Fuzzy cognitive maps (FCMs) for the analysis of team communication. (February 2020)

• Record Type: Journal Article
• Authors: Engome Tchupo, Dominique; Kim, Jung Hyup; Macht, Gretchen A.
• Is Part Of: Applied ergonomics. Volume 83(2020)
• Publication Date: 2020-02
• Subjects: Communication analysis -- Team mental models -- Naturalistic language analysis; (CM) Cognitive Map -- (FCM) Fuzzy Cognitive Map -- (FCMi) person-to-person FCMs -- (FCMt) team FCMs -- (SAs) Speech Acts -- (RI) Requesting Information -- (RC) Requesting Confirmation -- (RwI) Response with Information -- (RwoI) Response without Information -- (NR) No Response -- (PI) Providing Information -- (S) Statement -- (LPTs) Lower Performing Teams -- (HPTs) Higher Performing Teams; Human engineering -- Periodicals; 620.82
• DOI: 10.1016/j.apergo.2019.102979
• Languages: English
• ISSNs: 0003-6870

Abstract

Abstract: Communication in teams plays a vital role in team success. This work proposes Fuzzy Cognitive Maps (FCMs) as a formalized, team communication methodology for the analysis of content and flow, simultaneously, of naturalistic team communication in a structured environment. Several methods of analysis of team communication exist. Few of them, however, analyze the flow and content of communication simultaneously, and none with teams using naturalistic language. Team communication data is coded for flow and content (through speech acts), then turned into FCMs for visual and statistical analysis. Results show that when using the FCM methodology both flow and content of communication can be diagrammed at the individual level, however, when assessing at the team level, what is illustrated is the flow of content. The application of different statistical analyses to the FCM output provides the opportunity to answer various questions on team communication. This study demonstrates that by using speech acts (SAs) as nodes, FCMs can illustrate and analyze team communication flow and content for naturalistic language. The FCM methodology is a powerful tool for studying team communication, and further incremental work could advance knowledge in team communication dynamics and provide contributions to graph theory. This method provides a visual overview of team communication dynamics in a naturalistic language setting, thus allowing for the study of intra-team dynamics and inter-team dynamics simultaneously. Highlights: FCMs can illustrate team communication patterns assessing flow and content simultaneously. Augmented FCMs illustrate aggregations of pairwise, individual conversations as team communication patterns. Augmented FCMs via mathematical models and utilizing the SA methods represents team communication as flow between content. Future research should explore the use of FCMs and augmented FCMs in communication and expand knowledge on their comparison metrics.