Developing a sustainable development framework in the context ofmining industries: AHP approach

Lixin Shen a, Kamalakanta Muduli b,n, Akhilesh Barve b

a Transportation Management College, Dalian Maritime University, Dalian, Chinab Indian Institute of Technology, Bhubaneswar, Orissa, India

a r t i c l e i n f o

Article history:Received 20 April 2013Received in revised form24 October 2013Accepted 25 October 2013

Keywords:Sustainable developmentGSCMAHPIndian mining industries

a b s t r a c t

Although mining companies contribute positively to the social and economical components of sustain-able development (SD) by generating employment and wealth, they still negatively contribute to theecological component of SD. Therefore, mining companies are increasingly showing their inclinationtoward the adoption of green supply chain management (GSCM) in order to improve their ecologicalperformance. With an extensive literature survey, various criteria and sub-criteria for improving theeffectiveness of GSCM implementation are identified from the literature. Analytic hierarchy process(AHP) is used to evaluate the competitive priorities of these criteria, and interested organizations can useit as a procedural guidance for GSCM implementation. It has been found that mining companies havenot given the “soft” factors of GSCM adequate attention. This study explores how the “appropriateimplementation approach” and “continuous improvement” are the weaker areas of GSCM practice in thecase of the Indian mining sector. Hence, mining industries need to focus on these weaker areas and bringnecessary improvements to these areas in order to enhance their GSCM performance.

& 2013 Elsevier Ltd. All rights reserved.

Introduction

Mining can be viewed as an important activity for the growthand development of society by providing raw materials neededto produce everyday items. Computers, televisions, large buildingstructures, electricity, and automobiles would only be dreamswithout the extraction of minerals. Without a doubt, all techno-logical and medical advancements today are virtually dependenton mining activities. If managed properly, the wealth derivedfrom mining and oil extraction could provide substantial financialnourishment, thus raising the living standards of poverty-strickenpopulations and “kick-starting” a number of manufacturing andservice sector industries in countries that are struggling to develop(Hilson, 2012). Recently, there has been a gradual convergencetoward the view that mineral resource wealth can, and should,serve as an engine of growth and of poverty reduction (Aubynn,2009). In responding to this, developing countries such as Indiaare increasingly focusing on mining activities for the generation ofwealth and employment.

India is ranked globally among the top 10 mineral-producingnations for having mineral deposits of 257.4 billion tons of coal,25.2 billion tons of iron ore, and 3.3 billion tons of bauxite ore,which constitute 10%, 3%, and 4%, respectively, of the world's

resources (Singh, 2009; Muduli et al., 2013). Besides these, 84other minerals—including three fuel minerals, three atomic miner-als, and 23 minor minerals—are produced in the country. As perCentral statistical organization's estimation, the present value ofmineral production in India is US$ 41790 million, which accountsfor 2.5% of national gross domestic product (GDP), in contrast withUS$ 13.5 million in 1947. Further, a strategy report from theMinistry of Mines, a branch of the Government of India (2011)estimates that unlocking the potential of the mining sector in Indiacould add about US$ 210 billion to US$ 250 billion, or 6 to 7%,to the GDP and create 13 to 15 million jobs through direct andindirect contribution by 2025. Despite the tremendous supportprovided for the country's economic development, Indian miningindustries are blamed for their adverse environmental and socialconsequences. One of the major issues is the generation of hugeamounts of mine waste, which was 1,841 million tons during2005–06 and is increasing continuously (Bhushan, 2008). In fact,this issue will be further intensified in coming years due to depletionof superior grades of ore (high metal content), leaving behind theinferior grades of ore to be extracted. The extraction of these inferiorgrades of ore involves a higher amount of energy consumption andthe emission of more greenhouse gas (Norgate and Haque, 2010).Historically, the mining industry has had significant environmentalimpacts through poor waste management, the lack of or poorrehabilitation, an emphasis on production over environmentalimpacts, and so on (Mudd, 2007). Dust, noise, light, water, visualpollution, contaminated water, gas emissions including green house

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n Corresponding author.E-mail address: kamalakantam@gmail.com (K. Muduli).

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gases such as CH4, CO2, NOx, and SOx, a significant amount of wastegeneration, acid mine drainage, and altered geologic and faunahabitat conditions are some of the adverse environmental impactsof mining (Worrall et al., 2009). In addition, several mines facecomplications in the form of toxic chemical additives, such ascyanide, mercury, and surfactants, which are generally used duringthe concentration process of minerals (Hilson and Nayee, 2002).Similarly, occupational diseases such as pneumoconiosis, silicosis,asbestosis, and lung cancer; other health-related problems of localresidents and employees due to environmental degradation;increased traffic volume; employee safety; and education are someof the social issues associated with mining (Azapagic, 2004). Besidesthese, accidents at mining sites are also an important issue. Eightmining disasters since 1973 have occurred in India, causing severalcausalities. In addition, major rivers in the country run the risk ofdisappearing, as most of the minerals in the country are found in thewatershed and catchment areas of some major rivers (Bhushan,2008). There have been several instances in the country when thelocal community had to face hardship because the mining operationchanged the hydrological regime due to the breaching of ground-water (Bhushan, 2008). More mining-related issues are presented inTable 1. Recently, Indian mining industries witnessed strong publicopposition to large projects such as Vedanta Aluminum, POSCO, andKalinganagar steel projects in Odisha, India, as well as the stoppageof mining activities in Ballary iron ore mining in Karnataka, India, in2011 (Mohanty and Goyal, 2012). This has raised concern forsustainable development in the Indian mining industry.

The severity of past mining disasters and the casualtiesassociated with them have raised the public's perception of miningas being a high-risk activity not only for the public's and workers'health but also for the environment (Botta et al., 2009; Muduliet al., 2013). With growing awareness of the adverse impacts ofmining, pressure on mining companies is increasing from societyas well as from the government to reduce their environmental andsocial impacts. Mining companies in many cases have respondedpositively to such pressures in an effort to avoid slow-ups andshutdowns that occur frequently these days due to the under-estimation of civil society's demands with regard to miningprojects (Prno and Slocombe, 2012). Besides, mining companieshave also begun to realize that their long-term success dependson their ability to align their economic interests with the values of

society (Esteves, 2008). Consequently, the past decade, in parti-cular, has seen an increasingly focused debate on the need to shiftmodern mining to a more sustainable framework, with manymining companies now reporting annually on their sustainabilityperformance alongside their financial results (Mudd, 2010). There-fore, this research on sustainable development issues in themining context is significant.

This research's objectives are as follows:

� To provide a framework for SD practices in mining industries� To evaluate the relative importance of various criteria of GSCM� To identify the extent of the impact of “soft” and “hard”

components of GSCM criteria on its effectiveness in the Indianmining context.

Literature review

This section's objective is to summarize the literature onsustainable development in the mining context, the concept ofGSCM, and GSCM's approach to achieving sustainable develop-ment in the mining industry. This section also summarizesliterature on the criteria of cleaner production (CP), environmentalmanagement system (EMS), and total quality management (TQM)implementation.

Sustainable development

Many conceptualizations of SD exist in literature and sustain-ability has become an important part of any business (Govindan,2013). In the context of mining, some of them are as follows:

SD is an integrated approach that recognizes the interdepen-dence of three dimensions: the economic, the environmental, andthe social performances of an organization (Chaabane et al., 2010).

SD is the integration of four spheres—economic development,social concerns, environmental pressures, and governance—thatmaximizes the contribution to the well-being of the currentgeneration with an equitable distribution of costs and benefitswithout compromising the potential for satisfying the needs ofmultiple future generations (Fleury and Davies, 2012).

Table 1Various sustainable development issues in the mining industries.

Economic issues Environmental issues Social issues

� Contribution to GDP and wealth creation� Reduction of costs� Increased sales and profits� Creation of new business opportunities� Distribution of revenues and wealth� Investments (capital, employees communities,

pollution prevention and impacts, mine closure )� Shareholder value� Value added� Wide spread smuggling activity leading to losses

to miners and government.

� Biodiversity loss� Emissions to air� Energy use� Global warming and other environmental

Impacts� Land use, management and rehabilitation� Product toxicity� Resource use and availability� Solid waste� Water use, effluents and leachates

(including acid mine drainage)� Noise pollution� Underground mine fires� Sedimentation of rivers and flooding

in nearby villages� Reduction in rainfall rates.� Lock-up of large areas of fertile land

under waste dump.� Mercury pollution.� Water scarcity

� Bribery and corruption� Creation of employment� Employee education and skills

development� Equal opportunities and

non-discrimination Health and safety� Human rights and business ethics� Labour/management relationship� Relationship with local communities� Stakeholder involvement� Wealth distribution� Displacement and loss of land� Destruction of traditional forms of livelihoods� Degradation of social customs� Occupational illness� Heavy vehicular traffic causing traffic

jams and accidents.

Adopted from Azapagic, 2004; Ghose, 2003a, b; Ghose, 2009; Sharma et al., 2009; Chikkatur et al., 2009.

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Generally, SD is the combination of enhanced socioeconomicgrowth and development, and improved environmental protectionand pollution prevention (Hilson and Murck, 2000)

“SD espouses the complex objective of giving commensurateemphasis to developing the economic and social dimensions whilesustaining the earth's ecological resources” (Perez-Batres et al., 2011).

It can be understood from the above definitions that SD has threedimensions: an economic goal, environmental performance, and socialaspects. In order to achieve SD, all three interdependent dimensionshave to be addressed simultaneously. Table 1 summarizes varioussustainability issues associated with the mining industries.

Current status of sustainable development practices in IndiaSoon after the Stockholm conference in 1972, India took legislative

steps and erected many laws pertaining to the environment. It becamethe first country in the world to make provisions for protection of theenvironment and its natural resources in a constitution (Sharma et al.,2009). However, movements in support of environmental concernsand SD began in the early 1980s, partly due to international develop-ments but mainly due to the devastating effects of the Bhopal gastragedy in 1984 (Mohanty and Goyal, 2012). Later, the need for asustainable development framework for the Indian mining industrywas first flagged in the report from the Hoda commission, which wasset up in 2005 (MoM, 2010). On the recommendation of the Hodacommission, ERM India developed an SD framework comprising SDprinciples, reporting initiatives, and best practice guidelines. A few bigcompanies have begun to exercise some of the SD practices such asscientific mining, environmental protection and mitigation, commu-nity stakeholder engagement, local socio-economic development inmining project areas, and transparency and accountability (Mohantyand Goyal, 2012).

Green supply chain management

The voluntary pursuit of any activity that encompasses theconcern for energy efficiency, the environment, water conservation,the use of recyclable products, and renewable energy is defined as“green” (Mudgal et al., 2010; Muduli and Barve, 2013). In greensupply chain management, the word “green” is used in conjunctionwith supply chain management to portray its environmental-friendliness image. GSCM has evolved as a proactive strategy withthe objective of enabling organizations to comply with environ-mental regulations by improving their ecological efficiency. It canbe defined as “the set of supply chain management policies held,actions taken and relationships formed in response to concernsrelated to the natural environment with regard to the design,acquisition, production, distribution, use, re-use and disposal ofthe firm's goods and services” (Zsidisin and Siferd, 2001; Diabat andGovindan, 2011; Muduli et al., 2013). There exist many literaturesdescribing about green supply chain management implementationpressures, drivers and barriers in the context of India and othercountries (Mathiyazhagan et al., 2013; Xu et al., 2013;Mathiyazhagan et al., in press; Govindan et al., 2013a; Azevedo etal., 2013; Govindan et al., 2013b; Jabbour et al., 2013). But thecontext of Indian mining industry is still unaddressed.

GSCM approach for sustainable development in mining sector

Material resources are finite; hence, mining operations that resultin the depletion of these material resources are unsustainable.According to Mikesell (1994), SD in the mining sector can be practicedby annually saving and investing the revenue that mining generates inactivities that will be available to multiple future generations. There-fore, if the wealth generated from mined finite resources can beincreased by practicing GSCM so that the excess wealth could beutilized in the development of alternative technologies or in theconstruction of facilities, such as roads, health care units, and educa-tional institutions, that could serve our future generations, thenmining operations will no longer be unsustainable. Berkel (2007)advocates that the use of cleaner production techniques improves therecovery of resources and improves recycling options, thus reducingthe depletion rates of mineral reserves and hence making the sectorsustainable. Additionally, by improving ecological responsiveness,GSCM can lead to a sustained competitive advantage, thereforeimproving long-term profitability (Paulraj, 2009). Extractive industriescan put themselves in comparatively better positions in several areas:waste management, cost savings due to a reduced number ofenvironmental accidents, cost savings due to the avoidance of unne-cessary cleanups, cost savings due to fewer expenditures on regulatoryfines, the usage of raw materials, energy utilization, problems asso-ciated with tailings pond leakage, the controlled emission of toxiceffluents, and easy access to financial help from financial groups,thereby gaining an economic benefit through the integration of GSCMactivities in their operations (Hilson and Nayee, 2002). Therefore,mining industries must reduce their environmental impacts througheffective environmental management programs in order to contributeto SD (Hilson and Murck, 2000). Moreover, the industry can improveits ability to meet challenges regarding worker and community safety,poor working conditions, and associated accidents, coupled withvarious occupational health hazards—which are the causes of poorworkforce quality, workforce shortages, and reduced productivity—byfollowing GSCM practices (Muduli and Barve, 2013) (Fig. 1).

Contemporary research

The scarcity of literature on GSCM criteria drove us to reviewsimilar kinds of literature on other managerial programs such asTQM, CP, and EMS, as several researchers have argued that thephilosophies of TQM (Daily and Huang, 2001), CP, and EMS (Zhuet al., 2010) are either parallel to or overlap with GSCM philosophy.

Tseng et al. (2008) investigated CP implementation criteria inreference to PCB manufacturers in Taiwan. They identified fourimportant decision criteria: “organizing,” “systems and technolo-gies,” “assessment and feedback,” and “training and people” alongwith 12 sub-criteria, three under each main decision criterion.They used the fuzzy analytic network process (FANP) to tackle thedifferent decision criteria involved in the selection of competitivepriorities. Later on, Tseng et al. (2009) used the fuzzy analytichierarchy process (FAHP) to study the competitive priorities of CPimplementation criteria in reference to printed wire board (PWB)manufacturers in Taiwan. They proposed 16 criteria under four

Effective GSCM Practices

Sustainable Development

Practices

Economical Objective

Social Objective

Ecological Objective

Fulfilment of GSCM Criteria

Fig. 1. Framework for Sustainable Development.

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Yes

No

Literature Review

Develop a tentative list of GSCM Criteria and Sub-Criteria

Establish interrelationships among identified factors and sub-factors

Construct pair-wise comparison matrices

Measure consistency of matrices

Are all the matrices

consistent?

Calculate eigen vector of all the matrices

Calculate local priority weights by normalizing the eigen vectors

Calculate global priority weights of the criteria w.r.t objective by multiplying the local weights with the weights of the

elements at the corresponding upper levels

Expert Consultation

Fig. 2. Conceptual framework for the study.

Goal Level-1

Criteria Level-2

Sub-Criteria Level- 3 & 4

C1. Structure and Responsibility C11 C12 C13 C14 C2. Communication C21 C22 C23

C3. EMS Document Control C31 C32

C33 C34

C4. Operational Control C41 C42 C43 C44 C5. Emergency Preparedness C51 C52 C53

D1. Monitoring D11 D12

D2. Assessment of Non-Conformance and Corrective Action

D21 D22 D23

D3. Records D31 D32 D33 D34

D4. EMS Audit D41 D42 D43

D5. Management Review D51 D52 D53 D54

A1. Environmental Policy A11 A12 A13 A14

A2. Planning A21 A22 A23 A24

A3. Resource Based Strategy(RBS) A31 A32 A33

A4. Green Work Culture A41 A42 A43 A44

B1. Process Analysis B11 B12 B13 B14

B2. Analysis of Technical Aspects B21 B22 B23 B24

B3. Cultural Analysis B31 B32 B33

B4. LCA B41 B42 B43

Effectiveness of GSCM Implementation

Top Management Commitment

IER/Gap Analysis Appropriate Implementation Approach

Continual Improvement

Fig. 3. Hierarchical model for the selection of GSCM implementation criteria on priority basis.

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major criteria. A study by Sambasivan and Fei (2008), exploredfour factors and 14 sub-factors that are critical for effectiveimplementation of ISO 14001-based EMS in the electrical andelectronic sector in Malaysia. AHP methodology was used to findthe relative weights and priorities of these factors and sub-factors.Daily and Huang (2001) identified top management support,environmental training, employee empowerment, teamwork,and a reward system as the critical human resource factors forthe effective implementation of EMS. They also mentioned policy,planning, implementation and operation, checking and correctiveaction, and management review as the key categories of EMScriteria. Zutshi and Sohal (2004) investigated critical factors forEMS adoption and proposed management leadership and support,learning and training, internal analysis, and sustainability as fourkey factors for the effectiveness of EMS. Wee and Quazi (2005)conducted a survey on the success factors of environmentalmanagement practices in Singapore. They identified 64 factorsfrom a literature survey. They validated 62 criteria of EMS throughstatistical analysis using data collected from 186 respondents,including chief executives from various electronic and chemicalcompanies that were operating in Singapore, and extracted theminto seven major factors.

Lewis et al. (2006) identified top management commitment, gapanalysis, system deployment, and continual improvement as the fourmajor criteria for effective TQM implementation. They also identified15 sub-criteria and 54 components of these sub-criteria as havingsignificant impact on the effectiveness of TQM implementation.

It has been identified from the literature that some of thecriteria have the potential to influence human behavior. Thesecriteria are defined as “soft” criteria in this paper. These are largelyrelated to the behavioral aspects of working life, such as leader-ship, human resource management, supplier relations, and custo-mer focus (Lewis et al., 2006). Contrary to these “hard” factors arenon-behavioral factors. These do not have a direct influence onhuman behavior and are concerned mostly with systems and toolsthat extend their support to the implementation of various “soft”factors (Black and Porter, 1996; Lewis et al., 2006).

Research gapResearch by Tseng et al. (2008, 2009) and Sambasivan and Fei

(2008) explored and investigated 12, 16, and 14 success criteria ofCP implementation, respectively. None of these studies analyzed theproblems on a sub-criteria level. Wee and Quazi (2005) initiallyidentified 64 success factors of an environmental management systemfrom the literature. Further, results of their study indicate that 62 outof these 64 success factors are statistically valid. However, no attemptwas made in this study to prioritize these success factors. It was alsoobserved that most of the existing literatures on GSCM in the Indiancontext either discuss GSCM barriers or GSCM drivers, which areoverlooked GSCM success factors. Further, it is evident from pastliterature that empirical studies that determine the extent of theinfluence of the “hard” and “soft” criteria of GCSM are scarce in themining context. Surprisingly, not many studies identify the relativeimportance of various critical criteria of GSCM implementation in themining sector. An attempt has been made in this study to bridge thesegaps by determining the priority of the critical success factors of GSCMas well as to explore the degree of the impact of both “soft” and “hard”criteria on effective GSCM implementation in the mining context.

Criteria for effective GSCM implementation

The implementation of GSCM is a complex phenomenon, andits effectiveness depends on a large number of interrelated criteria.A literature review is conducted to identify these criteria. Variouscriteria considered in this study are as follows.

Top management commitment (TMC)

Without continued senior management commitment, theimplementation of any business process or practice within acompany soon wanes because it lacks active senior managementsupport, participation and leadership, and interest in new systemsand processes (Mudgal et al., 2010). Commitment from topmanagement is like a framework for environmental improvement.Top management has the responsibility of deciding the environ-mental strategy to be followed, deciding the level of training andcommunication required (Govindarajulu and Daily, 2004), allocat-ing adequate resources on time (Zutshi and Sohal, 2003a, b), andcultivating a strong culture that allows its employees the freedomto make environmental improvements without excessive manage-ment intervention (Daily and Huang, 2001).

Initial environmental review (IER)

Before the initiation of GSCM implementation, a comprehensiveinitial environmental review (IER), also commonly known as a gapanalysis, has to be conducted, and the organization's environmentalpolicy should be formulated on the basis of the results of the IER(Zutshi and Sohal, 2004). The IER is done to evaluate the organization'sability to consistently improve its ecological performance. Analysis ofthe organization's current process, technical aspects, work culture, andlife cycle are done to identify its feasibility in accommodating desiredchanges. The IER essentially enables organizations to identify theirstrengths, shortcomings, and best practices (Tseng et al., 2008).

Appropriate implementation approach (AIA)

This criterion addresses the procedures followed during imple-mentation of GSCM in an organization. This is one of the importantcriteria, as an inappropriate implementation strategy is found to be aninhibiting factor in effective GSCM implementation. The reason maybe that the management systems that the organizations try to followare sometimes highly complex and bureaucratic (Jackson, 1996); thisincreases paperwork, administrative delays, and apathy, thus leadingto a reduction in efficiency (Quazi, 1999). A proper organizationalstructure has to be developed, and the responsibility and authorityhas to be delegated to the employees involved. It is also necessaryto establish an effective two-way communication system, properdocumentation control system, operational control system, and emer-gency preparedness system in order to achieve the desired GSCMeffectiveness.

Continuous improvement (CI)

Continuous improvement is an essential GSCM criterion thatemphasizes the means rather than the end (Lewis et al., 2006). Itenables organizations to identify their achievements with respect tothe goal. It also helps organizations to improve the efficiency andeffectiveness of their processes and prevents them from having towait for problems to occur. Monitoring, the assessment of non-conformance and corrective action, record audits, and managementreview are the sub-criteria included under this criterion.

The various sub-criteria and attributes for effective GSCM imple-mentation that are included in this study are presented in Table 2.

Methodology

AHP has been chosen for this study due to some of itsadvantages over other multi-criteria decision-making (MCDM)tools such as ISM, ELECTRE, TOPSIS, and ANP. ISM can providea hierarchical structure for the interdependent variables involved

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in the study but fails to investigate the relative importance of thevariables with respect to the goal. Hence, it helps to identify thedirection of the relationship of the variables but still fails torecognize their priority weight. In general, MCDM tools such asELECTRE and TOPSIS have limited acceptance among scientificcommunities and practitioners (Mathiyazhagan et al., 2013).Another MCDM tool, ANP, requires several pair-wise comparisonmatrices and adds complexity to the survey process for non-expert

participants (Harputlugil et al., 2011) in comparison with AHP(Mathiyazhagan et al., 2013).

The idea behind choosing AHP methodology in this case isattributed to (Haq and Kannan, 2006; Kannan et al., 2009):

– its ability to decompose a complex decision problem intoseveral sub-problems in terms of hierarchical levels, whereeach level represents a set of criteria, sub-criteria, or attributes

Table 2Sub-criteria of GSCM.

Criteria no. Criteria References

A11. Commitment to continual improvement and pollution prevention Daily and Huang (2001), Petroni (2001), Whitelaw (2004)A12. Commitment to comply with legislation Daily and Huang (2001), Petroni (2001), Whitelaw (2004)A13. Framework for setting and reviewing environmental goals Quazi (1999), Daily and Huang (2001), Whitelaw (2004)A14. Commitment to documentation and implementation Quazi (1999), Daily and Huang (2001), Whitelaw (2004)A21. Legal and other requirements Petroni (2001), Daily and Huang (2001), Sambasivan and Fei (2008)A22 Environmental objectives and targets Daily and Huang (2001), Zutshi and Sohal (2004), Sambasivan and Fei (2008)A23 Environmental aspects determination Daily and Huang (2001), Sambasivan and Fei (2008)A24. structure of the environmental management program Petroni (2001), Daily and Huang (2001), Whitelaw (2004)A31 Environmental education and training Zutshi and Sohal (2004), Tseng et al. (2009), Wee and Quazi (2005)A32. Workforce development activities (WKDA) Lewis et al. (2006)A33. Employee reward and recognition scheme Quazi (1999), Lewis et al. (2006)A41. Management involvement (MINV) Quazi (1999), Lewis et al. (2006), Zutshi and Sohal (2004)A42. Employee involvement(EINV) Quazi (1999), Zutshi and Sohal (2004), Tseng et al. (2009), Hsu and Hu (2008)A43. Managing organisational change Quazi (1999), Tseng et al. (2009)A44. Green teamwork Quazi (1999), Lewis et al. (2006)B11 Strength of current process Tseng et al. (2009), Lewis et al. (2006)B12. Shortcomings of current process Tseng et al. (2009), Lewis et al. (2006)B13 Best practices Tseng et al. (2009), Lewis et al. (2006)B14 Cross functional requirement analysis Lewis et al. (2006)B21. Availability of monitoring and measuring equipment Sambasivan and Fei (2008)B22. Availability of assistance from environmental specialist Sambasivan and Fei (2008), Whitelaw (2004)B23. Feasibility of production process enhancement Sambasivan and Fei (2008)B24. Risk analysis Zutshi and Sohal (2004)B31. Identification of culture Lewis et al. (2006)B32. Alignment of culture with environmental program Lewis et al. (2006)B33 Monitoring culture change Lewis et al. (2006), Zutshi and Sohal (2004)B41. Quantity of energy used at each stage (Tseng et al. (2009), Zutshi and Sohal (2004), Hsu and Hu (2008)B42. Quantity of material usage at each stage Tseng et al. (2009), Zutshi and Sohal (2004), Hsu and Hu (2008)B43. Quantity of waste released at each stage Tseng et al. (2009), Zutshi and Sohal (2004), Hsu and Hu (2008)C11. Development of EMS organizational structure (Martin (1998), Whitelaw (2004)C12. Documentation of organizational chart Martin (1998), Whitelaw (2004)C13. Routing to all relevant employees Martin (1998), Whitelaw (2004)C14 Defining all EMS position responsibilities Martin (1998), Whitelaw (2004)C21. Communication between involving departments Petroni (2001), Zutshi and Sohal (2004), Hsu and Hu (2008)C22. Communication between top management and employees Petroni (2001), Zutshi and Sohal (2004), Hsu and Hu (2008)C23. Communication between organization and its supply chain partners Petroni (2001), Zutshi and Sohal (2004), Hsu and Hu (2008)C31. Updating documents Zutshi and Sohal (2004), Daily and Huang (2001), Whitelaw (2004)C32 Locating documents Zutshi and Sohal (2004), Daily and Huang (2001)C33 Discarding obsolete documents Zutshi and Sohal (2004), Daily and Huang (2001)C34. Integration of documentation system Zutshi and Sohal (2004)C41. Identification key activities requiring control Zutshi and Sohal (2004), Petroni (2001)C42 Establishment of operational control requirements of key activities Zutshi and Sohal (2004), Daily and Huang (2001)C43. Identification of specific portion of procedure requiring attention Martin (1998), Petroni (2001)C44. Review of monitoring results against requirements Martin (1998), Petroni (2001)C51. Systems to identify and respond to accidents Sambasivan and Fei (2008), Petroni (2001)C52. Procedures for preventing and mitigating environmental impact Sambasivan and Fei (2008), Petroni (2001)C53. Periodical review of emergency systems Sambasivan and Fei (2008), Petroni (2001)D11. Calibration of monitoring tools Martin (1998), Whitelaw (2004)D12. Maintenance of monitoring equipments Martin (1998), Whitelaw (2004)D21. Process performance Martin (1998), Whitelaw (2004)D22. Process reliability Martin (1998), Whitelaw (2004)D23. Process conformance Martin (1998), Whitelaw (2004)D31. Records should be comprehensive Petroni (2001), Daily and Huang (2001)D32. Records should be specialized Daily and Huang (2001), Wee and Quazi (2005)D33. Records should be traceable Daily and Huang (2001), Wee and Quazi (2005), Whitelaw (2004)D34. Retrievable and damage protected Daily and Huang (2001), Whitelaw (2004)D41. External audit Zutshi and Sohal (2004), Whitelaw (2004)D42. Internal audit Zutshi and Sohal (2004), Whitelaw (2004)D43. Independent audit Martin (1998)D51. Review environmental potentialities of organizational activities Martin (1998), Sambasivan and Fei (2008)D52. Review of extent of non-conformance to EMS standards Martin (1998), Whitelaw (2004)D53 Review of effectiveness of corrective action Martin (1998), Whitelaw (2004)D54 Review of adequacy of resources Martin (1998), Whitelaw (2004)

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related to each sub-problem as shown in Fig. 3 (Saaty, 1980,1990; Sambasivan and Fei, 2008)

– its ability to quantify both the experts' objective and subjectivejudgments in order to make a trade-off and to determinepriority weights (Lewis et al., 2006)

– its simplicity, flexibility, and logical consistency (Saaty, 1986,2000)

Problem-solving using AHP (Fig. 2) can be categorized into thefollowing three phases:

Phase-1 Conversion of the decision problem into a structuralhierarchyIn this phase, an analytical framework was initially constructed tofacilitate the study. A list of criteria and sub-criteria for effectiveGSCM implementation identified through a review of the literatureand expert consultation was structured in the hierarchical frame-work, with the objective of occupying the topmost level of thehierarchy, while the criteria and sub-criteria are occupying sub-sequent lower levels of the hierarchy.Phase-2 Collection of data and development of judgmental matrixThis phase involves the collection of data from a group ofexperts. The respondents were asked to examine the relativestrength of a criterion in relation to another criterion positionedabove it in the hierarchy and to assign relative scales in a pair-wise fashion (Saaty, 2000; Yang and Shi, 2002; Lewis et al., 2006).With the help of the experts' judgment, a set of comparisonmatrices were constructed for all elements in a level of thehierarchy with respect to an element of the immediately higherlevel so as to prioritize and convert individual comparativejudgments into ratio scale measurements (Kannan et al., 2008).A nine-point scale, as Saaty (2000) suggested, is used to quantifythe preferences (refer Table 3).Phase-3 Determination of priorities by computation of normalizedweightsAfter the development of judgmental matrices, normalized weightsof all criteria are computed. The data set is not entirely consistentin this case; hence, use of the normalized eigenvector method issuggested for calculating relative weights (Saaty, 1996, 2000; Lewiset al., 2006). Then, the global weight (relative importance of eachfactor with respect to the goal) and maximum eigen value (λmax)for each matrix are computed. The global priority weights ofeach hierarchy level can be calculated by multiplying normalizedpriority weights in the preceding levels (Lewis et al., 2006). Theλmax value is an important validating parameter used in AHP tocalculate the consistency ratio [CR] (Saaty, 2000) of the estimated

vector in order to validate whether the pair-wise comparisonmatrix provides a completely consistent evaluation (Kannanet al., 2008).

The CR can be calculated using the following formula.

CR¼ λmax �nðn�1ÞðRIÞ;

where “n” is the order of the matrix and “RI” is known as therandom consistency index.

RI values for matrices of the order 1–10 are given in the Table 4.An acceptable range of values of CR for a third-order matrix is

from 0.0 to 0.05; that for a fourth-order matrix is from 0.0 to 0.08,and that for other higher-order matrices is from 0.0 to 0.1 (Saaty,2000; Cheng and Li, 2001; Kannan et al., 2008). A CR value lyingwithin the prescribed range indicates an acceptable level ofconsistency for the pair-wise comparisons.

Application of the model to case illustration

Four mining companies that include three privately owned and astate-level gold category PSU, all of which are operating in Odisha,were selected for the study. These companies are engaged in theextraction of minerals including iron, coal, manganese, dolomite, andchromites. All of the companies have ISO 14001 certification forenvironmental management. Twelve professionals, three from eachof the four selected mines, were chosen. The experts chosen have thedesignations of environmental engineer, environmental scientist, orproject manager and have an average working experience of 10 years.

Besides these, three experts from academics were consulted, out ofwhich two have research backgrounds in environmental science andone has a special interest in small business and supply chain manage-ment. A total of three respondents from the state pollution controlboard of Odisha were selected. Two of them are environmentalscientists, and the other one is an environmental engineer who dealswith mining-related environmental issues. Two professionals fromthe directorate of mines in Odisha were chosen, including a miningengineer with more than 10 years of experience in his current positionand a total of 22 years of work experience. One respondent from theIndian Bureau of Mines (IBM) was consulted as well. Twenty-oneexperts in all were consulted. Opinions from this group of expertswere collected with the help of a set of semi-structured questions. Therespondents were not allowed to have interaction with one another inorder to avoid the dominance of some respondents. This was achievedby keeping the individual respondents' interaction limited to speakingwith the researcher only.

Computation of consistency ratio

One of the key steps in AHP is building multiple pair-wisecomparisons for the synthesis of results (Sambasivan and Fei,

Table 3Scale of preferences between two elements.

Preference scores Definition Explanation

1 Equally important Both the elements have equal priority3 Moderately important One element is moderately favoured over the other.5 Strongly important Experience and judgment strongly recommend to prefer one element over the other7 Very strong importance An element is given very strong preference over another and its dominance demonstrated in practice9 Extremely strong importance The evidence favouring one activity over another is of the highest degree possible of affirmation2, 4, 6, 8 Used to represent compromise between the preferences listed aboveReciprocals Reciprocals are used for inverse comparison

Table 4Average random consistency index (RI) based on matrix size.Source: Adapted from Saaty (2000).

n 1 2 3 4 5 6 7 8 9 10RI 0 0 0.52 0.89 1.11 1.25 1.35 1.40 1.45 1.49

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2008). In this regard, the consistency of pair-wise judgmentsbecomes important. Consistency ratios for all of the factors arecomputed and found to be within an acceptable range (lowest0.000 and highest 0.077), and the overall consistency ratio is0.0325.

Result analysis

Normalized local and global weights for the identified criteria,sub-criteria, and attributes were generated to determine therelative importance of these factors, sub-factors. and attributes

Table 5Priority weights of various criteria, sub-criteria and the attributes.

Level- 2 Level-3 Hard/soft Level- 4

Criteria Sub-criteria Attributes Hard/Soft Local weight Global Weight

TMC (0.5462) A1. Environmental policy (0.1998) S A11 S 0.0780 0.0085A12 S 0.5223 0.0570A13 H 0.1998 0.0218A14 S 0.1998 0.0218

A2. Planning (0.5223) H A21 H 0.5962 0.1700A22 H 0.1692 0.0483A23 H 0.0652 0.0186A24 S 0.1692 0.0483

A3. Resource based strategy (0.1998) S A31 S 0.2582 0.0282A32 S 0.1047 0.0114A33 S 0.6370 0.0695

A4. Green work culture (0.0780) S A41 S 0.6249 0.0266A42 S 0.0667 0.0028A43 H 0.1542 0.0065A44 S 0.1542 0.0065

IER (0.2323) B1. Process analysis (0.5469) H B11 H 0.375 0.0476B12 H 0.375 0.0476B13 H 0.1249 0.0159B14 H 0.1249 0.0159

B2. Analysis of technical aspects (0.1629) H B21 H 0.5636 0.0213B22 H 0.2576 0.0097B23 H 0.1095 0.0041B24 H 0.0693 0.0026

B3. Cultural analysis (0.0658) S B31 S 0.1634 0.0025B32 S 0.2969 0.0045B33 H 0.5396 0.0082

B4. LCA (0.2244) H B41 H 0.1047 0.0055B42 H 0.2582 0.0135B43 H 0.6370 0.0332

AIA (0.1377) C1. Structure and responsibility (0.1457) S C11 S 0.06792 0.0014C12 H 0.38989 0.0078C13 H 0.1523 0.0030C14 S 0.3898 0.0078

C2. Communication (0.0645) S C21 S 0.1884 0.0019C22 S 0.7306 0.0063C23 S 0.081 0.0007

C3. EMS document control (0.4709) H C31 H 0.5588 0.0362C32 H 0.1262 0.0082C33 H 0.0543 0.0035C34 H 0.2606 0.0169

C4. Operational control (0.2632) H C41 H 0.2323 0.0084C42 H 0.1377 0.0049C43 H 0.5463 0.0198C44 H 0.0837 0.0030

C5. Emergency preparedness (0.0557) H C51 H 0.1047 0.0008C52 H 0.2582 0.0019C53 H 0.6370 0.0049

CI (0.0838) D1. Monitoring (0.0672) H D11 H 0.7501 0.0042D12 H 0.2499 0.0014

D2. Assessment of non-conformance and corrective action(0.0972) H D21 H 0.1427 0.0011D22 H 0.4286 0.0035D23 H 0.4286 0.0035

D3. Records (0.4186) H D31 H 0.375 0.0132D32 H 0.1249 0.0044D33 H 0.1249 0.0044D34 H 0.375 0.0132

D4. EMS Audit (0.2625) H D41 H 0.1999 0.0044D42 H 0.6001 0.0132D43 H 0.1999 0.0044

D5. Management review (0.1599) H D51 H 0.078 0.0010D52 H 0.1998 0.0027D53 H 0.5223 0.0070D54 H 0.1998 0.0027

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with respect to effective GSCM implementation. Table 5 shows thelocal priority weights of different criteria levels.

It can be observed from Table 6 that evaluators have empha-sized the first criterion, “TMC,” with a priority percent of 54.6. Thisresult is in line with the findings of Sambasivan and Fei (2008),Tseng et al. (2009), and Kim and Rhee (2012). Criterion “IER,” witha priority percent of 23.2, was ranked as the second mostimportant criterion, while criterion “AIA,” with a priority percentof 13.8, and “CI,” with a priority percent of 8.4, were given lessimportance by the evaluators.

Rankings of the level-three and level-four criteria are donebased on their global weights (priority percent with respect to thegoal) and are represented in Table 6. Analysis of level-three criteriareveals that sub-criteria “Planning,” (A2) with a priority weight of0.2853 (28.53 priority percent), is the most important sub-criteria,which is similar to the findings of Sambasivan and Fei (2008)and Kim and Rhee (2012). Similarly “Process Analysis,” (B1) witha priority weight of 0.1270, is given higher emphasis by theevaluators and is ranked as the second most important level-three criterion, which is similar to the findings of Tseng et al.(2009). It can be observed from Table 6 that attribute “Legal andOther Requirements” (A21), with a priority weight of 0.17, is foundto be the most important level-four criteria for effective GSCMimplementation in the Indian mining industry.

The ranks of the “soft” and “hard” sub-criteria and their level-three and level-four attributes are shown in Table 7. It can also beobserved from Table 7 that “hard” factors in level three have a totalpriority percent of 69.5, whereas that of “soft” factors is 30.5. Theresults also show that out of the top five level-three criteria, fourbelong to the “hard” category, while only two belong to the “soft”category. Similarly, it can be observed that 18 out of the top 20attributes (level-four criteria) fall under the “hard” category, whileonly six fall under the “soft” category. This implies that less

emphasis is given to “soft” factors in comparison with “hard”factors while evaluating criteria for the implementation of GSCMpractices in the mining industry in India. These findings are similarto the findings of Lewis et al. (2006) in TQM criteria analysis.

A closer analysis of the priority percent scores in level threeand level four can be helpful in identifying the specific areas ofweakness and strengths in the Indian mining sector. It can be seenthat “Assessment of Non-conformance and Corrective Action”(D2), “Emergency Preparedness” (C5), and “Monitoring” (D1) arethe “hard” components of the weaker criteria and need attention.Similarly, “Cultural Analysis” (B3) and “Communication” (C2) arethe weakest areas of the “soft” components. This kind of situationrepresents a weaker compliance requirement in these areas. Bylooking at the level-four criteria, it can be found that “EmployeeReward and Recognition Scheme” (A33), “Commitment to Complywith Legislation,” and “Structure of the Environmental Manage-ment Program” (A24) are the strong components of the “soft”criteria, while “Legal and Other Requirements” (A21), “Environ-mental Objectives and Targets” (A22), “Strength of Current Pro-cess”(B11), and “Shortcomings of Current Process”(B12) are thestrong components of the level-four “hard” criteria. This mayrepresent a situation where compliance requirements are verystrong.

Managerial implications

GSCM not only emphasizes the customer's demand as thecentral criteria but also simultaneously emphasizes the recyclingof the materials and energy among the enterprises in the supplychain, and it emphasizes the unification of the economic objective,social objective, and environmental objective, thus leading to SD.This study makes an attempt to improve SD practices in the Indian

Table 6Overall ranking of criteria for effective GSCM implementation.

Sub-criteria Priority weights Rank Attributes Priority weights Rank Attributes Priority weights Rank

A2(H) 0.2853 1 A21(H) 0.1700 1 D53 (H) 0.0070 24B1(H) 0.1270 2 A33(S) 0.0695 2 C22 (S) 0.0065 25A1(S) 0.1091 3 A12(S) 0.0570 3 A43 (S) 0.0065 25A3(S) 0.1091 3 A22 (H) 0.0483 4 A44 (S) 0.0065 25C3(H) 0.0649 4 A24 (S) 0.0483 4 B41 (H) 0.0055 26B4(H) 0.0521 5 B11 (H) 0.0476 5 C42 (H) 0.0049 27A4(S) 0.0426 6 B12 (H) 0.0476 5 C53 (H) 0.0049 27B2(H) 0.0378 7 C31 (H) 0.0362 6 B32 (S) 0.0045 28C4(H) 0.0363 8 B43 (H) 0.0332 7 D43 (H) 0.0044 29D3(H) 0.0351 9 A31 (S) 0.0282 8 D41 (H) 0.0044 29D4(H) 0.0219 10 A41 (S) 0.0266 9 D32 (H) 0.0044 29C1(S) 0.0200 11 A13 (H) 0.0218 10 D33 (H) 0.0044 29B3(S) 0.0152 12 A14 (S) 0.0218 10 D11 (H) 0.0042 30D5(H) 0.0134 13 B21 (H) 0.0213 11 B23 (H) 0.0041 31C2(S) 0.0088 14 C43 (H) 0.0198 12 C33 (H) 0.0035 32D2(S) 0.0081 15 A23 (H) 0.0186 13 D22 (H) 0.0035 32C5(S) 0.0077 16 C34 (H) 0.0169 14 D23 (H) 0.0035 32D1(S) 0.0056 17 B13 (H) 0.0159 15 C13 (H) 0.0030 33

B14 (H) 0.0159 15 C44 (H) 0.0030 33B42 (H) 0.0135 16 A42 (S) 0.0028 34D31 (H) 0.0132 17 D52 (H) 0.0027 35D34 (H) 0.0132 17 D54 (H) 0.0027 35D42 (H) 0.0132 17 B24 (H) 0.0026 36A32 (S) 0.0114 18 B31 (H) 0.0025 37B22 (H) 0.0097 19 C52 (H) 0.0019 38A11 (S) 0.0085 20 C21 (S) 0.0017 39C41 (H) 0.0084 21 C11 (S) 0.0014 40B33 (H) 0.0082 22 D12 (H) 0.0014 40C32 (H) 0.0082 22 D21 (H) 0.0011 41C12 (H) 0.0078 23 D51 (H) 0.0010 42C14 (S) 0.0078 23 C51 (H) 0.0008 43

C23 (S) 0.0007 44

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mining industry by enhancing the effectiveness of GSCM imple-mentation. Following are the managerial insights that emergefrom this study.

� This study model can be helpful for mining companies inidentifying the gap between the desired and the currentconditions as well as in identifying the areas of improvements(Tseng et al., 2009).

� The study results can be helpful in identifying the factors'relative importance. This will be helpful particularly for thesmall mining industries that are suffering from various kindsof resource constraints, as it will aid in formulating suitablestrategies that rationale resource for its optimum utilization.

� The hierarchical representation of the GSCM criteria evaluationproblem can allow decision-makers to easily observe the effectof the changes of the priority in the upper levels on the priorityof the criteria at the lower levels (Lewis et al., 2006).

� The analysis can help managers or decision-makers to structuretheir problem by focusing on its differing aspects rather thanfocusing on only one or two aspects.

Conclusion

Although mining and allied activities have the ability togenerate wealth and employment, their harmful environmentalimpact creates a challenge for their survival. The challenge forgovernments, the mining industry, and the general population ishow to balance the socio-economic and environmental issues in away that maximizes benefits and minimizes or eliminates harmand degradation (Worrall et al., 2009). Various mining industrieshave increasingly adopted GSCM in an effort to address thesechallenges. However, many mining industries in India are stillstanding on the crossroads of GSCM adoption due to the lack of aclear picture of sector-specific GSCM success factors. This studymakes an attempt to explore the success factors that are essentialfor GSCM's effectiveness in the Indian mining context.

Sixty-six elements (level-four criteria) were selected via scan-ning the past literature. Three elements were discarded from thelist based on the feedback received from the experts. The other 63elements were considered for further analysis. These elementswere categorized under 18 sub-criteria (level-three criteria). Out ofthese 63 elements, 17 were found to fall under the “soft” categoryand 46 under the “hard” category. A multi-criteria decision-making methodology, AHP, has been proposed in this research toidentify the relative importance of various criteria, sub-criteria,and attributes that are critical for the effectiveness of GSCMimplementation in the Indian mining industry. The results indicatethat all of these 63 elements do not have equal impact on GSCMeffectiveness. The empirical study also indicates the degree ofinfluence of “soft” and “hard” criteria on GSCM implementation.The study explores that Indian mining industries give less empha-sis to “soft” factors (human resource factors), whereas a significantnumber of researches conclude that human resources—being theexecuters of any program—decides its success; hence, it should begiven significant weightage (Daily and Huang, 2001; Daily et al.,2007; Wee and Quazi, 2005; Kaur, 2011). The study's findingsaffirmed that top management plays a vital role in GSCM imple-mentation. This can be justified by the fact that various research-ers have advocated that top management's initiatives are alwaysbehind the origination of any management strategy. Managementhas the responsibility of formulating the environmental policy,allocating resources, arranging environmental training programs,and establishing as well as maintaining a green work culture(Strachan et al., 2003; Zutshi and Sohal, 2003a, b, 2004). Again,from the results, it is clear that the evaluators give AIA and CI lesspriority. This indicates that the Indian mining industries have atendency to follow their competitors' strategies or the best-in-class categories without analyzing these strategies' compatibilitywith their own work cultures and environmental policies. Never-theless, reviewing the performance of these implemented strate-gies is a neglected practice in the case of Indian mining industries.This is mainly due to the fact that the smaller mines lack adequatemonitoring equipments, and the state and central governmentagencies lack adequate manpower to monitor these companies'

Table 7Ranking of the “hard” and “soft” sub-criteria and attributes.

Level-3 criteria Level-4 criteria

Soft Priority percent Hard Priority percent Soft Priority percent Hard Priority percent Hard Priority percent

A1 10.91 A2 28.53 A33 6.95 A21 17.00 A43 0.65A3 10.91 B1 12.70 A12 5.70 A22 4.83 B41 0.55A4 4.26 C3 6.49 A24 4.83 B11 4.76 C42 0.49C1 2.00 B4 5.21 A31 2.82 B12 4.76 C53 0.49B3 1.52 B2 3.78 A41 2.66 C31 3.62 D43 0.44C2 0.88 C4 3.63 A14 2.18 B43 3.32 D41 0.44Total 30.5 D3 3.51 A32 1.14 A13 2.18 D32 0.44

D4 2.19 A11 0.85 B21 2.13 D33 0.44D5 1.34 C14 0.78 C43 1.98 D11 0.42D2 0.81 A44 0.65 A23 1.86 B23 0.41C5 0.77 C22 0.63 C34 1.69 C33 0.35D1 0.56 B32 0.45 B13 1.59 D22 0.35Total 69.5 A42 0.28 B14 1.59 D23 0.35

B31 0.25 B42 1.35 C13 0.30C21 0.19 D31 1.32 C44 0.30C11 0.14 D34 1.32 D52 0.27C23 0.07 D42 1.32 D54 0.27Total 30.5 B22 0.97 B24 0.26

C41 0.84 C52 0.19B33 0.82 D12 0.14C32 0.82 D21 0.11C12 0.78 D51 0.10D53 0.70 C51 0.08

Total 69.5

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environmental performance. However, an analysis of the process,technical aspects, and culture has to be conducted in order toidentify their compatibility with the company's environmentalobjectives. The organizational structure, process, and culture haveto be aligned with the goal in order to maximize GSCM effective-ness. In addition, appropriate monitoring instruments and peoplewith adequate technical expertise to handle these instruments arerequired to ensure continuous improvement in the process.

Limitation and scope of study

The sample size in this paper is small (21), which is a limitationof this paper; therefore, care should be taken while making anattempt at the generalization of the results. Further, only 63success factors were included in this study; hence, more successfactors can be considered in future studies, and statistical methodscould be followed to validate these success factors. Nevertheless,AHP has the limitation of capturing imprecision and vaguenessassociated with the experts' judgments. Therefore, the fuzzy AHP,which is capable of providing freedom to the experts to expresstheir judgments through natural languages, can be considered infuture studies.

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