Relational Analytics
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Relational Analytics
This guidebook goes beyond people analytics to provide a research-based, practice-tested methodology for doing relational analytics, based on the science of relational coordination. We are witnessing a revolution in people analytics, where data are used to identify and leverage human talent to drive performance outcomes. Today’s workplace is interdependent, however, and individuals drive performance through networks that span department, organization and sector boundaries. This book shares the relational coordination framework, with a validated scalable analytic tool that has been used successfully across dozens of countries and industries to understand, measure and influence networks of relationships in and across organizations, and which can be applied at any level in the private and public sectors worldwide. Graduate students and practitioners in human resource management, health policy and management, organizational behavior, engineering and network analysis will appreciate the methodology and hands-on guidance this book provides, with its focus on identifying, analyzing and building networks of productive interdependence. Online resources include data appendices and statistical commands that can be used to conduct all these analyses in readers’ own organizations.
Predictive Analytics in Human Resource Management
This volume is a step-by-step guide to implementing predictive data analytics in human resource management (HRM). It demonstrates how to apply and predict various HR outcomes which have an organisational impact, to aid in strategising and better decision-making. The book: Presents key concepts and expands on the need and role of HR analytics in business management. Utilises popular analytical tools like artificial neural networks (ANNs) and K-nearest neighbour (KNN) to provide practical demonstrations through R scripts for predicting turnover and applicant screening. Discusses real-world corporate examples and employee data collected first-hand by the authors. Includes individual chapter exercises and case studies for students and teachers. Comprehensive and accessible, this guide will be useful for students, teachers, and researchers of data analytics, Big Data, human resource management, statistics, and economics. It will also be of interest to readers interested in learning more about statistics or programming.
AI, Analytics and Strategic Decision-Making
AI, Analytics and Strategic Decision-Making is an insightful exploration into the transformative power of artificial intelligence and advanced analytics in business and policy contexts. This volume provides a unique blend of theoretical knowledge and practical applications, showcasing how AI and analytics are reshaping decision-making processes across multiple domains. From predicting high-growth firms using Random Forests to leveraging Large Language Models for process mining, this book offers an extensive look at the latest innovations in AI and their strategic implications. It explores diverse topics, including the role of knowledge management in Industry 4.0, AI-powered decision-making tools for individuals, and public policy-making through Internet Public Opinion Analysis. Each chapter provides a nuanced perspective on how AI and analytics tools—ranging from Social Network Analysis to agent-based modeling—can tackle real-world challenges, such as climate change, resource allocation, and productivity enhancement. AI, Analytics and Strategic Decision-Making will inspire entrepreneurs, policymakers, academics, and business strategists to think critically about the power of data-driven insights and AI technologies. This comprehensive resource dives into how innovative tools can foster informed decision-making, drive efficiency, and pave the way for strategic success in an increasingly complex world.