Final Project - National Center for Border Security and...

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



Outlook…………………………………………………………………………..19Outlook…………………………………………………………………………..19

Operations Research.…………………………….…………………………………..20Operations Research.…………………………….…………………………………..20



Outlook…………………………………………………………………………..21Outlook…………………………………………………………………………..21

KM/AI/IR…………………..…………………….…………………………………..22KM/AI/IR…………………..…………………….…………………………………..22



Outlook…………………………………………………………………………..24Outlook…………………………………………………………………………..24

Economics of Informatics ……………………….…………………………………..25Economics of Informatics ……………………….…………………………………..25



Outlook…………………………………………………………………………..27Outlook…………………………………………………………………………..27

2

Social Informatics ……………………………….…………………………………..28Social Informatics ……………………………….…………………………………..28



Outlook…………………………………………………………………………..30Outlook…………………………………………………………………………..30

Human-Computer Interaction…...……………….…………………………………..32Human-Computer Interaction…...……………….…………………………………..32



Outlook…………………………………………………………………………..34Outlook…………………………………………………………………………..34

Systems Analysis & Design.………..………….………...…………………………..35Systems Analysis & Design.………..………….………...…………………………..35



Outlook…………………………………………………………………………..37Outlook…………………………………………………………………………..37

Workflow…………………...…...……………….…………………………………..38Workflow…………………...…...……………….…………………………………..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

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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

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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.

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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.

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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

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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.

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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

http://www.cs.umd.edu/users/ben/index.html

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

APPENDIX A: REFERENCESAPPENDIX A: REFERENCES

40

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: [email protected]

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


41

mailto:[email protected]


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


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: [email protected]

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: [email protected]

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: [email protected]

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.


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.


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.


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.


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.


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


Organization: Dept. of Information Systems, City University of Hong Kong.

Contact Info: Tel.: (852) 27887560, E-mail: [email protected]

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


Organization: Department of Management Science & Information Systems,

University of Texas, Austin, TX.


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.


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.


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.


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.


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

mailto:[email protected]%20


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


Organization: Department of Computer Science, University of Toronto, Toronto,

Canada.


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

mailto:[email protected]%20

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: [email protected]

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: [email protected]

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 [email protected]

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.


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.


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)


Research Interests: stochastic processes, controlling mechanisms, Key publication:

Wiener, N. Cybernetics. Wiley, New York, (1948).

51


mailto:%[email protected]%20

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.


Research Interests: Artificial intelligence.

52

mailto:%[email protected]%20%20

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.


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

mailto:[email protected]%20(Marvin%20Minsky)

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: [email protected]

Research Interests: Natural language and information processing.

Key publication:

Jones, K.S. Natural language processing for information retrieval.


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.


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: [email protected]

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.


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


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.


Research Interests: Electronic business, electronic commerce, electronic

networks, financial markets.

Key Publication:

Mendelson, H. Pricing Computer Services – Queuing Effects.


40. Name: Carl Shapiro

Graduate Education (PhD): MIT, 1981.

Organization: Walter A. Haas School of Business, The University of California,

Berkeley, CA.


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.


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.


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.


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.


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: [email protected]

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.


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.


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.


Research Interests: Computers and information systems, information economics,

telecommunications, management science, operations

management, robotics, applied economics, productivity,

manufacturing systems, information resource management.

Key Publication:




Google Web Sites]

49. Name: Tridas Mukhopadhyay

Graduate Education (PhD): University of Michigan, Ann Arbor, 1987.

Organization: Carnegie Mellon University.


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:




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: [email protected]

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.


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.


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.


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.


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.


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.


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.


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


Organization: Columbia University, New York, NY.


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.


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.


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.


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.


Samuelson, P. Toward a new politics of intellectual property


62. Name: Peter G. Neumann


Organization: Computer Science Laboratory, Menlo Park, CA.


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: [email protected]

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.


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.


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.


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: [email protected]

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.


69

mailto:%[email protected]


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.


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

mailto:%20%[email protected]

Organization: Department of Industrial Engineering, Tsinghua University,

Beijing, P.R. of China.


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.


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: [email protected]

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.



Key Publication:

73


Language Reference Manual. New York: Addison-Wesley Object


81. Name: James Rumbaugh

Graduate Education (PhD): MIT.



Key Publication:


Language Reference Manual. New York: Addison-Wesley Object


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

http://stlweb.hpl.hp.com/ebps/bpi/defaul.asp

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.

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