A Meta-Analytic Examination of the Antecedents and Outcomes of Artificial Intelligence Utilization in Accelerating Development in Startup Businesses

Abstract

This study aims to identify, categorize, and synthesize the antecedents and outcomes of artificial intelligence utilization in accelerating development in startup businesses. A systematic meta-analytic approach was employed to integrate empirical findings from studies published between 2019 and 2024. Following PRISMA guidelines, a comprehensive search was conducted across databases including Scopus, Web of Science, IEEE Xplore, ScienceDirect, and PubMed. Studies were screened based on predefined inclusion criteria requiring quantitative designs, extractable effect sizes, and explicit examination of AI-related antecedents or outcomes in startup contexts. A structured extraction protocol captured bibliographic data, methodological features, and statistical indicators. Quality assessment procedures ensured the reliability of included studies. Random-effects models were applied using Comprehensive Meta-Analysis (CMA) and R packages (“metafor” and “meta”), with heterogeneity assessed via Q-statistics and I² indices. Moderator analyses explored variations across geographic regions, industry sectors, methodological designs, and types of AI applications. Publication bias was examined using funnel plots, Egger’s tests, and trim-and-fill adjustments. The meta-analysis synthesized 45 empirical studies and identified 113 unique concepts, which were organized into several axial categories. Significant antecedent predictors of AI-enabled acceleration included leadership orientation toward AI, human capital readiness, organizational agility, data infrastructure maturity, ecosystem support, and regulatory and ethical alignment. Outcomes demonstrated statistically significant improvements in innovation speed (p < .001), operational efficiency (p < .001), forecasting accuracy (p < .01), and decision-making quality (p < .001). Market and financial indicators—including customer acquisition speed, revenue growth, and valuation—also showed strong positive effects (p < .001). Moderator analyses revealed variations by industry and geographic region, confirming that contextual factors influence the strength of AI’s impact. AI-driven acceleration in startups is a multidimensional phenomenon shaped by strategic, organizational, technological, and ecosystem conditions, producing substantial gains in innovation speed, operational performance, and market outcomes.