In this study three data-centric process modelling methods are studied, in order to create a better understanding of both the claimed and perceived added value of these methods. The three methods that are selected are Data-Driven Process Structures, Product-Based Workflow Design and Artifact-Centric Process Modelling. Using the three research questions described in section 1.1, the findings of this study will be summarised.
First research question
The first question addresses the theoretical differences between the selected methods. After selecting five quality attributes (Functionality, Usability, Efficiency, Maintainability, and Flex- ibility), key articles for each of the methods are identified. Using Grounded Theory Coding, these articles are coded, and claims of the developers are determined. The result of this Theo- retical Evaluation is an overview of the claimed aspects and attributes of all three methods, which is provided in Table 7. This table can be used as a basis for the comparison of the different methods.
Second research question
Rather than focusing on the theoretical similarities and differences between the methods alone, an Empirical Evaluation is conducted that identifies experts’ perceptions towards the method. Three workshop sessions are conducted (one for each method), in which participating experts study the method, make exercises, and discuss their experiences. Again, Grounded Theory Coding is used; this time to extract the participants’ perceptions towards the methods.
From the Empirical Evaluation it becomes clear that data-centric modelling methods are perceived as fairly difficult. Although the participants were able to apply the methods to simple examples, various difficulties were encountered. The creation of models was often perceived as complex or ambiguous, and the often abstract models were perceived as difficult to interpret for business users.
Third research question
In order to obtain the main strengths and weaknesses of this approach, the results from the Theoretical Evaluation are combined with the results of the Empirical Evaluation. In this way, claimed and perceived aspects and attributes of the methods are combined and compared, thereby identifying the strengths and weaknesses of the approaches. In the Discussion (Chapter 6), further explanations for the obtained results are provided, and the methods are compared to each other. The five selected quality attributes are used to identify the methods’ strengths and weaknesses; not only can these strengths and weaknesses be used for selecting one of these data-centric methods, they can also be used for the improvement of the methods. One could for example investigate the opportunity to increase the reusability of models for the Artifact- Centric approach, by incorporating some of the aspects of DDPS. Another opportunity can be found in the flexibility of DDPS: while DDPS currently is applied in a manufacturing domain, it might be interesting to apply this method in a service domain.
CONCLUSION This study combines insights obtained from theory, with insights obtained in an empirical setting. The results of this study therefore do not only focus on theoretical similarities and differences, but take user perceptions into account as well. In the empirical study, some inter- esting weaknesses were identified; these weaknesses did not only relate to individual methods, but often addressed data-centric methods in general. As data-centric methods are not widely known yet, they may encounter opposition. This study however tries to show that although adjustments might be required for all of the methods, the initiatives are interesting, often provide added value to the current activity-centric approaches, and are worth further investi- gation.
7.1 Limitations and Future Research
Some limitations to this study need to be mentioned. While some of these limitations posit opportunities for further research, others are merely mentioned in order to contribute to a complete and transparent study. The limitations are divided into limitations with respect to the materials used, and limitations that consider the evaluation method.
7.1.1 Materials
Although the materials used in this study are created with great care, they are still created by a non-expert. Despite the fact that the contents of the tutorials are revised by the developers of the corresponding method, and the understandability is reviewed by two Master Students, it is still possible that the tutorials are incomplete, or somewhat unclear at given points. It might therefore be that problems perceived by the participants are caused by an error or ambiguity in the tutorial, hence not caused by limitations of the method.
Furthermore, the questionnaire could have been better matched with the contents of the workshop and its desired results. Though ‘Perceived Ease of Use’ and ‘Perceived Usefulness’ do contribute to the evaluation, questions that more specifically addressed the selected quality attributes might have provide some interesting contributions.
7.1.2 Evaluation
As the information obtained in the questionnaire and discussion all is based on the participants’ perceptions, all information in the study can be seen as perceived information. In order to acquire some information on for example actual efficiency, one should make use of some quan- titative metrics. The time required for creating a model for example, is a perfect illustration of such a quantitative metric.
Furthermore, the fairest comparison between methods would have been a comparison based on the same case. This in addition enables measuring the time required to complete the model,
CONCLUSION
7.2 Future Research
Future studies can try to focus on a better triangulation: integrating questionnaires, exercises, and discussion even further. When these sources are better aligned, the results actively support each other. In addition, it would be interesting to evaluate the methods using a variety of cases; cases that apply the method in environments for which it is not originally developed. Furthermore, quantitative metrics could be included, in order to obtain quantitative infor- mation as well.
Other future studies could aim at a deeper understanding of one quality attribute. While this study used a variety of quality attributes, a more in depth study would be interesting as well. One could for example select only aspects that relate to Usability, in order to obtain detailed insights on for example the understandability of models and the ease of use of the method.
Another interesting direction is to test data-centric methods in real life situations. The methods in this study often use hypothetical examples, or variations on the same example. Using the methods in a practical situation might provide some interesting insights as well, as problems and difficulties are more likely to occur in real (often complex) situations.
REFERENCES