Research Novelty in Information Systems Journals After ChatGPT: Differences Across Institutional Language Contexts

Large language models are increasingly used in scholarly work, yet it remains unclear whether their productivity gains are accompanied by changes in research novelty.

We examine how relative abstract-level semantic novelty in Information Systems journals changed after ChatGPT became widely available and whether this change differed across institutional language contexts.

We analyze 13,847 articles published from 2020 to 2025 in 44 A* and A Information Systems journals.

Using SPECTER2 representations of titles and abstracts, we measure each article's semantic distance from its nearest recent predecessors and estimate a comparative pre/post model.

Articles whose first authors were affiliated with institutions in non-English-dominant countries show a 0.176 standard deviation larger post-2022 decline in relative semantic novelty than articles from English-dominant affiliations, equivalent to about 7 percentile points.

The pattern is similar across several alternative specifications, although the balanced-author estimate is less precise.

We interpret this finding through a tension in generative AI-supported knowledge work.

GenAI can widen access to prior knowledge and support new combinations, but it can also make established frames easier to reproduce.

Because individual LLM use is not observed, the result identifies a heterogeneous post-2022 shift rather than an effect of LLM adoption.

The study extends research on LLMs and scholarly productivity by shifting attention from publication counts to the semantic positioning of published articles and by showing that post-2022 change differs across institutional contexts.