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A Big-Data Approach to Understanding the Thematic Landscape of the Field of Business Ethics, 1982-2016

Journal of business ethics, 2019-11, Vol.160 (1), p.127-150 [Peer Reviewed Journal]

Springer Nature B.V. 2019 ;Springer Science+Business Media B.V., part of Springer Nature 2018 ;Journal of Business Ethics is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0167-4544 ;EISSN: 1573-0697 ;DOI: 10.1007/s10551-018-3806-5

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  • Title:
    A Big-Data Approach to Understanding the Thematic Landscape of the Field of Business Ethics, 1982-2016
  • Author: Liu, Ying ; Mai, Feng ; MacDonald, Chris
  • Subjects: Academic publications ; Big Data ; Business and Management ; Business Ethics ; Content analysis ; Data analysis ; Disclosure of information ; Education ; Ethics ; History ; Landscape ; Management ; Original Paper ; Philosophy ; Quality of Life Research ; Studies ; Topics
  • Is Part Of: Journal of business ethics, 2019-11, Vol.160 (1), p.127-150
  • Description: This study focuses on examining the thematic landscape of the history of scholarly publication in business ethics. We analyze the titles, abstracts, full texts, and citation information of all research papers published in the field's leading journal, the Journal of Business Ethics, from its inaugural issue in February 1982 until December 2016—a dataset that comprises 6308 articles and 42 million words. Our key method is a computational algorithm known as probabilistic topic modeling, which we use to examine objectively the field's latent thematic landscape based on the vast volume of scholarly texts. This "big-data" approach allows us not only to provide time-specific snapshots of various research topics, but also to track the dynamic evolution of each topic over time. We further examine the pattern of individual papers' topic diversity and the influence of individual papers' topic diversity on their impact over time. We conclude this study with our recommendation for future studies in business ethics research.
  • Publisher: Dordrecht: Springer
  • Language: English
  • Identifier: ISSN: 0167-4544
    EISSN: 1573-0697
    DOI: 10.1007/s10551-018-3806-5
  • Source: ProQuest One Psychology
    ProQuest Central
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