Interactions in engaged work teams: a qualitative study

Abstract

Purpose The aim of this study is to describe work-engaged teams in terms of interpersonal interaction. Design/methodology/approach Six teams (N = 31 individuals) were videotaped during a decision-making task, for one hour. Based on a priori defined categories, the authors coded the videos in terms of the degree of interaction between team members, the physical distance between members, the degree of team’s activation and the valence of their interaction. The videos were also coded in terms of motivational and affective processes. Team work engagement was assessed using questionnaires. Findings Highly engaged team members work physically close and have an increment on their interactions up until the task’s temporal midpoint. They have an initial peak of activation and show more positive emotional valence in the first and the last moments of the task. The most interpersonal processes used are affective. The worst performing team had the highest initial interaction levels followed by an abrupt decrease both in their levels of interaction and in their levels of activation. Simultaneously, they present higher peaks of positive emotional valence. Practical implications Although engaged teams are essentially characterized by the presence of positive interactions, it is fundamental to alternate more “exited” and fun moments with more task focused ones and collective interaction moments with individual work. Originality/value This study answers to Kozlowski and Chao’s (2012) call for studying emergence in a more direct way, using qualitative analysis of video data.

Observational Research in Face-to-Face Small Groupwork: Capturing Affect as Socio-Dynamic Interpersonal Phenomena

“…Across the 21 studies affect phenomena were typically examined for their function toward groups’ purpose, with socioemotional constructs conceptualized as playing a functional role in how group members got along together to serve group aims. This includes for example, the role of socioemotional phenomena in different learning processes in CL (e.g., science argumentation, Isohätäläa et al, 2018), or in the evolution of highly engaged group task focus (e.g., Costa et al, 2017). Four of the 21 studies that used socioemotional constructs, investigated affect also for its functional role explicitly as social.…”

“…The observational focus on affect as either positive (eight studies), negative (four studies), or both (nine studies) was identified. Four studies (Barsade, 2002; Bartel & Saavedra, 2000; Costa et al, 2017; Lehmann-Willenbrock et al, 2011) were grounded in a circumplex affect model (e.g., Larsen & Diener, 1992; Russell, 1980), which conceptualizes affect on two dimensions of valence on a negative-positive scale, and level of activation. The focus on affective valence rather than discrete emotions is consistent with emotion measure reviews (e.g., Mauss & Robinson, 2009) which have suggested that a dimensional perspective (valence; arousal) may be more suitable for measuring group behavioral data, capturing broad affect phenomena (i.e., mood, climate).…”

“…Noteworthy is the finding that 18 studies identified affect within verbal content, as the predominant focus (10 studies) or in combination with nonverbal affect expression (eight studies), which reflects the importance attributed to affect phenomena in talk in recent research. In the eight studies in which both verbal and nonverbal affect data were examined, some studies analyzed nonverbal and verbal affect independently, for example by employing different coding instruments for each data source (e.g., Costa et al, 2017; Lehmann-Willenbrock et al, 2011). Both nonverbal and verbal affect expressions were also explored together in narrative analysis.…”

“…The third column of Table 2 shows how affect, as visible data in groupwork interactions, has typically been captured through either quantifying coded categories investigating its sequential evolution, or through qualitative analysis. Although interaction analysis may be classified as qualitative research (e.g., Costa et al, 2017), the analysis of observed affect data in the 21 studies contrasts narrative to quantitative analysis of frequencies and numerically derived patterns of coded indicators. Three approaches were identified: quantitative analysis of coded affect indicators; narrative analysis of observed affect as temporally unfolding; and, combination of narrative analysis and quantified indicators to temporally explore affect dynamics.…”

An Overview on the Team Interaction Process

“…showed that team learning is faster, broader and deeper than individual learning. Positivity in team interaction is good for team learning and performance (Patrícia et al, 2017; Lehmann-Willenbrock et al, 2017)[13] [14]. However, there is no denying that there is a certain "knowledge protection barrier" in team learning.…”