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AAMITMIT D DEOKAREOKAR
KKELLYELLY F FADELADEL
JJIEXUNIEXUN L LII
JJACINTOACINTO M MAQUERAAQUERA
MMARKARK P PATTONATTON
SSURENDRAURENDRA S SARNIKARARNIKAR
XXIAOYUNIAOYUN S SUNUN
RRONGONG Z ZENGENG
MIS 696A: RMIS 696A: READINGSEADINGS I INN MIS MIS
FINAL PROJECTFINAL PROJECT
FALL 2002FALL 2002
PRESENTEDPRESENTED TOTO: D: DRR. J. JAYAY N NUNAMAKERUNAMAKER, J, JRR..
GGROUPROUP M MEMBERSEMBERS::
MODEL OF MIS, MODEL OF MIS,
KEY RESEARCHERS & CONTRIBUTIONSKEY RESEARCHERS & CONTRIBUTIONS
TTABLEABLE O OFF C CONTENTSONTENTS
Introduction……………………………………………………………………………4Introduction……………………………………………………………………………4
MIS Defined…………………………………………………………………………...6MIS Defined…………………………………………………………………………...6
Our Model………………………………………………………………………...…...7Our Model………………………………………………………………………...…...7
Database………………………………………….…………………………………..11Database………………………………………….…………………………………..11
Overview…………………………………………………………………………11Overview…………………………………………………………………………11
Seminal Works…………………………………………………………………...11Seminal Works…………………………………………………………………...11
Outlook…………………………………………………………………………..13Outlook…………………………………………………………………………..13
Collaboration Technology.…………………………….…………………………….14Collaboration Technology.…………………………….…………………………….14
Overview…………………………………………………………………………14Overview…………………………………………………………………………14
Seminal Works…………………………………………………………………...15Seminal Works…………………………………………………………………...15
Outlook…………………………………………………………………………..19Outlook…………………………………………………………………………..19
Operations Research.…………………………….…………………………………..20Operations Research.…………………………….…………………………………..20
Overview…………………………………………………………………………20Overview…………………………………………………………………………20
Seminal Works…………………………………………………………………...20Seminal Works…………………………………………………………………...20
Outlook…………………………………………………………………………..21Outlook…………………………………………………………………………..21
KM/AI/IR…………………..…………………….…………………………………..22KM/AI/IR…………………..…………………….…………………………………..22
Overview…………………………………………………………………………22Overview…………………………………………………………………………22
Seminal Works…………………………………………………………………...22Seminal Works…………………………………………………………………...22
Outlook…………………………………………………………………………..24Outlook…………………………………………………………………………..24
Economics of Informatics ……………………….…………………………………..25Economics of Informatics ……………………….…………………………………..25
Overview…………………………………………………………………………25Overview…………………………………………………………………………25
Seminal Works…………………………………………………………………...25Seminal Works…………………………………………………………………...25
Outlook…………………………………………………………………………..27Outlook…………………………………………………………………………..27
2
Social Informatics ……………………………….…………………………………..28Social Informatics ……………………………….…………………………………..28
Overview…………………………………………………………………………28Overview…………………………………………………………………………28
Seminal Works…………………………………………………………………...29Seminal Works…………………………………………………………………...29
Outlook…………………………………………………………………………..30Outlook…………………………………………………………………………..30
Human-Computer Interaction…...……………….…………………………………..32Human-Computer Interaction…...……………….…………………………………..32
Overview…………………………………………………………………………32Overview…………………………………………………………………………32
Seminal Works…………………………………………………………………...33Seminal Works…………………………………………………………………...33
Outlook…………………………………………………………………………..34Outlook…………………………………………………………………………..34
Systems Analysis & Design.………..………….………...…………………………..35Systems Analysis & Design.………..………….………...…………………………..35
Overview…………………………………………………………………………35Overview…………………………………………………………………………35
Seminal Works…………………………………………………………………...35Seminal Works…………………………………………………………………...35
Outlook…………………………………………………………………………..37Outlook…………………………………………………………………………..37
Workflow…………………...…...……………….…………………………………..38Workflow…………………...…...……………….…………………………………..38
Overview…………………………………………………………………………38Overview…………………………………………………………………………38
Seminal Works…………………………………………………………………...38Seminal Works…………………………………………………………………...38
Outlook…………………………………………………………………………..39Outlook…………………………………………………………………………..39
Appendix A.………………...…...……………….…………………………………..40Appendix A.………………...…...……………….…………………………………..40
Database References………………………..……………………………………41Database References………………………..……………………………………41
Collaboration Technology References…………………………………………...43Collaboration Technology References…………………………………………...43
Operations Research References…………..……………………………………..48Operations Research References…………..……………………………………..48
KM/AI/IR References……………………....……………………………………49KM/AI/IR References……………………....……………………………………49
Economics of Informatics References…………………………………………...54Economics of Informatics References…………………………………………...54
Social Informatics References……………………….…………………………..61Social Informatics References……………………….…………………………..61
Human-Computer Interaction References………………….……………………67Human-Computer Interaction References………………….……………………67
Systems Analysis & Design References…………...……..……………………...71Systems Analysis & Design References…………...……..……………………...71
Workflow References…..………………………………………………………..75Workflow References…..………………………………………………………..75
3
IINTRODUCTIONNTRODUCTION
Management Information Systems (MIS) is a rich, heterogeneous, and applied discipline.
It constantly intersects with virtually all fields of academic and industrial activity, and, as
such, presents a wide vista of research along many dimensions. MIS research is
commonly and conveniently categorized along sub-domains by prominent research media
and members of the MIS community. We have chosen to adopt such a classification for
the structure of this paper. We have divided MIS into the following sub-domains:
1. Database
2. Collaboration Technology
3. Operations Research
4. KM/AI/IR
5. Economics of Informatics
6. Social Informatics
7. Human-Computer Interaction
8. Systems Analysis & Design
9. Workflow
This classification borrows from work done by Marshall, et al., in The MIS Disciplines,
Founding Papers Current Research, and Future Direction. Admittedly, these sub-
domains cannot hope to completely or tidily classify all MIS research; the very
interdisciplinary nature of MIS gives natural rise to multifaceted research that often spans
logical boundaries. For the purposes of understanding and presenting the development of
the discipline, however, we feel that such classification is useful, if not necessary.
One important difference between our classification and that of Marshall, et al., is that we
do not associate database research with systems analysis and development. While
databases frequently appear on the systems development scene in practice, we feel that
4
underlying principles, research foci, and researchers differ enough among the two to
warrant their separation.
Identifying “key” or “most influential” researchers and papers in MIS is a prodigious
task, involving a fair degree of subjective assessment. Since significant gray area exists
between “core” and “peripheral” research work, there is likely to be some disagreement
as to the importance of many contributions to the discipline. In the interest of
maintaining the appropriate degree of succinctness and manageability, we have attempted
to identify a handful of researchers in each field whose contributions have been
foundational or, at least, highly influential. Our selections are guided by subjective
evaluations and by the number of times a particular work has been cited by subsequent
research.
The main body of the paper is organized along the sub-domains presented above, with
each section divided further into the following sections: overview of the sub-domain,
summary of key researchers and their contributions, and future outlook of the sub-
domain. The paper concludes with an appendix outlining each key researcher, contact
and education information, and seminal papers and contributions. Our aim is to provide a
clear, concise, narrative-style survey highlighting the important events that have shaped
each area, together with developing directions in the research domain. We feel that such
an approach is superior, for purposes of understanding and pedagogy, to a simple
enumeration of researchers and their work.
5
MIS DMIS DEFINEDEFINED
The literature is replete with definitions of MIS. These definitions differ along many
dimensions, but most seem to converge on the idea that MIS involves the dynamic and
often complex interactions between technology, information, people, and organizations.
Worth noting is the distinction between the definition of a Management Information
System (a physical, composite artifact) and Management Information Systems (a
discipline). The difference is illustrated by contrasting the definitions chosen by Lowry,
et al. and Marshall, et al., respectively:
A management information system is the complement of people, machines, and procedures that develops the right information and communicates it to the right managers at the right time. (quoted from Brabbi)
Management Information Systems (sic) is an applied discipline which focuses on how information and information technology is used by, is managed by and affects organizations.
To a great degree, these definitions are mutually reinforcing. What constitutes a
Management Information System is largely determined by the discipline (or confluence
of disciplines) whose aim is to study, define, build, and refine such systems. In a similar
vein, the discipline is guided by real-world, practical requirements of those who
implement and rely on information systems. This is especially true for an applied field
such as MIS; the need for the MIS artifact is, at the core, the raison d’etre of the MIS
discipline.
For our purposes, a hybrid of these definitions is most appropriate. Our focus is on MIS
as a discipline, but it is the multifaceted MIS artifact that has spawned the need to both
draw from and contribute to other disciplines. Our model (described below) depicts the
interaction among MIS and its reference disciplines and the way in which MIS draws
from fundamental principles to produce artifacts used in the application domain. i Brabb, George J. Computers and Information Systems in Business, Houghton Mifflin Co., Boston, (1976), 26-37.
6
OOURUR M MODELODEL
Figure 1 - Model of MIS
The MIS model (Figure 1) has been developed by our team as an effort toward
understanding and visualizing the research in MIS. Our model flows from the need to
capture both the underlying research process in MIS as well as its interdisciplinary
nature. The model is intended for an academic audience and is useful in helping
researchers map their research interests within the field. It is not intended to represent all
the aspects of MIS research, but instead give a more profound picture of MIS as a
discipline.
7
We have built our model as an extension to Dr. Nunamaker’s9 system model (Figure 2),
proposed in his paper Systems development in information systems research. Dr.
Nunamaker’s model captures the essence of the research process in typical MIS research
work, which is the ‘systems development’ process taking place through the interaction of
‘theory building’, ‘experimentation’, and ‘observation’. We understand that this lies at
the core of any research effort; therefore, it is located at the center of our model.
Figure 2 – Dr. Nunamaker’s System Model
The model shows 5 primary regions. The central region characterizes research in MIS
domain, as discussed above. The left region shows the fields related to MIS that can be
characterized as more technical, while the right region shows the fields related to MIS
that can be characterized as more behavioral. These regions depict the multi-disciplinary
nature of MIS research. Collaboration with these fields is an intrinsic part of MIS
research. The top region in the model shows the application domain, which is composed
of the fields for which MIS builds tools. The bottom region shows logic and reasoning,
which is a cardinal factor in any research in MIS.
8
The color transition from red to blue shown in our model depicts the research on a ‘tool-
domain’ spectrum. The systems development process in MIS starts with understanding
the theory or domain related to the research problem (shown by blue color in the model),
going through the iteration and interaction of ‘theory building’, ‘experimentation’, and
‘observation’ and finally building tools or applications for different disciplines or fields
(shown by red color in the model).
The red-to-blue transition depicted in the model captures three important observations
about MIS research. First, the transition is indicative of the dynamic role of MIS in
transforming elements of logic and reasoning into practical applications. MIS was born as
a separate field to achieve a blend of computing and information science and provide
applications/tools for industry and other domains. Our domain-tool spectrum shows this
facet of MIS. This perspective is consistent with Dr. Nunamaker’s view that research in
MIS is inextricably linked to systems building for applications. Second, the spectrum
captures the holistic view in MIS research. For example, any large research effort or a
sub-domain in MIS can be perceived as a process of moving towards the ‘tool’ region of
the spectrum from the ‘domain’ region. Finally, any given research paper can be
classified according to its position on the domain-tool spectrum. For example, consider
the research paper A foundation for the study of group decision support system by
DeSanctis11 and Gallupe12. The paper discusses the technical developments in electronic
communication, computing, and decision support, coupled with new interest on the part
of organizations to improve meeting effectiveness. The sub-domain of collaboration
technology as such, lies more towards the tool side in the domain-tool spectrum, trying to
develop collaboration tools for organizations. At the same time, this paper, which is
addressing a particular research problem, lies on the domain side of the domain-tool
spectrum, since it is trying to present the fundamentals of GDSS, understanding the
domain of group dynamics, decision support, computing technologies.
Another key feature of our model is the technical-behavioral (shown as green-yellow in
the model) spectrum. Lowry, et.al. tried to classify MIS research on the technical-
behavioral scale. Instead of trying to classify research methodologies, we decided to take
9
a different approach. Our model classifies the sub-domains in MIS on a technical-
behavioral spectrum. Although research within any sub-domain can be either technical or
behavioral, the overall research in that sub-domain can be characterized as mainly
technical or behavioral. For example, database research would be classified as more
technical and mathematically rigorous as compared to Human-Computer Interaction,
which is more behavioral and descriptive. Additionally, we perceived MIS research as
interacting dynamically with other fields, which could be technical or behavioral. For
example, MIS research depends heavily on Computer Science, Engineering, etc., which
are technical and mathematically rigorous fields. On the other hand, MIS research also
depends heavily on Management Science, Communication, etc., which are more
behavioral and descriptive fields. We feel that the classifications like technical-behavioral
and rigor-relevance (or descriptive), qualitative-quantitative, etc. are embedded in every
research effort for any sub-domain in MIS. We feel it is more important to show the
comprehensive view of MIS and other closely related fields.
10
DDATABASEATABASE
Overview
Database technology and its applications are, arguably, at the core of MIS and have far-
reaching effects in virtually all other disciplines. Few other research arenas have
experienced rapid growth and pervasive influence comparable to that of database
research. Silberschatzii, et al., observe:
The history of database system research is one of exceptional productivity and startling economic impact. Barely 20 years old as a basic science research field, database research has fueled an information services industry estimated at $10 billion per year in the U.S. alone. Achievements in database research underpin fundamental advances in communications systems, transportation and logistics, financial management, knowledge-based systems, accessibility to scientific literature, and a host of other civilian and defense applications. They also serve as the foundation for considerable progress in basic science in various fields ranging from computing to biology.
The Relational Database Management System (RDBMS) that is prevalent today has
evolved over the past four decades. Magnetic tapes used for data storage in the 1950s
and early 1960s gradually gave way to the hard disk storage media, which gained
widespread use in the late 1960s and early1970s. These disks freed data from the
constriction of sequentiality, and allowed programmers to define data structures, such as
lists and trees, that allowed direct data access. File-based, hierarchical, and network
DBMSs entered the scene. Though they represented definite improvement in data
storage and access capabilities, these DBMSs still suffered from a number of limitations,
including lack of data independence and the necessity of navigational programming.
Seminal Works
A 1970 landmark paper by E.F. Codd1, A Relational Model of Data for Large Shared
Data Banks, introduced the foundational principles of today’s RDBMS. This paper
presented the relational model, together with a non-procedural way of querying data.
ii Silberschatz A., Stonebraker M., Ullman J., Database systems: Achievements and opportunities. Communications of the ACM, 34, 10, (1991), 110-120.
11
Though the relational model posed numerous advantages, it was not immediately adopted
as a technology due to perceived performance deficiencies. However, IBM’s System R
project was eventually undertaken to produce an efficient relational database system.
DB2, a currently-used RDBMS, and the SQL query language evolved from System R.
In 1976 Peter Pin-Shan Chen2 proposed the entity-relationship model in The Entity-
Relationship Model--Toward a Unified View of Data. This model proved an invaluable
tool in incorporating real-world semantic-based relationships in database design. The
entity-relationship diagram, now widely used as a tool for logical database design, was
also proposed. One of the primary strengths of the ER model is that it is simple enough
to readily understand, yet powerful enough for scientific and technical applications. The
ER model has since been extended by many researchers and is still a fundamental tool in
conceptual database design.
The establishment of the relational model and the utility of the ER model, together with
advances in data storage technology and query processing capabilities, have fueled
database research along myriad fronts. Researchers at UC Berkeley, led by Michael
Stonebraker3, began the Ingres project in 1972, which resulted in relational optimization
techniques, a language binding technique, and innovative storage strategies. The Ingres
RDBMS is also a result of this research, and is now owned and distributed by Computer
Associates. The 1986 Postgres project, a spinoff of the Ingres project, became the basis
of a new object-relational system, and subsequently developed into the more robust
Postgres95 and PostgreSQL.
The emergence of object oriented systems in recent years has prompted researchers to
investigate how an object-oriented DBMS might address some of the weaknesses of the
relational model. Won Kim4, an especially prolific researcher in this area, has produced a
number of papers on both relational and object-oriented database systems. Among the
most important are Querying object-oriented databases, which presents a novel language
for querying object-oriented databases, and Integrating an object-oriented programming
12
system with a database system, which explores issues related to the integration of a
relational database system and an object-oriented programming language.
As databases are increasingly used in broad and complex applications, the necessity to
integrate data stored in multiple locations and formats has become paramount. A number
of researchers have examined this problem. Among them are Salvatore T. March5, of the
University of Minnesota, and Sudha Ram6, of the University of Arizona. In Allocating
Data and Operations to Nodes in Distributed Database Design, March, et al., develops a
comprehensive mathematical modeling approach to address issues such as concurrency
control, optimization of retrieval and update operations, and data allocation and
replication. In Heterogeneous Distributed Database Systems, Ram proposes the Unifying
Semantic Model (USM) as a model to achieve semantic reconciliation among
heterogeneous data sources.
Outlook
Though database research is regarded by many as “mature” and “commercialized”, the
future promises many more research problems that will require creative solutions.
Forthcoming research areas include the following:
Support for multimedia objects, including tertiary storage capabilities, new data
types, improved quality of service, multiresolution queries, and user interface
support
New issues regarding distributed information, such as degree of autonomy of
participants, accounting and billing for access to remote data, replication and
reconciliation, data integration and conversion, and ensuring data quality
Emerging uses for database systems, including data mining, data warehousing,
and the management of data repositories containing both data and metadata
Workflow and transaction management issues
Improving ease of use for end users, application programmers, and administrators
13
CCOLLABORATIONOLLABORATION T TECHNOLOGYECHNOLOGY
Overview
The term ‘collaboration’ implies working together, especially in a joint intellectual effort.
Collaboration technologies are a specific class of information systems that facilitate this
process, taking into consideration the group dynamics that is an indispensable part of the
ongoing process. Group Support Systems (GSS) or Group Decision Support Systems
(GDSS) are the fundamental element of Collaboration technologies. In fact, these terms
are used interchangeably in the literature. There is little agreement in the literature about
what constitutes a GSS or Collaborative technology system. DeSanctis11 and Gallupe12
quote Gerrity (1971) in their paper Group decision support systems: A new frontier
(1985), who originally articulated the concept of a Decision Support System (DSS) as a
system that involves “an effective blend of human intelligence, information technology
and software which interact closely to solve complex problems.” DeSanctis and Gallupe,
themselves, built on this idea of DSS to define GDSS as “an interactive computer-based
system which facilitates solution of unstructured problems by a set of decision makers
working together as a group.” They also mention the components of GDSS as hardware,
software, people and procedures, which are arranged to support a group of people,
usually in the context of a decision-related meeting.
Although specific research in Collaboration Technology can be either technical or
behavioral, the overall sub-domain tends towards the behavioral side on the technical-
behavioral scale. This is an observation made after reviewing the literature in this area
and analyzing the research questions. Although building systems is at the core of GSS
research, the effect of these systems is tested by considering their impact on groups.
Hence group dynamics or the behavioral study of groups is an implicit part of GSS
research.
A decade of research in GSS has led to some maturity of the sub-domain and
understanding of some of the key underlying principles. Hence, on the ‘domain-tool’
spectrum, we can say that GSS has moved towards the ‘tool’ side, in general, over the
14
past few years. In spite of this, there are new research problems that are being studied.
These research problems can be said to be near the ‘domain’ side of the ‘domain-tool’
scale.
Seminal Works
Delphi and its potential impact on information systems (1971), authored by Turoff7,
depicts Delphi method as “a method for the systematic solicitation and collation of
informed judgments on a particular topic”. This method, founded by Olaf Helmer and
Norman Dalkey, is a communication structure aimed at producing detailed critical
examination and discussion, not at forcing a quick compromise, in asynchronous
problem-solving groups. The application of Delphi method to build various design
communication systems has been discussed by Turoff in Computer-mediated
communication requirements for group support (1991). The research on Dephi method
occurred concurrently with the GSS research. Though both the methods were trying to
increase efficiency in a group decision making process, the literature seems to shows
differences of opinions amongst both the researchers on various underlying issues. For
example, the latter paper mentions GDSS development being carried out under the
fallacious presumption of automation. Also, it disagrees on the issue of synchronous
problem solving techniques. Their viewpoint is that it is not clear if the claimed
effectiveness of elaborate and expensive decision rooms outperforms a well-structured
‘focus’ group with normal meeting room facilities.
A seminal work in the area of GDSS is the book Decision support systems: An
organizational perspective (1978) by Keen and Morton8. This publication provided a rich
set of perspectives and methodologies for studying decision making, for the years to
follow. The concepts in the book have evolved from two main areas of research: the
theoretical studies of organizational decision-making done at the Carnegie Institute of
Technology during the late 1950s and the early ‘60s and the technical work on interactive
computer systems, mainly carried out at the Massachusetts Institute of Technology. This
laid the foundation for the development of GSS research. Another prominent work, edited
by Morton is the book The corporation of the 1990s: Information technology and
15
organizational transformation (1991), which presents the ‘impact’ of the new
information technologies (IT) on organizations with the goal of determining how the
organizations of the 1990s – and beyond – will differ from then, i.e. 1991. Though this
book is from a decade before, it captures the ways in which advancement in information
systems create an impact on the organizations.
One of the pre-eminent researchers in the field of Collaboration Technology is
Nunamaker9, whose research and publications have created a long-lasting impact on the
field of MIS. Systems development in information systems research (Winter 1990-91) is
one of his significant works in which he proposes a framework to explain the nature of
systems development as a research methodology in Information Science (IS) research.
His paper Electronic meeting systems to support group work (1991) on Electronic
Meeting Systems (EMS) laid the foundation for GSS. The paper depicts how the effects
of the EMS technology are contingent on the situation. A model based on process gains
and process losses is used to explain this, with supporting observations in the field and
the laboratory. Nunamaker et.al’s another key paper Lessons from a dozen years of group
support systems research: a discussion of lab and field findings (Winter 1996-97)
presents an overview of GSS research conducted at The University of Arizona, where
researchers have built 6 generations of group support systems software, conducted over
150 research studies and facilitated over 4,000 projects. The paper also proposes
Groupware Grid, which is a theory-based heuristic model for evaluating the contributions
of groupware technology to team productivity.
Once the GSS concept was formalized with some pioneering works mentioned before,
many researchers have built systems to test it for different applications. The power of
GSS is revealed with such multi-faceted application. For example, Olson’s10 paper titled,
Groupwork close up: a comparison of the group design process with and without a
simple group editor (1993) presents the study with a simple collaborative tool, a shared
text editor called ShrEdit. The paper depicts some interesting observations like the groups
using ShrEdit generated fewer ideas, but apparently better ones. The observations imply
16
that small workgroups can capitalize on the free access they have to a shared workspace,
without requiring a facilitator or a work process embedded in the software.
Another prominent researcher in the arena of Collaboration is DeSanctis11, affiliated with
the Duke University. One of his noteworthy works is the paper titled, A foundation for
the study of group decision support systems (1987) presents a conceptual overview of
GDSS based on an information-exchange perspective of decision making. Three levels of
systems are described, representing varying degrees of intervention into the decision
process. The paper then proposes a multi-dimensional taxonomy of systems as an
organizing framework for research in the area of GDSS. Finally, three environmental
contingencies are identified as critical to GDSS design: group size, member proximity,
and the task confronting the group. Another paper Group decision support systems: a new
frontier (1985), authored by DeSanctis presents an overview of the GDSS concept and
explore issues related to the design, implementation, and study of these systems. It
categories GDSS as decision room, local decision network, teleconferencing, and remote
decision making, based on the proximity of group members. These and other works by
DeSanctis have laid the groundwork for GDSS research.
Gallupe12 et.al’s work titled Electronic brainstorming and group size (1992) summarizes
research to determine whether or not group size has an effect on electronic brainstorming
by using different group size. The authors found that larger groups using GSS indeed
generated more ideas and experienced higher levels of satisfaction than groups that did
not use technology. However, the effects of production blocking and evaluation
apprehension on group performance affected the small groups, due to which there were
very less differences between the 2 experimental groups. Gallupe’s another significant
work is titled Images of information systems in the early 21st century (2000), in which he
presents a “fresh eyes” look at the field of IS, where it is now and where it is going. The
paper acknowledges the infancy of the field of IS, but also appreciates the high rate of
progress in IS as compared to other fields. It presents different metaphors like game,
orchestra, soap opera, machine, garden, and journey to view MIS from different
perspectives. Finally, Gallupe discusses the challenges for IS in the 21st century.
17
Vogel13 is one of the important contributor’s to the GDSS research in MIS. He has often
collaborated with Nunamaker et. al. He was earlier a professor at the University of
Arizona and is now affiliated with the City University of Hong Kong. Group decision
support system impact: Multi-methodological exploration (1990) is one of his significant
publications. The paper documents multi-methodological exploration of the impact of
GDSS. Examples of studies at the University of Arizona have been used to illustrate the
use of six methodologies: mathematical simulation, software engineering (including
prototyping), case, survey, field study, lab experiment, and conceptual
(subjective/argumentative) based on an established taxonomy of MIS research methods.
Vogel et. al hope that through this multi-methodological approach, we can make use of
the best that humans and technology jointly have to offer in addressing complex
questions.
Group & organizational DSS as well as global information technology are important
aspects of Collaboration Technology research. Jarvenpaa14 is a renowned researcher in
this area, affiliated with the University of Texas, Austin. Her paper Is anybody out there?
Antecedents of trust in global virtual teams (1998) is significant in proposing a model for
explaining trust in global virtual teams. A global virtual team is an example of a
boundaryless network organization form where a temporary team is assembled on an as-
needed basis for the duration of a task and staffed by members from different countries.
The paper shows, through an experiment in a global virtual team, that in the early phases
of teamwork, team trust is predicted strongest by perceptions of other team members’
integrity, and weakest by perceptions of their benevolence. The effect of other members’
perceived ability on trust is seen to decrease over time. The members’ own propensity to
trust is observed to have a significant, though unchanging, effect on trust.
An important aspect of Collaboration Technology is its use in practice. Wanda
Orlikowski15, a key researcher at the Sloan School, MIT has been instrumental in this
area. She has made significant contributions with publications like Improvising
organizational transformation over time: a situated change perspective (1996) and Using
18
technology and constituting structures: a practical lens for studying technology in
organizations (2000).
Sara Kiesler16 has been doing extensive research on the social and behavioral aspects of
computers and computer-based communication technologies over the past decade or so.
Her experiments show that compared with a face-to-face meeting, a computer-mediated
discussion leads to delays; more explicit and outspoken advocacy; “flaming;” or more
equal participation among group members; and more extreme, unconventional, or risky
decisions. These results have been published in her paper titled, Group decision making
and communication technology. Internet paradox revisited is another of her key
publications, where she shows how Internet tends to be consistent with a “rich get richer”
model by studying the impact of the internet on the society.
Outlook
Over the last 2 decades, research has helped in establishing a strong foundation for
collaborative technologies and computer-mediated communications. As we advance into
the new millennium, the use of these technologies would cause more and more impact on
the society. Virtual organizations seem to be a clear goal for the near future.
Advancement in technology like web services, etc. would certainly aid distributed
collaboration. However, research is still in its infancy for such applications. Facilitation
in a distributed collaboration, semantic barriers due to languages, etc. are the key issues,
which are being addressed by the current research in this area.
19
OOPERATIONSPERATIONS R RESEARCHESEARCH
Overview
The term operations research (or management science) means a scientific approach to
decision making, which seeks to determine how best to design and operate a system,
usually under conditions requiring the allocation of scarce resources. Operations research
covers a large number of topics, including operations management, logistics, supply
chain, decision sciences, scheduling, material resource planning etc. It has applications in
a wide range of industries, including manufacturing, telecommunications, information
systems, finance and transportation. The domain of operations research is closely related
to MIS domain. Operations research is a predominantly technical domain dealing
primarily with optimal allocation and utilization of resources. Some of the research areas
in MIS heavily related to concepts and tools from operations research are supply chain
management, operations management and decision support systems. As areas of applied
and theoretical operations research (i.e. computational complexity, approximation
algorithms, mathematical programming) develop and mature, they become useful tools
aiding further research in MIS-related areas like supply chain and decision support
systems.
Seminal Works
An important milestone in the history of operations research was the development of the
simplex algorithm by George Dantzig19 in 1947. In his paper on linear programming
Dantzig detailed an algorithm to solve a set of linear equations to optimize an objective
function. The simplex algorithm is the single most widely used algorithm in operations
research and has led to rapid development of the domain. Another development in the
field of operations research can be traced to Stephen Cook’s20 paper on computational
complexity, where he defines a way to identify and prove complex problems. This led to
rapid developments in identification of complex problems and their solutions. Some
current research in operations focuses on the effect of information technology on
processes. One such area is supply chain management. New developments in IT,
20
reduction in cost, and increase in speed of information are all affecting various industry
processes. Hau Lee’s18 paper on E-Commerce studies this phenomenon. The
convergence of IT and operations research has created many new research avenues for
MIS. Hau Lee at Stanford and Marshall Fisher17 from Wharton School are the leading
researchers in the area of information technology and operations research.
Outlook
As detailed in Marshall Fisher’s paper on information sharing and inventory
management, combining information technology with operations research can result in
huge benefits and cost savings to organizations. This is likely to be a major research area
for the next few years.
21
KM/AI/IRKM/AI/IR
Overview
Knowledge Management is the collection of processes that govern the creation,
dissemination, and utilization of knowledge. Research within MIS seeks systems and
managerial approaches to collecting, processing, and organizing the intellectual assets for
business functions and decisions. The system, or technology, provides the basis for the
managerial approach, which, in turn, defines the way to use the technology. Knowledge
management draws from a wide range of disciplines and technologies, such as Artificial
Intelligence and Information Retrieval. In our model, knowledge management is located
closer to the middle area between the technology side and behavior side, since it is based
on both of them.
Artificial Intelligence (AI) is the science and engineering of making intelligent machines,
especially intelligent computer systems. AI by itself is a vast, multi-disciplinary field of
research which developed in parallel with computer science and software engineering
while also building on and overlapping with other subjects like linguistics, philosophy,
psychology, biology, mathematics, and logic. Information retrieval (IR) seeks systems for
indexing, searching, and recalling data, particularly text or other unstructured forms in a
collection. In the past 20 years, the area of information retrieval has grown well beyond
its primary goals. Nowadays, research in IR includes modeling, document classification
and categorization, systems architecture, user interfaces, data visualization, filtering,
languages, etc. In our model, both AI and IR are located in the technology side because
their development coincides with the development of other technology, such as computer
sciences and engineering.
Seminal Works
Knowledge management evolved to be official discipline in the 1990s. In 1990, Peter
Senge26 popularized the "Learning organization" in The Fifth Discipline: The Art and
Practice of the Learning Organization. He described the organization as an organism
22
with the capacity to enhance its capabilities and shape its own future. Almost at the same
time, Peter Drucker21 identified knowledge as the new basis of competition in the modern
society and Stanford professor Paul Romer25 called knowledge the only unlimited
resource, the one asset that grows with use. Although a number of management theorists
have contributed to the evolution of KM, Ikujiro Nonaka22 and Hirotaka Takeuchi23 made
knowledge management an official discipline. In 1995, they introduced the concept of a
“knowledge company” in their publication The Knowledge-Creating Company, which is
considered a groundbreaking study of knowledge generation. Later, Thomas Davenport24
investigated effective mechanisms by which an organization can promote knowledge
sharing and keep hold of the knowledge in its premises. The concept of knowledge
management has become more prevalent in business practices and other areas. KM uses
tools from other disciplines, including AI, data mining, data warehousing, digital library
and information visualization. The research within MIS fosters involvement of these key
related disciplines in knowledge management.
Compared to KM, AI has a relatively long history of evolution. In the 1940s, Warren
McCulloch and Walter Pitts proposed a model of artificial neurons with which they
suggested that suitably defined networks could learn. Their work is now generally
recognized as the first work of AI. Norbert Wiener27, the first American to make
observations on the principle of feedback theory, suggested that all intelligent behavior
was the result of feedback mechanisms, or conditioned responses, and that it was possible
to simulate these responses using a computer. In 1951, Marvin Minsky31 and Dean
Edmonds built the first neural network computer. In 1955, Allen Newell28 and Herbert
Simon29 developed The Logic Theorist, which became an essential step in developing AI.
In 1956, John McCarthy30 organized a conference for “The Dartmouth summer research
project on artificial intelligence” to attract others interested in machine intelligence. This
was where the term "Artificial Intelligence" was adopted. In 1957, the General Problem
Solver (GPS), by Newell and Simon, was tested. GPS was an extension of the feedback
mechanism, and was capable of solving a wide variety of common-sense problems. In
1958, McCarthy developed LISP, a programming language widely used by AI developers
that allowed computer programs to operate upon themselves. By the 1970s, computer
23
programs were developed to emulate human-like activities, such as games and puzzles. In
1980s, expert systems were created that could predict the probability of a solution under
set conditions. In the 1990s, AI techniques have been applied to more business practices,
including fraud-detection, financial prediction, and customer behavior analysis.
IR has evolved through several generations. In the 1960s, Hans Peter Luhn32 proposed the
representation of a document by statistical information about the distribution of its words.
Since then, many automatic methods for indexing have been developed based on his early
work. In the 1970s, Gerald Salton33 proposed vector space model, which became the
foundation for representing documents in modern IR systems and web search engines.
After the1980s, more techniques were adapted for information retrieval. For example, as
proposed by Karen Sparck Jones34, Natural Language Processing (NLP) has been used to
automatically generate concept thesauri, generate document summaries, handle natural
language queries, and reduce the feature space for vector space models. Other AI
techniques such as neural networks and genetic algorithms have also been used in IR.
Outlook
Knowledge management is still new to many organizations. While KM is fueled by more
studies and examples that demonstrate a linkage between knowledge management and
reduced costs or increased revenues, the KM service market will attract more investment.
The development of new IT techniques, including e-portals, work flow, data warehouses,
data mining, intelligent agents and other techniques from AI and IR, will also accelerate
the growth of KM. IDC predicts the service market of KM to be worth $8 billion dollars
by next year. This challenges MIS researchers with the requirement of appropriate
managerial models that can direct the application of the cutting-edge IT techniques in
different organizational contexts. New AI and IR techniques are being used more and
more, not only in business but also in areas such as medicine, biology, and education.
The application of AI and IR techniques to multiple applications in various domains
requires that MIS researchers be able to identify the specific requirements and constrains
associated with each domain. Even though uncertainty exists ahead, the development of
KM, AI and IR will bring exciting opportunities for future success.
24
EECONOMICSCONOMICS O OFF I INFORMATICSNFORMATICS
Overview
We define the Economics of Informatics as the study of how economic efficiencies are
impacted by the application of information technologies to business functions. In
practice, this breaks down into multiple overlapping areas:
The Economics of Informatics – The Business Perspective,
The Economics of Informatics – The Market Perspective, and
The Economics of Informatics – The Developers Perspective.
Due to the close relations between these different areas, many researchers have worked in
more than one area. Relating this to the overall model of MIS, the Economics of
Informatics is depicted as being analytical versus being applied, and as balanced between
behavioral and technical.
We deliberately avoid using the term E-Commerce to define any portion of the overall
information systems model or the economics of informatics, as the term is overly
ambiguous and can generally be construed to cover any business interaction that utilizes
information technology. The term can not only be used to describe economic areas of
study, but can also be applied to organizational, operational, and systems portions of the
MIS discipline.
Seminal Works
The Economics of Informatics – The Business Perspective
The study of the Economics of Informatics began with the study of the economics of
applying information technology to business issues. In 1985, Haim Mendelson359 wrote a
seminal paper on computer services, Pricing Computer Services – Queuing Effects, and
in 1986 Timothy Bresnahan44 wrote Measuring the Spillovers from Technical Advance -
Mainframe Computers in Financial Services.
25
The examination of the economics of information technology investments continued
rapidly. In 1991, Eric Clemons37 wrote Evaluation of Strategic Investments in
Information Technology; in 1993 Eric Brynjolsson36 wrote The Productivity Paradox of
Information Technology, which was followed by a collaborative effort between
Brynjolsson and Lorin Hitt46 in 1996, Paradox lost? Firm-level evidence on the returns to
information systems spending. At about the same time, Tridas Mukhopadhyay49, Charles
H. Kriebel48, and Anitesh Barua43 wrote the 1995 paper, Information Technologies and
Business Value - An Analytic And Empirical-investigation, and Tridas Mukhopadhyay
collaborated on the 1995 article, Business Value Of Information Technology - A Study Of
Electronic Data Interchange.
The Economics of Informatics – The Market Perspective
As businesses applied informatics to new processes, it became apparent that there would
be market effects, both in terms of existing markets and emerging new markets. Thomas
Malone38 is considered by many to be the father of this research field. In 1987 he
coauthored Electronic Markets and Electronic Hierarchies: Effects of Information
Technology on Market Structure and Corporate Strategies. Vijay Gurbaxani45 then
coauthored The Impact of Information-systems on Organizations and Markets in 1991.
The emerging Internet age saw the introduction of a number of works in this field,
starting with Electronic commerce: building blocks of new business opportunity, which
was written in 1996 by Andrew B. Whinston42 along with 4 other authors, and The
Emerging Role of Electronic Marketplaces on the Internet, written in 1998 by Yannis
Bakos35. Following close behind were Carl Shapiro40 and Hal Varian41, who in 1999
collaborated to write Versioning: The smart way to sell information, and a Yannis Bakos
and Eric Brynjolsson collaboration, Bundling Information Goods: Prices, Profits, And
Efficiency.
The Economics of Informatics – The Developers Perspective
At the same time work was being done on the economic effects of informatics
technology, a limited amount of work was also underway on developing the technologies
26
themselves. This field of research was significantly strengthened by Chris F. Kemerer’s47
1987 article, An Empirical Validation of Software Cost Estimation Models.
The Economics of Informatics – The Overall Perspective
Needless to say, some effort had to be made to provide a complete perspective on all the
different knowledge that had been gained from the collision of information systems,
Internet technologies, and their applications to economic efficiency and commerce. So, in
1998 Andrew B. Whinston coauthored the book, Frontiers of Electronic Commerce,
which covered many of these topics.
Outlook
In the future, a more formal distinction between economics and the economics of
informatics is likely to emerge. By definition, informatics is an applied field, and,
consequently, the study of its economics is also an applied field. Thus, more rigorous
research showing the impact of information technology investments, especially from the
business perspective, is needed. This research will be characterized by better research
methodologies and a more thorough accounting of “uncontrolled” business variables.
In the field of markets, new market mechanisms will undoubtedly undergo rigorous study
and definition, followed in many instances by trial applications as firms continue to seek
a competitive advantage. Market research is likely to be further segregated between
economists studying new “pure” market forms and MIS researchers investigating these
markets on an applied basis. Governmental incorporation of new market mechanisms is
also likely to emerge for purposes such as frequency auctions.
Similar growth in research of economics of developing informatics tools is, at this point,
uncertain. Although work will continue to be done, this area doesn’t appear to have
nearly the support enjoyed by the business and market research segments.
27
SSOCIALOCIAL I INFORMATICSNFORMATICS
Overview
Social Informatics (SI) refers to the body of research and study that examines social
aspects of computerization -- including the roles of information technology in social and
organizational change and the ways that the social organization of information
technologies are influenced by social forces and social practices. SI includes studies and
other analyses that are labeled as social impacts of computing, social analysis of
computing, studies of computer-mediate communication (CMC), information policy,
"computers and society," organizational informatics, interpretive informatics, and so on.
28
Social Informatics
Information Policy
PrivacySecurity
Intellectual Property
E-governmentE-voting
Social impact of computerization
Computer Phobia
Organizational informatics
Computer-mediated communication
Figure 3 – Social Informatics
As shown in Figure 1, research of social informatics is often combined with other
behavioral research such as management, arts & humanities, communication,
psychology. These disciplines help to define what an information system is with respect
to the impact on human beings.
Seminal Works
It is often assumed that SI started with the Internet. However, it actually began with
studies of computerization in workplaces and organizations that date back to the early
1970s, although the specific label of “social informatics” was not yet being used (Kling,
Rob50. 1980. Social Issues and Impacts of Computing: From Arena to Discipline). The
term “social informatics” first came into popular use in North America in 1996, and both
integrated and built on bodies of research that were previously known by labels such as
"computers and society," "social impacts of computing," "social issues of computing,"
"social analysis of computing," and "behavioral information systems" (Kling, Rob.
1999. What is Social Informatics and Why Does it Matter?).
In the 1980s, the range of topics studied in this area expanded to include new types of
issues. Examples include studies on the extent to which people would communicate
more or less effectively with organizational e-mail systems. (Kiesler, Sara & Sproull,
Lee57. Reducing Social-Context Cues: Electronic Mail in Organizational communication
and A two-level perspective on electronic mail in organizations) The authors used ideas
about how social context cues within a communication setting affect information
exchange, the paper argues that electronic mail does not simply speed up the exchange of
information but leads to the exchange of new information as well. The authors explored
effects of electronic communication related to self-absorption, status equalization, and
uninhibited behavior.
With regard to information policy, Dorothy E. Denning60, Pamela Samuelson61, Kenneth
L. Kraemer52 and Mary J. Culnan53 are the representative researchers who have
contributed much to this domain. Dorothy E. Denning’s Cryptography and Data Security
is the first book on the topic of cryptography in the field, and is heavily cited. The book
29
deals with cryptography from an algorithms approach. Data security is also covered,
especially for secure operating systems. Reviews on Amazon list the book as essential
introduction into cryptography. In 1994, Pamela Samuelson’s paper Copyright’s Fair use
Doctrine and Digital Data appeared in Communications of the ACM. In this paper,
Samuelson defines four factors that are considered when determining whether a copyright
infringement has taken place.
Outlook
There are many new areas where IT plays a central role, such as distance education,
knowledge management, the formation of online support groups, efforts to support
"virtual teams" in organizations, development of "collaboratories" to support scientists
who work at large distances from each other, and e-commerce. Social informatics
researchers have learned that each of these areas involves not only the provision of
appropriate technologies, but also subtle social behavior that has to be understood and
taken into account. If this does not happen, potential problems may not simply involve
nonuse, but may result in broader negative consequences of poor design, such as students
in a distance education class losing interest in a whole topic due to frustration. Issues
such as these make social informatics a crucial research field in the successful human
adoption of numerous technologies. Following are some of the defining research
problems of social informatics:
Kling, R. Critical Professional Discourses About Information and Communications
Technologies and Social Life in the U.S. in Human Choice and Computers: Issues of
Choice and Quality of Life in the Information Society, Kluwer Academic Publishers,
(2002), 1-20.
Kling R.; and Callahan, E. Electronic Journals, the Internet, and Scholarly
Communication in Annual Review of Information Science and Technology (ARIST),
37, (in press).
30
Kling, R.; and McKim, G. A Bit More to IT: Scholarly Communication Forums as
Socio-Technical Interaction Networks. Journal of the American Society for
Information Science, (November 2002).
Kling, R.; Fortuna, J.; King, A. The Real Stakes of Virtual Publishing: The
Transformation of E-BioSci into PubMed Central. (under review).
Noam, E.M. Ownership and Concentration of American Media ( forth coming).
Denning, D.E. Is Cyber Terror Next? essays after September 11, Social Science
Research Council, (November 2001).
Toward a "New Deal" for Copyright for an Information Age. Forthcoming in 100
Michigan L. Rev., (2002).
31
HHUMANUMAN-C-COMPUTEROMPUTER I INTERACTIONNTERACTION
Overview
Human-computer interaction (HCI) is a discipline concerned with the design, evaluation
and implementation of interactive computing systems for human use and with the study
of major phenomena surrounding them. As its name suggests, the research domain of
HCI is at the interface of human and computer research, which are respectively related to
the behavioral and technical research of MIS.
HCI can be categorized into five interrelated sub-fields, as follows: (N) the nature of
human-computer interaction, (U) the use and context of computers, (H) human
characteristics, (C) computer system and interface architecture, (D) the development
process, and (P) project presentations and examinations. Some of the interrelationships
among these topics are represented in the following figure.
Figure 4 - Human-Computer Interaction
32
Seminal Works
Human-computer interaction arose as a field from intertwined roots in computer graphics,
operating systems, human factors, ergonomics, industrial engineering, cognitive
psychology, and the systems part of computer science. Organizations and researchers
have aimed at different targets in these research areas. Though it is clearly impossible to
list every key person and milestone in the history of HCI research in a selection of this
scope, our motivation is to give an overview of this interesting research field.
There has been a mistaken impression that much of the important work in HCI occurred
in industry, and that if university HCI research is not supported, industry work will carry
on anyway. This is simply not true. Actually, many of the most famous HCI successes
developed by companies are deeply rooted in university research.
In this paper, we list several marked publications, most of which are from university
research. In 1983, Stuart K. Card68 et al. wrote the book, The Psychology of Human-
Computer Interaction, discussing the design of human-computer interfaces from a
perspective of psychology. E. Tufte67 in his book, The Visual Display of Quantitative
Information (1983), teaches some basics on how to most effectively present quantitative
information using various sorts of graphs and charts. In 1986, Ben Shneiderman64, in his
book, Designing the User Interface: Strategies for Effective Human-Computer
Interaction, offers practical techniques and guidelines for interface design, discusses
underlying issues, and supports conclusions with empirical results. G.W. Furnas’s70
paper, Generalized fisheye views (1986), explores fisheye views presenting, in turn,
naturalistic studies, a general formalism, a specific instantiation, a resulting computer
program, example displays and an evaluation. Donald Norman’s66 book, The Design of
Everyday Things (1989), introduces the new knowledge gained by the discipline,
documents our inability to make good gadgets, and shows how the former can help fix
the latter. Additionally, Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman wrote
the groundbreaking book, Readings in Information Visualization: Using Vision to Think,
defining the emerging field of information visualization and offering the first-ever
33
collection of 47 classic papers of the discipline, with introductions and analytical
discussions of each topic and paper.
Outlook
We expect that the future of HCI research will unveil some of the following
developments:
Ubiquitous communication
High functionality systems
Mass availability of computer graphics
Mixed media
High-bandwidth interaction
Large and thin displays
Embedded computation
Group interfaces
User Tailorability
Information Utilities
34
SSYSTEMSYSTEMS A ANALYSISNALYSIS A ANDND D DESIGNESIGN
Overview
System analysis concerns the analysis of an existing or proposed system, which helps in
determining the managerial information requirements of the system. The analysis phase
also identifies and evaluates the benefits to be derived through the computerization of
that system. Systems design phase involves the proposing a feasible and “good” design
for the improved system. It also involves the development of the system’s specifications
for programming. The systems analysis phase, then, produces the system’s logical design,
followed by the systems design phase, which produces the system’s physical design. The
analysis and the design phases may also be referred to as systems engineering and
software engineering respectively.
35
Seminal Works
Ludwig von Bertalanffy73 first introduced systems development as a formal discipline
through various lectures, which he presented in the 1930s, and his various publications
after World War II. He referred to the discipline as General Systems Theory. Modern
systems analysis and design still follows the basic framework presented by Bertalanffy 70
years ago.
It was perhaps only in the 1970’s, however, that systems analysis and design started to
take shape as a rigorous academic field. This can be attributed to J. Daniel Couger74,
whose publication, Evolution of System Development Techniques, presented the software
development lifecycle, essentially still in use today. Couger also provided content to the
framework first proposed by von Bertalanffy.
Advances in software programming also influenced advances in systems development. In
the 1960’s, Donald Knuth and E. W. Dijkstra developed the structured programming
methodology to facilitate the development of software for systems, specifically for highly
complex systems. The principles behind structured programming were later adopted for a
systems development methodology now referred to as structured analysis and system
design (SASD). In Structured Analysis (SA): A language for communicating ideas,
Douglas Ross75 outlined how the language of structured analysis can be adapted for
systems analysis. The role of systems analysis in the SA framework marks the
significance of Ross’ paper. The goal of structured analysis, Ross stipulates, is to derive a
structured, modular model of the system.
Larry Constantine76 laid the foundation for the design side of SASD, in association with
co-workers at IBM, Wayne Stevens and Glenford Myers. Edward Yourdon83 through his
book, The Practical Guide to Structured System Design, later popularized SASD. This
book compiled all of the important ideas of the then existing design techniques along
with the structured analysis.
More recently, a new way of looking at analysis and design has become popular - Object
Oriented Analysis and Design (OOAD). While similar to SASD, it looks at a system from
36
a different viewpoint. OOAD views the systems in terms of objects as opposed to
functions or processes as in SASD.
Like SASD, OOAD has its roots in programming techniques. Ole-Johan Dahl77 and
Kristen Nygaard78 introduced object-oriented concepts through the SIMULA
programming language. Object oriented analysis and design then became established
through the Unified Modeling Language (UML) an object oriented modeling language
that has been approved as a standard by the Object Management Group (OMG). Rational
Software co-workers Grady Booch79, Ivar Jacobson80 and James Rumbaugh81 developed
the UML.
Computer aided software analysis and design has also helped to facilitate systems
development. Perhaps the first significant attempt at automating systems analysis and
design was the Information System Design and Optimization System (ISDOS) system,
established by Daniel Teichroew82 in 1967. The project to develop ISDOS started at Case
Western Reserve University and was later continued at the University of Michigan. Jay
Nunamaker, founder of the University of Arizona MIS department, was also an ISDOS
co-founder.
Outlook
Very diverse and highly distributed systems are finding extensive applications, primarily
because of the popularity of the internet. The development of more formal analysis and
design specifications for existing system development methodologies should help
facilitate the development of such complex system environment. The OMG, for example
is now considering the addition of a more formal, mathematical description language for
the UML. An example of such a formal mathematical approach is the PSL/PSA feature of
the Teichroew’s ISDOS. Consequently, new problems may end up being resolved by
classic solutions.
37
WWORKFLOWORKFLOW
Overview
Workflow research is a very active field, perhaps because of keen commercial interest in
workflow applications. Businesses over the past decade have discovered workflow
applications to be very effective productivity tools.
Seminal Works
Although workflow researchers are very prolific, the field is relatively young;
consequently, it is still too early to identify the most influential publications in the field.
Still, certain researchers may be considered noteworthy, and they are identified in the
appendix. Many advances have also been made in the field, and some degree of
consensus has been reached in terms of what workflow actually means, primarily through
the efforts of the Workflow Management Coalition (WFMC). The WFMC defines
workflow as:
The automation of a business process, in whole or part, during which documents, information or tasks are passed from one participant to another for action, according to a set of procedural rules.
The implementation of a workflow system allows users to automate routine processes.
This allows users to attend to more “value added” tasks, although they still need to pay
attention to exceptions generated by the workflow system.
Current workflow systems may be classified into one of the following 3 different types:
Image-based Workflow Systems, which automate the flow of paper
through an organization, by transferring the paper to digital "images" (first
workflow systems).
Form-based Workflow Systems, which intelligently route forms through
an organization. They are text-based and consist of editable fields, unlike
images.
38
Coordination-based Workflow Systems, which facilitate the completion of
work by providing a framework for coordination of action. These systems
help facilitate business processes.
It is common for an image-based workflow to be a subsystem of a form-based workflow
which, in turn, may be a subsystem of a coordination-based workflow system.
Outlook
Future work will probably involve techniques for workflow verification, which find
logical errors in a workflow design and provide guidelines to help the designer correct
errors. Workflow performance improvement will also likely be the issue that will be
addressed. Design methodologies, such as ones that use Petri nets, are now active and
will probably continue to be so. Workflow systems, with the extensive applications of
internet, need to be designed in a way similar to Petri nets, which provide a framework
for distributed and concurrent systems.
39
DDATABASEATABASE R REFERENCESEFERENCES
1. Name: E.F. Codd
Graduate Education (PhD): University of Michigan, 1963
Organization: Retired, IBM Research Laboratory San Jose, California
Research Interests: Dr. Codd invented the relational data model in a series of
research papers published commencing in 1970. The
relational data model is particularly well suited for business
data management. In this model, data are organized into
tables. The data can be manipulated using a relational
algebra. SQL is a standard language for talking to a
relational database. Dr. Codd also introduced the concept
and rules of data normalization.
Key publication:
Codd, E.F. A Relational Model of Data for Large Shared Data Banks.
Communications of the ACM, 13, 6, (1970), 377-387.
2. Name: Peter Pin-Shan Chen
Graduate Education (PhD): Harvard University, 1973
Organization: Louisiana State University, Department of Computer Science
Contact Info: Tel: (225) 578-1495, Email: pchen@lsu.edu
Research Interests:
Key publication:
Chen, P.P. The Entity-Relationship Model: Toward a unified view of data.
TODS, 1, 1, (1976), 9-36.
3. Name: Michael Stonebraker
Graduate Education (PhD): University of Michigan
Organization: University of California, Berkely
Contact Info: Tel: (510) 780-1700, Email: mike@cohera.com
41
Research Interests: DBMS support for visualization environments and next-
generation distributed DBMSs
Key publication:
Stonebraker, M. The design and implementation of INGRES. ACM, 1, 3,
(1976), 189-122.
4. Name: Won Kim
Graduate Education (PhD): University of Illinois in Urbana-Champaign, 1980
Organization: Cyber Database Solutions
Contact Info: Tel: (512) 349-9757, Email: info@cyberdb.com
Research Interests: Relational, Object-Oriented, & Object-relational database
systems, Data Warehousing, Business intelligent systems
(OLAP, Data Mining), Internet software infrastructure
technology (HTML/XML, e-Commerce systems, etc.)
Key publication:
Won, K. Integrating an object-oriented programming system with a
database system. ACM Conference Proceedings, (1988), 142-152.
Kifer, M.; Won, K.; and Sagiv, Y. Querying object-oriented databases.
Proceedings of the ACM SIGMOD, (1992).
5. Name: Salvatore T. March
Graduate Education (PhD): Cornell University, 1978
Organization: Vanderbilt University, Owen Graduate School of Management
Contact Info: Tel: 615-322-2534, Email: salvatore.t.march@Vanderbilt.edu
Research Interests: Information System Development, Electronic Commerce,
Logical and Physical Database Design, Distributed Database
Design, and Object-Oriented Languages, Development
Tools, and Methodologies
42
Key publication:
March, S. T.; and Rho, S. Allocating Data and Operations to Nodes in
Distributed Database Design. IEEE Transactions in Knowledge and Data
Engineering, 72, (1995), 305-317.
6. Name: Sudha Ram
Graduate Education (PhD): University of Illinois at Urbana-Champaign, 1985
Organization: University of Arizona
Contact Info: Tel: 520-621-2748, Email: ram@bpa.arizona.edu
Research Interests: Interoperability among Heterogeneous Database Systems,
Semantic Modeling, Data Allocation, Schema and View
Integration, Intelligent Agents for Data Management, and
Tools for database design
Key publication:
Ram, S. Heterogeneous Distributed Database Systems. IEEE Computer,
24, 12, (1991), 7-11.
CCOLLABORATIONOLLABORATION T TECHNOLOGYECHNOLOGY R REFERENCESEFERENCES
7. Name: Murray Turoff
Graduate Education (PhD): Brandies University, 1965.
Organization: Information Systems Dept., NJIT, Newark, NJ.
Contact Info: Tel.: (973) 596-3366, E-mail: turoff@njit.edu
Research Interests: Computer mediated communication systems, delphi design,
collaborative systems and group decision support systems,
social impacts of computer and information systems.
Key publication:
Turoff, M. Delphi and it potential impact on information systems. AFIPS
Conference Proceedings, Fall Joint Computer Conference, 39, (1971),
317-326.
43
Turoff, M. Computer mediated communication requirements for group
support. Journal of Organizational Computing, 1, (1991), 85-113.
8. Name: Michael S. Scott Morton
Graduate Education (PhD): Harvard University, 1967.
Organization: MIT Sloan School of Management, Cambridge, MA.
Contact Info: Tel.: (617) 253-2676, E-mail: mssm@mit.edu
Research Interests: Corporate strategy, strategic options, information technology.
Key Publications:
Morton, M.S.S.; and Keen, P.G.W. Decision support systems: an
organizational perspective. Addison-Wesley, Boston, (1978).
Morton, M.S.S.; editor, The corporation of the 1990s: Information
technology and organizational transformation. Oxford University Press,
(1991).
9. Name: Jay F. Nunamaker, Jr.
Graduate Education (PhD): Case Western Reserve University, 1969.
Organization: Department of Management Information Systems, The University
of Arizona, Tucson, AZ.
Contact Info: Tel.: (520) 621-4475, E-mail: nunamaker@bpa.arizona.edu
Research Interests: Computer supported collaboration and decision support to
improve productivity and communication.
Key Publications:
Nunamaker, J.F., Jr.; Dennis, A.R.; Valacich, J.S.; Vogel, D.R.; and
George, J.F. Electronic meeting systems to support group work.
Communications of the ACM, 34, 7 (July 1991), 40-61.
Nunamaker, J.F., Jr; Chen, M.; and Purdin, T.D.M. Systems development
in information systems research. Journal of Management Information
Systems, 7, 3 (Winter 1990-91), 89-106.
Nunamaker, J.F., Jr.; Briggs, R.O.; Mittleman, D.D.; Vogel, D.R.; and
Balthazard, P.A. Lessons from a dozen years of group support systems
44
research: a discussion of lab and field findings. Journal of Management
Information Systems, 13, 3 (Winter 1996-97), 163-207.
10. Name: Judith S. Olson
Graduate Education (PhD): University of Michigan, 1969.
Organization: School of Information, Computer Information Systems, Dept. of
Psychology, University of Michigan, Ann Arbor, MI.
Contact Info: Tel.: (734) 647-4606, E-mail: jsolson@umich.edu
Research Interests: Computer supported cooperative work, small group behavior,
cognitive psychology, human computer interaction, business
process re-engineering.
Key Publication:
Olson, J.S.; Olson, G.M.; Storrosten, M.; Carter, M. Groupwork close up:
a comparison of the group design process with and without a simple group
editor. ACM Transactions on Information Systems (TOIS), 11, 4 (1993),
321-348.
11. Name: Gerardine DeSanctis
Graduate Education (PhD): Texas Tech University, 1980.
Organization: The Fuqua School of Business, Duke University, Durham, NC.
Contact Info: Tel.: (919) 660-7848, E-mail: gd@duke.edu
Research Interests: Electronic communication, organization design, information
technology management, and teams.
Key Publications:
DeSanctis, G.; and Gallupe, R.B. A foundation for the study of group
decision support systems. Management Science, 33, 5 (1987), 589-609.
DeSanctis, G.; and Gallupe, R.B. Group decision support systems: a new
frontier. Data Base, 16, 2 (1985), 2-10.
12. Name: R. Brent Gallupe
Graduate Education (PhD): University of Minnesota, 1985.
45
Organization: School of Business, Queen's University, Kingston, Canada.
Contact Info: Tel.: (613) 533-2361, E-mail: gallupeb@post.queensu.ca
Research Interests: Computer support for groups and teams, knowledge
management systems, global information management.
Key Publications:
Gallupe, R.B.; Dennis, A.R.; Cooper, W.H.; Valacich, J.S.; Bastinutti,
L.M.; and Nunamaker, J.F., Jr. Electronic brainstorming and group size.
Academy of Management Journal, 35, (1992), 350-369.
Gallupe, R.B. Images of information systems in the early 21st century.
Communications of the Association for Information Systems, 3, 3 (2000),
2-16.
13. Name: Douglas Vogel
Graduate Education (PhD): University of Minnesota, 1985.
Organization: Dept. of Information Systems, City University of Hong Kong.
Contact Info: Tel.: (852) 27887560, E-mail: isdoug@cityu.edu.hk
Research Interests: Group support systems, business process improvement,
executive support systems, technology support for learning
environments, electronic commerce, virtual organizations.
Key Publication:
Vogel, D.; and Nunamaker, J.F., Jr.; Group decision support system
impact: Multi-methodological exploration. Information and Management,
18, (1990), 15-28.
14. Name: Sirkka L. Jarvenpaa
Graduate Education (PhD): University of Minnesota, 1986.
Organization: Department of Management Science & Information Systems,
University of Texas, Austin, TX.
Contact Info: Tel.: (512) 471-1751, E-mail: sjarvenpaa@mail.utexas.edu
Research Interests: Global information technology, group and organizational
DSS, and strategic use of information technology.
46
Key Publication:
Jarvenpaa, S.L.; Knoll, K.; and Leidner, D.E. Is anybody out there?
Antecedents of trust in global virtual teams. Journal of Management
Information Systems, 14, 4 (Spring 1998), 29-64.
15. Name: Wanda J. Orlikowski
Graduate Education (PhD): New York University.
Organization: MIT Sloan School of Management, Cambridge, MA.
Contact Info: Tel.: (617) 253-0443, E-mail: wanda@mit.edu
Research Interests: Information technology and organizational change, working
virtually, knowledge sharing.
Key Publications:
Yates, J.; and Orlikowski, W.J. Genres of organizational communication:
A structural approach to studying communication and media. Academy of
Management Review, 17, 2 (1992), 299-326.
Orlikowski, W.J. Improvising organizational transformation over time: a
situated change perspective. Information Systems Research, 7, 1 (1996),
63-67.
Orlikowski, W.J. Using technology and constituting structures: a practice
lens for studying technology in organizations. Organization Science, 11, 4
(2000), 404-428.
16. Name: Sara Kiesler
Graduate Education (PhD): Ohio State University.
Organization: Human-Computer Interaction Institute, Carnegie Mellon
University, Pittsburgh, PA.
Contact Info: Tel.: (412) 268-2888, E-mail: kiesler@cs.cmu.edu
Research Interests: Social and behavioral aspects of computers, group dynamics,
and computer-based communication technologies.
47
Key Publications:
Kraut, R.; Kiesler, S.; Boneva, B.; Cummings, J.; Helgeson, V.; and
Crawford, A. Internet paradox revisited. Journal of Social Issues, 58, 1
(Spring 2002), 49-74.
Kiesler, S.; and Sproull, L. Group decision-making and communication
technology. Organizational Behavior and Human Decision Processes, 52,
1 (1992), 96-123.
OOPERATIONSPERATIONS R RESEARCHESEARCH R REFERENCESEFERENCES
17. Name: Marshall Fisher
Graduate Education (PhD): Massachusetts Institute of Technology, 1970.
Organization: Department of Operation and Information Management, The
Wharton School, University of Pennsylvania.
Contact Info: Tel.: (215) 898-5872, E-mail: fisher@wharton.upenn.edu
Research Interests: Supply chain management, retailing.
Key Publication:
Fisher, M. Supply chain inventory management and the value of shared
information. Management Science, 46, (August 2000), 1032-1050.
18. Name: Hau L. Lee
Graduate Education (PhD): University of Pennsylvania, 1983.
Organization: Stanford Graduate School of Business, Stanford University, Palo
Alto, CA.
Contact Info: Tel.: (650) 723-0514, E-mail: haulee@stanford.edu
Research Interests: Supply chain management, global logistic system design and
control, manufacturing and distribution strategy.
Key Publication:
Lee, H. E-Fulfillment: Winning the last mile of E-Commerce. Sloan
Management Review, 42, 4 (2001).
48
19. Name: George B. Dantzig
Graduate Education (PhD): University of California, 1946.
Organization: Professor Emeritus, Stanford University, Palo Alto, CA.
Research Interests: Linear Programming, Combinatorial Mathematics,
Optimization.
Key Publication:
Dantzig, G.B. Maximization of a linear function of variables subject to
linear inequalities. In Koopmans T.C. (ed.) “Activity alaysis of production
and allocation” John Wiley and Sons, New York, (1951), 339-347.
20. Name: Stephen A. Cook
Graduate Education (PhD): Harvard University, 1966.
Organization: Department of Computer Science, University of Toronto, Toronto,
Canada.
Contact Info: Tel.: (416) 978-5183, E-mail: sacook@cs.toronto.edu
Research Interests: Computational complexity, combinatorial mathematics.
Key Publication:
Cook, S.A. The complexity of theorem proving procedures. Proc of the 3rd
annual ACM symposium on Theory of Computing, ACM, New York, 151-
158.
KM/AI/IR RKM/AI/IR REFERENCESEFERENCES
21. Name: Peter F. Drucker
Graduate Education (PhD): University of Frankfurt in 1930’s in Germany.
Organization: Department of Social Sciences, Claremont Graduate School.
Research Interests: Strategy and policy for businesses and social sector
organizations.
Contact Info: Tel: (909) 607-9064
49
Key publication:
Drucker, P.F. Post-capitalist society. Oxford, UK: Butterwoth-Heinemann,
(1993).
22. Name: Ikujiro Nonaka
Graduate Education (PhD): University of California, Berkley.
Organization: Japan Advanced Institute of Science and Technology.
Contact Info: E-mail: kouhou@jaist.ac.jp
Research Interests: Organizational Theory, Corporate Strategy.
Key publication:
Nonaka, I.; and Takeuchi, H. The knowledge-Creating Company. New
York: Oxford University Press, (1995).
23. Name: Hirotaka Takeuchi
Graduate Education (PhD): University of California, Berkley.
Organization: Dean of the Graduate School of International Corporate Strategy,
Hitotsubashi University in Tokyo.
Contact Info: Fax: 813-4212-3006, E-mail: info@ics.hit-u.ac.jp
Research Interests: The knowledge creation process within organizations,
competitiveness of Japanese firms in global industries, new
product development process, and international corporate
strategy.
Key publication:
Takeuchi, H.; and Nonaka, I. The knowledge-Creating Company. New
York: Oxford University Press, (1995).
24. Name: Thomas H. Davenport
Graduate Education (PhD): Harvard University.
Organization: The Accenture Institute for Strategic Change.
Contact Info: Tel: E-mail institute@accenture.com
50
Research Interests: Information and knowledge management, reengineering,
enterprise systems, and the use of information technology
systems in business
Key publication:
Davenport, T.H., Working Knowledge: How Organizations Manage What
they Know. Boston, MA: Harvard Business School Press.
25. Name: Paul M Romer
Graduate Education (PhD): University of Chicago.
Organization: Graduate School of Business, Stanford University.
Contact Info: E-mail: Paul.Romer@Stanford.edu
Research Interests: The dynamics of wealth creation.
Key publication:
Romer, P.M. Two strategies for economics development: using ideas and
producing ideas. Proceedings of the World Bank Annual Conference on
development economics, the World Bank, (1993).
26. Name: Peter Senge
Graduate Education (PhD): MIT.
Organization: Organizational Learning Center, MIT.
Contact Info: Tel: (617) 253-1572, Email: jean@sol-ne.org
Research Interests: Organizational learning, organizational change
Key publication:
Senge, P. The fifth discipline: the art and practice of the learning
organization. New York: Doubleday, (1990).
27. Name: Norbert Wiener (1894-1964)
Graduate Education (PhD): Harvard University, 1912.
Research Interests: stochastic processes, controlling mechanisms, Key publication:
Wiener, N. Cybernetics. Wiley, New York, (1948).
51
28. Name: Allen Newell (1927-1992)
Graduate Education (PhD): Carnegie Institute of Technology.
Organization: Computer Science at Carnegie Mellon University.
Research Interests: Computer simulation as the key research tool for
understanding and modeling the human mind. Produced the
Logic Theorist, the General Problem Solver, and the NSS
chess program.
Key publications:
Newell, A. Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall,
Inc., (1972).
Newell, A. Unified theories of cognition. Cambridge, MA: Harvard
University Press, (1990).
29. Name: Herbert A. Simon (1916-2001)
Graduate Education (PhD): University of Chicago.
Organization: Computer Science and Psychology at Carnegie Mellon University.
Research Interests: Learning from examples; CaMeRa (a model using visual
imagery in reasoning); finding good problem representations;
EPAM, (a unified theory simulating perception and memory)
and the psychology of scientific discovery (BACON and
other programs).
Key publications:
Simon, H.A. Experiments with a heuristic compiler. Journal of the
association for computing machinery, 10, (1963), 493-506.
Simon, H.A. The science of artificial intelligence. MIT Press, (1981).
30. Name: John McCarthy
Graduate Education (PhD): Princeton University, 1951.
Organization: Computer Science at Stanford University.
Contact Info: E-mail: jmc@cs.stanford.edu
Research Interests: Artificial intelligence.
52
Key publication:
McCarthy, J. Some philosophical problems from the standpoint of
Artificial Intelligence. In B. Meltzer and D. Michie, editors, Machine
Intelligence 4, Edinburgh University Press, (1969), 463-502.
31. Name: Marvin Minsky
Graduate Education (PhD): Princeton University.
Organization: Media Arts and Sciences, E.E. and C.S. at MIT.
Contact Info: E-mail: minsky@media.mit.edu
Research Interests: Artificial intelligence, cognitive psychology, neural networks
and the theory of Turing machines.
Key publications:
Minsky, M. A framework for representing knowledge, Psychology of
Computer Vision, ed. P. Winston, McGraw Hill, (1975).
Minsky M. Perceptrons: An Introduction to Computational Geometry. The
MIT Press, (1969).
32. Name: Hans P. Luhn (1896-1964)
Organization: IBM, the American Documentation Institute.
Research Interests: Automatic indexing.
Key publication:
Luhn, H.P. The automatic creation of literature abstracts. IBM Systems
Journal, 2, (1958), 159-165.
33. Name: Gerard Salton (1927-1995)
Graduate Education (PhD): Harvard University.
Organization: Cornell University.
Research Interests: Automatic language processing.
Key publications:
Salton, G. Automatic text processing: the transformation, analysis, and
retrieval of information by computer. Addison Wesley, (1989).
53
Salton, G. The SMART retrieval system: experiments in automatic
document processing. Prentice-Hall Series in Automatic Computation,
Englewood Cliffs, New Jersey, (1971), Chapters 14-17.
34. Name: Karen Sparck Jones
Organization: Computer Laboratory, University of Cambridge.
Contact Info: email: ksj@cl.cam.ac.uk
Research Interests: Natural language and information processing.
Key publication:
Jones, K.S. Natural language processing for information retrieval.
Communications of the ACM, 39, 1, (1996), 92-101.
EECONOMICSCONOMICS O OFF I INFORMATICSNFORMATICS R REFERENCESEFERENCES
35. Name: Yannis Bakos
Graduate Education (PhD): The MIT Sloan School of Management.
Organization: Leonard N. Stern School of Business at New York University.
Contact Info: Tel.: (212) 998-0841, E-mail: bakos@stern.nyu.edu
Research Interests: The impact of information technology on markets, how
internet-based electronic marketplaces will affect pricing and
competition, pricing strategies for information goods.
Key Publication:
Bakos, Y.; and Brynjolfsson, E. Bundling information goods: Prices,
profits, and efficiency. Management Science, 45, 12, (1999), 1613-1630.
36. Name: Eric Brynjolfsson
Graduate Education (PhD): MIT, 1991.
Organization: The MIT Sloan School of Management.
Contact Info: Fax: (617) 258-7579, E-mail: bakos@stern.nyu.edu
54
Research Interests: How businesses can effectively use information technology
(IT) in general and the internet in particular.
Key Publications:
Brynjolfsson, E. The Productivity Paradox of Information Technology.
Communications of the ACM, 35, 12, (1993), 66-77.
Brynjolfsson, E.; and Hitt, L. Paradox lost? Firm-level evidence on the
returns to information systems spending. Management Science, 42, 4,
(1996), 541-558.
Bakos, Y.; and Brynjolfsson, E. Bundling information goods: Prices,
profits, and efficiency. Management Science, 45, 12, (1999), 1613-1630.
37. Name: Eric K. Clemons
Graduate Education (PhD): Cornell University, 1976.
Organization: The Wharton School at the University of Pennsylvania
Research Interests: Information technology and business strategy; information
technology and financial markets; making the decision to
invest in strategic information technology ventures;
managing the risk of strategic information technology
implementations; strategic implications of electronic
commerce for channel power and profitability.
Key Publications:
Clemons, E.K. Evaluations of Strategic Investments in Information
Technology. Communications of the ACM, 34, 1, (1991), 22-36.
Clemons, E.K.; Reddy, S.P.; and Row, M.C. The impact of information
technology in the organization of economic activity: The ‘move to the
middle’ hypothesis. Journal of Management Information Systems, 10, 2,
(1993), 9-35.
38. Name: Thomas W. Malone
Graduate Education (PhD): Stanford University.
Organization: The MIT Sloan School of Management
55
Contact Info: Tel.: (617) 253-6843, E-mail: malone@mit.edu
Research Interests: How new organizations can be designed to take advantage of
the possibilities provided by information technology.
Key Publication:
Malone, T.W.; Yates, J.; and Benjamin, R.I. Electronic Markets and
Electronic Hierarchies: Effects of Information Technology on Market
Structure and Corporate Strategies. Communications of the ACM, 30, 6,
(1987), 484-497.
39. Name: Haim Mendelson
Graduate Education (PhD): Tel Aviv University 1979.
Organization: The MIT Sloan School of Management.
Contact Info: Tel.: (650) 725-8927, E-mail: haim@stanford.edu
Research Interests: Electronic business, electronic commerce, electronic
networks, financial markets.
Key Publication:
Mendelson, H. Pricing Computer Services – Queuing Effects.
Communications of the ACM, 28, 3, (1985), 312-321.
40. Name: Carl Shapiro
Graduate Education (PhD): MIT, 1981.
Organization: Walter A. Haas School of Business, The University of California,
Berkeley, CA.
Contact Info: Tel.: (510) 642-5905, E-mail: shapiro@haas.berkeley.edu
Research Interests: Antitrust economics, intellectual property and licensing,
product standards and compatibility, and the economics of
networks and interconnection.
Key Publications:
Shapiro, C.; and Varian, H.R. Versioning: The smart way to sell
information. Harvard Business Review, (Nov-Dec 1998). [5 Citations, 196
google sites]
56
Farrell, J.; and Shapiro, C. Dynamic competition with switching costs.
Rand Journal of Economics, 19, (1988), 123-137. [120 google sites]
41. Name: Hal R. Varian
Graduate Education (PhD): The University of California Berkley, 1973.
Organization: The University of California, Berkeley.
Contact Info: Tel.: (510) 642-9980, E-mail: hal@sims.berkeley.edu
Research Interests: Economic theory, econometrics, industrial organization,
public finance, and the economics of information technology.
Key Publication:
Shapiro, C.; and Varian, H.R. Versioning: The smart way to sell
information. Harvard Business Review, (Nov-Dec 1998). [5 Citations, 196
google sites]
42. Name: Andrew B. Whinston
Graduate Education (PhD): Carnegie-Mellon University, 1962.
Organization: The University of Texas at Austin.
Contact Info: Tel.: (512) 471-8879, E-mail: abw@mail.utexas.edu
Research Interests: Electronic Commerce, its impact on business protocols and
processes, on organizational structure and corporate
networks, electronic publishing, electronic education,
complementarity of convergent computational paradigms and
business value of IT.
Key Publications:
Whinston, A.B.; and Kalakota, R. Frontiers of Electronic Commerce
Addison-Wesley, (1998). [1290 google listings]
Applegate, L.M.; Holsapple, C.W.; Kalakota, R.; Radermacher, F.J.; and
Whinston, A.B. Electronic commerce: building blocks of new business
opportunity. Journal of Organizational Computing and Electronic
Commerce, 6, 1, (1996), 1-10. [49 google listings]
57
43. Name: Anitesh Barua
Graduate Education (PhD): Carnegie Mellon University, 1991.
Organization: McCombs School of Business, the University of Texas at Austin.
Contact Info: E-mail: barua@mail.utexas.edu
Research Interests: The business value of Internet related Information
Technologies, measuring economic aspects of the Internet
Economy, and the efficiency of electronic markets.
Key Publication:
Barua, A.; Kriebel, C.; and Mukhopadhyay, T. Information Technologies
And Business Value - An Analytic And Empirical-investigation.
Information Systems Research, 6, 1, (1995), 3-23. [52 Citations, 102
Google Web Sites]
44. Name: Timothy F. Bresnahan
Graduate Education (PhD): Princeton University.
Organization: Stanford University.
Contact Info: E-mail: Tbres@stanford.edu
Research Interests: Competition in high technology industries; technical change
by users of information technologies; employment and
growth in the new economy.
Key Publication:
Bresnahan, T.F. Measuring the Spillovers From Technical Advance -
Mainframe Computers In Financial Services. American Economic Review,
76, 4, (1986), 742-755. [37 Citations, 77 Google Web Sites]
45. Name: Vijay Gurbaxani
Graduate Education (PhD): University of Rochester.
Organization: Graduate School of Managament, University of California, Irvine.
Contact Info: e-mail: vgurbaxa@uci.edu
Research Interests: The impact of emerging information technologies on new
business strategies and structures. He explores ways firms
58
can use technology to more efficiently execute existing
strategies. He develops and evaluates business driven
strategies for information sourcing.
Key Publication:
Gurbaxani, V.; and Whang, S. The Impact of Information-systems On
Organizations And Markets. Communications of the ACM, 34, 1, (1991),
59-73. [93 Citations, 293 Google Sites]
46. Name: Lorin M. Hitt
Graduate Education (PhD): Massachusetts Institute of Technology, 1996
Organization: Wharton School, The University of Pennsylvania.
Contact Info: e-mail: lhitt@wharton.upenn.edu
Research Interests: Information technology and productivity; information
systems and organization; economics of electronic
commerce; intangible assets; applied econometrics.
Key Publications:
Brynjolfsson, E.; and Hitt, L. Paradox lost? Firm-level evidence on the
returns to information systems spending. Management Science, 42, 4,
(1996), 541-558. [68 citations, 278 Google Web Sites]
47. Name: Chris F. Kemerer
Graduate Education (PhD): Carnegie-Mellon University.
Organization: Katz School, The University of Pittsburgh.
Contact Info: e-mail: ckemerer@katz.business.pitt.edu
Research Interests: Software project planning, software project cost estimation,
software measurement, software development
methodology/tool evaluation, software maintenance.
Key Publication:
Kemerer, C. An Empirical Validation of Software Cost Estimation
Models. Communications of the ACM, 30, 5, (1987), 416-429. [98
Citations, 164 Google Web Sites]
59
48. Name: Charles H. Kriebel
Graduate Education (PhD): Massachusetts Institute of Technology, 1964.
Organization: Carnegie Mellon University.
Contact Info: E-mail: ck04@andrew.cmu.edu
Research Interests: Computers and information systems, information economics,
telecommunications, management science, operations
management, robotics, applied economics, productivity,
manufacturing systems, information resource management.
Key Publication:
Barua, A.; Kriebel, C.; and Mukhopadhyay, T. Information Technologies
And Business Value - An Analytic And Empirical-investigation.
Information Systems Research, 6, 1, (1995), 3-23. [52 Citations, 102
Google Web Sites]
49. Name: Tridas Mukhopadhyay
Graduate Education (PhD): University of Michigan, Ann Arbor, 1987.
Organization: Carnegie Mellon University.
Contact Info: e-mail: tridas@andrew.cmu.edu
Research Interests: Electronic commerce, strategic use of IT, business-to-business
commerce, economics of information system management.
Current research interests include adoption of e-commerce,
loyalty on the Internet, business value of information
technologies, software cost management..
Key Publications:
Barua, A.; Kriebel, C.; and Mukhopadhyay, T. Information Technologies
And Business Value - An Analytic And Empirical-investigation.
Information Systems Research, 6, 1, (1995), 3-23. [52 Citations, 102
Google Web Sites]
60
Mukhopadhyay, T.; Kekre, S.; and Kalathur, S. Business Value Of
Information Technology - A Study Of Electronic Data Interchange. MIS
Quarterly, 19, 2, (1995), 137-156. [52 Citations, 153 Google Web Sites]
SSOCIALOCIAL I INFORMATICSNFORMATICS R REFERENCESEFERENCES
50. Name: Rob Kling
Graduate Education (PhD): Stanford University, 1971.
Organization: School of Library and Information Science, The University of Bloomington, IN.
Contact Info: Tel (812) 855-9763, E-mail: kling@indiana.edu
Research Interests: Social informatics, organizational informatics, information
systems, information technology and social change.
Key Publications:
Kling, R. Social Analyses of Computing: Theoretical Perspectives in
Recent Empirical Research. Computing Surveys, 12,1, (March 1980), 61-
110.
Kling, R. Computerization and Social Transformations. Science
Technology and Human Values, 16, 3, (Summer 1991), 342-367.
51. Name: John L. King
Graduate Education (PhD): University of California, Irvine, CA.
Organization: School of Information, University of Michigan, MI.
Contact Info: Tel (734) 647-3576, E-mail: jlking@umich.edu
Research Interests: Development of high-level requirements for information
systems design and implementation, study of organizational
and institutional forces that shape the development of
information technology.
61
Key Publication:
King, J.L.; Gurbaxani, V.; Kraemer, K.L.; McFarlan, W.; Raman, K.S.;
and Yap, C.S. Institutional Factors in Information Technology Innovation.
Information Systems Research, 5, 2, (June 1994), 139-169.
52. Name: Kenneth L. Kraemer
Graduate Education (PhD): University of Southern California, 1967.
Organization: School of Management, University of California, Irvine, CA.
Contact Info: Tel (949) 824-5246, E-mail: kkraemer@uci.edu
Research Interests: National computer policy, social impacts of information
systems, management of information technology, payoffs
from IT investments, globalization of e-commerce.
Key Publications:
Kraemer, K.L.; and Dedrick, J. From nationalism to pragmatism: IT policy
in China. IEEE Computer, 28, 8, (1995), 64-73.
Kraemer, K.L., Dedrick, J. and Jarman, S. Supporting the free market:
Information technology policy in Hong Kong. The Information Society,
10, 4, (1994), 223-246.
53. Name: Mary J. Culnan
Graduate Education (PhD): University of California at LA, 1980.
Organization: The McDonough School of Business,
Georgetown University, Washington D.C.
Contact Info: Tel.: (202) 687-3802, E-mail: culnanm@msb.edu
Research Interests: Social and public policy impacts of information technology,
information privacy, consumer attitudes toward privacy and
electronic marketing.
Key Publications:
Mary, J.C.; Kling, R.; and Wetherbe, J.C. Social Issues of IS: Reshaping
Our Research Agenda for 2001. ICIS , (1992), 297.
62
Mary, J.C.; O'Reilly III, C.A.; and Chatman, J.A. Intellectual structure of
research in organizational behavior, 1972-1984: A cocitation analysis.
Journal of the American Society for Information Science, 41, 6, (1990),
453-458.
54. Name: Michael D. Cohen
Graduate Education (PhD): University of California, Irvine, 1972.
Organization: School of Information, University of Michigan, MI.
Contact Info: Tel.: (734) 647-8027, E-mail: mdc@umich.edu
Research Interests: Organizational learning and routines and their interactions
with information technology, research using laboratory
studies, field studies, and computational models.
Key Publication:
Cohen, M.D.; March, J.G.; and Olsen, J.P. A Garbage Can Model of
Organizational Choice. Administrative Science Quarterly, 17, (1972), 1-
25.
55. Name: Robert Benjamin
Graduate Education (PhD): California Institute of Technology, 1970.
Organization: School of Information Studies, Syracuse University.
Contact Info: Tel.: (757) 496-9689, E-mail: ribenjamin@cox.net
Research Interests: Management of information technology-enabled change,
strategic application of information technology, the evolution
of information infrastructures and the societal implications of
information technology.
Key Publication:
Benjamin, R.; Malone, T.; and Yates, J. Electronic markets and electronic
hierarchies. Communications of the ACM, 30, 6, (1987), 484-497.
56. Name: Seymour (Sy) Goodman
Graduate Education (PhD): California Institute of Technology, 1970.
63
Organization: College of Computing, Georgia Institute of Technology, GA.
Contact Info: Tel.: (404) 385-1461, E-mail: goodman@cc.gatech.edu
Research Interests: International diffusion and the national absorption of
information technology, national and international security
dimensions of information technology, with a primary
emphasis on policy issues such as cyber-crime, and terrorism,
critical IT-based infrastructure protection.
Key Publication:
Goodman, S.E.; Wolcott, P.; and Burkhart, G. An Examination of High-
Performance Computing Export Control Policy in the 1990s. IEEE
Computer Society Monograph, Los Altos CA, (1996), 115 pages.
57. Name: Lee S. Sproull
Graduate Education (PhD): Stanford University, 1977
Organization: Leonard N. Stern School of Business, New York University, NY.
Contact Info: Tel.: (212) 998-0804, E-mail: lsproull@stern.nyu.edu
Research Interests: Implications of computer-based communication technologies
for managers, organizations, communities, and society, how
technology induces changes in interpersonal interaction,
group dynamics and decision making, and organizational or
community structure.
Key Publications:
Sproull, L.S., and Kiesler, S. A two-level perspective on electronic mail in
organizations. Journal of Organizational Computing, 1, 2, (1991), 125-
134.
Sproull, L.S., and Hofmeister, K. Thinking about implementation. Journal
of Management, 12, (1986), 43-60.
Sproull, L., & Kiesler, S. (1986). Reducing social context cues: Electronic
mail in organizational communication. Management Science, 32, 1492-
1512.
64
58. Name: Eli M. Noam
Graduate Education (PhD): Harvard University, 1975.
Organization: Columbia University, New York, NY.
Contact Info: Tel.: (212) 854-8332, E-mail: noam@columbia.edu
Research Interests: Public choice and regulation, public finance, economics of
criminal justice, communication.
Key Publications:
Noam, E.M. The Efficiency of Direct Democracy, Journal of Political
Economy, 88, 4, (August 1980).
Noam, E.M. Electronics and the Dim Future of the University. Science,
270, (October 1995), 247-249.
59. Name: Richard Mason
Graduate Education (PhD): University of California Berkeley, 1968.
Organization: Edwin L Cox School of Business,
Southern Methodist University, Dallas, TX.
Contact Info: Tel.: (214) 768-3145, E-mail: rmason@mail.cox.smu.edu
Research Interests: Business strategy and information systems, social and ethical
implications of information systems, ethics and genetics, and
the history of information systems.
Key Publication:
Mason, R.O.; and Mitroff, I.I. A Program for Research on Management
Information Systems. Management Science, 19, 5, (1973), 475-487.
60. Name: Dorothy E. Denning
Graduate Education (PhD): Purdue University.
Organization: Georgetown Institute for Information Assurance.
Contact Info: Tel.: (202) 687-5703, E-mail: denning@georgetown.edu
Research Interests: Cyber crime and cyber terrorism, information warfare and
security, the impact of technology on society.
65
Key Publication:
Denning, D.E. Crime and Crypto on the Information Superhighway,
Journal of Criminal Justice Education, 6, 2, (Fall 1995), 323-336.
61. Name: Pamela Samuelson
Graduate Education (J.D.): Yale Law School, 1976.
Organization: University of California, Berkeley, CA.
Contact Info: Tel.: (510) 642-6775, E-mail: pam@sims.berkeley.edu
Research Interests: Intellectual property law, challenges that new information
technologies are posing for public policy and traditional legal
regimes.
Key Publications:
Samuelson, P. Copyright’s Fair use Doctrine and Digital Data.
Communications of the ACM, 37, 1, (1994), 21-27.
Samuelson, P. Toward a new politics of intellectual property
Communications of the ACM, 44, 3, (2001), 98-99.
62. Name: Peter G. Neumann
Graduate Education (PhD): Harvard University, 1961.
Organization: Computer Science Laboratory, Menlo Park, CA.
Contact Info: Tel.: (650) 859-2375, E-mail: Neumann@csl.sri.com
Research Interests: Security, crypto applications, overall system survivability,
reliability, fault tolerance, safety, software-engineering
methodology, systems in the large, applications of formal
methods, and risk avoidance.
Key Publications:
Jarvenpaa, S.L.; Knoll, K.; and Leidner, D.E. Is anybody out there?
Antecedents of trust in global virtual teams. Journal of Management
Information Systems, 14, 4 (Spring 1998), 29-64.
66
Landau, S.; Kent, S.; Brooks, C.; Charney, S.; Denning, D.; Diffie, W.;
Lauck, A.; Miller, D.; Neumann, P.; and Sobel, D. Crypto Policy
Perspectives Communications of the ACM, 37, 8, (1994), 115-121.
63. Name: Steve Woolgar
Graduate Education (PhD): Cambridge University, 1978.
Organization: Saïd Business School, University of Oxford, England.
Contact Info: Tel: 44 (0) 1865 288667, E-mail: steve.woolgar@sbs.ox.ac.uk
Research Interests: social studies of science and technology, social problems and
social theory.
Key Publications:
Woolgar, S. Configuring the user: The case of usability trials, in J. Law
ed., A Sociology of monsters: essays on power, technology and
domination, London: Routledge, (1991).
Woolgar, S. Reflexivity is the ethnographer of the text, in S. Woolgar, ed.,
Knowledge and reflexivity: New frontiers in the sociology of knowledge,
London: Sage, (1988).
HHUMANUMAN-C-COMPUTEROMPUTER I INTERACTIONNTERACTION R REFERENCESEFERENCES
64. Name: Ben Shneiderman
Graduate Education (PhD): State University of New York at Stony Brook, 1973.
Organization: CS, ISR, UMIACS, University of Maryland, College Park, MD.
Contact Info: Tel.: (301) 405-2680, E-mail: ben@cs.umd.edu
Research Interests: Human-computer interaction, user interface design.
Key Publications:
Shneiderman, B. Direct manipulation: a step beyond programming
languages. Computer, 16, 8, (August 1983), 57-69.
Shneiderman, B. Designing the User Interface: Strategies for Effective
Human-Computer Interaction. Addison Wesley, (1986).
67
Shneiderman, B. Software Psychology: Human Factors in Computer and
Information Systems. Little, Brown Computer Systems Series: Little,
Brown & Company, (1980), 49.
65. Name: Jakob Nielsen
Graduate Education (PhD): Technical University of Denmark.
Organization: Nielsen Norman Group, Fremont, CA.
Contact Info: Tel.: (408) 720-8808, E-mail: nielsen@nngroup.com
Research Interests: designs of websites and information architecture, task design.
Key Publications:
Nielsen, J. How to write for the Web (based on how people read on the
Web) (1997).
Nielsen, J. Survey of Usability Laboratories (1994).
Nielsen, J. Guerrilla HCI: Using Discount Usability Engineering to
Penetrate the Intimidation Barrier (1994).
66. Name: Don Norman
Graduate Education (PhD): University of Pennsylvania.
Organization: Department of Computer Science, Northwestern University.
Contact Info: E-mail: norman@northwestern.edu
Research Interests: The human-centered design process, physical objects with
embedded computation and telecommunication.
Key Publications:
Norman, D.A.; and Draper, S. (eds.) User Centered System Design: New
Perspectives on Human-Computer Interaction. Hillsdale, NJ: Lawrence
Erlbaum Associates, (1986).
Norman, D.A. The design of everyday things. New York: Doubleday,
(1990).
67. Name: Edward R. Tufte
Organization: Yale University.
68
Contact Info: Tel: (203) 272-9187, E-mail: tufte@graphicspress.com
Research Interests: Statistical evidence, information design, interface design,
digital video, sculpture, and printmaking.
Key Publications:
Tufte, E.R. The Visual Display of Quantitative Information. Graphics
Press, Cheshire, CT, (1983).
Tufte, E.R. Envisioning Information, Connecticut: Graphics Press,
(1990).
Tufte, E.R. Visual Explanations: Images and Quantities, Evidence and
Narrative. Connecticut: Graphics Press, (1997).
68. Name: Stuart K. Card
Graduate Education (PhD): Carnegie Mellon University.
Organization: Xerox Palo Alto Research Center.
Research Interests: study of input devices, theories of human-machine interaction
including the Model Human Processor, the GOMS theory of
user interaction, and information foraging theory. New
paradigms of human-machine interaction, including the
Rooms Workspace Manager and the Information Visualizer.
Key Publications:
Card, S.K.; Moran, T.P.; and Newell, A. The psychology of human-
computer interaction, Hillsdale, N.J., L. Erlbaum Associates, (1983).
Card, S.K.; Mackinlay, J.D.; and Shneiderman, B. Readings in
Information Visualization: Using Vision to Think, Morgan Kaufmann
Publishers, (1999).
69. Name: Brad Myers
Graduate Education (PhD): University of Toronto.
Organization: Human Computer Interaction Institute,
School of Computer Science, Carnegie Mellon University.
Contact Info: Tel.: (412) 268-5150, E-mail: bam+@cs.cmu.edu
69
Research Interests: User interface development systems, programming by
example, visual programming, interaction techniques,
window management, and programming environments.
Key Publications:
Myers, B.A. A Brief History of Human Computer Interaction Technology.
ACM interactions, 5, 2, (March 1998), 44-54.
Cypher, A.; Daniel C.; Kurlander, D.; Lieberman, H.; Maulsby, D.; Myers,
B.A.; and Turransky, A. (eds.) Watch What I Do: Programming by
Demonstration. Cambridge, MA: The MIT Press, (1993).
70. Name: George W. Furnas
Graduate Education (PhD): Stanford University.
Organization: School of Information, University of Michigan.
Contact Info: Tel: (734) 763-0076, E-mail: furnas@umich.edu
Research Interests: human computer interaction, information access and
visualization, multivariate statistics and graphical reasoning,
statistical semantics, adaptive indexing, latent semantic
indexing, generalized fisheye views, purely graphical
deduction systems, the prosection method for high
dimensional visualization, multitrees, space-scale diagrams
and information navigation.
Key Publications:
Furnas, G.W. Generalized fisheye views. In Proceedings of CHI '86: ACM
Conference on Human Factors in Software, (1986), 16-23.
Furnas, G.W. Effective view navigation. In Proceedings of CHI '97:
Human Factors in Computing Systems, Atlanta, Georgia. Association for
Computing Machinery, (1997).
71. Name: Gavriel G. Salvendy
Graduate Education (PhD): University of Birmingham, United Kingdom.
70
Organization: Department of Industrial Engineering, Tsinghua University,
Beijing, P.R. of China.
Contact Info: Tel.: (765) 494-5426, E-mail: salvendy@ecn.purdue.edu
Research Interests: design, operation, and management of advanced engineering
systems.
Key Publications:
Salvendy, G.G. (ed.) Handbook of Human Factors. New York: John Wiley
& Sons, (1987).
Salvendy, G.G. and Smith, M.J. (eds.) Human-Computer Interaction:
Software and Hardware Interfaces. Amsterdam, Netherlands: Elsevier
Science Publishers, (1993).
72. Name: Brenda Laurel
Graduate Education (PhD): Ohio State University.
Organization: Art Center College of Design in Pasadena, CA.
Contact Info: Tel: (408) 741-5865, E-mail: blaurel@tauzero.com
Research Interests: human-computer interaction, & cultural aspects of
technology.
Key Publication:
Laurel, B. The Art of Human-Computer Interface Design. Addison-
Wesley, (1990).
SSYSTEMSYSTEMS A ANALYSISNALYSIS A ANDND D DESIGNESIGN R REFERENCESEFERENCES
73. Name: Ludwig von Bertalanffy (death -1972)
Graduate Education (PhD): University of Vienna.
Organization: State University of New York.
Research Interests: General systems theory.
Key Publication:
71
Bertalanffy, L. General Systems Theory. Foundations, Development,
Applications. New York: George Braziller, (1968).
74. Name: J. Daniel Couger (death - 1998)
Graduate Education (PhD): University of Colorado.
Organization: University of Colorado, Colorado Springs.
Research Interests: Systems analysis techniques.
Key Publication:
Couger, J.D. System Analysis Techniques, New York: Wiley and Sons,
(1974).
75. Name: Douglas Ross
Graduate Education (S.M.): MIT.
Organization: SofTech, Inc.
Research Interests: Systems analysis and design, software development and
architecture.
Key Publication:
Structured Analysis (SA): A Language for Communicating Ideas, IEEE
Transactions on Software Engineering, SE-3, (Jan. 1977), 16-34.
76. Name: Larry. L. Constantine
Graduate Education (PhD): Massachusetts Institute of Technology.
Organization: University of Technology, Sydney, Australia.
Contact: E-mail: larry@it.uts.edu.au
Research Interests: Computer programming, systems analysis and design.
Key Publications:
Constantine, L.L.; Stevens, W.P.; and Myers, G.J. Structured design. IBM
Systems Journal, (1974), 115-139.
Constantine, L.L. The Practical Guide to Structured System Design,
Englewood Cliffs, N.J. : Prentice-Hall, (1975).
77. Name: Ole-Johan Dahl (death – 2002)
72
Graduate Education (PhD): University of Oslo.
Organization: University of Oslo, Norway.
Research Interests: Computer programming, object oriented analysis, operations
research.
Key Publication:
Dahl, O.; Nygaard, K. The Simula Programming Manual, (1965).
78. Name: Kristen Nygaard (death – 2002)
Graduate Education (PhD): University of Oslo.
Organization: University of Oslo, Norway.
Research Interests: Computer programming, object oriented analysis, operations
research.
Key Publication:
Dahl, O.; Nygaard, K. The Simula Programming Manual, (1965).
79. Name: Grady Booch
Graduate Education (M.S.): University of California, Santa Barbara.
Organization: Rational Software.
Research Interests: Object oriented analysis and design.
Key Publication:
Booch, G.; Jacobson; and Rumbaugh. The Unified Modeling
Language Reference Manual. New York, Addison-Wesley Object
Technology Series, (1998).
80. Name: Ivar Jacobson
Graduate Education (PhD): Royal Institute of Technology.
Organization: Rational Software.
Research Interests: Object oriented analysis and design.
Key Publication:
73
Booch, G.; Jacobson; and Rumbaugh. The Unified Modeling
Language Reference Manual. New York: Addison-Wesley Object
Technology Series, (1998).
81. Name: James Rumbaugh
Graduate Education (PhD): MIT.
Organization: Rational Software.
Research Interests: Object oriented analysis and design.
Key Publication:
Booch, G.; Jacobson; and Rumbaugh. The Unified Modeling
Language Reference Manual. New York: Addison-Wesley Object
Technology Series, (1998).
82. Name: Daniel Teichroew
Graduate Education (PhD): University of North Carolina.
Organization: University of Michigan.
Research Interests: Systems analysis and design.
Key Publications:
Teichroew, D. Problem Statement languages in MIS. Proceedings,
International Symposium of BIFOA, Cologne, (July 1970), 253-270.
Teichroew, D. Problem Statement Analysis: Requirements for the
Problem Statement Analyzer (PSA). ISDOS Working paper, U of
Michigan, Ann Arbor, (1971), 20-53
Teichroew, D.; Sayani, H. Automation of System Building.
Datamation, (Aug. 1971). 25-30.
83. Name: Edward Yourdon
Graduate Education (B.S.): MIT.
Research Interests: Computer programming, systems analysis and design
Key Publication:
74
Yourdon, E. The Practical Guide to Structured System Design
Englewood Cliffs, N.J. : Prentice-Hall, (1975).
WWORKFLOWORKFLOW R REFERENCESEFERENCES
84. Name: Fabio Casati
Graduate Education (PhD): Politecnico Di Milano.
Organization: Hewlett Packard Research Labs, Palo Alto.
Research Interests: Business process intelligence (details available on the HP
internal web), service composition, e-services analysis and
management.
Key Publications:
Casati, F. Workflow evolution. Data and Knowledge Engineering,
Elsevier Science, (January 1998).
Casati, F. An environment for designing exceptions in workflows.
Information Systems, 24, 3, (1999), 255-273.
85. Name: Kees van Hee
Graduate Education (PhD): TU Eindhoven.
Organization: Bakkenist Management Consultants.
Research Interests: Process modeling, workflow design.
Key Publication:
Hee, K.; and W.M.P. van der Aalst. Workflow Management:
Modellen, Methoden en Systemen, Dutch Academic Service, (2002).
86. Name: Akhil Kumar
Graduate Education (PhD): University of California, Berkeley.
Organization: Smeal College of Business, Penn State University, PA.
75
Research Interests: workflow systems, e-services, database systems, distributed
information systems and intelligent systems .
Key Publication:
Kumar, A.; W.M.P. van der Aalst; and H.M.W. Verbeek. Dynamic
work distribution in workflow management systems: how to balance
quality and performance? Journal of MIS, 18, 3, (Winter 2001-2002),
157-193.
87. Name: Willibrordus Martinus Pancratius van der Aalst
Graduate Education (PhD): Eindhoven University of Technology, Netherlands.
Organization: Eindhoven University of Technology, Netherlands.
Research Interests: Information systems, simulation, petri nets, process models,
workflow management systems, verification techniques,
enterprise resource planning systems, computer supported
cooperative work, interorganizational business processes.
Key Publication:
W.M.P. van der Aalst. Dealing with workflow change: identification
of issues and solutions. International Journal of Computer Systems,
Science and Engineering, 15 5, (2000), 267-276.
W.M.P. van der Aalst. Loosely coupled interorganizational
workflows: modeling and analyzing workflows crossing organizational
boundaries. Information and Management, 37, 2, (March 2000), 67-75.
76
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