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Purpose: Academic and other researchers have limited tools with which to address the current proliferation of predatory and hijacked journals. These journals can have negative effects on science, research funding, and the dissemination of information. As most predatory and hijacked journals are not error free, this study used ChatGPT, an artificial intelligence (AI) technology tool, to conduct an evaluation of journal quality. Methods: Predatory and hijacked journals were analyzed for reliability using ChatGPT, and the reliability of result have been discussed. Results: It shows that ChatGPT is an unreliable tool for journal quality evaluation for both hijacked and predatory journals. Conclusion: To show how to address this gap, an early trial version of Journal Checker Chatbot has been developed and is discussed as an alternative chatbot that can assist researchers in detecting hijacked journals. [ABSTRACT FROM AUTHOR]

There are many misleading scientific metrics that are not known to the scientific community, particularly novice researchers. There is limited research in the area of misleading metrics, particularly related to business journals. Therefore, this research aims to examine the use of misleading metrics by business journals, the most popular misleading metrics, and countries contributing to the website traffic for such metrics. We used Scimago ranking for business journals and examined the website of each for the use of misleading metrics. Further, we used a domain-based approach by gathering data from Search Engine Optimization websites (i.e., Alexa and Ubersuggest). Only a few Scopus-indexed, low-quality business journals used misleading metrics on their website. The most common misleading metrics were International Scientific Institute, Open Academic Journals Index, CiteFactor, IndexCopernicus, and International Scientific Indexing. In addition, Indian authors were the most frequent visitors of the websites of misleading metrics. [ABSTRACT FROM AUTHOR]