More Than Words? Computer-Aided Text Analysis in Organizational Behavior and Psychology Research

Abstract

Computer-aided text analysis (CATA) offers great promise for scholars who aspire to capture the beliefs, cognitions, and emotions of individuals as reflected in their narratives and written texts. We review advancements in the use of CATA in organizational studies and highlight the increased momentum towards incorporating rigor when using CATA. We review key CATA software and note the variance in possibilities of approaches when extracting meaning from languages. We then outline the potential for this technique to build knowledge surrounding organizational psychology and organizational behavior. Specifically, we highlight specific research streams that might benefit from the use of CATA and highlight cross-cultural perspectives that might further advance the use of this technique.

The promise of open survey questions—The validation of text-based job satisfaction measures

“…Organizational scientists have been leveraging written texts in their study of psychological constructs and business phenomena for decades [1–3]. While manual human coding of texts continues to be the gold standard for annotating text, rapid advances in computer-aided text analysis (CATA) have opened up a venue for analyzing open texts in a drastically more efficient but still reliable manner [4].…”

In Their Own Words: A Call for Increased Use of Organizational Narratives in Family Business Research

“…One key technique in family business research, which follows this first method of narrative analysis, has been described as the "bag of words" approach, where scholars carefully select words-utilizing generic or specially-created lists of keywords-to reflect critical constructs of interest. This approach generally leverages an empirical software such as Atlas.ti, NVivo, LIWC, DICTION, or CAT Scanner (Short, McKenny, & Reid, 2018). For example, our previously mentioned FBR article utilized word list software counts reflecting the five entrepreneurial orientation dimensions of autonomy, competitive aggressiveness, innovativeness, proactiveness, and risk taking (Short et al, 2009).…”

Time to go wild: How to conceptualize and measure process dynamics in real teams with high-resolution

“…Others have used archival data from sports teams to measure team coordination (Halevy et al, 2012;Swaab, Schaerer, Anicich, Ronay, & Galinsky, 2014). If the researchers have some sort of text data (e.g., virtual chat logs, email transcripts), they could rely on various tutorials that explain how to use automatic text analytic approaches (Banks, Woznyj, Wesslen, & Ross, 2018;Bonito & Keyton, 2018;Short, McKenny, & Reid, 2018;Gonzales, Hancock, & Pennebaker, 2010). While text-analytic approaches are more advanced with respect to the automation of data transformation, they may still involve transcription efforts (for example, if the researchers had decided to transcribe video-recorded communication, e.g., Bonito & Keyton, 2018) and close collaboration with computer scientists (Büngeler et al, 2016).…”

“…For examples how to transform archival sport team data into "team phenomena", see Halevy et al (2012) For examples/overviews how to transform electronic data from online teams, see Riedl & Wooley (2017), Gibson (2018) For overviews/tutorials how to use automatic text analytic approaches to transform text into team constructs, see Banks et al (2018), Bonito & Keyton (2018), Short et al (2018), Gonzales et al (2010). Available Tools for "Computer-Aided Text Analysis": http://www.amckenny.com/CATScanner/ http://liwc.wpengine.com/ For construct validity of data from wearables with surveys (in lab and field contexts), see Chaffin et al (2017) • Example: "The researchers code the videos with Co-Act (a validated scheme that captures team coordination).…”