Cross-Sectional Analysis of AI Adoption and Firm Performance Across Industries

Thesis Abstract

The rapid integration of artificial intelligence (AI) technologies across diverse industries has intensified concerns about how adoption translates into measurable firm performance, particularly given variations in organizational context, capability, and strategic focus. This study addresses the problem of inconsistent evidence on the performance impact of AI by performing a cross-sectional analysis across manufacturing, services, and technology sectors to delineate how adoption intensity, deployment scope, and human–machine collaboration influence performance outcomes. The aim is to understand the differential effects of AI adoption on firm performance and to identify contextual moderators that explain cross-industry variation. Specific objectives are (1) to quantify AI adoption intensity at the firm level using a validated multi-item scale; (2) to assess the relationship between AI adoption and financial performance (ROI, ROA, andEBIT margin) and non-financial performance (operational efficiency, innovation outcomes, and customer satisfaction); (3) to examine the moderating roles of organizational capability, data governance maturity, and top management support; (4) to test for industry-specific differential effects of AI on performance; and (5) to provide implications for strategy and governance. The theoretical framework integrates the Resource-Based View (RBV) and the Dynamic Capabilities perspective to explain how AI capabilities translate into performance advantages and how organizational processes enable or constrain value realization. The study employs a cross-sectional design with a stratified sample of 600 firms drawn from three industries—manufacturing (n=200), services (n=200), and technology (n=200)—selected from a comprehensive corporate database and supplemented by industry associations to ensure representation of small, medium, and large enterprises. Data are collected via a structured survey instrument administered to senior managers, complemented by archival financial data from public disclosures and vendor records to triangulate performance measures. Instrument validity and reliability are established through confirmatory factor analysis (CFA) and Cronbach’s alpha, with composite reliability thresholds above 0.70 and average variance extracted (AVE) above 0.50. The primary data analysis combines descriptive statistics, multivariate regression, and structural equation modeling (SEM) to test the hypothesized relationships and to assess mediation by organizational capabilities and data governance maturity. Robustness checks include propensity score matching to address endogeneity concerns and multigroup SEM to compare across industries. Anticipated findings suggest that AI adoption intensity is positively associated with both financial and non-financial performance, with stronger effects in technology and manufacturing sectors where data availability and automation opportunities are pronounced. The effects are expected to be enhanced when data governance maturity and top management support are high, indicating significant moderation by organizational capabilities. It is also anticipated that the innovation outcomes and customer experience gains mediate the relationship between AI adoption and performance, particularly in services. The study contributes to knowledge by offering a nuanced, theory-grounded understanding of how AI investments translate into performance across different industry contexts, clarifying boundary conditions under which AI yields the greatest value. It extends the RBV and dynamic capabilities literature by empirically linking AI-enabled capabilities with superior performance and governance mechanisms necessary for value realization. Practically, the results provide managers with evidence-based guidance on prioritizing AI initiatives, building data governance structures, and aligning AI strategy with organizational capabilities to maximize performance gains. The study concludes that while AI adoption generally enhances performance, the magnitude and pathways of impact are contingent on industry characteristics and internal competencies; thus, strategic investments should be tailored to sector-specific data ecosystems, governance maturity, and leadership commitment. Recommendations include developing standardized AI governance frameworks, investing in data quality and interoperability, and fostering cross-functional teams to accelerate knowledge integration from AI systems into core processes. Further research is advised to explore longitudinal effects and to incorporate behavioral and ethical considerations in AI-driven decision making.