These studies were able to identify individuals and organizations that may be influential, but were not able to determine the extent of this influence. Whilst an emerging stream of tourism research has begun to employ inferential techniques, such as the Quadratic Assignment Procedure (Liu, Huang, & Fu, 2017), most Social Network Analysis (SNA) research relies on descriptions of networks to explain relationships among entities (Shumate & Palazzolo, 2010). However, these approaches do not enable researchers to determine if patterns identified in networks could have occurred by chance (Hunter & Handcock, 2006). Researchers have raised concerns when attempting to infer network characteristics from descriptive metrics; for example, clustering coefficient values, which indicate that entities or actors are important in networks, can be observed in randomly created networks (Newman, Strogatz, & Watts, 2001). This suggests these metrics will require additional qualitative or quantitative data about network actors or characteristics in order to support robust research. The aim of this paper is to examine the emergent network identity in a DMO network by identifying relational and node property influences on the structure of a communications network in a DMO. Using data collected from the Destination Milton Keynes initiative, the communication network of a DMO was modelled using an Exponential Random Graph approach. These models identified the extent to which node (organizational characteristics) and structure influence the distribution of communication ties in the network. Literature Review Network theory (Granovetter, 1973) and the analytical approach of SNA can be used to examine the arrangement of relationships between interacting entities, such as individuals, groups and organisations (Wang & Xiang, 2007). In the tourism and management domain, this perspective advocates that organisations no longer act solely as individual entities but through relational networks where value is created by initiating and nurturing collaboration (Fyall et al. 2009). SNA examines structural and relational properties of networks, such as density (Table 1), to identify patterns that can be used to explain social behaviour (Prell, 2012). SNA literature in business and management (Borgatti & Foster, 2003) seeks to demonstrate how the concept is able to visualise otherwise invisible social networks. Once depicted, invisible social networks, such as communication structures, may be leveraged for visible results in organisations (Conway, 2014). However, to date, little research has been undertaken to examine communication among destination organizations, particularly through the lens of SNA (Asero, Gozzo, & Tomaselli, 2016). SNA has often been perceived as a network tool that produces largely descriptive data without providing deeper insights (Prell 2012). Within this context, scholars have argued that social network studies often overemphasise the quantity of network relationships and interactions rather than their quality (Conway 2014). Table ...