Advancing Explainable Artificial Intelligence and Predictive Business Analytics to Strengthen Digital Transformation, Operational Resilience, and Productivity among U.S. Small and Medium-Sized Enterprises

This research aimed to shed light on the value of Explainable Artificial Intelligence (XAI) capability and predictive business analytics for Small and Medium Enterprises (SMEs) in the United States in driving digital transformation, and how these capabilities contribute to better operational resilience and productivity for SMEs. The approach adopted to select the sample was a positivist, quantitative, cross-sectional design that involved sampling 385 SME-knowledgeable decision-makers, evenly distributed across industries, firm sizes, and the four Census Regions. Partial least-squares structural equation modeling with 10,000 bootstrap resamples, mediation, multi-group, importance–performance, endogeneity, heterogeneity, necessary-condition, and predictive-ability analyses were performed on the data. Explainable AI and predictive analytics were the top factors for advancing digital transformation, with the same β values of 0.405, and in turn, operational resilience with the same β values of 0.181 and 0.157, respectively. Combinations of digital transformation and resilience affecting productivity (β=0,294 and β=0,408), as well as the impact of digital transformation on resilience (β=0,504), had significant positive effects. The robustness tests confirmed the absence of endogeneity in the materials, with increases in the percentages for digital transformation (51.2%), resilience (56.6%), and productivity (56.4%). The transparent intelligence that is turned into productivity in the integration of a digital process and adaptable strengths of operation. That is why SMEs need to pay attention to the following themes: explainable governance, analytics integration, workforce capacity, and resilient Digital operations amid constant uncertainty.