Analysis of Critical Success Factors to Design E-waste Collection Policy in India: A Fuzzy DEMATEL Approach

The design of an e-waste collection policy is challenging, especially for a country like India, where the economy is a developing state, and there is a large diversity in socio-economic factors. The e-waste collection policy impacts the various stakeholders such as the manufacturer, the raw material producers, the assemblers, the retailers, the generator (households and bulk consumers), the scrap dealers, the smelters, the recyclers, and the regulators. The design of an e-waste collection policy needs to consider the appropriate set of Critical Success Factors (CSFs), which will maximise the e-waste collection providing business sustainability to the stakeholders while satisfying the environmental regulations in the operating locations. Twenty-three CSFs identified and categorised in six implication dimensions for the e-waste collection policy framework based on a literature survey and experts committee view. The fuzzy DEMATEL approach is employed to analyse the CSFs to design an e-waste collection policy in India from a comprehensive perspective. Cause and effect interrelationship is established among the CSFs, and also their impacts are evaluated to segregate the CSFs into cause group (prominent influencing and independent) and effect group (influenced and dependent). The CSFs such as technology involvement, green practices, environmental program, certification and licensing, public ethics and stakeholder's awareness for circular economy are prominent influencing CSFs for e-waste collection policy in India. The current study is expected to provide a platform for policymakers to design the e-waste collection policy.


Introduction
Handling policies of Electronic Waste (e-waste) or Waste of Electrical and Electronics Equipment (WEEE) are an essential aspect of the environmental ethics of a country. Electrical and Electronic Equipment(EEEs) are generally categorised based on their usage and useful life for drafting the e-waste policy. While EEEs play a vital role in modern life in terms of safety, comfort, education and entertainment, WEEE poses a severe environmental risk if not treated and disposed of appropriately. Due to rapid technological advancement, higher affordability, increasing purchase power, decreasing cost, shorter useful life cycle, increased customisation, and promotional events like exchange schemes, there is a tremendous increase in consumption of EEEs worldwide. For example, the present penetration rate of mobile subscribers worldwide is 67 % and expected to reach 71% in 2025 (GSMA 2019). The expected life cycle of EEEs like mobile phones at present is shorter than two years, while computers have a useful life of about three years (Öztürk 2015). This ever-shortening life span of EEEs contributes to a great extent to WEEE growth. According to various sources (Ayodeji 2011;Baldé et al. 2017), the worldwide increase in e-waste generation at present is assessed to be at a rate of 10% -20% annually. There is great concern about the lack of government support and ineffective regulatory framework on e-waste handling in many countries, resulting in a continuous increase in informal recycling activities that pose a severe risk to the environment as they can bypass environmental regulations (Al-Anzi et al. 2017). Unscientific e-waste handling is causing severe damage to the environment by contaminating soil, water and atmosphere, ultimately affecting human lives (Cao et al. 2016). Hence, appropriate policy implication in e-waste handling is of critical importance for a country and its well-being, and it necessitates social awareness drive to tackle the problem.
A recent study by Forti et al. (2020) revealed that only 78 countries worldwide have national legislation on e-waste handling. Although there is great diversity across the world, majority of the developed countries such as Europe, America, and Oceania have implemented robust e-waste policy and established necessary infrastructure for the collection and processing of e-waste (Wath et al. 2010). They also successfully managed their social awareness programs and education in the past. For instance, more than 50% of Switzerland's citizens expressed a desire to place the highest emphasis on environmental issues as recorded in a survey (Chaudhary and Vrat 2018). Most of the developed nations have also introduced provisions for steep penalties for improper handling of ewaste (Wath et al. 2010).
In contrast, in developing countries such as India, the regulatory framework and vigilance are inadequate for mitigating WEEE related issues at the same time, general public awareness is also deficient. As a result, a large part of e-waste gets dumped or transported from developed countries as a donation, or hands-down etc. (Garlapati 2016). Some of the direct causes identified by (Thavalingam and Karunasena 2016) in developing nations are insufficient corrective measures, unclear roles of stakeholders, and insufficient resource investment in the e-waste management sector. These are challenges that are believed to be arising out of lack of social awareness and inadequate legal & enforcement framework. The same gives rise to the informal or grey sector handling a vast amount of e-waste, as observed and recorded for Bangladesh, Malaysia, Indonesia, the Philippines and Brazil (Rodrigues et al. 2020). The situation is not much different in the case of India.
Apart from technological shortcomings, one of the problems for India is its substantial population. India is currently ranked third globally in terms of e-waste generation, which assessed as 3.23 million tons (Forti et al. 2020). The estimated volume of e-waste generation in India grossly exceeds that of e-waste processing capacity (0.78 million tons), as CPCB (2019) observed. As per the assessment carried out in 2017 (Awasthi et al. 2018), the various sources that prominently contribute to the growth of e-waste are household appliances (42%), information and telecom equipment (34%), consumer electronics (14%), and other electronic equipment (10%). Many regulatory agencies also pointed out laxity in implementing e-waste policies and lack of enforcement as a prominent drawback in India (Jecton and Timothy 2013).
In India, the e-waste collection channels can be classified into two main types based on the particular sector, i.e. informal and formal. The informal sector dominates in most places and it is estimated that around 95% of the e-waste collection is recycled by this sector, hindering the formal channels related to e-waste collection (Kumar et al. 2017). The informal collection sector includes scrap dealers (Kabbadiwala), paddlers, and rag pickers who collect e-waste directly from residents at a minimal cost (Garg 2021). The collection network of this sector is vast and labour intensive as the door to door collection channel is mainly used, which reduces their logistics cost compared to the formal sector (Singh et al. 2020). The informal sector is unregulated and allegedly do not follow scientific methods as well as safety standards. It also poses a significant threat to the sustainability of e-waste collection by the formal sector. In contrast, the formal sector includes registered and authorised recyclers, collectors, dismantlers, producers, and retailers (Patil and Ramakrishna 2020). This sector procures e-waste through proper collection channels as per government guidelines and primarily cater to bulk consumers such as multi-national companies, universities, government/private offices, etc. Some formal players do facilitate door-to-door collection on customer demand. Some also support exchange schemes through the online portal or at retail stores and collection centres (Batoo et al. 2021). The authorised sector faces a tough challenge from the informal sector as the latter conducts collection activities that bypass labour regulations, various safety standards, and even unethical practices. Slack enforcement of regulatory policies and poor consumer awareness is considered the crux of the problem.
For the successful implication of e-waste collection policy, it is paramount to monitor its outcomes from time to time and identify the need for government involvement in policy amendments (Wang et al. 2017). Further, the stakeholders need to play a responsible role in implementing e-waste collection policy and spreading environmental awareness. Okorhi et al. (2017) presented a survey of various effects of the implementation of e-waste collection policy, including the involvement of local government and solid waste handling agencies. They suggested that e-waste management is distinctly different from municipal solid waste management and emphasised the need of setting up independent standards for e-waste regulation by the government. Wath et al. (2011) argued that the framework for e-waste collection policy should maintain a balance between the economics involve and environmental and public health & safety concerns. Morris and Metternicht (2016) stated that for enhancement in the effectiveness of e-waste collection policy, it is essential to re-assess the roles and responsibilities of stakeholders. Furthermore, identical enablers are required for cases involving the local and the federal government to engage and educate the public on the need for separate e-waste management and the various priorities of the e-waste collection policy.
The motivation of this research to accomplish the following objectives: & To identify and shortlisted Critical Success Factors (CSFs) for designing an e-waste collection policy based on a literature survey and content validity with the expert's committee. & To evaluate and categorised CSFs as cause group and effect groups, those need to be managed. & To perform sensitivity analysis for examining the robustness of the result minimise biases during the decisionmaking process. & To set appropriate standards and controls to regulate the action of stakeholders associated with e-waste handling in the public and private sector.
The present work attempts to identify the CSFs influencing the designing of e-waste collection policy in the Indian context. A Multi-Criteria Decision Making (MCDM) technique through an appropriate framework is adopted. Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) chosen for evaluating the CSFs. DEMATEL analysing the influencing behaviour of CSFs on other CSFs. The method is utilised primarily to develop two sets of CSFs that is cause group and effect group considering multiple expert judgments and the fuzziness associated with their decisions.
The study provides a novel research contribution in the e-waste management field, focusing on designing e-waste collection policy in the Indian context. The methodology exploits interrelationship among the various influencing factors and enables policymakers to incorporate those factors to strengthen the e-waste policy in terms of acceptable collection strategy. Prioritising CSF requires attention to the factors which are critical causal CSFs to others. The study provides research implications for researchers and practitioners to understand the evaluation process of CSFs better and identify significant CSFs while drafting an e-waste collection policy. A sensitivity analysis is also conducted to check the robustness of the result.

Evolution of e-waste policy in India
With a motive to mitigate short and long-term impacts on environmental and human health arising out of e-waste, the Indian government has enacted several e-waste policies from time to time. The evolution of the e-waste policy in India may be classified into five phases (CPCB 2019), as illustrated in Fig. 1.
The period during 1986 to 2003 can be termed as initiation phase as several pioneering initiatives were taken during this phase to identify, categorise and assign the responsibility of handling various waste streams. Acts such as Environment protection Act 1986 was brought in, to identify various types of pollutants to the environment. After about three years, Hazardous (Management and Handling) Rules 1989 was introduce to define hazardous wastes and their sources. Subsequently, a series of Hazardous (Management and Handling) Amendments in 2000 and 2003 were brought forward which identifies and categorises various harmful wastes into twelve categories. The term e-waste was introduced and recognised as a waste stream with contamination potential under schedule three of the hazardous waste rules in 2003. There was no separate regulation explicitly formulated to address the e-waste related problem. However, this can still be considered a profound first step to subsequently develop specific legislation for various waste streams and environmental issues arising out of it.
During 2004 to 2008, several studies were commissioned, and detailed guidelines were drawn, this time interval can be defined as the Draft phase. An explicit formulation of guidelines for environmentally sound management of e-waste was initiated by the Ministry of Environment and Forest (MoEF) and Central Pollution Control Board (CPCB), Government of India in the year 2008. Under this initiation, identification and assessment of various sources of e-waste were made. Classification of e-waste according to its components, composition, and harmful effects were attempted. These documents also addressed the recycling potential of e-waste for economic benefit. However, the guidelines of e-waste did not adequately explain the roles and responsibilities of various monitoring agencies such as State Pollution Control Boards (SPCBs), local bodies, as well as other stakeholders.
The period from 2009 to 2011 can be termed as a Development Phase-I as the first consolidated policy framework on e-waste management was debated during 2010-11 and was passed in 2011 which was termed as E-waste (Management and Handling) Rules, 2011. The policy laid down the roles and responsibilities of stakeholders and monitoring agencies. It also introduced the concept of Extended Producer Responsibilities (EPR) in India although was in place in developed nation. This policy defined various stakeholders of a business model around e-waste management, grouped under manufacturer, producers, collectors, dismantlers, and recyclers. However, the Development Phase-I fell short of defining transboundary movement of ewaste under various schemes as a hazardous and a mitigation plan for the same. Other criticisms are that it did not adequately address economic implications, merits, demerits, barriers and drivers of e-waste management in India (Wath et al. 2010).
The next distinct phase identified is from 2012 to 2016 as Development Phase-II. The levels of responsibilities of the various government bodies were explained in the E-waste (Management) Rules, 2016, and it is presented in Table 1. The prominent feature of this policy was a target-based approach for e-waste collection under EPR. The adoption of the same policy was based on existing international best practices which demonstrated a higher success rate for implementation of EPR. The policymakers took references from many countries like the Netherlands (recycling rate 45%-75%), Japan (recycling rate 50%-60%), South Korea (recycling rate 55%-70%) and UK (recycling rate 50%-80%) (CPCB 2019). In these countries, the e-waste management policy was in a much more mature phase with set targets of recycling rate. Whereas in India, successful and sustainable collection infrastructure was not yet established. Further, the implementation plan of this rule for the producer under EPR provision was to subsequently set a guideline of collection target. During the first two years, the recommended target was 30%, and the subsequent bi-yearly target was increased by 40%, and so on up to 70% (CPCB 2019). Under the rule, the producers were required to share the details of EEEs and collection target of the forthcoming years based on sales forecast to the CPCB in a prescribed format.
The development during the year from 2017 to till date can be term as implementation year. The policymakers duly considered the feedback obtained from various stakeholders to formulate a comprehensive E-waste (Management) Amendment Rules, 2018. The provision in this rule was that producers should be liable to share collection targets with government authorities. Another condition was that in the event of any violation of environmental law, strict action might be taken in the form of cancellation of registration.
As of 27 th July 2019, CPCB had registered 312 authorised recycling/dismantling units across India (CPCB 2019). The units are located in 18 states, as shown in Fig. 2, and the overall registered recycling capacity was 0.78 million tons. The highest number of recycling units are located in Maharashtra (75 units), followed by 71 units in Karnataka and 41 units in Uttar Pradesh. Out of the total formal recyclers, only 51 units had installed capacity exceeding 5000 tons per annum. The total installed capacity of registered recyclers was 0.78 million tons, four times lesser than the projected e-waste generation of 3.23 million tons. Several studies have revealed that the implemented e-waste policy in India and other developing countries face unique challenges (Patil and Ramakrishna 2020;Singh et al. 2020). These include a thriving informal sector in the absence of strict enforcement of regulation, lack of public awareness, and lack of financial resources to implement the necessary intervention steps to manage e-waste.

Materials and Methods
This section highlights the identification of e-waste collection policy CSFs, research gaps, and the importance of fuzzy-DEMATEL method in relation to the proposed methodology.

CSFs identification for e-waste collection policy
In order to identify the CSF of the e-waste collection policy, it is imperative to create a theoretical foundation to explore the various factors involved during the design of the e-waste collection policy. An important driver of e-waste collection policy is the prevailing culture related to consumption patterns, public awareness and disposal behaviour. Several researchers have conducted studies on e-waste management and its sustainability. Particularly, the researchers stressed the e-waste management-related policy. Li et al. (2016) studied the impact of e-waste regulations on improving collection activities and overall sustainable management. Various countries have started to pay attention to the e-waste policy and identify those local variables or drivers that assist in developing a sustainable formal recycling and safe disposal system (Triguero et al. 2016). A study by Carisma (2009) measured the socioculture and economic aspects as prominent drivers of ewaste management policy in the Philippines. Similarly, in Taiwan (Shih 2017), a Recycling Fund Management Board (RFMB) analysed the e-waste policy, intending to maximise the recycling rate and improve the fund allocation system. It found that flexibility, fairness and promotion are the key drivers of improvement in the e-waste recycling rate. Triguero et al. (2016) also reviewed the waste management policies of 28 European countries and identified three primary drivers that influence the waste management policy. These are Government responsibility to pay subsidies for waste management; Consumer responsibility to deposit the appropriate quantity of unsorted waste; Producer responsibility to pay the cost of waste management already included in the final prices of EEEs. Yu et al. (2014) argued that the policy instruments must consider developing the ewaste rules. It reviewed the e-waste policy of China and identified potential improvement areas like monitoring and auditing system, identification of the location of the informal sector, sharing of information about treatment technologies with the government, and the need to spread awareness among the public in the hinterland. Leclerc and Badami (2020) reviewed the EPR program of e-waste policy in Canada. It identified a few policy drivers that tend to add up in e-waste regulation, such as enforcement mechanisms & penalties, visibility of environmental handling fees and modulation of 3R (Reuse, Reduce and Recycling). Parajuly et al. (2020) argue that the circular economy is an essential enabler for policy intervention in European countries. This driver may fill the gap between conventional drivers such as awareness campaigns, economic incentives, stricter regulations, transboundary movement and consumer behaviour towards an e-waste policy. It suggested that the circular economy concept can influence the socio-economic culture and promote green practices. However, Borthakur and Govind (2017) argued that in developing countries like India, various factors like socio-cultural, economic, political, technological, infrastructural and environmental differences play a pivotal role in public acceptance of e-waste collection policy. The designing of the e-waste collection policy of India adopted a few points from the legislation at various developed nations in Europe, the U.S., and Japan. Some examples of different schemes experimented in India are 'deposit refund scheme', 'polluter pay', 'EPR system', 'collection target', stakeholders definitions and responsibility setting etc. However, the policy focuses more on the EPR system, implying the producer's responsibility to recycle e-waste. This is done, possibly to boost India's limited recycling capacity and nudge private investment in the same (Awasthi and Li 2017). While in China, the policy focuses more on minimising raw material consumption and in developing sustainable recycling technology and developing infrastructure for recycling activities (Patil and Ramakrishna 2020). Singapore initially implemented regulations to restrict the transboundary shipment of hazardous waste. National Environmental Agency (NEA) in Singapore monitors the movement of e-waste and regulate the stakeholders to follow e-waste policy (Patil and Ramakrishna 2020). Canada also focused on EPR policy for managing their e-waste. The main emphasis was on the design of EPR policy to generate local employment through take-back programmes and to encourage reuse and reduce to address environmental impacts of EEEs (Leclerc and Badami 2020). Most of the developing countries adopted policies that have similarities with developed countries. However, successful policy designing and implementation hinge on the culture and unique circumstances of the country. In some countries, the policies fell short of receiving public acceptance, and in many, the government's efforts were not matching the requirement.
The other essential opportunities are to improve the policy taking a broader outlook like considerations in 'United Nations Agenda 2030' for Sustainable Development Goals (SDGs). The aim will be to identify the CSFs resulting from more exhaustive policy analysis and establish casual relationships from such a perspective. The main priorities of the policy strategies are to focus on the formulation of the following area of policies, enact legislation to reduce waste generation, promote responsible public behaviour on waste management, and promote waste segregation at source. Other priorities are the 3Rs & recovery energy from the waste, promotion of waste treatment, and establishing environmentally sound infrastructure for e-waste management (ITU 2018).
The identification of CSFs was based primarily on an extensive literature review. A list of CSFs prepared through various available electronic databases such as Google Scholar, Web of Science and Scopus. Appropriate search strings used to identify related keywords for CSFs, the phrases used included e-waste, electronic waste, collection policy, regulation, and legislation in relation to the collection system. After selecting and reviewing the literature, a comprehensive list of CSFs have been identified, keeping in mind the design of the e-waste collection policy. Based on this list, discussions and content verification was conducted with the expert committee to finalise the CSFs. After several rounds of discussion, feedback is taken from the expert committee. Based on that, changes were made in the wordings and text of the original description of CSFs. The process improved the clarity of thought process, and the expert's committee involvement brought validity. A total of twenty-three CSFs got shortlisted. These were later grouped under six different broad aspects taking the help of experts. These six aspects are (1) Research & development, (2) Education & social behaviour, (3) Economic instrument, (4) Traceability, (5) Responsibility, and (6) Legislation & Regulation and the same is tabulated with explanation in Table 2.

Research gaps
An extensive literature review shows that drafting an effective e-waste collection policy has been a significant concern for policymakers, consumers, and various other stakeholders who are directly involved in e-waste management activities. The various previous studies have focused on the barriers or critical analysis of the implementation of e-waste management issues. Minimal research concentrated on the interrelation among the CSFs of e-waste collection policy in developing countries like India. Further, no studies have found that identifies the influence and efficiency of e-waste collection policy or identify those factors that contribute to the foremost. Hence, the current research undertakes to evaluate the CSFs for filling the gap and promoting the importance of e-waste policy. Moreover, it is hoped that this study can better understand ewaste collection and its policy development. The inclusion outcomes of the study for e-waste policy development will lead maximise the e-waste collection in India.

Proposed research framework
The framework consists of two phases. The first phase identifies and lists CSFs related to e-waste collection policy based on an extensive literature survey and experts discussion. The second phase involves applying the fuzzy DEMATEL technique to develop the interrelationship among the CSFs for analysis of causal relation between one CSF over another. Finally, the findings are discussed with the expert's committee to assist them in reframing e-waste collection policy and develop tactical schemes by policymakers for successful and broad acceptance level of e-waste collection policy. The various steps adopted in the proposed research framework for evaluating the CSFs are illustrated in Fig. 3.

Research method
The objective of the current study is to evaluate and identify the causal relationship between the CSFs. Various MCDM methods are available such as Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Interpretive Structural Modelling (ISM), and DEMATEL. Among these methods, AHP could not determine the relationship between factors (Parmar and Desai 2020). ANP only quantify and develop inner or direct dependency between factors (Tseng 2009). While the ISM technique establishes the hierarchical interrelationships between the factors, it does not significantly influence the factors. It does not divide the factors into cause and effect groups (Mangla et al. 2016). DEMATEL is one of MCDM methods. It develops the direct and indirect influence relationship within factors and converts the influence factors into the cause group and effect group (Gupta and Barua 2018). DEMATEL is a mathematical computational method invented by Research Centre of Science and Human Affairs Program of the Battelle Memorial Institute Geneva (Gabus and Fontela 1973). DEMATEL can be an effective way to solve the various complex management problems by developing complex causal relationships with matrices or diagraphs. The matrices or diagraphs portray a contextual relationship among the factors or elements of the system. The results of DEMATEL represent the relationship between factors by categorising them into cause and effect group (Tsai et al. 2020). Considering the common biases and vagueness in human judgment in an actual situation, a fuzzy set theory has been used to extend the traditional DEMATEL into fuzzy-DEMATEL (Karuppiah et al. 2020).
In waste management applications, the fuzzy-DEMATEL method has been utilised by various researchers in analysing the problem. Tseng and Lin (2009)     The fuzzy-DEMATEL method consists of the following steps: Step1. To establish an expert committee Literature review and brainstorming/critical discussion are necessary to ascertain the research problem. For critical discussion, three committees were formed, and each committee consisted of six experts. The experts range from academia, the recycling industry and waste management consultants; most have more than ten years of experience in their respective fields. All experts are assumed capable of problem-solving and having significant knowledge of e-waste management comprising policymaking, designing of e-waste handling practice, and e-waste management in the Indian context. The responses are collected through telephonic interviews and email conversation.
Step2. To construct the initial fuzzy direct relationship matrix e Α for each expert (k) In this step, for qualitative judgment, expert committee is asked to rate CSFs on a Triangular fuzzy number (TFN) scale as presented in Table 3. Here, e a ijk ¼ l ijk À ; m ijk ; u ijk Þ indicates the degree of influence factor i on factor j while k indicates number of experts, and n indicates number of factors. Then for each expert a n × n non-negative fuzzy direct relationship matrix is established as shown in equation (1).
Step3. To develop a defuzzified direct relationship matrix Initial fuzzy direct relationship matrix of each expert is defuzzified into crisp form. The development of Defuzzified Direct Relationship Matrices (DDRM) is through the conversion of the fuzzy numbers into a crisp score that is CFCS, as proposed by Opricovic and Tzeng  (2003). The stepwise procedure of CFCS is mentioned from equation (2) to (8).
(1) Normalisation: Where (2) Compute left (ls) and right (us) spread of normalised fuzzy numbers: (3) Compute total normalised crisp value: (4) Compute crisp value: Step4. Development of an average direct relationship matrix (A ij ), normalised direct relationship matrix (D) and Total Relation Matrix (TRM) ′T′ The aggregate DDRM is obtained from each expert by developed average direct relationship matrix (ADRM) using equation (9).
The normalisation of ADRM is done using equation (10) The total relationship matrix T is computed by equation (11) Step5. To calculate row sum (R i ) and column sum (C j ) of TRM (T). Equation 12 and 13 are used to find R i and C j .
Step6. To establish the cause and effect relationship The value of R i and C j determine the cause or effect nature of a factor based on the computation of (R i + C j ) and (R i -C j ). Where, (R i + C j ) represent the degree of prominence of the factor "i" in the entire system and (R i -C j ) represent net cause and effects that factor "i" contributes to the system. Furthermore, the interrelation among the CSFs is developed based on the threshold value α, which is calculated by using equation (14).
The threshold value α is computed from the matrix T. When values in the matrix T exceed α, it indicates a strong interrelation between factors. The weak relationship between the factors are generally eliminated.

Case analysis and application of fuzzy-DEMATEL for ewaste collection policy
The authors developed a framework for examining CSFs related to acceptance of e-waste policy in Indian scenario, incorporating the perspectives of various stakeholders that are necessary for a successful implication of a sustainable e-waste management system. The data required for the fuzzy-DEMATEL analysis is collected in the state of Rajasthan in India. Some recently reported studies in other application areas have applied DEAMTEL as a tool for analysis in fuzzy situations, taking the inputs from three to five expert committees (Parmar and Desai 2020;Singhal et al. 2020).
Based on the expert's committee's opinion, the initial pairwise matrices are built. The inputs are provided by experts   The highest degree of influential impact power of R K23 -Certification and licensing has the highest degree of influential impact power of R equal to 1.332, which means K23 factor has the highest impact on the other CSFs.
2 HighestR-C score in the group K23 -Certification and licensing has the highest value of R-C equal to 1.335, which indicates K23 is also least influenced by all other factors. 3 Lowest R -C score in the group K8 -Public ethics CSFs has the least value of R-C equal to 0.83, which indicates K8 highly is influenced by most other factors.

4
Highest R+C score K5 -Government initiatives has the highest R+C score equals to 1.64, and this CSFs has the potential to improve the system, and it requires attention by the policymakers.
committees on a 0 to 4 scale (Table 3) depending upon the influence of one factor over another factor and the same is then converted using a fuzzy linguistic scale. Further, fuzzy responses matrices of individual committees are converted in crisp value by using equation (5-8). Thereafter, the metrics of the three committees were averaged using equation (9) and then normalized to the ADRM using equation (10) presented in Table 4. The TRM is constructed using equation (11), as shown in Table 5. Table 6 represent the rankings of the CSFs, where R and C are computed using equation (12).

Results and discussions
To evaluate the interconnect among the listed CSFs, the R and C values are computed from the TRM (Table 5), and a causal diagraph is drawn as presented in Fig. 4. The X-axis denotes (R + C), which depicts the prominence of factors (i.e. the cause group), and Y-axis indicates (R -C), which is the effect group, the same is also known as a receiver group. The positive value of CSFs on the Y-axis in the figure will represent the cause group, while negative value as effect group. The advantage of the causal diagraph is that it becomes easier to capture the complexity in decision making. The relative value of various CSFs are used to determine the influencing and influenced factors. The diagraph should help make it easier for policymakers to consider CSFs for designing the e-waste collection policy.

Ranking of CSFs based on R+C values
The order of CSFs are evaluated through the degree of prominence measured by (R+C) values. A PARETO chart is developed based on the degree of prominence to identify the group of significant CSFs. The PARETO chart (Fig. 5  The highest degree of influential impact power of R K4 -Environmental program has the highest degree of influential impact power R equals to 0.71, which means K4 has the most operative factors of the group.
2 H i g h e s tR-C score in the group K6 -Training & empowerment has the highest value of R-C equals -0.105, which indicates it is highly influenced by cause group CSFs.
3 L o w e s tR-C score in the group K2 -Technology involvement has the least value of R-C equals to -1.04, which indicates all other factors least influence K2.

4
Highest R+C score K2 -Technology involvement has the highest R+C score equals to 2.30, and this CSFs is the most prominent factor among the other CSFs in the group.
Infrastructure development (K3), Public ethics (K8) and Government initiatives (K5). Therefore, it is recommended that policymakers pay more attention to these CSFs for designing the e-waste collection policy.

The categorisation of CSFs into cause group and effect group based on R-C values
Based on their R-C score (Table 9), CSFs are classified into cause and effect groups. Out of the twenty-three CSFs, fifteen factors have a positive (R-C) value and are put in the cause group. While the rest eight with negative (R-C) values, are identified as the effect group. The listed CSFs in the cause group indicate prominent independent factors that significantly influence other factors. The highest positive value of R-C is obtained for Certification and licensing (K23) followed by Public ethics (K18), and Collection mechanism (K19). The R-C values of cause group CSFs are presented in Fig. 6 in order of prominence. The primary factors or indicators of the cause group are summerised in Table 7. It is recommended that the prominent cause group CSFs be critically analysed by policymakers before any implementation decision.
The effect group CSFs are listed in Fig. 7, the prominent CSFs of the effect group are Technology involvement (K2), Infrastructure development (K3), Green practices (K1) and CSR (K14). The significance of effect CSFs are explained in Table 8. The key indicator for the effect group CSFs needs to be identified to measure the performance of the CSFs.
In order to check the robustness of the cause and effect relationship, a sensitivity analysis is carried out using a scheme prescribed by Rajesh and Ravi (2015). For sensitivity analysis the weights of particular professional is varied, and the overall effect is analysed were changed at random. Here we present three cases of such changes are investigated by assigning new weights to experts committee as same is presented in Table 9.
The cause and effect values and ranking of CSFs are obtained again and is tabulated in Table 10. Sensitivity analysis shows uniform ranking order (minimal variation in CSFs) is observed in each investigation. This implies robustness of the overall exercise and fidelity of the result and it is concluded that there is no significant bias in the expert's committee input.

Establishment of strategy interrelation map between causes and effects
To develop the strategy interrelation map among the CSFs, the calculated threshold value (α) was obtained as 0.029 and computed using equation (14). This value is used to eliminate the weak interrelations among the CSFs and thereby highlight only those CSFs whose value in TRM is greater than the threshold value. A total of 146 interrelations are developed between CSFs based on their values. Since the number of interrelations is large,  it is difficult to represent all the interrelations in one diagram. Therefore, strategy interrelations map is divided into three parts, one depicts interrelations between effects, the second shows interrelations between causes and third shows interrelations between causes (Fig. 8). The number of interrelations among CSFs in the cause group is found more than that of the effect group. It is evident from Fig. 8

Research implications
The research brings forth significant insight into the complex interrelation among various factors influencing e-waste collection policy and their implications, which can ultimately help frame appropriate policy. In the present study, twentythree CSFs have been evaluated based on the input provided by an experts' committee. The study illustrates relations between the CSFs and establishes the prominence of CSFs in cause and effect sequence. A causal diagraph shows the connection among CSFs. The results are deemed helpful for policymakers and practitioners to comprehend the implications of policy design and implementation strategies for ewaste management.  & It is found that Public ethics (K8) is one of the cause group factors with high relevance. Policymakers need to pay due attention to this factor, encourage the morality aspects, support responsible public behaviour, and increase ewaste disposal activity along with emphasis on continued education and improvement in policy framework. & Stakeholder's awareness about the circular economy (K9) is also the most crucial factor, and this also provides a direct economic benefit. Therefore, the circular economy concept needs to be promoted and awareness to be increased and it is essential for a sustainable e-waste management (Singh et al. 2021). To ensure full utilisation of e-waste under reuse, reduce, recycle and recover under circular economy concept.

Conclusion
E-waste management is a complex but crucial issue in developing countries, especially in India, where the unorganised sector conducts activities in an unethical manner that harms the ecosystem. The e-waste policy has nation-wide implications and stakeholders need to comply with and assist the implementation of the robust e-waste management system. In the context of 'United Nations Agenda 2030' for SDGs of e-waste management, the policymakers are under pressure to design a robust e-waste collection policy that is largely acceptable to various stakeholders. Here, we presented an in-depth analysis of the CSFs based on discussion with the experts

Inter-relationship between effects Inter-relationship between causes
Inter-relationship between causes and effect committee. There is a lack of research to determine the relationship between the various CSFs of e-waste collection policy. To the best of our knowledge, this is a novel study that determines the CSFs facilitating the design of e-waste collection policy and examines the degree of criticality of the factors. The insights from the current study are believed to be beneficial for the stakeholders, practitioners, and policymakers to enhance acceptance of e-waste collection policy in the Indian context for successful implementation.
This research explored twenty-three CSFs with the help of a literature review and experts views. The Fuzzy-DEMATEL method has been applied to develop the interrelation among the CSFs and examined the degree of prominence of the CSFs and categorisation into cause group and effect group. The research reveals that technology involvement (K2) and green practice (K1) factors have the highest importance among CSFs, implying that sustainable e-waste management practices require more responsiveness. So, the policymakers may give more attention to the proponents of research & development in e-waste handling. The stakeholders need to invest in improving their existing methods. Moreover, awareness about the circular economy among stakeholders is an essential factor for sustainable economic benefit. Further findings are that the legislation & regulation CSFs fall under the cause group and are the most vital driver for designing of e-waste collection policy.
This study also pointed out the important limitations of the research, which can be viewed as a future scope. The present study is based on limited expert input that is subjective in nature. An alternative technique of ISM-MICMAC can also be utilised to develop hierarchical relations between factors. Further, the authors also recommend empirical analysis, i.e. structural equation modelling that can be employed to conduct quantitative evaluation from a large sample to validate interrelation among CSFs. This study is conducted in the western part of India. The results of this study may differ from other studies conducted at the states of India and in different countries.
Taxonomies e Α , Fuzzy direct relationship matrix; e a ijk ¼ l ijk À ; m ijk ; u ijk Þ , Degree of influence factor i on factor j; k , Number of experts; (xl ij , xm ij , xu ij ), Normalised value of (l ij , m ij , u ij ); minl k ij , Column minimum value of l ij ; maxu k ij , Column maximum value of u ij ; xls k ij , Left spread measure of normalised fuzzy number; xus k ij , Right spread measure of normalised fuzzy number; x k ij , Total normalised crisp value; z k ij , Crisp value defuzzified from TFN Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article