## 4 Method
### Stimuli and Experimental Design  
For the experiment, we built digital prototypes that could be interactively manipulated to investigate videos recommended by the participants. The prototypes were developed using Framer, which is a popular design and prototyping graphical user interface tool in the industry [54]. The participants were all Korean; therefore, all experimental materials were designed in the Korean language. The prototype design was inspired by the Netflix interface design. We expected that this interface design would allow participants to become accustomed to using our prototype without any additional training because Netflix is the most popular SVOD service not just globally but also in South Korea [55]. The lists of video recommendations in the prototype were created using data from the actual accounts of the three Netflix users who provided their details. We did not build our own recommendation system for this research because the purpose of this study was not to objectively evaluate recommendations but rather the user's perceptions of the recommendations. Our experiment was designed as a 2 (information source: online friend or influencer) \(\times\) 2 (recommender type: algorithm or human) between-subjects design. Four interactive prototypes were developed based on these experimental conditions. The details of the manipulation of each condition are described below. To separate the two conditions of information source operationally (online friend vs. influencer), we controlled an instruction message for social recommendation on the starting page. On this page, the participants were asked to select one profile from among the three people. For this selection, the message \"these are friends' playlists\" was displayed in the online-friend condition, whereas \"these are influencers' playlists\" was displayed in the influencer condition (see Figure 3). Especially in the influencer condition, for their immersion in this selection, participants were informed that the three influencers were famous video content reviewers who had over three hundred thousand followers on YouTube. The same profiles were provided between the two conditions, including usernames and avatars, to control for any potential nuisance effects. Figure 2: Answers to “how many days do you use SVOD services in a week?”. Next, we offered several welcome messages for content exploration at the top of the recommendation page for the manipulation of the recommender type (algorithm vs. human). Our prototype provides a message to facilitate the exploration of a recommended list created by a chosen profile or a list generated by an algorithm in which the profile might be interested, depending on the recommender type. More specifically, the message \"these are [recommended] content X may like\" was displayed in the algorithm condition, whereas \"these are content X would like to recommend\" was displayed in the human condition (see Figure 4). An identical recommendation list was provided for all experimental conditions to reduce any unintended consequences of various recommendations. The prototype design and functionalities of the main page are described in more detail in Figure 4. First, an avatar and welcome message are shown at the top of the page. A menu  
Figure 4: Stimuli representing both recommendation lists made by human (**left**, “Content X would like to recommend”) and algorithm (**right**, “Content X may like”). Figure 3: Stimuli of both information sources’ conditions: influencer (**left**) and online-friend (**right**). is provided below that area to filter genres (romance, documentary, comedy, horror, and reality). If a participant selects one item, the list of recommended content below is provided for the selected genre. Below the genre area are lists of recommended content with their titles in each row. The title briefly describes the criterion on which the content was selected (e.g., watch it again, trending now, and continuing watching).