The performance evaluation of parallel computing environments is crucial for the design of parallel job schedulers, as well as policy definitions. The analysis of user behavior is fundamental to unveil individual behaviors and reactions to different system performances (e.g., scarce resources, low throughput, etc.). In this paper, we present an analysis of parallel computing users based on responses to the Questionnaire for User Habits of Computer Clusters (QUHCC). The survey is composed of 7 measures and 53 items, and was answered by 23 users of computer clusters at TU Dortmund University. We investigate several influences on working behavior, including the influence of slow responses on working times, strategies to cope with high contention and poor performance, user's experience, and user satisfaction. Analysis results reveal that user satisfaction is negatively correlated to the application slowdown; users tend to work after hours to improve their efficiency; informal agreements between users are established to coordinate executions and reduce the system load; and scientific experiments may include several clusters, thus the user submission behavior should be seen from a multi-dimensional perspective. We then compare and discuss the analysis results with conclusions obtained from statistical trace analysis to reveal unknown and hidden correlations and feedbacks between system characteristics and the subsequent job submissions. Our findings indicate that the user characteristics together with the historical information (traces) are crucial to build a concise understanding of feedback effects between the user satisfaction, their job submission behavior, and the system performance. Additionally, this paper also provides a first overview of which user reactions may be the most relevant for dynamic performance evaluation.