University at Buffalo, State University of New York, USA
Technological advances by Web 2.0 media and mobile phones have recently enabled users to become producers of their own media content. Users are able to create and share photos and videos with speed and ease. A much different trend has emerged with these new technological affordances, though. Individuals are utilizing this new media and are creating and sharing sexually explicit user-generated content (SEUGC) of themselves. Four hundred undergraduate students completed an online survey to assess their likelihood to create and share SEUGC in the future. Social cognitive theory (Bandura, 1986) was the framework used to analyze the factors that propel this behavior. Results suggest the influence of viewing pornography, sexual self-efficacy, and entertainment and arousal outcomes as triggers and motivators for engaging in this behavior. The model also revealed a desensitization effect towards negative consequences that might occur from creating and sharing SEUGC.
Keywords: social cognitive theory, user-generated content, sexuality, mobile phones, Web 2.0, cybersex
Throughout history, new media have significantly impacted human sexuality. Today, numerous forms of pornography engendered by new media have been assimilated within the Internet. Until recently, the majority of pornography has been produced by professionals within the industry (Cronin & Davenport, 2001) while Internet users have traditionally been considered passive consumers of online pornography (Doring, 2009). With new Internet technologies, users have become producers of their own sexually explicit content and are able to create and share sexually explicit messages, photos and videos Additionally, most cell phones now have the capability to capture and attach sexually-laden images and videos to text messages that can be sent and received from practically anywhere (Weisskirch & Delevi, 2011). This new form of sexually self-produced material is known as sexually explicit user-generated content (SEUGC).
SEUGC is defined as non-professional photos, videos, or written messages (e-mail, instant messages, blogs, mobile phone text messages) that are created by individuals planning, soliciting, or actually engaging in sexual activities for non-profit. Such sexual activities may include visual depictions or written descriptions of an individual’s private areas, and/or visual depictions or written descriptions of an individual masturbating, or engaging in sexual activities with another person or persons. Sharing is defined as any transmission of this sexually explicit content via Internet or mobile phone technology and may occur privately (one-to-one) or publicly (one-to-many).
Numerous studies exist that have examined the influence of the Internet on sexual behavior. In their study on cybersex compulsivity, Cooper, Delmonico, and Burg (2000) report that a majority of individuals in their sample spent 10 hours or less engaging in cybersex activity. This study, as well as a follow-up study (Cooper, Griffin-Shelly, Delmonico, & Mathy, 2001), concluded that both of their samples were not at high risk for online sexual compulsivity, however. Cybersex has also been linked to an increase of risky sexual behaviors amongst its users. Numerous studies conclude that a significant amount of both men and women engage in unprotected vaginal or anal intercourse with sex partners met online (Liau et al., 2006; McFarlane, Kachur, Bull, & Rietmeijer, 2004). Internet pornography is not strictly limited to explicit sexual behaviors, however. Gray and Klein (2006) reveal that a majority of Internet users search for sex information online such as HIV/AIDS prevention information (Ybarra, Kiwanuka, Emenyonu, & Bangsberg, 2006). Rietmeijer and Shaw (2007) list several services associated with online sex education such as training courses used to increase sexual communication skills, and sex chats monitored by social workers and sex experts.
None of these studies, however, have exclusively examined the democratization of the Internet and mobile phones that have equipped individuals with the ability to create and share SEUGC of themselves. While two nation-wide studies (MTV-AP, 2009; National Campaign to Prevent Teen Pregnancy & Cosmogirl.com, 2008) have created a foundation for exploring SEUGC, no studies exist that have applied a theoretical framework to understand this behavior. The current study proposes a theoretical approach to explain the motivations and behaviors of individuals who are likely to create and share SEUGC of themselves via the Internet and mobile phones. By using social cognitive theory (Bandura, 1986), we will examine who is likely to create this content and why they are likely to create and share this content.
Social cognitive theory (SCT) is a complex, multi-faceted theoretical framework that seeks to understand human thought processes and behavior. Past research using SCT (formerly known as social learning theory) to explain sexual behavior has traditionally focused on pornography and its effect on men’s aggression towards women (Malamuth & Check, 1985; Donnerstein, 1984; Mulac, Jansma, & Linz, 2002), adolescent sexual behavior and contraception (Kegeles, Adler, & Irwin, 1988; Byrne, Kelley, & Fisher, 1993; Guyer, Strobino, Ventura, & Singh, 1995) and the development of human sexuality (Hoult, 1983; Van Wyk & Geist, 1984; Oliver & Hyde, 1993). Studies associating Internet sexual behavior and SCT remain scarce. A few exceptions are evident to this date. Goodson et al., (2000) used SCT to create an instrument to explore college students' sexual behaviors and attitudes while searching the Internet. A second study implemented the instrument revealing sexual gratification and sexual curiosity as the main motivators for accessing this information (Goodson et al. 2001). A recent study by Siegfried, Lovely, and Rogers (2008) used SCT to examine the behaviors of individuals who consume child pornography on the Internet. While these studies have certainly added to the research field of SCT to help explain certain sexual behaviors, there yet remains a study that has applied SCT to analyze why users are shifting towards becoming active producers of sexual content rather than passive consumers.
Within SCT, behavioral, personal, and environmental factors influence people’s thoughts and behaviors simultaneously in a reciprocal fashion (Bandura, 1978). Behavior is contingent on expected outcomes which represent what individuals might expect to achieve from performing a specific behavior and serve as motivators that guide people’s behaviors. Furthermore, behavior may be determined by an individual’s own direct experience or mediated vicariously by observing others (LaRose & Eastin, 2004). Within our framework, vicarious experience will be specifically referred to as vicarious pornography experience. We expand this construct to include exposure to both SEUGC and professionally produced sexually explicit content. Specific content includes professionally produced print and electronic media as well as SEUGC in the form of messages, photos, and videos. In addition to expected outcomes and vicarious experience, thought processes and behaviors are also driven by self-efficacious beliefs. There has been a paucity of research focusing on individual sexual self-efficacy; more specifically, one’s ability to perform sexual techniques and to arouse their partner sexually. We intend to add a new dimension to the concept of sexual self-efficacy by examining its influence on creating and sharing SEUGC.
In addition to direct experiences, by exercising forethought, individuals are able to form expected outcomes without directly engaging in a behavior (Bandura, 1991). Doring (2009) states online sexual activity offers many of the gratifications associated with offline sex including but not limited to, emotional fulfillment, sexual arousal, and feelings of self acceptance (McKenna, Green, & Smith, 2001). LaRose and Eastin (2004) describe expected outcomes as second order constructs that are grouped within a set of first-order constructs known as behavioral incentives. This study posits six dimensions of behavioral incentives: (1) entertainment incentives, (2) arousal incentives (3) social incentives, (4) self-evaluative incentives, (5) self-reactive incentives, and (6) adverse social outcomes.
Entertainment outcomes are predicated on the motivation to seek enjoyment. Jayson (2008) reports that about a third of young adults 20-26 and 20% of teens say they’ve sent or posted naked photos or videos of themselves online, mostly to have fun or be flirtatious and Rose (2010) describes a major driver for sending SEUGC via mobile phones is for the fun and excitement of it.
Arousal incentives represent motivators for seeking and providing sexual pleasure. Online sexual activity, most commonly referred to as cybersex, can be viewed as any online activity that involves engaging in sexual stimulation and gratification (Maheu & Subotnik, 2001). Mills (1998) found that some people spend more than 70 hours a week on the Internet engaging in online sexual activity while Goodson et al. (2000) report sexual gratification as a primary motivator for engaging in this behavior.
Social incentives represent reasons for social integration and acceptance. The Internet enables individuals who do not share mainstream sexual interests to locate and communicate with like-minded individuals without experiencing social stigmatization (Doring, 2009). The Internet also serves as an outlet for sexually disenfranchised groups (Tepper & Owens, 2002) and Cooper et al. (2002) mention several reasons for engaging in online sexual activity including the need to socialize with people who share the same sexual interests, to meet people to date, and to have offline sexual activities with.
LaRose, Lin, and Eastin (2003) describe self-evaluative incentives as rewards intended to leave individuals with a general sense of self-satisfaction that is derived from accomplishing an activity that meets desired standards. Bandura (1986) explains, direct feedback is most effective in fostering development of standards when it is supported by feedback from one another. Feedback about progress influences the goals set for prospective actions, thus providing a continuing source of self-motivation (Bandura, 1986). Under the current study, without feedback from other individuals, those who are likely to create and share SEUGC are unaware as to whether or not their audiences will generally like their content.
Self-reactive incentives (LaRose & Eastin, 2004) may be intended to alleviate negative feelings and moods such as stress and low self-esteem. Bandura, (1986) suggests that as people experience moments of self-inflicted distress, they may be inclined to seek relief through certain behaviors designed to lessen the discomfort. Cooper et al. (2000) describe several reasons for engaging in online sexual activity including ways to alleviate depression and dysphoria, mitigate stressful moods, and to cope with feelings.
H1: Expected entertainment, arousal, social, self-evaluative, and self-reactive outcomes will be positively related to the likelihood to create and share SEUGC.
Adverse social outcomes represent a different dimension of behavioral incentives. Unlike the aforementioned behavioral incentives that encourage behavior, these motivators discourage individuals from creating and sharing SEUGC. Findings from a study by Jayson (2008) show that 73% of teenagers surveyed said they knew sending sexually suggestive content can have serious negative consequences. The consequences of this behavior may jeopardize ones reputation, career, and overall livelihood (Athensreview.com; Grant, 2008). The likelihood of achieving positive expected outcomes of a behavior should cause future performances of that behavior. On the other hand, actions that are likely to engender negative outcomes will generally be ignored (Bandura, 1986). Individuals who view the expected outcomes produced from creating and sharing SEUGC as positive will most likely disregard the negative outcomes that are attached to this behavior.
H2: Expected adverse social outcomes will be negatively related to the likelihood to create and share SEUGC.
Behavioral, cognitive, and affective learning can be achieved not only via direct experience but also vicariously by observing the actions of others and the outcomes that they engender (Bandura, 2008). Through vicarious experience, one is able to infer how new behavior patterns are performed and on later occasions, the symbolic construction serves as a guide for action (Bandura, 1971). According to Bandura (1986), sexual modeling can affect sexual behavior in several ways: “It can teach amorous techniques, reduce sexual inhibitions, alter sexual attitudes, and shape sexual practices in a society by conveying norms about what sexual behaviors are permissible and which exceed socially acceptable bounds” (p.263).
Bandura (1986) comments, people who observe other individuals gain desired outcomes by performing a specific behavior may be more inclined to mimic that behavior. Pornography, according to Bridges (2010) acts as a learning model that informs individuals what they might expect when performing in sexual situations. We predict that positively rewarded behaviors depicted in professional pornography will most likely influence viewers to create and share SEUGC. Individuals who create and share SEUGC often do so with the intention to “hook-up” offline (National Campaign to Prevent Teen Pregnancy, 2008), a behavior that is theoretically mediated by vicarious experience. Individuals who consume specialized professional pornography that caters to their unique sexual interests often results in them seeking out like-minded people on the Internet who also share the same sexual interests (Doring, 2009). This may lead to an exchange of information including social support and political activism (Doring, 2009) and possibly, self-produced SEUGC. Furthermore, individuals who observe peers who create and share SEUGC of themselves and achieve positive outcomes from doing so may be more inclined to also create and share. In their research on adolescent sexual behavior, DiBlasio and Benda (1990), report that association with sexually active acquaintances lead to increased sexual activity among adolescents when coupled with positive outcomes expected from engaging in sexual behaviors.
Pornography has often been criticized for communicating unrealistic body images to its audience (Albright, 2008) and has also been associated with sexual insecurity (Peter & Valkenburg, 2008b). These studies lead us to believe that vicarious pornography experience will not be related to self-evaluative outcomes. If individuals do not feel sexually attractive there should be no use in receiving confirmation from others that they are, as self-evaluative outcomes are intended to do. Doring (2009), however, comments that studies such as these do not permit any determinations of causality and that sexually insecure individuals may be more likely to turn to pornography rather than avoid it. We seek to test this relationship to rule out any indirect or unknown causality.
H3: Vicarious pornography experience will be positively related to entertainment, arousal, social, self-evaluative and self-reactive expected outcomes.
Observation of a behavior, coupled with positive outcomes may contribute to desensitization towards the negative consequences of a behavior (Bandura, 1978). Basuerman (1996), comments that in order for socially learned behaviors to occur within the context of pornographic consumption, there would have to be a portrayal of positive consequences and a lack of negative consequences. Individuals who have been influenced to create and share SEUGC because of observational learning should thus be less fearful of potential negative outcomes.
H4: Vicarious pornography experience will be negatively related to expected adverse social outcomes.
Pornography, according to Bridges (2010), can teach viewers how to engage in sexual behaviors and also how to sexually please their partners. Likewise, Borzekowski and Rickert (2001) conclude that a majority of individuals consume pornography as a form of sexual learning and more pertinent to this study, how to have sex. Bandura (1977) states, individuals do not rely solely on individual mastery as their sole source of self-efficacy. Observing other individuals perform activities without negative consequences can generate positive self-efficacious expectations (Bandura, 1977). Individuals thus persuade themselves that if others can achieve certain goals then they should be able to achieve at least some improvement in performance (Bandura & Barab, 1973). Using this theoretical framework, we expect the more pornography one is exposed to, the higher their sexual self-efficacy will be.
H5: Vicarious pornography experience will be positively related to sexual self-efficacy.
There seems to be very little research done that defines and examines sexual self-efficacy from a sexual performance perspective. Most research on sexual self-efficacy has focused on self-efficacious beliefs in regards to condom use (Bandura, 1990; Basen-Enquist & Parcel, 1992; Wufert & Wan, 1993; Wufert, et al., 1994) and also in terms of being able to resist sexual encounters (Rosenthal, Moore, & Flyn, 1991; Seal, Minichiello, & Omodei, 1997; Rostosky et al., 2008). Studies remain scarce or non-existent that have analyzed self-efficacious beliefs about sexual performance and its relation to a variety of sexual behaviors. Research findings by Rosenthal et al. (1991) conclude that individuals with high assertive sexual self-efficacy also were more inclined to engage in high sexual risk-taking. Seal et al. (1997) further report a positive relationship between an individual’s sexual self-efficacy and increased sexual risk-taking with spontaneous partners. Although one may argue that creating and sharing SEUGC is not tantamount with sexually risky behaviors such as having unprotected sex, individuals with a higher sense of self-efficacy tend to exhibit less resistance in regards to sexual activities (Rosenthal, Moore, & Flyn, 1991). Therefore, we might expect a positive relationship with sexual self-efficacy and creating and sharing SEUGC.
H6: Sexual self-efficacy will be positively related to the likelihood to create and share SEUGC.
The types of outcomes people anticipate depend largely on their judgments of how well they will be able to perform in given situations (Bandura, 1986). LaRose and Eastin (2004) suggest that self-efficacy should influence expected behavioral outcomes. Individuals need to learn how to successfully obtain desired outcomes and their achievement of these outcomes is partially based on self-efficacious beliefs (LaRose & Eastin, 2004). We suspect that one’s sexual self-efficacy should also be dependent on what they might achieve from creating and sharing SEUGC. Sexually active individuals who achieve positive rewards based on their sexual self-efficacy might also expect to achieve the same positive rewards when engaging in other sexual behaviors such as creating and sharing SEUGC.
H7: Sexual self-efficacy will be positively related to expected entertainment, arousal, social, self-evaluative and self-reactive outcomes.
Figure 1. Hypothesized Relationships.
Participants consisted of 404 college students enrolled in an introductory undergraduate Communication course at a large Northeastern university. All subjects received course credit for their participation. Subjects agreed to participation via an online consent form. Participants were guaranteed complete anonymity and all data was used by the authors for the purpose of this study only.
An online questionnaire created by the research investigators was created to collect data. The survey questions were designed to measure who is likely to create and share SEUGC and why they are likely to create and share this content. Recent research has shown a shift towards multimodal media use that has come to fulfill certain audience needs and expectations (Vishwanath, 2008). We extend this research to multimodal media (Internet and mobile phone) and its relation with technology creators rather than audiences. Moreover, in lieu of analyzing creating and sharing content independently, we have chosen to collectively analyze these behaviors by conjoining them into one construct. We believe that individuals create SEUGC with the intent to share this content. A recent MTV-Associate Press poll revealed that 30% of young adults aged 14-24 have created and shared some form of SEUGC. A 2008 study conducted by the National Campaign to Prevent Teen and Unplanned Pregnancy and Cosmogirl.com report that 20% of teens surveyed have shared naked or semi-naked photos or videos of themselves that they produced either online or via text messaging services. In addition to these two studies, several stories in the media have also reported individuals engaging in this behavior including well-known athletes and government officials. These instances have come to the media and public’s attention because these individuals not only created this content but ultimately because this content was shared, making it accessible to others.
Our main dependent variable was constructed to explore the likelihood of creating and sharing SEUGC (M = 2.36, SD = 1.03). Respondents were presented with a 5-item Likert scale where 1 indicated very unlikely and 5 indicated very likely. The majority of constructs in the study were measured using 5-point Likert type scales ranging from ‘strongly disagree’ to ‘strongly agree’ unless otherwise noted. Wherever possible, the scales used for the instrument were taken from previously validated measures, reworded, and designed to fit within the context of the study. Most of the constructs all had high to reasonably high Cronbach alphas. The means and standard deviations for each scale and item can be found in Table 1.
Expected Outcomes. A 31-item scale was issued asking respondents to indicate what they expect or might expect from creating and sharing SEUGC. Entertainment outcomes were measured using 6 items created by the researchers (α = .86). Arousal outcomes contained 3-items previously tested by Goodson et al. (2000) in addition to 1 item created by the researcher (α = .94). To measure social outcomes, 3 modified items by LaRose and Eastin (2004) were used along with 2 added items (α = .92). Self-evaluative outcomes were measured using 3-items developed by the researchers (α = .96). Self-reactive outcomes consisted of 5 items created by the researchers (α = .95). To analyze negative expected outcomes, 8 items developed by the researchers were utilized (α = .95).
Vicarious Sexual Experience. A 7-item scale created by the researchers was developed to measure the amount of sexually explicit media individuals have been exposed to. Items measured included exposure to both professionally produced pornography and SEUGC (α = .69).
Sexual Self-efficacy. Sexual self-efficacy was defined as the belief in one’s ability to perform sexually and to give sexual pleasure to their partners. To measure this construct, 3 items from the Sexual Attitudes Scale (Offer, 1969) and 1 item from the Sexual Self-Efficacy Scale – Erectile Functioning were used and modified (Libman et al., 1985) (α = .84).
Table 1. Scale/Items, Means, and Standard Deviations.
Evaluation of structural model
To analyze our hypothesized relationships, structural equation modeling was employed. The use of a student sample in our study design controlled for two constructs related to the production of sexual content online: direct technology experience (how long individuals have been using the Internet and mobile phones) and technological self-efficacy (the ability to use this technology). Controls using this method occur when the control happens because the sample either shares or does not share a trait(s). In the case of using students, most people share these traits, making them invariant across the sample and unnecessary to measure and control. College students continue to be early adopters of new Internet tools and applications in comparison to the general U.S. Internet–using population (Jones, Johnson-Yale, Millermaier, & Perez 2009, Vishwanath & Goldhaber, 2003). Furthermore, college students are often the focus of much of the prior research on Internet due to their accessibility to Internet connections (e.g., LaRose et al., 2001; Papacharissi & Rubin, 2000) and Internet addiction (e.g.,Chou & Hsiao, 2000; Morahan-Martin & Schumacher, 2000; Pratarelli et al., 1999). For this reason, we expected minimal variance within our college sample for direct technology experience and technology self-efficacy, thus creating a ceiling effect.
The first step in assessing our model was to test for the model’s ‘reasonableness’. There are four criteria according to Joreskog and Sorbom (1986) that must be met when testing for reasonableness: (1) the standard parameter estimates should not be large; (2) the parameter estimates should not be highly correlated; (3) all variances should be positive; (4) all absolute correlations should not exceed 1. All criteria was reasonably met for the hypothesized model. The next step was to test for the goodness of fit for our model. We did this first by conducting a χ2/df ratio (df: degree of freedom). According to Bentler (1989), models indicating a good fit should not exceed a ratio of .5. Under this guideline, the value of 3.106 (p < .001) is a good fit for the hypothesized model (χ2 = 40.375, df = 13).
In addition to the χ2/df ratio, several other tests including the GFI (goodness of fit index), the CFI (comparative fit index), and the RMSEA (root mean square error of approximation) were executed. A standard rule for assessing the GFI and CFI states that the closer the GFI and CFI are in unison, the better the model fits the data, with an ideal cut-off value of 0.95. In assessing the RMSEA, values less than .05 indicate a good fit, values as high as .08 represent a mediocre fit, and values exceeding .10 denote a poor fit. Overall, our model was a good fit. The measures of goodness of fit can be found in Table 2.
As a total sample (N = 404), 49% were male (n = 196), 51% were female (n = 205) and .5% identified as transgendered (n = 2). 88% of respondents were between the ages of 17-21 (n = 354) with 9% between the ages of 22-24 (n = 37), 3% between the ages of 25 – 29 (n = 10) and .5% over the age of 30 (n = 2). In regards to race/ethnicity, 56% respondents were white (n = 226), 16% were Asian (n = 63), 13% were white, non-Hispanic, or Latino (n = 51), 10% were black or African American (n = 42), 4% were Hispanic/Latino (n = 17), .7% were American Indian or Alaskan Native (n = 2), and .2% were Native Hawaiian or Pacific Islander (n = 1). The majority of participants, 95%, identified themselves as heterosexual (n = 385). 2% reported being homosexual (n = 9), 1% identified as bi-sexual (n = 4) and 2% refused to answer (n = 6).
The overall model predicted 61% of the variance in the dependent variable (likelihood to create and share SEUGC in the future). The variances explained for each of the endogenous variables were as follows: expected outcomes (R2 =.342), entertainment outcomes (R2 =.468), arousal outcomes (R2 =.416), social outcomes (R2 =.208), self-evaluative outcomes (R2 =.221), self-reactive outcomes (R2 =.220), adverse social outcomes (R2 =.129), sexual self-efficacy (R2 =.116). The hypothesized relationships, their t-values, standardized estimates, and p-values can be found in table 2.
Table 2. Hypotheses and structural model results.
Hypothesis 1 predicted a relationship between expected entertainment, arousal, social, self-evaluative, and self-reactive outcomes and the likelihood to create and share SEUGC. Our data supported this relationship. These motivators best helped us understand what may influence individuals to participate in this form of sexual behavior in the future. Entertainment outcomes emerged as the main motivator for creating and sharing SEUGC (M = 2.4, SD =.92). Participants are more likely to create and share to be flirtatious (M = 3.1, SD = 1.4) and to have fun (M = 2.9, SD = 1.3). Additionally, participants are also more likely to create and share to joke around (M = 2.5, SD = 1.2). Arousal outcomes were revealed to be the second primary motivator. Amongst these motivators, participants are more likely to create and share SEUGC to stimulate sexual fantasies (M = 2.5, SD = 1.3) and to satisfy their curiosity about sex (M = 2.3, SD = 1.2).
Hypothesis 2 predicted that expected adverse social outcomes would be negatively related to the likelihood to create and share SEUGC. This finding was also supported.
Hypothesis 3 predicted that vicarious pornography experience would be positively related to entertainment, arousal, social, self-evaluative and self-reactive expected outcomes. Our data supported this relationship.
Hypothesis 4 stated that vicarious pornography experience would be negatively related to expected adverse social outcomes. This hypothesis was found to be significant.
Hypothesis 5 predicted a relationship between vicarious pornography experience and sexual self-efficacy. This relationship was supported by our data.
Hypothesis 6 stated that sexual self efficacy would be positively related to the likelihood to create and share SEUGC. This relationship was found to be significant.
Hypothesis 7 stated that sexual self-efficacy would be related to expected entertainment, arousal, social, self-evaluative, and self-reactive outcomes. This relationship, however, was not significant.
The study presented here used SCT and employed a structural model to assist in gaining an understanding of the thought processes and motivations of individuals who share sexually explicit user-generated content of themselves. The overall results support the foundations of social cognitive which are predicated on vicarious learning, self-efficacy, and expected outcomes. Furthermore, results confirm the general structure of the model and support the key propositions in H1, H2, H3, H4, H5, and H6: Expected entertainment, arousal, social, self-evaluative, and self-reactive outcomes predicted one’s likelihood to create and share SEUGC; and adverse social outcomes negatively predicted one’s likelihood to create and share SEUGC. Vicarious pornography experience predicted entertainment, arousal, social, self-evaluative and self-reactive expected outcomes; vicarious pornography experience negatively predicted adverse social outcomes; vicarious pornography experience predicted sexual self-efficacy; and sexual self efficacy predicted one’s likelihood to create and share SEUGC;
Expected entertainment, arousal, social, self-evaluative, and self-reactive outcomes were all found to be positively related to creating and sharing SEUGC. These motivators best helped us understand what may drive individuals to participate in this form of sexual behavior. Entertainment and arousal outcomes were major motivators for the likelihood to create and share SEUGC. Individuals are more likely to create and share to seek enjoyment and to sexually gratify themselves. Recent work by Jayson (2008) and Rose (2010) names entertainment motivators as a primary predictor of creating and sharing SEUGC and extensive work by Cooper et al. (1997, 1999, 2000, 2002) reports cybersex as a significant motivator for online sexual activity. Adverse social outcomes were intended to dissuade individuals from engaging in creating and sharing SEUGC. Expected outcomes only influences future behavior if an individual perceives those outcomes to be beneficial. Negative outcomes that occur from participating in specific behaviors will primarily be neglected (Bandura, 1986). In conjunction with our hypothesis, individuals who were more likely to create and share SEUGC primarily viewed the expected outcomes associated with SEUGC as beneficial to themselves. This perception lead participants to disregard the negative repercussions associated with creating and sharing SEUGC. As predicted, individuals with less fear of adverse social were more likely to participate in the behavior.
Vicarious pornography exposure was found to be an influence on the likelihood to create and share SEUGC. This relationship was mediated by expected entertainment, arousal, social, self-evaluative and self-reactive outcomes. This finding is consistent with the work of Emmers-Sommer (2006) who found that behaviors depicted in pornography that produce positive rewards (i.e. sexual pleasure) are more likely to be modeled by viewers who observe this behavior. Vicarious learning under SCT is a major source for shaping the expected outcomes associated with a behavior (LaRose & Eastin, 2004). Those people who are consumers of professionally produced pornography and SEUGC are more likely to model these behaviors when positive outcomes are observed. People who observe positive outcomes engendered by sexual behaviors within either professionally produced pornography or SEUGC are more likely to create SEUGC that may also bring forth the same positive outcomes.
One may expect that observing negative consequences of other individuals as a result of creating and sharing SEUGC would dissuade individuals from creating and sharing their own content. We hypothesized an opposite effect, one in which exposure to pornographic content on a regular basis would desensitize the individuals’ concern of experiencing negative outcomes from creating and sharing SEUGC of their own. Our results were confirmed. Seeing others perform threatening activities without adverse consequences can generate positive expectations within observers (Bandura, 1977). Likewise, anxiety arousal to threatening consequences is reduced by a combination of desensitizing effects caused by massive exposures to adverse behaviors and behavioral modeling (Bandura & Barab, 1973).
The findings also suggested as individuals consume more pornography (professionally based and SEUGC) their sexual self-efficacy increases. Findings by Boies (2002) reveals participants are likely to learn sexual techniques from viewing sexually explicit material. An earlier study by Check (1995) names pornography as the most significant source of sexual education. According to SCT, self-efficacy mediates the relationship between a person’s knowledge for performing a specific behavior (Bandura, 1986) and their belief in performing that behavior. Our findings support this framework in that people learn sexual techniques from viewing pornography which in turn increases their sexual self-efficacy for performing these behaviors.
Sexual self-efficacy was also directly related to the likelihood to create and share SEUGC. Sexual self-efficacy has been linked to other sexual behaviors such as proper contraceptive use, the willingness to view pornography, and the ability to masturbate (Rosenthal et al., 1991). Findings in our study are also confirmed by research by Seal et al. (1997). The authors found that individuals with a higher sense of sexual self-efficacy were more likely to initiate sexual interactions. Sexual self-efficacy can now be attributed to initiating another sexual behavior, that being the likelihood to create and share SEUGC.
Achievement of positive expected outcomes is partially dependent on self-efficacious beliefs (LaRose & Eastin, 2004). As previously discussed, the types of outcomes people anticipate depend largely on their self-efficacious beliefs of how well they will be able to perform in given situations (Bandura, 1986). SCT would suggest that expected outcomes obtained during offline sexual encounters are based on an individual’s sexual self-efficacy. The expected outcomes of online sexual encounters often gratify individuals in the same way offline sexual encounters do (Doring, 2009). This prior research in addition to the influence of self-efficacy on expected outcomes under SCT lead us to believe expected outcomes would be positively related to sexual-self efficacy. This relationship, however, was not supported.
Future Research and Limitations
The definition of SEUGC was widely defined in our study. We expanded it to include creating and sharing content via both the Internet and mobile phone. Future research may specifically examine the medium in which individuals are creating and sharing. Both media are outlets for engaging in this behavior and while the overwhelming evidence is that these different media are in fact converging towards a device-agnostic, unified user experience, it could still be argued that the types of behaviors enacted on each medium interacts with the design, purpose, and limitations of the medium, the device used to access the content, and its user as both the source and the audience for the content. Tidwell and Walther (2008) report that many Internet users are currently disclosing personal information on their Web space without explicit reciprocity. We speculate that SEUGC shared through the Internet may be intended for a mass, general audience while content shared through mobile phones may be intended for close, personal contacts.
Furthermore, future studies should extend to include a more diverse sample. The current study was primarily restricted to college freshmen. The homogenous sample should be expanded to include more diverse subpopulations for future examination. Additionally, the age of our participants may have accounted for the negative relationship between negative consequences and creating and sharing SEUGC. College freshmen are perhaps less worried about damaging effects that might occur such as having their career jeopardized because they have yet to begin their professional careers. The sensitive subject matter of our study may also have dissuaded some participants to answer honestly. Although our model was theoretically sound and the majority of our hypotheses were supported, it is likely that some of our participants did not respond honestly which may have prevented our model from achieving a better fit.
The variables, professionally produced pornography and SEUGC were generated under one construct, vicarious pornography experience. We understand this to be a limitation to our study due to the difference in content for both variables. One posits professionally produced material whereas the other is strictly DIY pornography. The difference in variables could account for the somewhat weak internal consistency of the vicarious pornography experience scale. Future studies employing this construct should analyze these variables independently.
The construct, sexual self-efficacy also needs further examination. Studies addressing sexual self-efficacy (Rosenthal, Moore, & Flyn, 1991; Seal, Minichiello, & Omodei, 1997; Rostosky et al., 2008) have examined this construct in terms of healthy sexual behaviors and the ability to resist sexual encounters. We conceptualized this construct as the ability to perform sexual behaviors and to arouse partners sexually. Rosenthal et al. also used the Sexual Attitudes Scale (Offer, 1969) in their study, however, they used this measurement to analyze sexual self-esteem which is similar to how we defined our construct of sexual self-efficacy. Despite this similarity, our definition of sexual self-efficacy needs further clarification and should be re-tested for future studies.
Overall, the study is noteworthy because it adds a new dimension to the field of scientific research on sexuality and technology that has not been previously explored. This study potentially serves as a seminal groundwork within this area of research and hopefully will inspire scholars to conduct future studies related to the behavior examined herein. The theoretical model tested, moreover provides a good foundation for understanding this new form of sexual behavior. We intend to continue our research on this sexual behavior by applying the framework and findings of this study to future explorations.
Albright, J. (2008). Sex in America online: An exploration of sex, marital status, and sexual
identity in internet sex seeking and its impacts. Journal of Sex Research , 45,
Ballard, S. (1998). Sources of sexuality information for university students. The Journal of Sex Education and Therapy, 23, 278-287.
Bandura, A. (1971). Psychological modeling: Conflicting theories. Chicago: Aldine-Atherton.
Bandura, A., & Barab, P. (1973). Processes governing disinhibitory effects through symbolic modeling. Journal of Abnormal Psychology, 82, 1-9.
Bandura, A. (1978). The Self System in Reciprocal Determinism. American Psychologist, 33, 344-358.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. New Jersey: Prentice Hall.
Bandura, A. (1990). Perceived self-efficacy in the exercise of control over AIDS infection.
Evaluation and Program Planning, 13, 9-17.
Bandura, A. (1991). Social Cognitive Theory of Self-Regulation. Organizational Behavior and Human Decision Processes, 50, 248-287.
Bandura, A. (2001). Social cognitive theory: an agentic perspective. Annual Review Psychology, 50, 1-26.
Bandura, A. (2008). Social cognitive theory of mass communication. In J. Bryant & M. Oliver (Eds.), Media effects: Advances in theory and research (pp. 94-125). New York: Routledge.
Baumeister, R. (1982). A Self-Presentational View of Social Phenomena. Psychological Bulletin, 91, 3-26.
Basen-Engquist, K. &. (1993). Attitudes, norms, and self-efficacy: A model of adolescents' HIV- related sexual risk behavior. Health Education Quarterly, 20, 263-277.
Bauserman, R. (1996). Sexual Aggression and Pornography: A Review of Correlation Research. Basic and Applied Social Psychology, 18, 405-427.
Bridges, A. J. (2010). Methodological considerations in mapping pornography content. In K.
Boyle (Ed.), Everyday Pornography (pp. 34-50). New York: Routledge.
Byrne, D., Kelley, K., & Fisher, W. A. (1993). Unwanted teenage pregnancies: Incidence,
interpretation, and intervention. Applied and Preventive Psychology, 2, 101- 113.
Catania, J., Gibson, D., Chitwood, D., & T.J., C. (1990). Methodological problems in AIDS
behavioral research: influences on measurement error and participation bias in studies of sexual behavior. Psychological Bulletin, 108, 339-362.
Cooper, A., Morahan-Martin, J., Mathey, R., Maheu, M. (2002). Toward an increased
understanding of user demographics in online sexual activities. Journal of Sex & Marital Therapy, 28, 105-129.
Cooper, A., Delmonico, D., & Burg, R. (2000). Cybersex users, abusers, and compulsives: New findings and implications. Sexual Addiction & Compulsivity, 19, 5-30.
Cooper, A., Putnam, D., Planchon, L., & Boies, S. (1999). Online sexual compulsivity: Getting tangled in the Net. Sexual Addiction & Compulsivity, 6, 79-104.
Cooper, A., Sportolari, L. (1997). Romance in cyberspace: Understanding online attraction.
Journal of Sex Education & Therapy, 22, 7-14
Cronin, B., & Davenport, E. (2001). E-rogenous zones: Positioning pornography in the digital economy. Information Society, 17, 33-48.
Cummings, L.L., Schwab, D.P., Rosen, M. (1971). Performance and knowledge of results as
determinants of goal setting. Journal of Applied Psychology, 17, 526-530.
DiBlasio, F. A., & Benda, B. B. (1990). Adolescent sexual behavior: Multivariate analysis of a social learning model. Journal of Adolescent Research, 5, 449-466.
Donnerstein, E. (1984). Pornography: Its effects on violence against women. In N.
Malamuth & E. Donnerstein (Eds.), Pornography and sexual aggression (pp. 53–81). New York: Academic Press.
Doring, N. M. (2009). The Internet's impact on sexuality: A critical review of 15 years research. Computers in Human Behavior, 5, 1089-1101.
Eastin, M. L. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of Computer Mediated Communication, 6. Retrieved from: http://jcmc.indiana.edu/vol6/issue1/eastin.html
Eastin, M. (2005). Teen Internet Use: Relating Social Perceptions and Cognitive Models to
Behavior. Cyberpsychology & Behavior, 8, 62-75.
Emmers-Sommer, T., Perry, P., Hanzal, A., & Triplett, L. (2006). Love, Suspense, Sex, and
Violence: Mens and Women's Film Predilections, Exposure to Sexually Violent Media, and their Relationship to Rape Myth Acceptance. Sex Roles, 55, 311-320.
Goffman, E. (1959). The Presentation of Self in Everyday Life. New York: Doubleday.
Goodson, P. M. (2000). Sex and the Internet: A survey instrument to assess college students'
behavior and attitudes. CyberPsychology & Behavior, 3, 129-149.
Guyer, B., Strobino, D. M., Ventura, S. J., & Singh, G. K. (1995). Annual summary of vital
statistics. Pediatrics, 96, 1029-1039.
Haffner, D. W. (1987). Adolescent Sexuality in the Media. SIECUS Rep. March/April, 9–12.
Hoult, T. F. (1983). Human sexuality in bio-logical perspective: Theoretical and methodological considerations. Journal of Homosexuality, 9, 137-155.
Jayson, S. (2008). Study shows many teens, young adults share nude images.
Retrieved from: http://abcnews.go.com/US/story?id=6433047&page=1
Jensen, J. (2008). User Generated Content - a mega-trend in the new media landscape. Retrieved from http://homepages.cwi.nl/~garcia/euroitv2007/tutorials/user-generated-content2.pdf
Jones, S., Johnson-Yale, C., Millermaier, F., & Seoane-Perez, S. (2009). Everyday
life, online: U.S. college students use of the Internet. First Monday, 14. Retrieved from: http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/2649/2301
Kalichman, S., Rompa, D., DiFonzo, K., Simpson, D., Kyomugisha, F., Austin, J., & Webster, L. (2001). Initial Development of Scales to Assess Self-Efficacy for Disclosing HIV Status and Negotiating Safer Sex in HIV-Positive Persons. AIDS and Behavior, 5, 291-296.
Kegeles, S. M., Adler, N. E., & Irwin, C. E. (1988). Sexually active adolescents and condoms: Changes over one year in knowledge, attitudes, and use. American Journal of Public Health, 78, 460-461.
Koch, P. B. (1998). Sexual knowledge and education. In R. T. Francouer (Ed.), Sexuality in America: Understanding our sexual values and behavior. New York: Continuum.
LaRose, R., Lin, C., & Eastin, M. (2003). Unregulated Internet Usage: addiction, habit, or
deficient self-regulation. Media Psychology, 5, 225-253.
LaRose, R., & Eastin, M. (2004). A social cognitive theory of Internet uses and gratifications: Toward a new model of media attendance. Journal of Broadcasting and Electronic Media, 48, 358-376.
Lee, D., Im, S., & Taylor, C. (2008). Voluntary Self-Disclosure of Information on the Internet: A Multimethod Study of the Motivations and Consequences of Disclosing Information on Blogs. Psychology & Marketing, 25, 692-710.
Lee, D. L. (2007). A Socio-cognitive model of video game usage. Journal of Broadcasting & Electronic Media, 51, 632-650.
Lenhart, A. M. (2007). Social networking websites and teens. Washington, DC: Pew Internet & American Life Project. Retrieved from: http://www.pewinternet.org/Reports/2007/Social-Networking-Websites-and-Teens.aspx
Liau, A., Millett, G., & Marks, G. (2006). Meta-analytic examination of online sex-seeking and sexual risk behavior among when have sex with men. Sexually Transmitted Diseases, 33, 576-584.
Libman, E., Rothenberg, I., Fichten, C., & Amsel, R. (1985). The SSES-E: A measure of sexual self-efficacy in erecticle functioning. Journal of Sex and Marital Therapy, 11, 237-247.
Lin, C. (2009). Effects of the Internet. In J. Bryant & M. Oliver (Eds.), Media effects: advances in theory and research 3rd edition (pp. 567-589). Beverly Hills: Sage.
Lo, V.-H., & Wei, R. (2005) Exposure to internet pornography on Taiwanese adolescents’ sexual attitudes and behavior. Journal of Broadcasting & Electronic Media, 46, 13-34
Locke, E., Cartledge, N., & Koeppel, J. (1968). Motivational effects of knowledge of results. Psychological Bulletin, 70, 474-485.
Maheu, M. M., & Subotnik, R. B. (2001). Infidelity on the Internet: Virtual relationships and real betrayals. Naperville, IL: Source books.
Malamuth, N. C. (1985). The effects of aggressive pornography on beliefs in rape myths:
Individual differences. Journal of Research in Personality, 19, 299-320.
McFarlane, M., Kachur, R., Bull, S., & Rietmeijer, C. (2004). Women, the Internet, and sexually transmitted infections. Journal of Women's Health, 13, 689-694.
McKenna, K. G. (2002). Relationship formation on the Internet: What's the big attraction.
Journal of Social Issues, 58, 9-31.
McKenna, K., Green, A., & Smith, P. (2001). Demarginalizing the sexual self. Journal of Sex Research, 38, 302-311.
Mulac, A. J. (2002). Men’s Behavior Toward women after viewing sexually-explicit films: Degradation makes a difference. Communication Monographs, 69, 311-328.
Offer, D. (1969). The Psychological World of the Teenager: A Study of Normal Adolescent Boys. New York: Basic Books.
Oliver, M. B., & Hyde, J. S. (1993). Gender differences in sexuality: A meta-analysis. Psychological Bulletin, 114, 29-51.
Peter, J., & Valkenburg, P. (2008b). Adolescents' exposure to sexually explicit internet material, sexual uncertainty, and attidues toward uncommitted sexual exploration: Is there a link? Communication Research, 35, 579-601.
Quaid, L. (2009). 'Sexting' is More Common Than you Might Think. Associated Press. Retrieved from: http://www.deseretnews.com/article/705349025/Sexting...think.html?pg=all
Ropelato, J. (2006). Internet Poronography Statistics. Retrieved from:
Rose, M. (2010). Naked in cyberspace: Sexting a new tech trend. Retrieved from: http://kbleck.files.wordpress.com/2010/02/naked-in-cyberspace.pdf
Rosenthal, D., Moore, S., & Flynn, I. (1991). Adolescent self-efficacy, self-esteem and sexual risk-taking. Journal of Community & Applied Social Psychology, 1, 77–88.
Ruggiero, T. (2000). Uses and gratifications theory in the 21st century. Mass Communication & Society, 3, 3-37.
Seal, A., Minichiello, V., & Omodei, M. (1997). Young women's sexual risk taking behaviour: re-visiting the influences of sexual self-efficacy and sexual self-esteem. International Journal of STD and AIDS, 8, 159-165.
Seigfried, K., Lovely, R., & Rogers, M. (2008) Self-reported online child pornography behavior: a psychological analysis. International Journal of Cyber Criminology, 2, 286-297.
Sieving, R., Bearinger, L., Resnick, M., Pettingell, S., & Skay, S. (2007). Adolescent Dual
Method Use: Relevant Attitudes, Normative Beliefs and Self-Efficacy. Journal of Adolescent Health, 40, 1-16.
Tepper, M., & Owens, A. (2002). Access to Pleasure: Onramp to Specific Information on Disability, Illness, and Other Expected Changes throughout the Lifespan. In A. Cooper (Ed.), Sex and the Internet: A guidebook for clinicians (pp. 72-92). New York: Brunner-Routledge.
Tidwell, L. C., & Walther, J. B. (2002). Computer-mediated communication effects on
disclosure, impressions, and interpersonal evaluations: Getting to know one another a bit at a time. Human Communication Research, 28, 317-348.
Van Wyk, P. H., & Geist, C. S. (1984). Psychosocial development of heterosexual, bi-sexual and homosexual behavior. Archives of Sexual Behavior, 13, 505-544.
Vishwanath, A., & Goldhaber, G. (2003). An examination of the factors contributing to adoption decisions among late-diffused technology products. New Media & Society, 5, 547-572.
Vishwanath, A. (2008). The 360 degree news experience: Audience connections to the
ubiquitous news organization. Journalism & Mass Communication Quarterly, 85, 7-22.
Yoder, V. V. (2005). Internet pornography and loneliness: An association? Sexual Addiction and Compulsivity, 12, 19-44.
Weisskirch, R., & Delevi, R. (2011). “Sexting” and adult romantic attachment. Computers in Human Behavior, 28, 1697–1701.
Wulfurt, E., & Wan, C. (1993). Condom use: a self-efficacy mode. Health Psychology, 12, 346-53.
Joseph M. Sirianni
Department of Communication
University at Buffalo, North Campus
359 Baldy Hall
Buffalo, New York 14260-1020