Learning and Knowledge Sharing in a Manufacturing Company

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  • Title: Learning and Knowledge Sharing in a Manufacturing Company
  • Author(s): Shuang Geng, K. B. Chuah, C.K. Cheung
  • Publisher: Common Ground Research Networks
  • Collection: Common Ground Research Networks
  • Series: Organization Studies
  • Journal Title: Knowledge Management: An International Journal
  • Keywords: Learning and Knowledge Sharing, Influencing Factors
  • Volume: 16
  • Issue: 4
  • Year: 2016
  • ISSN: 2327-7998 (Print)
  • ISSN: 2327-9249 (Online)
  • DOI: https://doi.org/10.18848/2327-7998/CGP/v16i04/13-32
  • Citation: Geng, Shuang, K. B. Chuah, and C.K. Cheung. 2016. "Learning and Knowledge Sharing in a Manufacturing Company." Knowledge Management: An International Journal 16 (4): 13-32. doi:10.18848/2327-7998/CGP/v16i04/13-32.
  • Extent: 32 pages


Learning and knowledge sharing is important for organization knowledge development. Individual knowledge ought to be aligned with enterprise knowledge and concentrate on improving the competitive advantage of an organization in today’s dynamic marketplace. There has been much research about knowledge learning and sharing in the organizational context. Evidently, learning and knowledge sharing processes in organizations are influenced by various factors. These influencing factors interact with each other. We proposed a model to explore the interrelationship between the influencing factors of knowledge learning and sharing in electronic component manufacturing companies. This study collected data from a manufacturing company producing high technology electronic components but uses high laborer intensive processes. It is a typical China based manufacturing company of today which is finding that cheap labor and low cost are no longer sufficient to maintain competitiveness. The company needs to have the capability to learn and improve with advancing technology. In our proposed model, we divide these factors into external factors (organization policy, organizational value, information technology) and internal factors (individual attitude, self-perceived knowledge competency). We used quantitative data to validate the proposed model and test our hypothesis. This study would be of particular interest to industry practitioners of organizational learning and developments.