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

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.

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Publication Details

Journal
Journal of Information Technology
Published
2025-05-19
DOI
https://doi.org/10.1177/02683962251340699
Citations
2
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
4.57
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article

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

Jan Marco Leimeister, Ivo Blohm, Shuk Ying Ho, Shaila Miranda
2 citations
Journal of Information Technology
Big Data and Business Intelligence
4.57
article

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

Jan Marco Leimeister, Ivo Blohm, Shuk Ying Ho, Shaila Miranda
article en
2 citations

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.

Journal of Information TechnologyVol. 40(2)
University of Kassel (DE), Australian National University (AU), University of St.Gallen (CH), University of Arkansas at Fayetteville (US)
Openalex Percentile: Top 6%
Big Data and Business Intelligence
4.57
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