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Management Information Systems
A Model of the MIS Domain and its Important Papers, Key Contributors, and Leading Research Universities
MIS696A Final ProjectDr. Jay F. Nunamaker, Jr.Fall 2004
Prepared By:Ahmed AbbasiJessica BaggerDaning HuXin LiJon Marthaler
Nicole Forsgren MeekMatt PearsallDave ShimkoTao WangJerod Wilkerson
.........Introduction...............................................................................................1
Project Objectives................................................................................1
The Field of Management Information Systems (MIS)...........................1
Our Contribution to the Project.............................................................2
MIS Domain – Presented and Explained..............................................3
Classification Framework.....................................................................4
Artificial Intelligence...................................................................................5
Introduction.........................................................................................5
School Listing......................................................................................6
Timeline..............................................................................................6
Key Contributors..................................................................................7
Key Papers.......................................................................................15
Collaboration...........................................................................................20
Introduction.......................................................................................20
School Listing....................................................................................21
Timeline............................................................................................21
Key Contributors................................................................................22
Key Papers.......................................................................................29
Data Management...................................................................................32
Introduction.......................................................................................32
School Listing....................................................................................34
Timeline............................................................................................35
Key Contributors................................................................................36
Key Papers.......................................................................................49
Decision Sciences/Operations Management.............................................53
Introduction.......................................................................................53
School Listing....................................................................................54
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.........Timeline............................................................................................55
Key Contributors................................................................................56
Key Papers.......................................................................................60
Economics of Informatics.........................................................................63
Introduction.......................................................................................63
School Listing....................................................................................64
Timeline............................................................................................65
Key Contributors................................................................................66
Key Papers.......................................................................................76
Human-Computer Interaction...................................................................81
Introduction.......................................................................................81
School Listing....................................................................................82
Timeline............................................................................................82
Key Contributors................................................................................83
Key Papers.......................................................................................95
Social Informatics....................................................................................98
Introduction.......................................................................................98
School Listing....................................................................................99
Timeline..........................................................................................100
Key Contributors..............................................................................101
Key Papers.....................................................................................112
Systems Analysis & Design....................................................................117
Introduction.....................................................................................117
School Listing..................................................................................118
Timeline..........................................................................................119
Key Contributors..............................................................................120
Key Papers.....................................................................................132
School Listings......................................................................................137
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.........Methodology...................................................................................137
Limitations.......................................................................................137
School Listing..................................................................................138
School Rankings.............................................................................139
Quick School Snapshots..................................................................142
Methodology...................................................................................142
School Bios.....................................................................................143
Future Research........................................................................................1
Future Extensions of Our Project..........................................................1
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Section1IntroductionProject Objectives
In this project, we aim to:
1) Build on existing mappings of the MIS domain (by previous classes);
2) Identify the top academic contributors, using prior class documents and utilizing a “completeness check,” whereby an expert in each sub-domain offers feedback regarding researchers;
3) Identify research papers within each MIS domain and re-classify them employing a modified framework;
4) Display the landmark events for each discipline in a timeline format; and
5) Identify the top research institutions worldwide within the MIS domain.
The Field of Management Information Systems (MIS)
The field of Management Information Systems (MIS) is a rich and varied discipline, which makes use of both theoretical and applied approaches. It intersects with a multitude of academic and industrial specialties, providing exciting opportunities for research along many dimensions. MIS research is commonly categorized into defined categories. Following this tradition, we present our domains and sub-domains:
i. Artificial Intelligence1. Knowledge Management2. Information Retrieval (data mining, text mining)3. Bioinformatics
ii. Collaboration1. Collaborative Systems Development2. Collaborative Systems Use and Implications
iii. Data Management1. Database architecture and design2. Database optimization3. Distributed databases4. Streaming data in databases5. Data mining6. Information Exchange and Integration
iv. Decision Science/Operations Research1. Operations Management2. DSS3. E-Commerce
v. Economics of Informatics1. Evaluation of Information Systems2. Effects of Information Technology on Markets
vi. Human Computer Interaction
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1. User Interfaces2. Intelligent Agents3. Information Dissemination
vii. Social Informatics1. Social Issues2. Legal Issues3. Ethical Issues
viii. System Analysis & Design 1. Requirements Determination/Systems Analysis2. Software Development Methodologies3. Software Design4. Project Planning/Management 5. Workflow
Our intent is to build on previous class documents by improving each existing section (key MIS contributors and significant papers) while extending the work by identifying leading Information Systems departments within the United States. To that end, we have structured the paper as follows:
The main body of the paper is organized along the given fields. Under each area, we give a domain-specific introduction, we discuss the top MIS departments identified for this domain (our main contribution to this project, to be discussed in detail below), key contributors, and significant papers, which will be subcategorized if appropriate. Following is a discussion of the major contribution of this paper – the identification of major research departments in each domain, identified with prospective PhD students as the audience. The conclusion of the paper includes future research for future class projects.
Our Contribution to the Project
In past years, first-year student groups have attempted to add to the project by refining it—adding more areas, more researchers, and generally attempting to more completely define the academic area of the MIS domain. We have continued to do this; we believe this year’s edition of the project is a more complete, more concise, and more accurate reflection of the MIS academic domain than ever before. We have made an effort to tie together the efforts of previous groups, and we have made an effort to cross-check our work with the extensive knowledge of the University of Arizona MIS department faculty. Consequently, we believe that important improvements and adjustments have been made, and we are confident in the strength of our classifications.
Our major contribution is an entirely new addition to this ongoing project. Past years have focused on the key research, and on the important researchers, within the MIS domain. We have turned to what we believe is part three of this equation: the key institutions in our domain. After all, academic institutions are the bedrock upon which the first two parts rest; the universities of the world are the domain’s research leaders, and they provide the opportunity for the key researchers to conduct the research that changes the future MIS field.
Our contribution has made an effort to select the top academic institutions within the MIS domain, group them into general categories based on this selection, and categorize each institution based on the type of domain research that is conducted in each university. We have done this by selecting five previous studies and rankings that have allowed us to identify the top 66 universities worldwide in the field of MIS. We have grouped them into tiers based on their rankings within these five studies that will allow us, at a glance, to identify the top programs. We have also categorized these institutions based on the type of MIS research that takes precedence at each school. This allows us to quickly identify the top universities that are conducting research in each sub-domain.
Our overall classification of the domain thus takes three parts: the classification of research, of researchers, and now of institutions. Our contribution to the paper is the refinement of the first two and the addition of the third.
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.........MIS Domain – Presented and Explained
Figure 1: Model of MIS Domain
The domain of MIS has its foundations in many disciplines. MIS has borrowed heavily from the studies of Computer Science, Mathematics, Engineering, Economics, Communications, Psychology, and Management, to build theories and refine techniques for understanding and expanding the role of information systems in managing data, processes, and organizations. Our model is adapted from those of both the 2002 and 2003 classes, to which we have made few fundamental changes to the foundations and sub-domains, choosing instead subtle modifications that emphasize both the intrinsic connections between disciplines and the central role of the MIS research process to all sub-domains.
Our model is laid out in concentric layers, with the foundation disciplines making up the outermost level. The eight sub-domains of MIS (Artificial Intelligence, Collaboration, Data Management, Economics of Informatics, Human-Computer Interaction, Operations Management, Social Informatics, and Systems Analysis and Design) make up the next ring, each having its genesis in one or more of the foundation fields. At the core of our model lies a variation of Dr. Nunamaker’s system model, proposed in his paper Systems Development in
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.........Information Systems Research. At the heart of each sub domain lays the MIS research process, in which ‘systems development,’ ‘experimentation,’ and ‘observation’ are the tools of the researcher that both draw from and lend to “theory building.’
One characteristic which distinguishes the MIS domain from other fields is that the study of MIS directly connects technology advancement, especially for information systems, with the management of individuals in organizations. Our overall model recognizes that most fields of science, and sub-domains of an interdisciplinary field such as MIS can be classified by how technically or behaviorally focused research in each is. Those foundations and sub-domains to the left of our model are more technically oriented, while those to the right are more behaviorally oriented. The technical-behavioral continuum in our model is captured by an orange-yellow spectrum, with technical areas more orange, behavioral areas more yellow, and those that lie between a shade of either or both depending on the strength of their technical-behavioral influences.
Classification Framework
Classifying papers can be an enormous and complex undertaking. A sound classification model is a dire necessity for accomplishment of such a task, but this in itself is no small feat. In order to tackle this arduous task, we began my analyzing the model created by the 2003 project. The classifications used in this model were based on three dimensions as follows: Theory – Application; Behavioral – Technical; and, Rigor – Relevance.
Our evaluation of the 2003 model led us to derive two conclusions. Firstly, we could not discern a significant difference between the Theory – Application and Rigor – Relevance dimensions. Although one could argue the existence of differences between the two dimensions, we felt that these differences were mere subtleties. Our conclusion was that the two dimensions were undeserving of independent consideration, and hence, only one was incorporated in our model. Theory-Application was selected because we felt that it had superior intuitive appeal. We also kept the Behavioral – Technical dimension.
Another problem with the previous model was the lack of some form of measure to place papers according to their roles, or contributions, within the respective domains: How much insight does the theoretical or technical nature of a paper provide? In order to overcome this deficiency, we devised a new dimension to facilitate the characterization of papers in a more descriptive and useful manner. A description of this new dimension called “Contribution Type” is given below:
Foundational – papers that lay the framework for research in a certain domain. Foundational papers tend to encompass a broader range, create a basis for thought in a discipline, and generally receive many citations from latter works in their respective fields.
Extension – papers that provide a contribution to an existing theory or research framework.
Exploratory – papers that explore new areas within an existing research area. Exploratory papers tend to be specific, with a more concentrated focus, hence differentiating them from Foundational works.
Review – review papers typically do not make a significant new contribution to their area, but provide an invaluable perspective due to their convergence of important topics and research in their specific field.
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.........Section2
Artificial IntelligenceIntroduction
Artificial Intelligence (AI) is a branch of science which deals with helping machines find solutions to complex problems in a more human-like fashion. Its evolution can be partitioned into three phases. The first phase is called the General Problem Solving (GPS) phase, which mainly took place in the 1970s. In this phase, researchers tried to use computers to emulate human problem solving. During this phase, the tasks to be solved were usually general problems, such as games. The second phase of AI’s evolution is normally called the Expert System or Knowledge-Based System phase; the rise of this phase was during the 1980s. In this phase, researchers moved their research interest from general tasks to specific tasks, such as the medicine industry or the oil-drilling industry. Furthermore, the systems learned from experts to solve the problem, rather than learning from so-called “kids” to solve the general problem (the major technology of the first phase). In the 1990s, AI evolved into its third phase, which is normally called the Data Mining – Machine Learning phase. Symbolic learning and interactive learning are two foundational technologies in this field. The difference between the second and third phases is that by applying machine learning technologies, the system can learn from the examples. Put another way, the system is able to learn by itself without the direct input from the expert.
Currently there are about a dozen major research directions within AI. But only three of them are closely related to the MIS domain (all of the others are mainly within Computer Science domain). The three major research directions are: NLP and Information Retrieval, Machine Learning & Data Mining, and Knowledge Representation and Reasoning.
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.........School Listing
Artificial Intelligence
School Tier
Res
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b
Facu
lty In
tere
sts
Lead
ing
Res
earc
hers
Dep
artm
ent N
ame
MIT I X X University of Arizona I X X University of Pittsburgh II X Arizona State University III X X University of Michigan III X University of Illinois V X Drexel University Research I X Northeastern University Research II X George Washington University Research III X Georgetown University Research III X Bentley College Teaching I X Stanford University Teaching I X
Timeline
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.........Key Contributors
Hsinchun Chen
– ProfessorDepartment of MISEller College of ManagementUniversity of Arizona (Tucson, Arizona)
CONTACT INFORMATION
7841 E. Sabino Crest Pl.Tucson, Arizona 85750Office: (520) 621-2748FAX: (520) [email protected]://ai.bpa.arizona.edu/hchen/
EDUCATION
Ph.D. – Information Systems, New York University, 1989M.S. – Information Systems, New York University, 1987MBA -- Management Information Systems, State University of New York at Buffalo, 1985B.S. – Management Science, National Chiao-Tung University in Taiwan, 1981
RESEARCH INTERESTS
AI, border protection, terrorism research, infectious disease informatics, digital library and digital government.
KEY PUBLICATIONS
Chen, H., H. Fan, et al. (2003). "Testing a Cancer Meta Spider." International Journal of Human Computer Studies 59(5): 775-776.
Chen, H. (1999). "Semantic Research for Digital Libraries." D-Lib Magazine 5(10/11).
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.........Edward Feigenbaum
-Professor of Computer Science-Co-Scientific Director, Knowledge Systems LaboratoryStanford University (Stanford, California)
CONTACT INFORMATION
Knowledge Systems LaboratoryDepartment of Computer ScienceGates Computer Science Building, 2AStanford UniversityStanford, CA 94305-9020
(650) 723-3444 Telephone
http://ksl-web.stanford.edu/people/eaf/
EDUCATION
Ph.D. – Carnegie Mellon UniversityBS – Carnegie Mellon University
RESEARCH INTERESTS
Knowledge-Based Systems Research & Applications; Computer Industry Research; Defense Technology and Technology Policy
KEY PUBLICATIONS
Feigenbaum, E., R. K. Lindsay, et al. (1993). "DENDRAL: A Case Study of the First Expert System for Scientific Hypothesis Formation." Artificial Intelligence 61(2): 209-261.
Feigenbaum, E. A. (1993). "Tiger in a Cage: The Applications of Knowledge-based Systems." Association of American Artificial Intelligence.
Feigenbaum, E. A., P. E. Friedland, et al. (1994). "Knowledge-Based Systems Research and Applications in Japan, 1992." AI Magazine 15(2): 19-43.
Feigenbaum, E. A. and J. A. Hendler (2001). "Knowledge Is Power: The Semantic Web Vision." Web Intelligence 2001: 18-29.
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.........John McCarthy
-Professor EmeritusComputer Science Department Stanford University (Stanford, California)
CONTACT INFORMATION
Room 208, Gates Building 2AComputer Science DepartmentStanford UniversityStanford, California 94305-9020
(650) 723-4430 Telephone
http://www-formal.stanford.edu/jmc/index.html
EDUCATION
Ph.D. – Mathematics, Princeton University, 1951B.S. – Mathematics, California Institute of Technology, 1948
RESEARCH INTERESTS
His main artificial intelligence research area has been the formalization of common sense knowledge.
KEY PUBLICATIONS
McCarthy, J. (1960). "Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I." Communications of the ACM 3(4): 184-195.
McCarthy, J. (1968). Programs with Common Sense. Semantic Information Processing. Cambridge, MA, MIT Press: 403-418.
McCarthy, J. and P. J. Hayes (1969). Some Philosophical Problems from the Standpoint of Artificial Intelligence. Machine Intelligence 4. B. M. a. D. Michie. Edinburgh, Edinburgh University Press: 463-502.
McCarthy, J. (1979). First order theories of individual concepts and propositions. Machine Intelligence 9: 129-147.
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.........Marvin Minsky
- Toshiba Professor of Media Arts and Sciences- Professor of E.E. and C.S.Massachusetts Institute of Technology (Cambridge, MA)
CONTACT INFORMATION
The Media LaboratoryBuilding E1577 Massachusetts AvenueCambridge, MA 02139-4307
617-253-5960 Telephone
http://web.media.mit.edu/~minsky/
EDUCATION
Ph.D. – Mathematics, Princeton University, 1954BA – Mathematics, Harvard University, 1950
RESEARCH INTERESTS
AI, cognitive psychology, mathematics, computational linguistics, robotics, and optics. In recent years, he has worked chiefly on imparting to machines the human capacity for commonsense reasoning.
KEY PUBLICATIONS
Minsky, M. (1969). Perceptrons: An Introduction to Computational Geometry. Cambridge, MA, MIT Press.
Minsky, M. (1975). A Framework for Representing Knowledge. The Psychology of Computer Vision. P. Winston, McGraw-Hill.
Minsky, M. (1980). "K-Lines: A Theory of Memory." Cognitive Science 4(2): 117-130.
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.........Allen Newell (1927-1992)
- ProfessorComputer Science, Carnegie Mellon University(Pittsburgh, Pennsylvania)
INFORMATION
Memorial website: http://stills.nap.edu/readingroom/books/biomems/anewell.html
EDUCATION
Ph.D. – Carnegie Institute of Technology
RESEARCH INTERESTS
Computer simulation as the key research tool for understanding and modeling the human mind.
KEY PUBLICATIONS
Simon, H. A., J. C. Shaw, et al. (1959). "Report on a general problem-solving program." Communications of the ACM 2(19).
Newell, A., C. G. Bell, et al. (1971). The CMU Multiminiprocessor Computer: Requirements, Overview of the Structure, Performance, Cost and Schedule. Pittsburgh, Computer Science Department, Carnegie Mellon University.
Newell, A. and P. Freeman (1971). A model for functional reasoning in design. Second International Joint Conference on Artificial Intelligence, London, England.
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.........Paul Romer
– Professor Graduate School of Business, Stanford University (Stanford, California)
CONTACT INFORMATION
Graduate School of BusinessStanford UniversityStanford, CA [email protected] http://www.stanford.edu/~promer/
EDUCATION
Ph.D. – Economics, University of Chicago, 1983BS – Math, University of Chicago, 1977
RESEARCH INTERESTS
New growth theory
KEY PUBLICATIONS
Romer, P. (1986). "Cake Eating, Chattering and Jumps: Existence Results for Variational Problems." Econometrica 54(4): 897-908.
Romer, P. (1986). "Increasing Returns and Long-Run Growth." Journal of Political Economy 94: 1002-37.
Romer, P. (1987). "Growth Based on Increasing Returns Due to Specialization." American Economic Review 77: 56-62.
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.........Gerard Salton (1927-1995)
– Professor (1965-1995)Computer Science DepartmentCornell University (Ithaca, New York)
INFORMATION
Memorial website: http://www.cs.cornell.edu/Info/Department/Annual95/Faculty/Salton.html
EDUCATION
Ph.D. – Computer Science, Harvard University, 1958MA – Mathematics, Brooklyn College, 1950BA – Mathematics, Brooklyn College, 1952
RESEARCH INTERESTS
Natural-language processing, especially information retrieval, SMART information retrieval system in the 1960s (allegedly, SMART is known as "Salton's Magical Automatic Retriever of Text") as the main research tool.
KEY PUBLICATIONS
Salton, G. (1980). "Automatic information retrieval." Computer 12(5): 41-57.
Salton, G., H. Wu, et al. (1981). "The measurement of term importance in automatic indexing." Journal of the ASIS 32(3): 175-186.
Salton, G., C. T. Yu, et al. (1982). "Term Weighting in Information-Retrieval Using the Term Precision Model." Journal of the ACM 29(1): 152-170.
Salton, G. and M. J. McGill (1983). Introduction to Modern Information Retrieval. New York, NY, McGraw-Hill.
Salton, G., E. A. Fox, et al. (1983). "Extended Boolean Information-Retreival." Communications of the ACM 26(11): 1022-1036.
Salton, G. (1989). Automatic Text Processing. Reading, MA, Addison-Wesley.
Salton, G. and C. Buckley (1991). "Global Text Matching for Information Retrieval." Science 253(5023): 1012-1015.
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.........Herbert A. Simon (1916-2001)
– ProfessorComputer Science, Psychology, Social & Decision Science, and Industrial Administration DepartmentsCarnegie Mellon University (Pittsburgh, Pennsylvania)
INFORMATION
Memorial website: http://www.psy.cmu.edu/psy/faculty/hsimon/hsimon.html
EDUCATION
Ph.D. – Political Science, University of Chicago, 1943BA – Political Science, University of Chicago, 1936
RESEARCH INTERESTS
AI, information Processing systems, intelligence & epistemology, social implications, Economics & management, and the Philosophy of science.
KEY PUBLICATIONS
Simon, H. A. (1996). The Science of Artificial. Cambridge, MA, MIT Press.
Simon, H. A., J. C. Shaw, et al. (1959). "Report on a general problem-solving program." Communications of the ACM 2(19).
Simon, H. A. (1968). Administrative Behavior. International Encyclopedia of the Social Sciences. D. L. Sills. New York, NY, Macmillan and the Free Press. 1: 74-79.
Simon, H. A. (1980). "Cognitive Science: The newest science of the artificial." Cognitive Science 4(1): 33-46.
14
.........Key Papers
Sub-category 1: General Problem Solving
Preliminary description of General ProblemSolving -I (GPS-I)Newell, J. C. Shaw, and H. A. Simon. Technical Report Report CIP Working Paper 7, Carnegie Institute of Technology, Pittsburgh, PA, 1957.Category: technical, application, foundational
A Framework for Representing Knowledge, Psychology of Computer Vision Minsky, MarvinSemantic Information Processing, MIT Press, Cambridge, MA, 1968, pages 403-418Classification: technical, application, foundational
A major influence in persuading AI workers and psychologists to consider representing commonsense knowledge in relatively large structures called "frames," which exemplify typical instances or cases. Frames inherit default assumptions that can be displaced when more specific information is available.
Programs with Common Sense McCarthy, JohnSemantic Information Processing, MIT Press, Cambridge, MA, 1968Classification: technical, application, foundational
Arguably the first paper on logical AI (i.e. AI in which logic is the method of representing information in computer memory and not just
the subject matter of the program). This paper discusses programs to manipulate in a suitable formal language (most likely a part of the predicate calculus) common instrumental statements. The basic program will draw immediate conclusions from a list of premises. These conclusions will be either declarative or imperative sentences.
Applications of differential equations in general problem solvingR. W. Klopfenstein Communications of the ACM, 8, 9Category: technical, application, extension
A large class of problems leading to digital computer processing can be formulated in terms of the numerical solution of systems of ordinary differential equations. Powerful methods are in existence for the solution of
such systems. A good general purpose routine for the solution of such systems furnishes a powerful tool for processing many problems. This is true from the point of view of ease of programming, ease of debugging, and minimization of computer time. A number of examples are discussed in detail
On applications of differential equations in general problem solving Robert N. KubikCommunications of the ACM Volume 9 Issue 2Category: technical, application, extension
15
.........A large class of problems leading to digital computer processing can be formulated in terms of the numerical solution of systems of ordinary differential equations. Powerful methods are in existence for the solution of such systems. A good general purpose routine for the solution of such systems furnishes a powerful tool for processing many problems. This is true from the point of view of ease of programming, ease of debugging, and minimization of computer time. A number of examples are discussed in detail.
Sub-category 2: Expert Systems
Dendral and Meta-dendral: Roots of Knowledge Systems and Expert System ApplicationsFeigenbaum E. A. and Buchanan B. G.Artificial Intelligence 59, 1993, 233-240 Category: technical, application, extension
The significance of DENDRAL system was that it was the first successful knowledge-intensive system. The success of DENDRAL was instrumental in convincing the AI research community of importance of knowledge representation. The DENDRAL program was an existence proof that computers could couple technical knowledge with simple inference mechanisms.
AI and computational science: Implementing Fuzzy Expert System for intelligent buildings Carlos A. Reyes-Garcia, Elva. Corona March 2003 Proceedings of the 2003 ACM symposium on Applied computing Category: technical, application, extension
In this paper, we present the design of an intelligent dome, as well as the design and implementation of the Fuzzy Expert System for the module controlling Ventilation and Air Conditioning. The system was designed as a Client/Server, Blackboard Architecture, implemented in FuzzyCLIPS, and intensively tested under different conditions. We describe here all the design and implementation stages as well as some of the results obtained.
Sub-category 3: NLP and information retrieval
Transporting the linguistic string project system from a medical to a Navy domain Elaine Marsh, Carol Friedman ACM Transactions on Information Systems, vol. 3, issue 2Category: technical, application, extension
The Linguistic String Project (LSP) natural language processing system has been developed as a domain-independent natural language processing system. Initially utilized for processing sets of medical messages and other texts in the medical domain, it has been used at the Naval Research Laboratory for processing Navy messages about shipboard equipment failures. This paper describes the structure of the LSP system and the features that make it transportable from one domain to another. The processing procedures encourage the isolation of domain-specific information, yet take advantage of the syntactic and semantic similarities between the medical and Navy domains. From our
16
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experience in transporting the LSP system, we identify the features that are required for transportable natural language systems.
Inheritance in natural language processing Walter Daelemans, Koenraad De Smedt, Gerald Gazdar Computational Linguistics, vol. 18 Issue 2Category: technical, theory, extension
In this introduction to the special issues, we begin by outlining a concrete example that indicates some of the motivations leading to the
widespread use of inheritance networks in computational linguistics. This example allows us to illustrate some of the formal choices that have to be made by those who seek network solutions to natural language processing (NLP) problems. We provide some pointers into the extensive body of Al knowledge representation publications that have been concerned with the theory of inheritance over the last dozen years or so. We go on to identify the three rather separate traditions that have led to the current work in NLP. We then provide a fairly comprehensive literature survey of the use that computational linguists have made of inheritance networks over the last two decades, organized by reference to levels of linguistic description. In the course of this survey, we draw the reader's attention to each of the papers in these issues of Computational Linguistics and set them in the context of related work.
Improving accuracy in word class tagging through the combination of machine learning systems Hans van Halteren, Walter Daelemans, Jakub Zavrel Computational Linguistics Volume 27 Issue 2Category: technical, application, extension
We examine how differences in language models, learned by different data-driven systems performing the same NLP task, can be exploited to yield a higher accuracy than the best individual system. We do this by means of experiments involving the task of morphosyntactic word class tagging, on the basis of three different tagged corpora. Four well-known tagger generators (hidden Markov model, memory-based, transformation rules, and maximum entropy) are trained on the same corpus data. After comparison, their outputs are combined using several voting strategies and second-stage classifiers. All combination taggers outperform their best component. The reduction in error rate varies with the material in question, but can be as high as 24.3% with the LOB corpus.
Sub-category 4: Knowledge representation and reasoning
Knowledge representation for commonsense reasoning with textKathleen Dahlgren, Joyce McDowell, Edward P. Stabler September 1989 Computational Linguistics Volume 15 Issue 3Category: technical, application, extension
Naive semantics is a level of cognitive representation of concepts that can be discovered empirically and represented in a principled way with
FOL without resort to a special knowledge representation language. Because NS representations are linked to ordinary words and do not depend on a special knowledge representation level, they should be transportable from one text to another. The KT system demonstrates that these rich representations are powerful in resolving many of the large residue of ambiguities that remain after the work of a purely syntactic parser is completed.
Knowledge compilation and theory approximationBart Selman, Henry Kautz March 1996 Journal of the ACM (JACM), Volume 43 Issue 2Category: technical, theory, extension
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.........Computational efficiency is a central concern in the design of knowledge representation systems. In order to obtain efficient systems, it has been suggested that one should limit the form of the statements in the knowledge base or use an incomplete inference mechanism. The former approach is often too restrictive for practical applications, whereas the latter leads to uncertainty about exactly what can and cannot be inferred from the knowledge base. We present a third alternative, in which knowledge given in a general representation language is translated (compiled) into a tractable form—allowing for efficient subsequent query answering. We show how propositional logical theories can be compiled into Horn theories that approximate the original information. The approximations bound the original theory from below and above in terms of logical strength. The procedures are extended to other tractable languages (for example, binary clauses) and to the first-order case. Finally, we demonstrate the generality of our approach by compiling concept descriptions in a general frame-based language into a tractable form.
Learning to reason Roni Khardon, Dan Roth September 1997 Journal of the ACM (JACM), Volume 44 Issue 5Category: technical, theory, extension
We introduce a new framework for the study of reasoning. The Learning (in order) to Reason approach developed here views learning
as an integral part of the inference process, and suggests that learning and reasoning should be studied together. The Learning to Reason framework combines the interfaces to the world used by known learning models with the reasoning task and a performance criterion suitable for it. In this framework, the intelligent agent is given access to its favorite learning interface, and is also given a grace period in which it can interact with this interface and construct a representation KB of the world W. The reasoning performance is measured only after this period, when the agent is presented with queries a from some query language, relevant to the world, and has to answer whether W implies a. The approach is meant to overcome the main computational difficulties in the traditional treatment of reasoning which stem from its separation from the “world”. Since the agent interacts with the world when constructing its knowledge representation it can choose a representation that is useful for the task at hand. Moreover, we can now make explicit the dependence of the reasoning performance on the environment the agent interacts with. We show how previous results from learning theory and reasoning fit into this framework and illustrate the usefulness of the Learning to Reason approach by exhibiting new results that are not possible in the traditional setting. First, we give Learning to Reason algorithms for classes of propositional languages for which there are no efficient reasoning algorithms, when represented as a traditional (formula-based) knowledge base. Second, we exhibit a Learning to Reason algorithm for a class of propositional languages that is not known to be learnable in the traditional sense.
Sub-category 5: Data Mining & Machine Learning
Applications of machine learning and rule induction Pat Langley, Herbert A. Simon Communications of the ACM, Volume 38, issue 11 Category: technical, application, extension
Machine learning is the study of computational methods for improving performance by mechanizing the acquisition of knowledge from
experience. Expert performance requires much domain-specific knowledge, and knowledge engineering has produced hundreds of AI expert systems that are now used regularly in industry. Machine learning aims to provide increasing levels of automation in the knowledge engineering process, replacing much time-consuming human activity with automatic techniques that improve accuracy or efficiency by discovering and exploiting regularities in training data. The ultimate test of machine learning is its ability to produce systems that are used regularly in industry, education, and elsewhere.
Machine learning in automated text categorization Fabrizio Sebastiani
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March 2002 ACM Computing Surveys (CSUR), Volume 34 Issue 1Category: technical, theory, extension
The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
Machine learning comprehension grammars for ten languagesPatrick Suppes, Lin Liang, Michael Böttner September 1996 Computational Linguistics Volume 22 Issue 3Category: technical, application, extension
Comprehension grammars for a sample of ten languages (English, Dutch, German, French, Spanish, Catalan, Russian, Chinese, Korean,
and Japanese) were derived by machine learning from corpora of about 400 sentences. Key concepts in our learning theory are: probabilistic association of words and meanings, grammatical and semantical form generalization, grammar computations, congruence of meaning, and dynamical assignment of denotational value to a word.
19
.........Section3CollaborationIntroduction
With his “mother of all demos” [Link], Doug Englebart (traditionally considered a leader in human-computer interaction) presented one of the first computer-aided collaborative meetings. Most in attendance thought the live video conference with his lab 30 miles away was a hoax.
Collaboration research in MIS pulls from many different disciplines, including communication (human communication and group interaction) and decision sciences. While group collaboration productivity without technology support tends to decrease as the number of participating individuals increases, Group Support Systems (GSS) and Group Decision Support Systems (GDSS) collaboration technologies provide the support and structure to allow more efficient and effective group work. GSS and GDSS do this by providing group memory, anonymity, and parallel communication to better utilize time (Nunamaker et al, 1991).
As with all research done in MIS, there are both behavioral (Collaborative Systems Use and Implications) and technical (Collaborative Systems Development) aspects present.
We would like to express our thanks to Dr. Jay Nunamaker for his help in verifying key contributors and important events in this area.
20
.........School Listing
Collaboration
School Tier
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MIT I X University of Arizona I X X University of Texas-- Austin I X X Georgia State University II X University of Georgia II X Arizona State University III X Indiana University III X University of Michigan III X Georgia Institute of Technology V X Florida State University Research I X National University of Singapore Research I X Queen's University Research I X X X Tel Aviv University Research II X George Washington University Research III X Duke University Teaching II X
Timeline
21
.........Key Contributors
Gerardine DeSanctis
- Thomas F Keller Professor of Business Administration The Fuqua School of BusinessDuke University (Durham, North Carolina)
CONTACT INFORMATION
The Fuqua School of BusinessDuke UniversityBox 90120Durham, NC 27708-0120
(919) 660-7848 Telephone(919) 681-6245 Facsimile
[email protected] http://www.fuqua.duke.edu/faculty/alpha/gd.htm
EDUCATION
PhD - Business Administration, Texas Tech University, 1982MA - Psychology, Fairleigh Dickinson University, 1977BA - Psychology, Villanova University, 1975
RESEARCH INTERESTS
Electronic Communication, Organization Design, Distributed Teams, and Online Communities
KEY PUBLICATIONS
Desanctis, G. and R. B. Gallupe (1987). "A Foundation for the Study of Group Decision Support Systems." Management Science 33(5): 589-609.
Desanctis, G., M. S. Poole, et al. (1991). "Using Computing in Quality Team Meetings: Initial Observations from the IRS--Minnesota Project." Journal of Management Information Systems 8(3): 7 (20 pages).
Desanctis, G. and J. F. Courtney (1983). "Toward Friendly User MIS Implementation." Communications of the Acm 26(10): 732-738.
Watson, R. T., G. DeSanctis, et al. (1988). "Using a GDSS to facilitate group consensus: some intended an unintended consequences." MIS Quarterly 12(3): 463-478.
22
.........R. Brent Gallupe
- Associate Dean of Faculty Development Professor- Director, Queens Centre for Knowledge-Based EnterprisesDepartment of Management Information SystemsQueens University (Ontario, Canada)
CONTACT INFORMATION
Department of Management Information SystemsSchool of BusinessQueens UniversityOntario, Canada
(613) 533-2361 ext. 32361 Telephone(613) 533-2013 Facsimile
http://business.queensu.ca/research/faculty/files/cv961329023.pdf
EDUCATION
Ph.D. – Business Administration, University of Minnesota, 1985MBA – York University, 1977BS – Honors Bachelor of Mathematics, University of Waterloo, 1974
RESEARCH INTERESTS
Electronic brainstorming in management information systems
KEY PUBLICATIONS
Desanctis, G. and R. B. Gallupe (1987). "A Foundation for the Study of Group Decision Support Systems." Management Science 33(5): 589-609.
Gallupe, R. B., A. R. Dennis, et al. (1992). "Electronic Brainstorming and Group-Size." Academy of Management Journal 35(2): 350-369.
23
.........George P. Huber
- Charles and Elizabeth Prothro Regents Chair in Business AdministrationDepartment of ManagementThe University of Texas at Austin (Austin, Texas)
CONTACT INFORMATION
Department of ManagementMcCombs School of BusinessThe University of Texas at AustinAustin, Texas 78712
(512) 471-9609 Telephone
http://www.mccombs.utexas.edu/dept/management/faculty/profiles/index.asp?addTarget=41
EDUCATION
Ph.D. – Purdue UniversityBSME – University of MissouriMSIE – University of Missouri
RESEARCH INTERESTS
Organizational change, organizational design, and organizational decision making.
KEY PUBLICATIONS
Huber, G. P. (1984). "Issues in the Design of Group Decision Support Systems." Mis Quarterly 8(3): 195-204.
Huber, G. P. (1983). "Cognitive style as a basis for MIS and DSS designs: Much ado about nothing?" Management Science 29(5): 567.
Huber, G.P. (1982). “Organizational information systems: determinants of their performance and behavior.” Management Science 28(2): 138-155.
24
.........Robert Johansen
- Senior Vice President and Distinguished FellowInstitute for the Future (Palo Alto, California)
CONTACT INFORMATION
Institute for the Future124 University Avenue, 2nd FloorPalo Alto, California 94301
(650) 854-6322 Telephone(650) 854-7850 Fax
http://www.iftf.org/people/bjohansen.html
EDUCATION
Ph.D. – Northwestern UniversityBS – University of Illinois
RESEARCH INTERESTS
Forecasting and exploring business, social, and organizational effects of new technologies; future of religion and its impacts on business, society, and individuals.
KEY PUBLICATIONS
Johansen, R. (1988). Groupware: Computer Support for Business Teams. New York : London, Free Press; Collier Macmillan.
Johansen, R. (1977). "Social Evaluations of Teleconferencing." Telecommunications Policy 1(5): 395-419.
25
.........Jay F. Nunamaker, Jr.
- Regents & Soldwedel Professor of MIS, Computer Science, and Communication- Director, Center for the Management of InformationDepartment of Management Information SystemsThe University of Arizona (Tucson, Arizona)
CONTACT INFORMATION
Department of Management Information SystemsEller College of Business and Public AdministrationThe University of ArizonaTucson, Arizona 85721
(520) 621-4475 Telephone(520) 621-2433 Facsimile
http://www.cmi.arizona.edu/home/Jay%20Nunamaker.html
EDUCATION
Ph.D. – Operations Research & Systems, Case Western Reserve University, 1969MS – Industrial & Systems Engineering, University of Pittsburgh, 1965BS – Industrial Management, Carnegie Mellon University, 1964BS – Mechanical Engineering, University of Pittsburgh, 1960
RESEARCH INTERESTS
Computer supported collaboration and decision support to improve productivity and communication.
KEY PUBLICATIONS
Nunamaker, J. F., A. R. Dennis, et al. (1991). "Electronic Meeting Systems to Support Group Work." Communications of the Acm 34(7): 40-61.
Nunamaker, J. F., Jr., M. Chen, et al. (1990-1991). "Systems Development in Information Systems Research." Journal of Management Information Systems 7(3): 89 (18 pages).
Nunamaker, J. F., Jr., R. O. Briggs, et al. (1996-1997). "Lessons from a Dozen Years of Group Support Systems Research: A Discussion of Lab and Field Findings." Journal of Management Information Systems 13(3): 163-207.
26
.........Murray Turoff
- Distinguished Professor- Hulburt Professor of MIS Information Systems Department New Jersey Institute of Technology (Newark, NJ)
CONTACT INFORMATION
Information Systems DepartmentNew Jersey Institute of TechnologyUniversity HeightsNewark, NJ 07102
(973) 596-3366 Telephone(973) 596-5777 [email protected] http://eies.njit.edu/~turoff/
EDUCATION
Ph.D. – Physics, Brandeis University, 1965B. A. – Mathematics and Physics, University of California at Berkeley, 1958
RESEARCH INTERESTS
Information Systems, Computer Mediated Communication Systems, Delphi Design, Policy Analysis, Planning Methodologies, Interface Design, Systems Evaluation, Technological Forecasting & Assessment, Collaborative Systems & Group DSSs, Office Automation, Management Information Systems, Social Impacts of Computer & Information Systems, and Management of Computer and Information Systems
KEY PUBLICATIONS
Turoff, M. (1971). "Delphi and its Potential Impact on Information Systems." AFIPS Conference Proceedings, Fall Joint Comptuer Conference 39: 317-326.
Turoff, M. (1991). "Computer Mediated Communication Requirements for Group Support." Journal of Organizational Computing 1(1): 85-113.
27
.........Joseph S. Valacich
- The Marian E. Smith Presidential Endowed Chair- The George and Carolyn Human Distinguished Professor
in Information SystemsSchool of Accounting, Information Systems, and Business LawWashington State University (Pullman, Washington)
CONTACT INFORMATION
School of Accounting, Information Systems & Business LawCollege of Business and EconomicsWashington State UniversityPullman, Washington 99164
(509) 335-1112 Telephone
http://www.cbe.wsu.edu/~jsv
EDUCATION
Ph.D. – Management Information Systems, University of Arizona, 1989MBA – General Management, University of Montana, Missoula, 1983BS – Computer Science, University of Montana, Missoula, 1982
RESEARCH INTERESTS
Electronic commerce, the diffusion of technology in organizations, group decision behavior, and distance learning.
KEY PUBLICATIONS
Nunamaker, J. F., A. R. Dennis, et al. (1991). "Electronic Meeting Systems to Support Group Work." Communications of the Acm 34(7): 40-61.
Nunamaker, J. F., A. R. Dennis, et al. (1991). "Information Technology for Negotiating Groups - Generating Options for Mutual Gain." Management Science 37(10): 1325-1346.
Connolly, T., L. M. Jessup, et al. (1990). "Effects of Anonymity and Evaluative Tone on Idea Generation in Computer-Mediated Groups." Management Science 36(6): 689-703.
28
.........Key Papers
Within the context of our model, the key collaboration papers include:
Electronic Brainstorming and Group SizeGallupe, Brent R., Dennis, Alan R., Cooper, William H., Valacich, Joseph, Bastianutti, Lana M., Nunamaker Jay F.Academy of Management Journal, 35(5), 1992, 350-369. [Link]Classification: Behavioral, Application, Extension
Two experiments were conducted with small and large groups. The quality of ideas was compared between the groups. The larger groups generated more unique, high quality ideas and the members were more satisfied when electronic brainstorming was used. There were few differences for smaller groups. Electronic brainstorming reduces the effects of production blocking and evaluation apprehension.
Issues in the Design of Group Decision Support SystemsHuber, G.P. MIS Quarterly, 4, 1984, 195-204. [Link]Classification: Behavioral, Theory, Foundational
Abstract:The need for GDSS systems is driven by the clash of two forces: The
environmentally-imposed demand for more information sharing and the resistance to allocating more managerial attention and professional time to sharing information. In addressing this clash, the paper focuses on three issues in the design of these systems: System capabilities, system delivery modes, system design strategies. Each of these issues has a connection to the system’s survival.
Electronic Meeting Systems to Support Group WorkNunamaker, Jay F., Dennis, Alan R., Valacich, Joseph S., Vogel, Douglas R., George, Joey F.Communications of the ACM, 34(7), 1991, 40-61. [Link]Classification: Behavioral, Application, Foundational
Abstract:This paper provides an introduction to electronic meeting systems. Advantages of the system include the fact that all participants work simultaneously, all have an equal opportunity for participation, counterproductive behavior is discouraged, larger groups can work together effectively, outside information is easily accessible and an automatic organizational memory is generated. Disadvantages are that anonymity may mean that individuals may not participate at all, reaction to comments is slowed by the mechanics of typing, there is less richness in the communication process and separation of people from comments can lead to a feeling of dehumanization among participants.
Beyond the Chalkboard: Computer Support for Collaboration and Problem Solving in MeetingsStefik, M., Foster, G., Bobrow, D.G., Kahn, K., Lanning, S. & Suchman, L.Communications of the ACM, 30(1), 1987, 33-47. [Link]Classification: Behavioral, Application, Exploratory
29
.........
An experimental meeting room called the Colab at Xerox PARC has been created to study computer support of collaborative problem solving in face-to-face meetings. The long-term goal is to understand how to build computer tools to make meetings more effective.
Delphi and its Potential Impact on Information SystemsTuroff, M.AFIPS Conference Proceedings, Fall Joint Computer Conference, 39, 1971, 317-326. [Link]Classification: Behavioral, Theory, Foundational
This work proposes that the Delphi method can be used to establish a meaningful group communication structure. The paper illustrates that the Delphi method is “a method for the systematic solicitation and collation of informed judgments on a particular topic”
Lessons from a Dozen Years of Group Support Systems Research: A Discussion of Lab and Field FindingsNunamaker, J.F., Jr.; Briggs, R.O.; Mittleman, D.D.; Vogel, D.R.; and Balthazard, P.A.Journal of Management Information Systems, 13, 3 (Winter 1996-97), 163-207. [Link]Classification: Behavioral, Application, Foundational
A theoretical foundation for the Groupware Grid, a tool for designing and evaluating GSS, is presented. Lessons are presented from 9 key domains: 1. GSS in organizations, 2. cross-cultural and multicultural issues, 3. designing GSS software, 4. collaborative writing, 5. electronic polling, 6. GSS facilities and room design, 7. leadership and facilitation, 8. GSS in classroom and 9. Business process reengineering.
Computer Mediated Communication Requirements for Group SupportTuroff, MurrayJournal of Organizational Computing, Volume 1, Number 1. (1991), 85-113. [Link]Classification: Technical, Application, Extension
This article provides an overview of historical evolution of computer mediated communication systems within the context of designing for group support. A number of examples of design features to support specific group tasks are illustrated. The result of this is a synthesis of a number of observations on the assumptions and goals for the design of CMC systems. An emphasis is placed on the advantages offered by asynchronous support of communication process, self-tailoring of communication structures by users and groups, and the integration into the communication system of other computer resources and information systems.
Information Technology for Negotiating Groups: Generating Options for Mutual GainNunamaker, J. F., Jr., Dennis, Alan R., Valacich, Joseph S., Vogel, Douglas R.Management Science. Linthicum: Oct 1991. Vol. 37, Iss. 10; pg. 1325-47. [Link]Classification: Behavioral, Application, Extension
This paper addressed the important initial stage of negotiation: generating options for mutual gain. An integrated series of laboratory and field studies is presented that investigated various aspects of computer-supported option generation for groups that meet at the same place and time. The use of anonymity to separate personalities from the issues and promote more objective evaluation was found to improve option generation in some circumstances, particularly those with increased criticalness or power differences among
COLLABORATION
30
.........
the participants. Larger groups were found to be more effective than smaller groups, several smaller groups combined, and nominal groups.
Future Research in Group Support Systems: Needs, Some Questions and Possible DirectionsNunamaker, J. F.International Journal of Human-Computer Studies, 47, 3, 1997, 357-385. [Link]Classification: Behavioral, Application, Exploratory
This paper discusses the future of GSS research in terms of what is needed, some important research questions, and offers some possible directions. Sections 1-4 describes the fundamental background information for GSS research, the need for GSS research, the multi-methodological approach and several major issues in applying GSS in organizational settings. Sections 5-7 explores important GSS questions, and keys to successful distributed collaboration from our experience and provide some answers to the difficult question "what is needed for a distributed workspace?" Section 8-10 clarifies just what virtual reality can offer for distributed collaboration, the justification for a virtual reality representation of the distributed office and what we need to get real work done in a virtual workspace including: support for sense making during the process, automating bottlenecks in the process, modeling through simulation and animation, multiple languages, education, crisis response and software inspection.
Effects of anonymity and evaluative tone on idea generation in computer-mediated groupsConnolly, Terry, Jessup, Leonard M., Valacich, Joseph S. LinthicumManagement Science Jun 1990, 36, 6, p. 689 – 703. [Link]Classification: Behavioral, Theory, Extension
The effects of anonymity and evaluative tone on computer-mediated groups were evaluated using a group decision support system to perform an idea-generation task. Evaluative tone was manipulated through a confederate group member who entered supportive or critical comments into the automated brainstorming system. Groups working anonymously and with a critical confederate produced the greatest number of original solutions and overall comments, but average solution quality per item and average solution rarity did not differ across conditions. Identified groups working with a supportive confederate were the most satisfied and had the highest levels of perceived effectiveness, but they produced the fewest original solutions and overall comments.
Groupware: Some Issues and ExperiencesEllis, Clarence A., Gibbs, Simon J., Rein, Gail L.Communications of the ACM, Jan 1991, 34, 1, pp. 38-59. [Link]Classification: Technical, Application, Extension
The goal of groupware is to assist groups in communicating, in collaborating, and in coordinating their activities. The most familiar
example of groupware is the computer-based message system, which supports the asynchronous exchange of textual messages between groups of users. The conceptual underpinning of groupware - the merging of computer and communications technology - applies to a broad range of systems. Information sharing in the groupware context leads to unexplored problems in distributed systems and user interface design that emphasizes group interaction.
COLLABORATION
31
.........Section4Data ManagementIntroduction
Data Management, a key research area within the MIS field, has seen great progress in the past 50 years. The field, which was driven by the basic requirement of storing data, has generated one of the most important techniques that support many key applications, especially enterprise applications and government applications.
Databases can be separated into 4 categories according to the database’s structure: Network Databases; Heretical Databases; Relational Databases; and Object Oriented Databases.
In 1961 Charles Bachman at the General Electric Company introduced the first network database—an integrated data store (IDS) system. This kind of database was very successful at that time. The biggest software company at that time, Cullinet (founded in 1973), was a network database provider. In 1971, the Data Base Task Group of the Conference on Data Systems Languages had already begun to define the network database standard, which is called CODASYL.
The second database system to become popular was the hierarchical database. One famous example of a hierarchical database is the Information Management System (IMS) developed by IBM in 1968. Although the hierarchical database was not as successful as network database, some of the products, such as IMS (currently IMS v6), are still used in many important areas.
The milestone of the development of the field of data management is E. F. Codd’s paper “A Relational Model of Data for Large Shared Data Banks” (1970), which gave birth to the relational database. Codd put all his efforts into beautifying relational theory, the foundation of the powerful relational database. Relational databases proved quite successful. At the time, however, the relational model was not embraced and adopted. In 1974, there was a famous argument on the pros and cons of relational databases, between the researchers led by Codd who supported relational databases, and the researchers led by Bachman who supported network databases. Codd’s eventual victory in this argument propelled the development of the relational database.
After Codd’s theoretical research, other researchers created the prototype of relational database. The two most famous groups are the SYSTEM R group in IBM , which led to IBM's SQL/DS & DB2, Oracle, HP's Allbase etc, and the INGRES group led by Michael Stonebraker and Eugene Wong at the University of California—Berkeley, which ultimately led to Ingres Corp., Sybase, MS SQL Server, and others. Another byproduct of SYSTEM R was the SQL language invented by Ray Boyce and Don Chamberlin in 1974, which is now the standard database query language. The two prototypes had such great success that both of them were awarded the “Software System Award” by ACM in 1988.
Object-oriented is another database technology. In 1985 Michael Stonebraker’s INGRES group changed into the Postgres group; the group’s focus moved to the research on object-oriented database. In 1986 the first commercial objected-oriented database, Gbase, was invented by GRAPHEL Co. In the past 20 years, researchers have investigated many aspects on how an object-oriented DBMS might address some of the weaknesses of the relational model. Although the theory is very powerful, the application of it is still very
32
.........narrow—the relational database is so successful that the companies do not take the risk of merging into another new database model.
Besides the evolution of the of database structure, much research has been done in database design, database integration, distributed database, and other such topics.
In 1976, Peter Pin-Shan Chen published his famous paper “The Entity-Relationship Model--Toward a Unified View of Data”, which incorporated real-world semantic-based relationships in database design. The ER model proposed in the paper is simple to understand, and powerful for design. The entity-relationship model has been the most widely used design method in conceptual database design and extended by many researchers in multiple fields. For example Teorey, Yang, et al proposed “A Logical Design Methodology For Relational Databases Using The Extended Entity-Relationship Model” in 1986.
As there are so many kinds of different databases in the world, database integration is also a very important area. Spaccapietra (1992) discussed the assertion-based approach of integrating different schemas in “Model Independent Assertions For Integration Of Heterogeneous Schemas”, with which the data administrator need not to define all the details of schema elements but only the basic corresponding elements and the nature of the correspondence in between. Batini provided a unifying framework for the problem of schema integration in “A Comparative Analysis of Methodologies for Database Schema Integration” in 1986. The paper also reviewed the research in this area.
Distributed Data Management is another solution to improve the database’s capability and scalability. In 1980, Mohan discussed some research issues in “Distributed Data Base Management: Some Thoughts and Analyses”. In 1995 Salvatore T. March and Sangkyu Rho did some research on the concurrency control, retrieval optimization, and data allocation and replication in Heterogeneous Distributed Database Systems, in their paper titled “Allocating Data and Operations to Nodes in Distributed Database Design”.
In recent years, the rapid development of the internet and the World Wide Web have given rise to the need to store and query unstructured text, audio, and video data in a database. XML is a technical language which is used in this field. Now, the database which combines XML and database techniques has appeared. In 1999 AG invented the Tamino, a native XML database, which stored the original XML objects and used XML query techniques to search information in the database.
33
.........School Listing
Data Management
School Tier
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MIT I X X New York University I X University of Arizona I X X X University of Texas-- Austin I X Georgia State University II X X Arizona State University III X Indiana University III X University of Washington IV X Georgia Institute of Technology V X National University of Singapore Research I X Texas A&M University Research I X Boston University Research II X Northeastern University Research II XUniversity of South Florida Research II X George Washington University Research III X Georgetown University Research III X SUNY- Buffalo Research III X University of Colorado-- Denver Research III X Bentley College Teaching I X Purdue University Teaching I X University of Connecticut Teaching II X
34
.........Timeline
35
.........Key Contributors
Raymond F. Boyce (deceased 1974)
- ResearcherIBM Research Laboratory Database Research Division
*Picture not available*
EDUCATION
Ph.D. – Computer Science, Purdue University, 1971
RESEARCH INTERESTS
Structured query language, relational databases
KEY PUBLICATIONS
Chamberlin, D.D., R.F. Boyce (1974). “SEQUEL: A Structured English Query Language.” SIGMOD Workshop. 1 : 249-264
36
.........Peter Pin-Shan Chen
- Murphy J. Foster Professor of Computer Science Department of Computer ScienceLouisiana State University (Baton Rouge, Louisiana)
CONTACT INFORMATION
Louisiana State UniversityDepartment of Computer Science298 Coates Hall, Tower RoadBaton Rouge, LA 70803
(225) 578-1495 Telephone (225) 578-1465 Facsimile
http://bit.csc.lsu.edu/~chen/
EDUCATION
Ph.D. – Computer Science / Applied Mathematics, Harvard University, 1973MS – Computer Science / Applied Mathematics, Harvard University, 1970BS – Electrical Engineering, National Taiwan University, 1968
RESEARCH INTERESTS
Database design, Entity-Relationship Model, Software Engineering in particular Computer-Aided Software Engineering
KEY PUBLICATIONS
Chen, P. P.-S. (1976). "The entity-relationship model toward a unified view of data." ACM Transactions on Database Systems (TODS) 1(1): 9-36.
37
.........Edgar F. Codd (1924-2003)
- Researcher- Founder of Relational DatabasesIBM Research Laboratory (San Jose, CA)
CONTACT INFORMATION
Memorial website: http://www.sis.pitt.edu/~mbsclass/hall_of_fame/codd.htm
EDUCATION
Ph.D. – Computer Science, University of Michigan at Ann Arbor, 1965BS – Mathematics / Chemistry, Oxford University, England
RESEARCH INTERESTS
Relational database technologies
KEY PUBLICATIONS
Codd, E. F. (1970). "A Relational Model of Data for Large Shared Data Banks." Communications of the Acm 13(6): 377-387.
Codd, E. F. (1979). "Extending the database relational model to capture more meaning." ACM Transactions on Database Systems (TODS) 4(4): 397 - 434.
Codd, E. F. (1972). "Relational Completeness of Data Base Sublanguages." Database Systems: 65-98.
38
.........Alen R. Hevner
- ProfessorInformation Systems and Decision SciencesUniversity of South Florida (Tampa, FL)
CONTACT INFORMATION
Information Systems and Decision SciencesCollege of Business AdministrationUniversity of South Florida4202 East Fowler Avenue, CIS 1040Tampa, FL 33620-7800
(813) 974-6753 Telephone(813) 974-6749 Facsimile
http://www.coba.usf.edu/hevner
EDUCATION
Ph.D. - Purdue University, Computer Science, September 1979M.S. - Purdue University, Computer Science, December 1976B.S. - Purdue University, May 1973
RESEARCH INTERESTS
Information system, Distributed Database, Decision Science
KEY PUBLICATIONS
Apers, P. M. G., A. R. Hevner, et al. (1983). "Optimization algorithms for distributed queries." IEEE Transactions Software Engineering 9(1): 57-68.
Elmasri, R., J. Weeldreyer, et al. (1985). "The category concept: an extension to the entity-relationship model." Data & Knowledge Engineering 1(1): 75-116.
39
.........Stuart E. Madnick
- John Norris Maguire Professor of Information TechnologyLeaders for Manufacturing Professor of Management Science Sloan School of Management Massachusetts Institute of Technology (Cambridge, MA)
CONTACT INFORMATION
77 Massachusetts Ave. Building E53-321Cambridge, MA 02139-4307 [email protected]
http://web.mit.edu/smadnick/www/home.html
EDUCATION
Ph.D. – Computer Science, Massachusetts Institute of Technology, 1972MS – Management, Massachusetts Institute of TechnologyBS – Electrical Engineering, Massachusetts Institute of Technology
RESEARCH INTERESTS
Connectivity among disparate distributed information systems, database technology, software project management, and the strategic use of information technology
KEY PUBLICATIONS
Abdel-Hamid, T. and S. E. Madnick (1991). Software project dynamics: an integrated approach, Prentice-Hall, Inc.
Goh, C. H., S. E. Madnick, et al. (1994). "Context interchange: overcoming the challenges of large-scale interoperable database systems in a dynamic environment." Proceedings of the third international conference on Information and knowledge management: 337-346.
Wang, Y. R. and S. E. Madnick (1989). The Inter-Database Instance Identification Problem in Integrating Autonomous Systems. Proceedings of the Fifth International Conference on Data Engineering.
40
.........Salvatore T. March
- David K. Wilson Professor of Management (Information Technology)Owen Graduate School of ManagementVanderbilt University (Nashville, TN)
CONTACT INFORMATION
Owen Graduate School of Management Vanderbilt UniversityNashville, TN 37203
(615) 322-7043 Telephone (615) 343-7177 Facsimile
http://mba.vanderbilt.edu/Sal.March/
EDUCATION
Ph.D. – Operations Research / Information Processing, Cornell University, 1978M.S. – Operations Research, Cornell University, 1975B. S. – Industrial Engineering / Operations Research, 1972
RESEARCH INTERESTS
Database development, design and integration. Data modeling.
KEY PUBLICATIONS
March, S. T. and D. G. Serverance (1977). "The determination of efficient record segmentations and blocking factors for shared data files." ACM Transactions on Database Systems (TODS) 2(3): 279 - 296.
Kim, Y. G. and S. T. March (1995). "Comparing Data Modeling Formalisms." Communications of the Acm 38(6): 103-115.
41
.........Shamkant B. Navathe
- ProfessorCollege Of ComputingGeorgia Institute Of Technology (Atlanta, GA)
CONTACT INFORMATION
College of ComputingGeorgia Institute of Technology801 Atlantic Dr., NWAtlanta, GA 30332-0280
(404) 894-0537 Telephone(404) 894-9442 Facsimile
http://www.cc.gatech.edu/~sham/
EDUCATION
Ph.D. Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 1976M.S. Computer and Information Science, Ohio State University, Columbus, OH 1970B.E. Electrical Communications Engineering, Indian Institute of Science 1968B.Sc. Physics, Mathematics, University of Poona, India 1965
RESEARCH INTERESTS
Database Modeling and Design, Distributed and Mobile Databases, Engineering and Manufacturing Applications, Intelligent Information Retrieval, Knowledge Management, Genome Data Management, Web Transaction and Applications modeling, Text Mining
KEY PUBLICATIONS
Batini, C., M. Lenzerini, et al. (1986). "A comparative analysis of methodologies for database schema integration." ACM Computing Surveys (CSUR) 18(4): 323 - 364.
Elmasri, R. and S. B. Navathe (1989). Fundamentals of database systems, Benjamin-Cummings Publishing Co., Inc.
Larson, J. A., S. B. Navathe, et al. (1989). "A Theory of Attributed Equivalence in Databases with Application to Schema Integration." IEEE Transactions on Software Engineering 15(4): 449 - 463.
42
.........Sudha Ram
- Eller Professor of Management Information SystemsDepartment of Management Information SystemsEller College of ManagementUniversity of Arizona (Tucson, AZ)
CONTACT INFORMATION
Department of Management Information SystemsEller College of ManagementThe University of ArizonaTucson, Arizona 85721
(520) 621-2748 Telephone(520) 621-4113 Facsimile
http://vishnu.bpa.arizona.edu/ram/index.html
EDUCATION
Ph.D. – MIS / Corporate Finance, University of Illinois at Urbana Champaign, 1985.MBA – MIS, Indian Institute of Management, Calcutta, India, 1981 BS – Chemistry / Mathematics / Physics, University of Madras, India, 1979
RESEARCH INTERESTS
E-business infrastructure and strategy, Automate design tools for database design, distributed and heterogeneous database systems, intelligent agents and digital libraries, design of distributed knowledge based systems
KEY PUBLICATIONS
Ram, S. (1995). "Intelligent database design using the unifying semantic model." Information and Management 29(4): 191 - 206.
Hayne, S. and S. Ram (1990). Multi-User View Integration System (MUVIS): An Expert System for View Integration. Proceedings of the Sixth International Conference on Data Engineering.
Ram, S. (1991). "Heterogeneous Distributed Database-Systems." Computer 24(12): 7-10.
43
.........Stefano Spaccapietra
- Professor Department of Management Information SystemsSwiss Federal Institute of Technology (Lausanne (EPFL), Switzerland)
CONTACT INFORMATION
IN J 236Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
+41 (21) 693 5210 Telephone
http://lbdwww.epfl.ch/~stefano/e-index.html
EDUCATION
PhD – Computer Science - University of Paris VI, 1978M.S. – Computer Science - University of Paris VI, 1969
RESEARCH INTERESTS
Semantic interoperability, spatio-temporal data modeling, multimedia databases and various modeling issues (views, schema evolution, data model translation).
KEY PUBLICATIONS
Spaccapietra, S., C. Parent, et al. (1992). "Model independent assertions for integration of heterogeneous schemas." The VLDB Journal — The International Journal on Very Large Data Bases 1(1): 81 - 126.
Spaccapietra, S. and C. Parent (1994). "View Integration - a Step Forward in Solving Structural Conflicts." Ieee Transactions on Knowledge and Data Engineering 6(2): 258-274.
Devogele, T., C. Parent, et al. (1998). "On spatial database integration." International Journal of Geographical Information Science 12(4): 335 - 352.
44
.........Michael Stonebraker
- Professor of Electrical Engineering and Computer Science Department of Electrical Engineering and Computer ScienceUniversity of California at Berkeley (Berkeley, CA)
CONTACT INFORMATION
Department of Electrical Engineering and Computer Science621 Soda HallBerkeley, CA 94720-1770
(510) 642-5799 Telephone
http://epoch.cs.berkeley.edu:8000/nasa_e2e/mike.html
EDUCATION
Ph.D. – Information and control engineering, University of Michigan, 1971MS – University of Michigan BS – Princeton University
RESEARCH INTERESTS
Operating systems and expert systems, DBMS support for visualization environments and next-generation distributed DBMSs
KEY PUBLICATIONS
Stonebraker, M. (1987). The Design of the POSTGRES Storage System. Proceedings of the 13th International Conference on Very Large Data Bases.
Stonebraker, M., G. Held, et al. (1976). "The design and implementation of INGRES." ACM Transactions on Database Systems (TODS) 1(3): 189-222.
45
.........Veda Storey
- Professor of Computer Information Systems- Professor of Computer ScienceCollege of Business AdministrationGeorgia State University (Atlanta, GA)
CONTACT INFORMATION
Department of Computer Information SystemsJ. Mack Robinson College of Business AdministrationGeorgia State UniversityP.O. Box 4015Atlanta, Georgia 30302-4015
(404)-651-3894 Telephone
http://www.cis.gsu.edu/~vstorey/
EDUCATION
Ph.D. – Management Information Systems, University of British Columbia, 1986MBA – Queen’s University, 1980BS – Mathematics / Computer Science, Mt. Allison University, 1978
RESEARCH INTERESTS
Database management systems, intelligent systems, and ontology development.
KEY PUBLICATIONS
Sugumaran, V. and V. C. Storey (2002). "Ontologies for conceptual modeling: their creation, use, and management." Data & Knowledge Engineering 42(3): 251-271.
Storey, V. C. and D. Dey (2002). "A Methodology for Learning Across Application Domains for Database Design Systems." IEEE Transactions on Knowledge and Data Engineering 14(1): 13-28.
46
.........Toby J. Teorey
- Professor, EECSComputer Science and Engineering Division (CSE)Department of Electrical Engineering and Computer Science (EECS)University of Michigan (Ann Arbor, MI)
CONTACT INFORMATION
1301 Beal AvenueAnn Arbor, MI 48109-2122 USA
734-763-8154 Telephone734-763-1503 Facsimile
http://www.eecs.umich.edu/~teorey/
EDUCATION
PhD, Computer Science, University of Wisconsin, 1972 MS, Electrical Engineering, University of Arizona, 1965 BS, Electrical Engineering, University of Arizona, 1964
RESEARCH INTERESTS
Database and data warehouse design, On-Line Analytical Processing (OLAP), Performance analysis of computer systems
KEY PUBLICATIONS
Teorey, T. J., D. Yang, et al. (1986). "A logical design methodology for relational databases using the extended entity-relationship model." ACM Computing Surveys (CSUR) 18(2): 197 - 222.
Teorey, T. J. and T. B. Pinkerton (1972). "A comparative analysis of disk scheduling policies." Communications of the ACM 15(3): 177 - 184.
47
.........Kim Won
- CEOCyber Database Solutions (Austin, TX)
CONTACT INFORMATION
Cyber Database SolutionsAustin, Texas
(512) 771-9376 Telephone(512) 329-0244 Facsimile
http://www.cyberdb.com/index.html
EDUCATION
Ph.D. – University of Illinois in Urbana-Champaign, 1980M.S./ B. S. – Massachusetts Institute of Technology
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 PUBLICATIONS
Banerjee, J., H. T. Chou, et al. (1987). "Data Model Issues for Object-Oriented Applications." Acm Transactions on Office Information Systems 5(1): 3-26.
Banerjee, J., W. Kim, et al. (1987). Semantics and implementation of schema evolution in object-oriented databases. Proceedings of the 1987 ACM SIGMOD international conference on Management of data.
Won, K. (1982). "On optimizing an SQL-like nested query." ACM Transactions on Database Systems (TODS) 7(3): 443 - 469.
Kifer, M., W. Kim, et al. (1992). "Querying object-oriented databases." Proceedings of the 1992 ACM SIGMOD international conference on Management of data 393-402.
48
.........Key Papers
Sub-Category 1: Database architecture and design
A Relational Model of Data for Large Shared Data BanksCodd, E. F.Communications of the ACM, 12(6), 1970, 377-387. Classification: Technical, Theory, Foundational
A model based on n-ary relations, a normal form for data base relations, and the concept of a universal data sublanguage are
introduced in this paper. Moreover, certain operations on relations (other than logical inference) are discussed and applied to the problems of redundancy and consistency in the user's model. Codd’s model laid the theoretical foundation for relational databases.
The Entity-Relationship Model – Toward a Unified View of DataChen, P. P.ACM Transactions on Database Systems (TODS), 1(1) 1976, 9-36. Classification: Technical, Theory, Extension
A data model, called the entity-relationship model, is proposed. This model incorporates some of the important semantic information about
the real world, and can be used as a basis for unification of different views of data. The entity-relationship model is now widely used in conceptual database design.
The design and implementation of INGRES Michael Stonebraker, G. H., Eugene Wong, Peter Kreps ACM Transactions on Database Systems (TODS) 1976 1(3): 189-222.Classification: Technical, Application, Exploratory
The paper described the design and implementation of INGRES database management system. INGRES is a multi-user relational
database management system which supports two high level nonprocedural data sublanguages, and runs as a collection of processes. The paper discussed the multi-process structure, process management, access management, concurrency and recovery control of the system. INGRES is the precedent of many modern RDBMS.
Database abstractions: aggregation and generalization Smith, J. M. and D. C. P. SmithACM Transactions on Database Systems (TODS), 1977, 2(2): 105-133.Classification: Technical, Theory, Extension
The paper defined two kinds of important abstraction methods in database design—aggregation and generalization. Using aggregation and generalization, data models could be defined as a set of aggregation hierarchies intersecting with a set of generalization hierarchies. This high level structure provides a discipline for the organization of relational databases. The methodology makes the data model easier to design, maintain, understand, use and optimize. The paper also proposed a triggering mechanism to fulfill the need of this mechanism, which is now widely used in RDMBS
49
......... Data Model Issues for Object-Oriented Applications Banerjee, J., H. T. Chou, et al. ACM Transactions on Office Information Systems 1987 5(1): 3-26.Classification: Technical, Application, Exploratory
The paper proposed a prototype database system that adds persistence and sharability to objects created and manipulated in
object-oriented applications. The ORION data model proposed three enhancements to the conventional object-oriented data model: schema evolution, composite objects, and versions. These enhancements are strongly motivated by the data management requirements of the ORION applications from the domains of artificial intelligence, computer-aided design and manufacturing, and office information systems with multimedia documents.
SEQUEL: A Structured English Query LanguageChamberlin, D.D., R.F. Boyce (1974)SIGMOD Workshop, 1: 249-264Classification: Technical, Theory, Foundational
In this paper, they introduced the data manipulation facility for a structured English query language (SEQUEL) which can be used for
accessing data in an integrated relational database and which is so popular and widely used in current database management industry.
Sub-Category 2: Database Integration
A comparative analysis of methodologies for database schema integrationBatini, C., M. Lenzerini, et al. ACM Computing Surveys (CSUR) 1986 18(4): 323 - 364.Classification: Technical, Application, Review
The paper is provided a unifying framework for the problem of schema integration, then a comparative review of the work done thus far in this area. Such a framework, with the associated analysis of the existing approaches, provides a basis for identifying strengths and weaknesses of individual methodologies, as well as general guidelines for future improvements and extensions.
Semantic database modeling: survey, applications, and research issues Hull, R. and R. King ACM Computing Surveys (CSUR) 1987 19(3): 201-260.Classification: Technical, Application, Review
The paper reviews the philosophical motivations of semantic models, including the need for high-level modeling abstractions and the reduction of semantic overloading of data type constructors. It then introduced the primary components of semantic models. The paper surveyed the prominent semantic models. The paper also discussed a number of related topics based on these models.
Model independent assertions for integration of heterogeneous schemas Spaccapietra, S., C. Parent, et al. The International Journal on Very Large Data Bases 1992, 1(1): 81 - 126.Classification: Technical, Application, Exploratory
50
.........This paper investigates the assertion-based approach, in which the DBA's action is limited to pointing out corresponding elements in the schemas and to defining the nature of the correspondence in between, instead of restructuring schema elements from existing local schemas and of solving inter-schema conflicts. This methodology is capable of: ensuring better integration by taking into account additional semantic information (assertions about links); automatically solving structural conflicts; building the integrated schema without requiring conforming of initial schemas; applying integration rules to a variety of data models; and performing view as well as database integration.
51
......... Context Interchange: Overcoming the Challenges of Large-Scale Interoperable Database Systems in a Dynamic EnvironmentCheng Hian Goh, Stuart E. Madnick, Michael SiegelCIKM 1994: 337-346. Classification: Technical, Application, Extension
This paper highlights the problem of receiver heterogeneity, scalability, and evolution which have received little attention in the literature, provides an overview of the Context Interchange approach to interoperability, illustrates why this is able to better circumvent the problems identified, and forges the connections to other works by suggesting how the context interchange framework differs from other integration approaches in the literature.
Sub-Category 3: Distributed Database
Distributed data base management: Some thoughts and analyses.Mohan, C. Proceedings of the ACM 1980 annual conference 1980: 399 - 410.Classification: Technical, Application, Review
This paper initially presents a brief outline of the nature of research that has been performed in distributed database. It also discussed some of the important issues (like integrity and security constraints, deadlocks, concurrency control, etc.) and analyses some proposed mechanisms. Then, it presented a brief sketch of an adaptive architecture for distributed data base management systems.
Allocating Data and Operations to Nodes in Distributed Database Design. March, S. T., & Rho, S.IEEE Transactions in Knowledge and Data Engineering, 72, 1995, 305-317. Classification: Technical, Application, Exploratory
A comprehensive mathematical modeling approach to allocating data and operations to nodes in a computer network is proposed. The approach first generates units of data to be allocated from a logical data model representation. Retrieval and update activities are then decomposed into relational operations on these fragments. Both fragments and operations on them are then allocated to nodes using a mathematical modeling approach.
52
.........Section5Decision Sciences/Operations ManagementIntroduction
Decision Sciences consists of three primary categories: Decision Support Systems (DSS), E-Commerce/Supply Chain Management, and Operations Research (OR). Although the three sub-disciplines are quite contrasting with respect to their research focus, methodologies, origins, and lifespan, they share a common objective which binds them together. That objective is to aid the decision-making process.
The most traditional of these categories is Operations Research, which has been around for well over a half-century. Operations Research evolved from being a method of solving tactical war-time problems to becoming a form of “practical mathematics” that could be applied to business problems. This explains the highly technical focus of most Operations Research. OR is comprised of two major areas: Deterministic research and Stochastic research. Deterministic topics relate to optimization, facility layout, and inventory control, whereas Stochastic topics include quality control, forecasting, and simulation.
Decision Support Systems became popular in the 1980’s as part of a focus towards providing managers with tools to enhance decision-making abilities. Whereas OR was geared towards highly structured, model-based problems, DSS was aimed at less structured problems. Although much of the earlier DSS research dealt with laying the framework for such systems, more recent work has focused on studying Decision Support Systems in domain-specific situations or to assess Group DSS, which has become its own research area.
E-Commerce and Supply Chain Management have emerged as major categories of Decision Sciences in the past decade. These areas have spawned as a result of the emergence of the internet as a powerful business medium, increased customer centrism, and globalization.
We would like to give special thanks to Dr. Moshe Dror (University of Arizona) and Dr. France Belanger (Virginia Tech).
53
.........School Listing
Decision Science / Operations Mgmt.
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Carnegie Mellon University I X MIT I X X New York University I X XUniversity of Arizona I X X University of Minnesota I X X XUniversity of Pennsylvania I X X X XUniversity of Texas-- Austin I X X Georgia State University II X X University of California-- Irvine II X XUniversity of Georgia II X University of Pittsburgh II X Arizona State University III X X Harvard University III X XIndiana University III X XUniversity of California-- Los Angeles III X XUniversity of Maryland III X University of Rochester IV X University of Southern California IV X X XUniversity of Washington IV X Georgia Institute of Technology V X University of Illinois V X Drexel University Research I X Florida International University Research I X XFlorida State University Research I X National University of Singapore Research I X XPenn State University Research I X X XQueen's University Research I X XTexas A&M University Research I X University of British Columbia Research I X X University of Houston Research I X University of South Carolina Research I X Auburn University Research II X X Boston University Research II X Case Western Reserve University Research II X Ecole Des Hautes Etudes Comm. Research II X Florida Atlantic University Research II XHong Kong University of S&T Research II X X Northeastern University Research II X
54
.........Rutgers University Research II X Southern Methodist University Research II X Tel Aviv University Research II X University of Colorado-- Boulder Research II X University of Hawaii Research II X University of Memphis Research II X University of South Florida Research II X XUniversity of Toledo Research II X XUniversity of Western Ontario Research II XGeorge Washington University Research III X Georgetown University Research III X London Business School Research III X X XSUNY- Buffalo Research III X Syracuse University Research III X XTennessee Technological Research III X University of Colorado-- Denver Research III X Bentley College Teaching I X Northwestern University Teaching I X XStanford University Teaching I X X X XUniversity of California - Berkeley Teaching I X Duke University Teaching II X X XMichigan State University Teaching II X XRochester Institute of Technology Teaching II X University of Connecticut Teaching II X X
Timeline
55
.........Key Contributors
George Dantzig
- Professor, Operations Research and Computer Science Systems Optimization LaboratoryStanford University (Stanford, CA)
CONTACT INFORMATION
Retired. Information not available.
EDUCATION
Ph.D. – Mathematics, University of California at Berkeley, 1946MS – Mathematics, University of Michigan, 1937BSS – Physics, University of Maryland, 1936BSS – Mathematics, University of Maryland, 1936
RESEARCH INTERESTS
Optimization, Linear programming.
KEY PUBLICATIONS
Dantzig, G. (1963). Linear Programming and Extensions. Princeton, NJ, Princeton University Press.
G. (1951). Maximization of a linear function of variables subject to linear inequalities. Activity Analysis of Production and Allocation, Wiley.
Dantzig, G. (1960). "Decomposition Principle for Linear-programs." Operations Research 8(1): 101-111.
Dantzig, G. (1957). "Discrete-Variable Extremum Problems." Operations Research 5(2): 266-277.
56
.........Hau Lee
- Professor, Operations, Information and Technology- Director, Stanford Global Supply Chain Management ForumStanford University (Stanford, CA)
CONTACT INFORMATION
Stanford Graduate School of BusinessLittlefield 253518 Memorial WayStanford UniversityStanford, CA 94305-5015
(650) 723-0514 Telephone(650) 725-0468 Facsimile
http://gobi.stanford.edu/facultybios/bio.asp?ID=98
EDUCATION
Ph.D. – Operations Research, University of Pennsylvania, 1983MS – Operations Research, University of Pennsylvania, 1979MSc – Operational Research, London School of Economics, 1975BSS – Economics and Statistics, University of Hong Kong, 1974
RESEARCH INTERESTS
Supply chain management, Global logistic system design and control, Multi-echelon inventory systems, Manufacturing and distribution strategy, and Design for supply chain management
KEY PUBLICATIONS
Lee, H. (2001). "E-Fulfillment: Winning the Last Mile of E-Commerce." Sloan Management Review 42(4).
Lee, H., V. Padmanabhan, et al. (1997). "Information distortion in a supply chain: The bullwhip effect." Management Science 43(4): 546-558.
Lee, H. and C. S. Tang (1998). "Variability reduction through operations reversal." Management Science 44(2): 162-172.
Lee, H. and C. Billington (1992). "Managing Supply Chain Inventory - Pitfalls and Opportunities." Sloan Management Review 33(3): 65-73.
57
.........Marshall Fisher
- UPS Transportation Professor for the Private Sector; Professor of Operations and Information Management- Co-Director, Fishman-Davidson Center for Service and Operations ManagementThe Wharton SchoolUniversity of Pennsylvania (Philadelphia, PA)
CONTACT INFORMATION
Operations & Information Management DepartmentThe Wharton School, University of Pennsylvania500 Jon M. Huntsman HallPhiladelphia, PA 19104-6340
(215) 898.5872 Telephone(215) 898.3664 Facsimile
http://opim.wharton.upenn.edu/home/faculty/fisher/index.html
EDUCATION
Ph.D. – Operations Research, MIT, 1970SM - Management, MIT, 1969SB – Electrical Engineering, MIT, 1965
RESEARCH INTERESTS
Supply Chain Management and Retailing
KEY PUBLICATIONS
Fisher, M. and G. P. Cachon (2000). "Supply chain inventory management and the value of shared information." Management Science 46(8): 1032-1048.
Fisher, M. (1997). "What is the right supply chain for your product?" Harvard Business Review 75(2): 105.
Fisher, M., J. H. Hammond, et al. (1994). "Making Supply Meet Demand." Harvard Business Review 72(3): 83-93.
58
.........Ralph Sprague
- ProfessorDecision Sciences DepartmentUniversity of Hawaii at Manoa (Honolulu, HI)
CONTACT INFORMATION
Decision Sciences Department College of Business Administration University of Hawaii at Manoa2404 Maile WayHonolulu, HI 96744
(808) 956-7082 Telephone (808) 956-9889 Facsimile
http://www.cba.hawaii.edu/sprague/home.htm
EDUCATION
PhD – Decision Sciences, Indiana University
RESEARCH INTERESTS
Decision Support Systems, Strategic Systems Planning, Management of Information Systems, and Electronic Document Management
KEY PUBLICATIONS
Sprague, R. (1980). "A Framework for the Development of Decision Support Systems." MIS Quarterly 4(4): 1-26.
Sprague, R. (1996). "Towards a Better Understanding of Electronic Document Management." HICSS 5: 53-61.
Sprague, R. (1995). "Electronic Document Management: Challenges and Opportunities for Information Systems Manage." MIS Quarterly 19(1).
59
.........Key Papers
DECISION SUPPORT SYSTEMS
A Framework for the Development of Decision Support SystemsSprague, R. MIS Quarterly, 4(4), 1980, 1-26.Classification: Technical, Application, Foundational
This article proposes a framework to explore the nature, scope, and content of the evolving topic of Decision Support Systems (DSS). The
first part of the framework considers ((a) three levels of technology which have been designated DSS, (b) the developmental approach that is evolving for the creation of a DSS. and (c) the roles of several key types of people in the building and use of a DSS. The second part develops a descriptive model to assess the performance objectives and the capabilities of a DSS as viewed by three of the major participants in their continued development and use. The final section outlines several issues in the future growth and development of a DSS as a potentially valuable type of information system in organizations.
DSS Design: A Systemic View of Decision SupportAriav, G; Ginzberg, M.Communications of the ACM, 28(10), 1985, 1045-1052.Classification: Behavioral, Theory, Extension
This paper suggests a systemic view of DSS. The author illustrates the premise of the systemic view by considering the following five aspects
simultaneously: environment, role, components, arrangement of components, and the resources required to support the system. Though the topic of this article is DSS, the purpose of this article is to present a comprehensive view of DSS using a systemic framework as an organizing concept. The concepts of systems theory integrate the disparate perspectives in the DSS literature into a consistent and coherent body of knowledge.
An Experimental Investigation of the Impact of Computer-Based Decision Aids on Decision-Making StrategiesTodd, P.; Benbasat, I.Information Systems Research, 2(2), 1991, 87-115.Classification: Behavioral, Application, Extension
This paper proposes the use of a cognitive effort model of decision making to explain decision-maker behavior when assisted by DSS. The central proposition of the article is that specific features can be incorporated within a DSS that will alter the effort required to implement a particular strategy, and thus influence strategy selection by the decision-maker. Based on three empirical experiments, the author contends that decision makers tend to adapt their strategy selection to the type of decision aids available in such a way as to reduce cognitive effort.
ELECTRONIC COMMERCE / SUPPLY CHAIN MANAGEMENT
Electronic Commerce: Structures and IssuesZwass, V.International Journal of Electronic Commerce, 1(1), 1996, 3-23. Classification: Behavioral, Application, Foundational
60
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This paper presents a hierarchical framework of E-commerce development, as well as of analysis, range from the wide-area telecommunications infrastructure to electronic marketplaces and electronic hierarchies enabled by E-commerce. Several nodal problems were discussed that defined future development in E-commerce, including integrating electronic payment into the buying process, building a consumer marketplace, the governance of electronic business, and the new intermediation.
Frictionless Commerce? A Comparison of Internet and Conventional RetailersBrynjolfsson, E.; Smith, M.Management Science, 46(4), 2000, 563-585. Classification: Behavioral, Application, Extension
This paper examines the hypothesis that electronically mediated markets would have less friction than comparable conventional markets, through an empirical price comparison between the two types of markets. The conclusion is that while there is lower friction in many dimensions of Internet competition, branding, awareness, and trust remain important sources of heterogeneity among Internet retailers. The authors conclude by questioning whether these differences are a symptom of an immature market or reflect more permanent characteristics of Internet retailing.
E-Fulfillment: Winning the Last Mile of E-CommerceLee, H.; Whang, S.Sloan Management Review, 42(4), 2001, 75-90. Classification: Technical, Application, Extension
This paper provides an overview of how Internet technologies have allowed existing supply chain strategies to be applied in new and
innovative ways to fulfill orders more quickly and efficiently. In their analysis, the authors explain two core concepts for E-enabled supply chains, as well as five of the more prominent supply chain strategies they underlie. The paper concludes by pointing out ways that companies can extend E-supply chain management and E-fulfillment beyond mere cost containment.
Supply Chain Inventory Management and the Value of Shared InformationCachon, G.P.; Fisher, M.Management Science, 46(8), August 2000, 1032-1048. Classification: Technical, Theory, Exploratory
This paper builds a simple supply chain model and runs simulations to compare the performance of the model with and without the full information sharing made possible by information technology (IT). Upon comparing these metrics, the authors then take their analysis a step further by comparing the information sharing-related performance improvements with other IT-related performance improvements. In the end, they conclude that it is more valuable to use IT to help speed the physical flow of goods through a supply chain rather than to expand the flows of information in a supply chain.
Information Distortion in a Supply Chain: the Bullwhip EffectLee, H.; Padmanabhan, V.; Whang, S.Management Science, 43, 1997, 546-558.Classification: Technical, Application, Exploratory
This paper discusses how wide variations in orders result in information distortion effects that are compounded as they move
downward through a supply chain. The authors have named this phenomenon the bullwhip effect, and claim that it is caused by four major factors that they describe in detail. In addition, they conclude with four suggestions of how companies can counteract and control the bullwhip effect in their supply chains.
61
......... Trust-Related Arguments in Internet Stores: A Framework for EvaluationKim, D.; Benbasat, I.Journal of Electronic Commerce Research, , 4(2), 2003, 49-64.Classification Technical, Application, Extension
This paper discusses the trust related issues and arguments (evidence) Internet stores need to provide in order to increase consumer trust. Based on a model of trust from academic literature, in addition to a model of the customer service life cycle, the paper develops a framework that identifies key trust-related issues and organizes them into four categories: personal information, product quality and price, customer service, and store presence. It is further validated by comparing the issues it raises to issues identified in a review of academic studies, and to issues of concern identified in two consumer surveys. The framework is also applied to ten well-known web sites to demonstrate its applicability. The proposed framework will benefit both practitioners and researchers by identifying important issues regarding trust, which need to be accounted for in Internet stores. For practitioners, it provides a guide to the issues Internet stores need to address in their use of arguments. For researchers, it can be used as a foundation for future empirical studies investigating the effects of trust-related arguments on consumers’ trust in Internet stores.
OPERATIONS RESEARCH
Decomposition Principle for Linear ProgramsDantzig, G.B.; Wolfe, POperations Research, 8(1), 1960, 101-111Classification: Technical, Application, Exploratory
This paper presents an approach for solving linear programs that entails the breaking-up of complex problems into simpler sub-problems
and a coordinating program obtained from the parts, by linear transformation. This coordinator finds the optimum mix of sub-problems based on supply and demand, which generates new prices, which in turn generates new supply and demand. This iterative process eventually leads to an optimal solution.
The Lagrangian Relaxation Method for Solving Integer Programming ProblemsFisher, M.LManagement Science, 27(1), 1981, 1-18Classification: Technical, Application, Extension
This paper reviews the idea that many hard integer programming problems can be viewed as easy problems complicated by a relatively small set of side constraints. This approach, known as Lagrangian relaxation, has led to dramatically improved algorithms for a number of problems in the areas of routing, scheduling, location, and assignment.
Branch-and-Price: Column Generation for Solving Huge Integer ProgramsBarnhart, C.; Johnson, E.L.; Nemhauser, G.L.; Savlesbergh, M.W.P.; Vance, P.H.Operations Research, 46(3), 1998, 316-329Classification: Technical, Application, Exploratory
This paper discusses formulations of integer programs with a huge number of variables and their solution by column generation methods, i.e., implicit pricing of non-basic variables to generate new columns or to prove LP optimality at a node of the branch-and-bound tree. The authors present classes of models for which this
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approach decomposes the problem, provides tighter LP relaxations, and eliminates symmetry. They then discuss computational issues and implementation of column generation, branch-and-bound algorithms, including special branching rules and efficient ways to solve the LP relaxation. The authors also discuss the relationship with Lagrangian duality.
63
.........Section6Economics of InformaticsIntroduction
The application of economics in the MIS realm focuses on two primary areas of investigation: determining the value of information technology investments to organizations and examining the impact of technology on financial markets. Most Economics researchers in MIS have focused largely on the creation and evaluation of economic productivity, valuation methods, and transactions costs. Organizations invest in information systems in order to increase efficiency, quality and productivity, to provide vital information to organizational decision makers, and to gain measures of the success of business processes. By applying economic tools and theories, researchers attempt to assess these purported benefits of IT investments.
The nature of open financial markets depends on the availability of timely and pertinent information to investors. Economists study electronic markets and the impact of information on trade, pricing, and decision-making. As well, they examine indirect effects of technology such as bundling and pricing issues, switching costs, intellectual property rights, and the evolution of IT as an enabler of organizational change. As these fields are closely aligned, researchers tend to work in multiple areas, applying similar approaches to a number of different issues.
Economics of Informatics is founded on fundamentals of economic theory, and is based in large part on Ronald Coase’s 1937 paper, “The Nature of the Firm,” which launched virtually all economic research that is applied to information technology. It first proposed the notion of “transactions costs” and illustrated the strategic importance of these costs to all firms, regardless of market environment. Most subsequent research in this field has its genesis in these concepts.
Our thanks to Dr. Matt Thatcher of the University of Arizona Management Information Systems department for his review of the researchers in this domain.
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Economics of Informatics
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Carnegie Mellon University I X MIT I X X X New York University I X X University of Arizona I X University of Minnesota I X X University of Pennsylvania I X X X University of Texas-- Austin I X Georgia State University II X X University of California-- Irvine II X X Harvard University III X X University of California-- Los Angeles III X X University of Maryland III X University of Rochester IV X University of Washington IV X Florida State University Research I X Southern Methodist University Research II X George Washington University Research III X Georgetown University Research III X Queensland Research III X SUNY- Buffalo Research III X Syracuse University Research III X University of Colorado-- Denver Research III X Northwestern University Teaching I X XStanford University Teaching I X X X University of California - Berkeley Teaching I X Duke University Teaching II X University of Connecticut Teaching II X
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.........Timeline
66
.........Key Contributors
Lynda Applegate
- Henry R. Byers Professor of Business AdministrationHarvard Business SchoolHarvard (Cambridge, MA)
CONTACT INFORMATION
Graduate School of BusinessHarvard UniversityCambridge, Massachusetts 02138
(617) 495-6362 Telephone(617) 496-2910 Facsimile
http://pine.hbs.edu/external/facPersonalShow.do?pid=6411
EDUCATION
Ph.D. – University of Arizona
RESEARCH INTERESTS
Electronic markets. Influence of information technology on markets and organizations. Evolution of electronic commerce and role of IT as an enabler of flexible and adaptive organizational designs. Network organizations
KEY PUBLICATIONS
Appelgate, L. M. and et.al. (1996). "Electronic Commerce: Building Blocks of New Business Opportunity." Journal of Organizational Computing and Electronic Commerce 6(1): 1-10.
67
.........Yannis Bakos
- Associate Professor of ManagementLeonard M. Stern School of BusinessNew York University (New York, NY)
CONTACT INFORMATION
Leonard N. Stern School of BusinessNew York University44 West 4th Street, Room 8-83New York, NY 10012-1126
(212) 998-0841 Telephone
EDUCATION
Ph.D. – Management, MIT Sloan School of ManagementMBA – Finance, MIT Sloan School of ManagementMS – Electrical Engineering, MIT Department of Electrical Engineering and Computer ScienceMS – Computer Science, MIT Department of Electrical Engineering and Computer ScienceBS – Computer Engineering, MIT Department of Electrical Engineering and Computer Science
RESEARCH INTERESTS
Economic and business implications of information technology, the Internet, and online media.
KEY PUBLICATIONS
Bakos, Y. and E. Brynjolfsson (1999). "Bundling information goods: Pricing, profits, and efficiency." Management Science 45(12): 1613-1630.
Bakos, Y. (1998). "Toward Friction-free Markets: The Emerging Role of Electronic Marketplace on the Internet." Communications of the ACM 41(8): 35-42.
Bakos, Y. (1991). "A Strategic Analysis of Electronic Marketplaces." MIS Quarterly 15(3): 295-310.
68
.........Anitesh Barua
- Professor of Information SystemsMcCombs School of BusinessUniversity of Texas at Austin (Austin, TX)
CONTACT INFORMATION
McCombs School of BusinessUniversity of Texas at Austin MGMT SCI & INFO SYSTEMS, B65001 University StationAustin, TX 78712
(512) 471-3322 Telephone
EDUCATION
Ph.D. – , Carnegie Mellon University, 1991MS – Carnegie Mellon University, 1987BE – Jadavpur University, 1984
RESEARCH INTERESTS
Measuring business value of information technology, analyzing strategic information technology investments, enterprise modeling using information economics, and economics of software development and maintenance.
KEY PUBLICATIONS
Barua, A., C. H. Kriebel, et al. (1995). "Information technologies and business value: An analytic and empirical investigation." Information Systems Research 6(1): 3-23.
Susarla, A., A. Barua, et al. "Understanding the Service Component of Application Service Provision: An Empirical Analysis of Satisfaction with ASP Services." MIS Quarterly 27(1): 91-123.
69
.........Erik Brynjolfsson
- George and Sandi Schussel Professor of Management- Director of the Center for eBusiness Sloan School of ManagementMassachusetts Institute of Technology (Cambridge, MA)
CONTACT INFORMATION
Sloan School 50 Memorial Drive, E53-313 MIT Cambridge, MA 02142
(617) 253-4319 Telephone
http://ebusiness.mit.edu/erik
EDUCATION
Ph.D. – Managerial Economics, Massachusetts Institute of Technology, 1991SM – Math/Decision Science, Harvard University, 1984AB – Applied Mathematics, Harvard University, 1984
RESEARCH INTERESTS
Information Technology and Economics, including: Information technology and the organization of work; Information technology and productivity; Pricing and sharing of digital information
KEY PUBLICATIONS
Brynjolfsson, E. (1993). "The productivity paradox of information technology." Association for Computing Machinery. Communications of the ACM 36(12): 67-77.
Brynjolfsson, E. and L. Hitt (1996). "Paradox Lost? Firm-level evidence on the returns to information systems spending." Management Science 42(4): 541-558.
Bakos, Y. and E. Brynjolfsson (1999). "Bundling information goods: Pricing, profits, and efficiency." Management Science 45(12): 1613-1630.
70
.........Eric Clemons
– Professor of Operations and Information Management and ManagementThe Wharton School University of Pennsylvania (Philadelphia, PA)
CONTACT INFORMATION
Operations and Information ManagementThe Wharton School of the University of Pennsylvania572 Jon M. Huntsman Hall3730 Walnut StreetPhiladelphia, PA 19104-6340
(215) 898-7747 Telephone
EDUCATION
Ph.D. - Operations Research, Cornell University, 1976MS - Operations Research, Cornell University, 1974SB - Physics, Massachusetts Institute of Technology, 1970
RESEARCH INTERESTS
Information technology and business strategy; 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. (1991). "Evaluation of Strategic Investments in Information Technology." Association for Computing Machinery. Communications of the ACM 34(1): 22-36.
Clemons, E. K. and B. W. Weber (1997). "Information Technology and Scfreen-Based Securities Trading: Pricing the Stock and Pricing the Trade." Management Science 43(12).
71
.........Ronald Coase
– Clifton R. Musser Professor Emeritus of Economics – Winner of the 1991 Nobel Prize in EconomicsUniversity of Chicago (Chicago, Illinois)
CONTACT INFORMATION
University of Chicago Law School1111 East 60th StreetChicago, IL 60637 USA
(773) 702-7342 Telephone
EDUCATION
London School of Economics, 1929-1931.University of London, B. Com. 1932, D.Sc. (Econ.) 1951.University of Cologne, Dr. Rer. Pol. h.c., 1988.Yale University, D. So. Sc., (honorary) 1989.Washington University in St. Louis, LL.D., (honorary) 1991.University of Dundee, Scotland, LL.D., (honorary) 1992.University of Buckingham, England, D.Sci, (honorary) 1995.Beloit College, D.H.L., (honorary) 1996.Universite de Paris, docteur, (honoris causa) 1996
RESEARCH CONTRIBUTIONS
Dr. Coase won the 1991 Nobel Prize in Economics "for his discovery and clarification of the significance of transaction costs and property rights for the institutional structure and functioning of the economy"
KEY PUBLICATIONS
"The Nature of the Firm", 1937, Economica.
"The Marginal Cost Controversy", 1946, Economica.
"The Problem of Social Cost", 1960, Journal of Law and Economics.
"Durability and Monopoly", 1972, Journal of Law and Economics.
"The Lighthouse in Economics", 1974, Journal of Law and Economics.
"Marshall on Method", 1975, Journal of Law and Economics.
"The Wealth of Nations", 1977, Economic Inquiry.
"Economics and Contiguous Disciplines", 1978, Journal of Legal Studies.
"The New Institutional Economics", 1984, Journal of Institutional and Theoretical Economics.
“The Firm, the Market and the Law”, 1988.
"The Institutional Structure of Production", 1992, AER
"The Institutional Structure of Production", 1993, in Williamson, editor, Nature of the Firm.
72
.........Vijay Gurbaxani
– Professor, Information ManagementGraduate School of ManagementUniversity of California, Irvine (Irvine, CA)
CONTACT INFORMATION
Graduate School of ManagementUniversity of California, Irvine GSM 315Irvine, CA 92697
(949) 824-5215 Telephone
EDUCATION
Ph.D. - Business Administration, University of Rochester, 1987MS - Business Administration, University of Rochester, 1983MS - Computer Science, Indian Institute of Technology, 1980
RESEARCH INTERESTS
Economics of Information Systems Management; Information Technology and Business Strategy; E-commerce; Information Systems Budgets; Sourcing of Information Systems Services.
KEY PUBLICATIONS
Dedrick, J., V. Gurbaxani, et al. (2003). "Information technology and economic performance: A critical review of the empirical evidence." ACM Computing Surveys 35(1): 1-28.
King, J. L., V. Gurbaxani, et al. (1994). "Institutional Factors in Information Technology Innovation." Information Systems Research 5(2): 139-169.
Gurbaxani, V. and S. Whang (1991). "The Impact of Information Systems on Organizations and Markets." Association for Computing Machinery. Communications of the ACM 34(1): 59-73.
73
.........Kenneth L. Kraemer
- Professor of Information Systems- Director of the Center for Research on Information Technology and OrganizationsGraduate School of ManagementUniversity of California (Irvine, California)
CONTACT INFORMATION
University of California, Irvine3200 Berkeley PlaceIrvine, California 92697
(949) 824-5246 Telephone
http://www.crito.uci.edu/kraemer.asp
EDUCATION
Ph.D. – Public Administration, University of Southern California, 1967M.P.A. – Public Administration, University of Southern California, 1965M.S.C. & R.P. – City and Regional Planning, University of Southern California, 1964B. Architecture – University of Notre Dame, 1959
RESEARCH INTERESTS
Use and Impact of Information Technology in Organizations; Globalization of Information Technology Production and Use; Management of Information Systems
KEY PUBLICATIONS
Kraemer, K. and S. Dewan (2000). "Information Technology and Productivity: Evidence from Country-level Data." Management Science 46(4): 548-562.
74
.........Haim Mendelson
– General Atlantic Partners Professor of Electronic Business and Commerce, and Management; – Codirector, Center for Electronic Business and Commerce– Codirector, Strategic Uses of Information Technology Executive Program Graduate School of BusinessStanford University (Stanford, California)
CONTACT INFORMATION
Graduate School of BusinessLittlefield 253518 Memorial WayStanford UniversityStanford, California 94305
(650) 725-8927 Telephone
[email protected] http://gobi.stanford.edu/facultybios/bio.asp?ID=104 http://faculty-gsb.stanford.edu/mendelson/
EDUCATION
Ph.D. – School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel, 1979MSc – Management Sciences, Tel Aviv University, Tel Aviv, Israel, 1977BSc, Mathematics and Physics, Hebrew University, Jerusalem, Israel, 1972
RESEARCH INTERESTS
Electronic business, electronic networks, and financial markets.
KEY PUBLICATIONS
Mendelson, H. (1985). "Pricing Computer Services - Queuing Effects." Communications of the ACM 28(3): 312-321.
Mendelson, H. (1987). "Economies of Scale in Computing: Grosch's Law Revisited." Communications of the ACM 30(12): 1066-1072.
Mendelson, H. (1986). "Incomplete Information Costs and Database Design." ACM Transactions on Database Systems 11(2): 159-185.
Mendelson, H. and R. Ravindran (1998). "Clockspeed and Informational Response: Evidence from the Information Technology Industry." Information Systems Research 9(4): 415-433.
Mendelson, H. (2000). "Organizational Architecture and Success in the IT Industry." Management Science 46(4): 513-529.
75
.........Andrew B. Whinston
–Director, Center for Research in Electronic Commerce –Fellow IC2
– Hugh Roy Cullen Centennial Chair ProfessorMSIS, Computer Science, Economics DepartmentsUniversity of Texas (Austin, Texas)
CONTACT INFORMATION
The University of Texas at AustinMgmt Sci & Info Systems1 University Station Stop B6500Austin, TX 78712
(512) 471-8879 Telephone(512) 471-7962 Facsimile
http://cism.bus.utexas.edu/abw/main.html
EDUCATION
Ph.D. – Management, Carnegie Mellon University, 1962
RESEARCH INTERESTS
Bringing technological, business, economic, public policy, sociological, cryptographic and political concerns together in laying the theoretical and practical foundations of a digital economy
KEY PUBLICATIONS
Whinston, A. B., D. O. Stahl, et al. (1997). The Economics of Electronic Commerce. New York, New York, Macmillan Technical Publishing.
76
.........Key Papers
We have selected the following papers as significant contributions to the domain, based on how frequently they have been cited and the impact they have had on the field.
Management Misinformation Systems Ackoff, R.L.Management Science, 14(4), Dec-67, b147-B157.Classification: Behavioral, Theory, Foundation
Five assumptions commonly made by designers of management information systems are identified. It is argued that these are not
justified in many (if not most) cases and hence lead to major deficiencies in the resulting systems. These assumptions are: (1) the critical deficiency under which most managers operate is the lack of relevant information, (2) the manager needs the information he wants, (3) if a manager has the information he needs his decision making will improve, (4) better communication between managers improves organizational performance, and (5) a manager does not have to understand how his information system works, only how to use it. To overcome these assumptions and the deficiencies which result from them, a management information system should be imbedded in a management control system. A procedure for designing such a system is proposed and an example is given of the type of control system which it produces.
Common systems development assumptions are presented. The paper argues that these assumptions result in system deficiencies and that a management control system should be used to overcome the assumptions.
Bundling information goods: Prices, profits, and efficiencyBakos, Y., & Brynjolfsson, EManagement Science, 45(12) 1999. Classification: Technical, Theory, Exploratory
The strategy of bundling a large number of information goods, such as those increasingly available on the Internet, and selling them for a fixed
price, is studied. The optimal bundling strategies for a multiproduct monopolist are studied, and it is found that bundling very large numbers of unrelated information goods can be surprisingly profitable. The reason is that the law of large numbers makes it much easier to predict consumers' valuations for a bundle of goods than their valuations for the individual goods when sold separately. Digital information goods have properties that distinguish them from traditional goods. The pricing strategy of digital information goods is discussed. Models bundling information goods are proposed and back by empirical analysis.
Information Technologies And Business Value - An Analytic And Empirical-investigation Barua, A.; Kriebel, C.; and Mukhopadhyay, TInformation Systems Research, 6(1) 1995, 3-23. Classification: Technical, Application, Extension
An important management question today is whether the anticipated economic benefits of information technology (IT) are being realized. In an analysis, this problem is considered to be measurement related, and a new process-oriented methodology for ex post measurement is proposed and tested to audit IT impacts on a strategic business unit (SBU) or profit center's performance. The IT impacts on a given SBU are measured relative to a group of SBUs in the industry. The methodology involves a 2-stage analysis of intermediate and higher level output variables that also accounts for industry and economy wide exogenous variables for tracing and measuring IT contributions. The data for testing the proposed model were obtained from SBUs in the manufacturing sector. The results show significant positive
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impacts of IT at the intermediate level. The study provides a practical management tool to address the question of why (or why not) certain IT impacts occur.
Contradictions about productivity gains from IT exist primarily due to problems with the measurement of data. A process oriented model to measure and understand the impact of IT is introduced.
The Productivity Paradox of Information TechnologyBrynjolfsson, E.Communications of the ACM, 35(12) 1993, 66-67. Classification: Technical, Theory, Review
The relationship between information technology (IT) and productivity is widely discussed, but little understood. The increased interest in the
productivity paradox, as it has become known, has engendered a significant amount of research, but thus far, this has only deepened the mystery. Recent work suggests that the return to IT spending may in fact be much higher than previously estimated. What is known and not known is summarized, the central issues are distinguished from diversions, and the questions that can be profitably explored in future research are clarified. After reviewing and assessing the research to date, it appears that the shortfall of IT productivity is as much due to deficiencies in the measurement and methodological tool kit as to mismanagement by developers and users of IT. The research presented reflects the results of a computerized literature search of 30 of the leading journals in both information systems and economics, and includes discussions with leading researchers in the field. The key findings and essential research references are highlighted and discussed
Productivity is the fundamental measure of a technology’s contribution. However, the relationship between productivity and technology is not understood. After observing the data of IT performance as explained in this article, much of the “productivity paradox” can be attributed to errors in measurement.
The Nature of the FirmCoase, Ronald H.Economica, 4(16) 1937, 386-405.Classification: Behavioral, Theory, Foundation
This article proposes a clear and widely accepted theory suggesting why firms exist. It argues that transaction costs define the boundary of
the firm. Transaction costs are the main factor that determine whether a transaction be conducted within the market or within the hierarchical organization. Lower transaction costs make the firm, planned economy, to be efficient and justify the existence of the firm.
Evaluations of Strategic Investments in Information TechnologyClemons, E.K.Communications of the ACM, 34(1) 1991, 22-36. Classification: Behavioral, Application, Extension
Developing a strategic application intended to make a company more flexible and more responsive with information technology differs from
investments in back office automation. Computer technology applications are discussed.
Strategic application development is fundamentally different than back office automation. Strategic applications increase flexibility, responsiveness, and adaptability. Back office automation reduces expenses or increases capacity. Several cases and lessons learned about implementing strategic systems are presented in the paper.
Information Technology and Economic Performance: A Critical Review of the Empirical EvidenceDedrick, J., Gurbaxani, V. & Kraemer, K.ACM Computing Surveys, 35(1) 2003, 1-28. Classification: Behavioral, Theory, Review
78
.........For many years, there has been considerable debate about whether the IT revolution was paying off in higher productivity. Studies in the 1980s found no connection between IT investment and productivity in the US economy, a situation referred to as the productivity paradox. Since then, a decade of studies at the firm and country level has consistently shown that the impact of IT investment on labor productivity and economic growth is significant and positive. This article critically reviews the published research, more than 50 articles, on computers and productivity. It develops a general framework for classifying the research, which facilitates identifying what is known, how well it is known, and what is not known.
This paper critically reviews more than 50 published researches on computers and productivity, which show that the impact of IT investment on labor productivity and economic growth has been significant and positive in the last decade. It develops a general framework for classifying the research, which facilitates identifying what is known, how well it is known, and what is not known.
Information Technology and Productivity: Evidence from Country-level DataDewan S., & Kraemer, K.Management Science, 46(4) 2000, 548-562. Classification: Behavioral, Application, Review
A key driver of the demand for the products and services of the global IT industry is studied - returns from IT investments. An intercountry production function relating IT and non-IT inputs to GDP output is estimated, on panel data from 36 countries over the 1985-1993 period. Significant differences are found between developed and developing countries with respect to their structure of returns from capital investments. For the developed countries in the sample, returns from IT capital investments are estimated to be positive and significant, while returns from non-IT capital investments are not commensurate with relative factor shares. The situation is reversed for the developing countries subsample, where returns from non-IT capital are quite substantial, but those from IT capital investments are not statistically significant.
This paper studies returns from IT investments, a key driver of the demand for the products and services of the global IT industry. An inter-country production function relating IT and non-IT inputs to GDP output is estimated, on panel data from 36 countries over the 1985-1993 period. Significant differences are found between developed and developing countries with respect their structure of returns from capital investments.
Dynamic competition with switching costsFarrell, J., & Shapiro, C.Rand Journal of Economics, 19, 1988, 123-137.Classification: Behavioral, Theory, Exploratory
Analysis using an overlapping-generations model shows that switching costs, which arise when a buyer changes suppliers, can cause
inefficiency by encouraging entry into the market to serve new customers, even if the entry is inefficient. The model assumes 2 sellers, both of whom can produce a good at a constant average cost. Consumers live for 2 periods, and each young consumer buys from the seller with the lowest price. At the beginning of the next period, the consumer is faced with the choice of buying from the same firm (the incumbent) or from the other firm (the entrant). Switching from the incumbent to the entrant involves a switching cost that is the same for all buyers and is common knowledge. Buyers require one unit of good in each period and try to minimize outlays. Sellers maximize present values of their profits and cannot discriminate between old and new consumers. The seller's price can be set at any time during the period but cannot be changed during that period.
The paper argues that switching costs can cause inefficiency in a surprising way: far from forming an entry barrier, they encourage entry to serve new customers, even when such entry is inefficient.
The Impact of Information Systems on Organizations and MarketsGurbaxani, V.Communications of the ACM, 34(1), 1991, 59-73. Classification: Behavioral, Application, Extension
79
.........The impact of information technology on 2 attributes of firms is examined: firm size and the allocation of decision-making authority among the various actors in a firm. The approach taken builds on existing economic theories of organization: agency theory and transaction cost economics. A firm can use information systems (IS) to decentralize some decision rights and to centralize others, exploiting the merits of both systems and leading to a hybrid structure. When information technology (IT) plays a significant role in reducing internal coordination costs, a firm may find it advantageous to grow horizontally and vertically. Based on the model presented, it is shown that a firm's use of IT can result in an increase or decrease in either the horizontal or vertical dimension of firm size. IS should be assessed and compared with regard to specific managerial contexts. A more focused investigation is necessary in order to understand the impact of IT on organizations and markets.
This paper builds on the existing economic theories of the agency theory and transaction and cost economics. A firm can use information systems (IS) to decentralize some decision rights and to centralize others, exploiting the merits of both systems and leading to a hybrid structure. When information technology (IT) plays a significant role in reducing internal coordination costs, a firm may find it advantageous to grow horizontally and vertically.
An Empirical Assessment of the Organization of Transnational Information SystemsKing, W. & Sethi, V.Journal of MIS, 15(4) 1999, 7-28. Classification: Behavioral, Theory, Exploratory
The objective of this study is to aid in understanding of the organization of information systems in organizations whose activities cross national boundaries. The increasing globalization of business has led firms to seek new, and more appropriate, organizational structures, processes, and cultures. This has required the establishment of appropriate information technology platforms to coordinate business processes and to provide coalition mechanisms. This study is based on five important dimensions of transnational strategy - the configuration of value chain activities, the coordination of value chain activities, centralization, strategic alliances, and market integration - that define a comprehensive taxonomy of transnational strategy. The results support the proposition that the organizational characteristics of centralization, dispersal, and coordination are differentially reflected in the IT configurations of various kinds of multinational corporations. In a centrally coordinated business structure, IT is also globally centralized. In addition, local autonomy was shown to affect the deployment of IT in global firms.
This paper is based on five important dimensions of transnational strategy—the configuration of value chain activities, the coordination of value chain activities, centralization, strategic alliances, and market integration—to support the proposition that the organizational characteristics of centralization, dispersal, and coordination are differentially reflected in the IT configurations of various kinds of multinational corporations. In a centrally coordinated business structure, IT is also globally centralized. In addition, local autonomy was shown to affect the deployment of IT in global firms.
Institutional Factors in Information Technology InnovationKing, J., Gurbaxani, V., Kraemer, K., McFarlan, F., et al.Information Systems Research, 5(2) 1994, 139-170.Classification: Behavioral, Theory, Foundation
The lack of coherent policy advice for creating government policy for information technology (IT) innovation reveals a shortfall in research
understanding of the role of government institutions, and institutions more broadly in IT innovation. Long-established intellectual perspectives on innovation from neoclassical economics and organization theory are not adequate to explain the dynamics of actual innovation change in the IT domain. A broader view adopted from economic history and the new institutionalism in sociology provides a stronger foundation for understanding the role of institutions in IT innovation. Institutional intervention in IT innovation can be constructed at the intersection of the influence and regulatory powers of institutions and the ideologies of supply-push and demand-pull models of innovation. Institutional policy formation regarding IT innovation is
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facilitated by the understanding of the multifaceted role of institutions in the innovative process and the contingencies governing the institution-innovation mix. This paper states that long-established intellectual perspectives on innovation from neoclassical economics and organization theory are not adequate to explain the dynamics of actual innovation change in the IT domain. A broader view adopted from economic history and the new institutionalism in sociology provides a stronger foundation for understanding the role of institutions in IT innovation. Institutional intervention in IT innovation can be constructed at the intersection of the influence and regulatory powers of institutions and the ideologies of supply-push and demand-pull models of innovation.
Versioning: The smart way to sell informationShapiro, C. and Varian, H. R.Harvard Business Review, Nov-Dec 1998, 106-114. Classification: Behavioral, Application, Exploratory
The rapid rise, and even more rapid fall, of CD telephone directories stands as a cautionary tale for the purveyors of information products,
particularly those sold in digital form. It reveals that the so-called new economy is still subject to the old laws of economics. Because the marginal cost of reproducing information tends to be very low, the price of an information product, if left to the marketplace, will tend to be low as well. The only viable strategy is to set prices according to the value a customer places on the information. In order to set different prices for basically the same information without incurring high costs or offending customers, it is possible to offer the information in different versions designed to appeal to different types of customers. With this strategy, called versioning, customers in effect segment themselves. The version they choose reveals the value they place on the information and the price they are willing to pay for it.
Digital information products are subject to the laws of economics. After several companies have lowered production costs, competitive forces move marginal costs down. Low reproduction costs make products economically perilous. Success depends on traditional product-management skills: determining customer needs, achieving true differentiation, and developing an adept positioning and pricing plan.
81
.........Section7Human-Computer InteractionIntroduction
Human-Computer Interaction (HCI) is an MIS discipline that focuses on the human aspects of information systems. HCI has its roots in a variety of disciplines including operating systems, industrial engineering, computer science, ergonomics, and psychology. HCI covers a wide variety of subjects, and there is a continuous debate over its scope. HCI became a factor in the development of operating systems because they required techniques for interfacing input/output devices, for synchronizing computer response times to human interaction times, for multiprocessing, and for supporting a graphical user interfaces (GUI). GUIs were invented by Xerox PARC but built on previous research at Stanford and MIT. Another important contribution of HCI, hypertext, was created by Ted Nelson in 1965 but the idea was first introduced by Vannevar Bush in his seminal work “As We Think” in the infamous concept of “Memex.” There are many exciting aspects to HCI, including some research in the fields of natural language processing and virtual reality that was introduced long ago but is ongoing.
Bush, in his 1945 paper “As We Think,” laid the foundations for HCI by introducing the need to have the ability to effectively manage information. In the 1950s, Engelbart began to define information processing and social effects of computing and collaboration. In his paper, “Augmenting Human Intellect,” Engelbart defines future goals of HCI research. Ivan Sutherland is credited with inventing the first user interface that uses a light pointer to control objects on the screen. He also introduced concepts of object-oriented programming and Computer-Aided Design. Licklider’s “man-machine symbiosis” (1960) proposed a set of goals to achieve in technology, and with his position at ARPA, was able to set priorities to support the invention of such items as the mouse, the graphical user interface, and the building of the internet (initially know as ARPANET).
HCI can be roughly broken down into these sub-categories:
The nature of human-computer interaction;
Systems and software development; and,
The visualization and usability of data/information.
The scope of (and distinctions between) these sub-categories are highly fluid and tend to change often. As such, attempts to further sub-classify the watershed HCI research is well beyond the scope of this paper.
82
.........School Listing
Human-Computer Interaction
School Tier
Res
earc
h La
b
Facu
lty In
tere
sts
Lead
ing
Res
earc
hers
Dep
artm
ent N
ame
University of Texas-- Austin I X Georgia State University II X University of Michigan III X University of Maryland III X University of British Columbia Research I X X Hong Kong University of S&T Research II X Tel Aviv University Research II X Bentley College Teaching I X Michigan State University Teaching II X University of Virginia Teaching II X
Timeline
83
.........Key Contributors
Vannevar Bush (1890-1974)
– Director, U.S. Office of Scientific Research & Development– Vice President and Dean School of EngineeringMassachusetts Institute of Technology (Cambridge, MA)
EDUCATION
Ph.D. – Engineering, Harvard and MIT, 1916-1917MS – Tufts College, 1913BS – Tufts College
RESEARCH INTERESTS
Hypertext research.
KEY PUBLICATIONS
Bush, V. (1945). As We May Think. The Atlantic Monthly. 176: 101-108.
84
.........Stuart K. Card
- Senior Research Fellow - Area Manager, User Interface Research (UIR) Group Information Sciences & Technologies LaboratoryPalo Alto Research Center, Inc. (Palo Alto, California)
CONTACT INFORMATION
Palo Alto Research Center, Incorporated3333 Coyote Hill RoadPalo Alto, California 94304
(650) 812-4000 Telephone
[email protected] (or [email protected])
EDUCATION
Ph.D. – Psychology/AI/Computer Science, Carnegie Mellon University, 1978 (Chair: Allen Newell)AB – Physics, Oberlin College, 1966
RESEARCH INTERESTS
Human factors for input devices (e.g. mice), information visualization, and information scent.
KEY PUBLICATIONS
Card, S., T. Moran, & A. Newell (1983). The Psychology of Human-Computer Interaction. Hillsdale, NJ: Lawrence Erlbaum.
Newell, A. and S. K. Card (1985). "The Prospects for Psychological Science in Human-Computer Interaction." Human-Computer Interaction 1(3): 209.
Card, S., Mackinlay, J., & Shneiderman, B. (1999). Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann Publishers.
85
.........John M. Carroll
– Professor, Information Sciences and Technology– Director, Center for Human-Computer Interaction, Virginia Tech Virginia Tech
CONTACT INFORMATION
School of Information Sciences and Technology 307H Information Sciences and Technology The Pennsylvania State University University Park, PA 16802
Bldg Phone: 1.814. 863.2476Fax: 1.814. 865.6426E-mail: [email protected]
EDUCATION
Ph.D. Psychology, Columbia University, 1976M.A. Psychology, Columbia University, 1974M.Phil. Psychology, Columbia University, 1975B.A., Mathematics and Information Sciences, Lehigh University, 1972
RESEARCH INTERESTS
Scenario-based methods for design and development, minimalist techniques for making information efficient, computer support for collaborative work and education, community-oriented computing, and social impacts of computing.
KEY PUBLICATIONS
Carroll, J. M. (1995). Scenario-based design: envisioning work and technology in system development, New York :; Wiley.
Carroll, J. M. (2000). Making use: scenario-based design of human-computer interactions. Cambridge, Mass., MIT Press.
86
.........Douglas C. Englebart
– Director, Augmentation Research, SRI – Founder, Bootstrap Institute Stanford Research LabsStanford University (Stanford, California)
CONTACT INFORMATION
Bootstrap Institute6505 Kaiser DriveFremont, CA 94555
(510) 713-3550 Telephone(510) 792-3506 Facsimile
http://www.bootstrap.org/index.html
EDUCATION
Ph.D. – Electrical Engineering, University of California-Berkeley, 1955BS – Electrical Engineering, Oregon State University, 1948
RESEARCH INTERESTS
Augmenting human intellect – via graphical design, human factors, and hypertextual information display.
KEY PUBLICATIONS
Englebart, D. (1962). Augmenting Human Intellect: A Conceptual Framework, Stanford Research Institute.
English, W. K., Engelbar.Dc, et al. (1967). "Display-Selection Techniques for Text Manipulation." Ieee Transactions on Human Factors in Electronics HFE8(1): 5-&.
87
.........George W. Furnas
- Professor, School of Information- Professor, Computer Science and EngineeringThe University of Michigan (Ann Arbor, Michigan)
CONTACT INFORMATION
School of Information University of Michigan 310 West Hall 550 East University AvenueAnn Arbor, Michigan 48109-1092
(734) 763-0076 Telephone(734) 764-2475 Facsimile
http://www.si.umich.edu/~furnas/
EDUCATION
Ph.D. – Psychology, Stanford University, 1980BA – Psychology, Harvard University, 1974
RESEARCH INTERESTS
HCI: information access, visualization, computer-supported cooperative work & graphical reasoning
KEY PUBLICATIONS
Furnas, G. W., T. K. Landauer, et al. (1987). "The Vocabulary Problem in Human System Communication." Communications of the Acm 30(11): 964-971.
Furnas, G. W. (1986). "Generalized fisheye views." ACM SIGCHI Bulletin 17(4): 16-23.
Furnas, G. W. (1997). Effective View Navigation. Conference on Human Factors in Computing Systems, Proceedings of CHI 1997: Human Factors in Computing Systems.
88
.........J.C.R. Licklider (1915-1990)
– Founding DirectorInformation Processing Techniques OfficeAdvanced Research Projects Agency (Washington, DC)
INFORMATION
http://www.ibiblio.org/pioneers/licklider.html
EDUCATION
Ph.D. – Psychoacoustics (the psychophysiology of the auditory system), MITBA – Mathematics, Washington State UniversityBS – Physics, Washington State UniversityBS – Psychology, Washington State University
RESEARCH INTERESTS
Human-computer interaction.
KEY PUBLICATIONS
Licklider, J. C. R. (1960). "Man-Computer Symbiosis." IRE Transactions on Human Factors in Electronics, HFE-1: 4-11.
Licklider, J. C. R., R. W. Taylor, et al. (1968). "The Computer as a Communication Device." Science and Technology(76): 21-&.
89
.........Brad A. Myers
–Professor, School of Computer ScienceHuman Computer Interaction InstituteCarnegie Mellon University (Pittsburgh, Pennsylvania)
CONTACT INFORMATION
Human Computer Interaction InstituteCarnegie Mellon University5000 Forbes AvenuePittsburgh, PA 15213-3891
(412) 268-5150 Telephone(412) 268-1266 Facsimile
http://www-2.cs.cmu.edu/~bam/
EDUCATION
Ph.D. -- Computer Science, University of Toronto, 1987. MS -- Computer Science, Massachusetts Institute of Technology, 1980BS -- Computer Science and Engineering, Massachusetts Institute of Technology, 1980
RESEARCH INTERESTS
User Interface Software, Hand-held computers, Demonstrational Interfaces, User Interface Design, Window Managers, Visual Programming, Programming Environments.
KEY PUBLICATIONS
Brad A. Myers. Creating User Interfaces by Demonstration. Boston, MA: Academic Press, May 1988.
Brad A. Myers, ed. Languages for Developing User Interfaces. Boston: Jones and Bartlett, 1992.
Myers, B. A. (1998). "A Brief history of Human Computer Interaction Technology." ACM Interactions 5(2): 44-54.
90
.........Allen Newell (1927-1992)
– Professor, School of Information– Professor, Computer Science and EngineeringCarnegie-Mellon University (Pittsburgh, Pennsylvania)
EDUCATION
Ph.D. – Industrial Administration, Carnegie Institute of Technology (now CMU), 1957BA – Physics, Stanford University, 1949
RESEARCH INTERESTS
Computer science, artificial intelligence, and cognitive psychology
KEY PUBLICATIONS
Newell, A. (1990) Unified Theories of Cognition. Cambridge, MA: Harvard University Press.
Card, S. K., T. P. Moran, et al. (1980). "The Keystroke-Level Model for User Performance Time with Interactive Systems." Communications of the ACM 23(7): 396-410.
Robertson, G., A. Newell, et al. (1977). ZOG: A Man-Machine Communication Philosophy. Carnegie Mellon University Technical Report, Carnegie Mellon University.
Newell, A. and H. A. Simon (1976). "Computer Science as Empirical Inquiry - Symbols and Search." Communications of the Acm 19(3): 113-126.
91
.........Jakob Nielsen
– Co-Founder & PrincipalNielsen Norman Group (Fremont, California)
CONTACT INFORMATION
Nielsen Norman Group48921 Warm Springs BoulevardFremont, California 94539-7767
(408) 720-8808 Telephone
http://www.nngroup.com/
EDUCATION
Ph.D. – User Interface Design, The Technical University of Denmark
RESEARCH INTERESTS
Web usability, interface design, information architecture, and task design
KEY PUBLICATIONS
Nielsen, J. (2000) Designing Web Usability: The Practice of Simplicity. Indianapolis, IN: New Riders Publishing.
Nielsen, J. (1992). Finding usability problems through heuristic evaluation. Conference on Human Factors in Computing Systems, Monterey, California, ACM Press.
Nielsen, J. and J. M. Faber (1996). "Improving system usability through parallel design." Computer 29(2): 29-35.
92
.........Donald A. Norman
– Co-Founder & principal, Nielsen Norman Group– Professor, Depts. of Computer Science & Psychology, Northwestern University.– Professor emeritus, Cognitive Science, University of California - San Diego.– Trustee, Institute of Design, Illinois Institute of Technology.
CONTACT INFORMATION
Nielsen Norman Group2841 Manor DriveNorthbrook, Illinois 60062
(847) 498-4292 Telephone(847) 272-6631 Facsimile
http://www.nngroup.com/
EDUCATION
Ph.D. – Mathematical Psychology, University of Pennsylvania, 1962.MS – Electrical Engineering, University of Pennsylvania, 1959.BS – Electrical Engineering, Massachusetts Institute of Technology, 1957.
RESEARCH INTERESTS
The human-centered design process; physical objects with embedded computation and telecommunication.
KEY PUBLICATIONS
Norman, D. A. and Draper, S. (1986) User Centered System Design: New Perspectives on Human-Computer Interaction. Hillsdale, NJ: Lawrence Erlbaum.
Norman, D.A. (1990) The design of everyday things. New York: Doubleday.
93
.........Ben Shneiderman
– Professor, Department of Computer Science– Member, Institute for Systems Research– Member, Institute for Advanced Computer Studies– Founding Director, Human-Computer Interaction LabDepartment of Computer ScienceThe University of Maryland (College Park, Maryland)
CONTACT INFORMATION
Department of Computer ScienceUniversity of Maryland College Park, MD 20742
(301) 405-2680 Telephone(301) 405-6707 Facsimile
http://www.cs.umd.edu/users/ben/
EDUCATION
Ph.D. – Computer Science, State University of New York at Stony Brook, 1973MS – Computer Science, State University of New York at Stony Brook, 1972BS – Mathematics/Physics, City College of New York, 1968
RESEARCH INTERESTS
Human-computer interaction and user interface design.
KEY PUBLICATIONS
Shneiderman, B. (1983). "Direct Manipulation - a Step Beyond Programming-Languages." Computer 16(8): 57-69.
Shneiderman, B. (1987). Designing the User Interface: Strategies of Effective Human-Computer Interaction (3rd ed.). Pearson Addison Wesley.
Shneiderman, B. (1980). Software Psychology: Human Factors in Computer and Information Systems. Little, Brown and Co.
94
.........Terry A. Winograd
– Professor, Department of Computer Science– Director, Human-Computer Interaction teaching programs– HCI Research Director, Stanford Interactivity LabDepartment of Computer ScienceStanford University (Stanford, California)
CONTACT INFORMATION
Stanford University Gates Computer Science 3B, Room 388 Stanford, California 94305-9035
(650) 723-2780 Telephone(650) 723-0033 Facsimile
http://hci.stanford.edu/~winograd/
EDUCATION
Ph.D. – Applied Mathematics, Massachusetts Institute of Technology, 1970MA -- Linguistics, University College (London), 1967BA – Mathematics, The Colorado College, 1966
RESEARCH INTERESTS
Human-computer interaction design, with a focus on the theoretical background and conceptual models.
KEY PUBLICATIONS
Winograd, T. (1988). "A Language/Action Perspective on the Design of Cooperative Work." Human-Computer Interaction 3(1): 3-28.
Winograd, T. (1972). Understanding Natural Language, Academic Press, 1972.
Winograd, T. (1983). Language as a Cognitive Process: Syntax, Addison-Wesley, 1983.
Winograd T. and F. Flores (1986). Understanding Computers and Cognition: A New Foundation for Design, Addison-Wesley, 1987.
Adler, P. and T. Winograd (1992), Usability: Turning Technologies into Tools, Oxford, 1992.
Winograd, T., J. Bennett, et al. (1996), Bringing Design to Software, Addison Wesley, 1996.
95
.........Key Papers
Organizational Information Requirements, Media Richness and Structural DesignDaft, R. L., Lengel, R. H.Management Science, 32 (5). 1986. 554-571. [Link]Classification: Behavioral, Application, Extension
This paper answers the question, "Why do organizations process information?" Uncertainty and equivocality are defined as two forces that influence information processing in organizations. Organization structure and internal systems determine both the amount and richness of information provided to managers. Models are proposed that show how organizations can be designed to meet the information needs of technology, interdepartmental relations, and the environment. One implication for managers is that a major problem is lack of clarity, not lack of data. The models indicate how organizations can be designed to provide information mechanisms to both reduce uncertainty and resolve equivocality.
The Vocabulary Problem in Human-System CommunicationFurnas, G. W., Landauer, T. K., and Gomez, L. M.Communications of the ACM, 30 (11). 1987. 964-971. [Link]Classification: Behavioral, Theory, Exploratory
In almost all computer applications, users must enter correct words for the desired objects or actions. For success without extensive training,
or in first-tries for new targets, the system must recognize terms that will be chosen spontaneously. We studied spontaneous word choice for objects in five application-related domains, and found the variability to be surprisingly large. In every case two people favored the same term with probability <0.20. Simulations show how this fundamental property of language limits the success of various design methodologies for vocabulary-driven interaction. For example, the popular approach in which access is via one designer's favorite single word will result in 80-90 percent failure rates in many common situations. An optimal strategy, unlimited aliasing, is derived and shown to be capable of several-fold improvements.
Information Foraging Pirolli, P. and Card, S.Psychological Review, 106 (4). 1999. 643-675.Classification: Behavioral, Theory, Review
Information foraging theory is an approach to understanding how strategies and technologies for information seeking, gathering, and
consumption are adapted to the flux of information in the environment. The theory assumes that people, when possible, will modify their strategies or the structure of the environment to maximize their rate of gaining valuable information. The theory is developed by (a) adaptation (rational) analysis of information foraging problems and (b) a detailed process model (adaptive control of thought in information foraging [ACT-IF]). The adaptation analysis develops (a) information patch models, which deal with time allocation and information filtering and enrichment activities in environments in which information is encountered in clusters; (b) information scent models, which address the identification of information value from proximal cues; and (c) information diet models, which address decisions about the selection and pursuit of information items. ACT-IF is instantiated as a production system model of people interacting with complex information technology.
96
......... The Usability Engineering Life CycleNielsen, J.IEEE Computer, 25 (3). 1992. 12-22. [Link]Classification: Behavioral, Application, Extension
A practical usability engineering process that can be incorporated into the software product development process to ensure the usability of
interactive computer products is presented. It is shown that the most basic elements in the usability engineering model are empirical user testing and prototyping, combined with iterative design. Usability activities are presented for three main phases of a software project: before, during, and after product design and implementation. Some of the recommended methods are not really single steps but should be used throughout the development process.
Direct Manipulation: A Step Beyond Programming LanguageShneiderman, B.IEEE Computer. 1993. 57-69.Classification: Behavioral, Theory, Extension
Shneiderman's theory of direct manipulation describes interactive systems where a user manipulates the files and folders within their
system by using methods other than typed commands. Direct manipulation systems also display a visual representation of the system's activity or progression, which result in an increase in the user's perception of control. The central themes of user control are rooted in: The visibility of system objects and actions; rapid, reversible, incremental actions; and the replacement of complex command syntax with simple visual alternatives.
A Language/Action Perspective on the Design of Cooperative WorkWinograd, T.Human-Computer Interaction, 3 (1). 1988. 3-30. [Link]Classification: Behavioral, Theory, Extension
This paper examines the language/action perspective on the design of cooperative work. It includes: A description of the coordinator communication tool from a language/action perspective; aspects of coordinated work; and an illustration of the language/action perspective in the studies of nursing work in a hospital ward.
Computer-Mediated Communication for Intellectual TeamworkGalegher J., Kraut, R.E.Information Systems Research, 5 (2). 1994. 110-138. Classification: Behavioral, Application, Extension
This paper focuses on a study which examined the effects of computer-mediated communication on group processes and
performance. It includes a manipulation of communication modality; association of evaluations and work activities; and discusses the effects of communication modality on social experience.
Generalized Fisheye ViewsFurnas, G. W.CHI 1986: ACM Conference on Human Factors in Software / ACM Press. 1986. 16-23. [Link]Classification: Technical, Application, Exploratory
97
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In many contexts, humans often represent their own “neighborhood” in great detail, yet only generalize major landmarks further away. This suggests that such views (“fisheye views”) might be useful for the computer display of large information structures like programs, data bases, online text, etc. This paper explores fisheye views presenting, in turn, naturalistic studies, a general formalism, a specific instantiation, a resulting computer program, example displays and an evaluation.
98
.........Section8
Social InformaticsIntroduction
Social Informatics (SI) refers to an interdisciplinary field that studies the design, uses, and results of information and communication technologies. In other words, SI investigates the social side of the technology, accounting for their interactions with institutional and cultural contexts. These social issues are often divided into three broad categories: social, legal, and ethical issues. Examples of social issues include legislation and company computer policies. Examples of the social impact of computerization include telecommunications and organizational informatics. Research of social informatics is often combined with other behavioral research such as management, arts & humanities, communication, psychology, making it a cross-disciplinary field. Indeed, one finds SI researchers in a variety of departments aside from MIS, such as computer science, information science, education, anthropology or political science. These disciplines help to define what an information system is with respect to the impact on human beings. However, this interdisciplinary approach may also impede the advancement of knowledge in this field as it is hard for scholars to know where to look for published materials.
SI is a problem-driven field and its research cuts across multiple levels in terms of analysis and is done at the group, departmental, organizational, national and/or societal levels. By being problem-driven, SI is similar to other fields such as human computer interaction or software engineering, for example. In addition to solving problems, SI is empirically driven in that SI researchers attempt to investigate the problem as opposed to utilize theories or methods in the same extent as other fields, such as operations research or linguistic analysis.
SI has become an increasingly important area within the MIS field, as the last half of the 1990s saw an increase of information and communication technologies. Computers, the Internet and the World Wide Web have become an integral part of life in the Western world. As these technologies are spread globally, social and ethical issues will be increasingly debated.
Our thanks to Dr. Suzie Weisband of the University of Arizona Management Information Systems department for her review of the researchers in this domain.
99
.........School Listing
Social Informatics
School Tier
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MIT I X X X New York University I X University of Minnesota I X X X University of Texas-- Austin I X X Georgia State University II X University of California-- Irvine II X University of Georgia II X X Harvard University III X Indiana University III X X University of Michigan III X University of Rochester IV X University of Southern California IV X Drexel University Research I X Florida International University Research I X Florida State University Research I X National University of Singapore Research I X Texas A&M University Research I X University of British Columbia Research I X X University of South Carolina Research I X X Ecole Des Hautes Etudes Comm. Research II X Hong Kong University of S&T Research II X Tel Aviv University Research II X University of Hawaii Research II X University of Memphis Research II X University of Toledo Research II X University of Western Ontario Research II X George Washington University Research III X Georgetown University Research III X Queensland Research III X X University of Colorado-- Denver Research III X Bentley College Teaching I X University of California - Berkeley Teaching I X Duke University Teaching II X X Michigan State University Teaching II X University of Connecticut Teaching II X University of Virginia Teaching II X X
100
.........Timeline
101
.........Key Contributors
Gordon B. Davis
-Honeywell Professor of Management Information SystemsCurtis L. Carlson School of ManagementUniversity of Minnesota (Minneapolis, Minnesota)
CONTACT INFORMATION
Information/Decision Sciences3-341 CSOM321 19th Ave SMinneapolis, MN 55455
http://www.carlsonschool.umn.edu/Page1471.aspx
EDUCATION
Ph.D. – Business Administration, Stanford University, 1959M.B.A – Business Administration, Stanford University, 1957B.S – Political Science and Accounting, Idaho State University, 1955
RESEARCH INTERESTS
MIS planning, information requirements determination, management of knowledge work, conceptual foundations for information systems
KEY PUBLICATIONS
Davis, G. (1974). Management Information Systems: Conceptual Foundations, Structure, and Development, McGraw-Hill.
Davis, G. and S. Hamilton (1993). Managing Information: How Information Systems Impact Organizational Strategy, Business One Irwin.
102
.........Sirkka L. Jarvenpaa
– James L. Bayless/Rauscher Pierce Refsnes, Inc. Chair in Business AdministrationMcCombs School of BusinessUniversity of Texas (Austin, Texas)
CONTACT INFORMATION
Department of Management Science & Information SystemsMcCombs School of BusinessUniversity of TexasAustin, TX 78712-1178
(512) 471-1751 Telephone (512) 232-6112 Facsimile
http://www.mccombs.utexas.edu/dept/msis/faculty/profiles/index.asp?addTarget=316
EDUCATION
PhD – University of Minnesota, 1986MBA – University of Minnesota, 1982 BS – Bowling Green State University, 1981
RESEARCH INTERESTS
Clicks and Mortars, Customer Insight, E-Commerce, and Information Systems.
KEY PUBLICATIONS
Jarvenpaa, S. and D. B. Stoddard (1998). "Business Process Redesign: Radical and Evolutionary Change." Journal of Business Research 41(1): 15-27.
Jarvenpaa, S. L., K. Knoll, et al. (1998). "Is anybody out there? Antecedents of trust in global virtual teams." Journal of Management Information Systems 14(4): 29-65.
Jarvenpaa, S. (1995). Reengineering the Organization, Harvard Business School Publishing.
103
.........Sara Kiesler
– Hillman Professor of Computer Science and Human-Computer InteractionHuman-Computer Interaction Institute Carnegie Mellon University
CONTACT INFORMATION
Human-Computer Interaction InstituteCarnegie Mellon University5000 Forbes Ave. Pittsburgh, PA 15213
http://www-2.cs.cmu.edu/~kiesler/index.html
EDUCATION
PhD - Ohio State University
RESEARCH INTERESTS
Social and behavioral aspects of computers, group dynamics, and computer-based communication technologies.
KEY PUBLICATIONS
Kiesler, S. (1997). Culture of the Internet. NJ, US, Lawrence Erlbaum Associates, Inc.
Sproull, L. and S. Kiesler (1991). Connections: new ways of working in the networked organization. Cambridge, MIT Press.
Kraut, R., M. Patterson, et al. (1998). "Internet paradox: A social technology that reduces social involvement and psychological well-being?" American Psychologist 53(9): 1017-1031.
104
.........Rob Kling (1944 – 2003)
– Professor of Information Systems and Information Science– Director of the Center for Social Informatics– Adjunct Professor of Computer ScienceSchool of Library and Information ScienceIndiana University at Bloomington
EDUCATION
Ph.D. – Artificial Intelligence, Stanford University, 1971Undergraduate – Columbia University, 1965
RESEARCH INTERESTS
Social informatics; the effective use of electronic media to support scholarly and professional communication.
KEY PUBLICATIONS
Kling, R. (1991). "Computerization and Social Transformations." Science, Technology, & Human Values 16(3): 342-367.
Kling, R. (1996). Computerization and Controversy: Value Conflicts and Social Choices. San Diego, Academic Press.
105
.........John L. King
– Dean– ProfessorSchool of InformationUniversity of Michigan
CONTACT INFORMATION
School of InformationUniversity of Michigan304 West Hall550 E. University AvenueAnn Arbor MI 48109-1092Telephone: +1(734)647-3576Direct Fax: +1(734)764-2475
http://www.si.umich.edu/~jlking/
EDUCATION
PhD – Administration, UC Irvine, 1977MS – Administration, UC Irvine, 1974BA – Philosophy, UC Irvine, 1972
RESEARCH INTERESTS
Problem of design and development of socio-technical information infrastructures. Development of high-level requirements for information systems design and implementation, study of organizational and institutional forces that shape the development of information technology.
KEY PUBLICATION
King, J. L., V. Gurbaxani, et al. (1994). "Institutional Factors in Information Technology Innovation." Information Systems Research 5(2): 139-169.
106
.........Kenneth L. Kraemer
– CRITO & I/UCRC Director- Professor of Information SystemsGraduate School of Management and CRITOUniversity of California at Irvine (Irvine, CA)
CONTACT INFORMATION
Information SystemsGraduate School of Management and CRITOUC Irvine3200 Berkeley Place(949) 824-5246
http://www.gsm.uci.edu/~kkraemer/
EDUCATION
PhD - Public Administration, University of Southern California, 1967MPA - Public Administration, University of Southern California, 1965MSC & R.P. - City and Regional Planning, University of Southern California, 1964B. Architecture - University of Notre Dame, 1959
RESEARCH INTERESTS
Use and Impact of Information Technology in Organizations; Globalization of Information Technology Production and Use; Management of Information Systems.
KEY PUBLICATIONS
Kraemer, K. L. and J. Dedrick (1995). "From nationalism to pragmatism: IT policy in China." IEEE Computer 28(8): 64-73.
Kraemer, K. L., J. Dedrick, et al. (1994). "Supporting the Free Market: Information technology policy in Hong Kong." The Information Society 10(4): 223-246.
107
.........Wanda J. Orlikowski
– Professor of Information Technologies and Organization Studies– Eaton-Peabody Chair of Communication Sciences Sloan School of ManagementMassachusetts Institute of Technology
CONTACT INFORMATION
MIT Sloan School 50 Memorial Drive (E53-325) Cambridge, MA 02142-1347
(617) 253-0443 Telephone(617) 258-7579 Facsimile
http://ccs.mit.edu/Wanda.html
EDUCATION
PhD – Stern School of Business, New York University, 1985
RESEARCH INTERESTS
Dynamic interactions between organizations and information technology.
KEY PUBLICATIONS
Orlikowski, W. J. (2000). "Using technology and constituting structures: A practice lens for studying technology in organizations." Organization Science 11(4): 404-428.
108
......... Orlikowski, W. J. and J. J. Baroudi (1991). "Studying information technology in organizations: Research
approaches and assumptions." Information Systems Research 2(1): 1-28.
109
.........Pamela Samuelson
– Professor– Co-Director of Berkeley Center for Law and Technology School of Information Management and SystemsSchool of LawUniversity of California (Berkeley, California)
CONTACT INFORMATION
University of California at Berkeley 102 South Hall Berkeley, CA 94720-4600
(510) 642-6775 Telephone(510) 642-5814 Facsimile
http://www.sims.berkeley.edu/~pam/
EDUCATION
JD – Yale Law School, Yale University, 1976MA – Political Science, University of Hawaii at Honolulu, 1972BS – History, University of Hawaii at Honolulu, 1971
RESEARCH INTERESTS
Intellectual property law, public policy for information technology and traditional legal regimes.
KEY PUBLICATIONS
Samuelson, P. (1994). "Copyright's fair use doctrine and digital data." Communications of the ACM 37(1): 21-28.
Samuelson, P. (2001). "Toward a new politics of intellectual property." Communications of the ACM. 44(3): 98-100.
110
.........Lee S. Sproull
– Leonard N. Stern School Professor,– Vice-Dean of the Faculty– Director, Digital Economy InitiativeStern School of BusinessNew York University (New York, New York)
CONTACT INFORMATION
Stern School of BusinessNew York University44 West 4th Street, 11-55 New York, NY 10012212-998-0804 – Telephone212-995-4228 – FAX
http://pages.stern.nyu.edu/~lsproull/
EDUCATION
Ph.D. – Stanford University, 1977M. A. – Stanford University, 1975M.A.T – Wesleyan University, 1969BA – Wellesley College, 1967
RESEARCH INTERESTS
Implications of computer-based communication technologies for managers, organizations, communities, and society and how technology induces changes in interpersonal interaction, group dynamics and decision making and organizational or community structure.
KEY PUBLICATIONS
Sproull, L. S. and K. R. Hofmeister (1986). "Thinking about implementation." Journal of Management 12(1): 43-60.
Sproull, L. S. (1986). "Using electronic mail for data collection in organizational research." Academy of Managment Journal 29(1): 156-169.
Sproull, L. (1984). "The nature of managerial attention." Advances in Information Processing in Organizations 1(1): 9-27.
111
.........JoAnne Yates
– Sloan Distinguished Professor of ManagementMassachusetts Institute of Technology (Cambridge, MA)
CONTACT INFORMATION
MIT Sloan School 50 Memorial Drive, E52-544 Cambridge, MA 02142 Fax: 617-253-2660 Tel: 617-253-7157
http://ccs.mit.edu/yates.html
EDUCATION
PhD - University of North Carolina, Chapel HillBA - Texas Christian University
RESEARCH INTERESTS
How use of communication and information within firms shapes and is shaped over time by its changing organizational, managerial, and technological contexts.
KEY PUBLICATION
Yates, J. and W. J. Orlikowski (1992). "Genres of organizational communication: A structural approach to studying communication and media." Academy of Managment Review 17(2): 299-326.
112
.........Robert W. Zmud
– Editor-in-Chief and Senior Editor of MIS Quarterly– Professor and Michael F. Price Chair in MIS University of Oklahoma (Norman, OK)
CONTACT INFORMATION
Division of MISMichael F. Price College of BusinessUniversity of OklahomaNorman, OK 73019-4006
(405) 325-0791 Telephone(405) 325-1957 Facsimile
http://faculty-staff.ou.edu/Z/Robert.W.Zmud-1/
EDUCATION
PhD – Management, University of ArizonaMS – Management, Massachusetts Institute of TechnologyBAE – Aerospace Engineering, University of Virginia
RESEARCH INTERESTS
The impact of information technology in facilitating a variety of organizational behaviors and on organizational efforts involved with planning, managing, and diffusing information technology.
KEY PUBLICATIONS
Cooper, R. and R. Zmud (1990). "Information Technology Implementation Research: A technology diffusion approach." Management Science 34(2): 123-139.
Shank, M. E., A. C. Boynton, et al. (1985). "Critical Success Factor Analysis as a Methodology for MIS Planning." MIS Quarterly 9(2): 121-129.
113
.........Key Papers
Within the context of our model, the key social informatics papers include:
Electronic Markets and Electronic HierarchiesMalone, Thomas W., Yates, JoAnne, Benjamin, Robert I.Communications of the ACM, 30(6), 1987, 484-497. Classification: Behavioral, Theory, Extension
Electronic interconnections can be seen as the result of three forces: the electronic communication effect, the electronic brokerage effect,
and the electronic integration effect.
By reducing the costs of coordination, information technology will lead to an overall shift toward proportionately more use of markets - rather than hierarchies - to coordinate economic activity.
Thinking about ImplementationSproull, Lee S., Hofmeister, K.Journal of Management, 12(1), 1986, 43-60. Classification: Behavioral, Application, Exploratory
Ideas from cognitive psychology are used to explore the role of different perceptions, attributions and inferences in how people think
about implementation. Some perceptions, attributions and inferences shifted over time, but initial major differences associated with organizational position and commitment to the innovation did not change.
Contends that 3 cognitive processes (interpretation, attribution, and inference) contribute to people's mental representations of an innovation and illustrates this contention in a case study involving a monitoring-achievement program introduced by a new superintendent in a 45,000-pupil school system. Assessment of implementation of the management-by-objectives program suggests that some perceptions, attributions, and inferences shifted over time, but initial major differences associated with organizational position and commitment to the innovation did not change. It is suggested that the study illustrates a paradox of positive value based on a simple model of human preference change. It is further suggested that innovation managers need to acknowledge in advance that things will go wrong and to rely on participant initiative for solutions or courses of action for program elements deliberately left unplanned or unspecified.
Reducing Social Context Cues: Electronic Mail in Organizational CommunicationSproull, Lee. S., Kiesler, Sara.Management Science, 32(12), 1986, 1492-1512. Classification: Behavioral, Application, Extension
Electronic mail speeds up communication and leads to the exchange of new information as well. This paper explores the effects of electronic communication related to self-absorption, status equalization, and uninhibited behavior. Decreasing social context cues has a substantial deregulating effect on communication. Much of the information shared in electronic mail would not have been conveyed through any other medium.
It is argued that electronic mail not only speeds up the exchange of information but also causes an exchange of new information. The argument is based on ideas of how social context clues affect information exchange within a communication setting. Questionnaires are employed to study the use of electronic mail at all levels in the organization of a Fortune 500 company. The questionnaires are supplemented by the actual messages sent by electronic mail in the company. Five hypotheses are proposed: 1. Social context clues are relatively weak when an electronic mail system (EMS) is used. 2. EMS behavior is relatively self-focused. 3. EMS behavior is relatively undifferentiated by the status of the sender. 4. EMS behavior is relatively nonconforming
114
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and uninhibited. 5. EMS provides new information. The findings confirm the validity of all 5 hypotheses. It is felt that unreliable technology, inexperienced users, or lack of access could not account for the results.
Toward a New Politics of Intellectual PropertySamuelson, Pamela.Communications of the ACM, 44(3) 2001, 98-99. Classification: Behavioral, Application, Exploratory
Traditionally copyright was on the periphery of law because it involved technical rules for a highly specialized industry. Copyright law has
therefore become highly complex and effectively unreadable. A new politics of intellectual property is proposed due to the fact that copyright deeply affects the information environment for us all. Articulating a positive case for an open information environment is probably the single most important thing the new politics of intellectual property might do.
Until recently, copyright was on the periphery of law because it involved technical rules for a highly specialized industry. Copyright law has, as a consequence, become highly complex and effectively unreadable. Two important legacies of the old politics of intellectual property are: 1. copyright industry groups shave cultivated relationships with policymakers in the executive and legislative branches over time, and 2. the public has gotten used to the idea that copyright does not concern them.
Copyright's Fair Use Doctrine and Digital DataSamuelson, Pamela.Communications of the ACM, 37(1) 1994, 21-27. Classification: Behavioral, Application, Extension
The application of copyright law’s Fair Use doctrine to works in digital form is discussed. The fair use doctrine provides a flexible and
adaptable way to balance the interests of copyright owners and of the public so as to maintain adequate incentives to produce creative works while at the same time allowing the public to make reasonable uses of copyrighted materials. Several court cases are examined to illustrate how courts are likely to analyze the fairness of certain uses in the U.S.
The copyright law's fair use doctrine applies to all copyrighted works, including those in digital form. The fair use of a copyrighted work concerns four factors: the defendant's purpose, the nature of the copyrighted work, the substantiality of the taking, and the potential for harm to the copyrighted work's market. Some of the situations in which copyrighted material can be fairly used include clip art and clip sound programs, scanning of correspondence and documents received into an electronic repository, reformatting of electronic documents for reading on a different computer or software, placing electronic 'tags' on documents for database retrieval, and automatic translations of documents. Scanning or photocopying entire print magazines or journals is not acceptable. Private, noncommercial copying of on-line messages or quoting parts of someone's comments on a bulletin board is considered fair use.
Using Technology and Constituting Structures: A Practice Lens for Studying Technology in OrganizationsOrlikowski, Wanda. J.Organization Science, 11(4) 2000, 404-428. Classification: Behavioral, Theory, Extension
A proposal is made of an extension to the structural perspective on technology that develops a practice lens to examine how people, as they interact with a technology in their ongoing practices, enact structures which shape their emergent and situated use of that technology. After developing this lens, an example of its use in organizational research is offered, and then some implications for the study of technology in organizations are suggested.
As both technologies and organizations undergo dramatic changes in form and function, organizational researchers are increasingly turning to concepts of innovation, emergence, and improvisation to help explain the new ways of organizing and using technology evident in practice. With a similar intent, a proposal is made
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of an extension to the structural perspective on technology that develops a practice lens to examine how people, as they interact with a technology in their ongoing practices, enact structures which shape their emergent and situated use of that technology.
Is Anybody Out There? Antecedents of Trust in Global Virtual TeamsJarvenpaa, Sirkaa, Knoll K., Leidner, D.Journal of Management Information Systems, 14(4) 1998, 29-64. Classification: Behavioral, Application, Exploratory
Seventy-five teams, consisting of members in different countries, are studied to explore the antecedents of trust in a global virtual team setting. The two-week trust-building exercises did have a significant effect on the team members’ perceptions of the other members’ ability, integrity, and benevolence. A qualitative analysis of e-mails explores the strategy of “swift” trust. A research model for explaining trust in global virtual teams is then advanced.
A global virtual team is an example of a boundary less 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. In such teams, coordination is accomplished via trust and shared communication systems. The focus of the reported study was to explore the antecedents of trust in a global virtual-team setting. Seventy-five teams, consisting of four to six members residing in different countries, interacted and worked together for eight weeks. The two-week trust-building exercises did have a significant effect on the team members' perceptions of the other members' ability, integrity, and benevolence. In the early phases of teamwork, team trust was 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 decreased over time. The members' own propensity to trust had a significant, though unchanging, effect on trust. A qualitative analysis of six teams' electronic mail messages explored strategies that were used by the three highest trust teams, but were used infrequently or not at all by the three lowest trust teams. The strategies suggest the presence of 'swift' trust. The paper advances a research model for explaining trust in global virtual teams.
Computer Support for Meetings of Groups Working On Unstructured Problems: A Field ExperimentJarvenpaa, Sirkka, Rao, V., Huber, G.MIS Quarterly, 12(4) 1988, 645-666. Classification: Behavioral, Application, Exploratory
A preliminary study was conducted to investigate the consequences of computer support for teams working on unstructured, high-level conceptual software design problems in face-to-face group settings. A networked workstation technology and electronic blackboard technology were contrasted with conventional communication technology. Significant team differences were found in performance and interaction measures. The results indicate that the theory of compute-based meeting support technology must be extended to account for team differences.
A preliminary study was conducted to investigate the consequences of computer support for teams working on unstructured, high-level conceptual software design problems in face-to-face group settings. A networked workstation technology and electronic blackboard (EBB) technology were contrasted with conventional communication technology such as paper notepads. Twenty-one software designers and computer scientists assigned to 3 teams performed team tasks that involved generating ideas and reaching consensus. Positive effects on the thoroughness of information exchange and quality of team performance were found in meetings in which EBB technology was available. The networked workstation provided mixed results. Significant team differences were found in performance and interaction measures among the different experimental groups. The results indicate that the theory of computer-based meeting support technology must be extended to account for team differences.
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......... Computerization and Social TransformationsKling, RobScience Technology and Human Values, 16 (3), 1991, 342-367. Classification: Behavioral, Application, Review
This article examines the relationship between the use of computer-based systems and transformations in parts of the social order.
Answers to this question rest heavily on the way computer-based systems are consumed -not just produced or disseminated. The article examines qualitative case studies of computerization in welfare agencies, urban planning, accounting, marketing, and manufacturing to examine the ways that computerization alters social life in varied ways: sometimes restructuring relationships and in other cases reinforcing existing social relationships. The article also examines some of the theoretical issues in studies of computerization, such as drawing boundaries.
This article examines the relationship between the use of computer-based systems and transformations in parts of the social order. Answers to this question rest heavily on the way computer-based systems are consumed -not just produced or disseminated. The discourse about computerization advanced in many professional magazines and the mass media is saturated with talk about "revolution," and yet substantial social changes are often difficult to identify in carefully designed empirical studies. The article examines qualitative case studies of computerization in welfare agencies, urban planning, accounting, marketing, and manufacturing to examine the ways that computerization alters social life in varied ways: sometimes restructuring relationships and in other cases reinforcing existing social relationships. The article also examines some of the theoretical issues in studies of computerization, such as drawing boundaries. It concludes with some observations about the sociology of computer science as an academic discipline.
Institutional Factors in Information Technology InnovationKing, John Leslie; Gurbaxani, Vijay; Kraemer, Kenneth L.; McFarlan, F. Warren; Raman, K.S.Yap, C.S.Information Systems Research, 5(2), 1994, 139-169.Classification: Behavioral, Application, Extension
A research model is developed that links two major dimensions of IS planning – the quality of the planning process and planning effectiveness with a set of eight organizational factors derived from the contingency research in IS planning. The model is validated from surveys from senior IS executives. The “ends” and “means” of IS planning are equally important.
Provides a synthesis of perspectives to assist research on the institutional aspects of information technology (IT) innovation. Description of innovation and institutions in IT; Role of innovation in national economic development; Social role of institution; Institutional intervention in IT innovation
Critical Success Factor Analysis as a Methodology for MIS PlanningShank, Michael E.; Boynton, Andrew C.; Zmud, Robert WMIS Quarterly, 9(2), 1985, 121-129. Classification: Behavioral, Application, Extension
This article addresses the use and benefits of the Critical Success Factor (CSF) methodology in identifying corporate information needs and, subsequently, in developing a corporate information systems plan. The outcome of the CSF study has been a fundamental rethinking of the nature of the corporation, and its impact far surpassed the initial expectations of everyone involved. The case presented here, combined with information drawn from the CSF literature, can provide a number of meaningful insights on the use of the CSF methodology as a procedure for MIS planning and for building support for using information technologies throughout a user population.
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This article addresses the use and benefits of the Critical Success Factor (CSF) methodology in identifying corporate information needs and, subsequently, in developing a corporate information systems plan. The conclusions presented are drawn from an analysis of a CSF study conducted at Financial Institutions Assurance Corporation (FIAC). Interestingly, the initial purpose of this study was to evaluate the firm's existing data processing system in light of intermediate-term corporate objectives. However, the outcome of the CSF study has been a fundamental rethinking of the nature of the corporation, and its impact far surpassed the initial expectations of everyone involved. The case presented here, combined with information drawn from the CSF literature, can provide a number of meaningful insights on the use of the CSF methodology as a procedure for MIS planning and for building support for using information technologies throughout a user population.
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.........Section9
Systems Analysis & DesignIntroduction
Information Systems Analysis and Design is the process of determining what an information system needs to do and how it will do it. Ludwig von Bertalanffy can be considered the father of Systems Analysis and Design. He introduced the study of complex systems, which he called General System Theory, in a series of lectures he delivered in the 1930’s and 40’s. His book, General System Theory, was published in 1968.
Ole-Johan Dahl and Kristen Nygaard made one of the most important contributions to Systems Analysis and Design with their invention in 1966 of object-oriented programming, through the development of the Simula programming language. However, it took almost two decades of additional advances on systems design concepts—and the invention of the Smalltalk and C++ programming languages—before object-oriented design emerged in the mid to late 1980’s as the dominant approach to systems analysis, design and development.
Several important advancements were made in the 1970’s, beginning with the introduction of the waterfall method in 1970. This was followed by the development of structured design in 1974 and structured analysis in 1976. During this period the concepts of modular programming and component-based development were also introduced and refined. These advancements were made by several different researchers whose individual contributions are described in the descriptions of key research that follow this section.
Barry Boehm introduced the spiral model of system development in 1988, which immediately began to replace the traditional waterfall method. The spiral model is used in virtually all current software development methodologies.
In 1994, Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides (commonly referred to as The Gang of Four), showed how the concept of architectural design patterns—long used by building architects—could be applied to software design. This started a whole new sub-field of systems design research.
From the mid 1980s to the mid 1990s, several different object-oriented development methodologies were developed. The most widely accepted of these was the Booch Method, developed by Grady Booch. In the early 1990s, Booch’s employer (Rational Software) hired the principal developers of two of the leading competitors to the Booch method. This allowed Grady Booch, Ivar Jacobson, and James Rumbaugh to combine their notations and methodologies to form the Unified Modeling Language (UML) and the Unified Software Development Process.
UML was accepted as a standard object-oriented design notation by the Object Management Group in 1997, and has since become the only widely used notation for object-oriented systems and the only notation supported by most system design tools. The Unified Modeling Language, although widely used, has not become dominant and seems to be losing ground to more lightweight methodologies.
Object-oriented analysis and design has dominated Systems Analysis and Design for about the last twenty years. However, a recently introduced technology called aspect-oriented programming may have a large impact on systems design in the future. Aspect-oriented programming is an extension of object-oriented programming that adds the ability to write code modules (aspects) that are dynamically discovered and loaded
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.........at runtime to add functionality to existing system in ways not directly supported by traditional object-oriented systems.
School Listing
Systems Analysis and Design
School Tier
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Facu
lty In
tere
sts
Lead
ing
Res
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Dep
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University of Texas-- Austin I X Georgia State University II X Arizona State University III X University of Washington IV X Georgia Institute of Technology V X Drexel University Research I X Florida International University Research I X Florida State University Research I X University of British Columbia Research I X X Boston University Research II X X Case Western Reserve University Research II X Hong Kong University of S&T Research II X Tel Aviv University Research II X University of South Florida Research II X University of Toledo Research II X
George Washington UniversityResearch III X
Georgetown UniversityResearch III X
SUNY- BuffaloResearch III X
Syracuse UniversityResearch III X
Bentley College Teaching I X Purdue University Teaching I X University of California - Berkeley Teaching I X University of Virginia Teaching II X
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.........Timeline
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.........Key Contributors
Barry Boehm
- Professor- Director, USC Center for Software EngineeringComputer Science DepartmentUniversity of Southern California (Los Angeles, CA)
CONTACT INFORMATION
USC Center for Software EngineeringUniversity of Southern CaliforniaLos Angeles, CA 90007
(213) 740-8163 Telephone(706) 740-4927 Facsimile
http://sunset.usc.edu/Research_Group/barry.html
EDUCATION
Ph.D. – Mathematics, UCLA, 1964M.S. – Mathematics, UCLA, 1961BA – Mathematics, Harvard, 1957
RESEARCH INTERESTS
Software process modeling, software requirements engineering, software architectures, software metrics and cost models, software engineering environments, and knowledge-based software engineering.
KEY PUBLICATIONS
Boehm, B. W., J. R. Brown, et al. (1978). Characteristics of Software Quality. North Holland, Amsterdam.
Boehm, B. W. (1981). Software Engineering Economics. Engelwood Cliffs, N.J., Prentice Hall.
Boehm, B. W. (1988). "A Spiral Model of Software Development and Enhancement." IEEE Computer 21(5): 61-72.
Boehm, B. W. (1996). "Anchoring the Software Process." IEEE Software.
Boehm, B. W. and T. DeMarco (1997). "Software Risk Management." IEEE Software 14(3): 17-19.
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.........Grady Booch
- Chief ScientistRational Software
CONTACT INFORMATION
Not available.
http://www-306.ibm.com/software/rational/bios/booch.html
EDUCATION
M.S. – Electrical Engineering, University of California at Santa Barbara, 1979B.S. – United States Air Force Academy, 1977
RESEARCH INTERESTS
Object-oriented analysis and design.
KEY PUBLICATIONS
Booch, G. (1986). "Object-Oriented Development." IEEE Transactions on Software Engineering SE-12(2): 211-221.
Booch, G. (1993). Object-Oriented Analysis and Design with Applications. Redwood City, CA, The Benjamin/Cummings Publishing Company.
Booch, G., J. Rumbaugh, et al. (1998). The Unified Modeling Language User Guide, Addison-Wesley.
Jacobson, I., G. Booch, et al. (1999). The Unified Software Development Process, Addison-Wesley.
123
.........Ole-Johan Dahl (deceased 2002)
- Professor Emeritus Department of InformaticsUniversity of Oslo (Oslo, Norway)
EDUCATION
Ph.D. – University of Oslo
RESEARCH INTERESTS
Computer programming, object-oriented analysis, and operations research.
KEY PUBLICATIONS
Dahl, O.-J. and K. Nygaard (1966). "Simula--an ALGOL-Based Simulation Language." Communications of the ACM 9(9): 671-678.
Dahl, O.-J., E. W. Dijkstra, et al. (1972). Structured Programming, Academic Press.
124
.........Tom DeMarco
- PrincipalAtlantic Systems Guild (Camden, ME)
CONTACT INFORMATION
The Atlantic Systems GuildCamden, ME
Phone: (207) 236-4735
http://www.systemsguild.com/GuildSite/TDM/TDMBio.html
EDUCATION
Diplome - University of Paris at the SorbonneHonorary Doctor of Science - City University, London, 2003M.S. - Columbia University BSEE - Cornell University
RESEARCH INTERESTS
System software specification, risk management, project management, requirements engineering, and litigation of software-intensive contracts.
KEY PUBLICATIONS
DeMarco, T. and P. J. Plauger (1979). Structured Analysis and System Specification, Prentice Hall.
DeMarco, T. (1986). Controlling Software Projects: Management, Measurement, and Estimates, Prentice Hall.
Boehm, B. W. and T. DeMarco (1997). "Software Risk Management." IEEE Software 14(3): 17-19.
DeMarco, T. and T. Lister (1999). Peopleware: Productive Projects and Teams, Dorset House.
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.........Edsgar W. Dijkstra (deceased 2002)
EDUCATION
Ph.D. – Computing Science, University of AmsterdamM.S. – Theoretical Physics, University of LeydenB.S. – Mathematics, University of Leyden
RESEARCH INTERESTS
Data structures and algorithms.
KEY PUBLICATIONS
Dijkstra, E. W. (1959). "A Note on Two Problems in Connection with Graphs." Numerische Mathematic.
Dijkstra, E. W. (1968). "Go To Statement Considered Harmful." Communications of the ACM 11(3): 147-8.
Dijkstra, E. W. (1969). Notes on Structured Programming.
Dahl, O.-J., E. W. Dijkstra, et al. (1972). Structured Programming, Academic Press.
Dijkstra, E. W. (1976). A Discipline of Programming. Englewood Cliffs, NJ, Prentice Hall.
126
.........Clarence Ellis
- ProfessorDepartment of Computer Science University of Colorado at Boulder (Boulder, CO)
CONTACT INFORMATION
Department of Computer ScienceEngineering Center ECOT 747430 UCBBoulder, CO 80309-0430
(303) 492-5984 Telephone(303) 492-2844 Facsimile
http://www.cs.colorado.edu/~skip/Home.html
EDUCATION
Ph.D. – Computer Science, University of Illinois, 1969BS - Math and Physics, Beloit College in Wisconsin
RESEARCH INTERESTS
Workflow Technology, Groupware, Cognitive Science (Group cognition), Computer Supported Cooperative Work, Object Oriented Systems, Systems Modeling, Distributed Interaction Systems, and Group User Interfaces.
KEY PUBLICATIONS
Ellis, C. A. and G. J. Nutt (1980). "Office Information Systems and Computer Science." Computing Surveys 12(1): 27-60.
Ellis, C. A., S. J. Gibbs, et al. (1991). "Groupware - Some Issues and Experiences." Communications of the Acm 34(1): 38-58.
Ellis, C. A. (1994). "Goal Based Workflow Systems." International Journal of Collaborative Computing 1(1): 61-86.
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.........Ivar Jacobson
- Vice President of Business EngineeringRational Software
CONTACT INFORMATION
Jaczone, Inc.800 Third Avenue, 23rd flNew York, NY 10022
EDUCATION
Ph.D. – Royal Institute of Technology
RESEARCH INTERESTS
Object-oriented analysis and design.
KEY PUBLICATIONS
Jacobson, I., M. Christerson, et al. (1992). Object Oriented Software Engineering: A Use Case Driven Approach, Addison-Wesley.
Jacobson, I., M. Ericsson, et al. (1994). The Object Advantage: Business Process Reengineering with Object Technology, Addison-Wesley.
Booch, G., J. Rumbaugh, et al. (1998). The Unified Modeling Language User Guide, Addison-Wesley.
Jacobson, I., G. Booch, et al. (1999). The Unified Software Development Process, Addison-Wesley.
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.........Jeff Kramer
- Professor- Head, Department of Computing of Imperial College- Director, Distributed Software Engineering Research CenterImperial College (London, England)
CONTACT INFORMATION
Distributed Software Engineering Department of ComputingImperial College180 Queen’s GateLondon SW7 2AZ UNITED KINGDOM
44-20-7594-8271 Telephone44-20-7594-8282 Facsimile
http://www.doc.ic.ac.uk/~jk/
EDUCATION
Ph.D. – Computer Science, Imperial College, London, England, 1979M.S. – Computer Science, Imperial College, London, England, 1972B.S. – Electrical Engineering, University of Natal, South Africa, 1970
RESEARCH INTERESTS
Distributed systems, software architecture, specification and design methods, behavior analysis, requirements analysis.
KEY PUBLICATIONS
Kramer, J. and J. Magee (1985). "Dynamic Configuration for Distributed Systems." IEEE Transactions on Software Engineering SE-11(4): 424-36.
Kramer, J., J. Magee, et al. (1989). "Managing Evolution in Distributed Systems." Software Engineering Journal 4(6): 321-9.
J. and J. Magee (1990). "The Evolving Philosophers Problem: Dynamic Change Management." IEEE Transactions on Software Engineering SE-16(11): 1293-1306.
Finkelstein, A. C. W., D. Gabbay, et al. (1994). "Inconsistency Handling in Multiperspective Specifications." IEEE Transactions on Software Engineering 20(8): 569-78.
129
.........Kristen Nygaard (deceased 2002)
- Professor Emeritus Department of Informatics University of Norway (Oslo, Norway)
EDUCATION
Ph.D. – University of Oslo
RESEARCH INTERESTS
Computer programming, object-oriented analysis, and operations research.
KEY PUBLICATIONS
Dahl, O.-J. and K. Nygaard (1966). "Simula--an ALGOL-Based Simulation Language." Communications of the ACM 9(9): 671-678.
130
.........David Lorge Parnas
- Director, Software Quality Research LaboratoryFaculty of Engineering Computing and Software DepartmentMcMaster University (Hamilton, Ontario, Canada)
CONTACT INFORMATION
McMaster UniversityDepartment of Computing & Software,1280 Main St. West, Hamilton, OntarioCanada L8S 4L7
(905) 525-9140 ext. 27353 Telephone
http://www.cas.mcmaster.ca/sqrl/parnas.homepg.html
EDUCATION
Ph.D. – Electrical Engineering, Carnegie Mellon UniversityM.S. – Electrical Engineering, Carnegie Mellon UniversityB.S. – Electrical Engineering, Carnegie Mellon University
RESEARCH INTERESTS
Computer system design.
KEY PUBLICATIONS
Parnas, D. L. (1972). "On the Criteria To Be Used in Decomposing Systems into Modules." Communications of the ACM 15(12): 1053-8.
Parnas, D. L. (1972). "A Technique for Software Module Specification with Examples." Communications of the ACM 15(5).
Parnas, D. L. (1976). "On the Design and Development of Program Families." IEEE Transactions on Software Engineering.
Parnas, D. L. (1999). "Software Engineering Programs are not Computer Science Programs." IEEE Software 16(6): 19-30.
131
.........Amit P. Sheth
- Professor- Director, Large Scale Distributed Information System Lab- CTO and co-founder of Semagix, IncDepartment of Computer Science University of Georgia (Athens, GA)
CONTACT INFORMATION
Large Scale Distributed Information System LabDepartment of Computer ScienceUniversity of Georgia415 Graduate Studies Research CenterAthens, GA 30602-7404
(706) 542-2310 Telephone
(706) 542-4771 Facsimile
http://lsdis.cs.uga.edu/~amit/
EDUCATION
Ph.D. – Computer & Information Science, Ohio State University, 1985MS. – Computer & Information Science, Ohio State University, 1983BE – Electrical & Electronics Engineering, Birla Institute of Tech. & Science, 1981
RESEARCH INTERESTS
Semantic Web and Semantic Information Brokering, Semantic Web Processes, Management of Rich Media Content and Information Sources, and Semantic Applications in Financial, Healthcare and National Security.
KEY PUBLICATIONS
Georgakopoulos, D., M. Hornick, et al. (1995). "An Overview of Workflow Management: From Process Modeling to Workflow Automation Infrastructure." Distributed and Parallel Databases 3: 119-153.
Sheth, A. P. and N. Krishnakumar (1995). "Managing Heterogeneous Multi-System Tasks to Support Enterprise-wide Operations." Distributed and Parallel Databases 2(2): 155-86.
Shah, K. and A. P. Sheth (1999). "InfoHarness: An Information Integration Platform for Managing Distributed, Heterogeneous Information." IEEE Internet Computing: 18-28.
Sheth, A. P., C. Bertram, et al. (2002). "Semantic Content Management for Enterprises and the Web." IEEE Internet Computing: 80-7.
132
.........Edward Yourdon
- Independent consultant, author, lecturer- Chairman, Cutter Consortium
CONTACT INFORMATION
37 BroadwayArlington, Massachusetts 02474
(212) 214-0775 Telephone(212) 214-0775 Facsimile
http://www.yourdon.com
EDUCATION
B.S. – Applied Mathematics, MIT
KEY PUBLICATIONS
Yourdon, E. and L. L. Constantine (1979). Structured Design. Englewood Cliffs, N.J., Prentice Hall.
Yourdon, E. (1994). Object-Oriented Systems Design: An Integrated Approach. Englewood Cliffs, N.J., Prentice Hall.
133
.........Key Papers
General System Theory: Foundations, Development, Applicationsvon Bertalanffy, L. (1968). New York, George Braziller.Classification: Technical, Theory, Foundational
Although this book was not published until 1968, it is based on concepts taught by the author in the 1930's and 40's. General System
Theory is the study of complex systems and it is the basis of information systems analysis and design.
Simula—An Algol-Based Simulation LanguageDahl, O.-J. and K. Nygaard (1966). Communications of the ACM 9(9): 671-678.Classification: Technical, Application, Foundational
This is the classic paper that introduced the concept of object-oriented programming. It introduced "Simula" which was the first object-
oriented language. Although Simula was created in the 1960's, object-oriented programming did not become common until the 1980's.
Notes on Structured ProgrammingDijkstra, 1969Classification: Technical, Application, Foundational
This manuscript established structured programming. It was circulated privately by the author starting in 1969. It was finally published as part
of a book co-authored by the author in 1972 see (Dahl, Dijkstra, & Hoare, 1972).
Managing the Development of Large Software Systems: Concepts and TechniquesRoyce, W. W. (1970). Western Electronic Show and Convention, Los Angeles, Wescon Technical Papers.Classification: Technical, Application, Foundational
This paper established the "waterfall" method of software development—probably the first widely accepted systems analysis and design method. Although it does not use the word "waterfall" to describe the method, it recommends that the following steps be followed for any non-trivial software development project: System Requirements, Software Requirements, Analysis, Program Design, Coding, Testing, and Operations. Most organizations have moved or are now moving to an iterative methodology; however, the waterfall approach established by this paper in 1970 is still heavily used and is a predecessor to most other methodologies.
Program Development by Stepwise RefinementWirth, N. (1971)Communications of the ACM 14(4): 221-227.Classification: Technical, Application, Extension
This paper introduced the idea of solving complex programming problems by dividing them into smaller and smaller pieces until each
piece becomes easy to solve. This is one of the fundamental building blocks of modular programming.
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......... On the Criteria to be Used in Decomposing Systems into ModulesParnas, D. L. (1972) Communications of the ACM 15(12): 1053-8.Classification: Technical, Application, Extension
This was one of the early papers on modular programming. Although it did not invent the concept, it established important criteria to be used when applying it.
Structured DesignStevens, W. P., G. J. Myers, et al. (1974) IBM Systems Journal(2): 115-39.Classification: Technical, Application, Foundational
This paper established the concept of "structured design" as a way to reduce the complexity of programs. It proposed a formal notation for describing program modules.
Programming-in-the-Large versus Programming-in-the-SmallDeRemer, F. and H. Kron International Conference on Reliable Software, ACM Sigplan Notices.Classification: Technical, Application, Foundational
This paper extended modular programming to establish the concept of component-based programming.
The Mythical Man-Month: Essays on Software EngineeringBrooks, F. P., Jr. (1975)Addison-Wesley.Classification: Technical, Application, Review
This is a classis book on project management. It consists of a set of essays that explore various problems that prevent large software
projects from being completed on-time and on-budget. According to the author, the book is an answer to the question: "Why is programming hard to manage?"
Structured Analysis (SA): A Language for Communicating IdeasRoss, D. T. (1976)IEEE Transactions on Software Engineering SE-3(1): 16-34.Classification: Technical, Application, Extension
This paper extended the concepts and notations of structured programming and structured design into a systems development
methodology known as structured analysis.
Office Information Systems and Computer ScienceEllis, C. A. and G. J. Nutt (1980) Computing Surveys 12(1): 27-60.Classification: Technical, Application, Extension
This was one of the early papers on office information systems (workflow). It describes office information systems by giving examples,
discusses some of the problems of office information systems, and discusses future trends in this area of research.
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......... The Draco Approach to Constructing Software from Reusable ComponentsNeighbors, J. M. (1984) IEEE Transactions on Software Engineering 10(5): 564-74.Classification: Technical, Application, Extension
This paper describes an approach called Draco to the construction of software systems from reusable software parts. The approach organizes reusable software components by problem area or domain. Statements of programs in these specialized domains are then optimized by source-to-source program transformations and refined into other domains. Draco was the first system to support the transformation of high-level domain specific programs to executable code.
Dynamic Configuration for Distributed SystemsKramer, J. and J. Magee (1985). IEEE Transactions on Software Engineering SE-11(4): 424-36.Classification: Technical, Application, Extension
This paper presents a model for performing dynamic modification, extension, and configuration of running programs. It describes the
properties required by languages and runtime environments to support this dynamic capability.
Object-Oriented DevelopmentBooch, G. (1986)IEEE Transactions on Software Engineering SE-12(2): 211-221.Classification: Technical, Application, Review
This paper provides a detailed overview and introduction to object-oriented programming. It compares object-oriented design to
traditional structured design.
A Spiral Model of Software Development EnhancementBoehm, B. W. (1988) IEEE Computer 21(5): 61-72.Classification: Technical, Application, Foundational
This is the classic and heavily cited paper that introduced the spiral method of software development. Spiral development extends the
traditional "waterfall" method by adding the concept of iteration. The concepts of spiral development have been heavily used in object-oriented development methodologies as well as almost all other development methodologies that have been created since this paper's publication.
Process ModelingCurtis, B., M. I. Kellner, et al. (1992)Communications of the ACM 35(9): 75-90.Classification: Technical, Application, Review
This paper illustrates process modeling from functional, behavioral, organizational, and informational perspectives. The authors explain
five dominant process modeling paradigms: programming models, functional models, plan-based models, Petri-net models, and quantitative models. Additionally, the authors suggest several future Workflow research focus areas: multi-paradigm representations, workflow usage in process improvement, and process-based software development environments.
136
......... Design Patterns: Elements of Reusable Object-Oriented Software Gamma, E., R. Helm, et al. (1995). Addison-Wesley.Classification: Technical, Application, Foundational
This is the heavily cited book (over 6,000 citations on Google Scholar) that first applied the concept of architectural design patterns to
software development. The authors suggest a practice of software design similar to what architects do when they design physical structures—reuse existing designs to solve similar problems. The book describes 23 patterns that are commonly used to handle common programming problems. Each pattern is described in detail with design diagrams, code examples, and a discussion of how and when to use the pattern.
Inconsistency Handling in Multiperspective SpecificationsFinkelstein, A. C. W., D. Gabbay, et al. (1994)IEEE Transactions on Software Engineering 20(8): 569-78.Classification: Technical, Application, Extension
This paper describes an approach to handling inconsistencies that appear in the specifications of large software systems. The
inconsistencies occur when specifications are obtained from different people with different perspectives on the system. The authors recommend a solution that is a combination of the ViewPoints framework and a logic-based approach.
An Overview of Workflow Management: From Process Modeling to Workflow Automation InfrastructureGeorgakopoulos, D., M. Hornick, et al. (1995). Distributed and Parallel Databases 3: 119-153.Classification: Technical, Application, Review
This paper provides a high-level overview of workflow management methodologies and software products. The authors also discuss the emerging infrastructure technologies that will support increased workflow automation in complex real-world environments involving heterogeneous, autonomous, and distributed information systems. The article provides a discussion of how distributed object management and customized transaction management can support further advances in Workflow.
Software Architecture: Perspectives on an Emerging DisciplineShaw, M. and D. Garlan (1996) Prentice Hall.Classification: Technical, Application, Review
This book provides an overview of software architecture patterns and styles. It uses several case studies to compare and contrast various
architectural solutions.
Software Risk ManagementBoehm, B. W. and T. DeMarco (1997). IEEE Software 14(3): 17-19.Classification: Technical, Application, Review
This paper provides an overview of risk management. It focuses on the need to mange risk (instead of ignoring it) and briefly introduces
several other papers that provide more specific information on how to manage software project risk.
137
......... The Unified Modeling Language Reference ManualBooch, G., J. Rumbaugh, et al. (1998). Addison-Wesley.Classification: Technical, Application, Extension(Booch, Rumbaugh, & Jacobson, 1998)
This book describes the Unified Modeling Language, which was accepted as a standard software design notation by the Object Management Group (OMG) in 1997. UML represents the combination of the notations and diagrams previously used separately by the authors in their own methodologies.
Aspect-Oriented ProgrammingElrad, T., R. E. Filman, et al. (2001)Communications of the ACM 44(10): 29-32.Classification: Technical, Application, Review(This paper provides an overview of aspect-oriented programming—a new technology that began to emerge at the end of the 1990's. The
paper describes aspect-oriented programming as an extension of object-oriented programming that addresses some limitations of object-oriented programming by providing a mechanism to dynamically identify load and execute code modules to augment the behavior of an object-oriented system.
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.........Section10School ListingsMethodology
Our contribution to this project is a listing of the top academic institutions in the area of MIS. To that end, we have compiled a list of 66 academic institutions around the world, 57 of who are in the United States, based on the following methodology.
We took into account five different rankings when determining which schools to include. The first three were studies that were handed out in class. These studies were chosen because they presented what we felt was a fairly recent picture of research productivity in the MIS field.
The first of these was the study in the December/January 1998 issue of Decision Line by Im, Kim, and Kim, titled “An Assessment of Individual and Institutional Research Productivity in MIS.” The second study was a follow-up to this study that addressed the concerns of others; this appeared in the September/October 1998 issue of Decision Line, and was titled “A Response to ‘Assessing Research Productivity: Important But Neglected Considerations,’” by the same three authors. The final study was conducted by Athey and Plotnicki and appeared in the March 2000 issue of the Communications of the Association for Information Systems; it was titled “An Evaluation of Research Productivity in Academic IT.”
We also felt that we needed to get “the student’s perspective” in our rankings; in other words, we wanted to take into account the experience of the student in academic MIS. Therefore, we chose to include two student-focused rankings in our listing. These two were the 2004 and 2005 editions of the “Best Graduate Schools” rankings that are published annually by US News & World Report. Specifically, we used the “specialty rankings” that focused on the best MIS programs in business schools.
Both studies in Decision Line ranked 50 schools. The study that appeared in CAIS ranked 24 schools. The 2004 “Best Graduate Schools” ranking included 26 schools; the 2005 edition of the same ranking included 28 schools. To be included in our listing, a school only had to appear on any one of the five rankings. Also, we were concerned only with traditional academic institutions; therefore, the two non-academic research groups that appeared in the Decision Line studies (IBM and Bellcore) were discarded in the final rankings.
Limitations
Our listing has certainly been limited by several factors, notably the following:
The US News & World Report rankings have come under fire in the past several years for failing to provide a true snapshot of the academic world as it is. In the case of these “specialty rankings” (the rankings of MIS programs), the rankings were conducted by asking business school deans and program heads to nominate up to 10 schools that were excellent in this specific area. Thus, these rankings are merely a subjective survey and are not objectively measured as the research studies were. However, we feel that these are the best rankings available to measure the field in a “student-centric” way. In addition, we believe that the excellence of an MIS program is not only measured by the research productivity of that program, but by the perceived excellence of the program among other academic institutions. This is what the US News & World Report rankings measure.
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......... The newest of our research productivity rankings is now four and a half years old. In addition, this is in
contrast to the US News & World Report rankings, which are very recent. However, we are confident in the applicability of these research rankings. Not only are they the most recent rankings of which we are aware, but also their importance is highlighted by their presence in our coursework. Also, given that the US News & World Report rankings are notoriously slow to change (because of their survey-based nature), we believe that the time delay between the two sets of listings should not be a significant factor. We remain confident in the strength of the three studies, despite these limitations.
School Listing
We can now present our list of institutions. It is our belief that any attempt to rank these institutions from 1-66 would result in many forced, artificial choices. Instead, we have grouped them into ten tiers based on the five rankings used. Our rationale for the groupings is as follows:
The first five tiers of our grouping encompass schools that were mentioned in both the “research-centric” rankings (the three published studies) and the “student-centric” US News & World Report rankings.
Tier I Schools (7 schools): These schools were always listed in the top 10 in all five of our studies.
Tier II Schools (4): These schools were always listed in the top 25 in all five of our studies.
Tier III Schools (6): These schools were listed in both US News & World Report rankings; in addition, they were either ranked in the CAIS study or both Decision Line studies, but not in all three.
Tier IV Schools (3): These schools were listed in both Decision Line rankings (the larger of the rankings), but not the CAIS rankings. In addition, they were listed once in the US News & World Report rankings.
Tier V Schools (2): These schools were mentioned in exactly one of the three “research-centric” rankings, as well as exactly one of the “student-centric” rankings.
The next three tiers are devoted to schools that were mentioned in the “research-centric” rankings but never in the “student-centric” rankings.
Tier I Research Schools (10): These schools were mentioned in all three “research-centric” rankings.
Tier II Research Schools (16): These schools were listed either in both Decision Line studies, or in the CAIS study, but not in all three.
Tier III Research Schools (8): These schools were ranked in either Decision Line study but not both.
The final two tiers are the schools that were listed only in the “student-centric” rankings and not in the “research-centric” rankings.
Tier I Teaching Schools (5): These schools were listed in both US News & World Report rankings.
Tier II Teaching Schools (5): These schools were listed in one US News & World Report ranking but not both.
We made no attempt to objectively rank the schools within each of the tiers. The schools are listed in no particular order within the tiers except some general order to highlight the rankings.
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Tiered Results
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Tier I SchoolsUniversity of Arizona 1 1 1 4 4MIT 3 4 3 1 1University of Minnesota 2 3 8 5 4Carnegie Mellon University 4 5 10 2 2New York University 5 6 2 9 8University of Pennsylvania 7 7 5 6 6University of Texas-- Austin 8 8 9 3 3
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Tier II SchoolsGeorgia State University 17 15 11 10 8University of Georgia 11 9 14 17 17University of California-- Irvine 16 11 24 13 12University of Pittsburgh 18 10 15 19 17
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Tier III SchoolsUniversity of California-- Los Angeles 15 19 14 26Indiana University 23 20 11 14University of Maryland 25 29 8 7University of Michigan 41 45 14 11Harvard University 50 41 23 16Arizona State University 22 17 19
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Tier IV SchoolsUniversity of Southern California 28 30 20University of Washington 44 42 26University of Rochester 47 40 26
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Tier V SchoolsUniversity of Illinois 32 24Georgia Institute of Technology 46 21
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Tier I Research SchoolsUniversity of South Carolina 6 2 7National University of Singapore 12 13 4Drexel University 10 14 6University of British Columbia 9 12 16Florida International University 14 16 12Florida State University 13 17 19Texas A&M University 19 18 21Penn State University 20 21 20Queen's University 22 23 17University of Houston 27 35 18
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Tier II Research SchoolsUniversity of Colorado-- Boulder 21 22Auburn University 24 25Case Western Reserve University 26 27Ecole Des Hautes Etudes Comm. 29 26
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.........University of Hawaii 34 28Hong Kong University of Sci. and Tech. 42 24University of Western Ontario 36 31Southern Methodist University 33 34Boston University 31 37Florida Atlantic University 32 43Rutgers University 46 36University of South Florida 49 38Tel Aviv University 43 44Northeastern University 39 49University of Memphis 13University of Toledo 23
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Tier III Research SchoolsLondon Business School 33Georgetown University 35Syracuse University 38Queensland 40George Washington University 45University of Colorado-- Denver 48SUNY- Buffalo 48Tennessee Technological 50
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Tier I Teaching SchoolsStanford University 7 10University of California - Berkeley 12 15Purdue University 14 13Northwestern University 19 22Bentley College 21 20
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Tier II Teaching SchoolsDuke University 23University of Virginia 23Michigan State University 26
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.........Rochester Institute of Technology 25University of Connecticut 22
Quick School Snapshots
We have compiled a short overview of each of the schools in our rankings. This overview includes some basic information about the school, and its position in our rankings. In addition, we have highlighted the areas within the MIS domain in which each of the included schools participates.
Methodology
Our methodology for the inclusion of each “area” included four categories. First, we considered the research interests of the faculty at that particular school. If at least 3, or at least 20%, of the MIS faculty at a particular school listed an area as a research interest, then we considered that area to be a “topic of interest” at that school. Secondly, we noted the presence of funded research groups and labs. If the school had such a center that conducted research in a certain area and students appeared to be involved in the research, that area was included in our listing as a topic of interest for that school. Our third criterion was the presence of a faculty member at that school who has been identified as a “key researcher” in a certain area. We believe that this indicates that this area should be included in our listing for that school. Finally, our last criterion accounted for schools that have a department named after one of our certain areas, rather than a broad “MIS” department. We believe this indicates a commitment to this topic within the department.
144
.........School Bios
Arizona State University
Location: Tempe, Arizona
Rankings #22 – CAIS Study#17 – US News & World Report 2003#19 – US News & World Report 2004
Categorization: Tier III
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Artificial Intelligence Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Collaboration A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
145
.........Auburn University
Location: Auburn, Alabama
Rankings #24 – January 1998 Decision Line#25 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
146
.........Bentley College
Location: Waltham, Massachusetts
Rankings #21 – US News & World Report 2003#20 – US News & World Report 2004
Categorization: Tier I Teaching
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Artificial Intelligence A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
Human-Computer Interface Department includes a funded research lab in this specialty
147
.........Boston University
Location: Boston, Massachusetts
Rankings #31 – January 1998 Decision Line#37 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
Systems Analysis and Design Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
148
.........Carnegie Mellon University
Location: Pittsburgh, Pennsylvania
Rankings #4 – January 1998 Decision Line#5 – October 1998 Decision Line#10 – CAIS Study#2 – US News & World Report 2003#2 – US News & World Report 2004
Categorization: Tier I
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Economics of Informatics A qualifying number of faculty list this is a research interest
(No other information was available for this university.)
149
.........Case Western Reserve University
Location: Cleveland, Ohio
Rankings #26 – January 1998 Decision Line#27 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
150
.........Drexel University
Location: Philadelphia, Pennsylvania
Rankings #10 – January 1998 Decision Line#14 – October 1998 Decision Line#6 – CAIS Study
Categorization: Tier I Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Artificial Intelligence A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
Systems Analysis & Design A qualifying number of faculty list this is a research interest
151
.........Duke University
Location: Durham, North Carolina
Rankings #23 – US News & World Report 2003
Categorization: Tier II Teaching
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest The department is named for this specialty
Collaboration Dr. Gerardine DeSanctis, one of the leading researchers in this specialty, works for Duke.
Social Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Economics of Informatics Department includes a funded research lab in this specialty
Key People at Duke Dr. Gerardine Desanctis— Thomas F. Keller Professor of Business Administration in the Fuqua School of Business at Duke University. Is a leading researcher in the Collaboration field. Got her Ph.D from Texas Tech in 1982.
152
.........Ecoles Des Hautes Etudes Commerciales
Location: Lausanne, France
Rankings #29 – January 1998 Decision Line#26 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty
Social Informatics Department includes a funded research lab in this specialty
153
.........Florida Atlantic University
Location: Boca Raton, Florida
Rankings #32 – January 1998 Decision Line#43 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management The department is named for this specialty
(No other information was available for this university.)
154
.........Florida International University
Location: Miami, Florida
Rankings #14 – January 1998 Decision Line#16 – October 1998 Decision Line#12 – CAIS Study
Categorization: Tier I Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Systems Analysis and Design A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
155
.........Florida State University
Location: Tallahassee, Florida
Rankings #13 – January 1998 Decision Line#17 – October 1998 Decision Line#19 – CAIS Study
Categorization: Tier I Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Collaboration A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
Economics of Informatics A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
156
.........George Washington University
Location: Washington, D.C.
Rankings #45 – January 1998 Decision Line
Categorization: Tier III Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Collaboration A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
Economics of Informatics A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
Artificial Intelligence A qualifying number of faculty list this is a research interest
157
.........Georgetown University
Location: Washington, D.C.
Rankings #35 – January 1998 Decision Line
Categorization: Tier III Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Artificial Intelligence A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
Economics of Informatics A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
158
.........Georgia Institute of Technology
Location: Atlanta, Georgia
Rankings #46 – October 1998 Decision Line#21 – US News & World Report 2003
Categorization: Tier V
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Collaboration A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
159
.........Georgia State University
Location: Atlanta, Georgia
Rankings #17 – January 1998 Decision Line#15 – October 1998 Decision Line#11 – CAIS Study#10 – US News & World Report 2003#8 – US News & World Report 2004
Categorization: Tier II
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Economics of Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest Dr. Veda Storey, one of the leading researchers in this specialty, works for GSU.
Social Informatics A qualifying number of faculty list this is a research interest
Collaboration A qualifying number of faculty list this is a research interest
Human-Computer Interaction Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Key People at Georgia State Dr. Veda Storey— Professor of Information Systems and Professor of Computer Science at the Computer Information Systems department in the College of Business at Georgia State University. One of the leading researchers in the Data Management field. Got her Ph.D from the University of Michigan, 1976.
160
.........Harvard University
Location: Cambridge, Massachusetts
Rankings #50 – January 1998 Decision Line#41 – October 1998 Decision Line#23 – US News & World Report 2003#16 – US News & World Report 2004
Categorization: Tier III
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Social Informatics A qualifying number of faculty list this is a research interest
Economics of Informatics A qualifying number of faculty list this is a research interest Dr. Lynda Applegate, one of the leading researchers in this specialty, works for Harvard.
Key People at Harvard Dr. Lynda Applegate— Henry R. Byers Professor of Business Administration at the Harvard Business School. One of the leading researchers in the Economics of Informatics field. Got her Ph.D. from the University of Arizona.
161
.........Hong Kong University of Science and Technology
Location: Hong Kong
Rankings #42 – January 1998 Decision Line#24 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Human-Computer Interaction A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
162
.........Indiana University
Location: Bloomington, Indiana
Rankings #23 – January 1998 Decision Line#20 – October 1998 Decision Line#11 – US News & World Report 2003#14 – US News & World Report 2004
Categorization: Tier III
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Collaboration A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest Dr. Rob Kling, one of the leading researchers in this specialty, works for IU.
Data Management A qualifying number of faculty list this is a research interest
Key People at Indiana Dr. Rob Kling—Professor of Information Systems and Information Science in the School of Library and Information Science at Indiana University. Director of the Center of Social Informatics. Got his Ph.D. from Stanford University in 1976.
163
.........London Business School
Location: London, England
Rankings #33 – October 1998 Decision Line
Categorization: Tier III Research
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest The department is named for this specialty
164
.........Michigan State University
Location: East Lansing, Michigan
Rankings #26 – US News & World Report 2003
Categorization: Tier II Teaching
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Human-Computer Interaction A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
165
.........Massachusetts Institute of Technology
Location: Cambridge, Massachusetts
Rankings #3 – January 1998 Decision Line#4 – October 1998 Decision Line#3 – CAIS Study#1 – US News & World Report 2003#1 – US News & World Report 2004
Categorization: Tier I
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Artificial Intelligence Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Collaboration Department includes a funded research lab in this specialty
Data Management Department includes a funded research lab in this specialty Dr. Stuart E. Madnick, one of the leading researchers in this specialty, works for MIT.
Economics of Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest Dr. Erik Brynjolfsson, one of the leading researchers in this specialty, works for MIT.
Social Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest Dr. Wanda Orlikowski, one of the leading researchers in this specialty, works for MIT. Dr. Joanne Yates, one of the leading researchers in this specialty, works for MIT.
Key People at MIT Dr. Wanda Orlikowski —Professor of Information Technologies and Organization Studies at the Sloan School of Management. One of the leading Social Informatics researchers in the world. Got her Ph.D. from New York University in 1985. Dr. Stuart E. Madnick—John Norris Maguire Professor of Information Technology at the Sloan School of Management. One of the leading researchers in the Data Management field. Was awarded his Ph.D. from MIT in 1972. Dr. Erik Brynjolfsson—George and Sandi Schussel Professor of Management at the Sloan School. Director for the Center for E-Business at MIT. One of the leading researchers in the field of Economics of Informatics. Got his Ph.D. from MIT in 1991. Dr. Joanne Yates— Sloan Distinguished Professor of Management at the Sloan School of Management. A leading Social Informatics researcher. Got her Ph.D. from the University of North Carolina at Chapel Hill.
166
.........National University of Singapore
Location: Singapore
Rankings #12 – January 1998 Decision Line#13 – October 1998 Decision Line#4 – CAIS Study
Categorization: Tier I Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Collaboration A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
167
.........New York University
Location: New York, New York
Rankings #5 – January 1998 Decision Line#6 – October 1998 Decision Line#2 – CAIS Study#9 – US News & World Report 2003#8 – US News & World Report 2004
Categorization: Tier I
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Data Management A qualifying number of faculty list this is a research interest
Economics of Informatics A qualifying number of faculty list this is a research interest Dr. Yannis Bakos, one of the leading researchers in this specialty, works for NYU.
Social Informatics Dr. Lee S. Sproull, one of the leading researchers in this specialty, works for NYU.
Key People at New York Dr. Yannis Bakos—Associate Professor of Management at the Stern School of Business. One of the leading Economics of Informatics field researchers. Was awarded his Ph.D. from MIT. Dr. Lee S. Sproull—Leonard N. Stern School Professor at the Stern School of Business. Director of the Digital Economy Initiative. One of the leading Social Informatics researchers in the world. Got her Ph.D. from Stanford University in 1977.
168
.........Northeastern University
Location: Boston, Massachusetts
Rankings #39 – January 1998 Decision Line#49 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management The department is named for this specialty
Data Management The department is named for this specialty
Artificial Intelligence A qualifying number of faculty list this is a research interest
169
.........Northwestern University
Location: Evanston, Illinois
Rankings #19 – US News & World Report 2003#22 – US News & World Report 2004
Categorization: Tier I Teaching
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Economics of Informatics A qualifying number of faculty list this is a research interest The department is named for this specialty
170
.........Penn State University
Location: State College, Pennsylvania
Rankings #20 – January 1998 Decision Line#21 – October 1998 Decision Line#20 – CAIS Study
Categorization: Tier I Research
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest The department is named for this specialty
171
.........Purdue University
Location: West Lafayette, Indiana
Rankings #14 – US News & World Report 2003#13 – US News & World Report 2004
Categorization: Tier I Teaching
Research Areas
Data Management A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
172
.........Queen’s University
Location: Kingston, Ontario, Canada
Rankings #22 – January 1998 Decision Line#23 – October 1998 Decision Line#17 – CAIS Study
Categorization: Tier I Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Collaboration Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest Dr. R. Brent Gallupe, one of the leading researchers in this specialty, works for QU.
Key People at Queen’s Dr. R. Brent Gallupe— Associate Dean of Faculty Professor in the Management Information Systems department at Queen’s University. Director of the Queen’s Center for Knowledge-Based Enterprises. One of the leading Collaboration researchers in the world. Received his Ph.D. from the University of Minnesota in 1985.
173
.........Queensland
Location: Brisbane, Australia
Rankings #40 – January 1998 Decision Line
Categorization: Tier III Research
Research Areas
Economics of Informatics A qualifying number of faculty list this is a research interest
Social Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
174
......... Rochester Institute of Technology
Location: Rochester, New York
Rankings #25 – US News & World Report 2004
Categorization: Tier II Teaching
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
175
.........Rutgers University
Location: New Brunswick, New Jersey
Rankings #46 – January 1998 Decision Line#36 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
176
.........Southern Methodist University
Location: Dallas, Texas
Rankings #33 – January 1998 Decision Line#34 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Economics of Informatics A qualifying number of faculty list this is a research interest
177
.........Stanford University
Location: Palo Alto, California
Rankings #7 – US News & World Report 2003#10 – US News & World Report 2004
Categorization: Tier I Teaching
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest Dr. Hau Lee, one of the leading researchers in this specialty, works for SU. The department is named for this specialty
Economics of Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest Dr. Haim Mendelson, one of the leading researchers in this specialty, works for SU.
Artificial Intelligence Dr. Paul M. Romer, one of the leading researchers in this specialty, works for SU.
Key People at Stanford Dr. Hau Lee— Professor of Operations, Information, and Technology at the Stanford Graduate School of Business. Director of the Stanford Global Supply Chain Management Forum. One of the leading Decision Sciences/Operations Management researchers. Got his Ph.D. from the University of Pennsylvania in 1983. Dr. Haim Mendelson—General Atlantic Partners Professor of Electronic Business, Commerce, and Management at the Stanford Graduate School of Business. One of the leading Economics researchers in the world. Got his Ph.D. from Tel Aviv University in 1979. Dr. Paul Romer— Professor at the Stanford Graduate School of Business. Recognized as one of the Artificial Intelligence field’s leading researchers. Received his Ph.D. from the University of Chicago in 1983.
178
.........State University of New York - Buffalo
Location: Buffalo, New York
Rankings #48 – January 1998 Decision Line
Categorization: Tier III Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Economics of Informatics A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
179
.........Syracuse University
Location: Syracuse, New York
Rankings #38 – January 1998 Decision Line
Categorization: Tier III Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Economics of Informatics A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
180
.........Tel Aviv University
Location: Tel Aviv, Israel
Rankings #43 – January 1998 Decision Line#44 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
Collaboration A qualifying number of faculty list this is a research interest
Human-Computer Interaction A qualifying number of faculty list this is a research interest
181
.........Tennessee Technological University
Location: Cookeville, Tennessee
Rankings #50 – October 1998 Decision Line
Categorization: Tier III Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
182
.........Texas A&M
Location: College Station, Texas
Rankings #19 – January 1998 Decision Line#18 – October 1998 Decision Line#21 – CAIS Study
Categorization: Tier I Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
183
.........University of Arizona
Location: Tucson, Arizona
Rankings #1 – January 1998 Decision Line#1 – October 1998 Decision Line#1 – CAIS Study#4 – US News & World Report 2003#4 – US News & World Report 2004
Categorization: Tier I
Research Areas
Collaboration Department includes a funded research lab in this specialty Dr. Jay Nunamaker, one of the leading researchers in this specialty, works for UA.
Data Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest Dr. Sudha Ram, one of the leading researchers in this specialty, works for UA.
Artificial Intelligence Department includes a funded research lab in this specialty Dr. Hsinchun Chen, one of the leading researchers in this specialty, works for UA.
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Economics of Informatics Department includes a funded research lab in this specialty
Key People at Arizona Dr. Jay F. Nunamaker, Jr.—founded the MIS department at Arizona. Regents & Soldwedel Professor of MIS, Computer Science, and Communication at the Eller College of Management. Director of the Center for the Management of Information. One of the key researchers in the field of Collaboration. Received his Ph.D. from Case Western Reserve University in 1969. Dr. Sudha Ram—Eller Professor of Management Information Systems at the Eller College, UA. Director of the Advanced Database Research Group. One of the key researchers in the field of Data Management. Received her Ph. D. from the University of Illinois in 1985. Dr. Hsinchun Chen—Professor in the MIS department at the University of Arizona. Director of the Artificial Intelligence Laboratory at the Eller College of Management. One of the key researchers in the field of Artificial Intelligence. Got his Ph.D. from New York University, 1989.
184
.........University of British Columbia
Location: Vancouver, British Columbia, Canada
Rankings #9 – January 1998 Decision Line#12 – October 1998 Decision Line#16 – CAIS Study
Categorization: Tier I Research
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Human-Computer Interaction Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Social Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
System Analysis and Design Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
185
.........University of California - Berkeley
Location: Berkeley, California
Rankings #12 – US News & World Report 2003#15 – US News & World Report 2004
Categorization: Tier I Teaching
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Economics of Informatics A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
Social Informatics Pamela Samuelson, one of the leading researchers in this specialty, works for UCB.
Key People at Cal-Berkeley Pamela Samuelson— Professor in the School of Information Management and Systems. Director of Berkeley Center for Law and Technology. One of the leading researchers in the field of Social Informatics.
186
.........University of California - Irvine
Location: Irvine, California
Rankings #16 – January 1998 Decision Line#11 – October 1998 Decision Line#24 – CAIS Study#13 – US News & World Report 2003#12 – US News & World Report 2004
Categorization: Tier II
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Economics of Informatics A qualifying number of faculty list this is a research interest Dr. Kenneth L. Kraemer, one of the leading researchers in this specialty, works for UCI. Dr. Vijay Gurbaxani, one of the leading researchers in this specialty, works for UCI.
Social Informatics Dr. Kenneth L. Kraemer, one of the leading researchers in this specialty, works for UCI.
Key People at Cal-Irvine Dr. Kenneth L. Kraemer— Professor of Information Systems in the Graduate School of Management. One of the leading researchers in both the Economics of Informatics field and in the field of Social Informatics. Received his Ph.D. from the University of Southern California in 1967. Dr. Vijay Gurbaxani— Professor of Information Systems in the Graduate School of Management. Recognized as a leading Economics of Informatics researcher. Got his Ph.D. from the University of Rochester in 1987.
187
.........University of California – Los Angeles
Location: Los Angeles, California
Rankings #15 – January 1998 Decision Line#19 – October 1998 Decision Line#14 – US News & World Report 2003#26 – US News & World Report 2004
Categorization: Tier III
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Economics of Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
188
.........University of Colorado – Boulder
Location: Boulder, Colorado
Rankings #21 – January 1998 Decision Line#22 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
189
.........University of Colorado - Denver
Location: Denver, Colorado
Rankings #48 – January 1998 Decision Line
Categorization: Tier III Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Economics of Informatics A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
190
.........University of Connecticut
Location: Storrs, Connecticut
Rankings #22 – US News & World Report 2004
Categorization: Tier II Teaching
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Economics of Informatics A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
191
.........University of Georgia
Location: Athens, Georgia
Rankings #11 – January 1998 Decision Line#9 – October 1998 Decision Line#14 – CAIS Study#17 – US News & World Report 2003#17 – US News & World Report 2004
Categorization: Tier II
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Collaboration A qualifying number of faculty list this is a research interest
Social Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
192
.........University of Hawaii
Location: Manoa, Hawaii
Rankings #34 – January 1998 Decision Line#28 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Social Informatics A qualifying number of faculty list this is a research interest
Decision Sciences/Operations Management Dr. Ralph Sprague, one of the leading researchers in this specialty, works for UH.
Key People at Hawaii Dr. Ralph Sprague— Professor in the Decision Sciences department at UH. One of the leading DS/OM researchers in the world. Was awarded his Ph.D by Indiana University.
193
.........University of Houston
Location: Houston, Texas
Rankings #27 – January 1998 Decision Line#35 – October 1998 Decision Line#18 – CAIS Study
Categorization: Tier I Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
194
.........University of Illinois
Location: Champaign-Urbana, Illinois
Rankings #32 – October 1998 Decision Line#24 – US News & World Report 2004
Categorization: Tier V
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Artificial Intelligence A qualifying number of faculty list this is a research interest
195
.........University of Maryland
Location: College Park, Maryland
Rankings #25 – January 1998 Decision Line#29 – October 1998 Decision Line#8 – US News & World Report 2003#7 – US News & World Report 2004
Categorization: Tier III
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty
Economics of Informatics A qualifying number of faculty list this is a research interest
Human-Computer Interaction Department includes a funded research lab in this specialty
196
.........University of Memphis
Location: Memphis, Tennessee
Rankings #13 – CAIS Study
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
197
.........University of Michigan
Location: Ann Arbor, Michigan
Rankings #41 – January 1998 Decision Line#45 – October 1998 Decision Line#14 – US News & World Report 2003#11 – US News & World Report 2004
Categorization: Tier III
Research Areas
Artificial Intelligence A qualifying number of faculty list this is a research interest
Collaboration Department includes a funded research lab in this specialty
Human-Computer Interaction Dr. George W. Furnas, one of the leading researchers in this specialty, works for UM.
Social Informatics Dr. John King, one of the leading researchers in this specialty, works for UM.
Key People at Michigan Dr. George W. Furnas— Professor in the School of Information at the University of Michigan. One of the leading researchers in the field of Human-Computer Interaction. Got his Ph.D. from Stanford University in 1980. Dr. John King— Dean of, and Professor in, the School of Information at UM. Recognized as one of the leading Social Informatics researchers in the world. Received his Ph.D. from the University of California at Irvine, 1977.
198
.........University of Minnesota
Location: Minneapolis, Minnesota
Rankings #2 – January 1998 Decision Line#3 – October 1998 Decision Line#8 – CAIS Study#5 – US News & World Report 2003#4 – US News & World Report 2004
Categorization: Tier I
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest The department is named for this specialty
Economics of Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Social Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest Dr. Gordon Davis, one of the leading researchers in this specialty, works for UM.
Key People at Minnesota Dr. Gordon Davis—Honeywell Professor of MIS at the Carlson School of Management. Founded the first Information Systems department in the nation at the University of Minnesota. Founder of the Management Information Systems Research Center. One of the key researchers in the field of Social Informatics. Received his Ph.D. from Stanford University in 1959.
199
.........University of Pennsylvania
Location: Philadelphia, Pennsylvania
Rankings #7 – January 1998 Decision Line#7 – October 1998 Decision Line#5 – CAIS Study#6 – US News & World Report 2003#6 – US News & World Report 2004
Categorization: Tier I
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest Dr. Marshall Fisher, one of the leading researchers in this specialty, works for UPenn. The department is named for this specialty
Economics of Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest Dr. Eric Clemons, one of the leading researchers in this specialty, works for UPenn.
Key People at Penn Dr. Marshall Fisher— UPS Transportation Professor for the Private Sector; Professor of Operations and Information Management at the Wharton School. One of the leading Decision Sciences/Operations Management researchers in the world. Received his Ph.D. from MIT, 1970. Dr. Eric Clemons— Professor of Operations and Information Management and Management at the Wharton School. Recognized as one of the leading researchers in the field of Economics of Informatics. Got his Ph.D. from Cornell University in 1976.
200
.........University of Pittsburgh
Location: Pittsburgh, Pennsylvania
Rankings #18 – January 1998 Decision Line#10 – October 1998 Decision Line#15 – CAIS Study#19 – US News & World Report 2003#17 – US News & World Report 2004
Categorization: Tier II
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Artificial Intelligence A qualifying number of faculty list this is a research interest
201
.........University of Rochester
Location: Rochester, New York
Rankings #47 – January 1998 Decision Line#40 – October 1998 Decision Line#26 – US News & World Report 2004
Categorization: Tier IV
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Economics of Informatics Department includes a funded research lab in this specialty
Social Informatics A qualifying number of faculty list this is a research interest
202
.........University of South Carolina
Location: Columbia, South Carolina
Rankings #6 – January 1998 Decision Line#2 – October 1998 Decision Line#7 – CAIS Study
Categorization: Tier I Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Social Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
203
.........University of South Florida
Location: Tampa, Florida
Rankings #49 – January 1998 Decision Line#38 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Systems Analysis and Design A qualifying number of faculty list this is a research interest
Data Management Dr. Alan R. Hevner, one of the leading researchers in this specialty, works for USF.
Key People at South Florida Dr. Alan R. Hevner— Professor in the Information Systems and Decision Sciences department at the University of South Florida. One of the leading Data Management researchers in the world. Received his Ph.D. in 1979 from Purdue University.
204
.........University of Southern California
Location: Los Angeles, California
Rankings #28 – January 1998 Decision Line#30 – October 1998 Decision Line#20 – US News & World Report 2004
Categorization: Tier IV
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest The department is named for this specialty
Social Informatics A qualifying number of faculty list this is a research interest
205
.........University of Texas - Austin
Location: Austin, Texas
Rankings #8 – January 1998 Decision Line#8 – October 1998 Decision Line#9 – CAIS Study#3 – US News & World Report 2003#3 – US News & World Report 2004
Categorization: Tier I
Research Areas
Decision Sciences/Operations Management Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
Collaboration A qualifying number of faculty list this is a research interest Dr. George P. Huber, one of the leading researchers in this specialty, works for UT.
Social Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest Dr. Sirkaa L. Jarvenpaa, one of the leading researchers in this specialty, works for UT.
Economics of Informatics Dr. Andrew B. Whinston, one of the leading researchers in this specialty, works for UT. Dr. Anitesh Barua, one of the leading researchers in this specialty, works for UT.
Key People at Texas Dr. Sirkaa L. Jarvenpaa— James L. Bayless Chair in Business Administration at the McCombs School of Business. One of the leading researchers in the field of Social Informatics. Received her Ph. D. from the University of Minnesota in 1986. Dr. Andrew B. Whinston— Hugh Roy Cullen Centennial Chair Professor in the MSIS, Computer Science, and Economics Departments at UT. Director of the Center for Research in Electronic Commerce. One of the leading researchers in the field of Economics of Informatics. Received his Ph.D. from Carnegie Mellon University in 1962. Dr. George P. Huber— Charles and Elizabeth Prothro Regents Chair in Business Administration at the McCombs School. One of the leading researchers in the Collaboration field. Received his Ph.D. from Purdue University. Dr. Anitesh Barua— Professor of Information Systems at the McCombs School. One of the leading Economics of Informatics researchers in the world. Received his Ph.D. from Carnegie Mellon University in 1991.
206
.........University of Toledo
Location: Toledo, Ohio
Rankings #23 – CAIS Study
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest The department is named for this specialty
Systems Analysis and Design A qualifying number of faculty list this is a research interest
Social Informatics A qualifying number of faculty list this is a research interest
207
.........University of Virginia
Location: Charlottesville, Virginia
Rankings #23 – US News & World Report 2003
Categorization: Tier II Teaching
Research Areas
Systems Analysis and Design A qualifying number of faculty list this is a research interest
Human-Computer Interaction A qualifying number of faculty list this is a research interest
Social Informatics Department includes a funded research lab in this specialty A qualifying number of faculty list this is a research interest
208
.........University of Washington
Location: Seattle, Washington
Rankings #44 – January 1998 Decision Line#42 – October 1998 Decision Line#26 – US News & World Report 2004
Categorization: Tier IV
Research Areas
Decision Sciences/Operations Management A qualifying number of faculty list this is a research interest
Economics of Informatics A qualifying number of faculty list this is a research interest
Data Management A qualifying number of faculty list this is a research interest
Systems Analysis and Design A qualifying number of faculty list this is a research interest
209
.........University of Western Ontario
Location: London, Ontario, Canada
Rankings #36 – January 1998 Decision Line#31 – October 1998 Decision Line
Categorization: Tier II Research
Research Areas
Decision Sciences/Operations Management The department is named for this specialty
Social Informatics A qualifying number of faculty list this is a research interest
210
Section11Future ResearchFuture Extensions of Our Project
This section of the paper serves as a place for our own thoughts about “where to go from here.” There are several avenues to be explored that we did not probe ourselves, either because of time constraints or because we felt to do so would be beyond the scope of our contribution. Here, then, are a few ideas of future extensions to our school listing.
Using social networks and other such theories, map the MIS domain by cataloguing the links between schools; for example, map out the links between professors and the schools that are linked to them (where they are now, where they conducted important research, where they received their Ph. D., etc.)
In the same way, map out these links based on research areas; for example, map out the spread of collaboration research based on these links.
Using some set of objective criteria, attempt to rank the schools discretely, i.e. 1-66.
Any other modifications that attempt to more objectively or more narrowly categorize the academic institutions within the MIS domain, or attempt to categorize the links between such institutions.
