# E-Waste Reverse Supply Chain: A Review and Future Perspectives
## 5 Research Gaps and Potential Research Directions

Table 6: _Cont._issues, financial concerns and etc., consumers' behaviour is one of the most vital factors which contributes to the success of e-waste RSC implementation. Their return intention is a crucial variable for the prediction of the quantity of e-waste, and as a result, this will become important inputs for electronic firms in implementing the design of their RSC systems. Even though a few studies examined customers' behaviour and residents' awareness for returning e-waste issues, their research just focused on some particular products such as mobile phones or computers. Hence, further studies can enhance the behaviour of customers for a variety of electronic products, like household devices or IT and telecommunications equipment. Most studies in this category try to improve the awareness of inhabitants about e-waste by the public media, educational programmes, etc. The roles of government and industry through incentive campaigns to encourage consumers to return their end-of-life products can be an interesting topic in the future. There is a lack of studies focusing on e-waste RSC implementations in rural areas. The residents and local governments in the countryside, especially in developing countries have less awareness of e-waste recycling and environmental protection [46]. Hence, research on factors affected e-waste RSC implementations in rural areas can be conducted in future studies. Extended producer responsibility (EPR) policy aims to manage and treat their end-of-life products. This plays an important role in e-waste RSC implementations at an industry perspective, which can be enhanced to the implementation at the national level. More practical research specifying on EPR implementation for e-waste would be potential future studies. Risk assessment is also an important factor in RSC operation but it has been not examined by existing studies. In future research, risk assessment will be an interesting topic to explore. Research group 2: Performance evaluation and decision making E-waste is normally recognized as a complex issue to make a proper decision. The complexity is influenced by various elements such as various sources of collection, the flow of returned products, the numbers of decision-makers. Hence, it requires an appropriate decision to improve the RSC performance which should be based on the collective decision. In this context, focusing on the vagueness of different decision-makers using Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS) and more criteria to interpret their interaction on the RSC performance would be potential topics in future. To comply with governmental regulations and create a competitive advantage, there is a need for electronics industries to collaborate with third-party reverse supply chain providers to effectively manage their returned products [67]. Although some existing studies conducted the evaluation and selection of third-party RSC providers, the limited number of studies focused on multiple attribute evaluation of electronics companies for RSC collaborations. Hence, more research on this issue by considering the vagueness of the decision-making process could be a promising study direction in the future. Another potential topis could be conducting game theory to examine the strategic interactions in the decision-making process among different partners in RSC operations for e-waste. Some recent research has considered three suitable dimensions such as social, economic and environmental aspects. It would be an interesting direction if future studies could integrate the technology dimension into the sustainable RSC system. The applications of radio-frequency identification, the internet of things can implement for inventory management, tracking the flows of materials, the management of recovery information could be a new study direction in this category. * Most existing studies estimate the number of product returns by examining different variables such as data sales, the lifespan of products, material composition, etc.