Artificial Intelligence Adoption, Predictive Analytics, and Digital Innovation as Determinants of Developer Productivity: Evidence from San Diego, USA

Abstract

Purpose— The study investigates how artificial intelligence adoption, predictive analytics, and digital innovation affect developer productivity within enterprise software environments of technology firms in San Diego, USA.

Study Design/methodology/approach— A quantitative research design was employed using survey data collected from 350 professionals. Structural Equation Modeling (SEM) was applied to validate the proposed framework and examine the relationships among the key variables.

Findings— Results reveal that AI adoption enhances defect traceability and operational efficiency, predictive analytics strengthens proactive release management, and digital innovation supports usability improvements and cross-functional collaboration. Collectively, these factors demonstrate a significant positive influence on developer productivity, confirming the robustness of the model.

Research Practical Implications— The findings provide actionable insights for product managers and organizational leaders seeking to optimize enterprise platforms, improve user experience, and maintain competitive advantage through technology-enabled productivity strategies.

Originality/value— This research offers empirical evidence from a prominent U.S. technology hub, extending the literature on productivity in enterprise software environments and highlighting the critical role of advanced technologies in shaping developer performance.