Abstract:
This thesis is concerned with receptivity and response encountered at different
levels within organisations when a novel approach to the learning of fault diagnosis
skills is introduced. Essentially, the work involved the transfer of a learning
technology from research and development on the one hand to the workplace on the
other.
With only a few exceptions, previous research had taken a highly focused, machinecentred
view of fault diagnosis. The same view has been adopted towards the
limited range of training that is currently offered in this subject. The overall aim
here was to introduce a holistic approach by viewing fault diagnosis as a social
process that is conducted within a technical context. To do this, account had to be
taken of the complex interactions found between a number of disciplines such as,
design, production, quality assurance, buying, maintenance and management.
The learning technology that served as a vehicle for the transfer of this systems
approach was a series of open learning modules. The modules were produced as
part of the project.
The methodology was based upon an inductive approach that involved the
interpretation of qualitative data; this was done using a triangulation of research
methods: case studies, critical incidents, and survey questionnaire. The sample, of
both large and small organisations, was designed to provide a mix of different types
of manufacturing and service industries. In each case, the practice of fault
diagnosis skills continues to be a critical influence upon business performance.
Different factors arose at different levels within each organisation, and betweenorganisation
factor differences are also identified.
Apart from the production of open learning material, the contribution made to the
subject area is of new insights into the mechanism used for technology transfer
within companies, and the identification of factors that either facilitate or hinder
transfer of this kind. There is also a contribution to the debate about how the theory
of systems thinking can be applied in a prescriptive way as opposed to the more
common descriptive delivery.
Recommendations are made for further developmento f the learning technology.
