Artificial intelligence

: understanding the influence of BI&A systems and firm performance in technology adoption at firm level

Abstract

Business Intelligence & Analytics (BI&A) systems have emerged as powerful tools for extracting insights from enormous data sets, giving businesses the ability to make wise decisions and obtain a competitive advantage. In a rapidly evolving Artificial Intelligence (AI) landscape, the world faces significant unknowns and potential disruptions, from the impact of automation and robotics on employment to security concerns regarding the use of autonomous AI systems. In today's quickly changing business environment, understanding the dynamics of BI&A and AI adoption is paramount for organizations seeking sustainable growth and competitive advantage. This work investigates the intricate relationships between BI&A maturity, firm performance and AI adoption within organizational contexts, shedding light on the factors shaping technology integration strategies, an area requesting further investigation. For these reasons, we designed an innovative and comprehensive theoretical framework that integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with Dinter's BI&A maturity model and the firm performance construct from Law and Ngai (2007). Structural Equation Modelling (SEM) was employed to analyse empirical data gathered from a sample of various organizations from a European country. Our findings reveal compelling insights into the factors influencing AI adoption and firm performance. BI&A maturity perception emerges as a significant predictor of both AI adoption intention and firm performance, highlighting the pivotal role of mature BI&A systems in facilitating technology adoption and enhancing organizational performance. Additionally, the intention to adopt AI is found to positively influence AI actual use, underscoring the importance of fostering a culture of innovation and technology readiness within organizations. Overall, this work contributes to knowledge by providing a nuanced understanding of the interplay between the perception of BI&A maturity, firm performance and AI adoption, thereby providing new and useful insights about strategic decision-making processes and guiding future research endeavours in the field of technology adoption and organizational performance.

Date of Award	13 Nov 2024
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Gonçalo da Costa Aleixo Monteiro Melhorado Baptista (Supervisor)