Big data analytics–AI performance through organisational learning: institutional theory and KBV perspectives

Abstract

Purpose The purpose of this study is to investigate how organisational learning enhances performance in big data analytics (BDA)–artificial intelligence (AI) environments by integrating institutional theory and the knowledge-based view. Specifically, it examines the influence of competitive pressure and data-driven culture (DDC) on performance through organisational learning (OL), and whether analytics talent capability moderates the learning–performance relationship. Design/methodology/approach This study surveyed 374 Chinese managers and IT leaders involved in BDA–AI across industries. Data were collected via an online platform using a seven-point Likert scale. The research model was tested with PLS-SEM (SmartPLS 4.0) and 5,000 bootstraps, controlling for firm size, industry type and common method bias. Findings The study finds that a DDC influences organisational performance only indirectly through OL. Competitive pressure (CP) positively impacts OL, which strongly improves performance. Additionally, BDA–AI analytics talent significantly moderates the OL–performance link, indicating that greater talent enhances an organisation's ability to translate learning into better performance outcomes. Research limitations/implications This study reveals that a DDC improves organisational performance only when mediated by OL, providing important insights for institutional theory and the KBV. It challenges the assumption that culture alone drives performance, emphasising the essential role of learning in converting knowledge into strategic value. Additionally, BDA–AI talent significantly moderates the learning–performance relationship, highlighting the critical influence of human capital. Future research should examine the interplay of institutional pressures, learning capabilities and talent in data-intensive contexts. Practical implications Organisations must foster a DDC, invest in analytics talent and support continuous learning to maximise BDA–AI benefits. Data-based decision-making, upskilling and interdisciplinary training are essential. Without skilled talent, learning processes falter. Aligning culture, learning and talent is key to driving innovation, adaptability and sustained performance in data-intensive environments. Originality/value This study offers a novel perspective by integrating institutional theory and the knowledge-based view to examine how external pressures and internal capabilities jointly influence performance in BDA–AI adoption. It uniquely highlights OL as a core mechanism and introduces BDA–AI talent capability as a key moderator. Addressing a gap in existing research, the paper emphasises that technology alone is insufficient – organisational processes and human capital are essential for realising AI's strategic value, offering both theoretical and practical contributions to the field.