“Impact of Technology Adoption on Quality Assurance Processes and their Benefits and Challenges”

Abstract

This study strengthens its methodological foundation by integrating additional analytical techniques beyond correlation, specifically regression and model validation, to enhance the interpretive depth and analytical rigor. In today’s increasingly digitized global market, Quality Assurance (QA) is undergoing a significant technological transformation, driven by automation tools, artificial intelligence (AI), machine learning, and real-time data analytics. These technologies offer opportunities for increased efficiency, improved accuracy, and reduced human error in QA processes. However, the integration of new technologies introduces complexities such as high implementation costs, cybersecurity risks, resistance to change, and integration difficulties with legacy systems. Adopting the Goal-Action-Data (GAD) framework, this study investigates the multifaceted impact of technology adoption on QA outcomes, including efficiency, accuracy, and organizational performance.