| Summary: | Rapid growth in the production of new homes in the United Kingdom (UK) is putting build quality under pressure as evidenced by the increase in the number of defects. Housing associations (HAs) contribute 21% to the UK’s annual supply of new homes. HAs are experiencing the challenge of government funding cuts and rental revenue reductions. Maximising the benefit of learning from defects is recognised as being a key opportunity for HAs to help address these challenges. Learning from defects is argued as a means of reducing the persistent defect problem within UK house building, yet how HAs learn from defects is under-researched. There is also a lack of research exploring which impacts of defects are perceived as important by the key stakeholders, which has caused confusion over which types of defect HAs should focus their learning on. The aim of this research was to better understand how UK HAs, in practice, learn from past defects to reduce the prevalence of defects in future new homes. The theoretical lens for this research was organisational learning (OL). An action research approach consisting of diagnosis, action planning, action taking, action evaluation and specifying learning was adopted. Further, the principles of soft systems methodology were incorporated in the action planning phase in order to explore an ill-structured real world problem to identify desirable and feasible changes (action interventions) within a HA. Data collection consisted of questionnaires, semistructured interviews, a focus group and organisational documents. Data analysis techniques included thematic analysis and simple statistical analysis. The key findings suggested that OL can potentially reduce defects and revealed that the health and safety implications and home occupant disruption caused by defects are the priorities. OL in HAs appeared to be viewed as a secondary task which consisted of a defects management team capturing and analysing defect data to identify improvement opportunities, with a primary focus on designing out the identified defects. Opportunities for data analysis fluctuated with workload. The use of live data analysis protected against workload spikes. The key findings further enabled the development of a specific OL from defects model for HAs. The findings revealed the importance of a dual approach to learning consisting of a codification approach of designing out defects combined with a personalisation approach of networking to tackle workmanship and other defects that can’t be designed out. The practical challenges of AR were highlighted when the interventions were abandoned due to changes in key personnel, despite the changes of a live data dashboard being shown to be beneficial after implementation.
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