<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" "http://jats.nlm.nih.gov/publishing/1.2/JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.2" xml:lang="en">
    <front>
        <journal-meta>
            <journal-id journal-id-type="pmc">F1000Research</journal-id>
            <journal-title-group>
                <journal-title>F1000Research</journal-title>
            </journal-title-group>
            <issn pub-type="epub">2046-1402</issn>
            <publisher>
                <publisher-name>F1000 Research Limited</publisher-name>
                <publisher-loc>London, UK</publisher-loc>
            </publisher>
        </journal-meta>
        <article-meta>
            <article-id pub-id-type="doi">10.12688/f1000research.177261.1</article-id>
            <article-categories>
                <subj-group subj-group-type="heading">
                    <subject>Research Article</subject>
                </subj-group>
                <subj-group>
                    <subject>Articles</subject>
                </subj-group>
            </article-categories>
            <title-group>
                <article-title>DOES BEHAVIOURAL SPILLOVER OCCUR IN URBAN SUSTAINABLE CONSUMPTION? EVIDENCE FROM SWITCHING AND DISPOSAL PRACTICES IN MALAYSIA</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 1; peer review: 1 approved with reservations]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>El Bachir El Mouden</surname>
                        <given-names>Ahmed</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <uri content-type="orcid">https://orcid.org/0009-0006-4654-6158</uri>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Ahmad</surname>
                        <given-names>Rohizan Binti</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <uri content-type="orcid">https://orcid.org/0009-0009-7107-2435</uri>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Dada</surname>
                        <given-names>Morakinyo</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Software</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <uri content-type="orcid">https://orcid.org/0000-0002-0358-5070</uri>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Manickam</surname>
                        <given-names>Gobinathan A/L</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Project Administration</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0009-0001-7987-6552</uri>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Ohajionu-Addae</surname>
                        <given-names>Uchechi Cynthia</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>Marketing and Management, Asia Pacific University of Technology &amp; Innovation, Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia</aff>
                <aff id="a2">
                    <label>2</label>School of Business, Knutsford University College, Accra, Greater Accra Region, Ghana</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:morakinyo@apu.edu.my">morakinyo@apu.edu.my</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>2</day>
                <month>5</month>
                <year>2026</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2026</year>
            </pub-date>
            <volume>15</volume>
            <elocation-id>655</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>19</day>
                    <month>3</month>
                    <year>2026</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 El Bachir El Mouden A et al.</copyright-statement>
                <copyright-year>2026</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <self-uri content-type="pdf" xlink:href="https://f1000research.com/articles/15-655/pdf"/>
            <abstract>
                <p>Urban sustainable consumption has become a critical priority in Malaysia&#x2019;s transition toward low-impact lifestyles. Yet, a persistent attitude-behavior gap continues to undermine green progress. This study explores how green purchase intention (GPI) translates into broader green actions through behavioral spillover, focusing on green switching (GSB) and green disposal behaviors (GDB). Grounded in the Theory of Planned Behavior, Value-Belief-Norm, and Spillover frameworks, the study examines five predictors, attitude, perceived behavioral control, subjective norms, environmental concern, and price sensitivity, using data from 263 urban consumers analyzed via SmartPLS 4. Findings reveal that attitude, control, concern, and price sensitivity significantly drive GPI, while subjective norms remain weak. GPI further mediates green purchase behavior (GPB), which sequentially spills over into switching and disposal actions, demonstrating sustainability as a cumulative behavioral chain rather than isolated intent. Although greenwashing (GWS) showed no moderating effect, it highlights consumers&#x2019; resilience against deceptive marketing. The study advances theoretical integration of TPB, VBN, and Spillover theories, aligns with SDG 12, SDG 11, and SDG 15, and urges policies that reduce price barriers, enforce marketing transparency, and empower sustained green lifestyles. It is among the first in Malaysia to empirically validate behavioral spillover across the green consumption lifecycle.</p>
            </abstract>
            <kwd-group kwd-group-type="author">
                <kwd>Urban sustainable consumption</kwd>
                <kwd>Green purchase intention</kwd>
                <kwd>Green purchase behavior</kwd>
                <kwd>Behavioral spillover</kwd>
                <kwd>Greenwashing</kwd>
            </kwd-group>
            <funding-group>
                <funding-statement>The author(s) declared that no grants were involved in supporting this work.</funding-statement>
            </funding-group>
        </article-meta>
    </front>
    <body>
        <sec id="sec1" sec-type="intro">
            <title>1. Introduction</title>
            <sec id="sec2">
                <title>1.1 Background of study</title>
                <p>
Environmental degradation driven by urbanization, industrialization, and overconsumption has escalated sharply in Malaysia, raising urgent concerns for sustainable consumption (
                    <xref ref-type="bibr" rid="ref69">Manisalidis et al., 2020</xref>; 
                    <xref ref-type="bibr" rid="ref108">Waris &amp; Hameed, 2020</xref>). Humanity&#x2019;s ecological footprint now exceeds Earth&#x2019;s biocapacity by 70%, requiring 1.7 Earths annually (
                    <xref ref-type="bibr" rid="ref107">Walsh, 2024</xref>), reflecting a sharp increase from previous estimates (
                    <xref ref-type="bibr" rid="ref111">WWF, 2020</xref>). In Malaysia, urban expansion and industrial growth have exacerbated environmental stress, including deforestation at 0.5% annually (
                    <xref ref-type="bibr" rid="ref58">Koons, 2024</xref>), rising air pollution causing 32,000 premature deaths, and waste generation at 39,000 tons per day with only 33.16% recycled (
                    <xref ref-type="bibr" rid="ref54">International Trade Administration, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref63">Lee, 2024</xref>). Limited infrastructure, with just one waste-to-energy plant compared to Singapore&#x2019;s five and China&#x2019;s 700, further constrains sustainable waste management (
                    <xref ref-type="bibr" rid="ref16">Bernama Staff, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref83">Reccessary, 2024</xref>).</p>
                <p>National frameworks such as the Circular Economy Blueprint, Renewable Energy Roadmap, and the National Climate Change Bill aim to improve environmental performance, yet gaps remain, including fragmented policy execution and misalignment between federal and state priorities (
                    <xref ref-type="bibr" rid="ref73">Nadar, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref53">Institut MASA, 2024</xref>). Despite policy support and corporate initiatives like Aeon&#x2019;s &#x201c;No Plastic Bag Day&#x201d; (
                    <xref ref-type="bibr" rid="ref1">AEON, 2021</xref>; 
                    <xref ref-type="bibr" rid="ref115">Yue &amp; Mohd Nor, 2024</xref>), consumer adoption of green products remains inconsistent, with only 39% practicing responsible consumption despite 80% awareness (
                    <xref ref-type="bibr" rid="ref105">Visa, 2023</xref>). Price sensitivity, perceived product quality, and availability continue to hinder sustainable behavior (
                    <xref ref-type="bibr" rid="ref82">Qi &amp; Ploeger, 2021</xref>; 
                    <xref ref-type="bibr" rid="ref60">Kumarasinghe et al., 2023</xref>).</p>
                <p>
Plastic pollution compounds these challenges, as Malaysia ranks among the top 20 global producers of plastic waste, generating 0.81 million tons in 2021 and importing an additional 35.13 thousand tons in 2022, with 73.1 thousand tons released into oceans (
                    <xref ref-type="bibr" rid="ref68">Macheca et al., 2024</xref>). Consumers&#x2019; willingness to adopt green alternatives, particularly among educated urban populations, has become critical for curbing ecological impacts and promoting circular practices (
                    <xref ref-type="bibr" rid="ref99">Tan &amp; Goh, 2018</xref>; 
                    <xref ref-type="bibr" rid="ref103">Unnisa &amp; Kalpana, 2025</xref>). These insights underscore the importance of understanding behavioral determinants that drive green purchasing, switching, and disposal, particularly amid rising environmental consciousness and policy-driven opportunities.</p>
                <p>This study addresses these gaps by investigating psychological, environmental, and economic drivers of urban sustainable consumption in Malaysia, providing empirical evidence crucial for shaping policy, corporate strategies, and societal shifts toward lasting environmental stewardship.</p>
            </sec>
            <sec id="sec3">
                <title>1.2 Problem statement</title>
                <p>
Rapid urbanization, industrial expansion, and climate change have intensified environmental pressures in Malaysia, from escalating waste generation of 39,000 tons daily with only 33.16% recycled (
                    <xref ref-type="bibr" rid="ref54">International Trade Administration, 2024</xref>) to persistent deforestation impacting 38% of Malaysians&#x2019; top concerns (
                    <xref ref-type="bibr" rid="ref58">Koons, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref109">Wei et al., 2022</xref>). Despite growing environmental awareness, a pronounced gap remains between consumer knowledge and actual green purchasing, with only 39% engaging in sustainable practices despite 80% recognizing environmental impacts (
                    <xref ref-type="bibr" rid="ref105">Visa, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref19">Bin Rusli et al., 2022</xref>). This intention-behavior discrepancy is aggravated by price sensitivity, as over 55% of Malaysians resist paying premiums for green products, compounded by modest wage growth and inflation limiting discretionary spending (
                    <xref ref-type="bibr" rid="ref17">Bernama, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref52">Ikram, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref106">Wahab, 2024</xref>). Structural barriers such as limited retail availability and perceived behavioral control further inhibit consistent adoption of green practices (
                    <xref ref-type="bibr" rid="ref2">Afridi et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref109">Wei et al., 2022</xref>). Complicating these consumer-level challenges is widespread greenwashing, undermining trust as firms exaggerate environmental claims, while nascent regulatory frameworks fail to ensure verification and accountability (
                    <xref ref-type="bibr" rid="ref18">Bhanu, 2025</xref>; 
                    <xref ref-type="bibr" rid="ref67">Loong et al., 2025</xref>; 
                    <xref ref-type="bibr" rid="ref37">Greenpeace Malaysia, 2024</xref>). The misalignment of policy, enforcement, and stakeholder engagement amplifies the challenge, as initiatives like the Environmental Quality (Amendment) Bill 2023 are hampered by fragmented governance and insufficient consumer involvement (
                    <xref ref-type="bibr" rid="ref73">Nadar, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref36">Greenpeace Malaysia, 2023</xref>). Moreover, prior research remains limited, focusing disproportionately on Generation Z or specific urban clusters, restricting insight into diverse demographic and geographic influences on sustainable consumption (
                    <xref ref-type="bibr" rid="ref84">Rou &amp; Lian Kang, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref115">Yue &amp; Mohd Nor, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref3">Ahmed et al., 2023</xref>). These converging environmental, economic, behavioral, and regulatory constraints reveal a systemic barrier to translating awareness into action, particularly in urban Malaysia where micro-level consumer decisions accumulate to shape macro-level sustainability outcomes. Addressing this requires an integrative understanding of the psychological, environmental, and economic determinants that drive green purchase intention, purchasing, switching, and disposal behaviors, offering critical guidance to policymakers, businesses, and NGOs seeking to foster genuine, scalable, and enduring sustainable consumption.</p>
            </sec>
        </sec>
        <sec id="sec4">
            <title>2. Literature review</title>
            <sec id="sec5">
                <title>2.1 Hypothesis development</title>
                <p>

                    <bold>2.1.1 Green Switching Behavior (GSB)</bold>
                </p>
                <p>Green switching behavior reflects consumers&#x2019; deliberate transition from conventional to green products, signaling a profound commitment to environmental responsibility (
                    <xref ref-type="bibr" rid="ref24">Castro Santa et al., 2024</xref>; 
                    <xref ref-type="bibr" rid="ref71">Maslikhah et al., 2024</xref>). Beyond purchase, it entails lifestyle shifts driven by environmental awareness, social norms, and perceived efficacy of actions (
                    <xref ref-type="bibr" rid="ref119">Zhuang et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref49">Hussain &amp; Huang, 2022</xref>). Mediated by repeated green purchases, switching strengthens habitual sustainability despite price sensitivity and skepticism (
                    <xref ref-type="bibr" rid="ref80">Perez-Castillo &amp; Vera-Martinez, 2021</xref>; 
                    <xref ref-type="bibr" rid="ref26">Chen et al., 2022</xref>). Neuroscientific evidence highlights its affective and identity-based dimensions, making GSB a pivotal lever for sustainable consumption transitions in urban Malaysia (
                    <xref ref-type="bibr" rid="ref117">Zhang et al., 2022</xref>; 
                    <xref ref-type="bibr" rid="ref24">Castro Santa et al., 2024</xref>).</p>
                <p>

                    <bold>2.1.2 Green Disposal Behavior (GDB)</bold>
                </p>
                <p>Green disposal behavior embodies intentional actions like recycling, reusing, and donating to minimize ecological harm and close the product lifecycle loop (
                    <xref ref-type="bibr" rid="ref92">Singh, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref64">Leonard &amp; Meilina, 2024</xref>). It reflects ethical commitment, informed by environmental knowledge, personal values, and social norms, bridging consumption and waste (
                    <xref ref-type="bibr" rid="ref93">Sneddon et al., 2022</xref>; 
                    <xref ref-type="bibr" rid="ref23">Camilleri &amp; Ferrari, 2024</xref>). Mediated by prior green purchase behavior, disposal practices sustain sustainable consumption while reinforcing circular economy principles (
                    <xref ref-type="bibr" rid="ref65">Li et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref4">Aiguobarueghian et al., 2024</xref>). Accessible infrastructure and community support enhance engagement, highlighting GDB as a pivotal endpoint in urban Malaysia&#x2019;s sustainability transition (
                    <xref ref-type="bibr" rid="ref120">Zoli &amp; Congiu, 2024</xref>; 
                    <xref ref-type="bibr" rid="ref116">Yusoff et al., 2023</xref>).</p>
                <p>

                    <bold>2.1.3 Green Purchase Behavior (GPB)</bold>
                </p>
                <p>
Green purchase behavior reflects the deliberate selection of products that minimize environmental harm, embodying consumers&#x2019; ethical alignment with sustainability and resource conservation (
                    <xref ref-type="bibr" rid="ref116">Yusoff et al., 2023</xref>). Beyond transactional decisions, it integrates environmental awareness, moral norms, and perceived efficacy, motivating actions even amid cost or convenience trade-offs (
                    <xref ref-type="bibr" rid="ref88">Sheikh et al., 2023</xref>; 
                    <xref ref-type="bibr" rid="ref20">Brinsi &amp; L&#x00e1;szl&#x00f3;, 2024</xref>). This behavior catalyzes green switching, where initial purchases trigger habitual replacement of conventional products with greener alternatives through psychological spillover and action-follow-up mechanisms (
                    <xref ref-type="bibr" rid="ref24">Castro Santa et al., 2024</xref>; 
                    <xref ref-type="bibr" rid="ref94">Stangherlin et al., 2023</xref>). Simultaneously, green purchase behavior strengthens environmental self-identity and perceived consumer effectiveness, promoting green disposal practices such as recycling, reusing, and donating, thereby closing the product lifecycle loop (
                    <xref ref-type="bibr" rid="ref93">Sneddon et al., 2022</xref>; 
                    <xref ref-type="bibr" rid="ref91">Siew et al., 2025</xref>). These interlinked behaviors are further reinforced by social norms, accessibility, transparent eco-labeling, and trust in green products, while threats such as greenwashing or infrastructural barriers can impede their continuity (
                    <xref ref-type="bibr" rid="ref119">Zhuang et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref70">Margariti et al., 2024</xref>). By bridging intention, purchase, switching, and disposal, green purchase behavior emerges as the pivotal engine of sustainable consumption, transforming ethical awareness into systemic environmental stewardship in Malaysia&#x2019;s urban context (
                    <xref ref-type="bibr" rid="ref78">Ogiemwonyi et al., 2023</xref>; 
                    <xref ref-type="bibr" rid="ref24">Castro Santa et al., 2024</xref>).</p>
                <p>

                    <bold>2.1.4 Mediating Effect of Green Purchase Behavior</bold>
                </p>
                <p>Green purchase behavior serves as the pivotal bridge translating consumers&#x2019; intentions into tangible green actions, mediating the pathway from green purchase intention to both green switching and disposal behaviors (
                    <xref ref-type="bibr" rid="ref119">Zhuang et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref50">Huynh et al., 2024</xref>). By converting cognitive readiness into action, GPB strengthens environmental self-identity, perceived consumer effectiveness, and habitual competence, enabling consistent switching to greener alternatives and responsible disposal practices (
                    <xref ref-type="bibr" rid="ref28">De Sio et al., 2022</xref>; 
                    <xref ref-type="bibr" rid="ref22">Butar Butar et al., 2024</xref>). Attitudes, perceived behavioral control, and subjective norms reinforce this process, while accumulated experience mitigates perceived risk and price sensitivity (
                    <xref ref-type="bibr" rid="ref119">Zhuang et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref77">Nitika et al., 2022</xref>). This mediating role situates GPB as the engine of behavioral spillover, operationalizing intention into a systemic, post-purchase sustainability trajectory that advances urban Malaysian consumption toward enduring environmental stewardship (
                    <xref ref-type="bibr" rid="ref72">Munir et al., 2023</xref>; 
                    <xref ref-type="bibr" rid="ref119">Zhuang et al., 2021</xref>).</p>
                <p>

                    <bold>2.1.5 Moderating Effect of Greenwashing (GWS)</bold>
                </p>
                <p>Greenwashing (GWS) represents a critical disruptor in sustainable consumption, undermining trust, amplifying skepticism, and weakening the progression from green purchase intention to actual behavior (
                    <xref ref-type="bibr" rid="ref32">Feghali et al., 2025</xref>; 
                    <xref ref-type="bibr" rid="ref87">&#x015e;enyapar, 2024</xref>). By creating deceptive impressions of environmental responsibility, firms erode consumer confidence and inflate perceived risk, even among those with strong pro-environmental values (
                    <xref ref-type="bibr" rid="ref21">Bulut et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref34">Ganesh &amp; Priya, 2024</xref>). While prior studies focused on its pre-intention influence, the moderating effect of greenwashing on the intention-behavior link remains underexplored, representing a crucial vulnerability in the TPB framework (
                    <xref ref-type="bibr" rid="ref5">Ajzen, 1991</xref>; 
                    <xref ref-type="bibr" rid="ref100">Tarabieh, 2021</xref>). Exposure to misleading green claims, as in high-profile cases like H&amp;M or Volkswagen, can derail actual green purchases despite strong intentions, highlighting the fragility of behavioral realization under market deception (
                    <xref ref-type="bibr" rid="ref11">Alizadeh et al., 2024</xref>; 
                    <xref ref-type="bibr" rid="ref86">Sawayda et al., 2022</xref>). This positions greenwashing as a structural barrier, revealing whether intentions act as resilient anchors or are susceptible to manipulation (
                    <xref ref-type="bibr" rid="ref89">Shi &amp; Omar, 2024</xref>).</p>
                <p>

                    <bold>2.1.6 Green Purchase Intention (GPI)</bold>
                </p>
                <p>Green purchase intention (GPI) embodies a deliberate consumer commitment to prioritize green products, reflecting both cognitive evaluations of ecological benefits and emotional engagement with sustainability goals (
                    <xref ref-type="bibr" rid="ref59">Kumar et al., 2020</xref>). Empirical evidence consistently positions GPI as a critical antecedent to green purchase behavior (GPB), translating environmental motivation into concrete action (
                    <xref ref-type="bibr" rid="ref119">Zhuang et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref102">Tian et al., 2022</xref>). Yet, a persistent intention-behavior gap reveals that favorable attitudes and subjective norms alone do not guarantee actual green consumption, with factors such as perceived behavioral control, price sensitivity, and skepticism influencing the translation from intention to behavior (
                    <xref ref-type="bibr" rid="ref70">Margariti et al., 2024</xref>; 
                    <xref ref-type="bibr" rid="ref101">Tawde et al., 2023</xref>). In Malaysia&#x2019;s urban markets, rising environmental awareness and knowledge bolster GPI, but structural and psychological barriers can inhibit GPB, emphasizing the need for strategies that convert intention into measurable, sustained green consumption (
                    <xref ref-type="bibr" rid="ref9">Alhomssi &amp; Abbass Ali, 2022</xref>).</p>
                <p>

                    <bold>2.1.7 Mediating Effect of Green Purchase Intention</bold>
                </p>
                <p>Green Purchase Intention (GPI) acts as the psychological engine transforming consumer motivation into real green behavior. It bridges how attitude, perceived behavioral control, subjective norms, environmental concern, and price sensitivity evolve from abstract beliefs into concrete purchasing choices (
                    <xref ref-type="bibr" rid="ref119">Zhuang et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref61">Kun et al., 2025</xref>). Positive evaluations of green products and strong perceived capability (
                    <xref ref-type="bibr" rid="ref5">Ajzen, 1991</xref>) significantly heighten intention, while social norms and environmental concern reinforce moral alignment and pro-social motivation (
                    <xref ref-type="bibr" rid="ref78">Ogiemwonyi et al., 2023</xref>; 
                    <xref ref-type="bibr" rid="ref112">Xu et al., 2022</xref>). Even price sensitivity, when balanced with perceived value, channels through intention rather than acting as a direct barrier (
                    <xref ref-type="bibr" rid="ref88">Sheikh et al., 2023</xref>). Structural modeling consistently confirms that intention fully mediates these relationships, closing the attitude-behavior gap and establishing GPI as the decisive motivational conduit linking cognition, affect, and green purchasing behavior (
                    <xref ref-type="bibr" rid="ref114">Yuan et al., 2023</xref>; 
                    <xref ref-type="bibr" rid="ref119">Zhuang et al., 2021</xref>).</p>
                <p>

                    <bold>2.1.8 Attitude (ATT)</bold>
                </p>
                <p>Attitude (ATT) encapsulates an individual&#x2019;s evaluative, cognitive, and emotional disposition toward objects, behaviors, or issues, shaping their beliefs, feelings, and behavioral tendencies (
                    <xref ref-type="bibr" rid="ref57">Kang et al., 2025</xref>). In sustainable consumption, a positive environmental attitude strongly predicts green purchase intention (GPI), as consumers perceive green products as morally and socially desirable, and linked to advantageous outcomes (
                    <xref ref-type="bibr" rid="ref47">Hoang Yen &amp; Phuong Hoang, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref119">Zhuang et al., 2021</xref>). Empirical evidence across product categories, from organic foods to green packaging, confirms that favorable attitudes consistently drive intention, although external constraints like affordability may modulate actual behavior (
                    <xref ref-type="bibr" rid="ref25">Chekima et al., 2016</xref>; 
                    <xref ref-type="bibr" rid="ref12">Amoako et al., 2020</xref>).</p>
                <p>

                    <bold>2.1.9 Perceived Behavioral Control (PBC)</bold>
                </p>
                <p>Perceived Behavioral Control (PBC) reflects an individual&#x2019;s perception of their ability to perform a behavior, integrating internal capabilities and external resources such as time, money, and access (
                    <xref ref-type="bibr" rid="ref8">Albanese et al., 2025</xref>). In sustainable consumption, higher PBC enhances green purchase intention (GPI) as consumers feel empowered to overcome financial, informational, and logistical barriers (
                    <xref ref-type="bibr" rid="ref119">Zhuang et al., 2021</xref>). Empirical studies across Malaysia, Vietnam, and India confirm that perceived control strongly predicts intention to buy green products, though contextual factors like availability, affordability, and prior experiences modulate this effect (
                    <xref ref-type="bibr" rid="ref75">Nguyen et al., 2016</xref>; 
                    <xref ref-type="bibr" rid="ref48">Huong Nguyen et al., 2019</xref>; 
                    <xref ref-type="bibr" rid="ref27">Choi &amp; Johnson, 2019</xref>).</p>
                <p>

                    <bold>2.1.10 Subjective Norms (SN)</bold>
                </p>
                <p>Subjective norms (SN) profoundly shape green purchase intentions by reflecting perceived social pressures from family, peers, and significant referents, driving individuals to align behaviors with collective expectations (
                    <xref ref-type="bibr" rid="ref97">Sus &amp; Drew, 2023</xref>). Empirical evidence demonstrates that both injunctive and descriptive norms reinforce sustainable consumption across diverse green product categories, including cosmetics, clothing, and general green goods (
                    <xref ref-type="bibr" rid="ref81">Pop et al., 2020</xref>; 
                    <xref ref-type="bibr" rid="ref119">Zhuang et al., 2021</xref>). In collectivist contexts such as Malaysia, SN amplifies intentions by embedding ethical behavior within social identity and peer validation, highlighting the social contagion of pro-environmental actions (
                    <xref ref-type="bibr" rid="ref7">Alalei &amp; Jan, 2023</xref>).</p>
                <p>

                    <bold>2.1.11 Environmental Concern (EC)</bold>
                </p>
                <p>Environmental concern (EC) reflects the cognitive, affective, and conative engagement of individuals with environmental issues, encompassing awareness, emotional responses, and motivation to act (
                    <xref ref-type="bibr" rid="ref62">Laheri et al., 2023</xref>). Strong environmental concern drives green purchase intention (GPI) by linking ethical responsibility, ecological emotions, and perceived moral obligation to tangible consumption choices (
                    <xref ref-type="bibr" rid="ref76">Niehoff et al., 2023</xref>; 
                    <xref ref-type="bibr" rid="ref119">Zhuang et al., 2021</xref>). Empirical studies in Malaysia and emerging markets demonstrate that heightened EC correlates with increased adoption of green products across diverse categories, from organic skincare to eco-labeled goods, reinforcing sustainable consumption through both emotional commitment and behavioral motivation (
                    <xref ref-type="bibr" rid="ref6">Al Mamun et al., 2020</xref>; 
                    <xref ref-type="bibr" rid="ref113">Yadav &amp; Pathak, 2017</xref>).</p>
                <p>

                    <bold>2.1.12 Price Sensitivity (PS)</bold>
                </p>
                <p>Price sensitivity (PS) reflects consumers&#x2019; responsiveness to price changes, shaping perceived value and purchasing decisions (
                    <xref ref-type="bibr" rid="ref56">Kagan, 2025</xref>). In sustainable consumption, elevated PS acts as a barrier to green purchase intention (GPI), as green products often carry premium costs due to eco-certifications and environmentally responsible processes (
                    <xref ref-type="bibr" rid="ref118">Zhao &amp; Zhong, 2015</xref>). Empirical evidence in Malaysia and developing regions demonstrates that high PS dampens adoption of green products despite favorable attitudes or environmental concern, highlighting economic constraints as a critical influencer in sustainable consumption (
                    <xref ref-type="bibr" rid="ref10">Ali et al., 2021</xref>; 
                    <xref ref-type="bibr" rid="ref109">Wei et al., 2022</xref>).</p>
                <p>
                    <xref ref-type="fig" rid="f1">
Figure 1</xref> below presents the conceptual framework of the study. The model examines the influence of attitude, perceived behavioural control, subjective norms, environmental concern, and price sensitivity on green purchase intention, which subsequently influences green purchase behaviour and spillover responses in the form of green switching and green disposal behaviours among urban consumers. Greenwashing is incorporated as a moderating factor within the intention&#x2013;behaviour pathway.</p>
                <fig fig-type="figure" id="f1" orientation="portrait" position="float">
                    <label>
Figure 1. </label>
                    <caption>
                        <title>The Proposed Research Framework for the Study.</title>
                    </caption>
                    <graphic id="gr1" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/195455/8a3dbaa8-ea41-4d3d-b18f-4dbf692538b3_figure1.gif"/>
                </fig>
            </sec>
            <sec id="sec6">
                <title>2.2 Theories and theoretical grounding</title>
                <p>

                    <bold>2.2.1 Theory of Planned Behavior (TPB)</bold>
                </p>
                <p>
The Theory of Planned Behavior (TPB) by 
                    <xref ref-type="bibr" rid="ref5">Ajzen (1991)</xref> provides a powerful lens for decoding green consumer behavior, linking attitude, subjective norms, and perceived behavioral control to intention and actual action. Unlike the Theory of Reasoned Action, TPB integrates perceived control, reflecting individuals&#x2019; confidence and external constraints that shape their ability to act sustainably (
                    <xref ref-type="bibr" rid="ref5">Ajzen, 1991</xref>; 
                    <xref ref-type="bibr" rid="ref55">Joshi &amp; Rahman, 2015</xref>). Its predictive precision has been proven across green consumption contexts (
                    <xref ref-type="bibr" rid="ref66">Liobikien&#x0117; et al., 2016</xref>; 
                    <xref ref-type="bibr" rid="ref104">Varah et al., 2021</xref>), showing that favorable attitudes, strong social influence, and high self-efficacy collectively heighten green purchase intention. Extensions incorporating environmental concern and price sensitivity further refine TPB&#x2019;s explanatory scope, positioning it as the most robust and contextually relevant framework for understanding Malaysia&#x2019;s sustainable consumption dynamics (
                    <xref ref-type="bibr" rid="ref85">Rui Wen, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref46">Ho &amp; Huynh, 2022</xref>).</p>
                <p>

                    <bold>2.2.2 Value-Belief-Norm (VBN)</bold>
                </p>
                <p>The Value-Belief-Norm (VBN) theory explains pro-environmental behavior as a cascade from deeply held values, biospheric, altruistic, egoistic, to beliefs about ecological consequences and personal responsibility, which activate moral norms guiding green actions (
                    <xref ref-type="bibr" rid="ref95">Stern, 2000</xref>; 
                    <xref ref-type="bibr" rid="ref42">Han, 2015</xref>). Individuals aware of environmental impacts and motivated by ethical obligation are more likely to adopt sustainable consumption practices, transcending economic trade-offs, and fostering green purchase intention grounded in personal commitment and ecological consciousness (
                    <xref ref-type="bibr" rid="ref43">Hau Nguyen et al., 2025</xref>; 
                    <xref ref-type="bibr" rid="ref44">Hein, 2022</xref>).</p>
                <p>

                    <bold>2.2.3 Blending Theory of Planned Behavior and Value-Belief-Norm</bold>
                </p>
                <p>Blending the Theory of Planned Behavior (TPB) with the Value-Belief-Norm (VBN) theory provides a powerful lens to understand green consumer behavior, capturing both rational decision-making and moral obligation. TPB explains intention through attitude, subjective norms, and perceived control (
                    <xref ref-type="bibr" rid="ref5">Ajzen, 1991</xref>), while VBN emphasizes biospheric and altruistic values that trigger personal norms and ecological awareness (
                    <xref ref-type="bibr" rid="ref95">Stern, 2000</xref>). In Malaysia, this synergy reveals how internal values, social pressures, and economic considerations collectively shape sustainable consumption, overcoming cost barriers through moral motivation and ethical commitment (
                    <xref ref-type="bibr" rid="ref44">Hein, 2022</xref>; 
                    <xref ref-type="bibr" rid="ref84">Rou &amp; Lian Kang, 2023</xref>; 
                    <xref ref-type="bibr" rid="ref43">Hau Nguyen et al., 2025</xref>).</p>
                <p>

                    <bold>2.2.4 Spillover Theory</bold>
                </p>
                <p>Behavioral spillover captures how initial green actions trigger broader sustainable practices, linking purchase, switching, and disposal behaviors in a cascading effect (
                    <xref ref-type="bibr" rid="ref13">Austin et al., 2011</xref>; 
                    <xref ref-type="bibr" rid="ref74">Nash et al., 2019</xref>). Positive spillover strengthens environmental self-identity and efficacy, fostering brand switching and responsible disposal, while negative spillover may offset gains through compensatory consumption (
                    <xref ref-type="bibr" rid="ref24">Castro Santa et al., 2024</xref>; 
                    <xref ref-type="bibr" rid="ref30">Fanghella et al., 2019</xref>). In Malaysia, spillover reveals how early green engagement shapes enduring sustainable consumption patterns (
                    <xref ref-type="bibr" rid="ref94">Stangherlin et al., 2023</xref>; 
                    <xref ref-type="bibr" rid="ref15">Behn et al., 2025</xref>).</p>
            </sec>
        </sec>
        <sec id="sec7">
            <title>3. Methodology</title>
            <p>
This study employs a quantitative, theory-driven methodology to unravel the mechanisms shaping urban sustainable consumption in Malaysia, integrating Theory of Planned Behavior, Value-Belief-Norm, and Spillover frameworks (
                <xref ref-type="bibr" rid="ref14">Barroga et al., 2023</xref>). Guided by a positivist paradigm (
                <xref ref-type="bibr" rid="ref79">Park et al., 2020</xref>) and a deductive approach (
                <xref ref-type="bibr" rid="ref110">Williams, 2024</xref>), the study adopts an explanatory design to capture causal relationships among five predictors, attitude, perceived behavioral control, subjective norms, environmental concern, and price sensitivity, on green purchase intention and its spillover into switching and disposal behaviors (
                <xref ref-type="bibr" rid="ref35">George &amp; Merkus, 2025</xref>). A cross-sectional survey using structured questionnaires captured primary data from urban consumers (
                <xref ref-type="bibr" rid="ref96">Stewart, 2025</xref>; 
                <xref ref-type="bibr" rid="ref90">Sieber, 2023</xref>; 
                <xref ref-type="bibr" rid="ref98">Taherdoost, 2022</xref>), with a sample size of 160 determined via G*Power analysis (
                <xref ref-type="bibr" rid="ref31">Faul et al., 2007</xref>). Data collection combined online distribution through Google Forms and selective in-person engagement to maximize inclusivity and representation. Prior to participation, respondents were provided with information outlining the purpose of the study, the voluntary nature of participation, and assurances of anonymity and confidentiality. Informed consent was obtained electronically before participants proceeded to complete the questionnaire. Only individuals aged 18&#x00a0;years and above were eligible to participate in the survey. Participants provided electronic informed consent before accessing the questionnaire and proceeding with the survey. Pilot testing with 55 respondents ensured reliability, while rigorous cleaning, coding, and tabulation prepared the dataset for analysis. IBM SPSS provided descriptive profiling of demographics and construct central tendencies, establishing a robust foundation for interpretation (
                <xref ref-type="bibr" rid="ref51">IBM, 2024</xref>). Core structural relationships, mediations, and moderation effects were tested using SmartPLS 4 through confirmatory factor analysis, path modeling, and bootstrapping, ensuring construct validity and predictive precision (
                <xref ref-type="bibr" rid="ref40">Hair et al., 2022</xref>). This approach delivers reliable, generalizable insights that deepen theoretical and practical understanding of sustainable consumption behavior in Malaysia.</p>
        </sec>
        <sec id="sec8">
            <title>4. Results and analysis</title>
            <sec id="sec9">
                <title>4.1 Demographic profile</title>
                <p>
                    <xref ref-type="table" rid="T1">
Table 1</xref> below presents the demographic profile of the respondents. The sample comprised 263 participants, with males accounting for 57% and females 43%. The majority of respondents were aged 18&#x2013;25&#x00a0;years (87.8%) and were predominantly Malaysian (70.3%). Most participants were students (83.3%), with the majority reporting a monthly income of RM1000 or below (68.4%). In terms of environmental consumption behaviour, 78.7% indicated awareness of green products, while 91.3% reported previous purchase experience.</p>
                <table-wrap id="T1" orientation="portrait" position="float">
                    <label>
Table 1. </label>
                    <caption>
                        <title>Demographic profile (n&#x00a0;=&#x00a0;263).</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top">Demographic profile</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Category</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Frequency</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Percentage (%)</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="2" valign="top">Gender</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Male</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">150</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">57</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Female</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">113</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">43</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="4" valign="middle">Age Group</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">18&#x2013;25&#x00a0;years</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">231</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">87.8</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">26&#x2013;35&#x00a0;years</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">17</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">6.5</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">36&#x2013;45&#x00a0;years</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">8</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">3</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Above 45&#x00a0;years</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">7</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">2.7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="2" valign="middle">Nationality</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Malaysian</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">185</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">70.3</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Non-Malaysian
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">78</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">29.7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="5" valign="middle">Occupation</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Student</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">219</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">83.3</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Full-time Employee</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">18</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">6.8</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Part-time Employee</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">15</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">3.7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Self-employed
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">5</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">1.9</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Unemployed</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">6</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">2.3</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="6" valign="middle">Level of Study</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Pre-University (e.g., High School, Foundation, A-Levels, IB, STPM, etc.)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">82</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">31.2</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Diploma</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">63</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">39.5</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Bachelor&#x2019;s Degree</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">104</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">63.4</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Master&#x2019;s Degree</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">10</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">3.8</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">PhD</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">3</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">1.1</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Professional Certificate (ACCA, PMP, CFA, etc.)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">1</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.4</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="7" valign="middle">Income Level</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">RM1000 and below</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">180</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">68.4</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">RM1001 - RM2000</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">37</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">14.1</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">RM2001 - RM3000</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">14</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">5.3</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">RM3001 - RM4000</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">14</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">5.3</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">RM4001 - RM5000</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">3</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">1.1</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">RM5001 - RM10000</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">5</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">1.9</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Above RM10000</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">10</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">3.8</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="3" valign="middle">Awareness of Green Products</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Yes</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">207</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">78.7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Somewhat</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">45</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">17.1</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">No</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">11</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">4.2</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="2" valign="middle">Purchase Experience of Green Products</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Yes</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">240</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">91.3</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">No</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">23</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">8.7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="5" valign="top">Purchase Frequency</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">Never</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">23</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">8.7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Rarely (e.g., once every few months)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">91</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">34.6</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Sometimes (e.g., once a month)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">101</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">38.4</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Often (e.g., a few times a month)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">36</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">13.7</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="middle">Always (e.g., regularly as part of your routine)</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">12</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">4.6</td>
                            </tr>
                        </tbody>
                    </table>
                </table-wrap>
            </sec>
            <sec id="sec10">
                <title>4.2 Assessment of measurement model</title>
                <p>As shown in 
                    <xref ref-type="table" rid="T2">
Table 2</xref> below, all reflective constructs demonstrated strong psychometric robustness across reliability, validity, and collinearity diagnostics. Internal consistency was excellent, with all Cronbach&#x2019;s Alpha and Composite Reliability (CR) values exceeding the 0.70 benchmark (
                    <xref ref-type="bibr" rid="ref38">Hair Jr. et al., 2021</xref>), confirming that the indicators coherently represent their latent variables, including complex constructs such as intention, behavioral spillover, and greenwashing. Convergent validity (AVE) was firmly established, as all AVE values surpassed the 0.50 threshold (
                    <xref ref-type="bibr" rid="ref41">Hair et al., 2013</xref>), indicating that each construct captured substantial shared variance despite the conceptual diversity of psychological, behavioral, and post-consumption measures. Multicollinearity (VIF) was not a concern, with all item-level VIF values ranging between 1.336 and 2.543, well below the critical ceiling of 5 (
                    <xref ref-type="bibr" rid="ref39">Hair Jr et al., 2014</xref>), demonstrating that predictors, mediators, and outcome variables remained empirically distinct without inflating structural paths.</p>
                <table-wrap id="T2" orientation="portrait" position="float">
                    <label>
Table 2. </label>
                    <caption>
                        <title>Assessment of reliability, convergent validity, and multiple linearity.</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top">Construct</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Item</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Loading</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">CA</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">CR</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">AVE</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">VIF</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="4" valign="top">Attitude (ATT)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">ATT1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.775</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.817</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.879</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.646</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.503</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">ATT2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.842</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.992</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">ATT3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.792</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.675</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">ATT4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.805</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.818</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="4" valign="top">Perceived Behavioral Control (PBC)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">PBC1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.858</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.789</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.864</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.613</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.044</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">PBC2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.766</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.563</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">PBC3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.747</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.579</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">PBC4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.757</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.474</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="3" valign="top">Subjective Norms (SN)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">SN1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.820</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.831</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.899</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.749</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.574</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">SN3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.875</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.323</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">SN4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.899</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.543</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="4" valign="top">Environmental Concern (EC)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">EC1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.810</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.838</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.891</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.672</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.595</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">EC2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.851</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.135</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">EC3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.821</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.985</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">EC4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.796</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.818</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="2" valign="top">Price Sensitivity (PS)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">PS1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.917</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.806</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.912</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.838</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.837</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">PS3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.913</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.837</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="3" valign="top">Green Purchase Intention (GPI)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">GPI1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.870</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.855</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.912</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.775</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.074</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GPI2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.888</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.224</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GPI3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.883</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.085</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="4" valign="top">Green Purchase Behavior (GPB)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">GPB1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.793</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.815</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.878</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.644</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.728</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GPB2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.734</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.411</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GPB3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.845</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.948</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GPB4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.835</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.009</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="4" valign="top">Greenwashing (GWS)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">GWS1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.862</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.870</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.911</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.719</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.238</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GWS2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.880</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.536</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GWS3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.847</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.202</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GWS4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.801</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.822</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="4" valign="top">Green Switching Behavior (GSB)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">GSB1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.809</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.848</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.897</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.685</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.599</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GSB2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.822</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.026</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GSB3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.837</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.114</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GSB4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.843</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.074</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="4" valign="top">Green Disposal Behavior (GDB)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">GDB1</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.763</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.809</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.873</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.633</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.336</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GDB2</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.796</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.904</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GDB3</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.824</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.923</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">GDB4</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.798</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.786</td>
                            </tr>
                        </tbody>
                    </table>
                </table-wrap>
                <p>As shown in 
                    <xref ref-type="table" rid="T3">
Table 3</xref> below, discriminant validity, assessed using HTMT (
                    <xref ref-type="bibr" rid="ref45">Henseler et al., 2015</xref>), further confirmed that the constructs were sufficiently distinct. Although two HTMT values involving Green Purchase Intention (GPI), specifically with Environmental Concern (0.904) and Perceived Behavioral Control (0.924), slightly exceeded the 0.90 conventional guideline, they remained within the upper permissible boundary of 1.00 (
                    <xref ref-type="bibr" rid="ref33">Franke &amp; Sarstedt, 2019</xref>). Given that the majority of HTMT values fell well below 0.90, the overall pattern supports satisfactory discriminant validity and indicates that the constructs maintain adequate empirical separation. Collectively, these results confirm that the reflective measurement model is exceptionally stable, theoretically coherent, and empirically precise, providing a rigorous foundation for testing the structural pathways that drive sustainable consumption and behavioral spillover in urban Malaysia.</p>
                <table-wrap id="T3" orientation="portrait" position="float">
                    <label>
Table 3. </label>
                    <caption>
                        <title>Assessment of the discriminant validity using HTMT.</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top">Construct</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">1</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">2</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">3</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">4</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">5</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">6</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">7</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">8</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">9</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">10</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">1. Attitude (ATT)</td>
                                <td colspan="1" rowspan="1" style="background-color:#FFF2CC"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">2. Environmental Concern (EC)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.855</td>
                                <td colspan="1" rowspan="1" style="background-color:#FFF2CC"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">3. Green Disposal Behavior (GDB)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.777</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.763</td>
                                <td colspan="1" rowspan="1" style="background-color:#FFF2CC"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">4. Green Purchase Behavior (GPB)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.697</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.658</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.724</td>
                                <td colspan="1" rowspan="1" style="background-color:#FFF2CC"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">5. Green Purchase Intention (GPI)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.852</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.904</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.802</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.859</td>
                                <td colspan="1" rowspan="1" style="background-color:#FFF2CC"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">6. Green Switching Behavior (GSB)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.571</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.530</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.685</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.893</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.709</td>
                                <td colspan="1" rowspan="1" style="background-color:#FFF2CC"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">7. Greenwashing (GWS)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.539</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.609</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.640</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.633</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.662</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.471</td>
                                <td colspan="1" rowspan="1" style="background-color:#FFF2CC"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">8. Perceived Behavioral Control (PBC)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.837</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.799</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.800</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.872</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.924</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.769</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.588</td>
                                <td colspan="1" rowspan="1" style="background-color:#FFF2CC"/>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">9. Price Sensitivity (PS)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.518</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.485</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.534</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.833</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.633</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.829</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.411</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.659</td>
                                <td colspan="1" rowspan="1" style="background-color:#FFF2CC"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">10. Subjective Norms (SN)</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.536</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.518</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.587</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.701</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.602</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.709</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.412</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.665</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.724</td>
                                <td colspan="1" rowspan="1" style="background-color:#FFF2CC"/>
                            </tr>
                        </tbody>
                    </table>
                </table-wrap>
            </sec>
            <sec id="sec11">
                <title>4.3 Assessment of structural model and bootstrapping</title>
                <p>The bootstrapping procedure with 5,000 resamples was employed to test the significance of all hypothesized paths. As presented in 
                    <xref ref-type="table" rid="T4">
Table 4</xref>, the results showed that four of the five predictors of Green Purchase Intention (GPI): attitude (H1) (t&#x00a0;=&#x00a0;2.630, p&#x00a0;=&#x00a0;0.009), perceived behavioral control (H2) (t&#x00a0;=&#x00a0;5.513, p&#x00a0;=&#x00a0;0.000), environmental concern (H4) (t&#x00a0;=&#x00a0;6.557, p&#x00a0;=&#x00a0;0.000), and price sensitivity (H5) (t&#x00a0;=&#x00a0;2.228, p&#x00a0;=&#x00a0;0.026) were significant, whereas subjective norms (H3) were non-significant (t&#x00a0;=&#x00a0;0.518, p&#x00a0;=&#x00a0;0.604). GPI strongly predicted Green Purchase Behavior (H6) (t&#x00a0;=&#x00a0;11.598, p&#x00a0;=&#x00a0;0.000). In addition, Green Purchase Behavior generated substantial spillover effects into green switching behavior (H9) (t&#x00a0;=&#x00a0;21.572, p&#x00a0;=&#x00a0;0.000) and green disposal behavior (H10) (t&#x00a0;=&#x00a0;12.950, p&#x00a0;=&#x00a0;0.000).</p>
                <table-wrap id="T4" orientation="portrait" position="float">
                    <label>
Table 4. </label>
                    <caption>
                        <title>Assessment of bootstrapping, effect size, and path coefficients.</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top">Relationship</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">

                                    <italic toggle="yes">t</italic> value</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">

                                    <italic toggle="yes">p</italic> value</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">&#x0192;
                                    <sup>2</sup>
                                </th>
                                <th align="left" colspan="1" rowspan="1" valign="top">&#x03b2;</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H1) ATT&#x00a0;&#x2192;&#x00a0;GPI</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.630</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.009</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.041</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.160</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H2) PBC&#x00a0;&#x2192;&#x00a0;GPI</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">5.513</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.000</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.154</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.314</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H3) SN&#x00a0;&#x2192;&#x00a0;GPI</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.518</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.604</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.024</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H4) EC&#x00a0;&#x2192;&#x00a0;GPI</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">6.557</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.000</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.256</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.397</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H5) PS&#x00a0;&#x2192;&#x00a0;GPI</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.228</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.026</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.032</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.119</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H6) GPI&#x00a0;&#x2192;&#x00a0;GPB</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">11.598</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.000</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.535</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.637</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H7a) ATT&#x00a0;&#x2192;&#x00a0;GPI&#x00a0;&#x2192;&#x00a0;GPB</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.513</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.012</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H7b) PBC&#x00a0;&#x2192;&#x00a0;GPI&#x00a0;&#x2192;&#x00a0;GPB</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">4.937</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.000</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H7c) SN&#x00a0;&#x2192;&#x00a0;GPI&#x00a0;&#x2192;&#x00a0;GPB</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.519</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.604</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H7d) EC&#x00a0;&#x2192;&#x00a0;GPI&#x00a0;&#x2192;&#x00a0;GPB</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">6.080</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.000</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H7e) PS&#x00a0;&#x2192;&#x00a0;GPI&#x00a0;&#x2192;&#x00a0;GPB</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2.106</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.035</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H8) GWS x GPI&#x00a0;&#x2192;&#x00a0;GPB</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.122</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.262</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.007</td>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H9) GPB&#x00a0;&#x2192;&#x00a0;GSB</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">21.572</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.000</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.300</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.752</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H10) GPB&#x00a0;&#x2192;&#x00a0;GDB</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">12.950</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.000</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.578</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.605</td>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H11) GPI&#x00a0;&#x2192;&#x00a0;GPB&#x00a0;&#x2192;&#x00a0;GSB</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">9.692</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.000</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                            <tr>
                                <td align="left" colspan="1" rowspan="1" valign="top">H12) GPI&#x00a0;&#x2192;&#x00a0;GPB&#x00a0;&#x2192;&#x00a0;GDB</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">7.869</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.000</td>
                                <td colspan="1" rowspan="1"/>
                                <td colspan="1" rowspan="1"/>
                            </tr>
                        </tbody>
                    </table>
                </table-wrap>
                <p>The mediation tests further revealed significant indirect effects through intention for attitude (H7a) (t&#x00a0;=&#x00a0;2.513, p&#x00a0;=&#x00a0;0.012), perceived behavioral control (H7b) (t&#x00a0;=&#x00a0;4.937, p&#x00a0;=&#x00a0;0.000), environmental concern (H7d) (t&#x00a0;=&#x00a0;6.080, p&#x00a0;=&#x00a0;0.000), and price sensitivity (H7e) (t&#x00a0;=&#x00a0;2.106, p&#x00a0;=&#x00a0;0.035), while subjective norms (H7c) remained non-significant (t&#x00a0;=&#x00a0;0.519, p&#x00a0;=&#x00a0;0.604). Moreover, significant spillover mediation pathways were observed from GPI to GPB to green switching behavior (H11) (t&#x00a0;=&#x00a0;9.692, p&#x00a0;=&#x00a0;0.000) and from GPI to GPB to green disposal behavior (H12) (t&#x00a0;=&#x00a0;7.869, p&#x00a0;=&#x00a0;0.000). The moderating effect of greenwashing (H8) on the intention&#x2013;behavior relationship was non-significant (t&#x00a0;=&#x00a0;1.122, p&#x00a0;=&#x00a0;0.262), indicating that the intention&#x2013;behavior pathway remained statistically stable. Effect size results reported in 
                    <xref ref-type="table" rid="T4">
Table 4</xref> further underscore this behavioral cascade, with Green Purchase Behavior exerting extremely large effects on switching (f
                    <sup>2</sup>&#x00a0;=&#x00a0;1.300) and disposal (f
                    <sup>2</sup>&#x00a0;=&#x00a0;0.578), while intention demonstrated a substantial effect on behavior (f
                    <sup>2</sup>&#x00a0;=&#x00a0;0.535). Among the antecedents of intention, environmental concern and perceived behavioral control showed the strongest influence, while subjective norms played a minimal role.</p>
                <p>The coefficient of determination and predictive relevance statistics are reported in 
                    <xref ref-type="table" rid="T5">
Table 5</xref>. The coefficient of determination (R
                    <sup>2</sup>) indicates that the model explains a substantial proportion of variance in the endogenous constructs. Specifically, green purchase intention recorded a high explanatory power (R
                    <sup>2</sup>&#x00a0;=&#x00a0;0.737), while green purchase behavior also demonstrated strong explanatory capability (R
                    <sup>2</sup>&#x00a0;=&#x00a0;0.543). Furthermore, the results confirm meaningful downstream spillover effects, with switching behavior (R
                    <sup>2</sup>&#x00a0;=&#x00a0;0.565) and disposal behavior (R
                    <sup>2</sup>&#x00a0;=&#x00a0;0.366) showing moderate explanatory power. Predictive relevance (Q
                    <sup>2</sup>) further reinforces these findings, as intention (Q
                    <sup>2</sup>&#x00a0;=&#x00a0;0.722) and behavior (Q
                    <sup>2</sup>&#x00a0;=&#x00a0;0.531) exhibit strong predictive accuracy, while switching (Q
                    <sup>2</sup>&#x00a0;=&#x00a0;0.388) and disposal (Q
                    <sup>2</sup>&#x00a0;=&#x00a0;0.395) demonstrate moderate yet meaningful predictive capability.</p>
                <table-wrap id="T5" orientation="portrait" position="float">
                    <label>
Table 5. </label>
                    <caption>
                        <title>Assessment of coefficient of determination and predictive relevance.</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top">Variables</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">R-Square (R
                                    <sup>2</sup>)</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Q
                                    <sup>2</sup> Predict</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="center" colspan="1" rowspan="1" valign="middle">
                                    <bold>Green Disposal Behavior (GDB)</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.366</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.395</td>
                            </tr>
                            <tr>
                                <td align="center" colspan="1" rowspan="1" valign="middle">
                                    <bold>Green Purchase Behavior (GPB)</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.543</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.531</td>
                            </tr>
                            <tr>
                                <td align="center" colspan="1" rowspan="1" valign="middle">
                                    <bold>Green Purchase Intention (GPI)</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.737</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.722</td>
                            </tr>
                            <tr>
                                <td align="center" colspan="1" rowspan="1" valign="middle">
                                    <bold>Green Switching Behavior (GSB)</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.565</td>
                                <td align="left" colspan="1" rowspan="1" valign="middle">0.388</td>
                            </tr>
                        </tbody>
                    </table>
                </table-wrap>
                <p>
                    <xref ref-type="table" rid="T6">
Table 6</xref> reports the predictive model fit statistics. The model fit indices, including SRMR, d_ULS, d_G, and NFI, fall within acceptable thresholds for complex behavioral models, indicating an adequate model fit. These statistics suggest that the proposed structural model demonstrates acceptable predictive performance and theoretical parsimony despite the complexity of the behavioral relationships examined.</p>
                <table-wrap id="T6" orientation="portrait" position="float">
                    <label>
Table 6. </label>
                    <caption>
                        <title>Assessment of predictive model fit.</title>
                    </caption>
                    <table content-type="article-table" frame="hsides">
                        <thead>
                            <tr>
                                <th align="left" colspan="1" rowspan="1" valign="top"/>
                                <th align="left" colspan="1" rowspan="1" valign="top">Saturated model</th>
                                <th align="left" colspan="1" rowspan="1" valign="top">Estimated model</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td align="center" colspan="1" rowspan="1" valign="middle">
                                    <bold>SRMR</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.080</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.121</td>
                            </tr>
                            <tr>
                                <td align="center" colspan="1" rowspan="1" valign="middle">
                                    <bold>d_ULS</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">4.220</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">9.813</td>
                            </tr>
                            <tr>
                                <td align="center" colspan="1" rowspan="1" valign="middle">
                                    <bold>d_G</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.275</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1.513</td>
                            </tr>
                            <tr>
                                <td align="center" colspan="1" rowspan="1" valign="middle">
                                    <bold>Chi-square
</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">1915.092</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">2139.115</td>
                            </tr>
                            <tr>
                                <td align="center" colspan="1" rowspan="1" valign="middle">
                                    <bold>NFI</bold>
</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.716</td>
                                <td align="left" colspan="1" rowspan="1" valign="top">0.683</td>
                            </tr>
                        </tbody>
                    </table>
                </table-wrap>
                <p>Taken together, the results confirm a coherent and high-performing structural model in which strong intentions translate into purchasing behavior and subsequently extend into wider sustainable practices through behavioral spillover, offering a theoretically grounded and empirically robust explanation of urban sustainable consumption.</p>
            </sec>
        </sec>
        <sec id="sec12">
            <title>5 Discussion, implications, limitations, recommendations, and conclusion</title>
            <sec id="sec13">
                <title>5.1 Discussion of hypotheses results&#x2019; findings</title>
                <p>This study uncovers the psychological architecture driving urban sustainable consumption in Malaysia and extends established behavioral theories in a context where their predictions have long been assumed rather than tested. The results reveal a decisive shift from collectivist expectations toward individual agency: Attitude, Perceived Behavioral Control, Environmental Concern, and Price Sensitivity significantly strengthen Green Purchase Intention (H1, H2, H4, H5 accepted), whereas Subjective Norms do not (H3 rejected). This divergence from classic TPB assumptions in collectivist cultures indicates that urban consumers in Malaysia now anchor their green choices in internalized ecological values and personal efficacy rather than social pressure, offering a major theoretical recalibration. The positive effect of Price Sensitivity (H5) on intention further challenges dominant literature portraying green products as cost barriers, suggesting that consumers increasingly perceive them as value-aligned, cost-effective investments, an economically grounded logic rarely observed in emerging markets.</p>
                <p>The translation of intention into behavior reinforces this pattern of empowered decision-making. The strong relationship between Green Purchase Intention and Green Purchase Behavior (H6 accepted) demonstrates a narrowing intention-behavior gap, while the non-significant moderation of greenwashing (H8 rejected) indicates that once intention is internalized, it evolves into a stable psychological contract sustained by identity consistency, value internalization, and cognitive alignment rather than susceptibility to misleading claims. This clarifies greenwashing&#x2019;s true position within the behavioral sequence: influential during attitude formation but negligible in post-intention execution.</p>
                <p>The mediating role of GPI further illuminates this motivational circuitry. GPI significantly channels the effects of Attitude, PBC, Environmental Concern, and Price Sensitivity into behavior (H7a, H7b, H7d, H7e accepted), while mediation from Subjective Norms remains absent (H7c rejected). Collectively, these findings portray the urban Malaysian green consumer as intrinsically driven, efficacy-oriented, and economically rational, an emerging behavioral profile with substantive theoretical and practical importance.</p>
                <p>The most novel contribution lies in the empirical confirmation of downstream behavioral spillover. Green Purchase Behavior powerfully triggers both Green Switching and Green Disposal actions (H9, H10 accepted), and GPB fully mediates the pathway from intention to these higher-order behaviors (H11, H12 accepted). This demonstrates that intention alone is insufficient for extended sustainability practices; behavioral spillover materializes only after the identity-reinforcing act of purchase reconfigures self-perception and activates further pro-environmental commitments.</p>
                <p>Overall, this study advances TPB by revealing an intention-behavior system shaped more by personal agency than collectivist norms, challenges price-barrier assumptions, clarifies greenwashing&#x2019;s stage-specific influence, and empirically validates a cascading spillover mechanism. These insights deepen scholarly understanding of sustainable consumption in emerging economies and provide actionable guidance for policymakers and marketers seeking to cultivate durable, value-driven green behavior in urban Malaysia.</p>
            </sec>
            <sec id="sec14">
                <title>5.2 Implications of the study</title>
                <p>Urban Malaysia&#x2019;s sustainable consumption landscape generates powerful implications across theory, practice, policy, society, and methodology. Theoretically, this study reshapes dominant behavioral models by showing that intention is only the mid-point of a broader behavioral cascade: personal attitudes, perceived control, environmental values, and economic evaluations, not social norms, drive intention, while green purchase behavior becomes the pivotal behavioral anchor that converts intention into switching and disposal actions, extending TPB, enriching VBN&#x2019;s moral-ecological lens, and empirically structuring spillover as a sequential mechanism rather than an incidental effect. Managerially, the results position first green purchases as the gateway to long-term loyalty and circularity, urging firms to lower economic and psychological entry barriers, design credible and transparent green claims, embed recycling and take-back infrastructures, and anticipate fluid switching as empowered consumers gravitate toward authentically green brands. For policymakers, the evidence underscores the strategic value of stimulating initial green purchases through fiscal incentives, eco-literacy campaigns, strict anti-greenwashing regulation, and investment in urban recycling systems that convert willingness into consistent disposal behavior. Societally, the confirmation of positive spillover, absent moral licensing, reveals an emerging culture of ecological consistency in Malaysia where early green actions reinforce identity, inspire community diffusion, and accelerate collective transitions toward circular consumption. Methodologically, the study advances sustainability research by demonstrating the value of PLS-SEM enriched with predictive relevance (Q
                    <sup>2</sup>), sequential mediation modelling, and boundary-condition testing of greenwashing, showing that rigorous structural modelling can both validate theoretical resilience and generate policy-relevant forecasts. Together, these implications offer a unified roadmap for scholars, regulators, and industry leaders seeking to convert individual green intentions into systemic, self-reinforcing sustainability transformations in urban Malaysia.</p>
            </sec>
            <sec id="sec15">
                <title>5.3 Limitations and recommendations</title>
                <p>Urban Malaysia&#x2019;s green consumption reveals nuanced limitations and transformative pathways for future research and practice. Methodologically, the cross-sectional design captures a snapshot of GPI, GPB, GSB, and GDB, yet cannot establish temporal causality or track spillover persistence, suggesting longitudinal approache to capture dynamic behavioral evolution. Geographically and demographically, data were concentrated in Kuala Lumpur, Selangor, and Penang, primarily among younger, educated adults, potentially inflating normative and cognitive effects; future studies should expand across diverse urban centers, socio-economic strata, and generational cohorts to fully capture Malaysia&#x2019;s heterogeneous urban consumer landscape. Conceptually, broad green product categories limit granularity, as motivations for food, electronics, or apparel differ, highlighting the need for category-specific analyses to inform precise managerial strategies. The novelty of the constructs, particularly behavioral spillover from GPB to GSB and GDB and the moderating role of greenwashing, introduces theoretical originality but limits triangulation and comparability, warranting replication to validate findings. Macroeconomic and infrastructural factors, such as regulatory incentives, and recycling systems were excluded, leaving systemic enablers of disposal and switching underexplored, while the greenwashing requires dynamic contextual reconceptualization alongside variables like environmental identity and habit strength. Firms should lower entry barriers, integrate verifiable eco-labels, employ dual moral-economic framing, and embed product take-back and circular design to convert intentions into lasting behaviors, while policymakers must enhance eco-literacy, fiscal incentives, anti-greenwashing enforcement, and urban recycling infrastructure to reinforce spillover effects. Academically, future research should extend TPB-VBN models with additional antecedents such as moral obligation, environmental identity, and product trustworthiness, explore multi-outcome spillover including reduction, repair, and collaborative consumption, and employ longitudinal-mixed methods (qualitative and quantitative) to dissect cultural and infrastructural moderators shaping behavioral pathways. Together, these insights provide a holistic roadmap for converting intention into systemic sustainability, bridging individual psychology, market strategy, and policy infrastructure, and offering a robust template for ASEAN and emerging economies confronting urban environmental challenges.</p>
            </sec>
        </sec>
        <sec id="sec16">
            <title>Ethics statement</title>
            <p>Ethical approval for this study was obtained from the Asia Pacific University Research Ethics Committee (APU REC) (Reference No: APU/FLT/EA/2026/006). The study was conducted in accordance with institutional and international ethical guidelines for research involving human participants. Participation in the study was voluntary, and all respondents were informed about the purpose of the research, the anonymity and confidentiality of their responses, and their right to withdraw at any time without consequences. Only individuals aged 18&#x00a0;years and above were eligible to participate, and informed consent was obtained electronically prior to completion of the questionnaire.</p>
        </sec>
    </body>
    <back>
        <sec id="sec19" sec-type="data-availability">
            <title>Data availability</title>
            <p>The dataset supporting the findings of this study is openly available in the Figshare repository at: 
                <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.31083940">https://doi.org/10.6084/m9.figshare.31083940</ext-link>
            </p>
            <p>El Mouden, A. E. B. E., Ahmad, R. B., Dada, M., Manickam, G. A. L., &amp; Ohajionu-Addae, U. C. (2026). 
                <italic toggle="yes">Dataset for: Does behavioural spillover occur in urban sustainable consumption? Evidence from switching and disposal practices in Malaysia.</italic> Figshare. 
                <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.31083940">https://doi.org/10.6084/m9.figshare.31083940</ext-link> (
                <xref ref-type="bibr" rid="ref29">Dada, Morakinyo et al., 2026</xref>).</p>
            <p>The repository contains the survey dataset and variable descriptions used to conduct the statistical analyses reported in this article. The dataset is shared under the 
                <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC-BY 4.0) licence</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>
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    <sub-article article-type="reviewer-report" id="report492343">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.195455.r492343</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Nguyen</surname>
                        <given-names>Khanh Huy</given-names>
                    </name>
                    <xref ref-type="aff" rid="r492343a1">1</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0009-0007-8525-5983</uri>
                </contrib>
                <aff id="r492343a1">
                    <label>1</label>University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>30</day>
                <month>6</month>
                <year>2026</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2026 Nguyen KH</copyright-statement>
                <copyright-year>2026</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport492343" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.177261.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>The manuscript examines whether green purchase intention translates into green purchase behaviour and subsequently into green switching and green disposal behaviours among urban consumers in Malaysia. The topic is timely, especially in relation to sustainable consumption, greenwashing, and behavioural spillover. The integration of TPB, VBN, and spillover theory is potentially valuable, and the authors provide an openly available dataset, which is a strength.</p>
            <p> However, the manuscript requires major revision. The strongest problem is that the study uses a cross-sectional self-report survey but repeatedly claims to demonstrate a behavioural chain, spillover, and causal progression from intention to purchase, then to switching and disposal. These claims are not supported by the design. In addition, the sample is heavily skewed toward young students, the measurement model shows discriminant validity problems, the PLS-SEM model-fit interpretation is questionable, and several theoretical interpretations go beyond the statistical evidence.&#x00a0;</p>
            <p> </p>
            <p> Major comments&#x00a0;</p>
            <p> 1. The claim of &#x201c;behavioural spillover&#x201d; is too strong for a cross-sectional design&#x00a0;</p>
            <p> The manuscript argues that green purchase behaviour &#x201c;spills over&#x201d; into green switching and green disposal behaviour. However, all variables appear to have been measured at one time point using the same questionnaire. This design cannot establish temporal ordering or behavioural carry-over.</p>
            <p> </p>
            <p> The authors should either:</p>
            <p> - Reframe the findings as&#x00a0;associations consistent with a spillover interpretation, or</p>
            <p> - Conduct a longitudinal, experimental, or behavioural-tracking study that can genuinely test spillover over time.</p>
            <p> </p>
            <p> At present, claims such as &#x201c;sequentially spills over,&#x201d; &#x201c;cumulative behavioural chain,&#x201d; &#x201c;triggers switching and disposal,&#x201d; and &#x201c;behavioural spillover materializes&#x201d; are overstated.</p>
            <p> </p>
            <p> 2. The sample does not represent &#x201c;urban Malaysian consumers&#x201d;</p>
            <p> The manuscript describes the sample as &#x201c;urban consumers in Malaysia,&#x201d; but Table 1 shows that&#x00a0;87.8% are aged 18-25,&#x00a0;83.3% are students, and&#x00a0;68.4% earn RM1000 or below. Nearly&#x00a0;30% are non-Malaysian. This is closer to a student-dominant urban convenience sample than a general urban consumer sample.</p>
            <p> The authors should revise the title, abstract, and discussion to avoid overgeneralising to urban Malaysia. A more accurate framing would be: &#x201c;young urban consumers&#x201d; or &#x201c;student-dominant urban consumers in Malaysia.&#x201d; The authors should also report recruitment location, recruitment channel, sampling method, data collection period, response rate, and city-level distribution.</p>
            <p> </p>
            <p> 3. The theory integration is underdeveloped</p>
            <p> The manuscript claims to integrate TPB, VBN, and Spillover Theory. In practice, the model is mainly an extended TPB model with environmental concern and price sensitivity added. VBN theory is not fully operationalised because the study does not measure core VBN components such as values, awareness of consequences, ascription of responsibility, or personal norms.</p>
            <p> The authors should clarify whether VBN is genuinely tested or only used as background theory. If VBN is retained as a theoretical foundation, the model should include VBN-specific constructs. Otherwise, the manuscript should be reframed as an extended TPB model with spillover-related downstream behaviours.</p>
            <p> </p>
            <p> </p>
            <p> 4. Hypotheses need to be explicitly stated</p>
            <p> The literature review provides descriptions of constructs, but the hypotheses are not clearly stated in a formal way before the results. Readers should not need to infer H1-H12 from the figure and tables.</p>
            <p> The authors should add a clear list of hypotheses, for example:</p>
            <p> &#x201c;H1:&#x00a0;Attitude positively influences green purchase intention.&#x201d;</p>
            <p> This is particularly important for H7a-H7e, H8, H11, and H12 because the mediation and moderation logic is currently difficult to follow.</p>
            <p> </p>
            <p> 5. Measurement reporting is insufficient</p>
            <p> The manuscript does not provide enough information about the measurement items. It reports loadings, reliability, AVE, and VIF, but it does not clearly state:</p>
            <p> -&#x00a0;The exact item wording.</p>
            <p> - The source of each scale.</p>
            <p> - Whether items were adapted.</p>
            <p> - The response scale used.</p>
            <p> - Whether the questionnaire was pretested for content validity.</p>
            <p> - Why some items appear to be removed, for example SN2 and PS2 are missing from Table 2.</p>
            <p> The authors should include a measurement-item table, preferably in an appendix, showing item wording, sources, scale anchors, deleted items, and justification for deletion.</p>
            <p> </p>
            <p> 6. Discriminant validity is problematic</p>
            <p> Table 3 reports HTMT values above the conventional 0.90 threshold, specifically GPI with Environmental Concern = 0.904 and GPI with PBC = 0.924. The manuscript states that these values are acceptable because they are below 1.00. This is not a convincing interpretation.</p>
            <p> Values above 0.90 suggest potential discriminant validity problems. The authors should not describe the measurement model as &#x201c;exceptionally stable&#x201d; or &#x201c;empirically precise.&#x201d; They should address the issue by reporting HTMT confidence intervals, checking item cross-loadings, reviewing conceptual overlap, and considering whether constructs are empirically distinguishable.</p>
            <p> </p>
            <p> 7. Model fit is misinterpreted</p>
            <p> Table 6 reports an estimated model SRMR of&#x00a0;0.121&#x00a0;and NFI of&#x00a0;0.683. These values do not clearly support the claim that the model fit is acceptable. SRMR above 0.10 is commonly treated as problematic, and NFI below 0.90 is weak.</p>
            <p> The authors should revise the interpretation of model fit and avoid claiming strong model fit. They should also clarify whether they are reporting saturated or estimated model indices and explain why the estimated model SRMR is substantially worse than the saturated model SRMR.</p>
            <p> </p>
            <p> 8. The use of price sensitivity is theoretically inconsistent</p>
            <p> The literature review describes price sensitivity as a barrier to green purchase intention, but the results show a positive relationship between price sensitivity and GPI. The discussion then interprets this as evidence that consumers view green products as value-aligned or cost-effective.</p>
            <p> This interpretation is not justified unless the item wording measures something like perceived value or willingness to pay rather than price sensitivity. The authors need to clarify the operational definition of price sensitivity. If high scores mean greater sensitivity to price increases, the expected effect should logically be negative. If high scores mean price-value consciousness, the construct should be renamed.</p>
            <p> </p>
            <p> 9. Moderation by greenwashing is not adequately tested or interpreted</p>
            <p> The non-significant greenwashing interaction is interpreted as evidence that consumers are resilient against deceptive marketing. This conclusion is too strong. A non-significant moderation effect may reflect low statistical power, poor measurement, restricted variance, or model misspecification.</p>
            <p> The authors should report the main effect of greenwashing, the interaction method used in SmartPLS, whether variables were mean-centred or standardised, confidence intervals, and simple slope plots if relevant. They should avoid interpreting non-significance as psychological resilience.</p>
            <p> </p>
            <p> 10. Mediation analysis is incomplete</p>
            <p> The mediation results only report t-values and p-values. The authors should report standardised indirect effects, bootstrapped confidence intervals, total effects, direct effects, and variance accounted for where appropriate. Claims of &#x201c;full mediation&#x201d; require evidence that direct paths were tested and became non-significant when the mediator was included.</p>
            <p> For H11 and H12, the authors should clarify whether these are sequential mediation hypotheses or direct spillover hypotheses. The figure and table should be aligned.</p>
            <p> </p>
            <p> 11. Common method bias is not addressed</p>
            <p> All variables appear to be collected from the same respondents using the same instrument at the same time. This creates a high risk of common method bias, especially because many constructs are attitudinal and conceptually close.</p>
            <p> The authors should report procedural remedies and statistical checks, such as a marker variable, full collinearity VIF, Harman&#x2019;s single-factor test, or a common latent factor approach. This is especially important given the very high path coefficient from GPB to GSB and the high HTMT values among several constructs.</p>
            <p> </p>
            <p> </p>
            <p> 12. Table 1 contains numerical errors</p>
            <p> Some demographic percentages in Table 1 appear incorrect. For example:</p>
            <p> -&#x00a0;Part-time employee: 15 out of 263 is approximately&#x00a0;5.7%, not 3.7%.</p>
            <p> - Diploma: 63 out of 263 is approximately&#x00a0;24.0%, not 39.5%.</p>
            <p> - Bachelor&#x2019;s degree: 104 out of 263 is approximately&#x00a0;39.5%, not 63.4%.</p>
            <p> The authors must recalculate all percentages in Table 1 and check whether labels or values have been misplaced.</p>
            <p> </p>
            <p> </p>
            <p> Minor comments 
                <list list-type="order">
                    <list-item>
                        <p>The manuscript mixes British and American spelling, such as &#x201c;behavioural&#x201d; and &#x201c;behavioral.&#x201d; The authors should choose one style and apply it consistently.</p>
                    </list-item>
                    <list-item>
                        <p>The abstract is too promotional. Phrases such as &#x201c;consumers&#x2019; resilience,&#x201d; &#x201c;cumulative behavioural chain,&#x201d; and &#x201c;among the first&#x201d; should be softened unless strongly supported.</p>
                    </list-item>
                    <list-item>
                        <p>The literature review is overly long and descriptive. It should be more analytical and focused on the specific logic behind each hypothesis.</p>
                    </list-item>
                    <list-item>
                        <p>Several references are not ideal for a scholarly article, including sources such as Investopedia, Scribbr, Helpful Professor, ResearchGate pages, and media articles. These should be replaced with peer-reviewed academic sources where possible.</p>
                    </list-item>
                    <list-item>
                        <p>The manuscript relies heavily on recent 2024-2025 sources, but some foundational literature on behavioural spillover, intention-behaviour gap, greenwashing, and green consumer behaviour should be more deeply engaged.</p>
                    </list-item>
                    <list-item>
                        <p>The implications section is too broad and sometimes repeats the discussion. The authors should separate theoretical, managerial, and policy implications more clearly.</p>
                    </list-item>
                    <list-item>
                        <p>The limitations section is useful but should more directly acknowledge the most serious limitation: the study cannot establish temporal spillover.</p>
                    </list-item>
                    <list-item>
                        <p>The phrase &#x201c;longitudinal approache&#x201d; should be corrected to &#x201c;longitudinal approaches.&#x201d;</p>
                    </list-item>
                    <list-item>
                        <p>The authors should use &#x201c;p &lt; .001&#x201d; instead of &#x201c;p = 0.000.&#x201d;</p>
                    </list-item>
                    <list-item>
                        <p>The manuscript would benefit from professional copyediting. There are several spacing, formatting, and style issues, especially in the introduction and references.</p>
                    </list-item>
                </list>
            </p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Yes</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Yes</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Yes</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Yes</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>Marketing, Psychology</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p>
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    </sub-article>
</article>
