Violent Behavior and Social Network Antisocial and Prosocial Behaviors

Abstract

While the youth’s social network has affected violent behavior, the influences of different kinds of networks and their behaviors have been unclear. Accordingly, the antisocial and prosocial of the peer friend (less than 5 years older or younger) network, elder friend (aged > 40 years) network, and the linking or resourceful network are likely influential based on social learning, social bond, and social capital theories. For a contest of the network behaviors, this study surveyed 2,363 youths (aged 16–24 years) in the Chinese society of Hong Kong. Results reveal that the prosocial behavior of peer and elder networks significantly inversely predicted the youth’s violent behavior, whereas the antisocial behavior of the linking network was significantly positively predictive. These network effects were not significantly different between the migrant and native and between the younger (aged below 20 years) and older (aged 20+ years) youths. The results imply the value of preventing the youth’s violent behavior by raising the youth’s bonding with prosocial friends and preventing the youth’s antisocial linking network with the guidance of the theories.

Keywords

violent behavior antisocial behavior prosocial behavior peer network elder network linking network social bonding social learning social capital

A youth’s violent behavior or using force to hurt or damage has risen or receded with his or her peers’ antisocial behavior or prosocial behavior, which more broadly means harming or helping social life, respectively (Niño et al., 2017; Walters, 2020). The antisocial or prosocial behavior of the youth’s friends or social network has encouraged or discouraged the youth’s violent behavior, respectively (Foshee et al., 2013; Pettit et al., 2006). Specifically, the social network divides into the bonding or peer network, bridging or elder network, and linking network (Johnston et al., 2013; Quetulio Navarra et al., 2018). The peer bonding network comprises youth friends, the elder bridging network comprises friends (including cohabiting partners) aged 40 years or older, and the linking network comprises resourceful acquaintances such as entrepreneurs and officials outside the youth’s life sphere (Stadtfeld & Pentland, 2015; Yu et al., 2019). While the peer network has been prominent in existing research on youth violence, the elder and linking networks have not. As a gap in research regarding the social network, the latter is necessary for a complete social network investigation. Meanwhile, through their supervision or support, the elder and linking networks are presumably crucial concerning the youth’s violent behavior (Hein et al., 2015; James et al., 2015; Pellegrini & Blatchford, 2000). This study thus contests the effects of the antisocial and prosocial behaviors of the youth’s peer friend, elder friend, and linking networks on the youth’s violent behavior.

Youth’s violent behavior represents a prevalent antisocial behavior or externalizing problem (Stoddard et al., 2011; Van Heel et al., 2019). The problem is severe in its devastating and life-threatening consequences (Cooper et al., 2019; Stoddard et al., 2011). Such consequences include more serious crimes and recidivism, detrimental to oneself and others, thus undermining society (Asendorpf et al., 2008; Boehnke & Bergs-Winkels, 2002; Nirel et al., 1997). Notably, violent behavior has fomented conflict with others and impaired life chances, leading to depression, shamefulness, and self-derogation (Asendorpf et al., 2008; Del Bove et al., 2008; Jasko et al., 2019; Morrison, 2006). Violent behavior also reflects victimization (Palacios et al., 2019). Hence, the youth’s violent behavior calls for social policy and practice for its prevention, including boosting prosocial influences and eliminating antisocial influences from social networks (Gottfredson & Gottfredson, 2018; Luengo Kanacri et al., 2020).

The youth’s social network, consisting of peer and elder friends and linking acquaintances, is influential on the youth (Bassani, 2009; Johnston et al., 2013). Such influence lies in the delivery of domination, norms, and resources from the social network sought by the youth, particularly because of his or her attachment to the network (Dika & Singh, 2002; Johnston et al., 2013; Laser & Leibowitz, 2009). Through socializing, the delivery is emotionally, informationally, instrumentally, and socially influential (Laser & Leibowitz, 2009). The influence meets the youth’s need for social comparison for following and validating something (Merchant et al., 2017). Moreover, the social network is influential as a precursor to a community, vital in connection, engagement, participation, and commitment to magnify the influence (Ferguson, 2006). The influence also rests on an obligation to follow the social network in practice (Gu et al., 2014; Pettit et al., 2011). Among the social network, the influence of the peer network is prominent because of the youth’s orientation or susceptibility to his or her peers (Dishion & Tipsord, 2011). Such orientation establishes the youth’s attachment to peers and reception of their pressure (Zou et al., 2018). Besides peer friends, elder or adult friends in the social network are also prevalent and influential through arrangements by the school, youth programs, or others (Battistich, 2008; Velazquez et al., 2011). Elder friends (aged 40+ years) in the Chinese context are likely teachers who engage in youth’s daily life (L. Tian et al., 2013; Wright, 2015). Notably, elder friends are prominent in Chinese people because of their culturally respectable status (Wright, 2015). These friends’ influence is natural, normal, and normative, essential to socialization, social integration, and community building for meeting the youth’s need for recourse (Battistich, 2008; Clayton et al., 2021). In addition, the linking or resourceful acquaintance network, resembling parents, is influential in advocating for, empowering, modeling for, nurturing, and training the youth (Birman & Morland, 2014; Harris & Lee, 2019; Liang et al., 2008; Waldeck, 2019).

Antisocial behavior, such as joining gangs and breaking the law, is prevalent in the youth’s social network, particularly the peer network, which is rarely supervised (Güroğlu et al., 2007; Soller et al., 2017). Such behavior, being contagious and harmful to oneself and others, invokes public disapproval and related preventive policy and practice (Backman & Nilsson, 2011; Kornienko et al., 2018; Taylor et al., 2016; van der Leun & Koemans, 2013).

Prosocial behavior, such as conformity to norms and conventions, volunteering, and supporting law enforcement, is noteworthy in the youth’s social network (Aronson & Brown, 2013; Hughes et al., 2016). Such noteworthiness stems from the contributions of prosocial behavior to the caring, socializing, and well-being of oneself and others (Dovidio et al., 2006; Mastain, 2007). Those contributions to others spring from exchange and interdependence among people (Adongo et al., 2019; Van Lange et al., 2012). Hence, prosocial behavior is a goal for promotion through education, mentoring, propaganda, and other programing, policies, and practices (Leadbeater et al., 2016; Mesurado et al., 2019; Waldeck, 2019).

Impacts on Violent Behavior by Social Network Antisocial and Prosocial Behaviors

According to an integrated model of social learning theory, social bond theory, and social capital theory, the youth’s violent behavior is a function of the antisocial and prosocial behaviors of his or her social network. Social learning theory emphasizes behavioral change through learning, incorporating attention, coding, experiencing, identification, imitating, modeling, observing, and receiving reinforcement, as exemplified in education, particularly from the authority (Braun & Vliegenthart, 2008; Jung, 2016; Kaplan et al., 2013). The theory has explained the contribution of learning to antisocial and prosocial behavior (Brayne, 2014; Nejati & Shafaei, 2018). Moreover, the theory posits that learning prosocial behavior demeans and thus displaces antisocial behavior (Kaplan et al., 2013). Alternatively, social bond theory highlights the inhibition of antisocial behavior by conventional bonding, characterized by attachment, belief, commitment, and involvement concerning conventional or prosocial behavior (Herman-Kinney, 2003; Kaplan et al., 2013). In addition, social capital theory stresses the impacts of the comparable or resourceful social network (Bassani, 2009; Yu et al., 2019). The impacts have been evident on the youth’s antisocial and prosocial behavior (Dufur et al., 2013; King & Furrow, 2004). Integrating social learning, social bond, and social capital theories suggests that the antisocial and prosocial behaviors of authoritative, comparable, and resourceful social networks are influential (Bonell et al., 2013). The peer network is comparable, the elder adult network is authoritative, and the linking network is authoritative and resourceful. Hence, the youth’s violent behavior is likely to be predictable by the antisocial and prosocial behaviors of his or her peer friend network, elder friend network, and linking acquaintance network. Hence, the social network is an influence crucial in social learning, social bond, and social capital theories (Bonell et al., 2013; Fujiyama et al., 2021).

The youth’s violent behavior is likely to be positively predictable by the antisocial behavior of his or her peer friend network, elder friend network, and linking acquaintance network. According to social learning theory, the youth’s violent behavior evolves from learning from others’ antisocial behavior, including aggression, corporal punishment, gang involvement, irritation, rejection, violence, other delinquent acts, and related training (Hoffman et al., 2005; Howell & Howell, 2007; Kaufman-Parks et al., 2017; Kornienko et al., 2018; Kuczynski & Parkin, 2007; Van Ryzin & Dishion, 2013; Vitaro et al., 2007). Friends have also been role models for the youth to learn violent behavior (Hurd et al., 2011; Vitaro et al., 2007). Herein, role modeling is prominent among friends or social network members through their interaction and learning (Johnson et al., 2016). Antisocial behavior has been a crucial foundation of violent behavior (Kornienko et al., 2018). Thus, the youth’s violent behavior has risen with his or her experiences of others’ antisocial behavior, including aggression, bullying, corporal punishment, exclusion, hostility, maltreatment, rejection, threatening, troubling, violence, and other delinquent acts (Folger & Wright, 2013; Gershoff, 2002; Giordano et al., 2015; Grych & Kinsfogel, 2010; Hurd et al., 2011; Ishoy, 2017; James et al., 2015; Kim et al., 2017; Masui et al., 2013; Na & Paternoster, 2019; Nino et al., 2017; Richards et al., 2014; Vitaro et al., 2007; Yeh et al., 2010). More specifically, the youth’s violent behavior has grown with antisocial behavior, including aggression, violence, and other delinquent acts in his or her social network (Brendgen et al., 2008; Farrell et al., 2017; Hagan & Foster, 2001; Pettit et al., 2006). Similarly, the youth’s violent behavior has escalated with his or her friends’ antisocial behavior, including deviation and violence. (Giordano et al., 2015; Ribeaud & Eisner, 2015). Such social network influences follow social capital theory, emphasizing influence amplification through friendship or resourceful networks (Benton, 2016; Flap & Volker, 2013).

The youth’s violent behavior is likely inversely predicted by the prosocial behavior of his or her peer friend network, elder friend network, and linking acquaintance network. According to social learning, the youth’s violent behavior diminishes with learning from others’ prosocial behavior, including cooperation and responsibility-taking (Choi et al., 2011; Morrison, 2006). Prosocial behavior has displaced violent behavior (Malonda et al., 2019). Thus, the youth’s violent behavior has receded with his or her experiences of others’ prosocial behavior, including cohesiveness, collective service, nurturing, support, and warmth (De Coster et al., 2006; Han & Grogan-Kaylor, 2013; Hassija et al., 2018; Lahlah et al., 2014; Richards et al., 2014; Vazsonyi et al., 2007; Walters, 2020). Alternatively, social bond theory proposes that bonding with prosocial people defuses violent behavior (Cecen-Celik & Keith, 2019; J. Lee & Randolph, 2015). Hence, the youth’s behavior has declined with his or her bonding with prosocial classmates, friends, parents or family members, peers, or teachers (Foshee et al., 2013; Lee & Song, 2012; Vazsonyi et al., 2007; Walters, 2020; Wei et al., 2010). Such social network influences also reflect social capital theory (Benton, 2016; Flap & Volker, 2013).

Hypothesis Testing

Given their uncertainty, the following hypotheses based on the theories require empirical testing in the current study.

The youth’s violent behavior is positively predictable by the antisocial behavior of his or her

1.1 Peer friend network

1.2 Elder friend network

1.3 Linking network

The youth’s violent behavior is inversely predictable by the prosocial behavior of his or her

2.1 Peer friend network

2.2 Elder friend network

2.3 Linking network

Testing the hypotheses requires controlling for prior violent behavior and demographic characteristics, possibly confounding the hypothesized predictions. The youth’s prior violent behavior is likely to positively and inversely predict the antisocial and prosocial behavior of the youth’s social network, respectively. This prediction reflects selectivity due to homophily, emphasized in social capital theory (Hampton & Duncan, 2011; Shin et al., 2019). Accordingly, social capital hinges on and thus incorporates homophily to realize power. Hence, the youth’s violent behavior has predicted his or her peers’ antisocial behavior (Prinstein & Wang, 2005). This prediction mirrors the inverse prediction of the youth’s antisocial behavior by his or her peers’ prosocial behavior (Criss et al., 2017). Similarly, the youth’s prosocial behavior has been inversely predictable by antisocial behavior (Rusby et al., 2019). In addition, the youth’s violent behavior has reduced with age, education, female gender, religious faith, and parents’ marriage, and increased with family size and social welfare reception (Farrington, 2007; Giordano et al., 2015; Jang & Franzen, 2013; Na & Paternoster, 2019; Walters, 2020). Meanwhile, the youth’s peers have been more antisocial when the youth has been older and male (Vander Ven et al., 2001; Warr, 2005).

Studying in Hong Kong

Hong Kong, a special administrative region of China since 1997, was the study site for consolidating knowledge worldwide, particularly across comparable developed places. Accordingly, Hong Kong is globally comparable and relevant to other developed places through globalization, modernization, and Westernization (Cheung, 2015; Hui et al., 2018). Similar to other developed places, violence and other antisocial behavior, prosocial behavior, and social networking are salient and attract public attention (X. Tian, 2016; Zamecki, 2018). Such comparability warrants generalizing findings between Hong Kong and outside. Meanwhile, Hong Kong has distinctive cultural and urban features due to Chinese inheritance and the dense and compact settlement (Mak & Day, 2012; Zhu, 2018). These features heighten social networking, including intergenerational networking, and thus its prominence (Forrest & Xian, 2018; Schwartz et al., 2009). Such prominence would be higher in migrants from Mainland China to Hong Kong, whose Westernization is weaker than that of the locally born or natives (Chou et al., 2014). Comparing the social network impacts between migrants and natives thus reveals differentials due to Westernization to qualify the generalization of findings. Besides Westernization, aging may make a difference in the social network impacts, either increasing or decreasing autonomy or dependence on social networks (Pinquart & Pfeiffer, 2013; Seibert & Kerns, 2009). Such age differentials also inform generalizing findings from this youth study.

Method

This study employed data from a random-sample telephone survey of 2,363 Chinese youths in Hong Kong (aged 16–24 years) before the COVID-19 outbreak. The sampling proceeded with a random draw of household telephone numbers in the whole territory to contact households and randomly select a youth from each household to respond to the survey. Conducted by trained interviewers on weekday evenings and weekend day time, and evenings, this survey attained 2,398 responses, representing a response rate of 51.9% from 4,620 households contacted. the response rate, resulting from repeated canvassing until ending with the selected youths’ consent, fared well among surveys, which averaged 25% (Keeter et al., 2006). Nevertheless, 35 responses dropped from the analysis due to incompleteness.

The surveyed youths had an average of 20.2 years in age, 12.5 years in education, and 4.2 persons in the family (see Table 1). Among them, 50.2% were female, 76.7% were locally born, 87.5% were irreligious, 1,0% were married, 95.6% had married parents, and 1.9% received social welfare or lived in low-income families.

Table 1.

Means and Standard Deviations (N = 2,363).

Variable	Scoring	M/%	SD
Female	0, 100	50.2	50.0	female
Age	years	20.2	2.5	age
Education	years	12.5	3.2	eduy
Locally born	0, 100	76.7	42.3	bornhk
Irreligious	0, 100	87.5	33.1	atheist
Welfare reception	0, 100	1.9	13.8	cssa
Married	0, 100	1.0	10.1	marry
Parents married	0, 100	95.6	20.6	p.marry
Family size	persons	4.2	0.8	fsize
Violent behavior, past 3 months	0–100	19.0	16.6	act.1
Peer network antisocial behavior, past 6 months	0–100	14.0	17.7	antip
Peer network prosocial behavior, past 6 months	0–100	37.4	19.9	prop
Elder network antisoc8iality, past 6 months	0–100	12.6	17.0	antio
Elder network prosocial behavior, past 6 months	0–100	33.2	25.8	proo
Linking network antisocial behavior, past 6 months	0–100	8.4	15.3	antik
Linking network prosocial behavior, past 6 months	0–100	22.3	23.5	prok
Violent behavior, last year	0–100	19.0	16.3	act

Measurement

Multiple rating items measured the youth’s violent behavior in the past 3 months and last year and the perceived antisocial and prosocial behavior of the youth’s peer friend network (age difference less than 5 years), elder friend network (aged 40+ years), and linking network in the past 6 months (see Table 2). Violent behavior, measured by six items, included hitting, bumping others, and vandalism (Bruner & Hensel, 1992; Enosh et al., 2015; Kornienko et al., 2018; Schmid, 2012). Antisocial behavior, measured by six items, included joining gangs and breaking the law (Hawes & Dadds, 2007). Prosocial behavior, measured by three items, included volunteering and supporting police law enforcement (Wilson et al., 2009). Linking acquaintances referred to those resourceful people such as entrepreneurs or officials outside the youth’s life sphere.

Table 2.

Standardized Factor Reliability/Loadings.

Factor/Indicator	Trait	Method
Peer network antisocial behavior, past 6 months	.658
Using force	.529	.347	ANTIP1
Breaking the law	.599	.322	ANTIP2
Joining gangs	.646	.295	ANTIP3
Peer network prosocial behavior, past 6 months	.588
Doing volunteer service	.693	.217	PROP1
Advancing social harmony	.568	.186	PROP2
Elder network antisocial behavior, past 6 months	.701
Using force	.571	.436	ANTIO1
Breaking the law	.673	.356	ANTIO2
Joining gangs	.585	.363	ANTIO3
Elder network prosocial behavior, past 6 months	.707
Doing volunteer service	.517	.445	PROO1
Advancing social harmony	.757	.444	PROO2
Supporting police law enforcement	.535	.350	PROO3
Linking network antisocial behavior, past 6 months	.735
Using force	.547	.164	ANTIK1
Breaking the law	.854	.211	ANTIK2
Joining gangs	.623	.182	ANTIK3
Linking network prosocial behavior, past 6 months	.725
Doing volunteer service	.742	.145	PROK1
Advancing social harmony	.744	.184	PROK2
Supporting police law enforcement	.518	.214	PROK3
Violent behavior, past 3 months	.767
Hitting others	.510	.237	ACT11
Injuring others	.289	.306	ACT12
Bumping others	.648	.213	ACT13
Jostling police	.669	.183	ACT14
Vandalism	.714	.222	ACT15
Participating in collecting shocking	.605	.237	ACT16
Violent behavior, last year	.781
Hitting others	.670	.300	ACT1
Injuring others	.686	.322	ACT2
Bumping others	.626	.312	ACT3
Jostling police	.633	.287	ACT4
Vandalism	.593	.278	ACT5
Participating in collecting shocking	.277	.100	ACT6

Analysis

Structural equation modeling integrated confirmatory factor analysis for identifying factors and structural relation analysis for testing hypotheses, with the robust maximum likelihood estimate accommodating data with various distributions (via Mplus, Muthen & Muthen, 2006). Factors identified were eight traits factors, representing the youth’s violent behavior in the past 3 months and last year and the antisocial and prosocial behavior of the youth’s peer friend network, elder network, and linking network in the past 6 months, given an independent method factor of acquiescence representing the common rating method artifact. Thus, the trait factors were independent of the method artifact (Podsakoff et al., 2003). With the trait factors, structural relation analysis simultaneously predicted the youth’s violent behavior with the antisocial and prosocial behaviors of the youth’s social networks and the youth’s prior violent behavior and background characteristics. The analysis also ran the prediction of the antisocial and prosocial behavior of the social networks with the youth’s prior violent behavior and background characteristics. This analysis gauged the risk of reverse causation or selection, such that the youth’s violent behavior affected the behaviors of social networks due to selecting friends and other social network members. In addition to modeling with all youths, two-group modeling with migrant and native and younger (aged below 20 years) and older (aged 20+ years) youths enabled testing differential effects between groups (with Wald’s test in Mplus).

Results

Structural equation modeling, incorporating confirmatory factor analysis and structural relation analysis, with all youths showed a good fit to warrant the adequacy of loadings and effects estimated (L²(377) = 1,202, SRMR = .024, RMSEA = .030, CFI = .952, Marsh et al., 2004). A good fit also emerged from structural equation modeling with the two groups and migrant and native youths for testing differential effects between the groups (L²(737) = 1,892, SRMR = .030, RMSEA = .036, CFI = .944). Similarly, two-group modeling differentiating younger (aged below 20 years) and older youths (aged 20+ years) yielded a good fit (L²(771) =2,023, SRMR = .030, RMSEA = .037, CFI = .940). Notably, the modeling could not include the youth’s maritage because of the tiny number of married youths (1%, see Table 1).

The confirmatory factor analysis part of structural equation modeling identified the 8 trait factors independent of the method factor. These factors showed convergence to achieve acceptable reliability (.588–.781, see Table 2). Notably, the trait factor of peer network prosocial behavior was less reliable because of its fewer items found adequate to represent the behavior. Supporting police law enforcement thus did not represent peer friends’ prosocial behavior. Simultaneously, these trait factors entered the structural relation analysis part of structural equation modeling for testing hypotheses.

Concerning Hypothesis 1, Hypothesis 1.3 but not Hypotheses 1.1 and 1.2 found support from the analysis. Accordingly, the youth’s violent behavior in the past 3 months was significantly positively predictable by the antisocial behavior of the youth’s linking network in the past 6 months (β = .098, see Table 3) but not by that of the youth’s peer or elder network. Notably, the youth’s violent behavior was significantly predictable by the antisocial behavior of the youth’s elder network when not controlling for the youth’s prior violent behavior (β = .099, p < .05). Hence, the antisocial behavior of the elder network was spurious and became nonsignificant when controlling for the youth’s prior violent behavior. By contrast, the antisocial behavior of the peer network did not significantly predict the youth’s violent behavior even without controlling for the youth’s prior violent behavior (β = .017, p > .05).

Table 3.

Standardized Effects: all (N = 2,363).

Predictor	Violence	Peer antisocial	Peer prosocial	Elder antisocial	Elder prosocial	Linking antisocial	Linking prosocial	Violence, last year
Peer network antisocial behavior	.043								ANTIP
Peer network prosocial behavior	–.138**								PROP
Elder network antisocial behavior	.088								ANTIO
Elder network prosocial behavior	–.177***								PROO
Linking network antisocial behavior	.098*								ANTIK
Linking network prosocial behavior	–.016								PROK
Violent behavior, last year	.232***	.054	–.041	.011	–.149***	.226***	.023		ACT
Female	.039	–.013	.130***	.016	.052*	–.002	.065**	–.052*	FEMALE
Age	–.018	–.062*	–.100***	–.024	.032	.010	.010	.078*	AGE
Education	.189***	–.011	.118***	–.018	.074**	.025	.023	–.038	EDUY
Locally born	–.019	–.107***	–.083**	–.124***	–.068**	–.120***	–.165***	–.041	BORNHK
Irreligious	–.014	–.029	–.045	–.039	.008	–.005	–.022	.001	ATHEIST
Welfare reception	.036	–.004	–.007	.021	.028	–.027	.006	.053	CSSA
Parents married	.062**	–.071*	.075*	–.091*	.041	–.093**	–.009	–.054	PMARRY
Family size	.003	.058*	.003	.126***	–.004	.150***	.051*	.058	FSIZE
R ²	.154	.030	.050	.047	.041	.108	.040	.022

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

Regarding Hypothesis 2, Hypotheses 2.1 and 2.2 attained support, while Hypothesis 2.3 did not. Accordingly, the youth’s violent behavior in the past 3 months was significantly negatively predictable by the prosocial behavior of the youth’s peer and elder networks (β = −.138 and −.177, see Table 3), but not that of the youth’s linking network. Notably, the prosocial behavior of the youth’s linking network did not significantly predict the youth’s violent behavior even without controlling for the youth’s prior violent behavior (β = −.043, p > .05).

Meanwhile, the youth’s prior violent behavior significantly positively predicted the youth’s violent behavior and the antisocial behavior of the youth’s linking network (β = .232 & .226, see Table 3) and significantly negatively predicted the antisocial behavior of the youth’s elder network (β = −.149). Nevertheless, the youth’s prior violent behavior did not significantly predict the antisocial behavior of the youth’s peer and elder networks and the prosocial behavior of the youth’s peer and linking networks. The inconsistent and trivial effects of the youth’s prior violent behavior on the behaviors of social networks lessened the risk of reverse causation from the youth to his or her social networks.

Two-group modeling with migrant and native youths revealed some differentials in the effects. Among the effects of social networks on the youth’s violent behavior, the most remarkable differential arose when the effect of the antisocial behavior of the elder network was significantly positive in the migrant but not significant in the native (β = .245 vs. .021, see Table 4). However, the differential was nonsignificant (p = .068,). The next most remarkable differential appeared when the effect of the prosocial behavior of the peer network was significantly negative on the native but was not significant on the migrant (β = −.156 vs. −.098). This differential, nevertheless, was not significant (p = .298). The other differential effects of social networks on the youth’s violent behavior were also nonsignificant. Hence, the effects of antisocial and prosocial behaviors in various social networks were indifferent between the migrant and native.

Table 4.

Standardized Effects on Violent Behavior in Migrants and Natives.

Predictor	Migrant	Native	χ ²	p-value
Peer network antisocial behavior	–.029	.084	1.566	.211
Peer network prosocial behavior	–.098	–.156**	1.083	.298
Elder network antisocial behavior	.245*	.021	3.324	.068
Elder network prosocial behavior	–.274**	–.145**	1.240	.266
Linking network antisocial behavior	.115	.072	0.099	.753
Linking network prosocial behavior	–.015	.000	0.019	.891
Above 6			4.631	.592
Violent behavior, last year	.196*	.245***	1.565	.223
Age	.115*	–.075*	10.450	.001
Education	.057	.239***	12.845	.0003
Parents married	.142**	.034	2.089	.148

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

Two-group modeling differentiating younger and older youths showed some differences in social network effects, but the differentials were nonsignificant. Notably, peer network prosocial behavior significantly inversely predicted the youth’s violent behavior in the older youth but not in the younger youth (β = −.155 vs. −.098, see Table 5). The differential, however, was nonsignificant (p = .650). Conversely, linking network antisocial behavior significantly positively predicted the youth’s violent behavior in the older youth but in the younger youth (β = .113 vs. .083). The differential was nonsignificant (p = .680). Differentials in background effects on violent behavior were also nonsignificant.

Table 5.

Standardized Effects on Violent Behavior in Younger and Older Youth.

Predictor	Younger (< 20 years) (n = 942)	Older (20+ years) (n = 1,421)	χ ²	p-Value
Peer network antisocial behavior	.042	.053	0.028	.868
Peer network prosocial behavior	–.098	–.154**	0.206	.650
Elder network antisocial behavior	.037	.104	0.535	.465
Elder network prosocial behavior	–.146*	–.174**	0.187	.666
Linking network antisocial behavior	.083	.114*	0.171	.680
Linking network prosocial behavior	–.011	–.029	0.034	.854
Above 6			2.820	.831
Violent behavior, last year	.238***	.225***	0.067	.807
Age	.005	–.018	0.209	.647
Education	.181***	.169***	0.000	.994
Parents married	.061	.050	0.057	.811

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

In addition, some background characteristics significantly predicted the behaviors of the youth and his or her social networks (see Table 3). Accordingly, the antisocial and prosocial behaviors of the peer network diminished with the youth’s age. The prosocial behavior of peer friend, elder friend, and linking networks was higher in the female than in the male youth. The youth’s violent behavior and the prosocial behavior of the peer and elder networks increased with the youth’s education. The antisocial and prosocial behaviors of peer friend, elder friend, and linking networks were higher in the locally born than in the migrant. The youth’s violent behavior and the prosocial behavior of the peer network were higher in the youth with married parents than in the other. This youth with married parents had lower antisocial behavior in elder and linking networks than did the other. The antisocial behavior of peer, elder, and linking networks, and the prosocial behavior of the linking network rose with family size.

Two differentials in the background effects, due to age and education, on the youth’s violent behavior were significant between the migrant and native. Age displayed a significant positive effect on violent behavior in the migrant but a significant inverse effect in the native (β = .115 vs. −.074, see Table 4). Meanwhile, the education effect was significantly positive on violent behavior in the native but not significant in the migrant (β = .239 vs. .057). The effects of other background characteristics on violent behavior were not significantly different between the migrant and the native.

Discussion

The youth’s violent behavior was significantly positively predictable by the antisocial behavior of his or her linking network and was significantly inversely predictable by the prosocial behavior of his or her peer and elder friend networks. These predictions, which were not significantly different between migrants and natives and between younger and older youths in Hong Kong, support Hypotheses 1.3, 2.1, and 2.2, predicated on integrating social learning theory, social bond theory, and social capital theory. Such integration emphasizes influence from learning from the authoritative and resourceful social network and bonding with conventional networks (Bassani, 2009; Braun & Vliegenthart, 2008; Herman-Kinney, 2003; Jung, 2016; Kaplan et al., 2013; Yu et al., 2019). More specifically, the youth learned to behave violently from antisocial linking acquaintances who were authoritative and resourceful. The youth also followed control by prosocial peer friends and elder friends over violent behavior, possibly because they are readily available and close. Social control from social bonding works through closeness based on attachment, commitment, and involvement (Cecen-Celik & Keith, 2019; Costello & Hope, 2016). By contrast, the youth was free from control by linking acquaintances, possibly because they are physically or socially detached, remote, and unavailable. Hence, linking acquaintances are models for the youth’s learning, whereas peer friends and elder friends control the young’s violent behavior. The influences of learning and control accommodate the social network properties maintained in social capital theory (Johnson et al., 2016). Thus, the youth learns from and obeys different social networks selectively.

The youth did not learn from peer friends and elder friends, who were not authoritative and resourceful generally. Specifically, the youth did not learn to behave violently from his or her antisocial peer or elder friends, likely because these friends were not good models or teachers for the youth. In the first place, the youth selects authoritative and resourceful people to admire and thus learn (Ivaldi & O’Neill, 2008). Hence, the youth would not select similar others for learning. Moreover, antisocial people are hostile, repulsive, unfriendly, thus not good models or teachers (Allen et al., 2005; Vauclair & Fischer, 2011).

As the youth’s behavior was not significantly predictable by the antisocial behavior of the youth’s peer and elder friend networks, social learning theory does not apply to the prediction by the networks. Instead, the theory suggests that antisocial and prosocial behaviors of the networks positively and inversely predict the youth’s violent behavior, respectively (Kabiri et al., 2020; Walters, 2020). By contrast, social bond theory posits that conventional or prosocial bonding inhibits violent behavior (Cecen-Celik & Keith, 2019; J. Lee & Randolph, 2015). The inverse predictions of the youth’s violent behavior by the prosocial behavior of the youth’s peer and elder networks thus testify the control process of social bond theory. Hence, social bond theory is preferable to social learning theory for explaining the predictions by peer and elder networks.

Neither the youth’s birthplace nor age significantly impacted social network effects on the youth’s violent behavior. As the local birthplace indicates Westernization, Westernization or culture does not seem to moderate the effects or value of social networks. The lack of moderation may stem from indifference in attachment to others between Chinese and Western cultures (D. K.-S. Chan et al., 2010). Attachment has fostered networking and social networks (D. Y. Lee, 2013; Lin, 2015). Meanwhile, the indifference to age may reflect the uncertain age effect on the value of social bonding (Akers & Lee, 1999; Watkins et al., 2000). The indifferences suggest that the social network effects may not be specific to the developmental stage and sociocultural context.

Nevertheless, age and education exhibited differential effects on the violent behavior of the migrant and native. The positive effect of age on the migrant’s violent behavior reflects the emphasis on age as social power or status in Chinese culture (Peng et al., 2015). Such power or status has translated into violent behavior (Petering et al., 2016). By contrast, Western culture envisions the development of morality and social appropriateness with aging (Connolly & McIsaac, 2009; Gibbs et al., 2007). Such development has eroded the youth’s violent behavior (Leadbeater et al., 2016; Van Heel et al., 2019). Meanwhile, Chinese culture trusts education to be prosocial and virtuous (K. Chan & McNeal, 2003). Such trust has restrained the youth’s violent behavior (Malonda et al., 2019). By contrast, education in Western culture emphasizes challenging, competing, criticalness, and empowering (Biggeri & Santi, 2012; Elmore, 2009; Killick, 2018). These emphases have fueled the youth’s violent behavior (Choi et al., 2011; Kaufman-Parks et al., 2017). Hence, Westernization would raise the positive effect of education on violent behavior.

The youth’s prior violent behavior significantly predicted the prosocial behavior of the elder network and the antisocial behavior of the linking network but not the antisocial behavior of the peer and elder networks and the prosocial behavior of the peer and linking network. Such nonsignificant prediction reflects the lack of friend selection due to the youth’s violent behavior. This lack echoes the rejective effect of violent behavior on inhibiting friendship and networking (Vitaro et al., 2007). This rejective effect remarkably reduced making friends with prosocial adults, who are conventional in befriending peers more than younger people (Richmond et al., 2019). By contrast, predicting the antisocial behavior of the linking or resourceful network by the youth’s prior violent behavior represents a selection that possibly involves gang or criminal networking (Esbensen et al., 2001). Such networking emerges because it strengthens antisocial behavior and its gain, as the formidable gangster affords and desires to recruit violent youths (Panfil, 2014). Hence, the association between the violent youth and the antisocial linking network is special homophily based on illicit gain.

Limitations and Future Research

The present contest of social network effects on the youth’s violent behavior has limitations requiring future research to minimize. The limitations lie in the cross-sectional design relying on self-report measurement in a single place. Such a design prohibits ascertaining the temporal and, thus, causal order in measures involved in prediction. The study cannot eliminate the risk of reverse causation due to selecting friends and other social network members. The self-measurement furthermore did not tap measured behaviors with objectively assured validity. Meanwhile, the single place limits generalizing the findings to the full range of sociocultural contexts, even though social network effects did not significantly differ between the migrant and native. Thus, future research is necessary to corroborate present findings with designs, measurements, and samples to ensure the prediction and causality, validity, and generalizability. The research can repeatedly measure behaviors from multiple informants to optimize objective validity, predictability, and causality to gauge the impacts of earlier measures on later ones. Meanwhile, sampling sites need to represent diverse sociocultural contexts surrounding the population worldwide to optimize the generalizability. The diversity can enable the examination of contextual moderation regarding behavior prediction.

Future research can also strengthen theoretical explanations for social network effects based on social bond, social learning, and social capital theories with mediating and moderating measures. For social bond theory, the measures include bonding with prosocial networks, comprising attachment, belief, commitment, and involvement. Regarding social learning theory, the measures include learning, identification, modeling, and reinforcement concerning antisocial and prosocial network. With social capital theory, the measures include the resource, function, and structure of the social network for amplifying its effect. The extended measures can explicitly explain the differential effects of the antisocial and prosocial behavior of peer, elder, and linking networks on the youth’s violent behavior.

Implications

Promoting youth’s networking with prosocial peer and elder friends and preventing the youth’s antisocial linking network is promising to prevent youth’s violent behavior. The emphasis of the promotion is on strengthening the youth’s bonding with prosocial friends. Based on social bond theory, leveraging social bonding or control from conventional persons such as parents is helpful to change behaviors, including extending bonding and networking with others (Bagwell & Schmidt, 2011). Likewise, leveraging social bonding or control from one source can detach networking with others (Bai et al., 2017). Building conventional bonds thus help foster further bonding with conventional or prosocial persons and detaching from the antisocial linking network. Based on social learning theory, it is necessary to undercut the youth’s learning from the antisocial linking network or its learnability. The theory suggests the worth of learning about the harm of learning from the antisocial network (Jurik et al., 2014). Based on social capital theory, alternatively, depriving the linking network’s resources, function, and structure help prohibit its influence (Flap & Volker, 2013; Munro, 2009). By contrast, removing the youth’s networking with antisocial friends, who do not foment violent behavior, is less vital than fostering the youth’s bonding with prosocial friends.

Footnotes

Data Availability

The data supporting this study’s findings are available from the corresponding author upon reasonable request.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding support for the study comes from a grant (2015.A1.023.15C) of the Public Policy Research Scheme of the Government of Hong Kong, China.

Ethical Approval

This study secured approval from the university research ethics committee.

ORCID iD

Chau-kiu Cheung

Author Biography

Chau-kiu Cheung, Ph.D., is an associate professor at the City University of Hong Kong, China. He has recently published research results concerning civility, social inclusion, resilience, character education, moral development, peer influence, and class mobility. His current research addresses issues of protest, thriving, early child development, organizational culture, and drug and vocational rehabilitation. Over the years, he has endeavored to scrutinize and advance knowledge concerning sociomoral development, social capital, and life quality. He actively engages in applied research on various social services for youth, elders, people with disabilities, education, policing, correctional services, and other community concerns. [email: ssjacky@cityu.edu.hk]

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