Make It Your Own: Digital Applications for Customized Information in Museums : Conception and Evaluation of an App

Sammanfattning: Visiting a museum is a popular activity for many people. Be it to find out about certain museum objects or to feel entertained. An important contribution to a satisfying museum experience is the information about the objects. On the one hand, the provided information should be about them, but at the same time reflect the interests of the visitor. Since the information presented on information labels is often only rudimentary, visitors can follow human guides or use digital tools that display additional information. The problem, however, that exists despite a wide range of offers, is that the information is designed for the general public and cannot be customized by the visitor. In this way, information is presented that is either not of interest to the visitor or, in the worst case, that the visitor does not understand because prior knowledge is lacking. Even if existing research deals with the presentation of information and the preparation of digital applications, the customizing aspect is often missing. This master thesis shows to what extent information and functions embedded in an app can be customized. A prototype of an app was constructed that, based on an example museum, presents filter options too utput customized information about a museum object. The prototype was tested in a user study by six participants. The participants tested the prototype by processing test tasks and answered an evaluation questionnaire. Three of the six participants also completed a group interview. The goal was to find out to what extent the filters offered reflect the needs of the participants and which functions and aspects are important for customization. The results show that despite the current lack of focus on prior knowledge, filtering based on it is not very effective. In addition, suggestions were made as to how groups could benefit from the more individual use of the app. Future research can build on the achieved results by integrating the proposed functions and testing them in further studies to expand the customization step by step.