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Institution Publication Feature Analysis Based on Time-Series Clustering.

Weibin Lin1,2, Mengwen Jin1, Feng Ou1

  • 1College of Business Administration, Huaqiao University, Quanzhou 362021, China.

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|July 27, 2022
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Summary

This study introduces three novel analysis methods to examine publication characteristics over time. These methods help evaluate discipline development and journal preferences within institutions for strategic subject construction.

Keywords:
associated networkfeature analysisinstitution publicationnumerical valuestime series clusteringtrend analysis

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Area of Science:

  • Bibliometrics
  • scientometrics
  • management science research

Background:

  • Analyzing publication data is crucial for understanding academic discipline development.
  • Existing methods may not fully capture the nuances of institutional publication trends and journal preferences.

Purpose of the Study:

  • To propose and validate three new methods for analyzing the characteristics of agency publications.
  • To offer insights into discipline construction and development within institutions.
  • To identify journal selection patterns for strategic academic planning.

Main Methods:

  • Time series analysis of articles from 30 key management science journals.
  • Numerical distribution analysis.
  • Trend analysis.
  • Correlation network analysis.

Main Results:

  • Identified similar discipline development levels and trends across institutions.
  • Enabled a more objective academic stratification.
  • Revealed institutional journal preferences, offering valuable data for subject development.

Conclusions:

  • The proposed analysis methods provide a robust framework for evaluating academic publications.
  • These methods support informed decision-making in subject construction and institutional development.
  • Understanding publication patterns is key to enhancing research impact and strategic planning.