Revolutionizing Research Methodologies: The Emergence of Research 5.0 through AI, Automation, and Blockchain

Abstract

This integrative literature review (ILR) explores the significant impact of incorporating artificial intelligence (AI), automation, and blockchain technology into research methodologies, collectively known as Research 5.0. The study addresses the shortcomings of traditional research methods, which need help managing the complexities and demands of modern scientific inquiry, thereby affecting the reliability and efficiency of research across various fields. The ILR aims to critically assess how these advanced technologies can enhance research processes, guided by a conceptual framework centered on AI, automation, and blockchain. The research method involved a comprehensive literature review and the analysis of qualitative data to identify patterns, challenges, and opportunities for implementing these technologies. The findings reveal that while AI significantly improves research efficiency and accuracy, it also introduces challenges such as algorithmic bias and transparency issues, which can be mitigated through Research 5.0 Explainable AI (RXAI) framework and comprehensive researcher training. Automation enhances consistency but risks reducing human oversight, necessitating hybrid systems that blend human expertise with automated precision. Blockchain strengthens data integrity and transparency yet faces complexity and energy consumption challenges, underscoring the need for scalable and sustainable solutions. The study concludes that while Research 5.0 technologies offer substantial potential, their successful integration requires careful consideration of ethical, technical, and operational challenges. Future research should focus on developing transparent AI systems, hybrid automation models that retain human judgment, and scalable blockchain solutions to advance research methodologies effectively.

Keywords:

Research 5.0, Artificial intelligence, Automation in technology, Blockchain, Research methodologies, Data integrity, Research 5.0 explainable AI, Hybrid systems