Model-Driven Software Engineering (MDSE) is a widely used approach to deal with the increasing complexity in software. Model transformations are essential operations in MDSE. Due to the increasing complexity in software, corresponding input models and model transformations applied to them become larger and more complex as well. This means that the execution performance of model transformations is gaining in importance. While improving the performance of model transformation execution engines has been a focus of the MDSE-community in the past, there does not exist any empirical study on how developers of model transformation deal with performance issues. Consequently, we conducted an exploratory mixed method study consisting of a quantitative online survey and a qualitative interview study. We used a questionnaire to investigate whether the performance of a transformation is actually important for them and whether transformation engineers have already tried to improve the performance of a model transformation. Subsequently, we conducted semi-structured interviews based on the answers to the questionnaire to investigate how transformation engineers deal with performance issues, what causes and solutions they found and also what they think what could help them to easier find causes. The results of the quantitative online survey show that 43 of 81 participants have already tried to improve the performance of a transformation and 34 of the 81 are sometimes or only rarely satisfied with the execution performance. Based on the answers from our 13 interviews, we identified different strategies to prevent or find performance issues in model transformations as well as different types of causes of performance issues and solutions. Finally, we compiled a collection of additional tool features perceived helpful by the interviewees to address performance issues.