Using interbrain synchrony to study teamwork: A systematic review and meta-analysis

It has been proposed that interbrain synchrony (IBS) may help to elucidate the neural mechanisms underpinning teamwork. As hyperscanning studies have provided abundant findings on IBS in team environments, the current review aims to synthesize the findings of hyperscanning studies in a way that is relevant to the teamwork research. A systematic review was conducted. Included studies were classified according to the IPO (i.e. input, process, output) model of teamwork. Three multi-level meta-analyses were performed to quantify the associations between IBS and the three IPO variables. The methodology followed PRISMA guidelines and the protocol was pre-registered (https://osf.io/7h8sa/). Of the 229 studies, 41 were included, representing 1326 teams. The three meta-analyses found statistically significant positive effects, indicating a positive association between IBS and the three IPO teamwork variables. This study provides evidence that IBS is a relevant measure of the teamwork process and argues for the continued use of IBS to study teamwork.

Optimizing brand loyalty through user-centric product package design: A study of user experience in dairy industry

Objective

With the arrival of the experience economy era, changes in the marketing environment, and the evolution of consumer psychological needs, a good user experience will bring them freshness. Based on user experience, this paper analyzes the relationship among product brand image, brand trust, and brand loyalty, aiming to promote product values and improve brand loyalty and trust.

Methods

Through case analysis, consumers' favorite brands were selected and conducted positioning analysis on brand color, image, package form, and so on. The study proposed a hypothetical model of user experience on brand loyalty and performed a questionnaire survey on 357 consumers. The relational model of the impact of user experience on consumers was verified using the SEM (Structural Equation Model) method.

Results

It is shown that sensory experience, emotional experience, behavioral experience, and thinking experience have significant impacts on brand image; brand image apparently affects brand trust; and brand trust and image remarkably influence brand loyalty.

Conclusions

Extending the concept of user experience to the fast-moving consumer goods industry will contribute to the package design of products and the theory and practice of brand loyalty. The research findings can provide effective strategies and approaches for marketers to improve product market competitiveness and enhance consumer brand stickiness.

The development and validation of an Army team resilience measure

Given the demanding nature of its mission, the collective units of the Army, not just individual Soldiers, need to be able to withstand and adapt to a wide range of challenges. Therefore, it is important to be able to effectively assess resilience at the team-level and to understand the factors that can enable or diminish it. This article describes the development of a construct valid and psychometrically-sound measure of team resilience - the Team Resilience Scale (TRS). A theoretical framework of team resilience and related constructs is introduced. We then summarize the procedures for developing the TRS and related constructs, providing evidence of the content validity of the TRS. Finally, we assess the psychometric soundness and construct validity of the TRS in two Army field studies. Our analyses support the convergent validity of items and indicate that the measure can be used to examine three first-order dimensions of resilience (i.e., physical, affective, and cognitive) or as a single overall resilience composite. Results show the TRS was positively related to team performance in both samples and it co-varied with stressors and team actions. Practical recommendations for use of the measure and suggestions for future research are offered.

Topic modeling and clustering in the trace data-driven analysis of job demands among teachers

Psychosocial work environment characteristics like job demands have traditionally been studied using survey data. We propose an alternative approach utilizing work related trace data collected from the information systems that employees use to achieve organizational goals. We analyze the job demands of teachers from two universities of applied sciences using trace data collected from the educational online platform Moodle over a period of 90 weeks. The data contain pairs of targets and actions (like message_sent) performed by teachers on Moodle. The timestamps of the target-action pairs allow us to study the dynamic nature of job demands, which is not possible by using periodically collected survey data. We show how trace data can be used to analyze processes related to job demands using data-driven approaches. We have identified topics, themes, temporal processes, and employee clusters from Moodle data representing the work tasks of teachers. The information obtained is action-oriented, context-specific, and dynamic, meeting the current needs for information about changing working life. The approach we have provided could be widely utilized in organizations as well as in research on occupational wellbeing. It is useful in identifying targets for intervention and it could be expanded to include prediction models on different outcomes.

An organizational digital footprint for interruption management: a data-driven approach

Purpose

Interruptions are prevalent in knowledge work, and their negative consequences have driven research to find ways for interruption management. However, these means almost always leave the responsibility and burden of interruptions with individual knowledge workers. System-level approaches for interruption management, on the other hand, have the potential to reduce the burden on employees. This paper’s objective is to pave way for system-level interruption management by showing that data about factual characteristics of work can be used to identify interrupting situations.

Design/methodology/approach

The authors provide a demonstration of using trace data from information and communications technology (ICT)-systems and machine learning to identify interrupting situations. They conduct a “simulation” of automated data collection by asking employees of two companies to provide information concerning situations and interruptions through weekly reports. They obtain information regarding four organizational elements: task, people, technology and structure, and employ classification trees to show that this data can be used to identify situations across which the level of interruptions differs.

Findings

The authors show that it is possible to identifying interrupting situations from trace data. During the eight-week observation period in Company A they identified seven and in Company B four different situations each having a different probability of occurrence of interruptions.

Originality/value

The authors extend employee-level interruption management to the system-level by using “task” as a bridging concept. Task is a core concept in both traditional interruption research and Leavitt's 1965 socio-technical model which allows us to connect other organizational elements (people, structure and technology) to interruptions.

Westerink JHD, Rispens S. A Review of Using Wearable Technology to Assess Team Functioning and Performance

Wearable technology enables collecting continuous in situ data from multiple people in various modalities, which can enhance team research and support, as the dynamic coupling of signals between interacting individuals (i.e., team coordination dynamics) is believed to reflect underlying processes and states of team functioning and performance. We conducted a systematic review on existing literature to evaluate the prospective use of wearable technology in research and practice. Using the IMOI framework as an organizing tool, our review revealed considerable support linking team coordination dynamics in different modalities to team functioning and performance, but also explicated the field’s nascent status.

50 Years of Sports Teams in Work Teams Research: Missed Opportunities and New Directions for Studying Team Processes

For the last 50 years, sports team samples have played a significant role in mainstream management literature. Prior research has analyzed different kinds of data, such as archival, survey-based, and interviews/observations, from a wide range of sports teams. These teams differ greatly in terms of contexts (i.e., recreational to intercollegiate to professional) and types of interdependence. We explore this body of work and focus on what is germane for the work teams literature as we examine 255 relevant articles spanning the years 1972–2021 in major management, organizational behavior (OB), human resource management (HRM), and strategy journals. Using the input-process-outcome model in our coding process, we identify the relative absence of the study of team processes. We develop a conceptual framework linking team interdependence, team processes, and the initiation and maintenance of these processes. This framework is intended to help guide future research on team processes in the context of sports team samples and enhance the generalizability of this research to the work teams domain. Additionally, we identify an agenda for future research using sports team samples for work groups/teams researchers. Overall, we intend to spur thoughtful and creative future research in the work groups/teams area using the rich field environment that sports teams present.

Capturing the Temporal Dynamics of Language Style Matching in Groups and Teams

This article presents a dynamic conceptualization for the assessment of language style matching (LSM) over time. LSM is a team’s mutual adaption of function words like pronouns, articles, or prepositions. LSM is a nonconsciously but frequently occurring communication behavior allowing researchers unobtrusive insights into teams’ internal dynamics. Building on guidelines for the alignment of construct and measurement, a dynamic conceptualization and method for LSM are introduced. Simulated examples and interactions of N = 160 individuals in 26 teams indicate that dynamic LSM allows for a truer estimation of LSM than the hitherto used static method. Implications for future application are discussed.

Examining Diurnal Differences in Multidisciplinary Care Teams at a Pediatric Trauma Center Using Electronic Health Record Data: Social Network Analysis

BACKGROUND

The care of pediatric trauma patients is delivered by multidisciplinary care teams with high fluidity that may vary in composition and organization depending on the time of day.

OBJECTIVE

This study aims to identify and describe diurnal variations in multidisciplinary care teams taking care of pediatric trauma patients using social network analysis on electronic health record (EHR) data.

METHODS

Metadata of clinical activities were extracted from the EHR and processed into an event log, which was divided into 6 different event logs based on shift (day or night) and location (emergency department, pediatric intensive care unit, and floor). Social networks were constructed from each event log by creating an edge among the functional roles captured within a similar time interval during a shift. Overlapping communities were identified from the social networks. Day and night network structures for each care location were compared and validated via comparison with secondary analysis of qualitatively derived care team data, obtained through semistructured interviews; and member-checking interviews with clinicians.

RESULTS

There were 413 encounters in the 1-year study period, with 65.9% (272/413) and 34.1% (141/413) beginning during day and night shifts, respectively. A single community was identified at all locations during the day and in the pediatric intensive care unit at night, whereas multiple communities corresponding to individual specialty services were identified in the emergency department and on the floor at night. Members of the trauma service belonged to all communities, suggesting that they were responsible for care coordination. Health care professionals found the networks to be largely accurate representations of the composition of the care teams and the interactions among them.

CONCLUSIONS

Social network analysis was successfully used on EHR data to identify and describe diurnal differences in the composition and organization of multidisciplinary care teams at a pediatric trauma center.

Use of Technology in the Study of Team-Interaction and Performance

Direct observation of groups is labor-intensive. As a result, current research on small groups often relies on retrospective ratings. Recent developments in sensor-technology have eased data gathering, leading to a renewed interest in direct observation of groups. Sensor technology has potential, but also limitations; research has been technology- and data-driven with less recognition of the large body, and long history, of research and theory building. We review the literature on technology in small group research, argue for more interdisciplinary research and propose combining sensor technology with methods of interaction analysis, and the theories that underlie them, developed prior to 1980.

Team Physiological Dynamics: A Critical Review

OBJECTIVE

Review the use of physiological measurement in team settings and propose recommendations to improve the state of the science.

BACKGROUND

New sensor and analytical capabilities enable exploration of relationships between team members' physiological dynamics. We conducted a review of physiological measures used in research on teams to understand (1) how these measures are theoretically and operationally related to team constructs and (2) what types of validity evidence exist for physiological measurement in team settings.

METHOD

We identified 32 articles that investigated task-performing teams using physiological data. Articles were coded on several dimensions, including team characteristics. Study findings were categorized by relationships tested between team physiological dynamics (TPD) and team inputs, mediators/processes, outputs, or psychometric properties.

RESULTS

TPD researchers overwhelmingly measure single physiological systems. Although there is research linking TPD to inputs and outputs, the research on processes is underdeveloped.

CONCLUSION

We recommend several theoretical, methodological, and statistical themes to expand the growth of the TPD field.

APPLICATION

Physiological measures, once established as reliable indicators of team functioning, might be used to diagnose suboptimal team states and cue interventions to ameliorate these states.

The challenges of working with “real” teams: Introduction to the second installment

We introduce the next two papers in our running special section on the challenges studying modern teams—those that may not have identifiable boundaries, stable membership, or members who belong only to that single team. Our perspective is that many of the assumptions about teams themselves are no longer correct, so rather than further exploiting our traditional approaches, the field should explore new or different ways to analyze the team experience. Thus, in these special sections, we present theoretical arguments made based on disciplined imagination and actual experience for why such new approaches are credible. This installment presents two papers that should enrich researchers’ sophistication in their ontological assumptions about teams. They are excellent complements to each other, as both are about questions of meaning and both have clear methodological implications for research design, but one zooms in to the nature of teams and the other zooms out to the nature of knowledge itself.

The challenges of working with “real” teams: Challenges, needs, and opportunities

Teams are ubiquitous in organizations, yet work contexts now make traditional teams—those that have identifiable boundaries, stable membership, and members who belong only to that single team—a rarity. Teamwork has evolved along with work itself, making the traditional means of studying and validating team experiences (e.g., agreement statistics) inadequate. Yet it is not merely that current measures are antiquated, many of the assumptions about teams themselves are no longer correct. We felt that rather than simply trying to further exploit our traditional approaches to studying teams, the field should explore new or different ways to capture the team experience. New ideas about how to study teams will necessarily start out as theoretical—arguments made based on disciplined imagination and actual experience for why such new approaches are credible. If those who study new forms of teams can then validate these theories, then such new approaches expand the field’s capabilities. Thus, over the next few issues of OPR, we will be featuring papers that present new stances on how to study real teams. Such papers will provide arguments as to why these approaches are legitimate and necessary, to hopefully help bring these new approaches to future empirical work on teams in the real world.

Using live disaster exercises to study large multiteam systems in extreme environments: Methodological and measurement fit

Multiteam systems (MTSs) are comprised of two or more teams working toward shared superordinate goals but with unique subgoals. In large MTSs operating in extreme environments, coordination difficulties have repeatedly been found, which compromise response effectiveness. Research is needed that examines MTSs in situ within extreme environments to develop temporal theories of inter-team processes and understanding of how coordination may be improved within these challenging contexts. Live disaster exercises replicate the complexities of extreme environments, providing a valuable avenue for observing inter-team processes in situ. This article seeks to contribute to MTS research by highlighting (i) a mixed-method framework for collecting data during live disaster exercises that uses both inductive and deductive approaches to promote methodological and measurement fit; (ii) ways in which data can be collected and combined to meet the appropriate standards of their methodological class; and (iii) a case example of a National exercise.

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

Team processes are interdependent activities among team members that transform inputs into outputs, vary over time, and are critical for team effectiveness. Understanding the temporal dynamics of team processes and related team phenomena with a high-resolution lens (i.e., methods with high sampling rates) is particularly challenging when going “into the wild” (i.e., studying teams operating in their full situated context). We review quantitative field studies using high-resolution methods (e.g., video, chat/text data, archival, wearables) and map out the various temporal lenses for studying team dynamics. We synthesize these different lenses and present an integrated temporal framework that is of help in theorizing about team dynamics. We also provide readers with a “how to” guide that summarizes four essential steps along with analytical methods (e.g., sequential and pattern analyses, mixed-methods research, abductive reasoning) that are applicable to the broad scope of high-resolution methods.

The Behavioral Biology of Teams: Multidisciplinary Contributions to Social Dynamics in Isolated, Confined, and Extreme Environments

Teams in isolated, confined, and extreme (ICE) environments face many risks to behavioral health, social dynamics, and team performance. Complex long-duration ICE operational settings such as spaceflight and military deployments are largely closed systems with tightly coupled components, often operating as autonomous microsocieties within isolated ecosystems. As such, all components of the system are presumed to interact and can positively or negatively influence team dynamics through direct or indirect pathways. However, modern team science frameworks rarely consider inputs to the team system from outside the social and behavioral sciences and rarely incorporate biological factors despite the brain and associated neurobiological systems as the nexus of input from the environment and necessary substrate for emergent team dynamics and performance. Here, we provide a high-level overview of several key neurobiological systems relevant to social dynamics. We then describe several key components of ICE systems that can interact with and on neurobiological systems as individual-level inputs influencing social dynamics over the team life cycle-specifically food and nutrition, exercise and physical activity, sleep/wake/work rhythms, and habitat design and layout. Finally, we identify opportunities and strategic considerations for multidisciplinary research and development. Our overarching goal is to encourage multidisciplinary expansion of team science through (1) prospective horizontal integration of variables outside the current bounds of team science as significant inputs to closed ICE team systems and (2) bidirectional vertical integration of biology as the necessary inputs and mediators of individual and team behavioral health and performance. Prospective efforts to account for the behavioral biology of teams in ICE settings through an integrated organizational neuroscience approach will enable the field of team science to better understand and support teams who work, live, serve, and explore in extreme environments.

Using Sensors in Organizational Research-Clarifying Rationales and Validation Challenges for Mixed Methods

Sensor-based data are becoming increasingly widespread in social, behavioral, and organizational sciences. Far from providing a neutral window on "reality," sensor-based big-data are highly complex, constructed data sources. Nevertheless, a more systematic approach to the validation of sensors as a method of data collection is lacking, as their use and conceptualization have been spread out across different strands of social-, behavioral-, and computer science literature. Further debunking the myth of raw data, the present article argues that, in order to validate sensor-based data, researchers need to take into account the mutual interdependence between types of sensors available on the market, the conceptual (construct) choices made in the research process, and the contextual cues. Sensor-based data in research are usually combined with additional quantitative and qualitative data sources. However, the incompatibility between the highly granular nature of sensor data and the static, a-temporal character of traditional quantitative and qualitative data has not been sufficiently emphasized as a key limiting factor of sensor-based research. It is likely that the failure to consider the basic quality criteria of social science measurement indicators more explicitly may lead to the production of insignificant results, despite the availability of high volume and high-resolution data. The paper concludes with recommendations for designing and conducting mixed methods studies using sensors.

The Development and Construct Validity of a Team Processes Survey Measure

Marks, Mathieu, and Zaccaro (2001) advanced a theory and framework of team processes that has garnered much attention and guided numerous investigations. They proposed that 10 first-order constructs (e.g., strategy formulation, coordination, conflict management) would map to three second-order constructs (i.e., transition, action, and interpersonal). Despite the popularity of this framework, we are unaware of any validated multiitem measures of the team processes they identified. Accordingly, we develop and demonstrate content and construct validity of 50-, 30-, and 10-item versions of a survey measure of team processes. Using data from over 700 teams, we test Marks et al.’s higher-order model and find results that are largely consistent with both their 10 first-order dimensions and the 10:3 second-order framework. Using samples of global virtual knowledge teams and health care employees, we provide evidence of the discriminant validity of our team process measure versus a measure of team empowerment. We provide recommendations for the use of these measures in future research and practice and encourage their use as part of a portfolio of measures of team processes.

Embracing Complexity: Reviewing the Past Decade of Team Effectiveness Research

We conceptualize organizational teams as dynamic systems evolving in response to their environments. We then review the past 10 years of team effectiveness research and summarize its implications by categorizing studies under three main overlapping and coevolving dimensions: compositional features, structural features, and mediating mechanisms. We highlight prominent work that focused on variables in each of these dimensions and discuss their key relationships with team outcomes. Furthermore, we review how contextual factors impact team effectiveness. On the basis of this review, we advocate that future research seek to examine team relationships through a dynamic, multilevel perspective, while incorporating new and novel measurement techniques. We submit that the future of teams research may benefit from a conceptualization of them as dynamic networks and modeling them as small complex systems.

Integrating the Split/Analyze/Meta-Analyze (SAM) Approach and a Multilevel Framework to Advance Big Data Research in Psychology

Abstract. Though big data research has undergone dramatic developments in recent decades, it has mainly been applied in disciplines such as computer science and business. Psychology research that applies big data to examine research issues in psychology is largely lacking. One of the major challenges regarding the use of big data in psychology is that many researchers in the field may not have sufficient knowledge of big data analytical techniques that are rooted in computer science. This paper integrates the split/analyze/meta-analyze (SAM) approach and a multilevel framework to illustrate how to use the SAM approach to address multilevel research questions with big data. Specifically, we first introduce the SAM approach and then illustrate how to implement this to integrate two big datasets at the firm level and country level. Finally, we discuss theoretical and practical implications, proposing future research directions for psychology scholars.

Team Performance Archetypes: Toward a New Conceptualization of Team Performance Over Time

We examine the concept of team performance and propose a framework to understand patterns of change over time. Following a literature review on team performance (focusing on empirical articles published between 2007 and 2017) and drawing on Greek and Roman mythology, we identify five team performance trajectories: “Jupiter” (consistently high performing), “Neptune” (relatively steady, average performance), “Pluto” (low performing), “Icarus” (initially high performing, with a downward spiral), and “Odysseus” (initially low to midrange performing, with an upward spiral), which we refer to as “team performance archetypes.” We discuss how they might be used in conjunction with growth modeling methodology to help facilitate theory building and data collection/analysis with respect to team performance. In addition, we discuss the future research implications associated with using the archetypes to help conceptualize patterns of team performance over time.

Language and Group Processes: An Integrative, Interdisciplinary Review

This article reviews research that examines the use of language in small interacting groups and teams. We propose a model of group inputs (e.g., status), processes and emergent states (e.g., cohesion, influence, and innovation), and outputs (e.g., group effectiveness and member well-being) to help structure our review. The model is integrated with how language is used by groups to both reflect group inputs but also to examine how language interacts with inputs to affect group processes and create emergent states in groups, and then ultimately helps add value to the group with outputs (e.g., performance). Using cross-disciplinary research, our review finds that language is integral to how groups coordinate, interrelate, and adapt. For example, language convergence is related to increased group cohesion and group performance. Our model provides the theoretical scaffolding to consider language use in interacting small groups and suggests areas for future research.