Abstract Participatory enterprise modeling (PEM) is presumed to have a positive impact on commitment, ownership feelings and further appraisals by domain experts with respect to the model. However, there has not been a lot of research into whether PEM actually has the desired effects. In this paper we report on an investigation of the effects of three settings with different levels of involving domain experts: an overall model was created (1) from four individual interviews, (2) from four individual models, or (3) in a joint meeting of domain and modeling consultants. The results show that the non-participatory interview setting led to less favorable appraisals, e.g., the possibility to participate and the value of the model were perceived as lower and the contribution of the modeling consultants was perceived as higher. Our findings should help practitioners in weighing possible benefits of participatory enterprise modeling against the organizational effort and monetary cost it involves.

In data-driven research within Model-Driven Engineering (MDE), the extraction of conceptual models, such as UML diagrams, from software repositories is a crucial step for analyzing software design, evolution, and quality. However, these extracted models often contain inconsistencies, redundancies, and noise because most model repositories are not curated. Without effective data cleansing, the reliability of empirical and machine learning (ML)-based MDE studies working with these repositories is seriously threatened. This paper proposes a data cleansing pipeline designed to effectively cleanse model repositories. Our approach systematically addresses common data quality issues by offering a sequence of automated pre-processing, validation, and filtering steps based on rule-based heuristics and ML techniques. By integrating conceptual modeling-specific data cleansing techniques into an automated pipeline, our approach reduces manual intervention, enhances reproducibility, and supports scalable analysis of model repositories. In an experimental evaluation of open-source UML diagram repositories, we demonstrate the effectiveness of our method in cleansing models. In two reproducibility studies, we further show the statistically significant effect the use of our MCP4CM pipeline has on downstream tasks.