Usage frequency and application variety of research methods in library and information science: Continuous investigation from 1991 to 2021
arXiv:2606.31081v1 Announce Type: cross Abstract: The present study analyzed over 26,000 research articles published between 1991 and 2021 in twenty-one major LIS (Library and Information Science) journals, using the machine learning (ML) approach to categorize the research methods used by LIS scholars. The findings of this study are significant. Firstly, there has been a shift in the research strategy from conceptual research (e.g., "Theoretical approach") to empirical research (e.g., "Interview") in LIS investigations over the past 31 years. Secondly, the research topics explored by LIS scho
The proliferation of machine learning tools enables large-scale, automated analysis of academic trends, making such continuous investigations more feasible and granular than ever before.
Understanding the evolution of research methods in fields like Library and Information Science offers insights into broader academic shifts and the increasing adoption of empirical and computational approaches across disciplines.
Academic research in LIS is clearly shifting from conceptual to empirical methods, with machine learning playing an increasing role in meta-analysis itself, indicating a broader trend in academic methodology.
- · Machine Learning Researchers
- · Empirical Research Methods
- · LIS Scholars (adopting ML)
- · Purely Conceptual Research
- · Traditional Bibliometric Approaches
Increased empirical rigor and data-driven insights within LIS and potentially other social sciences.
Greater demand for computational skills and data literacy among LIS students and faculty, reshaping curricula.
The application of AI/ML to analyze academic trends could become a standard methodology, leading to more dynamic and real-time mapping of knowledge landscapes.
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