The world is complex. Therefore the structure (who, what, where) of many organizations is complex. Therefore the processes (how and in which order) in many organizations are complex. When managers think and deliberate about problems in organizations they like to rely on schemes. A clear picture can summarize the complex structure and the processes of an organization in a clear way. All kinds of drawings that describe the structure or the processes of an organization are called models. I concentrate mainly on process models: the drawings that describe a series of followed actions or actions to be followed. To be correct, I have to admit that a model isn't always a drawing. It is a representation of reality and therefore it could also be a piece of text. But mostly a model is a graphical representation.
The value of business process models heavily depends on the quality of the process model. There are some aspects involved in process model quality. One of them is correctness, another one is understandability. Each of these aspects can be measured. The measurement of correctness is objective and relates to counting errors of different levels: structural error, syntactical errors, semantical errors, etc. The measurement of understandability is subjective and relates to the capabilities of the "reader" of the models. Therefore, researchers have found other variables that influence understandability and that can be measured objectively: size of a model, layout aspects (e.g. number of crossing edges), complexity (e.g. number of arrows from or to a node), etc. In other words, there are a large number of accepted objective metrics to quantify the quality of a process model. So, it is possible to assess which of two models is better. But how can we help modelers to increase the quality of their models?
We know what better models are, but we do not know very well how better models can be constructed. Therefore, we study the properties of the process of process modeling. Which way of making models leads to better results? To get insights in the modeling process, we measure properties of the process and try to find that properties for which we are able (i) to show that an improvement of the process property will result in an improvement of the resulting process model and (ii) to instruct or support the modeler to model in such a way that these properties will improve (and in this way the resulting model will be of better quality).
I'm hoping to have given you a clear image off what I'm doing. If you are interested, but didn't understand what I explained, please let me know. Not only I will try to help you understand, you will also help me to point out which part of the text is difficult. If you are no researcher, please also don't hesitate to ask. Your input is perhaps the most important because people who are not familiar with these things may ask the most relevant questions (e.g. 'all right, but is anyone waiting for this'). You can find my contact info here.
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