1. Methodological Review of Key Research by Key People within Management Information Systems
Presented 6th Dec 2006

2. Key Researchers for this Project
Yi-da Chen (Alan)
Yan Dang(Mandy)
Zheren Hu (Ben)
Sean Humpherys
Chang Heon Lee
Kevin Moffitt
Jing Sun
Sven Thoms
Roopali Wakhare
Yulei (Gavin) Zhang

3. Acknowledgements
Dr. Jay F. Nunamaker, Jr.
Chris Diller

4. To familiarize participants with key IS researchers
Project Objectives
To familiarize participants with key IS researchers
and key research along with the main methodologies
used in said research

5. Past Contributions Class 1998 Class 1999 Class 2000 Class 2001
Defined MIS with 7 subdomains
Identified 45 key researchers
Class 1999
Defined MIS with 10 subdomains
Identified 47 key researchers
Class 2000
Identified 90 key researcher
Group researcher by 15 research areas
Class 2001
Redefined MIS into 8 subdomains
Added basic timeline of events

6. Class 2002 Class 2003 Past Contributions
Visually represented MIS with 9 subdomains
Described seminal works grouped by subdomain
Class 2003
Visualization of research characteristics via 3d chart
Identify 101 key researchers with key papers
Researchers grouped by MIS subdomain
Detailed timelines by subdomain
Provided End Note file of key research

7. Class 2004 Class 2005 Past Contributions
Identified University department of key researchers
Class 2005
Combines MIS models from previous three years into new model
Reclassified key researchers using new model
Quantified research statistics by subdomain

8. Discuss and compare positivism and interpretism
Class 2006 Contributions
Identify and discuss common research methodologies and methodological paradigms
Discuss and compare positivism and interpretism
Categories key research and key MIS researchers by methodology to provide precedence for methodologies from key research
Identify new key research and key individuals since the previous studies
Compile 300 PDF files of key research into a class repository for use by future classes
Update the End Note reference database for use by future classes

9. Defining MIS
“A management information system is the complement of people, machines, and procedures that develops the right information and communicates it to the right managers at the right time.”
-Brabb

10. Foundations

11. Research Philosophy and Main Paradigms
Reality
/Truth
Knowledge
Ontology
What it truly is?
Epistemology
What we know?
How do we know what we know?
Mythology examples
“Reality is separated from the individual who observes it”
Cat, 猫, Katzen
“Knowledge exists beyond the human mind”
Principles/laws
Explanation, prediction, and control
Lab experiment
Positivism
Practices
Theory
Conspicuous Consumption
The theory of the leisure class
Interview
“Reality cannot be separated from the individual who observes it”
Prejudice
“Knowledge is intentionally constituted”
The way subjective meanings are created and sustained
Interpretivism

12. Research Paradigms in IS
Positivism says that objects / entities exist independently.
Ontology is an important concept in Positivism.
Objects, and the subjects who observe these objects, are separate and independent.
Because objects “exist”, they can be measured and compared to other objects.

13. Research Paradigms in IS
Interpretivism says that objects, and the subjects who observe these objects cannot be separated.
It is based on Epistemology
Anything can only be studied in context.
The act of observation changes that which is being observed.
"If a tree falls in the woods and no one is around to hear it, does it make a sound?"

14. Discussion of Research Methodologies

15. Presentation of Findings

16. Design Science DEFINITION: Methodology Usage Rate = 41 / 161 = 25%
Creates and evaluates IT artifacts intended to solve identified organizational problems. Design science is inherently a problem solving process.
DS seeks to create innovations that define the idea, practices, technical capabilities, and products through which the analysis, design, implementation, management, and use of IS can be effectively and efficiently accomplished.
DS can use a combination of other research methodologies to achieve the research aim.
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).
Methodology Usage Rate = 41 / 161 = 25%

17. Design Science Design Science Research Guidelines:
1. Design as an artifact
Design science research requires the creation of an innovative, purposeful artifact.
2. Problem relevance
Develop technology-based solutions to important and relevant business problems.
3. Design evaluation
Well-executed evaluation methods. The selection of evaluation methods must be matched appropriately with the designed artifact and the selected.
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).

18. Design Science 4. Research contribution 5. Research rigor
Provide clear and verifiable contribution in the area. One or more contributions must be found in a given research project. The contributions are the design artifact, foundations or methodologies.
5. Research rigor
Rely upon rigorous methods in both construction and evaluation.
6. Design as a search process
Search for an effective artifact.
7. Communication of research
Design science research must be presented effectively both to technology-oriented and management-oriented audiences.
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).

19. Design Science Sudha Ram Ivar Jacobson Stefano Spaccapietra
University of Arizona
Data Management
Intelligent database design using the unifying semantic model (1995)
Ivar Jacobson
Technology Vice President of Business Engineering, Rational Software
Systems Analysis and Design
The Object Advantage: Business Process Reengineering with Object Technology (1994)
Stefano Spaccapietra
Swiss Federal Institute of Technology
Data Management
Model independent assertions for integration of heterogeneous schemas (1992)

20. Pluralistic Methodology
DEFINITION:
This methodology differs from the reset in that if encourages using multiple research methodologies to achieve the scientific goal.
Pluralistic methodology relies on synthetic unification to integrate conclusions derived from different sets of analytical methods.
A pluralistic methodology that involves making comparisons between any theories at all forces defendants to improve the articulation of each theory. In this way, scientific pluralism improves the critical power of science.
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).
Methodology Usage Rate = 3 / 161 = 2%

21. Pluralistic Methodology=
Pluralistic Methodology
Jay F. Nunamaker, Jr.
The University of Arizona
Collaboration
Electronic Meeting Systems to Support Group Work
Information Technology for Negotiating Groups - Generating Options for Mutual Gain
Vijay Gurbaxani
University of California, Irvine
Economics of Information
The Impact of Information Systems on Organizations and Markets

22. Laboratory Experiment
DEFINITION:
This methodology is the most common and consists of empirical and quantitative research done in a controlled environment.
A test is made to demonstrate a known truth, examine the validity of a hypothesis, or determine the efficacy of something previously untried.
Tests or investigations carried out in a laboratory.
Compare to Field experiment
Field experiment: Experiment carried out on a substance or on an organism in the open air as opposed to in a laboratory.
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).
Methodology Usage Rate = 11 / 161 = 7 %

23. Laboratory Experiment
Douglas C. Engelbart
Stanford University
Human Computer Interaction
Display-Selection Techniques for Text Manipulation(1967)
Allen Newell
Carnegie-Mellon University
Artificial Intelligence
Computer Science as Empirical Inquiry - Symbols and Search(1976)
Hsinchun Chen
University of Arizona
Artificial Intelligence
A Machine Learning Approach to Inductive Query by Examples: An Experiment Using Relevance Feedback, ID3, Genetic Algorithms, and Simulated Annealing (1998)

24. System Development Methodology
DEFINITION:
The development of system can provide a perfectly acceptable piece of evidence (an artifact) in support of a ‘proof’, where proof is taken to be any convincing argument in support of a worthwhile hypothesis.
System development could be thought of as a ‘proof-by-demonstration’ and may bridge the gap between the technological and the social sides of IS research.
The legitimacy of research and development as a valid research activity within the technical domain of IS has been debated extensively and justified by Nunamaker and Chen (1990) and Parker et al (1994)
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).
Methodology Usage Rate = 17 / 161 = 10 %

25. System Development Methodology
The Central Nature of Systems Development in the Research Life Cycle

26. System Development Methodology
Principle Parts of a Methodology :
1. Construct a Conceptual Framework:
State a meaningful research question
Investigate the system functionalities and requirements
2. Develop a System Architecture:
Define functionalities of system components
3. Analyze & Design the System:
Design knowledge base schema and processes to carry out system functions
4. Build the (Prototype) System:
Gain insight about the problems and the complexity of the system
5. Observe & Evaluate the System:
Consolidate experiences learned
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).

27. System Development Methodology
Amit P. Sheth
University of Georgia
Semantic Web and Applications
Semantic content management for enterprises and the Web (2002)
Michael Stonebraker
Massachusetts Institute of Technology
Distributed Database Systems, Operating Systems
Mariposa: a wide-area distributed database system (1996)
Kim Won
Sungunkwan University
Object-Relational Database Systems
Semantic Semantics and implementation of schema evolution in
object-oriented databases (1987)

28. System Development Methodology
David Lorge Parnas
McMaster University
Computer System Design
On the design and development of program families (1976)
Terry A. Winograd
Stanford University
Human Computer Interaction
A language/action perspective on the design of cooperative Work (1988)

29. Prototyping Methodology
DEFINITION:
Prototyping is used as a proof-of-concept to demonstrate feasibility.
Distinguishable Features:
Focus on more specific functionalities or algorithms
Usually prototype system evaluation do not include observation of the use of the system by case studies and field studies
Methodology Usage Rate = 16 / 161 = 10 %

30. Prototyping Methodology
Veda Storey
Georgia State University
Data Management, Ontology Development
Ontologies for conceptual modeling: their creation, use, and management." Data & Knowledge Engineering (2002)
Gerardine DeSanctis (deceased 2005)
Duke University
Electronic Communication
Using computing in quality team meetings: Some initial observations
from the IRS-Minnesota project (2002)

31. Argumentative Methodology
DEFINITION:
An argumentative paper presents an analysis of evidence in favor of a particular conclusion. It is used to convince a reader to adopt a particular point of view. The writer supports their position with research from other papers, and logical inferences. Other view points are acknowledged, but the majority of evidence supports the author’s own conclusion.
This is the modus operandi for fortune sellers. Traces of this methodology can be found in the “Discussion” and “Results” sections in many IS papers.
Three of the articles discuss the potential impact of research on the future. Six of the articles criticize or analyze a current trend an offer tips for improvement.
Methodology Usage Rate = 9 / 161 = 5 %

32. Argumentative Methodology
Russell L. Ackoff
University of Pennsylvania
Systems Thinking
Management Misinformation Systems (1967)
Allen Newell
Carnegie Mellon University
Computer Science and Engineering
The Prospects for Psychological Science in Human Computer Interactions (1985)
Murray Turoff
New Jersey Institute of Technology
Object-Relational Database Systems
Delphi and its Potential Impact on Information Systems (1971)

33. Field Study Methodology
DEFINITION:
Research done not in a laboratory but in the natural environment; it may be observational only, it may include experimental interaction with the subjects in the field. Limited time-span, as opposed to ethnography.
Methodology Usage Rate = 5 / 161 = 3 %

34. Field Study Methodology
Erik Brynjolfsson
MIT, Professor of Management
IT and Organizations, Internet Commerce, It & Business Value
Frictionless Commerce? A Comparison of Internet and
Conventional Retailers (2000)
Wanda Orlikowski
MIT, Professor of IT and Organizational Studies
IT Work Practices, Emergent Use of Technology
Using Technology and Constituting structures: A Practice Lens for Studying Technology in Organizations (2000)
Studying Information Technology in Organizations: Research
Approaches and Assumptions (1991)
Given that "production could be carried on without any organization [that is, firms] at all", Coase asks, why and under what conditions should we expect firms to emerge?
Coase argues that the size of a firm (as measured by how many contractual relations are "internal" to the firm and how many "external") is a result of finding an optimal balance between the competing tendencies of the costs outlined above. In general, making the firm larger will initially be advantageous, but the decreasing returns indicated above will eventually kick in, preventing the firm from growing indefinitely.
Robert Coase
University of Chicago, Professor Emeritus of Economics
Transaction Cost, Durable Goods Monopoly Firms have no Power Conjecture (Future Pricing Pressure)
The Nature of the Firm (1937)

35. Ethnography Methodology
DEFINITION:
Genre of writing or research that presents qualitative description of human social phenomena, based on fieldwork. Ethnography presents the results of a holistic research method founded on the idea that a system's properties cannot necessarily be accurately understood independently of each other. Such holistic research was not present in any of the papers surveyed.
Methodology Usage Rate = 0 / 161 = 0 %

36. Survey Methodology
DEFINITION:
This is a research methodology that relies on gathering research data via interviews and survey instruments. The data is quantitative but usually reflects subjective opinions on qualitative matters. Proper survey question design or interview question design are essential to avoid bias.
Methodology Usage Rate = 5 / 161 = 3 %

37. Survey Methodology William R King Susan Athey Paul Beckman
Editor in Chief MIS Quarterly
Katz School of Business, University of Pittsburgh
An Empirical Assessment of the Organization of
Transnational Information Systems (1999)
Susan Athey
Associate Professor of CIS at Colorado State University
IS ethics, IS pedagogies, IS education
An evaluation of research productivity in academic IT (2000)
Paul Beckman
Associate Professor, IS Department, San Francisco State University
UI, Visualization, Virtual Environments, Force-Feedback Devices
Kevin Bacon, Degrees-of-Separation, and MIS Research (2002)

38. Case Studies DEFINITION: Methodology Usage Rate = 5.39%
A case study is a particular method of primarily qualitative research. Rather than using large samples and following a rigid protocol to examine a limited number of variables, case study methods involve an in-depth, longitudinal examination of a single instance or event: a case.
The Major Advantages
An emphasis of case studies on detail provides valuable insight for problem solving, evaluation, and strategy.
It is known that important scientific propositions have the form of universals, and a universal can be falsified by a single counter-instance. Thus, a single, well-designed case study can provide a major challenge to a theory and provide a source of new hypotheses and constructs simultaneously.
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).
Methodology Usage Rate = 5.39%

39. Case Studies Kenneth L. Kraemer Edward Feigenbaum Jakob Nielsen
University of California (Irvine, California)
Use and Impact of Information Technology (IT)
Payoffs from IT Investments
Information technology and economic performance: A critical review of the empirical evidence (2003)
Information technology and productivity: Evidence from country-level data
Edward Feigenbaum
Standford University
Knowledge-Based Systems Research & Application
Dendral: A Case Study of the First Expert System For Scientific Hypothesis Formation (1993)
Jakob Nielsen
Nielsen Norman Group
Human-Computer Interaction (HCI)
Designing Web Usability: The Practice of Simplicity

40. Action Research DEFINITION: Methodology Usage Rate = 0.00 %
In action research, the researcher wants to try out a theory with practitioners in real situations, gain feedback from this experience, modify the theory as a result of this feedback and try it again. Action research consists of many iteration. Each iteration of the action research process adds to the theory so it is more likely to be appropriate for a variety of situation.
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).
Methodology Usage Rate = 0.00 %

41. Action Research
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).

42. Game-Role Playing DEFINITION: Methodology Usage Rate = 0.00%
As-if experiments in which the subject is asked to behave as if he or she were a particular person in a particular situation. It requires a human subject playing a role or simulating a behavior. It may be conducted in a laboratory or interactively in the field.
In the investigations identified as role playing studies, subjects were asked how they would feel or behave, or how another would feel or behave, if they or that other person were in a certain situation. That is, subjects knowingly played the role of a person in a situation not actually encountered.
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).
Methodology Usage Rate = 0.00%

43. Field Experiment DEFINITION:
A field experiment applies the scientific method to experimentally examine an intervention in the real world rather than in the laboratory. Field experiments, like lab experiments, generally randomize subjects into treatment and control groups and compare outcomes between these groups.
Compared with Laboratory Experiments
The major difference between field experiment and laboratory experiment is that in field experiment there is a limited scope to control the variables, while laboratory experiment has adequate scope to rigorously control the variables.
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).

44. Field Experiment Conclusion:
Research done in a less controlled natural environment, must be quantitative and include research principles such as random selection.
Methodology Usage Rate = 3 / 161 = 2 %

45. Field Experiment Sirkka L. Jarvenpaa Robert Johansen
University of Texas (Austin, Texas)
Social Informatics
computer support for meetings of groups working on unstructured problems: A field experiment (1998)
Robert Johansen
Institute for the Future (Palo Alto, CA)
Collaboration
Social evaluations of teleconferencing (1977)

46. Historical Methodology
DEFINITION:
The historical method comprises the techniques and guidelines by which historians use primary sources and other evidence to research and then to write history.
The question of the nature, and indeed the possibility, of sound historical method is raised in the philosophy of history, as a question of epistemology.
The examination of societies of social unites over time and in comparison with one another.
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).
Methodology Usage Rate = 1 / 161= 0.62%

47. Historical Methodology=
Historical Methodology
Murray Turoff
New Jersey Institute of Technology (Newark, NJ)
Collaboration
Computer Mediated Communication Requirements for Group Support (1991)

48. Description Research DEFINITION:
A description consists of an enumeration of the quantitative and qualitative parameters which seek to provide a definition of some thing, such as what that thing looks like, sounds like, or feels like.
A complete description allows for not merely defining but distinguishing one state from another and general characteristics commonly noticed which in popular culture define or distinguish something. It can represent subtle differences in states.
A complete description is created and used in scientific disciplines as technical terminology.
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).
Methodology Usage Rate = 29 / 161= 18 %

49. Description Research Pamela Samuelson Carl Shapiro Erik Brynjolfsson
University of California (Berkeley, California)
Social Informatics
Copyright’s Fair use Doctrine and Digital Data (1994)
Carl Shapiro
University of California at Berkeley
Economics of Information
Versioning: The smart way to sell information (1998)
Erik Brynjolfsson
Massachusetts Institute of Technology (Cambridge, MA)
Economics of Information
The productivity paradox of information technology

50. Mathematical Models Methodology
DEFINITION:
Abstract model using mathematics to describe system behavior; include independent variables, constants; may be derivable through proof; has a quantitative outcome that is verifiable via a proof or via experiments.
A mathematical model is a abstract model that uses mathematical language to describe the behavior of a system. Mathematical models are used particularly in the natural sciences and engineering disciplines (such as physics, biology, and electrical engineering) but also in the social sciences (such as economics, sociology and political science); physicists, engineers, computer scientists, and economists use mathematical models most extensively.
IS research has been perceived by some as purely a social science, thus ignoring the technology side of it. However, this view is changing as more researchers recognize that information systems involve an unavoidable technical component (Cecez-Kecmanovic 1994, Parker et al 1994).
Methodology Usage Rate =8.7 % or 14/161

51. Mathematical Models Methodology
Hau L. Lee
Stanford University
Operations Management
Information distortion in a Supply Chain: The Bullwhip Effect(1997)
Salvatore T. March
Vanderbilt University
Data Management
Allocating data and operations to nodes in distributed database design (1995)
JoAnne Yates
Massachusetts Institute of Technology
Social Informatics
Electronic Markets and Electronic Hierarchies(1987)

52. Simulation Methodology
DEFINITION:
Enhances purely mathematical models when elementary functions alone cannot describe the solution. Generates samples of data from running multiple simulation, usually includes variables too numerous to put into one mathematical model or that cannot be derived via a mathematical proof and is generally not experimentally verifiable in the full-scale natural environment.
Methodology Usage Rate = 1.8% or 3/161

53. Simulation Methodology
George W. Furnas
The University of Michigan
Human Computer Interaction
The Vocabulary Problem in Human System Communication (1987)
J. Teorey
University of Michigan
Data Management
A comparative analysis of disk scheduling policies (1972)
Patrick Suppes
Stanford University
Psychology, Language and Logic
Machine learning comprehension grammars for ten languages(1996)