The dark triad,dating app use and online disinhibition positively predict technology-facilitated sexual violence perpetration

Abstract

Technology-Facilitated Sexual Violence (TFSV) describes the perpetration of a variety of sexual offences committed through digital communication technologies. The current study aims to investigate the similarity of risk factors between TFSV and in-person sexual offending. The sample consisted of 105 young adult, Australian males who responded to measures of sexual violence acceptance, dating app usage, online disinhibition, and the dark triad. Overall, 45.3% of the sample reported at least one instance of TFSV perpetration in the past 5 years. Standard and hierarchical linear regressions revealed dating app usage, the dark triad, and online disinhibition positively predicted TFSV perpetration. Neither measure of attitudes accepting of sexual violence predicted TFSV perpetration, but their positive correlation is indicative of cultural norms enabling sexual violence against women. As illuminated here, the congruence between TFSV and in-person violence extends beyond conceptual and prevalence trends to predictive risk factors. Recommendations for interventions and prevention include raising awareness of risk-generating app features that create vulnerability to sexual harm and identifying heterogenous groups of TFSV offenders based on key characteristics to develop optimal intervention strategies.

Keywords

Technology-Facilitated Sexual Violence online disinhibition dating app risk factors dark triad perpetration

Emerging technologies that provide new ways to communicate and interact with others also serve as novel methods to enact crime and create sexual harm. Technology-Facilitated Sexual Violence (TFSV) is a digital counterpart to in-person sexual violence (hereafter referred to as non-TFSV) whereby a perpetrator can commit online sexual harassment, cyberstalking, Image-Based Sexual Abuse (IBSA), sexual coercion and extortion, and/or creates opportunities to commit in-person sexual assaults through digital communication technologies including social media platforms and, dating applications (“dating apps”) (Henry & Powell, 2018; Pooley & Boxall, 2020; Powell & Henry, 2019). TFSV can be used to generate sexual harms without physical proximity to the victim, or extend and prolong non-TFSV sexual harms (Bailey et al., 2021; Henry & Powell, 2018). Although there is a wealth of literature focussing on non-TFSV offenders, these approaches are only now gradually being extended into the domain of TFSV. The current study aims to evaluate a risk model of TFSV offending informed by previously identified risk factors of non-TFSV including sexism, dark personality traits, behavioural disinhibition, and technology-related risk factors.

TFSV and non-TFSV

Despite unique modalities, TFSV and non-TFSV mirror one another in many ways. Both are forms of interpersonal aggression with sexual elements involved in motivations or behaviour, are enabled by cultural norms and relational power imbalances (Cama, 2021; Megarry, 2014; Powell, 2022), and can be described, assessed, and compared based on common elements across each offence (i.e., relationship to offender, visibility to others, role of technology, etc.) (Boyle, 2019; Kelly, 1987). Furthermore, there are similarities in prevalence trends, such as the disproportionate victimisation of women (Albury et al., 2019; Douglass et al., 2018; Dreßing et al., 2014; Karasavva & Forth, 2022; Reyns et al., 2012; Wolbers et al., 2022) and the overrepresentation of males as offenders (Henry et al., 2019; Powell, 2022; Powell et al., 2019, 2020; Valentine et al., 2022). These reflect non-TFSV trends (Australian Bureau of Statistics, 2016, 2023) although TFSV demonstrates a greater prevalence of victimisation.

Australian TFSV victimisation rates range from 62% to 72% (Henry et al., 2019; Wolbers et al., 2022). High prevalence is demonstrated across TFSV domains: online sexual harassment (29%–60%; Douglass et al., 2018; Powell & Henry, 2019), cyberstalking (23%–28%; Powell & Henry, 2019; Wolbers et al., 2022), and IBSA (23% and 38%; Powell et al., 2019, 2020). Given the pervasiveness of digital sexual harms, this area urgently requires empirical focus. Previous research has indicated that being a young adult male is associated with self-reported TFSV perpetration (Powell & Henry, 2019; Powell et al., 2019, 2020). This group is therefore the focus of this study.

In light of demonstrated overlaps between TFSV and non-TFSV, the current research aims to evaluate how previously identified non-TFSV risk factors, such as sexism, dark personality traits, and behavioural disinhibition, contribute to TFSV offending risk models. Previously applied empirical frameworks may further elucidate TFSV risk factors.

Routine Activity Theory (RAT)

Research on in-person crime has utilised various theoretical frameworks to explain necessary environmental factors that enable a crime to occur. Cohen and Felson's (1979) RAT explains crime occurring from the convergence in space and time of a motivated offender, a suitable target, and an absence of capable guardians to prevent the crime from occurring. Lifestyle-Routine Activity Theory (L-RAT; Cohen et al., 1981) expands RAT by conceptualising target attractiveness and distinguishing between physical (i.e., locks, alarms, cameras, etc.) and social (i.e., guards, bystanders, etc.) guardians. The focus on environmental factors enables insight into how the risk and occurrence of crime may vary across social contexts and locations (Lersch & Hart, 2023). However, given the ubiquity of digital communication technologies in contemporary life and the differences between in-person and cybercrimes, it is questioned whether environmental theories of crime can be effectively applied to technology-facilitated crimes (Yar, 2005).

Compared to in-person contexts, contemporary technology platforms enable unique opportunities to perpetrate, extend the reach of the offender (Choi et al., 2018), create separation between one's online identity, one's in-person identity, and other users (Suler, 2005), and facilitate anonymity or identity shielding (Citron, 2014; De Fazio & Sgarbi, 2016; Rowse et al., 2022). Certain platform features may be exploited by offenders such as the ability to erase evidence of contact with victims (Wolbers et al., 2022), provide multiple streams of information to the offender about the victim (Reyns et al., 2011) such as linked social media profiles or geolocation tracking features of dating apps (Centelles, 2019; Phan et al., 2021). These functionalities create differences in TFSV and non-TFSV experiences but do not necessarily preclude the utility of environmental criminological theories of crime.

Previous research has shown the applicability of L-RAT to victimisation of online crime types including online sexual harassment (Marcum et al., 2010; Wolfe et al., 2014), cyberstalking (Boillot Fansher, 2017; Reyns et al., 2011, 2012), and domains of TFSV and technology-facilitated sexual assault (Centelles, 2019). This body of research conceptualised core constructs of RAT in the context of the investigated crime and provided information about relevant risk factors. For instance, Reyns et al. (2011) reported that L-RAT constructs of online proximity to offenders, online guardianship, and target attractiveness were significant predictors of cyberstalking victimisation. The application of L-RAT to online instances of sexual harm provides insight into certain aspects of the digital environment that increase one's risk of victimisation. Yet, the focus on environmental factors neglects broader social and structural aspects that produce greater probabilistic risk of certain demographic to experience victimisation (Hayes & Maher, 2023) – for instance, the overrepresentation of females in the majority of sexual violence domains. Additionally, L-RAT should be applied to assess how environmental factors enable the perpetration of technology-facilitated sexual harms, rather than just using this lens to examine victimisation.

Thus, the ability to identify risk factors related to core theoretical constructs of L-RAT across digital and physical contexts indicate the presence of core pillars which enable and predict criminal behaviour (Wolfe et al., 2014). However, these environmental factors need to be located in a broader social context to incorporate aspects that are more fundamental to the incidence of sexual violence. Given the similarity of predictive constructs, non-TFSV risk factors may also demonstrate predictive utility in TFSV.

Risk factors for sexual violence perpetration

Sexism

Sexist attitudes contribute to an increased risk of perpetrating both non-TFSV (Bouffard & Goodson, 2017; Cleveland et al., 2019; de Heer et al., 2020; Malamuth et al., 2021) and TFSV (Flynn et al., 2023; Powell, 2022). These attitudes function to accept and excuse sexual violence by shifting responsibility to the social environment, the victim, or male biology (Polaschek & Ward, 2002). Males typically report greater acceptance of sexist attitudes compared to females (Gluck et al., 2020; Henry et al., 2019). Other research reported that aside from males, individuals that had reported experiencing or perpetrating IBSA were more likely to minimise the harms of IBSA and attribute responsibility of victimisation to victims (Flynn et al., 2023; Powell et al., 2022). On a macro scale, cultural and subcultural adherence to sexist ideologies also serve as risk factors, by behaviourally reinforcing sexual violence (Thompson & Morrison, 2013). The notion of group norms aligns with the construct of subjective norms described by the theory of planned behaviour as beliefs about others’ attitudes (Ajzen, 1991). In the case of TFSV, this extends to online spaces as the internet can function to facilitate deviant online peer networks on dedicated websites or social media groups (Funnell & Hush, 2018; Henry & Flynn, 2019; Henry & Powell, 2014). Demonstrably, elitist club membership and perception of peer approval of forced sex predicted TFSV over a 3 year period (Thompson & Morrison, 2013). Motivations for IBSA include the victim being sexually attractive, not perceiving IBSA as serious, and bragging to others often resulting in peer acceptance (Clancy et al., 2020). The current research aims to evaluate the contribution of acceptance of sexist attitudes to TFSV offending risk (Hypothesis 1). Furthermore, the present study examines the relationship between TFSV acceptance and TFSV offending, through a scale created for this research. Alongside individual and social attitudes, sexual violence may be partially explained by personality traits (Gluck et al., 2020; Pina et al., 2017).

Dark personality traits

The dark tetrad describes four subclinical, malevolent personality traits: Machiavellianism, psychopathy, narcissism, and sadism, with higher scores indicating dispositions to aggression, deception, and manipulative social strategies (Chabrol et al., 2009; Paulhus & Williams, 2002). These traits have been demonstrated to significantly contribute to non-TFSV (Malamuth et al., 2021) and TFSV (Branson & March, 2021; Clancy et al., 2019). It is noteworthy that, in a meta-analysis, sadism has shown a moderate correlation with TFSV perpetration, amongst other forms of aggression (Thomas & Egan, 2022). However, other traits are tentative as only narcissism was reported to predict IBSA perpetration in regression analyses (Clancy et al., 2020; Karasavva & Forth, 2022). Some traits may increase dispositions to behaviour that increase the risk of both victimisation and perpetration (Karasavva & Forth, 2022), and this may be explained by the associated behavioural-level manifestations, rather than the traits themselves.

Belonging to elitist clubs and narcissism are both identified as risk factors for IBSA perpetration. Indeed elitist club membership may reflect one's level of narcissism, which is associated with grandiosity and superiority (Paulhus & Williams, 2002). Since the behavioural-level trait – elitist club membership – has been identified as a risk factor it may help to identify groups with cultures endorsing sexual violence offering an intervention focus (Thompson & Morrison, 2013). Conversely, narcissism may be more useful in explaining why elitist club membership is a risk factor for IBSA perpetration. The relationship between personality factors and behavioural manifestations should be considered in future research whether it be theory-building or identifying at-risk groups. The contribution of dark triad traits to TFSV offending risk will be examined in the current research (Hypothesis 1).

Online disinhibition

The contribution of alcohol to risk profiles of non-TFSV offending is, in part, explained by the disinhibitory psychopharmacological effects of alcohol, affecting cognitive processes and behaviour (Abbey et al., 2006). Behavioural disinhibition may also be relevant to TFSV offending, as the process of deindividuated online communication was a risk factor for cyberbullying (Lowry et al., 2016; Udris, 2014; Wright et al., 2019). Alongside software design elements that enable anonymous usage, psychological mechanisms influence one's disposition to atypically pro- or anti-social behaviour in online contexts, described by the online disinhibition effect (Suler, 2005). Several researchers have tentatively implicated online disinhibition in the high prevalence of TFSV (Choi et al., 2018; National Crime Agency, 2016; Rowse et al., 2022) but little empirical evidence has been found to support this notion. Evidence to date indicates online disinhibition is associated with technology-facilitated intimate partner violence perpetration (Maftei & Dănilă, 2021). Others have found that alongside online disinhibition, historical perpetration of intimate partner violence predicted the technology-facilitated counterpart of this behaviour (Duerksen & Woodin, 2019). Therefore, those with the greatest risk of TFSV offending were individuals with a history of sexual violence perpetration who also experienced a greater degree of online disinhibition (Zhong et al., 2020). The current research aims to examine the role of the online disinhibition effect in TFSV offending (Hypothesis 2). This examination of literature reveals some parallels between predictive risk factors of TFSV and non-TFSV which is evaluated by the current study.

The present study

The current study aimed to examine a risk model of TFSV offending based on previously identified risk factors of non-TFSV offending. Predictions were as follows. Risk factors for non-TFSV (attitudes accepting of rape-myth and sexual, dark triad) will be positively associated with TFSV perpetration (Hypothesis 1). Technology-related risk factors (online disinhibition, dating app usage) will be positively associated with TFSV perpetration (Hypothesis 2). Hypothesis 3 is that rape-myth and TFSV acceptance, online disinhibition, and the dark triad will each uniquely contribute to the prediction of TFSV perpetration.

Method

Participants

Participants were 115 males aged between 18 and 25 years of age (M = 22.28, SD = 1.85) living in Australia. Three respondents were identified as outliers and seven had missing data values (>10%). The final sample consisted of 105 participants.

Measures

Demographics

Age, gender, and whether one was living in Australia were assessed to provide information about participants and evaluate the screening criteria.

Dating app use

One's engagement with dating apps was assessed through two continuous variables measuring the number of dating app accounts and number of individuals met in-person from dating apps in the past 5 years.

Rape-myth acceptance

The Rape-Myth Scale-Subtle Version (Thelan & Meadows, 2021) assesses the degree to which sexual violence is perceived as culturally normative/acceptable. A total of 13 items from the subscales “She asked for it” and “He didn’t mean to” were included here as they measure the extent to which responsibility is assigned to a victim and an offender's behaviour is excused. Compared to the original scale (Payne et al., 1999), this version avoids defensive, biased responding by updating emotionally charged terms; for instance, “rape” was changed to “sexual assault”. Participants rated each item using a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). In the pilot study, this scale had good internal consistency (α = .93) (Thelan & Meadows, 2021) and prior iterations of this scale with the original wording of items also demonstrated internal reliability (α = .92) (PettyJohn et al., 2023).

Acceptance of TFSV

An eight-item Measure of TFSV Acceptance (MOTA) was developed for this study to assess the degree to which one has an accepting attitude towards TFSV occurrences. Items were measured on a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. This scale was developed as no scales were located that measure this construct and it is a likely precursor to TFSV offences. Items comprising the scale and application of attitudinal scales specified to contexts of technology-facilitated sexual harms were inspired by the sexual image-based abuse myth acceptance scale (Powell et al., 2019). Scale development procedures were facilitated by an exploratory factor analysis for measure validation. The data were reduced to a two-factor oblique factor solution using a promax rotation and a principal components extraction method. The “minimisation” factor explained 42.6% of the variance and measured the perception that TFSV victims were overreactive or overly sensitive to TFSV instances (“Women tend to overreact to situations like this”). The “invulnerability” factor assessed the perception that TFSV victimisation would not cause oneself distress (“If it happened to me, I probably would not care that much” and “I would be flattered if people did this sort of thing to me”) and explained 13.4% of the variance. Together these factors explained 55.9% of the variance. Both the minimisation factor (α = .79) and the invulnerability factor (α = .77) demonstrated considerable internal reliability but did not exceed .80. These factors reflect components of non-TFSV scales that measure minimisation, suggesting similar attitudinal mechanisms that are significantly related (Table 1). The MOTA scale and item-level correlations are available through the Open Science Framework (https://doi.org/10.17605/OSF.IO/TQ5AP).

Table 1.

Bivariate correlations of variables.

Variable	M	Α	1	2	3	4	5
1. Dating app usage	3.89	–	–	–	–	–	–
2. TFSV acceptance	13.99	.83	.02	–	–	–	–
3. Rape-myth acceptance	25.52	.85	.01	.36**	–	–	–
4. Dark triad	39.27	.83	.08	.11	.19	–	–
5. Online disinhibition	32.07	.90	−.01	−.003	.05	.32**	–
6. TFSV perpetration	1.03	.84	.34**	−.003	.02	.38*	.22*

Note. M = mean value; α = Cronbach's alpha; TFSV = technology-facilitated sexual violence.

* p < .05. ** p < .001.

Dark triad

The Dirty Dozen measure (Jonason & Webster, 2010) has 12 items with four items assessing each aspect of the dark triad: Machiavellianism, psychopathy, and narcissism. Participants rated items on a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree). Sample items are “I tend to lack remorse” (psychopathy) and “I tend to seek prestige or status” (narcissism). This measure is widely used, with considerable validity (Moor & Anderson, 2019) and has demonstrated good internal reliability (α = .87) (Gluck et al., 2020).

Online disinhibition

The 12-item Measure of Online Disinhibition (Stuart & Scott, 2021) assessed the degree of behavioural change in online contexts on a 5-point Likert scale from 1 = not at all like me to 5 = very like me. Sample items are “I am more confident online than I am offline” and “I say things on the internet that I would not say in person”. The scale has good internal reliability (α = .93) (Scott et al., 2022).

Perpetration of TFSV

The dependent variable was assessed through behavioural domains of TFSV perpetration (online harassment, cyberstalking, and IBSA) and their frequency in the past 5 years with 14 behaviour-focused items, rated on a Likert scale (1 = never, 5 = more than 10 times). These items were adapted from a scale of TFSV behaviours employed by the Australian Institute of Criminology (Wolbers et al., 2022). The wording of some items was adjusted to include colloquial terms, such as “mates”, making items appear more casual and relatable to participants in the present study's cultural context to reduce defensive responding. Sample items include “Sent someone a sexual image without them asking for it” and “Shown or sent some of your mates a sexual image of somebody when the person in the photo didn’t know about it”. The scale and item-level prevalence rates are available through the Open Science Framework (https://doi.org/10.17605/OSF.IO/TQ5AP).

Procedure

To reach a broad sample of Australian, young adult males both the participant crowdsourcing service Prolific and paid advertisements on social media were utilised. The anonymous survey was completed in an average time of 9 min. Participants were provided with the contact details of support services and the researchers in case of distress. The study was approved by the Human Research Ethics Committee of RMIT University (ID: 25326).

Data analysis

Data cleaning and assumption testing were facilitated by SPSS Version 29. It should be noted that the inclusion of individuals indicating zero instances of TFSV perpetration created a positively skewed dependent variable. Three outliers were identified and removed before bivariate correlations were calculated (to assess Hypotheses 1 and 2) and regression analyses were conducted (to assess Hypothesis 3).

Results

Descriptive statistics

Overall, 45.3% of the sample self-reported at least one instance of perpetration in the past 5 years. The mean score of total TFSV perpetration was 1.4 (SD = 2.3). Of the measured domains 35.8% reported online sexual harassment, 23.6% cyberstalking, and 12.3% IBSA. Of those reporting TFSV offending, 25% reported having never used dating apps.

Correlational analysis

Of the non-TFSV risk factors, the dark triad, but neither rape-myth acceptance nor TFSV acceptance, was significantly associated with TFSV perpetration (Hypothesis 1). Of the technology-related risk factors, both online disinhibition and dating app usage were significantly associated with TFSV perpetration (Hypothesis 2). The dark triad and online disinhibition were positively related, as were TFSV and rape-myth acceptance (see Table 1).

Linear regression analyses

To evaluate the variables’ predictive validity a standard and hierarchical linear regression were calculated (Hypothesis 3). TFSV perpetration was the dependent variable in each regression analysis and was calculated by summing the three domains. A standard linear regression indicated TFSV perpetration was predicted by dating app usage (t = 3.629, p < .001) and the dark triad (t = 2.560, p < .001) which explained 26.3% of the variance associated with TFSV perpetration (R²= .263, adjusted R²= .226). None of the other independent variables were significant predictors of TFSV perpetration.

When each of the dark triad traits were entered individually each was significant: narcissism (t = 2.397, p = .018), psychopathy (t = 2.467, p = .015), and Machiavellianism (t = 2.561, p = .012). However, when these individual traits were concurrent in the linear regression none were significant. Using the collective measure of the dark triad produced greater increases in explained variance and had a greater standardised coefficient than any of the individual traits.

A hierarchical multiple regression was calculated to investigate potential mediational or moderational relationships (Table 2). Model 1 contained just one predictor, dating app usage which explained 11.4% of the variance. Model 2 added online disinhibition which increased the prediction of variance by 5.2%. The final iteration of the model included dating app usage, online disinhibition, and the dark triad which predicted 25.2% of the variance.

Table 2.

Hierarchical regression coefficients for models and variables.

Predictor	R ²	ΔR²	B	β	t	p
Model 1	.114	.114			58.734	<.001
Dating apps			.135	.338	3.642	<.001
Model 2	.166	.052			19.160	<.001
Dating apps			.137	.343	3.795	<.001
Online disinhibition			.050	.227	2.514	.013
Model 3	.252	.086			13.528	<.001
Dating apps			.125	.313	3.620	<.001
Online disinhibition			.025	.115	1.250	.214
Dark triad			.067	.316	3.412	<.001

Note. N = 105, R²= .252.

While dating app usage and the dark triad are significant individually, online disinhibition is a significant predictor only in Model 2. The reduction of significance once the dark triad was entered (and the significant bivariate relationship), indicates the relationship between online disinhibition and TFSV perpetration was completely mediated by the dark triad. Those who experienced a greater degree of online disinhibition tend to have a greater level of dark triad traits and also tend to perpetrate TFSV more (Figure 1). Potential moderating effects were tested by combining standardised values of online disinhibition and dark triad scores; however, this was not significant (t = 1.265, p = .209).

Figure 1.

Regression model of predictors of TFSV perpetration.

Discussion

The study advanced the literature by examining a risk model of TFSV offending based on previously established non-TFSV offending risk factors and emerging technology-related risk factors. Approximately half of the sample reported TFSV offending, and one-quarter of these individuals reported having never used a dating app. Of non-TFSV risk factors, the dark triad predicted TFSV offending, however, neither measure of acceptance of sexual violence was significant – which partially supported Hypothesis 1. Of technology-related risk factors, online disinhibition and dating app usage were significant predictors, supporting Hypothesis 2. Predictors of TFSV perpetration involved a confluence of risk factors increasing dispositions to sexual violence across physical and digital contexts (dark triad), and risk factors specified to the technological contexts of TFSV (dating app usage and online disinhibition). This partially supported Hypothesis 3 as neither attitudinal measure contributed to prediction of TFSV perpetration.

Recent research has identified an increasing prevalence of technology-facilitated sexual harms (National Crime Agency, 2016; Valentine et al., 2022), and thus dating app usage is implicated as a risk factor for sexual violence (Centelles, 2019; Choi et al., 2018; Powell, 2022). These trends reflect the growing ubiquity of digital communication technologies in everyday life and contemporary dating scripts. In line with RAT (Cohen & Felson, 1979), dating app usage increases visibility and accessibility of individuals to potential offenders through digital profiles. Furthermore, communication functions of platforms providing a means to evaluate target suitability and attractiveness and initiating coercive strategies (Centelles, 2019; Choi et al., 2021; Reyns et al., 2011). While the current findings did not empirically evaluate RAT, these results can be interpreted in the context of extant literature applying RAT to cyberbullying (Navarro & Jasinski, 2012), online sexual harassment (Karasavva et al., 2023; Marcum et al., 2010; Wolfe et al., 2014), cyberstalking (Boillot Fansher, 2017; Reyns et al., 2011), and TFSV (Centelles, 2019). Alongside this body of research, the current findings indicate applicability of environmental criminological theory to digital contexts to elucidate risk factors of technology-facilitated crime, which is imperative given the overwhelming and growing presence of technology in crime.

Consistent with previous literature, the dark triad was associated with TFSV perpetration (Branson & March, 2021). Other research reported that only individual traits predicted forms of TFSV perpetration (Clancy et al., 2019; Karasavva & Forth, 2022) and were uniquely linked to various motivations to engage with TFSV (Karasavva et al., 2023). In general support of these findings, the current study adds support to the already well-established role of the dark triad in individual dispositions to anti-social behaviour. Moreover, the current findings provide insight into how the dark triad functions to create sexually aggressive dispositions. The contribution of online disinhibition was explained by a perpetrator's dark triad traits, suggesting those scoring higher on dark triad traits experience greater online disinhibition and are at a greater risk of TFSV perpetration. These findings contribute to the extant but limited body of evidence reporting online disinhibition as a significant predictor of technology-facilitated violence (Zhong et al., 2020), technology-facilitated intimate partner violence (Maftei & Dănilă, 2021), and cyberbullying (Wright et al., 2019). Furthermore, it offers insight into how higher-order personality traits function to increase risk through technology-related aspects of behaviour such as online disinhibition. Future research should seek to evaluate the role of online disinhibition in cybercrime and how this may be mitigated in TFSV prevention efforts.

The online disinhibition effect may explain the insignificance of both attitudinal variables in the current study. Psychological distance between online behaviour and social identity may dispel the contribution of attitudes since there is less pressure to conform to typical social behaviours. Future research may wish to consider the moderating role of online disinhibition between attitudes and online behaviours. Another consideration relates to Ajzen's (1991) theory of planned behaviour which indicates that individual attitudes alone do not predict behaviour, but adds to social norms, locus of control, and behavioural intentions to improve predictive validity. However, the current findings are inconsistent with previous research that reported global sexism measures in an all-male sample (Thompson & Morrison, 2013) and attitudes accepting of IBSA in a mixed sample (Powell et al., 2022) significantly predicted TFSV perpetration. Furthermore, in the current study, neither attitudinal measure of acceptance of technology-facilitated or in-person sexual violence were significantly correlated to TFSV perpetration. It should be noted that these attitudes represent lower-order expressions of broader traits such as gender-discriminatory attitudes or sexism, and measuring these may have produced unique results. Given the divergence between findings of the current study and previous study findings, there may be methodological issues associated with the application of these measures in the current study.

Theoretical implications are drawn from the current research which identify how risk factors of sexual violence perpetration exist in technology-facilitated and in-person counterparts. To effectively predict perpetration, variables are required to be recontextualised to suit the mode of sexual violence that is being investigated. Demonstrably, RAT constructs of proximity and exposure to perpetrators are core mechanisms of risk and are uniquely measured across digital and in-person contexts. Non-TFSV risk increases with a greater number of sexual partners (Bouffard & Goodson, 2017; Zinzow & Thompson, 2019), and the current research identifies the technological counterpart reporting that greater dating app usage increases TFSV risk (Choi et al., 2018; Powell, 2022). Therefore, the current research supports the notion that despite the impact of technology on behaviour, the fundamental, underlying mechanisms of risk remain unchanged (Agustina, 2015).

Limitations and future research directions

The model presented in the current study may have benefitted from the addition of other risk factors identified in existing literature. The inclusion of previous TFSV victimisation (Powell, 2022), non-TFSV perpetration (Boillot Fansher, 2017; Duerksen & Woodin, 2019; Zhong et al., 2020), sadism (Karasavva et al., 2023; Thomas & Egan, 2022), and peer-level norms of in-person communities (Thompson & Morrison, 2013) would be worthy of investigation. Other variables that have not yet been tested including peer-level norms of online communities (Henry & Flynn, 2019), self-concept, alcohol and other drug intoxication, and the concepts arising from the theory of planned behaviour may also explain TFSV perpetration. Furthermore, TFSV takes different forms including technology-facilitated sexual assaults and various forms of IBSA (i.e., webcam hacking, using photo editing software or artificial intelligence to create images of abuse, see e.g., Flynn et al., 2022) and future research should investigate these in light of coercive control tactics, motivations to perpetrate, and the relationship between perpetrator and victim.

There are some methodological considerations related to the cross-sectional design that do not enable identification of the direction of causality, or temporal ordering between observed variables and future research using longitudinal designs is required to address this. Furthermore, participants may have responded in a socially desirable or defensive manner especially considering the sensitive nature of the research topic, although the anonymous nature of the survey was an attempt to mitigate this. Of the attitudinal measures, only two of five individual subscales of the rape-myth scale were used for parsimony, which may have produced different results than the full scale. Additionally, given the sample was comprised of young adults, Australian males future research is required to validate the MOTA scale (TFSV acceptance) in diverse samples to evaluate its psychometric properties. The nature of a small, homogenous sample presents practical considerations for the degree of generalisability and predictive power of the current results.

Indeed, the heterogeneity of TFSV offenders warrants investigation with a view to developing a typological analysis of offenders. For instance, there may be factors that distinguish clusters of individuals based on behavioural characteristics, internal narratives, self-concept, and motivations to offend. Future prevention efforts should consider how to address risk factors for TFSV. These efforts should include a specific focus on reducing the proclivity to experience online disinhibition across platforms through software design on dating apps and social media. Diverse prevention and intervention efforts are required to address cultural and contextual risks, barriers, and harms for individual and social intersectionality with respect to gender and sexual identities, racial and ethnic diversity or Indigenous status, younger and older individuals, and individuals with lower access to technology. Offender-orientated approaches to reduce sexual violence may benefit from expanding the focus of sexually distorted cognitions to incorporate technology-related elements. Furthermore, TFSV should be evaluated in the context of a theoretical perspective to expand the current knowledge base beyond the variable-analytic approach to integrate theoretical implications of TFSV and criminological theory.

Conclusion

This research contributes to the growing body of literature investigating risk factors for TFSV and provides evidence for the significant contribution of online disinhibition, dark triad traits, and dating app usage to TFSV perpetration. Given the emerging nature of TFSV research and the overlap between TFSV and non-TFSV, criminological frameworks and approaches can be applied to online contexts to generate novel information about the similarities and differences between these forms of sexual violence. This approach is likely to advance the understanding of technology-facilitated crime, inform interventions and provide novel insights into criminal behaviour in general.

Footnotes

Data availability statement

The data supporting the findings of this study are available from the Open Science Framework: .

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Adam Gunnoo

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