In the wake of the 2014 Flint Water Crisis, researchers, regulators, and utility professionals have given increased attention to understanding drivers of (CWS) Safe Drinking Water Act (SDWA) compliance by community water systems (CWSs). Most of this research has only explored system traits while ignoring the vital role of human capital, especially the operator. The status of CWS operators can vary widely between different systems. More critically, scholars have not investigated how effective external linkages between CWS operators have impacted SDWA compliance. Drawing from the theories of Organizational Learning’s inter-organizational learning, Innovation Systems’ knowledge transfers, and Agglomeration Economics’ knowledge spillovers, I hypothesized that increased interactions between CWS operators, facilitated in part by geographic proximity, would lead to more information sharing, increased CWS performance, and fewer SDWA violations. Remarkably little is known about the drivers of inter-operator interactions or whether such interactions improve SDWA compliance, and this research helped fill the data gap through a large-sample survey of CWS operators in Michigan to capture the frequency of interactions along with a range of operator and system characteristics which may explain why some operators participate in more inter-operator interactions than others. With this novel dataset, along with publicly available system and community data, this research first investigated what endogenous operator characteristics were associated with more reported inter-operator interactions. Through multiple methods on reported operator interactions, the Utility and Contract operators and operators with memberships in professional organizations appear more likely to report more interactions than Non-Affiliated operators and all operators who were not members of professional organizations. Second, based on Tobler’s first law of geography, there should be some spatial autocorrelation in the number of reported interactions, and this was tested using variogram modeling. Observed spatial autocorrelation indicated location-based differences in the number of reported interactions. Third, we used multiple methods to explore the primary research question to identify endogenous and spatial drivers of reported inter-operator interactions. Multiple models found that rural districts had a higher probability of fewer SDWA violations with increased interactions, while the urban districts had the inverse relationship. Fourth, the research incorporated CWS-specific and operator-specific variables, as the operator-specific data were not independent of the CWS observations (since some operators run multiple CWSs). I used a Generalized Linear Mixed-Model to estimate these relationships accounted for the multiple levels and found that more interactions increased the probability of SDWA compliance for certain types of operators. The broader implications of this research encourage stakeholders to pursue more inter-operator interactions as a low-cost mechanism to increase SDWA compliance. Seven avenues to increase interactions are outlined, ranging from open operator contact lists to operator focus groups to identify common problems and solutions to creating a state-level operator mentorship program to support new operators.
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