Development and validation of quality management ...

Int. J. Services and Operations Management, Vol. 17, No. 4, 2014

Development and validation of quality management constructs for software industries: an empirical investigation from India A.K. Digalwar* Department of Mechanical Engineering, Birla Institute of Technology and Science, Pilani (Rajasthan) – 333031, India E-mail: [email protected] *Corresponding author

P. Haridas Department of Mechanical Engineering, College of Engineering, Trivendrum – 695574, India E-mail: [email protected]

I. Nelson Joseph Lourdes Matha College of Science and Technology, Lourdes Hills, Kuttichal, Trivandrum – 695574, India E-mail: [email protected] Abstract: Quality management (QM) is a powerful technique for managing, monitoring, analysing and improving the operational performance. Various studies have been done in the manufacturing and service sectors to find out the relation between quality management practices and operational performance. Very little studies are found from software industries. The aim of this paper is to present results of a survey on QM in the software industries. The focus is on understanding the critical success factors (CSFs) for successful implementation of QM in the software industry. In total, 12 critical success factors (CSFs) with 114 variables were developed from the literature and discussions with software quality professionals. To validate the data obtained from a survey of software industries in India, reliability and validity analysis was carried out. These results provide an increased understanding of how to better implement QM in the software industry, and provide managers with improved guidelines for identifying the most important factors that will lead to success. Indian software companies are leading exporters to Europe and the USA. Considering the growth of the Indian software industry and increased inclination towards acquiring quality certifications and quality improvement techniques, a better understanding of the implementation of QM can provide companies with a stronger competitive advantage. Keywords: quality management; critical success factors; factor analysis; software industry; India.

Copyright © 2014 Inderscience Enterprises Ltd.

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A.K. Digalwar et al. Reference to this paper should be made as follows: Digalwar, A.K., Haridas, P. and Joseph, I.N. (2014) ‘Development and validation of quality management constructs for software industries: an empirical investigation from India’, Int. J. Services and Operations Management, Vol. 17, No. 4, pp.453–478. Biographical notes: A.K. Digalwar received his PhD from the BITS Pilani, India. Currently, he is working as an Assistant Professor in Mechanical Engineering. He has over 15 years of teaching experience at graduate and post-graduate levels. His areas of interest are performance measurement systems, world class manufacturing, total quality management, lean and sustainable/green manufacturing. He has published more than 40 papers in national/international conferences and international journals. He is a Life Member of Indian Society of Technical Education and a member of Performance Measurement Association, UK. Also, he is a Secretary General for Indian Subcontinent Decision Sciences Institute (ISDSI) USA. P. Haridas has completed his BSc (Eng.) in Mechanical Engineering in 1969 from the College of Engineering, Trivandrum, later he did MTech in Industrial Engineering and Operations Research from the IIT Kharagpur and completed his PhD from the BITS Pilani. He has more than 40 years of teaching experience at various levels. He has published many papers in the national and international conferences and journals. His area of research includes total quality management, performance measurement system, and operations management. I. Nelson Joseph did BSc (Eng.) in Mechanical Engineering in 1975 from the Kerala University and PGDIE from the NITIE Mumbai. He has completed his PhD in Total Quality Management from the IIT Madras. He has more than 28 years of teaching experience and five years of industrial experience. At present, he is working as a Principal at the Marian Engineering College Trivandrum. He has published many papers in the national and international conferences and journals.

1

Introduction

In this era of globalisation, with intensive competition, it is very tough for an organisation to survive. In the prevailing global economy, the long term success depends upon the organisations ability to reorganise and to improve its operations to match the new challenges in the external environment. Customers’ needs become increasingly difficult to meet ever increasingly forced to achieve customers demand for faster response, better value for money, products or services, more product varieties and expect lower prices, reliable delivery and product reliability and integrity. To remain competitive, organisations introduce quality management and improvement programmes one after another. Companies started giving total quality management (TQM) as a management philosophy and a way of companies life that helps in managing organisations to improve its overall effectiveness and performance towards achieving world-class status for the past few decades. In the present days of globalisation and liberalisation, ability to export successfully is becoming crucial to the economic well-being of any country. Even though USA and UK were dominating in the world market, today it cannot be disputed that the Japanese have

Development and validation of quality management constructs

455

achieved a position of superiority in the world market. This is achieved by the Japanese companies by producing universally acceptable quality products. The reputation of quality has enabled Japanese companies to increase their market share at a rapid and incredible rate. It is a fact that companies are increasingly forced to achieve world-class manufacturing capability in order to compete and in many cases to survive. The ability to respond to these domestic and world class challengers is the difference between survival and extinction. TQM is a way to establish that differentiation in any company (Haridas, 2012). In the present day context, Indian companies have a lot of challenges ahead and the picture is not very encouraging. After the liberalisation process during early ‘90s, these processes have become imperative for the survival of many organisations. Business units in India are ever increasingly forced to achieve world class manufacturing capabilities in order to compete and in many cases to survive. This can be achieved only through the practice of quality management. A survey conducted by National productivity Council revealed that quality improvement was considered vital to strengthen the competitiveness of Indian business and industry. Many survey responses showed that a majority of senior managers are unaware of the benefits of quality costing, an integral element of TQM. From the survey one could conclude that the function of quality assurance is well accepted in manufacturing organisations in India. However, there seems to be some erroneous understanding since many organisations considers quality assurance to be same as TQM (Haridas and Digalwar, 2012). Many surveys and studies conducted in the last decade in many of the Indian organisations shows that they have already geared to accept changes and adaptation to bring about new management thinking based on TQM. However, there have been differences in the approach to TQM. Introduction of quality assurance through ISO 9000 is the most popular approach towards TQM in Indian companies and institutions (Karuppusami and Gandhinathan, 2006; Burli et al., 2012). In quality management, the main focus is on continuous improvement. When the organisation adopts TQM practices, they need new methods of performance measurement to check for continuous improvement. In India most of the manufacturing and service industries are still very far from world class practices and as such they are facing competition both from imports and from multinational companies in the domestic markets. Although many studies have been done in the manufacturing sector (Saraph et al., 1989; Flynn et al., 1994; Black and Porter, 1996; Ahire et al., 1996; Andersen and Jordan, 1998; Gunasekaran et al., 1998; Mohanty and Lakhe, 1998; Motwani, 2001; Wali et al., 2003; Kounis and Panagopoulos, 2007; Sangwan and Digalwar, 2008; Sugumaran et al., 2013), and in service sectors like banking, hotels, hospitality, transportations, education, software projects (Anderson, 1995; Gupta et al., 2005; Psomas et al., 2010; Ueno, 2010; Bayraktaroglu and Atrek, 2010; Gupta et al., 2012; Sharma and Garg, 2012; Shirouyehzad et al., 2012; Burli et al., 2012; Fraser et al., 2013; Adil et al., 2013; Sudhakar, 2013). It is observed that no work has yet been reported on the development and validation of a set of CSFs which cover the entire domain of software industries. Adopting the methodology used by Saraph et al. (1989) in the field of quality management and Digalwar and Sangwan (2007) in the field of world class manufacturing, this paper attempts to identify and validate a set of critical success factors for software industries in India. The reliability and validity analysis were carried out by

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A.K. Digalwar et al.

using SPSS 11.5 for windows statistical tool on the data collected from software industries.

2

Indian software industry

The modern business and marketing have a major role to play in the new liberalised and globalised business era. In India, the trend of developing business has been positive until now. For this progress the information technology (IT) field has played an important role. There was a time probably till a decade ago, when no Indian company would dream of becoming a global player. Then the software industry showed what was possible with a combination of enterprise and quick thinking. Now, the entire world thinks of India as one of the places from where to execute its IT strategy. The IT industry is India’s first truly global industry. The Indian IT industry has been steering the growth of the Indian economy like no other industry in the past decade by generating jobs, pushing exports, enticing FDI, creating wealth, bolstering foreign exchange reserves and in umpteen other visible and invisible ways. It has grown at an incredible rate of 50% per annum over the past few years and has the potential to grow even further and faster. It is highly export oriented and extremely knowledge intensive. The nation is pinning high hopes on this industry in its developmental agenda (Haridas and Digalwar, 2012). The size of the software business in the domestic market in the 1970s was not big enough for these companies to generate revenues year after year. As a result they had to look towards foreign shores to generate revenues. In the 1980s, technological changes in the IT sector offered new opportunities for the Indian software firms. The big opportunity and potential to earn huge revenues attracted firms like TCS, Wipro, HCL, Infosys and other to enter the software business. These firms acquired considerable expertise in various types of IT related work. The excellent skill sets possessed by Indian engineers and the 12-hour time difference between India and the USA attracted a few foreign companies to locate their software development centres in India. Indian seems to have comprehensive understanding that software is treasure in knowledge economy era. India Government plays an important role during software industry growing up promptly. In 1980, India Government established long-range strategic planning for developing software; in 1986, India Government made policies to encourage software export, software development and training; in 1991, India Government made set a plan to build STPI; in 1998, India Government made 108 pieces of measures to develop software industry. This plan asked the government to make policy environment for an aim of $50 billion in 2008, becoming one of the biggest countries of software product and export. Detail measures include zero-tariff policy, zero circulation tax and zero service tax, and immunity bank loan and risk investment. Moreover, government’s adverting to IT leads public passion in India (Beamish and Armistead, 2001; Feixue and Zhijie, 2012). Feixue and Zhijie (2012) explained in their paper, that in June 1991, India Government established the first software park in Bangalore and took preferential policies – exemption from import and export tax to encourage overseas and domestic companies, regulated limitation for small and medium-sized enterprises to introduce computer technology, and allowed foreign enterprises to control 75% to 100% of shares, software businessmen whose product is aboard-oriented could be exempted from income tax. Preferential policies mentioned above stimulate both here and abroad investment and


457

‘brain drain’ disappears. Thus, many famous IT companies establish research centres and production base in India, such as Microsoft, Intel, Apple and IBM. Some companies even divert more than half of software and development project to India. Along with coming into India, these famous companies bring about not only precious fund but also advanced supervise and technology. At present, India has 18 software parks and 7,500 listed companies, government also plan to establish new software park in undeveloped areas. These positive policies basically become great power for India software development. Overseas markets occupy 95% of India software industry, mainly software outsourcing. The development of software industry is dependent on economic background of the country, because software industry cannot exist without environment. According to statistics from Culture Development Report of UN, culture development index of India is up to 0.446 in 1995, but only ranks the 139th in 174 countries. India is an agriculture country with over 1 billion population, about 30 million people live in poverty; imperfect equipment manufacturing system, uncompleted department and small scale cannot extend domestic market and provide value added service. Therefore, India software industry is lack of domestic market as firm supporter. The structure of Indian information industry is irrational to some degree and hardware manufacturing is almost a blank. It is disadvantageous for long term development of Indian software without the support of domestic hardware manufacturers, since hardware is the bearer of software and the relation between software and hardware is imitate. Development of software can promote development of hardware and vice verse (Feixue and Zhijie, 2012). The value of exports of software industry of Israel is $2.5 billion in 2000; Ireland is $8.5 billion; and the sum of population of these two countries is less than 10 million. Moreover, software industry of Korea, Taiwan, Philippine challenges India. Technology of software industry change fast, software export of India is not export of product and intellectual property but export of labour service. India software companies primarily process for US software companies and intellectual property of most software products still belongs to US companies. The world software market is growing rapidly due to fast technological change, free trade and strong competitive pressures. To move further up the value chain, improving productivity, targeting new geographies, vertical domains the business and compete with the big US IT Vendors and threats from other software developing countries, the Indian software firms realised the need to move away from being bracketed as low price and low quality producers to competitively priced and high quality producers.

3

Literature review on quality management constructs

Until a study conducted by Saraph et al. (1989), no comprehensive measure of quality management was found in the literature that could systematically measure the extent of quality management in business units. This study developed measures of quality management, and identified 76 operating-system elements of quality management programme. Using a sample of 162 managers, the authors validated scales for the identified constructs. Further, testing and refinement of the Saraph et al. (1989) instrument is required to understand quality management practice better, and to develop theories and models that relate the critical factors of QM program to an organisation’s performance. Saraph et al. (1989) considered their study to be preliminary, and they

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called for follow up studies to further develop the systematic measurement of quality management practice in industry. Saraph et al. (1989) identified eight critical factors of quality management that were synthesised from the literature on quality management at the business unit level. The instrument contained operational measures (items) for each of the factors. The measures could be used in combination to produce a profile of quality management within a business unit as a whole, or with respect to individual factors. The measures proposed by authors were empirically-based and shown to be reliable and valid. Flynn et al. (1994) developed dimensions of quality management. A study of 42 manufacturing plants from three industries, which sought multiple responses from managers and workers from various functions, formed the basis for empirical validation and refinement of these constructs. Based on the seven dimensions of quality management identified, a set of 14 perceptual scales was developed. The scales consisted of quality leadership, quality improvement rewards, process control, feedback, maintenance, cleanliness and organisation, new product quality, product design simplicity, inter-functional design process, labour skills, selection for team work potential, team work, supplier relationship, and customer interaction. Black and Porter (1996) proposed an empirical frame work for QM implementation. The research extracted a series of items from the Malcolm Baldrige model and the established literature. These items formed the basis of a questionnaire sent to over 200 managers. Data was examined using several well established analytical techniques that identified ten critical factors of QM. The factors included corporate quality culture, strategic quality management, performance measurement system, human resource management, operational quality planning, corporate image, supplier partnership, and infrastructure for process improvement, customer satisfaction orientation and improvement assessment. These factors were shown to be reliable and valid. Ahire et al. (1996) conducted a similar study and suggested an empirical framework for QM implementation. Through a detailed analysis of the literature, they identified 12 constructs of integrated quality management strategies. Using a survey of 371 manufacturing firms and the constructs were then validated. Finally, a framework to examine the effects of integrated quality management strategies on a firm’s product quality was suggested. The integrated quality management constructs included top management commitment, customer focus, supplier quality management, design quality management, benchmarking, SPC usage, and internal quality information usage, employee empowerment, employee involvement, employee training, product quality, supplier and performance. Reed et al. (1996) suggested that cost leadership and differentiation strategy in the companies would enhance the execution effect of QM activities. Based on literature review, Seetharaman et al. (2006) generalised six key success factors on the execution of QM activities: high-rank manager’s support, employee’s active involvement in the execution, methods to measure the performance, culture of continuous improvement, value on customer’s needs, education and employment training. Ismail and Ebrahimpour (2003) selected 76 literatures related to QM activities from 1989 to 2000 and recognised the key success factors of effective QM activities execution, including leadership, strategic planning, customer and market orientation, data analysis, human resource management and process management. Antony et al. (2002) suggested 11 key success factors of the execution of QM activities, including educational training, quality data and figure analysis, the manager’s commitment, customer satisfaction orientation, role of quality control department, communication for quality improvement, continuous


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improvement, product and service design, the supplier’s quality management, manufacturing management and employee relationship. Motwani (2001) generalised seven constructs of the execution of QM activities high rank manager’s support, quality measurement and benchmarking, manufacturing management, product design, employee training and empowerment, supplier quality management and customer participation and satisfaction. Chung et al. (2010) divided the implementation of QM activities into five constructs: leadership, data analysis, human resource management, process management and market orientation. Prajogo and Sohal (2001) suggested that the implementation strategies of QM activities included differentiation strategy and cost leadership strategy. QM activities upon differentiation strategy aimed to provide better products or services to satisfy the customer’s needs. Cost leadership strategy aimed to reduce the cost and avoid the flaws and wastes. According to Sohail and Teo (2003) some researchers have pointed out the opinion that the ISO 9000 certification is the first step towards the implementation of QM while some researchers still prefer to maintain focus on QM only. They indicated that even though some authors praise the ISO 9000 concept, others view it as a ritualised form of quality management that should not be used in isolation from QM principles. One study by Sun (2000) found that ISO 9000 standards are partially related to the implementation of QM and the improvement of business performance and therefore it is recommended by the study that ISO 9000 should be incorporated with the philosophy and methods of QM. Parzinger and Nath (2000) examined the link between QM and software quality and found that QM implementation improves software quality and performance and thus increases customer satisfaction. Hasan and Kerr (2003) studied the relationship between QM practice and organisational performance in service organisations and discovered that QM practices like top-management commitment; employee involvement; training; supplier quality; service design; quality techniques; bench marking; and customer satisfaction leads to higher productivity and quality performance. A study by Jain and Guptha (2011) indicates that quality certifications help the implementation of quality management programmers based on QM principles and that the quality certification has an impact on performance and helps the organisations to achieve business excellence. Also among the quality certified organisations, CMM certified software organisations have better QM practices and business excellence as compared to ISO certified organisations. Because ISO 9000 provides general guidelines for all the organisations ,which can be used for QM purposes ,whereas CMM stresses on process improvement and provides guidance for stable, capable and mature processes by identifying the key processing areas in software development. Shirouyehzad et al. (2012) evaluated three, four and five-star hotels to identify the most critical service quality dimensions. Study applies the principle behind the SERVQUAL and performance methods to measure service quality of hotels. Also, data envelopment analysis approach is exploited to evaluate the efficiency of hotels. Inoue and Yamada (2013) examined four scenarios, which are beneficial for process improvement in pharmaceutical industries, they are explored through application of the Key-Graph. The critical factors identified are project scheduling, automation, resource management, and process improvement methodology. The project leaders also addressed critical factors, such as top management support, shared goals, and a data-driven approach. Table 1 shows the various QM practices that have been proposed by select authors and Table 2 gives a list of critical factors recommended by select authors.

Five (5) QM factors

Agus and Abdullah (2000) Eleven (11) QM practices

Eight (8) QM factors

Agus and Abdullah (2000)

Antony et al. (2002)

Ten (10) QM factors

Ten (10) major QM practices

Yusof and Aspinwal (1999)

Black and Porter (1996)

Twelve (12) implementation constructs of QM

Ahire et al. (1996)

Number of practices used Eight (8) critical success factors (CSFs) for QM implementation

Saraph et al. (1989)

Practices

Management commitment, role of quality department, training and education, employee involvement, continuous improvement, supplier partnership, product/service design, quality policies, quality data and reporting, communication to improve quality, and customer satisfaction orientation.

Top management commitment, supplier relationship, training, employee focus, customer focus

Top management commitment, customer focus, supplier relationship, training, employee focus, quality process, measurement, and zero defect.

Management leadership, continuous improvement systems, education and training, supplier quality management, systems and processes, measurement and feedback, human resources management, improvement tools and techniques, resources, and work environment and culture.

People and customer management, supplier partnerships, communication of improvement information, customer satisfaction orientation external interface management, teamwork structures for improvement, operational quality planning, quality improvement measurement systems, and corporate quality culture.

Top management commitment, employee training, design quality management, supplier quality management, internal quality information usage, employee involvement, employee empowerment, customer focus, benchmarking, and SPC usage.

Top management leadership, role of the quality department, training, product design, supplier quality management, process management, quality data reporting and employee relations.

Table 1

Author (year)

460 A.K. Digalwar et al.

QM practices proposed by select authors

Number of practices used

Sudhakar (2013)

Inoue and Yamada (2013) Seven (7) software project factors

Eight (8) process improvement factors

Ten (10) TQM factors

Seventeen (17) TQM factors

Talib et al. (2010)

Fotopoulos and Psomas (2010)

Nine (9) QM practices

Eight (8) critical QM factors

Twelve (12) TQM major practices

Ooi et al. (2005)

Temtime and Solomon (2002)

Sureshchandar et al. (2002)

Practices

Communication factors, technical factors, organisational factors, environmental factors, product factors, team factors and project management factors.

Project scheduling, automation, resource management, process improvement methodology, top management support, shared goals, and a data-driven approach.

Leadership, strategic quality planning, employee management and involvement, supplier management, customer focus, process management, continuous improvement, information and analysis, knowledge management and education.

Top-management commitment (TMC), customer focus (CF), training and education (TE), continuous improvement and innovation (CII), supplier management (SM), employee involvement (EI), information and analysis (IA), process management (PM), quality systems (QS), benchmarking (BM), quality culture (QC), human resource management (HRM), strategic planning (SP), employee encouragement (EE), teamwork (TW), communication (COM), and product and service design (PSD).

Top management, education and training, employee participation, customer focus, organisational culture, teamwork, job involvement, career satisfaction, commitment.

Managerial leadership and commitment, customer satisfaction, continuous improvement, employee empowerment and involvement, supplier partnership, quality culture and philosophy, resources and working environment, and measurement and feedback.

Top management commitment and visionary leadership, human resource management, technical system, information and analysis system, benchmarking, continuous improvement, customer focus, employee satisfaction, union intervention, social responsibility, services capes, and service culture.

Table 1

Author (year)

Development and validation of quality management constructs QM practices proposed by select authors (continued)

461

Continuous improvement

Work environment and culture

Benchmarking

Communication of information √ √

√

√

√

√

√

√

√ √

√

√

√

√

√

√

√

√

√

√

√

√

√

√

Strategic quality management

√

√

√

√

√

√

√

√

√

√

√

√

Garvin (1986) √

Ishikawa (1988)

Customer focus and satisfaction

√

Role of quality department

√

√

√

√

√

√

Feigenbaum (1983) √

Saraph et al. (1989) √

√

Quality planning/product design (service)

√

√

Deming (1981) √

Lu and Sohal (1993)

Team work structures

√

Supplier quality management

√

√ √

Training and involvement

√

Quality measurement system/quality data

Quality technology/process design (SQC)

√

√

√

√

Quality policies/process management

√

Top management leadership

Juran (1978) √

Crosby 1(979)

Employee relation/empowerment and satisfaction

Authors Factors

Porter and Parker (1993) √

√

√

√

√

√

√

√

Motwani et al. (1994) √

√

√

√

√

√

√

√

√

√

Powel (1995) √

√

√

√

√

√

√

√

√

√

√

√

Black and Porter (1996) √

√

√

√

√

√

√

√

√

Ahire et al. (1996) √

√

√

√

√

√

√

√

√

√

Yusof and Aspinwal (1999) √

√

√

√

√

√

Agus and Abdullah (2000) √

√

√

√

√

√

√

Motwani (2001) √

√

√

√

√

√

√

√

√

Sureshchandar et al. (2002) √

√

√

√

√

√

√

√

Ooi et al. (2005) √

√

√

√

√

√

Samat et al. (2008) √

√

√

√

√

√

√

√

√

√

√

√

Kumar et al. (2011)

3

7

5

7

3

9

7

7

9

11

10

17

13

13

18

19

Table 2 Total

462 A.K. Digalwar et al.

List of QM critical factors recommended by select authors


4

463

Methodology

4.1 Development of QM constructs In order to determine the critical success factors and variables of QM, the first step is to carry out an exploratory study. Studies are performed by various researchers addressing performance measures/critical success factors/essentials/foundation blocks for QM. The literature is reviewed, with an objective to find out all the CSF related to the QM, these are called foundation blocks/essentials/requirements/enablers/success factors or performance measures. The next step is screening the CSFs and their variables with the help of QM practitioners/experts. Many experts were approached, having a very good exposure of QM deployment in software industries, most of them were Project head/President/Vice-President/General Manager/Senior Manager for QM activities, to involve in the study but because of some or other reasons supports from only nine experts could be obtained. The available literature about QM constructs was discussed with them. However, it can be stated that it is an exploratory research. A numbers of brainstorming sessions were held to screen final list of 12 CSFs and 174 variables. Finally, unanimously agreed upon CSFs are: Top Management Commitment and Leadership; Quality Policy; Training to Employees; Product Design and Operating System; Quality Information System; Employee Participation and Empowerment; Human Resource Management; Customer focus and Satisfaction; Team work for Continuous Improvement; Organisational Culture; Infrastructure and Facilities; Financial growth.

4.2 Data collection A structured questionnaire was developed based on the above selected CSFs and their variables. A comprehensive questionnaire was used to find out number of QM related issues in Indian software industries. A sample of 250 experts from software industries were identified through various sources like technopark list, NASSCOM list CMM list. Snowball sampling method has been adopted for the collection of data. As snowball sampling is useful in locating members of rare population by referrals. Reduced sample size and costs are clear cut advantages of snowball sampling. Also in-person survey was considered. Finally, 74 responses received on a Likert’s scale of 1–5; where a rating of 5 indicated the very important and 1 indicated the least important. The responses were classified based on region, in terms of regional distribution, the respondents were evenly divided among the four regions: north region – 14%, East region – 7%, West region – 30% and South region – 49%. Response was very less from the East region. This might be because the eastern region has fewer states and relatively less industry concentration. Experience of the respondents’ range from 5 years to 20 years. They have average total experience of 15.82 years (median 15.5 years). A greater proportion of respondents have experience between 10 years and 20 years. Result of descriptive analysis shows that all the respondents selected were already exposed to a good number of QM practices. Table 3 shows the results of descriptive analysis.

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Table 3

Results of descriptive analysis %Response

Regional distribution of respondents North

14

East

07

West

30

South

49

Respondents by experience 0–5 years

06

6–10 years

25

11–15 years

34

More than 15 years

35

Respondents by designation

5

Projects engineers

32

Project leaders

38

DGM/GM/VP

30

Data analysis and discussions

5.1 Importance index analysis The numerical scores from the questionnaire provided a measure of strength of opinion of the effect of each item on the success of project. These are subsequently transformed into relative importance index using the following formula (adapted from Digalwar and Sangwan, 2011). Importance index of item/variable: ⎛ 5 ai xi ⎜ ⎜ i =1 ( ) x Ix = ⎜ 5 ⎜ 5 xi ⎜ ⎝ i =1

∑

∑

⎞ ⎟ ⎟ ⎟ × 100% ⎟ ⎟ ⎠

(1)

where ai

constant expressing weight given to i

xi

variable expressing frequency of response for i

i

1, 2, 3, 4, 5.

The importance indices range from 0 to 100. These indices reflect the relative importance of the variables/items listed in the questionnaire. As would be expected, while some items have high leverage, and others do not. The importance indices have been classified into four categories to reflect the respondents’ ratings as follows:

Development and validation of quality management constructs •

very important : 75 < I ≤ 100

•

important : 50 < I ≤ 75

•

less important : 25 < I ≤ 50

•

not important : 0 < I ≤ 25.

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Based on the classification, out of 150 variables 58 variables/items are rated ‘very important’ 92 variables/items are rated as ‘important’ and no variable is rated as either ‘less important’ or ‘not important’ category. These variables according to the ranking are listed in Table 4. Ideally, all the variables are considered for further analysis purposes. The frequency of favourable response to CSFs indicates that a representative majority of software industry professionals have recognised the concept and it is potential. Table 4 S. no.

Summary of importance index analysis CSF

Very important

Important

Less important

Not important

1

Total management commitment and leadership

8

10

-

-

2

Quality policy

2

10

-

-

3

Employee training

2

8

-

-

4

Software product design

4

6

-

-

5

Quality information system

4

4

-

-

6

Employee participation

8

10

-

-

7

Human resource management

4

21

-

-

8

Customer focus

8

3

-

-

9


3

6

-

-

10

Organisation culture

7

9

-

-

11

Infrastructure and facilities

3

1

-

-

12

Financial growth

5

4

-

-

58

92

Total

5.2 Reliability assessment and item analysis Reliability concerns the extent to which an experiment, test or any measuring procedure yields the same results on repeated trials (Carmines and Zeller, 1979). Internal consistency analysis was carried out to measure the reliability of the items under each critical factor using Cronbach’s alpha. Item(s) was/were eliminated in order to achieve maximisation of the alpha coefficient, where needed. During the initial analysis, none of the items were dropped to improve reliability. Values of Cronbach’s alpha are above the significance value of 0.60 for all the critical factors (ranging from 0.756 to 0.902), which is acceptable for the internal consistence of the scale (Nunnally, 1978; Saraph et al., 1989; Digalwar and Sangwan, 2007).

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After assessing reliability, detailed item analysis was carried out by adopting Nunnally (1978) method for proper assignment of items to each scale (measure). After the elimination of items during reliability assessment, scale scores were calculated by averaging the scores of the remaining items under each factor. Item score to each scale score correlations were used to examine if an item belonged to the factor/measure assigned. Since scale scores were the average of the respective hypothesised items under each factor/measure, high correlation between each scale score and its respective items were expected. If an item correlates higher to its scale score relative to other scale score, it is deemed that the particular item has been properly assigned to that factor. Any item that correlated higher to other scale scores relative to its own was dropped. Both, reliability tests and item analysis were performed again and the results are shown in Table 5. Final Cronbach’s alpha ranges from 0.8401 to 0.9178, which means that the items/variables of all the factors are highly reliable. The values of correlation coefficients also have high values, which prove that the items/variables had been properly assigned to their respective scales. Table 5 shows the summary of item analysis. Table 5 Sl. no.

Summary of item analysis results CSF

Original no. of items

Item deleted

Remaining no. of items

Final Cronbach’s alpha

1

Total management commitment and leadership

18

7

11

0.9162

2

Quality policy

12

0

12

0.9132

3

Employee training

10

3

7

0.8779

4

Software product design and OP

10

2

8

0.8401

5


8

-

8

0.8868

6

Employee participation and empowerment

18

6

12

0.9086

7

Human resources management

25

13

12

0.9174

8


11

-

11

0.9006

9


9

-

9

0.8962

10


16

5

11

0.9178

11


4

-

4

0.8575

12

Financial growth

9

-

9

0.8788

5.3 Validity assessment The validity of a measure refers to how well it measures what it sets out to measure (Litwin, 1995). Content validity and construct validity are generally considered for validity assessment.


467

•

Content validity: A performance measure has content validity if there is general agreement that the measure has items that covers all aspects of the variable being measured. Content validity is not evaluated numerically. It is subjectively judged by the researchers. Since the 12 factors of QM were developed based on exhaustive review of literature and detailed evaluation by the practitioners, they were considered to have content validity. Content validity depends on how well the researchers created the measurement items to cover the content domain of the variable being measured (Nunnally, 1978).

•

Construct validity: A measure has construct validity if it measures the theoretical construct that it is designed to measure. Muttar (1985) stated three methods of determining construct validity: multi-trait multi-method analysis, factor analysis and, correlation and partial correlation analysis. Out of these three methods, factor analysis is usually used to identify items, which should be included in a consistent measuring instrument (Muttar, 1985). Given that one of the objectives of this study is to develop items/variables to assess each construct, factor analysis is chosen to evaluate construct validity, which is consistent with the literature (Nunnally, 1978; Saraph et al., 1989; Flynn et al. 1994; Badri et al., 1995; Quazi et al., 1998; Digalwar and Sangwan, 2007). Appropriateness of the data for factor analysis is also determined by examining the minimum number of observations required per variable. The sample meets the general rule that there must be at least five times as many observations as there are variables (Hair et al., 1995). According to Flynn et al. (1997) a sample size of 30 or more is statistically sufficient for the analysis. Next, the appropriateness of the factor model is determined by examining the strength of the relationship among the items/variables. Correlation matrix, Barlett’s test of sphericity and Kaiser-Meyer-Oklin (KMO) measure of sampling adequacy are the three measures recommended in the literature (Norusis, 1994; Hair et al., 1995; Digalwar and Sangwan, 2007) for the purpose of determining the strength of relationship before carrying out the factor analysis.

•

Correlation matrix: Visual inspection of the correlation values between the items in each measure shows that all the correlations are greater than 0.3. This implies that the respective items under each measure are likely to have common factors (Norusis, 1994; Hair et al., 1995).

•

Barlett’s test of sphericity: Barlett’s test assesses the overall significance of the correlation matrix. If the value of the test statistic for sphericity is large and the associated significance level is small, it can be concluded that the variables are correlated. Barlett’s test of sphericity demonstrated sufficiently high values for all the 12 CSFs (at p < = 0.0001).

•

KMO measure of sampling adequacy: The test results given in Table 6 show KMO measures ranging from 0.776 to 0.908, which is above the suggested minimum standard of 0.5 required for running factor analysis (Norusis, 1994; Hair et al., 1995; Digalwar and Sangwan, 2007). Hence, based on the above tests, it is concluded that all the 12 factors are suitable for applying factor analysis.

468 •

A.K. Digalwar et al. Factor analysis: Factor analysis was conducted on items under each measure based upon principal components analysis with varimax rotation using the statistical computing package SPSS 11.5 for Windows. A total of 12 analyses were carried out. The number of factors to be extracted in each analysis was determined by the eigenvalue. Number of factors was extracted in accordance to the number of eigenvalue over one. The results of the factor analysis are shown in Table 7. As observed from Table 7, all measures (constructs) are uni-factorial. This means that items hypothesised under the factors each formed into a single factor.

Table 6

KMO measures of different factors

Performance measure

KMO

Top management commitment and leadership

0.881


0.908

Quality policy

0.892


0.848

Employee training

0.866


0.855


0.777


0.874


0.776


0.814


0.899

Financial growth

0.855

Table 7

Performance measure

KMO

Summary of factor analysis results

CSF

Item loading % Eigenvalues range variance

Number of factors extracted

Top management commitment and leadership 0.571–0.857

6.074

55.216

1

Quality policy

6.216

51.798

1

0.568–0.832

Employee training

0.643–0.849

4.077

58.243

1


0.556–0.745

4.425

44.255

1


0.706–0.786

4.469

55.865

1


0.571–0.837

6.054

50.452

1


0.603–0.840

6.346

52.880

1


0.571–0.797

5.588

50.796

1


0.606–0.822

4.955

55.051

1


0.648–0.826

6.070

55.177

1


0.811–0.870

2.804

70.096

1

Financial growth

0.662–0.761

4.582

50.910

1

Next, an analysis of the factor loading was performed. The factor loading represents the correlation between the variables and their respective factor. The squared loading of each variable is the amount of the variable’s total variance accounted for by its factor. Based upon the sample size of 74, factor loading is considered to be significant if they are greater than ±0.45 (Hair et al., 1995). The factor loading of all the items under all 12 factors have met the above criteria, with the lowest loading at 0.571 (see Table 7). Since the factor loadings exceeded ±0.45, all items contributed to the factor represented. Thus, the findings indicate that the QM factors have the construct validity. A set of reliable and valid 12 CSFs for QM with their variables for software industries are shown in Table 8.

Development and validation of quality management constructs Table 8

469

Reliable and valid quality management constructs and their variables

Variable Variable description code Top management commitment and leadership TMC1

Extent to which the top executive assumes responsibility for quality performance of the products and services of the company.

TMC2

Level of top executives’ dynamism in leading the quality programme.

TMC3

Extent to which the quality mission forms the basis of strategic planning and decision making.

TMC4

Willingness of top management to identify and remove the root-cause problems of quality.

TMC5

Extent to which the top management supports long-term quality management policy.

TMC6

Extent to which the quality goals and policy are understood by the work force in the division.

TMC7

Amount of review of quality issues in top management meetings.

TMC8

Extent to which the top management believes quality improvement as a means to increase profits.

TMC9

Level of participation of major department heads in the quality improvement programme.

TMC10

Extent to which quality data such as cost of quality, defects, errors, rework, etc., are used as tools to manage quality.

TMC11

Extent to which the feedback data from after sales are used for continuous improvement of quality in the company as a whole. Quality policy

QP1

Extent to which the company has a clear long-term vision statement.

QP2

The level at which the company’s short term business plan influence the quality management practices.

QP3

The goals of QM and their benefits to people in achieving them are made known to all the employees.

QP4

Extent to which various quality plans and policies are communicated to the employees.

QP5

Amount of coordination between quality department and other departments.

QP6

Effectiveness of quality department in improving quality.

QP7

Degree to which divisional top management is evaluated for quality performance.

QP8

Extent to which the top management supports long term quality improvement programmes.

QP9

Extent to which quality data obtained after the implementation of QM practice are used to evaluate middle level management and their supervisory performance.

QP10

Extent to which the goals and policies with respect to quality management are understood by the employees.

QP11

Role and contribution of quality department with respect to quality policy, new software product development, specification, etc.

QP12

The ability of the firm for customer retention

470 Table 8

A.K. Digalwar et al. Reliable and valid quality management constructs and their variables (continued)

Variable Variable description code Employee training ET1

Amount of training provided to employees in quality principles and policies.

ET2

Resources available for employee quality training.

ET3

Involvement of top management in quality training to employees.

ET4

Extent to which the management considers employee education and training as an investment.

ET5

Specific work skill training, technical and vocational given to employees throughout the division.

ET6

Extent of training in the advanced statistical techniques such as failure mode analysis, regression analysis, Six Sigma, etc.

ET7

Additional training required for the workers Software product design and OP

SPD1

Extent to which the customer requirements are thoroughly considered in new product design.

SPD2

Level of participation of various departments in new product development.

SPD3

How far the new programmes/products are thoroughly reviewed before they are marketed.

SPD4

Coordination among various departments in the product/process development process.

SPD5

Extent to which quality data, control charts are displayed at the employee workplace

SPD6

The extent of customer satisfaction by the company due to new practices.

SPD7

The ability in the early detection of defects.

SPD8

The ability to predict the project delivery date. Quality information system

QIS1

System existing in the organisation to collect the cost of quality.

QIS2

Extent to which quality data are available to managers and supervisors.

QIS3

Extent to which the quality data are used to evaluate supervisor and managerial performance.

QIS4

Extent to which the quality feedback from the clients is taken care-off for further improvement.

QIS5

Extent to which the customer-contact personnel communicate with middle and top management on matters related to customer requirements and satisfaction.

QIS6

The overall effectiveness of communication process in the organisation.

QIS7

Extent to which reports on the effectiveness of quality management programme are communicated to the employees.

QIS8

Extent to which the quality data are available to workers.


471

Reliable and valid quality management constructs and their variables (continued)

Variable Variable description code Employee participation and empowerment EPE1

All employee suggestions are evaluated in our company.

EPE2

Employees are encouraged to deal with the customer’s complaints and needs.

EPE3

Extent of company’s cross-functional teams’ involvement in quality.

EPE4

Our company has several quality control circles (within one function)

EPE5

Employees are very committed to the success of our company.

EPE6

Reporting of work problems are encouraged in our company.

EPE7

The degree to which the employees are given freedom and authority for operational independence and experimentation with respect communicated to the employees to project management activities.

EPE8

Extent to which the employees are given freedom to express their opinions, comments and criticisms on organisational functioning.

EPE9

Extent to which the involvement of team members at various stages in software projects are encouraged.

EPE10

The level of staff morale in the organisation.

EPE11

How far the company allows flexible work practices.

EPE12

The ability of the employees for innovative ideas. Human resources management

HRM1

Extent of effort to recruit quality manpower.

HRM2

Effectiveness of strategies adopted for retaining talented and experienced people.

HRM3

Quality emphasis by marketing and sales personnel.

HRM4

The effectiveness with which the company aligns human resource planning and management with company’s strategic plans.

HRM5

Extent of effectiveness of management development programme for the improvement of quality and productivity.

HRM6

Effectiveness of quality improvement teams in the division.

HRM7

The effectiveness with which the company evaluates and improves its human resource planning and management using employee related data.

HRM8

Degree of participation and contribution by major departmental heads in the quality improvement process.

HRM9

Degree to which employees are trained in team building and group dynamics for achieving the quality mission.

HRM10

Degree to which the employees are trained for developing their communication skills (written and verbal).

HRM11

The level of training of employees in quality management system such as CMM, ISO 9000, etc.

HRM12

The level of labour turn-over in the company.

472 Table 8

A.K. Digalwar et al. Reliable and valid quality management constructs and their variables (continued)

Variable Variable description code Customer focus and satisfaction CFS1

People in my work unit care about our customers.

CFS2

We monitor customer complaints and feedback and use these items as basis for determining customer satisfaction.

CFS3

We compare our customer satisfaction with our competitors.

CFS4

The level of customer involvement during the specification and design stages of the software project.

CFS5

The level of customer involvement during the development and testing stages of the software project.

CFS6

Extent to which the organisation encourages the interaction between the customers and employees.

CFS7

Extent to which service is provided after project completion/project delivery.

CFS8

Extent to which attempts are made to satisfy the explicit, implicit and delight needs of the customers.

CFS9

Quality related customer complaints are treated with top priority.

CFS10

The online delivery of the projects by the organisation.

CFS11

The degree of service level provided by the firm. Continuous improvement

CI1

Extent to which the ability to work in team is taken as a criteria in employee selection.

CI2

The level of spirit of cooperation and team work in the organisation.

CI3

Extent to which the members of the team are from various departments.

CI4

Extent to which the data are monitored for efficiency and effectiveness.

CI5

The regularity in monitoring of quality of products and services.

CI6

How far the problem – solving tools for continuous improvement in quality is used.

CI7

The involvement of each and every member of the organisation in improving quality.

CI8

Extent of quality related bench marking system in the organisation.

CI9

The extent to which the software development processes are systematically measured and evaluated. Organisation culture

OC1

Degree to which the employees accept quality as a strategic weapon to gain competitive advantage.

OC2

Degree to which the employees realise the importance of customer satisfaction in achieving quality.

OC3

The level of trust and openness between employees and management.

OC4

The level of coordination between the team members and their leader.

OC5

The level of coordination between project teams and top management.

OC6

The level of coordination between project teams and customers.

OC7

The degree of respect and fairness in treatment that the employee to get within the organisation.


473

Reliable and valid quality management constructs and their variables (continued)

Variable Variable description code Organisation culture OC8

Availability of opportunities for career advancement.

OC9

Presence of incentive schemes based on performance to motivate employees.

OC10

Extent to which achievements in quality are recognised and rewarded.

OC11

Extent to which the roles and responsibilities of employees are specified clearly. Infrastructure and facilities

IF1

Adequacy of hardware facilities provided.

IF2

Adequacy of software facilities provided.

IF3

Adequacy of information facilities such as internet, access to journals and other publications, training and other facilities.

IF4

The degree to which the physical layout of the workplace and the environment at the work place are comfortable to the employees. Financial growth

FG1

Extent of overall increase in profitability for the last three years.

FG2

Extent of increase in return on investment.

FG3

The overall revenue growth of the company

FG4

The level of operating cost of the company.

FG5

Trends in cost relative to competitors.

FG6

The ability to complete the project within budget.

FG7

The value added per employee to the organisation.

FG8

The extent of savings in cost due to improvement.

FG9

The extent of getting repeat business in the firm.

6

Conclusions and directions for future work

Global competitions, rapidly changing technologies and the shorter life cycles of the products have made the software area very competitive. Nowadays, companies face significant uncertainties and continuous changes, also global depression in software area. Customer requires low priced high quality products at specific target time. So, new methods and technologies are to be developed. A firm which follows TQM principles can meet the challenges. Progress can be made only with continuous improvement in all the areas. In this work, development and validation of QM constructs have been focused. A set of reliable and valid CSFs for QM with their variables for software industries are shown in Table 8. These were developed from the review of the literature and by statistical analysis. The reliability and validity analysis of the critical factors were carried out by using SPSS® 11.5 for MS Windows®. All the 12 CSFs are found to be reliable as the value of the reliability coefficient, Cronbach’s alpha for the CSFs ranges from 0.8401 to 0.9178, these are greater than the generally accepted value of 0.6. However, during the detailed item analysis, 36 of the items correlated highly to other construct scores relative to their own construct score and have been dropped. This demonstrates that the CSFs

474


have relatively high scores of reliability. The computed value of correlation matrix, Barlett’s test of sphericity and KMO measure of sampling (0.776 to 0.908) adequacy show a high strength of relationship among the remaining items. The factor analysis shows that all the proposed 12 CSFs of QM are one-dimensional with adequate item loading ranges. From these results, it can be concluded that all the proposed CSFs have construct validity. Developed CSFs of QM have several important implications for managers. First, quality management constructs permits managers/practitioners to obtain a better understanding of QM practices and follow academic researchers to proceed with the task of developing and testing theories of QM. Second, managers can use these significant operating variables to obtain a better understanding of the existing quality management practices and to assign responsibilities within the organisation for achieving organisation-wide improvements in quality. Finally, after plans for improvement have been made and implemented, the methodology reported in this study can again be used by managers to re-evaluate the organisation’s position. This study covered only software companies in India. Similar study can be undertaken among a large number of other industries and it can be compared. Critical success factors were developed based upon self-reported information from the respondents. Questions were subjective in nature. Respondents were asked to rate the items based upon their perception, as to the extent to which the items were applicable in their respective companies. Hence, a certain amount of bias might have been introduced into the data collected. This study can be extended to compare the practices followed in other countries also for competing efficiently in the global market. Research can be done to find out whether any other critical factors can enhance the validity and reliability of the study. Also, a comparison can be done between clustered and non-clustered companies for further enhancement of the idea.

Acknowledgements We would like to acknowledge the reviewers for their constructive and helpful comments.

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