| dc.description.abstract | This master’s thesis takes us on a journey through the assumption of algorithms and data via a quantitative approach. This guides us through the complexities of Spotify and TikTok’s algorithm and data, illuminating the key concepts and ideas that are essential for understanding this fascinating and an important topic. As we read, we are struck by two important research questions:
• RQ1: Can Spotify and TikTok users discover new music and content with the guidance of algorithms?
• RQ2: Does the algorithm affect the user behavior’s data positively or negatively within these platforms?
This study is based on the following hypotheses; users will have the ability to discover new music and sound easily on both platforms determined by the algorithm. Additionally, the user’s data within behavior will be more or less affected by the algorithm positively and negatively.
The research will be conducted through quantitative methods utilizing a survey. In this case, Google Survey will be used for data collection from respondents and will explore user interactions with the recommendation algorithm on Spotify and TikTok, as well as examine consumer behavior and the perceived effects of the algorithms on personal data. Additionally, the study will consider the influence of the COVID-19 pandemic on platform usage. | |
