This study applied a uses and gratifications approach to investigate social games — the game applications integrated in social networking platforms. Users’ expected social gratifications and game gratifications from playing social games were examined. The investigation focused on three dimensions of game play: frequency, duration, and engagement of game activities. A hierarchical regression analysis found that social interaction and diversion are positive predictors of game play. Results suggest that there is a distinctly social aspect to social games that reflects their social networking characteristics. Social games should be described as social media rather than as just one category of online computer games.
Growing crops in your virtual gardens, helping out on friends’ farms, or even rustling piggies and stealing fruit from their fields, over 15 million players in China spend more than five hours a day  on Happy Farm, one of the most popular games running on social networking sites (SNS).
Social games, which are gaming applications integrated into social networking platforms such as Facebook and MySpace, have enjoyed enormous popularity. Facebook imported Happy Farm in April 2009. The game peaked with over two million monthly active players within that year (Facebook, 2009), and brought in 30 to 40 percent of Facebook’s monthly revenue . Top social game companies have gained a massive user pool over the past three years: Zynga  has a user base with 242 million monthly active users; Five Minutes  has 73 million; and Playfish  has 50 million. The social gaming market generated revenues of US$76 million in 2008, US$639 million in 2009, and a predicted US$826 million in 2010 (Caoili, 2010).
This explosive growth in social games could have its own important implications for research. A traditional core concern for media scholars is the possible negative social impacts brought about by the diffusion of media such as television, cable, and the Internet. Based on the observation of the general decline in social engagement in contemporary America during the rise of television, Putnam (2000) has proposed that people are leading more isolated lives in which time is spent on passive media at the expense of social and community activities. This concern has led to a debate as to whether Internet–based activities displace valuable social activities. Some scholars have argued that the Internet makes people lonelier, encroaching on time that could otherwise be spent with family and friends (Kraut, et al., 1998; Nie and Erbring, 2002). Others have argued that the Internet, unlike traditional passive media such as television, is capable of connecting people across time and space and thus facilitates social interaction and communicative activities (Williams, 2006; Wellman, 2001; Boase, et al., 2006). While both sides of the debate have received empirical support, a growing number of scholars are suggesting a more nuanced approach to examine Internet use (Shen and Williams, 2010; Steinkuehler and Williams, 2006; Williams, 2007). People use the Internet in different ways. They are able to engage in a variety of online activities — surfing Web sites for information, watching videos, writing e–mail messages, chatting with friends, participating in virtual communities, and others — along a continuum from asocial to social. It would be more rigorous to consider the impact of differentiated uses on social engagement afforded by the underlying social mechanics of each activity.
Residing on the social networking platforms, social games may have the capacity to serve as a unique venue for socialization in a playful manner. The designed–in structures of social games indeed encourage users to interact with their friends in order to play well in the game. Moreover, a January 2010 survey by PopCap Games  found that 24 percent of the 4,917 respondents played social games at least once a week, which indicated that social games have reached an enormous portion of the population. And contrary to prevailing stereotypes, the average age of social gamers was 43, and females (55 percent) were more likely to play social games than males (45 percent). This appeal of social games to a vast, formerly ‘nongamer’ audience, combined with their particular social mechanism, might suggest an unusual fashion of social game use among players for both game enjoyment and social engagement.
Yet few published research studies have focused on social games. This means that we still have limited knowledge of what social games are and why people play them, and that we have even less understanding of whether their particular social structures provide new “places” for social interaction (Putman, 2000; Steinkuehler and Williams, 2006). The present study attempts to fill this research gap.
Uses and gratifications are useful for studying the motives and usage patterns of computer game players (Lee, et al., 2009; Williams, et al., 2008). This research framework assumes the centrality of people who choose particular communication to satisfy certain needs or interests (Baran and Davis, 2006; Rubin, 2009). Gamers are not passive recipients of the influence of games. Rather they direct the action and make choices throughout the play experience (Williams, et al., 2008). Gamers with different demographic and social backgrounds have always had different expectations and uses of the same media (Sherry, et al., 2006). They purposely engage in a game and expect certain kinds of gratifications from playing (Sherry, 2001; Lee, et al., 2009). The uses and gratifications approach is well fitted for studying the interactive nature of game playing (Sherry, 2001). It provides a foundation to gain insight on the impact of game experiences, the role of players’ choices and actions, and their usage patterns (Willams, et al., 2008). This study applies a uses and gratifications approach to examine the motives of social game use.
The following section discusses the four key characteristics of social games that distinguish them from the other types of digital games. Subsequently, uses and gratifications theory is introduced, and possible social motives and game motives of social game players are hypothesized. This paper finds that older players who expect to interact with others and to divert themselves through social games tend to be involved in greater use of those games.
Social games refer to the game applications that are integrated in the social networking platforms. Key components, which differentiate social games from other computer games, are: (1) social platform–based; (2) multiplayer; (3) real identity; and, (4) casual gaming.
The first three components of social games can be considered together as the social feature. Being one category of applications on social networking platforms, social games must operate on social platforms, such as social networking sites (SNS). A player’s social connections are an important part of the game. The main game play of a typical social game involves social activates like trading, chatting, flirting, or mischief–making. In other words, social activities taking place within a game’s design provide interaction among players in whatever manner the game affords, be it fighting, cooperating, or romancing.
A social game is not social unless it is played with other people whether it’s two or two hundred. As many SNS users do not necessarily develop a network in order to meet new people but rather to communicate with people who are already a part of their existing social networks (boyd and Ellison, 2007), social games enable players to interact with real–time friends, like old classmates, work buddies, and family members. Players can browse through friends’ game pages and see their activity logs. Yet it is not necessary for all the friends to be online at the same time to play the game. The game world persists whether the user logs on or not, which is the same as in a Massively–Multiplayer Online game (MMO) (Yee, 2006; Schmid, 2008). Events and interactions, which are driven by other users, occur in the game world even when the user is not logged on. Therefore, users themselves are an integral part of the social games. Ideally, in order to play well in a social game, players need to pay attention to their friends’ activities.
SNS also provide users with an identity to play their game applications. This means social game players do not need to apply for an extra subscription. Some SNS require a real–name registration. For example, Facebook initially targeted itself as a campus–oriented and real–name registration Web site (Facebook, 2007). Although some SNS have abandoned such policy or less than stringently enforced it less stringently, most SNS users still register with their real name so that their friends can find them. Especially for Chinese SNS users, who appreciate the environment abounding with personalization and self–expression, real–names are very much the “norm” (Kuo, 2008). As such, a social game players usually use real–name identities.
The fourth component of a social game highlights its game feature, which is close to a casual game. Being casual means that a social game is easy to pick up as opposed to an intense and complex hardcore game. Most social games have simple interfaces and they enable players to make visible progress relatively quickly. Interacting with friends can be achieved in the game without any barriers. For example, in Happy Farm, players can perform the farming task by simply clicking on several icons. Lots of small rewards are given out during the game, while no negative consequences are given (e.g., players’ levels are always going up, and you never get kicked out unless you choose to). Yet traditional competitive structures, like skill progression and friends–only leaderboards, are still included in the games.
Although some studies have explored the motives and uses of different types of digital games (Williams, et al., 2008; Yee, 2006; Molen and Jongbloed, 2007; Sun, et al., 2006; Lucas and Sherry, 2004; Bartle, 1996), social games can be distinguished from the others. Since social games are social–platform grounded, they can take advantage of a Web sites’ social networking capabilities. That is, players have already got an established network of friends and acquaintances to play with. The fact that social gamers mainly play with real–life friends makes social games different from a general MMO, whose players are mostly virtual friends (Yee, 2006) who have never met in real life (Nardi and Harris, 2006). People act differently when there is shared history. Studies have consistently documented that increased friendship and closeness (friends vs. strangers) generally lead to an expansion of cooperative acts (Majolo, et al., 2006), mutual support and toleration (Cords, 1997), and greater accuracy of social judgments, such as mind–reading ability (Funder, 1995; Thomas and Fletcher, 2003). Playing games with friends can have a different dynamic from playing with strangers. A social game is also different from a role–playing game (RPG), in which players assume the role of characters in a fantasy setting, because in a social game people represent themselves since most social networking sites require real name registration. According to many game design experts, social games are poised to set off a revolution in the game industry (Chen, 2009).
But why do people engage in social games? What do users expect from playing? Studies found that motivations behind game play are good predictors of players’ usage patterns (Williams, et al., 2008; Sherry, et al., 2006). The predominant research framework for studying media use and other forms of communication activities from the user’s perspective is uses and gratifications.
Uses and gratifications in the Internet environment
The uses and gratifications approach assumes that individuals actively use certain media to satisfy certain needs (Baran and Davis, 2006). It has been regarded as better suited for the studies of Internet use (Morris and Ogan, 1996; Newhagen and Rafaeli, 1996; Ruggiero, 2000), since users are more actively engaged communication participants in the Internet environment as compared to degrees of engagement in other traditional media (Ruggiero, 2000). Stafford (2008) and Stafford, et al. (2004) found that internet gratifications could be broken into three categories: process, content, and social gratifications. Ko, et al. (2005) identified four dimensions of Internet uses and gratifications investigating a marketing Web site, namely information, convenience, entertainment, and social–interaction motivations. Starkman (2007) pointed out that the primary motivations for using the Internet are the desires for relaxation, fun, encouragement, and status.
An expectancy–value approach of uses and gratifications
However, as many earlier Internet–related studies have reconfirmed that gratification seeking did not explain media behavior very well (LaRose, et al., 2001; Palmgreen, et al., 1985), a departure from the gratifications sought/gratifications obtained (GS/GO) formulation has been the innovative operational definitions of gratifications, or what might be called prospective gratifications. This approach proposes expectancy–value models of media uses and gratifications (Rayburn and Palmgreen, 1984).
The expectancy–value models predict gratification seeking from communication channels based on an expected outcome. This approach links users’ expectations to their individual goal satisfaction (Palmgreen, 1984; Vroom, 1995). According to this approach, gratifications expected “influence the seeking of gratifications, which influence media consumption” . Gratifications expected have been formed over time and are constantly being reevaluated through the degree of satisfaction of past experiences. An individual’s feeling of satisfaction derived from past media experience can increase the gratifications expected in the prospective future behavior.
Studies that have employed prospective measures (e.g., Lin, 1999; Charney and Greenberg, 2002; LaRose, et al., 2001) ask what the gratifications are that respondents expect from the media are in the future as opposed to those that they desire or have obtained in the past. Studies (LaRose and Eastin, 2004; LaRose, et al., 2001) found that expected outcomes (e.g., “when using the Internet it is likely that I will have fun”) improve the predictive power of Internet use compared to both gratifications sought (e.g., “I use the Internet because I want fun”) and gratifications obtained (e.g., “I use the Internet to have fun”). Thus, gratifications expected reflect current beliefs and perceptions about the outcomes of prospective media use. This study uses gratification expected to predict the use of social games.
Social games share important attributes with social networking sites in that the former are based on the later. There have been a few attempts to explore uses and gratifications of SNS (Urista, et al., 2009; Joinson, 2008; Subrahmanyam, et al., 2008; Cha, 2010). SNS offers a combination of several computer–mediated communication (CMC) applications, including walls, blogs, music, photo albums, video, and emails (Urista, et al., 2009); previous studies explored the multifunctional uses of SNS that are able to fulfill a variety of needs in one location. As social games are one of the many applications grounded in SNS, it is plausible to examine whether users play social games for the similar social motives identified in prior research of SNS.
Some recent studies have investigated individuals’ motivations for using SNS. Urista, et al. (2009) examined two leading online SNS: MySpace and Facebook. Through focus groups, they found that “1) efficient communication, 2) convenient communication, 3) curiosity about others, 4) popularity, and 5) relationship formation and reinforcement”  are the factors that motivate young people to use SNS to fulfill their needs. Similarly other studies found that college students used Facebook and MySpace to maintain connections with friends (Lampe, et al., 2007; Ellison, et al., 2007; Cha, 2010) family and relatives (Subrahmanyam, et al., 2008), and because all their friends had SNS accounts (Subrahmanyam, et al., 2008).
Joinson (2008), using an online survey, identified six unique motivations for using Facebook: “social connection (α = .89), shared identities (α = .74), content (α = .74), social investigation (α = .75), social network surfing (α = .79), and status updating (α = .71)” . The third motivation for using Facebook — content gratifications — investigated the gratifications obtained through the use of applications within Facebook, such as “playing games”, and “quizzes” . Among the six factors, status updates significantly predict the site visiting frequency, while content gratifications predict the number of hours spent online.
Although these studies have focused on different SNS servers and populations, the motivations found from them are largely overlapping. According to Joinson’s (2008) findings, to upkeep “social connections” reflects “relationship formation and reinforcement”  motives; ; the motive to obtain “shared identities” may be the result of the consideration that “all friends had SNS accounts” (Subrahmanyam, et al., 2008) and users do not want to feel isolated; to conduct “social investigation” and “networking surfing” are highly correlated (r = .54, ρ < .01) and can be combined together. They cover the motives for “efficient and convenient communication”  with friends as well as for fulfilling the needs of being “curious about others” .
Comparing and synthesizing the motives for SNS use that are identified in prior research (Joinson, 2008; Urista, et al., 2009; Subrahmanyam, et al., 2008; Cha, 2010), the present study applied all of the U&G factors found in these studies except “content” gratifications (Joinson, 2008). As mentioned earlier in Joinson’s study (2008), the content factor measures the gratifications obtained from using different applications within SNS. Social game is one of the SNS applications. Since this study aimed to develop a social game specific measure, the content factor did not apply here.
Accordingly, after eliminating replicated motives, this study located five factors that motivate SNS use and applied them to social games use. These factors are social connection (to keep in touch with friends and maintain relationships); social investigation (to see what friends do and to make new friends); shared identity (to join friends’ groups to avoid being left out); popularity (to become a popular figure among friends); and, self–expression (to update one’s status or to let friends know your news). It is expected that these motives for using SNS may be salient reasons for playing social games, therefore:
H1a, 1b, 1c, 1d, & 1e. Expected gratifications for social connection (1a), social investigation (1b), shared identity (1c), popularity (1d), and self–expression (1e) from social games will be positively related to the frequency of game play.
H2a, 2b, 2c, 2d, & 2e. Expected gratifications for social connection (2a), social investigation (2b), shared identity (2c), popularity (2d), and self–expression (2e) from social games will be positively related to the duration of game play.
H3a, 3b, 3c, 3d, & 3e. Expected gratifications for social connection (3a), social investigation (3b), shared identity (3c), popularity (3d), and self–expression (3e) from social games will be positively related to the engagement of game activities.
Social games are one category of general digital games. As such, this study also considers the game motives of social game players. In order to generate gratification items for video game play, Lucas and Sherry (2004) reopened the basic question of what we use the media for by beginning with focus group and interviews. They identified six general categories of video game play motivation: “(a) competition (α = .86) — to compete with other players in the game; (b) challenge (α = .80) — to attempt to beat the game; (c) social interaction (α = .81) — to interact with friends through the game; (d) diversion (α = .89) — to pass time or to stop boredom, (e) fantasy (α = .88) — to do things that are impossible in real life; and, (f) arousal (α = .85) — to play the game because it is exciting” .
Sherry, et al. (2006) found that challenge was the most reported motivation for playing video games. Based on the same scale, a recent study of free online games among children (9–13 year–olds) in the Netherlands revealed that among all children challenge was the most popular reason for playing games, followed by fantasy, arousal, social interaction, diversion, and competition (Molen and Jongbloed, 2007). Sun, et al. (2006) found that the gratifications sought by Chinese online gamers, in the order of importance, are: diversion, competition, interaction, meeting strangers, and self–expression. Williams, et al. (2008) found that the motivations for achievement highly determined the total playing time among MMO players. Sherry, et al. (2006) found that video game players’ motivations for diversion and social interaction were the most important predictors of the total number of hours they spent playing during a typical week. Yee (2006) found that motivations for escapism were correlated with hours of usage per week for both male and female MMORPGs players.
Social games have some common characteristics with traditional video games in that both are digital games that involve gaming mechanisms and playful features. Therefore, the uses and gratifications scale developed by Sherry, et al. (2006) is employed to explore the game motives of social games players. Accordingly, it is expected that:
H4a, 4b, 4c, 4d, 4e, & 4f. Expected gratifications for competition (4a), challenge (4b), social interaction (4c), diversion (4d), fantasy (4e), and arousal (4f) from social games will be positively related to the frequency of game play.
H5a, 5b, 5c, 5d, 5e, & 5f. Expected gratifications for competition (5a), challenge (5b), social interaction (5c), diversion (5d), fantasy (5e), and arousal (5f) from social games will be positively related to the duration of game play.
H6a, 6b, 6c, 6d, 6e, & 6f. Expected gratifications for competition (6a), challenge (6b), social interaction (6c), diversion (6d), fantasy (6e), and arousal (6f) from social games will be positively related to the engagement of game activities.
Social game selection: Happy Farm
Happy Farm is a virtual agriculture game running on a Chinese SNS Kaixin001. The basic task in the game involves raising crops and livestock. As users spend more time in the game and become sophisticated players, their skill levels go up. And players’ “farm cash” accumulate through selling their harvests. Yet perhaps what makes the game extremely popular is perhaps that it also allows players to visit friends’ farms, to send them handiwork, and to steal their products. Such social elements within the game’s design can be found in many other social games. The present study was targeted at the users of Happy Farm because of its popularity and its representativeness of general social games.
Cross–sectional data were collected through self–administered online questionnaires. Participants were Happy Farm players recruited using a snowball sampling technique. Messages were sent via the SNS with a request for participation as well as a link to the questionnaire. Several players were sent a private message asking them to forward the survey link to their friends who are also Happy Farm players. In the final analysis, a total of 140 Happy Farm players responded to the survey, and 93 of them (66.4 percent) provided usable data: 66.2 percent female, and 33.8 percent male. Other data entries with considerable missing values were removed from the analysis. The mean age was 25.65 (SD = 2.36), ranging from 20 to 37.
Three main categories of variables were measured in the survey: (1) expected social gratifications for playing Happy Farm; (2) expected game gratifications for playing Happy Farm; and, (3) game play variables. A survey instrument describing the measurement of these variables can be found in the Appendix.
Expected social gratifications. To measure expected social gratifications, an 18–item scale was created based on SNS U&G factors developed in previous studies (Joinson, 2008; Urista, et al., 2009). Respondents were asked to what extent they agree (1 = strongly disagree; 7 = strongly agree) that they would expect five different kinds of social gratifications from playing Happy Farm: social connection, social investigation, shared identity, popularity, and self–expression.
Expected game gratifications. To measure expected game gratifications, a 22–item scale developed by Lucas and Sherry (2004) was applied in the questionnaires. Each item was measured using Likert scale questions, which asked the respondents to assess the level of agreement with each scale item (1 = strongly disagree; 7 = strongly agree). The scale consisted of six expected game gratifications: competition, challenge, social interaction, diversion, fantasy, and arousal.
Game play variables. Prior studies have suggested the need to distinguish different types of scales of measuring the amount of media/game use such as frequency and duration (Tidwell and Walther, 2002; Cha, 2010). Joinson (2008) found that, among Facebook users, status updates significantly predict the site visiting frequency, while content gratifications predict the number of hours spent on the site. Therefore, the predictors of media use might depend on what usage measurement is employed.
The present study differentiated three game play variables that measured: (1) the frequency of game play. Respondents were asked to report how often (1 = less than once every couple of months, 12 = more than five times every day) they logged onto Happy Farm; (2) the duration of game play. Respondents were also asked to report the average amount of time (1 = less than 10 minutes every time, 5 = more than two hours every time) they spent on Happy Farm; and, (3) the engagement of game activities. Respondents were asked how often (1 = never, 5 = very often) they played Happy Farm within several special categories of gaming activities. The categories were: collecting harvest; stealing friends’ harvests; watering plants for friends, using strategies; growing crops on friends’ farms; using ‘tools’, such as fertilizer and pesticide; looking for tips to play well; and, paying attention to rankings. The measures of activities were summed and averaged to create an index of the engagement of game activities.
Across the total sample, players logged onto Happy Farm five times a week on average (M = 8.82, SD = 2.97), and the average amount of time played each session was around 10 minutes (M = 1.87, SD = 1.05). Among all the gaming activities, players reported that they often grew crops on friends’ farms (M = 3.68, SD = 1.63), while they seldom paid attention to the rankings among friends (M = 1.99, SD = .89).
To ensure the reliability of the gratification scales, item analyses were firstly performed. This was followed by an analysis of multicollinearity in order to preclude inappropriate regression analyses.
Construct reliability check
In the present study, the Cronbach’s alpha values of the five social gratifications indices were as follow (Table 1): social connection (M = 4.58, SD = .88, α =.90), social investigation (M = 4.16, SD = .95, α = .63), shared identity (M = 3.95, SD = .83, α = .81), popularity (M = 3.53, SD =.91, α = .88), and self–expression (M = 3.86, SD = .99, α = .70). The reliability coefficient of social investigation was low, indicating that the construct had poor internal consistency.
Table 1: Analysis of social expected gratifications. Item number Item n M SD α Social connection 78 4.58 .88 .90 3 I will reconnect with people I’ve lost contact with. 81 4.52 1.21 5 I will receive some friends’ requests. 80 4.40 1.04 6 I will find people I haven’t seen for a while. 80 4.74 1.15 7 I will find out what old friends are doing now. 81 4.78 1.04 9 I will be able to contact long distance friends. 81 4.54 1.03 18 I will maintain relationships with people I may not get to see very often. 80 4.29 1.01 Social investigation 79 4.16 .95 .70 4 I will want to see what my friends are doing. 80 4.35 1.24 8 I will use advanced search to look for specific types of people. 81 4.47 1.18 10 I will meet new people. 80 3.39 1.27 (removed) 13 I will stalk some people (pay great attention). 80 3.36 1.17 Shared identity 80 3.95 .83 .81 11 I will join events or activities of my friends. 80 4.26 .92 14 I will belong to the group. 80 3.75 1.03 17 I will decrease the likelihood of being left out. 80 3.82 .97 Popularity 78 3.53 .91 .88 2 I will be more popular among friends. 81 3.35 1.26 12 My friends will think I am very active in the group. 79 3.67 .92 15 I will be more famous among friends. 79 3.51 .89 Self–expression 80 3.86 .99 .70 1 I will attract my friends to view my status and my photos. 81 3.54 1.24 16 I will be able to let my friends know my updates. 80 4.14 1.05
Item analyses were conducted on the social investigation index. Initially, each of the four items was correlated with the total score for social investigation (with the item removed). All the correlations were greater than .45 except for item 10 (M = 3.39, SD = 1.27): “I will meet new people” (r = .23). The correlation between item 10 and the other three social investigation items — to see what others are doing (M = 4.35, SD = 1.24, r = .12, ρ = .30), to look for a special person (M = 4.47, SD = 1.18, r = .12, ρ = .28), and to stalk some people (M = 3.66, SD = 1.17, r = .30, ρ = .01) — were especially low. Item 10 differed in content from the other three items in that it emphasized getting to know new people and networking through games. And according to the above mean scores, players reported they did not expect to meet new people on Happy Farm as much as to get involved in other social investigation activities, which is in line with boyd and Ellison’s (2007) findings that SNS users do not necessarily develop a network to meet new people. Based on these results, this item assessing new network development was eliminated from the index. When item 10 was removed, the alpha reliability coefficient of social investigation rose to .70. Through item analyses, the Cronbach’s alpha values of the five social gratifications indices were all above .70.
The Cronbach’s alpha values of the six game gratification indices in this study were as follow (Table 2): competition (M = 3.26, SD = .94, α = .80), challenge (M = 3.38, SD = .89, α = .84), social interaction (M = 4.38, SD = .97, α = .79), diversion (M = 3.89, SD = .90, α = .81), fantasy (M = 3.45, SD = .92, α = .84), and arousal (M = 3.25, SD = .91, α = .89). Reliability coefficients exceeded .79 for all indices, indicating a reliable measure.
Table 2: Analysis of game expected gratifications. Item number Item n M SD α Competition 82 3.26 .94 .80 7 I will prove my friends that I am good at this game. 82 3.29 1.34 13 I will immediately want to play again if I lose to someone. 82 3.60 1.15 16 It will be important to me to be the most skilled person playing the game. 82 3.39 1.14 20 I will get upset when I lose to my friends. 82 2.74 1.13 Challenge 80 3.38 .89 .84 3 I will feel proud when I master an aspect of a game. 82 3.41 1.14 9 I will feel very rewarding to get to the next level. 81 3.56 1.17 14 I will play until I complete a level or win a game. 81 3.14 1.08 (removed) 22 I will enjoy finding new and creative ways to work through the game. 82 3.34 1.03 Social interaction 82 4.38 .97 .79 10 My friends and I use this video game as a reason to get connected. 82 4.35 1.12 17 Often, a group of friends and I will play the games together. 82 4.41 1.01 Diversion 80 3.89 .90 .81 4 I will forget some of the real-life problems I have. 81 3.67 1.30 5 I will play video games when I have other things to do. 81 3.89 1.13 8 I will vent and relieve stress from the day. 82 3.71 1.17 12 I will feel relaxed. 82 4.27 1.02 Fantasy 81 3.45 .92 .84 1 I will be able to do things I can’t do in real life. 82 4.11 1.12 21 I will be able to pretend I am someone/somewhere else. 82 3.17 1.09 15 I will like to do something that I could not normally do in real life. 81 3.28 1.18 18 I will enjoy the excitement of assuming an alter ego in a game. 82 3.29 1.11 Arousal 81 3.25 .91 .89 2 My level of adrenaline will be raised. 82 3.49 .98 6 I will be kept on the edge of my seat. 82 2.98 1.10 11 My emotions will be stimulated. 82 3.49 1.03 19 The game will excite me. 81 3.07 1.09
Since the zero–order correlations among the 11 expected gratifications ranged from .21 (ρ = .06) to .83 (ρ < .001), the possibility of multicollinearity was checked. The condition number κ  (Kappa) was 14, not exceeding the conventional threshold value of 30 above which serious problems of multicollinearity are indicated . Therefore, multicollinearity did not significantly affect the interpretation of results.
Three hierarchical regression models were used to examine the contributions of background characteristics, and motivations in explaining the frequency, the duration, and the engagement level of social game play. The demographic control variables were entered in the first block, followed by the eleven gratification variables in the second blocks. The results of the three regression models are reported in Table 4.
Table 3: Amount of game play, engagement in game activity, and demographic variables. Variable n M SD Frequency of game play 95 8.87 2.97 Duration of game play 94 1.87 1.05 Engagement in game activity 91 2.74 .59 Collecting harvest 91 2.67 .90 Stealing friends’ harvest 91 3.42 1.09 Watering plants for friends 91 3.10 1.06 Using strategies 91 2.18 1.24 Growing crops in friends’ farms 91 3.68 1.63 Using ‘tool’ functions 90 2.30 1.07 Looking for tips to play well 89 2.17 .97 Paying attention to rankings 91 1.99 .89 Gender Female 50 66.7% Male 25 33.8% Age
(range: 20 to 37 yrs. old)
73 25.6 2.34
Table 4: Multiple hierarchical regressions explaining frequency of game play, duration of game play, and engagement in game activity.
Note: Regression entries are standardized beta coefficients.
* ρ < .10, ** ρ < .05, *** ρ < .01.
Predictors Frequency Duration Engagement ΔR2 Beta ΔR2 Beta ΔR2 Beta Block 1. Control .01 .09* .03 Gender .16 .02 .20 Age -.07 .29** .12 Block 2. .33** .27 .39*** Connection -.27 -.37 -.09 Investigation -.03 .18 .24 Shared identity .08 .20 -.24 Popularity .09 -.42 .08 Self–expression .09 .27 -.08 Competition .21 .34 .30 Challenge .36 -.20 .10 Social interaction .37* .54** .59*** Diversion .46** .04 .35* Fantasy -.64** -.42* -.53** Arousal -.29 .22 -.07 Total R2 .33 .36 .42** F 1.74* 1.87* 2.45** n 59 58 59
Predicting the frequency of playing Happy Farm
The results indicated that diversion (β4d = .46, ρ = .037) and social interaction ((β4c = .37, ρ = .092) were the significant positive contributors in the model. Another significant contributor was found of expected gratification for fantasy (β4e = -.64, ρ = .013), but the relationship was negative and counterintuitive (see Table 4 for the coefficients of other predictors). The final equation accounted for 33.4 percent of the variance in the frequency of game play. Accordingly, the expected gratifications of filling time and relaxing, and interacting with others predicted increased frequency of playing, whereas the expected gratifications of fantasy was a predictor of playing less frequently.
Predicting the duration of playing Happy Farm
Age (β = .29, ρ = .037), and social interaction (β5c = .54, ρ = .015) were significantly associated with the duration of game play. Fantasy motive (β5e = -.42, ρ = .099) was a statistically significant yet negative contributor in the model. The overall equation accounted for 36 percent of the variance in the amount of time spent on the game. Therefore, the results suggested that older users who played Happy Farm for the purpose of interacting with others stayed in the game longer than did their counterparts.
Predicting the engagement of Happy Farm activities
Social interaction motive (β6c = .59, ρ = .005), and diversion motive (β6d = .35, ρ = .092) were significant contributors of the engagement of game activities. Again fantasy motive (β6e= -.53, ρ = .028) was a negatively significant predictor in the model. The final equation accounted for 42 percent of the variance in the engagement of game activities. Accordingly, Happy Farm players who expected to interact with others and find diversion reported that they became more engaged in the game activities.
While social games are experiencing an explosive growth in popularity, they are still very unfamiliar terrain for researchers. This paper presented a quantitative explorative study of social games using the uses and gratifications approach. Based on the special characteristics of social games, the present study proposed that the expected gratifications of social game players include both social motives and game motives. Three dimensions of the game play — frequency, duration, and the engagement of game activities — were examined separately.
The findings indicated that respondents played social games more frequently and became more engaged in different kinds of game activities for the purpose of diversion. That is, social games are used to relax, escape from stress, and avoid responsibilities. Diversion has long been considered a reason for playing video games (Sherry, et al., 2006; Sun, et al., 2006; Yee, 2006; Molen and Jongbloed, 2007). This motivation may not be surprising here. Social games are casual, easy to pick up, “masterable” to a vast, formerly “nongamer” audience, and they require fewer mental resources to process gaming tasks. In addition, “Casual game design commonly features excessive positive feedback for every successful action the player performs” . Such designed–in structures of social games make them cheerful and easygoing ‘places’: lots of small rewards are given out during the game, and progress is made quickly and is visible. More traditional hardcore game designs, on the other hand, require large amounts of time and resources commitment. In addition, hardcore games generally challenge and punish the player for failing. Players with little knowledge of game conventions and low tolerance for difficulty in hardcore games are likely to become frustrated rather than relax. In prior studies, challenge and competition have been documented as the most popular reasons for playing video games (Lucas and Sherry, 2004; Sherry, et al., 2006; Molen and Jongboled, 2007; Sun, et al., 2006). But neither was a significant motive for playing social games. It is possible that social gamers do not necessarily enjoy the challenge of “beating the game,” or beating friends. The “lenient difficulty/punishment structures” [18 of casual design affords that almost all social game players can master the game. Even if they play badly, no punishments are given. Thus, social games do not enforce winning or competing, rather they accommodate more flexible playing styles. Such a carefree and optimistic playing manner may be able to provide desirable places for users to temporarily avoid, forget about, and escape from real–life stress and problems.
There is also a distinctly social aspect to social games. The findings demonstrated that respondents played social games more frequently, spent more time on the game, and got more engaged in game activities for the purpose of social interaction. In fact, the social interaction motive was a stronger predictor of game play variables than the diversion motive. Players want to interact with friends and to keep up social connections through social games. This social reason for playing games is intriguing. It is consistent with what the game developers have envisioned: “social game design isn’t about creating a game that is strategically deep as much as it is about making sure that the game, in turn, creates interesting interaction between players” [19. Social networking platforms provide opportunity for the social embedding of games. Applied together, the platforms and the designed–in structures make the social context a critical component of the game play and enjoyment.
The results also revealed that social interaction predicted both frequency and duration of game playing, while diversion only predicted only frequency. As discussed above, social games generally allow the players to make progress or complete a game within short periods. But the game design is also very flexible. In other words, social games require small commitments while allowing the player to spend more time with the game if desired. The findings may suggest that users, who play games for the purpose of interaction with close ties, are willing to play with large time investments. While the underlying structure of social games encourages social interaction in general, users do not passively accept what the technology affords but actively appropriate technology to accomplish their own goals (Bargh and McKenna, 2004). As social games are played in social settings, social considerations may become part of the player’s deliberations (Juul, 2010). It is likely that users who relish the social values of game actions and desire to enhance their social standing in the game tend to play long sessions at a time. For instance, they want to help as many friends as they can in the game, or they try to choose specific gifts for particular friends.
The negative relationship between the fantasy factor and game play intensity was unexpected. However, prior studies on MMO (Williams, et al., 2008) have found negative association between the players’ motive to be immersed in a fantasy world and the total playing time. They have suggested that some MMO players may not find fantasy elements of the game–play satisfying, such as role–playing. As mentioned, social game players usually use real–name identities in order to interact with real–life friends. They may find that interacting with friends through games does not differ much from real–life social exchanges. For example, close friends who help each other in real life may also help each other out on Happy Farm. Another possible explanation is that as the major game play is to interact with friends, the fantasy factor is not an important element for the players to enjoy the games.
It is worth noting that research on MMO consumption (Shaw, et al., 2006) has indicated that “habit strength” [20 is a powerful predictor of game usage. Habit and consciously planned behavior have been found to be separate and opposing processes (Landis, et al., 1978; Shaw, et al., 2006). This study only investigates only the expected gratifications from social game play. However, the interplay between habit and active decision–making of game play is not considered. Happy Farm players may be initially drawn to the game by the expectation of being popular figures among friends. However, over time game play becomes an automatic, conditioned response and the original motivations are no longer actively reflected during the play (Shaw, et al., 2006). Most users play social games not once or twice but instead repeatedly on an ongoing basis. Logging onto Happy Farm may be triggered by sensory cues and a consistent context (e.g., the sight of the Happy Farm link when surfing the Internet at leisure hours of the day) rather being a planned decision based on expected outcomes. Thus, some expected gratifications may become less salient upon repetition (LaRose and Eastin, 2004). And the game play becomes habitual.
The playing habits may also explain the non–salient predictors of motives for game competition and challenge because most of the players in the present sample may have long since acquired the skills needed to master their game, besides the reason that the design of social games is simple and casual itself.
The author cautions against overly zealous interpretations of the findings from this study; it is not without limitations. In particular, this study relies on a non–probabilistic sampling method (e.g., snowball sampling); the selection of participants was primarily based on a self–chosen strategy. It is worthwhile to select a diverse population to replicate the results. The participant pool presented in the present study makes it problematic to generalize findings to the entire population of social game players. The findings revealed a significant interaction between age and the duration of game play, suggesting that older participants may stay in the game longer. With a relatively small age range of the respondents, the results should be interpreted with caution. Studies have shown that there are significant differences in the preference of game genres across age groups (Sherry, et al., 2003). It is possible that social games are liked best by older people, who have less knowledge of game conventions and can only make small commitments because of a life with parenting, jobs, and general adult responsibilities.
Future research should explore a broader spectrum of social and psychological characteristics that might influence the amount of social game play. Previous studies have consistently shown gender differences in playing computer games (Williams, et al., 2008; Eglesz, et al.,, 2005; Lucas and Sherry, 2004; Shaw, et al.,, 2006). Due to the snowball sampling technique, demographic variances are limited in this study. Further inclusion of individual differences into the study can help us understand the impact of demographic variance on social game playing patterns. In addition, personality also predicts the frequency of game playing (McClure and Mears, 1984). As the current findings suggest that social games are a means of social interaction with friends, it may allow individuals with certain characteristics to become connected in society more comfortably. For instance, one prior study (Sheeks and Birchmeier, 2007) found that people who are shy but wish to be social were able to develop a better relationship with others online. Thus, individual characteristics such as shyness, sociability, and communication skills could be investigated in future research to determine their impact on social game experience.
Although this study suggests that social games have the potential to provide a unique venue for social activities, it is unclear how the use of social games might influence the psychosocial well–being of the players. Recent studies found that online interactions with strangers can come at the cost of meaningful activities with off–line friends and family, while interacting with one’s existing social ties through the Internet can enhance social involvement (Shen and Williams, 2011). Social gamers mainly play with close ties. Future research can examine whether social games could serve as a particular site that produces meaningful interaction with high–quality communication. One may also want to take into consideration the size of the in–game network and a player’s desire to manage the social situation through games, when investigating the psychosocial outcomes of social game play.
Future research might also focus on various phases of a new medium diffusion process, such as the initiation and termination of social game play and the processes through which habits are formed and broken. According to Hiltz and Turoff (1978, 1981; Rice, 1993), the uses of new media evolve as users become more familiar with them. A combination of uses and gratifications and diffusion of innovations may extend the theoretical approach to understand the uses of a specific game.
Taken as a whole, the findings suggest that players use social games to interact with social ties and to find diversion from real–life stress. These gratifications are also afforded by the particular underlying game structure. Social games are potential avenues to enhance one’s social circles, and they should be described as social media rather than as just one of many online computer games.
About the author
Jinghui (Jove) Hou is a doctoral student at the Annenberg School of Communication and Journalism, University of Southern California. Her research primarily focuses on the social psychological factors related to human–computer interaction and technology–mediated communication.
E–mail: jinghuih [at] usc [dot] edu
1. Global Times (2009).
3. Zynga is a U.S.–based social game developer. It also claims to be the largest social gaming company in terms of the number of active users. The company develops FarmVille and CityVille that are popular on Facebook.
4. Five Minutes is a Chinese social game company that has developed Happy Farm, the first SNS farming game and likely the most popular social game in China.
5. Playfish is a U.S. social game developer. One of Playfish’s hit products is Pet Society, which attracts 21.5 million monthly active users on Facebook.
6. PopCap Games is an American video game developer and publisher. Its games are available for Web, PC, Xbox, cell phones, PDAs, and etc. Its flagship titles include Bejeweled and Plants vs. Zombies.
7. Rayburn and Palmgreen, 1984, p. 540.
8. Urista, et al., 2009, p. 221.
9. Joinson, 2008, pp. 3—10.
10. Joinson, 2008, p. 5.
11. Urista, et al., 2009, p. 221.
13. Urista, et al., 2009, p. 221.
14. Lucas and Sherry, 2004, p. 503.
16. Cohen, et al., 2003, p. 424.
17. Juul, 2010, p. 50.
18. Juul, 2010, p. 54.
19. Juul, 2010, p. 121.
20. Shaw, et al., 2006, p. 14.
Stanley J. Baran and Dennis K. Davis, 2006. Mass communication theory: Foundations, ferment, and future. Fourth edition. Belmont, Calif.: Thomas Wadsworth.
John A. Bargh and Katelyn Y.A. McKenna, 2004. “The Internet and social life,” Annual Review of Psychology, volume 55, pp. 573–590.http://dx.doi.org/10.1146/annurev.psych.55.090902.141922
Richard A. Bartle, 1996. “Players who suit MUDs,” Journal of MUD Research, volume 1, number 1, at http://www.mud.co.uk/richard/hcds.htm, accessed 4 March 2010.
Jeffrey Boase, John B. Horrigan, Barry Wellman, and Lee Rainie, 2006. The strength of Internet ties: The Internet and emails aid users in maintaining their social networks and provide pathways to help when people face big decisions. Washington, D.C.: Pew Internet & American Life Project, and at http://www.pewinternet.org/Reports/2006/The-Strength-of-Internet-Ties.aspx, accessed 5 November 2009.
danah boyd and Nicole Ellison, 2007. “Social network sites: Definition, history, and scholarship,” Journal of Computer–Mediated Communication, volume 13, number 1, at http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html, accessed 2 November 2009.
Eric Caoili, 2010. “Study: Social games to generate over $1 billion in 2012” (20 July), at http://www.gamasutra.com/view/news/29511/Study_Social_Games_To_Generate_Over_1_Billion_In_2012.php, accessed 5 April 2011.
Jiyoung Cha, 2010. “Factors affecting the frequency and amount of social networking site use: Motivations, perceptions, and privacy concerns,” First Monday, volume 15, number 12, at http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/ fm/article/view/2889/2685, accessed 11 March 2011.
T. Charney and B.S. Greenberg, 2002. “Uses and gratifications of the Internet,” In: Carolyn A. Lin and David J. Atkin (editors). Communication technology and society: Audience adoption and uses. Cresskill, N.J.: Hampton Press, pp. 379–407.
Sande Chen, 2009. “The social network game boom” (29 April), at http://www.gamasutra.com/view/feature/4009/the_social_network_game_boom.php, accessed 28 September 2009.
Marina Cords, 1997. “Friendship, alliances, reciprocity and repair,” In: Andrew W. Whiten and Richard W. Byrne (editors). Machiavellian intelligence II: Extensions and evaluations. Cambridge: Cambridge University Press, pp. 24–49.
Denes Eglesz, Istvan Fekete, Orhidea Edith Kiss, and Lajos Izso, 2005. “Computer games are fun? On professional games and players’ motivations,” Educational Media International, volume 42, number 2, pp. 117–124.http://dx.doi.org/10.1080/09523980500060274
Nicole Ellison, Charles Steinfield, and Cliff Lampe, 2007. “The benefit of Facebook ‘friends’: Exploring the relationship between college students’ use of online social networks and social capital,” Journal of Computer–Mediated Communication, volume 12, number 4, at http://jcmc.indiana.edu/vol12/issue4/ellison.html, accessed 3 November 2009.
Facebook, at http://www.facebook.com/, accessed 28 September 2009.
David C. Funder, 1995. “On the accuracy of personality judgment: A realistic approach,” Psychological Review, volume 102, number 4, pp. 652–670.http://dx.doi.org/10.1037/0033-295X.102.4.652
Global Times, 2009. “‘Farm theft’ shows today’s society anxiety,” at http://opinion.globaltimes.cn/chinese-press/2009-09/463097.html, accessed 10 November 2009.
Starr Roxanne Hiltz and Murray Turoff, 1981. “The evolution of user behavior in a computerized conferencing system,” Communications of the ACM, volume 24, number 11, pp. 739–751.http://dx.doi.org/10.1145/358790.358794
Starr Roxanne Hiltz and Murray Turoff, 1978. The network nation: Human communication via computer. Reading, Calif.: Addison–Wesley.
Adam N. Joinson, 2008. “‘Looking at’, ‘looking up’ or ‘keeping up with’ people? Motives and uses of Facebook, CHI 2008 (5–10 April, Florence, Italy), at http://people.bath.ac.uk/aj266/pubs_pdf/1149-joinson.pdf, accessed 5 December 2009.
Jesper Juul, 2010. A casual revolution: Reinventing video games and their players. Cambridge, Mass.: MIT Press.
Hanjun Ko, Chang–Hoan Cho, and Marilyn S. Roberts, 2005. “Internet uses and gratifications: A structural equation model of interactive advertising,” Journal of Advertising, volume 34, number 2, pp. 57–70.http://dx.doi.org/10.1080/00913367.2005.10639191
Robert Kraut, Michael Patterson, Vicki Lundmark, Sara Kiesler, Tridas Mukopadhyay, and William Scherlis, 1998. “Internet paradox: A social technology that reduces social involvement and psychological well–being?” American Psychologist, volume 53, number 9, pp. 1,017–1,031.
Kaiser Kuo, 2008. “China’s SNS and privacy issues,” Digital Watch, at http://digitalwatch.ogilvy.com.cn/, accessed 22 November 2009.
Cliff Lampe, Nicole Ellison, and Charles Steinfield, 2007. “A familiar Face(book): Profile elements as signals in an online social network,” CHI ’07: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, at https://www.msu.edu/~nellison/lampe_et_al_2007.pdf, accessed 10 December 2009.
Dan Landis, Harry C. Triandis, and John Adamopoulos, 1978. “Habit and behavioral intentions as predictors of social behavior,” Journal of Social Psychology, volume 106, number 2, pp. 227–237.http://dx.doi.org/10.1080/00224545.1978.9924174
Robert LaRose and Matthew S. Eastin, 2004. “A social cognitive theory of Internet uses and gratifications: Toward a new model of media attendance,” Journal of Broadcasting & Electronic Media, volume 48, number 3, pp. 358–377.http://dx.doi.org/10.1207/s15506878jobem4803_2
Robert LaRose, Dana Mastro, and Matthew S. Eastin, 2001. “Understanding Internet usage: A social cognitive approach to uses and gratifications,” Social Science Computer Review, volume 19, number 4, pp. 395–413.http://dx.doi.org/10.1177/089443930101900401
Kwan Min Lee, Wei Peng, and Namkee Park, 2009. “Effects of computer/video games and beyond,” In: Jennings Bryant and Mary Beth Oliver (editors). Media effects: Advances in theory and research. Third edition. New York: Routledge, pp. 551–566.
Carolyn A. Lin, 1999. “Online–service adoption likelihood,” Journal of Advertising Research, volume 39, number 2, pp. 79–89.
Kristen Lucas and John L. Sherry, 2004. “Sex differences in video game play: A communication–based explanation,” Communication Research, volume 31, number 5, pp. 499–523, and at http://icagames.comm.msu.edu/cr.pdf, accessed 10 November 2009.
Bonaventura Majolo, Kaye Ames, Rebecca Garratt, Kate Hall, and Natasha Wilson, 2006. “Human friendship favours cooperation in the iterated prisoner’s dilemma,” Behaviour, volume 143, number 11, pp. 1,383–1,395.
Robert F. McClure and F. Gary Mears, 1984. “Video game players: Personality characteristics and demographic variables,” Psychological Reports, volume 55, number 1, pp. 271–276.http://dx.doi.org/10.2466/pr0.19220.127.116.111
Juliette Walma Van Der Molen and Wieteke Jongbloed, 2007. “Free online games: An exploratory uses and gratifications study of free games on the Internet,” paper presented at the 57th Annual Conference of the International Communication Association (San Francisco, 24–28 May).
Merrill Morris and Christine Ogan, 1996. “The Internet as mass medium,” Journal of Computer–Mediated Communication, volume 1, number 4, at http://jcmc.indiana.edu/vol1/issue4/morris.html, accessed 18 November 2009.
Bonnie Nardi and Justin Harris, 2006. “Strangers and friends: Collaborative play in World of Warcraft,” CSCW –06: Proceedings of the 2006 20th Anniversary Conference on Computer Supported Cooperative Work (Banff, Alberta, 4–8 November), pp. 149–158.
John E. Newhagen and Sheizaf Rafaeli, 1996. “Why communication researchers should study the Internet: A dialogue,” Journal of Communication, volume 46, number 1, pp. 4–13.http://dx.doi.org/10.1111/j.1460-2466.1996.tb01458.x
Norman H. Nie and Lutz Erbring, 2002. “Internet and society: A preliminary report,” IT & Society, volume 1, number 1, pp. 275–283, and at http://www.vermario.com/wiki/lib/exe/fetch.php/internet_society_report.pdf, accessed 6 November 2009.
Philip Palmgreen, 1984. “Uses and gratifications: A theoretical perspective,” In: Robert N. Bostrom (editor). Communication yearbook, volume 8, pp. 20–55.
Philip Palmgreen, Lawrence A. Wenner, and Karl E. Rosengren, 1985. “Uses and gratifications research: The past ten years,” In: Karl E. Rosengren, Lawrence A. Wenner, and Philip Palmgreen (editors). Media gratifications research: Current perspectives. Beverly Hills, Calif.: Sage, pp. 11–37.
Robert D. Putnam, 2000. Bowling alone: The collapse and revival of American community. New York: Simon & Schuster.
J.D. Rayburn and Philip Palmgreen, 1984. “Merging uses and gratifications and expectancy–value theory,” Communication Research, volume 11, number 4, pp. 537–562.http://dx.doi.org/10.1177/009365084011004005
Ronald E. Rice, 1993. “Media appropriateness: Using social presence theory to compare traditional and new organizational media,” Human Communication Research, volume 19, number 4, pp. 451–484.http://dx.doi.org/10.1111/j.1468-2958.1993.tb00309.x
Alan M. Rubin, 2009. “Uses and gratifications perspective on media effects,” In: Jennings Bryant and Mary Beth Oliver (editors). Media effects: Advances in theory and research. Third edition. New York: Routledge, pp. 165–184.
Thomas E. Ruggiero, 2000. “Uses and gratifications theory in the 21st century,” Mass Communication & Society, volume 3, number 1, pp. 3–37.http://dx.doi.org/10.1207/S15327825MCS0301_02
Ryan Schmid, 2008. “Real text in virtual worlds,” Technical Communication, volume 55, number 3, pp. 277–284.
Patrick Shaw, Robert LaRose, and Christina Wirth, 2006. “Reaching new levels in massively multiplayer online games: A social cognitive theory of MMO usage,” paper presented at the Annual Conference of the International Communication Association, Dresden, Germany.
Miranda S. Sheeks and Zachary P. Birchmeier, 2007. “Shyness, sociability, and the use of computer–mediated communication in relationship development,” CyberPsychology & Behavior, volume 10, number 1, pp. 64–70.http://dx.doi.org/10.1089/cpb.2006.9991
Cuihua Shen and Dmitri Williams, 2011. “Unpacking time online: Connecting Internet and massively multiplayer online game use with psychosocial well–being,” Communication Research, at http://crx.sagepub.com/content/early/2010/11/30/0093650210377196, accessed 3 January 2010.
John L. Sherry, 2001. “The effects of violent video games on aggression: A meta–analysis,” Human Communication Research, volume 27, number 3, pp. 309–331.
John L. Sherry, Kristen Lucas, Bradley S. Greenberg, and Ken Lachlan, 2006. “Video game uses and gratifications as predictors of use and game preference,” In: Peter Vorderer and Jennings Bryant (editors). Playing video games: Motives, responses, and consequences. Mahwah, N.J.: Lawrence Erlbaum Associates, pp. 213–224.
John L. Sherry, Rebecca de Souza, Bradley S. Greenberg, and Ken Lachlan, 2003. “Relationship between developmental stages and video game uses and gratifications, game preference and amount of time spent in play,” paper presented at the Annual Conference of the International Communication Association, San Diego, Calif.
Thomas F. Stafford, 2008. “Social and usage–process motivation for consumer Internet access,” Journal of Organizational and End User Computing, volume 20, number 3, pp. 1–21.http://dx.doi.org/10.4018/joeuc.2008070101
Thomas F. Stafford, Marla Royne Stafford, and Lawrence L. Schkade, 2004. “Determining Uses and Gratifications for the Internet,” Decision Sciences, volume 35, number 2, pp. 259–288.http://dx.doi.org/10.1111/j.00117315.2004.02524.x
Neal Starkman, 2007. “Going the distance,” T.H.E. Journal, volume 34, number 2, pp. 18–24.
Constance Steinkuehler and Dmitri Williams, 2006. “Where everybody knows your (screen) name: Online games as ‘third places’,” Journal of Computer–Mediated Communication, volume 11, number 4, at http://jcmc.indiana.edu/vol11/issue4/steinkuehler.html, accessed 3 December 2009.
Kaveri Subrahmanyam, Stephanie M. Reich, Natalia Waechter, and Guadalupe Espinoza, 2008. “Online and offline social networks: Use of social networking sites by emerging adults,” Journal of Applied Developmental Psychology, volume 29, number 6, pp. 420–433.http://dx.doi.org/10.1016/j.appdev.2008.07.003
Tao Sun, Bu Zhong, and Jun Zhang, 2006. “Uses and gratifications of Chinese online gamers,” China Media Research, volume 2, number 2, pp. 58–63.
Geoff Thomas and Garth Fletcher, 2003. “Mind–reading accuracy in intimate relationships: Assessing the roles of the relationship, the target, and the judge,” Journal of Personality and Social Psychology, volume 85, number 6, pp. 1,079–1,094.
Lisa Collins Tidwell and Joseph B. Walther, 2002. “Computer–mediated communication effects on disclosure, impressions, and interpersonal evaluations,” Human Communication Research, volume 28, number 3, pp. 317–348.http://dx.doi.org/10.1111/j.1468-2958.2002.tb00811.x
Mark A. Urista, Qingwen Dong, and, Kenneth D. Day, 2009. “Explaining why young adults use MySpace and Facebook through uses and gratification theory,” Human Communication, volume 12, number 2, pp. 215–229, and at http://www.uab.edu/Communicationstudies/humancommunication/07_Urista_final.pdf, accessed 20 November 2009.
Victor H. Vroom, 1995. Work and motivation. San Francisco: Jossey–Bass.
Barry Wellman, 2001. “Computer networks as social networks,” Science, volume 293, number 5537, pp. 2,031–2,034.
Dmitri Williams, 2007. “The impact of time online: Social capital and cyberbalkanization,” CyberPsychology & Behavior, volume 10, number 3, pp. 398–406.http://dx.doi.org/10.1089/cpb.2006.9939
Dmitri Williams, 2006. “On and off the ’Net: Scales for social capital in an online era,” Journal of Computer–Mediated Communication, volume 11, number 2, at http://jcmc.indiana.edu/vol11/issue2/williams.html, accessed 1 July 2011.
Dmitri Williams, Nick Yee, and Scott E. Caplan, 2008. “Who plays, how much, and why? Debunking the stereotypical gamer profile,” Journal of Computer–Mediated Communication, volume 13, number 4, pp. 993–1,018.
Nick Yee, 2006. “The demographics, motivations and derived experiences of users of massively multi–user online graphical environments,” Presence: Teleoperators and Virtual Environments, volume 15, number 3, pp. 309–329.http://dx.doi.org/10.1162/pres.15.3.309
Happy Farm use survey.
Note: The original questionnaire is in Chinese. It was translated by the researcher, who is bilingual in English and Chinese.
Amount of Happy Farm play Frequency: How often do you play Happy Farm on average? (Reverse coded)
- I play every day, and I play more than 5 times per day.
- I play every day, and I play 2–5 times per day.
- I play every day, and I play 1–2 times per day.
- I do NOT play every day. I play 5–6 times per week.
- I play 4 times per week.
- I play 3 times per week.
- I play 2 times per week.
- I play 1 times per week.
- I play less than once a week, but more than once every half a month.
- I play less than once every half a month, but more than once every month.
- I play once every several months.
- Since the installation, I have played only for once or twice.
Duration: On average, when you play, about how long do you play for? (Reverse coded)
- More than 2 hours every time.
- About 1–2 hours every time.
- About 30 minutes to 1 hour every time.
- About 15 minutes to 30 minutes.
- About 10 minutes to 15 minutes.
- Less than 10 minutes every time.
Engagement in Happy Farm game activities How often do you collect harvest on average?
- Very often
How often do you steal friends’ harvest on average?
- Very often
How often do you water plants and clean weeds and bugs for friends on average?
- Very often
How often do you use some strategies to play the game on average?
- Very often
How often do you grow crops in friends’ farms on average?
- Very often
How often do you use ‘tool’ functions on average, such as fertilizers, and pesticides?
- Very often
How often do you look for expert tips, and advices in order to play the game well?
- Very often
How often do you pay attention to the rankings among your farm neighbors (i.e., other players) on average?
- Very often
Happy Farm social gratifications Please rate the following statements on how strongly you agree or disagree with them (1= strongly disagree, 7 = strongly agree). When playing Happy Farm:
- I will attract my friends to view my status and my photos.
- I will be more popular among friends.
- I will reconnect with people I’ve lost contact with.
- I will want to see what my friends are doing.
- I will receive some friends’ requests.
- I will find people I haven’t seen for a while.
- I will find out what old friends are doing now.
- I will use advanced search to look for specific types of people.
- I will be able to contact long distance friends.
- I will meet new people.
- I will join events or activities of my friends.
- My friends will think I am very active in the group.
- I will stalk some people (pay great attention).
- I will feel the belonging to the group.
- I will be more popular among friends.
- I will be able to let my friends know my updates.
- I will decrease the likelihood of being left out.
- I will maintain relationships with people I may not get to see very often.
Happy Farm social gratifications Please rate the following statements on how strongly you agree or disagree with them (1= strongly disagree, 7 = strongly agree). When playing Happy Farm:
- I will be able to do things I cant do in real life.
- My level of adrenaline will be raised.
- I will feel proud when I master an aspect of a game.
- I will forget some of the real–life problems I have.
- I will play video games when I have other things to do.
- I will be kept on the edge of my seat.
- I will prove my friends that I am good at this game.
- I will vent and relieve stress from the day.
- I will feel very rewarding to get to the next level.
- My friends and I use this video game as a reason to get connected.
- My emotions will be stimulated.
- I will feel relaxed.
- I will immediately want to play again if I lose to someone.
- I will play until I complete a level or win a game.
- I will like to do something that I could not normally do in real life.
- It will be important to me to be the most skilled person playing the game.
- Often, a group of friends and I will play the games together.
- I will enjoy the excitement of assuming an alter ego in a game.
- The game will excite me.
- I will get upset when I lose to my friends.
- I will be able to pretend I am someone/somewhere else.
- I will enjoy finding new and creative ways to work through the game.
Demographic control variables Gender:
Received 6 April 2011; accepted 19 June 2011.
“Uses and gratifications of social games: Blending social networking and game play” by Jinghui Hou is licensed under a Creative Commons Attribution–NonCommercial 3.0 Unported License.
Uses and gratifications of social games: Blending social networking and game play
by Jinghui Hou.
First Monday, Volume 16, Number 7 - 4 July 2011
A Great Cities Initiative of the University of Illinois at Chicago University Library.
© First Monday, 1995-2014.