Current Status and Evolution of Shared Mental Models Based on Bibliometric Analysis

Kristina KONSTANTINOVA, Siran FANG, Xiaoxu ZHANG

系统科学与信息学报(英文) ›› 2024, Vol. 12 ›› Issue (4) : 433-456.

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系统科学与信息学报(英文) ›› 2024, Vol. 12 ›› Issue (4) : 433-456. DOI: 10.21078/JSSI-2023-0173

    Kristina KONSTANTINOVA1(), Siran FANG1(), Xiaoxu ZHANG2,3,*()
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Current Status and Evolution of Shared Mental Models Based on Bibliometric Analysis

    Kristina KONSTANTINOVA1(), Siran FANG1(), Xiaoxu ZHANG2,3,*()
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Abstract

The cornerstone of most management research revolves around improving productivity and team performance. A crucial factor influencing team performance is the shared mental model that team members hold regarding task and team member related issues. This concept has garnered increasing attention from scholars, resulting in an abundance of literature. However, there remains a noticeable scarcity of a comprehensive literature review that combines both quantitative and qualitative analyses of shared mental model studies. In order to grasp a comprehensive understanding of the current status and future trends in the area, this study employs a bibliometric approach to review the literature on shared mental models published in the Web of Science Core Collection database from 1992 to 2023. Co-citation analysis is employed to thoroughly scrutinize the structure of this research area, computationally highlighting research hotspots, revealing potential future research directions and applications, as well as pinpointing pivotal turning points and landmarks in the field. Through scientific bibliometric analysis of knowledge structures and emerging trends, this review makes a substantial contribution to the contemporary literature on shared mental models, providing valuable insights for researchers and practitioners in the field.

Key words

shared mental models / team mental model / shared cognition / co-citation analysis

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Kristina KONSTANTINOVA , Siran FANG , Xiaoxu ZHANG. . 系统科学与信息学报(英文), 2024, 12(4): 433-456 https://doi.org/10.21078/JSSI-2023-0173
Kristina KONSTANTINOVA , Siran FANG , Xiaoxu ZHANG. Current Status and Evolution of Shared Mental Models Based on Bibliometric Analysis. Journal of Systems Science and Information, 2024, 12(4): 433-456 https://doi.org/10.21078/JSSI-2023-0173

1 Introduction

Shared Mental Models (SMM) have developed into a captivating and essential concept in the realms of teamwork, decision-making, and organizational performance over the past few decades. This appealing concept, often interchangeably referred to as Team Mental Models (TMM), shared cognition, and team cognition, explores the specifics of how teams create common representations of working environment. The origins of the SMM concept within organizational studies can be traced back to the early work of visionaries like Cannon-Bowers, Salas, and Converse[1], who defined it as 'knowledge structures held by members of a team that enable them to form accurate explanations and expectations for the task, and, in turn, coordinate their actions and adapt their behavior to demands of the task and other team members'[2]. As the field progressed, the focus of interest shifted from individual knowledge structures to collective creation and utilization of shared mental representations.
The notion of TMMs closely aligns with the concept of SMM, depicting 'organized mental representations of the key elements within a team's relevant environment that are shared across team members'[3]. While subtle distinctions may exist between SMM and TMM in the literature, they ultimately represent different facets of the same overarching theme: The characterization of cognitive structures and shared understanding among teammates.
Scholars are expanding the scope of research within the field by delving into different types and components of SMM, including team and task-related SMM[4], temporal SMM[5], perceived SMM[6], SMM similarity[7], and accuracy[8]. Furthermore, researchers are investigating a broad range of contexts and applications, encompassing military teams[9, 10], medical teams[11-13], decision-making teams[14-16], sport teams[17-19], virtual teams[20-22], human-robot teams[23], among others.
As the study of SMMs has evolved, researchers have encountered the challenge of developing effective measurement tools. Although there is widespread agreement on the positive relationship between SMM and team performance[24-28], accurately capturing this correlation requires a variety of methods adjusted to specific work environments. Consequently, the design and implementation of scaling tools have remained a pertinent issue[29, 30].
The concept of SMM has a history spanning over three decades and has received significant attention from the academic community. Existing reviews primarily focus on specific domains, applications, or measurement issues, while a scientometric analysis surfacing the whole landscape of the topic on SMM in the organizational/ business economics studies is in lack. Klimoski and Mohammed[31] conducted a comprehensive review that critically examined the concept, encompassing its content, form, function, antecedents, and consequences of TMMs. Doyle and Ford[32] contributed by refining the concept of mental models, proposing a new definition of "mental models of dynamic systems". DeChurch and Mesmer-Magnus[33] applied a meta-analytical approach to examine the relationships among cognition, behavior, motivation, and performance, offering a novel interpretation of its impact. Mohammed, et al.[3] provided a detailed account of empirical data on the outcomes and antecedents in the literature on TMMs, discussing the conceptual foundations and measurement technics employed. Burtscher and Manser[34] analyzed the methodologies utilized in the SMM literature and offered discussion on its application in healthcare teams. De Mol, et al.[35] elucidated the interaction of entrepreneurial team cognition with other determinants within an input-mediator-output framework. The construct of SMM was reviewed in the context of health professions learners, representing clinical teams, by Floren, et al.[36]. Tasca[37] examined the theories of team cognition in relation to theories of reflective functioning, exploring how these research domains can mutually enrich each other. Andrews, et al.[38] synthesized the SMM literature, with a focus on definition and measurement, further proposing the utilization of the SMM concept in the context of human-AI interactions.
Despite the abundance of SMM research literature, a comprehensive review that combines both quantitative and qualitative analysis of this research field is notably missing. This review, therefore, employs bibliometric analysis to thoroughly examine the overall progress, key research domains, and future trends within the realm of SMM concept. It offers a comprehensive evaluation of the entire knowledge landscape.
The database of SMM literature continues to expand, with relevant studies being published daily. Given the large volume of articles, the use of computer programs is essential for comprehensive analysis. In this study, Citespace was employed to conduct co-citation analysis, offering a comprehensive overview of emerging trends and pivotal points in the SMM research area[39].
Our study makes several significant contributions to the existing body of research. Firstly, the computational approach allowed to analyze a vast corpus of literature spanning over 30 years. Secondly, emerging trends were computationally identified without human intervention or prejudice. Thirdly, co-citation analysis focused solely on the co-citation network within the dataset, excluding citation counts from studies outside the SMM domain. Thus, co-citation links are more important in evaluating the research paper's contribution specifically to SMM studies.

2 Methodology

This review encompasses literature published from 1992 to 2023, extracted from the Web of Science Core Collection database in October 2023. A query-based search was applied to identify relevant studies using the keywords "shared mental model" or "team mental model". To improve the quality and validity of dataset, the search was limited to the "Management" category of the Web of Science, "Article" or "Review" publication types. Consequently, the initial result of 10059 articles was narrowed down to 585 articles, including 41 review articles and 544 research articles.
The methodology of this review encompasses both descriptive statistical analysis and co-citation analysis. Descriptive statistical analysis serves to delineate the research area and its overall growth in publications. Additionally, it identifies the research institutions that have significantly contributed to the field of SMM. On the other hand, co-citation analysis is instrumental in identifying research focal points and emerging trends. This technique operates on the premise that references frequently cited together are associated. Thus, citation links serve as indicators of the current state and future trajectory of a specific research area. The co-citation analysis for this review was conducted using the analytical tool Citespace 6.2.R5, which generated visual representations offering valuable insights into the research landscape[39].
Data was segmented into 1-year intervals and constrained by the G-index, employing a scale factor k=5. This selection process ensured that the chosen publications are indicative of the field's progression and transformation over time. The visual representation of the analysis incorporates the following details: 1) The node size corresponds to the number of citations per reference; 2) the thickness of purple rings encircling the nodes indicates the level of betweenness centrality; 3) references experiencing citation bursts are highlighted with red citation rings.
Cluster analysis of co-cited articles is a method used to identify research clusters based on the cited references of publications. Cluster determination is vital for analyzing the mainstream of the research area. The co-citation network divides the dataset into clusters. The size of each cluster determined by the number of publications within it. Larger clusters are more informative as they are formed by greater number of references. Each cluster is assigned an ID and label, with the IDs ranked according to the size of the cluster (numbers of publications in the cluster). Cluster labeling was composed by noun phrases from keywords of citing articles. Another quality marker is the silhouette value of a cluster, which ranges from -1 to 1. The higher the silhouette value of a cluster, the higher the homogeneity of articles within that cluster[40]. Terms, the most frequently used by citing articles, may indicate the research direction over time.
To delve into the network structure, several key metrics were assessed: Centrality, burstness, citation count, and sigma. References with high centrality (betweenness centrality) act as pivotal points in the field, linking various clusters. The most cited references are those that have made substantial contributions to the development of SMM, often regarded as landmarks. Citation burst identifies areas that have attracted significant attention from the scholarly community, gauged by both intensity (number of citations) and duration (how long the burst persists). Sigma is a metric that encapsulates both centrality and citation burstness. Works with elevated values in both aspects are likely to have a higher sigma value than those with a strong value in either centrality or burstness alone. Chen, et al. characterized sigma as a gauge of research novelty, indicating that papers with a high sigma level are likely to introduce innovative ideas to the field[41].

3 Results and Discussion

3.1 Descriptive Statistical Analysis

Descriptive statistical analysis is a widely used method of statistical data summary and description. In this review, descriptive analysis was applied to highlight the development of the area in terms of growth (subsection 3.1.1), its application to diverse research areas (subsection 3.1.2) and the presence of influential actors (subsection 3.1.3).

3.1.1 Overall Growth Analysis

A preliminary understanding of a research area's progress can be gleaned from quantitative metrics like the volume of publications and their corresponding citations. These metrics indicate the level of research engagement, the widening scope of the field, and the increasing resonance of the subject in academic circles. The comprehensive growth analysis is outlined in Figure 1 and elaborated upon in the subsequent discussion.
Figure 1 Number of publications and citations during 1992–2023

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Figure 1 features a dual representation, employing both bar and line graphs. The bars depict the quantity of published articles, while the line traces the annual citation count from 1992 to 2023. Notably, there is a consistent upward trend throughout this period in both the number of publications and citations. A substantial surge occurred in 2019, witnessing a 50% increase in publication count, and this reached its zenith in 2020 with 47 publications. As for citations, the pinnacle was reached in 2021 and 2022, with 4322 and 4103 citations respectively. It's worth mentioning that data from 2023 was only partially included in this analysis due to the timing of data collection and analysis, accounting for the observed dip in both publication count and citations. The statistical analysis drew from the Web of Science database in October 2023.

3.1.2 Research Area Analysis

The application of the SMM concept spans various research domains, as detailed in Appendix A. The most extensive domain is "Business Economics", encompassing all 585 articles, a consequence of the initial search constraints within the "Management" category on the Web of Science database. It's worth noting that many publications are interdisciplinary, implying that a single publication may be counted multiple times and could pertain to multiple research areas. Following "Business Economics", the other top five research areas include "Psychology" with 223 publications, "Information Science Library Science" with 43 research papers, "Social Sciences Other Topics" with 29 articles, "Computer Science" with 24 publications, and "Operations Research Management Science" with 15 records. Remarkably, interdisciplinary articles in psychology and business economics account for over 38% of all publications.
The interdisciplinary nature of SMM research, as revealed in the analysis of research areas, underscores the adaptability and versatility of this concept. Its substantial presence in both business economics and psychology highlights its potential to significantly enhance our comprehension of team cognition processes. Additionally, Table 1 provides insight into the top 10 journals with the highest citation counts in the field of SMM, offering a snapshot of the development and sustained interest among researchers in this domain.
Table 1 Top 10 journals with highest citation count in the topic of SMM
Citation count Journal Impact factor 2023
447 Journal of Applied Psychology 9.9
440 Journal of Management 13.5
431 Academy of Management Review 16.4
419 Academy of Management Journal 10.5
379 Administrative Science Quarterly 10.4
374 Journal of Organizational Behavior 6.8
341 Organization Science 4.1
286 Organizational Behavior and Human Decision Processes 4.6
258 Small Group Research 2.5
256 Journal of Personality and Social Psychology 7.6
Note: Table l displays co-citation count in the analyzed database (585 publications).
The citations listed for each journal in Table 1 represent the number of citations within the co-citation network, as extracted through the utilization of the Citespace program. This serves as a testament to the significance of the topic and the overall quality of publications within the research field.

3.1.3 Influenced Institutions Analysis

Table 2 provides a spotlight on the top 10 institutions that have played pivotal roles in advancing the field of SMM. The information regarding affiliations and departments was extracted from the results analysis page of the Web of Science database. To prevent duplication stemming from single publications being attributed to multiple units within an institution, different departments under the same affiliation were consolidated into a single unit of institution.
Table 2 Top 10 influential affiliations by number of publications
No. Affiliations Number of publications % of 585
1 State University System of Florida 39 6.667
2 Pennsylvania Commonwealth System of Higher Education 22 3.761
3 University Of North Carolina 20 3.419
4 Maastricht University 19 3.248
5 University system of Georgia 19 3.248
6 University of Connecticut 17 2.906
7 Michigan State University 16 2.735
8 Pennsylvania State University 15 2.564
9 George Mason University 13 2.222
10 Arizona State University 11 1.88
The State University System of Florida claims the top spot in this ranking with an impressive 39 publications in the field of SMM. In terms of geographical distribution among the top 10 affiliations, nine are based in the USA, while one, Maastricht University, is situated in the Netherlands. It's worth noting that some of the affiliated organizations listed in Table 2 encompass multiple institutions. For instance, the State University System of Florida oversees 12 public universities in Florida (USA), and the University System of Georgia encompasses 26 public colleges and universities in Georgia (USA). As a result, while the affiliation ranking offers valuable insights into major contributors, it may not offer an entirely precise depiction of the most influential institutions in this research area, providing instead a general overview of key players in the field.
Upon delving into specific publications, it's evident that institutions like the State University System of Florida and the Pennsylvania Commonwealth System of Higher Education have made substantial contributions. For instance, a noteworthy work by Salas, et al. from the State University System of Florida delves into the essential components of teamwork, proposing the Big Five elements: Team leadership, mutual performance monitoring, backup behavior, adaptability, and team orientation[42]. Meanwhile, within the Pennsylvania Commonwealth System of Higher Education, Mohammed stands out with a series of influential papers. These works shed light on the interdisciplinary nature of TMMs[43], develop measurement tools for TMMs[3, 44], and present an integrated conceptual framework for team cognition research[45]. These contributions have significantly enriched the field.
The sole European university in this ranking has demonstrated a strong focus on team adaptation processes[46-48] and team learning behavior[49-51]. This emphasis on these critical aspects of team dynamics highlights their valuable contribution to the broader understanding of SMMs in teamwork.
To conclude, this subsection showcases the firm development of the area through the number of publications and citations, while SMM concept has contributed to different research areas and holds research perspectives for further exploration in even more subject fields.

3.2 Co-citation Analysis

The co-citation analysis was performed using Citespace 6.2.R5[39]. The most frequently cited publications in each year, determined by the G-index with a scale factor k=5, were selected to create a refined dataset for constructing a synthesized network of references. To ensure the reliability of each cluster, the citing sources were thoroughly examined. Initially, the network consisted of 7 clusters, but one was deemed unreliable as it was formed by only two citing articles. Additionally, this cluster included an untraceable cited reference from an anonymous author (see Appendix B). Consequently, this reference was excluded from the analysis, and the network was restructured.
The newly organized network used for analysis contains 288 references and 33 co-citation clusters. The largest connected components (k=1) were employed in subsequent analysis, displaying the six largest clusters with 206 nodes, which accounts for 71% of the entire network.

3.2.1 Clustering

The evaluation of the network, constructed by Citespace, involved checking the values of network modularity and silhouette values[40]. Modularity of the network equals Q=0.7576, which is high enough to state that the clusters within the network are representative and can be used for further analysis. The weighted mean silhouette value S=0.9282, which points on a high level of clusters homogeneity. The co-citation analysis consists of 6 largest clusters of co-cited references, presented in Figure 2.
Figure 2 Clusters of co-cited articles

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Cluster #0, highlighted in red in Figure 2, boasts the highest number of publications (46 in total). Following closely is Cluster #1, marked in orange, which is the most recent cluster in the dataset. Meanwhile, Cluster #5 and Cluster #6, with 19 publications each, represent the smallest clusters depicted in Figure 2.
As indicated in Section 2, cluster IDs are arranged based on cluster size. It's worth noting that Cluster #4 was not presented by Citespace due to limitations with the Largest K Connected Components, even though its size surpasses those of both Cluster #5 and Cluster #6.
Table 3 provides detailed clusters' information, such as the average publication year, clusters' ID and labels, the number of cited articles attributed to each cluster, the silhouette value of the cluster and terms most frequently used by citing articles. Each cluster was labeled using keywords from citing articles associated with the cluster.
Table 3 General information of 6 major clusters of co-cited articles policy
Average publication year of articles in cluster Cluster ID Cluster label Number of articles per cluster Silhouette Key terms by log likelihood ratio
1999 2 Team training 41 0.915 team training; strategic consensus; team ability; measurement; aggregation
2003 6 Shared work values 19 0.935 shared work values; social network analysis; lab experiments; team characteristics; computer-mediated communication
2006 3 Shared cognition 37 0.868 shared cognition; adaptive behaviors; team mental model; implicit coordination; group decision making
2011 0 Team situation model 46 0.936 team situation model; temporal conflict; collective leadership; adaptation; multiteam systems
2014 5 composition 19 0.94 Composition; structure; mutual performance monitoring; accuracy; exploration and exploitation
2017 1 Mental model 44 0.975 mental models; team adaptation; team dynamics; team performance; constraints
The arrangement of clusters in Table 3 corresponds to the chronological order of the average publication year within each cluster, beginning with the earliest Cluster #2 and concluding with the most recent Cluster #1. It's important to note that all clusters listed in Table 3 exhibit a high degree of homogeneity, as evidenced by their silhouette values, which range from 0.868 to 0.975. For detailed information on the most cited references in each cluster, please refer to Appendix C.
Cluster #2 centers around researchers' endeavors to define the landscape of SMM research, extending and adapting empirical evidence from diverse literature domains including information sharing, transactive memory, group learning, and cognitive consensus[43]. The discussion within this cluster also encompasses the value of shared cognition and addresses fundamental research questions highlighted in the literature[52], Stout, et al. contributed by exploring the relationship between team planning, SMMs, and coordinated team decision-making and performance[53], while Rentsch and Klimoski aim to delineate and test several antecedents of cognition in teams[54].
In Cluster #6, a wealth of laboratory and field studies enriches the understanding of SMMs. These studies provide evidence of a positive and significant relation between SMM and team processes and performance[55]. The cluster further examines the interaction of different SMM types, such as positional-goal interdependencies and cue-strategy associations, and their impact on team effectiveness[28]. Other investigations in this cluster delve into antecedents to team performance, such as TMM similarity and accuracy[56], as well as transactive memory systems in mature, continuing groups[57].
Moving to Cluster #3, researchers within this cluster recognize the complex nature of teams, engaging in discussions about their characteristics, emphasizing the mediating role of team training in strengthening TMM. Mathieu, et al., within this cluster, stressed the imperative use of both quantitative and qualitative methodologies, coupled with time-sensitive approaches, to capture the multiplex nature of teamwork[58]. Kozlowski and Ilgen's thorough review on team effectiveness highlighted team training, alongside team design and team leadership, as an effective intervention shaping team processes and elevating overall team performance[59]. Edwards, et al. underscored the importance of team training in fostering accuracy and similarity in TMM[60]. Literature review of Ilgen, et al. in this context pays heed to the temporal and qualitative aspects of affective, behavioral, or cognitive mediators[61]. Additionally, Rico, et al. contributed by discussing cognitive factors, exemplifying the value of team cognition in comprehending essential team processes, such as coordination[62].
In Cluster #0, attention is directed toward the team situation model and the changing environment. Uitdewilligen, et al. explored the role of cognitive knowledge structures in team adaptation to a changing task context[47]. Cronin, et al. advocated for viewing and studying groups as dynamic entities[63]. Extending into leadership considerations, Morgeson, et al. considered development of SMM and transactive memory as leaders' responsibility[64]. A comprehensive meta-analysis within this cluster scrutinizes shared cognition in its relation to team performance, incorporating controls for behavioral and motivational dynamics[33]. Measurement issues were also addressed by researchers from this Cluster[3, 65]. DeChurch and Mesmer-Magnus highlighted that the manner in which SMMs were measured and represented at the team level can unveil meaningful distinctions in observed relationships, as evidenced in their meta-analysis on measuring SMMs. This cluster thus weaves together insights into team dynamics, leadership considerations and methodological nuances, enriching our understanding of the intricate interplay within teams.
Cluster #5 embrace research on virtual teams[66], emphasizing the need to adapt to a rapidly changing environment. Baard, et al. contributed to the cluster by delivering a comprehensive review of individual and team performance adaptation[67]. Hoch and Kozlowski evaluated traditional hierarchical leadership, structural supports, and shared team leadership in relation to virtual team performance[68]. D'Innocenzo, et al. supplemented these insights with meta-analytic evidence supporting the positive relationship between shared leadership and overall team performance[69]. Finally, in Cluster #1, the focus is on the importance of team learning and adaptation. Christian, et al. explored the nature of the adaptive stimulus[70], while Santos, et al. studied how team adaptation mediates the relationship between team learning and performance[5]. Waller, et al. contributed to the group and team dynamics literature by presenting elements of dynamism using the concept of emergence[71]. Maynard, et al. reviewed the team adaptation literature, emphasizing factors that serve as antecedents of team adaptation[72]. Grand, et al. contributed to a process-oriented theory of team knowledge emergence, examining dynamic processes through which collectively held knowledge emerges from the individual to the team level[73].
The cluster analysis undertaken in this exploration has provided prominent insights into the developing landscape of SMM research area. By sketching the flow of the main research vectors over time, the clusters have effectively displayed the dynamic nature of the field. As the clusters define distinct areas of centering, they serve as a roadmap for scholars, guiding further evolution of SMM research.

3.2.2 Turning Points and Landmarks

References with high betweenness centrality play a pivotal role in the network. They not only serve as links within a cluster but also act as crucial bridges between clusters, serving as turning points in the co-cited references network. As outlined in the methodology section, these high betweenness centrality references are visually indicated by the presence of purple rings in the network visualization (see Figure 3). For a detailed information on references with high betweenness centrality, please refer to Table D.1 in Appendix D.
Figure 3 Turning points on co-cited publications

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Nodes marked with purple rings, indicating high betweenness centrality, were strategically positioned at the edges of clusters to enhance the readability of the visualization. These nodes, denoted by the names of the first author and the publication year, hold significant importance in the network. The majority of these high centrality records originate from Cluster #3 and Cluster #0.
Work of Baard, et al.[67] from Cluster #5 boasts the highest centrality level. This publication delves into individual and team performance, adaptation, and performance constructs. It acts as a bridge, connecting two clusters: Cluster #5, which addresses team structure and performance evaluation, and Cluster #0, which centers on team modeling and adaptation.
The second highest centrality value belongs to Mohammed, et al.[3]. Together with Hollenbeck, et al.[74], both publications represent Cluster #0 and connect to Cluster #3. These works focus on team cognition and dynamics. Work of Mohammed, et al.[3] provides a comprehensive review of the conceptual structure of TMM, encompassing the measurement tools employed and the empirical outcomes presented in the literature. Meanwhile, publication of Hollenbeck, et al.[74] introduces team categorization based on skill differentiation, authority differentiation, and temporal stability, indicating team maturity and the sharedness of mental models.
Another noteworthy reference connecting Cluster #3 and Cluster #0 is study of Rico, et al.[62], which delves into implicit coordination and its influence on the TMM construct. Work of Ilgen, et al.[61] from Cluster #3 discusses affective, behavioral, and cognitive mediators (with SMM as the predominant concept) between team input and output, thus linking Cluster #3 with Cluster #6. It's worth noting that all five references with the highest centrality levels are review articles.
The most co-cited references in the database spotlight influential researchers who have made substantial contributions to the research area Table D.2 in Appendix D. Cluster #0 and Cluster #3 each have three articles in the top 10 cited references, while Clusters #6 has two. Clusters #2 and Cluster #5 each contribute one article to the ranking. Topping the list is work of Mohammed, et al.[3] with 37 citations from Cluster #0. Concluding the top 10 most cited references is study of Baard, et al.[67] from Cluster #5, also representing the most recent publication in the list.
The ranking provided here is based on the citation counts of co-cited references within a dataset of 585 articles. It's important to note that the total citation count may encompass citations from various research fields, resulting in significantly higher figures. For example, publication of Mohammed, et al.[3] has garnered 467 citations in the Web of Science database, whereas work of Mathieu, et al.[24] boasts a total of 1428 citation counts.

3.2.3 Citation Burst and Sigma

Publications with the most pronounced citation bursts are references that have received a surge of citations within a specific time frame Table D.3 in Appendix D. Among these, three references originate from Cluster #0, four from Cluster #3, two represent Cluster #6, and one is from Cluster #2. It's worth noting that 9 out of the top 10 references in terms of strongest citation bursts are also featured in the list of most highly cited references Table D.2 in Appendix D. This suggests that publications with higher citation counts are more likely to experience a burst in citations. However, their positions in the rankings may not align precisely. For instance, while publication of Mathieu, et al.[24] holds the third position in the list of top-cited articles, it ranks second in terms of citation burst. This discrepancy arises because citation burst considers not only the number of citations but also the duration of the burst event. As illustrated in Figure 4, article of Mathieu, et al.[24] from Cluster #2 exhibits a slightly longer burst duration compared to work of DeChurch and Mesmer-Magnus[33] from Cluster #0.
Figure 4 Timeline of burstness events by clusters

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Figure 4 provides a visual representation of the timeline view of burst events, with each cluster represented by a distinct color. Cluster labels are presented on the right side of the graph, and references with the strongest burstness are indicated on the graph with the same color as their cluster IDs and labels.
DeChurch is mentioned twice on the graph, because DeChurch in co-authorship with Mesmer-Magnus, conducted two meta-analyses in the same year, both with high level of burstness. The first meta-analysis combines findings of 65 independent studies on team cognition in relation to work processes, motivation and performance[33]. And the second publication is meta-analysis of 23 empirical studies on SMM in relation to team processes and performance[65]. Both reviews aggregate previous findings in the area and contribute to the understanding of teamwork and SMMs. The first publication discusses the cognitive underpinnings of effective teamwork, while the second publication focuses on the measurement of shared TMMs, which is an essential element in understanding and enhancing team performance.
Lastly, the most pivotal references in the dataset are those exhibiting a high sigma value see Table D.4 in Appendix D. The top five references, with the highest sigma values, are discussed chronologically from the earliest publication by Mathieu, et al. in the year 2000[24] to the most recent publication by Baard, et al. in 2014[67]. Publication of Mathieu, et al. on SMM and team performance, emphasizing the necessity of team guidance and feedback and illustrating unique effects of task and TMMs through PC-based flight combat simulation[24], has a sigma value of 2.49. The findings from this study garnered attention from subsequent researchers. Smith-Jentsch, et al. emphasized the significance of measuring various types of SMMs[28]. Lewis conducted field research to measure a concept similar to TMM, known as the transactive memory system[75]. Ellis highlighted the adverse impact of acute stress on mental models and transactive memory[76]. Edwards, et al. investigated the relationship between the similarity and accuracy of TMMs[60]. Furthermore, Mathieu, et al. continued their research with PC-based flight simulations to measure the relationship between teammates' team and task mental model sharedness and team performance[55].
Rico, et al.'s work on team situation models and implicit coordination behaviors[62] boasts a sigma value of 3.32. Their exploration of team knowledge structures in coordination processes sparked theoretical discussion and empirical research. For instance, Fisher, et al. studied the mediating role of implicit coordination between TMM similarity and team outcomes[77]. Vashdi, et al. examined the mediating role of workload sharing and team helping between action team learning and performance[78], drawing on Rico, et al.'s concept of implicit coordination characterized by "proactively sharing a workload or helping a colleague". Highly cited publications by DeChurch[33] and Mohammed[3], part of Cluster #0, also recognized Rico's observations in their reviews.
The highest sigma value, 21.07, is attributed to the review on TMMs by Mohammed, et al.[3]. Their 15-year review encompassed numerous empirical studies and reconfirmed the positive association between the sharedness of TMMs and team performance. This work also emphasized the need for further research on the accuracy and stability of TMMs, which subsequently became a focal point of interest in the following years (refer to Table 3, Cluster #5).
Another significant publication from Cluster #0 with a high sigma value is the work of DeChurch and Mesmer Magnus[33], who meta analytically demonstrated the presence of a cognitive foundation in teamwork. Their suggestions for future research directions, such as considering the importance of time, became a focal point in the research of Mohammed, et al.[79], who incorporated time-related aspects in their work. Another prompt consideration for possible research directions was the exploration of multiple forms of coordination, as subsequently explored by Konradt, et al.[80].
The most recent publication among the top five references with highest sigma value is the work of Baard, et al. from Cluster #5[67]. Their conceptual architecture defines the nature of adaptation, focusing on the what, where, and how of adaptation and linking it to antecedent factors, such as individual differences, interventions, and support systems.
In conclusion, the co-citation analysis provides an extensive overview of the SMM research landscape. It not only highlights critical publications and influential researchers but also identifies major clusters, offering valuable insights into the progression of research topics within the field.

4 Conclusion

This review aimed to investigate SMM studies using co-citation analysis, with the primary goal to identify influential publications — landmarks in the field of SMM research — and uncover emerging trends in the literature. Employing descriptive statistical analysis and co-citation analysis, the study yielded several significant findings:
1) The field of SMM boasts a rich history and has experienced a notable increase in scholarly interest, as evidenced by overall growth trends.
2) High citation counts in high-impact journals across diverse research domains, including applied and social psychology, management, and organizational sciences, highlight the interdisciplinary nature of the SMM research.
3) Co-citation analysis provided computationally validated results, shedding light on the evolving research focus. It emphasized crucial publications and influential researchers, as well as presented valuable insights into the evolution of research focus within the field.
4) Cluster investigation revealed insights into the research trajectory over the analyzed period. Initially, researchers contributed to concept development, defining the landscape in Cluster #2. A surge in laboratory and field studies brought to light methodological issues (Cluster #6). The discourse in Cluster #3 centered on team training and coordination. Subsequent clusters addressed the challenges of a rapidly changing environment, engaging with topics of collective leadership and adaptation (Cluster #0 and Cluster #5). The latest and largest cluster emphasized increasing interest of the scholarly community in team dynamics and adaptations (Cluster #1). Table 3 displays the most frequently used terms in citing articles, offering insights into both the current state of research in the cited references of the cluster and potential direction for research in the subsequent cluster. As the research mainstream continues, citations from the last cluster (Cluster #1) are likely to pave the way for a new cluster in the coming years, indicating research interest in team dynamics.
5) Values of centrality, citation count, burstness and sigma showcased the most prominent works in the area of SMM. These works contributed significantly by conceptualizing and synthesizing the research area, providing reader with comprehensive overview of the research state and guiding future research directions.
Despite the valuable insights provided by this review, it is important to acknowledge its limitations. First, dataset was confined to 585 articles from Web of Science Core Collection. Numerous influential works was not included in the dataset, such as work of Cannon-Bowers, Salas and Converse[1]. This research is particularly noteworthy as it played a pivotal role in initiating concept development within the organizational sciences.
Future research could consider expanding the dataset using a citations-based expansion method, encompassing all potentially relevant articles that could contribute to the area of SMM. Citation based expansion may enhance representation of interdisciplinary research publications. Different sources of data could also benefit dataset expansion.
In conclusion, the current literature review has provided valuable insights into the development of the body of knowledge over the past few decades. The concept of SMM has shown a consistent development trajectory and holds significant potential for further growth within the domain of business economics.

Appendix A Research Area Distribution

Figure A1 Top 10 research areas of SMM studies

Full size|PPT slide

Appendix B Citing Articles and Cited References of Cluster N8

Figure B1 Information about cited references and citing articles of Cluster #8 from initially comprised network

Full size|PPT slide

Citing articles placed on the left side. Reference, excluded from the analysis highlighted with green marker.

Appendix C Most Cited References in Major Clusters

Table C.1 Most cited references from Cluster #0
Network co-citation count References from Cluster #0
37 Mohammed S, Ferzandi L, Hamilton K. Metaphor no more: A 15 year review of the team mental model construct. Journal of Management, 2010, 36: 876–910.
29 DeChurch L A, Mesmer-Magnus J R. The cognitive underpinnings of effective teamwork: A meta-analysis. Journal of Applied Psychology, 2010, 95(1): 32–53.
19 DeChurch L A, Mesmer-Magnus J R. Measuring shared team mental models: A meta-analysis. Group Dynamics: Theory, Research, and Practice, 2010, 14(1): 1–14.
10 Uitdewilligen S, Waller M J, Pitariu A H. Mental model updating and team adaptation. Small Group Research, 2013, 44(2): 127–158.
9 Cronin M A, Weingart L R, Todorova G. Dynamics in groups: Are we there yet? The Academy of Management Annals, 2011, 5(1): 571–612.
Table C.2 Most cited references from Cluster #1
Network co-citation count References from Cluster #1
9 Grand J A, Braun M T, Kuljanin G, et al. The dynamics of team cognition: A process-oriented theory of knowledge emergence in teams. Journal of Applied Psychology, 2016, 101(10): 1353–1385.
8 Santos C M, Passos A M, Uitdewilligen S. When shared cognition leads to closed minds: Temporal mental models, team learning, adaptation and performance. European Management Journal, 2016, 34(3): 258–268.
8 Christian J S, Christian M S, Pearsall M J, et al. Team adaptation in context: An integrated conceptual model and meta-analytic review. Organizational Behavior and Human Decision Processes, 2017, 140: 62–89.
7 Waller M J, Okhuysen G A, Saghafian M. Conceptualizing emergent states: A strategy to advance the study of group dynamics. The Academy of Management Annals, 2016, 10(1): 561–598.
7 Maynard M T, Kennedy D M, Sommer S A. Team adaptation: A fifteen-year synthesis (1998–2013) and framework for how this literature needs to "adapt" going forward. European Journal of Work and Organizational Psychology, 2015, 24(5): 652–677.
Table C.3 Most cited references from Cluster #2
Network co-citation count References from Cluster #2
19 Mathieu J E, Heffner T S, Goodwin G F, et al. The influence of shared mental models on team process and performance. Journal of Applied Psychology, 2000, 85(2): 273–283.
10 Mohammed S, Dumville B C. Team mental models in a team knowledge framework: Expanding theory and measurement across disciplinary boundaries. Journal of Organizational Behavior, 2001, 22(2): 89–106.
9 Marks M A, Mathieu J E, Zaccaro S J. A temporally based framework and taxonomy of team processes. Academy of Management Review, 2001, 26(3): 356–376.
9 Marks M A, Sabella M J, Burke C S, et al. The impact of cross-training on team effectiveness. Journal of Applied Psychology, 2002, 87(1): 3–13.
6 Stout R J, Cannon-Bowers J A, Salas E, et al. Planning, shared mental models, and coordinated performance: An empirical link is established. Human Factors: The Journal of the Human Factors and Ergonomics Society, 1999, 41(1): 61–71.
Table C.4 Most cited references from Cluster #3
Network co-citation count References from Cluster #3
16 Mathieu J, Maynard M T, Rapp T, et al. Team effectiveness 1997–2007: A review of recent advancements and a glimpse into the future. Journal of Management, 2008, 34(3): 410–476.
15 Edwards B D, Day E A, Arthur W, et al. Relationships among team ability composition, team mental models, and team performance. Journal of Applied Psychology, 2006, 91(3): 727–736.
12 Rico R, Sánchez-Manzanares M, Gil F, et al. Team implicit coordination processes: A team knowledge-based approach. Academy of Management Review, 2008, 33(1): 163–184.
11 Kozlowski S W J, Ilgen D R. Enhancing the effectiveness of work groups and teams. Psychological Science in the Public Interest, 2006, 7(3): 77–124.
10 Ilgen D R, Hollenbeck J R, Johnson M, et al. Teams in organizations: From input-process-output models to IMOI models. Annual Review of Psychology, 2005, 56(1): 517–543.
Table C.5 Most cited references from Cluster #5
Network co-citation count References from Cluster #5
12 Baard S K, Rench T A, Kozlowski S W J. Performance adaptation. Journal of Management, 2013, 40(1): 48–99.
10 Hoch J E, Kozlowski S W J. Leading virtual teams: Hierarchical leadership, structural supports, and shared team leadership. Journal of Applied Psychology, 2014, 99(3): 390–403.
6 D'Innocenzo L, Mathieu J E, Kukenberger M R. A meta-analysis of different forms of shared leadership-team performance relations. Journal of Management, 2016, 42(7): 1964–1991.
6 Gilson L L, Maynard M T, Jones Young N C, et al. Virtual teams research. Journal of Management, 2014, 41(5): 1313–1337.
4 Edmondson A C, Lei Z. Psychological safety: The history, renaissance, and future of an interpersonal construct. Annual Review of Organizational Psychology and Organizational Behavior, 2014, 1(1): 23–43.
Table C.6 Most cited references from Cluster #6
Network co-citation count References from Cluster #6
18 Mathieu J E, Heffner T S, Goodwin G F, et al. Scaling the quality of teammates' mental models: Equifinality and normative comparisons. Journal of Organizational Behavior, 2004, 26(1): 37–56.
16 Smith-Jentsch K A, Mathieu J E, Kraiger K. Investigating linear and interactive effects of shared mental models on safety and efficiency in a field setting. Journal of Applied Psychology, 2005, 90(3): 523–535.
10 Lim B C, Klein K J. Team mental models and team performance: A field study of the effects of team mental model similarity and accuracy. Journal of Organizational Behavior, 2006, 27(4): 403–418.
9 Austin J R. Transactive memory in organizational groups: The effects of content, consensus, specialization, and accuracy on group performance. Journal of Applied Psychology, 2003, 88(5): 866–878.
5 De Dreu C K W, Weingart L R. Task versus relationship conflict, team performance, and team member satisfaction: A meta-analysis. Journal of Applied Psychology, 2003, 88(4): 741–749.

Appendix D Structural Measurements of Co-cited References

Table D.1 Cited publications with highest betweenness centrality
# Centrality References Cluster ID
1 0.34 Baard SK, 2014, J MANAGE, V40, P48 5
2 0.23 Mohammed S, 2010, J MANAGE, V36, P876 0
3 0.21 Rico R, 2008, ACAD MANAGE REV, V33, P163 3
4 0.21 Hollenbeck JR, 2012, ACAD MANAGE REV, V37, P82 0
5 0.19 Ilgen DR, 2005, ANNU REV PSYCHOL, V56, P517 3
6 0.16 Burke CS, 2006, J APPL PSYCHOL, V91, P1189 3
7 0.14 DeChurch LA, 2010, J APPL PSYCHOL, V95, P32 0
8 0.11 Mathieu J, 2008, J MANAGE, V34, P410 3
9 0.10 Smith-Jentsch KA, 2005, J APPL PSYCHOL, V90, P523 6
10 0.10 Christian JS, 2017, ORGAN BEHAV HUM DEC, V140, P62 1
Table D.2 Most cited references
# Citation Counts References Cluster ID
1 37 Mohammed S, 2010, J MANAGE, V36, P876 0
2 29 DeChurch LA, 2010, J APPL PSYCHOL, V95, P32 0
3 19 Mathieu JE, 2000, J APPL PSYCHOL, V85, P273 2
4 19 DeChurch LA, 2010, GROUP DYN-THEOR RES, V14, P1 0
5 18 Mathieu JE, 2005, J ORGAN BEHAV, V26, P37 6
6 16 Mathieu J, 2008, J MANAGE, V34, P410 3
7 16 Smith-Jentsch KA, 2005, J APPL PSYCHOL, V90, P523 6
8 15 Edwards BD, 2006, J APPL PSYCHOL, V91, P727 3
9 12 Rico R, 2008, ACAD MANAGE REV, V33, P163 3
10 12 Baard SK, 2014, J MANAGE, V40, P48 5
Table D.3 References with the strongest citation burst
# Bursts References Cluster ID
1 14.93 Mohammed S, 2010, J MANAGE, V36, P876 0
2 11.51 Mathieu JE, 2000, J APPL PSYCHOL, V85, P273 2
3 10.90 DeChurch LA, 2010, J APPL PSYCHOL, V95, P32 0
4 9.14 Mathieu JE, 2005, J ORGAN BEHAV, V26, P37 6
5 8.56 DeChurch LA, 2010, GROUP DYN-THEOR RES, V14, P1 0
6 8.31 Mathieu J, 2008, J MANAGE, V34, P410 3
7 8.10 Smith-Jentsch KA, 2005, J APPL PSYCHOL, V90, P523 6
8 7.66 Edwards BD, 2006, J APPL PSYCHOL, V91, P727 3
9 6.20 Rico R, 2008, ACAD MANAGE REV, V33, P163 3
10 6.08 Kozlowski SWJ, 2006, PSYCHOL SCI, V0, PP77 3
Table D.4 Structurally and temporally significant references
# Sigma References Cluster ID
1 21.07 Mohammed S, 2010, J MANAGE, V36, P876 0
2 4.81 Baard SK, 2014, J MANAGE, V40, P48 5
3 4.30 DeChurch LA, 2010, J APPL PSYCHOL, V95, P32 0
4 3.32 Rico R, 2008, ACAD MANAGE REV, V33, P163 3
5 2.49 Mathieu J, 2000, J APPL PSYCHOL, V85, P273 2

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The first author's contribution was supported by CAS-TWAS President's fellowship for international doctoral students. We express our gratitude to Professor Chaomei Chen for providing valuable and timely feedback on Citespace products.

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