惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Google DeepMind News
Google DeepMind News
D
DataBreaches.Net
C
Check Point Blog
I
InfoQ
A
About on SuperTechFans
Engineering at Meta
Engineering at Meta
月光博客
月光博客
Recent Announcements
Recent Announcements
酷 壳 – CoolShell
酷 壳 – CoolShell
T
Tailwind CSS Blog
Y
Y Combinator Blog
博客园 - Franky
博客园_首页
罗磊的独立博客
量子位
美团技术团队
T
The Blog of Author Tim Ferriss
Last Week in AI
Last Week in AI
大猫的无限游戏
大猫的无限游戏
爱范儿
爱范儿
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Martin Fowler
Martin Fowler
博客园 - 叶小钗
aimingoo的专栏
aimingoo的专栏

cs.SI updates on arXiv.org

Hiding in Plain Sight: Finding MAHA on Reddit Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Algorithmic Cultivation: How Social Media Feeds Shape User Language Universal Dynamics of Punctuated Progress AI-Mediated Communication Can Steer Collective Opinion CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity Explainable Detection of Depression Status Shifts from User Digital Traces Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles Humanwashing -- It Should Leave You Feeling Dirty When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks Predicting Channel Closures in the Lightning Network with Machine Learning Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence When Can Digital Personas Reliably Approximate Human Survey Findings? RAwR: Role-Aware Rewiring via Approximate Equitable Partition GravityGraphSAGE: Link Prediction in Directed Attributed Graphs Structure-Centric Graph Foundation Model via Geometric Bases Attention-based graph neural networks: a survey When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge Scalable inference of spatial regions and temporal signatures from time series Can LLMs Emulate Human Belief Dynamics? Predicting Post Virality with Temporal Cross-Attention over Trend Signals H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
Leadership Network and Team Performance in Interactive Co...
Satyam Mukherjee · 2016-06-07 · via cs.SI updates on arXiv.org

Over the years, the concept of leadership has experienced a paradigm shift - from solitary leader (centralized leadership) to de-centralized leadership or distributed leadership. This paper explores the idea that centralized leadership, as earlier suggested, negatively impacts team performance. I applied the hypothesis to cricket, a sport in which leaders play an important role in team's success. I generated batting partnership network and evaluated the central-most player in the team, applying tools of social network analysis. Analyzing 3420 matches in one day international cricket and 1979 Test matches involving 10 teams, I examined the impact of centralized leadership in outcome of a contest. I observed that the odds for winning a one day international match under centralized leadership is 30% higher than the odds for winning under de-centralized leadership. In both forms of cricket (Test and one day international ), I failed to find evidence that distributed leadership is associated with higher team performance. These results suggest important implications for cricket administrators in development and management of working teams.