The Moderating Role of Online Privacy Awareness in the Relationship Between Smartphone Addiction and Online Grooming Risk in Adolescents

Abstract

Adolescents’ increasing dependence on smartphones has heightened concerns about vulnerability to online risks, including online grooming risk. However, the processes linking smartphone addiction to online grooming risk, and the roles of online privacy awareness and phubbing behavior (ignoring others in face-to-face interactions due to smartphone use), remain insufficiently understood, especially in clinical populations. This cross-sectional study involved 182 adolescents aged 13 to 17 years who were referred to a child and adolescent psychiatry outpatient clinic in Türkiye. Participants completed self-report measures of smartphone addiction, online grooming risk, phubbing behavior, and online privacy awareness. Data were analyzed using correlation analyses, hierarchical regression analyses, and moderation analyses conducted with Hayes’ PROCESS macro. Smartphone addiction was positively associated with online grooming risk and phubbing behavior and negatively associated with online privacy awareness. Hierarchical regression analyses indicated that smartphone addiction significantly predicted online grooming risk after controlling for sociodemographic and clinical variables. Online privacy awareness acted as a protective factor by directly reducing online grooming risk and by moderating the relationship between smartphone addiction and online grooming risk. Phubbing behavior did not independently predict online grooming risk in multivariate models. Smartphone addiction represents a direct risk factor for online grooming among adolescents, whereas online privacy awareness plays an important protective role.

Keywords

smartphone addiction online grooming online privacy awareness phubbing adolescent

Introduction

Today, smartphones, with their features that incorporate many different functions, have become an indispensable part of daily life, reaching billions of users worldwide (Statista, 2024; Zhang et al., 2021). This proliferation has reached striking proportions, particularly among the adolescent population. Current data show that 95.7% of young people in Spain (González-Cabrera et al., 2017) and 86.2% of 11 to 15 year olds in Türkiye own a smartphone and check their devices very frequently (Turkish Statistical Institute, 2024). However, this technological integration also brings with it various risks that threaten individuals’ bio-psycho-social well-being (George & Odgers, 2015). The foremost of these risks is smartphone addiction, defined as the excessive use of smartphones to the extent that it impairs an individual’s physical and mental health (Liu et al., 2017). Studies conducted in different countries reveal that the prevalence of smartphone addiction among adolescents varies within a wide and alarming range of 10% to 31% (Cha & Seo, 2018; Haug et al., 2015; Lopez-Fernandez et al., 2014; Roh et al., 2018). This intense and uncontrolled relationship that adolescents establish with their devices makes them more vulnerable to potential risks in online environments. Importantly, this vulnerability may emerge not only through the amount of time adolescents spend online, but also through the ways smartphone overuse reshapes their social attention, interpersonal functioning, and responses to online interactions.

One socially observable way in which smartphone addiction may affect adolescents’ daily functioning is through “phubbing,” a behavior that reflects the displacement of face-to-face attention by smartphone use. Phubbing is defined as an individual being preoccupied with their smartphone instead of communicating with the person in front of them in a social setting, thereby neglecting their conversation partner (Chotpitayasunondh & Douglas, 2016; Karadağ et al., 2015). In the literature, this behavior is considered not only a matter of politeness, but also a factor contributing to the weakening of social bonds, the isolation of individuals, and a decline in the quality of face-to-face communication (Roberts & David, 2016). As a result of this situation, adolescents experiencing social isolation or feeling rejected in real life may need more attention and approval from strangers in online platforms and thus become more vulnerable to risks such as online grooming. From this perspective, phubbing is relevant not simply as a problematic social habit, but as a behavioral indicator of a broader shift toward digitally mediated interaction, which may increase adolescents’ exposure to risky online contact.

When adolescents increasingly orient their social attention toward digital environments and away from face-to-face interaction, they may become more exposed to forms of online contact that carry interpersonal and sexual risk. One of the most serious of these risks is online grooming. First defined by Salter in the literature, “online grooming” is a process-oriented phenomenon in which an adult manipulates a child or adolescent for the purpose of sexual abuse and exploitation. This process involves psychological manipulation based on establishing trust and intimacy through digital platforms; it can begin with the sharing of sexually explicit material and extend to real-life abuse (Pasca et al., 2022; Salter, 1995). The anonymity provided by the internet and the blurring of the distinction between the virtual and real worlds have made this dark side more dangerous (Boyd et al., 2020) and have made online abuse one of the most common types of abuse today (Whittle et al., 2013). Research indicates that approximately 17.2% of adolescents are exposed to this risk (Montiel et al., 2016), that the vast majority of victims (70%) are between the ages of 13 and 15 (Davidson et al., 2012) and that girls are at higher risk (Hernández et al., 2021). The excessive and uncontrolled use of smartphones, which are the primary channel for internet access today, is considered a risk factor that could directly increase adolescents’ likelihood of being exposed to these manipulative processes. At the same time, exposure alone is unlikely to explain why some adolescents are more vulnerable than others. Protective individual-level factors, particularly those related to how adolescents understand privacy, disclosure, and online boundaries, may shape whether such online contact develops into grooming risk.

Online privacy awareness is increasingly emerging as a protective factor in adolescents’ digital lives. In today’s digital environments, privacy is not limited to the sharing of personal information; it also encompasses broader dimensions such as digital footprints, tracking, and datafication (Farthing et al., 2024). Recent studies show that adolescents have a certain level of awareness regarding online privacy, but this awareness is not always equally developed (Farthing et al., 2024; Williams et al., 2023). Furthermore, it is thought that this level of knowledge and awareness can be influenced by socialization processes such as parent-child communication (Williams et al., 2023). Furthermore, new findings reveal that online safety education can make positive contributions in terms of reducing risky information sharing, strengthening account security behaviors, and limiting online interactions with strangers (Pham et al., 2024). Since adolescence is a developmental stage characterized by increased impulsivity and intense online interaction, deficiencies in privacy awareness may make young people more vulnerable to risky online experiences. When evaluated specifically in terms of the risk of online grooming, this awareness can help adolescents recognize suspicious interactions, manage the sharing of personal information in a more controlled manner, and approach communications initiated by strangers with greater caution. When all these findings are considered together, it is clear that online privacy awareness should be regarded as an effective, developable, and protective resource in adolescents’ exposure to online risks. Therefore, efforts to understand the risk of online grooming among adolescents should focus not only on risk-increasing processes associated with problematic smartphone use but also on protective processes based on online privacy awareness.

A review of the literature reveals that, to the best of our knowledge, there are no studies that examine the interaction between phubbing behavior, which is a result of smartphone addiction, and online privacy awareness, which is a protective mechanism, with the risk of online grooming within a comprehensive model framework in a clinical sample. Investigating these variables together is critical for several reasons. First, it allows for a deeper understanding of the social displacement mechanism, whereby smartphone addiction leads to phubbing, creating a social and emotional void that adolescents may attempt to fill through risky online interactions. Elucidating this path is essential for identifying behavioral milestones that precede online victimization. Furthermore, testing the moderating role of online privacy awareness in a clinical population provides an empirical basis for preventive psychiatry. If such awareness serves as a buffer, it can be integrated into clinical assessments and tailored intervention programs as a modifiable protective factor. Consequently, this holistic approach moves beyond merely identifying risks and offers a proactive framework for enhancing digital safety in high-risk adolescent groups. Based on this fundamental motivation, the present study aims to examine the roles of phubbing and online privacy awareness in the relationship between smartphone addiction and online grooming risk among adolescents. The findings are expected to fill this gap in the literature and provide a theoretical basis for preventive interventions and digital awareness programs targeting adolescents. The hypotheses determined in line with the theoretical framework and relevant literature of this study are presented below:

H1: Smartphone addiction in adolescents positively and significantly predicts online grooming risk.

H2: Smartphone addiction is positively associated with phubbing behavior in adolescents.

H3: Online privacy awareness negatively predicts online grooming risk among adolescents.

H4: Online privacy awareness moderates the relationship between smartphone addiction and online grooming risk, such that this association is weaker at higher levels of online privacy awareness.

Materials and Methods

Participants

This study used a cross-sectional design. The sample consisted of adolescents aged 13 to 17 years who attended the Child and Adolescent Psychiatry Outpatient Clinic at Pamukkale University Medical Faculty Hospital in Türkiye between September and November 2025. Participants were recruited from among adolescents who met the study’s inclusion and exclusion criteria and agreed to participate after being invited face-to-face during their clinic visit. Detailed information about the study was provided to both adolescents and their parents/legal guardians, and those who agreed to participate were enrolled after informed consent procedures were completed. No public advertisement or external solicitation was used. Each participant completed the study measures only once. The inclusion criteria were: (1) being aged 13 to 17 years, (2) providing informed consent from the adolescent and their legal guardian, and (3) completing all assessment scales. The exclusion criteria were having a diagnosis of intellectual disability, autism spectrum disorder, psychotic disorder, or bipolar disorder. Participants’ diagnostic status and eligibility with respect to the exclusion criteria were confirmed by DSM-5-based clinical interviews conducted by child and adolescent mental health specialists and through retrospective review of hospital medical records.

A power analysis performed with G*Power 3.1.9.7 software prior to the study determined that at least 111 participants were needed to achieve a medium effect size (lpl = 0.30), 95% confidence level, and 80% power. A total of 187 adolescents meeting the specified criteria were reached; however, 5 participants who did not complete the scales were excluded, and data collection was completed with a total of 182 adolescents (N = 182).

Data Collection Tools

Sociodemographic Data Form: A form created by researchers that asks participants about their age, gender, family structure, family income, parents’ education level, academic success, peer relationships, relationship with parents, smartphone ownership, smartphone usage time, and smartphone usage purposes.

Online Grooming Risk Scale: Developed by Pasca et al. (2022), this 8-item measure was validated in Turkish by Aktu (2024). The scale consists of two dimensions, “relationship intimacy” and “secrecy,” and has a 5-point Likert-type rating. High scores indicate an increased risk of online grooming. In the current study, the Cronbach alpha’s coefficient was found to be .836.

Smartphone Addiction Scale: It is a 33-item measure developed by Kwon et al. (2013) and adapted into Turkish by Şata et al. (2016). It assesses smartphone addiction across six dimensions and features a 6-point Likert-type scale. Higher scores indicate increased levels of addiction. In the current study, the Cronbach alpha’s coefficient was determined to be .944.

General Phubbing Scale: The 15-item measure developed by Chotpitayasunondh and Douglas (2018), is the adolescent form of the scale, and its Turkish validity and reliability study was conducted by Gavcar et al. (2023). It evaluates phubbing behavior in four subdimensions: nomophobia, interpersonal conflict, self-isolation, and problem acknowledgment, and uses a 7-point Likert scale. High scores indicate increased phubbing behavior. In the current study, the Cronbach alpha’s coefficient was determined to be .934.

Online Privacy Awareness Scale: The scale was developed by Korkmaz and colleagues in 2021. It consists of 17 items and is designed as a 5-point Likert scale. The scale assesses three subfactors: attention, security, and communication and sharing. Items in the communication and sharing subfactor are reverse-coded. As the score obtained from the scale increases, the participant’s awareness of online privacy increases (Korkmaz et al., 2021). In the current study, the Cronbach alpha’s coefficient was determined to be .865.

Ethical Principles

Ethics approval was obtained from the Pamukkale University Non-Interventional Clinical Research Ethics Committee (Reference No: E-60116787-020-748243). We performed all the study procedures following the Declaration of Helsinki. Informed consent was obtained from the participants and their parents/legal guardians.

Statistical Analysis

Statistical analyses were conducted using IBM SPSS Statistics for Mac, version 27.0 (IBM Corp., Armonk, NY). Descriptive statistics for continuous variables are presented as mean ± standard deviation, while categorical data are presented as frequency and percentage. Pearson correlation analyses were performed to determine relationships between variables. Hierarchical regression analyses were conducted to identify predictors of online grooming risk. In addition, moderation analyses were performed using the PROCESS Macro (Model 1) developed by Hayes to test the hypotheses (Hayes, 2018; Preacher & Hayes, 2008). A 95% confidence interval and a significance level of p < .05 were used to evaluate the results.

Prior to the main analyses, the normality of the data distribution was assessed using skewness and kurtosis coefficients, complemented by visual inspection of Normal Q-Q plots. All variables yielded skewness and kurtosis values within the acceptable range of −2 to +2, indicating that the data met the assumption of normal distribution. Additionally, assumptions of linearity, homoscedasticity, independence of errors, and multicollinearity were examined. Visual inspection of scatterplots and residual plots supported linearity and homoscedasticity. Durbin–Watson values indicated independence of errors, and variance inflation factor (VIF) values were below commonly accepted thresholds, suggesting no multicollinearity issues.

Results

In this study, data from 182 adolescents aged 13 to 17 who visited a child and adolescent psychiatry outpatient clinic were examined; the relationships between online grooming risk, smartphone addiction, phubbing, and online privacy awareness were investigated. The findings obtained from the analyses are summarized below under the following headings.

Sociodemographic and Clinical Characteristics of the Participants

The study was conducted with a total of 182 participants with a mean age of 15.12 ± 1.40 (range: 13–17). 56.0% (n = 102) of the participants were female, and 44.0% (n = 80) were male. While 17.0% of the sample (n = 31) did not have any psychiatric diagnosis, 83.0% (n = 151) had at least one psychiatric disorder diagnosis. The most common diagnoses among participants were attention deficit hyperactivity disorder (37.9%, n = 69), depressive disorder (33.0%, n = 60), and anxiety disorders (25.3%, n = 46). According to the diagnostic classification used in the analysis, 57.1% of adolescents (n = 104) were in the internalizing disorders group, while 40.1% (n = 73) were in the externalizing disorders group. In terms of digital habits, 95.6% of participants (n = 174) were found to have their own smartphone. When smartphone usage purposes were examined in order of priority, the most frequent purpose was social media use (43.4%, n = 79), followed by playing games and listening to music (11.0%, n = 20 for both). The second most frequent purpose of use was listening to music (22.5%, n = 41) and browsing the internet (18.7%, n = 34), while the third most frequent purpose of use was watching movies/videos (15.9%, n = 29) and listening to music (15.4%, n = 28). The detailed sociodemographic and clinical characteristics of the participants are presented in Table 1.

Table 1.

Sociodemographic and Clinical Characteristics of the Participants.

Variables	Mean ± SD/Range
Demographic Characteristics
Age (Years)	15.12 ± 1.40 (13–17)
	n	%
Gender
Female	102	56.0
Male	80	44.0
Family Income Status
Low	26	14.3
Middle	108	59.3
High	48	26.4
Family Structure
Intact Family	145	79.7
Fragmented Family	37	20.3
Academic and Psychosocial Factors
Academic Success
Good	51	28.0
Moderate	113	62.1
Poor	18	9.9
Peer Relationships
Good	89	48.9
Moderate	68	37.4
Poor	25	13.7
Relationship with parents
Good	73	40.1
Moderate	83	45.6
Poor	26	14.3
Smartphone Usage Habits
Personal Smartphone Ownership (Yes)	174	95.6
Daily Usage Duration
<4 hr	104	57.2
4–8 hr	63	34.6
>8 hr	15	8.2
Clinical Profile and Diagnoses
Any Psychiatric Diagnosis (Yes)	151	83.0
Individual Diagnoses*
ADHD	69	37.9
Depressive Disorder	60	33.0
Anxiety Disorders	46	25.3
Conduct Disorder	10	5.5
OCD	6	3.3
Specific Learning Disorder	2	1.1
Clinical Classification (Regression Groups)
Internalizing Disorders (Total)	104	57.1
Externalizing Disorders (Total)	73	40.1

Note. ADHD = Attention Deficit Hyperactivity Disorder; OCD = Obsessive-Compulsive Disorder.

Participants could have more than one diagnosis.

Correlation Analyses

According to the analysis results, smartphone addiction was positively correlated with online grooming risk (r = .454, p < .001) and phubbing behavior (r = .813, p < .001) and negatively correlated with online privacy awareness (r = −.277, p < .001). Positive correlations were found between online grooming risk and phubbing behavior (r = .352, p < .001), and negative correlations with online privacy awareness (r = −.369, p < .001). Finally, a negative significant relationship was found between phubbing behavior and online privacy awareness (r = −.203, p = .006). The results of the Pearson correlation analysis conducted to determine the relationships between variables are presented in Table 2.

Table 2.

Descriptive Statistics and Pearson Correlation Matrix for Study Variables.

Variables	Mean (M)	Std. Dev. (SD)	1	2	3	4
1. Online Grooming Risk Scale Scores	12.70	5.91	1	.454*	−.369*	.352*
2. Smartphone Addiction Scale Scores	89.81	32.63	.454*	1	−.277*	.813*
3. Online Privacy Awareness Scale Scores	68.24	11.21	−.369*	−.277*	1	−.203*
4. General Phubbing Scale Scores	45.08	22.12	.352*	.813*	−.203*	1

p < .01 (2-tailed), N = 182. Boldfaced values indicates statistically significant values.

Hierarchical Regression Analyses

A three-stage hierarchical regression analysis was conducted to identify factors predicting online grooming risk. In the first step of the analysis, it was determined that the sociodemographic and clinical control variables included in the model (age, gender, internalizing disorders, externalizing disorders, parental adjustment, peer relationships, smartphone ownership, and smartphone usage time) explained 9.5% of the total variance, and the model as a whole was statistically significant (F [8,173] = 2.28, p = .024). Within this block, smartphone usage time was found to be a significant positive predictor of online grooming risk (β = .196, p < .010). In the second step, the smartphone addiction variable was added to the model. At this stage, a significant increase of 13.4% in the explained variance was recorded (ΔR² = .134, p < .001), and it was determined that smartphone addiction positively and significantly predicted the risk (β = .503, p < .001). After smartphone addiction was entered into the model, smartphone usage time was no longer a significant predictor (p = .205). In the third and final step, the variables of online privacy awareness and phubbing behavior were included in the model. It was found that the final model explained 29.8% of the total variance (Adjusted R² = .252) and was statistically significant (F [11,170] = 6.55, p < .001). When examining the full model, smartphone addiction was found to be the strongest predictor continuing to increase the risk of online grooming (β = .452, p < .001), while online privacy awareness was found to be a protective factor that significantly reduced the risk (β = −.276, p < .001). On the other hand, no significant predictive effect of phubbing behavior on the risk of online grooming was found in the regression model (β = −.006, p = .956). Neither smartphone ownership nor smartphone usage time remained significant in the final model. The results of the hierarchical regression analyses are presented in Table 3.

Table 3.

Hierarchical Regression Analysis Predicting Online Grooming Risk.

Predictors	B	SE	β	t	p
Step 1
Age	.414	.324	.098	1.279	.203
Gender (Female)	1.009	.994	.085	1.015	.312
Internalizing Disorders	.142	.964	.012	.147	.883
Externalizing Disorders	.026	1.003	.002	.026	.979
Peer Relationship (Good)	−.887	.920	−.075	−.965	.336
Relationship with parents (Good)	−.714	.926	−.059	−.772	.441
Smartphone ownership	.582	2.146	.020	.271	.787
Smartphone usage time	.976	.373	.196	2.615	.010
Step 2
Smartphone Addiction Scale Scores	.091	.017	.503	5.473	<.001
Step 3
Smartphone Addiction Scale Scores	.082	.022	.452	3.715	<.001
Online Privacy Awareness Scale Scores	−.145	.036	−.276	−4.047	<.001
General Phubbing Scale Scores	−.002	.031	−.006	−.056	.956

Note. Dependent variable: Online Grooming Risk. N = 182. Step 1: R² = .095, p = .024; Step 2: ΔR² = .134, p < .001; Step 3: ΔR² = .068, p < .001. Total Model: F(11,170) = 6.55, p < .001, R² = .298, adjusted R² = .252. Boldfaced values indicates statistically significant values.

Regression assumptions were checked prior to analysis. When examining the multicollinearity issue, it was observed that the VIF values ranged between 1.08 and 3.58 (VIF < 5) and that the tolerance values remained within appropriate limits. Furthermore, the Durbin-Watson coefficient of 1.991 confirmed that there was no autocorrelation among the errors.

Moderation Analyses

The moderating role of online privacy awareness in the relationship between smartphone addiction and the risk of online grooming was tested using Model 1. The analysis results showed that the model explained 29.9% of the total variance (F [3,178] = 25.31, p < .001) and that the interaction term between Smartphone Addiction Scale (SAS) and Online Privacy Awareness Scale (OPAS) was significant (β = −.0027, 95% CI [−0.0048, −0.0007], p = .0077). According to the conditional effect analysis results, as the OPAS level increases, the predictive power of smartphone addiction on online grooming risk decreases (Low OPAS: β = .10, p < .001; High OPAS: β = .03, p = .019). The Johnson-Neyman analysis results revealed that when the OPAS score exceeded the threshold of 12.75, the effect of smartphone addiction on the risk of online grooming became statistically insignificant (p > .05). Information regarding the moderation analysis is presented in Table 4. Additionally, an interaction graph related to the moderation effect is presented in Figure 1.

Table 4.

Moderating Role of Online Privacy Awareness in the Relationship Between Smartphone Addiction and Online Grooming Risk.

Variables	B	SE	t	p	95% CI
SAS	0.0695	0.0118	5.8705	.000	[0.0462, 0.0929]
OPAS	−0.1450	0.0345	−4.1959	.000	[−0.2131, −0.0768]
Interaction (SAS × OPAS)	−0.0027	0.0010	−2.6976	.0077	[−0.0048, −0.0007]
Model Summary	R ²	F	df	p	ΔR²
	.2991	25.3187	(3, 178)	<.001	.0287

Note. All continuous variables were mean-centered. The interaction term (SAS × OPAS) significantly contributes to the model (ΔR² = .0287, p = .0077), confirming the moderation. Johnson-Neyman analysis indicated that the effect of addiction on risk becomes non-significant (p > .05) when privacy awareness scores exceed 12.75. SAS = Smartphone Addiction Scale; OPAS = Online Privacy Awareness Scale. Boldfaced values indicates statistically significant values.

Figure 1.

Simple slopes for the interaction between smartphone addiction scale scores and online privacy awareness scores on online grooming risk scores.

Discussion

Principal Findings

This study examined the relationship between smartphone addiction and online grooming risk among adolescents within a comprehensive model, considering the variables of phubbing behavior and online privacy awareness. The findings revealed that smartphone addiction directly predicted online grooming risk. It was also found that higher levels of online privacy awareness functioned as a protective factor that significantly reduced online grooming risk. In addition, online privacy awareness moderated the relationship between smartphone addiction and online grooming risk, such that the association was weaker among adolescents with higher privacy awareness. Although phubbing behavior showed a strong relationship with smartphone addiction, it did not emerge as an independent predictor of online grooming risk. This suggests that phubbing behavior creates a contextual ground involving adolescents’ orientation toward digital environments and a decrease in protective social interactions, rather than being a behavior that directly increases online grooming risk. Overall, the findings indicate that the risks of exploitation adolescents may encounter in digital environments are closely related not only to usage intensity but also to individual awareness processes such as online privacy awareness.

Smartphone Addiction as a Risk Factor for Online Grooming

Our study found that smartphone addiction significantly and positively predicts the risk of online grooming among adolescents. This finding suggests that adolescents’ increasing and persistent online presence in digital environments may create a breeding ground for exposure to online grooming attempts. Indeed, a population-based study conducted in Germany reported that a significant proportion of adolescents reported encountering online sexually suggestive requests and that such experiences are common among adolescents (Sklenarova et al., 2018). These findings show that the risk of online grooming is not exceptional but can be an important part of adolescents’ online experiences. In this context, smartphone addiction represents not only the frequency of digital tool use, but also a pattern of use that enables adolescents to have constant, portable, and often adult-supervision-free access to online environments. This suggests that risks in the digital environment can be addressed within the framework of Routine Activity Theory (Cohen & Felson, 1979). According to the adaptation of this theory to the cyber world, the fundamental factor determining the risk of victimization is the convergence of a motivated aggressor (abuser) and a suitable target (adolescent) in the digital realm, in the absence of a capable protector (parental supervision) (Herrero, 2022). This theoretical approach makes it possible to understand why adolescents’ access patterns in digital environments and the intensity of their online interactions play a critical role in the emergence of online grooming risks.

Large-scale European data reveals that a significant proportion of children and adolescents access the internet via mobile devices, can communicate with strangers online, and that some of them take these online contacts offline (Livingstone et al., 2011). In this type of digital context, adolescents with smartphone addiction spending longer periods of time online in an unmonitored manner may increase their likelihood of encountering potentially exploitative online interactions. Supporting this interpretation, a recent study with adolescents showed that indicators of compulsive smartphone use, such as “inability to put down the phone,” strongly predicted the risk of online grooming (Melo Laclote et al., 2025). This finding indicates that the effect of smartphone addiction on online grooming risk may operate through increased digital exposure and accessibility rather than indirect psychological processes. Therefore, the findings of the present study suggest that smartphone addiction can be considered a direct exposure factor that increases the risk of online grooming for adolescents, which is theoretically and empirically meaningful.

The Protective Role of Online Privacy Awareness

In the present study, it was found that online privacy awareness significantly reduced adolescents’ risk of online grooming and played a protective role in the relationship between smartphone addiction and online grooming risk. This finding shows that online risks are closely related not only to the level of exposure to digital environments but also to how adolescents perceive and manage the situations they encounter in these environments. Indeed, large-scale studies conducted across Europe have revealed that not all children who encounter online risks report negative experiences; children’s responses to such situations, their level of knowledge about online safety, and the way they share their experiences with others are related to the level of harm reported (Livingstone et al., 2011). In this context, online privacy awareness can serve a protective function for adolescents against risky online interactions through processes such as being aware of the limits of sharing personal information, exercising caution in online communication, and recognizing potentially exploitative interactions. Findings on preventive interventions also support this interpretation. Short-term educational interventions targeting adolescents have been shown to reduce risky responses to sexually explicit online interactions by increasing knowledge and awareness about grooming (Calvete et al., 2022). Similarly, qualitative studies discuss that young people may experience significant uncertainty in recognizing and assessing their online interactions as risky; this situation can lead to grooming behaviors often being perceived as normal forms of online communication (Wood & Wheatcroft, 2020). Furthermore, studies focusing on parental and child awareness reveal that the lack of clarity surrounding personal information sharing and privacy boundaries in online environments is among the contextual factors that can increase vulnerability (Dorasamy et al., 2021).

It has been observed that the effect of smartphone addiction on online grooming risk is significantly weakened in adolescents with high online privacy awareness. These results indicate that online privacy awareness should be approached as an active individual awareness process that limits risks in digital environments, rather than a passive level of knowledge. Therefore, the current findings show that psycho-educational approaches aimed at strengthening adolescents’ online privacy awareness, in addition to limiting digital use, are also critical in interventions aimed at preventing online grooming risk.

Phubbing Behavior in the Proposed Model

Our study found that phubbing behavior showed significant relationships with online grooming risk at the bivariate level; however, when smartphone addiction and online privacy awareness were included in the model, it lost its significance as a direct predictor. This finding indicates that phubbing behavior should be considered a contextual behavior pattern that influences adolescents’ orientation toward digital environments and the quality of face-to-face social interactions, rather than an independent risk factor that directly increases the risk of online grooming. Indeed, phubbing is defined as a behavior that arises primarily from the prioritization of smartphone use in social interaction contexts and is closely related to problematic smartphone use (Karadağ et al., 2015). Therefore, since phubbing behavior largely reflects a usage pattern that overlaps with smartphone addiction, it is expected that when the effect of smartphone addiction is controlled for in multivariate models, phubbing behavior will not contribute uniquely to the risk of online grooming. However, the indirect and contextual effects of phubbing behavior should not be overlooked. Previous studies have shown that phubbing can negatively affect the quality of face-to-face social interactions and lead to disruptions in interpersonal communication (David & Roberts, 2017). Such interactive disconnections may limit adolescents’ opportunities to share their online experiences and receive social feedback, thereby creating conditions where online grooming risks are less likely to be noticed. In this context, phubbing behavior may create a contextual ground that indirectly affects adolescents’ processes of evaluating and sharing their online experiences, rather than being a direct determinant of online grooming risk.

Limitations and Future Directions

The findings of this study should be evaluated within certain methodological limitations. First, due to the cross-sectional design of the study, the findings do not allow for establishing causal relationships but only indicate correlational patterns between variables. Future longitudinal studies would be useful to examine in more detail the temporal dimension of the relationships between smartphone addiction, online privacy awareness, and online grooming risk. Second, all measures used in the study are based on self-report instruments. This implies that factors such as social desirability tendencies and recall bias may have influenced the findings, especially when sensitive content is involved. However, ensuring participant confidentiality and relying on voluntary participation may have contributed to reducing such biases. The fact that the study was conducted with a clinical sample limits the direct generalization of the findings to adolescents in the general population. The findings should also be interpreted in light of the demographic and contextual characteristics of the sample. The study was conducted with adolescents aged 13 to 17 years recruited from a child and adolescent psychiatry outpatient clinic in Türkiye and included both female and male participants. Therefore, the results may reflect the developmental, clinical, and sociocultural characteristics of this specific population. In addition, diversity-related characteristics such as ethnicity, migration background, sexual orientation, and gender identity were not assessed. Future studies should examine these associations in more diverse and representative samples. Finally, this study assessed online grooming through risk and vulnerability indicators rather than direct experiences. This may limit the extent to which the findings reflect actual grooming experiences. Future studies combining different data sources may contribute to a more comprehensive understanding of online grooming risks.

Conclusion

This study examined the relationship between smartphone addiction and online grooming risk among adolescents in the context of online privacy awareness and phubbing behavior, offering a comprehensive perspective on the multidimensional nature of digital risks. The findings showed that smartphone addiction directly predicted online grooming risk. In addition, online privacy awareness emerged as a protective factor that weakened the association between smartphone addiction and online grooming risk, highlighting its potential importance as a target for preventive intervention. However, the fact that phubbing behavior did not emerge as an independent predictor in multivariate models suggests that this behavior may be part of a broader digital use pattern rather than a distinct risk factor. Overall, these findings emphasize the importance of considering both digital exposure and individual awareness processes in understanding adolescents’ vulnerability to online grooming risk. Future longitudinal and multi-source studies may help clarify the temporal dynamics of these relationships and inform more effective prevention strategies.

Footnotes

Acknowledgements

We thank all participants and their families who participated in the study. The authors acknowledge the use of ChatGPT 5.4 for grammatical corrections and language editing during the drafting of this manuscript.

ORCID iD

Erdal Görkem Gavcar

Ethical Considerations

Ethics approval was obtained from the Pamukkale University Non-Interventional Clinical Research Ethics Committee (Reference No: E-60116787-020-748243). We performed all the study procedures following the Declaration of Helsinki. Informed consents were obtained from the participants and their parents/legal guardians.

Authors’ Contributions

Conceptualization: E.G.G, A.B, B.K.B

Methodology: E.G.G, A.B, B.K.B

Resources: E.G.G, A.B, B.K.B; Ö.B; M.A.T

Investigation: E.G.G

Data Curation: E.G.G

Formal analysis: E.G.G

Validation: E.G.G, A.B, B.K.B; Ö.B; M.A.T

Writing–Original Draft: E.G.G

Writing–Review & Editing: E.G.G, A.B, B.K.B; Ö.B; M.A.T

Supervision: A.B, B.K.B; Ö.B; M.A.T

All authors interpreted the data, contributed to writing and revision of the manuscript, and approved the final version to be published.

Funding

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

Declaration of Conflicting Interests

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

Data Availability Statement

The data supporting this study’s findings are available on request from the corresponding author. The data are not publicly available to preserve the privacy of research participants.*

Author Biographies

Erdal Görkem Gavcar, MD, completed his medical education at Pamukkale University (in Denizli Province, Türkiye) and his specialization training in Child and Adolescent Psychiatry at the Pamukkale University. He currently works as an Assistant Professor in the Department of Child and Adolescent Psychiatry at Pamukkale University. The author has articles in internationally published journals.

Ahmet Büber, MD, completed his medical education at Suleyman Demirel University (in Isparta Province, Türkiye) and his specialization training in Child and Adolescent Psychiatry at Pamukkale University. He currently works as an Associate Professor in the Department of Child and Adolescent Psychiatry at Pamukkale University. The author has articles in internationally published journals.

Bürge Kabukçu Başay, MD, completed her medical education at Hacettepe University (in Ankara Province, Türkiye) and her specialization training in Child and Adolescent Psychiatry at Ege University (in İzmir Province, Türkiye). She currently works as an Associate Professor in the Department of Child and Adolescent Psychiatry at Pamukkale University. The author has articles in internationally published journals.

Ömer Başay, MD, completed his medical education at Ondokuz Mayıs University (in Samsun Province, Türkiye) and his specialization training in Child and Adolescent Psychiatry at Ege University (in İzmir Province, Türkiye). He currently works as an Associate Professor in the Department of Child and Adolescent Psychiatry at Pamukkale University. The author has articles in internationally published journals.

Merve Aktaş Terzioğlu, MD, completed her medical education at Akdeniz University (in Antalya Province, Türkiye) and her specialization training in Child and Adolescent Psychiatry at Pamukkale University. She currently works as an Associate Professor in the Department of Child and Adolescent Psychiatry at Pamukkale University. The author has articles in internationally published journals.

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