A Grey-DEMATEL approach for implicating e-waste management practice: Modeling in context of Indian scenario

Atul Kumar Sahu (Department of Industrial and Production Engineering, Institute of Technology, Guru Ghasidas University, Bilaspur, India)
Harendra Kumar Narang (Department of Mechanical Engineering, National Institute of Technology, Raipur, India)
Mridul Singh Rajput (Department of Mechanical Engineering, National Institute of Technology, Raipur, India)

Grey Systems: Theory and Application

ISSN: 2043-9377

Publication date: 5 February 2018

Abstract

Purpose

The use of smart electronic gadgets is proportionately increased during last decades as these gadgets are crafting coziness and relief to the society by making their work easier, effective, etc. These gadgets are the need of today’s working environment for effective planning and work execution. Today, people pertaining to almost every corner of the world are addicted to smart mobile phones, and nowadays, these mobile handsets have become very essential and it is not possible to survive without using them. On the other hand, these smart mobile handsets become inefficient and obsolete over time due to which there is a need to replace the old phones by the new ones, thus creating e-waste. The purpose of this paper is to recognize the significant enablers which are responsible for replacing the existing working mobile phones with the new ones by the end consumers.

Design/methodology/approach

The Grey-DEMATEL (Decision-Making Trial and Evaluation Laboratory) approach is proposed by the authors to compute the decision results. The present work is supported by the structural modeling equations for supporting sustainability throughout and recognizes the most significant enablers responsible for creating e-waste by replacing the working mobile phones with the new ones.

Findings

The implication for reducing e-waste using a qualitative approach is presented by easy computation steps for collaborating green issues in the present work. The authors explained numerous enablers, which are responsible for handsets replacement by the consumers. The work can aid the companies as well as the government legislations to identify the significant enablers, drivers, factors, attributes, etc., in moving toward green environmental issue; the generation of e-waste by the obsolete existing working handsets due to non-identification of deficient enablers can be insignificant to the society.

Research limitations/implications

The implication of developed Grey-DEMATEL techniques is presented by its integration with the application field of e-waste generation by mobile handsets. The authors attempt to devise a conceptual framework linked with knowledge-based theory. The work is illustrated by the case research to understand its applicability and validity in the present scenario.

Originality/value

The authors attempt to propose a decision model, which will aid in identifying the most significant factorial condition responsible for replacing the existing mobile phones with the new ones by the end consumers. The proposed appraisement module can be used as an investigative tool to build and fabricate a planned environmental progress map for overall business considering environmental domain by the companies.

Keywords

Citation

Sahu, A., Narang, H. and Rajput, M. (2018), "A Grey-DEMATEL approach for implicating e-waste management practice", Grey Systems: Theory and Application, Vol. 8 No. 1, pp. 84-99. https://doi.org/10.1108/GS-11-2017-0037

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

The exploitation of electronic gadgets has multiplied proportionately during recent decades. The use of electronic devices (i.e. personal computers, mobile phones and entertainment electronics), which necessitate proper disposal, has grown rapidly throughout the world (Widmer et al., 2005). Electronic equipment’s production and consumption have grown exponentially during the last two decades due to speedy changes in equipment features, capabilities and prices, and the growth in internet use. This has generated a huge quantity of obsolete electronic devices, which has taken the form of e-waste in developed countries (Nnorom and Osibanjo, 2008a). Today, people in almost every corner of the world are addicted to smart mobile phones, and nowadays, these mobile handsets have become very essential and it is sometimes perceived that survival without them is impossible. Countless human dealings, transactions and connections are directly or indirectly carried out with the help of mobile handsets. Mobiles are defined as necessary communication tools related to everyday life and they can affect human relationships and interactions in countless ways; both directly and indirectly (Chen and Katz, 2009). Mustafa (2013) studied the numerous influential variables associated with feelings of shyness and loneliness, which affect the use of mobile handsets. People around the world often become highly emotional based on the information delivered by mobile handsets (Chen and Katz, 2009). The primary motivation for using mobile handsets is to call family members and send text messages to our loved ones, but recently, mobiles have also been crucially important to their users as they provide numerous functions and, nowadays, addiction to cellular phones among college students is regarded as a serious health-problem (Çağan et al., 2014). The market for mobile handsets around the world is rapidly and turbulently changing owing to numerous important reasons, based on the strong materialization of smart phones (Medhi and Mondal, 2015).

Today, countless companies produce mobile handsets to satisfy the needs of consumers, but, after some time, consumers are compelled to replace their existing mobiles, even if they do not want to change them, due to various conditions, embedded in several factors, which are the causes of generated e-waste. Waste is always a chief concern because of its generation in high volumes, its hazardous nature and due to the deficiencies of the disposal policies applicable to handle them (Jecton and Timothy, 2013). Electronic waste, known as e-waste, has emerged as an important concern in numerous developing countries and it also holds great sway over the economic growth and development of those countries. E-waste is defined as being made up of unnecessary, used electrical or electronic devices. Immature treatment of such waste can have adverse effects on human health and the environment. E-waste recycling and disposal operations warrant risk pertaining to the health of workforces and society as a whole, due to the contaminants from harmful substances such as lead, cadmium, beryllium, etc. Preventing contamination while disposing of these materials is a major concern in today’s world. E-waste contains high levels of toxic materials, which generate environmental pollution and are risky to human health, if not properly handled (Ejiogu, 2013). The generation of e-waste can also be a concern in cases where electronic goods have long half-lives (Kiddee et al., 2013).

E-waste contains hazardous constituents and, due to inadequate protocols to manage it, this waste can be buried in landfills, burnt in open air or discarded into water bodies, which are highly polluting activities. Numerous countries have legislated to direct electronics manufacturers and importers to take back used electronic products at the ends of their lives, based on the principle of extended producer responsibility (Nnorom and Osibanjo, 2008b). E-waste is the fastest rising evil affecting the environment and threatening human health due to the presence of a variety of toxic by-products that can become hazardous if handling practices are not carefully managed. Practical approaches are needed to be developed for the industries for the sustainable management of e-waste streams (Davis and Wolski, 2009). Nations have started to draft legislation pertaining to their handling and have set certain rules and regulations to improve the reuse, recycling and other modes of recovery for these wastes for the sake of reducing their illicit disposal (Nnorom and Osibanjo, 2008a).

It is evident that e-waste impinges on the green environment and its existence is reflective of an undesirable outcome, which is not welcomed by the society. The present work strives to identify the most significant factorial conditions which are responsible for generating e-waste and the extent to which mobile handsets are responsible for creating e-waste, as Petruzzellis (2010) found that that the factors that influence communities to use mobile phones also influence companies to launch new products, both from a technological point of view and from a marketing one. Petruzzellis (2010) also tried to capture different, relevant aspects that influence consumers’ decisions regarding technological products. The decision support arena can help to portray the governing behavior of any system precisely (Yang and Tzeng, 2011). Thus, in the present work, the authors have framed a decision support system by way of a computational framework to model the significant enablers which are responsible for replacing existing mobile phones with new ones for end consumers, thereby creating e-waste. The finished work will be helpful in promoting green supply activities by suggesting the significant factors, which will be helpful in decreasing the extent of e-waste and support environmental sustainability. The presentation level of any system can be effectively assessed by computational models and, thus, research is always obligatory to be conducted for devising and improving new directions of computational tools and models (Sahu et al., 2017). Thus, the implication for reducing e-waste, using a qualitative approach, is presented by easy computational steps for collaborating on green issues in the present work. In this work, the authors have assembled an effortless decision support arena based on a Grey-DEMATEL approach to facilitate the mobile companies modeling of e-waste practices in their decision making. The presented decision model is furnished by structuring equations, and companies can easily implement it in their decision making. The work is illustrated by a case research in an Indian context to make it easily understandable.

2. Grey sets theory (GsT)

Real-world decision information is normally incomplete and vague. To represent this information, Deng (1982) developed the concept of grey sets theory. The grey degree represents the incompleteness or uncertainty which a system experiences (Khuman et al., 2016; Liu et al., 2016). GsT is an effective method for handling uncertainty under conditions of discrete data and incomplete information (Liu et al., 2011). Liu, Zhang, Jian, Yuan and Yang (2015) applied GsT for the assessment of supplier performance at the production and development stages of the Commercial Aircraft Corporation of China Ltd. They utilized a grey cluster evaluation model, based on triangular end-points, and considered weight functionality to evaluate significant vendors to undertake development tasks for the C919 program of the considered firm. Liu, Liu and Fang (2015) presented a fractional reverse accumulative grey Verhulst model to improve the stability and the prediction accuracy of the system’s characteristics. Definitions representing the mathematical environment of GsT are as follows (Deng, 1982):

Definition 2.1.

A grey system (Xie and Liu, 2011) holds uncertain information, namely, a grey number and grey variables. The grey system is shown in Figure 1.

Definition 2.2.

The grey set (Gs) under universal (X) is defined by two mappings:

(1) { μ ¯ G ( x ) : x [ 0 , 1 ] μ ̲ G ( x ) : x [ 0 , 1 ]
where μ ¯ G ( x ) μ ̲ G ( x ) , x X , X = R , and μ ¯ G ( x ) and μ ̲ G ( x ) are the upper and lower membership functions in Gs, respectively.
Definition 2.3.

In Gs, the precise value is unidentified, while the upper and/or the lower confines are approximated. Generally, a grey set is written as ( G = G | μ ̲ μ ¯ ) .

Definition 2.4.

The grey number is known as a lower limit grey number, when the lower edge of G is probably approximated:

(2) G = [ G ̲ , ]
Definition 2.5.

The grey number is known as an upper limit grey number, when the upper edge of G is probably approximated:

(3) G = [ , G ]
Definition 2.6.

If the lower and upper grey degrees are approximated, then it is known as an interval grey set:

(4) G = [ G ̲ , G ¯ ]
Definition 2.7.

The basic operations of grey sets x 1 = [ x ̲ 1 , x ¯ 1 ] & x 2 = [ x ̲ 2 , x ¯ 2 ] are articulated by the following equation:

(5) x 1 + x 2 = [ x ̲ 1 + x ̲ 2 , x ¯ 1 + x ¯ 2 ] x 1 x 2 = [ x ̲ 1 x ̲ 2 , x ¯ 1 x ¯ 2 ] x 1 × x 2 = [ x ̲ 1 x ̲ 2 , x ¯ 1 x ¯ 2 ] x 1 ÷ x 2 = [ x ̲ 1 , x ¯ 1 ] × [ 1 x ̲ 2 , 1 x ¯ 2 ] }
Definition 2.8.

The whitened value of ⊗x is a deterministic quantity. The whitened value x(λ) of any x = [ x ̲ , x ¯ ] can be obtained as (Liu and Lin, 2006):

(6) x ( λ ) = λ x ̲ + ( 1 λ ) x ¯ ,
where λ is a whitening coefficient such that λ∈[0,1].

3. Decision-making trial and evaluation laboratory (DEMATEL)

The DEMATEL technique was developed by the Battelle Memorial Institute of the Geneva Research Center (Fontela and Gabus, 1976). The technique computes an interaction relationship surrounded by decision variables. It is effective in transforming the causes and effective relationships of complex systems. The technique explicates the degree of impact and interrelations between the considered criteria and alternatives (Tzeng et al., 2007; Lee et al., 2013). This technique is supportive in analyzing the role of the implicated independent criteria in the process of decision making (Wu and Lee, 2007).

The technique starts with the recognition of the controlling factors or variables and, hence, the crucial factors controlling the system are listed by means of documentation, measurement, brainstorming, etc. Next, professional judgments surrounding the decision problems are collected based on the survey points and, consequently, their estimations are aggregated in a pair-wise comparisons matrix to construct a direct-relation matrix (K), as shown by the following equation. The measurement scale based on GsT is used in this work to establish the pair-wise comparison matrix for determining the causal relationships among variables:

(7) K = | 0 k 12 k 1 n k 21 0 k 2 n k n1 k n2 0 |
here kij indicates the influence of ith criterion on jth criterion. Afterwards, the normalized initial direct-relation matrix N I d = [ N I d i j ] n n is constituted. Equation (8) is utilized to constitute the N I d = [ N I d i j ] n n , which is represented by Equation (9), where the elements in NId denote the normalized influence of ith criterion on jth criterion:
(8) N I d = γ K , suchthat γ = min { 1 / M a x 1 < i < n i = 1 n | K i j | , 1 / M a x 1 < i < n j = 1 n | K i j | }
Hence:
(9) N I d = | 0 N I d 12 N I d 1 n N I d 21 0 N I d 2 n N I d n1 N I d n2 0 |
Such that:

Subsequently, the total relation matrix (TRMnn) known as the total influence matrix is formed by utilizing N I d = [ N I d i j ] n n to identify the φ-indirect effects of N I d = [ N I d i j ] n n . The following equation conceptualizes TRMnn from a normalized initial direct-relation matrix (Lee et al., 2013):

( N I d ) 2 , ( N I d ) 3 , ( N I d ) φ
Hence:
(10) TRM n n = N I d ( I N I d ) 1
where I signifies an identity matrix. The elements (TRMij) in TRMnn indicate the possible impact and cause of ith criterion on jth criterion, such that i, j=1, 2, …, n. Subsequently, the following equations determine the total influence (cause and effect) of ith criterion on jth criterion:
(11) d i = i = 1 n TRM i j ( i = 1 , 2 , 3 , , n )
(12) r j = j = 1 n TRM i j ( j = 1 , 2 , 3 , , n )

Accordingly, the row and column vectors, known as prominence (di⊕rj) and relation (dirj), declare the cause and receiver behavior of criteria (Yang and Tzeng, 2011). The cause and effect relationship diagram structures the impact relationship between prominence and relation amongst criteria.

4. Factorial conditions challenging replacement of existing working mobile handsets by the consumers

Electronic gadgets and instruments are necessities in today’s scenarios. These gadgets and instruments aid in achieving efficiency in working systems, as well as in the fulfillment of daily work. Mobile handsets are electronic gadgets, which are used as means of communication around the world and, in particular, the new generation smart mobiles are technological tools that offer many services, i.e. camera features, gaming, internet access, short message services, calculating, video player functionality, watching TV, shopping, navigation, etc. (Tan et al., 2013). In today’s world, consumers replace their handsets with news ones frequently, due to many factorial conditions, which are responsible for e-waste generation. The major intention of the present work is to draft a framework for identifying significant drivers of those factorial conditions which are responsible for e-waste and can aid in minimizing it, to an extent. Defining the net contribution of any factor in any decision-making process can reinforce it and is necessary to be synchronized for seeking effective solutions in decision-making processes (Sahu et al., 2016). Therefore, in this section, the authors have defined several factorial conditions to enlarge the view of audiences, practitioners and academicians, which are responsible for the replacement of mobiles by consumers in today’s dynamic scenarios. The following points replicate numerous conditions, termed as enablers, embedded by several factors, which lead to the replacement of existing working mobile handsets by consumers and also indicate the necessity of a strict management system to cope with the generated e-waste caused by discarded mobiles.

4.1 Enabler 4.1: Inadequate allocation of RAM and internal application storage memory (RAMINT)

RAMINT replicates the conditions for replacement of existing mobile handsets by consumers due to inadequate allocation of RAM and internal application storage memory by companies. It has been found that numerous people purchase new mobiles of the same or different brands repeatedly because of issues that occur due to insufficient memory and internal application storage memory. This persistent problem is reflected in degraded RAM performance, which is initiated in handsets due to the introduction of novel applications and software over time.

4.2 Enabler 4.2: Malfunctioning of mobiles due to hardware failure (MALHARD)

MALHARD replicates the condition of the replacement of existing mobile handsets by consumers due to hardware failure in their handsets. This provides an idea of the reliability and maintainability of the purchased devices.

4.3 Enabler 4.3. Concern/interest in consumers (public) to sell/purchase old mobiles through e-commerce, etc. (CONOLD)

CONOLD signifies the concern/interest for selling/purchasing old mobiles by consumers in cases where respective users are considering purchasing new ones. This indicates whether, in cases of purchasing new mobiles from companies, old, obsolete mobiles are concernedly sold and interestedly purchased via e-commerce, etc., or not creating e-waste. It mainly signifies the utilization of old, obsolete mobiles by other consumers, and in such cases purchases of new mobiles accompanied by the continued use of replaced mobiles do not create e-waste.

4.4 Enabler 4.4: Replacement of old phones due to keen use of new aesthetic appearances of new ones in the market (REPAPP)

REPAPP replicates the condition of replacement of existing mobile handsets by consumers due to their keenness on the new aesthetic appearances of handsets launched by mobile companies. This indicates an interest among consumers in paying extra for purchasing new mobiles based just on their new appearances.

4.5 Enabler 4.5: Likely to alter brand and ready to pay extra cost (BRAEXT)

BRAEXT signifies curiosity in changing brand, which, in turn, leads to the replacement of existing mobiles. In BRAEXT, the consumer is happy with their existing mobile, but likely to replace it just for the sake of changing brands without having any complaint about their existing phone, since brand attitudes can be positively related to consumers’ intentions to use (purchase) specific mobile phones over others (Petruzzellis, 2010).

4.6 Enabler 4.6: Bored by using one mobile over time (BOROVT)

BOROVT replicates the condition of replacement of existing mobile handsets by consumers for reasons of being bored by using the same mobile for a long period of time and feeling a need for its replacement. This elaborately reflects the condition of becoming dispassionate about using the same thing over time.

4.7 Enabler 4.7: New purchasing due to new launch for reflecting their class in society (LAUCLASS)

LAUCLASS signifies the condition of purchasing new mobiles by end users, again and again over a short period of time, just to reflect their status in society. This condition deals with the existence of an elite class in societies that have high statuses, power and significant money wealth.

4.8 Enabler 4.8: Costly upgrading or non-upgrading of an existing mobile set (UPGRAD)

UPGRAD replicates the condition of replacement of existing mobile handsets by consumers due to costly upgrading or non-upgrading of the elements of existing handsets, i.e. camera upgrading, RAM upgrading, other distinguishing features that prioritize distinguishing by consumers, etc. UPGRAD indicates the extent of interchangeability or possible upgrading facilities that are available in existing handsets compared to their costs, and signifies whether software or other amenity upgrades are required by handsets over time.

4.9 Enabler 4.9: Alteration in technology like 3G, 4G, etc. (ALTTECH)

ALTTECH replicates the condition of replacement of existing mobile handsets by consumers due to changes in technology, concepts, etc. This signifies developments and implementations of new milestones in new handsets due to technological innovations – for example, the speedy replacement of existing handsets with new ones by end users due to 4G in India.

The above-mentioned points replicate enablers, which can be the factorial conditions that generate e-waste due to the replacement of existing mobile handsets by consumers. In this work, the authors developed a structural framework to identify the most significant conditionembedded in several factors, so that proper appreciation and focus could be concentrated in regard to segmental development and expansion, which, in turn, would help to reduce e-waste to some extent.

5. Structural modeling and discussions

In this study, the authors have attempted to propose a decision model by framing structural modeling equations to support e-waste sustainability throughout by recognizing the most significant factorial conditions responsible for creating e-waste caused by the discarding of mobile phones. The implication for reducing e-waste using quantitative approaches is presented by easy computational steps for supporting green issues in the present work. The work has been initiated by cataloging the numerous, responsible conditions embedded in several factors, which can influence the replacement decisions made pertaining to mobiles. This can be done by means of documented surveying of the public (consumers), who are using mobiles handsets. The numerous factorial conditions which are used and can influence the replacement decisions taken pertaining to mobiles are discussed in Section 4. The factorial conditions responsible for manipulating the performance of handsets are generally named as enablers, as they possess the capabilities to alter the performance level of the mobile handsets and, if they are not properly synchronized, then they result in the generation of e-waste.

The synchronization of these enablers can only be made possible by identifying their importance in the system. Thus, the present work has been conceptualized to suggest the framework for identifying the role of general possible enablers among mobile handset users. In general, the majority of the enablers driving the system have subjectivity and, thus, subjective decision support frameworks are required to evaluate them. In this study, the end consumers from different regions act as the decision makers (DMs), whose feelings about the replacement of their old handsets were shared during different conducted brainstorming sessions. The application of grey theory was utilized and grey scale, as shown in Table I, was utilized for grasping the uncertainties and subjective views of the DMs. A grey-based approach can be utilized as a solution step to capture inherent uncertainties within a system (Zhan et al., 2015; Liu, Liu, Liu and Liu, 2015). Subsequently the identified enablers, as discussed in Section 4, were transformed in the form of questionnaires for collecting linguistic data from the survey. Linguistic variables are best for characterizing the subjective judgment of DMs on behalf of qualitative evaluation factors. The present modeling confined the views of five DMs, in which their linguistic priority weights were captured in the form of a direct relationship linguistic matrix to import the DEMATEL technique, as shown by Equation (7). The structured direct relationship linguistic matrixes are shown in Tables II-VI. Next, the collected subjective views were transformed into grey sets, which were further approximated and aggregated to define the numeric domain. The aggregated direct relationship matrixes pertaining to the responsible driving factors are furnished by Table VII. Next, the normalized direct relationship matrix was formed by importing Equation (8), for structuring the TRM, which could be done by using Equation (10). The normalized direct relationship matrix and TRM are furnished by Tables VIII and IX in the present work.

Next, Equations (11) and (12) were used to determine the prominence level and relational level amongst the derived enablers, which could be used to determine the most significant factorial conditions responsible for the creation and generation of e-waste in the present study. Tables X and XI represent the cause and receiver level information and summarize the priority importance of the criteria. The cause and effect relationship diagram is shown in Figure 2 to understand the impact relationship between prominence and relations amongst enablers. Prominence exemplifies the total impact given by the enablers and thus informs about the character of the enablers by briefly indicating its degree of importance in the system. Relations exemplify the causer and receiver nature of the enablers. Thus, the positive value of (dirj) characterizes the impact given by the enablers and the negative value of (dirj) characterizes the impact received by the enablers.

This study facilitates the processing of linguistic preferences and incomplete information to recognize optimal results, as linguistic preferences can be easily acquired from the large audience selected as DMs during a survey design. This study can assist companies to build strategic plans to improve weak performing enablers. The proposed work can be efficient in dealing with situations, where subjective evaluation is required. A Grey-DEMATEL theory has been developed and its application presented in this work facilitates a qualitative decision model. The presented grey-based appraisement platform can ease the computation and overall presentation of selected enablers to declare their responsible role in reducing e-waste, and also aid in identifying weak performing enablers for future improvement. In a wider sense, the proposed appraisement module can be used as an investigative tool to build and fabricate a planned environmental progress map for businesses considering the environmental domain. The work has tried to ignite the feeling of paying tribute to the environment by end consumers as well as by companies by providing a way for reducing e-waste by all of them. The present work is supported by the structural modeling equations for supporting sustainability throughout and recognizes the most significant factors responsible for creating e-waste from mobile handsets. The proposed appraisement index system’s implementation could effectively process survey data to obtain results pertaining to the identification of the most momentous enabler affecting the replacement of handsets by end consumers, resulting in the creation of e-waste. The implication for reducing e-waste using quantitative approaches is presented by easy computational steps for supporting green issues in the present work.

6. Conclusions

The present work drafted a framework to model the factorial picture of decision making pertaining to the generation of e-waste caused by the replacement of obsolete working mobile handsets by consumers. The study highlighted the possible decision enablers, which can aid in responsibly minimizing the quantity of e-waste to an extent during the replacement of mobile handsets. The present work has suggested a decision framework to companies as well as government legislators, which can be utilized to reduce e-waste by considering actual driving factors, which can be easily found by conducting a short survey on the aforesaid issue. The work can also be utilized to identify the significant role of distinguishing decision variables in distinguishing decision problems by taking into consideration momentous factors leading to the existence of decision problems. The implication of developed Grey-DEMATEL techniques is presented by its integration with the application field of e-waste generation caused by mobile handsets. The author has also explained numerous enablers, which can be responsible for handsets’ replacement by consumers. The work is illustrated by the case research to help with the understanding of its applicability and validity in the presented application domain.

In the present work, the authors found RAMINT and ALTTECH as the first and second most significant enablers with average scores of 5.0386 and 4.6274, respectively, and LAUCLASS as the least significant enabler with an average score of 3.3833, which exemplified that companies should focus on developing mobile handsets by mainly concentrating on offering advancements and developments of high allocations of RAM and internal application storage memory at low costs. The work also clarifies that the high allocation of RAM and internal application storage memory should be a priority, offering it at consistent selling costs by curtailing other extra features, and thereby supporting sustainability by reducing some quantity of e-waste in their supply chain activities. In Figure 2, the enablers RAMINT and ALTTECH are in the first quadrant with RAMINT illuminating its high prominence, which demonstrates its importance amongst other enablers and shows that advancements toward synchronizing RAMINT will help in reducing the replacement of mobile handsets by end users, which will automatically reduce e-waste in the wider sense. The priority importance of enablers is explained by plotting a pie chart, which is shown in Figure 3.

The study found that the development of advanced RAM and its high composition should be the first main provision for mobile handset companies wishing to ameliorate environmental issues by reducing the quantity of e-waste. Hence, more advancement toward the allocation of RAM and internal application storage memory should be looked at as part of initial quoted prices, while reducing some less favored features in mobiles would help to remove e-waste from the environment. This work can aid companies as well as government legislators by identifying the significant enablers, drivers, factors, attributes, etc., which can help toward advancing green environmental issues by limiting the generation of e-waste caused by the discarding of obsolete, existing working handsets. Otherwise, the non-identification of deficient enablers could be significant for societies.

Figures

The grey system illustration

Figure 1

The grey system illustration

The prominence and relationship diagram amongst enablers

Figure 2

The prominence and relationship diagram amongst enablers

Pie chart dispersing priority importance of enablers

Figure 3

Pie chart dispersing priority importance of enablers

The ⊗G attribute weight scale

Attribute w
Very Poor (VP) [0.00, 0.10]
Poor (P) [0.10, 0.30]
Medium Poor (MP) [0.30, 0.40]
Fair (F) [0.40, 0.50]
Medium Good (MG) [0.50, 0.60]
Good (G) [0.60, 0.90]
Very Good (VG) [0.90, 1.00]

Direct relationship linguistic matrix by DM (first)

Enablers RAMINT MALHARD CONOLD REPAPP BRAEXT BOROVT LAUCLASS UPGRAD ALTTECH
RAMINT 0 VG G VG MG G MG VG G
MALHARD F 0 MP P G MP VG P MP
CONOLD VP MG 0 VP MG G MG P F
REPAPP G MP F 0 MP G G VP P
BRAEXT P MG G P 0 VG P MG MP
BOROVT G MG P VP P 0 MG VP VG
LAUCLASS F G VP F F VP 0 G MP
UPGRAD G MP P MG VP VG MP 0 VG
ALTTECH VG G MG VG MG MG G G 0

Direct relationship linguistic matrix by DM (second)

Enablers RAMINT MALHARD CONOLD REPAPP BRAEXT BOROVT LAUCLASS UPGRAD ALTTECH
RAMINT 0 VP VG MG G MG G MG VG
MALHARD VP 0 MP MP VP MP MP MP P
CONOLD VG MP 0 F MG G VP MP F
REPAPP P VP P 0 P VP VG MG VP
BRAEXT VG VP MG MP 0 MG F VP MP
BOROVT MP VG P G MP 0 VP MP VG
LAUCLASS MG MP F MP F G 0 P VP
UPGRAD VP G P MP VP MG MP 0 MG
ALTTECH VG P VG MG VG VP G MP 0

Direct relationship linguistic matrix by DM (third)

Enablers RAMINT MALHARD CONOLD REPAPP BRAEXT BOROVT LAUCLASS UPGRAD ALTTECH
RAMINT 0 VG G VG MG G VP VG P
MALHARD VP 0 MP VP P MP F MG P
CONOLD G P 0 F VG VP G VP G
REPAPP VG P P 0 F MG P VG VP
BRAEXT VP VG MG P 0 VP VP P G
BOROVT MP VP G VG MP 0 VP P VP
LAUCLASS G F VP P VP G 0 F MG
UPGRAD P VP VP MP MP VP VP 0 VP
ALTTECH VG G VG VG VP MG P VG 0

Direct relationship linguistic matrix by DM (fourth)

Enablers RAMINT MALHARD CONOLD REPAPP BRAEXT BOROVT LAUCLASS UPGRAD ALTTECH
RAMINT 0 G VG VP VG VG P G G
MALHARD MP 0 MP F MP MG VP F G
CONOLD MP F 0 F P VP F MP VP
REPAPP G VP MP 0 VG P VP VG MG
BRAEXT VP MG MG VG 0 VP MG MP VP
BOROVT VG VP G VP MP 0 VP P G
LAUCLASS VP P P F VP F 0 VP VP
UPGRAD MP F F VG MG VP MP 0 VP
ALTTECH VP VG MP G G VG VP MG 0

Direct relationship linguistic matrix by DM (fifth)

Enablers RAMINT MALHARD CONOLD REPAPP BRAEXT BOROVT LAUCLASS UPGRAD ALTTECH
RAMINT 0 VG VG VP VG VP G MG VG
MALHARD F 0 MP MG MP P G F P
CONOLD G VP 0 MP MG VG P G VP
REPAPP P MP MG 0 F P MG VG MP
BRAEXT VG F G P 0 P MP VP VP
BOROVT VP VG MP MP VP 0 F MP G
LAUCLASS MG MP F G F VP 0 VP VP
UPGRAD P VP VG G MG MP P 0 F
ALTTECH G MG MP G VP VG VG VG 0

Aggregated direct relationship matrix for driving factors

Metrics RAMINT MALHARD CONOLD REPAPP BRAEXT BOROVT LAUCLASS UPGRAD ALTTECH
RAMINT [0.000,0.000] [0.660,0.800] [0.780,0.960] [0.460,0.560] [0.680,0.820] [0.520,0.700] [0.360,0.560] [0.680,0.820] [0.620,0.820]
MALHARD [0.220,0.320] [0.000,0.000] [0.300,0.400] [0.260,0.380] [0.260,0.420] [0.300,0.420] [0.440,0.580] [0.340,0.460] [0.240,0.440]
CONOLD [0.480,0.660] [0.260,0.380] [0.000,0.000] [0.300,0.400] [0.500,0.620] [0.420,0.600] [0.320,0.480] [0.260,0.420] [0.280,0.420]
REPAPP [0.460,0.680] [0.140,0.260] [0.280,0.420] [0.000,0.000] [0.420,0.540] [0.260,0.440] [0.420,0.580] [0.640,0.740] [0.180,0.300]
BRAEXT [0.380,0.500] [0.460,0.560] [0.540,0.720] [0.300,0.460] [0.000,0.000] [0.300,0.420] [0.260,0.380] [0.180,0.300] [0.240,0.380]
BOROVT [0.420,0.560] [0.460,0.560] [0.340,0.560] [0.360,0.500] [0.200,0.320] [0.000,0.000] [0.180,0.280] [0.160,0.300] [0.600,0.780]
LAUCLASS [0.400,0.540] [0.340,0.500] [0.180,0.300] [0.360,0.520] [0.240,0.340] [0.320,0.500] [0.000,0.000] [0.220,0.380] [0.160,0.260]
UPGRAD [0.220,0.400] [0.260,0.400] [0.300,0.440] [0.520,0.660] [0.260,0.360] [0.340,0.440] [0.200,0.320] [0.000,0.000] [0.360,0.460]
ALTTECH [0.660,0.800] [0.540,0.740] [0.580,0.680] [0.700,0.880] [0.400,0.540] [0.560,0.660] [0.440,0.640] [0.640,0.780] [0.000,0.000]

Normalized direct relationship matrix (enablers)

Metrics RAMINT MALHARD CONOLD REPAPP BRAEXT BOROVT LAUCLASS UPGRAD ALTTECH
RAMINT 0.0000 0.1352 0.1611 0.0944 0.1389 0.1130 0.0852 0.1389 0.1333
MALHARD 0.0500 0.0000 0.0648 0.0593 0.0630 0.0667 0.0944 0.0741 0.0630
CONOLD 0.1056 0.0593 0.0000 0.0648 0.1037 0.0944 0.0741 0.0630 0.0648
REPAPP 0.1056 0.0370 0.0648 0.0000 0.0889 0.0648 0.0926 0.1278 0.0444
BRAEXT 0.0815 0.0944 0.1167 0.0704 0.0000 0.0667 0.0593 0.0444 0.0574
BOROVT 0.0907 0.0944 0.0833 0.0796 0.0481 0.0000 0.0426 0.0426 0.1278
LAUCLASS 0.0870 0.0778 0.0444 0.0815 0.0537 0.0759 0.0000 0.0556 0.0389
UPGRAD 0.0574 0.0611 0.0685 0.1093 0.0574 0.0722 0.0481 0.0000 0.0759
ALTTECH 0.1352 0.1185 0.1167 0.1463 0.0870 0.1130 0.1000 0.1315 0.0000

Total relation matrix (enablers)

Metrics RAMINT MALHARD CONOLD REPAPP BRAEXT BOROVT LAUCLASS UPGRAD ALTTECH
RAMINT 0.2373 0.3465 0.3838 0.3207 0.3410 0.3258 0.2796 0.3499 0.3246
MALHARD 0.1789 0.1242 0.1919 0.1855 0.1776 0.1855 0.1977 0.1932 0.1716
CONOLD 0.2532 0.2080 0.1623 0.2156 0.2395 0.2351 0.2015 0.2094 0.1993
REPAPP 0.2484 0.1834 0.2180 0.1526 0.2230 0.2057 0.2137 0.2629 0.1774
BRAEXT 0.2217 0.2258 0.2548 0.2080 0.1356 0.2008 0.1811 0.1831 0.1813
BOROVT 0.2421 0.2378 0.2383 0.2303 0.1919 0.1496 0.1771 0.1962 0.2526
LAUCLASS 0.2076 0.1949 0.1738 0.2014 0.1691 0.1913 0.1094 0.1771 0.1509
UPGRAD 0.1930 0.1868 0.2029 0.2356 0.1803 0.1964 0.1635 0.1338 0.1890
ALTTECH 0.3471 0.3217 0.3353 0.3544 0.2889 0.3158 0.2842 0.3380 0.1977

Cause & receiver level information

Factors RAMINT MALHARD CONOLD REPAPP BRAEXT BOROVT LAUCLASS UPGRAD ALTTECH
Di 2.9093 1.6062 1.9238 1.8851 1.7922 1.9160 1.5754 1.6814 2.7831
Rj 2.1293 2.0290 2.1612 2.1041 1.9469 2.0060 1.8079 2.0437 1.8444
(dirj) 0.7800 −0.4229 −0.2373 −0.2190 −0.1547 −0.0900 −0.2325 −0.3623 0.9387
Nature Given Receiver Receiver Receiver Receiver Receiver Receiver Receiver Given

Priority importance (enablers)

Factors RAMINT MALHARD CONOLD REPAPP BRAEXT BOROVT LAUCLASS UPGRAD ALTTECH
(dirj) 5.0386 3.6352 4.0850 3.9892 3.7391 3.9220 3.3833 3.7250 4.6274
Rank 1 8 3 4 6 5 9 7 2

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Further reading

Sahu, A.K., Sahu, N.K. and Sahu, A.K. (2014), “Appraisal of CNC machine tool by integrated MULTI MOORA-IGVN circumstances: an empirical study”, Grey Systems: Theory and Application, Vol. 4 No. 1, pp. 104-123.

Sahu, A.K., Sahu, N.K. and Sahu, A.K. (2015), “Benchmarking CNC machine tool using hybrid fuzzy methodology a multi indices decision making approach”, International Journal of Fuzzy System Applications, Vol. 4 No. 2, pp. 28-46.

Corresponding author

Atul Kumar Sahu is the corresponding author and can be contacted at: atul85sahu@gmail.com

About the authors

Atul Kumar Sahu is an Assistant Professor in the Department of Industrial and Production Engineering, Guru Ghasidas Vishwavidyalaya (Central), Bilaspur, India. He is pursuing PhD from the Department of Mechanical Engineering at National Institute of Technology, Raipur, India.

Dr Harendra Kumar Narang is an Assistant Professor in the Department of Mechanical Engineering at National Institute of Technology, Raipur, India and obtained his PhD Degree from Indian Institute of Technology, Roorkee, India.

Dr Mridul Singh Rajput is an Assistant Professor in the Department of Mechanical Engineering at National Institute of Technology, Raipur, India and obtained his PhD Degree from Indian Institute of Technology, Delhi, India.