Process
Data Collection:
Three methods were applied to the data collection process.
The primary research method was user interviews. Users were selected to participate in a structured, individual interview that comprised mainly of seventeen open-ended questions. Users of the “Design Squad: UX Community” Discord server and a private, unnamed Discord server volunteered themselves to participate. Each interview lasted approximately 30 minutes. Two users were interviewed on Discord, and the remaining was interviewed on the phone. The users were required to meet a minimum threshold of two requirements to participate in the interview. The first requirement was that the person must have had experience running before. The second requirement was that the person must have used running-related technology before.
The secondary data collection method was to evaluate published scholarly articles that relate to running-related technologies and their users. Articles published from an educational or governmental institution have been accepted as a valid form of secondary research for this study.
The third data collection method was a competitive audit consisting of the top three running-related applications used on smartwatches was conducted to understand the current state of running-related applications. The three applications that were chosen to be audited are Strava, Apple Workouts, and Nike Run Club.
Data Interpretation:
Two methods were used to interpret the data and consolidate results.
The interpretation team consisted of three members. Team member one reviewed the interview transcripts and secondary research data with members two and three. Members two and three asked the reviewer additional questions to draw out more details such as interviewees’ tones, attitudes, possible motives, and environment during the interview. Members two and three also asked questions about the secondary data to measure against their initial assumptions made about the target user groups. Team member two reviewed the competitive audit. The other two members asked questions regarding whether the insights gathered during the interviews and secondary research data were in line with the competitive audit.
The interpretation team captured user insights as affinity diagram session notes using FigJam. Notes were captured at random by all three members of the interpretation team. After insights were considered complete, the team unanimously removed notes considered similar in nature (i.e., “User aspires to be active” and “Wants to be active” would be considered similar). Insights were then sorted into clusters without speaking. Once the clustering was complete, each cluster was discussed, and the label for each cluster was determined. Although there were parallels among each runner classification, there were discernable differences between their motivations.
Data Representation: Personas and HTA
Two methods were chosen to represent the data. The first data representation was user personas. These user persona types were identified in the labeled clusters during the affinity diagram process. Each attribute of the user persona is based on the interpretation team’s insights on the data collected in the primary user interviews and secondary data research. The second data representation was a hierarchical task analysis. This task analysis is a generalization of the insights found during the competitive audit and secondary research. It is based on how users currently use running-related smartwatch applications.
