Collaborative Network Learning in Manufacturing


  • Wee Hock Quik Auckland University of Technology
  • Nevan J. Wright Auckland Institutes of Studies
  • Ammar Rashid University of Central Punjab
  • Halimin Herjanto Auckland University of Technology



collaborative networked learning, socio-technical systems theory, workplace learning


This study aims to investigate the antecedents of collaborative networked learning (CNL), to develop an integrative CNL framework and to bridge the gap between theory and praxis in manufacturing. It provides a holistic perspective of CNL within the complexity of the manufacturing environment, including empirical investigation using survey questionnaires. The findings and discussions draw upon socio-technical systems (STS) theory, and present the theoretical context and interpretations through the lens of manufacturing employees. Results of the study show the existence of significant positive influences of organizational support, promotive interactions, positive interdependence, internal-external learning, perceived effectiveness and perceived usefulness of CNL among manufacturing employees. The study offers a basis for empirical validity for measuring CNL in organizational learning, knowledge and information sharing in manufacturing.

Author Biographies

Wee Hock Quik, Auckland University of Technology

Previously, Lecturer from Department of Information System. Currently, Lean Manager for Pentair Valves and Controls.

Nevan J. Wright, Auckland Institutes of Studies

Professor and Head of Academic of Business Administration Programmes

Ammar Rashid, University of Central Punjab

Previously, Lecturer from Department of Information System. He is now an Associate Professor with University of Central Punjab, Punjab, Pakistan

Halimin Herjanto, Auckland University of Technology

Lecturer, Department of Marketing.




How to Cite

Quik, W. H., Wright, N. J., Rashid, A., & Herjanto, H. (2014). Collaborative Network Learning in Manufacturing. International Journal of Advanced Corporate Learning (iJAC), 7(4), pp. 4–12.