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

Ivo Blohm et al.

Journal of Information Technology2025https://doi.org/10.1177/02683962251340699article
AJG 4ABDC A*
Weight
0.41

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.

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https://doi.org/https://doi.org/10.1177/02683962251340699

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@article{ivo2025,
  title        = {{Next-generation IS research methods – towards a better understanding of complex and dynamic phenomena … and generative AI as the elephant in the room}},
  author       = {Ivo Blohm et al.},
  journal      = {Journal of Information Technology},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/02683962251340699},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.