The Influence of Sexually Explicit Internet Material on Sexual Risk Behavior: A Comparison of Adolescents and Adults

Abstract

This study had three goals: first, to investigate whether sexually explicit Internet material (SEIM) affects sexual risk behavior; second, to study whether these effects differ between adolescents and adults; and third, to analyze, separately for adolescents and adults, whether gender and age moderate an influence of SEIM on sexual risk behavior. The authors conducted a 2-wave panel survey among nationally representative random samples of 1,445 Dutch adolescents and 833 Dutch adults. SEIM use increased sexual risk behavior among adults, but not among adolescents. More specifically, moderator analyses showed that SEIM use increased sexual risk behavior only among male adults, but not among female adults. In the adolescent sample, no moderating gender effect occurred. Neither among adolescents nor among adults did age moderate the effects. Our study shows that SEIM may influence outcomes related to people's sexual health. It also suggests that male adults may present a potential risk group for adverse effects of SEIM.

Acknowledgments

This research was supported by a grant from The Netherlands Organisation for Scientific Research to the first and the second author.

Notes

¹Several other remedies were not applicable to our particular data. Data transformations, such as the log-transformation of skewed variables, often do not solve the problem of nonnormal distributions, in particular when interaction effects are investigated (Russell & Dean, 2000). Moreover, regression types designed for skewed dependent variables, such as Poisson and Tobit regression, were not useful because neither did we have a true count-data dependent variable (for Poisson regression, see Osgood, Finken, & McMorris, 2002) nor homoscedastic residuals (for Tobit regression, see Hutchinson & Holtman, 2005).

Note. The results for the adolescent sample appear in the lower triangle and appear in bold (N = 1,444). The upper triangle displays the results for the adult sample (N = 833).

*p < .05. **p < .01. ***p < .001 (two-tailed).

Note. SEIM = sexually explicit Internet material, HCSE = heteroscedasticity-consistent standard error (HC3), bca 95% CI = bootstrap bias-corrected accelerated 95% confidence interval (1,000 bootstrap samples, N = 1,444 each). All predictors were measured at Wave 1.

*p < .05. **p < .01. ***p < .001 (two-tailed).

Note. SEIM = sexually explicit Internet material, HCSE = heteroscedasticity-consistent standard error (HC3), bca 95% CI = bootstrap bias-corrected accelerated 95% confidence interval (1,000 bootstrap samples, N = 833 each). All predictors were measured at Wave 1.

*p < .05. **p < .01. ***p < .001 (two-tailed).

²Sexual risk behavior may not only be a consequence of exposure to SEIM, but also an antecedent. Individuals who have engaged in sexual risk behavior may be more likely to use SEIM as this material may reflect their sexual preferences better than other sexual media content. To test this possibility, we ran—separately for adolescents and adults—the same OLS regression model with heteroscedasticity-consistent standard errors and bca 95% CIs as described earlier with exposure to SEIM as the dependent variable. Among adults, sexual risk behavior (Wave 1) did not affect exposure to SEIM (Wave 2), B = .047, HCSE = .049, ns, (bca 95% CI: −.033/.182). Among adolescents, sexual risk behavior (Wave 1) negatively predicted exposure to SEIM (Wave 2), B = − .126, HCSE = .059, p < .05, (bca 95% CI: −.239/−.014). Thus, the less often adolescents engaged in sexual risk behavior, the more likely they were to use SEIM.

³It is plausible that our results may also be moderated by people's relationship status. Therefore, we ran the interaction-effect models with an interaction effect between relationship status (Wave 2) and SEIM use (Wave 1). No significant interaction effect emerged, neither among adolescents, B = .028, HCSE = .036, ns (bca 95% CI: −.019/.127), nor among adults, B = .049, HCSE = .050 (bca 95% CI: −.033/.174). Researchers should replicate this finding with bigger samples to preclude that power issues affected our results.