This study investigates the organizational factors influencing technological readiness for Artificial Intelligence (AI) adoption, using a theoretical lens grounded in Sociotechnical Systems Theory (STS). A survey of 210 participants from educational and industrial institutions in China was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results confirm that AI adoption significantly enhances organizational efficiency and employee engagement, which in turn mediate its impact on technological readiness. By incorporating mediation analysis, the study provides a nuanced understanding of the indirect mechanisms through which AI adoption influences readiness, offering both theoretical and managerial insights for AI-enabled transformation. The integration of STS and rigorous empirical modeling presents a methodologically sound and practically relevant contribution to the field.

As artificial intelligence (AI) technologies become increasingly integrated into organizational operations, understanding the factors that drive AI-driven innovation performance is crucial. This study examines the roles of Digital Leadership (DL), Knowledge Management Effectiveness (KME) and AI Integration Capability (AIIC) in promoting Organizational Agility (OA) within Chinese industries. Using a mixed-methods approach and Structural Equation Modeling (SEM) with Smart Partial Least Squares (Smart PLS), the research analyzes data from 540 respondents across various sectors. The findings reveal significant direct effects of DL and AIIC on OA, as well as indirect effects mediated by DL and AIIC. Additionally, the study identifies moderation effects where AI-Driven Innovation Performance (AIDIP) enhances the relationships between KME and AIIC, as well as between AIIC and OA. The results highlight the importance of strategic leadership, effective knowledge management and robust AI integration capabilities in achieving organizational agility. The study contributes to the literature by providing a comprehensive model of AI-driven innovation performance and offers practical insights for organizations seeking to leverage AI technologies for competitive advantage. Limitations and future research directions are discussed, including the potential for global and longitudinal studies to further explore these relationships.

Strong data processing inequalities (SDPI) are an important object of study in Information Theory and have been well studied for$f$-divergences. Universal upper and lower bounds have been provided along with several applications, connecting them to impossibility (converse) results, concentration of measure, hypercontractivity, and so on. In this paper, we study Renyi divergence and the corresponding SDPI constant whose behavior seems to deviate from that of ordinary-divergences. In particular, one can find examples showing that the universal upper bound relating its SDPI constant to the one of Total Variation does not hold in general. In this work, we prove, however, that the universal lower bound involving the SDPI constant of the Chi-square divergence does indeed hold. Furthermore, we also provide a characterization of the distribution that achieves the supremum when is equal to 2 and consequently compute the SDPI constant for Renyi divergence of the general binary channel.