Machine Learning Applications in Knowledge Management

Purpose: The general objective of the study was to explore machine learning applications in knowledge management. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to machine learning applications in knowledge management. Preliminary empirical review revealed that integrating machine learning (ML) into knowledge management (KM) systems significantly enhanced decision-making processes, knowledge sharing, and collaboration within organizations. ML-powered tools improved efficiency and accuracy by automating tasks and providing predictive insights, leading to better organizational performance and innovation. However, the study also highlighted the challenges of data quality, integration, and user adaptation, emphasizing the need for comprehensive strategies and investments to maximize ML benefits in KM. Ultimately, the study underscored ML's transformative potential in creating a more efficient, innovative, and competitive organizational environment. Unique Contribution to Theory, Practice and Policy: The Knowledge-Based View (KBV) of the Firm, Technology Acceptance Model (TAM) and Socio-Technical Systems Theory may be used to anchor future studies on machine learning applications in knowledge management. The study recommended integrating dynamic ML capabilities into theoretical frameworks, emphasizing the interplay between ML algorithms and human cognition. It advised organizations to invest in robust ML infrastructure, foster a culture of continuous learning, and adopt user-centric design principles. Policymakers were urged to establish ethical standards and incentivize best practices in data governance. Practical recommendations included automating routine tasks to enhance efficiency, using ML to foster collaborative innovation, and adopting continuous improvement and adaptation mindsets to keep ML applications relevant and effective.