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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Psychol.</journal-id>
<journal-title>Frontiers in Psychology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Psychol.</abbrev-journal-title>
<issn pub-type="epub">1664-1078</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fpsyg.2022.1093999</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Psychology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>The effect of COVID-19 risk perception on pro-environmental behavior of Chinese consumers: Perspectives from affective event theory</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Zhou</surname> <given-names>Shuai</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1639564/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>School of Economics and Management, Zhoukou Normal University</institution>, <addr-line>Zhoukou</addr-line>, <country>Henan, China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Asia-Europe Institute, University of Malaya</institution>, <addr-line>Kuala Lumpur</addr-line>, <country>Malaysia</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Jia Wei, Xi&#x2019;an Jiaotong University, China</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Mohammed Salem, University College of Applied Sciences, Palestine; Asif Khan, National Kaohsiung University of Science and Technology, Taiwan</p></fn>
<corresp id="c001">&#x002A;Correspondence: Shuai Zhou, <email>s2040929@siswa.um.edu.my</email></corresp>
<fn fn-type="other" id="fn004"><p>This article was submitted to Environmental Psychology, a section of the journal Frontiers in Psychology</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>04</day>
<month>01</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>13</volume>
<elocation-id>1093999</elocation-id>
<history>
<date date-type="received">
<day>09</day>
<month>11</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>05</day>
<month>12</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2023 Zhou.</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Zhou</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<sec>
<title>Introduction</title>
<p>COVID-19 has altered human cognition and changed the cultural values of society. However, there has not been much debate among scholars about whether these above changes have led to an increase in pro-environmental behavior (PEB) of Chinese consumers.</p>
</sec>
<sec>
<title>Methods</title>
<p>A comprehensive model was developed based on affective event theory. An online questionnaire was distributed, and 501 usable questionnaires were collected. In addition, two complementary approaches were employed: partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA).</p>
</sec>
<sec>
<title>Results</title>
<p>The PLS-SEM results showed that COVID-19 risk perception, nostalgia, the awe of nature, and attitude were found to have a positive effect on PEB; and the moderating effect of power distance belief (PD) between nostalgia (NO), attitude (AT) and PEB was confirmed. According to the NCA results, AT and NO are necessary conditions for the PEB of consumers.</p>
</sec>
<sec>
<title>Discussion</title>
<p>This study provides deeper insight into the understanding of consumers&#x2019; pro-environmental behavior in the context of COVID-19 through the combined use of PLS-SEM and NCA.</p>
</sec>
</abstract>
<kwd-group>
<kwd>pro-environmental behavior</kwd>
<kwd>necessary condition analysis</kwd>
<kwd>power distance</kwd>
<kwd>nostalgia</kwd>
<kwd>awe of nature</kwd>
<kwd>COVID-19 risk perception</kwd>
</kwd-group>
<counts>
<fig-count count="7"/>
<table-count count="7"/>
<equation-count count="1"/>
<ref-count count="166"/>
<page-count count="22"/>
<word-count count="13499"/>
</counts>
</article-meta>
</front>
<body>
<sec id="S1" sec-type="intro">
<title>1 Introduction</title>
<p>Urbanization and industrialization have had many negative impacts on ecosystems and human health, such as global warming, air pollution, urban waste, and loss of biodiversity, primarily due to the imbalance between human social development and ecological governance (<xref ref-type="bibr" rid="B105">Rosenmann et al., 2016</xref>; <xref ref-type="bibr" rid="B4">Alzubaidi et al., 2021</xref>). As a result of this unsustainable development mode combined with the destruction of the environment, a plague pandemic was eventually released that threatened public health as well as environmental safety around the globe (<xref ref-type="bibr" rid="B23">Daryanto et al., 2022</xref>; <xref ref-type="bibr" rid="B159">Zebardast and Radaei, 2022</xref>). Some scholars believe that COVID-19 is the revenge of nature due to the overacquisition of nature and the consumption of wild animals by humans (<xref ref-type="bibr" rid="B11">Cave and Dredge, 2020</xref>; <xref ref-type="bibr" rid="B73">Li et al., 2020</xref>; <xref ref-type="bibr" rid="B78">Liu et al., 2021</xref>). Global economies and societies have been impacted greatly by the COVID-19 crisis (<xref ref-type="bibr" rid="B111">Severo et al., 2021a</xref>; <xref ref-type="bibr" rid="B85">Milfont et al., 2022a</xref>). For example, in China, a widespread lockdown of cities, restrictions on public transportation and personal movement have been imposed by the Chinese government to prevent the spread of COVID-19 (<xref ref-type="bibr" rid="B16">Chen et al., 2020</xref>). Nevertheless, COVID-19 is also a turning point in promoting public awareness of the relationship between climate change, health, and sustainable living, as well as accelerating sustainable consumption (<xref ref-type="bibr" rid="B101">Ramkissoon, 2020</xref>; <xref ref-type="bibr" rid="B133">Tchetchik et al., 2021</xref>; <xref ref-type="bibr" rid="B86">Milfont et al., 2022b</xref>). Actually, a number of changes have taken place in the way consumers live and think due to the outbreak of COVID-19 (<xref ref-type="bibr" rid="B8">B&#x00FC;ssing et al., 2021</xref>; <xref ref-type="bibr" rid="B128">Sun et al., 2022</xref>; <xref ref-type="bibr" rid="B154">Yang et al., 2022</xref>). As environmental degradation continues to worsen, an increasing number of consumers are becoming aware that individual behavior has a substantial impact on environmental problems (<xref ref-type="bibr" rid="B109">Schwartz et al., 2020</xref>; <xref ref-type="bibr" rid="B87">Moon et al., 2021</xref>). In addition, it is becoming increasingly common for people to reflect on and re-evaluate their relationship with the environment (<xref ref-type="bibr" rid="B113">Shakil et al., 2020</xref>; <xref ref-type="bibr" rid="B84">Mi et al., 2021</xref>). Therefore, it is crucial to explore the key factors influencing consumers&#x2019; pro-environmental behavior in the context of COVID-19.</p>
<p>Over the last decade, scholars have become increasingly interested in consumers&#x2019; pro-environmental behavior (<xref ref-type="fig" rid="F1">Figure 1</xref>). A large and growing body of literature has investigated the antecedents of pro-environmental behavior, such as attitude (<xref ref-type="bibr" rid="B4">Alzubaidi et al., 2021</xref>; <xref ref-type="bibr" rid="B128">Sun et al., 2022</xref>), personal motivation (<xref ref-type="bibr" rid="B154">Yang et al., 2022</xref>), environmental knowledge and awareness (<xref ref-type="bibr" rid="B15">Chen et al., 2022</xref>), social and personal norms (<xref ref-type="bibr" rid="B92">Pearce et al., 2022</xref>), personality and environmental values (<xref ref-type="bibr" rid="B120">Simpson et al., 2021</xref>), self-efficacy and self-identification (<xref ref-type="bibr" rid="B57">Huang et al., 2022</xref>; <xref ref-type="bibr" rid="B114">Sharma et al., 2022b</xref>). Although scholars have provided explanations and insights based on a variety of theories and perspectives, some areas of research remain to be explored. First, a majority of previous studies regarding pro-environmental behavior have concentrated on individual psychological factors; however, these psychological factors may not be as important in predicting actual pro-environmental behavior as previously thought (<xref ref-type="bibr" rid="B89">Nielsen et al., 2022</xref>). Therefore, to promote pro-environmental behavior, it may be necessary to look beyond individual motivations to uncover some unexplained differences in attitude-behavior models by gaining a deeper understanding of how external factors influence behavior (<xref ref-type="bibr" rid="B130">Sun et al., 2019</xref>; <xref ref-type="bibr" rid="B77">Linder et al., 2021</xref>), for example, COVID-19.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption><p>Interest in pro-environmental behavior research over the past decade.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyg-13-1093999-g001.tif"/>
</fig>
<p>Second, there has been a surge of interest in the impact of COVID-19 on pro-environmental behavior recently; however, the relationship between COVID-19 risk perception and pro-environmental behavior is not clear, and the results of some previous studies have been contradictory (<xref ref-type="bibr" rid="B84">Mi et al., 2021</xref>; <xref ref-type="bibr" rid="B139">Urban and Braun Kohlova, 2022</xref>). The COVID-19 pandemic presents a significant threat to humanity and raises issues about the ecological ethics of humans (<xref ref-type="bibr" rid="B159">Zebardast and Radaei, 2022</xref>). During the postpandemic era, people live in the shadow of the pandemic and continue to experience the various negative effects of the pandemic. The social cognition and behavior of individuals may be significantly altered by major external environmental events such as COVID-19 (<xref ref-type="bibr" rid="B13">Chen, 2020b</xref>; <xref ref-type="bibr" rid="B159">Zebardast and Radaei, 2022</xref>), and it has been found that people&#x2019;s behavior varies considerably in different contexts (<xref ref-type="bibr" rid="B149">Wu et al., 2021</xref>). Therefore, it is necessary to further understand the relationship between the cognitive response to COVID-19 (i.e., COVID-19 risk perception) and consumer pro-environmental behavior from a new theoretical perspective.</p>
<p>Third, there is a relatively small body of literature that is concerned with the relationship between COVID-19 risk perception and consumer sentiment, such as nostalgia, environmental guilt, and awe of nature (<xref ref-type="bibr" rid="B84">Mi et al., 2021</xref>; <xref ref-type="bibr" rid="B126">Sun et al., 2021a</xref>; <xref ref-type="bibr" rid="B65">Kim et al., 2022</xref>). Individuals will not always act rationally (<xref ref-type="bibr" rid="B67">Koenig-Lewis et al., 2014</xref>; <xref ref-type="bibr" rid="B128">Sun et al., 2022</xref>); as a result, pro-environmental behavior is not always the result of a reasonable thought process or response to the environment (<xref ref-type="bibr" rid="B36">Gezhi and Xiang, 2022</xref>). Irrational factors such as emotions are ignored, which are to some extent more important than rational cognitive factors (<xref ref-type="bibr" rid="B67">Koenig-Lewis et al., 2014</xref>; <xref ref-type="bibr" rid="B127">Sun et al., 2021b</xref>). According to affective event theory, significant events in work scenarios can have an impact on employees&#x2019; emotions, and the positive and negative emotions generated by employees can have a significant impact on individuals&#x2019; attitudes and behaviors (<xref ref-type="bibr" rid="B145">Weiss and Cropanzano, 1996</xref>). Therefore, consumers&#x2019; behavior patterns may change when they are in the pandemic environment of COVID-19, considered a major external event that consumers have to contend with (<xref ref-type="bibr" rid="B84">Mi et al., 2021</xref>). As an important external factor, will people&#x2019;s experiences and perceptions of the risks caused by COVID-19 prompt a profound reflection on existing eco-ethical issues such as the relationship between humans and nature, as well as a further improvement in the practice of pro-environmental behavior? Surprisingly, there has been insufficient attention given to this issue by researchers (<xref ref-type="bibr" rid="B18">Chi, 2021</xref>; <xref ref-type="bibr" rid="B139">Urban and Braun Kohlova, 2022</xref>).</p>
<p>To fill the aforementioned research gaps, three research questions were focused on. <bold>RQ1</bold>. What is the effect of COVID-19 risk perception on consumers&#x2019; emotions (i.e., nostalgia, guilt, and awe) and on attitude and pro-environmental behavior? <bold>RQ2.</bold> How does power distance belief (PD) moderate the relationships between emotions (i.e., nostalgia, awe), attitude, and pro-environmental behavior? <bold>RQ3.</bold> What is the necessary factor of the pro-environmental behavior of consumers in the context of the COVID-19 pandemic? To solve these research questions and obtain a more comprehensive understanding of the causal relationship between the variables, this study uses two complementary research methods to explore the mechanisms influencing consumer pro-environmental behavior from the dual perspective of adequacy (PLS-SEM) and necessity (NCA). In addition, a rigorous model was constructed based on affective event theory. Affective event theory, derived from organizational behavior, focuses on the emotional impact of work events on employees, whose positive and negative emotions, in turn, influence their attitudes and behaviors. Analogous to employees&#x2019; work events, we treat COVID-19 as a major external shock faced by consumers and explore the cognitive and emotional responses to external environmental events and how these responses affect consumers&#x2019; attitudes and behaviors. This study contributes to existing knowledge in terms of developing pro-environmental behavior research within the context of affective event theory and in light of the perception of risk associated with COVID-19. In particular, we examine how consumers&#x2019; cognitive response to external event shocks (COVID-19 risk perception) affects three consumer emotions (guilt, awe of nature, and nostalgia), which influence their attitudes toward environmental protection and pro-environmental behaviors. In addition, we further narrowed the attitude-behavior gap in the current pro-environmental behavior study by adding a cultural contextual factor (power distance).</p>
</sec>
<sec id="S2">
<title>2 Literature review and hypothesis</title>
<sec id="S2.SS1">
<title>2.1 Pro-environmental behavior</title>
<p>Any behavior that benefits the environment or that strives to harm the environment as little as possible without causing significant harm to the environment is considered pro-environmental behavior (<xref ref-type="bibr" rid="B68">Kollmuss and Agyeman, 2002</xref>). In summary, a wide range of factors have been examined in the past to explore the factors influencing pro-environmental behavior, including sociodemographic characteristics, rational cognitive factors, and external situational factors. Scholars have focused on demographics in terms of gender (<xref ref-type="bibr" rid="B122">Sreen et al., 2018</xref>; <xref ref-type="bibr" rid="B74">Li et al., 2019</xref>), age (<xref ref-type="bibr" rid="B147">Wiernik et al., 2013</xref>), educational level (<xref ref-type="bibr" rid="B38">Gifford and Nilsson, 2014</xref>; <xref ref-type="bibr" rid="B82">Meyer, 2015</xref>), and income level (<xref ref-type="bibr" rid="B82">Meyer, 2015</xref>; <xref ref-type="bibr" rid="B75">Lim and Moon, 2022</xref>). In regard to rational cognitive factors, attitudes, norms, motivation, beliefs, and values play an important role (<xref ref-type="bibr" rid="B96">Poortinga et al., 2004</xref>; <xref ref-type="bibr" rid="B39">Gilg et al., 2005</xref>; <xref ref-type="bibr" rid="B95">Polonsky et al., 2012</xref>; <xref ref-type="bibr" rid="B10">Casal&#x00F3; et al., 2019</xref>; <xref ref-type="bibr" rid="B99">Punzo et al., 2019</xref>; <xref ref-type="bibr" rid="B14">Chen, 2020a</xref>; <xref ref-type="bibr" rid="B40">Gu et al., 2020</xref>), and the majority of these studies are based on the Theory of Planned Behavior (TPB), the Normative Activation Model (NAM), and the Value-Belief-Normative Theory (VBN). Additionally, external situational factors include infrastructure, individual capabilities, availability of technical equipment and products, governmental enforcement, public media, culture of society, and economic conditions (<xref ref-type="bibr" rid="B132">Tanner, 1999</xref>; <xref ref-type="bibr" rid="B22">Corraliza and Berenguer, 2000</xref>; <xref ref-type="bibr" rid="B42">Haddad, 2015</xref>; <xref ref-type="bibr" rid="B129">Sun et al., 2018</xref>; <xref ref-type="bibr" rid="B116">Shi et al., 2019</xref>; <xref ref-type="bibr" rid="B165">Zhou et al., 2019</xref>; <xref ref-type="bibr" rid="B48">He and Filimonau, 2020</xref>; <xref ref-type="bibr" rid="B61">Kaiser, 2021</xref>).</p>
</sec>
<sec id="S2.SS2">
<title>2.2 COVID-19 risk perception</title>
<p>Risk perception is considered a value judgment related to the uncertainty caused by a particular risk (<xref ref-type="bibr" rid="B137">Tversky and Fox, 1995</xref>) and can be understood from both cognitive and affective perspectives. People&#x2019;s perceptions of COVID-19 risk are influenced by the danger it poses to their lives and health, which in turn influences their coping strategies and behavioral changes (<xref ref-type="bibr" rid="B119">Shulman et al., 2022</xref>). In addition to having a significant impact on the normal functioning of society, COVID-19 has also had profound effects on human social cognition and emotions (<xref ref-type="bibr" rid="B127">Sun et al., 2021b</xref>; <xref ref-type="bibr" rid="B70">Lawrance et al., 2022</xref>).</p>
<p>COVID-19 has had a significant impact on the economy and lives of humans. The widespread lockdown triggered feelings of boredom and loneliness (<xref ref-type="bibr" rid="B166">Zhou et al., 2022</xref>), making people nostalgic for the past (<xref ref-type="bibr" rid="B37">Gibbs and Egermann, 2021</xref>; <xref ref-type="bibr" rid="B151">Xia et al., 2021</xref>). A sense of awe is associated with perceived grandeur, which means that the individual has encountered something shocking. Second, the feeling of awe triggers a need for adaptation, i.e., the individual&#x2019;s existing mental structure is insufficient to comprehend the awe-inspiring object (<xref ref-type="bibr" rid="B64">Keltner and Haidt, 2003</xref>). Additionally, widespread social blockades and restrictions on social distance have altered the way people live and consume (<xref ref-type="bibr" rid="B140">V&#x00E1;zquez-Mart&#x00ED;nez et al., 2021</xref>), which is consistent with the two core characteristics of awe-inspiring emotions. In addition, human arrogance and excessive claims of nature have led to an imbalance in the relationship between humans and nature, which has resulted in the COVID-19 pandemic. Consumers will be upset when they see the destruction of the ecological environment and serious social consequences, believe that humans are responsible for it, and feel guilty for not taking action to improve the environment (<xref ref-type="bibr" rid="B70">Lawrance et al., 2022</xref>). To alleviate this sense of guilt, individuals may engage in compensatory behaviors, such as green purchasing and pro-environmental behavior.</p>
<p>Previous studies have indicated that information and sensitivity about global crises such as COVID-19 positively impact individuals&#x2019; knowledge, attitudes, perceived behavioral control, and motivation and provide a basis for encouraging environmentally responsible behavior (<xref ref-type="bibr" rid="B128">Sun et al., 2022</xref>; <xref ref-type="bibr" rid="B159">Zebardast and Radaei, 2022</xref>). In addition, according to protective motivation theory, consumers&#x2019; perception of COVID-19 risk makes individuals vulnerable and feel that their lives and health are threatened; this state, in turn, shifts an individual&#x2019;s focus from materialism to pro-social values (<xref ref-type="bibr" rid="B119">Shulman et al., 2022</xref>). Consumers may be inclined to minimize social contact due to the perceived risk of COVID-19 (<xref ref-type="bibr" rid="B59">Im et al., 2021</xref>), which may result in a preference and a greater sense of connection with the natural environment. It is possible that individuals may be more inclined to act in an environmentally friendly manner as a result of this perceived connection (<xref ref-type="bibr" rid="B131">Tam, 2013</xref>). It has also been shown that perceptions of COVID-19 risk and vulnerability stimulate empathy in individuals, and the emotion of empathy is an important psychological factor in motivating pro-environmental behavior (<xref ref-type="bibr" rid="B157">Yin et al., 2021</xref>; <xref ref-type="bibr" rid="B58">Ienna et al., 2022</xref>). Therefore, it is hypothesized that:</p>
<disp-quote>
<p><bold>H1a:</bold> COVID-19 risk perception has a positive and significant effect on nostalgia.</p>
</disp-quote>
<disp-quote>
<p><bold>H1b:</bold> COVID-19 risk perception has a positive and significant effect on guilt.</p>
</disp-quote>
<disp-quote>
<p><bold>H1c:</bold> COVID-19 risk perception has a positive and significant effect on the awe of nature.</p>
</disp-quote>
<disp-quote>
<p><bold>H1c:</bold> COVID-19 risk perception has a positive and significant effect on pro-environmental behavior.</p>
</disp-quote>
</sec>
<sec id="S2.SS3">
<title>2.3 Nostalgia, guilt, and awe of nature</title>
<p>The emotion of nostalgia serves key psychological functions as a positive, self-relevant, and social emotion (<xref ref-type="bibr" rid="B110">Sedikides et al., 2008</xref>). Nostalgia is a psychological variable that stimulates emotions and cognition; it can trigger positive actions or behavioral tendencies; and it can enhance social connection, facilitate social interaction, and serve as a catalyst for social interaction (<xref ref-type="bibr" rid="B123">Srivastava et al., 2022</xref>). The experience of nostalgia is often associated with positive emotions, allowing people to mentally escape the fast pace of modern life and return to an earlier time for solace. Temporary escapes provide psychological buffers and promote positive emotional and social relationships. In addition to providing a sense of presence, it enhances the sense that one is living a meaningful life (<xref ref-type="bibr" rid="B161">Zhang et al., 2021</xref>) and helps increase social connectedness and promote pro-social behavior (<xref ref-type="bibr" rid="B20">Christou et al., 2018</xref>). In addition to expressing the inner self through nostalgia, consumers become concerned about the welfare of others and develop greater empathy, while focusing on the inner self predicts more altruistic behaviors, including pro-environmental behaviors (<xref ref-type="bibr" rid="B88">Newman et al., 2014</xref>). However, few studies have explored the role of nostalgia in consumer pro-environmental behavior. <xref ref-type="bibr" rid="B161">Zhang et al. (2021)</xref> found that the emotion of nostalgia creates a sense of meaning in life, which in turn encourages consumers to invest in more sustainable recycling practices. Similarly, <xref ref-type="bibr" rid="B144">Wang et al. (2020)</xref> found that consumers&#x2019; product disposal behavior is affected differently by individual and collective nostalgia because they trigger different mechanisms.</p>
<p>Typically, guilt is a negative experience that occurs when an individual engages in moral reflection and takes responsibility for his or her actions after having hurt others, which plays an important role in prosocial behavior (<xref ref-type="bibr" rid="B30">Eisenberg et al., 2005</xref>; <xref ref-type="bibr" rid="B134">Teper et al., 2015</xref>). Compensatory ethics theory suggests that when an individual commits an unethical act, he or she will experience a strong sense of discomfort, which is caused by guilt (<xref ref-type="bibr" rid="B164">Zhong et al., 2010</xref>). The emotion of guilt plays an important role in influencing the attitude and behavior of the pro-environment (<xref ref-type="bibr" rid="B32">Escadas et al., 2019</xref>), and researchers have also explored how guilt affects individuals&#x2019; pro-environmental behavior as a type of pro-social behavior. <xref ref-type="bibr" rid="B6">Bamberg and M&#x00F6;ser (2007)</xref> concluded, from a meta-analysis of 57 articles, that the feeling of guilt was one of the eight core variables that influence pro-environmental behavior. It is important to note that consumers&#x2019; feelings of guilt indicate that they have not achieved behavioral goals (e.g., environmentally friendly behaviors), which can drive motivation and change in attitudes toward environmentally friendly behaviors (<xref ref-type="bibr" rid="B1">Adams et al., 2020</xref>). It has also been shown in recent research that guilt plays a vital role in the change of attitudes toward environmental protection and the promotion of environmental behavior (<xref ref-type="bibr" rid="B1">Adams et al., 2020</xref>; <xref ref-type="bibr" rid="B31">Eom et al., 2021</xref>; <xref ref-type="bibr" rid="B118">Shipley and van Riper, 2021</xref>; <xref ref-type="bibr" rid="B47">Haj-Salem et al., 2022</xref>).</p>
<p>Awe is an emotional reaction to a stimulus that is perceived as powerful, vast, and beyond an individual&#x2019;s cognitive abilities (<xref ref-type="bibr" rid="B64">Keltner and Haidt, 2003</xref>). This study defines awe as a self-transcending positive emotion that allows individuals to pay more attention to their surroundings than to themselves (<xref ref-type="bibr" rid="B124">Stellar et al., 2017</xref>; <xref ref-type="bibr" rid="B163">Zhao et al., 2018</xref>). There is evidence that awe leads to a number of positive effects on an individual. From a cognitive perspective, awe not only expands an individual&#x2019;s perception of time&#x2019;s abundance (<xref ref-type="bibr" rid="B106">Rudd et al., 2012</xref>) but can also lead to systematic cognitive processes. As a positive emotion of self-transcendence, awe enables individuals to extend their self-concept to pay more attention to their surroundings and outside world (<xref ref-type="bibr" rid="B93">Piff et al., 2015</xref>; <xref ref-type="bibr" rid="B124">Stellar et al., 2017</xref>; <xref ref-type="bibr" rid="B163">Zhao et al., 2018</xref>), and an individual&#x2019;s pro-environmental behavior is stimulated when the natural environment is integrated into their self-concept (<xref ref-type="bibr" rid="B108">Schultz, 2001</xref>). By transforming the notion that humans are not above nature (<xref ref-type="bibr" rid="B142">Wang et al., 2019</xref>) but instead are part of the natural world, individuals feel that they are not isolated, resulting in an inescapable sense of responsibility for existing environmental consequences, which in turn promotes individual pro-environmental behavior (<xref ref-type="bibr" rid="B60">Jacobs and McConnell, 2022</xref>). Individuals with this mindset tend to be more conscious of the consequences of their actions, re-examine the relationship between humans and nature (<xref ref-type="bibr" rid="B142">Wang et al., 2019</xref>), and change their attitudes toward nature. From an emotional perspective, awe can also bring spiritual pleasure to individuals (<xref ref-type="bibr" rid="B106">Rudd et al., 2012</xref>), thereby enhancing their prosocial behavior (<xref ref-type="bibr" rid="B97">Prade and Saroglou, 2016</xref>). Therefore, it is hypothesized that:</p>
<disp-quote>
<p><bold>H2a:</bold> Nostalgia has a positive and significant effect on pro-environmental behavior.</p>
</disp-quote>
<disp-quote>
<p><bold>H2b:</bold> Nostalgia has a positive and significant effect on attitude.</p>
</disp-quote>
<disp-quote>
<p><bold>H3:</bold> Guilt has a positive and significant effect on attitude.</p>
</disp-quote>
<disp-quote>
<p><bold>H4a:</bold> Awe of nature has a positive and significant effect on attitude.</p>
</disp-quote>
<disp-quote>
<p><bold>H4b:</bold> Awe of nature has a positive and significant effect on pro-environmental behavior.</p>
</disp-quote>
</sec>
<sec id="S2.SS4">
<title>2.4 Attitude</title>
<p>A person&#x2019;s attitude toward the environment has a significant impact on his or her behavior and intentions in relation to the environment (<xref ref-type="bibr" rid="B17">Cheung and To, 2019</xref>). Despite the fact that scholars acknowledge the discrepancy between consumers&#x2019; attitudes and actual behavior (<xref ref-type="bibr" rid="B153">Yadav et al., 2019</xref>), environmental attitude is still considered to be one of the best predictors of actual environmental behavior (<xref ref-type="bibr" rid="B53">Herbes et al., 2020</xref>; <xref ref-type="bibr" rid="B33">Felix et al., 2022</xref>). According to the theory of planned behavior (<xref ref-type="bibr" rid="B3">Ajzen, 1991</xref>), an individual&#x2019;s behavior can be effectively controlled by his or her inner subjective consciousness and is a rational decision. An individual&#x2019;s behavioral attitudes influence their behavioral intentions, which in turn influence their actual behavior. In terms of cognitive dissonance theory, people tend to act on their beliefs to avoid mental discomfort (<xref ref-type="bibr" rid="B34">Festinger, 1962</xref>). It appears that people with a higher level of environmental awareness demonstrate more pro-environmental behaviors (<xref ref-type="bibr" rid="B76">Lin et al., 2022</xref>). Therefore, it is hypothesized that:</p>
<disp-quote>
<p><bold>H5:</bold> Attitude has a positive and significant effect on pro-environmental behavior.</p>
</disp-quote>
</sec>
<sec id="S2.SS5">
<title>2.5 Power distance belief</title>
<p>The concept of power distance is a cultural value that reflects the individual&#x2019;s acceptance of the gap in power (<xref ref-type="bibr" rid="B54">Hofstede, 1984</xref>, <xref ref-type="bibr" rid="B55">1989</xref>). In social cultures where power distance is generally high, vertical hierarchical relationships are strongly felt. As a result of accepting power differences, individuals are expected to act in accordance with their position in the social hierarchy and maintain the existing state of power distribution, and they may maintain this position in the social hierarchy. Compared to Westerners, Chinese people are heavily influenced by Confucian culture and tend to have a higher power distance orientation (<xref ref-type="bibr" rid="B135">Thompson et al., 2020</xref>). Due to the long-term influence of Confucian culture and the high-powered distance society, individuals view inequality as justified as a result of power distribution, and higher-ranking individuals will receive greater &#x201C;rent-seeking,&#x201D; resulting in an unequal distribution of income. As a result, Chinese consumers not only subconsciously accept their social class and position but also link their social responsibility to their social status. In light of the high cost of pro-environmental behavior (<xref ref-type="bibr" rid="B143">Wang and Chao, 2019</xref>; <xref ref-type="bibr" rid="B24">De Silva et al., 2021</xref>), many Chinese residents view pro-environmental behavior as a greater social responsibility that should be borne by external forces with more wealth and power, such as celebrities, large corporations, or the central government (<xref ref-type="bibr" rid="B25">Dendler and Dewick, 2016</xref>; <xref ref-type="bibr" rid="B24">De Silva et al., 2021</xref>).</p>
<p>Despite the fact that the Chinese have a high PD, there is a wide variation in the degree to which individuals accept unequal power distributions within institutions and organizations (<xref ref-type="bibr" rid="B66">Kirkman et al., 2009</xref>; <xref ref-type="bibr" rid="B83">Mi et al., 2020</xref>). As a result, individuals with high PDs are more sensitive to the concept of social status than those with low PDs (<xref ref-type="bibr" rid="B63">Kaynak et al., 2013</xref>). Previous research has indicated that emotions influence attitudes and behavior differently depending on cultural values (<xref ref-type="bibr" rid="B91">Onwezen et al., 2014</xref>). According to their study, both anticipated pride and guilt have an effect on environmental behavior; however, the extent of this effect varies between individuals in individualistic and collectivist countries (<xref ref-type="bibr" rid="B91">Onwezen et al., 2014</xref>). Therefore, the present study suggests that even individuals with the same attitudes and specific emotions may still exhibit inconsistent pro-environmental behaviors in different power distance contexts, implying that power distance moderates the relationship between attitudes and specific emotions and pro-environmental behaviors. As a result, it is hypothesized that:</p>
<disp-quote>
<p><bold>H6a:</bold> Power distance weakens the relationship between nostalgia and pro-environmental behavior.</p>
</disp-quote>
<disp-quote>
<p><bold>H6b:</bold> Power distance weakens the relationship between attitude and pro-environmental behavior.</p>
</disp-quote>
<disp-quote>
<p><bold>H6c:</bold> Power distance weakens the relationship between awe of nature and pro-environmental behavior.</p>
</disp-quote>
<disp-quote>
<p><bold>H6d:</bold> Power distance weakens the relationship between COVID-19 risk perception and pro-environmental behavior.</p>
</disp-quote>
<p>In sum, based on affective event theory, we developed a rigorous model. The conceptual model in <xref ref-type="fig" rid="F2">Figure 2</xref> includes cognitive elements, emotional elements, and contextual cultural influences that impact consumer behaviors.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption><p>Conceptual framework.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyg-13-1093999-g002.tif"/>
</fig>
</sec>
</sec>
<sec id="S3">
<title>3 Data collection, measurements, and samples</title>
<p>Due to the social distance restrictions in China, face-to-face interviews should be avoided; thus, this research was conducted online. Referring to the data collection procedure of previous studies (<xref ref-type="bibr" rid="B84">Mi et al., 2021</xref>; <xref ref-type="bibr" rid="B56">Hua and Dong, 2022</xref>; <xref ref-type="bibr" rid="B152">Xiang et al., 2022</xref>), the pilot study and formal survey were conducted through &#x201C;WJX,&#x201D; a famous online platform in China. To protect the privacy of the participants and to mitigate the effects of social desirability bias, we stated in the questionnaire that the data collection was for academic research only, that the research was anonymous and that all information would be kept strictly confidential. To ensure the quality of the questionnaire, an honorarium of 5&#x2013;10 RMB (roughly 0.7&#x2013;1.6 dollars) was awarded to each participant who answered the questionnaire.</p>
<p>Prior to conducting the formal survey, a pilot study was conducted to ensure the reliability and validity of the measurement scale. A total of 55 questionnaires were distributed through the online platform, the feedback collected from the pre-study was summarized, and the semantics and expressions of some scales were modified and adjusted appropriately. After the deletion of some questions, all the scales in the pilot study passed the reliability and validity tests and formed the final questionnaire. Before the formal survey, a priori power analysis was conducted in G power 3.1.9.6 to determine an appropriate sample size. With a medium effect size of 0.15 (<xref ref-type="bibr" rid="B26">Ducoffe, 1995</xref>), an alpha of 0.05, and a power of 0.90, a sample size of 146 was needed for the following study. The formal study was conducted in April 2022 and lasted for 1 month. After filtering for straight lining and missing data, 44 responses were deleted from a total of 545 questionnaires. Finally, 501 valid questionnaires were collected. Compared with the required sample size in G power, our sample size was deemed appropriate. As convenience sampling is widely used in marketing, consumer behavior and social science research (<xref ref-type="bibr" rid="B107">Santos and Gon&#x00E7;alves, 2019</xref>; <xref ref-type="bibr" rid="B41">Gupta et al., 2020</xref>), it is considered acceptable considering population size, time, and cost to use the convenience sampling technique (<xref ref-type="bibr" rid="B62">Kapoor and Dwivedi, 2020</xref>).</p>
<p>A questionnaire was developed to evaluate the effects of COVID-19 on pro-environmental behaviors (<xref ref-type="supplementary-material" rid="DS1">Supplementary Appendix 7</xref>). The proposed conceptual model was tested using widely used and validated measurement items, including COVID-19 risk perception (<xref ref-type="bibr" rid="B90">O&#x2019;Connor and Assaker, 2022</xref>), nostalgia (<xref ref-type="bibr" rid="B148">Wildschut et al., 2006</xref>), guilt (<xref ref-type="bibr" rid="B2">&#x00C1;goston et al., 2022</xref>), awe of nature (<xref ref-type="bibr" rid="B117">Shiota et al., 2006</xref>), attitude (<xref ref-type="bibr" rid="B100">Qin and Hsu, 2022</xref>), power distance (<xref ref-type="bibr" rid="B158">Yoo et al., 2011</xref>), and pro-environmental behavior (<xref ref-type="bibr" rid="B125">Stern, 2000</xref>). Research items were measured using a five-point Likert scale (1 being highly disagree and 5 being highly agree). In addition, four demographic variables, namely, gender, age, education level, and monthly income status (RMB), were selected as control variables. The demographic characteristics of the respondents are summarized in <xref ref-type="table" rid="T1">Table 1</xref>.</p>
<table-wrap position="float" id="T1">
<label>TABLE 1</label>
<caption><p>Sociodemographic profile of respondents.</p></caption>
<table cellspacing="5" cellpadding="5" frame="box" rules="all">
<thead>
<tr>
<td valign="top" align="left" style="color:#ffffff;background-color: #7f8080;">Measure</td>
<td valign="top" align="left" style="color:#ffffff;background-color: #7f8080;">Item</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Frequency</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Percent</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Cumulative percent</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Gender</td>
<td valign="top" align="left">Male</td>
<td valign="top" align="center">226</td>
<td valign="top" align="center">45.1</td>
<td valign="top" align="center">45.1</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">Female</td>
<td valign="top" align="center">275</td>
<td valign="top" align="center">54.9</td>
<td valign="top" align="center">100</td>
</tr>
<tr>
<td valign="top" align="left">Age</td>
<td valign="top" align="left">Below 20</td>
<td valign="top" align="center">32</td>
<td valign="top" align="center">6.4</td>
<td valign="top" align="center">6.4</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">21&#x2013;30</td>
<td valign="top" align="center">295</td>
<td valign="top" align="center">58.9</td>
<td valign="top" align="center">65.3</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">31&#x2013;40</td>
<td valign="top" align="center">131</td>
<td valign="top" align="center">26.1</td>
<td valign="top" align="center">91.4</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">41&#x2013;50</td>
<td valign="top" align="center">26</td>
<td valign="top" align="center">5.2</td>
<td valign="top" align="center">96.6</td>
</tr>
<tr>
<td valign="top" align="left">Edu</td>
<td valign="top" align="left">50 and over</td>
<td valign="top" align="center">17</td>
<td valign="top" align="center">3.4</td>
<td valign="top" align="center">100</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">Junior high school or below</td>
<td valign="top" align="center">17</td>
<td valign="top" align="center">3.4</td>
<td valign="top" align="center">3.4</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">Senior high school</td>
<td valign="top" align="center">56</td>
<td valign="top" align="center">11.2</td>
<td valign="top" align="center">14.6</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">Technical college</td>
<td valign="top" align="center">103</td>
<td valign="top" align="center">20.6</td>
<td valign="top" align="center">35.1</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">Junior college or university</td>
<td valign="top" align="center">259</td>
<td valign="top" align="center">51.7</td>
<td valign="top" align="center">86.8</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">Master&#x2019;s degree or PhD</td>
<td valign="top" align="center">66</td>
<td valign="top" align="center">13.2</td>
<td valign="top" align="center">100</td>
</tr>
<tr>
<td valign="top" align="left">Income/month (RMB)</td>
<td valign="top" align="left">Less than 3,500</td>
<td valign="top" align="center">178</td>
<td valign="top" align="center">35.5</td>
<td valign="top" align="center">35.5</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">3,500&#x2013;6,000</td>
<td valign="top" align="center">154</td>
<td valign="top" align="center">30.7</td>
<td valign="top" align="center">66.3</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">6,001&#x2013;8,000</td>
<td valign="top" align="center">94</td>
<td valign="top" align="center">18.8</td>
<td valign="top" align="center">85</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">8,001&#x2013;9,999</td>
<td valign="top" align="center">64</td>
<td valign="top" align="center">12.8</td>
<td valign="top" align="center">97.8</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">over 10,000</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">2.2</td>
<td valign="top" align="center">100</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">Total</td>
<td valign="top" align="center">501</td>
<td valign="top" align="center">100</td>
<td valign="top" align="center"/></tr>
</tbody>
</table></table-wrap>
</sec>
<sec id="S4" sec-type="results">
<title>4 Results</title>
<p>The proposed model was tested using multiple methods. The normality of the data was assessed by using a one-sample Kolmogorov&#x2013;Smirnov (KS) test. In <xref ref-type="supplementary-material" rid="DS1">Supplementary Appendix 4</xref> and the QQ plot (<xref ref-type="supplementary-material" rid="DS1">Supplementary Appendix 5</xref>), it appears that the two-tailed <italic>p</italic>-values are below 0.05, which indicates that the distribution does not follow a normal distribution. We then used partial least squares structural equation modeling (PLS-SEM) to calculate the net effect of independent variables due to its resilience to non-normal distributions (<xref ref-type="bibr" rid="B46">Hair et al., 2019</xref>).</p>
<sec id="S4.SS1">
<title>4.1 PLS-SEM result</title>
<sec id="S4.SS1.SSS1">
<title>4.1.1 Common method bias</title>
<p>To reduce the influence of common method bias (CMB) on the study results, the English scales extracted from foreign literature were back translated, and accurate language expressions were presented to the respondents through repeated comparisons and corrections, thus reducing the possibility of errors due to language expression ambiguity. In addition, as a statistical test, we conducted Harman&#x2019;s single factor to determine whether CMB was present using SPSS 26, and seven factors in this study were combined to produce a single factor. The result showed that the newly formed factor explained 39.506% of the variation, which is less than the 50% requirement (<xref ref-type="bibr" rid="B94">Podsakoff et al., 2003</xref>; <xref ref-type="bibr" rid="B71">Leong et al., 2020b</xref>). In addition, to further confirm that CMB is not a problem, we calculated the substantive variance and the method variance by converting each item into a single-item second-order construct (<xref ref-type="bibr" rid="B94">Podsakoff et al., 2003</xref>). According to <xref ref-type="table" rid="T2">Table 2</xref>, the average substantively explained variance of the indicators is 0.881, while the variance based on the average method is 0.013, with most factor loadings not significant. Furthermore, the ratio 60:1 between the substantive variance and the method variance confirms the lack of concern regarding CMB (<xref ref-type="bibr" rid="B72">Leong et al., 2020a</xref>). Additionally, as shown in <xref ref-type="fig" rid="F3">Figure 3</xref>, there was no particularly high correlation between the variables in this study (<italic>r</italic> &#x003E; 0.9). This indicates that CMB does not pose a threat to our findings (<xref ref-type="bibr" rid="B5">Bagozzi et al., 1991</xref>). In summary, based on the analysis above, CMB is not an issue in this study.</p>
<table-wrap position="float" id="T2">
<label>TABLE 2</label>
<caption><p>Common method bias analysis.</p></caption>
<table cellspacing="5" cellpadding="5" frame="box" rules="all">
<thead>
<tr>
<td valign="top" align="left" style="color:#ffffff;background-color: #7f8080;">Construct</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Indicator</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Substantive factor loading (R1)</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;"><italic>T</italic>-value</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">R1<sup>2</sup></td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Method factor loading (R2)</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;"><italic>T</italic>-value</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">R2<sup>2</sup></td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">AN</td>
<td valign="top" align="center">AN1</td>
<td valign="top" align="center">0.884</td>
<td valign="top" align="center">41.001</td>
<td valign="top" align="center">0.781</td>
<td valign="top" align="center">0.041</td>
<td valign="top" align="center">1.402</td>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">AN2</td>
<td valign="top" align="center">0.933</td>
<td valign="top" align="center">41.108</td>
<td valign="top" align="center">0.870</td>
<td valign="top" align="center">&#x2013;0.045</td>
<td valign="top" align="center">1.512</td>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">AN3</td>
<td valign="top" align="center">0.893</td>
<td valign="top" align="center">33.858</td>
<td valign="top" align="center">0.797</td>
<td valign="top" align="center">0.003</td>
<td valign="top" align="center">0.092</td>
<td valign="top" align="center">0.000</td>
</tr>
<tr>
<td valign="top" align="left">AT</td>
<td valign="top" align="center">AT1</td>
<td valign="top" align="center">0.853</td>
<td valign="top" align="center">36.701</td>
<td valign="top" align="center">0.728</td>
<td valign="top" align="center">0.043</td>
<td valign="top" align="center">1.446</td>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">AT2</td>
<td valign="top" align="center">0.816</td>
<td valign="top" align="center">30.295</td>
<td valign="top" align="center">0.666</td>
<td valign="top" align="center">0.077</td>
<td valign="top" align="center">2.346</td>
<td valign="top" align="center">0.006</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">AT3</td>
<td valign="top" align="center">0.963</td>
<td valign="top" align="center">38.391</td>
<td valign="top" align="center">0.927</td>
<td valign="top" align="center">&#x2013;0.125</td>
<td valign="top" align="center">3.769</td>
<td valign="top" align="center">0.016</td>
</tr>
<tr>
<td valign="top" align="left">CRP</td>
<td valign="top" align="center">CRP1</td>
<td valign="top" align="center">0.660</td>
<td valign="top" align="center">15.519</td>
<td valign="top" align="center">0.436</td>
<td valign="top" align="center">0.247</td>
<td valign="top" align="center">5.479</td>
<td valign="top" align="center">0.061</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">CRP2</td>
<td valign="top" align="center">0.675</td>
<td valign="top" align="center">17.152</td>
<td valign="top" align="center">0.456</td>
<td valign="top" align="center">0.210</td>
<td valign="top" align="center">5.222</td>
<td valign="top" align="center">0.044</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">CRP3</td>
<td valign="top" align="center">0.767</td>
<td valign="top" align="center">17.816</td>
<td valign="top" align="center">0.588</td>
<td valign="top" align="center">0.129</td>
<td valign="top" align="center">2.716</td>
<td valign="top" align="center">0.017</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">CRP4</td>
<td valign="top" align="center">1.104</td>
<td valign="top" align="center">30.405</td>
<td valign="top" align="center">1.219</td>
<td valign="top" align="center">&#x2013;0.266</td>
<td valign="top" align="center">6.183</td>
<td valign="top" align="center">0.071</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">CRP5</td>
<td valign="top" align="center">1.087</td>
<td valign="top" align="center">26.892</td>
<td valign="top" align="center">1.182</td>
<td valign="top" align="center">&#x2013;0.264</td>
<td valign="top" align="center">5.630</td>
<td valign="top" align="center">0.070</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">CRP6</td>
<td valign="top" align="center">0.894</td>
<td valign="top" align="center">16.350</td>
<td valign="top" align="center">0.799</td>
<td valign="top" align="center">&#x2013;0.096</td>
<td valign="top" align="center">1.622</td>
<td valign="top" align="center">0.009</td>
</tr>
<tr>
<td valign="top" align="left">GU</td>
<td valign="top" align="center">GU1</td>
<td valign="top" align="center">0.801</td>
<td valign="top" align="center">29.619</td>
<td valign="top" align="center">0.642</td>
<td valign="top" align="center">0.110</td>
<td valign="top" align="center">3.546</td>
<td valign="top" align="center">0.012</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">GU2</td>
<td valign="top" align="center">0.882</td>
<td valign="top" align="center">27.097</td>
<td valign="top" align="center">0.778</td>
<td valign="top" align="center">&#x2013;0.060</td>
<td valign="top" align="center">1.527</td>
<td valign="top" align="center">0.004</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">GU3</td>
<td valign="top" align="center">0.861</td>
<td valign="top" align="center">29.861</td>
<td valign="top" align="center">0.741</td>
<td valign="top" align="center">0.034</td>
<td valign="top" align="center">1.010</td>
<td valign="top" align="center">0.001</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">GU4</td>
<td valign="top" align="center">0.952</td>
<td valign="top" align="center">34.383</td>
<td valign="top" align="center">0.906</td>
<td valign="top" align="center">&#x2013;0.091</td>
<td valign="top" align="center">2.648</td>
<td valign="top" align="center">0.008</td>
</tr>
<tr>
<td valign="top" align="left">NO</td>
<td valign="top" align="center">NO1</td>
<td valign="top" align="center">0.878</td>
<td valign="top" align="center">33.214</td>
<td valign="top" align="center">0.771</td>
<td valign="top" align="center">0.035</td>
<td valign="top" align="center">1.080</td>
<td valign="top" align="center">0.001</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">NO2</td>
<td valign="top" align="center">0.925</td>
<td valign="top" align="center">35.503</td>
<td valign="top" align="center">0.856</td>
<td valign="top" align="center">&#x2013;0.039</td>
<td valign="top" align="center">1.197</td>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">NO3</td>
<td valign="top" align="center">0.902</td>
<td valign="top" align="center">35.308</td>
<td valign="top" align="center">0.814</td>
<td valign="top" align="center">0.003</td>
<td valign="top" align="center">0.094</td>
<td valign="top" align="center">0.000</td>
</tr>
<tr>
<td valign="top" align="left">PEB</td>
<td valign="top" align="center">PEB1</td>
<td valign="top" align="center">0.820</td>
<td valign="top" align="center">33.334</td>
<td valign="top" align="center">0.672</td>
<td valign="top" align="center">0.115</td>
<td valign="top" align="center">3.716</td>
<td valign="top" align="center">0.013</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PEB2</td>
<td valign="top" align="center">0.840</td>
<td valign="top" align="center">32.268</td>
<td valign="top" align="center">0.706</td>
<td valign="top" align="center">0.047</td>
<td valign="top" align="center">1.451</td>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PEB3</td>
<td valign="top" align="center">0.842</td>
<td valign="top" align="center">28.926</td>
<td valign="top" align="center">0.709</td>
<td valign="top" align="center">0.042</td>
<td valign="top" align="center">1.211</td>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PEB4</td>
<td valign="top" align="center">0.917</td>
<td valign="top" align="center">31.536</td>
<td valign="top" align="center">0.841</td>
<td valign="top" align="center">&#x2013;0.108</td>
<td valign="top" align="center">3.178</td>
<td valign="top" align="center">0.012</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PEB5</td>
<td valign="top" align="center">0.927</td>
<td valign="top" align="center">27.160</td>
<td valign="top" align="center">0.859</td>
<td valign="top" align="center">&#x2013;0.111</td>
<td valign="top" align="center">2.861</td>
<td valign="top" align="center">0.012</td>
</tr>
<tr>
<td valign="top" align="left">PD</td>
<td valign="top" align="center">PD1</td>
<td valign="top" align="center">0.908</td>
<td valign="top" align="center">125.277</td>
<td valign="top" align="center">0.824</td>
<td valign="top" align="center">0.027</td>
<td valign="top" align="center">1.462</td>
<td valign="top" align="center">0.001</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PD2</td>
<td valign="top" align="center">0.869</td>
<td valign="top" align="center">66.731</td>
<td valign="top" align="center">0.755</td>
<td valign="top" align="center">0.016</td>
<td valign="top" align="center">0.766</td>
<td valign="top" align="center">0.000</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PD3</td>
<td valign="top" align="center">0.911</td>
<td valign="top" align="center">98.184</td>
<td valign="top" align="center">0.830</td>
<td valign="top" align="center">&#x2013;0.037</td>
<td valign="top" align="center">2.076</td>
<td valign="top" align="center">0.001</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PD4</td>
<td valign="top" align="center">0.903</td>
<td valign="top" align="center">99.005</td>
<td valign="top" align="center">0.815</td>
<td valign="top" align="center">&#x2013;0.027</td>
<td valign="top" align="center">1.334</td>
<td valign="top" align="center">0.001</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PD5</td>
<td valign="top" align="center">0.888</td>
<td valign="top" align="center">78.078</td>
<td valign="top" align="center">0.789</td>
<td valign="top" align="center">0.022</td>
<td valign="top" align="center">1.073</td>
<td valign="top" align="center">0.000</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Average</bold></td>
<td valign="top" align="center"/>
<td valign="top" align="center"><bold>0.881</bold></td>
<td valign="top" align="center"/>
<td valign="top" align="center"><bold>0.785</bold></td>
<td valign="top" align="center">&#x2212;<bold>0.002</bold></td>
<td valign="top" align="center"/>
<td valign="top" align="center"><bold>0.013</bold></td>
</tr>
<tr>
<td valign="top" align="left"><bold>Ratio</bold></td>
<td valign="top" align="center" colspan="7"><bold>60</bold></td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn><p>AN, awe of nature; AT, attitude; CRP, COVID-19 risk perception; GU, guilt; NO, nostalgia; PD, power distance; PEB, pro-environmental behavior. Bold values are the average value of substantive factor loading and method factor loading and the ratio of them.</p></fn>
</table-wrap-foot>
</table-wrap>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption><p>Correlation between variables.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyg-13-1093999-g003.tif"/>
</fig>
</sec>
<sec id="S4.SS1.SSS2">
<title>4.1.2 Assessment of the measurement model</title>
<p>The PLS-SEM measurement model is evaluated based on three criteria: reliability, convergent validity, and discriminant validity (<xref ref-type="bibr" rid="B35">Fornell and Larcker, 1981</xref>). According to <xref ref-type="bibr" rid="B43">Hair et al. (2021)</xref>, Cronbach&#x2019;s alpha, composite reliability (CR), and rho_A can be used to measure internal consistency. All three values exceeded the accepted cut-off point of 0.7 (<xref ref-type="bibr" rid="B45">Hair et al., 2014</xref>), demonstrating the reliability of the scale (<xref ref-type="table" rid="T3">Table 3</xref>). In assessing the convergent validity of a measurement model, researchers can use the outer loading (&#x003E;0.708) and the average variance extracted (AVE) scores (&#x003E;0.5) of each observed item (<xref ref-type="bibr" rid="B35">Fornell and Larcker, 1981</xref>; <xref ref-type="bibr" rid="B5">Bagozzi et al., 1991</xref>). <xref ref-type="table" rid="T3">Table 3</xref> shows that all items exceeded the acceptable value, indicating the study had convergent validity.</p>
<table-wrap position="float" id="T3">
<label>TABLE 3</label>
<caption><p>Assessment of the reliability and convergent validity.</p></caption>
<table cellspacing="5" cellpadding="5" frame="box" rules="all">
<thead>
<tr>
<td valign="top" align="left" style="color:#ffffff;background-color: #7f8080;">Construct</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Item</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Loading</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Cronbach&#x2019;s alpha</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">rho_A</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">CR</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">AVE</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">AN</td>
<td valign="top" align="center">AN1</td>
<td valign="top" align="center">0.917</td>
<td valign="top" align="center">0.887</td>
<td valign="top" align="center">0.889</td>
<td valign="top" align="center">0.93</td>
<td valign="top" align="center">0.816</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">AN2</td>
<td valign="top" align="center">0.899</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">AN3</td>
<td valign="top" align="center">0.894</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">AT</td>
<td valign="top" align="center">AT1</td>
<td valign="top" align="center">0.886</td>
<td valign="top" align="center">0.848</td>
<td valign="top" align="center">0.855</td>
<td valign="top" align="center">0.908</td>
<td valign="top" align="center">0.766</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">AT2</td>
<td valign="top" align="center">0.88</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">AT3</td>
<td valign="top" align="center">0.86</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">CRP</td>
<td valign="top" align="center">CRP1</td>
<td valign="top" align="center">0.884</td>
<td valign="top" align="center">0.929</td>
<td valign="top" align="center">0.937</td>
<td valign="top" align="center">0.944</td>
<td valign="top" align="center">0.737</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">CRP2</td>
<td valign="top" align="center">0.868</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">CRP3</td>
<td valign="top" align="center">0.885</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">CRP4</td>
<td valign="top" align="center">0.863</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">CRP5</td>
<td valign="top" align="center">0.846</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">CRP6</td>
<td valign="top" align="center">0.801</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">GU</td>
<td valign="top" align="center">GU1</td>
<td valign="top" align="center">0.892</td>
<td valign="top" align="center">0.896</td>
<td valign="top" align="center">0.899</td>
<td valign="top" align="center">0.928</td>
<td valign="top" align="center">0.762</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">GU2</td>
<td valign="top" align="center">0.831</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">GU3</td>
<td valign="top" align="center">0.89</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">GU4</td>
<td valign="top" align="center">0.877</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">NO</td>
<td valign="top" align="center">NO1</td>
<td valign="top" align="center">0.907</td>
<td valign="top" align="center">0.885</td>
<td valign="top" align="center">0.886</td>
<td valign="top" align="center">0.929</td>
<td valign="top" align="center">0.812</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">NO2</td>
<td valign="top" align="center">0.891</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">NO3</td>
<td valign="top" align="center">0.906</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">PD</td>
<td valign="top" align="center">PD1</td>
<td valign="top" align="center">0.9</td>
<td valign="top" align="center">0.939</td>
<td valign="top" align="center">0.946</td>
<td valign="top" align="center">0.953</td>
<td valign="top" align="center">0.802</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PD2</td>
<td valign="top" align="center">0.863</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PD3</td>
<td valign="top" align="center">0.915</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PD4</td>
<td valign="top" align="center">0.908</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PD5</td>
<td valign="top" align="center">0.892</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">PEB</td>
<td valign="top" align="center">PEB1</td>
<td valign="top" align="center">0.907</td>
<td valign="top" align="center">0.917</td>
<td valign="top" align="center">0.92</td>
<td valign="top" align="center">0.938</td>
<td valign="top" align="center">0.752</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PEB2</td>
<td valign="top" align="center">0.878</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PEB3</td>
<td valign="top" align="center">0.874</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PEB4</td>
<td valign="top" align="center">0.835</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="center">PEB5</td>
<td valign="top" align="center">0.842</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
</tbody>
</table>
<table-wrap-foot>
<fn><p>AN, awe of nature; AT, attitude; CRP, COVID-19 risk perception; GU, guilt; NO, nostalgia; PD, power distance; PEB, pro-environmental behavior.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>To assess the discriminant validity, two types of assessments were conducted: the Fornell-Larcker criterion (<xref ref-type="bibr" rid="B35">Fornell and Larcker, 1981</xref>) and heterotrait-monotrait ratios (<xref ref-type="bibr" rid="B52">Henseler et al., 2015</xref>). The Fornell-Larcker criterion examines the correlation coefficient between the square root of the mean variance extraction and other latent variables of the measurement model. To satisfy the Fornell-Larcker criteria, the AVE of one latent variable must be larger than the squared correlation with the other latent variable (<xref ref-type="bibr" rid="B35">Fornell and Larcker, 1981</xref>). <xref ref-type="table" rid="T4">Table 4</xref> shows that the criteria have been met. The HTMT value is calculated by comparing the mean cross-correlation coefficient between observed variables of different latent profiles to the mean correlation coefficient between observed variables of the same latent profile. Discriminant validity is established if the value of HTMT is less than 0.85 (<xref ref-type="bibr" rid="B51">Henseler et al., 2014</xref>) or 0.90 (<xref ref-type="bibr" rid="B9">Carri&#x00F3;n et al., 2017</xref>). As seen in <xref ref-type="table" rid="T4">Table 4</xref>, all obtained values are below 0.85, demonstrating good discriminant validity.</p>
<table-wrap position="float" id="T4">
<label>TABLE 4</label>
<caption><p>Assessment of the discriminant validity.</p></caption>
<table cellspacing="5" cellpadding="5" frame="box" rules="all">
<thead>
<tr>
<td valign="top" align="left" style="color:#ffffff;background-color: #7f8080;"></td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">AN</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">AT</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">CRP</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">GU</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">NO</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">PD</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">PEB</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="center" colspan="8" style="color:#191b1a;background-color: #dcdcdc;"><bold>Fornell-Larcker criterion</bold></td>
</tr>
<tr>
<td valign="top" align="left">AN</td>
<td valign="top" align="center"><bold>0.903</bold></td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">AT</td>
<td valign="top" align="center">0.450</td>
<td valign="top" align="center"><bold>0.875</bold></td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">CRP</td>
<td valign="top" align="center">0.604</td>
<td valign="top" align="center">0.514</td>
<td valign="top" align="center"><bold>0.858</bold></td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">GU</td>
<td valign="top" align="center">0.510</td>
<td valign="top" align="center">0.520</td>
<td valign="top" align="center">0.631</td>
<td valign="top" align="center"><bold>0.873</bold></td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">NO</td>
<td valign="top" align="center">0.470</td>
<td valign="top" align="center">0.665</td>
<td valign="top" align="center">0.588</td>
<td valign="top" align="center">0.618</td>
<td valign="top" align="center"><bold>0.901</bold></td>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">PD</td>
<td valign="top" align="center">&#x2212;0.048</td>
<td valign="top" align="center">0.309</td>
<td valign="top" align="center">0.050</td>
<td valign="top" align="center">0.101</td>
<td valign="top" align="center">0.215</td>
<td valign="top" align="center"><bold>0.896</bold></td>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">PEB</td>
<td valign="top" align="center">0.535</td>
<td valign="top" align="center">0.482</td>
<td valign="top" align="center">0.542</td>
<td valign="top" align="center">0.435</td>
<td valign="top" align="center">0.468</td>
<td valign="top" align="center">&#x2212;0.222</td>
<td valign="top" align="center"><bold>0.867</bold></td>
</tr>
<tr>
<td valign="top" align="center" colspan="8" style="color:#191b1a;background-color: #dcdcdc;"><bold>HTMT</bold></td>
</tr>
<tr>
<td valign="top" align="left">AN</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">AT</td>
<td valign="top" align="center">0.516</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">CRP</td>
<td valign="top" align="center">0.655</td>
<td valign="top" align="center">0.571</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">GU</td>
<td valign="top" align="center">0.570</td>
<td valign="top" align="center">0.592</td>
<td valign="top" align="center">0.681</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">NO</td>
<td valign="top" align="center">0.530</td>
<td valign="top" align="center">0.763</td>
<td valign="top" align="center">0.642</td>
<td valign="top" align="center">0.693</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">PD</td>
<td valign="top" align="center">0.057</td>
<td valign="top" align="center">0.349</td>
<td valign="top" align="center">0.075</td>
<td valign="top" align="center">0.111</td>
<td valign="top" align="center">0.237</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/></tr>
<tr>
<td valign="top" align="left">PEB</td>
<td valign="top" align="center">0.592</td>
<td valign="top" align="center">0.540</td>
<td valign="top" align="center">0.575</td>
<td valign="top" align="center">0.473</td>
<td valign="top" align="center">0.516</td>
<td valign="top" align="center">0.240</td>
<td valign="top" align="center"/></tr>
</tbody>
</table>
<table-wrap-foot>
<fn><p>The off-diagonal values (bold) in the above matrix are the square correlations between the latent constructs and the diagonals are AVEs.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="S4.SS1.SSS3">
<title>4.1.3 Assessment of the structural model</title>
<p>Initially, the structural model was examined for collinearity by examining the variance inflation factors (VIFs) of all predictor constructs. In <xref ref-type="table" rid="T5">Table 5</xref>, it is evident that all variables&#x2019; VIF values were less than 3 (1.188&#x2013;2.528), indicating that there was no issue with collinearity (<xref ref-type="bibr" rid="B46">Hair et al., 2019</xref>; <xref ref-type="bibr" rid="B43">Hair et al., 2021</xref>). Next, bootstrapping was employed to test the significance of the hypothesis with 5,000 subsamples (<xref ref-type="bibr" rid="B43">Hair et al., 2021</xref>). As shown in <xref ref-type="fig" rid="F4">Figure 4</xref> and <xref ref-type="table" rid="T5">Table 5</xref>, the results showed that the effects of COVID-19 risk perception on nostalgia (&#x03B2; = 0.588, <italic>t</italic>-value = 16.958), moral anger (&#x03B2; = 0.631, <italic>t</italic>-value = 19.992), and awe of nature (&#x03B2; = 0.604, <italic>t</italic>-value = 20.004) were all significant, thereby supporting H1a, H1b, and H1c. In addition, nostalgia (&#x03B2; = 0.521, <italic>t</italic>-value = 12.284), moral anger (&#x03B2; = 0.125, <italic>t</italic>-value = 2.805), and awe of nature (&#x03B2; = 0.142, <italic>t</italic>-value = 3.631) demonstrated a significant positive effect on attitude, supporting H2b, H3, and H4a, respectively. Additionally, COVID-19 risk perception (&#x03B2; = 0.154, <italic>t</italic>-value = 3.284), nostalgia (&#x03B2; = 0.146, <italic>t</italic>-value = 2.905), awe of nature (&#x03B2; = 0.178, <italic>t</italic>-value = 4.214), and attitude (&#x03B2; = 0.232, <italic>t</italic>-value = 4.550) were found to have a positive effect on pro-environmental behavior, thus supporting H1d, H2a, H4b, and H5.</p>
<table-wrap position="float" id="T5">
<label>TABLE 5</label>
<caption><p>Assessment of structural model.</p></caption>
<table cellspacing="5" cellpadding="5" frame="box" rules="all">
<thead>
<tr>
<td valign="top" align="left" style="color:#ffffff;background-color: #7f8080;">Hypothesis</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Coefficient</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Std</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;"><italic>T</italic>-values</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;"><italic>P</italic>-values</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;"><italic>f</italic><sup>2</sup></td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">VIF</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Result</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">CRP - &#x003E; NO</td>
<td valign="top" align="center">0.588</td>
<td valign="top" align="center">0.035</td>
<td valign="top" align="center">16.958</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">0.530</td>
<td valign="top" align="center"/>
<td valign="top" align="center">Supported</td>
</tr>
<tr>
<td valign="top" align="left" colspan="8" style="background-color: #dcdcdc;"><bold>NO; <italic>R</italic><sup>2</sup> = 0.346; <italic>Q</italic><sup>2</sup> predict = 0.278</bold></td>
</tr>
<tr>
<td valign="top" align="left">CRP - &#x003E; GU</td>
<td valign="top" align="center">0.631</td>
<td valign="top" align="center">0.032</td>
<td valign="top" align="center">19.992</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">0.662</td>
<td valign="top" align="center"/>
<td valign="top" align="center">Supported</td>
</tr>
<tr>
<td valign="top" align="left" colspan="8" style="background-color: #dcdcdc;"><bold>GU; <italic>R</italic><sup>2</sup> = 0.398; <italic>Q</italic><sup>2</sup> predict = 0.301</bold></td>
</tr>
<tr>
<td valign="top" align="left">CRP - &#x003E; AN</td>
<td valign="top" align="center">0.604</td>
<td valign="top" align="center">0.03</td>
<td valign="top" align="center">20.004</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">0.574</td>
<td valign="top" align="center"/>
<td valign="top" align="center">Supported</td>
</tr>
<tr>
<td valign="top" align="left" colspan="8" style="background-color: #dcdcdc;"><bold>AN; <italic>R</italic><sup>2</sup> = 0.365; Q<sup>2</sup> predict = 0.294</bold></td>
</tr>
<tr>
<td valign="top" align="left">NO - &#x003E; AT</td>
<td valign="top" align="center">0.521</td>
<td valign="top" align="center">0.042</td>
<td valign="top" align="center">12.284</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">0.304</td>
<td valign="top" align="center">1.707</td>
<td valign="top" align="center">Supported</td>
</tr>
<tr>
<td valign="top" align="left">GU - &#x003E; AT</td>
<td valign="top" align="center">0.125</td>
<td valign="top" align="center">0.045</td>
<td valign="top" align="center">&#x2004;&#x2004;2.805</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">0.017</td>
<td valign="top" align="center">1.798</td>
<td valign="top" align="center">Supported</td>
</tr>
<tr>
<td valign="top" align="left">AN - &#x003E; AT</td>
<td valign="top" align="center">0.142</td>
<td valign="top" align="center">0.039</td>
<td valign="top" align="center">&#x2004;&#x2004;3.631</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">0.027</td>
<td valign="top" align="center">1.425</td>
<td valign="top" align="center">Supported</td>
</tr>
<tr>
<td valign="top" align="left" colspan="8" style="background-color: #dcdcdc;"><bold>AT; <italic>R</italic><sup>2</sup> = 0.476; <italic>Q</italic><sup>2</sup> predict = 0.358</bold></td>
</tr>
<tr>
<td valign="top" align="left">AN - &#x003E; PEB</td>
<td valign="top" align="center">0.178</td>
<td valign="top" align="center">0.042</td>
<td valign="top" align="center">&#x2004;&#x2004;4.214</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">0.039</td>
<td valign="top" align="center">1.775</td>
<td valign="top" align="center">Supported</td>
</tr>
<tr>
<td valign="top" align="left">AT - &#x003E; PEB</td>
<td valign="top" align="center">0.232</td>
<td valign="top" align="center">0.051</td>
<td valign="top" align="center">4.55</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">0.046</td>
<td valign="top" align="center">2.528</td>
<td valign="top" align="center">Supported</td>
</tr>
<tr>
<td valign="top" align="left">CRP - &#x003E; PEB</td>
<td valign="top" align="center">0.154</td>
<td valign="top" align="center">0.047</td>
<td valign="top" align="center">&#x2004;&#x2004;3.284</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">0.022</td>
<td valign="top" align="center">2.306</td>
<td valign="top" align="center">Supported</td>
</tr>
<tr>
<td valign="top" align="left">NO - &#x003E; PEB</td>
<td valign="top" align="center">0.146</td>
<td valign="top" align="center">0.05</td>
<td valign="top" align="center">&#x2004;&#x2004;2.905</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">0.019</td>
<td valign="top" align="center">2.412</td>
<td valign="top" align="center">Supported</td>
</tr>
<tr>
<td valign="top" align="left">PD - &#x003E; PEB</td>
<td valign="top" align="center">&#x2212;0.329</td>
<td valign="top" align="center">0.036</td>
<td valign="top" align="center">&#x2004;&#x2004;9.146</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">0.197</td>
<td valign="top" align="center">1.188</td>
<td valign="top" align="center">Supported</td>
</tr>
<tr>
<td valign="top" align="left" colspan="8" style="background-color: #dcdcdc;"><bold>PEB; <italic>R</italic><sup>2</sup> = 0.538 <italic>Q</italic><sup>2</sup> predict = 0.373</bold></td>
</tr>
<tr>
<td valign="top" align="left">PD &#x00D7; AN - &#x003E; PEB</td>
<td valign="top" align="center">0.048</td>
<td valign="top" align="center">0.039</td>
<td valign="top" align="center">&#x2004;&#x2004;1.228</td>
<td valign="top" align="center">0.220</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">Not supported</td>
</tr>
<tr>
<td valign="top" align="left">PD &#x00D7; NO - &#x003E; PEB</td>
<td valign="top" align="center">&#x2212;0.127</td>
<td valign="top" align="center">0.047</td>
<td valign="top" align="center">&#x2004;&#x2004;2.719</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;</xref></td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">Supported</td>
</tr>
<tr>
<td valign="top" align="left">PD &#x00D7; CRP - &#x003E; PEB</td>
<td valign="top" align="center">0.006</td>
<td valign="top" align="center">0.044</td>
<td valign="top" align="center">&#x2004;&#x2004;0.139</td>
<td valign="top" align="center">0.889</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">Not supported</td>
</tr>
<tr>
<td valign="top" align="left">PD &#x00D7; AT - &#x003E; PEB</td>
<td valign="top" align="center">&#x2212;0.136</td>
<td valign="top" align="center">0.044</td>
<td valign="top" align="center">&#x2004;&#x2004;3.103</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t5fn1">&#x002A;&#x002A;</xref></td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">Supported</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="t5fn1"><p>&#x002A;&#x002A;&#x002A;<italic>p</italic> &#x003C; 0.001, &#x002A;&#x002A;<italic>p</italic> &#x003C; 0.01.</p></fn>
</table-wrap-foot>
</table-wrap>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption><p>Inner model result of PLS-SEM. &#x002A;&#x002A;&#x002A;<italic>p</italic> &#x003C; 0.001, &#x002A;&#x002A;<italic>p</italic> &#x003C; 0.01.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyg-13-1093999-g004.tif"/>
</fig>
<p>In addition, a two-stage approach was used to examine the moderation effect using Smart-PLS (<xref ref-type="bibr" rid="B49">Henseler and Fassott, 2010</xref>). The results of the moderating effect test in <xref ref-type="table" rid="T5">Table 5</xref> showed that power distance (PD) had a significant negative effect on the relationship between nostalgia and pro-environmental behavior (&#x03B2; = &#x2212;0.1127, <italic>t</italic>-value = 2.719) and attitude and pro-environmental behavior (&#x03B2; = &#x2212;0.136, <italic>t</italic>-value = 3.103), indicating that different levels of power distance negatively impact the relationship between nostalgia and pro-environmental attitudes. Thus, H6a and H6b were supported. Unexpectedly, the interaction effects between awe of nature and power distance (&#x03B2; = 0.048, <italic>t</italic>-value = 1.228) and COVID-19 risk perception and power distance (&#x03B2; = 0.006, <italic>t</italic>-value = 0.139) on pro-environmental behavior were not significant. Therefore, H6c and H6d were not supported. Additionally, the Johnson&#x2013;Neyman technique was employed to further understand the moderating effect of power distance (<xref ref-type="bibr" rid="B121">Spiller et al., 2013</xref>). This method identifies the level from which the moderating variable has a moderating effect on the relationship between the independent and dependent variables. As shown in <xref ref-type="fig" rid="F5">Figure 5</xref>, the moderating effect of power distance on the relationship between nostalgia and pro-environmental behavior is not significant when PD is below 1.19 and above 2.08. Similarly, the moderating effect of power distance on the relationship between attitude and pro-environmental behavior is not significant when PD is below 1.20 and above 2.57.</p>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption><p>Moderating effect of power distance belief.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyg-13-1093999-g005.tif"/>
</fig>
<p>Next, the coefficient of determination (<italic>R</italic><sup>2</sup>) was used to evaluate the explained variance. According to <xref ref-type="bibr" rid="B19">Chin et al. (2003)</xref>, the <italic>R</italic><sup>2</sup>-value is considered small (0.19), moderate (0.33), and substantial (0.67). In <xref ref-type="table" rid="T5">Table 5</xref>, COVID-19 risk perception explained 34.6, 39.8, and 36.5% of the variation in nostalgia, moral outrage, and awe of nature, respectively, demonstrating significant explanatory power. In addition, to establish predictive criteria, we calculated effect sizes using prognostic relevance in Stone-Geisser <italic>Q</italic><sup>2</sup>. The <italic>Q</italic><sup>2</sup>-values were 0.278 for nostalgia, 0.301 for moral anger, 0.294 for awe of nature, 0.358 for attitude, 0.373 for pro-environmental behavior, all of which were above zero (<xref ref-type="bibr" rid="B44">Hair et al., 2013</xref>). In terms of effect size <italic>f</italic><sup>2</sup>, which measures the substantial influence of an exogenous variable, a value of 0.02, 0.15, and 0.35 represents a small, medium, and large effect of the corresponding exogenous variable (<xref ref-type="bibr" rid="B21">Cohen, 1988</xref>). As seen in <xref ref-type="table" rid="T5">Table 5</xref>, COVID-19 risk perception had a substantial effect on nostalgia (<italic>f</italic><sup>2</sup> = 0.530), moral anger (<italic>f</italic><sup>2</sup> = 0.662), and awe of nature (<italic>f</italic><sup>2</sup> = 0.574). Nostalgia and power distance had a moderate effect on attitude (<italic>f</italic><sup>2</sup> = 0.304) and pro-environmental behavior (<italic>f</italic><sup>2</sup> = 0.197), respectively.</p>
<p>Regarding the model fit of this study, both the standardized root mean square residual (SRMR) and goodness of fit (GOF) were used. The SRMR value was 0.050, which was below the recommended maximum of 0.08 (<xref ref-type="bibr" rid="B50">Henseler et al., 2016</xref>), indicating a good overall fitness of the proposed framework. The GOF is defined as the geometric mean of the extracted variance and the average of the <italic>R</italic><sup>2</sup> of all endogenous variables, and the GOF value of the research framework was calculated as follows.</p>
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<p>As suggested by <xref ref-type="bibr" rid="B146">Wetzels et al. (2009)</xref>, the GOF value was small (0.1), medium (0.25), and large (0.36). The GOF for this study is 0.575, which is larger than the recommended value. Taking into consideration both SRMR (0.050) and GOF values (0.575), it can be concluded that the research model in this study is appropriate.</p>
</sec>
</sec>
<sec id="S4.SS2">
<title>4.2 Necessary condition analysis (NCA) result</title>
<p>NCA is a new method of necessary condition analysis (NCA) based on complex causality; it not only identifies the necessary conditions of the outcome variables but also calculates the effect size and bottlenecks of these conditions quantitatively. It is used to determine the size of the &#x201C;necessary-but-not-sufficient-condition&#x201D; effects between independent variables and dependent variables (<xref ref-type="bibr" rid="B27">Dul, 2016</xref>). As a complement to traditional adequacy analysis techniques, NCA quantifies the number of antecedent conditions required for achieving a particular level of outcome variables by analyzing the effect size and bottleneck of antecedent conditions (<xref ref-type="bibr" rid="B28">Dul et al., 2020</xref>).</p>
<p>First, to conduct the NCA, latent variable scores were obtained using the PLS-SEM procedure (<xref ref-type="bibr" rid="B104">Richter et al., 2020</xref>), and then the NCA package in R was employed to perform NCA analysis (<xref ref-type="bibr" rid="B29">Dul et al., 2018</xref>). As a starting point, an NCA consists of drawing a ceiling line through the upper-left observations of an x-y plot, and the scatter plots for all relevant relations are shown in <xref ref-type="fig" rid="F6">Figure 6</xref>. Second, using a recommended random sample size of 10,000, we tested the statistical significance of the effect sizes (d) of the latent variable scores (<xref ref-type="bibr" rid="B27">Dul, 2016</xref>; <xref ref-type="bibr" rid="B28">Dul et al., 2020</xref>). Since the CE-FDH line is appropriate for survey data rated on a five-point Likert scale, we interpreted the NCA results using its parameters (<xref ref-type="bibr" rid="B141">Vis and Dul, 2018</xref>). The results in <xref ref-type="fig" rid="F7">Figure 7</xref> and <xref ref-type="table" rid="T6">Table 6</xref> meaningfully (<italic>d</italic> &#x2265; 0.1) and significantly (<italic>p</italic> &#x003C; 0.05) reveal that AT (<italic>d</italic> = 0.257, <italic>p</italic> &#x003C; 0.001) and NO (<italic>d</italic> = 0.131, <italic>p</italic> &#x003C; 0.001) are necessary conditions for the pro-environmental behaviors of consumers. Finally, the bottleneck technique was used to assist in specifying threshold levels for achieving a particular level of performance. As shown in <xref ref-type="table" rid="T7">Table 7</xref>, to achieve a low level of pro-environmental behavior (30%), AT (9.4%), CRP (0.2%) are necessary; to have a medium level of PEB (70%), AN (7.9%), AT (52.4%), CRP (6.5%), GU (6.6%), and NO (16.4%) are necessary. However, at a high level of pro-environmental behavior (100%), six necessary conditions should be present: AN at no less than 66.1%, AT at no less than 58.6%, CRP at no less than 58.9%, GU at no less than 62.4%, NO at no less than 67.3%, and PD at no more than 20.2%.</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption><p>Scatter plots of necessary condition analysis.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyg-13-1093999-g006.tif"/>
</fig>
<fig id="F7" position="float">
<label>FIGURE 7</label>
<caption><p>Histograms with bottleneck values.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyg-13-1093999-g007.tif"/>
</fig>
<table-wrap position="float" id="T6">
<label>TABLE 6</label>
<caption><p>Necessary condition analysis result (Method: CE-FDH).</p></caption>
<table cellspacing="5" cellpadding="5" frame="box" rules="all">
<thead>
<tr>
<td valign="top" align="left" style="color:#ffffff;background-color: #7f8080;">Construct</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Ceiling zone</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Scope</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Effect size (d)</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;"><italic>P</italic>-values</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Conditional inefficiency<break/> (%)</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Outcome inefficiency<break/> (%)</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left" colspan="7"><bold>PEB</bold></td>
</tr>
<tr>
<td valign="top" align="left">AN</td>
<td valign="top" align="center">1.123</td>
<td valign="top" align="center">11.557</td>
<td valign="top" align="center">0.097</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t6fns3">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">33.898</td>
<td valign="top" align="center">34.794</td>
</tr>
<tr>
<td valign="top" align="left">AT</td>
<td valign="top" align="center">3.728</td>
<td valign="top" align="center">14.495</td>
<td valign="top" align="center">0.257</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t6fns3">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">41.455</td>
<td valign="top" align="center">24.868</td>
</tr>
<tr>
<td valign="top" align="left">CRP</td>
<td valign="top" align="center">1.313</td>
<td valign="top" align="center">11.868</td>
<td valign="top" align="center">0.111</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t6fns3">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">41.058</td>
<td valign="top" align="center">20.114</td>
</tr>
<tr>
<td valign="top" align="left">GU</td>
<td valign="top" align="center">0.563</td>
<td valign="top" align="center">15.381</td>
<td valign="top" align="center">0.056</td>
<td valign="top" align="center">0.154</td>
<td valign="top" align="center">37.567</td>
<td valign="top" align="center">65.546</td>
</tr>
<tr>
<td valign="top" align="left">NO</td>
<td valign="top" align="center">1.667</td>
<td valign="top" align="center">12.728</td>
<td valign="top" align="center">0.131</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t6fns3">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">32.748</td>
<td valign="top" align="center">64.734</td>
</tr>
<tr>
<td valign="top" align="left">PD</td>
<td valign="top" align="center">0.576</td>
<td valign="top" align="center">11.274</td>
<td valign="top" align="center">0.051</td>
<td valign="top" align="center"><xref ref-type="table-fn" rid="t6fns3">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">20.239</td>
<td valign="top" align="center">80.070</td>
</tr>
<tr>
<td valign="top" align="left" colspan="7"><bold>AT</bold></td>
</tr>
<tr>
<td valign="top" align="left">AN</td>
<td valign="top" align="center">0.403</td>
<td valign="top" align="center">14.058</td>
<td valign="top" align="center">0.029</td>
<td valign="top" align="center">0.006</td>
<td valign="top" align="center">82.887</td>
<td valign="top" align="center">75</td>
</tr>
<tr>
<td valign="top" align="left">GU</td>
<td valign="top" align="center">0.563</td>
<td valign="top" align="center">15.381</td>
<td valign="top" align="center">0.037</td>
<td valign="top" align="center">0.064</td>
<td valign="top" align="center">87.339</td>
<td valign="top" align="center">59.433</td>
</tr>
<tr>
<td valign="top" align="left">NO</td>
<td valign="top" align="center">0.455</td>
<td valign="top" align="center">15.482</td>
<td valign="top" align="center">0.029</td>
<td valign="top" align="center">0.047</td>
<td valign="top" align="center">75</td>
<td valign="top" align="center">83.545</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="t6fns3"><p>&#x002A;&#x002A;&#x002A;<italic>p</italic> &#x003C; 0.001.</p></fn>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="T7">
<label>TABLE 7</label>
<caption><p>Bottleneck table (percentages).</p></caption>
<table cellspacing="5" cellpadding="5" frame="box" rules="all">
<thead>
<tr>
<td valign="top" align="left" style="color:#ffffff;background-color: #7f8080;">PEB</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">AN</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">AT</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">CRP</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">GU</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">NO</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">PD</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">0</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
</tr>
<tr>
<td valign="top" align="left">10</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
</tr>
<tr>
<td valign="top" align="left">20</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
</tr>
<tr>
<td valign="top" align="left">30</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">&#x2004;&#x2004;9.4</td>
<td valign="top" align="center">&#x2004;&#x2004;0.2</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
</tr>
<tr>
<td valign="top" align="left">40</td>
<td valign="top" align="center">&#x2004;&#x2004;7.9</td>
<td valign="top" align="center">18.0</td>
<td valign="top" align="center">&#x2004;&#x2004;6.5</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
</tr>
<tr>
<td valign="top" align="left">50</td>
<td valign="top" align="center">&#x2004;&#x2004;7.9</td>
<td valign="top" align="center">18.0</td>
<td valign="top" align="center">&#x2004;&#x2004;6.5</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
</tr>
<tr>
<td valign="top" align="left">60</td>
<td valign="top" align="center">&#x2004;&#x2004;7.9</td>
<td valign="top" align="center">18.0</td>
<td valign="top" align="center">&#x2004;&#x2004;6.5</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
<td valign="top" align="center">NN</td>
</tr>
<tr>
<td valign="top" align="left">70</td>
<td valign="top" align="center">&#x2004;&#x2004;7.9</td>
<td valign="top" align="center">52.4</td>
<td valign="top" align="center">&#x2004;&#x2004;6.5</td>
<td valign="top" align="center">&#x2004;&#x2004;6.6</td>
<td valign="top" align="center">16.4</td>
<td valign="top" align="center">NN</td>
</tr>
<tr>
<td valign="top" align="left">80</td>
<td valign="top" align="center">&#x2004;&#x2004;7.9</td>
<td valign="top" align="center">52.4</td>
<td valign="top" align="center">10.2</td>
<td valign="top" align="center">&#x2004;&#x2004;6.6</td>
<td valign="top" align="center">33.6</td>
<td valign="top" align="center">NN</td>
</tr>
<tr>
<td valign="top" align="left">90</td>
<td valign="top" align="center">25.0</td>
<td valign="top" align="center">58.6</td>
<td valign="top" align="center">58.9</td>
<td valign="top" align="center">18.5</td>
<td valign="top" align="center">50.0</td>
<td valign="top" align="center">85.1</td>
</tr>
<tr>
<td valign="top" align="left">100</td>
<td valign="top" align="center">66.1</td>
<td valign="top" align="center">58.6</td>
<td valign="top" align="center">58.9</td>
<td valign="top" align="center">62.4</td>
<td valign="top" align="center">67.3</td>
<td valign="top" align="center">20.2</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn><p>NN, not necessary.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec id="S5" sec-type="discussion|conclusion">
<title>5 Discussion and conclusion</title>
<sec id="S5.SS1">
<title>5.1 Theoretical implications</title>
<p>Overall, the majority of previous studies have examined the impact of rationality, affection, and culture separately on consumers&#x2019; pro-environmental behavior, while few have examined their combined effect. This study makes the following contributions.</p>
<p>First, drawing on affective event theory, this study constructs a comprehensive model that explores how external event shocks (COVID-19) affect consumers&#x2019; pro-environmental behavior. To our knowledge, this is one of the few studies that use affective event theory to investigate consumers&#x2019; pro-environmental behavior, expanding the perspective for understanding consumer behavior.</p>
<p>Second, an increasing number of scholars argue that cognitive and affective factors should be integrated into a unified rational and emotional theory of environmental behavior to better explain individuals&#x2019; pro-environmental behavior (<xref ref-type="bibr" rid="B136">Tian and Liu, 2022</xref>). Unfortunately, a limited number of studies have explored the interaction between emotional and cognitive factors. As a result, we intend to further investigate the interaction between rational and emotional factors in our study, as cognitive and emotional factors are mutually influential (<xref ref-type="bibr" rid="B80">Luo and Chea, 2018</xref>).</p>
<p>Third, the majority of previous studies focusing on the pro-environmental behavior of consumers used first-generation multivariate methods (such as multiple regression analysis) and second-generation multivariate methods [such as structural equation modeling (SEM)], which assume symmetrical relations between variables; these studies primarily explored the net effect of antecedent variables. However, using PLS-SEM and NCA, this study provides further insight into the mechanisms influencing pro-environmental behavior from the dual perspectives of adequacy and necessity.</p>
<p>Finally, whether consumers&#x2019; attitudes translate into actual behaviors depends on the specific context in which they live (<xref ref-type="bibr" rid="B160">Zhang et al., 2019</xref>; <xref ref-type="bibr" rid="B128">Sun et al., 2022</xref>). Most studies of people&#x2019;s behavioral and psychological reactions to the outbreak of COVID-19 have been based in Western countries where individualism is prevalent, and relatively few studies have been conducted in countries where collectivism and power distance are high (<xref ref-type="bibr" rid="B69">Kwon and Park, 2022</xref>). Thus, this study explores the pro-environmental behaviors of consumers with different PDs during the COVID-19 pandemic in China and broadens the boundary conditions for understanding pro-environmental behaviors.</p>
</sec>
<sec id="S5.SS2">
<title>5.2 Discussion of findings</title>
<p>In this study, we extend the body of knowledge on pro-environmental behavior during the COVID-19 pandemic. The purpose of this study is to contribute to the theoretical understanding of consumer behavior concerning environmental issues and to provide insight into how to develop ecological environments in a sustainable manner.</p>
<p>First, according to the PLS-SEM results, COVID-19 risk perception had a significant effect on pro-environmental behavior and specific emotions, which is consistent with previous studies. Consumers&#x2019; perception of COVID-19 risk creates a sense of crisis, which significantly influences people&#x2019;s pro-environmental behavior (<xref ref-type="bibr" rid="B81">Maleksaeidi and Keshavarz, 2019</xref>; <xref ref-type="bibr" rid="B65">Kim et al., 2022</xref>). Due to COVID-19, people are now more environmentally conscious in their consumption, believing that if we do not care for the environment, pandemics and disasters will happen again in the future (<xref ref-type="bibr" rid="B79">Lucarelli et al., 2020</xref>). However, the conclusion in this study is different from that of <xref ref-type="bibr" rid="B138">Urban and Braun Kohlov&#x00E1; (2022)</xref>, who found that there was no impact of the COVID-19 crisis on environmental attitudes or green decisions. The possible reason for the inconsistency in research is that previous studies ignored the role of individual emotions. Alternatively, only when a certain emotion is evoked in the consumer will he or she actually engage in pro-environmental behavior. As mentioned earlier, among the factors influencing consumers&#x2019; pro-environmental behavior, emotional factors can play a more decisive role than cognitive factors in some cases. According to affective events theory, it is important to recognize that an individual&#x2019;s behavior is not always dictated by his or her rational perceptions. External events in life can cause negative or positive emotional reactions, and these emotional reactions can have a significant impact on attitudes and behaviors.</p>
<p>Second, in terms of emotions, the PLS-SEM results revealed that nostalgia had a significant effect on attitude and pro-environmental behavior, which is consistent with previous studies. <xref ref-type="bibr" rid="B144">Wang et al. (2020)</xref> and <xref ref-type="bibr" rid="B161">Zhang et al. (2021)</xref> found that the emotion of nostalgia had a positive effect on consumers&#x2019; pro-environmental behaviors, such as sustainable recycling and product disposal. However, <xref ref-type="bibr" rid="B143">Wang and Chao (2019)</xref> found that green products are less preferred by consumers with a high feeling of nostalgia; consumers with a strong sense of nostalgia tend to dwell on the past, which inhibits their preference for green products, as green products are often associated with the future (<xref ref-type="bibr" rid="B4">Alzubaidi et al., 2021</xref>). This inconsistency in research occurs because nostalgia is a complex concept, and different types of nostalgia have different effects on consumer behavior (<xref ref-type="bibr" rid="B123">Srivastava et al., 2022</xref>). According to <xref ref-type="bibr" rid="B7">Boym (2008)</xref>, there are two types of nostalgia: &#x201C;restorative nostalgia&#x201D; and &#x201C;reflective nostalgia.&#x201D; &#x201C;Reflective nostalgia&#x201D; refers to reflecting on the remembered past for the purpose of the present and is often associated with establishing continuity. This kind of nostalgia is a positive experience that provides individuals with the opportunity to reflect on the past, which enhances self-worth, self-esteem, and social connectedness and promotes pro-social behavior (<xref ref-type="bibr" rid="B123">Srivastava et al., 2022</xref>; <xref ref-type="bibr" rid="B162">Zhang and Tao, 2022</xref>). There may be a tendency for individuals to invoke nostalgia as a coping mechanism to deal with the social distance restrictions of COVID-19 and the resulting isolation and social disconnection. This may be done as a means to regain a sense of self-continuity and meaning (<xref ref-type="bibr" rid="B151">Xia et al., 2021</xref>). We believe that the nostalgia triggered by COVID-19 is a &#x201C;reflective nostalgia,&#x201D; which promotes pro-environmental behavior. Additionally, according to the NCA result, nostalgia (<italic>d</italic> = 0.131, <italic>p</italic> &#x003C; 0.001) was a necessary condition for pro-environmental behavior.</p>
<p>In addition, the emotion of guilt was found to have a significant effect on pro-environmental behavior, which is consistent with previous studies. Increasingly, consumers are becoming aware that COVID-19 outbreaks are linked to unsustainable consumption patterns. When people realize they could have avoided it, they develop negative guilt (<xref ref-type="bibr" rid="B103">Rees et al., 2015</xref>), which may cause them to take responsibility for the environmental impact (<xref ref-type="bibr" rid="B23">Daryanto et al., 2022</xref>). An ingrained sense of personal responsibility and willingness to act morally compelled to mitigate COVID-19 triggered by guilt can also contribute to the promotion of actions that are environmentally responsible (<xref ref-type="bibr" rid="B86">Milfont et al., 2022b</xref>). Similarly, an awe of nature was found to have a positive and significant effect on attitude and pro-environmental behavior. An individual is more inclined to be generous (<xref ref-type="bibr" rid="B156">Yang and Hu, 2021</xref>), to engage in helping activities (<xref ref-type="bibr" rid="B150">Wu et al., 2022</xref>) and to devote more time to charitable activities when instilled with the feeling of awe (<xref ref-type="bibr" rid="B93">Piff et al., 2015</xref>). A previous study also found that awe significantly influences pro-environmental behavior as a manifestation of pro-social behavior (<xref ref-type="bibr" rid="B102">Ramus and Killmer, 2007</xref>).</p>
<p>Additionally, the moderating effects of PD were confirmed. PD was found to have a significant moderating effect between nostalgia and pro-environmental behavior, which echoes the inconsistency of findings from previous studies regarding nostalgia and pro-environmental behavior (<xref ref-type="bibr" rid="B143">Wang and Chao, 2019</xref>; <xref ref-type="bibr" rid="B144">Wang et al., 2020</xref>; <xref ref-type="bibr" rid="B161">Zhang et al., 2021</xref>). This result confirms that the influence of consumers&#x2019; specific emotions on pro-environmental behavior varies across cultural contexts. As indicated in previous studies, the perception of climate change by individuals is often closely related to their personal values and worldviews; and cultural orientation can have a significant impact on their environmental attitudes and behaviors (<xref ref-type="bibr" rid="B98">Price et al., 2014</xref>). Due to the inequitable distribution of social power, consumers with high PDs take their ignorance of pro-environmental behaviors for granted and justify it with a vengeance. In contrast, individuals in low power distance cultures believe that pro-environmental behavior is one of their responsibilities and obligations and that pro-environmental behavior is dependent upon their participation. Previous research has shown that Chinese consumers have a strong reliance on the government for pro-environmental activities (<xref ref-type="bibr" rid="B12">Chan, 2000</xref>; <xref ref-type="bibr" rid="B155">Yang and Weber, 2019</xref>). This is inextricably linked to China&#x2019;s Confucian culture. Consumers may feel that their individual efforts do not contribute to the improvement of such environmental issues in high power distance cultural contexts (<xref ref-type="bibr" rid="B4">Alzubaidi et al., 2021</xref>). However, even within the same cultural context, there are differences in the cultural perceptions of different consumers (<xref ref-type="bibr" rid="B83">Mi et al., 2020</xref>), which lead to different pro-environmental behaviors.</p>
</sec>
<sec id="S5.SS3">
<title>5.3 Managerial implications</title>
<p>For managerial implications, this study indicates that consumers&#x2019; perceptions of COVID-19 risk are associated with a variety of environmental affective responses, which can influence individuals&#x2019; attitudes toward the environment and, in turn, their pro-environmental behavior. Consequently, governments and marketers should pay attention to cognitive and emotional factors and their interactions to promote pro-environmental behavior among consumers. In the context of COVID-19, fears and negative messages about ecological problems can be included in marketing to induce consumers&#x2019; perception of COVID-19 risks so that consumers consciously attribute the degradation of the ecological environment to human failure in properly handling the moral relationship between humans and nature. In turn, this generates eco-ethical reflections and realizes the relationship between their own abilities and obligations, triggering consumers&#x2019; feelings of empathy and guilt. Furthermore, public service announcements and green advertisements on social media may be infused with awe or nostalgia. Inducing transcendent emotions such as awe and nostalgia can increase public awareness of the detrimental consequences of environmentally harmful behavior and attribute responsibility to them, as well as change consumer attitudes toward environmental protection, thereby contributing to pro-environmental behavior.</p>
<p>In countries with higher power distances, consumers are often less enthusiastic about ecological issues, believing that such costly public goods with no short-term benefits should be handled by the government or large corporations. In light of the findings of this study, Chinese consumers should develop a sense of environmental responsibility. Individuals who display environmental responsibility are more likely to take action to mitigate environmental problems. It is a result of their awareness of environmental problems and their perception of the importance of protecting the environment. The government or marketers should appeal to consumers&#x2019; concern for the environment in a higher power distance cultural context so that they will be motivated to express an emotional response, which, in turn, enhances their sense of environmental responsibility and drives pro-environmental behavior.</p>
</sec>
<sec id="S5.SS4">
<title>5.4 Limitations and future research directions</title>
<p>First, the mechanism through which COVID-19 influences consumers&#x2019; pro-environmental behavior is relatively complex. The purpose of this study is to construct a theoretical model based on a cognitive-emotional-attitudinal-behavioral model and affective event theory to examine the psychological mechanisms underlying consumers&#x2019; pro-environmental behavior development. Future research can be conducted to comprehensively investigate the mechanism of COVID-19&#x2019;s impact on consumers&#x2019; pro-environmental behavior by combining cognitive and emotional frameworks and different theoretical perspectives.</p>
<p>Second, this paper uses the questionnaire method, and the data are all from self-reports of the same subjects. Although the results of the data analysis indicate that CMB does not pose a significant threat, the rigor of the methodology could be further improved (<xref ref-type="bibr" rid="B115">Sharma et al., 2022a</xref>). It is possible for future research on consumer pro-environmental behavior to be supplemented by objective and subjective data to test the robustness of the findings.</p>
<p>To verify the model, this study concludes with a comprehensive application of SEM and a necessity analysis. Researchers may be able to further explore and expand the research model of consumers&#x2019; pro-environmental behavior by employing experimental methods and case studies in the future. Furthermore, the majority of research on consumer pro-environmental behavior is based on the analysis of correlations between influencing factors; COVID-19 is in the midst of a natural experiment (<xref ref-type="bibr" rid="B112">Severo et al., 2021b</xref>); therefore, future research can use causal inference methods to explore the mechanisms that influence consumers&#x2019; pro-environmental behavior.</p>
</sec>
</sec>
<sec id="S6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec id="S7" sec-type="author-contributions">
<title>Author contributions</title>
<p>SZ: conceptualization, methodology, data curation, writing&#x2014;original draft, figures production, and writing&#x2014;review and editing.</p>
</sec>
</body>
<back>
<sec id="S8" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="S9" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="S10" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1093999/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1093999/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.docx" id="DS1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<ref-list>
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