Modelling the enablers of sustainable consumer behaviour towards electronic products

Abstract

Purpose The consumption patterns of humanity are putting an enormous burden on the resources, which are beyond the fulfiling capacity of the earth. These consumption patterns need to be made sustainable. Therefore, this study aims to focus on identifying the key enablers driving the sustainable consumer behaviour. Design/methodology/approach Based on the literature review of 107 studies (1995 to 2018), which were published over a period of 23 years, fourteen key enablers of sustainable consumer behaviour were identified. These 14 enablers were validated with the help of empirical research done by surveying 200 consumers of electronic products. This was followed by the application of interpretive structural modelling (ISM) technique, to get an insight into how these enablers are influencing sustainable consumer behaviour. Findings ISM technique and empirical research aided in classifying the enablers based on their driving and dependence power. A further analysis was done to know about the contextual relationship between all 14 enablers and how these enablers are influencing each other and sustainable consumer behaviour. Attitude, subjective norm and perceived controlled behaviour were found dependent variables while education, government policy and advertisement emerged as an autonomous variable. Research limitations/implications This study will be helpful for future research in the field of sustainable consumer behaviour. Through understanding of the enablers of sustainable consumer behaviour, a scale can be developed having significant theoretical and managerial implications. This study may help in improving the sustainability initiatives by various stakeholders involved in the process of purchase, usage and dispose of electronic products. Originality/value This study is the first temporal and spatial study, which includes a holistic view of the enablers of sustainable consumer behaviour.

Benchmarking the barriers of sustainable consumer behaviour

“…Barriers were ranked and finalised on the basis of mean score i.e. a barrier was selected if it's mean score was more than 3 (Sheoran and Kumar, 2020). The cutoff limit is 3.1 for the barrier "inconvenience".…”

“…The increasing importance of such a complex issue necessitates comprehensive analysis which includes understanding the gaps in the existing literature, identifying enablers and barriers of sustainable consumer behaviour (Al-Esmael, 2019;Sheoran and Kumar, 2020). This article is an attempt to identify the barriers of sustainable consumer behaviour of Indian electronics product consumers and find out their respective weights.…”

Conceptualisation of sustainable consumer behaviour: converging the theory of planned behaviour and consumption cycle

“…According to Prothero et al (2011) and Sheoran and Kumar (2020a) in order to understand consumer behaviour, whole consumption cycle should be studied and not just initial choices because post choice behaviours like product usage, product life extension and product disposal also play a very important and equally significant impact on sustainability. Moreover, Hofmeister-Toth et al (2011) and Sheoran and Kumar (2020b) explained that to promote sustainable consumer consumption behaviour it is needed that key factors behind such behaviour should be identified and presented in a structured manner. Hofmeister-Toth et al (2011) conducted a survey to study eco-friendly consumption patterns in Hungary and analysed that females are more environmentally conscious and are more involved in such behaviours.…”

Modelling the Enablers for Branded Content as a Strategic Marketing Tool in the COVID-19 Era

“…Step 5: MICMACAnalysis: The purpose of MICMAC Analysis is to divide the identified factors into four different clusters as per the driving power and dependence of the factors. The four clusters thus identify and group the factors as autonomous, dependent, linkage, and independent factors [44,[60][61][62][63]. The grouping of factors in this study is presented in Figure 1 and discussed as below: Cluster I: This cluster groups together Autonomous Factors in the system.…”