Accelerating supply chain management learning: identifying enablers from a university-industry collaboration. Issue 4 (13th June 2016) - Gibson, Trish; Kerr, Donald; Fisher, Ron
Abstract : Purpose: There is an acknowledged need to advance the supply chain management (SCM) learning agenda, with an emphasis on integration. This paper discuss an Australian university–industry collaboration aimed at accelerating SCM learning and offers some insight into models for building a forward-looking SCM. Design/methodology/approach: The research is an exploratory case study of the industry–university collaboration, using grounded theory procedures. The primary data involved 25 interviews with key participants from the university and industry partners, and from the first cohort of students in the undergraduate program developed within the collaboration. Findings: A theoretical framework for accelerating SCM learning was developed from the case study data; it comprises six constructs that influence, at strategic and operational levels, the acceleration of SCM learning. Four cross-construct concepts from the framework that form the cornerstones of accelerated learning are discussed in some detail. Research limitations/implications: The framework facilitates an examination of successes and shortfalls in the case study collaboration and generates a deeper understanding of critical elements for progressing the SCM learning agenda, and expanding SCM education. As with all qualitative research, the results may not be generalisable; testing the relevance and usefulness of the framework with the field will be an important next step. Practical implications: As the framework identifies conditions, characteristics and capacities of organisations and individuals that support the acceleration of SCM learning, it can provide assistance in designing future university–industry collaborations for advancing SCM learning. Originality/value: The framework identifies critical success factors for alliances and partnerships aimed at accelerating learning in an emerging body of knowledge such as SCM.