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Article

Easy to Know, Hard to Act: How Do Green Attribute Centrality, Environmental Concern, and Trust Exert a Chain Effect on Purchase Decisions?

by
Kun Zhang
1,2,
Xiaoling Sun
3,†,
Baolong Yuan
1,*,†,
Yuxuan Tan
1 and
Caiyan Yang
1
1
School of Economics and Management, Central South University of Forestry and Technology, Changsha 410004, China
2
Institute of Green Development of Hunan Province, Changsha 410004, China
3
Department of Economics and Management, Hunan Chemical Vocational Technology College, Zhuzhou 412000, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2025, 17(23), 10540; https://doi.org/10.3390/su172310540
Submission received: 19 October 2025 / Revised: 18 November 2025 / Accepted: 20 November 2025 / Published: 25 November 2025
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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Abstract

Promoting the transformation of green consumption has emerged as a critical pathway to address ecological constraints. However, existing research exhibits significant discrepancies in understanding the relationship between green attributes and purchase intention and lacks a deep unpacking of the underlying individual psychological mechanisms. Employing three situational experiments, this study integrates the chain effects of green attribute centrality (GAC), environmental concern (EC), and green trust (GT) to systematically explore the formation mechanism of consumers’ green purchase decisions. Results show three key findings: (1) When consumers exhibit high EC, GAC enhances their green purchase intention (GPI) more effectively; however, when EC is low, GAC demonstrates no significant differential impact on GPI. (2) The interaction between GAC and EC influences GPI via green perceived value (GPV) (i.e., this interaction effect is mediated by GPV). (3) GT further moderates these relationships: under high GT conditions, consumers with high EC show a stronger preference for high-centrality green products; conversely, under low GT conditions, such consumers exhibit a greater preference for low-centrality green products. This study provides a novel theoretical explanation and practical pathway for bridging the “attitude-behavior gap” in green consumption. It not only deepens the understanding of the mechanisms underlying green consumption decision-making but also offers precise references for enterprises to implement differentiated green marketing strategies and for policymakers to guide public green consumption.

1. Introduction

Global environmental crises—including climate change, environmental pollution, and sharp biodiversity decline—are threatening ecological balance and sustainable human development at an unprecedented rate [1,2]. Confronted with these severe challenges, fostering a comprehensive green transition of the economy and society has become a global consensus. Guiding the public to adopt stable green consumption behaviors, thereby leveraging demand-side changes to drive production towards low-carbon and circular models, is regarded as a crucial pathway to addressing environmental constraints [3]. However, the Citizen Ecological Environment Behavior Survey Report (2022) issued by China’s Ministry of Ecology and Environment reveals a stark reality: although over half of the public highly recognize the importance of green consumption, only 60% translate this awareness into sustained consumption practices. This salient “attitude-behavior gap” highlights the pronounced divergence between cognition and action, which not only hampers the cultivation of the green consumption market but also constrains the full effectiveness of environmental governance [4,5]. Therefore, systematically investigating the influencing factors and underlying psychological mechanisms of consumers’ GPI is of significant theoretical and practical value. Such research is essential for bridging the “attitude-behavior gap,” unlocking the potential of green consumption, and ultimately providing demand-side momentum for the systematic resolution of environmental issues.
The green attributes of products play a critical role throughout the entire consumer decision-making process—from purchase and use to disposal—and have been identified as a key determinant of purchase intention. Their driving effect on green consumption has thus become a central topic in academic research [6,7]. The effectiveness of green attributes is often contingent on their centrality to the core value proposition of product. For instance, in the new energy vehicle sector, products featuring central green attributes (e.g., long driving range) significantly sway purchase decisions, whereas those with peripheral green features (e.g., eco-friendly packaging) see a markedly weaker effect, highlighting the pivotal role of GAC. However, there remains considerable divergence in the literature regarding the nature of the relationship between green attributes and purchase intention. On the one hand, studies grounded in consumer psychological traits—such as environmental values [8,9,10], motivation [11], and green self-efficacy [12]—often presume that consumers hold more positive and stable attitudes toward products with green attributes. On the other hand, other research has revealed that, under certain conditions, green attributes may inhibit purchase intention due to factors such as perceived quality trade-offs [13,14] or skepticism [15,16,17] toward green claims. Furthermore, market observations indicate that even with prominent green attributes, consumer response is still strongest among individuals with high Environmental Concern (EC), and this effect is fundamentally underpinned by a foundation of Trust (GT) in the brand’s claims, as evidenced by the success of Unilever’s Sustainable Living Plan. This discrepancy suggests that the link between green attributes and purchase intention is not simply linear or unidirectional; rather, it is embedded within complex contexts and operates as a dynamic process that requires deeper analysis incorporating individual differences and underlying mechanisms.
Although existing studies have explored green consumption behavior from multiple perspectives, research on the key individual psychological variable of EC remains relatively underdeveloped. Notably, individual differences may serve as a critical entry point for reconciling the aforementioned contradictions. EC reflecting an individual’s attitudinal tendency and degree of importance placed on environmental protection—is considered a significant antecedent predicting pro-environmental behavior [18,19,20]. In essence, GPI involves consumers’ trade-off between environmental attributes and traditional product utility, and its intensity varies dynamically with the level of EC [21]. Together, these divergent findings point to a fundamental question: Does the influence of green attributes on purchase intention exhibit nonlinear characteristics depending on the level of EC? Relying solely on product attributes or external contextual factors, without delving into the underlying psychological mechanisms of individuals, fails to explain why the same green attribute may yield a “double-edged” effect across different consumer groups. Therefore, there is an urgent need to integrate theoretical perspectives and conduct empirical testing to uncover the boundary conditions and mediating mechanisms of EC in the relationship between green attributes and purchase intention.
Grounding our research in the relatively under-explored perspective of GAC, this study moves beyond the oversimplified treatment of a “singular environmental attitude” in traditional green consumption research. By positioning GPV as a core mechanism, we systematically investigate the differentiated characteristics and boundary conditions of green consumption behaviors across varying levels of EC. To systematically address the core research questions, this study employs a sequence of three progressively designed experimental investigations. This approach establishes a comprehensive logical chain encompassing “whether it influences” “how it influences” and “under what conditions it influences”. Specifically, Study 1 examines the interaction effect between GAC and EC, aiming to clarify the fundamental boundary mechanisms of the “whether it influences” framework. Building upon these findings, Study 2 introduces GPV as a mediating variable to elucidate the underlying psychological mechanisms of “how it influences”. Finally, Study 3 incorporates GT as a moderating variable to delineate the external conditions, under which these influence mechanisms are amplified or altered. Collectively, this series of studies constructs an integrated theoretical model by providing empirical substantiation for the complete pathway through which GAC influences consumer decision-making.
Theoretically, this research not only addresses the core debate regarding the heterogeneous effects of green attributes across consumer segments but also enriches the micro-psychological explanatory framework of green consumption behavior. Practically, the findings offer a novel perspective for tackling the “attitude-behavior gap” in green consumption. They suggest that enterprises should implement differentiated green product designs—such as pairing high-centrality attributes with highly concerned consumer segments and focusing on trust-building for low-trust groups—and provide actionable insights for policymakers to develop stratified green guidance strategies.

2. Literature Review and Hypothesis Development

2.1. GAC, EC and GPI

Product attributes refer to the characteristics or elements that fulfill consumer needs, typically categorized into three fundamental dimensions: benefit attributes, image attributes, and physical attributes [22]. Green attributes can be regarded as an advanced form of traditional benefit attributes, integrating environmental features into existing functional characteristics to simultaneously satisfy consumers’ practical needs and ecological aspirations [23,24]. According to attribute centrality theory, consumers assign different weights to various product attributes during their psychological decision-making processes. An attribute is considered to have higher centrality when it is perceived as an integral part of the product’s core functionality [25]. Specifically, GAC reflects the degree to which environmental attributes are integrated into a product’s core functions. When green attributes occupy a central position—such as through the use of sustainable materials or environmentally friendly production processes—they are viewed as indispensable components of product functionality, directly influencing both product performance and environmental impact [26]. In contrast, peripheral green attributes (e.g., eco-friendly packaging) enhance the product’s overall environmental profile without altering its primary functions [27].
In green marketing contexts, GAC significantly influences consumers’ purchase decisions by conveying implicit information about environmental value and sustainable utility [28]. Research indicates that when green attributes are central to a product, consumers are more likely to perceive them as fundamental components, thereby strengthening their recognition of the product’s green benefits. This recognition leads consumers to perceive their purchase as not only fulfilling personal needs but also generating spillover benefits for ecological and social well-being, resulting in more positive value assessments [29].
However, the relationship between GAC and purchase intention remains debated in the academic literature. On one hand, studies suggest that central green attributes enhance the credibility of environmental claims, thereby improving consumers’ perception of green value and purchase intention [25]. On the other hand, researchers have found that central green attributes may raise consumer concerns about product performance and safety. For instance, products that come into direct contact with the skin and incorporate novel green materials may be perceived as risky or potentially unhygienic due to uncertainties about their long-term performance under normal usage conditions [30]. Furthermore, the “do-gooder derogation” stereotype—whereby ethical products are perceived as less competent—can lead consumers to worry that green attributes compromise key product characteristics such as durability, functionality, and overall quality. These concerns are particularly pronounced for central green attributes [13]. In comparison, while peripheral green attributes may be questioned regarding their authentic environmental benefits, they are less likely to trigger fundamental doubts about the product’s functional value [27]. These contradictory findings suggest that the driving effect of green attributes on consumer behavior is not simply linear but rather complex and context-dependent.
EC, as an important individual psychological variable, may play a crucial moderating role in this relationship. EC reflects the extent to which consumers value environmental issues and their willingness to contribute personally to solving these problems [31,32,33,34,35]. Consumers with high EC typically possess stronger environmental knowledge and greater motivation to process green information [36,37].Consequently, they are more likely to thoroughly evaluate the degree of GAC in their purchase decisions. Specifically, for highly environmentally concerned consumers, high GAC signals stronger environmental commitment and enhances the perceived green quality of products, thereby significantly increasing purchase intention [28]. In contrast, consumers with low EC demonstrate limited cognitive engagement with environmental issues, and their purchase decisions are predominantly influenced by traditional factors such as price and convenience [38]. Consequently, GAC is likely to have a weaker impact on their purchase intentions. In other words, due to their limited perception of green value and weak internal motivation, low environmentally concerned consumers are less likely to prioritize green attributes in their decision-making, resulting in insignificant differences in purchase intention across different levels of GAC.
Drawing upon the Elaboration Likelihood Model (ELM), we posit that EC determines the cognitive processing route for GAC. Consumers with high EC are motivated to engage in central route processing through evaluating the utility of high-GAC products to form stronger purchase intentions. Conversely, consumers with low EC rely on peripheral route processing, wherein GAC exerts a negligible influence as their decisions are guided by conventional product attributes (e.g., price, convenience). Therefore, EC is hypothesized as the essential factor to moderate the relationship between GAC and Green Purchase Intention (GPI). Based on the preceding analysis, the following hypothesis is proposed:
H1. 
EC positively moderates the relationship between GAC and GPI.
H1a. 
For consumers with high EC, high GAC (vs. low GAC) leads to higher GPI.
H1b. 
For consumers with low EC, GAC has no significant effect on GPI.

2.2. The Mediating Role of GPV

With the interaction between GAC and EC established, a fundamental question emerges: through what psychological mechanisms is this interaction effect mediated? Grounded in signal theory, we posit that GPV may serve as a pivotal mediator. According to signaling theory, in consumption environments characterized by information asymmetry, consumers rely on observable product attributes as cues to infer the intrinsic value of products [39]. GAC, as an important product signal, reflects the extent to which environmental commitment is integrated into the core functions of a product [25]. When consumers encounter green attributes with high centrality, they perceive them as reliable signals of the brand’s environmental commitment, thereby enhancing their recognition of the product’s green value [40].
However, the degree to which GAC influences GPV may vary depending on consumers’ level of EC. Consumers with high EC possess stronger motivation and ability to deeply process green product information. For these consumers, green attributes with high centrality not only convey a reliable signal of environmental commitment but also provide additional social identity value [41], thereby significantly enhancing their GPV. In contrast, consumers with low EC have limited cognitive investment in green issues and tend to be indifferent towards green products in their attitudes. They lack in-depth consideration of different levels of GAC and do not engage in extensive processing of this information to judge the actual green value of the product; thus, the influence of GAC on their GPV is relatively limited. GPV, as the outcome of this cognitive processing, plays a key role in influencing consumer behavioral intentions [42,43]. When consumers perceive higher environmental and functional value from green products, they develop stronger purchase intentions [44].
Based on the above theoretical analysis, this study proposes that GPV plays a key mediating role in the process through which GAC influences GPI, and the strength of this mediating path is moderated by EC. Specifically, for consumers with high EC, the mediating path where GAC enhances GPI by increasing GPV is more significant; whereas for consumers with low EC, this mediating path is relatively weaker.
Based on the above analysis, the following hypotheses are proposed:
H2. 
GPV mediates the interactive effect of GAC and EC on GPI.
H2a. 
For consumers with high EC, GAC positively influences GPI through the mediating role of GPV.
H2b. 
For consumers with low EC, the mediating effect of GPV on the relationship between GAC and GPI is not significant.

2.3. The Moderating Role of GT

To precisely delineate the applicability boundaries of the aforementioned causal mechanism, it is necessary to empirically investigate the key external contextual factors. GT embodies consumers’ confidence in the veracity of corporate environmental commitments and represents a significant moderating factor potentially influencing the entire causal pathway. Therefore, study investigates whether GT constitutes a moderating boundary condition within the proposed research model. GT refers to the beliefs and expectations formed by consumers based on the perceived ability, reliability, and benevolence of an enterprise producing green products, as well as the resulting willingness to rely on that enterprise and its products [45]. It reflects the consumer’s recognition of the enterprise’s environmental commitments and their confidence in the authenticity of the product’s environmental value [46]. Existing research generally agrees that GT is an important conduit between an individual’s pro-environmental value orientation and pro-environmental behavior [47], and can significantly promote the formation of GPI [48,49].
However, there are significant differences in the level of trust consumers place in green products. Yet, few studies have examined the differences in purchase intention, brand attitude, etc., resulting from varying levels of consumer GT. In fact, consumers lack a unified standard for judging the environmental attributes and value of green products, and their assessment of the motivations behind corporate environmental behaviors can also vary based on past experiences and information exposure [50]. In the context of this study, when consumers are confronted with green attributes of varying centrality, their level of trust significantly influences the process of interpreting environmental value. Consumers with a high degree of GT believe that green products can not only meet their own needs but also positively contribute to alleviating resource pressure and protecting the ecological environment. Therefore, a high level of GT is a prerequisite for the positive impact of the interaction between GAC and consumer EC on GPI. For consumers with a low degree of GT, due to their skeptical attitude towards the environmental attributes of green products [51], their purchase intention remains at a relatively low level and shows no significant difference regardless of whether they encounter products with high or low GAC.
According to trust transfer theory, GT is posited to moderate the GAC–EC interaction because trust alters risk perceptions. When GT is high, consumers with high EC can safely prioritize high-GAC products; when GT is low, even high-EC consumers may avoid high-GAC items due to skepticism. Based on the above analysis, this study proposes that GT moderates the interactive effect of GAC and EC on purchase intention. Thus, the following hypotheses are proposed:
H3. 
The interactive effect of GAC and EC on consumers’ GPI is moderated by GT. Specifically:
H3a. 
For consumers with a high level of GT, the GPI generated by high GAC will be stronger for those with high EC (vs. low EC), whereas no significant difference will occur when GAC is low.
H3b. 
For consumers with a low level of GT, regardless of having high or low EC, the GPI generated by GAC remains at a low level and shows no significant difference.
Based on the theoretical basis and the research hypotheses, the research model of the present study is proposed, as shown in Figure 1.

3. Research Design and Results Analysis

3.1. Study 1: The Interactive Effect of GAC and EC on GPI

3.1.1. Study Design and Procedure

As a pilot study, this research implemented a two-by-two (GAC: high vs. low × EC: high vs. low) factorial design to test Hypothesis H1, investigating whether EC moderates the direct relationship between GAC and GPI. 165 consumers (67 males, 98 females, SD = 5.23) were recruited as participants through an online survey platform (Table 1). To ensure that participants were actual consumers with relevant purchase experience, a screening criterion was implemented at the beginning of the survey. Only individuals who reported having purchased shampoo in the past six months were eligible to participate in this study.
To ensure the robustness of our study, a statistical power analysis was conducted by using G*Power 3.1.9.6 [52]. We specified a Fixed effects, special, main effects and interactions ANOVA with an effect size f = 0.25 (medium effect), a significance level (α) of 0.05, and a total sample size of 165 participants. The analysis revealed that the achieved statistical power was 0.89, which indicates an 89% probability of correctly detecting a significant effect. Given the specified parameters, 0.89 exceeds the conventional threshold of 0.80 and confirms the sufficiency of our sample size for testing the hypothesized interactions.
Drawing on the experimental stimuli from Chen and Wu [53], the stimulus used in this study was a green product—Denee Shampoo (a fictitious brand) produced by Company A. Shampoo was chosen as the experimental stimulus because hair care products are highly accessible and familiar to consumers. Furthermore, there are many green and non-green varieties of hair care products on the market, making it easier for participants to believe in the authenticity of the fictitious shampoo. Using the fictitious shampoo brand “Denee” as the stimulus aimed to prevent participants from forming associations with familiar shampoo brands currently on the market.
During the experiment, participants were randomly assigned to one of the two experimental conditions: core attribute green information or peripheral attribute green information. They first read the scenario: “You intend to buy a bottle of shampoo and see the following green product, Denee Shampoo, produced by Company A. Please read the material below and answer the related questions.” Following the experimental designs of Skard et al. [14] and Chen and Wu [53], high GAC was manipulated using product composition containing green ingredients, while low GAC was manipulated using green packaging. In the high GAC group, participants viewed an image and read the description of Denee Shampoo: “100% natural plant formula; Protect the environment, care for the Earth; Green raw materials, no burden on the planet.” In the low GAC group, participants viewed an image and read the description of Denee Shampoo: “Bottle made from 100% recycled materials; Protect the environment, care for the Earth; Green bottle, no burden on the planet.” Subsequently, participants completed scales measuring EC, GPV, and GPI.
To ensure the reliability and validity of the experiment, this study referred to mature scales and well-established measurement methods from domestic and international literature. The EC scale was adapted from Pagiaslis and Krontalis [54]. The scale for GPV was adapted from Xue [55]. The scale for GT came from Chen [56]. The scale for GPI was adapted from Cui [57]. After excluding invalid questionnaires with missing values and extreme values, a total of 165 valid questionnaires were collected for this study.

3.1.2. Study Results

Scale Reliability Test: The results showed that the Cronbach’s α coefficients for EC and GPI were 0.833 and 0.714, respectively, both exceeding the benchmark of 0.70. This indicates that the scales exhibited good reliability.
Common Method Bias Test: Since this study employed self-reported data, common method bias could be a potential concern. Procedural remedies such as anonymity and reverse-scored items were implemented during the survey administration. A confirmatory factor analysis was conducted on all self-rated items to assess common method bias. The results indicated a poor model fit: χ2/df = 5.337, CFI = 0.70, GFI = 0.72, AGFI = 0.60, NFI = 0.66, RMSEA = 0.16. Therefore, no serious common method bias was found in this study.
Manipulation Checks: First, the manipulation effect of GAC was tested. The results of statistical analysis showed no significant difference in the ratings of the green product between the two groups of participants (M_high = 5.50 vs. M_low = 5.34, t = 1.148, p > 0.05). However, participants considered high GAC (formula ingredients) to be a more important and decisive aspect of the shampoo compared to low GAC (bottle packaging) (M_high = 5.56 vs. M_low = 4.11, t = 8.158, p < 0.001). This indicates that the experimental manipulation of GAC was successful. Second, participants were divided into high and low EC groups based on the mean score of EC. The results showed a significant difference between the high and low EC groups (M_high = 6.12 vs. M_low = 4.711, t = 15.464, p = 0.000).
The Interactive Effect of GAC and EC on GPI: The main effect of GAC on GPI was significant (F (1, 161) = 24.884, p < 0.001). The main effect of EC on GPI was also significant (F (1, 161) = 40.022, p < 0.001). Furthermore, the significant interaction (F = 35.601, p < 0.001) supports H1, confirming that EC moderates the GAC–GPI link (Table 2). This aligns with the Elaboration Likelihood Model: high-EC consumers centrally process green attributes, whereas low-EC consumers rely on peripheral cues. Thus, the empirical result validates the theoretical assumption that individual differences in EC drive heterogeneous responses to GAC.
Further analysis showed that for consumers with high EC, high GAC elicited a significantly higher purchase intention compared to low GAC (M_high = 5.933 vs. M_low = 4.81, t = 8.416, p < 0.001). In contrast, for consumers with low EC, there was no significant difference in purchase intention between the low and high GAC conditions (M_high = 4.659 vs. M_low = 4.784, t = 0.758, p = 0.536). Based on these results, H1a and H1b were supported, confirming that GAC and EC interact to influence consumers’ GPI (Figure 2).

3.2. Study 2: The Mediating Role of GPV

3.2.1. Study Procedure

Building upon the initial study’s confirmation of interaction effects, the second study aims to investigate the underlying mechanisms. We replicated the experimental design from the first study and introduced GPV as a mediator to test Hypothesis 2 (H2), specifically to examine whether GPV mediates the interaction between GAC and EC on GPI. This study employed a 2 (GAC: high vs. low) × 2 (EC: high vs. low) between-subjects experimental design to test the hypotheses. A total of 197 consumers (86 males, 111 females, aged between 17 and 68) were recruited from an online survey platform and provided with a monetary incentive (Table 3). The specific experimental procedure was as follows:
First, similar to Study 1, participants were randomly assigned to one of two scenarios (A or B). The experimental stimulus chosen for this study was a green product—ECO Laundry Detergent (a fictitious brand) produced by Company A. To ensure participants were genuine consumers with relevant purchase experience, a screening protocol was implemented at the outset of the survey. Only respondents reporting laundry detergent purchases within the preceding six months were permitted to proceed the study. Laundry detergent was selected as the target product because cleaning products are common in the market and familiar to consumers as daily necessities. Furthermore, there are numerous green and non-green options available for cleaning products, making participants more inclined to believe in the authenticity of the fictitious laundry detergent. Using the fictitious brand “ECO” aimed to exclude the potential influence of brand familiarity or pre-existing brand impressions on the experimental results. This study utilized images and textual descriptions of the laundry detergent for the experimental manipulation. Apart from the differing textual descriptions (i.e., the manipulation of GAC), all other information about the stimuli was identical across conditions.
To ensure the robustness of our study, a statistical power analysis was conducted by using G*Power 3.1.9.6. We specified a Fixed effects, special, main effects and interactions ANOVA with an effect size f = 0.25 (medium effect), a significance level (α) of 0.05, and a total sample size of 197 participants. The analysis showed that the achieved statistical power was 0.93, which indicates an 93% probability of correctly detecting a significant effect. Given the specified parameters, 0.93 exceeds the conventional threshold of 0.80 and confirms the sufficiency of our sample size for testing the hypothesized interactions.
During the experiment, participants were randomly assigned to either the high or low GAC condition. They first read the scenario: “You intend to buy a bottle of laundry detergent and see the following green product, ECO Laundry Detergent, produced by Company B. Please read the material below and answer the related questions.”
Drawing on the experimental designs of Skard et al. [14] and Chen and Wu [52], high GAC was manipulated by emphasizing green ingredients in the product composition, while low GAC was manipulated by emphasizing green packaging. In the high GAC group, participants viewed an image and read the description of ECO Laundry Detergent: “100% natural plant formula; Protect the environment, care for the Earth; Green raw materials, no burden on the planet.” In the low GAC group, participants viewed an image and read the description of ECO Laundry Detergent: “Bottle made from 100% recycled materials; Protect the environment, care for the Earth; Green bottle, no burden on the planet.”
Subsequently, participants completed scales measuring EC, GPV, and GPI. The referenced mature scales were consistent with those used in Study 1. After excluding invalid questionnaires with missing values and extreme values, a total of 184 valid questionnaires were collected for this study.

3.2.2. Results Analysis

Reliability Test: Firstly, Cronbach’s α was used to test the reliability of the scales for EC, GPV, and GPI. The results showed that the Cronbach’s α coefficients for EC, GPV, and GPI were 0.86, 0.81, and 0.77, respectively, all exceeding 0.7. This indicates good reliability for all scales.
Common Method Bias Test: Because this study employed self-reported data, common method bias could be a potential concern. To address this issue, procedural remedies such as anonymity and reverse-scored items were implemented during the survey administration. A confirmatory factor analysis was conducted on all self-rated items, and the results indicated a very poor model fit: χ2/df = 6.622, CFI = 0.69, GFI = 0.68, AGFI = 0.54, NFI = 0.655, RMSEA = 0.18. Therefore, no serious common method bias was found in this study.
Manipulation Check: The manipulation effect of GAC was tested. The results showed that participants considered high GAC (formula ingredients) to be a more important and decisive aspect for the laundry detergent compared to low GAC (bottle packaging) (M_high = 5.77 vs. M_low = 4.64, t = 0.53, p = 0.000). This indicates that the experimental manipulation of GAC was successful.
The Interactive Effect of GAC and EC on GPV: A two-way ANOVA revealed a significant interaction effect between GAC and EC on GPV (F (1, 180) = 19.24, p < 0.001) (Table 4).
Further analysis showed that for consumers with high EC, high GAC elicited a significantly higher GPV compared to low GAC (M_high = 5.75 vs. M_low = 5.44, t = 6.16, p < 0.001). In contrast, for consumers with low EC, there was no significant difference in GPV between the high and low GAC conditions (M_high = 5.15 vs. M_low = 4.83, t = 1.63, p = 0.11).
Mediation Effect Test: To test the mediating role of GPV, Hayes’s PROCESS macro (Model 8) was employed using a bootstrapping method with 5000 resamples and a 95% confidence interval. The results showed that when EC was high, GAC influenced GPI through GPV, indicating a significant mediating effect (indirect effect = 0.19, 95% CI [0.01, 0.39]), and that explained 38.5% of the total effect (partial R2 = 0.385). Thus, H2a was supported. Conversely, when EC was low, GAC did not influence GPI through GPV, as the mediating effect was not significant (indirect effect = −0.04, 95% CI [−0.16, 0.04]). Thus, H2b was supported. The significant moderated mediation effect (Index = 0.24, 95% CI [0.01, 0.49]) provides the robust support for H2, revealing that GPV serves as a critical cognitive mechanism through which GAC and EC jointly influence GPI (Figure 3). This aligns with signaling theory that high GAC acts as a credible environmental cue, enabling high-EC consumers to infer greater green utility (e.g., functional and emotional benefits), and thus enhances GPV and subsequently GPI. In contrast, low-EC consumers lack the motivation to deeply process such signals, resulting in a non-significant mediation path. Consequently, the findings empirically validate that GPV functions as a value-based bridge that translates attribute centrality into behavioral intention, contingent on individual differences in environmental concern—a novel insight into the micro-foundations of green decision-making.

3.3. Study 3: The Moderating Role of GT

3.3.1. Study Design and Procedure

Based on the preceding two studies, this research incorporates GT as a critical boundary condition. Employing a 2 (GAC: high vs. low) × 2 (EC: high vs. low) × 2 (GT: high vs. low) inter-group experimental design, we empirically examine Hypothesis 3 (H3) to assess how GT moderates the interaction effects between GAC and EC, thus delineating the theoretical model’s applicable scope. We predicted that when consumers have a higher (vs. lower) level of GT, the GPI and GPV generated by high GAC for consumers with high EC would be enhanced, thereby testing H3a and H3b. A total of 299 consumers (157 females, 142 males) were recruited from an online survey platform and provided with a monetary incentive (Table 5). Given that bicycles constitute a high-involvement, low-frequency purchase, we established a screening criterion to ensure participants could fully engage with the experimental scenario. Familiarity was assessed using the item: “How familiar are you with bicycles…?” (Responses were recorded on a 7-point Likert scale anchored at 1 = Very unfamiliar and 7 = Very familiar. Only respondents self-reporting a familiarity level of 4 or higher were included in the study.
To ensure the robustness of our study, a statistical power analysis was conducted by using G*Power 3.1.9.6 [52]. We specified a Fixed effects, special, main effects and interactions ANOVA with an effect size f = 0.25 (medium effect), a significance level (α) of 0.05, and a total sample size of 299 participants. The analysis revealed that the achieved statistical power was 0.99, which explains an 99% probability of correctly detecting a significant effect. Given the specified parameters, 0.99 exceeds the conventional threshold of 0.80 and confirms the sufficiency of our sample size for testing the hypothesized interactions.
The experimental stimuli for this study were adapted from the research of Gong and Sheng [28]. A bicycle, a product highly relevant to consumers’ daily lives, was preliminarily selected as the target product for the formal experiment. In the high GAC condition, the scenario was described as follows: “Suppose you intend to purchase a bicycle. After some research, you find a suitable model. Compared to ordinary bicycles on the market, this bicycle’s frame utilizes environmentally friendly carbon steel supports, which not only offer high load-bearing capacity and wear resistance but also impose no burden on the environment.” In the low GAC condition, the scenario was described as: “Suppose you intend to purchase a bicycle. After some research, you find a suitable model. Compared to ordinary bicycles on the market, this bicycle’s saddle is made from environmentally friendly rubber material, which not only provides good shock absorption and waterproof effects but also imposes no burden on the environment.”
This study employed images and textual descriptions of the bicycle for the experimental manipulation. Apart from the differing textual descriptions (i.e., the manipulation of GAC), all other information about the stimuli was identical across conditions. The mature scales referenced were consistent with those used in Study 1.

3.3.2. Results Analysis

Reliability Test: Firstly, Cronbach’s α was used to test the reliability of the scales for GT, EC, GPV, and GPI. The results of the reliability analysis for EC, GPV, GT, and GPI showed Cronbach’s α coefficients of 0.81, 0.72, 0.83, and 0.84, respectively, all exceeding 0.7. This indicates good reliability for all scales.
Common Method Bias Test: Since this study employed self-reported data, common method bias could be a potential concern. Procedural remedies such as anonymity and reverse-scored items were implemented during the survey administration. A confirmatory factor analysis was conducted on all self-rated items to assess common method bias. The results indicated a poor model fit: χ2/df = 9.10, CFI = 0.37, GFI = 0.50, AGFI = 0.54, NFI = 0.35, RMSEA = 0.17. Therefore, no serious common method bias was found in this study.
Manipulation Check: The manipulation effect of GAC was tested. The results showed no significant difference in the ratings of the green product between the two participant groups (M_high = 5.61 vs. M_low = 5.64, t = −0.261, p = 0.298 > 0.05). However, participants considered high GAC (bicycle frame) to be a more important and decisive aspect for the bicycle compared to low GAC (bicycle saddle) (M_high = 5.65 vs. M_low = 4.71, t = 7.77, p = 0.000). This indicates that the experimental manipulation of GAC was successful.
Test of the Moderating Effect of GT: Subsequently, the moderating effect of GT was tested. As the measurement of GT was continuous, this study first divided all participants into high and low GT groups based on the mean ± 1 standard deviation (SD) of their GT scores, using a spotlight analysis and regression analysis approach. The moderating role of GT in the effect of the interaction between GAC and EC on GPI was then analyzed. The Bootstrap procedure (Model 8) was used for data analysis.
The results indicated a significant three-way interaction effect among GAC, EC, and GT on GPI (F (1291) = 6.009, p = 0.015). The significant three-way interaction (GAC × EC × GT; F = 24.848, p = 0.000) confirms H3, demonstrating that green trust establishes a critical boundary condition for the GAC–EC relationship (Table 6 and Figure 4). This finding resonates with trust transfer theory that high GT mitigates perceived risks, allowing high-EC consumers to confidently prioritize high-GAC products for their core environmental utility. Conversely, under low GT, skepticism triggers risk-aversion, leading high-EC consumers to prefer low-GAC products (e.g., packaging) as a “safer” alternative to fulfill environmental motives—a pattern divergent from H3b but consistent with behavioral risk models. Thus, GT not only moderates but reconfigures the interaction between attribute centrality and individual concern, highlighting that trust context can shift consumer strategies from value-maximization to risk-minimization.
Further spotlight analysis revealed that when consumer GT was high (Mean + 1SD = 5.89), and the green attribute was manifested in a core attribute (high centrality), the GPI of the high (vs. low) EC group (Mean + 1SD = 5.88) was significantly enhanced (M_high = 5.77 vs. M_low = 4.31; t = −6.92, p = 0.00). While when the green attribute was manifested in a peripheral attribute (low centrality), the difference in GPI between the high and low EC groups was not significant (M_high = 3.87 vs. M_low = 4.00; t = 0.65, p = 0.52). Based on the results, H3a was supported.
When consumer GT was low (Mean − 1SD = 4.12), and the green attribute was manifested in a core attribute (high centrality), there was no significant difference in GPI between the high and low EC groups (M_high = 3.81 vs. M_low = 3.83; t = 0.09, p = 0.93). However, when the green attribute was manifested in a peripheral attribute (low centrality), the GPI of the high EC group (Mean + 1SD = 5.88) was significantly enhanced compared to the low EC group (M_high = 4.48 vs. M_low = 3.98; t = −2.58, p = 0.01 < 0.05). This result is contrary to the hypothesis H3b.

4. Research Conclusions and Discussion

This study systematically elucidates the complex mechanisms through which GAC influences consumer purchasing decisions via three interconnected experiments. First, Study 1 demonstrated that the relationship wherein GAC promotes GPI is significantly contingent upon consumer EC levels. Subsequently, Study 2 revealed the internal mediating process: this interaction effect operates through the core psychological mechanism of enhanced GPV. Finally, Study 3 delineated the boundary condition: GT functions as a critical external moderator of the entire impact pathway; specifically, under low-trust conditions, high-EC consumers shift their preference from core to peripheral green attributes. Collectively, this research not only confirms and delineates the existence of the GAC effect but also elucidates its underlying mechanism and contextual boundaries, thereby providing robust empirical evidence for an integrated green consumption decision model. The findings demonstrate that GAC’s influence on purchase intention is systematically moderated by individual characteristics and mediated by psychological processes, rather than exhibiting a simple linear relationship. The principal findings are presented as follows:
Firstly, there is a significant interactive effect between GAC and EC on purchase intention. The purchase intention of consumers with high EC increases with the enhancement of GAC (M_high = 5.933 vs. M_low = 4.81, t = 8.416, p < 0.001), whereas the purchase intention of the low EC group is not affected by GAC (M_high = 4.659 vs. M_low = 4.784, t = 0.758, p = 0.536). This finding is consistent with the theoretical expectations of the Elaboration Likelihood Model and corroborates the conclusions of Kumar et al. [58,59]: a consumer’s level of EC determines their information processing route. High EC activates the central route, leading consumers to deeply scrutinize the value of core green attributes; whereas low EC leads to peripheral route processing, making differences in GAC irrelevant.
Secondly, GPV is provisionally supported as a mediating mechanism of this interactive effect. Green attributes with high centrality indirectly strengthen purchase intention by enhancing consumers’ perceived value of the product’s environmental utility (Index = 0.24, 95% CI [0.01, 0.49]). This conclusion echoes the assertions of Yan et al. [60], Roh et al. [61], and Chwialkowska et al. [62] that “GPV is the core driver of green consumption decisions,” while also revealing that EC operates through the chain path of “attribute centrality → perceived value → purchase intention,” thereby supplementing the explanatory map of the micro-psychological mechanisms of green consumption.
Finally, GT negatively moderates the “high EC—high centrality preference” relationship. Specifically, under conditions of low GT (Mean − 1SD = 4.12), the conventional interaction pattern undergoes significant modification: when green attributes demonstrate high centrality, no significant difference emerges in purchase intention among consumers with high versus low EC(M_high = 3.81 vs. M_low = 3.83; t = 0.09, p = 0.93). Conversely, when green attributes manifest low centrality, GPI among highly EC consumers becomes significantly elevated compared to their low-concern counterparts (M_high = 4.48 vs. M_low = 3.98; t = −2.58, p = 0.01 < 0.05). This phenomenon can be attributed to perceived risk induced by low trust [17]—regardless of individual differences in EC, consumer skepticism toward green products may elicit critical evaluation and decision hesitation [17,63], thereby uniformly suppressing GPI to lower levels. However, regarding low-centrality green attributes (such as packaging), when consumers suspect exaggerated environmental benefits, they are less inclined to question the functional value or outright reject the product [27]. This configuration provides highly environmentally concerned consumers with a risk-controllable alternative pathway to fulfill their environmental motivations. These consumers avoid core functional attributes potentially associated with “greenwashing” risks, placing trust in and selecting peripheral green attributes characterized by safer commitments [27]. Conversely, consumers with low EC exhibit weaker intrinsic motivation; even when confronted with low-risk peripheral green attributes, their GPI remains challenging to effectively activate.

5. Theoretical Contribution and Managerial Implications

5.1. Theoretical Contribution

This study makes theoretical contributions in the following three aspects: First, by revealing the moderating role of EC, it provides a new theoretical explanation for the divergent conclusions in green attribute research. Echoing the studies of Gong Siyu and Sheng Guanghua et al. [64], this research confirms that the level of EC is a key variable for understanding the heterogeneous effects of green attributes, providing empirical evidence for resolving long-standing theoretical debates and supplementing the findings of Gong Siyu and Sheng Guanghua et al. [64] Secondly, it constructs the mediating path of “GAC → GPV → GPI,” deepening the understanding of green consumption decision-making mechanisms. Aligning with Cam’s [65] view on the importance of GPV, this study further clarifies its key mediating role in the interactive influence of EC and GAC, addressing the insufficient exploration of explanatory mechanisms in previous research. Finally, it innovatively identifies the boundary role of GT, expanding the application scope of GT theory. Previous research on GT often treated it as an independent or mediating variable [46], rarely considering its role as a moderator. Meanwhile, this study first empirically demonstrates that the context of trust determines how environmental concern functions: in high-trust environments, it drives consumers to pursue core green value, whereas in low-trust environments, it prompts risk-averse behavior and shifts consumer preference toward peripheral green attributes. This finding provides a novel theoretical lens for understanding the complex decision-making mechanisms underlying green consumption in contexts of trust crisis. This discovery expands the application boundaries of GT theory and provides a new theoretical explanation for understanding how a lack of trust weakens the translation of environmental motivation into behavior.

5.2. Managerial Implications

The findings of this study provide the following practical guidance for enterprises to effectively implement sustainable development concepts and gain a green competitive advantage: First, enterprises must segment markets based on consumers’ environmental concern levels and adopt communication strategies aligned with these levels. For consumers with high environmental concern, companies should prioritize showcasing high-centrality green attributes by providing detailed, professional technical information. For instance, Tesla’s release of its 2024 Impact Report continuously discloses core data on battery recycling and energy efficiency to users and media, thereby addressing this segment’s need for in-depth verification of environmental utility. Conversely, for consumers with low environmental concern, green attribute centrality exerts minimal influence; thus, enterprises may position green attributes as supplementary information integrated with products’ functional benefits rather than as primary selling points. Secondly, when promoting green products, companies should strategically shape and communicate clear GPV. This study confirms that GAC effectively enhances GPI by elevating consumers’ GPV, implying that enterprises must not only showcase green attributes but also vividly articulate the specific environmental utility and emotional value these attributes deliver to consumers. For example, Patagonia’s Worn Wear initiative not only promotes its core green attributes (e.g., organic cotton) but also enhances consumers’ sense of environmental accomplishment through services like garment repair and sharing stories of used clothing, thereby substantially elevating their green perceived value. Thirdly, enterprises should establish a systematic GT management system. This study reveals that under conditions of low GT, even consumers with high EC tend to avoid products with high GAC. Therefore, building strong GT in green claims constitutes a crucial prerequisite for enterprises to address the “attitude-behavior gap” in sustainable consumption. In mature markets where consumers are familiar with sustainability, communication efforts can focus on promoting high GAC. Conversely, in emerging markets, marketers should emphasize the product’s direct functional advantages like cost-effectiveness while gradually building credibility for environmental claims.

6. Limitations and Future Research Directions

This study inevitably has some limitations, which simultaneously provide directions for future research. (1) Based on an online participant sample, we obtained a convenient and diverse consumer group, but its demographic representativeness may be limited compared with the national population. Future studies should take into account both expanding the sample size and diversity, and adopting field experiments in more realistic scenarios to enhance the external validity of the research. (2) To ensure consumers’ familiarity with and accessibility to the experimental products, all selected products in this study were utilitarian products. Although the manipulation of experimental stimuli was derived from previous mature academic research, the equivalence of stimuli in dimensions such as environmental efficacy and novelty was not directly measured. Future studies should consider different product types to test the generalizability of the conclusions to other green product categories, while strengthening manipulation checks to improve the internal validity of the research. (3) The scope of the present study focused on the core variables in the theoretical model, without examining other potential influencing factors. Future studies should expand the research scope to include external contextual factors such as eco-labels and social norms, as well as individual difference variables such as personal values and environmental knowledge to further refine the theoretical model of green consumption decision-making.

Author Contributions

Conceptualization, K.Z., X.S. and Y.T.; methodology, X.S. and K.Z.; investigation X.S. and C.Y.; resources, K.Z. and X.S.; data curation X.S., Y.T. and C.Y.; writing—original draft preparation, X.S.; writing—review and editing, K.Z., X.S. and B.Y.; supervision, K.Z. and B.Y.; funding acquisition, B.Y. and Y.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Foundation of China (No. 22BJY197) by National Office for Philosophy and Social Sciences, the Hunan Provincial Social Science Fund (24JL005) by Hunan Province Office for Philosophy and Social Sciences and the Hunan Provincial Social Sciences Review Committee Project (No. XSP25YBZ206) by Hunan Provincial Federation of Social Sciences.

Institutional Review Board Statement

Easy to Know, Hard to Act: How Do Green Attribute Centrality, Environmental Concern, and Trust Exert a Chain Effect on Purchase Decisions? is a research paper on consumer behavior studies. According to the Scientific Research Management Regulations of Central South University of Forestry and Technology, this research: 1. Utilizes legally obtained public data or data generated through non-interventional observation of public behaviors; 2. Employs anonymized information data that cannot identify specific individuals or be restored to original form; 3. Involves no sensitive personal information or commercial interests. Therefore, this study does not require ethics approval.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Our data is still in use and serves as the source for the paper under submission. Therefore, data sharing is not currently available.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
GACGreen Attribute Centrality
ECEnvironmental Concern
GTGreen Trust
GPVGreen Perceived Value
GPIGreen Purchase Intention

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Figure 1. The research model of the present study.
Figure 1. The research model of the present study.
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Figure 2. The interaction effect of GAC and EC on GPI.
Figure 2. The interaction effect of GAC and EC on GPI.
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Figure 3. The interaction effect of GAC and EC on GPV.
Figure 3. The interaction effect of GAC and EC on GPV.
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Figure 4. The moderating role of GT.
Figure 4. The moderating role of GT.
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Table 1. Demographic profile of respondents of Study 1.
Table 1. Demographic profile of respondents of Study 1.
VariableFrequencyPercentage
GenderMale6740.6.9
Female9859.4
Age (years)≤1531.9
18–2210865.4
12–254426.7
≥26106
Education levelJunior college31.8
Bachelor’s degree12575.8
Master’s degree3722.4
Income (CNY)≤10003320.0
1001–20007042.4
2001–30004426.1
≥30001810.9
Table 2. Analysis of the Interaction between GAC and EC in Study 1. Dependent Variable: GPI.
Table 2. Analysis of the Interaction between GAC and EC in Study 1. Dependent Variable: GPI.
SourceType III Sum of SquaresdfMean SquareFSignificance
Corrected Model42.532 a314.17732.9850.000
Intercept4179.42214179.4229723.9780.000
GAC10.695110.69524.8840.000
EC17.202117.20240.0220.000
GAC × EC15.302115.30235.6010.000
Error69.1991610.430
Total4303.667165
Corrected Total111.731164
a R2 = 0.381 (Adjusted R2 = 0.369).
Table 3. Demographic profile of respondents of Study 2.
Table 3. Demographic profile of respondents of Study 2.
VariableFrequencyPercentage
GenderMale8641.3
Female11158.7
Age (years)≤22126.1
23–324723.9
33–422613.2
43–523316.8
53–625326.9
≥632613.2
Education levelHigh school73.8
Junior college 6231.5
Bachelor’s degree10754.3
Master’s degree 891.8
Income (CNY)≤20005427.4
2001–30006533.0
3001–50003819.3
5001–80002814.2
≥8001126.1
Table 4. Test of the Interaction Effect between GAC and EC in Study 2. Dependent Variable: GPV.
Table 4. Test of the Interaction Effect between GAC and EC in Study 2. Dependent Variable: GPV.
SourceType III Sum of SquaresdfMean SquareFSignificance
Corrected Model56.704 a318.90137.5470.000
Intercept3632.51313632.5137215.8220.000
EC33.561133.56166.6680.000
GAC4.01914.0197.9840.005
EC × GAC9.68619.68619.2420.000
Error90.6141800.503
Total4377.563184
Corrected Total147.318183
a R2 = 0.385 (Adjusted R2 = 0.375).
Table 5. Demographic profile of respondents of Study 3.
Table 5. Demographic profile of respondents of Study 3.
VariableFrequencyPercentage
GenderMale14247.5
Female15752.5
Age (years)≤18175.7
18–259230.8
26–359431.4
36–456722.4
≥55299.7
Education levelHigh school144.7
Junior college 6421.4
Bachelor’s degree18160.5
Master’s degree 3812.7
Doctor’s degree 20.6
Income (CNY)≤20007224.1
2001–30007123.7
3001–50005719.1
5001–80006822.7
≥80003110.4
Table 6. Test of the Three—way Interaction among GAC, EC and GT. Dependent Variable: GPI.
Table 6. Test of the Three—way Interaction among GAC, EC and GT. Dependent Variable: GPI.
SourceType III Sum of SquaresdfMean SquareFSignificance
Corrected Model113.905 a716.27218.1040.000
Intercept5386.10315386.1035992.6040.000
GAC42.153142.15346.8990.000
EC15.122115.12216.8250.000
GT15.619115.61917.3780.000
GAC × EC21.064121.06423.4360.000
GAC × GT8.96518.9659.9750.002
EC × GT3.30413.3043.6760.056
GAC × EC × GT5.40115.4016.0090.015
Error261.5482910.899
Total5792.444299
Corrected Total375.453298
a R2 = 0.303 (Adjusted R2 = 0.287).
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Zhang, K.; Sun, X.; Yuan, B.; Tan, Y.; Yang, C. Easy to Know, Hard to Act: How Do Green Attribute Centrality, Environmental Concern, and Trust Exert a Chain Effect on Purchase Decisions? Sustainability 2025, 17, 10540. https://doi.org/10.3390/su172310540

AMA Style

Zhang K, Sun X, Yuan B, Tan Y, Yang C. Easy to Know, Hard to Act: How Do Green Attribute Centrality, Environmental Concern, and Trust Exert a Chain Effect on Purchase Decisions? Sustainability. 2025; 17(23):10540. https://doi.org/10.3390/su172310540

Chicago/Turabian Style

Zhang, Kun, Xiaoling Sun, Baolong Yuan, Yuxuan Tan, and Caiyan Yang. 2025. "Easy to Know, Hard to Act: How Do Green Attribute Centrality, Environmental Concern, and Trust Exert a Chain Effect on Purchase Decisions?" Sustainability 17, no. 23: 10540. https://doi.org/10.3390/su172310540

APA Style

Zhang, K., Sun, X., Yuan, B., Tan, Y., & Yang, C. (2025). Easy to Know, Hard to Act: How Do Green Attribute Centrality, Environmental Concern, and Trust Exert a Chain Effect on Purchase Decisions? Sustainability, 17(23), 10540. https://doi.org/10.3390/su172310540

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