Environ Sci Pollut Res DOI 10.1007/s11356-015-4841-8
RESEARCH ARTICLE
Consumer purchase intention towards environmentally friendly vehicles: an empirical investigation in Kuala Lumpur, Malaysia Rafia Afroz 1 & Muhammad Mehedi Masud 2 & Rulia Akhtar 1 & Md. Ashraful Islam 3 & Jarita Bt Duasa 1
Received: 11 January 2015 / Accepted: 4 June 2015 # Springer-Verlag Berlin Heidelberg 2015
Abstract This paper examines whether attitudes towards electric vehicles (ATEVs), subjective norms (SNs) and perceived behavioural control (PBC) have significant associations with consumer purchase intention (PI) and the purchase behaviour of environmentally friendly vehicles (EFVs). The results from the survey questionnaires are analysed using confirmatory factor analysis (CFA) and structural equation modelling (SEM). The findings of this paper indicate that ATEV, SN and PBC significantly influence PI. This finding also indicates that environmental consequence and individual preferences do not influence the PI of the respondents. We found that Malaysian car owners are largely unaware of the greenhouse effects on the environment or attach to it little importance, which is reflected in their PI towards EFVs. The outcomes of this study could help policymakers design programmes to influence attitudes, subjective norms, perceived behavioural control and purchase behaviour to prevent further air pollution and reduce CO2 emissions from the transportation sector.
Responsible editor: Philippe Garrigues * Rulia Akhtar [email protected] 1
Department of Economics, Faculty of Economics and Management Science, International Islamic University Malaysia, 50728 Kuala Lumpur, Malaysia
2
Faculty of Economics and Administration, University of Malaya, 50603 Kuala Lumpur, Malaysia
3
Research Management Centre, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
Keywords Attitudes . Subjective norm . Perceived behavioural control . Purchase intention . Electric vehicle . Carbon emissions
Introduction Malaysia is an industrialising country that is rapidly developing to achieve its Vision 2020. Its economy has shifted from an agricultural-based economy to a manufacturing-based economy. Consequently, Malaysia has experienced incredible economic achievement in the last three decades, which has accelerated urbanisation and led to an increase in the per capita income of its population. Rapidly increasing economic activities and rising income levels have led to an exponential increase in the demand for both freight and passenger transport services, especially in the rapidly growing urban areas. The total number of registered vehicles continues to increase in Malaysia. It increased from 7,686,684 in 1996 to 19,016,782 in 2009 (Ministry of Transport 2009). The total numbers of registered vehicles in Malaysia were 19,016,782 in 2009, of which 8,506,080 accounts for private passenger cars. This has made the land transport sector a prominent sector (Malaysian Environmental Quality Report 2012) but has contributed to the deterioration of the ambient air conditions in Malaysia (Malaysian Environmental Quality Report 2010). In Malaysia, the transport sector contributed approximately 97 % of carbon monoxide (CO), 18 % of particulate matter (PM10), 52 % of nitrogen oxide (NO2) and 7 % of sulphur oxide (SOx), from 1991 to 2009 in a 2-year interval. For this reason, transportation has been recognised as the fastestgrowing source of carbon dioxide (CO2) emissions (Ribeiro and Cardoso 2003). According to World Energy Statistics (IEA 2007), the total CO2 emission in Malaysia increased 21.30 % during 2000 to 2003, whereas during the same
Environ Sci Pollut Res
period, it is increased 7.31 % globally and 16.57 % in Asia. In Malaysia, the total CO2 emission increased more than the global and Asian average. The Department of Environment Malaysia (2003) stated that the Klang Valley was the most prone region to serious air pollution compared to other parts of the country. With mountains in the east and the Straits of Malacca to the west and being highly developed and densely populated, the Klang Valley provided a conducive environment for pollutants to accumulate particularly when atmospheric conditions are stable. For this reason, it has attracted serious concern from the authorities after recognising that it had a high potential of air pollution due to its inherent topography (Sham 1979). A study undertaken in 1987 by the Klang Valley Environmental Improvement Project reported that the total air pollution emission level was approximately 433,400 tons per year (Department of Environment 1987). The Malaysian government has expressed its strong commitment towards strengthening a sustainable transportation system. Since late 2011, the Malaysian government has been working towards accelerating developments in the transport sector towards zero-emission mobility. Their goal is to achieve a 10 % increase in the number of environmentally friendly vehicles (EFVs) on Malaysian roads by 2020 (Ministry of Transport 2010). The government is encouraging private firms to introduce the necessary infrastructure for efficient operation of these vehicles. Furthermore, the government is developing regulations and standards for firms that plan on setting up charging stations for EFVs. Such measures are needed to create an environment that generates public interest for the use of EFVs. Malaysia-based vehicle producer, Proton, and Japanbased vehicle producer, Nissan and Mitsubishi, are all running trials of EFVs in Malaysia to raise public awareness of the vehicle’s plug-in option and to test the cars’ viability. If Malaysia is to achieve its ambitious target of zero-emission mobility, it will need to move quickly to generate industry interest and public acceptance.
Literature review Numerous theories addressed human behaviour. The most frequently cited theory on the attitude-behaviour relation is the theory of planned behaviour (TPB) (Ajzen 1991). The theory of planned behaviour includes the following five essential dimensions that clarify human behaviour: (i) attitudes towards certain behaviour, which measures an individual’s general attitude and is identified as an evaluative response reflecting beliefs about the behaviour (Oskamp 1977); (ii) subjective norms, as the perceived social pressure to conduct the behaviour in which an individual is considered responsible to behave according to external opinions that influence what he or she does; and (iii) perceived behavioural control (PBC) refers
to an individual’s belief about their ability to perform a specific behaviour based on their capabilities and the external forces that can influence alternative (Staats 2003). These three variables determine (iv) the intention to perform the behaviour and (v) the intention which, in turn, does directly affect behaviour (Ajzen 1985). The TPB proposes that behaviour is affected by behavioural intentions, which, in turn, are affected by attitudes towards the behaviour. The attitude can be defined as Bthe result of a consumer’s assessment of particular behaviours^ (Ajzen 1991). Many researchers reported consumers having very positive attitudes towards preventing environmental damage using the TPB model (Vermeir and Verbeke 2006; Bhattacharya and Sen 2004; Sen and Bhattacharya 2001). Consumers even demand companies to produce their products in an environmentally friendly way (Bockman et al. 2009; Kanarattanavong and Ruenrom 2009; Schmeltz 2012). Many researchers studied the attitude-behaviour relation when it concerned green products, and all concluded that there was a wide gap between what consumers thought and what they did regarding making purchase decisions (Eck 2009; Anon 2009; Finisterra et al. 2009; Basu and Hicks 2008; Darnall et al. 2012; Ferguson 2011). Young (2004) named this gap the attitude-behaviour gap. In the case of green-purchasing behaviour, the attitude-behaviour gap is generally formed when a consumer is concerned about environmental consequences and individual consequences and thinks it is important for companies to be socially responsible and produce green products but do not interpret their positive attitudes when making a purchase. Oliver and Rosen (2010) argued that environmental values are powerful predictors of certain consumer actions and positively influence willingness to engage in actions that protect the environment (Oliver and Rosen 2010). For example, few researchers found that consumers who showed high levels of environmental awareness during their purchase were more likely to purchase environmentally friendly car compared to those consumers who were less environmentally concerned (Heffner et al. 2007). Similarly, Gallagher and Muehlegger (2011) found that social preferences for environmental quality and energy security were a major determinant for consumer adoption of environmentally friendly car. Gallagher and Muehlegger (2011) concluded that social preferences increased environmentally friendly car sales more than rising gas prices or tax incentives. On the other hand, a study by Ramayah et al. (2010) found that environmental consequences showed non-statistically significant effects on green purchase intention among Malaysians baby diaper users, which contradicted with previous findings. Individual consequences include measures of convenience, range of product sizes and perceived usefulness in relation to purchase intention such as when individuals want to buy a product, they will choose those options that maximise their
Environ Sci Pollut Res
utility based on their convenience, preference, usefulness of the product and their budget (Roche et al. 2010). Domina and Koch (2002) concluded that convenience is an important factor affecting recycling behaviour of textile (Domina and Koch 2002). Van Bree (2010) found that an increase in gas prices influences consumer behaviour. In a study on consumer adoption of hybrid electric vehicle (HEV), Gallagher and Muehlegger (2011) found that consumers usually make the decision to buy (HEVs) in response to an increase in gas prices and government incentives. This study employed the aforementioned five components of TPB to design and developed the questionnaire. Attitude towards environment showed respondents’ opinions regarding EFVs and their awareness of environmental issues. Figure 1 presents the research model proposed to identify the relationship among dependent and independent variables as derived from a review of the relevant literature. Here, the two important attitudinal variables of environmental consequences and individual consequences were added to the TPB model since they significantly affected the attitudes towards green products (Oliver and Rosen 2010; Heffner et al. 2007; Kahn 2007; Gallagher and Muehlegger 2011; Ramayah et al. 2010; Roche et al. 2010; Domina and Koch 2002; Van Bree 2010). In our proposed model, the purchase behaviour among the respondents (i.e. dependent variable) is the function of intention to purchase and the purchase intention is a function of attitudes towards EFVs, subjective norms and perceived behavioural control (i.e. independent variables). An attitude towards EFVs is a function of individual consequences and environmental consequences.
Fig. 1 Proposed research model (adopted from Ajzen (2002), theory of planned behaviour (TPB))
Sampling The survey was conducted from January to April 2014. All of the respondents were owners of vehicles and above 18 years. The questionnaires were distributed and collected through face-to-face interviews from parking areas in different shopping malls and university campuses. The study applied the convenient sampling method to collect the data because the population is too large with an unknown population size and it is impossible to include every individual. This sampling technique was chosen because it is fast and inexpensive and easy to collect data. The question of the adequacy of sample size remains a prime concern in the application of structural equation modelling (SEM). Since our population size is unknown, to obtain an appropriate sample size from this population, the following formula was used (Lind et al. 2012), which gave us a sample size of 384.16.
Methodological approach
n ¼ πð1−πÞðZ=E Þ2
Study area
where n is the size of the sample, π (0.50) is the population proportion, Z is the standard normal value corresponding to the desired level of confidence, and E is the maximum allowable error. Based on the formula for sample size, Z=1.96 (95 % confidence level), π=0.5 and E=5 %. Considering 5 % non-response error, we increase our sample size to 400. Conferring to Hoe (2008), a sample size of 200 possesses sufficient statistical strength for data analysis. However, if the sample size is too large, e.g. beyond 400, the SEM statistical analysis might be too sensitive, constructing goodnessof-fit measures that indicate poor fit (Hair et al. 2010). Therefore, based on the above-recommended guidelines, approximately 400 questionnaires were distributed and the response rate was 87.5 %. Out of 400, 17 respondents did not complete all the sections, 8 respondents did not response correctly, and 25 respondents did not agree to participate in this survey. Finally, 350 usable questionnaires were analysed in this study.
Kuala Lumpur, Putrajaya and Labuan are the three areas directly governed by the federal government of Malaysia. This study focused on the federal territory of Kuala Lumpur for several reasons. Among them is that it has the largest population and largest economy in terms of gross domestic product (GDP) in Malaysia (DOSM 2012). It also contributed to the state’s rapid development as Malaysia’s transportation and industrial hub (DOSM 2012). Kuala Lumpur (latitude 3° 8′ N, longitude 101° 44′ E) is situated in the Federal Territory of Kuala Lumpur, in the west Peninsular Malaysia. The total population of Kuala Lumpur in 2005 was estimated at 1.6 million people (City Hall of Kuala Lumpur 2008). The ethnicity compositions were Malays (41 %), Chinese (39 %), Indians (10 %) and foreign population (7 %). The Federal Territory of Kuala Lumpur consists of 11 districts as shown in Fig. 2.
ð1Þ
Environ Sci Pollut Res Fig. 2 Map of the Federal Territory of Kuala Lumpur
Research design This study used the theory of planned behaviour with five components to design and develop the theoretical framework, namely attitude towards EFVs (ATT), subjective norms (SNs), PBC, purchase intention (PI) and purchase behaviour (PB). Apart from the TPB, two attitudinal variables were considered in this model, namely individual consequences (ICs) and environmental consequences (ECs) adopted from Ramayah et al. (2010). The questionnaire consisted of sections A, B and C. Section A collected information on the respondents’ socioeconomic characteristics such as gender, age, race, education and income. These variables were adopted from a household survey questionnaire in Malaysia (DOSM 2012). Section B included several items to measure the respondents’ attitudes towards EFVs, subjective norm, perceived behavioural control, purchase intention towards EFVS and PB. All the construct domain and measurement items were adapted from a comprehensive review of past studies (Kalafatis et al. 1999; De Cannière et al. 2009; Oliver and Lee 2010; Ramayah et al. 2010). Most of the items were not taken as found in the existing literature but were developed and modified due to the different context of the study. Section C contains several questions that ask the respondents to explore their awareness,
knowledge and perception of EFVs. Hence, in order to measure awareness and knowledge and perception of EFVs, several measurement items were adapted from the past studies of Tran (2006), Bayard and Jolly (2007), Zirbel et al. (2011), Lillemo (2014) and Shoukry et al. (2012). All items for section B are measured using five-point Likert scales representing a range from 1 (strongly disagree) to 5 (strongly agree).
Results and discussion Descriptive statistical analysis The researchers successfully distributed 400 questionnaires among Malaysian car owners and received a total of 350 completed questionnaires, where 50 questionnaires were excluded from 400 questionnaires due to incomplete and incorrect responses. This showed a response rate of 87.5 %. Table 1 shows that males constituted 51.7 % while females 48.3 %. The greatest number of respondents (49.0 %) aged between 31 and 45 years. Most of the respondents are middle aged. The largest number of respondents were Melayu (64 %). 25.7 % had graduate education, while 24.2, 21.4, 20 and 8.5 % had
Environ Sci Pollut Res Table 1
Demographic information of the respondents (N=350)
Variables Gender Male Female Age (years) 18–30 31–45 46–60 Race Malay Indian Chinese Others Education Lower secondary school Higher secondary school Diploma Graduate Postgraduate Income RM 2000 and less than RM 2001 to RM 4000 RM 4001 to RM 6000 RM 6001 to RM 8000 RM 8001 and above
Table 2
Awareness and knowledge of environmental issues
Frequency
Percentage (%)
Items
181 169
51.7 48.2
142 170 38
40.0 49.0 11.0
223 37 85 5
64.0 10.5 24.1 1.4
75 70 85 90
21.4 20.0 24.2 25.7
Awareness of environmental issues I am aware of environmental problems in Malaysia I am aware that CO2 emission is increasing within Kuala Lumpur City I am aware that transportation sector contributes highly towards CO2 emission I am aware that EFVs can contribute to reduce CO2 Environmental knowledge I know about environmental issues I know Malaysia is facing environmental issues (like water, air pollution, solid waste, climate change, etc.) I understand that there are environmental indicators I know traditional vehicles are damaging our environment
30
8.5
28 146 138 24 14
8.0 41.7 39.4 6.8 4.0
diplomas, lower secondary, higher secondary and postgraduate education, respectively. The study found that only 8.0 % of the respondents had an income range of RM 2000 or less. The highest percentage of the respondents (41.7 %) had an income range of RM 2001 to RM 4000 per month, while 39.4 and 7.3 % of the respondents had an income range of RM 4001 to RM 6000 and of RM 6001 to RM 8000, respectively. Only 4.0 % of respondents had an income range of more than RM 8000 per month.
Automobile users’ awareness and knowledge of environmental issues In order to measure respondents’ awareness level of environmental issues, several statements were included in the questionnaire as shown in Table 2. The results indicate that 78 % of the respondents were aware of environmental problems, 90 % of the respondents confirmed that transportation is the largest contributor of CO2 emission, while 86 % admitted that CO2 emission is increasing in the Federal Territory of Kuala Lumpur, Malaysia. However, only 67 % of the respondents are aware of environmentally friendly cars. Similarly, to explore the knowledge of environmental issues, we found that
Agreed Disagreed (%) (%)
78
28
86
14
90
10
70
30
75 73
25 27
74
26
66
34
75 % of the respondents confirmed knowing about environmental issues while 73 % mentioned that Malaysia is confronting environmental issues like air pollution, water pollution, climate change, etc. Meanwhile, only 66 % mentioned that traditional cars are damaging our environment. Awareness of environmentally friendly vehicles To explore the respondents’ familiarity with EFVs, we included several types of EFVs in the questionnaire as shown in Fig. 3. The results revealed that 42.1 % of respondents were familiar with plug-in hybrid electric vehicles (PHEVs) while 40.5 and 17.4 % of the respondents were familiar with HEVs and battery electric vehicles, respectively. Users’ concern about EFVs In order to explore users’ concerns about EFVs, we stated several items on EFVs in the questionnaire, as show in Fig. 4. The figure shows that 36 % of the respondents identified battery range as the greatest challenge, followed by high
Aware of EVs Percentage (%) 42.1
40.5
17.4
Hybrid electric vehicle (HEV)
Plug-in hybrid electric vehicle (PHEV)
Fig. 3 Awareness of EFVs
Baery electric vehicle (BEV)
Environ Sci Pollut Res
Fig. 4 Apprehension about electric vehicles (EFVs)
cost (24 %), changing infrastructure (12 %), reliability (10 %) and safety (8 %). These concerns reaffirm some of the issues identified initially by respondents when asked about associations with EFVs. In order to measure their level of concern over the aforementioned associations of EFVs, we stated one questionnaire items with five-point scales representing a range from 1 (not concern at all) to 5 (very much concern). It indicated that 80 of the respondents are very much concerned about it while 15 % are concerned and 5 % of the respondents are not concerned. Ranking of EFV attributes In this study, the authors discussed the ranking of electronic vehicle attributes as shown in Table 3. Table 3 indicates that Malaysian car owners assign ranking no. 1 to design of the car while rankings 2–5 were assigned to safety, quality, performance and reduced greenhouse gas emission, respectively. This means that Malaysian car owners are more concern about the design of the car. It also shows that they assigned rank 5 to reduced greenhouse gas emission. This result indicates that Malaysian car owners are not aware of the greenhouse effects on the environment. In this regard, policymakers and social organisations should work together to create awareness. Tests for confirmatory factor analysis According to Kline (2010), the purpose of a measurement model points to the appropriateness of the observed indicators as a measurement instrument representing a latent variable. This is echoed by Hair et al. (2011), who observed that in Table 3
Ranking of EV attributes
Items
Mean (N=385) Standard (N=385) Ranking
Performance Safety Design Quality Reduced greenhouse gas
2.8421 2.3584 2.1870 2.4312 3.012
1.07778 1.08797 1.08318 1.12549 1.09823
4 2 1 3 5
measurement theory, the purpose is to estimate the relationship between the observed and the underlying latent variables. The adequacy of a measurement model is performed by confirmatory factor analysis (CFA). In doing so, four fit indices are checked to ascertain the fitting of the model with the data: chi-square statistic, normed chi-square, root mean square approximation (RMSEA) and comparative fit index (CFI). For an adequate model fit, general guidelines suggest cut-off values for such indices: normed chi-square and RMSEA are to be less than 5 and 0.088, respectively, while CFI values are to be above 0.9 (Hair et al. 2011; Byrne 2009). Prior to testing the structural equation model, CFA was performed on the entire set of measurement items simultaneously. The process of evaluating the measurement model resulted in deleting terms based on the factor loadings only of less than 0.40 (Field 2009). Based on the CFA tests, all the seven dimensions had adequate model-to-data fit: normed chi-square value below 2.41, CFI value above 0.95 and RMSEA value less than 0.080. These tests also evaluated the reliability and construct validity. Cronbach’s alpha measures the reliability coefficient, which indicates the consistency of the entire scale (Hair et al. 2011) or the overall reliability of the questionnaire (Field 2009). The results from this study showed that all the four dimensions had reliability values above 0.70 which indicated that the questionnaire was reliable and consistent (see Table 4). According to Hair et al. (2010), a standardised factor loading should be 0.40 or higher, ideally 0.70 or higher, which provides strong evidence of convergent validity. In this study, all the items had significant factor loadings, most of them greater than 0.60, which indicates adequate convergent validity.
Validating the measurement model In order to validate the measurement model, the convergent and discriminant validity was measured (Janssens et al. 2008; Kline 2005; Schumacker and Lomax 2004). Convergent validity is judged to be adequate when average variance extracted (AVE) equals or exceeds 0.50. The standardised factor loadings of all the items were larger than 0.50, ranging from 0.56 to 0.86, and were statistically significant at the 0.001 % level. This evidence supported the unidimensionality of each scale, which indicated that convergent validity was obtained. The reliability of the construct (Cronbach’s alpha and composite reliability) should also be assessed (Anderson and Gerbing 1988). The purpose of composite reliability is to measure the reliability of the internal consistency of the measured items representing a latent construct and must be established before construct validity can be assessed (Hair et al. 2010). According to Chinna (2009), the composite reliability, by convention, should be at least 0.70 to suggest good reliability and to indicate that internal consistency exists.
Environ Sci Pollut Res Table 4
Construct validity of confirmatory factory analysis
Items
Standard CR loadings
Perceived behavioural control (PBC) (normed χ2 =1.536, CFI=0.995, RMSEA=0.052) Respectfulness and politeness is important to me Self-control such as being restrained and self-disciplined is important to me Clean and tidy environment is important to me For the achievement of my life, being hardworking and aspiring is important to me Subjective norms (SNs) (normed χ2 =1.19, CFI=0.999, RMSEA=0.030) Conventional car can create air pollution Conventional car can create smog in large cities Conventional car produce greenhouse gases such CO2 and NO2 that contribute to global warming and climate change Attitudes towards EVs (ATT) (normed χ2 =2.246, CFI=0.991, RMSEA=0.079) Environmentally friendly car is a fuel-efficient car. So, it can reduce CO2 emission Environmentally friendly car can decrease the use of petroleum Environmentally friendly car can reduce the greenhouse gas emission Purchase intention (PI) (normed χ2 =1.335, CFI=0.999, RMSEA=0.041) I would buy an environmentally friendly car if the quality is lower than a conventional car I would buy an environmentally friendly car even if the performance is lower than a conventional car I would buy an environmentally friendly car even if it has a less-appealing design I would buy an environmentally friendly car even if it is less comfortable Purchase behaviour (PB) (normed χ2 =1.871, CFI=0.923, RMSEA=0.061) I often buy environmentally friendly car I often buy environmentally friendly car to reduce CO2 emission and air pollution I often buy environmentally friendly car to protect our natural environment I often think about environment and human health before purchasing anything Individual consequences (ICs) (normed χ2 =1.553, CFI=0.911, RMSEA=0.071) Environmentally friendly car is comfortable to use Environmentally friendly cars are safe in mode of transportation Environmentally friendly car can reduce carbon emissions Environmental consequences (ECs) (normed χ2 =1.442, CFI=0.921, RMSEA=0.081) Conventional car affects air and water quality because oil and particles get washed into lakes and rivers Air pollution caused by a conventional car has negative health effects especially for those people who has asthma and other respiratory problems Conventional car can contribute to environmental degradation
Composite reliability and AVE were calculated using a procedure outlined by Lowry and Gaskin (2014). Composite reliability was found as PBC (0.86), SN (0.83), ATT (0.81), PI (0.88) IC (0.77) and EC (0.80), as shown in Table 4. These values were greater than the minimum acceptable reliability of 0.70 (Hair et al. 2006; Malhotra and Birks 2007; Sekaran 2003). The AVE values for all the eight factors exceeded the minimum criteria (AVE>0.50). As shown in Table 4, the convergent validity for the proposed constructs of this study is adequate. Once convergent validity was achieved, it was appropriate to test for discriminant validity. Discriminant validity was present when the correlation between the two constructs was lower than the recommended value (r≤0.85) indicating the existence of discriminant validity (Kline 2005) (see Table 4).
Average
0.86 0.558 0.77 0.84 0.82 0.56 0.83 0.588 0.77 0.84 0.69 0.81 0.624 0.76 0.76 0.85 0.88 0.714 0.77 0.85 0.89 0.87 0.87 0.512 0.72 0.69 0.78 0.67 0.77 0.508 0.69 0.73 0.72 0.80 0.504 0.72 0.68 0.73
Test for structural equation modelling SEM is used to test the causal effect among the main constructs of a hypothesised model (Kline 2010). In this study, a structural model was tested to examine the relationship among ATTs, SNs, PBC, PI and PB (see Fig. 5). The model had an adequate fit to the data: normed chi-square=2.54, less than 3; CFI = 0.932, greater than 0.90; p = 0.015, less than p ≥ 0.005; and RMSEA = 0.077, less than 0.088 (Hair et al. 2010). As shown in Fig. 5, the R2 values for the two dependent (endogenous) variables were purchase intention=0.70 and purchase behaviour=0.69 which indicated that a large percentage of the variance in the dependent factors was explained by the independent (exogenous) factors. All hypotheses were
Environ Sci Pollut Res
Fig. 6 Mediating effects of purchase intention
Fig. 5 Integrated structural equation modelling
Discussions
supported in the SEM based on the significant level (p
RESEARCH ARTICLE
Consumer purchase intention towards environmentally friendly vehicles: an empirical investigation in Kuala Lumpur, Malaysia Rafia Afroz 1 & Muhammad Mehedi Masud 2 & Rulia Akhtar 1 & Md. Ashraful Islam 3 & Jarita Bt Duasa 1
Received: 11 January 2015 / Accepted: 4 June 2015 # Springer-Verlag Berlin Heidelberg 2015
Abstract This paper examines whether attitudes towards electric vehicles (ATEVs), subjective norms (SNs) and perceived behavioural control (PBC) have significant associations with consumer purchase intention (PI) and the purchase behaviour of environmentally friendly vehicles (EFVs). The results from the survey questionnaires are analysed using confirmatory factor analysis (CFA) and structural equation modelling (SEM). The findings of this paper indicate that ATEV, SN and PBC significantly influence PI. This finding also indicates that environmental consequence and individual preferences do not influence the PI of the respondents. We found that Malaysian car owners are largely unaware of the greenhouse effects on the environment or attach to it little importance, which is reflected in their PI towards EFVs. The outcomes of this study could help policymakers design programmes to influence attitudes, subjective norms, perceived behavioural control and purchase behaviour to prevent further air pollution and reduce CO2 emissions from the transportation sector.
Responsible editor: Philippe Garrigues * Rulia Akhtar [email protected] 1
Department of Economics, Faculty of Economics and Management Science, International Islamic University Malaysia, 50728 Kuala Lumpur, Malaysia
2
Faculty of Economics and Administration, University of Malaya, 50603 Kuala Lumpur, Malaysia
3
Research Management Centre, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
Keywords Attitudes . Subjective norm . Perceived behavioural control . Purchase intention . Electric vehicle . Carbon emissions
Introduction Malaysia is an industrialising country that is rapidly developing to achieve its Vision 2020. Its economy has shifted from an agricultural-based economy to a manufacturing-based economy. Consequently, Malaysia has experienced incredible economic achievement in the last three decades, which has accelerated urbanisation and led to an increase in the per capita income of its population. Rapidly increasing economic activities and rising income levels have led to an exponential increase in the demand for both freight and passenger transport services, especially in the rapidly growing urban areas. The total number of registered vehicles continues to increase in Malaysia. It increased from 7,686,684 in 1996 to 19,016,782 in 2009 (Ministry of Transport 2009). The total numbers of registered vehicles in Malaysia were 19,016,782 in 2009, of which 8,506,080 accounts for private passenger cars. This has made the land transport sector a prominent sector (Malaysian Environmental Quality Report 2012) but has contributed to the deterioration of the ambient air conditions in Malaysia (Malaysian Environmental Quality Report 2010). In Malaysia, the transport sector contributed approximately 97 % of carbon monoxide (CO), 18 % of particulate matter (PM10), 52 % of nitrogen oxide (NO2) and 7 % of sulphur oxide (SOx), from 1991 to 2009 in a 2-year interval. For this reason, transportation has been recognised as the fastestgrowing source of carbon dioxide (CO2) emissions (Ribeiro and Cardoso 2003). According to World Energy Statistics (IEA 2007), the total CO2 emission in Malaysia increased 21.30 % during 2000 to 2003, whereas during the same
Environ Sci Pollut Res
period, it is increased 7.31 % globally and 16.57 % in Asia. In Malaysia, the total CO2 emission increased more than the global and Asian average. The Department of Environment Malaysia (2003) stated that the Klang Valley was the most prone region to serious air pollution compared to other parts of the country. With mountains in the east and the Straits of Malacca to the west and being highly developed and densely populated, the Klang Valley provided a conducive environment for pollutants to accumulate particularly when atmospheric conditions are stable. For this reason, it has attracted serious concern from the authorities after recognising that it had a high potential of air pollution due to its inherent topography (Sham 1979). A study undertaken in 1987 by the Klang Valley Environmental Improvement Project reported that the total air pollution emission level was approximately 433,400 tons per year (Department of Environment 1987). The Malaysian government has expressed its strong commitment towards strengthening a sustainable transportation system. Since late 2011, the Malaysian government has been working towards accelerating developments in the transport sector towards zero-emission mobility. Their goal is to achieve a 10 % increase in the number of environmentally friendly vehicles (EFVs) on Malaysian roads by 2020 (Ministry of Transport 2010). The government is encouraging private firms to introduce the necessary infrastructure for efficient operation of these vehicles. Furthermore, the government is developing regulations and standards for firms that plan on setting up charging stations for EFVs. Such measures are needed to create an environment that generates public interest for the use of EFVs. Malaysia-based vehicle producer, Proton, and Japanbased vehicle producer, Nissan and Mitsubishi, are all running trials of EFVs in Malaysia to raise public awareness of the vehicle’s plug-in option and to test the cars’ viability. If Malaysia is to achieve its ambitious target of zero-emission mobility, it will need to move quickly to generate industry interest and public acceptance.
Literature review Numerous theories addressed human behaviour. The most frequently cited theory on the attitude-behaviour relation is the theory of planned behaviour (TPB) (Ajzen 1991). The theory of planned behaviour includes the following five essential dimensions that clarify human behaviour: (i) attitudes towards certain behaviour, which measures an individual’s general attitude and is identified as an evaluative response reflecting beliefs about the behaviour (Oskamp 1977); (ii) subjective norms, as the perceived social pressure to conduct the behaviour in which an individual is considered responsible to behave according to external opinions that influence what he or she does; and (iii) perceived behavioural control (PBC) refers
to an individual’s belief about their ability to perform a specific behaviour based on their capabilities and the external forces that can influence alternative (Staats 2003). These three variables determine (iv) the intention to perform the behaviour and (v) the intention which, in turn, does directly affect behaviour (Ajzen 1985). The TPB proposes that behaviour is affected by behavioural intentions, which, in turn, are affected by attitudes towards the behaviour. The attitude can be defined as Bthe result of a consumer’s assessment of particular behaviours^ (Ajzen 1991). Many researchers reported consumers having very positive attitudes towards preventing environmental damage using the TPB model (Vermeir and Verbeke 2006; Bhattacharya and Sen 2004; Sen and Bhattacharya 2001). Consumers even demand companies to produce their products in an environmentally friendly way (Bockman et al. 2009; Kanarattanavong and Ruenrom 2009; Schmeltz 2012). Many researchers studied the attitude-behaviour relation when it concerned green products, and all concluded that there was a wide gap between what consumers thought and what they did regarding making purchase decisions (Eck 2009; Anon 2009; Finisterra et al. 2009; Basu and Hicks 2008; Darnall et al. 2012; Ferguson 2011). Young (2004) named this gap the attitude-behaviour gap. In the case of green-purchasing behaviour, the attitude-behaviour gap is generally formed when a consumer is concerned about environmental consequences and individual consequences and thinks it is important for companies to be socially responsible and produce green products but do not interpret their positive attitudes when making a purchase. Oliver and Rosen (2010) argued that environmental values are powerful predictors of certain consumer actions and positively influence willingness to engage in actions that protect the environment (Oliver and Rosen 2010). For example, few researchers found that consumers who showed high levels of environmental awareness during their purchase were more likely to purchase environmentally friendly car compared to those consumers who were less environmentally concerned (Heffner et al. 2007). Similarly, Gallagher and Muehlegger (2011) found that social preferences for environmental quality and energy security were a major determinant for consumer adoption of environmentally friendly car. Gallagher and Muehlegger (2011) concluded that social preferences increased environmentally friendly car sales more than rising gas prices or tax incentives. On the other hand, a study by Ramayah et al. (2010) found that environmental consequences showed non-statistically significant effects on green purchase intention among Malaysians baby diaper users, which contradicted with previous findings. Individual consequences include measures of convenience, range of product sizes and perceived usefulness in relation to purchase intention such as when individuals want to buy a product, they will choose those options that maximise their
Environ Sci Pollut Res
utility based on their convenience, preference, usefulness of the product and their budget (Roche et al. 2010). Domina and Koch (2002) concluded that convenience is an important factor affecting recycling behaviour of textile (Domina and Koch 2002). Van Bree (2010) found that an increase in gas prices influences consumer behaviour. In a study on consumer adoption of hybrid electric vehicle (HEV), Gallagher and Muehlegger (2011) found that consumers usually make the decision to buy (HEVs) in response to an increase in gas prices and government incentives. This study employed the aforementioned five components of TPB to design and developed the questionnaire. Attitude towards environment showed respondents’ opinions regarding EFVs and their awareness of environmental issues. Figure 1 presents the research model proposed to identify the relationship among dependent and independent variables as derived from a review of the relevant literature. Here, the two important attitudinal variables of environmental consequences and individual consequences were added to the TPB model since they significantly affected the attitudes towards green products (Oliver and Rosen 2010; Heffner et al. 2007; Kahn 2007; Gallagher and Muehlegger 2011; Ramayah et al. 2010; Roche et al. 2010; Domina and Koch 2002; Van Bree 2010). In our proposed model, the purchase behaviour among the respondents (i.e. dependent variable) is the function of intention to purchase and the purchase intention is a function of attitudes towards EFVs, subjective norms and perceived behavioural control (i.e. independent variables). An attitude towards EFVs is a function of individual consequences and environmental consequences.
Fig. 1 Proposed research model (adopted from Ajzen (2002), theory of planned behaviour (TPB))
Sampling The survey was conducted from January to April 2014. All of the respondents were owners of vehicles and above 18 years. The questionnaires were distributed and collected through face-to-face interviews from parking areas in different shopping malls and university campuses. The study applied the convenient sampling method to collect the data because the population is too large with an unknown population size and it is impossible to include every individual. This sampling technique was chosen because it is fast and inexpensive and easy to collect data. The question of the adequacy of sample size remains a prime concern in the application of structural equation modelling (SEM). Since our population size is unknown, to obtain an appropriate sample size from this population, the following formula was used (Lind et al. 2012), which gave us a sample size of 384.16.
Methodological approach
n ¼ πð1−πÞðZ=E Þ2
Study area
where n is the size of the sample, π (0.50) is the population proportion, Z is the standard normal value corresponding to the desired level of confidence, and E is the maximum allowable error. Based on the formula for sample size, Z=1.96 (95 % confidence level), π=0.5 and E=5 %. Considering 5 % non-response error, we increase our sample size to 400. Conferring to Hoe (2008), a sample size of 200 possesses sufficient statistical strength for data analysis. However, if the sample size is too large, e.g. beyond 400, the SEM statistical analysis might be too sensitive, constructing goodnessof-fit measures that indicate poor fit (Hair et al. 2010). Therefore, based on the above-recommended guidelines, approximately 400 questionnaires were distributed and the response rate was 87.5 %. Out of 400, 17 respondents did not complete all the sections, 8 respondents did not response correctly, and 25 respondents did not agree to participate in this survey. Finally, 350 usable questionnaires were analysed in this study.
Kuala Lumpur, Putrajaya and Labuan are the three areas directly governed by the federal government of Malaysia. This study focused on the federal territory of Kuala Lumpur for several reasons. Among them is that it has the largest population and largest economy in terms of gross domestic product (GDP) in Malaysia (DOSM 2012). It also contributed to the state’s rapid development as Malaysia’s transportation and industrial hub (DOSM 2012). Kuala Lumpur (latitude 3° 8′ N, longitude 101° 44′ E) is situated in the Federal Territory of Kuala Lumpur, in the west Peninsular Malaysia. The total population of Kuala Lumpur in 2005 was estimated at 1.6 million people (City Hall of Kuala Lumpur 2008). The ethnicity compositions were Malays (41 %), Chinese (39 %), Indians (10 %) and foreign population (7 %). The Federal Territory of Kuala Lumpur consists of 11 districts as shown in Fig. 2.
ð1Þ
Environ Sci Pollut Res Fig. 2 Map of the Federal Territory of Kuala Lumpur
Research design This study used the theory of planned behaviour with five components to design and develop the theoretical framework, namely attitude towards EFVs (ATT), subjective norms (SNs), PBC, purchase intention (PI) and purchase behaviour (PB). Apart from the TPB, two attitudinal variables were considered in this model, namely individual consequences (ICs) and environmental consequences (ECs) adopted from Ramayah et al. (2010). The questionnaire consisted of sections A, B and C. Section A collected information on the respondents’ socioeconomic characteristics such as gender, age, race, education and income. These variables were adopted from a household survey questionnaire in Malaysia (DOSM 2012). Section B included several items to measure the respondents’ attitudes towards EFVs, subjective norm, perceived behavioural control, purchase intention towards EFVS and PB. All the construct domain and measurement items were adapted from a comprehensive review of past studies (Kalafatis et al. 1999; De Cannière et al. 2009; Oliver and Lee 2010; Ramayah et al. 2010). Most of the items were not taken as found in the existing literature but were developed and modified due to the different context of the study. Section C contains several questions that ask the respondents to explore their awareness,
knowledge and perception of EFVs. Hence, in order to measure awareness and knowledge and perception of EFVs, several measurement items were adapted from the past studies of Tran (2006), Bayard and Jolly (2007), Zirbel et al. (2011), Lillemo (2014) and Shoukry et al. (2012). All items for section B are measured using five-point Likert scales representing a range from 1 (strongly disagree) to 5 (strongly agree).
Results and discussion Descriptive statistical analysis The researchers successfully distributed 400 questionnaires among Malaysian car owners and received a total of 350 completed questionnaires, where 50 questionnaires were excluded from 400 questionnaires due to incomplete and incorrect responses. This showed a response rate of 87.5 %. Table 1 shows that males constituted 51.7 % while females 48.3 %. The greatest number of respondents (49.0 %) aged between 31 and 45 years. Most of the respondents are middle aged. The largest number of respondents were Melayu (64 %). 25.7 % had graduate education, while 24.2, 21.4, 20 and 8.5 % had
Environ Sci Pollut Res Table 1
Demographic information of the respondents (N=350)
Variables Gender Male Female Age (years) 18–30 31–45 46–60 Race Malay Indian Chinese Others Education Lower secondary school Higher secondary school Diploma Graduate Postgraduate Income RM 2000 and less than RM 2001 to RM 4000 RM 4001 to RM 6000 RM 6001 to RM 8000 RM 8001 and above
Table 2
Awareness and knowledge of environmental issues
Frequency
Percentage (%)
Items
181 169
51.7 48.2
142 170 38
40.0 49.0 11.0
223 37 85 5
64.0 10.5 24.1 1.4
75 70 85 90
21.4 20.0 24.2 25.7
Awareness of environmental issues I am aware of environmental problems in Malaysia I am aware that CO2 emission is increasing within Kuala Lumpur City I am aware that transportation sector contributes highly towards CO2 emission I am aware that EFVs can contribute to reduce CO2 Environmental knowledge I know about environmental issues I know Malaysia is facing environmental issues (like water, air pollution, solid waste, climate change, etc.) I understand that there are environmental indicators I know traditional vehicles are damaging our environment
30
8.5
28 146 138 24 14
8.0 41.7 39.4 6.8 4.0
diplomas, lower secondary, higher secondary and postgraduate education, respectively. The study found that only 8.0 % of the respondents had an income range of RM 2000 or less. The highest percentage of the respondents (41.7 %) had an income range of RM 2001 to RM 4000 per month, while 39.4 and 7.3 % of the respondents had an income range of RM 4001 to RM 6000 and of RM 6001 to RM 8000, respectively. Only 4.0 % of respondents had an income range of more than RM 8000 per month.
Automobile users’ awareness and knowledge of environmental issues In order to measure respondents’ awareness level of environmental issues, several statements were included in the questionnaire as shown in Table 2. The results indicate that 78 % of the respondents were aware of environmental problems, 90 % of the respondents confirmed that transportation is the largest contributor of CO2 emission, while 86 % admitted that CO2 emission is increasing in the Federal Territory of Kuala Lumpur, Malaysia. However, only 67 % of the respondents are aware of environmentally friendly cars. Similarly, to explore the knowledge of environmental issues, we found that
Agreed Disagreed (%) (%)
78
28
86
14
90
10
70
30
75 73
25 27
74
26
66
34
75 % of the respondents confirmed knowing about environmental issues while 73 % mentioned that Malaysia is confronting environmental issues like air pollution, water pollution, climate change, etc. Meanwhile, only 66 % mentioned that traditional cars are damaging our environment. Awareness of environmentally friendly vehicles To explore the respondents’ familiarity with EFVs, we included several types of EFVs in the questionnaire as shown in Fig. 3. The results revealed that 42.1 % of respondents were familiar with plug-in hybrid electric vehicles (PHEVs) while 40.5 and 17.4 % of the respondents were familiar with HEVs and battery electric vehicles, respectively. Users’ concern about EFVs In order to explore users’ concerns about EFVs, we stated several items on EFVs in the questionnaire, as show in Fig. 4. The figure shows that 36 % of the respondents identified battery range as the greatest challenge, followed by high
Aware of EVs Percentage (%) 42.1
40.5
17.4
Hybrid electric vehicle (HEV)
Plug-in hybrid electric vehicle (PHEV)
Fig. 3 Awareness of EFVs
Baery electric vehicle (BEV)
Environ Sci Pollut Res
Fig. 4 Apprehension about electric vehicles (EFVs)
cost (24 %), changing infrastructure (12 %), reliability (10 %) and safety (8 %). These concerns reaffirm some of the issues identified initially by respondents when asked about associations with EFVs. In order to measure their level of concern over the aforementioned associations of EFVs, we stated one questionnaire items with five-point scales representing a range from 1 (not concern at all) to 5 (very much concern). It indicated that 80 of the respondents are very much concerned about it while 15 % are concerned and 5 % of the respondents are not concerned. Ranking of EFV attributes In this study, the authors discussed the ranking of electronic vehicle attributes as shown in Table 3. Table 3 indicates that Malaysian car owners assign ranking no. 1 to design of the car while rankings 2–5 were assigned to safety, quality, performance and reduced greenhouse gas emission, respectively. This means that Malaysian car owners are more concern about the design of the car. It also shows that they assigned rank 5 to reduced greenhouse gas emission. This result indicates that Malaysian car owners are not aware of the greenhouse effects on the environment. In this regard, policymakers and social organisations should work together to create awareness. Tests for confirmatory factor analysis According to Kline (2010), the purpose of a measurement model points to the appropriateness of the observed indicators as a measurement instrument representing a latent variable. This is echoed by Hair et al. (2011), who observed that in Table 3
Ranking of EV attributes
Items
Mean (N=385) Standard (N=385) Ranking
Performance Safety Design Quality Reduced greenhouse gas
2.8421 2.3584 2.1870 2.4312 3.012
1.07778 1.08797 1.08318 1.12549 1.09823
4 2 1 3 5
measurement theory, the purpose is to estimate the relationship between the observed and the underlying latent variables. The adequacy of a measurement model is performed by confirmatory factor analysis (CFA). In doing so, four fit indices are checked to ascertain the fitting of the model with the data: chi-square statistic, normed chi-square, root mean square approximation (RMSEA) and comparative fit index (CFI). For an adequate model fit, general guidelines suggest cut-off values for such indices: normed chi-square and RMSEA are to be less than 5 and 0.088, respectively, while CFI values are to be above 0.9 (Hair et al. 2011; Byrne 2009). Prior to testing the structural equation model, CFA was performed on the entire set of measurement items simultaneously. The process of evaluating the measurement model resulted in deleting terms based on the factor loadings only of less than 0.40 (Field 2009). Based on the CFA tests, all the seven dimensions had adequate model-to-data fit: normed chi-square value below 2.41, CFI value above 0.95 and RMSEA value less than 0.080. These tests also evaluated the reliability and construct validity. Cronbach’s alpha measures the reliability coefficient, which indicates the consistency of the entire scale (Hair et al. 2011) or the overall reliability of the questionnaire (Field 2009). The results from this study showed that all the four dimensions had reliability values above 0.70 which indicated that the questionnaire was reliable and consistent (see Table 4). According to Hair et al. (2010), a standardised factor loading should be 0.40 or higher, ideally 0.70 or higher, which provides strong evidence of convergent validity. In this study, all the items had significant factor loadings, most of them greater than 0.60, which indicates adequate convergent validity.
Validating the measurement model In order to validate the measurement model, the convergent and discriminant validity was measured (Janssens et al. 2008; Kline 2005; Schumacker and Lomax 2004). Convergent validity is judged to be adequate when average variance extracted (AVE) equals or exceeds 0.50. The standardised factor loadings of all the items were larger than 0.50, ranging from 0.56 to 0.86, and were statistically significant at the 0.001 % level. This evidence supported the unidimensionality of each scale, which indicated that convergent validity was obtained. The reliability of the construct (Cronbach’s alpha and composite reliability) should also be assessed (Anderson and Gerbing 1988). The purpose of composite reliability is to measure the reliability of the internal consistency of the measured items representing a latent construct and must be established before construct validity can be assessed (Hair et al. 2010). According to Chinna (2009), the composite reliability, by convention, should be at least 0.70 to suggest good reliability and to indicate that internal consistency exists.
Environ Sci Pollut Res Table 4
Construct validity of confirmatory factory analysis
Items
Standard CR loadings
Perceived behavioural control (PBC) (normed χ2 =1.536, CFI=0.995, RMSEA=0.052) Respectfulness and politeness is important to me Self-control such as being restrained and self-disciplined is important to me Clean and tidy environment is important to me For the achievement of my life, being hardworking and aspiring is important to me Subjective norms (SNs) (normed χ2 =1.19, CFI=0.999, RMSEA=0.030) Conventional car can create air pollution Conventional car can create smog in large cities Conventional car produce greenhouse gases such CO2 and NO2 that contribute to global warming and climate change Attitudes towards EVs (ATT) (normed χ2 =2.246, CFI=0.991, RMSEA=0.079) Environmentally friendly car is a fuel-efficient car. So, it can reduce CO2 emission Environmentally friendly car can decrease the use of petroleum Environmentally friendly car can reduce the greenhouse gas emission Purchase intention (PI) (normed χ2 =1.335, CFI=0.999, RMSEA=0.041) I would buy an environmentally friendly car if the quality is lower than a conventional car I would buy an environmentally friendly car even if the performance is lower than a conventional car I would buy an environmentally friendly car even if it has a less-appealing design I would buy an environmentally friendly car even if it is less comfortable Purchase behaviour (PB) (normed χ2 =1.871, CFI=0.923, RMSEA=0.061) I often buy environmentally friendly car I often buy environmentally friendly car to reduce CO2 emission and air pollution I often buy environmentally friendly car to protect our natural environment I often think about environment and human health before purchasing anything Individual consequences (ICs) (normed χ2 =1.553, CFI=0.911, RMSEA=0.071) Environmentally friendly car is comfortable to use Environmentally friendly cars are safe in mode of transportation Environmentally friendly car can reduce carbon emissions Environmental consequences (ECs) (normed χ2 =1.442, CFI=0.921, RMSEA=0.081) Conventional car affects air and water quality because oil and particles get washed into lakes and rivers Air pollution caused by a conventional car has negative health effects especially for those people who has asthma and other respiratory problems Conventional car can contribute to environmental degradation
Composite reliability and AVE were calculated using a procedure outlined by Lowry and Gaskin (2014). Composite reliability was found as PBC (0.86), SN (0.83), ATT (0.81), PI (0.88) IC (0.77) and EC (0.80), as shown in Table 4. These values were greater than the minimum acceptable reliability of 0.70 (Hair et al. 2006; Malhotra and Birks 2007; Sekaran 2003). The AVE values for all the eight factors exceeded the minimum criteria (AVE>0.50). As shown in Table 4, the convergent validity for the proposed constructs of this study is adequate. Once convergent validity was achieved, it was appropriate to test for discriminant validity. Discriminant validity was present when the correlation between the two constructs was lower than the recommended value (r≤0.85) indicating the existence of discriminant validity (Kline 2005) (see Table 4).
Average
0.86 0.558 0.77 0.84 0.82 0.56 0.83 0.588 0.77 0.84 0.69 0.81 0.624 0.76 0.76 0.85 0.88 0.714 0.77 0.85 0.89 0.87 0.87 0.512 0.72 0.69 0.78 0.67 0.77 0.508 0.69 0.73 0.72 0.80 0.504 0.72 0.68 0.73
Test for structural equation modelling SEM is used to test the causal effect among the main constructs of a hypothesised model (Kline 2010). In this study, a structural model was tested to examine the relationship among ATTs, SNs, PBC, PI and PB (see Fig. 5). The model had an adequate fit to the data: normed chi-square=2.54, less than 3; CFI = 0.932, greater than 0.90; p = 0.015, less than p ≥ 0.005; and RMSEA = 0.077, less than 0.088 (Hair et al. 2010). As shown in Fig. 5, the R2 values for the two dependent (endogenous) variables were purchase intention=0.70 and purchase behaviour=0.69 which indicated that a large percentage of the variance in the dependent factors was explained by the independent (exogenous) factors. All hypotheses were
Environ Sci Pollut Res
Fig. 6 Mediating effects of purchase intention
Fig. 5 Integrated structural equation modelling
Discussions
supported in the SEM based on the significant level (p
