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Lucia Monacis Department of Humanities, University of Foggia, Foggia, Italy

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Valeria de Palo Department of Humanities, University of Foggia, Foggia, Italy

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Mark D. Griffiths International Gaming Research Unit, Division of Psychology, Nottingham Trent University, Nottingham, UK

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Maria Sinatra Department of Educational Sciences, Psychology, Communication, University of Bari Aldo Moro, Bari, Italy

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Aim

Research into social networking addiction has greatly increased over the last decade. However, the number of validated instruments assessing addiction to social networking sites (SNSs) remains few, and none have been validated in the Italian language. Consequently, this study tested the psychometric properties of the Italian version of the Bergen Social Media Addiction Scale (BSMAS), as well as providing empirical data concerning the relationship between attachment styles and SNS addiction.

Methods

A total of 769 participants were recruited to this study. Confirmatory factor analysis (CFA) and multigroup analyses were applied to assess construct validity of the Italian version of the BSMAS. Reliability analyses comprised the average variance extracted, the standard error of measurement, and the factor determinacy coefficient.

Results

Indices obtained from the CFA showed the Italian version of the BSMAS to have an excellent fit of the model to the data, thus confirming the single-factor structure of the instrument. Measurement invariance was established at configural, metric, and strict invariances across age groups, and at configural and metric levels across gender groups. Internal consistency was supported by several indicators. In addition, the theoretical associations between SNS addiction and attachment styles were generally supported.

Conclusion

This study provides evidence that the Italian version of the BSMAS is a psychometrically robust tool that can be used in future Italian research into social networking addiction.

Abstract

Aim

Research into social networking addiction has greatly increased over the last decade. However, the number of validated instruments assessing addiction to social networking sites (SNSs) remains few, and none have been validated in the Italian language. Consequently, this study tested the psychometric properties of the Italian version of the Bergen Social Media Addiction Scale (BSMAS), as well as providing empirical data concerning the relationship between attachment styles and SNS addiction.

Methods

A total of 769 participants were recruited to this study. Confirmatory factor analysis (CFA) and multigroup analyses were applied to assess construct validity of the Italian version of the BSMAS. Reliability analyses comprised the average variance extracted, the standard error of measurement, and the factor determinacy coefficient.

Results

Indices obtained from the CFA showed the Italian version of the BSMAS to have an excellent fit of the model to the data, thus confirming the single-factor structure of the instrument. Measurement invariance was established at configural, metric, and strict invariances across age groups, and at configural and metric levels across gender groups. Internal consistency was supported by several indicators. In addition, the theoretical associations between SNS addiction and attachment styles were generally supported.

Conclusion

This study provides evidence that the Italian version of the BSMAS is a psychometrically robust tool that can be used in future Italian research into social networking addiction.

Introduction

Over the last decade, social media use has become increasingly prevalent in daily activities. However, when people spend significant amounts of time online, Internet-related activities can lose their functional properties leading to negative outcomes including problematic and addictive behaviors (Griffiths, 2000; LaRose, Kim, & Peng, 2010). Among the various lines of research examining online addictive behaviors, a more recent concern has focused on addiction to social networking sites (SNSs).

SNSs are virtual communities where users can create individual public profiles, interact with real-life friends, and meet other people with similar interests to maintain both online and offline relationships (Griffiths, 2012; Kuss & Griffiths, 2011a,2011b). However, on the basis of a case study in which the use of SNSs had interfered significantly in the life of a young adult female who had developed anxiety and insomnia symptoms, Karaiskos, Tzavellas, Balta, and Paparrigopoulos (2010) conceptualized SNS addiction as an Internet spectrum addiction disorder. Other early empirical evidence was provided by Pelling and White (2009) who referred that self-identity and belongingness predicted SNS addiction. A study by Wilson, Fornasier, and White (2010) reported that high extraversion and low conscientiousness were the predictors of SNS-addictive tendencies. In light of Young’s (1999) identification of five types of Internet addictions (i.e., computer addiction, information overload, net compulsions, cybersexual addiction, and cyber-relationship addiction), Kuss and Griffiths (2011b) argued that the pathological use of SNSs seemed to fall in the fifth category, although Griffiths (2000) had criticized this typology explaining that most of these subtypes involve individuals having addictions on the Internet rather than to it.

Starting from a biopsychological perspective which foresaw similarities between substance-related addictions and behavioral addictions, Griffiths’ (2005) components model of addiction comprised six criteria that could be also applied to SNS addiction (Griffiths, 2005; Kuss & Griffiths, 2011b), namely mood modification (i.e., engagement in SNSs leading to a favorable change in emotional states), salience (i.e., behavioral, cognitive, and emotional preoccupation with the SNS usage), tolerance (i.e., ever increasing use of SNSs over time), withdrawal symptoms (i.e., experiencing unpleasant physical and emotional symptoms when SNS use is restricted or stopped), conflict (i.e., interpersonal and intrapsychic problems ensuing because of SNS usage), and relapse (i.e., addicts quickly reverting back in their excessive SNS usage after a period of abstinence). However, some criticism has been raised to the conceptualization of Internet-related disorders which has emphasized how everyday life behaviors tend to be too easily overpathologized and considered as addictions. Indeed, Billieux, Schimmenti, Khazaal, Maurage, and Heeren (2015) have suggested a shift from a mere criteria-based approach toward an approach focusing on the psychological processes involved.

Along with the operationalization of the core components of addiction, research interest has primarily focused on the development and validation of psychometrically robust scales, such as the six-item Bergen Facebook Addiction Scale (BFAS; Andreassen, Torsheim, Brunborg, & Pallesen, 2012) and the Facebook Intrusion Questionnaire (Elphinston & Noller, 2011). However, there has been criticism of these instruments particularly because they examine the problematic use of one specific commercial SNS (i.e., Facebook), rather than the activity itself (i.e., social networking) (e.g., Griffiths, 2012). For instance, the BFAS does not differentiate between potential addictions to gaming activity on Facebook (e.g., playing Farmville), gambling via Facebook, or constantly messaging Facebook friends. Griffiths (2013) has suggested using more reliable and valid addiction scales that focus upon social networking, so that current gaps in empirical knowledge can be more robustly developed.

Another line of investigation has attempted to understand the psychosocial factors influencing SNS addiction. Empirical research has reported strong relationships between SNS addiction and other forms of technological and behavioral addiction, such as Internet gaming addiction (Andreassen, Billieux, et al., 2016) and Internet addiction (Monacis, de Palo, Griffiths, & Sinatra, 2016; Wegmann, Stodt, & Brand, 2015), as well as between SNS addiction and interpersonal factors, such as demographic characteristics (Andreassen, 2015; Andreassen, Billieux, et al., 2016; Kuss & Griffiths, 2011a,2011b), personality traits based on the five-factor model (Andreassen et al., 2013; Wilson et al., 2010), impulsivity (Wu, Cheung, Ku, & Hung, 2013), narcissism (Andreassen, Pallesen, & Griffiths, 2016; Buffardi & Campbell, 2008; Mehdizadeh, 2010), and identity styles (Monacis, de Palo, Griffiths, & Sinatra, 2017; Sinatra, de Palo, Contini, & Volpicella, 2016).

A further important but understudied element regarding the association between personality and individual differences and predicting patterns of engagement in SNS addictions has been attachment theory (Mikulincer & Shaver, 2007). The theory is based on dispositional differences in the functioning of the attachment system, considered as a behavioral regulatory system that mediates close relationships (i.e., with parents and romantic partners), reflects individuals’ characteristic cognitions and emotions, and predicts different ways of interacting with acquaintances and strangers. Empirical evidence has supported the predictive role of attachment in Facebook and online social networking use, demonstrating that attachment style contributes to the conceptual integration of online social networks with personality characteristics (Rom & Alfasi, 2014; Yaakobi & Goldenberg, 2014). Research has shown that securely attached individuals have larger social networks and more social ties with others (Jenkins-Guarnieri, Wright, & Hudiburgh, 2012). Anxiously attached individuals use Facebook more frequently and are constantly concerned about how they are perceived by others on Facebook (Lin, 2015, 2016). High attachment avoidance is associated with less interest in Facebook and its use (Oldmeadow, Quinn, & Kowert, 2013). In addition (and given this study examines Italian participants), only one Italian study has investigated the relationship between Internet addiction and attachment styles suggesting a prevalent role is played by those with an anxious attachment attitude (i.e., the Preoccupation with Relationship; Schimmenti, Passanisi, Gervasi, Manzella, & Fama, 2014).

Given the general paucity on this topic within the Italian context and in light of the aforementioned considerations, this study contributes to increasing international knowledge in the field of SNS addiction by further testing the psychometric properties of the Bergen Social Media Addiction Scale (BSMAS; Andreassen, Billieux, et al., 2016) and by providing empirical data concerning the theoretical relationship between attachment styles and SNS addiction. More specifically, it was expected that the tendency to become addicted to SNSs would be predicted by a negatively secure attachment style and a positively avoidant/anxious attachment style.

Methods

Participants and procedure

A total of 769 questionnaires were initially collected. However, 35 of them were excluded from the subsequent analyses because they were not fully completed. Therefore, the final sample comprised 734 participants (aged 16–40 years, mean age = 21.63 years, SD = 3.95) with 415 males and 319 females. Four fifths of the participants’ parents were married or had common-law partners (80.1%), and three quarters were high school graduates (72.2%). The sample was split into two age categories: adolescents (aged 16–19 years; n = 270) and young adults (aged over 20 years; n = 464). Participants were recruited from Italian schools and universities and selected on the basis of their availability through convenience sampling. Students were voluntarily invited to take part in this study by completing a self-report questionnaire, which took approximately 15 min to complete. Data collection occurred from May to July 2016. Potential order effects were controlled by presenting the scales in three randomized orders. The BSMAS was translated from English into Italian separately by the Italian authors of this study. The resulting Italian version was then back-translated into English by a native speaker to establish the comparability and to resolve any discrepancies (Harkness, Pennell, & Schoua-Glusberg, 2004).

Measures

Socio-demographics

The survey included questions concerning gender, age, parents’ relationship status, and educational level to obtain a profile of the respondents’ socio-demographic features.

BSMAS

The Italian translation of the BSMAS (Andreassen, Billieux, et al., 2016) was used to assess the experiences in the use of social media over the past year. The BSMAS contains six items reflecting core addiction elements (i.e., salience, mood modification, tolerance, withdrawal, conflict, and relapse; Griffiths, 2005). Each item deals with experiences within a time frame of 12 months and is answered on a 5-point Likert scale ranging from 1 (very rarely) to 5 (very often). Sample items include: “How often during the last year have you used social media so much that it has had a negative impact on your job/studies?” and “How often during the last year have you felt an urge to use social media more and more?” (see Appendix).

Internet Gaming Disorder Scale – Short Form (IGD9-SF)

The Italian version of the IGDS9-SF (Monacis et al., 2016; original English version by Pontes & Griffiths, 2015) assesses the severity of Internet gaming disorder (IGD) and its detrimental effects by examining both online and/or offline gaming activities occurring over a 12-month period. The scale comprises nine items corresponding to the nine core criteria of IGD defined in the American Psychiatric Association’s (2013) DSM-5 (e.g., “Do you systematically fail when trying to control or cease your gaming activity?” “Have you deceived any of your family members, therapists or others because the amount of your gaming activity?” etc.). They are answered on a 5-point Likert scale ranging from 1 (never) to 5 (very often). Higher scores indicate higher degree of gaming disorder. In this study, the scale had an excellent Cronbach’s α coefficient (.97), comparable with the values reported in other studies (Pontes & Griffiths, 2015, 2016).

Attachment Style Questionnaire (ASQ)

The Italian version of the ASQ (Fossati et al., 2003) comprises 40 items and was used to assess the five dimensions of adult attachment. These are Confidence (C; eight items, e.g., “I feel confident that other people will be there for me when I need them”), Discomfort with Closeness (DwC; 10 items, e.g., “I prefer to depend on myself rather than other people”), Need for Approval (NfA; seven items, e.g., “I wonder why people would want to be involved with me”), Preoccupation with Relationship (PwR; eight items, e.g., “I often feel left out or alone”), and Relationship as Secondary (RaS; seven items, e.g., “To ask for help is to admit that you are a failure”). Each item is rated on a 6-point Likert scale ranging from 1 (total disagree) to 6 (totally agree). The five scales are associated with two latent factors, Anxiety and Avoidance, according to Hazan and Shaver’s (1987) and Bartholomew’s (1990) conceptualizations of attachment styles. Previous findings reported adequate internal consistency and test–retest reliability (Fossati et al., 2003). In this study, Cronbach’s α values for ASQ scales ranged from .72 to .85 and were in line with Fossati et al.’s (2003) study.

Statistical analyses

Before performing the data analysis, cleaning of the data set was conducted by the inspection of cases with missing values. The univariate normality of all items of the BSMAS was checked following Kim’s (2013) standard guidelines. More specifically, “[f]or sample sizes greater than 300 […] either an absolute skew value larger than 2 or an absolute kurtosis (proper) larger than 7 may be used as reference values for determining substantial non-normality” (Kim, 2013, p. 53). In addition, the univariate outliers were identified using the graphic approach (i.e., inspection of the box plot), whereas the multivariate outliers were inspected using Mahalanobis distances and the critical value for each case based on the χ2 distribution values. As a result of these procedures, no case was removed.

First, statistical analyses involved independent-samples t-tests to verify gender and age effects on the scores of the variables taken into account. Second, data were submitted to confirmatory factor analysis (CFA) to assess the construct validity of the BSMAS, as well as to multigroup analyses across sex and age to assess the measurement invariance (MI). CFA was performed with the mean- and variance-adjusted maximum likelihood (MLMV) estimation method, as it has been demonstrated as robust with respect to non-normality (Bentler & Dudgeon, 1996). The χ2 and its degrees of freedom (df), the comparative fit index (CFI), the root mean square error of approximation (RMSEA) plus its 90% confidence interval (CI), and the standardized root mean square residuals (SRMR) were used. For χ2, test values associated with p > .05 were considered good-fitting models, even though the p value of this test is sensitive to large sample size; for CFI, values greater than or equal to 0.90 were accepted as indicators of good fit (Bentler & Bonnett, 1980). Hu and Bentler (1999) demonstrated that RMSEA is one of the most informative criteria and recommended a value close to 0.06 in conjunction with an SRMR value of 0.08 or less. Furthermore, in line with Vandenberg and Lance’s (2000) recommendations, MI across age and gender was evaluated through the following steps: (a) testing for invariance of number of factors (configural invariance); (b) testing for the equality of factor loadings (weak or metric invariance); and (c) testing for the equality of indicator intercepts (strong or scalar invariance). The classical approach based on the χ² difference (Δχ2) test was used. As this method is sensitive to the model’s complexity and large sample size, it is recommended to compare two nested models using cutoff values of ΔCFI < 0.01 and ΔRMSEA < 0.015 for metric and scalar invariances (Chen, 2007; Cheung & Rensvold, 2002). As Bollen (1989) suggested, metric invariance is an important prerequisite for meaningful cross-group comparison.

Third, the scale reliability was examined using: (a) the average variance extracted (AVE) that assesses the extent to which the items of a specific factor converge or share a high proportion of variance (Hair, Black, Babin, & Anderson, 2010), values greater than 0.50 are considered adequate; (b) the standard error of measurement (SEM) that assesses the degree to which the observed scores fluctuate as a result of the measurement errors (Morrow, Jackson, Disch, & Mood, 2011). The criterion of acceptable precision was SEM ≤ SD/2 (Wuang, Su, & Huang, 2012); and (c) the factor determinacy coefficient of the internal consistency (Tabachnick & Fidell, 2013). As noted by Brown (2003), this coefficient represents an important result of factor analysis. In particular, a high degree of determinacy indicates that “the factor score estimates could serve as suitable substitutes for the factor itself” (Brown, 2003, p. 1418). Factor score determinacy represents the correlation between the estimated and true factor scores. It ranges from 0 to 1 and describes how well the factor is measured, with 1 being the best value (Muthén & Muthén, 1998–2012). The larger the coefficient (e.g., ≥0.70, Tabachnick & Fidell, 2013), the more stable the factors, in the sense that the observed variables account for substantial variance in the factor scores, whereas low values mean the factors are poorly defined by the observed variables.

Bivariate correlations were employed to analyze the patterns of associations between social networking addiction, IGD, attachment styles, and age. A multiple regression analysis was undertaken using forward method and entering demographic variables, including age and gender, to examine the predictive associations between attachment styles and social networking addiction. A p value of .05 was set as the critical level for statistical significance. All statistical analyses were performed using Mplus 7.2 and SPSS for Windows 20.0.

Ethics

The study procedures were carried out in accordance with the Declaration of Helsinki. The investigation was approved by the research team of the Ethics Committee of the Institutional Department (December 2015). Permission to conduct the research was required from heads and deans of schools and institutions. Written informed consent was obtained from students over 18 years of age, whereas parents or legal guardians provided written consent for students aged under 18 years to participate.

Results

Independent-samples t-test

Significant gender differences emerged in BSMAS scores [t(732) = 2.18, p < .05] and age differences [t(732) = −5.94, p < .001]. More specifically, females and young adults obtained higher scores. In addition, age differences within gender groups were found: both young adult females [t(287.399) = −3.00, p < .01] and males [t(345.026) = −5.41, p < .001] obtained higher scores (Table 1).

Table 1.

Means and standard deviations for BSMAS scores in sex and age groups

N Mean SD
Total sample 734 14.20 5.89
Sex
Male 415 4.61 6.49
Female 319 13.66 4.96
Age
Adolescents 270 12.54 4.82
Young adults 464 15.16 6.24
Female sample
Adolescents 119 12.51 4.20
Young adults 193 14.12 5.17
Male sample
Adolescents 135 12.11 4.94
Young adults 240 15.31 6.64

CFA

To replicate the factor structure of the BSMAS, a CFA was conducted with the MLMV method. The fit indices of the one-factor solution were acceptable (χ2 = 66.853, df = 9, p < .001; RMSEA = 0.094, 90% CI = 0.073–0.115; CFI = 0.97; SRMR = 0.03). As the RMSEA value was high, a careful inspection of the modification indices (MIs) suggested adding a covariance path between the error terms of Items 1 and 2 (MI = 45.735). After carrying out a second CFA, the indices showed a better degree of fit (χ2 = 25.900, df = 8, p < .001; RMSEA = 0.055, 90% CI = 0.032–0.080; CFI = 0.99; SRMR = 0.02). All factor loadings were significant and ranged from 0.46 to 0.86 (Figure 1).


            Figure 1.
Figure 1.

Summary of the confirmatory factor analysis results obtained from the six items of the Bergen Social Networking Addiction Scale (BSMAS) with two error terms correlated (N = 734)

Citation: Journal of Behavioral Addictions 6, 2; 10.1556/2006.6.2017.023

MI across gender and age groups

To evaluate the generalizability of the model across males and females, adolescents and young adults, two multigroup CFAs using MLMV estimation were performed. For each analysis, an unconstrained model with factor loadings free to vary between subgroups was compared with a constrained model, in which the factor loadings were held constant across subgroups. Before conducting multigroup analyses, separate CFAs were performed for age and gender subgroups. The results indicated a good fit of the data for each subgroup. The MI of the one-factor solution was supported at all levels for age, whereas at metric level for gender (Table 2).

Table 2.

Measurement invariance of BSMAS (Italian version) by sex and age

Model χ2 df Δχ2 Δdf Sig. CFI ΔCFI RMSEA ΔRMSEA
Sex
Female 23.666 8 0.97 0.078
Male 13.652 8 0.99 0.041
Configural 36.695 16 0.99 0.059
Metric 42.840 21 6.145 5 0.292 0.99 0.00 0.053 0.006
Scalar 86.593 26 43.753 5 <0.0001 0.97 0.02 0.080 0.027
Age
Adolescents 9.844 8 0.99 0.029
Young adults 23.229 8 0.99 0.064
Configural 32.806 16 0.99 0.053
Metric 42.618 21 9.812 5 0.081 0.99 0.00 0.053 0.000
Scalar 47.331 26 4.713 5 0.452 0.99 0.00 0.047 0.006

Reliability analysis

The internal consistency for the scale was examined using Cronbach’s α and was very good (α = .88). The inter-item correlations were relatively high (i.e., ≥0.30). The extent to which the items of the specific factor converged or shared a high proportion of variance was assessed through the AVE method. The results provided an adequate value (AVE = 0.57). The SEM was calculated to assess the degree to which the observed scores fluctuated as a result of the measurement errors. As expected, the value met the criterion (SEM = 2.02 ≤ SD/2 = 2.94). Finally, the factor score determinacy coefficient was 0.95, showing an excellent degree of internal consistency.

Correlation and regression analyses

Zero-order correlations were run to assess the associations between social networking addiction, IGD, attachment styles, and age. The results showed a strong positive association between the BSMAS and IGD-SF9 scores (r = .764, p < .001), positive interrelationships between BSMAS and the four attachment styles, except for Confidence which was negatively associated with BSMAS scores (Table 3), and positive associations between BSMAS scores and age (r = .286, p < .001).

Table 3.

Bivariate correlations among BSMAS, AGE, IGD, and ASQ dimensions

Age IGD C DwC RaS NfA PwR
Social media addiction 0.286** 0.764** −0.478** 0.209** 0.359** 0.425** 0.186**

Note. IGD: Internet gaming disorder; C: Confidence; DwC: Discomfort with Closeness; RaS: Relationship as Secondary; NfA: Need for Approval; PwR: Preoccupation with Relationship.

p < .001.

Findings from standard multiple regression analysis indicated (in general) that the set of the attachment style variables significantly predicted social media addiction [F(7, 679)  = 53.704, p < .001] accounting for 35.6% of the variance (adjusted R2 = .350). The significant predictors were Age, Confidence, Relationship as Secondary, and Need for Approval. Confidence was a negative predictor of SNS addiction, whereas Relationship as Secondary, Need for Approval, and Age were positive predictors of SNS addiction (Table 4).

Table 4.

Regression analysis with standardized and unstandardized coefficients

Model Unstandardized coefficients Standardized coefficients
B SE B
(Constant) 8.472 2.319
Age 0.354 0.045 0.237***
C −2.325 0.285 −0.303***
DwC −0.253 0.272 −0.033
RaS 0.684 0.231 0.112**
NfA 1.473 0.268 0.228***
PwR 0.453 0.292 0.057

**p < .01. ***p < .001.

Discussion

The aim of this study was twofold: (a) to provide empirical evidence of the psychometric properties of the BSMAS in a sample of Italian adolescents and young adults and (b) to explore whether attachment styles predicted addictive tendencies toward the use of SNSs. To achieve the first goal, construct validity and reliability were assessed in the Italian version of the BSMAS. Overall, and consistent with previous studies (e.g., Andreassen et al., 2012, 2013; Phanasathit, Manwong, Hanprathet, Khumsri, & Yingyeun, 2015; Pontes, Andreassen, & Griffiths, 2016; Wang, Ho, Chan, & Tse, 2015), the results confirmed the one-factor solution of the instrument, given that indices obtained from confirmatory analyses showed an excellent fit of the model to the data. All items significantly loaded on the hypothesized factor, although Items 1 and 2 shared an amount of variance that was not captured by the construct. In the present authors’ opinion, this covariance can be statistically and theoretically justified. Indeed, to improve the RMSEA value, this covariance represented a statistical artifact. Furthermore, the preoccupation that dominates the behavior, which corresponds with the criterion of salience (Item 1), could be theoretically related to an increased amount of the activity needed to achieve the satisfactory effects, and which also is subsumed within the criterion of tolerance (Item 2). In terms of MI, the results of multigroup analyses provided evidence of configural, metric, and strict invariances for age groups, thus indicating that the items and the underlying construct had the same meaning among adolescents and young adults. By contrast, the equivalence across gender groups was established only at configural and metric levels. Consequently, the factor structure and the pattern of factor loadings of the construct might be considered equivalent across males and females. As for reliability, the internal consistency of the BSMAS was supported by several indicators, such as the Cronbach’s α, AVE, SEM, and factor determinacy, whose values were found to be high, in line with those reported by other studies (Andreassen et al., 2012; Andreassen, Billieux, et al., 2016; Pontes et al., 2016). Future studies should replicate these findings in cross-cultural contexts to confirm the overall construct validity and reliability of the BSMAS.

Consistent with previous findings (Andreassen, Billieux, et al., 2016; Monacis et al., 2016), this investigation confirmed the strong association between SNS addiction and IGD. With regard to the theoretical associations between social SNS addiction and attachment styles, similarly to previous research the findings of this study generally supported the aforementioned hypotheses regarding the causal relationship between the constructs. More specifically, secure attachment orientation negatively predicted SNS addiction. Individuals characterized by high self-esteem, enjoyment in intimate relationships and in sharing feelings with others (Bowlby, 1969/1982, 1973, 1980), may be lower at risk of becoming addicted to social networking. The negative direction of the path suggests that secure attachment is a protective factor against SNS addiction, thus contrasting with some studies that have reported a positive relationship between secure attachment orientation and Facebook use. This association has been explained by the function of Facebook itself to maintain established, trustworthy friendships and to increase interpersonal competency (e.g., Jenkins-Guarnieri et al., 2012; Oldmeadow et al., 2013; Pempek, Yermolayeva, & Calvert, 2009). Indeed, as securely attached individuals use Facebook in a positive way and enjoy when their profile is viewed by important others, they may tend to use SNSs as tools to increase their sense of social belonging and connection. This is why they may be able to manage SNS use without developing addictive tendencies.

Moreover, and consistently with other findings (Hart, Nailling, Bizer, & Collins, 2015; Oldmeadow et al., 2013), the expected positive association between Need for Approval, referred to anxious style, and social networking addiction was found. Individuals characterized by this style manifest excessive desires and efforts for acceptance, and therefore, depend upon others. Consequently, they are more likely to engage in using SNSs for significant amounts of time to obtain approval and feedback, thus facilitating potentially addictive use. Consequently, these individuals frequently check online comments posted by others because they are motivated to seek out a positive feedback.

In contrast to some findings showing that avoidant attachment predicts restrained activities when using Facebook, given that avoidant individuals are more introverted and less concerned with socializing and meeting new people (Hart et al., 2015; Oldmeadow et al., 2013), the results of this study yielded a positive relationship between “Relationship as Secondary,” referred to the “avoidance” dimension, and SNS addiction. This association may be explained by the fact that these individuals tend to satisfy their need of social belonging using the online format, which affords such a dismissive approach to close relationships by maintaining a “safe” distance from others. It was also found that females were more addicted than males. This result supports Andreassen, Pallesen, et al.’s (2016), Barker’s (2009), and Durkee et al.’s (2012) studies, while for age, the result is not in line. In this study, young adults emerged as being more addicted than adolescents. Regression analyses also confirmed that age was a positive predictor of SNS addiction.

Taken as a whole, this study demonstrated that the testing of the Italian version of BSMAS resulted in a psychometrically robust instrument. This study also examined the relationship between SNS addiction and attachment style, because it has been argued that attachment style is “a better candidate to explain some aspects of social media engagement” (Hart et al., 2015, p. 34) than other previously explored variables. This study is not without its limitations. As participants were sampled on the basis of self-selected convenience sampling strategy, a more representative sample of the population is required to generalize the findings. This study is also limited by the use of a self-report methodology; therefore, the data were subject to well-known associated biases (most notably social desirability and recall biases). To consider the instrument as a valid diagnostic tool, future research should assess the optimal cutoff point based on clinical or empirical data. Moreover, the cross-sectional design of this study did not allow to control the effects of external variables that could have affected the associations. Finally, longitudinal research is needed to better explore the relationship between age and SNS addiction.

Authors’ contribution

All authors contributed to the writing of the paper.

Conflict of interest

The authors declare no conflict of interest.

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  • Andreassen, C. S. , Torsheim, T. , Brunborg, G. S. , & Pallesen, S. (2012). Development of a Facebook Addiction Scale. Psychological Reports, 110, 501517. doi:10.2466/02.09.18.PR0.110.2.501-517

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barker, V. (2009). Older adolescents’ motivations for social network site use: The influence of gender, group identity, and collective self-esteem. CyberPsychology & Behavior, 12, 209213. doi:10.1089/cpb.2008.0228

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bartholomew, K. (1990). Avoidance of intimacy: An attachment perspective. Journal of Social Personality Relationship, 7, 147178.

  • Bentler, P. M. , & Bonnett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bentler, P. M. , & Dudgeon, P. (1996). Covariance structure analysis: Statistical practice, theory, and directions. Annual Review of Psychology, 47, 563592.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Billieux, J. , Schimmenti, A. , Khazaal, Y. , Maurage, P. , & Heeren, A. (2015). Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. Journal of Behavioral Addictions, 4(3), 119123. doi:10.1556/2006.4.2015.009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley.

  • Bowlby, J. (1969/1982). Attachment and loss: Vol. 1. Attachment. New York, NY: Basic Books.

  • Bowlby, J. (1973). Attachment and loss: Vol. 2. Separation: Anxiety and anger. New York, NY: Basic Books.

  • Bowlby, J. (1980). Attachment and loss: Vol. 3. Sadness and depression. New York, NY: Basic Books.

  • Brown, T. A. (2003). Confirmatory factor analysis of the Penn State Worry Questionnaire: Multiple factors or method effects? Behaviour Research and Therapy, 41, 14111426. doi:10.1016/S0005-7967(03)00059-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buffardi, E. L. , & Campbell, W. K. (2008). Narcissism and social networking web sites. Personality and Social Psychology Bulletin, 34, 13031314. doi:10.1177/0146167208320061

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14, 464504. doi:10.1080/10705510701301834

    • Crossref
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  • Cheung, G. W. , & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233255. doi:10.1207/S15328007SEM0902_5

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  • Durkee, T. , Kaess, M. , Carli, V. , Parzer, P. , Wasserman, C. , Floderus, B. , Apter, A. , Balazs, J. , Barzilay, S. , Bobes, J. , Brunner, R. , Corcoran, P. , Cosman, D. , Cotter, P. , Despalins, R. , Graber, N. , Guillemin, F. , Haring, C. , Kahn, J. P. , Mandelli, L. , Marusic, D. , Mészáros, G. , Musa, G. J. , Postuvan, V. , Resch, F. , Saiz, P. A. , Sisask, M. , Varnik, A. , Sarchiapone, M. , Hoven, C. W. , & Wasserman, D. (2012). Prevalence of pathological internet use among adolescents in Europe: Demographic and social factors. Addiction, 107(12), 22102222. doi:10.1111/j.1360-0443.2012.03946.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elphinston, R. A. , & Noller, P. (2011). Time to face it! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychology, Behavior, and Social Networking, 14, 631635. doi:10.1089/cyber.2010.0318

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fossati, A. , Fenney, J. A. , Donati, D. , Domini, N. , Novella, L. , Bagnato, M. , Acquarini, E. , & Maffei, C. (2003). On the dimensionality of the Attachment Style Questionnaire in Italian clinical and nonclinical participants. Journal of Social Personal Relationships, 20(1), 5079. doi:10.1177/0265407503020001187

    • Search Google Scholar
    • Export Citation
  • Griffiths, M. D. (2000). Internet addiction – Time to be taken seriously? Addiction Research, 8(5), 413418. doi:10.3109/16066350009005587

  • Griffiths, M. D. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10, 191197. doi:10.1080/14659890500114359

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffiths, M. D. (2012). Facebook addiction: Concerns, criticisms and recommendations. Psychological Reports, 110, 518520. doi:10.2466/01.07.18.PR0.110.2.518-520

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffiths, M. D. (2013). Social networking addiction: Emerging themes and issues. Journal of Addiction Research & Therapy, 4, e118. doi:10.4172/2155-6105.1000e118

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hair, J. F. , Black, W. C. , Babin, B. J. , & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.

    • Search Google Scholar
    • Export Citation
  • Harkness, J. , Pennell, B. E. , & Schoua-Glusberg, A. (2004). Survey questionnaire translation and assessment. In S. Presser, J. M. Rothgeb, M. P. Couper, J. T. Lessler, E. Martin, J. Martin, & E. Singer (Eds.), Methods for testing and evaluating survey questionnaires (pp. 453473). Hoboken, NJ: John Wiley & Sons.

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    • Search Google Scholar
    • Export Citation
  • Hart, J. , Nailling, E. , Bizer, G. Y. , & Collins, C. K. (2015). Attachment theory as a framework for explaining engagement with Facebook. Personality and Individual Differences, 77, 3340. doi:10.1016/j.paid.2014.12.016

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hazan, D. , & Shaver, P. R. (1987). Romantic love conceptualized as an attachment process. Journal of Personality and Social Psychology, 52, 511524.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, L. , & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 155. doi:10.1080/10705519909540118

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jenkins-Guarnieri, M. A. , Wright, S. L. , & Hudiburgh, L. M. (2012). The relationships among attachment style, personality traits, interpersonal competency, and Facebook use. Journal of Applied Developmental Psychology, 33, 294301. doi:10.1037/a0030946

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karaiskos, D. , Tzavellas, E. , Balta, G. , & Paparrigopoulos, T. (2010). Social network addiction: A new clinical disorder? European Psychiatry, 25, 855. doi:10.1016/S0924-9338(10)70846-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H. Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 5254. doi:10.5395/rde.2013.38.1.52

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Appendix

Di seguito sono riportate alcune domande sul tuo rapporto con i social media e sull’suo che ne fai (Facebook, Twitter, Instagram, ecc.). Per ciascuna domanda scegli la risposta che ti descrive meglio.

Durante l’ultimo anno con quale frequenza… Molto raramente Raramente Qualche volta Spesso Molto spesso
1. hai trascorso molto tempo pensando ai social media o hai programmato di usarli? 1 2 3 4 5
2. hai sentito il bisogno di usare sempre di più i social media? 1 2 3 4 5
3. hai usato i social media per dimenticare i tuoi problemi personali? 1 2 3 4 5
4. hai provato a smettere di usare i social media senza riuscirci? 1 2 3 4 5
5. sei diventato ansioso o agitato se ti è stato proibito l’uso dei social media? 1 2 3 4 5
6. hai utilizzato i social media così tanto che il loro uso ha avuto un impatto negativo sul tuo lavoro/sui tuoi studi? 1 2 3 4 5
  • American Psychiatric Association . (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Association.

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  • Andreassen, C. S. (2015). Online social network site addiction: A comprehensive review. Current Addiction Reports, 2(2), 175184. doi:10.1007/s40429-015-0056-9

    • Crossref
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    • Export Citation
  • Andreassen, C. S. , Billieux, J. , Griffiths, M. D. , Kuss, D. J. , Demetrovics, Z. , Mazzoni, E. , & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30(2), 252262. doi:10.1037/adb0000160

    • Crossref
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    • Export Citation
  • Andreassen, C. S. , Griffiths, M. D. , Gjertsen, S. R. , Krossbakken, E. , Kvam, S. , & Pallesen, S. (2013). The relationships between behavioral addictions and the five-factor model of personality. Journal of Behavioral Addictions, 2(2), 9099. doi:10.1556/JBA.2.2013.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andreassen, C. S. , Pallesen, S. , & Griffiths, M. D. (2016). The relationship between excessive online social networking, narcissism, and self-esteem: Findings from a large national survey. Addictive Behaviors, 64, 287293. doi:10.1016/j.addbeh.2016.03.006

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andreassen, C. S. , Torsheim, T. , Brunborg, G. S. , & Pallesen, S. (2012). Development of a Facebook Addiction Scale. Psychological Reports, 110, 501517. doi:10.2466/02.09.18.PR0.110.2.501-517

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barker, V. (2009). Older adolescents’ motivations for social network site use: The influence of gender, group identity, and collective self-esteem. CyberPsychology & Behavior, 12, 209213. doi:10.1089/cpb.2008.0228

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bartholomew, K. (1990). Avoidance of intimacy: An attachment perspective. Journal of Social Personality Relationship, 7, 147178.

  • Bentler, P. M. , & Bonnett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bentler, P. M. , & Dudgeon, P. (1996). Covariance structure analysis: Statistical practice, theory, and directions. Annual Review of Psychology, 47, 563592.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Billieux, J. , Schimmenti, A. , Khazaal, Y. , Maurage, P. , & Heeren, A. (2015). Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. Journal of Behavioral Addictions, 4(3), 119123. doi:10.1556/2006.4.2015.009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley.

  • Bowlby, J. (1969/1982). Attachment and loss: Vol. 1. Attachment. New York, NY: Basic Books.

  • Bowlby, J. (1973). Attachment and loss: Vol. 2. Separation: Anxiety and anger. New York, NY: Basic Books.

  • Bowlby, J. (1980). Attachment and loss: Vol. 3. Sadness and depression. New York, NY: Basic Books.

  • Brown, T. A. (2003). Confirmatory factor analysis of the Penn State Worry Questionnaire: Multiple factors or method effects? Behaviour Research and Therapy, 41, 14111426. doi:10.1016/S0005-7967(03)00059-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buffardi, E. L. , & Campbell, W. K. (2008). Narcissism and social networking web sites. Personality and Social Psychology Bulletin, 34, 13031314. doi:10.1177/0146167208320061

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14, 464504. doi:10.1080/10705510701301834

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheung, G. W. , & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233255. doi:10.1207/S15328007SEM0902_5

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durkee, T. , Kaess, M. , Carli, V. , Parzer, P. , Wasserman, C. , Floderus, B. , Apter, A. , Balazs, J. , Barzilay, S. , Bobes, J. , Brunner, R. , Corcoran, P. , Cosman, D. , Cotter, P. , Despalins, R. , Graber, N. , Guillemin, F. , Haring, C. , Kahn, J. P. , Mandelli, L. , Marusic, D. , Mészáros, G. , Musa, G. J. , Postuvan, V. , Resch, F. , Saiz, P. A. , Sisask, M. , Varnik, A. , Sarchiapone, M. , Hoven, C. W. , & Wasserman, D. (2012). Prevalence of pathological internet use among adolescents in Europe: Demographic and social factors. Addiction, 107(12), 22102222. doi:10.1111/j.1360-0443.2012.03946.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elphinston, R. A. , & Noller, P. (2011). Time to face it! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychology, Behavior, and Social Networking, 14, 631635. doi:10.1089/cyber.2010.0318

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fossati, A. , Fenney, J. A. , Donati, D. , Domini, N. , Novella, L. , Bagnato, M. , Acquarini, E. , & Maffei, C. (2003). On the dimensionality of the Attachment Style Questionnaire in Italian clinical and nonclinical participants. Journal of Social Personal Relationships, 20(1), 5079. doi:10.1177/0265407503020001187

    • Search Google Scholar
    • Export Citation
  • Griffiths, M. D. (2000). Internet addiction – Time to be taken seriously? Addiction Research, 8(5), 413418. doi:10.3109/16066350009005587

  • Griffiths, M. D. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10, 191197. doi:10.1080/14659890500114359

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffiths, M. D. (2012). Facebook addiction: Concerns, criticisms and recommendations. Psychological Reports, 110, 518520. doi:10.2466/01.07.18.PR0.110.2.518-520

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffiths, M. D. (2013). Social networking addiction: Emerging themes and issues. Journal of Addiction Research & Therapy, 4, e118. doi:10.4172/2155-6105.1000e118

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hair, J. F. , Black, W. C. , Babin, B. J. , & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.

    • Search Google Scholar
    • Export Citation
  • Harkness, J. , Pennell, B. E. , & Schoua-Glusberg, A. (2004). Survey questionnaire translation and assessment. In S. Presser, J. M. Rothgeb, M. P. Couper, J. T. Lessler, E. Martin, J. Martin, & E. Singer (Eds.), Methods for testing and evaluating survey questionnaires (pp. 453473). Hoboken, NJ: John Wiley & Sons.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hart, J. , Nailling, E. , Bizer, G. Y. , & Collins, C. K. (2015). Attachment theory as a framework for explaining engagement with Facebook. Personality and Individual Differences, 77, 3340. doi:10.1016/j.paid.2014.12.016

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hazan, D. , & Shaver, P. R. (1987). Romantic love conceptualized as an attachment process. Journal of Personality and Social Psychology, 52, 511524.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, L. , & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 155. doi:10.1080/10705519909540118

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jenkins-Guarnieri, M. A. , Wright, S. L. , & Hudiburgh, L. M. (2012). The relationships among attachment style, personality traits, interpersonal competency, and Facebook use. Journal of Applied Developmental Psychology, 33, 294301. doi:10.1037/a0030946

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karaiskos, D. , Tzavellas, E. , Balta, G. , & Paparrigopoulos, T. (2010). Social network addiction: A new clinical disorder? European Psychiatry, 25, 855. doi:10.1016/S0924-9338(10)70846-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H. Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 5254. doi:10.5395/rde.2013.38.1.52

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The author instruction is available in PDF.
Please, download the file from HERE

Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

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2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 850 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Luke CLARK (University of British Columbia, Canada)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)
  • Attila SZABÓ (ELTE Eötvös Loránd University, Hungary)
  • Róbert URBÁN (ELTE Eötvös Loránd University, Hungary)
  • Aviv M. WEINSTEIN (Ariel University, Israel)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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