1. Introduction
In recent decades, green marketing has become essential to companies’ sustainability efforts, aiming to align marketing strategies with ecological responsibility. Green marketing is concerned with satisfying the needs of consumers and other stakeholders in a sustainable way [1]. Reflecting this shift in consumer priorities, the search for sustainable products has risen by 71% since 2016, especially in developed countries, signaling a movement toward sustainability [2]. To respond to this demand, many businesses leverage digital channels to promote environmental commitments and engage consumers. Among these digital platforms, social media stand out as effective channels for green marketing [3,4,5]. With 58.4% of the world’s population actively using social media—a number projected to grow from 4.89 billion in 2023 to 5.85 billion by 2027—these platforms offer a low-cost and continuous medium for consumer engagement [6,7]. By facilitating direct interaction between companies and their audiences, social media can build brand loyalty and foster customer acquisition [8]. While previous research has largely focused on the effects of companies’ communication strategies and their effectiveness from the companies’ perspective, this study sought to fill the gap by exploring consumer perspectives and the factors that shape their responses to eco-friendly content [9]. Research in this area suggests that consumer attitudes and awareness play a significant role in purchase intentions. For example, positive attitudes toward green products have been shown to enhance purchase intentions [10], and consumer awareness of the benefits of eco-friendly products influences perceived quality and value, which in turn affect purchase intentions [11]. However, the specific ways in which social media-based green marketing influences purchase behaviors and brand attitudes remain underexplored. This underscores the importance of understanding how consumers engage with green marketing content on social media and identifying the factors that drive consumer behavior in this context. Furthermore, despite companies’ efforts to communicate their sustainability practices and strategies, not all consumers are interested in engaging with these messages, making it essential to understand consumer behavior in this context [12,13]. Although some scholars have already studied consumers’ responses to corporate communication strategies, further research is needed across different industries [14,15,16]. By analyzing responses to environmentally focused content on social media, this research advances the discourse on green marketing’s impact on consumer engagement and behavior.
This study investigated whether green marketing activities on social media impact consumers’ purchase intentions and their level of word of mouth (WOM). The research question aimed to identify influential factors related to green marketing activities on social media that drive purchase intentions and WOM and to explore variations in consumer responses across different markets. To systematically address these objectives, this research intended to answer the following research question:
RQ: What are the relevant factors related to green marketing activities on social media that impact achieving purchase intention and WOM?
The paper’s outline is as follows. The theoretical background and existing literature are discussed in the Section 2, and the hypothesis development is presented. The Section 3 provides an overview of the methodology, including experimental design and stimuli, measurement instruments, and the sample selection process and data collection. The results are in the Section 4. The Section 5 presents the discussion and the implications of this research. In the Section 6, the conclusions are presented.
2. Theoretical Background and Development of Hypotheses
2.1. Green Marketing’s Impact on Consumer Perceptions
Informativeness, as described by Rotzoll et al. [17], is the degree to which a company provides sufficient information that enables customers to make informed choices. Gao and Koufaris [18] highlighted the significant role of informativeness in the digital world by emphasizing its impact on customer attitudes. Further, Taylor et al. [19] found a positive correlation between informativeness and customer attitudes in the social media field. Similarly, Lee and Hong’s [20] empirical research confirmed that a higher level of informativeness positively influences customer responses to social media content, which in turn may impact their purchase intentions.
Within the context of green marketing, informative content often serves to educate consumers about the environmental benefits of products or practices, such as carbon footprint reduction or sustainable sourcing [20,21]. By embedding detailed, factual information, green marketing-related posts on social media align with eco-conscious consumer values, empowering audiences to make environmentally aligned choices [22,23]. This alignment suggests that exposure to green marketing messages may increase perceived informativeness, as such posts are inherently designed to convey actionable insights about sustainability. Building on this theoretical foundation, this study evaluated how green marketing-related posts influence participants’ perceived informativeness, leading to the following hypothesis:
Participants exposed to green marketing-related posts report a higher level of informativeness than those exposed to non-green marketing posts.
Social media platforms, by their nature characterized by their high degree of novelty, generally provide users with a more joyful and entertaining experience [24]. Previous studies revealed that entertainment significantly impacts the value of social media content [25]. Kim and Ko [26] point out that consumer attitudes are positively affected by entertainment, and this leads to the generation of strong engagement between consumers and companies. Ebrahimi et al. [27] suggest that by creating an entertaining environment on social media, companies may offer enjoyable conditions to increase followers’ enjoyment and encourage them to search for and buy their eco-friendly products. According to that study, entertainment resulting from social network marketing positively influences sustainable consumer behaviors. Similarly, Tezer and Bodur [28] found that exposure to green products, compared to conventional products, increases consumers’ enjoyment levels, as consumers perceive an increase in their social worth. Additionally, the concept of self-congruence theory, which states that consumers find content more appealing when it is consistent with their self-concept, can also be applied here. Self-congruence plays an important role in predicting various consumer behavior aspects such as advertising effectiveness, brand loyalty, and satisfaction [29]. Most people consider being environmentally conscious part of their self-identity; therefore, eco-friendly content may be more entertaining to them. This theory is supported by research by Zaman et al. [30], which claims that there is a relationship between ethical self-identity and consumer behavior towards green products. Based on the existing literature, the following hypothesis is proposed:
Participants exposed to green marketing-related posts report a higher level of entertainment than those exposed to non-green marketing posts.
Relevance is described by Celsi and Olson [31] as “the degree to which consumers perceive an object to be self-related or in some way instrumental to accomplishing their personal goals and values”. According to the existing literature, followers are more likely to be interested in online content if they perceive it to be more relevant to their personal tastes and preferences. Moreover, they are likely to value and find online content useful when they feel it is relevant to their interests [24,32]. Because most internet users are time concerned, message relevancy is critical [33]. Internet users spend very little time reading content on social media—instead, they scan the page for relevant information. As a result, it is critical to deliver relevant messages to a certain online community, which is also significant in customers’ decision-making processes [34,35]. Moreover, the perceived relevance of a message to a user is a vital consideration when forming beliefs and making decisions about a company’s environmental practices [36]. Based on the aforementioned discussion, it can be argued that when participants are exposed to green marketing-related content, they will likely perceive it as more relevant to their needs and preferences compared to another group that is exposed to non-green marketing content. Therefore, the next hypothesis proposes the following:
Participants exposed to green marketing-related posts report a higher level of perceived relevance than those exposed to non-green marketing posts.
2.2. Green Marketing’s Impact on Brand Engagement
This section explores the impact of green marketing on brand engagement. Within this analysis, brand engagement refers to aspects like brand attitudes, purchase intentions, and word-of-mouth behavior. The concept of attitude toward the brand can be understood as an individual’s internal evaluation of the brand [37,38]. Brand attitude, which refers to an overall evaluation of a brand can be influenced by the activities initiated by companies [39]. For instance, Olsen et al. [40] reported that the introduction of eco-friendly products enhances brand attitude. Similarly, Yan et al. [41] confirmed that advertising messages that provide explicit information about environmentally friendly products can build positive attitudes toward brands. Another study also suggested that when consumers perceive a brand as a green brand, their attitudes toward it increase [42]. Drawing on this perspective, the next hypothesis postulates that participants’ level of brand will increase when they encounter posts related to green marketing.
Participants exposed to green marketing-related posts report a higher level of brand attitude than those exposed to non-green marketing posts.
In the era of digital communication, consumers are not passive recipients of marketing messages. They actively participate in social media discussions, share their opinions, and influence their peers and groups. Given this active role, the spread of information via WOM becomes particularly vital in the green marketing context. Chu and Chen [43] confirmed that when consumers encounter eco-friendly initiatives on social media, they are more likely to engage in word-of-mouth discussions about the brand and exhibit a higher intention to purchase. Purchase intention is defined as an individual’s deliberate and conscious desire to make an effort toward purchasing a brand [37]. Prior research identifies several key factors that influence consumer purchase intentions, particularly in the context of green marketing. For example, the adoption of eco-friendly products has been shown to positively impact purchase intentions, with environmental concerns playing a significant mediating role [44]. Furthermore, Jaganath [45] found that individuals with strong environmental attitudes are more inclined toward green purchasing behavior, indicating that sustainability-conscious consumers are more responsive to green marketing messages. This dynamic leads to H5 and H6, which posit that when participants are exposed to green marketing-related content, they will have a higher level of purchase intention and WOM.
Participants exposed to green marketing-related posts report a higher level of purchase intention than those exposed to non-green marketing posts.
Participants exposed to green marketing-related posts report a higher level of WOM than those exposed to non-green marketing posts.
Building on the idea that consumers’ perceptions significantly influence their attitudes toward brands, this study also proposes examining the impact of informativeness, entertainment, and perceived relevance variables on brand attitude. The existing literature suggests that these elements can enhance consumers’ attitudes toward a brand. For instance, Mattke et al. [46] point out that when the media is entertaining and informative, it leads to a positive brand attitude. Similarly, De Keyzer et al. [47] argue that consumers who perceive a social media advertisement as more personalized tend to find the advertisement more personally relevant. As a result of improved personal relevance, they develop a more favorable brand attitude. Moreover, a study conducted by Wottrich et al. [48] on game-related advertising shows that relevant ads, as a result of customization, increase a positive attitude toward the brand. These reflections lead to the formulation of the following hypothesis:
(a) Informativeness, (b) entertainment, and (c) perceived relevance have a positive impact on brand attitude.
WOM is an important element to explore, especially given that social networks enable members to easily disseminate viral messages and information [49]. According to the theory of reasoned action [50], there is a positive correlation between consumer attitude toward the brand/product and their likelihood to buy it. This framework can also be applied to explain why consumers choose to buy from businesses that participate in green marketing-related activities [51]. Kang and Hustvedt [52] suggest that brand attitude directly affects consumers’ perceptions of the brand’s social responsibility. This in turn causes customers to spread positive WOM and make purchases. According to Yan et al. [51], customers’ purchase intentions can be directly predicted by their attitudes toward the brands. Based on these premises, it is expected that brand attitude has a positive impact on both purchase intention and WOM. Therefore, H8a–b is proposed, as follows:
Brand attitude has a positive impact on (a) purchase intention and (b) WOM.
2.3. Greenwashing
The surge in environmental content has raised doubts about its credibility, leading consumers to question the sustainability aspect of business practices and to suspect companies of greenwashing [53]. Within this context, greenwashing is understood as the “selective disclosure of positive information about a company’s environmental or social performance, while withholding negative information on these dimensions” [36]. Early research suggests that consumers’ perceptions of a company’s level of greenwashing can significantly influence their behavior [54]. Based on the discussion, it was formulated that greenwashing may moderate the relationship between brand attitude and purchase intention, as well as brand attitude and WOM communication.
Greenwashing moderates the relationship between (a) brand attitude and purchase intention and (b) brand attitude and WOM.
2.4. Ecological Consciousness
The degree of consumers’ ecological consciousness can also be a factor in measuring the relationships among the variables of this study. Ecologically conscious consumer behavior encompasses behaviors that reflect the environmental concerns of consumers, such as pro-environmental purchasing behavior, recycling behavior, and engagement in pro-environmental activities [55]. The level of ecological consciousness among consumers influences their behaviors differently. Sudbury Riley et al. [56] found that consumers with higher levels of ecological consciousness behave differently from those who score lower on this scale. Following this line of reasoning, the next hypothesis is proposed:
Consumers’ ecologically conscious level moderates the relationship between (a) brand attitude and purchase intention and (b) brand attitude and WOM.
2.5. Cultural Variations in Engagement with Green Marketing
By examining the impact of pro-environmental content on social media across different cultural contexts, this study aimed to provide a more comprehensive understanding of the subject. Prior research has highlighted noticeable differences across markets in terms of engagement with brand pages on social media. For example, Tsai and Men [57] identified cultural disparities between Chinese and American consumers in their participation within online communities, illustrating how collectivist versus individualistic values shape digital interactions. Moreover, consumer perceptions and behaviors for participating in sustainable behaviors vary across cultures. Minton et al. [58] examined the level of sustainable behaviors exhibited on social media and observed that cultural differences lead to different levels of sustainable practices such as recycling, purchasing green products, and supporting sustainable charities.
The selection of Poland and the United States as comparative cases reflects distinct cultural, regulatory, and historical trajectories. Poland’s environmental consciousness has been shaped by its rapid integration into the European Union, which introduced stringent sustainability policies and fostered a collective, pragmatic approach to eco-friendly behaviors rooted in Central European cultural norms [59]. In contrast, the US exemplifies a consumerist, individualistic culture with decentralized environmental policies and a longer history of corporate green marketing tied to personal values rather than regulatory mandates. In the US, companies increasingly adopt green marketing strategies to attract environmentally aware consumers and drive sustainable growth, with improved consumer attitudes toward eco-friendly products reflecting this trend. Meanwhile, in Poland, green marketing effectiveness is influenced by socioeconomic factors and evolving environmental awareness, though recent shifts suggest growing interest in sustainable consumption [60].
These examples demonstrate how green marketing can influence consumer attitudes and behaviors globally, with cultural factors significantly shaping responses to eco-friendly marketing efforts. With these findings in mind, the intention is to determine whether nationality might influence the relationship between green marketing activities and consumer behavior, using Poland and the United States as representative markets. Following this line of reasoning, the next hypothesis is proposed:
The impact of green marketing-related posts on participants’ (a) informativeness, (b) entertainment, (c) perceived relevance, (d) brand attitude, (e) purchase intention and (f) WOM differs significantly between Polish and US consumers.
Figure 1 presents the research model and the formulated hypotheses.
3. Methodology
3.1. Experimental Design and Stimuli
To investigate the impacts of green marketing activities in the context of social media, an experimental study using a between-subjects design was conducted. A between-subjects design compares differences between subjects who have been exposed to different stimuli [61]. The between-subjects design allows for a clear comparison of the effects of different conditions on various subject groups and constitutes the most frequently used experimental type in the marketing literature [62].
Participants were randomly assigned to one of two groups and provided with brief information about a fictional protein bar brand called “First Power” (see Appendix A and Appendix B). Before answering questions, each group was shown four social media posts. The posts and brand elements, including the brief information and logo, differed between the groups in terms of the messages they contained. The social media posts displayed to the treatment group contained messages related to green marketing. However, the posts shown for the control group did not evoke any green messages. The purpose of these posts was to create a general impression and provide information about the fictional brand being presented.
In the next step, questions were asked to test how exposure to these social media posts impacts participants’ responses in terms of “informativeness”, “entertainment”, “perceived relevance”, “brand attitude”, “word of mouth”, and “purchase intention”. Afterward, the level of “greenwashing” and “ecologically conscious behavior” among consumers was measured.
3.2. Measurement Instruments
The research constructs, except for brand attitude, were measured using a five-point multi-item Likert scale that ranged from 1, representing “strongly disagree”, to 5, representing “strongly agree”. Brand attitude was measured using a five-point Likert-type scale with bipolar adjectives ranging from unappealing to appealing, bad to good, unpleasant to pleasant, unfavorable to favorable, and unlikable to likable. The study used established measurement instruments to select the items for the research constructs, which had been validated in previous studies. However, a few of the items were adjusted slightly to enhance their relevance to the context of the study. For Polish participants, the original scales in English were translated into Polish using the back-translation method suggested by Usunier [63]. Two bilingual marketing professors reviewed the inconsistencies between the original survey and the survey that was back-translated.
For informativeness, 6 items were adopted from Yadav and Rahman’s [64] and Triantafillidou and Siomkos’s [65] studies. To measure entertainment, 6 items were taken from Kim and Ko’s [26,66], and Triantafillidou and Siomkos’s [65] studies. For word of mouth, 4 items were adapted from Kim and Ko [26,66], and Yadav and Rahman [64]. Perceived relevance was operationalized using 6 items from prior research conducted by Alalwan [24]. To measure brand attitude, 5 items seeking respondents’ attitudes toward the displayed brand were adapted from Spears and Singh’s [37] study. To measure the purchase intention of participants, 4 items were taken from Erkan and Evans [67] and Hsu and Lin [68]. For greenwashing, 3 items from Chen and Chang’s [69] study were used. To measure consumers’ ecologically conscious behavior, 5 items were adapted from Bailey et al. [70].
3.3. Data Collection Process
In the first step, a pilot study was conducted to test and gain a better understanding of the experimental items. The pilot experiment was conducted on 130 management students. Pilot study participants were further asked to answer additional questions like if they had any suggestions regarding the survey itself and if they found any of the questions to be incomprehensible or irrelevant. Based on the results obtained, both the images and questionnaire items were subsequently modified.
After conducting the preliminary study, the main study was conducted. Respondents were recruited through an external market research agency, which helped to achieve a representative sample of respondents in terms of gender, age, and nationality. To ensure study reliability, responses were completed in under 5 min, and those from respondents who incorrectly answered two trap questions, such as “For this question, select the ‘Strongly disagree’ option”, indicating potential inattention, were excluded from the analysis. A total of 2400 questionnaires were distributed, and after applying these exclusion criteria, a final sample of 2035 participants remained for the analysis.
3.4. Research Sample
Descriptive statistics were used to analyze the demographic variables of the respondents and are shown in Table 1. The sample was comprised of 1018 attendees from the United States and 1017 attendees from Poland. In terms of gender, there were slightly more female participants in both the US and Poland groups, with 54.1% and 50.8%, respectively. With regard to age, the distribution of participants was fairly similar between the US and Poland groups. The largest proportion of attendees was aged 25–34 (22.9% and 26.2%, respectively). The second most common age group was between 35 and 44 years old (21.6% and 22%, respectively). Other age categories included 18–24, with 11.5% of Americans and 13.2% of Poles, and 45–54, with 21.8% of Americans and 19.7% of Poles. The smallest age group was people between 55 and 65 years old, with 22.2% of Americans and 18.9% of Poles in this category. In terms of treatment and control group participants, it was ensured that there were at least 500 participants in each group. Although the treatment group had slightly more participants than the control group, the proportion of participants was quite similar (n = 1032 and n = 1003, respectively).
3.5. Statistical Analysis
To empirically test the proposed model regarding the causal relationships among the variables, structural path modeling using PLS-SEM was employed [71]. SmartPLS 4 was used for PLS-SEM to test the assumed model and hypotheses related to causal relationships between the factors. The use of PLS-SEM was justified for this study due to the complexity of the structural model, which includes numerous constructs and relationships [72]. Furthermore, this analysis is suitable when the research has exploratory features and aims to test a theoretical framework from a predictive perspective [72,73]. This approach also allows the assessment of both the measurement and structural models at the same time and is known as an applicable method for testing both formative and reflective measurements [74,75].
4. Results
4.1. Measurement Model
The adequacy of the measurement model was assessed using PLS-SEM. First, the reliability and convergent validity of the scales were examined by calculating factor loadings, Cronbach’s α, composite reliability, and average variance extracted (AVE). An overview of the instruments used is presented in Table 2. In the presented table, all factor loadings are over 0.7 which indicates that each item has a strong relationship with its corresponding construct [76]. The internal consistency of each eight dimensions was determined by evaluating Cronbach’s α. It was found that the dimensions used possessed a high degree of reliability. Cronbach’s α ranged from 0.874 to 0.966, indicating that all values exceeded the “good” threshold of 0.80 [77]. The estimated AVE values ranged from 0.664 to 0.870, which suggested that all of the values surpassed the minimum accepted threshold, which is 50% [76]. The composite reliability values (rho_a and rho_c) ranged from 0.880 to 0.966 and 0.908 to 0.973. Both these ranges are above the minimum threshold of 0.70, which confirms the internal consistency reliability of the measures [78].
Additionally, to check for discriminant validity, two different criteria were applied. First, the AVE of each construct was compared to its squared correlations with other constructs as shown in Table 3. Based on these results, all the latent variables tested met the prerequisites of the Fornell–Larcker criterion [79], affirming the existence of discriminant validity. As a second step, discriminant validity was verified by employing the heterotrait–monotrait ratio (HTMT). All HTMT values were below 0.9, which is consistent with the threshold advocated by Hair Jr. et al. [71] (see Table 4). Furthermore, the variance inflation factor (VIF) scores of indicators were computed to check the degree for multicollinearity. The VIF ranged from 1.025 to 4.211, meaning that all VIF scores were below the cutoff value of 5 [80]. Therefore, no issue of multicollinearity existed with regard to the data.
4.2. Analysis-of-Variance Results for Experimental Conditions
To determine the impact of green marketing-related post-exposure on informativeness, entertainment, perceived relevance, word of mouth, brand attitude, and purchase intention, a one-way analysis of variance (ANOVA) was conducted. The conditions were compared using the variables outlined in Table 5.
With the exception of ecologically conscious consumer behavior and greenwashing, all other scales were statistically significant (p < 0.05). The reason for the lack of significance in these two variables (p = 0.056, p = 0.482) is that the participants’ levels of ecologically conscious behavior and greenwashing were not significantly different from each other. This finding confirms the notion that the participants were selected randomly and were not biased in terms of participants’ levels of ECCB or greenwashing.
ANOVA results indicated that exposure to presented content had a significant impact on participants’ reported informativeness level (F (1, 2033) = 63.55, p < 0.05). Participants who were exposed to green marketing-related content (M = 3.99, SD = 0.78) reported significantly higher levels of informativeness compared to those who were exposed to non-green marketing-related content (M = 3.70, SD = 0.87). These findings suggest that exposure to green content may enhance individuals’ enjoyment and engagement with media. Thus, H1 is supported.
Participants’ entertainment level (F (1, 2033) = 45.96, p < 0.05) also followed the same pattern as informativeness. Participants who viewed green marketing-themed media (M = 3.76, SD = 0.87) reported a significantly higher level of entertainment than the control group (M = 3.49, SD = 0.91). These results suggest that green marketing-related media may be more engaging and enjoyable for viewers. According to these results, the second hypothesis is accepted.
H3 addresses the differences in the perceived relevance of social media content, F (1, 2033) = 76.36, p < 0.05. The finding demonstrated that when the content was related to environmental topics (M = 3.63, SD = 1.02), individuals perceived the content to be more similar to their personal interests, values, or needs compared to non-environmental content (M = 3.21, SD = 1.11). Therefore, H3 is also supported.
Hypothesis H4 predicts that participants have a higher level of brand attitude when they encounter green marketing-related content. The results confirm this claim (M = 4.2, SD = 0.8 vs. M = 3.8, SD = 0.9). This claim is supported by the results (F (1, 2033) = 42.78, p < 0.05), with a mean brand attitude value of 4.30 (SD = 0.80) for the treatment group and 4.07 (SD = 0.84) for the control group.
As predicted in this study, when people viewed environmentally friendly posts, they were more likely to have a higher level of purchase intention (M = 3.59, SD = 1.05) than for the non-environmentally friendly posts (M = 3.44, SD = 1.08), F (1, 2033) = 10.02, p < 0.05. Hence, H5 receives support.
Social media users who viewed eco-friendly content (M = 3.39, SD = 1.09) reported significantly higher levels of word of mouth by talking about and recommending the brand and its products to friends compared to the other group (M = 3.12, SD = 1.15), with F (1, 2033) = 29.50, p < 0.05. Thus, the empirical evidence provides support for H6.
4.3. Structural Model
Figure 2 shows the results of the hypothesis testing of the structural model, including the estimated path coefficients and the variance explained (R2 value) by the dependent variables. The R2 values for the endogenous variables—brand attitude (0.583), purchase intention (0.564), and WOM (0.499)—indicate the model’s moderate predictive power [71].
The subsequent step involved performing PLS analysis (bootstrapping, 5000 subsamples) to test the hypothetical relationships between the variables in the model. The estimated path coefficients (β) of the structural model are given in Table 6.
Results presented in the table reveal that informativeness (β = 0.248, p < 0.05), entertainment (β = 0.257, p < 0.05) and perceived relevance (β = 0.316, p < 0.05) significantly affected the brand attitude. Thus, H7a, H7b and H7c are confirmed.
The next set of hypotheses focuses on the relationship between brand attitude and purchase intention (H8a), and brand attitude and WOM (H8b). The results support H8a and H8b, which represent the impact of brand attitude on purchase intention (β = 0.612, p < 0.05) and WOM (β = 0.542, p < 0.05).
4.4. Results of the Moderation Analysis
The following hypotheses (H9a-b) explore whether greenwashing moderates the relationship between brand attitude and purchase intention, as well as brand attitude and WOM. According to the results, greenwashing moderated the relationship between brand attitude and both purchase intention (β = 0.026, p < 0.05) and WOM (β = 0.039, p < 0.05). As brand attitude increases, the moderating effect of greenwashing weakens, but the effect is still present to some degree. Similarly, the moderation effects of ECCB between brand attitude and purchase intention (H10a) and brand attitude and WOM (H10b) were also analyzed. The coefficient of the interaction term (ECCB x brand attitude) on purchase intention (β = 0.055, p < 0.05) and WOM (β = 0.076, p < 0.05) indicated significant moderation effects. Accordingly, the empirical results support H9a-b and H10a-b.
4.5. A Comparison of the Effects of Green Marketing-Related Posts on Consumer Behavior: US vs. Poland
The last hypothesis (H11) suggests that there is a significant difference between Polish and US consumers regarding the impact of green marketing-related posts on various aspects, namely, (a) informativeness, (b) entertainment, (c) perceived relevance, (d) brand attitude, (e) purchase intention, and (f) WOM.
To test H11, a one-way ANOVA was conducted. This study compared the exposure to green marketing-related posts between consumers in the US and Poland with respect to the following variables: informativeness, entertainment, perceived relevance, brand attitude, purchase intention, and WOM (see Table 7).
As demonstrated in Table 7, the obtained results indicate that there are significant differences between Polish and US consumers in three out of the eight measured variables. These are informativeness (F = 22.426, p < 0.05), perceived relevance (F = 11.025, p < 0.05) and purchase intention (F = 9.173, p < 0.05). Compared to their Polish counterparts, respondents in the US reported higher levels of informativeness, perceived relevance, and purchase intention when exposed to green marketing-related content. These findings suggest that for US consumers compared to Polish consumers, eco-friendly posts may have a more noticeable impact on these three consumer behavior dimensions. Thus, H11a, H11c, and H11e were accepted.
On the other hand, no significant differences were found in other dimensions such as entertainment (F = 3.476, p = 0.063), brand attitude (F = 0.002, p = 0.963), WOM (F = 0.610, p = 0.435), ecologically conscious consumer behavior (F = 0.084, p = 0.773) or greenwashing (F = 1.711, p = 0.191). According to these results, H11b, H11d, and H11f were not supported.
5. Discussion
The findings of this study contribute to the literature across social media, green marketing, and business communication literature in many ways. According to the existing literature, pro-environmental social media content has a considerable influence on customers’ consumption behaviors [81]. To examine the effects of green marketing-related content on different consumer behavior variables, an experimental study was conducted.
The results align with previous research that suggests green marketing initiatives can positively influence respondents’ informativeness level. Specifically, the finding of this study shows that consumers exposed to pro-environmental content reported higher levels of informativeness compared to another group that was not exposed. Chiu et al. [82] imply that information sharing can be a useful method for green behavior change and providing sustainable messages can increase the information level of targeted consumers. Similarly, Uzunoğlu et al. [83] claim that providing consumers with CSR-related messages has the potential to facilitate consumer engagement and generate favorable outcomes. Having increased consumer knowledge about environmentally friendly goods and services also provides an advantage in reducing the price barrier to purchase and making it easier to convince consumers to purchase [84].
According to Tsai and Men’s study [57], information-seeking and entertainment were the two most dominant reasons to engage with brand social media pages. The results of my study demonstrate that there is a significant relationship between green marketing-related content and the entertainment levels of consumers. Every activity enabled by use of social media, such as providing factual information and customized content or building relationships with users, should be entertaining [26]. However, some types of content may be more entertaining than others. The finding of this study supports the notion that eco-friendly messages can make media more enjoyable for viewers compared to non-ecofriendly content. The online questionnaire with 450 respondents conducted by Ebrahimi et al. [27] revealed that entertainment plays a vital role in influencing consumers’ sustainable behavior.
The findings also demonstrate that green marketing-related content was perceived by respondents as highly relevant compared to non-green marketing posts. When customers perceive messages as relevant, they are more inclined to share the information or buy the product. Furthermore, this supports the findings of Knight et al. [36], which demonstrated that when social media posts meet the specific expectations of customers, it leads to an increased sharing of their sustainability messages. It is proposed that it is vital for companies to recognize that only the most fitting messages have a higher likelihood of capturing users’ interest and potentially being shared [35]. Thus, the degree to which a message is perceived to be relevant by a social media user plays an essential role. This perception influences how they form beliefs and make decisions about a company’s sustainability practices, ultimately determining if they will take action as a result [36].
Another important dimension in evaluating social media content is word of mouth. The findings indicate that participants have a higher level of WOM when they are exposed to green marketing-related posts. This result is in line with previous research. According to Ebrahimi et al. [27], presenting an eco-friendly attitude on social media positively increases the effect of word-of-mouth communication among consumers. Similarly, Salem and Alanadoly [85] claimed that consumers are motivated to disseminate information through word of mouth on products or brands that are aligned with the concept of sustainability.
The findings also demonstrate the value of using green messages on social media, as these can positively influence consumers’ purchase intention. When participants see pro-environmental posts on social media, they are more likely to have a higher level of purchase intention. This finding is in line with previous research suggesting that disseminating information about green products via a Facebook brand page can be effective in driving actual purchase behavior [84]. This strengthens and expands upon the current body of literature exploring the impact of green marketing on consumer engagement and behavior. Lu and Miller [84] add that when consumers have enough information about the offered eco-friendly products and services, they will be more likely to purchase these products regardless of their price premium. Likewise, Byrum [5] argues that positive CSR campaigns on social media have the ability to influence consumers’ purchase intention behavior.
Another significant contribution of this study is the evidence that companies can improve their followers’ attitudes toward the brand through green marketing-related content. This result is also consistent with Chu and Chen’s [43] research. Their study examined how environmentally themed content in social media affects consumers’ responses to brands and confirmed that environment-related activities in social media significantly enhance positive brand attitudes and identification with the brand. At the same time, this study contributes to the green marketing literature by empirically supporting the mediating role of brand attitude between factors such as informativeness, entertainment, and perceived relevance, and behavioral intentions such as purchase intention and word of mouth.
Another notable finding in this study is the existence of differences between countries in terms of the effect of the consumers’ perceptions and behaviors toward eco-friendly campaigns and initiatives. These results align with previous studies that suggest sustainability-focused messages can vary significantly across different markets and play a crucial role in shaping consumer perceptions [86]. In particular, pro-environmental claims in developed economies tend to have a stronger influence on consumer purchase intentions [58]. In the context of my findings, the influence of green marketing-related posts on respondents’ informativeness, perceived relevance, and purchase intention varied significantly between Polish and US consumers. However, some aspects of the study, such as levels of entertainment, brand attitude, and WOM, did not show significant differences between the two groups. This could be due to the universal appeal of these factors across different cultures, where entertainment and brand attitudes are shaped by the overall effectiveness of green messaging rather than by cultural nuances. Additionally, the type of product and the context in which it is presented may have moderated these effects, as consumers in both markets might have similar perceptions of such products, reducing the likelihood of strong cross-cultural differences in these specific areas [87]. It is important to note that prior studies examining the influence of eco-friendly social media strategies have largely focused on single-region contexts, such as Western economies [5,36] or culturally bounded settings [88,89]. By analyzing the impact of pro-environmental content on consumers in distinct cultural–regulatory ecosystems—exemplified by Poland and the United States—this research broadens the scope of analysis and provides a more comprehensive understanding of the subject. The results suggest that businesses should carefully consider cultural, regulatory, and regional priorities when developing and implementing marketing strategies. These cross-cultural findings advance the literature by highlighting how global variability in consumer responses to green marketing is shaped by localized frameworks, such as collective sustainability norms (e.g., EU-aligned policies in Poland) versus individualistic, market-driven engagement (e.g., the US). This contributes to the theoretical understanding of user behavior and engagement in the context of green marketing.
6. Conclusions
This article examined the effects of green marketing-related content on various consumer behavior variables through an experimental study. The research focused on identifying key factors associated with green marketing activities on social media that influence purchase intention WOM. Specifically, the study investigated how exposure to green marketing-related posts affects variables such as informativeness, entertainment, perceived relevance, WOM, brand attitude, and purchase intention. The findings support the proposed hypotheses, demonstrating that exposure to green marketing-related posts may have a significant impact on these variables. In addition, the effect of pro-environmental content on these variables was also tested between two different countries—the USA and Poland. The findings indicate that the impact of green marketing-related posts on participants’ informativeness level, perceived relevance, and purchase intention differs significantly between Polish and US consumers.
From a theoretical perspective, this study tested a proposed structural model and hypotheses regarding the causal relationships among the variables. The findings confirm that informativeness, entertainment, and perceived relevance significantly affect brand attitude. In addition, the results demonstrate a strong relationship between brand attitude and purchase intention, as well as brand attitude and WOM. Moreover, moderating effects of greenwashing and ecologically ECCB on the relationships between brand attitude, purchase intention, and WOM were tested. According to the findings, greenwashing moderates the relationship between brand attitude and both purchase intention and WOM. Furthermore, ECCB also moderates the relationship between brand attitude and purchase intention, as well as brand attitude and WOM.
The findings provide valuable insights into the use of social media for green marketing efforts and initiatives. For managers and practitioners, recognizing the types of content and strategies that effectively engage users can enhance the impact and effectiveness of their social media efforts. Furthermore, understanding the differences and similarities between social platforms is also essential for companies. They should not use the same strategies for all social networking sites. Thus, it can help them to better communicate their sustainability efforts to their audience. Moreover, this can build stronger relationships with consumers who are interested in environmental issues and who define themselves as “green consumers”. By applying these obtained results, businesses can adjust their pro-environmental messages, visuals and distribution methods to better suit their target audience and increase the chances of high engagement.
Another key takeaway is the importance of tailoring social media content to emphasize both informativeness and entertainment, which were shown to drive higher consumer engagement. Marketers should focus on creating content that not only educates consumers about the environmental benefits of their products but also entertains and resonates with their target audience. This can be achieved by using engaging visuals, storytelling, and interactive elements such as quizzes or polls that align with the brand’s sustainable marketing strategies. Customizing messages to fit the specific environmental priorities and concerns of the target market will enhance relevance and likely result in stronger consumer engagement and a positive brand attitude.
Furthermore, the cross-country differences observed in the study offer important insights for global green marketing strategies. The finding that Polish and US consumers reported different levels of informativeness, perceived relevance, and purchase intention when exposed to green marketing-related content suggests that marketers should consider regional preferences and values when designing content. For example, in markets where environmental consciousness is lower, marketers may need to invest more in educational content to raise awareness, whereas in markets with higher ecological consciousness, focusing on how the product aligns with consumers’ personal values may be more effective.
Limitations and Further Research Directions
Some limitations of this article must be acknowledged. This study may suffer from common method bias, which is recognized as a primary source of measurement error and can impact research outcomes [90]. In particular, the order in which survey questions are presented can trigger a cognitive phenomenon known as “priming”. Priming entails the subconscious activation of thoughts due to prior stimuli. In this context, the placement of the ecologically conscious consumer behavior scale can induce priming effects. These effects may in turn influence the judgment phase of the response process, because answering initial questions brings information into short-term memory that remains accessible when responding to later questions [90].
The findings of this research should not be generalized across all industries. In accordance with the research objectives and as part of the experimental design, only a fictional protein bar brand named “First Power” was included in the analysis. To improve generalization, it would be beneficial to widen the sample and conduct comparative research across different industries.
Ethical review and approval were waived for this study due to Legal Regulations based on NCN Ethical Guidelines.
Informed consent was obtained from all subjects involved in the study.
Data will be made available upon publication.
The author declares no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Demographic information of survey respondents.
| US | Poland | Total | |
|---|---|---|---|
| Gender | |||
| Male | 467 (45.9%) | 500 (49.2%) | 967 (47.5%) |
| Female | 551 (54.1%) | 517 (50.8%) | 1068 (52.5%) |
| Total | 1018 | 1017 | 2035 |
| Age | |||
| 18–24 | 117 (11.5%) | 134 (13.2%) | 251 |
| 25–34 | 233 (22.9%) | 266 (26.2%) | 499 |
| 35–44 | 220 (21.6%) | 224 (22%) | 444 |
| 45–54 | 222 (21.8%) | 200 (19.7%) | 422 |
| 55–65 | 226 (22.2%) | 193 (18.9%) | 419 |
| Total | 1018 | 1017 | 2035 |
| Group | |||
| Treatment group | 516 (50.7%) | 516 (50.7%) | 1032 |
| Control group | 502 (49.3%) | 501 (49.3%) | 1003 |
| Total | 1018 | 1017 | 2035 |
Convergent validity and reliability statistics.
| Constructs and Items | Factor | CA | CR rho_a | CR rho_c | AVE |
|---|---|---|---|---|---|
| Informativeness (I) [ | 0.905 | 0.913 | 0.927 | 0.678 | |
| I1: These posts offer accurate information on products. | 0.764 | ||||
| I2: These posts offer useful information. | 0.868 | ||||
| I3: The information provided by these posts is comprehensive. | 0.749 | ||||
| I4: These posts were highly educational for me. | 0.870 | ||||
| I5: These posts had made me more knowledgeable. | 0.844 | ||||
| I6: These posts stimulated my curiosity. | 0.839 | ||||
| Entertainment (E) [ | 0.935 | 0.937 | 0.949 | 0.756 | |
| E1: These posts are fun. | 0.852 | ||||
| E2: These posts seem interesting. | 0.868 | ||||
| E3: It is attractive to see these posts. | 0.890 | ||||
| E4: I experienced joy through these posts. | 0.866 | ||||
| E5: These posts gave me a good feeling. | 0.847 | ||||
| E6: I felt cheerful when I saw these posts. | 0.893 | ||||
| Perceived relevance (PR) [ | 0.966 | 0.966 | 0.973 | 0.855 | |
| PR1: These posts are relevant to me | 0.938 | ||||
| PR2: These posts are important to me. | 0.924 | ||||
| PR3: I found these posts meaningful. | 0.885 | ||||
| PR4: I think these posts fit my interests. | 0.930 | ||||
| PR5: I think these posts fit with my preferences. | 0.932 | ||||
| PR6: Overall, I think these posts fit me. | 0.938 | ||||
| Brand attitude (BA) [ | 0.958 | 0.959 | 0.968 | 0.857 | |
| How do you rate the brand described in the posts you just read? | |||||
| BA1: Unappealing/appealing | 0.915 | ||||
| BA2: Bad/good | 0.928 | ||||
| BA3: Unpleasant/pleasant | 0.928 | ||||
| BA4: Unfavorable/favorable | 0.933 | ||||
| BA5: Unlikable/likable | 0.925 | ||||
| Purchase Intention (PI) [ | 0.945 | 0.945 | 0.960 | 0.858 | |
| PI1: It is very likely that I will buy protein bars of this brand. | 0.936 | ||||
| PI2: I will buy this protein bar next time I need it. | 0.935 | ||||
| PI3: I will definitely try the protein bar of this brand. | 0.936 | ||||
| PI4: I will recommend the protein bar of this brand. | 0.898 | ||||
| Word of mouth (WOM) [ | 0.950 | 0.951 | 0.964 | 0.870 | |
| WOM1: I would like to pass on the information I acquired from these posts to my friends. | 0.940 | ||||
| WOM2: I would like to share opinions on the brand I acquired from these posts with my friends. | 0.938 | ||||
| WOM3: I would like to upload these posts on my social media account. | 0.912 | ||||
| WOM4: I would recommend my friends to visit this brand’s social media and see these posts. | 0.940 | ||||
| Greenwashing (G) [ | 0.879 | 0.880 | 0.926 | 0.806 | |
| G1: Companies often mislead in their environmental features. | 0.906 | ||||
| G2: Companies frequently possess a green claim that are untrue. | 0.891 | ||||
| G3: Companies usually exaggerate how their green functionality actually is. | 0.896 | ||||
| Ecologically conscious consumer behavior (ECCB) [ | 0.874 | 0.893 | 0.908 | 0.664 | |
| ECCB1: I buy environmentally friendly products whenever possible. | 0.870 | ||||
| ECCB2: I try to use products made from recycled materials. | 0.872 | ||||
| ECCB3: I reduce household waste. | 0.776 | ||||
| ECCB4: I would be willing to reduce my consumption to help protect the environment. | 0.809 | ||||
| ECCB5: I am the kind of concerned consumer who recycles. | 0.739 |
Note: CA—Cronbach’s α; CR—composite reliability; AVE—average variance extracted.
Measurement model: discriminant validity using Fornell–Larcker criterion.
| Constructs | BA | ECCB | E | G | I | PR | PI | WOM |
|---|---|---|---|---|---|---|---|---|
| BA | 0.926 | |||||||
| ECCB | 0.448 | 0.815 | ||||||
| E | 0.719 | 0.470 | 0.869 | |||||
| G | −0.085 | 0.070 | −0.111 | 0.898 | ||||
| I | 0.695 | 0.444 | 0.808 | −0.112 | 0.824 | |||
| PR | 0.717 | 0.520 | 0.830 | −0.106 | 0.761 | 0.925 | ||
| PI | 0.715 | 0.515 | 0.748 | −0.059 | 0.692 | 0.811 | 0.926 | |
| WOM | 0.655 | 0.500 | 0.799 | −0.108 | 0.715 | 0.832 | 0.798 | 0.933 |
Measurement model: discriminant validity using heterotrait–monotrait ratio.
| Constructs | BA | ECCB | E | G | I | PR | PI | WOM | G x BA | ECCB x BA |
| BA | ||||||||||
| ECCB | 0.486 | |||||||||
| E | 0.758 | 0.510 | ||||||||
| G | 0.093 | 0.085 | 0.122 | |||||||
| I | 0.740 | 0.486 | 0.868 | 0.125 | ||||||
| PR | 0.744 | 0.555 | 0.872 | 0.115 | 0.805 | |||||
| PI | 0.751 | 0.557 | 0.795 | 0.065 | 0.740 | 0.849 | ||||
| WOM | 0.685 | 0.534 | 0.847 | 0.118 | 0.763 | 0.868 | 0.841 | |||
| G x BA | 0.068 | 0.045 | 0.103 | 0.131 | 0.099 | 0.087 | 0.075 | 0.079 | ||
| ECCB x BA | 0.283 | 0.291 | 0.170 | 0.019 | 0.179 | 0.154 | 0.177 | 0.137 | 0.033 |
Results of the one-way ANOVA testing the effects of green marketing-related posts vs. non-green marketing-related posts.
| Means (Standard Deviations) | ||||
|---|---|---|---|---|
| Green Marketing-Related (n = 1032) | Non-Green Marketing-Related (n = 1003) | F-Value | p-Value | |
| Informativeness | 3.9889 (0.78257) | 3.6964 (0.87109) | 63.547 | <0.001 |
| Entertainment | 3.7573 (0.86648) | 3.4909 (0.90623) | 45.957 | <0.001 |
| Perceived relevance | 3.6276 (1.02369) | 3.2149 (1.10635) | 76.359 | <0.001 |
| Brand attitude | 4.3043 (0.79945) | 4.0660 (0.84376) | 42.777 | <0.001 |
| Purchase intention | 3.5904 (1.04477) | 3.4414 (1.07723) | 10.024 | 0.002 |
| WOM | 3.3874 (1.08556) | 3.1184 (1.14826) | 29.496 | <0.001 |
| ECCB | 3.8011 (0.82288) | 3.7301 (0.81112) | 3.846 | 0.056 |
| Greenwashing | 3.5714 (0.86499) | 3.5444 (0.86919) | 0.494 | 0.482 |
Bootstrapping results for the structural relationships.
| Constructs | Original Sample | Sample Mean | Standard Deviation | T-Statistics | p-Values |
|---|---|---|---|---|---|
| Brand attitude -> Purchase intention | 0.612 | 0.612 | 0.017 | 35.618 | 0.000 |
| Brand attitude -> WOM | 0.542 | 0.541 | 0.018 | 30.479 | 0.000 |
| Entertainment -> Brand attitude | 0.257 | 0.257 | 0.032 | 8.125 | 0.000 |
| Informativeness -> Brand attitude | 0.248 | 0.248 | 0.031 | 8.064 | 0.000 |
| Perceived relevance -> Brand attitude | 0.316 | 0.315 | 0.031 | 10.157 | 0.000 |
| Greenwashing x Brand attitude -> Purchase intention | 0.026 | 0.027 | 0.013 | 1.993 | 0.046 |
| Greenwashing x Brand attitude -> WOM | 0.039 | 0.041 | 0.013 | 3.004 | 0.003 |
| Ecologically conscious consumer behavior x Brand attitude -> Purchase intention | 0.055 | 0.055 | 0.013 | 4.371 | 0.000 |
| Ecologically conscious consumer behavior x Brand attitude -> WOM | 0.076 | 0.076 | 0.014 | 5.540 | 0.000 |
Results of the one-way ANOVA testing the effects of green marketing-related posts on consumers in the US and Poland.
| Poland (n = 516) | US (n = 516) | F-Value | p-Value | |
|---|---|---|---|---|
| Informativeness | 3.8747 (0.79291) | 4.1030 (0.75577) | 22.426 | <0.001 |
| Entertainment | 3.7070 (0.81870) | 3.8075 (0.90978) | 3.476 | 0.063 |
| Perceived relevance | 3.5223 (0.99751) | 3.7329 (1.03955) | 11.025 | <0.001 |
| Brand attitude | 4.3031 (0.75981) | 4.3054 (0.83796) | 0.002 | 0.963 |
| Purchase intention | 3.4922 (1.00807) | 3.6885 (1.07225) | 9.173 | 0.003 |
| WOM | 3.3609 (1.04309) | 3.4138 (1.12682) | 0.610 | 0.435 |
| ECCB | 3.8826 (0.72498) | 3.8682 (0.86287) | 0.084 | 0.773 |
| Greenwashing | 3.6066 (0.81706) | 3.5362 (0.90984) | 1.711 | 0.191 |
Appendix A. Experimental Conditions
Enlarge this image.Figure A1. Description of brand for treatment group (left) and control group (right).
Appendix B. Experimental Stimuli: Social Media Posts According to the Groups
References
1. Garg, A. Green Marketing for Sustainable Development: An Industry Perspective. Sustain. Dev.; 2015; 23, pp. 301-316. [DOI: https://dx.doi.org/10.1002/sd.1592]
2. WWF. An Eco-Wakening; WWF: New York, NY, USA, 2021.
3. Hughes, C.; Swaminathan, V.; Brooks, G. Driving Brand Engagement Through Online Social Influencers: An Empirical Investigation of Sponsored Blogging Campaigns. J. Mark.; 2019; 83, pp. 78-96. [DOI: https://dx.doi.org/10.1177/0022242919854374]
4. White, K.; Habib, R.; Hardisty, D.J. How to SHIFT Consumer Behaviors to Be More Sustainable: A Literature Review and Guiding Framework. J. Mark.; 2019; 83, pp. 22-49. [DOI: https://dx.doi.org/10.1177/0022242919825649]
5. Byrum, K. “Hey Friend, Buy Green”: Social Media Use to Influence Eco-Purchasing Involvement. Environ. Commun.; 2019; 13, pp. 209-221. [DOI: https://dx.doi.org/10.1080/17524032.2017.1308404]
6. Statista Number of Social Media Users Worldwide from 2017 to 2027. Available online: https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/ (accessed on 30 November 2023).
7. We Are SocialHootsuite. Digital 2022 Global Overview Report; We Are Social: London, UK, HootSuite: Vancouver, BC, Canada, 2022.
8. Tsimonis, G.; Dimitriadis, S. Brand Strategies in Social Media. Mark. Intell. Plan.; 2014; 32, pp. 328-344. [DOI: https://dx.doi.org/10.1108/MIP-04-2013-0056]
9. Lee, Y. Corporate Sustainable Development and Marketing Communications on Social Media: Fortune 500 Enterprises. Bus. Strateg. Environ.; 2017; 26, pp. 569-583. [DOI: https://dx.doi.org/10.1002/bse.1936]
10. Liao, Y.-K.; Wu, W.-Y.; Pham, T.-T. Examining the Moderating Effects of Green Marketing and Green Psychological Benefits on Customers’ Green Attitude, Value and Purchase Intention. Sustainability; 2020; 12, 7461. [DOI: https://dx.doi.org/10.3390/su12187461]
11. Wu, S.-I.; Chen, Y.-J. The Impact of Green Marketing and Perceived Innovation on Purchase Intention for Green Products. Int. J. Mark. Stud.; 2014; 6, 81. [DOI: https://dx.doi.org/10.5539/ijms.v6n5p81]
12. Ghoshal, M. Green Marketing-A Changing Concept in Changing Time. BVIMR Manag. Edge; 2011; 4, pp. 82-92.
13. Lee, Y. Communicating Sustainable Development: Effects of Stakeholder-centric Perceived Sustainability. Corp. Soc. Responsib. Environ. Manag.; 2020; 27, pp. 1540-1551. [DOI: https://dx.doi.org/10.1002/csr.1900]
14. Consiglio, I.; De Angelis, M.; Costabile, M. The Effect of Social Density on Word of Mouth. J. Consum. Res.; 2018; 45, pp. 511-528. [DOI: https://dx.doi.org/10.1093/jcr/ucy009]
15. Huang, L.; Clarke, A.; Heldsinger, N.; Tian, W. The Communication Role of Social Media in Social Marketing: A Study of the Community Sustainability Knowledge Dissemination on LinkedIn and Twitter. J. Mark. Anal.; 2019; 7, pp. 64-75. [DOI: https://dx.doi.org/10.1057/s41270-019-00053-8]
16. Reich, B.J.; Pittman, M. An Appeal to Intimacy: Consumer Response to Platform-Appeal Fit on Social Media. J. Consum. Psychol.; 2020; 30, pp. 660-670. [DOI: https://dx.doi.org/10.1002/jcpy.1154]
17. Rotzoll, K.B.; Haefner, J.E.; Hall, S.R. Advertising in Contemporary Society: Perspectives toward Understanding; University of Illinois Press: Champaign, IL, USA, 1996; ISBN 0252065425
18. Gao, Y.; Koufaris, M. Perceptual Antecedents of User Attitude in Electronic Commerce. ACM SIGMIS Database database Adv. Inf. Syst.; 2006; 37, pp. 42-50. [DOI: https://dx.doi.org/10.1145/1161345.1161353]
19. Taylor, D.G.; Lewin, J.E.; Strutton, D. Friends, Fans, and Followers: Do Ads Work on Social Networks?: How Gender and Age Shape Receptivity. J. Advert. Res.; 2011; 51, pp. 258-275. [DOI: https://dx.doi.org/10.2501/jar-51-1-258-275]
20. Lee, J.; Hong, I.B. Predicting Positive User Responses to Social Media Advertising: The Roles of Emotional Appeal, Informativeness, and Creativity. Int. J. Inf. Manag.; 2016; 36, pp. 360-373. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2016.01.001]
21. Wei, C.-F.; Chiang, C.-T.; Kou, T.-C.; Lee, B.C.Y. Toward Sustainable Livelihoods: Investigating the Drivers of Purchase Behavior for Green Products. Bus. Strateg. Environ.; 2017; 26, pp. 626-639. [DOI: https://dx.doi.org/10.1002/bse.1942]
22. Gleim, M.R.; Smith, J.S.; Andrews, D.; Cronin, J.J. Against the Green: A Multi-Method Examination of the Barriers to Green Consumption. J. Retail.; 2013; 89, pp. 44-61. [DOI: https://dx.doi.org/10.1016/j.jretai.2012.10.001]
23. Yadav, M.; Rahman, Z. The Influence of Social Media Marketing Activities on Customer Loyalty. Benchmarking Int. J.; 2018; 25, pp. 3882-3905. [DOI: https://dx.doi.org/10.1108/BIJ-05-2017-0092]
24. Alalwan, A.A. Investigating the Impact of Social Media Advertising Features on Customer Purchase Intention. Int. J. Inf. Manag.; 2018; 42, pp. 65-77. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2018.06.001]
25. Logan, K.; Bright, L.F.; Gangadharbatla, H. Facebook versus Television: Advertising Value Perceptions among Females. J. Res. Interact. Mark.; 2012; 6, pp. 164-179.
26. Kim, A.J.; Ko, E. Impacts of Luxury Fashion Brand’s Social Media Marketing on Customer Relationship and Purchase Intention. J. Glob. Fash. Mark.; 2010; 1, pp. 164-171. [DOI: https://dx.doi.org/10.1080/20932685.2010.10593068]
27. Ebrahimi, P.; Khajeheian, D.; Fekete-Farkas, M. A SEM-NCA Approach towards Social Networks Marketing: Evaluating Consumers’ Sustainable Purchase Behavior with the Moderating Role of Eco-Friendly Attitude. Int. J. Environ. Res. Public Health; 2021; 18, 13276. [DOI: https://dx.doi.org/10.3390/ijerph182413276]
28. Tezer, A.; Bodur, H.O. The Greenconsumption Effect: How Using Green Products Improves Consumption Experience. J. Consum. Res.; 2020; 47, pp. 25-39. [DOI: https://dx.doi.org/10.1093/jcr/ucz045]
29. Ahn, T.; Ekinci, Y.; Li, G. Self-Congruence, Functional Congruence, and Destination Choice. J. Bus. Res.; 2013; 66, pp. 719-723. [DOI: https://dx.doi.org/10.1016/j.jbusres.2011.09.009]
30. Zaman, K.; Iftikhar, U.; Rehmani, M.; Irshad, H. Embracing Biodegradable Bags: Effects of Ethical Self-Identity on Consumer Buying Behavior. Soc. Responsib. J.; 2023; 19, pp. 474-485. [DOI: https://dx.doi.org/10.1108/SRJ-03-2021-0099]
31. Celsi, R.L.; Olson, J.C. The Role of Involvement in Attention and Comprehension Processes. J. Consum. Res.; 1988; 15, pp. 210-224. [DOI: https://dx.doi.org/10.1086/209158]
32. Zhu, Y.-Q.; Chang, J.-H. The Key Role of Relevance in Personalized Advertisement: Examining Its Impact on Perceptions of Privacy Invasion, Self-Awareness, and Continuous Use Intentions. Comput. Human Behav.; 2016; 65, pp. 442-447. [DOI: https://dx.doi.org/10.1016/j.chb.2016.08.048]
33. Cheung, C.M.K.; Lee, M.K.O.; Rabjohn, N. The Impact of Electronic Word-of-mouth. Internet Res.; 2008; 18, pp. 229-247. [DOI: https://dx.doi.org/10.1108/10662240810883290]
34. Dunk, A.S. Product Life Cycle Cost Analysis: The Impact of Customer Profiling, Competitive Advantage, and Quality of IS Information. Manag. Account. Res.; 2004; 15, pp. 401-414. [DOI: https://dx.doi.org/10.1016/j.mar.2004.04.001]
35. Hussain, S.; Guangju, W.; Jafar, R.M.S.; Ilyas, Z.; Mustafa, G.; Jianzhou, Y. Consumers’ Online Information Adoption Behavior: Motives and Antecedents of Electronic Word of Mouth Communications. Comput. Human Behav.; 2018; 80, pp. 22-32. [DOI: https://dx.doi.org/10.1016/j.chb.2017.09.019]
36. Knight, H.; Haddoud, M.Y.; Megicks, P. Determinants of Corporate Sustainability Message Sharing on Social Media: A Configuration Approach. Bus. Strateg. Environ.; 2022; 31, pp. 633-647. [DOI: https://dx.doi.org/10.1002/bse.2941]
37. Spears, N.; Singh, S.N. Measuring Attitude toward the Brand and Purchase Intentions. J. Curr. Issues Res. Advert.; 2004; 26, pp. 53-66. [DOI: https://dx.doi.org/10.1080/10641734.2004.10505164]
38. Mitchell, A.A.; Olson, J.C. Are Product Attribute Beliefs the Only Mediator of Advertising Effects on Brand Attitude?. J. Mark. Res.; 1981; 18, pp. 318-332. [DOI: https://dx.doi.org/10.1177/002224378101800306]
39. Keller, K.L.; Lehmann, D.R. Brands and Branding: Research Findings and Future Priorities. Mark. Sci.; 2006; 25, pp. 740-759. [DOI: https://dx.doi.org/10.1287/mksc.1050.0153]
40. Olsen, M.C.; Slotegraaf, R.J.; Chandukala, S.R. Green Claims and Message Frames: How Green New Products Change Brand Attitude. J. Mark.; 2014; 78, pp. 119-137. [DOI: https://dx.doi.org/10.1509/jm.13.0387]
41. Yan, R.-N.; Hyllegard, K.H.; Blaesi, L.F. Marketing Eco-Fashion: The Influence of Brand Name and Message Explicitness. J. Mark. Commun.; 2012; 18, pp. 151-168. [DOI: https://dx.doi.org/10.1080/13527266.2010.490420]
42. Lim, D.J.; Youn, N.; Eom, H.J. Green Advertising for the Sustainable Luxury Market. Australas. Mark. J.; 2021; 29, pp. 288-296. [DOI: https://dx.doi.org/10.1177/1839334921999488]
43. Chu, S.; Chen, H. Impact of Consumers’ Corporate Social Responsibility-related Activities in Social Media on Brand Attitude, Electronic Word-of-mouth Intention, and Purchase Intention: A Study of Chinese Consumer Behavior. J. Consum. Behav.; 2019; 18, pp. 453-462. [DOI: https://dx.doi.org/10.1002/cb.1784]
44. Prakash, G.; Pathak, P. Intention to Buy Eco-Friendly Packaged Products among Young Consumers of India: A Study on Developing Nation. J. Clean. Prod.; 2017; 141, pp. 385-393. [DOI: https://dx.doi.org/10.1016/j.jclepro.2016.09.116]
45. Jaganath, R. A Study on Green Purchasing Behaviour of Young Consumers in Mettupalayam (Coimbatore District). Int. J. Appl. Res.; 2016; 2, pp. 678-681.
46. Mattke, J.; Müller, L.; Maier, C. Paid, Owned and Earned Media: A Qualitative Comparative Analysis Revealing Attributes Influencing Consumer’s Brand Attitude in Social Media. Proceedings of the 52nd Hawaii International Conference on System Sciences; Hawaii, HI, USA, 8 January 2019; 10.
47. De Keyzer, F.; Dens, N.; De Pelsmacker, P. How and When Personalized Advertising Leads to Brand Attitude, Click, and WOM Intention. J. Advert.; 2022; 51, pp. 39-56. [DOI: https://dx.doi.org/10.1080/00913367.2021.1888339]
48. Wottrich, V.M.; Verlegh, P.W.J.; Smit, E.G. The Role of Customization, Brand Trust, and Privacy Concerns in Advergaming. Int. J. Advert.; 2017; 36, pp. 60-81. [DOI: https://dx.doi.org/10.1080/02650487.2016.1186951]
49. Chu, S.-C. Viral Advertising in Social Media: Participation in Facebook Groups and Responses among College-Aged Users. J. Interact. Advert.; 2011; 12, pp. 30-43. [DOI: https://dx.doi.org/10.1080/15252019.2011.10722189]
50. Fishbein, M.; Ajzen, I. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research; Addison-Wesley: Boston, MA, USA, 1977.
51. Yan, R.-N.; Ogle, J.P.; Hyllegard, K.H. The Impact of Message Appeal and Message Source on Gen Y Consumers’ Attitudes and Purchase Intentions toward American Apparel. J. Mark. Commun.; 2010; 16, pp. 203-224. [DOI: https://dx.doi.org/10.1080/13527260902863221]
52. Kang, J.; Hustvedt, G. Building Trust between Consumers and Corporations: The Role of Consumer Perceptions of Transparency and Social Responsibility. J. Bus. ethics; 2014; 125, pp. 253-265. [DOI: https://dx.doi.org/10.1007/s10551-013-1916-7]
53. Lyon, T.P.; Montgomery, A.W. Tweetjacked: The Impact of Social Media on Corporate Greenwash. J. Bus. Ethics; 2013; 118, pp. 747-757. [DOI: https://dx.doi.org/10.1007/s10551-013-1958-x]
54. Topal, İ.; Nart, S.; Akar, C.; Erkollar, A. The Effect of Greenwashing on Online Consumer Engagement: A Comparative Study in France, Germany, Turkey, and the United Kingdom. Bus. Strateg. Environ.; 2020; 29, pp. 465-480. [DOI: https://dx.doi.org/10.1002/bse.2380]
55. Bailey, A.A.; Mishra, A.; Tiamiyu, M.F. Green Advertising Receptivity: An Initial Scale Development Process. J. Mark. Commun.; 2016; 22, pp. 327-345. [DOI: https://dx.doi.org/10.1080/13527266.2014.904812]
56. Sudbury Riley, L.; Kohlbacher, F.; Hofmeister, A. A Cross-Cultural Analysis of pro-Environmental Consumer Behaviour among Seniors. J. Mark. Manag.; 2012; 28, pp. 290-312. [DOI: https://dx.doi.org/10.1080/0267257X.2012.658841]
57. Tsai, W.-H.S.; Men, L.R. Consumer Engagement with Brands on Social Network Sites: A Cross-Cultural Comparison of China and the USA. J. Mark. Commun.; 2017; 23, pp. 2-21. [DOI: https://dx.doi.org/10.1080/13527266.2014.942678]
58. Minton, E.; Lee, C.; Orth, U.; Kim, C.-H.; Kahle, L. Sustainable Marketing and Social Media. J. Advert.; 2012; 41, pp. 69-84. [DOI: https://dx.doi.org/10.1080/00913367.2012.10672458]
59. Mazur-Wierzbicka, E. Measurement of Progress in the Environmental Area: Poland against the Countries of the European Union. Int. J. Environ. Res. Public Health; 2022; 20, 563. [DOI: https://dx.doi.org/10.3390/ijerph20010563]
60. Schmitt, F. How Cultural Differences Affect the Adoption and Effectiveness of Green Marketing Strategies in Germany. Int. J. Strateg. Mark. Pract.; 2024; 6, pp. 47-59. [DOI: https://dx.doi.org/10.47604/ijsmp.2457]
61. Viglia, G.; Zaefarian, G.; Ulqinaku, A. How to Design Good Experiments in Marketing: Types, Examples, and Methods. Ind. Mark. Manag.; 2021; 98, pp. 193-206. [DOI: https://dx.doi.org/10.1016/j.indmarman.2021.08.007]
62. Hernandez, J.M.; Basso, K.; Brandão, M.M. Experimental Research in Marketing. Rev. Bras. Mark.; 2014; 13, pp. 98-117. [DOI: https://dx.doi.org/10.5585/remark.v13i2.2692]
63. Usunier, J.-C. International and Cross-Cultural Management Research; Sage: New York, NY, USA, 1998; ISBN 1446264173
64. Yadav, M.; Rahman, Z. Measuring Consumer Perception of Social Media Marketing Activities in E-Commerce Industry: Scale Development & Validation. Telemat. Inform.; 2017; 34, pp. 1294-1307.
65. Triantafillidou, A.; Siomkos, G. The Impact of Facebook Experience on Consumers’ Behavioral Brand Engagement. J. Res. Interact. Mark.; 2018; 12, pp. 164-192. [DOI: https://dx.doi.org/10.1108/JRIM-03-2017-0016]
66. Kim, A.J.; Ko, E. Do Social Media Marketing Activities Enhance Customer Equity? An Empirical Study of Luxury Fashion Brand. J. Bus. Res.; 2012; 65, pp. 1480-1486. [DOI: https://dx.doi.org/10.1016/j.jbusres.2011.10.014]
67. Erkan, I.; Evans, C. The Influence of EWOM in Social Media on Consumers’ Purchase Intentions: An Extended Approach to Information Adoption. Comput. Human Behav.; 2016; 61, pp. 47-55. [DOI: https://dx.doi.org/10.1016/j.chb.2016.03.003]
68. Hsu, C.-L.; Lin, J.C.-C. What Drives Purchase Intention for Paid Mobile Apps?—An Expectation Confirmation Model with Perceived Value. Electron. Commer. Res. Appl.; 2015; 14, pp. 46-57. [DOI: https://dx.doi.org/10.1016/j.elerap.2014.11.003]
69. Chen, Y.-S.; Chang, C.-H. Greenwash and Green Trust: The Mediation Effects of Green Consumer Confusion and Green Perceived Risk. J. Bus. Ethics; 2013; 114, pp. 489-500. [DOI: https://dx.doi.org/10.1007/s10551-012-1360-0]
70. Bailey, A.A.; Mishra, A.S.; Tiamiyu, M.F. Application of Green Scale to Understanding US Consumer Response to Green Marketing Communications. Psychol. Mark.; 2018; 35, pp. 863-875. [DOI: https://dx.doi.org/10.1002/mar.21140]
71. Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: New York, NY, USA, 2021; ISBN 1544396333
72. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to Use and How to Report the Results of PLS-SEM. Eur. Bus. Rev.; 2019; 31, pp. 2-24. [DOI: https://dx.doi.org/10.1108/EBR-11-2018-0203]
73. Verhagen, T.; Vonkeman, C.; Feldberg, F.; Verhagen, P. Present It like It Is Here: Creating Local Presence to Improve Online Product Experiences. Comput. Human Behav.; 2014; 39, pp. 270-280. [DOI: https://dx.doi.org/10.1016/j.chb.2014.07.036]
74. Chin, W.W.; Marcolin, B.L.; Newsted, P.R. A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study. Inf. Syst. Res.; 2003; 14, pp. 189-217. [DOI: https://dx.doi.org/10.1287/isre.14.2.189.16018]
75. Hair, J.F.; Ringle, C.M.; Sarstedt, M. Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Plann.; 2013; 46, pp. 1-12. [DOI: https://dx.doi.org/10.1016/j.lrp.2013.01.001]
76. Henseler, J.; Ringle, C.M.; Sarstedt, M. A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. J. Acad. Mark. Sci.; 2015; 43, pp. 115-135. [DOI: https://dx.doi.org/10.1007/s11747-014-0403-8]
77. George, D.; Mallery, P. IBM SPSS Statistics 27 Step by Step: A Simple Guide and Reference; Routledge: London, UK, 2021; ISBN 1000486850
78. Dijkstra, T.K.; Henseler, J. Consistent Partial Least Squares Path Modeling. MIS Q.; 2015; 39, pp. 297-316. [DOI: https://dx.doi.org/10.25300/MISQ/2015/39.2.02]
79. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res.; 1981; 18, pp. 39-50. [DOI: https://dx.doi.org/10.1177/002224378101800104]
80. Rogerson, P.A. Statistical Methods for Geography: A Student’s Guide; Sage Publications: New York, NY, USA, 2019; pp. 1-432.
81. Alam, M.M.; Lutfi, A.; Alsaad, A. Antecedents and Consequences of Customers’ Engagement with Pro-Environmental Consumption-Related Content on Social Media. Sustainability; 2023; 15, 3974. [DOI: https://dx.doi.org/10.3390/su15053974]
82. Chiu, M.-C.; Kuo, T.-C.; Liao, H.-T. Design for Sustainable Behavior Strategies: Impact of Persuasive Technology on Energy Usage. J. Clean. Prod.; 2020; 248, 119214. [DOI: https://dx.doi.org/10.1016/j.jclepro.2019.119214]
83. Uzunoğlu, E.; Türkel, S.; Akyar, B.Y. Engaging Consumers through Corporate Social Responsibility Messages on Social Media: An Experimental Study. Public Relat. Rev.; 2017; 43, pp. 989-997. [DOI: https://dx.doi.org/10.1016/j.pubrev.2017.03.013]
84. Lu, Q.S.; Miller, R. How Social Media Communications Combine with Customer Loyalty Management to Boost Green Retail Sales. J. Interact. Mark.; 2019; 46, pp. 87-100. [DOI: https://dx.doi.org/10.1016/j.intmar.2018.12.005]
85. Salem, S.F.; Alanadoly, A.B. Personality Traits and Social Media as Drivers of Word-of-Mouth towards Sustainable Fashion. J. Fash. Mark. Manag. An Int. J.; 2021; 25, pp. 24-44. [DOI: https://dx.doi.org/10.1108/JFMM-08-2019-0162]
86. Minton, E.; Tan, S.J.; Tambyah, S.K.; Liu, R.L. Drivers of Sustainability and Consumer Well-Being: An Ethically-Based Examination of Religious and Cultural Values. J. Bus. Ethics; 2022; 175, pp. 167-190. [DOI: https://dx.doi.org/10.1007/s10551-020-04674-3]
87. Park, H.H.; Jeon, J.O. The Impact of Mixed EWOM Sequence on Brand Attitude Change: Cross-Cultural Differences. Int. Mark. Rev.; 2018; 35, pp. 390-411. [DOI: https://dx.doi.org/10.1108/IMR-06-2016-0118]
88. Gupta, M.; Syed, A.A. Impact of Online Social Media Activities on Marketing of Green Products. Int. J. Organ. Anal.; 2021; 30, pp. 679-698. [DOI: https://dx.doi.org/10.1108/IJOA-02-2020-2037]
89. Zhao, L.; Lee, S.H.; Copeland, L.R. Social Media and Chinese Consumers’ Environmentally Sustainable Apparel Purchase Intentions. Asia Pacific J. Mark. Logist.; 2019; 31, pp. 855-874. [DOI: https://dx.doi.org/10.1108/APJML-08-2017-0183]
90. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. J. Appl. Psychol.; 2003; 88, 879. [DOI: https://dx.doi.org/10.1037/0021-9010.88.5.879]
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