Abstract
Pornography use has become widespread and mainstream in American society, with estimates that 60% of men and 35% of women have viewed pornography at some time in the last year. Pornography use has been associated with both positive and negative outcomes depending on the user, and some of these conflicting results may stem from problematic measurement. Using a newly validated measure that assesses frequency, duration, arousal, and deliberate or accidental exposure to seven common types of pornography, we sought to understand whether the motivations to view pornography differed depending on biological sex of the user and the type of use they engaged in. With an MTurk.com sample of 312 participants, we used a variable selection to explore the most consistent predictors of pornography use. Results found that sexually based motivations were consistent motivations to use pornography for both males and females. Educationally based motivations reliably predicted accidental exposure to pornography, while emotions like sadness and tiredness reliably predicted longer durations of pornography use. These results indicate that motivations to view pornography are similar for males and females and that sexually based reasons and emotions are primary in an individual’s decision to use pornography.
Keywords
Pornography use has become widespread and mainstream in American society, with estimates that 60% of men and 35% of women have viewed pornography at some time in the last year (Price et al., 2016). Recent figures estimate that 40 million adults regularly view internet pornography, an industry that generates US$97 billion worldwide (Ropelato, 2007). Among young adult populations, pornography is generally accepted and used: 67% young men and 49% young women rated pornography consumption as acceptable, while 87% young men and 31% young women reported using pornography (Carroll et al., 2008). These figures suggest a general acceptance of pornography. With such wide-reported use, research has sought to understand what pornography actually entails, whether pornography is accompanied with positive or negative outcomes, and what drives individuals to consume pornography.
Defining Pornography
Notwithstanding its widespread use, both the society and the scientific community have struggled to reach consensus on a definition of pornography (Malamuth et al., 2000; Willoughby & Busby, 2016). Regarding the thorny problem of operationalizing pornography, Supreme Court Justice Potter Stewart is famously quoted as saying “I know it when I see it” (Gewirtz, 1996), and research participants have been asked to use a similar heuristic when responding to questions about pornography consumption in most existing studies. They are typically asked to self-determine whether they have consumed pornography, without direction from the researchers about what constitutes pornography.
One definition of pornography is media that portrays sexually explicit activities (Lottes et al., 1993). When participants were asked to rate a number of scenarios from least pornographic to most pornographic, most people consistently considered “an image of a heterosexual couple having sex which shows the man’s penis penetrating the woman” to be most pornographic, while other items like “a television program showing the filming of a swimsuit edition of a magazine that shows models being filmed in a variety of provocative poses but no full nudity of breasts or genitalia” were considered least pornographic (Willoughby & Busby, 2016). For the other 18 options between these two extremes, participants varied significantly in their judgment of what was pornographic.
Pornography may be defined by the viewer’s intention. For example, if a viewer picks up a Sears catalogue for the purposes of sexual arousal, then the catalogue could be termed pornography (Rea, 2001). On the other hand, pornography may be defined by the creator’s intention. A famous naked photograph of Marilyn Monroe appeared in Life magazine in October 1996 (Rea, 2001). It was not considered to be pornographic because of the venue—a widely read family magazine. However, if the same photograph had appeared in Hustler magazine, it may have been considered pornographic due to the reputation of the magazine (Rea, 2001). If we accept this logic, a wife taking a nude picture for her husband’s private arousal would be considered a pornographer (Rea, 2001).
Because of the complexities of trying to determine the boundaries of what is pornographic, definitions of pornography have varied from study to study. Some researchers have used vague or generic definitions when assessing their participant’s pornography consumption, while others were extremely specific. For example, Hald and Malamuth (2008) defined pornography as “men and women posing or acting [while] naked such as seen in Playboy/Playgirl” (p. 616). The Pornography Consumption Index included more than just nudity in their definition; they described pornography as media that “contains explicit exposure or descriptions of sexual acts involving the genitals, such as vaginal or anal intercourse, oral sex, or masturbation” (Reid et al., 2011, p. 364). Many scales simply assess pornography by asking the participant whether they viewed pornography in the past X days. By not imposing a definition, researchers allow their participants to identify for themselves if what they viewed was pornographic. Letting participants decide for themselves whether they have viewed pornography probably increases external validity, even as it limits control over what media fit potential inclusionary criteria. For the purposes of our study, we determined that leaving the term “pornography” largely undefined likely creates the most generalizable findings. In the present study, therefore, we only described different types of exposure (e.g., “threesome pornography”) but did not impose a strict definition of what constitutes pornography.
Outcomes for Pornography Consumption
If there are potentially negative or damaging implications associated with pornography consumption, understanding the motivation to use pornography may be beneficial in decreasing its use. One of the foremost reasons that pornography has been under scientific scrutiny is to establish whether there are positive or negative outcomes associated with its use. Findings have varied from study to study. The 1970 report of the Commission on Obscenity and Pornography concluded that its “empirical investigation provided no evidence that exposure to or use of explicit sexual materials plays a significant role in the causation of social or individual harm” (Silbert & Pines, 1984, p. 1). However, since then, multiple studies have implicated pornography consumption with social and individual harm.
On an individual level, relational satisfaction, sexual satisfaction, and sexual aggression are often studied as negative implications for an individual user or a partner. Lower relational and sexual satisfaction have been implicated with pornography consumption (Wright et al., 2017; Zillmann & Bryant, 1988); specifically, pornography consumption is associated with a less satisfying sexual relationship with a partner, including the ways the partner gives affection, the way the partner looks, and the partner’s sexual performance (Zillmann & Bryant, 1988). These findings were especially relevant for those who used paraphilic pornography or who used mainstream pornography from a young age (Štulhofer et al., 2010). It is important to note that pornography consumers were only impacted in their happiness and satisfaction within a sexual context; pornography consumption did not encumber their overall life happiness or satisfaction (Zillmann & Bryant, 1988).
Sexual aggression has also been associated as an outcome for individual pornography consumption (Malamuth et al., 2000). Scenes of male dominance and males exerting power over female counterparts are commonly seen in pornographic videos, which may lead users to seek these kinds of sexual encounters themselves (Brown, Conner, & Vennum, 2017). These findings are particularly poignant if users are watching violent types of pornography or are men who are already at high risk of sexual aggression (Malamuth et al., 2000). However, men who are already predisposed to sexual aggression are more likely to view violent pornography and therefore engage in sexually aggressive behaviors (Owens et al., 2012; Seto et al., 2001). In this case, the evidence suggests pornography may be a marker more than a predictor.
On a societal level, pornography consumption has been associated with public health concerns like more sexual partners, penetrative hookups while intoxicated, lowered condom use, paid-sex behavior, extramarital sex, and substance use (Braithwaite et al., 2015; Carroll et al., 2008; Wright, 2013). Not only have these behaviors been found to be associated with pornography consumption, but overall attitudes of acceptance and normality toward these behaviors have also increased (Brown, Conner, & Vennum, 2017; Carroll et al., 2008; Wright, 2013). Pornography consumers, especially young people, may be developing sexual scripts informed by the events depicted in pornographic material, which are unlike real-world sexual encounters (Brown, Conner, & Vennum, 2017). In this way, pornography could be normalizing behaviors that are seen as risky from a public health perspective.
Positive outcomes have also been associated with pornography consumption (Hald & Malamuth, 2008; Kvalem et al., 2016; Morrison et al., 2004). Higher sexual self-esteem and lower levels of sexual anxiety may be a positive effect of consuming pornography (Morrison et al., 2004). Additionally, body image concerns have been found to be unrelated to pornography use among samples of females and gay males (Borgogna, Lathan, & Mitchell, 2018; Kvalem et al., 2016). Notably, these positive outcomes are all related to one’s views of self rather than public health.
Whether pornography is related to positive or negative outcomes seems to be moderated by individual differences in the consumer. Notably, religiosity (especially scrupulosity) plays a key role in depressive symptoms among pornography users and may play a role in whether pornography users identify as addicted (Borgogna, Duncan, & McDermott, 2018; Bradley et al., 2016; Grubbs et al., 2018; Maddock et al., 2019). Individuals who are female, more religious, and less sexually active generally rate pornography as having negative outcomes (Lottes et al., 1993). Moreover, what many consider positive outcomes, such as greater sexual self-esteem leading to an increase in sexual partners, others might consider negative outcomes. Many studies rely on self-reported perception of effects among those who use, which may create demand characteristics to claim positive outcomes in hindsight (Hald & Malamuth, 2008). Different research methodologies play a role in whether outcomes of pornography consumption are seen as positive or negative. A meta-analytic review concluded that there was no link between pornography consumption and acceptance of rape myth among nonexperimental studies, but among experimental studies, there was a positive link between these two variables (Allen et al., 1995). Therefore, it is likely that many factors influence and confound whether pornography consumption is viewed as a positive or negative activity, perhaps including why people use pornography in the first place.
Similarly, the literature is mixed regarding outcomes for relational or coupled pornography consumption. Using pornography has been associated with lowered interest in physical intimacy, and lower levels of sexual satisfaction and quality, in both the partner who uses pornography and the partner who does not use pornography (Poulsen et al., 2013; Schneider, 2002; Yucel & Gassanov, 2010). A lowered interest in physical intimacy could stem from the feeling that while their partner is consistently using pornography, they are engaging in emotional infidelity (Whitty, 2003). This is often considered “traumatic” for female partners of heavy pornography consumers and is associated with their own lowered feelings of worth and desirability (Bergner & Bridges, 2002). If a partner feels that they are not as desirable, or that they are being cheated on, which in turn decreases the amount of physical intimacy in a relationship, it follows that the relationship will suffer as a result of this pornography consumption. However, these findings only highlight the outcomes for when one partner in a couple is using pornography.
When both partners in a couple use pornography, findings appear to be somewhat more positive. Even when both partners use pornography but not necessarily together, they report lower levels of dysfunction and a higher level of eroticism in their relationship (Daneback et al., 2009). Further, these couples are reported to have better sexual communication and less problems with arousal and self-desirability, generally reporting positive effects on their sexual relationship (Daneback et al., 2009; Hald & Malamuth, 2008). However, these findings are correlational and thus might reflect that those who have better sexual communication are more likely to use pornography as a couple. In fact, much of the research on pornography suffers from the limitations of correlational research. Yet the preponderance of evidence suggests that coupled pornography consumption is correlated with higher sexual quality and better sexual communication (Poulsen et al., 2013).
Motivations to Consume Pornography
Scientists have begun to consider why individuals may choose to consume pornography. Most of the conclusions from the literature on underlying motivations for pornography consumption can be clustered into four underlying factors: relational motivations, fantasy motivations, habitual use motivations, and mood management motivations (Paul & Shim, 2008). The most prominent motivations include seeking sexual entertainment, sexual gratification, to masturbate, to sexually arouse oneself or someone else, to make sex interesting, to enjoy sexual thrills, to learn about sex, in-person exploration, to fantasize about unrealistic events, to distract oneself, and out of curiosity (Boies, 2002; Burns, 2001; Paul & Shim, 2008). Sexually based reasons seem to comprise a vast majority of the motivations to consume pornography.
The motivation to consume pornography can differ depending on how often someone uses pornography. Individuals who typically choose to abstain from pornography reported only seeking pornography to satisfy curiosity and educate themselves about sex (Brown, Durtschi, Carroll, & Willoughby, 2017). On the other hand, individuals who accept and use pornography frequently were found to use pornography for the purpose of sexual arousal and masturbation (Brown, Durtschi, Carroll, & Willoughby, 2017). Thus, a sexual motivation appears to drive regular users toward pornography, while curiosity appears to drive abstainers to view pornography.
Motivations appear to differ depending on the gender, religiosity, and biologically based sexual tendencies and preferences of the individual (Brown, Durtschi, Carroll, & Willoughby, 2017; Paul & Shim, 2008). As males and females exhibit different patterns of pornography consumption, it follows that their motivations to consume may also be different. Curiosity is often cited as the predominant motivation for both males and females to consume pornography, but other studies conclude that it is regularly only females who use pornography to satisfy curiosity or learn about sex, while males tend to view pornography predominantly for sexual pleasure or excitement (Goodson et al., 2001; Sabina et al., 2008). This finding suggests that males and females may have different reasons to consume pornography, with males being more sexually driven, while females are driven more by curiosity to learn.
Conversely, some research shows that sexual arousal is the predominant motivation for both males and females to view pornography (Boies, 2002; McKenna et al., 2001). However, after sexual arousal as the primary motivation, males tend to use pornography in response to negative emotions like boredom, loneliness, depression, anger, or stress, while females use pornography when they cannot sleep (Baltazar et al., 2010). These findings suggest that males may use pornography to combat unwanted emotions, while female use is less emotionally tied. As with other pornography literature, these conflicting results show that the field has not yet reached a stable conclusion regarding the gender differences in motivations to consume pornography.
Although efforts have been made to understand the motivations for pornography consumption, the current body of literature is not without limitation. One explanation is that measurement problems can account for a lot of the varying outcomes in all pornography research. The same holds true for motivational research. Brown, Durtschi, Carroll, and Willoughby (2017) cited measurement as the main problem with their research. They believed that their use of a single-item measure, which assessed only frequency of use, was the key problem that limited their understanding of different motivations to consume pornography. If they had been able to ask about periods of time spent using pornography during a viewing episode, they may have been able to evaluate different spectrums of pornography consumption and participant’s underlying motivations (Brown, Durtschi, Carroll, & Willoughby, 2017). Additionally, if they asked participants about the content of their pornography consumption, they may have been able to perform a more thorough analysis of motivations underlying a whole spectrum of use.
To address the problems commonly seen in pornography motivations research, for this study, we used a new measurement tool named the Consumption of Pornography Scale (COPS) to measure participant’s pornography consumption (Hatch et al., under review). This measurement instrument is comprised of multiple subscales. The Frequency subscale measures how often an individual views pornography. The Duration subscale measures how long an individual views pornography for during a typical viewing episode. The Accidental and Deliberate Exposure subscale measures whether individuals viewed up to seven of the most common types of pornography purposefully or accidentally and also measures their associated levels of arousal with each type of pornography. In knowing how often and how long participants view specific types of pornography and how aroused they became, we hope to gain a clearer perspective on what prompts multiple types of pornography consumption. As far as we can tell, this is the first study of its kind to parse apart the specific motivations associated with different types of pornography consumption rather than simply assessing for general attitudes associated with frequency of pornography consumption.
Further, many pornography-related research studies do not comment on the psychometrics of their measurement tools. Some articles have no mention of psychometrics when reporting the measure they used for pornography consumption (Baltazar et al., 2010; Boies, 2002; Burns, 2001; Sabina et al., 2008), while others organically create just a few questions that assess pornography consumption and do not seek to validate these questions (Baltazar et al., 2010; Sabina et al., 2008). Without measurement validation, the field cannot ascertain the construct that is actually being studied (Hogan & Agnello, 2004), and poor measurement is also contributing to replication difficulties in the entire field of psychology (Loken & Gelman, 2017). In the present study, we chose to use the COPS because it covers more of the conceptual domain of pornography consumption—in addition to frequency, it examines duration, types of content viewed, how aroused the person became, whether their consumption was intention or accidental, and so on—and reports promising psychometric properties (Hatch et al., under review).
The Present Study
Previous research has struggled to define pornography and illustrates varying outcomes associated with pornography consumption. Motivations have been found to also differ depending on the demographic and usage of individual, but research has not been able to reach a sound conclusion based on the problems associated with measurement. Through using a psychometrically sound pornography consumption measure, the current study intends to investigate the motivations that underlie pornography consumption in a sample of anonymous survey takers.
We hypothesize that motivations for pornography consumption will differ for males and females. We hypothesize that males will view pornography for sexually based reasons like for sexual arousal or when they want to have sex, whereas females will view pornography for reasons like curiosity or sex education. Further, we hypothesize that frequency and duration of pornography use will be predicted by sexually based reasons, but that accidental and deliberate exposure and arousal will be predicted by situational variables like boredom, curiosity, being alone, or being with others.
Method
Sample
The sample consisted of 312 participants, comprised of 169 males, 139 females, and four individuals who chose to not disclose their biological sex. Mean age of participants was 36.32 (SD = 10.20), with male’s average age of 36.22 (SD = 10.07) and female’s average age of 36.43 (SD = 10.40). Participants self-identified as White American (75%), Black American (10%), Asian American (5%), Latino American (5%), Biracial American (3%), a race not listed (1%), or did not respond (1%). The majority of the sample were heterosexual (85%), with 10% bisexual, 3% homosexual, 1% nonspecified sexual orientation, and 1% of participants did not respond. Participants were recruited via TurkPrime.com, an online data collection tool. Online survey participation is a commonly used tool in pornography research; it was our hope that this group of online users reflects the population we hope to generalize to.
Data Collection
Data from this study were collected as part of a larger longitudinal study examining pornography consumption over time in 2017. MTurk.com users saw our study listed as measuring pornography use over time and read informed consent prior to participation. In this survey, participants were asked to complete a 20-min survey online wherever they chose to access the internet. The overall survey contained demographic questions, motivation questions, consumption of pornography questions, control questions to check for attention, and a measure of sexual sensation seeking. Participants were paid $0.50 for completing the survey and entered into a drawing for an Amazon.com gift certificate for $50. Gift certificates were sent electronically via MTurk.com directly to the winner’s account.
Measures
Motivation questions
To assess the different motivations to consume pornography, the authors wrote 15 items that represent typical motivations for pornography consumption. Items were derived from existing measures and theoretical literature on the subject (Reid et al., 2011). For example, a participant would read “I view pornography when…” followed by “I am horny,” “I am bored,” “I have nothing better to do,” “I haven’t had sex in a long time,” “I am tired,” “I am alone,” “I am with others,” “I am curious,” “I want to have sex,” “for sex education purposes,” “to learn about sex,” “I am sad,” “to get back at my spouse/partner,” “the opportunity arises,” and “for educational purposes.” Participants would choose from a 5-point Likert-type scale ranging from strongly disagree to strongly agree. Internal consistency for this brief motivation questionnaire was good (α = .83). To verify factor structure, we performed a confirmatory factor analysis (CFA), which demonstrated good model fit for three factors named “sexually motivated,” “educationally motivated,” and “boredom” as well as 8 stand-alone items (see Results section for model fit information).
The COPS
This is a 34-item test that measures four separate facets of pornography consumption: frequency of consumption, duration of each viewing episode, deliberate exposure and arousal to varying types of pornographic content, and accidental exposure and arousal to varying types of pornographic content (Hatch et al., under review). To test the frequency of consumption, participants were asked how often they had viewed pornography in the last year, 30 days, and 7 days. To answer, participants chose a response ranging from once to multiple times a day. To test the duration of each viewing episode, participants were asked three questions similar to “When I view pornography, I view it for….” Participants would then select an answer from seven possible responses ranging from less than 5 min to 46+ min. To test exposure and arousal to varying types of pornographic content, participants were asked to select whether they had ever viewed heterosexual, gay men, lesbian, solo, threesome, group sex, and sadomasochism (S&M) pornography. Each question had a definition for each genre of pornography in case participants were unclear on the terms used. For example, solo pornography was defined as “where one individual is portrayed showing genitals or masturbating.” Then, if respondents selected yes, they were asked how aroused they had become. Participants responded to each question in a Likert-type scale, where 0 is not at all aroused and 7 is very aroused.
Psychometrics for this scale was good. Both the Deliberate Exposure and Arousal subscale and the Accidental Exposure and Arousal subscale showed good internal consistency (ω t = .81, ω t = .85), while both the Frequency subscale and the Duration subscale demonstrated excellent internal consistency (ω t = .94, ω t = .90). The COPS showed good construct validity; the Frequency subscale correlated with biological sex (r = −.54, p < .001), showing that males view pornography more often than females, which is consistent with other research (Fisher & Byrne, 1978; Hald, 2006), and the overall scale score positively correlated with the Sexual Sensation Seeking Scale (r = .56, p < .001; Kalichman et al., 1994). For each participant, we summed a total score for each subscale and an overall COPS score. We used these scores with the motivation questions to run our analyses.
Biological sex
Biological sex and its relationship with pornography consumption has been one of the most reliable findings in the literature; males have consistently been found to use pornography far more than women (Fisher & Byrne, 1978; Hald, 2006). As part of our survey, participants were asked to indicate their biological sex. Males were coded as 1, and females were coded as 0.
Data Analysis
We used Stata Version 16 to verify the factor structure of the motivation items. As we had an a priori theory regarding how the motivations would group, we used a CFA. Once we could verify a good fit within the factor structure, we moved on to our variable selection analysis.
We used variable selection to explore which motives drive pornography use. The primary reason or using variable selection was because this area of research is fairly new and has shaky measurement foundations (Hatch et al., under review; Kohut et al., 2019). We used Austin and Tu’s (2004) variable selection methods for predictive models. Data were analyzed using the “bootstrap” command on RStudio. In bootstrapping, a sampling distribution is created to see how many times each factor and stand-alone item are a significant predictor of the outcome when a significant p value is specified to be below .05. We used a sampling distribution of 100 resamples and only reported the motivations that reliably predicted pornography use in 60% or more of the resamples (Austin & Tu, 2004). Two important units of information are given in the output. First, the reliability of each predictor from statistical significance, or the percentage of times the estimate of the regression coefficient of each variable was statistically significant when α was specified at .05. Second, the reliability from the direction of the regression coefficient or the percentage of times the estimate of the regression coefficient of each variable was positive or negative. For example, a motivation could reliably be significant in 80% of the resamples and 100% of the time go in a positive direction. Each hypothesis was tested by bootstrapping the overall (both males and females together) models for frequency, duration, accidental exposure and arousal, and deliberate exposure and arousal, then bootstrapping the models a second time but separately between males and females.
Results
CFA
We used Stata Version 16 to perform a CFA to verify the factor structure of the motivation items. We first hypothesized a priori that the items would load onto four factors named “sexually motivated,” “educationally motivated,” “situationally motivated,” and “emotionally motivated.” We expected the items “I am horny,” “I haven’t had sex in a long time,” and “I want sex” would load onto the “sexually motivated” factor; the items “I am curious,” “for sex education purposes,” “to learn about sex,” and “for educational purposes” would load onto the “educationally motivated” factor; the items “I am bored,” “the opportunity arises,” and “I have nothing better to do” would load onto the “situationally motivated” factor; lastly, the items “I am tired,” “I am sad,” and “to get back at my partner” would load onto the “emotionally motivated” factor. Fit indices suggested an adequate fit for our hypothesized model, χ2(61) = 199.99, p < .01, comparative fit index (CFI) = .92, Tucker–Lewis index (TLI) = .89, root mean square error of approximation (RMSEA) = .08, 90% confidence interval (CI) = [.07, .10], p < .01, standardized root mean residual (SRMR) = .12 (see Table 1), but poor factor loadings on some of the items led to a revision of the model (see Figure 1).
Model Fit Statistics for Model With Situational Construct.
Note. AIC = Akaike’s information criteria; BIC = Bayesian information criteria; CFI = comparative fit index; TLI = Tucker–Lewis index; SRMR = standardized root mean residual; CD = coefficient of determination.
*p > .05.
Original four-factor structure of the motivations for pornography use.
The item “I am curious” had a very low factor loading with the “educationally motivated” factor (r = .37), suggesting that curiosity is not represented by a desire for sexual education, so we removed this item from this factor. The item “I want sex” also had a low factor loading with the “sexually motivated” factor, suggesting that a desire for sex is distinct from a desire to view pornography for sexual reasons (r = .62). This item was removed from this factor. We removed the situationally motivated and emotionally motivated factors and replaced them with the more focused “boredom” factor, as the items in these factors did not seem to represent the factors with great precision. The items “I am sad” and “to get back at my partner” were previously included in the “emotionally motivated” factor but were best suited as stand-alone items, not represented by any of the factors. With these in mind, we created a second model that we estimated would better represent our theory of motivations to view pornography.
Our revised model consisted of three factors named “sexually motivated,” “educationally motivated,” and “boredom.” The “sexually motivated” factor was comprised of the items “I feel horny” and “I haven’t had sex”; the “educationally motivated” factor was comprised of the items “for sex education purposes,” “for educational purposes,” and “to learn about sex”; lastly, the “boredom” factor was comprised of the items “I feel bored” and “when I have nothing better to do.” We computed Raykov’s ρ, a measure of internal consistency, for each factor. The sexually motivated factor demonstrated adequate reliability (ρ = .68), but the educationally motivated factor and the boredom factor demonstrated good internal consistency (ρ = .93, ρ = .89). The remaining eight items fit the measurement model better as stand-alone rather than as part of a factor (see Figure 2). This model displayed good fit, χ2(67) = 143.49, p < .01, CFI = .95, TLI = .91, RMSEA = .06, 90% CI [.05, .07], p < .05, SRMR = .03 (see Table 2) so was the each of the factors—as well as the individual items—was used for variable selection.
Revised three-factor structure of the motivations for pornography use.
Model Fit Statistics for Model Without Situational Construct.
Note. AIC = Akaike’s information criteria; BIC = Bayesian information criteria; CFI = comparative fit index; TLI = Tucker–Lewis index; SRMR = standardized root mean residual.
*p > .05.
What Are the Consistent Predictors of Pornography Use?
Aim 1: Do males view pornography for sexually based reasons?
To test this hypothesis, we bootstrapped each of the motivations with male models of frequency of pornography use, duration of pornography use, accidental exposure to pornography, and deliberate exposure to pornography. We expected to see the sexually based reasons more reliably predict the different types of pornography use in each model.
Male frequency model
The most reliable predictor of how often a male used pornography was represented by the item “when the opportunity arises,” as it was statistically significant in 95% of the resamples with a positive coefficient in 100% of the resamples (see Table 3). Males were more likely to use pornography when they had the time. The second most reliable predictor of how often a male used pornography was represented by the “sexually motivated” factor, suggesting that feelings of sexual arousal or a desire to engage in sexual activity is a highly motivating force for a male to use pornography frequently. Sadness also predicted frequency of pornography use among males, indicating that males may use pornography more frequently when they are feeling sad. Overall, the results of this model suggest that sexual reasons do play a role in how frequently males use pornography, but that other factors also play a role like when an opportunity to use arises or when feeling sad.
Male Frequency Model.
Male duration model
The most reliable predictor of how long a male views pornography in one given episode was represented by the “sexually motivated” factor, which was significant in 79% of the resamples with a positive coefficient in 100% of the resamples (see Table 4). The second most reliable predictor of male duration of pornography use was tiredness, followed by a desire to get back at one’s partner. Further, “when the opportunity arises” and sadness reliably predicted male duration of pornography use. Overall, these findings suggest that males are most likely to view pornography for longer periods of time when feeling sexually motivated, tired, sad, and wanting to get back at a partner.
Male Duration Model.
Male Accidental Exposure Model.
Male deliberate exposure model
The most reliable predictor of a male deliberately seeking out pornography was represented by the “sexually motivated” factor, which was a significant predictor in 91% of the resamples with a positive coefficient in 100% of the resamples (see Table 6). Next, “when the opportunity arises,” curiosity, tiredness, and sadness predicted deliberate exposure to pornography for males. Overall, this model suggests that males are motivated to seek out pornography when they feel sexually aroused, have an opportunity, and feel curious, tired, or sad.
Male Deliberate Exposure Model.
To answer the question of Aim 1, the findings from these models suggest that males are primarily motivated to use pornography for sexually based reasons. Other motivations like tiredness or when the opportunity arises also play a role in male pornography use, but sexual motivation appears to be primary in male pornography use.
Aim 2: Do females view pornography for educationally based reasons?
To test this hypothesis, we bootstrapped only the female models for frequency of pornography use, duration of pornography use, deliberate exposure to pornography, and accidental exposure to pornography. We expected that the primary reliable predictors of each of these models would be represented by our “educationally motivated” factor.
Female frequency model
The most reliable predictor of how often a female used pornography was represented by the “sexually motivated” factor, which was significant in 100% of the resamples (see Table 7). This suggests that females are highly sexually motivated to view pornography. Boredom, tiredness, and a desire for sex were also reliable predictors of frequency of pornography use. Contrary to our hypothesis, these findings suggest that sexually based reasons account for a greater proportion of how often females view pornography.
Female Frequency Model.
Female duration model
The most reliable predictor of how long a female uses pornography during a given episode was sadness, which was significant in 80% of the resamples with a positive coefficient in 98% of the resamples (see Table 8). This suggests that when females feel sad, they are more likely to view pornography. Tiredness, “when the opportunity arises,” boredom, the “sexually driven” factor, and curiosity predicted female duration of pornography use. Interestingly, the educationally motivated factor was a reliable predictor with statistical significance in 72% of the resamples but with a negative coefficient in 96% of the resamples. This means that females who used pornography for greater durations were less likely to endorse doing so for educationally based reasons. Overall, these results suggest that females view pornography for greater amounts of time when they are feeling sad, tired, bored, sexually driven, and curious.
Female Duration Model.
Female accidental exposure model
The most reliable predictor of when a woman will accidentally see pornography was tiredness, which was significant in 83% of the resamples with a positive coefficient in 100% of the resamples (see Table 9). Further, sexually driven motivation and boredom, a desire for sex, and being alone were significant predictors of accidental exposure to pornography. This model suggests that females are more likely to be accidentally confronted with pornography when they are tired, alone, bored, and feeling sexually motivated.
Female Accidental Exposure Model.
Female deliberate exposure model
The most reliable predictor of deliberate exposure to pornography was represented by the “sexually motivated” factor, which was significant in 99% of the resamples with a positive coefficient in 100% of the resamples (see Table 10). Females tended to seek out pornography when they felt sexually aroused. “When the opportunity arises,” the desire to get back at a partner, and being with others predicted deliberate pornography use for females. Overall, the findings of this model suggest that females are most likely to deliberately choose to look at pornography when they are feeling sexually motivated, when the opportunity presents itself, and when they are with others in a group.
Female Deliberate Exposure Model.
Combined Frequency Model.
The findings from these models suggest that our hypothesis for Aim 2, that females will ultimately be motivated in all types of pornography use by an educational motivation, is incorrect. Sexually based motivations like a desire for sex or feelings of arousal, and situationally based motivations like boredom and tiredness, seem to play a greater role in female overall pornography consumption than educational reasons.
Aim 3: Are frequency and duration of pornography use predicted by sexually based reasons?
To test the hypothesis that frequency and duration of pornography use are primarily motivated by sexual reasons like feeling aroused or desiring sex, we bootstrapped the overall general models of frequency and duration of pornography use to see whether sexually based reasons were primary reliable predictors in how often and how long individuals, regardless of gender, use pornography.
Overall frequency model
The most reliable predictor of frequency of pornography use, among males and females, was represented by the factor “sexually motivated” which was significant in 100% of the resamples with a positive coefficient in 100% of the resamples (see Table 3). Generally, frequency of pornography use is best predicted by sexual arousal or a desire for sex. Sadness, “when the opportunity arises,” boredom, and a desire to get back at a partner were also consistent predictors of frequency of pornography use. This model’s findings suggest that both men and women use pornography with more frequency when they are sexually motivated, sad, bored, when the opportunity has presented itself, and when they wish to get back at a partner.
Overall duration model
The most reliable predictor of how long an individual, whether male or female, uses pornography is “when the opportunity arises,” which was significant in 95% of the resamples with a positive coefficient in 100% of the resamples (see Table 12). Feeling sexually motivated, sad, and curious also predicted duration of pornography use for both males and females. These findings imply that how long an individual views pornography for in a given episode is best predicted by an opportunity that presents itself, feeling sexually motivated, feeling sad, and feeling curious. To answer Aim 3, these models suggest that sexually based reasons are among the primary motivations for frequency and duration of pornography use, but that there are some situational factors that predict these types of use, only with weaker reliability.
Combined Duration Model.
Male accidental exposure model
The most reliable predictor for male accidental exposure to pornography was represented by the “educationally motivated” factor, which was significant in 78% of the resamples with a positive coefficient in 100% of the resamples (see Table 5). This suggests that when males want to learn about sex, they are accidentally viewing pornography. The second most reliable predictor of male accidental exposure was represented by the “sexually motivated” factor, followed by “when the opportunity arises” and tiredness. These findings show that males may accidentally see pornography when they feel aroused, have a spare moment, and feel tired.
Combined Accidental Exposure Model.
Aim 4: Are accidental and deliberate exposure to pornography motivated by situational factors?
This aim hypothesizes that individuals, whether male or female, view pornography on purpose or accidentally as a result of situational factors like boredom, tiredness, sadness, or being around other people/being alone rather than for sexual or educational purposes. To answer this question, we bootstrapped the overall general accidental exposure and deliberate exposure models, including both male and female participants.
Overall accidental exposure model
The most reliable predictor of accidentally being exposed to pornography for both males and females was tiredness, which was a significant predictor in 88% of the resamples with a positive coefficient in 100% of the resamples (see Table 13). Further, being sexually motivated, educationally motivated, when the opportunity presented itself, and boredom were significant predictors of individuals accidentally seeing pornography. Overall, these findings suggest that accidental exposure to pornography among males and females is best predicted by tiredness, boredom, for sexual and educational reasons, and when the opportunity presents itself.
Overall deliberate exposure model
The most reliable predictor of deliberately seeking out pornography was feeling sexually motivated, which was significant in 99% of the resamples with a positive coefficient in 100% of the resamples (see Table 14). Males and females were most likely to seek out pornography when they felt sexually aroused or wanted sex. Other significant predictors included when the opportunity arose, being tired, and being alone. Interestingly, a desire to get back at one’s partner was a reliable predictor of deliberate exposure to pornography in 83% of the resamples but had a negative coefficient in 100% of the resamples. This means that individuals who choose to look at pornography were less likely to endorse doing so to get back at a partner. The findings of this model suggest that individuals seek out pornography when they are feeling sexually motivated, when the opportunity presents itself, when they are tired, and when they are alone.
Combined Deliberate Exposure Model.
The findings from both of these models do not support our hypothesis that situational factors primarily predict accidental and deliberate exposure. Both sexual and educational motivations played a role in reliably predicting whether an individual will be accidentally exposed to pornography or will purposefully choose to view pornography.
Discussion
Pornography is a complex construct that, although widely used and accepted, has varying definitions and related outcomes. The research surrounding what motivates an individual to consume pornography is similarly mixed. One possible explanation as to why results vary from study to study includes poor measurement; most research into pornography uses unvalidated or single-item measures. To address this key concern in the field of pornography motivation research, we used a well-validated psychometrically sound measurement tool to assess pornography use. Additionally, we sought to psychometrically test our theory of motivations by conducting a CFA on a set of motivation questions that reflected typical motivations to use pornography as derived from the literature (Baltazar et al., 2010; Reid et al., 2011). Because of the shaky measurement foundations in the field (Hatch et al., under review; Kohut et al., 2019), we used a bootstrap analysis to discover the most reliable motivations of different behavioral facets of pornography use.
Our findings from Aim 1 replicated the common finding that males are most likely to consume pornography for sexually based reasons, which was consistently the first or second most reliable predictor of all facets of male pornography use. Other motivations like seeking sexual education, tiredness, sadness, and boredom were also consistently ranked among the most reliable predictors of male pornography use. These findings indicate that more of an emotional component can be linked to male pornography use, which replicates the findings of Baltazar et al. (2010) but disagrees with Brown, Durtschi, Carroll, and Willoughby’s (2017) findings that pornography is rarely used to self-soothe from negative emotions. However, their sample were college students so perhaps this tendency grows through maturation.
Our findings from Aim 2 also converge with those of Baltazar et al. (2010) that females are also predominantly motivated to use pornography for sexually based reasons. Despite evidence that males and females use pornography at different frequencies (Flood, 2007) and with different use patterns (Seidman, 2004), it appears that their motivations to use pornography do not vary significantly. Sexually based motivation as well as tiredness and boredom reliably predicted pornography use among both men and women. In conflict with Goodson et al. (2001) and Sabina et al. (2008) findings, our results found that, among both males and females, sexually based reasons were always primary in predicting pornography use while curiosity predicted pornography use with less reliability.
Our findings from Aims 3 and 4 indicated that sexually based motivations are also primary in determining how often and for how long both males and females view pornography and for whether individuals seek out pornography deliberately or are accidentally confronted with pornography. While sexually based motivation predicted accidental and deliberate pornography use, accidental use was also associated with tiredness. It makes sense that an individual who is tired may stumble across unwanted pornographic content while surfing the internet.
Despite sexually based motivations being the most reliable predictor of all types of pornography use among males and females, some of the less reliable predictors indicate interesting findings. First, emotions like tiredness, boredom, and sadness were reliable predictors of pornography use in multiple models. To our knowledge, this is the first study to discover the relationship between these emotions and pornography use in both males and females. These findings are useful in the context of helping individuals who may feel that their pornography use is problematic; if they can be aware of when their negative emotions arise, it may help them to lessen their pornography use. Clinicians who work with individuals with problematic pornography use may help their clients to recognize these emotional triggers and find alternative distractions when sad, tired, or bored.
Further, the motivation “to get back at my partner” had a negative coefficient in the combined deliberate exposure model. These findings may suggest that for the most part, pornography use is an individual’s choice to consume, not a result of revenge-seeking behaviors in the context of a relationship. Among our sample, pornography use was not usually used as a tactic for relationship games but was mostly a personal decision. Some research suggests that when one partner used pornography, the other partner felt that they were being cheated on (Whitty, 2003) which led to lowered feelings of self-worth. Our results in this area indicate that typically individuals do not use pornography to get back at a partner, instead their use is more about their own feelings rather than anything to do with their partner.
Lastly, the “educationally motivated” factor predicted some pornography use with reliability, particularly in the accidental exposure model. This finding implies that individuals who are looking online for sex education resources are instead finding pornography. As the sexual encounters portrayed in pornography can be somewhat unrealistic and misleading and can lead users to view sex as primarily physical and casual, not relational and affectionate (Peter & Valkenburg, 2010; Tsitsika et al., 2009), perhaps schools, parents, and even the internet should provide more realistic platforms for learning about sex.
The strengths of this study include trustworthy measurement of pornography use and motivations to use pornography. The COPS, a psychometrically validated measure, evaluated how motivations to use pornography may be different across multiple types of pornography use. Additionally, the motivation items were gathered from prior research and tested for reliability and factor structure. From these procedures, we feel we have minimized error to reliably and validly measure our two constructs. Additionally, our sample was taken from across the United States so should represent pornography use across the United States geographically; however, this sample could be a source of limitation in their generalizability. Those who use Turkprime may have skewed distribution of pornography use when compared to non-Turkprime users as they are likely on the internet more often to perform their work.
To conclude, this study highlights the similar motivation that males and females have to use pornography as well as the role that negative emotions may play in motivation to use pornography. Implications of these findings include the knowledge that this sexually based activity, pornography use, is predominantly motivated by sexually based reasons regardless of biological sex and the type of pornography use. Males and females appear to be more similar in their motivations to view pornography than previously thought. Additional findings can help partners of pornography users to know that their partner’s use is typically not about them or the relationship but more of a personal choice, and clinicians working with pornography users may want to measure negative emotions like tiredness or sadness or boredom to help those wishing to decrease their pornography use.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
