Senior Editor Emeritus: Prabhudev Konana

University of Maryland, College Park
Retired; no longer accepting new manuscripts

Term: January 1, 2020 – December 31, 2020

Research Interests

I am interested in a wide variety of topics from evaluating business value and user behaviors in technology-intensive environments and platforms, extracting insights from user generated content and new data sources (e.g., Internet of Things) for prediction and to inform decision-making, and designing IT capabilities from emerging technologies using a Design Science perspective.

Some of my recent projects include: understanding user search and information seeking and sharing behaviors (e.g., homophily, confirmation bias) in virtual communities and social networks to evaluate outcomes; evaluating behavioral changes with advanced technological capabilities (e.g., semi-autonomous vehicles, Internet-of-things, AI); understanding business value of emerging technologies (e.g., blockchain); designing metrics that incorporate attention and information flows within networks than simple nodal connections to better predict economic outcomes; extracting sentiments and value of information from large structured and unstructured data (e.g., news and user generated content) for prediction.

Given the nature of the research, I have drawn on theories and methods from a variety of disciplines including psychology, economics, sociology and computer science to inform my work.

Research Methods

My early work relied on Design Science and blending Design Science and economic principles to propose optimal resource allocation. More recently, I have employed psychology and social science research methods (e.g., survey methods, quasi-experiments) to study behaviors. I am using econometrics, qualitative, and computer science methods (text analytics, machine learning) in a wide variety of problem settings. I am comfortable handling papers that rely on different research methods or employ mixed-methods approaches.

However, I am not competent in evaluating papers that are purely analytical modeling and deeply mathematical.


I prefer papers that are simple to read and articulate the big ideas, research questions, and broader impact without overbearing language. I particularly like papers that challenge conventional wisdom, inform IS and other disciplines, and open new areas of research.


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