This chapter provides the demographic information and descriptive statistics of cotton yarn mills participated in the research. Secondly, correlation analysis between variables is studied. Thirdly, factor analysis is performed to reduce the quality management construct. Fourthly, correlation analysis between quality management constructs and performance measures is studied. Finally, the multiple linear regression models of performance measures against quality management constructs are reported.

  • Demographic Information

The stratification of the respondents spinning mills was performed based on size of mills. Spinning mills with 25,000 or more installed spindles were categorized as large mills and the mills with less than 25,000 spindles were attributed as small mills.

Province

No. of small mills

No. of large mills

Total no. of mills

Punjab

35

56

79

Sindh

17

5

22

N.W.F.P.

5

2

7

Baluchistan

2

0

2

Total

49

61

110

Table 4.1 Province wise Distribution of Sample

Table.1 shows the province wise distribution of sampled mills. Punjab is at the top in cotton growing and most of the industry is located here so Punjab has highest representation (72 %) in the sample. Sindh has second most representation (20%) in the sample. There are forty-nine (45%) small mills, and sixty-one (55%) large mills in the sample. All the participant mills are member of all Pakistan textiles manufacturing association (APTMA). All the respondent mangers were male with average age of thirty-seven years, and four years average experience of cotton yarn industry. There were thirty-one (28%) composite mills, and seventy-nine (72%) mills were involved only in manufacturing yarn. The average life of participant mills was seventeen years.

4.2 Descriptive statistics

Simple frequency, percent frequency, and cumulative frequency have been calculated to define the implementation level of each quality management practice by the cotton yarn mills. The mean and standard deviation have been calculated to establish overall implementation level of each quality management practice by the cotton yarn industry. Furthermore, histograms have been constructed along with curves to describe the implementation phenomena graphically.

Independent Variables

  • Teamwork = TWRK
  • Technical discussion between employees = TDE
  • Role of senior employees as trainers = RSET
  • Team reward system = TRS
  • Quality communication = QCOM
  • Evaluation of market trends = EMT
  • Internal customer focus = ICF
  • Organization as customer focused = OCF
  • Customer Needs Identification = CNI
  • Customer problem solving system = CPSS
  • Effectiveness of customer problem system = ECPS
  • Customer relation management = CRM
  • Targets for customer Satisfaction = TCS
  • Customer feedback as quality improvement tool = CFQT
  • Customer Encouragement for feedback = CEF
  • Employees' suggestions towards quality improvement = EIA
  • Quality as a management goal = QAMG
  • Top management support for change = TMSC
  • Setting of organizational targets = SOT
  • Delegation of authority with responsibility = DRWA
  • Investment in quality enhancement = IQE
  • Employees' empowerment = EEPW
  • Training programs = TRIP
  • Training for industry trends = TRIND
  • Training for new technology = TRTEC
  • Absence of discrimination at organization = ADISC
  • Documentation of procedure = DPRS
  • Benchmarking = BENCH
  • Organizational environment = OEN
  • Use of quality management tools for improvement = QMTI
  • Evaluation of suppliers' quality management systems = ESQM
  • Use of SQC for supplier management = SQCS
  • Investment in business process reengineering = IBPR
  • Complaint management = COM
  • Change of suppliers = COS

Dependent Variables

  • Rejection Rate = RRATE
  • Profit per unit = PPU
  • Sale volume = SVOL
  • Market share = MSHR
  • Organizational performance improvement = OPI

4.2.1. Teamwork

Against the question, “To encounter special work problems employees often perform combined effort”, following response were recorded from a sample of 110 mill managers. The question was labeled by teamwork and was coded as TWORK.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

18

16.4

16.4

16.4

3

15

13.6

13.6

30.0

4

63

57.3

57.3

87.3

5

14

12.7

12.7

100.0

Total

110

100.0

100.0

Table 4.2 Frequency Distribution for teamwork

Table (4.2) shows that 70 percent of the mill managers reported that there was a visible culture of teamwork among the employees at their mills and the employees perform collective efforts to resolve the problems they face at workplace. This response reflects the existence of prestigious professional integrity and work efficiency at these mills. 16.4 percent of the mill mangers responded neutrally. 13.6 percent of the mill managers reported the lack of integrity and team oriented approach at their mills. WORK has a mean response of 3.66, which shows that most of the mills possessed the team based work environment and there was a professional harmony among the workforce. A high standard deviation of 0.90 depicts that mills had large difference in terms of existence of teamwork among the workforce.

Figure 4.1 Histogram for teamwork

Fig.4.1 indicates the histogram constructed by using response on horizontal axis and the number of mills, against each level on vertical axis. The curve is negatively skewed (skewness -0.657) which shows that large proportion of mills had high level of teamwork among their workforce.

4.2.2. Technical Maturity

Against the question, “Technical discussion among workers is regularly occurred” following response were recorded from a sample of 110 mill managers. The question was labeled by technical maturity and was coded as TDE

Response

Frequency

Percent

Valid Percent

Cumulative Percent

1

2

1.8

1.8

1.8

2

12

10.9

10.9

12.7

3

26

23.6

23.6

36.4

4

62

56.4

56.4

92.7

5

8

7.3

7.3

100.0

Total

110

100.0

100.0

Table 4.3 Frequency Distribution for Technical Discussion between Employees

Table (4.3) shows that 63.7 percent of the mill managers reported that there was a scenario of good workmanship at the mills as workers were more involved in technical discussion about their work. This high percent of response reflects the existence of quality circles. 23.6percent of the mill mangers responded neutrally. 12.7 percent managers reported a lack of workforce's tendency to discuss the technical issues regarding their work. TDE has a mean response of 3.56, which shows that at most of the mills technical discussion among the employees was existed that reflected the professional maturity of workforce. A high standard deviation of 0.852 shows that mills were departed greatly with respect to technical maturity level of workforce.

Figure 4.2 Histogram of Technical Discussion between Employees

Fig.4.2 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.838) which shows that there were a culture of technical discussion among the employees to improve the work quality in large proportion of the sampled mills.

4.2.3. Role of Senior Employees as Trainers

Against the question, “The experienced employees are always keen to put efforts to guide and train new workers” following responses were recorded from a sample of 110 mill managers. The question was labeled by role of senior employees as trainers and was coded as RSET.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

8

7.3

7.3

7.3

2

8

7.3

7.3

14.6

3

30

27.3

27.3

41.9

4

60

54.5

54.5

96.4

5

4

3.6

3.6

100.0

Total

110

100.0

100.0

Table 4.4 Frequency Distribution for Role of Senior Employees as Trainers

Table (4.4) shows that 58.1 percent of the mill managers reported that experinced employees were postively contributing in training and development of less technical and untrained workforc at their respective mills. This response reflects that work environment at these mills was healty and supportive for the workforce, the senior workforce is highly motivated and own the organization. 41.9 percent of the mill mangers responded neutrally, this high percenatge shows that manager had no clear understanding of the level of contribution by senior workforce at their mills, and underweighted the importance of senior workforce's role in creating the congial work environment. RSET has a mean response of 3.40, which shows that at large proprtion of mills there were healty work enviroment created by the senior and experinced workforce through providing training and support to new workers. A high standard deviation of 0.95 reflects that mills managers had large disagreement in their observation regarding the senior workforce behaviuor.

Figure 4.3 Histogram for Role of Senior Employees as Trainer

Fig.4.3 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -1.145) which shows that at high proportion of mills senior workforce was contributing positively in creating healthy environment through providing training and workplace support to junior workforce.

4.2.4. Team Based Reward System

Against the question, “Teams are rewarded for good work rather than individuals”, following responses were recorded from a sample of 110 mill managers. The question was labeled by team based reward system and was coded as TRS.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

9

8.2

8.2

8.2

2

16

14.5

14.5

22.7

3

16

14.5

14.5

37.3

4

63

57.3

57.3

94.5

5

6

5.5

5.5

100.0

Total

110

100.0

100.0

Table 4.5 Frequency Distribution of Team Based Reward System

Table (4.5) shows that 62.8 percent of the mill managers reported that management supported the team-oriented environment by rewarding the employees for good work as a team rather than pushing the individuals to create a discriminating work environment. 14.5 percent of the mill mangers responded neutrally about the reward system existed at their mills. 22.7 percent of managers reported that management pushed individual by appreciating their sole contribution and ignoring the other contributors, this reflects reward system was not fair enough at these mills. TRS has a mean response of 3.37, which shows that most of the mills possessed the team based reward system. A high standard deviation (1.065) shows that mills had implemented large difference in the reward system they adopted.

Figure 4.4 Histogram for Team Based Reward System

Fig.4.4 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.935) which shows that large proportion of mills had team based reward system.

4.2.5. Quality Communication

Against the question, “Informal communication about work quality is frequent among employees”, following responses were recorded from a sample of 110 mill managers. The question was labeled by quality communication and was coded as QCOM.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

1

.9

.9

.9

2

17

15.5

15.5

16.4

3

35

31.8

31.8

48.2

4

52

47.3

47.3

95.5

5

5

4.5

4.5

100.0

Total

110

100.0

100.0

Table 4.6 Frequency Distribution of Quality Communication

Table (4.6) shows that 51.8 percent of the mill managers reported that informal communication was frequent among the employees to tackle day-to-day matters at their mills, this response reflects that at these mills the work environment was more open and less informal which helped employees to work with ease and less formal tightness. 31.8 percent of the mill mangers responded neutrally. 16.4 percent of the mill managers reported the absence of informal communication at their mills that shows that environment at these mills was highly formal and there was less openness and friendless work environment. QCOM has a mean response of 3.39, which shows that the level of informal communication was moderate in the sampled mills. This relatively high standard deviation of 0.836 depicts that mills had a large difference regarding the informal communication.

Figure 4.5 Histogram of Quality Communication

Fig.4.5 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.462) which shows that large proportion of mills had culture of informal quality communication among the employees.

4.2.6. Evaluation of Market Trends

Against the question, “We continuously evaluate the market”, following response were recorded from a sample of 110 mill managers. The question was labeled by evaluation of market trends and was coded as EMT.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

2

1.8

1.8

1.8

2

24

21.8

21.8

23.6

3

33

30.0

30.0

53.6

4

48

43.6

43.6

97.3

5

3

2.7

2.7

100.0

Total

110

100.0

100.0

Table 4.7 Frequency Distribution of Evaluation of Market Trends

Table (4.7) shows that 46.3 percent of the mill managers reported that to be competitive in the market they used to evaluate the market trends. The evaluation of prevailing trends in market could help to redesign the product and the processes to achieve better quality yarn. It was observed that the mills that were evaluating the market trends were in more innovative and responsive towards the existing and forecasted market challenges. 30.0 percent of the mill mangers responded neutrally. 23.6 percent of the mill managers reported the lack of interest of management at their mills in studying the market trends and hence were less competitive in the dynamic market. EMT has a mean response of 3.24 that shows that the overall level of mills involvement in evaluation of market trends was moderate and should be improved to meet the market challenges. A high standard deviation (0.888) shows that mills had large disagreement in their approach towards the use of market research in developing their processes and products.

Figure 4.6 Histogram of Evaluation of Market Trends

Fig.4.6 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.405) which shows that number of mills which involved in evaluation of market trends were larger than the mills which were reluctant towards the the market research.

4.2.7. Internal customer focus

Against the question, “We focus on internal customers to provide them with quality environment”, following response were recorded from a sample of 110 mill managers. The question was labeled by internal customer focused and was coded as ICF.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

12

10.9

10.9

10.9

3

21

19.1

19.1

30.0

4

66

60.0

60.0

90.0

5

11

10.0

10.0

100.0

Total

110

100.0

100.0

Table 4.8 Frequency Distribution of Internal Customer Focused

Table.(4.8) shows that 70 percent of the mill managers reported that management was concerned to provide a healthy and quality environment to their employees by focusing on employees organizational issues and interests. It was reported that organization which were internally smooth and employees- friendly were more productive against the efficient. This high percentage of mills shows that management at these mills was visionary and had supportive attitude towards the employees. 19.1 percent of the mill mangers responded neutrally. 10.9 percent of the mill managers reported that management was less focused on the employees' interests and environment provided at workplace was not supportive. ICF has a mean response of 3.69, which shows that most of the mills had productive and supportive work environment created by management through focusing on internal customers. A relatively low standard deviation of 0.798 shows that most of the mills were internal focused and there was less disagreement between the mills in their approach in dealing with their employees.

Figure 4.7 Histogram of Internal Focused

Fig.4.7 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.703) which shows that large proportion of mills were prioritizing the internal customers (employees) and focused on their issues to improve the organizational environment.

4.2.8. Organization as customer focused

Against the question, “Our organization is customer focused”, following response were recorded from a sample of 110 mill managers. The question was labeled by organization as customer focused, and was coded as OCF.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

8

7.3

7.3

7.3

3

17

15.5

15.5

22.7

4

69

62.7

62.7

85.5

5

16

14.5

14.5

100.0

Total

110

100.0

100.0

Table 4.9 Frequency Distribution of Organization as Customer Focused

Table (4.9) shows that 77.2 percent of the mill managers reported that their mills were more customer oriented and focused on customers' interests to improve the quality of their products and processes. This high percent of customer-focused mills shows the quality consciousness and their realization of prevailing trends. 15.5 percent of the mill mangers responded neutrally. 7.3 percent of mill managers reported an absence of customer-focused approach at their mills. OCF has a mean response of 3.85, which shows that most of the mills were customer oriented. A relatively low standard deviation of 0.756 revealed that mills had less difference in their approach towards the customers' management.

Figure 4.8 Histogram of Organization as Customer Focused

Fig.4.8 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.770) which shows that large proportion of mills was highly customer focused.

4.2.9. Customer Needs Identification

Against the question, “Customer needs are continuously identified”, following response were recorded from a sample of 110 mill managers. The question was labeled by customer needs identification and was coded as CNI.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

2

1.8

1.8

1.8

2

1

.9

.9

2.7

3

18

16.4

16.4

19.1

4

78

70.9

70.9

90.0

5

11

10.0

10.0

100.0

Total

110

100.0

100.0

Table 4.10 Frequency Distribution of Customer Needs Identification

Table (4.10) shows that 80.9 percent of the mill managers reported that there existed a mechanism that was followed by their mills to identify the customer needs. From this high percentage, it can be concluded that the importance of customer satisfaction was realized by these mills and hence they strived to read the customer minds. 19.1 percent of the mill mangers responded neutrally. Only 2.7 percent of the mill managers reported the lack of customer orientation at these mills. CNI has a mean response ( 3.86) which shows that large proportion of the mills had implemented the mechanism, to identify the customer' needs. A low standard deviation (0.670) depicts that mills had less difference in adoption of customer' needs identification system.

Figure 4.9 Histogram of Customer Needs Identification

Fig.4.9 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.1512) which shows that large proportion of mills had a culture in which customer needs were identified.

4.2.10. Customers' Problem Management System

Against the question, “We have well established mechanism to solve customer complaints”, following response were recorded from a sample of 110 mill managers. The question was labeled by effectiveness of customer's problem management system and was coded as CPSS.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

2

1.8

1.8

1.8

3

25

22.7

22.7

24.5

4

72

65.5

65.5

90.0

5

11

10.0

10.0

100.0

Total

110

100.0

100.0

Table 4.11 Frequency Distribution of Customers' Problem Management System

Table (4.11) shows that 75.5 percent of the mill managers reported that their mills had strong complaint management mechanism, that helped them to satisfy the customers and gain good repute in market. This very high percentage response shows that mills had established customer satisfaction as their core priority. 24.5 percent of the mill mangers responded neutrally. Only 1.8percent of the mill managers reported a poor system of customer' complaints management at their mills. CPSS has a mean response of 3.93, which shows that most of the mills had quality system of customer's complaint management. A low standard deviation (0.570) shows that large proportion of mills had strong customer's complaints management system with small difference.

Figure 4.10 Histogram for Customers' Problem Management System

Fig.4.10 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.314) which shows that large proportion of mills had strong system of customer's complaints management.

4.2.11. Effectiveness of Customers' Problem Management System

Against the question, “We solve complaints and problem of our customers effectively and continuously”, following responses were recorded from a sample of 110 mill managers. The question was labeled by customer relation management and was coded as ECPS.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

5

4.5

4.5

4.5

3

13

11.8

11.8

16.4

4

77

70.0

70.0

86.4

5

15

13.6

13.6

100.0

Total

110

100.0

100.0

Table 4.12 Frequency Distribution of Effectiveness of Customers' Problem Management System

Table 4.12 shows that 83.6 percent of the mill managers reported that their mills had a permanent feature of dealing with customer's issues effectively. This high percent response shows that mills had adopted customers' oriented approach. 16.4 percent of the mill mangers responded neutrally.4.5 percent of the mill managers reported the ineffectiveness of the problem solving systems at their mills. ECPS has a mean response of 3.84, which shows that overall industry had a culture of customers' management. A low standard deviation (0.614) shows that mills had less difference with respect to effectiveness of dealing in customers' issues.

Figure 4.11 Histogram of Effectiveness of Customers' Problem Management System

Fig.4.11 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.379) which shows that large proportion of mills had high level of effectiveness of customers' complaints and problems management system.

4.2.12. Evaluation of customers' relations

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

1

.9

.9

.9

3

28

25.5

25.5

26.4

4

73

66.4

66.4

92.7

5

8

7.3

7.3

100.0

Total

110

100.0

100.0

Against the question, “We evaluate customers relation to strengthen it”, following response were recorded from a sample of 110 mill managers. The question was labeled by targets for customer satisfaction and was coded as CRM.

Table 4.13 Frequency Distribution of Evaluation of customers' relations

Table.(4.13) shows that 73.7 percent of the mill managers reported that their mills developed the monitoring system to evaluate the organizational relations with customers to strengthen it on regular basis. Strong and interactive customer-suppliers' relationships could be proved effective quality management tool to enhance the quality of products and processes. This high percent response shows that mills were rating the customer-suppliers' relationship very high in the quality improvement efforts. 25.5 percent of the mill mangers responded neutrally. Only 0.9 percent of the mill managers reported that their mills were not considering suppliers' contribution important in the quality management process. CRM has a mean response (3.93) which shows that most of the mills had developed the constructive relation with customers, and evaluate them to strengthen reciprocated corporation for the achievement of continuous quality improvement. A standard deviation (0.660) shows that mills had difference in their approach to deal in customers-suppliers' relation management as a quality improvement tool.

Figure 4.12 Histogram of Evaluation of customers' relations

Fig.4.12 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.899) which shows that large proportion of mills had developed mechanism to reinforce the quality oriented relationship with the suppliers.

4.2.13. Targets for Customer Satisfaction

Against the question, “We set improvement targets for customers' satisfaction”, following response were recorded from a sample of 110 mill managers. The question was labeled by targets for customers satisfaction was coded as TCS.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

1

.9

.9

.9

3

28

25.5

25.5

26.4

4

73

66.4

66.4

92.7

5

8

7.3

7.3

100.0

Total

110

100.0

100.0

Table 4.14 Frequency Distribution of Targets for Customer Satisfaction

Table (4.14) shows that 73.7 percent of the mill managers reported that customers' satisfaction was considered crucial organizational target and the mills were continuously establishing the targets for customers' satisfaction through providing them with quality products and services. The high percentage response (73.7) shows that cotton yarn manufacturing industry rated the customers' satisfaction very high, and hence the customers were ultimately the dominating stakeholder. 25.5 percent of the mill mangers responded neutrally about the strategy of targeting the customers' satisfaction in their mills. Only 0.9 percent of mill managers reported the lack of organizational strategy for targeting the customers for their satisfaction. TCS has a mean response of 3.80, which shows that most of the mills were involved in sitting targets for customers' satisfaction continuously. A low standard deviation (0.571) shows that customers' satisfaction was considered the ultimate organizational goal by the industry with more homogeneity.

Figure 4.13Histogram of Targets for Customers' Satisfaction

Fig.4.13 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.288) which shows that large proportion of mills was considering customers' satisfaction critical organizational goal and continuously targeting the customers.

4.2.14. Customer Feedback as Quality Improvement Tool

Against the question, “Customer feedback is used as tool to initiate to set our quality targets”, following responses were recorded from a sample of 110 mill managers. The question was labeled by customer feedback and as quality improvement tools was coded as CFQT.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

1

.9

.9

.9

3

21

19.1

19.1

20.0

4

83

75.5

75.5

95.5

5

5

4.5

4.5

100.0

Total

110

100.0

100.0

Table 4.15 Frequency Distribution of Targets for Customer Satisfaction

Table (4.15) shows that 80.0 percent of the mill managers reported that customer feedback about the products and services quality was taken as quality improvement tool in their mills, and quality targets were linked with the needs and demands of customers, derived from their feedback. Customers' feedback could be effective tool to get informed of the customers' attitudes, behaviors, and interest that could be helpful to improve the quality of products and services. This high percent response (80.0) shows that customers' voice was ranked very high by the industry, and hence the customers were considered a dominant stakeholder in the yarn manufacturing industry. 19.1 percent of the mill mangers responded neutrally. Only 0.9 percent of mill managers reported that their mills were not taking the customer feedback as an effective quality improvement tool and revising their quality improvement targets. CFQT has a mean response of 3.84, which shows that most of the mills implemented the customer feedback as quality improvement tool, and used it regularly to revise their quality targets according to the demands of customers. A low standard deviation (0.498) shows that customers' feedback was used by the industry as quality improvement tool with more homogeneity.

Figure 4.15 Histogram for Targets for Customer Satisfaction

Fig.4.15 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.774) which shows that large proportion of mills were using customers' feedback to improve their process, products, and service quality.

4.2.15. Customer Encouragement for Feedback

Against the question, “We really encourage our customers to put feedback through different ways”, following response were recorded from a sample of 110 mill managers. The question was labeled by customer encouragement for feedback and was coded as CEF.

Response

Frequency

Percent

Valid Percent

Cumulative Percent

2

5

4.5

4.5

4.5

3

16

14.5

14.5

19.1

4

74

67.3

67.3

86.4

5

15

13.6

13.6

100.0

Total

110

100.0

100.0

Table 4.16 Frequency Distribution of Customer Encouragement for Feedback

Table (4.16) shows that 80.9 percent of the mill managers reported that their mills were encouraging the customers by multiple techniques to provide feedback against the quality of products and services they received. Customers' surveys, complaints forms, and appraisal forms were most common tools used by the industry to get customers' voice. The high percent response (80.9) shows that the mills developed the mechanisms with high priority to get the customers' feedback; different techniques were applied to encourage and motivate the customers to share their product and service experiences. 19.1 percent of the mill mangers responded neutrally. Only 4.5 percent of mill managers reported that a proper mechanism was not designed by their mills to encourage customer to provide feedback about the quality of products and services they received. CEF has a mean response (3.90) which shows that most of the mills designed and developed techniques to encourage customers to provide their feedback that would be used to improve the quality of yarn and level of service quality. A standard deviation (0.677) shows that mills were different in their approach in dealing with customers' feedback collecting techniques.

Figure 4.15 Histogram of Customer Encouragement for Feedback

Fig.4.15 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.782) that shows that large proportion of mills had designed and implemented techniques to encourage the customers to share their products and services quality experience.

4.2.16. Employees' Suggestions as Quality Improvement Tool

Against the question, “We appreciate employees suggestions towards quality improvement”, following response were recorded from a sample of 110 mill managers. The question was labeled by employees' suggestions as quality improvement tool was coded as EIAT.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

6

5.5

5.5

5.5

3

22

20.0

20.0

25.5

4

74

67.3

67.3

92.7

5

8

7.3

7.3

100.0

Total

110

100.0

100.0

Table 4.17 Frequency Distribution of Employees' Suggestions as Quality Improvement Tool

Table (4.17) shows that 74.6 percent of the mill managers reported that employees were encouraged and appreciated to suggest the quality improvement initiatives and steps both for processes and products. Employees are exposed and interacted with processes more frequently and are well known to the strengths and weaknesses of their organizations and their understanding and their knowledge could be productive in redesigning the process for continuous quality improvement. The high percent response (74.6) shows that employees' experience was highly ranked in the mills and employees' suggestions were used as quality improvement tool. 25.5 percent of the mill mangers responded neutrally. Only 5.5 percent of mill mangers reported that their mills ignored the experience of employees, and the employees' suggestions towards the quality improvement initiatives were not appreciated. EIAT has a mean response of 3.76, which shows that most of the mills appreciated their employees to participate and to provide suggestions towards the quality improvement initiatives. A standard deviation (0.663) shows that mills had difference in culture of appreciation for the employees to contribute in the quality improvement programs.

Figure 4.16 Histogram of Employees' Suggestions as Quality Improvement Tool

Fig.4.16 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.855) which shows that large proportion of mills was appreciating the contribution of employees towards the quality improvement programs through the suggestions.

4.2.17. Quality as a Management Goal

Against the question, “Quality is the ultimate goal of top management process improvement efforts”, following response were recorded from a sample of 110 mill managers. The question was labeled by quality as a management goal and was coded as QMAG.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

15

13.6

13.6

13.6

3

11

10.0

10.0

23.6

4

60

54.5

54.5

78.2

5

24

21.8

21.8

100.0

Total

110

100.0

100.0

Table 4.18 Frequency Distribution ofQuality as a Management Goal

Table (4.18) shows that 86.3 percent of the mill managers reported that the top management at their mills was visionary and well aware of the market trends therefore they were considering quality as a matter of their survival rather than as competitive edge. Top management at these mills was focused on quality management through the process management practices. This high percent response reflects the top management was perceived more innovative and quality conscious by the managers. 10.0 percent of the mill mangers responded neutrally. 13.6 percent of mill managers argued that their top management was not targeting quality as prime focus and hence proved themselves less market competitive and their survival was at threat because of inactive approach towards the changing market scenarios. QMAG has a mean response of 3.85, which shows that top management in large proportion of mills was focused on quality improvement and were more reactive towards the dynamic market challenges. A high standard deviation of 0.92 depicts that there was large difference in mills regarding the level of top management focus in quality management as an ultimate organizational goal.

Figure 4.17 Histogram of Quality as a Management Goal

Fig.4.17 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed( skewness -0.764) which shows that in large proportion of mills top management had quality oriented approach towards implementation of process improvement practices.

4.2.18. Top Management Support for Change

Against the question, “Our management encourages and facilitates the productive organizational changes”, following response were recorded from a sample of 110 mill managers. The question was labeled by top management support for change and was coded as TMSC.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

3

2.7

2.7

2.7

2

9

8.2

8.2

10.9

3

15

13.6

13.6

24.5

4

57

51.8

51.8

76.4

5

26

23.6

23.6

100.0

Total

110

100.0

100.0

Table 4.19 Frequency Distribution of Top Management Support for Change

Table (4.19) shows that 75.4 percent of the mill managers reported the work environment was not stagnant at their mills and productive changes through constructive ideas were appreciated and promoted by the mill management. The challenges imposed by the globalized market demands the proactive approach by the spinning industry. This high percent response reflects the existence of innovative and creative work environment created by the management at these mills. 10.9percent of the mill mangers responded neutrally. Only 2.7 percent of the managers reported the lack of support from the management for the change of organization status quo through the implementation of modern era management concepts and theories at workplace. TMSC has a mean response of 3.85, which shows that most of the mills featured with facilitating and supporting management for the organizational improvement. A high standard deviation of 0.966 depicts that there were large difference between mills with respect to management response towards the organizational changes.

Figure 4.18 Histogram of Top Management Support for Change

Fig.4.18 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -1.01) which shows that large proportion of mills had management that was facilitating and supporting for the organizational changes proposed by the workforce.

4.2.19. Setting of Organizational Targets

Against the question, “Our management has established organization's targets for both short and long term”, following response were recorded from a sample of 110 mill managers. The question was labeled by setting of organizational targets and was coded as SOT.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

18

16.4

16.4

16.4

3

14

12.7

12.7

29.1

4

72

65.5

65.5

94.5

5

6

5.5

5.5

100.0

Total

110

100.0

100.0

Table 4.20 Frequency Distribution of Setting of Organizational Targets

Table (4.20) shows that 71.0 percent of the mill managers reported that management had established the future organizational targets for both short and long term. Defining the organizational goals could be effective roadmap tool to monitor the organizational performance. The high percent response (71.0) shows that shows that mills were defining and planning for the future organizational targets by devising organizational milestones and goals. 29.1 percent of the mill mangers responded neutrally. 16.4 percent of mill managers reported that their mills had poor planning mechanism and organizational future targets were established unsteadily. SOT has a mean response of 3.60, which shows that most of the mills had established their organizational targets progressively for both short and long term. A high standard deviation (0.826) shows that mills had large difference in their approach to establish organizational future goals.

Figure 4.19 Histogram of Setting of Organizational Targets

Fig.4.19 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.923) which shows that large proportion of mills had established their future organizational goals.

4.2.20. Delegation of Authority

Against the question, “Our management delegates responsibility to employees with authority”, following response were recorded from a sample of 110 mill managers. The question was labeled as delegation of authority and was coded as DRWA.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

9

8.2

8.2

8.2

2

7

6.4

6.4

14.5

3

18

16.4

16.4

30.9

4

76

69.1

69.1

100.0

Total

110

100.0

100.0

Table 4.21Frequency Distribution of Delegation of Authority

Table.(4.21) shows that 69.1 percent of the mill managers reported that management created healthy work environment for the employees through distribution of authority to them with assigned responsibilities. Authoritative and commanding employees at workplace could be more motivate and productive for the parent organization. The high percent response reflects that the managers were convinced and appreciated the culture of well-balanced distribution between the authority and responsibility in their mills. 16.4 percent of the mill mangers responded neutrally. 14.6 percent reported the absence of imbalance between the magnitude of work responsibility and the authority designated by the management to the employees. The culture of imbalance distribution of authority against the responsibility level could extend the organizational hierarchy chain that would be result in the motiveless and unproductive workforce. DRWA has a mean response of 3.46, which shows that most of the mills had culture in which the distribution of authority to the employees against the magnitude of responsibility was comparatively balanced. A high standard deviation (0.935) shows that the mills had large cultural difference in distribution of authority and responsibilities to employees.

Figure 4.20 Histogram of Delegation of Authority

Fig.4.20 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -1.674) which shows that large proportion of mills had appreciable balance between authority and responsibility distribution.

4.2.21. Investment in Quality Improvement

Against the question, “Our top management is investing a lot for quality improvement”, following response were recorded from a sample of 110 mill managers. The question was labeled by investment in quality improvement and was coded as IQI.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

3

2.7

2.7

2.7

2

17

15.5

15.5

18.2

3

19

17.3

17.3

35.5

4

68

61.8

61.8

97.3

5

3

2.7

2.7

100.0

Total

110

100.0

100.0

Table 4.22 Frequency Distribution of Investment in Quality Improvement

Table.(4.22) shows that 74.5 percent of the mill managers reported that their mills were quality conscious and investing in the quality improvement projects and programs. Globalization had created a market scenario in which only quality providing organization could find a survival. The prevailed investment trends in the yarn industry reflect the level of realization of market demands and response shown by the industry. This high percent response reflects that mills were investing highly in quality improvement to meet the dynamic market challenges. 17.3 percent of the mill mangers responded neutrally. 18.2 percent of mills managers reported that management at their mills was reluctant and ignorant to the changing market scenarios and was not prioritizing the investing in quality improvement areas. IQI has a mean response of 3.46, which shows that most of the mills were investing in quality improvement activities to meet the modern era market challenges. A high standard deviation (0.885) shows that mills had difference in their approach to invest in quality improvement activities.

Figure 4.21 Histogram of Investment in Quality Improvement

Fig.4.21 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -1.061) which shows that large proportion of mills was investing in quality improvement programs.

4.2.22. Employees Empowerment

Against the question, “We are empowered to take decision regarding corrective measures”, following response were recorded from a sample of 110 mill managers. The question was labeled by employees' empowerment and was coded as EEPW.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

11

10.0

10.0

10.0

2

11

10.0

10.0

20.0

3

40

36.4

36.4

56.4

4

45

40.9

40.9

97.3

5

3

2.7

2.7

100.0

Total

110

100.0

100.0

Table 4.23 Frequency Distribution of Employees Empowerment

Table.(4.23) shows that 43.6 percent of the mill managers reported that employees were empowered with decision-making authority in their area of responsibility. Employees could take corrective and advanced process measures for the continuous quality improvement in the organization. The low percent response (43.6) shows that the there were relatively few sampled mills which were empowered the employees in their area of responsibility to take the work related decisions to reduce inefficiency of processes because of unnecessary long hierarchical chain. 36.4 percent of the mill mangers responded neutrally that shows the lack of understanding of managers about their work environment. 20.0 percent of mill managers reported that work environment was more than formal and employees were not empowered to take innovative, corrective, and advanced process measures. EEPW has a mean response of 3.16, which shows that cotton yarn industry had mixed culture of employees empowerment, in which some mills were empowered the employees with decision making and others were not. A high standard deviation of 1.000 depicts that mills had large difference in their culture of employees' empowerment at their workplace.

Figure 4.22 Histogram of Employees Empowerment

Fig.4.22 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is positively skewed (skewness -0.784) which shows that relatively large proportion of mills was empowered their employees to take managerial decisions independently in their area of responsibility.

4.2.23. Training Programs

Against the question, “We have organization wide training and development process for all employees”, following response were recorded from a sample of 110 mill managers. The question was labeled by training programs and was coded as TRIP.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

8

7.3

7.3

7.3

3

28

25.5

25.5

32.7

4

68

61.8

61.8

94.5

5

6

5.5

5.5

100.0

Total

110

100.0

100.0

Table 4.24 Frequency Distribution of Training Programs

Table.(4.24) shows that 67.3 percent of the mill managers reported that their mills had organization wide training and development processes for all the employees. Training and development processes were considered important by these mills' management, and hence each employee was provided with training in the area of his job responsibilities. The high percent response (67.3) shows that cotton yarn industry had developed training and development processes for the employees to enhance their job and organizational related responsibilities. 25.5 percent of the mill mangers responded neutrally about their existence of training and development programs for the employees. TRIP has a mean response (3.65) which shows that most of the mills had developed training and development programs for their employees. A standard deviation (0.696) shows that mills had large difference in the training and development processes for the employees.

Figure 4.23 Histogram of Training Program

Fig (4.23) indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.738) which shows that in the large proportion of mills there were well established training and development processes for the employees.

4.2.24. Management Training Programs

Against the question, “We train our management for new advancement in the industry”, following response were recorded from a sample of 110 mill managers. The question was labeled by management training programs and was coded as TRIND

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

20

18.2

18.2

18.2

3

32

29.1

29.1

47.3

4

58

52.7

52.7

100.0

Total

110

100.0

100.0

Table 4.25 Frequency Distribution of Management Training Programs

Table.4.25 shows that 52.7 percent of the mill managers reported that their mills had culture of learning and development, for their management personals, for the advanced and innovative management techniques to be more successful and efficient organization. The large percent response (52.7) shows that mills were considering the learning and development of their management personnel indispensable to deal with modern era challenging issues in the organizations. 47.7 percent of the mill mangers responded neutrally, it was the highest proportion of neutral response that shows that mill managers were not well aware of the status of learning and development programs structure in their mills. 18.2 percent of managers reported the there were no specific learning and development programs for the management personnel planned and arranged by their mills. TRIND has a mean response of 3.35, which shows that the most of the mills had arranged learning and training programs for their management personnel to prepare them for the modern era business challenges. A standard deviation (0.696) shows that mills had different approach to deal with learning and development matters of management personnel.

Figure 4.24 Histogram of Management Training Programs

Fig.4.24 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.687) which shows that number of mills, which were focused on the development of human resource for the organizational challenges of modern age, were in greater proportion than mills which were reluctant and less reactive towards the development of human resources.

4.2.25. Training for New Technology

Against the question, “We train our workers for new machine handling when a change in technology is observed”, following response were recorded from a sample of 110 mill managers. The question was labeled by training for new technology and was coded as TRTEC.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

3

2.7

2.7

2.7

2

22

20.0

20.0

22.7

3

32

29.1

29.1

51.8

4

48

43.6

43.6

95.5

5

5

4.5

4.5

100.0

Total

110

100.0

100.0

Table 4.26 Frequency Distribution of Training for New Technology

Table.(4.26) shows that 48.1 percent of the mill managers reported that their mills provided training to its workforce to equip them with new technologies. Cotton spinning industry is considered a very dynamic industry and the sharp development is observed on continuous basis, hence to meet the challenges mills were involved in up gradation of their technology, and trained their employees for these advancements. These mills were working on enhancement and development of their technology and were continuously spending on training and development of their employees for the use of these technologies. 29.1 percent of the mill mangers responded neutrally. 22.7 percent of mill managers reported that their mills were paying less attention on meeting with the changing technology challenges. TRTEC has a mean response of 3.27, which shows that the level of adoption of new technology and training and development of workforce for ever-changing technologies was above the average for the cotton spinning industry. A high standard deviation (0.928) shows that there was large difference among the mills in the prevailing trends of new technology adoption and training and development of their employees.

Figure 4.25 Histogram of Training for New Technology

Fig.4.25 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.433) which shows that number of mills, which had developed training programs to meet the challenges posted by the ever-changing market, were greater than mills which were failed to develop training and development programs in the technology management for the employees.

4.2.26. Absence of Discrimination in the Organization

Against the question, “No discrimination, what so ever, exists in the organization”, following responses were recorded from a sample of 110 mill managers. The question was labeled by discrimination at organization and was coded as ADISC.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

35

31.8

31.8

31.8

2

7

6.4

6.4

38.2

3

27

24.5

24.5

62.7

4

24

21.8

21.8

84.5

5

17

15.5

15.5

100.0

Total

110

100.0

100.0

Table 4.27 Frequency Distribution of Absence of Discrimination in the Organization

Table.(4.27) shows that 37.3 percent of the mill managers reported that their mills had an indiscriminative and merited work environment, while the employees were dealt on merit and performance. The very low proportion of mills had appreciable culture regarding the appraisal and rewarding system. The presence of indiscriminative, fair, and unbiased culture of assessment of job performance in the organizations could motivate and satisfy the employees to give their maximum contribution towards the organization without a fear of being underrated. 24.5 percent of the mill mangers responded neutrally. 38.2 percent of mill managers reported the presences of discriminative and biased culture of appraisal and rewarding system. The large proportion of mills had a discriminative culture that could be result in frustrated, dishearten and unproductive employees. DISC has a mean response of 2.86, which shows most of the mills had discrimination and biased culture of appraisals and rewarding systems. A high standard deviation of 1.471 depicts that mills had large disagreement in the culture of appraisals and rewarding systems.

Figure 4.26 Histogram of Absence of Discrimination in the Organization

Fig.4.26 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (-.013) which shows that large proportion of mills had highly discriminative and biased work environment.

S4.2.27. Documentation of Procedures

Against the question, “We have well defined work procedures and are enforced implicitly and explicitly through rules and norms”, following response were recorded from a sample of 110 mill managers. The question was labeled by documentation of procedures and was coded as DPRS.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

7

6.4

6.4

6.4

3

31

28.2

28.2

34.5

4

71

64.5

64.5

99.1

5

1

.9

.9

100.0

Total

110

100.0

100.0

Table 4.28 Frequency Distribution of Documentation of Procedures

Table (4.28) shows that 65.4 percent of the mill managers reported that their mills had documented all the work procures, which were adopted and implemented thoroughly in the organization. The documented and well-defined work procedures could be effective in pacing up the routine matters and provide a road map for employees to be familiar with organizational processes in mills with low retention rate. 28.2 percent of the mill mangers responded neutrally. 6.4 percent of mill mangers reported that their mills were failed to document the work procedures that caused lack of their implementation in the organization. DPRS has a mean response of 3.60, which shows that most of the mills had well-defined and established documented work procedures to maintain a reasonable pace of organizational activities. A small standard deviation (0.624) depicts that mills had small difference in documenting the work procedures and implementing them.

Figure 4.27 Histogram of Documentation of Procedures

Fig. 4.27 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -1.082) which shows that large proportion of mills had well defined documented work procedures.

4.2.28. Benchmarking

Against the question, “We always evaluate the prevailing trends in our competitors to improve ourselves”, following response were recorded from a sample of 110 mill managers. The question was labeled by benchmarking and was coded as BENCH.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

2

1.8

1.8

1.8

2

24

21.8

21.8

23.6

3

33

30.0

30.0

53.6

4

50

45.5

45.5

99.1

5

1

.9

.9

100.0

Total

110

100.0

100.0

Table 4.29 Frequency Distribution of Benchmarking

Table.(4.29) shows that 46.4 percent of the mill managers reported that their mills were involved in benchmarking activities to revise and improve their quality management approach. The understanding of the competitors' business management approach towards the market trends through benchmarking could extend the horizons of strategic planning for the organizations. The low percent response (46.4) shows that benchmarking was performed by less number of mills. 23.6 percent of the mill mangers responded neutrally. 23.6 percent of mill managers reported that benchmarking activities could not gain management support, and their mills were not performing the benchmarking to evaluate the competitors' market approach, and the market trends. BENCH has a mean response of 3.22, which shows that most of the mills were performing the benchmarking activities to improve the quality their processes and products. A high standard deviation of 0.861 depicts that mills had large difference in implementation of benchmarking to examine the market trends; competitors' strategies to revise the organizational strategies for the improvement.

Figure 4.28 Histogram of Benchmarking

Fig.4.28 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is positively skewed (skewness -0.528) which shows that reasonable proportion of mills was applying benchmarking as a tool of understanding the competitors' approach and market trends to devise advance measures of improving the organizational performance.

4.2.29. Organizational Environment

Against the question, “Our organization has healthy environment that makes it best place to work”, following response were recorded from a sample of 110 mill managers. The question was labeled by organizational environment and was coded as OEN.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

8

7.3

7.3

7.3

3

32

29.1

29.1

36.4

4

65

59.1

59.1

95.5

5

5

4.5

4.5

100.0

Total

110

100.0

100.0

Table 4.30 Frequency Distribution of Organizational Environment

Table.(4.30) shows that 63.6 percent of the mill managers reported that their mills had healthy work environment that facilitate employees to work independently with relief of mind. This high percent response (63.6) shows that work environment at these mills were supportive and constructive for the employees. 29.1 percent of the mill mangers responded neutrally. 7.3 percent of mill managers reported that work environment in their mills was poor and the employees were not satisfied and motivated in working with the organization. OEN has a mean response of 3.61, which shows that most of the mills had healthy work environment. A moderate standard deviation (0.692) shows that mills had small difference in their level of work environment.

Figure 4.29 Frequency Distribution of Organizational Environment

Fig.4.29 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.655) which shows that large proportion of mills had dynamic and motivational work environment for the employees.

4.2.30. Quality Management Tools for Improvement

Against the question, “Quality management tools are widely used at workplace in our organization”, following response were recorded from a sample of 110 mill managers. The question was labeled by quality management tools for improvement and was coded as QMTI.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

16

14.5

14.5

14.5

3

15

13.6

13.6

28.2

4

74

67.3

67.3

95.5

5

5

4.5

4.5

100.0

Total

110

100.0

100.0

Table 4.31 Frequency Distribution of Quality Management Tools for Improvement

Table.(4.31) shows that 71.8 percent of the mill managers reported that use of quality management tools were a permanent feature at their mills. Quality management tools were used to plan and design the quality-oriented processes, implementation of designed plans and the maintenance of the processes through monitoring activities. This high percent response (71.8) shows that mills, which were implementing the quality management tools, were well aware of the modern era market challenges, and were involved deeply in quality improvement efforts. 13.6 percent of the mill mangers responded neutrally. 14.5 percent of mill managers reported the lack of use of quality management tools at their workplace .This reluctance against the use of quality management tools could be attributed to insufficient technical skills and deficiency in commitment towards modernization by the management. QMTI has a mean response of 3.73, which shows that most of the mills were implementing the quality management tools. A moderate standard deviation (0.619) shows that mills had difference in their approach towards the implementation of quality management tools.

Figure 4.30 Histogram of Quality Management Tools for Improvement

Fig.4.30 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -1.025) which shows that large proportion of mills were implementing quality management tools to improve the product and process quality.

4.2.31. Evaluation of Supplier Quality Management Systems

Against the question, “We evaluate the efforts of of suppliers towards quality improvement initiative regularly”, following response were recorded from a sample of 110 mill managers. The question was labeled by evaluation of suppliers' quality management systems and was coded as ESQM.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

6

5.5

5.5

5.5

3

22

20.0

20.0

25.5

4

78

70.9

70.9

96.4

5

4

3.6

3.6

100.0

Total

110

100.0

100.0

Table 4.32 Frequency Distribution of Evaluation of Supplier Quality Management Systems

Table.(4.32) shows that 74.5 percent of the mill managers reported that their mills were evaluating the efforts of of suppliers towards quality improvement initiative on the regular basis. Evaluation of suppliers for quality improvement purpose could develop a healthy relationship between suppliers and customer organizations. This high percent response shows that mills considered the contribution of suppliers very important in improving the quality of their products and hence strived to develop a constructive quality oriented culture with suppliers. 20.0 percent of the mill mangers responded neutrally. ESQM has a mean response 3.73, which shows that most of the mills had developed the mechanism, to evaluate the suppliers' quality management systems. Only 5.5 percent of mill managers reported that their mills had failed to develop any rational mechanism to evaluate the suppliers' quality management systems. A moderate standard deviation (0.619) depicts that mills had different level of approach towards evaluation of suppliers' quality management systems.

Figure 4.31 Histogram of Evaluation of Supplier Quality Management Systems

Fig.4.31 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -1.162) which shows that large proportion of mills had implemented mechanism to evaluate the suppliers' quality management systems.

4.2.32. Suppliers' Management

Against the question, “We keep on change our suppliers if they fail to maintain quality”, following response were recorded from a sample of 110 mill managers. The question was labeled by suppliers' management and was coded as SMAG.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

3

2.7

2.7

2.7

2

27

24.5

24.5

27.3

3

21

19.1

19.1

46.4

4

42

38.2

38.2

84.5

5

17

15.5

15.5

100.0

Total

110

100.0

100.0

Figure 4.33 Histogram of Evaluation of Supplier Quality Management Systems

Table.(4.33) shows that 53.7 percent of the mill managers reported that their mills had a strict monitoring and controlling system to ensure quality supplies from suppliers. These mills were strict against any nonconformance and kept on change of suppliers those failed to maintain the agreed quality level. 19.1 percent of the mill mangers responded neutrally. 30.0 percent of mill managers reported the lack of well-established suppliers' quality management system in their mills. SMAG has a mean response of 3.39, which shows that mills had better suppliers; quality management system and most of mills had policy to change suppliers with poor quality supplies. A high standard deviation of 1.101 depicts that mills had very large difference in their suppliers' quality management systems and the way of dealing with non-conforming suppliers.

Figure 4.32 Histogram of Evaluation of Supplier Quality Management Systems

Fig.4.32 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.242) which shows that large proportion of mills had the policy to change suppliers with inconsistent quality supplies on regular basis, however, there were also a good proportion of mills that were reacted tenderly against suppliers who even failed to maintain agreed quality level.

4.2.33. Use of SQC for Supplier Management

Against the question, “The use of statistical quality control is most frequent in assessing the quality of our suppliers' quality systems”, following response were recorded from a sample of 110 mill managers. The question was labeled by use of SQC for suppliers' management and was coded as SQCS.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

4

3.6

3.6

3.6

2

19

17.3

17.3

20.9

3

33

30.0

30.0

50.9

4

52

47.3

47.3

98.2

5

2

1.8

1.8

100.0

Total

110

100.0

100.0

Table 4.34 Frequency Distribution of Use of SQC for Supplier Management

Table (4.34) shows that 49.1 percent of the mill managers reported that statistical quality control techniques were implemented to evaluate and monitor the suppliers' quality management systems. The evaluation and monitoring of suppliers' quality management systems on regular basis could be effective in promoting the culture of quality in supplier organizations and hence reduced the magnitude of nonconformance. 30.0 percent of the mill mangers responded neutrally. 20.9 percent of the mill managers reported that statistical quality control was rarely applied at their mills because of lack of understanding by the management and process complexity. SQCS has a mean response of 3.26, which shows that relatively large proportion of mills had implemented the statistical quality control techniques to evaluate the quality management systems of supplier as continuous quality improvement efforts. A high standard deviation (0.871) depicts that mills had large difference in the implementation of statistical quality control techniques to evaluate and monitor suppliers' quality management systems.

Figure 4.33 Histogram of Use of SQC for Supplier Management

Fig.4.33 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.705) which shows that large proportion of mills had implemented statistical quality control for the supplier management.

4.2.34. Complaints Management

Against the question, “Customer complaints are reduced through better”, following response were recorded from a sample of 110 mill managers. The question was labeled by complaints management and was coded as CMAG.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

2

1.8

1.8

1.8

2

5

4.5

4.5

6.4

3

26

23.6

23.6

30.0

4

70

63.6

63.6

93.6

5

7

6.4

6.4

100.0

Total

110

100.0

100.0

Table 4.35 Frequency Distribution of Complaints Management

Table.(4.35) shows that 68.0 percent of the mill managers reported that their mills had customers' complaints management system to satisfy the customers and gain and retain good repute in the customers community. This high percent response reflects that most of mills were customers oriented and considered satisfaction as benchmark for their business success; hence, they worked on solving the complaints and issues of the customers. 23.6 percent of the mill mangers responded neutrally. Only 4.5 percent of the mill managers reported that their mills were less focused on customers' satisfaction, as they were reluctant to solve customers' problems. The existence of poor complaints management system in the organization reflects the low level of organizational focus on customers' satisfaction. CMAG has a mean response of 3.68, which shows that most of the mills had well established and active complaint management system. A high standard deviation (0.741) shows that mills had developed different level of customers' complaint management system.

Figure 4.34 Histogram of Complaints Management

Fig.4.34 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness-1.201) which shows that large proportion of mills had well developed and active customers' complaints management system.

4.2.35. Investment in Business Process Reengineering

Against the question, “We are investing in restructuring as a strategic tool to improve the organizational performance”, following response were recorded from a sample of 110 mill managers. The question was labeled by investment in business process reengineering and was coded as IBPR.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

5

4.5

4.5

4.5

2

17

15.5

15.5

20.0

3

25

22.7

22.7

42.7

4

57

51.8

51.8

94.5

5

6

5.5

5.5

100.0

Total

110

100.0

100.0

Table 4.36 Frequency Distribution of Investment in Business Process Reengineering

Table (4.36) shows that 57.3 percent of the mill managers reported that their mills had realized the prevailed business trends in the industry and were investing on business process reengineering, to gain the competitive edge through modernization. Rational investment in business process reengineering could change both the working methods of organization and the mode of production. This relatively low percent response reflects that the level of adoption of reengineering was low against the other quality management practices in the cotton spinning industry. 22.7 percent of the mill mangers responded neutrally in reporting the presence of business process reengineering trend. 24.5percent of mill managers reported that there was no trend prevailing of business process reengineering in their mills. From this trend in the mills, it can be concluded that either management were ignorant of business process reengineering advantages or it was less concerned with it. IBPR has a mean response of 3.38, which shows that the level of business process reengineering adopted in sampled mills was moderate and a high proportion of mills was not using the business process reengineering. A high standard deviation of 0.967 depicts that mills had large difference in terms of their adoption of business process reengineering.

Figure 4.35 Histogram of Investment in Business Process Reengineering

Fig. 4.35 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.774) which shows that large proportion of mills was investing in business process reengineering to improve the quality of their processes and products.

4.2.36. Internal perceived level of organizational achievements

Against the question, “We are far better organization than we were five years back”, following responses were recorded from a sample of 110 mill managers. The question was labeled by internal perceived quality and was coded as IPQ.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

8

7.3

7.3

7.3

2

15

13.6

13.6

20.9

3

13

11.8

11.8

32.7

4

58

52.7

52.7

85.5

5

16

14.5

14.5

100.0

Total

110

100.0

100.0

Table 4.37 Frequency Distribution of Internal perceived level of organizational achievements

Table.(4.37) shows that 57.2 percent of the mill managers reported that their mills were improving continuously as an organization in last five years period. This perceived level of achievement could prove to be catalyst to enhance internal employees' motivation and loyalty towards the organization. The high percent response shows that managers in most of the mills were convinced and satisfy with the pace of growth and development in their mills. 11.8 percent of the mill mangers responded neutrally. 20.9 percent of mill managers were unsatisfied and disappointed with the performance and achievement of their mills in last five years, this dissatisfaction, and disappointment could cause to disturbance among employees and low retention rate. IPQ has a mean response of 3.54, which shows that most of the mills managers reported that their mills improved as organization in last five years. A high standard deviation (1.123) shows that mills had different level of organizational achievement in last five years.

Figure 4.36 Histogram of Internal perceived level of organizational achievements

Fig.1 indicates the histogram constructed by using response on horizontal axis and the number of mills, against each level on vertical axis. The curve is negatively skewed (skewness -0.865) which shows that large proportion of mills was experienced an improvement as an organization.

4.2.37. Reduced Rejection Rate

Against the question, “Rejection rates are reduced regularly”, following response were recorded from a sample of 110 mill managers. The question was labeled by reduced rejection rate and was coded as RRATE.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

9

8.2

8.2

8.2

3

25

22.7

22.7

30.9

4

65

59.1

59.1

90.0

5

11

10.0

10.0

100.0

Total

110

100.0

100.0

Table 4.38 Frequency Distribution of Reduced Rejection Rate

Table (4.38) shows that 69.1 percent of the mill managers reported that through implementation of process quality management techniques they reduced rejection rate of production on regular basis. 22.7 percent of the mill mangers responded neutrally. 8.2 percent of mill managers reported the failure of process management and hence high rejection rate that ultimately reflects the poor process performance. RRATE has a mean response of 3.71, which shows that most of the mills had improved, quality-oriented processes that helped to reduce the rejection rate of mills. A low standard deviation of 0.0.758 depicts that mills had less difference in their rejection rate management achievements.

Figure 4.37 Histogram of Reduced Rejection Rate

Fig.4.37 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.612) which shows that large proportion of mills had improved production processes that resulted in reduced rejection rate.

4.2.38. Realization of Profit

Against the question, “Our overall profitability is improved”, following response were recorded from a sample of 110 mill managers. The question was labeled by realization of profit and was coded as PRFT.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

20

18.2

18.2

18.2

3

16

14.5

14.5

32.7

4

69

62.7

62.7

95.5

5

5

4.5

4.5

100.0

Total

110

100.0

100.0

Table 4.39 Frequency Distribution of Realization of Profit

Table.(4.39) shows that 67.3 percent of the mill managers reported that their mills were realizing the profit in their business. Realizing profit is an exclusive objective of business-oriented organization while the severe competition and modernization in the cotton spinning industry proved a great hurdle to achieve this ultimate goal. This high percent response reflects that yarn industry was on path of business success through the virtue of adaptation of modern quality management techniques. 14.5 percent of the mill mangers responded neutrally. 18.2 percent of mill managers reported the downfall of their mills in profit realization, decreased in profit of these mills reflected inadequate performance that could be attributed to the failure of mills in adopting quality management practices. PRFT has a mean response of 3.54, which shows that most of the mills were realizing the business profit. A high standard deviation of 0.824 depicts that mills had difference in their business profitability.

Figure 4.38 Histogram of Realization of Profit

Fig.4.38 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.820) which shows that large proportion of mills had realized profit in their business.

4.2.39. Market Share

Against the question, “Our market share is increasing rapidly”, following response were recorded from a sample of 110 mill managers. The question was labeled by market share and was coded as MSHR.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

1

23

20.9

20.9

20.9

2

10

9.1

9.1

30.0

3

26

23.6

23.6

53.6

4

50

45.5

45.5

99.1

5

1

.9

.9

100.0

Total

110

100.0

100.0

Table 4.40 Frequency Distribution of Market Share

Table.(4.40) shows that 46.4percent of the mill managers reported that their mills were realizing an increase in their business market share. Globalization and modernization posted a challenge to a dominated and successful yarn industry of Pakistan to sustain and maintain its status in national and international market. The low percent response reflects that the realization of increased market share in the market was really a challenge, and only less than half of sampled mills gained it. 23.6 percent of the mill mangers responded neutrally. 30 percent of mill managers reported their mills were failed to gain any business market share. MSHR has a mean response of 2.96, which shows that less than half of mills were earned increased market share while overall industry faced a challenge to retain it. A high standard deviation of 1.196 depicts that mills had large difference in earning business market share.

Figure 4.39 Histogram of Market Share

Fig.4.39 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.650) which shows that large proportion of mills had observed an increase in their market business share.

4.2.40. Sale Volume

Against the question, “Our sale volume is continuously increased”, following response were recorded from a sample of 110 mill managers. The question was labeled by sale volume and was coded as SVOL.

Response

Frequency

Percent

Valid Percent

Cumulative

Percent

2

15

13.6

13.6

13.6

3

17

15.5

15.5

29.1

4

63

57.3

57.3

86.4

5

15

13.6

13.6

100.0

Total

110

100.0

100.0

Table 4.41 Frequency Distribution of Sale Volume

Table.(4.41) shows that 70.9 percent of the mill managers reported that sale volume of their mills' production increased. The increase in sale volume reflects the increased demand of mills products because of enhanced mills reliability and quality. This high percent response shows that most of the mills were enjoying good growth in their sale volumes and proved productive and efficient. 15.5 percent of the mill mangers responded neutrally. 13.6 percent of mill managers reported that their mills were facing a decline in sale volume because of inefficient business process management. SVOL has a (3.71) mean response which shows that most of the mills had realized an increase in sale volume through better business process management. A high standard deviation (0.871) shows that mills had large difference in their achievements of increase in sale volume.

Figure 4.40 Histogram of Sale Volume

Fig.4.40 indicates the histogram constructed by using response on horizontal axis and the number of mills against each level on vertical axis. The curve is negatively skewed (skewness -0.666) which shows that large proportion of mills had experienced a smooth increase in their sale volume.

4.3 Correlation analysis between the variables

The correlation analysis is used to measure the interrelationship between the quality management techniques and the results are reported along with Pearson correlation coefficient (r) and level of significance (p).

Correlation analysis between TWOK and all other independent variables was studied, which revealed that TWOK has positive correlations with the other quality management techniques. The highest correlation of TWOK is observed with TRS (r = 0.820, p=0.000), and the lowest positive correlation of TWOK is with CFQT (r = 0.285, p = 0.03). The correlation between TWOK and quality performance indicators shows that TWOK has positive correlations with OPI (r = 0.570, p = 000), SVOL( r = 0.575,p = 0.000), RRATE ( r = 0.728, p =0.000), PRFT (r = 0.723) and MSHR ( r = 0.695, p = 0.000).

Correlation analysis between TDE and all other independent variables was studied, which revealed that TDE has positive correlations with the other quality management techniques. The highest correlation of TDE is observed with TWOK (r = 0.739, p=0.000), and the lowest positive correlation of TDE is with CFQT (r = 0.349, p = 0.000). The correlation between TDE and quality performance indicators shows that TDE has positive correlations with OPI ( r = 0.564, p = 000), SVOL( r = 0.656,p = 0.000), RRATE ( r = 0.753, p =0.000), PRFT (r = 0.738,p = 0.000) and MSHR ( r = 0.759, p = 0.000).

Correlation analysis between RSET and all other independent variables was studied, which revealed that RSET has positive correlations with the other quality management techniques. The highest correlation of RSET is observed with TRS (r = 0.794, p=0.000), and the lowest positive correlation of RSET is with ECPS (r = 0.285, p = 0.03). The correlation between RSET and quality performance indicators shows that RSET has positive correlations with OPI (r = 0.451, p = 000), SVOL( r = 0.552, p = 0.000), RRATE ( r = 0.698, p =0.000), PRFT (r = 0.646, p = 0.000) and MSHR ( r = 0.602, p = 0.000).

Correlation analysis between TRS and all other independent variables was studied, which revealed that TRS has positive correlations with the other quality management techniques. The highest correlation of TRS is observed with TWOK (r = 0.820, p=0.000), and the lowest positive correlation of TRS is with ADISC (r = 0.440, p = 0.000). The correlation between TRS and quality performance indicators shows that TRS has positive correlations with OPI (r = 0.529, p = 000), SVOL( r = 0.662, p = 0.000), RRATE ( r = 0.737, p =0.000), PRFT (r = 0.726, p = 0.000) and MSHR ( r = 0.659, p = 0.000).

Correlation analysis between QCOM and all other independent variables was studied, which revealed that QCOM has positive correlations with the other quality management techniques. The highest correlation of QCOM is observed with RRATE (r = 0.731, p=0.000), and the lowest positive correlation of QCOM is with ECPS (r = 0.412, p = 0.000). The correlation between QCOM and quality performance indicators shows that QCOM has positive correlations with OPI (r = 0.693, p = 000), SVOL( r = 0.662, p = 0.000), RRATE ( r = 0.731, p =0.000), PRFT (r = 0.677, p = 0.000) and MSHR ( r = 0.657, p = 0.000).

Correlation analysis between EMT and all other independent variables was studied, which revealed that EMT has positive correlations with the other quality management techniques. The highest correlation of EMT is observed with BENCH (r = 0.789, p=0.000), and the lowest positive correlation of EMT is with CFQT (r = 0.317, p = 0.000). The correlation between EMT and quality performance indicators shows that EMT has positive correlations with OPI (r = 0.664, p = 000), SVOL( r = 0.588, p = 0.000), RRATE ( r = 0.594, p =0.000), PRFT (r = 0.602, p = 0.000) and MSHR ( r = 0.691, p = 0.000).

Correlation analysis between ICF and all other independent variables was studied, which revealed that ICF has positive correlations with the other quality management techniques. The highest correlation of ICF is observed with OEM (r = 0.759, p=0.000), and the lowest positive correlation of ICF is with CFQT (r = 0.333, p = 0.000). The correlation between ICF and quality performance indicators shows that ICF has positive correlations with OPI (r = 0.637, p = 000), SVOL( r = 0.687, p = 0.000), RRATE ( r = 0.562, p =0.000), PRFT (r = 0.712, p = 0.000) and MSHR ( r = 0.699, p = 0.000).

Correlation analysis between OCF and all other independent variables was studied, which revealed that OCF has positive correlations with the other quality management techniques. The highest correlation of OCF is observed with CNI (r = 0.646, p = 0.000), and the lowest positive correlation of OCF is with CFQT and TRIP (r = 0.333, p = 0.000). The correlation between OCF and quality performance indicators shows that OCF has positive correlations with OPI (r = 0.433, p = 000), SVOL(r = 0.613, p = 0.000), RRATE (r = 0.513, p =0.000), PRFT (r = 0.578, p = 0.000) and MSHR ( r = 0.531, p = 0.000).

Correlation analysis between CNI and all other independent variables was studied, which revealed that CNI has positive correlations with the other quality management techniques. The highest correlation of CNI is observed with TRIND (r = 0.713, p = 0.000), and the lowest positive correlation of CNI is with TCS (r = 0.335, p = 0.000). The correlation between CNI and quality performance indicators shows that CNI has positive correlations with OPI (r = 0.449, p = 000), SVOL(r = 0.639, p = 0.000), RRATE ( r = 0.535, p =0.000), PRFT (r = 0.618, p = 0.000) and MSHR ( r = 0.566, p = 0.000).

Correlation analysis between CPSS and all other independent variables was studied, which revealed that CPSS has positive correlations with the other quality management techniques. The highest correlation of CPSS is observed with TRIND (r = 0.713, p = 0.000), and the lowest positive correlation of CPSS is with TCS (r = 0.335, p = 0.000). The correlation between CPSS and quality performance indicators shows that CPSS has positive correlations with OPI (r = 0.535, p = 000), SVOL(r = 0.511, p = 0.000), RRATE (r = 0.608, p =0.000), PRFT (r = 0.521, p = 0.000) and MSHR ( r = 0.521, p = 0.000).

Correlation analysis between ECPS and all other independent variables was studied, which revealed that ECPS has positive correlations with the other quality management techniques. The highest correlation of ECPS is observed with CPSS (r = 0.648, p = 0.000), and the lowest positive correlation of CFQT (r = 0.272, p = 0.004). The correlation between ECPS and quality performance indicators shows that ECPS has positive correlations with OPI (r = 0.555, p = 000), SVOL(r = 0.562, p = 0.000), RRATE (r = 0.488, p =0.000), PRFT (r = 0.562, p = 0.000) and MSHR ( r = 0.529, p = 0.000).

Correlation analysis between CRM and all other independent variables was studied, which revealed that CRM has positive correlations with the other quality management techniques. The highest correlation of CRM is observed with CEF (r = 0.970, p = 0.000), and the lowest positive correlation of DRWA (r = 0.219, p = 0.022). The correlation between CRM and quality performance indicators shows that CRM has positive correlations with OPI (r = 0.413, p = 000), SVOL(r = 0.474, p = 0.000), RRATE (r = 0.453, p =0.000), PRFT (r = 0.500, p = 0.000) and MSHR (r = 0.462, p = 0.000).

Correlation analysis between TCS and all other independent variables was studied, which revealed that TCS has positive correlations with the other quality management techniques. The highest correlation of TCS is observed with OEN (r = 0.636, p = 0.000), and the lowest positive correlation of OCF (r = 0.267, p = 0.022). The correlation between TCS and quality performance indicators shows that TCS has positive correlations with OPI (r = 0.598, p = 000), SVOL(r = 0.601, p = 0.000), RRATE (r = 0.605, p =0.000), PRFT (r = 0.587, p = 0.000) and MSHR (r = 0.580, p = 0.000).

Correlation analysis between CFQT and all other independent variables was studied, which revealed that CFQT has positive correlations with the other quality management techniques. The highest correlation of CFQT is observed with TRIP (r = 0.655, p = 0.000), and the lowest positive correlation of CEF (r = 0.223, p = 0.019). The correlation between CFQT and quality performance indicators shows that CFQT has positive correlations with OPI (r = 0.486, p = 000), SVOL(r = 0.418, p = 0.000), RRATE (r = 0.528, p =0.000), PRFT (r = 0.430, p = 0.000) and MSHR (r = 0.313, p = 0.001).

Correlation analysis between CEF and all other independent variables was studied, which revealed that CEF has positive correlations with the other quality management techniques. The highest correlation of CEF is observed with CRM (r = 0.970, p = 0.000), and the lowest positive correlation of CFQT (r = 0.223, p = 0.019). The correlation between CEF and quality performance indicators shows that CEF has positive correlations with OPI (r = 0.486, p = 000), SVOL(r = 0.433, p = 0.000), RRATE (r = 0.426, p =0.000), PRFT (r = 0.465, p = 0.000) and MSHR (r = 0.426, p = 0.001).

Correlation analysis between EIA and all other independent variables was studied, which revealed that EIA has positive correlations with the other quality management techniques. The highest correlation of EIA is observed with IQE (r = 0.752, p = 0.000), and the lowest positive correlation of RSET (r = 0.223, p = 0.000). The correlation between EIA and quality performance indicators shows that EIA has positive correlations with OPI (r = 0.702, p = 000), SVOL(r = 0.675, p = 0.000), RRATE (r = 0.519, p =0.000), PRFT (r = 0.657, p = 0.000) and MSHR (r = 0.591, p = 0.000).

Correlation analysis between QAMG and all other independent variables was studied, which revealed that QAMG has positive correlations with the other quality management techniques. The highest correlation of QAMG is observed with TMSC (r = 0.831, p = 0.000), and the lowest positive correlation of CEF (r = 0.314, p = 0.001). The correlation between QAMG and quality performance indicators shows that QAMG has positive correlations with OPI (r = 0.756, p = 000), SVOL(r = 0.710, p = 0.000), RRATE (r = 0.658, p =0.000), PRFT (r = 0.628, p = 0.000) and MSHR (r = 0.695, p = 0.000).

Correlation analysis between TMSC and all other independent variables was studied, which revealed that TMSC has positive correlations with the other quality management techniques. The highest correlation of TMSC is observed with QAMG (r = 0.831, p = 0.000), and the lowest positive correlation of OCF and CRM (r = 0.358, p = 0.000). The correlation between TMSC and quality performance indicators shows that TMSC has positive correlations with OPI (r = 0.775, p = 000), SVOL(r = 0.745, p = 0.000), RRATE (r = 0.706, p =0.000), PRFT (r = 0.683, p = 0.000) and MSHR (r = 0.671, p = 0.000).

Correlation analysis between SOT and all other independent variables was studied, which revealed that SOT has positive correlations with the other quality management techniques. The highest correlation of SOT is observed with DRWA (r = 0.848, p = 0.000), and the lowest positive correlation of CEF (r = 0256, p = 0.000). The correlation between SOT and quality performance indicators shows that SOT has positive correlations with OPI (r = 0.768, p = 000), SVOL(r = 0.717, p = 0.000), RRATE (r = 0.676, p =0.000), PRFT (r = 0.733, p = 0.000) and MSHR (r = 0.728, p = 0.000).

Correlation analysis between DRWA and all other independent variables was studied, which revealed that DRWA has positive correlations with the other quality management techniques. The highest correlation of DRWA is observed with SOT (r = 0.848, p = 0.000), and the lowest positive correlation of CEF (r = 0190, p = 0.047). The correlation between DRWA and quality performance indicators shows that DRWA has positive correlations with OPI (r = 0.827, p = 000), SVOL(r = 0.741, p = 0.000), RRATE (r = 0.709, p =0.000), PRFT (r = 0.706, p = 0.000) and MSHR (r = 0.745, p = 0.000).

Correlation analysis between IQE and all other independent variables was studied, which revealed that IQE has positive correlations with the other quality management techniques. The highest correlation of IQE is observed with TMSC (r = 0.745, p = 0.000), and the lowest positive correlation of CFQT (r = 0.403, p = 0.047). The correlation between IQE and quality performance indicators shows that IQE has positive correlations with OPI (r = 0.736, p = 000), SVOL(r = 0.736, p = 0.000), RRATE (r = 0.654, p =0.000), PRFT (r = 0.722, p = 0.000) and MSHR (r = 0.710, p = 0.000).

Correlation analysis between EEPW and all other independent variables was studied, which revealed that EEPW has positive correlations with the other quality management techniques. The highest correlation of EEPW is observed with ICF (r = 0.742, p = 0.000), and the lowest positive correlation of CFQT (r = 0.395, p = 0.000). The correlation between EEPW and quality performance indicators shows that EEPW has positive correlations with OPI (r = 0.640, p = 000), SVOL(r = 0.719, p = 0.000), RRATE (r = 0.620, p =0.000), PRFT (r = 0.613, p = 0.000) and MSHR (r = 0.611, p = 0.000).

Correlation analysis between TRIP and all other independent variables was studied, which revealed that TRIP has positive correlations with the other quality management techniques. The highest correlation of TRIP is observed with QMTI (r = 0.792, p = 0.000), and the lowest positive correlation of TCS (r = 0.332, p = 0.000). The correlation between TRIP and quality performance indicators shows that TRIP has positive correlations with OPI (r = 0.662, p = 000), SVOL(r = 0.589, p = 0.000), RRATE (r = 0.659, p =0.000), PRFT (r = 0.616, p = 0.000) and MSHR (r = 0.591, p = 0.000).

Correlation analysis between TRIND and all other independent variables was studied, which revealed that TRIND has positive correlations with the other quality management techniques. The highest correlation of TRIND is observed with TRTEC (r = 0.905, p = 0.000), and the lowest positive correlation of CEF (r = 0.383, p = 0.000). The correlation between TRIND and quality performance indicators shows that TRIND has positive correlations with OPI (r = 0.653, p = 000), SVOL(r = 0.656, p = 0.000), RRATE (r = 0.706, p =0.000), PRFT (r = 0.686, p = 0.000) and MSHR (r = 0.760, p = 0.000).

Correlation analysis between TRTEC and all other independent variables was studied, which revealed that TRTEC has positive correlations with the other quality management techniques. The highest correlation of TRTEC is observed with TRIND (r = 0.905, p = 0.000), and the lowest positive correlation of CEF (r = 0.278, p = 0.003). The correlation between TRTEC and quality performance indicators shows that TRTEC has positive correlations with OPI (r = 0.598, p = 000), SVOL(r = 0.576, p = 0.000), RRATE (r = 0.648, p =0.000), PRFT (r = 0.633, p = 0.000) and MSHR (r = 0.687, p = 0.000).

Correlation analysis between ADISC and all other independent variables was studied, which revealed that ADISC has positive correlations with the other quality management techniques. The highest correlation of ADISC is observed with QMAG (r = 0.658, p = 0.000), and the lowest positive correlation of CEF (r = 0.296, p = 0.002). The correlation between ADISC and quality performance indicators shows that ADISC has positive correlations with OPI (r = 0.540, p = 000), SVOL(r = 0.490, p = 0.000), RRATE (r = 0.415, p =0.000), PRFT (r = 0.446, p = 0.000) and MSHR (r = 0.529, p = 0.000).

Correlation analysis between DPRS and all other independent variables was studied, which revealed that DPRS has positive correlations with the other quality management techniques. The highest correlation of DPRS is observed with EMT (r = 0.752, p = 0.000), and the lowest positive correlation of CEF (r = 0.361, p = 0.000). The correlation between DPRS and quality performance indicators shows that DPRS has positive correlations with OPI (r = 0.781, p = 000), SVOL(r = 0.679, p = 0.000), RRATE (r = 0.702, p =0.000), PRFT (r = 0.674, p = 0.000) and MSHR (r = 0.780, p = 0.000).

Correlation analysis between BENCH and all other independent variables was studied, which revealed that BENCH has positive correlations with the other quality management techniques. The highest correlation of BENCH is observed with EMT (r = 0.989, p = 0.000), and the lowest positive correlation of CFQT (r = 0.319, p = 0.001). The correlation between BENCH and quality performance indicators shows that BENCH has positive correlations with OPI (r = 0.685, p = 000), SVOL(r = 0.612, p = 0.000), RRATE (r = 0.604, p =0.000), PRFT (r = 0.622, p = 0.000) and MSHR (r = 0.703, p = 0.000).

Correlation analysis between OEN and all other independent variables was studied, which revealed that OEN has positive correlations with the other quality management techniques. The highest correlation of OEN is observed with DPRS (r = 0.761, p = 0.000), and the lowest positive correlation of CEF (r = 0.406, p = 0.000). The correlation between OEN and quality performance indicators shows that OEN has positive correlations with OPI (r = 0.745, p = 000), SVOL(r = 0.753, p = 0.000), RRATE (r = 0.778, p =0.000), PRFT (r = 0.819, p = 0.000) and MSHR (r = 0.792, p = 0.000).

Correlation analysis between QMTI and all other independent variables was studied, which revealed that QMTI has positive correlations with the other quality management techniques. The highest correlation of QMTI is observed with EMT (r = 0.732, p = 0.000), and the lowest positive correlation of OEF (r = 0.361, p = 0.000). The correlation between QMTI and quality performance indicators shows that QMTI has positive correlations with OPI (r = 0.782, p = 000), SVOL(r = 0.691, p = 0.000), RRATE (r = 0.686, p =0.000), PRFT (r = 0.724, p = 0.000) and MSHR (r = 0.743, p = 0.000).

Correlation analysis between ESQM and all other independent variables was studied, which revealed that ESQM has positive correlations with the other quality management techniques. The highest correlation of ESQM is observed with IQE (r = 0.701, p = 0.000), and the lowest positive correlation of DPRS (r = 0.356, p = 0.000). The correlation between ESQM and quality performance indicators shows that ESQM has positive correlations with OPI (r = 0.661, p = 000), SVOL(r = 0.634, p = 0.000), RRATE (r = 0.611, p =0.000), PRFT (r = 0.722, p = 0.000) and MSHR (r = 0.581, p = 0.000).

Correlation analysis between SQCS and all other independent variables was studied, which revealed that SQCS has positive correlations with the other quality management techniques. The highest correlation of SQCS is observed with COM (r = 0.764, p = 0.000), and the lowest positive correlation of CFQT (r = 0.221, p = 0.000). The correlation between SQCS and quality performance indicators shows that SQCS has positive correlations with OPI (r = 0.597, p = 000), SVOL(r = 0.758, p = 0.000), RRATE (r = 0.600, p =0.000), PRFT (r = 0.687, p = 0.000) and MSHR (r = 0.755, p = 0.000).

Correlation analysis between COM and all other independent variables was studied, which revealed that COM has positive correlations with the other quality management techniques. The highest correlation of COM is observed with QAMG (r = 0.734, p = 0.000), and the lowest positive correlation of CFQT (r = 0.305, p = 0.001). The correlation between COM and quality performance indicators shows that COM has positive correlations with OPI (r = 0.692, p = 000), SVOL(r = 0.850, p = 0.000), RRATE (r = 0.748, p =0.000), PRFT (r = 0.805, p = 0.000) and MSHR (r = 0.794, p = 0.000).

Correlation analysis between COS and all other independent variables were studied, which revealed that COS has negative correlations with all the other quality management techniques. The highest negative correlation of COS is observed with IQE (r = -0.527, p = 0.000), and the lowest positive correlation of TRIND (r = -0.193, p = 0.044). The correlation between COS and quality performance indicators shows that COS has negative correlations with OPI (r = -0.379, p = 000), SVOL(r = -0.492, p = 0.000), RRATE (r = -0.368, p =0.000), PRFT (r =-0.376, p = 0.000) and MSHR (r = -0.68, p = 0.000).

Correlation analysis between IBPR and all other independent variables was studied, which revealed that IBPR has positive correlations with the other quality management techniques. The highest correlation of IBPR is observed with COM (r = 0.798, p = 0.000), and the lowest positive correlation of CFQT (r = 0.416, p = 0.001). The correlation between IBPR and quality performance indicators shows that IBPR has positive correlations with OPI (r = 0.748, p = 000), SVOL(r = 0.776, p = 0.000), RRATE (r = 0.816, p =0.000), PRFT (r = 0.827, p = 0.000) and MSHR (r = 0.845, p = 0.000).

The correlation analysis of variables revealed that quality management techniques used in the cotton yarn mills were correlated positively to each other, which further suggests that implementation of one technique facilitate the implementation of other correlated techniques. The positive correlations between the variables provide strong argument to use the quality management collectively to realize the claimed benefits of quality management techniques.

4.4 Factor analysis

Factor analysis was used to reduce the quality management practices scales to the smaller number of underlying factors. The principal component method was used to extract factor that have Eigen values greater than one and the varimax rotation was used to make the factors more interpretable.

4.4.1 Validation of factor analysis

The Bartlett test of sphericity and Kaiser-Mayer-Olkin measure of sampling adequacy were used to validate the use of factor analysis.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.855

Bartlett's Test of Sphericity

Approx. Chi-Square

5174.403

Df

595

Sig.

.000

Table 4.42 Bartlett test of sphericity and Kaiser-Mayer-Olkin measure of sampling adequacy

Table (4.42 ) shows that Bartlett test of sphericity is significant with p = 0.00 < 0.05, it means that correlation matrix is an identity matrix and variables are highly correlated to each other, therefore, factor analysis is appropriate

4.4.2 Communalities analysis

To observe the importance of investigated variables, communality analysis was performed. Initial and extracted communalities values were reported.

Variable

Question asked

Initial

Extract

TWOK

To encounter special work problems employees often perform a combined effort.

1.000

.775

TDE

A technical discussion among workers is regularly occurred.

1.000

.731

RSET

Experienced employees are always keen to put efforts to guide and train new workers.

1.000

.865

TRS

Teams are rewarded for good work rather than individuals.

1.000

.789

QCOM

Informal communication about work quality is frequent among employees.

1.000

.738

EMT

We continuously evaluate the trends of market.

1.000

.881

ICF

We focus on internal customer to provide them with quality.

1.000

.794

OCF

Our organization is customer focused.

1.000

.759

CNI

Customer needs are continuously identified.

1.000

.692

CPSS

We have a well-established mechanism to solve customers' complaints.

1.000

.785

ECPS

We solve complaints and problems of our customers effectively and continuously.

1.000

.711

CRM

We evaluate customer relation to strengthen it.

1.000

.950

TCS

We set improvement targets for customer satisfaction.

1.000

.629

CFQT

Customer feedback is used as tool to initiate to set our quality targets.

1.000

.736

CEF

We really encourage our customer to put feed back through different ways.

1.000

.939

EIA

We appreciate employee suggestion towards improvement.

1.000

.700

QAMG

Quality is the ultimate goal of top management's process improvement efforts.

1.000

.754

TMSC

Our Management encourages and facilitates the productive organizational changes.

1.000

.831

SOT

Management has established organization target for both short and long term.

1.000

.817

DRWA

Our management delegates Responsibility to employees with authority.

1.000

.873

IQE

Our top management is investing a lot for quality improvement.

1.000

.828

EEPW

We are empowered to take decision regarding corrective measure.

1.000

.697

TRIP

We have an organization wide training and development process for all employees.

1.000

.870

TRIND

We train our management for new advancement in industry.

1.000

.881

TRTEC

We train our workers for new machine handling when a change in technology is made.

1.000

.804

ADISC

No discrimination, what so ever, exists in the organization?

1.000

.640

DPRS

We have, well defined procedures, which are enforced, implicitly and explicitly through rules and norms.

1.000

.795

BENCH

We always evaluate the prevailing trends in our competitors' organizations to improve ourselves.

1.000

.895

OEN

Our organization has healthy environment that makes it best place to work.

1.000

.794

QMTI

Quality management tools are widely used at workplace.

1.000

.809

ESQM

We evaluate the efforts of supplier towards quality improvement initiatives regularly.

1.000

.546

SQCS

The use of SQC is most frequent in assessing the quality of our suppliers' quality systems.

1.000

.760

COM

Customer complaints are reduced through better management.

1.000

.739

COS

We keep on change our suppliers if they fail to maintain quality.

1.000

.817

IBPR

We are investing in restructuring as a strategic tool to improve organizational performance.

1.000

.820

Table 4.43 Communalities Analysis

Table (4.43) shows that the the highest variation (95%) is explained by customer relation management practice (CRM), which suggested that CRM is most important quality management practice. It is observed that statistically least important variable is ESQM among the other variables because the variation explained by ESQM is 54.6 percent.

4.4.3 Eigen value analysis

Component

Initial Eigen values

Rotation Sums of Squared Loadings

Total

% of Var

Cum. %var

Total

% of Var.

Cum. %var

QOS

19.790

56.542

56.542

8.302

23.720

23.720

TCE

2.273

6.494

63.036

6.379

18.226

41.946

TMR

1.680

4.801

67.837

3.922

11.205

53.151

SCR

1.424

4.067

71.904

3.713

10.608

63.760

DAE

1.180

3.371

75.275

3.380

9.657

73.417

QIC

1.098

3.136

78.411

1.748

4.994

78.411

Six factors were extracted from the quality management practices scales. These are comprised of existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC). The six factors account for 78.4 percent variation against 100 percent variation with thirty-five items. Percentage of variation explained by each variable with cumulative percentage of variation is given in table (4.44).

Table (4.44) Percentage of variation explained by each factor

Table.( 4.44) shows that existence of quality-oriented system (QOS) explained 23.72 percent of total variation, therefore it is most highly contributing factor and continuous improvement for customer satisfaction (QIC) explained only 4.99 percent variation of total variation, which is least contributing factor in six selected factors.

4.5 Reliability Analysis

To evaluate the reliability of the quality management constructs, existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC), formed by factor analysis, Cronbatch α is calculated.

Factors

Cronbach's Alpha

No. of Items

QOS

0.957

14

TCE

0.942

10

TMR

0.851

4

SCR

0.717

4

DAE

0.708

2

QIC

*

1

Table 4.45 Reliability Statistics

* factor 6 comprised of just one item while at least two items are required to calculate Cronbach's Alpha

Table 4.45 shows the reliability of the quality management constructs, it was observed that the highest reliability was observed of factor labeled as existence of quality-oriented system (QOS) and the lowest reliability was of factor labeled as designating authority with accountability to employees (DAE). It was also observed that all the factors were reliable with α > 0.70.

4.6 Results of correlation analysis

The correlation analysis between the composite quality management constructs and performance measures is used to testify the hypotheses stated in the study.

4.6.1 Correlation analysis of profit per unit (PRFT)

Correlation between the profit per unit (PRFT) and quality-oriented systems (QOS) implemented in the mills was studied, which revealed that implementation of quality oriented system is significantly correlated with the profit per unit realized by the mills (r = 0.438, p =0.000). The correlation analysis result shows that presence and efficient execution of quality-oriented systems in the cotton yarn mills could increase the realization of profit by the cotton yarn mills all the way through minimizing the process failures, unwelcome work delays, unfastened monitoring, and controlling practices.

Correlation between the profit per unit (PRFT) and team based, customer- oriented environment (TCE) prevailed in the cotton yarn mills was studied ,which revealed that team based and customer- oriented environment in the mills is significantly correlated with the profit per unit realized by the mills (r = 0.602, p = 0.000). The correlation analysis result shows that mills, which had a culture of teamwork between the workforce and more customer focused, were in better position to realize profit.

Correlation between the profit per unit (PRFT) and top management role in developing employees' career (TMR) prevailed in the cotton yarn mills was studied ,which revealed that top management role in developing employees' career is significantly correlated with the profit per unit realized by the mills (r = 0.249, p = 0.009). The correlation analysis result shows that top management had crucial role to play in the development of the organizational loyalty in employees through providing support to them towards the victorious career path, which eventually result in better performance by the employees and hence increased the profit of the cotton yarn mills.

Correlation between the profit per unit (PRFT) and supplier and customer relations management (SCR) prevailed in the cotton yarn mills was studied ,which revealed that supplier and customer relations management in the mills is significantly correlated with the profit per unit realized by the mills (r = 0.201, p = 0.035). The correlation analysis result shows that mills, which had an efficient mechanism of supplier and customer relations management, were realizing more profit than the mills without the well-established mechanism of supplier and customer relations management.

Correlation between the profit per unit (PRFT) and policy of designating authority with accountability to the employees (DAE) prevailed in the cotton yarn mills was studied ,which revealed that the existence of policy of designating authority with accountability to the employees in the mills is significantly correlated with the profit per unit realized by the mills (r = 0.308, p = 0.001). The correlation analysis result shows that mills, which had devised the indiscriminative policy of designating level of authority with rational and quantified accountability mechanism, were realizing more profit than the mills with more discriminative distribution of authority, responsibility, and accountability mechanism.

Correlation between the profit per unit (PRFT) and continuous quality improvement for customer satisfaction (QIC) in the cotton yarn mills was studied ,which revealed that continuous quality improvement for customer satisfaction in the mills is significantly correlated with the profit per unit realized by the mills (r = 0.195, p = 0.042). The correlation analysis result shows that mills, which were actively focused on customers' satisfaction through implementation of continuous quality improvement practices, were realizing more profit than the mills, which were reluctant or ignorant to modern quality improvement practices.

Table 4.46 Testing of hypothesis of profit per unit

Null Hypothesis (Ho)

Alternative Hypothesis (Ha)

Result

Profit per unit (PRFT) is independent of the quality-oriented systems (QOS) implemented in cotton yarn industry.

Profit per unit (PRFT) is correlated with the quality-oriented system (QOS) implemented in cotton yarn industry.

H1o is rejected

Profit per unit (PRFT) is independent of team based and customer- oriented environment (TCE) prevailed in cotton yarn industry.

Profit per unit (PRFT) is correlated with the team based and customer- oriented environment (TCE) prevailed in cotton yarn industry.

H2o is rejected

Profit per unit (PRFT) is independent of top management role in developing employees' career (TMR) prevailed in cotton yarn industry.

Profit per unit (PRFT) is correlated with the top management role in developing employees' career (TMR) prevailed in cotton yarn industry.

H3o is rejected

Profit per unit (PRFT) is independent of supplier and customer relations management (SCR) in cotton yarn industry.

Profit per unit (PRFT) is correlated with the supplier and customer relations management (SCR) in cotton yarn industry.

H4o is rejected

Profit per unit (PRFT) is independent of policy of designating authority with accountability to the employees (DAE) in cotton yarn industry.

Profit per unit (PRFT) is correlated with the policy of designating authority with accountability to the employees (DAE) in cotton yarn industry.

H5o is rejected

Profit per unit (PRFT) is independent of continuous quality improvement for customer satisfaction (QIC) in cotton yarn industry.

Profit per unit (PRFT) is correlated with the continuous quality improvement for customer satisfaction (QIC) in cotton yarn industry.

H6o is rejected

Table 4.46 shows that all the null hypotheses were rejected at level of significance (α=.05). Further, it is concluded that all six quality management construct had positive correlation with the profit per unit realized by the cotton yarn mills.

4.6.2. Correlation analysis of market share (MSHR)

Correlation between the market share (MSHR) and quality-oriented systems (QOS) implemented in the mills was studied, which revealed that implementation of quality oriented system is significantly correlated with the market share earned by the mills (r = 0.599, p =0.000). The correlation analysis result shows that presence and efficient execution of quality-oriented systems in the cotton yarn mills could increase the market share of the cotton yarn mills by minimizing the process failures, unwelcome work delays, unfastened monitoring, and controlling practices. This improvement in organizational quality scenario leads the organization towards success path in gaining and retaining the confidence of customers and good repute at market place.

Correlation between the market share (MSHR) and team based, customer- oriented environment (TCE) prevailed in the cotton yarn mills was studied ,which revealed that team based and customer- oriented environment in the mills is significantly correlated with market share earned by the mills (r = 0.557, p = 0.000). The correlation analysis result shows that mills, in which employees had developed a culture of teamwork and informal quality circles and had customer focused work environment, were more appreciated, and owned by the customers. These mills were observing more growth in market share than the mills without integrated workforce and not as much of customers-oriented work environment.

Correlation between the market share (MSHR) and top management role in developing employees' career (TMR) prevailed in the cotton yarn mills was studied ,which revealed that the correlation between top management role in developing employees' career and the market share earned by the mills is insignificant (r = 0.166, p = 0.083) with level of significance ( α =0.05, 2-tailed). However, the correlation is significant with level of significance (α = 0.10, 2-tailed). The correlation analysis result shows that although top management had crucial role to play in the development of the organizational loyalty in employees through providing support to employees towards the victorious career path but it has relatively low effect on the growth observed by the mills in their market share.

Correlation between the market share (MSHR) and supplier and customer relations management (SCR) prevailed in the cotton yarn mills was studied ,which revealed that supplier and customer relations management in the mills is not correlated significantly with the market share growth observed by the mills (r = 0.143, p = 0.137). The correlation analysis result shows that the mechanism of supplier and customer relations management, in the cotton yarn mills, is not significantly contributing in mills' growth rate of market share.

Correlation between the market share (MSHR) and policy of designating authority with accountability to the employees (DAE) prevailed in the cotton yarn mills was studied ,which revealed that the existence of policy of designating authority with accountability to the employees in the mills is significantly correlated with the growth in market share observed by the mills (r = 0.200, p = 0.036). The correlation analysis result shows that mills, which were devised the indiscriminative policy of designating level of authority with rational and quantified accountability mechanism observed higher growth rate in their market share than the mills with more discriminative distribution of authority, responsibility, and accountability mechanism.

Correlation between the market share (MSHR) and continuous quality improvement for customer satisfaction (QIC) in the cotton yarn mills was studied ,which revealed that continuous quality improvement for customer satisfaction in the mills is significantly correlated with the market share growth observed by the mills (r = 0.226, p = 0.018). The correlation analysis result shows that mills, which were prioritizing the customers' satisfaction and implementing of continuous quality improvement practices to achieve the customers' satisfaction, were observing higher growth rate in their market share than the mills that were reluctant or ignorant to modern quality improvement practices.

Table 4.47 Testing of hypothesis of market share (MSHR)

Null Hypothesis (Ho)

Alternative Hypothesis (Ha)

Result

Market share (MSHR) is independent of the quality-oriented systems (QOS) implemented in cotton yarn industry.

Market share (MSHR) is correlated with the quality-oriented system (QOS) implemented in cotton yarn industry.

H1o is rejected

Market share (MSHR) is independent of team based and customer- oriented environment (TCE) prevailed in cotton yarn industry.

Market share (MSHR) is correlated with the team based and customer- oriented environment (TCE) prevailed in cotton yarn industry.

H2o is rejected

Market share (MSHR) is independent of top management role in developing employees' career (TMR) prevailed in cotton yarn industry.

Market share (MSHR) is correlated with the top management role in developing employees' career (TMR) prevailed in cotton yarn industry.

H3o is rejected

Market share (MSHR) is independent of supplier and customer relations management (SCR) in cotton yarn industry.

Market share (MSHR) is correlated with the supplier and customer relations management (SCR) in cotton yarn industry.

H4o is accepted

Market share (MSHR) is independent of policy of designating authority with accountability to the employees (DAE) in cotton yarn industry.

Market share (MSHR) is correlated with the policy of designating authority with accountability to the employees (DAE) in cotton yarn industry.

H5o is rejected

Market share (MSHR) is independent of continuous quality improvement for customer satisfaction (QIC) in cotton yarn industry.

Market share (MSHR) is correlated with the continuous quality improvement for customer satisfaction (QIC) in cotton yarn industry.

H6o is rejected

Table 4.47 shows that four null hypotheses (H1o,H2o H5o and H6o ) are rejected at level of significance (α =.05) and H3o null hypothesis is rejected at level of significance (α = 0.10). The H4onull hypothesis is not rejected.

4.6.3. Correlation analysis of sale volume (SVOL)

Correlation between the sale volume (SVOL) and quality-oriented systems (QOS) implemented in the mills was studied, which revealed that implementation of quality oriented system is significantly correlated with the growth rate observed in sale volume by the mills (r = 0.502, p =0.000). The correlation analysis result shows that presence and efficient execution of quality-oriented systems in the cotton yarn mills could increase the growth rate of sale volume by minimizing the process failures, unwelcome work delays, unfastened monitoring, and controlling practices that helps to be successful in gaining and retaining the confidence of customers and good repute at market place.

Correlation between the sale volume (SVOL) and team based, customer- oriented environment (TCE) prevailed in the cotton yarn mills was studied ,which revealed that team based and customer- oriented environment in the mills is significantly correlated with the growth rate observed in sale volume by the mills (r = 0.492, p = 0.000). The correlation analysis result shows that mills, which had a culture of teamwork between the workforce and more customer focused, were observing higher growth rate of sale volume than the mills with lack of team based work approach among the employees and not as much customer focused.

Correlation between the sale volume (SVOL) and top management role in developing employees' career (TMR) prevailed in the cotton yarn mills was studied ,which revealed that top management role in developing employees' career is significantly correlated with the growth rate observed in sale volume by the mills (r = 0.198, p = 0.039). The correlation analysis result shows that top management had crucial role to play in the development of the organizational loyalty in employees through providing support to employees towards the victorious career path. Being motivated and loyal, employees could prove real asset for the cotton yarn mills to enhance the organizational quality and repute to attract the potential customers, and hence the higher growth rate of sale volume could be observed by the mills.

Correlation between the sale volume (SVOL) and supplier and customer relations management (SCR) prevailed in the cotton yarn mills was studied ,which revealed that the correlation between supplier and customer relations management and the growth rate observed in sale volume by the cotton yarn mills is not significant(r = 0.177, p = 0.064), with level of significance ( α =0.05, 2-tailed). However, the correlation is significant with level of significance (α = 0.10, 2-tailed). The correlation analysis result shows that mills, which had an efficient mechanism of supplier and customer relations management, were in better position to realize relatively higher growth in sale volume than mills without the supplier and customer relations management system.

Correlation between the sale volume (SVOL) and the policy of designating authority with accountability to the employees (DAE) prevailed in the cotton yarn mills was studied. It revealed that the existence of policy of designating authority with accountability to the employees in the mills is significantly correlated with the growth rate observed in sale volume by the mills (r = 0.432, p = 0.000). The correlation analysis result shows that mills, which were devised the indiscriminative policy of designating level of authority with rational and quantified accountability mechanism were enjoying higher growth rate in sale volume than the mills with more discriminative distribution of authority ,responsibility and accountability mechanism.

Correlation between the sale volume (SVOL) and continuous quality improvement for customer satisfaction (QIC) in the cotton yarn mills was studied ,which revealed that the correlation between continuous quality improvement for customer satisfaction and the growth rate observed in sale volume by the mills is not significant (r = 0.161, p = 0.092) with level of significance (α=0.05, 2-tailed). However, the correlation is significant with level of significance (α = 0.10, 2-tailed). The correlation analysis result shows that mills, which were actively focused on customers' satisfaction through implementation of continuous quality improvement practices, were observing relatively higher growth rate in sale volume than the mills, which were reluctant or ignorant to modern quality improvement practices.

Table 4.48 testing of hypothesis of sale volume (SVOL)

Null Hypothesis (Ho)

Alternative Hypothesis (Ha)

Result

Sale volume (SVOL) is independent of the quality-oriented systems (QOS) implemented in cotton yarn industry.

Sale volume (SVOL) is correlated with the quality-oriented system (QOS) implemented in cotton yarn industry.

H1o is rejected

Sale volume (SVOL) is independent of team based and customer- oriented environment (TCE) prevailed in cotton yarn industry.

Sale volume (SVOL) is correlated with the team based and customer- oriented environment (TCE) prevailed in cotton yarn industry.

H2o is rejected

Sale volume (SVOL) is independent of top management role in developing employees' career (TMR) prevailed in cotton yarn industry.

Sale volume (SVOL) is correlated with the top management role in developing employees' career (TMR) prevailed in cotton yarn industry.

H3o is rejected

Sale volume (SVOL) is independent of supplier and customer relations management (SCR) in cotton yarn industry.

Sale volume (SVOL) is correlated with the supplier and customer relations management (SCR) in cotton yarn industry.

H4o is rejected

Sale volume (SVOL) is independent of policy of designating authority with accountability to the employees (DAE) in cotton yarn industry.

Sale volume (SVOL) is correlated with the policy of designating authority with accountability to the employees (DAE) in cotton yarn industry.

H5o is rejected

Sale volume (SVOL) is independent of continuous quality improvement for customer satisfaction (QIC) in cotton yarn industry.

Sale volume (SVOL) is correlated with the continuous quality improvement for customer satisfaction (QIC) in cotton yarn industry.

H6o is rejected

Table 4.48 shows that four null hypotheses (H1o,H2o ,H3o and H5o ) are rejected at level of significance (α=.05). While, the null hypotheses (H4o and H6o) were rejected at level of significance (α =0.10).

4.6.4. Correlation analysis of reduced rejection rate (RRATE)

Correlation between the reduced rejection rate (RRATE) and quality-oriented systems (QOS) implemented in the mills was studied, which revealed that implementation of quality oriented system is significantly correlated with the reduced rejection rate achieved by the mills (r = 0.351, p =0.000). The correlation analysis result shows that presence and efficient execution of quality-oriented systems in the cotton yarn mills reduces the rejection rate of production by curtailment of production process failures, quality assurance of raw material, training of workforce, documentation of work procedure, and fastened the monitoring, and controlling practices. The use of proactive quality management system proved effective in reducing the rejection rate of cotton yarn mills.

Correlation between the reduced rejection rate (RRATE) and team based, customer- oriented environment (TCE) prevailed in the cotton yarn mills was studied ,which revealed that team based and customer- oriented environment in the mills is significantly correlated with the reduced rejection rate achieved by the mills (r = 0.623, p = 0.000). The correlation analysis result shows that mills, which had a culture of teamwork between the workforce and more customer focused, were in better position to realize profit.

Correlation between the reduced rejection rate (RRATE) and top management role in developing employees' career (TMR) prevailed in the cotton yarn mills was studied ,which revealed that top management role in developing employees' career is significantly correlated with reduced rejection rate achieved by the mills (r = 0.373, p = 0.000). The correlation analysis result shows that top management had crucial role to play in the development of the organizational loyalty in employees through providing support to human resources towards the victorious career path, which ultimately result in better performance by the employees and hence increased the profit of the cotton yarn mills.

Correlation between the reduced rejection rate (RRATE) and supplier and customer relations management (SCR) prevailed in the cotton yarn mills was studied ,which revealed that the correlation between supplier and customer relations management and reduced rejection rate achieved by the mills is not significant (r = 0.174, p = 0.07) with level of significance ( α =0.05, 2-tailed). However, the correlation is significant with level of significance (α = 0.10, 2-tailed). The correlation analysis result shows that mills, which had developed congenial supplier and customer relations through the better management, were successful in improving the quality of raw materials, and semi-finished products from the suppliers that reduced the rejection rate of the cotton yarn manufacturing processes.

Correlation between the reduced rejection rate (RRATE) and policy of designating authority with accountability to the employees (DAE) prevailed in the cotton yarn mills was studied ,which revealed that the existence of policy of designating authority with accountability to the employees in the mills is significantly correlated with the reduced rejection rate achieved by the mills (r = 0.196, p = 0.004). The correlation analysis result shows that mills, which had developed the indiscriminative policy of designating level of authority with rational and quantified accountability mechanism, made the employees more motivated, loyal, and efficient at their workplace. Motivated workforce with positive frame of mind help to minimize the number of process failures and the proportion of waste activities to gain a reduction in the defective rates in cotton yarn mills.

Correlation between the reduced rejection rate (RRATE) and continuous quality improvement for customer satisfaction (QIC) in the cotton yarn mills was studied ,which revealed that continuous quality improvement for customer satisfaction in the mills is significantly correlated with reduced rejection rate achieved by the mills (r = 0.286, p = 0.002). The correlation analysis result shows that mills, which were actively focused on customers' satisfaction through providing the quality products manufactured through quality oriented production processes ensured by the implementation of continuous quality improvement practices as statistical quality control, and quality assurance techniques to achieve reduction in the rejection rates.

Table 4.49 Testing of hypothesis of reduced rejection rate (RRATE).

Null Hypothesis (Ho)

Alternative Hypothesis (Ha)

Result

Reduced rejection rate (RRATE) is independent of the quality-oriented systems (QOS) implemented in cotton yarn industry.

Reduced rejection rate (RRATE) is correlated with the quality-oriented system (QOS) implemented in cotton yarn industry.

H1o is rejected

Reduced rejection rate (RRATE) is independent of team based and customer- oriented environment (TCE) prevailed in cotton yarn industry.

Reduced rejection rate (RRATE) is correlated with the team based and customer- oriented environment (TCE) prevailed in cotton yarn industry.

H2o is rejected

Reduced rejection rate (RRATE) is independent of top management role in developing employees' career (TMR) prevailed in cotton yarn industry.

Reduced rejection rate (RRATE) is correlated with the top management role in developing employees' career (TMR) prevailed in cotton yarn industry.

H3o is rejected

Reduced rejection rate (RRATE) is independent of supplier and customer relations management (SCR) in cotton yarn industry.

Reduced rejection rate (RRATE) is correlated with the supplier and customer relations management (SCR) in cotton yarn industry.

H4o is rejected

Reduced rejection rate (RRATE) is independent of policy of designating authority with accountability to the employees (DAE) in cotton yarn industry.

Reduced rejection rate (RRATE) is correlated with the policy of designating authority with accountability to the employees (DAE) in cotton yarn industry.

H5o is rejected

Reduced rejection rate (RRATE) is independent of continuous quality improvement for customer satisfaction (QIC) in cotton yarn industry.

Reduced rejection rate (RRATE) is correlated with the continuous quality improvement for customer satisfaction (QIC) in cotton yarn industry.

H6o is rejected

Table 4.49 shows that the five null hypotheses (H1o, H2o, H3o, H5o and H6o) were rejected at level of significance (α =.05).While, the null hypothesis (H4o) is rejected at level of significance (α=0.10).

4.6.5. Correlation analysis of overall improvement in organizational performance

Correlation between the overall improvement in organizational performance (OPI) and quality-oriented systems (QOS) implemented in the mills was studied, which revealed that implementation of quality oriented system is significantly correlated with overall improvement in organizational performance realized by the mills (r = 0.593, p =0.000). The correlation analysis result shows that presence and efficient execution of quality-oriented systems in the cotton yarn mills supports to achieve the organizational goal and overall organizational performance by minimizing the process failures, unwelcome work delays, unfastened monitoring, and controlling practices.

Correlation between the overall improvement in organizational performance (OPI) and team based, customer- oriented environment (TCE) prevailed in the cotton yarn mills was studied ,which revealed that team based and customer- oriented environment in the mills is significantly correlated with overall improvement in organizational performance realized by the mills (r = 0.302, p = 0.001). The correlation analysis result shows that mills, which had a culture of teamwork between the workforce and more customer focused were realized improvement in their overall performance.

Correlation between the overall improvement in organizational performance (OPI) and top management role in developing employees' career (TMR) prevailed in the cotton yarn mills was studied, which revealed that top management role in developing employees' career is significantly correlated with the overall improvement in organizational performance realized by the mills (r = 0.309, p = 0.001). The correlation analysis result shows that top management had crucial role to play in the development of the organizational loyalty in employees through providing support to employees towards the victorious career path, which eventually result in better performance by the employees towards the improvement of overall organizational performance.

Correlation between the overall improvement in organizational performance (OPI) and supplier and customer relations management (SCR) prevailed in the cotton yarn mills was studied ,which revealed that the correlation between supplier and customer relations management and the overall improvement in organizational performance achieved by the is not significant (r = 0.137, p = 0.153). The correlation analysis result shows that mechanism of supplier and customer relations management is not effectively contributing towards the improvement of overall organizational performance.

Correlation between the overall improvement in organizational performance (OPI) and the policy of designating authority with accountability to the employees (DAE) prevailed in the cotton yarn mills was studied. It revealed that the existence of policy of designating authority with accountability to the employees in the mills is significantly correlated with overall improvement in organizational performance realized by the mills (r = 0.342, p = 0.000). The correlation analysis result shows that mills, which were devised the indiscriminative policy of designating level of authority with rational and quantified accountability mechanism were enjoying higher improvement in overall organizational performance than the mills with more discriminative distribution of authority ,responsibility and accountability mechanism.

Correlation between the overall improvement in organizational performance (OPI) and continuous quality improvement for customer satisfaction (QIC) in the cotton yarn mills was studied ,which revealed that continuous quality improvement for customer satisfaction in the mills is significantly correlated with overall improvement in organizational performance realized by the mills (r = 0.329, p = 0.000). The correlation analysis result shows that mills, which were actively focused on customers' satisfaction through implementation of continuous quality improvement practices were, realized higher improvement in overall organizational performance than the mills, which were reluctant or ignorant to modern quality improvement practices.

Table 4.50 Testing of hypothesis of overall improvement in the organizational performance

Null Hypothesis (Ho)

Alternative Hypothesis (Ha)

Result

Overall improvement in the organizational performance (OPI) is independent of the quality-oriented systems (QOS) implemented in cotton yarn industry.

Overall improvement in the organizational performance (OPI) is correlated with the quality-oriented system (QOS) implemented in cotton yarn industry.

H1o is rejected

Overall improvement in the organizational performance (OPI) is independent of team based and customer- oriented environment (TCE) prevailed in cotton yarn industry.

Overall improvement in the organizational performance (OPI) is correlated with the team based and customer- oriented environment (TCE) prevailed in cotton yarn industry.

H2o is rejected

Overall improvement in the organizational performance (OPI) is independent of top management role in developing employees' career (TMR) prevailed in cotton yarn industry.

Overall improvement in the organizational performance (OPI) is correlated with the top management role in developing employees' career (TMR) prevailed in cotton yarn industry.

H3o is rejected

Overall improvement in the organizational performance (OPI) is independent of supplier and customer relations management (SCR) in cotton yarn industry.

Overall improvement in the organizational performance (OPI) is correlated with the supplier and customer relations management (SCR) in cotton yarn industry.

H4o is rejected

Overall improvement in the organizational performance (OPI) is independent of policy of designating authority with accountability to the employees (DAE) in cotton yarn industry.

Overall improvement in the organizational performance (OPI) is correlated with the policy of designating authority with accountability to the employees (DAE) in cotton yarn industry.

H5o is rejected

Overall improvement in the organizational performance (OPI) is independent of continuous quality improvement for customer satisfaction (QIC) in cotton yarn industry.

Overall improvement in the organizational performance (OPI) is correlated with the continuous quality improvement for customer satisfaction (QIC) in cotton yarn industry.

H6o is rejected

Table 4.50 shows that the five null hypotheses (H1o, H2o, H3o, H5o and H6o) were rejected at level of significance (α =.05).While, the H4o null hypothesis is not rejected at level of significance (α=0.10).

4.7 Regression Analysis

Multiple linear regression models are developed for each quality parameter using six independent quality management constructs. For each regression model, firstly ANOVA is performed to analyze the significance of model. Secondly, coefficient of determination (Adjusted R-Squared) is calculated to determine the percentage of variations explained by the independent variables in the model.

4.7.1. Multiple Linear Regression Model for Profit per Unit

Multiple linear regression analysis was performed to measure the relationship of independent variables, existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC).with dependent variable profit per unit (PRFT).

4.7.1.1. Parameter estimates of profit per unit model

Parameter estimates for the profit per unit were displayed in Table. (4.51).

Model

B

Std.Error

t

Sig.

PRFT

(Constant)

3.536

.038

93.083

.000

QOS

.369

.038

9.667

.000

TCE

.507

.038

13.280

.000

TMR

.210

.038

5.493

.000

SCR

.169

.038

4.439

.000

DAE

.259

.038

6.789

.000

QIC

.164

.038

4.295

.000

Table 4.51 Model Parameter Estimates

Following model was established based on experimental results obtained:

PRFT = 3.536 + 0.369 QOS+ 0.507 TCE+0.210 TMR+ 0.169 SCR+ 0.259 DAE+0.164 QIC

A significance relationship was observed between the profit per unit and all the six independent factors. All the factors contribute with positive coefficients in defining the regression model for profit per unit (PRFT) i.e. high level of adoption of each factor would result in increase of profit per unit. From the above model, it was concluded that a unit level change in adoption of quality-oriented system (QOS) would cause an increase of profit per unit by 0.369, which was the highest effect against other factors. Continuous improvement for customer satisfaction (QIC) had lowest effect (0.164) in defining the profit per unit (PPU) of cotton spinning mills.

4.7.1.2. ANOVA of organizational profit per unit model

Model significance was tested by ANOVA and result was displayed in Table 4.52.

Model

Sum of Squares

df

Mean Square

F

Sig.

PRFT

Regression

61.001

6

10.167

64.036

.000

Residual

16.353

103

.159

Total

77.355

109

From the Table 4.52, the p-value of ANOVA was 0.000, which shows that the model is highly significant as p-value is less than 0.05.

4.7.1.3. Summary of profit per unit model

The model summary is provided, which is comprised of coefficient of Determination (R-square), adjusted R- square and standard error of estimate.

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

PRFT

.888

.789

.776

.398

Table 4.53 Model Summary

From the Table 4.53, the regression model had an Adjusted R-Squared value 0.789, which showed that 78.9 % variation in the model was explained, for profit per unit (PRFT), by the linear relationship of independent variables. High Adjusted R-Squared value (> 0.70) also depicted that model was significant. The standard error of estimate was 0.398, showed that observed data was not far away from the estimated line and the model was justified.

4.7.1.4. Normal P-P Plot of Regression model standardized residuals

The normal P-P plot of regression model standardized residuals is constructed to rationale the use of linear regression analysis under the assumption of normality for the profit per unit (PRFT).

Figure 4.41 Normal P-P of Regression Standardized Residuals

Figure (4.41) shows that the residuals of regression line follows a normal distribution, hence the validity of regression analysis is proved.

The multiple linear regression discovered that existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC), are significant variables to define the profit per unit (PRFT) of the cotton yarn mills. The six quality management constructs explained 78.9 percent of the variability in the regression model of profit per unit (PRFT) in cotton yarn mills. The regression analysis further revealed that cotton yarn mills, which have devised quality-oriented systems to deal with internal and external customers in successful manner, with the support from top management's indiscriminative behavior towards the organizational developments, and the initiated collaboration with suppliers in enhancing the yarn quality, can realize higher business profit in the scenario of competition-oriented cotton yarn market.

4.7.2. Multiple Linear Regression Model for Sale Volume

Multiple linear regression models were used to measure the relationship of independent variables existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC) with sale volume (SVOL).

4.7.2.1. Parameter estimates of organizational performance model

Parameter estimates for the sale volumes were displayed in Table 4.54.

Model

B

Std.Error

t

Sig.

SVOL

(Constant)

3.709

.040

91.891

.000

QOS

.437

.041

10.784

.000

TCE

.429

.041

10.569

.000

TMR

.172

.041

4.245

.000

SCR

.154

.041

3.807

.000

DAE

.376

.041

9.273

.000

QIC

.140

.041

3.449

.001

Table 4.54 Model Parameter Estimates

Following model was established based on statistical analysis:

SVOL= 3.709 + 0.437 QOS+ 0.429 TCE+0.172 TMR+ 0.154 SCR+ 0.376 DAE+0.140 QIC

A significance relationship was observed between the sale volume (SVOL) and all the six independent factors. All the factors contribute with positive coefficients in defining the regression model for the sale volume i.e. high level of adoption of each factor would result in increase of sale volume (SVOL) of the cotton spinning mills. From the above regression model, it was concluded that organizations with well-established quality oriented system (QOS) achieved higher sale volume (SVOL). Continuous improvement for customer satisfaction (QIC) had lowest effect (0.140) in defining the increasing rate of sale volume of cotton spinning mills.

4.7.2.2. ANOVA of sale volume model

Model significance was tested by ANOVA and results were displayed in Table 4.55.

Model

Sum of Squares

df

Mean Square

F

Sig.

SVOL

Regression

64.231

6

10.705

59.733

.000

Residual

18.459

103

.179

Total

82.691

109

Table.8 (b) Model Usability

From the Table 4.55, the p-value of ANOVA was 0.000, which shows that the model is highly significant as p-value is less than 0.05.

4.7.2.3. Summary of sale volume model

The model summary is provided, which is comprised of coefficient of Determination (R-square), adjusted R- square and standard error of estimate.

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

SVOL

.881

.777

.764

.423

Table.4.56 Model Summary

From the Table 4.56, the regression model had an Adjusted R-Squared value 0.764, which showed that 76.4 % variation in the model for sale volume was explained by selected independent variables. High Adjusted Squared value (> 0.70) also depicted that model was significant. The standard error of estimate was 0.423 was very small which showed that observed data was not far away from the estimated line and the model was justified.

4.7.2.4. Normal P-P Plot of Regression model standardized residuals

The normal P-P plot of regression model standardized residuals is constructed to rationale the use of linear regression analysis under the assumption of normality for the sale volume (SVOL).

Figure 4.42 Normal P-P of Regression Standardized Residuals

Figure (4.42) shows that the residuals of regression line follows a normal distribution, hence the validity of regression analysis is proved.

The multiple linear regression revealed that the independent variables in the sale volume (SVOL) model, existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC), are significant. The six quality management predictors explained 76.4 percent of the variability in the sale volume (SVOL) in cotton yarn mills. The regression analysis shows that cotton yarn mills which were implementing the quality management techniques realizing significance growth in their sale volume because of increase in efficiency and yarn quality improvement. Further, the regression model revealed that the cotton yarn mills, which have devised quality-oriented systems to deal with internal and external customers in productive manner, with the support from top management's indiscriminative behavior towards the organizational developments, and the initiated collaboration with suppliers in enhancing the yarn quality would realize an increase in sale volume (SVOL).

4.7.3. Multiple Linear Regression Model for Market Share

Multiple linear regression models were used to measure the relationship of independent variables existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC).with the predicted variable production market share (MSHR).

4.7.3.1. Parameter estimates of organizational performance mode

Parameter estimates for the performance efficiency were displayed in Table.4.57.

Model

B

Std.Error

t

Sig.

MSHR

(Constant)

2.964

.051

57.687

.000

QOS

.716

.052

13.881

.000

TCE

.666

.052

12.911

.000

TMR

.198

.052

3.846

.000

SCR

.171

.052

3.305

.001

DAE

.239

.052

4.628

.000

QIC

.270

.052

5.227

.000

Table 4.57 Model Parameter Estimates

Following model was established based on analysis;

MSHR = 2.964 + 0.716 QOS+ 0.666 TCE+0.198 TMR+ 0.171 SCR+ 0.239 DAE+0.270 QIC

A statistically significance relationship was observed between the market share (MSHR) of mills and the quality management practices adopted by them. All the six factors contribute with positive coefficients in defining the regression model for the sale volume i.e. high level of adoption of each factor would result in increase of market share of the cotton spinning mills. From the above regression model, it was concluded that with unit level change in quality-oriented system (QOS) would cause an increase of market share of mills by 0.716, which was the highest effect against other factors. Supplier and customer relation management (SCR) had lowest effect (0.171) in defining the market share (MSHR) of cotton spinning mills.

4.7.3.2. ANOVA of market share model

Model significance was tested by ANOVA and result was displayed in Table 4.58.

Model

Sum of Squares

df

Mean Square

F

Sig.

MSHR

Regression

125.951

6

20.992

72.305

.000

Residual

29.903

103

.290

Total

155.855

109

Table 4.58 Model Usability

From the Table 4.58, the p-value of ANOVA was 0.000, which shows that the model was highly significant as p-value was less than 0.05.

4.7.3.3. Summary of market share model

The model summary is provided, which is comprised of coefficient of Determination (R-square), adjusted R- square and standard error of estimate.

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

MSHR

.899

.808

.797

.539

Table 4.59 Model Summary

From the Table 4.59, the regression model for market share had an Adjusted R-Squared value 0.797, which showed that 79.7 % variation in the model, was because of selected independent variable and 21.3% variation was unexplained that might be attributed to other than selected factors. High Adjusted R-Squared value (> 0.70) also depicted that model was significant. The standard error of estimate was .539; it showed that observed data was not far away from the estimated line and the model was justified.

4.7.3.4. Normal P-P Plot of Regression model standardized residuals

The normal P-P plot of regression model standardized residuals is constructed to rationale the use of linear regression analysis under the assumption of normality for the market share (MSHR).

Figure 4.43Normal P-P of Regression Standardized Residuals

Figure (4.43) shows that the residuals of regression line follows a normal distribution, hence the validity of regression analysis was proved.

The multiple linear regression revealed that the model predictors, existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC) are significant to define the market share (MSHR). The six quality management constructs explained 79.7 percent of the variability in the market share (MSHR) in cotton yarn mills. Further, the regression analysis revealed that a cotton yarn mills which have devised quality-oriented systems to deal with internal and external customers in flourishing manner with the support from top management's indiscriminative behavior towards the organizational developments, and the initiated collaboration with suppliers in enhancing the yarn quality, would realize higher business performance and increase in market share.

4.7.4. Multiple Linear Regression Model for Reduced Rejection Rate

Multiple linear regression models were used to measure the relationship of independent variables existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC).with reduced rejection rate (RRATE).

4.7.4.1. Parameter estimates of organizational performance model

Parameter estimates for the performance efficiency were displayed in Table.4.60.

Model

B

Std.Error

t

Sig.

RRATE

(Constant)

3.709

.033

111.526

.000

QOS

.266

.033

7.975

.000

TCE

.472

.033

14.135

.000

TMR

.283

.033

8.459

.000

SCR

.132

.033

3.940

.000

DAE

.149

.033

4.446

.000

QIC

.217

.033

6.483

.000

Table 4.60 Model Parameter Estimates

Following model was established based on analysis;

RRAT = 3.709 + 0.266QOS+ 0.472 TCE+0.283 TMR+ 0.132 SCR+ 0.149 DAE+0.217 QIC

A statistically significance relationship was observed between the reduced rejection rate (RRATE) of mills and the quality management practices adopted by them. All the six factors contribute with positive coefficients in defining the regression model for the reduced rejection rate i.e. high level of adoption of each factor would result in reduced rejection rate in the cotton spinning mills. From the above regression model, it was concluded that with unit level change in adoption of quality management practices comprised of Teams based customer-oriented environment (TCE) would reduce the rejection rate by 0.472, which was the highest effect against other factors. Supplier and customer relation management (SCR) had lowest effect (0.132) in defining the reduced rejection rate (RRATE) in the cotton spinning mills.

4.7.4.2. ANOVA of reduced rejection rate model

Model significance was tested by ANOVA and result was displayed in Table 4.61.

Model

Sum of Squares

df

Mean Square

F

Sig.

RRATE

Regression

50.159

6

8.360

68.711

.000

Residual

12.532

103

.122

Total

62.691

109

Table 4.61 Model Usability

From the Table 4.61, the p-value of ANOVA was 0.000. Which shows that the model is highly significant as p-value is less than 0.05.

4.7.4.3. Summary of reduced rejection rate model

The model summary is provided, which is comprised of coefficient of Determination (R-square), adjusted R- square and standard error of estimate.

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

RRATE

.894

.800

.788

.349

Table 4.62 Model Summary

From the Table 4.62, The regression model for reduced rejection rate had an Adjusted R-Squared value 0.788, which showed that 78.8 % variation in the model, was because of selected independent variables and 21.2% variation was unexplained that might be attributed to factors other than selected in study. High Adjusted R-Squared value (> 0.70) also depicted that model was significant. The standard error of estimate was small (0.349) which showed that observed data was not far away from the estimated line and the model was justified.

4.7.4.4. Normal P-P Plot of Regression model standardized residuals

The normal P-P plot of regression model standardized residuals is constructed to rationale the use of linear regression analysis under the assumption of normality for the reduced rejection rate (RRATE).

Figure 4.44 Normal P-P of Regression Standardized Residuals

Figure (4.44) shows that the residuals of regression line follows a normal distribution, hence the validity of regression analysis is proved.

The multiple linear regression revealed that existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC), are significant variables to define the profit per unit (PRFT) of the cotton yarn mills. The six quality management constructs explained 78.8 percent of the variability in the reduced rejection rate (RRATE) in cotton yarn mills. The regression analysis further revealed that a cotton yarn mills which have devised quality-oriented systems to deal with internal and external customers in successful manner, with the support from top management's indiscriminative behavior towards the organizational developments, and the initiated collaboration with suppliers in enhancing the yarn quality that would result in process improvement and observed reduction in rejection rate.

4.7.5. Multiple Linear Regression Model for Organizational Performance

Multiple linear regression models were used to measure the relationship of independent variables existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC).with predicted variable organizational performance (OPI).

4.7.5.1. Parameter estimates of organizational performance model

Parameter estimates for the organizational performance were displayed in Table.4.63.

Model

B

Std.Error

t

Sig.

OPI

(Constant)

3.536

.051

68.876

.000

QOS

.666

.052

12.912

.000

TCE

.339

.052

6.569

.000

TMR

.347

.052

6.722

.000

SCR

.154

.052

2.987

.004

DAE

.384

.052

7.438

.000

QIC

.370

.052

7.167

.000

Table 4.63 Model Parameter Estimates

OPI= 3.536 + 0.666QOS + 0.339 TCE + 0.347 TMR + 0.154 SCR + 0.384 DAE + 0.370 QIC

Following model was established based on analysis:

A statistically significance relationship was observed between organizational performance (OPI) of mills and the quality management practices adopted by them. All the six factors contribute with positive coefficients in defining the regression model for the organizational performance (OPI) i.e. high level of adoption of each factor would result in increase organizational performance of the cotton spinning mills. From the above regression model, it was concluded that with unit level change in adoption a quality-oriented system (QOS) would have an effect of 0.666 in defining the organizational performance level, which was the highest effect against other factors. Supplier and customer relation management (SCR) had lowest effect (0.154) in defining the level of organizational performance in the cotton spinning mills.

4.7.5.2. ANOVA of overall improvement in organizational performance model

Model significance was tested by ANOVA and result was displayed in Table 4.64.

Model

Sum of Squares

df

Mean Square

F

Sig.

OPI

Regression

107.486

6

17.914

61.777

.000

Residual

29.868

103

.290

Total

137.355

109

Table 4.64 Model Usability

From the Table 4.64, the p-value of ANOVA was 0.000, which shows that the model is highly significant as p-value is less than 0.05.

4.7.5.3. Summary of overall improvement in organizational performance model

The model summary is provided, which is comprised of coefficient of Determination (R-square), adjusted R- square and standard error of estimate.

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

OPI

.885

.783

.770

.539

Table 4.65 Model Summary

From the Table 4.65, the regression model for organizational performance had an Adjusted Squared value 0.770, which showed that 77 % variation in the model, was because of selected independent variable and 23% variation was unexplained. High Adjusted R-Squared value (> 0.70) also depicted that model was significant. The standard error of estimate was 0.539, which showed that observed data was not far away from the estimated line and the model was justified.

4.7.5.4. Normal P-P Plot of Regression model standardized residuals

The normal P-P plot of regression model standardized residuals is constructed to rationale the use of linear regression analysis under the assumption of normality for the overall organizational performance (OPI).

Figure 4.45 Normal P-P of Regression Standardized Residuals

Figure (4.45) shows that the residuals of regression line follows a normal distribution, hence the validity of regression analysis was proved.

The multiple linear regression discovered that existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC), are significant variables towards the overall organizational performance (OPI) of the cotton yarn mills. The six quality management constructs explained 77.0 percent of the variability of the overall organizational performance (OPI) in cotton yarn mills. The regression analysis further revealed that a cotton yarn mills, which have devised quality-oriented systems to deal with internal and external customers in successful manner, with the support from top management's indiscriminative behavior towards the organizational developments, and the initiated collaboration with suppliers, creates a productive work environment that would positively contribute in overall organizational performance.

Summary

This chapter was mainly focused on results of five statistical analyses; firstly, the descriptive statistics, comprised of frequency tables and histograms, of the quality management techniques was reported. The descriptive statistics revealed that cotton yarn industry was using quality management techniques with average to high level with high inter variability between the mills. Secondly, correlation analysis between the variables was reported, which revealed that quality management techniques were inter-related positively. The positive correlation suggested that adoption of any of the quality management technique facilitated the organization to adopt other quality management techniques with more productive results. Further, the correlation analysis of quality management techniques with performance measures revealed that implementation of quality management techniques by the cotton yarn mills had positive impact on the performance measures. These results supported the results of effectiveness of quality management techniques in other industries (Issac et al,2004, Mile,2006, Lassaad et al ,20006). Thirdly, factor analysis was used to establish independent quality management constructs from the correlated quality management techniques. Six quality management constructs were extracted from the quality management techniques, which explained 78.4 percent of variation against 100 percent variation with thirty-five items. The extracted quality management factors were comprised of existence of quality-oriented system (QOS), teams based customer-oriented environment (TCE), top management role in developing employees' career (TMR ), supplier and customer relation management (SCR), designating authority with accountability to employees (DAE), and continuous quality improvement for customer satisfaction (QIC). Thirdly, result of reliability analysis with Cronbatch α, was reported, which revealed that all the quality management factors were reliable (α > 0.70) except QIC, for which Cronbatch α could not be calculate as it was comprised of one variable only. Fourthly, results of correlation analysis of quality management factors against each performance measures were reported. The correlation analysis revealed that quality management factors had positive impact on performance measures in the cotton yarn industry, which supported the results of quality management in other industries. Finally, the results of regression models for each organizational performance measure were reported. The regression models revealed quality management factors were significant in defining the organization performance measures profit per unit (PRFT), sale volume (SVOL), reduced rejection rate (RATE), market share (MSHR) and the organizational performance improvement (OPI). Further, the results from regression models suggested that the implementation of quality management techniques by the cotton yarn mills had positive impact on organizational performance of mills.