## 3 Methodology
### Notification Perception Analysis  
Preparation of data and coding: All open-ended responses were collected and listed to answer the first RQ. Once all responses were compiled into one single document, we then proceeded by assigning codes to them. Each statement was coded by assigning meaningful labels. Open coding was used according to the practical guide of qualitative research [44]. The labels were expected to characterize notification-related experiences (e.g., \begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Notification Aspects** & **Associated Questions** \\ \hline \multirow{3}{*}{Experience} & How was your experience with the mobile push notifications you received today? \\  & Scale 1 (bad)–5 (good) \\ \cline{2-3}  & Could you elaborate on the experience of the notifications you received on this particular day, if any? (Open-ended) \\ \hline \multirow{3}{*}{Perceived Frequency} & How many push notifications _do you think_ you received today? \\  & Scale 1 (Very few)–5 (A lot) \\ \cline{2-3}  & Would you like to add any feedback or comments regarding the notification count you received today? (Open-ended) \\ \hline \multirow{3}{*}{Relevance and Importance} & How important or relevant were the notifications you received today? \\  & Scale 1 (Irrelevant)–5 (Important) \\ \cline{2-3}  & Could you explain why? What seemed important to you, or what was considered irrelevant, if any? (Open-ended) \\ \hline \multirow{3}{*}{Additional} & Any additional comments you would like to explain? (frustrations, experiences good or bad, something that caught your attention, annoying aspects, etc.) (Open-ended) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Diary Questions. stress, perceived relevance, etc.) but also context (personal, work-related, leisure-related), behaviors or intentions (using the silent mode, disabling, etc.) and other, unexpected characteristics, not fitting into the groups above. Coding was first completed independently by two of the authors who are experts in HCI, more specifically in interaction design and UX. Later they discussed the results to reach a mutual agreement between each other. Altogether, 135 out of 140 statements were targeted and labeled. Five statements were missing because some participants failed to submit their daily entries. It was important to mention that each statement with all the labels assigned belonged to a certain participant as well as to a certain day in the diary. Cluster analysis: The open coding process was expected to reveal a large number of distinct labels that could be grouped into logical categories. These categories were expected to help make further sense of participants' notification experiences. Hierarchical cluster analysis was used to sort the labels by similarity. The statistical computing software R function \"hclust\" was used to sort the labels hierarchically according to their similarities between each other. The similarities were expressed in the form of a dissimilarity matrix, which recorded dissimilarity distances between all labels. The greater the distance between any two labels, the larger the difference they have between them. Notification perception analysis was based on participant statements requested once per day from all participants. The dissimilarity matrix was compiled upon how the labels were assigned to the statements. If different labels were assigned to the same statement, the dissimilarity distance score would be increased by 1 for the inter-label distances between all these labels. Eventually, taking into account all label assignments, the distances should represent various perceptions across different participants as well as notification days. Hierarchical cluster analysis was preferred to other similarity grouping methods, because of the graphical representation of results via a dendrogram. The dendrogram helps experts notice possible commonalities and outsiders across labels before the best fit for clusters is decided. Such visual clues were expected to help experts in the following thematic analysis.