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reasoning behind their rankings of the motives previously sent out. For a full list of the questions, see appendix D. 3.2.5 Data Analysis In order to analyze the empirical data gathered through the interviews Content Analysis was used. Content Analysis allows researchers to sift through dense and rich data to identify themes and meaningful interpretations (Erlingsson & Brysiewicz, 2017). This was deemed essential when conducting qualitative research, based on its complex and subjective nature. Thus, the research- team followed the systematic steps of Content Analysis in order to interpret what was said during the interviews, to grasp and understand the underlying motivations behind the participants’ answers. Firstly, the recordings from the interviews were transcribed. This was done in Swedish to avoid valuable meaning to get lost in translation. The transcripts were also kept on the researchers’ computers, out of reach from others, to fulfill and respect the anonymity and privacy rights of the participants. Following this, the researchers read and re-read the transcripts independently and noted down first impressions of each interview. During the next step, researchers highlighted relevant or interesting information found in the transcripts and developed ‘meaning units’ by summarizing the relevant information to a shorter sentence. Descriptive labels, also referred to as codes, were then created for each meaning unit. When coding, the researchers attempted to summarize the idea behind the meaning unit into one or two words, to facilitate generalization. The researchers made sure to develop meaning units before coding, in order to minimize the risk of starting to interpret the data too soon, and thereby losing the essential meaning behind a statement. An example of what the coding tables looked like can be seen in Table 2 (appendix E). During the next step of the analysis, the researchers attempted to group similar codes into overarching categories. Once these had been established, the researchers compared and contrasted the categories derived from the empirical data with prior literature and the established categories from the UGT. The similarities and differences helped identify general themes and a deeper understanding concerning what motivates Gen Z to participate in #challenges on TikTok. By including key passages, quotes and ideas from primary data comprehensive depiction of findings were created. Deriving from these findings, a revised UGT for the context of TikTok was 28PDF Image | It’s time to TikTok
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