- Search memory
- Thèse de Doctorat en Sciences de Gestion : Institut des Sciences et Technologies, Mines ParisTech : 2014
- Item type
- Study and report
- Dissertation
- Language
- English
- Publication year
- 2014
As cluster became a new buzzword, governments around the world increasingly implement
cluster policies. However, a relevance gap is growing between cluster research and practice. Scholars
build theories about the roles of clusters as powerful entities fostering innovation and competitiveness.
Meanwhile, governments and policy-driven cluster organizations struggle to manage these highly
entangled objects. This thesis addresses such a relevance gap. A systematic literature review (SLR) is
conducted on cluster policy research, which demonstrates that governments and policy-driven cluster
managers constantly face a multitude of organizational dilemmas, i.e. a set of decisions and choices for
which there is no one best choice, in matters such as how to implement cluster policy (political
dilemmas), how to manage policy-driven cluster members (managerial dilemmas) and how to adapt the
policy-driven cluster to the local reality (structural dilemmas). Answering these dilemmas is constitutive
of the management of policy-driven clusters, but it generates side-effect pathologies that need to be
monitored and evaluated. In this thesis, we conduct a qualitative empirical investigation of a French
policy-driven cluster located in the Paris Region: we analyse in detail the organisational dilemmas and
their related side-effect pathologies. Four different pathologies are identified: inefficiency (driven by
leadership dilemmas), distrust (driven by subsidies dilemmas), non-conformity (driven by structural
dilemmas), and pragmatism (driven by managing innovation and collaboration dilemmas). The deeper
knowledge of these pathologies contributes to improve cluster policy implementation and cluster
evaluations. Finally, this thesis argues that academics need to shift from studying the anatomy of
clusters to studying the pathology of clusters.