Navigating the knowledge current: Unraveling the dynamics of knowledge transfer and learning in outsourcing for enhanced business streamlining
University of edinburgh MA Social Policy and Economics, United Kingdom.
Review Article
Magna Scientia Advanced Research and Reviews, 2024, 12(01), 265–274
Article DOI: 10.30574/msarr.2024.12.1.0163
Publication history:
Received on 02 September 2024; revised on 17 October 2024; accepted on 20 October 2024
Abstract:
This study examines the pivotal role of knowledge transfer and continuous learning in optimizing outsourcing arrangements to achieve streamlined business operations. Amidst rapid globalization and technological advancements, outsourcing has evolved from a cost-reduction strategy to a complex mechanism involving core business functions. This dissertation explores the processes and dynamics of knowledge transfer between outsourcing partners, emphasizing their impact on operational efficiency. Using a mixed-methods approach, the research integrates qualitative insights from interviews and quantitative data from surveys to identify key factors such as trust, communication, and cultural alignment that enhance knowledge transfer effectiveness. Findings reveal a positive correlation between robust knowledge exchange mechanisms and improved business performance, supported by thematic analysis and regression models. Practical recommendations include fostering trust, implementing continuous learning programs, and aligning organizational cultures to optimize outsourcing partnerships. The study contributes to academic and practical understanding by addressing gaps in knowledge transfer literature, offering actionable strategies to mitigate challenges like knowledge loss and communication barriers. These insights enable organizations to enhance their outsourcing strategies, ensuring sustainable competitive advantage in dynamic global markets.
Keywords:
Knowledge Transfer; Outsourcing; Continuous Learning; Organizational Efficiency; Cultural Alignment; Global Supply Chains.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0