The purpose of this study is to investigate how enjoying nature serves as a precursor to consumer responsibility for sustainable consumption (CRSC), and how this responsibility influences consumer preferences for local brands. It provides a theoretically grounded explanation of how sustainability-oriented consumers engage with local products.
Drawing on the theory of reasoned action and the norm activation model and using data from 430 Mexican and 450 Spaniards consumers, the authors examine the direct impact of CRSC on local brand purchase likelihood. The authors also assess mediating mechanisms – local brand attitude (LBA), local brand perceived quality (LBQ) and familiarity (LBF) – and explore enjoying nature as a novel antecedent of CRSC.
Results confirm that CRSC positively influences local brand purchase likelihood, with mediation effects through LBA, local brand perceived quality and local brand familiarity. Enjoying nature significantly predicts CRSC, revealing a deeper motivational base for sustainability.
This paper contributes to sustainability and branding literature by demonstrating how CRSC translates into support for local brands, identifying a previously untested antecedent (enjoying nature) and clarifying key mediators of sustainable consumption behavior.
1. Introduction
Sustainability, as defined by the United Nations, refers to development that meets current needs without compromising the ability of future generations to meet theirs (Brundtland, 1987). This concept is increasingly significant in marketing and consumer behavior, evidenced by actions like obtaining sustainability certifications and purchasing locally sourced products (Minton et al., 2018). Growing concerns about the impact of consumption on sustainability have been highlighted (Jackson, 2014), in line with the principles of Agenda 21 (United Nations, 1992). In this context, promoting sustainable food consumption by promoting eco-conscious choices – both among consumers and within the food industry – is imperative, as it is expected to generate substantial environmental benefits (European Commission, 2020).
Consumer preferences increasingly favor locally sourced food, driven by proximity and social connections, leading to the resurgence of farmers’ markets and local food brands (Feldmann and Hamm, 2015). Consumers who choose local brands often prioritize products from responsible producers (Pícha and Skořepa, 2018). Given these trends, and as climate change and environmental degradation intensify, promoting responsible consumption has become a critical goal for societies seeking sustainable development.
In this context, local brands emerge as a meaningful way to encourage responsible consumption, particularly when they embody environmental and ethical practices that reflect collective interests. Despite the growing attention to sustainability, little is known about the mechanisms that foster consumer responsibility for sustainable consumption (CRSC) and how this sense of responsibility influences consumers’ intention to purchase local brands (LBPL). Moreover, while constructs like enjoying nature have been linked to environmental concern (Mayer and Frantz, 2004; Mayer et al., 2009), their role in activating CRSC and shaping brand preferences remains underexplored. This study addresses these gaps by investigating how enjoying nature serves as a precursor to CRSC and how this responsibility, in turn, influences LBPL in two culturally and economically distinct markets: Mexico and Spain.
Despite the extensive literature on brand studies (Park et al., 2020; Rubio et al., 2019), there is a gap in research that integrates sustainable consumption as a social practice when analyzing preferences for global or local brands. Previous studies have explored sustainable luxury brands (Wang et al., 2021) and co-creating sustainable corporate brands (Lahtinen and Närvänen, 2020). Still, our review identified only one paper that analyzes local brands and sustainable consumption together (Liu, 2020). Global brands maintain consistency across markets (Özsomer and Altaras, 2008), while local brands emphasize uniqueness and community contribution within limited regions (Özsomer, 2012). Consumers weigh the merits of local versus global brands due to market globalization (Özsomer, 2012), leading to persistent competition for local brands from global counterparts (Ger, 1999). Understanding consumer behavior toward local brands is crucial for developing effective marketing strategies to position and sustain them.
To advance this understanding, the present study is grounded in the Theory of Reasoned Action (TRA; Fishbein and Ajzen, 1975) and the norm activation model (NAM; Schwartz, 1977). While TRA suggests that behavior is influenced by rational evaluations and social norms, NAM highlights the role of moral obligations in activating prosocial behaviors. Enjoying nature, as the hedonic pleasure experienced in natural settings, may strengthen personal norms and thereby promote CRSC, which could ultimately shape purchasing decisions aligned with sustainable values. However, the direct and indirect pathways through which enjoying nature shapes sustainable consumer behavior – particularly in relation to local brand preferences – have yet to be fully articulated. To address this, we further examine the mediating roles of local brand attitude (LBA), local brand perceived quality (LBQ) and familiarity (LBF) to provide a more granular understanding of the CRSC–LBPL link.
To explore whether these mechanisms hold across different societal conditions, we consider two distinct national contexts. The connection between customer responsibility for social consumption and local brands is significantly influenced by various factors such as socio-economic status, cultural values and environmental concerns. Different contexts exhibit different stages of economic development and consumption patterns; some prioritize local brands to support domestic industries amid rapid growth, while others value local products for cultural preservation and financial support. Cultural values also influence consumer behavior (Beugelsdijk et al., 2017; Shao et al., 2020; Vollero et al., 2020), with differences in emphasis on collective harmony or regional identities. These cultural differences also shape environmental concerns, leading to diverse policies addressing pollution and resource depletion. Market dynamics and globalization also affect local brand landscapes, impacting competition and brand loyalty. Comparing contexts provides insights for businesses and policymakers to promote sustainable consumption and support local economies. For this reason, we compare two distinct contexts: Mexico and Spain.
Sustainability is commonly understood as a balance between three pillars: environment, society and economy (Reisch et al., 2013). In Mexico, key sustainability challenges arise from its large population, sprawling urban centers, and high per capita water consumption (Estrada et al., 2020). As a major global manufacturing hub, the country faces growing scrutiny over its environmental footprint, prompting many companies to incorporate sustainability into their business practices. By contrast, Spain’s more diversified economy – centered on tourism and services – has fostered a strong commitment to environmental preservation and sustainable development, particularly through responsible tourism initiatives (Balsalobre-Lorente et al., 2021; Teruel-Serrano and Vinals, 2020).
Despite these structural differences, evidence on consumer attitudes and behaviors toward sustainability across the two countries remains mixed. Some studies report similar levels of environmental awareness and eco-friendly behavior among consumers in both countries (Cavazos-Arroyo and Sánchez-Lezama, 2022). Others note that Spaniards tend to score higher on environmental dimensions of corporate social responsibility, while Mexicans show stronger engagement with economic and social aspects (Curras-Perez et al., 2023). Further disparities emerge in areas like waste management and availability of eco-friendly products, which may partly explain differing levels of pro-environmental behavior (Chamorro and García-Gallego, 2023). Additionally, Thøgersen (2010) emphasized that differences in sustainable consumption among countries can be attributed to variations in political regulations. Considering national cultural norms and infrastructural setups, Mexico and Spain exhibit markedly distinct political systems and administrative frameworks (Basco et al., 2020) and significant behavioral differences among their populations, as confirmed by Hofstede’s Insights (Hofstede, 2009). In this paper, we investigate whether and how enjoying nature can activate CRSC, and how this, in turn, influences preference for local brands across both national contexts.
This study makes three key contributions to the literature on sustainable consumption and branding. First, it introduces enjoying nature as a novel psychological antecedent of CRSC. We argue that the pleasure derived from natural experiences fosters a sense of responsibility to protect what is valued, thus highlighting a motivational mechanism based on positive reinforcement, complementing previous approaches that emphasize belonging or emotional attachment. Second, the study positions CRSC as a central driver of local brand preference, clarifying the role of individual pro-sustainability values in shaping marketplace behavior. Third, by comparing two distinct cultural and socioeconomic contexts – Spain and Mexico – we provide cross-national evidence on how contextual factors shape the psychological and behavioral mechanisms underlying sustainable consumption. This comparative lens enriches our theoretical framework and offers practical insights into how sustainability strategies and local brand positioning should adapt across different markets.
2. Theoretical background and hypotheses
2.1 Theory of reasoned action and norm activation model
The TRA, developed by Fishbein and Ajzen (1975), predicts behavior based on personal choice, emphasizing intention as the immediate precursor to behavior. TRA’s accuracy extends to various contexts, including environmental behaviors (Kim et al., 2012) and recycling (Bagozzi and Dabholkar, 1994). It incorporates attitude and subjective norms in predicting intention, where attitude reflects behavior evaluation and correlates positively with intention (Ajzen, 1991; Cheng et al., 2006). Subjective norms reflect pressure to meet important individuals’ expectations (Ajzen, 1991; Fishbein and Ajzen, 1975).
The NAM, introduced by Schwartz (1977), focuses on drivers influencing pro-environmental behaviors (Onwezen et al., 2013). NAM highlights the role of personal norms in predicting altruistic actions, with pro-environmental behaviors reducing harmful effects on natural systems (Steg and de Groot, 2010). Personal norms are crucial in predicting behaviors (De Groot and Steg, 2009), and perceiving pro-environmental actions as socially acceptable can enhance personal responsibility (Yazdanpanah et al., 2014).
Combining the TRA and the NAM allows consideration of both pro-social and self-interested motivations, offering comprehensive insights into pro-environmental behavior. Subjective norms reflect societal views, determining behavior perceptions as positive or negative, with unique standards exerting social pressures and ingrained as personal norms (Liu et al., 2017). Thus, perceiving pro-environmental behaviors as socially acceptable may prompt consumers to feel personally responsible for performing those behaviors.
The literature suggests that both the TRA and the NAM (NAM) are useful for predicting pro-environmental behaviors (Kim et al., 2012). The TRA describes how responsibility for sustainable consumption, shaped by personal norms and attitudes, translates into specific behaviors, such as purchasing local brands. In this manner, the attitude toward a particular behavior (in this context, the attitude toward local brands) and other perceived factors, such as local brand quality (LBQ) and local brand familiarity (LBF) – which can affect the perception of ease or perceived behavioral control in making the purchase – enhance the intention to buy local brands.
On the other hand, the NAM highlights how enjoying nature, as an emotionally rich experience, activates personal norms and moral motivations to preserve it. It raises environmental awareness and the sense of personal responsibility as consumers become aware of the consequences for the environment and contribute to pro-environmental behaviors (Bamberg and Möser, 2007; Kaiser et al., 1999).
Integrating both theories is crucial to understanding how a responsible sustainability orientation (CRSC) affects the intention and likelihood of purchasing local brands. This encompasses rational and attitudinal evaluations described by TRA and moral considerations explained by NAM. The causal chain from nature connection to purchase decision includes mediators such as LBA, LBQ and LBF, which serve as facilitators that translate consumer responsibility into specific actions within the context of local brands.
2.2 Enjoying nature and consumer responsibility for sustainable consumption
The human–nature relationship has been widely studied through constructs that reflect its emotional, cognitive and experiential complexity (Ives et al., 2018). Among the most prominent are connectedness to nature (Mayer and Frantz, 2004) and love of nature (Dong et al., 2020), which include dimensions such as emotional affinity (Kals et al., 1999), nature-relatedness (Nisbet et al., 2009) and the integration of nature into one’s self-concept (Schultz, 2001). Connectedness to nature refers to a person’s subjective sense of belonging to the natural world, built through affective and experiential ties (Mayer and Frantz, 2004; Schultz, 2001). Ives et al. (2018) further highlight its multidimensional character – material, experiential, emotional, cognitive and philosophical.
This connection often leads to love of nature, a deep emotional bond characterized by intimacy, passion and commitment toward the environment (Dong et al., 2020). While most research emphasizes the cognitive and emotional facets of this bond (Dong et al., 2020; Perrin and Benassi, 2009), the experiential dimension – namely, direct contact with nature through recreational or immersive activities – has received less attention. In this regard, the concept of enjoyment of nature (Bogner and Wiseman, 1999, 2002) offers a distinct perspective: it captures the hedonic pleasure and positive sensations experienced in natural settings. This immediate and situational enjoyment provides a valuable affective foundation for understanding how positive interactions with nature can activate consumer responsibility for sustainability.
We assume that emotional connection to nature predicts environmental concern and sustainable behavior (Mayer and Frantz, 2004; Mayer et al., 2009), extending to consumer preferences such as support for local products, often viewed as more environmentally responsible (Schultz, 2000). Enjoying nature – defined as the immediate pleasure derived from natural settings (Bogner and Wiseman, 1999) – reinforces this connection and has been shown to encourage sustainable consumption (Lim, 2017; Piligrimienė et al., 2020).
The TRA explains behavior through intentions shaped by attitudes and social norms, while the NAM highlights personal moral norms as drivers of pro-social actions. When individuals enjoy and feel connected to nature, they are more likely to perceive environmental consequences, assume responsibility (Kaiser et al., 1999) and activate personal norms related to environmental care (Hosta and Zabkar, 2020; Kostadinova, 2016). This enhances their commitment to sustainability and increases the likelihood of choosing eco-friendly options, such as local brands (Dong et al., 2020; Hosta and Zabkar, 2020).
By integrating TRA and NAM, we argue that enjoyment of nature strengthens moral responsibility for sustainable consumption and promotes behavior aligned with environmental values. This leads us to propose:
Enjoying nature (EN) positively influences customer responsibility for sustainable consumption (CRSC).
2.3 Consumer responsibility for sustainable consumption and local brand purchase likelihood
Evidence confirms a sustainable consumption attitude-behavior gap, reflecting the disparity between positive attitudes toward sustainability and actual consumption behaviors (Prothero et al., 2011). Luchs et al. (2015) related CRSC to this gap, using the consumers’ felt responsibility for sustainability (CFRS) scale (Luchs and Miller, 2015). CFRS reflects consumers’ obligation to consume in ways that promote self-oriented and pro-social/pro-environmental values (Luchs and Miller, 2015). Their findings suggest that consumer responsibility for sustainability predicts behavior better than attitudes, with a positive interactive effect between both predictors. Additionally, felt responsibility isn’t necessarily linked to positive attitudes.
Literature examines consumer behavior toward brands through various concepts like purchase intentions, brand attitudes and brand preferences (Batra et al., 2000; Boubker and Douayri, 2020; Dodds et al., 1991; Grimm, 2005; Khan and Fatma, 2017; Putrevu and Lord, 1994). Studies adopting the TRA support the positive effect of brand attitude on purchase intentions (Ajzen and Fishbein, 1974; López et al., 2019). In this study, following Llonch et al. (2013), the dependent variable is local brand purchase likelihood to determine which constructs influence consumer behavior toward local brands. Given the consumer responsibility’s role in sustainable consumption behavior, we hypothesize its influence on brand preference:
Consumer responsibility for sustainable consumption (CRSC) positively impacts local brand purchase likelihood (LBPL).
2.4 Consumer responsibility for sustainable consumption, local brand attitude and local brand purchase likelihood
Understanding attitudes is crucial for analyzing behavior because attitudes significantly shape people’s thoughts, emotions and behaviors (Batra et al., 2000). CRSC reflects an individual’s sense of duty to engage in consumption practices that minimize environmental impact and promote social well-being. Consumers who feel a high sense of responsibility for sustainable consumption are likely to develop favorable attitudes toward brands that align with their values (Čapienė et al., 2022; Paswan et al., 2017).
Local brands often emphasize their commitment to sustainability and community welfare, resonating with consumers’ responsibility for sustainable consumption. This alignment leads to positive emotional responses and beliefs about the brand, forming a favorable LBA. As consumer attitudes are relatively stable and long-lasting, their buying decisions reflect their brand attitude and intention to purchase (Lee et al., 2023). Therefore, we propose the following hypothesis:
Consumer responsibility for sustainable consumption (CRSC) positively influences local brand attitude (LBA).
On the other hand, brand attitude refers to consumers’ emotional response to a brand based on their emotional preference. These attitudes are general evaluations based on beliefs or affective reactions that can predict behaviors (Ajzen, 1987, 1991; Watson and Wright, 2000). Positive brand attitudes have been shown to increase brand purchase likelihood, loyalty and acceptance (López et al., 2019; Miller et al., 2022; Saini and Singh, 2020; Steenkamp and De Jong, 2010; Watson and Wright, 2000).
Local brands, perceived as being more closely aligned with the consumer’s sustainable values, generate positive attitudes, which in turn increase the likelihood of purchasing those brands (Nijssen and Douglas, 2011; Teng et al., 2022). Therefore, a positive LBA, driven by CRSC, is expected to lead to a higher likelihood of purchasing local brands. We thus propose the following hypothesis:
Local brand attitude (LBA) positively influences local brand purchase likelihood (LBPL).
Combining the TRA and the NAM, we argue that CRSC shapes consumers’ attitudes toward local brands, influencing their purchase decisions. While CRSC drives the initial positive evaluation of local brands, it is the developed LBA that directly impacts the purchase likelihood. Consumers with high levels of CRSC are likely to perceive local brands as being aligned with their values, leading to positive attitudes toward these brands, which ultimately increase their purchase likelihood. This mediating effect of LBA can be conceptualized as follows: CRSC leads to the formation of favorable attitudes toward local brands (LBA), which, in turn, increase the local brand purchase likelihood (LBPL). Therefore, the mediating hypothesis can be stated as:
Local brand attitude (LBA) mediates the relationship between consumer responsibility for sustainable consumption (CRSC) and local brand purchase likelihood (LBPL).
2.5 Consumer responsibility for sustainable consumption, local brand quality and local brand purchase likelihood
Perceived quality is a consumer’s judgment about a product’s excellence or superiority (Zeithaml, 1988). It is influenced by both intrinsic and extrinsic cues, such as country of origin, price and brand name (Dodds et al., 1991; Thakor and Katsanis, 1997). For consumers with a high sense of responsibility for sustainable consumption, the perceived quality of a local brand may be enhanced by the brand’s alignment with their values. Such consumers are likely to evaluate the quality of a brand not only based on traditional metrics but also on the brand’s commitment to sustainability and social responsibility.
Research has shown that perceived brand quality positively influences customer perceived value (Chen and Hu, 2010; Sweeney and Soutar, 2001). Therefore, consumers who prioritize sustainable consumption are likely to perceive higher quality in local brands that demonstrate sustainable practices. This leads to the following hypothesis:
Consumer responsibility for sustainable consumption (CRSC) positively influences local brand quality (LBQ).
Perceived brand quality has been identified as a key determinant for brand evaluation and positively influences brand attitude and purchase intention (López et al., 2019). High perceived quality enhances the likelihood of purchasing the brand, as quality is directly linked to purchase intentions (Chi et al., 2009; Wang and Tsai, 2014), repurchase intentions (Ranjbarian et al., 2012) and willingness to pay a price premium (Cronin et al., 2000). In the context of local brands, higher perceived quality is expected to increase the likelihood of purchase, as consumers favor brands that meet their quality expectations and align with their values. Thus, the following hypothesis is:
Local brand quality (LBQ) positively influences local brand purchase likelihood (LBPL).
By integrating the TRA and the NAM, we propose that CRSC influences consumers’ perceptions of LBQ, subsequently impacting their likelihood of purchase. While CRSC drives the initial positive evaluation of a brand’s quality, it is the perceived quality that directly impacts purchase decisions. Consumers with high levels of CRSC are likely to perceive local brands as superior in quality due to their alignment with sustainable values, leading to a higher likelihood of purchasing these brands. This mediating effect of LBQ can be conceptualized as follows: CRSC leads to the formation of positive perceptions of LBQ, and these perceptions increase the likelihood of purchasing the local brand. Therefore, the mediating hypothesis can be stated as:
Local brand quality (LBQ) mediates the relationship between consumer responsibility for sustainable consumption (CRSC) and local brand purchase likelihood (LBPL).
2.6 Consumer responsibility for sustainable consumption, local brand familiarity and local brand purchase likelihood
CRSC reflects the consumer’s commitment to environmentally and socially responsible purchasing decisions, leading them to seek out and become more familiar with brands that align with their values, particularly local brands that often emphasize sustainability and community support. Local brand familiarity refers to the extent to which a consumer recognizes and is knowledgeable about a local brand.
Research indicates that consumers with a high sense of CRSC are more likely to engage with brands that exhibit sustainable practices, increasing their familiarity with such brands. Familiarity with a brand reduces uncertainty and increases trust, which is essential for consumers committed to sustainable consumption (Keller, 1993; Laroche et al., 2001). Therefore, we propose the following hypothesis:
Consumer responsibility for sustainable consumption (CRSC) positively influences local brand familiarity (LBF).
Likewise, brand familiarity has been shown to play a critical role in consumer decision-making. Familiar brands are perceived as more reliable and of higher quality, which increases the likelihood of purchase (Aaker, 1991; Alba and Hutchinson, 1987). When consumers are familiar with a local brand, they are more likely to develop positive attitudes toward it and consider it in their purchase decisions. Research has also demonstrated that localism, access to clear information and low levels of consumer confusion positively contribute to perceived value, which in turn enhances purchase intention (Benhissi and Hamouda, 2025). In this regard, familiarity with local brands serves as a cognitive shortcut that reduces uncertainty, facilitates evaluations, builds trust and lowers perceived purchase risks. As such, consumers are more likely to buy familiar local brands, especially those aligned with their values of sustainable consumption (Chaudhuri and Holbrook, 2001; Keller, 2003). Thus, we hypothesize:
Local brand familiarity (LBF) positively influences local brand purchase likelihood (LBPL).
By combining the TRA and the NAM, we suggest that CRSC affects consumers’ familiarity with local brands, which in turn impacts their purchase likelihood. CRSC drives consumers to seek out and engage with local brands that are perceived as sustainable, increasing their familiarity with these brands. Increased familiarity then leads to higher purchase likelihood due to enhanced trust and reduced perceived risks. This mediating effect of local brand familiarity can be conceptualized as follows: CRSC leads to greater familiarity with local brands, which enhances the likelihood of purchasing these brands. Therefore, the mediating hypothesis can be stated as:
Local brand familiarity (LBF) mediates the relationship between consumer responsibility for sustainable consumption (CRSC) and local brand purchase likelihood (LBPL).
2.7 The relevance of the context
The relationship between CRSC and local brands can be significantly influenced by context. Factors such as socio-economic status (Sivapalan et al., 2021), cultural values (Minton et al., 2018) and environmental concerns (Yan et al., 2021) contribute to shaping this connection. Diverse contexts encompass varying stages of economic development and consumption patterns (Patwa et al., 2021). In rapidly industrializing regions, there might be a prioritization of local brands to bolster domestic industries amidst swift expansion (Chien et al., 2021). Conversely, in more established contexts, local products may be valued for their role in preserving cultural heritage and supporting local economies (Foster, 2020).
Culture has been studied across academic fields to uncover potential differences in human behavior (Beugelsdijk et al., 2017; Shao et al., 2020; Vollero et al., 2020). Cultural values significantly impact consumer behavior, with some cultures prioritizing collective harmony (Duong et al., 2023) while others emphasize regional identities and local craftsmanship (Zhang et al., 2023). Environmental concerns and policies vary widely (Tandon et al., 2020), market dynamics (Zhang and Watson, 2020) and globalization (Mandler et al., 2021) also exert influence, leading to disparities in competition, consumer preferences and brand loyalty, all of which shape the landscape for local brands.
Drawing comparisons between contexts can offer valuable insights into how CRSC influences preferences for local brands, especially in the presence of variations in socio-economic status, cultural values and environmental concerns. Such comparative analysis assists businesses and policymakers in devising tailored strategies to foster sustainable consumption practices and bolster local economies. Thus, it was proposed the following hypotheses:
Context moderates the effect of consumer responsibility for sustainable consumption (CRSC) on local brand purchase likelihood (LBPL).
Therefore, the proposed model considering all relationships is depicted in Figure 1.
The model displays variables represented by ovals connected through directional arrows labeled H 1 to H 12. E N connects to C R S C through H 1. C R S C links to L B P L, L B A, L B Q, and L B F through multiple hypotheses. L B A, L B Q, and L B F also have directional paths leading to L B P L, showing interrelated effects. The variable context is positioned below and connects upward through H 12, indicating a moderating effect.Research framework
Source: Figure by authors
The model displays variables represented by ovals connected through directional arrows labeled H 1 to H 12. E N connects to C R S C through H 1. C R S C links to L B P L, L B A, L B Q, and L B F through multiple hypotheses. L B A, L B Q, and L B F also have directional paths leading to L B P L, showing interrelated effects. The variable context is positioned below and connects upward through H 12, indicating a moderating effect.Research framework
Source: Figure by authors
3. Research design
3.1 Sample and data collection
The focus of the current study involves the inhabitants of Mexico and Spain, selected for various reasons. Numerous studies have compared Mexico and Spain, as they share a common language and historical, cultural and religious heritage; are categorized as high-context cultures (Ueltschy, 2010); and are situated in the same cultural zone cluster (Beugelsdijk et al., 2017). However, cultural differences between the two countries are also highlighted in some of Hofstede’s dimensions (Ueltschy, 2010), with individualism versus collectivism scores of 30 for Mexico and 51 for Spain, indicating that Spain is significantly more individualistic than Mexico (Hofstede, 2025). Furthermore, both countries differ in their socioeconomic, institutional and political conditions (Ayuso and Navarrete-Báez, 2018; Ueltschy, 2010). When comparing the GDP per capita of Spain (US$33.509.0) with that of Mexico (US$13.790.0) (World Bank, 2023), it becomes clear that Mexico, classified as an emerging market, faces greater survival challenges due to socioeconomic issues such as low income, rapid economic growth, high demographic pressures and significant social disparities compared to Spain (Curras-Perez et al., 2023).
The data were collected through a questionnaire in Mexico and Spain, respectively, including demographic questions that worked as conditional questions to ensure the sample was a good representation of the province population by age and gender attending data from the Mexican Census Bureau (INEGI, 2020; Mexico) and the National Statistics (INE, 2023; Spain), respectively.
In Spain, we aimed to obtain a sample representing the entire Spanish population aged 18–85 (38.624,561 individuals as of 1 January 2023). We administered the survey to 5.394 invited people, and 2.589 completed it using the Netquest online panel. Our sample was representative, considering a 95% confidence level, an error rate of 1.9% and a proportion rate of 50%. To avoid problems related to sample size disparities and ensure that the two contexts can be compared without the sample size affecting the robustness of the results, we selected a subsample of 450 subjects from Spain comparable to the one from Mexico. When we compare the sociodemographic characteristics of the entire Spanish sample with those of the random sample, the representativeness and characteristics of the sample remain consistent. Demographic representation was ensured by controlling for age, gender and geographical area quotas. Regarding Spaniard participant characteristics, men accounted for 51.4% of the sample, and the average age was 49.75. Predominant were the following groups: 35–54-year-olds (38%), those with middle-to-high incomes (54.6% above €2,001) and those with upper secondary/postgraduate education (around 86.2%).
In Mexico, the selection process was based on criteria set by the Mexican Census Bureau (INEGI, 2020), using quota sampling based on sociodemographic variables to ensure a representative sample. The sample’s composition involved stratified random sampling to ensure that key demographic groups within the Mexican population were adequately represented. Participants completed an anonymous survey in an online survey database in Pollfish and were included in a Web-based panel study by a national panel provider. A total of 430 (49.8% females and 50.2% males) aged between 18 and 54 years (Mage = 37.25, SD = 12.77) participated. Predominant groups were as follows: aged 35–54 (39.6%), middle incomes (41.16% above MXN 20,000) and upper secondary/postgraduate (95.8%).
3.2 Measures
The measurement scales of each construct were adapted from the sources indicated in Table 1. The translation process involved translating the scales to Spanish by the authors of this research, back-translating to identify discrepancies performed by research colleagues, reviewing for cultural and linguistic equivalence by a panel of four marketing professors, pilot testing with a small sample of the target population for clarity and relevance; refining the scales based on pilot testing feedback; conducting a final back-translation for meaning equivalence, and finalizing the scales with all adjustments. Each item was rated on a five-point Likert scale (1 = “strongly disagree” and 5 = “strongly agree”).
Constructs and items
| Constructs | Items | Sources |
|---|---|---|
| Enjoy nature (EN) | EN1: “I really like going on trips into the countryside, for example, to forests or fields” | Milfon and Duckitt (2010) |
| EN2: “Sometimes, when I am unhappy, I find comfort in nature” | ||
| EN3: “Being out in nature is a great stress reducer for me” | ||
| EN4: “I enjoy spending time in natural settings just for the sake of being out in nature” | ||
| EN5: “I have a sense of well-being in the silence of nature” | ||
| Consumer responsibility for sustainable consumption (CRSC) | CRSC1: “I feel obligated to try to implement sustainable practices where appropriate” | Luchs and Miller (2015) |
| CRSC2: “It is up to me to bring about improvements in sustainability” | ||
| CRSC3: “I feel a personal sense of responsibility to be more sustainable in my product choices” | ||
| Local brand purchase likelihood (LBPL) | LBPL1: “I would buy local brands” | Dodds et al. (1991), Putrevu and Lord (1994) |
| LBPL2: “I would certainly buy local brands” | ||
| LBPL3: “It is very likely that I will buy local brands” | ||
| LBPL4: “The next time I need a certain product, I will buy it from a local brand” | ||
| LBPL5: “When I had to buy a product, I will definitely try a local brand first” | ||
| Local brand attitude (LBA) | LBA1: “I like local brands” | Batra et al. (2000) |
| LBA2: “I have a positive opinion of local brands” | ||
| LBA3: “Local brands seem attractive to me” | ||
| Local brand quality (LBQ) | LBQ1: “Local brands are well made” | Sweeney and Soutar (2001) |
| LBQ2: “Local brands offer a high level of quality” | ||
| LBQ3: “Local brands would perform consistently” | ||
| Local brand familiarity (LBF) | LBF1: “This brand is very familiar to me” | Steenkamp et al. (2003) |
| LBF2: “I’m very knowledgeable about this brand” | ||
| LBF3: “I have seen many advertisements for it in magazines, radio, or TV” |
| Constructs | Items | Sources |
|---|---|---|
| Enjoy nature ( | EN1: “I really like going on trips into the countryside, for example, to forests or fields” | |
| EN2: “Sometimes, when I am unhappy, I find comfort in nature” | ||
| EN3: “Being out in nature is a great stress reducer for me” | ||
| EN4: “I enjoy spending time in natural settings just for the sake of being out in nature” | ||
| EN5: “I have a sense of well-being in the silence of nature” | ||
| Consumer responsibility for sustainable consumption ( | CRSC1: “I feel obligated to try to implement sustainable practices where appropriate” | |
| CRSC2: “It is up to me to bring about improvements in sustainability” | ||
| CRSC3: “I feel a personal sense of responsibility to be more sustainable in my product choices” | ||
| Local brand purchase likelihood ( | LBPL1: “I would buy local brands” | |
| LBPL2: “I would certainly buy local brands” | ||
| LBPL3: “It is very likely that I will buy local brands” | ||
| LBPL4: “The next time I need a certain product, I will buy it from a local brand” | ||
| LBPL5: “When I had to buy a product, I will definitely try a local brand first” | ||
| Local brand attitude ( | LBA1: “I like local brands” | |
| LBA2: “I have a positive opinion of local brands” | ||
| LBA3: “Local brands seem attractive to me” | ||
| Local brand quality ( | LBQ1: “Local brands are well made” | |
| LBQ2: “Local brands offer a high level of quality” | ||
| LBQ3: “Local brands would perform consistently” | ||
| Local brand familiarity ( | LBF1: “This brand is very familiar to me” | |
| LBF2: “I’m very knowledgeable about this brand” | ||
| LBF3: “I have seen many advertisements for it in magazines, radio, or |
4. Results
4.1 Partial least squares analysis
Given the complexity of the research model, which includes multiple mediators and moderators, we used partial least squares structural equation modeling (PLS-SEM) for hypothesis testing. This technique is appropriate when the primary research objective is theory development and when the data do not meet the assumption of multivariate normality (Hair et al., 2017). Furthermore, PLS-SEM is well-suited for estimating complex models with smaller to moderate sample sizes and can handle models with higher-order constructs, interaction effects and formative indicators (Cassel et al., 1999; Kock and Hadaya, 2018).
While our full data set from Spain comprised 2,589 observations, we chose to randomly extract a subsample of 450 Spanish respondents to match the final validated sample of 430 Mexican respondents. This decision was taken to ensure balanced group sizes in the cross-national analysis and to avoid estimation biases or disproportionate influence from one country. As noted by Cheah et al. (2020), unequal sample sizes across the moderator-based subgroups would decrease statistical power and lead to the underestimation of moderating effects (Hair et al., 2017). Our final data set thus includes 880 respondents, with relatively balanced samples from both countries, allowing for more robust and interpretable comparisons.
To ensure that the sample size was adequate to detect meaningful effects, we conducted an a priori power analysis using G*Power. For a two-tailed t-test with a small effect size (d = 0.20), α = 0.05, and a desired power level of 0.85, the analysis indicated that a total sample size of 900 (450 per group) would be required. Although our final sample size (N = 430) falls below this conservative estimate, our use of PLS-SEM is appropriate given its robustness in handling small-to-medium samples and non-normal data distributions, as previously mentioned. Moreover, the observed effect sizes in our model exceed the small-effect threshold for small effects, supporting the adequacy of our sample for detecting significant relationships.
Likewise, we applied both heuristic and statistical power assessment techniques. First, following the 10-times rule recommended by Hair et al. (2022), we verified that the sample size exceeded the minimum threshold commonly suggested in PLS-SEM research. This rule states that the sample size should be at least 10 times the maximum number of structural paths directed at any latent construct in the model. In our case, the most complex endogenous construct (LBPL) is predicted by four constructs, suggesting a minimum sample size of 40. Both subsamples (Spain = 450; Mexico = 430) comfortably exceed this requirement. Second, we conducted a post hoc power analysis based on Cohen’s f2 effect size estimates, which assess the relative impact of each predictor on its respective endogenous construct. According to Cohen (1988), f2 values of 0.02, 0.15 and 0.35 correspond to small, medium and large effects, respectively. Our results show that the majority of paths exhibit at least small effects. In particular, the relationships EN → CRSC and LBA → LBPL show medium-sized effects in the Spanish subsample (f2 = 0.222 and 0.271, respectively), confirming adequate statistical power for key relationships. The only exception is LBQ → LBPL, which consistently falls below the small-effect threshold, indicating limited predictive power for this path. These findings indicate that the sample size is sufficient to detect the majority of the hypothesized effects with adequate statistical power.
4.2 Measurement model
First, we established that common method bias (CMB) was not an issue when implementing Harman’s one-factor/single-factor test. According to Harman’s, it can be confirmed that the study is not affected by CMB, as neither the Spanish nor the Mexican context reaches the 50% reference (Kock et al., 2021). Additionally, the variance inflation factor (VIF) was calculated for both countries, with the highest parameter in the outer model being 3.819 for Mexico and 3.710 for Spain. Then, the reliability of the first-order reflective constructs is evaluated using a confirmatory factor analysis (CFA) (see Table 2).
Internal consistency and convergent validity
| Items | Outerloadings | Cronbach’s alpha | Composite reliability (rho_c) | Average variance extracted (AVE) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Complete | Mexico | Spain | Complete | Mexico | Spain | Complete | Mexico | Spain | Complete | Mexico | Spain | |
| CRSC | 0.793 | 0.684 | 0.825 | 0.879 | 0.825 | 0.896 | 0.707 | 0.611 | 0.741 | |||
| CRSC1 | 0.844 | 0.803 | 0.865 | |||||||||
| CRSC2 | 0.804 | 0.712 | 0.816 | |||||||||
| CRSC3 | 0.874 | 0.826 | 0.899 | |||||||||
| EN | 0.889 | 0.873 | 0.901 | 0.918 | 0.907 | 0.926 | 0.692 | 0.663 | 0.716 | |||
| EN1 | 0.804 | 0.780 | 0.845 | |||||||||
| EN2 | 0.852 | 0.851 | 0.845 | |||||||||
| EN3 | 0.822 | 0.774 | 0.836 | |||||||||
| EN4 | 0.854 | 0.835 | 0.875 | |||||||||
| EN5 | 0.827 | 0.828 | 0.828 | |||||||||
| LBA | 0.894 | 0.857 | 0.919 | 0.934 | 0.913 | 0.949 | 0.826 | 0.778 | 0.861 | |||
| LBA1 | 0.901 | 0.879 | 0.920 | |||||||||
| LBA2 | 0.925 | 0.911 | 0.936 | |||||||||
| LBA3 | 0.900 | 0.856 | 0.928 | |||||||||
| LBF | 0.777 | 0.739 | 0.769 | 0.869 | 0.848 | 0.866 | 0.692 | 0.654 | 0.685 | |||
| LBF1 | 0.892 | 0.890 | 0.891 | |||||||||
| LBF2 | 0.882 | 0.855 | 0.884 | |||||||||
| LBF3 | 0.708 | 0.661 | 0.693 | |||||||||
| LBPL | 0.919 | 0.898 | 0.928 | 0.940 | 0.925 | 0.945 | 0.757 | 0.712 | 0.776 | |||
| LBPL1 | 0.880 | 0.871 | 0.876 | |||||||||
| LBPL2 | 0.902 | 0.883 | 0.907 | |||||||||
| LBPL3 | 0.890 | 0.870 | 0.900 | |||||||||
| LBPL4 | 0.847 | 0.802 | 0.866 | |||||||||
| LBPL5 | 0.828 | 0.790 | 0.855 | |||||||||
| LBQ | 0.895 | 0.869 | 0.914 | 0.935 | 0.920 | 0.946 | 0.827 | 0.792 | 0.853 | |||
| LBQ1 | 0.902 | 0.886 | 0.914 | |||||||||
| LBQ2 | 0.917 | 0.913 | 0.924 | |||||||||
| LBQ3 | 0.908 | 0.871 | 0.933 | |||||||||
| Items | Outerloadings | Cronbach’s alpha | Composite reliability (rho_c) | Average variance extracted ( | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Complete | Mexico | Spain | Complete | Mexico | Spain | Complete | Mexico | Spain | Complete | Mexico | Spain | |
| 0.793 | 0.684 | 0.825 | 0.879 | 0.825 | 0.896 | 0.707 | 0.611 | 0.741 | ||||
| CRSC1 | 0.844 | 0.803 | 0.865 | |||||||||
| CRSC2 | 0.804 | 0.712 | 0.816 | |||||||||
| CRSC3 | 0.874 | 0.826 | 0.899 | |||||||||
| 0.889 | 0.873 | 0.901 | 0.918 | 0.907 | 0.926 | 0.692 | 0.663 | 0.716 | ||||
| EN1 | 0.804 | 0.780 | 0.845 | |||||||||
| EN2 | 0.852 | 0.851 | 0.845 | |||||||||
| EN3 | 0.822 | 0.774 | 0.836 | |||||||||
| EN4 | 0.854 | 0.835 | 0.875 | |||||||||
| EN5 | 0.827 | 0.828 | 0.828 | |||||||||
| 0.894 | 0.857 | 0.919 | 0.934 | 0.913 | 0.949 | 0.826 | 0.778 | 0.861 | ||||
| LBA1 | 0.901 | 0.879 | 0.920 | |||||||||
| LBA2 | 0.925 | 0.911 | 0.936 | |||||||||
| LBA3 | 0.900 | 0.856 | 0.928 | |||||||||
| 0.777 | 0.739 | 0.769 | 0.869 | 0.848 | 0.866 | 0.692 | 0.654 | 0.685 | ||||
| LBF1 | 0.892 | 0.890 | 0.891 | |||||||||
| LBF2 | 0.882 | 0.855 | 0.884 | |||||||||
| LBF3 | 0.708 | 0.661 | 0.693 | |||||||||
| 0.919 | 0.898 | 0.928 | 0.940 | 0.925 | 0.945 | 0.757 | 0.712 | 0.776 | ||||
| LBPL1 | 0.880 | 0.871 | 0.876 | |||||||||
| LBPL2 | 0.902 | 0.883 | 0.907 | |||||||||
| LBPL3 | 0.890 | 0.870 | 0.900 | |||||||||
| LBPL4 | 0.847 | 0.802 | 0.866 | |||||||||
| LBPL5 | 0.828 | 0.790 | 0.855 | |||||||||
| 0.895 | 0.869 | 0.914 | 0.935 | 0.920 | 0.946 | 0.827 | 0.792 | 0.853 | ||||
| LBQ1 | 0.902 | 0.886 | 0.914 | |||||||||
| LBQ2 | 0.917 | 0.913 | 0.924 | |||||||||
| LBQ3 | 0.908 | 0.871 | 0.933 | |||||||||
The internal consistency of each construct is assessed through the composite reliability (CR) values and Cronbach’s alpha (α) coefficients, for which previous studies (Chin, 1998; Fornell and Larcker, 1981) set an acceptable threshold of 0.7. However, “while Cronbach’s alphas are the standard value reported for scale reliability, this value tends to underestimate the internal consistency of scales consisting of fewer than 10 items” (Herman, 2015, p. 8), resulting in indicators that are slightly low (0.680) and reasonable (0.670) for small item scales (Taber, 2018). All values in the “complete” columns are above the benchmark, supporting the internal consistency. Regarding convergent validity, the average variance extracted (AVE) and the standardized factor loading should exceed 0.5 and 0.7, respectively (Fornell and Larcker, 1981). Both measures for all constructs meet the criteria; therefore, convergent validity is deemed appropriate.
According to Clark and Watson (1995) and Kline (2011), the constructs present discriminant validity if the value of the Heterotrait-Monotrait ratio (HTMT) is lower than 0.85. In line with this criterion, discriminant validity is supported for the complete data (see Table 3).
Discriminant validity: Heterotrait-Monotrait ratio (HTMT)
| Complete | Mexico | Spain | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Construct | CRSC | EN | LBA | LBF | LBPL | CRSC | EN | LBA | LBF | LBPL | CRSC | EN | LBA | LBF | LBPL |
| EN | 0.461 | 0.376 | 0.491 | ||||||||||||
| LBA | 0.382 | 0.408 | 0.358 | 0.360 | 0.376 | 0.428 | |||||||||
| LBF | 0.400 | 0.351 | 0.643 | 0.312 | 0.381 | 0.821 | 0.354 | 0.306 | 0.525 | ||||||
| LBPL | 0.484 | 0.408 | 0.763 | 0.674 | 0.392 | 0.379 | 0.812 | 0.703 | 0.479 | 0.407 | 0.730 | 0.607 | |||
| LBQ | 0.355 | 0.307 | 0.737 | 0.594 | 0.626 | 0.352 | 0.277 | 0.862 | 0.687 | 0.688 | 0.330 | 0.311 | 0.642 | 0.515 | 0.564 |
| Complete | Mexico | Spain | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Construct | |||||||||||||||
| 0.461 | 0.376 | 0.491 | |||||||||||||
| 0.382 | 0.408 | 0.358 | 0.360 | 0.376 | 0.428 | ||||||||||
| 0.400 | 0.351 | 0.643 | 0.312 | 0.381 | 0.821 | 0.354 | 0.306 | 0.525 | |||||||
| 0.484 | 0.408 | 0.763 | 0.674 | 0.392 | 0.379 | 0.812 | 0.703 | 0.479 | 0.407 | 0.730 | 0.607 | ||||
| 0.355 | 0.307 | 0.737 | 0.594 | 0.626 | 0.352 | 0.277 | 0.862 | 0.687 | 0.688 | 0.330 | 0.311 | 0.642 | 0.515 | 0.564 | |
4.3 Structural model
The hypothesized relationships between constructs were assessed using PLS, and to determine the significance of the parameters, a bootstrapping of 5,000 resamples was implemented (Chin, 1998). According to Ringle et al. (2024), at a 5% significance level, the 95% bootstrap confidence interval derived from the percentile method with bias correction concludes that all relationships in the structural model are significant in the complete (see Appendix) model and for each country separately. The empirical results, including all available observations and the confidence intervals, suggest that EN has a significant positive impact on CRSC (0.391; CI 0.330–0.452) and CRSC has a significant positive impact on LBPL (0.163; CI 0.111–0.213); thus, H1 and H2 are supported, respectively (see Table 4). CRSC has a positive and significant impact on LBA (0.323; CI 0.257–0.390), LBQ (0.299; CI 0.234–0.365) and LBF (0.324; CI 0.261–0.388); therefore, H3, H6 and H9 are also supported. Similarly, LBA (0.439; CI 0.351–0.525), LBQ (0.113; CI 0.037–0.191) and LBF (0.230; CI 0.164–0.296) also have a positive and significant impact on LBPL, which supports H4, H7 and H10. Finally, considering the coefficients and their significance for the specific indirect effects in Table 4, we can also confirm the partial mediation effect of LBA (0.142; CI 0.102–0.186), LBQ (0.034; CI 0.011–0.061) and LBF (0.075; CI 0.050–0.103) (H5, H8, H11) on the relationship between CRSC and LBPL. Using the same approach suggested by Falk and Miller (1992), the predictive ability of the model is confirmed since the R2 for CRSC, LBA, LBQ, LBF and LBPL thus exceeds the limit of 0.1.
Path coefficients of the structural model
| Complete | Mexico | Spain | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Hypothesis | Path file | Coeff. | STDEV | T statistics | p-values | Coeff. | STDEV | T statistics | p-values | Coeff. | STDEV | T statistics | p-values |
| H1 | EN → CRSC | 0.391 | 0.031 | 12.690 | 0.000 | 0.300 | 0.049 | 6.149 | 0.000 | 0.427 | 0.039 | 10.889 | 0.000 |
| H2 | CRSC → LBPL | 0.163 | 0.026 | 6.202 | 0.000 | 0.102 | 0.035 | 2.910 | 0.004 | 0.174 | 0.038 | 4.602 | 0.000 |
| H3 | CRSC → LBA | 0.323 | 0.034 | 9.615 | 0.000 | 0.280 | 0.049 | 5.721 | 0.000 | 0.329 | 0.045 | 7.284 | 0.000 |
| H4 | LBA → LBPL | 0.439 | 0.045 | 9.852 | 0.000 | 0.464 | 0.063 | 7.362 | 0.000 | 0.455 | 0.060 | 7.559 | 0.000 |
| H5 | CRSC → LBA → LBPL | 0.142 | 0.021 | 6.651 | 0.000 | 0.130 | 0.029 | 4.504 | 0.000 | 0.150 | 0.030 | 4.910 | 0.000 |
| H6 | CRSC → LBQ | 0.299 | 0.033 | 9.007 | 0.000 | 0.273 | 0.048 | 5.649 | 0.000 | 0.287 | 0.045 | 6.338 | 0.000 |
| H7 | LBQ → LBPL | 0.113 | 0.040 | 2.835 | 0.005 | 0.134 | 0.057 | 2.335 | 0.020 | 0.107 | 0.052 | 2.069 | 0.039 |
| H8 | CRSC → LBQ → LBPL | 0.034 | 0.013 | 2.633 | 0.008 | 0.036 | 0.018 | 2.079 | 0.038 | 0.031 | 0.016 | 1.918 | 0.054* |
| H9 | CRSC → LBF | 0.324 | 0.032 | 10.044 | 0.000 | 0.247 | 0.053 | 4.667 | 0.000 | 0.288 | 0.046 | 6.277 | 0.000 |
| H10 | LBF → LBPL | 0.230 | 0.033 | 6.885 | 0.000 | 0.181 | 0.053 | 3.413 | 0.001 | 0.218 | 0.044 | 4.920 | 0.000 |
| H11 | CRSC → LBF → LBPL | 0.075 | 0.014 | 5.453 | 0.000 | 0.045 | 0.017 | 2.634 | 0.008 | 0.063 | 0.017 | 3.637 | 0.000 |
| R-square | |||||||||||||
| CRSC | 0.153 | 0.090 | 0.182 | ||||||||||
| LBA | 0.104 | 0.078 | 0.108 | ||||||||||
| LBF | 0.105 | S0.061 | 0.083 | ||||||||||
| LBPL | 0.569 | 0.552 | 0.550 | ||||||||||
| LBQ | 0.090 | 0.074 | 0.082 | ||||||||||
| SRMR | 0.044 | 0.054 | 0.046 | ||||||||||
| d_ULS | 0.497 | 0.741 | 0.527 | ||||||||||
| d_G | 0.265 | 0.348 | 0.298 | ||||||||||
| Chi-square | 1,478.473 | 853.529 | 941.281 | ||||||||||
| NFI | 0.886 | 0.833 | 0.881 | ||||||||||
| Complete | Mexico | Spain | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Hypothesis | Path file | Coeff. | T statistics | p-values | Coeff. | T statistics | p-values | Coeff. | T statistics | p-values | |||
| H1 | 0.391 | 0.031 | 12.690 | 0.000 | 0.300 | 0.049 | 6.149 | 0.000 | 0.427 | 0.039 | 10.889 | 0.000 | |
| H2 | 0.163 | 0.026 | 6.202 | 0.000 | 0.102 | 0.035 | 2.910 | 0.004 | 0.174 | 0.038 | 4.602 | 0.000 | |
| H3 | 0.323 | 0.034 | 9.615 | 0.000 | 0.280 | 0.049 | 5.721 | 0.000 | 0.329 | 0.045 | 7.284 | 0.000 | |
| H4 | 0.439 | 0.045 | 9.852 | 0.000 | 0.464 | 0.063 | 7.362 | 0.000 | 0.455 | 0.060 | 7.559 | 0.000 | |
| H5 | 0.142 | 0.021 | 6.651 | 0.000 | 0.130 | 0.029 | 4.504 | 0.000 | 0.150 | 0.030 | 4.910 | 0.000 | |
| H6 | 0.299 | 0.033 | 9.007 | 0.000 | 0.273 | 0.048 | 5.649 | 0.000 | 0.287 | 0.045 | 6.338 | 0.000 | |
| H7 | 0.113 | 0.040 | 2.835 | 0.005 | 0.134 | 0.057 | 2.335 | 0.020 | 0.107 | 0.052 | 2.069 | 0.039 | |
| H8 | 0.034 | 0.013 | 2.633 | 0.008 | 0.036 | 0.018 | 2.079 | 0.038 | 0.031 | 0.016 | 1.918 | 0.054* | |
| H9 | 0.324 | 0.032 | 10.044 | 0.000 | 0.247 | 0.053 | 4.667 | 0.000 | 0.288 | 0.046 | 6.277 | 0.000 | |
| H10 | 0.230 | 0.033 | 6.885 | 0.000 | 0.181 | 0.053 | 3.413 | 0.001 | 0.218 | 0.044 | 4.920 | 0.000 | |
| H11 | 0.075 | 0.014 | 5.453 | 0.000 | 0.045 | 0.017 | 2.634 | 0.008 | 0.063 | 0.017 | 3.637 | 0.000 | |
| R-square | |||||||||||||
| 0.153 | 0.090 | 0.182 | |||||||||||
| 0.104 | 0.078 | 0.108 | |||||||||||
| 0.105 | S0.061 | 0.083 | |||||||||||
| 0.569 | 0.552 | 0.550 | |||||||||||
| 0.090 | 0.074 | 0.082 | |||||||||||
| 0.044 | 0.054 | 0.046 | |||||||||||
| d_ULS | 0.497 | 0.741 | 0.527 | ||||||||||
| d_G | 0.265 | 0.348 | 0.298 | ||||||||||
| Chi-square | 1,478.473 | 853.529 | 941.281 | ||||||||||
| 0.886 | 0.833 | 0.881 | |||||||||||
*p < 0.1
4.4 Moderation analysis
This research examines context as a potential moderator of the analyzed relationship; however, it is imperative to assess the reliability and validity of the measurement instruments for the subgroups delineated by context and, subsequently, to analyze the possible moderation effect, for which multigroup analysis will be used. Values in Tables 2 and 3 (for columns Mexico and Spain) confirm convergent and discriminant validity in the two contexts considered.
Additionally, we test the measurement invariance to eliminate the possibility that the differences found in the inner model coefficients are derived from errors in the measurement model. As suggested by Hair et al. (2012), the measurement invariance of composite models (MICOMs) was used to test the measurement invariance, analyzing the configurational invariance, the compositional invariance and the scalar invariance (the equality of the composite means and variances) (Henseler et al., 2016). Compositional invariance (step 2 in MICOM, see Table 5) is proved; however, the equality of the composite means and variances cannot be proved for the variable context, which shows the partial invariance of the measurement instrument. Although full scalar invariance (Step 3: equality of means and variances) was not achieved for all constructs, the establishment of compositional invariance indicates that partial measurement invariance is present (Hair et al., 2022; Henseler et al., 2016). According to the SmartPLS guidelines, partial invariance is sufficient to justify the use of multi-group analysis (MGA), allowing meaningful comparisons of group-specific path coefficients. Therefore, based on the earlier findings, we can proceed to assess the moderating effect of context on the proposed relationships (Byrne, 2006; Hair et al., 2006), for which it is necessary to carry out a multigroup analysis (Henseler et al., 2016).
MICOM step 2
| Construct | Original correlation | Correlation permutation mean | 5.0% | Permutation p-value |
|---|---|---|---|---|
| CRSC | 0.999 | 0.999 | 0.996 | 0.483 |
| EN | 1.000 | 0.999 | 0.998 | 0.719 |
| LBA | 1.000 | 1.000 | 1.000 | 0.056 |
| LBF | 0.999 | 0.999 | 0.997 | 0.441 |
| LBPL | 1.000 | 1.000 | 1.000 | 0.563 |
| LBQ | 1.000 | 1.000 | 1.000 | 0.050 |
| Construct | Original correlation | Correlation permutation mean | 5.0% | Permutation p-value |
|---|---|---|---|---|
| 0.999 | 0.999 | 0.996 | 0.483 | |
| 1.000 | 0.999 | 0.998 | 0.719 | |
| 1.000 | 1.000 | 1.000 | 0.056 | |
| 0.999 | 0.999 | 0.997 | 0.441 | |
| 1.000 | 1.000 | 1.000 | 0.563 | |
| 1.000 | 1.000 | 1.000 | 0.050 |
Following the procedures suggested by Keil et al. (2000) and Chin (2000), the multigroup path coefficient differences were examined based on PLS Bootstrap MGA. Table 6 shows the multigroup comparison test results obtained for the moderation hypothesis testing. Consistent with Chin (1998), bootstrapping (5.000 resamples) was used to generate the t-values.
Multigroup analysis. Path coefficients-Bootstrap MGA
| Hypothesis | Relationships | Mexico | Spain | Difference (Mexico-Spain) | Two-tailed (Mexico Spain) p-value | ||
|---|---|---|---|---|---|---|---|
| Coeff. | p-values | Coeff. | p-values | ||||
| H1 | EN → CRSC | 0.300 | 0.000 | 0.427 | 0.000 | −0.127 | 0.043 |
| H2 | CRSC → LBPL | 0.102 | 0.004 | 0.174 | 0.000 | −0.072 | 0.164 |
| H3 | CRSC → LBA | 0.280 | 0.000 | 0.329 | 0.000 | −0.049 | 0.461 |
| H4 | LBA → LBPL | 0.464 | 0.000 | 0.455 | 0.000 | 0.009 | 0.920 |
| H5 | CRSC → LBA → LBPL | 0.130 | 0.000 | 0.150 | 0.000 | −0.020 | 0.634 |
| H6 | CRSC → LBQ | 0.273 | 0.000 | 0.287 | 0.000 | −0.014 | 0.834 |
| H7 | LBQ → LBPL | 0.134 | 0.020 | 0.107 | 0.039 | 0.027 | 0.729 |
| H8 | CRSC → LBQ → LBPL | 0.036 | 0.038 | 0.031 | 0.054 | 0.006 | 0.812 |
| H9 | CRSC → LBF | 0.247 | 0.000 | 0.288 | 0.000 | −0.041 | 0.560 |
| H10 | LBF → LBPL | 0.181 | 0.001 | 0.218 | 0.000 | −0.037 | 0.594 |
| H11 | CRSC → LBF → LBPL | 0.045 | 0.008 | 0.063 | 0.000 | −0.018 | 0.449 |
| Hypothesis | Relationships | Mexico | Spain | Difference (Mexico-Spain) | Two-tailed (Mexico Spain) p-value | ||
|---|---|---|---|---|---|---|---|
| Coeff. | p-values | Coeff. | p-values | ||||
| H1 | 0.300 | 0.000 | 0.427 | 0.000 | −0.127 | 0.043 | |
| H2 | 0.102 | 0.004 | 0.174 | 0.000 | −0.072 | 0.164 | |
| H3 | 0.280 | 0.000 | 0.329 | 0.000 | −0.049 | 0.461 | |
| H4 | 0.464 | 0.000 | 0.455 | 0.000 | 0.009 | 0.920 | |
| H5 | 0.130 | 0.000 | 0.150 | 0.000 | −0.020 | 0.634 | |
| H6 | 0.273 | 0.000 | 0.287 | 0.000 | −0.014 | 0.834 | |
| H7 | 0.134 | 0.020 | 0.107 | 0.039 | 0.027 | 0.729 | |
| H8 | 0.036 | 0.038 | 0.031 | 0.054 | 0.006 | 0.812 | |
| H9 | 0.247 | 0.000 | 0.288 | 0.000 | −0.041 | 0.560 | |
| H10 | 0.181 | 0.001 | 0.218 | 0.000 | −0.037 | 0.594 | |
| H11 | 0.045 | 0.008 | 0.063 | 0.000 | −0.018 | 0.449 | |
There is only one significant difference in all the examined relationships: the effect of enjoying nature (EN) on CRSC is significantly different in Spain than in Mexico. The other effects do not present significant differences between the two contexts.
To further interpret the identified moderation effect, we examined the marginal impact of EN on CRSC in Mexico vs Spain. The interaction plot indicates that while EN positively influences CRSC in both countries, the strength of this relationship is more pronounced in Spain. This suggests that Spanish consumers who experience a strong connection with nature are more likely to internalize this enjoyment as a personal sense of responsibility toward sustainable consumption. In contrast, the relationship, while still significant, appears weaker in Mexico, possibly due to differences in environmental infrastructure, cultural emphasis on nature or the visibility of sustainable initiatives (see Figure 2).
The interaction plot presents predicted C R S C values against E N factor scores, showing differences between Mexico and Spain. The horizontal axis represents E N factor scores from negative three to positive three, while the vertical axis shows predicted C R S C values from one to four point five. The line for Spain has a steeper positive slope than that for Mexico, suggesting that increases in E N correspond to greater C R S C values in the Spanish context. The comparison highlights how contextual differences modify the E N to C R S C relationship.Interaction between consumer responsibility for sustainable consumption and context (Mexico vs. Spain) on local brand purchase likelihood
Source: Authors’ own work
The interaction plot presents predicted C R S C values against E N factor scores, showing differences between Mexico and Spain. The horizontal axis represents E N factor scores from negative three to positive three, while the vertical axis shows predicted C R S C values from one to four point five. The line for Spain has a steeper positive slope than that for Mexico, suggesting that increases in E N correspond to greater C R S C values in the Spanish context. The comparison highlights how contextual differences modify the E N to C R S C relationship.Interaction between consumer responsibility for sustainable consumption and context (Mexico vs. Spain) on local brand purchase likelihood
Source: Authors’ own work
As we can see, the relationship between EN and CRSC is stronger in Spain, suggesting that contextual factors enhance the psychological translation of nature appreciation into sustainability-oriented behavior.
As shown in Table 7, the coefficients enable us to support all the hypotheses.
Estimates and hypotheses supported
| Hypotheses | Coeff. | Results | |
|---|---|---|---|
| H1 | Enjoying nature (EN) positively influences customer responsibility for sustainable consumption (CRSC) | 0.391*** | Supported |
| H2 | Consumer responsibility for sustainable consumption (CRSC) positively impacts local brand purchase likelihood (LBPL) | 0.163*** | Supported |
| H3 | Consumer responsibility for sustainable consumption (CRSC) positively influences local brand attitude (LBA) | 0.323*** | Supported |
| H4 | Local brand attitude (LBA) positively influences local brand purchase likelihood (LBPL) | 0.439*** | Supported |
| H5 | Local brand attitude (LBA) mediates the relationship between consumer responsibility for sustainable consumption (CRSC) and local brand purchase likelihood (LBPL) | 0.142*** | Supported |
| H6 | Consumer responsibility for sustainable consumption (CRSC) positively influences local brand quality (LBQ) | 0.299*** | Supported |
| H7 | Local brand quality (LBQ) positively influences local brand purchase likelihood (LBPL) | 0.113*** | Supported |
| H8 | Local brand quality (LBQ) mediates the relationship between consumer responsibility for sustainable consumption (CRSC) and local brand purchase likelihood (LBPL) | 0.034*** | Supported |
| H9 | Consumer responsibility for sustainable consumption (CRSC) positively influences local brand familiarity (LBF) | 0.324*** | Supported |
| H10 | Local brand familiarity (LBF) positively influences local brand purchase likelihood (LBPL) | 0.230*** | Supported |
| H11 | Local brand familiarity (LBF) mediates the relationship between consumer responsibility for sustainable consumption (CRSC) and local brand purchase likelihood (LBPL) | 0.075*** | Supported |
| H12 | Context moderates the effect of consumer responsibility for sustainable consumption (CRSC) on local brand purchase likelihood (LBPL) | Supported (base on the significant difference in the coefficients of Mexico and Spain in the relationships EN → CRSC) | |
| Hypotheses | Coeff. | Results | |
|---|---|---|---|
| H1 | Enjoying nature ( | 0.391 | Supported |
| H2 | Consumer responsibility for sustainable consumption ( | 0.163 | Supported |
| H3 | Consumer responsibility for sustainable consumption ( | 0.323 | Supported |
| H4 | Local brand attitude ( | 0.439 | Supported |
| H5 | Local brand attitude ( | 0.142 | Supported |
| H6 | Consumer responsibility for sustainable consumption ( | 0.299 | Supported |
| H7 | Local brand quality ( | 0.113 | Supported |
| H8 | Local brand quality ( | 0.034 | Supported |
| H9 | Consumer responsibility for sustainable consumption ( | 0.324 | Supported |
| H10 | Local brand familiarity ( | 0.230 | Supported |
| H11 | Local brand familiarity ( | 0.075 | Supported |
| H12 | Context moderates the effect of consumer responsibility for sustainable consumption ( | Supported (base on the significant difference in the coefficients of Mexico and Spain in the relationships | |
***p-values < 0.01
5. Discussion
This study explores the intricate relationships between CRSC and the likelihood of purchasing local brands in Mexico and Spain. By integrating the TRA and the NAM, we provide a comprehensive understanding of the motivations behind pro-environmental consumer behaviors and how they translate into local brands purchasing decisions.
Our findings reveal several important insights into the antecedents and mediators that influence these behaviors, contributing to the broader literature on sustainable consumption and local branding. In this line, the study establishes that enjoying nature significantly impacts CRSC, supporting our first hypothesis (H1). This suggests that individuals who derive pleasure from natural environments are more likely to feel responsible for engaging in sustainable consumption practices. This finding aligns with previous research indicating that a connection to nature can foster environmental concern and pro-environmental behavior (Kals et al., 1999). By introducing enjoying nature as an antecedent to CRSC, we add a novel dimension to understanding the drivers of sustainable consumer behavior. This insight is particularly valuable for marketers and policymakers aiming to promote sustainable practices, as fostering positive sensations derived from enjoying nature may enhance consumer responsibility.
However, the moderation analysis reveals that this effect is stronger in Spain than in Mexico, suggesting that political, structural and cultural differences may influence the strength of this relationship (Vicente-Molina et al., 2013). One possible explanation lies in urban planning and unequal access to natural environments between Spain and Mexico. Mexican cities experience a deficit in access to urban green spaces, exacerbated by horizontal urban sprawl and car dependence (Huerta, 2022). In contrast, Spanish cities, being more compact and pedestrian-friendly, facilitate access to public parks and natural areas (Ruiz-Apilánez et al., 2023). Furthermore, in Spain, higher per capita income, lower inequality and better environmental quality (5.9 on a scale of 10 in Spain vs 3.6 in Mexico) (OECD, 2025), as well as a better work-life balance (8.4 on a scale of 10 in Spain vs 0.4 in Mexico) (OECD, 2025), enable more people to have free time and resources to participate in recreational activities such as enjoying nature. Finally, a more stable cultural and economic context in Spain can foster a greater concern for environmental issues, strengthening the connection with nature and promoting sustainable consumption practices (Bassi, 2023).
On the other hand, the positive relationship between CRSC and local brand purchase likelihood (H2) highlights the direct influence of consumer responsibility on purchasing decisions. This finding is consistent across Mexico and Spain, indicating that CRSC is a robust predictor of local brand purchases in diverse cultural contexts.
Our research confirms that LBA, LBQ and local brand familiarity mediate the relationship between CRSC and local brand purchase likelihood, supporting hypotheses H5, H8 and H11, respectively. Each of these mediators independently contributes to enhancing the local brand purchase likelihood. The positive influence of CRSC on LBA (H3) and the subsequent impact of LBA on local brand purchase likelihood (H4) underscores the importance of consumer attitudes in shaping purchase intentions. Consumers who feel responsible for sustainable consumption will likely develop favorable attitudes toward local brands, driving their purchasing decisions. Similarly, the significant impact of CRSC on LBQ (H6) and the effect of LBQ on local brand purchase likelihood (H7) highlight the role of perceived quality. Consumers with a sense of responsibility for sustainability tend to perceive local brands as higher quality, influencing their purchase likelihood. Finally, the positive relationship between CRSC and local brand familiarity (H9) and between local brand familiarity and local brand purchase likelihood (H10) indicates that familiarity with local brands mediates the effect of consumer responsibility on purchase decisions. Familiarity may enhance trust and preference for local brands, facilitating sustainable consumption choices.
6. Conclusions
6.1 Theoretical contribution and managerial implications
This study offers several theoretical and practical contributions by integrating the TRA and the NAM, offering a holistic view of the motivations driving sustainable consumer behavior. This integration enables a nuanced understanding of how societal and personal norms interact to influence purchasing decisions. While previous research has applied TRA and NAM separately to predict various pro-environmental behaviors, this study uniquely combines them to explore how CRSC affects local brand purchase likelihood. This expands the application of these theories, showing how self-interested and pro-social motivations shape sustainable consumption practices.
The confirmation of enjoying nature as an antecedent to CRSC adds a new layer to the role of experiential connections with nature in fostering environmental responsibility. This provides new theoretical insights into how pro-environmental responsibility is formed, complementing existing approaches that emphasize perceptions of belonging or cognitive and emotional attachment to nature (Dong et al., 2020; Perrin and Benassi, 2009).
Previous studies have primarily focused on the direct effects of environmental attitudes on behavior. However, this study proposes that “enjoying nature” can reinforce consumers’ personal norms, increasing their sense of responsibility toward sustainable consumption. This perspective extends the theoretical framework by demonstrating how personal enjoyment and connection to nature can significantly influence pro-environmental attitudes and behaviors (Kostadinova, 2016; Piligrimienė et al., 2020). Once the enjoyment of nature encourages the internalization of environmental values, it enables a compromise on sustainable consumption.
Findings indicate that the impact of enjoying nature on CRSC is stronger in Spain than in Mexico. This suggests that each country’s infrastructural and/or cultural differences may influence individual accountability for sustainable consumption (Vicente-Molina et al., 2013), as well as the relationship between individuals and nature (Milfont and Schultz, 2016). In this vein, Spain, which preserves natural resources and promotes responsible tourism (Teruel-Serrano and Vinals, 2020), has well-established recycling systems and extensive public transportation, placing a stronger cultural emphasis on preserving nature and supporting sustainable behaviors, which leads to greater engagement with sustainable practices (Piligrimienė et al., 2020). In contrast, Mexico’s socio-economic landscape is marked by industrialization, urbanization, economic development and the influence of local industries (Estrada et al., 2020). Structural limitations, such as limited access to recycling facilities and public transport systems, indicate that economic factors may affect CRSC.
These findings highlight the importance of considering the cultural and ecological context when promoting pro-environmental values. For example, in Spain, where environmental values and eco-friendly habits may already be culturally reinforced, emphasizing the emotional and moral benefits of engaging with nature could further enhance CRSC. In Mexico, where structural limitations or alternative environmental priorities may influence this pathway, campaigns might benefit from connecting the experience of nature more explicitly with local community well-being and long-term environmental resilience. This cross-contextual insight provides a richer understanding of how enjoying nature (EN) translates into sustainable responsibility (CRSC) and underscores the necessity for context-specific environmental messaging strategies. The structural differences and access to green spaces between the two countries could lead to more frequent and meaningful interactions with nature for Spanish citizens. As nature becomes more integrated into the daily lives of Spaniards and as they have more direct experiences with the natural environment, this fosters greater environmental awareness, positive attitudes and mindful consumption. Therefore, it is crucial to enhance city infrastructure and invest in urban spaces to ensure that, in accordance with the World Health Organization’s (WHO, 2016) recommendations, every person has access to green space within a 300-meter walk from their residence. This will enable individuals to enjoy nature more and strengthen their connection to it, thereby increasing their emotional involvement in sustainable consumption behaviors.
The study also advances the understanding of how CRSC interacts with other consumer perceptions, such as LBA, LBQ and local brand familiarity, to influence local brands purchasing decisions in different cultural contexts. This multi-dimensional exploration offers a more nuanced view of how sustainable consumption values and attitudes are translated into consumer behavior, filling a gap in the literature on the interaction between positive attitudes toward sustainability and actual consumption behaviors (Prothero et al., 2011).
Our findings highlight the importance of fostering CRSC as a strategic lever to enhance local brand preference. In alignment with other studies (e.g. Gumede and Hattingh, 2025), who emphasize how professional discretion drives effective Corporate Social Responsibility, our study underscores the critical role of responsible consumer behavior in influencing purchase decisions toward sustainable local brands. Local brand managers should leverage this responsibility as a strategic asset. By engaging consumers’ connection to nature and fostering positive brand attitudes, local brands can enhance perceived quality and familiarity, gaining an edge over global competitors in sustainability-driven markets. Familiarity with local brands also provides a sense of security and reduces ambiguity, especially when ethical attributes complement functional product quality.
From a managerial perspective, this implies that companies – especially those promoting local brands – should align their sustainability initiatives with consumer values emphasizing personal responsibility for sustainable consumption. Additionally, they should craft comprehensive strategies that not only articulate their sustainability endeavors but also foster consumers’ sense of responsibility and emotional attachment to nature. This means going beyond traditional environmental messaging and designing experiences, narratives and brand interactions that make sustainable consumption feel personal and purposeful. Managers should also invest in educational marketing, ensuring consumers understand the impact of their choices, which can help reduce skepticism and avoidance when consumers do not fully understand the value or benefits of sustainable products (Benhissi and Hamouda, 2025). In doing so, companies can transform sustainability from a value proposition into a shared mission, increasing long-term loyalty and brand advocacy among eco-conscious consumers.
In this context, local brands hold a comparative advantage over global ones: they can communicate sustainability in ways that feel more direct, credible and culturally embedded. Their proximity to local communities allows them to craft narratives aligned with regional values and traditions. This approach can cultivate a positive LBA among consumers, particularly in regions where local cultural values and collective harmony are emphasized. To compete more effectively with global brands, local brand managers should use local cultural stories, contextual sensitivity and community engagement to strengthen the emotional and symbolic relevance of their sustainability efforts. Additionally, they should emphasize the brand’s role in supporting local economies and preserving cultural heritage to enhance brand loyalty (Nijssen and Douglas, 2011; Teng et al., 2022).
These factors help consumers interpret brand initiatives as intrinsically motivated and aligned with long-standing local values – rather than externally imposed or opportunistic (Baghi and Antonetti, 2025). This intrinsic motive attribution enhances perceived authenticity and emotional connection, which are essential for fostering consumer responsibility. Managerially, this means going beyond standard environmental claims to tell compelling, place-based stories that link the brand’s identity with the well-being of the community and local environment. Emphasizing contributions to cultural tradition, local employment and region-specific environmental challenges can further differentiate local brands from global competitors. In doing so, brands frame sustainability as accessible and personally meaningful, encouraging consumers to view responsible consumption not as a distant ideal, but as a reflection of their own values and lived experiences (Baghi and Antonetti, 2025; Groza et al., 2011). By contrast, global brands – despite their scale and resources – often struggle to establish the same level of contextual relevance. Their sustainability messages may rely on universal standards and distant goals, which can lead to consumer skepticism, perceived greenwashing or cause hypocrisy (Baghi and Antonetti, 2021). To counteract this, global brands must localize their strategies and demonstrate authentic commitment through meaningful partnerships and transparent impact at the community level.
Marketing strategies should also be tailored to Spain and Mexico’s cultural and economic contexts. In Spain, marketers might focus on promoting the environmental and cultural benefits of local brands, leveraging the strong pro-environmental attitudes and CRSC observed among consumers. This could include highlighting the preservation of local traditions, sustainable tourism practices and the natural beauty of Spain’s landscapes in brand messaging. In Mexico, strategies might need to emphasize the economic benefits of supporting local industries and the role of local brands in community development, appealing to consumers in a rapidly industrializing economy and growing population.
In markets like Mexico and Spain, characterized by differing institutional and political dynamics, managers of local sustainable brands should proactively align their environmental, social and governance (ESG) performance communication with local socio-political expectations, as it has been shown that political awareness can significantly strengthen the positive impact of ESG efforts on firm performance (Kumar and Joseph, 2025). By embedding ESG messaging within a framework that reflects local cultural and political values, local brands can enhance perceived authenticity and consumer trust – thereby reinforcing the motivational pathways from enjoying nature → consumer sustainable responsibility → brand attitude, perceived quality and familiarity that drive preference for local brands.
In summary, this study makes several important contributions to the literature on sustainable consumption and local branding. Our primary contribution lies in demonstrating that CRSC plays a central role in driving preference for local brands, particularly in the context of emerging markets like Mexico. We further enhance understanding by showing that this relationship is mediated by consumers’ attitudes, perceptions of quality and familiarity with local brands. By recognizing the enjoyment of nature as a driver of consumer responsibility and the likelihood of purchasing local brands, we broaden the practical implications; prioritizing pleasurable experiences in natural environments to nurture this connection. Collectively, these findings offer a more comprehensive framework for understanding how sustainability values are translated into local brand purchase behaviors.
6.2 Limitations and future research
While this study provides valuable insights, it has limitations. The data is cross-sectional, limiting the ability to infer causality. To enhance the generalizability of the results, future research should include a more varied sample and consider longitudinal designs to validate these relationships over time. Although previous research has found a significant association between nature connection and pro-environmental behaviors (Mayer and Frantz, 2004; Mayer et al., 2009; Nisbet et al., 2009), other variables, such as environmental awareness and ecological values (Dong et al., 2020), as well as values toward nature that differ across cultures (Boeve-de Pauw and Van Petegem, 2013), could also influence CRSC and the preference for local brands. Therefore, future studies should examine the effects of these variables and other aspects of pro-environmental behavior to better understand cross-country variations (Vicente-Molina et al., 2013). It is also essential to investigate the formation and activation of moral norms that foster ethical consumer behavior toward the environment (Bamberg and Möser, 2007). Furthermore, future research could focus on the differences between nature connection and nature love, as well as conduct cross-cultural comparisons that take into account Hofstede’s other dimensions.
Cultural differences are complex and multi-faceted, and further research could explore subcultural differences or investigate how regional, urban-rural, generational or segmentation of consumer variations within countries impact sustainable consumption behaviors. Exploring cultural contexts or economic factors beyond Mexico and Spain can also offer a broader understanding of the impact of sustainable consumption patterns. Although this study focuses on local brands, it does not dismiss the importance of global brands with a sustainability-oriented positioning operating within these countries, nor does it overlook how they interact with or adapt to local consumer values. Future research may explore whether the proposed mediation model is applicable in the context of global brands and investigate how their sustainability narratives align with consumers’ pro-environmental norms and their sense of responsibility for sustainable consumption. For instance, global brands such as Patagonia may invoke similar mechanisms by leveraging consumers’ connection to nature and their commitment to sustainable consumption. While our focus is on local brands, this does not exclude the possibility of comparable effects arising from global brands; rather, it presents opportunities for future research to compare these dynamics. Understanding the ways in which global brands align with – or challenge – culturally rooted environmental values could enrich the discourse on sustainable brand management and intercultural consumer behavior. Such inquiries are vital for advancing the fields of environmental psychology and sustainable marketing practices globally. Further cross-cultural investigations in environmental psychology are necessary to determine effective strategies for promoting consumer responsibility as a fundamental driver of sustainable brand practices.
Acknowledgements
The authors confirm that this research received no specific grant from any funding agency, commercial or not-for-profit sectors, and there are no acknowledgements to declare.
References
Appendix
Path coefficients and confidence intervals
| Path | Original sample (O) | Sample mean (M) | 2.50% | 97.50% |
|---|---|---|---|---|
| EN → CRSC | 0.391 | 0.392 | 0.330 | 0.452 |
| CRSC → LBPL | 0.163 | 0.163 | 0.111 | 0.213 |
| CRSC → LBA | 0.323 | 0.325 | 0.257 | 0.39 |
| LBA → LBPL | 0.439 | 0.439 | 0.351 | 0.525 |
| CRSC → LBQ | 0.299 | 0.301 | 0.234 | 0.365 |
| LBQ → LBPL | 0.113 | 0.114 | 0.037 | 0.191 |
| CRSC → LBF | 0.324 | 0.326 | 0.261 | 0.388 |
| LBF → LBPL | 0.230 | 0.230 | 0.164 | 0.296 |
| Path | Original sample (O) | Sample mean (M) | 2.50% | 97.50% |
|---|---|---|---|---|
| 0.391 | 0.392 | 0.330 | 0.452 | |
| 0.163 | 0.163 | 0.111 | 0.213 | |
| 0.323 | 0.325 | 0.257 | 0.39 | |
| 0.439 | 0.439 | 0.351 | 0.525 | |
| 0.299 | 0.301 | 0.234 | 0.365 | |
| 0.113 | 0.114 | 0.037 | 0.191 | |
| 0.324 | 0.326 | 0.261 | 0.388 | |
| 0.230 | 0.230 | 0.164 | 0.296 |
Specific indirect effects and confidence intervals
| Path | Original sample (O) | Sample mean (M) | 2.50% | 97.50% |
|---|---|---|---|---|
| EN → CRSC → LBA → LBPL | 0.055 | 0.056 | 0.037 | 0.077 |
| EN → CRSC → LBQ → LBPL | 0.013 | 0.013 | 0.004 | 0.025 |
| EN → CRSC → LBF → LBPL | 0.029 | 0.029 | 0.019 | 0.042 |
| EN → CRSC → LBA | 0.126 | 0.127 | 0.093 | 0.163 |
| EN → CRSC → LBF | 0.127 | 0.128 | 0.096 | 0.162 |
| EN → CRSC → LBPL | 0.064 | 0.064 | 0.041 | 0.088 |
| EN → CRSC → LBQ | 0.117 | 0.118 | 0.085 | 0.152 |
| CRSC → LBA → LBPL | 0.142 | 0.142 | 0.102 | 0.186 |
| CRSC → LBQ → LBPL | 0.034 | 0.034 | 0.011 | 0.061 |
| CRSC → LBF → LBPL | 0.075 | 0.075 | 0.050 | 0.103 |
| Path | Original sample (O) | Sample mean (M) | 2.50% | 97.50% |
|---|---|---|---|---|
| 0.055 | 0.056 | 0.037 | 0.077 | |
| 0.013 | 0.013 | 0.004 | 0.025 | |
| 0.029 | 0.029 | 0.019 | 0.042 | |
| 0.126 | 0.127 | 0.093 | 0.163 | |
| 0.127 | 0.128 | 0.096 | 0.162 | |
| 0.064 | 0.064 | 0.041 | 0.088 | |
| 0.117 | 0.118 | 0.085 | 0.152 | |
| 0.142 | 0.142 | 0.102 | 0.186 | |
| 0.034 | 0.034 | 0.011 | 0.061 | |
| 0.075 | 0.075 | 0.050 | 0.103 |

