Previous research has maintained that the capacity to manage alliances is a distinct capability, defined as the ability to identify, negotiate, manage, monitor and terminate collaborations. This paper focuses on an important but hitherto neglected aspect of alliance capability by investigating how partnering firms may learn how to better manage their dyadic R&D collaborations. In particular, we seek to test the Nonaka and Takeuchi (1995) model of dynamic knowledge creation by establishing a link between the facilitation of four knowledge conversion processes -socialisation, externalisation, combination and internalisationand an improved capability to manage inter-organisational R&D processes. We specify and extend the model by identifying and testing several critical interactions between these knowledge conversion processes. Relying on data from 105 R&D partnerships in the global telecommunications industry, we suggest that the failure to support one of these knowledge conversion processes has the potential to hamper the proper functioning of the other knowledge conversion processes and thus the emergence of capability to manage dyadic R&D collaborations.

Encouraging experiences from a case study show that when lean manufacturing is implemented as an innovation process, and social stimulation games are applied, the resulting new systems are both economic and organizational successes. The lead time of change is short and resistance to change is avoided. The lean principles can spill over to other business processes, which ultimately creates a “lean” enterprise. To guide this development, top‐down change management should be replaced by “lean” visions and guidelines. Under this umbrella, the individual development projects should unfold as consciously managed innovation processes that encourage bottom‐up creativity and learning.

Structured abstract:Purpose -The study compares the coordination of supply chain networks in contractually different complex construction projects. Design/methodology/approach -A comparative case study of the coordination of collaborative work in two successful hospital construction projects was conducted. One of the projects applied multiple dyadic contracts, whereas the other project applied one multiparty contract between the parties. The projects were located in the USA. Data was collected by observing the coordination on the construction sites for six weeks and by conducting 72 interviews. Findings -The paper shows that depending on the contract type, the timing and extent of complementary procedural coordination differs during projects. Compared with one multiparty contract, the multiple dyadic contracts needed to be complemented during the design phase with three additional procedural coordination mechanisms: 1) organizational design, 2) processes for collaborative work, and 3) integrated concurrent engineering sessions. Additionally, common rules of conduct were taken into use during the construction phase. However, regardless of the contract type, procedural coordination mechanisms, such as colocated working, collaborative decision making in inter-organizational meetings, a liaison role, and shared project goals were needed throughout the projects. Practical implications -If multiple dyadic contracts are applied, procedural coordination mechanisms have to be co-created by all supply chain parties at the beginning of the project. Originality/value -The study provides understanding on successful contractual and complementary procedural coordination mechanisms of supply chain networks in complex construction projects.

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Researchers stress the importance of understanding knowledge transformation in projects. To explore how knowledge is transformed across organizational and specialization boundaries in project networks, we observed 22 knowledge transformation cases in two project networks. We found that new knowledge was created across specialization boundaries and that knowledge was altered across organizational boundaries. When both organizational and specialization boundaries were crossed, new knowledge was created. From these findings, we developed a set of propositions and formulated these into a model of knowledge transformation in project networks. This research contributes to a better understanding of knowledge transformation processes and outcomes in project networks.