THE IMPACT OF PREDICTIVE INTELLIGENCE AND REAL-TIME DATA INSIGHTS ON CORPORATE FINANCIAL SUSTAINABILITY

Abstract

Purpose: This study examines the impact of Predictive Intelligence (PI) and Real-Time Data Insights (RDI) on Corporate Financial Sustainability (CFS), while controlling for organizational factors, industry factors, and AI-related risk factors. The research aims to determine whether these analytic capabilities (PI and RDI) individually and jointly contribute to long-term financial resilience and performance in modern organizations.

Theoretical Framework: The study is grounded in three complementary theoretical perspectives: the Resource-Based View, which conceptualizes PI and RDI as strategic organizational capabilities; Dynamic Capability Theory, which explains how sensing (PI) and seizing/reconfiguration (RDI) functions enable firms to adapt to changing environments; and the Data-Driven Decision-Making perspective, which emphasizes the role of timely, analytically processed data in improving decision quality and financial outcomes.

Methodology: A quantitative, cross-sectional research design was adopted, with data collected from 320 managerial and analytical professionals across medium and large organizations in multiple countries and industries, including Financial Services, Manufacturing, Information Technology, and Retail/Trade. Reliability and validity tests, including Cronbach's alpha, Exploratory Factor Analysis (EFA), and Harman's Single Factor Test for common method bias, were conducted, and measurement methods were adapted from validated scales.

Key Findings: The results provided strong support for all three hypotheses. PI had a significant positive effect on CFS (β = 0.410, p < 0.001, ΔR² = 0.144), and RDI similarly demonstrated a significant positive influence (β = 0.370, p < 0.001, ΔR² = 0.133). When entered together, PI and RDI jointly explained an additional 19.7% of variance in CFS (ΔR² = 0.197, p < 0.001), confirming their complementary and synergistic contributions. Among controls, OF consistently showed a strong positive effect, IF was positive but marginally significant, and AR exhibited a significant negative relationship with CFS across all models.

Originality/Value: This study contributes to the literature by empirically distinguishing PI and RDI as separate but complementary analytics capabilities, demonstrating that their combined use provides a more comprehensive explanation of financial sustainability than either construct alone. It also highlights the importance of managing AI-related risks to realize the full potential of analytics investments. The findings offer practical guidance for organizations seeking to leverage AI-enabled predictive and real-time analytics for long-term financial resilience.