Social network theories can be used to describe the relations and interactions between members of a certain network. One of the most influential theories in this field is the theory of network cohesion as proposed by Coleman (1988) which states that dense networks have better opportunities to acquire social capital. Social capital can be seen as the “embedded value” of the network connections. The capacity for information flow (Coleman, 1988) within a network is such a form of social capital. If there are a relatively high number of connections in a network which are willing to share information with you, then you are in a favorable position to acquire social capital. On the other hand, Burt (2001) proposes a different view on the matter. He argues that “structural holes” within a network increase the opportunities for the acquisition of social capital. The definition of structural holes, however, is somewhat problematic but it is strongly associated with being in a position of entrepreneurial opportunities. This is because of the unique information that can be acquired by members of a network who have structural holes. Furthermore, the theory predicts that actors in a social network who are in structurally equivalent positions are likely to act in the same way. This is because actor A takes the actions of actor B as a reference frame for his own action if they are in a structurally equivalent position. This means that both dense networks and structural holes facilitate information flow and adoption in some way. This is useful when considering the adoption process of MM within a social network. However, while both theories make claims regarding the benefits resulting from the relations between entities in a network, it could be noted that they are defined too broadly to make inferences about specific types of information flow. For example, actors A and B can be related to each other within a certain network but that is not to say they discuss topics x, y and z. They could only talk about topic x. When it comes to MM, this can be problematic as personal finances are inherently a private affair and it is thus plausible that relevant information is not always spread among members of a network. Maertens and Barrett (2013) suggest that knowing a certain member of your social network does not mean that you can benefit from the information that member possesses. A certain flow of information is necessary to make this knowledge available. Murendo et al. (2017) found this to be the case in the situation regarding MM as well. Therefore, we believe the number of people in your network will influence your chances of adoption of MM, but only if you exchange information about MM with those people. This leads to the first hypothesis, H1.