Role of Artificial Intelligence in Employee Development and Engagement Abstract The advent of Artificial Intelligence (AI) has brought about a paradigm shift in the field of Human Resource Management (HRM) as far as the methods employed in developing and engaging employees are concerned. Increasingly, firms use technologies driven by AI including machine learning and […]
SUPPLY CHAIN RESILIENCE AND RISK MANAGEMENT STRATEGIES IN THE POST-PANDEMIC ERA Abstract The COVID-19 pandemic exposed deep and systemic vulnerabilities in global supply chains, disrupting the movement of goods, raw materials, and finished products across virtually every industry. This paper investigates supply chain resilience and risk management strategies adopted by organizations in the post-pandemic […]
AN INTERDISCIPLINARY SYSTEMATIC LITERATURE REVIEW: VALUATION AND PRIORITISATION IN EVERYDAY LIFE ACROSS ECONOMICS, BIOLOGY, AND BEHAVIORAL SCIENCE Abstract Decision-making in everyday life requires individuals to continuously assign value to available options and prioritize among competing alternatives. The processes of valuation and prioritisation have been widely examined across disciplines such as economics, biology, and behavioral […]
UNVEILING PERSONALITY PROFILES: A DATA-DRIVEN APPROACH TO BIG FIVE TRAITS, SENTIMENT, AND TEXTUAL SELF-DESCRIPTION Abstract This study presents a data-driven framework for understanding personality traits, emotional expression, and self-description using Natural Language Processing (NLP) and psychometric analysis. The research integrates the Big Five Inventory (BFI), sentiment analysis, semantic embeddings, clustering, and regression modeling to […]
VALUATION AND PRIORITISATION IN EVERYDAY LIFE: AN INTERDISCIPLINARY REVIEW ACROSS ECONOMICS, BIOLOGY, AND BEHAVIORAL SCIENCE Abstract Human decision-making is fundamentally an allocation problem under conditions of scarcity and uncertainty. Individuals continuously assign value to alternatives and prioritize actions while operating with limited time, energy, and resources. These processes of valuation and prioritisation have been […]
ANALYZING THE DYNAMIC IMPACT OF MONETARY POLICY SHOCKS ON INFLATION AND UNEMPLOYMENT Abstract This paper investigates the dynamic impact of monetary policy shocks on inflation and unemployment using a Structural Vector Autoregression (SVAR) framework. Using high-frequency data obtained from the Federal Reserve Economic Data (FRED) database, the study examines the transmission mechanism of changes […]
SENTIMENT ANALYSIS OF SOCIAL MEDIA DATA FOR BRAND REPUTATION MANAGEMENT: A REVIEW AND COMPARATIVE ANALYSIS Abstract Brand reputation is no longer shaped solely by news reports or periodic customer surveys; instead, it evolves continuously through millions of social media interactions generated every day. This paper presents a comprehensive review of sentiment analysis techniques for […]
ROLE OF INDUSTRY 4.0 TECHNOLOGIES IN ENHANCING MANUFACTURING AND SERVICE OPERATIONS Abstract The Fourth Industrial Revolution, commonly known as Industry 4.0, is fundamentally transforming the way organizations design, produce, deliver, and experience goods and services. Built upon interconnected technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), cyber-physical systems, robotics, additive manufacturing, […]
REAL-TIME ANALYTICS FOR INTELLIGENT BUSINESS DECISION-MAKING: AN INTEGRATIVE FRAMEWORK AND EMPIRICAL SYNTHESIS Abstract The exponential growth of digital data within corporate ecosystems has fundamentally transformed business decision-making, creating the need for advanced analytical techniques capable of processing and interpreting large volumes of information in real time. This paper presents a comprehensive review of Real-Time […]
COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR CUSTOMER CHURN PREDICTION IN BANKING Abstract Acquiring new customers is significantly more expensive than retaining existing ones, making customer churn prediction a critical challenge for organizations. This paper reviews the application of machine learning techniques for churn prediction over the past five years, examining models ranging from […]