class discussion. Homework (Individual) HW#1: Ch3 -­‐ Problem 59 (p.153), Ch4 -­‐ Problems 1-­‐3 (p.173), Ch5 -­‐ Problem 16 & 18 (p.222), Ch6 -­‐ Problem 24 (p.272) HW#2: Ch7 -­‐ Problems 9 (p.296), 19 (p.308), 23 (p.309), 41 (p.321); Ch8 – Problems 5 (p.333), 12 & 13 (p.342), 27 (p.346); Ch9 – Problems 1 & 4 (p.370), 15 & 17 (p.388), 23 & 24 (p.396) HW#3: Ch10 – Problems 1 (p.424), 13 (p.436), 19 (p.443), 24 (p.444); Ch11 – Problem 1 (p.483), 6 (p.484) HW#4: Ch12 – Problems 5 (p.521), 18 (p.532), 26 (p.544); Ch13 – Problems 2 (p.592), 24 (p.607) “Stats Application” Project The “Stats Application” project has two components: a presentation and a paper. Students will choose their own project topic, based on their own interests and preferences. Students should discuss their project topic with the instructor as soon as possible to ensure it is appropriate. The topic should relate somehow to the practice of Applied Business Statistics. The following are possible project types: •
Data analysis and descriptive statistics, or 54 •
Data analysis and statistical tests. Presentations should last approximately 10 -­‐ 15 minutes. It would be helpful if presentations could involve the rest of the class somehow, e.g., by including a problem or problems that could serve as the basis for class participation. Written reports are due on the last day of class. Reports should include references to current research related to the project topic. At least two current references should be cited in the report. Class Participation Class participation includes participating in Excel practice, case analysis and discussion, in addition to class attendance. Communication
Email is probably the most effective way to communicate with the instructor. The instructor
may occasionally communicate with students using class lists on StagWeb.
FAIRFIELD UNIVERSITY ACADEMIC HONESTY POLICY: In all academic work, students are expected to submit materials that are their own. Examples of dishonest are listed in the link of my website: faculty.fairfield.edu/xhe In the event of such dishonesty, professors are to award a grade of zero for the project, paper or
examination in question, and may record an F for the course itself. When appropriate, expulsion
may be recommended. A notation of the event is made in the student’s file in the academic
dean’s office. The student will receive a copy.
55 Sample Course Syllabus
QA 500 – Business Forecasting & Predictive Analytics
Course Description
This course introduces analytical techniques used to assist in managing under uncertainty. Topics
include time series and other forecasting techniques, as well as Monte Carlo simulation to assess
the risk associated with managerial decisions. Specifically, we will cover decision support
systems, collecting data, data sources, time dependent models and analysis, advanced solver,
time series techniques, exponential smoothing, moving averages, and Box-Jenkins (ARIMA)
models. Application examples include: Financial models - Stock prices, Risk management Bond ratings, Behavior models - Customer attrition, Customer likes/dislikes, Buying patterns Propensity to buy, Politics - Identify swing voters, and Sales. Prerequisite: QA 400.
Learning Outcomes
1. Identify the appropriate techniques to use to analyze time series data
2. Identify when Monte Carlo simulation is appropriate for assessing risk
3. Use computer software for implementing forecasting
4. Use computer simulation software to perform risk analyses
Text
Principles of Business Forecasting, Keith Ord & Robert Fildes, South-Western, Cengage
Learning, 2013, ISBN 978-0-324-31127-3.
Grading Policy
Participation
Homework
Quizzes
Mid-Term Exam
Final Exam
Total
10%
30%
30%
20%
30%
100%
Participation
Students are expected to attend and participate in each class session. Participation means being
prepared, being punctual, being respectful, actively listening and responding to questions.
Homework
All homework assignments will be submitted through Blackboard.
56 Quizzes
There will be a total of five (5) quizzes.
Exams
One mid-term exam and one final exam will be given. In case of an emergency, the student must
contact the instructor to request an excused absence. All students must take the mid-term exam
and the cumulative final exam. Exams will be a combination of multiple choice and
computational questions that may require the use of software.
Outline of Course Content: Session Topics Reading 1 Ch 1 Minicase 1.1 & Minicase 1.4
Ch 2 Exercises 2.1 & 2.4, Minicase 2.1
Ch 3 Exercises 3.1, 3.3 & 3.4
• Forecasting: Why and How 2 • Basic Tools for Forecasting 3 • Forecasting Trends: Exponential Smoothing 4 • Seasonal Series: Forecasting & Ch 4 Exercises 4.1, 4.2 & 4.4
Minicase 5.2
Decomposition 5 • State-­‐Space Models for Time Series 6 Ch 5 • ARIMA Models 7 • Simple Linear Regression for Forecasting 8 9
10
• Multiple Regression for Forecasting Ch 6 Exercises 6.1
Exercises 7.5
Ch 7 Ch 8 Minicase 8.3
• Model Building
Ch 9 • Advanced Methods of Forecasting
Ch 10 Exercises 9.4
Exercises 10.4
• Judgment-Based Forecasting
Ch 11
Exercises 11.10
11
Case & Homework 57 12
• Putting Forecasting Methods to Word
Ch 12
• Forecasting in Practice
Ch 13
13
14
• Final Exam
Exercises 12.16
58 Appendix D
IS/OM Faculty and CVs
Name/Title
Degree/Institution
Expertise
Teaching
James He
Professor
Chris Huntley
Associate Professor
Patrick Lee
Associate Professor
Yasin Ozcelik
Associate Professor
Vishnu Vinekar
Associate Professor
Additional Line
TBD
Ph.D. (1992)
Penn State
Ph.D. (1996)
University of Virginia
Ph.D. (1984)
Carnegie Mellon University
Ph.D. (2005)
Purdue University
Ph.D. (2007)
University of Texas at Arlington
Ph.D.
?
Operations Management and
Quantitative Analysis
Information Systems
OM 500*
QA 400, 500*
IS 500, 520, 550*
Operations Management and
Quantitative Analysis
Information Systems
OM 400, 525
QA 500*
IS 500, 540*, 550*
Information Systems
IS 500, 540*, 550*
Information Systems and Big
Data Management
IS 540*, 550*
*new course in MSBA program
59 James He Professor Department: Information Systems & Operations Management School: Dolan School of Business Date of Appointment: September 2000 Date of Rank: September 2006 Years of Service: 14 Employment 2000 -­‐ Professor (2006) , Information Systems & Operations Management, Present Fairfield University 1991 -­‐ 2000 Assistant Professor (1991-­‐1997) and Associate Professor (1997-­‐2000), School of Business, South Carolina State University, Orangeburg, SC. Degrees Ph.D. Management Science and Information Systems, Pennsylvania State University, 1992 MBA University of Shanghai for Science and Technology, 1987 BS Zhejiang University, 1982 Honors 2012 DSB Outstanding Research Paper Award 2006 Distinguished as a Fellow of the International Information Management Association (IIMA) 2006 Elected as President of the International Information Management Association (IIMA) Publications Articles •
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Xin James He and Myron Sheu, Efficacy of Functional User Impact on Information System Development, Management Research Review, Vol. 37 (9), Sept. 9, 2014 (Forthcoming). Xin James He, The Effect of Supply Chain Strategy on Quality and U.S. Competitiveness, Communications of the IIMA, Vol. 13 (2), 2013, pp. 15-­‐27. Xin James He, Enhancing Excel Skills in Teaching Undergraduates in Business, Communications of the IIMA, 11(4) 2012, 13-­‐30. Myron Sheu and Xin James He, An Empirical Study of Integration Issues in IS Development, The Journal of Academy of Business and Economics, 11(4) 2011, 154-­‐159. 60 •
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Xin James He, Factors Affecting Business Process Reengineering in China, International Journal of Internet and Enterprise Management, 7(2) 2011, 172-­‐
196. Xin James He, Xiaobo Xu and Jack C. Hayya, The Impact of Stochastic Lead Time: The Mean or the Variance, International Journal of Information Technology & Decision Making, 10(1) 2011, 175-­‐185. Jack C. Hayya, Terry Harrison and Xin James He, The Impact of Stochastic Lead Time Reduction on Inventory cost under Order Crossover, European Journal of Operational Research, 211(2) 2011. James He, Analysis of Strategic Supply Management: A Switching Cost View, 2011 Proceedings of the International Conference on Service Systems and Service Management, 2011, 255-­‐260. Wei Gao, Xin James He and Hengshan Wang, The Impact of Knowledge Integration on Firm Performance, Journal of International Technology and information Management, 18(2) 2009, 239-­‐258. Xiaobo Xu and Xin James He, Impact of Team Altitude and Behavior on IS Project Success, Communications of the IIMA, 8(4) 2008, 41-­‐52. Xin James He and Myron Sheu, Enterprise Documentation: A Formal-­‐Model Approach, Communications of the IIMA, 7(2) 2007, 51-­‐59. Jun Zhuo and Xin James He, Analysis of E-­‐Marketplace for Textile Industry in China, International Journal of Management and Enterprise Development, 4(3) 2007, 337-­‐353. Xin James He and Myron Sheu, Measured Decision Support for Online Customers, Journal of Business Management and Change, 1(1) 2006, 27-­‐38. Xin James He and Myron Sheu, Data Warehousing for Supply Chain Management with Case Analysis, International Journal of Management and Enterprise Development, 3(5) 2006, 438-­‐452. Xin James He, A Comparative Study of Business Process Reengineering in China, Communications of IIMA, 5(1) 2005, 25-­‐30. Xin James He, ERP Implementation in the Global Marketplace: The Case of China and the United State of America, International Journal of Knowledge, Culture, and Change Management, 4 2005, 889-­‐905. Xin James He, J.G. Kim and Jack C. Hayya, The Cost of Lead-­‐time Variability: The Case of the Exponential Distribution, International Journal of Production Economics, 97(2) 2005, 113-­‐122. Xin James He and Hengshan Wang, Logistics Information System in China: An Overview, Communications of the IIMA, 4(3) 2004, 113-­‐122. J.G. Kim, D. Sun, Xin James He and Jack C. Hayya, The (s, Q) Inventory Model with Erlang Lead Time and Deterministic Demand, Naval Research Logistics, 51(6) 2004, 906-­‐923. Xin James He, The ERP Challenge in China: A Resource-­‐Based Perspective, Information Systems Journal, 14(2) 2004, 153-­‐167. Myron Sheu and Xin James He, Intelligent Web User Interfaces, Communications of the IIMA, 3(2) 2003, 1-­‐15. Jack C. Hayya, Dean Chatfield, Xin James He and Chao-­‐Hsien Chu, Supply Chain Information Technology Metrics, International Journal of Operations and Quantitative Management, 9(1) 2003, 33-­‐48. 61 •
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Myron Sheu and Xin James He, Intelligent Machanism -­‐ Vital for the Success of E-­‐
Commerce, Journal of Object Technology, 2(4) 2003, 101-­‐112. Xin James He, Will ERP Fit Enterprises in China, Communications of the IIMA, 2(1) 2002, 25-­‐36. Xin James He and Jack C. Hayya, The Impact of Just-­‐in-­‐time Production on Food Quality, Total Quality Management, 13(5) 2002, 651-­‐670. Conference Proceedings •
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Xin James He and Myron Sheu, Efficacy of Functional User Impact on Information System Development, 2013 Proceedings of Southwest Decision Sciences Institute Annual Meeting, (2013), 581-­‐588. Xin James He, Analysis of Strategic Supply Management: A Switching Cost View in 2011 Proceedings of the International Conference on Service Systems and Service Management. (2011). Ying Yi and Xin James He, Analysis of Switching Costs on Customer Loyalty in e-­‐
commerce in 2010 Proceedings of INFORMS Service Science Conference. (2010), 98-­‐105. Xin James He, The Impact of Stochastic Lead Time: the Mean or the Variance in 2009 Proceedings of International Multi-­‐Conference of Engineering and Computer Scientists. (2009), 2076-­‐2080. Xin James He and Wenjie Wu, Factors Affecting Adoption of ERP in China in Joint 2006 Proceedings of the International Conference on CIMCA and IAWTIC. Sydney, Australia: (2006). Jack Hayya, Dean Charfield, Xin James He and Andrew Pan, Exploiting Linear Independence in the Analysis of Stochastic Inventory Systems in 2006 Proceedings of Decision Sciences Institute. San Antonio, TX: (2006), 32271-­‐
32276. Xin James He, Business Process Reengineering in China: An Empirical Study in Proceedings of the 2nd European Conference on IS Management, Leadership and Governance. Paris, France: (2006), 61-­‐70. Jack C. Hayya, U. Bagchi, Xin James He, J.G. Kim and Andrew C. Pan, Supply Chain Analysis: RFID & Inventory Control in 2005 Proceedings of Decision Sciences Institute. San Francisco, CA: (2005), 11861-­‐11866. Xin James He, Early Trends of ERP Implementation in China in 2003 Proceedings of Decision Sciences Institute. Washington, DC: (2003), 1-­‐6. Xin James He, Inventory Management under ERP Environment in Proceedings of the 3rd International Conference of Electronic Commerce Engineering. Hangzhou, China: (2003), 249-­‐252. Xin James He, A Resource-­‐Based View on ERP Implementation in Proceedings of the 7th International Conference of the Decision Sciences Institute. Shanghai, China: (2003), CD-­‐ROM. Jack C. Hayya, Dean Chatfield, Xin James He and Chao-­‐Hsien Chu, Supply Chain Information Technology Metrics in 2003 Proceedings of Western Decision Sciences Institute. Island of Kauai, Hawaii: (2003), 500-­‐502. 62 •
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Xin James He and Jack C. Hayya, Lead-­‐Time Variability and Inventory Cost in 2002 Proceedings of Decision Sciences Institute. San Diego, CA: (2002), 1922-­‐
1927. J.G. Kim, Xin James He and Jack C. Hayya, Stochastic Lead-­‐Time Inventory Models in 2002 Proceedings of Western Decision Sciences Institute. Las Vegas, NV: (2002), 748-­‐750. Xin James He, The Impact of ERP Implementation in China in Proceedings of the 1st International Conference on e-­‐Business in Hong Kong. (2001), CD-­‐ROM. Chao-­‐Hsien Chu, Xin James He and Jack C. Hayya, A Data Warehousing Approach to Supply Chain Integration in 2001 Proceedings of Decision Sciences Institute. San Francisco, CA: (2001). Presentations Conferences •
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Xin James He, “Critical Skills Required in Big Data Business Analytics.” Paper presented to the Academy of Business Research Fall 2013 Conference, Montego Bay, Jamaica, November 13-­‐15, 2013. Xin James He, “The Impact of Global Supply Chain on the Product Quality: The Case of Boeing 787.” Paper presented to 26th Euro-­‐Institute for Operations Research and Management Science Joint Conference, Rome, Italy, July 1-­‐ 4, 2013. Xin James He and Myron Sheu, “Efficacy of Functional User Impact on Information System Development.” Paper presented to the 2013 Southwest Decision Sciences Institute Annual Meeting, Albuquerque, NM on March 16, 2013. Christine Beggan and Xin James He, “The Effect of Outsourcing and the Global Trade Imbalance on US Economy.” Paper presented to the The 23rd Annual Meeting of International Information Management Association, Chicago, IL on October 16, 2012. Xin James He, “The Effect of Global Supply Chain on U.S. Manufacturing: A Switching Cost View.” Paper presented to the 2012 Institute for Operations Research and Management Science International Conference, Beijing, China on June 27, 2012. James He and Myron Sheu, “An Empirical Study of Integration Issues in IS Development.” Paper presented to the 2011 International Academy of Business and Economics Conference, Las Vegas, NV on October 17, 2011. Ying Yi and James He, “Analysis of Strategic Supply Management: A Switching Cost View.” Paper presented to the 8th International Conference on Service Systems and Service Management, Tianjin, China on June 26, 2011. Xin James He and Ying Yi, “Analysis of Switching Costs on Customer Loyalty in e-­‐
commerce.” Paper presented to the 2010 INFORMS Service Science Conference, Taipei, Taiwan on July 9, 2010. Xin James He, “The Impact of Stochastic Lead Time: The Mean or the Variance.” Paper presented to the International Multi-­‐Conference of Engineering and Computer Scientists 2009 on Operations Management, Hong Kong on March 20, 2009. 63 •
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Xin James He, “Stochastic Lead Time: Is it the mean or the variance that matters more.” Paper presented to the 19th Annual Conference of International Information Management Association, San Diego, CA on October 13, 2008. Xin James He and Myron Sheu, “Enterprise Documentation: A Formal-­‐Model Approach.” Paper presented to the 18th Annual Conference of International Information Management Association, Beijing, China on October 16, 2007. Xin James He and Wenjie Wu, “Factors Affecting Adoption of ERP in China.” Paper presented to the Joint 2006 International Conference on CIMCA and IAWTIC, Sydney, Australia on November 30, 2006. Jack Hayya, Dean Charfield, Xin James He and Andrew Pan, “Exploiting Linear Independence in the Analysis of Stochastic Inventory Systems.” Paper presented to the 2006 Decision Sciences Institute Annual Meeting, San Antonio, TX on November 19, 2006. Xin James He, “The Impact of Lead Time Variability on Supply Chain Management.” Paper presented to the 17th Annual Meeting of International Information Management Association, New Rochelle, NY on October 5, 2006. Xin James He, “Business Process Reengineering in China: An Empirical Study.” Paper presented to the 2nd European Conference on IS Management, Leadership and Governance, Ministry of Research, Paris, France on July 12, 2006. Jack C. Hayya, U. Bagchi, Xin James He, J.G. Kim and Andrew Pan, “Supply Chain Analysis: RFID & Inventory Control.” Paper presented to the 2005 Decision Sciences Institute Annual Meeting, San Francisco, CA on November 21, 2005. Xin James He, “Analysis of Critical Success Factors for Business Process Engineering.” Paper presented to the 4th International Conference on Information and Management Science, Kunming, China on July 3, 2005. Jack C. Hayya, J.G. Kim, Xin James He and D. Sun, “A Regression Approach to Inventory Control with Erlang Lead Time and Deterministic Demand.” Paper presented to the 2004 Decision Sciences Institute Annual Meeting, Boston, MA on November 21, 2004. Xin James He, “ERP Implementation in the Global Marketplace: USA vs. China.” Paper presented to the 4th International Conference on Knowledge, Culture, and Change in Organizations, London, UK on August 5, 2004. Xin James He, “Early Trends of ERP Implementation in China.” Paper presented to the 2003 Decision Sciences Institute Annual Meeting, Washington, DC on November 23, 2003. Xin James He, “Inventory Management under ERP Environment.” Paper presented to the 3rd International Conference on Electronics Commerce Engineering, Hangzhou, China on October 23, 2003. Xin James He and Jack Hayya, “Lead-­‐time Variability and Inventory Cost.” Paper presented to the 2002 Decision Sciences Institute Annual Meeting, San Diego, CA on November 24, 2002. J.G. Kim, Xin James He and Jack Hayya, “Stochastic Lead-­‐time Inventory Models.” Paper presented to the 2002 Western Decision Sciences Institute Annual Meeting, Las Vegas, NV on April 3, 2002. 64 •
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Xin James He, “The ERP Implementation in China.” Paper presented to the 1st International Conference on Electronic Business, Hong Kong, China on December 20, 2001. Chao-­‐Hsien Chu, Xin James He and Jack Hayya, “A Data Warehousing Approach to Supply Chain Integration.” Paper presented to the 2001 Decision Sciences Institute Annual Meeting, San Francisco, CA on November 18, 2001. Xin James He, “Analysis of Switching Costs on Customer Loyalty in e-­‐commerce.” Paper presented to the , on. Grants •
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James He, The Impact of Global Supply Chain Strategy on Product Quality: The Case of Boeing 787, Dolan School of Business: $5,000 (June 1 – December 15, 2013). James He, The Effect of Global Supply Chain on U.S. Manufacturing: A Switching Cost View, Dolan School of Business: $5,000 (June 1 -­‐ December 15, 2012). James He, Strategic Supply Chain Measurement: A Switching Cost Perspective, Dolan School of Business: $5,000 (June 1 -­‐ December 15, 2011). James He, Critical Success Factors on IS Project Performance, Dolan School of Business: $5,000 (June 1 -­‐ December 15, 2010). James He, Stochastic Lead Time: Is it the mean or the variance that matters more, Dolan School of Business: $5,000 (June 1 -­‐ December 15, 2008). Xin James He, Enterprise Documentation: A Formal-­‐Model Approach, Dolan School of Business: $5,000 (June 1 -­‐ December 15, 2007). Xin James He, Analysis of E-­‐marketplace in the Textile Industry in China, Dolan School of Business: $4,000 (May 1 -­‐ September 1, 2006). Xin (James) He, Analysis of Critical Success Factors for Business Process Reengineering, Dolan School of Business, Fairfield University: (2005). Xin (James) He, Data Warehouse in Supply Chain Management, Fairfield University: $3,500 (June 1 -­‐ December 15, 2004). Xin (James) He, The (s, Q) Inventory Model with Stochastic Lead Time and Deterministic Demand, Dolan School of Business, Fairfield University: $4,000 (June 1, 2004 -­‐ February 10, 2005). Service Committee Dolan School of Business •
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Continuous Improvement & Assessment Committee September 2008 – June 30 2011 DSB Undergraduate Curriculum Committee September 2005 -­‐ August 2008 General Faculty •
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Academic Council September 2012 -­‐ June 2014 Undergraduate Curriculum September 2009 -­‐ June 2012 Academic Council September 2008 -­‐ December 2008 Faculty Development & Evaluation September 2006 -­‐ August 2008 65 •
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Academic Council September 2004 -­‐ August 2006 Undergraduate Curriculum September 2003 -­‐ August 2006 Chair: September 2004 -­‐ August 2005 •
University Advancement September 2001 -­‐ August 2004 Chair: September 2003 -­‐ August 2004 University Administration •
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Self-­‐Study 5: Faculty August 2006 -­‐ August 2007 Self-­‐Study 6: Students August 2006 -­‐ August 2007 Institutional Service •
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Continuous Improvement & Assessment Committee. July 1, 2011 -­‐ Present. Academic Council. September 1, 2011 -­‐ May 30, 2014. Academic Council. September 1, 2008 -­‐ December 15, 2008. Professional Service •
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President of International Information Management Association. November 1, 2006 -­‐ November 1, 2007. Vice President of International Information Management Association. In charge of IIMA 2007 Annual Conference. November 1, 2005 -­‐ November 1, 2006. Program Chair and Vice President of International Information Management Association. November 1, 2004 -­‐ November 1, 2005. Community Service •
Coordinator of Fairfield Chinese Academy. January 7, 2002 -­‐ Present. 66 Christopher L Huntley Associate Professor Department: Information Systems & Operations Managment School: Dolan School of Business Date of Appointment: September 1997 Date of Rank: September 2004 Years of Service: 16 Employment 1997 -­‐ Associate Professor (2004) , Information Systems & Operations Present Managment, Fairfield University 1996 -­‐ 1997 Visiting Assistant Professor, Management Science and Information Systems, University of California at Riverside, Riverside, CA . 1995 -­‐ 1996 Director of Network Analysis, Service Design and Planning, Consolidated Rail Corporation, Philadelphia, PA. 1994 -­‐ 1995 Senior Operations Analyst/Architect, Information Systems, Consolidated Rail Corporation, Philadelphia, PA. 1992 -­‐ 1994 Software Developer and Consultant, Commonwealth Computer Research, Charlottesville, VA. Degrees Ph.D. University of Virginia, 1996 M.S. University of Virginia, 1989 B.S. University of Virginia, 1987 Publications Articles •
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Christopher L. Huntley, Onshore Mobile App Development: Successes and Challenges, IEEE Computer, 44(9) 2011. Christopher L. Huntley, The Developer's Perspective, IEEE Computer, 44(5) 2011, 88-­‐90. Vishnu Vinekar and Christopher L. Huntley, Agility vs. maturity: Is there really a trade-­‐off?, IEEE Computer, 43(5) 2010, 87-­‐89. Christopher L. Huntley, A Developmental View of Systems Security, IEEE Computer, 39(1) 2006, 113-­‐114. Jeffrey Arthur and Christopher L. Huntley, Ramping up the Learning Curve: Assessing the Impact of Deliberate Learning on Plant Performance Under Gainsharing, Academy of Management Journal, 48(6) 2005, 1159-­‐1170. 67 •
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Christopher L. Huntley, Richard Mathieu and George Schell, An Initial Assessment of Remote Access Computer Laboratories for IS Education, Journal of Information Systems Education, 15(4) 2004, 397-­‐406. Christopher L. Huntley, Organizational Learning in Open Source Software Projects: An Analysis of Debugging Data, IEEE Transactions on Engineering Management, 50(4) 2003, 485-­‐493. Suzanne Hetzel Campbell and Christopher L. Huntley, Limits to Growth: A Private Family Health Practice, International Journal of Entrepreneurship and Innovation, 4(2) 2003, 133-­‐139. Douglas Lyon and Christopher L. Huntley, There’s More Than One Way to Build a Bridge, IEEE Computer, 2002. S. Erevelles, V. Horton, E. Rolland and Christopher L. Huntley, Incorporating Geographic Information Systems into Marketing Education, Journal of Database Marketing, 6(4) 1999, 357-­‐371. Christopher L. Huntley and Donald Brown, Parallel Genetic Algorithms with Local Search, Computers and Operations Research, 23 1996, 559-­‐572. Christopher L. Huntley, Freight Routing and Scheduling at CSX Transportation, Interfaces, 25(3) 1995, 58-­‐71. Donald Brown and Christopher L. Huntley, A Practical Application of Simulated Annealing to Clustering, Pattern Recognition, 14 1992, 402-­‐412. Christopher L. Huntley and Donald Brown, A Parallel Heuristic for Quadratic Assignment Problems, Computers & Operations Research, 12 1991, 275-­‐289. Christopher L. Huntley, Donald Brown and Paul Garvey, Clustering of Homogeneous Subsets, Pattern Recognition Letters, 12 1991, 401-­‐408. Chapters •
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Christopher L. Huntley and Suzanne Hetzel Campbell, Limits to Growth: A Case Study of a Private Family Medical Practice -­‐ updated. in P. Westhead, M. Wright and G. McElwee (Eds.), Entrepreneurship – Perspectives and Cases. Essex, England: Pearson Education Limited (2011), pp. 411-­‐416. Mousumi Bhattacharya and Christopher L. Huntley, Social Network Mapping Software: New Frontiers in HRM in T. Torres-­‐Coronas and M. Arias-­‐Olivia (Eds.), E-­‐Human Resources Management. New York: Idea Group (2005), pp. 68-­‐85. Conference Proceedings •
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Christopher L. Huntley, Caritas in Veritase in Business Technology Courses: Anomaly or Teaching Moment?. in Proceedings of the Colleagues in Jesuit Business Education Conference. (2010). Christopher L. Huntley and Stephanie Lucca, Small World Effects in Extended OSS Communities: An Analysis of Project Dependency Data in B. Wray (Ed.), Proceedings of the 2008 Meeting of the Southeastern Decision Sciences Institute. Atlanta, GA: Decision Sciences Institute (2008). Suzanne H. Campbell and Christopher L. Huntley, Growing pains at a private breastfeeding practice in World Association for Case Method Research & Case 68 Method Application(WACRA), 19th International Conference. Mannheim, Germany: WACRA (2002). Presentations Conferences •
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Christopher L. Huntley, “Caritas in Veritate in Business Technology Courses: Anomaly or Teaching Moment?” Paper presented to the Colleagues in Jesuit Business Education, Milwaukee, WI on July 9, 2010. Christopher L. Huntley and Winston M. Tellis, “Servant Leadership as it Appears in Open Source Software.” Paper presented to the Colleagues in Jesuit Business Education Conference, Rockhurst University, Kansas City, KS on July 16, 2009. Christopher L. Huntley, “Small World Effects in Extended OSS Communities: An Analysis of Project Dependency Data.” Paper presented to the 2008 Meeting of the Southeast Decision Sciences Institute, Orlando, FL on February 18, 2008. Mousumi Bhattacharya and Christopher L. Huntley, “Social network analysis and human resource management.” Panel presented at the Academy of Management Meetings, New Orleans, LA on August 8, 2004. Lectures & Seminars •
Winston M. Tellis, Christopher L. Huntley and Vishnu Vinekar, IS In Developing Countries: Employee Motivation for ERP In Microfinance, Paper presented to the Dolan School of Business, Fairfield University, Fairfield, CT, on February 21, 2009. Service Committee Center for Academic Excellence •
FLC -­‐ Diversity 2007-­‐08 September 1997 -­‐ Present General Faculty •
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Academic Council September 2012 -­‐ June 2014 Athletics September 2010 -­‐ June 2013 Academic Council September 2006 -­‐ August 2008 University College September 2003 -­‐ August 2006 Chair: September 2004 -­‐ August 2005 •
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Educational Technologies September 2000 -­‐ August 2003 Library September 1999 -­‐ August 2002 Chair: September 2001 -­‐ August 2002 Institutional Service 69 •
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Co-­‐Chair, Fairfield Business Plan Competition. Coordinate all operations and all web services for the initial offering of the competition. October 1, 2011 -­‐ Present. Stags Council. Work with administrators and staff from across campus on student-­‐life issues for the varsity and club athletes. September 1, 2011 -­‐ Present. DSB Graduate Admissions Committee. Review graduate applications and make admisssion decisions for marginal candidates. September 1, 2011 -­‐ Present. University Web Advisory Committee. Monthly meetings to review and advise on Fairfield University's web sites and services. September 1, 2011 -­‐ Present. NCAA Faculty Athletics Representative. Act as ombudsman on the behalf of student-­‐athletes. In this capacity I administer annual surveys of all athletes and advise the athletics department on academic matters. July 1, 2011 -­‐ Present. Professional Service •
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Editorship. Editorship of the "In Development" column in Computer, the flagship publication of the IEEE Computer Society. May 1, 2011 -­‐ Present. Board member, IEEE Computer Society. In addition to my column editor duties, I also participate in annual board meetings to set the direction for the society. February 1, 2011 -­‐ Present. Community Service •
Web Development Projects 2010-­‐2012. Completed professional-­‐quality interactive websites for the following organizations: * Beardsley Zoo * Operation Hope * CT CAHS/VITA Program * Bridgeport Schools * JustFaith Ministries January 1, 2011 -­‐ Present. Development •
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Teaching MIS, Prentice-­‐Hall, Boston, MA (April 6, 2006 -­‐ April 6, 2006). Teaching MIS, Prentice-­‐Hall, Boston, MA (April 5, 2006). Research •
Christopher L. Huntley and Stephanie Lucca Small Worlds in OSS Communities: An Analysis of Project Dependency Data, April 1, 2005. 70 Patrick S Lee Associate Professor Department: Information Systems & Operations Managment School: Dolan School of Business Date of Appointment: September 1995 Date of Rank: September 1999 Years of Service: 19 Employment 1995 -­‐ Associate Professor (1999) , Information Systems & Operations Present Managment, Fairfield University Degrees Ph.D. Industrial Administration, Carnegie-­‐Mellon, 1984 Publications Articles •
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Patrick S. Lee, Coordinating pricing and inventory purchasing decisions of a supply chain for e-­‐tailers in face of quantity discount, Academy of Information and Management Sciences Journal, 16(2,2) 2013. Gerard M. Campbell, Patrick S. Lee and Annemarie Van Parys, Cross-­‐Training and Allocation of Highly-­‐Skilled Workers at Surgental, Inc. -­‐-­‐ a Spreadsheet-­‐
Based Teaching Case, International Journal of Case Method Research & Application, XXV(1) 2013, 41-­‐47. Patrick S. Lee, Continuously Increasing Price in a Gradual Usage Inventory Cycle: An Optimal Strategy for Coordinating Production with Pricing for a Supply Chain, Academy of Information and Management Science Journal, 15(1) 2012, 105-­‐116. Prafulla Joglekar, Patrick S. Lee and Alireza M. Farahani, Continuously Increasing Price in an Inventory Cycle: An Optimal Strategy for E-­‐tailers, Journal of Applied Mathematics and Decision Sciences, (Article ID: 483267) 2008, 14 pages. [ doi:10.1155/2008/483267 ] •
Patrick S. Lee and prafulla joglekar, A dual pricing model for price sensitive products subject to obsolescence, Journal of Business and Economics Studies, 2005. 71 •
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Prafulla joglekar and Patrick S. Lee, Responding to a one-­‐time-­‐only-­‐sale (OTOS) of a product subject to obsolescence, Academy of Information and Management Sciences Journal, 2003. Prafulla Joglekar, Patrick S. Lee, and Madjid Tavana, The Development and Validation of a Campus Recruiting Expert System Using Expert Opinions and Historical Data, Expert Systems with Applications, 1994. Patrick S. Lee, Investigation of Cost Variances Within a Linear Programming Framework, Accounting Inquiries, 1993. Patrick S. Lee, A Two-­‐Stage Approach to Multi-­‐Period Allocation of Savings Among Investment Plans, Annals of Operations Research, 1993. Patrick S. Lee, Consequences of Removing Subjects in Item Calibration, Objective Measurement : Theory into Practice, 2 1993. Patrick S. Lee, An Exact Formulation of Inventory Costs and Optimal Lot Size in Face of Sudden Obsolescence, Operations Research Letters, 1993. Patrick S. Lee, Constraint Optimization: a Perspective of IRT Parameter Estimation, Objective Measurement: Theory into Practice, 1 1991. Patrick S. Lee, Effect of Auto-­‐Correlation on Single-­‐Subject Single Facet Crossed-­‐
Design Generalizability Assessment, Behavioral Assessment, 1990. Patrick S. Lee, An Exploration of an Individual’s Decision-­‐Making Regarding Tax-­‐
Deferred Investment Plans, Journal of Risk Insurance, 1990. Patrick S. Lee, A Critical Review of the S/L Realibility Index, Behavioral Assessment, 1985. Patrick S. Lee, The Estimation of Kappa From Agreement Interobserver Reliability, Behavioral Assessment, 1984. Patrick S. Lee, The Effect of the Use and Percentage agreement on Behavioral Observation Reality: a Reassessment, Behavioral Assessment, 1984. Patrick S. Lee, Optimal Advertising for the Nerlove-­‐Arrow Model with Replenishable Budget, Optimal control: Applications and Methods, 1981. Patrick S. Lee, A Parametric Linear Complementary Technique for the Computation of Equilibrium in a Single Commodity Spatial Model, Mathematical Programming, 1980. Abstract •
Bruce M. Bradford and Patrick S. Lee, Investigation of cost variances within a linear programming framework, Proceedings of the Mid-­‐Atlantic American Accounting Association Meeting, 1993, 73. Conference Proceedings •
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Patrick S. Lee and Patricia M. Poli, Does Quality Pay: A DEA Study of the Motor Carriers' Performances in Allied Academies International Conference. Jacksonville, FL: (2007). P. Joglekar and P. Lee, Continuously Increasing Price in an Inventory Cycle: An Optimal Strategy for E-­‐tailers in, Honolulu: HIC International Conference, Honolulu 2005 (2005). 72 •
Prafulla Joglekar, and Patrick S. Lee, Dynamic pricing in a B2B environment with price elasticity in Allied Academies International Conference, Mississippi 2004. Presentations Conferences •
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Patrick S. Lee, “Coordinating pricing and inventory purchasing decisions of a supply chain for an e-­‐tailer in face of quantity discount.” Paper presented to the Allied academies, las vegas, 2012, las vegas on October 10, 2012. Gerard M. Campbell, Patrick S. Lee and Annemarie Van Parys, “Cross-­‐Training and Allocation of Highly-­‐Skilled Workers at Surgental, Inc. -­‐-­‐ A Spreadsheet-­‐Based Teaching Case.” Paper presented to the Twenty-­‐ninth International Conference of the World Association for Case Method Research & Application (WACRA), Stockholm, Sweden on June 27, 2012. Patrick S. Lee, “Continuously Increasing Price in a Gradual Usage Inventory Cycle: An Optimal Strategy for Products with Price Elasticity*.” Paper presented to the INFORMS, 2010, Buenos Aires, Buenos Aires, Argentina on June 1, 2010. Ari P. Vepsalainen and Patrick S. Lee, “Community-­‐based modeling of service networks.” Paper presented to the Production and Operations Management Society, Dallas, TX on May 12, 2007. Patricia M. Poli and Patrick S. Lee, “Does Quality Pay?” Presentation to the INFORMS, Hong Kong on June 1, 2006. Joan L. Van Hise, Patrick S. Lee and Barbara Porco, “Jesuit Business Education -­‐ Are we different?” Paper presented to the Colleagues in Jesuit Business Education Annual Conference, Loyola University -­‐ Chicago on July 22, 2005. Patrick S. Lee, “Dynamic Pricing in a B2b Environment With Price Elasticity.” Paper presented to the 2005 IFOR Meeting, Honolulu, Hawaii on July 15, 2005. Patrick S. Lee, “Continuously Increasing Price in an Inventory Cycle: an Optimal Strategy for E-­‐tailers.” Paper presented to the Hawaiian International Conference 2005, Honolulu, Hawaii on May 25, 2005. Patrick S. Lee, “Investigation of Cost Variances Within a Linear Programming Framework.” Paper presented to the Presented at the Mid-­‐Atlantic Accounting Association Annual Meeting, Washington, DC, on June 1, 1993. Patrick S. Lee, “Effects if Deleting Perfect and Zero Raw Scors in IRT.” Paper presented to the Presented at the International objective Measurement Workshop V at the University of Chicago, on June 1, 1991. Patrick S. Lee, “Constraint Optimization: a Perspective or IRT Parameter Estimation.” Paper presented to the Presented at the International Objective Measurement Workshop IV at the University of California at Berkly, CA, on June 1, 1989. Lectures & Seminars •
Gerard M. Campbell and Patrick S. Lee, Cross-­‐Training and Allocation of Highly-­‐
Skilled Workers in a Multiple Flow-­‐Shop Environment, Paper presented to the Dolan School of Business Research Seminar, on February 22, 2012. 73 •
Patrick S. Lee, Barbara Porco and Joan L. Van Hise, Jesuit Business Education: Are We Different?, Paper presented to the Charles F. Dolan School of Business Faculty Seminar Series, on October 1, 2005. Grants •
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Patrick S. Lee, Coordinating pricing and inventory purchasing decisions of a supply chain for an e-­‐tailer in face of quantity discount, DSB Summer Grant: $5000 (2012) Patrick S. Lee, The American Council of Education: $5,000 (2010). Patrick S. Lee, On Exploring Personal Portfolio Selection System-­‐Wealth Maximization, DSB: $5,000 (2008). Patrick S. Lee, dynamic pricing during a gradual replenishing inventory cycle: an optimal strategy for e-­‐tailers, DSB summer research grant: $4,000 (June 1, 2005 -­‐ March 1, 2006). Service Committee Dolan School of Business •
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Research and Grant Committee 2013 Graduate Programs Committee September 2009 -­‐ August 2011 Undergraduate Programs Committee September 2006 – August 2009 Continuous Improvement & Assessment Committee September 2003 -­‐ August 2006 General Faculty •
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Rank & Tenure October 2002 -­‐ August 2003 Faculty Development Committee (1995-­‐1998), Chair, 1998 Professional Service •
Opponent-­‐-­‐Doctoral Disssertation:Policy Variants for Coordinating Supply Chain Inventory Replenishments by Kaij E. Karrus, Aalto University, Helsinki, Finland, 2012. 74 Yasin Ozcelik
Office Address Home Address Dolan School of Business, Dept. of IS & OM 44 Strawberry Hill Ave #7B Fairfield University, Fairfield, CT 06824 Stamford, CT 06902 Phone: (203) 254-­‐4000 ext. 2821 Phone: (203) 554-­‐4748 E-­‐mail: [email protected] Website: www.misworld.org EDUCATION
Ph.D. Degree PURDUE UNIVERSITY, West Lafayette, IN Krannert School of Management Department of Management Information Systems Major Field: Management Information Systems Degree Conferred: August 2005 Dissertation Title: “Essays on the Effects of Information Technology and the Internet on Business Environments” M.S. Degree PURDUE UNIVERSITY, West Lafayette, IN Krannert School of Management Department of Economics Major Field: Economics Degree Conferred: May 2001 B. S. Degree BILKENT UNIVERSITY, Ankara, Turkey Faculty of Science Department of Mathematics Major Field: Mathematics Degree Conferred: May 1999 WORK EXPERIENCE
2011-­‐Present 2005-­‐2011 2001-­‐2005 1999-­‐2001 Associate Professor, Department of Information Systems & Operations Management, Dolan School of Business, Fairfield University Assistant Professor, Department of Information Systems & Operations Management, Dolan School of Business, Fairfield University Graduate Instructor & Research Assistant, Department of Management Information Systems, Krannert School of Management, Purdue University Teaching Assistant & Research Assistant, Department of Economics, Krannert School of Management, Purdue University 75 1998-­‐1999 Website developer and sales representative, MOS Computer, Turkey PROFESSIONAL AFFILIATIONS
1. Registered Member, Association for Information Systems (AIS) 2. Registered Member, Institute for Operations Research and Management Sciences (INFORMS) 3. Registered Member, Information Systems Society 4. Registered Member, American Association of University Professors (AAUP) RESEARCH INTERESTS
Economics of Information Systems Business Models in Electronic Commerce Business Value of Information Technology TEACHING INTERESTS
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• Information Systems • Electronic Commerce • Database Management Systems TEACHING EXPERIENCE
Fairfield University
1. IS 100 – Introduction to Information Systems •
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Spring 2013 (3 sections), Fall 2013 (2 sections) Spring 2012 (3 sections), Fall 2012 (3 sections) Spring 2011 (3 sections), Fall 2011 (2 sections) Spring 2010 (3 sections), Fall 2010 (2 sections) Spring 2009 (3 sections), Fall 2009 (2 sections) Spring 2008 (2 sections), Fall 2008 (2 sections) Spring 2007 (2 sections) Spring 2006 (2 sections), Fall 2006 (2 sections) Fall 2005 (3 sections) 2. OM 101 – Operations Management •
Spring 2013 (1 section) 3. IS 310 – Information Systems in Organizations •
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Fall 2013 (1 section) Fall 2011 (1 section) Fall 2010 (1 section) Fall 2009 (1 section) Spring 2008 (1 section), Fall 2008 (1 section) Fall 2006 (1 section) 4. IS 395 – Systems Capstone Project •
Spring 2006 (1 section) 76 5. IS 500 – Information Systems -­‐ MBA Level •
Spring 2007 (1 section) Purdue University
1. MGMT 547 – Computer Communications Systems (Fall 2003, 2 sections) • Purdue University Graduate Student Award for Outstanding Teaching, Fall 2003 • Krannert School of Management Outstanding Graduate Instructor Award, Fall 2003 2. MGMT 547 – Computer Communications Systems (Fall 2002, 2 sections) • The Krannert Dean's Certificate of Recognition for Teaching Excellence, Fall 2002 3. ECON 210 – Principles of Economics (Fall 1999, 1 section) PUBLICATIONS
Journal Articles
1. Altinkemer, K., Ozcelik, Y., Ozdemir, Z. “Productivity and Performance Effects of Business Process Reengineering: A Firm-­‐level Analysis,” Journal of Management Information Systems, 27(4), 2011, pp. 129-­‐162. 2. Ozcelik, Y., Ozdemir, Z. “Market Transparency in Business-­‐to-­‐Business e-­‐Commerce: A Simulation Analysis,” International Journal of E-­‐Business Research, 7(4), 2011, pp. 62-­‐
78. 3. Ozdemir, Z., Altinkemer, K., De, P., Ozcelik, Y. “Donor-­‐to-­‐Nonprofit Online Marketplace: An Economic Analysis of the Effects on Fundraising,” Journal of Management Information Systems, 27(2), 2010, pp. 213-­‐242. 4. Ozcelik, Y. “Do Business Process Reengineering Projects Payoff? Evidence from the United States,” International Journal of Project Management, 28(1), 2010, pp. 7-­‐13. 5. Ozcelik, Y. “Six Sigma Implementation in the Service Sector: Notable Experiences of Major Firms in the United States,” International Journal of Services and Operations Management, 7(4), 2010, pp. 401-­‐418. 6. Ozcelik, Y. “The Rise of Teleworking in the USA: Key Issues for Managers in the Information Age,” International Journal of Business Information Systems, 5(3), 2010, pp. 211-­‐229. 7. Ozcelik, Y. “Effects of Online Service Providers for Nonprofits on Fundraising Markets: An Economic Analysis,” International Journal of Information Systems in the Service Sector, 1(4), 2009, pp. 15-­‐32. 8. Altinkemer, K. and Ozcelik, Y. “Cash-­‐back Rewards versus Equity-­‐based Electronic Loyalty Programs in E-­‐commerce,” Information Systems and E-­‐Business Management, 7(1), 2009, pp. 39-­‐55. 77 9. Ozcelik, Y. “Globalization and the Internet: Digitizing the Nonprofit Sector,” Journal of Global Business Issues, 2(1), 2008, pp. 149-­‐152. Book Chapters
1. Ozcelik, Y. “Does the Internet Increase Fundraising Revenues of Nonprofit Organizations? An Economic Analysis,” Information Systems and New Applications in the Service Sector: Models and Methods, editor: John Wang, pp. 293-­‐308, IGI Global Publishing, 2011. 2. Ozcelik, Y., Carter, C., Clark, M., Martinez, A. “Digitizing Healthcare: Electronic Medication Administration (eMAR) and Bedside Medication Verification (BMV),” Biomedical Knowledge Management: Infrastructures and Processes for E-­‐Health Systems, editors: Wayne Pease, Malcolm Cooper, and Raj Gururajan, pp. 31-­‐41, IGI Global Publishing, 2010. 3. Ozcelik, Y. “IT-­‐Enabled Reengineering: Productivity Impacts (reprint),” IT Outsourcing: Concepts, Methodologies, Tools, and Applications, editor: Kirk St. Amant, Volume 1, pp. 371-­‐376, IGI Global Publishing, 2009. 4. Ozcelik, Y. “IT-­‐Enabled Reengineering: Productivity Impacts,” Encyclopedia of Information Communication Technology, editors: Antonio Cartelli and Marco Palma, Volume 2, pp. 498-­‐502, Idea Group Reference, 2008. 5. Ozcelik, Y. “Electronic Loyalty Programs: Comparative Survey,” Encyclopedia of Information Communication Technology, editors: Antonio Cartelli and Marco Palma, Volume 1, pp. 286-­‐290, Idea Group Reference, 2008. 6. Rees, J., Koehler, G.J., and Ozcelik, Y. “Information Privacy and e-­‐Business Activities: Key Issues for Managers,” Managing e-­‐Business in the 21st Century, editors: Sushil K. Sharma and Jatinder N.D. Gupta, pp. 139-­‐151, Heidelberg Press, 2003. Refereed Articles in Conference Proceedings
1. Ozcelik, Y. and Altinkemer, K. “Impacts of Information Technology Outsourcing on Organizational Performance: A Firm-­‐Level Empirical Analysis,” Proceedings of the 17th European Conference on Information Systems (ECIS 2009), Verona, Italy, June 8-­‐10, 2009. 2. Ozcelik, Y. and Ozdemir, Z. “Market Transparency in Business-­‐to-­‐Business (B2B) E-­‐
Commerce,” Proceedings of the 3rd Midwest United States Association for Information Systems Conference (MWAIS 2008), Eau Claire, Wisconsin, May 23-­‐24, 2008, Paper no. 4. 3. Altinkemer, K., De, P., Ozcelik, Y., and Ozdemir, Z. “Fundraising and the Internet,” Proceedings of the 13th Americas Conference on Information Systems (AMCIS 2007), Keystone, Colorado, August 9-­‐12, 2007. 78 4. Altinkemer, K., Ozcelik, Y., and Ozdemir, Z. “Productivity and Performance Effects of IT-­‐
Enabled Reengineering: A Firm-­‐Level Analysis,” Proceedings of the 15th European Conference on Information Systems (ECIS 2007), pp. 985-­‐993, St. Gallen, Switzerland, June 7-­‐9, 2007. 5. Altinkemer, K., De, P., and Ozcelik, Y. “Donor-­‐to-­‐Organization Marketplace (D2O),” Proceedings of the 7th INFORMS Telecommunications Conference, pp. 233-­‐235, Boca Raton, Florida, March 7-­‐10, 2004. 6. Altinkemer, K. and Ozcelik, Y. “Incentive Compatible Electronic Loyalty (ICEL),” Proceedings of the EURO/INFORMS Conference, Istanbul, Turkey, July 6-­‐10, 2003. 7. Altinkemer, K. and Ozcelik, Y. “Incentive Compatible Electronic Loyalty,” Proceedings of the 5th International Conference on Electronic Commerce Research (ICECR), Montreal, Canada, October 23-­‐27, 2002. PROFESSIONAL PRESENTATIONS
Research Consortia and Workshops
1. “Does Information Technology (IT) Outsourcing Matter? Empirical Evidence from the US Companies,” The International Institute of Industrial Engineers (IIE) Conference, Istanbul, Turkey, June 26-­‐28, 2013. 2. “Multi-­‐dimensional Effects of Information Technology (IT) Outsourcing on Company Performance: A Firm-­‐level Empirical Analysis,” 32nd National Congress on Operations Research and Industrial Engineering, Istanbul, Turkey, June 20-­‐22, 2012. 3. “Performance Effects of Information Technology Outsourcing: A Firm-­‐Level Empirical Analysis,” 30th National Congress on Operations Research and Industrial Engineering, Istanbul, Turkey, June 30-­‐July 2, 2010. 4. “Process Reengineering and Firm Productivity,” The Decision Sciences Institute (DSI) 40th Annual Meeting 2009, New Orleans, LA, November 14-­‐17, 2009. 5. “An Internet-­‐enabled Donor-­‐to-­‐Nonprofit (D2N) Marketplace,” MIS Research Workshop, Purdue University, West Lafayette, Indiana, October 5, 2007. 6. “An Economic Analysis of Emerging e-­‐Commerce Models in the New Era,” Americas Conference on Information Systems (AMCIS) Doctoral Consortium, New York City, New York, August 5, 2004. 7. “An Economic Analysis of Emerging e-­‐Commerce Models in the New Era,” The Big Ten IS Research Consortium, East Lansing, Michigan, April 30-­‐May 1, 2004. 8. “Incentive Compatible Electronic Loyalty (ICEL),” Workshop on Information Systems and Economics (WISE 2002), Barcelona, Spain, December 14-­‐15, 2002. 79 Invited Presentations
1. “Using Wikis for the Introductory Information Systems (IS) Classes,” Fairfield University Faculty Development Day, December 11, 2009. 2. “Application of Critical Thinking Principles to Information Systems Classes,” Fairfield University Dolan School of Business Faculty Meeting, May 2, 2008. 3. “Market Transparency in Business-­‐to-­‐Business (B2B) E-­‐Commerce: An Experimental Analysis,” Fairfield University Dolan School of Business Research Seminar Series, April 23, 2008. 4. “Productivity and Performance Effects of IT-­‐Enabled Reengineering: A Firm-­‐Level Analysis,” Fairfield University Dolan School of Business Research Seminar Series, November 8, 2006. 5. “Incentive Compatible Electronic Loyalty,” Faculty Candidate Seminar, Koç University, Department of Operations & Information Systems, College of Administrative Sciences & Economics, Turkey, December 20, 2004. 6. “Incentive Compatible Electronic Loyalty,” Faculty Candidate Seminar, Sabanci University, School of Management, Turkey, December 20, 2004. 7. “Incentive Compatible Electronic Loyalty,” Faculty Candidate Seminar, Fairfield University, Department of Information Systems & Operations Management, Dolan School of Business, December 6, 2004. 8. “Incentive Compatible Electronic Loyalty,” Faculty Candidate Seminar, University of Tulsa, Department of Management Information Systems, College of Business Administration, October 13, 2004. REVIEWER SERVICES FOR JOURNALS, CONFERENCES, AND BOOKS
2013 1. Management Information Systems for the Information Age (textbook review) 2012 1. Journal of Management Information Systems 2011 1. International Journal of E-­‐commerce 2010 1. Communications of the Association for Information Systems 2. International Conference on Mobile Business 3. International Journal of Project Management (three different papers reviewed) 80 4. Journal of Management Information Systems (two different papers reviewed) 5. M: Information Systems (McGraw-­‐Hill) 2009 1. Decision Sciences Institute (DSI) Annual Meeting 2. Electronic Commerce Research and Applications 3. Encyclopedia of e-­‐Business Development & Management in the Digital Economy (IGI Global) 4. International Journal of Project Management 2008 1. Communications of the Association for Information Systems 2. Decision Support Systems 3. Information Systems and E-­‐Business Management 4. Journal of International Technology and Information Management 5. The Handbook of Technology Management (Wiley) 2007 1. Decision Support Systems 2. Information Systems and E-­‐Business Management 3. International Conference on Information Systems (ICIS) 4. Journal of E-­‐commerce Research 2005 1. European Journal of Operational Research UNIVERSITY AND ACADEMIC SERVICES
University Services
1. Member, Dolan School of Business Strategic Task Force, Fairfield University, Spring 2013. 2. Member, Library Committee, Fairfield University, Spring 2011. 3. Member, Undergraduate Curriculum Committee, Fairfield University, Spring 2010. 4. Member, Educational Technologies Committee, Fairfield University, 2006 – 2009. 5. Member, Dolan School of Business Undergraduate Curriculum Committee, Fairfield University, September 2009 – August 2012. 6. Member, Dolan School of Business Committee on Graduate Admissions, Fairfield University, 2008 – 2011. 7. Member, Dolan School of Business Graduate Programs Committee, Fairfield University, 2006 – 2009. 8. Chair, Graduate Curriculum Development Subcommittee for Department of Information Systems & Operations Management, Dolan School of Business, Fairfield University, Spring 2007 & Spring 2008. 9. Member, Faculty Search Committee for Department of Information Systems & Operations Management, Dolan School of Business, Fairfield University, Fall 2006. 81 10. Member, Undergraduate Curriculum Development Subcommittee for Department of Information Systems & Operations Management, Dolan School of Business, Fairfield University, Spring 2006. 11. Faculty advisor for business undergraduate students, Fairfield University, 2005 – Present. Academic Services
1. Program Committee Member, 9th International Conference on Mobile Business (ICMB 2010) & 9th Global Mobility Roundtable (GMR 2010), Athens, Greece, June 13-­‐15, 2010. 2. TV interview about “Cyber Monday” on the Connecticut 12 TV Channel, November 28, 2005. 3. Session Chair, 7th INFORMS Telecommunications Conference, Electronic Commerce session, Boca Raton, Florida, March 7-­‐10, 2004. 4. United Way Captain, Krannert School of Management Division, Purdue University, Fall 2003. COMPETITIVE RESEARCH GRANTS
1. Dolan School of Business Summer Research Support Award (Summer 2011) •
“Does Outsourcing Information Technology Projects Improve Performance of the U.S. companies?”, awarded $5,000. 2. Fairfield University Summer Research Stipends Program Award (Spring 2009) • “Do Six Sigma Projects Improve Firm Performance? Empirical Evidence from the U.S. Companies”, awarded $3,500. 3. Fairfield University Faculty Research Committee Grant (Spring 2008) • “Impacts of Information Technology Outsourcing on Company Performance: A Firm Level Empirical Analysis”, awarded $1,000. 4. Fairfield University Pre-­‐tenure Research Leave Grant (Fall 2007) •
“Open Source Software Development Networks: An Empirical Analysis of Social Structure and Success Factors”. 5. Dolan School of Business Summer Research Support Award (Summer 2007) • “Market Transparency in Business-­‐to-­‐Business (B2B) E-­‐Commerce: An Experimental Analysis”, awarded $5,000. SPECIAL AWARDS AND HONORS
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Purdue University Graduate Student Award for Outstanding Teaching (Fall 2003) Krannert School of Management Outstanding Graduate Instructor Award (Fall 2003) The Krannert Dean’s Certificate of Recognition for Teaching Excellence (Fall 2002) Full assistantship by Purdue University, Krannert School of Management (1999-­‐2005) Full scholarship by Bilkent University, Department of Mathematics, Turkey (1994-­‐
1999) 6. Graduated with Honor Degree from Bilkent University, Turkey PROFESSIONAL TRAINING
82 1. Jesuit Mission and Identity Gathering, Fairfield University, April 29, 2010 2. Service Learning Roundtable, organized by the Center for Faith and Public Life, Fairfield University, February 17, 2010 3. Jesuit Mission and Identity Gathering, Fairfield University, April 7, 2009 4. Harvard Business School Case-­‐based Teaching Workshop, Bryant University, Smithfield, Rhode Island, March 24, 2009 5. The 6th Pedagogy, Technology & Course Redesign Conference, Fairfield University, Fairfield, Connecticut, June 7-­‐9, 2006 6. Project Management Institute Southern New England Chapter Project Management Conference (SNEC-­‐PMI 2006), Connecticut Convention Center, Hartford, Connecticut, May 23, 2006 COMPUTER KNOWLEDGE
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Programming Languages: Visual Basic, C++, Turbo Pascal, ASP, PHP, HTML, LaTeX. Scientific Computing Software: Maple, Matlab, Mathematica. Statistical Software: SAS, Gauss, STATA, LimDep. Database Management Systems Software: Microsoft Access, SQL, MySQL. Other Software: MikTeX, Microsoft Frontpage, Flash, Adobe Photoshop, Paint Shop Pro. REFERENCES
Prof. Kemal Altinkemer Associate Professor Department of Management Information Systems Krannert Graduate School of Management, Purdue University 403 W. State Street, West Lafayette, IN 47907 Phone: (765) 494-­‐9009 E-­‐mail: [email protected] • Prof. Prabuddha De Accenture Professor of Information Technology and Professor of Management Department of Management Information Systems Krannert Graduate School of Management, Purdue University 403 W. State Street, West Lafayette, IN 47907 Phone: (765) 494-­‐0699 E-­‐mail: [email protected] • Prof. Jackie Rees Associate Professor Department of Management Information Systems Krannert Graduate School of Management, Purdue University 403 W. State Street, West Lafayette, IN 47907 Phone: (765) 494-­‐0320 E-­‐mail: [email protected] •
83 Prof. Christopher Huntley Associate Professor Chair, Department of Information Systems & Operations Management Dolan School of Business, Fairfield University 1073 North Benson Road, Fairfield, CT 06824 Phone: (203) 254-­‐4000 X 2874 E-­‐mail: [email protected] • Additional references are available upon request. •
84 Vishnu Vinekar
Associate Professor of Information Systems & Operations Management
Charles F. Dolan School of Business, Fairfield University, 1073 N Benson Rd Fairfield CT 06824
http://faculty.fairfield.edu/vvinekar
Email: [email protected]
Ph: 203-254-4000 x 2831
Fax: 203-254-4105
EDUCATION Ph.D. in Business Administration
Major: Information Systems, Minor: Management, G.P.A.: 3.875/4.0
University of Texas at Arlington, 2007
Dissertation: “A Two-Order Effect Model of IT Business Value: Theoretical Development and
Empirical Test”
Successfully defended on July 11th 2007.
Master of Science in Information Systems, G.P.A.: 3.909/4.0
Texas A&M International University, 2002
Bachelor of Architecture
Manipal Institute of Technology, India, 1999
ACADEMIC EXPERIENCE Associate Professor, Information Systems & Operations Management,
Fairfield University
September 1st 2014 – Present
Assistant Professor, Information Systems & Operations Management,
Fairfield University
September 1st 2007 – 2014
Graduate Assistant, Information Systems & Operations Management,
University of Texas at Arlington
2003 – 2007
RESEARCH REFEREED JOURNAL PUBLICATIONS At Assistant Professor rank:
Vinekar, V. & Teng, J. T. C. (2012) IT Impacts in Information and Physical Product Industries Journal
of Computer Information Systems, Vol. 53(1), pp. 65-71.
Sow PG, Gaye A, Ka O, Bop M, Kasse Y, Seck M, Vinekar VV, Tellis WM (2012) Global experiences
in Health and Technology Information Communication (TIC) between Fairfield University in
USA and Bambey University in Senegal, Journal of Medicine and Medical Sciences Vol. 3(9)
pp. 546-549.
Sow PG, Coume M, Ka O, Gaye A, Vinekar V (2012) Determinants of Medication Adherence among
People Living with HIV/AIDS in Senegal. International Journal of Modern Biology and
Medicine, 1(3), pp. 156-165
Vinekar, V. & Teng, J. T. C. (2012) The roles of IT in physical and information product industries:
Testing a contingency model of the IT-productivity relationship. Journal of Information
Technology Management, 23(2), pp. 1-16.
Vinekar, V. & Teng, J. T. C. (2012) The Resource Based View of IT Business Value: Complementary
Investments or Embedded Knowledge? Journal of Information & Knowledge Management,
11(1), pp. 1250005 1-13.
Vinekar, V., Langran, E. & Tellis, W. (2011) Using ICT and Service Learning in Rural Senegal: Project
funded by USAID/HED. Issues in Information Systems, 12(1), pp. 464-472
Vinekar, V. & Huntley, Christopher L. (2010) Agility versus Maturity: Is There Really a Trade-Off?
IEEE Computer, 43(5), pp. 87-89.
Vinekar, V., Teng, J. T. C., & Chennamaneni, A. (2009) A Unified Framework of Knowledge
Management and Business Intelligence; Journal of International Technology and Information
Management 18(2), pp. 143-159 - Keynote Paper
85 Lavelle, J., Brockner, B., Konovsky, M., Price, K., Henley, A., Taneja, A., & Vinekar, V. (2009)
Commitment, procedural fairness and organizational citizenship behavior: A multifoci analysis.
Journal of Organizational Behavior, 30(3), pp. 337-357.
Prior to Assistant Professor rank:
Vinekar, V., Slinkman, C., & Nerur, S. (2006) Can agile and traditional systems development approaches
co-exist? An ambidextrous view; Information Systems Management, 23(3), pp. 31-42
REFEREED CONFERENCE PROCEEDINGS At Assistant Professor rank:
Vinekar, V., Langran, E. & Tellis, W. (2011) Using ICT and Service Learning in Rural Senegal: Project
funded by USAID/HED. Proceedings of the 51st Annual International Association of Computer
Information Systems International Conference, October 5-8, 2011 in Mobile, Alabama, USA
Vinekar V. & Tellis, W. (2010) Corporate Social Responsibility and the academic world, Proceedings of
the 2010 Annual Meeting of the Academy of International Business, U.S. Northeast Chapter
Sept. 30-Oct. 2, Quinnipiac University, Hamden, Conn.
Vinekar V. (2008) Strategies for Learning: Reconceptualizing the Strategy-Environment-Performance
Relationship through a Knowledge-Based Perspective, Proceedings of the 39th Annual Meeting of
the Decision Sciences Institute, Nov 22-25 2008, Baltimore, MD
Vinekar, V. (2008) Empowerment of Slum Children in Developing Countries through Information
Technology: Human Capabilities versus Environmental Determinism. Proceedings of the
Fourteenth Americas Conference on Information Systems, Aug. 14-17 2008, Toronto, Canada
Price, K., Henley, A., Lavelle, J., Taneja, A., Vinekar, V. (2008) Multiple Facets of Team Voice: the
Role of Relational Demography, Personality, and Team Structure; Proceedings of the 2008
Annual Meeting of the Academy of Management, August 8-13, 2008, Anaheim, California.
Vinekar, V. (2008) Knowledge Flows in International Business: Review, Integration, and Future
Directions. Proceedings of the Northeast Decision Sciences Institute, Mar. 28-30, 2008,
Brooklyn, New York
Prior to Assistant Professor rank:
Vinekar, V. (2006) Towards a Unified Framework on Outsourcing: Integrating Multiple Theoretical
Viewpoints; Proceedings of the Twelfth Americas Conference on Information Systems, Aug. 4-6
2006, Acapulco, Mexico – Winner Best Paper Award
Vinekar, V.; Gokhale, R.; & Teng, J. T. C. (2006) Motivational determinants of vendor side project
performance on outsourced ISD projects; Proceedings of the Twelfth Americas Conference on
Information Systems, Aug. 4-6 2006, Acapulco, Mexico
Vinekar, V.; Gokhale, R. (2006) Towards a dynamic view for striking a balance in exploitation versus
exploration; Proceedings of the Thirty-seventh Annual Meeting of the Decision Sciences
Institute, Nov. 18-21 2006, San Antonio, Texas
Vinekar, V. (2006) Towards a theoretical framework of information systems development strategy: The
contingent effects of organizational culture and project uncertainty; Proceedings of the Ninth
Annual Conference of the Southern Association of Information Systems, Mar. 11-12, 2006,
Jacksonville, Florida
86 AWARDS • Best Paper Award, Americas Conference on Information Systems 2006
• Lawrence Schkade Award for Excellence in Research, University of Texas at Arlington, 2007
TEACHING
FAIRFIELD UNIVERSITY Course
IS 100: Introduction to Information Systems
IS 500: Information Systems (Graduate)
IS 240: Systems Analysis
IS 350: International Information Systems
IS 220: Technology & Society
IS 300: Special Topics: Web Development
UNIVERSITY OF TEXAS AT ARLINGTON Course
BUSA 2303: Introduction to Information
Systems
First taught
Fall 07
Summer 08
Fall 09
Spring 11
Spring 13
Spring 13
Last taught
Spring 14
Summer 11
Spring 2014
Spring 14
Spring 13
Spring 13
First taught
Fall 04
Last taught
Spring 06
Sections
23
3
6
3
1
1
Semesters
12
3
6
3
1
1
Sections Semesters
4
4
SERVICE UNIVERSITY COMMITTEE MEMBERSHIPS Handbook Committees: • Educational Technologies Committee, Fall 2012 - Present
• Academic Council, Fairfield University, Fall 2011-Spring 2012
• Faculty Development And Evaluation Committee, Fairfield University, Fall 2008 – Spring 2011
• Student Life committee, Fairfield University, Spring 2008 DSB Committees: • DSB Undergraduate Curriculum Committee, Fall 2012 - present • DSB Graduate Programs Committee, Fall 2012 - present Other Committees: • International Studies Coordination Committee, Fairfield University, Fall 2008 – Fall 2011
• Service learning Advisory Committee. Fairfield University, Fall 2012 - Present
SESSION CHAIR • Thirty-seventh Annual Meeting of the Decision Sciences Institute, Nov. 18-21 2006, San Antonio,
Texas
Track:
Business
Value
Generating
Innovative
Technologies
Session:
Decision Sciences
• Thirty-seventh Annual Meeting of the Decision Sciences Institute, Nov. 18-21 2006, San Antonio,
Texas Track: Organizational Behavior/Organizational Theory – Session: Decision Making 87 REVIEWER • Journal of Systems & Software
• European Journal of Information Systems
• Information Systems Management
• Journal of International Technology and Information Management
• International Conference on Information Systems
• Americas Conference on Information Systems
• Decision Science Institute Conference
• International Information Management Association Conference
• Southern Association of Information Systems Conference
88