KnE Social Sciences | The First International Research Conference on Economics and Business (IRCEB) | pages: 411–425

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Theenvironmental pollution problems, especially air pollution becomes one of the main concern of global issues nowadays. Consumers, environmental observer groups, and government increasingly want to have company's involvement in terms of social responsibility to the environment [1]. This intention is supported by Ferraro's (2009) research that states 35% of Australians realize that environmental degradation is a bigger problem than the economy, terrorism, war, and security problems. This phenomenon becomes an important issue as the impact of global destruction. Therefore, the term `green' arises then which means the attention of the environmental, social, and economical impacts [5,16]. The Green Movement develops due to several important issues, such as environmental degradation, global warming, and the excessive use of unrenewable resources (it is the less wise-consumption habits of people). Therefore, green product (environmentally friendly) is very important to be developed by each industry to participate in the environment maintenance as well as to be its competitive advantage. According to the Energy Efficiency Workshop (2012), green product can be achieved through material recycling, clean technology, raw material change, and improvement of the internal management efforts.

Green marketing refers to the strategies to promote a product / service by using environmental claims on company systems, company policies, and processes of production or marketing. Green marketing has bridged between the attitude of the company to protect the environment and the needs of the stakeholders [9], so, in other words, green marketing is an opportunity to innovate by creating a unique differentiationto enhance the competitive advantage of company.

Nowadays,the consumers are increasingly aware of the importance of consuming green product. However, companies must understand what kind of green product that theconsumers demand. Thecompanies get difficulty to innovate its products to begreen product-based without understanding the consumers' interest inbuying the green product. So that the interest of consumers against rising green product, then the company must find more complex information about changes in the environment, including the pollution of the environment. In other words, tounderstand more the importance of green marketing as a competitive advantage, the company should increase the consumers' interest in purchasing. To attract the consumers' interest, the green product attributes should be different from non-green product attributes In this study, theconsumers' perceptions of green marketing focusing on green product that is the product of cheap and environmentally friendly cars were examined. Such cars in Indonesia are known as low cost green car (LCGC). This term is associated with the phenomenon of worsening air pollution in Indonesia that is partly due to the use of motor vehicles. Green marketing observed in this study, namely green perceived value and green perceived risk [13,2].

Recognizing the phenomenon of environmental damage due to a variety of pollution, especially air pollution, the Indonesian government issued guidelines policy on low-cost and environmentally friendly or low-cost green car (LCGC) in the form of the Rules of Industrial Ministry No. 33 / M-IND / PER / 7/2013 on the Development of Four Wheels-Motor Vehicle Production that saves energy and its price is affordable. The technical guidelines are derived from low carbon emissions car program or low emission carbon (LEC) which has been stipulated in Government Regulation No. 41 Year 2013about taxable luxury goods in the form of Motor Vehicles taxed withSales Tax on Luxury Goods.

Under the government regulations, thecar manufacturers have being started to launch products of the environmentally friendly car called Low-Cost Green Car (LCGC). There are several players in this LCGC industry, namely Toyota Agya, Daihatsu Ayla, Honda Brio, Suzuki Wagon R, and Datsun Go. As reported by the finance seconds (2013), there are several requirements that should be fulfilled by LCGC, firstly, for the internal of piston engine fire, the cylinder content capacity was 980-1200 ccs with the consumption of oil-fuel (BBM) at least 20km/liter or other similar fuels. For compression-ignition engine (diesel),cylinder content capacity was up to 1,500 ccs with fuel consumption of at least 20km / liter or other equivalent fuel. Secondly, the rules concerning the types of fuel must fulfill the minimum specifications of Research Octane Number (RON) 92 or PERTAMAX for internal of piston engine fire and Cetane Number (CN) 51 for diesel. Thirdly, there are obligations that govern the use of additional brands, models, and logos that represent Indonesia. Fourth, regulating the highest LCGC products prices that is Rp 95 million based on the location of the headquarters agent licensee office. Regarding the price, within the guidelines it has been mentioned that the price can be adjusted to the changes of conditions or economic indicators that include the inflation, exchange rate, or the price of raw materials. It also includes the use of an automatic transmission and or passenger safety technology.

Malang is a large city with high population density during day time if it is compared to the evening. This is due to many workers beyond Malang having work in Malang. They go to Malang in the morning and go back to their hometowns in the afternoon. As a result, the traffic density in the working-day is very high. This phenomenon causes the increase ofair pollution in Malang.

The purposes of this study are to: (1) determine the effect of green perceived value on consumers'interest in purchasing LCGC products in Malang and (2) determine the effect of green perceived risk on the consumers' interest in purchasing LCGC products in Malang.


The Interest in Purchasing "Green" Product (Green Purchase Intention)

According to Netemeyer et al., (2005), the interest in the purchase of "green" (green purchase intentions) is a consumer desire to buy a special product because of the need for the neighborhoods where this desire is very likely to materialize. Suki (2013) states that in green marketing product purchase, consumers should concern on the green product. Marketers try to influence purchasing decisions by providing information about green product that they market.

Green Marketing

The term “green marketing” appeared for the society awareness of the importance of protecting the environment from various pollutions. Nowadays, people are increasingly need green products. Thus, companies might use green marketing to improve their competitive advantage. Green marketing is also divided into several stages. According to Lee (2008), since 2000, green marketing is growing towards the third stage. Peattie and Crane (2005) identifies five marketing strategies that lead to failure during this period, they are: (1) Green Spinning: Taking a reactive approach to relations with the public, for it concerns or discrediting criticism from the public against the Company's common. (2) Green Selling: Taking the opportunity policies by adding several claims toward available products to increase the sales. (3) Green Harvesting: Being passionate to the environment only when this strategy is able to lower costs. (4) Entrepreneur marketing: Building innovation of environment-friendly products to the market without understanding clearly the consumers' exact desires. (5) Compliance Marketing: implementing regulations of the environment as one of the opportunities to promote products based on requirements determinedby the government without initiating to go beyond the rules.

Green Perceived Value

Definition of green perceived value according to Patterson and Spreng (1997) is the consumers' ratings of "green" products that they consume by comparing the benefits of what they accept to their sacrifice to obtain the product, which involves their needs of environmentally friendly products. Green perceived value is important for the company because it may increase the consumers' interest in purchasing (purchase intention) [17]. According to Grimmer and Wolley (2014), when consumers decide to purchase, they will maximize the value that can be obtained, compare the perceived cost to the perceived value, and buy products that provide greater perceived value.

Currently, the perceived value becomes increasingly important, and therefore companies can increase the consumers's interest in purchasing by increasing the perceived value [17]. According to Sweeney et al, (1999), perceived value is a set of attributes related to the consumer's perception of the product value, and therefore the higher the perceived value, themore positive word-of-mouth that are created and the interest in purchasing increases. Chang and Chen (2008) in his research state that when consumers are increasingly getting the high perceived value of the products they consume, they increasingly want to buy the product. The study shows that there is a positive influence between green perceived value to the interest in purchasing “green” product. When the product value is developed, the product sales will increase. Therefore, it is assumed that green perceived value is an important component that affects the consumers' interest in purchasing “green” product. Therefore, the first hypothesis of this study was formulated as follow:

H1: Perceived value positively correlates to the interest in purchasing “green” product

Green Perceived Risk

According to Chen and Chang (2013) in his research, the definition of green perceived risk is the possibility of acceptance of negative results related to the purchasing "green" product. Therefore, the consumer purchase decision depends on the risk of consuming the “green” product. green perceived risk could also be hampered if there is a relationship between the consumer and the company. Expectations of the bignegative consequences are related to purchasing behavior [15]. Previous research indicates that there is a negative relationship between perceived risk and interest in purchasing green product [2,10]. The negative relationship occurs because perceived risk might lower product purchase behavior. Someone who cares of environmentally friendly products, he/she will check whether there is a specific loss of the product that can harm the environment. Therefore, to increase the interest in purchasingproducts, thegreen perceived risk should be reduced [3]. Thus, the secondhypothesis is formulated as follow:

H2: Perceived risk negatively affects the interest in purchasing“green” product


The research objective is to test the effect of independent variables that are green perceived value and perceived risk on the dependent variable, namely green purchase intention. This study design is a confirmatory study that aims at testing a hypothesis with a cross-sectional research periodl. The data were collected by using a questionnaire. The questionnaire contained items of research questions that described the variables studied. Before conducted the study, the researchers conducted measurement tests on 50 respondents. Measurement test was intended to pre-test research instruments, whether or not the instrument could meet the validity and reliability. The populations in this study were the residents in Malang which had a potential income to buy LCGC products. The samples in this study were approximately 250 respondents. They were the citizens who worked and had income in Malang. The total of respondents had fulfilled the adequacy of samples 16. This statistical analysis was used to test some separated regression equations but they were correlated simultaneously and statistically. The data collection was conducted by using closed questionnaires, so that the respondents only chose the provided answer considered as the appropriate one. The power analysis used was Structural Equation Modelling (SEM) by using AMOS application.


Therespondents'characteristics in this research setting were described in the category of the respondents' gender and age. The distributions of respondents based on their gender were 127 respondents (79.4%) consisting of men and 33 respondents (20.6%) were women. In terms of the respondent's age, from 160 questionnaire data collected, 68 respondents (42.5%) were 36-40 years old, 32 respondents (20%) were 41-45 years old, 21 respondents (13.1%) were 31-35 years old, 9 respondents (5.6%) were 26-30 years old and 9 respondents (5.6%) were 20-25 years old as well.

Outliers Testing Results

Outlier is an observation appearing with extreme values having unique characteristics for single or combination variables and it is very different from other observations (Hair et al, 2006, p. 73). The treatment of outliers is by removing it from the data and they are not included in the subsequent calculations. In multivariate analysis, the presence of outliers can be tested by seeing the value ofMahalanobis d-squared value with a significance level p1 or p2 < 0.001. If the value of p1 or p2 in Amos output 16 indicates a value less than 0001, it can be an indication of outlier. Table 1 shows outlier data based on output AMOS 21.

Table 1

Multivariate Outliers.

Based on the criteria of mahalanobis distance value, the value of which was considered as outlier in Table 4.3 contained 4 outliers. Based on Table 1, the 4 outliers were excluded from the research data. Thus, further analysis used 156 samples.

Testing Results of Construct Reliability and Variance Extracted

Testing construct reliability and variance extracted was still in test validity, in this case, the convergent validity. Convergent validity is dealing with to the extent to which a score of one measurement scale isassociated with another scale score designed to assess the same construct (Cooper and Schindler, 2006, p. 320). Table 2 shows the results of testing construct reliability and variance extracted.

Table 2

Construct Reliability and Variance Extracted of Every Measurement Construct.

Construct Construct Reliability Variance Extracted
Green Perceived Value 0.852 0.594
Green Perceived Risk 0.957 0.847
Purchase Intention 0.917 0.688

Table 2 shows the construct reliability for all variables tested in this study is 0.7. The test results show that the reliability constructs each construct the measurement meets the requirements of internal consistency and indicators of measurement used is sufficient to represent the constructs of research. Variance extracted for each construct is 0.5, meaning that more than 50% of indicators that is used to explain each construct.

Discriminant Validity

Discriminant validity is the extent to which a construct is completely different from the other constructs. The method used to assess the discriminant validity was by comparing the variance extracted (VE) for each construct to the square of the correlation between the construct with other constructs in the model. Models has enough discriminant validity if the value of VE for each construct is greater than the square of the correlation between the construct with other constructs in the model (Hair et al, 2006: 778).

Table 3

Correlations among constructs.

Table 3 above shows that the VE value of each construct had a higher value than the square of the correlation among constructs one another. Thus, it can be concluded that the measurement (indicator) used in this study met the criteria of discriminant validity.

Estimation and Testing Structural Model

Structural relationship model tested was a causality relationship among the green constructs perceived value, green perceived risk, and purchase intention. There were some assumptions that must be considered before doing the test by using Structural Equation Modeling (SEM).

Sufficiency Sample Assumptions

The total number of respondents in this study was 160 respondents. The sample size in the study was claimedto have fulfilled the criteria of the minimum sample size of studies using statistical tools Structural Equation Modeling (SEM) with the procedure of Maximum Likelihood Estimation (MLE), that was 150 to 400 (Hair et al, 2006: 741).

Normality Assumption

Other assumptions associated with Structural Equation Modeling (SEM) approach is the data that must be distributed in multivariate and normally. The statistical value to test the normality was Z value (Critical Ratio or CR in output Amos 21) of skewness and kurtosis size of data. When the CR value was greater than the critical value, it could be presumed that the data distribution was not normal. The critical value could be determined based on the 0.01 level that was equal to ± 2:58. Normality test results can be seen in Table 4.6.

Table 4

Normality Test.

Based on the normality test results in Table 4.6, the value in multivariate CR was 13 739. This value was far above the value of ± 2:58 at the 0.01 level. It can be said that the data were not normally distributed. This phenomenon happened because the data used in this study were derived from the primary data. The primary data were based on respondents' answers that were very diverse, so it was difficult to obtain the data following a normal distribution perfectly. Although Maximum Likelihood Estimation (MLE) technique requires a normal distribution of data, but if it turns out the normality assumption cannot be fulfilled, the subsequent analysis can still be performed for the estimation technique is quite robust.

Assumptions Goodness of Fit

Measurement of the goodness of fit is performed to measure the extent to which the model can predict the covariance or correlation matrix observed. A model reporting χ2 value and the degree of freedom, CFI and RMSEA will provide enough information to evaluate a model (Hair et al, 2006, p. 752). Assessment of the cutoff value should also be based on the characteristics of the model. The simple model and small sample should be evaluated rigorously than the complex models and larger sample. However, Mueller in Purwanto (2008) states that the assessment of the model fit can also be based on a normed Chi-square (CMIN / DF), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Root Mean Square Residual (RMR) and Root Mean Square Error of Approximation (RMSEA). Table 5 shows the goodness of fit analysis of the structural model based on a cutoff value given in the literature.

Table 5

Goodness of Fit Structural Model Results.

No Index Recommended Value Result
1 χ 2 Accepted p significant value χ 2 = 325.013 (df = 51, p = 0.000)
2 CMIN/DF < 3 6,373
3 GFI > 0.90 0.775
4 AGFI > 0.90 0.656
5 RMSEA < 0.07 0.184
6 CFI > 0.92 0.779
7 TLI > 0.92 0.714

Table 5 shows that in general, the structural model did not fit. All indicators were below the recommended value. The unfit model's was possibly caused by the homogeneity of the data in the study,. As we know that, the model of this research consisted of only three variables and thirteen indicators. The number of respondents who wasonly 160 respondents was also assumed to be the cause of this unfit model.

Evaluation of Causality Relationship

The hypothesis that described the influence of a construct to the other constructs was measured by looking at whether the regression weight estimate that connected the two constructs with a significance level of 0.05, had a critical ratio (CR) > 1.96 (Byrne, 2001, p. 76). In addition,we should see the relationship direction was in accordance with the hypothesis. Table 6 shows the analysis results of regression weights in this study.

Table 6

Weights Regression Analysis Results.

Hypothesis Relation Standardized Regression Weights Critical Ratio P Description
H1 GPV MB 0.180 2.240 0.025 Sig
H2 GPR MB 0.845 9.342 *** Sig
*** = p < 0.001; ns = not significant; sig = significant

According to Table 6, it was found that there was a causality relationship among the constructs tested. Green Perceived Value (GPV) had a positive influence on the direction of purchase intention (MB). Green Perceived Risk (PEN) influenced positively the purchase intention (MB).


Hypothesis Testing

Hypothesis testing was done by analyzing the significance level of causality among the constructs in the model by looking at a P value of < 0.05 and value C.R. > 1.96, as well as standardized structural view (path) coefficients of each hypothesis, especially on the suitability path toward relationships with the direction of the relationship that had been previously hypothesized. If the direction of the relationship was in accordance with the hypothesized, a P value of < 0.05 and its value C.R. also fulfilled the recommended requierement, so it can be concluded that the tested hypothesis was supported 1.

Hypothesis 1

The first hypothesis testing was conducted to determine causality relationship of Green Perceived Value (GPV) with purchase intention(MB) on research. Significance was obtained when the CR > 1.96 value was at the 0.05 significance level. Based on the results of data processing, the value of CR on the causality relationship between Green Perceived Value (GPV) with purchase intention (MB) was 2.240 (Table 6). These results indicate that the hypothesis 1 (H1) in this study was received at a significance level of 5%, which means Green Perceived Value influenced positively the LCGC product purchase intention.

Steenkamp and Geyskens (2006) state that Green perceived value is important for the company because it may increase the interest of consumer purchases (purchase intention). Grimmer and Wolley (2014) also states that when determining the purchase decision, consumers will maximize the value that can be obtained, compare the perceived cost to the perceived value and buy products that provide greater perceived value. Currently, green perceived value becomes increasingly important, and therefore companies can increase consumer interest in purchasing by increasing its perceived value [17]. According to Sweeney et al (1999) perceived value is a set of attributes related to the consumer's perception of the value of the product, and therefore the higher the perceived value, the more positive word-of-mouth will be created and the interest in purchase will increase.

Chang and Chen (2008) in their research state that when consumers are increasingly getting the high perceived value of the products they consume, they increasingly want to buy the product. The study shows that there was a positive influence of green perceived velue to green purchase intention When the product value was developed, the ability to sell these products would increase.

Hypothesis 2

The second hypothesis testing was conducted to determine the causality relationship between green perceived risk (GPR) and purchase intention (MB). Significance was obtained when the CR > 1.96 value was at the 0.05 significance level. Based on the results of data processing, the value of CR on the green causal relationship between the perceived risk (GPR) and purchase intention (MB) was 9342 (Table 6). These results indicated that the hypothesis 2 (H2) in this study was received at a significance level of 5%, which meant green perceived risk influenced positively the purchase intention.

The results were consistent with the results of the study conducted by Chang and Chen (2008) which showed that the decline in perceived risk could significantly increase a person's interest in buying, both men and women. So, the lower the perceived risk of using a product, the higher the intention to buy was. Expectations of the big negative consequences were associated with purchasing behavior [15]. Previous research indicated that there was a negative relationship between perceived risk and interest green purchasing [2,10]. The negative relationship occured because perceived risk would lower the product purchase behavior. Someone who cares for environmentally friendly products, he/she will check whether there is a specific loss of the product that can harm the environment. Therefore, to increase the interest in the purchase of products, thegreen perceived risk should be reduced [3].


The purpose of this study is to examine the effect of green perceived value and perceived risk on the LCGC green products purchase intention. In general, the results show the entire predictor variables in this study had a positive influence on purchase intention ofthe LCGC products. The study results can be concluded as follows: (1) Green Perceived Value had an influence on purchase intention of LCGC products. The results of this study indicated that the perception of green value attached to the LCGC still appeals to the respondent. Numerous publications in the media which gave the impression that the LCGC car was environmentally friendly had succeeded in improving the perception of the green value in this LCGC product. (2) Green Perceived Risk had a negative influence on the purchase intention of LCGC products. The results showed that the perception of the risk in using LCGC products still affected interest in buying this product. Perception of the risk of LCGC new product which incidentally caused the respondents felt that this product had not been tested, either its efficiency or durability. This phenomenon caused the perception of the risk inbuying a car was still high, so the respondents still considered the perceived risk whether they would buy LCGC products.



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