From Information to Action: Exploring the Mediating Effects of Environmental Attitude, Personal Responsibility, and Government Performance on Green Purchase Intentions

From Information to Action: Exploring the Mediating Effects of Environmental Attitude, Personal Responsibility, and Government Performance on Green Purchase Intentions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article From Information to Action: Exploring the Mediating Effects of Environmental Attitude, Personal Responsibility, and Government Performance on Green Purchase Intentions Chunyu YANG, Fuchuan Chen, Xiaonan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6590941/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Understanding how environmental information provision (IP) translates into green purchase intention (GPI) is crucial for promoting sustainable consumption, particularly in rapidly developing urban contexts like Chinese megacities. While the influence of individual motivation is known, the interplay with perceived institutional factors remains less explored. This study investigates the pathways linking IP to GPI, specifically examining the mediating roles of environmental attitude (EA), personal responsibility (PR), and perceived government performance (PGP), guided by the Information-Motivation-Behavioral Skills (IMB) model. Utilizing survey data from 4,966 residents (“Green Lifestyle Survey of Residents in Chinese Megacities”), structural equation modeling (SEM) and multi-group analysis tested the hypothesized relationships. Results confirm IP significantly boosts GPI both directly and indirectly. EA, PR, and PGP all function as significant mediators in this process. Notably, personal responsibility (PR) exerted the strongest mediating effect, highlighting its critical role. Furthermore, individual factors (EA, PR) did not significantly predict PGP, potentially reflecting cultural nuances where personal duty operates independently of institutional evaluation. Significant age and income differences also emerged, with PGP more influential for older individuals and PR resonating more with younger consumers. This research enhances the IMB framework by integrating institutional context (PGP) and reveals actionable, demographically-targeted insights for fostering GPI through appeals to either personal duty or governmental effectiveness. Social science/Environmental studies Social science/Psychology Green Purchase Behavior (GPI) Information Provision (IP) Environmental Attitude (EA) Perceived Government Performance (PGP) Personal Responsibility (PR) Information-Motivation-Behavioral Skills (IMB) Model Figures Figure 1 Figure 2 Introduction The escalating climate crisis and accelerating resource depletion underscore the urgent need for a global transition towards sustainable consumption patterns (IPCC, 2023 ). Within this transition, green consumption, defined as purchasing decisions prioritizing environmental sustainability (Joshi & Rahman, 2015 ), represents a critical pathway for mitigating ecological degradation. Central to understanding green consumption is the concept of Green Purchase intention (GPI), that signifies consumers’ willingness and conscious plan to choose environmentally-friendly products over conventional alternatives(Chan & Lau, 2002 ). GPI serves as a crucial precursor to actual green purchasing behavior, making it a key focal point for research and intervention efforts aimed at fostering sustainability. Despite growing public awareness of environmental issues, a significant and persistent discrepancy exists between consumers’ stated pro-environmental attitudes and their actual purchasing behavior. This phenomenon, widely recognized as the ‘attitude-behavior gap’(Fennis et al., 2011 ; Kollmuss & Agyeman, 2002 ),highlights the complexity of translating environmental concern into tangible action. One commonly-employed strategy to bridge this gap is environmental information provision (IP), that aims to equip consumers with the knowledge necessary to make informed, sustainable choices (Thøgersen & Ölander, 2002 ).Theoretically, the Information-Motivation-Behavioral Skills (IMB) model suggests that effective behavior change necessitates not only identification of relevant information but also sufficient motivation to act upon it and the requisite behavioral skills(Fisher & Fisher, 2023 ). However, while the IMB model provides a valuable framework, its comprehensive application to elucidate specific pathways from environmental information exposure to formation of green purchase intentions remains limited. Several critical gaps persist in the current understanding of this information-to-intention process. First, although the direct positive effect of environmental information on purchase intentions has been documented (Bang et al., 2000 ), the crucial mediating mechanisms through which information translates into intention have been insufficiently explored. Specifically, the nature of psychological ‘motivations’ (such as attitudes and feelings of responsibility) activated by information, as posited by the IMB model, requires deeper investigation within the green consumption context (Kollmuss & Agyeman, 2002 ).Second, existing research tends to focus predominantly on individual-level psychological factors (e.g., attitudes, values, perceived effectiveness) (Kim et al., 2019 ; Liu et al., 2012 ), often neglecting potentially significant influence of the broader institutional context such as citizens’ perceptions of government performance in environmental protection (Perceived Government Performance PGP)(Stern, 2000 ). Despite its potential importance, the interplay between individual motivations and these contextual perceptions has been particularly understudied, especially in contexts where government action is highly visible. Third, there is a consequent lack of integrated empirically-applied behavioral frameworks that holistically capture the interaction between informational inputs, individual psychological drivers (motivation), and relevant contextual factors in shaping GPI (Davis et al., 2009 ) This fragmentation hinders the development of comprehensive and effective strategies. While demographic factors and barriers like price sensitivity are acknowledged influences (Kim & Lee, 2023 ; Liu et al., 2021 ; White et al., 2019 ; Young et al., 2009 ), and negative factors like greenwashing perceptions can also undermine trust (Zhang et al., 2018 ), and understanding the core motivational pathway initiated by information remains paramount(Fennis et al., 2011 ; Kollmuss & Agyeman, 2002 ; Stern, 2000 ; Thøgersen & Ölander, 2002 ).This study aims to bridge these research gaps by developing and empirically testing an integrated theoretical framework grounded in the IMB model. We specifically investigate how environmental information provision (IP) influences GPI among residents in major Chinese cities, a context characterized by rapid urbanization and significant environmental challenges. We examine the crucial mediating roles of EA and PR as key individual motivational factors, along with PGP as a representation of the perceived institutional context. By incorporating both individual and institutional factors as mediators, we seek to unravel the nuanced pathways transforming information into intention within this specific socio-cultural setting(Ajzen, 1991 ; Stern, 2000 ).To guide our investigation, this study addresses the following research questions: How does environmental information provision (IP) directly and indirectly influence GPI? To what extent do environmental attitude (EA), personal responsibility (PR), and perceived government performance (PGP) individually and collectively mediate the relationship between IP and GPI? What are the relative strengths and potential interplay among EA, PR, and PGP within the pathway from information to intention? By addressing these questions, this study offers two primary contributions. From a theoretical perspective, it extends the application of the IMB model by proposing and testing a multi-mediation framework that integrates both individual psychological factors (EA, PR) and perceptions of institutional context (PGP) to explain the information-to-intention link in a non-Western context. From a practical perspective, the findings offer actionable insights for marketers, policy makers, and environmental advocates seeking to design more effective information-based interventions to promote sustainable consumption. Understanding the mediating roles of attitudinal, normative, and contextual factors can help stakeholders develop targeted strategies that address multiple determinants of green purchasing behavior. Theoretical framework Information-Motivation-Behavioral Skills (IMB) Model. The Information-Motivation-Behavioral Skills (IMB) model, originally developed to explain health-related behaviors (Fisher & Fisher, 1992 ), provides a robust framework for understanding the drivers of pro-environmental actions, including GPI(Fisher & Fisher, 2023 ).The model posits that performing a specific behavior is a function of possessing adequate Information related to the behavior, sufficient motivation to enact it, and the necessary behavioral skills to execute it effectively. Within the realm of sustainable consumption, these components translate into specific factors influencing consumer choices. 1.Information (IP) : In the context of GPI, the ‘Information’ component refers to consumer knowledge and awareness regarding environmental problems, the environmental impact of products, and the benefits associated with choosing eco-friendly alternatives. This information is often acquired through various channels such as eco-labels on products, corporate sustainability reports, media coverage, educational campaigns, and government initiatives(Seacat & Northrup, 2010 ). An effective environmental information provision (IP) serves as the initial trigger, potentially activating subsequent motivational and behavioral components of the IMB model. 2.Motivation (EA & PR) : The “Motivation” component within the IMB framework encompasses the diverse factors that propel individuals toward action, bridging the gap between knowing and intending. This motivation can be conceptualized along dual pathways: personal attitudes and social/normative influences(Stern, 2000 ). In this study, we operationalize motivation through two key psychological constructs: Environmental Attitude (EA) Reflecting the cognitive and affective evaluation of environmental protection, EA represents an individual’s favorable or unfavorable disposition towards environmental issues and green product(Liu et al., 2012 ). It aligns with the personal/cognitive dimension of motivation. Personal Responsibility (PR) Capturing the normative dimension, PR refers to an individual’s sense of moral obligation or duty to contribute to environmental solutions through his or her actions(Stern, 2000 ). Since empirical evidence supports that consumers possessing strong environmental attitudes and a heightened sense of personal responsibility are significantly more inclined to form intentions to purchase sustainable products(Yi & Yi, 2024 ),EA and PR are hypothesized as crucial motivational mediators translating environmental (Kavitha & Kumar, 2023 ; Xu et al., 2018 ) information into GPI. 3.Behavioral Skills (Facilitated by PGP) : The Behavioral Skills component of the IMB model encompasses perceived self-efficacy—an individual’s confidence in their ability to successfully perform a behavior(Fisher & Fisher, 2023 ). For green purchasing, this involves the perceived capacity to effectively identify, access, afford, and utilize green products. Such self-efficacy is not formed in isolation but is significantly shaped by the perceived quality and supportive character of the institutional environment(Newig & Fritsch, 2009 ), particularly in contexts like China where government performance strongly influences public life (Kavitha & Kumar, 2023 ; Wang et al., 2014 ; Xu et al., 2018 ). We therefore propose that Perceived Government Performance (PGP) regarding environmental protection serves as a robust proxy for these contextually-dependent Behavioral Skills. High PGP fosters confidence in credible government commitment and effectiveness(Ehret et al., 2020 ), leading individuals to perceive a more reliable enabling environment, including perceiving functional systems such as eco-labeling, accessible information, adequate green infrastructure (e.g., recycling, EV charging), and trustworthy regulatory oversight (Andika et al., 2023 ; Yi & Yi, 2024 ). Critically, this perception of effective governmental facilitation directly translates into enhanced consumer self-efficacy. Individuals feel more capable and confident navigating the green marketplace when they perceive that the government is performing well in creating supportive conditions(Deryugina & Shurchkov, 2016 ; Ehret et al., 2020 ; Seacat & Northrup, 2010 ). By reflecting the perceived ease and feasibility conferred by effective governance, PGP encapsulates a crucial dimension of individuals’ perceived ability to act, thus functioning as a valid indicator for Behavioral Skills in the context of green consumption. This research utilizes the IMB model to examine how IP influences GPI among urban Chinese residents. We specifically investigate the mediating pathways through EA and PR, representing the core “Motivation” component, and PGP, conceptualized as a crucial factor shaping the perceived “Behavioral Skills”. This integrated approach allows for a more nuanced understanding of how information translates into intention via both individual psychological drivers and perceptions of the institutional environment in non-Western urban settings. Hypotheses Development Information Provision and Green Purchase Intention IP and GPI . The Information Provision plays a critical role in shaping consumer decision-making, particularly concerning green purchase intention (GPI). Drawing upon the Information-Motivation-Behavioral Skills (IMB) model(Fisher & Fisher, 1992 ). Studies show that an accessible environmental information (e.g., carbon footprints, certifications) enhances awareness, fostering GPI (Deryugina & Shurchkov, 2016 ; Yi & Yi, 2024 ). Different types of information matter; factual details reduce gaps (Andika et al., 2023 ), while persuasive or emotional content can align values or increase urgency(Franzen, 2003 ; Stern, 2000 ). Effectiveness also varies by context; government sources may build trust (Ehret et al., 2020 ), while overload or “greenwashing” can erode it (Seacat & Northrup, 2010 ), and cultural differences exist(Andika et al., 2023 ).While some findings are context-dependent(Stern, 2000 ), since a positive IP and GPI link is broadly supported (Fisher & Fisher, 2023 ), this study proposed the following hypothesis: H1: IP positively influences GPI. Environmental Attitude and Green Purchase Intention EA and GPI. Environmental Attitude (EA) signifies an individual’s evaluation of environmental protection, encompassing beliefs, values, and concerns(Dunlap et al., 2000 ; Franzen, 2003 ; Stern, 2000 ), often reflecting dimensions like ecological worldview and perceived responsibility(Schultz et al., 2004 ). As a psychological precursor, EA consistently shows a strong positive relationship with GPI (Chan & Lau, 2002 ; Kim et al., 2019 ). Consumers with stronger EA align green products with their values (Xiao et al., 2022 ), feel a greater moral obligation (Surira et al., 2024 ), and may prioritize environmental benefits, reducing dissonance(Liu et al., 2012 ). Perceived consumer effectiveness (PCE)—the belief that individual actions matter—also mediates this relationship, bridging attitudes to actionable intentions(Zhang et al., 2018 ). Considering EA as a motivational factor within frameworks like the IMB model, this study hypothesizes: H2: Environmental attitude (EA) positively affects Green Purchase Intention (GPI). IP, EA and GPI. The IMB model suggests information influences behavior via motivation (Fisher & Fisher, 1992 ). Information Provision (IP) can shape Environmental Attitude (EA) by enhancing understanding of consequences(Deryugina & Shurchkov, 2016 ). Several mechanisms underpin this relationship; environmental information can increase awareness and heighten concern regarding environmental issues(Surira et al., 2024 ),challenge misconceptions and thereby improve attitudes toward green products(Andika et al., 2023 ), and bolster positive attitudes by highlighting the effectiveness of individual contributions, thus reducing perceived helplessness (Stern, 2000 ).This mediational pathway is consistent with value-belief-norm theory, suggesting that information influences personal norms and behavior by altering beliefs and attitudes (Stern, 2000 ), and finds empirical support(Seacat & Northrup, 2010 ). This study also proposed the following hypothesis (H): H3: EA positively mediates the relationship between IP and GPI Personal Responsibility (PR) and Green Purchase Intention PR and GPI. Personal Responsibility (PR) constitutes an individual’s perceived obligation to mitigate environmental harm, grounded in moral norms and awareness of consequences (Schwartz, 1994 ; Schwartz & Bilsky, 1990 ), and encompassing dimensions like responsibility acceptance and attribution (Bamberg & Möser, 2007 ). A strong positive relationship between PR and pro-environmental behaviors, including Green Purchase Intention (GPI), has been consistently demonstrated in empirical research (Kim et al., 2019 ; Thøgersen, 2006 ; Thøgersen & Ölander, 2002 ). Specifically, consumers internalizing greater PR perceive green purchasing as fulfilling an ethical duty (X. Liu et al., 2012 ), aligning with moral imperatives (Kim et al., 2019 ). This sense of responsibility acts as a potent normative motivator for GPI, potentially exceeding the influence of purely cognitive factors (Chan & Lau, 2002 ), translating perceived obligation into purchase intent. Accordingly, this study proposed the following hypothesis: H4: PR positively affects GPI. IP, PR and GPI. Beyond potential direct effects, Personal Responsibility (PR) is also theorized to function as a crucial mediator within the information-behavior pathway. Specifically, IP can enhance an individual’s awareness of environmental problems and clarify their personal role in either contributing to or mitigating these issues (Deryugina & Shurchkov, 2016 ). Since this heightened awareness and understanding fostered by information are believed to activate a stronger sense of PR, compelling individuals to align their actions with perceived moral obligations(Surira et al., 2024 ), this activated sense of PR, stimulated by the provided information, is expected to translate into a greater GPI, as consumers view green purchasing as a viable means to fulfill their felt responsibility (Liu et al., 2012 ). Accordingly, this study proposed the following hypothesis: H5: PR positively mediates the relationship between IP and GPI. Perceived Government Performance (PGP) and Green Purchase Intention PGP and GPI . Perceived Government Performance (PGP) captures citizens’ evaluations of governmental effectiveness, transparency, and legitimacy in addressing environmental challenges(Newig & Fritsch, 2009 ). This perception influences pro-environmental actions by fostering trust in collective solutions and legitimizing individual efforts within that framework (Kollmuss & Agyeman, 2002 ). Empirical studies consistently find a positive relationship between PGP and GPI. When individuals perceive effective government commitment (e.g., through policies and enforcement), trust is enhanced in the broader environmental strategy, motivating personal alignment such as green purchasing (Surira et al., 2024 ) related to enhanced consumer confidence. Furthermore, active government promotion and regulation lend legitimacy to sustainable practices, reinforcing green consumption as socially normative and personally meaningful (Bamberg & Möser, 2007 ), thus strengthening GPI. Accordingly, this study proposed the following hypothesis: H6: PGP positively affects GPI. EA , PR and PGP . The IMB model posits that motivation enhances behavioral capacity, including factors facilitating action (Fisher & Fisher, 1992 ). Within this framework, we examine how motivational components EA and PR influence PGP. PGP, reflecting trust in institutional effectiveness, can be seen as enabling individual green actions. Specifically, individuals with stronger EA may evaluate government environmental initiatives more positively, perceiving greater credibility when policies align with their pro-environmental values (Surira et al., 2024 ; Zhang et al., 2018 ). Similarly, a heightened sense of PR likely leads to more favorable citizen satisfaction(Nie & Wang, 2022 ; Zhang et al., 2018 ), because individuals holding governments accountable expect policies reflecting collective action norms (Bamberg & Möser, 2007 ). Effective institutional support indeed strengthens trust (Kollmuss & Agyeman, 2002 ). Based on this theoretical and empirical foundation, the following hypotheses are proposed: H7a: EA positively affects PGP. H7b: PR positively affects PGP. IP , PGP and GPI . Integrating the Information-Motivation-Behavioral Skills (IMB) model with theories of institutional trust and policy legitimacy, we investigate the role of PGP in shaping GPI. We posit that IP, particularly related to governmental environmental initiatives, enhances PGP by increasing public awareness of institutional efforts (Kavitha & Kumar, 2023 , 2023 ; Deryugina & Shurchkov, 2016 ; Liu et al., 2021 ). This elevated PGP is in turn proposed to foster GPI. The mechanism operates through enhanced institutional trust, mitigating perceived risks in green markets (Tsang et al., 2009 ), and bolstering policy legitimacy, framing GPI as an institutionally-endorsed behavior(Kavitha & Kumar, 2023 ; Liu et al., 2021 ). Furthermore, PGP fosters perceptions of regulatory assurance, increasing confidence in green products (Ioannou et al., 2022 ; Shojaei et al., 2024 ). The nature of IP, such as its transparency or government alignment, also shapes PGP’s mediating strength (Andika et al., 2023 ; Surira et al., 2024 ). This mediating pathway (IP → PGP → GPI) is expected to be particularly salient in contexts such as China, characterized by high institutional trust and collectivist values, where governmental actions significantly influence public attitudes and behaviors (Chan & Lau, 2002 ; Ehret et al., 2020 ). Accordingly, this study proposed the following hypothesis: H8: PGP positively mediates the relationship between IP and GPI. Based on the above theories and research hypotheses, a framework model of the mechanism influencing GPI has been constructed (Fig. 1 ). Data, Variables and Methods Data collection The data for this study were drawn from the ‘Green Lifestyle Survey of Residents in Chinese Megacities,’ conducted between June 2023 and January 2024. The online survey targeted urban residents for collecting data; urban residents account for 38% of national apparel consumption (NBS,2024). Given China’s vast population (1.41 billion by 2023) and 31 provincial-level regions, nationwide sampling poses methodological challenges. The survey strategically targeted seven megacities (Beijing, Shanghai, Guangzhou, Shenzhen, Chongqing, Tianjin, and Chengdu; selected based on populations exceeding 10 million, as per the 2021 ‘Seventh National Census’(NBS,2023). These cities collectively generate over 2.1 million tons of discarded clothing annually, representing 45% of China’s total post-consumer textile waste (CNTAC, 2025). The sampling design meticulously followed established sampling principles. To enhance accuracy and scientific rigor while maintaining feasibility, we used a multistage sampling approach that integrated probability proportional to size sampling with equal probability sampling. Considering the economic efficiency and sampling errors, cost implications were factored into determining and allocating sample sizes. For scientific, efficient, and practical management of issues related to sample data weighting, a sampling scheme was carefully devised and robust methods for adjusting the sample data weights were developed. A total of 5,188 valid questionnaires were collected; after excluding invalid responses, 4966 valid questionnaires were retained. Ethical clearance was obtained from the Psychology Ethics Committee (Protocol XXXXXXX), that approved the study design, recruitment process, and data management practices. The demographic characteristics of the participants are provided in Table 1 . As shown in Table 1 , the survey’s male-to-female ratio was fair and matched China’s age distribution. The sampling number of questionnaires gathered in the examined cities was adequate, and the sample data distribution was similar among the seven mega-cities. A variety of data helped to identify the target populations for the seven mega-cities. Table 1 Socio-demographic characteristics of the sample population. variables Characteristics Frequency Percentage Characteristics Frequency Percentage Gender Male 2480 49.90% Monthly family income (RMB) < 2999 95 1.91% Female 2486 50.10% 3000–4999 375 7.55% Age 60 91 1.83% 40000–59999 114 2.30% Education Junior High School and below 258 5.20% > 60000 79 1.59% High School 689 13.87% City Shanghai 863 17.38% Junior College / Associate Degree 1173 23.62% Beijing 744 14.98% Bachelor's Degree 2565 51.65% Shenzhen 621 12.51% Master's Degree 240 4.83% Chongqing 996 20.06% Doctoral Degree 41 0.83% Guangzhou 623 12.55% Marital Status Single 978 19.69% Chengdu 622 12.53% Married 3988 80.30% Tianjin 497 10.01% Household Registration Type Urban 3116 62.75% Rural 1850 37.25% [Data source] Data were compiled based on this research project. Variables and Measures Independent Variable. This study combines the Information-Motivation-Behavioral (IMB) model with existing research on IP. The independent variable IP was operationalized as the composite frequency of individuals’ exposure to environmental information across three primary media channels: (1) new/social media (e.g., Weibo, NetEase, WeChat, Douyin, Xiaohongshu), (2) interpersonal networks (e.g., family, friends, colleagues), and (3) traditional media (e.g., TV, newspapers, magazines, radio). Responses were recorded on a 5-point Likert scale: 5 = “several times daily”, 4 = “weekly”, 3 = “monthly”, 2 = “yearly”, and 1 = “never”. The IP score was calculated by summing the standardized frequencies across all items, with higher scores indicating greater environmental information exposure. Dependent Variable and mediating variable Environmental attitude (EA), personal responsibility (PR), perceived government performance (PGP), and green purchase intention (GPI). All variables,except for the dependent variable (GPI) are assessed using a five-point Likert scale, where responses range from 1 (strongly disagree) to 5 (strongly agree). For GPI, a five-point Likert scale is used, with 5 indicating “several times every day” and 1 representing “never.” The measurement tools for each variable, along with their validity indices and references, are presented in Table 2 . Table 2 Items ’loadings (k) and the constructs’Cronbach’s α coefficients and AVEs. Variable Names Items Code Indicator Factor loadings Cronbach'α CR AVE Literature Green Purchase Intention (GPI) GPI1 Reduce the frequency of purchasing new clothing, shoes, and hats. 0.691 0.673 0.757 0.443 Chan & Lau, 2002 ; Kim & Lee, 2023 ; Kim, et al., 2019 . GPI2 Avoid pursuing products with elaborate packaging. 0.717 GPI3 Minimize the use of disposable items and utensils when traveling or ordering takeout. 0.728 GPI4 Within an acceptable price range, prefer green appliances or energy-efficient products. 0.501 Environmental Attitude (EA) EA1 Inconvenience in purchasing or using green products. 0.699 0.802 0.802 0.504 Bang et al., 2000 ; Dunlap et al., 2000 ; Stern, 2000 EA2 Lack of trust in current green products. 0.697 EA3 Purchasing green products does not contribute to personal social image. 0.724 EA4 Green products have minimal benefits for the environment. 0.719 Perceived Government Performance (PGP) PGP1 The government is highly professional in promoting low-carbon living. 0.673 0.774 0.776 0.465 Newig & Fritsch, 2009 ; Ioannou et al., 2022 ; Shojaei et al., 2024 PGP2 The government is committed to promoting low-carbon living for the public good. 0.638 PGP3 The government is determined to promote low-carbon living. 0.654 PGP4 The government is highly efficient in promoting low-carbon living. 0.757 Personal Responsibility (PR) PR1 I have the responsibility and obligation to protect the environment. 0.592 0.681 0.743 0.371 Ajzen et al., 1996; Brekke et al., 2010; Gifford & Nilsson, 2014; Kim et al., 2019 PR2 Despite the small impact of an individual, I should contribute to environmental protection. 0.524 PR3 Low-carbon and environmental actions should start with small things. 0.637 PR4 Those who cause environmental pollution should be held accountable for ecological deterioration and environmental degradation. 0.749 PR5 Participating in environmental governance is a duty and responsibility of the public. 0.515 Results Measurement model. Prior to hypothesis testing, Cronbach's α coefficients were calculated to evaluate the reliability of each construct. As shown in Table 2 , the factor loadings of the indicators ranged from 0.501 to 0.757 (all exceeding 0.45), and Cronbach's α values varied from 0.673 to 0.802 (all greater than 0.6) (Hair et al., 1998). These results indicate that the reliability of all constructs in the research model is acceptable. Convergent validity can be measured by the average variance extracted (AVE). The discriminant validity of the overall measurement model can be measured by the average variance extracted (AVE), according to Henseler et al (Henseler et al., 2014 , 2016 ). Discriminant validity refers to the extent to which a construct is distinct from other constructs (Henseler et al., 2014 ). Table 3 illustrates that the square root of the AVE for each construct (diagonal elements in bold) exceeds the correlations among constructs, confirming good discriminant validity. Furthermore, each AVE value is greater than 0.36, and the CR values are greater than 0.70, providing reasonable support for the convergent validity of the scales(Henseler et al., 2014 ). These findings confirm both the reliability and validity of the proposed model. Table 3 Assessment of discriminant validity. Variable GPI EA PR PGP GPI 0.666 EA 0.052 ** 0.710 PR 0.388 ** -0.074 ** 0.682 PGP 0.107 ** 0.033 * 0.061 ** 0.610 Note: ** Significant at the 0.01 level (two-tailed). * Significant at the 0.05 level (two-tailed). Structural model. Structural Equation Modeling (SEM) was employed to test the hypotheses outlined above. The goodness of fit indices are presented in Table 4 . Model fit summary. The model’s fit was assessed using the GFI, AGFI, CFI, IFI, and TLI indicators, which yielded values of 0.987, 0.981, 0.977, 0.977, and 0.971, respectively. The RMSEA value was 0.028, and the Chi-square to degrees of freedom ratio (χ²/df) was 4.838. These results suggest that the fit indices meet acceptable levels(Bagozzi, 1981 ). Table 4 Main fitness test index values. Fitness index x 2 /df RMSEA GFI AGFI CFI IFI TLI Standard < 5 0.9 > 0.90 > 0.90 > 0.90 > 0.90 Index value 4.838 0.028 0.987 0.981 0.977 0.977 0.971 Result Ideal Ideal Ideal Ideal Ideal Ideal Ideal Hypotheses test. The path analysis results, shown in Table 5 , also reveal that both PI and EA are positively associated with GPI, with standardized path coefficients of 0.284 (p < 0.001, C.R. = 14.160) and 0.107 ( p < 0.001, C.R. = 5.925), respectively, thus supporting H1 and H2. Additionally, PR and PGP are positively related to GPI, with standardized path coefficients of 0.391 ( p < 0.001, C.R. = 14.851) and 0.079 ( p < 0.001, C.R. = 4.066), respectively, confirming support for H4 and H6. However, since EA and PR are not significantly related to PGP, with standardized path coefficients of 0.033( p = 0.071, C.R.=1.803) and 0.043( p = 0.063, C.R.=1.863), H7a and H7b were rejected. Table 5 Path analysis. path Std.Estimate S.E. C.R. P IP→EA 0.114 0.008 7.235 *** IP→PGP 0.112 0.005 5.603 *** IP→PR 0.454 0.004 23.339 *** H1 IP→GPI 0.284 0.006 14.160 *** H2 EA→GPI 0.107 0.011 5.925 *** H4 PR→GPI 0.391 0.037 14.851 *** H6 PGP→GPI 0.079 0.024 4.066 *** H7aEA→PGP 0.033 0.009 1.803 ns H7bPR→PGP 0.043 0.026 1.863 ns Note: * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001. ns indicates not significant. [Data source] The data above are compiled based on this research project. Mediation effect test. As shown in Fig. 2 and Table 6 , we applied the bootstrap confidence intervals method with 5,000 bootstrap samples to examine how the effect of PI on GPI is mediated through EA, PR, and PGP. The bootstrap test results revealed significant indirect effects of PI on GPI through EA, PR, and PGP, with coefficient values of 0.011, 0.178, and 0.009, respectively, all with p -values less than 0.001. Since these findings support H3, H5, and H8, except for H7a and H7b, all proposed hypotheses are supported. After theoretical refinement and empirical validation, hypotheses H7a and H7b were removed, resulting in the final confirmatory model for the mechanisms influencing green purchase intentions. Table 6 Mediation Effect Test. path SE Std.Estimate Bias-corrected-95%CI Lower Upper P H3 PI→EA→GPI 0.003 0.011 0.006 0.016 *** H5 PI→PR→GPI 0.016 0.178 0.150 0.212 *** PI→EA→PGP→GPI 0.000 0.000 0.000 0.001 * PI→PR→PGP→GPI 0.001 0.002 0.000 0.004 ns H8 PI→PGP→GPI 0.003 0.009 0.006 0.019 *** Multi-group analysis. To explore the heterogeneity of green purchase intentions mechanisms influencing among groups with varying demographic characteristics, we further analyzed mechanisms influencing GPI across multiple groups. The observations were divided into two groups based on each demographic factor. Table 7 provides a description of the multi-group categories and their corresponding sample sizes. Our analysis includes a total of 10 models, categorized as follows: gender (male, female), age (< 40, ≥ 40), residence (First-tier, New first-tier), income (< 8000, ≥ 8000), and education level (below bachelor’s degree, bachelor’s degree or above). Table 7 Multi-group description and samples. Variable Category Sample Size Percentage Gender Male 2480 49.94% Female 2486 50.06% Age Below 40 2073 41.74% Above 40 2893 58.26% City of Residence First-tier: Shanghai,Beijing,Shenzhen,Guangzhou 2851 57.41% New First-tier: Chengdu,Chongqing,Tianjing 2115 42.59% Income An income of less than 8000 RMB per month 1645 33.13% An income of more than 8000 RMB per month 3321 66.87% Educational Level Below Bachelor's Degree 2120 42.69% Bachelor's Degree or Above 2846 57.31% Note: “First-tier cities”: Designated by the National Bureau of Statistics of China, Beijing, Shanghai, Guangzhou, and Shenzhen are classified as first-tier cities. “New first-tier cities”: Based on multiple dimensions, including commercial resource concentration and urban connectivity, _The First Financial Weekly_ of China assesses Chengdu, Chongqing, Tianjin, and others as new first-tier cities. Table 8 demonstrates that all representative fit indices are within acceptable levels, indicating that the multi-group structural equation model fits well (Bagozzi & Yi, 1988). Table 9 presents the estimated coefficients for the influencing paths across the 10 models. Notable differences were observed in the mechanisms of these models. Key findings include that all paths in the 10 models, except for the EA→GPI path ( β = 0.044, p > 0.05) and the PI→EA→GPI path ( β = 0.006, p > 0.05) in the age ≥ 40 group, were significant and positive. Overall, PI had a significant positive impact on EA, PR, and PGP, while PI, EA, PR, and PGP significantly positively influenced GPI. Table 8 Fitness test of the structural model. Fitness Gender Age City Income EL x 2 /df 3.602 3.556 3.494 3.400 3.482 RMSEA 0.023 0.023 0.022 0.022 0.220 GFI 0.979 0.980 0.980 0.981 0.980 AGFI 0.972 0.973 0.973 0.974 0.973 CFI 0.967 0.968 0.969 0.970 0.969 IFI 0.968 0.968 0.969 0.970 0.969 TLI 0.960 0.961 0.962 0.964 0.962 Note: EL = Education Level The following provides a detailed discussion of the path characteristics across the 10 models. First, regarding gender differences, male respondents were more sensitive to all paths, except for PI→GPI ( β = 0.245, p < 0.01), compared to females ( β = 0.254, p < 0.001). Second, all paths were significant and positive for respondents aged 0.05; β = 0.006, p > 0.05). Respondents aged ≥ 40 were also more sensitive to the path PI→PGP ( β = 0.160, p < 0.001) than those aged < 40 ( β = 0.111, p < 0.01). Conversely, for paths such as PGP→GPI, PI→PR, and PI→PR→GPI, respondents aged < 40 were more sensitive ( β = 0.083, p < 0.001; β = 0.468, p < 0.001; β = 0.209, p < 0.001) than their older counterparts ( β = 0.081, p < 0.001; β = 0.443, p < 0.001; β = 0.202, p < 0.001). Third, in the city of residence group, respondents in New first-tier cities exhibited greater sensitivity to all paths, except for PR→GPI and PI→PR→GPI ( β = 0.444, p < 0.001; β = 0.204, p < 0.001), compared to those in first-tier cities ( β = 0.465, p < 0.001; β = 0.212, p < 0.001). Fourth, respondents with an income < 8000 RMB/month were more sensitive to paths PI→EA, EA→GPI, PI→PGP, PGP→GPI, PI→EA→GPI, and PI→PGP→GPI ( β = 0.173, p < 0.001; β = 0.087, p < 0.01; β = 0.136, p < 0.001; β = 0.089, p < 0.01; β = 0.015, p < 0.01; β = 0.012, p < 0.01) than respondents earning ≥ 8000 RMB/month ( β = 0.098, p < 0.001; β = 0.068, p < 0.01; β = 0.133, p < 0.001; β = 0.077, p < 0.001; β = 0.007, p < 0.001; β = 0.010, p < 0.001). For the remaining four paths, respondents with higher income were more sensitive. Fifth, among education level groups, respondents with education below a bachelor’s degree were more sensitive to the paths EA→GPI, PI→PR, PI→EA→GPI, and PI→PR →GPI ( β = 0.097, p < 0.001; β = 0.482, p < 0.001; β = 0.010, p < 0.001; β = 0.215, p < 0.001) than those with a bachelor’s degree or higher ( β = 0.054, p < 0.01; β = 0.430, p < 0.001; β = 0.007, p < 0.05; β = 0.198, p < 0.001). In contrast, for the other six paths, those with higher education levels were more sensitive. Table 9 Multi-group analysis of the mechanisms that influence GPI. path Gender Age City Income Education Level Male Female Below 40 Above 40 First-tier New First-tier lower 1 upper 1 lower 2 upper 2 PI→EA 0.123 *** 0.109 *** 0.102 *** 0.136 *** 0.109 *** 0.127 *** 0.173 *** 0.098 *** 0.105 *** 0.129 *** EA→GPI 0.088 *** 0.052 * 0.093 *** 0.044 0.063 ** 0.082 ** 0.087 ** 0.068 ** 0.097 *** 0.054 * PI→PGP 0.152 *** 0.117 *** 0.111 ** 0.160 *** 0.118 *** 0.155 *** 0.136 *** 0.133 *** 0.098 *** 0.164 *** PGP→GPI 0.089 *** 0.072 * 0.083 *** 0.081 ** 0.073 ** 0.090 ** 0.089 ** 0.077 *** 0.067 * 0.093 *** PI→PR 0.492 *** 0.424 *** 0.468 *** 0.443 *** 0.455 *** 0.459 *** 0.412 *** 0.476 *** 0.482 *** 0.430 *** PR→GPI 0.487 *** 0.422 *** 0.447 *** 0.457 *** 0.465 *** 0.444 *** 0.401 *** 0.487 *** 0.447 *** 0.459 *** PI→GPI 0.245 ** 0.254 *** 0.254 *** 0.253 *** 0.252 *** 0.253 *** 0.217 *** 0.267 *** 0.213 *** 0.284 *** PI→EA→GPI 0.011 *** 0.006 * 0.009 *** 0.006 0.007 ** 0.010 ** 0.015 ** 0.007 *** 0.010 *** 0.007 * PI→PGP→GPI 0.014 *** 0.008 ** 0.009 *** 0.013 ** 0.009 ** 0.014 *** 0.012 ** 0.010 *** 0.007 * 0.015 *** PI→PR→GPI 0.240 *** 0.179 *** 0.209 *** 0.202 *** 0.212 *** 0.204 *** 0.165 *** 0.232 *** 0.215 *** 0.198 *** Note: Lower1 = An income of less than 8000 RMB per month, Upper1 = An income of more than 8000 RMB per month; Lower2 = Below Bachelor's Degree, Upper2 = Bachelor's Degree or Above. Discussion This study empirically validates an integrated structural model, grounded primarily in the Information-Motivation-Behavioral Skills (IMB) framework, to elucidate the complex mechanisms linking IP and GPI. Our model distinctively incorporates pathways from IP to both intrinsic EA and extrinsic PR motivations, as well as to behavioral skills (PGP), ultimately influencing GPI. Notably, we extend prior frameworks by explicitly testing the direct influence of information on motivation(Andika et al., 2023 ), finding empirical support for these hypothesized relationships (Fig. 1 ). Since the pathways and corresponding hypotheses were validated through empirical results, this study confirms not only the impacts of information on intrinsic and extrinsic motivations and behavioral skills, but also the influence of these motivations on behavioral skills and their subsequent effects on behavioral intentions. IP was also found to directly enhance GPI (H1 supported) among 4,966 residents from seven major Chinese cities: Shanghai, Beijing, Shenzhen, Chongqing, Guangzhou, Chengdu, and Tianjin. This result can be attributed to China’s strong emphasis on environmental protection, which encourages green product purchases through platforms such as Sina and NetEase, as well as traditional media like television and radio(Zhao et al., 2022 ). Information sharing on these platforms addresses social needs such as interaction and information exchange (Bedard & Tolmie, 2018 ). Residents of these cities, influenced by the rapid development of the internet and the proliferation of smartphones, have access to a wider array of information channels, including word-of-mouth from family and friends ( Zhang et al., 2018 ). Furthermore, companies leverage both new and traditional media as sales channels and organize public welfare activities to promote green concepts and products. These cities, the most developed in China, play a leading role in implementing green purchase policies and measures (Liu et al., 2021 ). For example, Kang & Kim ( 2017 ) found that information sharing via social media can motivate consumers to adopt green products and enhance their purchase behaviors. Based on the IMB model, this study provides valuable insight into successful implementation of China’s green consumption policies and the promotion of green products by businesses through information dissemination(Kang & Kim, 2017 ). This research also explored the mechanisms through which IP influences GPI, focusing on the mediating roles of EA, PR, and PGP. The findings support the proposed framework, demonstrating that improved IP enhances both motivation and behavioral demonstration. Consistent with prior studies(Kim et al., 2019 ; Stern, 2000 ), providing environmental knowledge strengthens consumers' intentions to purchase green products, aligning with the IMB model that asserts that information is a key driver of behavioral change(Yi & Yi, 2024 ). Both EA and PR were found to mediate the IP-GPI relationship (H2-H5 supported). According to the Theory of Planned Behavior(Ajzen, 1991 ), EA significantly mediates the impact of IP on GPI, confirming that information fosters pro-environmental beliefs, in turn motivating sustainable consumption. Frequent IP can promote positive environmental attitudes(Steg & Vlek, 2009), increasing the intention to conserve resources(Willis et al, 2011 ) and triggering responses to environmental crises (Yazdanpanah et al., 2014 ), thus influencing protective behaviors (Unlocking water sustainability: The role of knowledge, attitudes, and practices among women (AlHaddid et al., 2024 )). PR also mediates this relationship, supporting the Norm Activation Model (NAM): when individuals perceive a moral obligation to act sustainably, they are more likely to translate knowledge into action(Surira et al., 2024 ). Beyond individual motivations, PGP was also positively related to GPI (H6 supported) and mediated the relationship between IP and GPI (H8 supported), suggesting that trust in government policies (e.g., regulations, incentives) reduces barriers to green consumption. This supports Institutional Theory, that highlights the role of external support systems in enhancing behavioral control(Greve & Teh, 2018 ) In China, government environmental programs and policies are widely disseminated through various information channels, exerting a systemic influence on enhancing citizens' PGP. The government not only provides infrastructure (e.g., EV charging stations) to facilitate large-scale green consumption but also implements several programs aimed at encouraging green purchasing behaviors (e.g., green vehicle tax exemptions, subsidies). To some extent, altering extrinsic motivation (PR) through IP is easier than influencing intrinsic factors (EA). It is also easier to influence GPI through PR than through EA. Previous research indicates that the role of IP and sharing in shaping consumer behavior is growing(Mangold & Faulds, 2009 ). As an external factor, IP can influence consumers’ psychological states, leading to collective evaluations of environmental responsibility. PR generates social pressure(Ajzen & Fishbein, 1977 ). Compared to consumers in other countries, green consumption behavior in China is significantly influenced by collectivist values (Yan et al., 2021 ), including social pressure, that make the PR exhibited by a group a strong predictor of individual behavior (Ralston et al., 1998). Therefore, for consumers in a collectivist culture like China’s, PR plays a crucial role in shaping their intention to purchase green products. Finally, the non-significant effects of EA and PR on perceived government PGP (H7a and H7b rejected) reveal important nuances in how individuals perceive the role of environmental governance in collectivist contexts. Two interrelated explanations emerge. First, cultural collectivism decouples individual motivation from institutional trust. In China’s top-down environmental governance system, citizens often view environmental protection as a state responsibility rather than an individual obligation (Zhang & Sun, 2024 ). Strong EA or PR may even reduce expectations of personal action, as individuals assume that the government will lead systemic solutions (Liu et al., 2021 ). This contrasts with individualist cultures, where personal norms often align with evaluations of institutional performance (Bamberg & Möser, 2007 ). Second, the IMB model’s assumption regarding “behavioral skills” faces boundary conditions. While the model suggests that motivation (EA/PR) enhances actionable skills(Fisher & Fisher, 1992 ), PGP functions as an external enabler (e.g., policy credibility) rather than an individual capability. This misalignment calls for cross-level theories (e.g., institutional theory) to bridge macro-level governance perceptions with micro-level psychographics(Greve & Teh, 2018 ). The multi-group analysis revealed significant heterogeneity in the mechanisms influencing GPI across demographic segments. Our findings demonstrate that the components of the IMB model operate differently depending on individual characteristics, offering important insights for designing targeted interventions. All multi-group models demonstrated satisfactory fit indices (Table 8 ), confirming the structural model's validity across demographic groups. Nearly all hypothesized paths were significant across the ten models, with two exceptions observed in the older age group (≥ 40). In this group, EA did not significantly influence GPI (EA→GPI), nor did it mediate the relationship between IP and GPI (IP→EA→GPI). These exceptions suggest that while the IMB framework is broadly applicable, path strengths vary significantly across demographic profiles. Age-Related Variations. Substantial variation was observed between age groups. Among younger respondents (< 40), all hypothesized pathways were significant, indicating robust IMB mechanisms. However, in the older group (≥ 40), the pathway from EA to GPI was non-significant. This suggests that EA may be less influential in shaping purchase decisions for older individuals who may prioritize other considerations. Notably, older participants exhibited greater sensitivity to the effect of IP on PGP (IP→PGP), potentially indicating increased institutional trust or concern with age. In contrast, younger respondents showed stronger mediation effects via personal responsibility, implying a greater tendency to internalize environmental messages as moral or civic obligations. Income-Based Differences . Income also moderated the pathways within the IMB model. Lower-income respondents (< 8000 RMB/month) exhibited stronger associations between IP and both EA and PGP, along with more pronounced effects of these mediators on GPI. This suggests that for this group, IP effectively activates both attitudinal and institutional mechanisms. In contrast, higher-income individuals showed stronger effects via personal responsibility pathways, possibly reflecting a greater sense of agency and self-efficacy linked to financial security. These findings extend the IMB model by highlighting how its components function heterogeneously across demographic segments. IP strategies should be customized to align with the dominant pathways within each group. For older consumers, strategies emphasizing institutional performance and direct informational appeals may be more effective than those focused on shifting attitudes. For lower-income populations, campaigns should leverage attitudinal change and highlight government actions. In contrast, messaging for higher-income individuals might focus on personal responsibility and empowerment. Conclusion Main Conclusions. This study illuminates how IP (IP) influences green purchase intentions (GPI) in urban China, mediated by environmental attitude (EA), personal responsibility (PR), and perceived government performance (PGP). IP directly enhances GPI, with its effect amplified through PR and, to a lesser extent, EA. PGP’s mediating role underscores the critical importance of institutional trust in fostering sustainable consumption. Theoretical Contributions. We extend the IMB model by positioning EA, PR, and PGP as key motivational drivers of green consumption within collectivist cultures. Providing empirical support for the mediating pathways linking IP to GPI, we acknowledge that our findings are context-dependent and must be interpreted with caution.. Our multi-group analysis further reveals the moderating influence of demographics, demonstrating how diverse consumer segments respond to information and motivational cues. However, it is important to note that our study’s scope is limited to specific cultural contexts, and caution is needed when applying these results to other settings. We specifically highlight the crucial role of cultural context in shaping the interplay between individual motivations (EA, PR) and institutional trust (PGP), which may differ from Western-centric models and requires further investigation in other cultural contexts. Practical Implications. These findings offer practical insights for targeting information campaigns.Tailoring campaigns to specific demographics is crucial: for younger consumers, emphasizing PR and social norms for younger consumers while for older audiences, focusing on government initiatives and policy credibility may yield better results. For lower-income consumers, strengthening EA and reinforcing the positive role of PGP may be more impactful,, while higher-income consumers might respond more to PR and social influence. Governments should focus on improving and communicating environmental performance to bulid public trust, which can amplify the positive impact of IP on GPI. Moreover, educational programs aimed at fostering EA and PR, particularly among younger and less educated groups, can strengthen the foundation for sustainable consumption.However, these implications must be considered within the constraints of the study’s cultural and demographic focus. Limitations and Future Research Directions. While our study provides valuable insights, it is important to acknowledge several limitations. First, the cross-sectional design limits causal inferences and longitudinal studies should be necessary to track behavioral changes over time. Second, the study’s focus on specific demographics within a limited geographic area restricts generalizability. Future research should explore cultural and regional variations to better understand how these dynamics function in diverse contexts. Although EA, PR, and PGP were identified as significant mediators, additional factors, such as social norms, perceived behavioral control, product availability, specific product attributes,should be investigated to provide a more comprehensive understanding of GPI. Furthermore, exploringhow these factors interact with cultural values and institutional structures across diverse national contexts will be essential for enhancing the robustness of the finding. Declarations Ethical Approval: All procedures involving human participants were conducted in accordance with the ethical standards of the relevant national research committee and the 1964 Helsinki Declaration (including its subsequent amendments or comparable ethical standards). Formal ethical approval for this study was obtained on May 15, 2023, from the institutional ethics review board (Approval No. 202304001). Informed Consent: Written informed consent was obtained from all participants prior to their participation in this study, during the period of [July 1, 2023] to [June 30, 2024]. Participants were thoroughly informed about the study’s purpose, which was to investigate urban residents’ awareness and behaviors regarding green lifestyles. This included a detailed explanation of the topics covered, such as environmental protection, energy conservation, green consumption, low-carbon travel, and waste management. They were explicitly informed of their right to withdraw from the study at any time without penalty or loss of benefits, and that their participation was entirely voluntary. Data collected are intended solely for academic publication and knowledge dissemination and will not be used for longitudinal studies or other applications, as no personal contact information or directly identifiable personal data was recorded. To ensure confidentiality, all data were anonymized prior to analysis, with only aggregated demographic profiles used to support the research findings. Conflicts of Interest: The authors declare no conflict of interest. Author Contribution Conceptualization, Xiaonan Wang. and Chunyu Yang.; methodology, Chunyu Yang. and Fuchuan Chen.; software, Fuchuan Chen.; writing—original draft preparation., Xiaonan Wang, Chunyu Yang. and Fuchuan Chen.; writing—review and editing, Xiaonan Wang. and Chunyu Yang.; visualization, Xiaonan Wang, Chunyu Yang. and Fuchuan Chen.; All authors have read and agreed to the published version of the manuscript. Data Availability Statement: Data is not publicly available, though the data may be made available on request from the corresponding author. References Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes , 50 (2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-t Ajzen, I., & Fishbein, M. (1977). 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Exploring influences of environmental information, beliefs and self‐efficacy on nurses’ climate health behaviours and their relationships. Journal of Advanced Nursing . https://doi.org/10.1111/jan.16269 Young, W., Hwang, K., McDonald, S., & Oates, C. J. (2009). Sustainable consumption: green consumer behaviour when purchasing products. Sustainable Development , n/a-n/a. https://doi.org/10.1002/sd.394 Zhang, L., Li, D., Cao, C., & Huang, S. (2018). The influence of greenwashing perception on green purchasing intentions: the mediating role of green word-of-mouth and moderating role of green concern. Journal of Cleaner Production , 187 , 740–750. https://doi.org/10.1016/j.jclepro.2018.03.201 Zhang, H. , & Sun, S. (2024). The (in)congruence effects of organizational green compensation and employee green conscientiousness on pro-environmental behavior: evidence from china. BMC Psychology, 12(1), 1-23. China. BMC Psychol 12, 623 (2024). https://doi.org/10.1186/s40359-024-02122-9 Zhao, L., Sun, J., Zhang, L., & Ma, B. (2022). Traditional media or social media? corporate green media communication and consumer intention to cocreate green value in post-covid-19 china. Asia Pacific Journal of Marketing and Logistics , 35 (3), 745–774. https://doi.org/10.1108/apjml-09-2021-0663 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6590941","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":505689812,"identity":"7f9b30ed-aa93-4524-8059-079d31d6f00e","order_by":0,"name":"Chunyu YANG","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chunyu","middleName":"","lastName":"YANG","suffix":""},{"id":505689813,"identity":"a0f04969-a707-41c4-a49d-7af64ae50795","order_by":1,"name":"Fuchuan Chen","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Fuchuan","middleName":"","lastName":"Chen","suffix":""},{"id":505689814,"identity":"c9b1f1d9-a9b4-4926-ad55-00de7d0ed1fb","order_by":2,"name":"Xiaonan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYNCDDwY2dqTpYJxRkJZMmhZmng+HGBsIqTJnP3vwMw/DYXlz/jVmj20MDjAzsB8+ugGfFsuevGRpoBbDnTOepRvnGNzhY+BJS7uBT4vBgRwzZh6G24wbbhw+Jp1j8IyZQYLHDL+W82/AWuw33DjYJm1hcJixgaCWGxBbEjecbz4mzUCcljfGknMY/idvuMGWJtljkJbMRtAv53MMP7xhSLPdcP6MmcSPPzZ2/OyHj+HVAgJMvP+ApEQChMdGSDkIMP4AkfwHiFE7CkbBKBgFIxEAAKWPSe8Dc0KdAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Open University","correspondingAuthor":true,"prefix":"","firstName":"Xiaonan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-05-05 03:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6590941/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6590941/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90152957,"identity":"92134014-90a6-4a3e-ba11-f3b6ff3daa32","added_by":"auto","created_at":"2025-08-29 07:36:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6590941/v1/a8290878e1b2e56fb100051f.png"},{"id":90152504,"identity":"cfbb73e9-4238-4564-90d9-47e1c196d4bb","added_by":"auto","created_at":"2025-08-29 07:28:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68235,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of SEM\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6590941/v1/ac65f13c6104b828cabb5fa5.png"},{"id":90153930,"identity":"25a3d76a-0c0d-4766-b322-743510fa00a1","added_by":"auto","created_at":"2025-08-29 07:44:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1775899,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6590941/v1/05cd29a9-0e52-4974-9db7-47761d211d20.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Information to Action: Exploring the Mediating Effects of Environmental Attitude, Personal Responsibility, and Government Performance on Green Purchase Intentions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe escalating climate crisis and accelerating resource depletion underscore the urgent need for a global transition towards sustainable consumption patterns (IPCC, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Within this transition, green consumption, defined as purchasing decisions prioritizing environmental sustainability (Joshi \u0026amp; Rahman, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), represents a critical pathway for mitigating ecological degradation. Central to understanding green consumption is the concept of Green Purchase intention (GPI), that signifies consumers\u0026rsquo; willingness and conscious plan to choose environmentally-friendly products over conventional alternatives(Chan \u0026amp; Lau, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). GPI serves as a crucial precursor to actual green purchasing behavior, making it a key focal point for research and intervention efforts aimed at fostering sustainability.\u003c/p\u003e\u003cp\u003eDespite growing public awareness of environmental issues, a significant and persistent discrepancy exists between consumers\u0026rsquo; stated pro-environmental attitudes and their actual purchasing behavior. This phenomenon, widely recognized as the \u0026lsquo;attitude-behavior gap\u0026rsquo;(Fennis et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kollmuss \u0026amp; Agyeman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e),highlights the complexity of translating environmental concern into tangible action. One commonly-employed strategy to bridge this gap is environmental information provision (IP), that aims to equip consumers with the knowledge necessary to make informed, sustainable choices (Th\u0026oslash;gersen \u0026amp; \u0026Ouml;lander, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).Theoretically, the Information-Motivation-Behavioral Skills (IMB) model suggests that effective behavior change necessitates not only identification of relevant information but also sufficient motivation to act upon it and the requisite behavioral skills(Fisher \u0026amp; Fisher, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, while the IMB model provides a valuable framework, its comprehensive application to elucidate specific pathways from environmental information exposure to formation of green purchase intentions remains limited. Several critical gaps persist in the current understanding of this information-to-intention process. First, although the direct positive effect of environmental information on purchase intentions has been documented (Bang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), the crucial mediating mechanisms through which information translates into intention have been insufficiently explored. Specifically, the nature of psychological \u0026lsquo;motivations\u0026rsquo; (such as attitudes and feelings of responsibility) activated by information, as posited by the IMB model, requires deeper investigation within the green consumption context (Kollmuss \u0026amp; Agyeman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).Second, existing research tends to focus predominantly on individual-level psychological factors (e.g., attitudes, values, perceived effectiveness) (Kim et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), often neglecting potentially significant influence of the broader institutional context such as citizens\u0026rsquo; perceptions of government performance in environmental protection (Perceived Government Performance PGP)(Stern, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Despite its potential importance, the interplay between individual motivations and these contextual perceptions has been particularly understudied, especially in contexts where government action is highly visible. Third, there is a consequent lack of integrated empirically-applied behavioral frameworks that holistically capture the interaction between informational inputs, individual psychological drivers (motivation), and relevant contextual factors in shaping GPI (Davis et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) This fragmentation hinders the development of comprehensive and effective strategies. While demographic factors and barriers like price sensitivity are acknowledged influences (Kim \u0026amp; Lee, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; White et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Young et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and negative factors like greenwashing perceptions can also undermine trust (Zhang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and understanding the core motivational pathway initiated by information remains paramount(Fennis et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kollmuss \u0026amp; Agyeman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Stern, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Th\u0026oslash;gersen \u0026amp; \u0026Ouml;lander, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).This study aims to bridge these research gaps by developing and empirically testing an integrated theoretical framework grounded in the IMB model. We specifically investigate how environmental information provision (IP) influences GPI among residents in major Chinese cities, a context characterized by rapid urbanization and significant environmental challenges. We examine the crucial mediating roles of EA and PR as key individual motivational factors, along with PGP as a representation of the perceived institutional context. By incorporating both individual and institutional factors as mediators, we seek to unravel the nuanced pathways transforming information into intention within this specific socio-cultural setting(Ajzen, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Stern, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).To guide our investigation, this study addresses the following research questions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow does environmental information provision (IP) directly and indirectly influence GPI?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo what extent do environmental attitude (EA), personal responsibility (PR), and perceived government performance (PGP) individually and collectively mediate the relationship between IP and GPI?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat are the relative strengths and potential interplay among EA, PR, and PGP within the pathway from information to intention?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eBy addressing these questions, this study offers two primary contributions. From a theoretical perspective, it extends the application of the IMB model by proposing and testing a multi-mediation framework that integrates both individual psychological factors (EA, PR) and perceptions of institutional context (PGP) to explain the information-to-intention link in a non-Western context. From a practical perspective, the findings offer actionable insights for marketers, policy makers, and environmental advocates seeking to design more effective information-based interventions to promote sustainable consumption. Understanding the mediating roles of attitudinal, normative, and contextual factors can help stakeholders develop targeted strategies that address multiple determinants of green purchasing behavior.\u003c/p\u003e"},{"header":"Theoretical framework","content":"\u003cp\u003e\u003cb\u003eInformation-Motivation-Behavioral Skills (IMB) Model.\u003c/b\u003e The Information-Motivation-Behavioral Skills (IMB) model, originally developed to explain health-related behaviors (Fisher \u0026amp; Fisher, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), provides a robust framework for understanding the drivers of pro-environmental actions, including GPI(Fisher \u0026amp; Fisher, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).The model posits that performing a specific behavior is a function of possessing adequate Information related to the behavior, sufficient motivation to enact it, and the necessary behavioral skills to execute it effectively. Within the realm of sustainable consumption, these components translate into specific factors influencing consumer choices.\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.Information (IP)\u003c/b\u003e: In the context of GPI, the ‘Information’ component refers to consumer knowledge and awareness regarding environmental problems, the environmental impact of products, and the benefits associated with choosing eco-friendly alternatives. This information is often acquired through various channels such as eco-labels on products, corporate sustainability reports, media coverage, educational campaigns, and government initiatives(Seacat \u0026amp; Northrup, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). An effective environmental information provision (IP) serves as the initial trigger, potentially activating subsequent motivational and behavioral components of the IMB model.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.Motivation (EA \u0026amp; PR)\u003c/b\u003e: The “Motivation” component within the IMB framework encompasses the diverse factors that propel individuals toward action, bridging the gap between knowing and intending. This motivation can be conceptualized along dual pathways: personal attitudes and social/normative influences(Stern, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In this study, we operationalize motivation through two key psychological constructs:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEnvironmental Attitude (EA)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eReflecting the cognitive and affective evaluation of environmental protection, EA represents an individual’s favorable or unfavorable disposition towards environmental issues and green product(Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It aligns with the personal/cognitive dimension of motivation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePersonal Responsibility (PR)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eCapturing the normative dimension, PR refers to an individual’s sense of moral obligation or duty to contribute to environmental solutions through his or her actions(Stern, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Since empirical evidence supports that consumers possessing strong environmental attitudes and a heightened sense of personal responsibility are significantly more inclined to form intentions to purchase sustainable products(Yi \u0026amp; Yi, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e),EA and PR are hypothesized as crucial motivational mediators translating environmental (Kavitha \u0026amp; Kumar, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) information into GPI.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.Behavioral Skills (Facilitated by PGP)\u003c/b\u003e: The Behavioral Skills component of the IMB model encompasses perceived self-efficacy—an individual’s confidence in their ability to successfully perform a behavior(Fisher \u0026amp; Fisher, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For green purchasing, this involves the perceived capacity to effectively identify, access, afford, and utilize green products. Such self-efficacy is not formed in isolation but is significantly shaped by the perceived quality and supportive character of the institutional environment(Newig \u0026amp; Fritsch, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), particularly in contexts like China where government performance strongly influences public life (Kavitha \u0026amp; Kumar, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We therefore propose that Perceived Government Performance (PGP) regarding environmental protection serves as a robust proxy for these contextually-dependent Behavioral Skills. High PGP fosters confidence in credible government commitment and effectiveness(Ehret et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), leading individuals to perceive a more reliable enabling environment, including perceiving functional systems such as eco-labeling, accessible information, adequate green infrastructure (e.g., recycling, EV charging), and trustworthy regulatory oversight (Andika et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yi \u0026amp; Yi, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Critically, this perception of effective governmental facilitation directly translates into enhanced consumer self-efficacy. Individuals feel more capable and confident navigating the green marketplace when they perceive that the government is performing well in creating supportive conditions(Deryugina \u0026amp; Shurchkov, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ehret et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Seacat \u0026amp; Northrup, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). By reflecting the perceived ease and feasibility conferred by effective governance, PGP encapsulates a crucial dimension of individuals’ perceived ability to act, thus functioning as a valid indicator for Behavioral Skills in the context of green consumption.\u003c/p\u003e\u003cp\u003eThis research utilizes the IMB model to examine how IP influences GPI among urban Chinese residents. We specifically investigate the mediating pathways through EA and PR, representing the core “Motivation” component, and PGP, conceptualized as a crucial factor shaping the perceived “Behavioral Skills”. This integrated approach allows for a more nuanced understanding of how information translates into intention via both individual psychological drivers and perceptions of the institutional environment in non-Western urban settings.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eHypotheses Development\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003eInformation Provision and Green Purchase Intention\u003c/h2\u003e\u003cp\u003e\u003cb\u003eIP\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eGPI\u003c/b\u003e. The Information Provision plays a critical role in shaping consumer decision-making, particularly concerning green purchase intention (GPI). Drawing upon the Information-Motivation-Behavioral Skills (IMB) model(Fisher \u0026amp; Fisher, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Studies show that an accessible environmental information (e.g., carbon footprints, certifications) enhances awareness, fostering GPI (Deryugina \u0026amp; Shurchkov, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yi \u0026amp; Yi, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Different types of information matter; factual details reduce gaps (Andika et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while persuasive or emotional content can align values or increase urgency(Franzen, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Stern, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Effectiveness also varies by context; government sources may build trust (Ehret et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while overload or “greenwashing” can erode it (Seacat \u0026amp; Northrup, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and cultural differences exist(Andika et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).While some findings are context-dependent(Stern, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), since a positive IP and GPI link is broadly supported (Fisher \u0026amp; Fisher, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), this study proposed the following hypothesis:\u003c/p\u003e\u003cp\u003eH1: IP positively influences GPI.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eEnvironmental Attitude and Green Purchase Intention\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eEA and GPI.\u003c/b\u003e Environmental Attitude (EA) signifies an individual’s evaluation of environmental protection, encompassing beliefs, values, and concerns(Dunlap et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Franzen, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Stern, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), often reflecting dimensions like ecological worldview and perceived responsibility(Schultz et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). As a psychological precursor, EA consistently shows a strong positive relationship with GPI (Chan \u0026amp; Lau, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consumers with stronger EA align green products with their values (Xiao et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), feel a greater moral obligation (Surira et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and may prioritize environmental benefits, reducing dissonance(Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Perceived consumer effectiveness (PCE)—the belief that individual actions matter—also mediates this relationship, bridging attitudes to actionable intentions(Zhang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Considering EA as a motivational factor within frameworks like the IMB model, this study hypothesizes:\u003c/p\u003e\u003cp\u003eH2: Environmental attitude (EA) positively affects Green Purchase Intention (GPI).\u003c/p\u003e\u003cp\u003e\u003cb\u003eIP, EA and GPI.\u003c/b\u003e The IMB model suggests information influences behavior via motivation (Fisher \u0026amp; Fisher, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Information Provision (IP) can shape Environmental Attitude (EA) by enhancing understanding of consequences(Deryugina \u0026amp; Shurchkov, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Several mechanisms underpin this relationship; environmental information can increase awareness and heighten concern regarding environmental issues(Surira et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e),challenge misconceptions and thereby improve attitudes toward green products(Andika et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and bolster positive attitudes by highlighting the effectiveness of individual contributions, thus reducing perceived helplessness (Stern, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).This mediational pathway is consistent with value-belief-norm theory, suggesting that information influences personal norms and behavior by altering beliefs and attitudes (Stern, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), and finds empirical support(Seacat \u0026amp; Northrup, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This study also proposed the following hypothesis (H):\u003c/p\u003e\u003cp\u003eH3: EA positively mediates the relationship between IP and GPI\u003c/p\u003e\n\u003ch3\u003ePersonal Responsibility (PR) and Green Purchase Intention\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003ePR and GPI.\u003c/b\u003e Personal Responsibility (PR) constitutes an individual’s perceived obligation to mitigate environmental harm, grounded in moral norms and awareness of consequences (Schwartz, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Schwartz \u0026amp; Bilsky, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), and encompassing dimensions like responsibility acceptance and attribution (Bamberg \u0026amp; Möser, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). A strong positive relationship between PR and pro-environmental behaviors, including Green Purchase Intention (GPI), has been consistently demonstrated in empirical research (Kim et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Thøgersen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Thøgersen \u0026amp; Ölander, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Specifically, consumers internalizing greater PR perceive green purchasing as fulfilling an ethical duty (X. Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), aligning with moral imperatives (Kim et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This sense of responsibility acts as a potent normative motivator for GPI, potentially exceeding the influence of purely cognitive factors (Chan \u0026amp; Lau, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), translating perceived obligation into purchase intent. Accordingly, this study proposed the following hypothesis:\u003c/p\u003e\u003cp\u003eH4: PR positively affects GPI.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIP, PR and GPI.\u003c/b\u003e Beyond potential direct effects, Personal Responsibility (PR) is also theorized to function as a crucial mediator within the information-behavior pathway. Specifically, IP can enhance an individual’s awareness of environmental problems and clarify their personal role in either contributing to or mitigating these issues (Deryugina \u0026amp; Shurchkov, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Since this heightened awareness and understanding fostered by information are believed to activate a stronger sense of PR, compelling individuals to align their actions with perceived moral obligations(Surira et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), this activated sense of PR, stimulated by the provided information, is expected to translate into a greater GPI, as consumers view green purchasing as a viable means to fulfill their felt responsibility (Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Accordingly, this study proposed the following hypothesis:\u003c/p\u003e\u003cp\u003eH5: PR positively mediates the relationship between IP and GPI.\u003c/p\u003e\n\u003ch3\u003ePerceived Government Performance (PGP) and Green Purchase Intention\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003ePGP and GPI\u003c/b\u003e. Perceived Government Performance (PGP) captures citizens’ evaluations of governmental effectiveness, transparency, and legitimacy in addressing environmental challenges(Newig \u0026amp; Fritsch, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This perception influences pro-environmental actions by fostering trust in collective solutions and legitimizing individual efforts within that framework (Kollmuss \u0026amp; Agyeman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Empirical studies consistently find a positive relationship between PGP and GPI. When individuals perceive effective government commitment (e.g., through policies and enforcement), trust is enhanced in the broader environmental strategy, motivating personal alignment such as green purchasing (Surira et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) related to enhanced consumer confidence. Furthermore, active government promotion and regulation lend legitimacy to sustainable practices, reinforcing green consumption as socially normative and personally meaningful (Bamberg \u0026amp; Möser, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), thus strengthening GPI. Accordingly, this study proposed the following hypothesis:\u003c/p\u003e\u003cp\u003eH6: PGP positively affects GPI.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEA\u003c/b\u003e, \u003cb\u003ePR\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003ePGP\u003c/b\u003e. The IMB model posits that motivation enhances behavioral capacity, including factors facilitating action (Fisher \u0026amp; Fisher, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Within this framework, we examine how motivational components EA and PR influence PGP. PGP, reflecting trust in institutional effectiveness, can be seen as enabling individual green actions. Specifically, individuals with stronger EA may evaluate government environmental initiatives more positively, perceiving greater credibility when policies align with their pro-environmental values (Surira et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, a heightened sense of PR likely leads to more favorable citizen satisfaction(Nie \u0026amp; Wang, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), because individuals holding governments accountable expect policies reflecting collective action norms (Bamberg \u0026amp; Möser, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Effective institutional support indeed strengthens trust (Kollmuss \u0026amp; Agyeman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Based on this theoretical and empirical foundation, the following hypotheses are proposed:\u003c/p\u003e\u003cp\u003eH7a: EA positively affects PGP.\u003c/p\u003e\u003cp\u003eH7b: PR positively affects PGP.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIP\u003c/b\u003e, \u003cb\u003ePGP\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eGPI\u003c/b\u003e. Integrating the Information-Motivation-Behavioral Skills (IMB) model with theories of institutional trust and policy legitimacy, we investigate the role of PGP in shaping GPI. We posit that IP, particularly related to governmental environmental initiatives, enhances PGP by increasing public awareness of institutional efforts (Kavitha \u0026amp; Kumar, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Deryugina \u0026amp; Shurchkov, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This elevated PGP is in turn proposed to foster GPI. The mechanism operates through enhanced institutional trust, mitigating perceived risks in green markets (Tsang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and bolstering policy legitimacy, framing GPI as an institutionally-endorsed behavior(Kavitha \u0026amp; Kumar, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, PGP fosters perceptions of regulatory assurance, increasing confidence in green products (Ioannou et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shojaei et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The nature of IP, such as its transparency or government alignment, also shapes PGP’s mediating strength (Andika et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Surira et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This mediating pathway (IP → PGP → GPI) is expected to be particularly salient in contexts such as China, characterized by high institutional trust and collectivist values, where governmental actions significantly influence public attitudes and behaviors (Chan \u0026amp; Lau, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Ehret et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Accordingly, this study proposed the following hypothesis:\u003c/p\u003e\u003cp\u003eH8: PGP positively mediates the relationship between IP and GPI.\u003c/p\u003e\u003cp\u003eBased on the above theories and research hypotheses, a framework model of the mechanism influencing GPI has been constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e"},{"header":"Data, Variables and Methods","content":"\u003ch2\u003eData collection\u003c/h2\u003e\u003cp\u003eThe data for this study were drawn from the ‘Green Lifestyle Survey of Residents in Chinese Megacities,’ conducted between June 2023 and January 2024. The online survey targeted urban residents for collecting data; urban residents account for 38% of national apparel consumption (NBS,2024). Given China’s vast population (1.41\u0026nbsp;billion by 2023) and 31 provincial-level regions, nationwide sampling poses methodological challenges. The survey strategically targeted seven megacities (Beijing, Shanghai, Guangzhou, Shenzhen, Chongqing, Tianjin, and Chengdu; selected based on populations exceeding 10\u0026nbsp;million, as per the 2021 ‘Seventh National Census’(NBS,2023). These cities collectively generate over 2.1\u0026nbsp;million tons of discarded clothing annually, representing 45% of China’s total post-consumer textile waste (CNTAC, 2025).\u003c/p\u003e\u003cp\u003eThe sampling design meticulously followed established sampling principles. To enhance accuracy and scientific rigor while maintaining feasibility, we used a multistage sampling approach that integrated probability proportional to size sampling with equal probability sampling. Considering the economic efficiency and sampling errors, cost implications were factored into determining and allocating sample sizes. For scientific, efficient, and practical management of issues related to sample data weighting, a sampling scheme was carefully devised and robust methods for adjusting the sample data weights were developed. A total of 5,188 valid questionnaires were collected; after excluding invalid responses, 4966 valid questionnaires were retained. Ethical clearance was obtained from the Psychology Ethics Committee (Protocol XXXXXXX), that approved the study design, recruitment process, and data management practices. The demographic characteristics of the participants are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the survey’s male-to-female ratio was fair and matched China’s age distribution. The sampling number of questionnaires gathered in the examined cities was adequate, and the sample data distribution was similar among the seven mega-cities. A variety of data helped to identify the target populations for the seven mega-cities.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-demographic characteristics of the sample population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003evariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eMonthly family income (RMB)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; 2999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.91%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3000–4999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.55%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt; 30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.92%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5000–7999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e23.66%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31–40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.83%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8000–11999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e31.31%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41–50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.05%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12000–19999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e23.48%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51–60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.37%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20000–39999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt; 60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.83%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40000–59999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJunior High School and below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026gt; 60000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.59%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.87%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eCity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eShanghai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e17.38%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJunior College / Associate Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.62%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBeijing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e14.98%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBachelor's Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.65%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eShenzhen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12.51%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaster's Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.83%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChongqing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e20.06%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoctoral Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.83%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGuangzhou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12.55%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.69%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChengdu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12.53%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTianjin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.01%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHousehold Registration Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.25%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e[Data source] Data were compiled based on this research project.\u003c/p\u003e\u003ch3\u003eVariables and Measures\u003c/h3\u003e\u003cp\u003e\u003cb\u003eIndependent Variable.\u003c/b\u003e This study combines the Information-Motivation-Behavioral (IMB) model with existing research on IP. The independent variable IP was operationalized as the composite frequency of individuals’ exposure to environmental information across three primary media channels: (1) new/social media (e.g., Weibo, NetEase, WeChat, Douyin, Xiaohongshu), (2) interpersonal networks (e.g., family, friends, colleagues), and (3) traditional media (e.g., TV, newspapers, magazines, radio). Responses were recorded on a 5-point Likert scale: 5 = “several times daily”, 4 = “weekly”, 3 = “monthly”, 2 = “yearly”, and 1 = “never”. The IP score was calculated by summing the standardized frequencies across all items, with higher scores indicating greater environmental information exposure.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDependent Variable and mediating variable\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eEnvironmental attitude (EA), personal responsibility (PR), perceived government performance (PGP), and green purchase intention (GPI). All variables,except for the dependent variable (GPI) are assessed using a five-point Likert scale, where responses range from 1 (strongly disagree) to 5 (strongly agree). For GPI, a five-point Likert scale is used, with 5 indicating “several times every day” and 1 representing “never.” The measurement tools for each variable, along with their validity indices and references, are presented in \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eTable\u0026nbsp;2 Items ’loadings (k) and the constructs’Cronbach’s α coefficients and AVEs.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVariable Names\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eItems Code\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eIndicator\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFactor loadings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCronbach'α\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eLiterature\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eGreen Purchase Intention (GPI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPI1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReduce the frequency of purchasing new clothing, shoes, and hats.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c9\" namest=\"c8\" rowspan=\"4\"\u003e\u003cp\u003eChan \u0026amp; Lau, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kim \u0026amp; Lee, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kim, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPI2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAvoid pursuing products with elaborate packaging.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.717\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPI3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMinimize the use of disposable items and utensils when traveling or ordering takeout.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPI4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithin an acceptable price range, prefer green appliances or energy-efficient products.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEnvironmental Attitude (EA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInconvenience in purchasing or using green products.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c9\" namest=\"c8\" rowspan=\"4\"\u003e\u003cp\u003eBang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Dunlap et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Stern, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLack of trust in current green products.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePurchasing green products does not contribute to personal social image.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.724\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGreen products have minimal benefits for the environment.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePerceived Government Performance (PGP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePGP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe government is highly professional in promoting low-carbon living.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c9\" namest=\"c8\" rowspan=\"4\"\u003e\u003cp\u003eNewig \u0026amp; Fritsch, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ioannou et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shojaei et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePGP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe government is committed to promoting low-carbon living for the public good.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePGP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe government is determined to promote low-carbon living.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePGP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe government is highly efficient in promoting low-carbon living.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.757\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003ePersonal Responsibility (PR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePR1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI have the responsibility and obligation to protect the environment.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c9\" namest=\"c8\" rowspan=\"5\"\u003e\u003cp\u003eAjzen et al., 1996; Brekke et al., 2010; Gifford \u0026amp; Nilsson, 2014; Kim et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDespite the small impact of an individual, I should contribute to environmental protection.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.524\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePR3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow-carbon and environmental actions should start with small things.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePR4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThose who cause environmental pollution should be held accountable for ecological deterioration and environmental degradation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePR5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eParticipating in environmental governance is a duty and responsibility of the public.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eMeasurement model.\u003c/b\u003e Prior to hypothesis testing, Cronbach's α coefficients were calculated to evaluate the reliability of each construct. As shown in \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e, the factor loadings of the indicators ranged from 0.501 to 0.757 (all exceeding 0.45), and Cronbach's α values varied from 0.673 to 0.802 (all greater than 0.6) (Hair et al., 1998). These results indicate that the reliability of all constructs in the research model is acceptable.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConvergent validity\u003c/b\u003e can be measured by the average variance extracted (AVE). The discriminant validity of the overall measurement model can be measured by the average variance extracted (AVE), according to Henseler et al (Henseler et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiscriminant validity\u003c/b\u003e refers to the extent to which a construct is distinct from other constructs (Henseler et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates that the square root of the AVE for each construct (diagonal elements in bold) exceeds the correlations among constructs, confirming good discriminant validity. Furthermore, each AVE value is greater than 0.36, and the CR values are greater than 0.70, providing reasonable support for the convergent validity of the scales(Henseler et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These findings confirm both the reliability and validity of the proposed model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssessment of discriminant validity.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePGP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.666\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.052\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.710\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.388\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.074\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.682\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePGP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.107\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.033\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.061\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.610\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNote: \u003csup\u003e**\u003c/sup\u003e Significant at the 0.01 level (two-tailed).\u003csup\u003e*\u003c/sup\u003e Significant at the 0.05 level (two-tailed).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eStructural model.\u003c/b\u003e Structural Equation Modeling (SEM) was employed to test the hypotheses outlined above. The goodness of fit indices are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel fit summary.\u003c/b\u003e The model\u0026rsquo;s fit was assessed using the GFI, AGFI, CFI, IFI, and TLI indicators, which yielded values of 0.987, 0.981, 0.977, 0.977, and 0.971, respectively. The RMSEA value was 0.028, and the Chi-square to degrees of freedom ratio (χ\u0026sup2;/df) was 4.838. These results suggest that the fit indices meet acceptable levels(Bagozzi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1981\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMain fitness test index values.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitness index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003csup\u003e2\u003c/sup\u003e/df\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAGFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndex value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.971\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIdeal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIdeal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIdeal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIdeal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIdeal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIdeal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eIdeal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHypotheses test.\u003c/b\u003e The path analysis results, shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, also reveal that both PI and EA are positively associated with GPI, with standardized path coefficients of 0.284 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, C.R. = 14.160) and 0.107 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, C.R. = 5.925), respectively, thus supporting H1 and H2. Additionally, PR and PGP are positively related to GPI, with standardized path coefficients of 0.391 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, C.R. = 14.851) and 0.079 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, C.R. = 4.066), respectively, confirming support for H4 and H6. However, since EA and PR are not significantly related to PGP, with standardized path coefficients of 0.033(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.071, C.R.=1.803) and 0.043(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.063, C.R.=1.863), H7a and H7b were rejected.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePath analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003epath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStd.Estimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC.R.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIP\u0026rarr;EA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIP\u0026rarr;PGP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIP\u0026rarr;PR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1 IP\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2 EA\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4 PR\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6 PGP\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH7aEA\u0026rarr;PGP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH7bPR\u0026rarr;PGP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: \u003csup\u003e*\u003c/sup\u003e indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. ns indicates not significant.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Data source] The data above are compiled based on this research project.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMediation effect test.\u003c/b\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, we applied the bootstrap confidence intervals method with 5,000 bootstrap samples to examine how the effect of PI on GPI is mediated through EA, PR, and PGP. The bootstrap test results revealed significant indirect effects of PI on GPI through EA, PR, and PGP, with coefficient values of 0.011, 0.178, and 0.009, respectively, all with \u003cem\u003ep\u003c/em\u003e-values less than 0.001. Since these findings support H3, H5, and H8, except for H7a and H7b, all proposed hypotheses are supported.\u003c/p\u003e\u003cp\u003eAfter theoretical refinement and empirical validation, hypotheses H7a and H7b were removed, resulting in the final confirmatory model for the mechanisms influencing green purchase intentions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediation Effect Test.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003epath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStd.Estimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eBias-corrected-95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3 PI\u0026rarr;EA\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5 PI\u0026rarr;PR\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePI\u0026rarr;EA\u0026rarr;PGP\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePI\u0026rarr;PR\u0026rarr;PGP\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH8 PI\u0026rarr;PGP\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMulti-group analysis.\u003c/b\u003e To explore the heterogeneity of green purchase intentions mechanisms influencing among groups with varying demographic characteristics, we further analyzed mechanisms influencing GPI across multiple groups. The observations were divided into two groups based on each demographic factor. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e provides a description of the multi-group categories and their corresponding sample sizes. Our analysis includes a total of 10 models, categorized as follows: gender (male, female), age (\u0026lt;\u0026thinsp;40, \u0026ge;\u0026thinsp;40), residence (First-tier, New first-tier), income (\u0026lt;\u0026thinsp;8000, \u0026ge;\u0026thinsp;8000), and education level (below bachelor\u0026rsquo;s degree, bachelor\u0026rsquo;s degree or above).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMulti-group description and samples.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSample Size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.94%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.06%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelow 40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.74%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove 40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.26%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCity of Residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst-tier: Shanghai,Beijing,Shenzhen,Guangzhou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.41%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNew First-tier: Chengdu,Chongqing,Tianjing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.59%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIncome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAn income of less than 8000 RMB per month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.13%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAn income of more than 8000 RMB per month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.87%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEducational Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelow Bachelor's Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.69%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBachelor's Degree or Above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.31%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: \u0026ldquo;First-tier cities\u0026rdquo;: Designated by the National Bureau of Statistics of China, Beijing, Shanghai, Guangzhou, and Shenzhen are classified as first-tier cities. \u0026ldquo;New first-tier cities\u0026rdquo;: Based on multiple dimensions, including commercial resource concentration and urban connectivity, _The First Financial Weekly_ of China assesses Chengdu, Chongqing, Tianjin, and others as new first-tier cities.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e demonstrates that all representative fit indices are within acceptable levels, indicating that the multi-group structural equation model fits well (Bagozzi \u0026amp; Yi, 1988). Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e9\u003c/span\u003e presents the estimated coefficients for the influencing paths across the 10 models. Notable differences were observed in the mechanisms of these models. Key findings include that all paths in the 10 models, except for the EA\u0026rarr;GPI path (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and the PI\u0026rarr;EA\u0026rarr;GPI path (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the age\u0026thinsp;\u0026ge;\u0026thinsp;40 group, were significant and positive. Overall, PI had a significant positive impact on EA, PR, and PGP, while PI, EA, PR, and PGP significantly positively influenced GPI.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFitness test of the structural model.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEL\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex\u003csup\u003e2\u003c/sup\u003e/df\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.482\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.960\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eNote: EL\u0026thinsp;=\u0026thinsp;Education Level\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe following provides a detailed discussion of the path characteristics across the 10 models. First, regarding gender differences, male respondents were more sensitive to all paths, except for PI\u0026rarr;GPI (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.245, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), compared to females (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.254, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eSecond, all paths were significant and positive for respondents aged\u0026thinsp;\u0026lt;\u0026thinsp;40, while most paths were significant for those aged\u0026thinsp;\u0026ge;\u0026thinsp;40, with the exceptions of EA\u0026rarr;GPI and PI\u0026rarr;EA\u0026rarr;GPI (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Respondents aged\u0026thinsp;\u0026ge;\u0026thinsp;40 were also more sensitive to the path PI\u0026rarr;PGP (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.160, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than those aged\u0026thinsp;\u0026lt;\u0026thinsp;40 (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.111, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conversely, for paths such as PGP\u0026rarr;GPI, PI\u0026rarr;PR, and PI\u0026rarr;PR\u0026rarr;GPI, respondents aged\u0026thinsp;\u0026lt;\u0026thinsp;40 were more sensitive (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.083, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.468, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.209, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than their older counterparts (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.081, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.443, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.202, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eThird, in the city of residence group, respondents in New first-tier cities exhibited greater sensitivity to all paths, except for PR\u0026rarr;GPI and PI\u0026rarr;PR\u0026rarr;GPI (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.444, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.204, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), compared to those in first-tier cities (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.465, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.212, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Fourth, respondents with an income\u0026thinsp;\u0026lt;\u0026thinsp;8000 RMB/month were more sensitive to paths PI\u0026rarr;EA, EA\u0026rarr;GPI, PI\u0026rarr;PGP, PGP\u0026rarr;GPI, PI\u0026rarr;EA\u0026rarr;GPI, and PI\u0026rarr;PGP\u0026rarr;GPI (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.173, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.087, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.136, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.089, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) than respondents earning\u0026thinsp;\u0026ge;\u0026thinsp;8000 RMB/month (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.098, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.068, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.133, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.077, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eFor the remaining four paths, respondents with higher income were more sensitive. Fifth, among education level groups, respondents with education below a bachelor\u0026rsquo;s degree were more sensitive to the paths EA\u0026rarr;GPI, PI\u0026rarr;PR, PI\u0026rarr;EA\u0026rarr;GPI, and PI\u0026rarr;PR \u0026rarr;GPI (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.097, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.482, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.215, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than those with a bachelor\u0026rsquo;s degree or higher (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.054, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.430, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.198, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, for the other six paths, those with higher education levels were more sensitive.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMulti-group analysis of the mechanisms that influence GPI.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003epath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eCity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eIncome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBelow 40\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAbove 40\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFirst-tier\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNew First-tier\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003elower\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eupper\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003elower\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eupper\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePI\u0026rarr;EA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.123\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.109\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.102\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.136\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.109\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.127\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.173\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.098\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.105\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.129\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEA\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.088\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.052\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.093\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.063\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.082\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.087\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.068\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.097\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.054\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePI\u0026rarr;PGP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.152\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.117\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.111\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.160\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.118\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.155\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.136\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.133\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.098\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.164\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePGP\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.089\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.072\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.083\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.081\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.073\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.090\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.089\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.077\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.067\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.093\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePI\u0026rarr;PR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.492\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.424\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.468\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.443\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.455\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.459\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.412\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.476\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.482\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.430\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.487\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.422\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.447\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.457\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.465\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.444\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.401\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.487\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.447\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.459\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePI\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.245\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.254\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.254\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.253\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.252\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.253\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.217\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.267\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.213\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.284\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePI\u0026rarr;EA\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.011\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.010\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.015\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.007\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePI\u0026rarr;PGP\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.014\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.015\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePI\u0026rarr;PR\u0026rarr;GPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.240\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.179\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.209\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.202\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.212\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.204\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.165\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.232\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.215\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.198\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eNote: Lower1\u0026thinsp;=\u0026thinsp;An income of less than 8000 RMB per month, Upper1\u0026thinsp;=\u0026thinsp;An income of more than 8000 RMB per month; Lower2\u0026thinsp;=\u0026thinsp;Below Bachelor's Degree, Upper2\u0026thinsp;=\u0026thinsp;Bachelor's Degree or Above.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study empirically validates an integrated structural model, grounded primarily in the Information-Motivation-Behavioral Skills (IMB) framework, to elucidate the complex mechanisms linking IP and GPI. Our model distinctively incorporates pathways from IP to both intrinsic EA and extrinsic PR motivations, as well as to behavioral skills (PGP), ultimately influencing GPI. Notably, we extend prior frameworks by explicitly testing the direct influence of information on motivation(Andika et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), finding empirical support for these hypothesized relationships (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Since the pathways and corresponding hypotheses were validated through empirical results, this study confirms not only the impacts of information on intrinsic and extrinsic motivations and behavioral skills, but also the influence of these motivations on behavioral skills and their subsequent effects on behavioral intentions.\u003c/p\u003e\u003cp\u003eIP was also found to directly enhance GPI (H1 supported) among 4,966 residents from seven major Chinese cities: Shanghai, Beijing, Shenzhen, Chongqing, Guangzhou, Chengdu, and Tianjin. This result can be attributed to China\u0026rsquo;s strong emphasis on environmental protection, which encourages green product purchases through platforms such as Sina and NetEase, as well as traditional media like television and radio(Zhao et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Information sharing on these platforms addresses social needs such as interaction and information exchange (Bedard \u0026amp; Tolmie, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Residents of these cities, influenced by the rapid development of the internet and the proliferation of smartphones, have access to a wider array of information channels, including word-of-mouth from family and friends ( Zhang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, companies leverage both new and traditional media as sales channels and organize public welfare activities to promote green concepts and products. These cities, the most developed in China, play a leading role in implementing green purchase policies and measures (Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For example, Kang \u0026amp; Kim (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that information sharing via social media can motivate consumers to adopt green products and enhance their purchase behaviors. Based on the IMB model, this study provides valuable insight into successful implementation of China\u0026rsquo;s green consumption policies and the promotion of green products by businesses through information dissemination(Kang \u0026amp; Kim, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis research also explored the mechanisms through which IP influences GPI, focusing on the mediating roles of EA, PR, and PGP. The findings support the proposed framework, demonstrating that improved IP enhances both motivation and behavioral demonstration. Consistent with prior studies(Kim et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Stern, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), providing environmental knowledge strengthens consumers' intentions to purchase green products, aligning with the IMB model that asserts that information is a key driver of behavioral change(Yi \u0026amp; Yi, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Both EA and PR were found to mediate the IP-GPI relationship (H2-H5 supported). According to the Theory of Planned Behavior(Ajzen, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), EA significantly mediates the impact of IP on GPI, confirming that information fosters pro-environmental beliefs, in turn motivating sustainable consumption. Frequent IP can promote positive environmental attitudes(Steg \u0026amp; Vlek, 2009), increasing the intention to conserve resources(Willis et al, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and triggering responses to environmental crises (Yazdanpanah et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), thus influencing protective behaviors (Unlocking water sustainability: The role of knowledge, attitudes, and practices among women (AlHaddid et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)). PR also mediates this relationship, supporting the Norm Activation Model (NAM): when individuals perceive a moral obligation to act sustainably, they are more likely to translate knowledge into action(Surira et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Beyond individual motivations, PGP was also positively related to GPI (H6 supported) and mediated the relationship between IP and GPI (H8 supported), suggesting that trust in government policies (e.g., regulations, incentives) reduces barriers to green consumption. This supports Institutional Theory, that highlights the role of external support systems in enhancing behavioral control(Greve \u0026amp; Teh, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) In China, government environmental programs and policies are widely disseminated through various information channels, exerting a systemic influence on enhancing citizens' PGP. The government not only provides infrastructure (e.g., EV charging stations) to facilitate large-scale green consumption but also implements several programs aimed at encouraging green purchasing behaviors (e.g., green vehicle tax exemptions, subsidies).\u003c/p\u003e\u003cp\u003eTo some extent, altering extrinsic motivation (PR) through IP is easier than influencing intrinsic factors (EA). It is also easier to influence GPI through PR than through EA. Previous research indicates that the role of IP and sharing in shaping consumer behavior is growing(Mangold \u0026amp; Faulds, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). As an external factor, IP can influence consumers\u0026rsquo; psychological states, leading to collective evaluations of environmental responsibility. PR generates social pressure(Ajzen \u0026amp; Fishbein, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Compared to consumers in other countries, green consumption behavior in China is significantly influenced by collectivist values (Yan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), including social pressure, that make the PR exhibited by a group a strong predictor of individual behavior (Ralston et al., 1998). Therefore, for consumers in a collectivist culture like China\u0026rsquo;s, PR plays a crucial role in shaping their intention to purchase green products.\u003c/p\u003e\u003cp\u003eFinally, the non-significant effects of EA and PR on perceived government PGP (H7a and H7b rejected) reveal important nuances in how individuals perceive the role of environmental governance in collectivist contexts. Two interrelated explanations emerge. First, cultural collectivism decouples individual motivation from institutional trust. In China\u0026rsquo;s top-down environmental governance system, citizens often view environmental protection as a state responsibility rather than an individual obligation (Zhang \u0026amp; Sun, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Strong EA or PR may even reduce expectations of personal action, as individuals assume that the government will lead systemic solutions (Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This contrasts with individualist cultures, where personal norms often align with evaluations of institutional performance (Bamberg \u0026amp; M\u0026ouml;ser, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Second, the IMB model\u0026rsquo;s assumption regarding \u0026ldquo;behavioral skills\u0026rdquo; faces boundary conditions. While the model suggests that motivation (EA/PR) enhances actionable skills(Fisher \u0026amp; Fisher, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), PGP functions as an external enabler (e.g., policy credibility) rather than an individual capability. This misalignment calls for cross-level theories (e.g., institutional theory) to bridge macro-level governance perceptions with micro-level psychographics(Greve \u0026amp; Teh, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe multi-group analysis revealed significant heterogeneity in the mechanisms influencing GPI across demographic segments. Our findings demonstrate that the components of the IMB model operate differently depending on individual characteristics, offering important insights for designing targeted interventions.\u003c/p\u003e\u003cp\u003eAll multi-group models demonstrated satisfactory fit indices (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e), confirming the structural model's validity across demographic groups. Nearly all hypothesized paths were significant across the ten models, with two exceptions observed in the older age group (\u0026ge;\u0026thinsp;40). In this group, EA did not significantly influence GPI (EA\u0026rarr;GPI), nor did it mediate the relationship between IP and GPI (IP\u0026rarr;EA\u0026rarr;GPI). These exceptions suggest that while the IMB framework is broadly applicable, path strengths vary significantly across demographic profiles.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAge-Related Variations.\u003c/b\u003e Substantial variation was observed between age groups. Among younger respondents (\u0026lt;\u0026thinsp;40), all hypothesized pathways were significant, indicating robust IMB mechanisms. However, in the older group (\u0026ge;\u0026thinsp;40), the pathway from EA to GPI was non-significant. This suggests that EA may be less influential in shaping purchase decisions for older individuals who may prioritize other considerations. Notably, older participants exhibited greater sensitivity to the effect of IP on PGP (IP\u0026rarr;PGP), potentially indicating increased institutional trust or concern with age. In contrast, younger respondents showed stronger mediation effects via personal responsibility, implying a greater tendency to internalize environmental messages as moral or civic obligations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncome-Based Differences\u003c/b\u003e. Income also moderated the pathways within the IMB model. Lower-income respondents (\u0026lt;\u0026thinsp;8000 RMB/month) exhibited stronger associations between IP and both EA and PGP, along with more pronounced effects of these mediators on GPI. This suggests that for this group, IP effectively activates both attitudinal and institutional mechanisms. In contrast, higher-income individuals showed stronger effects via personal responsibility pathways, possibly reflecting a greater sense of agency and self-efficacy linked to financial security.\u003c/p\u003e\u003cp\u003eThese findings extend the IMB model by highlighting how its components function heterogeneously across demographic segments. IP strategies should be customized to align with the dominant pathways within each group. For older consumers, strategies emphasizing institutional performance and direct informational appeals may be more effective than those focused on shifting attitudes. For lower-income populations, campaigns should leverage attitudinal change and highlight government actions. In contrast, messaging for higher-income individuals might focus on personal responsibility and empowerment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e\u003cb\u003eMain Conclusions.\u003c/b\u003e This study illuminates how IP (IP) influences green purchase intentions (GPI) in urban China, mediated by environmental attitude (EA), personal responsibility (PR), and perceived government performance (PGP). IP directly enhances GPI, with its effect amplified through PR and, to a lesser extent, EA. PGP\u0026rsquo;s mediating role underscores the critical importance of institutional trust in fostering sustainable consumption.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTheoretical Contributions.\u003c/b\u003e We extend the IMB model by positioning EA, PR, and PGP as key motivational drivers of green consumption within collectivist cultures. Providing empirical support for the mediating pathways linking IP to GPI, we acknowledge that our findings are context-dependent and must be interpreted with caution.. Our multi-group analysis further reveals the moderating influence of demographics, demonstrating how diverse consumer segments respond to information and motivational cues. However, it is important to note that our study\u0026rsquo;s scope is limited to specific cultural contexts, and caution is needed when applying these results to other settings. We specifically highlight the crucial role of cultural context in shaping the interplay between individual motivations (EA, PR) and institutional trust (PGP), which may differ from Western-centric models and requires further investigation in other cultural contexts.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePractical Implications.\u003c/b\u003e These findings offer practical insights for targeting information campaigns.Tailoring campaigns to specific demographics is crucial: for younger consumers, emphasizing PR and social norms for younger consumers while for older audiences, focusing on government initiatives and policy credibility may yield better results. For lower-income consumers, strengthening EA and reinforcing the positive role of PGP may be more impactful,, while higher-income consumers might respond more to PR and social influence. Governments should focus on improving and communicating environmental performance to bulid public trust, which can amplify the positive impact of IP on GPI. Moreover, educational programs aimed at fostering EA and PR, particularly among younger and less educated groups, can strengthen the foundation for sustainable consumption.However, these implications must be considered within the constraints of the study\u0026rsquo;s cultural and demographic focus.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations and Future Research Directions.\u003c/b\u003eWhile our study provides valuable insights, it is important to acknowledge several limitations. First, the cross-sectional design limits causal inferences and longitudinal studies should be necessary to track behavioral changes over time. Second, the study\u0026rsquo;s focus on specific demographics within a limited geographic area restricts generalizability. Future research should explore cultural and regional variations to better understand how these dynamics function in diverse contexts. Although EA, PR, and PGP were identified as significant mediators, additional factors, such as social norms, perceived behavioral control, product availability, specific product attributes,should be investigated to provide a more comprehensive understanding of GPI. Furthermore, exploringhow these factors interact with cultural values and institutional structures across diverse national contexts will be essential for enhancing the robustness of the finding.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical Approval:\u003c/h2\u003e\u003cp\u003eAll procedures involving human participants were conducted in accordance with the ethical standards of the relevant national research committee and the 1964 Helsinki Declaration (including its subsequent amendments or comparable ethical standards). Formal ethical approval for this study was obtained on May 15, 2023, from the institutional ethics review board (Approval No. 202304001).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed Consent:\u003c/strong\u003e\u003cp\u003eWritten informed consent was obtained from all participants prior to their participation in this study, during the period of [July 1, 2023] to [June 30, 2024]. Participants were thoroughly informed about the study\u0026rsquo;s purpose, which was to investigate urban residents\u0026rsquo; awareness and behaviors regarding green lifestyles. This included a detailed explanation of the topics covered, such as environmental protection, energy conservation, green consumption, low-carbon travel, and waste management. They were explicitly informed of their right to withdraw from the study at any time without penalty or loss of benefits, and that their participation was entirely voluntary. Data collected are intended solely for academic publication and knowledge dissemination and will not be used for longitudinal studies or other applications, as no personal contact information or directly identifiable personal data was recorded. To ensure confidentiality, all data were anonymized prior to analysis, with only aggregated demographic profiles used to support the research findings.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Xiaonan Wang. and Chunyu Yang.; methodology, Chunyu Yang. and Fuchuan Chen.; software, Fuchuan Chen.; writing\u0026mdash;original draft preparation., Xiaonan Wang, Chunyu Yang. and Fuchuan Chen.; writing\u0026mdash;review and editing, Xiaonan Wang. and Chunyu Yang.; visualization, Xiaonan Wang, Chunyu Yang. and Fuchuan Chen.; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e\u003cp\u003eData is not publicly available, though the data may be made available on request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAjzen, I. 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The (in)congruence effects of organizational green compensation and employee green conscientiousness on pro-environmental behavior: evidence from china. \u003cem\u003eBMC Psychology, \u003c/em\u003e12(1), 1-23. China. BMC Psychol 12, 623 (2024). \u003cu\u003ehttps://doi.org/10.1186/s40359-024-02122-9\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eZhao, L., Sun, J., Zhang, L., \u0026amp; Ma, B. (2022). Traditional media or social media? corporate green media communication and consumer intention to cocreate green value in post-covid-19 china. \u003cem\u003eAsia Pacific Journal of Marketing and Logistics\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(3), 745\u0026ndash;774. https://doi.org/10.1108/apjml-09-2021-0663\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Green Purchase Behavior (GPI), Information Provision (IP), Environmental Attitude (EA), Perceived Government Performance (PGP), Personal Responsibility (PR), Information-Motivation-Behavioral Skills (IMB) Model","lastPublishedDoi":"10.21203/rs.3.rs-6590941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6590941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding how environmental information provision (IP) translates into green purchase intention (GPI) is crucial for promoting sustainable consumption, particularly in rapidly developing urban contexts like Chinese megacities. While the influence of individual motivation is known, the interplay with perceived institutional factors remains less explored. This study investigates the pathways linking IP to GPI, specifically examining the mediating roles of environmental attitude (EA), personal responsibility (PR), and perceived government performance (PGP), guided by the Information-Motivation-Behavioral Skills (IMB) model. Utilizing survey data from 4,966 residents (\u0026ldquo;Green Lifestyle Survey of Residents in Chinese Megacities\u0026rdquo;), structural equation modeling (SEM) and multi-group analysis tested the hypothesized relationships. Results confirm IP significantly boosts GPI both directly and indirectly. EA, PR, and PGP all function as significant mediators in this process. Notably, personal responsibility (PR) exerted the strongest mediating effect, highlighting its critical role. Furthermore, individual factors (EA, PR) did not significantly predict PGP, potentially reflecting cultural nuances where personal duty operates independently of institutional evaluation. Significant age and income differences also emerged, with PGP more influential for older individuals and PR resonating more with younger consumers. This research enhances the IMB framework by integrating institutional context (PGP) and reveals actionable, demographically-targeted insights for fostering GPI through appeals to either personal duty or governmental effectiveness.\u003c/p\u003e","manuscriptTitle":"From Information to Action: Exploring the Mediating Effects of Environmental Attitude, Personal Responsibility, and Government Performance on Green Purchase Intentions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-29 07:28:22","doi":"10.21203/rs.3.rs-6590941/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-17T05:22:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T09:21:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-19T08:43:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298572822690902785302399175852984737390","date":"2025-11-18T11:30:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155718069915815045560025719291732599415","date":"2025-11-17T04:41:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125543883850482655836236131230118386061","date":"2025-11-15T17:53:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52144376573920864141763391420002516955","date":"2025-11-15T15:18:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189045764794624825724367631028800418962","date":"2025-11-15T12:28:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-21T03:32:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-30T10:14:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-21T17:21:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-10T07:14:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-06-10T07:10:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"516a2512-0624-4c6c-80aa-15f5a2b7e3a7","owner":[],"postedDate":"August 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53707785,"name":"Social science/Environmental studies"},{"id":53707786,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-02-09T09:24:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-29 07:28:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6590941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6590941","identity":"rs-6590941","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}