chapter-3 research methodology 3.1 introduction 3.2...

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CHAPTER-3

RESEARCH METHODOLOGY

3.1 INTRODUCTION

This chapter shall elaborate on the methodology adopted by the researcher to

conduct the proposed study. This chapter shall throw light on the research design

adopted, nature of data collected, sources of data, the sampling plan proposed to be used,

the research instrument to be utilized for the research and details relating to the

representation and analysis of the collected data.

3.2 RESEARCH DESIGN

The proposed research study is descriptive in nature, covering manufacturing

industries situated in Union Territory of Puducherry.

3.3 NATURE AND SOURCE OF DATA

Both primary and secondary data have been used for this research. Primary data

was collected using a well structured questionnaire, which was administered personally to

the executives of manufacturing undertakings in Union Territory of Puducherry.

Secondary data was collected from the findings of Published Papers, Articles, Books,

Prior Studies, Organizations‘ Bulletins, Annual Reports of the manufacturing units and

from various web sites.

3.4 DATA COLLECTION INSTRUMENT

3.4.1 Initial Items Generation

The survey instrument was initially developed based on review of previous

literature, which addressed the basic theoretical constructs of Business Environment

Characteristics of Manufacturing undertakings, Advanced Manufacturing Technologies

of Manufacturing undertakings, Competitive Priorities of Manufacturing undertakings

and Business Performance of Manufacturing undertakings. These constructs were further

sub-divided into various domains, each consisting of number of statements.

The Business Environment Characteristics construct is divided into six domains

namely, Labour Availability, Business cost, Competitive Hostilities, Dynamism, Political

Environment and Government Laws and Regulations. Similarly, the Advanced

Manufacturing Technologies construct is sub-divided into three domains namely,

Advanced Manufacturing Technology Implementation, Direct Advanced Manufacturing

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Technology and Indirect Advanced Manufacturing Technology. The third construct of

Competitive Priorities is divided into six domains namely, Quality, Cost, Delivery,

Flexibility, Customer Focus and Knowhow. The fourth and final construct of Business

Performance consists of five statements. Totally 109 variables were included in the

preliminary schedule, of which 37 were included in the first construct, 24 in the second

construct, 43 in the third construct, and 5 variables were included in the fourth and final

construct.

The executives of the manufacturing firms were asked to provide perceptual

information on the performance of their company. Dess and Robinson (1984) have

recommended the utilization of perceived measures if objective measures are not

available. It is difficult to collect financial data of manufacturing undertakings from the

executives as the data may be confidential. Further, the executives may not have the data

in their memory and may not be able to recall the desired data for the study when the

survey is undertaken. Swamidass and Newel (1987) have also confirmed the difficulties

of using objective measures of performance due to the reluctance of the companies to

provide financial data. Hence, the executives of manufacturing units have been required

to rate the comparative market share and growth of sales of their undertaking in relation

to their competitors, in a Likert‘s Five Point Scale. Vickery et al. (1993) has also used

perceptual information to study the trends of Return of Investment and Return on Sales of

undertakings. Hence, the researcher decided to use the perceived measurement of

performance of the manufacturing units.

3.4.2 Qualitative Inquiry

Researcher held extensive consultations with the research supervisor and other

subject experts and industrial experts in the field of Operations Management. An In-depth

interview was conducted with a panel of ten best academicians, consisting of four experts

from Operations Management, two Statisticians, two Finance experts, one English expert

and one representative of the State Government. These experts from top and eminent

institutions such as Pondicherry University, NIT Tiruchy, and Anna University, Chennai.

Furthermore, the researcher held extensive consultations with ten industrial experts in the

rank of Chief General Managers and Managers at operational level and got valuable

inputs in drafting the schedule.

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3.4.3 Face Validity and Content Analysis

The experts consulted were requested to carefully go through the research

constructs in the light of the objectives of the research. The experts evaluated the 109

constructs and selected the constructs to be included for the study based on the

compatibility and, representativeness, suitability and importance of the constructs, and

the capacity of the constructs to match with domain area and yield best accurate results,

those constructs not satisfying these parameters shall be summarily rejected and removed

from the schedule. Further, additional constructs suggested by the experts shall be

included in the schedule. Furthermore, the experts were requested to evaluate the

complete schedule for its simplicity, clarity, unambiguity, composition of the schedule,

feasibility of obtaining the desired information from the respondents and the length of the

schedule, and accordingly refine the schedule. The refined scale was further improved

based on the suggestions of industrial experts. Upon the successful completion of this

process, the number of variables constituting the Business Environment Characteristics of

Manufacturing firms were reduced to five for Business cost domain, five for Labour

Availability domain, five for Competitive Hostilities domain, four in respect of

Government Laws and Regulations domain, five in respect of Political Environment and

four in respect of Dynamism. Similarly, the number of variables constituting the

Advanced Manufacturing Technologies construct is arrived at six for Advanced

Manufacturing Technology Implementation domain, six for Direct Advanced

Manufacturing Technology domain and five in respect of the Indirect Advanced

Manufacturing Technology domain. The number of variables constituting the third

construct of Competitive Priorities is fixed at six for Quality domain, five for Cost

domain, five for Delivery domain, four for Flexibility domain, four for Customer Focus

and six in respect of the Knowhow domain. The fourth and final construct of Business

Performance consists of five variables without any sub-divisions of domains.

Hence, the total number of variables pertaining to Business Environment

Characteristics was reduced to 28, while those relating to Advanced Manufacturing

Technologies were reduced to 17, those relevant to Competitive Priorities were reduced

to 30 and the number of variables relating to Business Performance of the Manufacturing

undertakings was arrived at 5. Hence, the total numbers of constructs were reduced from

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109 to 80. Further, the number of questions relating to the industrial profile of the

manufacturing units studied was enhanced from 10 to 13.

3.4.4 Pilot Study

Prior to the full-fledged resumption of the research process, a pilot study was

conducted on some 52 manufacturing undertakings located in Puducherry. Based on the

feedback obtained from the Pilot study, the researcher made minor modifications in the

questions pertaining to the industrial profile of the manufacturing units studied. These

questions were redesigned in statement forms to accommodate the respondent‘s

recommendations. Further, based on their feedback, some technical terms which were not

easily understandable for the respondents were suitably modified and substituted with

simpler terms.

Based on the inputs obtained from the Pilot study, the final schedule was drafted.

The final schedule consisted of five sections. The first section consisted of 13 questions

relating to the industrial profile of the units studied, while the second section consisted of

28 key determining variables to measure the business environment characteristics of the

manufacturing units. The third section endeavours to measure the advanced

manufacturing technologies of the manufacturing undertakings with the help of 17

variables in respect of 3 domains, while the fourth section tries to measure the

competitive priorities of the manufacturing units through 30 variables. The fifth and final

section consists of 5 key determining variables to measure the business performance of

the manufacturing undertakings. Five point Likert‘s Scale has been used in respect of

these 80 variables, with the scale values ranging from low level of priority attached to

high level of priority.

3.4.5 Reliability test for Data Collection Instrument

The next step is to test the reliability of the schedule. Reliability shall reveal the

accuracy and consistency of the results from the survey instrument. The result of the Pilot

study was tested for Reliability using the Cronbach alpha and the value of reliability was

found to be more than satisfactory level of 0.6 in respect of all the categories.

In addition to testing the reliability, the researcher has also tested the

Communality, which measures the percent of variance explained by the factors in a given

item. Furthermore, the researcher has tested the Normality, which indicates the normal

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distribution of the data. While plotting the data in a graph, if a bell shaped curve is

arrived at, then the mean value is 0 and the value of standard deviation is 1, which

indicates that there is a standard normal distribution of the data (Lewis-Beck, Bryman &

Liao, 2004; Groebner & Shannon, 1990). Testing Normality is absolutely important for

multivariate data analysis (Hair et al. 2006).

The researcher then proceeded to test the Homogeneity of the data, which shall

indicate the uniqueness of the population. In addition, the researcher proceeded to test the

Multicollinearity, which presence when more than two independent variables represent

the common thing. Tabachnick & Fidell (2007) in his study suggested items from the

same construct in the data set, the correlation value higher than 0.90 between any

variables will create some problematic conditions in analysis. If it is more than above

author suggested value it is better to exclude a particular item is advisable. To evaluate

Multicollinearity, item to item correlations were calculated between each item. It helps to

solve the multicollinearuty problem in the data set.

Next, the researcher tested the Linearity of data, which reveals the existence of

linear relationships among the variables, which is important in multivariate analysis

techniques. Most multivariate techniques (including covariance structure modeling)

employed in this study implicitly believe that relationships between variables are linear.

Departures from linearity have an effect on calculated correlations between variables in

this study were carefully evaluated the linearity. The next step is to test the Individual

item reliability using factor loading to analyse the individual reliability of the variables.

Carmines and Zeller (1979) proposed factor loadings greater than or equal to 0.707.

However, several authors (Barclay et al., 1995; Chin, 1998) recommended that this

principle not to be supposed to strictly followed in exploratory studies; in some

circumstances factor loadings up to 0.5 or 0.6 can be accepted.

The next step is to test the Construct reliability, which sure that the internal

consistency of all the variables when they compute the similar idea by assessing how

aggressively the observable variables evaluate the latent variable (Fornell and Larcker,

1981). Construct reliability value should be more than 0.6 and it shows acceptable

reliability of the measurement items (Chen and Paulraj, 2004; Nunnally, 1978; Cronbach,

1951). Further, the researcher has proceeded to test the Convergent validity, which

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establishes the relationship between observed constructors. It is the process of analyzing

scores of one constructor correlated with the scores of another measure, may be similar or

different. Fornell and Larcker (1981) assessed convergent validity, by using the average

variance extracted (AVE) and value should be 0.5 or above. The researcher has also

tested the Discriminant validity, which specifies that how much the given construct with

another construct in a model. This validity can be calculated by matching the AVE value

with the square of the correlations of the constructs.

3.5 SAMPLING

3.5.1 Sample Frame

Union Territory of Puducherry is the sample frame for the study. All the four

regions of Union Territory namely, Puducherry, Karaikal, Yanam, and Mahe. With a

current population of 11.1 lakhs and existence of well established 72 large scale

industries, 176 medium scale industries and 7950 small scale industries and the this

number being on the ever increase, offers tremendous scope for choosing Puducherry as

the sample frame for the study.

3.5.2 Sample Population

Business units engaged in manufacturing and those located in the four regions of

Pondicherry, Karaikal, Mahe and Yanam shall be the sample population for this study.

executives with titles of Directors, Chief Executives, Managing Directors, General

Managers and Senior Level Managers who have the leadership in different functional

areas like Operations, Marketing, Human Resources and Finance shall constitute the

sample population, from which sample shall be drawn for the conduct of this study.

3.5.3 Sample technique

The sample technique used for the study is Simple Random sampling method.

The names of 8588 units engaged in manufacturing as on 2011 were listed out and 365

sample units were drawn from this list using the Lottery Method.

3.5.4 Sample size

The most important part of any research is the proper calculation of appropriate

sample size for the survey. In the present research work, the formula

205.096.1 n has been used to calculate the appropriate sample size for the

study. Pilot study was conducted on manufacturing firms located in Puducherry. Based

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on the findings of the pilot survey, a possible sample size of 337 with a 5% error in mean

estimate was finalized for the study. Out of 365 questionnaires which were administered,

15 were rejected for invalid and incomplete responses and 350 valid questionnaires were

considered for further analysis which is more than the desired figure of 337.

3.6 DATA COLLECTION METHOD

Personal Interview method was employed to collect data. The researcher

administered the schedule personally to the respondents and collected the necessary data.

3.7 DATA REPRESENTATION

The raw data collected were coded suitably and represented in tabular and

diagrammatic forms to facilitate the usage of traditional and sophisticated statistical tools

for analyzing the data.

3.8 DATA ANALYSIS TOOLS

Both traditional and sophisticated statistical tools were applied for data analysis.

The data collected were fed in to Excel sheet and the statistical packages of SPSS 19

Version and LISREL were employed. The statistical tools of Mean, Standard Deviation,

Chi-square, ANOVA, Correlation, Cluster Analysis, Discriminant Analysis,

Correspondence Analysis, Fuzzy TOPSIS, Confirmatory Factor Analysis and Structural

Equation Model were used to analyse the data and arrive at meaningful conclusions.

3.9 DATA EXAMINATION AND PREPARATION

This section explains how characteristics of the data were studied for consistency

with distributional assumptions. Checking the reliability and validity of the research

instrument is more important before starting any kind of analysis, especially in respect of

conducting multivariate analysis with structural equation modeling. The first step shall be

to ensure that the data is properly prepared and thoroughly examined. This will help to

minimize measurement error and maximize the validity and reliability of the data. The

requirement level of data can be verified using many tests such as Reliability,

Communality, Normality, Homogeneity, Multicollinearity, Linearity, Individual item

reliability, Construct reliability, Convergent validity, and Discriminat validity. These

tests shall study the entire anatomy of the data set.

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3.9.1 Reliability

Internal consistency of the data can be verified using Reliability test. Cronbach

alpha has been applied to verify the internal consistency and reliability of the data.

Cronbach coefficient alpha is commonly used to measure the reliability of a set of two or

more construct indicators (Cronbach, 1951). It is calculated on the internal consistency

based on average correlation among items. The value of Cronbach alpha should exceed

the threshold limit of 0.60 to consider the data as reliable (Nunnally, 1978). Cronbach's

alpha value is directly related to the number of items. More the number of items included

in the study, higher shall be the. The Cronbach's alpha value in respect of various items

included in this study is displayed in the following table.

Table 3.1 Cronbach's alpha reliability test for Business Environment

Characteristics domains

SL.

No

Variables Cronbach's

Alpha if

Item Deleted

Cronbach'

s Alpha

Business cost

1 Mounting labor cost .846

.848

2 Mounting material cost .818

3 Mounting transportation cost .813

4 Mounting utility cost .812

5 Mounting rent .793

Labour availability

6 Scarcity of managerial Personnel .883

.903

7 Dearth of technicians .884

8 Deficiency of clerical Personnel .876

9 Scarcity of skilled and Specialized Personnel .879

10 Shortage of Direct Labour .884

Competitive hostility

11 Stumpy profit margins .870

.896

12 Dilapidating Local demand .865

13 Dilapidating International demand .861

14 Dilapidating Product Standards .888

15 Dilapidating Quality of Acquired Inputs .877

Government laws and regulations

16 Complex governmental regulations and

procedures .833

.863 17 Ambiguous government laws and regulations .814

18 Red Tapism and Delays .804

19

Government‘s protectionism Policy Towards

Industries .848

52

Table 3.1 Cronbach's alpha reliability test for Business Environment

Characteristics domains (continued)

SL.

No

Variables Cronbach's

Alpha if

Item Deleted

Cronbach'

s Alpha

Political environment

20 Country balance of payment status .874

.889

21 Bilateral and Multi-lateral Governmental

Agreements .857

22 Nation‘s Political Stability .861

23 Regulatory Mechanism for Protecting Investments .865

24 Military Coalitions with fellow countries .867

Dynamism

25 Tempo of innovative operations processes .827

.834 26 Changing customer Aspirations in the industry .763

27 Emerging challenges from competitors .754

28 Rate of information diffusion .778

The above table displays that the value of Cronbach's α coefficient of all the

factors included under the BEC domains range from 0.834 to 0.903. This establishes the

reliability of all the factors included under the BEC domain. Furthermore, the estimated

value of Cronbachs Alpha in respect of all the variables exceeds the ―Alpha if Item

Deleted" value and hence, no item needs to be dropped from the study.

Table 3.2 Cronbach's alpha reliability test for AMT domains

SL.

No Variables

Cronbach's

Alpha if Item

Deleted

Cronbach's

Alpha

AMT implementation

1 Planning .889

.895

2 Requirement Analysis .870

3 Cost/Benefit Analysis .870

4 Technology assessment .873

5 Development and Implementation .870

6 Training .884

Direct AMT

7 Computer numerical control (CNC) machines .882

.890 8 Robotics (Ro) .873

9 Flexible manufacturing system (FMS) .870

10 Automated material handling systems (AMHS) .866

53

Table 3.2 Cronbach's alpha reliability test for AMT domains (continued)

SL.

No Variables

Cronbach's

Alpha if Item

Deleted

Cronbach's

Alpha

11 Automated guided vehicles (AGV) .862

12 Rapid prototyping (RP) .874

Indirect AMT

13 Computer aided design (CAD) .902

.912

14 Material requirement planning (MRP) .883

15 Statistical process control (SPC) .879

16 Bar coding (BC) .893

17 Material resource planning (MRPII) .900

The above table displays that the Cronbach's α coefficient of all the factors

included under the AMT domain range from 0.890 to 0.912. This indicates that all the

factors included under the AMT domain have good internal consistency. Similarly, the

estimated value of Cronbachs Alpha value exceeds the ―Alpha if Item Deleted" value in

respect of all the items and hence all the items included under the three factors need not

be dropped from the study.

Table 3.3 Cronbach's alpha reliability test for Competitive Priority domains

SL.

No Variables

Cronbach's Alpha

if Item Deleted

Cronbach's

Alpha

Quality

1 Low defect rate .877

.877

2 Performance quality .846

3 Product Reliability .836

4 Environmental aspect .861

5 Certification .860

6 Product durability .855

Cost

7 Low costs .797

.830

8 Value added costs .780

9 Quality costs .779

10 Activity based measurement .795

11 Continuous improvement .828

Delivery

12 Fast delivery .855

.872 13 On time delivery .834

14 Right quality .840

54

Table 3.3 Cronbach's alpha reliability test for Competitive Priority domains

(continued)

SL.

No Variables

Cronbach's Alpha

if Item Deleted

Cronbach's

Alpha

15 Right amount .833

16 Dependable promises .863

Flexibility

17 Design adjustments .772

.804 18 Volume change .715

19 Product Mix changes .754

20 Broad product line .777

Customer focus

21 After sales service .833

.842 22 Product customization .773

23 Customer information .782

24 Measurement of satisfaction .777

Know how

25 Knowledge management .908

.916

26 Creativity .894

27 Continuous learning .894

28 Problem solving skills .897

29 Training/education .901

30 R&D .909

It can be inferred from the above table that the Cronbach's α coefficient of all the

items included under the Competitive Priority domain range from 0.804 to 0.916. This

indicates that all the items included under the six factors of the CP domain command

good degree of internal consistency. Further, the estimated value of Cronbach‘s Alpha

value exceeds the ―Alpha if Item Deleted" value in respect of all the items, and hence all

the items can be included for the study.

Table 3.4 Cronbach's alpha reliability test for Business Performance domain

SL.

No Variables

Cronbach's Alpha

if Item Deleted

Cronbach's

Alpha

1 Market share .914

.925

2 Sales growth .899

3 Profit margin .899

4 Return on assets (ROA) .922

5 Return on investment (ROI) .905

55

It can be inferred from the above table that the Cronbach's α coefficient in respect

of all the variables included under the Business Performance domain far exceeds the

required threshold limit of 0.6. Further, the estimated value of Cronbachs Alpha value

exceeds the ―Alpha if Item Deleted" value. This signifies that all the items shall be

included for this study.

3.9.2 Communality

Higher communalities are better at the time of model formulation and the

minimum threshold limit for establishing the Communality of the data is 0.5. All

variables with communality value of less than 0.5 should be removed.

Table 3.5 Communality test for Business Environment Characteristics domain

SL.

No Variables Communality

Business cost

1 Mounting labor cost .574

2 Mounting material cost .625

3 Mounting transportation cost .642

4 Mounting utility cost .648

5 Mounting rent .728

Labour availability

6 Scarcity of managerial Personnel .713

7 Dearth of technicians .708

8 Deficiency of clerical Personnel .749

9 Scarcity of skilled and Specialized Personnel .731

10 Shortage of Direct Labour .706


11 Stumpy profit margins .717

12 Dilapidating Local demand .747

13 Dilapidating International demand .770

14 Dilapidating Product Standards .616

15 Dilapidating Quality of Acquired Inputs .679


16 Complex governmental regulations and procedures .688

17 Ambiguous government laws and regulations .743

18 Red Tapism and Delays .767

19 Government‘s protectionism Policy Towards Industries .639

56

Table 3.5 Communality test for Business Environment Characteristics domain

(continued)

SL.



20 Country balance of payment status .648

21 Bilateral and Multi-lateral Governmental Agreements .734

22 Nation‘s Political Stability .711

23 Regulatory Mechanism for Protecting Investments .694

24 Military Coalitions with fellow countries .685

Dynamism

25 Tempo of innovative operations processes .567

26 Changing customer Aspirations in the industry .747

27 Emerging challenges from competitors .764

28 Rate of information diffusion .712

It can be observed from the above table that the communality value in respect of

all the items exceed the threshold limit of 0.5 and hence all items may be included for the

proposed study.

Table 3.6 Communality test for Advance Manufacturing Technology domain

SL.


AMT Implementation

1 Planning .546

2 Requirement Analysis .704

3 Cost/Benefit Analysis .709

4 Technology assessment .680

5 Development and Implementation .706

6 Training .597

Direct AMT

7 Computer numerical control (CNC) machines .558

8 Robotics (Ro) .643

9 Flexible manufacturing system (FMS) .668

10 Automated material handling systems (AMHS) .705

11 Automated guided vehicles (AGV) .744

12 Rapid prototyping (RP) .618

57

Table 3.6 Communality test for Advance Manufacturing Technology domain

(continued)

SL.


Indirect AMT

13 Computer aided design (CAD) .682

14 Material requirement planning (MRP) .790

15 Statistical process control (SPC) .812

16 Bar coding (BC) .745

17 Material resource planning (MRPII) .728


all the items exceed the threshold limit of 0.5 and hence all items may be included for the

proposed study.

Table 3.7 Communality test for Competitive Priority domain

SL.


Quality

1 Low defect rate .477

2 Performance quality .693

3 Product Reliability .768

4 Environmental aspect .593

5 Certification .597

6 Product durability .634

Cost

7 Low costs .598

8 Value added costs .678

9 Quality costs .660

10 Activity based measurement .619

11 Continuous improvement .456

Delivery

12 Fast delivery .619

13 On time delivery .721

14 Right quality .688

15 Right amount .725

16 Dependable promises .571

58

Table 3.7 Communality test for Competitive Priority domain (continued)

SL.


Flexibility

17 Design adjustments .584

18 Volume change .727

19 Product Mix changes .640

20 Broad product line .580

Customer focus

21 After sales service .503

22 Product customization .753

23 Customer information .737

24 Measurement of satisfaction .753

Know how

25 Knowledge management .642

26 Creativity .759

27 Continuous learning .767

28 Problem solving skills .741

29 Training/education .699

30 R&D .624


all but one items exceed the threshold limit of 0.5 and hence all but that one items may be

included for the proposed study. The communality value in respect of low defect rate is

below 0.5 and hence this item is dropped from the study.

Similarly, the communality value in respect of the item ―Continuous

improvement‖ is less than the recommended value of 0.5 and hence this item is also

dropped from the study.

Table 3.8 Communality test for Business Performance domain

SL.


1 Market share .737

2 Sales growth .824

3 Profit margin .824

4 Return on assets (ROA) .677

5 Return on investment (ROI) .791

59

The above table displays that the communality value in respect of all the items far

exceed the minimum desirable value of 0.5 and hence all the items may be included for

the study without dropping any of them.

3.9.3 Normality

In general terms, normality specifies that the data are normally distributed.

Normally distributed data will result in the formation of a bell-shaped curve. Data with

high Normality will yield a mean of zero and standard deviation of one. (Groebner &

Shannon, 1990; Lewis-Beck et al. 2004). Normality of data is indispensable for arriving

at SEM using LISREL and lack of normality will adversely affect the goodness-of-fit

indices and standard error (Hair et al. 2006; Jöreskog and Sörbom, 1996; Baumgartner

and Homburg, 1996). Hence, the normality of data has been tested and the results are

discussed in the following sections. Normality of data can be tested using Kurtosis and

Skewness. Skewness may be positive (if the tail of the curve points towards left) or

negative (if the tail of the curve points towards right) (Groebner & Shannon, 1990).

Similarly, Kurtosis indicates the peakedness of the distribution curve. Positive Kurtosis

will lead to the curve with high peak, while negative Kurtosis will lead to a flat curve

(Everitt, 2006). Kurtosis should be in the range of +3 and -3, while Skewness should be

in the range of +1 to -1 (Lewis-Beck et al. 2004; Hair et al. 2006).

Table 3.9 Normality test for Business Environment Characteristics domain

SL.

No Variables Skewness Kurtosis

Business cost

1 Mounting labor cost -0.549 0.127

2 Mounting material cost -0.212 -1.219

3 Mounting transportation cost -0.439 -0.93

4 Mounting utility cost -0.414 -0.856

5 Mounting rent -0.333 -1.23

Labour availability

6 Scarcity of managerial Personnel 0.32 -0.136

7 Dearth of technicians 0.353 -0.322

8 Deficiency of clerical Personnel 0.421 -0.471

9 Scarcity of skilled and Specialized Personnel 0.199 -0.726

10 Shortage of Direct Labour 0.314 -0.783

60

Table 3.9 Normality test for Business Environment Characteristics domain

(continued)

SL.



11 Stumpy profit margins -0.677 -0.389

12 Dilapidating Local demand -0.709 -0.396

13 Dilapidating International demand -0.635 -0.477

14 Dilapidating Product Standards -0.905 0.624

15 Dilapidating Quality of Acquired Inputs -0.912 0.378


16 Complex governmental regulations and procedures -0.134 -0.583

17 Ambiguous government laws and regulations 0.041 -0.555

18 Red Tapism and Delays -0.04 -0.712

19 Government‘s protectionism Policy Towards Industries -0.091 -0.545


20 Country balance of payment status 0.142 -0.225

21 Bilateral and Multi-lateral Governmental Agreements 0.061 -0.722

22 Nation‘s Political Stability 0.072 -0.513

23 Regulatory Mechanism for Protecting Investments 0.013 -0.698

24 Military Coalitions with fellow countries 0.094 -0.712

Dynamism

25 Tempo of innovative operations processes -0.493 -0.101

26 Changing customer Aspirations in the industry -0.473 -0.128

27 Emerging challenges from competitors -0.639 0.287

28 Rate of information diffusion -0.414 -0.231

The above table shows that the data in respect of all the variables easily pass the

normality test as the Skewness value is in the range of +1 to -1 and the value of Skewness

ranges between +3 and -3. Hence, all the variables are normally distributed.

Table 3.10 Normality test for Advance Manufacturing Technology domains

SL.


Implementation AMT

1 Planning -0.847 0.283

2 Requirement Analysis -0.665 -0.322

3 Cost/Benefit Analysis -0.631 -0.446

4 Technology assessment -0.776 0.066

5 Development and Implementation -0.724 -0.185

6 Training -0.759 -0.297

61

Table 3.10 Normality test for Advance Manufacturing Technology domains

(continued)

SL.


Direct AMT

7 Computer numerical control (CNC) machines 0.623 -0.495

8 Robotics (Ro) 1.12 0.154

9 Flexible manufacturing system (FMS) 1.012 -0.3

10 Automated material handling systems (AMHS) 1.103 -0.179

11 Automated guided vehicles (AGV) 1.303 0.498

12 Rapid prototyping (RP) 0.806 0.08

Indirect AMT

13 Computer aided design (CAD) 0.556 -1.2

14 Material requirement planning (MRP) 0.156 -1.206

15 Statistical process control (SPC) 0.254 -1.287

16 Bar coding (BC) 0.251 -1.444

17 Material resource planning (MRPII) -0.307 -0.853

The above table displays that the normality conditions in respect of the data is met

with the value of Kurtosis and Skewness falling within the desired range.

Table 3.11 Normality test for Competitive Priority domains

SL.


Quality

1 Performance quality -0.839 0.326

2 Product Reliability -0.804 0.244

3 Environmental aspect -0.967 0.379

4 Certification -0.896 0.433

5 Product durability -0.767 0.061

Cost

6 Low costs 0.031 -0.595

7 Value added costs -0.426 0.02

8 Quality costs -0.626 0.289

9 Activity based measurement -1.146 0.353

Delivery

10 Fast delivery -1.045 1.326

11 On time delivery -1.155 1.279

12 Right quality -1.305 1.779

13 Right amount -1.097 1.055

14 Dependable promises -0.796 0.19

62

Table 3.11 Normality test for Competitive Priority domains (continued)

SL.


Flexibility

15 Design adjustments -0.332 -0.388

16 Volume change -0.473 -0.745

17 Product Mix changes -0.549 0.127

18 Broad product line -0.631 -0.278

Customer focus

19 After sales service -0.484 -0.214

20 Product customization -0.512 -0.055

21 Customer information -0.457 -0.275

22 Measurement of satisfaction -0.421 -0.243

Know how

23 Knowledge management -0.886 0.345

24 Creativity -0.953 0.17

25 Continuous learning -0.996 0.409

26 Problem solving skills -1.054 0.465

27 Training/education -1.049 0.274

28 R&D -1.104 0.512

From the above table 3.11 the normality tests are conducted for six domains of

competitive priorities such as Quality, Cost, Delivery, Flexibility, Customer Focus and

Know-how the results shows that all the value are within the range of -2 to +2 of

skewness and kurtosis. This indicate that the above six domains are considered to be

normally distributed.

Table 3.12 Normality test for Business Performance domain

SL.


1 Market share -0.355 -0.343

2 Sales growth -0.467 -0.419

3 Profit margin -0.329 -0.491

4 Return on assets (ROA) -0.144 -0.614

5 Return on investment (ROI) -0.088 -0.547

It can be observed from the above table that the data is normally distributed as the

desired norms relating to Skewness and Kurtosis is well met.

63

3.9.4 Linearity

Testing for linear relationships between the variables is important in multivariate

analysis techniques. Most multivariate techniques (including covariance structure

modeling) implicitly believe that relationships between variables are linear. Departures

from linearity have an effect on calculated correlations between variables. Statistical tools

such as regression, correlation and SEM can be performed only if the data is linear.

Table 3.13 Linearity test

Equation

Model Summary Parameter Estimates

R

Square F df1 df2 Sig. Constant b1 b2 b3

Linear .099 38.178 1 348 .000 2.178 .308

Logarithmic .096 37.042 1 348 .000 2.101 .970

Inverse .077 29.103 1 348 .000 4.015 -2.217

Quadratic .099 19.164 2 347 .000 1.940 .452 -.020

Cubic .099 12.739 3 346 .000 1.932 .461 -.023 .000

Compound .091 34.816 1 348 .000 2.241 1.099

Power .091 34.801 1 348 .000 2.177 .302

S .075 28.330 1 348 .000 1.377 -.702

Growth .091 34.816 1 348 .000 .807 .095

Exponential .091 34.816 1 348 .000 2.241 .095

Logistic .091 34.816 1 348 .000 .446 .910

Dependent Variable: Low costs

Independent Variable: Performance quality

64

Figure 3.1 Linearity test

The author has randomly picked two variables (low cost and performance quality)

to verify the Linearity of the data. The above table shows that the R2 value is 0.091,

which is high enough to denote that the data are linear.

3.9.5 Homogeneity

Homogeneity of data is a prerequisite to applying any statistical technique in all

research. Homogeneous data can be obtained from a unique population. Usually some

outside forces will affect, change and abrupt the data while carrying the research. Frankly

speaking maintaining homogeneity data is critical one. To test the homogeneity of the

data set, the author used one demographic variable of Nature of product dealt by the

enterprise and BEC domain.

Table 3.14 Test of Homogeneity of Variances for BEC domain

SL.

No

Variables Levene

Statistic

df

1 df2 Sig.

Business cost

1 Mounting labor cost 0.514 1 348 0.474

2 Mounting material cost 1.109 1 348 0.293

3 Mounting transportation cost 3.44 1 348 0.064

4 Mounting utility cost 0.777 1 348 0.379

5 Mounting rent 3.118 1 348 0.078

65

Table 3.14 Test of Homogeneity of Variances for BEC domain (continued)

SL.

No

Variables Levene

Statistic

df

1 df2 Sig.

Labour availability

6 Scarcity of managerial Personnel 0.526 1 348 0.469

7 Dearth of technicians 1.654 1 348 0.199

8 Deficiency of clerical Personnel 2.175 1 348 0.141

9 Scarcity of skilled and Specialized Personnel 1.326 1 348 0.25

10 Shortage of Direct Labour 2.455 1 348 0.118


11 Stumpy profit margins 0.034 1 348 0.854

12 Dilapidating Local demand 3.118 1 348 0.078

13 Dilapidating International demand 0.101 1 348 0.751

14 Dilapidating Product Standards 0.722 1 348 0.396

15 Dilapidating Quality of Acquired Inputs 0.34 1 348 0.56


16 Complex governmental regulations and

procedures 0.062 1 348 0.804

17 Ambiguous government laws and regulations 0.721 1 348 0.396

18 Red Tapism and Delays 0.785 1 348 0.376

19 Government‘s protectionism Policy Towards

Industries 0.025 1 348 0.874


20 Country balance of payment status 0.733 1 348 0.392

21 Bilateral and Multi-lateral Governmental

Agreements 1.654 1 348 0.199

22 Nation‘s Political Stability 2.91 1 348 0.089

23 Regulatory Mechanism for Protecting

Investments 1.409 1 348 0.236

24 Military Coalitions with fellow countries 2.51 1 348 0.114

Dynamism

25 Tempo of innovative operations processes 3.055 1 348 0.071

26 Changing customer Aspirations in the industry 3.863 1 348 0.05

27 Emerging challenges from competitors 2.85 1 348 0.09

28 Rate of information diffusion 2.978 1 348 0.085

The above table displays that, using Levene Statistic, there is no significant

relationship among all the factors studied. It can be seen that the value of significance in

respect of the demographic variable of Nature of Product dealt by the enterprise and BEC

domain is more than 0.05. This shows that there is no element of homogeneity present in

the data set. Hence, it can be said that the data are in Heterogeneous form.

66

Table 3.15 Test of Homogeneity of Variances for AMT domains

SL.

No

Variables Levene

Statistic df1 df2 Sig.

Implementation AMT

1 Planning 1.756 1 348 0.186

2 Requirement Analysis 1.203 1 348 0.273

3 Cost/Benefit Analysis 1.001 1 348 0.318

4 Technology assessment 1.123 1 348 0.255

5 Development and Implementation 0.268 1 348 0.605

6 Training 3.619 1 348 0.055

Direct AMT

7 Computer numerical control (CNC) machines 1.231 1 348 0.268

8 Robotics (Ro) 3.629 1 348 0.058

9 Flexible manufacturing system (FMS) 0.511 1 348 0.475

10 Automated material handling systems (AMHS 0.032 1 348 0.858

11 Automated guided vehicles (AGV) 3.151 1 348 0.077

12 Rapid prototyping (RP) 0.002 1 348 0.963

Indirect AMT

13 Computer aided design (CAD) 2.725 1 348 0.1

14 Material requirement planning (MRP) 1.374 1 348 0.242

15 Statistical process control (SPC) 2.582 1 348 0.109

16 Bar coding (BC) 1.347 1 348 0.247

17 Material resource planning (MRPII) 0.285 1 348 0.594

The above table shows that Levene Statistic is not significant among the factors as

the significance value in respect of the demographic variable of Nature of product dealt

by the enterprise and AMT exceeds 0.05, suggesting absence of homogeneity in the data

set.

Table 3.16 Test of Homogeneity of Variances for CP domains

SL.

No

Variables Levene


Quality

1 Performance quality 1.732 1 348 0.189

2 Product Reliability 2.093 1 348 0.149

3 Environmental aspect 0.392 1 348 0.532

4 Certification 2.087 1 348 0.149

5 Product durability 0.369 1 348 0.544

Cost

6 Low costs 0.206 1 348 0.65

7 Value added costs 3.629 1 348 0.058

8 Quality costs 1.08 1 348 0.299

9 Activity based measurement 2.095 1 348 0.149

67

Table 3.16 Test of Homogeneity of Variances for CP domains (continued)

SL.

No

Variables Levene


Delivery

10 Fast delivery 0.385 1 348 0.535

11 On time delivery 0.17 1 348 0.68

12 Right quality 0 1 348 0.987

13 Right amount 1.216 1 348 0.271

14 Dependable promises 0.166 1 348 0.684

Flexibility

15 Design adjustments 3.126 1 348 0.062

16 Volume change 1.093 1 348 0.297

17 Product Mix changes 0.514 1 348 0.474

18 Broad product line 3.719 1 348 0.055

Customer focus

19 After sales service 0.603 1 348 0.438

20 Product customization 0.866 1 348 0.353

21 Customer information 3.619 1 348 0.052

22 Measurement of satisfaction 2.751 1 348 0.098

Know how

23 Knowledge management 0.616 1 348 0.433

24 Creativity 0.008 1 348 0.928

25 Continuous learning 0.005 1 348 0.945

26 Problem solving skills 1.984 1 348 0.16

27 Training/education 2.169 1 348 0.142

28 R&D 0.088 1 348 0.766

The above table shows that Levene Statistic is not significant among the factors

and the value of significance in respect of the demographic variable of Nature of Product

dealt by the enterprise and CP domain exceed the 0.05 mark, suggesting the absence of

homogeneity in the data set.

Table 3.17 Test of Homogeneity of Variances for BP domain

SL.

No

Variables Levene


1 Market share 3.111 1 348 0.063

2 Sales growth 3.752 1 348 0.054

3 Profit margin 3.719 1 348 0.055

4 Return on assets (ROA) 0.094 1 348 0.759

5 Return on investment (ROI) 0.462 1 348 0.497

68

It can be observed from the above table that the Levene Statistic is not significant among the factors. This suggests the

absence of homogeneity among the data. Hence, the data available can be used for statistical analysis.

3.9.6 Multicollinearity

Multicollinearity presents if two or more independent variables assess the same thing. Tabachnick & Fidell (2007)

suggested that the correlation values exceeds 0.90 in respect of variables in the same data set, can cause statistical problems

and such variables should be dropped from the study.

Table 3.18 Correlations for Business Environment Characteristics domain

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

1 1.00

2 0.37 1.00

3 0.50 0.50 1.00

4 0.42 0.57 0.56 1.00

5 0.50 0.65 0.59 0.60 1.00

6 0.29 0.18 0.20 0.24 0.23 1.00

7 0.35 0.23 0.28 0.25 0.26 0.67 1.00

8 0.27 0.16 0.21 0.24 0.25 0.68 0.65 1.00

9 0.30 0.20 0.19 0.21 0.21 0.62 0.65 0.68 1.00

10 0.29 0.11 0.13 0.20 0.16 0.63 0.60 0.66 0.68 1.00

11 0.14 0.38 0.30 0.33 0.32 0.13 0.13 0.04 0.06 0.05 1.00

12 0.23 0.37 0.34 0.35 0.31 0.14 0.16 0.08 0.06 0.06 0.70 1.00

13 0.16 0.32 0.31 0.31 0.33 0.16 0.11 0.05 0.06 0.07 0.73 0.69 1.00

14 0.17 0.30 0.35 0.30 0.28 0.10 0.09 0.07 0.06 0.09 0.53 0.61 0.58 1.00

15 0.17 0.25 0.30 0.23 0.30 0.18 0.18 0.08 0.08 0.09 0.59 0.62 0.66 0.60 1.00

16 0.21 0.08 0.10 0.12 0.03 0.39 0.38 0.34 0.39 0.44 0.05 0.00 0.02 0.07 0.12 1.00

17 0.15 0.17 0.15 0.14 0.12 0.41 0.43 0.40 0.48 0.47 0.08 0.05 0.05 0.12 0.20 0.63 1.00

18 0.20 0.06 0.09 0.06 0.04 0.45 0.40 0.39 0.50 0.46 -.01 -.02 -.01 0.05 0.06 0.63 0.69 1.00

19 0.07 0.06 0.04 0.05 -.02 0.30 0.31 0.33 0.33 0.36 -.03 -.06 -.08 0.01 0.04 0.53 0.57 0.61 1.00

20 0.06 0.11 0.10 0.18 0.09 0.32 0.25 0.34 0.32 0.39 0.05 0.02 0.03 0.10 0.08 0.32 0.40 0.41 0.41 1.00

21 0.09 -.04 0.02 0.06 0.01 0.33 0.28 0.36 0.27 0.42 -.03 -.02 -.05 0.03 0.04 0.42 0.42 0.47 0.49 0.63 1.00

22 0.12 0.00 -.01 0.02 -.07 0.33 0.30 0.36 0.37 0.37 -.09 -.06 -.10 0.05 0.00 0.44 0.40 0.45 0.44 0.60 0.62 1.00

23 0.07 -.09 -.02 0.00 -.13 0.30 0.25 0.37 0.28 0.39 -.10 -.03 -.09 0.01 0.06 0.42 0.36 0.45 0.51 0.53 0.68 0.66 1.00

24 0.05 0.00 -.02 0.04 0.00 0.36 0.28 0.38 0.34 0.43 -.06 -.03 -.03 0.05 0.04 0.34 0.39 0.43 0.40 0.60 0.63 0.63 0.59 1.00

25 0.17 0.26 0.32 0.30 0.33 0.12 0.09 0.12 0.09 0.09 0.10 0.18 0.04 0.14 0.10 0.22 0.11 0.16 0.13 0.25 0.18 0.13 0.08 0.09 1.00

26 0.39 0.40 0.45 0.42 0.42 0.21 0.20 0.17 0.20 0.18 0.22 0.31 0.20 0.23 0.13 0.24 0.15 0.18 0.08 0.23 0.16 0.15 0.11 0.11 0.44 1.00

27 0.35 0.45 0.47 0.38 0.38 0.20 0.20 0.13 0.15 0.18 0.20 0.30 0.23 0.22 0.19 0.23 0.13 0.16 0.09 0.25 0.14 0.14 0.08 0.13 0.49 0.69 1.00

28 0.29 0.44 0.43 0.47 0.45 0.17 0.14 0.13 0.09 0.15 0.24 0.30 0.23 0.22 0.20 0.11 0.09 0.10 0.05 0.19 0.14 0.06 0.07 0.11 0.41 0.67 0.65 1.00

69

Variable

code

Variable Variable

code

Variable Variable

code

Variable

1 Mounting labor cost 11 Stumpy profit margins 20

Country balance of payment

status

2 Mounting material cost 12 Dilapidating Local demand 21

Bilateral and Multi-lateral

Governmental Agreements

3 Mounting transportation

cost 13

Dilapidating International

demand 22 Nation‘s Political Stability

4 Mounting utility cost 14

Dilapidating Product

Standards 23

Regulatory Mechanism for

Protecting Investments

5 Mounting rent 15

Dilapidating Quality of

Acquired Inputs 24

Military Coalitions with

fellow countries

6 Scarcity of managerial

Personnel 16

Complex governmental

regulations and procedures 25

Tempo of innovative

operations processes

7 Dearth of technicians 17

Ambiguous government

laws and regulations 26

Changing customer

Aspirations in the industry

8 Deficiency of clerical

Personnel 18 Red Tapism and Delays 27

Emerging challenges from

competitors

9 Scarcity of skilled and

Specialized Personnel 19

Government‘s protectionism

Policy Towards Industries 28 Rate of information diffusion

10 Shortage of Direct Labour

It can be observed from the above table that the correlation values in respect of all the variables does not exceed the

prescribed value of 0.90, and hence, it can be concluded that there is no multicollinearuty problems in the data.

70

Table 3.19 Correlations for Advance Manufacturing Technology

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 1.00

2 0.61 1.00

3 0.56 0.68 1.00

4 0.51 0.60 0.63 1.00

5 0.50 0.63 0.65 0.66 1.00

6 0.44 0.54 0.55 0.60 0.63 1.00

7 0.21 0.16 0.12 0.18 0.18 0.10 1.00

8 0.04 0.01 -.02 0.03 -.08 -.04 0.51 1.00

9 0.16 0.08 0.05 0.13 0.06 0.07 0.55 0.57 1.00

10 0.12 0.09 0.04 0.10 0.10 0.09 0.49 0.61 0.68 1.00

11 0.06 0.04 -.03 0.04 0.04 0.00 0.56 0.63 0.65 0.71 1.00

12 0.00 0.01 0.00 0.05 0.03 0.02 0.56 0.58 0.51 0.57 0.62 1.00

13 0.23 0.02 0.05 0.10 0.03 0.01 0.48 0.45 0.54 0.52 0.44 0.42 1.00

14 0.12 0.11 0.03 0.15 0.10 0.09 0.47 0.46 0.51 0.52 0.46 0.45 0.67 1.00

15 0.06 -.04 -.02 0.06 -.01 0.05 0.49 0.49 0.56 0.55 0.50 0.50 0.70 0.77 1.00

16 0.16 0.06 0.03 0.12 0.05 0.05 0.47 0.51 0.60 0.52 0.50 0.43 0.62 0.69 0.73 1.00

17 0.14 0.09 0.05 0.14 0.03 0.05 0.42 0.44 0.54 0.51 0.45 0.44 0.61 0.71 0.69 0.69 1.00

Variable

code

Variable Variable

code

Variable

1 Planning 10 Automated material handling systems (AMHS)

2 Requirement Analysis 11 Automated guided vehicles (AGV)

3 Cost/Benefit Analysis 12 Rapid prototyping (RP)

4 Technology assessment 13 Computer aided design (CAD)

5 Development and Implementation 14 Material requirement planning (MRP)

6 Training 15 Statistical process control (SPC)

7 Computer numerical control (CNC) machines 16 Bar coding (BC)

8 Robotics (Ro) 17 Material resource planning (MRPII)

9 Flexible manufacturing system (FMS)

It can be observed from the above table that the correlation values in respect of all the variables does not exceed the

prescribed value of 0.90, and hence, it can be concluded that there is no multicollinearuty problems in the data.

71

Table 3.20 Correlations for Competitive Priority domains 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

1 1.00 2 0.72 1.00

3 0.53 0.61 1.00

4 0.54 0.61 0.53 1.00 5 0.52 0.61 0.58 0.65 1.00

6 0.31 0.30 0.27 0.25 0.33 1.00

7 0.41 0.39 0.32 0.35 0.35 0.63 1.00 8 0.35 0.38 0.32 0.38 0.41 0.53 0.57 1.00

9 0.39 0.41 0.36 0.38 0.39 0.47 0.59 0.52 1.00

10 0.39 0.41 0.38 0.40 0.41 0.39 0.34 0.38 0.41 1.00 11 0.42 0.46 0.39 0.43 0.45 0.32 0.32 0.30 0.40 0.67 1.00

12 0.48 0.49 0.43 0.45 0.44 0.33 0.33 0.33 0.43 0.54 0.61 1.00

13 0.44 0.42 0.40 0.40 0.48 0.33 0.34 0.33 0.44 0.53 0.64 0.68 1.00 14 0.43 0.46 0.40 0.42 0.44 0.31 0.33 0.33 0.31 0.47 0.52 0.54 0.59 1.00

15 0.23 0.30 0.28 0.23 0.25 0.25 0.29 0.31 0.33 0.19 0.28 0.27 0.33 0.32 1.00

16 0.35 0.31 0.31 0.27 0.31 0.37 0.36 0.36 0.39 0.32 0.37 0.34 0.40 0.34 0.50 1.00 17 0.30 0.24 0.23 0.22 0.23 0.29 0.24 0.31 0.31 0.33 0.32 0.30 0.36 0.31 0.49 0.62 1.00

18 0.28 0.25 0.31 0.23 0.25 0.24 0.34 0.31 0.29 0.21 0.25 0.23 0.28 0.30 0.46 0.56 0.42 1.00

19 0.19 0.27 0.20 0.27 0.23 0.26 0.29 0.29 0.28 0.31 0.25 0.21 0.28 0.27 0.21 0.18 0.20 0.16 1.00 20 0.18 0.34 0.26 0.26 0.18 0.24 0.30 0.26 0.25 0.27 0.18 0.20 0.19 0.23 0.31 0.20 0.14 0.20 0.51 1.00

21 0.23 0.34 0.28 0.33 0.27 0.22 0.28 0.29 0.35 0.27 0.20 0.22 0.24 0.18 0.28 0.19 0.20 0.17 0.46 0.66 1.00 22 0.22 0.32 0.29 0.34 0.32 0.25 0.31 0.30 0.34 0.37 0.25 0.29 0.24 0.22 0.22 0.20 0.16 0.18 0.47 0.68 0.69 1.00

23 0.32 0.37 0.39 0.37 0.37 0.40 0.37 0.39 0.40 0.41 0.44 0.35 0.36 0.37 0.36 0.36 0.31 0.23 0.31 0.36 0.33 0.35 1.00

24 0.32 0.39 0.37 0.42 0.41 0.28 0.35 0.32 0.39 0.40 0.45 0.45 0.42 0.33 0.37 0.35 0.27 0.31 0.31 0.35 0.37 0.41 0.72 1.00 25 0.34 0.39 0.39 0.38 0.37 0.32 0.32 0.33 0.39 0.40 0.39 0.42 0.38 0.40 0.36 0.40 0.32 0.31 0.27 0.32 0.29 0.32 0.65 0.70 1.00

26 0.31 0.31 0.31 0.39 0.37 0.32 0.35 0.33 0.38 0.32 0.37 0.34 0.37 0.33 0.36 0.30 0.26 0.29 0.31 0.28 0.29 0.30 0.62 0.70 0.73 1.00

27 0.32 0.34 0.31 0.32 0.35 0.28 0.38 0.36 0.38 0.31 0.34 0.35 0.38 0.39 0.40 0.42 0.33 0.29 0.24 0.32 0.25 0.30 0.58 0.59 0.73 0.67 1.00 28 0.21 0.24 0.30 0.26 0.32 0.27 0.31 0.27 0.32 0.31 0.31 0.32 0.31 0.24 0.29 0.34 0.18 0.24 0.19 0.24 0.23 0.27 0.49 0.67 0.59 0.61 0.64 1.00

72

Variable

code

Variable Variable

code

Variable

1 Performance quality 15 Design adjustments

2 Product Reliability 16 Volume change

3 Environmental aspect 17 Product Mix changes

4 Certification 18 Broad product line

5 Product durability 19 After sales service

6 Low costs 20 Product customization

7 Value added costs 21 Customer information

8 Quality costs 22 Measurement of satisfaction

9 Activity based measurement 23 Knowledge management

10 Fast delivery 24 Creativity

11 On time delivery 25 Continuous learning

12 Right quality 26 Problem solving skills

13 Right amount 27 Training/education

14 Dependable promises 28 R&D

It can be observed from the above table that the correlation values in respect of all

the variables does not exceed the prescribed value of 0.90, and hence, it can be concluded

that there is no multicollinearuty problems in the data.

Table 3.21 Correlations for Business Performance domain

Items Market

share

Sales

growth

Profit

margin

Return

on assets

(ROA)

Return on

investment

(ROI)

Market share 1.00

Sales growth 0.78 1.00

Profit margin 0.73 0.80 1.00

Return on assets (ROA) 0.56 0.67 0.69 1.00

Return on investment (ROI) 0.70 0.73 0.76 0.71 1.00

It can be observed from the above table that the correlation values in respect of all

the variables does not exceed the prescribed value of 0.90, and hence, it can be concluded

that there is no multicollinearuty problems in the data.

It can be observed from the aforesaid discussion that the data available is

absolutely qualitative to proceed to the next step of analysis to yield reliable results.

Hence, the data screening process has concluded successfully and the data is ready to be

used for further analysis, especially multivariate analysis.

73

Hence, it can be concluded that the research instrument used for this study and the data collected using the same is

absolutely reliable and valid. The blue print of the entire study is depicted in the following flow chart.

3.10 Blueprint of the Proposed Study

A snapshot of the entire study is portrayed in the following flow chart.

Figure 3.1 Blueprint of the Proposed Study

chapter-3 research methodology 3.1 introduction 3.2...

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