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https://doi.org/10.1177/0022042621994547
Journal of Drug Issues
1 –22
© The Author(s) 2021
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DOI: 10.1177/0022042621994547
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Original Article
Why Parents Misuse Prescription
Drugs to Enhance the Cognitive
Performance of Healthy Children:
The Influence of Peers and
Social Media
Sebastian Sattler1,2 , Guido Mehlkop3, Vanessa Bahr3,
and Cornelia Betsch3
Abstract
The mechanisms affecting parents’ misuse of prescription stimulant drugs to boost healthy
children’s school performance are hardly unknown. Using four web-based factorial vignette
surveys (2×2 between-subjects design experiment), we investigated the willingness of U.S.
parents with school-aged children to medicate a fictitious 13-year-old child whose grades had
declined. We examined mechanisms of informational and normative social influence on their
decision-making: others’ behavior (NExperiment 1 = 359), others’ definitions (NExperiment 2 = 326),
social control (NExperiment 3 = 325), and others’ experience (NExperiment 4 = 313). In addition, we
explored the moderating role of influential sources (close friends vs. social media). Parents
were more willing to engage in said behavior when others reported engagement in this behavior
or positive drug experiences, especially if both influences were transmitted via social media.
Others’ definitions and social control had no effect. Thus, social media might be a channel for
the prevention of pharmacological cognitive enhancement.
Keywords
cognitive enhancement, substance misuse, descriptive norm, injunctive norm, social influence
Introduction
If other children at school were hypothetically taking prescription drugs to increase cognitive
functions such as concentration or memory without medical necessity, approximately every third
respondent in a nonrepresentative survey (conducted by the magazine Nature among their
[potential] readers) would feel pressure to give such drugs to their child (Maher, 2008). Such
nonmedical use of prescription drugs has gained growing academic interest in recent years
(d’Angelo et al., 2017; Heyes & Boardley, 2019; O’Connor & Nagel, 2017; Teter et al., 2020).
Candidate drugs for this so-called “cognitive enhancement” (CE) include methylphenidate,
1University of Cologne, Köln, Germany
2Institut de recherches cliniques de Montréal, Quebec, Canada
3University of Erfurt, Germany
Corresponding Author:
Sebastian Sattler, Institute of Sociology and Social Psychology, University of Cologne, Universitaetsstrasse 24, 50931
Köln, Germany.
Email: sattler@wiso.uni-koeln.de
994547JODXXX10.1177/0022042621994547Journal of Drug IssuesSattler et al.
research-article2021
2 Journal of Drug Issues 00(0)
modafinil, and amphetamines. These drugs are usually prescribed to treat attention-deficit hyper-
activity disorder (ADHD), sleep disorders, or narcolepsy.
A recent study among U.S. high school students found that 2.5% self-reported CE for study
purposes (Teter et al., 2020). Among the several hundred thousand adolescents (aged 12–17) in
the United States who misused prescription stimulants (Substance Abuse and Mental Health
Services Administration, 2019), cognitive performance enhancement is one key motive (Schepis
et al., 2020). The prevalence rates of CE that have been assessed in different populations (e.g.,
students or employed people) vary greatly (Maier & Schaub, 2015; Sattler, 2016; Smith & Farah,
2011). Recent research, however, found indications for increasing prevalence rates in several
countries (Maier et al., 2018). Currently, little is known about parents’ administration of drugs to
enhance the cognitive performance of their children (Sattler, 2020a). According to a survey with
parents from the United States, 1% of parents of children aged 13 to 17 (who had not been pre-
scribed stimulants to treat ADHD) assumed that their children use “study aids,” and another 4%
were not sure (C.S. Mott Children’s Hospital National Poll on Children’s Health, 2013).
Most research investigating the etiology of CE has involved college and university students,
that is, persons using the drugs for themselves (reviews: Caviola & Faber, 2015; Ragan et al.,
2013; Schelle et al., 2014). We aim to explore the decision-making process of parents to give
these drugs to their children (here defined as 18 years and below) without medical necessity
because it can be assumed that at least for younger children, parents may contribute to children’s
CE. Parents are important stakeholders in decisions regarding whether a child will engage in CE
(Sattler & Wörn, 2019) because children depend on their parents’ stewardship (Coleman, 1994).
A certain proportion of parents seem to have high expectations toward their children’s school
performance, driven by the aim of improving their children’s prospects or own status motives,
often together with competitive thinking (Doepke et al., 2019; Nadesan, 2002; Rasmussen &
Troilo, 2016). This might make some parents tempted to administer CE. Moreover, parents are
also the gatekeepers of drugs, and as they are often role models, their attitudes may impact their
children’s attitudes and potential substance use subsequently in life (Epstein et al., 2007). While
researchers forecasted a rise of CE in children (Colaneri et al., 2018), also “because parents want
the best for their child” (O’Connor & Nagel, 2017, p. 5), parents fear such a rise (Forlini &
Racine, 2009; Hiltrop & Sattler, under review).
Factors that may facilitate an increase or prevent a decrease in CE include a high availability
of drugs due to more (invalid) diagnoses, competitive admission criteria and grading, increased
performance and peer pressure in schools, parents who may convince physicians that their chil-
dren have ADHD when they are not satisfied with their child’s grades or behavior, or the spread
of information through online sources (Conrad & Bergey, 2014; ExpressScripts, 2014; Forlini &
Racine, 2009; King et al., 2014; McCabe et al., 2005; Singh et al., 2013). Given this, parents
might be susceptible to rationalizing CE as an instrument to achieve their goals (Arria & DuPont,
2010), similar to engagement in other intensive parenting practices such as private tutoring
(Wells et al., 2016).
Such developments should also consider the serious criticisms that CE in children raised due
to its individual and social consequences (Colaneri et al., 2018; Graf et al., 2013; Nagel, 2019;
Sattler & Singh, 2016). These criticisms include concerns about fairness, side effects, social dis-
advantages, and peer pressure. Thus, gaining insight into how parents decide for or against such
drugs is crucial for prevention and regulation policies and to protect children from harm.
However, limited research has investigated parents’ motivations and goals regarding CE (Ball &
Wolbring, 2014; Forlini & Racine, 2009; Sattler, 2020b; Sattler & Wörn, 2019; Smith & Farah,
2011). The initial, mainly qualitative research, however, indicates that parents would administer
CE to children under certain conditions (Ball & Wolbring, 2014; Cutler, 2014). They would
surprisingly do so, despite no clinical research on the effectiveness of CE drugs in children
(Sattler & Singh, 2016). Reviews for research in healthy adults, however, indicated moderate
Sattler et al. 3
enhancement effects (Battleday & Brem, 2015; Caviola & Faber, 2015), whereby the first clinical
research with healthy college students suggests mixed objective but strong subjective effects
(Weyandt et al., 2018). Therefore, to judge safety and effectiveness, parents must rely on their
personal experience or information from other sources. Thus, when parents lack personal experi-
ence, peers or media reports might play an important role in this process. Qualitative and survey
research shows that social influences play a role in parents’ decisions to (hypothetically) give
children CE drugs (Forlini & Racine, 2009; Maher, 2008; Sattler & Wörn, 2019), but research on
the causal role of social influences is missing.
We aim to close this gap by performing four factorial survey experiments investigating the
effect of social influence on parents’ decisions to give CE drugs to children.
Theory: The Importance of Social Influence Processes
Social influence through peer groups and social media is an important driver of individual behav-
ior. Several studies document social influences on various health-related behaviors or motiva-
tions, such as vaccination (Betsch et al., 2010), eating (Higgs & Thomas, 2016), doping in sports
(Kabiri et al., 2019), or legal and illegal substance use (Borsari & Carey, 2009; Napper et al.,
2016). Some researchers assume that peers’ behavior and norms are even more important for
actual behavior than individuals’ personal attitudes (Keuschnigg & Kratz, 2017; LaBrie, Hummer,
Neighbors, et al., 2010).
In our study, we aim to test the assumptions embedded in social learning theory (Akers &
Sellers, 2013; Ford & Ong, 2014; Peralta & Steele, 2010; Watkins, 2016) and the social norms
approach (Cialdini & Goldstein, 2004; Napper et al., 2016), which describe several mechanisms
underlying how others influence one’s behavior. These mechanisms are applicable not only to
explaining social influences from friends or other peers but also to social influences from the
internet and social media, which is important since medical information from online sources has
gained popularity (Betsch et al., 2010; Elmore et al., 2017). Based on a classical typology by
Deutsch and Gerard (1955), social influences can be grouped into informational and normative
influences. Information can be derived from interactions with others and by observing the actions
and experiences of others. This might result in the imitation (of role models) and reinforcement
of a behavior (Akers & Sellers, 2013). Relying on others might be especially important for the
onset of new behavior (Cochran et al., 2017). Thus, following others or deriving information
from their behavior and via interactions can be an adaptive strategy (Hertwig & Hoffrage, 2013)
because acquiring information privately can be more costly (e.g., time consuming or dangerous)
(Webster & Laland, 2008). Information from others can also reinforce one’s behavior because it
provides insight regarding its reward and cost structure. However, others can also be the source
of incorrect or dangerous information (Betsch et al., 2015; Epstein et al., 2007).
Normative influences can also be based on others’ actions in terms of indirectly signaling their
definitions of right and wrong, i.e., their normative expectations to conform, or directly by oth-
ers’ thoughts and speech (i.e., speaking about their definitions), and whether they reinforce
behavior by exercising social control (Akers & Sellers, 2013; Deutsch & Gerard, 1955; Napper
et al., 2016; Peralta & Steele, 2010).
These informational and normative influence processes can be direct, subtle, indirect, and
outside of awareness. Several processes of this influence have been described in the literature
related to substance use, including alcohol, opioids, and the nonmedical use of prescription drugs
(Borsari & Carey, 2009; e.g., Maahs et al., 2016; Napper et al., 2016; Watkins, 2016). For exam-
ple, research has found that the onset of the latter is driven by exposure to other users and learn-
ing about their positive and negative drug experiences (Mui et al., 2014). Minimal research on
social influence, however, exists on CE. However, since individuals often may have minimal
personal knowledge regarding CE, they may have an incentive to rely on others. Hence, social
4 Journal of Drug Issues 00(0)
influences and anecdotal evidence might play a crucial role in forming attitudes toward CE
(London-Nadeau et al., 2019).
In our factorial survey experiments, we assess the influence of others, namely, others’ behav-
ior (Experiment 1), others’ definitions, (Experiment 2), others’ control (Experiment 3), and oth-
ers’ experience (Experiment 4). In each experiment, we also examine whether social influence
differs depending on whether it occurs via face-to-face interactions with close friends or social
media. The following paragraphs present the hypotheses related to each factor.
Others’ Behavior
Individuals can directly learn via interactions with others (i.e., through differential associations
with others) (Ford & Ong, 2014; Peralta & Steele, 2010; Watkins, 2016). Individuals can also
indirectly derive information from others’ (typical) behavior since their “doing” serves as a
descriptive norm (Cialdini & Goldstein, 2004; Napper et al., 2016) that may lead to imitation
(Akers & Sellers, 2013). Moreover, exposure to others’ behavior can signal their attitudes and
normative evaluations. For example, if peers use CE, observing this can serve as behavioral con-
firmation of others’ acceptance of CE and others’ benefit-risk ratio for these drugs, thereby
revealing the preferences of peers (Borsari & Carey, 2009; Ford & Ong, 2014; Sattler, 2020a;
Sattler et al., 2014; Watkins, 2016). Knowing or believing that others engage in deviant behavior
can help neutralize or justify one’s own deviant behavior, such as by reducing personal responsi-
bility (Cutler, 2014; Heyes & Boardley, 2019). Such knowledge may also produce conformity
pressure or indirect coercion to behave similarly to avoid falling behind (Forlini & Racine, 2009;
Sattler et al., 2014; Sattler & Wörn, 2019). Previous focus group research found that parents and
health care providers are aware of peer pressure if students use CE (Forlini & Racine, 2009).
Although this knowledge rendered the parents sad and worried, the health care providers assumed
that peer pressure led to higher demands and increased the need to use CE. While another qualita-
tive study found that some parents did not react to external pressures (Ball & Wolbring, 2014), a
survey showed that classmates’ drug use would exert pressure to also medicate their children
(Maher, 2008). Similarly, survey research (Maier et al., 2016b; Molloy et al., 2019) and experi-
mental studies investigating CE in populations other than children (with hypothetical scenarios)
provide evidence of contagion effects (London-Nadeau et al., 2019; Sattler et al., 2014).
Therefore, we predict that the higher the number of others who engage their children in CE, the
greater the willingness to give CE to a child (others’ behavior hypothesis).
Others’ Definitions
By observing others’ behavior, individuals might indirectly infer the moral acceptability of this
behavior, but information regarding its moral status can also be directly communicated. Social
learning theory discusses how the moral status of a behavior affects decision-making through
definitions. These definitions are an individual’s own beliefs about or moral judgments of whether
a behavior is right or wrong (Cutler, 2014; LaBrie, Hummer, Lac, et al., 2010). Such definitions
include neutralization techniques with regard to whether the violation of norms can be justified.
The perception of the definitions of others’ has been termed injunctive norms in the social norms
approach (Napper et al., 2016). These norms can influence an individual’s definitions, since
knowledge of others’ definitions can help an individual understand a situation and deduce infor-
mation regarding which behavior is appropriate, expected, or justified, and this knowledge can
help defuse shame and guilt if the individual engages in deviant behavior. This knowledge may
also result in an individual conforming to others’ views (Napper et al., 2016) by aligning both
attitudes and behavior, which might be achieved to obtain approval from others (Cialdini &
Goldstein, 2004). However, whether parents’ CE decisions are affected by others’ moral
Sattler et al. 5
considerations remains an unanswered question. Only the study by Forlini and Racine (2009)
found that parents are receptive to the acceptance of CE among peers. We predict that the more
others find the use of CE in children morally acceptable, the greater the willingness to give CE
to a child (others’ definitions hypothesis).
Others’ Control
Knowing the degree to which others accept a behavior might be of less relevance to individuals’
behavior than whether others turn words into action in the form of social control through rein-
forcement. Others may make the effort and show (dis)approval to enforce a social norm by apply-
ing informal incentives or punishments that are either beneficial or costly to those targeted (Akers
& Sellers, 2013; Sattler et al., 2014; Watkins, 2016). Some studies have found that recommenda-
tions from friends to take CE (positive reinforcement) are rare (Maier et al., 2016b) and that only
a few individuals would use CE if employers recommended it (Franke et al., 2012). Regarding
pediatric CE, in two qualitative studies, parents said that they would lose respect for other parents
who pressure their children to take drugs for enhancement (Ball & Wolbring, 2014; Hiltrop &
Sattler, under review). Studies involving students demonstrate that hypothetical disapproval
reduces the willingness to use CE (Sattler et al., 2014), while expected approval increases the
intention to use CE (Ponnet et al., 2015). In an era of online hate speech and “shitstorms” as
extreme examples of others’ control and generally negative feedback that can be provided anony-
mously online, it is important to study whether others’ critiques voiced via social media affect
parents’ CE decisions. Here, we predict that the more strongly others show disapproval (e.g., by
criticizing) of using CE among children, the lower the willingness to give CE to a child (others’
control hypothesis).
Others’ Experiences
While observing others’ behavior is a direct influence process in the case of imitation and a more
indirect influence process when information from others’ behavior must be inferred, there is also
the possibility that individuals hear directly about others’ evaluations of and experience with a
certain behavior. Information about others’ experiences with CE can be a primary source of rein-
forcement for this behavior in addition to reinforcement via social control (Cochran et al., 2017;
London-Nadeau et al., 2019). Such vicarious experiences of a behavior include its perceived
benefits and costs; in the context of drugs, there are desired effects and unwanted side effects
(Akers & Sellers, 2013; Ford & Ong, 2014; Peralta & Steele, 2010; Sattler et al., 2014; Watkins,
2016). Schelle et al. (2014) assume that it is not others’ behavior per se that matters but rather
whether that behavior leads to positive results, thereby creating pressure to use prescription stim-
ulant drugs. This emergence of implicit pressure has also been described in qualitative research
as a reason for initiating CE use (Heyes & Boardley, 2019). Concerns regarding side effects are
mirrored in studies involving parents, who perceive CE as emotionally and physically harmful
and want to avoid harming children (Ball & Wolbring, 2014; Forlini & Racine, 2009). However,
no study has investigated the influence of personally or vicariously experienced side effects in
this context. We predict that the higher others rate the positive effects of a drug relative to the
negative effects when giving CE to children, the greater the willingness to give CE to a child
(others’ experience hypothesis).
Influence Source
The described forms of influence can occur through different sources, such as relatively proximal
or more distal reference groups (Cochran et al., 2017; Napper et al., 2016). Influences from
6 Journal of Drug Issues 00(0)
proximal others might mainly occur via direct interactions (Akers & Sellers, 2013; Cochran
et al., 2017; LaBrie, Hummer, Neighbors, et al., 2010). Influences from distal others might more-
frequently occur indirectly through media or identification with these distant others. In line with
social comparison theory (Festinger, 1954) and social impact theory (Latané, 1981), some
researchers assume that more proximal reference groups are more influential than distant refer-
ence groups (LaBrie, Hummer, Neighbors, et al., 2010; Napper et al., 2016) and; thus, the learn-
ing of certain behavior mainly occurs in intimate personal groups rather than via “interpersonal
agencies of communication, such as movies and newspapers” (Sutherland et al., 1995, p. 67).
However, influences from distal groups might exist even if these others are not directly
observed because people can learn about others’ actual or planned behavior by consulting the
internet (Hertwig & Hoffrage, 2013). Thus, media represent a source of influence from distant
reference groups (Akers & Sellers, 2013; Watkins, 2016) and searching for health or medical
information is among the most popular online activities (Fox & Fallows, 2003). Therefore, how
the media, especially the internet, including social media, can influence CE behavior remains a
highly relevant but still open question. Media outlets continuously cover the topic of CE; the
amount of related content on the internet is increasing, and the impact of media, including inter-
net forums, on CE perceptions and use has been previously expected but not tested (Conrad &
Bergey, 2014; Racine & Forlini, 2010; Vargo & Petróczi, 2016). Research in other areas indicates
that information obtained from internet sources can affect health-related behavior and perceived
social norms because users of such sources learn about others’ reported behavior, experiences,
reasoning, and (dis-)approval, such as drinking alcohol, smoking, or vaccination (Betsch et al.,
2010; Elmore et al., 2017; Moreno & Whitehill, 2014), which can have unfavorable effects
(Betsch et al., 2013; Elmore et al., 2017). Therefore, we investigate whether the four mechanisms
of social influence differ depending on the source that exerts the social influence, that is, proxi-
mate (i.e., friends) and distant (i.e., others represented in media or unknown persons involved in
discussions within an internet forum) reference groups. This question is particularly important
because information spreads easily and quickly on the internet and can influence many people
simultaneously. Therefore, investigating its influence has practical relevance by informing inter-
vention strategies (Crocco et al., 2002; Moreno & Whitehill, 2014). Given this reasoning, we
pose the research question of whether the effects of others’ behavior, others’ control, others’ defi-
nitions, and others’ experience depend on whether the sources of influence are close friends or
communication via a social media site (source question).
Methods
Experimental Vignette Design
To test the hypotheses, we presented participants with hypothetical situations (vignettes) in an
experimental design. This approach is a useful research strategy when it is difficult to directly
observe behavior or when it is ethically problematic to conduct real experiments (Graeff et al.,
2014; Rettinger & Kramer, 2009). This approach is also known to reduce socially desirable
responses (Choong et al., 2002). Conducting experiments with vignettes instead of relying only
on traditional survey research can advance research concerning social influences and substance
use, which is often correlational and thus does not provide insight into causation (Watkins, 2016).
Such correlational research also has difficulties differentiating between effects capturing how an
individual’s behavior is influenced by peers and effects resulting from selecting peers whose
behaviors are very similar to the individual’s behavior or experiences with CE (i.e., homophily)
or those exposed to similar contexts (e.g., to other parents with children at the same competitive
school). The differentiation between these effects can be better addressed with an experimental
design (Akers & Sellers, 2013). Such designs avoid problems of unobserved heterogeneity and
Sattler et al. 7
allow causal claims to be made. Experiments also have an advantage over perceptual measures
that can provoke projection biases, that is, when respondents project their behavior or attitudes
onto others’ behavior or attitudes.
Pretesting
We conducted cognitive pretests (N = 5) using the think-aloud technique and probing questions
to evaluate and improve the study procedure and comprehensibility of the instruments.
Participants
For each experiment, we recruited respondents through Clickworker, a commercial platform with
more than 2 million accounts worldwide, of which approximately 46% are located in North
America; 35% of account holders speak U.S. American English (Clickworker, 2021). Similar to
MTurk and comparable platforms (Buhrmester et al., 2011; Clifford et al., 2015), individuals can
earn additional income on Clickworker for different tasks, such as survey participation. Therefore,
such platforms have received cross-disciplinary attention for data collection (Irani, 2015).
Motivators for completing tasks are often monetary but differ by task and include fun and educa-
tion (Al-Ani & Stumpp, 2016; Dunn, 2020; Ipeirotis, 2010). Research with MTurk samples indi-
cates a sufficiently high replication rate of the direction and significance of treatment effects that
have been found in probability samples (Mullinix et al., 2015). The criteria for participation
included consent to participate, no participation in a prior experiment of this type, passing the
attention check,1 U.S. citizenship, being a parent of at least one child in elementary/primary
school, middle school, or high school (junior/senior),2 and no item nonresponse. Those who com-
pleted the study received a small monetary incentive. Participation was voluntary and anonymous.
We recruited 359 parents (25.9% women, mean age: 36.27) in Experiment 1, 326 participants
(27.3% women, mean age: 36.86) in Experiment 2, 325 participants (20.9% women, mean age:
38.11) in Experiment 3, and 319 participants (25.2% women, mean age: 36.15) in Experiment 4.3
Instruments
Experimental treatments. The four experiments used 2×2 between-subjects designs (social influ-
ence × influence source). The participants were asked to carefully read a scenario that described
parents who were worried about the declining school performance of their child who would have
a high school proficiency exam (Supplemental Appendix A for the cover story, which was identi-
cal across all experiments). Consistent with the definition of CE, the vignette said that the child
was in good psychological and physical health; thus, the use of these drugs was not necessary for
medical reasons. We randomly assigned the respondents to the following influence source condi-
tions: they either read that parents were discussing with close friends who were also parents or
reading about parents from a social media site for parents (i.e., an online discussion forum) about
the use of CE drugs to enhance their child’s cognitive performance. In both versions, we explicitly
stated that such drugs are usually prescribed to children with ADHD only but are also used off-
label for purposes for which they are not officially approved. The story randomly continued with
one of four binary social influence conditions displayed for one of two influence sources (Supple-
mental Appendix B for a detailed description of the wording). In Experiment 1, the respondents
read that either none or most others gave such drugs to their healthy children to enhance school
performance. In Experiment 2, the others stated that CE is morally acceptable or unacceptable. In
Experiment 3, the others strongly criticized parents who gave CE drugs to children or advised
parents to administer such drugs. In Experiment 4, the others reported either that the positive
consequences of such drug use outweighed the negative consequences or the opposite.
8 Journal of Drug Issues 00(0)
In all experiments, we set the age at 13 years because at this age, parents may still have con-
siderable influence over their children’s actions and whether they take medication (which might
decrease with age). Moreover, at this age, children are close to changing to a 4-year high school;
thus, their performance is important. Students also start taking the Preliminary Scholarly Aptitude
Test (PSAT) and National Merit Scholarship Qualifying Test (NMSQT) to qualify for scholar-
ships. Further studies may replicate our experiments with other ages. Due to the experimental
design, both treatments are uncorrelated (Supplemental Appendix C). Almost all correlations
between these treatments and respondent characteristics were insignificant, while two statisti-
cally significant but relatively weak correlations existed with age (Experiment 2) and sex
(Experiment 4). We therefore control our results for the respective respondent characteristics.
Manipulation check. We displayed the vignette for a minimum of 45 seconds before the respon-
dents could continue, self-paced, to the next page for the manipulation check and the dependent
variable. We used a manipulation check that referred to the content of the manipulated social influ-
ence in each experiment (items adapted from Watkins, 2016, Supplemental Appendix D).
Dependent variable. Each experiment then assessed the behavioral willingness to give a prescrip-
tion stimulant drug to a child by asking “If you were one of Charlie’s parents, would you give a
prescription stimulant drug to Charlie?” (adapted from Sattler et al., 2014). The response options
ranged from 0 “definitely not” to 100 “definitely yes.” Although we cannot measure actual
behavior, willingness is an important predictor of behavior according to the theory of planned
behavior (Ajzen & Fishbein, 2004; Epstein et al., 2007).
Prior CE. We assessed prior experience with CE in children by asking parents whether they gave
prescription stimulant drugs to their child (-ren) to enhance their children’s cognitive abilities
(e.g., for mental concentration, memory, or vigilance) without medical necessity (Sattler & Wie-
gel, 2013). Response options were “No, never,” “Yes, within the last 30 days,” “Yes, between the
last 30 days and 6 months,” “Yes, between the last 6 months and 1 year,” and “Yes, more than 1
year ago.” We dichotomized the responses into no [0] and yes [1].
Together with sex and age, we consider this variable exploratorily in the analysis to better
understand this underexplored phenomenon since prior research indicates a covariation with par-
enting and/or CE drug use (willingness) (Borra & Sevilla, 2019; Láng, 2018; Sattler & Wiegel,
2013; Substance Abuse and Mental Health Services Administration, 2019; Wall, 2010; Wilens
et al., 2008).
Debriefing
Given the risk of influencing parent decision-making regarding prescription drug use by pro-
viding false information, we thoroughly debriefed the participants at the end of the survey
(Supplemental Appendix E).
Ethics Approval
The ethics committee of the University of Erfurt approved all four experiments (EV-20170725).
Statistical Analysis
To test the hypotheses for each experiment, first, we ran linear regression models to regress the
willingness to give CE on the manipulated social influence factor and the influence source, and
second, we added the resulting interaction term. Simple slopes were computed to gain more
Sattler et al. 9
detailed insights into the relationship between social influence and the information source. Sex,
age, and prior CE administration were used as controls.
Results
The manipulation checks in Experiments 1 to 4 showed the expected statistically significant dif-
ferences between the social influence conditions (Table 1).
The average willingness to give prescription stimulant drugs to a child was similar across the
experiments as follows: Mean (M) = 9.95 (standard deviation, SD = 20.163) in Experiment 1,
M = 10.02 (SD = 20.412) in Experiment 2, M = 11.40 (SD = 21.763) in Experiment 3, and
M = 12.62 (SD = 22.461) in Experiment 4. The assessed prior experience with CE in children
in the form of a self-reported lifetime prevalence of giving prescription stimulant drugs to their
child (-ren) to enhance their children’s cognitive abilities was 5.57% in Experiment 1, 2.45% in
Experiment 2, 2.15% in Experiment 3, and 5.75% in Experiment 4.
In Experiment 1, the linear regression analysis showed that consistent with the others’ behav-
ior hypothesis, the willingness was 4.10 scale points higher when most other parents in the
vignette said that they gave CE drugs to their children than when no other parents did that (p =
.047, Model 1, Table 2). The willingness under the friends condition was also higher than that
under the social media condition (p = .048). Respondents who already gave CE drugs to their
children had an elevated willingness (p < .001). The respondent’s sex (p = .214) and age (p =
.110) had no effects. When entering the interaction term of both treatments, the conditional main
effects remained significant, and there was no evidence of an interaction (p = .227). An explor-
ative simple slope analysis suggested that the social influence effect in Model 1 was mainly
driven by the social media condition and probably due to the relatively low willingness when no
other parents under the social media condition engage in CE (see the difference between the
respective white bars in Figure 1, B = 6.606, t = 2.26, p = .024). Under the friends condition,
the parents were not affected by others (see respective black bars in Figure 1, B = 2.881, t =
0.58, p = .565). As indicated by the nonsignificant interaction, the two slopes did not signifi-
cantly differ.
In Experiment 2, willingness did not differ with regard to others’ definitions (inconsistent with
the others’ definitions hypothesis) or the influence of source treatment (p = .580 and p = .208,
respectively; Model 3). However, the willingness was higher for parents with prior CE engage-
ment (p = .015) as well as for mothers than it was for fathers (p = .016). Again, age was unre-
lated (p = .247). Furthermore, we did not observe an interaction effect (p = .653, Model 4).
Table 1. T-Tests of the Manipulation Checksa in Experiments 1–4.
Experiment N Mean (SD)t value
1. Others’ behavior 359 none most 55.59***
1.17 (0.858) 5.66 (0.638)
2. Others’ definitions 326 unacceptable acceptable 16.11***
1.78 (1.938) 5.65 (2.378)
3. Others’ social control 325 criticize advise 26.99***
1.17 (0.666) 5.79 (2.097)
4. Others’ experience 313 negative positive 15.98***
2.74 (2.180) 6.08 (1.339)
Note. N = number of observations.
aSee wordings in Supplemental Appendix C.
***p < .001 (two-tailed).
10
Table 2. Linear Regression Models of the Willingness to Give CE Drugs to a Healthy Child (B-Coefficients, Standard Errors in Brackets).
Experiment 1: others’ behavior Experiment 2: others’ definitions Experiment 3: others’ control Experiment 4: others’ experiences
Predictors 1 2 3 4 5 6 7 8
T1: Social influencea4.10* (2.06) 6.61* (2.92) −1.23 (2.22) −0.26 (3.10) −0.08 (2.38) −1.53 (3.50) 10.31*** (2.35) 15.33*** (3.35)
T2: Friends (Ref. Social
media)
4.05* (2.04) 6.35* (2.78) 2.84 (2.25) 3.79 (3.09) 2.75 (2.38) 1.43 (3.33) −2.96 (2.35) 1.60 (3.20)
Women (Ref. Men) 2.89 (2.33) 2.75 (2.33) 6.08* (2.51) 6.17* (2.52) 1.56 (2.94) 1.48 (2.95) 5.63* (2.70) 5.83* (2.68)
Age −0.18 (0.11) −0.18 (0.11) −0.15 (0.13) −0.16 (0.13) −0.27* (0.14) −0.27* (0.14) −0.09 (0.13) −0.08 (0.13)
Prior CE 23.43*** (4.43) 23.44*** (4.43) 17.66* (7.24) 17.58* (7.25) 28.60*** (8.19) 28.51*** (8.20) 30.04*** (5.00) 29.94*** (4.97)
T1 × T2 — −4.95 (4.09) −2.02 (4.50) — 2.69 (4.76) — −9.72* (4.65)
Constant 10.17* (4.54) 9.28* (4.60) 12.66* (5.28) 12.44* (5.31) 19.42*** (5.70) 20.15*** (5.85) 9.42 (5.21) 6.63 (5.35)
Observations 359 359 326 326 325 325 313 313
Note. CE = cognitive enhancement; T = treatment.
aThe respective type of social influence is indicated in the header of the table.
*p < .05. ***p < .001 (two-tailed).
Sattler et al. 11
In Experiment 3, social control (p = .972, Model 5) and the influence source (p = .250) had
no statistically significant effects. Thus, the others’ control hypothesis was not supported. Prior
CE engagement again increased willingness (p = .001). While younger respondents also showed
a higher willingness (p = .049), sex was unrelated (p = .616). We found no interaction effect
between the treatments (p = .573; Model 6).
In Experiment 4, we found a statistically significant positive social influence effect (p < .001;
Model 7) as follows: when others reported that the positive consequences of drug administration
were higher than the negative consequences, compared to the negative consequences being
higher, the willingness was higher, which supports the others’ experience hypothesis. There was
no significant main effect of source (p = .210). Prior CE administration (p < .001) and being a
woman (p < .037) were associated with a higher willingness. Age was unrelated (p < .484).
Model 8 shows a significant interaction effect between the treatments (p = .038). A simple slope
analysis showed that the social influence effect only occurred under the social media condition
(see different heights of the respective white bars in Figure 1, B = 15.329, t = 4.58, p < .001).
No such effect existed under the friends condition (see respective black bars, B = 5.607, t =
1.73, p = .085).
Discussion
While students’ motive to use prescription stimulants nonmedically for better performance while
studying has been frequently reported in student samples of different ages (Faraone et al., 2020;
Garnier-Dykstra et al., 2012; Rabiner et al., 2009; Schepis et al., 2020; Teter et al., 2020), parents’
willingness to initiate such behavior and the etiology behind their decision-making have rarely
been studied. To the best of our knowledge, this is the first set of theory-driven research
Experiment 1:
Others’ behavior
(N=359)
Experiment 2:
Others’ definitions
(N=326)
Experiment 3:
Others’ control
(N=325)
Experiment 4:
Others’
experience
(N=313)
0
5
10
15
20
25
30
none most unacceptable acceptable criticize advise negative>
positive
positive>
negative
Predicted behavioral willingness
Figure 1. Predicted values (with standard errors) of behavioral willingness to give prescription
stimulant drugs to children depending on the experimental treatments and their interactions based on
the linear regression models shown in Table 2. The friends’ condition is indicated by black bars (),
and the social media condition is indicated by white bars ().
12 Journal of Drug Issues 00(0)
experiments investigating parents’ willingness to give CE to healthy children and the question of
the drivers that lead parents to do so. We therefore used four experiments to explore the mecha-
nisms of social influence and the potential moderating effect of the influence source. The general
findings are that a fraction of parents are willing to administer drugs for better school perfor-
mance and that social influence plays a role, but its impact depends on the type of influence and
partially on its source.
Experiment 1 tested the influence of exposure to others’ behavior. Similar to other findings
regarding the effects of others’ behavior in different contexts of (il-)legal drug use by students
(Maahs et al., 2016; Sattler et al., 2014) and the first descriptive research investigating parents’
willingness to give prescription stimulant drugs to their healthy children for CE (Maher, 2008;
Sattler & Wörn, 2019), our results show that this willingness was higher when most other parents
were also engaged in this behavior compared to when parents were not engaged in this behavior,
supporting the others’ behavior hypothesis. This effect was independent of the influence source
(source question). Others’ CE engagement can signal that this behavior is morally acceptable, has
a good benefit-risk ratio, and should be imitated; it is thus an informational and normative influ-
ence. It can also lead to conformity pressure, the justification of deviant behavior, and expected
disadvantages if one does not engage in said behavior. The results also imply that others’ nonen-
gagement in CE can serve as an informational and normative deterrent regarding CE.
Obviously, there is more than one explanation for the underlying mechanism of the effect of
social influence found in Experiment 1. Thus, in Experiment 2, we directly investigated the effect
of injunctive norms and definitions of others grasping the acceptability of CE, but we found nei-
ther the postulated effect of others’ definitions (others’ definitions hypothesis) nor a difference
between close friends and social media (source question). Other research with students found
that while descriptive norms mattered for CE, this was not always the case for injunctive norms
(Molloy et al., 2019; Ponnet et al., 2015). It has been assumed that descriptive norms are more
heuristic, whereby injunctive norms require more reasoning (Gibbons et al., 2015), and since
most parents already judge this behavior as not very acceptable, normative information might
have less influence. Others’ CE-disapproving definitions might also not imply that parents must
fear negative reactions.
As such reactions are often expensive or invasive and might have differing relevance if they
are derived from friends rather than reading social media content, it is of interest to consider the
effect of such responses. Therefore, Experiment 3 tested the effect of whether others could exert
social control and, thus, attempt to reward or punish CE behavior. While earlier work concerning
social control in the context of drug use among students is inconclusive (Bavarian et al., 2013;
Conn & Marks, 2014; Peralta & Steele, 2010; Ponnet et al., 2015; Sattler et al., 2014; Watkins,
2016), we found no effect of social control (others’ control hypothesis) and no difference between
the influence sources (source question). Receiving strong criticism for giving prescription stimu-
lant drugs to children (compared to receiving advice to perform such behavior) might not be a
sufficient sanction to cause an effect, or our respondents may not have wanted others to tell them
how to make parental decisions. This finding is consistent with earlier qualitative research
involving pediatric CE showing that some parents said that they would not react to external pres-
sure (Ball & Wolbring, 2014; Hiltrop & Sattler, under review).
Finally, Experiment 4 investigated others’ experience with CE drugs in children. Prior research
with students indicates that positive rather than negative personal experiences increase the use of
different classes of drugs (Peralta & Steele, 2010; Watkins, 2016), although whether such effects
also occur via peer reports about drug effects is unclear. In Experiment 4, we found a positive
effect of such peer reports: when the benefits outweighed the negative consequences, the willing-
ness to give prescription stimulants to children increased, corroborating the others’ experience
hypothesis. Prepared with this direct information about others’ CE experiences, parents may
engage in benefit–cost deliberation regarding whether to attempt this behavior. However, we
Sattler et al. 13
found that this effect was stronger under the social media condition (source question). Post hoc
explanations might be that information from social media is considered more reliable than infor-
mation from a more selective and biased group of friends; thus, the respondents could make a
better inference about the general population when given information under the social media
condition. The wisdom of the crowd on a social media site might also be perceived as more
knowledgeable, and the number of parents reporting might be considered larger than that under
the friends condition; thus, under the social media condition, the information may have been
more convincing. In summary, the effect of others’ experience under the social media condition
tended to be relatively strong compared to that under all other conditions (Figure 1), underlining
the persuasive power of information on the internet.
Moreover, we found a few inconsistent effects of sex and age, that is, mothers compared to
fathers and younger compared to older parents, on willingness to administer CE. The preferred
routes to success and the duty of childrearing might vary by sex (Richards et al., 1991), and younger
parents might be more familiar with CE and more open to adopt risk-taking than older parents (cf.
Turner & McClure, 2003; Wiegel et al., 2016). Further studies should elaborate on this.
We found surprisingly high levels of parents’ prior experience with administering CE drugs to
children. However, comparative data are rare, and older survey data (C.S. Mott Children’s Hospital
National Poll on Children’s Health, 2013) provide only an indirect indication of the phenomenon
because parents were asked about their awareness of their child using prescription stimulants for
studying without indication. The only data that we know are from U.S. parents with children, and
these data indicate a lifetime prevalence of 6.8% of parents giving drugs to children for CE (Sattler
et al., under review). Thus, further research is warranted. However, consistent with research on CE
users, our exploratory analysis also showed that in all four experiments, parents with experience
in giving CE to their child(-ren) had an elevated willingness to administer CE. This can be attrib-
uted to, for example, positive experience and habituation (Wiegel et al., 2016).
Strengths, Limitations, and Avenues for Future Research
Our study advances prior research in the context of prescription stimulant drug use in healthy
children and research concerning the social influences on drug misuse in general. Such research
has previously mainly employed qualitative or correlational designs. This study stands out from
prior research because the experimental design ensures high internal validity and allows for
causal claims about social influence by avoiding the risk that exposure to similar environments
or selection of peers rather than their influence is at play. This design also avoids problems
related to projection biases of perceptual measures of peer behavior/experience. Although several
analyses revealed null results, thereby revealing insight into seemingly irrelevant factors, the
manipulation check analyses provide some confidence that the manipulations worked as intended.
Further studies should use a representative sample of the U.S. population to increase the gener-
alizability of our results. Such a sample may include permanent residents, and researchers should
consider assessing whether the children of such individuals are enrolled in school in the U.S. or
abroad. In addition, samples from varying countries should be employed because the susceptibil-
ity to social influence (i.e., conformity pressure) might vary between countries with individualistic
and collectivistic orientations (Cialdini & Goldstein, 2004). Our understanding of parents’ deci-
sions could be further enriched by investigating whether personality variables (such as self-control
or parental self-efficacy) moderate the effect of social influences (Epstein et al., 2007).
The use of self-reports is a common strategy in substance research (Watkins, 2016). However,
reporting the willingness to give prescription drugs to one’s child can be considered a sensitive
topic that can result in socially desirable responses and item nonresponse. This problem seems less
profound in web-based surveys, which have been found to increase the accuracy of responses and
the likelihood of reporting sensitive information (Crutzen & Göritz, 2010; Kreuter et al., 2008). In
14 Journal of Drug Issues 00(0)
addition, our approach of using hypothetical situations could further reduce socially desirable
responses (Choong et al., 2002). We also ensured an anonymous procedure to protect the respon-
dents’ answers. When including a measure of anonymity perception concerning our survey in our
models (Patrzek et al., 2015), we found that in Experiment 1, the main effect of social influence
was only marginally significant in Model 1, while the anonymity perceptions were not significant
(Supplemental Appendix F). However, in Model 2, the conditional main effect of social influence
was statistically significant, and the anonymity perceptions were still insignificant. Moreover,
these anonymity perceptions were only significant in Experiment 3, but this effect did not change
the other effects in the model and showed an unexpected negative direction. We have no clear
interpretation of this particular effect, but in summary, our results seem to be relatively unaffected
by social desirability bias. While we cannot rule out such bias, the lack of item nonresponse in our
experiments may indicate reliable responses, although this cannot be confirmed.
Although the use of hypothetical situations with willingness measures has the abovemen-
tioned advantages, it is a general limitation of such hypothetical vignette studies that we cannot
conclude that voiced willingness will translate into actual behavior (Exum & Bouffard, 2010;
Grasmick & Bursik, 1990). For example, parents might be willing to use medications but may
not know how to access them, or they may seek other, more preferable means to enhance a child’s
performance. Nevertheless, such intentional measures showed relatively high correlations with
behavior (Beck & Ajzen, 1991; Pogarsky, 2004), and effects estimated with such designs matched
behavioral benchmarks (Hainmueller et al., 2015). This is especially important because designs
with manipulations in the real world would be ethically problematic.
Future studies may also investigate other influential sources. We only investigated whether
the effects vary between information obtained from close friends and that obtained from social
media content. Influences through real contact or other media, such as TV, radio broadcasts, or
newspapers, could differ in their attributed trustworthiness and, hence, its effects.
While the focus of our experiments was to obtain initial insights into social influences on par-
ents’ decisions regarding CE, we held the gender and age of the child in the vignette constant.
Future studies may, however, examine whether these characteristics modify parental decision-mak-
ing. For example, for parents with traditional gender roles and gendered parenting practices, the
importance of school performance and the means to achieve it may differ between sons and daugh-
ters (Hadjar et al., 2007). Moreover, parents showing hierarchical self-interest (reflecting factors
such as competitiveness, success orientation, or acceptance of inequality) may engage in parenting
styles in which girls are educated as more passive caretakers, whereas boys are trained to be domi-
nant and successful (Hagan et al., 2000). These are exemplary reasons why the willingness to
administer CE drugs might also be gendered and why parents might be more open to social influ-
ences suggesting CE for boys. However, a recent meta-analysis (Endendijk et al., 2016) dampens
such expectations, as currently, the parenting practices for boys and girls differ only minimally.
Parents’ decisions may also be influenced by the child’s age. On one hand, parents’ level of
control over whether a child takes medication should decrease with age, and parents’ influence-
ability by others should thus also decrease. However, with increasing age, school performance
might become more consequential (e.g., with regard to educational transitions, qualification for
scholarships, labor market entry). Thus, parents may want to support the child’s educational suc-
cess. They might then be particularly open to others’ suggestions regarding CE. Future research
should examine these diverging assumptions.
Conclusion
Few scholars are optimistic about CE in healthy children, for example, to compensate for disad-
vantages that emerge from understaffed and overcrowded schools (Ray, 2016). Most scholars
share the view that such behavior should be prevented, monitored, regulated, or even
Sattler et al. 15
sanctioned—also from a medical point of view (Graf et al., 2013). Our study found that a fraction
of parents are willing to engage their children in a practice, which, on the one hand, may help
them realize a benefit in terms of enhanced performance and better grades but, on the other hand,
may be dangerous for the health and brain development of children and unfair to others, and it
may infringe on the autonomous decisions of children (Sattler, 2020a). Even if this is only a frac-
tion of parents, this practice is also concerning because the onset and continuation of prescription
stimulant use among students is affected by students’ peers (Ford & Ong, 2014; London-Nadeau
et al., 2019; Mui et al., 2014; Sattler et al., 2014). Thus, if parents initiate CE in their children,
they may indirectly affect the instigation and preservation of CE by other students. This is likely
to happen via influence effects, and children could also be the source of the substances used by
their classmates because it has been shown that peers are one of the most frequent drug sources
for students (Garnier-Dykstra et al., 2012; McCabe et al., 2019; Poulin, 2001).
Our study provides evidence for contagion effects between parents. It shows the effects of
other parents’ behavior and especially of experiences with drug effects. However, contrary to the
theoretical predictions and different from several studies with students (Helmer et al., 2016;
Ponnet et al., 2015; Sattler et al., 2014), other parents’ moral evaluations and others’ exercise of
social control had no effects. Moreover, the significant effects that we found occurred only under
the social media condition. This finding indicates that social influences indeed play a role in
parental decisions and that instrumental information seems to be important, whereas normative
aspects directly transferred by others do not affect such decisions. The results imply that the
prevalence of pediatric performance enhancement could increase, as expected by several schol-
ars (Colaneri et al., 2018), if more effective drugs with fewer side effects were available or the
perception existed that such drugs have these properties despite clinical evidence because such
evidence in healthy children is lacking. Our results warrant attention because media reports
include information about prevalence rates and are sometimes enthusiastic about a forthcoming
trend of prescription drug use. Thus, media reports can impact (potential) users to start or con-
tinue such a practice (Forlini & Racine, 2009). The results also deserve attention because media
reports and comments on internet platforms represent only the perspectives of single users
(exemplars). Such exemplars can have greater effects on attitudes and behavior than verified
statistical information on health topics (Krämer & Peter, 2020). This is concerning because peo-
ple increasingly browse the internet for parenting- and health-related information and thereby
rely on nonevidence-based websites containing purely anectodical advice and false information
(Bernhardt & Felter, 2004; Herrmann-Werner et al., 2019; Stukus, 2019; Zillmann, 2002).
Public health authorities should be aware of this potential impact and may aim to search for
counterstrategies. Prevention strategies and health promotion via social media (e.g., links to
online interventions or educational messages) could be effective in influencing parents’ behavior
and reducing the potentially negative effects of prescription stimulant use by healthy children (cf.
Moreno & Whitehill, 2014). Such campaigns might be justified because the amount of user-
generated content on the internet is escalating. Given that numerous studies show that individuals
often massively overestimate the prevalence of substance misuse, presenting the real and rather
low prevalence of CE among children might lead to an adjustment of this perception (Faraone
et al., 2020). Such information needs to be carefully framed to avoid provoking curiosity among
those who are not aware of the risks of such drugs (Sattler & Wiegel, 2013). Nonmedical web-
sites might need disclaimers explaining that anecdotal evidence can be dangerous (Moreno &
Whitehill, 2014). Moreover, forum moderators need to correct exaggerated hopes or downplayed
risks and provide links to support services or online interventions. Parents might also need to
improve their media literacy skills to better classify information found online (Elmore et al.,
2017). As suggested by Singh et al. (2013), clinicians should be careful about being pushed by
parents to diagnose nonexistent diseases and help parents and their children with uncertainties
regarding the need for and dangers of medication use.
16 Journal of Drug Issues 00(0)
Acknowledgments
The authors thank Jonathan Woern for assisting with the preparation for this study. We also thank Johanne
Stuempel for conducting the cognitive pretests, Floris van Veen for programming the experiments, and
Monique Mitchell for the language edits of the vignettes.
Declaration of Conflicting Interests
The author(s) 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 publi-
cation of this article: This work was supported by a grant to the first author [Sebastian Sattler] from the John
Templeton Foundation via The Enhancing Life Project.
Ethics Approval
The questionnaire and methodology for this study were approved by the Ethics Committee of the University
of Erfurt—20170725.
Informed Consent
Informed consent was obtained from all study participants.
ORCID iD
Sebastian Sattler https://orcid.org/0000-0002-6491-0754
Supplemental Material
Supplemental material for this article is available online.
Notes
1. Despite the effort of platforms like Clickworker to validate accounts (e.g., blocking duplicate accounts),
an attention check was used to remove both bots and participants who quickly clicked through the survey
to obtain the payment (Berinsky et al., 2014; Shamon & Berning, 2019). We used a question that appeared
to ask the participants about their favorite color but with a request to select certain colors and solve a
simple addition problem involving two numbers adapted from Berinsky et al. (2014). We acknowledge
that our attention check is comparatively difficult compared to other checks. In Experiment 1, 58.3%
of the respondents failed this check, and in Experiments 2, 3% and 4, 62.4%, 60.1%, and 54.8% of the
respondents failed this check, respectively. These high rates indicate a strong obstacle but ensure that
only participants who paid sufficient attention were allowed to continue. Such ex ante filtering of careless
respondents has been suggested to increase data quality and to be a motivational influence (Shamon &
Berning, 2019). For vignettes that require the careful reading of text, this can be especially useful.
2. This was performed because parents should have an inside view when making judgments about children.
3. The demographic descriptive information shown in brackets is based on slightly lower-case numbers
due to missing values. Given the comparatively low number of women in the sample, we examined
whether women and men reacted differently to the treatments by incorporating two-way interactions
between sex and each treatment. We also used an additional round of models that added three-way
interactions between the two treatments and sex. However, we found that none of these interaction
effects were significant (results available upon request).
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Author Biographies
Sebastian Sattler is a lecturer in sociology. His research interests include sociological theory, decision-
making, sociology of health, criminology, drug use, and quantitative methods. His work has been published
in journals, including European Sociological Review, Frontiers in Psychology, and PLOS ONE.
Guido Mehlkop is a professor of empirical research methods. His research interests include criminology,
rational choice theory, methodology, and quantitative methods. He has written a monograph regarding
the explanation of crime as a rational choice. His work has also been published in journals, including
Criminology, and the European Sociological Review.
Vanessa Bahr is a master’s psychology student focusing on cognitive psychology and cognitive neuro-
science. Her research interests include cognitive enhancement, dementia, and behavioral medicine.
Cornelia Betsch is a professor of health communication. She is a trained psychologist, and her work aims
to obtain an understanding of the principles of health behavior by applying a judgment and decision-making
and strategic-interaction perspective. Her work has been published in Nature, Nature Human Behavior, and
Health Psychology.
