During the last several decades, we see a tendency towards openly distributed knowledge. Whereas we experienced an open source movement in the 80's, we now see that open learning and open innovation have become popular. Akin to open source code encouraging transformational creativity (Boden, 2004), open or networked innovation may lead to more effective organisational learning (Sloep, 2009a). This process of open knowledge exchange involves short time commitments, similar to those in Ad-Hoc Transient Communities (AHTC). We would like to pose a new view on the interpersonal ties in networked innovation, that is, the view of interpersonal ties as coalitions. Networked innovation occurs on three levels: the micro level, the meso level, and the macro level. Micro level coalitions are formed on the personal level. Meso level coalitions are formed by units of people within organisations. Organisations form coalitions or alliances with other parties on the macro level of networked innovation. Each of these levels has their own problems that hinder the process of networked innovation. Examples are self-interest (micro), negative ties between units (meso) and so-called logrolling. People need to be informed of the value of their candidate coalitions so as to develop intrinsic motivation for co-operation. We propose the view of interpersonal ties within networked innovation as coalitions. We compare characteristics of Granovetter's characteristics of interpersonal ties (Granovetter, 1973)with the characteristics that were identified by Begley et al. (2008)to underscore this view. Afterwards, we propose an initial model of the antecedents of coalitions. These antecedents were described earlier by Brass et al. (2004)and we suggest an extension of this list of antecedents. Besides, we provide an initial model that visualises the relations between the antecedents and coalitions. As this is part of ongoing research, we provide a methodology for further investigation of the process of coalition formation within networked innovation. This includes an extensive literature review, model development, simulation and verification through an online experiment. This will hopefully answer our questions on how coalitions are formed within networked innovation, what the structure of these coalitions is, how they are sustained and how the payoff is divided.