inflibnet - development and validation of drivers for, barriers to...

Development and Validation of Drivers for, Barriers to and Stakeholders of

Green Manufacturing

THESIS

Submitted in partial fulfilment

of the requirements for the degree of

DOCTOR OF PHILOSOPHY

by

VARINDER KUMAR MITTAL

Under the Supervision of

PROF. KULDIP SINGH SANGWAN

Department of Mechanical Engineering

BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE, PILANI

2013

......dedicated

to

my beloved parents......

ii

ACKNOWLEDGEMENTS

It is indeed, a privilege as well as a pleasant duty to express my gratitude to all those

who have made it possible for me to complete this thesis.

I would like to convey my profound gratitude and regards to my supervisor, Prof. Kuldip

Singh Sangwan, Associate Professor and Head, Mechanical Engineering Department,

Birla Institute of Technology & Science, Pilani, for his keen interest and painstaking

supervision throughout the period of this study. I am grateful for his incisive comments

and insightful suggestions that have helped me to remove many lacunae from this thesis.

I wish to thank Prof. L. K. Maheshwari (Professor Emeritus-cum Advisor and former

Vice-Chancellor, BITS Pilani), Prof. B. N. Jain (Vice-Chancellor, BITS Pilani), Prof. G.

Raghurama, (Director, Pilani Campus), Prof. R. K. Mittal, (Director, Dubai Campus),

Prof. R. N. Saha, (Deputy Director, Research & Educational Development and

Administration), Prof. G. Sundar (Deputy Director, Off-Campus Programmes), Prof. S.

K. Verma, (Dean, Academic Research, PhD Programmes), Prof. M. S. Dasgupta, (Unit

Chief, Placement), Prof. N. N. Sharma, (Dean, Academic Registration and Counselling

Division), Prof. Hemant R. Jadav, (Professor-in-charge, Academic Research Division,

Ph.D. Programme) for giving me an opportunity to do the research in the area of my

interest. I feel obliged to Prof. Ravi Prakash (Director, Amity School of Engineering and

Technology and former Dean, Research and Consultancy Division, BITS Pilani) for his

support.

I thank the members of Doctoral Advisory Committee, Dr. Abhijeet K. Digalwar

(Assistant Professor, Mechanical Engineering Department) and Dr. Jyoti Tikoria,

(Assistant Professor, Management Department) for their support and suggestions to

carry out this work effectively.

iii

I am extremely thankful to Prof. Christoph Herrmann, Dr. Philipp Halubek, Ms. Patricia

Egede and Mr. Christian Wulbusch, of IWF, TU, Braunschweig, Germany, for extending

help and support in completing my work.

I am also grateful to Dr. Devika Sangwan (Assistant Professor, Humanities and

Languages Department, BITS Pilani), Prof. P. J. Singh (Associate Professor,

Mechanical Engineering Department, PEC University of Technology, Chandigarh),

Prof. A. P. Singh (Dean, Instruction Division), Dr. Ashish M. Gujrathi (Assistance

Professor, Chemical Engineering Department, BITS Pilani), Prof. Kumar Neeraj

Sachdev (Associate Professor, Humanities and Languages Department, BITS Pilani) for

providing me the guidance whenever I needed.

I owe thanks to all my friends and colleagues for their help and co-operation at every

step in this study. I thank all experts from industry and academia for their valuable

inputs to the study.

My work would be incomplete without the constant support and inspiration of my family.

A very special expression of appreciation is extended to my father Sh. Parkash Chand

Mittal, mother Smt. Murti Devi Mittal, wife Mrs. Shammu Mittal, son Master Sahas

Mittal, and daughter Baby Gracy Mittal. Without their encouragement, patience, and

understanding this endeavour would not have been possible. My special expression of

appreciation is also extended to my brother Mr. Satish Kumar Mittal, sisters Annu Garg

and Asha Garg.

Last but not the least, I pray and thank to ALMIGHTY GOD for showering HIS divine

blessings and giving me an inner strength and patience.

Varinder Kumar Mittal

iv

BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE

PILANI, RAJASTHAN, INDIA

CERTIFICATE

This is to certify that the thesis entitled “Development and Validation of Drivers for,

Barriers to and Stakeholders of Green Manufacturing” submitted by Varinder

Kumar Mittal, ID. No. 2007PHXF433P for the award of PhD Degree of the Institute

embodies the original work done by him under my supervision.

Signature in full of the Supervisor

Name in capital block letters PROF. KULDIP SINGH SANGWAN

Designation Associate Professor and Head

Department of Mechanical Engineering

BITS Pilani (Pilani Campus), Rajasthan, India

Date: November 01, 2013

v

ABSTARCT

Manufacturing firms consume large amount of energy and natural resources in highly

unsustainable manner and release huge amounts of green house gases leading to many

economic, environmental and social problems from climate change to local waste

disposal. This has lead to a new manufacturing paradigm of green manufacturing (GM).

There are various drivers for, barriers to, and stakeholders of green manufacturing which

play a vital role in its implementation. These drivers, barriers, and stakeholders are to be

understood properly and analyzed for relationships among them. This is expected to

provide government and industry insights to prioritize the policies and focus to leverage

basic drivers and to mitigate root barriers for effective implementation of GM.

This thesis aims at identifying the drivers for, barriers to, and stakeholders of GM,

developing a model of these drivers, barriers, and stakeholders using statistical analysis

and testing the model using structural equation modelling technique.

The identification, ranking and validation of the models of drivers, barriers and

stakeholders is expected to provide better knowledge to decision makers in government

to develop policies and prioritize them to facilitate green manufacturing adoption and

diffusion. The ranking and hierarchy of drivers and barriers will provide better

knowledge to the top management in industry to develop and prioritize their business

strategies to facilitate smooth implementation of green manufacturing. In addition, the

interrelationship among drivers/barriers and their driver-driven relationship is expected

to provide the managers/executives the better understanding of the complex relations to

develop effective implementation plan. The comparison of drivers/barriers among the

Indian and German industries will help industry and government in both the countries to

learn from each other.

vi

The identification and development of models of stakeholders is expected to help

government and industry to come to a common platform from the environmental,

economical and social perspectives. The identification of the stakeholders will help

managers/executives in the industry to build better relations with these stakeholders. The

ranking of the stakeholders is expected to help government to romp in the important

stakeholders to develop the future policies related to environmental issues.

The study is significant for researchers working in the field of green manufacturing and

other similar systems as the study provides an exhaustive review of literature and traces

the origin and evolution of green manufacturing, environmentally conscious

manufacturing, sustainable manufacturing, sustainable production, environmentally

benign manufacturing, environmentally responsible manufacturing, clean manufacturing,

and cleaner production. The study will help the future researchers in the area, as it

provides definitions, scope, similarities, and differences among these eight systems/terms

from the literature as perceived by the researchers. This is expected to be a boon for the

research in the area as the lack of definition of green manufacturing has been identified

as a big challenge to the research in this area.

Further, this study suggests an action plan for policy makers in government and industry

to help green manufacturing implementation in India.

vii

TABLE OF CONTENTS

Page No.

Acknowledgements ii

Abstract v

Table of contents vii

List of figures xi

List of tables xiii

List of symbols and abbreviations xvi

CHAPTER 1: INTRODUCTION 1

1.1 OVERVIEW OF GREEN MANUFACTURING 1

1.2 RESEARCH MOTIVATION 2

1.3 OBJECTIVES OF THE STUDY 4

1.4 METHODOLOGY 4

1.5 SIGNIFICANCE OF THE STUDY 5

1.6 ORGANIZTION OF THE THESIS 8

CHAPTER 2: LITERATURE REVIEW 10

2.1 INTRODUCTION 10

2.2 SEARCH METHODOLOGY 14

2.3 EVOLUTION OF SEARCH TERMS 16

2.3.1 Sustainable Production 16

2.3.2 Clean Manufacturing 22

2.3.3 Cleaner Production 22

2.3.4 Environmentally Conscious Manufacturing 24

2.3.5 Green Manufacturing 25

2.3.6 Environmentally Responsible Manufacturing 29

2.3.7 Environmentally Benign Manufacturing 29

2.3.8 Sustainable Manufacturing 30

2.4 OBSERVATIONS, ANALYSIS AND DISCUSSION 33

2.4.1 Publications and Research Trend 33

2.4.2 Scope of the Eight Search Keywords 35

2.5 LITERATURE REVIEW ON DRIVERS FOR GM 45

2.6 LITERATURE REVIEW ON BARRIERS TO GM 59

2.7 LITERATURE REVIEW ON STAKEHOLDERS OF GM 76

2.8 RESEARCH GAPS 94

CHAPTER 3: DRIVERS FOR GREEN MANUFACTUIRNG

IMPLEMENTATION

96

3.1 DRIVERS FOR GM IMPLEMENTATION 96

3.1.1 Current Legislation 96

3.1.2 Future Legislation 97

3.1.3 Incentives 98

3.1.4 Public Pressure 99

3.1.5 Peer Pressure 100

3.1.6 Cost Savings 100

viii

Page No.

3.1.7 Competitiveness 101

3.1.8 Customer Demand 102

3.1.9 Supply Chain Pressure 103

3.1.10 Top Management Commitment 104

3.1.11 Public Image 105

3.1.12 Technology 105

3.1.13 Organizational Resources 106

3.2 RANKING OF GM DRIVERS USING FUZZY TOPSIS 106

3.2.1 Overview of Fuzzy TOPSIS 106

3.2.2 Development of Fuzzy TOPSIS Method for Ranking

GM Drivers

110

3.2.3 Results and Discussion 120

3.3 DEVELOPMENT OF A MODEL OF GM DRIVERS

USING INTERPRETIVE STRUCTURAL MODELLING

120

3.3.1 Overview of Interpretive Structural Modelling 121

3.3.2 ISM Procedure 122

3.3.2.1 Structural self-interaction matrix 122

3.3.2.2 Initial reachability matrix 123

3.3.2.3 Final reachability matrix 124

3.3.2.4 Level partitions 125

3.3.2.5 ISM model 126

3.3.3 MICMAC Analysis 126


3.4 DEVELOPMENT OF A MODEL OF GM DRIVERS

USING STRUCTURAL EQUATION MODELLING

129

3.4.1 Overview of Structural Equation Modelling 129

3.4.2 Research Methodology 129

3.4.2.1 Survey instrument development 130

3.4.2.2 Data collection 131

3.4.2.3 Data analysis 132

3.4.3 Development of Model Using SEM 136

3.4.3.1 Exploratory factor analysis 136

3.4.3.2 Confirmatory factor analysis 138

3.4.3.3 Structural model 141


3.5 COMPARISION OF DRIVERS IN INDIA AND

GERMANY

144

3.5.1 Descriptive Statistics 144

3.5.2 Comparing Means Using Independent T-Test 145

3.5.3 Effect Size for Independent T-Test 148


3.6 SUMMARY 150

CHAPTER 4: BARRIERS TO GREEN MANUFACTUIRNG

IMPLEMENTATION

153

ix

Page No.

4.1 BARRIERS TO GM IMPLEMENTATION 153

4.1.1 Weak Legislation 153

4.1.2 Low Enforcement 154

4.1.3 Uncertain Future Legislation 155

4.1.4 Low Public Pressure 155

4.1.5 High Short-Term Costs 156

4.1.6 Uncertain Benefits 156

4.1.7 Low Customer Demand 157

4.1.8 Trade-Offs 157

4.1.9 Low Top Management Commitment 158

4.1.10 Lack of Organizational Resources 159

4.1.11 Technological Risk 159

4.1.12 Lack of Awareness/ Information 160

4.2 RANKING OF GM BARRIERS USING FUZZY TOPSIS 161


GM Drivers

161


4.3 DEVELOPMENT OF A MODEL OF GM BARRIERS

USING INTERPRETIVE STRUCTURAL MODELLING

175

4.3.1 ISM Procedure 175

4.3.1.1 Structural self-interaction matrix 175

4.3.1.2 Initial reachability matrix 176

4.3.1.3 Final reachability matrix 176

4.3.1.4 Level partitions 177

4.3.1.5 ISM model 178

4.3.2 MICMAC Analysis 179


4.4 DEVELOPMENT OF A MODEL OF GM BARRIERS

USING STRUCTURAL EQUATION MODELLING

181



4.4.2 Development of Model Using SEM 184


4.4.2.2 Confirmatory factor analysis 186

4.4.2.3 Structural model 187


4.5 COMPARISION OF BARRIERS IN INDIA AND

GERMANY

191

4.5.1 Descriptive Statistics 192

4.5.2 Comparing Means Using Independent T-Test 193

4.5.3 Effect Size for Independent T-Test 195


4.6 SUMMARY 196

x

Page No.

CHAPTER 5: STAKEHOLDERS OF GREEN MANUFACTUIRNG

IMPLEMENTATION

198

5.1 STAKEHOLDERS OF GM IMPLEMENTATION 198

5.1.1 Government 198

5.1.2 Employees 199

5.1.3 Consumers 200

5.1.4 Market 200

5.1.5 Media 201

5.1.6 Local Politicians 202

5.1.7 Local Community 202

5.1.8 Suppliers 203

5.1.9 Trade Organizations 203

5.1.10 Environmental Advocacy Groups 204

5.1.11 Investors/Shareholders 204

5.1.12 Partners 205

5.1.13 Owners 206

5.1.14 CEOs 206

5.2 RANKING OF GM STAKEHOLDERS USING FUZZY

TOPSIS

207


GM Stakeholders

207


5.3 CLASSIFICATION OF GM STAKEHOLDERS 217


5.3.1.1 Questionnaire development 218

5.3.1.2 Data collection 218




5.4 COMPARATIVE ANALYSIS OF SMEs AND LARGE

ENTERPRISES 224


5.5 SUMMARY 227

CHAPTER 6: CONCLUSIONS 228

REFERENCES 237

APPENDIX - A SURVEY QUESTIONNAIRE FOR

DRIVERS/BARRIERS

A 1

APPENDIX - B SURVEY QUESTIONNAIRE FOR

STAKEHOLDERS

A 4

APPENDIX - C LIST OF PUBLICATIONS A 6

APPENDIX - D BRIEF BIOGRAPHY OF THE CANDIDATE AND

SUPERVISOR

A 8

xi

LIST OF FIGURES

Figure No. Title of the Figure Page No.

1 Plan of work 6

2.1 Term-wise publication on GM and similar terms 18

2.2 Year-wise publications on GM and similar terms with

first time appearance of these terms

19

2.3 Trend of year-wise publications on GM and similar terms 20

2.4 Sustainable manufacturing – goal, pillars and objectives 33

2.5 Product life cycle 41

2.6 Environmentally conscious manufacturing: systems

approach

41

2.7 Evolution of sustainable manufacturing 43

2.8 Year-wise literature contribution for GM drivers 58

2.9 Year-wise literature contribution for GM barriers 72

2.10 Year-wise literature distribution on GM stakeholders 91

3.1 Triangular fuzzy number a~ 109

3.2 A hierarchical structure for ranking the drivers for GM 111

3.3 Aggregated closeness coefficient of GM drivers 118

3.4 Closeness coefficient (CCi) of drivers (government,

industry and expert perspectives)

119

3.5 An ISM model of drivers for GM implementation 126

3.6 Driver-Dependence Diagram 127

3.7 Research Methodology Outline 130

3.8 Classification of drivers for GM implementation 138

3.9 Path diagram representing the measurement model of

drivers for GM implementation

139

3.10 Full structural model of drivers for GM implementation 142

3.11 Independent t-test procedure 146

4.1 A hierarchical structure for ranking the barriers to GM 164

4.2 Aggregated closeness coefficient of GM barriers 172

4.3 Closeness coefficient (CCi) of GM barriers (government,

industry and expert perspectives)

172

4.4 The ISM model of barriers to GM implementation 179

4.5 Driver - Dependence Diagram 180

4.6 Classification of barriers to GM implementation 185

xii

LIST OF FIGURES

Figure No. Title of the Figure Page No.

4.7 Path diagram representing the measurement model of

barriers to GM implementation

186

4.8 Proposed full structural model of barriers to GM

implementation

189

4.9 Final full structural model of barriers to GM

implementation

190

5.1 A hierarchical structure for ranking the stakeholders of

GM

207

5.2 Closeness coefficient (CCi) of GM stakeholder

(aggregate)

215

5.3 Closeness coefficient (CCi) of GM stakeholders

(economic, social and environmental perspectives)

215

5.4 Research methodology 218

5.5 Classification of stakeholders of GM implementation 223

xiii

LIST OF TABLES

Table No. Title of the Table Page No.

2.1 Share of global growth (Times of India; June 9, 2013) 12

2.2 Summary of article search on GM and similar terms 15

2.3 Year-wise publications on GM and similar terms 17

2.4 Analysis of GM and similar systems/terms in extant

literature

37

2.5 6R definitions 43

2.6 Similarity among the search keywords by various

researchers

44

2.7 Distribution of the reviewed articles on GM drivers 46

2.8 Review of literature on GM drivers 54

2.9 Region-wise literature contribution for GM drivers 59

2.10 GM driver summary 60

2.11 Distribution of the reviewed articles on GM barriers 62

2.12 Review of literature on GM barriers 69

2.13 Region-wise literature contribution for GM barriers 73

2.14 GM barrier summary 73

2.15 Various definitions of stakeholders from extant literature 77

2.16 Distribution of the reviewed articles on GM stakeholders 78

2.17 Review of literature on GM stakeholders 88

2.18 Various classifications of stakeholders from extant

literature

91

2.19 Region-wise literature contribution on GM stakeholders 92

2.20 Research area-wise literature contribution on GM

stakeholders

92

2.21 GM stakeholder summary 93

3.1 Description of GM drivers 107

3.2 Criteria for ranking drivers for GM. 111

3.3 Linguistic variables and fuzzy ratings for the alternatives

and criteria

112

3.4 Linguistic assessment of the criteria 112

3.5 Linguistic assessment of the alternatives (drivers) 112

3.6 Aggregate fuzzy weights of the criteria 113

3.7 Aggregate fuzzy weights of alternatives (drivers) 114

3.8 Normalized alternatives (drivers) 115

xiv

LIST OF TABLES


3.9 Weighted normalized alternatives (drivers) 115

3.10 Distance of drivers from FPIS and FNIS 117

3.11 Aggregated closeness coefficients for alternatives

(drivers)

117

3.12 Closeness coefficients for individual criterion

(perspectives)

118

3.13 Ranking of GM drivers 119

3.14 Structural self-interaction matrix (SSIM) of drivers 123

3.15 Initial reachability matrix of drivers 124

3.16 Final reachability matrix of drivers 124

3.17 Level identification (Iterations 1-5) 125

3.18 Descriptive statistics of data 135

3.19 Factor loadings of GM drivers by exploratory factor

analysis

137

3.20 Factor loadings of GM drivers by EFA (within each

factor)

137

3.21 Confirmatory factor analysis statistics 140

3.22 Correlation and covariance of latent variables 141

3.23 Results of hypothesis test for GM drivers 142

3.24 Group statistics for drivers 146

3.25 Independent t-test statistics to compare drivers for India

and Germany

148

3.26 Results of comparison for drivers 149

4.1 Description of GM barriers 162

4.2 Criteria for ranking barriers to GM 164


4.4 Linguistic assessment of the alternatives (barriers) 166


4.6 Aggregate fuzzy weights of the alternatives (barriers) 167

4.7 Normalized decision matrix (barriers) 168

4.8 Weighted normalized alternatives (barriers) 169

4.9 Distance of barriers from FPIS and FNIS 170

4.10 Aggregated closeness coefficient for alternatives

(barriers)

171

4.11 Closeness coefficients for different criteria (perspectives) 171

xv

LIST OF TABLES


4.12 Ranking of GM barriers 173

4.13 Structural self-interaction matrix (SSIM) of barriers 175

4.14 Initial reachability matrix 176

4.15 Final reachability matrix 177

4.16 Level partitions 178

4.17 Descriptive statistics of data 183

4.18 Factor loadings of GM barriers by exploratory factor

analysis

184

4.19 Factor loadings of GM barriers by EFA (within each

factor)

185

4.20 Confirmatory factor analysis statistics 187

4.21 Goodness-of-fit statistics (CFA) 188

4.22 Results of hypothesis test 190

4.23 Group statistics for barriers to GM implementation 192

4.24 Independent t - test to compare barriers for India and

Germany

194

4.25 Results of comparison of barriers to GM 195

5.1 Criteria for ranking stakeholders of GM 208


5.3 Linguistic assessment of the alternatives (stakeholders) 209


5.5 Aggregate fuzzy weights of the alternatives

(stakeholders)

210

5.6 Normalized alternatives (stakeholders) 211

5.7 Weighted normalized alternatives (stakeholders) 212

5.8 Distance of stakeholders from FPIS and FNIS 213

5.9 Aggregate closeness coefficient for alternatives

(stakeholders)

214

5.10 Closeness coefficients for different criteria (perspectives) 214

5.11 Ranking of GM stakeholders 216

5.12 Descriptive and reliability analysis of stakeholders for

SMEs and large enterprises

221

5.13 Factor loadings of all stakeholders through EFA. 222

5.14 Results of Mann-Whitney U test 225

xvi

LIST OF SYMBOLS AND ABBREVIATIONS

Symbol/

Abbreviation

Description

AGFI Adjusted Goodness of Fit Index

AHP Analytic Hierarchy Process

AMOS Analysis of Moment Structures

BRICS Brazil, Russia, India, China and South Africa

CFA Confirmatory Factor Analysis

CFI Comparative Fit Index

CII Confederation of Indian Industry

CITC Corrected Item – Total Correlation

CM Clean Manufacturing

CP Cleaner Production

CSR Corporate Social Responsibility

DF Degrees of Freedom

EB Economy Barriers

EBM Environmentally Benign Manufacturing

ECM Environmentally Conscious Manufacturing

ED Economy Drivers

EFA Exploratory Factor Analysis

EMS Environmental Management System

EPR Extended Producer Responsibility

ERM Environmentally Responsible Manufacturing

EVA Equal Variance Assumed

EVNA Equal Variance Not Assumed

EU European Union

FI Fairly Important

FNIS Fuzzy Negative Ideal Solution

FPIS Fuzzy Positive Ideal Solution

GDP Gross Domestic Product

GFI Goodness of Fit Index

GM Green Manufacturing

GP Green Purchasing

GS Google Scholar

H High

I Important

IB Internal Barriers

ID Internal Drivers

ISIC International Standard Industrial Classification

ISM Interpretive Structural Modelling

ISO International Organization for Standardization

KMO Kaiser-Meyer-Oklin

L Low

http://en.wikipedia.org/w/index.php?title=Comparative_Fit_Index&action=edit&redlink=1

xvii

LIST OF SYMBOLS AND ABBREVIATIONS

Symbol/

Abbreviation

Description

LCA Life Cycle Assessment

LI Less Important

M Medium

Mgt. Management

MLE Maximum Likelihood Estimation

MSMEs Micro, Small and Medium Enterprises

NASA National Aeronautics and Space Administration

NFI Normed Fit Index

NGO Non Governmental Organization

NI Not Important

OECD Organisation for Economic Co-operation and Development

OEM Original Equipment Manufacturer

Org. Organizational

PB Policy Barriers

PD Policy Drivers

RMB Renminbi (Chinese currency) ¥

RMR Root Mean Residual

RMSEA Root Mean Square Error of Approximation

SD Sustainable Development

SEM Structural Equation Modelling

SM Sustainable Manufacturing

SMEs Small and Medium Enterprises

SP Sustainable Production

SPSS Statistical Package for the Social Sciences

SSIM Structural self-interaction matrix

TOPSIS Technique for Order of Preference by Similarity to Ideal Solution

UK United Kingdom

USA United States of America

VH Very High

VI Very Important

VL Very Low

WEEE Waste Electrical and Electronic Equipment

α Cronbach's Alpha

d Cohen's d (Effect size)

http://en.wikipedia.org/w/index.php?title=Standardized_Root_Mean_Residual&action=edit&redlink=1

CHAPTER 1

INTRODUCTION

This chapter comprises the overview of green manufacturing, research motivation,

objectives, methodology, significance, and organization of the thesis.

1.1 OVERVIEW OF GREEN MANUFACTURING

Sustainable development (SD) has become an important issue across the globe because of

the rapid increase in consumption of natural resources; green house gas emissions; landfill

problems; unhealthy degradation of air, soil and water; etc. The manufacturing sector plays a

vital role in sustainable development as it consumes a significant portion of energy and

resources of any country (Energy Information Administration, USA). Manufacturing firms

consume the natural resources in highly unsustainable manner and release large amounts of

green house gases leading to many economic, environmental and social problems from

global warming to local waste disposal. This has led to a new manufacturing paradigm of

Green Manufacturing (GM). It is also known by plethora of names like environmentally

conscious manufacturing, environmentally benign manufacturing, sustainable

manufacturing, sustainable production, cleaner production, clean manufacturing, etc.

Principles of green manufacturing deal with developing methods for manufacturing products

from conceptual design to final delivery, and ultimately to the end of life disposal, that

satisfy environmental standards and requirements (Ilgin and Gupta, 2010). It broadly implies

the development of innovative manufacturing sciences and technologies across the life cycle

of products and services which minimize negative environmental impacts; conserve energy

and natural resources; safe for employees, communities, and consumers; and are

economically sound (International Trade Administration, 2010).

Introduction

2 | P a g e

Green manufacturing (GM) maintains sustainability of environmental, economical and

social objectives in the manufacturing domain and attempts to establish a solid foundation

for all three pillars to achieve sustainability in business operations. It is a method to develop

technologies to transform materials without emission of greenhouse gases, use of non-

renewable or toxic materials or generation of waste (Allwood, 2009). Green manufacturing

helps in minimizing the use of resources and the environmental impact of a product.

Successful implementation of green manufacturing requires going beyond small standalone

initiatives and adopting an integrated three-step framework: (i) planning for green as a core

part of business strategy, (ii) executing green initiatives across the value chain by shifting

towards green energy, green products and green processes, and (iii) communicating and

promoting green initiatives and their benefits to all stakeholders (Bhattacharya et al., 2011).

The goals of green manufacturing are frequently achieved through product and process

design (Thomas, 2001; Dornfeld, 2009). Green manufacturing encompasses all factors

associated with environmental concerns in manufacturing by continuously integrating eco-

friendly industrial processes and products (Chuand and Yang, 2013).

1.2 RESEARCH MOTIVATION

If everybody on the earth lives the lifestyle of the people from the technologically developed

countries, which is not even one-fifth of the current population, the earth population would

consume around 3-6 globes per year (Seliger, 2007). It is projected that the world population

would rise to 8.3 billion people by the year 2025 (Furukawa, 1996). More population means

more demand for material and energy which will further lead to the challenges like global

warming, climate change, landfill problems, natural resources depletion, unhealthy living

conditions because of excessive air, water and sand pollution, etc.

Introduction

3 | P a g e

Manufacturing sector is one of the key industry sectors which not only decide the economic

well being of any country but also directly affect the life style of the people and Gross

Domestic Product (GDP) of any country. Manufacturing is one of the important elements of

sustainable development as it produces goods which are required to cater to the needs of the

society. Manufacturing is an input–output system, in which the resources are transformed

through manufacturing processes into products or semi-products (Liu et al., 2002). Energy

and materials are the two primary inputs to the manufacturing which are obtained by

exploiting the natural resources like fossil fuels and material ores. The emerging, developing

and underdeveloped countries are trying to uplift the living style of their rising population.

At the same time, developed countries do not want to sacrifice their current living standard

(O’Brien, 2002). Therefore, the average global consumption pattern keeps increasing as

living standards keep growing, which means that the growth of manufacturing is inevitable.

This has led to a highly unsustainable situation as currently industry consumes about half of

the world’s energy (Ross, 1992) and the consumption of critical raw materials (such as steel,

aluminium, copper, nickel, zinc, wood, etc) for industrial use has increased worldwide.

Manufacturing sector is growing worldwide at a very fast pace in order to meet the demand

of the goods required. The countries, particularly developing countries are working hard to

have a high growth in manufacturing sector to boost their economy. However, high growth

of the manufacturing sector has tremendous impact on the environment, which is the prime

global concern these days and the biggest challenge among industries, academia,

governments, and international communities.

The challenge of sustaining high growth of the manufacturing sector without harming the

environment can be addressed by implementation of approaches, systems, strategies,

Introduction

4 | P a g e

processes, etc. which can manufacture goods while taking environment, economy and

society into focus. Hence, the implementation of GM in industry is one option with the

mankind to have sustainable development of the manufacturing sector. However, the

implementation of GM is not as simple as it seems, particularly in developing and under-

developed countries. It is necessary to understand the motivating factors which help the

industry to implement green manufacturing systems. It is also important to understand the

barriers to these newer systems so that barriers can be mitigated before hand for easy

adoption of these systems. It becomes pertinent to understand the stakeholders which help in

adoption of these systems.

1.3 OBJECTIVES OF THE STUDY

The objectives of this study are to:

Develop, rank, validate, and model the drivers for green manufacturing

implementation.

Develop, rank, validate, and model the barriers to green manufacturing

implementation.

Develop, rank and classify stakeholders of green manufacturing implementation.

Compare the importance of drivers for and barriers to green manufacturing

implementation between India (emerging country) and Germany (developed country).

Compare the importance of stakeholders of green manufacturing in SMEs and large

enterprises.

1.4 METHODOLOGY

The objectives of the study are to be achieved through the accomplishment of the following

tasks:

Introduction

5 | P a g e

A thorough review of literature to trace the origin and evolution of green

manufacturing and similar systems/terms.

A thorough review of the literature of drivers for, barriers to and stakeholders of green

manufacturing.

Ranking of drivers for, barriers to and stakeholders of green manufacturing using

fuzzy TOPSIS multi-criteria decision model.

Development of interpretive structural model of drivers for and barriers to green

manufacturing implementation.

Development and validation of structural model of drivers for and barriers to green

manufacturing implementation.

Classification of stakeholders of green manufacturing implementation using

exploratory factor analysis.

Comparison of importance of the drivers for and barriers to green manufacturing

implementation in India and Germany using independent t-test.

Comparison of the importance of stakeholders of green manufacturing implementation

in SMEs and large enterprises using Mann-Whitney U test.

The plan of work of the thesis is given in figure 1.

1.5 SIGNIFICANCE OF THE STUDY

The facilitation of drivers, mitigation of barriers, and involvement of stakeholders are vital

for easy, smooth and effective implementation of green manufacturing. The identification,

ranking, development and validation of the various models of drivers based on the data from

Indian industry is expected to provide better knowledge to decision makers in government to

Introduction

6 | P a g e

Introduction

Literature review on Green Manufacturing (GM) and similar systems

Conclusions

Literature review on

GM drivers

Ranking of GM

drivers using fuzzy

TOPSIS

Development of

ISM model of GM

drivers

Development of

SEM model of GM

drivers

Case study to

compare GM

drivers in India and

Germany


GM barriers

Ranking of GM

barriers using fuzzy

TOPSIS

Development of

ISM model of GM

barriers

Development of

SEM model of GM

barriers

Case study to

compare GM

barriers in India

and Germany


GM stakeholders

Ranking of GM

stakeholders using

fuzzy TOPSIS

Classification of

GM stakeholders

using exploratory

factor analysis

Case study to

compare GM

stakeholders in

SMEs and large

enterprises

Figure 1: Plan of work

develop policies and prioritize them to facilitate green manufacturing adoption and diffusion

from Indian perspective. The ranking and the hierarchy of the drivers will provide the

decision makers with the required understanding to prioritize policies in a sequential manner

Introduction

7 | P a g e

based on the rank and hierarchy of the driver. High ranking and bottom level drivers in

hierarchy require priority over the other drivers. Similarly, the ranking and hierarchy will

provide better knowledge to the top management in industry to develop and prioritize their

business strategies to facilitate smooth implementation of green manufacturing. In addition,

the inter-relationship among drivers is expected to provide the managers/executives with the

better understanding of the complex relations to develop effective implementation plan.

Similarly, the developed models of the barriers are expected to help decision makers in

government to develop policies and prioritize them to mitigate green manufacturing barriers

from Indian perspective. The ranking and the hierarchy of the barriers will provide these

decision makers with the required understanding to prioritize policies in a sequential manner

based on the rank and hierarchy of the barrier. The developed models will be helpful to the

top management in industry to develop and prioritize their business strategies for smooth

implementation of green manufacturing. The inter-relationship among barriers and their

driver-driven relationship is expected to help the managers/executives to develop an

effective plan to mitigate the root barriers before others. The comparison of drivers/barriers

between the Indian and German industries will help industry and government in both the

countries to learn from each other.

The identification and development of models of stakeholders are expected to help

government and industry to come to a common platform from the environmental,

economical and social perspectives. The identification of the stakeholders will help

managers/executives in the industry to build better relations with stakeholders. The ranking

of the stakeholders is expected to help government to romp in the important stakeholders to

develop the future environmental policies.

Introduction

8 | P a g e

The study is significant for researchers working in the field of green manufacturing and

other similar systems as the study will provide an exhaustive review of literature and trace

the origin and evolution of green manufacturing, environmentally conscious manufacturing,

sustainable manufacturing, sustainable production, environmentally benign manufacturing,

environmentally responsible manufacturing, clean manufacturing, and cleaner production.

The study will help the future researchers, as it provides various definitions, scope,

similarities, and differences among these eight systems/terms from the literature. This is

expected to be a boon for the research in the area as the Dornfeld et al. (2013) has identified

the lack of definition of green manufacturing as a big challenge to the research in this area.

Further, this study suggests an action plan to help green manufacturing implementation in

India.

1.6 ORGANIZATION OF THE THESIS

Chapter 1 presents the introduction of the thesis. Chapter 2 presents a review of literature on

origin and evolution of green manufacturing and similar systems/terms. It also identifies

drivers for, barriers to, and stakeholder of green manufacturing. Chapter 3 discusses drivers

for green manufacturing and presents the ranking of these drivers using fuzzy TOPSIS

model. An interpretive structural model and a structural equation model of the drivers are

also presented in this chapter. A comparison of these drivers has been carried out between

Indian and German industries using independent t-test. Similarly, chapter 4 discusses

barriers to green manufacturing and presents the ranking of these barriers using fuzzy

TOPSIS model. An interpretive structural model and a structural equation model of the

barriers are also presented in this chapter. A comparison of barrier importance has been done

between Indian and German industries using independent t-test. Chapter 5 discusses the

Introduction

9 | P a g e

development and classification of stakeholders of green manufacturing using statistical

analysis. This chapter also presents a comparison of stakeholders of SMEs and large

enterprises. Finally, chapter 6 gives the conclusions of the research work along with

limitations, action plan to implement GM in India and scope for future work.

CHAPTER 2

LITERATURE REVIEW

In this chapter a thorough review of the literature on the green manufacturing and similar

systems/terms has been done. The objectives of the chapter are (i) to trace the evolution of

green manufacturing and similar systems/terms, (ii) to identify green manufacturing drivers,

(iii) to identify green manufacturing barriers, and (iv) to identify green manufacturing

stakeholders.

2.1 INTRODUCTION

The 1980s have witnessed a fundamental change in the way governments and development

agencies think about environment and development. The two were no longer regarded as

mutually exclusive. It has been recognized that a healthy environment is essential for

healthy economy and Sustainable Development (SD). The broad concept of SD was widely

discussed in the early 1980s, but was placed firmly on the international agenda with the

publication of Brundtland report titled "Our Common Future" in 1987. The process of

bringing together environmental and socio-economical issues was expressed in the

Brundtland report’s definition of sustainable development as 'meeting the needs of the

present without compromising the ability of future generations to meet their own needs'

(WCED, 1987). The concept of SD was the result of the growing awareness of the global

links among mounting environmental problems, socio-economical issues (poverty and

inequality) and concerns about a healthy future for humanity (Hopwood et al., 2005). The

'Brundtland Report' has pointed out the planet-wide interconnections of environmental

problems. The Brundtland report offered a valuable documentation on problems like

environment, energy, resources, industry, and development (Trainer, 1990). Later, Kyoto

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Protocol in 1997 and Copenhagen Protocol in 2009 brought the international community on

a common platform to discuss about the SD. Neffke et al. (2008) stated that SD has gained

importance because of the fact that the humanity since 1985 started consuming resources

more than that can be regenerated; 1.2 globes in 2001. If everybody on the earth lives the

lifestyle of the people from the technologically developed countries, which is not even one-

fifth of the current population, the earth population would consume around 3-6 globes per

year (Seliger, 2007). Moreover, the global population is growing at a fast rate and will reach

to 9 billion by 2050 (Lutz et al., 2008). More population means more demand for material

and energy which will further lead to the challenges like global warming; climate change;

landfill problems; depleting natural resources; unhealthy living conditions because of

excessive air, water and sand pollution; etc.

Manufacturing is one of the important elements of SD as it produces goods which are

required to cater to the needs of the society. Manufacturing is an input–output system in

which the resources are transformed into products or semi-products (Liu et al., 2002).

Energy and materials are the two primary inputs to the manufacturing which are obtained by

exploiting the natural resources like fossil fuels and material ores. The emerging, developing

and underdeveloped countries are trying to uplift the living style of their rising population.

At the same time developed countries do not want to sacrifice their current living standard

(O’Brien, 2002). Therefore, the average global consumption pattern keeps increasing as

living standards grow, although these consumption patterns may slightly vary from region to

region; driven by local cultural, societal and economic factors. This means that the growth

of manufacturing is inevitable. This has led to a highly unsustainable situation as in the last

50 years the consumption of energy by the industrial sector has more than doubled and

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industry currently consumes about half of the world’s energy (Ross, 1992) and the

consumption of critical raw materials (such as steel, aluminum, copper, nickel, zinc, wood,

etc) for industrial use has increased worldwide. The competition for natural resources is

accelerating in the BRICS (Brazil, Russia, India, China, and South Africa) countries and

many other developing and underdeveloped economies.

Despite the increasing world-wide concerns on environmental issues in order to monitor the

environmental impact of human activities including manufacturing, the situation today

seems to be rather alarming (Chryssolouris et al., 2008). Currently, the situation is more

worrying because of the accelerating growth of emerging economies, viz. China and India as

evident from table 2.1. In a traditional textbook, world economy growth is concentrated in

the US, Japan and Europe. However, western world is no longer expected to drive world

GDP growth in the decades ahead, India and China are expected to take over as shown in

table 2.1. By the turn of the millennium, China's consistent 10% annual growth rate puts it

on the top of the list of countries in the world. India and US are the only competitors to

China as the European Union (EU) is expected to make up only 6% of the world's growth

rate (Times of India; June 9, 2013).

Table 2.1: Share of global growth (Times of India; June 9, 2013)

Period Share of global growth (in % age)

Leading Nations Emerging Nations Advanced Nations

1982-1987 US (29.8), China (9.9), Japan (10.3) 31 69

1992-1997 US (24.2), China (18.9), Japan (3.8) 46 54

2002-2007 US (12.6), China (23.6), India (7.7) 67 33

2012-2017 US (13.9), China (33.6), India (9.4) 74 26

The rapid growth in manufacturing has created many economic, environmental and social

problems from global warming to local waste disposal (Sangwan, 2011). There is a strong

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need, particularly in emerging and developing economies to improve manufacturing

performance so that there is less industrial pollution, less material & energy consumption,

less wastage, etc. One such potential system is Green Manufacturing (GM). GM is also

known by plethora of different names or terms: clean manufacturing, environmentally

conscious manufacturing, environmentally benign manufacturing, environmentally

responsible manufacturing, sustainable manufacturing, or sustainable production. These

terms have appeared in the literature from late 1980s. Generally, these terms put forward

similar but not same views and have different scope. The term "green" has been misused a

lot in recent times by authors as well firms because there is no unambiguous definition and

scope of this term. Dornfeld (2009) has said that one of the challenges in studying green

manufacturing is the definition of terms.

Therefore, one of the objectives of this chapter is to explore the literature on the evolution of

terms – Environmentally Conscious Manufacturing (ECM), Cleaner Production (CP), Clean

Manufacturing (CM), Green Manufacturing (GM), Environmentally Benign Manufacturing

(EBM), Environmentally Responsible Manufacturing (ERM), Sustainable Manufacturing

(SM), and Sustainable Production (SP) to:

trace the origin of these eight systems/terms in literature,

find the meanings of these eight systems/terms as reported by various researchers,

identify the scope of these eight systems/terms,

report the publications on these eight systems/terms, and

reflect the research trend in these eight systems/terms.

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2.2 SEARCH METHODOLOGY

The literature was searched by using the Google Scholar (GS) database. Articles were

collected using the keywords "cleaner production", "environmentally conscious

manufacturing", "environmentally responsible manufacturing", "sustainable manufacturing",

"green manufacturing", "environmentally benign manufacturing", "clean manufacturing",

and "sustainable production". The GS database has been used for literature search due to its

broader data coverage (e.g. including conference proceedings, working papers and books)

instead of the Thomson ISI Web of Knowledge database, which is considered the most

commonly used source of bibliometric data. GS database coverage is not as strictly

methodological as the Thomson ISI database (Harzing and Wal, 2007; Schiederig et al.,

2011). However, analysis based on GS data results in more comprehensive citation

coverage, particularly in the field of management and international business (Harzing and

Wal, 2007). The evidence also exists in the literature to verify that the data extracted from

the GS database covers the relevant literature (Schiederig et al., 2011). The literature search

has been conducted by topic and not by (top) journal to include "all" published articles in

this field as suggested by Webster and Watson (2002). The extracted article types included

journals, conference proceedings, books, book chapters, and working papers. One drawback

of both methods is that all papers published prior to 1990 may have not been digitalized and

therefore may have not been included in the online databases.

The GS search using the eight keywords resulted in 62,33,100 articles (table 2.2, method 1).

It was not possible to review all these articles within the scope of the present study.

Therefore, the search was narrowed down to articles having these keywords in the title of

the article. However, these keywords may be as an exact phrase or all the words of the

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keyword may be randomly present in the title. This is one of the drawbacks of GS search by

topic. The patents and citations were also excluded. This narrowed down the number of

articles to 2,570 as shown in table 2.2 (method 2). This data was further divided into yearly

data as shown in table 2.3 and figure 2.1. The number of articles in table 2.3 are 2395. This

difference in the total number of articles in tables 2.2 and 2.3 is because there are many

articles which don't have year of publication. For example, a nine page article entitled

"Multi-objective decision making for environmentally responsible manufacturing" by Basu

and Sutherland (1999) in the 6th

International Seminar on Life Cycle Engineering does not

show up in the year-wise search but available in GS search without time frame.

Table 2.2: Summary of article search on GM and similar terms

S. No. Keyword No. of articles

(Method 1)

No. of articles

(Method 2)

1 Sustainable Production 17,50,000 477

2 Clean Manufacturing 8,45,000 23

3 Cleaner Production 3,49,000 1,200

4 Environmentally Conscious Manufacturing 35,300 76

5 Green Manufacturing 18,90,000 397

6 Environmentally Responsible Manufacturing 75,900 10

7 Environmentally Benign Manufacturing 37,900 49

8 Sustainable Manufacturing 12,50,000 338

TOTAL 62,33,100 2,570

Method 1: Using "all the words" option in keywords on GS normally with no addition constraint

(including patents and citations).

Method 2: Using "exact phrase" option in keyword of the title of the article on GS (excluding patents and

citations) .This means that if all the keywords are even randomly present in the title are also included.

Figure 2.2 provides year-wise publication trend of the literature (2395 articles). It also

provides the year of appearance of the different keywords in the literature for the first time.

The search for first time appearance of terms in scholarly articles was done separately using

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GS without any constraints. Figure 2.3 provides the trend of papers published on all eight

terms on a common chart.

2.3 EVOLUTION OF SEARCH TERMS

This section presents a compilation of evolution, connotation and scope of these eight search

terms by various researchers. Intention is to compile the scholarly articles on these terms

showing how environmental and societal concerns have been integrated in manufacturing

over a period of time.

2.3.1 Sustainable Production (SP)

Holdgate (1987) used the term sustainable production in the article "The reality of

environmental policy", published in the Journal of the Royal Society of Arts. This term

appeared in the title for the first time in the master' thesis of Kowey, B.N. entitled "An

example of planning for sustainable production: the dry-cell battery problem", School of

Community and Regional Planning, The University of British Columbia, September, 1990.

But some authors have written that the term sustainable production was introduced at the

1992 UNCED conference in Rio de Janeiro as a guide to help companies and governments

transition towards sustainable development (Rosen and Kishawy 2012; Guerry and Boots

2012; Carruthers, 1996).

Sustainable production is defined as ‘the creation of goods and services using processes and

systems that are non‐polluting; conserving energy and natural resources; economically

viable; safe and healthy for employees, communities and consumers; and socially and

creatively rewarding for all working people (Lowell Center for Sustainable Production,

1998). Six main aspects of SP are: energy and material use; natural environment; social

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Table 2.3: Year-wise publications on GM and similar terms

S. No. Year Number of articles (Method 2)

SP CM CP ECM GM ERM EBM SM TOTAL

1 2013 (Partial) 11 1 33 1 24 0 0 26 96

2 2012 68 1 91 1 49 1 0 81 292

3 2011 57 1 106 4 55 0 2 59 284

4 2010 52 2 108 3 55 1 0 51 272

5 2009 38 2 85 6 36 0 2 25 194

6 2008 41 0 68 3 27 2 3 31 175

7 2007 18 0 77 3 21 0 2 19 140

8 2006 23 0 65 5 31 1 3 13 141

9 2005 16 0 57 2 19 0 1 6 101

10 2004 17 1 59 4 11 0 2 2 96

11 2003 11 1 76 0 7 1 2 3 101

12 2002 9 0 39 0 11 0 21 3 83

13 2001 15 0 41 6 10 0 4 3 79

14 2000 9 1 60 2 7 1 1 2 83

15 1999 7 1 42 5 9 2 0 2 68

16 1998 9 2 28 1 4 0 1 0 45

17 1997 3 1 25 2 4 0 0 1 36

18 1996 5 3 25 1 0 0 1 0 35

19 1995 4 0 22 6 1 0 0 0 33

20 1994 1 1 14 8 0 0 0 0 24

21 1993 2 0 2 4 0 0 0 0 8

22 1992 0 0 3 1 0 0 0 0 4

23 1991 0 0 1 1 0 0 0 0 2

24 1990 1 0 1 0 0 0 0 0 2

25 1989 0 1 0 0 0 0 0 0 1

26 1988 0 0 0 0 0 0 0 0 0

27 1987 0 0 0 0 0 0 0 0 0

28 Before 1987 0 0 0 0 0 0 0 0 0

Total 417 19 1128 69 381 9 45 327 2395

Cleaner Production (CP); Environmentally Conscious Manufacturing (ECM); Sustainable Manufacturing (SM); Green Manufacturing (GM); Sustainable

Production (SP); Environmentally Benign Manufacturing (EBM); Clean Manufacturing (CM); Environmentally Responsible Manufacturing (ERM)

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Figure 2.1: Term-wise publication on GM and similar terms

0

200

400

600

800

1000

1200

1400

Environmentally Responsible

Manufacturing

Clean Manufacturing

Environmentally Benign

Manufacturing

Environmentally Conscious

Manufacturing

Sustainable Manufacturing

Green Manufacturing

Sustainable Production

Cleaner Production

Nu

mb

er

of

arti

cle

s

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Figure 2.2: Year-wise publications on GM and similar terms with first time appearance of these terms

0

50

100

150

200

250

300

350 N

um

be

r o

f ar

ticl

es

Timeline

Term "Environmentally Benign Manufacturing" appeared in Literature

Term "Environmentally Responsible Manufacturing" appeared in Literature

Term "Environmentally Conscious Manufacturing" and "Green

Manufacturing" appeared in Literature

Term "Cleaner Production" appeared in Literature

Term "Clean Manufacturing" appeared in Literature

Term "Sustainable Production"

appeared in Literature

Term "Sustainable Manufacturing" appeared in Literature

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Figure 2.3: Trend of year-wise publications on GM and similar terms

0

20

40

60

80

100

120

Nu

mb

er

of

arti

cle

s

Timeline

Cleaner Production

Environmentally Conscious Manufacturing

Environmentally Responsible Manufacturing


Green Manufacturing

Environmentally Benign Manufacturing

Clean Manufacturing

Sustainable Production

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justice and community development; economic performance; workers; and products (Veleva

and Ellenbecker, 2001). SP is defined as an industrial activity resulting in products that meet

the needs and wishes of the present society without compromising the ability of future

generations to meet their needs and wishes. As a consequence of this definition, sustainable

production minimizes all kinds of waste as well as the use of natural resources and energy.

A possible way to fulfil these requirements is by a continuous improvement of industrial

activities with respect to (i) cost and time efficiency, (ii) product and process quality, (iii)

effectiveness, and (iv) usage of virgin raw materials and energy (de Ron, 1998). There are

three possible strategies to integrate sustainable production with company business strategy:

a ‘follower’ strategy complying with all legal requirements, a ‘market-oriented’ strategy

driven by the market conditions where sustainable production is subordinate to but supports

the fulfilling of the business strategy, and a ‘sustainability-oriented’ strategy in which

sustainable production is seen as a key factor and is fully integrated with the business

strategy (de Ron, 1998). SP emphasizes a life‐cycle perspective in the manufacture, use,

recycling and disposal of goods and services, instead of the traditional focus on discrete

activities, as well as encourages continuous improvement in efficiency of the use of energy

and resources (Falkman, 1996).

The nature of SP systems varies according to the industry sector, but the generic

characteristics of any SP system (O'Brien, 1999) are: (i) environmental consciousness must

pervade the culture of the whole organisation, (ii) both product and process designs must

address sustainable issues and incorporate them into basic design procedure, (iii) make

maximum use and reuse of recycled components and materials, (iv) product life-cycle

concepts must be applied to the whole manufacturing system, (v) organisations must be lean

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as well as clean, (vi) re-engineering must address environmental and sustainable issues, (vii)

kaizen must address environmental issues, (viii) company's metrics must address sustainable

issues, (ix) manufacturers must support extended life cycles, and (x) use of clean

technologies.

2.3.2 Clean Manufacturing (CM)

Nagaraj et al. used this term in 1989 in the article entitled "Particulate Generation in Devices

Used in Clean Manufacturing" published in Particles in Gases and Liquids by Springer. It

involves continuous incremental improvement of environmental attributes of products,

processes and operations (Richards, 1994). Mohanty and Deshmukh (1998) discussed how

green productivity can be increased through clean manufacturing by evolving a mind-set for

total waste minimization, creating a sense of urgency for clean manufacturing and directing

the efforts in multiple dimensions. Karp (2005) opined that clean manufacturing is an

expanded strategy of lean manufacturing by including environmental considerations. This

expanded strategy involves broadening the definition of waste to include air and water

emissions, solid and hazardous waste generation and toxics use. The results attained by

combining “lean” and “clean” manufacturing into one approach can be staggering: savings

to individual companies in the hundreds of thousands of dollars, improvements in

production efficiencies, and enhancement of overall environmental performance (Karp,

2005).

2.3.3 Cleaner Production (CP)

This term was coined in 1989 by United Nations Environment Programme Industry and

Environment (UNEP IE) to create awareness about cleaner production through information

dissemination. UNEP IE launched many cleaner production implementation programmes in

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many countries with the active support of United Nations Industrial Development

Organization, Universities, World Bank, and other lending organizations. It is a continuous

application of an integrated preventive environmental strategy to processes, products and

services to increase efficiency and reduce risks to humans and the environment (UNEP,

1994). It is a preventive way to deal with pollution and seeks to avoid waste generation at

source rather than treating the symptoms of generated waste (Siaminwe et al., 2005).

Cleaner production activities promote strategies, policies and practices to prevent pollution

from processes, products and services. It is a problem solving strategy rather than a solution

– CP takes the waste generating process (root problem) as given and employs a preventive

mindset to develop alternative solutions (Berkel et al., 1997). It encompasses a thorough

review of all aspects of business operations and identifies opportunities where improvements

will help business's economy as well as the environment (Khan, 2008). It further adds that in

addition to economical and environmental benefits, cleaner production saves staff from

undue injuries, raises staff morale, improves legislative compliance, prevents or controls

spills, and raises business's profile amongst its competitors. CP reduces resource use and /or

pollution at the source by using cleaner production methods (Frondel et al., 2007). Cleaner

production requires new attitudes, knowledge and skills for all professionals to ensure that

preventive environmental strategies are integrated into planning and development activities

across society (Unnikrishnan and Hegde, 2007). Cleaner production reduces resource use

and/or pollution at the source by using cleaner production methods, whereas end-of-pipe

technologies curb pollution emissions by implementing add-on measures. Thus, cleaner

production technologies are frequently seen as being superior to end-of-pipe technologies

for both environmental and economic reasons (Frondel et al., 2007). Cleaner production is a

promising approach to control pollution in an economically feasible way and takes into

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account the operating and environmental variables as well as the economic aspects and

social relationships (Frijns and Vliet, 1999).

2.3.4 Environmentally Conscious Manufacturing (ECM)

The first GS citation for the term comes from Granoff (1991) in the article "Environmentally

conscious manufacturing at Sandia National Laboratory in Environmentally Conscious

Manufacturing/Technology Workshop, Albuquerque, NM (USA), during February 20-21,

1991. Environmentally conscious manufacturing refers to those processes that reduce the

harmful environmental impacts of manufacturing, including minimization of hazardous

waste, reduction of energy consumption, improvement of material utilization efficiency, and

improvement of operational safety. Approaches involve substitution of non-hazardous for

hazardous materials, replacement of existing processes with new waste-free processes, and

increased use of recycle. Reducing waste at the source, through ECM, saves energy and

money and provides value addition for the production and process (Granoff, 1991).

Matysiak (1993) stated that the focus of the industry has been on reducing the environmental

impacts of products and processes because of the rising compliance costs and stringent

regulatory requirements in U.S. This has the designers, engineers, and managers to use life

cycle analysis, design for environment, and environmentally conscious manufacturing as

tools to help in evaluating the alternatives from environment and company perspectives.

Darnall et al. (1994) refers ECM as transformation of materials into useful products through

a value-added process that simultaneously enhances economic well-being and sustains

environmental quality. It is concerned with developing methods for manufacturing new

products from conceptual design to final delivery and ultimately to the end-of-life (EOL)

disposal such that the environmental standards and requirements are satisfied (Gungor and

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Gupta, 1999; Rao, 2009; Ilgin and Gupta, 2010). With ECM, the manufacturer resolves to

exceed regulated environmental standards and strives toward the goal of pollution

prevention (Kutz, 2007). The ECM involves planning, developing and implementing

manufacturing processes and technologies that minimize or eliminate hazardous waste and

reduce scrap. A major objective of ECM is to design products that are recyclable or can be

remanufactured or reused (Sarkis and Rasheed, 1995). It consists of methods and tools to

achieve sustainable production through process optimization across the supply chain with

environmental costs in mind (Reich-Weiser et al., 2010).

Some authors also use term environmentally conscious design and manufacturing

(Weissman and Sekutowski, 1991; Zhang et al., 1997). However, the objective is same, i.e.

design, synthesis, processing, and use of products in continuous or discrete manufacturing

industries taking into consideration the social and technological aspects (Zhang et al., 1997).

An ECM provides safer and cleaner factories, increased worker protection, reduced future

disposal costs, reduced environmental and health risks, improved product quality at lower

cost, better public image, and higher productivity (Weissman and Sekutowski, 1991). The

ECM is a proactive approach which aims to reduce the resource consumption, hazardous

emission and energy usage throughout the product life cycle by re-engineering the design

and manufacturing processes and selecting appropriate materials.

2.3.5 Green Manufacturing (GM)

The first GS citation for the term comes from Lewis in the article "The games children play

- even Verminous Skumm is made of recycled material" published in 17 EPA Journal in

1991. The article educates the children about the environmental issues in manufacturing

while they play games. The first article with "Green Manufacturing" in the title of the article

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is found in year 1995 by Dickinson et al. entitled "Green product manufacturing" published

in AT&T technical journal. The GM is the application of sustainable science to the

manufacturing industry (Hua et al., 2005). The term GM was coined to reflect the new

manufacturing paradigm that employs various green strategies and techniques to become

more eco-efficient. This strategy includes creating products/systems that consume less

material and energy, substituting input materials, reducing unwanted outputs, and converting

outputs to inputs (recycling) (Deif, 2011).

Deif (2011) opined that sustainability is a concept and GM is a methodology to the design

and engineering activities involved in product development and/or system operation to

minimize environmental impact. Green manufacturing is a concept of production which

connects the design of products and processes that reduces waste, eliminates costly end-of-

the-pipe treatments, provides safer products and reduces use of energy and resources

(Burchart-Korol, 2011). The fundamentals of GM are related to minimizing the use of

resources and the environmental impact of a product. Successful implementation of GM

requires going beyond small standalone initiatives and adopting an integrated three-step

framework: (i) planning for green as a core part of business strategy, (ii) executing green

initiatives across the supply chain by shifting towards green energy, green products and

green processes, and (iii) communicating and promoting green initiatives and their benefits

to all stakeholders.

The GM is an ongoing process of continually improving manufacturing techniques with an

ultimate goal of sustainability and it is a process with sustainability as the ultimate albeit

distant goal considering three areas of knowledge - specificity of sustainability, triple bottom

line, and technology wedge which refers to action affecting the use of technology, materials,

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and energy eventually leading to sustainability (Guerry and Boots, 2012). Balan (2008)

opined that in addition to faster and cheaper, several other factors such as materials used in

manufacturing; generation of waste, effluents and their treatment (or possible elimination);

life of the product; and finally treatment of the product after its useful life are also important

considerations in manufacturing a product or evaluating an existing process line.

Green manufacturing deals with maintaining sustainability of environmental, economical

and social objectives in the manufacturing domain and attempts to establish a solid

foundation for all the three pillars to achieve sustainability in business operations. It is a

method to develop technologies to transform materials without emission of greenhouse

gases, use of non-renewable or toxic materials or generation of waste (Allwood, 2009). The

GM is a modern manufacturing strategy integrating all the issues of manufacturing with goal

of reducing and minimizing environmental impact and resource consumption during a

product life cycle; which includes design, synthesis, processing, packaging, transportation,

and the use of products in continuous or discrete manufacturing industries (Melnyk and

Smith, 1996; and Liu et al., 1999). Green has moved from being perceived as a "necessary

evil" to being seen as "good business". The companies take their problem solving approach

to the next level and develop innovative techniques towards effective solutions, which result

in cost savings from reduced work handling, effluent control, process automation, etc. All

these efforts are applications of green manufacturing. This manufacturing concept is not just

restricted to addressing the social and environmental impact of a pollution-centric process.

The main objectives of GM include pollution prevention, waste reduction, materials and

energy consumption reduction, political traction, brand enhancement, regulatory

compliance, talent retention, consumer retention and attraction, cost savings, etc. (Deif,

2011; Sangwan, 2006, 2011; Bhattacharya et al., 2011).

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The goals of green manufacturing are frequently achieved through product and process

design (Thomas, 2001; Dornfeld 2009). Green manufacturing encompasses all factors

associated with environmental concerns in manufacturing by continuously integrating eco-

friendly industrial processes and products (Chuand and Yang, 2013). Green manufacturing

can mitigate air, water and land pollution, reduce waste at the source and minimise health

risks to humans and other species (Berkel et al., 1997; Hui et al., 2001). For processes,

green manufacturing strives to conserve materials and energy, eliminate toxic substances

and reduce waste produced; for products, green manufacturing attempts to minimise

environmental impacts throughout the product life cycle (Berkel et al.,1997). Green

manufacturing is an advanced manufacturing system which aims to improve process

efficiency and minimise environmental impact and resource consumption during

manufacturing (Sivapirakasam et al., 2011; Tan et al., 2002). According to Chuand and

Yang (2013), GM is a manufacturing method that minimizes waste and pollution and is a

subset of sustainable manufacturing.

The research on green manufacturing can be divided into two groups: first, the work that

deals with the overall concept of green manufacturing (Naderi, 1996; Mohanty and

Deshmukh, 1998; Jovane et al., 2003; Sangwan, 2006; Wang and Lin, 2007) and second, the

work that provides various analytical tools and models to realize green manufacturing at

different levels (Fiksel, 1996; Melnyk et al., 2001; Hui et al., 2002; Krishnan et al., 2004;

Deif, 2011).

However, the introduction of “green” manufacturing strategy is a very complex issue, since

it presents a multi-dimensional impact on performance and often induces a significant

modification in management procedures (Azzone et al., 1997; Hutchinson, 1996; Roome,

1992).

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2.3.6 Environmentally Responsible Manufacturing (ERM)

Larrabee (1993) used the term environmentally responsible manufacturing at the

International Symposium on Semiconductor Manufacturing at Texas AN for the

manufacturing of IC (Integrated Circuits) in an environmentally responsible manner.

However, the first article with environmentally responsible manufacturing in the title of the

article is found in year 1999 in "Environmentally Responsible Manufacturing: Past

Research, Current Results, and Future Directions for Research" by Curkovic et al. (1999).

This article is available at two different locations. At location one, the title of the article is

"Environmentally Responsible Manufacturing" and at other location, the title of the article is

"Environmentally Responsible Manufacturing: Past Research, Current Results, and Future

Directions for Research", however, both the locations are leading to same article. ERM is an

economically driven, system-wide and integrated approach to the reduction and elimination

of all waste streams associated with the design, manufacture, use and/or disposal of products

and materials (Curkovic and Landeros, 2000; Handfield et al., 1997; Melnyk et al., 2001).

An environmentally responsible or environmentally conscious manufacturing program

addresses the environmental impact of the interrelated decisions that are made at various

stages of product life: design, raw materials consumption, processing, delivery, use,

recycling, and/or disposal (Rao, 2008).

2.3.7 Environmentally Benign Manufacturing (EBM)

Allen and Arvizu (1994) used the term EBM in the article titled "Technology Transfer at

Sandia National Laboratories" in Proceedings of the Twenty-Seventh Annual Hawaii IEEE

International Conference on System Sciences, at Wailea, HI, USA, Jan 4-7, 1994 to discuss

the technology transfer from government to the private sector that has assumed important

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new dimensions with the declining competitiveness of key U.S. industries in world markets.

In the same year, Schmitt also used the term EBM in the article "Do manufacturing

technologies need federal policies? published in the journal of vacuum science & technology

B: microelectronics and nanometer structures. EBM involves the technologies, operational

practices, analytical methods, and strategies for sustainable production within the industrial

ecology framework. According to Durham (2002), EBM specifically addresses the

development and implementation of benign material processing to meet the challenges of

sustainable materials in a use and reuse environment with a goal of zero waste. It also

addresses remanufacturing, reuse and recycling issues in a total environmental management

context (Durham, 2002).

The EBM is the manufacturing part of the industrial ecology movement which attempts to

reconcile economic growth and environmental protection (Gutowski, 2002). Its goal is

sustainability and its methods are based upon scientific understanding and technology

development in concert with policy development (Gutowski, 2002). It may be observed that

EBM places the emphasis not only upon manufacturing, but also recognizes that design is

extremely important and that doing a proper eco-design will decrease the environmental

impact of a product before it even gets to market (Jeswiet and Hauschild, 2005).

2.3.8 Sustainable Manufacturing (SM)

Stephen et al. (1990) wrote the book entitled "Investing in sustainable manufacturing: a

study of the credit needs of Chicago's metal finishing industry". However, this is more about

the economic sustainability of an industry segment rather than the economic sustainability of

the manufacturing or design. This term also appeared as conference theme "Sustainable

Manufacturing For Global Business" in 1st International Conference on Managing

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Enterprises - Stakeholders, Engineering, Logistics, and Achievement (ME-SELA 97), July

22-24, 1997 at Loughborough University, Loughborough, England. Sustainable

manufacturing evolved from the concept of sustainable development to address concerns

about environmental impact, economic development, globalization, inequities and other

factors (Rosen and Kishawy, 2012). A comprehensive scope of sustainable manufacturing

has been given in a report entitled "Sustainable Manufacturing Initiatives" by Organisation

for Economic Co-operation and Development (OECD) in 2011 which says the evolution of

sustainable manufacturing concepts and practices involves pollution control by

implementation of non-essential technologies (end-of-pipe solutions), cleaner production by

modifying products and production methods (process optimization and substitution of

materials), eco-efficiency by systematic environmental management (environmental

strategies and monitoring environmental management systems), life cycle thinking by

extending environmental responsibility (green supply chain management and corporate

social responsibility), closed-loop production by restructuring of production methods

(minimizing or eliminating virgin materials), and industrial ecology by integrating systems

of production (environmental partnerships and eco-industrial parks). OECD says SM is all

about minimising the diverse business risks inherent in any manufacturing operation while

maximising the new opportunities that arise from improving processes and products (Guerry

and Boots, 2012). Goals of SM as articulated by OECD are to reduce the intensity of

materials use, energy consumption, emissions, and unwanted by-products while maintaining

or improving the value of products to society and to organizations. The OECD also relates

the term ‘sustainable manufacturing’ to ‘eco-innovation’. The latter is described as the

trigger for developing a green economy and thus assisting manufacturing to become

sustainable.

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Most of the literature on SM broadly refers to balancing the triple bottom line; a term first

coined by John Elkington (1994) referring the three P’s – people, profit and planet. As stated

by OECD, the triple bottom line is a reference to the economic, environmental and social

value added, captured or destroyed during the process of wealth creation. The US

Department of Commerce defines sustainable manufacturing as “the creation of

manufactured products using processes that minimize negative environmental impacts,

conserve energy and natural resources, are safe for employees, communities, and consumers

and are economically sound. The Queensland Government defines sustainable

manufacturers as those who “use world‐class manufacturing and environment friendly

practices to improve the profitability of their businesses and reduce their impact on the

environment. The SM broadly implies the development of innovative manufacturing

sciences and technologies that span the life cycle of products and services to minimize

negative environmental impacts; conserve energy and natural resources; are safe for

employees, communities, and consumers; and are economically sound (International Trade

Administration, 2010).

Jawahir (2008) defined SM as design and manufacture of high quality/performance products

with improved/enhanced functionality using energy-efficient, toxic-free, hazardless, safe and

secure technologies and manufacturing methods utilizing optimal resources and energy by

producing minimum waste and emissions, and providing maximum recovery, recyclability,

reusability, remanufacturability, redesign features, and all aimed at enhanced societal

benefits and economic impact. The SM is creating a product in a way that considers the

entire product’s life cycle and its full impact surrounding the use and reuse of raw materials

and auxiliary materials, environment and the surrounding community.

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Sustainable manufacturing is the ability to smartly use natural resources for manufacturing,

by creating products and solutions that; thanks to new technology, regulatory measures and

coherent social behaviours; are able to satisfy economical, environmental and social

objectives, thus preserving the environment while continuing to improve the quality of

human life (Garetti and Taisch, 2012). This is to further ensure the betterment of people,

planet and prosperity (Jawahir, 2008) as shown in figure 2.4.


Society Environment Economy

People Planet

ü Improved health

ü Safety

ü Enhanced quality

of life

ü Ethics

ü Cleaner air, water,

and soil

ü Eco-balance and

efficiency

ü Greater

implementation of

regulations, codes,

etc.

ü New employment

ü Product and

process innovation

ü Large scale new

business

opportunities

Goal

Pillars

Means what??

Objectives

Prosperity

Figure 2.4: Sustainable manufacturing - goal, pillars and objectives (Jawahir, 2008)

2.4 OBSERVATIONS, ANALYSIS AND DISCUSSION

2.4.1 Publications and research trend

The two most populated economies of the world, India and China, had a big surge in their

Gross Domestic Product (GDP) in early 1990's. In early 1991, India started market driven

economic reforms to integrate Indian economy with world economy and from early 1990's

got a big push to its GDP growth. China, in contrast, started the market driven economic

reforms in 1978 but big surge in GDP growth came only in early 1990's with the creation of

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Shanghai SEZ. These two countries have not looked back since then in GDP growth and

these days the combined growth of China and India is more than 30%. But this growth has

also resulted in the negative effects in term of pollution, depleting natural resources,

inequality, etc. It can be seen from the figure 2.2 that the research in GM and similar terms

also started from early 1990s (assuming it used to take 1 to 2 years to publish in 1990s).

During 1987-1998, the growth in number of publications has been linear and all the eight

keywords of search were coined during this interval. The earth summit in 1992 generated

awareness on sustainable development and policy makers in governments became aware of

the need of sustainable development. The Kyoto protocol in 1997 generated more awareness

about green house gas emissions. The green house gas emissions are relatively more due to

manufacturing activities so it seems there is more research on greening the manufacturing

during this period. Post 1998, the growth in number of publications is exponential and the

growth is relatively higher during 2005-2011 period as seen in figure 2.2. The Doha talks in

2001 where environment was first time introduced as a measure and Copenhagen

declaration in 2009 were remarkable stimulants for the research community in this field.

The number of publications on cleaner production are far more than other keywords (figure

2.1 and 2.3). It has been observed that two of the reasons for this are (i) the launching of

cleaner production implementation programmes in many countries by United Nations

Environment Programme Industry and Environment (UNEP IE) and (ii) the launch of the

Journal of Cleaner Production in 1993. Many of the early papers in this journal have the

term cleaner production in their title. The terms SM, GM and SP are also more often used in

research than the terms ECM, ERM and EBM as seen from figures 2.1 and 2.3. The majority

of the articles in the data set as given in table 2.2 relates to the notion "cleaner production"

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(46.69%), followed by "sustainable production" (18.56%), "green manufacturing" (15.44%),

and "sustainable manufacturing" (13.15%). The number of articles on "environmentally

responsible manufacturing" (0.38%) are lowest. The number of articles on "environmentally

conscious manufacturing" (2.95%), "environmentally benign manufacturing" (1.90%) and

"clean manufacturing" (0.89%) are also less.

2.4.2 Scope of the eight search keywords

Various authors have defined the scope of these eight keywords differently. Sustainable

production is defined as an activity (Lowell Centre for Sustainable Production, 1998; de

Ron, 1998), a perspective (Falkman, 1996) and an approach (Alting and Jøgensen, 1993);

clean manufacturing as a strategy (Biehl and Gaimon, 1999; Karp, 2005) and an approach

(Richards, 1994); cleaner production as an approach (Frinjn and Vliet, 1999), a tool (Hillary

and Thorsen, 1999), a strategy (UNEP, 1994; Fresner, 1998), and a way (Siaminwe et al.,

2005); environmentally conscious manufacturing as a tool (Matysiak, 1993), a method/tool

(Reich-Weiser et al., 2010), a program (Rao, 2008), a process (Darnall et al., 1994), and a

method (Gungor and Gupta, 1999; Ilgin and Gupta, 2010); green manufacturing as an

activity (Balan, 2008), a system (Tan et al., 2002), a method (Chuand and Yang, 2013;

Allwood, 2009), a process/system (Dornfeld et al., 2013), an approach/system/strategy

(Burchart-Korol, 2011), an approach (Deif, 2011), and a strategy (Liu et al., 1999);

environmentally responsible manufacturing as an approach (Handfield et al, 1997; Melnyk

et al., 2001), a system (Melnyk and Handfield, 1995) and a program (Rao, 2008);

environmentally benign manufacturing as a concept (Tan et al., 2011), an activity (Jeswiet

and Hauschild, 2005), a process/system (Dornfeld et al., 2013), and practices/methods

(Durham, 2002); and sustainable manufacturing as an activity (US Department of

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Commerce, 2009; Madu, 2001; Jawahir, 2008), a system/process (Dornfeld et al., 2013), a

way (Sustainable Manufacturing Hub, 2009), and an ability (Garetti and Taisch, 2012).

Different authors define these terms as activity or strategy or way or tool or method or

process or program or approach or perspective, etc. It is more confusing when different

authors call same term by different perspective and different terms by same perspective.

Table 2.4 provides a summary of literature review on the scope of these keywords. Some of

the common issues which have been part of the scope of many of these search terms are:

Triple bottom line

When it comes to sustainable manufacturing all authors have categorically included

environmental, economical and societal aspects (table 2.4). Most of the authors have also

included these three aspects in sustainable production and some authors have included these

three aspects in green manufacturing. Most of the authors have considered only

environmental and economical aspects in ECM, EBM, ERM, and CP. However, clean

manufacturing is considered from environmental perspective by all authors except Richards

(1994) who considered clean manufacturing from all three perspectives.

Product life cycle engineering

Many of the authors view product life cycle approach as an inevitable component of these

search terms. An exception is the cleaner production, which is viewed more as a production

of goods by avoiding pollution and waste generation and hence reducing all types of waste

and making efficient use of resources. Product life cycle engineering covers any design

activity which aims at improving the environmental performance of a product through its

life cycle. The product life cycle engineering is a closed loop of six stages starting from

material extraction to treatment and disposal as shown in figure 2.5. Various tools used in

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Table 2.4: Analysis of GM and similar systems/terms in extant literature

S.

No.

Keyword Author Point of view Definition/Scope Triple Bottom Line

ENA ECA SOA

1 SP Lowell Centre for

Sustainable Production,

1998

Activity Creation of goods and services using processes and

systems which confirm to environmental, economical

and social dimensions.

Yes Yes Yes

Falkman, 1996 Perspective Considers product's entire life cycle and continuous

improvement in efficiency of the use of energy and

resources.

Yes Yes No

de Ron, 1998 Activity Resulting in products that meet the needs and wishes of

the present society without compromising the ability of

future generations to meet their needs and wishes.

Yes Yes Yes

Alting and Jøgensen, 1993 Approach Product design for whole life cycle with minimum

influence on environment, occupational health and use

of resources.

Yes Yes Yes

2 CM Richards, 1994 Approach Environmental life cycle to production that includes

relevant safety, health and social factors across the life

time of product, process, material, technology or

service.

Yes No Yes

Karp, 2005 Strategy Expanded strategy of lean manufacturing by including

environmental considerations.

Yes No No

Biehl and Gaimon, 1999 Strategy Focuses on reducing the amount and toxicity of waste

from manufacturing processes

Yes No No

3 CP UNEP, 1994 Strategy Integrated preventive environmental strategy for

processes, products and services.

Yes Yes Yes

Siaminwe et al., 2005 Way Avoidance of pollution and waste generation at source Yes No No

Fresner, 1998 Strategy Strategy to prevent emissions at the source and to

initiate a continuous preventive improvement of

environmental performance of organizations.

Yes Yes No

Hillary and Thorsen, 1999 Tool Industrial processes and products aimed at reducing all

wastes, minimizing risks to the environment and

making efficient use of resources and raw materials.

Yes Yes No

Frinjn and Vliet, 1999 Approach Control pollution in an economical feasible way taking

into account environmental aspects and social

relationship

Yes Yes Yes

Environmental aspect (ENA); Economic aspect (ECA); Societal aspect (SOA)

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Table 2.4: Analysis of GM and similar systems/terms in extant literature (contd.)

S.

No.


ENA ECA SOA

4 ECM Ilgin and Gupta, 2010 Method Dealing with green principles for manufacturing from entire

product's life cycle perspective.

Yes No No

Gungor and Gupta, 1999 Method Manufacturing new products from entire product's life cycle

perspective.

Yes No No

Darnall et al., 1994 Process Conversion of materials into useful products through value

added processes that enhance economic well-being and sustain

environmental quality.

Yes Yes No

Rao, 2008 Program Addresses the environmental impact of decisions made from

entire product's life cycle perspective.

Yes No No

Reich-Weiser et., 2010 Method/Tool Sustainable production through process optimizations across the

supply chain with environmental cost in mind.

Yes Yes No

Matysiak, 1993 Tool Evaluation of alternatives which are best for environment and

company.

Yes Yes No

5 GM Liu et al., 1999 Strategy Reduction and minimization of environmental impact and

resource consumption during entire product's life cycle.

Yes No No

Allwood, 2009 Method Development of technologies to transform materials without

emission of greenhouse gases, use of non-renewable or toxic

materials or generation of waste.

Yes Yes No

Deif, 2011 Approach Design and engineering activities involved in product

development and/or system operation to minimize

environmental impact.

Yes Yes Yes

Burchart-Korol, 2011 Approach/Strategy A sustainable approach to the design and engineering activities

involved in product development and/or system operation to

minimize environmental impact.

Yes Yes Yes

Dornfeld et al., 2013 Process/System Process/system which has minimal, nonexistent, or negative

impact on the environment.

Yes No Yes

Chuand and Yang, 2013 Method Manufacturing method that minimises waste and pollution and

is a subset of sustainable manufacturing.

Yes Yes No

Tan et al., 2002 System Integrates manufacturing-related environmental issues to

mitigate adverse environmental impacts and resource

consumption throughout the product life cycle.

Yes Yes No

Balan, 2008 Activity Elimination of environmental waste and reduction of energy

consumption by redefining existing production process/system.

Yes Yes No


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S.

No.


ENA ECA SOA

6 ERM Melnyk et al., 2001 Approach Reduction and elimination of all waste streams

for the entire product's life cycle perspective. Yes Yes No

Rao, 2008 Program Addresses the environmental impact of decisions

made from entire product's life cycle

perspective.

Yes No No

Melnyk and Handfield, 1995 System Integrates product and process design issues

with issues of manufacturing production

planning and control in such a manner as to

identify, quantify, assess and manage the flow of

environmental waste with the goal of reducing

and ultimately minimizing its impact on the

environment while also trying to maximize

resource efficiency.

Yes Yes No

Handfield et al., 1997 Approach Economically driven, system-wide, and

integrated approach to the reduction and

elimination of all waste streams associated with

the design, manufacture, use and/or disposal of

products and materials.

Yes Yes No

7 EBM Durham, 2002 Practice/ method Sustainable production within the industrial

ecology framework.

Yes Yes No

Dornfeld et al., 2013 Process/system Addresses the dilemma of maintaining a

progressive worldwide economy without

continuing to damage our environment.

Yes No Yes

Jeswiet and Hauschild, 2005 Activity Enabling economic progress while minimizing

pollution and waste, and conserving resources.

Yes Yes No

Tan et al., 2011 Concept Considers the environmental impact, resource

efficiency and resource consumption.

Yes Yes No


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S.

No.


ENA ECA SOA

8 SM US Department of

Commerce, 2009

Activity Manufacturing products that are

environmentally, socially and economically

sound.

Yes Yes Yes

Garetti and Taisch, 2012 Ability Smart use of natural resources by creating

products and solutions to improve the quality of

human life.

Yes Yes Yes

Jawahir, 2008 Creation High quality/performance products with

improved/enhanced functionality using

technologies with least impact on environment,

society and economy.

Yes Yes Yes

Dornfeld et al., 2013 System/process Manufacturing system/process that addresses the

impacts on the environment, economy and

society.

Yes Yes Yes

Madu, 2001 Activity Developing and practicing technologies to

transform materials into finished products with

reduction in; energy consumption, emission of

greenhouse gases, generation of waste, use of

non-renewable or toxic materials.

Yes Yes Yes


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life cycle engineering include eco-design (Gottberg et al., 2006; Knight and Jenkins, 2009;

Bovea and Gallardo, 2006), life cycle assessment (Bishop, 2000; Klöpffer, 1997; Duda and

Shaw, 1997; Curran, 1996), life cycle costing (Woodward, 1997; Thumann, 1988; Gluch

and Baumann, 2004). Some authors also call product life cycle engineering as a systems

approach, e.g. Sarkis (1995) as shown in figure 2.6.

Figure 2.5: Product life cycle

Raw

Material

Virgin

MaterialFabrication Assembly Consumer

Reuse

Remanufacture

Recycle

Disposal

Reduce

Waste Waste Waste Waste Waste

Procurement Production Distribution

Figure 2.6: Environmentally conscious manufacturing: systems approach (Sarkis, 1995)

Use

Retirement

Treatment and Disposal

Material Extraction

Material Processing

Manufacturing

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Resource and energy efficiency

A common thread among these terms is resource and energy efficiency throughout the life

cycle of the product. However, CP does not talk about energy efficiency other than

production phase.

System approach

Generally, researchers talk about systems approach when it comes to SM, GM, SP, and CP

wherein the approach is to integrate these systems with business strategies of the company.

The integration of environmental policy with business strategies generates stakeholder value

and provides innovative and comprehensive environmental solutions.

Pollution prevention

All these terms focus on the pollution prevention in all forms, i.e. air, water or soil pollution.

Some of the papers on SM, GM, ECM, SP, and CP write about it explicitly but it is

implicitly included in other terms also. Pollution prevention practices on one hand increase

the manufacturing cost due to innovative technology requirement and on the other hand have

the potential to decrease the manufacturing cost due to decrease in resource and energy

consumption, waste generation and recycling.

6R concept

Jawahir et al. (2007) tried to distinguish some of these terms based on the 'R' concept as

shown in figure 2.7. It can be observed from literature that SM is about all 6R; GM, ECM,

EBM, ERM and SP are based on 3R concept of reuse, remanufacture and recycle; and CM

and CP do not call for end-of-life strategies. '6R concept', proposed by Joshi et al. (2006), is

a good tool in sustainable design as it maximizes the life of a product and builds

improvements into the product after first life cycle (Yan and Feng, 2013). 6R concept has

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much broader focus compared to the conventional 3R for practical sustainable design and

manufacturing as shown in figure 2.7. One approach to attain closed-loop flow is application

of the 6R methodology for sustainable manufacturing (Joshi et al., 2006). Table 2.5

provides brief description of 6R definitions.

Figure 2.7: Evolution of sustainable manufacturing (Jawahir et al., 2007)

Table 2.5: 6R definitions

Term Definition

Reduce It refers to the resource reduction in pre-manufacturing; energy and material

consumption reduction in manufacturing; and reduction of all forms of wastes

during post-manufacturing. It involves activities that seek to simplify the current

design of a given product to facilitate future post-use activities.

Recover It represents the activities of collecting, disassembly and dismantling of specific

components from a product at the end-of-life for subsequent post-use activities.

Redesign It is redesigning the product to simplifying future post-use processes. It extends

product usage life cycle or use less energy/resource; use modular design for easy

recycling, reuse and remanufacture; provides unique identity to the returned

product, etc.

Remanufacture Remanufacture is the reprocessing of used products in such a manner that product

quality is as good or better than the new in terms of appearance, reliability and

performance.

Recycling Recycling is the process of recovering material after a product has been discarded.

Reuse Reuse means continuing to use an item after it has been relinquished by its previous

user, rather than destroying, dumping or recycling it. Reuse ‘as is’ refers to the reuse

of a product with minimal reprocessing. Further use is the use of a used product for

a different purpose than was originally intended.

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The application of such innovation-based sustainable manufacturing practices can help

increase the potential benefits to all stakeholders as opposed to conventional manufacturing

practices. These practices incorporated with optimized technological improvements at the

process level (Jawahir and Dillon, 2007) and integrated across supply chain with life-cycle

based approach at the systems level (Badurdeen et al., 2009; Badurdeen and Liyanage,

2011) are needed for sustainable manufacturing.

Are these terms similar?

There is no unambiguous single definition of any of these eight systems/terms which

explicitly defines the scope and limitation of the terms. Some researchers claim many of the

terms to be same (table 2.6) and few are differentiating them, for example Jawahir et al.,

(2007).

Table 2.6: Similarity among the search keywords by various reseachers

Sr. Author (s) SP CM CP ECM GM ERM EBM SM

1 Melnyk and Smith (1996) ü ü ü ü

2 Mbohwa (2002) ü ü

3 Drizo and Pegna (2006) ü ü ü ü

4 Rao (2009) ü ü

5 Allwood (2009) ü ü

6 Li et al. (2010) ü ü ü ü

7 Sangwan (2011) ü ü ü ü ü

8 Burchart-Korol (2011) ü ü ü ü ü ü

9 Duhan et al. (2012) ü ü ü ü ü ü

10 Schmitter (2012) ü ü

11 Dornfeld et al. (2013) ü ü

12 Martin et al. (2004) ü ü

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Over the years many different terminologies have emerged as there is no universal definition

for these terms. It is apparent from the literature that many of the elements of these concepts

overlap and supplement each other. Therefore, to frame the discussion about green

manufacturing in this thesis all the eight keywords will be used interchangeably; means

designing, manufacturing, delivering, and disposing products that produce minimum

negative effect on environment and society and are economically viable as has been

vouchsafed by Shakespeare in Romeo and Juliet (II, ii, 1-2)

What's in a name? That which we call a rose

By any other name would smell as sweet.

Some basic aspects observed during literature review, which can be used to standardize the

terminology are:

Use of life cycle engineering approach. It should clearly define which of the phases –

material extraction, material processing, manufacturing, use/service, transportation, and

storage – have been considered.

Clarity on the end-of-life strategies used.

Clarity in use of various components of triple bottom line perspectives of economy,

environment and society, i.e. the perspective(s) should be clear and unambiguous.

Inclusion of the whole supply chain and integration of environmental improvement

strategies with the business strategy.

2.5 LITERATURE REVIEW ON DRIVERS FOR GM

The successful adoption of GM initiatives in the industry can be understood by analyzing

motivations for the firms to launch GM practices. Manufacturing firms face multiple

https://www.enotes.com/romeo-text/act-ii-scene-ii#rom-2-2-45

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motivations called 'drivers' which are motivating and/or forcing the industry to adopt GM.

The driving factors play active role in adoption and diffusion of GM in industry. Availability

of comprehensive overview of the drivers would raise awareness and convince the firms to

justify investments on newer systems. There are number of factors that act as drivers for the

implementation of GM. Understanding of these drivers is necessary to implement GM

effectively. This section identifies through review of 55 articles (table 2.7) from various

journals, conferences, thesis/dissertation, etc.

Table 2.7: Distribution of the reviewed articles on GM drivers

S.

No.

Journal/Conference No. of

articles

Publisher

1 Resources, Conservation and Recycling 1 Elsevier

2 Journal of Cleaner Production 8 Elsevier

3 Energy Policy 1 Elsevier

4 CIRP Annals - Manufacturing Technology 2 Elsevier

5 European Management Journal 1 Elsevier

6 Journal of Purchasing & Supply Management 1 Elsevier

7 International Journal of Production Economics 2 Elsevier

8 Minerals Engineering 1 Elsevier

9 Applied Energy 1 Elsevier

10 Corporate Social Responsibility and Environmental

Management

3 Wiley

11 Business Strategy and the Environment 4 Wiley

12 Book 1 Wiley

13 Environmental Quality Management 1 Wiley

14 CIRP Life Cycle Engineering Conference 1 Springer

15 Frontiers of Environmental Science & Engineering in China 1 Springer

16 Journal of Business Ethics 1 Springer

17 Environment, Development and Sustainability 1 Springer

18 International Journal of Operations & Production Management 2 Emerald

19 Social Responsibility Journal 1 Emerald

20 Journal of Manufacturing Technology Management 1 Emerald

21 Technology Analysis & Strategic Management 1 Taylor & Francis

22 International Journal of Sustainable Engineering 1 Taylor & Francis

23 *Other Journals 7 -----------------

24 Conferences 3 -----------------

25 Miscellaneous (Theses/Working papers/Reports/Books) 8 -----------------

Total 55

*Manufacturing Engineering by Society of Manufacturing Engineers, Manufacturing Engineer by IET

Digital Library, Journal of Advanced Manufacturing Systems by World Scientific, Journal of Industrial

Engineering and Management by Open Journal Systems, International Journal of Engineering Sciences by

International Journals of Multidisciplinary Research Studies (IJMRS), American Journal of Economics by

Scientific & Academic, Environment and Planning C: Government and Policy by Environment and

Planning.

Literature Review

47 | P a g e

Singh et al. (2012) identified 14 drivers motivating green manufacturing practices from the

survey of Indian industry. These 14 drivers are: employee motivation, health and safety;

global climatic pressure and ecological benefits; environmental concerns and legislature;

green image, global marketing and competitiveness; social and environmental responsibility;

organizational capabilities and awareness; government rules and legislation; scarcity of

resources, higher waste generation and waste disposal problem; customer awareness,

pressure and support; demand for environment friendly products; economic benefits or cost

reduction benefits; society or public pressure; supplier pressure and willingness; investor

and shareholder pressure. Law and Gunasekaran (2012) identified key motivating factors to

adopt sustainable development strategies in Hong Kong – strategy/policy, mindset, system,

measures, needs to advance, performance, laws, regulations, social pressure, market trend,

competition, and company's willingness and readiness.

Kapetanopoulou and Tagaras (2011) conducted a study to assess the current state of affairs

in the product recovery domain as perceived by Greek industry through a questionnaire

based survey of 312 questionnaire responses. The study found customer service, green

image, competition, profitability, and legislation as the driving factors. Diabat and Govindan

(2011) developed a model of the drivers affecting the implementation of green supply chain

management using an interpretive structural modelling technique. The identified drivers are:

certification of suppliers’ environmental management system, environmental collaboration

with suppliers, collaboration between product designers and suppliers to reduce and

eliminate product environmental impacts, government regulation and legislation, green

design, ISO 14001 certification, integrating quality environmental management into the

planning and operation process, reducing energy consumption, reusing and recycling

materials and packaging, environmental collaboration with customers, and reverse logistics.

Literature Review

48 | P a g e

Zhu and Geng (2013) developed drivers of extended supply chain practices for energy

saving and emission reduction among Chinese manufacturers by analyzing a total of 299

usable questionnaires from traditional heavy polluters and high energy consuming

industries. The drivers were categorized as coercive (national environmental regulations,

national resource saving and conservation regulations, regional environmental regulations,

and regional resource saving and conservation regulations), normative (export,

environmental requirements from domestic customers, environmental awareness of Chinese

consumers, the news media follows our industry closely, and public environmental

awareness), and mimetic (green strategy of same product producers, green strategy of

substitute product producers and industrial professional group activities). ElTayeb et al.

(2010) examined the effect of four drivers, namely regulations, customer pressures, social

responsibility, and expected business benefits on green purchasing (GP) in the Malaysian

manufacturing sector through a mail survey of 132, ISO 14001 certified manufacturing

firms. Massoud et al. (2010) assessed the factors influencing the implementation of ISO

14001 EMS in Lebanese food industry to help build foundations for developing strategies

and policy reforms. The identified eight drivers are: meet company requirements, meet

customer demand, use marketing tool, export barrier overcome, reduce operational cost,

enhance company image, improve environmental performance, and follow international

food industry trends.

Rahimifard et al. (2009) presented some of the main issues related to the establishment of

sustainable product recovery and recycling, and highlighted the main market drivers in a

number of key areas. The study examined a range of drivers (end-of-life levies for the

consumer at point of sale, take-back levies from manufacturers, landfill taxation, free-

Literature Review

49 | P a g e

market, and end-of-life value) in research and development for the next generation of

product recovery initiatives in UK market. Mont and Leire (2009) summarized the

information on drivers collected from interviews with 20 private and public Swedish

companies to engage in socially responsible purchasing. Stakeholder influences,

organizational values, media, NGO attention, and employee concern were found to be the

main drivers. Zhang et al. (2009) pointed out 13 drivers to engage enterprises in

environmental management initiatives in China - legal requirement, competitive advantage,

social responsibility requirements, demand from customers, cost reduction, supply chain

requirements, demand from employees, demand from local community, demand from

shareholders, avoiding environmental risks, government support, demand from NGOs, and

demand from banks. Birkin et al. (2009) explored the need to establish new sustainable

business models in China through a mixed methodology, using questionnaire survey and

interviews in 2005 with a small sample of 20 manufacturing companies to examine the

reality of the level of sustainable development and drivers. The identified drivers are

compliance with industry standard, social response, improve resource use, improve

efficiency, improve productivity, right thing to do, main board requirement, supplier

requirement, customer requirement, cost effective, competitive advantage, and legal

requirement.

Montalvo (2008) presented a selective survey of papers from 1997-2007 concerning the

factors affecting adoption as a primary condition to diffusion and exploitation of cleaner

technologies. These factors are - public policy, economics, markets, communities and social

pressure, attitudes and social values, technological opportunities and capabilities, and

organizational capabilities. Walker et al. (2008) identified internal and external drivers for

Literature Review

50 | P a g e

environmental supply chain management practices in UK companies through an exploratory

study conducted based on face-to-face semi-structured interviews with senior managers

from seven different private and public sector organizations. The study identified three

internal drivers namely organization values, value champions, cost reduction and seven

external drivers namely access to environmental information, regulatory compliance,

environmental risk minimization, monitor environmental performance,

pressure/encouragement by customers, regeneration of local areas, and gain of competitive

advantage. Yu et al. (2008) listed seven drivers (environmental regulations, governmental

green procurement law, market/customer demand, cost reduction, competitive advantages,

pressure from media/NGOs, internal environmental commitments) for adopting eco-design

and extended producer responsibility (ERP) in electrical and electronics companies

operating in China. Luken and Rompaey (2008) illustrated the findings of a survey of 105

plants in nine developing countries across four manufacturing sub-sectors on factors

affecting environmentally sound technology adoption. The survey identifies 10 drivers

namely current regulations, financial incentives, future regulations, environmental image,

high cost of production inputs, product specifications in foreign markets, requirements of

owners and investors, supply chain demand, public pressure, and peer pressure for adopting

environmentally sound technology. Yuksel (2008) identified; from the questionnaire survey

of 105 big firms in Turkey about cleaner production practices; drivers to enhance the

implementation of cleaner production practices. The three identified drivers are increased

support of government, increased and punitive sanctions of environmental laws, and

establishment of environmental information network leads.

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51 | P a g e

Studer et al. (2006) analyzed drivers (competitive advantage, reputation/ brand

enhancement, consistent with corporate ethics, stakeholder demand, risk reduction, supply

chain requirement, government encouragement, cost reduction, and reduced need for

regulation) to engage Hong Kong businesses with voluntary environmental initiatives

through an exploratory questionnaire survey of 55 companies and compared their relevance

for companies of different sizes namely SME’s and large companies. Williamson et al.

(2006) presented the empirical research on the environmental practices of 31 small and

medium-sized manufacturing enterprises located in the West Midlands region of the UK

through recorded semi-structured interviews of owners or managers to show that ‘business

performance’ and ‘regulation’ drive environmental behavior of manufacturing firms.

Veshagh and Li (2006) examined the status of eco-design and manufacturing in automotive

SMEs in Midlands, United Kingdom by analyzing the results of a questionnaire designed to

identify the drivers (government legislation, environmental benefits, customer requirements,

company image, cost reduction, improved product quality, voluntary actions, profit, market

opportunities, competition, and investors) for their move towards greater sustainability in

automotive product design and manufacture. Lawrence et al. (2006) examined the

motivations (cost reduction, shareholder value, investor pressure, board influence, outside

pressure groups, employees, reputation/brand, risk management, and government

regulations) other than economic ones which drive sustainability practices in New Zealand

SMEs. Dummett (2006) identified drivers for corporate environmental responsibility

through face-to-face interviews of 25 senior business leaders from major Australian and

international companies using a set of open ended questions. The identified drivers are –

government legislation or threat, government incentive policies, cost savings, market

advantage, protect or enhance reputation and/or brand, avoid risk or response to accident,

Literature Review

52 | P a g e

champion, pressure from shareholders, pressure from consumers, pressure from NGOs, and

societal expectation.

Zhu et al. (2005) conducted an empirical study of drivers using a questionnaire survey from

Chinese enterprises for the adoption of green supply chain management practices. The

identified drivers are – central government environmental regulations, regional

environmental regulations, exports, sales to foreign customers in China, supplier’s advances

in developing environmentally friendly goods, supplier’s advances in developing

environment friendly packaging, environmental partnership with suppliers, competitors’

green strategies, industrial professional group activities, enterprise’s environmental mission,

cost of disposal of hazardous materials, cost of environmentally friendly goods, and cost of

environmentally friendly packaging. Gutowski et al. (2005) identified; from the study of

Japan, Europe and USA industry; motivating factors for EBM. The motivating factors are

regulatory mandates (emissions standards, e.g. air, water, solid waste; worker exposure

standards; product take-back requirements in EU and Japan; banned materials and reporting

requirements, e.g. EPA Toxic Release; inventory), competitive economic advantages

(reduced waste treatment and disposal costs; conservation of energy, water and materials;

reduced liability; reduced compliance costs; first to achieve cost-effective product take-back

system; first to achieve product compliance; and supply chain requirements), and proactive

green behavior (corporate image, regulatory flexibility, employee satisfaction, ISO 14001

certification, market value of company, Dow Jones Sustainability Group Index, investor

responsibility research centre, green purchasing, and eco-labelling).

Perez-Sanchez et al. (2003) developed a strategy for implementing an environmental

management system after analyzing drivers (increasing legislation/regulation, increasing

Literature Review

53 | P a g e

customer pressure, competition, increasing cost of waste disposal and landfill cost,

environmental pressure groups, depletion of finite resources, energy consumption, and

recycling issues) of environmental performance in SMEs.

Murphy (2001) found the drivers for EBM as take-back legislation, landfill bans, material

bans, life cycle assessment tool and database development, recycling infrastructure,

economic incentives, cooperative/joint efforts with industry, financial and legal liability,

ISO 14000 certification, supply chain involvement, and EBM as a business strategy. Allen

(2001) stated that in order to identify critical research needs in EBM, it is first necessary to

define the objectives of EBM and identify the forces driving its implementation. If this

strategic framing of goals is not done, then EBM becomes just a collection of loosely

connected technologies. The identified drivers are – consumers, regulations and policies,

non-government organizations, supply chain, and economics.

Gunningham and Sinclair (1997) identified drivers to the adoption of cleaner production by

industry on the basis of industry consultations and literature review. The identified drivers

are classified as internal motivators and drivers (environmental management systems and

continuous improvement, voluntary initiatives, environmental leadership, corporate

environmental reports, environmental accounting, and improvements in productivity) and

external motivators and drivers (innovative regulations and pollution prevention, negotiated

self-regulations, economic incentives, codes of practice, education and training, industry

networking, buyer supplier relations, financial institutions, community perceptions and

involvement, environmental auditors, green consumers, and international trade incentives).

A summary of 55 articles reviewed for identifying green manufacturing drivers is presented

in table 2.8. The number of reviewed papers could have been increased easily as many of the

Literature Review

54 | P a g e

Table 2.8: Review of literature on GM drivers

S. No. Author(s) & Year Country/ Continent Research Area Industry Sector/Segment/Type/Size

1 Law and Gunasekaran (2012) Hong Kong Sustainable development

strategies

High-tech manufacturing firms

2 Singh et al. (2012) India Green manufacturing Not-specified

3 Diabat and Govindan (2011) India Green supply chain management Manufacturing firms

4 Kapetanopoulou and Tagaras (2011) Greece Product recovery Manufacturing companies

5 Zhu and Geng (2013) China Extended supply chain practices Manufacturing firms

6 Massoud et al. (2010) Lebanon ISO 14001 EMS Food industry

7 ElTayeb et al. (2010) Malaysia Green purchasing adoption EMS 14001 certified manufacturing

companies

8 Rahimifard et al. (2009) UK Sustainable product recovery and

recycling

Manufacturing industries

9 Birkin et al. (2009) China Sustainable development Manufacturing companies

10 Zhang et al. (2009) China Environmental management

initiatives

SMEs

11 Mont and Leire (2009)

Sweden Supply chains Private & public organizations

12 Luken and Rompaey (2008) Brazil, China, India,

Mexico, Vietnam,

Thailand, Tunisia,

Keyna & Zimbabwe

Environmentally sound

technology adoption

Pulp & paper, iron & steel, textiles and

leather manufacturing industries

13 Yu et al. (2008) China Eco-design and extended

producer responsibility

Electrical and electronic companies

14 Yuksel (2008) Turkey Cleaner production Big firms

15 Walker et al. (2008) UK Environmental supply chain

management

Private and public sector organizations

16 Montalvo (2008) Not-specified Cleaner technologies Not-specified

17 Dummett (2006) Australia Corporate environmental

responsibility

Major Australian and international

companies

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55 | P a g e

Table 2.8: Review of literature on GM drivers (contd.)


18 Veshagh and Li (2006) UK Eco-design and manufacturing Automotive SMEs

19 Lawrence et al. (2006) New Zealand Sustainability practices SMEs

20 Williamson et al. (2006) UK Environmental practices Manufacturing SMEs

21 Studer et al. (2005) Hong Kong Voluntary environmental

initiatives

SMEs

22 Zhu et al. (2005) China Green supply chain management Manufacturing organizations

23 Gutowski et al. (2005) Japan, Europe and

USA

Environmentally benign

manufacturing

Not-specified

24 Perez-Sanchez et al. (2003) UK Environmental management

system

SMEs

25 Allen (2001) Japan, Europe and

USA


manufacturing

Not-specified

26 Gunningham and Sinclair (1997) Australia Cleaner production Not-specified

27 Adebambo et al. (2013) Malaysia Sustainable environmental

manufacturing

Food and beverages companies

28 Cagno and Trianni (2013) Italy Energy efficiency Italian manufacturing enterprises

29 Chkanikova and Mont (2012) Sweden Sustainable supply chain Food retail

30 Pajunen et al. (2012) Finland Industrial material use Not-specified

31 Bey et al. (2013) Denmark Environmental strategies Manufacturing companies

32 Baines et al. (2012) UK Green production Manufacturing companies

33 Amrina and Yusof (2012) Malaysia Sustainable manufacturing Automotive companies

34 Lee et al. (2006) Singapore Sustainable design and

manufacturing

Manufacturing industry

35 Taylor (2006) Canada Cleaner production Manufacturing industry

36 Lee (2012) Korea Energy efficiency Steel industry

37 Bhattacharya et al. (2011) India Green manufacturing Manufacturing industry

38 Yalabik and Fairchild (2011) UK Environmental innovation Not-specified

Literature Review

56 | P a g e

Table 2.8: Review of literature on GM drivers (contd.)


39 Parker et al. (2009) Australia Environmental improvements SMEs

40 Silvia et al. (2010) USA Sustainable manufacturing Manufacturing industry

41 Remmen (2001) Denmark Greening industry SMEs

42 Millar and Russell (2011) Trinidad and Tobago,

Jamaica, Guyana, St

Lucia and Barbados

Sustainable manufacturing Manufacturing companies

43 Mondal et al. (2010) Bangladesh Renewable energy technologies Rural areas

44 Schonsleben et al. (2010) Switzerland Sustainability Energy intensive industries

45 Okereke (2007) UK Carbon management UK FTSE 100

46 Ghazinoory and Huisingh (2006) Iran Cleaner production Manufacturing companies

47 Horvath et al. (1995) USA Environmentally conscious

manufacturing

Not-specified

48 Sangwan (2006) India Green manufacturing Manufacturing companies

49 Allen et al. (2002) Europe, Japan, and

the USA


manufacturing

Manufacturing companies

50 Del Río González (2008) Spain Sustainable technologies Not-specified

51 Dwyer (2007) Not-specified Sustainability Not-specified

52 Jaafar et al. (2007) USA Product design for sustainability Manufacturing companies

53 Schroeder and Robinson (2010) USA Green excellence Manufacturing companies

54 Seidel et al. (2009) New Zealand Environmentally benign

manufacturing

SMEs

55 Ioannou and Veshagh (2011) UK Sustainability Manufacturing industry

Literature Review

57 | P a g e

reviewed articles are based on review and refer to many previous studies. Also, further

literature review during the description development of the identified drivers did not yield

new driver.

Observations and discussion

The review of literature reveals that GM driver studies have been done on a good mix of

industry sectors/segments/types/sizes; from small sized to big sized industry, from process

type to discrete parts manufacturing, from manufacturing to service sector, from public to

private sector. It shows that 40% of the studies were carried out on manufacturing industry

without specifying the exact category of manufacturing, 20% of the studies were on small

and medium enterprises (SMEs), 20% of the studies have not specified any industry

sector/segment/type/size. The remaining 20% articles conducted studies on specific industry

sectors namely food & beverages, pulp & paper, iron & steel, electrical & electronics,

automotive, textiles, leather, etc.

The review also shows that various researchers have studied the drivers on various aspects

of GM. The motive was to study any aspect which helps to reduce the negative

environmental effect. Therefore, topics ranges from drivers for voluntary environmental

initiatives, green purchase, EMS, sustainability, eco-design, corporate environmental

responsibility, carbon management, green excellence, green supply chains, etc. 50% of the

articles conducted studies on green manufacturing and similar terms, 10% of the studies

were on green supply chain management, 8% of the studies were on

sustainability/sustainability practices. The rest 32% of the articles carried out research on

various research area namely sustainable development in manufacturing, sustainable product

recovery, environmental management initiatives, corporate environmental responsibility,

eco-design, energy efficiency, carbon management, green excellence, etc.

Literature Review

58 | P a g e

Figure 2.8 shows that more literature on the topic is available after 2004. The literature on

the topic during 1990s and early 2000s may be less because during this period there was

more conceptual thinking to build up the topic. It is only when the implementation started in

late 1990s; there were more studies on drivers. The studies on the topic have been carried

out around the world except Africa (table 2.9). However, the number of literature on Asia

and Europe is large (about two third). It is pertinent to mention here that I do not claim that

no paper has been missed in the survey.

Figure 2.8: Year-wise literature contribution for GM drivers

The review of research articles shows that the research in the area of GM drivers is mostly

empirical based. The empirical studies of different industrial sectors and countries by

researchers have lead to divergent names of the drivers. The researchers used different

names and taxonomy to describe the same driver. For example, the 'current legislations'

driver is described as 'current regulations', 'regulations', 'environmental regulations',

0

1

2

3

4

5

6

7

8

9

Nu

mb

er

of

arti

cle

s (f

or

dri

vers

)

Year

Literature Review

59 | P a g e

Table 2.9: Region-wise literature contribution for GM drivers

S. No. Continent Countries Total studies

1 Asia Hong Kong (2), India (4), China (5), Lebanon (1), Malaysia

(3), Turkey (1), Singapore (1), Korea (1), Iran (1), Bangladesh

(1)

20

2 Europe Greece (1), UK (9), Sweden (2), Italy (1), Finland (1),

Denmark (2), Spain (1), Switzerland (1)

18

3 North America Canada (1), USA (4) 5

4 Australia Australia (3), New Zealand (2) 5

5 Africa ---------------------------- 0

6 Miscellaneous Brazil, China, India, Mexico, Vietnam, Thailand, Tunisia,

Keyna & Zimbabwe (1), Japan, Europe and USA (3), Trinidad

and Tobago, Jamaica, Guyana, St Lucia and Barbados (1)

5

7 Not-specified Not - specified (2) 2

Total number of studies reviewed 55

'regulatory pressure', 'government regulations', and 'threat of legislation' in the studies

conducted in the past. Similarly, the 'public pressure' drivers is referred as 'market pressure',

'pressure from stakeholders', and 'pressure from social communities' in the earlier research

which gives the same meaning. Also, the driver 'public image' is referred as 'environmental

image' and 'green image' in some studies. There is a strong need to bring these divergent

names to some standard generic nomenclature to give a specific direction to the research in

this field. Therefore, after review of these research articles a generic list of drivers was

developed. This list was discussed with practitioners and academicians working in the area.

Some modifications to the name of the drivers were made based on the suggestions by these

academicians and experts from industry to improve the clarity of the drivers. Table 2.10

provides the list of these 13 drivers and the authors who have contributed to these drivers.

2.6 LITERATURE REVIEW ON BARRIERS TO GM

Industry may understand the importance of GM implementation but many times it may not

be possible to implement it. There may be a number of reasons for this – lack of

Literature Review

60 | P a g e

Table 2.10: GM driver summary

S.

No.

Drivers

Author (s) and Year

Cu

rren

t L

egis

lati

on

Fu

ture

Leg

isla

tio

n

Ince

nti

ves

Pu

bli

c P

ress

ure

Pee

r P

ress

ure

Co

st S

avin

gs

Co

mp

etit

iven

ess

Cu

sto

mer

Dem

and

Su

pp

ly C

hai

n P

ress

ure

To

p M

anag

emen

t C

om

mit

men

t

Pu

bli

c Im

age

Tec

hn

olo

gy

Org

aniz

atio

nal

Res

ou

rces

1 Zhu and Geng (2013) ü ü ü

ü

2 Cagno and Trianni (2013) ü ü

ü

ü ü ü

3 Bey et al. (2013) ü ü ü ü ü ü

4 Adebambo et al. (2013) ü ü ü ü

5 Singh et al. (2012) ü ü ü ü ü ü ü ü

6 Law and Gunasekaran (2012) ü ü ü

ü ü

7 Lee (2012) ü ü ü ü ü ü ü

8 Amrina and Yusof (2012) ü ü ü ü ü ü

9 Pajunen et al. (2012) ü ü ü ü ü ü ü

10 Chkanikova and Mont (2012) ü ü ü ü ü

ü

11 Baines et al. (2012) ü ü ü ü ü

ü ü

12 Kapetanopoulou and Tagaras (2011) ü

ü ü

ü

13 Diabat and Govindan (2011) ü ü

ü

14 Ioannou and Veshagh (2011) ü ü ü ü ü ü ü ü

15 Yalabik and Fairchild (2011) ü ü

ü ü ü

16 Bhattacharya et al. (2011)

ü ü

ü

17 Millar and Russell (2011)

ü ü

ü ü

18 ElTayeb et al. (2010) ü

ü ü

19 Massoud et al. (2010)

ü ü ü

ü

20 Schönsleben et al. (2010) ü ü ü ü ü ü ü ü ü

21 Schroeder and Robinson (2010) ü ü ü ü

ü ü

ü

22 Silvia et al. (2010) ü ü ü

ü ü

23 Remmen (2001) ü ü ü ü

ü

24 Mondal et al. (2010)

ü

ü ü

ü ü

25 Seidel et al. (2009) ü ü ü ü ü ü

26 Parker et al. (2009) ü ü ü ü ü ü

27 Rahimifard et al. (2009)

ü

28 Mont and Leire (2009) ü

ü

ü ü

Literature Review

61 | P a g e

Table 2.10: GM driver summary (contd.)

S.

No.

Drivers

Author (s) and Year

Cu

rren

t L

egis

lati

on

Fu

ture

Leg

isla

tio

n

Ince

nti

ves

Pu

bli

c P

ress

ure

Pee

r P

ress

ure

Co

st S

avin

gs

Co

mp

etit

iven

ess

Cu

sto

mer

Dem

and

Su

pp

ly C

hai

n P

ress

ure

To

p M

anag

emen

t C

om

mit

men

t

Pu

bli

c Im

age

Tec

hn

olo

gy

Org

aniz

atio

nal

Res

ou

rces

29 Zhang et al. (2009) ü ü ü ü ü ü

30 Birkin et al. (2009) ü

ü ü ü ü

31 Montalvo (2008) ü ü ü

ü

ü

ü ü

32 Walker et al. (2008) ü ü ü ü ü

33 Yu et al. (2008) ü

ü

ü ü ü ü

34 Luken and Rompaey (2008) ü ü ü ü ü

ü ü

35 Yuksel (2008) ü ü

ü ü

ü

36 Del Río González (2008) ü ü ü ü ü ü ü ü ü ü ü ü ü

37 Dwyer (2007) ü ü ü ü ü ü ü

ü

38 Jaafar et al. (2007) ü ü ü ü ü ü ü

39 Okereke (2007) ü ü ü ü

ü ü

ü

40 Studer et al. (2006) ü

ü

ü ü ü ü ü

41 Williamson et al. (2006) ü ü ü ü

ü ü

42 Veshagh and Li (2006) ü ü ü ü ü ü ü

43 Lawrence et al. (2006) ü ü ü

ü ü

44 Dummett (2006) ü ü ü ü ü ü ü

45 Sangwan (2006) ü

ü ü ü ü ü ü ü ü

46 Ghazinoory and Huisingh (2006) ü ü ü

47 Taylor (2006) ü ü ü ü

ü

48 Lee et al. (2006) ü ü

ü ü

ü ü

49 Zhu et al. (2005) ü ü ü

ü ü

50 Gutowski et al. (2005) ü

ü ü ü ü ü

51 Perez-Sanchez et al. (2003) ü ü ü ü ü ü ü

52 Allen et al. (2002) ü

ü ü ü ü ü ü

53 Allen (2001) ü ü ü ü ü ü

54 Gunningham and Sinclair (1997) ü ü ü ü ü ü ü

ü

55 Horvath et al. (1995) ü ü

ü

Literature Review

62 | P a g e

infrastructure, organizational factors, regional factors, political systems, legislative factors,

etc. These factors which act as hindrance or inhibitors to the successful adoption of GM are

termed as "barriers" to the implementation of GM. There are number of hindering factors

that act as barriers to the implementation of GM. Proper understanding of these barriers is

necessary to implement GM effectively. This section identifies the barriers to GM

implementation through review of 62 research articles from various journals, conferences,

thesis/dissertations, reports, working papers, etc. as given in table 2.11.

Table 2.11: Distribution of the reviewed articles on GM barriers

S.

No.

Journal/Conference Name No. of

articles

Publisher

1 Energy Policy 2 Elsevier

2 CIRP Annals - Manufacturing Technology 2 Elsevier

3 European Management Journal 1 Elsevier

4 Journal of Cleaner Production 14 Elsevier

5 Minerals Engineering 2 Elsevier

6 Journal of Purchasing & Supply Management 1 Elsevier

7 Energy 1 Elsevier

8 International Journal of Hospitality Management 1 Elsevier

9 Clean Technologies and Environmental Policy 1 Springer

10 CIRP Life Cycle Engineering Conferences 6 Springer

11 Frontiers of Environmental Science & Engineering in China 1 Springer

12 Corporate Social Responsibility & Environmental Management 2 Wiley

13 Journal of Industrial Ecology 1 Wiley

14 Environmental Quality Management 1 Wiley


16 International Journal of Operations & Production Management 1 Emerald


18 Journal of Organizational Change Management 1 Emerald

19 Journal of Environmental Science and Health 1 Taylor & Francis

20 *Other Journals 11 --------------

21 Conferences 3 --------------

22 Miscellaneous (Theses/Working papers/Reports/Books) 4 --------------

Total 62

*Manufacturing Engineer by IET Digital Library, Journal of Industrial Engineering and Management by

Open Journal Systems, British Journal of Management by Blackwell, International Journal of Engineering

Sciences by International Journals of Multidisciplinary Research Studies, Manufacturing Engineering by

Society of Manufacturing Engineering, Journal of Advanced Manufacturing Systems by World Scientific,

International Business Management by Medwell, IEEE International Symposium, Environment and

Planning C: Government and Policy by Environment and Planning, IEEE, SPIE Proceedings by SPIE

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Singh et al. (2012) identified 12 barriers affecting green manufacturing practices from the

survey of Indian industry. The twelve barriers are: lack of research and empirical studies;

lack of customer, supplier and shareholder awareness; increment in overall cost or financial

burden; lack of awareness in companies; inadequate coordination between different

departments; need of development of new analytical tools and models; incompatibility with

different management and manufacturing systems; lack of management commitment; lack

of necessary tools; management skills and knowledge; loose government legislation; and

inability to adopt adequate environmental treatment measures.

Koho et al. (2011) inferred from an online survey of Spanish companies that lack of

standardized metrics/performance benchmarks, lack of demand from customers and

consumers, and lack of specific ideas were regarded as the biggest barriers to sustainability.

However, few critical barriers like technology risk, top management commitment, trade-

offs, and low enforcement are missing.

Massoud et al. (2010) assessed the factors influencing the implementation of ISO 14001

EMS in Lebanese food industry to help build foundations for developing strategies and

policy reforms to reduce the barriers to implement ISO 14001 EMS. The assessed barriers

are time demand, lack of in-house knowledge, not seen as apriority by management, cost of

certification, not required for export, no customer demand, benefits not clear, not legal

requirement, and lack of government support. Herren and Hadley (2010) found extra

financial burden, lack of time to devote, lack of information about environmentally

sustainable practices, lack of motivation, stakeholders, slow or no communication, company

culture (i.e. business specific barrier), and legal regulations as barriers that SME’s face in

Literature Review

64 | P a g e

implementing environmental practices in their businesses in USA. However, 'uncertain

future legislation' and 'low enforcement' are not considered in the study.

Zhang et al. (2009) pointed out 10 barriers (not a legal demand, no demand from

employees, no demand from local communities, costly, lack of technology, creates

competitive disadvantage, no demand from stakeholders, lack of government support,

cannot improve reputation, and no demand from banks) to engage enterprises in

environmental management initiatives in China through a questionnaire survey of 443

companies. Seidel et al. (2009) described the barriers faced by SMEs in moving toward

environmentally benign manufacturing. These barriers – undeveloped organizational

environmental culture, ignorance of own environmental impacts, lack of knowledge and

experience with environmental issues, absence of effective environmental legislation, lack

of awareness about environmental trends or not believing that sustainability will benefit the

company, limited financial and staff resources available for environmental projects, and

perceived conflicts between environment friendly practices and other business objectives –

can affect the uptake of environmentally benign manufacturing practices in SMEs,

particularly where strong market and legislative drivers are just emerging.

Wang et al. (2008) identified 13 barriers – lack of awareness of energy saving, lack of

experience in technology and management, lack of funding or financing difficulties, limited

policy framework, lack of research personnel or trained manpower, lack of public

participation, inadequate data and information, reluctance to invest because of high

investment risk, objections from the vested interests groups, inappropriate industrial

framework, lack of strategic planning, lack of appropriate production technologies, and lack

of incentive support – to energy saving in China through the review of literature and opinion

Literature Review

65 | P a g e

of experts from energy industry and academia. Yu et al. (2008) identified six barriers to eco-

design in Chinese electrical and electronics companies. The six barriers are: high cost, lack

of pressure from regulations, lack of market demand, unfair competition, lack of internal

environmental commitments, and technological challenges/lack of expertise. Luken and

Rompaey (2008) illustrated the findings of a survey of 105 plants in nine developing

countries across four manufacturing sub-sectors on factors affecting environmentally sound

technology adoption. The survey identifies barriers (lack of information, high

implementation cost, no alternative chemical/raw material input, no alternative process

technology, uncertainty about performance impact, and lack of tradition/skills) to adopt

environmentally sound technologies as perceived by plant managers and key informants.

Montalvo (2008) presented a selective survey of papers from 1997-2007 representing the

general wisdom concerning the factors (public policy, economics, markets, communities and

social pressure, attitudes and social values, technological opportunities and capabilities, and

organizational capabilities) affecting adoption, diffusion and exploitation of cleaner

technologies. Yuksel (2008) identified barriers to implementation of cleaner production

practices in Turkey through the well designed questionnaire survey of 105 big firms. The

identified barriers are: environmental issue as cost driver, cost of environmental

technologies, firms prefer reactive approach than proactive, lack of environmentally

consciousness within company, lack of environmentally consciousness within society, and

lack of customer demand. Shi et al. (2008) applied an Analytic Hierarchy Process (AHP) to

examine and prioritize underlying barriers to adoption of cleaner production (CP) by SMEs

in China from the perspectives of government, industry and expert groups. Authors

identified 20 barriers – lax environmental enforcement, absence of economic incentive

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66 | P a g e

policies, lack of market preference/demands, inadequate industrial self-regulation, weak

public awareness and pressure, high initial capital cost, difficulty in accessing financial

capital, poor financial performance of CP, lack of effective evaluation measures for CP, lack

of financing service for SMEs, limited in-plant expertise/capability, lack of access to

external technical support, difficulty to access information on CP, additional infrastructure

requirements, lack of technical training on the workshop floor, higher priorities to

production expansion/market share, concern about competitiveness, management resistance

to change, lack of awareness of CP, and inadequate management capacity.

Studer et al. (2006) analyzed barriers and incentives to engage Hong Kong businesses with

voluntary environmental initiatives and compares their relevance for SMEs and large

companies. The analyzed barriers are: not a legal requirement, no demand from customers,

not seen as priority by senior management, lack of incentives, no demand from stakeholders,

lack of resources, costly, corporate inertia, lack of in-house knowledge/skills, and

competitive disadvantage. Mitchell (2006) explored why cleaner production has not been

widely adopted by industry in Vietnam, despite the promotion of cleaner production by

government, academia and research institutions. Author found that overall policy

environment, growing dependence of firms on outside financial and technical assistance,

traditional corporate culture, and internal management and accounting systems are major

reasons for lack of cleaner production adoption in Vietnam. Veshagh and Li (2006)

examined the status of eco-design and manufacturing in automotive SMEs of United

Kingdom through a questionnaire designed to identify the barriers faced by SMEs in their

move towards greater sustainability in automotive product design and manufacture. The

identified barriers are: lack of financial incentives, no justification for investment, not yet

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67 | P a g e

required by customers, infrastructure change, no clear environmental benefits, insufficient

capacity, not yet required by legislation, and not yet required by parent companies.

Siaminwe et al. (2005) identified eleven barriers – financial problems, lack of awareness,

lack of knowledge, no technical competence, poor/weak enforcement of environmental laws,

no national policy, no company policy, options are too technical to implement, absence of

subsidies, insufficient return on investment, and unwillingness to change to cleaner

production – hindering the process of CP implementation in Zambian industry. Moors et al.

(2005) identified six barriers – economic barriers, systemic characteristics, knowledge

infrastructure, legislative context, organization and culture of the firm, and stage of

technology development – that impede the implementation of more radical solutions in the

base metals producing industry from the present situation of using end-of-pipe technologies.

Zhang (2000) identified lack of pollution prevention and environmental awareness, lack of

governmental programs and cooperation for promotion, lack of financial support, lack of

research and development, and lack of CP promotion audit in China as key barriers to CP.

Cooray (1999) summarizes the SME specific barriers to implement CP schemes in Sri

Lankan SMEs through an industrial survey of food & beverages, hospitality and steel

industries. Author listed 12 barriers to the implementation of CP and categorized into four

groups. First group of systemic barriers contains lack of professional management skills and

poor record keeping; second group of organizational barriers contains concentration of

decision making powers, over-emphasis on production and non-involvement of workers;

third group of technical barriers contains limited technical capabilities, limited access to

technical information, limited skilled human capital, lack of in-house monitoring and

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68 | P a g e

deficiencies in maintenance; and fourth group of economic barriers contains financial

soundness of the company, high cost and low availability of capital for cleaner production.

The review of 62 research articles for identifying GM barriers is presented in table 2.12.

Observations and discussion

The review of literature reveals that GM barrier studies have been done on a good mix of

industry sectors/segments/types/sizes; from small sized to big sized industry, from process

to discrete parts manufacturing, from manufacturing to service sector, from aerospace to

mining industry, from public to private sector. It shows that 25% of the studies were carried

out on manufacturing industry without specifying the exact category of manufacturing, 15%

of the studies were on SMEs, 20% of the studies have not specified any industry

sector/segment/type/size. The other 40% articles conducted studies on specific industry

sectors namely metal, machinery, food & drink, chemicals, pulp & paper, textiles, cement,

leather, iron & steel, electrical & electronics, oil & construction, mining, automotive, hotel,

rubber, plastic, wood, etc.

The review also reveals that various researchers have studied barriers on various aspects of

GM. The motive was to study any aspect of the green manufacturing which helps to reduce

the negative environmental effect. Some of the topics include, barriers to energy efficiency

investment, energy efficiency, sustainable businesses, eco-design, sustainability, carbon

management, EMS, green supply chains, etc. The review reveals that 47% of the articles

conducted studies on green manufacturing and similar terms, 8% of the studies were on

green supply chain management, 8% of the studies were on sustainability/sustainability

practices, 6% of the studies were conducted on energy efficiency/saving, 6% of the studies

were conducted on environmental management initiatives/systems.

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Table 2.12: Review of literature on GM barriers

S. No. Author(s) & Year Country/Continent Topic Industry Sector/Segment/Type/Size

1 Sardianou (2008) Greece

Industrial energy efficiency

investments

Metals, machinery, food and drink,

chemicals, paper and textiles

2 Shi et al. (2008) China Cleaner production implementation SMEs

3 Cooray (1999) Sri Lanka Cleaner production assessment SMEs

4 Walker et al. (2008) U.K. Environmental supply chain

management practices

Public and private sectors

5 Zhang (2000) China

Promote cleaner production Not -specified

6 Zhang et al. (2009) China

Environmental management

initiatives

SMEs

7 Luken and Rompaey (2008) Brazil, China, India,

Mexico, Vietnam,

Thailand, Tunisia, Keyna

& Zimbabwe

Environmentally sound technology

adoption

Pulp and paper, iron and steel, textiles and

leather manufacturing industries

8 Yuksel (2008) Turkey Cleaner production practices Big/large firms

9 Ries et al. (1999) Switzerland Integration of environmental aspects

in product design

Electronic and mechanical product firms

10 Siaminwe et al. (2005) Zambia

Implementation of cleaner

production

Food, beverages, tobacco, metal, wood,

chemical, rubber, plastic, paper, energy,

textile, leather, cement and service

11 Taylor (2006) Canada Cleaner production measures Aerospace, automotive, textile, coffee, diary,

sugar, wine and wood

12 Moors et al. (2005) The Netherlands Cleaner production Base metals producing industry

13 Studer et al. (2006) Hong Kong Environmental change SMEs and large firms

14 Birkin et al. (2009) China

Sustainable businesses Electrical goods, electronics, logistics, oil

and construction

15 Hilson (2000) Americas Cleaner technologies and cleaner

production

Mining industry

16 Gunningham and Sinclair

(1997)

Australia Adoption of cleaner production

practices

Not -specified

17 Post and Altman (1994) Not-specified Environmental change Not-specified

18 Montalvo (2008) China Adoption of cleaner production Not -specified

19 Yu et al. (2008)

China Producer responsibility and eco-

design

Electrical and electronic manufacturers

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Table 2.12: Review of literature on GM barriers (contd.)


20 Gonzalez-Torre et al. (2010)

Spain

Environmentally oriented reverse

logistics

Automotive industry sector

21 Lawrence et al. (2006) New Zealand Sustainability practices SMEs

22 Mitchell (2006) Vietnam Root cause of barriers to cleaner

production

Not -specified

23 Ghazinoory and Huisingh

(2006)

Iran Cleaner production Not -specified

24 Massoud et al. (2010) Lebanon Environmental management

systems

Food industry

25 Herren and Hadley (2010) USA

Environmental sustainability Small businesses

26 Mukherjee (2011)

India Cleaner production

Foundry sector

27 Mont and Leire (2009)

Sweden Supply chains Private and public companies

28 Wang et al. (2008) China Energy saving Not -specified

29 Okereke (2007) UK Carbon management UK FTSE 100 companies

30 Seidel et al. (2009) Not -specified Environmentally benign

manufacturing practices

SMEs

31 Dwyer (2007) Not-specified Sustainability Not-specified

32 Del Río González (2008) Spain Sustainable technologies Not-specified

33 Sangwan (2006) India Green manufacturing Manufacturing companies

34 Schonsleben et al. (2010) Switzerland Sustainability Energy intensive industries

35 Zhu and Geng (2013) China Extended supply chain practices Manufacturing firms

36 Singh et al. (2012) India Green manufacturing Not-specified

37 Veshagh and Li (2006) UK Eco-design and manufacturing Automotive SMEs

38 Ioannou and Veshagh (2011) United Kingdom Sustainability Manufacturing industry

39 Mondal et al. (2010) Bangladesh Renewable energy technologies Rural areas

40 Silvia et al. (2010) USA Sustainable manufacturing Manufacturing industry

41 Lee (2012) Korea Energy efficiency Steel industry

42 Parker et al. (2009) Australia Environmental improvements SMEs

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Table 2.12: Review of literature on GM barriers (contd.)


43 Amrina and Yusof (2012) Malaysia Sustainable manufacturing Automotive companies

44 Bey et al. (2013) Denmark Environmental strategies Manufacturing companies

45 Millar and Russell (2011) Trinidad and Tobago,

Jamaica, Guyana, St Lucia

and Barbados

Sustainable manufacturing Manufacturing companies

46 Pajunen et al. (2012) Finland Industrial material use Not-specified

47 Kapetanopoulou and Tagaras

(2011)

Greece Product recovery Manufacturing companies

48 Baines et al. (2012) United Kingdom Green production Manufacturing Companies

49 Chkanikova and Mont (2012) Sweden Sustainable supply chain Food retail

50 Mittal and Sangwan (2011) India Environmentally conscious technology Manufacturing companies

51 Koho et al. (2011) Spain Sustainable manufacturing Manufacturing companies

52 Mittal et al. (2012) India Environmentally conscious

manufacturing


53 Mittal et al. (2013) India Green manufacturing Manufacturing companies

54 Kaebernick and Kara (2006) Australia, Austria, Belgium,

Germany, Singapore,

Taiwan, USA

Environmentally sustainable

manufacturing


55 Del Rıo et al. (2010) Spain Eco-innovation Not-specified

56 Nagesha and Balachandra

(2006)

India Energy efficiency Small scale industry clusters

57 Murillo-Luna et al. (2011) Spain Proactive environmental strategies Industrial firms

58 Sarkis et al. (2006) Not-specified Environmentally conscious

manufacturing


59 Wooi and Zailani (2010) Malaysia Green supply chain SMEs

60 Goan (1996) USA Environmentally conscious design and

manufacturing


61 Hillary (2004) UK Environmental management systems SMEs

62 Chan (2008) Hong Kong Environmental management systems Hotel industry

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The rest 25% of the articles presents research on various research area namely

environmental product design, environmental change, sustainable businesses, producer

responsibility & eco-design, environmental reverse logistics, carbon management,

environmental strategies/ improvements, material use, etc.

Figure 2.9 provides a glimpse of number of studies in literature on barriers. It shows there is

less literature upto year 2005. The number of studies on barriers have increases from 2006.

It is pertinent to mention here that I do not claim that no paper has been missed on the

subject. One of the reasons for the less number of papers during 1990s and early 2000s is

that during this period the topic was emerging and there were more concept building work

on the topic. Later, when these topics were implemented then hindrances to their adoption/

implementation were reported. The studies on the GM barriers have been carried throughout

all the continents (table 2.13).

Figure 2.9: Year-wise literature contribution for GM barriers

0

2

4

6

8

10

12

Nu

mb

er

of

arti

cle

s (f

or

bar

rie

rs)

Year

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73 | P a g e

However, there are more studies in Asia and Europe. These two continents account for about

two third studies. The large number of literature on Chinese and Indian industry also reflects

that these two countries are leading in manufacturing sector. Table 2.14 provides the list of

the 12 barriers and author(s) who contributed to identify these barriers.

Table 2.13: Region-wise literature contribution for GM barriers

S. No. Continents Countries Total studies

1 Asia China (8), Sri Lanka (1), Hong Kong (2), Turkey (1), India

(7), Lebanon (1), Iran (1), Vietnam (1), Bangladesh (1),

Malaysia (2), Korea (1)

26

2 Europe Greece (2), UK (6), Spain (5), Switzerland (2), Sweden (2),

Finland (1), Denmark (1), The Netherlands (1)

20

3 North America Canada (1), USA (3) 4

4 Australia Australia (2), New Zealand (1) 3

5 Africa Zambia (1) 1

6 Miscellaneous Brazil, China, India, Mexico, Vietnam, Thailand, Tunisia,

Keyna & Zimbabwe (1), Americas (1), Trinidad and Tobago,

Jamaica, Guyana, St Lucia and Barbados (1), Australia,

Austria, Belgium, Germany, Singapore, Taiwan, USA (1)

4

7 Not-specified Not-specified (4) 4


Table 2.14: GM barrier summary

S.

No.

Barrier

Author (s) and Year

Wea

k L

egis

lati

on

Lo

w E

nfo

rcem

ent

Un

cert

ain

Fu

ture

Leg

isla

tio

n

Lo

w P

ub

lic

Pre

ssu

re

Hig

h S

ho

rt-T

erm

Co

sts

Un

cert

ain

Ben

efit

s

Lo

w C

ust

om

er D

eman

d

Tra

de-

Off

s

Lo

w T

op

Man

agem

ent

Co

mm

itm

ent

Lac

k o

f O

rgan

izat

ion

al R

eso

urc

es

Tec

hn

olo

gic

al R

isk

Lac

k o

f A

war

enes

s /

Info

rmat

ion

1 Bey et al. (2013)

ü ü ü

2 Mittal et al. (2013) ü ü ü ü ü ü ü ü ü ü ü ü

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74 | P a g e

Table 2.14: GM barrier summary (contd.)

S.

No.

Barrier

Author (s) and Year

Wea

k L

egis

lati

on

Lo

w E

nfo

rcem

ent

Un

cert

ain

Fu

ture

Leg

isla

tio

n

Lo

w P

ub

lic

Pre

ssu

re

Hig

h S

ho

rt-T

erm

Co

sts

Un

cert

ain

Ben

efit

s

Lo

w C

ust

om

er D

eman

d

Tra

de-

Off

s

Lo

w T

op

Man

agem

ent

Co

mm

itm

ent

Lac

k o

f O

rgan

izat

ion

al R

eso

urc

es

Tec

hn

olo

gic

al R

isk

Lac

k o

f A

war

enes

s /

Info

rmat

ion

3 Zhu and Geng (2013)

ü ü

ü

4 Singh et al. (2012) ü

ü ü ü

ü

5 Pajunen et al. (2012) ü ü ü ü

ü

ü

6 Baines et al. (2012) ü

ü

ü ü ü ü

7 Chkanikova and Mont (2012) ü ü ü ü

ü ü

8 Amrina and Yusof (2012)

ü

ü ü ü ü

9 Lee (2012)

ü ü

ü ü ü ü

10 Mittal et al. (2012) ü ü ü ü ü ü ü ü ü ü ü ü

11 Mittal and Sangwan (2011) ü ü ü ü ü

ü ü ü

12 Ioannou and Veshagh (2011) ü

ü ü ü ü ü ü ü

13 Koho et al. (2011)

ü ü

ü

14 Murillo-Luna et al. (2011) ü ü ü ü ü ü

ü ü

15 Millar and Russell (2011) ü

ü ü ü ü ü ü

16 Kapetanopoulou and Tagaras (2011)

ü ü ü ü ü

17 Herren and Hadly (2010) ü

ü

ü

ü

18 Massoud et al. (2010) ü

ü ü ü

ü

19 Schönsleben et al. (2010) ü

ü ü ü

ü

20 Del Río et al. (2010) ü ü

ü ü ü ü ü

ü ü

21 Mukherjee (2011) ü ü ü

ü ü ü

22 Mondal et al. (2010)

ü ü ü

ü

ü

23 Silvia et al. (2010) ü ü

ü

ü

24 Wooi and Zailani (2010) ü ü ü ü

ü ü ü ü

25 Seidel et al. (2009) ü ü

ü ü ü

26 Zhang et al. (2009) ü ü ü ü

ü

27 Birkin et al. (2009) ü ü

ü

ü

ü

28 Parker et al. (2009) ü ü ü ü ü

ü

29 Gonzalez-Torre et al. (2010) ü ü ü ü ü ü ü ü ü ü

30 Mont and Leire (2009) ü ü ü ü ü ü ü

ü

Literature Review

75 | P a g e


S.

No.

Barrier

Author (s) and Year

Wea

k L

egis

lati

on

Lo

w E

nfo

rcem

ent

Un

cert

ain

Fu

ture

Leg

isla

tio

n

Lo

w P

ub

lic

Pre

ssu

re

Hig

h S

ho

rt-T

erm

Co

sts

Un

cert

ain

Ben

efit

s

Lo

w C

ust

om

er D

eman

d

Tra

de-

Off

s

Lo

w T

op

Man

agem

ent

Co

mm

itm

ent

Lac

k o

f O

rgan

izat

ion

al R

eso

urc

es

Tec

hn

olo

gic

al R

isk

Lac

k o

f A

war

enes

s /

Info

rmat

ion

31 Yuksel (2008) ü ü ü ü ü

32 Chan (2008) ü ü

ü

ü ü ü

33 Sardianou (2008) ü ü

ü ü ü

34 Luken and Rompaey (2008) ü ü ü

ü ü ü

35 Yu et al. (2008) ü

ü ü

ü

ü ü

36 Del Río González (2008) ü ü ü ü ü ü

ü ü ü ü

37 Shi et al. (2008) ü ü ü ü ü ü ü ü

38 Montalvo (2008) ü ü ü ü ü ü ü

39 Wang et al. (2008) ü ü

ü ü ü ü

40 Walker et al. (2008) ü ü ü

ü ü

41 Okereke (2007) ü ü ü

ü

42 Dwyer (2007) ü ü ü ü ü ü

ü

43 Kaebernick and Kara (2006) ü

ü

44 Ghazinoory and Huisingh (2006) ü ü

ü ü

45 Sarkis et al. (2006)

ü ü

ü ü

46 Nagesha and Balachandra (2006) ü ü

ü ü ü ü ü

47 Veshagh and Li (2006) ü ü ü ü ü ü

48 Studer et al. (2006) ü ü ü ü ü ü

49 Mitchell (2006) ü

ü ü ü

50 Taylor (2006) ü ü ü ü

ü

ü

51 Lawrence et al. (2006)

ü

ü ü ü

52 Sangwan (2006) ü ü

ü ü ü ü

53 Siaminwe et al. (2005) ü ü ü

ü ü ü

54 Moors et al. (2005) ü ü ü

ü ü ü ü

55 Hillary (2004)

ü ü ü

ü ü ü

56 Zhang (2000) ü

ü ü ü

57 Hilson (2000) ü ü ü ü ü

ü ü ü

58 Ries et al. (1999) ü ü

ü ü ü ü

Literature Review

76 | P a g e


S.

No.

Barrier

Author (s) and Year

Wea

k L

egis

lati

on

Lo

w E

nfo

rcem

ent

Un

cert

ain

Fu

ture

Leg

isla

tio

n

Lo

w P

ub

lic

Pre

ssu

re

Hig

h S

ho

rt-T

erm

Co

sts

Un

cert

ain

Ben

efit

s

Lo

w C

ust

om

er D

eman

d

Tra

de-

Off

s

Lo

w T

op

Man

agem

ent

Co

mm

itm

ent

Lac

k o

f O

rgan

izat

ion

al R

eso

urc

es

Tec

hn

olo

gic

al R

isk

Lac

k o

f A

war

enes

s /

Info

rmat

ion

59 Cooray (1999)

ü ü ü ü

60 Gunningham and Sinclair (1997) ü ü ü ü ü ü ü

61 Goan (1996) ü ü ü ü ü ü ü ü ü

62 Post and Altman (1994) ü

ü ü ü ü ü

2.7 LITERATURE REVIEW ON STAKEHOLDERS OF GM

The involvement of the stakeholders in the decision making about the environmental

initiatives is a vital issue. In last more than two decades, researchers from the engineering

and management areas attempted to define the GM stakeholders with different thoughts and

from different perspectives. Since 1983, a number of stakeholder definitions are proposed by

various engineering and management researchers. Typical definitions of stakeholder from

the literature are listed in table 2.15. Out of the 14 definitions listed, the one by Freeman

(1984) seems to be more suitable and widely accepted which states stakeholder as those

groups who can affect or are affected by the achievement of an organization's objectives.

A literature review of 46 research articles (table 2.16) from year 1998 to 2013 is carried out

to identify and examine the stakeholders of GM.

Literature Review

77 | P a g e

Table 2.15: Various definitions of stakeholders from extant literature

S. No. Author(s) Definition

1 Stanford Research Institute,

1983

Those groups on which the organization is dependent for its

continued survival

2 Freeman, 1984 Those groups who can affect or are affected by the achievement

of an organization's objectives

3 Alkhafaji, 1989 Groups to whom the corporation is responsible

4 Thompson et al., 1991 Groups in relationship with an organization

5 Nutt and Backoff, 1992 All parties who will be affected by or will affect the

organization’s strategy

6 Bryson, 1995 Any person, group or organization that can place a claim on the

organization’s attention, resources, or output, or is affected by

that output

7 Clarkson, 1995 These are persons or groups that have or claim, ownership,

rights, or interests in a corporation and its activities in past,

present or future

8 Donaldson and Preston, 1995 A group qualifies as a stakeholder if it has legitimate interest in

performance aspects of the organization’s activities

9 Eden and Ackermann, 1998 People or small groups with the power to respond to, negotiate

with, and change the strategic future of the organization

10 Greenwood, 2001 Group or individual who can affect or is affected by the

corporation

11 Johnson and Scholes, 2002 Those individuals or groups who depend on the organization to

fulfil their own goals and on whom, in turn, the organization

depends

12 Post et al., 2002 The individuals and constituencies that contribute, either

voluntarily or involuntarily, to its wealth-creating capacity and

activities, and therefore its potential beneficiaries and/or risk

bearers

13 Bryson, 2004 Persons, groups or organizations that must somehow be taken

into account by leaders, managers and front-line staff

14 Foley, 2005 Those entities and/or issues, which a business identifies from

the universe of all who are interested in and/or affected by the

activities or existence of that business, and are capable of

causing the enterprise to fail, or could cause unacceptable

levels of damage if their needs are not met

Ditlev-Simonsen and Wenstop (2013) investigated the perceptions of the relative importance

of different stakeholders (owners, employees, customers, NGOs and governmental

authorities) in motivating managers to engage in corporate social responsibility. Roy et al.

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Table 2.16: Distribution of the reviewed articles on GM stakeholders

S.

No.

Journal/Conference Name No. of

articles

Publisher


2 Industrial Management & Data Systems 1 Emerald

3 Management Decision 1 Emerald

4 European Journal of Marketing 2 Emerald

5 Corporate Governance 1 Emerald

6 Business Process Management Journal 1 Emerald


8 Strategic Management Journal 3 Wiley

9 Corporate Social Responsibility and Environmental

Management

2 Wiley

10 Journal of Operations Management 1 Elsevier

11 Environmental Modelling & Software 1 Elsevier

12 Journal of World Business 1 Elsevier

13 International Journal of Production Economics 1 Elsevier

14 Accounting, Organizations and Society 1 Elsevier

15 Total Quality Management & Business Excellence 1 Taylor & Francis

16 Total Quality Management & Business Excellence 1 Taylor & Francis

17 Production Planning & Control: The Management of

Operations

1 Taylor & Francis

18 Public Management Review 1 Taylor & Francis

19 Construction Management and Economics 1 Taylor & Francis

20 Journal of Environmental Planning and Management 1 Taylor & Francis

21 International Journal of Management Reviews 2 Blackwell

22 Business Ethics: A European Review 2 Blackwell

23 Journal of Management Studies 1 Blackwell

24 Journal of Business Ethics 3 Springer

25 Journal of the Academy of Marketing Science 1 Springer

26 *Other Journals 10 -------------

27 Miscellaneous (Working paper) 1 -------------

Total 46

*The Academy of Management Journal by Academy of Management, Journal of Business Ethics by Kluwer

Academic, Journal of the Academy of Marketing Science by Academy of Marketing Science, Corporate

Reputation Review by Palgrave Macmillan, Harvard Business Review by FSG, Ecology and Society by

Resilience Alliance, Journal of General Management by Wilfrid Laurier University, International Journal

of Environmental Science and Technology by Scientific Information Database, Academy of Management

Review by Academy of Management, BELGEO by University of Zurich

(2013) examined the specific motivations and resources of SMEs from the data of 254 ISO

9000 and ISO 14000 certified Canadian SMEs. The stakeholders mentioned are: customer,

markets, shareholder, employees, owner/manager's social responsibility.

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Gunasekaran and Spalanzani (2012) specified that the pressure promoting sustainable

business practices is external in terms of government regulations, profit and non-profit

organizations, community, and suppliers; and internal in terms of strategic objectives, top

management vision, employee safety and well being, cost savings, productivity and quality,

and consumer. An attempt has been made to understand the complexities of sustainable

business development (SBD), the challenges and their sources, and the advances made so far

to address the SBD issues. Bryde and Schulmeister (2012) investigated the effect of using

lean on the refurbishment of a municipal building in Germany. Participant observation,

archival project documentation and semi-structured interviews were used to collect data on

the use of lean. The key stakeholders involved were: suppliers, customers, shareholders,

contractors, and subcontractors. Dey and Cheffi (2012) developed and deployed an

analytical framework for measuring the environmental performance of manufacturing

supply chains, in three major areas of supply chain management, environmental

management and performance measurement in three manufacturing organisations in the UK.

The stakeholders mentioned were: customers, suppliers, employees, shareholders, managers,

environmental advocacy groups, regulations, unions, and community. Chang and Chen

(2012) developed an integral conceptual model of green intellectual capital to explore its

managerial implications and determinants by integrating the theories of CSR and green

management. CSR extends beyond the traditional duty of shareholders, managers and

employees to the mission of stakeholders such as societal groups, customers, employees,

suppliers, and social communities. Wolf (2012) analysed the three competing models of the

relationship among sustainable supply chain management, stakeholder pressure and

corporate sustainability performance using a dataset of 1,621 organizations for the statistical

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80 | P a g e

comparison of these three models of the potential stakeholders (customers, competitors,

governments, employee, suppliers, non-governmental organizations, local communities,

partners).

Cronin Jr et al. (2011) proposed an investigative framework that identifies the various

stakeholders potentially impacted through the environmentally friendly efforts of a firm

through an integration of the marketing, management, and operations literature. The core

stakeholders considered for the study were consumers, competitors, government and NGOs,

investors, supply chain partners, employees, and society. Shah (2011) presented a neo-

institutional perspective of the perceptions of corporate environmentalism held by

stakeholder groups relative to each other and the influence that specific firm-level

characteristics, such as size, ownership, compliance record, and location, have on these

perceptions. The stakeholders were classified into three groups: business-chain stakeholders

(suppliers and service providers), NGOs/community based organizations (CBOs)

stakeholders (community groups), and regulatory stakeholders (govt agencies, e.g.

environmental management authority). Ayuso et al. (2011) empirically analysed an

international sample of 656 large companies to investigate the engagement with different

stakeholders that promote sustainable innovation. It was stated that today’s companies need

to innovate by reinventing the way they relate to their multiple stakeholders, viz. employees,

customers, suppliers, NGOs/activists, communities, governments, and competitors.

Lutzkendorf et al. (2011) differentiated the major actors among financial stakeholders

(investors, managers and employees), their roles, interests, motives and options for

influencing property and construction markets for sustainable development. Azadi et. al.

(2011) presented a conceptual framework of multi-stakeholder involvement (MSI) by a

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mixed method approach, to identify the main factors influencing urban green space

performance using content analysis. The stakeholders that were mentioned in relation to

urban green space performance are: government agencies, regulations, society, citizens,

NGOs, and managers.

Voinov and Bousquet (2010) reviewed the different types of stakeholder modelling namely

participatory modelling, group model building, mediated modelling, companion modelling,

etc. and compared participatory modelling to other frameworks that involve stakeholder

participation. The various stakeholders that have been considered are local, federal, private

and public organizations as well as individual citizens and interest groups. Darnall et al.

(2010) contributed to the development of stakeholder theory by deriving a size moderated

stakeholder model and applying it to a firm’s adoption of proactive environmental practices

by using the data collected from manufacturing sectors in six countries. The developed

stakeholders are classified into three groups: value chain stakeholders (household

consumers, commercial buyers, supplier of goods and services), internal stakeholders

(management employees and non-management employees), societal stakeholders

(environmental groups, community organisations, labour unions, industry or trade

organisations). Gonzalez- Benito and Gonzalez- Benito (2010) investigated the effects of six

relevant variables (size, internationalization, location of manufacturing activities, position in

the supply chain, industrial sector, and managerial values and attitudes) on stakeholder

(governments and regulatory agents, customers/consumers, employees/unions, shareholders,

financial institutions, communities and social groups, non-governmental organizations,

competitors, and media) and the environmental pressure perceived by industrial companies.

The effect is theoretically determined by distinguishing between pressure intensity and

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perception capacity and empirically tested with a survey sample of 186 Spanish

manufacturers. Sarkis et al. (2010) established the influence of stakeholder pressure on the

adoption of environmental practices which are further mediated, causally, by the level of

training in companies by investigating the Spanish automotive industry. The study also

focused on supporting the relationship between stakeholder and resource based theory on

various stakeholder pressures such as clients, governments, shareholders, employees, NGOs,

community, and supply chain partners. Garvare and Johansson (2010) presented a

conceptual model of stakeholder management, elaborating on the relationship between

organisational sustainability and global sustainability. Two groups of stakeholders have been

referred as primary stakeholders (governments, shareholders, suppliers, and managers), and

secondary stakeholders (co-workers and customers). Adewuyi and Olowookere (2010)

examined the contributions of WAPCO Plc. (a major cement company) to sustainable

development of the host communities through its CSR activities by adopting 15 CSR factors

from the literature. Stakeholders in the development activities include all individual

economic agents or groups, shareholders, employees, customers, communities, and

government. Marshall et al. (2010) developed a set of hypotheses, based on stakeholder

theory, regarding drivers of the adoption of environmental practices in the wine industries of

New Zealand and United States. The hypotheses are tested using data from survey

questionnaires collected in each country. Environmental stakeholders in the wine industry

include regulatory agencies, manager's attitude, employees, community members,

associations, media, and customers.

Azorin et al. (2009) discussed stakeholders (suppliers, customers, employees, and

management) identified through literature review in order to propose and analyse

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dimensions for quality management, environmental management, quality and environmental

management, and firm performance. Darnall et al. (2009) used international manufacturing

data to show that significant variations in the use of environmental audits are associated with

differences in stakeholder, viz internal (management and non-management employees),

societal (environmental groups, community and labour unions), supply chain (commercial

buyers and suppliers). Reyers et al. (2009) presents how stakeholders (government

departments, landowners, non-governmental organisations, and municipalities) can build a

more sustainable future for the Little Karoo region and attempted to address the information

gap between land-cover change and consequences of land cover change for ecosystem

services and human well-being at local scale. Gadenne et al. (2009) hypothesised that

external influences of various existing and potential stakeholder groups (suppliers,

customers, and legislation) moderated by particular SME characteristics create an impact on

the environmental awareness and attitudes of SME owners/managers, which in turn is

associated with their environmental practices.

Peloza and Papania (2008) examined the relationship between CSR and corporate financial

performance by considering the ability of stakeholders (media, consumers, community,

employees, shareholders, governmental agencies, and managers) to reward or punish the

firm. Braun and Starmanns (2008) analysed the factors which influence company managers

in their environmental decision making and to prioritize stakeholder claims (owners, clients,

local governments, national/state governments, suppliers, business associations, consumers,

local communities, media, banks, local/international environmentalist groups, and trade

unions) by applying and modifying the stakeholder salience model through data of 250

German manufacturing firms. Murillo-Luna et al. (2008) analysed the strategies or patterns

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of adaptation in responding to environmental requirements or expectations, specifically the

influence of different pressure groups or stakeholders on the degree of proactivity of these

patterns. The study concluded that in general, managers give high importance to pressure

from external social stakeholders, corporate government stakeholders, internal economic

stakeholders, external economic stakeholders, and regulatory stakeholders.

Chien and Shih (2007) investigated the green supply chain management practices likely to

be adopted through in depth interviews and questionnaire surveys among ISO 14001

certified electrical and electronic industry in Taiwan. The various stakeholders considered in

the study are: suppliers, competitors, government regulations, consumers, supply chain

partners, media, community, and managers. Byrd (2007) investigated the stakeholder

inclusion and involvement in the basic concept of sustainable tourism development (STD). It

was further investigated that the main key to the success and implementation of STD in a

community is the support of stakeholders, for example citizens, entrepreneurs and

community leaders. Srivastava (2007) identified stakeholders of green manufacturing from

literature: managers, consumers, employees, and natural environment. Jones et al. (2007)

identified shareholders, employees, customers, competitors, media, radical activist groups,

government agencies, suppliers, creditors, managers, and neighbouring communities as

stakeholders to develop a framework to highlight business ethics. David et al. (2007)

considered managers, regulatory agencies, shareholders, social organisations, community,

consumer pressure, suppliers, and employees as stakeholders to study the relationships

among shareholder proposal activism, managerial response and corporate social

performance.

Porter and Kramer (2006) introduced a framework that companies can use to identify all the

positive and negative effects on the society. A new way is proposed to look at the

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relationship among business and society, governments, activists and the media that does not

treat corporate success and social welfare. Morsing and Schulyz (2006) developed three

strategies for CSR communication in order to conceptualize how managers inform, engage

with and involve important stakeholders like external stakeholders (customers, suppliers,

competitors, media, environmental groups) and employees.

Hein et al. (2005) examined how stakeholders at different spatial scales attach different

values to ecosystem services in The Netherlands. The various stakeholders of the ecosystem

considered for the study are: government departments, landowners, non-governmental

organisations, municipalities, consumers and community. Neville et al. (2005) presented a

model of corporate social performance and financial performance relationship considering

shareholders, consumers, employees, business partners, governments, media, local

community, and the natural environment. Zink (2005) adopted European Foundation for

Quality Management excellence model to deal with the relevance of a stakeholder

orientation in a frame of corporate social responsibility as a precondition for sustainability.

The four groups of stakeholders identified in this model are: customers, shareholders,

employees, and society. Maignan et al. (2005) presented community leaders, business

partners, NGO’s, environmental groups, community, suppliers, investors, customers,

employees, owners/manager attitude as the stakeholders for implementing CSR in industry.

Steurer et al. (2005) took consumers, central government, decentralized authorities, civil

society, private sector, employees, communities, suppliers, and management as stakeholders

to analyse the achievements of sustainable development.

Maignan and Ferrell (2004) discussed the CSR initiatives as the actions undertaken to

display conformity to organizational and stakeholder norms to discuss the managerial

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processes needed to monitor, meet and even exceed stakeholder norms (employees,

investors, community leaders, business partners, customers, NGO’s, community, media, and

managers). Greenley et al. (2004) theorized that firms with different MSOPs (Multiple

Stakeholder Orientations) approach their strategic planning in different ways. The various

stakeholder considered in the study are: customer, competitor, employee, and shareholder.

Delmas and Toffel (2004) provided a model that described how stakeholders, including

regulators, customers, activists, competitors, local communities, industry associations, and

shareholders impose coercive and normative pressures on plants and their parent companies.

Wheeler et al. (2003) presented a simple navigational tool that assist managers in navigating

the relationship between business and society in the context of value creation. The

stakeholders for this study are: investors, customers, employees, suppliers, competitors,

business partners, and local communities. Vos (2003) explored the extent to which

modelling methodology, i.e. critical systems heuristics can help resolving the managerial

problem of identifying stakeholders, particularly the affected (citizens and community) and

the witness (action groups, pressure groups, and media).

Kassinis and Vafeas (2002) empirically investigated the determinants of the likelihood that

firms violate environmental laws by emphasizing on the corporate governance and

stakeholder theories. The study focused on potential explanatory factors that included

characteristics of the firm’s governance structure and its external stakeholders. The

indentified external stakeholders are: communities, political/legislative actors and

regulators.

Swift (2001) presented an overview of definitions of accountability and trust along with a

brief recap on current organisational practise and corporate social performance. The various

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stakeholders mentioned are: management, employees, shareholders, society, societal interest

groups, consumers, quasi-law or regulatory bodies, and agents.

Henriques and Sardosky (1999) conducted an empirical test of two related theoretical

models – the Roome (1992) and the Hunt and Auster (1990) models – followed by cluster

analysis and classified them as regulatory stakeholders (government, trade associations,

competitors), community stakeholders (local community, environmental organizations),

organisational stakeholders (shareholders, employees, customers, suppliers), and media. A

review of the literature is presented in table 2.17 for identifying the stakeholders of GM.

Observations and Discussion

The literature review on stakeholders provided few classifications by researchers. Most of

the researchers have classified the stakeholders into internal or organizational stakeholders,

external or societal or value chain stakeholders and regulatory stakeholders as shown in

table 2.18. Some researchers in the past analyzed the stakeholders either theoretically or by

using some mathematical/statistical tool and provided the classification of various

stakeholders into relatively few stakeholder factors for better understanding of the

stakeholders. Various classifications of stakeholders proposed by the various researchers in

the past are given in table 2.18.

Figure 2.10 presents the year-wise literature on stakeholders from 1998 till early 2013. It is

clearly evident from table 2.19, that most of the studies are either carried out in multiple

countries/continents but many of the studies do not identify the geographical regions. One

study has been conducted in India. A large number of these (15/46) studies have compared

the stakeholders for more than one country as shown in table 2.19.

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Table 2.17: Review of literature on GM stakeholders

S. No. Author(s) & Year Country/Continent Research Area Industry Sector/Segment/Type/Size

1 Hummels (1998) Nigeria, Amsterdam Corporate environmentalism Oil and gas, service

2 Henriques and Sardosky

(1999)

Canada Environmental management Oil and gas, packaging and plastic.

3 Moir (2001) Not-specified Corporate environmentalism Not-specified

4 Swift (2001) U.K and Europe Green manufacturing Not-specified

5 Kassinis and Vafeas (2002) U.S.A. Corporate environmentalism Not-specified

6 Vos (2003) Not-specified Corporate environmentalism Not-specified

7 Wheeler et al. (2003) Nigeria, U.S.A, U.K., Canada,

Australia, Europe

Corporate environmentalism Oil and gas, chemical industry, service

industry, FMCG

8 Delmas and Toffel (2004) U.S.A. and European Union Environmental management Chemical industry and multinational

corporations of various industries.

9 Maignan and Ferrell (2004) U.S.A. and Britain Corporate social responsibility

and environmental management

Petroleum, service, retail, FMCG,

telecommunication

10 Steurer et al.(2005) Not-specified Sustainable environmentalism Not-specified

11 Neville et al. (2005) Not-specified Corporate environmentalism Not-specified

12 Zink (2005) U.S., Europe, Dubai. Corporate environmentalism Not-specified

13 Maignan et al. (2005) U.S.A. Corporate environmentalism Petroleum, pharmaceutical, service, corporate

14 Morsing and Schulyz

(2006)

Denmark, Sweden, Norway Corporate environmentalism Oil and gas, service, FMCG

15 Porter and Kramer (2006) Not-specified Corporate environmentalism Service, FMCG, automotive, infrastructure.

16 Bryson (2004) Not-specified Green manufacturing Not-specified

17 Chien and Shih (2007) Taiwan Green manufacturing Electrical and electronics

18 David et al. (2007) Not-specified Corporate environmentalism Not-specified

19 Srivastava (2007) Not-specified Green manufacturing Not-specified

20 Jones et al. (2007) Not-specified Corporate environmentalism Not-specified

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Table 2.17: Review of literature on GM stakeholders (contd.)

S. No. Author(s) & Year Country/Continent Research Area Industry Sector/Segment/Type/Size

21 Murillo-Luna et al. (2008) Spain Environmental management Not-specified

22 Braun and Starmanns (2008) Germany and Spain Corporate environmentalism Manufacturing sector

23 Peloza and Papania (2008) Europe, U.S.A., South Africa, Corporate environmentalism Not-specified

24 Azorín et al.(2009) Not-specified Quality and environmental

management

Not-specified

25 Gadenne et al. (2009) Queensland: Australia Environmental management Manufacturing, service, and retail

26 Reyers et al. (2009) South Africa Sustainable environmentalism Tourism

27 Darnall et al. (2009) France, Germany, Norway,

U.S.A. , Canada, Hungary

Accounting and environmental

management

Manufacturing sector

28 Voinov and Bousquet (2010) Not-specified Environmental management Not-specified

29 Garvare and Johansson (2010) Not-specified Sustainable environmentalism Not-specified

30 Adewuyi and Olowookere (2010) Africa Corporate environmentalism Cement industry

31 Darnall et al.(2010) France, Germany, Norway,

U.S.A. , Canada, Hungary

Environmental management Manufacturing sector

32 Gonzalez-Benito and Gonzalez-

Benito (2010)

Spain Corporate environmentalism Chemical products, electric and

electronic, furniture and fixtures.

33 Marshall et al. (2010) New Zealand and U.S.A. Environmental management Wine industry

34 Sarkis et al. (2010) Spain Environmental and operations

management

Automotive industry

35 Ditlev-Simonsen and Midttun

(2011)

Hong Kong, Norway Corporate social responsibility and

environmental management

Not-specified

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Table 2.17: Review of literature on GM stakeholders (contd.)

S.

No.

Author(s) & Year Country/

Continent

Research Area Industry Sector/Segment/Type/Size

36 Cronin et al. (2011) Not-specified Green manufacturing Not-specified

37 Shah (2011) Trinidad and Tobago: West

Indies

Corporate environmentalism Pollution intensive oil, gas and petrochemical

38 Ayuso et al. (2011) Not-specified Sustainable environmentalism Not-specified

39 Azadi et al. (2011) USA, Canada, Sweden,

UK, Germany, China,

Japan, Italy, Scotland,

Australia, Singapore,

Russia, Jordan,

Switzerland, Brazil

Sustainable environmentalism Not-specified

40 Chang and Chen (2012) Taiwan Green manufacturing Manufacturing industries

41 Gunasekaran and Spalanzani

(2012)

China Manufacturing management Manufacturing and service sector

42 Bryde and Schulmeister

(2012)

Germany, USA, Saudi

Arabia

Manufacturing management Infrastructure and construction

43 Wolf (2012) Not-specified Corporate environmentalism FMCG

44 Dey and Cheffi (2012) U.K. Manufacturing management Manufacturing industry

45 Roy et al. (2013) Canada Corporate environmentalism Manufacturing industry

46 Ditlev-Simonsen and

Wenstop (2013)

Norway Corporate environmentalism Not-specified

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Table 2.18: Various classifications of stakeholders from extant literature

S. No. Author(s) Classification

1 Clarkson, 1995 Public primary stakeholders

Primary stakeholders

Secondary stakeholders

2 Henriques and Sadorsky, 1999 Regulatory stakeholders

Organizational stakeholders

Community stakeholders

Media

3 Buysse and Verbeke, 2003 Regulatory stakeholders

External primary stakeholders

Internal primary stakeholders

Secondary stakeholders

4 Murillo-Luna et al., 2008 External social stakeholders

Corporate government stakeholder

Internal economic stakeholders

External economic stakeholders

Regulatory stakeholders

5 Darnall et al., 2008 Value chain stakeholders

Internal stakeholders

Societal stakeholders

6 Shah, 2011 Business-chain stakeholders

NGO/CBO stakeholders

Regulatory stakeholders

Figure 2.10: Year-wise literature distribution on GM stakeholders

0

1

2

3

4

5

6

7

8

Nu

mb

er

of

arti

cle

s (f

or

stak

eh

old

ers

)

Year

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Table 2.19: Region-wise literature contribution on GM stakeholders

S. No. Continents Countries Total studies

1 Asia China (1), Taiwan (2) 03

2 Europe Spain (3), Norway (1), UK (1) 05

3 North America USA (2), Canada (2), Trinidad and Tobago: West Indies (1) 05

4 Australia Australia (1) 01

5 Africa South Africa (1), Africa (1) 02

6 Miscellaneous USA & EU (1), UK & Europe (1), New Zealand & USA (1),

Hong Kong & Norway (1), France, Germany, Norway, USA ,

Canada, & Hungary (2), Germany & Spain (1), Denmark,

Sweden & Norway (1), USA, Europe & Dubai (1), Germany,

USA & Saudi Arabia (1), Nigeria & Amsterdam (1), USA &

Britain (1), Europe, USA & South Africa (1), USA, Canada,

Sweden, UK, Germany, China, Japan, Italy, Scotland,

Australia, Singapore, Russia, Jordan, Switzerland & Brazil

(1), Nigeria, USA, UK, Canada, Australia & Europe (1)

15

7 Not-specified ---- 15


Table 2.20 clearly reveals that many of the studies have been conducted for 'corporate

environmentalism' and significant number of studies investigated 'environmental

management', 'green manufacturing', and 'sustainable environmentalism'. Two studies were

carried out on the 'corporate social responsibility' and 'environmental management' together.

The categorization of various areas studied in the literature review is presented in table 2.20.

Table 2.20: Research area-wise literature contribution on GM stakeholders

S. No. Research Area No. of studies

1 Corporate Environmentalism 20

2 Environmental Management 7

3 Quality and Environmental Management 1

4 Environmental and Operations Management 1

5 Accounting and Environmental Management 1

6 Corporate Social Responsibility and Environmental Management 2

7 Manufacturing Management 3

8 Green Manufacturing 6

9 Sustainable Environmentalism 5

Total 46

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Table 2.21 provides the list of 46 articles on the stakeholders of GM.

Table 2.21: GM stakeholder summary

S.

No.

Stakeholder

Author(s) and Year

Go

ver

nm

ent

Em

plo

yee

s

Co

nsu

mer

s

Mar

ket

Med

ia

Lo

cal

Po

liti

cian

s

Lo

cal

Co

mm

un

ity

Su

pp

lier

s

Tra

de

org

anis

atio

ns

En

vir

on

men

tal

Ad

vo

cacy

Gro

up

s

Inv

esto

rs/S

har

eho

lder

s

Par

tner

s

Ow

ner

s

CE

Os

1 Roy et al. (2013) ü ü ü

ü ü ü

2 Ditlev-Simonsen and Wenstop (2013) ü ü ü ü ü ü

3 Wolf (2012) ü ü ü ü

ü ü ü ü

4 Chang and Chen (2012)

ü ü

ü ü ü ü ü ü

5 Dey and Cheffi (2012) ü ü ü

ü ü ü ü ü

ü

6 Bryde and Schulmeister (2012)

ü

ü ü ü

7 Gunasekaran and Spalanzani (2012) ü ü ü ü ü ü ü ü

ü

8 Ditlev-Simonsen and Midttun (2011)

ü ü ü

ü ü

9 Cronin Jr et al. (2011) ü ü ü ü ü

ü ü ü ü

10 Shah (2011) ü ü ü

ü ü ü ü ü ü

11 Ayuso et al. (2011) ü ü ü ü ü ü ü

12 Azadi et al. (2011) ü

ü ü

ü

13 Marshall et al. (2010) ü ü ü ü ü

ü ü ü

14 Sarkis et al. (2010) ü ü ü ü ü ü ü ü

15 Darnall et al. (2010)

ü ü

ü ü ü ü

16 Gonzalez- Benito and Gonzalez-

Benito (2010) ü ü ü ü ü ü ü ü ü ü

17 Voinov and Bousquet (2010) ü

ü ü ü ü

18 Adewuyi and Olowookere (2010) ü ü ü

ü ü ü ü

19 Garvare and Johansson (2010) ü ü ü

ü ü ü ü ü ü

20 Darnall et al. (2009) ü ü

ü ü ü ü

ü

21 Gadenne et al. (2009) ü ü

ü

ü ü

22 Azorín et al. (2009) ü ü

ü

ü

23 Reyers et al. (2009) ü

ü ü ü

ü

24 Peloza and Papania (2008) ü ü ü ü ü ü ü ü

ü ü

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Table 2.21: GM stakeholder summary (contd.)

S.

No.

Stakeholder

Author(s) and Year

Go

ver

nm

ent

Em

plo

yee

s

Co

nsu

mer

s

Mar

ket

Med

ia

Lo

cal

Po

liti

cian

s

Lo

cal

Co

mm

un

ity

Su

pp

lier

s

Tra

de

org

anis

atio

ns

En

vir

on

men

tal

Ad

vo

cacy

Gro

up

s

Inv

esto

rs/S

har

eho

lder

s

Par

tner

s

Ow

ner

s

CE

Os

25 Murillo-Luna et al. (2008) ü ü ü ü ü ü ü ü ü ü ü ü ü

26 Braun and Starmanns (2008) ü ü ü ü ü ü ü ü ü ü ü ü ü

27 Jones et al. (2007) ü ü ü ü ü ü ü ü ü ü ü

28 David et al. (2007) ü ü ü ü ü ü ü ü

29 Chien and Shih (2007) ü ü ü ü ü ü

ü ü

30 Srivastava (2007) ü ü

ü

ü

31 Morsing and Schulyz (2006) ü ü ü ü ü ü ü

ü

32 Porter and Kramer (2006) ü

ü ü

ü

33 Neville et al. (2005) ü ü ü ü

ü

ü ü

34 Steurer et al.(2005) ü ü ü

ü ü ü ü ü ü ü ü

35 Maignan et al. (2005) ü ü ü ü ü

ü ü ü ü ü

36 Zink (2005) ü ü ü

ü

37 Maignan and Ferrell (2004)

ü ü ü ü ü ü ü ü ü

ü

38 Bryson (2004) ü ü ü ü ü ü ü ü ü ü ü ü ü ü

39 Delmas and Toffel (2004) ü ü ü

ü ü

ü ü ü

40 Wheeler et al. (2003) ü ü ü

ü ü

ü ü

41 Vos (2003)

ü ü

ü ü

42 Kassinis and Vafeas (2002) ü

ü ü

ü ü

43 Swift (2001) ü ü ü

ü ü ü ü ü

44 Moir (2001) ü ü ü ü ü ü ü ü ü ü

45 Henriques and Sardosky (1999) ü ü ü ü ü ü ü ü ü ü ü

46 Hummels (1998) ü ü ü ü ü ü ü ü ü ü ü ü ü

2.8 RESEARCH GAPS

It is observed that various researchers have found drivers and barriers based on literature

review. Further, few researchers validated these drivers/barriers through statistical tools.

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However, none of the researchers have developed a model reflecting hierarchy and

relationship among the drivers/barriers of green manufacturing. The inter-relationship and

hierarchy among drivers/barriers are needed to identify the root drivers/barriers to facilitate

drivers and mitigate barriers in order to have effective and smooth GM implementation.

Therefore, in this study drivers/barriers would be modelled to get the hierarchy and inter-

relationship among drivers/barriers. Review of literature also reveals that there is lack of

research articles providing the ranking of drivers and barriers which is important to focus on

vital few. Further, drivers for and barriers to GM implementation in India have not been

compared with any other country. Moreover, there is no study which has developed the

models through confirmatory factor analysis and structural equation modelling. It is clearly

evident from the review of literature that stakeholders play a vital role in the implementation

of GM in industry. However, there is lack of research pertaining to identification and

validation of stakeholders for different industry sizes namely SMEs and large enterprises.

It has also been observed from the literature that there are many terms – green

manufacturing, environmentally conscious manufacturing, environmentally benign

manufacturing, environmentally responsible manufacturing, sustainable manufacturing,

sustainable production, clean manufacturing, cleaner production – defined by various

researchers. But some researchers call many of these systems/terms similar and a few

researchers different. There is a lack of definition and scope of these systems/terms which is

hampering the research in this area. There is a need to compare the scope of these terms and

find the common thread in these systems/terms.

CHAPTER 3

DRIVERS FOR GREEN MANUFACTURING

IMPLEMENTATION

Manufacturing firms face multiple motivations called 'drivers' which are motivating

and/or forcing the industry to adopt GM. These motivating factors (drivers) play active

role in adoption and diffusion of GM in industry. Detailed literature on drivers has been

provided in chapter 2. Availability of comprehensive studies of the drivers would help

industry to implement GM effectively. Hence, identification, development, ranking,

establishing hierarchy and inter-relationships, and validation of these drivers is the first

step towards effective implementation of GM. This chapter provides:

Development of GM drivers.

Ranking of the drivers using fuzzy TOPSIS multi-criteria decision model.

Establishment of hierarchy and inter-relationship among the drivers using

interpretive structural modelling.

Validation of the drivers through an empirical study and statistical analysis.

A case study to compare the GM drivers for India and Germany.

3.1 DRIVERS FOR GM IMPLEMENTATION

This section develops brief descriptions of drivers identified in the last chapter based on

literature and the discussion held with experts from industry and academia.

3.1.1 Current Legislation

The European Union (EU) has formulated a number of prescriptive directives

encompassing the design, production and treatment of a range of industrial and consumer

products (Rahimifard et al., 2009). Contrary to the traditional thought of considering

market as a main driving force, the command-and-control regulation has over many

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97 | P a g e

years promoted diffusion of environmental technologies, such as waste-water treatment

plants, chimney emission filters, environmental control technologies, etc (Remmen,

2001). An analysis of Australian survey data revealed that government legislation or

threat of legislation is the most important driver for the corporate social responsibility.

As said by Professor John Hewson, it is clearly evident that if a legislative, regulatory

and compliance framework is present; companies tend to perform better in terms of

social responsibility because they are required to comply (Dummett, 2006). Another

Swedish study found that government legislation is the key factor which drove them to

have good environmental credentials (Emtairah et al., 2002). A survey conducted in

three regions of the world – Asia Pacific, Europe and USA – established that for one

third of the respondents environmental legislation is the main driving force, whereas two

thirds of the respondents feel that they are affected by the environmental regulations set

by the government of their countries (Kaebernick and Kara, 2006). The financial

penalties such as taxes and levies can also encourage firms with low environmental

commitment to engage in environmental improvements within their operations (Parker et

al., 2009). Environmental department of Canada has a legal obligation to manage the

risks associated with the use and release of toxic substances, requiring the companies to

develop and implement pollution prevention technologies (Taylor, 2006). One of the

major drivers of GM is the environmental regulation (Zhu and Geng, 2013).

Governments all over the world are enforcing legislations to protect the environment.

Based on the international agreement on climate change (Kyoto Protocol) and legislation

of the European Union, German companies have to buy certificates to be allowed to emit

green house gases.

3.1.2 Future Legislation

Industry feels not only pressure from the current legislation, but also from anticipated

future regulations. The stringency of laws in some countries is still lacking and future


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improvements can easily motivate companies to enhance their environmental

performance (Luken and Rompaey, 2008). In India, for instance, not all companies are

complying with the legislative requirements, because of loopholes in the laws and

ineffective regulations (Mejia, 2009). The European Parliament views this concept as so

critical to the future of the EU that current and future legislation must integrate

sustainability into implementation orders (American Chamber of Commerce in Europe,

2004). For instance, market expectations for the next generation of gasoline engines are:

improved performance, lower toxic emissions to meet future legislation, and reduced fuel

consumption to help meet future legislation linked to green house gas emissions

including CO2 (Picron et al., 2008).

3.1.3 Incentives

Financial incentives improve the green level of businesses with attractive loans, grants or

tax exemptions for capital investments. Empirical evidences support that financial

incentives, like tax breaks or duty free imports, influence the company's investment

strategy for environmental technologies (Luken and Rompaey, 2008). In Germany, the

Federal Ministry for the Environment (2011) , Nature Conservation and Nuclear Safety

(BMU) provides, together with the state-owned bank KfW, loans and grants for

companies to invest in environment friendly production technologies. In India

environmental research in industry is supported by the government through priority

programs, and financial and institutional support (Ministry of Environment and Forests,

2006). Incentive priority programs started by the government educate businessed on the

benefits of corporate environmental responsibility (CER). Economic incentives act as

driver for encouraging CER. An Austrian survey found that government incentives are a

key driver to this change. Study further found respondents claiming economic incentives

and deterrents have a massive role in CER implementation (Dummett, 2006). Financial


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support to engage SMEs in environmental improvement can come in the form of

subsidies (Mir and Feitelson, 2007), grants, loans, and tax concessions (Bradford and

Fraser, 2008; Clement and Hansen, 2003). Economic incentives, through the use of

instruments such as taxes, subsidies, and load-based licences, may be employed to

accelerate the adoption of cleaner production. Incentives may be positive, in the form of

subsidies and tax deductions, or negative, in the form of taxes and charges. Either way,

the incentives work by using a price signal to bring to the attention of management to

cleaner production opportunities that would otherwise go unnoticed (Gunningham and

Sinclair, 1997).

3.1.4 Public Pressure

Another driver for industry is the public awareness of environment and sustainability

issues and the active pressure of various stakeholders to change industrial environmental

behaviour (Montalvo, 2008). These stakeholders can be local communities, Non-

Governmental Organizations (NGOs), consumer groups, media or political green parties.

These stakeholders are forcing companies to implement environmental practices in order

to be perceived as having legitimate organizational activities (Zhu and Geng, 2013). In

India public pressure also plays an important role. In the state of Orissa, for instance,

environmental activists have prevented the development of a bauxite mine on protected

forestland over several years with the help of environmental organizations like

Kalpavriksh and magazines like Down To Earth (Mejia, 2009). In some instances,

external pressures from authorities or environmental movements initiated by the public

can motivate companies to think about alternatives for cleaner production (Moors et al.,

2005). The manifestation of growing public concern for the environment is in term of the

rising public complaints regarding environmental damage. This change in public

attitudes toward the environment has strengthened the ability of the Korean Ministry of


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Environment to develop the capacity to maintain an ambitious monitoring, inspections,

and enforcement program (Aden et al., 1999).

3.1.5 Peer Pressure

The peer pressure like public pressure is also a driver. In developing countries, NGOs are

shaping how organizations and companies do their business. A different form of social

pressure can be exerted by industrial peers like trade and business associations (Luken

and Rompaey, 2008). These networks are placed within industry and promote GM.

These synergies can be shared through networks and thus companies are pressurizing

each other to enhance their environmental performance. In India, industrial experts are

sharing ideas about GM on summits like green manufacturing summit of the

Confederation of Indian Industry (CII). The same is true for Germany, where business

associations like the chamber of industry and commerce are holding events and are

providing information about sustainable developments. The environmental audits and

reviews involving external party visits to examine business practices of SMEs to identify

opportunities for environmental improvements may have a encouraging impact (Parker

et al., 2009). The voluntary environmental initiatives in manufacturing may be

encouraged through industry peer pressure, for example, via the membership of industry

associations, business networks, and through observation of competitors and

benchmarking of performance against other firms (Gunningham and Sinclair, 1997).

3.1.6 Cost Savings

It has become clear to many industries that investments in cleaner technologies can

reduce costs, for example in USA, by saving on green taxes imposed by Environmental

Protection Act of the government which demands the implementation of best available

technologies (Remmen, 2001). Past statistics show that in the last 25 years, total

expenses of the US business sector was over ten trillion US dollars. The annual expense


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in eliminating and controlling pollution was about 1.2 trillion US dollars (Berry and

Rondinelli, 1998), which demonstrates a great potential of huge cost saving on operating

businesses. Cost savings associated with measures such as energy and waste reductions,

especially for manufacturing companies, mean sizeable savings in production costs

(Dummett, 2006). Some SMEs accept that better environmental practices could save

costs and improve relationships with customers (Rutherford et al., 2000). GM provides

opportunity to save money through reduced consumption of energy and material,

resulting in simultaneous environmental protection. Moreover, green manufacturing

technologies are following the principle of preventing pollution generation at the source

rather than reducing it in the production process after it has been produced (Graedel and

Howard-Grenville, 2005). Studies in India revealed that green manufacturing has a high

potential to reduce the waste handling, storage and disposal, as well as packaging and

maintenance costs (Sangwan, 2011).

3.1.7 Competitiveness

Another economic driver is the increased competitiveness of companies implementing

GM technologies (Dwyer, 2007). GM provides organizations advantages in their cost

structures through a higher degree of efficiency which enables them to act more

independently in the markets. A study investigating drivers for eco-innovation in the

European Union revealed that managers expect a future increase in energy prices and this

is the main driver for eco-innovation and development in Europe (European

Commission, 2011). The reasons for greening the industries go beyond ethical issues to

gain competitive advantages. For instance, the well-known 3M program, demonstrates

the potential of cutting costs with environmental initiatives (Shrivastava, 1996). Cost

savings, green image, waste reduction, etc. as a result of GM implementation provides

competitiveness to the organizations. A study of Korean manufacturing SMEs by Lee


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(2009) revealed that systematic green management reduced water demand by 21 percent,

wastewater generation by 16 percent and minor material usage by 13 percent, which

resulted in competitiveness in the market as overall production costs were reduced by

494.5 million Korean Won. The manufacturers saw the creation of new markets and

increased market share as well as the ability to differentiate themselves from their

competitors. Manufacturers may willingly adopt sustainable practices, motivated by the

potential long‐term competitiveness of their firms rather than being forced to do so by

legislation or mandatory compliance (Millar and Russell, 2011).

3.1.8 Customer Demand

Demand from the customer is not a very strong driver as found from the interviews

conducted in 2003, but the trend of demand for green products is continuously

increasing, and it would become more important in future. For example, everyone wants

to buy a car which uses less fuel to operate (Dummett, 2006). Consumers are

undoubtedly an increasingly important force that shapes the social responsibility of

organizations (Mont and Leire, 2009). Demand from end-customers in the markets is

also a driver for GM (Ioannou and Veshagh, 2011). The purchase and consumption

behaviour is more and more formed by ethical criteria and customers prefer buying

environmental friendly manufactured products (Dwyer, 2007). Green products are

ethically superior as they can comply with sustainability standards (Luken and Rompaey,

2008), and green products may have an economic advantage as they are consuming less

energy and material. Altogether, customers are playing an active role in the GM adoption

by companies. Growing public sensitivity to environmental issues is reflected in

consumer behaviour. Collectively, such consumers have the economic muscle to demand

that environmentally unsound products are either improved or replaced (Gunningham

and Sinclair, 1997).


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3.1.9 Supply Chain Pressure

Every company is part of a supply chain, consisting of different suppliers and

distributors, interacting with each other. These business partners can drive an

organization to implement green manufacturing technologies and practices (Luken and

Rompaey, 2008). Organizations try to improve their environmental performance, like

suppliers need to change processes in order to enable an increase of the overall

performance of the entire supply chain. In the exemplary case of Subaru Indiana

Automotive the steel supplier changed the dimensions of its steel coils to enable scrap

reduction during a green manufacturing initiative (Schroeder and Robinson, 2010).

Following the EU parliament’s approval of the European Union (EU) directives on

Waste Electrical and Electronic Equipment (WEEE), Restriction of Hazardous

Substances (RoHS), and Eco-design for Energy using products (EuP), a leading group of

companies in the electronics and consumer products industry; including Samsung, LG,

Sony, Toshiba, NEC, IBM, HP, and Dell; have adopted 'green' standards in their supply

chain management (Lee, 2009). Final manufacturers often exercise buying power to

pressurise their suppliers to achieve superior environmental performance. As part of the

RoHS-compliance program, many larger companies are asking their suppliers to verify

parts and components compliance to secure compliance of the final products (Cusack

and Perrett, 2006). Larger firms may be able to impose product and process preferences

on other firms using their market power to influence the behaviour of upstream suppliers

and downstream buyers. For example, firms may require their suppliers to comply with

certain cleaner production processing standards and may in fact subject them to an

independent assessment of their environmental performance. The interchange between

industrial buyers and suppliers generates incentives to innovate and to respond to market

demands (Gunningham and Sinclair, 1997).


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3.1.10 Top Management Commitment

Top management commitment has a crucial influence over the organizational culture of

the company (Petts, 1998; Trice and Beyer, 1991). One of the important characteristics

in aggressive environmental management is the active support and participation of top

management in environmental protection affairs (Henriques and Sadorsky, 1999). Top

management support and involvement has a crucial impact on the major company

initiatives (Maidique and Zirger, 1984), and GM is no exception. Personal commitment

of the individuals including owners and founders of the firms has been found to be an

organizational factor motivating green management (New et al., 2000). The commitment

of a corporate management for environmental issues favours the implementation of

environmental technologies (Del Rio et al., 2010). Without the support from the

company’s leaders an environmental pro-active strategy is not thinkable. Leadership has

to provide a vision needed to achieve a green level in manufacturing (Schroeder and

Robinson, 2010). Volkswagen AG has committed to become more environment friendly

and expresses this by celebrating World Environment Day at their site in Pune

(http://cars.sulekha.com/volkswagen-india-celebrates-world-environment-day_volkswag

en _press-releases_977 (checked 12/11/2011). Leadership commitment is vital for the

uptake of GM in organizations. This active role can be taken by the owner of a company,

the top management or by shareholders. This is often referred to as corporate social

responsibility, where companies have programs of actions to contribute to the

improvement of social welfare (Hediger, 2010). Harnessing the power of environmental

leadership can be a potent tool in the furtherance of environmental initiatives.

Environmental leadership which refers to the management process within firms in which

senior management demonstrates a strong commitment to the principle and practice of

environmental initiatives, is likely to experience a 'trickle down' effect whereby all layers


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of the firm experience a much greater corporate environmental commitment

(Gunningham and Sinclair, 1997).

3.1.11 Public Image

The reputation or image of the company in public is very important to survive in the

market. The need to protect the image and to enhance the same is essential part of any

future business. Some companies see environmental initiatives not as a responsibility,

but their future strategy and survival (Dummett, 2006). The companies in developing

countries have already started to have a green image, so that they can distinguish

themselves from competitors. Nowadays, there is a trend in large industries to have a

green reputation and to be open and willing to co-operate in environmental issues that

are important for the society as a whole. Besides the corporate green image, the

personnel at lower levels in the organization need to become conscious of environmental

aspects (Moors et al., 2005). One of the motivating factor for GM is the importance of

maintaining an environmentally responsible corporate image (Allen et al., 2002). The

positive public perception of a company can be used for green marketing to gain new

environmentally conscious customers. Moreover, enterprises are increasingly motivated

to implement environmental practices in order to be perceived as having legitimate

organizational activities (Zhu and Geng, 2013). In an Indian study, managers rated a

‘better organization image in public’ as one of the most important benefits of GM

(Sangwan, 2011).

3.1.12 Technology

A major part of GM is the implementation and usage of new green technologies. The

availability of proven environment friendly technology to the industry can motivate

companies to implement GM. Green technologies have specific characteristics that foster


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energy and resource efficiency and effectiveness (Montalvo, 2008). Examples are the

usage of energy-savings lamps, metalworking fluids based on renewable resources or

energy-efficient electric motors. Often the performance of the technology can be

important regardless of its environmental impact.

3.1.13 Organizational Resources

Organizational resources refer to all capabilities of the organization to carry out and

innovate in GM. A key factor for that is having skilled and motivated staff in a company

(Schroeder and Robinson, 2010). If green metrics and goals are implemented in the

corporate strategy, achievements are visible and people are accountable for them. This

can continuously motivate green improvements. Another example is the available

capital. A healthy financial situation makes it more likely for a company to invest in

green technologies, especially in capital-intensive cleaner technologies (Del Rio et al.,

2010). Table 3.1 provides the description of drivers for implementation of GM. The

small and medium-sized enterprises (SMEs) often lack the knowledge, expertise, skills,

finance and human resources to make the desired changes within their manufacturing

system of organizations (Lee, 2008). In addition, it is often observed that the approaches

are narrowly focused to specific features of the production process or the product when

the SMEs attempted to change. Thus, SMEs often have a limited view on the direction of

future innovation and tend to tackle green issues in an ad hoc manner (Lee, 2008;

Nawrocka, 2008).

3.2 RANKING OF GM DRIVERS USING FUZZY TOPSIS

3.2.1 Overview of Fuzzy TOPSIS

Two major different kinds of uncertainties, i.e. ambiguity and vagueness, exist in the real

life. While ambiguity is associated with one-to-many relations, that is, situations in


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Table 3.1: Description of GM drivers

S. No. Drivers Description References

1 Current Legislation Pollution control norms, landfill taxes, emission

trading, polluted water discharge norms, eco-

label, etc.

Singh et al. (2012), Law and Gunasekaran (2012), Yu et al.

(2008), Zhang et al. (2009), Luken and Rompaey (2008), Yuksel

(2008), Birkin et al. (2009), Veshagh and Li (2006), Walker et

al. (2008), Lawrence et al. (2006), ElTayeb et al. (2010), Zhu

and Geng (2013), Diabat and Govindan (2011), Perez-Sanchez

et al. (2003), Kapetanopoulou and Tagaras (2011), Allen (2001),

Sangwan (2006)

2 Future Legislation Expected development of stricter laws and

increased level of enforcement.

Luken and Rompaey (2008), Dwyer (2007), Montalvo (2008),

Del Río González (2008) Seidel et al. (2009), Jaafar et al. (2007)

3 Incentives Investment subsidies, awards, R&D support, tax

exemptions, duty free imports, etc.

Zhang et al. (2009), Luken and Rompaey (2008), Yuksel (2008),

Studer et al. (2006), Gunningham and Sinclair (1997), Murphy

(2001)

4 Public Pressure Local communities, politicians, NGOs, media,

insurance companies, banks, etc.

Singh et al. (2012), Law and Gunasekaran (2012), Yu et al.

(2008), Mont and Leire (2009), Zhang et al. (2009), Luken and

Rompaey (2008), Montalvo (2008), Zhu and Geng (2013),

Gunningham and Sinclair (1997), Allen (2001)

5 Peer Pressure Trade and business associations, networks,

experts, etc.

Luken and Rompaey (2008), Lawrence et al. (2006), Zhu and

Geng (2013), Perez-Sanchez et al. (2003), Gunningham and

Sinclair (1997), Zhu et al. (2005)

6 Cost Savings Reduction of energy consumption, reduction in

virgin material use, less waste, etc.

Singh et al. (2012), Yu et al. (2008), Massoud et al. (2010),

Zhang et al. (2009), Studer et al. (2006), Birkin et al. (2009),

Montalvo (2008), Veshagh and Li (2006), Walker et al. (2008),

Lawrence et al. (2006), ElTayeb et al. (2010), Diabat and

Govindan (2011), Zhu et al. (2005)


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Table 3.1: Description of GM drivers (contd.)

S. No. Drivers Description References

7 Competitiveness Better process performances, higher product

quality, higher efficiency, competing with best-

practices in sector, etc.

Singh et al. (2012), Law and Gunasekaran (2012), Yu et al. (2008),

Zhang et al. (2009), Studer et al. (2006), Birkin et al. (2009),

Veshagh and Li (2006), Walker et al. (2008), Perez-Sanchez et al.

(2003), Kapetanopoulou and Tagaras (2011)

8 Customer Demand End-user demand for environmentally friendly

products

Singh et al. (2012), Yu et al. (2008), Massoud et al. (2010), Zhang

et al. (2009), Birkin et al. (2009), Veshagh and Li (2006), Walker

et al. (2008), ElTayeb et al. (2010), Perez-Sanchez et al. (2003),

Allen (2001)

9 Supply Chain Pressure Demand of suppliers, distributors, OEM,

compliance with legislation in global markets

Singh et al. (2012), Zhang et al. (2009), Luken and Rompaey

(2008), Studer et al. (2006), Birkin et al. (2009), Diabat and

Govindan (2011), Murphy (2001), Allen (2001), Kara et al. (2010)

10 Top Management Commitment Management, owner or investors are highly

committed to enhance environmental

performance, ethics, social values, etc.

Yu et al. (2008), Luken and Rompaey (2008), Walker et al. (2008),

Lawrence et al. (2006), Murphy (2001), Zhu et al. (2005),

Sangwan (2006)

11 Public Image Importance of a positive public perception of

company, green image, etc.

Massoud et al. (2010), Luken and Rompaey (2008), Studer et al.

(2006), Veshagh and Li (2006), Lawrence et al. (2006),

Kapetanopoulou and Tagaras (2011)

12 Technology Opportunities, advantages or performance of

available green and efficient technology

Montalvo (2008), Del Río González (2008), Sangwan (2006)

13 Organizational Resources Availability of skilled and motivated staff to

implement GM and financial resources.

Singh et al. (2012), Montalvo (2008), Gunningham and Sinclair

(1997), Allen et al. (2002), Del Río González (2008), Sangwan

(2006), Schönsleben et al. (2010), Schroeder and Robinson (2010),

Sangwan (2011b)


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which the choice between two or more alternatives is left unspecified; vagueness is

associated with the difficulty of making sharp or precise distinctions in the world, that is,

some domain of interest is vague if it cannot be delimited by sharp boundaries. The

fuzzy mathematical programming is developed for treating such uncertainties in the

setting of optimization problems. (Klir, 1987). Fuzzy theory is applied to model

parameters for decision making to rank drivers. In fuzzy set theory, a triangular fuzzy

number a~ can be defined by a triplet (a1,a2,a3) as shown in figure 3.1 and the conversion

scales are applied to transform the linguistic terms into fuzzy numbers. The membership

function )(~ xa is defined as (refer equation 1):

)(~ xa =

otherwise

axaaa

xa

axaaa

ax

,0

,

,

32

23

3

21

12

1

(1)

Fig. 3.1: Triangular fuzzy number a~

Fuzzy sets were introduced by Zadeh in 1965 to represent/manipulate data and

information processing nonstatistical uncertainties (Zadeh, 1965). It is specifically

designed to represent mathematical uncertainties and vagueness and to provide

formalized tools for dealing with the imprecision intrinsic to many problems. Fuzzy logic

provides an inference morphology that enables approximate human reasoning

capabilities to be applied to knowledge-based systems. The theory of fuzzy logic

a1 a2 a3 0

1


110 | P a g e

provides a mathematical strength to capture the uncertainties associated with human

cognitive processes, such as thinking and reasoning.

Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is a practical

and useful technique for ranking and selection of a number of possible alternatives

through measuring Euclidean distances. TOPSIS was first developed by Hwang and

Yoon in 1981. Triantaphyllou and Lin (1996) developed a fuzzy version of the TOPSIS

method based on fuzzy arithmetic operations, which leads to a fuzzy relative closeness

for each alternative. TOPSIS is based upon the concept that the chosen alternative should

have the shortest distance from the positive ideal solution (PIS), i.e. the solution that

maximizes the benefit criteria and minimizes the cost criteria; and the farthest from the

negative ideal solution (NIS), i.e. the solution that maximizes the cost criteria and

minimizes the benefit criteria (Wang and Elhag, 2006). Fuzzy TOPSIS provides a proper

tool to encounter the uncertain and complex environments by measuring the inherent

ambiguity of concepts associated with decision maker’s subjective judgment in multi-

criteria decision making atmosphere. TOPSIS method is rational and easily

programmable computation procedure (Awasthi et al., 2011; Ding, 2011; Salehi and

Tavakkoli-Moghaddam, 2008).

3.2.2 Development of Fuzzy TOPSIS Method for Ranking GM Drivers

Figure 3.2 provides an hierarchical structure used to rank the 13 drivers for GM

implementation using three perspectives – government, industry and expert. The 13

drivers (D1 to D13) are at the bottom of the hierarchy and the criteria used to rank the

drivers are at the middle of the hierarchy.

Further, various criteria chosen for ranking the drivers for GM implementation, their

definition and type are presented in table 3.2. A scale of 1–9 is applied for rating the


111 | P a g e

criteria and the alternatives. The linguistic variables and fuzzy ratings for the alternatives

and criteria are shown in table 3.3.

GOAL:

Ranking of Drivers

Industry perspective

[C2]

Government

perspective [C1]

Expert perspective

[C3]

D6 D7D5 D8D4 D9D3D2D1 D12D11D10 D13

Figure 3.2: A hierarchical structure for ranking the drivers for GM

Table 3.2: Criteria for ranking drivers for GM.

Criteria Definition Criteria type

Government perspective View of officials from government departments

handling industrial environmental policies Importance

(the more the

better)

Industry perspective View of executives from industry handling industrial

and environmental policies

Experts perspective View of experts working on environmental issues

The steps of fuzzy TOPSIS methodology for ranking drivers for GM are presented

below:

Step 1: Assignment of ratings to the criteria and alternatives

Let us assume that, there are 'j' possible drivers called D = {D1, D2 . . . Dj} which are to

be evaluated against 'm' criteria, C = {C1, C2 . . . Cm}. The criteria weights are denoted

by wi (i = 1, 2 . . . m). The performance ratings of each decision maker DMk (k = 1, 2, . . .

, K) for each alternative Dj (j = 1, 2, . . , n) with respect to criteria Ci (i = 1, 2, . . . ,m) are

denoted by ijkk xR ~~

(i = 1, 2, . . . ,m; j = 1, 2, . . . , n; k = 1, 2, . . . , K) with membership

function )(~ xkR

.


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Table 3.3: Linguistic variables and fuzzy ratings for the alternatives and criteria

Linguistic terms for alternative ratings Linguistic terms for criteria ratings

Linguistic Term Membership Function Linguistic Term Membership Function

Not Important (NI) (1,1,3) Very Low (VL) (1,1,3)

Less Important (LI) (1,3,5) Low (L) (1,3,5)

Fairly Important (FI) (3,5,7) Medium (M) (3,5,7)

Important (I) (5,7,9) High (H) (5,7,9)

Very Important (VI) (7,9,9) Very High (VH) (7,9,9)

In the present case we have thirteen alternatives (drivers), three criteria (perspectives)

and three decision makers. Table 3.4 and table 3.5 present linguistic assessments for all

three criteria and thirteen alternatives respectively in consultation with decision makers.

The inputs from a group of three respondents from each category are clubbed to one and

named as DM1, DM2, and DM3 for government, industry and expert respectively. It is

apparent that all criteria belong to the direct category, that is, the higher the value, the

more preferable the alternative.

Table 3.4: Linguistic assessment of the criteria

Criteria DM1 DM2 DM3

Government perspective (C1) VH L L

Industry perspective (C2) L VH VH

Experts perspective (C3) H H H

Table 3.5: Linguistic assessment of the alternatives (drivers)

S. No. Drivers Government Industry Experts

D1 Current Legislation VI I LI

D2 Future Legislation I LI I

D3 Incentives FI VI VI

D4 Public Pressure I FI LI

D5 Peer Pressure LI LI FI

D6 Cost Savings I FI I

D7 Competitiveness I I VI

D8 Customer Demand FI FI I

D9 Supply Chain Pressure FI FI I

D10 Top Management Commitment VI LI VI

D11 Public Image FI FI I

D12 Technology FI I VI

D13 Organizational Resources I I I


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Step 2: Compute aggregate fuzzy ratings for the criteria

If the fuzzy ratings of all decision makers are described as triangular fuzzy numbers

kR~

(ak, bk, ck), k = 1, 2. . . K, then the aggregated fuzzy rating is given by (Table 3.6)

as:

kR~

(a, b, c), k = 1, 2... K,

where

a = }{min kk a ,

K

k

kbK

b1

1 and c = }{max kk c

The fuzzy decision matrix for the criteria (W~

) is constructed as:

)~,.......~,~(~

21 nwwwW

Table 3.6: Aggregate fuzzy weights of the criteria

Criteria DM1 DM2 DM3 Aggregate Fuzzy Weight

Government perspective (C1) (7,9,9) (1,3,5) (1,3,5) (1,5,9)

Industry perspective (C2) (1,3,5) (7,9,9) (7,9,9) (1,7,9)

Experts perspective (C3) (5,7,9) (5,7,9) (5,7,9) (5,7,9)

Step 3: Compute the fuzzy decision matrix

The fuzzy decision matrix for the alternatives )~

(D is constructed below (Table 3.7) using

the following relation:

nccc ...21

mnmm

n

n

m xxx

xxx

xxx

B

B

B

D

~...~~............

~...~~

~...~~

...

~

21

22221

11211

2

1


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Table 3.7: Aggregate fuzzy weights of alternatives (drivers)


D1 Current Legislation (7,9,9) (5,7,9) (1,3,5)

D2 Future Legislation (5,7,9) (1,3,5) (5,7,9)

D3 Incentives (3,5,7) (7,9,9) (7,9,9)

D4 Public Pressure (5,7,9) (3,5,7) (1,3,5)

D5 Peer Pressure (1,3,5) (1,3,5) (3,5,7)

D6 Cost Savings (5,7,9) (3,5,7) (5,7,9)

D7 Competitiveness (5,7,9) (5,7,9) (7,9,9)

D8 Customer Demand (3,5,7) (3,5,7) (5,7,9)

D9 Supply Chain Pressure (3,5,7) (3,5,7) (5,7,9)

D10 Top Management Commitment (7,9,9) (1,3,5) (7,9,9)

D11 Public Image (3,5,7) (3,5,7) (5,7,9)

D12 Technology (3,5,7) (5,7,9) (7,9,9)

D13 Organizational Resources (5,7,9) (5,7,9) (5,7,9)

Step 4: Normalize the fuzzy decision matrix

The raw data is normalized using a linear scale transformation to bring the various

criteria scales on a comparable scale. The normalized fuzzy decision matrix R~

shown in

table 3.8 is computed as:

nmijrR ]~[~

, i = 1, 2, . . . ,m ; j = 1, 2, . . . , n

Where

***,,~

j

ij

j

ij

j

ij

ijc

c

c

b

c

ar and

}{max*

ijij cc …. (Benefit or Importance Criteria)

Step 5: Compute the weighted normalized matrix

The weighted normalized matrix V~

for criteria is computed by multiplying the weights

)~( jw of evaluation criteria with the normalized fuzzy decision matrix ijr~ as:

nmijvV ]~[~

, i = 1, 2. . . m; j = 1, 2. . . n where jijij wrv ~(.)~~


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Table 3.8: Normalized alternatives (drivers)


*

jc 9 9 9

D1 Current Legislation (0.78,1,1) (0.56,0.78,1) (0.11,0.33,0.56)

D2 Future Legislation (0.56,0.78,1) (0.11,0.33,0.56) (0.56,0.78,1)

D3 Incentives (0.33,0.56,0.78) (0.78,1,1) (0.78,1,1)

D4 Public Pressure (0.56,0.78,1) (0.33,0.56,0.78) (0.11,0.33,0.56)

D5 Peer Pressure (0.11,0.33,0.56) (0.11,0.33,0.56) (0.33,0.56,0.78)

D6 Cost Savings (0.56,0.78,1) (0.33,0.56,0.78) (0.56,0.78,1)

D7 Competitiveness (0.56,0.78,1) (0.56,0.78,1) (0.78,1,1)

D8 Customer Demand (0.33,0.56,0.78) (0.33,0.56,0.78) (0.56,0.78,1)

D9 Supply Chain Pressure (0.33,0.56,0.78) (0.33,0.56,0.78) (0.56,0.78,1)

D10 Top Management Commitment (0.78,1,1) (0.11,0.33,0.56) (0.78,1,1)

D11 Public Image (0.33,0.56,0.78) (0.33,0.56,0.78) (0.56,0.78,1)

D12 Technology (0.33,0.56,0.78) (0.56,0.78,1) (0.78,1,1)

D13 Organizational Resources (0.56,0.78,1) (0.56,0.78,1) (0.56,0.78,1)

The weighted normalized matrix is given in table 3.9.

Table 3.9: Weighted normalized alternatives (drivers)


D1 Current Legislation (0.78,5,9) (0.56,5.46,9) (0.55,2.31,5)

D2 Future Legislation (0.56,3.9,9) (0.11,2.31,5) (2.8,5.46,9)

D3 Incentives (0.33,2.8,7) (0.78,7,9) (3.9,7,9)

D4 Public Pressure (0.56,3.9,9) (0.33,3.92,7) (0.55,2.31,5)

D5 Peer Pressure (0.11,1.65,5) (0.11,2.31,5) (1.65,3.92,7)

D6 Cost Savings (0.56,3.9,9) (0.33,3.92,7) (2.8,5.46,9)

D7 Competitiveness (0.56,3.9,9) (0.56,5.46,9) (3.9,7,9)

D8 Customer Demand (0.33,2.8,7) (0.33,3.92,7) (2.8,5.46,9)

D9 Supply Chain Pressure (0.33,2.8,7) (0.33,3.92,7) (2.8,5.46,9)

D10 Top Management Commitment (0.78,5,9) (0.11,2.31,5) (3.9,7,9)

D11 Public Image (0.33,2.8,7) (0.33,3.92,7) (2.8,5.46,9)

D12 Technology (0.33,2.8,7) (0.56,5.46,9) (3.9,7,9)

D13 Organizational Resources (0.56,3.9,9) (0.56,5.46,9) (2.8,5.46,9)

FPIS (B+) (9,9,9) (9,9,9) (9,9,9)

FNIS (B-) (0.11,0.11,0.11) (0.11,0.11,0.11) (0.55,0.55,0.55)


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Step 6: Compute the fuzzy positive ideal solution (FPIS) and the fuzzy negative ideal

solution (FNIS)

The FPIS and FNIS of the alternatives given in table 3.9, are computed as follows:

)~,......~,~( **

2

*

1

*

nvvvA

where }{max~3

*

ijij vv , i = 1, 2. . . m; j = 1, 2, . . . , n

)~,......~,~( 21

nvvvA

where }{min~3ijij vv , i = 1, 2. . . m; j = 1, 2, . . . , n

Step 7: Compute the distance of each alternative from FPIS and FNIS

The distance (

ii dd ,* ) of each weighted alternative i = 1, 2. . . m from the FPIS and the

FNIS is computed as follows:

Let a~ = (a1, a2, a3) and

b~

= (b1, b2, b3) be two triangular fuzzy numbers.

The distance between them is given by following relation using vertex method

][3

1)

~,~(

2

33

2

22

2

11 babababad

n

j

jijvi vvdd1

** )~,~( i = 1, 2. . . m

n

j

jijvi vvdd1

)~,~( i = 1, 2. . . m

Where )~

,~( badv is the distance measurement between two fuzzy numbers a~ and b

~. The

distances of each weighted alternative from FPIS and FNIS are shown in table 3.10

below.


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Table 3.10: Distance of drivers from FPIS and FNIS

Distance C1 C2 C3 Distance C1 C2 C3

d(D1,D+) 5.27 5.28 6.63 d(D1,D-) 5.87 5.99 2.76

d(D2,D+) 5.69 6.82 4.12 d(D2,D-) 5.58 3.09 5.79

d(D3,D+) 6.25 4.88 3.16 d(D3,D-) 4.28 6.50 6.43

d(D4,D+) 5.69 5.91 6.63 d(D4,D-) 5.58 4.54 2.76

d(D5,D+) 7.04 6.82 5.28 d(D5,D-) 2.95 3.09 4.24

d(D6,D+) 5.69 5.91 4.12 d(D6,D-) 5.58 4.54 5.79

d(D7,D+) 5.69 5.28 3.16 d(D7,D-) 5.58 5.99 6.43

d(D8,D+) 6.26 5.91 4.12 d(D8,D-) 4.28 4.54 5.79

d(D9,D+) 6.26 5.91 4.12 d(D9,D-) 4.28 4.54 5.79

d(D10,D+) 5.27 6.82 3.16 d(D10,D-) 5.87 3.09 6.43

d(D11,D+) 6.26 5.91 4.12 d(D11,D-) 4.28 4.54 5.79

d(D12,D+) 6.26 5.28 3.16 d(D12,D-) 4.28 5.99 6.43

d(D13,D+) 5.69 5.28 4.12 d(D13,D-) 5.58 5.99 5.79

Step 8: Compute the closeness coefficient (CCi) of each alternative

The closeness coefficient CCi represents the distances to the fuzzy positive ideal solution

( *A ) and the fuzzy negative ideal solution ( A ) simultaneously. The closeness

coefficient of each alternative is calculated as follows:

CCi = )( *

ii

i

dd

d

, i = 1, 2. . . m

The closeness coefficients representing importance for alternatives are given in table

3.11 and figure 3.3. Similarly the closeness coefficients for different criteria are

computed and given in table 3.12 and figure 3.4.

Table 3.11: Aggregated closeness coefficients for alternatives (drivers)

Driver *

id

id CCi

D1 17.18 14.62 0.459

D2 16.63 14.46 0.465

D3 14.29 17.21 0.546

D4 18.23 12.88 0.414

D5 19.14 10.28 0.349

D6 15.72 15.91 0.503

D7 14.13 18.00 0.560

D8 16.29 14.61 0.473

D9 16.29 14.61 0.472

D10 15.25 15.39 0.502

D11 16.29 14.61 0.472

D12 14.70 16.70 0.531

D13 15.09 17.36 0.534


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Table 3.12: Closeness coefficients for individual criterion (perspectives)

Driver Closeness coefficient (CCi)

Government perspective Industry perspective Expert perspective

D1 0.52693 0.5315 0.29393

D2 0.49512 0.311806 0.584258

D3 0.406458 0.571178 0.67049

D4 0.49512 0.43445 0.29393

D5 0.295295 0.311806 0.445378

D6 0.49512 0.43445 0.584258

D7 0.49512 0.5315 0.67049

D8 0.406072 0.43445 0.584258

D9 0.406072 0.43445 0.584258

D10 0.52693 0.311806 0.67049

D11 0.406072 0.43445 0.584258

D12 0.406072 0.5315 0.67049

D13 0.49512 0.5315 0.584258

Figure 3.3: Aggregated closeness coefficient of GM drivers

0.3 0.35 0.4 0.45 0.5 0.55 0.6

Competitiveness

Incentives

Organizational Resources

Technology

Cost Savings

Top Management Commitment

Customer Demand

Supply Chain Pressure

Public Image

Future Legislation

Current Legislation

Public Pressure

Peer Pressure

Closeness Coefficient (CCi)

Importance of GM Drivers


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Figure 3.4: Closeness coefficient (CCi) of drivers (government, industry and expert

perspectives)

Step 9: Rank the alternatives (drivers)

Rank the alternatives according to the closeness coefficient (CCi) in decreasing order and

select the alternative with the highest closeness coefficient for final implementation. The

best alternative is closest to the FPIS and farthest from the FNIS. The aggregate ranking

of the drivers according to the three criteria, i.e. government, industry, and experts

perspectives is given in table 3.13:

Table 3.13: Ranking of GM Drivers

S. No. Driver Name Rank

1 Competitiveness [D7] 1

2 Incentives [D3] 2

3 Organizational Resources [D13] 3

4 Technology [D12] 4

5 Cost Savings [D6] 5

6 Top Management Commitment [D10] 6

7 Customer Demand [D8] 7

8 Supply Chain Pressure [D9] 8

9 Public Image [D11] 9

10 Future Legislation [D2] 10

11 Current Legislation [D1] 11

12 Public Pressure [D4] 12

13 Peer Pressure [D5] 13

0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7

Current Legislation

Future Legislation

Incentives

Public Pressure

Peer Pressure

Cost Savings

Competitiveness

Customer Demand



Public Image

Technology


Closeness Coefficent (CCi)

Experts perspective Industry perspective Government perspective


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3.2.3 Results and Discussion

The fuzzy TOPSIS results clearly show that driver 'competitiveness' is the highest ranked

driver (rank 1/13) and 'incentives' is the second highest ranked driver (rank 2/13),

followed by organizational resources (rank 3/13). In other words, these three drivers are

rated as most important for the implementation of GM in industry. The 'competitiveness'

among the organizations to grab more and more market share can motivate them to adopt

the GM, provided the government supports in terms of incentives, tax exemptions, and

subsidies. The availability of the skilled manpower to implement GM can further make it

possible for small companies like SMEs to adopt the GM. Availability of green and

efficient technology (rank 4/13) to the organizations is vital for diffusion of GM in the

industry which can reduce cost (rank 5/13) of manufacturing by consuming lesser energy

and material. Also, the willingness of the management (rank 6/13) to adopt GM

voluntarily is an important driver, which is an outcome of corporate social responsibility

of the organizations. Customer demand (rank 7/13) and supply chain pressure (rank 8/13)

are moderately important in India perhaps because of less demand of green products by

price sensitive customers. Public image (rank 9/13), future legislation (rank 10/13),

current legislation (rank 11/13), public pressure (rank 12/13), and peer pressure (rank

13/13) are least important in emerging countries like India because of lack of

information/awareness about the importance of green products and processes, which

creates the importance of public image in the mind of producers. The pressure from

public and peer are also least important drivers.

3.3 DEVELOPMENT OF A MODEL OF GM DRIVERS USING

INTERPRETIVE STRUCTURAL MODELLING

This section provides the overview of Interpretive Structural Modelling (ISM) and

development of an ISM model of 13 drivers for GM implementation:


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3.3.1 Overview of Interpretive Structural Modelling (ISM)

ISM is a systematic application of some elementary notions of graph theory in such a

way that theoretical, conceptual and computational leverage is exploited to efficiently

construct a directed graph or network representation of the complex pattern of a

contextual relationship among a set of elements (Malone, 1975). First proposed by J.

Warfield in 1973, ISM is a computer assisted learning process that enables individuals or

groups to develop a map of the complex relationships among the many elements

involved in a complex situation. ISM is an interactive learning process whereby a set of

different directly and indirectly related elements are structured into a comprehensive

systemic model (Thakkar et al., 2008). The model so formed portrays the structure of a

complex issue in a carefully designed pattern employing graphics as well as words

(Sage, 1977). ISM is often used to provide fundamental understanding of complex

situations, as well as to put together a course of action for solving a problem. It has been

used worldwide by many prestigious organizations including NASA.

ISM provides an ordered directional framework for complex problems, and gives

decision makers a realistic picture of the situation and the variables involved (Attri et al.,

2013). Its basic idea is to use practical experience and knowledge of experts to

decompose a complicated system into several sub-systems (elements) and construct a

multilevel structural model (Gorvett and Liu, 2007). ISM develops insights into

collective understandings of relationships. The ISM is interpretive in the sense that the

judgment of the group of experts decides whether and how the drivers are related to each

other. It is structural in the sense that on the basis of relationship an overall structure is

extracted from the complex set. Developing inter-relationships among variables through

the expert opinion has been used and recommended by many researchers in the extant

literature (Talib et al., 2011; Jharkharia and Shanker, 2005; Soti et al., 2010; Mohammed


122 | P a g e

et al., 2008; Jindal and Sangwan, 2011). It is a modeling technique as the specific

relationships and overall structure are portrayed in a graphical model. It is primarily

intended as a group learning process but can also be used individually.

3.3.2 ISM procedure

The various procedural steps involved in the ISM methodology (Warfield, 1974; Soti et

al., 2010) are:

• Identifying the elements, which are relevant to the problem or issue. This could be

done by a literature survey or any group problem solving technique.

• Establishing a contextual relationship among elements of the system.

• Developing a structural self-interaction matrix (SSIM) of elements indicating pair-

wise relationship among elements of the system.

• Developing a reachability matrix from the SSIM and checking the matrix for

transitivity.

• Partitioning the reachability matrix into different levels and drawing ISM model.

• Review of the ISM model to check for conceptual inconsistency and make the

necessary modifications.

Transitivity of the contextual relation is a basic assumption in ISM which states that if

element A is related to B and B is related to C, then A is necessarily related to C. The

following sections shows the procedure and development of an ISM model of 13 drivers

for GM implementation in Indian industry:

3.3.2.1 Structural Self-interaction Matrix (SSIM)

Relative relationship input (Table 3.14) is provided by the academic and industry experts

among GM drivers. The ISM methodology suggests the use of expert opinions based on

management techniques such as brain storming, nominal group technique, etc. The


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following four symbols have been used to denote the direction of relationship between

drivers i and j for analyzing the drivers in developing SSIM (Warfield, 1974; Mandal and

Deshmukh, 1994):

V = Driver i leads to driver j; A = Driver j leads to driver i;

X = Driver i and j will lead each other; O = Driver i and j are unrelated.

Table 3.14: Structural Self-Interaction Matrix (SSIM) of drivers

S.

No.

Drivers Drivers

2 3 4 5 6 7 8 9 10 11 12 13

1 Current Legislation V V A A V V A V V O V V

2 Future Legislation V A A V V A O V A V V

3 Incentives A A X X A A A A A A

4 Public Pressure O V V O V V V V V

5 Peer Pressure V V O V V V V V

6 Cost Savings X A A A A A A

7 Competitiveness A A A A A A

8 Customer Demand V V V V V

9 Supply Chain Pressure V A V V

10 Top Management Commitment A O O

11 Public Image V V

12 Technology O

13 Organizational Resources

3.3.2.2 Initial Reachability Matrix

The SSIM has been converted into a binary matrix called the initial reachability matrix

(Table 3.15) by substituting V, A, X and O by 1 and 0 as per the following rules:

• If the (i, j) entry in the SSIM is V, the (i, j) entry in the reachability matrix becomes 1

and the (j, i) entry becomes 0.

• If the (i, j) entry in the SSIM is A, the (i, j) entry in the reachability matrix becomes 0


• If the (i, j) entry in the SSIM is X, the (i, j) entry in the reachability matrix becomes 1

and the (j, i) entry also becomes 1.

• If the (i, j) entry in the SSIM is O, the (i, j) entry in the reachability matrix becomes 0



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Table 3.15: Initial Reachability Matrix of drivers

S.

No.

Drivers Drivers

1 2 3 4 5 6 7 8 9 10 11 12 13

1 Current Legislation 1 1 1 0 0 1 1 0 1 1 0 1 1

2 Future Legislation 0 1 1 0 0 1 1 0 0 1 0 1 1

3 Incentives 0 0 1 0 0 1 1 0 0 0 0 0 0

4 Public Pressure 1 1 1 1 0 1 1 0 1 1 1 1 1

5 Peer Pressure 1 1 1 0 1 1 1 0 1 1 1 1 1

6 Cost Savings 0 0 1 0 0 1 1 0 0 0 0 0 0

7 Competitiveness 0 0 1 0 0 1 1 0 0 0 0 0 0

8 Customer Demand 1 1 1 0 0 1 1 1 1 1 1 1 1

9 Supply Chain Pressure 0 0 1 0 0 1 1 0 1 1 0 1 1

10 Top Management Commitment 0 0 1 0 0 1 1 0 0 1 0 0 0

11 Public Image 0 1 1 0 0 1 1 0 1 1 1 1 1

12 Technology 0 0 1 0 0 1 1 0 0 0 0 1 0

13 Organizational Resources 0 0 1 0 0 1 1 0 0 0 0 0 1

3.3.2.3 Final Reachability Matrix

Table 3.16 presents the final reachability matrix developed from the initial reachability

matrix after incorporating the transitivities as discussed in previous section. The driving

power and dependence of each driver are also shown in table 3.16.

Table 3.16: Final Reachability Matrix of drivers

Drivers Drivers D

P 1 2 3 4 5 6 7 8 9 10 11 12 13

1. Current Legislation 1 1 1 0 0 1 1 0 1 1 0 1 1 9

2. Future Legislation 0 1 1 0 0 1 1 0 0 1 0 1 1 7

3. Incentives 0 0 1 0 0 1 1 0 0 0 0 0 0 3

4. Public Pressure 1 1 1 1 0 1 1 0 1 1 1 1 1 11

5. Peer Pressure 1 1 1 0 1 1 1 0 1 1 1 1 1 11

6. Cost Savings 0 0 1 0 0 1 1 0 0 0 0 0 0 3

7. Competitiveness 0 0 1 0 0 1 1 0 0 0 0 0 0 3

8. Customer Demand 1 1 1 0 0 1 1 1 1 1 1 1 1 11

9. Supply Chain Pressure 0 0 1 0 0 1 1 0 1 1 0 1 1 7

10. Top Management Commitment 0 0 1 0 0 1 1 0 0 1 0 0 0 4

11. Public Image 0 1 1 0 0 1 1 0 1 1 1 1 1 9

12. Technology 0 0 1 0 0 1 1 0 0 0 0 1 0 4

13. Organizational Resources 0 0 1 0 0 1 1 0 0 0 0 0 1 4

Dependence 4 6 13 1 1 13 13 1 6 8 4 8 8

DP - Driving Power


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Driving power for each driver is the total number of drivers (including itself), which it

may help to achieve. On the other hand dependence is the total number of drivers

(including itself), which may help achieving it. The driving power and dependency will

be used later in the classification of drivers.

3.3.2.4 Level Partitions

From the final reachability matrix, the reachability and antecedent sets for each driver

are found. The reachability set consists of the element itself and other elements, which it

may help achieve, whereas the antecedent set consists of the element itself and other

elements, which may help achieving it. The intersection of these sets is derived for all

elements. The element for which the reachability and intersection sets are same is the

top-level element in the ISM hierarchy. The top-level element of the hierarchy would not

help achieve any other element. Once the top-level element is identified, it is separated

out from the other elements. This process continues till all elements are assigned levels.

The identified levels help in building the final model. In the present case the drivers

along with their reachability set, antecedent set, intersection set, and the levels are shown

in table 3.17.

Table 3.17: Level Identification (Iterations 1-5)

I. No. Driver Reachability Set Antecedent Set I. Set Level

1 4 1,2,3,4,6,7,9,10,11,12,13 4 4 I

1 5 1,2,3,5,6,7,9,10,11,12,13 5 5 I

1 8 1,2,3,6,7,8,9,10,11,12,13 8 8 I

2 1 1,2,3,6,7,9,10,12,13 1,4,5,8 1 II

2 11 2,3,6,7,9,10,11,12,13 4,5,8,11 11 II

3 2 2,3,6,7,10,12,13 1,2,4,5,8,11 2 III

3 9 3,6,7,9,10,12,13 1,4,5,8,9,11 9 III

4 10 3,6,7,10 1,2,4,5,8,9,10,11 10 IV

4 12 3,6,7,12 1,2,4,5,8,9,11,12 12 IV

4 13 3,6,7,13 1,2,4,5,8,9,11,13 13 IV

5 6 3,6,7 1,2,3,4,5,6,7,8,9,10,11,12,13 3,6,7 V

5 7 3,6,7 1,2,3,4,5,6,7,8,9,10,11,12,13 3,6,7 V

5 3 3,6,7 1,2,3,4,5,6,7,8,9,10,11,12,13 3,6,7 V

I. No. - Iteration Number; I. Set - Intersection Set


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3.3.2.5 ISM model

The structural model is generated by means of vertices/nodes and lines of edges. A

relationship between the driver j and i is shown by an arrow which points from i to j or j

to i depending upon the relationship between i and j. ISM model developed after

removing the transitivities as described in ISM methodology is shown in figure 3.5. All

the 13 drivers for GM implementation have been divided into five levels.

Customer

Demand

Public

Pressure

Peer

Pressure

Public ImageCurrent

Legislation

Future

Legislation

Supply Chain

Pressure

Top Management

CommitmentTechnology

Organizational

Resources

Incentives Cost Savings Competitiveness Level V

Level IV

Level III

Level II

Level I

Figure 3.5: An ISM model of drivers for GM implementation

3.3.3 MICMAC Analysis

Drivers are classified into four clusters (Mandal and Deshmukh, 1994), namely

autonomous drivers, dependent drivers, linkage drivers, and independent drivers.

Autonomous drivers (first cluster) have weak driving power and weak dependence, so


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these drivers are generally disconnected from the system. The second cluster is named

dependent drivers. These drivers have weak driving power and strong dependence

power. Five drivers namely incentives, cost savings, competitiveness, top management

commitment, technology, and organizational resources (3, 6, 7, 10, 12 and 13

respectively) belong to this cluster. The third cluster is named as linkage drivers having

strong driving power and strong dependence power. In this study, no driver lies in first

and third clusters.

Dri

vin

g P

ow

er

13

12

11 4,5

8

10

9 1

11

8

7 2,9

6

5

4 10,12

13

3 3,6

7

2

1

1 2 3 4 5 6 7 8 9 10 11 12 13

Dependence

Figure 3.6: Driver-Dependence Diagram

The fourth cluster is named as independent drivers which has strong driving power and

weak dependence power. Seven drivers namely current legislation, future legislation,

public pressure, peer pressure, customer demand, supply chain pressure, and public

image (1, 2, 4, 5, 8, 9 and 11respectively) belong to this cluster. The graph between

dependence power and driving power for the drivers is given in figure 3.6. Higher value

I

Autonomous

Variables

II

Dependent

Variables

III

Linkage

Variables

IV

Driver

Variables


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of 'dependence' for a driver means that other drivers in the network are to be addressed

first. High value of 'driving force' of a driver means that these drivers are to be

addressed before taking up the other drivers.


The developed ISM model consists of five levels of hierarchy as shown in figure 3.2.

The first level, consisting of public pressure, peer pressure and customer demand drivers,

is termed as 'awareness level'. These three drivers have the maximum driving power and

minimum dependence as shown in the MICMAC results in figure 3.6. This means that

the policy makers in government have to spread the awareness of GM, which in turn

force the individual organizations to adopt GM. These are the root drivers for GM

implementation and help all other drivers for effective implementation of GM. Second

and third level drivers are external to the organizations in nature and these drivers force

the organizations to adopt GM. For example, the emission norms for vehicles in different

countries are forcing the organizations to adopt GM to fulfil the current legislations and

be ready for future improved legislations. Similarly, many multinational organizations

are forcing the SMEs in their supply chain to implement GM. The legislation enforced

by the government for environmental management motivates the organizations to adopt

GM. Once, the organization has been motivated (by level I drivers) or forced (by level II

and III drivers) to implement GM, next is to develop human and technological resources

in the organization. There are three drivers at level IV. These three drivers are internal to

organizations. Top management may be forced to adopt GM by coercive drivers at level

II and III but it is very difficult to motivate managers at middle and lower levels to

implement GM if its implementation does not improve productivity and quality.

Therefore, it has been observed that GM implementation may require specific human

resources at middle and lower levels (Singh et al 2013). GM implementation generally


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requires better and efficient technologies. Top level (level V) drivers are the expected

benefits of the GM implementation provided by the state in terms of tax exemptions,

subsidized loans, allotment of land, etc. or cost savings achieved through consumption of

lesser amount of energy and materials or improved competitiveness among the peers.

3.4 DEVELOPMENT OF A MODEL OF GM DRIVERS USING STRUCTURAL

EQUATION MODELLING

This section provides an overview of Structural Equation Modelling (SEM), develops

and validates SEM model of drivers for GM implementation.

3.4.1 Overview of Structural Equation Modelling

SEM is a statistical methodology that takes a confirmatory, i.e., hypothesis-testing

approach to the analysis of a structural theory bearing on some phenomenon. Typically,

this theory represents “causal” processes that generate observations on multiple variables

(Bentler, 1988). The term structural equation modelling conveys two important aspects

of the procedure: (a) that the causal processes under study are represented by a series of

structural (i.e., regression) equations and (b) that these structural relations can be

modelled pictorially to enable a clearer conceptualization of the theory under study. The

hypothesized model can then be tested statistically in a simultaneous analysis of the

entire system of variables to determine the extent to which it is consistent with the data.

If goodness-of-fit is adequate, the model argues for the plausibility of postulated

relations among variables; if it is inadequate, the tenability of such relations is rejected.

3.4.2 Research Methodology

The basic steps of methodology are driver development, survey instrument development,

data collection, data analysis, model proposition and model validation. The outline of the

research methodology is given in figure 3.7.


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Driver Development

Survey Instrument Development

Data Collection

Model proposition

(Exploratory Factor Analysis)

Model Validation

(Confirmatory factor Analysis and

Structural Equation Modeling)

Data Analysis

Figure 3.7: Research Methodology Outline

In the first step, thirteen drivers for GM implementation presented in section 3.2 were

developed using literature and discussion held with practitioners and academicians.

Survey instrument development, data collection and data analysis are presented in this

section. Model proposition and model validation are presented in the next section.

3.4.2.1 Survey instrument development

A questionnaire was developed based on the drivers in the last section. This survey

questionnaire asked the participants to rate the importance of drivers for GM

implementation on 5 point Likert scale, where 1 means no impact, 2 means low impact, 3

means medium impact, 4 means high impact, and 5 means very high impact. This type of

scale is often used in research and due to the equal spacing between the single scoring

number, an interval scale is simulated to allow further statistical analysis. This type of

scale is used in an effort to force respondents to make an exclusive and decisive choice.

Pre-testing was carried out in two stages. In the first stage, a draft of the questionnaire

was provided to two academicians and they were requested to critically evaluate the


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items from the standpoint of item specificity and clarity of construction. Based on the

critique received, some items were revised to improve their specificity and clarity.

The second pre-test involved administering the questionnaire to industrial professionals.

The professionals were asked to complete the revised questionnaire and indicate any

ambiguity or other difficulty they experienced in responding to the items, as well as to

offer any suggestions they deemed appropriate. The pre-testing was done with 5 top

executives from Indian manufacturing industry in "1st Green Manufacturing Summit" at

New Delhi organized by Confederation of Indian Industry (CII) during February 2011.

This assessment should be a personal judgment of the impact each factor has in the

respondent’s company. Therefore, driver description was provided to ensure that the

participants get the right meaning of the drivers. The questionnaire developed for the

research is given in Appendix A.

3.4.2.2 Data collection

Once the survey instrument is ready, the next step of paramount importance is the

selection of sample for data collection. A sample is a part of population, which is

selected for obtaining the necessary information. Nunnally (1967) argued that, when a

measuring instrument is used for data collection, the subjects/samples used should be

those for whom the instrument is intended. Since the primary objective of the study was

to develop an instrument to measure the participants’ perception of GM drivers,

managers and above were considered as appropriate samples. The General Managers,

Directors, Divisional General Managers, Sr. Managers, Chief Engineers are likely to be

“thought” leaders with respect to environmental activities in their organizations,

therefore, they were taken as the samples for this study. The selection of samples for this

survey has been made based on the following criteria:


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i. Participant should be holding the position not below the level of manager.

ii. Participant should be having working experience of at least 5 years in

manufacturing activities.

iii. Participant should be involved in the decision making about the environmental

improvement activities of the organization.

Next, to select the sample industries a brief literature review was done and it was found

that from the Indian perspective, the major manufacturing sectors involving major

environmental challenges are textile, chemical, rubber/plastics, cement, fabrication,

machinery, electrical and electronics, automotive, pharmaceutical, steel/ iron, food, etc.

The questionnaire was used for an online survey via www.surveymonkey.com website

during February to May 2011. An email was sent to about 500 senior executives (senior

manager and above) working in the manufacturing/production departments or corporate

social responsibility (CSR) heads of different manufacturing firms. The respondents

were selected from Industry Directory by Confederation of India Industry of 2010. The

CSR heads were requested to forward the mail to the appropriate person responsible for

the environmental initiatives in the company. This email contained the web link of the

survey website, explained the background and the objective of the study. The email also

assured the confidentiality of the data as given in appendix - A. The low response rate

was the major concern during the initial stage of the survey. In order to increase the

response rate, email reminders were sent repeatedly and even in some cases telephonic

calls were made. In total 95 usable responses were collected. The response rate was 19%.

3.4.2.3 Data analysis

The drivers will be useful for different applications, by different researchers, in different

studies, only if they are statistically reliable and valid. Reliability reflects the driver's


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ability to consistently yield the same response. Reliable drivers will produce the same

results each time it is administered to the same person in the same setting. Validity refers

to the degree to which drivers truly measure the factors which they intend to measure.

There are four methods to assess the reliability of empirical measurements – the retest

method, the alternative form method, the split-halves method, and the internal

consistency method. The first three methods have major limitations (particularly for field

studies) such as requiring two independent administrators of the instrument on the same

group of people or requiring two alternate forms of the measuring instrument. In

contrast, the internal consistency method works quite well in field studies because it

requires only one administrator. The internal consistency of a set of measurement items

refers to the degree to which items in the set are homogeneous. Internal consistency can

be estimated using reliability coefficient such as Cronbach’s alpha. Internal consistency

analysis was carried out by using SPSS 16.0, to measure the reliability of the items under

each driver in term of Cronbach’s alpha. An alpha value of 0.70 is often considered as

the criteria for establishing internally consistency but a value of 0.6 is also considered

good for the new measures like the present one. Items are eliminated in order to improve

the Cronbach’s alpha, if needed.

A driver has construct validity if it measures the theoretical construct that it is designed

to measure. Muttar (1985) stated three methods of determining construct validity – multi-

trait multi-method analysis, factor analysis, and correlational and partial correlational

analyses. Out of these three methods, factor analysis is usually used to identify items,

which should be included in a consistent measuring instrument. Given that one of the

objectives of this study is to develop items/variables to assess each driver, factor analysis

is chosen to evaluate construct validity, which is consistent with the literature (Flynn et

al., 1994; Quazi, 1999; Badri et al., 1995). Appropriateness of the data for factor analysis


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is also determined by examining the minimum number of observations required per

variable. According to Flynn et al. (1994) a sample size of 30 or more is statistically

sufficient for the analysis. The appropriateness of the factor model is determined by

examining the strength of the relationship among the items/variables. Correlation matrix,

Barlett’s test of sphericity and Kaiser-Meyer-Oklin (KMO) measure of sampling

adequacy are the three measures recommended in the literature for the purpose of

determining the strength of relationship before carrying out the factor analysis (Hair et

al., 1995; Norusis, 1994).

Correlation matrix: Visual inspection of the correlation values between the items in each

measure shows that all the correlations are greater than 0.3. This implies that the

respective items under each measure are likely to have common factors (Hair et al.,

1995; Norusis, 1994).

Barlett’s test of sphericity: Barlett’s test assesses the overall significance of the

correlation matrix. If the value of the test statistic for sphericity is large and the

associated significance level is small, it can be concluded that the variables are

correlated. Barlett’s test of sphericity demonstrated approximate Chi-square value of

457.834, degree of freedom value (df) of 66.000,and significance level value of 0.000,

which are sufficient values for all the thirteen drivers.

KMO measure of sampling adequacy: The test result shows KMO measure of 0.772,

which is above the suggested minimum standard of 0.5 required for running factor

analysis. Hence, based on the above tests, it is concluded that all the thirteen drivers are

suitable for applying factor analysis.

Item - total correlation refers to a correlation of an item or indicator with the composite

score of all the items forming the same set. Corrected item - total correlation (CITC)


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does not include the score of the particular item in question when calculating the

composite score, thus it is labeled 'corrected'. Items from a given scale exhibiting item-

total correlations less than 0.50 are usually candidate for elimination (Koufteros, 1999).

The driver – peer pressure – is eliminated for further analysis as its CITC value is 0.278,

which is less than the minimum accepted value of 0.5. The four drivers – future

legislation, cost savings, competitiveness and public image have CITC values of 0.474,

0.423, 0.457 and 0.469 respectively. These values are very close to 0.5, so these drivers

were not eliminated. Secondly, one driver i.e. current legislation have CITC value of

0.407, but not eliminated as it is very important drivers for GM implementation and also

have a high value of Cronbach's alpha as shown in table 3.18.

Table 3.18: Descriptive statistics of data

Drivers Mean SD CITC SMC CAID

Current Legislation 3.4211 1.13530 0.407 0.563 0.837

Future Legislation 3.5158 0.99854 0.474 0.631 0.831

Incentives 3.1158 1.07053 0.555 0.463 0.825

Public Pressure 2.8105 1.06486 0.520 0.523 0.828

Peer Pressure 2.7579 0.84697 0.278 0.145 0.842

Cost Savings 4.0000 0.85053 0.423 0.311 0.834

Competitiveness 3.8947 0.97275 0.457 0.449 0.832

Customer Demand 3.5158 1.06054 0.638 0.537 0.819

Supply Chain Pressure 3.0842 1.09800 0.554 0.442 0.825

Top Management Commitment 3.9158 1.06854 0.527 0.547 0.827

Public Image 3.6316 1.04222 0.469 0.586 0.832

Technology 3.5053 1.06065 0.531 0.531 0.827

Organizational Resources 3.5263 1.00892 0.525 0.482 0.828

SD - Standard Deviation; CITC - Corrected Item-Total Correlation; SMC - Squared Multiple

Correlation; CAID - Cronbach's alpha if Item Deleted

It can be concluded from the correlation matrix, Barlett's test of sphericity, and KMO

measure of sampling adequacy that the collected data is reliable and is suitable for

further analysis and model development.


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3.4.3 Development of Model using SEM

The number of drivers are large, so the possibility of identifying an underlying structure

(i.e. model) is explored. This allows to consider few constructs representing drivers.

Model development has two parts - model proposition using exploratory factor analysis

(section 3.4.3.1), and model validation using confirmatory factor analysis and structural

equation modelling (sections 3.4.3.2 and 3.4.3.3).

3.4.3.1 Exploratory factor analysis (EFA)

The EFA is used to determine the number of latent variable/factors which represent the

complete set of items. The EFA has been done to find major factors/latent variables

reflecting the major categories and also factor loadings of drivers to these latent variables

as shown in table 3.19. In other words, a model of drivers for GM implementation is

proposed. Factor analysis was conducted on drivers based upon principal components

analysis with Varimax rotation. During EFA, three uni-factorial factors/latent variable

with eigen values greater than one evolved.

The factor loadings for all drivers, which represent the correlation between the variables

and their respective factors, are also found to be satisfactory. The minimum factor

loading is 0.446 for ‘organizational resources’ which is almost equal to the minimum

recommended values of ± 0.45 by Hair et al. (1995). However, to be more confident,

factor analysis within each of the three factors was conducted and the results confirm

that the drivers are well represented by the three explored factors as given in table 3.20.

Hence, it can be concluded that all drivers contribute highly to the represented factors

and have construct validity. After carefully analyzing the group of drivers under each

factor, these three factors are named as: Policy Drivers (PD); Internal Drivers (ID); and

Economy Drivers (ED) as shown in figure 3.8.


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Table 3.19: Factor loadings of GM drivers by exploratory factor analysis

Drivers Factor 1 Factor 2 Factor 3

Current Legislation -0.034 0.802 0.136

Future Legislation -0.028 0.875 0.143

Incentives 0.188 0.623 0.305

Public Pressure 0.428 0.732 -0.070

Cost Savings -0.098 0.286 0.707

Competitiveness 0.180 -0.047 0.812

Customer Demand 0.407 0.277 0.584

Supply Chain Pressure 0.292 0.206 0.621

Top Management Commitment 0.759 0.161 0.169

Public Image 0.878 0.020 0.095

Technology 0.722 0.060 0.301

Organizational Resources 0.446 0.038 0.584

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

Rotation converged in 5 iterations.

Table 3.20: Factor loadings of GM drivers by EFA (within each factor)

Drivers Factor 1 Factor 2 Factor 3

Current Legislation ---- 0.785 ----

Future Legislation ---- 0.872 ----

Incentives ---- 0.721 ----

Public Pressure ---- 0.767 ----

Cost Savings ---- ---- 0.647

Competitiveness ---- ---- 0.783

Customer Demand ---- ---- 0.825

Supply Chain Pressure ---- ---- 0.768

Top Management Commitment 0.798 ---- ----

Public Image 0.825 ---- ----

Technology 0.805 ---- ----

Organizational Resources 0.719 ---- ----

% of variance explained 62.078 62.144 57.559

KMO 0.641 0.703 0.731


Single component extracted each time.


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Drivers for GM

implementation

Policy Drivers (PD)Internal Drivers (ID) Economy Drivers (ED)

Current Legislation

Incentives

Future Legislation

Public Pressure

Top Mgt. Commitment

Org. Resources

Technology

Cost Savings

Competitiveness

Customer Demand

Supply Chain PressurePublic Image

Figure 3.8: Classification of drivers for GM implementation

3.4.3.2 Confirmatory factor analysis (CFA)

The EFA is not sufficient to assess all the essential measurement properties of the

constructs like unidimensionality (Koufteros, 1999). CFA is done to examine the

unidimensionality to ensure the theoretical relationships among the observed variables

(or indicators) with their respective factors (or constructs). Unidimensionality here

means the existence of one unobserved latent variable underlying a set of observed

variables. This is important because weak associations between theoretical constructs

and observed variables may lead to incorrect inferences and misleading conclusions

about relationships among the underlying theoretical constructs of interest (Koufteros,

1999). CFA is a multivariate analysis technique for assessing the model further, which is

pre-specified by EFA (Hair et al., 2006). The proposed model of EFA was transferred to

AMOS 16.0, an SEM tool to carry out the CFA as shown in figure 3.9. Here, the path

diagram represents a measurement model containing three latent variables or constructs

and corresponding twelve indicators or observed variables. The rectangular blocks

represent the observed variables, which act as indicators of the latent or unobserved


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variables represented by the oval block. The circular blocks connecting to the observed

variables through a single headed arrow represents measurement errors (e1, e2,…….,

e12) in measuring the value of an observed variable. The double headed arrow represents

the true correlation among latent variables.

Current Legislation

Incentives

Future Legislation

Public Pressure

Policy

Drivers

Top Mgt. Commitment

Org. Resources

Technology

Public Image

Internal

Drivers

Cost Savings

Competitiveness

Customer Demand


Economy

Drivers

e11

e21

e31

e41

e51

e61

e71

e81

e91

e101

e111

e121

1

1

1

Figure 3.9: Path diagram representing the measurement model of drivers for GM

implementation

The table 3.21 shows the standardized and unstandardized regression weights of the data.

In the unstandardized regression weights, the regression weight of one item under each

factor is fixed and rest are estimated. The regression weights of current legislation, top

management commitment, and cost savings are fixed randomly. The unstandardized

regression weights signify that when latent construct goes up by 1, then the individual


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item goes up by the unstandardized regression weight mentioned against that item.

Similarly, the standardized regression weights signify that when latent construct goes up

by 1 standard deviation, then the standard deviation of individual item goes up by the

standardized regression weight mentioned against that item.

Table 3.21: Confirmatory factor analysis statistics

Drivers Regression Weights* Regression

Weights** Estimate Standard Error Critical Ratio

Current Legislation 1.000 ---- ---- 0.761

Future Legislation 1.016 0.141 7.225 0.879

Incentives 0.702 0.135 5.198 0.567

Public Pressure 0.752 0.134 5.617 0.611

Cost Savings 1.000 ---- ---- 0.475

Competitiveness 1.525 0.392 3.892 0.634

Customer Demand 2.158 0.507 4.255 0.822

Supply Chain Pressure 1.868 0.463 4.036 0.687

Top Management Commitment 1.000 ---- ---- 0.738

Public Image 1.020 0.157 6.482 0.772

Technology 0.942 0.156 6.021 0.700

Organizational Resources 0.778 0.147 5.290 0.608

P < 0.001 (for all coefficients)

* Unstandardized

** Standardized

The minimum value of 'critical ratio' which is a ratio of item estimate to the standard

error is 3.892 which is much above the |2| (|2| is generally considered significant at the

0.01 level). Testing of measurement model (CFA model) using maximum likelihood

estimation (MLE) showed degrees of freedom = 51, CMIN = 120.395, p = 0.000, GFI =

0.819, CFI = 0.833, IFI = 0.839, TLI = 0.784, RMSEA = 0.120, and RMR = 0.106.

These model fit indicators are either within the acceptable value range or very close to

acceptable value. Hence, it is concluded that the measurement model presented in figure

3.6 is accepted (confirmed) and the full structural model can be tested to validate the

final model. The covariance between the latent variables varies from '0' to '1' where '0'

means that the constructs are measuring entirely different variables and '1' means that


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both the constructs are measuring the same variables. The correlations and covariances

among all three latent variables are shown in table 3.22. These values show that the

explored latent variables measure/represent different drivers but not completely

independent drivers.

Table 3.22: Correlation and covariance of latent variables

Drivers Correlation Covariance

Internal Drivers - Economy Drivers 0.655 0.207

Policy Drivers - Internal Drivers 0.265 0.179

Policy Drivers - Economy Drivers 0.428 0.148

3.4.3.3 Structural Model

The factor analysis conducted in the last section has a limitation of examining only one

relationship at a time but it is required to study a set of relationships at a time, which

created a need of further analysis using SEM, which is an extension of factor analysis

and multiple regression analysis (Hair et al., 2006). SEM is a statistical technique for

testing and estimating causal relations using a combination of statistical data and

qualitative causal assumptions. Confirmatory modelling usually starts with hypotheses

that get represented in causal models. Following hypotheses are proposed based on the

careful examination of measurement model to test the full structural model.

Hypothesis (H1): The internal drivers for the implementation of GM are positively

related to policy drivers.

Hypothesis (H2): The internal drivers for the implementation of GM are positively

related to economy drivers.

Hypothesis (H3): The policy drivers for the implementation of GM are positively

related to economy drivers.

Testing of full structural equation model using maximum likelihood estimation (MLE)

was undertaken because MLE has been found to provide valid results even with sample


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sizes as small as 50 (Hair et al., 2006). These model fit indicators are either within the

acceptable value range or very close to it. One probable cause of the little variation of fit

indices may be the small sample size of 95, which is used for the analysis. The fit of the

model can further be improved by correlating the error terms. The proposed structural

model is shown in figure 3.10.

Current Legislation

Incentives

Future Legislation

Public Pressure

Policy

Drivers



Technology

Public Image

Internal

Drivers

Cost Savings

Competitiveness

Customer Demand


Economy

Drivers

H1

H2

H3

Figure 3.10: Full structural model of drivers for GM implementation

The loading estimates have not changed substantially from the CFA model tested in the

last section which indicates the parameter stability and supports the validity of

measurement model. The validation of the model is not complete without examining the

individual parameter estimates. The results of the hypothesis test are shown in table 3.23.

Table 3.23: Results of hypothesis test for GM drivers

Hypothesis β value p value Result

H1 The internal drivers for the implementation of

GM are positively related to policy drivers.

0.291 0.036 Accepted

H2 The internal drivers for the implementation of

GM are positively related to economy drivers.


H3 The policy drivers for the implementation of

GM are positively related to economy drivers.



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All the three hypotheses are accepted as the values of β and p confirms the acceptance.

The hypothesis is accepted if p < 0.05 and β is positive. Therefore, the figure 3.10 shows

the final structural model.


The literature review found 13 generic drivers for GM implementation, however, one

driver, i.e. 'peer pressure' was eliminated during data analysis because of low CITC

value. The results of the online survey show that these 12 drivers have mean value

ranging from 2.7579 to 4.0000 on a scale of 5. It indicates that the industry perceives

these drivers as important drivers for GM implementation. These drivers were tested for

their reliability through Cronbach's alpha value. The Cronbach's alpha values for all the

drivers are good as these are above 0.8 (the good value range). The EFA grouped all

drivers in three categories – policy drivers, internal drivers and economy drivers. This

categorization or factorization has been confirmed by the confirmatory factor analysis.

The factor loadings of all the drivers to their respective factors provided the construct

validity to the drivers. In other words, the policy, internal and economy drivers (factors)

are truly measured by the respective variables (drivers) given in figure 3.8.

Hypothesis testing through SEM has provided interesting results. Accepted hypotheses

show that 'internal drivers' cause policy and 'economy driver's, and 'policy driver's also

cause 'economy drivers'. It infers that for the effective implementation of GM, 'internal

drivers' are to be investigated first as these are the root drivers for GM implementation.

For example, the existence and availability of 'organizational resources' in terms of

human and technical resources facilitates and motivates the government to establish the

effective legislation to implement GM. Also, the availability of technology motivates the

government and other agencies to think of new legislation which can be forced in future,

for example, the availability of European Union emission standards 'euro IV' will cause


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the establishment of similar standards in developing countries. The 'incentives' will lower

the cost of manufacturing of product, hence increases the 'competitiveness'. The

availability of 'technology' which is proven better and more efficient than the existing

technology will generate cost savings, which makes it possible for the companies to

manufacture products of better quality at lesser cost making the business more

competitive. 'Public image' causes the 'competitiveness' as well as 'customer demand'.

Similarly, the 'public pressure' causes the 'customer demand' in the market for

environment friendly products.

3.5 COMPARISION OF DRIVERS IN INDIA AND GERMANY

A case study has been done to compare the proposed GM implementation drivers in a

developed country (Germany) and an emerging country (India). To compare the drivers

for GM, a survey was conducted in Germany using face-to-face interviews followed by

responses in the questionnaire. The data for India is same as in the last section. The

number of filled in questionnaire from German industry were 22 but as the

questionnaires were filled after discussion, the quality of data is expected to be high.

Firstly, the mean values are calculated to assess the importance of the drivers. Very low

mean values of any factor gives a clue that the particular factor is not important and

should be eliminated from the study. Secondly, the standard deviation values are

calculated because the mean value is not always sufficient to measure the central

tendency of the data. Lastly, an 'independent t-test' is done to assess the significance of

the differences as shown below:

3.5.1 Descriptive Statistics

Group statistics for drivers are presented in table 3.24. The minimum value of mean for

drivers is more than 2.38 on a scale of 5, which represents that all the drivers are

important in both countries. The internal consistency analysis is carried out using the


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software SPSS 16.0 for Windows®, to measure the reliability of each driver in term of

the Cronbach’s alpha. In this study, during the initial analysis, none of the factors were

eliminated to improve the reliability as during the initial analysis, Cronbach’s alpha

values were very high for all the 12 drivers. The Cronbach’s alpha value of 0.893 for the

drivers is achieved on the combined data in India and Germany which is considered good

and therefore it is concluded that the data is highly reliable. Hence, it is approved to use

this data for further analysis.

The examination of the mean values of all drivers suggests that the 'top management

commitment' is the most important in India (mean value 4.05) and 'cost savings' is most

imporatnt driver in Germany (mean value 4.03). 'Public pressure' is least important both

in India and Germany with mean values of 2.95 and 2.38 respectively. In addition, to

express the variability of a population, the standard deviation is commonly used to

measure confidence in statistical conclusions. A useful property of the standard deviation

is that it is expressed in the same unit as the data. The standard deviation of data from

both the countries varies from a minimum value of 0.935 for 'customer demand' in India

and maximum value of 1.336 for 'future legislation' in India as shown in table 3.24.

3.5.2 Comparing Means Using Independent t - test

An independent t-test (two-tailed) is conducted on two entirely different and independent

samples of respondents from Indian and German companies to compare the importance

of drivers. The independent t-test is done to know, whether the difference in the drivers

is statistically different for the two countries or not. The procedure to conduct an

independent t-test is shown in figure 3.11. The hypotheses defined for the independent t-

test are:

H0: µIndia = µGermany (null hypothesis)

H1: µIndia ≠ µGermany (alternate hypothesis)


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Table 3.24: Group statistics for drivers

Drivers Country Mean Std. Deviation

Current Legislation India 3.05 1.214

Germany 3.41 1.188

Future Legislation India 3.50 1.336

Germany 3.25 1.047

Incentives India 3.55 1.143

Germany 2.69 1.030

Public Pressure India 2.95 0.950

Germany 2.38 1.129

Cost Savings India 3.82 1.053

Germany 4.03 1.062

Competitiveness India 3.91 1.109

Germany 3.94 0.982

Customer Demand India 3.73 0.935

Germany 3.22 1.099

Supply Chain Pressure India 3.59 1.008

Germany 2.75 1.191

Top Management Commitment India 4.05 1.046

Germany 3.41 1.188

Public Image India 3.86 1.037

Germany 2.91 1.174

Technology India 3.86 1.082

Germany 2.91 1.146

Organizational Resources India 3.73 1.032

Germany 3.34 1.181

Define null and alternate hypotheses

State alpha (α)

Calculate degrees of freedom

State decision rule

Calculate test statistic

State results

State conclusion

Figure 3.11: Independent t-test procedure


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The alpha level used for the study is 0.05, which is a commonly accepted in statistical

studies. The 't-distribution for critical value' for 52 degrees of freedom and 0.05 alpha

level is 2.007 as obtained from the t-table. The decision rule for the parameter states that

the calculated 't' value should be between ± 2.007 to accept the null hypothesis and for

the 't values beyond this range, the null hypothesis will be rejected because of a strange

and unlikely case. The 't' value is calculated using the following formula:

where

Next, in the Levene's test for equality of variances, if the variances are equal in both

groups then the p-value ("Sig.") will be greater than 0.05. However, if the "Sig." value is

less than 0.05, the variances are unequal. If variances are unequal then 'equal variances

not assumed (EVNA)' row need to be used, otherwise 'equal variances assumed (EVA)'

row is taken for interpreting t-test for equality of means. The p-value of more than 0.05

is obtained for all the drivers for Levene's test so it is concluded that the variances are

equal and 'EVA' column have to be selected. Looking down this column, it was seen that

the group means are significantly different as the value in the "Sig. (2-tailed)" row is less

than 0.05 as the t-test is conducted at a confidence interval of 95%. Based on this t-test

for equality of means, the significance of difference of importance for drivers in both the

countries are presented in table 3.25.


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3.5.3 Effect Size for Independent t - test

After performing independent t-test, it is important to calculate the 'effect size' which

measures the magnitude of mean differences. It explains whether the difference in the

'means' are a little different or very different. This is usually calculated after rejecting the

null hypothesis in a statistical test. If the null hypothesis is not rejected, then the 'effect

size' has little or no meaning. 'Cohen's d' is used to assess the effect size as given below:

‐

- -

-

Table 3.25: Independent t-test statistics to compare drivers for India and Germany

Drivers Levene's Test* T-test for Equality of Means Cohen's

d*** F Sig. t df Sig.** MD# SED

$

Current Legislation EVA 0.057 0.812 -1.087 52 0.282 -0.361 0.332

-0.2997 EVNA -1.083 44.631 0.285 -0.361 0.333

Future Legislation EVA 1.518 0.223 0.770 52 0.445 0.250 0.325

0.20829 EVNA 0.736 37.901 0.466 0.250 0.340

Incentives EVA 0.242 0.625 2.876 52 0.006 0.858 0.298

0.79046 EVNA 2.820 42.087 0.007 0.858 0.304

Public Pressure EVA 2.458 0.123 1.974 52 0.054 0.580 0.294

0.54631 EVNA 2.038 49.780 0.047 0.580 0.284

Cost Savings EVA 0.001 0.974 -0.727 52 0.471 -0.213 0.293

-0.1985 EVNA -0.728 45.561 0.470 -0.213 0.293

Competitiveness EVA 0.324 0.571 -0.099 52 0.921 -0.028 0.287

-0.0286 EVNA -0.097 41.557 0.923 -0.028 0.293

Customer Demand EVA 1.502 0.226 1.772 52 0.082 0.509 0.287

0.49985 EVNA 1.826 49.559 0.074 0.509 0.278

Supply Chain Pressure EVA 1.297 0.260 2.709 52 0.009 0.841 0.310

0.76135 EVNA 2.795 49.680 0.007 0.841 0.301

Top Management

Commitment

EVA 1.962 0.167 2.038 52 0.047 0.639 0.314 0.57180

EVNA 2.088 48.779 0.042 0.639 0.306

Public Image EVA 0.992 0.324 3.085 52 0.003 0.957 0.310

0.85769 EVNA 3.157 48.693 0.003 0.957 0.303

Technology EVA 0.104 0.748 3.085 52 0.003 0.957 0.310

0.85243 EVNA 3.118 46.960 0.003 0.957 0.307

Organizational

Resources

EVA 0.653 0.423 1.233 52 0.223 0.384 0.311 0.35166

EVNA 1.265 48.951 0.212 0.384 0.303

* for Equality of Variances; ** 2-tailed; #Mean Difference;

$Standard Error Difference;

*** to assess effect size


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'Cohen' d' is used to assess the magnitude of difference. A value of 'cohen'd' upto 0.3 can

be considered as small effect and a value of 0.7 and above can be considered as large

effect. In between values are generally taken as medium effect.

Table 3.26 presents the final results of the comparison of drivers and their effect size

based on 'cohen's 'd' value.

Table 3.26: Results of comparison for drivers

Drivers Comparison Effect size

Current Legislation Equal ----------

Future Legislation Equal ----------

Incentives Significantly different Large

Public Pressure Significantly different Medium

Cost Savings Equal ----------

Competitiveness Equal ----------

Customer Demand Equal ----------

Supply Chain Pressure Significantly different Large

Top Management Commitment Significantly different Medium

Public Image Significantly different Large

Technology Significantly different Large

Organizational Resources Equal ----------


Results clearly show the statistical significance either 'statistically different' or 'equal' in

column 2 of table 3.26 along with the magnitude of the difference in column 3, if it

exists. Large effect size means that the importance of driver is highly different in both

the countries. This has provided a broad perspective on the drivers for GM in two

different economies – a developed economy and an emerging economy.

The four drivers – incentives, supply chain pressure, public image, and technology –

have large differences in the two countries. The impact of 'incentives' in India is higher

than in Germany. It means the implementation of GM in India is rather driven by

economic benefits than legislation. The 'supply chain pressure' is more important in India


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as the manufactured goods are supplied to many developed nations which have more

stringent regulations for imported parts/products. As many Indian organizations have not

yet implemented GM, they are striving for a better 'public image' which can create a

competitiveness over other organizations in the market. The scarcity and cost of the

newer green 'technology' in India makes it a better motivator than in Germany, where the

technology is easily available.

The two drivers public pressure and top management commitment are significantly

different but have medium differences in the two countries. The 'public pressure' is more

important in India because of increasing awareness among agencies like banks, insurance

companies, NGOs, etc. and the involvement of peers by the government in policy

making. The influence of the 'top management commitment' is higher in India, which can

trigger voluntary initiatives amongst the top management to implement GM. Rest of the

drivers current legislation, future legislation, cost savings, competitiveness, customer

demand, and organizational resources have same importance in both the countries.

3.6 SUMMARY

The 13 drivers for GM implementation have been developed using literature and

discussion with practitioners and academicians.

The 13 drivers for GM implementation were ranked using fuzzy TOPSIS multi-criteria

decision model from government, industry and expert perspectives. This provided a

proper tool to encounter the uncertain and complex environment by measuring the

inherent ambiguity of decision maker’s subjective judgment. The study concluded that

competitiveness, incentives, organizational resources and technology are the four top

ranking drivers and should be facilitated first by the government and industry to help

industry in implementing GM.


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A model of the 13 drivers for GM implementation has been developed using interpretive

structural modelling showing hierarchy and inter-relationship. It has been found that

'customer demand', 'public pressure' and 'peer pressure' are the root drivers for GM

implementation and these drivers help other drivers for effective implementation of GM.

The developed model divided the identified drivers into five levels of hierarchies

showing inter-relationship among these drivers. The developed model will be highly

useful for the policy makers in government and industry to strategically leverage their

resources in a systematic way for successful implementation of GM.

A statistically reliable and valid model of GM implementation drivers is presented using

statistical tools, namely SPSS 16.0 and AMOS 16.0. The drivers were purified using

statistical analysis. One of the drivers namely 'peer pressure' was eliminated during this

process. The remaining 12 GM drivers were divided into three categories – internal

drivers, policy drivers, and economy drivers – using exploratory factor analysis. The top

management commitment, the availability of human resources in the organization,

environment friendly technology, and need of green image of the organization represent

internal drivers for GM implementation in an organization. The policy drivers are

symbolized by current and future legislations related to the operations and products of

the organization, incentives provided by the governments, and the pressure build by the

media, NGOs, banks, insurance companies, local politicians, etc. The economy drivers

are reflected by cost savings, competitiveness, customer demand, and supply chain

pressure. Secondly, the confirmatory factor analysis is done to confirm the classification

of the drivers. The final model has been tested using structural equation modelling

technique wherein hypotheses affirm that internal drivers cause policy and economic

drivers and policy drivers further causes economy drivers.


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Lastly, a case study is carried out to compare the importance of drivers in an emerging

country (India) and a developed country (Germany) using independent t-test. The study

concluded that four drivers – incentives, supply chain pressure, public image, and

technology – have large differences in the two countries. Public pressure and top

management commitment drivers are significantly different but have medium differences

in the two countries. Rest of the drivers – current legislation, future legislation, cost

savings, competitiveness, customer demand, and organizational resources – have same

importance in both the countries. This has provided a broad perspective on the drivers for

GM in two different economies.

CHAPTER 4

BARRIERS TO GREEN MANUFACTURING

IMPLEMENATATION

Manufacturing firms face multiple barriers hindering GM implementation. The mitigation of

these barriers would help industry to effectively implement GM. The literature on GM

barriers has been provided in chapter 2. This chapter provides:

Development of GM barriers.

Ranking of the barriers using fuzzy TOPSIS multi-criteria decision model.

Establishment of hierarchy and inter-relationship among the barriers using interpretive

structural modelling.

Validation of the barriers through an empirical study and statistical analysis.

A case study to compare the GM barriers in India and Germany.

4.1 BARRIERS TO GM IMPLEMENTATION

This section develops brief descriptions of the 12 barriers identified in the second chapter.

The development of these barriers is based on the literature and the discussion held with

experts from industry and academia.

4.1.1 Weak Legislation

The weak environmental legislation is an important barrier to the implementation of GM.

This includes the complete absence of environmental laws and the complexity or

ineffectiveness of the legislation. Some companies especially SMEs invest in environment

friendly technologies only when they are forced to do (Masurel, 2007). Environmental

legislations should not only be simple to understand and implement but also strong and

Barriers to Green Manufacturing Implementation

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effective to force the organizations to accept them in totality. Zhang (2005) has pointed out

the loopholes within the environmental legislation in China as a barrier to the

implementation of green government procurement. It has been observed in some countries

that the regulations are so weak that the cost of paying fine for not complying with

environmental laws is less than complying with the environmental laws. For instance,

according to the Chinese environmental regulations, the local environmental protection

bureau can only fine companies a maximum of 100,000 RMB whenever they pollute and

break discharge limits of waste (Zhang, 2006). The absence of international environmental

legislation and the lack of harmony among national legislations often hinder major

environmental improvements (Moors et al., 2005).

4.1.2 Low Enforcement

Another aspect of legislation is the low enforcement of otherwise strong regulations. In

some countries, the enforcement of the environmental regulations is a challenge for

authorities due to varied reasons such as lack of organizational infrastructure, lack of trained

human resources, cost of monitoring, and dishonest officials. In Netherlands, funding is

provided by the central government so that local authorities can monitor the implementation

of environmental laws but this is not evident in countries such as UK (Rutherfoord et al.,

2000). The literature suggests that the low cost and effective methods for legislation

enforcement are: site visits, membership of industry associations on regulatory compliance,

independent audits, and annual progress reports (Revell and Blackburn, 2007; Gunningham

and Sinclair, 2002). Moreover, corruption can be a problem to enforce regulations as it is

easier to pay bribe to the officials instead of improving production conditions (Robbins,

2000). The problem of corruption in many countries like India is clearly hindering the


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implementation of GM particularly in micro, small and medium enterprises (Singh et al.,

2012).

4.1.3 Uncertain Future Legislation

Another important legislative barrier to GM implementation is uncertainty of current

legislations or future legislations. When companies think of investing into green

technologies, possible upcoming legislations with unforeseen impacts can be a threat to

them (Seidel et al., 2009). Thus, investments are withheld for future regulations. This shows

the fear of decision makers to put efforts into projects, which may excel today’s

environmental standards, but may not be sufficient after few years. This uncertainty arose

from frequent policy changes experienced in the past (Del Rio Gonzalez, 2005). It may be a

very crucial decision for the management to invest in newer environmental technologies if it

takes long time to implement. It may happen that by the time new technology is

implemented, the regulations are changed and are far more stringent than that can be met

with by the new technology. Hence, the huge investments made by the company are not

sufficient to comply with the newer regulations, posing threat to the existence of the

company. Lee and Dimitris (1997) found from an empirical study of UK based chemical

manufacturing firms that the frequent changes in the existing legislation about the

environmental performance is a constraint in encouraging the firms for environmental

initiatives.

4.1.4 Low Public Pressure

Low public pressure is a barrier to GM implementation (Del Rio et al., 2010). The absence

of pressure by key social actors like local communities, media, NGOs, banks, insurance

companies or politicians may not provide the necessary push for companies to eco-innovate.


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The various public pressure groups include local communities, local politicians, local

bodies, media and NGOs (Sangwan, 2011). In recent past, the demonstrations and agitations

by local communities had forced the Indian government to shut down polluting industries or

revisit the mining agreements with industries. The media has played a crucial role in

highlighting these agitations and creating a general awareness among masses. Lack of fund

and high insurance premium for non-GM organizations may force these organizations to

adopt GM. Andrew-Speed (2004) has found through a case study that the lack of public

participation is a major barrier to energy saving.

4.1.5 High Short-term Costs

On the economic side, high short-term costs of implementation are found to be barrier to

GM (Zhu and Geng, 2013). Investing in a new and efficient equipment or machinery

requires financial resources. The cost of buying the newer and efficient technology is

generally very high and also it requires lot of changes in the shop floor leading to higher

costs of implementation. Thus, manufacturers fear extra costs can affect their profits or

market share (Dwyer, 2007). In the long run, green technologies can save costs because of

higher efficiency, but higher initial capital cost of cleaner technologies prevents companies

from implementing it (Shi et al., 2008). Moors et al. (2005) concluded that enormous capital

investment is required for fundamental technological changes and the returns on huge

investment are long-term.

4.1.6 Uncertain Benefits

In addition to the high investment and implementation costs, the economic benefits of GM

can be uncertain (Del Rio Gonzalez, 2005). The green economy business costs are not yet

fully understood, technology is new and not fully matured; and costs are still evolving


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(Sangwan 2011). This leads to the big challenge for the top level management to justify the

shift from existing conventional technologies to newer green technologies in term of

economic benefits. Clean technologies are profitable in the medium or long run; sometimes,

they are not profitable at all in comparison to traditional technologies. The slow rate of

return on the investments made by the companies on environment friendly technologies is

another issue that makes economic benefit calculations complex or sometimes impossible.

GM implementation requires a long investment period due to the length of the entire product

cycle which increases the risk (Hua et al., 2005).

4.1.7 Low Customer Demand

Low customer demand for environment friendly products and for their production conditions

is a barrier to GM implementation (Dwyer, 2007). Most customers, particularly in

developing countries, are price sensitive and are interested in cheaper products. Customers

often cannot verify if a product was produced in an environmentally conscious manner or

not. This is also true for many attributes of environment friendly complex products (Yim,

2007). Studies within the European Union have revealed that the uncertain demand from the

market is one of the most important barriers to eco-innovation uptake and development

(European Commission, 2011). As the initial investment is very high in GM systems, the

cost of the product produced is also high and customer is reluctant to buy a costly product,

though produced in green way. This reluctance becomes more significant in emerging

countries like India where the customers are highly sensitive to the price.

4.1.8 Trade-Offs

Organizations outsource their non-environmental friendly manufacturing work to

developing or emerging markets where environmental laws are less stringent. This reduces


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their share of emission. In other words, organizations have traded-off their emissions to

other countries by outsourcing the manufacturing. This has led to the trading of the emission

rather than reduction (Dwyer, 2007). A growing share of products are manufactured in

BRICS countries for the western economies. The import of these products hides the carbon

emissions behind the international borders. CO2 footprints of some countries or companies

are decreasing due to the change of manufacturing site of suppliers in other countries.

Pollution is growing on the global scale with the assumption that the BRICS countries do

not have the same environmental effectiveness as the western countries. This easy way of

problem outsourcing is a barrier to GM implementation.

4.1.9 Low Top Management Commitment

Top management has significant ability to influence, support and champion the actual

formulation and deployment of environmental initiatives across the organization (Sangwan,

2011). GM implementation requires top management commitment and its lack hinders GM

implementation. Studies have identified that companies particularly SMEs ignore their own

environmental impact because of lack of management support (Seidel et al., 2009). Top

management feels that sustainability does not have the potential to benefit the companies.

Compulsory environmental regulations might force the companies for compliance-driven

behavior instead of environmental commitment, which is essential for effective green

initiatives (Tilley, 1999). There are evidences in the literature that profit-driven and

compliance-driven firms have low environmental commitment because compliance-driven

firms lack strategic mindset for the change (Condon, 2004). The absence of top level

encouragement or commitment towards cleaner production could be an important barrier to

major innovations for environmental improvements (Moors et al., 2005).


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4.1.10 Lack of Organizational Resources

Limited technical, human, and economic resources always affect the ability of firms to adopt

new practices like GM (Hadjimanolis and Dickson, 2000). Cost is the most serious obstacle

for taking environmental factor into manufacturing system (Min and Galle, 2001). It’s very

costly to change existing investments, information systems, and habits (Wycherley, 1999).

Due to the lack of resources, SMEs are not able to find alternative solution in designing their

products to fulfill the design for environment requirements (Van Hemel and Cramer, 2002).

Another difficulty which the firms face is the lack of organizational resources in terms of

skilled personnel required for installation of new technology (DeCanio, 1993; Kablan, 2003;

DeCanio, 1998; Zilahy, 2004; Velthuijsen, 1993). In China, lack of experience has been

found to be a serious barrier affecting energy saving because of the high energy

consumption and low efficient economic development pattern (Wang et al., 2008). Lack of

trained manpower, lack of technical assistance and professional training for technicians, and

lack of ability for testing lessen the potential efficiency of economic measures (Andrews-

Speed, 2004). Tight financial resources, low technological competency and a low priority

given to environmental issues can be obstacles to eco-innovation (Del Rio et al., 2010). In

Sri Lanka, lack of professional management skills, low quality of record keeping, over

emphasis on production, and non involvement of workers have been observed as important

barriers (Cooray, 1999). Lack of trained human resources hinders the adoption of GM. The

main organizational risk factors are the integration of the management approach, the

knowledge level of the lead group, and the knowledge level of the personnel (Hua et al.,

2005).

4.1.11 Technology Risk

Since the concept of GM is relatively new, its theories and technologies are still being

developed. Only experience will show whether or not each technology can be used in GM


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projects to create extended benefits for industry, society and ecology. Therefore, there are

many technological risk factors, including its reliability, maintenance, and applicability (Hua

et al., 2005). State of the art information on new technologies, materials, operations and

industrial processes is often not available to the top management, particularly in SMEs.

Moreover, technical support is not often updated in SMEs (Wooi and Zailani, 2010). There

are evidences that managers are deterred from initiating risky energy efficiency projects if

the personal consequences of failure seems to be high (DeCanio, 1993). Upscaling of a new

unproven technological development is a risky activity, which will lead to losses if the

technology does not work. This increases the perceived risk to industries considering huge

investments in new technologies (Moors et al., 2005). Reluctance to invest in newer

technologies because of high risk is a vital barrier to energy saving practices too. A large

amount of capital is needed to buy new equipment and develop better technologies. The

longer investment cycle increases the level of risk to such an extent that companies of all

sizes would rather not pioneer new energy efficient technologies in their production.

Potential producers of new energy efficient technologies may not invest and produce new

products without guaranteed sale of the new technologies (Andrews-Speed, 2004). The

newer green technologies may even be incompatible with the current manufacturing system,

so hinders the implementation.

4.1.12 Lack of Awareness/Information

Lack of awareness or information in a company about latest environment friendly

manufacturing systems is another major barrier. In emerging countries, plant managers often

have insufficient information about the available technology choices (Luken and Rompaey,

2008) and have limited access to green literature or the information diffusion. Even in

western economies information seems to be lacking. The British Engineering Employers


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Federations found that most of their members do not know the sustainability meaning

(Dwyer, 2007). Lack of exposure is a common problem faced by the SMEs. Management

does not have information on initiatives taken by other organizations and their success to

implement GM (Wooi and Zailani, 2010). Some managers are resistant to technological

changes for energy conservation because they do not know how to implement an energy

conservation project or how to quantify energy saving benefits (Kablan, 2003; Harris et al.,

2000; Tonn and Martin, 2000). Increase in information and awareness of environmental

issues have a considerable support in literature as it is expected to encourage environmental

commitment among SMEs (Simpson et al., 2004; Condon, 2004; Tilbury et al., 2005).

SME's response to challenges of improving the environmental performance is slow because

of the lack of skills and knowledge required for environmental action (Rowe and

Hollingsworth, 1996). A study conducted in Zambia revealed that the lack of awareness

regarding cleaner production re-enforces why many companies still relies on the traditional

approaches of waste management by concentrating on pollution control and end-of-pipe

abatement rather than pollution prevention at source (Siaminwe et al., 2005). Lack of

information and awareness is rated as an important barrier to energy saving methods in

China (Wang et al., 2008).

4.2 RANKING OF GM BARRIERS USING FUZZY TOPSIS

4.2.1 Development of a Fuzzy TOPSIS Method for Ranking GM Barriers

Figure 4.1 provides an hierarchical structure used to rank the 12 barriers to GM

implementation. The 12 barriers (B1 to B12) are at the bottom of the hierarchy, the criteria

used to rank the barriers are at the middle of the hierarchy, and the goal of ranking the

barriers is at the top of the hierarchy.


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Table 4.1: Description of GM barriers

S. No. Barriers Description References

1 Weak Legislation Complete absence of environmental laws

or complex and ineffective environmental

legislations.

Singh et al. (2012), Massoud et al. (2010), Herren and Hadly (2010), Yu

et al. (2008), Zhang et al. (2009), Seidel at al. (2009), Studer et al.

(2006), Veshagh and Li (2006), Moors et al. (2005), Kaebernick and

Kara (2006), Mittal et al. (2012), Mittal et al. (2013), Ioannou and

Veshagh (2011), Del Río et al. (2010), Schönsleben et al. (2010),

Sangwan (2006), Del Río González (2005)

2 Low Enforcement Ineffective enforcement of environmental

laws because of lack of organizational

infrastructure, lack of trained human

resources, cost of monitoring, dishonest

officials, etc.

Shi et al. (2008), Siaminwe et al. (2005), Mittal et al. (2012), Mittal et

al. (2013), Del Río et al. (2010), Del Río González (2005)

3 Uncertain Future

Legislation

Possibility of upcoming legislations with

unforeseen impacts on the huge

investments on newer technologies.

Dwyer (2007), Sangwan (2006, 2011)

4 Low Public Pressure The absence of pressure by key social

actors like local communities, media,

NGOs, banks, insurance companies or

politicians.

Wang et al. (2008), Shi et al. (2008), Zhang et al. (2009), Montalvo

(2008), Studer et al. (2006), Mittal et al. (2012), Mittal et al. (2013), Del

Río et al. (2010), Del Río González (2005)

5 High Short-Term Costs High cost of buying newer efficient

technology and its implementation.

Massoud et al. (2010), Herren and Hadly (2010), Wang et al. (2008), Yu

et al. (2008), Shi et al. (2008), Cooray (1999), Zhang et al. (2009),

Luken and Rompaey (2008), Montalvo (2008), Studer et al. (2006),

Siaminwe et al. (2005), Moors et al. (2005), Mittal et al. (2012), Mittal

et al. (2013), Dwyer (2007), Ioannou and Veshagh (2011), Zhu and

Geng (2013), Del Río et al. (2010), Schönsleben et al. (2010), Del Río

González (2005)

6 Uncertain Benefits Uncertainty of achievable benefits after

making huge investments in newer

technologies

Massoud et al. (2010), Seidel at al. (2009), Luken and Rompaey (2008),

Montalvo (2008), Veshagh and Li (2006), Moors et al. (2005), Mittal et

al. (2012), Mittal et al. (2013), Ioannou and Veshagh (2011), Zhu and

Geng (2013), Del Río et al. (2010), Schönsleben et al. (2010), Del Río

González (2005)


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Table 4.1: Description of GM barriers (contd.)

S. No. Barriers Description References

7 Low Customer Demand Low customer demand for environment

friendly products and processes

because of price-sensitive and

uninformed customers.

Koho et al. (2011), Massoud et al. (2010), Yu et al. (2008), Shi et al. (2008),

Studer et al. (2006), Veshagh and Li (2006), Mittal et al. (2013), Dwyer

(2007), Ioannou and Veshagh (2011), Del Río et al. (2010), Schönsleben et

al. (2010), Del Río González (2008)

8 Trade-Offs Outsourcing of dirty manufacturing

work to developing or emerging

markets where environmental laws are

less stringent.

Dwyer (2007), Del Río et al. (2010), Sangwan (2006)

9 Low Top Management

Commitment

Low top management commitment

deterring ability to influence, support

and champion the actual formulation

and deployment of environmental

initiatives across the organization.

Singh et al. (2012), Massoud et al. (2010), Herren and Hadly (2010), Wang

et al. (2008), Yu et al. (2008), Shi et al. (2008), Cooray (1999), Montalvo

(2008), Studer et al. (2006), Mitchell (2006), Siaminwe et al. (2005), Moors

et al. (2005), Kaebernick and Kara (2006), Mittal et al. (2012), Mittal et al.

(2013), Dwyer (2007), Ioannou and Veshagh (2011), Zhu and Geng (2013),

Sangwan (2006), Del Río González (2005)

10 Lack of Organizational

Resources

Limited technical and human resources

affect the ability of firms to adopt new

practices like green manufacturing.

Singh et al. (2012), Wang et al. (2008), Yu et al. (2008), Shi et al. (2008),

Cooray (1999), Seidel at al. (2009), Luken and Rompaey (2008), Montalvo

(2008), Studer et al. (2006), Mitchell (2006), Veshagh and Li (2006),

Siaminwe et al. (2005), Moors et al. (2005), Mittal et al. (2012), Mittal et al.

(2013), Dwyer (2007), Ioannou and Veshagh (2011), Zhu and Geng (2013),

Del Río et al. (2010), Sangwan (2006), Del Río González (2005)

11 Technology Risk State of the art technologies, materials,

operations and industrial processes are

often unproven and their

implementation is always risky.

Wang et al. (2008), Yu et al. (2008), Cooray (1999), Zhang et al. (2009),

Luken and Rompaey (2008), Montalvo (2008), Siaminwe et al. (2005),

Mittal et al. (2012), Mittal et al. (2013), Ioannou and Veshagh (2011), Del

Río et al. (2010), Schönsleben et al. (2010), Sangwan (2006, 2011), Del Río

González (2005), Mittal and Sangwan (2011)

12 Lack of Awareness/

Information

Insufficient information about the

available technology choices and

limited access to green literature or the

information diffusion.

Singh et al. (2012), Massoud et al. (2010), Herren and Hadly (2010), Wang

et al. (2008), Shi et al. (2008), Cooray (1999), Zhang (2000), Seidel at al.

(2009), Luken and Rompaey (2008), Siaminwe et al. (2005), Moors et al.

(2005), Mittal et al. (2013), Dwyer (2007), Ioannou and Veshagh (2011),

Zhu and Geng (2013), Del Río González (2005)


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GOAL:

Ranking of Barriers

Industry perspective

[C2]

Government

perspective [C1]

Expert perspective

[C3]

B6 B7B5 B8B4 B9B3B2B1 B12B11B10

Figure 4.1: A hierarchical structure for ranking the barriers to GM

Table 4.2 lists various criteria chosen for ranking the barriers to GM implementation, their

definition and type. These criteria have been obtained from literature review and discussion

with Indian government, industry and experts. Three different criteria namely government

perspective, industry perspective and expert perspective were chosen to determine the

ranking of barriers to GM implementation for Indian scenario. Ranking based on the

combined criteria is useful to judiciously prioritize the barriers. A scale of 1–9 is applied for

rating the criteria and the alternatives. Table 3.3 (chapter 3) presents the linguistic variables

and fuzzy ratings for the alternatives and criteria.

Table 4.2: Criteria for ranking barriers to GM


Government perspective View of officials from government departments

handling industrial and environmental policies Importance

(the more the

important)

Industry perspective View of executives from industry handling

industrial and environmental policies

Experts perspective View of experts working on environmental issues

The second step of the methodology involves evaluation of all barriers against the selected

criteria, i.e. the perspective in this case using fuzzy TOPSIS. The fuzzy TOPSIS approach


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chooses the alternative that is closest to the positive ideal solution and farthest from the

negative ideal solution. A positive ideal solution is composed of the best performance values

for each attribute whereas the negative ideal solution consists of the worst performance

values. The various steps of the fuzzy TOPSIS method used to rank barriers are as follows:


Let us assume there are 'j' possible barriers called B = {B1, B2 . . . Bj} which are to be

evaluated against m criteria, C = {C1, C2 . . . Cm}. The criteria weights are denoted by wi (i

= 1, 2 . . . m). The performance ratings of each decision maker Dk (k = 1, 2, . . . , K) for each

alternative Bj (j = 1, 2, . . , n) with respect to criteria Ci (i = 1, 2, . . . , m) are denoted by

ijkk xR ~~ (i = 1, 2, . . . ,m; j = 1, 2, . . . , n; k = 1, 2, . . . , K) with membership function

)(~ xkR

. In the present case there are twelve alternatives (barriers), three criteria

(perspectives) and three decision makers as discussed in chapter 2. Table 4.3 and table 4.4

present linguistic assessments for all three criteria and twelve alternatives respectively in

consultation with decision makers. It is apparent that all criteria belong to the important

category, i.e. the higher the value, the more important the alternative.



Government perspective (C1) VH L L

Industry perspective (C2) L VH VH

Experts perspective (C3) H H H


If the fuzzy ratings of all decision makers are described as triangular fuzzy numbers


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kR~

(ak, bk, ck), k = 1, 2. . . K, then the aggregated fuzzy rating is given by kR~

(a, b, c), k

= 1, 2... K, where

Table 4.4: Linguistic assessment of the alternatives (barriers)

S. No. Barriers Government Industry Experts

B1 Weak Legislation LI FI I

B2 Low Enforcement FI FI VI

B3 Uncertain Future Legislation I VI FI

B4 Low Public Pressure I FI LI

B5 High Short-Term Costs FI VI I

B6 Uncertain Benefits I I I

B7 Low Customer Demand FI FI FI

B8 Trade-Offs VI LI FI

B9 Low Top Management Commitment I LI VI

B10 Lack of Organizational Resources I I I

B11 Technology Risk FI I VI

B12 Lack of Awareness/Information LI FI I

a = }{min kk a ,

K

k

kbK

b1



) is constructed below:

)~,.......~,~(~

21 nwwwW

Table 4.5 presents the aggregate fuzzy weights for the criteria on the basis of ratings given

by the decision makers.



Government perspective (C1) (7,9,9) (1,3,5) (1,3,5) (1,5,9)

Industry perspective (C2) (1,3,5) (7,9,9) (7,9,9) (1,7,9)

Expert perspective (C3) (5,7,9) (5,7,9) (5,7,9) (5,7,9)


167 | P a g e



(D is constructed below (Table 4.6) using the

following relation:

nccc ...21

mnmm

n

n

m xxx

xxx

xxx

B

B

B

D

~...~~............

~...~~

~...~~

...

~

21

22221

11211

2

1

Table 4.6: Aggregate fuzzy weights of the alternatives (barriers)


B1 Weak Legislation (1,3,5) (3,5,7) (5,7,9)

B2 Low Enforcement (3,5,7) (3,5,7) (7,9,9)

B3 Uncertain Future Legislation (5,7,9) (7,9,9) (3,5,7)

B4 Low Public Pressure (5,7,9) (3,5,7) (1,3,5)

B5 High Short-Term Costs (3,5,7) (7,9,9) (5,7,9)

B6 Uncertain Benefits (5,7,9) (5,7,9) (5,7,9)

B7 Low Customer Demand (3,5,7) (3,5,7) (3,5,7)

B8 Trade-Offs (7,9,9) (1,3,5) (3,5,7)

B9 Low Top Management Commitment (5,7,9) (1,3,5) (7,9,9)

B10 Lack of Organizational Resources (5,7,9) (5,7,9) (5,7,9)

B11 Technology Risk (3,5,7) (5,7,9) (7,9,9)

B12 Lack of Awareness/Information (1,3,5) (3,5,7) (5,7,9)


The raw data is normalized using a linear scale transformation to bring the various criteria

scales onto a comparable scale. The normalized fuzzy decision matrix R~

shown in table 4.7

is computed as:

nmijrR ]~[~

, i = 1, 2, . . . , m ; j = 1, 2, . . . , n

Where


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***,,~

j

ij

j

ij

j

ij

ijc

c

c

b

c

ar and }{max*


Table 4.7: Normalized decision matrix (barriers)

S.

No.

Barriers Government Industry Experts

*

jc 9 9 9

B1 Weak Legislation (0.11,0.33,0.56) (0.33,0.56,0.78) (0.56,0.78,1)

B2 Low Enforcement (0.33,0.56,0.78) (0.33,0.56,0.78) (0.78,1,1)

B3 Uncertain Future Legislation (0.56,0.78,1) (0.78,1,1) (0.33,0.56,0.78)

B4 Low Public Pressure (0.56,0.78,1) (0.33,0.56,0.78) (0.11,0.33,0.56)

B5 High Short-Term Costs (0.33,0.56,0.78) (0.78,1,1) (0.56,0.78,1)

B6 Uncertain Benefits (0.56,0.78,1) (0.56,0.78,1) (0.56,0.78,1)

B7 Low Customer Demand (0.33,0.56,0.78) (0.33,0.56,0.78) (0.33,0.56,0.78)

B8 Trade-Offs (0.78,1,1) (0.11,0.33,0.56) (0.33,0.56,0.78)

B9 Low Top Management Commitment (0.56,0.78,1) (0.11,0.33,0.56) (0.78,1,1)

B10 Lack of Organizational Resources (0.56,0.78,1) (0.56,0.78,1) (0.56,0.78,1)

B11 Technology Risk (0.33,0.56,0.78) (0.56,0.78,1) (0.78,1,1)

B12 Lack of Awareness/Information (0.11,0.33,0.56) (0.33,0.56,0.78) (0.56,0.78,1)



for criteria is computed by multiplying the weights )~( jw

of evaluation criteria with the normalized fuzzy decision matrix ijr~ as:

nmijvV ]~[~

, i = 1, 2. . . m; j = 1, 2. . . n

where jijij wrv ~(.)~~



solution (FNIS)


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The FPIS and FNIS of the alternatives given in table 4.8 are computed as follows:

Table 4.8: Weighted normalized alternatives (barriers)


B1 Weak Legislation (0.11,1.67,5) (0.33,3.89,7) (2.78,5.45,9)

B2 Low Enforcement (0.33,2.78,7) (0.33,3.89,7) (3.89,7,9)

B3 Uncertain Future Legislation (0.56,3.89,9) (0.78,7,9) (1.67,3.89,7)

B4 Low Public Pressure (0.56,3.89,9) (0.33,3.89,7) (0.56,2.34,5)

B5 High Short-Term Costs (0.33,2.78,7) (0.78,7,9) (2.78,5.45,9)

B6 Uncertain Benefits (0.56,3.89,9) (0.56,5.45,9) (2.78,5.45,9)

B7 Low Customer Demand (0.33,2.78,7) (0.33,3.89,7) (1.67,3.89,7)

B8 Trade-Offs (0.78,5,9) (0.11,2.34,5) (1.67,3.89,7)

B9 Low Top Management Commitment (0.56,3.89,9) (0.11,2.34,5) (3.89,7,9)

B10 Lack of Organizational Resources (0.56,3.89,9) (0.56,5.45,9) (2.78,5.45,9)

B11 Technology Risk (0.33,2.78,7) (0.56,5.45,9) (3.89,7,9)

B12 Lack of Awareness/Information (0.11,1.67,5) (0.33,3.89,7) (2.78,5.45,9)

FPIS (B+) (9,9,9) (9,9,9) (9,9,9)

FNIS (B-) (0.11,0.11,0.11) (0.11,0.11,0.11) (0.56,0.56,0.56)

)~,......~,~( **

2

*

1

*

nvvvA where }{max~3

*

ijij vv , i = 1, 2. . . m; j = 1, 2, . . . , n

)~,......~,~( 21

nvvvA where }{min~3ijij vv , i = 1, 2. . . m; j = 1, 2, . . . , n


The distance (

ii dd ,* ) of each weighted alternative i = 1, 2. . . m from the FPIS and the FNIS

is computed as follows:

Let a~ = (a1, a2, a3) and b~



][3

1)

~,~(

2

33

2

22

2

11 babababad


170 | P a g e

n

j

jijvi vvdd1

** )~,~( i = 1, 2. . . m

n

j

jijvi vvdd1

)~,~( i = 1, 2. . . m

Where )~

,~( badv is the distance measurement between two fuzzy numbers a~ and b~

. The

distances of weighted alternatives from FPIS and PNIS are shown in table 4.9.

Table 4.9: Distance of barriers from FPIS and FNIS


d(B1,B+) 7.04 5.92 4.13 d(B1,B

-) 2.96 4.54 5.78

d(B2,B+) 6.27 5.92 3.17 d(B2,B

-) 4.27 4.54 6.42

d(B3,B+) 5.70 4.88 5.29 d(B3,B

-) 5.58 6.51 4.23

d(B4,B+) 5.70 5.92 6.62 d(B4,B

-) 5.58 4.54 2.76

d(B5,B+) 6.27 4.88 4.13 d(B5,B

-) 4.27 6.51 5.78

d(B6,B+) 5.70 5.29 4.13 d(B6,B

-) 5.58 5.99 5.78

d(B7,B+) 6.27 5.92 5.29 d(B7,B

-) 4.27 4.54 4.23

d(B8,B+) 5.28 6.82 5.29 d(B8,B

-) 5.87 3.10 4.23

d(B9,B+) 5.70 6.82 3.17 d(B9,B

-) 5.58 3.10 6.42

d(B10,B+) 5.70 5.29 4.13 d(B10,B

-) 5.58 5.99 5.78

d(B11,B+) 6.27 5.29 3.17 d(B11,B

-) 4.27 5.99 6.42

d(B12,B+) 7.04 5.92 4.13 d(B12,B

-) 2.96 4.54 5.78



( *A ) and the fuzzy negative ideal solution ( A ) simultaneously. The closeness coefficient of

each alternative (Tables 4.10 and 4.11) is calculated as

CCi = )( *

ii

i

dd

d

, i = 1, 2. . . m


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The closeness coefficients for alternatives are given in table 4.10 and figure 4.2. Similarly

the closeness coefficients for different criteria are computed and given in table 4.11 and

figure 4.3.

Table 4.10: Aggregated closeness coefficient for alternatives (barriers)

Barrier *

id

id CCi

B1 17.10 13.28 0.4371

B2 15.36 15.23 0.4979

B3 15.87 16.32 0.5071

B4 18.24 12.88 0.4139

B5 15.29 16.55 0.5198

B6 15.12 17.35 0.5344

B7 17.48 13.04 0.4273

B8 17.38 13.21 0.4318

B9 15.68 15.11 0.4907

B10 15.12 17.35 0.5344

B11 14.72 16.68 0.5312

B12 17.10 13.28 0.4371

Table 4.11: Closeness coefficients for different criteria (perspectives)

Barriers Closeness coefficient (CCi)

Government perspective Industry perspective Experts perspective

B1 0.2962 0.4338 0.5828

B2 0.4051 0.4338 0.6697

B3 0.4950 0.5712 0.4448

B4 0.4950 0.4338 0.2943

B5 0.4051 0.5712 0.5828

B6 0.4950 0.5313 0.5828

B7 0.4051 0.4338 0.4448

B8 0.5266 0.3128 0.4448

B9 0.4950 0.3128 0.6697

B10 0.4950 0.5313 0.5828

B11 0.4051 0.5313 0.6697

B12 0.2962 0.4338 0.5828


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Figure 4.2: Aggregated closeness coefficient of GM barriers

Figure 4.3: Closeness coefficient (CCi) of GM barriers (government, industry and

expert perspectives)

0.4 0.42 0.44 0.46 0.48 0.5 0.52 0.54

Uncertain Benefits

Lack of Organizational Resources

Technology Risk

High Short-Term Costs

Uncertain Future Legislation

Low Enforcement

Low Top Management Commitment

Weak Legislation

Lack of Awareness/Information

Trade-Offs

Low Customer Demand

Low Public Pressure


Importance of GM barriers

0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7

Weak Legislation

Low Enforcement


Low Public Pressure


Uncertain Benefits

Low Customer Demand

Trade-Offs



Technology Risk



Expert perspective Industry perspective Government perspective


173 | P a g e

Step 9: Rank the alternatives (barriers)


select the alternative with the highest closeness coefficient for mitigation. The best

alternative is closest to the FPIS and farthest from the FNIS. The aggregate ranking of the

barriers according to the three criteria, i.e. government, industry, and experts perspectives is

given in table 4.12:

Table 4.12: Ranking of GM barriers

S. No. Barriers Name Rank

1 Uncertain Benefits [B6] 1

2 Lack of Organizational Resources [B10] 2

3 Technology Risk [B11] 3

4 High Short-Term Costs [B5] 4

5 Uncertain Future Legislation [B3] 5

6 Low Enforcement [B2] 6

7 Low Top Management Commitment [B9] 7

8 Weak Legislation [B1] 8

9 Lack of Awareness/Information [B12] 9

10 Trade-Offs [B8] 10

11 Low Customer Demand [B7] 11

12 Low Public Pressure [B4] 12


Figure 4.3 clearly shows that the 'low public pressure' (rank 12/12) and low customer

demand (rank 11/12) have the lowest ranking. In other words, these barriers are perceived as

the least significant barriers to GM implementation. The three barriers related to economical

perspective have high rankings. The 'uncertain benefits' (rank 1/12) is perceived as the most

significant barrier followed by 'technology risk' (3/12) and high short-term costs (4/12). It

implies that the evolving green technologies/methodologies (low life cycle), slow rate of


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return, high initial cost and complex green economy benefits make the industry reluctant to

implement GM. These barriers together with uncertain future legislation (rank 5/12) and

'low top management commitment' (rank 7/12) barriers drive the management to have ‘wait

and watch’ approach to GM rather than to embrace it immediately. Enforcement of the

environmental legislations (rank 6/12) is even weaker to force the organizations to

implement GM. A low rank to trade-offs (rank 10/12) reflects that India has not still become

a manufacturing hub for low quality (high polluting) outsource manufacturing.

The second ranked barrier is 'lack of organizational resources' (rank 2/12). It shows the

Indian industry does not have the green technology and people who can implement it.

Coupled with the 'lack of awareness/information' (rank 9/12), it reflects that Indian industry,

academia and government have to come together to develop programmes to train the

industry people on various aspects of GM.

Figure 4.3 and table 4.11 show the results of government, industry and expert perspective.

As per the results, government thinks that the most significant barriers are trade-offs, lack of

organizational resources, low top management commitment, uncertain benefits, low public

pressure and uncertain future legislation. Government perceives that weak legislation and

lack of awareness/information are not strong barriers. Indian industry perceives that

uncertain future legislation and high short term costs are strong barriers to GM

implementation. However, the industry seems to agree that its low top management

commitment and trade-offs are significant barriers. Experts view low enforcement of

legislation, low top management commitment from industry and technology risks as the

most significant barriers. As per experts, the low public pressure is the least significant

barrier to GM implementation in India.


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4.3 DEVELOPMENT OF A MODEL OF GM BARRIERS USING INTERPRETIVE

STRUCTURAL MODELLING

4.3.1 ISM Procedure

The following steps show the development of an interpretive structural model of 12 barriers

to GM implementation in Indian industry:

4.3.1.1 Structural self-interaction matrix (SSIM)

Experts from the Indian industry and academia were consulted in identifying the nature of

contextual relationships (see table 4.13) among the GM barriers. The ISM methodology

suggests the use of expert opinions based on management techniques such as brain storming,

nominal group technique, etc. For analyzing the barriers in developing SSIM, the following

four symbols have been used to denote the direction of relationship between barriers i and j:

V = Barrier i will help achieve barrier j; A = Barrier j will be achieved by barrier i;

X = Barrier i and j will help achieve each other; O = Barrier i and j are unrelated.

Table 4.13: Structural self-interaction matrix (SSIM) of barriers

S. No. Barriers Barriers

2 3 4 5 6 7 8 9 10 11 12

1 Weak Legislation V V X V V X V X V V A

2 Low Enforcement O A O V A V A V V A

3 Uncertain Future Legislation A O V A V A V V A

4 Low Public Pressure V V X V X V V A

5 High Short-Term Costs V A V A V V A

6 Uncertain Benefits A X A X V A

7 Low Customer Demand V X V V A

8 Trade-Offs A X V A

9 Low Top Management Commitment V V A

10 Lack of Organizational Resources V A

11 Technology Risk A

12 Lack of Awareness/ Information


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4.3.1.2 Initial reachability matrix

The SSIM has been converted into a binary matrix called the initial reachability matrix by

substituting V, A, X and O by 1 and 0, as shown in table 4.14, as per the following rules:

• If the (i, j) entry in the SSIM is V, the (i, j) entry in the reachability matrix becomes 1


• If the (i, j) entry in the SSIM is A, the (i, j) entry in the reachability matrix becomes 0


• If the (i, j) entry in the SSIM is X, the (i, j) entry in the reachability matrix becomes 1


• If the (i, j) entry in the SSIM is O, the (i, j) entry in the reachability matrix becomes 0


Table 4.14: Initial reachability matrix

Barriers Barriers

1 2 3 4 5 6 7 8 9 10 11 12

1. Weak Legislation 1 1 1 1 1 1 1 1 1 1 1 0

2. Low Enforcement 0 1 0 0 0 1 0 1 0 1 1 0

3. Uncertain Future Legislation 0 0 1 0 0 1 0 1 0 1 1 0

4. Low Public Pressure 1 1 1 1 1 1 1 1 1 1 1 0

5. High Short-Term Costs 0 0 0 0 1 1 0 1 0 1 1 0

6. Uncertain Benefits 0 0 0 0 0 1 0 1 0 1 1 0

7. Low Customer Demand 1 1 1 1 1 1 1 1 1 1 1 0

8. Trade-Offs 0 0 0 0 0 1 0 1 0 1 1 0

9. Low Top Management Commitment 1 1 1 1 1 1 1 1 1 1 1 0

10. Lack of Organizational Resources 0 0 0 0 0 1 0 1 0 1 1 0

11. Technology Risk 0 0 0 0 0 0 0 0 0 0 1 0

12. Lack of Awareness/ Information 1 1 1 1 1 1 1 1 1 1 1 1

4.3.1.3 Final reachability matrix

The final reachability matrix (Table 4.15) is developed from the initial reachability matrix

after incorporating the transitivities. Transitivity of the contextual relation is a basic


177 | P a g e

assumption in ISM which states that if element A is related to B and B is related to C, then A

is necessarily related to C.

Table 4.15: Final reachability matrix

S.

No. Barriers

Barriers DP

1 2 3 4 5 6 7 8 9 10 11 12

1 Weak Legislation 1 1 1 1 1 1 1 1 1 1 1 0 11

2 Low Enforcement 0 1 0 0 0 1 0 1 0 1 1 0 5

3 Uncertain Future Legislation 0 0 1 0 0 1 0 1 0 1 1 0 5

4 Low Public Pressure 1 1 1 1 1 1 1 1 1 1 1 0 11

5 High Short-Term Costs 0 0 0 0 1 1 0 1 0 1 1 0 5

6 Uncertain Benefits 0 0 0 0 0 1 0 1 0 1 1 0 4

7 Low Customer Demand 1 1 1 1 1 1 1 1 1 1 1 0 11

8 Trade-Offs 0 0 0 0 0 1 0 1 0 1 1 0 4

9 Low Top Management Commitment 1 1 1 1 1 1 1 1 1 1 1 0 11

10 Lack of Organizational Resources 0 0 0 0 0 1 0 1 0 1 1 0 4

11 Technology Risk 0 0 0 0 0 0 0 0 0 0 1 0 1

12 Lack of Awareness/ Information 1 1 1 1 1 1 1 1 1 1 1 1 12

Dependence 5 6 6 5 6 11 5 11 5 11 12 1 84

DP - Driving Power

The driving power and dependence of each barrier are also shown in table 4.15. Driving

power of each barrier is the total number of barriers (including itself), which it may help

achieve. On the other hand dependence is the total number of barriers (including itself),

which may help achieving it. The driving power and dependency will be used later in the

classification of barriers.

4.3.1.4 Level partitions

From the final reachability matrix, the reachability and antecedent sets for each barrier are

found. The reachability set consists of the element itself and other elements, which it may

help achieve, whereas the antecedent set consists of the element itself and the other

elements, which may help achieving it. The intersection of these sets is derived for all

elements. The element for which the reachability and intersection sets are same is the top


178 | P a g e

level element in the ISM hierarchy. The top level element of the hierarchy would not help

achieve any other element. Once the top level element is identified, it is separated out from

the other elements. This process continues till all elements are assigned levels. The

identified levels help in building the final model. In the present case the barriers with their

reachability set, antecedent set, intersection set, and the levels are shown in table 4.16.

Table 4.16: Level partitions

Iteration Barrier Reachability Set Antecedent Set Intersection Set Level

1 11 11 1,2,3,4,5,6,7,8,9,10,11,12 11 V

2 6 6,8,10 1,2,3,4,5,6,7,8,9,10,12 6,8,10 IV

2 8 6,8,10 1,2,3,4,5,6,7,8,9,10,12 6,8,10 IV

2 10 6,8,10 1,2,3,4,5,6,7,8,9,10,12 6,8,10 IV

3 2 2 1,2,4,7,9,12 2 III

3 3 3 1,3,4,7,9,12 3 III

3 5 5 1,4,5,7,9,12 5 III

4 1 1,4,7,9 1,4,7,9,12 1,4,7,9 II

4 4 1,4,7,9 1,4,7,9,12 1,4,7,9 II

4 7 1,4,7,9 1,4,7,9,12 1,4,7,9 II

4 9 1,4,7,9 1,4,7,9,12 1,4,7,9 II

5 12 1,4,7,9,12 12 12 I

4.3.1.5 ISM model

The structural model is generated by means of vertices/nodes and lines of edges. A

relationship between the barriers i and j is shown by an arrow which points from i to j or j to

i depending upon the driver-driven relationship between i and j as discussed above. ISM

model developed after removing the transitivities as described in ISM methodology is

shown in figure 4.4. All the twelve barriers to GM implementation have been divided into

five levels.


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Lack of

Awareness/

Information

Low Customer

Demand

Low Top

Management

Commitment

Low Public

Pressure

Weak

Legislation

Uncertain

Future

Legislation

Low

Enforcement

High Short

Term Cost

Uncertain

benefitsTrade-Offs

Lack of

Organizational

Resouces

Technology

Risk

Level I

Level II

Level III

Level IV

Level V

Figure 4.4: The ISM model of barriers to GM implementation

4.3.2 MICMAC Analysis

Barriers are classified into four clusters – autonomous, dependent, linkage, and driver

barriers as shown in figure 4.5. Autonomous barriers (first cluster) have weak driving power

and weak dependence, so these barriers are generally disconnected from the system. The

second cluster is named dependent barriers. These barriers have weak driving power but

strong dependence power. Four barriers, namely uncertain benefits, trade-offs, lack of

organizational resources, and technological risk belong to this cluster.


180 | P a g e

Dri

vin

g P

ow

er

12 12

11 1,4

7,9

10

9

8

7

6

5 2,3

5

4 6,8

10

3

2

1 11

1 2 3 4 5 6 7 8 9 10 11 12

Dependence

Figure 4.5: Driver - Dependence Diagram

The third cluster is named as linkage barriers having strong driving power and strong

dependence power. In this study, no barrier lies in this cluster. The fourth cluster is named as

driving barriers having strong driving power and weak dependence power. Five barriers,

namely weak legislation, low public pressure, low customer demand, low top management

commitment, and lack of awareness/information belong to this cluster.


The developed ISM model consists of five levels of hierarchy as shown in figure 4.4. The

first level, consisting of lack of information and awareness among the public, government,

and industry is the root barrier to GM implementation which in turn influences the public

pressure, customer demand, top management commitment, and legislative structure. This

barrier has strong driving power and weak dependence. Scarcity of general awareness

alleviates the lack of pressure from public to incorporate environmental thinking. It also

IV

Driver

Variables

III

Linkage

Variables

I

Autonomous

Variables

II

Dependent

Variables


181 | P a g e

alleviates the lack of demand from the customer which forces the industry to manufacture

green products and lack of management commitment to implement GM. The lack of

information and awareness among government officials leads to insufficient legal structure

which is essential to force the industry to implement GM. The high short term cost of

switching over to newer energy efficient and pollution free technologies, the low

enforcement of existing regulations at ground level, and uncertainty among industries for

any legislation which may appear in future are level III barriers. Lack of organizational

resources in terms of finance, technology and human resources, trade-offs and uncertain

benefits of GM are level IV barriers to GM implementation. Generally, any new technology

has its own risk depending upon the maturity level and technology risk is level V barrier to

GM implementation.

Although, three barriers, namely low enforcement, uncertain future legislation, and high

short term cost lies in autonomous cluster, but these barriers lie near the line dividing the

cluster 1 and 2, so these barriers have properties of the barriers of cluster 2 also. Higher

value of 'dependence' for a barriers means that other barriers in the network are to be

addressed first. High value of 'driving force' of a barriers means that these barriers are to be

addressed before mitigating the other barriers.

4.4 DEVELOPMENT OF A MODEL OF GM BARRIERS USING STRUCTURAL

EQUATION MODELLING


The basic research methodology adopted in development of a model of GM barriers model

using Structural Equation Modelling (SEM) is same as used in chapter 3. The questionnaire

developed for the research is given in Appendix A.


182 | P a g e


The barriers will be useful for different applications, by different researchers, in different

studies, only if they are statistically reliable and valid. Reliability reflects the barrier's ability

to consistently yield the same response. Validity refers to the degree to which barriers truly

measure the factors which they intend to measure (Peter, 1979). Internal consistency can be

estimated using reliability coefficient such as Cronbach’s alpha (Schmitt, 1996). An alpha

value of 0.70 is often considered as the criteria for establishing internally consistency. In this

study, during the initial analysis, Cronbach’s alpha values were very high for all the twelve

barriers as shown in table 4.17. Therefore, all the barriers are reliable for GM

implementation.

A barrier has construct validity if it measures the theoretical construct that it is designed to

measure. Construct validity evidence involves the empirical and theoretical support for the

interpretation of the construct (barrier in this case). It refers to the validity of inferences that

observations or measurement tools actually represent or measure the construct being

investigated. Muttar (1985) stated three methods of determining construct validity – multi-

trait multi-method analysis, factor analysis, and correlational and partial correlational

analyses. Out of these three methods, factor analysis is usually used to identify items, which

should be included in a consistent measuring instrument (Floyd and Widaman, 1995). Given

that one of the objectives of this study is to develop items/variables to assess each barrier,

factor analysis is chosen to evaluate construct validity, which is consistent with the literature

(Flynn et al.,1994; Quazi, 1999; Badri et al., 1995; Digalwar and Sangwan, 2007).

Appropriateness of the data for factor analysis is also determined by examining the

minimum number of observations required per variable. According to Flynn et al. (1994) a

sample size of 30 or more is statistically sufficient for the analysis. The appropriateness of


183 | P a g e

the factor model is determined by examining the strength of the relationship among the

items/variables. Correlation matrix, Barlett’s test of sphericity and Kaiser-Meyer-Oklin

(KMO) measure of sampling adequacy are the three measures recommended in the literature

for the purpose of determining the strength of relationship before carrying out the factor

analysis (Hair et al., 1995, Norusis, 1994). Barlett’s test of sphericity demonstrated

sufficiently high values for all the twelve barriers at p ≤ 0.0001. The test result show KMO

measure of 0.829, which is above the suggested minimum standard of 0.5 required for

running factor analysis.

Items from a given scale exhibiting item-total correlations less than 0.50 are usually

candidate for elimination (Koufteros, 1999). Corrected item-total correlation (CITC) was

used to purify the scales. The two barriers – high short-term costs and trade-offs – have

CITC values less than 0.5 but these barriers were not eliminated because (i) the values of

CITC (0.480 and 0.495) are close to 0.5 and secondly, these barriers have high values of

Cronbach's alpha as shown in table 4.17.

Table 4.17: Descriptive statistics of data

Barriers Mean SD CITC SMC CAID

Weak Legislation 2.7158 1.16984 0.650 0.650 0.865

Low Enforcement 2.4632 1.22419 0.637 0.678 0.866

Uncertain Future Legislation 2.7053 1.03764 0.574 0.507 0.870

Low Public Pressure 2.4105 1.02348 0.583 0.486 0.869

High Short-Term Costs 3.2842 1.08538 0.480 0.525 0.875

Uncertain Benefits 3.2632 1.11943 0.581 0.630 0.869

Low Customer Demand 3.2000 1.21368 0.529 0.417 0.873

Trade-Offs 2.6632 1.05520 0.495 0.336 0.874

Low Top Mgt. Commitment 2.3158 1.21945 0.575 0.487 0.870

Lack of Organizational Resources 2.7368 1.15663 0.613 0.591 0.867

Technology Risk 2.7368 1.05118 0.636 0.548 0.866

Lack of Awareness/ Information 2.5368 1.05721 0.536 0.454 0.872

SD - Standard Deviation; CITC - Corrected Item-Total Correlation; SMC - Squared Multiple

Correlation; CAID - Cronbach's alpha if Item Deleted


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4.4.2 Development of the Model Using SEM

Model development has two parts; one, model proposition using exploratory factor analysis

and two, model validation using confirmatory factor analysis and structural equation

modelling.

4.4.2.1 Exploratory factor analysis (EFA)

The EFA was done to find major factors reflecting the major categories of barriers affecting

GM implementation. In other words, a model of barriers to GM implementation is proposed.

Factor analysis was conducted on barriers under each factor based upon principal

component analysis with Varimax rotation. During EFA, three uni-factorial factors/latent

variable with eigen values greater than one evolved. After carefully analyzing the group of

barriers under each factor, these three factors are named as: Policy Barriers (PB); Internal

Barriers (IB); and Economy Barriers (EB) as shown in figure 4.6. The factor loadings for all

barriers, which represent the correlation between the variables and their respective factors,

are also found to be greater than 0.57 (this is greater than the minimum recommended values

of ± 0.45 by Hair et al. (1995)) as shown in table 4.18.

Table 4.18: Factor loadings of GM barriers by exploratory factor analysis

Barriers Factor 1 Factor 2 Factor 3

Weak Legislation 0.824 0.264 0.138

Low Enforcement 0.836 0.307 0.063

Uncertain Future Legislation 0.782 0.167 0.158

Low Public Pressure 0.711 0.109 0.307

High Short-Term Costs 0.028 0.197 0.800

Uncertain Benefits 0.090 0.266 0.833

Low Customer Demand 0.386 0.032 0.674

Trade-Offs 0.225 0.168 0.635

Low Top Mgt. Commitment 0.295 0.727 0.104

Lack of Organizational Resources 0.136 0.790 0.273

Technology Risk 0.225 0.729 0.281

Lack of Awareness/ Information 0.157 0.813 0.092



Rotation converged in 5 iterations.


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However, to be more confident, factor analysis within each of the three factors was

conducted and the results confirm that the barriers are well represented by the three explored

factors as given in table 4.19. Hence, it can be concluded that all the items contribute highly

to the represented factors and have construct validity.

Table 4.19: Factor loadings of GM barriers by EFA (within each factor)

Barriers Factor 1 Factor 2 Factor 3

Weak Legislation 0.856 ---- ----

Low Enforcement 0.888 ---- ----

Uncertain Future Legislation 0.728 ---- ----

Low Public Pressure 0.643 ---- ----

High Short-Term Costs ---- 0.699 ----

Uncertain Benefits ---- 0.787 ----

Low Customer Demand ---- 0.777 ----

Trade-Offs ---- 0.718 ----

Low Top Management Commitment ---- ---- 0.736

Lack of Organizational Resources ---- ---- 0.876

Technology Risk ---- ---- 0.601

Lack of Awareness/ Information ---- ---- 0.572

% of variance explained 70.414 61.034 66.701

KMO 0.793 0.739 0.802

Extraction Method: Principal Component Analysis; Single component extracted each time.

Barriers to GM

implementation

Policy Barriers (PB)Internal Barriers (IB) Economy Barriers (EB)

Weak Legislation

Low Enforcement


Low Public Pressure

Low Top Mgt. Commitment

Lack of Org. Resources

Technology Risk

Lack of Awareness/ Info.


Uncertain Benefits

Low Customer Demand

Trade-Offs

Figure 4.6: Classification of barriers to GM implementation


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4.4.2.2 Confirmatory Factor Analysis (CFA)

The exploratory factor analysis is not sufficient to assess all the essential measurement

properties of the constructs like unidimensionality (Koufteros, 1999). CFA is done to

examine the unidimensionality to ensure the theoretical relationships among the observed

variables (or indicators) with their respective factors (or constructs). Figure 4.7 shows the

path diagram representing the measurement model with the three latent variables and 12

barriers. The statistics obtained from the CFA of the measurement model are summarized in

table 4.20.

Weak Legislation

Low Enforcement


Low Public Pressure

Policy

Barriers



Technology Risk


Internal

Barriers


Uncertain Benefits

Low Customer Demand

Trade-Offs

Economy

Barriers

e1

e2

e3

e4

1

1

1

1

e9

e10

e11

e12

1

1

1

1

e5

e6

e7

e8

1

1

1

1

1

1

1

Figure 4.7: Path diagram representing the measurement model of barriers to GM

implementation


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Table 4.20: Confirmatory factor analysis statistics

Barriers Regression Weights* Regression

Weights** Estimate Standard Error Critical Ratio

Weak Legislation 1.000 ---- ---- 0.856

Low Enforcement 1.086 0.075 14.427 0.888

Uncertain Future Legislation 0.754 0.067 11.238 0.728

Low Public Pressure 0.658 0.069 9.534 0.643

High Short-Term Costs 1.000 ---- ---- 0.736

Uncertain Benefits 1.227 0.122 10.048 0.876

Low Customer Demand 0.913 0.119 7.659 0.601

Trade-Offs 0.756 0.104 7.296 0.572

Low Top Management Commitment 1.000 ---- ---- 0.699

Lack of Organizational Resources 1.068 0.114 9.387 0.787

Technology Risk 0.959 0.103 9.299 0.777

Lack of Awareness/ Information 0.891 0.102 8.706 0.718

P < 0.001 (for all coefficients)

* Unstandardized

** Standardized

The factor loadings of the barriers, as shown in table 4.19, show a minimum value of 0.572

for ‘lack of awareness/information’ variable. The minimum value of critical ratio is 7.2

which is much above the |2| (|2| is generally considered significant at the 0.01 level). The

goodness of fit statistics for CFA are shown in table 4.21. It presents the estimated and

recommended values of all the goodness of fit indices, which clearly shows that all the

values are either within the recommended value range or very close to it. Hence, it is

concluded that the proposed measurement model is accepted (confirmed) and the full

structural model can be tested to validate the final model of barriers.

4.4.2.3 Structural model

Structural equation modelling is a statistical methodology that takes a confirmatory (i.e.

hypothesis testing) approach to the analysis of a structural theory (Byrne, 2001). Typically,

this theory represents 'causal' processes that generate observations on multiple variables


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(Bentler, 1989). SEM is applied to test the full structural model for assessing the impact of

factors/latent variables on each other. SEM methodology has been widely used in various

areas of research for empirical testing of frameworks in sustainable manufacturing (Vinodh

and Joy, 2012), pull production (Koufteros, 1999), operations management (Shah and

Goldstein, 2006), and environmentally conscious purchasing behavior (Arslan et al., 2012).

Confirmatory modelling usually starts with hypothesis (ses) that get represented in causal

models.

Table 4.21: Goodness-of-fit statistics (CFA)

Indexes Estimated

value

Recommended

value

Reference

Chi-square 125.080 ----- -----

Degree of freedom (DF) 51 ----- -----

P-value < 0.001 ≈ 0.0 -----

Chi-square/DF 2.45 < 5.0 Marsch and Hocevar, 1985

Root Mean Square Error of

Approximation (RMSEA)

0.088 Close to zero Hair et al., 2006

Root mean square residual (RMR) 0.084 < 0.08 Hu and Bentler, 1999

Goodness-of-Fit Index (GFI) 0.912 Close to one Dawes et al., 1998

Adjusted goodness of fit (AGFI) 0.865 Close to one -----

Comparative fit index (CFI) 0.929 > 0.90 Byrne, 2001

Following hypotheses are proposed, based on the careful examination of measurement

model, after confirmatory factor analysis to test the full structural model.

Hypothesis (H1): The internal barriers to the implementation of GM are positively related

to policy barriers.

Hypothesis (H2): The internal barriers to the implementation of GM are positively related

to economy barriers.

Hypothesis (H3): The policy barriers to the implementation of GM are positively related to

economy barriers.


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Based on the three hypotheses the proposed structural model is shown in figure 4.8.

Weak Legislation

Low Enforcement


Low Public Pressure

Policy

Barriers



Technology Risk


Internal

Barriers


Uncertain Benefits

Low Customer Demand

Trade-Offs

Economy

Barriers

H1

H2

H3

Figure 4.8: Proposed full structural model of barriers to GM implementation

Before hypotheses testing, testing of full structural equation model using maximum

likelihood estimation was undertaken to check the fitness of the model. It showed 51 degrees

of freedom, Chi-square = 125.080, p = 0.000, GFI = 0.912, CFI = 0.929, IFI = 0.930, TLI =

0.908, RMSEA = 0.088, and RMR = 0.084. These model fit indices support the fit of the

model. The results of the hypothesis test are shown in table 4.22. The first two hypotheses

are accepted as the values of β and p confirms the acceptance, but the data did not support

the third hypothesis as the value of p does not confirm the acceptance. The value of p should


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be less than 0.05 for 95% confidence level. The final full structural model after hypotheses

testing is shown in figure 4.9.

Weak Legislation

Low Enforcement


Low Public Pressure

Policy

Barriers



Technology Risk


Internal

Barriers


Uncertain Benefits

Low Customer Demand

Trade-Offs

Economy

Barriers

H1

H2

Figure 4.9: Final full structural model of barriers to GM implementation

Table 4.22: Results of hypothesis test

Hypothesis β value p value Result

H1 The internal barriers to the implementation of GM are

positively related to policy barriers.

0.695 < 0.001 Accepted

H2 The internal barriers to the implementation of GM are

positively related to economy barriers.

0.459 < 0.001 Accepted

H3 The policy barriers to the implementation of GM are

positively related to economy barriers.

0.110 < 0.159 Rejected


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Accepted hypotheses show that internal barriers cause policy and economic barriers. It

reflects that for the effective implementation of GM, internal barriers are to be mitigated

first as these are the root barriers to GM implementation. For example, lack of human and

technical resources to implement GM forces the governments to weaken the legislations and

enforcement as the real issue with managers is not to buy new technology, but about how

these new technologies will be implemented and deployed. Unavailability of new

technologies or future technologies forces the state/central governments not to develop long

term legislations. It also deters the organizations from computing future benefits. Lack of

awareness lowers the public pressure as well as the customer demand. Lack of top

management commitment to GM implementation makes the organizations decide to shift the

polluting manufacturing work to other nations (trade-offs). The final model of GM barriers

reveals that in order to move to the next level in the environmental performance, it is

prudent to start mitigating internal barriers, which automatically affect the economy and

policy barriers. Integrated efforts of policy makers in government and industry can bring this

change for better environmental performance.

4.5 COMPARISION OF GM BARRIERS IN INDIA AND GERMANY

This section presents a case study to compare the proposed GM implementation barriers in a

developed country (Germany) and an emerging country (India). To compare the barriers to

GM, a survey was conducted in Germany using face-to-face interviews followed by

responses in the questionnaire. The data for India is same as in the last section. The number

of filled in questionnaire for German industry were 22 but as the questionnaires were filled

after discussion, the quality of data is expected to be high. Firstly, the mean values are


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calculated to assess the importance of the barriers. Very low mean values of any factor gives

a clue that the particular barrier is not important and should be eliminated from the study.

Secondly, the standard deviation values are calculated because the mean value is not always

sufficient to measure the central tendency of the data. Lastly, an 'independent t-test' is done

to assess the significance of the differences as shown below:

4.5.1 Descriptive Statistics

The mean values for barriers are presented in table 4.23. The minimum value of mean for

barriers is more than 1.93 on a scale of 5, which means that all the barriers are important in

both the countries. The Cronbach’s alpha value of 0.907 for the barriers is achieved which is

considered good and therefore it is concluded that the data is highly reliable. Hence, it is

approved to use this data for further analysis.

Table 4.23: Group statistics for barriers to GM implementation

Barriers Country Mean Std. Deviation

Weak Legislation [B1] India 2.95 1.322

Germany 2.54 1.071

Low Enforcement [B2] India 2.81 1.289

Germany 2.11 1.066

Uncertain Future Legislation [B3] India 2.67 1.155

Germany 2.75 1.041

Low Public Pressure [B4] India 2.52 1.078

Germany 2.29 1.182

High Short-Term Costs [B5] India 2.90 1.044

Germany 3.43 1.230

Uncertain Benefits [B6] India 2.76 1.136

Germany 3.43 1.230

Low Customer demand [B7] India 3.10 1.261

Germany 3.25 1.351

Trade-Offs [B8] India 2.71 0.784

Germany 2.67 1.109

Lack of Top Management Commitment [B9] India 2.43 1.248

Germany 1.93 0.979

Lack of Org. Resources [B10] India 2.81 1.401

Germany 2.68 1.090

Technology Risk [B11] India 2.67 1.278

Germany 2.54 0.999

Lack of Awareness/Information [B12] India 2.81 1.327

Germany 2.18 0.819


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The examination of means for various barriers suggest that the 'low customer demand' is the

highly important barrier with mean value of 3.10 in India and the 'uncertain benefits' and

'high short term costs' are the highly important barriers with mean values of 3.43 in

Germany. Further, it is evident from the group statistics that 'lack of top management

commitment' is least important barrier in India and Germany with mean values of 2.43 and

1.93 respectively. The standard deviation of data from both the countries varies from a

minimum value of 0.784 for 'trade-offs' in India and maximum value of 1.401 for 'lack of

organizational resources' again in India.

4.5.2 Comparing Means Using Independent t - test

An independent t-test (two-tailed) is conducted on two entirely different and independent

samples of respondents from Indian and German companies to compare the importance of

barriers. The independent t-test is done to know, whether the difference in the barriers are

statistically different for the two countries or not. The procedure to conduct an independent

t-test is shown in figure 3.11 (chapter 3).

The hypotheses defined for the independent t-test are:

H0: µIndia = µGermany (null hypothesis)

H1: µIndia ≠ µGermany (alternate hypothesis)

The alpha level used for the study is 0.05, which is a commonly accepted in statistical

studies. The 't-distribution for critical value' for 52 degrees of freedom and 0.05 alpha level

is 2.007 as obtained from the t-table, The decision rule states that the calculated 't' value

should be between ± 2.007 to accept the null hypothesis and for the t values beyond this

range, the null hypothesis will be rejected because of a strange and unlikely case.


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The p-value is more than 0.05 for all the barriers for Levene's test except barrier B11 and B12

i.e. 'technology risk' and 'lack of awareness and information' respectively. So it is concluded

that the variances are equal and 'EVA' row have to be selected for barriers B1 to B10 and

'EVNA' for barriers B11 and B12. Based on this t-test for equality of means, the significance

of difference of impact for barriers in both the countries are presented in table 4.24.

Table 4.24: Independent t-test to compare barriers for India and Germany

Barriers Levene's Test* T-test for Equality of Means Cohen's

d*** F Sig. t Df Sig.** MD$ SED

#

B1 EVA 1.441 0.236 1.219 47 0.229 0.417 0.342

0.34079 EVNA 1.182 37.757 0.244 0.417 0.352

B2 EVA 1.377 0.247 2.086 47 0.042 0.702 0.337 0.59183

EVNA 2.030 38.308 0.049 0.702 0.346

B3 EVA 0.004 0.952 -0.265 47 0.792 -0.083 0.315 -0.0727

EVNA -0.261 40.627 0.796 -0.083 0.320

B4 EVA 0.367 0.548 0.724 47 0.472 0.238 0.329 0.20332

EVNA 0.734 45.141 0.467 0.238 0.324

B5 EVA 1.700 0.199 -1.571 47 0.123 -0.524 0.333 -0.4645

EVNA -1.609 46.214 0.114 -0.524 0.326

B6 EVA 0.071 0.791 -1.939 47 0.059 -0.667 0.344 -0.5659

EVNA -1.962 44.915 0.056 -0.667 0.340

B7 EVA 0.284 0.597 -0.408 47 0.685 -0.155 0.379 -0.1147

EVNA -0.412 44.704 0.682 -0.155 0.375

B8 EVA 4.067 0.050 0.167 46 0.868 0.048 0.285 0.04165

EVNA 0.174 45.642 0.863 0.048 0.274

B9 EVA 2.083 0.156 1.573 47 0.122 0.500 0.318 0.44579

EVNA 1.519 36.892 0.137 0.500 0.329

B10 EVA 2.566 0.116 0.368 47 0.714 0.131 0.356 0.10357

EVNA 0.355 36.699 0.724 0.131 0.369

B11 EVA 5.704 0.021 0.403 47 0.689 0.131 0.325 0.11333

EVNA 0.389 36.814 0.700 0.131 0.337

B12 EVA 14.173 0.000 2.052 47 0.046 0.631 0.308 0.57134

EVNA 1.921 31.167 0.064 0.631 0.328

* for Equality of Variances; **2-tailed; $Mean Difference;

# Standard Error Difference;

***To assess effect size


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4.5.3 Effect Size for Independent t-test

The effect size of the difference is computed using 'cohen's d' as shown in chapter 3. Table

4.25 presents the final results of the comparison of barriers to GM implementation and their

effect sizes.

Table 4.25: Results of comparison of barriers to GM

Barriers Comparison Effect Size

B1 Equal ----------

B2 Significantly different Medium

B3 to B12 Equal ----------

It clearly shows the statistical significance either 'statistically different' or 'equal' in column 2

of table 4.25 along with the magnitude of the difference if it exists in column 3.


The 'low enforecement' is the only barrier, which is seen significantly different between

India and Germany with medium difference. The enforecement of the legislation is not

possible to the full extent in India. This may be because of the problems with corruption and

a lack of supervisory infrastructure. The situation is different in Germany where the rules

and regulations are enfored to a large extent. All other barriers are found to be equal in both

countries.

These elaborations have been driven by the objectives to mitigate barriers as well as to find

opportunities for the two countries to learn from each other for collaborative efforts. The

decision makers in the manufacturing organizations can consider to adopt the same

strategies to mitigate the barriers if these factors have equal importance and can reduce the

risk of adopting unproven strategies which are not yet tested.


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4.6 SUMMARY

The 12 barriers to GM implementation have been developed using literature and discussion

with practitioners and academicians.

The barriers to GM implementation were ranking based on fuzzy TOPSIS multi-criteria

decision model which provides a proper tool to encounter the uncertain and complex

environments by measuring the inherent ambiguity of decision maker’s subjective judgment

using government, industry and expert perspectives. The research shows that uncertain

benefits, lack of organizational resources, technology risk, high short term costs, uncertain

future legislation, and low enforcement of legislation are top six barriers to GM

implementation in industry. The ranking of these barriers is expected to help the government

and industry to focus on mitigation of the top few important barriers within limited

resources. Low demand from public and customer are the two least important barriers to GM

implementation.

A model of the barriers to GM implementation is developed using interpretive structural

modelling which shows the hierarchy and inter-relationship among barriers. It has been

found that lack of information and awareness among the public, government and industry

personnel is the root barrier to GM implementation which in turn influences the public


barrier has strong driving power and weak dependence. Lack of general awareness alleviates

the lack of pressure from public to incorporate environmental thinking. It also alleviates the

lack of demand from the customer which forces the industry to manufacture green products

and lack of management commitment to use GM. The lack of information and awareness

among government officials leads to insufficient legal structure which is crucial to force the


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industry to implement GM. The developed model divided the identified barriers into five

levels of hierarchies showing their inter-relationship and depicting the driving-dependence

relationship.

A statistically reliable and valid model of GM implementation barriers is presented using

statistical tools namely SPSS 16.0 and AMOS 16.0. The 12 GM barriers were divided into

three categories – internal barriers, policy barriers, and economy barriers – using exploratory

factor analysis. Secondly, the confirmatory factor analysis has been done to confirm the

classification of the barriers. The final model has been tested using structural equation

modelling technique wherein hypotheses affirm that internal barriers cause policy and

economic barriers.

Lastly, a case study is carried out to compare the importance of barriers in a emerging

country (India) and developed country (Germany) using independent t-test. The study

concluded that the 'low enforecement' is the only barrier, which is seen significantly

different between India and Germany with medium difference. All other barriers are found

to have same importance in both countries. This has provided a broad perspective on the

barriers to GM implementation in the two different countries.

CHAPTER 5

STAKEHOLDERS OF GREEN MANUFACTURING

IMPLEMENTATION

The stakeholders are those individuals/groups which can affect or are affected by the

objectives of the organization. The literature review on stakeholders of GM has been

provided in chapter 2. This chapter provides:

Development of GM stakeholder.

Ranking of the stakeholders using fuzzy TOPSIS multi-criteria decision model.

Classification of the stakeholders using exploratory factor analysis.

A comparison of importance of GM stakeholders in SMEs and large enterprises.

5.1 STAKEHOLDERS OF GM IMPLEMENTATION

A list of 14 stakeholders has been provided in chapter 2 after the literature review. This

section provides the description and evolution of these stakeholders.

5.1.1 Government

Government regulation is a force of law setup by a competent authority, relating to the

actions of those under the authority's control. Government regulations are an important

stakeholder to pressurise the adoption of environmentally conscious manufacturing in

industry (Regens et al., 1997). Regulatory environment of the government having multiple

laws and rules may create an important source of pressure on firms (Rugman and Verbeke,

1998; Kassinis and Vafeas, 2002). At the plant level, institutional actors like governments

are believed to directly influence environmental practices like green initiatives (Hoffman,

2001; Delmas and Toffel, 2004). The most apparent stakeholders that influence adoption of

Stakeholders of Green Manufacturing Implementation

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environmental practices within companies are various government structures because

legislation authorizes the agencies to circulate and enforce rules and regulations (Delmas,

2002; Carraro et al., 1996; Majumdar and Marcus, 2001; Rugman and Verbeke, 1998). The

increasing environmental ethics of the public, the statutory requirements due to government

policies and regulations, international agreements, and pressure from organized groups are

generally considered to be the factors that pressurise companies in adopting a green

manufacturing or environmental management systems (Hui et al., 2001). There exist

departments and agencies within the government structure which are responsible for

enforcing regulatory conformity and penalizing the defaulters (Carmins et al., 2003;

Fineman and Clarke, 1996.

5.1.2 Employees

An employee contributes labor, skill and expertise for an employer and is typically hired to

perform precise duties. The term 'employee' refers to a specific defined relationship between

an individual and a corporation, which differs from those of customer or client. Employees

are directly related to a firm and have the ability to impact its bottom line directly

(Henriques and Sadorsky, 1999). The employee is a major source of a company's success,

and successful environmental policy planning requires active participation from an

employee (Buzzelli, 1991; Henriques and Sardosky, 1999). All the employees who are

supportive of a firm’s environmental goals are more likely to seek work within it, and hence

continue their employment. Employees may also engage in public whistle-blowing ending

up in exposing the firm’s potentially negligent environmental practices (Henriques and

Sadorsky, 1996; Darnall et al., 2009). Within the organization, the adoption of

environmental practices motivated by other stakeholders is carried out by employees (Sarkis

et al., 2010).


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5.1.3 Consumers

A customer, popularly known as a client, buyer, or purchaser is the receiver of

a good, service, product, or idea, obtained from a seller, vendor, or supplier for a monetary

or other valuable considerations. The customers or consumers are the key source of

information on environmental issues and practices (Williamson and Lynch-Wood, 2001;

Gadenne et al., 2009). At the plant level customers are believed to be most likely to directly

influence environmental practices (Hoffman, 2001; Delmas and Toffel, 2004). The firms

that adopt environmental management practices are motivated by customer concerns.

(Henriques and Sadorsky, 1996; Delmas and Toffel, 2004). Customer demands are the most

important type of external pressure (Doonan, et al., 2005; Chien and Shih, 2007). The results

of a survey stated that in the U.S.A. an estimated 75% of consumers claim that their

purchasing decisions are influenced by a company’s environmental reputation and 80%

would be willing to pay more for environmentally friendly goods (Lamming and Hampson,

1996; Chien and Shih, 2007). They respond positively to a company's actions by purchasing

its product and expressing their satisfaction to the managers of the company, or by voicing

their discontent by boycotting its product or by filing a suit against it (Greeno and Robinson,

1992; Henriques and Sardosky, 1999).

5.1.4 Market

A market is one of many varieties of systems, institutions, procedures, social

relations and infrastructures whereby parties engage in exchange. There are two roles in

markets, i.e. buying and selling. The market facilitates trade and enables the distribution

and allocation of resources in a society. Competition among the firms is a major part of

market pressure. Competitiveness is identified as one of the major motivations for


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environmental/ecological responsiveness (Bansal and Roth, 2000). Market competition

within an industry affects the rate of diffusion of environmental management practices.

(Delmas and Toffel, 2004). Thus, the greater the environmental demand perceived by

managers, the firm tends to adopt beyond the mandatory environmental requirements

established by the authorities, and even beyond market or society expectations (Murillo-

Luna et al., 2008). This is more evident when the sellers are larger in number than the

buyers.

5.1.5 Media

The media plays an important role in increasing awareness among public and formation of

their views and attitudes toward certain issues. Media is of three types: print media,

electronic media, and social media. The media plays a vital role to protect natural

environment by pressurising the firms (Henriques and Sadorsky, 1999). When

environmental crisis occurs, the media can influence society's perception of a company

(Sharbrough and Moody, 1995; Mitroff et al., 1989; Shrivastava and Siomkos, 1989;

Henriques and Sadorsky, 1999). The influence of the media come from the information they

convey about a company. It serves as a medium which reflects owner, employee, customer,

community and other stakeholder expectations. The importance of the media will be highest

for reactive firms, second highest for defensive firms, and third highest for accommodative

firms, and lowest for proactive firms (Henriques and Sadorsky, 1999). Negative press stories

can damage a business more than that of unhappy customers (Thomas et al., 2004).

Secondary stakeholders like the media sometimes can be viewed with greater concern than

employees or customers (Maignan et al., 2005).


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5.1.6 Local Politicians

Local politicians, i.e. member of local governing bodies or member of legislative assembly

or member of parliament which are elected by the people can influence the environmental

performance of the company as it influences the life of the people. Local politicians organize

the demonstrations by the people to pressurise the companies to adopt environmental

friendly manufacturing systems. Political/legislative environment is the most important

source of pressure on firms (Rugman and Verbeke, 1998; Gonzalez-Benito and Gonzalez-

Benito, 2010; Kassinis and Vafeas, 2002). They can bend the public opinion in favour of or

against a corporation's environmental performance (Clair et al., 1995; Turcotte, 1995; Sarkis

et al., 2010). Politicians can also raise the issue in parliament, assembly or local bodies and

these institutions may bring new stringent regulations.

5.1.7 Local Community

A local community is a group of interacting people sharing an environment. Community

stakeholders are defined as those people who are not necessarily involved in the partnership

but have knowledge of the community and the organization (Nelson, et al., 1999; Chien and

Shih, 2007). At the plant level the institutional actors like community are most likely to

directly influence environmental practices. (Hoffman, 2001; Delmas and Toffel, 2004).

Outside stakeholders like community generally apply pressure based on the environmental

record of a firm (Hart, 1995; Gladwin, 1993; Kassinis and Vafeas, 2002). The firms that are

exposed to pressures from surrounding communities are less likely to violate environmental

laws (Kassinis and Vafeas, 2002). A company’s decisions to implement environmental

management practices are effected by their desire to improve or maintain relations with local

communities. Local communities also impose coercive pressure on companies through their


203 | P a g e

vote in local and national elections or by environmental activism through environmental

NGOs or by filing citizen lawsuits. (Delmas and Toffel, 2004). The community stakeholders

have the ability to influence society’s perception of a firm (Henriques and Sadorsky,1996).

5.1.8 Suppliers

A supplier is a party that supplies goods or services. A supplier can exert its influence by

stopping delivery or it can pressure the firm to employ a more environmentally acceptable

substitute. Suppliers influenced the decision to follow certification and standard to certify

like ISO 14001, etc. (Delmas and Toffel, 2004). They contribute to the overall

environmental performance of a supply chain (Sarkar and Mohapatra, 2006; Gunasekaran

and Spalanzani, 2012). For developing a sustainable competitive advantage for the

manufacturer, supplier - manufacturer relationships are considered important (Cannon and

Homburg, 2001; Sheth and Sharma, 1997). A key deciding factor for environmental

performance in many organizations is the screening of suppliers. (Clark, 1999; Chien and

Shih, 2007). The interaction and coordination among companies in the supply chain reduce

environmental impact (Handfield et al., 1997). Stakeholders such as integrated supply chain

members, as evident from the automotive industry are dependent on environmentally sound

partners (Sarkis et al., 2010). Larger companies in competitive supply chains realize that

green supply chains are necessary to maintain a reliable source of components and material.

5.1.9 Trade Organisations

A trade association, also known as an industry trade group, business association or sector

association is an organization founded and funded by businesses that operate in a specific

industry. An industry trade association participates in public relation activities such

as advertising, education, political donations, lobbying, and publishing; but its main focus is


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collaboration between companies and standardization. Associations may offer other

services, such as organizing conferences, networking or charitable events or offering classes

or educational materials. In the context of proactive environmental strategy, industry and

trade associations are important stakeholders (Darnall et al., 2010). Trade organisations

consist of environmental regulators, and are individuals within government who have the

authority to create environmental requirements as well as inspect the firm’s compliance with

these requirements (Cordano and Frieze, 2000; Carmins et al., 2003; Fineman and Clarke,

1996; Darnall et al., 2009).

5.1.10 Environmental Advocacy Groups

They include all the environmental groups and NGO’s. At the plant level the institutional

actors like environmental interest groups are most likely to directly influence environmental

practices (Hoffman, 2001; Delmas and Toffel, 2004). To improve corporate

environmentalism, they can apply strong normative institutional pressure on firms even

though they are not directly involved in the firm’s economic transactions (Waddock and

Graves, 1997; Klassen and McLaughlin, 1996; Mitchell et al., 1997; Shah, 2011).

NGO/CBO stakeholders have the capacity to mobilize public opinion in favour of or in

opposition to a firm and can use public protests to emphasize their point of view (Freeman,

1984; Shah, 2011). Societal stakeholders generally use indirect approaches such as public

protests and strikes to influence a firm’s behaviour because they lack a direct economic

stake in the organization (Sharma and Henriques, 2005; Darnall et al., 2009).

5.1.11 Investors/Shareholders

A shareholder or stockholder is an individual or institution (including a corporation) that

legally owns a share of stock in a public or private corporation. Shareholders are the


205 | P a g e

stakeholders who are directly related to an organization and have the ability to impact its

bottom line. A firm is said to be serious about environmental plans if it communicates its

plans to employees and shareholders (Henriques and Sadorsky, 1999). By communicating

with shareholders a company establishes publicly that it is committed to the environment

and shows how that commitment could be interpreted as improved environmental

performance in the firm (Vos et al., 2009). The shareholders are the most fundamental

stakeholders and businesses must respond to them by maximizing their value (Reinhardt et

al., 2008). The reduction of risks and liability from proactive environmental practices and

programs adds to the shareholder value (Goldstein and Wiest, 2007; Reinhardt, 1999).

Stakeholders can voice their concerns by expressing their views at shareholder meetings

and/or by simply selling their shares (Greeno and Robinson, 1992; Henriques and Sadorsky,

1999).

5.1.12 Partners

Business partners in case of joint ventures or acquired/merged business are stakeholders in

decision making about the environmental performance. For instance, the joint venture may

adopt the best environmental practices existing among all the partners of the joint ventures.

The partnership between McDonald’s and the Environmental Defence Fund on packaging

issues is an example of influence of partners in the environmental performance of the

company (Rondinelli and Berry, 2000). In case of joint venture, MSMEs are coerced and/or

facilitated by partners to adopt environmental practices/technologies. Partners in the joint

ventures also influence the GM implementation decisions to save their reputation at other

locations. A partner takes part in an undertaking with another or others, especially in a

business or company with shared risks and profits.


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5.1.13 Owners

Owners include the owner of the firm as well as the board of directors. The board of

directors is at the apex of the decision making process in public corporations. Every major

decision, including a firm’s policy toward the environment, must go through the board. The

boards are finally responsible for corporate environmental strategy, be it proactive or passive

(Kassinis and Vafeas, 2002). Individual concern is the motivation for environmental

responsiveness (Bansal and Roth, 2000). Environment friendly processes and procedures

might be chosen by small business owners whether they are required to do so by law, or they

believe in increase in profits. Individual’s beliefs and attitudes affect an individual’s

behaviour and treatment of the environment is an ethical issue for a few of them (Ajzen and

Fishbein, 1980). Firms with owners/managers who have positive environmental attitudes are

important to suppliers, have a relatively higher level of environmental support practices in

turn affecting the buying decisions of customers. (Gadenne et al., 2009).

5.1.14 CEOs

CEO is the highest ranking executive in a company, whose main responsibilities include

developing and implementing high level strategies, making major corporate decisions,

managing the overall operations and resources of a company, and acting as the main point of

communication between the board of directors and the corporate operations. It was

established, during the discussion held with the senior managers, that many Indian MSMEs

have introduced GM as philanthropy of its chief executive officers. MSMEs may not have a

CEO by designation so it is assumed as management of the company.

Goodstein et al. (1994) suggest that large boards are not as appropriate to initiate strategic

action, in line with the view that larger boards are less participative and cohesive than


207 | P a g e

smaller ones. This may allow opportunistic CEOs to sidestep unwelcome board monitoring

on environmental policy matters. Further, Mitchell et al. (1997) also supported the positive

role of CEOs in environmental decision making of the company. The relationship between

economic and environmental performance influences the chief executive's belief and attitude

towards green manufacturing.

5.2 RANKING OF GM STAKEHOLDERS USING FUZZY TOPSIS

Figure 5.1 provides an hierarchical structure used to rank the 14 stakeholders of GM

implementation. The 14 stakeholders (S1 to S14) are at the bottom of the hierarchy and the

criteria used to rank the stakeholders are at the middle of the hierarchy.

GOAL:

Ranking of Stakeholders

Social perspective

[C2]

Environmental

perspective [C1]

Economic perspective

[C3]

S7 S8S6 S9S5 S10S4S3S2 S13S12S11 S14S1

Figure 5.1: A hierarchical structure for ranking the stakeholders of GM

5.2.1 Development of a Fuzzy TOPSIS Method for Ranking GM Stakeholders

Table 5.1 lists various criteria chosen for ranking the stakeholder of GM implementation,

their definition and type. These criteria have been obtained from literature review and

discussion held with Indian government officials, industry managers and experts. Three

different criteria namely environmental, social, and economic perspectives were chosen to

determine the ranking of stakeholders of GM implementation. Ranking based on the

combined criteria is useful to judiciously prioritize the stakeholders. A scale of 1–9 is


208 | P a g e

applied for rating the criteria and the alternatives. Table 3.3 (chapter 3) presents the

linguistic variables and fuzzy ratings for the alternatives and criteria.

Table 5.1: Criteria for ranking stakeholders of GM


Environmental perspective Influence of stakeholders on environmental

performance of the company Importance

(the more the

important)

Social perspective Influence of stakeholders on social performance of

the company

Economic perspective Influence of stakeholders on economic

performance of the company

The second step of the methodology involves evaluation of all stakeholders against the

selected criteria, i.e. the perspective in this case using fuzzy TOPSIS. The fuzzy TOPSIS

approach chooses the alternative that is closest to the positive ideal solution and farthest

from the negative ideal solution. A positive ideal solution is composed of the best

performance values for each attribute whereas the negative ideal solution consists of the

worst performance values. The various steps of fuzzy TOPSIS method developed for

ranking stakeholders are as follows:


Let us assume there are 'j' possible stakeholders called S = {S1, S2 . . . Sj} which are to be

evaluated against 'm' criteria, C = {C1, C2 . . . Cm}. The criteria weights are denoted by wi (i

= 1, 2 . . . m). The performance ratings of each decision maker Dk (k = 1, 2, . . . , K) for each

alternative Sj (j = 1, 2, . . , n) with respect to criteria Ci (i = 1, 2, . . . , m) are denoted by

ijkk xR ~~ (i = 1, 2, . . . ,m; j = 1, 2, . . . , n; k = 1, 2, . . . , K) with membership function

)(~ xkR

. In the present case we have 14 alternatives (stakeholders), three criteria

(perspectives) and three decision makers as discussed in chapter 3. Table 5.2 and table 5.3


209 | P a g e

present linguistic assessment for all three criteria and 14 alternatives respectively in

consultation with decision makers.



Environmental perspective (C1) VH VH H

Social perspective (C2) M M H

Economic perspective (C3) VH H VH

It is apparent that all criteria belong to the important category, i.e. the higher the value, the

more important the alternative.

Table 5.3: Linguistic assessment of the alternatives (stakeholders)

S. No. Stakeholders Environmental Social Economic

S1 Government VI I FI

S2 Local Politicians I VI FI

S3 Local Community VI VI LI

S4 Suppliers FI I VI

S5 Trade Organisations FI FI VI

S6 Investors/Shareholders LI LI VI

S7 Employees I VI FI

S8 Consumers VI VI LI

S9 Market FI I I

S10 Environmental Advocacy Groups VI VI LI

S11 Media VI VI LI

S12 Partners FI LI I

S13 Owners LI FI VI

S14 CEOs FI FI VI


If the fuzzy ratings of all decision makers are described as triangular fuzzy numbers kR~

(ak, bk, ck), k = 1, 2. . . K, then the aggregated fuzzy rating is given by kR~

(a, b, c), k = 1,

2... K, where

a = }{min kk a ,

K

k

kbK

b1



210 | P a g e


) is constructed below:

)~,.......~,~(~

21 nwwwW

Table 5.4 presents the aggregate fuzzy weights for the criteria on the basis of ratings given

by the decision makers.



Environmental perspective (C1) (7,9,9) (7,9,9) (5,7,9) (5,8.33,9)

Social perspective (C2) (3,5,7) (3,5,7) (5,7,9) (3,5.66,9)

Economic perspective (C3) (7,9,9) (5,7,9) (7,9,9) (5,8.33,9)



(D is constructed below (Table 5.5) using the

following relation:

nccc ...21

mnmm

n

n

m xxx

xxx

xxx

S

S

S

D

~...~~............

~...~~

~...~~

...

~

21

22221

11211

2

1

Table 5.5: Aggregate fuzzy weights of the alternatives (stakeholders)


S1 Government (7,9,9) (5,7,9) (3,5,7)

S2 Local Politicians (5,7,9) (7,9,9) (3,5,7)

S3 Local Community (7,9,9) (7,9,9) (1,3,5)

S4 Suppliers (3,5,7) (5,7,9) (7,9,9)

S5 Trade Organisations (3,5,7) (3,5,7) (7,9,9)

S6 Investors/Shareholders (1,3,5) (1,3,5) (7,9,9)

S7 Employees (5,7,9) (7,9,9) (3,5,7)

S8 Consumers (7,9,9) (7,9,9) (1,3,5)

S9 Market (3,5,7) (5,7,9) (5,7,9)

S10 Environmental Advocacy Groups (7,9,9) (7,9,9) (1,3,5)

S11 Media (7,9,9) (7,9,9) (1,3,5)

S12 Partners (3,5,7) (1,3,5) (5,7,9)

S13 Owners (1,3,5) (3,5,7) (7,9,9)

S14 CEOs (3,5,7) (3,5,7) (7,9,9)


211 | P a g e


The raw data is normalized using a linear scale transformation to bring the various criteria

scales onto a comparable scale. The normalized fuzzy decision matrix R~

shown in table 5.6

is computed as:

nmijrR ]~[~

, i = 1, 2, . . . , m ; j = 1, 2, . . . , n

Where

***,,~

j

ij

j

ij

j

ij

ijc

c

c

b

c

ar and }{max*


Table 5.6: Normalized alternatives (stakeholders)


*

jc 9 9 9

S1 Government (0.78,1,1) (0.56, 0.78,1) (0.33, 0.56, 0.78)

S2 Local Politicians (0.56, 0.78,1) (0.78,1,1) (0.33, 0.56, 0.78)

S3 Local Community (0.78,1,1) (0.78,1,1) (0.11, 0.33, 0.56)

S4 Suppliers (0.33, 0.56, 0.78) (0.56, 0.78,1) (0.78,1,1)

S5 Trade Organisations (0.33, 0.56, 0.78) (0.33, 0.56, 0.78) (0.78,1,1)

S6 Investors/Shareholders (0.11, 0.33, 0.56) (0.11, 0.33, 0.56) (0.78,1,1)

S7 Employees (0.56, 0.78,1) (0.78,1,1) (0.33, 0.56, 0.78)

S8 Consumers (0.78,1,1) (0.78,1,1) (0.11, 0.33, 0.56)

S9 Market (0.33, 0.56, 0.78) (0.56, 0.78,1) (0.56, 0.78,1)

S10 Environmental Advocacy Groups (0.78,1,1) (0.78,1,1) (0.11, 0.33, 0.56)

S11 Media (0.78,1,1) (0.78,1,1) (0.11, 0.33, 0.56)

S12 Partners (0.33, 0.56, 0.78) (0.11, 0.33, 0.56) (0.56, 0.78,1)

S13 Owners (0.11, 0.33, 0.56) (0.33, 0.56, 0.78) (0.78,1,1)

S14 CEOs (0.33, 0.56, 0.78) (0.33, 0.56, 0.78) (0.78,1,1)



for criteria is computed by multiplying the weights )~( jw

of evaluation criteria with the normalized fuzzy decision matrix ijr~ (Table 5.7) as:

nmijvV ]~[~

, i = 1, 2. . . m; j = 1, 2. . . n where jijij wrv ~(.)~~


212 | P a g e



solution (FNIS)

The FPIS and FNIS of the alternatives given in table 5.7 are computed as follows:

)~,......~,~( **

2

*

1

*

nvvvA where }{max~3

*

ijij vv , i = 1, 2. . . m; j = 1, 2, . . . , n

)~,......~,~( 21

nvvvA where }{min~3ijij vv

, i = 1, 2. . . m; j = 1, 2, . . . , n

Table 5.7: Weighted normalized alternatives (stakeholders)


S1 Government (3.9,8.33,9) (1.68, 4.41,9) (1.65, 4.66, 7.02)

S2 Local Politicians (2.8, 6.49,9) (2.34,5.66, 9) (1.65, 4.66, 7.02)

S3 Local Community (3.9,8.33,9) (2.34, 5.66, 9) (0.55, 2.74, 5.04)

S4 Suppliers (1.65, 4.66, 7.02) (1.68, 4.41, 9) (3.9,8.33, 9)

S5 Trade Organisations (1.65, 4.66, 7.02) (0.99, 3.16, 7.02) (3.9, 8.33, 9)

S6 Investors/Shareholders (0.55, 2.74, 5.04) (0.33, 1.86, 5.04) (3.9, 8.33, 9)

S7 Employees (2.8, 6.49,9) (2.34, 5.66, 9) (1.65, 4.66, 7.02)

S8 Consumers (3.9,8.33,9) (2.34, 5.66, 9) (0.55, 2.74, 5.04)

S9 Market (1.65, 4.66, 7.02) (1.68, 4.41, 9) (2.8, 6.49, 9)

S10 Environmental Advocacy Groups (3.9,8.33,1) (2.34, 5.66, 9) (0.55, 2.74, 5.04)

S11 Media (3.9,8.33,9) (2.34, 5.66, 9) (0.55, 2.74, 5.04)

S12 Partners (1.65, 4.66, 7.02) (0.33, 1.86, 5.04) (2.8, 6.49, 9)

S13 Owners (0.55, 2.74, 5.04) (0.99, 3.16, 7.02) (3.9, 8.33, 9)

S14 CEOs (1.65, 4.66, 7.02) (0.99, 3.16, 7.02) (3.9, 8.33,9)

FPIS (S+) (9,9,9) (9,9,9) (9,9,9)

FNIS (S-) (0.55,0.55,0.55) (0.33,0.33,0.33) (0.55,0.55,0.55)


The distance (

ii dd ,* ) of each weighted alternative i = 1, 2. . . m from the FPIS and the FNIS

is computed as follows:

Let a~ = (a1, a2, a3) and b~



213 | P a g e


][3

1)

~,~(

2

33

2

22

2

11 babababad

n

j

jijvi vvdd1

** )~,~( i = 1, 2. . . m

n

j

jijvi vvdd1

)~,~( i = 1, 2. . . m

Where )~

,~( badv is the distance measurement between two fuzzy numbers a~ and b~

. The

distances of weighted alternatives from FPIS and PNIS are shown in table 5.8 below.

Table 5.8: Distance of stakeholders from FPIS and FNIS


d(S1,S+) 2.9698 4.9883 5.0589 d(S1,S

-) 6.9078 5.5868 4.4708

d(S2,S+) 3.8618 4.3016 5.0589 d(S2,S

-) 6.1032 5.9894 4.4708

d(S3,S+) 2.9698 4.3016 6.4877 d(S3,S

-) 6.9078 5.9894 2.8842

d(S4,S+) 5.0589 4.9883 2.9698 d(S4,S

-) 4.4708 5.5868 6.9078

d(S5,S+) 5.0589 5.8363 2.9698 d(S5,S

-) 4.4708 4.2111 6.9078

d(S6,S+) 6.4877 6.8758 2.9698 d(S6,S

-) 2.8842 2.8592 6.9078

d(S7,S+) 3.8618 4.3016 5.0589 d(S7,S

-) 6.1032 5.9894 4.4708

d(S8,S+) 2.9698 4.3016 6.4877 d(S8,S

-) 6.9078 5.9894 2.8842

d(S9,S+) 5.0589 4.9883 3.8618 d(S9,S

-) 4.4708 5.5868 6.1032

d(S10,S+) 5.4912 4.3016 6.4877 d(S10,S

-) 4.8974 5.9894 2.8842

d(S11,S+) 2.9698 4.3016 6.4877 d(S11,S

-) 6.9078 5.9894 2.8842

d(S12,S+) 5.0589 6.8758 3.8618 d(S12,S

-) 4.4708 2.8592 6.1032

d(S13,S+) 6.4877 5.8363 2.9698 d(S13,S

-) 2.8842 4.2111 6.9078

d(S14,S+) 5.0589 5.8363 2.9698 d(S14,S

-) 4.4708 4.2111 6.9078



( *A ) and the fuzzy negative ideal solution ( A ) simultaneously. The closeness coefficient of

each alternative (Tables 5.9 and 5.10) is calculated as


214 | P a g e

CCi = )( *

ii

i

dd

d

, i = 1, 2. . . m

The closeness coefficients for alternatives are given in table 5.9 and figure 5.2. Similarly the

closeness coefficients for different criteria are computed and given in table 5.10 and figure

5.3.

Table 5.9: Aggregate closeness coefficient for alternatives (stakeholders)

S. No. Stakeholders

S1 Government 13.0171 16.9654 0.5658

S2 Local Politicians 13.2223 16.5634 0.5561

S3 Local Community 13.7591 15.7814 0.5342

S4 Suppliers 13.0171 16.9654 0.5658

S5 Trade Organisations 13.8650 15.5897 0.5293

S6 Investors/Shareholders 16.3333 12.6512 0.4365

S7 Employees 13.2223 16.5634 0.5561

S8 Consumers 13.7591 15.7814 0.5342

S9 Market 13.9091 16.1608 0.5374

S10 Environmental Advocacy Groups 16.2805 13.7710 0.4582

S11 Media 13.7591 15.7814 0.5342

S12 Partners 15.7965 13.4332 0.4596

S13 Owners 15.2938 14.0031 0.4780

S14 CEOs 13.8650 15.5897 0.5293

Table 5.10: Closeness coefficients for different criteria (perspectives)

S.

No.

Stakeholders Environmental

perspective

Social

perspective

Economic

perspective

S1 Government 0.6993 0.5283 0.4691

S2 Local Politicians 0.6125 0.5820 0.4691

S3 Local Community 0.6993 0.5820 0.3078

S4 Suppliers 0.4691 0.5283 0.6993

S5 Trade Organisations 0.4691 0.4191 0.6993

S6 Investors/Shareholders 0.3078 0.2937 0.6993

S7 Employees 0.6125 0.5820 0.4691

S8 Consumers 0.6993 0.5820 0.3078

S9 Market 0.4691 0.5283 0.6125

S10 Environmental Advocacy Groups 0.4714 0.5820 0.3078

S11 Media 0.6993 0.5820 0.3078

S12 Partners 0.4691 0.2937 0.6125

S13 Owners 0.3078 0.4191 0.6993

S14 CEOs 0.4691 0.4191 0.6993


215 | P a g e

Figure 5.2: Closeness coefficient (CCi) of GM stakeholder (aggregate)

Figure 5.3: Closeness coefficient (CCi) of GM stakeholders (economic, social and

environmental perspectives)

0.4 0.45 0.5 0.55 0.6

Government

Suppliers

Local Politicians

Employees

Market

Local Community

Consumers

Media

Trade Organisations

CEOs

Owners

Partners

Environmental Advocacy Groups

Investors/Shareholders


Importance of GM stakeholders

0.2 0.3 0.4 0.5 0.6 0.7 0.8

Government

Local Politicians

Local Community

Suppliers

Trade Organisations


Employees

Consumers

Market

Environmental Advocacy Groups

Media

Partners

Owners

CEOs


Economic perspective Social perspective Environmental perspective


216 | P a g e

Step 9: Rank the alternatives (stakeholders)


select the alternative with the highest closeness coefficient for mitigation. The best

alternative is closest to the FPIS and farthest from the FNIS. The aggregate ranking of the

barriers according to the three criteria, i.e. environmental, social, and economic perspectives

is given in table 5.11.

Table 5.11: Ranking of GM stakeholders

S. No. Stakeholders Rank

1 Government [S1] 1

2 Suppliers [S4] 1

3 Local Politicians [S2] 3

4 Employees [S7] 3

5 Market [S9] 5

6 Local Community [S3] 6

7 Consumers [S8] 6

8 Media [S11] 6

9 Trade Organisations [S5] 9

10 CEOs [S14] 9

11 Owners [S13] 11

12 Partners [S12] 12

13 Environmental Advocacy Groups [S10] 13

14 Investors/Shareholders [S6] 14


The fuzzy TOPSIS results clearly show that government (1/14) and suppliers (1/14) are the

top ranked stakeholders; local politicians (3/14) and employees (3/14) are the second highest

ranked stakeholders; followed by market (5/14) The active involvement of these top

stakeholders, in the decision making about the environmental initiatives of company and

hence the implementation, is important. Local community (6/14), consumers (6/14), and


217 | P a g e

media (6/14) are the forth level stakeholders as these stakeholders influence the company's

environmental performance by demanding green products and processes and highlighting

the importance of environmental performance in the society. Surprisingly, the

investors/shareholders is at the bottom of the ranking. It shows that in Indian stakeholders

are more interested into the economic aspects of the companies and not bothered by their

environmental performance as seen from figure 5.3.

Figure 5.3 also shows that CEOs, owners, investors/shareholders, trade organizations, and

suppliers are more concerned about economic aspects compared to environmental and social

aspects. On the other hand media, consumers, local community, and government are more

concerned about environmental issues. Only environmental advocacy groups seem to give

highest weightage to social aspects. Local politicians and employees have rational

weightage to environmental, social and economic perspectives. Partners in joint ventures

and shareholders provided least importance to social aspects.

5.3 CLASSIFICATION OF GM STAKEHOLDERS


The basic steps of the research methodology used for this study are literature review,

development of stakeholders, questionnaire development, data collection, data analysis,

model proposition, and a case study of stakeholder comparison in SMEs and large

enterprises as shown in figure 5.4. In the first step, 46 peer-reviewed research articles have

been reviewed in chapter 2. Secondly, 14 stakeholders of GM implementation were

identified through the review of literature and in consultation with academicians and experts

working in field of GM. Rest of the steps of the methodology are presented in figure 5.4:


218 | P a g e

Stakeholders Development

Survey Instrument Development

Data Collection

Model proposition

(Exploratory Factor Analysis)

Data Analysis

Figure 5.4: Research methodology

5.3.1.1 Questionnaire development

A questionnaire was developed based on the stakeholders identified in chapter 2. The survey

questionnaire asked the participants to rate the importance of 14 stakeholders of GM

implementation on 5 point Likert scale (Gartner, 1989; Sangwan et al., 2012; Singh et al.,

2013). The respondents were further asked to reply with few more details namely, size of the

company and industry sector. The questionnaire was discussed with few academicians and

experts to finalize it. The survey questionnaire is given in Appendix B.

5.3.1.2 Data collection

The Confederation of Indian Industry (CII) directory was used to select the manufacturing

organizations to get responses. The questionnaire survey was appended with a cover letter

mentioning the objective of the study and assuring the confidentiality of the data to the

respondents. The paper questionnaire was sent to more than 2000 executives (senior

manager and above) of different manufacturing companies in September 2008 through post


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and 252 valid (completely filled up questionnaires) responses were selected for the study,

which resulted in 12.6 % response rate. The number of valid filled questionnaire from SMEs

and large enterprises were 201 and 51 respectively.


The suitability of the data collected for analysis is assessed using reliability analysis. The

results of the reliability analyses carried out on the two different set of data are given in

tables 5.12. The mean value of the data is ≥ 2.58 for SMEs and ≥ 2.59 for large enterprises

on a scale of 5, which means that all the stakeholders are rated important by the respondents.

The reliability analysis on a sample data of 201 SMEs yielded Cronbach alpha value of

0.904 and the Cronbach alpha based on standardized items is 0.905. Also, the reliability

analysis on the sample of 51 large enterprises data yielded Cronbach alpha value of 0.898

and the Cronbach alpha based on standardized items is 0.895. The descriptive statistics like

standard deviation, scale mean if item deleted, scale variance if item deleted are presented in

table 5.12. The item-total statistics, as interpreted by Gliem and Gliem (2003) discussed that

'scale mean if item deleted' is the mean of the summated all items excluding the individual

item listed. The 'scale variance if item deleted' is the variance of the summated all items

excluding the individual item listed. The 'corrected item-total correlation' is the correlation

of the item designated with the summated score for all other items. The 'squared multiple

correlation' is the predicted correlation coefficient obtained by regressing the identified

individual item on all the remaining items. The 'alpha if item deleted' represents the scale’s

Cronbach alpha for internal consistency if the individual item is removed from the scale.

The CITC refers to the correlation of an item or indicator with the composite score of all

other items forming the same set. Items from a given scale exhibiting CITC value less than


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0.50 are usually candidate for elimination (Koufteros, 1999). The one stakeholder in SMEs,

i.e. 'government' have CITC value less than 0.5 but this stakeholder was not eliminated

because 'government' is an important stakeholder and has achieved high values of Cronbach

alpha as shown in table 5.12. The three stakeholders in large enterprises – 'government',

'local community', and 'employees' have CITC values less than 0.5 but still the stakeholders

were not eliminated because 'government' is an important stakeholder has high value of

Cronbach alpha as shown in table 5.12 and other two stakeholder have CITC values of 0.467

and 0.449 which are very close to minimum value of 0.5.

Barlett’s test assesses the overall significance of the correlation matrix. If the value of the

test statistic for sphericity is large and the associated significance level is small, it can be

concluded that the variables are correlated. Barlett’s test of sphericity demonstrated

approximate chi-square value of 2529.270, degree of freedom value (df) of 91, and

significance level value of 0.000, which are sufficient values for all the 14 stakeholders to

conclude that the variables are correlated. The test result showed KMO measure of 0.853,

which is above the suggested minimum standard of 0.5 required for running factor analysis.

Hence, based on the above tests, it is concluded that all the 14 stakeholders are suitable for

applying factor analysis.

5.3.1.4 Exploratory factor analysis

Exploratory factor analysis is a statistical method used to uncover the underlying structure of

a relatively large set of variables. The EFA is a widely utilized and broadly applied

statistical technique in various fields of manufacturing and operations research (Singh et al.,

2012). In the present case, the factor analysis is used to explore few latent variables


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Table 5.12: Descriptive and reliability analysis of stakeholders for SMEs and large enterprises

Stakeholders Mean S.D. S.M.I.D. S.V.I.D. C.I.T.C. S.M.C. C.A.I.D.

SMEs LEs SMEs LEs SMEs LEs SMEs LEs SMEs LEs SMEs LEs SMEs LEs

Government 3.86 4.39 1.23 0.80 39.25 43.55 118.92 125.57 0.29 0.13 0.40 0.31 0.91 0.90

Local Politicians 2.58 2.59 1.30 1.35 40.53 45.35 110.21 112.23 0.59 0.50 0.64 0.65 0.89 0.89

Local Community 2.89 3.12 1.28 1.45 40.22 44.82 112.10 112.18 0.53 0.46 0.61 0.76 0.90 0.89

Suppliers 2.63 3.02 1.30 1.12 40.48 44.92 108.08 111.87 0.68 0.65 0.71 0.73 0.89 0.88

Trade Organisations 2.79 3.31 1.25 1.27 40.32 44.63 109.95 107.27 0.63 0.74 0.68 0.80 0.89 0.88

Investors/Shareholders 2.78 3.41 1.30 1.28 40.33 44.53 107.02 107.77 0.72 0.72 0.75 0.81 0.89 0.88

Employees 3.56 4.08 1.19 1.12 39.55 43.86 113.74 116.44 0.51 0.44 0.58 0.57 0.90 0.89

Consumers 3.20 3.57 1.24 1.20 39.91 44.37 110.10 112.03 0.64 0.59 0.79 0.80 0.89 0.89

Market 3.21 3.61 1.22 1.25 39.90 44.33 110.46 108.26 0.63 0.72 0.83 0.84 0.89 0.88

Environmental Advocacy Groups 3.01 3.14 1.06 1.26 40.09 44.80 112.39 112.56 0.65 0.54 0.56 0.69 0.89 0.89

Media 3.20 3.12 1.19 1.33 39.91 44.82 113.54 110.98 0.52 0.56 0.66 0.70 0.90 0.89

Partners 2.85 3.16 1.12 1.10 40.26 44.78 110.49 111.29 0.69 0.69 0.60 0.60 0.89 0.88

Owners 3.31 3.69 1.11 1.30 39.80 44.25 111.55 108.83 0.65 0.66 0.73 0.90 0.89 0.88

CEOs 3.24 3.75 1.10 1.29 39.87 44.20 111.68 107.92 0.66 0.70 0.73 0.92 0.89 0.88

SMEs - Small and Medium Enterprises; LEs - Large Enterprises; S.D. - Standard Deviation; SMID - Scale Mean if Item Deleted; SVID - Scale Variance if

Item Deleted; CITC - Corrected Item Total Correlation; SMC - Squared Multiple Correlation; CAID - Cronbach Alpha if Item Deleted


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(stakeholder factors) which represent relatively large number of observed variables

(stakeholders) of GM. EFA is more appropriate as the purpose of the study is to literally

explore the data and not to confirm or support any theory. Factor analysis was carried out by

using SPSS 16.0 statistical tool and the results are presented in table 5.13.

Table 5.13: Factor loadings of all stakeholders through EFA.

Stakeholders

Factor loadings

Factor 1 Factor 2 Factor 3

Government 0.154 0.565 -0.158

Local Politicians 0.149 0.155 0.854

Local Community 0.356 -0.050 0.744

Suppliers 0.526 0.124 0.643

Trade Organisations 0.635 0.066 0.560

Investors/Shareholders 0.767 0.161 0.424

Employees 0.482 0.667 -0.190

Consumers 0.238 0.814 0.171

Market 0.190 0.831 0.254

Environmental Advocacy Groups 0.139 0.523 0.578

Media -0.088 0.775 0.377

Partners 0.577 0.475 0.242

Owners 0.828 0.215 0.169

CEOs 0.844 0.203 0.179



a. Rotation converged in 10 iterations.

As a rule of thumb, the factor loading of 0.32 is considered good for the minimum loading

of an item (Tabachnick and Fidell, 2001). In this case, all the stakeholders have factor

loadings of more than 0.32. The explored three latent factors have at least four observed

variables. After carefully analyzing the group of stakeholders under each factor, these three

stakeholder factors are named as: social stakeholders; internal stakeholders; and local

stakeholders as shown in figure 5.5.


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Stakeholder of GM

Implementation

Internal StakeholdersSocial Stakeholders Local Stakeholders


Partners

Owners

CEOs

Government

Employees

Consumers

Local Politicians

Local Community

Suppliers

Trade OrganizationsMarket

Media Environmental Advocacy

Groups

Figure 5.5: Classification of stakeholders of GM implementation


The 14 stakeholders are classified into three groups namely social stakeholders, internal

stakeholders, and local stakeholders using exploratory factor analysis. The exploratory

factor analysis is done to determine the few latent stakeholders which represent the

relatively large number of stakeholders. The classification of the stakeholders obtained by

exploratory factor analysis is: social stakeholders – government, employees, consumers,

market, and media; internal stakeholders – investors/shareholders, partners, owners, and

CEOs; and local stakeholders – local politicians, local community, suppliers, trade

organisations, environmental advocacy groups. It is interesting to observe that 'employees' is

categorized as a social stakeholder. It is because 'employees' have no concerned from

occupational health and safety perspective, i.e. the GM implementation will improve

employee's occupational health and safety. Table 5.13 show that 'employees' has a large


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factor loading to internal stakeholders also. Another interesting aspect is of 'environmental

advocacy groups' stakeholder. It has grouped under local stakeholders (factor loading 0.578)

but can be grouped under social stakeholders group also (factor loading 0.523). It can be

construed that in India, 'environmental advocacy groups' are working at local level rather

than at national level.

5.4 COMPARATIVE ANALYSIS OF SMEs AND LARGE ENTERPRISES

It was found that the data collected for the study is not normally distributed rather severely

non-normal. Various potential attempts were made to make it normal using data

transformation methods, but all fails, so it was decided to use non-parametric testing where

normality in the data is not required. Hence, non-parametric tests, i.e. Mann-Whitney U test

was conducted to compare the mean ranks of the stakeholders for SMEs and large

enterprises. The Mann-Whitney U test does not assume normality in the data and is much

less sensitive to outliers, therefore, it can be used for non-normal data.

The Mann-Whitney U test is used to compare differences between two independent groups

when the dependent variable is either ordinal or interval/ratio, but not normally distributed.

The data should obey four assumptions required for a Mann-Whitney U test to give a valid

result. The first assumption is that the dependent variable should be measured at the ordinal

or interval/ratio level. The second assumption is that independent variable should consist of

two categorical independent groups. The third assumption is the independence of

observations, which means that there is no relationship between the observations in each

group or among the groups. The fourth and last assumption is that the variables are not

normally distributed. However, for a Mann-Whitney U test to be able to provide a valid

result, both distributions must be of the same shape. The four assumption were confirmed


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Table 5.14: Results of Mann-Whitney U Test

Stakeholders Mean Std. Dev. Mean Rank

SMEs/Large

Enterprises

Mann-Whitney

U

Wilcoxon W Asymp. Sig.

(2-tailed)

Government 3.97 1.177 120.64/149.60 3947.50 24248.50 0.007

Local Politicians 2.58 1.314 126.44/126.75 5113.00 25414.00 0.978

Local Community 2.94 1.319 124.17/135.69 4657.00 24958.00 0.303

Suppliers 2.71 1.275 121.79/145.08 4178.00 24479.00 0.037

Trade Organisations 2.89 1.275 120.60/149.75 3940.00 24241.00 0.009

Investors/Shareholders 2.91 1.319 119.65/153.50 3748.50 24049.50 0.002

Employees 3.66 1.198 119.69/153.35 3756.00 24057.00 0.002

Consumers 3.27 1.240 122.27/143.16 4276.00 24577.00 0.058

Market 3.29 1.237 121.48/146.30 4115.50 24416.50 0.024

Environmental Advocacy Groups 3.04 1.103 124.43/134.67 4709.00 25010.00 0.349

Media 3.19 1.221 127.21/123.69 4982.00 6308.00 0.746

Partners 2.91 1.126 122.64/141.72 4349.50 24650.50 0.081

Owners 3.38 1.163 121.40/146.62 4099.50 24400.50 0.022

CEOs 3.35 1.159 119.71/153.26 3760.50 24061.50 0.002

SMEs (Sample Size 201)

Large enterprises (Sample Size 51)

Grouping Variable: Group

Asymp. Sig. - Asymptotic Significance


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for the present case. Therefore, Mann-Whitney U test was carried out using SPSS 16.0

statistical tool and the results are given in table 5.14.


Table 5.14 shows that the asymptotic significance value for the government, suppliers, trade

organizations, investors/shareholders, employees, market, owners, and CEOs is less than

0.05. It means these stakeholders are statistically different for the SMEs and large

enterprises. The rest of the stakeholders, i.e. local politicians, local community, consumers,

environmental advocacy groups, media, and partners are statistically similar between the

two groups of companies.

Government is the most important stakeholder pressure in large scale industries. Owing to

their huge establishment the impact of government rule and policies on large scale industries

is inevitable, whereas its pressure is less for SMEs. Suppliers to the large enterprises are

differently important than SMEs. As the profits are less in a SMEs, so the idea of

philanthropy isn’t that relevant. Trade organizations and investors/shareholders are different

because of the influence and the investment corpus. Shareholders are important stakeholder

group for large enterprises as compared to SMEs. SMEs are small in size so they have no

shareholders or very few shareholders, hence their pressure is less. Employees stakeholder

pressure is more important for large company than SMEs. Market behave differently to the

both industry size companies. Owners and CEOs social philanthropy is relatively a more

important stakeholder for large enterprises compared to SMEs.

Media as a stakeholder is equally important to both SMEs and large enterprises. Media is a

link between the company and the market/consumer. A company’s reputation is in one way


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handled by the media. All the companies are a part of the community, hence, local

community and local politicians act as important stakeholders for both SMEs and large

enterprises.

5.5 SUMMARY

The 14 stakeholders of GM implementation have been developed using literature and

discussion with practitioners and academicians.

The 14 stakeholders of GM implementation were ranking based on fuzzy TOPSIS multi-

criteria decision model which provides a proper tool to encounter the uncertain and complex

environments by measuring the inherent ambiguity of decision maker’s subjective judgment

using environmental, social and economic perspectives. The ranking of these stakeholders is

expected to help the government and industry to focus on few important stakeholders to

facilitate the GM implementation within limited resources.

The stakeholders are classified into three categories – social, internal, and local stakeholders

– through the application of exploratory factor analysis using statistical tool, SPSS 16.0.

Further, the importance of stakeholders are compared between SMEs and large enterprises

using Mann-Whitney U test and found that the pressure exerted by different stakeholders is

quite different for SMEs and large enterprises.

The stakeholders namely government, suppliers, trade organizations, investors/shareholders,

employees, market, owners, and CEOs are statistically different for the SMEs and large

enterprises. The rest of the stakeholders, i.e. local politicians, local community, consumers,

environmental advocacy groups, media, and partners are statistically similar for the two

groups of enterprises.

CHAPTER 6

CONCLUSIONS

Nowadays, almost every function within organizations has been influenced by external and

internal pressures to become green. Issues such as green consumerism, green products, green

processes, environmental footprints, etc. have affected/influenced the image of the company

in public, hence, marketing. The traditional reactive responses to these pressures are now

being supplemented and replaced by more proactive, strategic and competitive responses.

Many businesses have begun to realize that there are economical benefits of green

manufacturing in addition to environmental and social benefits. To facilitate the easy and

faster adoption and diffusion of green manufacturing in the industry, there is a need to

understand and analyze the drivers for, barriers to, and stakeholders of green manufacturing.

The study has focused on the development and validation of drivers for, barriers to, and

stakeholders of green manufacturing.

In chapter 2, the evolution of the green manufacturing and similar systems/terms using

online scholarly research articles on Google scholar has been traced. The term sustainable

production appeared in 1987, clean manufacturing in 1989, cleaner production in 1990,

environmentally conscious manufacturing and green manufacturing in 1991,

environmentally responsible manufacturing in 1993, environmentally benign manufacturing

in 1994, and sustainable manufacturing in 1997. However, there is no unambiguous

definition of any of these eight systems/terms which explicitly defines the scope and

limitation of the systems/terms. Some researchers feel that many of the terms are same,

whereas a few feel that they are different. These terms have been defined as activity or

strategy or way or tool or method or process or program or approach or perspective, etc. It is

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confusing when different researchers refer to the same term from different perspectives and

different terms by same perspective. Lack of unambiguous definitions for these

systems/terms over the years has led to the emergence of different terminologies. It is

apparent from the literature that many of the elements of these concepts overlap and

supplement each other. Some basic aspects observed during literature review, which can be

used to standardize the terminology, are:

Use life cycle engineering approach.

Provide clarity about the end of life strategies used.

Provide clarity in the use of various components of triple bottom line perspectives of

economy, environment and society.

Include the whole supply chain and integrate the environmental improvement strategies

with the business strategy.

Thirteen drivers for and twelve barriers to green manufacturing implementation have been

identified from the review of 55 and 62 research articles respectively. These drivers and

barriers were discussed with practitioners and academic experts in the field to make them

generic. The review reveals that GM driver and barrier studies have been done on a good

mix of industry sectors/segments/types/sizes – from small sized to big sized industry, from

process to discrete parts manufacturing, from manufacturing to service sector, from public to

private sector; and a wide range of industry sectors like metal, machinery, food & drink,

chemicals, pulp & paper, textiles, cement, leather, iron & steel, electrical & electronics, oil

& construction, mining, automotive, hotel, rubber, plastic, wood, etc. The review of research

articles has also shown that the research in the area of GM drivers and barriers is mostly

empirical based. The empirical studies of different industrial sectors and countries by

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230 | P a g e

researchers have led to divergent names of the drivers and barriers. The researchers use

different names and taxonomy to describe the same driver/barrier.

Various researchers have found drivers and barriers based on literature review, but only few

researchers have validated these drivers/barriers through statistical tools. However, models

reflecting hierarchy and relationship among the drivers/barriers of green manufacturing have

not been developed. The inter-relationship and hierarchy among drivers/barriers are needed

to identify the root drivers/barriers to facilitate drivers and mitigate barriers in order to have

effective and faster GM implementation. The review of literature has also revealed the lack

of articles providing the ranking of drivers and barriers.

The review of 46 research articles has shown that researchers in the past analyzed the

stakeholders either theoretically or by using some mathematical/statistical tool and provided

the classification of various stakeholders into relatively few stakeholders. 14 stakeholders of

green manufacturing have been identified from the review of the 46 research articles.

In chapter 3, the 13 drivers for GM implementation, identified in chapter 2, have been

developed and ranked using fuzzy TOPSIS multi criteria decision model from government,

industry and expert perspectives. This provided a proper tool to encounter the uncertain and

complex environments by measuring the inherent ambiguity of decision maker’s subjective

judgment. The study has concluded that competitiveness, incentives, organizational

resources and technology are the four top ranking drivers and should be facilitated first by

the government and industry to help industry in implementing green manufacturing.

A model of the 13 drivers for GM implementation has been developed using interpretive

structural modelling showing hierarchy and inter-relationship among these drivers. It has

Conclusions

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been found that 'customer demand', 'public pressure' and 'peer pressure' are the root drivers

for GM implementation and these drivers help other drivers for effective implementation of

GM. The developed model divides the identified drivers into five levels of hierarchies

showing inter-relationship among these drivers. The developed model will be highly useful

for the policy makers in government and industry to strategically leverage their resources in

a systematic way for successful implementation of green manufacturing.

A statistically reliable and valid model of green manufacturing implementation drivers is

presented using statistical tools. The drivers were purified using statistical analysis. One of

the drivers namely 'peer pressure' was eliminated during this process. The remaining 12

green manufacturing drivers were divided into three categories – internal, policy and

economy drivers – using exploratory and confirmatory factor analyses. The top management

commitment, the availability of human resources in the organization, environment friendly

technology, and need of green image of the organization represent internal drivers. The

policy drivers are represented by current and future legislations related to the operations and

products of the organization, incentives provided by the governments, and the pressure build

by the media, NGOs, banks, insurance companies, local politicians, etc. The economy

drivers are reflected by cost savings, competitiveness, customer demand, and supply chain

pressure. The final model has been tested using structural equation modelling technique

wherein hypotheses affirm that internal drivers cause policy and economic drivers and

policy drivers further cause economy drivers.

The case study carried out to compare the importance of drivers in an emerging country

(India) and a developed country (Germany) using independent t-test has shown that four

drivers – incentives, supply chain pressure, public image, and technology – have large

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differences in the two countries. Public pressure and top management commitment drivers

are significantly different but have medium differences in the two countries. Rest of the

drivers – current legislation, future legislation, cost savings, competitiveness, customer

demand, and organizational resources – have same importance in both the countries.

In chapter 4, the 12 barriers to the green manufacturing, identified in chapter 2, have been

developed and ranked using fuzzy TOPSIS multi criteria decision model. The research

shows that uncertain benefits, lack of organizational resources, technology risk, high short

term costs, uncertain future legislation, and low enforcement of legislation are top six

barriers to GM implementation in industry. The ranking of these barriers is expected to help

the government and industry to mitigate the top few important barriers to implement GM

within limited resources. Low demand from public and customer are the two least important

barriers to GM implementation.

A model of the barriers to GM implementation is developed using interpretive structural

modelling which shows the hierarchy and inter-relationship among barriers. It has been

found that lack of information and awareness among the public, government and industry

personnel is the root barrier to GM implementation which in turn influences the public


barrier has strong driving power and weak dependence. Lack of general awareness alleviates

the lack of pressure from public to incorporate environmental thinking. It also alleviates the

lack of demand from the customer which might force the industry to manufacture green

products and lack of management commitment to use GM. The lack of information and

awareness among government officials leads to insufficient legal structure which is crucial

to force the industry to implement GM. The developed model divides the identified barriers

Conclusions

233 | P a g e

into five levels of hierarchies showing their inter-relationship and depicting the driving-

dependence relationship.

A statistically reliable and valid model of GM implementation barriers is presented using

statistical tools. The 12 GM barriers were divided into three categories – internal barriers,

policy barriers, and economy barriers – using exploratory and confirmatory factor analyses.

The final model has been tested using structural equation modelling technique wherein

hypotheses affirm that internal barriers cause policy and economic barriers.

The case study carried out to compare the importance of barriers in a emerging country

(India) and a developed country (Germany) using independent t-test has shown that the 'low

enforecement' is the only barrier, which is seen statistically different in India and Germany

with medium difference. All other barriers are found to have same importance in both

countries.

In chapter 5, the 14 stakeholders of GM implementation have been developed and ranked

using fuzzy TOPSIS multi-criteria decision model using environmental, social and economic

perspectives. The results have shown that government, suppliers, local politicians,

employees, and market are high ranked stakeholders. The investors/shareholders are at the

bottom of the ranking. It shows that in Indian investor/shareholder is more interested the

economic aspects of the companies and not bothered by their environmental performance.

The CEOs, owners, investors/shareholders, trade organizations, and suppliers also are more

concerned about economic aspects compared to environmental and social aspects. On the

other hand, media, consumers, local community, and government are more concerned about

environmental issues. Only environmental advocacy groups seem to give highest weightage

Conclusions

234 | P a g e

to social aspects. Local politicians and employees have given rational weightage to

environmental, social and economic perspectives. Partners in joint ventures and shareholders

have provided least importance to social aspects.

The stakeholders have been classified into three categories – social, internal and local

stakeholders – through the application of exploratory factor analysis using statistical tool

SPSS 16.0. Further, the comparison of the importance of stakeholders between SMEs and

large enterprises using Mann-Whitney U test shows that the pressure exerted by different

stakeholders is quite different for SMEs and large enterprises. The pressure of government,

suppliers, trade organizations, investors/shareholders, employees, market, owners, and

CEOs has been found to be different for SMEs and large enterprises. The pressure of local

politicians, local community, consumer, environmental advocacy groups, media, and

partners has been found to be statistical similar for SMEs and large enterprises.

This study suggests following action plan to help green manufacturing implementation in

India:

Green manufacturing awareness campaigns should be organized for industry personnel

and public.

Government should come up with a comprehensive long term roadmap of

environmental standards with milestones for different industries so that industry has

more confidence in term of future legislations and benefits.

Formal human skill development programmes should be launched to provide

competitive human resources training on green manufacturing implementation.

The government should provide financial incentives to organizations to implement

green manufacturing.

Conclusions

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The government should develop infrastructure and mechanisms to enforce the

environmental rules and regulations stringently.

Industry should come forward to commit to green manufacturing implementation.

Government, industry and experts should come together to develop a comprehensive

programme on GM and to develop human resources in the field on the lines of ‘cleaner

production' programme of Chinese government.

Government, industry and experts should come together to develop a comprehensive

green index for Indian industry on the lines of NASDAQ OMX Green Economy Global

Benchmark Index. The ability to benchmark green and sustainable companies in a clear

and comprehensive manner will provide investors/shareholders the opportunity to

participate in the growth of green manufacturing implementation.

Specific Research Contribution of the Thesis

Some of the specific contributions of the research are:

The origin and evolution of green manufacturing and similar systems/terms have been

systematically traced for proper reference.

The meaning and scope of green manufacturing and similar systems/terms from the

extant literature have been clarified.

The significant and latest publications on green manufacturing and similar systems/terms

are included.

The research trend in green manufacturing and similar systems/terms have been

organized in a proper format for reference.

A proper and systematic identification of the drivers for, barriers to and stakeholders of

green manufacturing implementation is carried out.

Conclusions

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The drivers for, barriers to and stakeholders of green manufacturing implementation

using fuzzy TOPSIS multi-criteria decision model have been conveniently ranked for

better clarity on their importance.

The interpretive structural models of drivers for and barriers to green manufacturing

implementation showing hierarchy and inter-relationship among the drivers/barriers are

presented.

The models of drivers for and barriers to green manufacturing implementation using

structural equation modelling are developed and validated which might be used for

policy making in government and industry.

The importance of drivers and barriers between an emerging country (India) and a

developed country (Germany) is compared leading to an international finding.

The importance of stakeholders between SMEs and large enterprises is compared.

Limitations and Future Scope of Work

It is important to explicitly acknowledge the limitations of this research. This study targeted

the entire Indian industry which consists of many different sectors, products and sizes. When

these differences are large, they are translated into an analogous variability in the responses.

So, it would be better to do these studies on different sectors/segments/sizes of industry.

The developed models have been confirmed and tested using statistical tools but the data for

the study came from India. Therefore, there may be some bias in the data towards emerging

nations. It will be pertinent to test the model using data from some other developed and

developing countries. It will also be interesting to investigate the drivers, barriers and

stakeholders of single type of industry sector/size. Lastly, the fit of the model can be

improved by collecting more data for the analysis. The work can be further extended to

develop implementation model of green manufacturing.

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APPENDIX - A

A1

From: Varinder Kumar Mittal <[email protected]>

Subject: Survey regarding Drivers/Barriers for Environmentally Conscious

Manufacturing/Green Manufacturing

Dear Sir/Madam,

I am Varinder Kumar Mittal working as a Lecturer and pursuing my doctoral thesis

on the topic of Environmentally Conscious Manufacturing (ECM)/Green Manufacturing

(GM) at Birla Institute of Technology and Science, Pilani. It gives me immense pleasure to

interact with you on this topic.

With growing awareness of environmental issues – from global warming to local

waste disposal – business and government have come under increasing pressure to reduce

the environmental impacts involved in the production and consumption of goods and

services. Lack of comprehensive list of drivers/barriers for ECM/GM is a big challenge for

emerging countries like India.

In this context, I request you to kindly fill the attached questionnaire, which is one of

the important components of my research work. Your judicious response will assure

substantial judgment in this exercise and help to carry out the same successfully. I will be

happy to acknowledge the same.

Please make it convenient to spare your valuable time to fill in the questionnaire. It

will take maximum 15 minutes. The collected information will be kept confidential and

utilized for research purpose only. If you wish not to disclose your and/or your company’s

identity, then you can skip that information. I welcome your suggestions. It will be my

pleasure to answer your queries. If you are not comfortable to fill both the questionnaires

(drivers and barriers) then I would be highly obliged if you fill at least one of them.

If you are not associated with this subject then forwarding this mail to the concerned

person will be of a great help.

Please click here to fill the questionnaire https://www.surveymonkey.com/s/BITS-Pilani

Thanking you.

Yours truly,


+91-99505-19001 (Mobile)

https://www.surveymonkey.com/s/BITS-Pilani

APPENDIX - A

A2

Name:__________________________ Company: _________________________

E-mail:__________________________ Designation:_______________________

Industrial Experience:__________Years, Department_______________________

Do you want to contacted for further clarifications: Yes/No

Please rate the following factors which drive your company to implement ECM/GM on a scale from 1 to 5; where ‘1’ means no impact driver and ‘5’ means very high impact driver by √ mark in the appropriate box:

No im

pact

Low

im

pact

Me

diu

m

impact

Hig

h im

pact

Very

hig

h

impact

Driver Description 1 2 3 4 5

Current Legislation pollution control, landfill taxes, emissions trading, eco-label, etc.

Future Legislation expected development of stricter law, increased level of enforcement

Incentives investment subsidies, awards, R&D support

Public Pressure local communities, politicians, NGOs, media, insurance companies, banks promote environmental topics

Peer Pressure trade and business associations, networks, experts

Cost Savings reduction of energy consumption compared to rising energy costs, less waste output

Competitiveness better process performances, higher product quality, higher efficiency, competing with best-practices in sector

Customer Demand end-user demand for environmentally friendly products

Supply Chain Pressure demand of suppliers, distributors, OEM, compliance with legislation in global markets


management, owner or investors are highly committed to enhance environmental performance, ethics, social values

Public Image importance of a positive public perception of your company

Technology opportunities, advantages or performances of available green technology


skilled and motivated staff, healthy financial situation or performance measurements

Missing drivers (if any):

Any Comment(s):

APPENDIX - A

A3

Name:__________________________ Company: _________________________


Industrial Experience:__________Years, Department________________________


Please rate which of the following factors hinder your company to implement ECM/GM on a scale from 1 to 5; where ‘1’ means no impact barrier and ‘5’ means very high impact barrier: by √ mark in the appropriate box:

No im

pact

Low

im

pact

Me

diu

m

impact

Hig

h im

pact

Very

hig

h

impact

Barrier Description 1 2 3 4 5

Weak Legislation absence of environmental laws, complexity of law, ineffective legislation

Low Enforcement weak or no enforcement of laws, corruption


uncertain developments in legislation, withholding investments for future regulations

Low Public Pressure absence of pressure through local communities, media, NGOs or politicians

High Short-Term Costs investment and implementing costs

Uncertain Benefits uncertain or insignificant economic advantage, slow return on investment, amortization of older investments is prior

Low Customer Demand customers are price sensitive, interest in cheaper products, environment does not carry enough weight in the market

Trade-Offs rather trading emissions than reducing them, outsourcing of environmental problems, short product life cycles


management or owner is not committed to green issues, “our company has not an impact in the world”


lack of skilled staff, lack of experiences, no financial resources or capital access, green issues have low priority

Technological Risk risk of implementing new technology, fear of problems, no compatibility with existing systems, technological complexity


no awareness of green trends, limited access to green literature, not enough or not understandable information

Missing barriers (if any):

Any Comment(s):

APPENDIX - B

A4

To

..............................................................

..............................................................

.............................................................. Dated:....../...../..........

Subject: Survey regarding Stakeholders of Environmentally Conscious Manufacturing

Dear Sir/Madam,

I am Varinder Kumar Mittal working as a Lecturer and pursuing my doctoral thesis

on the topic of Environmentally Conscious Manufacturing (ECM) at Birla Institute of

Technology and Science, Pilani. It gives me immense pleasure to interact with you on this

topic.

With growing awareness of environmental issues – from global warming to local

waste disposal – business and government have come under increasing pressure to reduce

the environmental impacts involved in the production and consumption of goods and

services. Lack of comprehensive list of stakeholders of ECM is a big challenge for emerging

countries like India.

In this context, I request you to kindly fill the attached questionnaire, which is one of

the important components of my research work. Your judicious response will assure

substantial judgment in this exercise and help to carry out the same successfully. I will be

happy to acknowledge the same.

Please make it convenient to spare your valuable time to fill in the questionnaire. It

will take maximum 10 minutes. The collected information will be kept confidential and

utilized for research purpose only. If you wish not to disclose your and/or your company’s

identity, then you can skip that information. I welcome your suggestions. It will be my

pleasure to answer your queries.

If you are not associated with this subject then forwarding this mail to the concerned

person will be of a great help.

Thanking you.

Yours truly,


+91-99505-19001 (Mobile)

APPENDIX - B

A5

Name:__________________________ Company: _________________________


Industrial Experience:______________ Company Size: Micro/SME/Large

Department:___________________________


Please rate the degree or extent of practice for each variable on 1 to 5 scale where:

(1 – Very Low, 2 – Low, 3 – Medium, 4 –High, 5 –Very High)

OR

(1 –Completely Disagree, 2 –Rarely Agree, 3 –Partly agree, 4 –Rather Agree, 5 –Completely Agree)

A typical example is shown below:

Co

mp

lete

ly

Dis

ag

ree

Ra

rely

A

gre

e

Pa

rtly

a

gre

e

Ra

the

r A

gre

e

Co

mp

lete

ly

Ag

ree

Organization has an explicit environment policy/vision 1 2 3 4 5

S. No. Stakeholders ----------Rate----------

1 Government 1 2 3 4 5

2 Local politicians 1 2 3 4 5

3 Local community 1 2 3 4 5

4 Suppliers 1 2 3 4 5

5 Trade organisations 1 2 3 4 5

6 Shareholders 1 2 3 4 5

7 Employees 1 2 3 4 5

8 Consumers 1 2 3 4 5

9 Market 1 2 3 4 5

10 Environmental advocacy groups 1 2 3 4 5

11 Media 1 2 3 4 5

12 Partners 1 2 3 4 5

13 Owners 1 2 3 4 5

14 CEOs 1 2 3 4 5

5

APPENDIX - C

A6

PEER-REVIEWED INTERNATIONAL JOURNAL PUBLICATIONS (Published, In

Press, or Accepted)

[1] Mittal, V.K., Sangwan, K.S. (2014) Development of a Structural Model of

Environmentally Conscious Manufacturing Drivers, Journal of Manufacturing

Technology Management, Vol. 25, No. 8, pp. xx-xx. (In press)

[2] Mittal, V.K., Sangwan, K.S. (2013) Development of a Model of Barriers to

Environmentally Conscious Manufacturing Implementation, International Journal of

Production Research, DOI: http://dx.doi.org/10.1080/00207543.2013.838649

(Earlycite) 2012 Impact factor – 1.46

[3] Mittal, V.K., Sangwan, K.S. (2013) Fuzzy TOPSIS method for ranking barriers to

environmentally conscious manufacturing implementation: government, industry and

expert perspectives, International Journal of Environmental Technology and

Management, Vol. 16, No. 5, pp. xx-xx. (In press)

[4] Mittal, V.K., Sangwan, K.S. (2014) Modelling Drivers for Successful Adoption of

Environmentally Conscious Manufacturing, Journal of Modelling in Management, Vol.

9, No. 2, pp. xx-xx. (In Press)

[5] Mittal, V.K., Sangwan, K.S. (2013) Assessment of inter-relationships and hierarchy

among barriers to Environmentally Conscious Manufacturing, World Journal of

Science, Technology and Sustainable Development, Vol. 10, No. 4, pp. 297-307.

[6] Singh, P.J., Mittal, V.K., Sangwan, K.S. (2013) Development and validation of

performance measures for environmentally conscious manufacturing, International

Journal of Services and Operations Management, Vol. 14, No. 2, pp. 197-220.

[7] Sangwan, K.S., Mittal, V.K., Singh, P.J. (2012) Stakeholders for environmentally

conscious technology adoption: An empirical study of Indian micro, small and medium

enterprises, International Journal of Management and Decision Making, Vol. 12, No.1,

pp. 36-49.

PEER-REVIEWED INTERNATIONAL JOURNAL PUBLICATIONS

(Communicated)

[8] Mittal, V.K., Sangwan, K.S. (2013) Ranking of Drivers for Green Manufacturing

Implementation using Fuzzy TOPSIS method, Journal of Multi-Criteria Decision

Analysis, Manuscript ID: MCDA-13-0038 (Communicated on 13/08/2013)

[9] Mittal, V.K., Sangwan, K.S. (2013) A Bibliometric Analysis of Green Manufacturing

and Similar Systems, International Journal of Production Research, Manuscript ID:

TPRS-2013-IJPR-1074 (Communicated on 25/07/2013)

APPENDIX - C

A7

PEER-REVIEWED CIRP CONFERENCE PUBLICATIONS (Abroad) - Available

online on SpringerLink

[1] Mittal, V.K., Sangwan, K.S., Herrmann, C., Egede, P. (2013) 'Comparison of Drivers

and Barriers to Green Manufacturing: A Case of India and Germany'. Re-engineering

Manufacturing for Sustainability, ISBN: 978-981-4451-47-5 (Eds: Nee, Song and

Ong), In: proc. Of the 20th

CIRP International Conference on Life Cycle Engineering

(LCE 2013), Singapore, pp. 723-728.

[2] Mittal, V.K., Sangwan, K.S., Herrmann, C., Egede, P., Wulbusch, C. (2012) 'Drivers

and Barriers to Environmentally Conscious Manufacturing: A Comparative study of

Indian and German organizations. Leveraging Technology for a Sustainable World,

ISBN: 978-3-642-29069-5 (Eds: Dornfeld and Linke), In: proc. Of the 19th

CIRP

International Conference on Life Cycle Engineering (LCE 2012), University of

California, Berkeley, CA, USA, pp. 97-102.

[3] Mittal, V.K., Sangwan, K.S. (2011) Development of an interpretive structural model of

obstacles to environmentally conscious technology adoption in Indian industry.

Glocalized Solutions for Sustainability in Manufacturing, ISBN: 978-3-642-19692-8

(Eds: Hesselbach and Herrmann), In: proc. Of the 18th

CIRP International Conference

on Life Cycle Engineering (LCE 2011), Technische Universität, Braunschweig,

Germany, pp. 382-388.

CONFERENCE PUBLICATIONS (Within India)

[1] Mittal, V.K., Sangwan, K.S. (2012) Environmentally Conscious Manufacturing

Initiatives: Investigation on the Barriers in Indian Industry. in proc. of 4th International

& 25th All India Manufacturing Technology Design and Research Conference - 2012,

December 14 - 16, 2012, Jadavpur University, Kolkata, West Bengal, India.

[2] Nayagam, P.V., Mittal, V.K., Sangwan, K.S. (2012) Ranking of Drivers for

Sustainable Manufacturing using Analytical Hierarchy Process. in proc. of 3rd National

Conference on Recent Advances in Manufacturing, June 27-29, 2012, SVNIT Surat,

Gujarat, India.

[3] Mittal, V.K., Singh, P.J., Sangwan, K.S. (2011) Role of human and technology

resources in green manufacturing: a case of India. In: proc. Of the International

Conference on Sustainable Manufacturing, BITS Pilani, India.

[4] Singh, P.J., Mittal, V.K., Sangwan, K.S. (2011) Product and process characteristics for

green manufacturing: evidence from Indian large scale enterprises. In: proc. of the

International Conference on Sustainable Manufacturing, BITS Pilani, India.

[5] Mittal, V.K., Singh, P.J., Sangwan, K.S. (2009) Benefits and stakeholders of green

manufacturing: A study of Indian industry. In: Proc. of the 7th Global Conference on

Sustainable Manufacturing, IIT Madras, Chennai, India.

APPENDIX - D

A8

About the author (Varinder Kumar Mittal)

Varinder Kumar Mittal is a Lecturer in the Department of

Mechanical Engineering at Birla Institute of Technology and

Science, Pilani, Rajasthan, INDIA. He is a graduate in the

discipline of mechanical engineering from Punjab Technical

University, Jalandhar, INDIA in 1999, post-graduate in

mechanical engineering with specialization in production engineering from Punjab

Technical University, Jalandhar, INDIA in 2004 and pursuing his PhD in green

manufacturing from BITS Pilani, INDIA. His teaching and research interests are primarily

in the field of manufacturing engineering and management and operations research along

with analysis of problems using structural equation modelling, statistical analysis,

interpretive structural modelling, and fuzzy TOPSIS, etc. In addition to an experience of two

years in core manufacturing industry, he is engaged in teaching with various institutes of

repute in India from last more than 10 years.

About the supervisor (Prof. Kuldip Singh Sangwan)

Prof. Kuldip Singh Sangwan is an Associate Professor and Head in

the Department of Mechanical Engineering at Birla Institute of

Technology and Science, Pilani, Rajasthan. He did his B.E. and

M.E. from Punjab Engineering College, Chandigarh, and PhD from

BITS Pilani. He is an active researcher in the field of green

manufacturing, reverse logistics, lean manufacturing, sustainable

manufacturing, cellular manufacturing systems, and simulation and analysis of machining

processes on Titanium alloy. He has guided 4 PhD's and 5 PhD's are in progress in addition

to large number of research practices, dissertations, and thesis supervised. He is also an

active person in research activities in collaboration with foreign universities like TU

Braunschweig, Germany, Mondragon University, Mondragon, Spain, etc. In addition to the

teaching and research, he has been on administrative posts like Assistant Dean, Engineering

Services Division and Chief, Workshop Unit of BITS Pilani.

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