THE ECONOMICS OF DIGITAL SCIENCE: EVALUATING THE TECHNICAL ANDSTRATEGIC DETERMINANTS OF FIRM PERFORMANCE IN THE AI ERA

Abstract

The rapid proliferation of artificial intelligence (AI) and digital technologies has
ushered in a transformative era for firm competitiveness, where digital science—defined as the
systematic integration of advanced computational tools, machine learning algorithms, data analytics,
and digital infrastructure—serves as a foundational mechanism for value creation, operational
efficiency, and strategic differentiation. Despite substantial investments in AI and digital platforms,
empirical evidence reveals significant variance in performance outcomes across firms, suggesting that
technological adoption alone is insufficient for sustained competitive advantage. This study addresses
this gap by systematically evaluating the technical and strategic determinants of firm performance
in the AI-driven digital economy. Grounded in the Resource-Based View (RBV), Knowledge-Based
View (KBV), and Dynamic Capabilities Theory, the research develops a comprehensive conceptual
framework that links core technical determinants—namely, AI capability, data infrastructure quality,
and digitalization maturity—to firm performance through the mediating roles of strategic
determinants, including digital transformation strategy, organizational agility, ecosystem
participation, and innovation culture. The model posits that technical excellence establishes a
foundation of digital science maturity, but superior performance emerges only when these capabilities
are dynamically orchestrated through adaptive strategies and organizational learning mechanisms.
Empirical validation is drawn from a synthesis of global studies spanning diverse contexts: ecommerce platforms in China, manufacturing firms in Taiwan, SMEs in emerging markets, and large
enterprises in the United States. Key findings confirm that:
 AI capability significantly enhances innovation and decision quality, with indirect effects mediated by creativity
and strategic alignment;
 High-quality data infrastructure reduces operational costs and enables scalable analytics, but its impact is
contingent on robust governance and interoperability;
 Digitalization (e.g., cloud computing, IoT) drives productivity gains, yet returns diminish without
complementary organizational agility;
 Strategic factors—particularly digital transformation strategy and innovation culture—amplify the performance
impact of technical investments, while ecosystem participation extends market reach and accelerates learning.
The study further introduces a hypothetical performance curve illustrating diminishing marginal returns to AI
capability in the absence of strategic complements, underscoring the necessity of co-evolution between technical and
strategic domains. A proposed integrated research model offers testable hypotheses for future empirical work,
emphasizing mediation and moderation effects.
Theoretical contributions include an extension of RBV and KBV into digital contexts by conceptualizing digital
science as a higher-order dynamic capability, and the identification of interaction effects among technical, strategic, and
environmental factors. Practical implications urge managers to prioritize strategic coherence alongside technological
investment, while policymakers are encouraged to support digital infrastructure, workforce reskilling, and regulatory
frameworks that foster ethical AI deployment and ecosystem collaboration.
Keywords: Artificial Intelligence, Digital Science, Firm Performance, Resource-Based View, Organizational Agility,
Digital Transformation, Data Quality, Strategic Determinants