UX Researcher
- Qualitative User Research
- Market Research
- User Interviews, Focus Groups
- Presentation
R Studio, Invision, Microsoft Excel, Lucid Chart, PoweBi
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In this study, we explored user needs for a technology-based product to help people engage in sports in their communities. We began the study with scenario-based observations with eight participants, all of who had either previously participated in a team or solo sport. We asked participants to demonstrate how they would find a tennis partner with a similar skill level to theirs. The participants used their laptops to conduct their search to try to find a solution to the scenario. The participants were given as much time as needed to complete the task. Each observation took about 20 minutes. The participants used this time to conduct research, read articles, and find methods to reach out to other athletes. Screen capture recordings were used to observe their behaviors. We used the AEIOU (activities, environment, interaction, object, and users) framework to organize our findings.
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Heart disease resulting from a lack of exercise and poor diet accounts for over half a million deaths in the U.S. every year (CDC, 2019). In the U.S. alone, over 647,000 people die per year from some form of heart disease. The main causes of heart disease are lack of exercise and poor diet (CDC, 2019). A sedentary lifestyle is a very serious worldwide problem, especially in North America and Europe. Unfortunately, physical inactivity has progressively increased over the past several decades (Knight, 2012). Sports are a great way to improve our health. As the number of physical activities decreases, so do the number of opportunities and players to partake in them. Many local sports leagues and teams have had to cancel their seasons due to a lack of participants (Castaneda, 2017). The lack of leagues and teams has contributed to the difficulty in finding players to compete with, especially those with a similar skill level. Additionally, people face challenges in finding appropriate courts to play at due to lack of accessibility. There are people willing to play, they face the problem of accessibility to nearby sports locations (Lee, S.A., Ju, Y.J., Lee, J.E. et al, 2016)
We identified two potential competitors :
1) Bvddy :
The Bvddy app is described as a “Tinder for athletes.” Its aim is to help athletes find people to play sports with and allow them to make searches based on skill level and sports preferences. The app’s creators are sports enthusiasts and recognized how challenging it is to find fellow athletes to play sports with, especially at their desired skill level. One of the biggest drawbacks of the app is the location accuracy, even though they have multiple users, it matches you with players who are much farther than your current location and geographical area. The app was not a commercial success and is now discontinued.
2) Sportido :
The Sportido mobile app is a location-based sporting application that allows sports and fitness enthusiasts to search for trainers, venues, and players. Despite the promising concept, its functionality is poor. The Facebook-only sign-up greatly limits its user base especially for those that do not have a Facebook account or have not used it in some time. The limitations of the app include a lack of details about the venue like busiest hours, court types, conditions of the fields, and court availability.
We recruited our participants via DePaul University’s CDM/COMM Participant Pool. We also distributed the anonymous link in our social networks and to our classmates via Slack. We had the criteria for participants to be over 18 years to be able to participate in the survey. We collected the responses from 47 participants between the age of 18 to 54 years with a maximum percentage of 48.78% participants between the age group of 18-24 years and minimum of 2.44% participants between the age group of 49-54 years (see figure 1).
We had a balance of gender variations between Male (48.78%) and Females (51.22%). Most of the respondents (46.34%) were intermediately-skilled players (see figure 2 below).
We used R studio to analyze the survey data and also used the Qualtrics Report Analysis feature to represent our data. Based on data we formulated the following hypotheses:
1) Players who play with other people will have a higher performance rate than players who play by themselves.
2) Players that play and train in a group will experience more enjoyment.
3) Players that use their own equipment when they play/train have are more effective than the players that rent equipment.
To support these hypotheses, we conducted non-parametric tests on our survey data using Kruskal-Wallis
We observed eight participants (four male, four female) between the ages of 18 and 30. We recruited participants through personal connections. The table shows Interview participants :
As a team of 4 members, each of us conducted two interviews. Seven interviews were conducted through Zoom video calls and one on Google Meet. We audio-recorded and transcribed the interviews to help with coding and analyzing the data. We first introduced ourselves and provided a background of our project and then asked them to review the Informed consent form. Since all the interviews conducted were remote, we read out the form to them. The participants were asked a few warm-up questions to make them feel comfortable and prepare them for the next set of questions. The questions provided us with information on the background of their past sports experiences.
The interviews were individually transcribed and coded using Atlas.ti. We first polished our individual transcriptions before coding. The coding was a combination of systematic and descriptive coding. Each of us created an affinity diagram of our codes and created generalized themes and categories based on patterns found throughout our data. We then combined all our data into one cohesive affinity diagram.
After dropping all of my data onto sticky notes, I organized them onto my Affinity Map with themes and color-coded them by streaming service.
From our observations, interviews, and surveys, we found three recurring themes around how people find similarly skilled athletes to compete with and the common issues they face.
- Social Media. Our participants described the importance of social media to reach out to people and join sports communities. We learned from a new set of participants that social media plays a large role in their recruitment process as well. While Facebook was the most commonly used social media platform amongst our participants, they also used programs like WhatsApp and Chapter as ways to keep in touch with other players.
Our survey participants were asked if they preferred to play and train alone or with someone else. 93% of our participants answered that they preferred to play with other people as opposed to by themselves. We conducted a Kruskal-Wallis test to investigate the effectiveness of training for people that trained alone compared to people that trained with a partner or group. We found the workout production and effectiveness of those that trained in a group to be statistically significant (H(2)=14.31, p<.05, n^2=0.41).
- Enjoyment & Exercise. Our participants' main motivations for continuing to play sports in their adult lives were for a love of the game itself and because they are a great source of exercise.
Our survey participants were asked about whether they played sports for the following reasons based on a five-point Likert scale. Participants had to choose a number between one and five, one being “strongly disagree” and five beings “strongly agree.” The mean of the responses for providing enjoyment was 4.54 and for improving, overall health was 4.60. We conducted a Kruskal-Wallis test to investigate the level of enjoyment for people that trained alone compared to people that trained with a partner or group. We found the level of enjoyment for those that trained with a group to be statistically significant (H(2)=32.45, p<.05, n^2=0.43.
- Facility & Equipment Availability. The access our participants had to the necessary equipment and sports facilities played a large role in how often they played and who they played with.
our survey, we asked our participants-based on a Likert scale- whether or not they agreed with the statement, “ Equipment availability plays a large part in whether or not I play my sport.” 58.08% of our participants stated that they either somewhat or strongly agree with this statement. We conducted a Kruskal-Wallis test to investigate the effectiveness of training for people that used their own equipment compared to those that rented their equipment. We found the training effectiveness for those that used their own equipment was statistically significant (H(2)=21.12, p<.05, n^2=0.50.
The purpose of personas is to create reliable and realistic representations of your key audience segments for reference. These representations should be based on qualitative and quantitative user research and web analytics.
Susie is a 37-year-old that lives in Portland, OR. Susie was never really into sports growing up but has recently grown a fondness for tennis. She works at a sports company so she is surrounded by people that are very fit and active. She uses her work atmosphere as motivation to improve her own health and fitness, which has recently taken the form of playing tennis 3 days a week after work. Susie enjoys playing tennis because it is an individual sport and she doesn’t have to rely on anyone else in order to win. Susie has been frustrated recently because the people she has been playing against are not as good as her and she feels like she is not being challenged. Susie would love a way to find an opponent that is as skilled as she is and prefers to play casually based on each other’s availability.
Trevor is a 24-year-old from Boston, MA. He just recently moved to Chicago to attend graduate school and is a huge fan of soccer. In Boston, Trevor had a group of friends he would meet with to play games about 2-3 times a week. He liked playing with them because they were all former teammates of his on his high school soccer team so they all had a similar skill level. Trevor desires to find another group of people like his friends in Boston, in Chicago. Trevor does not want to just play with any stranger, however. He really only enjoys playing when his teammates and opponents are taking the game seriously and can keep up with his fast level of play. Currently, the only games he knows about in his community are a 45-minute drive away from his apartment. Trevor would love a way to see local leagues/games going on in his area and the game’s play style, whether it be casual or competitive.
A user journey map is useful for getting a more complete view of the user’s experience for a specific task. The goal is to find the significant low points or problems that the user is facing. It’s also a great time to highlight the thoughts and emotional aspects of the user’s experience.
The user's journey is centered around finding players with the same skill sets. First, the user may think about the surrounded active and fit people, which will motivate them to play any sports and remain fit.
The user experiences low points when:
- Trying to find players with the same skill set and schedule.
- No platform available where users can get relative results.
- Not being challenged.
The user's journey is centered around finding players in neighborhoods.
The user experiences low points when:
- Trying to find players in close neighborhoods.
- Not frequent games per week.
- No players with the same skill set.
Next, I created the user flow based on this to create a visual representation of the path that the user will take to find the resources.
The observation, interview, and survey data provided our group with a number of insights that have led to new design implications. Below are design implications that would be valuable additions to include based on our participant responses.
Due to Covid-19, the research for the case study had to be done remotely which caused limitations in observations. Our observation, interview, and survey had limitations including a limited age range (young adults) and relatively small sample size that may not generalize to a larger audience. In our future work, we will attempt to recruit from a more diverse age group. Additionally, the artificial context of a scenario-based observation and the suggestion of a specific sport biased our findings. All interviews were conducted via Zoom and google meet; this made it difficult to establish a personal connection with the participant. Also, we did not define a codebook for our interviews, so the researchers’ methods for coding were inconsistent across different interviews. For our survey, we had to eliminate a lot of questions due to the time it would take to complete, and also we were unable to gather data from participants above 55 years which would add more insights to our research questions.
