Next-generation IS research methods – towards a better understanding of complex and dynamic phenomena … and generative AI as the elephant in the room

Authors

Ivo Blohm, University of St.Gallen, Institute for Information Systems and Digital Business, Switzerland
Shaila Miranda, University of Arkansas, Walton College of Business, USA
Shuk Ying Ho, Australian National University, College of Business and Economics, Australia
Jan Marco Leimeister, Kassel University, Chair for Information Systems, Germany

Document Type

Other

Abstract

Emerging phenomena like the increasing volume and variety of available data, machine learning techniques, and, most recently, Generative AI are reshaping our research practices, affording new research methods for testing and developing theory. In this editorial, we discuss our two major observations from running the ‘Next-generation IS Research Methods’ Special Issue: (1) the need for research methods that enhance our understanding of complex and dynamic phenomena and (2) Generative AI as (potential) productivity enhancer. We compile these observations into an organizing framework and discuss possibilities for applying Generative AI in the fields of qualitative, quantitative, and engaged research. We highlight challenges that might occur when applying Generative AI in research and shed light on the changing role of researchers in such settings of human–AI collaboration.

DOI

10.1177/02683962251340699

Recommended Citation

Blohm, Ivo; Miranda, Shaila; Ho, Shuk Ying; and Leimeister, Jan Marco (2025) "Next-generation IS research methods – towards a better understanding of complex and dynamic phenomena … and generative AI as the elephant in the room," Journal of Information Technology: Vol. 40: Iss. 2, Article 1.
DOI: 10.1177/02683962251340699
Available at: https://aisel.aisnet.org/jit/vol40/iss2/1