Evidence-Based HRM and HR Analytics

Evidence-Based HRM

• Uses data analysis and evaluation of HR practices to inform decision-making
• Messages from benchmarking and research are used
• Evidence-based questions establish the situation, needed actions, and required information
• Sources include practitioner expertise, local context evidence, and critical evaluation of research
• Validity, relevance, and usefulness of evidence are evaluated
• Decisions are made by weighing up the evidence

HR Analytics

• Uses statistical analysis of data to assess HRM practices impact
• Analyses organizational performance to inform decision-making
• Analyzes data, measures, information, and knowledge
• Levels include descriptive, multi-dimensional, and predictive analytics
• Descriptive uses data to illustrate aspects like employee turnover
• Multi-dimensional combines data sets to find correlations
• Predictive analyzes data to predict future trends
• People must be kept informed and have easy access to data via dashboards

E-HRM

• Involves using digital technologies for HRM
• Includes web-based applications, computer hardware and software, AI, cloud technologies, chatbots, social media, smartphones and blockchain
• Digital HRM is an equivalent term using SMAC technologies (social, mobile, analytics and cloud)
• Goals include efficiency, enhanced service delivery, strategic orientation, and empowerment of HR professionals

AI in HRM

• Components include Machine Learning, NLP, Deep learning, and Neural Networks for tasks like decision-making, text mining, sentiment analysis, and speech recognition
• Applications include HR analytics, recruitment, talent management, and learning & development
• AI can reduce administrative workload, improve decision-making and significantly enhance people management
• Risks of potential bias in algorithms, data protection, a lack of transparency, and over-reliance on machines must be considered in AI implementation

Knowledge Management

• Deals with knowledge stocks (expertise, encoded) and flows (knowledge transfer)
• Strategies include codifying knowledge in databases ('codification') and sharing through direct interaction ('personalization')

Competency-Based HRM

• Focuses on employee characteristics for effective performance
• Uses the concept of competency for recruitment, selection, learning, and development, performance management
• Types of competencies include behavioral, technical, and NVQS/SNVQS.

Ethical Dimensions in HRM

• Equity, equality, and morality are central values
• Ethical decisions affect others and involve choices
• Deontological theory focuses on principles, utilitarianism on outcomes, and stakeholder theory on considering all affected parties
• Fairness means treating people right in processes and managing resources justly
• Ethical HRM concerns guidelines for actions and interactions, to consider individuals fairly and encourage ethical conduct
• Ethical guidelines in HRM include treating employees right, good interactions, and appropriate selection practices, and fair performance management

Corporate Social Responsibility (CSR)

• Combines business activities and social concerns
• Businesses consider a range of stakeholders (customers, workers, communities, etc.)
• CSR can integrate with business strategies leading to greater influence/impact and positive brand image.

Explore the fundamentals of Evidence-Based HRM and HR Analytics in this quiz. Delve into the data analysis techniques and strategic insights that shape effective HR practices and decision-making. Learn how statistical measures and data interpretation can enhance organizational performance and employee management.