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 4/29/2019

Agenda Introduction Conceptual understanding of the IS discipline Prototype development Pilot study Implications/future research/lessons learned 4/29/2019

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 4/29/2019

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. 4/29/2019

Analyzing the IS Discipline 4/29/2019

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 4/29/2019

Gauge the success of our project 4/29/2019

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 4/29/2019

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]. 4/29/2019

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 . 4/29/2019

Relationship of Rigor and Relevance from Hevner et al. (2004) 4/29/2019

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 4/29/2019

At Last, The Awesome! Automated self-organizing map IS space classification system WordNet WordNet::Similarity MATLAB SOM Toolbox 4/29/2019

Pilot Study Demonstration 4/29/2019

Implications of the Prototype Proof-of-concept is a success Limitations Technical Analytical 4/29/2019

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. 4/29/2019

Future Research Fix shortcomings of the prototype More comprehensive dataset Test other dimensions Analyze data from other disciplines to determine the IS essential core 4/29/2019

Lessons Learned Value of design science Familiarity of the IS discipline and the challenges of identifying IS identity Value of objective, automated classification 4/29/2019

Questions? 4/29/2019