Traditional notions of design engineering have been a multifaceted area of focus for product designers (Shiau et al., 2010), which have required a multitude of theoretical and conceptual models developed to mutually fulfill all performance and aesthetic expectations by prospective buyers. Some of these areas are related to industrial engineering and marketing, which further divide the objectives of each buying stakeholder group (Frischknecht, 2009). For a firm seeking to be environmentally friendly, design considerations must also include material choices that are both affordable and improve the environmental footprint of the product. European businesses based in France and Germany have strived toward the development of “Clean Technologies” which they cite as being enabling factors toward the continuous improvement of green performance in products. Therefore, these physical characteristics may also play a role in governing the marketing campaigns in terms of transferring the cost of environmental protection from the consumer to the industry to facilitate environmental protection within itself (Wen Wan et al., 2017). Chen et al. (2014) have outlined several product design requirements (also known as the 5Es) that they consider as being critical for consideration for consumer requirements and environmental regulations, i.e., the quality of end product, ease of usage, economies of scale and scope, energy-efficiency, and minimal environmental effects.

Goldman (1995) propose “Perceptions of products (e.g., that something is beautiful) are what it is noticed from the products.” He suggested eight categories for concepts that describe the perception of products, including, broadly evaluative, formal, evocative, emotional, behavioral, representational, perceptual, and historical. Some of the perceptions in these categories depend upon the experience of the consumer or comparison with other products. Perceptual attributes deal with the phenomenon of people’s judgments about the objective qualities of a product being influenced by design properties (Reid et al., 2010). The process of quantifying the perceptual attributes is difficult, and the only method is “behavioral sciences” that describes how many perceptual attributes are desirable. Existing research highlighted the fact that designers and users perceive perceptual attributes differently (Schnurr et al., 2017). Consumers use subjective attributes for narrowing down their choices when they are presented with many similar alternatives. Product design is an important factor that affects sales, so, designing products that are aligned with perceptual attributes are critical (Landwehr et al., 2011). The ability to integrate green criteria in terms of product design and performance guidelines can be used to enhance the perceptual effects of environmental friendliness on its witnesses and bystanders who see the product in action (Reid et al., 2010).

Past research has supported similar findings of environmentally aware consumers’ willingness to pay more for those products which they deem to be superior in terms of minimizing environmental overheads (Woo and Kim, 2019). There is also a trust-based element in terms of how environmentally aware consumers can be attracted to a particular product concerning its sustainability-oriented attributes, which can be further termed as being that of “Green Trust” (Chen, 2013).

In reflexive attributes, rationalization and intellectualization of a product occur in the minds of consumers and are subjected to training, education, experiences, and culture of an individual. It is conscious and might be the highest degree of feeling, emotion, and cognition (Norman, 2004). Emotions are functional because they form our position associated with our own environment, attracting us toward certain objects, actions, people, and ideas, and pushing us away from certain others (Frijda, 1986). Satisfying emotions pull us toward the stuff that are (or intended to be) advantageous, whereas negative emotions will drag us away from those that are (or supposed to be) harmful for our welfare (Desmet, 2002).

The first dealing with the product is through the sensory system, which establishes the initial impression or perception about the product. In the next stage, the person appraises the usability of the product, which is dependent on an individual’s culture and experiences. In the last stage, the person reflects on the product and its meaning in relation to him or her, and this is the phase where emotions appear. Emotions at that stage can vary from individual to individual and are dependent on every person’s own situation (Perez Mata et al., 2017).

Organizations communicate their strategies of green marketing with the aim of creating consumers’ attitudes and positive feelings toward the organization (Mercade Mele et al., 2019). Literature has also proven that these attitudes have an influence on customer behavioral outcomes in a diverse manner, such as the intention to purchase or recommend (Pérez and Del Bosque, 2015).

Green product attributes in recent years have gained increasing attention because of consumers’ eco-friendliness (Sharma and Foropon, 2019). Consumers’ purchase decisions are shifting toward products having green attributes because of their consumption-related environmental problems (Paul et al., 2016). Physical attributes of a product such as size and weight, product quality, design, price, and packaging incline consumer trust in that specific product (Aburumman and Nieto, 2019). Consumers’ trust probably determines their final purchasing decision and this trust is mainly based on the seller’s attitude of not being opportunistic, behaving ethically, and the attributes of the products they sell (Marakanon and Panjakajornsak, 2017). There is a difference between the perception of products perceived by designers and users (Magnier et al., 2019). Consumers when buying a product often make their decision by using subjective reasoning. Consumers also use subjective attributes for narrowing down their choice of the set when they are presented with many similar alternatives, and the same is the case with objective attributes (André et al., 2018). As the design is an important factor that affects sales, designing those products that are aligned with perceptual attributes is most important (Landwehr et al., 2011). The attributes perceived by a customer in a product mainly come from the quality, value, and risk, as concluded in the previous studies (De Medeiros et al., 2016). All these attributes play an important role in the phase of decision-making for that particular product and building long-term purchase trust with it.

The positive image of a product manufacturing firm is sometimes perceived to be a positive attribute to building trust (Nguyen et al., 2019). Attributes perceived by consumers also include country stereotypes and experiences of a product from the country (Perez Mata et al., 2017). The reflexive attribute of an event or product is an appraisal of an event or product as potentially beneficial or harmful for one’s well-being (Scherer, 2001). Appraisal is an assessment of the importance of a stimulus for one’s personal well-being, such as a desire toward purchasing a green product can be appraised as matching with our concern for an environmentally responsible one. However, people belonging to different cultures may appraise the similar (green) product in diverse ways and will experience different emotions regarding its purchase (Desmet et al., 2007). One who is sensitive to environmental issues may respond with the intentions of buying a green product as he/she trusts the green products.

Environmental issues have always been the focus of people’s attention, and with the emergence of green consumption, companies are also actively responding to market demands by implementing green marketing (Wang and Kuah, 2018). Companies’ environmental efforts are now being widely regarded as part of their corporate social responsibility, it is all because of the consumers’ awareness regarding environmental responsibility (Nyilasy and Gangadharbatla, 2016).

Jiang and Kim (2015) have proposed that consumers’ understanding of green marketing communications affects their ultimate buying intentions for the business. A strong relationship exists between green marketing and consumer trust because green marketing tools, such as environmental advertisement, eco-labeling, and eco-brand, can earn the consumer’s trust and guide them for purchasing such products (Uddin and Khan, 2018). Therefore, executing a green marketing strategy and sending information about the usage of green products will make consumers feel the advantages of green consumption (for example, the impact on social benefits such as health, the environment, and future generations), which can help consumers to move from intention of purchase to action (Cheung and To, 2019).

Today’s consumers are more conscious about the environment than before (Dewald et al., 2014), thus making companies responsible to meet consumers’ demands for eco-friendly (green) products. In the current setting, business companies are using green marketing strategies such as “recyclable and bio gradable” shape to gain a competitive advantage and appeal to environmentally conscious consumers (Szabo and Webster, 2021). Customers not only prefer green products, but they also prefer eco-friendly packaging (Hao et al., 2019). The basic idea behind green marketing is that would-be consumers will see the “greenness” of a good or the service as a gain and shape their buying decisions accordingly. The concept of green marketing strategy has been strengthened with the popularity of terms such as “ozone-friendly” and “recyclability,” which is further extended to include not only the consumer industry but also industrial products and services (Yang and Zhao, 2019).

Thus, to retain loyal customers for long, companies are exploring novel ways to communicate with the customers, such as adopting green marketing. Increased awareness of global warming, climate change, and other environmental issues has prompted eco-friendly manufacturers to produce green products. Consumers respond positively to eco-branded products with environmental features (Chatterjee, 2009). Roberts (1996) investigated the factors that govern environmentally driven consumer consumption patterns and found that consumers’ behaviors are in part due to an internal sense of altruism as well as their willingness to accept responsibility for the ongoing degradation of the environment. The main objective that companies should consider when designing marketing campaigns is the ability to convince consumers that their purchase choices will have an impact on the fate of the ecological health of the planet, thereby empowering them to make environmentally conscious purchase decisions (Schill and Godefroit-Winkel, 2019).

Companies can persuade certain consumers to purchase green products at premium prices that they perceive to be relatively “environmentally friendly” compared to alternative products (Wei et al., 2018). Green marketing campaigns are designed to convince consumers that their purchases play a role in mitigating the ongoing degradation of the environment by way of emphasizing the positive impact they are having in this scenario (Lin and Huang, 2012). Companies are revising their marketing strategies by adopting green marketing standards (including consideration of pricing, promotions, features, and distribution linkages) to meet these needs (Boztepe, 2012).

Based on the above hypotheses we propose the conceptual model given in Figure 1.

This analysis refers to an appropriate reliability measurement method for theoretical construct space (Chin and Todd, 1995) by showing the relationship between the observed items and the construct they measure. Table 2 describes the loading of the items used in the survey and shows that all the measures are significant on their path loading, representing acceptable convergent validity.

To perform different tests, we first checked the reliability and validity of the data. For this purpose, reliability was checked by Cronbach’s alpha, whereas convergent and discriminant validities were calculated through factor analysis using Smart PLS 3.0, as shown in Table 3. The results show that t Cronbach’s alpha for the constructs ranged from 0.890 to 0.937, showing a high reliability of the model. To measure convergent validity, composite reliability (CR) and mean-variance extracted (AVE) were used, which have a threshold value of 0.7 and 0.5, respectively. Table 3 shows the values of composite reliability (i.e., 0.822–0.864) and AVE (0.515–0.656) greater than the threshold values.

The results confirm that there is no issue of validity i.e., “the degree to which items differentiate between variables” (Thong, 2001, p.152). Therefore, the priority should be a strong correlation within the elements of the same construct. This can be achieved by checking the Fornell–Larcker criterion using correlation analysis and the square root of average variance extracted to inner-construct correlations (Lee et al., 2014). In Table 4, the square root of AVE (bold values in bracket) exceeds the value of the inter-construct. An additional test to validate discriminant validity is determined by the range of values in which the variables lie, that is, they are neither too high (> 0.90) nor too low (< 0.10).

As the reliability and validity of the data are now confirmed, hypothesis testing is done by bootstrapping the results.

To check a model good fit, the threshold value of chi-square (χ²/df) should be > 5.

The threshold values for comparative fit index (CFI) and goodness-of-fit index (GFI) should be ≥ 0.9, and normed fit index (NFI) should be ≥ 0.9 (Bentler, 1983, 1988; Browne and Cudeck, 1993). The threshold value for (RMSEA) should be ≤ 0.08 (Jöreskog and Sörbom, 1993; Dudgeon, 2004). The adjusted goodness of fit index (AGFI) should be ≥ 0.8. At last, IFI should be ≥ 0.9 (Bollen, 1989). The structural model of this study shows significant results i.e., χ²/df is (2), GFI is 0.65, AGFI is 0.89, CFI is 0.97, RMSEA is 0.06, NFI is 0.95, and IFI is 0.97.

To evaluate the structural model, the bootstrapping method was used with 396 samples to find values for T-statistics and to find how significant the variables are. In the path analysis, 4 out of 7 paths were found to be significant whereas the remaining three paths were not significant. Perceptual attributes and reflexive attributes were found to be statistically significant with consumer trust weighting (β = −0.390, p ≤ 0.05) and (β = 0.338, p ≤ 0.05), respectively. Perceptual attributes were also found to be statistically significant with green marketing (β = 0.375, p ≤ 0.05). Green marketing was found to be statistically significant with consumer trust weighting (β = 0.512, p ≤ 0.05). This implies that hypotheses H2, H3 H5, and H7 are supported.

Physical attributes are not statistically significant with consumer trust (β = 0.201, p = 0.156) and green marketing (β = 0.163, p = 0.164). Reflexive attributes were not statistically significant with green marketing weighting (β = 0.212, p = 0.075). Thus, this proves that hypotheses H1, H4, and H6 are not supported because they failed to meet the benchmark t-stat = 1.96 and p < 001.

Figure 2 shows the overall results of the proposed model in which we found the negative effect of control variables.

In this study, we checked the indirect effect of product attributes on consumer trust; we followed the recommendation of Zhao et al. (2010) and conducted the Sobel test (Sobel, 1982). The Sobel test results indicate a partial mediating role of green marketing between product attributes and consumer trust (β = 2.584, p < 0.001).

In the survey method common method bias (CMB) can be an issue, and to minimize CMB, different organizations including manufacturing, service, and education all over Pakistan were targeted. In Pakistan along with the national language, i.e., Urdu and other regional languages, English is a major language used in daily life. Hence, the questionnaires were prepared in English language and sent to the respondents. We follow Harman (1976) single-factor analysis to calculate CMB. The exploratory factor analysis should be less than 50% to avoid CMB. Results show that the total variance for the single factor was less than 50%, which suggests that CMB was not an issue in the current study (Podsakoff et al., 2012).