Lessons learnt from applying action research to support strategy formation processes in long-term care networks

This study demonstrates how action research (AR) that is aimed at scaling-up experiments can be applied to support a strategy formation process (SFP) in a subsidized long-term care network. Previous research has developed numerous AR frameworks to support experiments in various domains, but has failed to explain how to apply AR and action learning (AL) on the strategic level of organizational networks. Given this situation, we used a generic AR framework to explore its usefulness in supporting SFPs. The framework consists of four steps: (1) identifying the problem situation, (2) planning a solution, (3) taking action, and (4) reflecting on the action. The results show that utilizing AL in AR helps actors to reflect on and understand the challenges in forming a joint strategy in a network. We demonstrate that it can help to visualize the process and to create a common ground for discussion, to create a shared vision as well as commitment to scaling-up experiments. These insights should be used in future SFPs in networks. However, the results also show that the key barrier, the lack of executive commitment, was only identified at a late stage. This paper constitutes a first step toward a more sophisticated AR framework for strategy research. The mistakes highlighted here should help others avoid them in the futur