Team Performance Science Guide · Part VII · evidence chain
Performance Science Library
Every evidence pill in Performix traces back through this library. Source papers and books are ingested by the Research-to-Model Engine; constructs, validated measures, and survey items are derived from them and weighted by evidence strength.
196 sources
818 findings
915 constructs
525 survey items
last update 2026-07-08
BOOK
Skelton, M., & Pais, M. (2019). Team Topologies: Organizing Business and Technology Teams for Fast Flow. IT Revolution Press.
Ingested 2026-06-13 · 29 findings extracted · 78 constructs derived
The chapter leans on the foundational concepts of Conway’s Law, emphasized in research on organizational design and software engineering.
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Constructs derived (5)
Communication Structures of an OrganizationSUPPORT
Team Topologies: A New Way of Thinking about TeamsSUPPORT
The Revival of Conway’s LawSUPPORT
Cognitive Load and BottlenecksSUPPORT
Summary: Rethink Team Structures, Purpose, and InteractionsSUPPORT
Team Topologies, introduced by Matthew Skelton and Manuel Pais, are referenced to demonstrate how teams should be structured according to their interactions and unique responsibilities.
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The chapter discusses the implications of cognitive load as articulated in recent studies that link cognitive psychology to team performance and productivity.
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The chapter invokes examples from historical software development projects, highlighting the failures of organizations that did not heed Conway’s Law.
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Specific attention is given to studies illustrating the correlation between communication breakdowns and software inefficiencies.
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The chapter references case studies where the Reverse Conway Maneuver demonstrated tangible improvements in project outcomes.
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The chapter leverages theories from organizational psychology and cognitive load research to underscore its arguments.
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Static Team Topologies
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The chapter references various studies and frameworks that emphasize the significance of team dynamics in achieving organizational goals.
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Notably, it highlights research from Google’s Project Aristotle, which underscores the importance of team composition and dynamics.
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Case studies of high-performing technology organizations illustrate the successful implementation of the four topologies.
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The emphasis on creating a platform that is 'just big enough' draws from agile methodologies, underscoring industry standards for effective team interactions.
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The chapter draws upon cognitive load theory to support its arguments, emphasizing the direct correlation between well-defined boundaries and enhanced team performance.
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It references real-world examples from the manufacturing sector, illustrating how effective boundary-setting facilitates operational clarity.
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Evolve Team Structures with Organizational Sensing (part 1/3)
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Evolve Team Structures with Organizational Sensing (part 2/3)
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The chapter references best practices derived from well-regarded frameworks, including the analysis presented in 'Accelerate,' which examines high-performing software delivery.
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Data indicates that successful organizations operate with a significantly higher proportion of stream-aligned teams, validating the benefits of this structure.
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Anecdotal evidence illustrates the pitfalls of traditional infrastructure models that inhibit the flow of work and require a significant cultural shift towards modern team dynamics.
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The concept of dynamic incident response teams draws upon industry research demonstrating improved problem-solving efficiency when teams are aligned to specific streams of work.
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The chapter references studies from 'Accelerate,' which highlight the negative impacts of tightly coupled architectures on team autonomy and efficiency.
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The author cites Eric Evans’ work on domain-driven design and Martin Fowler’s insights, emphasizing the significance of bounded contexts for effective team alignment.
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Real-life examples, such as those from Poppulo, illustrate how a meticulously defined transition from monolithic architectures can yield positive results in terms of team autonomy and system performance.
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Further empirical data is provided through anecdotal evidence about typical obstacles faced by organizations splitting software improperly, leading to distributed monoliths and exacerbated inter-team dependencies.
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Conclusion
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The chapter references studies showing that intermittent collaboration can yield solutions of equal or higher quality compared to constant interaction.
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The concept of Conway's Law is illustrated to demonstrate how organizational boundaries impact software architecture, suggesting that management practices must align with technical goals.
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Anecdotes and case studies concerning organizations like IBM provide practical insights into how teams can effectively transition between interaction modes based on project requirements.
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Team Interaction Modes (part 2/2)
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