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Running head: SOCIAL NORMS AND PLASTIC AVOIDANCE
This is the pre-peer reviewed version of the following article:
Borg, K., Curtis, J., Lindsay, J. (2020). Social norms and plastic avoidance: Testing the theory of
normative social behaviour on an environmental behaviour. Journal of Consumer Behaviour.
Which has been published in final form at:
https://onlinelibrary.wiley.com/doi/abs/10.1002/cb.1842
This article may be used for non-commercial purposes in accordance with Wiley Terms and
Conditions for Use of Self-Archived Versions.
SOCIAL NORMS AND PLASTIC AVOIDANCE 1
Social norms and plastic avoidance: Testing the theory of normative social behaviour on an
environmental behaviour
Kim Borg (e: kim.borg@monash.edu; p: +61 3 9905 9854)1*
Jim Curtis1
Jo Lindsay2
1BehaviourWorks Australia, Monash Sustainable Development Institute, Monash
University; 8 Scenic Boulevard, Clayton, 3800, Victoria, Australia
2 School of Social Sciences, Arts Faculty, Monash University; 20 Chancellors Walk,
Clayton 3800, Victoria, Australia
* Corresponding author
Acknowledgements
This research was completed as part of a PhD undertaken at Monash University, supported by
the Australian Government Research Training Program, the Victorian Department of
Environment, Land, Water, and Planning, and Sustainability Victoria. We would like to
acknowledge the survey participants for their time and insights and the data collection agency,
Dynata.
Author biographies
Kim Borg is a Research Fellow at BehaviourWorks Australia, specialising in social and
government research. She is particularly interested in the impact of human behaviour on the
environment and is undertaking a Behaviour Change PhD via the Monash Graduate Research
Industry Partnership (GRIP) program.
Dr Jim Curtis is a Senior Research Fellow at BehaviourWorks Australia. His research
focuses on gaining a theoretical and applied understanding of the influences impacting on the
behaviour of a range of target audiences. He has acquired an intimate understanding of how the
public sector interacts with research and is committed to applying these learnings to ensure that
research delivers real value to critical decisions around policy.
Professor Jo Lindsay is Associate Dean Enterprise in the Arts Faculty and Professor of
Sociology in the School of Social Sciences. Professor Lindsay is a leading sociologist in the
fields of families, consumption and environmental sociology with a strong track record in
interdisciplinary research and supervision. Jo is passionate about research partnerships, research
innovation and working with partners and communities to solve complex social issues.
SOCIAL NORMS AND PLASTIC AVOIDANCE 2
Abstract
Plastic pollution is a critical global sustainability challenge. But the social norms associated with
single-use plastics are changing. These new norms could be encouraging consumer behaviour
change by highlighting which behaviours are common and acceptable. This paper sought to
explore the role of social norms in predicting plastic avoidance by testing the Theory of
Normative Social Behaviour (TNSB) in a dynamic real-world context. A representative survey
(N=1001) was conducted measuring consumer behaviour in relation to four single-use plastic
items (bags, straws, coffee cups, and take-away containers). Descriptive norms were found to be
the strongest predictor of plastic avoidance and most of the remaining variables were found to
moderate the norms-behaviour relationship. Specifically, the model for plastic bag avoidance
suggests that descriptive norms are even more important in predicting consumer behaviour in a
dynamic policy context. These findings indicate that there is an opportunity to use social norm
messaging to close the perception-action gap among consumers in order to address a global
environmental sustainability problem.
Keywords: consumer behaviour, social norms, single-use plastic, Theory of Normative Social
Behaviour, sustainability.
SOCIAL NORMS AND PLASTIC AVOIDANCE 3
Social norms and plastic avoidance: Testing the theory of normative social behaviour on an
environmental behaviour
Social norms are the unwritten rules which guide how people should behave based on what
others are doing (descriptive norms) and what others approve and disapprove of (injunctive
norms) (Cialdini, Kallgren, & Reno, 1991; Cialdini, Reno, & Kallgren, 1990; Kallgren, Reno, &
Cialdini, 2000). But norms are not static. They are socially negotiated, context dependent, and
malleable, changing to fit the time and place (Rimal & Lapinski, 2015). By influencing norms at
a societal level we could help to address large-scale environmental sustainability problems, such
as plastic pollution (Nyborg et al., 2016). However, such change requires long term and large-
scale behaviour and attitude change which can be slow and difficult.
Consumers are more likely to engage in new ‘green’ behaviours if they believe the
behaviours are normal (Rettie, Burchell, & Barnham, 2014). Providing such normative
information has been found to influence a variety of environmental behaviours, such as littering,
recycling, towel reuse, and energy consumption (Abrahamse & Steg, 2013; Cialdini et al., 1990;
Farrow, Grolleau, & Ibanez, 2017). Normative messaging has also been found to influence
single-use plastic avoidance (Arı & Yılmaz, 2017; de Groot, Abrahamse, & Jones, 2013).
However, social norm messaging can also have unintended consequences. In the context of
energy consumption, Schultz, Nolan, Cialdini, Goldstein, and Griskevicius (2007) found that
providing householders with information about their neighbours’ energy consumption decreased
energy use among high-consumption households, but it also increased energy use among low-
consumption households. This ‘boomerang effect’ only disappeared when an injunctive norm
component was added to the message. This highlights the importance of testing how and when
social norm perceptions affect different consumer behaviours.
The aim of the current research was to explore the role of social norms in predicting
consumer behaviours related to single-use plastic avoidance. By understanding this relationship,
behaviour change practitioners and policymakers could develop more effective interventions to
further promote avoidance of single-use plastics in the future.
Theorising Normative Behaviour
According to the Theory of Normative Social Behaviour, behavioural intentions are
influenced by perceived descriptive norms, moderated by perceived injunctive norms as well as
outcome expectations, group identity, and behavioural identity (Lapinski & Rimal, 2005; Rimal
& Real, 2003, 2005). Outcome expectations and group identity are drawn from Social Learning
Theory (Bandura, 1977) and refer to beliefs about the benefits of engaging in a particular
behaviour (outcome expectations) and how alike people perceive themselves to be compared to
SOCIAL NORMS AND PLASTIC AVOIDANCE 4
referent others (group identity) (Lapinski & Rimal, 2005). Behavioural identity, also known as
ego involvement, is the extent to which an individual’s self-identity aligns with the enactment of
a certain behaviour (Lapinski, Rimal, DeVries, & Lee, 2007).
While some previous studies have investigated environmental behaviours using TNSB,
such as recycling (Lapinski, Zhuang, Koh, & Shi, 2017) and water conservation (Lapinski et al.,
2007), most research involving the TNSB has explored health and safety behaviours, such as
cervical cancer screening (Walter, Murphy, Frank, & Ball-Rokeach, 2019), hand washing
(Lapinski, Maloney, Braz, & Shulman, 2013), speeding while driving (Geber, Baumann, &
Klimmt, 2019), and, most notably, alcohol consumption (Real & Rimal, 2007; Rimal, 2008;
Rimal & Real, 2005; Tankard & Paluck, 2016; Yang & Zhao, 2018). This represents an
important gap in the literature given that the influence of social norms on behaviour is greater for
behaviours which benefit the collective (environmental) rather than the individual (health)
(Lapinski et al., 2007). This is particularly relevant in relation to the plastic pollution crisis,
where a systematic behavioural shift away from single-use plastics is required, given that norms
have a greater influence when a behaviour is ambiguous or new (e.g. in response to a new
policy), public (e.g. shopping at a supermarket), and when the behaviour and consequences are
well known (e.g. when media coverage is high) (Lapinski & Rimal, 2005).
The Problem with Single-Use Plastics
It is estimated that about 8 million tonnes enters the marine environment each year
(Jambeck et al., 2015). Plastic negatively impacts wildlife through entanglement, ingestion, and
starvation; human health through the build-up and release of toxins; and has high costs
associated with accumulation and disposal (Ritch, Brennan, & MacLeod, 2009). Plastic pollution
has been identified as a cross-cutting sustainability crisis which is inextricably linked with
climate change, biodiversity loss, and human health (Vince & Stoett, 2018).
While waste management and recycling can help address the problem, the primary focus
should be avoidance, as per the waste hierarchy (Cleary, 2014). In particular, avoidance of
single-use ‘throw away’ consumer products – such as single-use plastic bags, straws, and food
and beverage containers. These items are used for a very short time and are typically discarded
as waste. For example, plastic bags are used for an average of 12 minutes, after which they might
be reused once as a bin liner but are ultimately destined for landfill (NSW EPA, 2016). Many of
these items can be avoided if consumers change their behaviour by using reusable alternatives
(e.g. reusable coffee cups) or by refusing unnecessary items (e.g. drinking straight from the glass
instead of using a straw).
SOCIAL NORMS AND PLASTIC AVOIDANCE 5
The Current Study
The good news is that change is happening. We are in the middle of a global social
movement away from single-use plastics (Vince & Stoett, 2018). Evidence for this shift can be
seen in the spread of plastic bag reduction policies at various country and jurisdiction levels
(Schnurr et al., 2018); in recent bans on a range of single-use plastic items (BBC News, 2018);
and in increased prevalence of media content reporting on the impact of plastic pollution (e.g.
BBC’s Blue Planet II
1
).
Australia is also in the middle of a single-use plastic transition. By the end of 2018, most
States and Territories had banned lightweight plastic bags (Victorian Government, 2018) and
Victoria had committed to banning bags by the end of 2019
2
. In addition, the two largest
Australian supermarket chains, Coles and Woolworths, had phased out free lightweight plastic
bags (Dulaney, 2017) and some state and local governments also proposed to ban other single-
use items, such as plastic straws, cutlery, and drink stirrers (Henriques-Gomes, 2019). During the
consultation process for Victoria’s plastic bag ban, it was found that the community was also
concerned about other single-use items such as plastic straws, coffee cups, and takeaway food
containers (Victorian Government, 2018). In response, the State government sought to develop a
plan to reduce other types of plastic pollution
3
.
Given the changing policy context across Australia and Victoria regarding single-use
plastic items, we anticipated that the social norms associated with avoidance of such items were
also changing. This provided a unique opportunity to study social norms in a dynamic, real-
world context, where events in the environment (e.g. policy changes) may have been shaping
new social norms (Rimal & Lapinski, 2015). The first hypothesis of the current study was:
H1. Consistent with the TNSB, we anticipated that perceived injunctive norms, outcome
expectations, group identity, and behavioural identity would moderate the relationship between
perceived descriptive norms and intentions to avoid single-use plastics.
Although evidence highlights the strong relationship between intention and action
(Armitage & Conner, 2001), it should be noted that consumers generally have a strong intention
to avoid single-use plastics, but intentions do not always reflect behaviour (Ertz, Huang, Jo,
Karakas, & Sarigöllü, 2017). Therefore, the second hypothesis was that:
H2. The TNSB would account for less of the explained variance of past behaviour
compared to behavioural intentions.
1
https://www.imdb.com/title/tt6769208
2
https://www.premier.vic.gov.au/victoria-says-no-to-plastic-waste/
3
https://www.environment.vic.gov.au/sustainability/plastic-pollution
SOCIAL NORMS AND PLASTIC AVOIDANCE 6
While the TNSB proposes that outcome expectations, operationalised as perceived
benefits, moderate the norm-behaviour relationship, preliminary research conducted by the
authors indicates that individual’s beliefs about undesirable outcomes may also influence
behaviour (In review). Specifically, individuals expect that engaging in plastic avoidance would
be difficult (or easy) and that it would be associated with high (or low) financial costs. The role
of self-efficacy as a moderator between norms and intentions has been found in relation to
drinking refusal (Jang, Rimal, & Cho, 2013). In addition, research on green purchasing supports
the importance of purchase costs (Papista, Chrysochou, Krystallis, & Dimitriadis, 2018).
Similarly, Rimal and Lapinski (2015) recognise that monetary costs associated with a behaviour
are an important behavioural attribute. Thus, the third hypothesis in the current study was that:
H3. Perceived barriers (self-efficacy and anticipated costs) would moderate the
relationship between descriptive norms and plastic avoidance.
Finally, the TNSB emphasises the importance of behavioural attributes in considering the
influence of social norms (Lapinski & Rimal, 2005). With this in mind, each behaviour included
in the current study was required to have the same behavioural attributes: repeated behaviours
(rather than once-off), occurring in public (rather than private), and with an alternative
behavioural option which does not impede the underlying objectives of the consumer (e.g. they
can still purchase the good but without the single-use item). The aggregate of the chosen
behaviours (described in the Methods section) constitutes the behavioural domain of single-use
plastic avoidance. However, in behavioural theory it is generally agreed that it is easier to predict
a specific behaviour rather than a class of behaviours (Fishbein, 2008). So, the fourth hypothesis
for the current study was that:
H4. The variables in the TNSB would vary in their predictive power for each plastic
avoidance behaviour.
Methods
Research Design
This study involved a random sample of 1,001 adults (aged 18 years or over) living in
Victoria, Australia. A research company was engaged to recruit participants who were randomly
drawn from their panel of members and sent an email invitation to complete the online survey.
Post-stratification cell weights were calculated so that the sample reflected the population for age
group by gender to compensate for low-responding cohorts (e.g. young males). The research was
approved by the author’s University Human Research Ethics Committee (project #20064).
SOCIAL NORMS AND PLASTIC AVOIDANCE 7
Measures
The specific behaviours included in the survey were: single-use plastic bags when
shopping; plastic straws when buying a drink at a café, restaurant or bar; disposable coffee cups
when buying a hot beverage; and plastic take-away containers when buying take-away food.
These reflect single-use items that were of concern to the Victorian public (based on the
Victorian Government’s public consultation (Victorian Government, 2018)) as well as some of
the most common plastic litter items found during beach clean-ups (Keep Australia Beautiful,
2016/17).
Past behaviours. Participants were first asked what percentage of the time they used the
four single-use plastic items during the previous month; where 0=Always avoided the plastic
item and 100=Always used the plastic item (Lapinski et al., 2013). If respondents did not
participate in the underlying behaviour (e.g. they did not buy hot beverages) they selected ‘Not
Applicable’ and were excluded from subsequent questions about the behaviour (n=bags: 958,
straws: 830, coffee cups: 817, take-away containers: 832). The mean across the four items was
calculated and used for the plastic avoidance behavioural domain (α=.81).
Behavioural intentions. Participants who engaged in the underlying behaviour were also
asked what percentage of time they intended to use each item during the next month; where
0=Always avoid the plastic item and 100=Always use the plastic item. The mean across the four
items was calculated and used for the plastic avoidance behavioural domain (α=.85).
Perceived descriptive norms. All respondents were asked to estimate what percentage
of time they believed most Victorians engaged in the four behaviours during the previous month;
where 0=Always avoided the plastic item and 100=Always used the plastic item. The aim of this
question was to mirror the behavioural questions while focusing on ‘most others’ in order to
determine the gap between perceptions of others’ behaviour and the collective sample’s
behaviour. Most Victorians was chosen rather than society in general due to the context of
plastic-reduction policies in Victoria compared to the rest of the country. The mean across the
four items was calculated and used for the plastic avoidance behavioural domain (α=.78).
Perceived injunctive norms. Perceptions about social approval (injunctive norms) were
measured by asking all respondents four questions: “In your opinion, to what extent would most
people in Victoria approve or disapprove of you [using item in context]”. Each statement was
rated on a scale from 1=Strongly disapprove to 7=Strongly approve. This question was based on
a modified version of the measure used by Real and Rimal (2007) where respondents were asked
to estimate how favourably society in general judged the target behaviour. The mean across the
four items was calculated and used for the plastic avoidance behavioural domain (α=.87).
SOCIAL NORMS AND PLASTIC AVOIDANCE 8
Outcome expectations-perceived benefits. In the context of alcohol consumption,
outcome expectations have been measured as perceived benefits to oneself and anticipatory
socialisation (Real & Rimal, 2007; Rimal, 2008). In the context of plastic avoidance, perceived
benefits to the self was retained (operationalised as “If I avoid [item], then I will feel good”),
however the behaviour is not a social activity (compared to drinking alcohol). Anticipatory
socialisation was therefore replaced with perceived benefits to the environment (operationalised
as “If I avoid [item], then the environment will benefit) as a relevant perceived benefit. Items
were rated on a scale from 1=Strongly disagree, to 7=Strongly agree. The mean approval rating
across the items was calculated and used for the plastic avoidance behavioural domain for
perceived self-benefits (α=.93) and perceived environmental benefits (α=.94).
Outcome expectations-perceived barriers. Two measures of perceived barriers of
plastic avoidance were included: self-efficacy and anticipated costs. Anticipated costs were
operationalised in a similar manner to perceived benefits where respondents were asked to rate
their level of agreement with the statement: “If I avoid [item], I will have to spend money on
alternatives”. Self-efficacy was measured using the following articulation: “I am confident that I
can always [avoid plastic item] when [specific context]” (Koletsou & Mancy, 2011). The mean
approval rating for the self-efficacy items (α=.75) and anticipated cost items were calculated
(α=.85) and used for the plastic avoidance behavioural domain.
Group identity. Given the target group for the study was Victorians, identification with
the group was measured using the following two items: “Being Victorian is an important part of
my identity” (adapted from Lapinski et al., 2013) and “I identify with people in Victoria”
(Postmes, Haslam, & Jans, 2013). Respondents were asked to rate their response from
1=Strongly disagree to 7=Strongly agree and responses to the two items were averaged into an
index of group identity (α=.90).
Behavioural identity. Two items were adapted from Lapinski et al. (2017) to measure
behavioural identity: “Being a single-use plastic avoider is part of how I see myself” and
“Single-use plastic avoidance is an important part of my identity”. Statements were rated from
1=Strongly disagree to 7=Strongly agree and averaged into an index of behavioural identity
(α=.89).
Socio-demographic characteristics. Previous research has indicated that socio-
demographic characteristics often correlate with pro-environmental attitudes and behaviours. For
example, despite young people having a higher level of environmental concern (Chan-Halbrendt,
Fang, & Yang, 2009) older generations tend to engage in waste reduction behaviours more often
(Kurisu & Bortoleto, 2011). Similarly, women are typically more willing to engage in pro-
SOCIAL NORMS AND PLASTIC AVOIDANCE 9
environmental behaviours, including using reusable shopping bags, compared to men (Kollmuss
& Agyeman, 2002; Sharp, Høj, & Wheeler, 2010). The demographic variables included in the
survey were age, gender, region, employment status, education level, household income,
language spoken at home, and country of birth.
Analyses
First, behavioural intentions, past behaviours, descriptive norms and injunctive norms
were reverse coded so that all variables represented perceptions about plastic avoidance (rather
than use). For example, if a respondent reported using single-use plastic bags 20 percent of the
time when shopping, it was recoded to indicate that they avoided plastic bags 80 percent of the
time. H1, H2 and H3 were tested using Hierarchical Multiple Regression to determine whether
the variables proposed by the TNSB, and the additional perceived barriers, moderated the
relationship between descriptive norms and behavioural intentions as well as past behaviours.
This was done by first adding control variables to the regressions, followed by descriptive
norms, then adding each moderating variable, and finally adding the interaction term. Variables
used in the regressions were standardised and centred and the product of descriptive norms and
the moderator was used as the interaction term (Aiken, West, & Reno, 1991). This process
mirrors that used by Rimal (2008) where the TNSB was tested in relation to alcohol consumption
among college students.
H4 was tested using a series of Standard Multiple Regressions for behavioural intentions
and past behaviours for each behaviour/item. For each test, all variables were entered in a single
step, providing an indication of how much variance in each behaviour was explained by the full
model and how much unique variance each variable contributed while controlling for the
influence of the other variables (Pallant, 2013). A similar method was used by Real and Rimal
(2007) to examine the role of peer communication in predicting alcohol consumption.
Results
Table 1 presents the mean percentage of time that respondents avoided each item in the
previous month, intended to avoid each item during the next month, and believed most
Victorians avoided each item. Across all four items intentions to avoid single-use plastics were
slightly greater than reported avoidance in the previous month, but there was a consistent
perception that others avoided single-use plastics much less often.
[INSERT TABLE 1 HERE]
Identifying Control Variables
Standard Multiple Regressions were conducted to determine the influence of
demographic variables on behavioural intentions and past behaviours. The demographic model
SOCIAL NORMS AND PLASTIC AVOIDANCE 10
accounted for 13.1% of variance in behavioural intentions and 17.8% of variance in past
behaviours. Only two variables made statistically significant contributions to both models; age
(intentions: β = .30, p < .001; past behaviour: β = .34, p < .001) and gender (Female = 1, Male =
2) (intention: β = –.10, p < .001; past behaviour: β = –.12, p < .001). Consistent with previous
research, this suggests that older respondents and females generally avoid single-use plastics
more often than younger respondents and males.
H1
The first hypothesis used behavioural intentions of the plastic avoidance behavioural
domain as the dependent variable. For each analysis, Step 1 included just the control variables of
age and gender, which accounted for 12.1% of the variance in intentions. In Step 2 of each
analysis, perceived descriptive norms was added, which accounted for an additional 13.6% (β =
.37, p < .001) of the variance. Each analysis repeated Steps 1 and 2 and then changed the
variables included in Steps 3 and 4 to include the moderator of interest (e.g. perceived injunctive
norms), followed by the interaction term between descriptive norms and the moderator.
As shown in the left section of Table 2, each moderator variable (Steps 3A, 3B, 3C, 3D,
and 3E), with the exception of group identity, made a statistically significant contribution to the
variance in behavioural intentions. The greatest contributions were from the perceived benefit
measures; self-benefits (ΔR2 = 12.6%, β = .36, p < .001) and environmental benefits (ΔR2 =
10.0%, β = .32, p < .001). The interaction terms between descriptive norms and the moderators
(Steps 4A, 4B, 4C, 4D, and 4E) also made small but significant contributions to the variance in
behavioural intentions, with the exception of group identity and behavioural identity. The
greatest contributing interaction term was descriptive norms x injunctive norms (ΔR2 = 1.4%, β =
–.12, p < .001).
[INSERT TABLE 2 HERE]
To determine the pattern of the interaction, the relationship between the independent
variable (perceived descriptive norms) and the dependent variable (behavioural intentions) were
graphically plotted at low (1 standard deviation below the mean) and high (1 standard deviation
above the mean) values of the moderator (e.g. injunctive norms) (Aiken et al., 1991). The
interaction plots are shown in Figure 1.
As shown in the figure, the effect of descriptive norms on behavioural intentions was
strengthened by respondents’ perceptions about the social approval of plastic avoidance
(injunctive norms) and by higher levels of perceived self- and environmental benefits. The
second row in Figure 1 shows that the relationship between descriptive norms and behavioural
intentions did not change at higher or lower levels of identification as a Victorian or as a plastic
SOCIAL NORMS AND PLASTIC AVOIDANCE 11
avoider. However, lower levels of identification as a plastic avoider was associated with lower
avoidance intentions. Therefore, H1 was partially supported as perceived injunctive norms and
outcome expectations moderated the relationship between perceived descriptive norms and
behavioural intentions but group identity and behavioural identity did not. The implications of
these findings are addressed in the Discussion.
[INSERT FIGURE 1 HERE]
H2
The second hypothesis was tested by repeating the analyses from H1 but using past
behaviour as the dependent variable. For each analysis, Steps 1 through 4E were repeated where
the control variables of age and gender were added first, followed by perceived descriptive
norms, then each moderator variable, and finally the interaction term.
The right section of Table 2 shows the past behaviours regressions. Age and gender
accounted for more of the initial variance in past behaviours (16.6%), compared to intentions.
Perceived descriptive norms added another 12.2% (β = .35, p < .001) of the variance. Each
moderator also contributed to a small amount of variance (except for group identity). However,
the contributions were smaller for past behaviour compared to intentions – e.g. self-benefits
contributed 12.6% to the variance in intentions but only 6.2% (β = .25, p < .001) to the variance
in past behaviours. These findings support H2 because the TNSB moderators were weaker
predictors of past behaviours compared to behavioural intentions.
H3
The third hypothesis was that perceived barriers, self-efficacy and anticipated costs,
would also moderate the relationship between descriptive norms and behavioural intentions/past
behaviours. The steps from H1 and H2 were replicated but with self-efficacy and anticipated
costs as the moderators (see Table 3). In Step 3F, self-efficacy was added, contributing to 11.5%
(β = .35, p < .001) of the variance in intentions and 9.0% (β = .31, p < .001) of the variance in
past behaviours. The interaction term between self-efficacy and descriptive norms added in Step
4F was not significant for intentions but it did make a significant, albeit small, contribution to
past behaviours (ΔR2 = 0.5%, β = –0.07, p < .01). Anticipated costs, added in Step 3G, also made
a significant contribution to the variance in intentions (ΔR2 = 5.5%, β = 0.25, p < .001) and in
past behaviours (ΔR2 = 3.1%, β = –0.19, p < .001). The interaction term between anticipated
costs and descriptive norms was also significant for intentions (ΔR2 = 1.8%, β = 0.14, p < .001)
and past behaviours (ΔR2 = 1.3%, β = 0.12, p < .001).
[INSERT TABLE 3 HERE]
SOCIAL NORMS AND PLASTIC AVOIDANCE 12
The interaction terms were plotted for behavioural intentions and past behaviours. As
shown in Figure 2, the relationship between descriptive norms and intentions did not change at
higher or lower levels of self-efficacy but the relationship between descriptive norms and past
behaviour was strengthened by higher levels of self-efficacy. The second column in Figure 2
shows that the relationship between descriptive norms and intentions/past behaviours was
strengthened by lower anticipated costs of avoiding single-use plastics.
Therefore, H3 was partially supported as self-efficacy moderated the relationship
between descriptive norms and past behaviour, but not behavioural intentions. And anticipated
costs moderated the relationship between descriptive norms and both intentions and past
avoidance behaviours.
[INSERT FIGURE 2 HERE]
H4
Finally, H4 proposed that the variables in the TNSB would vary in their predictive power
for each plastic avoidance behaviour. This was tested through a series of analyses where
behavioural intentions and past behaviours for each item were the dependent variables and
descriptive norms, injunctive norms, perceived self-benefits, perceived environmental benefits,
self-efficacy, anticipated costs for each item, along with group identity, behavioural identity, age,
and gender were the independent variables. Results are presented in Table 4.
The full model predicted around 40% of the variance in avoidance intentions and past
behaviours for each item. The model provided the greatest predictive power for avoiding plastic
straws when buying a drink (intention: R2 = 48.7%, past behaviour: R2 = 45.2%) and the smallest
predictive power for avoiding plastic take-away containers when buying take-away food
(intention: R2 = 41.6%, past behaviour: R2 = 35.5%). While descriptive norms consistently made
one of the strongest contributions to the variance in both intentions and past behaviours, it was
second to self-efficacy for avoiding disposable coffee cups when buying a hot beverage
(intention: β = 0.33, p<.001; past behaviour: β = 0.39) and avoiding plastic take-away containers
when buying take-away food (intention: β = 0.27, p<.001; past behaviour: β = 0.26).
Group identity did not make a significant contribution to the variance in any behaviour.
Whereas behavioural identity contributed to the variance in intentions to avoid plastic bags (β =
–0.09, p < .01) and disposable coffee cups (β = –0.08, p < .05) but not plastic straws or take-
away containers. Age made a significant contribution to the variance in intentions and past
behaviours across all items, however gender was only a significant contributor for plastic bag
avoidance (intention: β = –0.06, p < .05; past behaviour: β = –0.09, p < .001) and for past
SOCIAL NORMS AND PLASTIC AVOIDANCE 13
disposable coffee cup avoidance (β = –0.06, p < .05). In other words, females were more likely
to have avoided plastic bags and disposable coffee cups in the previous month than males.
Injunctive norms were most relevant for intentions to avoid plastic bags (β = 0.16, p <
.001) and coffee cups (β = 0.15, p < .001). Self-efficacy made a significant contribution to past
avoidance of plastic bags (β = 0.10, p < .01) but not to intentions. Anticipated costs made a
significant contribution to avoiding plastic straws (intention & past behaviour: β = –0.20, p <
.001), disposable coffee cups (intention: β = –0.14, p < .001; past behaviour: β = –0.09, p < .01),
and plastic take-away containers (intention: β = –0.15, p < .001; past behaviour: β = –0.12, p <
.001) but not plastic bags. These findings confirm H4 by demonstrating that each variable in the
TNSB varies in their predictive power for each plastic avoidance behavioural intention and past
behaviour.
[INSERT TABLE 4 HERE]
Discussion
The aim of this research was to explore the role of social norms in predicting consumer
behaviour related to single-use plastic avoidance using the TNSB (which has typically been
applied to health and safety behaviours). Findings indicate that most of the variables proposed by
the TNSB moderate the relationship between descriptive norms and single-use plastic avoidance,
with the exception of group identity; although the strength of this effect was weaker for past
behaviours compared to behavioural intentions. Results indicate that perceived barriers also
moderate the relationship between descriptive norms and plastic avoidance. Finally, the relative
importance of each variable within the TNSB in predicting avoidance was found to vary
depending on the specific item/behaviour.
Implications for Theory
The null finding for group identity may be due to the size of the reference group in the
current study. Previous research involving the TNSB and drinking behaviour has often focused
on University student samples where the reference group is other students at the target
University (Lapinski et al., 2013; Real & Rimal, 2007; Rimal, 2008; Rimal, Lapinski, Cook, &
Real, 2005). Our results indicate that group identity moderates the relationship between
descriptive norms and behaviour when the target group is psychologically close and easy to
identify, but not when it is expanded to wider society. This distinction was made by Park and
Smith (2007) where multiple types of norms were measured and compared, including social
norms of ‘valued others’ and social norms of ‘wider society’. They found that different types of
social norms interacted differently with behaviour, noting that if they had only used one measure
they may not have found any effects. Interestingly, despite the psychologically distant reference
SOCIAL NORMS AND PLASTIC AVOIDANCE 14
group in the current study, perceptions about descriptive norms in wider society still consistently
predicted plastic avoidance behaviours. This may be evidence of the stronger influence of social
norms on environmental behaviours compared to health behaviours (Lapinski et al., 2007).
Adding anticipated costs and self-efficacy to the TNSB had mixed results. While both
factors contributed to the variance in behaviour, self-efficacy did not moderate the relationship
between norms and intentions, but it did moderate the relationship between norms and past
behaviours – particularly in relation to avoiding ‘take-away’ items. In the context of resource
consumption, it has been found that many factors can often override intentions, including
difficulty and time constraints (Newton & Meyer, 2013). In fact, self-efficacy has been identified
as the greatest predictor of actual observed behaviour regarding reusable bag use (Lam & Chen,
2006). Similarly, anticipated costs made a significant contribution to predicting avoidance of
straws, coffee cups, and take-away containers but not plastic bags. These differences reiterate the
importance of behavioural attributes in predicting behaviour (Lapinski & Rimal, 2005) and may
highlight the role of policy (i.e. official rules) in influencing social norms (i.e. social rules) –
discussed below.
Implications for Practice
Given the recent supermarket plastic bag ban in Australia, plastic bag avoidance may be
motivated differently to other items. Combined with the impending State-wide ban, Victorians
may have been encouraged to adopt plastic bag avoidance behaviours without necessarily
changing their antecedent beliefs. This would explain why perceived benefits and barriers were
weaker predictors of bag avoidance compared to other items, which still largely rely on
voluntary behaviour change. The weaker attribution of self-efficacy for plastic bag avoidance
may also be a function of the relatively higher incidence of past avoidance behaviours, given that
direct experiences generally have a stronger influence than indirect experience (Kollmuss &
Agyeman, 2002). This pattern of behaviour change without attitude change in response to new
policy was also found when the first Australian plastic bag ban was introduced in South
Australia (Sharp et al., 2010). However, results from the current study demonstrate that in a
changing policy context, the role of perceived descriptive norms becomes even more important.
In contrast, plastic straws have yet to see the widespread policy implementation of plastic
bags, however, there has been an increase in local campaigns and consideration for how to
reduce plastic straw consumption without disadvantaging people who rely on them (e.g. the
disabled) (Schnurr et al., 2018). Rather than banning plastic straws altogether, our findings
indicate a less obtrusive intervention could be effective. Straw avoidance was primarily driven
by beliefs that others are also avoiding them and self-efficacy to avoid straws when purchasing a
SOCIAL NORMS AND PLASTIC AVOIDANCE 15
drink. If retailers adopted simple ‘nudge’ techniques (Thaler & Sunstein, 2009), such as
changing the default to only provide plastic straws on request, avoidance could increase
substantially without resorting to a ban. When a similar nudge technique was trialled on plastic
bags in Japan, plastic bag use decreased by 40% after only six months (Ohtomo & Ohnuma,
2014). The authors also found that the change in practice increased consumer beliefs that others
were also using plastic bags less often, reinforcing their decision to avoid plastic bags.
Results from the current study also point to a mismatch between how often people avoid
single-use plastic items and how often they believe most others avoid them. In communication
studies, this phenomenon is known as pluralistic ignorance (Shepherd, 2017) – where most
members of a group actually reject certain norms (e.g. using single-use plastics) but they believe
that most others accept them (Arias, 2019). This difference, combined with findings on the
importance of descriptive norm perceptions in predicting avoidance behaviour, highlight an
opportunity to correct misperceptions about plastic avoidance in order to further encourage
voluntary behaviour change. However, this also raises the question: how can misperceptions
about social norms be changed at a societal-level?
According to Burchell, Rettie, and Patel (2013), social marketing may hold the answer.
As the authors highlight, research has demonstrated that social marketing campaigns which
adopt a social norms approach are not only successful in correcting misperceptions about others,
but they are also typically associated with positive changes in behaviour. However, it should be
recognised that such campaigns may also be in conflict with other sources of information from
mass media which could also be influencing individual social norm perceptions. For example,
news coverage and documentaries often aim to draw attention to the scale of a problem (such as
the prevalence of littering) which can actually increase perceptions that the undesirable
behaviour is common and normal (Walsh-Childers & Brown, 2009), potentially reinforcing the
undesirable norm and negatively influencing behaviour.
Limitations & Future Research
The methodological limitations of this study are largely related to the use of a self-report
questionnaire and survey panel. For one, online panels typically use non-probability recruitment
methods which can lead to sample bias. Similarly, hosting the survey online may have biased the
sample towards respondents who have access to the internet (Borg & Smith, 2018). To
compensate for these issues, and to ensure that the sample broadly reflected the target
population, sampling quotas were applied and the data used in all analyses were weighted by age
and gender. Second, self-report surveys are subject to a number of limitations such as
randomised responding and extreme responding (Paulhus & Vazire, 2007). Quality appraisal
SOCIAL NORMS AND PLASTIC AVOIDANCE 16
procedures were followed in an attempt to identify and correct these errors where practicable –
for example, respondents who completed the survey too quickly or answered all Likert-type
questions with mid-point responses were excluded. Furthermore, while the use of past behaviour
is likely to be closer to actual behaviour than behavioural intentions, it still relies on a self-report
measure which is subject to recall error and social desirability and does not necessarily reflect an
individual’s true behaviour. Future research which employs alternative measures of behaviours,
such as observation, is recommended.
Further research is also recommended to explore the role of media messaging on
normative perceptions and on the moderators of plastic avoidance behaviours, such as perceived
benefits of avoidance. This gap was noted by Mead, Rimal, Ferrence, and Cohen (2014) who
recommend future research to understand the extent to which exposure from different
environmental cues, such as the media, affects different variables in the TNSB.
Conclusion
To address the global challenge that is plastic pollution, we need to reduce our reliance
on single-use plastics. This requires large-scale behaviour change as part of a broader systems
change in which many actors have a role to play (Löhr et al., 2017). While governments and
businesses are starting to do their part by introducing plastic reduction policies, individual-level
consumer behaviour change can also have a significant impact on plastic pollution. Our findings
indicate that not only are consumer perceptions about social norms the strongest predictor of
plastic avoidance behaviours, but there is an opportunity to close the perception-action gap to
further encourage change. In order to address this gap, practitioners and policy makers are
advised to use social norm messaging which highlights that avoidance of single-use plastics is
becoming more common and normal. Based on our findings, such messaging will be most
effective if it also emphasises: 1) how easy it is to avoid the item in question, 2) the
environmental benefits and positive feelings associated with avoidance, 3) how to keep the cost
of avoidance down, and 4) the social approval of avoidance.
SOCIAL NORMS AND PLASTIC AVOIDANCE 17
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SOCIAL NORMS AND PLASTIC AVOIDANCE 23
Tables
Table 1
Descriptive statistics – percentage of time spent avoiding single-use plastics by item (Mean and
Standard Deviation)
Past
behaviours
Behavioural
intentions
Descriptive
norms
M
SD
M
SD
M
SD
Single-use plastic bags
74.1
31.6
75.9
33.3
55.7
27.4
Plastic straws
71.2
34.3
74.9
33.2
45.9
27.0
Disposable coffee cups
57.2
38.1
61.6
38.0
35.5
25.9
Plastic take-away containers
53.0
35.9
60.2
36.0
32.4
26.7
Behavioural domain
65.2
29.0
69.4
30.1
42.4
20.8
SOCIAL NORMS AND PLASTIC AVOIDANCE 24
Table 2
Hierarchical Multiple Regression: TNSB predictors of intentions to avoid and of past avoidance
of single-use plastics
Behavioural intentions
Past behaviours
βa
ΔR2 (%)
R2(%)
β
ΔR2 (%)
R2(%)
Step 1:
12.1***
12.1
16.6***
16.6
Age
0.33***
0.39***
Gender
-0.11**
-0.13***
Step 2: DNb
0.37***
13.6***
25.8
0.35***
12.2***
28.8
Step 3A: IN
0.17***
2.6***
28.4
0.09***
0.8***
29.6
Step 4A: DNxIN
-0.12***
1.4***
29.8
-0.10***
1.0***
30.6
Step 3B: OES
0.36***
12.6***
38.3
0.25***
6.2***
35.0
Step 4B: DNxOES
-0.08***
0.6***
38.9
-0.08**
0.7**
35.7
Step 3C: OEE
0.32***
10.0***
35.7
0.25***
6.1***
34.9
Step 4C: DNxOEE
-0.07*
0.4*
36.2
-0.07**
0.5**
35.5
Step 3D: IDV
0.04
0.1
25.9
0.02
0.0
28.9
Step 4D: DNxIDV
0.02
0.0
25.9
0.04
0.1
29.0
Step 3E: IDB
0.18***
3.3***
29.1
0.14***
2.0***
31.1
Step 4E: DNxIDB
-0.03
0.1
29.1
-0.04
0.2
31.2
Note: N=1,001; Gender: 1=female, 2=male; DN= Descriptive norms; IN=Injunctive norms; OES=Outcome
expectations-self-benefits; OEE=Outcome expectations-environmental benefits; IDV=Group identity (Victorian);
IDB=Behavioural identity.
a Standardized beta coefficient up to the given step in the model. b Steps 3A through 4E include variables up to Step
2, one moderator and the interaction term with DN.
*p < .05. **p < .01. ***p < .001.
SOCIAL NORMS AND PLASTIC AVOIDANCE 25
Table 3
Hierarchical Multiple Regression: Perceived barriers as predictors of intentions to avoid and of
past avoidance of single-use plastics
Behavioural intentions
Past behaviour
βa
ΔR2 (%)
R2(%)
β
ΔR2 (%)
R2(%)
Step 1:
12.1***
12.1
16.6***
16.6
Age
0.33***
0.39***
Gender
-0.11**
-0.13***
Step 2: DNb
0.37***
13.6***
25.8
0.35***
12.2***
28.8
Step 3F: SE
0.54***
11.5***
37.0
0.31***
9.0***
37.6
Step 4F: DNxSE
-0.04
0.2
37.1
-0.07**
0.5**
38.0
Step 3G: AC
-0.25***
5.5***
31.0
-0.19***
3.1***
31.7
Step 4G: DNxAC
0.14***
1.8***
32.8
0.11**
1.3**
32.9
Note: N=1,001; Gender: 1=female, 2=male; DN= Descriptive norms; SE=Self-efficacy; AC=Anticipated costs.
a Standardized beta coefficient up to the given step in the model. b Steps 3F through 4G include variables up to Step
2, one moderator and the interaction term with DN.
*p<.05. **p<.01. ***p<.001.
SOCIAL NORMS AND PLASTIC AVOIDANCE 26
Table 4
Standard Multiple Regression: Standardized Beta Coefficients for all TNSB predictor variables – by single-use item
Behavioural intentions
Past behaviours
Bags
Straws
Coffee
cups
Take-away
containers
Bags
Straws
Coffee
cups
Take-away
containers
TNSB model (R2)
40.6%
48.7%
47.5%
41.6%
37.3%
45.2%
38.8%
35.5%
Age
0.18***
0.14***
0.16***
0.17***
0.21***
0.19***
0.21***
0.26***
Gender (Female=1, Male=2)
-0.06*
0.00
-0.04
0.00
-0.09***
-0.03
-0.06*
-0.04
Descriptive norms (item)
0.39***
0.28***
0.26***
0.24***
0.38***
0.30***
0.23***
0.22***
Injunctive norms (item)
0.16***
0.09***
0.15***
0.11***
0.09**
0.01
0.09**
0.08**
Outcome: self-benefit (item)
0.19***
0.23***
0.15***
0.24***
0.08
0.14***
0.02
0.15***
Outcome: environmental benefit (item)
0.10**
0.06
0.10**
0.08*
0.12**
0.04
0.09*
0.05
Group identity (Victorian)
-0.01
-0.03
-0.03
-0.06
-0.05
0.00
-0.04
-0.04
Behavioural identity (plastic avoider)
-0.09**
-0.04
-0.08*
-0.07
-0.04
-0.04
-0.05
-0.06
Self-efficacy (item)
0.04
0.20***
0.33***
0.27***
0.10**
0.23***
0.39***
0.26***
Anticipated costs (item)
-0.03
-0.20***
-0.14***
-0.15***
-0.03
-0.20***
-0.09**
-0.12***
Note: N=bags: 958, straws: 830, coffee cups: 817, take-away containers: 832.
*p<.05. **p<.01. ***p<.001.
SOCIAL NORMS AND PLASTIC AVOIDANCE 27
Figures
Figure 1. Plotting the relationship between descriptive norms and intention to avoid single-use plastics by low (-1SD) and high (+1SD) standardized
values of the moderating variables of TNSB.
DN=Descriptive norms; IN=Injunctive norms; OES=Outcome expectations-self-benefits; OEE=Outcome expectations-environmental benefits; IDV=Group identity (Victorian);
IDB=Behavioural identity.
SOCIAL NORMS AND PLASTIC AVOIDANCE 28
Figure 2. Plotting the relationship between descriptive norms and intention to avoid single-use plastics and past avoidance of single-use plastics by
low (-1SD) and high (+1SD) standardized values of Self-efficacy and Anticipated costs.
DN= Descriptive norms; SE=Self-efficacy; AC=Anticipated costs.
