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Article

How E-Commerce Product Environmental Information Influences Green Consumption: Intention–Behavior Gap Perspective

by
Xintian Wang
1,†,
Meng Peng
2,†,
Yan Li
3,*,
Muhua Ren
1,
Tao Ma
2,*,
Weidong Zhao
2 and
Jiayu Xu
1
1
School of Environment, Tsinghua University, Beijing 100084, China
2
Institute of Energy Conservation and Environmental Protection, China Center for Information Industry Development, Beijing 100048, China
3
School of Ecology and Environment, Renmin University of China, Beijing 100872, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2025, 17(6), 2337; https://doi.org/10.3390/su17062337
Submission received: 8 February 2025 / Revised: 27 February 2025 / Accepted: 5 March 2025 / Published: 7 March 2025
(This article belongs to the Section Sustainable Products and Services)
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Abstract

:
Product environmental information is considered a useful strategy to translate consumers’ intention into actual green product purchase behaviors. The underlying theoretical explanations of the importance of information await exploration. This research applies a theory of planned behavior lens to study the theorical mechanism of how product environmental information affects consumer behaviors in the e-commerce context. E-commerce product environmental information is covered in the theoretical model as a new variable which can be studied for its impact on consumer behaviors. As results show, the intention and perceived behavioral control can both positively impact behavior. Specifically, information on behavioral consequences, mediated by perceived control of behavior, can significantly improve purchase behavior for durable products such as air conditioners. Meanwhile, information frequency can positively moderate the relation of perceived behavior control to behavior. For fast-moving consumer goods, information such as online buyer reviews and product ratings can enhance behavior through a chained mediation effect due to intention linked with subjective norms. Information also has heterogeneous impacts on consumers with different demographic attributes. These results can provide practical implications for market practitioners adopting diverse information presentation strategies to promote green products on e-commerce platforms and addressing the gap between intention and behavior.

1. Introduction

The concept of green consumption has been widely recognized and accepted by consumers [1,2,3]. The sustainable characteristics of green products are credence attributes, which make it difficult for consumers to verify the reliability of a green attribute (e.g., no pesticide residues in organic vegetables, etc.) during the purchasing stage or even during the use stage [4,5]. As a result, customers have to make up-front payments for green premiums if they purchase a green product [6]. However, many consumers are skeptical of green products because they may perform worse than traditional alternatives, and it is hard for consumers to verify their environmental advantages [7,8]. The uncertainty of the consequences may hinder consumers from actually making the purchase even if they exhibit attitudes in favor of green products. Such a discrepancy is known as the green consumption intention–behavior gap, which has been noticed by both market data and academic studies [9,10,11].
Product environmental information refers to information cues that consumers obtain in a variety of ways about the benefits of a product for personal well-being, the environment, and resource conservation, which can eliminate information asymmetry between buyers and sellers [12,13,14]. Previous studies suggest that product environmental information can significantly impact consumer behaviors, which supports bridging the green consumption intention–behavior gap [4,15]. Many studies have conducted choice experiments to study the influences of environmental information factors on consumer behaviors, primarily focusing on traditional retail contexts [14,16,17]. Giving customers comprehensive information about environmental performances can make green attributes more significant when making decisions [18]. Zha et al. suggested that presenting energy-efficient labels can improve a consumer’s willingness to pay (WTP) for energy-efficient appliances [17].
Limited attention has been given to the disparity between the green consumption intention–behavior gap in the e-commerce context in current studies. In comparison to traditional offline shopping, the distinctive features of e-commerce prevent consumers from physically interacting with products, thereby increasing their demand for product information and reliance on external cues for decision-making [19,20]. On the other hand, online platforms can compensate for the absence of physical contact by employing technical means of information display. In a brick and mortar retail context, only a small amount of product environmental information can be conveyed to consumers through texts and labels due to the limited space on the package [14,21,22]. Other than a small amount of text, online consumers can also browse online product details pages and check platform certifications as well as consumer reviews [23,24]. Star ratings, like the amount of reviews, are considered an electronic word-of-mouth (e-WOM), and have proven to be greatly positive in their impacts on product sales [25]. How the label is designed and presented on the e-commerce platform also has significant influences on a consumer’s decision-making [26]. There are many sorts of information display methods, such as images and videos [27,28,29]. Multimedia’s technological capabilities also allow for targeted distribution of customized information to customers, considerably expanding the amount of product information that customers may access [30].
However, most of the studies concentrated on the influences of general online information, and more investigation into the influences of environmental information of products on consumer behaviors is needed. Few studies have explored the theoretical mechanisms of how product environmental information influences consumer behaviors and bridges the intention–behavior gap, especially on e-commerce platforms. Meanwhile, most of these studies on how information impacts consumers’ green buying decisions concentrate on general green products or a particular category, such as green food [21,31], appliances [17,22,32], and electric vehicles [33,34]. When buying different types of products, consumers may respond differently to the same information stimulation. Customers’ shopping behavior is more influenced by habits and shopping experiences when they purchase a particular sort of goods regularly [35,36]. Consumers tend to use outside sources of information when making decisions for less often purchased products [37]. As a result, the frequency of product purchases can directly influence how consumers’ green purchasing behavior is impacted by product environmental information.
Therefore, this study tries to understand influences attributable to e-commerce product environmental information on consumers’ green purchasing behaviors and the underlying theoretical mechanism. Whether there are differentiated impacts between two different product types, durables, and fast-moving consumer goods, is also studied. The results of this study can contribute to the theories as well as managerial practices. This study adds a novel factor to the theory framework of TPB, verifying that online product environmental information can bridge the intention–behavior gap by enhancing consumers’ perceived behavioral control. The findings also add nuance to the current literature by showing that consumers have different emphases on environmental information in the online context when purchasing different types of products.

2. Theoretical Framework and Hypotheses

2.1. Theory of Planned Behavior

Ajzen’s theory of planned behavior has been widely used and verified in green consumption studies to discuss consumer behaviors [3,8,38]. TPB believes an individual’s perceived behavioral control (PBC) impacts intention (INT), and intention and PBC further influence behavior [39]. The possibility of a consumer to purchase a green product is growing with his/her intention to perform this behavior. Meanwhile, the consumer’s perception of his or her ability to carry out this purchasing behavior also influences whether it can be performed [40].
H1: 
INT positively influences e-commerce green purchasing behavior.
H2: 
PBC positively influences e-commerce green purchasing behavior.
TPB believes individuals’ attitude (ATT), subjective norms (SN), and PBC together impact INT. A favorable attitude towards green products may lead to positive green consumption intention. Furthermore, a consumer’s intention to buy green products may also be affected by the social pressure they feel about whether or not to carry out green purchasing, i.e., subjective norms [38].
H3: 
ATT positively influences e-commerce green purchasing intention.
H4: 
SN positively influences e-commerce green purchasing intention.
H5: 
PBC positively influences e-commerce green purchasing intention.
PBC measures individuals’ perceived ease of performing an action and its behavioral consequences, which can directly promote behavior in addition to intention [41]. Therefore, enhancing consumers’ PBC can effectively promote behavior associated with green consumption and bridge the intention–behavior gap [15,42].
Furthermore, ATT, SN, and PBC are influenced by behavioral beliefs (BB), normative beliefs (NB), and control beliefs (CB), respectively. The influences of these beliefs on behavior is mediated by ATT, SN, and PBC, respectively. Especially, control beliefs are related to the consequences of a behavior and its ease or difficulty which can be measured by external information and past experience [39,43]. Therefore, information is one of the important factors influencing a consumer’s PBC [39].
For green products, product environmental information can change consumer control beliefs and influence consumer behavior by either reducing the perceived difficulty of purchasing a green product or increasing their perception of the purchase consequences. For example, organic labels can serve as simplified information tools that ease consumers’ identification of organic products at a relatively low search cost and cognitive cost [14]. Energy-efficient labeling of appliances allows consumers to perceive the reduction in energy consumption over the future life cycle of purchasing an energy-efficient appliance [44]. The following hypotheses are proposed:
H6: 
BB positively influences e-commerce green purchasing ATT.
H7: 
NB positively influences e-commerce green purchasing SN.
H8: 
CB positively influences e-commerce green purchasing PBC.
H9: 
E-commerce product environmental information influences purchase behavior through the mediating effect of PBC.

2.2. Frequency of Information

PBC is influenced by control power in addition to control beliefs which are related to external information [40,41]. Therefore, the intensity of information can also impact the relationship between PBC and behavior. Compared to traditional offline retail, an e-commerce platform can transform a greater volume of information, and how the information is presented is more diverse [27,28]. For example, an online video presentation of touching products can serve as visual design and significantly improve consumers’ WTP for organic agricultural products [45]. Live e-commerce can alleviate consumers’ perceived uncertainty and reduce information asymmetry through video product demonstrations and real-time interactive experiences [46]. Some studies suggest that high-frequency information interventions have a greater impact in comparison to low-frequency information interventions [47]. Tiefenbeck et al. suggest that intensive information interventions as well as real-time feedback contribute to energy-saving behaviors at the household level [48]. However, other studies argue that recipients who receive information interventions more frequently than once a week may become desensitized to messages as a result of information fatigue [49].
H10: 
E-commerce information frequency moderates the effect of PBC on behavior.

2.3. Product Type

Control beliefs are influenced by past experiences in addition to external information [39,43]. Consumers can obtain sensory experience about the activity through previous purchasing experiences, which influences PBC and makes consumers assume the behavior is easier to accomplish than anticipated [50,51]. Therefore, the frequency of purchases influences the shopping experiences of customers, which in turn influences how difficult they think it is to make a purchase and how much control they feel they have over the results of that action [7]. Based on the purchase frequency, products can be divided into two categories, durable goods and fast-moving consumer goods (FMCG) [52]. Consumers have rich experiences with regularly purchased products which leads to a higher level of PBC. Therefore, they do not require as much product information while buying FMCG [51]. Consumers have less shopping experience with durables, which are relatively expensive and purchased infrequently. As a result, they are more selective when making decisions and need to gather more information to support their decisions [51,53].
Some of the literature has examined factors influencing the purchase of durable goods or FMCG, respectively [4,7,54]. Consumers have a higher desire and sensitivity for information on product performances, such as technical specifications, energy performance, and products’ long-term affordability, when buying energy-efficient appliances, which have a longer life cycle and a higher price [44,54]. On the contrary, consumers have more shopping experiences for food and daily necessities which are purchased more frequently and relatively inexpensive. Therefore, consumers show less sensitivity concerning product information; purchasing habits influence their decisions to a greater extent [20,36,55]. The theoritical model and hypotheses can be seen in Figure 1.

3. Materials and Methods

3.1. Data Collection

We collected consumer data using an online survey based on a questionnaire. Quota sampling was adopted to select participants from seven major cities in China (Shanghai, Beijing, Shenzhen, Chongqing, Guangzhou, Chengdu, and Tianjin [56]) from the sample pool of the questionnaire survey website Wenjuanxing, based on the approximate population proportion. In the sample pool of each city, respondents were selected randomly to participate in the survey. According to market research, the highest e-commerce penetration rate is currently found in Tier 1 cities, which reaches about 80% [57]. Therefore, consumers in Tier 1 cities have a higher awareness of online shopping, and the respondents in this study were able to cover a more representative sample population.
Air conditioners (AC) and milk have a high e-commerce penetration rate in China and are easier to purchase on e-commerce platforms [58]. Therefore, energy-saving air conditioners and organic milk are chosen to represent, respectively, green durable goods and green FMCG. Two versions of the questionnaire were designed for each of the two product types. Respondents were allocated randomly to one of the two versions during the survey implementation stage.
The questionnaire includes two sections, the first of which collects the respondents’ demographic data, including age, income, gender, and education. The second section collects information on factors that influence participants’ green purchasing from e-commerce platforms. Measurements of influencing factors were adapted from a well-established scale questions of TPB in previous studies [59,60]. In this study, the question items of beliefs and their power were designed to measure product environmental information in the e-commerce platform. To measure the latent variables of three beliefs (BB, NB, and CB), the weighted beliefs (such as cbpp1, cbpp2) were employed as formative indicators. Reference group information such as shopping website ratings and consumer reviews were used as formative indicators of NB, and product information related to the consequences of shopping behavior such as energy-saving performance of air conditioners, green labels, and organic properties of milk were used as formative indicators of CB. Detailed formative items are shown in Appendix A. The assessment method was based on a five-point Likert scale (five very important, one not important).
The study conducted a pilot study with a sample of 109 university students in Beijing in June 2023, and the questionnaire was reviewed and edited based on respondents’ feedback and the results of the pilot study. Suggestions from experts were also considered to improve the questionnaire. The questionnaire was officially distributed online from 4 July 2023 to 20 July 2023. Trap questions and answer time screening were adopted as quality control measures. After excluding invalid responses, 1102 valid responses were obtained in total. A total of 59% of respondents were female and 41% were male. A total of 88.66% of the respondents were between the ages of 25 to 40. Over 80% of the interviewees have an educational level of college and above. These demographics are consistent with the general e-commerce platform consumers in China [57]. Therefore, the respondents of the study can be identified as a representative sample of the targeted population (Table 1).

3.2. Data Analysis Methods

Partial least squares structural equation modeling (PLS-SEM) method was used for data analysis. PLS-SEM is capable of handling relationships between latent variables. In contrast to the conventional structural equation method (covariance-based SEM), PLS-SEM is more appropriate for an exploratory analysis of theories [61]. Since the study incorporated product information as a new variable into the original theoretical framework, the PLS-SEM method was selected for our data analysis. When performing the calculation, the outcomes of the belief scores (bb, nb, cb) multiplied by their respective weighting factors (oe, mc, pp) were employed as formative indicators for the three belief constructs [60]. In this research, the significance of the mediating effect is tested through a bootstrapping method to calculate the variance accounted for (VAF) of indirect effect and total effect [61].

4. Results

4.1. Model Reliability and Validity

Data for the milk group and air-conditioning group were analyzed separately. The indicators showed that the measurement items used in both groups had good reliability and validity, as shown in Table 2. The factor loadings for all measurement items exceeded 0.70, which indicates good reliability. The Cronbach’s alpha of each construct was above or close to the recommended value of 0.70, and the composite reliability (CR) was larger than 0.70, indicating good internal consistency. The average variance extracted (AVE) all exceeded the threshold of 0.50, which indicates that the constructs have good convergent validity [62]. We used the Fornell–Larcker criterion to assess discriminant validity of construct. The square root of the AVE on the diagonal being greater in comparison to other values indicated good discriminant validity (Table 3) [62].
Common method biases of formative indicators were assessed in terms of VIF value for both groups. The indicators’ VIF values are less than three, suggesting that the result does not have the common method bias issue (Table 4). The q2 value of behavior (BH) was 0.191 and 0.046 for the milk and AC group, which were above 0, indicated that the model had good predictive accuracy.

4.2. Structural Results

For organic milk consumers, although PBC positively influences consumer behaviors, this influence is not statistically significant (β = 0.061, p > 0.05), as shown in Table 5 and Figure 2a. Meanwhile, the mediating role of CB through the path of PBC to promote behavior (CB -> PBC -> BH) was not significant, either (p > 0.05). In contrast, normative beliefs (NB) could significantly and positively influence organic milk purchasing behavior (β = 0.062, p < 0.05) through the chained mediating effect of subjective norms to intention (NB -> SN -> INT -> BH). This suggests that while buying FMCG items like milk, consumers are positively but not significantly influenced by product information related to behavioral outcomes and cognitive costs, including nutritional value and organic labeling. On the contrary, information related to word-of-mouth and opinions of important reference groups, such as buyer’s reviews on the e-commerce platforms and product ratings, can significantly facilitate purchasing behaviors. The finding that e-WOM can significantly impact customers’ decisions to buy organic food supports findings from earlier studies [63].
For air conditioners, the results of the structural analysis indicate that PBC can positively and significantly influence consumer behavior (β = 0.546, p < 0.001), as shown in Table 5 and Figure 2b. Meanwhile, control beliefs were able to significantly promote consumer purchasing behavior through the mediating effect of PBC (CB -> PBC -> BH, β = 0.222, p < 0.001). Normative beliefs were also able to significantly and positively influence the purchase behavior of energy-efficient air conditioners (β = 0.099, p < 0.05) through the chained mediation of subjective norms-intention (NB -> SN -> INT -> BH). This suggests that when purchasing durable products such as air conditioners, product information on behavioral consequences as well as opinions of important reference groups both have significant influences on purchase behavior. This finding supports the existing literature; green labels, information on energy-saving performance, as well as product ratings all have positive influences on home appliance consumption [17,25].

4.3. IPMA Analysis

IPMA analysis was carried out to grasp contributions of influencing factors on purchasing behaviors and how the two groups differed from one another. Besides the total effects, the IPMA method also covers the dimension of average latent variable scores, providing a visualized result of influencing factors’ contributions by combining importances (x-axis, total effects) with performances (y-axis, average latent variable scores). IPMA can identify the most critical impacting factors, with the noteworthy variables usually located on the lower right side of the map. Factors in this region have higher importance but relatively lower performances, indicating a greater potential to influence the dependent variable by improving the performance of these factors [64].
The IPMA results for the milk and air conditioning groups are shown in Figure 3. In the case of milk (Figure 3a), the two most important factors influencing consumers’ purchase behavior are intention and attitude. On the contrary, consumers’ PBC is the second most important influencing factor in addition to intention when purchasing AC (Figure 3b). It is worth noting that although PBC ranks second in terms of importance, its performance ranks after intention, attitude, and subjective norms, resulting in PBC’s location at the bottom right of the picture. This suggests that PBC can have substantial influences on consumers when purchasing energy-efficient air conditioners on the e-commerce platform, but its potential has not been fully released. Therefore, the significance of consumers’ PBC on facilitating consumers’ green purchasing behaviors should be further explored through a more effective presentation of product information.

4.4. The Moderating Effect of Information Frequency

Table 6 reports results concerning moderation effects attributable to information frequency for the two groups. For the milk group, the moderating effect of information frequency on the path of PBC-BH is not significant. This indicates that when purchasing milk, consumers’ high-frequency exposure to product information does not promote their actual purchasing behavior. For the AC group, a higher frequency of information stimulation can positively and significantly moderate the PBC-BH path, as shown in Figure 4. This indicates that a high frequency of exposure to product information is linked with a greater possibility that consumers will show PBC-enhancing purchasing behaviors, thus promoting the transformation of consumers’ real purchasing behaviors.
Results from the PLE-SEM indicate that in the context of purchasing AC, product information can promote consumers’ purchasing behavior through the mediating effect of PBC, i.e., it bridges the intention–behavior gap through enhancing PBC. While in the context of purchasing milk, the influence of information on PBC is not statistically significant. Therefore, H2 and H9 are partially supported. The differences in structural and IPMA results between the two groups demonstrated that the influence of product information has distinct influences on the purchasing behavior of different product categories. Product information frequency may have a positive moderating impact on the relation of PBC to AC purchase behavior, indicating that H10 is supported. However, the moderation effect does not show statistical significance in the context of milk purchase (Table 7).

4.5. Heterogeneity Analysis Based on Consumer Characteristics

Demographic characteristics also have impacts on consumer’s intention and behaviors. Therefore, this section uses the multi-group analysis (MGA) method in PLS-SEM to analyze the heterogeneity of different consumer groups, as shown in Table 8 and Table 9.
Among energy-efficient AC consumers, male consumers are more likely than female consumers to turn their purchase intention into behavior, as seen by the male group’s path coefficient being higher than the female group’s. This result is consistent with past research conducted in China [65]. When analyzing the heterogeneity of age, income, and education, the sample was divided into two groups based on the average number [66]. Elderly consumers have a more positive attitude towards energy-saving AC than do young consumers. The higher income group had a more positive attitude and intention than the lower income group.
Among organic milk consumers, gender, age and income have no significant heterogenous impacts on consumers’ intention and behavior. The education level has a heterogeneous effect on both milk and AC consumers. Normative beliefs or behavioral beliefs have a higher impact on PBC in the group with less education experience.

5. Discussion

5.1. Findings

This study discusses how product environmental information in the e-commerce context influences consumers’ green purchasing behaviors within the framework of TPB. The findings suggest that e-commerce product environmental information can close the intention–behavior gap and encourage consumers to adopt green purchase practices, but the influences of information differ between durables and FMCG. Demographic characteristics also have impacts.
For durable goods such as air conditioners, product environmental information related to the consequences of consumer behavior, such as carbon emission reduction performance and energy consumption performance, can significantly promote consumer’s green purchases and bridge the intention–behavior gap. This type of information can reduce consumers’ cognition costs and reduce information asymmetry, enabling consumers to clearly understand the environmental and personal benefits of their purchasing green products [22,67]. At the same time, due to the great demand for product information when purchasing durables, it is likely that a consumer will buy a green product in proportion to the frequency with which they become exposed to and perceive product environmental information, i.e., there is a positive moderating effect attributable to the product information frequency on PBC affecting purchasing behavior. Previous studies have found that providing product environmental information can alter consumers’ preferences for products’ green attributes [18,32]. These findings support and complement current studies on the effects of product information interventions, underlining the importance of information intervention frequency.
For FMCG such as milk, product information on the consequences of purchasing behaviors such as traceability information and green labels have limited power in influencing consumers to purchase green products. Meanwhile, high-frequency information interventions are not effective in bridging the intention–behavior gap. This result supports the existing literature, whereby simply using green labels is insufficient to influence customer decisions regarding organic foods [14,68]. This may result from the low perceived risk of behavioral consequences when purchasing FMCG. Even if one is disappointed with the product, the switching cost of repurchasing is small due to relatively low prices, high substitutability between alternative products, and FMCG’s high purchase frequency. Customers are therefore uninterested in product information regarding the consequences of their FMCG purchases [7,69]. It is worth noting that though product environmental information related to behavioral consequences is not effective in bridging the intention–behavior gap, signals with social and interactive attributes do have positive effects in influencing consumer behaviors [70]. Promotions on e-commerce platforms, buyers’ reviews, and product ratings can promote consumers’ purchases of organic milk by enhancing their intention. This could be the outcome of the fact that this type of product information enhances consumers’ trust in a product, and this in turn promotes their purchasing behavior [71,72].
Results of heterogeneity analysis showed that information has differentiated impacts on consumers of different gender, age, education and income. Due to relatively higher environmental awareness, Chinese males are better able to understand the environmental impacts of their purchases of AC products [73]. Elderly consumers have a more positive attitude toward environmental protection as a result of their education and social discipline [74]. Higher income groups are more willing to pay a premium for green products due to their high disposable income and relatively low price sensitivity [75]. Previous studies based on offline shopping believe that people with a higher education level have stronger purchase intention for green products [75,76]. The results of this study show that various product environmental information in the online shopping context can make up for the lack of environmental knowledge [15,77]. Consumers with less school experience are more inclined to refer to external product information to assist decision-making when shopping online.

5.2. Theoretical Implications

This article’s theoretical contributions are as follows. First, the study explores the theoretical mechanism of how e-commerce product environmental information promotes consumers’ green purchasing behaviors within the framework of TPB, supporting and supplementing previous empirical studies. Many studies have tested the positive role of product environmental information to influence consumers’ buying green products through experiments [17,44,67]. This study constructed the control belief (CB) items to measure product environmental information according to classical models of TPB. The PLS-SEM results showed the mechanism of product environmental information promoting green purchasing behaviors, i.e., through the mediation effect of PBC. This result confirms that information intervention can effectively bridge the green purchase intention–behavior gap. Second, this study extended the theoretical model of TPB, including information frequency as a new variable. The results confirm that information frequency has a positive moderating role in the path of PBC affecting purchasing behavior. This moderating effect is significant in the purchase of durable goods but not in the purchase of FMCG. The results of this study can offer theoretical and empirical evidence on how product environmental information intervention can bridge the intention–behavior gap and promote green consumption, as well as explore the differences in impact due to product environmental information on purchases in various categories of products.

5.3. Practical Implications

This study can provide relevant market practitioners with practical insights. The findings reveal that while purchasing different categories of products, e-commerce users focus on distinct forms of information. For FMCG such as organic milk, consumers are more interested in product reputation, branding, and advertising exposure. Therefore, e-commerce platforms should focus more on improving eWOM when promoting green FMCG products. Actions can be taken to improve the display and screening mechanism of buyers’ reviews and comments, as well as maintenance of product ratings. At the same time, expanding product marketing and promotion is required to improve product recognition.
For durables such as energy-efficient air conditioners, consumers pay more attention to products’ attributes during usage stages. Therefore, e-commerce platforms need to focus on aspects such as product performance when promoting green durable products. When it comes to online shopping, e-commerce platforms are able to present abundant product information through multiple types of signals such as pictures, videos, and detailed description pages, which is hardly feasible in traditional offline retail. For example, a few studies have noticed and studied the impacts of virtual reality technology displaying products and providing interactive experiences in affecting e-commerce buyers’ behaviors [78,79]. E-commerce platforms should take advantage of the information display capability, employing pictures, videos, and interactive functions to emphasize the positive behavioral consequences of their purchases, including personal benefits (e.g., saving on electricity bills) and benefits for the environment (e.g., carbon emission reductions). At the same time, more efforts should be made to promote marketing to deepen consumers’ cognition of products through a higher frequency of information intervention, which is more conducive to the transformation of consumers’ intentions into actual purchases.
In addition, results of heterogeneity analysis indicate that different consumers may response differently to product environmental information. Especially for AC products, consumers show higher heterogeneity. Male consumers who have more life and school experience with a higher income are more likely to be influenced by e-commerce product environmental information to purchase green products. For milk consumers, only education level has heterogeneous impacts. Therefore, e-commerce platforms should reinforce targeted marketing to online consumers, presenting customized promotion information based on consumer characteristics, especially when promoting green durable products.

5.4. Limitations and Future Research Directions

This article is not without its limitations. We used self-reported data to investigate influences on consumers’ purchasing behaviors attributable to information. Respondents in green consumption studies tend to overestimate their subjective intentions and behaviors, which may cause self-reported bias due to social desirability error [80]. This bias can hardly be fully avoided though measures have been taken in this study, such as a statement to ensure anonymity before the survey begins. Therefore, future studies could consider using sales data from real transactions to measure consumers’ purchases, which can improve the accuracy of the data. Also, the long-term impacts of this information on consumer behaviors is an interesting topic to be explored.
Second, the measurements in this study related to online product environmental information as well as online purchasing behaviors are constructed for general e-commerce shopping. Many e-commerce companies have now adopted dual-platform strategies, developing both web and mobile platforms. These two forms of e-commerce show different features in terms of screen size and page design, thus consumers on these two platforms may be driven by different contributing factors and show different preferences [81,82]. Therefore, future research could consider studying the influences of information intervention on consumer behaviors in web and mobile e-commerce separately.

6. Conclusions

In this study, the mechanism of e-commerce product environmental information to bridge the green purchase intention–behavior gap is proposed and verified within the framework of TPB. The findings first indicate that the e-commerce product environmental information can effectively bridge the green purchase intention–behavior gap. Second, the influences of product environmental information differ between product types. For durable goods, information can promote purchasing behavior and bridge the intention–behavior gap through the mediating effect of consumers’ PBC. At the same time, the higher the frequency of exposure to e-commerce product environmental information, the stronger the influences of PBC on behavior. Therefore, when marketing green durable goods in the e-commerce context, providing behavioral consequences information on product performance and environmental benefits and increasing marketing promotion efforts will be conducive to enhancing consumers’ green purchasing behavior. For FMCG, buyer’s online reviews and product ratings in the e-commerce context are more effective in promoting consumers’ purchasing behavior through the chained mediating effect of subjective norms to intention. Accordingly, when marketing green FMCG, it is more important to focus on building good e-WOM within the consumer group. Consumer characteristics also influence how information affects their purchase intention and behavior. Especially for durable products such as AC, customized information streaming to target consumers may lead to more efficient green consumption promotion.

Author Contributions

Conceptualization, X.W., M.P. and Y.L.; data curation, X.W. and M.P.; formal analysis, X.W. and M.P.; funding acquisition, M.P., Y.L. and J.X.; investigation, Y.L., T.M. and J.X.; methodology, X.W. and Y.L.; project administration, M.P., Y.L. and J.X.; resources, M.P., Y.L., T.M. and J.X.; software, X.W. and M.R.; supervision, Y.L., T.M. and J.X.; validation, M.P., Y.L. and T.M.; visualization, X.W., M.P. and Y.L.; writing—original draft, X.W. and M.P.; writing—review and editing, X.W., M.P., Y.L., M.R., T.M. and W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Xinjiang production and Construction Corps Key areas of scientific and technological research project (grant number 2023AB042), the Renmin University Foundation Project “The Impact of Eco-Product Certification Model on Consumer Acceptance”, and the China Postdoctoral Innovative Talents Program (Grant No. BX201907170).

Institutional Review Board Statement

Ethical review and approval were waived for the research since the questionnaire survey did not involve ethical issues and was conducted in accordance with general ethical guidelines and legal requirements.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. It was promised that the survey was anonymous and that the result would only be used for academic purposes.

Data Availability Statement

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Measurements of formative indicators.
Table A1. Measurements of formative indicators.
Behavioral Beliefs
WeightedBeliefsOutcome evaluationOutcomes (milk)Outcomes (AC)
bboe1bb1oe1environment protectionenvironment protection
bboe2bb2oe2nutritional valueelectricity bill savings
bboe3bb3oe3food safetycooling/heating performance
Normative beliefs
WeightedBeliefsMotivation to complyReferents (milk)Referents (AC)
nbmc1nb1mc1friends and familyfriends and family
nbmc2nb2mc2online advertisingonline advertising
nbmc3nb3mc3online reviews and product ratingsonline reviews and product ratings
nbmc4nb4mc4brandbrand
Control beliefs
WeightedBeliefsPerceived powerControl factors (milk)Control factors (AC)
cbpp1cb1pp1traceability informationcarbon reduction information
cbpp2cb2pp2nutrient information such as protein contentenergy saving performance
cbpp3cb3pp3organic labelsenergy efficiency labels
cbpp4cb4pp4promotion and salespromotion and sales

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Sustainability 17 02337 g001
Figure 1. Theoretical model and research hypotheses.
Figure 1. Theoretical model and research hypotheses.
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Figure 2. Structural results for milk group (a) and air conditioner group (b). Note: * p < 0.05, ** p < 0.01, *** p < 0.001, n.s. not significant.
Figure 2. Structural results for milk group (a) and air conditioner group (b). Note: * p < 0.05, ** p < 0.01, *** p < 0.001, n.s. not significant.
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Figure 3. IPMA map of constructs for milk group (a) and AC group (b).
Figure 3. IPMA map of constructs for milk group (a) and AC group (b).
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Figure 4. Moderation effect of information frequency (AC group).
Figure 4. Moderation effect of information frequency (AC group).
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Table 1. Respondents information.
Table 1. Respondents information.
GroupNumberPercentage
Age25 and below19918.06%
26~4077870.60%
41~50988.89%
51~60232.09%
60 and above40.36%
GenderMale45241.02%
Female65058.98%
Income (Yuan/month)3000 and below615.54%
3001–500011110.07%
5001–800024822.50%
8001–10,00025022.69%
10,001–20,00032029.04%
20,000 and above11210.16%
Educationprimary school and below10.09%
senior high141.27%
high school655.90%
graduate91983.39%
postgraduate and above1039.35%
Sample Size1102100%
Table 2. Reliability and validity of measurement items.
Table 2. Reliability and validity of measurement items.
ItemsMilkAC
LoadingsCronbach’s AlphaCRAVELoadingsCronbach’s AlphaCRAVE
att10.8230.7660.8650.6810.7870.6620.8160.597
att20.828 0.741
att30.826 0.788
int10.8480.7780.8710.6930.7460.6790.8240.609
int20.799 0.782
int30.849 0.812
pbc10.8310.7650.8640.6800.8590.7400.8530.659
pbc20.804 0.763
pbc30.838 0.810
sn10.8210.7600.8620.6760.7980.7400.8530.659
sn20.814 0.786
sn30.832 0.787
Table 3. Discriminant validity by the Fornell–Larcker criterion.
Table 3. Discriminant validity by the Fornell–Larcker criterion.
Milk ATTINTPBCSN
ATT0.825
INT0.6930.832
PBC0.3070.3990.825
SN0.6130.6010.3240.822
AC ATTINTPBCSN
ATT0.772
INT0.5470.780
PBC0.4630.3960.812
SN0.4450.4020.2860.790
Table 4. VIF of formative indicators.
Table 4. VIF of formative indicators.
Formative ConstructMilkAC
Formative ItemsVIFFormative ItemsVIF
Behavioral Beliefbboe11.159bboe11.080
bboe21.181bboe21.051
bboe31.227bboe31.070
Control Beliefcbpp11.213cbpp11.072
cbpp21.180cbpp21.098
cbpp31.275cbpp31.147
cbpp41.010cbpp41.027
Normative Beliefnbcm11.250nbcm11.211
nbcm21.193nbcm21.308
nbcm31.268nbcm31.182
nbcm41.291nbcm41.182
Table 5. Results of structural analysis.
Table 5. Results of structural analysis.
PathModel 1 (Milk)Model 2 (AC)
βp Valueβp Value
BB -> ATT0.6220.000 ***0.3900.000 ***
CB -> PBC0.4140.000 ***0.5690.000 ***
NB -> SN0.6430.000 ***0.1690.000 ***
INT -> BH0.3950.000 ***0.4880.000 ***
PBC -> BH0.0610.151 n.s.0.5460.000 ***
ATT -> INT0.4900.000 ***0.3900.000 ***
PBC -> INT0.1690.000 ***0.1370.002 **
SN -> INT0.2460.000 ***0.1630.001 **
ATT -> INT -> BH0.1940.000 ***0.1820.000 ***
BB -> ATT -> INT -> BH0.1200.000 ***0.0660.000 ***
BB -> ATT -> INT0.3050.000 ***0.0380.001 **
CB -> PBC -> BH0.0250.163 n.s.0.2220.000 ***
CB -> PBC -> INT0.0700.000 ***0.0670.003 **
NB -> SN -> INT0.1580.000 ***0.0800.001 **
NB -> SN -> INT -> BH0.0620.000 ***0.0990.000 ***
SN -> INT -> BH0.0970.000 ***0.0170.006 **
PBC -> INT -> BH0.0670.000 ***0.0310.004 **
CB -> PBC -> INT -> BH0.0280.000 ***0.0270.008 **
Note: ** p < 0.01, *** p < 0.001, n.s. not significant.
Table 6. Moderation effect of information frequency.
Table 6. Moderation effect of information frequency.
GroupPathCoefficientsp Value
MilkModerating Effect -> BH−0.0310.379 n.s.
ACModerating Effect -> BH0.0870.039 *
Note: * p < 0.05, n.s. not significant.
Table 7. Results of main hypotheses testing.
Table 7. Results of main hypotheses testing.
HypothesesMilkAC
H2PBC positively influences e-commerce green purchasing behaviorNot supportedSupported
H9Product environmental information influences behavior through the mediating effect of PBCNot supportedSupported
H10Information frequency moderates the relationship between PBC and behaviorNot supportedSupported
Table 8. Heterogeneity analysis of AC consumers.
Table 8. Heterogeneity analysis of AC consumers.
PathGender
(Male vs. Female)		Age
(Young vs. Old)		Income
(Low vs. High)		Education
(Low vs. High)
p-ValueSignificancep-ValueSignificancep-ValueSignificancep-ValueSignificance
ATT -> INT0.289n.s.0.523n.s.0.196n.s.0.735n.s.
BB -> ATT0.453n.s.0.003**0.019**0.581n.s.
CB -> PBC0.495n.s.0.373n.s.0.789n.s.0.016**
INT -> BH0.030**0.396n.s.0.004**0.086n.s.
NB -> SN0.896n.s.0.070n.s.0.635n.s.0.944n.s.
PBC -> BH0.815n.s.0.121n.s.0.134n.s.0.962n.s.
PBC -> INT0.802n.s.0.881n.s.0.860n.s.0.616n.s.
SN -> INT0.893n.s.0.781n.s.0.283n.s.0.590n.s.
Note: ** p < 0.01, n.s. not significant.
Table 9. Heterogeneity analysis of milk consumers.
Table 9. Heterogeneity analysis of milk consumers.
PathEducation (Low vs. High)
p-ValueSignificance
ATT -> INT0.289n.s.
BB -> ATT0.453n.s.
CB -> PBC0.495n.s.
INT -> BH0.030**
NB -> SN0.896n.s.
PBC -> BH0.815n.s.
PBC -> INT0.802n.s.
SN -> INT0.893n.s.
Note: ** p < 0.01, n.s. not significant.
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Wang, X.; Peng, M.; Li, Y.; Ren, M.; Ma, T.; Zhao, W.; Xu, J. How E-Commerce Product Environmental Information Influences Green Consumption: Intention–Behavior Gap Perspective. Sustainability 2025, 17, 2337. https://doi.org/10.3390/su17062337

AMA Style

Wang X, Peng M, Li Y, Ren M, Ma T, Zhao W, Xu J. How E-Commerce Product Environmental Information Influences Green Consumption: Intention–Behavior Gap Perspective. Sustainability. 2025; 17(6):2337. https://doi.org/10.3390/su17062337

Chicago/Turabian Style

Wang, Xintian, Meng Peng, Yan Li, Muhua Ren, Tao Ma, Weidong Zhao, and Jiayu Xu. 2025. "How E-Commerce Product Environmental Information Influences Green Consumption: Intention–Behavior Gap Perspective" Sustainability 17, no. 6: 2337. https://doi.org/10.3390/su17062337

APA Style

Wang, X., Peng, M., Li, Y., Ren, M., Ma, T., Zhao, W., & Xu, J. (2025). How E-Commerce Product Environmental Information Influences Green Consumption: Intention–Behavior Gap Perspective. Sustainability, 17(6), 2337. https://doi.org/10.3390/su17062337

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