Adolescents’ intention to take risks: The influence of moral perception and
injunctive norms
Vera Verhoef University of Amsterdam Vera Verhoef 11704810Bachelor thesis Psychobiology
Department of Developmental Psychology University of Amsterdam
Daily Supervisor: Ana da Silva Pinho, MSc Official Supervisor: Dr. Wouter van den Bos June 19th, 2020
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Abstract
Previous research showed the importance of studying individuals’ intention to engage in risk-taking behavior given its strong impact on actual risk-taking behavior among
adolescents. Perceived social norms and individuals’ beliefs are proposed by theoretical models as two of the most prominent sources of influence on one’s behavioral intention. By combining components of two theoretical models, the present study investigated the influence of perceived injunctive norms and adolescents’ moral perception/beliefs about behavior on their intention to engage in risk-taking behavior. The sample (N = 87) consisted of Dutch high school students aged between 11 and 18 years (Mage = 14.4 years; 53 % female), who completed self-reported questionnaires and a behavioral task. Using a linear mixed-effects model, the results of this study showed that individual moral perception significantly predicted adolescents’ intention to engage in risk-taking behavior. Whereas perceived injunctive norms were not a significant predictor of risk-taking intention. The results demonstrate the importance of taking into account adolescents’ own beliefs and mental representations of behavior while studying their intention and motivation to engage in risky behavior. Driven by theoretical models, this study provides novel insights into adolescents’ behavioral intention.
Keywords: adolescence, perceived injunctive norms, moral perception, risk-taking behavior, behavioral intention
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Introduction
Adolescence is a period between childhood and adulthood (Crone & Dale, 2012),
characterized by several changes at emotional, hormonal and behavioral levels as well as brain maturation (Chein & Steinberg, 2013; Steinberg, 2005). Adolescents go through a period of social re-orientation: they start spending more time with their peers and, inversely, less time with their parents and family (Van den Bos, 2013; Berkowitz, 2004). During this developmental phase, adolescents become more susceptible to peer influence. This influence can be described in a broad way as the effect that a peer or peers may have on one’s behavior and attitudes (Møller & Haustein, 2014; Berkman, 2000). Peer influence is often associated with adolescents’ risk-taking behavior and adolescents are more
susceptible to peer influence compared to adults (Gardner & Steinberg, 2012). Individual perception of social norms within the peer context is one of the most important sources of social influence on adolescent behavior (Zaleski & Aloise-Young, 2013; Cestac, Paran & Delhomme, 2014). Yet, little is known about the influence of perceived social norms on adolescents’ intention to engage in risk-taking behavior.
Individuals’ intention of behavior can be described as individual motivation to engage in certain behaviors (Baker & White, 2010). A widely applied model to study behavioral intention is the Theory of planned behavior (TPB, Azjen, 1991), which is an extended version of the Theory of reasoned action (TRA) developed by Fishbein (1979). Both theories propose that behavioral intention is the most relevant determinant of behavior in general (Montaño & Kasprzyk, 2015). Therefore, an individual’s intention of behavior is an important predictor of future behavior. Interestingly, empirical evidence has suggested that the likelihood of engaging in actual taking behavior is higher when the intention to be engaged in risk-taking behavior is stronger (Baker & White, 2010). Hence, it is important to understand how behavioral intention is formed and which factors may be influential.
Recently, Montaño and Kasprzyk (2015) developed an integrated theoretical model inspired by TPB and TRA, in which both perceived social norms and individuals’ own
behavioral beliefs are proposed as two of the most relevant sources of influence on
individuals’ intention of behavior. When referring to perceived social norms a distinction can be made between two types of norms: descriptive and injunctive norms (Rimal, 2008). Descriptive norms refer to the prevalence of a behavior in a given social context - “what
4 most people do”, while injunctive norms consider the approval (or disapproval) of a certain behavior by others – “what people should do”- within a given social context (Cialdini & Reno, 1990). Specifically, it has been shown that individuals’ perception of descriptive and
injunctive norms influence individuals’ risk-taking behavior, such as gambling, substance use and speeding behavior of drivers (Meisel & Goodie, 2014; Eisenberg et al., 2014; Tankard & Paluck, 2016, Møller & Haustein, 2014).
During adolescence, individuals may copy or adjust their behaviors to match those of peers (Crone & Konijn, 2018). Individuals, as social beings, have the desire to affiliate with groups that are formed by shared core values and beliefs (Cialdini & Trost, 1998). The need to be accepted and the strong desire to belong to the peer group (Albert, Chein, & Steinberg, 2013) is particularly prominent during adolescence. Hence, adolescents’ perception of how behaviors are judged by their peers, (i.e., approval or disapproval of a behavior - injunctive norm) within their group may play a role in adolescents’ intention to behave in a certain way. Indeed, in recent years researchers have devoted attention to the influence of
perceived norms on adolescents’ intention of behavior. However, mixed results have been reported in the literature about the impact of perceived injunctive norms on adolescent intention to engage in risky behavior. For instance, no effect of perceived norms on
adolescent intention to engage in online risky behavior (Sasson & Mesch,2016) or offline risk behavior (i.e., smoking behavior) was found (Harakeh et al.,2004). While, in contrary, it has been reported that adolescents perception of friends’ disapproval of offline risk-taking predicts adolescents’ motivation to engage in this behavior (Zaleski & Aloise-Young, 2013; Rimal, 2008; Paek, 2009). Currently, it is not clear to what extent perceived injunctive norms impact adolescent intention of risk-taking behavior.
In addition to perceived injunctive norms, a factor that may influence intention of behavior is individual’s moral perception of behavior or moral beliefs. Morality can be defined as the perceived correctness of social behavior; a set of attitudes to determine whether a behavior in a given situation is good or bad/ right or wrong (Forney, Crutsinger & Forney, 2006; Crocetti et al., 2018). Moral development is mostly taking place during the adolescence phase (Hart & Carlo, 2005). Also, moral judgement affects decision making in both negative and positive ways, resulting in different behavioral intentions (Yoon, 2011). Indeed, empirical evidence from developmental studies has shown the influence of
5 adolescents with a lower level of moral competence reported a higher intention of
engagement in risky behavior (Shek & Zhu, 2018), and individuals’ approval of risky behavior had a positive effect on adolescents’ intention to engage in risk-taking behavior (Forney et al., 2006). The evidence suggests that higher levels of individuals’ own approval of risky behaviors lead to more willingness of adolescents to adopt these behaviors (Morell-Gomis, 2018; Cristea & Gheorghiu, 2016). However, little is known about the influence of
adolescents’ moral perception of risk-taking behavior on their intention to engage in this type of behavior.
Previous findings suggest similarity between moral perception of behavior and the attitude component described in the TPB (Cristea & Gheorghiu, 2016, Montaño & Kasprzyk, 2015, Morell-Gomis et al., 2019). Hence, we propose to combine components of the TPB and the newly developed model of Montaño and Kasprzyk (2015) to deepen our understanding of intention to engage in risk-taking behavior in adolescence. This study will provide a deeper insight in the dynamic interplay between adolescents’ motivation to engage in risky behavior and individuals own moral perception of behavior as well as their perception of social norms. Since intention of behavior is a very strong predictor of actual risky behavior (Reniers et al., 2016; Lambert & Liard, 2016), it is of practical relevance to focus on
behavioral intention. A better understanding of the relationships between these variables will be informative for interventions on risk-taking behavior among adolescents as well as prevention strategies.
Current study
In the present study we aim to address relevant questions in a developmental population. First, we aim to investigate the impact of perceived injunctive norms on adolescents’ intention of risk-taking behavior. Second, we will investigate the influence of adolescents’ own moral perception of behavior on their intention to engage in risky
behavior. To do so, adolescents from a Dutch high school completed multiple questionnaires regarding perceived social norms and intention to engage in risky behavior as well as a task addressing adolescents’ moral perception of risky behavior. Based on previous studies on offline risk-taking, it is hypothesized that perceived injunctive norms will influence
adolescents’ intention to engage in risk-taking behavior. More specifically, it is expected that more perceived peer approval, thus more permissive injunctive norms, will lead to a higher
6 motivation to perform risky behavior (Hypothesis 1). Additionally, we believe that moral perception will influence adolescents’ intention to engage in risk-taking behavior.
Particularly, it is expected that more individual approval, thus more permissive moral beliefs, will lead to a higher motivation to perform risky behavior (Hypothesis 2). Just like their important role in the construction of social norms, significant others also play a role in the formation of one’s moral perception of behavior. One’s social network has an impact on how a person’s own moral perception of different behaviors is formed and shaped, especially peers or close friends (Genina, Rose & Vitell, 2016; Crocetti et al., 1990; Svensson et al., 2017). Therefore, this study was performed within the high school social network of adolescents, providing a more naturalistic and real social context to study the proposed dynamics between norms, beliefs and intention of behavior.
Materials and methods
Procedure. The current study is part of a larger research project called “Social Smart”
conducted in the Connected Minds lab at the University of Amsterdam. Generally, this project investigates processes regarding social information use and decision making in adolescents. Data for the larger project were collected in two waves using questionnaires and behavioral tasks. Several measures were assessed, however only data collected in the first wave will be used in the present study. The study protocol was approved by The Ethics Review Board of the Faculty of Social and Behavioral Sciences (ERB).
Dutch high schools were contacted and being informed about the research conducted in the Social Smart lab. Schools showing interest in participating in the project were recruited. First, a digital informed consent was sent to the parents of adolescents aged below 16 years old in the participating classes. Additionally, parents received an information letter about the aim and structure of the study. Parents reported back if they discussed possible participation with their offspring as well as permission for participation in this project. Participants aged 16 or above could give their own consent for participation. A monetary compensation was given to each participating class, consisting of € 5 per
participant and per each data collection wave. Also, one participant per class could win a €40 bol.com voucher in a lottery. The lottery tickets could be obtained by earning points in some questions and behavioral tasks.
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Data collection. Participants completed several questionnaires about their social networks
and behavior and also performed behavioral tasks in a two-wave data collection study. Between the two waves there was a time interval of approximately 3 weeks. Data collection took place at the high schools in classroom setting. Each session lasted no longer than 45 minutes. Beforehand participants were instructed about the structure of the test session. They were asked to complete the questionnaires and tasks individually, at their own pace. Additionality, participants were instructed that there were no right or wrong answers, to make sure that their answers were based on their own perception. Both the questionnaires as the behavioral tasks were performed on a tablet.
Participants. In total, 176 individuals agreed on participating in the larger study from which
141 participants filled in the questionnaires and the task in wave 1. Due to time constraints and missing data in some items, some participants were excluded from the main analysis. The final sample in which the main analysis was performed consisted of 87 participants. Participants’ age range was from 11 to 18 years, with a mean age of 14.4 years (SD = 2.45). Participants were from 7 different classes which educational level varied (i.e., MAVO, HAVO or VWO). Table 1 displays an overview of participants’ gender and age per grade.
n (%) Gender Female 46 (53) male 41 (47) Mean (SD) Age 1st grade 12.4 (.61) 3rd grade 14.7 (.52) 5th grade 16.8 (.81)
Table 1. Descriptive statistics of the final sample (demographics). Mean age per grade is showed
along with the percentage male and female participants.
Questionnaire design. A self-report questionnaire about social norm perception, risky
behavior and behavioral intention was filled in by the participants. Information about participants’ age, gender and grade was obtained beforehand. The first part of the questionnaire consisted of questions regarding participants’ social networks to map their
8 relationships with peers (e.g., questions about perceived popularity or about friendship). The second part of the survey addressed perceived social norms and behavioral intention (see appendix A.1 for the questionnaire).
Task design. The goal of the behavioral task in wave 1 was to measure adolescents’ moral
perception of behavior by using statements of several risky behaviors. In wave 2 adolescents were asked to rate some of the same items again, but now they were provided with social information from their social networks. In this study, only data on risk taking behavior from wave 1 is used. Risky behaviors included in the task were divided into more observable actions, such as smoking behavior, and less observable actions, such as the time spent on online games.
The task was designed in a way that some of the used risk-taking items matched the items included in the questionnaires about perception of social norms and intention of risk-taking behavior. Examples of risky behaviors addressed in both the questionnaire and the task are: substance use related behaviors (cigarettes, alcohol, weed) and skipping school (see appendix A.2 for an example of the task). In total, the risky behavior dimension used in this study consists of 4 offline risky behavior items.
Measures
Perceived injunctive norms. To assess the perception of injunctive norms participants were
asked to rate different risky behavior items; ‘I believe that my classmates think that
smoking/drinking alcohol/ skipping school is…’. Responses were rated by using a slider on a 11-point scale, in which (1) was very bad/wrong behavior and (11) very good/right behavior. The average injunctive norms score across the 4 risky behavior items was calculated for each participant. A higher score represented more perceived peer approval of risky behavior.
Individual moral perception. Moral perception of risky behavior was assessed by using a task in which participants were asked to either approve or disapprove different actions. For example, ‘I think smoking weed is…’. The responses could be rated on a slider ranging from very bad/wrong (1) to very good/right (11), with (6) being neutral. A score between (1) and (5) would be considered as individual disapproval of the action, a score between (7) and (11) indicates individual approval of the action. In total, participants were exposed to 24 risky
9 behavior statements. The average moral perception of each individual was calculated using the 4 risky behavior statements mentioned above. Higher average scores represented higher levels of individual approval of risky behaviors.
Behavioral intention. The behavioral intention indicator was measured with the question: ‘How likely are you to engage in the following action (i.e., smoking) within a year?’. A 5-point scale was used, ranging from very unlikely (1) to very likely (5). The average intention score was calculated per individual across the 4 risky behavioral items with a higher score
indicating a higher self-reported individual likelihood to engage in risk-taking behavior.
Data analysis. Data analysis was conducted using R studio for Windows, version 4.0.0 (Team,
2020). First, descriptive data analysis (e.g., checking assumptions, means, correlations) was conducted, followed by the main statistical analysis: linear mixed-effects model. Prior to the analysis the data were checked for missing values, duplicated participant numbers and data assumptions (e.g., normality). Duplicated participant numbers were due to, for example, a restart of the moral perception task. Participants with missing values were excluded in order to have complete data per participant. No distinction was made between male and female participants, since previous empirical research suggest no significant gender differences regarding intention of risk-taking behavior (Brown at al., 2010; Reniers et al., 2016). Four items from the risky behavior category regarding drinking alcohol, smoking weed, smoking cigarettes and skipping school were included in this study. A mean score of these 4 items was computed per participant. Pearson’s correlation was performed in order to test the strength of the relationship between perceived injunctive norms, moral perception of risky behavior and participants self-reported intention to engage in risk-taking behavior.
Linear mixed-effects model was conducted to test our hypotheses: the effect of perceived injunctive norms on adolescents’ intention of risk-taking behavior (H1) and the effect of individual moral perception of risk-taking behavior on adolescents’ intention to engage in risk-taking behavior (H2). This was done by using the lmer function of the lmerTest R package (Kuznetsova, Brockhoff & Christensen, 2017). Linear mixed-effects model used the maximum likelihood fitting method and we included random intercepts for each class in the model in order to account for differences within participants’ perception of norms dynamics per classroom.
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Results Descriptive Analysis.
Mean scores of behavioral intention and the predictors. All data was skewed to the left due to the scale of the questions (see appendix B). Risky behavior is generally perceived as negative, causing the scores to fall on the lower side of the scale. The perceived injunctive norms scale ranged from very wrong (1) to very right (11), while most adolescents
perceptions were falling into the category very wrong to wrong (1-5). Therefore, almost 40 % of the adolescents think that their classmates are not that disapproving of risk-taking
(M = 2.85, SD = 1.53, see appendix B.1). The same scale was used to assess individual moral perception. Here approximately 60% of the adolescents are disapproving about risk-taking behavior, falling in the ‘bad/wrong’ side of the scale (M = 2.83, SD = 1.33, see appendix B.2). Hence, adolescents are more permissive in terms of their own beliefs of risk-taking
compared to their perception of peer approval. Furthermore, the behavioral intention data showed low levels of intention to engage in risky behavior (M = 1.76, SD = .871, see appendix B.3).
Table 2 shows mean scores per grade in the sample. The perceived peer approval score of risky behavior is increasing per grade, with the highest score for 5th grades. Likewise, higher levels of individual approval of risk-taking behavior are showed in 5th grades
compared to 3rd and 1st grades. The same pattern was found for behavioral intention. Additionally, adolescents from the 1st grade perceive their peers as being less permissive towards risky behavior compared to their own beliefs, while adolescents from the 5th and 3rd grade perceived themselves as being less permissive (see appendix C).
11 Grade Mean SD Perceived injunctive normsa 1 1.81 0.98 3 2.80 0.75 5 4.28 1.04 Moral perception of risky behaviora 1 2.15 0.92 3 2.50 1.13 5 3.82 1.25 Intention of risk-takingb 1 1.22 0.39 3 1.69 0.30 5 2.49 0.90
Table 2. Means and standard deviations per grade for all tested variables. 1st grade data consists of
four classes, while 3rd grade data consists of only one class. Lastly, 5th grade data consists of two classes.
Independent variables are perceived injunctive norms and moral perception of risky behavior. a. Scale range = 1-11
b. Scale range = 1-5
Correlation analysis.
Associations between behavioral intention and the predictors. Pearson correlation tests revealed significant, positive, between-person relations among all variables. It is important to note that higher values for perceived injunctive norms and individual moral perception reflect greater approval of risky behavior. Moreover, greater values for behavioral intention reflect a higher self-reported likelihood of engaging in risky behavior. The strongest positive relationship was found between moral perception of risky behavior and intention of risky behavior (r(85) = .74, p < .001), indicating that higher values of individual approval of risk-taking are associated with increased values of adolescents’ behavioral intention (figure 1.A.). Likewise, between perceived injunctive norms and behavioral intention a moderate positive correlation was found (r(85) = .56, p < .001), hence increasing values of perception of peer approval are related to increasing levels of intention of adolescents to engage in risk-taking behavior (figure 1.B.). Furthermore, a positive moderate correlation was found between perceived peer approval of risky behavior and individual approval of risky behavior (r(85) = .55, p < .001, figure 1.C.).
12 Figure 1. A. Correlation between intention of behavior and perceived injunctive norms. A significant,
positive correlation was found between intention to engage in risk-taking behavior and perceived peer approval of risky behavior. B. Correlation between intention of behavior and moral perception. A significant, positive correlation was found between intention to engage in risk-taking behavior and individual approval of risky behavior. C. Correlation between moral perception and perceived
injunctive norms. A significant, positive relation was found between individual approval of risky
behavior and perceived peer approval of risky behavior.
Statistical modelling: Linear mixed-effects model.
Perceived injunctive norms and individual moral perception as predictors of risk-taking intention. It was hypothesized that both perceived injunctive norms and individual moral perception would be predictors of adolescents’ intention to engage in risky behavior. In order to check whether it would be relevant to control for differences between classes, we perform a one-way independent ANOVA for both predictive variables. Specifically, with respect to the perceived injunctive norms since these were asked in the context of each
A
C
13 classroom. There was a significant effect of class on the perceived injunctive norms score (F(1, 85) = 35.9, p <.001, α = .05), hence the perceived peer approval data showed to be dependent on class (see appendix D.1). Also, a significant effect of class on individual moral perception was found (F(1,85) = 24.9, p < .001, α = .05), however these differences seem to be most present due to higher scores of the 5th grades (see appendix D.2). Therefore, as we expected we included the variable ‘Classes’ in our mixed-effects model as random effects to control for differences between classes.
Additionally, we checked assumptions of multicollinearity and normality of residuals. Variance inflation factor (VIF) values were within the normal range (i.e., below 5 with VIF moral perception= 1.12 and VIF injunctive norm = 1.12). Also, a QQ-plot showed no violation of normality of residuals (see appendix E).
Hypothesis 1: Results of our linear mixed-effects model showed no significant effect of perceived injunctive norms on behavioral intention of adolescents when controlling for class differences (β = .05, t = .94, p < .35), suggesting that perceived injunctive norms do not predict adolescents’ behavioral intention to engage in risk-taking behavior.
Hypothesis 2: As expected, results from our model showed a positive effect of individual moral perception of risky behavior on adolescents’ behavioral intention when controlling for differences between classes (β = .37, t = 6.76, p < .001), hence individual moral perception is found to be a significant predictor of adolescents’ intention to engage in risky behavior (for more details see table 3 in appendix F).
The linear mixed-effects model explained 45% of the variance of adolescents’ intention of risk-taking behavior (R2=.45). Additionally, 6% of the variance was explained by differences between classes.
Discussion
The current study investigated the influence of perceived injunctive norms and individual moral perception on adolescents’ intention to engage in risky behavior. This study combined factors from the TPB by Azjen (1991) and the extended model proposed by Montaño & Kasprzyk (2015), to further advance our understanding of motivation to engage in risk-taking behavior in adolescence. In contrary to our first hypothesis, perceived injunctive norms of
14 adolescents did not significantly predict adolescents’ intention to engage in risk-taking behavior. In line with our second hypothesis, adolescents’ own moral perception of risk-taking behavior significantly predicted adolescents’ intention to engage in risky behavior. Moreover, the direction of this effect (i.e., positive) meets our expectation: more permissive individual perception of risky behavior leads to higher levels of intention to engage in risk-taking behavior.
Unexpectedly, results suggested that perceived injunctive norm is not a significant predictor of behavioral intentions. This finding is not in line with results of some previous studies suggesting a significant effect of injunctive norms on adolescents’ intention to engage in risky behavior (Zaleski & Aloise-Young, 2013; Cestac, Paran & Delhomme, 2014; White et al., 2009). A possible explanation for our finding could be that only a minority of the individuals are driven primarily by the perception of social pressure through social norms (Trafimow & Finlay, 1996), however it seems unlikely that the effect is small. Another
plausible explanation for this finding could be a bidirectional relationship between morality and injunctive norm perception. After all, our results indicated a significant positive
correlation between injunctive norm perception and individual moral perception. Research found that adolescents use a combination of their own values and their perception of friends’ approval to construct the perception of social norms, suggesting an interaction between individual moral perception and perceived norms (Zaleski & Aloise-Young, 2013). Moreover, empirical research states that individuals are most likely to be influenced by their friends, which might suggest that injunctive norms of friends influence intention of behavior (Simons-Morton & Farhat, 2010; Haye et al., 2011). The current study measured perceived injunctive norms by using individual perceptions of classmates. By not focussing specifically on the influences of friends, the effect of injunctive norm perception may have been less prominent.
In line with our second hypothesis, results suggest that individual moral perception of risky behavior has a positive effect on behavioral intention. Particularly, being more
permissive of beliefs about behavior leads to an increased motivation of adolescents to engage in risk-taking behavior in the near future. This finding is similar to findings of
previous research suggesting a strong positive influence of attitude on behavioral intention, which is comparable to the influence of moral perception (Cristea & Gheorghiu, 2016; Mentrikoski et al., 2019). Also, research found that moral beliefs have an independent effect
15 on behavioral intention, suggesting that a more positive moral judgement toward a behavior leads to a greater behavioral intention of a person (Sparks & Shepherd, 2002). Taken
together, this study suggests that individual moral perception is a relevant predictive component of behavioral intention.
Furthermore, the average score of all variables increased among grade, showing more perceived peer approval and more individual approval of risky behavior for higher grades. Remarkably, it was found that 1st grades perceived their peers as being less
permissive compared to their own beliefs, showing a lower average score of perceived peer approval compared to a higher score of individual approval. Whereas the opposite trend was found for both 3rd and 5th grades, in which adolescents seem to think that their peers are more permissive toward risky behavior compared to their own beliefs, showing a higher average score of perceived peer approval compared to a lower score of individual approval (see appendix C). Besides, adolescents from 1st grades showed the lowest intention to engage in risk-taking behavior which could be due to the fact that young adolescents have less experience with risky situations, and therefore rated actions as being riskier then other age-groups (Halpern-Felsher et al., 2001). Additionally, differences between grades might be explained by the fact that the social-influence effect decreases with age (Knoll et al., 2015; Sumter et al., 2009). More specifically, susceptibility to peer influence increases during early adolescence and individuals become more resistant to peer influence (Steinberg &
Monahan, 2007). Younger adolescents from the 1st grade are more influenced by their peers compared to older adolescents from 3rd and 5th grades (Knoll et al., 2017). Therefore, it could be that younger adolescents perceived peer norms more negatively compared to their own beliefs, whereas the opposite is the case for the higher grades. However, only 8% of the adolescents from the sample came from the 3rd grade, hence the results regarding
differences between grades have to be interpret with caution.
Study limitations.
This study is not without limitations. As discussed earlier, the sample mostly consisted of 1st and 5th grade students, resulting in an unequal distribution of age among the sample.
Specifically, not much data was obtained from adolescents aged around 14 years old. Hence it was not fully possible to examine differences between age groups among adolescents.
16 Second, cultural differences regarding the importance to comply to perceived injunctive norms were not taken into account. Empirical studies showed the importance of culture in the variation of injunctive norms, which could lead to differences in intention of behavior (Buchtel & Norenzayan, 2008; Moon, Weick & Uskul, 2017; Fischer, Karl & Fischer, 2019).
Furthermore, the validity of this study can be limited due to the potential limitations of data obtained through self-report. Even though participation was completely voluntary, and the questionnaires were completed individually, the data relies on the honesty of the participants. Moreover, participants could interpret particular questions differently or have a varying degree of understanding certain questions. This might result in incomparable or inaccurate responses. However, participants were always offered the possibility to ask for clearance about the questionnaires and task in case of confusion.
Future research.
Further research could, for instance, include both intention of behavior and actual behavior in the research model, making the model about adolescent risk-taking behavior more comprehensive and more informing for developing interventions. Moreover, future studies could test the influence of perceived descriptive norms on behavioral intention. Empirical research using TPB as framework identified descriptive norms as possible influence factor of adolescents’ intention to engage in risky behavior as well as their intention to engage in prosocial behavior (Cristea & Gheorghiu, 2016; White et al., 2009). Finally, nowadays social media is used a lot among adolescents, which create digital forms of risky behavior or even causes so called “social media addiction” (Rosenthal-von der Pütten et al., 2019; Barker, 2009; Andreassen, Pallesen & Griffiths, 2017; Bányai et al., 2017). Future research could focus on adolescents’ motivation to use social media platforms and examine the intention to perform risky behavior online.
In summary, findings from our study suggest that perceived injunctive norms as newly proposed component in the model of Montaño & Kasprzyk (2015) may not work as a predictor of behavioral intention like the original proposed component of subjective norms in the model of TPB by Azjen (1991). Besides, our results also indicate that moral perception could be an important additional component to predict behavioral intention. However, more research is needed: 1) to determine the influence of perceived norms on the intention to perform risky behavior; 2) to replicate the predictive value of moral perception in
17 combination with the other components in the models of Azjen (1991) and Montaño & Kasprzyk (2015). Nonetheless, this finding is of practical importance since it could be used to improve interventions by adding components focussed on adolescents’ mental
representations of behavior. Especially components which focus on adolescents’ own beliefs of risk-taking.
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Appendices Appendix A. Questionnaire and task design
A.1 Questionnaires regarding perceived injunctive norms and behavioral intention. Only questions addressing
injunctive norms and behavioral intention of the 4 risky behavior items used in this study are showed (i.e., smoking cigarettes, smoking weed, drinking alcohol and skipping school). Beforehand, participants were asked to report their age and complete questions about their social network.
Perceived injunctive norms. This part of the questionnaire is about what you believe that classmates are thinking about a certain behavior. So: if they would approve or disapprove it. In the next questions, you can indicate what classmates are thinking according to you.
I believe that my classmates think that… 1. Smoking cigarettes is ()
Very bad 1 2 3 4 5 6 7 8 9 10 11 Very good 2. Drinking alcohol is ()
Very bad 1 2 3 4 5 6 7 8 9 10 11 Very good 3. Smoking weed is ()
Very bad 1 2 3 4 5 6 7 8 9 10 11 Very good 4. Skipping school is ()
Very bad 1 2 3 4 5 6 7 8 9 10 11 Very good Behavioral intention. How likely are you to engage in the following actions within a year?
1. Smoking cigarettes
Very unlikely 1 2 3 4 5 Very likely 2. Drinking alcohol
Very unlikely 1 2 3 4 5 Very likely
3. Smoking weed
Very unlikely 1 2 3 4 5 Very likely 4. Skipping school
Very unlikely 1 2 3 4 5 Very likely
A.2 Task regarding individual moral perception. Only one example in Dutch (‘alcohol drinken’) addressing
individual moral perception of risk-taking behavior is showed. Participants had to move the slider according to their moral beliefs.
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Appendix B. Distribution of the variables
B.1. Distribution of perceived injunctive norm scores. The distribution of perceived peer approval
scores (M = 2.85, SD = 1.53) was not normally distributed (W = 0.928, p < .001). The distribution was moderately skewed to the left (Skewness = .509; Kurtosis = -.735). The blue dotted line represents the mean perceived peer approval score.
B.3. Distribution of behavioral intention scores.
The distribution of adolescents’ intention scores to engage in risky behavior (M = 1.76, SD = .871) was not normally distributed (W = .822, p <.001). The distribution was highly skewed to the left (Skewness = 1.41, Kurtosis = 1.94). The blue dotted line represents the mean intention score.
B.2 Distribution of moral perception scores. The
distribution of individual approval scores of risk taking (M = 2.83, SD = 1.33) was not normally distributed (W = .950, p <.05). The distribution was moderately skewed to the left (Skewness = .611, Kurtosis = -.221). The blue dotted line represents the mean individual approval score.
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Appendix C. Scores among grade
C.1. Visualisation of mean perceived injunctive norm scores per grade. Perceived peer approval of
risky behavior is increasing with grade.
C.2. Visualisation of mean moral perception scores per grade. Individual approval of risky behavior is
increasing with grade.
C.3. Visualisation of mean behavioral intention scores per grade. Intention to engage in future
Appendix E. Distribution of the residuals
E.1. QQ-plot of normality of residuals. Residuals
are normally distributed.
Appendix D. Data dependency of classes
D.1. Boxplot of median perceived injunctive norm score per class. Perceived peer approval showed to
be dependent on class(F(1, 85) = 35.9, p <.001, α = .05). However, it seems that perceived injunctive norms are more dependent on class compared to individual moral perception.
D.2. Boxplot of median individual moral perception score per class. Perceived peer approval showed to
be dependent on class (F(1,85) = 24.9, p < .001, α = .05). Classes 116 and 117 represent the 5th grades.
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Appendix F. Table containing detailed results of the tested linear mixed-effects model
Fixed effects Estimate (B) SE t P value
(intercept) .53 .21 2.57 .02 *
Perceived injunctive norms .05 .06 .94 .35
Moral perception .37 .05 6.76 <.001 **
Random effect Variance SD
Classes .06 .25
F.1. Table 3: Linear mixed-effects model for predicting intention of risk taking behavior. The output table
of the tested linear mixed-effectsmodel. Predictors are perceived injunctive norms and individual moral perception of risk-taking behavior, while controlling for classes as random effect. Only the moral perception score ** was a significant predictor of behavioral intention. The linear mixed-effects model is presented as: Intention of behavior = perceived injunctive norms + moral perception + (1 | class ID).
