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Preference for online social interactions among young people: Direct and indirect effects of emotional intelligence
Personality and Individual Differences 54 (2013) 524–529
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Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Preference for online social interactions among young people: Direct and indirect effects of emotional intelligence Silvia Casale a,⇑, Lisa Tella b, Giulia Fioravanti a a b
Department of Psychology, via di San Salvi 12, Firenze, Italy Faculty of Psychology, via della Torretta 16, Firenze, Italy
a r t i c l e
i n f o
Article history: Received 24 July 2012 Received in revised form 17 October 2012 Accepted 26 October 2012 Available online 30 November 2012 Keywords: Problematic internet use Preference for online social interactions Computer mediated communication Emotional intelligence Internet Attribute Perception
a b s t r a c t The social skills model of generalized problematic internet use predicts that individuals who perceive themselves as having low social competencies are at risk to develop a preference for online social interactions (POSI), which, in turn, might lead to compulsive use of Internet communication services. The present study aims to investigate if self-reported emotional intelligence (EI)—interpersonal and intrapersonal abilities—has an effect on POSI levels, and if this effect is mediated by the subjective importance attached to the major controllability (RC) and reduced non-verbal cues (RNVC) of computer mediated communication. 192 high schools and college students were recruited. Results from structural equation modeling show that Intrapersonal EI predicts both RC and RNVC, which, in turn, predicts the level of POSI. A partial mediation effect was found. On the other hand, Interpersonal EI is significantly associated with POSI, but this association cannot be explained by RC and RNVC. Its effect on POSI seems to be either direct or explained by variables not considered in the present study. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction The evidence that adolescents who report negative outcomes associated with Internet use are especially drawn to the social functions of the web (e.g., van den Eijnden, Meerkerk, Vermulst, Spijkerman, & Engels, 2008) has been accompanied by the development of theories that explain why widespread use of web social services become problematic for certain individuals. According to the social skill model of problematic internet use (PIU, Caplan, 2005), those who report a use of communicative services associated with negative outcomes have developed, as consequence of self-perception of social incompetence, a preference for online social interaction (POSI). POSI is defined as a ‘‘cognitive individualdifference construct characterized by beliefs that one is safer, more efficacious, more confident, and more comfortable with online interpersonal interactions and relationships than with traditional face to face (FtF) social activities’’ (Caplan, 2003, p. 629). According to Caplan (2005), POSI is already a cognitive symptom of PIU. More specifically, it is a cognitive precursor of the tendency to use the web for regulating negative mood states, the compulsive use of the web, and the presence of negative outcomes in real life. Caplan (2003, 2010) found an association between cognitive and behavioral symptoms where high levels of POSI predict compulsive use and negative outcomes. ⇑ Corresponding author. Tel.: +39 3287440547; fax: +39 0556236047. E-mail address: silvia.casale@unifi.it (S. Casale). 0191-8869/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.paid.2012.10.023
Since POSI is a key component of PIU, some studies (e.g., Caplan, 2007; Fioravanti, Dèttore, & Casale, 2012) have explored whether psychological factors associated with social skills are likely to predispose an individual to the development of POSI. Consistent results have been found with regard to the predictive role of social anxiety (Caplan, 2007) and low self-esteem (Fioravanti et al., 2012). Moreover, in accordance with Caplan’s model (2003), POSI mediated the relationship between these psychological factors and compulsive use of the web. Furthermore, Lee and Stapinski (2012) have recently shown that POSI tends to exacerbate social avoidance, which, in turn, could reinforce a tendency to return to computer mediated relationships. The peculiar characteristics of computer mediated communication (CMC) are often cited for explaining this sense of comfort and safety found online by people who perceived themselves as having low social skills. Cues filtered in theories (Walther, 1996) emphasize CMC’s diminished non-verbal cues, arguing that CMC’s unique properties represent an appealing advantage for those with interpersonal difficulties. In other words, online interpersonal interactions may offer a decreased social threat perception (Amichai-Hamburger & Furnham, 2007), which, in turn, increases the tendency to escape from FtF interaction. In fact, Lee and Stapinski (2012) have recently shown that higher social anxiety is associated with a perception of greater control during online social interactions. Fioravanti et al. (2012) found that the importance attached to controllability was associated with POSI and low selfesteem. Surprisingly, both studies failed to find a role for the
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reduction of non-verbal cues: the greater anonymity offered by the web does not explain the association between social anxiety and the reduction of the perceived consequences of negative evaluation (Lee & Stapinski, 2012), as neither is significantly correlated with low self-esteem and POSI (Fioravanti et al., 2012). However the latter study failed to distinguish between source anonymity and audiovisual anonymity. Among the social skills considered as predisposing risk factors for POSI, emotional intelligence (EI) has received less attention. This is surprising, as many studies have identified poor affect regulation abilities as important risk factors for the development of a variety of addiction related problems among young people (e.g., Parker, Taylor, Eastabrook, Schell, & Wood, 2008). Moreover, EI can be conceptualized and measured as a combination of skills (Goleman, 1998) that can protect the wellbeing of interpersonal relationships, as they are especially linked to social adaptation with the environment (Bar-On, 2005). Using different EI measures (ability scales or self-reports) and controlling for personality traits, empirical research shows that EI predicts positive relations with others (Lopes, Salovey, & Straus, 2003), social network size (Austin, Saklofske, & Egan, 2005) and mental health among young people (Davis & Humphrey, 2012). If EI is negatively associated with interpersonal problems (Ghiabi & Ali Besharat, 2011), it would also be negatively associated with POSI. As expected, the few studies that have explored the relationship between EI and Internet Addiction (IA) found a negative association (Oktan, 2011; Yanesari, Homayouni, & Gharib, 2010). Emotion-decoding capacity seems to be responsible for about 20% of the variance of IA among college students (Engelberg & Sjöberg, 2004). Among the different conceptualizations of EI, Bar-On’s view focused on those social competencies whose absence has been considered a risk factor for problematic use of communicative services, particularly POSI symptoms. Bar-On (2005) conceptualizes EI as a collection of emotional and social knowledge and skills that predict emotional and social adaptation within environments. This set of abilities enables individuals to generate, recognize, express, understand, and evaluate their own (and other people’s) emotions in order to cope with environmental demands and pressures (Van Rooy & Viswesvaran, 2004). According to Bar-On (2005), EI is composed of five dimensions: interpersonal abilities, intrapersonal abilities, adaptability, stress management, and general mood. Interpersonal and intrapersonal skills, in particular, strongly recall the types of social skills that Caplan (2005) suggests might be a protective factor for the development of POSI. Interpersonal competencies refer to the ability to cooperate with others, the ability to be attentive to and understand the feelings of others, and the ability to establish and maintain mutually satisfying relationships. Intrapersonal competencies include assertiveness, the ability to be self-reliant and self-directed in one’s thinking and actions, and the ability to realize one’s potential capacities. Among the studies about the association between EI and PIU, only one (Parker et al., 2008) adopted the emotional intelligence Bar On’s view. This study found a significant association between PIU and the Interpersonal component of EI for both younger and older adolescents. For both groups, however, the association between PIU and the Intrapersonal EI was low or not significant. However, Parker’s study focused on the association between EI and the behavioral aspects of an otherwise unspecified Internet addiction, rather than cognitive precursors—such as POSI—of a compulsive use of social communicative services. It is plausible to suppose that POSI is determined, in part, by an inability to be self-reliant and selfdirected in terms of both thinking and acting in FtF relationships. The present study aims to extend previous findings regarding the role of perceived social skills in the development of POSI, and the mechanism that might explain this association. First, it is hypothesized that self-reported emotional intelligence will have
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a negative influence on POSI levels. Then, on the basis of previous studies (Caplan, 2005; Fioravanti et al., 2012; Lee & Stapinski, 2012) regarding the mediating role of CMC’s characteristic in the relationship between low social skills and POSI, it is hypothesized that self-reported emotional intelligence will negatively affect the importance attached to controllability and the reduction of nonverbal cues, which, in turn, tend to enhance POSI levels. 2. Method 2.1. Participants and procedure A total of 192 persons between the ages of 16 and 23 were recruited in the study (M = 18.6 ± 1.9; 52% females and 43% singles). About 60% (59 M; 57 F) was recruited from a senior class in a public high school in Florence. The remaining sample (41 M; 35 F) was recruited from the Faculty of Psychology in Florence (Italy). The students were approached at the end of the lectures by a female research assistant. General information about the purposes of the study were announced to the participants. The participation was voluntary and anonymous. 2.2. Measures 2.2.1. Internet use To estimate the amount of time participants spent using different applications, the questionnaire asked students to report how many hours they spent in a typical week on: e-mail, searching for information, downloading, shopping, public chat rooms, discussion forums, instant messages, and blogging. Participants were asked to ignore study-related use in their estimations. This specification was considered necessary because students may spend an excessive amount of time online in order to complete an assignment. 2.2.2. Emotional intelligence Emotional intelligence was measured through the Italian version (Franco & Tappatà, 2009) of the 133 item Emotion Quotient Inventory (EQ-I; Bar-On, 1997, 2002). The EQ-I is a self-report measure of socially and emotionally intelligent behavior. Items are rated on a 5 point Likert scale, ranging from ‘‘very seldom or not true of me’’ (1) to ‘‘very often true of me or true of me’’ (5). Higher scores indicate a higher level of emotional intelligence. Five composite scale scores can be calculated. However, for the purposes of the present study, only the Intrapersonal Competencies scale and the Interpersonal Competencies scale were calculated. The Intrapersonal skills scale measures the awareness of one’s own emotions, strengths, and weaknesses, and the participants’ ability to express their own feelings and thoughts. Interpersonal skills tied to social consciousness and interpersonal relationships measure the extent to which users know how to recognize the emotions and feelings of others, and their ability to establish and maintain cooperative and satisfying relationships. In the current study, the Cronbach’s Alpha was a = .93 and a = .86 respectively for the Intrapersonal skills scale and the Interpersonal skills scale. 2.2.3. POSI The preference for online social interaction was measured through three items based on a measure that was developed by Caplan (2010) and adapted for (and used) on Italian students (Fioravanti et al., 2012). Participants rated their agreement on a scale ranging from 1 = strongly disagree to 5 = strongly agree. A sample item is: ‘‘Online social interaction is more comfortable for me than face-to-face interaction.’’ In the current study, the POSI scale was reliable (a = .81).
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2.2.4. Perceived relevance of CMC attributes Perceived relevance of reduced non-verbal cues (RNVC) and perceived relevance of controllability (RC) were measured by five items adapted from Schouten, Valkenburg, and Peter (2007). Respondents were asked to indicate on a Likert scale (from 1 = very unimportant to 5 = very important) how relevant reduced non verbal cues and controllability were during CMC. A sample item for perceived relevance of RNVC is: ‘‘others cannot hear how my voice sounds’’. A sample item for perceived RC is: ‘‘I have time to think about how I say something’’. In the current study, Cronbach’s alphas were approximately equal to those of the original version (for RNVC: a = 0.72 in Schouten et al. and a = 0.79 in the present study; for RC: a = 0.87 in both studies). 2.3. Statistical analysis Structural Equation Modeling (SEM) was performed to test the hypothesized effects of EI on POSI through the perceived relevance of CMC’s characteristics. SEM was conducted using LISREL 8.8 with the Robust Maximum Likelihood (RML) estimation method (Jöreskog & Sörbom, 2006). The following profile of goodness of fit indices was considered: the v2 (and its degrees of freedom and p-value), the Standardized Root Mean square Residual (SRMR- Jöreskog & Sörbom, 1993) ‘‘close to’’ 0.09 or lower and the Comparative Fit Index (CFI- Bentler, 1995) ‘‘close to’’ 0.95 or higher (Hu & Bentler, 1999) and the Root Mean Square Error of Approximation (RMSEA- Steiger, 1990) less than 0.08 (Browne & Cudeck, 1993). Because the v2 is sensitive to sample size, Kline (2005) suggested that a model demonstrates reasonable fit if the statistic adjusted by its degrees of freedom v2/df does not exceed 3. 3. Results
4. Discussion
There was no statistically significant difference in the proportion of females and males between the high school group (G1) and the university students group, G2 (v2 = 0.175, p = .676). No statistically significant differences were found between the two groups neither in the predictors’ levels (Interpersonal skills means: G1 = 110.10 ± 13.13; G2 = 110.12 ± 12.70; F(1, 190) = 0.001, p = .99, g2 = .00; Intrapersonal skills means: G1 = 134.88 ± 20.18; G2 = 140.81 ± 21.26; F(1, 190) = 3.790, p = .06, g2 = .01) or in the criterion variable (POSI means: G1 = 2.59 ± 1.27; G2 = 2.47 ± 1.44; F(1, 190) = 0.387, p = .53, g2 = .00). Hence, the study hypotheses were tested on the entire sample. Table 1 shows mean and standard deviation of time spent on the various Internet services in a typical week. The correlations between hours spent online and POSI are also shown in Table 1. As Table 1 Mean, standard deviation, and correlations among POSI and hours spent on different Internet applications in a typical week. Hrs/week M (SD) Type of service E-mail Information Downloading Shopping Chat rooms News/discussion Instant messaging Facebook Blogging Online games Adult websites * **
p < .05. p < .001.
expected, significant positive correlations between POSI and time spent in all communicative services (Instant Messaging, Facebook, Chat Rooms, Blogging) were found. Table 2 shows Product moment correlations between the study variables. The associations between EI components and POSI were negative and significant. On the other hand, correlation coefficients between POSI and the perceived relevance of CMC’s characteristics were positive and significant. Since no associations between Interpersonal EI and perceived relevance of CMC’s characteristics were found, the mediation effect of these attributes in the relationship between Interpersonal EI and POSI was not tested. Using an explorative approach, two alternative models were compared. In model A the effect of Intrapersonal EI on POSI was fully mediated by the importance attached to controllability and to reduced non-verbal cues. Model B was defined as a partially mediated model in which Intrapersonal EI affected POSI both indirectly through the perceived relevance of the above mentioned CMC’s characteristics and directly too. Table 3 shows the fit indices for both models. Although both the assessed structural models produced good fit to the data, the goodness of fit indices suggest that the partially mediated model (model B) explained the relations among factors better than the fully mediated model (model A). When the structural models were compared, model A fitted significantly worse than model B (Dv2 significant). Therefore, model B was selected as the most plausible representation of the phenomenon under study. Fig. 1 shows the standardized beta coefficients of model B; all the direct and indirect effects hypothesized were significant. All coefficient estimated for the measurement model and the estimates of error variances were significant too (p < .001). The explained variance of POSI was 48%.
0.72 (0.86) 3.22 (2.54) 2.35 (2.81) 0.22 (0.67) 4.40 (4.35) 0.72(1.98) 1.81 (2.93) 7.52 (5.28) 0.55 (1.70) 1.21 (3.10) 0.57 (1.28)
r with POSI
.133 .045 .216** .006 .309** .120 .255** .299** .157* .111 .026
The effect of EI on PIU has rarely been investigated. Moreover, no study has considered the different dimensions (cognitive versus behavioral) of problematic use of the web, and few studies have investigated what the individual is actually addicted to (e.g., communicative services rather than online gaming). Cognitive precursors of a maladaptive use of communicative services need to be investigated, as negative outcomes seem to be especially linked to a massive use of social network sites, especially among adolescents (e.g., van den Eijnden et al., 2008). The effect of emotional intelligence components on the preference for online social interaction has been hypothesized in the present study. As expected, self-reported emotional intelligence (both the Intrapersonal and the Interpersonal component) is negatively associated with the preference for online social interaction, which, in turn, is associated with more time spent online, with particular emphasis on communicative services. Previous studies have highlighted the protective role of emotional intelligence with respect to the compulsive use of web services (Oktan, 2011; Parker et al., 2008; Yanesari et al., 2010). These findings have been confirmed and elaborated on by the current study’s results, as an association between emotional intelligence and the cognitive dimension of PIU (POSI) was found. These findings are important because compulsive communicative services use is, at least in part, a consequence of the belief that the user feels safer and more comfortable in CMC (Caplan, 2005). The association between emotional intelligence and compulsive use could be entirely or partially mediated by POSI. In contrast with the findings of Parker et al. (2008), this study suggests a more important role for the Intrapersonal component of emotional intelligence than the Interpersonal component. This is plausible because the study by Parker et al. (2008) neither discriminated between compulsive use of specific types of services
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S. Casale et al. / Personality and Individual Differences 54 (2013) 524–529 Table 2 Mean, standard deviation, and correlations among perceived relevance of CMC attributes, emotional intelligence dimensions, and POSI. M (SD) Perceived relevance of CMC attributes Reduction of non-verbal cues (1) Controllability (2) Emotional Intelligence dimensions Intrapersonal (3) Interpersonal (4) Preference for online social interaction (5) **
1
1.62 (0.73) 2.75 (1.09)
2
3
4
5
– .338** .342** .087 .395**
137.22 (20.77) 110.11 (12.93) 2.54 (1.33)
– .445** .123 .429**
– .538** .524**
– .365**
–
p < .001.
Table 3 Goodness of fit indices of the tested structural equation models. Model
v2 (df)
p
v2/df
RMSEA (90% CI)
CFI
SRMR
Model A (fully mediated) Model B (partially mediated) Model A –Model B
102.58 (60) 81.95 (59)
<.001 .025
1.70 1.38
.06(.04–.08) .04 (.01–.06)
.98 .99
.07 .04
Dv2
Ddf
p
20.63
1
<.001
Note: RMSEA, root mean square error of approximation; 90% CI, 90% confidence interval; CFI, comparative fit index; SRMR, standardized root mean square residual; Dv2, v2 difference; Df, difference in degrees of freedom between models.
Fig. 1. Model B. Effect of Intrapersonal EI (IEI) on POSI partially mediated by the perceived relevance of CMC’s characteristics controllability (RC) and reduced non verbal cues (RNVC). ⁄p < .001.
nor did it consider the different dimensions of PIU. The stronger association between the Intrapersonal EI and POSI may be explained by noting that a perceived lack of autonomy and selfdirectiveness in thought and action among users could suggest a lack of self-monitoring abilities. CMC offers a high level of controllability—or the perception of it—as it is asynchronous and permits the user to reflect on what has been written before sending the message (Valkenburg & Peter, 2011). In the present study, the lower the level of assertiveness, self-reliance, and self-directiveness, the higher the importance of controllability. Moreover, the perceived relevance of controllability significantly mediates the relationship between this intrapersonal intelligence ability and POSI levels. The Intrapersonal component is associated with the importance attached to the reduction of non-verbal cues, which, in turn, influences POSI levels. This result is not consistent with a previous study (Fioravanti et al., 2012) that failed to find a role for anonymity in the relationship between low self-esteem and POSI. This
same study failed to distinguish between source anonymity and audiovisual anonymity. It takes no great leap of logic to suggest that a lack of self-reliance puts a person in a position to appreciate a reduction of evaluative non-verbal cues (that is, audiovisual anonymity) rather than put him or her in a situation of preferring complete anonymity (that is, source anonymity). Low self-esteem and the fear of judgment are quite common among adolescents, many of whom tend to frequently use social network sites like Facebook to communicate with existing friends (Valkenburg & Peter, 2011). In the present study, anonymity in terms of the reduction of nonverbal cues was considered, and the importance attached to it in the relationship between low Intrapersonal EI and POSI was found. However, the mediating role of the perceived relevance of controllability and of the reduction of non-verbal cues in the association between Intrapersonal intelligence and POSI was partial, indicating the presence of a direct effect too and suggesting that the relationship could be explained by other variables. For
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example, individuals who are characterized by low self-reliance and low self-directiveness might prefer online social interaction because it affects personal thoughts and actions in a less threatening way than FtF interaction – that is, because they think it minimizes other people’s opinions with regards to their own thoughts and actions. Further research is needed in order to investigate this hypothesis. No association between Interpersonal intelligence and CMC’s characteristics was found. However, since a moderate and significant association between Interpersonal intelligence and POSI exists, one could safely assume that this relationship is direct or mediated by different aspects of CMC. Indeed, the association could be direct, as people who do not have the ability to recognize emotions, feelings, and the needs of others may perceive FtF relationships as being extremely difficult and stressful. Previous studies have shown that those who score lower in EI measures— particularly in scales measuring the ability to understand emotions—are more likely to report negative relations with others, including close friends (Lopes et al., 2003), and often perceive real life situations to be stressful (Por, Barriball, Fitzpatrick, & Roberts, 2011). This latter association has been confirmed using the Bar-On measure among young students (Forushani & Besharat, 2011). Online interactions could be seen as less stressful by people with low Interpersonal EI because, if CMC implies a reduction of verbal and non-verbal cues, feelings and emotions are displayed only using written words, without the need to decode emotions through facial expressions and non-verbal signs. The cross-sectional nature of our research represents an important limitation of the current study. Even if emotional intelligence is considered a trait (Bar-On, 2005), it is possible to suppose that a preference for online social interaction could have a negative impact on self-reliance and self-directiveness in thoughts and actions and in the ability to express one’s own feelings, as POSI tends to exacerbate social avoidance (Lee & Stapinski, 2012). Further research is needed in order to clarify whether or not the association between EI and POSI is bidirectional. Longitudinal research, in particular, is needed in order to clarify the direction of all the associations found in this study. In addition, as it has already occurred in other fields, further research is required in order to clarify the additional contribution of EI compared with personality traits. One more limitation worth noting is that a self-report measure was used for the assessment of emotional intelligence. While the preference for online social interaction is by definition the result of a subjective evaluation, subjective assessments of emotional intelligence occasionally fail to reflect accurately what they intend to measure—the actual presence of traits and skills. It is noteworthy that absent or very low correlations are observed between self-reported and other-reported EI. In fact, Choi and Kluemper (2012) have recently found that other-report EI is the strongest predictor of trust levels in social relationships. Future research could clarify if a preference for online social interaction is associated with a subjective perception of low emotional intelligence and/or a real lack of social skills. Even so, Caplan’s model (2005) stresses the importance of subjective evaluation rather than actual competencies. Last, the means for internet usage are not particularly high. This could indicate inaccurate perception of internet usage, or it could indicate that the sample does not use the internet heavily. Especially the latter case could result in compromised generalizability because both high school and undergraduate students are generally heavy internet users. References Amichai-Hamburger, Y., & Furnham, A. (2007). The positive net. Computers in Human Behavior, 23, 1033–1045.
Austin, E. J., Saklofske, D. H., & Egan, V. (2005). Personality, well-being and health correlates of trait emotional intelligence. Personality and Individual Differences, 38(3), 547–558. http://dx.doi.org/10.1016/j.paid.2004.05.009. Bar-On, R. (1997). EQ-i Bar-On Emotional Quotient Inventory. Toronto, Canada: MultiHealth Systems. Bar-On, R. (2002). Bar-On Emotional Quotient Inventory (EQI): Technical manual. Toronto, Canada: Multi-Health Systems. Bar-On, R. (2005). The Bar-On model of emotional-social intelligence (ESI). Psicothema, Special Issue on Emotional Intelligence, 17, 1–28. Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage. Caplan, S. E. (2003). Preference for online social interaction: A theory of problematic internet use and psycho-social well-being. Communication Research, 30, 625–648. http://dx.doi.org/10.1177/0093650203257842. Caplan, S. E. (2005). A social skill account of problematic internet use. Journal of Communication, 55, 721–736. http://dx.doi.org/10.1111/j.1460-2466.2005. tb03019.x. Caplan, S. E. (2007). Relation among loneliness, social anxiety, and problematic internet use. Cyberpsychology & Behavior, 10, 234–242. http://dx.doi.org/ 10.1089/cpb.2006.9963. Caplan, S. E. (2010). Theory and measurement of generalized problematic internet use: A two-step approach. Computers in Human Behavior, 26, 1089–1097. http:// dx.doi.org/10.1016/j.chb.2010.03.012. Choi, S., & Kluemper, D. H. (2012). The relative utility of differing measures of emotional intelligence: Other-report EI as a predictor of social functioning. Revue Européenne de Psychologie Appliquée, 62, 121–127. http://dx.doi.org/ 10.1016/j.erap. 2012.01.002. Davis, S. K., & Humphrey, N. (2012). Emotional intelligence predicts adolescent mental health beyond personality and cognitive ability. Personality and Individual Differences, 52(2), 144–149. http://dx.doi.org/10.1016/j.paid.2011. 09.016. Engelberg, E., & Sjöberg, L. (2004). Internet use, social skills, and adjustment. Cyberpsychology & Behavior, 7, 41–47. http://dx.doi.org/10.1089/ 109493104322820101. Fioravanti, G., Dèttore, D., & Casale, S. (2012). Adolescent internet addiction: testing the association between self-esteem, the perception of internet attributes and preference for online social interactions. Cyberpsychology, Behavior and Social Networking, 15(6), 318–323. http://dx.doi.org/10.1089/cyber.2011.0358. Forushani, N. Z., & Besharat, M. A. (2011). Relation between emotional intelligence and perceived stress among female students. Procedia – Social and Behavioral Sciences, 30, 1109–1112. Franco, M., & Tappatà, L. (2009). Emotional Quotient Inventory (EQ-i): Manuale. Firenze: Giunti O.S. Organizzazioni Speciali. Ghiabi, B., & Ali Besharat, M. (2011). Emotional intelligence, alexithymia, and interpersonal problems. Procedia – Social and Behavioral Sciences, 30, 98–102. Goleman, D. (1998). Working with emotional intelligence. New York, NY: Bantam Books. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. http://dx.doi.org/10.1080/10705519909540118. Jöreskog, K. G., & Sörbom, D. (2006). LISREL 8.8 for Windows [Computer software]. Lincolnwood, IL: Scientific Software International Inc. Jöreskog, K. G., & Sörbom, D. (1993). LISREL8 user’s reference guide. Chicago, IL: Scientific Software International. Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford Press. Lee, B. W., & Stapinski, L. A. (2012). Seeking safety on the internet: Relationship between social anxiety and problematic internet use. Journal of Anxiety Disorders, 26, 197–2005. http://dx.doi.org/10.1016/j.janxdis.2011.11.001. Lopes, P. N., Salovey, P., & Straus, R. (2003). Emotional intelligence, personality, and the perceived quality of social relationship. Personality and Individual Differences, 35(3), 641–658. http://dx.doi.org/10.1016/S0191-8869(02)00242-8. Oktan, V. (2011). The predictive relationship between emotion management skills and internet addiction. Social Behavior and Personality: An International Journal, 39(10), 1425–1430. http://dx.doi.org/10.2224/sbp.2011.39.10.1425. Parker, J. D. A., Taylor, R. N., Eastabrook, J. M., Schell, S. L., & Wood, L. M. (2008). Problem gambling in adolescence: Relationships with internet misuse, gaming abuse and emotional intelligence. Personality and Individual Differences, 45, 174–180. http://dx.doi.org/10.1016/j.paid.2008.03.018. Por, J., Barriball, L., Fitzpatrick, J., & Roberts, J. (2011). Emotional intelligence: Its relationship to stress, coping, well-being and professional performance in nursing students. Nurse Educational Today, 31, 855–860. http://dx.doi.org/ 10.1016/j.nedt.2010.12.023. Schouten, A. P., Valkenburg, P. M., & Peter, J. (2007). Precursor and underlying processes of adolescents’ online self-disclosure: Developing and testing an ‘‘Internet-Attribute-Perception’’ model. Media Psychology, 10, 292–315. http:// dx.doi.org/10.1080/15213260701375686. Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173–180. Valkenburg, P. M., & Peter, J. (2011). Online communication among adolescents: An integrated model of its attraction, opportunities, and risks. Journal of Adolescent Health, 48(2), 121–127. http://dx.doi.org/10.1016/j.jadohealth.2010.08.020.
S. Casale et al. / Personality and Individual Differences 54 (2013) 524–529 van den Eijnden, R. J. J. M., Meerkerk, G., Vermulst, A. A., Spijkerman, R., & Engels, R. C. M. E. (2008). Online communication, compulsive internet use, and psychosocial well-being among adolescents: A longitudinal study. Developmental Psychology, 44(3), 655–665. http://dx.doi.org/10.1037/0012-1649.44.3.655. Van Rooy, D. L., & Viswesvaran, C. (2004). Emotional intelligence: A meta-analytic investigation of predictive validity and nomological net. Journal of Vocational Behavior, 65, 71–95. http://dx.doi.org/10.1016/S0001-8791(03)00076-9.
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Walther, J. B. (1996). Computer mediated communication: Impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23, 3–43. http://dx.doi.org/10.1177/009365096023001001. Yanesari, M. K., Homayouni, A., & Gharib, K. (2010). Can emotional intelligence predict addiction to internet in university students? European Psychiatry, 25(1), 748. http://dx.doi.org/10.1016/S0924-9338(10)70742-2.
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Preference for online social interactions among young people: Direct and indirect effects of emotional intelligence Silvia Casale a,⇑, Lisa Tella b, Giulia Fioravanti a a b
Department of Psychology, via di San Salvi 12, Firenze, Italy Faculty of Psychology, via della Torretta 16, Firenze, Italy
a r t i c l e
i n f o
Article history: Received 24 July 2012 Received in revised form 17 October 2012 Accepted 26 October 2012 Available online 30 November 2012 Keywords: Problematic internet use Preference for online social interactions Computer mediated communication Emotional intelligence Internet Attribute Perception
a b s t r a c t The social skills model of generalized problematic internet use predicts that individuals who perceive themselves as having low social competencies are at risk to develop a preference for online social interactions (POSI), which, in turn, might lead to compulsive use of Internet communication services. The present study aims to investigate if self-reported emotional intelligence (EI)—interpersonal and intrapersonal abilities—has an effect on POSI levels, and if this effect is mediated by the subjective importance attached to the major controllability (RC) and reduced non-verbal cues (RNVC) of computer mediated communication. 192 high schools and college students were recruited. Results from structural equation modeling show that Intrapersonal EI predicts both RC and RNVC, which, in turn, predicts the level of POSI. A partial mediation effect was found. On the other hand, Interpersonal EI is significantly associated with POSI, but this association cannot be explained by RC and RNVC. Its effect on POSI seems to be either direct or explained by variables not considered in the present study. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction The evidence that adolescents who report negative outcomes associated with Internet use are especially drawn to the social functions of the web (e.g., van den Eijnden, Meerkerk, Vermulst, Spijkerman, & Engels, 2008) has been accompanied by the development of theories that explain why widespread use of web social services become problematic for certain individuals. According to the social skill model of problematic internet use (PIU, Caplan, 2005), those who report a use of communicative services associated with negative outcomes have developed, as consequence of self-perception of social incompetence, a preference for online social interaction (POSI). POSI is defined as a ‘‘cognitive individualdifference construct characterized by beliefs that one is safer, more efficacious, more confident, and more comfortable with online interpersonal interactions and relationships than with traditional face to face (FtF) social activities’’ (Caplan, 2003, p. 629). According to Caplan (2005), POSI is already a cognitive symptom of PIU. More specifically, it is a cognitive precursor of the tendency to use the web for regulating negative mood states, the compulsive use of the web, and the presence of negative outcomes in real life. Caplan (2003, 2010) found an association between cognitive and behavioral symptoms where high levels of POSI predict compulsive use and negative outcomes. ⇑ Corresponding author. Tel.: +39 3287440547; fax: +39 0556236047. E-mail address: silvia.casale@unifi.it (S. Casale). 0191-8869/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.paid.2012.10.023
Since POSI is a key component of PIU, some studies (e.g., Caplan, 2007; Fioravanti, Dèttore, & Casale, 2012) have explored whether psychological factors associated with social skills are likely to predispose an individual to the development of POSI. Consistent results have been found with regard to the predictive role of social anxiety (Caplan, 2007) and low self-esteem (Fioravanti et al., 2012). Moreover, in accordance with Caplan’s model (2003), POSI mediated the relationship between these psychological factors and compulsive use of the web. Furthermore, Lee and Stapinski (2012) have recently shown that POSI tends to exacerbate social avoidance, which, in turn, could reinforce a tendency to return to computer mediated relationships. The peculiar characteristics of computer mediated communication (CMC) are often cited for explaining this sense of comfort and safety found online by people who perceived themselves as having low social skills. Cues filtered in theories (Walther, 1996) emphasize CMC’s diminished non-verbal cues, arguing that CMC’s unique properties represent an appealing advantage for those with interpersonal difficulties. In other words, online interpersonal interactions may offer a decreased social threat perception (Amichai-Hamburger & Furnham, 2007), which, in turn, increases the tendency to escape from FtF interaction. In fact, Lee and Stapinski (2012) have recently shown that higher social anxiety is associated with a perception of greater control during online social interactions. Fioravanti et al. (2012) found that the importance attached to controllability was associated with POSI and low selfesteem. Surprisingly, both studies failed to find a role for the
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reduction of non-verbal cues: the greater anonymity offered by the web does not explain the association between social anxiety and the reduction of the perceived consequences of negative evaluation (Lee & Stapinski, 2012), as neither is significantly correlated with low self-esteem and POSI (Fioravanti et al., 2012). However the latter study failed to distinguish between source anonymity and audiovisual anonymity. Among the social skills considered as predisposing risk factors for POSI, emotional intelligence (EI) has received less attention. This is surprising, as many studies have identified poor affect regulation abilities as important risk factors for the development of a variety of addiction related problems among young people (e.g., Parker, Taylor, Eastabrook, Schell, & Wood, 2008). Moreover, EI can be conceptualized and measured as a combination of skills (Goleman, 1998) that can protect the wellbeing of interpersonal relationships, as they are especially linked to social adaptation with the environment (Bar-On, 2005). Using different EI measures (ability scales or self-reports) and controlling for personality traits, empirical research shows that EI predicts positive relations with others (Lopes, Salovey, & Straus, 2003), social network size (Austin, Saklofske, & Egan, 2005) and mental health among young people (Davis & Humphrey, 2012). If EI is negatively associated with interpersonal problems (Ghiabi & Ali Besharat, 2011), it would also be negatively associated with POSI. As expected, the few studies that have explored the relationship between EI and Internet Addiction (IA) found a negative association (Oktan, 2011; Yanesari, Homayouni, & Gharib, 2010). Emotion-decoding capacity seems to be responsible for about 20% of the variance of IA among college students (Engelberg & Sjöberg, 2004). Among the different conceptualizations of EI, Bar-On’s view focused on those social competencies whose absence has been considered a risk factor for problematic use of communicative services, particularly POSI symptoms. Bar-On (2005) conceptualizes EI as a collection of emotional and social knowledge and skills that predict emotional and social adaptation within environments. This set of abilities enables individuals to generate, recognize, express, understand, and evaluate their own (and other people’s) emotions in order to cope with environmental demands and pressures (Van Rooy & Viswesvaran, 2004). According to Bar-On (2005), EI is composed of five dimensions: interpersonal abilities, intrapersonal abilities, adaptability, stress management, and general mood. Interpersonal and intrapersonal skills, in particular, strongly recall the types of social skills that Caplan (2005) suggests might be a protective factor for the development of POSI. Interpersonal competencies refer to the ability to cooperate with others, the ability to be attentive to and understand the feelings of others, and the ability to establish and maintain mutually satisfying relationships. Intrapersonal competencies include assertiveness, the ability to be self-reliant and self-directed in one’s thinking and actions, and the ability to realize one’s potential capacities. Among the studies about the association between EI and PIU, only one (Parker et al., 2008) adopted the emotional intelligence Bar On’s view. This study found a significant association between PIU and the Interpersonal component of EI for both younger and older adolescents. For both groups, however, the association between PIU and the Intrapersonal EI was low or not significant. However, Parker’s study focused on the association between EI and the behavioral aspects of an otherwise unspecified Internet addiction, rather than cognitive precursors—such as POSI—of a compulsive use of social communicative services. It is plausible to suppose that POSI is determined, in part, by an inability to be self-reliant and selfdirected in terms of both thinking and acting in FtF relationships. The present study aims to extend previous findings regarding the role of perceived social skills in the development of POSI, and the mechanism that might explain this association. First, it is hypothesized that self-reported emotional intelligence will have
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a negative influence on POSI levels. Then, on the basis of previous studies (Caplan, 2005; Fioravanti et al., 2012; Lee & Stapinski, 2012) regarding the mediating role of CMC’s characteristic in the relationship between low social skills and POSI, it is hypothesized that self-reported emotional intelligence will negatively affect the importance attached to controllability and the reduction of nonverbal cues, which, in turn, tend to enhance POSI levels. 2. Method 2.1. Participants and procedure A total of 192 persons between the ages of 16 and 23 were recruited in the study (M = 18.6 ± 1.9; 52% females and 43% singles). About 60% (59 M; 57 F) was recruited from a senior class in a public high school in Florence. The remaining sample (41 M; 35 F) was recruited from the Faculty of Psychology in Florence (Italy). The students were approached at the end of the lectures by a female research assistant. General information about the purposes of the study were announced to the participants. The participation was voluntary and anonymous. 2.2. Measures 2.2.1. Internet use To estimate the amount of time participants spent using different applications, the questionnaire asked students to report how many hours they spent in a typical week on: e-mail, searching for information, downloading, shopping, public chat rooms, discussion forums, instant messages, and blogging. Participants were asked to ignore study-related use in their estimations. This specification was considered necessary because students may spend an excessive amount of time online in order to complete an assignment. 2.2.2. Emotional intelligence Emotional intelligence was measured through the Italian version (Franco & Tappatà, 2009) of the 133 item Emotion Quotient Inventory (EQ-I; Bar-On, 1997, 2002). The EQ-I is a self-report measure of socially and emotionally intelligent behavior. Items are rated on a 5 point Likert scale, ranging from ‘‘very seldom or not true of me’’ (1) to ‘‘very often true of me or true of me’’ (5). Higher scores indicate a higher level of emotional intelligence. Five composite scale scores can be calculated. However, for the purposes of the present study, only the Intrapersonal Competencies scale and the Interpersonal Competencies scale were calculated. The Intrapersonal skills scale measures the awareness of one’s own emotions, strengths, and weaknesses, and the participants’ ability to express their own feelings and thoughts. Interpersonal skills tied to social consciousness and interpersonal relationships measure the extent to which users know how to recognize the emotions and feelings of others, and their ability to establish and maintain cooperative and satisfying relationships. In the current study, the Cronbach’s Alpha was a = .93 and a = .86 respectively for the Intrapersonal skills scale and the Interpersonal skills scale. 2.2.3. POSI The preference for online social interaction was measured through three items based on a measure that was developed by Caplan (2010) and adapted for (and used) on Italian students (Fioravanti et al., 2012). Participants rated their agreement on a scale ranging from 1 = strongly disagree to 5 = strongly agree. A sample item is: ‘‘Online social interaction is more comfortable for me than face-to-face interaction.’’ In the current study, the POSI scale was reliable (a = .81).
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2.2.4. Perceived relevance of CMC attributes Perceived relevance of reduced non-verbal cues (RNVC) and perceived relevance of controllability (RC) were measured by five items adapted from Schouten, Valkenburg, and Peter (2007). Respondents were asked to indicate on a Likert scale (from 1 = very unimportant to 5 = very important) how relevant reduced non verbal cues and controllability were during CMC. A sample item for perceived relevance of RNVC is: ‘‘others cannot hear how my voice sounds’’. A sample item for perceived RC is: ‘‘I have time to think about how I say something’’. In the current study, Cronbach’s alphas were approximately equal to those of the original version (for RNVC: a = 0.72 in Schouten et al. and a = 0.79 in the present study; for RC: a = 0.87 in both studies). 2.3. Statistical analysis Structural Equation Modeling (SEM) was performed to test the hypothesized effects of EI on POSI through the perceived relevance of CMC’s characteristics. SEM was conducted using LISREL 8.8 with the Robust Maximum Likelihood (RML) estimation method (Jöreskog & Sörbom, 2006). The following profile of goodness of fit indices was considered: the v2 (and its degrees of freedom and p-value), the Standardized Root Mean square Residual (SRMR- Jöreskog & Sörbom, 1993) ‘‘close to’’ 0.09 or lower and the Comparative Fit Index (CFI- Bentler, 1995) ‘‘close to’’ 0.95 or higher (Hu & Bentler, 1999) and the Root Mean Square Error of Approximation (RMSEA- Steiger, 1990) less than 0.08 (Browne & Cudeck, 1993). Because the v2 is sensitive to sample size, Kline (2005) suggested that a model demonstrates reasonable fit if the statistic adjusted by its degrees of freedom v2/df does not exceed 3. 3. Results
4. Discussion
There was no statistically significant difference in the proportion of females and males between the high school group (G1) and the university students group, G2 (v2 = 0.175, p = .676). No statistically significant differences were found between the two groups neither in the predictors’ levels (Interpersonal skills means: G1 = 110.10 ± 13.13; G2 = 110.12 ± 12.70; F(1, 190) = 0.001, p = .99, g2 = .00; Intrapersonal skills means: G1 = 134.88 ± 20.18; G2 = 140.81 ± 21.26; F(1, 190) = 3.790, p = .06, g2 = .01) or in the criterion variable (POSI means: G1 = 2.59 ± 1.27; G2 = 2.47 ± 1.44; F(1, 190) = 0.387, p = .53, g2 = .00). Hence, the study hypotheses were tested on the entire sample. Table 1 shows mean and standard deviation of time spent on the various Internet services in a typical week. The correlations between hours spent online and POSI are also shown in Table 1. As Table 1 Mean, standard deviation, and correlations among POSI and hours spent on different Internet applications in a typical week. Hrs/week M (SD) Type of service E-mail Information Downloading Shopping Chat rooms News/discussion Instant messaging Facebook Blogging Online games Adult websites * **
p < .05. p < .001.
expected, significant positive correlations between POSI and time spent in all communicative services (Instant Messaging, Facebook, Chat Rooms, Blogging) were found. Table 2 shows Product moment correlations between the study variables. The associations between EI components and POSI were negative and significant. On the other hand, correlation coefficients between POSI and the perceived relevance of CMC’s characteristics were positive and significant. Since no associations between Interpersonal EI and perceived relevance of CMC’s characteristics were found, the mediation effect of these attributes in the relationship between Interpersonal EI and POSI was not tested. Using an explorative approach, two alternative models were compared. In model A the effect of Intrapersonal EI on POSI was fully mediated by the importance attached to controllability and to reduced non-verbal cues. Model B was defined as a partially mediated model in which Intrapersonal EI affected POSI both indirectly through the perceived relevance of the above mentioned CMC’s characteristics and directly too. Table 3 shows the fit indices for both models. Although both the assessed structural models produced good fit to the data, the goodness of fit indices suggest that the partially mediated model (model B) explained the relations among factors better than the fully mediated model (model A). When the structural models were compared, model A fitted significantly worse than model B (Dv2 significant). Therefore, model B was selected as the most plausible representation of the phenomenon under study. Fig. 1 shows the standardized beta coefficients of model B; all the direct and indirect effects hypothesized were significant. All coefficient estimated for the measurement model and the estimates of error variances were significant too (p < .001). The explained variance of POSI was 48%.
0.72 (0.86) 3.22 (2.54) 2.35 (2.81) 0.22 (0.67) 4.40 (4.35) 0.72(1.98) 1.81 (2.93) 7.52 (5.28) 0.55 (1.70) 1.21 (3.10) 0.57 (1.28)
r with POSI
.133 .045 .216** .006 .309** .120 .255** .299** .157* .111 .026
The effect of EI on PIU has rarely been investigated. Moreover, no study has considered the different dimensions (cognitive versus behavioral) of problematic use of the web, and few studies have investigated what the individual is actually addicted to (e.g., communicative services rather than online gaming). Cognitive precursors of a maladaptive use of communicative services need to be investigated, as negative outcomes seem to be especially linked to a massive use of social network sites, especially among adolescents (e.g., van den Eijnden et al., 2008). The effect of emotional intelligence components on the preference for online social interaction has been hypothesized in the present study. As expected, self-reported emotional intelligence (both the Intrapersonal and the Interpersonal component) is negatively associated with the preference for online social interaction, which, in turn, is associated with more time spent online, with particular emphasis on communicative services. Previous studies have highlighted the protective role of emotional intelligence with respect to the compulsive use of web services (Oktan, 2011; Parker et al., 2008; Yanesari et al., 2010). These findings have been confirmed and elaborated on by the current study’s results, as an association between emotional intelligence and the cognitive dimension of PIU (POSI) was found. These findings are important because compulsive communicative services use is, at least in part, a consequence of the belief that the user feels safer and more comfortable in CMC (Caplan, 2005). The association between emotional intelligence and compulsive use could be entirely or partially mediated by POSI. In contrast with the findings of Parker et al. (2008), this study suggests a more important role for the Intrapersonal component of emotional intelligence than the Interpersonal component. This is plausible because the study by Parker et al. (2008) neither discriminated between compulsive use of specific types of services
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S. Casale et al. / Personality and Individual Differences 54 (2013) 524–529 Table 2 Mean, standard deviation, and correlations among perceived relevance of CMC attributes, emotional intelligence dimensions, and POSI. M (SD) Perceived relevance of CMC attributes Reduction of non-verbal cues (1) Controllability (2) Emotional Intelligence dimensions Intrapersonal (3) Interpersonal (4) Preference for online social interaction (5) **
1
1.62 (0.73) 2.75 (1.09)
2
3
4
5
– .338** .342** .087 .395**
137.22 (20.77) 110.11 (12.93) 2.54 (1.33)
– .445** .123 .429**
– .538** .524**
– .365**
–
p < .001.
Table 3 Goodness of fit indices of the tested structural equation models. Model
v2 (df)
p
v2/df
RMSEA (90% CI)
CFI
SRMR
Model A (fully mediated) Model B (partially mediated) Model A –Model B
102.58 (60) 81.95 (59)
<.001 .025
1.70 1.38
.06(.04–.08) .04 (.01–.06)
.98 .99
.07 .04
Dv2
Ddf
p
20.63
1
<.001
Note: RMSEA, root mean square error of approximation; 90% CI, 90% confidence interval; CFI, comparative fit index; SRMR, standardized root mean square residual; Dv2, v2 difference; Df, difference in degrees of freedom between models.
Fig. 1. Model B. Effect of Intrapersonal EI (IEI) on POSI partially mediated by the perceived relevance of CMC’s characteristics controllability (RC) and reduced non verbal cues (RNVC). ⁄p < .001.
nor did it consider the different dimensions of PIU. The stronger association between the Intrapersonal EI and POSI may be explained by noting that a perceived lack of autonomy and selfdirectiveness in thought and action among users could suggest a lack of self-monitoring abilities. CMC offers a high level of controllability—or the perception of it—as it is asynchronous and permits the user to reflect on what has been written before sending the message (Valkenburg & Peter, 2011). In the present study, the lower the level of assertiveness, self-reliance, and self-directiveness, the higher the importance of controllability. Moreover, the perceived relevance of controllability significantly mediates the relationship between this intrapersonal intelligence ability and POSI levels. The Intrapersonal component is associated with the importance attached to the reduction of non-verbal cues, which, in turn, influences POSI levels. This result is not consistent with a previous study (Fioravanti et al., 2012) that failed to find a role for anonymity in the relationship between low self-esteem and POSI. This
same study failed to distinguish between source anonymity and audiovisual anonymity. It takes no great leap of logic to suggest that a lack of self-reliance puts a person in a position to appreciate a reduction of evaluative non-verbal cues (that is, audiovisual anonymity) rather than put him or her in a situation of preferring complete anonymity (that is, source anonymity). Low self-esteem and the fear of judgment are quite common among adolescents, many of whom tend to frequently use social network sites like Facebook to communicate with existing friends (Valkenburg & Peter, 2011). In the present study, anonymity in terms of the reduction of nonverbal cues was considered, and the importance attached to it in the relationship between low Intrapersonal EI and POSI was found. However, the mediating role of the perceived relevance of controllability and of the reduction of non-verbal cues in the association between Intrapersonal intelligence and POSI was partial, indicating the presence of a direct effect too and suggesting that the relationship could be explained by other variables. For
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example, individuals who are characterized by low self-reliance and low self-directiveness might prefer online social interaction because it affects personal thoughts and actions in a less threatening way than FtF interaction – that is, because they think it minimizes other people’s opinions with regards to their own thoughts and actions. Further research is needed in order to investigate this hypothesis. No association between Interpersonal intelligence and CMC’s characteristics was found. However, since a moderate and significant association between Interpersonal intelligence and POSI exists, one could safely assume that this relationship is direct or mediated by different aspects of CMC. Indeed, the association could be direct, as people who do not have the ability to recognize emotions, feelings, and the needs of others may perceive FtF relationships as being extremely difficult and stressful. Previous studies have shown that those who score lower in EI measures— particularly in scales measuring the ability to understand emotions—are more likely to report negative relations with others, including close friends (Lopes et al., 2003), and often perceive real life situations to be stressful (Por, Barriball, Fitzpatrick, & Roberts, 2011). This latter association has been confirmed using the Bar-On measure among young students (Forushani & Besharat, 2011). Online interactions could be seen as less stressful by people with low Interpersonal EI because, if CMC implies a reduction of verbal and non-verbal cues, feelings and emotions are displayed only using written words, without the need to decode emotions through facial expressions and non-verbal signs. The cross-sectional nature of our research represents an important limitation of the current study. Even if emotional intelligence is considered a trait (Bar-On, 2005), it is possible to suppose that a preference for online social interaction could have a negative impact on self-reliance and self-directiveness in thoughts and actions and in the ability to express one’s own feelings, as POSI tends to exacerbate social avoidance (Lee & Stapinski, 2012). Further research is needed in order to clarify whether or not the association between EI and POSI is bidirectional. Longitudinal research, in particular, is needed in order to clarify the direction of all the associations found in this study. In addition, as it has already occurred in other fields, further research is required in order to clarify the additional contribution of EI compared with personality traits. One more limitation worth noting is that a self-report measure was used for the assessment of emotional intelligence. While the preference for online social interaction is by definition the result of a subjective evaluation, subjective assessments of emotional intelligence occasionally fail to reflect accurately what they intend to measure—the actual presence of traits and skills. It is noteworthy that absent or very low correlations are observed between self-reported and other-reported EI. In fact, Choi and Kluemper (2012) have recently found that other-report EI is the strongest predictor of trust levels in social relationships. Future research could clarify if a preference for online social interaction is associated with a subjective perception of low emotional intelligence and/or a real lack of social skills. Even so, Caplan’s model (2005) stresses the importance of subjective evaluation rather than actual competencies. Last, the means for internet usage are not particularly high. This could indicate inaccurate perception of internet usage, or it could indicate that the sample does not use the internet heavily. Especially the latter case could result in compromised generalizability because both high school and undergraduate students are generally heavy internet users. References Amichai-Hamburger, Y., & Furnham, A. (2007). The positive net. Computers in Human Behavior, 23, 1033–1045.
Austin, E. J., Saklofske, D. H., & Egan, V. (2005). Personality, well-being and health correlates of trait emotional intelligence. Personality and Individual Differences, 38(3), 547–558. http://dx.doi.org/10.1016/j.paid.2004.05.009. Bar-On, R. (1997). EQ-i Bar-On Emotional Quotient Inventory. Toronto, Canada: MultiHealth Systems. Bar-On, R. (2002). Bar-On Emotional Quotient Inventory (EQI): Technical manual. Toronto, Canada: Multi-Health Systems. Bar-On, R. (2005). The Bar-On model of emotional-social intelligence (ESI). Psicothema, Special Issue on Emotional Intelligence, 17, 1–28. Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage. Caplan, S. E. (2003). Preference for online social interaction: A theory of problematic internet use and psycho-social well-being. Communication Research, 30, 625–648. http://dx.doi.org/10.1177/0093650203257842. Caplan, S. E. (2005). A social skill account of problematic internet use. Journal of Communication, 55, 721–736. http://dx.doi.org/10.1111/j.1460-2466.2005. tb03019.x. Caplan, S. E. (2007). Relation among loneliness, social anxiety, and problematic internet use. Cyberpsychology & Behavior, 10, 234–242. http://dx.doi.org/ 10.1089/cpb.2006.9963. Caplan, S. E. (2010). Theory and measurement of generalized problematic internet use: A two-step approach. Computers in Human Behavior, 26, 1089–1097. http:// dx.doi.org/10.1016/j.chb.2010.03.012. Choi, S., & Kluemper, D. H. (2012). The relative utility of differing measures of emotional intelligence: Other-report EI as a predictor of social functioning. Revue Européenne de Psychologie Appliquée, 62, 121–127. http://dx.doi.org/ 10.1016/j.erap. 2012.01.002. Davis, S. K., & Humphrey, N. (2012). Emotional intelligence predicts adolescent mental health beyond personality and cognitive ability. Personality and Individual Differences, 52(2), 144–149. http://dx.doi.org/10.1016/j.paid.2011. 09.016. Engelberg, E., & Sjöberg, L. (2004). Internet use, social skills, and adjustment. Cyberpsychology & Behavior, 7, 41–47. http://dx.doi.org/10.1089/ 109493104322820101. Fioravanti, G., Dèttore, D., & Casale, S. (2012). Adolescent internet addiction: testing the association between self-esteem, the perception of internet attributes and preference for online social interactions. Cyberpsychology, Behavior and Social Networking, 15(6), 318–323. http://dx.doi.org/10.1089/cyber.2011.0358. Forushani, N. Z., & Besharat, M. A. (2011). Relation between emotional intelligence and perceived stress among female students. Procedia – Social and Behavioral Sciences, 30, 1109–1112. Franco, M., & Tappatà, L. (2009). Emotional Quotient Inventory (EQ-i): Manuale. Firenze: Giunti O.S. Organizzazioni Speciali. Ghiabi, B., & Ali Besharat, M. (2011). Emotional intelligence, alexithymia, and interpersonal problems. Procedia – Social and Behavioral Sciences, 30, 98–102. Goleman, D. (1998). Working with emotional intelligence. New York, NY: Bantam Books. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. http://dx.doi.org/10.1080/10705519909540118. Jöreskog, K. G., & Sörbom, D. (2006). LISREL 8.8 for Windows [Computer software]. Lincolnwood, IL: Scientific Software International Inc. Jöreskog, K. G., & Sörbom, D. (1993). LISREL8 user’s reference guide. Chicago, IL: Scientific Software International. Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford Press. Lee, B. W., & Stapinski, L. A. (2012). Seeking safety on the internet: Relationship between social anxiety and problematic internet use. Journal of Anxiety Disorders, 26, 197–2005. http://dx.doi.org/10.1016/j.janxdis.2011.11.001. Lopes, P. N., Salovey, P., & Straus, R. (2003). Emotional intelligence, personality, and the perceived quality of social relationship. Personality and Individual Differences, 35(3), 641–658. http://dx.doi.org/10.1016/S0191-8869(02)00242-8. Oktan, V. (2011). The predictive relationship between emotion management skills and internet addiction. Social Behavior and Personality: An International Journal, 39(10), 1425–1430. http://dx.doi.org/10.2224/sbp.2011.39.10.1425. Parker, J. D. A., Taylor, R. N., Eastabrook, J. M., Schell, S. L., & Wood, L. M. (2008). Problem gambling in adolescence: Relationships with internet misuse, gaming abuse and emotional intelligence. Personality and Individual Differences, 45, 174–180. http://dx.doi.org/10.1016/j.paid.2008.03.018. Por, J., Barriball, L., Fitzpatrick, J., & Roberts, J. (2011). Emotional intelligence: Its relationship to stress, coping, well-being and professional performance in nursing students. Nurse Educational Today, 31, 855–860. http://dx.doi.org/ 10.1016/j.nedt.2010.12.023. Schouten, A. P., Valkenburg, P. M., & Peter, J. (2007). Precursor and underlying processes of adolescents’ online self-disclosure: Developing and testing an ‘‘Internet-Attribute-Perception’’ model. Media Psychology, 10, 292–315. http:// dx.doi.org/10.1080/15213260701375686. Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173–180. Valkenburg, P. M., & Peter, J. (2011). Online communication among adolescents: An integrated model of its attraction, opportunities, and risks. Journal of Adolescent Health, 48(2), 121–127. http://dx.doi.org/10.1016/j.jadohealth.2010.08.020.
S. Casale et al. / Personality and Individual Differences 54 (2013) 524–529 van den Eijnden, R. J. J. M., Meerkerk, G., Vermulst, A. A., Spijkerman, R., & Engels, R. C. M. E. (2008). Online communication, compulsive internet use, and psychosocial well-being among adolescents: A longitudinal study. Developmental Psychology, 44(3), 655–665. http://dx.doi.org/10.1037/0012-1649.44.3.655. Van Rooy, D. L., & Viswesvaran, C. (2004). Emotional intelligence: A meta-analytic investigation of predictive validity and nomological net. Journal of Vocational Behavior, 65, 71–95. http://dx.doi.org/10.1016/S0001-8791(03)00076-9.
529
Walther, J. B. (1996). Computer mediated communication: Impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23, 3–43. http://dx.doi.org/10.1177/009365096023001001. Yanesari, M. K., Homayouni, A., & Gharib, K. (2010). Can emotional intelligence predict addiction to internet in university students? European Psychiatry, 25(1), 748. http://dx.doi.org/10.1016/S0924-9338(10)70742-2.