## 4 Evaluation
Table 4) confirmed, that the SemDaServ approach is logically correct, consistent, and fills a research gap in the field of SSSE. Regarding the success criteria (cf. Table A3), it was pointed out that more experiments are needed to measure the impact of the SemDaServ regarding success criteria 20 (efficiency of smart service development methods) and 22 (efficiency of the application of domain knowledge). This is caused by the fact that the SemDaServ approach itself generates efforts that need to be compared to approaches not using the SemDaServ approach (e.g., trial and error mixed with explorative data analysis using established data science tools and methods). For all other success criteria, it could be validated from a logical perspective that they are well addressed by the SemDaServ approach. The second workshop (participating experts: IDs 3, 4, and 5; cf. Table 4) confirmed, that the SemDaServ approach is logically correct, applicable, and useful for real-world applications. The comprehensibility and low access barrier of the artifacts used and the process steps were rated as very good. It was pointed out that scaling SemDaServ according to real-world project scenarios (budget, time, quality as well as the availability of experts) is an important aspect. Therefore, guidelines on tailoring SemDaServ to different project scenarios will be developed in future research. It was confirmed that the relevant stakeholders in the workshop participants' company can understand the artifacts resulting from SemDaServ and therefore a benefit is generated for the collaboration of product and service development. From a data scientist perspective, it was confirmed that the resulting semantic data specification provides great added value, as SemDaServ systematically describes relevant data and domain knowledge relevant for data understanding. Thus, iterations during smart service development can be prevented by developing a suitable data basis at an early stage. The interview with expert 6 (cf. Table 4) was focused on the topic of MBSE and the related use of SysML. In this interview, it was confirmed that the SemDaServ approach is compatible with the MBSE procedures and that the chosen representation type in the block definition diagram, as well as the use of the diagrams optionally mentioned in Section 3.3.4, is reasonable and logically correct. The interview with experts 7 and 9 (cf. Table 4) focused on current best practices established in the industry regarding the collaboration of product and service development related to data specification for AI applications. It turned out that in practice relevant data is mainly searched for according to a data-driven trial and error approach. For this purpose, the data scientists ask domain experts known to them from the past in an unstructured way which data is relevant for the application. Since the data scientists have little knowledge of the application domain, the questioning about relevant data usually remains at too general a level, as a result of which important details are lost. This process is time-consuming and causes many smart service projects to fail due to insufficient data. The systematic approach SemDaServ was evaluated as a suitable solution for reducing try and error iteration loops and thus increasing the efficiency in smart service development resulting from an improvement of the cooperation between product and service development. The interview with expert 8 (cf. Table 4) focused on the connection between product and service structure, the compatibility of the SemDaServ approach with other methods of SSSE, and the trade-off between document-oriented and model-oriented approaches. As a result, it was determined that there are methods such as MESSIAH [38] that should be used to develop an initial service structure. The resulting elements of the service structure can then be related to the product structure via the information items and information flows described in SemDaServ.