How Green Consumption Value Affects Green Consumer Behaviour: The Mediating Role of Consumer Attitudes Towards Sustainable Food Logistics Practices

Abstract

The aim of the study is to identify the green consumption values and production patterns and understand how they impact consumer behaviour and purchase intention of green consumers, in particular, the environment-conscious youth in the city of Bangalore. The focus of the study is on the pre-purchase sustainable logistics of food products and how they impact behaviour and attitudes of green consumers. The theory of planned behaviour and social cognitive theory form the theoretical base for the current research. An online survey was conducted among 284 participants in and around the scope area. Confirmatory factor analysis and structural equation modelling were used to test the hypotheses. The results showed that green consumption values and consumer attitudes towards sustainable food logistics either directly or indirectly influence green purchase intention and environmentally conscious behaviour towards food products. The main contribution of this study is the identification of new multidimensional constructs which can be used to measure green consumer attitude in terms of sustainable food logistics practices in the Indian context. These findings will empower managers and future researchers to understand how sustainable food logistics practices can create green consumer attitudes. They will also assist food production companies to identify possible opportunities, developments and other benefits they can derive from following sustainable food logistics practices.

Keywords

Green Consumption Value Sustainable Food Logistics Green Attitude Green Consumer Behaviour Green Consumer Attitude

Introduction

The member countries of the United Nations in their 2030 Sustainable Development Agenda (United Nations, 2015) enacted 17 Sustainable Development Goals (SDGs) as part of a new sustainable development strategy. SDG number 12 states: ‘Ensure sustainable consumption and production patterns’ (Colglazier, 2015; Gasper et al., 2019; Pradhan et al., 2017; United Nations, 2015). In this study, we discuss SDG number 12 from consumer behaviour and psychological viewpoint. Sustainability has emerged as one of the major themes of the modern era with many discussions focused on how sustainable consumption can help to arrest detrimental environmental impacts (Dolan, 2002; Schaefer & Crane, 2005). Several studies have explored all major aspects related to sustainable consumption and production (Govindan, 2018; Stöckigt et al., 2019; Tseng et al., 2013). However, it is seen that most studies have focused on either consumption or production individually, but not on both collectively. To this end, this research attempts to study both green consumption values and production patterns to study the sustainability attitudes concerning the pre-purchase logistics of food products and how they affect buying intentions and consumer behaviour of green consumers amongst the Indian youth in Bangalore.

Sustainability is impacted by all players in the food chain (producers, consumers, retailers and government); the various processes involved in food production and packaging and also the transportation of food products across multiple channels (Ghose & Chandra, 2020; Govindan, 2018; Stöckigt et al., 2019). However, consumer food choices can be a breakpoint in the chain and motivate sustainable food production. Sustainable solutions have to stand the market test in order to gain legislative approval.

Hypothesis Development

The Green Consumption Value, the Green Purchase Intention and Consumer Behaviour

Green consumption value occurs when an individual’s purchase and consumption behaviour reflects respect for the environment (Haws et al., 2014; Kottala & Singh, 2015; Moser, 2016; Nguyen et al., 2016). In other words, green consumption value refers to an individual’s desire to consume only those food products that meet their environmental and sustainability expectations (Patterson & Spreng, 1997; Varshneya et al., 2017; Wu & Chen, 2014). The green consumption values highly govern green consumer behaviour such that they purchase more green products (Kim & Moon, 2012; Nguyen et al., 2016). These consumers are well aware that their purchase patterns will not harm the environment (Varshneya et al., 2017; Wang & Lin, 2017).

Sustainable food practices are an emerging trend in India (Mani et al., 2016; Tait et al., 2016). Consumers who are alive to environmental issues will acquire positive consumer attitude towards sustainable food logistics (Jaiswal & Kant, 2018; Varshneya et al., 2017). In the Indian context, environmental awareness continues to be low and so it is fair to assume that consumers have not yet developed green consumption patterns and so they exhibit low green consumption value (Bamberg, 2003; Ghose & Chandra, 2020; Wang et al., 2014; Wu & Chen, 2014; Zhao et al., 2014). Many studies conclude that green consumption values influence environmental behaviour and consumer attitude that act as mediators between green purchase intention and environmental behaviour (Hauser et al., 2013; Pinto et al., 2011; Varshneya et al., 2017; Vermeir & Verbeke, 2008). Similar outcomes are seen in existing literature, particularly in the food product context (Grunert, 2011; Stöckigt et al., 2019; Vermeir & Verbeke, 2006). However, the relationship between green consumption value and consumer attitude towards sustainable food logistics has not been adequately addressed. Even though food sustainability is at a nascent stage (Mani et al., 2016), it is seen that the green purchase intention influences green consumption values of food products (do Paço et al., 2019; Varshneya et al., 2017; Woo & Kim, 2019), green consumer behaviour (Joshi & Rahman, 2015; Suki, 2016; Taufique & Vaithianathan, 2018) and consumer attitude towards sustainable food logistics. Further, green purchase intention may act as a mediator between green consumption value and green consumer behaviour (Hauser et al., 2013). Studies show that green purchase intention acts as a mediator between consumer attitude and green consumer behaviour (do Paço et al., 2019; Jaiswal & Kant, 2018; Malik & Singhal, 2017; Taufique & Vaithianathan, 2018).

Marketing literature argues that consumer attitude is one of the main determinants of consumer’s purchase intention and behaviour (Ghose & Chandra, 2020; Hauser et al., 2013; Krystallis et al., 2012; Malik & Singhal, 2017; Mann, & Kaur, 2019; Stöckigt et al., 2019; Wu & Chen, 2014). However, consumer attitude is a very subjective concept which varies the nature of the product and product set. Consumer attitude is different from typical behavioural setting and environmental behaviour setting (Stöckigt et al., 2019). Most consumer attitude scales are unidimensional and may not solve the complicated and multidimensional nature of consumer attitudes in a sustainable consumer behaviour setting. Hence, there is a gap in the sustainable consumer behaviour field to measure consumer attitude towards sustainability. Stöckigt et al. (2019) developed a bidimensional scale to estimate individual attitude towards sustainability in a food logistics context. These scale items were used to measure consumer attitude related to environmental and social issues; later, consumer attitude related to environmental and social issues was divided into two categories: willingness attitude and behavioural consistency attitude. Stöckigt et al. (2019) found that attitudes towards sustainability in the food logistics constructs (willingness attitude, behavioural consistency attitude) had a significant and positive impact on consumers’ shopping behaviour and their characteristics. Based on the above discussion, we develop the following hypotheses to measure of the impact of various aspects of consumer attitude on sustainable food logistics.

H₁: Green consumption value positively influences the Willingness Attitude towards sustainable food logistics.

H₂: Green consumption value positively influences the Behavioural Consistency towards sustainable food logistics.

H₃: Green consumption value positively influences the green consumer behaviour.

H_3.1: Willingness attitude towards sustainable food logistics mediates the relationship between green consumption value and green consumer behaviour.

H_3.2: Behavioural Consistency attitude towards sustainable food logistics mediates the relationship between green consumption value and green consumer behaviour.

H_3.3: Willingness attitude towards sustainable food logistics and green purchase intentions mediates the relationship between green consumption value and green consumer behaviour.

H_3.4: Behavioural Consistency attitude towards sustainable food logistics and green purchase intentions mediates the relationship between green consumption value and green consumer behaviour.

H_3.5: Green purchase intentions mediate the relationship between green consumption value and green consumer behaviour.

H₄: Green consumption value positively influences the green purchase intention.

H_4.1: Willingness attitude towards sustainable food logistics mediates the relationship between green consumption value and green purchase intention.

H_4.2: Behavioural Consistency attitude towards sustainable food logistics mediates the relationship between green consumption value and green purchase intention.

Attitudes Towards Sustainable Food Logistics, Green Purchase Intention and Consumer Behaviour

Literature shows that attitude greatly impacts consumer action, and in the context of this study, consumer attitude influences green purchase intention and behaviours (Ajzen, 1991; do Paço et al., 2019; Verma & Chandra, 2018; Woo & Kim, 2019). Accordingly, customer attitude towards sustainability should affect the green buying intention (Taufique & Vaithianathan, 2018; Woo & Kim, 2019; Wu & Chen, 2014; Zhao et al., 2014). More specifically, a positive attitude of willingness towards sustainability logistics products should lead to the purchase intention of sustainable products, leading to green consumer behaviour (Bamberg, 2003; Jaiswal & Kant, 2018; Stöckigt et al., 2019; Zerbini et al., 2019). The pre-purchase supply chains and logistics patterns have some sustainability consequences, such as the environmental impact of transport and storage, and waste management (Chkanikova & Mont, 2015; Filimonau & Gherbin, 2017; Fritz & Schiefer, 2008; Wang et al., 2014). However, it is observed that consumers remain largely unaware that these are also elements of sustainability. While existing literature has explored the patterns of consumption, particularly in behavioural economics, it has paid little attention to investigating attitudes towards sustainable logistics and their relationship with sustainable green purchasing intentions (Stöckigt et al., 2019). Therefore, this research aims to identify how attitudes towards sustainable food logistics, that is, the environmental impact of shipping and product storage, relate to sustainable buying behaviour. The above literature review leads to the following hypotheses.

H₅: Willingness attitude towards sustainable food logistics positively influences the green purchase intentions.

H₆: Behavioural Consistency attitude towards sustainable food logistics positively influences the green purchase intentions.

H₇: Willingness attitude towards sustainable food logistics positively influences the green consumer behaviour.

H₈: Behavioural Consistency attitude towards sustainable food logistics positively influences the green consumer behaviour.

Green Purchase Intention and Green Consumer Behaviour

Willingness to perform a specific action or behaviour, often driven by individual value and attitude, is known as behavioural intention, and such willingness influences the individual’s action (Jaiswal & Kant, 2018; Lai & Cheng, 2016). Consumers are concerned about ecological product quality and environmental consequences related to the product when they make their purchase decisions (do Paço et al., 2019; Moser, 2016; Tait et al., 2016). Many authors have studied the relationship between green purchase intention and green consumer behaviour (do Paço et al., 2019; Haws et al., 2014; Suki, 2016; Verma & Chandra, 2018; Zhao et al., 2014); however, there is very little study in the Indian setting (Jaiswal & Kant, 2018; Varshneya et al., 2017). In environment and sustainability literature, green purchase intention is the main predictor to measure green behaviour (Hauser et al., 2013; Jaiswal & Kant, 2018; Lai & Cheng, 2016; Moser, 2016; Woo & Kim, 2019). Hence, the present study tries to build this essential relationship in the Indian context.

In environmental behaviour, green purchase behaviour refers to selecting products that are not harmful to the environment (Wu & Chen, 2014; Zhao et al., 2014). Some studies refer to green purchase behaviour as purchasing ecological-friendly products (Bamberg, 2003; Haws et al., 2014; Kottala & Singh, 2015). Some other studies argue that purchasing sustainable products refers to green purchase behaviour (do Paço et al., 2019; Oosterveer & Spaargaren, 2013; Pinto et al., 2011). Products that can be recycled and are beneficial to the society and environment are known as sustainable products (Jaiswal & Kant, 2018). As discussed in the earlier sections, green consumer behaviour is evaluated based on consumers’ willingness to purchase green products. This willingness is driven by their green consumption values and a positive attitude towards sustainable products. The theory of planned behaviour suggests purchase intentions, incorporated with a positive attitude, determine consumer behaviour (Ajzen, 1991; Taufique & Vaithianathan, 2018; Verma & Chandra, 2018). Many authors have used the theory of planned behaviour to understand the relationship between purchase intention and consumer behaviour. In order to validate green purchase intention and green consumer behaviour in the field of environment behavioural research, there is need for modified behavioural measures in the Indian context. Thus, the above literature motivates the development of the following hypothesis and conceptual model (see Figure 1).

Figure 1.

Source: The authors.

H₉: Green purchase intention positively influences green consumer behaviour.

Methods

A well-structured questionnaire was developed based on the research framework. In total, five constructs were used to prove the hypothesized framework. To evaluate the green consumption value, six items were adopted from Haws et al. (2014). Ten items were adopted from Stöckigt et al. (2019) to measure consumers’ attitude towards sustainable food logistics practices, and these were classified into behavioural consistency attitude (six items) and willingness attitude (four items) towards sustainable food logistics. Three items were adopted to assess consumers’ green purchase intention (Woo & Kim, 2019). Finally, six items, suggested by Taufique and Vaithianathan (2018) and later modified by Roberts (1996) were used to measure the ecologically conscious consumer behaviour. All these constructs were adopted from various studies in literature and modified to the current research context. The initial questionnaire was administered to 100 students and ten university professors to test the construct validity and reliability for all the measurement items on a five-point Likert-type scale (1 = strongly disagree; 5 = strongly agree) (see the Appendix).

The questionnaire was administered to respondents residing in the city of Bangalore, a notable metro city in the state of Karnataka in India, which is home to a wide range of people from different economic, social and cultural settings. And so even though the respondents were from one city, their diverse backgrounds added richness to the research. Qualtrics online data collection tool was used to collect data from the respondents. They were provided an online link to the questionnaire link with three months completion time, thus giving them ample time to answer the questions and also share it with people in their circle for their responses. Overall, 440 questionnaires were collected. It is easy to review respondents’ bias by assessing the level of progress and completion time in any online setting (Malhotra, 2008; Steelman et al., 2014). Responses with less than 50 seconds of completion time and without 100% progress data were deleted from the research for better accuracy (Malhotra, 2008). After data cleaning, 284 samples were considered for final analysis. Most of the respondents belonged to the 18- to 25-year-old age group (68%), and 23% were between 26 and 35 years old. Of the total respondents, 58% were males and 42% were females; 66% had a postgraduate degree and 30% had a graduate-level degree.

Results

The maximum likelihood estimator was first used to test the measurement model and structural model, using AMOS 25; however, the present study violated the multivariate normality assumptions, with insufficient sample size to apply distribution-free estimation methods in AMOS 25. To fix this issue, the maximum likelihood estimation with the bootstrap resampling method (2000 samples) was used to obtain an accurate estimation of standard errors, as reflected in the P values and confidence intervals (Arbuckle, 2008; Arifin & Yusoff, 2016; Ievers-Landis et al., 2011; Nevitt & Hancock, 2001). The bias-corrected confidence interval was set at the 95% confidence level (Arbuckle, 2008; Hesterberg, 2015; Carpenter & Bithell, 2000).

Since the questionnaire was adapted from various literature studies, the confirmatory factor analysis was done directly with the previous construct structure (Brown, 2014). The confirmatory factor analysis results are detailed in Table 1. The Cronbach alpha reliability (α) scores are higher than the cut-off range 0.7, implying that a high level of internal consistency exists between the samples (Bonett & Wright, 2015).

Table 1.

Results of Confirmatory Factor Analysis and Descriptive Statistics

Indicator	Construct	β value	t value	α value	Mean (SD)
GCV1	Green consumption value	0.869	18.251**	0.932	3.96 (0.74)
GCV2		0.863	18.052**
GCV3		0.840	17.294**
GCV4		0.796	15.924**
GCV5		0.850	17.611**
GCV6		0.801	16.057**
CASFL3	Willingness attitude	0.879	18.175**	0.869	4.02 (0.62)
CASFL4		0.819	16.281**
CASFL5		0.736	13.942**
CASFL6		0.743	14.15**
CASFL1	Behavioral consistency attitude	0.807	16.132**	0.915	3.91 (0.67)
CASFL2		0.748	14.444**
CASFL7		0.846	17.367**
CASFL8		0.777	15.267**
CASFL9		0.801	15.955**
CASFL10		0.858	17.767**
GPI1	Green purchase intentions	0.791	15.095**	0.822	3.90 (0.79)
GPI2		0.737	13.688**
GPI3		0.808	15.561**
ECCB1	Environmentally conscious consumer behavior	0.784	15.549**	0.928	3.92 (0.74)
ECCB2		0.839	17.23**
ECCB3		0.841	17.287**
ECCB4		0.808	16.251**
ECCB5		0.821	16.672**
ECCB6		0.891	18.992**

Source: The authors.

Note: **P < 0.01; α, Cronbach alpha coefficient.

The convergent validity of the item-factor loadings was assessed by the estimation (β value) and statistical significance (Hair et al., 2013). This was followed by an assessment of the average variance extracted (AVE) and composite reliability (CR) of the constructs (Hair et al., 2013; Malhotra & Dash, 2011). Convergent validity was indicated by an item-factor loading (β value) ≥ 0.5 (Hair et al., 2013) and P < 0.05, AVE ≥ 0.5 and CR ≥ 0.7 (Fornell & Larcker, 1981). Finally, AVE and the square root of AVE were found to be higher than inter-construct correlations, thus supporting discriminant validity of the constructs (Hu & Bentler, 1999) and testifying to the uniqueness of each construct in the research (Fornell & Larcker, 1981). In confirmatory factor analysis, the items with factor loadings < 0.5 were considered for removal (Arifin & Yusoff, 2016; Hair et al., 2013). From the results listed in the table, it can be seen that the standardized beta values (β) are higher than 0.5 (see Table 1), and so no item was deleted in this study. All research constructs exhibit CR with the minimum acceptable level of 0.7 [CR > 0.7], indicating excellent composite reliability (Malhotra & Dash, 2011). The AVE values for all constructs are higher than normal levels [AVE ≥ 0.5] (Fornell & Larcker, 1981; Hu & Bentler, 1999). Thus, the convergent validity of the constructs is established. MSV is less than AVE, and the square root of AVE is higher than the inter-constructed correlations (Malhotra & Dash, 2011), which supports the discriminant validity of the constructs; refer to Table 2. The main components of the construct validity, such as convergent validity and discriminant validity, are proved, and so there are no validity concerns in this research.

Table 2.

Reliability and Validity Statistics

Constructs	CR	AVE	GCV	ECCB	BCA	GPI	WA
GCV	0.934	0.701	(0.837)
ECCB	0.931	0.691	0.793**	(0.831)
BCA	0.918	0.651	0.794**	0.711**	(0.807)
GPI	0.823	0.607	0.766**	0.777**	0.735**	(0.779)
WA	0.873	0.634	0.763**	0.795**	0.656**	0.740**	(0.796)

Source: The authors.

Note: **P < 0.01; values represented in parentheses represent squared root of AVE.

Abbreviations: BCA, behavioral consistency attitude; ECCB, environmentally conscious consumer behavior; GCV, green consumption value; GPI, green purchase intentions; WA, willingness attitude.

Before testing the structural model, the validation of the measurement model is carried out. Figure 2 shows the R² values for the green purchase intention (R² = 0.675, P < 0.01) and environmentally conscious consumer behaviour (R² = 0.744, P < 0.01). R² values indicate a good level of predictive accuracy, which is explained by the model (Hair et al., 2013). The model concludes that 74% of the variance in green consumer behaviour is explained by green consumption values, and consumer attitude towards sustainable logistics and green purchase intention, either directly or indirectly. Table 3 details the direct and indirect relations with path coefficients, upper and lower intervals and relevant statistical significance. By evaluating the model fit indices, the structural model is assessed. A model is predicted to be a good fit if the comparative chi-square (χ²/df) value starts to be sensible roughly at five or less (Arbuckle, 2008; Wheaton et al., 1977). The incremental fit indices, such as CFI, GFI and AGFI values close to 1, indicate a perfect fit (Hu & Bentler, 1999; Schreiber et al., 2006). The badness of the model fit is measured by root mean square error of approximation (RMSEA) and root mean square residual (RMR) (Browne & Cudeck, 1992). The value of the RMSEA and RMR of about 0.08 or less would show a close fit of the model (Brown, 2014). χ² = 441.61; df = 266 [χ²/df = 1.66]; GFI = 0.889; AGFI = 0.864; CFI = 0.969; RMR = 0.023; RMSEA = 0.048 and PClose = 0.629, and, accordingly, these values show a reasonable model fit (see Figure 2).

Table 3.

Hypothesis Testing

Direct Effect
Path Link	β (P)	UCL	LCL	Hypothesis
GCV® WA	0.769 (0.001**)	0.844	0.68	H₁ supported
GCV® BCA	0.799 (0.001**)	0.849	0.73	H₂ supported
GCV® ECCB	0.258 (0.012*)	0.441	0.065	H₃ supported
GCV® GPI	0.266 (0.039*)	0.496	0.012	H₄ supported
WA® GPI	0.340 (0.001**)	0.553	0.163	H₅ supported
BCA® GPI	0.304 (0.002**)	0.489	0.111	H₆ supported
WA® ECCB	0.352 (0.001**)	0.529	0.196	H₇ supported
BCA® ECCB	0.094 (0.276)	0.266	–0.08	H₈ not supported
GPI® ECCB	0.250 (0.003**)	0.431	0.067	H₉ supported
Indirect Effect
WA® GPI® ECCB	0.085 (0.003**)	0.171	0.021	H_7.1 supported Partial mediation
BCA ® GPI® ECCB	0.076 (0.004**)	0.166	0.014	H_8.1 supported Full mediation
GCV® WA® GPI	0.261			H_4.1 supported
GCV® BCA® GPI	0.237			H_4.2 supported
GCV® GPI	0.505 (0.001**)	0.726	0.303	Partial mediation
GCV® WA® ECCB	0.271			H_3.1 supported
GCV® BCA® ECCB	0.075			H_3.2 supported
GCV® WB® GPI® ECCB	0.065			H_3.3 supported
GCV® BCA® GPI® ECCB	0.059			H_3.4 supported
GCV® GPI® ECCB	0.067			H_3.5 supported
GCV® ECCB	0.539 (0.001**)	0.706	0.373	Partial mediation

Source: The authors.

Note: *P < 0.05; **P < 0.01.

Figure 2.

Source: The authors.

Among the nine direct path relations, eight direct paths attain statistical significance. The green consumer value significantly and positively influences both willingness attitude towards food sustainable logistics (β = 0.769, P < 0.01) and behavioural consistency attitude towards food sustainable logistics (β = 0.799, P < 0.01). Green consumption values predict 64% of the behavioural consistency attitude (R² = 0.638, P < 0.01) and 59% of willingness attitude (R² = 0.638, P < 0.01), thus supporting hypotheses H₁ and H₂. The green consumer value significantly and positively affects the green purchase intentions (β = 0.266, P < 0.05) and environmentally conscious consumer behaviour (β = 0.258, P < 0.05). This result supports H₃ and H₄. Both willingness attitude towards food sustainable logistics (β = 0.340, P < 0.01) and behavioural consistency attitude towards food sustainable logistics (β = 0.304, P < 0.01) are seen to positively influence the green purchase intention, thus supporting H₅ and H₆. However, the willingness attitude towards food sustainable logistics influences the willingness attitude towards food sustainable logistics (β = 0.352, P < 0.01) while behavioural consistency attitude towards food sustainable logistics does not affect the environmentally conscious consumer behaviour (β = 0.094, P = 0.276). These results support H₇ but reject H₈. Green purchase intention positively influences environmentally conscious consumer behaviour (β = 0.250, P < 0.01), thus supporting H₉.

The green purchase intention (β = 0.085, P < 0.01) controls the relationship between willingness attitude towards food sustainable logistics and environmentally conscious consumer behaviour. Since the direct relationship between willingness attitude and environmentally conscious consumer behaviour is significant, and green purchase intention acts as a partial mediator, H_7.1 is supported. In other words, green purchase intention acts as a full mediator between behavioural consistency attitude towards food sustainable logistics and environmentally conscious consumer behaviour (β = 0.076, P < 0.01). Also, there is no direct relationship between behavioural consistency attitude towards food sustainable logistics and environmentally conscious consumer behaviour; our results, thus, support H_8.1.

The willingness attitude towards food sustainable logistics (β = 0.261) and behavioural consistency attitude towards food sustainable logistics (β = 0.237) act as mediators between green consumption value and green purchase intentions, thus supporting H_4.1 and H_4.2. From these two results, it can be inferred that consumer attitude towards food sustainable logistics acts as a partial mediator between green consumption value and green purchase intention (β = 0.505, P < 0.01).

Also, consumer attitude towards food sustainable logistics (willingness (β = 0.271) and behavioural consistency attitude (β = 0.075) control the relationship between green consumption value and environmentally conscious consumer behaviour, thus supporting H_3.1 and H_3.2. However, the relationship between green consumption value and environmentally conscious consumer behaviour is controlled by consumer attitude towards food sustainable logistics (willingness attitudes [β = 0.065] and behavioural consistency attitudes [β = 0.059]) and green purchase intention, thus H_3.3 and H_3.4 are supported. Green purchase intention controls the prediction variation between green consumption value and environmentally conscious consumer behaviour (β = 0.067), thus H_3.5 is supported. The above five results conclude that consumer attitude towards food logistics and green purchase intention act as partial mediators between the green consumption value and environmentally conscious consumer behaviour (β = 0.539, P < 0.01).

Discussion and Conclusion

The main aim of the present study is to understand the green consumption value and consumer attitude towards sustainable food logistics and their impact on green purchase intentions and green consumer behaviour among young Indian consumers. Previous studies on the subject point to a gap in understanding consumer attitudes towards green products and sustainability (Lin & Hsu, 2015; Stöckigt et al., 2019; Taufique & Vaithianathan, 2018). Consumer green values and behaviours have been extensively studied. However, consumer attitudes in relation to sustainable logistics have not been adequately addressed (Stöckigt et al., 2019). Literature also reveals that sustainable practices influence green purchase intentions and green behaviour (Grunert, 2011; Krystallis et al., 2012; Mani et al., 2016). There is a need to create a multidimensional construct to measure consumer attitudes towards food products based on sustainable food logistics practices (Stöckigt et al., 2019). Most research is based on the theory of planned behaviour, which only measures behavioural intentions and not the individual behaviours towards food sustainability or, in other words, green behaviour (Taufique & Vaithianathan, 2018). While few studies have addressed this problem by including environmentally conscious consumer behaviour constructs to measure the overall green behaviour, these were not focused on understanding the impact of green behaviour on food products. Therefore, there was a need for a modified version of the environmentally conscious consumer behaviour construct to measure green consumer behaviour, primarily in the context of food products. This study strives to fill this gap in literature by developing a model based on the theory of planned behaviour (Liobikienė et al., 2016; Taufique & Vaithianathan, 2018; Wu & Chen, 2014) and the social cognitive theory (Lin & Hsu, 2015; Phipps et al., 2013), with specific focus on studying green consumer behaviour towards food products.

Our results suggest that green consumption values either directly or indirectly influence the green purchase intention and environmentally conscious consumer behaviour. For the Indian youth, environmental concerns are a primary factor that influences their purchase intentions directly, which is in line with many studies (Basha & Lal, 2019; Haws et al., 2014; Kottala & Singh, 2015; Sangroya & Nayak, 2017; Woo & Kim, 2019). This result also shows that green consumption value is one of the primary antecedents of consumer attitude towards sustainable food logistics. The direct relationship between consumer attitude towards sustainable food logistics and green purchase intention and green consumer behaviour is consistent with western studies (do Paço et al., 2019; Woo & Kim, 2019; Wu & Chen, 2014) and Indian studies (Jauhari & Manaktola, 2007; Kumar & Ghodeswar, 2015; Taufique & Vaithianathan, 2018; Verma & Chandra, 2018), which suggests that consumer attitude towards green practices and sustainability as significant predictors of green behaviour and green purchase intentions. The Indian government has introduced many schemes to reduce food waste at the consumer and retail level and also food losses along production and supply chains, including post-harvest losses (Gasper et al., 2019). The SDG 12 is closely related to other SDGs such as goal zero hunger (2), good health and well-being (3), quality education (4), clean water and sanitation (6), affordable and clean energy (7), decent work and economic growth (8), industry, innovation and infrastructure (9), sustainable cities and communities (11), climate action (13), life below water (14) and life on land (15) (NITI Aayog, 2018). These government schemes also aim to increase awareness about sustainability amongst the Indian youth, since it plays a vital role in influencing their green consumption value and motivating them to perform more environmentally friendly actions. In green production, food production companies tend to focus on a green product brand rather than the lifecycle impact (Liu et al., 2005). Over the last decade, these companies adopted green logistics, and these have now become integral to the supply chain management discourse and practices (Ala-Harja & Helo, 2014; Bloemhof et al., 2015). They have also taken care to promote these green policies to achieve competitive advantage, and at the same time, influence green values and attitude of the Indian consumers (Baines et al., 2012; Nanath & Pillai, 2017). Many companies have now begun to invest in green practices for long-term competitive gains and to build a robust and environmentally friendly brand (Jauhari & Manaktola, 2007). Consumers are well aware of what constitutes environmentally friendly production practices and are motivated to select environmentally friendly food production companies (Kumar & Ghodeswar, 2015; Kumar et al., 2017). The present study identifies that the most significant factor influencing their green consumer behaviour is their willingness attitude towards sustainable food logistics practices. Consumers who are willing to pay more for food products based on a green value chain and socially fair conditions are seen to have high level of green purchase intentions and green consumer behaviour. From this study, we can conclude that the Indian youth can play a crucial role in promoting sustainability, specifically food sustainability. Based on the results, it can be said that the Indian consumer’s purchase choices are highly influenced by the social and environmental issues, and this can be interpreted as a positive sign to achieve sustainability.

Implications

Green behaviour and green logistics are emerging concepts, and while studies have been conducted on the subjects, the Indian market context remains largely unaddressed. There is need to study consumer attitude towards green logistics and sustainable practices and how they influence the green consumption values and govern their green intention and behaviour, particularly towards food products. All our results are in line with the social cognitive theory and theory of planned behaviour in the context of green consumer behaviour towards food products (Lin & Hsu, 2015; Taufique & Vaithianathan, 2018). Our findings provide additional insights to academia on green consumer behaviours and particularly on how their green attitude can promote sustainable food logistics. This study brings to light new multidimensional constructs to measure green consumer attitude in terms of sustainable food logistics practices in the Indian context. These constructs will add to the understanding of managers and future researchers on how sustainable logistics practices create green consumer attitudes (Stöckigt et al., 2019).

The findings will also empower food production companies to recognize possible opportunities, developments and other benefits arising from sustainable food logistics. India is a big market with enormous potential for green food products. The green consumption pattern is a fast growing trend, implying that environmental issues are impacting consumer attitudes and behaviour. Hence, green food producers should focus more on producing sustainable food products to benefit from this market. Their marketing content and campaigns should present them as ethical brands, while, at the same time, raising awareness amongst the general population to build green behaviour and adopt sustainable practices.

As is always the case, this study has its limitations. The theory of planned behaviour and social cognitive theory form a theoretical stance for the current research; however, only a few of the constructs from the two theories have been included. Future research is advised to consider more theories and constructs. Most of the respondents fall in the youth category and so results cannot be generalized to an entire population. Hence, future studies should consider larger samples from other age groups for more light on the subject.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendix

Code	Statements
Green Consumption Value
GCV1	It is important to me that the food products I use do not harm the environment
GCV2	I consider the potential environmental impact of my actions when making many of my decisions
GCV3	My food purchase habits are affected by my concern for our environment
GCV4	I am concerned about wasting the food resources of our planet
GCV5	I would describe myself as environmentally responsible
GCV6	I am willing to be inconvenienced in order to take actions that are more environmentally friendly
Willingness Attitude
CASFL3	I would prefer a food product with environmentally friendly logistics to a comparable food product
CASFL4	I would prefer a food product that was stored and moved under socially fair conditions to a comparable food product
CASFL5	I would be willing to pay more for a food product with environmentally friendly logistics
CASFL6	I would be willing to pay more for a food product that was stored and moved under socially fair conditions
Behavioural Consistency Attitude
CASFL1	It is important to me that the logistics of the food products I buy are environmentally friendly
CASFL2	It is important to me that the logistics of the food products I buy are socially fair
CASFL7	I try to find out whether a food product’s logistics are environmentally friendly before I buy it
CASFL8	I try to find out whether a food product was stored and moved under socially fair conditions before I buy it
CASFL9	If a food product I intend to buy was not stored and moved in an environmentally friendly manner, I decline to buy it
CASFL10	If a food product I intend to buy was not stored and moved under socially fair conditions, I decline to buy it
Green purchase Intentions
GPI1	My willingness to repurchase the green food product is very high
GPI2	Overall, I am glad to repurchase green food product because it is environmentally friendly
GPI3	I intend to rebuy green food product because of environmental concern
Environmentally Conscious Consumer Behaviour
ECCB1	When there is a choice, I always choose the food product that contributes to the least amount of pollution
ECCB2	If I understand the potential damage to the environment that some food products can cause, I do not purchase those food products
ECCB3	I have switched food products for ecological reasons
ECCB4	Whenever possible, I buy food products packaged in reusable containers
ECCB5	When I have a choice between two equal food products, I always purchase the one less harmful to other people and the environment
ECCB6	I do not buy food products that harm the environment

Source: The authors.

References

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Ala-Harja

, & Helo

(2014). Green supply chain decisions: Case-based performance analysis from the food industry. Transportation Research Part E: Logistics and Transportation Review, 69, 97–107.

Arbuckle

(2008). Amos 17.0 user’s guide. SPSS Inc.

Arifin

W. N.

, & Yusoff

M. S.

(2016). Confirmatory factor analysis of the Universiti Sains Malaysia Emotional Quotient Inventory among medical students in Malaysia. SAGE Open, 6(2), 2158244016650240.

Baines

, Brown

, Benedettini

, & Ball

(2012). Examining green production and its role within the competitive strategy of manufacturers. Journal of Industrial Engineering and Management (JIEM), 5(1), 53–87.

Bamberg

(2003). How does environmental concern influence specific environmentally related behaviors? A new answer to an old question. Journal of Environmental Psychology, 23(1), 21–32.

Basha

M. B.

, & Lal

(2019). Indian consumers’ attitudes towards purchasing organically produced foods: An empirical study. Journal of Cleaner Production, 215, 99–111.

Bloemhof

J. M.

, van der Vorst

J. G.

, Bastl

, & Allaoui

(2015). Sustainability assessment of food chain logistics. International Journal of Logistics Research and Applications, 18(2), 101–117.

Bonett

D. G.

, & Wright

T. A.

(2015). Cronbach’s alpha reliability: Interval estimation, hypothesis testing, and sample size planning. Journal of Organizational Behavior, 36(1), 3–15.

10.

Brown

T. A.

(2014). Confirmatory factor analysis for applied research. Guilford Publications.

11.

Browne

M. W.

, & Cudeck

(1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258.

12.

Carpenter

, & Bithell

(2000). Bootstrap confidence intervals: When, which, what? A practical guide for medical statisticians. Statistics in Medicine, 19(9), 1141–1164.

13.

Chkanikova

, & Mont

(2015). Corporate supply chain responsibility: Drivers and barriers for sustainable food retailing. Corporate Social Responsibility and Environmental Management, 22(22), 65–82.

14.

Colglazier

(2015). Sustainable development agenda: 2030. Science, 349(6252), 1048–1050.

15.

do Paço

, Shiel

, & Alves

(2019). A new model for testing green consumer behavior. Journal of cleaner production, 207, 998–1006.

16.

Dolan

(2002). The sustainability of ‘sustainable consumption’. Journal of Macromarketing, 22(2), 170–181.

17.

Filimonau

, & Gherbin

(2017). An exploratory study of food waste management practices in the UK grocery retail sector. Journal of Cleaner Production, 167, 1184–1194.

18.

Fornell

, & Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

19.

Fritz

, & Schiefer

(2008). Food chain management for sustainable food system development: A European research agenda. Agribusiness: An International Journal, 24(4), 440–452.

20.

Gasper

, Shah

, & Tankha

(2019). The framing of sustainable consumption and production in SDG 12. Global Policy, 10, 83–95.

21.

Ghose

, & Chandra

(2020). Models for predicting sustainable durable products consumption behavior, a review article. Vision, 24(1), 81–89.

22.

Govindan

(2018). Sustainable consumption and production in the food supply chain: A conceptual framework. International Journal of Production Economics, 195, 419–431.

23.

Grunert

K. G.

(2011). Sustainability in the food sector: A consumer behavior perspective. International Journal on Food System Dynamics, 2(3), 207–218.

24.

Hair

J. F.

, Black

W. C.

, Babin

B. J.

, & Anderson

R. E.

(2013). Multivariate data analysis. Pearson Education Limited.

25.

Hauser

, Nussbeck

F. W.

, & Jonas

(2013). The impact of food-related values on food purchase behavior and the mediating role of attitudes: A Swiss study. Psychology & Marketing, 30(9), 765–778.

26.

Haws

K. L.

, Winterich

K. P.

, & Naylor

R. W.

(2014). Seeing the world through GREEN-tinted glasses: Green consumption values and responses to environmentally friendly products. Journal of Consumer Psychology, 24(3), 336–354.

27.

Hesterberg

T. C.

(2015). What teachers should know about the bootstrap: Resampling in the undergraduate statistics curriculum. The American Statistician, 69(4), 371–386.

28.

L. T.

, & Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

29.

Ievers-Landis

C. E.

, Burant

C. J.

, & Hazen

(2011). The concept of bootstrapping of structural equation models with smaller samples: An illustration using mealtime rituals in diabetes management. Journal of Developmental & Behavioral Pediatrics, 32(8), 619–626.

30.

Jaiswal

, & Kant

(2018). Green purchasing behavior: A conceptual framework and empirical investigation of Indian consumers. Journal of Retailing and Consumer Services, 41, 60–69.

31.

Jauhari

, & Manaktola

(2007). Exploring consumer attitude and behavior towards green practices in the lodging industry in India. International Journal of Contemporary Hospitality Management, 19(5), 364–377.

32.

Joshi

, & Rahman

(2015). Factors affecting green purchase behavior and future research directions. International Strategic Management Review, 3(1–2), 128–143.

33.

Kim

, & Moon

S. G.

(2012). Determinants of the pro-environmental behavior of Korean immigrants in the US. International Review of Public Administration, 17(3), 99.

34.

Kottala

S. Y.

, & Singh

(2015). A review of sustainability, deterrents, personal values, attitudes and purchase intentions in the organic food supply chain. Pacific Science Review B: Humanities and Social Sciences, 1(3), 114–123.

35.

Krystallis

, Grunert

K. G.

, de Barcellos

M. D.

, Perrea

, & Verbeke

(2012). Consumer attitudes towards sustainability aspects of food production: Insights from three continents. Journal of Marketing Management, 28(3–4), 334–372.

36.

Kumar

, Manrai

A. K.

, & Manrai

L. A.

(2017). Purchasing behavior for environmentally sustainable products: A conceptual framework and empirical study. Journal of Retailing and Consumer Services, 34, 1–9.

37.

Kumar

, & Ghodeswar

B. M.

(2015). Factors affecting consumers’ green product purchase decisions. Marketing Intelligence & Planning, 33(3), 330–347.

38.

Lai

C. K.

, & Cheng

E. W.

(2016). Green purchase behavior of undergraduate students in Hong Kong. The Social Science Journal, 53(1), 67–76.

39.

Lin

H. Y.

, & Hsu

M. H.

(2015). Using social cognitive theory to investigate green consumer behavior. Business Strategy and the Environment, 24(5), 326–343.

40.

Liobikienė

, Mandravickaitė

, & Bernatonienė

(2016). Theory of planned behavior approach to understand the green purchasing behavior in the EU: A cross-cultural study. Ecological Economics, 125, 38–46.

41.

Liu

, Yin

J. X.

, Cao

H. J.

, & He

(2005). Investigations and practices on green manufacturing in machining systems. Journal of Central South University of Technology, 12(2), 18–24.

42.

Malhotra

(2008). Completion time and response order effects in web surveys. Public Opinion Quarterly, 72(5), 914–934.

43.

Malhotra

N. K.

, & Dash

(2011). Marketing Research an Applied Orientation. Pearson Publishing.

44.

Malik

, & Singhal

(2017). Consumer environmental attitude and willingness to purchase environmentally friendly products: An SEM approach. Vision, 21(2), 152–161.

45.

Mani

, Gunasekaran

, Papadopoulos

, Hazen

, & Dubey

(2016). Supply chain social sustainability for developing nations: Evidence from India. Resources, Conservation and Recycling, 111, 42–52.

46.

Mann

B. J.

, & Kaur

(2019). Sustainable supply chain activities and financial performance: An Indian experience. Vision: The Journal of Business Perspective, 24(1), 60–69.

47.

Moser

A. K.

(2016). Consumers’ purchasing decisions regarding environmentally friendly products: An empirical analysis of German consumers. Journal of Retailing and Consumer Services, 31, 389–397.

48.

Nanath

, & Pillai

R. R.

(2017). The influence of green is practices on competitive advantage: Mediation role of green innovation performance. Information Systems Management, 34(1), 3–19.

49.

Nevitt

, & Hancock

G. R.

(2001). Performance of bootstrapping approaches to model test statistics and parameter standard error estimation in structural equation modeling. Structural Equation Modeling, 8(3), 353–377.

50.

Nguyen

T. N.

, Lobo

, & Greenland

(2016). Pro-environmental purchase behavior: The role of consumers’ biospheric values. Journal of Retailing and Consumer Services, 33, 98–108.

51.

NITI Aayog (2018). SDG India index: Baseline report.

52.

Oosterveer

, & Spaargaren

(2013). Green consumption practices and emerging sustainable food regimes: The role of consumers. In Food Practices in Transition (pp. 151–172). Routledge.

53.

Patterson

P. G.

, & Spreng

R. A.

(1997). Modelling the relationship between perceived value, satisfaction and repurchase intentions in a business-to-business, services context: An empirical examination. International Journal of Service Industry Management, 8(5), 414–434.

54.

Phipps

M. O.

, Luchs

M. G.

, Subrahmanyan

, Kapitan

, Catlin

J. R.

, & Weaver

(2013). Understanding the inherent complexity of sustainable consumption: A social cognitive framework. Journal of Business Research, 66(8), 1227–1234.

55.

Pinto

D. C.

, Nique

W. M.

, Añaña

E. D.

, & Herter

M. M.

(2011). Green consumer values: How do personal values influence environmentally responsible water consumption? International Journal of Consumer Studies, 35(2), 122–131.

56.

Pradhan

, Costa

, Rybski

, Lucht

, & Kropp

J. P.

(2017). A systematic study of Sustainable Development Goal (SDG) interactions. Earth’s Future, 5(11), 1169–1179.

57.

Roberts

J. A.

(1996). Green consumers in the 1990s: Profile and implications for advertising. Journal of Business Research, 36(3), 217–231.

58.

Sangroya

, & Nayak

J. K.

(2017). Factors influencing buying behavior of green energy consumer. Journal of Cleaner Production, 151, 393–405.

59.

Schaefer

, & Crane

(2005). Addressing sustainability and consumption. Journal of Macromarketing, 25(1), 76–92.

60.

Schreiber

J. B.

, Nora

, Stage

F. K.

, Barlow

E. A.

, & King

(2006). Reporting modeling analysis and confirmatory results: Equation factor review. The Journal of Educational Research, 99(6), 323–337.

61.

Steelman

Z. R.

, Hammer

B. I.

, & Limayem

(2014). Data collection in the digital age: Innovative alternatives to student samples. Journal of Consumer Psychology, 23(2), 212–219.

62.

Stöckigt

, Strube

, Lubjuhn

, & Brand

(2019). Assessment of consumer attitudes toward sustainability in food logistics and the role of shopping behavior and personal characteristics. In Innovative Logistics Services and Sustainable Lifestyles (pp. 87–104). Springer.

63.

Suki

N. M.

(2016). Consumer environmental concern and green product purchase in Malaysia: Structural effects of consumption values. Journal of Cleaner Production, 132, 204–214.

64.

Tait

, Saunders

, Guenther

, & Rutherford

(2016). Emerging versus developed economy consumer willingness to pay for environmentally sustainable food production: A choice experiment approach comparing Indian, Chinese and United Kingdom lamb consumers. Journal of Cleaner Production, 124, 65–72.

65.

Taufique

K. M.

, & Vaithianathan

(2018). A fresh look at understanding green consumer behavior among young urban Indian consumers through the lens of theory of planned behavior. Journal of Cleaner Production, 183, 46–55.

66.

Tseng

M. L.

, Tan

R. R.

, & Siriban-Manalang

A. B.

(2013). Sustainable consumption and production for Asia: Sustainability through green design and practice. Journal of Cleaner Production, 40, 1–5.

67.

United Nations (2015). Transforming our world: The 2030 agenda for sustainable development. General Assembly 70 session.

68.

Varshneya

, Pandey

S. K.

, & Das

(2017). Impact of social influence and green consumption values on purchase intention of organic clothing: A study on collectivist developing economy. Global Business Review, 18(2), 478–492.

69.

Verma

V. K.

, & Chandra

(2018). An application of theory of planned behavior to predict young Indian consumers’ green hotel visit intention. Journal of Cleaner Production, 172, 1152–1162.

70.

Vermeir

, & Verbeke

(2006). Sustainable food consumption: Exploring the consumer “attitude–behavioral intention” gap. Journal of Agricultural and Environmental ethics, 19(2), 169–194.

71.

Vermeir

, & Verbeke

(2008). Sustainable food consumption among young adults in Belgium: Theory of planned behavior and the role of confidence and values. Ecological economics, 64(3), 542–553.

72.

Wang

E. S.

, & Lin

H. C.

(2017). Sustainable development: The effects of social normative beliefs on environmental behavior. Sustainable Development, 25(6), 595–609.

73.

Wang

, Liu

, & Qi

(2014). Factors influencing sustainable consumption behaviors: A survey of the rural residents in China. Journal of Cleaner Production, 63, 152–165.

74.

Wheaton

, Muthen

, Alwin

D. F.

, & Summers

G. F.

(1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84–136.

75.

Woo

, & Kim

Y. G.

(2019). Consumer attitudes and buying behavior for green food products: From the aspect of green perceived value (GPV). British Food Journal, 121(2), 320–332.

76.

S. I.

, & Chen

J. Y.

(2014). A model of green consumption behavior constructed by the theory of planned behavior. International Journal of Marketing Studies, 6(5), 119.

77.

Zerbini

, Vergura

D. T.

, & Latusi

(2019). A new model to predict consumers’ willingness to buy fair-trade products. Food Research International, 122, 167–173.

78.

Zhao

, Gao

, Wu

, Wang

, & Zhu

(2014). What affects green consumer behavior in China? A case study from Qingdao. Journal of Cleaner Production, 63, 143–151.