Critical Success-Related Factors Influencing the Adoption and Use of Artificial Intelligence in Saudi Small and Medium-Sized Enterprises

Abstract

The current decade presents significant opportunities for businesses to harness the transformative power of artificial intelligence (AI). Nonetheless, organisations of all sizes, including small and medium-sized enterprises (SMEs), continue to encounter challenges related to the critical success factors influencing AI adoption. Understanding the interplay between AI adoption, its utilisation, and these success factors remains pivotal to enhancing technology-enabled business operations. Therefore, this thesis investigates the critical success factors affecting AI adoption and use within Saudi SMEs. To construct a robust conceptual framework, three theoretical perspectives are employed based on a structured evaluation of relevant literature: the Technology-Organisation-Environment (TOE) framework, the Human-Organisation-and-Technology Fit (HOT-FIT) model, and Institutional Theory. The resulting framework addresses the technical, social, organisational, and environmental contexts critical to supporting SMEs in adapting to and utilising AI. It identifies eight key factors: skilled personnel, organisational readiness, data strategies, security concerns, system quality, government regulation, AI vendors, and trust in AI. A quantitative research methodology is employed to collect data from a sample of 300 SMEs across Saudi Arabia, utilising a simple random sampling technique within a cross-sectional survey design. Various statistical techniques are used to analyse and validate the proposed framework, including partial least squares structural equation modelling (PLS-SEM), which facilitates the testing and validation of the conceptual framework for AI adoption and use among Saudi SMEs. The findings indicate that critical success factors significantly impact AI adoption and use by Saudi SMEs. Notably, skilled personnel, government regulation, AI vendors, and trust in AI are identified as primary determinants of AI adoption. Additionally, skilled personnel, organisational readiness, government regulation, AI vendors, and trust in AI emerge as essential for the effective and sustainable use of AI. A statistically significant variation in AI adoption and usage is observed among Saudi SMEs of different sizes. The primary contribution of this study lies in extending existing information technology adoption literature to encompass the context of critical success factors for AI. Moreover, the findings offer a comprehensive perspective on the organisational dynamics influencing AI adoption and use by integrating human, organisational, technological, environmental, and social dimensions into a single framework. From a practical standpoint, the research provides valuable insights for technology consultants, policymakers, and regulatory authorities. Specifically, Saudi SMEs can leverage these findings to effectively enhance their capacity to adopt and sustain AI-driven innovations effectively.