3.1 The relationships between benefits, commitment, trust, satisfaction, and customer

In the literature, several studies have been extensively used to inform our understanding of user behavior on social brand fan pages related to intention to purchase a product, trust, economic benefits, social benefits, brand relationship quality, commitment, and satisfaction (Akrout & Nagy, 2018; Nadeem et al., 2020; Verma et al., 2016). Social Exchange Theory (SET) (Lambe et al., 2001) and Commitment-Trust Theory of Relationship Marketing (CTRM) (Morgan & Hunt, 1994) are vital to understanding an individual's behavior in a social community. SET is a concept based on human behavior and social interactions with benefits (Thibault & Kelley, 1959). These benefits include tangible, intangible, or any rewards provided by the exchange must increase on a long-term relationship; otherwise, the partner gradually disengages and terminate the exchange relationship. According to Culnan et al. (2010), when the customer regularly engages with a brand via social network platforms and co-creates content, then the relationship quality improves, and as reported by Akrout and Nagy (2018) that fans minimize the communication cost between them and the brand by liking or follow them on social platforms to get more than tangible rewards, be more knowledgeable about their products or services and updates about the organizations. On the other hand, CTRM considers potential factors, trust, and commitment that must exist for a relationship to be successful (Annekie, 2009; Brink & Berndt, 2008). Based on these theories, our research aims to develop and test a more integrative conceptual framework, which allows us to simultaneously figure out the respective impacts of relationship benefits on commitment, trust, satisfaction, relationship quality, and subsequent impacts on customer expectations continuity.

According to Verma et al. (2016), relationship benefits can be identified as various functional or social benefits received from an exchange partner. They further explained that it might include convenience, time-saving, and price reduction to encourage consumers/partners to form a long-lasting relationship with them. Also, relationship benefits can be defined as benefits derived from partnerships that can be further added value and more useful to be characterized as valued benefits that support the realization of strategies designed to secure competitive advantage (Kelly & Scott, 2012). Accordingly, Kelly and Scott (2012) suggested that relationship benefits are divided into four dimensions: cost, service, image, and flexibility benefits. By their definition, cost benefits are characterized as the benefits that reduce costs through improved systems and procedures; service benefits are characterized as benefits that enhance service delivery; image benefits as brand name, promotion, and reputation benefits and flexibility benefits are categorized as benefits that enhance responsiveness to competitor actions and customer demands through innovation and competitive advantages.

The relationship benefits are one of the critical factors the organization used to let regular customers understand that they are likely to enjoy additional benefits because of their long-term commitment to the firm. However, customers and sellers are committed to and benefit from each other to achieve successful relationship exchanges (Ryu & Lee, 2017). According to Dagger and David (2012), satisfaction does not always guarantee greater customer loyalty even when relationship benefits are involved, but when customer involvement with the service relationship increases, the perceived relationship benefits grow. Therefore, they suggested that the relationship between satisfaction and loyalty can be enhanced by firms offering additional value through confidence, social, and unique treatment benefits, and future studies can focus on relationship investment and quality. In addition, Conze et al. (2010) found that the benefits of retaining customers increase revenues, and help to monitor up and cross-selling behavior of loyal customers. Furthermore, the support of customer acquisition via referrals benefits associated with the product, such as confidence benefits, social benefits of the customer needs, customization benefits, economic benefits, affective, cognitive benefits, and symbolic benefits are all helpful in boasting customer loyalty. Therefore, the customer's need to perceive high relationship benefits is vital to an organization to achieve more significant differentiation and gain a competitive advantage (Palmatier et al., 2007). Similarly, Radzia et al. (2018), social benefits, functional benefits, unique treatment benefits are all significantly influenced relationship commitment. Therefore the authors hypothesized that:

Trust is based on experience between the customers and the organization, and its effect is only in the future, which consequently mitigates relationship complexity (Brynes & Mujtaba, 2008). Trust is one of the central constructs in relationship marketing theory. Specifically, the literature sees trust and commitment as mediating effects of such factors like communication, opportunistic behavior, and relationship benefits on vital outcomes such as performance and loyalty (Palmatier et al., 2006, 2007). Therefore we hypothesized that:

Achieving high customer satisfaction is one of the predominant goals that service organizations seek due to the long-term benefits attached to it, which has been proved in the literature. Prior studies have found that hedonic benefits and confidence benefits directly significantly influence customer relationship satisfaction and indirectly significantly influence customer loyalty (El-Adly, 2019; Radzia et al., 2018). According to Akrout and Nagy (2018), hedonic and economic benefits positively affect trust and customer commitment within the brand fan page, as a vital quality relationship mediates the link between them and word-of-mouth. The authors further emphasized that a high level of engagement and strong relationship resulted in spreading positive word-of-mouth. Therefore, we posited that:

3.2 The relationship between commitment and expectation of continuity

The word “commitment” in marketing means focusing on the development and maintaining of a relationship with an individual. The aim is to achieve a cooperative goal without fear of opportunistic behavior (Van Vuuren et al., 2012). In the views of (Tsao & Hsieh, 2012), the concept of commitment is an exchange partner's belief that it should be an ongoing relationship. Commitment is part of serial responsibilities and cohesion between relevant partners, and its main intention is sustained by the relationship associated with a particular service, which forms the critical determinants over time and can also affect intention to continue or terminate a relationship (Sohn et al., 2013). This committed relationship is essential in customer relationship management (CRM) to establish long-term relationships and maintain a consistent competitive advantage over other brands. Kurn Park and An (2004) opined that customer brand and customer commitment require long-term relationships with customers and create loyalty; this means that committed customers are inclined to develop a cooperative relationship with the firm and be loyal to the firm. More so, a committed consumer has a desire to continue the relationship with the eco-friendly brand, enjoy purchasing, use them, and experiences a sense of loyalty and belongingness. Lin et al. (2003) argued that economic, social, and structural bonds are vital elements that uplift customer commitment. As economic and structural bonds are expected to have substantial effects on the instrumental factor because they raise the customers' costs while the social aspect of the customers and service providers relationship may help develop shared values and a psychological attachment that might lead to long-time commitment. Therefore, we posited that:

3.3 The relationship between trust and expectation of continuity

Trust is the way a firm proves its benignity and credibility from its achievement through its services (Van Tonder & Petzer, 2018); trust, as a multi-dimension aspect of CRM, believes to have imperative elements in developing relationships with customers (Mahmoud et al., 2018). Trust refers to the belief in others' intentions within the relationship (Sayil et al., 2018) and enables the two parties to solve power conflict and low profitability. Trust is one of the key elements to enhance the relationship between a consumer and a specific brand (Kim & Ko, 2010), and it represents the core variable of long-term relationships with customers. Kantsperger and Kunz (2010) also conclude that consumer's trust should be the firms' utmost priority with customers' general service interest (Tabrani et al., 2018). Generally, trust improves a customer's feelings towards the company, which is likely to increase a customer's intention to keep buying from the company. Social media users have also read reviews and ratings of a product or service before engaging with the organization. This results from their familiarity with social media and is likely to increase trust and expectation of continuity. Generally, trust is recognized as a critical antecedent of SCRM due to its inherently virtual nature of the digital environment and the customer's total willingness to form a positive buying intention towards any transaction (Van Vuuren et al., 2012). Therefore, we posited that:

3.4 The relationship between relationship-satisfaction and expectation of continuity

Customer satisfaction is generally being studied in past research as a unidimensional construct that measures overall satisfaction in CRM research and practice (El-Adly, 2019). Customer satisfaction is defined by Wikhamn (2019) as a reasonable indicator for non-financial performance and is generally conceptualized as customers overall and cumulative impression of the service provider's service, production, and product delivery performance (Altinay et al., 2018). However, customer satisfaction and overall satisfaction approaches are often used interchangeably because the conceptualizing customer satisfaction focuses on customer's emotional response to recent transactional experience with the firm while overall satisfaction focuses on customers' cumulative or overall impression of a firm's service performance or summing the satisfaction associated with specific products (Srivastava & Kaul, 2014). The derivation of satisfaction is not for self-service but based on customer's perceptions between performance and expectations. Therefore, the customer is satisfied if expectations from a product or service are met or exceeded (Santouridis & Veraki, 2017). Customer satisfaction is the extent to which customers are happy and satisfied with the organization's products and services (Haverila et al., 2013; Jeon & Choi, 2012; Ntale & Ngoma, 2019). Also, satisfaction is a necessary precondition for continuity expectations (Sayil et al., 2018). It is likely the customer has the right spot for the brand and can recommend the services as repurchase intention, the expectation of continuity, through word of mouth, and become committed customers (Sayil et al., 2018). Therefore, we posited that:

3.5 The relationship between relationship-quality and expectation of continuity

The original relationship quality in the theory and research is to recognize and fulfill the needs of customers while focusing on building relationships in the field of CRM (He et al., 2018; Kuhn & Mostert, 2018). The overall objective of relationship quality is to diagnose how organizations are managing their relationships and identifying the corrective actions to strengthen outcomes (Kang et al., 2013). By definition, relationship quality is considered an overall assessment of a relationship (Santouridis & Veraki, 2017). However, analysis has shown from the literature that there are possible links between CRM practices, relationship quality, and other factors (Mysen et al., 2012). Most focus their studies on measuring relationship quality with other dimensions such as trust, loyalty, commitment, and satisfaction (Chen & Myagmarsuren, 2011; Hsu et al., 2018; Kuhn & Mostert, 2018; Santouridis & Veraki, 2017). However, Fletcher et al. (2000) stated that the model would not need to posit a higher-order node, such as the perceived quality of the relationship. On the other hand, a standard social-cognitive approach might postulate the existence of a stored higher-order attitude that would exert downward pressure on individual assessment areas. Moreover, the quality of the relationship was not considered a higher-order concept in our study because previous studies have suggested that the quality of the relationship should be the subject of further empirical studies. The impacts of treating relationship quality components on relationship quality due to its larger exploratory power when predicting customer loyalty (Chen et al., 2014; Papista & Dimitriadis, 2019). Woo and Ennew (2004) argued that conceptualizing the relationship quality should be accepted as a general perspective on what it means instead of on identifying the constructs that create relationship quality. Thus, trust, satisfaction, and commitment are best known as mediating or intermediating factors for the expectation of continuity in numerous studies such as Mysen et al. (2012) and Amoako et al. (2018). Whereas, several scholars have proved that relationship quality is a significant variable for loyalty (Wu et al., 2013; Wongkitrungrueng & Assarut, 2018; Hsu et al., 2018). Also, Park and Tran (2018) found that relationship quality is valuable for organizations to obtain several positive outcomes. Tsai (2014) supports the view that both marketing practitioners and academic researchers should adopt traditional methods that significantly influence relationship quality (RQ) on relationship marketing outcomes. It is stated that relationship quality mediates the relationships between its antecedents and relationship marketing outcomes - customer loyalty, word-of-mouth (WOM), the expectancy of intention, and repurchase intention (Akrout & Nagy, 2018; Amoako et al., 2018; Ndubisi et al., 2012). According to Kuhn and Mostert (2018). It is essential to determine customers' relationship intentions first and strengthen their relationship quality perceptions when building customer loyalty. Also, Tajvidi et al. (2017) use interactivity, social support, and relationship quality as an antecedent to intention to co-create brand value. Relationship quality also enforces significant impacts of social support and relationship quality on consumers' intention to co-create brand value in a social commerce environment. Papista and Dimitriadis (2019) developed and tested a relationship-building model for green brands, which revealed that the relationship quality and satisfaction with the green brand significantly impact all three behavioral outcomes. Taylor et al. (2018) also suggested that the purpose of relationship quality is to maintain a seamless partnership to provide opportunities for more behavioral intentions. The authors further stated that the current study only focuses on assessing customers' various behavioral intentions, but none ever worked on any specific behavioral aspects of loyalty such as positive word-of-mouth (WOM), intentions, revisit intentions, or willingness-to-pay (WTP). Specifically, understanding what specific outcomes can be achieved by building relationship quality for the eco-friendly brand in the SCRM environment is quite necessary. Therefore, we posited that:

3.6 Expectation of continuity

An expectation of continuity shows the customer's intention to keep the relationship in the future and seize the likelihood of continued purchases (Lussier et al., 2017). However, high expectations of continuity can be demonstrated by consumers who perceive dependence on sellers as high switching costs or have time constraints. Customer loyalty is a deeply held commitment to rebuy or patronize a preferred product or service consistently in the future (Verma et al., 2016). The expectation of continuity is an attitudinal variable for measuring customers' future contributions to a brand, whereas customer equity is a behavioral variable accounting for actual purchasing records. As forecasting of consumers' future behavior becomes a critical issue for a firm, that future behavior should be estimated more critically.

5.1 Questionnaire design for data collection

The items used for this study were developed based on the literature review on SCRM. It was adapted and revised accordingly. The questionnaire examined online SCRMs with six components with 18 items – Relationship benefits, commitment, trust, relationship quality, relationship satisfaction, and expectation of continuity. All the 18 items were measured as shown in Table 1 on a 5-point Likert scale from 5-strongly agree to 1-strongly disagree.

The survey was done based on an empirical study performed in Northern Cyprus among the population who had experience buying eco-friendly products, especially online. The survey was done using a purposive sampling method on various eco-friendly shops within the metropolitan areas. Furthermore, more than 30 eco-friendly shops and supermarkets were visited between January and March 2019. We calculated the sample size using Yamane (1967) calculator. It is characterized as: Sample size (n) = where, n is the sample size, N is the population size, e is the marginal error. This study assumes 95% confidence level and an error margin (e) = 0.05. In substituting N = 1200 and other parameters in the sample size calculation formula, a minimum sample size (n) of 300 should be selected for the survey. A total of 400 questionnaires were distributed among the customers that are distributed directly to the consumers of these products, and a total of 320 respondents participated. However, 10 questionnaires were invalid due to personal reasons, and 30 of the respondents commented that they do not use SM to purchase such products, leaving 280 questionnaires as valid and returned for the analysis. The study sample consists of 57% male and 43% female respondents with an age group of 18–50 years. In total 163 (58.2%) respondents are graduates, 47 are postgraduate (16.7%), 64 are under-graduates (22.8%) while 6 are PhD students (2.1%). This comprises the different buying eco-friendly products location (supermarket: 41%, only eco-friendly shops: 35% and both supermarket and eco-friendly shops: 24%). The results show that the mean and standard deviation scores of all the variables we measured in this study varied from 3.786 to 4.239 and 0.627 to 0.360, respectively. Specifically, the results show that variables being used in the study obtained moderately high mean scores between 3 and 5.

5.2 Data and statistical analysis

The theoretical framework was analyzed using JASP (Jeffreys's Amazing Statistics Program) version 0.9.2.0. The two-way approach proposed by Anderson and Gerbing (1988) has been the basis for testing confirmatory factor analysis (CFA) and the hypothesized model structure. The first step used CFA to evaluate the reliability and validity of the measurement model. The second step tested the structural model by estimating and assessing the overall model fit.

6.1 Measurement model

First, a preliminary measure of Principal and Exploratory Factor Analysis (EFA) was performed to confirm the validity of data obtained. As a result, we tested the eigenvalues of derived variable components ranged from 1.398 to 7.381 and the percentage of total proportion variance ranged from 11.60% to 65.1%. In addition, skewness and kurtosis indices were analyzed for the normality of the data. Skewness and kurtosis indices must not exceed |2.3| to guarantee the normality of data (Shrestha, 2021). All the value of skewness and kurtosis indices of the items falls within the recommended range. Therefore, respondent data from this study is considered suitable for further factor analysis. Also, the Kaiser–Meyer–Olkin (KMO) and Bartlett sphericity test was used to measure the suitability of the sample. The results shown significant statistics of χ² (7979.43) and df = 66.000 (p = 0.000 > 0.05) and the KMO measure = 0.597 > 0.500. Consequently, the data gathered is appropriate for further analysis. Second, the measurement model was measured to examine the validity and reliability of constructs. First, Cronbach's alpha coefficient values were calculated to determine each construct's reliability, and the values of Cronbach's alpha coefficient are between 0.713 and 0.887, which indicates good internal consistency reliability. Furthermore, the convergent validity, which comprises composite reliability (C.R), factor loading, and average variance extracted (AVE), were measured. Also, the value of composite reliability ranged from 0.67 to 0.83, which implies that all constructs met the required values that must be ≥0.6 as recommended by (Bagozzi & Yi, 1998). Also, the factor loading of each item ranged from 0.538 to 0.833, which must be above 0.5 (Kline, 2011), and the AVE values ranged from 0.619 to 0.757, above the acceptable value must be ≥0.50 as recommended by (Fornell & Larcker, 1981) as shown in Table 2. Also, to ensure full collinearity of each item in the partial least square approach, the common method bias (CMB) is used to detect full Collinearity assessment (Kock, 2015). According to Hair et al. (2017), the VIF values should be <3.3 thresholds to be free from CMB among the items, and all VIF values were less than 3.3. In addition, we adopted the recommendations that Podsakoff et al. (2003) suggested. First, we asked for permission from the owner of each shop, supermarket and participation was voluntary. Second, we assured the anonymity and confidentiality of the respondents. Thirdly, it was stated on the questionnaire that there were no right or wrong responses, and they should answer the items with honesty.

To measure the discriminant validity, the square root of AVE of each construct must be greater than the correlation between the constructs, as stated by (Chin, 1997) and as shown in Table 3. The reliability, convergent validity, and discriminant validity summarized the theoretical model and are adequate to proceed to CFA.

6.2 Analysis of structural model and hypothesis testing

It is commonly good to measure the model fit based on the following statistical estimates—Goodness of Fit Index (GFI), Normed Fit Index (NFI), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA). In this study, the CFA presented an acceptable model fit. (X² = 229.163, and df = 127, X²/df = 1.81, Goodness of Fit index (GFI) = 0.997, Adjusted Goodness of fit index (AGFI) = 0.996, Normed fit index (NFI) = 0.958, Incremental Fit Index (IFI) = 0.981, Tucker-Lewis Index (TLI) = 0.977, Comparative Fit Index (CFI) = 0.981, Root Mean Square Error of Approximation (RMSEA) = 0.054, p ≤ 0.05. that is, RMSEA is ≥ level of 0.08 as recommended by (Hooper et al., 2008; Hu & Bentler, 1999). All the above fit indices meet the criterion recommended by (Bagozzi & Yi, 1998; Hair et al., 2006). After the analysis, the measurement result shows that our proposed model is fit.

Standardized regression coefficients (β) and p values determine the fitness, as stated in the conceptual model. Table 4 indicates β values, ranges from 0.09 to 0.99. However, there is one exception; trust is insignificant to the expectation of continuity (H6). This suggests that H6 is rejected. Although organizational research has also shown that trust in organizations positively impacts loyalty and can be more critical to loyalty than satisfaction (Van Tonder & Petzer, 2018; Wetsch, 2006). This could be because of the organization's service quality or e-service quality, which has been proved in the literature to affects customer loyalty and expectation of continuity (Boonlertvanich, 2019; Zulfadli & Permana, 2020). Nevertheless, the other hypotheses are positive and significant, confirming that the hypothesized relationships are accepted.

7.1 Discussion

This study contributes to the literature in two main ways. First, this study examined the relationships between SCRM benefits concerning customer commitment, trust, relationship satisfaction, relationship quality, and revealed its subsequent impacts on continuity expectations. The study's context was in eco-friendly products, and the participants were people buying eco-friendly products online only. As numerous types of social media continue to be the dominant form of communication for a large number of people and organizations nowadays as it is easy to disseminate information on social media platforms (Lu & Miller, 2019), then, this study target social media users purchasing and using eco-friendly products and the outcome of this research informs the increasingly important SCRM by identifying a positive relationship between relationships benefits and expectation of continuity via customer's commitment, trust, relationship satisfaction, and quality as they were significant. The structural model testing also confirms that SCRM benefits and its relationships are an extension of customer relationship marketing (Kim & Ko, 2012; Swani, Brown & Milne, 2014; Yeon Kim & Chung, 2011). Previous studies acknowledge that antecedent for the effectiveness of relationship benefits, satisfaction, trust, commitment, and relationship quality on SCRM (Godey et al., 2016; Kim & Ko, 2012; Seo & Park, 2018). Therefore, our study findings add to the body of knowledge by providing a more detailed explanation of SCRM and the extent to which these relationship marketing factors are interrelated with one another. The results appear that relationship benefits impact relationship marketing factors-commitment, trust, relationship satisfaction, and relationship quality (H1–H4). In line with Verma et al. (2016) that support the relationship benefits of different functional or social benefits operate in a different mode of the customer relationship to benefits received such as cost, service, image, and flexibility benefits. Also, other studies show that relationship benefits support customer acquisition via referral rewards e.g. confidence benefits, social benefits of the customer needs, customization benefits, economic benefits, affective and cognitive benefits, and symbolic benefits (Conze et al., 2010; Radzia et al., 2018). A general-purpose explanation of customers' relationship perception by Ryu and Lee (2017) explain the antecedent role of relationship benefits in customer relationship. The study further stated that the core service performance of customer relationships is to capture the differential impact between the expectancy of continuity and different dimensions of relationship benefits. Also, the results of the findings show that relationship commitment has a positive and significant impact on the expectation of continuity toward the eco-friendly brand (H5), the result of this study on relationship commitment on the expectation of continuity explains the highest degree of customer loyalty to the eco-friendly brand and the proximate way of any brand is firmly attached-to as the foremost brand for any customer among other brands (Amoako et al., 2018). Agreeing with this point; is because customer commitment is a strong predictor in CRM; Even though customer commitment is weighted more than other predictors and as a strong mediating variable to strengthening CRM. Furthermore, the results of the findings show that customer satisfaction has a positive and significant impact on the expectation of continuity towards eco-friendly brands (H7), customer satisfaction is a reasonable indicator for non-financial performance (Wikhamn, 2019). Also, a necessary precondition for loyalty (Sayil et al., 2018). Therefore, customers are satisfied, the expectations from eco-friendly brands are either met or exceeded. Also, relationship quality affects the expectation of continuity (H8). In line with the overall assessment of the strength of a relationship quality (Santouridis & Veraki, 2017). This results finding shows that relationship quality is an important determinant of customers' relationship intentions and to fulfill customer expectations and needs with the intention of customers having a long term relationship and to be able to enjoy the benefits of the continued purchase of eco-friendly brands. In general, our findings are consistent with Verma et al. (2016) that suggests that customers who positively benefit from eco-friendly products are effectively committed and satisfy with the quality of eco-friendly brands tend to rebuy or patronize eco-friendly brands consistently in the future. In summary, we found that customers who are positively benefiting from eco-friendly brands, are effectively committed and satisfied with the eco-friendly brands' quality.

7.2 Managerial implications

From a managerial perspective, it is first evident from the reviews of the study that a long term relationship is a key for SCRM, so that customer's needs and expectations will lead to customer trust with other benefits, satisfaction, commitment, and relationship quality to the product (Kelly & Scott, 2012). First, the relationship benefits impact relationship marketing factors-commitment, trust, relationship satisfaction, and relationship quality of the eco-friendly product. First, our findings suggest that customer relationship marketers should find means of exceeding customer expectation of continuity and guard against counterfeit products that can affect the competitive nature of the eco-friendly product, as it is often easy for relationship benefits to help to inflate customer expectation of continuity through improving the specific business model. As other factors related to benefits are positive and significant towards the repurchase of eco-friendly products, overall established the positive impact of commitment, satisfaction, and relationship quality on the expectation of continuity (El-Adly, 2019; Kuhn & Mostert, 2018). Second, service rendering by customer relationship marketers should be flexible with the inclusion of more relationship benefits so that customers are satisfied with the product also provided customer relationship marketers should always put customer's interest first and ensure that eco-friendly brands are reliable—customers who trust the provider are likely to be committed and are emotionally satisfied with the product. Third, our results suggest that social media posts of eco-friendly products and brand have a positive relationship with both customer's commitment and relationship satisfaction; therefore, eco-friendly organizations should pay more attention to an engaging post that will make the customer feel a sense of belonging and as well increase their knowledge about it, to achieve a long-term commitment to the organization. Fourth, we suggested that eco-friendly products package and all advertisement methods regarding the products should reflect the purpose of the products, contributing to the ecological footprint. Lastly, we suggested that the organizations selling eco-friendly products should make customer service a priority, reward customers, seek customer's advice on their products, and be loyal to attract loyalty.

7.3 Limitations and directions for further research

Our research has several limitations. This study was conducted based on the research on eco-friendly brands' purchases, using an SCRM approach. Firstly, the data collected and analyzed were drawn from any type of eco-friendly products, which seems that people might have a different opinion on each of the eco-friendly products category. Secondly, the data are from a single city in Northern Cyprus, which means this study's findings cannot be generalized. Therefore, future studies should focus on specific eco-friendly products in different industries, and cross-country analysis and longitudinal approach to the study might be a more effective method on a larger scale. Future studies should also get data about the customers' social media exposure to justify their knowledge about eco-friendly products via social media platforms.