Exploring Nexus among Big Data Analytics, Artificial Intelligence and Operational Performance
Department of Computer Science, University of Sindh @Laar Campus, Sindh Pakistan
Kavita Tabbassum
Information Technology Centre, Sindh Agriculture, University, TandoJam Sindh Pakistan
Muhammad Irfan
Department of Computer Science, University of Sindh, Pakistan
Kirshan Kumar Luhana
Information Technology Centre, Sindh Agriculture, University, TandoJam Sindh Pakistan
Muhammad Yaqoob Koondhar
Information Technology Centre, Sindh Agriculture, University, TandoJam Sindh Pakistan
Atia Bano Memon
Department of Computer Science, University of Sindh, Pakistan
Zulfikar Ahmed Maher
Information Technology Centre, Sindh Agriculture, University, TandoJam Sindh Pakistan
Abida Ali
Information Technology Centre, Sindh Agriculture, University, TandoJam Sindh Pakistan
Abstract
This study delves into the intricate nexus between Big Data analytics, Artificial Intelligence (AI) capability, and operational performance among manufacturing Small and Medium-sized Enterprises (SMEs) in the United Arab Emirates (UAE). Employing Structural Equation Modeling-Partial Least Squares (SEM-PLS) alongside simple random sampling, the investigation draws on a substantial sample of 550 manufacturing SMEs in the UAE. The primary objective is to uncover how Big Data analytics and AI capabilities synergize to influence operational performance in this vital sector. The results have showcased that the integration of Big Data analytics with advanced AI capabilities significantly enhances operational performance among manufacturing SMEs. This study introduces novel insights by demonstrating the complementary role of Big Data analytics and AI capabilities in driving operational efficiency and effectiveness, underscoring the critical importance of technological adoption and integration in the competitive landscape of manufacturing. Furthermore, the findings highlight the strategic implications for SMEs in the manufacturing sector, suggesting that investments in Big Data and AI technologies are pivotal in achieving superior operational performance. This research not only enriches the academic discourse on the interplay between Big Data analytics, AI, and operational performance but also offers practical guidelines for SMEs aiming to harness the power of these technologies for enhanced operational outcomes. In conclusion, this investigation provides a comprehensive understanding of the dynamic relationship between technological capabilities and operational performance, offering valuable insights for policymakers, industry practitioners, and academics in the realm of manufacturing SMEs.