Social Network Determinants of Self-Perceived Influence among Minority and Non-Minority STEM Faculty

This dissertation explores the significance of social networks of academic science, technology, engineering and mathematic (STEM) faculty in determining their amount of self-perceived influence in organizational decision making. Particular focus is placed on understanding how self-perceived influence varies between underrepresented minority (URM) and non-URM STEM faculty. This dissertation is motivated by several factors. First, there is the prevalent marginalization experienced by URM STEM faculty, which lessens the likelihood of them having a role in key organizational decision making. Second. while there is evidence suggesting that social relationships help URM STEM faculty succeed in academic organizations, less is known about the composition of their networks (i.e. structure, relationships, and etc.) and the impact that network composition has on their experiences in the organization. Furthermore, while URM STEM faculty have frequently recounted their experiences in academia in the context of how they are perceived by others in their environment, less is known about how URM STEM faculty perceive their own positions. Lastly, while research has addressed the relationship between networks and how they shape the perceived influence of people (as conceived by others) in decision making, less research has addressed how networks impact individual’s self-perceived influence in organizational decision making—especially in the academic STEM environment. Thus, the proposed dissertation seeks to fill gaps in literature by linking the structure of social networks of URM STEM faculty to how they impact URM’s self-perceived influence in organizational decision making. Primary research questions are: 1) Does network structure significantly explain the level of self-perceived influence held by academic science faculty in organizational decision making?; 2) Are there differences in the level of self-perceived influence held by URM academic science faculty versus non-URM academic science faculty; and 3) do URM and non-URM social networks explain self-perceived influence in the same way?. These questions are addressed by using structural equation modeling to understand the direct and indirect effects of race and network variables on self-perceived influence. Data from a national online survey of academic scientists is used for the analysis.