TYPE Original Research
PUBLISHED 10 November 2022
DOI 10.3389/frsus.2022.1023605

OPEN ACCESSEDITED BY

T. A. Kurniawan,
Xiamen University, China

Knowing the rules caneffectively enhance plastic
waste separation on campus

REVIEWED BY

Grigorios L. Kyriakopoulos,
National Technical University ofAthens, Greece
Yasir Ali Soomro,
King Abdulaziz University, Saudi Arabia

Takuro Uehara1*, Misuzu Asari2 and Ryo Sakurai1
1

College of Policy Science, Ritsumeikan University, Ibaraki, Japan, 2 Graduate School of Global
Environmental Studies, Kyoto University, Kyoto, Japan*CORRESPONDENCE

Takuro Uehara
takuro@fc.ritsumei.ac.jp
SPECIALTY SECTION

This article was submitted toWaste Management,a section of the journal
Frontiers in Sustainability
RECEIVED 19 August 2022
ACCEPTED 27 October 2022
PUBLISHED 10 November 2022
CITATION

Uehara T, Asari M and Sakurai R (2022)
Knowing the rules can effectively
enhance plastic waste separation on
campus. Front. Sustain. 3:1023605.
doi: 10.3389/frsus.2022.1023605
COPYRIGHT© 2022 Uehara, Asari and Sakurai. Thisis an open-access article distributed
under the terms of the Creative
Commons Attribution License (CC BY).
The use, distribution or reproduction
in other forums is permitted, provided
the original author(s) and the copyrightowner(s) are credited and that theoriginal publication in this journal iscited, in accordance with accepted
academic practice. No use, distribution
or reproduction is permitted which
does not comply with these terms.

Although proper plastic waste separation is critical to efficient disposal,
people do not always comply with designated rules, possibly resulting froma lack or misunderstanding of the correct rules related to local variability.
Accordingly, this study aimed to understand how learning the plastic waste
separation rules changed college students’ behavior on campus, besides
how said knowledge spreads across campuses. To evaluate the impact,
quasi-experiments were conducted from May to July 2022, including a hybridof before–after and treatment–control designs. The treatment group included
college students at a university in Japan (N = 295 and 188 for the ﬁrst
and second surveys, respectively); the control group also included college
students in the same region (N = 395 and 219 for the ﬁrst and second
surveys, respectively). Before learning the rules, 12.8% of the treatment group
followed the correct rules. After acquiring the rules, this number increased to38.3%. The difference-in-differences analysis veriﬁed its statistically signiﬁcant
improvement over the control group. Structural equation models based on thetheory of planned behavior before and after learning the rules revealed thatthe intention–behavior gap was bridged by the learning, indicating limitations
due to a lack of knowledge. The best–worst scaling showed that habits should
be targeted to further improve separation behaviors; whereas an egocentric
network analysis demonstrated that knowledge spread among peers, albeit toa limited extent. In summary, this study revealed that knowing designated rules
is a simple but effective approach to enhancing plastic waste separation in acertain context.
KEYWORDS

college student, difference-in-differences, egocentric network analysis, plastic waste
separation, structural equation modeling, theory of planned behavior, best-worst
scaling

Introduction
Recent research on the safe operating space claimed that the boundary ofnovel entities is already exceeded, with plastic pollution being of particular concern(Persson et al., 2022). Indeed, the annual production and release of plastics surpass
the global collective capacity for assessment and monitoring. Recent simulations ofbusiness-as-usual scenarios share an unequivocal view that plastic pollution will continue
increasing into the future (Cordier and Uehara, 2019; Lebreton and Andrady, 2019;
Borrelle et al., 2020; Hohn et al., 2020; Lau et al., 2020), claiming that society must do

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everything in its power, from technological solutions (e.g.,cleanup devices, pyrolysis; Dai et al., 2022, and flotation
separation; Wang et al., 2019) to non-technological solutions(e.g., waste management) to reduce the flow of plastic pollution.
Furthermore, such comprehensive efforts aimed at regulating
plastic pollution flow (e.g., reducing emissions and cleaning
up that which has been emitted) may not be sufficient to
reduce polluting stocks (Cordier and Uehara, 2019; Hohn et al.,2020; Lau et al., 2020), for as long as the net flow of plastic
pollution (i.e., rate of emission–rate of removal) is positive,
the plastic stock will accumulate in the environment (especiallymarine ecosystems; Uehara, 2020). Furthermore, as plastic waste
degrades into microplastics with time (Lebreton et al., 2019),
eliminating the possibility of efficient complete removal fromthe environment, immediate and substantial efforts to reverse
the net flow are urgent (Uehara, 2020).
Among possible non-technological solutions, changing
individuals’ behavior is foundational (Heidbreder et al., 2019),
and is often the primary aim of governmental policies(Great Britain Parliament. House of Lords Science Technology
Committee, 2011; Government of Japan, 2019). For example,
the reduction of plastic use and proper management ofplastic waste (e.g., avoiding littering and separating plastic
waste) can substantially impact plastic pollution (Cordier andUehara, 2019; Borrelle et al., 2020; Lau et al., 2020); whereas
other technological solutions, such as removing marine plastic
pollution, become unnecessary under practices of proper plastic
waste management. However, a recent study by the Organization
for Economic Co-operation and Development (OECD, 2022)
reported that globally only 9% of plastic waste is recycled.
Although a comprehensive use of policy instruments (e.g.,
regulation, economic instruments, infrastructure, technology,
data and communication, and voluntary commitments) is
desirable, particularly the use of economic and regulatory
instruments is fragmented (OECD, 2022). The report also assertsthat making recycling and sorting profitable is promising.
While market (i.e., price- and rights-based) and regulatory
instruments have been dominant policy directives aimed
at sustainable transformation (World Bank, 1997; Sterner
et al., 2019), an interest in behavioral instruments hasgrown (Great Britain Parliament. House of Lords Science
Technology Committee, 2011; OECD, 2017). Accordingly,
significant theoretical and empirical advancements in behavioral
sciences have occurred (e.g., nudges; Thaler and Sunstein, 2009;
Great Britain Parliament. House of Lords Science Technology
Committee, 2011; OECD, 2017), which have been applied
to plastic pollution issues (Alpizar et al., 2020). Nudges areinterventions that assist people in making certain decisions
but without forcing them (Thaler and Sunstein, 2009). For
example, OECD (2017) proposed seven types of instruments
aimed at behavioral adjustments: simplification and framingof information, changes to the physical environment, changes
to the default policy, use of social norms and comparisons,Frontiers in Sustainability

use of feedback mechanisms, reward and punishment schemes,
as well as goal setting and commitment devices. Further, theGreat Britain Parliament. House of Lords Science Technology
Committee (2011) identified seven types of non-market
and non-regulatory instruments: non-fiscal incentives anddisincentives, persuasion, provision of information, changes
to physical environments, changes to the default policy,
use of social norms, and salience; however, the empiricalevidence on the efficacy and effective use of behavioral
instruments remains limited (Great Britain Parliament. Houseof Lords Science Technology Committee, 2011; Carlsson
et al., 2019). As the context-dependency of policy instrument
effectiveness complicates the situation, it is essential toaccumulate correlated empirical evidence surrounding the useof behavioral instruments (Sterner et al., 2019; Grilli and Curtis,
2021; Fogt Jacobsen et al., 2022).
While all stakeholders are essential to fill the various
roles required (Heidbreder et al., 2019), the improvement ofplastic waste management among college campus students
represents a promising avenue. Universities and their students
hold a unique and critical position in society regarding
the sustainability transformation of not only campuses, butsocieties as well (Velazquez et al., 2006; Stephens et al.,2008; Uehara and Ynacay-Nye, 2018). Amaral et al. (2015)
suggested that it is the social responsibility of universities
to address environmental issues through education, research,community outreach, campus operations, etc. In the short term,
students could disseminate any acquired knowledge to theirfamily members and society (Stephens et al., 2008; Dardanoni
and Guerriero, 2021); whereas over the long term, college
students will become future leaders, researchers, consumers,
and entrepreneurs (Vicente-Molina et al., 2013; Amaral et al.,2015). Research addressing the factors affecting campus college
students’ waste separation behavior, however, remains lacking(Cho, 2019).
Given the importance of empirical research regarding
behavioral instruments of college students, this study aimed
to understand how knowledge of the plastic waste separation
rules changed college students’ plastic separation behavior
on campus (Research question 1), and how said knowledge
spread among campuses (Research question 2). Educating
people and spreading information are two common purposesof the provision of information (Matiiuk and Liobikiene, 2021).
Specifically, the correct separation rules in Japan may not be
simple, as the rules may differ by region and entities (e.g.,households, businesses, including universities). This complexity
may prevent many, even well-meaning people intending tofollow the rules, from properly participating (Knickmeyer,2020); therefore, the simplification and framing of information
can help people correct or change their behavior (Mont et al.,2014; Lehner et al., 2016; OECD, 2017).
To address these research questions, quasi-experiments wereconducted (Reichardt, 2019); namely, a hybrid of before–after02

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effectiveness remains undefined (Barr, 2007; Minelgaite andLiobikiene, 2019; Knickmeyer, 2020; Wang et al., 2020; Hu
et al., 2021; Matiiuk and Liobikiene, 2021; Wang C. et al., 2021;
Wang Y. et al., 2021). For example, Hu et al. (2021) revealed
that environmental knowledge (e.g., waste separation rules
and general environmental education) positively influenced
attitudes toward household waste separation intention, whereas
Matiiuk and Liobikiene (2021) found that environmental
knowledge (e.g., waste separation rules and the importanceof separation) positively impacted waste separation behavior
in Lithuania. In China, Wang et al. (2020) concluded thatwaste separation knowledge was positively correlated withattitudes, perceived behavioral control, and waste separation
intention. Alternatively, a comparative study across 27 EU
countries showed that greater waste separation information
was the least effective tool for encouraging its behavior(Minelgaite and Liobikiene, 2019). Accordingly, the mixed
results of the efficacy of information intervention on waste
separation behavior support the importance of further study(Wang C. et al., 2021).
Because efficacy may depend on a combination of content
type and context, intervention measures should be carefully
designed. In addition, the advancement of behavioral sciences
and nudges stand to shed further light on the relatively historicuse of information intervention (Great Britain Parliament.
House of Lords Science Technology Committee, 2011; OECD,
2017; Sunstein et al., 2019). Namely, simplification andframing of information are among the most widely applied
behavioral intervention methods (OECD, 2017), where theformer aims to help people understand complex information,
specifically through how it is framed and conveyed (Lehneret al., 2016). Further, among the two types of knowledge—
general environmental knowledge (e.g., environmental impact
of carbon dioxide) and specific knowledge of the context(Barr, 2007)—the latter appears more effective in the current
context, as knowledge of the correct waste disposal rules isa prerequisite for correct behavior (Knickmeyer, 2020; Hu
et al., 2021). Accordingly, the first hypothesis was defined
as follows:

and treatment–control designs, two common approaches forencouraging pro-environmental behaviors (Grilli and Curtis,
2021). Four methodological characteristics were included inthis study: First, two models were built based on thetheory of planned behavior (TPB; Ajzen, 1991), before andafter knowledge acquisition, to capture the changes to thepsychological constructs regarding plastic waste separation
intention and behavior. This method, inspired by Hu et al.
(2021), is distinct in that most TPB studies have beencross-sectional (i.e., a single model applied once). Second,a difference-in-differences (DID) analysis tested the impactof knowing the rules with other factors (e.g., time effect)against a control group, and measured the corresponding
degree of impact. Notably, the DID can estimate an unbiased
counterfactual by referring to a control group (Gertler et al.,2016). Third, using the best–worst scaling method (BWS;
Louviere et al., 2015), the relative criticality of other barriers
to following the correct plastic waste separation behavior, aside
from knowledge of the correct rules, was explored. Fourth,an egocentric network analysis based on social networking(Knoke and Yang, 2020) assessed the potential impact of ruledissemination. This network analysis technique has been widely
applied previously, including to college students (Hendrickson
et al., 2011; Meisel et al., 2015), although never directly to waste
problems on campus.
The remainder of the paper is structured as follows: Section
Literature review and hypotheses provides an overview of
the research framework and hypotheses; Section Materials
and methods introduces the materials and methods used;
Section Results reports the findings of this study, followed
by a discussion of their implications; and Section Discussion
provides a summary conclusion of the research.

Literature review and hypotheses
Knowledge acquisition throughsimpliﬁed information
Information intervention is one of the most used methods
for promoting environmentally sustainable behaviors (VicenteMolina et al., 2013; Sallaku et al., 2019; Grilli and Curtis, 2021).
Notably, it can be an effective measure on campuses, as it canbe implemented locally at across an array of organizational
levels, in contrast to market incentives or regulatory measures
put forth by the government (Kirakozian, 2016). Furthermore,
relative population stability is paramount to acquiring andimplementing the correct rules in context (Knickmeyer, 2020),
and the low population mobility on campus over can facilitate
the efficacy of teaching those who frequently use disposal bins
(i.e., students).
While various studies assessing information intervention
on waste separation behavior have been conducted, its overall

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H1: Knowledge of the plastic waste separation rules
on campus can positively impact the correlated
separation behavior.
Although there are various waste separation rules by type(e.g., plastics and combustibles) and location (e.g., campuses andhouseholds), the current study focused on campus plastic waste
separation as an information simplification strategy. Similarly,
no other information was provided, such as the environmental
damage caused by the failure to correctly follow the rules.
Furthermore, participants were not asked to follow the rules, as
it is another behavioral strategy (i.e., persuasion; Great Britain
Parliament. House of Lords Science Technology Committee,2011) to focus on the efficacy of rule acquisition.

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Theory of planned behavior

the person’s perceived ease of performing said behavior,
which can reflect the person’s experience and/or anticipatedbarriers. In accordance with previous related studies (Cho,2019; Wang et al., 2020), the following hypotheses were
proposed here:

The TPB provides a social psychological model tounderstand which psychological factors (e.g., attitudes andsocial norms) predict individuals’ behaviors. Notably, thistechnique encompasses an array of actions, from eating habits
to voting participation (Ajzen, 1991). The TPB, and its various
extensions, have been some of the most widely used frameworks
for identifying the determinants of pro-environmental
behaviors, and its use has been growing (Yuriev et al., 2020).
Specifically, the TPB and its extensions have also been applied
to waste separation behaviors (Xu et al., 2020; Hu et al., 2021;
Wang Y. et al., 2021; Wang C. et al., 2021; Govindan et al.,2022).
The TPB is suitable for this study as it extends the principal
points for choosing the TPB as a theoretical foundation (Yuriev
et al., 2020). First, we intend to understand the psychologicalfactors associated with plastic waste separation behavior. Whilevarious studies have applied the TPB to waste issues, thoseincluding modeled behavior are relatively scarce, even though an
intention–behavior gap has been frequently observed (VicenteMolina et al., 2013; Nielsen, 2017; Wang et al., 2020).
Importantly, applying the TPB to pro-environmental behaviors
has helped identify the intention–behavior gap as one of thetwo promising avenues for future research (Yuriev et al., 2020).
Second, the target behavior is exact: whether students on campus
sort plastic waste correctly. Third, the context (i.e., campus
college students in Japan) is unique and valuable. Althoughorganizational context (e.g., companies and universities) isessential for promoting pro-environmental behaviors, 81% of
the correlated TPB studies on pro-environmental behavior
have lacked organizational context, and instead focused onhouseholds (Yuriev et al., 2020). Accordingly, TPB studies
assessing campus waste management are lacking (Cho, 2019).
Furthermore, studies have indicated that the predictive powerof each influencing factor varies by context (e.g., country andculture; Lizin et al., 2017; Wang C. et al., 2021). Fourth,though it is not in the principal points made by Yuriev
et al. (2020), this study is unique for its estimation of
two TPB models: before and after knowledge acquisition.
To the best of the authors’ knowledge here, no such study
has compared changes to psychological factors and behavior
in TPB before and after knowledge acquisition. Although
Hu et al. (2021) surveyed similar changes to psychologicalfactors in TPB, their study did not include behavior, butbehavioral intention.
The TPB comprises three conceptually independent
determinants to predict behavioral intentions: attitude(ATT), subjective norm (SN), and perceived behavioral
control (PBC; Ajzen, 1991). ATT toward behavior refers toa person’s favorable or unfavorable evaluation of the actionin question; whereas SN refers to the person’s perceived
social pressure to perform the behavior, and PBC represents

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H2: ATT toward plastic waste separation is positively related
to college students’ plastic waste separation intention.
H3: SN is positively related to college students’ plastic waste
separation intention.
H4: PBC is positively related to residents’ plastic waste
separation intention.
H5: Plastic waste separation intention is positively related tocollege students’ plastic waste separation behavior.

Social network analysis
As people interact, previously acquired information canbe transmitted to others; thus, information acquisition can
indirectly change others’ behavior through their social network.
Accordingly, social network analysis provides tools for problemsolving by, for example, identifying influential people andunderstanding how information spreads across the network(Tabassum et al., 2018).
Campus can be a social setting unit for social network
analysis, as it represents the locations of college students
acquiring and disseminating knowledge among fellow students.
The social network analysis has thus been applied todiverse topics on campus, including friendship networks ofinternational students (Hendrickson et al., 2011), alcohol use,and co-occurring addictive behavior (Meisel et al., 2015);
however, to the best of the author’s knowledge, no such study
has been conducted on addressing campus waste.
Four levels of network analysis exist: egocentric network,
dyadic network, triadic relations, and whole network (Knoke
and Yang, 2020). The egocentric network comprises one
actor (ego), and all other actors (alters). Here, the ego is a
focal node of the network, and alters are all other nodes
connected to it (Tabassum et al., 2018). Although the egocentric
network analysis is the simplest level of analysis, it producesrich information regarding insights into the whole network.
Furthermore, it can be more accurate than higher levels ofanalysis due to actors’ memory constraints (Tabassum et al.,2018), as the information required to describe a network grows
exponentially as higher levels are targeted. Furthermore, agrowing number of studies have applied egocentric network
analysis, it remains in its infancy (Tabassum et al., 2018). This
study can contribute to this burgeoning field. In accordance withthe previous research (e.g., Maeda and Ritchie, 2003; Patterson
et al., 2021), the present proposed a final hypothesis:
H6: The acquired knowledge of plastic waste separation rules
spreads on campus.

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Materials and methods

differ by analysis as some respondents did not answer questions
necessary for analysis. Surveys 1, 2, and complementary quizzes
for the treatment group (Table 2) were held online duringclasses on Introductory Social Research Methods, for which all
first-year students majoring in policy sciences were registered.
Surveys 1 and 2 for the control group were also held online.
Informed consent was obtained from all the participants inthis study. While attrition or dropout from study with time
is common in a panel study (Lugtig, 2014), the sample sizesof the treatment group diminished for two additional uniquereasons; 29 respondents did not dispose of any plastic waste
on campus during the experiment, and a further 26 did notunderstand the correct rule. Because our focus was on theimpact of acquiring the rules on plastic separation behavior, we
excluded participants who did not acquire the rules or did notdispose of any plastic waste on campus.

This study conducted quasi-experiments where participants
were not assigned at random (Reichardt, 2019). Of the hybrid
before–after (i.e., pre-test–post-test) and treatment–control
designs applied here, the latter are becoming more popular in
impact assessments (Gertler et al., 2016), although is not always
possible for practical, ethical, financial, or methodologicalreasons (Kerschberger et al., 2012). For example, to the bestof the authors’ knowledge, no treatment–control studies haveapplied the TPB using structural equation modeling. Of the 30
studies on encouraging pro-environmental behaviors for waste
management reviewed by Grilli and Curtis (2021), 20 adopted
a before–after design (e.g., Tonglet et al., 2004; Whitmarshand O’Neill, 2010; Lakhan, 2016; Weber et al., 2019); however,
here, before–after and treatment–control designs were carefully
applied to provide a more accurate interpretation of the impact.

Survey designs and interventions
Case study: College students in Japan
Table 2 indicates surveys and quizzes with accompanying
question types aimed at addressing the proposed researchquestions and hypotheses. Each type of question was
the same across the surveys to ensure comparability (seeSupplementary material 1). As a hybrid research design, not allquestions for the treatment group were asked to the control
group. Treatment group TPB models were compared before andafter the intervention, and the social network analysis was also
conducted only for the treatment group.
In designing the surveys, a pre-test was conductedcontaining 27 students in the same college as the treatmentgroup on April 7, 2022. None of the students had received theguidance for separating plastic waste on campus; thus, 56% of
the participants followed the separation rules stipulated by themunicipality where their houses they are currently living arelocated. Municipalities, however, are often required to separate
plastic waste only with plastic marks, whereas businesses(including universities) are required to separate plastic waste
despite these marks. The plastic mark is an identification
symbol required for plastic containers and packaging (except
for alcoholic and non-alcoholic beverages, and PET bottles for
specific seasonings) under the Law for Promotion of Effective
Utilization of Resources (Plastic Packaging Recycling Council,n.d). Thus, knowledge of the correct plastic waste sorting
rules on campus could likely correct some students’ separation
behavior. A separate pre-test was conducted with 12 students
on April 21, 2022, to test the questionnaire, and some questions
were amended accordingly.

This study’s treatment and control groups comprised of firstyear college students. Here, younger students were targeted, as
knowledge significantly differs by major, potentially impacting
students’ thoughts and behaviors as they progress throughschool, thus complicating the process of eliciting general
responses. Furthermore, the first semesters in most Japanese
universities commence in April (including the college assessedhere); thus, by starting the surveys in May, it was assumed thatdifferences in major did not make a significant difference.
The treatment group comprised first-year college students
majoring in policy science at Ritsumeikan University, one of
the largest in Western Japan. There are ∼35,000 students
at Ristumeikan University. Ritsumeikan University has set
guidelines for promoting pro-environmental behaviors andachieving campus sustainability (The Ritsumeikan Trust, 2014).
Accordingly, there are four types of garbage bins installedon campus: combustibles, plastics, plastic bottles, aluminumand steel bottles and cans. Plastic waste included plastic foodcontainers and packaging, cutlery, and shopping bags. During
garbage collection from the bins on campus, the cleaning
staff further sort them into the correct bins when necessary.
Following the Waste Disposal and Cleaning Law Government ofJapan (1970), the plastic waste collected by the staff on campus
was transported by the contractor for recycling.
Comparatively, the control group comprised firstyear college students living in the same region where
Ritsumeikan University is located. Namely, participants
were recruited through a survey company for which potential
participants registered. Table 1 indicates sample sizes withrespondent demographics before (Survey 1) and after (Survey2) intervention, where the correct rules for plastic waste
separation on campus were explained. The sample sizes may

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Interventions
Two types of interventions were implemented in this study:(1) explaining the correct rules on campus until the participants

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TABLE 1 Demographic information of respondents.

Treatment group

Control group

Survey 1

Survey 2

Survey 1

Survey 2(May 12, 2022)(July 21, 2022)(May 16–23, 2022)(July 16–23. 2022)

Variable

Freq.

%

Freq.

%

Freq.

%

Freq.

%

N

295

–

188

–

395

–

219

–

149

50.5%

86

45.7%

256

64.8%

159

72.6%

Female

143

48.5%

99

52.7%

139

35.2%

60

27.4%

Prefer not to disclose

3

1.0%

3

1.6%

–*

–

–

–

295

100.0%

188

100%

395

100.0%

219

100.0%

GenderMale

Grade
1st year* Online surveys for the control group did not contain the choice “Prefer not to disclose.”

TABLE 2 Key components and schedule of surveys/quizzes by group.

Question
types

Treatment

Control

Survey 1

QuizzesSurvey 2

Survey 1

Survey 2(May 12)(May 19, 26; June 2, 9)(July 21)

(May 16–22)(July 16–23)

Understanding of separation rules

✓

✓

✓

✓

✓

Separation behavior

✓

✓

✓

✓

TPB

✓

✓

Barriers to separating plastic waste✓

Social network✓

understand them and (2) asking them to disseminate thisknowledge among fellow students. The correct plastic waste
separation rule on campus was succinctly presented following asimplification and framing of information strategy (i.e., a nudge;
Lehner et al., 2016; OECD, 2017). The rules were explained
to the treatment group after Survey 1. With some backgroundinformation, such as explaining that rules on campus may differ
from those at home, the following seven possible methods were
presented, with an explanation that Rule 7 is correct, which
follows the Waste Disposal and Cleaning Law Government ofJapan (1970):

Rule 5. Discard all plastic waste in “plastics,” regardlessof cleanliness.
Rule 6. All plastic waste is thrown into “plastics”; but if it isdirty, it is thrown into “combustibles.”Rule 7. All plastic waste is thrown into “plastics”; but
if it is dirty, it should be cleaned, and then throwninto “plastics.”
Notably, students were not persuaded to follow therule, as asking them constitutes another type of behavioral
intervention (i.e., “persuasion”; Great Britain Parliament. Houseof Lords Science Technology Committee, 2011). While somemunicipalities require households to sort plastics only witha plastic mark (i.e., Rule 4 is correct for some households),
businesses, including universities, are required to collect allplastic waste, despite the mark, which is potentially major
cause of incorrect separation practices on campus. To help
students learn the correct rules, quizzes were given following
the first survey. Besides the separation rule information, thestudents were asked to disseminate this knowledge as apersuasion strategy.

Rule 1. Discard all plastic waste in “combustibles.”Rule 2. Discard only those with a plastic mark in “plastics,”
regardless of cleanliness (e.g., residual food).
Rule 3. Only those with a plastic mark are thrown away
in “plastics”; but if they are dirty, they are thrown away in“combustibles.”Rule 4. Only those with a plastic mark are thrown into
“plastics”; but if they are dirty, they should be cleaned andthen thrown into “plastics.”

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TPB questionnaire construction and separation
behavior

considered was limited to 10 to lessen the respondents’ burden,
as the egocentric network was only one component of thesurvey. While Hendrickson et al. (2011) asked college students
to list everyone without limitation, Tanaka et al. (1997) asked
college students to name ≤10 alters in their network, includingcollege staff, students, and off-campus associates. As Tanaka
et al. (1997) reported an average of 7.76 alters, a maximumof 10 seemed reasonable for this study. The strength of ties
was assessed using a 10-point scale (1. Acquaintance, . . . , 5.
Friend, . . . , 10. Close friend; Hendrickson et al., 2011), where“acquaintance,” “friend,” and “close friend” are commonly usedin the Japanese context (e.g., Miyato, 2013; Sawaguchi and
Shibuya, 2015), and have been described in more detail withrelated questions in SM3. In addition, contact frequency (eitherin person or online—e.g., emails and SNS) were inquired about.
Attributes included alters’ gender, year, main campus they
belong to (of the three university campuses), whether they arein the same major, and if they explicitly shared the correct rulewith them.

Items for the constructs were prepared based on relevant
previous literature (Tonglet et al., 2004; Wan et al., 2014; Cho,2019; Govindan et al., 2022). There were six items for ATT, SN,
and PBC, and five for intention (INT; see SM2), customized
from the two perspectives: First, they were adapted to thestudy’s context (i.e., plastic waste separation for on-campus
students). Second, each construct included five or six items, assuggested by Fishbein and Ajzen (2009). Because the numberof items influences Cronbach’s alpha, the measure of internalconsistency of the items should be neither big nor small (Tavakol
and Dennick, 2011). A seven-point Likert scale, ranging from“strongly disagree” (1) to “strongly agree” (7) was then used toevaluate each item.
While behavior can be measured as either a latent variable
comprising several items or a directly measurable behavior(Ajzen, 1991; Zhang et al., 2017; Govindan et al., 2022), the latter
was selected here as the specific behavior of interest (i.e., plastic
waste separation on campus). Further, participants were asked
to choose their behavior from the seven rules and assigned acorresponding weight (Rule 1 = 0; Rules 2, 3 =1; Rule 4, 5, 6
= 2; Rule 7 = 3).

Data analyses
R (v.4.2.1: https://cran.r-project.org/) was used for data
analysis; all raw data are provided in Supplementary material.
Two TPB models were estimated using structural equation
modeling (SEM; Ullman and Bentler, 2012) based on treatmentgroup Surveys 1 and 2. Before each estimate, the confirmatory
factor analysis and reliability analysis using Cronbach’s alphawere conducted to evaluate the constructs of the TPB models(Wang Y. et al., 2021).
Changes in plastic waste separation behavior were measured
according to the above-mentioned weighted separation behavior(i.e., Rule 1 = 0... Rule 7 = 3), and assessed using
DID analysis (Angrist and Pischke, 2009; Reichardt, 2019)
to compare the changes in outcomes over time between
the treatment and control groups. When compared with acontrol group, DID can avoid a counterfeit estimate of thecounterfactual ascribed to an intervention (Gertler et al., 2016).
The changes were then measured by the average treatment
effect on the treated (ATT) (Goodman-Bacon, 2021). This
study applied doubly robust DID estimators (Sant’Anna andZhao, 2020) to estimate ATT using a two-group/two-period(2 × 2) unbalanced panel data, comprising the treatment andcontrol groups.
To measure the relative criticality of barriers to plastic
waste separation, BWS was applied (Louviere et al., 2015).
This method repeatedly asked about the most and least criticalbarriers among subsets, rather than asking to rank all items
individually in a single time, producing more accurate results(Louviere et al., 2015). The counting approach (Aizaki et al.,2015) was adopted here, and the standardized best–worst score

Barriers to plastic waste separation other thanknowledge of correct rules
Under the assumption that a simplification and framingof information strategy should be complemented with otherintervention measures (Sterner et al., 2019; Alpizar et al., 2020),
respondents were asked about other critical barriers to following
the appropriate waste separation rule. Accordingly, questions
were posed directed at barriers derived from behavioral
intervention measure types (Great Britain Parliament. House ofLords Science Technology Committee, 2011; OECD, 2017) tohelp elicit policy implications (Table 3).

Egocentric network
For the egocentric network analysis, participants (ego) wereasked to list students (alters) at the university with whom they
had direct relations. Following Knoke and Yang (2020), four
categories of questions were prepared: name generator, strengthof tie, alters’ attributes, and dissemination. A name generator
requests alters’ first names and initials with whom the ego hasa specified relationship. Although ≤5 alters were commonly
employed in previous studies (Hendrickson et al., 2011; Merluzzi
and Burt, 2013; McCarty et al., 2019; Patterson et al., 2021),
respondents here were asked to list ≤10 to cover weak ties
(e.g., acquaintances, classmates doing a group work together),as well as strong ties (e.g., close friends, or those socializing off
campus), as the former may also be important for disseminating
information (Hendrickson et al., 2011). The number of alters

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TABLE 3 Barriers derived from behavioral intervention measure types.

Barrier

Behavioral intervention measure type1

Approaches to carry out the required behavior are unclear

Simplification and framing information

2

The necessary conditions or facilities to carry out the required behavior are

Changes to the physical environment

lacking3

Current behaviors are familiar or habitual4

Nobody is watching, or follow how people surrounding me behave

Use of social norms and salience5

Effectiveness and meaning of adopting the required behavior are unknown

Use of feedback mechanisms

Details of how much to do, or how well one is doing in terms of the required

Goal setting and commitment devices

6

Changes to the default policy

behavior are unclear
7

Nobody has persuaded me to engage in the required behavior

Persuasion (e.g., voluntary agreements, public participation)from focusing on plastic waste with plastic marks, a common
household rule, to correctly disposing of all plastic waste inplastic bins.
The DID estimate used 909 valid responses (285 and 188 forthe treatment group; 277 and 159 for the control group in May
and July, respectively). ATT was 0.7287, which was statisticallysignificant (p < 0.01), with 95% confidence intervals [0.474 and0.9833] (see SM5 for details). Accordingly, the treatment group
improved their disposal score by 0.7287 compared with thecontrol group.

was computed to measure the relative criticality of barrier i
according to Equation (1):Standardized BWi =Pn Bin −

P

Nr

n Win

(1)

where Bin and Win are the number of times barrier i was selected
as the most and least critical out of all questions by respondentn, respectively; r is the number of times barrier i appeared in allquestions; and N is the number of respondents. The choice set
used in the survey here satisfied the balanced incomplete block
design (Louviere et al., 2015) and included seven questions, eachpresenting three barriers selected from the seven total (see SM4
for the choice set).
For the egocentric network analysis, besides descriptive
statistics (e.g., number of alters and the students to disseminate),a logistic regression model was applied to explore factors
affecting the decision to disseminate knowledge (Agresti, 2018).

Psychological constructs explaining
separation intention and behavior
Figure 1 presents the SEM based on the TPB. The internalreliability test using Cronbach’s alpha (Table 5) showed that allitems measuring the corresponding construct in Surveys 1 and 2
were valid, as the values lied within 0.70 and 0.90 (Tavakol and
Dennick, 2011). Further, the confirmatory factor analysis for theitems in Surveys 1 and 2 (Table 5) passed the critical criteria forthe convergence validity of measurement items with one caveat:
factor loading >0.5, composite reliability (CR) >0.7, and averagevariance extracted (AVE) >0.5 (Wang Y. et al., 2021). While thefactor loading of PBC4 in Survey 1 was below 0.5 (0.439), thevalue in Survey 2 was above 0.5 (0.502). Therefore, we retained
PBC4 in our analysis.
Besides the root-mean-square error of approximation(RMSEA), a commonly used index of fitness, various otherindices were applied as well (Cho, 2019; Wang C. et al., 2021;
Govindan et al., 2022). While RMSEA (≤ 0.08) and χ 2 /df ratio(≤ 3) met the criteria for both models, the Tucker–Lewis index(TLI ≥ 0.90) and comparative fit index (CFI ≥ 0.90) were
not satisfied for either model; however, as shown in one study
assessing the impacts of the number of observed variables andsample sizes for RMSEA, CFI, and TLI (Shi et al., 2019), thelatter two were more likely to be biased, and underestimate their

Results
Behavioral change
Only 11.9% of the treatment group had previously received
an explanation regarding the correct separation rules on
campus, of which only 3.4% remembered the rules. Further,
51.2% of the students in this group referred to the rules intheir households, which were largely different from those on
campus, whereas 34% did not refer to any information but
judged by themselves.
Table 4 indicates the degree to which respondents in thetreatment group after acquiring the correct rule changed theirseparation behavior. Here, the number of respondents practicing
engaging in proper separation increased from 24 to 72, among
which all 72 answered that the previous explanation on thecorrect rule was useful. Notably, the respondents in the redrectangle of Table 4 were those who changed their practices

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TABLE 4 Changes in plastic waste separation behavior of the treatment group in Surveys 1 and 2.

Survey 1

Survey 2
Rule 1

Rule 2Rule 3Rule 4Rule 5Rule 6

Rule 7
6

15

6

2

4

14

5

9

14

32

5

11

9

Rule 1
1Rule 2Rule 32

Rule 4

2

1
1

1

4

Total

Rule 5

2

1

34

6

21

64

Rule 6

1

2

6

12

7

28

15

24

29

72

188

Rule 7

1

Total

5

4

1

2

6

7

70

population values when the sample size was small (N < 500).
As the current models have a smaller sample size (N = 290 and
188 for Surveys 1 and 2, respectively), CFI and TLI may not bevalid fit indices; thus, only the fitness of current models based on
RMSEA and the χ 2 /df ratio were applied here.
All variables, except for the relationship between separation
intention and behavior in Survey 1, were statistically significant.
Further, the confidence intervals of standardized regression
coefficients overlapped across the two surveys, except for thatbetween separation intention and behavior in Survey 1. Notably,
R2 for separation intention and behavior increased significantly
in Survey 2 (by 0.177 and 0.172, respectively).

Barriers to separation behavior
Figure 2 presents the standardized scores for the treatment
and control groups in Survey 2. The treatment group included
only respondents who correctly understand the plastic waste
separation rule on campus. Here, although knowledge (No. 1)
and environment (No. 2) were relatively lower barriers for thetreatment group, they were more critical for the control group.
Importantly, habit (No. 3) was a relatively strong barrier for thetreatment group to practicing the correct separation habits.

FIGURE 1

SEM estimation results. (A) Survey 1 (N = 290; Treatment group;
May 12, 2022). (B) Survey 2 (N = 188; Treatment group; July 21,2022). **p < 1%; *p < 5%; numbers in [ } indicate 95%
conﬁdence intervals. A dashed line means that the path isnon-signiﬁcant.

Social network analysis
One hundred and eighty eight egos aware of the correct
separation rule listed 1,036 alters (Mean = 5.51, SD = 3.65,
Min = 0, Max = 10, Mode = 10). One hundred and seventy
three egos (92.0%) nominated at least one friend, 94 egos(50%) talked to 237 alters (22.9%) about the separation rule,
whereas 219 alters of them (92.4%) were first-year students inthe same major; thus, they also listened to our explanation of theseparation rule in the experiment (see SM6 for further egocentric
network characteristics).

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Table 6 shows the logistic model explaining the determinantsassociated with egos’ decision to discuss the correct rule. The
same gender with the ego, the strength of connection, thefrequency of communication, and the same campus were all
statistically significant factors, whereas different genders, yearsof alters, and disposal scores were not associated with thedecision to discuss.

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TABLE 5 Results of reliability and conﬁrmatory factor analysis.

Survey 1
Factor loading

S.E.

CR

AVE

α

ATT1

0.860

0.033

0.774

0.520

0.721

ATT2

0.780

0.037

ATT3

0.677

0.039

0.758

0.042

ATT4

0.815

0.030

0.817

0.041

ATT5

0.572

0.040

0.870

0.033

ATT6

0.568

0.044

0.720

0.044

SN1

0.843

0.020

0.890

0.019

SN2

0.884

0.016

0.796

0.026

SN3

0.728

0.027

0.820

0.024

SN4

0.851

0.019

0.869

0.021

SN5

0.647

0.039

0.729

0.035

SN6

0.769

0.025

0.768

0.031

PBC1

0.842

0.022

0.895

0.023

PBC2

0.887

0.016

0.800

0.027

PBC3

0.734

0.025

0.623

0.044

PBC4

0.439

0.037

0.502

0.055

PBC5

0.830

0.018

0.759

0.032

0.822

0.025

Measurement item

PBC6

0.645

0.032

INT1

0.715

0.029

INT2

0.688

INT3
INT4
INT5

0.889

0.863

0.852

0.626

0.555

0.576

0.863

0.837

0.829

Survey 2
Factor loading

S.E.

CR

AVE

α

0.927

0.036

0.888

0.686

0.837

0.858

0.037

0.909

0.662

0.892

0.894

0.556

0.827

0.895

0.662

0.879

0.820

0.026

0.035

0.773

0.029

0.790

0.025

0.829

0.023

0.889

0.020

0.831

0.025

0.690

0.032

0.814

0.026

FIGURE 2

Standardized BW scores comparing between treatment and control groups.

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TABLE 6 Logistic model explaining factors associated with spreading knowledge.

Coefficients

Std. Errorz value

Pr (>|z|)Intercept−7.041

0.861−8.1750.000

Gender (1 if male, 2 if female)−0.1180.171−0.694

0.488

Disposal score (Rule 1 = 0; Rule 2 or 3 = 1; Rule 4, 5, or 6 = 2; Rule 7 = 3)

0.242

0.139

1.747

0.081

**

Same gender (1 if same gender, 0 otherwise)

1.224

0.362

3.383

0.001

**

Connection (1. acquaintance, . . . , 10. close friend)

0.207

0.041

5.101

0.000

**
**

Frequency (1. less than once per week, . . . , 5. almost daily)

0.518

0.082

6.283

0.000

Year (1. first, . . . , 6. graduate student)

0.028

0.226

0.123

0.902

1.347

0.499

2.702

0.007

Campus (1. same campus, 0 other)
AIC

955.22

N

1,850

**

** p < 0.01; * p < 0.05.

Discussion

separation behavior, they have rarely confirmed if participantsrequired the correct knowledge directly related to the behavior
of interest, whereas this study tested recipients’ knowledge of thecorrect rule, for which 188 participants responded correctly. For
example, Matiiuk and Liobikiene (2021) asked respondents theiropinions [Strongly disagree (1)–Strongly agree) = (4)] regarding
four statements of waste separation behavior, whereas Wanget al. (2020) inquired about levels of understanding using a
Likert scale [strongly disagree (1) to strongly agree (5)]. Wang C.
et al. (2021) asked respondents about the number of media fromwhich they learned about municipal solid waste separation, andWang Y. et al. (2021) asked respondents about their familiarity
with household waste separation using a Likert scale [strongly
agree (7) to strongly disagree (1)]. Therefore, it was concluded
here that this approach provided a more accurate measurementof knowledge acquisition. Second, a DID analysis was employed
here to estimate the counterfactual, corroborating the findings
by avoiding potential biases. While it is common to use a
psychological model to address the impact of knowledge on
waste separation behavior (e.g., TPB and its extensions; Wanget al., 2020; Hu et al., 2021; Matiiuk and Liobikiene, 2021; Wang
C. et al., 2021; Wang Y. et al., 2021), to the best of the authors’knowledge, none have implemented a DID analysis.
Two further points are necessary for the appropriate
interpretation of the results: First, acquiring the correct
separation rule is particularly important in the current context;that is, a university campus where population mobility remains
relatively low, as students return to campus multiple times per
week. Besides the varying separation rules in different locations(e.g., residential areas and buildings), population mobility is alearning barrier (Knickmeyer, 2020); therefore, educating people
as to specific rules in places with high population mobility(e.g., train stations) may be less effective. Second, exploringan effective strategy of educating people was not the primary
purpose of this study. Employing the information simplification
strategy, and repeating tests following the explanation (Table 2)

Impact of knowledge on college
students’ separation behavior
The results revealed that knowing the correct plastic waste
separation rules on campus could positively impact correlated
behaviors (H1). This is in line with most previous studies(Barr, 2007; Wang et al., 2020; Hu et al., 2021; Matiiukand Liobikiene, 2021; Wang C. et al., 2021; Wang Y. et al.,2021), although one comparative study of 27 EU countries
indicated a mixed, and even negative, result of knowledge
dissemination (Minelgaite and Liobikiene, 2019). Of the 188
participants who acquired the correct rule, the number of
those who followed the rule increased by 25.8%, from 24 to
72. Out of the 57 participants who sorted plastic waste onlywith the plastic mark (as is often the rule in households), 49(86.0%) successfully changed their behavior to sort all types ofplastic waste, indicating those who cared about the separation
rule, but relied on an incorrect understanding, successfully
learned from the corrective explanation. One hundred andseventy three of 188 participants (92.0%) responded that the
explanation of the rule was useful, whereas 25.8% of participants
knew the correct rule before the explanation, indicating theexistence of misunderstandings, or a lack of correct knowledge.
Furthermore, of the 72 practicing students using the correct
separation behavior, 100% answered that the explanation was
useful; thus, the explanation was still useful even for those who
previously knew the correct rule, thus providing confirmation
value. To confirm the counterfactual of the intervention, a
DID analysis was conducted by referring to the control group,with the results indicating that knowledge of the correct rule
had a statistically significant impact on changing plastic waste
separation behavior on campus.
This study is unique in two aspects: First, althoughprevious studies have revealed the importance of knowledge on

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barrier to correct separation for the treatment group after having
learned the rule (−0.204), although it remained the most criticalbarrier for the control group (0.236), indicating the importanceof learning the correct rule, and the effectiveness of approachhere by providing knowledge. Second, the critical necessary
conditions of facilities differ by location. For example, treatmentgroup campuses here installed the same types of bins to assist
students with engaging in correct separation behavior; however,this may not necessarily be the case for all control groupcampuses. Third, after learning the correct rule, habit became
the next critical barrier (0.397) to behavior; therefore, studies
on changing people’s habits will be necessary and effective.
For example, changes to the physical environment and default
policies can be effective at tackling habits in the behavioral
sciences and nudges (OECD, 2017).

revealed that 87.9% of treatment group participants who
disposed of plastic waste during the experiment acquired therule. Notably, it may not always be possible to repeat tests intarget populations.
The TPB models in Figure 1 indicated that knowing thecorrect rule on campus bridged the intension–behavior gap(Vicente-Molina et al., 2013), supporting and verifying H2–H5
following participants’ education. Notably, the TPB model
predicted the separation intention but not the behavior (e.g., the
TPB model for Survey 1); however, the TPB model for Survey 2,
including only participants who acquired the correct separation
knowledge, showed that separation intention was associatedwith separation behavior. Some participants sorted only plastic
waste with marks, displaying an intention to contribute towaste separation, but by following incorrect rules, caused an
intention–behavior gap. The survey showed that 86.0% of theseparticipants changed to sort all types of plastic waste (i.e., Rule5, 6, or 7), despite the plastic mark, though some did not clean
dirty plastic waste (i.e., Rule 5 or 6). It was not always easy toclean them for some reasons; for example, there was no facility
with water close to bins to wash dirty plastic waste. Of this group,
87.8% of those who changed their behavior responded that the
explanation of the rule was useful.
Further, two more points are worth mentioning in relation
to the previous studies. First, although the intension–behavior
gap has been raised in the pro-environmental behavior
literature (Vicente-Molina et al., 2013; Sheeran and Webb, 2016;
Echegaray and Hansstein, 2017) and in this study, previous
research on waste separation reported there was no gap (Wanet al., 2014; Wang et al., 2020; Wang Y. et al., 2021). This could be
due to the uniqueness of the current context in which the rule on
campus differed from that in households, confusing those witha positive intention to participate in separation. Therefore, thisstudy implies that knowledge acquisition is particularly effective
when people intending to cooperate misunderstand the rule,and population mobility is low. Second, except for Hu et al.
(2021) whose approach we followed, all studies assessing theimpact of knowledge and information on separation behavior
using psychological constructs (including TPB models) applieda cross-sectional study. Previous studies also showed thatknowledge and information influenced various psychologicalconstructs; for example, Wang et al. (2020) revealed that waste
separation knowledge was positively associated with ATT, PBC,
and intention of Chinese residents. Wang Y. et al. (2021)
revealed that knowledge played a moderating role between PBC
and household waste separation in China; however, although
the methods were different, this study found no such significant
changes in psychological constructs of the TPB, save for theintention–behavior relationship in Survey 2, as the confidence
intervals overlapped across two periods.
The BWS in Figure 2 further corroborated the results, andput forth additional implications: First, the comparisons here
showed that knowing the rule (No. 1) became a relatively weaker

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Knowledge spread among peers on
campuses
22.9% of the alters were spoken about the rule, thus
supporting H6. Although the absence of relevant previous
research prohibits a comparison of this spread’s significance,these findings revealed that half of the egos (50%) talked to someof their alters (22.9%), implying the utility of social networks
for disseminating knowledge. There are, however, four caveats
and implications for this finding: First, since most of the alters(92.4%) were first-year students in the same major, the majority
had heard of the explanation in the experiment; therefore,
spreading largely confirmed knowledge, rather than informing
students of the rule for the first time. Second, although we
asked students to list ≤10 acquaintance-to-close friend based on
previous studies (Hendrickson et al., 2011, the mode of alters
was 10, indicating that the present list of alters underestimatedtheir network sizes. Third, as indicated by the logistic model(Table 5), it would be reasonable to focus on close friends foranalyzing the egocentric network regarding separation rules for
future research, as this topic was disseminated more frequently
among closer peers. Fourth, the spread itself does not speak to
its impact on alters’ behaviors; thus, investigating this aspectremains important.

Synthesis of the tested hypotheses
Table 7 presents the summary of hypotheses tests from H1 to
H6. All hypotheses were supported.

Conclusion
Given the importance of further empirical studies
on behavioral instruments of campus college students in

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TABLE 7 Synthesis of hypotheses tests.

Hypothesis

MethodResultTest

H1: Knowledge improves separation behavior

DID analysis

ATT = 0.7287, p < 0.01

Supported

H2: Attitude is positively related to intention

TPB and SEM

β = 0.347, p < 0.01

Supported

H3: Social norm is positively related to intention

TPB and SEM

β = 0.222, p < 0.01

Supported

H4: Perceived behavioral control is positively related to intention

TPB and SEM

β = 0.488, p < 0.01

Supported

H5: Intention is positively related to behavior

TPB and SEM

β = 0.418, p < 0.01

Supported

H6: Knowledge spreads on campus

Egocentric network analysis

22.9% of alters were spoken about the rule

Supported

an organizational context (Amaral et al., 2015; Yuriev
et al., 2020), this study aimed to understand how
learning plastic waste separation rules changed correlated
separation behavior (Research question 1), besides
how this knowledge spread across campuses (Researchquestion 2).
This study revealed that learning plastic waste separation
rules on campus improved students’ correlated separation
behavior. Although the finding may not be a surprise but rather
expected, the novelty of this study was to empirically verify thistaken-for-granted fact by using the DID analysis which involved
treatment and control groups to make an accurate evaluation
and avoid a counterfeit counterfactual evaluation. Furthermore,
the SEM in this study revealed that learning rules can fill theintention–behavior gap, a gap between people’s intention to do
the correct plastic waste separation and their actual behavior to
do so. Although the study was conducted in a specific context(i.e., first-year college students in Japan), helping people learn
the specific knowledge of the context can apply to similar
contexts where people with pro-environmental intention but
without correct knowledge are unintentionally misbehaving ina low population mobility context. Although it needs further
investigation, it can be applied to other organizational levels
including companies in any regions and countries, in contrast
to market incentives or regulatory measures put forth by thegovernment (Kirakozian, 2016). It is, however, also a limitationof this study that this finding can be effective only in a place
where population mobility remains relatively low. For example,
it is not feasible to educate tourists and participants in an
event to learn the rules in the destinations. Instead, othersolutions such as putting easily recognizable labels and signs on a
garbage bin may be more effective (Ministry of the Environment
Japan, 2017). This study also demonstrated that the knowledge
spread among peers across campuses, but its spread was rather
limited. Although our study revealed some factors influencing
the spread (e.g., connection strength and meeting frequency),
the policy implications for improving the knowledge spread
was limited.
Future research should consider latent growth modeling(Duncan and Duncan, 2004; Lemoyne et al., 2016), as it

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enables the statistical assessment of how psychologicalconstructs (e.g., TPB) explain waste separation behaviorof target populations change over time in coordination
with the behavioral instruments implemented over theperiod. As such, analyses require ≥ 3 periods, the present
data were not applicable. A secondary promising research
topic would be to assess the results of behavioral changes(e.g., changes in the composition of plastic waste insideof bins because of separation behavior), besides a selfreported survey of behavior, as recorded in this study [e.g.,
Yano and Sakai (2016)]. Lastly, there is an absolute lackof social network analysis studies on how environmental
knowledge spreads among peers. Understanding of theknowledge spread assists researchers and policymakers
to design an effective measure to help people learn theenvironmental knowledge.

Data availability statement
The original contributions presented in the study areincluded in the article/Supplementary material, further inquiries
can be directed to the corresponding author.

Ethics statement
Ethical review and approval was not required for the study
on human participants in accordance with the local legislationand institutional requirements. The patients/participants
provided their written informed consent to participate inthis study.

Author contributions
TU: data curation, formal analysis, project administration,
resources,
software,supervision,visualization,
andwriting–original draft preparation. TU and MA: funding
acquisition, investigation, and validation. MA and RS:
writing–review and editing. All authors contributed

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to conceptualization,
submitted version.

10.3389/frsus.2022.1023605

methodology,

andapproved

Publisher’s notethe

All claims expressed in this article are solely thoseof the authors and do not necessarily represent thoseof their affiliated organizations, or those of the publisher,
the editors and the reviewers. Any product that may beevaluated in this article, or claim that may be made byits manufacturer, is not guaranteed or endorsed by the
publisher.

Funding
This research was funded by the Environment
Research and Technology Development Fund of theEnvironmental Restoration and Conservation Agency ofJapan [JPMEERF21S11920].

Conﬂict of interest

Supplementary material

The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could
be construed as a potential conflict of interest.

The Supplementary Material for this article can befound online at: https://www.frontiersin.org/articles/10.3389/
frsus.2022.1023605/full#supplementary-material

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