consumers’ actual purchase behaviour towards green

MALAYSIAN JOURNAL OF CONSUMER AND FAMILY ECONOMICS Vol 25, 2020

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Consumers’ Actual Purchase Behaviour towards Green Products in Bangladesh

Md. Sakibur Rahman1, Mst. Anjuman Ara1, Md. Abdul Alim1, Teck Weng Jee2, Rodney Thiam Hock Lim2

1Department of Marketing, University of Rajshahi, Bangladesh 2School of Business, Swinburne University of Technology Sarawak

Campus, Malaysia

Abstract Global climate change is increasing at an alarming rate worldwide, with many developing countries reaching the limits of their ability to cope. While these countries must adopt environmentally sustainable behaviours, problems with awareness and purchase behaviours among the population remain formidable challenges to overcome. Within the context of Bangladesh, this study attempts to test consumers’ actual purchase behaviour towards green products. Employing the Theory of Planned Behaviour (TPB), a survey of 240 consumers, and Partial Least Squares-based Structural Equation Modelling (PLS-SEM) analysis, it was found that consumer attitude towards the environment, subjective norms, and perceived behavioural control have a significantly positive influence on behavioural intention, while behavioural intention has a significantly positive influence on actual purchase behaviour. The discussions along with the theoretical and practical implications presented at the end of this study are helpful for policy-makers, investors, and the related stakeholders. The conclusions, limitations, and future research directions are also discussed.

Keywords: Actual purchase behaviour, behavioural intention, Bangladesh, and green product.

Introduction In the past few decades, rapid growth in the world economy,

fuelled by overconsumption and overutilization of natural resources has led to serious degradation of the natural environment which has manifested in the form of the present climate crisis, problems with pollution, waste disposal, natural disasters, social inequalities, and other humanitarian issues (Campbell-Lendrum & Prüss-Ustün, 2019; Dermody, 2011; Schaffartzik et al., 2019; Wiedmann et al., 2020). One


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of these major issues currently at the forefront of the global green awareness agenda has been the climate change crisis where recent studies have gathered strong evidence of growing public awareness about global warming (Energy Policy Institute at the University of Chicago & The AP-NORC Centre for Public Affairs Research, 2019; Leiserowitz, et al., 2018). In these studies, respondents have indicated concerns about human activities that drive global warming and expressed an increasing desire to engage with these issues. As experts continue to highlight the severity of the matter on human populations (Bologna & Aquino, 2020; Wiedmann et al., 2020), policymakers in international bodies, governments, and businesses are facing increasing pressure to respond urgently by raising awareness and promote sustainability through green consumerism. Green consumerism refers to consumption behaviours that promote positive environmental effects (Sachdeva et al., 2015; Testa et al., 2020) and involves environmentally sustainable production and consumption of goods and services such as those that incorporate renewable and clean energy, organic and low toxicity products, minimized wastage, zero emissions, and so on (Connoly & Prothero, 2008).

While awareness about the importance of preserving the natural environment has gathered much momentum in recent years, the epicentre of this global green movement continues to be entrenched in the more affluent countries in North America and Europe. Correspondingly, research interest on green consumption behaviours has also mostly focused on these more developed countries (Cho et al., 2013; Gracia & de Magistris, 2007; Kanchanapibul et al., 2014; Liobikiene et al., 2016; Matthes et al. 2014; Onwezen et al., 2014; Testa et al., 2020). In comparison, there has been relatively less research into green consumption behaviour in the context of developing countries (Adrita, 2020; Bhatia & Jain, 2013; Cherian & Jacob, 2012; Khare, 2015; Obayelu, 2019), including Asian countries (Maichum et al., 2016; Mei et al., 2012).

Developing countries hold great potential for green products due to their growing economies, large market sizes, and increasing concerns over environmental issues. At the same time, research findings from the more developed western settings may not apply to other less developed settings as geographical and country-specific factors have been found to affect social and behavioural characteristics of environmentally conscious consumers (Arbuthnot & Lingg, 1975; Testa et al., 2020). Therefore, building a more robust theory of green


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consumption behaviour requires more emphasis on research in diverse contexts including developing countries. In line with this, this study aims to add insights on green consumption behaviours in the context of a developing country, namely Bangladesh. Using the Theory of Planned Behaviour (TPB) as the foundation of the study, this study investigates consumers' actual purchase behaviour towards green products in the context of Bangladesh. The green concept was introduced in Bangladesh in 1993 and formally recognized through the banning of polythene bags in 2002 by the Ministry of Environment and Forest (MoEF). The country offers several environmental awareness campaigns and programs aimed at making her people conscious about green products (Diamantopoulos et al., 2003).

The paper will next present a literature review followed by a proposed framework and hypotheses development. Next, the methods of the study along with the data analysis, results, discussions, and implications will be presented. Finally, the paper concludes with the limitations of the study and future research directions.

Theoretical Framework

This study utilized the Theory of Planned Behaviour (TPB) (Ajzen, 1985, 1988, 1991) which is a popular behavioural model that is used to explain and predict changes in behaviours (Ajzen 1991). It was developed from the Theory of Reasoned Action (TRA), which suggests that a person’s intentions to perform a behaviour is a function of their attitude toward the behaviour and subjective norms ((Fishbein & Ajzen, 1975). Since behaviour was found to not be completely voluntary and cannot always be controlled, the concept of perceived behavioural control was added to the TRA model to derive the TPB.

Perceived behavioural control is defined as an individual's perception of the ease or difficulty of performing a particular behaviour (Ajzen, 1988). How strong an attempt the individual makes to engage in the behaviour and how much control that individual has over the behaviour (behavioural control) are influential in whether he or she engages in the behaviour. Thus, behavioural intention is produced from a combination of attitude toward the behaviour, subjective norm, and perceived behavioural control. Behavioural control is similar to self-efficacy and depends on the individual's perception of how difficult it is going to be to engage in the behaviour. The more favourable a person's attitude is towards behaviour and subjective norms and the greater the


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perceived behavioural control, the stronger that person will intend to perform the behaviour.

Furthermore, the Theory of Planned Behaviour has been used in many previous studies to investigate purchase behaviour (Chaudhary & Bisai, 2018; Taufique & Vaithianathan, 2018;). Therefore, based on its broad acceptance and applicability, the Theory of Planned Behaviour has been applied as the theoretical foundation for this study. The current study’s theoretical framework (Figure 1) consisting of five basic constructs, namely attitude towards the environment, subjective norms, perceived behavioural control, behavioural intention, and actual purchase behaviour, has been formulated to investigate consumers’ actual purchase behaviour towards green products. The theoretical framework shows attitude towards the environment, subjective norms, perceived behavioural control, and behavioural intention as the proposed exogenous constructs that affect actual purchase behaviour.

Many previous studies have advocated that attitude towards the environment, subjective norms, and perceived behavioural control have a significant positive effect on behavioural intention (Carfora et al., 2019; Chaudhary & Bisai, 2018; Taufique & Vaithianathan, 2018). However, a few other studies have also claimed that subjective norms do not have a positive effect on behavioural intention (Chaudhary & Bisai, 2018; Taufique & Vaithianathan, 2018). Furthermore, Carfora et al. (2019) and Chaudhary and Bisai (2018) have also contended that consumers’ positive behavioural intention has a significant influence on actual purchase behaviour.

Figure 1 : Theoretical framework

Attitude towards the environment

Subjective norms

Perceived behavioural control

Behavioural intention

Actual purchase behaviour

H1

H2

H3

H4


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Literature Review 3.1 Green Products

The concept of green products came into prominence in the 1970s and 1980s amidst awareness that environmental issues such as overconsumption are the result of marketing activities (Fisk, 1974). An outcome of this growing awareness was the introduction of green products (Peattie & Crane, 2005) which fuelled a shift towards green marketing initiatives. To date, there has been a lack of a uniform definition of the term green products. Although it can actually encompass several dimensions such as political, corporate social responsiveness, fair trade, conservation, non-profit, ethical, and so on (McDonagh & Prothero, 1996), the concept 'green' is usually taken to mean environmental-friendliness and sustainability (Zhao & Chen, 2019). Within this perspective, green products do not just reduce impacts on the environment, but can bring about greater benefits to the environment compared to conventional products (Reinhardt, 1998) in terms of conserving energy and/or resources and reducing or eliminating the use of toxic agents, pollution, and waste (Ottman et al., 2008). They are not harmful to consumers (Lian et al., 2016), do not harm the environment or do not damage natural resources, and can be recycled or conserved (Kim et al., 2013). These products are often cutting edge, modern, cost-effective, and generate less waste. The consumption of green products ensures sound health and an opportunity to contribute to environmental sustainability (Leonidou et al., 2013). Thus, green products revolve around the production and marketing of sustainable and socially responsible products and services and involve a broad range of activities across the entire supply chain including using renewable resources, modifications to the product, its production processes, advertising, packaging, and so on to reduce the detrimental impacts of their consumption and disposal on the environment (Mishra & Sharma, 2012; Sivesan et al., 2013).

The successful adoption of green products by consumers is dependent on their awareness, knowledge of, and concerns with environmental issues. In many Asian countries, awareness programs have contributed to the increasing popularity of green products (Gurau & Ranchhod, 2005; Khan & Kirmani, 2015). As a result, many national and multinational manufacturing companies have begun to devote attention to the production and marketing of green products to satisfy the emerging needs of customers (Kim et al., 2013). Despite this, the


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literature shows that consumers' awareness and concerns about the environment and their favourable attitude towards green purchase do not always lead to the actual purchase of green products (Hughner et al., 2007; Liobikiene & Bernatoniene, 2017). A study by McKinsey and Company in 2007 showed that although 87% of consumers were aware and concerned about the environmental and social impact of the products they buy, only around 33% of them were willing to actually buy green goods (Bonini & Oppenheim, 2008). Thus, awareness of environment-friendly products is not necessarily reflected in the actual purchase behaviour. Apart from environmental awareness, consumers’ actual purchase decision towards green products is also affected by how well they are informed about the benefits and attributes of green products (Sheikh et al., 2014).

These issues are relevant to a developing country like Bangladesh which has been trying to embrace green production to establish green consumerism despite having an inadequate number of producers of green products (Nekmahmud & Fekete-Farkas, 2020; Sarker & Itohara, 2008). To establish a sustainable environment through green products consumption, various industries in Bangladesh have, in recent years, provided close attention to producing more green products in various customer categories such as food, textile, leather, agriculture, plastic, and cosmetics. Thus, efforts to encourage the purchasing of green products among consumers in Bangladesh is contingent on green awareness (Nekmahmud & Fekete-Farkas, 2020, p. 22).

Also, varying socio-economic circumstances in developed and developing countries raise the possibility of differences in purchasing behaviours towards green products. In developed countries, consumers have been found to have high levels of awareness and concern for environmental issues and have expressed intentions to purchase green products (Hughner et al., 2007; Tulley & Winer, 2014). In developing countries, the marketing of green products is confronted with unique barriers to consumer adoption including a lack of information and awareness about environmental issues, problems with quality and credibility, and lower purchasing power among segments of the population (Nguyen et al., 2017). For example, there are indications that customers in developing countries may still prioritize their purchase decisions according to non-green attributes such as value for money, product performance, and safety rather than on green propositions (Johri & Sahasakmontri, 1998). These raises doubt about


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consumer buying responses to green marketing initiatives in developing countries in general. Nevertheless, various studies have found that consumers in various developing countries are influenced by green marketing initiatives and are willing to purchase green products, even as they are priced at a premium (Biswas & Roy, 2016; Hossain & Khan, 2018). The lack of consistent findings in the literature suggests that more work is needed to better understand the determinants of consumers’ purchase behaviour in developing countries.

3.2 Attitude towards the Environment

Attitude is one of the major determining factors of behavioural intention in the TPB framework. Attitude towards a behaviour refers to the extent of a person’s favourable or unfavourable evaluation of a particular behaviour (Ajzen, 1991). Attitude has been described as favourable or unfavourable cognitive evaluations, emotional experiences, or behavioural tendencies that people constantly hold for certain situations or ideas (Blackwell et al., 2006). In terms of the environment, attitude can be defined as “cognitive and affective evaluation of the object of environmental protection” (Bamberg, 2003), or simply an individual’s concern for the environment and environmental issues (Kim & Choi, 2005). More specifically, environmental attitudes refer to ecological concern that is likely to result in a pro-environmental consumption behaviour (Rex & Baumann, 2007). Consumers who are aware of the environment are thought to be more likely to have an intention to endorse environmentally responsible consumer behaviour (Ellen et al., 1997).

The link between attitudes towards the environment and behavioural intentions including intentions to purchase green products has attracted numerous research interest and has covered many categories of green products such as organic products (Aaijaz & Ibrahim, 2009; Zagata, 2012), public transportation (Chen, 2016), green hotels and eateries (Chen & Tung, 2014; Kim et al., 2013), skincare products (Hsu et al., 2017), and others. However, the findings in these studies have often been varied. Several studies on pro-environmental consumer behaviour have acknowledged that attitude towards the environment has a significant positive effect on behavioural intentions (Carforaa et al., 2019; Chaudhary & Bisai, 2018; Chuah & Lu, 2019; Maichum et al., 2016; Taufique & Vaithianathan,


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2018). On the other hand, other researchers have reported weak links. For example, Joshi and Rahman’s (2017) study of young consumers in India found environmental attitudes to be least effective in explaining green purchase behaviour. This was similar to Bamberg’s (2003) findings among German university students concerning green electricity brochures. This leads to the following hypothesis:

H1: Consumer attitude towards the environment have a positive

influence on behavioural intentions of green products purchases

3.3 Subjective Norms

Subjective norms refer to the individual’s assessment of others’ preference and support for behaviour (Tarkiainen & Sundqvist, 2005). The preferences of friends, family members, co-workers, or any reference group may exert an influence on the intention of others to act in certain ways. Many social programs have used this notion of adherence to social norms to shape behaviours’ by emphasizing the desirable behaviour of the majority (Perkins, 2003). Thus, when consumers are unsure about how to act in a specific situation, they try to find support from their close contacts. As such, the reference group (subjective norms) is a commonly used construct in consumer behaviour literature (Taufique & Vaithianathan, 2018). The positive impact of significant others like friends, family, peers, and relatives towards green products has been reported in prior studies in having a significant influence on green consumers’ actual purchase intention (Dean et al., 2012). In this regard, many previous studies have acknowledged the significant positive influence that subjective norms have on green consumers’ behavioural intentions (Carfora et al., 2019). On the other hand, other studies have shown that subjective norms associated with green consumers does not influence their behavioural intentions (Chaudhary, & Bisai, 2018; Taufique & Vaithianathan, 2018). This inconsistent finding supports the need for further study on the effects of subjective norms on the actual purchase behaviour of green consumers. This leads to the following hypothesis:

H2: Subjective norms have a positive influence on the behavioural

intentions of green products purchases.


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3.4 Perceived Behavioural Control

Perceived behavioural control refers to 'the perceived ease or difficulty of performing a behaviour’ (Ajzen, 1991, p. 183). Perceived behavioural control can be divided into two broad categories, namely the internal perceived behavioural control (e.g. required personal skills, confidence, plan, etc.) and the external behavioural control (i.e. the ability to overcome external constraints) (Kidwell & Jewell, 2003). However, the Theory of Planned Behaviour highlighted the necessity of perceived behavioural control in anticipating the intentions as well as actual behaviours when the concerning behaviour is within discretional control of a person (Thogersen, 2007). Despite the numerous studies on consumers’ purchase behaviour, only few studies have been conducted on green products in the consumer behaviour literature (Carfora et al., 2019; Chaudhary & Bisai, 2018). These studies have shown that perceived behavioural control exert significant influences on green consumers’ behavioural intention. This leads to the following hypothesis:

H3: Perceived behavioural control has a positive influence on the

behavioural intentions of green products purchases.

3.5 Behavioural Intention and Actual Purchase Behaviour

Behavioural intention means a person’s degree of determination and willingness to perform a specific behaviour which is often determined by attitude and subjective norms (Ajzen, 1988). Ajzen (1988) also suggested that higher levels of willingness to conduct specific behaviour will have the best possibility of the idea being actually conducted. According to the Theory of Planned Behaviour, actual purchase behaviour is determined by several factors namely attitude, subjective norms, perceived behavioural control, and consumers’ behavioural intention (Azjen, 1991). Where green products have emerged to help conserve the environment (Harris, 2006), green purchasing behaviour is generally associated with purchasing done in a responsible, ethical, sustainable, and environmentally friendly way. Green purchasing behaviour includes buying energy efficient products, avoiding over-packaged goods, exhibiting a preference for biodegradable and recycled articles, and buying fair trade and locally sourced products. It contributes not only to equity and wellbeing in the community but also to the reduction of pollution and the preservation


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of the planet. Many studies conducted on green purchasing behaviour (Carfora et al., 2019; Chaudhary & Bisai, 2018;) advocate that behavioural intention has great power to influence actual purchase behaviours of green consumers. These studies suggest that if a green product marketer can build positive behavioural intentions among its customers, they can be influenced to make actual purchases. This leads to the following hypothesis:

H4: Behavioural intention has a positive influence on the actual

purchase of green products.

Research Methodology 4.1 Survey Measures

A quantitative survey was carried out by collecting data to test the significance of the proposed relations in the conceptual framework. The study adopted all items from previously validated instruments. The questionnaire featured 22 items and five basic demographic questions (i.e. age, gender, marital status, education, and income). Specifically, the items that measure attitude towards the environment, subjective norms, and behavioural intention were adopted from Taufique and Vaithianathan (2018), while the items that measure perceived behavioural control and actual purchase behaviour were adopted from Chaudhary and Bisai (2018). The measurement items were further validated by performing factor loading, Cronbach’s Alpha, composite reliability, and average variance extracted. These items were assessed on a six-point rating scale where 1 = strongly disagree, and 6 = strongly agree.

4.2 Sampling, Sample and Data Collection

The respondents for the data collection were recruited via purposive sampling technique. The data was collected in September 2019 through emails sent to members of Rajshahi University Marketing Alumni Association in Bangladesh where a link for participation was provided in the email. The association has a database of 20,000 registered alumni in Bangladesh. These alumni were chosen as they were likely to be employed and had the means to purchase consumer goods. Survey invitations were sent to 1000 email contacts with follow-up emails sent every seven days, involving a total of four follow-up emails. Respondents were asked screening questions in the first


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section of the questionnaire to confirm that they were consumers of green products. This criterion was established to ensure that respondents had actual knowledge of green products and had experience in purchasing or consuming green products in the past. After undergoing a filtration process for outliers and incompleteness, data from 2401 responses out of a total of 247 completed responses collected were used to test the conceptual model. The sample size in the current study meets the required sample sizes of 30 cases, determined as 10 cases x the maximum of three arrows (i.e. attitude towards the environment, subjective norms, and perceived behavioural control) pointing at a latent research construct [behavioural intention], 74 cases based on power analysis using G*Power, and 100 to 200 cases for a meaningful structural (or path) analysis (Hair et al., 2017; Hoyle, 1995).

4.3 Data Analysis Technique and Procedures

This study employed PLS-SEM to test the causal relations between the constructs of the proposed model. This analytical technique is more appropriate for confirmatory research that aims to examine the extent of the relationships between the exogenous and endogenous constructs where independent constructs predict the ultimate dependent construct (Hair et al., 2017; Ringle, Wende, & Becker, 2015). This technique has been increasingly applied in marketing research and other business studies (Henseler et al., 2009).

The three steps of PLS-SEM procedure for assessing the conceptual framework were drawn for this study. First, this study tested common method bias using Harman’s (1976) single-factor test and Bagozzi’s (1991) correlation threshold of 0.90 and for collinearity of indicators by computing the variance inflation factor (Hair et al., 2017). This was followed by the second step where established convergent and discriminant validity in the measurement model was tested by conducting confirmatory factor and correlation analyses to evaluate the results against the recommended threshold values for factor loadings, composite reliability, average variance extracted (AVE), and heterotrait-monotrait (HTMT) (Byrne, 2010; Fornell & Larcker, 1981; Hair et al., 2010; Henseler et al., 2015; Sarkar et al., 2001). Finally, the

1 24% response rate (240 usable responses / 1000 email survey invitations) were received, consistent with the suggested response rates for most online survey research (Deutskens et al., 2004).


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significance and effect size of the path relationships and predictive relevance of the structural model was tested through bootstrapping and blindfolding procedures (Hair et al., 2017; Ringle et al., 2015).

Findings

5.1 Respondents’ Profiles

Table 1 illustrates the demographic information of the respondents in this study. It shows that the majority of the respondents was in the 21-30-year-old age range (83.8%), while the rest were below 20 years old and above 30 years old. In terms of gender and marital status, a substantial number of respondents were men (75%), and single (76.7%). Most of the respondents were highly educated with 50% of them having undergraduate degrees while 42.5% of the sample have postgraduate degrees. In terms of monthly income, 38.8% of the respondents or their families earned less than BDT 20,000 while only 6.3% earned above BDT 60,000. However, 27.9% of the respondents earned between BDT 20001 to BDT 30000, followed by 15% with incomes between BDT 30001– BDT 40000 or approximately between 350 USD to 470 USD. Such findings are consistent with the sampling frame discussed earlier where the respondents are likely to be employed and have independent means for consumption of consumer goods.

Table 1 : Demographic results

Characteristics Frequency Percentage (%) Age: Below 20 years 16 6.7% 21-30 years 201 83.8% 31-40 years 15 6.3% 41-50 years 6 2.5% Above 50 years 2 0.8% Gender: Male 180 75.0% Female 60 25.0% Marital Status: Single 184 76.7% Married 56 23.3%


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Table 1 : Demographic results- continued

Characteristics Frequency Percentage (%) Education: Undergraduate Degree 120 50.0% Postgraduate Degree 102 42.5% Others 18 7.5% Monthly Income: BDT Below 20,000 93 38.8% BDT 20,001-30,000 67 27.9% BDT 30,001-40,000 36 15.0% BDT 40,001-50,000 20 8.3% BDT 50,001-60,000 9 3.8% BDT Above 60,000 15 6.3%

5.2 Manipulation Checks

The test for common method bias was executed by performing Harman's (1976) single-factor test. This was done by entering all the study constructs into a principal component factor followed by the elimination method of a principal component of one fixed factor without applied rotation (Podsakoff et al., 2003; Podsakoff et al., 2012). The test results showed that only one factor explains less than 40.7% (i.e. 33.06%) of the variance emerged. Un-rotated factor analysis was also applied and the used items produced four factors and explained 62.76% of the variance in total. Therefore, this study did not observe the common method variance as a major concern (Podsakoff et al., 1986).

The study also tested multi-collinearity of the indicators determined by the variance inflation factor and was found to be below the cut-off point of five (Hair et al., 2017). Hence, collinearity of indicators was not an issue in the measurement and structural models to estimate the path relations for PLS-SEM analysis of this study. Next, a two-step procedure was employed to calculate the measurement model for the used constructs and discriminant validity and then to test the drawn hypotheses in the structural model (Anderson & Gerbing, 1988).

5.3 Assessment of Measurement Model

The measurement model performs factor loading, Cronbach’s Alpha, composite reliability (CR), and average variance extracted


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(AVE) to establish the convergent validity (Fornell & Larcker, 1981). More specifically, Table 2 illustrates that the loading values of all items exceeded the suggested threshold value of 0.5 (Hair et al., 2011; Wong, 2013) while CR values of the study constructs exceeded the standard critical level of the recommended value of 0.708 (Hair et al., 2017). Lastly, the study found that the AVE value of this study is greater than the suggested value of 0.5 which was adequate for convergent validity (Bagozzi & Yi, 1988; Fornell & Larcker, 1981). Therefore, all three requisites for the convergent validity of the reflective measurement model were met.

Table 2 : Results of the measurement model

Item Description Loadings Attitude Towards the Environment (CA=0.89, CR=0.93, AVE=0.81) Raising environmental concern among our people in Bangladesh (ATE1)

0.89

More environmental protection works are needed in Bangladesh (ATE2) 0.93 It is essential to promote green living in Bangladesh (ATE3) 0.88 Subjective Norms (CA=0.91, CR=0.93, AVE=0.65) Most of my friends think I should recycle household garbage (SN1) 0.79 Most of my neighbours think I should use green products (SN2) 0.81 Most of my neighbours think I should recycle (SN3) 0.85 Most of my co-workers think I should use environmentally friendly household products (SN4)

0.80

Most of my co-workers think I should recycle (SN5) 0.82 Most of my family members think I should use green products (SN6) 0.79 Most of my family members think I should recycle (SN7) 0.77 Perceived Behavioural Control (CA=0.75, CR=0.84, AVE=0.57) I believe I have the ability to purchase green products (PBC1) 0.76 If it is entirely up to me, I am confident that I will purchase green products (PBC2)

0.77

I see myself as capable of purchasing green products in the future (PBC3)

0.80

I have resources, time, and willingness to purchase green products (PBC4)

0.70


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Table 2: Results of the measurement model - continued

Item Description Loadings Behavioural Intention (CA=0.78, CR=0.84, AVE=0.57) I will join a group or club which is concerned with the environment (BI1) 0.70 I would pay more taxes to support government control of pollution (BI2) 0.77 I would pay more each month for electricity if it meant cleaner air (BI3) 0.72 I would stop buying products from companies who cause pollution even though it might be inconvenient for me (BI4) 0.69

I would make personal sacrifices for the sake of slowing down pollution (BI5) 0.75

Note: CA= Cronbach’s alpha, CR= composite reliability, and AVE= average variance extracted.

Discriminant validity was further assessed by using the test suggested by Fornell and Larcker’s (1981) criterion as the traditional method, and Henseler et al. (2015)’s Heterotrait-Monotrait Ratio of Correlations (HTMT) criterion as a superior method. The traditional method suggested that the square roots of the AVEs were greater than the correlation values for each research construct pairing (Table 3). The superior method suggested that the threshold value of below 0.90 between two study constructs was acceptable to establish discriminant validity. The HTMT0.85 (Table 4) means that the result is below the recommended critical value of 0.85 for each group-specific model estimation. Thus, it also met the tests of discriminant validity.

Table 3 : Fornell and Larcker criterion results

Actual Purchase Behaviour

(APB)

Attitude towards the Environmen

t (ATE)

Behavioural Intention

(BI)

Perceived Behavioural

Control (PBC)

Subjective Norm (SN)

Actual Purchase Behaviour (APB)

0.82

Attitude towards the Environment (ATE)

0.15 0.90

Behavioural Intention (BI)

0.51 0.53 0.73

Perceived Behavioural Control (PBC)

0.44 0.52 0.56 0.76

Subjective Norm (SN) 0.34 0.25 0.40 0.24 0.80 Note: Bold diagonal values represent the square root of the AVE, and the off-diagonal value represents the correlation of coefficient.

Table 4 : Heterotrait-Monotrait Ratio of Correlations (HTMT) criterion results


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Actual Purchase Behaviour

(APB)

Attitude towards the Environment

(ATE)

Behavioural Intention

(BI)

Perceived Behavioural

Control (PBC)


Actual Purchase Behaviour (APB)

Attitude towards the Environment (ATE)

0.19

Behavioural Intention (BI)

0.66 0.62

Perceived Behavioural Control (PBC)

0.58 0.64 0.71


0.41 0.23 0.46 0.30

Note: Discriminant validity is established at HTMT 0.85

5.4 Assessment of Structural Model

In the structural model, the bootstrapping procedure was used to test the path relations. This analytical technique ensures the exactness and significance of the path relations between the study constructs (Hair et al., 2017). In this case, a total of 5,000 subsamples were taken from the original sample. The results from the bootstrapping procedure (Table 5) illustrate that the direct effects of attitude towards the environment, subjective norm, perceived behavioural control on behavioural intention, and actual purchase behaviour were positive and significant. Therefore, hypotheses H1, H2, H3, and H4 were supported.

Next, the predictive relevance of the structural model was further evaluated by using the blindfolding procedure (Hair et al., 2017; Ringle et al., 2015). More specifically, the proportion of variance in the endogenous construct was assessed by using the coefficient of determination (R2) which was predicted from the independent variables. The structural model also assessed the cross-validated predictive relevance by using the criterion of Stone-Geisser’s (Q2). The R2 and Q2 values for BI were 0.45 and 0.22, and APB were 0.26 and 0.16 respectively. This suggested that 45% of the variance in Behavioural Intention was explained by attitude towards the environment, subjective norm, and perceived behavioural control, and 26% of the variance in actual purchase behaviour was explained by behavioural intention. The results of the structural model also established the predictive relevance as their Q2 values for behavioural intention and actual purchase behaviour were 0.22 and 0.16 respectively which were greater than 0.


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Also, the effect sizes (f2) that specified the extent of the relative effect of a particular independent variable on a dependent variable was substantial (Chin, 2010). The greater effect size of the construct was BI on APB (f2= 0,35). Table 5 also shows the other effect sizes between the used constructs of the study.

Table 5 : Results of the structural model

Direct Effect Beta S.E. t-value

p-value

5.00%

95.00% Decision f2 R2 VIF Q2

H1: ATE -> BI 0.30 0.07 4.40 0.00 0.16 0.44 Supported 0.12 0.45

1.39 0.22 H2: SN -> BI 0.26 0.05 5.44 0.00 0.17 0.34 Supported 0.11 1.08

H3: PBC -> BI 0.34 0.07 4.84 0.00 0.20 0.47 Supported 0.15 1.41 H4: BI -> APB 0.51 0.06 8.03 0.00 0.35 0.61 Supported 0.35 0.26 1.00 0.16 **p< 0.01, *p< 0.05, S. E= Standard error.

Discussions and Implications This study emphasizes on the constructs that have a direct

influence on consumers' actual purchase behaviour towards green products. The estimation of PLS-SEM indicates that consumers’ attitude towards the environment, subjective norm, and perceived behavioural control directly influence their behavioural intention towards green products in Bangladesh. Thus, the hypotheses H1, H2, and H3 were supported as its beta values were 0.30, 0.26, and 0.34. Such findings are consistent with the outcome suggested in earlier studies (Carfora et al., 2019; Chaudhary & Bisai, 2018; Dean et al., 2012). It implies that the consumers who describe themselves as environmentally responsible and are concerned about wasting the resources of the earth had a positive behavioural intention to purchase green products. It also indicates that the consumers who get support from the individuals who have preferences in practising and using green products had a positive intention to purchase green products. Furthermore, the result shows that an individual's behavioural intention is led by their perceived behavioural control. From this finding, it can be predicted that the available production of green products influences consumers to demonstrate favourable behavioural intention towards green products. This ensures that the green product policy of Bangladesh is in the right tract as it aims to establish a sustainable environment through green products consumerism.

Next, the fourth hypothesis explored that consumers’ behavioural intention has a positive influence on actual purchase


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behaviour of green products. Therefore, the hypothesis H4 was strongly supported as its beta value was 0.51, and effect size (f2) was large. This result is consistent with previous works done in the same vicinity of green marketing (Adrita, 2019; Bhatia & Jain, 2013; Khare, 2015). This finding implies that consumers are generally guided by a predetermined set of beliefs and perceptions. At the same time, they maintain a collective concern for the environment and the adverse impact of their product consumption on the environment. This means that desirable intentions such as a positive behavioural intention towards green products are likely to influence their general demand and consumption of green products. The estimated relationships in the findings provide some valuable theoretical insights as well as practical implications.

6.1 Theoretical Implications

Theoretically, this study affirms the applicability of the TPB framework in predicting behavioural intentions and actual purchase behaviours (refer to Table 5) concerning consumers of green products within the context of a developing economy such as Bangladesh. It reinforces the works of Khare (2015) and Yadav & Pathak (2017) who similarly applied the TPB framework to examine behavioural intentions and actual purchase behaviours of Indian consumers towards green products. Hence, this study confirms the overall robustness of the TPB framework in its ability to explain and predict consumer behaviours in varying contexts.

Where there has been relatively less research into green consumption behaviours in the context of a developing country (Adrita, 2019; Bhatia & Jain, 2013; Khare, 2015;), this study addresses a critical gap in the literature by demonstrating the positive relationship between behavioural intention towards green products and actual purchase of green products. This research provides insights into the socio-psychological processes underlying green buying behaviours by showing how attitudes towards the environment, subjective norms, and perceived behavioural control influence actual purchase behaviour through their influence on behavioural intentions. In doing so, the study uncovers the underlying dynamics of the relationship among the aforementioned variables.

The study largely provides support for the TPB with the showing of a direct association between subjective norms and behavioural


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intention. This suggests that social entities such as family and friends directly influence the sampled consumers’ behavioural intention. The main theoretical contribution of this paper is that it largely attests to the validity of the TPB framework in explaining green consumers’ buying behaviour in the context of a developing country like Bangladesh.

6.2 Managerial Implications

This study carries significant implications for marketing managers who are responsible for the marketing of green products in Bangladesh and other developing countries. The findings enrich marketers’ understanding of consumers’ intentions to buy green products. Since the behavioural intention was found to be a significant predictor of actual purchase behaviour (refer to H4 findings in Table 5), marketers need to make efforts directed at improving the explanatory power of the predicting variables as identified in the current study. In addition to focusing on the predictors of behavioural intention established by the well-developed socio-psychological model of planned behaviour, marketers may benefit by directing their attention towards other variables such as attitude towards the environment, subjective norms, and perceived behavioural control. Since these variables have a strong influence on behavioural intentions (refer to H1, H2, and H3 findings in Table 5), marketers and other associated stakeholders can better plan and strategize specific activities and programs to quicken consumers’ behavioural intentions, and subsequently their actual purchase of green products as follows:

1. Consumers’ attitudes towards the environment produce positive

effects on their behavioural intentions towards green products. Such findings provide information on how consumers’ attitude towards the environment (i.e. how consumers feel about the environment) greatly contributes to their behavioural intentions towards green products (i.e. action resulting from how they feel about green products). Thus, to elicit desirable behavioural intentions towards the purchase of green products, marketers working in a developing country context can implement actionable strategies and activities to shape consumers’ attitudes towards the environment.

2. Consumers’ subjective norms and perceived behavioural control, which is a form of socio-psychological factor, produce significant


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positive effects on consumers’ behavioural intentions towards green products. These findings provide empirical evidence to the strong links between consumers’ socio-psychological factors and their behavioural intentions towards green products in the context of a highly collectivist society such as Bangladesh. Thus, firms operating in such contexts should focus on developing programs that can exert positive influences on social norms and perceived behavioural control in order to shape the behavioural intentions of target consumers.

3. Consumers’ actual purchase behaviour towards green products are positively affected by their behavioural intentions and this finding reinforces the need to create consumers’ intentions in most purchase circumstances.

Conclusion and Recommendations

Major environmental problems and depletion of natural resources have forced human civilization to focus on environmentally responsible consumption. As many organizations offer more options for environmentally friendly products, consumers respond with greater willingness to purchase such products. As this research is one of the first to study green purchasing decisions by applying the Theory of Planned Behavior (TPB) in Bangladesh, it addresses a gap by determining the various factors behind consumers’ actual purchase behaviour towards green products in the country. The findings provide some valuable insights on consumers’ actual purchase behaviour towards green products and raise further discussions of the effects of the main constructs used in this study. This study establishes that attitude towards environment, subjective norms, and perceived behavioural control have a positive influence on behavioural intention, while behavioural intention has a significant positive influence on consumers’ actual purchase behaviour.

The study has limitations common with similar research work and hence provide opportunities for future research. First, this study has focused on a broad category of green products. In reality, green products can be sub-divided into various sub-product categories such as organic food, green electrical appliances, clothing, and so on. Thus, it is suggested that future research should examine the impact of the variables used in this study on various categories and subcategories of green products. Next, where this study has used some basic constructs based on TPB, other relevant constructs can be incorporated in future


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research such as environmental concerns, self-identity, and consumers’ egoistic and personal values. This might provide further clarity for practitioners in implementing marketing strategies for green products. Finally, the sample size in this study may limit the generalizability of the findings. Data were also collected online. Thus, future research can improve the generalizability of the findings by extending the sample size.

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