‘Benign Neglect’ – Towards an Understanding of the Cultural Enablers and Barriers to Learning Transfer
2012-08-16T14:56:09Z (GMT) by
This study explores the ways in which organisational culture affects the transfer of learning. Much of the research in this area has focussed on the transfer of learning from formal training courses taking a positivist approach to examine specific influencing factors. This study takes a wider view exploring learning transfer through the lens of organisational culture. This study also takes a more holistic view of learning, exploring the ways in which current models of transfer might apply to both formal and informal types of learning. The study focuses on the UK civil service, as a sector not yet considered by the literature. The methodology takes a social constructionist approach and uses qualitative research methods to build a contextualised view of learning transfer, using individuals’ perceptions of their organisations. A series of one to one and group interviews were used to gather data from three samples groups. A system of thematic analysis was chosen to identify a variety of categories and themes for analysis. The study concludes that existing models of transfer do not reflect the complex and iterative nature of learning transferred from a wider range of learning experiences. It also concludes that in the civil service a transfer-supportive culture relies on the creation of a supportive ethos that encourages transfer through sub-cultures and informal practices, rather than imposed formal systems and active line management practices. The success of these informal practices is because they reflect more closely the cultural assumptions learners. This study recognises that this approach is dependent on a positive individual disposition towards learning and a management practice of benign neglect. By taking a holistic approach to learning and a wider perspective of organisational influences, via the lens of organisational culture, this study has added to our understanding of learning transfer beyond existing models.