Exploring knowledge management systems in exponential organizations: insights from non-financial reporting practices

Purpose This study aims to investigate how knowledge management systems (KMS) can be strategically leveraged to enhance the distinct attributes of exponential organizations (ExOs). By bridging the gap between traditional knowledge management practices and the dynamic, high-growth environments of ExOs, this research work seeks to identify how KMS can optimize knowledge flows, facilitate decision-making and support the innovation-driven goals that define ExOs. Furthermore, this study evaluates the integration of artificial intelligence (AI) within KMS to amplify their role in fostering operational efficiency. Design/methodology/approach The study conducts a content analysis of 130 non-financial reports from Nasdaq-listed companies. By leveraging Leximancer software for conceptual and relational analysis, the research identifies key patterns and relationships in the data, offering a structured understanding of the integration of KMS and ExOs. Findings The study’s results reveal critical insights into how KMS are disclosed within the non-financial reports of ExOs and how these disclosures reflect practices that support knowledge flows and decision-making. The integration of AI plays a crucial role, automating complex data processes, improving decision-making and fostering real-time adaptability. By aligning with the core (external and internal) attributes of ExOs, KMS support their massive transformative purpose. Originality/value This research develops a theoretical framework connecting KMS, AI and ExOs, presenting novel insights into their interdependence. It provides valuable implications for organizations seeking to implement KMS to navigate dynamic and competitive markets, advancing both theoretical understanding and practical applications.