Data collection

The data used in this thesis consists of eight interviews that were conducted in October of 2017. In order to find informants, I published a Facebook post in September of 2017 in which I described the general topic of my thesis and inquired for potential volunteers to be interviewed. The post was published in two groups – in Music Studies of University of Tampere and Music scholars of Finland – and in my own page. I also encouraged my social network to share the post forward. In this post, I told that the only requirement to be eligible for the interview was that one would be interested in the topic and willing to share one’s experiences in relation to it. Thus, the interviewees were not based on any specific reference group but rather on the replies I got. Unfortunately, I forgot to announce in the post that I cannot nor want to interview people that I am familiar with. Due to that I had to exclude a few volunteers I knew.

The lack of specific reference group is not an ideal situation since informants should represent some sort of group which in turn is defined by research interests. (Hirsjärvi & Hurme 2008, 60, 83). Despite this, all of the volunteers turned out to be citizens who are young adults with an academic background. This fits together well with the fact that according to the report published by Finnish National Group of IFPI and Teosto (2016), age groups of 16–25 and 26–35 use distinctly more streaming services compared with the older age groups. That being said, the reference group of the informants is young adults with academic background and interest in the topic.

Furthermore, it has to be noted that five of the interviewees, a majority, had an academic background in music studies unlike the rest of them. This is not ideal but unfortunately there was no possibility to frame the data only in people with this background. However, having an academic background in music is just one aspect that I have taken into account in my analysis. Still, it is an issue that may heavily affect the reliability of the research. A research is seen as reliable if other researchers can produce relatively similar results with relatively similar

research setting. Should this study be conducted again with more heterogenic informants, it may produce quite different results. However, as Schreirer (2012) notes, qualitative research emphasizes validity over reliability which is to say that the repeatability of research is not a big concern after all. On the other hand, the fact that most of the interviewees are musically oriented or informed can also be counted as an advantage of this research. These interviewees are, however, people who have already put attention to the topic, thus being able to reflect it more analytically.

The fact that a majority of interviewees were studying music or perceived it as a profession is something that has to be taken into account in the analysis because it is the reason why so many interviewees told how important music is in their everyday lives. For these interviewees especially, music was not only listened simply for fun or enjoyment but it was also an object of academic or professional interest. Thus, music could be listened to acquire inspiration for one's own work (of producing music), to maintain a good perception of what kind of music is produced in contemporary society or just accumulate knowledge of history of music.

The interviews were based on predetermined themes but questions within them changed a bit over time. I did not use a sample interview to test my interview frame so I had to make some minor changes after the first interview. This is in line with the idea that the analysis begins already during data collection which is discussed more in detail in the next chapter. The length of the interviews varied from 40 to over 70 minutes although the themes were the same. This was due to that every person has a different way of speaking and telling their ideas and opinions. Some of the interviewees turned out to be more capable of reflecting their ideas which was not surprising. After the interviews, I listened to the records and transcribed them carefully. Only filling words and totally irrelevant parts of discussion were marked out. The transcribe process resulted in approximately 70 pages of text which is the actual data to be analyzed.

The themes used in the interviews can be found after references at the end of the thesis. I will, however, describe them quickly here. The first theme was about the practices of listening to

music. This theme was chosen because I wanted to know how the informants use music in their

everyday life: how much do they listen, where do they listen, for what purposes, what do they like and what may have influenced their preferences? The idea was to get a basic insight of what music means for them which, in turn, was central in order to analyze how they perceive the features of recommender systems.

The second theme was about experiences of discovering music. This theme was chosen in order to discuss how the informants search and discover new music from a diversity of sources. The interviewees were asked their ideals of a good recommendation in terms of what do they value in music and in what circumstances. They were also asked to tell what source they use and provide examples of good or bad recommendations as well as reflect them.

The third theme was about usage of streaming services. This theme was chosen to find out what streaming services the informants had used since there were no restrictions in the interview invitation. The informants were asked to tell what was good or bad about them and in what situations they are used. The hypothesis was that different streaming platforms have very different properties and functionalities so they probably have a diversity of uses.

In the fourth theme, the discussion was guided closer to the core of this thesis: the usage of

music recommender systems. The informants were first asked what kind of ‘systems’ they had

used within those streaming services, how long and for what purposes. The idea of this thesis was to get concrete data of how the informants use the systems in their everyday life. Some of the interviewees had a mobile at hand so they could show some features they preferred or not. Observing was not an actual method in this thesis but it turned out that it could be used in further research.

The fifth theme was closely related with fourth one but more focused in the interpretations and

perceptions of algorithmic recommendations. In this theme, the interviewees were asked to

share their ideas about the relevance of the music they were recommended. The informants were explicitly requested to reflect the recommendations in relation to what they had told about their conceptions of a good recommendation during the second theme. They were also asked whether the algorithmic playlists had influenced their practices of listening to music in any way.

The last theme was about the conceptions of artificial intelligence. The interviewees were asked their prejudices and ideas of artificial intelligence in general: what might be its strengths and weaknesses. After that, the interviewees were asked to reflect the ability of AI to recommend music and compare this with so called ‘traditional’ recommenders such as cultural intermediaries discussed in literature review. The interviewees were explicitly asked to reflect what kind of differences there are between AI-driven recommendation and human curation.

These six themes stayed the same during all the interviewees but questions within them varied depending on the flow of the discussion. Some interviewees willingly shared information that I had not yet asked while others needed more explicit questions in order to be able to answer. The hierarchical interview frame, suggested by Eskola and Vastamäki (2015, p. 37–38) turned out to be useful in these situations. As usually happens, it turned out that the informants had ideas I had not expected so they were discussed more in detail. Every interview cumulated accumulated my knowledge of the topic somehow so it can be argued that the final interviews were somewhat ‘better’ than the first ones. I felt, however, that the data saturated in the end which is to say that there were no profoundly new ideas during the last interview.

4.3 Theory-related content analysis