Abstract
The research software Spotivey allows for enriching online surveys with data on an individual's music consumption through the Spotify Application Programming Interface (API). It seamlessly merges online surveys, which are popular and efficient data collection tools in the social sciences, with behavioral data without requiring further apps or tools. Hence, it addresses timely and relevant drawbacks of self-reports regarding any (digital) behavior and provides a solution for a specific topic of investigation.
Researchers typically resort to self-reports on behavior due to their ease of obtainment. However, such data are subject to well-known sources of error such as false recollection, social desirability, and satisficing (e.g., Schwarz & Oyserman, 2001). Spotivey lets researchers obtain actual behavioral data without using additional tools, which circumvents these problems in studies involving music reception. It further reduces the burden on researchers and participants alike, as it does not require programming skills to set up and is implemented right within the context of an online survey. It is currently only available for Spotify and in LimeSurvey. Naturally, researchers would benefit from the possibility to use it with other music streaming services (e.g., Apple Music, Amazon Music, Deezer) and other survey tools (e.g., Unipark, Qualtrics, LamaPoll), which the developers are working on already. However, the initial choice of Spotify and LimeSurvey is reasonable, as the former is currently the most popular music streaming service worldwide (RouteNote, 2022) and the latter is open-source software (LimeSurvey, 2020). Further, the developers commendably provide a server researchers can use for their projects free of charge. They also provide a Git repository that contains everything necessary to set up Spotivey on a custom server, if desired.
The developers have prepared a well-structured and easy-to-use web interface. Researchers register and log in using an e-mail address and a password. They are then provided with information on the motivations for and mechanics of Spotivey and a step-by-step tutorial on its use. For the tool to work, researchers need to link it with an existing LimeSurvey form. Once this connection is established, they can select which information they require from participants (last played songs, most played songs and artists, artists followed, created playlists). For songs and artists, researchers can limit the number of individual entries to be retrieved. It is further possible to set up a follow-up survey form with questions that directly relate to the provided behavioral data. This process is partially automated. Overall, the setup process is very straightforward, especially considering that Spotivey not only needs to establish a connection with the Spotify servers but also link retrieved information with up to two LimeSurvey forms. Programming is not required at any point, which significantly increases and benefits the target audience. Essentially, anyone who can set up an online survey can use Spotivey. The setup can be repeated with different options for different projects, if desired.
On the side of the participants, the process is also very simple, which is even more important. They are forwarded to the Spotivey server and asked to accept a data privacy notice that is automatically adjusted based on the required data researchers selected before. After logging into their Spotify account to allow access to their data, participants can select which of the songs, artists, and playlists are actually attributable to them, in case other people are using the same account. Finally, they are forwarded to the follow-up survey, if applicable. The switch from the survey to Spotivey in a pop-up window (and back) is seamless and should not be perceived as disruptive. Also, if participants are using a device that is already logged into their Spotify account, the login step is skipped, which is very convenient. Given that the smartphone has become the tool of choice for most people to access the Internet (and online surveys) and music streaming mostly takes place on the smartphone, too (e.g., NET-Metrix, 2020), it is likely that many study participants will benefit from that.
The developers addressed the issue of data protection as well as possible. They prepared a dynamic data privacy notice that appears right before granting access to use data, and Spotivey merely retrieves data necessary for the study. Personal information such as names, e-mail addresses, and years of birth are not accessed at all. However, no matter how considerate and well-designed the technical process may be, participants’ anonymity is compromised as soon as person-related information is assessed in the survey, as it is directly linked to the use data. While this is not an issue with Spotivey itself, it is a risk researchers should keep in mind when using it.
Giving participants the opportunity to select which information they would like to provide and which they would not is a reasonable feature, especially when using a shared account. However, this reintroduces some of the social desirability bias present in self-reporting that Spotivey initially avoided, as participants may omit anything they deem awkward or unacceptable in addition to data that are actually not associated with them. At the same time, this option is useful for avoiding dropouts. The developers allowed for disabling this selection mechanism, so researchers can decide to avoid the bias completely. When enabled, Spotivey should still register whether a participant omitted anything or not. This information could then be used as a control variable when analyzing the data to account for possible biases it introduced. In this case, however, the effects of the use of a shared account and social desirability are confounded. To tackle this problem, I suggest asking participants whether they share their Spotify account with others before allowing them to omit entries.
Spotivey is a very useful tool for research on music consumption. It allows researchers to combine online surveys with the retrieval of actual behavioral data without requiring programming knowledge or the use of additional tools, which would heavily interfere with the flow of the survey and may deter participants from completing the study. The developers set an example for others who may find ways to assess other types of digital behavior in a similar way. They implemented many functions that make the procedure as simple and secure as possible for researchers and participants alike. Integrating other popular music streaming services and survey software will make Spotivey even more useful in the future. I highly recommend using this tool whenever the topic of investigation requires data on music consumption.
