Beyond Likert scales: studying counterproductive work behavior research with natural language processing

Amal Chekili and Ivan Hernandez

Chapter 27 in Handbook of Counterproductive Work Behavior, 2025, pp 485-503 from Edward Elgar Publishing

Abstract: This chapter illustrates how computer-assisted natural language processing (NLP) has been used to study counterproductive work behavior (CWB) and the new possibilities offered by NLP to facilitate CWB research. Modern NLP methods transform unstructured text collected from social and organizational settings (e.g., conversations, tweets, interviews, emails) into quantitative values that capture their underlying semantic meaning. This computational approach to understanding text allows researchers to at scale, and often unobtrusively, extract subtle themes of the text’s content and draw inferences about the text’s writer. These benefits are especially important for facilitating studying types of CWBs with communication. This chapter provides examples of research that has used NLP to (1) measure the presence of CWBs, (2) predict the future occurrence of CWBs, and (3) understand their dimensions, using various text-based sources. This chapter also highlights how multilingual models, text generation models, and novel ways to obtain data offer new opportunities to study CWBs.

Keywords: Natural language processing; Big data; Text analysis; Deep learning; Counterproductive work behavior (search for similar items in EconPapers)
Date: 2025
ISBN: 9781035306664
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