Social norms refer to a set rules that governs a group of people’s attitude, behaviour and regularities which makes the group different from other social groups (Mcdonald & Crandell, 2015), while Conformity is the act of matching one’s views, attitude and behaviours to those of the majority, even if if the majority response contradicts one’s own personal ideas (Jiang and Kim 2015). According to social identity theory, humans have the natural tendency to desire a form of identity, they want to feel the sense of belonging and they achieve this by identifying with a group (can be social class, family, friendship group, clubs and so on) which has a huge impact in the development and modification of self-esteem. Social Identity theory addresses phenomena such as conformity, prejudice, inter-group conflict and so on. Existing research on peer influence in adolescence has primarily focused on social influence on risky decision-making and risk perception (Reniers et al., 2017), but has recently been expanded to include cognitive performance (Wolf et al., 2015) and hypothetical prosocial behaviour (Foulkes et al., 2018). However, little research has been done on how peers influence risky decision-making. Hence, as conformity in adolescent involves a wide area of research, this essay will be exploring conformity to social norms in adolescent with a specific focus on how peers influence risky decision making amongst adolescents.

Why adolescents conform to social norms
An adolescent’s social life is often improved if he or she conforms, because failure to do so risks exclusion from the group (Williams et al., 2013). According to world health organisation, any person within the age range of 10 to 19 years old is referred to as an adolescent (Csikszentmihalyi, 2021). Adolescence stage is characterised by a period of developmental and transitional phase between adulthood and childhood. During this phase, a lot of physical, biological and psychological changes takes place which affects adolescent’s decision making, behaviour and actions. It is a period where the desire for a sense of identity, peer acceptance and social connection is really intense.
Adolescence is marked by a rise in risky behaviours (Mata et al., 2016; Willoughby et al., 2013), as well as heightened sensitivity to peer influence (Blakemore and Mills, 2014; Somerville, 2013). Schools are the most common learning setting for social skill development, social behaviour and the formation of essential social understandings that begin in early childhood (Eccles & Roeser, 2011). Hence, adolescents’ interactions with peers particularly in high school, are crucial information sources for youngsters learning how they are likely to be seen in society at large. Peers provide a wealth of information about who they appear to be and have the power to categorise other youth into social groups based on perceived social characteristics (Stone et al., 2008 as cited in Schall et al, 2016), both of which have a significant impact on the normative work of identity formation (Crosnoe& Johnson, 2011). When adolescents are with their peers, they are more likely to make health-harming risky decisions such as risky driving, substance abuse and so on, but peers can also have a positive influence by supporting prosocial development, promoting safer choices, and facilitating learning (Van Hoorn et al., 2016).
Adolescents are specifically prone to peer influence due to several reasons which includes, peer selection and socialisation process, desire to gain social status and fear of social exclusion. While Peer selection and socialisation involves the process whereby an individual selects a desired peer group and align their behaviours to fit with that of the group they belong or wish to belong to over time, social status involves gaining some sort of group membership which is a potential reward for conforming to group norms and social exclusion implies losing group membership. In order to avoid unpleasant social outcomes and social exclusion, adolescents are more likely to conform to group norms such as risk taking as they perceive the consequences of the risk to be lower than being socially excluded from their peer group (Bleize et al., 2021).
Two complementary theories from social psychology helps to explain how adolescents are influenced by peer socialisation and selection process, they include social learning theory and social identity theory. According to social learning theory, the behaviour of an adolescent is shaped by social reinforcement and modelling from their peers. This implies that adolescent pick up behaviour from their peers, observe, learn and imitate these behaviours and the reinforcement they get in the form of socialisation and their peer relationship encourages them to continue with such behaviour. Whereas with social identity theory, adolescents tend to identify with a group, conform to their social norms in order to improve their self-worth and self-esteem. Therefore, if a peer group an adolescent selects and socialises with are involved in risky behaviour such as substance misuse or abuse, they are more likely to learn and imitate those behaviour in order to continue being in that group and their group membership is their reinforcement. Using a sample of 647 early adolescent, Bleize et al., (2021) conducted a study to see if and how the social identity viewpoint on group behaviour might be used to explain cyber bullying on WhatsApp. They specifically looked at how social identification influences conformity to cyber assault. This research finding revealed that the centrality component of social identity and compliance to cyber violence have an indirect positive relationship, mediated by perceived social pressure to comply, according to hierarchical multiple regression. Findings from this study can be criticised because it was a cross-sectional study obtained using self-report data. Hence, it is possible that some of the results obtained might be due to common method variance. Therefore, future studies can use a longitudinal approach instead to eradicate the issue of common method of variance. However, Bleize et al (2021) study findings show consistency with previous studies on social identity influences on conformity like Bastiaensens et al., (2016) who used the social identity viewpoint and yielded useful insights into teen conformity to traditional (offline) bullying and cyberbullying. Another study by Lee (2006) found out that the effects of depersonalization on conformity were mediated by group identification (Bleize, 2021). Therefore, findings from these research in general can help in designing prevention and intervention programs for adolescents. Over subsequent decades, studies determining the presence of selection and/or socialisation effects for a variety of adolescent behaviours (most commonly aggressive and health-risk behaviours) has been dominated by peer influence field, largely offering general support for both processes’ relevance. Henneberger, Mushonga and Preston (2021) conducted a systematic review using forty studies to explore to what extent peer selection and socialisation process influence adolescence substance misuse. Findings from this review revealed peer selection and socialisation affects alcohol misuse in adolescents but few studies found peer effect on tobacco and drug use. Variations in this finding could be because of variation of study design employed by the studies selected for the systematic review. In respect to adolescent substance use and risk behaviours, observational and experimental research have evaluated the relative extent of active (peer pressure) and passive (imitation) peer impact. Other Studies have shown that adolescent smoking behaviour is influenced by imitation rather than peer pressure (Harakeh & Vollebergh (2012). Observational research found that emerging adults emulated the drinking behaviour of peer models when it came to alcohol consumption (Hustad et al., 2010). The relative impacts of both types of influence on risky decision-making were investigated in two experimental investigations. One study found greater evidence of imitation than peer pressure (Riedijk & Harakeh, 2018). Using a sample of 63 emerging adults with a confederate of same gender, Riedikk and Harakeh (2018) study investigated if emerging adults replicate their peers’ risky decisions and if peer susceptibility acts as a moderator. Findings from this study revealed that when the peer displayed hazardous decision-making, the participants engaged in more risky decision-making, according to linear regression analysis and there was no evidence that peer susceptibility was a significant modulator of this connection. However, ecological validity of this study is questionable because risky decision-making in this study was measured using stop-light game task and peers’ susceptibility was measured using a questionnaire. Hence, it is difficult to conclude that participant’s behaviour will be the same in other real-life circumstances. Another study conducted by Harakehand De-Boer(2019) found that a mix of imitation and peer pressure was the most predictive of risky conduct. Again, findings from these studies will be useful in planning and conducting programs (prevention as well as intervention programs) for adolescents. Understanding the underlying mechanisms that make teenagers vulnerable to peer influence is critical to preventing adolescent substance abuse and fostering healthy youth development. Regression models with longitudinal data are the usual method for assessing the effect of peer influence on adolescent substance use. In this method, an adolescent self-report his or her own substance usage as well as the substance use of his or her peers. The substance usage of friends is then utilised to forecast changes in own substance use over time. The obtained estimate is understood as a peer influence estimate. According to findings from traditional studies (Simons-Morton & Farhat 2010), peer drug usage is linked to an increase in teenage substance use over time. The traditional approach to research has limitations because it is unable to separate co-occurring selection and socialisation processes.

Motivation for Conformity

Conformity was considered a goal-directed behaviour by Cialdini and Trost (1998), who identified three possible motivations: (1) “the goal of effective action” (p. 162), which represents conforming to others’ opinions in order to make more accurate and valid judgments; (2) “the goal of building and maintaining social relationships” (p. 166), which represents conforming to gain approval and acceptance; and (3) “the goal of managing self-concept” (p. 168), which represents conforming to gain the task (difficulty, complexity, subjectivity, and prior commitment), the group (size, cohesion, credibility, and similarity between the group and the individual), and the individual (size, cohesion, credibility, and similarity between the group and the individual) can all influence levels of compliance including inclination to conform, social anxiety, need for affiliation, and fear of negative evaluation (Bobek et al , 2013). Jiang and Kim (2015) conducted two research to investigate the relationship between conformity and Korean teenagers’ views of social support, academic motivation, and achievement. Parental success pressure, perceived connection with parents, and sentiments of remorse towards parents all demonstrated favourable associations with conformity. Conformity was also associated to instructors’ and peers’ perceived support, student mastery-approach goals, and accomplishment in the English and mathematics domains. This study can be criticised for it’s limitations on methodology as data were obtained using survey (self-report sample). Therefore, the risk of social desirability in responses, which is a common limitation of self-report tools, also applies to this study. However, irrespective of it’s limitation, this study provided support for Y.Nie et al (2013) study who looked at how individual-oriented and social-oriented achievement objectives influenced how people approached and avoided achieving their goals. Findings revealed that only the two approach goals were positively predicted by individual-oriented achievement motive, but both approach and avoidance goals were positively predicted by social-oriented accomplishment motive.

Age effect on peer influence susceptibility

According to early study, age was a significant predictor of peer influence vulnerability. Berndt (1979) study revealed that age had a curvilinear effect on peer influence susceptibility, with a peak around ninth grade. Recent studies of the age–susceptibility link, on the other hand, reveal that between the ages of 14 and 18, susceptibility to peer influence diminishes linearly (Stewart & Shamdasani, 2014). Some fascinating work studying age-related correlates has generated a more consistent set of findings in the last ten years. Recent research, for example, suggests that socialisation vulnerability may be a product of developmentally appropriate psychosocial maturation, including identity formation. This idea is supported by findings on age-related vulnerabilities to peer influence (Sumter et al., 2009). Higher levels of self-regulation among late adolescents (Gardner, Dishion, & Connell, 2008) and inhibitory control among younger adolescents (Vitale et al., 2005) have also been linked to protection from deviant peer influences, according to research. In lab trials, it was discovered that when teenagers and young adults between the ages of 13 and 24 are with friends, they are more prone to take driving risks than when they are alone. Adults’ (25 years and older) driving dangers on the other hand are unaffected by their peers (Gardner & Steinberg, 2005). This is supported by data from car accidents, which show that the likelihood of an accident for young drivers increases when they have a passenger in the vehicle (Chen et al., 2000 as cited in Myers & Smith, 2012). A large-scale study by Knoll et al., 2015 found that the opinion of other teenagers has a strong influence on young adolescents’ risk perceptions. In this study, 563 persons aged 8 to 59 years old were asked to rate the riskiness of ordinary scenarios, after which they were presented with risk ratings from other people, either teenagers or adults, for the same situations (these provided ratings were in fact fictitious). After that, the participants were asked to rate the riskiness of the situations once more. Other people’s opinions influenced all age groups, however while children and adults were more influenced by adult ideas, young adolescents (12–14 years old) moved their assessments more toward those of teenagers than toward those of adults. Although this study has a high strength in regards to reliability due to use of large sample size, but it’s generalisability and validity is questionable due to the use of self report sample.

Motivation for Conformity
Conformity was considered a goal-directed behaviour by Cialdini and Trost (1998), who identified three possible motivations: (1) “the goal of effective action” (p. 162), which represents conforming to others’ opinions in order to make more accurate and valid judgments; (2) “the goal of building and maintaining social relationships” (p. 166), which represents conforming to gain approval and acceptance; and (3) “the goal of managing self-concept” (p. 168), which represents conforming to gain the task (difficulty, complexity, subjectivity, and prior commitment), the group (size, cohesion, credibility, and similarity between the group and the individual), and the individual (size, cohesion, credibility, and similarity between the group and the individual) can all influence levels of compliance including inclination to conform, social anxiety, need for affiliation, and fear of negative evaluation (Bobek et al , 2013).
Jiang and Kim (2015) conducted two research to investigate the relationship between conformity and Korean teenagers’ views of social support, academic motivation, and achievement. Parental success pressure, perceived connection with parents, and sentiments of remorse towards parents all demonstrated favourable associations with conformity. Conformity was also associated to instructors’ and peers’ perceived support, student mastery-approach goals, and accomplishment in the English and mathematics domains. This study can be criticised for it’s limitations on methodology as data were obtained using survey (self-report sample). Therefore, the risk of social desirability in responses, which is a common limitation of self-report tools, also applies to this study. However, irrespective of it’s limitation, this study provided support for Y.Nie et al (2013) study who looked at how individual-oriented and social-oriented achievement objectives influenced how people approached and avoided achieving their goals. Findings revealed that only the two approach goals were positively predicted by individual-oriented achievement motive, but both approach and avoidance goals were positively predicted by social-oriented accomplishment motive.

Age effect on peer influence susceptibility
According to early study, age was a significant predictor of peer influence vulnerability. Berndt (1979) study revealed that age had a curvilinear effect on peer influence susceptibility, with a peak around ninth grade. Recent studies of the age–susceptibility link, on the other hand, reveal that between the ages of 14 and 18, susceptibility to peer influence diminishes linearly (Stewart & Shamdasani, 2014). Some fascinating work studying age-related correlates has generated a more consistent set of findings in the last ten years. Recent research, for example, suggests that socialisation vulnerability may be a product of developmentally appropriate psychosocial maturation, including identity formation. This idea is supported by findings on age-related vulnerabilities to peer influence (Sumter et al., 2009). Higher levels of self-regulation among late adolescents (Gardner, Dishion, & Connell, 2008) and inhibitory control among younger adolescents (Vitale et al., 2005) have also been linked to protection from deviant peer influences, according to research. In lab trials, it was discovered that when teenagers and young adults between the ages of 13 and 24 are with friends, they are more prone to take driving risks than when they are alone. Adults’ (25 years and older) driving dangers on the other hand are unaffected by their peers (Gardner & Steinberg, 2005). This is supported by data from car accidents, which show that the likelihood of an accident for young drivers increases when they have a passenger in the vehicle (Chen et al., 2000 as cited in Myers & Smith, 2012). A large-scale study by Knoll et al., 2015 found that the opinion of other teenagers has a strong influence on young adolescents’ risk perceptions. In this study, 563 persons aged 8 to 59 years old were asked to rate the riskiness of ordinary scenarios, after which they were presented with risk ratings from other people, either teenagers or adults, for the same situations (these provided ratings were in fact fictitious). After that, the participants were asked to rate the riskiness of the situations once more. Other people’s opinions influenced all age groups, however while children and adults were more influenced by adult ideas, young adolescents (12–14 years old) moved their assessments more toward those of teenagers than toward those of adults. Although this study has a high strength in regards to reliability due to use of large sample size, but it’s generalisability and validity is questionable due to the use of self report sample.

Gender effect on peer influence susceptibility Gender has also been discovered to be a significant predictor of vulnerability to peer influence, with studies suggesting that both delinquent and non-delinquent peer influences are more prevalent in boys (Miller 2010). Studies have explored gender effect in relation to peer influence on risky behaviour for example Clark and Lothian (2007) investigated the effect of peer group influence in four different “risky behaviours” using data from the Add Health survey (smoking, drinking, drunkenness, and marijuana use). Findings from this study revealed significant peer group effects for alcohol and drunkenness in all three peer groups, particularly in the male peer group. Except for friends, there is little evidence that the female peer group influences individual behaviour. In general, not all of findings from this study was significant, it was therefore suggested that future research should focus on identifying demographic groups that are more reactive to social pressure. According to Steinberg and Monahan (2007), boys are more susceptible to neutral peer influences than girls, both during adolescence and early adulthood. However, there is some evidence that girls have a greater proclivity to conform than boys (see Chassin et al. 1986). Nonetheless, other studies have found no significant gender differences in susceptibility to peer influence (Costanzo and Shaw 1966 as cited in Meldrun et al 2012). Social influence and Conformity There is a widespread misconception that studying social influence and how it affects behaviour is the same as studying an individual who always conforms to group norm (Moscovici, 1976; see also Packer & Miners, 2012). For example, the classic line studies on social influence by Asch (1951) and Asch (1956) provide the framework for many contemporary research of social influence and conformity (Friedkin & Johnsen, 2011). His work is frequently cited as proof that a group majority may persuade people to agree with or conform to its views (Levine, 1999). Nonetheless, Asch’s experiments were designed to investigate resistance to social influence and group repression of nonconformity (Moscovici & Faucheax, 1972). The findings of the Asch, 1951, and Asch, 1956 tests show that individuals have the ability to resist the majority group’s influence (Wood et al., 1994). Later research has backed up this finding, implying that social support is a crucial tool to fight against conformity (Morgan et al ., 2015). However, according to Milgram (1963, 1964), Asch’s study on conformity was regarded non-significant (too trivial), thus Milgram (1963, 1964) undertook further investigation to see if participants would still conform or not using more serious consequences. In Milgram’s (1963, 1964) study, research participants were instructed to apply increasingly high-voltage shocks to an actor in another room, who would scream and then go silent as the shocks became greater. The shocks were not real, but they were manufactured to appear like they are real for this study. Findings revealed that up to 65% of participants obeyed experimenter and administered shock to learners up to the very end (450volts). Although Milgram’s study was criticised for not being ethical as participants initially believed they were causing harm to another, but findings from Milgram’s study has supported the notion that obedience to authority is inevitable. Other studies have replicated Milgram’s study and similar findings was found (Burger, 2009).

Conclusion

In summary, adolescence is marked by a rise in risky behaviours and heightened sensitivity to peer influence (Blakemore and Mills, 2014; Somerville, 2013). Adolescents are specifically prone to peer influence due to several reasons which includes, peer selection and socialisation process, desire to gain social status and fear of social exclusion. Two complementary theories from social psychology helps to explain how adolescents are influenced by peer socialisation and selection process, they include social learning theory and social identity theory. Age and gender have also been found to be a significant predictor of vulnerability to peer influence. Peer influence research on conformity amongst adolescent has grown substantially over time, contributing significant conceptual, methodological, and empirical contributions to an exciting and vital field of study. Much work has to be done, including further investigation of each of the domains of peer influence research. For future research, more longitudinal research, will be needed to help illuminate the developmental changes that may underpin the types of behaviours, moderators, and mechanisms that make adolescents vulnerable to peer influence.

REFERENCE
Aerts, A., Reniers, M., & Mousavi, M. R. (2017). Model-based testing of cyber-physical systems. In Cyber-Physical Systems (pp. 287-304). Academic Press.
Bastiaensens, S., Pabian, S., Vandebosch, H., Poels, K., Van Cleemput, K., DeSmet, A., & De Bourdeaudhuij, I. (2016). From normative influence to social pressure: How relevant others affect whether bystanders join in cyberbullying. Social Development, 25(1), 193-211.
Blakemore, S. J., & Mills, K. L. (2014). Is adolescence a sensitive period for sociocultural processing?. Annual review of psychology, 65, 187-207.
Bleize, D. N., Anschütz, D. J., Tanis, M., &Buijzen, M. (2021). The effects of group centrality and accountability on conformity to cyber aggressive norms: Two messaging app experiments. Computers in human behavior, 120, 106754.
Bobek, D. D., Hageman, A. M., &Kelliher, C. F. (2013). Analyzing the role of social norms in tax compliance behavior. Journal of business ethics, 115(3), 451-468.
Burger, J. M. (2009). Replicating Milgram: Would people still obey today?. American Psychologist, 64(1), 1.
Crosnoe, R., & Johnson, M. K. (2011). Research on adolescence in the twenty-first century. Annual Review of Sociology, 37, 439-460.
Csikszentmihalyi, M. (2021). Material virtue: Ethics and the body in early China. Brill.
Eccles, J. S., &Roeser, R. W. (2011). Schools as developmental contexts during adolescence. Journal of research on adolescence, 21(1), 225-241.
Foulkes, L., Leung, J. T., Fuhrmann, D., Knoll, L. J., & Blakemore, S. J. (2018). Age differences in the prosocial influence effect. Developmental science, 21(6), e12666.
Friedkin, N. E., & Johnsen, E. C. (2011). Social influence network theory: A sociological examination of small group dynamics (Vol. 33). Cambridge University Press.
Harakeh, Z., & de Boer, A. (2019). The effect of active and passive peer encouragement on adolescent risk-taking. Journal of Adolescence, 71, 10-17.
Harakeh, Z., &Vollebergh, W. A. (2012). The impact of active and passive peer influence on young adult smoking: An experimental study. Drug and alcohol dependence, 121(3), 220-223.
Henneberger, A. K., Mushonga, D. R., & Preston, A. M. (2021). Peer influence and adolescent substance use: A systematic review of dynamic social network research. Adolescent Research Review, 6(1), 57-73.
Hustad, J. T., Barnett, N. P., Borsari, B., & Jackson, K. M. (2010). Web-based alcohol prevention for incoming college students: A randomized controlled trial. Addictive behaviors, 35(3), 183-189.
Jiang, Y., Bong, M., & Kim, S. I. (2015). Conformity of Korean adolescents in their perceptions of social relationships and academic motivation. Learning and Individual Differences, 40, 41-54.
Jiang, Y., Bong, M., & Kim, S. I. (2015). Conformity of Korean adolescents in their perceptions of social relationships and academic motivation. Learning and Individual Differences, 40, 41-54.
Knoll, L. J., Magis-Weinberg, L., Speekenbrink, M., & Blakemore, S. J. (2015). Social influence on risk perception during adolescence. Psychological science, 26(5), 583-592.
Lee, E. J. (2006). When and how does depersonalization increase conformity to group norms in computer-mediated communication?. communication research, 33(6), 423-447.
Levine, J. M. (1999). Solomon Asch’s legacy for group research. Personality and Social Psychology Review, 3(4), 358-364.
Mata, R., Josef, A. K., &Hertwig, R. (2016). Propensity for risk taking across the life span and around the globe. Psychological science, 27(2), 231-243.
McDonald, R. I., & Crandall, C. S. (2015). Social norms and social influence. Current Opinion in Behavioral Sciences, 3, 147-151.
Meldrum, R. C., Young, J. T., &Weerman, F. M. (2012). Changes in self-control during adolescence: Investigating the influence of the adolescent peer network. Journal of Criminal Justice, 40(6), 452-462.
Milgram, S. (1964). Issues in the study of obedience: A reply to Baumrind.
Miller, H. V. (2010). If your friends jumped off of a bridge, would you do it too? Delinquent peers and susceptibility to peer influence. Justice Quarterly, 27(4), 473-491
Morgan, T. J., Laland, K. N., & Harris, P. L. (2015). The development of adaptive conformity in young children: effects of uncertainty and consensus. Developmental science, 18(4), 511-524.
Myers, D. G., & Smith, S. M. (2012). Exploring social psychology. New York: McGraw-Hill.
Nie, Y., &Liem, G. A. D. (2013). Extending antecedents of achievement goals: The double-edged sword effect of social-oriented achievement motive and gender differences. Learning and Individual Differences, 23, 249-255.
Packer, D. J., & Miners, C. T. (2012). At the first sign of trouble or through thick and thin? When nonconformity is and is not disengagement from a group. Journal of Experimental Social Psychology, 48(1), 316-322.
Riedijk, L., &Harakeh, Z. (2018). Imitating the risky decision-making of peers: An experimental study among emerging adults. Emerging adulthood, 6(4), 255-265.
Schall, J., Wallace, T. L., &Chhuon, V. (2016). ‘Fitting in’in high school: How adolescent belonging is influenced by locus of control beliefs. International Journal of Adolescence and Youth, 21(4), 462-475.
Simons-Morton, B. G., &Farhat, T. (2010). Recent findings on peer group influences on adolescent smoking. The journal of primary prevention, 31(4), 191-208.
Somerville, L. H. (2013). The teenage brain: Sensitivity to social evaluation. Current directions in psychological science, 22(2), 121-127.
Stewart, D. W., &Shamdasani, P. N. (2014). Focus groups: Theory and practice (Vol. 20). Sage publications.
van Hoorn, J., van Dijk, E., Meuwese, R., Rieffe, C., & Crone, E. A. (2016). Peer influence on prosocial behavior in adolescence. Journal of research on adolescence, 26(1), 90-100.
Williams, K. D., Forgas, J. P., & Von Hippel, W. (2013). The social outcast: Ostracism, social exclusion, rejection, and bullying. Psychology Press.
Willoughby, T., Good, M., Adachi, P. J., Hamza, C., & Tavernier, R. (2014). Examining the link between adolescent brain development and risk taking from a social–developmental perspective (reprinted). Brain and cognition, 89, 70-78.
Wolf, L. K., Bazargani, N., Kilford, E. J., Dumontheil, I., & Blakemore, S. J. (2015). The audience effect in adolescence depends on who’s looking over your shoulder. Journal of adolescence, 43, 5-14.