Using CATA and Machine Learning to Operationalize Old Constructs in New Ways: An Illustration Using U.S. Governors’ COVID-19 Press Briefings

Jason D. Marshall, Francis J. Yammarino, Srikanth Parameswaran, Minyoung Cheong

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Increased computing power and greater access to online data have led to rapid growth in the use of computer-aided text analysis (CATA) and machine learning methods. Using “big data”, researchers have not only advanced new streams of research, but also new research methodologies. Noting this trend and simultaneously recognizing the value of traditional research methods, we lay out a methodology that bridges the gap between old and new approaches to operationalize old constructs in new ways. With a combination of web scraping, CATA, and supervised machine learning, using labeled ground truth data (i.e., data with known inputs and outputs), we train a model to predict CIP (Charismatic-Ideological-Pragmatic) leadership styles from running text. To illustrate this method, we apply the model to classify U.S. state governors’ COVID-19 press briefings according to their CIP leadership style. In addition, we demonstrate content and convergent validity of the method.

Original languageEnglish (US)
Pages (from-to)705-750
Number of pages46
JournalOrganizational Research Methods
Volume26
Issue number4
DOIs
StatePublished - Oct 2023

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • Strategy and Management
  • Management of Technology and Innovation

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