Performance measurement (PM) systems are commonly known for both their potential to enable significant improvement in organizational performance and success as well as the difficulty of actually obtaining these results. A review of the literature suggests that most applications of PM systems prove to be less effective than the design suggests or, in some cases, may fail to be institutionalized. While there have been many recent advancements in this area, the focus from researchers has been primarily on the design and use of these systems. More recent research suggests that the problem with these systems may be the execution and implementation of the system rather than the design. In response, researchers have investigated both enablers and barriers (more generally referred to as success factors) for PM systems. They generally assert that being aware of these factors and attempting to mitigate their effects increases the likelihood of system success and enables practitioners to realize the full potential benefit from these systems. While significant research has been conducted in this area, review of the literature suggests that there is little consistency among the studies with no consensus among researchers concerning terminology or definitions of factors. Analysis of the published literature suggests that this area is at a relatively early stage of maturity with many significant opportunities for further advancement of the research area. In this work, the existing published literature was synthesized and a comprehensive set of 29 potential success factors was identified, along with corresponding definitions. In addition, five distinct dimensions of PM implementation success were identified from the literature synthesis. These literature review results were used to design a survey questionnaire to assess actual organizational practice in an empirical field study. An exploratory factor analysis was conducted to refine the constructs corresponding to potential success factors and implementation success outcomes. The resulting final variables were investigated using regression analysis to identify the factors most strongly associated with each dimension of implementation success.