Green Purchase Behavior: A Study on Malaysian and Chinese ...€¦ · behavior, which is referred...

MALAYSIAN JOURNAL OF CONSUMER AND FAMILY ECONOMICS

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Green Purchase Behavior: A Study on Malaysian and Chinese Millennials

Soo-Cheng Chuah1, Jiang Lu2 1Faculty of Business and Management, Universiti Teknologi MARA

Selangor Branch, Puncak Alam Campus, Malaysia 2Honors College, Beijing Institute of Technology Zuhai, China

Abstract The negative impact on the environment can be reduced by consumers

opting for green consumption. Therefore, this study examined the effect of

cognitive factors toward green purchase intention through the mediating role

of green purchase attitude, as well as toward green purchase behavior

among millennials in Malaysia and China. An extended Theory of Planned

Behavior (TPB) model was applied by considering the cognitive factors of

environmental concern, perceived consumer effectiveness, and perceived

environmental knowledge accordingly. The model was analyzed via PLS-

SEM by using the data collected from 183 and 134 Malaysian and Chinese

millennials, respectively. The findings revealed that the green purchase

intention was positive and significantly driven directly by green purchase

attitude for both countries. All three cognitive factors were found to, directly

and indirectly, drive the mediating role of green purchase attitudes in the

context of Malaysia. In contrast, environmental concern was the only

cognitive factor that was directly and significantly related to green purchase

attitude. Hence, green purchase intention was a significant factor and

measure in predicting green purchase behavior in the model.

Keywords: environmental concern, green purchase behavior, millennials,

perceived consumer effectiveness, perceived environmental knowledge

1.0 Introduction

Rapid economic development and consequential human activities such as the overconsumption of natural resources and extended industrial usage have led to severe environmental degradation. Such degradation has resulted in global warming, ozone depletion, and water, air, and land pollutions, whereby they are all issues that are collectively driven by irresponsible consumption.These serious environmental problems have thus augmented existing


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concerns regarding the overall consumption (Jaiswal & Kant, 2018). The population has documented increasing concerns about the

impacts of environmental degradation on society and embarked toward paying more attention to the overall environmental welfare. This is done by changing the consumption patterns and purchase behavior due to the emerging notions of green consumerism and pro-environmental and ecological consciousness behaviors. The emergence of green consumerism and environmentally-conscious consumers is a great indication of the green development behavior of such consumers (Mean, Chowdhury, & Jankovich, 1999). “Green” is defined as environmentally-friendly, environmentally-responsible, and eco-friendly (Manakotla & Jauhari, 2007; PIzam, 2009). Thus, green purchase behavior refers to the act of buying products and services that are environmentally-favorable (Mainieri, Barnett, Valdero, Unipan, & Oskamp, 1997).

A heightened environmental consciousness can affect consumers’ behavior toward displaying their environmental concerns via the purchase of eco-friendly or green products (Fisher, Bashyal, & Bachmen, 2012; Straughan & Roberts, 1999; Chan, 2001; Mostafa, 2006; Paul et al., 2016). However, research works have revealed that consumer expression of environmental concern is not inevitably reflected by their green purchase behavior (Yong et al., 2010; Roberts, 1996). This has been confirmed by Hughner, McDonagh, Prothero, Shultz, and Stanton (2007); this work has shown that consumers with a positive attitude toward green products do not pledge an actual purchase behavior of green products. Therefore, a gap can be observed between consumer attitude and their actual behavior, which is referred to as the attitude-behavior gap (Anvar & Venter de Villiers, 2014). This gap is a critical matter in consumer green purchase behavior; thus, it is crucial to examine the factors that influence consumer’s green purchase attitude and behavior. Such need is driven by the fact that attitudes will, in turn, form the actual behavior (Baker & Ozaki, 2008), which is implied by the Theory of Planned Behavior (TPB) (Ajzen, 1985, 1991).

The unanimous of the TPB model in predicting consumer ecological behavior has initiated many studies to incorporate cognitive factors in the model for green purchase behavior prediction (Jaiswal & Kant, 2018). Therefore, this study extends the model by integrating three cognitive pro-social factors, namely environmental concern,


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perceived consumer effectiveness, and perceived environmental knowledge in order to investigate the green purchase behavior of millennials in Malaysia and China.

Youths and their consumption behavior have greatly influenced the environment as they are the mainstream consumers with a high purchasing power. However, studies describing millennial sustainable behavior are relatively limited. This particular population is especially feasible in promoting green products to their friends (Smith, 2010), but most of them are unsuccessful in adapting pro-environmental attitudes and translating them into actual behavior (Gaudelli, 2009). 2.0 Literature Review

2.1 Green Purchase Behavior

Green purchase behavior (GPB) refers to the purchase of eco-friendly products that are not damaging toward the environment (Chan, 2001; Mostafa, 2007). Similarly, eco-friendly products also refer to green or sustainable products, which are recyclable and do not harm the environment and society. Examples of such products include eco-friendly carry bags, energy-saving bulbs, appliances, vehicles, and household products, among others (Lee, 2008; Joshi & Rahman, 2015).

In general, the Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975) and Theory of Planned Behavior (TPB) (Ajzen & Fishbein, 1980) are two commonly-utilized theories in implying consumer behavioral action. These models have been applied to determine consumer purchase behaviors for green products. According to the TRA, consumer behavior is established by intentions that are incorporated with attitude and subjective norms. Meanwhile, TPB is an extended version of TRA, which incorporates the additional variable of perceived behavioral control with the measure of attitude and subjective norms.

Typically, consumer behavior toward eco-friendly products is not only influenced by attitudinal factors, which has been observed by scholars specializing on general green products (Chan, 2001; Chan & Lau, 2002; Mostafa, 2006; Chen & Chan, 2010, Kim & Chung, 2011; Zhou et al., 2013; Lai & Cheng, 2016). As such, other cognitive factors such as environmental concern, perceived consumer effectiveness, and perceived environmental knowledge along with the measure of


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attitude are directly and/or indirectly associated with consumer purchase intention and behavior toward green products. 2.2 Green Purchase Intention

Green purchase intention (GPI) refers to a consumer’s willingness to purchase green products in order to position for environmental sustainability (Chan, 2001; Dagher & Itani, 2014; Akehurst et al., 2012). Consumers are generally concerned about the ecological attributes and environmental consequences of a product in making their purchasing decision. As indicated in the TPB model, purchase intention is an antecedent in predicting green purchase behavior. Various studies have found a direct and significant relationship between GPI and GPB for green products (Chan, 2001; Chan & Lau, 2002; Kim et al., 2013; Wu & Chen, 2014; Lai & Chen, 2016; Jaiswal & Kant; 2018). Lai and Cheng have found that the magnitude of consumer intention is an effective factor in explaining the consumer’s psyche for green purchase. Meanwhile, Chan (2001) has shown that purchase intention can predict an actual green product purchase behavior within a month. Similarly, Chan and Lau’s (2002) findings have revealed that 51% and 63% of the variance in an actual green purchase behavior can be explained by consumer purchase intention for a study sample consisting of Chinese and U.S. consumers, respectively. Correspondingly, Wu and Chen's (2014) outcomes indicate that 80% of the variance in an actual green purchase behavior measured by a preference for purchasing green decisions is explained by purchase intention among Taiwanese consumers. Furthermore, GPI directly and significantly affects the green purchase behavior per Jaiswal and Kant’s (2018) study undertaken with a sample of Indian consumers.

The existing literature has revealed that purchase intention is an underlying predictor to measure consumer behavioral action in environmental behavior research in accordance with the TPB model. Therefore, based on extant research works that utilize the TPB, this study develops the following hypothesis:

H1:

Purchase intention for green products has positive and significant effects on green purchase behavior


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2.3 Attitude Toward Green Products (GPA)

In the TRA and TPB models, attitude is one of the crucial predecessor measures of behavioral intention and actual behavior as implicated in various environmental behavior research works. Fishbein and Ajzen (1975) have defined attitude as “a learned predisposition to respond in a consistently favorable or unfavorable manner with respect to a given object” (p. 20). It is the psychological and emotional means of consumer evaluation toward a behavior, regardless of whether such assessment is favorable or unfavorable alike (Chen & Tung, 2014). Thus, attitude is the main factor to predict behavioral intention as implied in the TRA and TPB models.

In general, a positive relationship between attitude and behavior intention toward green products has been observed across various cultures (Mostafa, 2007). In green purchase settings particularly, scholars have underlined the positive relationship between attitude and purchase intention accordingly (Chan, 2001; Chan & Lau, 2002; Chen & Chai, 2010; Lai & Cheng, 2016; Jaiswal & Kant, 2018). Therefore, this study proposes the following hypothesis: H2:

Attitude toward green products has a positive and significant effect on green purchase intention.

2.4 Environmental Concern

Environmental concern (EC) is defined as “an evaluation of, or an attitude toward, the facts of one’s own behavior, or others’ behavior, with consequences for the environment” (Fransson & Gӓrling, 1999, p. 370). It refers to an individual’s level of concern regarding an environmental issue (Kim & Choi, 2005). EC has been recognized as one of the important cognitive measures in predicting one’s ecology-friendly behavior in green marketing and environmental behavior literature. This concept is indicative of the level of environmental consciousness consumers have toward environmental problems faced by the society and their subsequent tendency to find a solution (Straughan & Roberts, 1999; Kim & Choi, 2005) through daily life behaviors. Furthermore, it reveals the awareness of responsibility through the level of emotional appeal that one exhibits in view of their participation in environmental protection (Lee, 2008; Dagher & Itani, 2014). Accordingly, scholars have reported a direct


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and significant effect of EC on attitude toward green products (Mostafa, 2007; Jaiswal & Kant, 2018), which in turn influences consumer intention for purchasing green products (Paul et al., 2016).

Besides, Kim and Choi (2005) have underlined a significant and direct relationship between EC and green purchase behavior with a sample made up of USA undergraduate students. They have further positioned EC as the most influential factor in predicting green purchase behavior compared to other factors in the study, namely perceived consumer effectiveness and collectivism. Similarly, Maichun, Parichatnon, and Peng (2016) have stated the positive relationship found between EC and green product purchase intention among Thai consumers. Sinnappan and Abd Rhaman (011) have further substantiated the significant impact of EC on green purchasing behavior among Malaysian consumers, while another work’s (Tan, Ajo, & Thurasamy, 2019) findings also reveal the factor as significant in predicting such behavior among young consumers in the country. Moreover, Wang, Wang, Li, and Zhao (2018) have unearthed the positive effects of EC on the attitude and intention to visit green hotels among the Chinese population. Generally, consumers with a higher EC tend to present a positive attitude toward green products, which in turn brings about a high level of intention in purchasing green products. Thus, this study proposes the following hypotheses: H3:

Environmental concern has a positive and significant effect on attitude toward green products.

H4:

Environmental concern has a positive and significant effect on green purchase intention.

2.5 Perceived Consumer Effectiveness

Environmental awareness does not always lead to the actual desired behavior. In general, consumers may be concerned and are aware of any environmental issues, but they do not carry out actual environmental behavior (Pru’homme & Raymond, 2013). Such paradox regarding environmental awareness and actual environmental behavior has been reported in past studies (Huang & Liu, 2017; Yoon et al., 2016). PCE, in particular, has been found to be one of the significant factors in filling the gap observed between environmental concern and environmentally-friendly behavior


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(Robert, 1996). PCE is thus defined as a “domain-specific belief that the effects of a consumer can make a difference in solution” (Ellen, Wiener, & Cobb-Walgren, 1991, p.103). Accordingly, it measures an individual’s belief in their contribution to solving environmental issues. The work by Shamdasani, Choi, and Richmond (1993) has indicated that green consumers with a higher internal self-control ability have a high belief that their behavior can effectively protect the environment.

In past works, PCE had been interpreted as similar to perceived behavioral control and subjective norms in the TPB model (Ellen et al., 1991; Kim & Choi, 2005; Mostafa, 2006; Tan, 2011). Furthermore, Roberts (1996) has asserted PCE as a significant factor in explaining consumer environmental behaviors; those with a higher belief in their behavior toward changing the environment tend to perform a high degree of PCE, which leads to a higher purchase intention of green products. Regardless, PCE has been extensively applied by scholars to predict consumer green consumption behavior (Roberts, 1996; Mostafa, 2006; Kim & Choi, 2005; Tan, 2011; Kim, 2011; Dagher & Itani, 2014; Heo & Muralidharan, 2017; Choi & Johnson, 2019; Trivedi, Patel, & Acharya, 2018). In particular, Tan and Lau (2011) have found a significant relationship between PCE and green purchase behavior among undergraduate students in Malaysia. Meanwhile, Wang, Zhao, Gao, Wu Wang, and Zhu's (2014) findings have depicted its positive and significant relationship with green purchase behavior in China. Additionally, Wang et al. (2018) have further found the positive effects of PCE on the attitude and intention to visit green hotels among Chinese people.

PCE has been recognized as a significant factor in influencing consumer attitudes toward the purchase of green products, which in turn influence their green purchase decision. Its positive association with consumer attitude toward purchasing green products are heavily documented (Tan, 2011; Vermeir & Verbeke, 2006; Kang et al., 2013; Wesley, Lee, & Kim, 2012; Jaiswal & Kant, 2018). Hence, the following hypotheses are proposed in this study: H5:

Perceived consumer effectiveness has a positive and significant effect on the attitude toward green products.

H6:

Perceived consumer effectiveness has a positive and significant effect on green purchase intention.


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2.6 Perceived Environmental Knowledge

Perceived environmental knowledge (PEK) theatrically refers to one’s cognitive ability to comprehend about environmental issues (e.g., air, water, and land pollution; energy usage and efficiency, and recycling and waste generation) and the impacts of these issues on the society (Chan, 2001; Mostafa, 2006; Tan, 2011).

There are two knowledge mechanisms that can be used to measure environmental knowledge, which is a subjective and objective measure alike (Tan, 2011). The subjective measure of knowledge “is based on an individual’s perception or self-assessment of what and how much an individual is aware of the green phenomenon known as abstract knowledge or perceived environmental knowledge” (Jaiswal & Kant, 2018). In contrast, its objective measure is based on concrete behavioral knowledge (Tan, 2011), otherwise described as “the general knowledge of facts, concepts, and relationships concerning the natural environment and its major ecosystems” (Fryxell & Lo, 2003, p.48). The subjective measure of knowledge is commonly applied by scholars to predict green consumerism behavior, whereby it is found to be an effective measure for evaluating consumer environmental knowledge on green behavioral performance (Hines et al., 1987; Mostafa, 2006; Tan, 2011).

Furthermore, PEK has been considered in environmental behavioral studies in relation to environmental attitude (Ellen, 1994). Contrastingly, past findings have yielded inconsistent results on its relationship with the purchase intention of eco-friendly products. By utilizing a meta-analysis, Hines, Hungerford, and Tomera (1987) have found that environmental knowledge is significantly related to a consumer’s environmentally responsible behavior. Meanwhile, Chan and Lau (2000) have highlighted the manner in which Chinese consumers who are highly eco-literate express a higher purchase intention of green products. In contrast, some findings have indicated that PEK does not induce consumer purchase intention on green products in the context of emerging economies, such as China, India, and Malaysia (Zhao et al., 2014; Kumar et al., 2017; Ahmad & Ariffin, 2008). In particular, Ahmad and Ariffin (2018) have shown that a high PEK does not explain green behavior among university students in Malaysia.


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Moreover, past studies have postulated the positive association between PEK with consumer attitudes toward green products, which in turn influences the purchase intention of green products (Chan, 2001; Kumar et al., 2017; Jaiswal & Kant, 2018). By taking into account previously undertaken studies, the following hypotheses are proposed:

H7:

Perceived environmental knowledge has a positive and significant effect on the attitude toward green products.

H8:

Perceived environmental knowledge has a positive and significant effect on green purchase intention.

The TPB model (Ajzen, 1985, 1987) is applied and modified in

order to predict consumer purchase behavior toward green products in this study. The proposed research model is shown in Figure 1.

Figure 8 : Proposed Research Model

3.0 Methodology

3.1 Measurements

The constructs measured in this study were adopted from Jaiswal and Kant’s (2018) work, namely by using a multi-item scale and a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). GPI and GPB were measured by three and four items, respectively. In particular, four items measured EC and PCE, respectively, while

GP

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PCE

EA

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GPI GP

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H2 H

1

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five items measured PEK and two items measured GPA.

3.2 Data Collection

This study applied the convenience sampling method in collecting data through a self-administrated questionnaire distributed among the millennials from two cities, namely Klang Valley, Malaysia and Zhuhai, China. Herein, millennials referred to those born between the years 1981–1996 and within the age range of 23–38 years for the current work.

G-Power was used to determine the adequate sample size for the study (Faul, Erdfelder, Lang, & Buchner, 2007). With the statistical power of 80% and effect size of 0.15 as recommended by Cohen (1988) in the case of behavioral science research, a minimum sample size of 92 was determined for this study to test the model using five predictors. A total of 183 and 134 respondents were invited to participate from Malaysia and China, respectively.

3.3 Multivariate Normality

Multivariate normality was examined using the WE-Power online tool. The p-value of Mardia’s multivariate skewness and kurtosis coefficient was less than 0.05, thereby showing that the data obtained in this study were multivariate non-normality in nature.

3.4 Data Analysis

This study employed the variance-based partial least square–structural equation modeling (PLS-SEM) method in order to test the model by using Smart PLS 3.2.8 software (Ringle, Wende, & Becker, 2015). This decision was grounded by the nature of the study, which was exploratory with the non-normality issue.

4.0 Findings

4.1 Measurement Model

The measurement model was assessed by evaluating the outer model via a measurement of the convergent validity and discriminant validity.

Hair, Joseph, Tomas, Ringle, and Sarstedt (2014) have suggested that convergent validity is determined by the factor loading,


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average variance extracted (AVE), and composite reliability (CR). The measurement model of convergent validity is presented in Table 1.

The results showed that the loading, AVE, and CR values for all items were higher than 0.5, 0.5, and 0.7, respectively. This outcome indicated that the constructs included were valid and reliable. However, four items (i.e., CE1, C2, PCE1, and PCE4) were dropped from the Malaysian sample and three items (i.e., PEK4, GPB3, and GPB4) were dropped from the Chinese sample due to low factor loading.

Table 41 : Measurement Model: Convergent validity

Malaysian sample, n = 183 Items Loadings AVE CR EC1 EC2 EC3 EC4

0.790 0.748 0.716 0.797

0.583 0.848

PCE1 PCE2 PCE3 PCE4

0.785 0.702 0.750 0.795

0.576 0.844

PEK1 PEK2 PEK3 PEK5

0.720 0.794 0.804 0.711

0.575 0.844

GPA1 GPA2

0.882 0.889

0.784 0.879

GPI1 GPI2 GPI3

0.810 0.821 0.773

0.642 0.843

GPB1 GPB2

0.838 0.856

0.717 0.835

Chinese sample, = 134 EC3 EC4

0.895 0.885

0.791 0.884

PCE2 PCE3

0.741 0.763

0.565 0.722

PEK1 PEK2 PEK3 PEK4 PEK5

0.787 0.790 0.768 0.777 0.786

0.611 0.887

GPA1 0.924 0.833 0.909


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GPA2 0.901 GPI1 GPI2 GPI3

0.780 0.846 0.779

0.644 0.844

GPB1 GPB2 GPB3 GPB4

0.702 0.763 0.820 0.722

0.567 0.839

Meanwhile, discriminant validity was assessed by using the

HTMT criterion as suggested by Henseler, Ringle, and Sarstedt (2015). As shown in Table 2, the HTMT values were less than the threshold value of 0.85 (Kline, 2011) for both samples, thereby implying the discriminant validity was confirmed.

Table 42 : Discriminant Validity (HTMT criterion)

1 2 3 4 5 6 Malaysian sample

EC PCE PEK GPA GPI GPB

0.338 0.587 0.604 0.664 0.732

0.255 0.371 0.226 0.088

0.332 0.552 0.648

0.630 0.595

0.809

Chinese sample

EC PCE PEK GPA GPI GPB

0.815 0.486 0.637 0.637 0.698

0.611 0.778 0.713 0.751

0.495 0.591 0.505

0.695 0.769

0.805

4.2 Structural Model

Prior to testing the hypotheses, the variance inflation factor (VIF) was examined in order to assess for the existence of multicollinearity problem in the model. The VIF values were less than 5 and ranged from 1.000 to 2.088 for both samples. Therefore, no collinearity problems were indicated among the predictor latent variables (Hair, Hult, Ringle, & Sarstedt, 2017).

The hypotheses were tested by performing a bootstrapping procedure with a resample of 1,000 (Hair et al., 2017) in order to assess the beta coefficients, t-values, p-values, and bootstrapped confidence intervals. The results are presented in Table 3.


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The results revealed a statistically significant and positive influence of EC (! = 0.173, t = 2.133, p < 0.05); PCE (! = 0.425, t = 5.441, p < 0.01); and PEK (! = 0.112, t = 1.679, p < 0.05) on the GPA in the context of the Malaysian sample. Thus, H1, H2, and H3 were statistically supported for this particular sample. However, the Chinese sample yielded a statistically significant and positive influence of GPA on CE only (! = 0.414, t = 4.976, p < 0.01). Meanwhile, PCE and PEK were found to pose a statistically non-significant positive influence on GPA. Thus, only H1 was supported in terms of the Chinese sample.

The hypotheses results for the influence of EC (! = 0.168, t = 1.987, p < 0.05); PCE (! = 0.201, t = 2.187, p < 0.05); and PEK (! = 0.210, t = 2.824, p < 0.01) in the Malaysian sample, thus rendering H3, H4, and H5 as supported. In contrast, the Chinese sample revealed that only EC (! = 0.235, t = 2.693, p < 0.01) and PEK (! = 0. 242, t = 2.709, p < 0.01) were found as positively and significantly influencing the GPI, whereas PCE was not significant. Thus, only H3 and H5 were supported.

Furthermore, the outcomes obtained were indicative of GPS and its significant and positive effect on GPI for the Malaysian (! = 0.231, t = 2.956, p < 0.01) and Chinese (! = 0.664, t = 3.717, p < 0.01) samples alike. Similarly, GPI also showed its positive effect on GPB for both samples (Malaysian sample: ! = 0.78, t = 8.662, p < 0.01; Chinese sample: ! = 0.664, t = 13.057, p < 0.01), thus rendering H7 and H8 both as supported.

Meanwhile, the respective R2 value for GPA, GPI, and GPB was 0.371, 0.398, and 0.334 in the Malaysian sample, while the Chinese sample obtained R2 values of 0.235, 0.367, and 0.440 for the GPA, GPI, and GPB, respectively. According to Hair et al. (2014), examining the changes in R2 values is relevant to examine the effect size, f2. The suggested method requires omitting a specific exogenous construct from the model and seeing the resulting R2 change. It is used to evaluate the impact of the omitted construct on the endogenous constructs (Table 3).

In the Malaysian sample, EC and PEK both posed a small effect on GPA (f2 = 0.029 and f2 = 0.016, respectively), while PCE yielded a medium effect. Meanwhile, EC (f2 = 0.027), PC (f2 = 0.032), and PEK (f2 = 0.057) all resulted in a small effect. In particular, GPA had a small effect on GPI, whereas GPI had a large effect on GPB in


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the case of the Malaysian sample. In contrast, the Chinese sample revealed that the PCE (f2 = 0.013) and PEK (f2 = 0.008) had a small effect on GPA, while EC had a medium effect on GPA. Similarly, EC and PEK posed a small effect on GPI, but no effect was observed for PCE on GPI. Additionally, GPA yielded a small effect on GPI, whereas the effect of GPI on GPB was large.

Next, the predictive relevance of the model was examined through the blindfolding procedure (Hair, Hult, Ringle, & Sarstedt, 2014). The Q2 values for GPA (Q2 = 0.268), GPI (Q2 = 0.230), and GPB (Q2 = 0.224) were all > 0 in the case of the Malaysian sample. Similarly, the Q2 values for GPA (Q2 = 0.176), GPI (Q2 = 0.209), and GPB (Q2 = 0.228) were also > 0. This suggests that the model offers sufficient predictive relevance for both samples.

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MALAYSIAN

JOU

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ON

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MIC

S

Table 43 : Structural Model

Malaysian Sample Hypotheses Std Beta Std error t-value p-value BCI LL BCI UL Decision f2 R2 Q2 VIF H1 GPI ® GPB 0.578 0.067 8.662 0.000 0.454 0.676 Supported 0.502 0.334 0.224 1.000 H2 GPA ® GPI 0.231 0.078 0.956 0.002 0.108 0.356 Supported 0.056 1.590 H3 EC ® GPA 0.173 0.081 2.133 0.017 0.045 0.295 Supported 0.029 0.371 0.268 1.664 H4 EC ® GPI 0.168 0.084 1.987 0.024 0.041 0.303 Supported 0.027 0.398 0.230 1.712 H5 PCE ® GPA 0.425 0.078 5.441 0.000 0.291 0.559 Supported 0.159 1.801 H6 PCE ® GPI 0.201 0.092 2.187 0.015 0.031 0.345 Supported 0.032 2.088 H7 PEK ® GPA 0.112 0.067 1.679 0.047 0.001 0.213 Supported 0.016 1.268 H8 PEK ® GPI 0.210 0.074 2.824 0.002 0.079 0.333 Supported 0.057 1.288 Chinese Sample Hypotheses Std Beta Std error t-value p-value BCI LL BCI UL Decision f2 R2 Q2 VIF H1 GPI ® GPB 0.664 0.051 13.057 0.000 0.563 0.734 Supported 0.787 0.440 0.228 1.000 H2 GPA ® GPI 0.304 0.082 3.717 0.000 0.160 0.421 Supported 0.112 1.306 H3 EC ® GPA 0.414 0.083 4.976 0.000 0.271 0.540 Supported 0.172 0.235 0.176 1.299 H4 EC ® GPI 0.235 0.087 2.693 0.004 0.106 0.384 Supported 0.057 0.367 0.209 1.523 H5 PCE ® GPA 0.101 0.057 1.780 0.038 -0.009 0.187 Not Supported 0.013 1.026 H6 PCE ® GPI -0.000 0.048 0.009 0.496 -0.077 0.075 Not Supported 0.000 1.039 H7 PEK ® GPA 0.089 0.080 1.106 0.135 -0.057 0.216 Not Supported 0.013 1.026 H8 PEK ® GPI 0.242 0.089 2.709 0.003 0.091 0.381 Supported 0.072 1.285

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5.0 Discussion and Implications The current study applied a behavioral model that incorporated

the “attitude-intention behavior” with an extended TPB model on the behavior of green purchase. This was achieved by examining the relationship of environmental concern (EC), perceived consumer effectiveness (PCE), perceived environmental knowledge (PEK), and green purchase attitude (GPA) with the green purchase intention (GPI), as well as on the green purchase behavior (GPB) among millennials in the context of Malaysia and China as the settings. This work confirmed that the cognitive construct of EC influenced the buying attitude shown by millennials of both countries toward green products. Such finding is consistent with the findings offered by past empirical studies (Mostafa, 2006; Jaiswal & Kant, 2018). It further implies that the millennials are concerned about the state of environmental sustainability and their inherent self-responsibility in order to protect the environment, which is thus reflected in their purchasing decision.

Here, PEK was found to be insignificant toward green purchase attitude (PEK → GPA, ! =---, P > 0.05) in the case of Chinese millennials. This is in support of the findings by Jaiswal and Kant (2018) underlining PEK as not significant in influencing the attitude toward green products in India, along with the argument that this may due to the low level of awareness regarding current environmental issues. However, it was found to significantly affect green purchase intention in the case of Chinese millennials for this study. In particular, PCE was depicted as insignificant in influencing green purchase attitude (PCE → GPA,! =---, P > 0.05) and green purchase intention (PCE → GPI,! =---, P > 0.05) among Chinese millennials. This may be attributable toward the confidence level of Chinese millennials in solving environmental problems and it is not reflected by their favorable attitude toward purchasing green products.

On the other hand, perceived environmental knowledge and perceived consumer effectiveness were found to positively and significantly influence the green purchase attitude and green purchase intention among Malaysian millennials. This is consistent with past findings that directly relate perceived environmental knowledge with a favorable attitude toward green products, which further affects the purchase intention for such products (Chan, 2001; Yadaw & Pathak, 2016; Kumar et al., 2017). Similarly, the significant impact of


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perceived consumer effectiveness on green purchase attitudes among Malaysian millennials is in line with prior studies (Tan, 2011; Kang et al., 2013). It is in support of the argument stating that if consumers believe the purchase of eco-friendly products will positively affect the environment, the highly-perceived consumer effectiveness will be displayed in their positive behavior toward green products (Roberts, 1996; Straughan & Roberts, 1999).

Green purchase attitude (GPA) revealed a strong influence on green purchase intention among millennials in both countries as shown in this work, while the attitude toward green purchase showed a direct effect on such intention. This finding is consistent with past studies that reveal consumers with a more positive attitude toward green products would lead to a display a higher intention in purchasing green products (Lee, 2008; Chan & Chao, 2010). Alternatively, the green purchase attitude was positively and significantly driven by EC, PCE, and PEK in the context of Malaysia, whereas it was only significantly driven by EC in China. Additionally, GPA was found to significantly affect the green purchase intention directly and indirectly alike.

The results obtained revealed the green purchase intention as a significant influence on the purchase behavior for green products in both countries. This is consistent with previous studies on green consumer behavior (Kim at al., 2013; Lai & Cheng, 2016; Kumar et al., 2017; Jaiswal & Kant, 2018). They have collectively positioned that consumers are likely to express their willingness to purchase green products for the benefit of the environment through a purchase behavior toward green products (Chan, 2001).

6.0 Limitations and Suggestions

This study has a few limitations. The sample size in this study is relatively small, thus future studies should opt to increase the sample size by collecting data from different cities in Malaysia and China. Furthermore, other factors may further influence millennial green purchase intention and behavior, which should be explored in upcoming works. Besides, cultural factors should also be considered as the moderating variables in assessing millennials from both countries in future studies.


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