CHAPTER IV: DATA COLLECTION AND ANALYSIS

“Ishikawa’s biggest contribution is in simplifying statistical techniques for quality control in

industry. At the simplest technical level, his work has emphasised good data collection and presentation.” - Samuel K.M.Ho (100, p.35)

_______________________________________________

The Research Design and Methodology for data collection was described in

previous chapter. This chapter now presents the data collection and data analysis.

The complete chapter is divided into three sections. The first part of section 4.2

describes the organizational and quality profile of respondents. The separate analysis is

presented for the responses of random sample (SMEs) and purposive sample (QA

winning industries) with an attempt to highlight the differences of quality practices and

organizational culture.

The second part presents the assessment for scale stability prior to main

empirical analysis. This part provides factor analysis, reliability & validity analysis, chi-

square test of independence, and cogeneric model (composite score of all constructs)

for both SMEs and Large enterprises. To obtain more indicative inferences, the group of

SME is divided further into ISO practicing SMEs and TQM practicing. Thus, the

comparative analysis is obtained for three groups: Group I (only ISO certified firms, not

implemented any formal TQM programme); Group II (ISO certified firms, have

implemented formal TQM programme and continuing there efforts to bag the national

Quality Awards, and Group III (industries won the national Quality Awards)

The third and final part, as presented in Section 4.3, provides the analysis for

several hypotheses, based on research objectives of this study. The first research

question (R1) addressing the relationship between quality practices and performance

measures, is empirically investigated by testing the three hypotheses (H1-1) (H1-2) and

(H1-3). The second research question (R2) focusing the length of time with quality

adoption in industries and its impact on manufacturing practices and performance, has

been investigated by two hypotheses (H2-1) and (H2-2) respectively. The third research

question (R3) of this study, was intended to investigate the impact of different

approaches of quality adoption (Such as TQM before ISO 9000 and vice versa) in

SMEs, and investigations were carried by testing one hypothesis (H3-1). The fourth

research question (R4) was related to provide the empirical investigation between ISO

9000 and TQM, which quality practice is highly significant to winning the Quality Award,

and hypothesis (H4-1) provides the outcomes. The fifth research question (R5) of this

research, was intended to investigate the effect of other factors (such as age of firm,

size of firm, annual sales turnover, and type of manufacturing) on performance. Four

hypotheses (H5-1 to H5-4) were tested to obtain the empirical relationship. The last

research question (R6) was associated with the investigations for performance

indicators and there relationship with quality practices, was analyzed through two

hypotheses (H6-1) and (H6-2). The analysis for two open ended questions is also

presented in this section.

4.1 Data Collection:

This study shows the satisfactory response rate of 37.28 % (261 from 700)

from random sample and 26.66 % (40 from 150) from purposive sample. Regarding the

analysis according to the method of data collection, 80 % (209 from 261) respondents

from random sample responded to postal survey, while remaining preferred an e-mail to

respond. From the purposive sample of QA winning firms, the 37% (15 out of 40)

respondents preferred an e-mail; while 63 % (25 from 40) responded via postal survey.

Table 4.1 provides the summary of data collection.

Psycholgical studies have mentioned about the cognitive bias as the prime

source of errors in judgment, social attribution, and memory, which are common

outcome of human thought, and often drastically skew the reliability of anecdotal and

legal evidence [170]

After examining the collected data, it was observed few responses were prone to

cognitive bias (defined as the human tendency to make systematic errors in certain

circumstances based on cognitive factors rather than evidence) and heuristics in nature

(based on information-processing shortcuts) [70]. After excluding all inappropriate

responses, this study finally considered 189 responses from SMEs and 40 responses

from QA winning firms, as valid and appropriate one.

After retaining those 229 appropriate responses, this study provides a common

response rate of 27%, for both random and purposive sample. It was observed further

that all 40 respondents from purposive sample (QA winning firms) have responded

appropriately to the questionnaire, which reflects their sincere approach while

responding to this study.

Table 4.1: Analysis for responses (Random and Purposive sample)

Postal Survey Internet Survey (via E- Mail)

Total

Targeted Population for SMEs : 700 Manufacturing units Targeted Population for QA winning firms : 150 Manufacturing units

Response Received : Total Response 261 92 353 Invalid Response 85

(32.60%) 39 (42.39 %)

124

Total Valid Response

176 (76.85%)

53 (57.60%)

229

Response from Random sample of ISO certified SMEs

151 (79.90%)

38 (20.10%)

189

Response from Purposive sample of Quality Award winning Industries

25/40 (62.50%)

15/40 (37.5%)

40

Valid Response rate from overall population: From ISO certified SMEs: 189 out of 700 (27%)

From QA wining firms: 40 out of 150 (27%)

This study also represents the responses from all sectors of manufacturing. Table 4.2

provides the sector wise response from the central India-wide population, to indicate the

highest respondents (22.90%) were from mechanical based industries, followed by

Automobile industries (17.00%) and other sectors of manufacturing.

To identify the demographic response rate, another analysis is carried further

and details of the same are presented in Table 4.3. The analysis has represented that

around 67% (152 from 229) respondents of this study were from Maharashtra state,

followed by 9.60 % (22 from 229) from Madhya Pradesh, and other regions of central

India.

Table 4.2: Responses from different Manufacturing Sectors of central India –wide Population

SN Area of Manufacturing SMEs Large

Enter.

Respondents Percentage

1 Mechanical and Machine tools

Industries

47 06 53 22.90

2 Automobile and Auto Component

Industries

27 12 39 17.00

3 Steel and other Metal Industries 24 09 33 14.50

4 Electrical and Electronics Industries 27 04 31 13.60

4 Chemical Industries 20 06 26 11.40

5 Textile Industries 16 03 19 08.30

6 Plastic Industries 16 -- 16 07.00

7 Other Industries (Ancillary Units) 12 -- 12 05.30

TOTAL 189 40 229 100.00

Table 4.3: Demographic response received for the study (only valid responses)

SN State SMEs Large

Enterprises

Percent

1 Maharashtra 123 29 66.37%

2 Goa -- 01 00.44%

3 Chattisgarh 10 01 04.80%

4 Madhya Pradesh 22 -- 09.60%

5 Gujarat 16 02 07.86%

6 Andhra Pradesh -- 01 00.44%

7 Tamilnadu 07 02 03.93%

8 Karnataka 02 01 01.31%

9 Delhi 09 03 05.24%

TOTAL 189 40

4.2 Organizational Profile of Respondents

The questionnaire of this study contained the first part as respondent’s profile,

and the second part as research instrument.

The analysis of respondents profile has indicated that all respondents from

random sample were from SMEs while all forty respondents of QA winning firms

were belonging to large enterprise. This categorization was made on the basis of

eligibility criteria defined by BIS to participate in assessment process of Business

Excellence Model, RGNQA [193] and also prescribed in other national quality award

model GPNQA [76]. They defined SMEs are those units which are having less than

250 employees in their firm, and annual turnover not exceeding 300 Crore INR.

Regarding quality practices in SMEs, it was observed further that among 189

respondents only 60 respondents have mentioned about using the formal TQM

programme in their firms, while remaining 129 respondents were only ISO certified

and haven’t implemented any formal TQM programme in their organization. This

analysis has provided that there are two groups of quality practioner in SMEs, The

first group implemented formal TQM programme in their firm, and the second one

haven’t implemented any formal TQM programme in their firm. It was further

indicative that later group in the absence of formal TQM programme is still

continuing the principles of ISO 9000 certification for continual improvement in their

organization.

The TQM practicing SMEs also mentioned about following the Quality Award

Model guidelines of RGNQA and other popular national quality award models such

as GPNQA, IMC RBNQA, and CII EXIM Bank Award. Table 4.4 provides the details

of all 189 SMEs, representing age of firm, size of firm, annual sales turnover and

year with ISO certification. It is indicative from the table that, all manufacturing

organizations were having less than 250 employees and annual turnover around 100

Crore INR.

The second part of questionnaire as mentioned earlier, contained the

research instrument in two halves, the first part of 59 questions to collect the

information for quality practices in respondents firm and the second part containing

the attributes of 24 performance measures. The respondents’ weight age (means) to

these performance measures is presented in Appendix 4.1, to pertain the

respondents view regarding impact of certification on performance prior to obtaining

the empirical investigations. The table shows that the respondents of ISO certified

SMEs, view certification to mainly to improve the order processing (Mean =3.47) and

to reduce the cost for inspection (Mean =3.44). However, they all shown

disagreement with the statement that ISO certification helps in improving the market

share by allotting lowest weight (Mean = 3.14) and to another statement of ISO

certification increases the exports (Mean= 3.22) and provides product innovation

(Mean =3.20). The respondents view regarding impact of certification for

improvement in market shares and product innovation is acceptable to certain

extent, but their negative response towards reduction in exports have raised a

substantial question about the utility of ISO certification, since globally this

certification is recognised as “passport to exports” [236]

Table 4.4: Organization Profile of SMEs

N Minimum Maximum Mean Std. Deviation Variance

Age of Firm 189 5.00 15.00 6.96 2.60 6.765

No. Of Employees 189 100.00 225 137.56 35.73 1276.758

Ann. Sales Turn. In Crore (INR)

189 50 225 99.07 44.60 1989.165

Year with ISO 189 3.00 15.00 7.90 2.24 5.0176

Valid N (list wise) 189

Based on quality practices in SMEs, the SMEs were divided into two groups. Group I

contained those firms practicing only ISO 9000 principles for performance improvement

and the Group II , practicing TQM along with certification for performance

improvement. For the analysis purpose, the Group I is termed as ‘ISO certified SMEs’,

and Group II have been referred as ‘TQM practicing SMEs’. Table 4.5 provides the

organizational profile of two groups of SMEs.

The Descriptive of organizational profile provides the relative difference between

these two groups such as ISO certified SMEs were having only 132 employees in their

firms, and the modest annual sales turnover of 75 Crores INR. On the other hand,

TQM practicing SMEs were having the employee size of 148 in their firm and an annual

sales turnover of 151 Crores INR. The average year with ISO certification in ISO

certified SMEs were 5.5 years, while for TQM practicing firm it was almost double (10.2

years). TQM practicing SMEs also indicated 8.8 years of TQM practices in their firms.

Table 4.5: Organizational Profile of two groups of SMEs

ISO certified SMEs N Minimum Maximum Mean Std. Deviation

Experience (Yrs) 129 5.00 20.00 10.13 4.61

Age of Firm (Yrs) 129 5.00 10.00 6.39 2.25

No. of Employee 129 100 200 132 33.71

Ann Sales Turnover (In Crore INR)

129 50 150 74.81 21.03

Year with ISO 129 3.00 8.00 5.56 2.31

Valid N (list wise) 129 TQM SMEs N Minimum Maximum Mean Std. Deviation

Experience (Yrs) 60 5.00 20.00 12.15 6.00

Age of firm (Yrs) 60 7.00 15.00 8.20 2.88

No. of employee 60 100 225 147.50 38.15

Ann Sales Turnover (In Crore INR)

60 100 225 151.25 36.36

Year with ISO 60 4.00 12.00 10.25 2.17

Year with TQM 60 3.00 12.00 8.82 2.19

Valid N (list wise) 60

The purposive sample of 40 respondents, were from those industries that have

bagged (won) national Quality Award in recent past. While sending the questionnaire to

these respondents, researchers have chosen the sample of 150 Quality Award winning

SMEs from the list of RGNQA, and other three national quality awards, with an objective

to obtain the comparative analysis between ISO certified SMEs and QA winning SMEs.

This list contained the winners from SME category from 2000 to 2009, and after analysis

it was observed that all respondents replied to this study; have bagged these awards in

between 2000-2006. Since, they responded to this questionnaire in 2010, all of them are

now turned into large enterprise (more than 250 employee and annual sales turnover

exceeds 300 Crore INR) .The other details of QA winning firms is presented in Table

4.6, to represent that QA winning firms are having more experienced employees, more

years of establishment, larger employee size and an impressive annual turnover in

comparison with SMEs. Regarding ISO certification, all respondents from this group

have mentioned the year of certification in between 2004 to 2008, which indicated few

firms have gone for certification after receiving the quality award.

Appendix 4.2 also presents the respondents view of impact of QA practices on

24 performance indicators, prior to empirical investigations. The analysis indicates that

QA practices positively influences to achieve customer satisfaction, increase the

exports, and ROI of the firm (all Means =4.60), while little influence was noted for

reduction in rework (Mean =4.25), reduction in personnel expenses, and order

processing and customer services (Mean =4.32)

Table 4.6: Organizational Profile of QA Winning firms

QA Winning Large Enter.

N Minimum

Maximum Mean Std. Deviation

Experience (Yrs) 40 5.00 20 15.5 5.80

Age of Firm (Yrs) 40 10.00 20.00 14.42 2.26

No. Of Employee 40 300.00 600 432 118.37

Annual Sales Turnover (In Crore INR)

40 300.00 900.00 482 129.46

Year with ISO 40 4.00 8.00 6.00 3.08

Year with TQM 40 5.00 15.00 10.00 3.06

Year with QA 40 4.00 10.00 7.35 2.14

Valid N (list wise) 40

4.2.1 Quality Profile of Respondents

ISO certified SMEs (N=129) has represented that 47 firms (36.44%) received

this certification before the year 2005, and 82 firms (63.56%) were certified after 2005.

These figures indicate the trend in growth rate of ISO certification in Indian

SMEs. Appendix 4.3 represents growth rate of ISO certification in Indian SMEs.

About the reasons for attaining the ISO certification, 52 respondents (40 %)

thought the certification for improving the competitiveness and 38 respondents (30 %)

looked certification as a result of pressure from customers. The remaining respondents

thought the certification for obtaining the company prestige and to develop the quality

culture in their organization. Regarding respondent’s position in organization, one third

employee were marketing and HR managers; half were production and works mangers,

and remaining were proprietors of those firms.

The second group of TQM practicing SMEs (N=60) received the ISO 9000

certification in between 1998 to 2006. The respondent’s analysis also indicated an

important point that 27 respondents have implemented TQM before ISO 9000

certification, while 18 of them have implemented TQM after the certification. The

remaining 15 respondents have implemented both TQM and ISO 9000 certification

simultaneously Regarding reason to attain the certification 60% respondents (36 from

60) thought certification for improving the competitiveness, while remaining 40%(24

from 60) favoured the reason for certification to meet the pressure from customers. The

respondent profile also represented their position in firm. One third respondents were

quality managers, while half of them at senior positions such as General Managers/

Senior Managers and CEOs. The remaining respondents were proprietors of those

firms.

The respondents from third group, represented more years with TQM practices

(15 years) and less year with ISO certification (maximum eight years). In responding to

different award received in past, respondents also added the information of other

international quality awards (also known as Business Excellence Models) like EFQM

and JQM, Frost and Sullivan award, Deming Prize etc.

The other awards like Product design awards, Kaizen awards, Best

entrepreneur of the year award etc were also mentioned by them. Table 4.7 provides

the summary of different awards received by these firms. Regarding reason for ISO

certification, 70 % respondents (28 from 40) stated the reason for certification due to

external pressure from customers and stack holders; while remaining mentioned the

reason for certification is to maintain the company prestige.

In contrast to SMEs, three forth respondents from quality awards winning

firms, were CEO and General Managers while remaining were

Production/operation/quality Managers.

Table 4.7: Summary of Awards

Major National Quality Award

International Awards Other Awards Number

of Firms

RGNQA

(N=10)

EFQM Global Brand Award 02 JQM KAIZEN Award 01 Frost & Sullivan Award Engineering Award 03 Dun & Broad Street Award Product design Award 01 Innovation Award (UK) Udyog Ratna 02 Deming Prize Best Entrepreneur of

the Year Award 01

IMC RBNQA

(N=13)

JQM Scope Award for Excellence

01

Dow Jones Award Product Design Award 03 World’s Most Admired Fortune Company Award

TPM Excellence Award 04

National Award for Technology

02

Export Award 01

Deming Prize KAIZEN Award 02

GPNQA

(N=10)

EFQM Best Supplier Award 01 ASME Design Award(USA) Product Design Award 03 Frost & Sullivan Award Best Entrepreneur of

the Year Award 03

Gold Award for Safety 02

Engineering Award 01

CII EXIM BANK AWARD

(N=7)

World’s Most Admired Fortune Company Award

Export Award 02

Innovation Award (UK) Best Supplier Award 02

Scope Award for Excellence

01

National Award for Technology

02

TOTAL 40

Table 4.8 provides the classification of responses form SMEs and large

enterprises, practicing ISO 9000; TQM; and QA Models. Table also provides the

response rate of these firms from different manufacturing sectors.

Table 4.8: Sector wise responses for SMEs and LE practicing ISO 9000, TQM and QA

Models

SN Area of Manufacturing ISO SMEs

TQM SMEs

QA LE

Respondents Percentage

1 Mechanical and Machine tools Industries 34 13 06 53 22.90

2 Automobile and Auto Component

Industries 17 10 12 39 17.00

3 Steel and other Metal Industries 11 13 09 33 14.50

4 Electrical and Electronics Industries 21 06 04 31 13.60

4 Chemical Industries 12 08 06 26 11.40

5 Textile Industries 13 03 03 19 08.30

6 Plastic Industries 15 01 -- 16 07.00

7 Other Industries (Ancillary Units) 06 06 -- 12 05.30

TOTAL 129 60 40 229 100.00

4.3 Validity and Reliability assessment of scale

This section describes the preliminary analysis of 229 valid responses from

SMEs and LE. Prior to testing the several hypotheses of this study, the scale stability

was obtained by performing the reliability and validity assessment.

Reliability refers to the property of a measurement instrument that causes it to

give similar results for similar inputs. Cronbach's alpha [40] is a measure of reliability.

More specifically, alpha is a lower bound for the true reliability of the survey.

Mathematically, reliability is defined as the proportion of the variability in the responses

to the survey that is the result of differences in the respondents. The computation of

Cronbach's alpha [74] is based on the number of items on the survey (k) and the ratio of

the average inter-item covariance to the average item variance, and presented as,

α=k (cov/var) / 1+(k−1) (cov/var) (4.1)

Under the assumption that the item variances are all equal, this ratio simplifies to the

average inter-item correlation, and the result is known as the Standardized item alpha

(or Spearman-Brown stepped-up reliability coefficient) [176]

α = kr/ 1+(k−1) r (4.2)

Validity refers to the degree to which evidence and theory supports the

interpretations of test scores entailed by proposed uses of tests. Although classical

models divided the concept into content validity, criterion validity, and construct validity

the modern view is that validity is a single unitary construct (unidimensionality of scale)

[11]

Content validity refers to the extent to which a measure represents all facets of a

given construct. For example, a depression scale may lack content validity if it only

assesses the affective dimension of depression but fails to take into account the

behavioural dimension. Content validity is also related to face validity, pertains to

whether the test "looks valid" to the examinees who take it. Content validity requires

more rigorous statistical tests than face validity, which only requires an intuitive

judgement [70]

Criterion validity is a measure of how well one variable or set of variables

predicts an outcome based on information from other variables, and will be achieved if

a set of measures from a sample relate to a behavioural criterion on which

psychologists agree. A typical way to achieve this is in relation to the extent to which a

score on a personality test can predict future performance or behaviour [174]

Construct validity refers to whether a scale measures or correlates with the

theorized construct that it purports to measure. The scale seeks to operationalized the

concept, typically measuring several observable phenomena that supposedly reflect

the underlying psychological concept. Construct validity is a means of assessing how

well this has been accomplished. In lay terms, construct validity answers the question:

"Are we actually measuring (are these means a valid form for measuring) what (the

construct) we think we are measuring?” Finally, unidimensionality is a necessary

condition for reliability analysis and construct validation. In the absence of

unidimensionality, a single number cannot be used to represent the value of a scale.

Confirmatory factor analysis is suggested along with Cronbach’s alpha and Goodness-

of-Fit Index to obtain the unidimensionality of scale and to develop cogeneric model

[88,145]

Ahire [11] also proposed convergent and discriminate validity analysis for survey

based questionnaire methodology to administer the scale, aligned with developing the

cogeneric model. Hair et al.[88] , suggests that confirmatory factor analysis along with

KMO and Bartlett’s test of sphericity [128] will provide the convergent validity of scale

(an extent to which construct will yield same result) and cross- tabs with Chi-square test

of independence to examine the discriminant validity(constituent items of scale exhibits

only one construct) [26]

This study has adopted the modern approach of unidimensionality analysis, to

develop the cogeneric model for composite single score of all constructs as proposed

by Ahire et al. [11] for development and validation of QM implementation constructs.

Both convergent and discriminant validity assessments were performed, to yield same

result with single construct of all constituent items of scale.

All five performance measures were treated as “single construct” of quality

performance along with eight constructs of QM practices, leadership; planning;

customer focus; information analysis; people management; process management;

supplier relationship, and employee involvement to obtain the composite score of all

nine constructs. SPSS 15 software (73,220) was used for the analysis.

The scale stability assessment process is further described in three sections.

Section 4.3.1, presents the Data reduction process (item deletion) from the scale by

using factor analysis. Section 4.3.2 provides the composite score of all nine constructs

along with reliability and GFI. Finally, section 4.3.3, describes the measure of

association of scale data with cross- tabs along with chi-square test-of- independence.

Finally, the composite score of all nine constructs was obtained for unidimensionality of

scale for future analysis.

4.3.1 Data Reduction by using Component Factor Analysis

Prior to main analysis, the data reduction process was carried out by using

Principal component factor analysis with varimax. The process was supported by

Kaiser-Meyer-Olkin measure of sampling adequacy, Bartlett’s test of sphericity; and

Scree plots to examine the convergent validity and to check whether the data is

sufficiently correlated [88]

Principal components analysis (factor analysis) is used to obtain the initial factor

solution. It is a factor extraction method used to form uncorrelated linear combinations

of the observed variables. The first component has maximum variance. Successive

components explain progressively smaller portions of the variance and are all

uncorrelated with each other [128]. Factor analysis attempts to identify underlying

variables, or factors, that explain the pattern of correlations within a set of observed

variables. Factor analysis is often used in data reduction to identify a small number of

factors that explain most of the variance that is observed in a much larger number of

manifest variables. In validity assessment, confirmatory factor analysis is the first step

towards assessing the construct validity, and later building the cogeneric model of

composite scores of all constructs (128,176)

A measure has construct validity if it measures the theoretical construct that it

was designed to measure. The construct validity of each construct was evaluated by

factor analysing the measurement items of each of the factors. A rotation procedure,

Varimax, was applied in order to maximize the correlation of each item on a factor.

Varimax is orthogonal rotation method that minimizes the number of variables

that have high loadings on each factor. This method simplifies the interpretation of the

factors. A Scree plot of the variance is associated with each factor, used to determine

how many factors should be kept. Typically the plot shows a distinct break between the

steep slope of the large factors and the gradual trailing of the rest (70,176)

The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was adopted to

identify the proportion of variance in variables due to underlying factors. High KMO

values (close to 1.0) generally indicate that a factor analysis may be useful with data. If

the value is less than 0.50, the results of the factor analysis probably won't be very

useful. Bartlett's test of sphericity tests the correlation matrix is an identity matrix, which

would indicate that variables are unrelated and unsuitable for Cogeneric Model; small

values (P ≤ 0.05) of sphericity indicates that a factor analysis is useful [73]

All factors were loaded group wise to obtain the component matrix for each

constructs. The Eigen values are kept over 1. The summary of component matrix and

loading range of factors along with KMO values for SMEs and LE is presented in Table

4.9. To analyze proportion of responses in the categories conforms to a particular

pattern a goodness of fit test was employed further. It was observed that data is

normally distributed at 99 % of confidence interval. The Histograms for SMEs (ISO

certified firms) and LE (QA winning firms) is shown in Appendix 4.4

Finally component matrix for two groups of SMEs such as ISO certified SMEs

(N=129) and TQM practicing SMEs (N=60) are shown in Table 4.10. The factor plot of

ISO certified SMEs (N=129) is shown in Table 4.11 and Scree plot is shown in figure 4.1

Table 4.9: Summary of Component Matrix of SMEs and LE

(N = 189) ISO certified SMES

Component

Item loading range for

component 1

Eigen values

% variation explained

by component

1

KMO

Bartlett's Test of Sphericity

Lead 0.756-0.818 2.59 64.90 0.80 Χ2

=251.566, dof = 6 , P= 0.00

Plan 0.701-0.811 2.89 57.90 0.79 Χ2=321.759, dof = 10 , P= 0.00

Cust 0.719-0.832 2.53 63.35 0.75 Χ2

=244.620, dof = 6 , P= 0.00

Info 0.779-0.894 2.75 68.79 0.80 Χ

2=315.86, dof = 6 , P= 0.00

Peop 0.741 -0.859 2.52 63.09 0.70 Χ

2=276.035, dof = 6 , P= 0.00

Proc 0.706 -0.816 2.42 60.64 0.70 Χ

2=,231.216 dof = 6 , P= 0.00

Supp 0.870-0.905 2.16 72.17 0.73 Χ2= 185.652, dof = 3 , P= 0.00

Empl 0.803-0.880 2.38 79.35 0.73 Χ

2=279.925, dof = 6 , P= 0.00

Perf 0.690-0.894 2.58 64.50 0.76 Χ2

= 271.556, dof = 6, P= 0.00

(N= 40) Quality Award Winning Industries

Component

Item loading range for

component 1

Eigen values

% variation explained

by component

1

KMO

Bartlett's Test of Sphericity

Lead 0.727-0.844 2.55 63.90 0.60 χ2=44.466, dof = 6 , P= 0.00

Plan 0.665-0.857 2.82 60.71 0.59 χ2=45.197, dof = 6 , P= 0.00

Cust 0.700-0.843 3.05 61.18 0.57 χ

2=66.494, dof = 10 , P= 0.00

Info 0.708-0.788 2.15 53.85 0.61 χ2=25.985, dof = 6 , P= 0.00

Peop 0.620-0.882 2.88 57.68 0.59 Χ2=63.730, dof = 10 , P= 0.00

Proc 0.693-0.756 2.67 57.30 0.58 Χ2=57.099, dof = 10 , P= 0.00

Supp 0.791-0.828 1.95 65.03 0.63 χ2=22.790, dof = 3 , P= 0.00

Empl 0.803-0.902 1.62 81.30 0.58 χ2=8.104, dof = 1 , P= 0.00

Perf 0.754-0.888 4.74 69.25 0.61 χ2=45.091, dof = 6 , P= 0.00

Table 4.10: Summary of Component Matrix of two groups of SMEs

(N = 129) ISO certified SMES

Component

Item loading range for

component 1 Eigen values

% variation explained by component 1

KMO

Bartlett's Test of Sphericity

Lead 0.617-0.812 3.62 51.80 0.80 Χ2 = 195.654 , dof = 6 , P= 0.00

Plan 0.574-0.846 4.84 60.50 0.79 Χ2=286.497, dof = 15 , P= 0.00

Cust 0.644-0.823 3.63 72.80 0.67 Χ2 = 181.869, dof = 10 , P= 0.00

Info 0.779-0.894 2.62 65.67 0.71 Χ

2=184.595, dof = 10 , P= 0.00

Peop 0.648-0.881 3.82 76.03 0.69 Χ

2=233.388, dof = 10 , P= 0.00

Proc 0.637-0.788 4.93 61.62 0.68 Χ

2= 120.111,dof = 15 , P= 0.00

Supp 0.788-0.812 2.56 64.03 0.73 Χ2= 59.427 , dof = 6 , P= 0.00

Empl 0.803-0.880 2.84 71.01 0.68 Χ

2= 97.286 , dof = 6 , P= 0.00

Perf 0.460-0.801 14.92 62.33 0.71 Χ2 = 89.944, dof = 10, P= 0.00

(N= 60) ISO certified and TQM practicing SMEs

Component

Item loading range for

component 1 Eigen values

% variation explained by component 1

KMO

Bartlett's Test of Sphericity

Lead 0.745-0.858 1.94 64.88 0.65 Χ2= 69.189, dof = 15, P= 0.00

Plan 0.627-0.789 2.63 65.88 0.60 Χ2= 41.440 , dof = 6 , P= 0.00

Cust 0.622-0.795 2.08 52.00 0.60 Χ2= 40.392 , dof = 6 , P= 0.00

Info 0.603-0.892 2.49 65.67 0.61 Χ2= 82.797, dof = 6 , P= 0.00

Peop 0.693-0.832 2.20 55.05 0.62 Χ2=46.429, dof = 6 , P= 0.00

Proc 0.777-0.855 4.12 68.90 0.65 Χ2=120.111, dof = 15 , P= 0.00

Supp 0.692-0.833 2.33 58.37 0.63 Χ2=59.427, dof = 6 , P= 0.00

Empl 0.809-0.872 2.74 68.70 0.68 Χ2=97.286, dof = 6 , P= 0.00

Perf 0.707-0.893 3.55 71.04 0.61 Χ2=89.944, dof = 10, P= 0.00

Table 4.11: Component loading on single factor for ISO Certified SMEs (N=129)

Component

Initial Eigen values

Total % of Variance Cumulative %

Lead 5.795 64.391 64.391

Plan .703 7.810 72.201

Cust .548 6.084 78.285

Info .442 4.913 83.198

Peop .416 4.620 87.817

Proc .345 3.836 91.653

Supp .280 3.114 94.767

Empl .250 2.778 97.545

Perf .221 2.455 100.000

Extraction Method: Principal Component Analysis.

987654321

6

5

4

3

2

1

0

Component Number

Eige

nva

lue

Scree Plot of lead, ..., Perf

Figure 4.1: Scree Plot for component loading

4.3.2 Composite Score of all nine constructs

Reliability is frequently defined as the degree of consistency of measures. The

internal consistency of a set of measurement items, therefore, refers to the degree to

which items in the set are homogeneous [74]. Reliability analysis is a correlation-based

procedure and coefficient of alpha is basic measure for this. The Cronbach’s alpha

measuring between the values 0.00 to 1.00 predicts the internal consistency of scale,

and for newly developed scale, the alpha value more than 0.7 is acceptable [170].The

reliability of factors needs to be determined in order to support measures of validity,

and it helps in data reduction or elimination of items on the basis of maximisation of

alpha [40, 201]. Finally, reliability assessment with Goodness-of-Fit-Index provides both

construct validity and convergent validity and also support to develop the Cogeneric

Model by fitting the data to the nearest distribution for future analysis [88].The

unidimensionality (Convergent validity) requires that there be one single latent variable

of underlying the set of measurement items and it is measured by the GFI, the values

near to 0.9 are considered evidence of good fit [26]. Table 4.12, presents the

composite score of all nine constructs for SMEs (N =189), and Quality Award winning

Industries (N=40)

Table 4.12: Composite Score of all nine constructs (For SMEs and LE)

Item (N=189)

Item

Initial

Cronbach’s α initial

Item Final

Cronbach α

Final Mean

Std. Deviation

Variance

GFI

Lead 09 0.61 04 0.81 3.62 0.94 0.88 0.93

Plan 08 0.70 06 0.84 3.31 0.90 0.82 0.92

Cust 09 0.74 05 0.80 3.26 0.94 0.88 0.93

Info 07 0.86 04 0.84 3.17 0.97 0.94 0.96

Peop 10 0.77 05 0.82 3.23 0.96 0.94 0.93

Proc 08 0.74 04 0.78 3.11 0.93 0.86 0.92

Supp 04 0.80 03 0.80 3.06 1.06 1.13 0.91

Empl 04 0.82 03 0.87 3.16 1.12 1.27 0.94

Perf 24 0.74 06 0.84 3.32 0.93 0.86 0.95

Total 83 40

Item (N=40)

Item

initial

Cronbach’s α initial

Item Final

Cronbach α

Final Mean

Std. Deviation

Variance

GFI

Lead 09 0.61 04 0.80 4.52 0.50 0.25 0.96

Plan 08 0.74 04 0.78 4.48 0.53 0.29 0.93

Cust 09 0.74 05 0.83 4.43 0.55 0.33 0.92

Info 07 0.74 04 0.86 4.46 0.45 0.20 0.96

Peop 10 0.77 05 0.84 4.51 0.49 0.24 0.94

Proc 08 0.74 05 0.84 4.50 0.48 0.23 0.95

Supp 04 0.78 03 0.81 4.45 0.51 0.26 0.91

Empl 04 0.75 03 0.84 4.31 0.69 0.48 0.96

Perf 24 0.74 07 0.85 4.46 0.41 0.17 0.95

Total 83 40

To obtain the investigations for two groups of SMEs, Table 4.13 provides the composite

score of all nine constructs of ISO certified SMEs (N=129) and TQM practicing SMEs

(N=60)

Table 4.13: Composite Score of all Nine Constructs for two groups of SMEs

Item (N=129)

Item

initial

Cronbach’s α initial

Item Final

Cronbach α

Final Mean

Std. Deviation

Variance

GFI

Lead 09 0.61 04 0.80 3.4109 .99795 .996 0.93

Plan 08 0.80 06 0.81 3.0510 .86959 .756 0.92

Cust 09 0.74 05 0.76 2.9488 .90079 .811 0.93

Info 07 0.86 04 0.86 2.9347 .86750 .753 0.96

Peop 10 0.77 05 0.80 2.9271 .92396 .854 0.93

Proc 08 0.74 04 0.80 2.8031 .86710 .752 0.92

Supp 04 0.80 03 0.81 2.7390 1.00775 1.016 0.91

Empl 04 0.82 03 0.84 2.8498 1.12508 1.266 0.94

Perf 24 0.74 06 0.83 2.9643 .77005 .593 0.95

Total 83 40

Item (N=60)

Item

initial

Cronbach’s α initial

Item Final

Cronbach α

Final Mean

Std. Deviation

Variance

GFI

Lead 09 0.60 04 0.72 4.0767 .59042 .349 0.96

Plan 08 0.70 06 0.76 3.9458 .60906 .371 0.92

Cust 09 0.54 05 0.72 3.8875 .71371 .509 0.94

Info 07 0.56 04 0.71 3.7667 .77277 .599 0.93

Peop 10 0.67 05 0.71 3.9125 .68554 .470 0.93

Proc 08 0.74 04 0.80 3.8000 .71886 .517 0.94

Supp 04 0. 70 03 0.82 3.7688 .81854 .670 0.93

Empl 04 0.72 03 0.83 3.8500 .78411 .615 0.96

Perf 24 0.64 06 0.76 4.1228 .67469 .455 0.91

Total 83 40

4.3.3 Chi-square tests of Independence

After reliability and validity assessment, the relationship between two or more

categorical variable is summarised by using frequency tables or cross-classifications of

observations. Cross-tabs are employed to test independence and measures of

association and agreement for nominal and ordinal data initially, to predict the

relationship between all quality practices (qual) and performance (perf).

The Cross tabs procedure forms two-way and multiway tables and provides a

variety of tests and measures of association along with tests of independence for

nominal and ordinal data [70,176]. The chi-square test of independence is used to test

whether the variables are independent in population and also examine the discriminant

validity of scale based in chi-square statistics [241]. For the test of independence, a chi-

square probability of less than or equal to 0.05 (or the chi-square statistic being at or

larger than the 0.05 critical point) is commonly interpreted as justification for rejecting

the null hypothesis that the row variable is unrelated (that is, only randomly related) to

the column variable to predict the variables are independent in population, and two

constructs are distinct.

Table 4.14, provides the cross-tabulation, and Table 4.15 represents the chi-square

statistics for three groups of industries.

The cross tabs indicates , that maximum respondents from ISO certified SMEs were

neither disagree nor agree (neutral) about the influence of ISO certification on

performance. However, they were agreed to the statement that ISO certification

practices have medium (moderate) influence on performance. The maximum

respondents from TQM practising SMEs were agreed to the statement that TQM

practices leads to higher performance outcomes.

The maximum respondents from Quality Award winning firms were strongly

agree to the statement that quality award guidelines are congruent with high

performance.

Table 4.14: Qual*Perf Cross Tabulation of three Groups of Industries

Only ISO certified

SMEs (N =129)

Performancea

Total

Little Influence Medium Influence High Influence

Qual

Disagree 22 11 4 37

Neutral 11 30 15 56

Agree 10 14 12 36

Total 43 55 31 129

TQM practicing

SMES ( N=60)

Performancea Total

Little Medium High Very High

Qual

Disagree 1 0 0 0 1

Neutral 0 5 6 1 12

Agree 0 2 27 14 43

Strongly

Agree 0 0 1 3 4

Total 1 7 34 18 60

QA winning

Industries (N=40)

Performancea

Total Medium High Very High

Qual Neutral 0 3 0 3

Agree 1 9 0 10

Strongly

Agree 0 11 16 27

Total 1 23 16 40 a Dependent variable performance

Finally to investigate the independence of all scale items, Pearson’s

chi-square test of independence [174] was conducted and the results are shown in

Table 4.15. The symmetric measures of three groups of industries are shown in

Appendix 4.3, which indicates higher values of Phi- coefficient, Cramer's V, and

Contingency Coefficient (≥ 0.3) for QA winning firms to represent strong association

between quality practices and performance measures.

Table 4.15: Chi-Square Tests of Independence

Value Df Asymp. Sig. (2-sided)

ISO certified SMEs (N =129)

Pearson Chi-Square 95.587(a) 9 0.000

Likelihood Ratio

97.398 9 0.000

Linear-by-Linear Association 65.739 1 0.000

N of Valid Cases 129

TQM practicing SMES ( N=60)

Pearson Chi-Square 77.279(a) 9 0.000

Likelihood Ratio 24.870 9 0.003

Linear-by-Linear Association 18.186 1 0.000

N of Valid Cases 60

QA winning Industries (N=40)

Pearson Chi-Square 14.802(a) 4 0.005

Likelihood Ratio 19.154 4 0.001

Linear-by-Linear Association 10.769 1 0.001

N of Valid Cases 40

a. 7 cells (43.8%) have expected count less than 5. The minimum expected count is .02. b. 12 cells (75.0%) have expected count less than 5. The minimum expected count is .02. c. 6 cells (66.7%) have expected count less than 5. The minimum expected count is .08.

4.3.4 Summary of Validity and Reliability Assessment

The preliminary analysis of validity and reliability provides the inference that

scale is stable, reliable and valid. The summary of factor analysis, Composite score of

all nine constructs along with the results of Chi-square tests of independence is shown

in Table 4.16.

Table 4.16: Summary of Reliability and Validity Assessment of Scale

Method Type of Firm Findings Principal factor Analysis with varimax

ISO certified SMEs (N=129) TQM practicing SMEs (N =60) QA winning Industries (N=40)

All KMO values are > 0.5 and the Bartlett’s test for sphericity (p≤ 0.01) indicates scale maintains the convergent validity [73].The two tests Bartlett’s spherical test and the measurement of sample suitability KMO confirm the suitability of data [70, 88,176]

Composite Score of all constructs

ISO certified SMEs certified SMEs (N=129) TQM practicing SMEs (N =60) QA winning Industries (N=40)

Cronbach’s α ≥ 0.7, shows the scale is reliable and acceptable for further analysis [170]. The GFI values ≥ 0.9 provides evidence of good fit [26].Both α and GFI confirms that scale is statically significant, reliable and valid and provides the construct validity [88]

Chi-square Tests of Independence

ISO certified SMEs (N=129) TQM practicing SMEs (N =60) QA winning Industries (N=40)

The chi-square statistics are significant at p≤ 0.05, indicates that scale provides discriminant validity and variables are not independent from population [155]

The Appendix 3.3 shows the final items in research instrument after deleting the items

during factor analysis. Total 40 items were finalised for SMEs and LE.

4.4 Testing the Hypotheses (Main Analysis)

After data reduction process and primary analysis for scale reliability and validity, all

research questions were addressed through testing of proposed hypotheses. This

section now presents the testing of hypotheses to obtain the empirical findings.

4.4.1 Relationship between Quality Practices and Performance Measures

The first research question (R1) of this study was intended to define the empirical

investigations to obtain the relationship between quality practices and performance

measures. Three hypotheses were proposed to address this research question:

H1-1: There is significant and positive relationship between ISO 9000 quality practices

and performance measure

H1-2: There is significant and positive relationship between TQM practices and

performance measure

H1-3: There is significant and positive relationship between QA Model practices and

performance measure

The literature has indicated that Regression Analysis was mostly preferred by

several researchers [4, 9, 67,125, 217-219, 231-232, and 253 ] to establish the

empirical relationship between quality practices and performance, since it provides both

correlation coefficients and regression coefficients (significant ‘r’ and P value) along with

ANOVA (T / F value) to predict the significance and strength of the model.

Two variables in regression equation are categorised as:

Independent Variables: The proposed eight dimensions of quality practices are

treated as the independent variables for the regression equation. They are:

Leadership (X1), Planning (X2), Customer focus (X3), Information Analysis

(X4), People Management (X5), Process Management (X6), Supplier

Relationship (X7), and Employee Involvement (X8)

Dependent Variable (Y): The Performance Measures of the industries is treated

as dependent variable.

The mathematical representation of the regression equation is as follows:

Y = b0 + b1 X1 + b2 X2 + b3 X3 + b4 X4 + b5 X5 + b6 X6 + b7 X7 + b8 X8 (4.3)

Where,

b0 = Constant, Value of dependent variable when value of

Independent variables are zero

= Also called intercepts, because it determines where the

Regression line meets the Y-axis.

b1…… b8 = Coefficients, that represents the estimated change in

Mean value of dependent variable for each unit

Change in the independent variable values.

The two regression models of SMEs (N = 189) and QA winning large enterprise (N=40) are presented in Table 4.17

Table 4.17: Regression Models of ISO SMEs and QA winning firms

Model

Depended Variable R R Square

Adjusted R Square

SE F ∆F Sig.(P)

1 Perf 0.80(a) 0.642 0.626 0.56895 40.320 .000(a)

2 Perf 0.98(b) 0.964 0.954 0.09038 102.659 0.00(c) Model 1: ISO certified SMEs

a Predictors: (Constant), empl, lead, info, peop, plan, supp, proc, cust

Model 2:QA winning firms b Predictors: (Constant), empl, lead, info, peop, plan, supp, proc, cust

The next Table 4.18, provides the regression coefficients for Independent variables

(quality practices) and dependent variable (performance measures) for SMEs

Table 4.18: Regression coefficients for Quality practices and Performance

Measures of SMEs (N =189)

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

B Std. Error

Beta B Std. Error

1

(Constant) 0.331 0.181 1.826 0.069

Lead 0.090 0.070 0.091 1.286 0.200

Plan 0.195 0.079 0.191 2.481 0.014

Cust 0.178 0.072 0.186 2.482 0.014 Info 0.136 0.079 0.136 1.727 0.086

Peop 0.031 0.083 0.032 0.378 0.706

Proc 0.146 0.065 0.153 2.249 0.026

Supp 0.110 0.066 0.126 1.672 0.096

Empl 0.037 0.061 0.045 .604 0.547 a Dependent Variable: perf

Putting the coefficients from the Table 4.18, the regression equation for the model will be in the following form: Y = 0.331+ 0.090 X1 + 0.195 X2 +0.178 X3 + 0.136 X4 + 0.031 X5 + 0.146 X6 + 0.110 X7+ 0.037 X8 (4.4)

As mentioned previously, based on the quality practices in SMEs; two groups of ISO

certified SMEs and TQM practicing SMEs were empirically investigated to obtain the

difference in relationship of quality practices with performance. The relationship of

quality practices and performance QA winning firms was also investigated with two

groups of SMEs. Table 4.19, presents the regression models for three groups of

industries.

Table 4.19: Regression Models for SMEs and LE

Model Depend. Variable

R R2 Adj. R2 SE F

∆F Sig. (P)

Model I Perf 0.747

(a) 0.559 0.529 0.54204 18.987 0.000(a)

Model II Perf 0.750

(b) 0.563 0.545 0.47987 8.217 0.000(b)

Model III Perf 0.982(c) 0.964 0.954 0.09038 102.659 0.000(c) a. ISO certified SMEs (N =129)

b. TQM practicing SMEs (N=60)

c. Quality Award Winning Industries (N=40)

The results of Regression Analysis for three groups are presented further in Table 4.20

Putting the coefficients from the Table 4.20, the regression equation for three models will be in the following form: Y = 0.706+ 0.146 X1 + 0.106 X2 - 0.135 X3 + 0.218 X4 + 0.130 X5 + 0.116 X6 + 0.116 X7+ 0.061 X8 (4.5) Y = 0.384+ 0.101 X1 + 0.274 X2 + 0.386 X3 - 0.115 X4 + 0.173 X5 + 0.113 X6 - 0.035 X7+ 0.051 X8 (4.6) Y = 0.048+ 0.192 X1 + 0.230 X2 + 0.20 X3 - 0.028 X4 + 0.174 X5 + 0.133 X6 + 0.111 X7- 0.022 X8 (4.7)

Table 4.20: Regression coefficients for Three Models

Model Constructs

Un standardized Coefficients

Standardized Coefficients T Sig.

B Std. Error Beta B Std. Error

1 (Constant) 0.706 0.199 3.539 0.001

Lead 0.146 0.078 0.184 1.875 0.063

Plan 0.106 0.090 0.116 1.178 0.241

Cust -0.135 0.093 -0.154 -1.451 0.150

Info 0.116 0.096 0.124 1.216 0.227

Peop 0.130 0.081 0.153 1.610 0.110

Proc 0.218 0.074 0.258 2.923 0.004

Supp 0.116 0.071 0.148 1.635 0.105

Empl 0.061 0.066 0.086 .922 0.358

2 (Constant) 0.384 0.530 0.725 0.472

Lead 0.101 0.131 0.089 0.774 0.443

Plan 0.274 0.132 0.289 2.076 0.043

Cust 0.386 0.156 0.348 2.480 0.016

Info -0.115 0.104 -0.132 -1.106 0.274

Peop 0.173 0.121 0.176 1.437 0.157

Proc 0.113 0.115 0.121 0.989 0.327

Supp -0.035 0.127 -0.043 -0.278 0.782

Empl 0.051 0.116 0.059 0.438 0.663

3 (Constant) 0.048 0.210 0.231 0.819

Lead 0.192 0.061 0.205 3.130 0.004

Plan 0.230 0.064 0.253 3.577 0.001

Cust 0.200 0.052 0.214 3.850 0.001

Info -0.028 0.045 -0.028 -0.614 0.544

Peop 0.174 0.062 0.182 2.809 0.009

Proc 0.133 0.058 0.155 2.303 0.028

Supp 0.111 0.030 0.186 3.747 0.001

Empl -0.022 0.030 -0.028 -0.747 0.460

a. Dependent Variable: perf

The results and interpretation of above findings is described in Chapter V.

4.4.2 Length of time with quality adoption and impact on manufacturing practices and Performance The second research question of this study was intended to examine the impact of the

length of time with quality adoption, on manufacturing practices and performance.

Two hypotheses were proposed to address the above Research question (R2): H2-1: In SMEs, length of time with TQM implementation is significantly correlated with

Manufacturing practices and performance in SMEs

H2-2: In large firms, length of time with TQM implementation is significantly correlated

with Manufacturing practices and performance

In past Wiele and Brown [236], Agus et al. [7], and Prajogo and Brown [180] used Bi-

variate correlations and Multiple Regression Analysis (MRA) to examine the effect of

TQM implementation years on performance of manufacturing firms. This study also

used Bi-variate analysis and MRA to investigate the effect of length of time with TQM on

performance. However, to compare the outcomes with other typology, year with ISO

9000 certification is also accounted for empirical examination along with TQM year.

The Bi-Variate analysis measures the linear relationship between two

interval/ratio level variables. Pearson's r is also referred to as the Bi-variate correlation

coefficient. Pearson's correlation coefficient between two variables is definedas the

covariance of the two variables divided by the product of their standard deviations:

(4.8) The above formula defines the population correlation coefficient, commonly represented

by the Greek letter ρ (rho). Substituting estimates of the covariances and variances

based on a sample gives the sample correlation coefficient, commonly denoted r :

(4.9)

The absolute value of both the sample and population Pearson correlation

coefficients are less than or equal to 1. Correlations equal to 1 or -1 correspond to data

points lying exactly on a line (in the case of the sample correlation), or to a Bivariate

distribution entirely supported on a line (in the case of the population correlation). The

Pearson correlation coefficient is symmetric: corr(X,Y) = corr(Y,X).The correlation

coefficient ranges from −1 to 1. A value of 1 implies that a linear equation describes the

relationship between X and Y perfectly, with all data points lying on a line for which Y

increases as X increases. A value of −1 implies that all data points lie on a line for which

Y decreases as X increases. A value of 0 implies that there is no linear correlation

between the variables.

MRA is used when; there are more than one dependent variables (Y1, Y2…..)

are likely to be tested for obtaining the relationship with independent variables (X1,

X2….). The present study has accounted two dependent variables, Y1: accounting

manufacturing performance of firms with TQM (with out accounting the year with TQM

implementation) and Y2: accounting manufacturing performance of firm with TQM

(accounting year with TQM implementation)

The figure 4.2 provides the year of ISO 9000 and TQM implementation in SMEs.

The second group of TQM practicing SMEs (N=60) was selected for the analysis, since

first group of ISO certified SMEs haven’t implemented any formal TQM programme in

their firm. The figure represents the trend, that up to 2004, ISO 9000 certification was

implemented by 36 firms (60%) while for the same duration only 27 SMEs (45%) have

implemented the TQM programme. In 2007, same number of firms (15 from 60)

implemented both ISO 9000 and TQM. The trend also represented that in recent years,

TQM implementation remained firm but ISO 9000 implementation has been declined in

SMEs.

Table 4.21 shows the bi-variate analysis of length of time with ISO 9000 and

TQM and Table 4.22 presents the MRA for manufacturing performance of TQM

practising firms. The outcomes of this analysis provided the investigation for first

hypothesis (H2-1)

The trend for ISO 9000 implementation and TQM implementation in QA winning

firms is shown in figure 4.2. It is to be mentioned here, that TQM implementation is

represented as QA guidelines implementations, since these firms have focused the

Business Excellence Models guidelines as a TQM implementation programme.

Figure

4.2: Year of

ISO 9000 and

TQM

implementation in SMEs

Table 4.21: Bi-Variate Analysis for length of time with ISO certification and TQM

Perf Lead Plan Cust Info Peop Proc Supp Empl

ISO 9000 Year

Pearson Correlation

0.47 (**)

0.35 (**)

0.33 (**)

0.25 0.18 0.07 0.08 0.06 .030

Sig. (2-tailed) .000 .006 .010 .054 .149 .564 .515 .644 .821

N 60 60 60 60 60 60 60 60 60

TQM Year Pearson 0.76 0.47 0.48 0.40 0.08 0.19 0.24 0.16 0.30

Correlation (**) (**) (**) (**) (*)

Sig. (2-tailed) .000 .000 .000 .001 .537 .143 .050 .214 .020

N 60 60 60 60 60 60 60 60 60

** Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed).

Table 4.22: MRA for TQM and Performance

Model R R Square Adjusted R Square

Std. Error of the Estimate

F

∆ F sig.

TQM firms

a

0.75 (a) 0.56 0.54 0.47 8.21 0.000(a)

TQM firms

b 0.86(b) 0.74 0.73 0.35 80.946 0.000(b)

N=60

a Predictors: (Constant), Mfg. Perf of TQM firm b Predictors: (Constant), Mfg. Perf of TQM firm with the year of implementation being accounted

The figure 4.3 presents the ISO 9000 implementation and QA guidelines (TQM)

implementation in LE. It was observed that initiation for both quality practices were

started during 2000. Up to 2002, the trend has indicated more focus on implementation

of ISO certification. The implementation of Quality Award guidelines has shown gradual

increase from 2000 to 2006, and after 2003 these enterprises preferred TQM

implementation (QA guidelines) over ISO 9000 certification.

Implementation of QP

0

2

4

6

8

10

12

14

16

18

2000 2002 2004 2006 Year

ISO cert QA Guide

No of Firms

Figure 4.3: Implementation of quality practices in QA winning firms

To examine the correlation between manufacturing practices and performance of

QA winning large enterprises, bi-variate analysis was carried out. Table 4.23 provides

the analysis for manufacturing practices and performance.

Similar to previous hypothesis, Bi-variate analysis was employed again by accounting

length of time with ISO certification and QA guidelines to test the hypothesis (H2-2) and

the results are shown in Table 4.24

Table 4.23: Bi-variate analysis for Manufacturing Practices and Performance In QA winning firms

Perf Lead Plan Cust Info Peop Proc Supp Empl

Perf Pearson Correlation 1

Lead Pearson Correlation

.858 (**)

1

Sig. (2-tailed) .000

Plan Pearson Correlation

0.87 (**)

.80 (**)

1

Sig. (2-tailed) .000 .000

Cust Pearson Correlation

0.76 (**)

0.67 (**)

0.66 (**)

1

Sig. (2-tailed) .000 .000 .000

Info Pearson Correlation

0.49 (**)

0.47 (**)

.49 (**)

0.65 (**)

1

Sig. (2-tailed) .001 .002 .001 .000

Peop Pearson Correlation

0.85 (**)

0.65 (**)

0.70 (**)

0.55 (**)

.359 (*)

1

Sig. (2-tailed) .000 .000 .000 .000 .023

Proc Pearson Correlation

0.85 (**)

0.67 (**)

0.76 (**)

0.57 (**)

.341 (*)

0.78 (**)

1

Sig. (2-tailed) .000 .000 .000 .000 .031 .000

Supp Pearson Correlation

0.71 (**)

0.55 (**)

0.49 (**)

0.41 (**)

.232 0.69 (**)

0.59 (**)

1

Sig. (2-tailed) .000 .000 .000 .008 .150 .000 .000

Empl

Pearson Correlation 0.15 0.27 0.18 0.12 .066 0.12 -0.01 0.16 1

Sig. (2-tailed) 0.35 .008 0.26 0.42 0.68 0.43 0.93 0.31

N 40 40 40 40 40 40 40 40 40

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

Table 4.24: Bi-variate analysis with length of time with ISO Certification and

QA Guidelines

Perf Lead Plan Cust Info Peop Proc Supp Empl

ISO 9000 Year

Pearson Correlation

0.43 (**)

0.32 (*)

0.35 (*)

0.32 (*)

0.22 0.43 (**)

0.48 (**)

0.44 (**)

-0.13

Sig. (2-tailed) .005 .044 .024 .040 .168 .006 .001 .004 .420

N 40 40 40 40 40 40 40 40 40

QA Year Pearson

Correlation 0.70 (**)

0.81 (**)

0.68 (**)

0.68 (**)

0.66 (**)

0.68 (**)

065. (**)

0.28 0.25

Sig. (2-tailed) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.45

N 40 40 40 40 40 40 40 40 40

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

4.4.3 Approaches to adopting quality in SMEs and there impact on

Manufacturing Practices and Performance

Brown and Wiele [31] have proposed the assessment typology for quality

adoption in firms and to examine its impact on manufacturing performance. There

approach was already described in section 2.4 of Chapter II. This study has adopted the

methodology proposed by them and used Analysis of Variance (ANOVA) to obtain the

empirical investigation. In its simplest form ANOVA provides a statistical test of whether

or not the means of several groups are all equal, and therefore generalizes t-test to

more than two groups. ANOVAs are helpful because they possess an advantage over a

two-sample t-test. Doing multiple two-sample t-tests would result in an increased

chance of committing a type I error.

To test the hypothesis that all treatments have exactly the same effect, The

ANOVA F–test (of the null-hypothesis that all treatments have exactly the same effect)

is recommended as a practical test, because of its robustness against many alternative

distributions. In its simplest form, the assumption of unit-treatment additivity states that

the observed response yi,j from experimental unit i when receiving treatment j can be

written as the sum of the unit's response yi and the treatment-effect tj, that is

yi,j = yi + tj (4.10)

The assumption of unit-treatment addivity implies that, for every treatment j, the

jth treatment have exactly the same effect tj on every experiment unit

The fundamental technique is a partitioning of the total sum of squares S into

components related to the effects used in the model.

(4.11)

So, the number of degrees of freedom can be partitioned in a similar way and specifies

the chi-square distribution which describes the associated sums of squares.

(4.12)

The F-test is used for comparisons of the components of the total deviation. For

example, in one-way, or single-factor ANOVA, statistical significance is tested for by

comparing the F test statistic,

(4.13)

Eight one-way ANOVA tests were conducted, one for each construct. A one-way

analysis of variance is used when the data are divided into groups according to only one

factor. The questions of interest are usually: (a) Is there a significant difference between

the groups? And (b) If so, which group is significantly different from other?

The mean value for performance was taken as the dependent variable. The

second group of TQM practicing SMES (N =60) was chosen for the analysis, since all

manufacturing firms belonging to this group, were already ISO certified and practicing

TQM in their organization.

As mentioned previously, all responses were categorised on the basis of

typology of adoption of quality practices in three sub groups:

Group 1: Implemented TQM after the ISO 9000 (ISO first, TQM later) (N=18)

Group 2: Implemented TQM and ISO 9000 simultaneously (Simultaneous ISO and

TQM) (N=15)

Group3: Implemented TQM before ISO 9000 (TQM first, later ISO 9000) (N=27)

ANOVA was used to test the hypothesis,

H3-1: In SMEs, Implementation of TQM before ISO 9000 provides significant impact on

Manufacturing practices and performance

Prior to conduction of ANOVA, the performance means of three groups were

obtained. Table 4.25 provides the means for performance of three groups.

Table 4.25: Means of Performance Measures of three groups

N = 60 N Minimum Maximum Perf Std. Deviation Variance

TQM after ISO 18 4.00 5.00 4.22 0.42779 0.183

TQM simul ISO 15 2.00 4.00 3.48 1.40408 1.971

TQM before ISO 27 4.00 5.00 4.66 0.48038 0.231

Valid N (listwise) 60

Table 4.26 presents the ANOVA of three groups.

Table 4.26: ANOVA for three groups of TQM practicing SMEs

TQM SMEs

Group

a (N=18)

Group

b (N=15)

Group

c (N=27)

N=60 Mean F Sig. Mean F Sig. Mean F Sig.

LEAD 4.02 3.330 0.005 3.98 3.624 0.040 4.07 3.282 0.018

PLAN 3.46 0.750 0.601 3.16 1.165 0.254 3.94 4.005 0.007

CUST 3.72 0.190 0.960 3.61 0.869 0.556 3.88 4.806 0.003

INFO 3.71 0.227 0.944 3.50 0.457 0.822 3.76 1.422 0.254

PEOP 3.91 3.243 0.039 3.88 1.311 0.531 3.91 3.697 0.011

PROC 3.63 0.595 0.705 3.25 0.977 0.497 3.80 5.770 0.001

SUPP 3.81 0.746 0.604 3.61 1.624 0.189 3.78 1.624 0.189

EMPL 3.66 1.546 0.248 3.53 1.997 0.180 3.85 2.236 0.077

PERF 4.22 3.48 4.66

Yr with ISO

8.22 4.50 6.60

Yr with TQM

3.38 4.50 9.78

a. Implemented TQM after ISO 9000 certification b. Implemented TQM and ISO 9000 simultaneously c. Implemented TQM before ISO 9000

4.4.4 Quality Practice significantly associated with winning the Quality Awards Many scholars in past have recommended that TQM practices are significant

with Quality Awards winning, In fact, Sun [222] and Thawesaengskulthai and Tannock

[233] mentioned that Business Excellence Models are nothing but integrated

approaches to TQM. However few scholars have examined the linkage of ISO 9000

with MBNQA (Mann, 2000) and Inaki et al. [103] mentioned that ISO 9000 practices are

congruent with QA model guidelines. To examine this issue, the argument of Samson

and Terziovski [201] was considered to test the hypothesis,

H4-1: TQM practices are significantly associated with winning of Quality Awards ANOVA was employed again to examine the means of all nine constructs for three

groups of Industries. Although ISO certified SMEs are directly not involved to obtain the

association with QA winning (since these firms haven’t implemented TQM) but in order

to predict there performance outcomes, researchers feel that independent analysis of

these firms; is also necessary and thus ANOVA table includes all three groups.

The comparison of TQM practicing SMEs and QA winning firms is presented in

Table 4.27.

Table 4.27: ANOVA for three groups representing different Quality practices

Groupa

(N=129) ISO certified SMEs

Group

b (N=60)

TQM practicing SMEs

Group

c (N=40)

QA winning Large Enter.

Mean F Sig. Mean F Sig. Mean F Sig.

LEAD 3.41 3.555 0.000 4.07 2.102 0.053 4.51 16.718 0.000

PLAN 3.05 3.803 0.000 3.88 5.772 0.000 4.46 3.657 0.040

CUST 2.94 3.190 0.000 3.94 5.720 0.000 4.44 8.062 0.004

INFO 2.89 3.450 0.000 3.76 1.845 0.090 4.32 6.292 0.009

PEOP 2.92 4.506 0.000 3.91 3.861 0.001 4.44 1.989 0.175

PROC 2.80 5.200 0.000 3.80 2.133 0.049 4.46 7.126 0.006

SUPP 2.73 3.327 0.000 3.76 1.658 0.132 4.40 6.212 0.009

EMPL 2.84 3.537 0.000 3.85 2.240 0.039 4.36 .749 0.734

PERF 2.95 4.12 4.46

Yr with ISO

5.56 10.25 6.00

Yr with TQM

8.82 10.00

Yr with QA

7.35

4.4.5 Impact of other factors on Manufacturing Performance Following Hypotheses were proposed to examine the impact of other factors on

manufacturing performance in SMEs and LE.

H5-1: There is positive and significant impact of other factors on manufacturing

performance of SMEs

H5-2: There is positive and significant impact of other factors on manufacturing

performance of ISO Certified SMEs

H5-3: There is positive and significant impact of other factors on manufacturing

performance of TQM Practicing SMEs

H5-4: There is positive and significant impact of other factors on manufacturing

performance of large enterprise

H5-5: The impact of other factors on manufacturing performance differentiates

SMEs from large enterprises

Multivariate analysis (MVA) is based on the statistical principle of multivariate

statistics, which involves observation and analysis of more than one statistical variable

at a time. Many researchers, Quazi et al, 2002, Tanner et al , 2005, Rungtusanatham et

al.[199], Youssef et al. [252-253] in past preferred Multivariate Analysis to obtain

individual effect of intercepts and combined effect of all intercepts over performance.

They mentioned the advantage of Multivariate analysis over other statistical techniques,

to obtain Multiple Analysis of Variance (MANOVA) and Multiple Regression Analysis

(MRA) simultaneously. The Tests between subject effects (Individual and group effect

of variables on fixed factors) also provides the collective effect and individual effect of

variable on fixed factor to predict among many influencing variables, which variable

mostly contributes to lowering or maximizing the overall performance (Hair et al.,

2005). Infect in Multivariate procedure it is also possible to introduce covariates (another

factor along with main factor) which is not possible in simple regression analysis and

ANOVA [253]. The means for manufacturing practices and performance is entered as

fixed factor while the other four factors, age of Industry; size of Industry (No. of

employees); annual sales turnover; and type of manufacturing were loaded as

dependent variables. No other covariates were used in this analysis. The result of (H5-

1) is presented in Table 4.28. The tests between subject of effects to represent

MANOVA (Multiple Analysis of Variance) and MRA (Multiple Regression Analysis) is

also presented in Table 4.29.

Table 4.28: Multivariate Tests(c) for SMEs (N=189)

Effect All SMEs (N=189) Value F Hypothesis df Error df Sig. Partial Eta Squared

Intercept

Pillai's Trace .959 886.644(a) 4.000 152.000 0.00 0.95

Wilks' Lambda .041 886.644(a) 4.000 152.000 0.00 0.95

Hotelling's Trace 23.333 886.644(a) 4.000 152.000 0.00 0.95

Roy's Largest Root 23.333 886.644(a) 4.000 152.000 0.00 0.95

Perf

Pillai's Trace 1.338 2.361 132.000 620.000 0.00 0.33

Wilks' Lambda .143 2.904 132.000 607.534 0.00 0.38

Hotelling's Trace 3.223 3.675 132.000 602.000 0.00 0.44

Roy's Largest Root 2.370 11.133(b) 33.000 155.000 0.00 0.70

a Exact statistic b The statistic is an upper bound on F that yields a lower bound on the significance level. c Design: Intercept+perf

Table 4.29: Tests of Between-Subjects Effects for all SMEs (N=189)

Source Dependent Variable Type III Sum of Squares

Df Mean Square F Sig.

Partial Eta

Squared

Corrected Model Age_ind 299.256(a) 33 9.068 1.445 .071 .235

NO_Empl 82974.596(b) 33 2514.382 2.481 .000 .346

Area_mfg 137.970(c) 33 4.181 .909 .614 .162

Ann_Turn 245445.055(d) 33 7437.729 8.970 .000 .656

Intercept Age_ind 4027.823 1 4027.823 641.931 .000 .806

NO_Empl 1732995.306 1 1732995.306 1710.311 .000 .917

Area_mfg 1297.059 1 1297.059 281.901 .000 .645

Ann_Turn 811690.107 1 811690.107 978.945 .000 .863

Perf Age_ind 299.256 33 9.068 1.445 .071 .235

NO_Empl 82974.596 33 2514.382 2.481 .000 .346

Area_mfg 137.970 33 4.181 .909 .614 .162

Ann_Turn 245445.055 33 7437.729 8.970 .000 .656

Error Age_ind 972.554 155 6.275

NO_Empl 157055.827 155 1013.263

Area_mfg 713.173 155 4.601

Ann_Turn 128517.908 155 829.148

Total Age_ind 10449.000 189

NO_Empl 3816750.000 189

Area_mfg 3594.000 189

Ann_Turn 2229125.000 189

Corrected Total Age_ind 1271.810 188

NO_Empl 240030.423 188

Area_mfg 851.143 188

Ann_Turn 373962.963 188

a R Squared = .235 (Adjusted R Squared = .072) b R Squared = .346 (Adjusted R Squared = .206) c R Squared = .162 (Adjusted R Squared = -.016) d R Squared = .656 (Adjusted R Squared = .583)

To test the hypotheses (H5-2) and (H5-3), Multivariate tests were employed again for

ISO certified SMEs (N =129) and TQM practicing SMEs (N=60) separately. The results

are displayed in Table 4.30

Table 4.30: Multivariate Tests(c) for two groups of SMES

Effect ISO SMEs (N=129) Value F Hypothesis df Error df ∆ F sig. Partial

eta squared

Intercept Pillai's Trace 0.947 439.82(a) 4.00 99.00 0.00 0.84

Wilks' Lambda 0.053 439.82(a) 4.00 99.00 0.00 0.75

Hotelling's Trace 17.771 439.82(a) 4.00 99.00 0.00 0.72

Roy's Largest Root 17.771 439.82(a) 4.00 99.00 0.00 0.80

Perf Pillai's Trace 0.807 0.991 104.00 408.00 0.51 0.24(c)

Wilks' Lambda 0.402 0.983 104.00 395.30 0.53 0.19(c)

Effect TQM SMEs (N=60) Value F Hypothesis df Error df ∆F Sig. Partial

eta squared

Intercept Pillai's Trace 0.911 136.13(a) 4.00 53.00 0.00 0.82

Wilks' Lambda 0.089 136.13(a) 4.00 53.00 0.00 0.88

Hotelling's Trace 10.275 136.13(a) 4.00 53.00 0.00 0.51

Roy's Largest Root 10.275 136.13(a) 4.00 53.00 0.00 0.88

Perf Pillai's Trace 0.231 1.147 12.00 165.00 0.32 0.27(c)

Wilks' Lambda 0.784 1.130 12.00 140.51 0.34 0.65(c)

Hotelling's Trace 0.258 1.109 12.00 155.00 0.35 0.07(c)

Roy's Largest Root 0.152 2.08(b) 4.00 55.00 0.09 0.74(c)

a Exact statistic (age of firm, size of firm, type of mfg., annual sales) b The statistic is an upper bound on F that yields a lower bound on the significance level. c Design: Intercept +Perf (age of firm, size of firm, type of mfg., annual sales+ performance)

The results of Multivariate tests for QA winning large firms is presented in Table 4.31

Table 4.31: Multivariate Tests(c) for QA winning Firms

Effect Value F Hypothes

is df Error df ∆F Sig.

Partial eta squared

Intercept Pillai's Trace 0.999 1250.195

(a) 3.00 5.00 0.00 0.98

Wilks' Lambda 0.001 1250.195

(a) 3.00 5.000 0.00 0.99

Hotelling's Trace 750.117 1250.195

(a) 3.00 5.000 0.00 0.97

Roy's Largest Root 750.117 1250.195

(a) 3.00 5.000 0.00 1.000

Perf Pillai's Trace 2.657 1.696 96.00 21.000 0.08 0.85(c)

Hotelling's Trace 1.039 0.974 104.00 390.00 0.55 0.21(c)

Roy's Largest Root 0.399 1.56(b) 26.00 102.00 0.05 0.19(c)

Wilks' Lambda 0.001 1.610 96.00 15.864 0.14 0.89(c)

Hotelling's Trace 38.077 1.454 96.00 11.000 0.25 0.90(c)

Roy's Largest Root 26.459 5.788(b) 32.00 7.000 0.01 1.00(c)

a Exact statistic (age of firm, size of firm, type of mfg., annual sales) b The statistic is an upper bound on F that yields a lower bound on the significance level. c Design: Intercept+perf (age of firm, size of firm, type of mfg., annual sales + performance)

Finally to investigate the hypothesis (H5-5), the impacts of other factors

differentiate SMEs with large industries. ANOVA is employed again to examine the

difference in quality practices of SMEs and Large enterprises. In statistics, analysis of

variance (ANOVA) is a collection of statistical models, and their associated procedures,

in which the observed variance in a particular variable is partitioned into components

attributable to different sources of variation.

The MANOVA and MRA of three groups of industries are presented in Table

4.32, to obtain the comparative analysis for three groups.

Table 4.32: MRA and MANOVA for SMEs and LE

SME Type

Factors R2 Adj. R

2 F ∆ F Sig.

Partial Eta Squared

Only ISO certified

Age of firm 0.249 0.058 1.30 0.177 0.249

Size of firm 0.196 -0.009 0.95 0.535 0.196

Type of Mfg. 0.215 0.015 1.07 0.387 0.215

Ann sales turnover 0.197 -0.008 0.96 0.526 0.197

TQM practicing

QA winning

firms

Age of firm 0.173 0.043 1.330 0.250 0.273

Size of firm 0.656 0.639 3.192 0.043 0.656

Type of Mfg. 0.172 0.073 0.496 0.854 0.072

Ann sales turnover 0.744 .0 710 3.072 0.039 0.744

Age of firm 0.854 0.750 3.277 0.039 0.854

Size of firm 0.899 0.738 3.949 0.018 0.899

Type of Mfg. 0.902 0.851 4.002 0.017 0.902

Ann sales turnover 1.000 1.00 1.00

4.4.6 Comparison of Performance Measures of SMEs and Large Enterprises

The research question (R6) was intended to compare the differences in

performance indicators of SMEs and LE due to deployment of quality practices. For the

empirical investigation, all quality practices were considered as single construct (means

of all eight quality practices) and all valid and reliable performance indicators were

accounted for the analysis as: product quality; operational performance; financial

performance; customer satisfaction; and employee satisfaction. In other words, this

analysis is vice versa to first research question (R1) where performance measures were

treated as single construct, while here quality practices are treated as single construct.

For ISO certified SMEs, from total twenty four items, six performance measure

indicators were found valid and reliable. Similarly, seven valid and reliable indicators

were also obtained from the QA winning large industries.

Following two hypotheses (H6-1) and (H6-2) were proposed to obtain the empirical

investigations

H6-1: There is significant relationship between performance measures and quality

practice of SMEs

H6-2: There is significant relationship between performance measures and quality

practice of large enterprises

The regression analysis methodology is employed again to test the hypotheses.

Table 4.33 provides the descriptive statistics for performance measure indicators of

both groups along with means for all quality practices. Regression Analysis is employed

further to obtain the relationship between quality means and performance measure

indicators.

Table 4.34(a) provides the Regression Model for SMEs, and Table 4.34(b) presents the

Regression Model for QA winning large industries. The regression coefficients for both

groups are mentioned further in Table 4.35(a) and 4.35(b).

Table 4.33: Descriptive Statistics for performance indicators of both groups

ISO SMEs N Minimum Maximum Mean Std.

Deviation Variance

Cronbach α

GFI

Red inspection cost

189 1.00 5.00 3.47 1.11 1.250 0.83 0.94

Prod. Features 189 1.00 5.00 3.37 1.22 1.491 0.83 0.94

Customer service 189 1.00 5.00 3.37 1.19 1.437 0.82 0.93

Labour Prod. 189 1.00 5.00 3.31 1.24 1.548 0.83 0.95

Empl motivation 189 1.00 5.00 3.30 1.30 1.690 0.82 0.92

Red Defects 189 1.00 5.00 3.29 1.29 1.667 0.84 0.96

Quality * 189 1.00 5.00 3.24 0.82 .675 0.85 0.95

Valid N (list wise) 189

QA firms N Minimum Maximum Mean Std.

Deviation Variance

Cronbach α

GFI

Cust satisfaction 40 2.00 5.00 4.62 0.74 .548 0.86 0.94

ROA/ROI 40 3.00 5.00 4.52 0.59 .358 0.86 0.93

Empl involvement 40 3.00 5.00 4.47 0.67 .461 0.85 0.94

Prod. Innovation 40 2.00 5.00 4.45 0.71 .510 0.86 0.96

Red. operation cost

40 2.00 5.00 4.45 0.71 .510 0.85 0.95

Competitiveness 40 2.00 5.00 4.42 0.71 .507 0.87 0.97

Labour Prod. 40 1.00 5.00 4.35 0.80 .644 0.87 0.96

Quality” 40 3.00 5.00 4.60 0.63 .400 0.85 0.94

Valid N (list wise) 40

* considered as single construct (mean of all eight quality practices)

Table 4.34(a): Regression Model for SMEs

Model R R Square Adjusted R

Square Std. Error of the Estimate F Sig.

1 0.78(a) 0.611 .598 .52082 47.670 .000(a)

a Predictors: (Constant), perf21, perf8, perf17, perf4, perf11, perf20 b Dependent Variable: qual

Table 4.34(b): Regression Model for large Enterprises

Model R R Square Adjusted R

Square Std. Error of the Estimate F Sig.

1 0.83(a) 0.801 0.770 .54037 3.061 0.014(a)

a Predictors: (Constant), Perf20, Perf5, Perf15, Perf3, Perf19, Perf17, Perf23 b Dependent Variable: QUAL

Table 4.35 (a): Regression Coefficients for Performance Measures Of SMEs

Model

Un standardized Coefficients

Standardized Coefficients

T Sig.

B Std. Error Beta B Std. Error

1

(Constant) 0.925 0.145 6.371 0.000

Prod_feat 0.210 0.045 0.286 4.686 0.000

Labour_Pro 0.098 0.047 0.146 2.075 0.039

Insp_cost 0.159 0.047 0.232 3.382 0.001

Defects 0.033 0.044 0.050 0.750 0.454

Cust_serv 0.123 0.047 0.193 2.629 0.009

Empl_mot 0.065 0.039 0.102 1.659 0.099

a Dependent Variable: qual

Table 4.35 (b): Regression Coefficients of Performance Measures of QA winning firms

Model

Un standardized Coefficients

Standardized Coefficients

T Sig.

B Std. Error Beta B Std. Error

1

(Constant) 3.768 0.650 5.798 0.000

Lab_prod -0.293 0.127 -0.428 -2.311 0.027

Competit 0.092 0.176 0.107 .523 0.050

Prod_inno 0.361 0.169 0.444 2.140 0.040

Open_cost -0.086 0.125 -0.129 -.692 0.049

Empl_inv -0.360 0.224 -0.386 -1.607 0.118

ROA/ROI 0.967 0.205 0.976 4.725 0.000

Cust_sat -0.490 0.170 -0.496 -2.889 0.007

a Dependent Variable: qual

The regression coefficients provided the two regression equation for SMEs and large enterprises: Y = 0.925+0.210 X1 + 0.098 X2 + 0.159 X3 + 0.033 X4 + 0.123 X5 + 0.065 X6 (4.14) Y = 3.768- 0.192 X1 + 0.092 X2 + 0.361 X3 - 0.086 X4 - 0.360 X5 + 0.967 X6 - 0.490 X7 (4.15)

4.4.7 Analysis for open ended Questions

In Questionnaire of this study, two open ended questions were mentioned to obtain the

respondents opinion about impact of quality practices on performance measures in

industries, and quality practices/ manufacturing practices definitely leads to successful

implementation of TQM programme in industries.

It is important to mention that respondents from ISO certified SMEs (N=129),

didn’t replied to the second question; which was obvious since these units haven’t

implemented formal TQM programmes in their organization. In contrast, respondents

from these firms attempted the first question to provide their opinion about impact of

quality practices on performance measures.

From TQM practicing SMEs and QA winning large enterprise, respondents have

responded to questions. The statistical summary of their responses is presented in

Table 4.36.

Table represented a very high response rate from QA firms, followed by TQM

practicing SMEs. It is important to mention that response rate is quite low for only ISO

certified SMEs in comparison with other two groups.

The ranking of performance (means of performance) of all respondents are

provided in Appendix the opinion collected from all respondents about the attributes of

performance measures and quality practises associated with successful implementation

of TQM for three groups of industries is presented in Appendix 4.4 and 4.5. .

Table 4.36: Summary of response rate for Open ended questions

Response from Industries

Question ISO certified SMEs (N =129)

TQM Practicing SMES (N=60)

QA winning Large Enterprises (N=40)

Open ended Question :01

54 (41.86%) 41(68.33%) 33(82.50%)

Open ended Question: 02

NIL 48(80.00%) 38(95.00%)

4.5 DISCUSSION

This chapter has presented many empirical examinations and investigations for

obtaining the results and interpretations of the study. Section 4.2 of this chapter

presented the organization profile and quality profile of the respondents of this study.

The analysis has indicated that ISO certified SMEs are having less number of

employees and modest annual sales turnover in comparison with other two groups of

TQM practicing SMEs and QA winning firms. The respondent’s position of ISO certified

SMEs also represented that many practioner replied to the questionnaire were from

marketing and HR department, to indicate that there is scarcity of real quality

practitioners in those firms. In fact it was observed that many proprietors of the firm also

responded to the questionnaire to raise the question that they really made justice while

replying to the questionnaire? Since, it is generalise tendency that no proprietor would

like to provide the real picture of their firm (or to blame himself) and when the issue is

related with quality practices and performance, mostly they avoid to respond and in

case of responding, it may possible to have subjective bias.

The TQM practicing SMEs has shown better position of respondents, as

General Managers and senior Managers also responded to the questionnaire. However,

like ISO certified SMEs, few proprietors (also mentioned themselves as CEO of that

firm) have replied to the questionnaire, which also leads to possibility of bias as

described previously.

The last group of QA winning firm has indicated the organizational culture,

since one third respondents were production/operations and quality managers of those

firms. In fact; it was observed that few General Managers and CEO also replied to the

questionnaire. One more evidence, researchers would like to share at this space, that

around ten quality practitioners from QA winning industries; have telephonically

informed that they have sent the response (via post/ email) and would like to know the

outcomes of the study. There sincere approach was also noted during sorting of

appropriate data for analysis, as it was observed that all forty respondents have

responded with completely filled questionnaire and few of them have also used an

additional sheet/s while responding to open ended questions.

The results and interpretations of data analysis are presented in Chapter V of

this thesis.