chapter iv research methodologyshodhganga.inflibnet.ac.in/bitstream/10603/21082/6...chapter iv...

CHAPTER IV

RESEARCH METHODOLOGY

4.1 Introduction4.2 Problem Statement4.3 Scope of the study4.4 Research Objectives4.5 Proposed Research Model4.6 Research Hypotheses4.6.1 Hypothesis based on Dimensions of Environmental Concerns across Organizational Variables4.6.2 Hypothesis based on impact of different dimensions of environmental concerns4.7 Research Design4.8 Selection of Survey Method

4.8.1 Measurement Scale4.8.2 Question Content and Wording4.8.3 Response Format4.8.4 Sequence of Questions 4.8.5 Administration of Final Questionnaire

4.9 Questionnaire Development and Administration4.9.1 Specification of the Information Needed4.9.2 Structure and Content Validity of the Questionnaire

4.10 Reliability & Validity Analysis4.10.1 Reliability Analysis4.10.2 Exploratory Factor Analysis

4.10.3 KMO and Bartlett’s Test (Factor Analysis) for Testing the Validity of the Questionnaire4.11 Confirmatory Factor Analysis (CFA)4.12 Model Fit Assessment 4.13 Tools used for Data Analysis

4.13.1 Analysis of Variance4.13.2 Structural Equation Modeling (SEM)

4.14 Limitations of the Study

Chapter IV

RESEARCH METHODOLOGY

4.1 Introduction

This chapter highlights the problem statement, research objectives, research questions,

significance of the study, hypotheses formulation, research design, questionnaire design,

sampling methods, data collection and administration. In addition, this chapter delineates

the conceptual underpinnings based on which analysis has been carried out in the

subsequent chapter of the study. Finally, the limitations of the study relevant to this research

are detailed out.

4.2 Problem Statement

Although industrial activity is essential for a country’s socio-economic development, it

causes destruction to the natural environment in various forms of pollution and depletion of

natural resources. With the advancement in the standard of living of the populace, concerns

for a healthier environment grow at a rapid pace. Businesses need to develop and device

ways and means for the fulfillment of eco-friendly environment through changes at various

levels of manufacturing process. This poses a challenge for the business entities to

continually improve their operations so that the society and the environment can benefit and

lead a better life.

Due to globalization and development in the socio-economic conditions of the society,

demand for various goods has increased many folds. As a result, production of goods has

increased rapidly which has, in turn, led to production of waste and depletion of natural

resources to a large extent. At this point, an all-round effort for the conservation and

improvement of the environment is necessary so that the generations to come do not suffer

with the polluted and non-eco friendly environment.

4.3 Scope of the Study

The present study aims at studying the environmental issue and challenges in SMEs in

India. In this study the following topics have been focused upon:

• Environmental laws pertaining to India

• Problems faced by SMEs in incorporating the environmental laws

• Environmental pollution

• Air;

• Water;

• Noise; and

• Waste Management

With regard to Indian SMEs, this study primarily focuses on the following:

• Lock, Hardware & Allied

• Pottery/Ceramic

• Leather & Tannery

• Glass

For the present research work micro level industries have also been included in the ambit of

small and medium enterprises (MSME).

This research aims to focus on environmental issues and challenges in SMEs. This is

expected to ensure the productivity and welfare of the society contributing to sustainable

development. The research amalgamates operations with supply chain and environmental

management in order to balance a variety of corporate objectives such as resource

conservation, pollution prevention and competitiveness, etc. The functional perspective

brings together different functional fields like, Total Quality Management (TQM), Total

Quality Environmental Management (TQEM), Re-engineering, Waste management,

Reverse logistics, etc.

This research will be effective in developing optimal strategies that balance environmental

and economic costs. Further, it shall contribute to long-term betterment of industry and

society as a whole.

4.4 Research Objectives

The manufacturing sector in SMEs has been characterized by high consumption of natural

resources in one form or the other. It is also a potent source of waste generation, ecosystem

disruption and depletion of natural resources.

This study aims to focus on environmental concerns in the four different categories of

SMEs situated in the state of Uttar Pradesh (U.P) in India viz. Lock, hardware & allied,

Pottery/Ceramic, Leather & Tannery, and Glass. The study focuses on resource

conservation, pollution prevention (air, water, and noise), waste reduction (solid & liquid)

and total quality environmental management (TQEM) practices. Specifically, the study aims

• To identify environmental issues and challenges and the extent of implementation at

various levels.

• To identify the extent of implementation of environmental protection practices at various

levels of operation in select SMEs.

• To explore the differences, if any, with regard to implementation of environmental

protection procedures and techniques across select SMEs.

• To develop a conceptual model covering different aspects as regards different

environmental issues and challenges concerning selected SMEs and their impact and

benefits so derived.

• To ascertain the validity of the conceptual model interlinking various environmental

concerns with environmental performance and benefits derived.

• To ascertain the benefits derived as a result of implementation of environmental

protection procedures and techniques with regard to resource conservation,

competitiveness and economic performance.

4.5 Proposed Research Model

The proposed research model has been crystallized after thorough review of literature. The

review covered various aspects of business operations and helped identify seven latent

constructs. These constructs are viz. Issues, Challenges, Environmental Management

Practices (pollution related & others), Resource Conservation, Pollution Prevention,

Competitiveness and Economic Performance. The research model indicating the

relationship amongst these variables is presented as Exhibit 4.1

EXHIBIT 4.1: PROPOSED RESEARCH MODEL

ZZ

4.6 Research Hypotheses

Research hypotheses were formulated based on extensive literature survey/review and

discussions with professionals & experts. In all thirty-eight null hypotheses have been

framed and they are categorized into two sets. The first set comprises hypotheses relating

dimensions of environmental concerns (environmental issues, environmental challenges,

environmental management practices, resource conservation, pollution prevention,

competitiveness & economic performance) with organizational variables namely nature of

industry (Lock, hardware & allied; Pottery/Ceramic; Leather & Tannery; and Glass),

organization status (Micro scale; Small scale; or Medium scale), number of employees (<

25; 26 to 50; 51 to 100; > 100) and number of suppliers associated with (< 5; 6 to 10; 11 to

20; > 20). The second set comprises hypotheses ascertaining impact of different dimensions

of environmental concerns on each other.

Keeping in view the objectives of the study the following hypotheses were formulated:

ENVRN.

ISSUES

ENVRN. CHALLENGE

S

POLLUTION PREVENTION

RESOURCE CONSERVATIO

N

ECONOMIC PERFORMANCE

COMPETITIVENESS

ENVIRONMENTAL MANAGEMENT PRACTICES

Lean Manufacturing Improved Technology TQM Reengineering

Reverse Logistics Remanufacturing Finance/Cost

Waste Management Govt. Policies/Regul.

4.6.1 Hypothesis based on Dimensions of Environmental Concerns across

Organizational Variables

H01 There is no significant difference in the mean value of environmental issues with

respect to the nature of industry.


respect to organizational status.


respect to number of employees.


respect to the number of suppliers associated with.

H05 There is no significant difference in the mean value of environmental challenges

with respect to the nature of industry.


with respect to organizational status.


with respect to number of employees.


with respect to the number of suppliers associated with.

H09 There is no significant difference in the mean value of environmental management

practices with respect to the nature of industry.


practices with respect to organizational status.


practices with respect to number of employees.


practices with respect to the number of suppliers associated with.

H013 There is no significant difference in the mean value of prevention of environmental

pollution with respect to the nature of industry.


pollution to organizational status.


pollution with respect to number of employees.


pollution with respect to number of suppliers associated with.

H017 There is no significant difference in the mean value of resource conservation with

respect to nature of industry.






respect to number of suppliers associated with.

H021 There is no significant difference in the mean value of competitiveness with respect

to nature of industry.


to organizational status.


to number of employees.


to number of suppliers associated with.

H025 There is no significant difference in the mean value of economic performance with








4.6.2 Hypothesis based on impact of different dimensions of environmental concerns

H029 There is no significant impact of environmental issues on environmental

management practices with regard to select SMEs.

H030 There is no significant impact of environmental challenges on environmental


H031 There is no significant impact of environmental management practices on resource

conservation with regard to select SMEs.

H032 There is no significant impact of environmental management practices on pollution

prevention with regard to select SMEs.

H033 There is no significant impact of resource conservation on competitiveness of select

SMEs.

H034 There is no significant impact of resource conservation on economic performance of

select SMEs.

H035 There is no significant impact of pollution prevention on competitiveness of select

SMEs.

H036 There is no significant impact of pollution prevention on economic performance of

select SMEs.

H037 There is no significant of competitiveness on economic performance of select SMEs.

H038 There is no significant impact of economic performance on competitiveness of

select SMEs.

4.7 Research Design

According to Yin (2003), the research design is the “logical sequence that connects the

empirical data to a study’s initial research questions and, ultimately, to its conclusions” The

research design comprises the blueprint for the collection, measurement and analysis of

data. The research design states both the structure of the research problem and the plan of

exploration used to obtain empirical evidence in relation to the problem.

For this research purpose a conclusive research design approach has been used. In the first

place, a descriptive research design approach is used, where a conceptual model is

developed, comprising of the broad dimensions of the study. In the second part, in order to

validate the cause-effect relationship among the different dimensions (variables) of the

research, a causal research design approach is used.

Surveys were carried out using a questionnaire as research tool to collect the data. It is an

established approach to obtain respondents’ opinion on a range of issues related to a

research problem.

4.8 Selection of Survey Method

The decision to choose a survey method may be based on a number of factors which include

sampling, type of population, question form, question content, response rate, costs and

duration of data collection (Aaker, Kumar and Dey, 2002). Owing to the nature of study it

was decided to personally administer the structured research instrument developed for the

study. Simple random sampling technique was employed to collect the data from the

executives or the entrepreneurs.

The main benefits of the method adopted are listed below:

• The questionnaire can be answered by circling the proper response format and with an

interviewer present, respondents could seek clarity on any question (Aaker et.al., 2002;

Boyd, Westfall and Stasch, 2003).

• The respondents are more motivated to respond, as they are not obliged to admit their

confession or ignorance to the interviewer (Hayes, 1998; Boyd et. al., 2003).

• The higher response rate can be assured since the questionnaire was collected

immediately once they are completed (Malhotra, 2007).

• This method offers highest degree of control over sample collection (Malhotra, 2007).

However, it can be very time consuming if a wide geographical region is involved. The

method allows researcher to ensure that the data covered is free from biasness and the

sample represents complete population. Though there are bound to be some biasness in the

selection of the sample, it can be eliminated to some extent by covering the larger

population in the overall sample.

A cover letter was used, having the introduction of the researcher, the objectives of the

research and the importance of the survey undertaken. A supervisor’s permission and

support letter was also attached to confirm that the researcher has come from an academic

institution.

4.8.1 Measurement Scale

To increase the response rate and facilitate respondents, the questionnaire included close-

ended questions. A five point Likert’s scale was used for this purpose. Two types of

measurement scales were used in this research: Nominal and Interval. Nominal scales were

used for identification purposes because they have no numeric value (Hayes, 1998). Interval

scale was used to measure the subjective characteristics of the respondents. This scale was

used due to its strength in arranging the objects in a specified order as well as being able to

measure the distance between the differences in response ratings (Malhotra, 2007).

4.8.2 Question Content and Wording

The questions were designed to be short, simple and comprehensive. Care was taken to

avoid ambiguous, vague, estimation based; generalization type, leading, double barreled and

presumptuous questions (Boyd et. al., 2003).

4.8.3 Response Format

Two types of response formats were chosen: Dichotomous close ended and labeled scales.

In order to obtain information pertaining to respondent’s demographics, a dichotomous

close-ended question format was used. In addition, so as to obtain respondent’s response

towards importance of environmental concerns, labeled scale response format was used.

Apart from the simplicity and in administration, it was easy to code for statistical analysis

(Burns & Bush, 2002; Luck & Rubin, 1987).

Labeled scale response format is appropriate in research as it allows the respondents to

respond to attitudinal questions in varying degrees, which describes the dimensions being

studied (Aaker et. al., 2002; Boyd et. al., 2003). In relation to the number of scale points,

there is no clear rule indicating an ideal number. However, many researchers acknowledge

that opinions can be captured best with 5 to 7 point scale (Aaker et.al., 2002; Malhotra,

2007). Keeping the same in mind a five point Likert’s scale was used for data collection in

this research.

4.8.4 Sequence of Questions

The questionnaire began with less complex and less sensitive questions and progressed to

opinion-sought questions. The questionnaire had two sections. Section A dealt with the

organization’s profile. Section B focused on environmental concerns viz. issues, challenges,

environmental management practices, pollution prevention, resource conservation,

competitiveness and economic performance.

4.8.5 Administration of Final Questionnaire

The sampling process included several steps: definition of population, establishment of the

sample frame, specification of sampling method, determination of sample size and selection

of the sample (Malhotra, 2007).

Step 1 Population: The target population of the study was defined as Private sector SMEs,

which included OEM and suppliers to the manufacturing SMEs in the state of Uttar Pradesh

in India.

Step 2 Sampling Frame: The sampling frame comprised of private sector SMEs in

Aligarh, Khurja, Kanpur & adjoining areas, Ferozabad and Purdilnagar in U.P in India.

Step 3 Sampling Method: The convenience and judgmental sampling processes were

adopted for this research. Based on the subset, an attempt has been made to represent the

entire population by the chosen sample (Hayes, 1998; Zikmund, 2000; Boyd et. al., 2003;

Levin & Rubin, 2006).

Step 4 Sample Size: The next step involved is determining the sample size for this study.

The required sample size depends on factors such as the proposed data analysis techniques,

financial support and access to sample frame (Malhotra, 2007). The data analysis techniques

employed in this research were done using SPSS 16.0 software and AMOS, which is very

sensitive to sample size and less stable when estimations are made, based on small sample

(Tabachnick & Fidell, 2001; Garson, 2008). Thus it was decided to target a total of around

250-300 respondents from different companies of the selected sector located in the state of

Uttar Pradesh in India.

Step 5 Final Sample: A total of 275 questionnaires were personally administered at

different companies of the selected sectors in the state of Uttar Pradesh in India. These

companies were carefully selected from the directories of private sector SMEs which also

included OEM and suppliers in Lock and allied, Leather & Tannery, Pottery/Ceramic and

Glass industries. This survey was conducted during 2011-12. A total of 35 questionnaires

were incomplete and were discarded. So, only 240 questionnaires were analyzed.

4.9 Questionnaire Development and Administration

Development of research instrument involves identification of constructs, method of survey

to be employed, questionnaire design, re-testing of questionnaire and administration of the

final questionnaire. The broad methodology adopted in developing the survey instrument in

the study is illustrated in Exhibit 4.2. The same is followed by a discussion on the steps

involved in the design.

Exhibit 4.2: Steps Involved in Questionnaire Design Process

(*Source: Adapted from Malhotra, N K, 2007; Kassim N M, 2001; Hamid, N R A, 2006)

Specify Information and Source

Selection of Survey Method

Develop Questionnaire

Measurement ScalesQuestion Content and WordingResponse FormatSequence of QuestionsPhysical Layout

Revision in Questionnaire

Finalization of Questionnaire

Questionnaire Distribution and Administration

PopulationSample FrameSample MethodSample SizeFinal Sample

Assessment, Refinement and Validation of Measurement Scales

4.9.1 Specification of the Information Needed

The objectives at the first stage were two folds; identifying the information required and

determining the source from where the information could be obtained. This stage begins

with identifying the information needed to meet the research objectives. For this purpose, a

conclusive study was carried out. The industries selected for the research purpose included

Lock, hardware & allied, Pottery/Ceramic, Leather & Tannery and Glass. The selected

SMEs are highly polluting in nature, polluting the environment in one-way or the other.

Lock, hardware & allied causes water, air and noise pollution. Leather and Tannery industry

causes water, air as well as noise pollution while Pottery/Ceramic industry causes water, air

and soil pollution. Glass industry causes air and noise pollution.

The questionnaire was developed after the review of available literature and in depth

interviews and discussions with the top and middle management of different companies of

the selected sector (lock hardware & allied, leather & tannery, pottery/ceramic and glass)

located in the state of Uttar Pradesh in India. From these interviews, feedback was obtained

on the variables so that they can be considered for inclusion in preliminary questionnaire.

The questionnaire so developed had the scientific basis of evolvement of the questions,

which could be considered reliable. The questionnaire was developed in English and

translated into national language Hindi, which is also the local language. Ramachandran

(1991) suggested that, if needed, the questionnaire should be translated into a local language

to avoid miscommunication and misinterpretation.

4.9.2 Structure and Content Validity of the Questionnaire

A number of measures are available to measure the reliability of the research instrument.

Measures of variables should have validity and reliability in order to draw valid inferences

from the research (Cronbach, 1971; Nunally, 1978). Reliability means ‘consistency’ or

‘trustworthiness’. Reliability deals with how consistently similar measures produce similar

results (Rosental & Rosnow, 1984). Reliability is the internal consistency of the

measurement, which is the degree of inter-correlations among the various items in the

instruments that constitute the scale (Nunally, 1978). Content validity primarily depends on

an appeal to the proprietary of the content and the way it is presented (Nunally, 1978). The

selection of measurement items in the questionnaire was based on review of available

literature and evaluation by executives and academicians, thus ensuring the content validity

of the questionnaire. The construct validity was tested through an exploratory factor

analysis. Factor Analysis is a means of describing groups of highly correlated variables by a

single underlying construct, or factor that is responsible for the observed correlations. Kim

and Mueller (1978), has suggested that only those items, which had a factor loading of more

than 0.4 are to be retained in the questionnaire.

4.10 Reliability & Validity Analysis

According to Leedy and Ormrod (2005), reliability and validity are essential characteristics

of research because they ensure the adequacy of research and the validity of conclusions.

The ability to repeat tests over time with the same degree of accuracy and precision is one

of the most important parts of research design and instrumentation. Reliability is the internal

consistency of the measurement, which is the degree of inter-correlations among the various

items in the instruments that constitute the scale (Nunally, 1978). Reliability means

‘repeatability’ or ‘consistency’. Reliability analysis helps in analyzing whether the same set

of items would educe the same responses if the same questions are re-administered to the

same respondents. Validity of a measurement is defined as the extent to which the

instrument measures what it is supposed to measure. Reliability is defined as the extent to

which a score ensures an underlying construct with stability and consistency (Singleton &

Strait, 2005).

One of the most common ways of computing the correlation values among the questions on

the instruments is by using the Cronbach's alpha (Cronbach, 1951), which is numerical

coefficient of reliability. According to Schuessler, (1971) Cronbach’s Alpha value greater

than 0.60 suggests a good reliability. For our research purpose, Cronbach’s Alpha value

greater than 0.6 has been considered satisfactory for measurement of the realiability

estimates.

4.10.1 Reliability Analysis

To test the reliability of the questionnaire, Reliability analysis was carried out for each

question using Cronbach’s Alpha value. Items having Cronbach’s Alpha value greater than

0.60 are considered to have good reliability (Schuessler, 1971), For valid 240 cases the

analysis results are presented below in Table 4.1

Table 4.1: Reliability Statistics of the Questionnaire

Reliability Statistics

Cases Items Cronbach’s Alpha

240 65 .861

The Cronbach’s Alpha’s value is 0.861 which is more than 0.6, hence the reliability of the

questionnaire is proved, i.e., the questionnaire is reliable for the purpose of collecting the

data.

Table 4.2: Reliability & Scale Statistics

Dimensions N of Items Cronbach’s AlphaIssues 3 .709Challenges 6 .670Environmental Management Practices 25 .609Pollution Prevention 15 .834Resource Conservation 7 .829Competitiveness 6 .616Economic Performance 3 .686

4.10.2 Exploratory Factor Analysis

Exploratory factor analysis (EFA) is a multivariate statistical method where a multivariate

normal random vector defined mean and covariance matrix is reduced to linear

combinations of the random variables. It is applied as a data reduction or structure detection

method. It is used to uncover the underlying structure of a relatively large set of variables.

Factor analysis is a means of describing groups of highly correlated variables by a single

underlying construct or factor that is responsible for the observed correlations.

4.10.3 KMO and Bartlett’s Test (Factor Analysis) for Testing the Validity of the

Questionnaire

To test and verify the dimensionality, construct validity and reliability of the scale items,

KMO and Bartlett’s Test was conducted. These items are Issues, Challenges, Environmental

Management, Pollution Prevention, Resource Conservation, Competitiveness and Economic

Performance.

The Kaiser-Meyer-Olkin measure of sampling adequacy tests whether the partial

correlations among variables are small. Further it should be greater than 0.5 for a

satisfactory factor analysis to proceed. Larger values of the KMO measures denote that the

factor analysis of the variables is a possible option. Bartlett’s test of sphericity tests whether

the correlations matrix is an identity matrix, which would indicate that the factor model is

inappropriate. The Bartlett’s test of sphericity is used to test the null hypothesis and to

check that the variables in the population correlation matrix are uncorrelated. The Kaiser-

Meyer-Olkin measures of sampling adequacy is greater than 0.790, and the observed

significant level is .0000. It is small enough to reject the hypothesis. It is concluded that the

strength of the relationship among variables is strong and, therefore, we can proceed for

factor analysis of the data.

The factor analysis was carried out with SPSS through factor extraction and rotation method

(Annexure III & IV) and the results are as below:

Table 4.3: KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.790

Bartlett's Test of Sphericity Approx. Chi-Square 13115.758

Df 2080.000

Sig. .000

This suggested that the sample was sufficient to take the further analysis.

Table 4.4: Total Variance Explained

Component

Initial Eigenvalues

Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 15.797 24.302 24.302 15.797 24.302 24.302 11.468 17.643 17.643

2 7.298 11.227 35.530 7.298 11.227 35.530 5.991 9.217 26.859

3 4.128 6.351 41.881 4.128 6.351 41.881 4.717 7.257 34.117

4 3.050 4.692 46.573 3.050 4.692 46.573 4.376 6.733 40.850

5 2.642 4.064 50.637 2.642 4.064 50.637 3.637 5.595 46.445

6 2.127 3.273 53.910 2.127 3.273 53.910 3.407 5.241 51.686

7 1.749 2.690 56.601 1.749 2.690 56.601 3.194 4.915 56.601

Extraction Method: Principal Component Analysis.

From the above analysis it was observed that the eigen value for the first factor is quite

large, i.e. 15.797, than the eigen value for the next factor and this factor accounts for

24.302% of the total variance. This suggests that the scale item of this variable is uni-

dimensional.

Table 4.5: Results of Exploratory Factor Analysis (EFA)

S.N. Statement Dimension Variance Explained

Factor Loading

1. Government Policies and regulations.

ENVIRONMENTALISSUES

25.302

0.460

2. Green Procurement practices. 0.6233. Societal concern for protection of

natural environment.0.562

4. Lack of commitment from top management.

ENVIRONMENTALCHALLENGES

11.227

0.531

5. Inadequate adoption of reverse logistics practices.

0.519

6. Inadequate strategic planning. 0.5877. Non adoption of cleaner

technology.0.462

8. Proper workplace management/ housekeeping practices.

0.476

9. Lean manufacturing practices. 0.465

10. The issue of natural resource depletion is highly significant.

6.531

0.529

11. Use of hazardous chemicals & substances is a highly significant issue.

0.494

12. Low usage of renewable energy sources is highly significant.

0.590

13. Low level of environmental awareness of the work force (Eco-literacy) is highly significant.

0.598

14. Assignment of roles and responsibilities with respect to environmental programs has been significantly implemented.

0.545

15. Conduct of Environmental training program for the employees has been executed.

0.556

16. The practice of Benchmarking environmental performance has been significantly implemented.

0.541

ENVIRONMENTAL MANAGEMENT

PRACTICES

ENVIRONMENTAL. MANAGEMENT

PRACTICES

17. Use of cleaner technology/ production processes to minimize wastes and make savings has been significantly implemented.

0.484

18. Continuous environmental performance improvement program has been significantly executed.

0.643

19. The Optimization of processes to reduce air emissions is an important consideration.

0.715

20. The Optimization of processes to reduce water use is an important consideration.

0.924

21. The Optimization of processes to reduce solid waste is an important consideration.

0.771

22. The Optimization of processes to reduce noise is an important consideration.

0.505

23. Recycle from the waste streams and reutilizing them in the manufacturing process is generally practiced.

0.565

24. Packaging material is reused after repair or modification for further packaging.

0.613

25. Products that can be reused after repair or modification are generally used.

0.647

26. Redesigning a product to improve performance and reduce waste is generally practiced.

0.550

27. Products are manufactured that can be easily dismantled at the end-of-life and their parts/ components are reutilized.

0.555

28. Sorting valuable raw materials which can be recycled or sold in open market is a common practice.

0.534

29. Converting a discarded product into a new product through appropriate processing is commonly practiced.

0.744

30. Information on the current regulations by issuing guidelines.

0.515

31. Information on cleaner technologies.

0.642

32. Promotion on environmental labels/eco-marks.

0.450

33. Encouraging self assessment of regulatory compliances.

0.609

34. Expediting environmental clearance/permit.

0.656

35. The issue of Air emissions is highly significant.



4.692

0.699

36. The issue of Water pollution is highly significant.

0.942

37. The issue of Solid waste is highly significant.

0.857

38. The issue of Hazardous waste is highly significant.

0.724

39. The issue of Noise pollution is highly significant.

0.519

40. The issue of Liquid waste is highly significant.

0.753

41 The issue of Waste disposal is highly significant.

0.576

42 Increased efficiencies and productivity.

0.574

43 Improved worker safety. 0.510

44 Reduced or eliminated long-term liabilities.

0.578

45 Decreased use of raw materials. 0.413

46 Diminished need for onsite storage space.

0.542

47 Greater compliance with government regulations.

0.504

48 Protection of natural resources, providing for long term sustainability of the business.

0.686

49 Enhanced employee morale and employee retention.

0.528

50 Lower consumption of raw material is highly significant for resource conservation.

0.638

51 Quantity of water used is highly 0.934

significant for resource conservation.

RESOURCE CONSERVATION

4.064

52 Waste water generated is highly significant for resource conservation.

0.922

53 Quantity of water treated is significantly important for resource conservation.

0.905

54 Level of electricity consumption is highly significant for resource conservation.

0.558

55 The level of fuel consumption is highly significant for resource conservation.

0.468

56 Hazardous waste reduction is highly significant for resource conservation.

0.489

57 Better corporate image.

COMPETITIVENESS 3.273

0.41858 Improved working environment. 0.47659 Improved employees’ environmental

awareness.0.593

60 Reduced risk of litigation. 0.41661 Exploring international markets. 0.50662 Creating good business relations

with customers & other stake holders.

0.490

63 Improvement in return on investment.


2.690

0.461

64 Increased productivity. 0.63465 Better strategic planning through

awareness of challenges ahead.0.512

4.11 Confirmatory Factor Analysis (CFA)

Confirmatory factor analysis was conducted using AMOS 16.0. Anderson and Gerbing

(1988), have suggested that the measurement model (relationships between observed items

and latent constructs) should be analyzed before the structural model (relationships between

latent constructs). The reason for this is that it is essential to understand what one is

measuring prior to testing relationships (Vandenberg and Lance, 2000). Confirmatory

Factor Analysis (CFA) was carried out on both the dependent and independent variables

without any structural relationships.

In order to test the data structure, CFA was applied. The model so obtained has been shown

below and the results are discussed subsequently.

Exhibit 4.3: Path Diagram for Confirmatory Factor Analysis

I

.56

Q1a

e1

.75

.79

Q1b

e2

.89

.19

Q1d

e3

.44

C

.78

Q2a

e4

.88

.60

Q2b

e5

.78

.40

Q2c

e6

.63

.19

Q2d

e7

.43

.17

Q2f

e8

-.41

.14

Q2g

e9

-.38

EP

.20

Q3a e10

.44

.39

Q3b e11

.62

.07

Q3c e12

.26

.08

Q3d e13

.29

.15

Q4a e14

.39

.30

Q4b e15

.55

.00

Q4c e16.03

Q4d e17.11

Q4e e18

.33

.00

Q5a e19

.03

.61

Q5b e20

.78.83

Q5c e21.91.00

Q5d e22

.04

.52

Q6a e23

-.72

.30

Q6b e24

-.55

.35

Q6c e25

-.59

.00

Q6d e26

.00

.26

Q6e e27

-.51

.01

Q6f e28

-.07

.22

Q6g e29

-.47

.10

Q7a e30

.32

.04

Q7b e31

.20

.15

Q7d e32

.39

.13

Q7f e33

.36

.09

Q7g e34

.31

PP

.21

Q9je49

.46

.16

Q9ie48

.40

.09

Q9he47

.30

.30

Q9ge46

.55.00

Q9fe45

.06.16

Q9de44

.40.01

Q9be43

.08

.04

Q9ae42

.20

.36

Q8ge41 .60

.65

Q8fe40

.81

.04

Q8ee39

.19

.68

Q8de38

.82

.84

Q8ce37

.92

.81

Q8be36

.90

.00

Q8ae35

.06

RC

.27

Q10ge56

.52.02

Q10fe55

-.13.36

Q10ee54

.60.88

Q10de53

.94

.92

Q10ce52.96

.88

Q10be51 .94

.09

Q10ae50

.29

COMP.71

Q11h

e62

.84.83

Q11g

e61

.91

.05

Q11e

e60

.22.01

Q11c

e59

.11.01

Q11b

e58

.09.18

Q11a

e57

.42EC

.43

Q12d

e65

.66.20

Q12b

e64

.45.29

Q12a

e63

.54

.72

.60.08

.45

.70

-.43

.96.10

.56

.95

-.50

-.10

.32

.83

-.36

.72

.37

-.57

.75

-.54

-.64.17

* I=Issues, C=Challenges, EP=Environmental Management Practices, PP=Pollution Prevention, RC=Resource Conservation, COMP=Competitiveness, EC=Economic Performance

4.12 Model Fit Assessment

For maximum likelihood estimates (MLE) to provide the valid results, Hair et al., (1998)

has recommended the absolute minimum requirement of 100 respondents. In this study, the

total sample size is 240. Thus, the present study fulfills the minimum sample size

requirement.

The ratio of Chi-square value and the corresponding degrees of freedom determine the

significance of the overall model. In our case, the value of Chi-square/degrees of freedom =

2.515, which is within the recommended level (< 3.0). A Parsimony Goodness of Fit Index

(PGFI) larger than 0.5 is generally considered a good model fit. In the present case, the

value comes out to be 0.657 signifying that the present model is acceptable. A good model

fit also demands that the Root Mean Square Error of Approximation (RMSEA) value

should be smaller than or equal to 0.1. In our case, the RMSEA value is 0.082, which is

within the desired limit. This again suggests an acceptable model fit here.

The value of GFI and AGFI which are measures that represent overall degree of fit (squared

residuals from prediction compared to the actual data) comes out to be 0.979 and 0.903

respectively. The AGFI value is on the lower side. For both of these, higher values would

indicate better fit but no absolute threshold levels have been established (Hair et al., 1998).

Normed Fit Index (NFI) value is 0.977 and Comparative Fit Index (CFI) value is 0.926,

both of these values are more than the desirable value of 0.9, suggesting that the model can

be accepted.

Table 4.6: Fit Indices for the Model

Fit Statistics Recommended

Values*

Observed

ValuesNormal Theory Weighted Least Squares Chi-Square N.A. 9106.267

Degrees of Freedom N.A. 1995Chi-Square/ Degrees of Freedom < 3.0 2.515

Root Mean Square Error of Approximation (RMSEA) ≤ 0.1 0.082P-Value for Test of Close Fit < 0.05 0.000

Normed Fit Index (NFI) ≥ 0.90 0.977Comparative Fit Index (CFI) ≥ 0.90 0.926Goodness of Fit Index (GFI) ≥ 0.90 0.979

Adjusted Goodness of Fit Index (AGFI) ≥ 0.90 0.903Parsimony Goodness of Fit Index (PGFI) ≥ 0.50 0.657

(* As proposed by Chien & Shih (2007) and Schumacker & Lomax (2004))

4.13 Tools used for Data Analysis

The final step was to select the appropriate statistical tools for the analysis of the primary

data which was collected for the study by using the specifically developed research

questionnaire. Using different statistical tools such as SPSS 16.0 and AMOS 16.0 software,

the organized data were then analyzed. It involves steps such as coding the responses,

clearing and screening the data, and selecting the appropriate data analysis strategy

(Malhotra, 2007). For systematic approach, research element, namely the research problem,

objectives, characteristics of data and the underline properties of the statistical techniques

need to be understood (Malhotra, 2007).

Descriptive analysis refers to the conversion of raw data into a form so that it would provide

information to describe a set of factors in a situation that will make them easy to understand

and explain.

This analysis gives a meaning to data through frequency distribution, which are useful to

identify differences among groups while in order to test hypotheses, ANOVA was applied.

4.13.1 Analysis of Variance

Analysis of Variance (ANOVA) is a collection of statistical models and their associated

procedures, in which the observed variance is partitioned into components due to different

explanatory variables. This method generates values that can be tested to determine whether

a significant relationship exists between variables. Generally ANOVA is applied when

comparison of means for more than two samples is to be drawn. However, ANOVA method

can also be applied in case of means for two samples as well.

4.13.2 Structural Equation Modeling (SEM)

Structural Equation Modeling is widely used in theoretical research across various

disciplines (Jöreskog & Sörbom, 1982; Garver and Mentzer, 1999). SEM can be defined as

a class of methodologies that seek to represent hypotheses about the mean variances, and

covariance of observed data in terms of a smaller number of “structural” parameters defined

by a hypothesized underlying mode (Kaplan, 2000; Glaser, 2002). SEM with latent

variables is more and more often used for analysis in marketing and consumer research

(Bollen, 1989; Schumacker & Lomax, 1996; Batista- Foguet & Coenders, 2000; Bagozzi,

1994). Some reasons for the wide spread use of these models are their parsimony (they

belong to the family of linear model), their ability to model complex systems (where

simultaneous & reciprocal relation may be present), and their ability to model relationship

among non observable variables while taking measurement errors into account (Jöreskog &

Sörbom, 1989; 1993; Jöreskog et. al., 2000). The model was estimated by normal theory

maximum likelihood using the AMOS 16.0 software. Since this study required the models

to be tested for best fit, SEM seemed to be appropriate analysis method as it produces more

comprehensive overall goodness of fit, than those found in other traditional methods.

4.14 Limitations of the Study

Academic research on any topic is a continuous process. Therefore, each part of the

research has to have some limitations in the form of either the resource constraints, be it the

money and time or the self defined scope of the study. The present research work too had

some limitations which, in fact, were not confined to any particular stage of the work.

Following are the limitations of this study:

• In a survey based research, more specifically questionnaire based, the lack of

involvement and cooperation of the respondents is a serious issue. The same was

realized during the process of data collection in this study. Some respondents appeared

reluctant to participate in the survey. They apprehended that a study on environmental

issues and challenges in SMEs (particularly in the respondent’s firm) may bring out the

weak & lacking points on their part that can put the organization in some trouble.

• Generally the organizations were found to be apprehensive of possible misuse of the

information researcher seeks from them about their business. Therefore, the respondents

appeared less cooperative with regard to participation in the survey.

• Although the sample for this study is selected by census sampling method, the

researcher has included the entire population restricted to the following segments

a) Lock, Hardware & Allied

b) Pottery/Ceramic,

c) Leather and Tannery, and

d) Glass

Thus, the interpretation of the findings cannot be generalized to the larger population of the

SMEs.

The study focused upon some key dimensions viz. Environmental Issues, Environmental

Challenges, Environmental Management Practices, Pollution Prevention, Resource

Conservation, Competitiveness and Economic Performance only. However, there may be

other factors also, e.g. Green Supply Chain Management, Environmental Accounting etc.

that too could have been considered. However, the inclusion of all these factors would have

made the study unwieldy. Therefore, only some key factors were focused upon. This too

may be considered as a limitation of the study.

The study was restricted and confined to a limited geographical area of Uttar Pradesh in

India. Exploring data from other areas of the country would have made the task of data

collection a tedious one.

Paucity of time was also a constraint with regard to data collection as personally

approaching the select SMEs over a wide geographical area required a lot of time,

considerable effort and money.

This chapter illustrated the research design, steps involved in questionnaire design and

administration. It also provides an overview of data analysis. The study’s methodology,

hypotheses, data collection, pilot study, pilot sample, reliability of the instrument, sample

size determination, sampling plan and techniques for data analysis are also described. Data

analysis techniques used in evaluating the hypotheses included factor analysis, Cronbach’s

Alpha for reliability analysis and analysis of variance (ANOVA). To test the validity of the

conceptual model Confirmatory Factor Analysis was applied. Most of the fit indices so

obtained were within the desirable range. This suggests that the model is acceptable. In the

later part, limitations of the study were also discussed. The next chapter presents the

analysis of data and its interpretation.

CHAPTER V

DATA ANALYSIS

5.1 Introduction

5.2 Hypotheses Testing

5.2.1 Hypotheses based on Dimensions of Environmental Concerns

5.3 The Conceptual Model

5.4 Tests of Significance and Inference

5.5 Hypotheses testing for ascertaining impacts between dimensions

Chapter V

DATA ANALYSIS

5.1 Introduction

This chapter deals with the results of the questionnaire-based survey carried out to

investigate and explore the current state of environmental problems in the select SME’s in

the state of Uttar Pradesh in India. It has been divided into two parts, the first part deals with

the analysis of the hypotheses based on dimensions of environmental concerns across the

organizational variables, one-way ANOVA has been applied to test and validate the

hypotheses. The second part also deals with the analysis of hypotheses, and tries to examine

the impact of the various dimensions of environmental concern viz. Environmental Issues

(I), Environmental Challenges (C), Environmental Management Practices (EP), Pollution

Prevention (PP), Resource Conservation (RC), Competitiveness (Comp), and Economic

Performance (EC) have on each other. The data collected through the questionnaire based

survey have been used to identify the impact of the dependent and independent variables on

each other. Data analysis was performed using Structural Equation Modelling (SEM) with

AMOS (Analysis of Moment Structures) version 16.0 as it ensures complete analysis and

has a graphical user interface, which is easy to understand. Further, it has the compatibility

with SPSS and hence provides direct import of data. Later on, the key findings of the

survey have been discussed.

5.2 Hypotheses Testing

In order to analyse the data the formulated hypotheses were tested. One way ANOVA was

applied with the help of SPSS 16.0 software. The results of hypotheses testing have been

presented and are discussed in the following section.

5.2.1 Hypotheses based on Dimensions of Environmental Concerns

H01: There is no significant difference in the mean value of environmental issues with


Table 5.1: Environmental Issues versus Nature of Industry

Industry N Mean Std. Deviation

F Sig.

LOCK, HARDWARE & ALLIED 72 3.81 0.50

31.081 0.034*

POTTERY/CERAMIC 58 4.21 0.51

LEATHER AND TANNERY 50 4.24 0.36

GLASS 60 3.53 0.44

Total 240 3.93 0.54* Significant at 95% confidence level

Comment: With an objective to establish the difference in the mean value obtained in

environmental issues across the nature of industry i.e. Lock, Hardware & allied,

Pottery/Ceramic, Leather & Tannery, and Glass one-way ANOVA technique was applied.

The descriptive statistics of the sample along with the mean value and standard deviation

are presented in the table 5.1. Test results for one-way ANOVA show that there exists a

significant difference in the mean value of environmental issues across the nature of

industry.

Leather and Tannery obtained the highest mean value of 4.24 followed by Pottery/Ceramic

with mean values of 4.21, Lock, Hardware and allied 3.81 and Glass 3.53.

The results further show that F = 31.081 and sig. = 0.034, which is less than 0.05 (at 95%

confidence level).

This entails that there exists a significant difference in environmental issues with respect to

the nature of industry. In addition to this, the mean values indicate that leather and tannery

industries pay more importance to environmental issues and all their efforts are in the

direction of effective management of the environmental issues in order to achieve the

desired objectives.

Hence, hypothesis H01: There is no significant difference in the mean value of

environmental issues with respect to the nature of industry does not hold good and is

therefore not supported while alternate hypotheses is supported.


respect to the organizational status.

Table 5.2: Environmental Issues versus Organizational Status

Status N Mean Std. Deviation

F Sig.

MICRO 87 3.67 0.64

25.744 0.017*SMALL 80 3.92 0.38

MEDIUM 73 4.24 0.39


Comment: With a view to establish any difference in the mean value obtained in

environmental issues with regard to organizational status i.e. Micro, Small, or Medium,

one-way ANOVA was applied.

Table 5.2 illustrates the descriptive statistics of the sample along with the mean value and

standard deviation obtained by organizations according to their status. It was established

that there exists a significant difference in the mean value of environmental issues with

respect to the status of the organization.

Organizations with medium operations obtained the highest mean value of 4.24 followed by

small and micro organizations with mean values of 3.92 and 3.67 respectively.

Moreover, the results show that F = 25.744 and sig. = 0.017, which is less than 0.05 (at 95%

confidence level).

This shows that there is a significant difference in environmental issues with respect to the

status of the organization. The mean values indicate that organizations with medium level of

operations pay more attention towards environmental issues as compared to other

organizations with micro or small level of operation.


environmental issues with respect to organizational status is not supported whereas

alternate hypothesis is supported.



Table 5.3: Environmental Issues versus Number of Employees

No. of Employees

N Mean Std. Deviation

F Sig.

LESS THAN 25 113 3.69 0.59

20.36 0.020*

26 TO 50 82 4.06 0.4151 TO 100 31 4.39 0.24MORE THAN 100 14 4.05 0.39


Comment: One-way ANOVA was applied in order to find out the difference in the mean

value obtained in environmental issues with respect to the number of employees viz. less

than 25, 26 to 50, 51 to 100, or more than 100.


are represented in Table 5.3. The one-way ANOVA test results show that there exists a

significant difference in the mean value of environmental issues with respect to employees

in SMEs.

It was assessed that organizations having employees in the range of 51 to 100 obtained the

highest mean value of 4.39 followed by organizations having employees 26 to 50 with mean

value of 4.06. Organizations engaging more than 100 employees, and less than 25

employees obtained mean values of 4.05, and 3.69 respectively, which are low as compared

to mean value obtained by organizations employing in the range of 51 to 100.


confidence level).

This implies that there exists a significant difference in environmental issues with respect to

the number of employees working in the organization. Organizations having employees in

the range 51 to 100 pay more importance to environmental issues as compared to

organizations having employees either less than 51 or more than 100.


environmental issues with respect to the number of employees is not supported, on the

other hand alternate hypotheses is supported.



Table 5.4: Environmental Issues versus Number of Suppliers

No. of Suppliers N Mean Std. Deviation

F Sig.

LESS THAN 5 22 3.70 0.45

27.393 0.237*

BETWEEN 6 TO 10 59 3.89 0.51

BETWEEN 11 TO 20 88 4.04 0.47

MORE THAN 20 71 4.08 0.44

TOTAL 240 3.92 0.54* Significant at 95% confidence level

Comment: With a view to establish the difference in the mean value obtained in

environmental issues with respect to the number of suppliers associated with, ANOVA was

applied.


obtained as the results of ANOVA which was applied to find out the significant difference

in the mean value of environmental issues with respect to the number of suppliers, are

presented in tabular form in Table 5.4.

The results show that there is no significant difference in the mean value of environmental

issues against the number of suppliers associated with. Organizations having more than 20

suppliers obtained the highest mean value of 4.08 followed by suppliers in the range of 11

to 20 have mean value of 4.04. Organizations having 6 to 10 suppliers and suppliers less

than 5 obtained the mean values of 3.89 and 3.70 respectively.

Moreover, the results show that F = 27.393 and sig. = 0.237, which is more than 0.05 (at

95% confidence level).

This shows that there is no significant difference in environmental issues with respect to

number of suppliers associated with. This shows that the number of suppliers does not have

any significant bearing on the environmental issues faced by the industries.


environmental issues with respect to number of suppliers associated with is supported,

whereas an alternate hypothesis is not supported.

H05: There is no significant difference in the mean value of environmental challenges

with respect to the nature of industry.

Table 5.5: Environmental Challenges versus Nature of Industry

Industry N Mean Std. Deviation F Sig.


16.567 0.010*



GLASS 60 2.89 0.31


Comment: In order to ascertain the difference in the mean value obtained in environmental

challenges with respect to the nature of industry i.e. Lock hardware & allied,

Pottery/Ceramic, Leather & Tannery and Glass, ANOVA was applied.

Table 5.5 represents the descriptive statistics of the sample along with the mean value and

standard deviation obtained.

It indicates that there exists a difference in the mean value of environmental challenges with


Lock, Hardware & allied industries obtained the highest mean value of 3.04 followed by

Glass, Pottery/Ceramic and Leather & Tannery with mean values of 2.89, 2.77 and 2.61

respectively.


confidence level).

This reveals that there exists a significant difference in the environmental challenges with

respect to nature of industry. Organizations belonging to Lock, Hardware & allied industry

face more environmental challenges as compared to other SMEs in question.


environmental challenges with respect to the nature of industry is not supported while




Table 5.6: Environmental Challenges versus Organizational Status


F Sig.

MICRO 87 2.93 0.42

7.800 0.003*SMALL 80 2.89 0.37

MEDIUM 73 2.71 0.33


Comment: With a purpose to establish the difference in the mean value obtained in

environmental challenges with respect to organizational status i.e. Micro, Small, or

Medium, one-way ANOVA was applied. The descriptive statistics of the sample along with

the mean value and standard deviation obtained by different organizations are presented in

Table 5.6.

It was noticed that there exists a difference in the mean value of environmental challenges


Micro scale organizations obtained the highest mean value of 2.93 followed by small and

medium scale with mean values of 2.89 and 2.71 respectively.

Further, the results show that F = 7.800 and sig. = 0.003, which is less 0.05 (at 95%

confidence level).

This means that there is a significant difference in environmental challenges with respect to

organizational status i.e. micro, small and medium. Organizations at the Micro level have to

face more environmental challenges as compared to small and medium organizations.


environmental challenges with respect to organizational status is not supported while



with respect to the number of employees.

Table 5.7: Environmental Challenges versus Number of Employees

No. of Employees N Mean Std. Deviation

F Sig.

LESS THAN 25 113 2.95 0.41

7.205 0.000*

26 TO 50 82 2.81 0.37

51 TO 100 31 2.63 0.27

MORE THAN 100 14 2.71 0.28

Total 2402.85

0.39*Significant at 95% confidence level

Comment: With an aim to establish the difference in the mean value obtained in

environmental challenges with respect to number of employees, statistical technique one-

way ANOVA was used. The descriptive statistics of the sample along with the mean value

and standard deviation are presented in Table 5.7.

It was observed that there exists a difference in the mean value of environmental challenges

with respect to the number of employees.

Organizations employing less than 25 employees obtained the highest mean value of 2.95

followed by organizations employing 26 to 50 employees with mean value of 2.81.

Organizations employing more than 100 employees got mean value of 2.71, while

organizations employing 51 to 100 obtained mean values of 2.63.

Further, the results show that F = 7.205 and sig. = 0.000, which is less than 0.05 (at 95%

confidence level).

This signifies that there exists a significant difference in environmental challenges with

respect to the number of employees. Organizations employing less than 25 employees have

to cope with more environmental challenges as compared to organizations employing either

25 or more employees.


environmental challenges with respect to the number of employees is not supported while



with respect to number of suppliers associated with.

Table 5.8: Environmental challenges versus Number of Suppliers

No. of Suppliers N Mean Std. Deviation

F Sig.

LESS THAN 5 22 2.97 0.36

1.801 0.148*

BETWEEN 6 TO 10 59 2.90 0.36

BETWEEN 11 TO 20 88 2.84 0.41

MORE THAN 20 71 2.78 0.37

TOTAL 240 2.85 0.39

Comment: With a purpose to establish the difference in the mean value obtained in

environmental challenges with respect to number of suppliers associated with, ANOVA was

applied. The descriptive statistics of the sample along with the mean value and standard

deviation is presented in Table 5.8.

This implies that there is no significant difference in the mean value of environmental

challenges with respect to number of suppliers associated with.

Organizations having less than 5 suppliers obtained the highest mean value of 2.97 followed

by organizations having suppliers between 6 to 10 have the mean value of 2.90.

Organizations having suppliers between 11 to 20 and more than 20 secured the mean value

of 2.84 and 2.78 respectively.

However, the results show that F = 1.801 and sig. = 0.148, which is more than 0.05 (at 95%

confidence level).

This means that there is no significant difference in environmental challenges with respect

to number of suppliers associated with. This shows that the number of suppliers does not

have any significant bearing on the environmental challenges faced by the industries.


environmental challenges with respect to number of suppliers associated with is supported

while the alternate hypothesis is not supported.

H09: There is no significant difference in the mean value of environmental management

practices with respect to the nature of industry.

Table 5.9: Environmental Management Practices versus Nature of Industry



11.392 0.000*



GLASS 60 3.18 0.31

Total 240 3.34 0.27*Significant at 95% confidence level

Comment: With a point to establish the difference in the mean value obtained in

environmental management practices with respect to nature of industry i.e. Lock, hardware

& allied, Pottery/Ceramic, Leather & Tannery and Glass, one way ANOVA was applied.


obtained by each industry are presented in Table 5.9.

It was observed that there exists a significant difference in the mean value of environmental

management practices with respect to the nature of industry.

Pottery/Ceramic obtained the highest mean value of 3.43 followed by Locks, Hardware &

allied, Leather and Tannery and Glass with mean values of 3.39, 3.33 and 3.18 respectively.


confidence level).

This leads to the fact that there exists a significant difference in environmental management

practices with respect to the nature of industry. Organizations in Pottery/Ceramic pay more

importance to environmental management practices as compared to other SMEs.


environmental management practices with respect to the nature of industry is not

supported while alternate hypothesis is supported.


practices with respect to the organizational status.

Table 5.10: Environmental Management Practices versus Organizational Status


F Sig.

MICRO 87 3.19 0.31

21.466 0.000*SMALL 80 3.40 0.21

MEDIUM 73 3.43 0.18


Comment: With an idea to establish the difference in the mean value obtained in

environmental management practices with respect to organizational status i.e. Micro, Small,

or Medium, one-way ANOVA was applied. The descriptive statistics of the sample along

with the mean value and standard deviation obtained by each organization are shown in

Table 5.10.

It was concluded that there exists a significant difference in the mean value of

environmental management practices with respect to organizational status.

Medium scale organization obtained the highest mean value of 3.43 followed by small scale

and micro scale with mean values of 3.40 and 3.19 respectively.


confidence level).

This implies that there is a significant difference in environmental management practices

with respect to organizational status. Medium scale organizations pay more importance to

environmental management practices as compared to Micro and Small scale organizations.


environmental management practices with respect to status of the organization is not



practices with respect to the number of employees.

Table 5.11: Environmental Management Practices versus Number of Employees

No. of Employees N Mean Std. Deviation

F Sig.

LESS THAN 25 113 3.26 0.33

6.774 0.000*

26 to 50 82 3.41 0.20

51 to 100 31 3.32 0.14

MORE THAN 100 14 3.49 0.24


Comment: In order to find out the difference in the mean value obtained in environmental

management practices with respect to the number of employees, one way ANOVA was


deviation obtained by each are presented in Table 5.11.

The statistics of the table show that there exists a difference in the mean value of

environmental management practices with respect to the number of employees.

Organizations having more than 100 employees secured the highest mean value of 3.49

while organizations having employees in the range of 26 to 50 obtained mean values of

3.41. Organizations having employees in the range 51 to 100 and organizations having

employees less than 25 obtained the mean values of 3.32 and 3.26 respectively.


confidence level).

This shows that there is a significant difference in environmental management practices

with respect to the number of employees. Moreover, the mean values indicate that

organizations having more than 100 employees pay more importance to environmental

management practices in order to achieve the desired objectives.


environmental management practices with respect to the number of employees is not

supported whereas alternate hypothesis is supported.


practices with respect to number of suppliers associated with.

Table 5.12: Environmental Management Practices versus Number of Suppliers

No. of Suppliers n Mean Std. Deviation

F Sig.

LESS THAN 5 22 2.94 0.19

30.604 0.021*

BETWEEN 6 TO 10 59 3.28 0.29

BETWEEN 11 TO 20 88 3.37 0.22

MORE THAN 20 71 3.47 0.21

TOTAL 240 3.34 0.27*Significant at 95% confidence level

Comment: One way ANOVA was used to ascertain the difference in the mean value

obtained in environmental management practices with respect to the number of suppliers

associated with. The descriptive statistics of the sample along with the mean value and

standard deviation obtained are represented in Table 5.12.

The results show that there exists a difference in the mean value of environmental

management practices with respect to the number of suppliers associated with.

Organizations having more than 20 suppliers obtained the highest mean value of 3.47

followed by organizations having suppliers in between 11 to 20 having a mean value of

3.37. Organizations having suppliers in between 6 to 10 and organizations having less than

5 suppliers obtained the mean values of 3.28 and 2.94 respectively.


confidence level).

This suggests that there exists a significant difference in environmental management

practices with respect to the number of suppliers associated with. Organizations having

suppliers more than 20 pay more importance to environmental management practices as

compared to organizations having suppliers either less than or equal to 20.


environmental management practices with respect to number of suppliers associated with is

not supported while alternate hypothesis is supported.

H013: There is no significant difference in the mean value of prevention of

environmental pollution with respect to the nature of industry.

Table 5.13: Environmental Pollution versus Nature of Industry



24.925 0.003*



GLASS 60 2.74 0.25


Comment: With an objective to establish the difference in the mean value obtained in

prevention of environmental pollution with respect to nature of industry, ANOVA was


deviation obtained by each industry are shown in Table 5.13.

The statistics show that there exists a difference in the mean value of prevention of

environmental pollution with respect to the nature of industry.

Leather and Tannery obtained the highest mean value of 3.93 followed by Pottery/Ceramic,

Lock, hardware & allied and Glass with mean values of 3.82, 3.49 and 2.74 respectively.


confidence level).

This implies that there exists a significant difference in prevention of environmental

pollution with respect to the nature of industry. Further, the mean values indicate that

Leather and Tannery industries are more concerned towards the prevention of

environmental pollution in order to achieve the desired objectives.

Hence, hypothesis H013: There is no significant difference in the mean value of prevention

of environmental pollution with respect to the nature of industry is not supported on the

other hand alternate hypothesis is supported.


environmental pollution with respect to organizational status.

Table 5.14: Environmental Pollution versus Organizational Status

Status n Mean Std. Deviation

F Sig.

MICRO 87 3.23 0.55

36.626 0.000*SMALL 80 3.40 0.42

MEDIUM 73 3.85 0.38


Comment: With a view to find out the difference in the mean value obtained in prevention

of environmental pollution with respect to organizational status i.e. Micro, Small, or

Medium, ANOVA was applied. The descriptive statistics of the sample along with the mean

value and standard deviation obtained by each organization according to their status are

represented in Table 5.14.

The table above signifies that there exists a difference in the mean value of prevention of

environmental pollution with respect to organizational status.

Medium scale organizations obtained the highest mean value of 3.85 followed by small and

micro level organizations with mean values of 3.40 and 3.23 respectively.


confidence level).

The results suggest that there exists a significant difference in prevention of environmental

pollution with respect to organizational status. Organizations having Medium scale of

operations are more concerned towards the prevention of environmental pollution as

compared to Micro or Small organizations.


of environmental pollution with respect to organizational status is not supported while



environmental pollution with respect to number of employees.

Table 5.15: Environmental Pollution versus Number of Employees

No. of Employees

n Mean Std. Deviation

F Sig.

LESS THAN 25 113 3.28 0.53

15.829 0.032*

26 to 50 82 3.57 0.46

51 to 100 31 3.91 0.29

MORE THAN 100 14 3.51 0.53


Comment: One-way ANOVA technique was used in order to find out the difference in the

mean values obtained in prevention of environmental pollution with respect to the number

of employees. The descriptive statistics of the sample along with the mean value and

standard deviation are shown in Table 5.15.

From the results it was noticed that there exists a significant difference in the mean value of

prevention of environmental pollution with respect to the number of employees.

Organizations having employee strength in between 51 to100 obtained the highest mean

value of 3.91 followed by organizations having strength in between 26 to 50 with mean

value of 3.57. Organizations having employee strength either more than 100 or less than 25

obtained mean values of 3.51 and 3.28 respectively.


confidence level).

The results suggest that there is a significant difference in prevention of environmental

pollution with respect to employee strength. Organizations having employees in the range

of 51 to 100 are more concerned towards the prevention of environmental pollution as

compared to organizations having either less than 51 or more than 100 employees.


of environmental pollution with respect to the number of employees is not supported while



environmental pollution with respect to number of suppliers associated with.

Table 5.16: Environmental Pollution versus Number of Suppliers


F Sig.

LESS THAN 5 22 2.71 0.39

42.046 0.008*

BETWEEN 6 TO 10 59 3.24 0.47

BETWEEN 11 TO 20 88 3.60 0.45

MORE THAN 20 71 3.75 0.37


Comment: With an aim to ascertain the difference in the mean value obtained in the

prevention of environmental pollution with respect to number of suppliers associated with,

one-way ANOVA technique was applied. The descriptive statistics of the sample along with

the mean value and standard deviation are shown by Table 5.16.

The table statistics show that there is a significant difference in the mean value of

prevention of environmental pollution with respect to the number of suppliers associated

with.

Organizations with more than 20 suppliers obtained the highest mean value of 3.75

followed by organizations engaging between 11 to 20 suppliers secured mean value of 3.60.

Further, organizations engaging suppliers either in between 6 to 10 or less than 5 obtained

mean values of 3.24 and 2.71 respectively.

Results further show that F = 42.046 and sig. = 0.008, which is less than 0.05 (at 95%

confidence level).

These results suggest that there is a significant difference in prevention of environmental

pollution with respect to number of suppliers engaged with. Moreover, the mean values

indicate that organizations engaging more than 20 suppliers pay more importance to

prevention of environmental pollution activities as compared to organisations engaging up

to 20 suppliers.


of environmental pollution with respect to number of suppliers associated with is not


H017: There is no significant difference in the mean value of resource conservation with


Table 5.17: Resource Conservation versus Nature of Industry



411.680 0.212*



GLASS 60 3.85 0.23


Comment: One-way ANOVA was used to ascertain the difference in the mean value

obtained in resource conservation with respect to nature of industry. The descriptive

statistics of the sample along with the mean value and standard deviation obtained by

different industries are shown in Table 5.17.

It was observed that there is no significant difference in the mean value of resource

conservation with respect to the nature of industry.

Leather and Tannery industry obtained the highest mean value of 4.15; Pottery/Ceramic

obtained 4.08 followed by Lock, Hardware & Allied and Glass with mean values of 3.91

and 3.85 respectively.

Moreover, the results show that F = 411.680 and sig. = 0.212, which is more than 0.05 (at

95% confidence level).

The results show that there is no significant difference in resource conservation with respect

to the nature of industry. This means that nature of industry does not have any significant

bearing on Resource Conservation.

Hence, hypothesis H017: There is no significant difference in the mean value of resource

conservation with respect to nature of industry is supported whereas alternate hypothesis is

not supported.


respect organizational status.

Table 5.18: Resource Conservation versus Organizational Status


F Sig.

MICRO 87 3. 37 0.55

18.716 0.000*SMALL 80 3.57 0.77

MEDIUM 73 3.05 0.52


Comment: With an intention to find out the difference in the mean value obtained in

resource conservation with respect to status of the organization i.e. Micro, Small, or

Medium, one way ANOVA was applied. The descriptive statistics of the sample along with

the mean value and standard deviation obtained by each type of the organization are shown

in Table 5.18.

The results show that there exists a difference in the mean value of resource conservation


Small scale industries obtained the highest mean value of 3.57 followed by members with

Micro and Medium level operations with mean values of 3.37 and 3.05 respectively.


confidence level).

This implies that there is a significant difference in resource conservation with respect to

organizational status. Small scale organizations are more concerned towards the resource

conservation as compared to Micro or Small organizations.


conservation with respect to organizational status is not supported while alternate

hypothesis is supported.


respect to the number of employees.

Table 5.19: Resource Conservation versus the Number of Employees

No. of Employees


F Sig.

LESS THAN 25 113 3.40 0.72

15.516 0.004*

26 to 50 82 3.77 0.74

51 to 100 31 4.31 0.31

MORE THAN 100 14 3.84 0.67


Comment: One-way ANOVA was applied in order to find out the difference in the mean

value obtained in resource conservation with regard to the number of employees engaged in

the organization. The descriptive statistics of the sample along with the mean value and

standard deviation obtained by each member of the chain are presented in Table 5.19.

The table statistic shows that there exists a significant difference in the mean value of

resource conservation with respect to the number of employees.

Organizations having employee strength in the range 51 to 100 obtained the highest mean

value of 4.31 followed by organizations having employees having more than 100 with mean

value of 3.84 whereas organizations having strength between 26 to 50 and less than 25

obtained mean values of 3.77 and 3.40 respectively.

Moreover, the results show F = 15.516 and sig. = 0.004, which is less than 0.05 (at 95%

confidence level).

The results imply that there is a significant difference in resource conservation with respect

to the number of employees. Organizations having employees in the range of 51 to 100 pay

more importance to Resource Conservation as compared to organizations employing either

less than or equal to 50 or more than 100 employees.


conservation with respect to the number of employees is not supported while alternate




Table 5.20: Resource Conservation versus Number of Suppliers


F Sig.

LESS THAN 5 22 2.73 0.51

37.688 0.000*

BETWEEN 6 TO 10 59 3.18 0.76

BETWEEN 11 TO 20 88 3.89 0.66

MORE THAN 20 71 4.00 0.49


Comment: With a view to find out the difference in the mean value obtained in resource

conservation and the number of suppliers associated with, one-way ANOVA was applied.


obtained are shown in Table 5.20.

The table statistic shows that there exists a significant difference in the mean value of

resource conservation with respect to the number of suppliers associated with the

organization.

Organizations having more than 20 suppliers obtained the highest mean value of 4.00,

followed by organizations having suppliers in the range of 11 to 20 with mean value of

3.89, while the organizations having suppliers in the range of 6 to 10 and the organizations

having less than 5 suppliers obtained the mean values of 3.18 and 2.73 respectively.

The results moreover show that F = 37.688 and sig. = 0.000, which is less than 0.05 (at 95%

confidence level).

The results indicate that there is a significant difference in resource conservation with

respect to the number of suppliers associated with. Organizations having more than 20

suppliers pay more importance to resource conservation as compared to organizations

having suppliers either 20 or less.


conservation with respect to number of suppliers associated with is not supported whereas


H021: There is no significant difference in the mean value of competitiveness with


Table 5.21: Competitiveness versus Nature of Industry



55.035 0.020*



GLASS 60 2.78 0.28


Comment: One-way ANOVA was used in order to ascertain the difference in the mean

value obtained in competitiveness with respect to the nature of industry. The descriptive

statistics of the sample along with the mean value and standard deviation obtained are

shown in Table 5.21.

Results indicate that there exists a significant difference in the mean value of

competitiveness with respect to the nature of industry.

Leather and Tannery industry obtained the highest mean value of 3.96, followed by

Pottery/Ceramic industry which obtained the mean value of 3.86, whereas Lock, Hardware

& allied obtained the mean value of 3.28, and Glass Industry obtained the least mean value

of 2.78.


confidence level).

The obtained results suggest that there exists a significant difference in competitiveness

across the nature of industry. Moreover, the mean values indicate that Leather and Tannery

industry employs more competitive strategies as compared to other industries in study.


competitiveness with respect to the nature of industry is not supported while alternate

hypothesis is support.


respect to Organizational status.

Table 5.22: Competitiveness versus Organizational Status


F Sig.

MICRO 87 3. 41 0.40

50.457 0.012*SMALL 80 3.71 0.36

MEDIUM 73 3.99 0.32


Comment: With the intention to find out the difference in the mean value obtained in

competitiveness across the organizational status i.e. Micro, Small & Medium, one- way

ANOVA was applied. The descriptive statistics of the sample along with the mean value

and standard deviation obtained are presented in Table 5.22.

The results suggest that there exists a significant difference in the mean value of

competitiveness with respect to the status of the organization.


micro scale organizations with mean values of 3.71 and 3.41 respectively.


confidence level).

This suggests that there is a significant difference in competitiveness with respect to the

status of the organization i.e. Micro, Small or Medium. Medium scale organizations are

more competitive as compared to Micro or Small organizations.


competitiveness with respect to organizational status is not supported while alternate




Table 5.23: Competitiveness versus Number of Employees

No. of Employees


F Sig.

LESS THAN 25 113 3.50 0.41

17.045 0.000*

26 to 50 82 3.79 0.34

51 to 100 31 3.92 0.46

MORE THAN 100 14 4.01 0.42


Comment: With the purpose to find out the difference in the mean value obtained in

competitiveness with respect to number of employees, ANOVA was applied. The

descriptive statistics of the sample along with the mean value and standard deviation

obtained are represented in Table 5.23.

The results show that there exists a difference in the mean value of competitiveness with


Organizations employing more than 100 employees obtained the highest mean value of 4.01

followed by organizations having employees in the range of 51 to 100 obtained the mean

value of 3.92. Organizations having employees in the range of 26 to 50 and the

organizations having less than 25 employees obtained the mean values of 3.79 and 3.50

respectively.


confidence level).

The results indicate that there is a significant difference in competitiveness with respect to

the number of employees. Further, the mean values indicate that organizations having more

than 100 employees are more competitive as compared to other organizations employing up

to 100 employees.


competitiveness with respect to the number of employees is not supported while alternate




Table 5.24: Competitiveness versus Number of Suppliers


F Sig.

LESS THAN 5 22 3.55 0.32

6.795 0.218*

BETWEEN 6 TO 10 59 3.57 0.40

BETWEEN 11 TO 20 88 3.67 0.46

MORE THAN 20 71 3.79 0.40


Comment: One-way ANOVA was used for the purpose of ascertaining the difference in the

mean value obtained in competitiveness with respect to the number of suppliers associated

with the organizations. The descriptive statistics of the sample along with the mean value

and standard deviation obtained by each are given in Table 5.24.

It was observed that there is no significant difference in the mean value of competitiveness

with respect to the number of suppliers.

Organizations having more than 20 suppliers obtained the highest mean value of 3.79

followed by organizations having suppliers in the range 11 to 20 with a mean value of 3.67,

whereas organizations having suppliers in the range of 6 to 10 and organizations having less

than 5 suppliers obtained the mean values of 3.57 and 3.55 respectively.

The results further show that F = 6.795 and sig. = 0.218, which is more than 0.05 (at 95%

confidence level).

The results suggest that there is no significant difference in competitiveness with respect to

the number of suppliers associated with. This shows that the number of suppliers does not

have any significant affect with regard to competitiveness.


competitiveness with respect to the number of suppliers associated with is supported while

alternate hypothesis is not supported.

H025: There is no significant difference in the mean value of economic performance with


Table 5.25: Economic Performance versus Nature of Industry

Industry n Mean Std. Deviation F Sig.


28.412 0.000*



GLASS 60 3.75 0.41


Comment: With the purpose to ascertain the difference in the mean value obtained in

economic performance with respect to the nature of industry, one-way ANOVA was used.


obtained are shown in Table 5.25.

It indicated that there exists a difference in the mean value of economic performance with


Pottery/Ceramic industry obtained the highest mean value of 4.20, followed by Leather and

Tannery industry which obtained a mean value of 4.03, whereas Glass industry obtained a

mean value of 3.75, and Lock, Hardware & allied obtained the least mean value of 3.55.


confidence level).

This indicates that there exists a significant difference in economic performance with

respect to the nature of industry. Moreover, the mean values indicate that Pottery/Ceramic

industry performs economically better as compared to other industries in study.

Hence, hypothesis H025: There is no significant difference in the mean value of Economic

Performance with respect to the nature of industry is not supported while alternate



respect to Organizational status.

Table 5.26: Economic Performance versus Organizational Status


F Sig.

MICRO 87 3. 61 0.39

45.937 0.023*SMALL 80 3.77 0.38

MEDIUM 73 4.25 0.51


Comment: With the aim to ascertain the difference in the mean value obtained in economic

performance with respect to the organizational status i.e. Micro, Small & Medium, one-way

ANOVA was used. The descriptive statistics of the sample along with the mean value and

standard deviation obtained are shown in Table 5.26.

It indicates that there exists a significant difference in the mean value of economic

performance with respect to the status of the organization.


micro scale organizations with mean values of 3.77 and 3.61 respectively.


confidence level).

This suggests that there is a significant difference in economic performance with regard to

status of the organization i.e. Micro, Small or Medium. Medium scale organizations

perform economically better as compared to Micro or Small scale organizations.

Hence, hypothesis H026: There is no significant difference in the mean value of economic

performance with respect to the Organizational status is not supported; on the other hand




Table 5.27: Economic Performance versus Number of Employees

No. of Employees


F Sig.

LESS THAN 25 113 3.64 0.39

18.772 0.000*

26 to 50 82 4.04 0.56

51 to 100 31 3.97 0.35

MORE THAN 100 14 4.33 0.49


Comment: One-way ANOVA was used to find out the difference in the mean value

obtained in economic performance with respect to the employee strength. The descriptive

statistics of the sample along with the mean value and standard deviation obtained are

shown in Table 5.27.

It shows that there exists a difference in the mean value of economic performance with


Organizations employing more than 100 employees obtained the highest mean value of 4.33

followed by organizations having employees in the range of 26 to 50 with a mean value of

4.04. Organizations having employees in the range of 51 to 100 and the organizations

having less than 25 employees obtained the mean values of 3.97 and 3.64 respectively.


confidence level).

This suggests that there is a significant difference in economic performance with respect to

the number of employees. Organizations employing more than 100 employees perform

economically better than the organizations employing up to 100 employees.


performance with respect to the number of employees is not supported while alternate




Table 5.28: Economic Performance versus Number of suppliers


F Sig.

LESS THAN 5 22 3.56 0.36

16.902 0.034*

BETWEEN 6 TO 10 59 3.68 0.41

BETWEEN 11 TO 20 88 3.80 0.42

MORE THAN 20 71 4.16 0.56


Comment: With the purpose to ascertain the difference in the mean value obtained in

economic performance with respect to number of suppliers, one-way ANOVA was applied.


obtained by each are shown in Table 5.28.

It was observed that there exists a difference in the mean value of economic performance

with respect to the number of suppliers associated with the organization.

Organizations having suppliers more than 20 obtained the highest mean value of 4.16

followed by organizations having suppliers in the range 11 to 20 with a mean value of 3.80,

whereas organizations having suppliers in the range of 6 to 10 and organizations having less

than 5 suppliers obtained the mean values of 3.68 and 3.56.

The results, show that F = 16.902 and sig. = 0.034, which is less than 0.05 (at 95%

confidence level).

The results suggest that there is a significant difference in economic performance with

respect to the number of suppliers associated with. Organizations having more than 20

suppliers perform economically better than organizations having up to 20 suppliers.


performance with respect to the number of suppliers associated with is not supported

while alternate hypothesis is supported.

Table 5.29: Summary of Hypotheses Testing

S. No. Hypothesis F Sig. Result

1.There is no significant difference in the mean value of Environmental Issues with respect to nature of industry.

31.081 0.034 Not Supported

2.There is no significant difference in the mean value of Environmental Issues with respect to organizational status.


3.There is no significant difference in the mean value of Environmental Issues with respect to number of employees.


4.There is no significant difference in the mean value of Environmental Issues with respect to number of suppliers associated with.

27.393 0.237 Supported

5. There is no significant difference in the mean value of Environmental Challenges with respect to nature of industry.


6. There is no significant difference in the mean value of Environmental Challenges with respect to organizational status


7. There is no significant difference in the mean value of Environmental Challenges with respect to number of employees.


8. There is no significant difference in the mean value of Environmental Challenges with respect to number of suppliers associated with.


9. There is no significant difference in the mean value of Environmental Management Practices with respect to nature of industry.


10. There is no significant difference in the mean value of Environmental Management Practices with respect to organizational status.


11. There is no significant difference in the mean value of Environmental Management Practices with respect to number of employees.

6.774 0.000Not Supported

S. No. Hypothesis F Sig. Result12. There is no significant difference in the

mean value of Environmental Management Practices with respect to number of suppliers associated with.


13. There is no significant difference in the mean value of Prevention of Environmental Pollution with respect to nature of industry.


14. There is no significant difference in the mean value of Prevention of Environmental Pollution with respect to organizational status.


15. There is no significant difference in the mean value of Prevention of Environmental Pollution with respect to number of employees.


16. There is no significant difference in the mean value of Prevention of Environmental Pollution with respect to number of suppliers associated with.


17. There is no significant difference in the mean value of Resource Conservation with respect to nature of industry.

411.680 0.212 Supported

18. There is no significant difference in the mean value of Resource Conservation with respect to organizational status.


19. There is no significant difference in the mean value of Resource Conservation with respect to number of employees.


20. There is no significant difference in the mean value of Resource Conservation with respect to number of suppliers associated with.


21. There is no significant difference in the mean value of Competitiveness with respect to nature of industry.


22. There is no significant difference in the mean value of Competitiveness with respect to organizational status.


23. There is no significant difference in the mean value of Competitiveness with respect to number of employees.


24. There is no significant difference in the mean value of Competitiveness with respect to number of suppliers associated with.


25. There is no significant difference in the

S. No. Hypothesis F Sig. Resultmean value of Economic Performance with respect to nature of industry.


26. There is no significant difference in the mean value of Economic Performance with respect to organizational status.


27. There is no significant difference in the mean value of Economic Performance with respect to number of employees.


28. There is no significant difference in the mean value of Economic Performance with respect to number of suppliers associated with.


5.3 The Conceptual Model

The proposed research model/conceptual model as developed in Chapter IV has been talked

about earlier. The proposed model is being presented here again.

Exhibit 5.1: PROPOSED RESEARCH MODEL*

(*Developed by Researcher)

The data with respect to the different dimensions of the proposed conceptual model were

collected with the help of questionnaire based survey.

All the dimensions of the proposed conceptual model were then assessed for the validity of

the conceptual model using SEM technique with the help of AMOS 16.0

ENVRN.ISSUES

ENVRN.CHAL-LENGES


RESOURCE CONSERVATIO

N


COMPETITIVENESS


Lean Manufacturing

Improved Technology

TQM

Reengineering

Reverse Logistics

Remanufacturing

Finance/Cost

Waste Management

Govt. Policies/Regul.

Exhibit 5.2 Path Diagram for Structural Equation Modelling

*issues = Environmental Issues, challenges = Environmental Challenges, envrn. mgt. pr. = Environmental Management Practices, Pollution Prevention = Pollution Prevention, Resource Conservation = Resource Conservation, Competitiveness = Competitiveness, EconomicPerfor = Economic Performance

5.4 Tests of Significance and Inference

The test of absolute fit measures, involves measuring the overall model fit using a

likelihood ratio chi-square statistic. The chi-square statistic indicates that the matrices

between the hypothesized model and the actual data are statistically different at a designated

significance level. The objective of this research is to have the hypothesized model “fit” the

actual data; thus, the absolute fit measure would preferably indicate no significant

difference. However, since the chi-square statistic is sensitive to sample size, additional

measures of overall fit must be used. Therefore, the goodness-of-fit-index (GFI) and root

mean square error of approximation (RMSEA) must also be examined. GFI represents the

percent of observed co-variances explained by the researcher’s hypothesized structural

equation model. A GFI of 0.95 is preferred; however, a GFI of 0.90 is deemed acceptable

for the model’s acceptance (Hair et. al.; 1998). AGFI, a second but similar measure to the

GFI, instead uses the mean squares instead of the sums of squares in the numerator and

denominator of (1 – GFI) and is interpreted at acceptance levels similar to the GFI of 0.90

or higher.

RMSEA, or root mean square error of approximation, indicates the errors of fit in the

covariance matrix. Values of 0.1 or less are acceptable and a recommended lower level is

0.08. CFI, a comparative fit index, is used to compare the model fit to other models. A

range of 0.95 or above infers a good fit of the model to the actual data (Hu & Bentler,

1999).

Parsimony indices are typically lower than the normed fit measures and typically range in

the 0.50 to 0.60 range with values larger than 0.60 considered satisfactory (Blunch 2008).

The default model absolute fit indices include the following: RMSEA = 0.016, GFI = 0.979,

AGFI =0.903, PGFI = 0.655 and CFI = 0.969. These indices confirmed an adequate fit of

the model to data. The model RMSEA of 0.016 which is well below the recommended level

of 0.08, indicates that the errors in the fit of the covariance matrix are very small. A value of

0.08 or less indicates a reasonable error of approximation, while a value of 0.05 or less

indicates a close fit of the model in relation to the degree of freedom.

The CFI of 0.969 is a normed fit index with a range from 0 to 1 and it is particularly useful

for estimating model fit with small samples (Hu & Bentler, 1999). In summary, the absolute

fit indices provide evidence of a good model fit to the data.

Table 5.30: Fit Indices for the Model

Fit Statistics Recommended

Values*

Observed

ValuesNormal Theory Weighted Least Squares Chi-

Square

N.A. 9106.267

Degrees of Freedom N.A. 1995Chi-Square/ Degrees of Freedom < 3.0 2.515

Root Mean Square Error of Approximation

(RMSEA)

≤ 0.1 0.016

P-Value for Test of Close Fit < 0.05 0.000Normed Fit Index (NFI) ≥ 0.90 0.977

Comparative Fit Index (CFI) ≥ 0.95 0.969Goodness of Fit Index (GFI) ≥ 0.90 0.979

Adjusted Goodness of Fit Index (AGFI) ≥ 0.90 0.903Parsimony Goodness of Fit Index (PGFI) ≥ 0.50 0.655

(* As proposed by Chien & Shih (2007) and Schumacker & Lomax (2004))

5.5 Hypotheses Testing for ascertaining impacts between dimensions

The statistical significance of all of the structural parameter estimates was examined to

determine the validity of the hypothesised paths. The values have been tested for

significance on the basis of Critical Ratio (C.R.) value. According to Garson (2005), values

are significant if Critical Ratio is more than 1.96. The hypotheses have been tested and their

results discussed as under:

H029: There is no significant impact of environmental issues on environmental


From the results, it has been established that the relationship between environmental issues

and environmental management practices is statistically significant (C.R=3.424), which is

more than the standard C.R. value of 1.96. Further, the path coefficient value is equal to

0.215 which is positive. This suggests that environmental issues have a positive significant

impact on environmental management practices.

Thus, the hypothesis H029: There is no significant impact of environmental issues on

environmental management practices is not supported while alternate hypothesis is

supported.

H030: There is no significant impact of environmental challenges on environmental


From the results, it has been established that the relationship between environmental

challenges and environmental management practices is statistically insignificant

(C.R.=1.610), which is less than the standard C.R. value of 1.96. Moreover, the path

coefficient value is equal to 0.101 which is positive. This implies that environmental

challenges have a positive but insignificant impact on environmental management practices.

Thus, the hypothesis H030: There is no significant impact of environmental challenges on

environmental management practices is supported while alternate hypothesis is not

supported.

H031: There is no significant impact of environmental management practices on

resource conservation with regard to select SMEs.


management practices and resource conservation is statistically significant (C.R=18.878),

which is more than the standard C.R. value of 1.96. Moreover, the path coefficient value is

equal to 0.774 which is positive. This implies that environmental management practices

have positive significant impact on resource conservation.

Hence, the hypothesis H031: There is no significant impact of environmental management

practices on resource conservation not supported whereas alternate hypothesis is

supported.

H032: There is no significant impact of environmental management practices on pollution prevention with regard to select SMEs.


management practices and pollution prevention is statistically significant (C.R=15.173),

which is more than the standard C.R. value of 1.96. Further, the path coefficient value is

equal to 0.700 which is positive. This implies that environmental management practices

have a positive significant impact on pollution prevention.

Thus, the hypothesis H032: There is no significant impact of environmental management

practices on pollution prevention is not supported while alternate hypothesis is supported.

H033: There is no significant impact of resource conservation on competitiveness of

select SMEs.

From the results, it has been established that the relationship between resource conservation

and competitiveness is statistically significant (C.R= 2.539), which is more than the

standard C.R. value of 1.96. Further, the path coefficient value is equal to 0.301 which is

positive. This implies that resource conservation has a positive significant impact on

competitiveness.

Thus, the hypothesis H033: There is no significant impact of resource conservation on

competitiveness is not supported while alternate hypothesis is supported.

H034: There is no significant impact of resource conservation on economic performance

of select SMEs.

From the results, it has been established that the relationship between resource conservation

and economic performance is statistically insignificant (C.R= -9.645), which is less than the

standard C.R. value of 1.96. Further, the path coefficient value is equal to -0.893 which is

negative. This implies that resource conservation has a negative but insignificant impact on

economic performance.

Thus, the hypothesis H034: There is no significant impact of resource conservation on

economic performance is supported while alternate hypothesis is not supported.

H035: There is no significant impact of pollution prevention on competitiveness of select

SMEs.

From the results, it has been established that the relationship between pollution prevention

and competitiveness is statistically insignificant (C.R= -10.233), which is less than the

standard C.R. value of 1.96. Moreover, the path coefficient value is equal to -1.934 which is

negative. This indicates that pollution prevention has a negative but insignificant impact on

competitiveness.

Thus, the hypothesis H047: There is no significant impact of pollution prevention on

competitiveness is supported whereas alternate hypothesis is not supported.

H036: There is no significant impact of pollution prevention on economic performance of

select SMEs.

From the results, it has been established that the relationship between pollution prevention

and economic performance is statistically significant (C.R=21.480), which is more than the

standard C.R. value of 1.96. However, the path coefficient value is equal to 2.564 which is

positive. This implies that pollution prevention has a positive significant impact on


Hence, the hypothesis H036: There is no significant impact of pollution prevention on

economic performance is not supported whereas alternate hypothesis is supported.

H037: There is no significant of competitiveness on economic performance of select

SMEs.

From the results, it has been established that the relationship between competitiveness and

economic performance is statistically insignificant (C.R= -18.957), which is less than the

standard C.R. value of 1.96. However, the path coefficient value is equal to -1.678 which is

negative. This implies that competitiveness has a negative but insignificant impact on


Thus, the hypothesis H037: There is no significant impact of competitiveness on economic

performance is supported while alternate hypothesis is not supported.

H038: There is no significant impact of economic performance on competitiveness of

select SMEs.

From the results, it has been established that the relationship between economic

performance and competitiveness is statistically significant (C.R=18.949), which is more

than the standard C.R. value of 1.96. However, the path coefficient value is equal to .949

which is positive. This implies that economic performance has a positive significant impact

on competitiveness.

Thus, the hypothesis H038: There is no significant impact of economic performance on

competitiveness is not supported whereas alternate hypothesis is supported.

Hypotheses testing results and the structural parameter estimates have been represented in

Table 5.31

Table 5.31: Structure Parameters and Hypotheses Testing Results

S.

No.

Hypothesis Path Critical

Ratio (C.R.)

Results

1. H029 Environmental Issues

→Environmental

management practices

3.424 Not Supported

2. H030 Environmental Challenges→ Environmental management

practices

1.610 Supported

3. H031 Environmental management practices →Resource

conservation

18.878 Not Supported

4. H032 Environmental management practices →Pollution

prevention


5. H033 Resource conservation →Competitiveness

2.539 Not Supported

6. H034 Resource conservation →Economic performance

-9.645 Supported

7. H035 Pollution prevention→ Competitiveness

-10.233 Supported

8. H036 Pollution prevention → Economic performance


9. H037 Competitiveness → Economic performance

-18.957 Supported

10 H038 Economic performance→ Competitiveness


Hypotheses testing results show that there is a linear positive significant relationship

between environmental issues & environmental management practices, environmental

management practices & resource conservation, environmental management practices &

pollution prevention, resource conservation & competitiveness, pollution prevention &

economic performance and lastly economic performance & competitiveness. This signifies

that for the above dimensions, there is a direct positive relationship.

The impact of environmental challenges on environmental management practices too is

positive. However, that impact is statistically insignificant.

On the other hand, there is a negative relationship between resource conservation and

economic performance, pollution prevention and competitiveness, and competitiveness and

economic performance. However, these impacts are statistically insignificant as the C.R.

values for each of these are lower than the standard C.R. value of 1.96. This shows that

there is an overall positive impact of the dimensions on each other.

In this chapter, the proposed hypotheses relating to dimensions of environmental concerns

across the organizational variables as well as hypotheses developed in order to assess the

impact of the various dimensions of environmental concern viz. Environmental Issues (I),

Environmental Challenges (C), Environmental Management Practices (EP), Pollution

Prevention (PP), Resource Conservation (RC), Competitiveness (Comp), and Economic

Performance (EC) on each other and their cause-effect relationship, were tested using one

way ANOVA and Structural Equation Modelling.

CHAPTER VI

CONCLUSIONS AND RECOMMENDATIONS

6.1 Introduction

6.2 Key findings

6.3 Suggestions

6.4 Directions for future research

Chapter VICONCLUSIONS AND RECOMMENDATIONS

6.1 Introduction

This chapter summarizes the research work undertaken, presents the key findings and

discusses the hypotheses results. Suggestions and recommendations for future research are

also dealt with.

6.2 Key findings

The key findings of the present research work related to environmental issues,

environmental challenges, environmental concerns, pollution prevention, resource

conservation, competitiveness and economic performance are discussed below

• There exist significant differences in the mean values environmental issues,

environmental challenges, environmental management practices, pollution

prevention, competitiveness, and economic performance with respect to nature of

industry.

• Leather and Tannery industry pay highest importance to environmental issues as

compared to other industries.

• Lock, Hardware and Allied face more environmental challenges as compared to

other SMEs.

• Pottery /Ceramic industry pay more importance to environmental management

practices as compared to other industries.

• Leather and Tannery industry is more concerned towards the prevention of

environmental pollution in comparison to other industries.

• Leather and Tannery industry employ more competitive strategies as compared to

other industries.

• Pottery/Ceramic industry performs economically better as compared to other

industries.

• There exist significant differences in the mean values of environmental issues,


prevention, resource conservation, competitiveness and economic performance with


• Organizations with medium operation pay more attention towards environmental

issues as compared to small and micro level organizations.

• Micro level organizations face more environmental challenges as compared to small

and medium organizations.

• Medium scale organizations pay more importance to environmental management

practices as compared to micro and small scale organizations.

• Medium scale organizations are more concerned towards the prevention of

environmental pollution as compared to micro or small scale organizations.

• Small scale organizations are more concerned towards the resource conservation as

compared to micro and medium scale organization.

• Medium scale organizations are more competitive as compared to micro or small

organizations.

• Medium scale organizations perform economically better as compared to micro or

small scale organizations.

• There exist significant differences in the mean values of environmental issues,


prevention, resource conservation, competitiveness and economic performance with


• Organizations having employees in the range 51 to 100 pay more importance to

environmental issues as compared to organizations having less than 51 and more

than 100 employees.

• Organizations employing less than 25 employees have to deal with more

environmental challenges as compared to organizations employing either 25 or more

employees.

• Organizations having more than 100 employees pay more importance to

environmental management practices in comparison to organizations employing lee

than 100 employees.

• Organizations having strength of employees in the range of 51 to 100 are more

concerned towards the prevention of environmental pollution as compared to

organizations having less than 51 or more than 100 employees.

• Organizations employing employees in the range of 51 to 100 pay more importance

to resource conservation as compared to organizations employing either less than or

equal to 50 or more than 100 employees.

• Organizations engaging more than 100 employees are more competitive as

compared to other organizations engaging up to 100 employees.

• Organizations employing more than 100 employees perform economically better

than the organizations employing up to 100 employees.

• There exist significant differences in the mean values of environmental management

practices, pollution prevention, resource conservation, and economic performance

with respect to number of suppliers associated with. It has also been observed that

• Organizations having more than 20 suppliers pay more importance to environmental

management practices as compared to organizations having suppliers up to 20.

• Organizations engaging more than 20 suppliers pay more importance to prevention

of environmental activities as compared to organizations engaging up to 20

suppliers.

• Organizations associated with more than 20 suppliers pay more importance to

resource conservation as compared to organizations having suppliers either 20 or

less.

• Organizations having more than 20 suppliers perform economically better than

organizations having up to 20 suppliers.

• Nature of industry be it micro, small or medium does not have any significance with

regard to resource conservation practices.

• The number of suppliers does not have any significance as far as environmental

issues are concerned. Organizations having any number of suppliers encounter

similar environmental issues.

• The number of suppliers does not have any significance with respect to

environmental challenges. Organizations having any number of suppliers face

similar environmental challenges.

• The number of suppliers making the supplies to the industry does not have any

significance with regard to competitiveness. Organizations having any number of

suppliers encounter similar environmental challenges.

• There exists a positive significant impact of environmental challenges on

environmental management practices.

• There exists a negative but insignificant impact of resource conservation on


• There exists a negative but insignificant impact of pollution prevention on

competitiveness.

• There exists a negative but insignificant impact of competitiveness on economic

performance.

• There exists a positive significant impact of environmental issues on environmental

management practices.

• There is a positive significant impact of environmental management practices on

resource conservation.

• There exists a positive significant impact of environmental management practices on

pollution prevention.

• There exists a positive significant impact of resource conservation on competitiveness.

• There is a positive significant impact of pollution prevention on economic

performance.

• There exists a positive significant impact of economic performance on

competitiveness.

Environmental Issues

This has been found that the environmental issues vary significantly with the nature of

industry. This is quite clear as the industry vary with the nature of business and the diversity

of the process involved. The environmental issues are also dependent on the status of the

organization i.e. micro, small or medium. The result shows that the medium level

companies are more concerned towards environmental issues. Further, it has also been

found that environmental issues vary significantly with the size of the workforce, whereas

the number of suppliers associated with the organization does not have any significance

with regard to environmental issues.

Environmental Challenges

The hypotheses results show that environmental challenges vary significantly with the

nature of industry. This is evident from the fact that the nature of business plays a

significant role. It has also been found that the environmental challenges vary significantly

with the status of the organization. The micro level organizations have to tackle more

environmental challenges because of one reason or the other. The result also draws to the

conclusion that environmental challenges vary significantly with the number of the

employees. Further, we conclude that the number of suppliers associated with the

organization does not have any significant difference.

Environmental Management Practices

It has been concluded from the results that environmental management practices vary

significantly with the nature of the industry. Pottery and Ceramic SMEs are more

concerned towards the environment. Significant variations are also found in environmental

concerns with respect to the status of organization (micro, small & medium). The results

also have led to conclude that environmental concerns vary significantly with the

organizational workforce strength. Further, environmental concerns also vary significantly

with the number of organizational suppliers.

Pollution Prevention

The adoption of prevention strategies for environmental pollution varies significantly with

the nature of industries. Leather and Tannery industry are more conscious towards the

pollution prevention. The results also convey that pollution prevention also vary

significantly with the status of the organization. The results also bring about the presence of

significant variations in prevention of environmental pollution with respect to employees

strength and number of suppliers.

Resource Conservation

The results show that there is no significant variation in resource conservations with respect

to nature of industry. Leather and Tannery industries are seriously more concerned towards

the resource conservation as it may lead to economies of scale. It is also seen that resource

conservation varies significantly with the status of the organization. Small organizations are

more worried towards resource conservation. Further, it has also been observed from the

results that resource conservation varies significantly with the number of employees and

also with the number of suppliers.

Competitiveness

It has been noticed from the results that Competitiveness varies significantly with the nature

of industry. Leather and Tannery industry apply more competitiveness techniques as

compared to other industries. It has also been noted that there is a significant variation in

Competitiveness with respect to status of organization. The medium scale organizations are

more competitive as compared to micro or small scale organizations. Further, it has been

seen from the results that Competitiveness varies significantly with the number of

employees the organization have, whereas it was observed that the number of suppliers

making supplies to the organization does not vary significantly.

Economic Performance

Significant variations are observed from the results, Economic Performance varies

significantly with regard to the nature of industry. Pottery/Ceramic enjoys more economic

performance as compared to the other industries. It has also been observed that the medium

scale organizations perform better on the front of economic performance as compared to

micro or small scale organizations. Further, the results show that economic performance

varies significantly with the employee strength of the organization and the number of

suppliers making supplies to the organization.

6.3 Suggestions

In order to make this planet worth living, it becomes strictly important to keep a check on

the growing levels of environmental pollution in the Indian SMEs. Problems related with

the environment be it air, water, noise, or soil pollution, solid hard waste disposal, forest

and agricultural degradation of land, ozone layer depletion etc are the most sensitive issues

now a days. Government rules and regulations are not implemented to its full length.

Environmental awareness of the masses is required to be raised. Though government has

taken some steps in this direction by introducing environmental education in the curriculum

of schools and colleges, still a lot of other steps are required to be taken.

The government may provide financial as well as technical help to SMEs in order to ensure

proper implementation of suggested rules and regulations conforming to international

standards. Social activist’s role and the consumer awareness can help in protecting the

environment to some extent.

The firms, on their part, may realize their responsibilities of protecting the environment and

conserving the natural resources, guaranteeing better returns as a by-product. The business

organizations are required to take steps in this direction. As the awareness regarding the

environment increases customers’ demand for the green products will increase.

Technological advancement will help in curbing this problem. Pollution prevention

strategies, reverse logistics, TQEM, re-engineering lean manufacturing etc will benefit both

the organizations as well as customers.

All these steps, if implemented and strictly followed, may help in making this world a better

place to live.

6.4 Directions for future research

• Only four sectors of SMEs have been targeted, which may not reflect the entire status

of environmental issues and challenges in Indian SMEs. Further work and studies may

be carried out in other Indian SMEs. It may help to understand the status of

environmental issues and challenges in SMEs as a whole.

• The seven dimensions were identified for this research work, future research proposal

may include some other dimensions such as environmental awareness and

sustainability etc.

• The work was confined to a limited geographical area of Uttar Pradesh in India; the

future research may cover a wider geographical area of the country and cover other

industrial cluster.

• The sample size of this study was 240 which may be increased so that a better

understanding of the problem is possible.

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ANNEXURES

ANNEXURE I

Research Questionnaire

Dear Respondent I am Farrukh Rafiq, doing Ph.D in the Department of Business Administration, Aligarh Muslim University under the supervision of Prof. Khalid Azam Sb. The topic of my research is “Study of Environmental Issues and Challenges in Small & Medium Enterprises (SMEs).” This research work is undertaken in partial fulfillment of requirement for the award of Ph.D. degree. I would be grateful if you kindly spend some of your precious time and help in conducting this survey, by filling the enclosed questionnaire. The data collected will be used purely for research and academic purpose. I assure you that all the responses will be kept strictly confidential and will be used for academic purpose only. I look forward for your response. Thanks.

Section A

1. Name of the Organization:

2. Your organization has the production activity in (Please tick)

(a) Lock, Hardware and Allied (b) Pottery/Ceramics (c) Leather and Tannery (d) Glass

3. Indicate the category your organization belongs to:

(a) Micro Scale (b) Small Scale (c) Medium scale

4. Indicate the total number of employees in your organization

(a) Less than 25 (b) 26 to 50 (c) 51 to 100 (d) More than 100

5. Indicate the average number of suppliers engaged by your organization for supplying raw

material/semi finished components in the final products

(a) Less than 5 (b) Between 6 to 10(c) Between 11 to 20 (d) More than 20

Section B

ISSUES

Q1. The following issues are critically important in integrating environmental concerns in

your business processes.

*SD = Strongly Disagree, D = Disagree, N = Neutral, A = Agree, SA = Strongly Agree

a. Government policies and regulationsb. Green procurement practicesc. Financial constraintsd. Societal concern for protection of natural environmente. Lack of support and guidance from regulatory authorities

CHALLENGES

Q2. The following challenges hinder integration of environmental concerns in your business

processes.

a. Lack of commitment from top management

b. Inadequate adoption of reverse logistics practices

c. Inadequate strategic planning

d. Non adoption of cleaner technology

e. Lack of corporate social responsibility

f. Proper workplace management/housekeeping practices

g. Lean manufacturing practices


SD1

D N A SA5

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

Q3. Following are the statements as regards environmental concerns with respect to your

manufacturing sector. Please show your agreement or disagreement on a five point scale as mentioned

below.

a. The issue of natural resource depletion is highly significant

b. Use of hazardous chemicals & substances is a highly significant issue

c. Low usage of renewable energy sources is highly significant

d. Low level of environmental awareness of the work force (Eco-literacy) is highly significant

Q4. Following are the statements about implementation of Total Quality Environmental Management practices/ Green Business practices in your organization.

a. Assignment of roles and responsibilities with respect to environmental programs has been significantly implemented.

b. Conduct of Environmental training program for the employees has been executed

c. The practice of Benchmarking environmental performance has been significantly implemented

d. Use of cleaner technology/production processes to minimize wastes and make savings has been significantly implemented

e. Continuous environmental performance improvement program has been significantly executed

Q5. On a scale of five, rate the following factors while selecting the manufacturing processes.

a. The Optimization of processes to reduce air emissions is an important consideration

b. The Optimization of processes to reduce water use is an important consideration

c. The Optimization of processes to reduce solid waste is an important consideration

d. The Optimization of processes to reduce noise is an important consideration

SD D N A SA5

SD D N A SA5

SD D N A SA5

SD D N A SA5

SD D N A SA5

SD D N A SA5

SD D N A SA5

SD D N A SA5

SD D N A SA5

SD D N A SA5

SD D N A SA5

SD D N A SA5

SD D N A SA5

Q6. Rate on a scale of five, your agreement/disagreement about the extent of application of the

following statements in your organization.

a. Recycle from the waste streams and reutilizing them in the manufacturing process is generally practiced

b. Packaging material is reused after repair or modification for further packaging

c. Products that can be reused after repair or modification are generally used

d. Redesigning a product to improve performance and reduce waste is generally practiced

e. Products are manufactured that can be easily dismantled at the end-of-life and their parts/components are reutilized

f. Sorting valuable raw materials which can be recycled or sold in open market is a common practice

g. Converting a discarded product into a new product through appropriate processing is commonly practiced

Q7. On a scale of five, show your agreement/disagreement on the following statements regarding the level of support from the government for environmental regulation.

a. Information on the current regulations by issuing guidelines. b. Information on cleaner technologies c. Information regarding Tax incentivesd. Promotion on environmental labels/eco-markse. Environmental education to raise awarenessf. Encouraging self assessment of regulatory compliancesg. Expediting environmental clearance/permits


SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

Q8. The following are the statements regarding concern for environmental pollution with regard to

your manufacturing unit. On a scale of five, please show your agreement/disagreement.

a. The issue of Air emissions is highly significant

b. The issue of Water pollution is highly significant

c. The issue of Solid waste is highly significant

d. The issue of Hazardous waste is highly significant

e. The issue of Noise pollution is highly significant

f. The issue of Liquid waste is highly significant

g. The issue of Waste disposal is highly significant

Q9. The following factors of pollution prevention strategies benefit your organization while selecting

the manufacturing processes. Show your agreement/disagreement on the scale of five.

a. Increased efficiencies and productivityb. Improved worker safetyc. Lower operational and environmental compliance costsd. Reduced or eliminated long-term liabilitiese. Decreased disposal costsf. Decreased use of raw materialsg. Diminished need for onsite storage spaceh. Greater compliance with government regulationsi. Protection of natural resources, providing for long term sustainability of the business

j. Enhanced employee morale and employee retention

RESOURCE CONSERVATION

Q10. On the scale of five, show your agreement/disagreement regarding the importance of the following factors related to your organization.

a. Lower consumption of raw material is highly significant for resource conservation

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

b. Quantity of water used is highly significant for resource conservation

c. Waste water generated is highly significant for resource conservation

d. Quantity of water treated is significantly important for resource conservation

e. Level of electricity consumption is highly significant for resource conservation

f. The level of fuel consumption is highly significant for resource conservation

g. Hazardous waste reduction is highly significant for resource conservation

COMPETITIVENESS

Q11. The following competitive benefits are observed/ perceived through integration of

environmental concerns.

a. Better corporate image

b. Improved working environment

c. Improved employees environmental awareness

d. Better competitive advantage through green products

e. Reduced risk of litigation

f. Increased social acceptance

g. Exploring international markets

h. Creating good business relations with customers & other stake holders


Q12. The following economic benefits are observed/ perceived through integration of environmental

concerns.

a. Improvement in return on investment

b. Increased productivity

c. Risk reduction related to termination of business

d. Better strategic planning through awareness of challenges ahead

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

SD D N A SA

ANNEXURE II

Questionnaire (Hindi Format)

ANNEXURE III

Table1 Total Variance Explained

Component

Initial EigenvaluesExtraction Sums of Squared

LoadingsRotation Sums of Squared

Loadings

Total% of

VarianceCumulative

% Total% of

VarianceCumulative

% Total% of

VarianceCumulative

%

1 15.797 24.302 24.302 15.797 24.302 24.302 11.468 17.643 17.643

2 7.298 11.227 35.530 7.298 11.227 35.530 5.991 9.217 26.859

3 4.128 6.351 41.881 4.128 6.351 41.881 4.717 7.257 34.117

4 3.050 4.692 46.573 3.050 4.692 46.573 4.376 6.733 40.850

5 2.642 4.064 50.637 2.642 4.064 50.637 3.637 5.595 46.445

6 2.127 3.273 53.910 2.127 3.273 53.910 3.407 5.241 51.686

7 1.749 2.690 56.601 1.749 2.690 56.601 3.194 4.915 56.601

8 1.570 2.416 59.017

9 1.531 2.356 61.372

10 1.365 2.100 63.472

11 1.300 2.000 65.472

12 1.225 1.884 67.356

13 1.186 1.824 69.180

14 1.152 1.773 70.953

15 1.035 1.592 72.546

16 .966 1.486 74.032

17 .938 1.444 75.475

18 .882 1.357 76.832

19 .839 1.290 78.123

20 .808 1.244 79.366

21 .770 1.184 80.550

22 .721 1.109 81.659

23 .717 1.103 82.763

24 .669 1.029 83.791

25 .629 .968 84.759

26 .617 .949 85.708

27 .586 .901 86.609

28 .557 .858 87.466

29 .536 .824 88.291

30 .525 .807 89.098

31 .489 .753 89.850

32 .472 .726 90.576

33 .463 .712 91.288

34 .426 .656 91.944

35 .414 .636 92.580

36 .389 .598 93.178

37 .366 .563 93.742

38 .318 .488 94.230

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