1. Toward a Definition of IS Identity
Jeff Jenkins
Gray Ross
Ximing Yu
Ming Yang
Toward a Definition of IS Identity
Proposing a prototype system for conducting objective, explorative research of the IS discipline
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2. Agenda Introduction Conceptual understanding of the IS discipline
Prototype development
Pilot study
Implications/future research/lessons learned
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3. Introduction The IS Identity Debate
What distinguishes IS research?
What is the internal structure of the IS discipline?
Definitions of IS identity have been mostly subjective in nature.
-IT artifact and its immediate nomological net
-Work Systems
1998 – 7; 1999 – 10, 2000 –15, 2001 –8; 2002 and 2003 –9; 2004—8
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4. Our Contribution
A prototype system to aid researchers in performing an objective analysis of the internal structure of the IS discipline and also how the IS discipline is unique from other disciplines.
Objective: “this is what we did, and this is what we found”
Self-organizing map to categorize research.
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5. Analyzing the IS Discipline
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6. Scope
Proof-of-concept of using an objective, automated SOM-based tool to help define the IS identity
Stepping stone for future groups in developing a robust definition of the IS identity.
Proof of concept
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7. Gauge the success of our project
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8. Leveraging Past Projects
Database from 2008
Concept of using an automated and objective classification system
Past definitions of IS research
IS essential core
Subdomains of research
Dimensions that define IS: e.g., rigor and relevance
Seminal articles and metadata
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9. Rigor vs. Relevance -- Rigor
Rigor refers to the use of methods and theories appropriate to the task-at-hand.
The emphasis on methodological rigor is to support propositions and relationships between variables, while theoretical rigor in defining theoretical constructs and research ideas is to attain legitimacy in the eyes of the other disciplines with which IS research interacts.
In this regard, research in its request for methodological rigor and theoretical rigor, may end up sacrificing relevance [Moody 2000].
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10. Rigor vs. Relevance -- Relevance
Relevance in research refers to the extent to which the practitioner community finds the knowledge produced by research applicable to their real-world settings and problems.
Relevance in IS covers issues such as the criteria used to evaluate relevancy of research, the role played by the transitory nature of information technology in such evaluation, the institutional and political factors in IS research that contribute to relevance, and the very critical role of information in widening or narrowing the disconnect between academia and practice in contributing to relevance .
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11. Relationship of Rigor and Relevance from Hevner et al. (2004)
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12. Seminal Articles and Leading IS Researchers
Lessons Learned:
Helped us understand the breadth of information systems research and the complexities or defining an essential core
Gave us a background knowledge to gauge the success of our prototype system (e.g., are the clusters from our prototype meaningful)
Helped us become familiar with the influential researchers of the IS
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13. At Last, The Awesome!
Automated self-organizing map IS space classification system
WordNet
WordNet::Similarity
MATLAB SOM Toolbox
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14. Pilot Study Demonstration
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15. Implications of the Prototype
Proof-of-concept is a success
Limitations
Technical
Analytical
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16. Observations
High rigor and relevance can exist together (they are not opposites). Red area overlaps in the two matrixes.
Technical and behavioral research rarely coexist together (they are on opposite sides of matrix). This is true with applied and theoretical research also.
Technical research appears to be more rigorous than behavioral research; behavioral research appears to be more relevant than technical research.
Theoretical and applied research can occur regardless if the research is behavioral or technical.
Technical research is often categorized as more innovative than behavioral research.
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17. Future Research Fix shortcomings of the prototype
More comprehensive dataset
Test other dimensions
Analyze data from other disciplines to determine the IS essential core
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18. Lessons Learned Value of design science
Familiarity of the IS discipline and the challenges of identifying IS identity
Value of objective, automated classification
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19. Questions?
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