Advances in Research Methods

Track Description
Proliferation of unique and interesting digital phenomena and the explosion of data has created numerous opportunities for IS researchers to provide new methodological and theoretical insights. This track reflects advanced in research methods across all IS traditions: behavioral, economics, organizational, design science and data science/computational as well as at their intersections. Any paper that offers new methods that can address IS-related questions is welcome. This includes, but is not restricted to advances in empirical methods, field and lab experiments, netnographical approaches, machine learning, computational methods, grounded theory, statistical models and econometrics, causal inference, data and text mining, predictive/prescriptive analytics, visual analytics, and crowdsourcing.

Contributions in methods can be motivated by establishing shortcomings of extant approaches or could have the ability to address entirely new problems or applications relevant to the emerging digital world, including business and societal challenges.

Track Chairs
Indranil Bardhan, University of Texas at Austin, indranil.bardhan@mccombs.utexas.edu
Varun Grover, University of Arkansas, vgrover@uark.edu
Balaji Padmanabhan, University of South Florida, bp@usf.edu
Ulrike Schultze, Southern Methodist University, uschultz@smu.edu