Cyber Team Performance: A Systematic Literature Review

The performance of technical teams in cyber domains such as cybersecurity, e-gaming and software engineering is critical for modern organizational success. This systematic litera- ture review synthesizes 137 primary studies to assess whether established‌ team effectiveness models, including Hackman’s normative framework and the Input–Mediator‌–Output–‌Input (IMOI) model, explain pe‌rformance in these technolo‌g‌y-mediated contexts. We find that communication, coordination and leadership consistently predict performance, while domain factors such as distributed work, temporal pressure and adversarial threat constrain direct model transfer. The literature shows frequent ad hoc and hybrid model use, indicating a need for adaptable frameworks that account for emergent team properties and nonlinear interactions. We summarize domain-specific operationalizations of effectiveness and pro- pose directions for model adaptation and empirical validation. Our synthesis underlines the importance of context-contingent, multi-layered theory for teams operating in cyber envi- ronments.

Cybersecurity, E-gaming, Software Engineering, Team performance, Team success factors, Team dynamics, Team Effectiveness Models, Coordination, Leadership, Trust and Shared Mental Models 1. INTRODUCTION The performance of technical teams operating in cyber-relevant domains such as cyber- security, e-gaming and software engineering has become a central concern in both academic and applied research. These domains increasingly rely on collaborative efforts to accomplish tasks characterized by high complexity, temporal pressure, distributed coordination and dy- namic uncertainty. Whether managing an ongoing cyberattack, developing complex software systems, or engaging in real-time tactical gameplay, the ability of teams to operate effec- tively is a key determinant of success. As a result, a growing body of literature has emerged that investigates the nature of team performance, the structural and interpersonal variables that influence it and the theoretical models that seek to explain and enhance it [1, 2, 3]. However, while team effectiveness has been studied extensively in traditional organizational, military and healthcare contexts, questions remain about the extent to which these insights, rooted in co-located, synchronous, and hierarchically stable environments can be applied to cyber-relevant domains where communication itself is technologically transformed. The concept of team effectiveness has traditionally been grounded in frameworks developed in psychology and management studies. Pioneering work by [4, 5, 6] introduced structured models that frame team performance in terms of stages of development, enabling conditions, and input-mediator-output cycles. More recent elaborations such as the "Big Five" model of teamwork [7], focus on behavioral and cognitive competencies including communication, leadership, mutual performance monitoring, adaptability and trust. These models have be- come foundational in evaluating and improving team performance in co-located, hierarchical, and relatively stable environments. The defining characteristics of cyber-relevant teams challenge many of the assumptions underlying these models. Cyber teams are collaborative groups that perform technology- intensive tasks, requiring specialized digital competencies, distributed coordination through electronic systems and rapid information processing in dynamic environments [6, 8]. The [9] concept of "social physics"—the quantitative study of idea flow through behavioral sens- ing—reveals that in technology-mediated environments, communication patterns become simultaneously more traceable and more complex. Digital breadcrumbs from version control systems, incident response logs, and gameplay telemetry enable unprecedented visibility into team interaction dynamics. The same technological mediation introduces what [9] terms "en- gagement" challenges: maintaining the repeated exchanges, cooperation and trust-building that [10] identified as essential, now across distributed, asynchronous, and often anonymous contexts. This review focuses on three specific cyber domains that exemplify these characteristics while presenting distinct operational contexts. Cybersecurity teams often comprise highly skilled professionals operating in high-stress, high-stakes environments characterized by ad- versarial threats, uncertainty and the need for rapid incident response. Software engineer- ing teams, in contrast, typically operate in distributed configurations with asynchronous coordination patterns, employing iterative development methodologies such as Agile and DevOps over extended project timelines. E-gaming teams function under intense temporal constraints, executing complex strategies with precision, synchronization, an‌d real-time role adaptation. These three domains were selected because they represent distinct yet com- plementary facets of cyber teamwork, each emphasizing different dimensions of team effec- 2 tiveness. They were prioritized over other technology-mediated contexts (e.g., IT support, digital marketing, general remote work teams) because they involve inherently technical, task-intensive team activities with substantial research bases examining team dynamics and performance. However, as our systematic search revealed, the empirical literature is heavily skewed toward software engineering (n = 114 studies), with cybersecurity (n = 30) and, in particular, e-gaming (n = 4) substantially underrepresented. This imbalance limits the generalizability of cross-domain comparisons and reflects broader gaps in teamwork research outside organizational software development contexts. Across these settings, task environ- ments differ significantly from those in which many classical team models were initially developed and validated. Therefore, there is an open question regarding whether existing models adequately capture the dynamics of teamwork in such domains, or whether adapta- tions are necessary to account for technological, contextual and organizational variations. Prior research has begun to explore these challenges, but a comprehensive synthesis is lacking. For example, [11] reviewed the foundational constructs in team science, focusing primarily on conventional work teams. Also, [12] conducted a meta-analysis on shared cognition and team performance, again drawing primarily from healthcare, military, and corporate settings. Some of the authors have called for greater consideration of dynamic and multilevel models to understand teams in complex environments, yet empirical evidence from cyber domains remains sparse [8]. Recent studies such as [13, 14, 15] showed early efforts to adapt model-based team formation and behavioral analysis in cybersecurity and global IT teams, respectively, but broader theoretical alignment is lacking. In the cybersecurity domain, several studies have reported attempts to tailor team models to adversarial and time-sensitive operations. For example, [14] proposed a team formation framework based on historical performance indicators integrating domain-specific constraints such as skill complementarity and mission-critical task timelines. Other cybersecurity-focused works such as those by [16, 17] emphasized the impact of trust, coordination and temporal complexity on distributed team performance. These studies highlight the pressing need to reframe traditional models to accommodate operational volatility. Within software engineering, the literature is particularly rich in studies applying or indirectly reflecting on team effectiveness concepts. For instance, [18] employed decision optimization models for human resource allocation, drawing on team competencies and role fit (key constructs in Hackman’s model). Additionally, [15] explored role clarity and decision authority in Agile teams, revealing structural ambiguities that complicate leadership and communication (key constructs in Hackman’s model). Also, [19] conducted a review of Agile frameworks, raising concerns about the lack of formal behavioral modeling in such method- ologies despite their impact on psychological safety and adaptability. E-gaming, while less represented, contributes unique perspectives on teamwork under real-time, competitive con- ditions. In [17], they documented how fast-paced distributed team structures affect coordina- tion and mental model alignment. Although not framed explicitly through classical models the findings resonate with IMOI’s emphasis on emergent states and dynamic feedback loops. These insights suggest that certain constructs, particularly shared mental models, communi- cation bandwidth, and real-time adaptability—may be more salient in gaming contexts than in traditional organizational teams. This systematic review aims to address this gap by critically examining the extent to which formal models of team effectiveness have been applied, tested or adapted in cyber- 3 relevant domains. Specifically, it examines the applicability of models such as IMOI, the Big Five, Hackman’s normative framework and Tuckman’s developmental sequence within the fields of cybersecurity, software engineering, and e-gaming. In doing so, it assesses not only model usage but also the domain-specific constraints such as distributed work, high task complexity, adversarial environments and temporal urgency that may influence their relevance or require modification. This inquiry builds on a lineage of research into commu- nication structure and collective intelligence. Where [10] quantified how network topology affects problem-solving in small groups, and [9] extended this to "idea flow" in organizations and societies, this review examines whether these structural insights translate to domains where the communication medium itself (digital tools), the task structure (adversarial, iter- ative, competitive), and temporal dynamics (real-time, asynchronous, crisis-driven) diverge from traditional settings. Resea‌rch on collective intelligence offers a perspective b‌y empha- sizing the emerg‌ent capa‌biliti‌es of teams, such as adaptive problem-s‌olving and collaborative decision-making under dynamic condit‌ions [20]. This systematic review examines both es- tablished and emergent models of team effectiveness, including collective intelligence, to assess their applicability across cybersecurity, software engineering and e-gaming domains. The research question guiding this review is as follows: To what extent are team effectiveness models applicable to cyber domains (cy- bersecurity, e-gaming, software engineering), and what domain-specific factors constrain or support their adaptation? This review contributes to the literature in four principal ways. First, it synthesizes existing studies to determine the prevalence and nature of model usage across domains. Sec- ond, it analyzes which team factors (e.g., communication, coordination, leadership, trust, shared cognition) are most frequently studied and how they map onto formal models. Third, it examines methodological trends in the empirical investigation of team effectiveness in cyber domains, identifying limitations and strengths in the current evidence base. Ulti- mately, it highlights conceptual and empirical gaps, providing directions for future research and offering practical implications for team training, design, and evaluation in cyber-relevant contexts. The review contributes theoretically in twofold: (1) a systematic cross-domain syn- thesis that reveals the limited direct application of established models across cyber domains, and (2) a novel cascading influence network model Figure 4 that reframes team effective- ness as emerging from multi-layered factor interactions rather than independent pathways. This hybrid architecture, a direct outcome of our cross-domain analysis integrates insights from 137 studies to provide both domain-specific understanding and generalizable principles for technology-mediated teamwork. To achieve these objectives, the review adheres to the PRISMA guidelines for systematic reviews and employs a protocol-based methodology that ensures transparency, replicability and analytical rigor. Following a comprehensive search strategy across multiple databases, 137 primary studies were selected based on predefined inclusion and exclusion criteria. These studies were subjected to thematic coding to ex- tract data related to team models, domains, factors, methods, and performance outcomes. The results are presented thematically, with an emphasis on cross-domain synthesis and identification of adaptation patterns. In line with the call for greater contextualization in team science [8], this review is grounded in the recognition that cyber-relevant domains exhibit distinct ecological, techno- 4 logical and social dynamics. For example, the volatility and adversarial nature of cyber- security operations may privilege rapid sensemaking and distributed leadership structures, which traditional input-output models do not adequately capture. Similarly, the use of Agile methodologies and DevOps workflows in software engineering introduces novel team configu- rations and iterative cycles that complicate linear stage-based frameworks. E-gaming teams, often composed of self-organized players, rely on highly attuned coordination mechanisms and shared mental models developed through intensive interaction and feedback. These domain-specific characteristics necessitate a careful evaluation of the assumptions embedded in traditional models. These challenges echo [10] experimental finding that operational pat- terns emerge from the interaction between task requirements and structural constraints. In his "five squares puzzle," teams with centralized communication could form partial solutions quickly but struggled to restructure when those solutions proved suboptimal—a phenomenon directly analogous to cybersecurity teams’ difficulty in pivoting during incident response, or Agile teams’ challenge of abandoning technical debt. The "insight occurrence versus insight utilization" distinction that [10] identified where decentralized networks are more likely to recognize and propagate novel solutions, suggests that cyber domains’ emphasis on innova- tion and adaptation may require structural properties fundamentally different from those that optimize routine coordination. The review also draws on organizational and network perspectives to interpret observed adaptation mechanisms. Network-centric leadership and distributed command concepts, exemplified in the Team-of-Teams literature help explain how dynamic delegation and in- formation sharing can replace rigid hierarchies in crisis contexts [21]. Sensemaking theory provides an account of how teams construct shared situational awareness under uncertainty [22, 23, 24]. Complementary network sociology concepts—Burt’s structural holes, Granovet- ter’s strength of weak ties and empirically informed models of inclusion/exclusion, illumi- nate how brokerage, cohesion, and peripheral ties shape information diffusion and innovation adoption in technical teams [25, 26, 27]. The open-source "bazaar" model of innovation of- fers a helpful analogy for guided self-organization and distributed problem-solving in software and cyber teams [28]. Hackman’s framework for instance, assumes clear task boundaries and stable membership whichmaynotholdinfluidcybersecurityenvironments. TheIMOImodelpresupposesalevel of input predictability and mediator stability that is often absent in adversarial simulations or red/blue team exercises. Likewise, the Big Five model, while behaviorally comprehensive may lack granularity in explaining high-speed, non-verbal coordination prevalent in e-gaming scenarios. Through its structured synthesis, this review identifies both the applicability and

of these models offering insight into when, how, and why specific models succeed or fail across domains. It also documents instances of model adaptation, hybridization, or theoretical innovation, reflecting the ongoing evolution of team science in response to emergent demands. The ultimate goal is to advance a more context-sensitive understanding of team effectiveness that accounts for the operational realities of cybersecurity, e-gaming and software engineering teams. The remainder of the paper is structured as follows. Section 2 presents the systematic literature review of existing studies on team effectiveness models and their application in cyber-relevant domains. Section 3 details the methodology employed in conducting this sys- tematic review, including the search strategy, inclusion criteria and data analysis procedures. 5 Section 4 reports the results of the thematic synthesis, highlighting key patterns in model usage, team factors studied and methodological approaches across domains. Section 5 in- troduces a theoretical model that indicates the correlation between team factors. Section 6 discusses the implications of the findings and examines their relevance to theory and practice. Section 7 identifies directions for future research and concludes the paper by summarizing the main insights and contributions of this systematic review. 2. Literature Review 2.1. Overview of Teams Effectiveness Research The concept of team effectiveness has evolved through various disciplinary lenses in- cluding Organizational psychology, management science, systems engineering and human- computer interaction. Foundational theoretical models, such as Hackman’s normative model of team performance [5], Tuckman’s stages of group development [4] and the Input-Mediator- Output-Input (IMOI) framework [6] have shaped the scholarly understanding of team dy- namics across diverse domains. These frameworks emphasize antecedent conditions (inputs), dynamic interaction processes (mediators) and outcome measures such as performance, satis- faction, orinnovation(outputs), formingthebasisforanalyticalevaluationsofteamfunction- ality. Moreover, [7] "Big Five" model of teamwork introduces critical constructs, including team leadership, mutual performance monitoring, backup behavior, adaptability, and team orientation [29], which are essential for understanding the cognitive and behavioral aspects of collaboration. Recent research on collective intelligence (CI) has introduced a complementary perspec- tive to classical models of team effectiveness. Defined as a group’s general ability to perform a wide variety of tasks, CI emphasizes emergent group properties such as social sensitivity, equal participation, and collaborative problem-solving capacity [20]. The paper [20] have demonstrated that CI can predict group performance across task domains more effectively than average individual intelligence. In cyber-relevant domains where distributed coordi- nation and rapid adaptation are critical, CI provides a framework for understanding how team-level emergent behaviors influence outcomes under dynamic conditions. While these models have shown consistent utility in traditional, co-located teams op- erating under stable task conditions, their transferability to dynamic, high-pressure, and technologically complex cyber domains remains underexplored. In particular, cybersecurity teams often operate in distributed configurations, respond to adversarial threats in real-time, and engage in activities that require rapid decision-making under uncertainty, contexts that challenge the assumptions embedded in classical team models. Similarly, software engineer- ing and e-gaming teams employ non-traditional workflows that necessitate asynchronous coordination, technical expertise, and ongoing role negotiation, which are often inadequately represented in legacy models. 2.2. Previous Reviews and Need for This Study Despite the foundational contributions of earlier syntheses, the existing body of literature reveals a gap in systematic efforts to evaluate team effectiveness models within cyber-relevant domains. Reviews by [11, 8] provide rich conceptual overviews of team constructs, but these works primarily address conventional workplace teams in organizational settings. In [12],the 6 authors further extended these insights through meta-analytic techniques but remained fo- cused on traditional domains such as healthcare, aviation, and business. In contrast, cyber domains, comprising cybersecurity operations centers, e-gaming teams, and software devel- opment units; present unique constraints, including the need for distributed coordination, real-time threat response, and the pressure of adversarial activity. These domains increasingly rely on team-based structures but often lack empirical eval- uations of team effectiveness grounded in established models. For example [14], devel- oped a performance-based team formation framework in cybersecurity settings, focusing on skill complementarity and previous performance history rather than classical developmental stages. In [30], the authors analyzed global virtual IT teams, revealing the significance of communication, trust and leadership, but without aligning the findings to a specific formal model. Few reviews have interrogated whether classical frameworks are robust to the com- plexity and volatility of cyber team contexts. This SLR is therefore justified by the need for a focused synthesis that evaluates the adaptation, extension, or limitations of formal team effectiveness models when applied to cyber-relevant operational environments. Research on collective intelligence also presents promising avenues for understanding team performance in these domains. Although not extensively tested in cyber settings, pre- liminary evidence suggests that CI may play a crucial role in distributed team coordination and adaptive task performance [20]. Its emphasis on emergent team properties aligns with the operational realities of cyber-relevant teams, where adaptability, shared cognition, and collaborative problem-solving are paramount. 2.3. Domains of Application and Theoretical Grounding The IMOI framework [6] appeared in 11 studies, reflecting its emphasis on dynamic, iterative performance. The Big Five model [7] was used in 45 papers, often in training or simulation contexts. Hackman’s normative model [5] was cited in 20 studies, particu- larly in software engineering, where role clarity and structural features were key. Details of studies distribution across team model can be found in Figure A.2. Many other studies used context-specific constructs or hybrid frameworks derived from practice, underscoring a lack of standardization in theoretical application. Despite this diversity, only a fraction of the studies employed a formalized team effectiveness model. For instance [14], utilized a performance-informed framework rather than citing a classical behavioral model. In ad- dition, [18] developed a decision model for optimizing human resource allocation based on competency-task fit, reflecting core principles in Hackman’s approach. Authors [17, 16] ex- plored coordination challenges and trust development in distributed Agile teams focusing on factors aligned with the IMOI model but without formal reference to it. While explicit application of collective intelligence theory remains limited, the growing interest in emergent team properties across these studies indicates its potential relevance. Table 1 compares four prominent team effectiveness models by summarizing their core dimensions and relevance to cyber-relevant domains. The models vary in their emphasis on structure, adaptability, iterative processes, and emergent capabilities which influence their suitability across contexts such as cybersecurity operations, software engineering, and e-gaming. 7 Model Core Dimensions Applicability to Cyber Domains Hackman’s Model Team boundaries, en- abling structure, sup- portive context Relevant in structured environments like software engineering; limited in fluid, dy- namic teams (e.g., Capture-The-Flag) Salas’ Big Five Leadership, mutual mon- itoring, backup behavior, adaptability, team orien- tation Critical for high-pressure environments suchascyberdefenseande-gaming, where adaptability is paramount IMOI Model Inputs, Mediators, Out- puts, iterative processes Supports dynamic, iterative teams; suit- able for Agile and DevOps teams Collective Intel- ligence Emergent capabilities, distributed cognition Useful in distributed and self-organized teams, such as e-gaming groups Table 1: Summary of Team Effectiveness Models and Applicability to Cyber Domains 2.4. Gaps and Motivation for This Review Despite the breadth of studies three gaps emerged. First, existing studies lack com- parative assessments that test whether team effectiveness models developed in conventional domains are transferable to cyber-relevant contexts. Second, most empirical efforts focus on isolated team constructs rather than examining integrative frameworks capable of modeling complex interactions across variables. Third, reliance on simulations and short-term task observations constrains ecological validity obscuring team development processes observ- able only in operational or longitudinal settings. By systematically synthesizing theoretical models, team factors, methodological approaches, and domain-specific findings, this review provides an integrated understanding of team effectiveness in cyber domains. It also offers a foundation for future model adaptation, validation and practical implementation within cybersecurity, e-gaming, and software engineering teams. Figure A.2, Figure A.3, Figure A.4, and Figure A.5 in the appendix represent the visualization of the frequency of stud- ies across formal team models, the frequency of studies across domains, the distribution of team factors studied across domains, the unified occurrence of team factors across reviewed papers, and the number of papers published per year, respectively. 3. METHODOLOGY 3.1. Protocol Development This study conducted a systematic literature review (SLR) on cyber team performance followingthePRISMA(PreferredReportingItemsforSystematicReviewsandMeta-Analyses) guidelines. The PRISMA framework was selected to ensure methodological transparency, replicability, and comprehensive coverage of the literature. To further ensure rigor and min- imize bias, a detailed review protocol was developed before the commencement of the review process. The protocol outlined every step of the methodology including the specific databases and search strategies for identifying relevant research, precise criteria for screening and se- lecting studies, methods for data extraction, and approaches for synthesizing the findings. The review process followed the four standard phases of PRISMA: identification, screening, 8 eligibility, and inclusion, in adherence to this predefined protocol. Establishing and following this comprehensive protocol aimed to enhance the robustness and reliability of the findings. 3.2. Search Strategy The search strategy was conducted to ensure coverage of relevant literature in the cy- bersecurity domain. The process was iterative and guided by established systematic review protocols incorporating computational tools and human judgment to optimize recall and pre- cision. To begin, a preliminary set of keywords and Boolean strings was constructed based on the research question, prior literature and terminology used in foundational models rele- vant to team effectiveness in cyber-related domains. These initial terms were refined through exploratory scoping searches and consultation with subject-matter experts in cybersecurity and organizational behavior. The final search query combined controlled vocabulary and natural language keywords as detailed in Table 2. It was applied across ACM Digital Library, IEEE Xplore and Scopus Digital Library. These databases were selected for their comprehensive coverage of relevant peer-reviewed articles and the recognition that different databases index different sets of journals and conferences leading to varying coverage within the same research domain. The search query was adapted to meet the technical requirements of each platform. An advanced query logic was applied, utilizing nested Boolean operators, wildcard characters, and phrase searching to strike a balance between sensitivity and specificity. For example, combinations of terms such as ("team" AND "performance" AND ("cy- bersecurity" OR "software engineering" OR "e-gaming"))* were cross-referenced with terms relatingtoeffectivenessmodels(e.g., "input-process-output,""sharedmentalmodels,""team cognition"). During the search execution, semi-automated tools were employed to assist in deduplication, metadata extraction and keyword mapping. These tools were configured and verified manually to ensure they aligned with the inclusion criteria. Human oversight was maintained throughout, particularly during the screening, validation of search results, and refinement of query terms based on initial retrieval trends. The search period was limited to studies published between 2015 and 2025 and the search string was applied on March 2025. Following the execution of these queries, the initial results comprised 527 publications from ACM Digital Library, 12,529 publications from Scopus Digital Library, and 307 from IEEE Xplore, totaling 13,363 publications, as detailed in Table 3. A total of 120 duplicates were identified and removed using Microsoft Excel and Rayyan, a web-based platform de- signed to streamline the systematic review process as indicated in Appendix A, Figure A.1 [31]. Subsequently, 13,050 publications were excluded after initial screening of titles, ab- stracts, and keywords, as they did not meet the predefined inclusion criteria. A total of 193 publications underwent full-text assessment for eligibility, and 24 publications were removed due to the inaccessible full-text, resulting in 137 studies included in this review. 9 Digital Li- brary Query IEEE Xplore ((( "cybersecurity" OR "cyber" OR "cyber competitions" OR "competition" ) AND ( "team" ) AND ( "success factors" OR "performance" OR "effectiveness" ) AND ( "team role" OR "teamwork" OR "team formation" OR "team strategies" OR "team modeling" OR "management teams" OR "team management" ) AND ( "model*" OR "framework*" OR "transfer*" OR "adapt*" OR "applicability" OR "compar*" OR "cross-domain" )) AND NOT ( "classroom" OR "education" OR "teaching" OR "students" )) OR (( "military team" OR "e-gaming" OR "sports" OR "soft- ware engineering" OR "software" ) AND ( "team" ) AND ( "success factors" OR "performance" OR "effectiveness" ) AND ( "team role" OR "teamwork" OR "team formation" OR "team strategies" OR "team modeling" OR "management teams" OR "team management" ) AND ( "model*" OR "framework*" OR "transfer*" OR "adapt*" OR "applicability" OR "compar*" OR "cross-domain" ) AND NOT ( "classroom" OR "education" OR "teaching" OR "students" )) ACM [[[All: "cybersecurity"] OR [All: "cyber"] OR [All: "cyber competitions"] OR [All: "competition"]] AND [All: "team"] AND [[All: "success factors"] OR [All: "perfor- mance"] OR [All: "effectiveness"]] AND [[All: "team role"] OR [All: "teamwork"] OR [All: "team formation"] OR [All: "team strategies"] OR [All: "team model- ing"] OR [All: "management teams"] OR [All: "team management"]] AND [[All: "model*"] OR [All: "framework*"] OR [All: "transfer*"] OR [All: "adapt*"] OR [All: "applicability"] OR [All: "compar*"] OR [All: "cross-domain"]] AND NOT [[All: "classroom"] OR [All: "education"] OR [All: "teaching"] OR [All: "stu- dents"]]] OR [[[All: "military team"] OR [All: "e-gaming"] OR [All: "sports"] OR [All: "software engineering"] OR [All: "software"]] AND [All: "team"] AND [[All: "success factors"] OR [All: "performance"] OR [All: "effectiveness"]] AND [[All: "team role"] OR [All: "teamwork"] OR [All: "team formation"] OR [All: "team strategies"] OR [All: "team modeling"] OR [All: "management teams"] OR [All: "team management"]] AND [[All: "model*"] OR [All: "framework*"] OR [All: "transfer*"] OR [All: "adapt*"] OR [All: "applicability"] OR [All: "compar*"] OR [All: "cross-domain"]] AND NOT [[All: "classroom"] OR [All: "education"] OR [All: "teaching"] OR [All: "students"]]] SCOPUS TITLE-ABS-KEY((("cybersecurity" OR "cyber" OR "cyber competitions" OR "competition") AND ("team") AND ("success factors" OR "performance" OR "ef- fectiveness") AND ("team role" OR "teamwork" OR "team formation" OR "team strategies"OR"teammodeling"OR"managementteams"OR"teammanagement") AND ("model*" OR "framework*" OR "transfer*" OR "adapt*" OR "applicability" OR "compar*" OR "cross-domain")) AND NOT ("classroom" OR "education" OR "teaching" OR "students")) OR (("military team" OR "e-gaming" OR "sports" OR "software engineering" OR "software") AND ("team") AND ("success factors" OR "performance" OR "effectiveness") AND ("team role" OR "teamwork" OR "team formation" OR "team strategies" OR "team modeling" OR "management teams" OR "team management") AND ("model*" OR "framework*" OR "transfer*" OR "adapt*"OR"applicability"OR"compar*"OR"cross-domain")ANDNOT("class- room" OR "education" OR "teaching" OR "students")) Table 2: Search Queries 10 Digital Library Number of Publications IEEE Xplore 307 ACM DL 527 Scopus DL 12,529 Table 3: Number of Publications Retrieved from Digital Libraries 3.3. Study Selection In ensuring methodological consistency clear eligibility criteria were established for se- lecting relevant studies. The initial screening involved evaluating each paper’s title and

to determine its relevance. The review targeted peer-reviewed journal articles and conference proceedings published in English and within a specific time frame. Papers that satisfied the predefined inclusion criteria, as detailed in Table 4, were retained for full-text analysis. Studies that failed to meet these criteria were excluded, following the exclusion criteria also presented in Table 4. Inclusion Criteria Exclusion Criteria Focused on team-based environments within cyber domains (e.g., cybersecurity, software engineering, e-gaming). Focused solely on individual-level train- ing or skills without assessing team inter- action, even if conducted in conventional domains (e.g., sports, military, manage- ment). Examined determinants of team suc- cess such as communication, coordination, leadership, trust, shared mental models, team cognition, or adaptability. Discussed tools, platforms, or systems without analyzing human or team collab- oration. Analyzed or modeled team dynamics using empirical methods (quantitative or qual- itative) or formalized conceptual frame- works. Did not fall within the specified domains or lacked relevance to team performance (e.g., healthcare, education, or unrelated technical domains). Provided findings related to team perfor- mance or effectiveness in operational, sim- ulated, or competitive contexts. Thepaperswerepurelyconceptualorspec- ulative, lacking empirical data or grounded theoretical models. Published in peer-reviewed journals or conference proceedings between 2015 and 2025, with full text available in English. Papers not published in English, were un- available in full text, or were published outside the 2015–2025 window. Table 4: Inclusion and Exclusion Criteria The detailed breakdown of retrieved and selected publications is presented in a PRISMA flow diagram in Figure 1, illustrating the selection process. 11 Figure 1: PRISMA - Flow Diagram 3.4. Data Extraction Followinganinitialcalibrationphase, theresearchteamdevelopedastructuredextraction form capturing key attributes of each study, including publication details, domain, method- ological approach, theoretical models applied, team factors investigated, and key findings. Among the theoretical models, we included both classical team effectiveness frameworks; such as the Input-Mediator-Output-Input (IMOI) model [6], Hackman’s normative model [5], the "Big Five" model of teamwork [7] and emerging constructs like collective intelligence (CI) [20]. Studies were coded for CI if they explicitly referenced the concept or if their anal- ysis emphasized emergent team effectiveness capabilities such as adaptive problem-solving, distributed cognition, or collaborative intelligence in dynamic environments. To enhance consistency in coding and reduce subjective bias, an initial trial coding of a representative sample of studies was conducted to refine the extraction form and coding scheme. This pro- cess enabled the adjustment of thematic categories and the clarification of coding rules before application to the full dataset. To mitigate single-coder bias we implemented some quality control steps. A calibration exercise coded a random 10% subsample (n = 14) to refine the extraction form and resolve ambiguities. Ambiguous cases were flagged and re-examined af- 12 ter the full extraction pass, and all coding decisions were documented in Rayyan to provide an audit trail. These measures reduce subjective variance, but the absence of formal inter- rater reliability testing is acknowledged as a limitation. All studies were screened, organized, and coded using Rayyan, a web-based tool designed for systematic reviews, which supported structured screening and decision tracking. Extracted data were exported to Microsoft Excel for systematic analysis, thematic categorization, and visualization of key patterns related to team effectiveness models, team factors, and domain-specific applications. 3.4.1. Handling measurement heterogeneity: Team effectiveness was operationalized heterogeneously across studies. To preserve eco- logical validity we adopted a dimensional coding approach, classifying each study according to the primary effectiveness dimension measured. • Speed/Temporal: Time-to-detect, mean time to respon‌d and cycle time • Quality/Accuracy: Defect density, false positive/negative rates and analysis‌ com- pleteness • Innovation/Adaptation: Feature velocity, p‌ivot success and adaptive solutions‌ • Subjective Experience: Member satisfaction, psychological safety and retention. Analyses reported in Section 4 summarize patterns by these dimensions rather than aggregating into a single omnibus performance score, thereby highlighting trade-offs across effectiveness types. A methodologi‌cal challenge emerged during data e‌xtraction: the operationalization of "team effectiveness" v‌ar‌ied d‌ramatically across domains, complicating comparative synthe- sis. In cybersecurity‌ studies, effectiveness was predominantly measured through • Temporal metrics:Mean time to detect (MTTD), mean time to respond (MTTR), incident containment speed • Accuracy metrics: False positive rates in threat detection, comp‌leteness of forensic analysis • Prevention metrics: Number of vulnerabilities remediated, successful attack simu- lations defended Software en‌gineering stu‌dies operationalized e‌ff‌ectiveness thr‌oug‌h • Delivery metrics:Sprint velocity, release frequency and cycle time • Quality metrics:D‌efect densi‌ty, code review thoroughness and technical debt reduc- tion • Adaptation metrics: Requirements volatility accommoda‌tion and pivot success in Agile retrosp‌ecti‌ves E-gaming research measured effectivene‌ss via 13 • Competitive outcomes: Win-loss records, tournament ranki‌ngs an‌d ELO ratings • Executio‌n metrics:Actions pe‌r minute (APM), objective completion rates and re- source efficiency • Coordination metrics: Communication density, synchronized ability‌ usage and po- sitioning entropy The Elo rating system is a method for quantifying relative player performance based on match outcomes where rating changes depend on the expected result derived from the play- ers’ current ratings [32]. This measurement heterogeneity has profound theoretical impli- cations. Factors that predict rapid inc‌ident response (cybersecurity effe‌ctiveness) may ac- tively undermine technical debt management (software engineering effectiveness) if speed is achieved through shortcuts‌. Factors enabling high APM execution (e-gaming effectiveness) may not translate to patient, methodical code review (software engineering effectiveness). The challenge was addressed by coding studies based on theeffectiveness dimensionthey op- erationalized (speed, quality, innovation, satisfaction) rather than imposing a unified metric. Table A.1 preserves this granularity by distinguishing "Performance" (domain-specific task outcomes), "Outcomes/Productivity/Success" (project-level achievements), and "Member Satisfaction" (subjective experience). This tripartite structure acknowledges that teams can be simultaneously high-performing (with fast incident response), unsuccessful (due to high analyst turnover resulting from burnout), and satisfying to members (with strong social co- hesion despite organizational failure). Fut‌ure meta-analyses should resist the temptation to agg‌regate heterogeneous effectiveness metrics into omnibus "performance" scores. Instead, configural approaches that map factor profiles to specific effectiveness dimensions may better capture the trade-offs teams na‌vigate in practice. 3.5. Thematic Analysis Thematic synthesis was employed to identify, categorize, and interpret patterns emerg- ing from the extracted data. An inductive coding strategy guided the analysis, allowing themes to emerge from the data rather than imposing predefined categories. Codes were assigned based on recurring concepts related to team effectiveness models, key team factors (e.g., communication, leadership, trust, adaptability), and contextual influences specific to each domain. Following initial coding, related codes were grouped into higher-order themes representing theoretical frameworks, methodological trends, and domain-specific constraints or enablers. This process facilitated the synthesis of findings across heterogeneous studies, ensuring that both explicitly stated results and implicit insights were systematically cap- tured. Patterns and themes identified during this synthesis were organized to align with the research question, emphasizing the applicability of team effectiveness models to cyber- security, e-gaming, and software engineering domains. The thematic analysis also served as the foundation for interpreting methodological gaps and highlighting areas requiring further empirical exploration. 14 4. RESULTS 4.1. Overview of Findings This systematic literature review reviewed 137 primary studies (PS) to assess the applica- bilityof team effectiveness models incyberdomains. Our inquiryinto thistopic fell intothree areas: cybersecurity (30 studies in total), software engineering (114 studies), and e-gaming (4 studies). The cybersecurity studies mainly focused on teams in Security Operation Centers (SOCs), teams where participants engaged in Capture-the-Flag (CTF) exercises, and teams respondingtosimulatedincidents. Incontrast, thesoftwareengineeringstudieswerecentered on Agile teams, DevOps, and teams working in distributed environments. In comparison, e- gaming teams were predominantly focused on professional multiplayer teams engaging with time critical decision-making tasks under cognitive load. For analytic clarity we concep- tually clustered studies into three scenario groups—defensive/adversarial (cybersecurity), iterative/productive (software engineering), and competitive/real-time (e-gaming)—and re- port cross-scenario patterns that respect these distinctions. The publication trend displayed in Figure A.5 in the appendix demonstrates a consistent increase in research interest over the last decade for these aforementioned areas. Across 137 studies (software engineering, n=114; cybersecurity, n=30; e-gaming, n=4), three consistent patterns emerged: (1) core team processes—communication, coordination and leadership—are the most robust predictors of performance across domains; (2) domain factors (distribution, temporal urgency, adversariality) mediate and sometimes reverse stan- dard model relationships; and (3) researchers frequently adapt or hybridize classical models rather than apply them intact. These patterns suggest that transferable theory is possible, but only if models explicitly encode domain contingencies and emergent mediator roles. Our results demonstrate that existing team effectiveness models are primarily applicable in cyber domains, provided that those models are adapted to fit domain-related elements. This literature review provides us with the following key takeaways: Organizational and leadership elements are necessary for team autonomy and successful agile transformations. Team composition, dynamics, and communication were central to performance. Trust and shared mental models were of utmost importance, particularly in distributed environments. Hybridmodelsandspecializedtoolsarebeingadoptedmoreandmorewidelytofittheunique needs associated with complex tasks in these domains. The main challenges of utilizing these models include resistance of the organization, undefined comprehension of agile principles, and the difficulties of distributed coordination. The results are organized around these themes to provide a more detailed explanation of how team effectiveness models can be adapted for the unique needs of cyber domains. 4.2. Thematic Findings 4.2.1. Organizational and Leadership Factors, Team Composition, and Dynamics: The literature consistently shows that organizational and leadership factors heavily in- fluence team effectiveness in cyber domains. At the foundational level leadership style and organizational culture significantly influence team performance and the success of agile trans- formations. Thisisconsistentwithresearcherswhoargue, thatleadershipstyleisasignificant predictor for the decision-making structure of a project and is directly related to a project’s 15 success. For example, a study that included 433 IT project participants [3] found that lead- ership style is a strong predictor of a project’s decision-making structure, which in turn directly affects project success. Similarly, a survey of 245 practitioners by [33] found that leadership styles like transactional, transformational, and empowering leadership positively relate to team performance regardless of project methodology, underscoring the importance of investing in leadership development. Organizational culture also plays a pivotal role. A systematic review by [34] highlighted the effects of hierarchical cultures on agile projects, in contrasttogroup, rationalanddevelopmentalculturesthatpositivelyinfluencethemthrough empowerment and innovation. For example, [35] revealed that bottom-up community for- mation can help balance organizational control with team autonomy in large-scale software organizations. Management support is also important; in the studies of [36, 37], manage- ment resistance and lack of management support were identified as reasons for project failure and can have a significant impact on teamwork empowerment. To counter this management needs to set targets in combination with allowing the team to still be autonomous, a result from a longitudinal study conducted by [38]. On a more granular level, team composition and internal dynamics are key drivers of performance. Research shows that a team’s effectiveness depends on its structure, the per- sonalities of its members and their interactions. For example, a recommendation system by [39] successfully used social network analysis to form software project teams based on technical and non-technical skills, leading to increased productivity. In cybersecurity and e- gaming where strategic team formation is essential, research conducted by [14, 40] has shown that forming teams based on collaborative scores and predefined requirements can maximize performance. Complex dynamics govern the internal workings of teams. Communication and shared knowledge are paramount. A study using a system dynamics approach by [37] identified team management, motivation and customer satisfaction as key factors affecting agile teamwork productivity. Other studies, such as a qualitative analysis by [41], revealed that task interdependence increases the need for information sharing, which in turn enhances team performance. In virtual teams, trustworthiness emerged as a significant factor that in- fluence success followed by interpersonal communication and commitment [30]. However, a qualitative study by [42] noted that remote work can lead to a loss of spontaneous knowl- edge exchange and socialization. Team composition and dynamics also influence a team’s ability to adapt and perform under pressure. Studies in e-gaming and project management found that task dependence and cooperative behavior promote helping, while increased team size can sometimes inhibit it [43]. Research also shows that teams naturally balance their roles over time, and managers can influence this process [44]. Furthermore, a team’s emo- tional state, or "group affective tone," is positively related to performance [45], with effects strengthening over the course of a project. Overall, the literature confirms that understand- ing both the macro-level organizational context and the micro-level internal dynamics is essential for building effective teams in cyber domains. 4.2.2. Distributed/Virtual Teams, Communication, and Coordination Challenges: This theme addresses the unique complexities of managing and ensuring effectiveness within teams that are geographically or culturally dispersed, heavily relying on communi- cation and coordination. An early contribution, [1], a case study, found that a manager’s remote management style significantly influences a dispersed team’s perceived proximity and 16 cohesion, emphasizing the importance of interpersonal relationships and a balance of distant and face-to-face exchanges. [2] proposed a new virtual project management framework that uses standard documentation and change management to improve project outcomes in global software development teams, aiming to minimize issues related to trust, communication, and cultural barriers. The specific challenges of global teams were further explored by [16], which used a system dynamics simulation to determine the optimal onsite-offshore team strength in global software development to reduce project cost and improve the schedule. [46], a system- atic literature review, identified 48 distinct practices for adopting agile methods in a global software development context, providing a framework for managing distributed teams. [47] empirically found that involving the client in the testing process of a distributed team, using agile testing principles, significantly improved team productivity compared to traditional methods. The broader context of virtual teams was addressed by [48], which proposed and validated a conceptual framework showing that knowledge sharing mediates the relationship between individual, team, organizational, and technological factors and virtual team performance. [30] developed a model for high-performing virtual teams, revealing that trustworthiness is the most significant factor influencing success followed by interpersonal communication and commitment. In 2023, [42] qualitatively studied the challenges faced by agile teams working remotely during the COVID-19 pandemic, finding no negative impact on project deliveries but losses in socialization, spontaneous knowledge exchange, and interaction. Coordination in large-scale and hybrid environments presents its own set of challenges. [49], a case study, highlighted that coordination of knowledge work in a large-scale agile development program is achieved through various feedback-based mechanisms that evolve. [50] identified that hybrid traditional and agile development approaches often fail due to a lack of dependency awareness across teams, stemming from misaligned planning activities. Cultural aspects in distributed environments gained specific attention in [51], which em- pirically found that cultural and geographical dispersion influence communication and col- laboration problems in open-source communities, sometimes even mitigating negative phe- nomena like the "lone wolf" effect. [52], through expert interviews, identified six key chal- lenges in cross-cultural communication on IT rollout projects: language, hidden assumptions, work-life balance, confrontation, time management, and apparent lies. [53], a study on re- mote R&D teams, found that a leader’s cooperative conflict management style positively influences team psychological safety, which in turn improves team innovation performance. Further developments in managing virtual teams include [54] which confirmed that com- munication frequency and quality positively influence a team’s transaction memory system (TMS), leading to improved knowledge sharing and team creativity in virtual teams. [55] aims to develop a Virtual Team Management Challenges Mitigation Model (VTMCMM) to assist virtual teams in offshore software development by identifying and addressing their specific challenges. [56] developed and validated a process improvement framework for mon- itoring and controlling distributed teams in a global software development environment, addressing a significant challenge not explicitly covered by existing frameworks. 4.2.3. Project Success, Evaluation, and Tools/Frameworks: This theme encompasses research focused on defining, measuring, and enhancing project success through various methods, frameworks, and technological tools. [17], a systematic 17 literature review, found a poor understanding of how to manage complexity in agile software delivery, particularly at the organizational, team, and individual levels. [57], a longitudinal study, found that a team’s similar task and temporal mental models are crucial for translat- ing learning behaviors into improved performance. [58], a mixed-methods study, validated a theory that the effectiveness of Scrum teams depends on five high-level factors: respon- siveness, stakeholder concern, continuous improvement, team autonomy, and management support. In the context of specific challenges, [59] described ongoing work to integrate re- search on shared mental models with techniques for autonomous agent team formation to improve human-robot coordination in search and rescue scenarios. [60] developed a predic- tive model using agility metrics that accurately forecasts IT project outcomes, finding that teamwork, team capabilities and collective experience have the most significant impact on success. This model achieved 97% accuracy. [61] found that agile practices positively relate to project success through teamwork quality, but traditional project portfolio management practices can constrain this relationship. Technology and specialized tools are increasingly being leveraged for project manage- ment. [62] investigated an AI-based task allocation framework that significantly improved the efficiency of remote agile teams by optimizing task completion time and workload bal- ance. [63] found that providing team leaders with augmented reality feedback on team members’ stress levels enables more effective work allocation, which increases overall team performance. [64] demonstrated that adopting Behavior-Driven Development (BDD) along with a custom metrics framework significantly improves collaboration, communication, and product quality in Scrum teams. Further developments in tools and frameworks include [65], which compared traditional and agile software development, finding agile more effective in IT projects due to its fostering of teamwork and communication, addressing challenges in oral communication and confidence. [66] proposed, implemented, and validated a conceptual model for arranging office space to support Scrum teams and improve their efficiency. [67] used an optimization model to generate context-aware sprint plans, which significantly im- proved team performance by better aligning with sprint goals and delivering more business value. [68] explored how project managers customize hybrid methodologies based on team type and project goal, guiding on integrating various approaches. Specific domain applications also emerged. [69] used a case study approach to assess the state of cybersecurity practices in a critical infrastructure organization, providing a holistic picture of their posture. [70] evaluated methods for managing a small software company’s delivery team focusing on how a blend of methodologies impacts team performance and satisfaction. [71] proposed an integrated method and an efficiency index for evaluating IT projects managed with the Scrum methodology, which can increase project efficiency by improving time limits and productivity. [72], a large-scale survey, found that the choice of agile scaling strategy (e.g., SAFe, LeSS) does not have a significant influence on team effectiveness or stakeholder satisfaction, suggesting alignment with organizational culture is more important than the specific framework. The use of advanced modeling and simulation for project management is also a growing area. [73] developed a theoretical framework and software simulation to analyze and predict interaction demands within hybrid multi- human multi-robotic and autonomous systems (RAS) teams. [74] found that human-agent teams outperformed human-only teams, and that high spatial awareness helped human-agent teams develop better shared mental models. [75] found that a shared game vision acts as an 18 important cognitive foundation for effective coordination and cooperation in computer game development projects. Later, [76] evaluated three algorithms for optimizing the composition of agile software development teams based on project requirements and human resources, finding consistent performances across the algorithms. [77] explored trust in automation and team commu- nication in human-agent teaming finding that participants with lower trust in automation spoke more than their teammates with higher trust, altering typical team dynamics. [78] analyzed a cybersecurity competition and found that this immersive training experience suc- cessfully enhances teamwork and incident response capabilities. [79] used software simulation

to explore how factors like communication styles and expertise distribution affect information alignment and task performance in collaborative teams. Finally, [80]presentedamultiagent-basedsimulationmodelthatcananalyzeandoptimize knowledge practices and different knowledge structures (generalist vs. specialist) to improve a team’s work performance. [81] identified factors influencing trust in autonomous systems and proposed using simulation methodologies to address research gaps in this area. [82], a case study, demonstrated how a re-engineering project using Agile methodology positively impacted employee performance by overhauling IT systems and business workflows. [83] showed that cognitive models can be used to design and evaluate autonomous managers (AMs), with simulations indicating that simpler models are more robust for optimizing team- based workflows and improving performance through dynamic task reallocation. [84] found that collective behavior metrics based on player placement in a car-soccer video game can be used to assess team dynamics, as they correlate with team performance. [85] proposed a method to convert the Functional Resonance Analysis Method (FRAM) representation of a system into a social network, allowing for a joint analysis of functional and social connections to understand system resilience. [86], an experimental study, found that while mixed human-AI teams outperformed human-only teams, they reported significantly lower perceived team cognition. This suggests a trade-off between objective performance and subjective team experience. [87] found that as agile software development iterations progress, a team’s cognition and use of cognitive artifacts change, shifting from individual to more social interactions. 4.3. Quality and Methodological Trends The methodological analysis of the reviewed studies uncovered several dominant trends. Simulation-based research accounted for forty-one studies, particularly prevalent within the cybersecurity domain. While this preference aligns with the need for controlled environ- ments in which researchers can study team behavior under adversarial conditions without compromising operational security or ethical standards, it introduces significant methodolog- ical limitations. Simulations, by design, compress temporal dynamics, eliminate authentic stress responses, and may not capture the emergent coordination patterns that develop through sustained operational experience. The ecological validity of simulation findings is further constrained by artificial team compositions (often students rather than practition- ers), task simplification that removes contextual complexity and the absence of real-world consequences that shape risk assessment and trust development. For ex‌ample, studies such as [16, 17] examined incident response dynamics in contr‌olled settings but were unable to replicate the casc‌ading uncertainties, inter-organizational dependencies, and career stakes 19 that characterize actual cybers‌ecu‌rity operation. These limitations suggest that simulation findings should be interpreted as establishing theoretical possibilities rather than operational prescriptions, particularly regarding longitudinal team development and adaptation under sustained adversarial pressure.‌ In software engineering research, mixed-methods approaches were prominently used in thirty-nine studies. These methods typically combined telemetry data, detailed case studies and post-task debriefings to provide a varied view of team interactions and effectiveness. The work of [18] exemplifies this trend offering insights into both quantitative performance metrics and qualitative team behaviors. Empirical observational studies were widespread in e-gaming research where analyses frequently relied on performance data, gameplay analytics, and in-game communication patterns. However, these studies often lacked formal application of established team effectiveness frameworks indicating a gap between empirical observations and theoretical grounding. Despite the recognized importance of theoretical models, their use across the reviewed studies was limited. The IMOI framework, proposed by [6], and Hackman’s normative model [5] appeared in fewer than one-fifth of the studies, revealing a conceptual gap in the direct application of established models to cyber-relevant research contexts. The limited usage suggests either a lack of awareness of these models among researchers or a perceived inadequacy of the models in addressing the unique challenges presented by cyber domains. 4.4. Key Factors and Their Interconnections The systematic examination of 137 primary studies revealed some important factors as- sociated with team effectiveness across the cyber domains. Communication was the most frequently studied factor - 53 of the studies discussed communication. This increased fre- quency reflects the foundational nature of communication for coordinating tasks, making decisions, and sharing the same situational awareness when working in high-stakes contexts. Team performance was the second most common factor and appeared in 40 studies (see studies that represented both behavioral dynamics and output-theoretic performance mea- sures). Coordination (n = 23) and Leadership (n = 20), were next, indicating that these are also important factors for aligning team tasks across complex implementation domains. Coordination was, in many cases, important in both cybersecurity and software engineering considerations, and leadership was often associated with studies in domain stress situational conditions, examining team dynamics in Agile projects. Figure 2 presents details of the factors. 20 Figure 2: Unified Occurrence of Team Factors Across Reviewed Studies Other highly cited factors are Motivation and Agile practices and Adaptability, each cited in 13 studies. Across the literature, these were the common factors in the software engineering literature, corresponding with the significance of iterative development, adapted leadership methods, and agility to changing requirements in the context of modern project development situations. Team performance is also enabled by efficiency theory, productivity theory, and diversity (skills/background) theory, all of which were each moderately present across the corpus. Fewer times cited as relevant factors were Trust and Team Cohesion (n = 7), Data Analytics and Strategy (n = 6), and Decision Support Systems (n = 4). As shown in Figure 3, it is obvious that the concepts are intertwined and not stand-alone concepts. The main nodes were "performance," "teamwork" and "coordination," which had lines drawn to timelines with other pivotal concepts concerning "agile project," "produc- tivity" and "trust." The evidence presented in the study shows the closeness of concepts opening up new avenues for collaborative research. While various themes may dominate certain areas of research, the interconnections suggest the foundational principles of team science are relevant in a variety of cyber-related contexts. This dense clustering of concepts supports the view that core elements of team science remain relevant across different domains even as the contexts in which they operate become increasingly complex and distributed. The co-occurrence patterns also suggest a convergence of research interests from tradition- ally distinct disciplines highlighting the multidisciplinary nature of contemporary research on team effectiveness. 21 Figure 3: Keyword Co-occurrence Network in Team Effectiveness Literature 4.5. Detailed Thematic Findings The analysis of 137 primary studies (PS) suggests that team effectiveness models apply to cyber domains when tailored to domain-specific conditions. Supportive leadership and cul- ture enable adaptation, collaboration, and trust to sustain effectiveness, hybrid approaches extend relevance across cybersecurity, e-gaming, and software engineering. At the same time, resistance, distributed coordination and domain complexity remain key constraints as detailed in Table 5, Table 6, Table 7, and Table 8. 22 Table 5: Theme 1: Leadership, Management, and Organizational Culture PS Summarized Findings [3, 15, 58, 88, 8, 89, 90, 91, 92, 93, 38, 94, 70, 95, 96, 97, 36, 35, 98, 99, 34, 100, 33, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 53, 112, 113, 114, 115] [36, 35] highlight the necessity for a hands-off, enabling management style, but man- agers need guidance as resistance can negatively affect team empowerment. A bottom-up approach through community-based alignment balances autonomy and control in large organizations. [98] emphasizes that successful agile transformation requires structured changes in culture, competencies, structure, and approach, along with top management support and continuous learning. [3] shows empowering leadership promoting partic- ipative decision-making improves project success, particularly in developing countries. [15, 58] discuss the Product Owner role; [58] identifies five critical factors for Scrum team effectiveness: responsiveness, stakeholder concern, continuous improvement, team auton- omy, and management support. [88] finds Management 3.0 principles relevant for LeSS framework implementation, though differences exist. [89, 90] underscore that successful agile transformation enhances organizational outcomes, contingent on culture and team readiness. [91] finds top management team cognitive patterns critical for balancing ex- ploration and exploitation. [92] provides a maturity model for measuring and improving self-organization. [93] shows timing of top management team changes is key for corpo- rate turnarounds. [38] finds management must provide clear goals while preserving team autonomy for complex projects. [94, 70] identify critical factors for software product management and gaps between academic theory and small-organization practice. [95, 96] show leadership styles, team diversity, and CEO humility positively influence team co- operation and firm performance. [97] reinforces that management support is pivotal for sustained agile usage. [99] adds that different agile practices affect customer responsive- ness differently, with no single practice improving both effectiveness and efficiency. [34] underscores organizational culture’s significant impact on agile projects, with hierarchical cultures being detrimental. [100] finds project managers’ knowledge crucial for mitigating technical risk. [33, 106] show leadership development is more important than style and that organizational management motivates agile scaling. [101] shows large organizations can employ agile even if higher-level planning is non-agile, but challenges in autonomy, role definition, and coordination remain. [102] finds Scrum success depends on team ma- turity and adherence to Scrum roles and events. [103, 105] show dependency management challenges emerge from self-organizing teams, but shared decision-making can promote knowledge augmentation and innovation. [104] identifies management challenges, partic- ularly lack of commitment, as a major impediment in distributed environments. [107, 108] reinforce leadership importance; [107] finds both transformational and transactional lead- ership styles influence shared leadership, and [108] shows Shared Mental Models among leaders improve New Product Development efficiency. [109] examines architectural gov- ernance and autonomy in Spotify model teams, while [110] addresses empowering teams in knowledge-intensive work. [111, 53] connect leadership to team performance, show- ing positive team climate and psychological safety enhance innovation and knowledge management. [112] finds transformational leadership and organizational culture critical for virtual team change management. [113] suggests hybrid approaches combining tradi- tional project management with high-quality teamwork and some agile practices yield the highest project success. [114, 115] propose addressing human factors via gamification and social psychology to enhance productivity in autonomous agile teams. 23 Table 6: Theme 2: Team Dynamics and Collaboration PS Summarized Findings [1, 5, 116, 18, 30, 57, 39, 43, 61, 117, 47, 118, 48, 41, 119, 120, 42, 65, 49, 121, 44, 122, 37, 123, 124, 51, 45, 125, 126, 127, 128, 52, 75, 129, 130, 131, 132, 133, 134, 135, 79, 136, 137, 138, 80, 54, 84, 139, 140, 141, 87] [37, 124] identify key factors for teamwork productivity and social sustainability. Team effectiveness, management, motivation, and customer satisfaction are cru- cial, alongside trust, clear communication, and a learning culture. [1] finds e- communication management modes enhance shared identity and perceived proxim- ity in remote teams. [18, 39] stress human and communication aspects for successful team formation; non-technical skills improve productivity. [30] finds trustworthi- ness as critical for virtual teams. [57, 117, 90] show shared mental models support team learning, with agile teams as complex adaptive systems. [43] highlights task dependence and cooperative spillover promote helping behavior; larger teams may inhibit this. [61] finds agile practices improve project success via teamwork quality. [47, 118, 41] show team interaction and knowledge management enhance perfor- mance. [48, 119] optimize virtual teams holistically, while [120] identifies suitable personality traits for Requirements Engineering. [42] notes remote work reduces so- cialization and spontaneous knowledge exchange. [65] shows agile fosters teamwork and communication more effectively than traditional methods. [49, 50] emphasize feedback and dependency awareness. [121] uses faultlines theory in e-sports teams. [44] shows project teams balance roles over time. [122] stresses attitudinal and cognitive variables. [123] notes benefits of small teams and challenges of divergent understandings. [51, 130] examine geographical and cultural dispersion. [45, 137] connect group affective tone and agile practices to performance. [125, 79] explore team size and performance. [131] highlights agile mindset and openness to change. [132] emphasizes people and outcomes in agile maturity. [126, 129] stress compe- tencies and non-technical factors. [127, 75] highlight communication. [52] identifies cross-cultural challenges. [133] shows social capital predicts performance. [134] theorizes self-organizing teams enable exploration and exploitation. [135] shows regulating knowledge boundaries improves coordination. [136] highlights Meaning, Impact, Flexibility, and Socio-Human factors. [138] validates workflow improves teamwork. [80] uses multiagent simulation to analyze knowledge structures. [84] applies video game metrics for virtual team dynamics. [54, 87] stress communica- tion for transaction memory and knowledge sharing. [139] emphasizes team spirit and environment. [140] summarizes factors influencing teamwork. [141] finds psy- chological safety mediates communication and productivity. 24 Table 7: Theme 3: Hybrid Models and Specific Applications (Task design) PS Summarized Findings [2, 14, 16, 59, 46, 60, 62, 63, 64, 142, 40, 143, 66, 67, 68, 69, 71, 144, 73, 145, 74, 146, 147, 148, 76, 77, 149, 78, 150, 79, 151, 152, 81, 82, 83, 85, 86] [145] provides a hybrid agile model combining Waterfall and Scrum. [2, 16, 46] address global and hybrid project management; [2] proposes a framework outper- forming traditional methods for virtual projects. [16] highlights trade-offs between cost savings and performance, [46] identifies 48 distinct agile practices in Global Software Development. [14, 40, 143] confirm strategic team formation is crucial for performance in cybersecurity. [59, 74] investigate human-AI teams. [60] develops a predictive model for agile outcomes, with team capabilities, collective experience, and teamwork being most impactful. [62] shows AI-based task allocation improves efficiency. [63, 64] demonstrate technology-driven improvements. [142] applies agile in embedded space system development. [66, 67] address physical and procedural environments. [68] identifies team type and project goals for customizing methodol- ogy. [69] finds multidisciplinary staff coordination critical. [71] describes a method for project effectiveness. [144] provides measures for monitoring agile progress. [73] provides frameworks for multi-human multi-RAS teams. [146, 147] propose models for team composition using competencies and social compatibility. [148] presents Joint Cognitive System model for Incident Management. [76] uses optimization al- gorithms for team composition. [77] studies automation and team communication. [149] introduces Prompt Engineering to leverage AI for role clarification. [78] an- alyzes cybersecurity competitions enhancing teamwork. [150] develops simulation integrating leadership and human factors. [79] uses software simulation for team information alignment. [151] shows dynamic trust models improve human-machine team performance. [81, 83] study human-machine teams via simulation. [152] in- troduces the “scaling out” strategy for agile projects. [82] case study demonstrates agile methodology improves performance. [85] converts socio-technical systems to social networks. [86] finds human-AI mixed teams perform better but with lower perceived team cognition. 25 Table 8: Theme 4: Challenges and Factors Affecting Agile Adoption (Constraints) PS Summarized Findings [17, 153, 154, 50, 155, 156, 157, 158, 159, 55, 56] [155] identifies programming, interaction, and external interruptions in agile devel- opment, manageable through information retrieval and dependency reduction. [17] finds agile complexity arises from implementation gaps and lack of understanding. [153, 154] highlights challenges in large-scale adoption; [153] cites organizational politics and resistance. [154] emphasizes managing team dynamics and distributed teams. [50] shows hybrid projects often fail due to lack of dependency awareness. [157] identifies change resistance, sprint management, relative advantage, and com- plexity as key adoption factors. [158] stresses high-quality user requirements for project success. [159] examines barriers to autonomy in large-scale agile. [55] pro- poses a model for virtual team management in offshore projects. [56] develops a process improvement framework for Global Software Development. These findings provide a foundation for discussing how established team effectiveness models can be adapted to the unique demands of cyber domains. 4.6. Methodologies and Evidence Base Adiverserangeofmethodologicalapproacheswasobservedamongthe137studies. Quan- titative methods (n = 52) included controlled experiments, behavioral telemetry and survey- based studies with performance metrics. Qualitative studies (n = 37) employed interviews, ethnographic observation, and grounded theory coding. Mixed-methods designs (n = 39) often combined simulation data with post-task interviews or reflective protocols. Simulation- based studies were especially prevalent in cybersecurity (n = 41), likely due to access limita- tions and ethical concerns in real-world incident settings. For instance [17, 16], adopted case- based approaches to explore distributed Agile practices identifying performance bottlenecks linked to coordination breakdown. Authors [18], used simulation-based decision models while [14], validated their cybersecurity team formation logic through experimental performance evaluation. Software engineering studies favored longitudinal field studies and performance analytics within development teams. E-gaming research often uses log-file analysis, com- munication trace data, and gameplay video coding. Despite methodological diversity, only a minority of studies engaged in formal model testing, validation or cross-domain general- ization. This limits the cumulative strength of the evidence base and highlights the need for theoretically grounded, multi-method research strategies that can bridge domain-specific insights with generalizable frameworks. 5. Theoretical Model The paper proposes a theoretical model of team performance in cyber domains, including cybersecurity, e-gaming and software engineering, derived from a systematic review of the literature and as presented in Table A.1. The correlation analysis presented in Table A.1 reveals a complex network of interdependencies among team performance factors in cyber do- mains, providing empirical support for the proposed theoretical model. The table synthesizes 26 findings from 137 primary studies that document both positive and negative correlational relationships between input factors and output factors across cybersecurity, e-gaming and software engineering contexts. 5.1. Methodological Approach and Interpretation The correlation matrix employs a systematic coding scheme where positive correlations (+) indicate that increases in the input factor are associated with improvements in the corre- spondingoutputmeasure, whilenegativecorrelations(-)suggestinverserelationships. Mixed correlations (-, +), on the other hand, reflect contextual variability between two references sharing different perspectives on the factor-outcome relationship or study characteristics. Each correlation is supported by specific primary study references, ensuring traceability and enabling meta-analytic validation of the reported relationships. 5.2. Foundational Construct Validation The empirical evidence validates the four foundational constructs identified in the the- oretical model. Communication emerges as the most documented factor, demonstrating 14 distinct correlational pathways that span from basic coordination functions to complex psy- chological processes. Communication exhibits positive relationships across all major outcome categories, with the exception of proximity effects, where increased digital communication may reduce perceived physical closeness [1]. Leadership and management exhibit similarly robust empirical support, with 12 documented relationships, confirming their multi-pathway influence on team effectiveness through both direct performance enhancement and indirect effects via empowerment and structural mechanisms. 5.3. Complex Interaction Patterns Table A.1 reveals some theoretically significant interaction patterns that extend beyond simple bivariate relationships. Project management demonstrates the highest complexity with 20 distinct correlational pathways suggesting its role as a central coordinating mecha- nismthatinterfaceswithvirtuallyallotherteameffectivenessdimensions. Sometraditionally positive factors show contextual negative correlations for example, gamification negatively correlates with commitment [159], suggesting potential motivational crowding-out effects in certain implementations. 5.4. Domain-Specific Adaptations The correlation patterns provide evidence of negative correlations in the performance of the cyber team. Team size demonstrates negative correlations with performance across multiple studies [16, 125, 79, 140], suggesting that cyber domain tasks may be susceptible to coordination losses associated with larger team configurations. Collective intelligence shows uniformly positive correlations, indicating that cyber domains may uniquely benefit from distributed cognitive processes and collaborative problem-solving approaches. 27 5.5. Theoretical Implications The findings support a dynamic, multi-level model of team effectiveness where foun- dational constructs operate through cascading influence networks rather than independent pathways. The prevalence of bidirectional relationships such as the trust-knowledge sharing cycle and the communication-coordination reinforcement loop suggests that effective cyber teams develop emergent properties through positive feedback mechanisms. The theoretical model must therefore account for these nonlinear dynamics and the potential for both vir- tuous and vicious cycles in team development processes. The empirical foundation provides researchers and practitioners with a comprehensive mapping of team effectiveness relation- ships in cyber domains while highlighting areas where contextual factors may moderate these fundamental associations. 5.6. Visual Representation of the Theoretical Model Figure 4 presents the theoretical model as a multi-layered network architecture, visually representing the relationships from Table A.1 in the appendix. The model uses a systematic visual coding scheme to show the complexity and directionality of factor interactions across four levels: Input, Second Latent, First Latent, and Output. The various factors are grouped into five clusters. Each cluster is represented with a square as shown in Figure 4 and additional details of the clustering can be found in Table A.2. 28 Figure 4: Theoretical Model 29 5.7. Illustrative Application: Cybersecurity Incident Response Team To demonstrate the model’s practical utility, consider a Security Operations Center (SOC) team responding to a rans‌omware incident. At theInput layer, the team begins with • Leadership & Management(moderate): On-call incident commander with previous ran‌somwa‌re experience • Skills (high): Spe‌cialize‌d ma‌lware ana‌lysts, network forensics experts • Team Structure(distributed): 24/7 shift rotation with handoff protoc‌ols • Trust(low): New team composition due to recent reorganization At the‌Second Latent lay‌er, these inputs acti‌vate cascading processes. Low initial trust (yellow negative correlation in Figure 4) hinders knowledge sharing—analysts tend to hoard forensic findings rather than collaborating, thereby slowing the process of threat attribution. However, strong leadership intervenes by mandating structured communication protocols (positive pathway) which gradually build Social Cohesion through repeated successful exchanges. The distributed structure init‌ially hinders Coordination (negative correlation with Performance) but enables around-the-clock coverage, crea‌ting a balancing mechani‌sm (brown arrow). At theFirst Latent layer, these dynamics synthesize into. • Communication: Improving through f‌orced st‌ructure, enabling real-time threat in- telligence sharing • Coordination: Still suboptimal due to trust deficits delaying cross-f‌unctional align- ment At t‌heOutput layer, the team achieves moderate Performance (ransomware con- tained within 8 hours vs. 4-hour organizational target) with mixedMember Satisfaction (analysts report frustra‌tion with communication overhead introduced by new protocols). This example illustrates how the model cap‌tures 1. Non-linear dynamics: Low trust negatively impacts knowledge, but leadership com- pensates thr‌ough structural interventions 2. Feedback loops: Successful structured communication (blue bidirectional arrow) be- gins building trust, creating virtuous cycl‌e potential 3. Domain specificity: Distributed structure s‌how‌s context-d‌ependent effects — nega- tive for Coordination, positive for coverage 4. Multi-level emergence: Team-level performance emerges from ind‌ividual‌ attributes (skills), social processes (trust-building) and structural constraints (shift rotation) Practitioners can use this model to diagnose performance gaps (e.g., "Is our coordination problem due‌ to skill deficits or trust breakdown?") and design targeted interventions (e.g., "‌Invest in trust-building exercises v‌s. revise handoff protocols"). 30 5.8. Network Architecture and Visual Encoding The model uses color-coded connections to show relationship types. Positive correlations are black, while negative correlations are yellow. Positive and negative correlations mean that the research is not conclusive. The published research was influenced by other factors; hence, more research is needed. Bidirectional blue arrows represent reinforcing cycles, and brown arrows show balancing mechanisms. Related factors across layers are grouped into five clusters that span the hierarchical layers. Leadership and governance factors encom- pass all elements related to team direction and strategic oversight, including Leadership & Management, Project Management, and Situational Awareness. Social Integration factors encompass interpersonal and communication processes such as Communication, Social Co- hesion, Trust, Collaboration, and Team Dynamics. Cognitive resources include knowledge- based and learning-oriented elements, such as knowledge, Skills, Innovation, and Collective Intelligence. Structural Organization factors represent the formal and procedural aspects of team configuration including Team Structure, Task design, Coordination mechanisms, and resource Allocation. Individual Attributes encompass personal characteristics and psycho- logical states such as Autonomy, Adaptability, Emotional Intelligence, and Member Satis- faction. This cross-layer distribution suggests that effective team performance emerges from the integrated functioning of these five domain areas rather than from isolated factor effects. Table A.2 contains detailed clustering of the factors. 5.9. Hierarchical Information Flow and Dynamic Feedback The four-layer architecture visualizes the cascading influence patterns. Input factors serving as the foundational level represent initial conditions. The Second Latent layer acts as the central processing hub, transforming inputs through mechanisms such as leadership and management as well as collaboration. The First Latent layer synthesizes these into outcome domains, such as outcome (outcomes, productivity, success) and Communication, serving as immediate precursors to performance. The model also highlights feedback loops. Reinforcing cycles strengthen relationships, such as the Trust-Knowledge sharing loop. Bal- ancing mechanisms maintain stability. The concentration of these cycles within the Social and Communication, and Knowledge and Learning groups, aligns with empirical findings, supporting the idea of emergent team intelligence. 5.10. Theoretical contributions This synthesis provides three clarifications that extend beyond existing frameworks. It emphasizes a cascading influence architecture where factors can act as inputs, mediators, or emergent properties depending on context. It distinguishes reinforcing cycles from balancing mechanisms, clarifying when interventions must pass thresholds versus when optimization is required. It operationalizes domain-contingent pathways, explicitly marking where contex- tual reversals occur and calling for configural, not solely variable-centric, theorizing. These refinements aim to move the literature toward testable, context-sensitive hypotheses. 5.11. Theoretical Implications The visual model integrates empirical findings into a coherent theoretical framework. Instead of showing simple linear relationships it illustrates how team effectiveness emerges 31 from the dynamic interaction of multiple subsystems. The model provides a comprehen- sive mapping of validated relationships for researchers and a framework for practitioners to understand the systemic nature of team interventions in cyber domains. 6. DISCUSSIONS The findings of this systematic literature review demonstrate that foundational team fac- tors, such as communication, coordination, and leadership, are crucial for team effectiveness across cyber domains. The adaptability observed in cyber teams parallels self-organizing natural systems where local interactions yield coherent global order without centralized con- trol. This systems metaphor clarifies how guided self-organization, supported by selective protocols and tooling, can foster resilient coordination in incident response and open-source development contexts. This aligns with and supports established principles from classi- cal models of team performance such as the Big Five model of teamwork and Hackman’s normative framework. One other finding is the significant underutilization of these formal models in cyber-relevant research. For instance, [14]‌ deve‌loped a cybersecurity team forma- tion framework based‌ on collaborative sco‌res and‌ histor‌ical performance ind‌icators witho‌ut‌ explicitly grounding it in Hackman’s normative model, despite addressing core constructs such as task design, team composition, and enabling conditions. Had the authors explicitly operationalized Hackman’s framework, they migh‌t have systematically tested whether "en- abling conditions" (organizational context and rewards) moderate the relationship between skill complementarity and performance—a theoretical prediction they implicitly tested but did not frame as model validation. Similarly,‌ [15] explore role ambiguity in Ag‌ile teams, revealing structural ch‌al‌lenges in leadership and authority that directly map onto IMOI‌’s mediator constructs (team processes, emergent states). However, the study did not position findin‌gs as testing or extending IMOI predictionsabouthowinputambiguitycascadesthroughprocessdisrupti‌o‌nstooutputdegra- dation. These missed opportunities for cumulative theory-buil‌ding suggest that researchers ma‌y lack awareness of how domain-specific observations can validate, refine, or refute es- tablished models, resulting in fragmented knowledge that cannot easily generalize across contexts. Instead of directly applying and testing these frameworks, researchers frequently use context-specific constructs or develop ad hoc models, indicating a gap in cross-domain theoretical integration. This fragmentation underscores the need for adaptable, hybrid mod- els that take into account emergent team properties and nonlinear dynamics. The reliance on simulation-based studies, especially in cybersecurity limits opportunities to observe emer- gent, long-term team dynamics making it difficult to fully validate models designed for longitudinal and real-world contexts, such as Hackman’s framework. Our analysis reveals that domain-specific factors critically influence the manifestation and importance of these core team factors. In cybersecurity, the high-stakes, adversarial environment places a premium on adaptability, trust and shared mental models, which are central to models like IMOI’s focus on emergent states. For example, the adversarial nature of cyber threats necessitates rapid, decentralized decision-making, which challenges tradi- tional, hierarchical leadership structures assumed by some classical models. The finding supports the need for distributed and shared leadership a concept that is gaining traction in modern team science but is not explicitly detailed in all traditional frameworks. In software 32 engineering, the prevalence of Agile and DevOps methodologies introduces unique dynam- ics. Here, iterative processes and asynchronous, distributed coordination are paramount. The literature highlights the importance of adaptability and continuous feedback, which are core tenets of the IMOI framework. However, the reviewed studies often do not make a formal link to these models. For example, many studies in this domain explore how psycho- logical safety and communication impact innovation but they rarely frame it through the lens of a formal model, suggesting a theoretical disconnect between practice and established theory. E-gaming teams, with their intense time constraints and Laissez-Faire leadership, further challenge the assumptions of traditional stage-based models such as Tuckman’s de- velopmental sequence. Instead, their success hinges on emergent coordination and shared mental models, which are closely aligned with the principles of Collective Intelligence. The fast-paced environment requires highly attuned non-verbal communication and rapid role ad- justments, elements that are often not given sufficient granularity in conventional behavioral models. 6.1. Practical implications For cybersecurity teams, prioritize early trust-building, structured incident communi- cation protocols,‌ and h‌ybrid duty models balancing coverage w‌ith coordination. Also, for software engineering teams, pair Agile process ado‌ption with explicit team development in- terventions and manage team size to lim‌it coordination loss. For e-gaming teams, emphasize shared mental model training and systematic debriefs, and utilize communication density as a diagnostic tool for coordination state. 6.2. Theoretical Trade-offs in Cross-Domain Synthesis The decision to group cybersecurity, software engineering and e-gaming under a unified "cyber" domain enables comparative analysis but introduces analytical tensions that merit explicitacknowledgment. Eachdomainoperatesunderdistinctlogicsthatmayrendercertain team factors differentially salient or even conceptually incompatible. Temporal logic divergence: Cybersecurity incident response operates under crisis temporality characterized by compressed decision cycles and irreversible consequences (a misconfigured firewall rule cannot be "rolled back" once attackers exploit it). Software engi- neering operates under iterative temporality where mistakes can be corrected in subsequent sprints through version control and testing. E-gaming operates under competitive temporal- ity where performance is evaluated in discrete match episodes with reset conditions. These temporal structures fundamentally alter how "adaptability" manifests-cybersecurity teams must adaptduring a crisis without pause, software teams adaptbetween iterations through retrospectives, and gaming teams adaptacross matches through strategy refinement. Authority structure incompatibility:Leadership in cybersecurity often follows inci- dent command hierarchies with clear escalation protocols and legal accountability. S‌oftware engineering increasingly adopts distributed authority throug‌h Agile self-organization and collective code o‌wner‌ship. E-gaming exhibits emergent leadership that shifts dynamically in response to the in-game phase and tactical context. Applying a single "leadership" construct across these contexts risks conceptual stretching—what constitutes "effective leadership" in crisis command may represent authoritarian overreach in Agile teams or tactical rigidity in gaming contexts. 33 Performance measurement incomparability:Cybersecurity success is often nega- tively defined (incidents prevented, mean time to detection/response), software engineering success combines delivery speed with technical debt management and e-gaming success is a zero-sum competitive ranking. These non-comparable outcome metrics complicate efforts to identify universal predic‌tors of "effectiveness." The theoretical model presented in Figure 4 addresses these tensions through pattern- based rather than variable-based synthesis. Rather than claiming "trust predicts perfor- manceuniversally"wedocumentthattrustoperatesthroughdomain-specificpathways—enabling knowledge sharing in distributed software teams, supporting risk-taking in cybersecurity op- erations, and facilitating rapid role negotiation in e-gaming. The model’s mixed correlations (yellow edges) explicitly indicate where domain logics produce contradictory empirical rela- tionships signaling areas that require contextualized theory rather than universal laws. This approach sacrifices conceptual simplicity for ecological validity. A more minimalist model would abstract away domain differences to identify minimal sufficient conditions for team effectiveness. Our synthesis reveals that such abstraction would obscure precisely the contex- tual contingencies that practitioners navigate. The future research challenge is developing configural theories that specify how factor combinations produce effectiveness in particular contexts rather than seeking universal pairwise relationships. Methodologically, the reliance on simulation-based studies, especially in cybersecurity, reflects the practical challenges of studying real-world incidents. Simulations are valuable for controlled observation but may not fully capture the complex, emergent dynamics of operational teams. Simulation findings should be interpreted cautiously. Simulations often (1) rely on convenience samples rather than practitioners, (2) compress temporal dynamics that in the field evolve over weeks or months, (3) lack authentic consequences that shape risk behavior, and (4) simplify cross-organizational dependencies. Thus, simulation results indicate theoretical possibilities rather than operational certainty; future research should prioritize longitudinal field validation and hybrid simulation-field designs. Similarly, the short-term nature of many studies limits insights into the longitudinal development of teams, a significant aspect of models like Hackman’s. This indicates the need for future research to employ more longitudinal, multi-method designs that can bridge domain-specific insights with generalizable theoretical frameworks. The review confirms the importance of core team factors. It highlights the need for a concerted effort to adapt and integrate formal theoretical models to the complex realities of cyber domains. 7. CONCLUSION AND FUTURE WORK The systematic literature review successfully investigated the applicability of team effec- tiveness models in cyber domains, identifying key domain-specific factors that influence their adaptation and implementation. The 137 primary studies analyzed confirm that core team factors, such as communication, coordination, leadership, and motivation, are consistently critical to team performance, thereby validating their foundational importance across di- verse contexts. However, a significant finding is the limited direct application of established theoretical models, with many researchers preferring to develop context-specific frameworks that address the unique demands of their domains. The review highlights the critical need for flexible and adaptable theoretical models. In cybersecurity, the high-stakes, adversarial 34 environment demands rapid coordination and adaptability, challenging the stable structures of traditional models. Software engineering teams, with their Agile and DevOps method- ologies, prioritize iterative processes and distributed leadership, which align more with the IMO‌I framewo‌rk’s d‌ynamic nature. E-gaming teams demonstrate the power of emergent coordination and shared mental models, emphasizing the relevance of collective intelligence principles. E-gaming is substantially underrepresented (n = 4) and conclusions about this domain are provisional. To strengthen the evidence, future work should leverage archival match telemetry and public communication streams, sample amateur and semi-professional teams and employ cross-game comparative designs to test generalizability across different gameplay architectures. From a theoretical perspective, this review advocates for the expansion and refinement of existing models to better account for technological mediation, distributed work and high- tempo operational contexts. Practitioners should adopt flexible, evidence-based approaches to team design, acknowledging that a one-size-fits-all model is insufficient for the modern technological landscape. The review provides a synthesized understanding of teams’ effec- tiveness in cyber-relevant domains and identifies gaps in theory and practice. It calls for future research that rigorously tests and refines team effectiveness models in diverse oper- ational environments using multi-method and longitudinal designs to capture the evolving nature of teamwork in modern technological work. The review has some limitations that provide a foundation for future research. The study was conducted by a single researcher, which, despite the use of systematic review tools like Rayyan introduces a risk of subjective interpretation and potential bias in theme identification. The absence of inter-rater reliabil- ity checks limits the robustness of our thematic categorizations. Also, the study’s findings are constrained by the predominance of simulation-based studies especially in cybersecurity. While these environments offer valuable controlled observations they may not fully capture the complexities and emergent dynamics of real-world operational teams. This reliance on simulations limits the ecological validity and generalizability of our findings. The short-term nature of many of the studies restricts insights into longitudinal team de- velopment and adaptation. We were unable to fully explore how teams evolve over extended periods and adapt to changing task demands, a critical component of many team effective- ness models. For future research, we propose targeted research questions that address critical gaps revealed by this synthesis. RQ1 (Cybersecurity):How does distributed leadership emergence differ between rou- tine monitoring and active incident response in Security Operations Centers? The suggestion is to use a longitudinal field study using communication network analysis comparing routine and incident phases. RQ2 (Software Engineering):Do Agile/DevOps teams follow Tuckman’s stages or a perpetual norming-performing oscillation? The suggested method is multi-team longitudinal tracking of cohesion, sprint velocity, and qualitative interviews. RQ3 (E-gaming‌):Can collective intelligence metrics (social sensitivity, equal partici- pation) predict competitive seas‌on performance? The suggested method combines archival match data with communication trace analysis across a season. RQ4 (Cross-domain):Under what conditions does insight occurrence fail to translate to insight utilization across network structures? The Suggested method here is experimental tasks with manipulated network topologies across domain analogs 35 These questions require multi-method resear‌ch designs combini‌ng quantitative perfor- mance data with rich qualitative ins‌ights, ideally through indust‌ry partnersh‌i‌ps that enable operat‌ional access while protecting sensitive information. One limitation of the theoretical model is that it focuses on intra-team factors therby underrepresenting external exosystem influences. Organizational culture, regulatory environments, market dynamics, technological infrastructure and inter-organizational dependencies fundamentally shape team effectiveness but remain undertheorized in our current framework. The current design exhibits conceptual overlap among constructs such as trust, com- mitment, and social cohesion, which can increase the model’s complexity without adding explanatory value. Future research should investigate these definitions and empirically test their validity to ensure that each factor contributes uniquely to the framework. Some con- structs, such as ownership and progress, are abstract and difficult to operationalize. Their measurement may vary across studies, undermining comparability. Research is needed to propose concrete metrics or adapt validated scales from related fields, ensuring that these elements can be reliably assessed in different contexts. While the model is designed for general applicability it lacks domain-specific calibration. For example, a cybersecurity team faces challenges related to threat intelligence, while a software team may prioritize agile practices. Mapping generic constructs to domain-specific practices would significantly im- prove the model’s practical relevance. The output layer is also relatively narrow focusing on immediate results like performance and communication. It omits longer-term and systemic outcomes such as resilience, sustainability and innovation success. Expanding this layer would enable the model to capture a broader, more complete picture of a team’s long-term impact. 36 Appendix A. Supplementary Figures and Tables Figure A.1: Systematic Literature Review process in Rayyan Software Figure A.2: Frequency of Studies Across formal Models 37 Figure A.3: Frequency of Primary Studies Across Domains Figure A.4: Distribution of Team Factors Studied Across Domains 38 Figure A.5: Number of Papers Published per Year Table A.1: Correlations Between Team Factors Input Output References Correlation Accommodation Proximity [66] + Adaptability Outcomes/Productivity/Success [89, 150] + Affiliation knowledge [48] + Outcomes/Productivity/Success [75] + Proximity [1] + Performance [140] + Allocation Performance [62, 128] + Progress [62, 76] + Workload [62] + Member Satisfaction [62] + Outcomes/Productivity/Success [128, 76] + Autonomy Knowledge [88] + Risk Mitigation [88] + Empowerment [122] + Communication [140] + Performance [92, 140] + Project Management [106] + Task [152] + Continued on next page 39 Table A.1 – continued from previous page Input Output References Correlation Collaboration Performance [14, 92, 78, 53] + Team Dynamics [143] + Outcomes/Productivity/Success [14, 143, 65, 94, 75, 147, 114, 139] + Innovation [88, 113] + Communication [88] + Psychological [43, 53] + Collective Intelligence Social Cohesion [143] + Team Dynamics [143] + Outcomes/Productivity/Success [3] + Performance [41, 79, 86] + Commitment Outcomes/Productivity/Success [131, 136] + Performance [30, 131] + Communication Outcomes/Productivity/Success [1, 18, 2, 47, 65, 123, 124, 147, 136, 114, 139] + Stakeholder Involvement [65] + Workload [77] + Trust [77, 54] −, + Social Cohesion [133] + Mental Models [133] + Organizational [110] + Collective Intelligence [87] + Coordination [54] + Performance [30, 92, 129, 133, 79, 140] + Skills [18] + Leadership & Management [160] + Proximity [1] − Psychological [39] + Conflict Collaboration [125] + Outcomes/Productivity/Success [154, 52] + Performance [154] + Coordination Performance [89, 59, 49, 135, 138] + Skills [40] + Innovation [54] + Outcomes/Productivity/Success [71] + Diversity Knowledge [56, 111] + Performance [95, 140] + Conflict [95] + Collaboration [95] + Social Cohesion [95] + Continued on next page 40 Table A.1 – continued from previous page Input Output References Correlation Psychological [95] + Communication [140] + Emotional Intelligence Performance [39] + Communication [140] + Empowerment Performance [33, 131] + Mental Models [122] + Communication [98] + Outcomes/Productivity/Success [144, 131] + Progress [144] + Knowledge [111] + Gamification Collective Intelligence [84] + Project Management [68] + Performance [37, 159, 140] + Outcomes/Productivity/Success [60, 117, 147, 114] + Commitment [159] − Collaboration [95] + Social Cohesion [95, 114] + Communication [140] + Knowledge Outcomes/Productivity/Success [55] + Collaboration [41] + Performance [48, 41, 125, 105, 151, 80] + Communication [104, 140] −, + Skills [80] + Innovation [54] + Leadership & Management Performance [91, 63, 143, 154, 95, 96, 122, 37, 126, 129, 150, 151, 140] + Outcomes/Productivity/Success [117, 154, 38, 37, 123, 98, 156, 160, 147, 132, 150, 152, 114, 55, 139] + Leadership Consistency [3, 160] + Teamwork [94] + Social Cohesion [124] + Team Structure [85] + Communication [140] + Project Management [88, 97, 106] + Autonomy [58, 97, 122, 37] + Stakeholder Involvement [58, 97, 122, 37, 126] + Knowledge [48] + Risk Mitigation [100] + Continued on next page 41 Table A.1 – continued from previous page Input Output References Correlation Leadership Consistency Outcomes/Productivity/Success [3, 93] + Performance [92] + Project Management [124] + Member Satisfaction Outcomes/Productivity/Success [60, 42, 102, 131, 158] + Performance [131, 140] + Adaptability [42] + Mental Models Autonomy [35, 159] −, + Proximity [1] + Trust [133] + Performance [133] + Outcomes/Productivity/Success [147, 114] + Organizational Member Satisfaction [112] − Performance [140] + Outcomes/ Productivity/ Success Teamwork [122] + Mental Models [122] + Performance [64, 129] − Ownership Outcomes/Productivity/Success [99] + Performance Outcomes/Productivity/Success [18, 37, 156] − Stakeholder Involvement [100] + Progress Outcomes/Productivity/Success [60, 156, 150, 136] + Performance [64] + Project Management Outcomes/Productivity/Success [61, 117, 47, 153, 142, 154, 154, 65, 68, 71, 123, 99, 155, 156, 101, 102, 158, 137, 113] + Performance [2, 14, 70, 126, 145, 155, 101, 110, 113] −, + Member Satisfaction [70, 161, 158, 82] + Skills [70] − Task [17, 155] + Collaboration [65] + Communication [142, 65] + Trust [65] + Teamwork [61, 142] + Knowledge [142] + Progress [142, 145, 155] + Empowerment [142] + Psychological [141, 115] + Diversity [56] + Stakeholder Involvement [126] − Continued on next page 42 Table A.1 – continued from previous page Input Output References Correlation Mental Models [115] + Coordination [50] + Situational Awareness [50] − Autonomy [161] + Leadership & Management [160] − Proximity Social Cohesion [1, 115] − Performance [15, 59] + Communication [1, 15, 66] + Psychological Communication [140] + Outcomes/Productivity/Success [43, 92, 146, 147, 114] + Member Satisfaction [128] + Skills [40] + Performance [92, 124, 100, 141, 53] + Collaboration [124, 141, 53] + Situational Awareness Coordination [50] + Leadership & Management [91] + Skills Performance [14, 40, 70, 72, 122, 98, 78] + Adaptability [146] + Outcomes/Productivity/Success [60, 37, 123, 102, 146] + Collective Intelligence [143] + Collaboration [143] − Stakeholder Involvement [72] + Trust [98, 133, 78] + Social Cohesion Outcomes/Productivity/Success [2, 15, 39, 89, 66, 34, 52, 147, 131, 114, 139] + Performance [66, 98, 129, 131, 133, 138, 85] + Communication [2, 66, 140] + Project Management [130] − Gamification [89] + Collaboration [66] + Leadership & Management [37] + Team Dynamics [143] + Knowledge [48, 147] + Conflict [95] − Stakeholder Involvement Risk Mitigation [70] + Project Management [67] + Outcomes/Productivity/Success [155, 156] +, − Performance [58] + Continued on next page 43 Table A.1 – continued from previous page Input Output References Correlation Task Outcomes/Productivity/Success [109] + Performance [118, 41, 154, 49, 79] − Project Management [67, 157] + Risk Mitigation [67] − Affiliation [67] + Psychological [43] + Coordination [49] + Collaboration [41] + Team Dynamics Performance [129, 130] + Leadership & Management [96] + Knowledge [111] + Empowerment [111] + Team Size Performance [16, 125, 79, 140] − Progress [16] + Mental Models [57, 43, 119] −, −, + Task [119] + Collective Intelligence [143, 123] + Social Cohesion [143] + Conflict [104] + Coordination [49] + Team Structure Autonomy [109] + Performance [92, 40, 44, 147, 86] + Collective Intelligence [143, 86] − Situational Awareness [86] + Social Cohesion [143, 115] + Member Satisfaction [161] + Outcomes/Productivity/Success [15, 123, 102, 136] + Communication [77] + Trust [122] + Mental Models [122] + Coordination [101] + Innovation [54] + Knowledge [54] + Task [73] + Teamwork Progress [156] + Performance [156, 138, 113] + Outcomes/Productivity/Success [61, 150, 113] + Technology & Intelligent Assistance Performance [74, 76, 140] + Communication [87] + Knowledge [48] + Continued on next page 44 Table A.1 – continued from previous page Input Output References Correlation Progress [1] + Trust Knowledge [48, 54] + Performance [30, 143, 129, 133, 138] + Collaboration [124, 129] + Teamwork [81] + Communication [140] + Outcomes/Productivity/Success [124, 147, 114] + Workload Performance [62] + Table A.2: Team Performance Factors Grouped by Clus- ter Cluster Factors Leadership and Governance Leadership & Management, Leadership Consistency, Coordination, Project Management, Risk Mitigation, Empowerment, and Stakeholder Involvement Social Integration Social Cohesion, Collaboration, Trust, Affiliation, Team Dynamics, Teamwork, Conflict, Gamification, Communication, and Member Satisfaction Cognitive Resources Knowledge, Skills, Mental Models, Collective Intelli- gence, Situational Awareness, Emotional Intelligence, and Innovation Structural Organization Team Structure, Team Size, Task, Proximity, Tech- nology & Intelligent Assistance, Allocation, Work- load, and Organizational Individual Attributes Diversity, Autonomy, Psychological, Commitment, Adaptability, Accommodation, Ownership, Perfor- mance, Outcomes and Progress

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