UX Research

Trends in Interactive Video Game Streaming: Exploring Google’s Project Stream (now Stadia)

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Abstract

Advances in processing power technology have made possible videogame streaming services. Today, gamers have the flexibility to play videogames online without the use of gaming hardware. The pay-to-play subscription business model challenges the traditional gaming business model (based on fixed prices) and has attracted powerful industry contenders. In 2018, Microsoft and Google both announced videogame streaming initiatives (Tuttle, 2018; Hsiao, 2018). Unfortunately, limited scholarly research exists on videogame streaming services. To uncover trends in videogame streaming, 903 video gamers were recruited in a US cross-national online study, focusing on Google’s Project Stream. By administering the Consumer Motivation Scale (CMS; Barbopoulos & Johansson, 2017), gamers’ consumer motivation (toward videogame streaming services) was uncovered to be multifaceted in nature. Additionally, gamers’ consumer motivation and affective experiences differentially impacted self-forecasted consumer behavior. Practical implications and research limitations are described.

Keywords: videogame streaming, Google, cloud gaming, interactive

In a 2018 US cross-national examination of the gaming industry, the Entertainment Software Association (ESA), uncovered total gaming consumer spend to be $36 billion. Specifically, $4.7 and $29.1 billion of total consumer spend was spent on gaming hardware and gaming content, respectively (see ESA, 2018). In today’s age, advances in smartphone technology have also made possible mobile devices as a versatile gaming platform. In the US, 36% of households game on their smartphones, a percentage figure that, interestingly enough, ties with households that game via gaming consoles (ESA, 2018). The emergence of smartphone technology (which possesses greater processing power, memory and resolution than many handheld systems) has not only threatened handheld sales (i.e., cannibalization), but has also impacted industry business models (Marchan & Hennig-Thurau, 2013). Gaming consoles and hard-copy videogames, historically, were assigned fixed prices. Purchased gaming hardware could then be used however the gaming consumer saw fit (e.g., the consumer decides overall gameplay time). Advances in PC processing power technology, however, made gaming possible without the use of gaming hardware (see Shea et al. 2013). Today, many gaming business models incorporate an online subscription service. Services that provide access to games online, referred to as videogame streaming services, allow gamers to select and play videogames similar to the way in which online movie subscription services allow consumers to select and watch movies.[i] This alternative model involves gamers paying a monthly service fee and a key prerequisite is fast-speed internet access.

In early 2014, Sony Interactive Entertainment (hereinafter referred to as Sony), launched the PlayStation Now, one of the first videogame streaming services to be made publicly available (Cai et al. 2016). The pay-to-play subscription service allows gamers to play games from the PlayStation 2, 3, and 4 (gamers also have the option of switching gameplay from their PC to a PlayStation 4 console). Growing popularity in streaming services brought Nvidia’s GeForce Now (launched in 2015), Jump Gaming (launched in 2017), and in 2018, the Microsoft Corporation announced, “our cloud engineers are building a game streaming network to unlock console-quality gaming on any device” (Spencer, 2018; Tuttle, 2018). The videogame streaming model has also attracted historically non-gaming corporations. In late 2018, Google announced Project Stream, an interactive videogame streaming project: “we’ve been working on Project Stream, a technical test to solve some of the biggest challenges in streaming” (see Hsiao, 2018).

Despite corporate initiatives in the videogame streaming field, limited scholarly research exists on the subject. As of the date of the present paper, no research exists on a) gamers’ affect (i.e., emotions) toward upcoming streaming services (e.g., Google’s Project Stream), b) gamers’ consumer motivation toward videogame streaming services, c)and how gamers’ affect, cognition, and motivation impact present and future consumer behavior of videogame streaming services. With the emergence of greater videogame streaming services, researching the subject, from the lens of the consumer, is indeed timely.

The Present Research

The present research aims to investigate three questions. First, (a) can a blog article on an upcoming videogame streaming service impact gamers’ positive and negative affect? Since Microsoft has yet to reveal a videogame streaming platform, the present research focuses on Google’s Project Stream (see Hsiao, 2018). Second, (b) what are gamers’ consumer motivation toward videogame streaming services? To uncover gamers’ consumer motivation the present research utilizes the Consumer Motivation Scale (CMS; Barbopoulos & Johansson, 2017), a validated multi-dimensional scale examining consumer goals. Third, can impacted (a) emotions and (b) consumer motivations (c) impact self-forecasted present and future consumer behavior? That is, learning about an upcoming streaming service (i.e., Google’s Project Stream) may impact emotions, which may in turn, impact predicted consumer behavior. Furthermore, which dimension of the multi-dimensional consumer motivation scale will predict consumer behavior? The present research is the first of its kind in the field of interactive videogame streaming. To this point, no research has examined topics on this subject from a consumer perspective. Predictions on potential results, therefore, are not provided. Instead, this research represents the first consumer-centered exploratory-driven research on the subject of videogame streaming.

Video game streaming ought not to be confused with live-streaming. Live-streaming involves video gamers streaming gameplay to an online audience. Video game streaming involves gamers choosing and playing selected games via online streaming services.

Methods

Participants

The final sample is composed of 903 consenting adult participants, [i] residents of the US, from Amazon Mechanical Turk (MTurk). MTurk was utilized because it proves to hold a more reliable and diverse participant pool than other types of participant sampling (e.g., undergraduate sampling; see Paolacci & Chandler, 2014). To gather a sample composed of only video gamers, the following two questions were asked, “Would you consider yourself a video gamer?” (1 = ‘Yes’ & 2 = ‘No’) and “How much video gaming experience do you have?” (1 = ‘None at all’ to 5 = ‘A lot of experience’). Participants that responded “Yes” to the former question and did not indicate “Not at all” to the latter question proceeded into the study. Since the study required participants to answer open-ended questions, participants unable to write their thoughts in the English language did not proceed into the study. The sample is composed of approximately the same number of males (53.3%) and females (46.6%) and the average participant age is 32 years old (Mdn = 30). [ii] Ethically, the sample is composed of more White participants (69.4%) than Black (11.4%), Latino (8.3%) or Asian (6.1%) participants.

Videogame Experience: With respect to gaming experience, 43.5% of participants indicated they have “A lot of experience” followed by “Quite a bit” (33.6%), “Moderate” (20.4%), and lastly, “A little experience” (2.5%). Participants were asked, “Which gaming console do you use most frequently?”. The PlayStation 4 (31.1%) and Xbox One (26.4%) proved to be the most frequently used gaming consoles among participants (other consoles ranked less favorably, e.g., Nintendo Wii, 7.6% & Nintendo Switch, 5.6%). To uncover gaming experience across various consoles, participants were asked, “Which gaming consoles do you own?” The PlayStation 4 (n = 407, 45.1%), the Xbox One (n = 371, 41.1%) and the Nintendo Wii (n = 326, 36.1%) were the most popular consoles among participants.

Videogame Streaming Experience: To uncover videogame streaming experience participants were asked, “How often do you use videogame streaming services?” (1 = “Never” to 6 = “Extremely often”). 20.5% of participants indicated “Slightly often”, followed by, “Moderately often” (19.9%), “Very often” (17.4%), “Not often at all” (13.5%), “Never” (9.9%), and finally, “Extremely often” (9.1%). Next, participants were asked, “Which videogame streaming services have you used most frequently?”. The PlayStation Now (27.6%) was the most frequently used streaming service. Independent services, such as, Jump (3.3%) and GeForce Now (2.8%), proved to be less frequently used. To uncover experience across a variety of videogame streaming services, participants were asked, “What videogame streaming services have you used?”. Unsurprisingly, the most used streaming service is the PlayStation Now (35.1%).

Procedure

Participants were informed the researchers were investigating interactive videogame streaming and that they would be required to read an article on the topic. Participants were not informed the study focused on Google’s Project Stream until they were presented with the article they were instructed to read. No random assignment to condition was conducted. Instead, naturally occurring groups (e.g., Male vs. Female; Xbox One gamers vs. PlayStation 4 gamers) were compared on key dependent variables (described in greater detail below). Once baseline affect was measured, participants were presented with a blog article on Google’s Project Stream. The article, entitled, “Pushing the limits of streaming technology” (see Hsiao, 2018), described the aims of Project Stream and introduced readers to the potential of interactive videogame streaming. The article included a short video depicting the visual gameplay capture (i.e., at 1080p streamed at 60fps) of Assassin’s Creed Odyssey, a AAA videogame developed by Ubisoft Entertainment (2018). No time limit was set on how long participants could take reading the article but they could not proceed past the article until 60 seconds elapsed. Following the article, participants were presented with a package of dependent measures, completed a mood booster task (i.e., Velten, 1968), debriefed and remunerated.

Measures

Explicit Affect: To measure baseline affect (i.e., participants’ emotions at the start of the study, prior to reading the article) and post-manipulation affect (i.e., participants’ emotions after the presentation of the article) the International Positive and Negative Affect Schedule Short-Form (I-PANAS-SF) was administered (see Karim, Weisz & Rehman, 2011). The I-PANAS-SF is composed of ten emotion items (i.e., active, determined, attentive, inspired, alert, afraid, nervous, upset, hostile & ashamed). Participants were instructed to focus on their present emotions (i.e., “RIGHT NOW to what extent do you feel”; ratings were made on a 5-point scale; 1 = “Not at all” to 5 = “Extremely”). In line with Karim et al. (2011), items were collapsed to form four single explicit affect indices, which are: explicit baseline negative affect (M = 1.65, SD = .93, Cronbach’s α = .92), explicit baseline positive affect (M = 3.52, SD = .88, Cronbach’s α = .80), explicit post-manipulation negative affect (M = 1.56, SD = .95, Cronbach’s α = .94), and explicit post-manipulation positive affect (M = 3.66, SD = .90, Cronbach’s α = .83).

Consumer Motivation: To uncover consumer motivation toward interactive streaming services the Consumer Motivation Scale (CMS) was administered (see Barbopoulos & Johansson, 2017). Ratings were made on a 6-point scale (0 = “Not at all important” to 5 = “Extremely important”). The CMS is composed of seven dimensions, which help to explain varying consumer goals. These dimensions include: Value for Money (5-items, e.g., “The streaming service should not be a waste of money”; M = 4.18, SD = .74, Cronbach’s α =.78), Quality (5-items, e.g., “The streaming service should be well-made and well-performed”; M = 4.15, SD = .70, Cronbach’s α =.77), Safety (5-items, e.g., “The streaming service should make me feel calm and safe”; M = 3.70, SD = .83, Cronbach’s α = .72), Stimulation (4-items, e.g., “The streaming service should be stimulating”; M = 3.84, SD = .83, Cronbach’s α = .76), Comfort (4-items, e.g., “The streaming service should be smooth and comfortable”; M = 3.90, SD = .77, Cronbach’s α = .70), Ethics (5-items, e.g., “The streaming service should be compatible with my personal and moral obligations”; M = 3.16, SD = 1.15, Cronbach’s α = .84), and Social Acceptance (5-items, e.g., “The streaming service should be liked by people who are important to me”; M = 2.27, SD = 1.47, Cronbach’s α = .92; see Graph 4.0).

Self-Forecasted Behaviour: Participants were presented with two items regarding their self-forecasted behavior. The first item involved present-moment consumer behavior: “How likely would you be to use Google’s videogame streaming service if it were available today?” (ratings were made on a 5-point scale; 1 = “Not likely at all” to 5 = “Extremely likely”; M = 3.5, SD = 1.13; see Graph 3.1). The second item involved future consumer behavior: “Do you plan on playing videogames online using Google’s interactive video streaming service?” (5-point scale; 1 = “Very slightly or not at all” to 5 = “Extremely”; M = 3.04, SD = 1.17; see Graph 3.0). To uncover the extent to which participants responded differently to Assassin’s Creed Odyssey, they were also asked, “Do you plan on purchasing Assassin’s Creed Odyssey?” (Yes, 19.9%; Maybe, 45.6%; No, 22.5%).

Service Innovation, Necessity, Recommendation and Familiarity: Next, participants were asked four items to further investigate gamer perceptions of Project Stream. First, they were asked how innovative they perceived the streaming service to be, “How innovative is Google’s videogame streaming service?” (1 = “Not innovative at all” to 5 = “Extremely Innovative”; M = 3.55, SD = .99). They were then asked the following questions: “When you think about Google’s videogame streaming service, how necessary do you think it is for gamers today?” (1 = “Not necessary at all” to 5 = “Extremely necessary”; M = 3.25, SD = 1.07), “How likely would you be to recommend Google’s videogame streaming service to a friend or colleague?” (0 = “Not at all likely” to 10 = “Extremely likely”; M = 7.02, SD = 2.30), and “How familiar are you with the Google company?” (1 = “Not at all familiar” to 5 = “Extremely familiar”; M = 4.10, SD = .93).

Gaming Addiction: To uncover how gaming addiction relates to consumer behavior, participants were presented with the 9-item Internet Gaming Disorder Scale (IGDS9-SF; see APA, 2013; e.g., “Do you feel more irritability, anxiety or even sadness when you try to either reduce or stop your gaming activity?”). Ratings were made on a 5-point Likert scale (1 = “Never” to 5 = “Very often”; M = 21.06, SD = 7.80, Cronbach’s α = .90). In line with previous research (e.g., Pontes & Griffiths, 2015), all items were collapsed to form a single index of gaming addiction. The lowest and highest score participants could obtain are 9 and 45, respectively. According to Pontes and Griffiths (2015) participants who obtain a score above 35 may be classified as disordered gamers. To help inform the gaming addiction index, participants were asked how frequently they game in a week:

How much do you game in a week? By gaming activity, we mean any gaming-related activity that has been played either from a computer/laptop or from a gaming console or any other kind of device (e.g., mobile phone, tablet, etc.) both online and/or offline.

Participants made their ratings on 6-point scale: “less than 7 hours” (1), “8–14 hours” (2), “15–20 hours” (3), “21–30 hours” (4), “31–40 hours” (5), and “More than 40 hours” (6; M = 3.00, SD = 1.41).

Results*

*Contact me for the full research paper

Fig. 1.1 represents future consumer behavior, “Do you plan on playing videogames online using Google’s interactive video streaming service?” (5-point scale; 1 = “Very slightly or not at all” to 5 = “Extremely”; M = 3.04, SD = 1.17). Darkest shaded areas on the map represent higher scores on the scale.

Fig. 1.2. represents present-moment consumer behavior, “How likely would you be to use Google’s videogame streaming service if it were available today?” (ratings were made on a 5-point scale; 1 = “Not likely at all” to 5 = “Extremely likely”; M = 3.5, SD = 1.13). Darkest shaded areas on the map represent higher scores on the scale.

[i] Participants were presented with an online information consent form. Participants had the option to selection either: “I consent to participate in this study [clicking here will lead to the study]” or “I do not consent to participate in this study [clicking here will return to the browser]”.

[ii] The average participant age (i.e., M = 32) is in line with ESA (2018) research, which uncovered the average gamer age, in the United States, to be 34 years old. Similarly, the gender breakdown (i.e., female: 46.6%) was also similar to findings by ESA (2018; i.e., female: 45%).

References

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Researcher | medium.com/@hajera | Youtube: UX Psych Lab | Webpage: uwaterloo.ca/scholar/h2alhome