english writing

J Child Fam Stud (2018) 27:69–79 DOI 10.1007/s10826-017-0872-8

ORIGINAL PAPER

The Impact of Social Media on Social Comparison and Envy in Teenagers: The Moderating Role of the Parent Comparing Children and In-group Competition among Friends

Peerayuth Charoensukmongkol1

Published online: 6 October 2017 © Springer Science+Business Media, LLC 2017

Abstract Teenagers tend to gravitate towards a group that is highly susceptible to negative psychological and beha- vioral outcomes from social media use. Because teenagers’ behaviors are easily shaped by the social context to which they belong, it is likely that parents and friends might be the key persons who have a strong influence on the behavioral outcomes that teenagers develop from social media use. Given the concern about the negative consequences of social media use by teenagers, this research aims to explore the relationship between social media use intensity and the tendency of teenagers to engage in social comparison and envy. Survey data were collected from 250 teenagers using a snowball sampling. Results from a partial least-squares regression showed that the positive relationship between social media use intensity and envy was significantly higher in teenagers whose parents compared children and teenagers in a peer-group which was characterized by high in-group competition. However, the positive relationship that social media use intensity had with social comparison was sig- nificantly higher only in teenagers who are in a peer-group characterized by a high in-group competition.

Keywords Social media ● Social comparison ● Social learning ● Envy ● Teenagers ● Parenting

Introduction

Social media, particularly Facebook and Instagram, creates a virtual community where people can unite together with family and friends no matter where they are located. Advances in social media technology also allows users to present themselves and to share their life events with others through personal messages and photos. However, although social media helps individuals to stay updated about what is going on with their friends’ life events, exposing too much to content that friends have posted on social media can inevitably trigger individuals to compare themselves with their friends, eventually leading to envy (Chou and Edge 2012). In particular, teenagers tend to gravitate towards a group that is highly susceptible to negative psychological and behavioral outcomes from social media use (Living- stone 2008; Twyman et al. 2010). Research showed that teenagers are usually in the age group that highly deploys social media for self-presentation to impress others (Car- penter 2012; Mehdizadeh 2010). Therefore, exposing teenagers to favorable life events that friends may have posted in order to create impressions in social media can easily make teenagers engage more in social comparisons, thereby causing them to feel envious of what they view from their friends’ posts (Tandoc et al. 2015).

Literature on outcomes associated with social media reported both positive and negative consequences of social media use (Charoensukmongkol 2015; Zhan et al. 2016). On the positive side, social media promote the active par- ticipation of users in producing content, thereby making them a powerful tool for people to engage in content sharing and self-presentation on the Internet (Lee and Ma 2012). People also rely heavily on social media to develop and maintain relationships with family and friends (Ariate et al. 2015). Moreover, studies showed that social media can be

* Peerayuth Charoensukmongkol peerayuth.c@nida.ac.th

1 International College, National Institute of Development Administration 118 Moo 3, Sereethai Road, Klong-Chan, Bangkapi, Bangkok 10240, Thailand

used to promote social capital and facilitate knowledge sharing among users (Ellison et al. 2007; Nielsen 2016). In particular, Zhan et al. (2016) summarized that using social media can enhance life satisfaction through the benefits of increased social capital, perceived social support perceived connectedness, and increased self-esteem. Research also showed that using social media during work can help employees to lower job stress and to enhance job perfor- mance (Brooks and Califf 2016). However, on the opposite side, scholars criticized that inappropriate use of social media can negatively affect physical and psychological health (Bright et al. 2015). In addition, studies found that using social media extensively can deteriorate interpersonal relationship in real-life and can lead to social isolation (Nongpong and Charoensukmongkol 2016; Tang et al. 2016). Too much social media access during work was also found to associate with more burnout and lower job per- formance (Brooks and Califf 2016; Charoensukmongkol 2015; Charoensukmongkol et al. 2017). Moreover, studies showed that teenagers who obsessively engaged in self- presentation on social media could be more susceptible to psychological stress and narcissism (Chua and Chang 2016; Fox and Moreland 2015). Sharing personal information on social media could also make individuals expose to privacy and security risks (Tsay-Vogel et al. 2016).

In literature, it was documented that self-presentation and self-disclosure are among the major reasons that motivate individuals to use social media (Ellison et al. 2007; Hong et al. 2012). However, not all information that is shared in social media tends to reflect the reality of the user who has shared it. Generally, information posted in social media tends to be socially desirable, which aims to create an impression with others (Mehdizadeh 2010). Chou and Edge (2012) suggested that because people don’t know every friend on their social media profiles personally, they tend to rely on availability, including statements and pictures that their friends have posted to form impressions of their friends. As a result, when individuals see statements or photos that a friend has posted about his or her favorable life experiences, they will form a conclusion that their friend actually has a happy life, although this may not be true (Chou and Edge 2012).

Because contents in social media involve socially desir- able information that people have posted to enhance self- presentation, exposing photos and statements that friends have posted about their favorable life experiences can cause individuals to be more prone to social comparison. In par- ticular, the social comparison theory introduced by Festin- ger (1954) is normally used in research to explain why individuals who use social media more intensively tend to be susceptible to this behavior (Chou and Edge 2012; Johnson and Knobloch-Westerwick 2014). Generally, the theory suggests that individuals are motivated to compare

themselves with others who are similar to them in order to gauge their own ability and performance. Although social comparison normally occurs in daily life (e.g., comparison of their own performance with that of their coworkers’ in the workplace), the likelihood to engage in this behavior tends to be more intense in the online social media com- munity where users can expose many targets comparison, or normally their friends in social media. According to John- son and Knobloch-Westerwick (2014, p. 34), “As existing knowledge of friends’ qualities and characteristics could facilitate the ease of making the desirable comparisons, social media are well situated for social comparison phenomena.”

Exposing contents in social media not only makes people engage more in social comparisons, but can subsequently trigger psychological reactions as well. Although Johnson and Knobloch-Westerwick (2014) suggested that people tend to compare themselves with others in social media to manage moods, one particular negative reaction that arises from comparing oneself with others is the envious feeling that individuals develop when they are exposed to impressive life experiences of friends they also expect to have. Generally, the term ‘envy’ was defined by Smith and Kim (2007, p. 49) as “an unpleasant and often painful blend of feelings caused by a comparison with a person or group of persons who possess something we desire.” Krasnova et al. (2013, p. 3) referred to it as “A painful emotion that emerges as a result of upward comparison to advantaged others, who possess something, that one covets but lacks.” Smith et al. (1999, p. 1008) asserted that “envy involves some sense of inferiority brought on by an unfavorable social comparison.” Finally, Tandoc et al. (2015, p. 141) suggested that envy should be distinguished from jealousy because “envy occurs when someone else has something we want but cannot have, while jealousy concerns losing something [we have] to a rival”.

In literature, the effect of social media use on envy was documented by several scholars (Burke et al. 2010; Chou and Edge 2012; Krasnova et al. 2013). For example, Krasnova et al. (2013) suggested that continuously enga- ging in the passive following of information that friends share on social media, such as following posts or proac- tively examining the profiles of friends, can trigger negative feelings of oneself. In particular, this negative feeling normally arises when individuals perceive that their friends are more attractive or have more favorable life experiences than they do. According to Jordan et al. (2011), when individuals compare themselves with friends in social media, they tend to underestimate their friends’ negative experiences, but overestimate their friends’ positive experiences, and that subsequently causes individuals to feel emotional distress. In addition, a study by Tandoc et al. (2015) found that college students who spent more time on

70 J Child Fam Stud (2018) 27:69–79

Facebook tended to engage in what the authors labeled as Facebook envy.

Although social media use intensity was found to explain the tendency of teenagers to engage in social comparison and envy, family and friends can influence the impact that social media use has on these two behavioral outcomes. In particular, the influence of family and friends on teenagers’ behaviors can be explained by social learning theory which postulates that individuals normally learn by observing the behaviors of other people within their social group (Ban- dura 1973). Generally, people that individuals choose to observe (or the role models) provide examples of behavior to imitate (Bandura 1973). Particularly for teenagers, lit- erature suggested that family and friends are the key social groups that serve as the influential role models because they are the persons who have frequent interactions with teen- agers (Hanson et al. 1992; Kagitçibasi 2007). Thus, social learning theory was widely used as a framework to explain the influence of family members and peer groups on the tendency of teenagers to develop a wide arrays of behaviors (DiBlasio and Benda 1990; Hanna et al. 2013; Kunkel et al. 2006; Over and Carpenter 2012).

Although there are many aspects of family characteristics which influence the behaviors of teenagers, one important characteristic is parents comparing their children. Generally, the issue surrounding parents comparing their own children with the children of others or even with their siblings is regarded as a parenting mistake that can lead to teenagers developing negative personalities and behaviors such as jealousy, sibling rivalry, and loss of self-confidence (Fein- berg et al. 2000; Gerlsma et al. 1990). Constantly being a target of comparison by their own parents can significantly increase social comparison behavior in teenagers because it makes teenagers become overly aware of what others have vs. what they don’t have. Constantly being told by their own parents to behave or to become like other children who are superior to them can also cause teenagers to feel negatively or to be envious of those who are set as a frame of reference by their parents (Lamb et al. 1982). Due to the personalities and behaviors that teenagers develop when they are con- stantly compared by their own parents, it can be expected that teenagers who are in a family with this type of parenting tend to become more susceptible to engaging in social comparison and are more prone to be envious of others when they are exposed to content posted by their friends in social media.

Similar to the influence of parenting, the nature of the relationships among friends in a peer-group that teenagers are in can strengthen the positive relationships that social media use intensity has on social comparison and envy. Generally, studies showed that friends in a peer-group tend to play a significant role in shaping behaviors of teenagers across a wide range of areas (Festl et al. 2013; Shadur and Hussong 2014). Although there are many aspects of peer-group

relationships that influence teenagers’ behaviors, the role of in-group competition among friends can be very important.

The influence of in-group competition on behavioral outcomes associated with social media use can be clearly explained by social rank theory which emphasizes the role of competition among members as a driving force that makes individuals strive to gain more dominance over others (Gilbert 1992; Price et al. 1994). According to this theory, “emotions and moods are significantly influenced by the perceptions of one’s social status/rank; that is the degree to which one feels inferior to others and looked down on” (Gilbert 2000, p. 174). The gist of this theory is based on the idea that human social ranks and social relationships in general have evolved around the desire to appear attractive to others (Barkow 1989). According to Markus and Wurf (1987), individuals basically focus on whatever aspects of themselves (e.g., physical appearance, belongings) are most distinctive in a particular social setting, and this evaluation tends to enhance self-worth and self-esteem when they perceive that they possess characteristics that they think others will value rather than what they valued by self alone. However, when individuals are concerned that they possess something that other people do not value, it gives rise to perceptions of being inferior, which, in turn, lead to a lower sense of self-worth and self-esteem (Gilbert 2000) and eventually result in envy (Burke et al. 2010; Chou and Edge 2012; Krasnova et al. 2013). According to Tandoc et al. (2015 p. 140), “Social competition can refer to competition for power or attractiveness, among other things. Those who do not succeed, or those who perceive they have not suc- ceeded, feel subordinated.” In this regard, being in a peer- group in which members usually compete to be superior to others can cause teenagers to face tremendous peer pressure. Because teenagers in this type of peer-group normally strive to gain more dominance over other peers in a group, it can trigger them to develop a higher propensity to be overly aware of the friends they have, and to compare themselves with friends in social media. In addition, they are more prone to be envious of others whom they perceive are more attractive or superior to them (Tandoc et al. 2015).

All research hypotheses are summarized as follows. Considered the role of parent comparing children, the first hypothesis proposes that the positive relationship between social media use intensity and social comparison in teen- agers will be stronger in teenagers whose parents like to compare their own children, whereas the second hypothesis proposes that the positive relationship between social media use intensity and envy in teenagers will be stronger in teenagers whose parents like to compare their own children. Considered the role of in-group competition among friends, the third hypothesis proposes that the positive relationship between social media use intensity and social comparison in teenagers will be stronger in teenagers who are in a peer-

J Child Fam Stud (2018) 27:69–79 71

group characterized by high in-group competition, whereas the fourth hypothesis proposes that the positive relationship between social media use intensity and envy in teenagers will be stronger in teenagers who are in a peer-group characterized by high in-group competition.

Method

Participants

Participants for this research were Thai teenagers between 13 and 19 years of age.

Procedure

A sample was obtained through snowball sampling meth- ods. Graduate students who were enrolled in an advanced research methodology class at the public university in Thailand who have siblings or relatives between thirteen and 19 years of age were asked by the author to help dis- tribute the questionnaires to their siblings or relatives to complete. Students were informed that the survey distribu- tion was voluntary. A self-administered questionnaire sur- vey was employed to collect data. The questionnaire was also anonymous. Although students did not receive any compensation or extra class credit in return, they were informed that the results from this survey study will be used as the example case for the research class later. A total of 299 sets of questionnaires were taken by students; of this total, 250 usable questionnaires were returned to the author. The actual sample consists of 126 males (50.4%) and 124 females (49.6%). The mean age is 16.28 years (SD= 1.618). For birth order, 69 are firstborn (27.6%), 95 are middle children (38%), 61 are last born (24.4%), and 25 are only children (10%).

Measures

Social media use intensity was measured using the scale developed by Ellison et al. (2007). The original scale was designed specifically to measure perceptions that people have regarding their personal levels of attachment to Facebook. The authors performed wording modifications by replacing “Facebook” with “social media” and included Facebook, Instagram, and LINE as an example to clarify the public type of social media that are popular in Thailand. Sample items include “Social media has become part of my daily routine” and “I feel out of touch when I haven’t logged onto social media for a while.” All items were rated on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).

The measures of social comparison, envy, parent com- paring children, and in-group competition among friends were developed by the author. The measures of social comparison, envy, parents comparing children, and in-group competition among friends were developed by the author following the process suggested in the literature (DeVellis 1991). First, the initial item pool of the scales were developed from the review of prior research (Calladine 1983; Clasen and Brown 1985; Festinger 1954; Gibbons and Buunk 1999; Krasnova et al. 2013; Smith and Kim 2007; Smith et al. 1999). The author also invited an expert in the area of teen behaviors to provide suggestions and to help validate the items. Moreover, a small group of graduate students in the research methodology class were asked to provide in-depth comments about the clarity, readability, accuracy, and com- prehensiveness of the questions. After some modifications were made, the scales were pre-tested with the initial sample of graduate students. The results from the pre-test showed satisfactory scale reliability and validity.

Question items that are used to measure these constructs are presented in Table 1. For the measures of social com- parison and envy, respondents were asked to indicate the extent to which they felt that being exposed to content that friends had posted on social media makes them become prone to adopt a set of behaviors that represent each con- struct. All items were rated on a five-point Likert scale ranging from 1 (a little) to 5 (extensively). For the measures of parent comparing children and in-group competition among friends, respondents were asked to evaluate their parents and friends in their peer-group on three statements per each construct. All items were rated on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).

In addition to the role of social media use intensity that was hypothesized to affect outcome variables, the study also incorporated other control variables, including age, gender, and birth order. Age was measured in years. Gender was measured as a dummy variable whereby male was coded as “one” and female was coded as “zero”. In particular, pre- vious studies showed that people of a different age and gender tend to differ in social comparison and envy (Burke 1998; Saroglou and Fiasse 2003). Finally, birth order including firstborn, middle child, lastborn, and only-child were measured by a dummy variable. Middle child, last- born, and only-child were incorporated as the control vari- ables in the analysis because previous research showed that they tend to express more social comparison behavior and jealousy than the firstborn (Saroglou and Fiasse 2003).

Data Analyses

Partial least-squares (PLS) regression was used to analyze the data. PLS combines principal component analysis, path

72 J Child Fam Stud (2018) 27:69–79

analysis, and a set of regressions to generate estimates of the standardized regression coefficients for the model’s paths and factor loadings for the measurement items (Chin and Newsted 1999). PLS provides greater flexibility than other structural equation modeling (SEM) techniques because it does not require data to be normally distributed and it requires a smaller sample size (Kline 2005). PLS was sui- table for this study because the results from the Jarque-Bera test of normality indicated that all main constructs in the hypotheses are not normally distributed. PLS estimation was performed using WarpPLS version 5.0.

Results

First, convergence validity was assessed using factor load- ings. According to Hair et al. (2009), factor loading must be at least .5 to support adequate convergence validity. Factor

loadings of all reflective constructs, as reported in Table 1, met this requirement. Second, discriminant validity was evaluated by comparing the construct’s average variance extracted (AVE) with the inter-construct correlations. According to Fornell and Larcker (1981), each construct’s AVE should be higher than its squared correlation with any other construct. The results, as shown in Table 2, confirmed that all AVEs satisfied this requirement. Third, construct reliability was assessed by Cronbach’s alpha coefficient and composite reliability coefficient. According to Nunnally (1978), these two coefficients should be higher than .7. The results in Table 1 indicated that all reflective constructs had coefficients that meet the minimum requirement.

Multicollinearity between latent variables was evaluated using full variance inflation factor (VIF) statistics. Petter et al. (2007) recommended that full VIF should be lower than 3.3 to confirm that multicollinearity is not a serious issue. The results indicated that the maximum full VIF was lower than this threshold. Furthermore, Kock and Lynn (2012) argued that the full collinearity test can serve as a technique that captures the possibility of common method bias (CMB) in the PLS model analysis. They proposed that full collinearity VIF lower than the critical value of 3.3 provides some evidence that CMB may not be a major threat for the analysis.

Social desirability bias (SDB) was detected using the technique suggested by Barger (2002). The scale to measure SDB was developed by the author to make them applicable to Thai culture. Respondents were asked to indicate whether they ever engaged in ten aspects of activities including displaying selfishness, having dirty thoughts, telling lies, gossiping, swearing, covering up wrongdoings, stealing, breaking rules, littering, and blaming others. The response was coded “one” if a respondent reported that they have never engaged in an activity and was coded “zero” other- wise. The total SDB scores were then correlated with the key variables in the model. According to Barger (2002), if the answers to the question are not related to respondents’ SDB scores, the correlation coefficient should be near zero. Correlation analysis showed that SDB scores correlate weakly with social media use intensity (r= .002; p= .976), social comparison (r= .104; p= .1), and envy (r= .079; p = .215). The findings mitigated the concern of SDB in the measures.

Results from PLS analysis are summarized in Table 3. Standardized path coefficients and p-values are reported. All fit indices of the PLS model estimation, including the average path coefficient (APC), average r-squared (ARS), average full collinearity (AFVIF), Sympson’s paradox ratio (SPR), r2 contribution ratio (RSCR), and statistical sup- pression ratio (SSR) were satisfactory.

Model 1 and Model 2 present the main effect of social media use intensity and two outcome variables. The

Table 1 Measurement scale and factors loadings

Social comparison

• You usually compare yourself with other people (.89).

• You usually compare yourself with people who are better or worse than you (.909).

• You usually compare what you have with what other people have (.911).

• You usually compare yourself with someone who is superior or inferior to you (.847).

You don’t like to compare yourself with other people * (.5).

Envy

• You feel uneasy when you know that your friends are better than you (.863).

• It is painful to know that your friends are more successful than you (.862).

• Knowing that someone has a better life than you is a painful feeling (.905).

• You don’t like your friends to look better than you (.921).

• You feel glad when you know that your friends are more successful than you * (.842).

Parent comparing children

• Your parents like to compare you with other children (.903).

• You can tell that your parents expect you to become like other children (.912).

• Your parents usually complaint why don’t you be like other children (.906).

In-group competition among friends

• Friends in your peer-group like to brag that they are better than others in a group (.92).

• Friends in your peer-group always compete to gain dominance (.927).

• Friends in your peer-group like to show off that they are superior to others in a group (.936).

Notes: Factors loadings are in * reversed question

J Child Fam Stud (2018) 27:69–79 73

Table 2 Correlation among variables and square root of average variance extracted

Variables Composite reliability coefficients

Cronbach’s alpha coefficients

SMUI SC ENV PCC IGC AGE MALE MC LB OC

SMUI .86 .803 (.714)

SC .879 .819 .16* (.780)

ENV .891 .846 .192** .212** (.789)

PCC .96 .937 .235** .138* .117** (.943)

IGC .958 .934 .108 .206** .205** .168** (.941)

AGE – – −.075 −.113 −.033 .087 −.031 (1)

MALE – – .068 .03 .029 .04 .111 .036 (1)

MC – – .015 .024 −.025 −.22** −.01 −.032 .084 (1)

LB – – −.028 .119 −.01 −.01 −.002 −.155* −.163* −.445** (1)

OC – – .047 .030 −.003 .067 .032 −.032 .011 −.261** −.189** (1)

Notes: Square roots of average variance extracted of latent variables are shown in the parentheses

SMUI social media use intensity, SC social comparison, ENV envy, PCC parents comparing children, IGC in-group competition among peers, AGE age, MALE male dummy variable, MC middle-child dummy variable, LB lastborn dummy variable, OC only-child dummy variable.

**p < .01, *p < .05

Table 3 PLS results

Variables Main effect Parent comparing children moderating effect

In-group competition among friends moderating effect

Model 1 Social comparison

Model 2 Envy

Model 3 Social comparison

Model 4 Envy

Model 5 Social comparison

Model 6 Envy

Hypothesized variables

Social media use intensity .106* .144** .095* .127** .099* .141**

Social comparison n/a .164** n/a .159** n/a .141**

Parent comparing children .122* .024 .106* −.001 .1* .009

In-group competition among friends .168*** .153** .165** .149** .157** .148**

Social media use intensity× Parent comparing children

n/a n/a .057 .094* n/a n/a

Social media use intensity× In-group competition among friends

n/a n/a n/a n/a .159** .136**

Control variable

Age −.071 −.015 −.066 −.006 −.067 −.013

Male dummy variable .02 −.008 .021 −.005 .027 −.002

Middle-child dummy variable .161** −.069 .159** −.071 .164** −.063

Lastborn dummy variable .204*** −.069 .207*** −.063 .225*** −.046

Only-child dummy variable .09* −.053 .088 −.055 .09* −.05

Model fit indicators

R2 .108 .1 .11 .107 .132 .117

Average path coefficienta .092* .092* .088* .088* .097* .097*

Average R2b .149** .149** .154** .154** .124** .124**

Average full collinearity VIFc 1.235 1.235 1.252 1.252 1.225 1.225

Sympson’s paradox ratiod .905 .905 .913 .913 .947 .947

Notes: Standardized coefficients are reported

a. good if< 0.05

b. good if < 0.05

c. good if< 3.3

d. good if > 0.7

***p< .001, **p< .01, *p< .05

74 J Child Fam Stud (2018) 27:69–79

findings showed that social media use intensity positively and significantly related with social comparison (β= .106; p= .024; Cohen’s ƒ2= .02) and envy (β= .144; p= .004; Cohen’s ƒ2= .028). In Model 2, the findings also indicated that social comparison and envy were positively and significantly associated (β= .164; p= .001; Cohen’s ƒ2= .035).

Hypotheses 1 and 2 predicted that the positive linkage that social media use intensity has with social comparison and envy respectively will be stronger in teenagers whose parents like to compare children. Model 3 and Model 4 present the results of this moderating effect. The findings showed that the beta coefficients of the interaction term between social media use intensity and parents comparing children positively explained social comparison (β= .057; p= .144; Cohen’s ƒ2= .008) and envy (β= .094; p= .039; Cohen’s ƒ2= .02). However, only the interaction term in the model that has envy as the dependent variable was statis- tically significant. Therefore, Hypothesis 1 was not sup- ported; but Hypothesis 2 was supported.

Hypotheses 3 and 4 predicted that the positive effect of social media use intensity on social comparison and envy respectively will be stronger in teenagers who are in a peer- group characterized by high in-group competition among friends. Model 5 and Model 6 present the results from this moderating effect. The findings showed that the beta coef- ficients of the interaction term between social media use intensity and in-group competition among friends positively explained social comparison (β= .159; p= .002; Cohen’s ƒ2= .028) and envy (β= .136; p= .006; Cohen’s ƒ2= .026). All beta coefficients were also significant. Therefore, Hypothesis 3 and Hypothesis 4 were statistically supported.

Figure 1 and Fig. 2 present the nature of the moderating effects. The illustrations were generated from standardized data. Consistent with the results from hypothesis testing, the figures show that the regression lines between social media use intensity and the two outcome variables were more positive in teenagers whose parents usually compared their children and in respondents who were in a peer-group

Fig. 1 Moderating effect of parent comparing children

J Child Fam Stud (2018) 27:69–79 75

characterized by high in-group competition. Specifically, the regression line between social media use intensity and envy differed tremendously when comparing teenagers who were exposed to low and high degrees of in-group com- petition among friends. For those who reported low in- group competition among friends, social media use intensity just slightly associated with envy.

Regarding the effects of the control variables, the ana- lysis found that birth order significantly explained social comparison and envy. In particular, being a middle child and a lastborn child were associated with social comparison and envy more than being a first born child. These findings are consistent with prior research which found that later- borns are more likely to be jealous than firstborns (Buunk 1997; Feinberg et al. 2000). For example, from the evolu- tionary perspective, Buunk (1997) proposed that parents often invest their material and immaterial resources more in firstborns, thereby motivating the laterborns to compete for love and attention from their parents throughout their childhood. However, given that birth order was included in the analysis for exploratory purpose, the explanation why

and how birth order affected social comparison and envy on social media is the issue that need to be examined in more detail in future research.

Discussion

The objective of this research was to investigate the role of social media use intensity on social comparison and envy in teenagers. The results indicated that teenagers who rated themselves higher on social media use intensity measure tended to report a higher degree of social comparison and envy. These findings are consistent with results from pre- vious studies that investigated the role of social media on these two behavioral outcomes (Chou and Edge 2012; Hong et al. 2012; Krasnova et al. 2013; Salovey and Rodin 1986; Tandoc et al. 2015). More important, the analysis of the moderating effect provided extra evidence that the rela- tionship between social media use intensity on these two behavioral outcomes were strongly influenced by the social environment to which teenagers belong. In particular,

Fig. 2 Moderating effect of in-group competition among friends

76 J Child Fam Stud (2018) 27:69–79

competition among friends in a peer-group was found as a key factor that strongly inflated the tendency of teenagers to engage in social comparison and envy when they were exposed intensively to social media. Specifically, this role of peer-competition is consistent with existing studies that employed social rank theory to explain why social compe- tition tends to trigger behavioral outcomes from social media use (Tandoc et al. 2015). Regarding the role of family factor, the analysis suggested that the moderating effect of a parent comparing children only presents for envy, but not social comparison. For teenagers whose par- ents liked to compare their children, the positive linkage between social media use intensity and envy were higher than for teenagers who were not exposed much to this type of parenting. Consistent with research on social learning theory (Festl et al. 2013; Over and Carpenter 2012), the overall results supported the role of social environment that determines the degree to which behaviors of teenagers are shaped as a result of social media use.

Limitations and Future Research

Despite the key findings, there are several research limita- tions that the author needs to clarify. First, social media use intensity was measured in terms of perceptions that the respondents have regarding their personal levels of attach- ment to social media, not the actual amount of time spent on social media. In addition, the measure neither captured information about the extent to which respondents were exposed to content from friends that portray positive life events nor information about whether the respondents mostly engaged in upward or downward comparisons in social media. Therefore, future research will be required to investigate these specifics of social media use. Second, because cross-sectional data were used in the analysis, the results can only be interpreted as the relationship between constructs, not causality. Third, this research used self- reported measures which can be susceptible to subjective bias from the respondents. Fourth, using a nonprobability sampling method cannot guarantee that that the sample is representative of the entire population. Also, the sample only covered a small group of respondents, making the generalizability of the findings difficult to be inferred.

This research offers additional insight that extends our understanding about the behavioral impacts caused by social media use in teenagers. While exposure to social media content can trigger teenagers to engage more in social comparison and envy as previous research suggested, it is important to understand that the behaviors that teenagers exhibit are also strongly shaped by family and friends. This means that not every teenager will be affected by social media use at the same level, but those who face social pressure from their parents and peers are more prone to

develop such behaviors than others. Therefore, in order to understand why exposure to social media content may result in unhealthy behaviors in teenagers, we need to take into consideration the role of social environment that influences such behaviors as well. In particular, the role of parenting can take a crucial part in explaining these behaviors of teenagers because parents are persons who play a funda- mental role in shaping their values and behaviors. As a result, the behaviors that teenagers adopted from social media use can be a reflection of how they are treated by parents. More important, the pressure that teenagers face from peer competition should be considered another social factor that shape the behaviors that teenagers develop from social media use. Because peers are persons whom teen- agers normally have closer relationships with and are person whom teenagers tend to rely more on than their parents in early adulthood, it is not surprising that teenagers who are in peer-group characterized by high in-group competition may have to behave in the ways that help them adjust to peer pressure that they are exposed to in social media.

In conclusion, while exposure to social media content about the good life of friends was mentioned to cause individuals to be more susceptible to social comparison and envy, this research has shown that these behavioral out- comes of teenagers are strongly influenced by the char- acteristic of the social environment that teenagers are in. Therefore, although someone may argue that using social media can result in unhealthy behaviors, it is important to understand the influence of social environments that also take part in shaping such behaviors as well.

Compliance with Ethical Standards

Conflict of Interest The authors declare that they have no compet- ing interests.

Ethical Approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed Consent The ethical committee of the International Col- lege of the National Institute of Development Administration approved the research and data collection procedure. Informed consent was obtained from all individual participants included in the study.

References

Ariate, R. J., Cruz, R. J., Dimaculangan, J., & Tibayan, C. A. (2015). The role of facebook in sustaining relationship among families of ofw. LPU Laguna Journal of Arts and Sciences Communication Research, 2(1), 156–181.

Bandura, A. (1973). Aggression: A social learning analysis. Engle- wood Cliffs, NJ: Prentice-Hall.

J Child Fam Stud (2018) 27:69–79 77

Barger, S. D. (2002). The marlowe-crowne affair: Short forms, psy- chometric structure, and social desirability. Journal of Person- ality Assessment, 79(2), 286–305.

Barkow, J. H. (1989). Darwin, sex and status: Biological approaches to mind and culture. Toronto: University of Toronto Press.

Bright, L. F., Kleiser, S. B., & Grau, S. L. (2015). Too much face- book? An exploratory examination of social media fatigue. Computers in Human Behavior, 44, 148–155. https://doi.org/10. 1016/j.chb.2014.11.048.

Brooks, S., & Califf, C. (2016). Social media-induced technostress: Its impact on the job performance of it professionals and the mod- erating role of job characteristics. Computer Networks. https:// doi.org/10.1016/j.comnet.2016.08.020.

Burke, M., Marlow, C., & Lento, T. (2010). Social network activity and social well-being. Paper presented at the CHI ‘10 Proceed- ings of the SIGCHI Conference on Human Factors in Computing Systems, New York, NY.

Burke, N. (1998). Gender and envy. New York, NY: Routledge. Buunk, B. P. (1997). Personality, birth order and attachment styles as

related to various types of jealousy. Personality and Individual Differences, 23(6), 997–1006. https://doi.org/10.1016/S0191- 8869(97)00136-0.

Calladine, C. E. (1983). Sibling rivalry: A parent education perspective. Journal of Policy, Practice, and Program, 65(2), 421–427.

Carpenter, C. J. (2012). Narcissism on facebook: Self-promotional and anti-social behavior. Personality and Individual Differences, 52 (4), 482–486.

Charoensukmongkol, P. (2015). Mindful facebooking: The moderat- ing role of mindfulness on the relationship between social media use intensity at work and burnout. Journal of Health Psychology. https://doi.org/10.1177/1359105315569096.

Charoensukmongkol, P., Murad, M., & Gutierrez-Wirsching, S. (2017). Social media sites use intensity and job burnout among u. S. And thai employees. International Journal of Cyber Behavior, Psychology and Learning, 7(1), 34–51. https://doi.org/10.4018/ IJCBPL.2017010103.

Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. Hoyle (Ed.), Statistical strategies for small sample research (pp. 307–341). Thousand Oaks, CA: Sage.

Chou, H.-T. G., & Edge, N. (2012). They are happier and having better lives than i am: The impact of using facebook on perceptions of others’ lives. Cyberpsychology, Behavior, and Social Networking, 15(2), 117–121.

Chua, T. H. H., & Chang, L. (2016). Follow me and like my beautiful selfies: Singapore teenage girls’ engagement in self-presentation and peer comparison on social media. Computers in Human Behavior, 55(Part A), 190–197. https://doi.org/10.1016/j.chb. 2015.09.011.

Clasen, D., & Brown, B. B. (1985). The multidimensionality of peer pressure in adolescence. Journal of Youth and Adolescence, 14 (6), 451–468. https://doi.org/10.1007/BF02139520.

DeVellis, R. F. (1991). Scale development – theory and applications. Newbury Park, CA: Sage.

DiBlasio, F. A., & Benda, B. B. (1990). Adolescent sexual behavior: Multivariate analysis of a social learning model. Journal of Adolescent Research, 5(4), 449–466. https://doi.org/10.1177/ 074355489054005.

Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of facebook “friends:” social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168. https://doi.org/10.1111/j. 1083-6101.2007.00367.x.

Feinberg, M. E., Neiderhiser, J. M., Simmens, S., Reiss, D., & Hetherington, E. M. (2000). Sibling comparison of differential

parental treatment in adolescence: Gender, self-esteem, and emotionality as mediators of the parenting-adjustment associa- tion. Child Development, 71(6), 1611–1628. https://doi.org/10. 1111/1467-8624.00252.

Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7(2), 117–140.

Festl, R., Scharkow, M., & Quandt, T. (2013). Peer influence, internet use and cyberbullying: A comparison of different context effects among german adolescents. Journal of Children and Media, 7(4), 446–462. https://doi.org/10.1080/17482798.2013.781514.

Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Fox, J., & Moreland, J. J. (2015). The dark side of social networking sites: An exploration of the relational and psychological stressors associated with facebook use and affordances. Computers in Human Behavior, 45(0), 168–176. https://doi.org/10.1016/j.chb. 2014.11.083.

Gerlsma, C., Emmelkamp, P. M. G., & Arrindell, W. A. (1990). Anxiety, depression, and the perception of early parenting: A meta-analysis. Clinical Psychology Review, 10(3), 251–277. https://doi.org/10.1016/0272-7358(90)90062-F.

Gibbons, F. X., & Buunk, B. P. (1999). Individual differences in social comparison: Development of a scale of social comparison orientation. Journal of Personality and Social Psychology, 76(1), 129–142.

Gilbert, P. (1992). Depression: The evolution of powerlessness.. Guilford, NY: Lawrence Erlbaum Associates.

Gilbert, P. (2000). The relationship of shame, social anxiety and depression: The role of the evaluation of social rank. Clinical Psychology and Psychotherapy, 7, 174–189.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis. 7th ed Upper Saddle River, NJ: Pre- ntice Hall.

Hanna, R. C., Crittenden, V. L., & Crittenden, W. F. (2013). Social learning theory: A multicultural study of influences on ethical behavior. Journal of Marketing Education, 35(1), 18–25. https:// doi.org/10.1177/0273475312474279.

Hanson, C. L., De Guire, M. J., Schinkel, A. M., Henggeler, S. W., & Burghen, G. A. (1992). Comparing social learning and family systems correlates of adaptation in youths with iddm. Journal of Pediatric Psychology, 17(5), 555–572. https://doi.org/10.1093/ jpepsy/17.5.555.

Hong, S., Tandoc, E., Kim, E., Kim, B., & Wise, K. (2012). The real you? The role of visual cues and comment congruence in per- ceptions of social attractiveness from facebook profiles. Cyberpsychology, Behavior, and Social Networking, 15(7), 339–344.

Johnson, B. K., & Knobloch-Westerwick, S. (2014). Glancing up or down: Mood management and selective social comparisons on social networking sites. Computers in Human Behavior, 41(0), 33–39. https://doi.org/10.1016/j.chb.2014.09.009.

Jordan, A. H., Monin, B., Dweck, C. S., Lovett, B. J., John, O. P., & Gross, J. J. (2011). Misery has more company than people think: Underestimating the prevalence of others’ negative emotions. Personality and Social Psychology Bulletin, 37(1), 120–135. https://doi.org/10.1177/0146167210390822.

Kagitçibasi, Ç. (2007). ). Family, self, and human development across cultures: Theory and applications (2nd ed.). Mahwah, NJ: Routledge.

Kline, R. B. (2005). Principle and practice of structural equation modeling (2nd ed.). New York, NY: The Guiford Press.

Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based sem: An illustration and recommenda- tions. Journal of the Association for Information Systems, 13(7), 546–580.

78 J Child Fam Stud (2018) 27:69–79

Krasnova, H., Wenninger, H., Widjaja, T., & Buxmann, P. (2013). Envy on facebook: A hidden threat to users’ life satisfaction? Paper presented at the The 11th International Conference on Wirtschaftsinformatik (WI), Leipzig, Germany.

Kunkel, A., Hummert, M. L., & Dennis, M. R. (2006). Social learning theory: Modeling and communication in the family context. In D. O. Braithwaite & L. A. Baxter (Eds.), Engaging theories in family communication: Multiple perspectives (pp. 260–275). Thousand Oaks, CA: Sage.

Lamb, M. E., Sutton-Smith, B., & Sutton-Smith, B. (1982). Sibling relationships: Their nature and significance across the lifespan. New York, NY: Psychology Press.

Lee, C. S., & Ma, L. (2012). News sharing in social media: The effect of gratifications and prior experience. Computers in Human Behavior, 28(2), 331–339. https://doi.org/10.1016/j.chb.2011.10.002.

Livingstone, S. (2008). Taking risky opportunities in youthful content creation: Teenagers’ use of social networking sites for intimacy, privacy and self-expression. New Media & Society, 10(3), 393–411. https://doi.org/10.1177/1461444808089415.

Markus, H., & Wurf, E. (1987). The dynamic self-concept: A social psychological perspective. Annual Review of Psychology, 38(1), 299–337. https://doi.org/10.1146/annurev.ps.38.020187.001503.

Mehdizadeh, S. (2010). Self-presentation 2.0: Narcissism and self- esteem on facebook. Cyberpsychology, Behavior, and Social Networking, 13(4), 357–364.

Nielsen, L. R. K. K. P. (2016). What factors influence knowledge sharing in organizations? A social dilemma perspective of social media communication. Journal of Knowledge Management, 20 (6). https://doi.org/10.1108/JKM-03-2016-0112D.

Nongpong, S., & Charoensukmongkol, P. (2016). I don’t care much as long as i am also on facebook: Impacts of social media use of both partners on romantic relationship problems. The Family Journal, 24(4), 351–358. https://doi.org/10.1177/106648071 6663199.

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York, NY: McGraw-Hill.

Over, H., & Carpenter, M. (2012). Putting the social into social learning: Explaining both selectivity and fidelity in children’s copying behavior. Journal of Comparative Psychology, 126(2), 182–192.

Petter, S., Straub, D., & Rai, A. (2007). Specifying formative con- structs in information systems research. MIS Quarterly, 31(4), 623–656.

Price, J., Sloman, L., Gardner, R., Gilbert, P., & Rohde, P. (1994). The social competition hypothesis of depression. The British Journal of Psychiatry, 164(3), 309–315.

Salovey, P., & Rodin, J. (1986). The differentiation of social com- parison jealousy and romantic jealousy. Journal of Personality and Social Psychology, 50, 1100–1112. https://doi.org/10.1037/ 0022-3514.50.6.1100.

Saroglou, V., & Fiasse, L. (2003). Birth order, personality, and reli- gion: A study among young adults from a three-sibling family. Personality and Individual Differences, 35(1), 19–29. https://doi. org/10.1016/S0191-8869(02)00137-X.

Shadur, J. M., & Hussong, A. M. (2014). Friendship intimacy, close friend drug use, and self-medication in adolescence. Journal of Social & Personal Relationships, 31(8), 997–1018. https://doi. org/10.1177/0265407513516889.

Smith, R. H., & Kim, S. H. (2007). Comprehending envy. Psycholo- gical Bulletin, 133(1), 46–64.

Smith, R. H., Parrott, W. G., Diener, E. F., Hoyle, R. H., & Kim, S. H. (1999). Dispositional envy. Personality and Social Psychology Bulletin, 25(8), 1007–1020. https://doi.org/10.1177/0146167299 2511008.

Tandoc, Jr, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43(0), 139–146. https://doi.org/10.1016/j.chb.2014.10.053.

Tang, J.-H., Chen, M.-C., Yang, C.-Y., Chung, T.-Y., & Lee, Y.-A. (2016). Personality traits, interpersonal relationships, online social support, and facebook addiction. Telematics and Informatics, 33 (1), 102–108. https://doi.org/10.1016/j.tele.2015.06.003.

Tsay-Vogel, M., Shanahan, J., & Signorielli, N. (2016). Social media cultivating perceptions of privacy: A 5-year analysis of privacy attitudes and self-disclosure behaviors among facebook users. New Media & Society. https://doi.org/10.1177/1461444816 660731.

Twyman, K., Saylor, C., Taylor, L. A., & Comeaux., C. (2010). Comparing children and adolescents engaged in cyberbullying to matched peers. Cyberpsychology, Behavior, and Social Net- working, 13(2), 195–199.

Zhan, L., Sun, Y., Wang, N., & Zhang, X. (2016). Understanding the influence of social media on people’s life satisfaction through two competing explanatory mechanisms. Aslib Journal of Information Management, 68(3), 347–361. https://doi.org/10.1108/AJIM-12- 2015-0195.

J Child Fam Stud (2018) 27:69–79 79

Journal of Child & Family Studies is a copyright of Springer, 2018. All Rights Reserved.