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An empirical study of barriers to TQM implementation in Indian industries October 6, 2009

Posted by Bima Hermastho in TQM Domain.
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K. Subrahmanya Bhat, Department of Mechanical Engineering, NMAM Institute of Technology, Karkala, India

Jagadeesh Rajashekhar, Team Quality, Mysore, India SIET Institute of Management, Bangalore, India
Article Type:

Research paper

Journal: The TQM Magazine, Volume: 2, Number: 3, Year: 2009, pp: 261-272
Abstract

Purpose – The purpose of this paper is to identify the barriers of total quality management (TQM) implementation, in order to make them known to the managers of Indian industries.
Design/methodology/approach – In order to achieve this objective, an extensive literature review has been carried out to understand the barriers to TQM implementation. This was followed by a survey of quality award winning industries in India.
Findings – A total of 41 completed questionnaires were received and the overall response rate was 31 percent. The findings of this survey suggest that the most important TQM barriers in Indian industry are: “no benchmarking of other company’s practices” and “employees are resistant to change”. Factor analysis of the 21 potential barriers to TQM implementation revealed the following five underlying constructs: lack of customer orientation, lack of planning for quality, lack of total involvement, lack of management commitment, and lack of resources.
Originality/value – The findings based on this empirical research present a solution to the difficulties faced by the managers while implementing TQM effectively in their industries.

Introduction

Global competition and economic liberalization are creating opportunities for Indian organizations. They use “quality” to compete with other organizations to improve their market share. The well-known quality approaches like ISO 9000, total quality management (TQM) and Six Sigma have helped them to achieve their goal. TQM is one of the important quality improvement techniques, which many firms are using to achieve success. TQM has been widely implemented throughout the world across different industries and sectors. The implementation of TQM has given them positive results. Industries in India have been trying individually to improve their product quality, besides their overall performance through TQM practices (Rajashekhar, 1999). But organizations find it difficult to implement TQM in a satisfactory and efficient way. Also, they notice some barriers or obstacles, which hinder the implementation of TQM. Owing to these barriers, organizations have not achieved the benefits, which they have expected from TQM. Besides, poor results have given rise to the opinion that TQM does not work and in many cases the TQM initiatives have been abandoned declaring that TQM has failed. To help address this gap in understanding key TQM implementation barriers in Indian firms, this research focused on:

  • empirically investigating the barriers that Indian industries experience; and
  • comparing the findings with a prior research on TQM implementation problems encountered by US firms.

This study involved survey-based research on the barriers associated with the implementation of TQM. Like previous research in this area, factor analysis has been used to empirically derive the obstacles from items that represent commonly cited barriers in the literature.

The rest of the paper is organized into five sections. The first section provides a review of the literature relating to the barriers to implementation of TQM. The next section describes the quality awards considered for this study. The third section covers the methodology used for this research. The fourth section covers the results of the data analysis and discussion of the findings; and the main conclusions of the research study are summarized in the final section.

Literature review

TQM practitioners claim that if a company’s culture is not conducive to total quality, the culture must be changed before a total quality programme can be implemented. There appears to be a multitude of reasons why companies fail in their effort to implement a quality management system. However, two common problems appear to be a lack of strategic planning and a lack of appropriate culture supportive of TQM programmes (Sebastianelli and Tamimi, 2003). The study of Liu (1998) and Rahim and Whalen (1994) showed lack of top management support and lack of proper training as the main barriers for TQM implementation. The barriers to implementing TQM will show up in all sectors – manufacturing, services, government, and education. Therefore, it is important for all organizations to understand and avoid these barriers both before and during TQM implementation (Tamimi and Sebastianelli, 1998). An extensive literature review carried out by Masters (1996) found 15 distinct barriers to TQM that are common to all types of organizations.

Salegna and Fazel (2000) have listed 16 obstacles which companies have reported when implementing TQM. Further, Tamimi and Sebastianelli (1998) have identified many problems that companies might experience while implementing TQM. Their survey was aimed at determining the extent to which these obstacles were actually experienced by the responding organizations. They surveyed a national sample of quality professionals. Based on a review of the quality literature and personal interview with local managers, 25 potential barriers to TQM were identified. Jun et al. (2004) empirically investigated barriers that firms in the Mexico’s Maquiladora industry experience, based on 25 potential obstacles to TQM success and compared the findings with prior research done with US firms. The findings of their study suggest that a prevalent TQM barrier in the Maquiladora industry is “high employee turnover”.

The study of Amar and Zain (2002), established 11 factors seen to be the barriers against the successful implementation of TQM in Indonesian manufacturing organizations. Gunasekaran (1999) examined the enablers of TQM implementation in a British manufacturing company using structured interviews of employees from different functional areas of the organization. Emphasizing people-oriented factors, such as teamwork and empowerment, he found that poor communication between departments was a real barrier to implementing TQM. A survey conducted in India in 1998 revealed the following as barriers impeding the implementation of TQM: lack of long-term supplier relationship, continued dependence on traditional incentive schemes, numerical targets, performance rating, slogans for improving productivity, and not identifying and providing the right type of training for each and everyone as demanded for every job (Rajashekhar, 1999). Finally, Ngai and Cheng (1997) derived the following four factors as the barriers for the implementation of TQM from their 17-item scale:

  1. cultural and employee barrier;
  2. infrastructure barrier;
  3. managerial barrier; and
  4. organizational barrier.

Table I shows the important barriers for TQM implementation identified by various authors.

Quality awards in India and survey respondents

A major boost to the growth of TQM is the promotion of quality award models in many countries. These award frameworks are used by many organizations to assess and benchmark their level of TQM implementation. The broad aims of these awards are described as follows (Ghobadian and Woo, 1996):

  • Increase awareness of the importance of quality management because of its important contribution to superior competitiveness.
  • Encourage systematic self assessment against established criteria and market awareness simultaneously.
  • Stimulate sharing and dissemination of information on successfully deployed quality strategies and on benefits derived from implementing these strategies.
  • Promote understanding of the requirements for the attainment of quality excellence and successful deployment of quality management.
  • Stimulate organizations to introduce a quality management improvement process.

Each award is based on a perceived model of TQM. They provide a useful audit framework against which organizations can evaluate their quality management methods, the deployment of these methods, and the end results. Quality awards are the standardized quality models used by firms as a guide for quality management or in order to carry out self-evaluation of their quality practices. As Hendricks and Singhal (2000) said, quality awards are proxy for effective implementation of TQM. A review of various quality awards criteria confirm that the core concepts and values emphasized in the awards are those that are widely considered to be the building blocks of effective TQM implementations. Awards are given after the applicant goes through a multi-level evaluation process where internal or external experts judge the applicant. A stringent process seems to be in place to ensure that winners are effectively implementing and practicing TQM. Therefore, in this research it was decided to focus the survey on “quality award winners” to find out the issues connected with the implementation of TQM. It was surmised that these companies have demonstrated their ability to achieve higher quality levels in their organizations and hence would be better to point out the barriers they encountered in implementing TQM. This presupposes that the criteria followed by these awards in identifying these winners matches with the spirit of TQM in principle and practice.

Some of the important quality awards won by companies in India considered in this study are:

  • Deming Application Prize.
  • Rajiv Gandhi National Quality Award.
  • Golden Peacock National Quality Award.
  • CII-EXIM Bank Award.
  • Ramakrishna Bajaj National Quality Award.
  • JRDQV Award.
  • RPG Quality Award.

Table II shows the number of industries which have won the quality awards listed above and the number of industries, who have responded to this study.

Research methodology
Sample

Data for this study were gathered using a mail questionnaire. This questionnaire was sent to quality managers of the firms, which have won the quality awards discussed above. Information regarding the quality award winners was collected from journals, magazines, internet resources, and newspapers. From this it was found that there were 135 manufacturing and service organizations in India, which have won different quality awards.

Survey instrument

The questionnaire consisted of three sections, two of which were relevant for this study. The first section dealt with gathering background information, such as name and size of the organization, whether the organization was ISO certified and which quality award they have won. The other section involved obtaining respondents’ opinions about a series of statements or items representing the barriers to TQM implementation.

A total of 21 questionnaire items measuring major potential barriers to TQM were identified based on the work done by Tamimi and Sebastianelli (1998). Respondents to the survey were asked to indicate how true each of these statements was about their organization using a five-point Likert type scale (1, not at all true; 2, slightly true; 3, somewhat true; 4, mostly true; 5, completely true).

Analysis method

Exploratory principal component factor analysis was used to determine whether the observed correlations among 21 items representing the barriers to TQM could be explained by the existence of a small number of underlying constructs (Conca et al., 2004). The 21-item instrument was subjected to principal component analysis with varimax rotation using the Statistical Package for the Social Science (SPSS) program. Only factors that accounted for a variance greater than one (i.e. eigenvalue > 1) were extracted. The rationale for this approach is that factors with a variance less than one are no better than a single variable (Tamimi, 1995). Finally, reliability and validity analysis were conducted on the questionnaire instrument.

Results and discussions
Respondent profile

A total of 42 firms out of 135 returned the completed questionnaire for an overall response rate of 31 percent. Since one questionnaire response was incomplete, only 41 responses were considered for analysis. It is considered to be adequate for this kind of survey. Table III presents various respondent firms characteristics.

Descriptive statistics

Table IV presents the mean and standard deviation values for each of the questionnaire items in descending order by their mean. The higher the mean, the greater is the importance of the barrier. Majority of the quality managers perceived “the best practices of other companies are not benchmarked” and “employees are resistant to change” as the most significant barriers to TQM. Hence, these two factors received the highest mean rating of 3.00 and 2.92, respectively. These two items appear to be of central concern to Indian managers. The inadequate resources as well as ineffective quality measurement techniques were also found to be other important barriers for many Indian firms. The mean values for the questionnaire items ranged from 2.14 to 3.00 with an overall mean of 2.54. As a comparison, according to Tamimi and Sebastianelli (1998), American managers in US firms identified five barriers (items with a mean score >3.00) to TQM:

  1. Management’s compensation is not linked to achieving quality goals.
  2. The best practices of other companies are not benchmarked.
  3. Employees are not trained in quality improvement skills.
  4. Employees are not trained in problem identification and problems solving techniques.
  5. Employees are resistant to change.

Therefore, the findings of this study show some similarity with the study done by Tamimi and Sebastianelli in USA.

Factor analysis

Factor analysis groups variables (i.e. single questions) into factors based on their common correlation. Those variables, which are correlated with each other, will be grouped together. Such a group of variables are called a factor (Saraph et al.1989). In order to explain the intercorrelations among the 21 items representing barriers to TQM, factor analysis was performed to identify the underlying factors. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy for this analysis was determined to be 0.754. The KMO statistic varies between 0 and 1. A value close to one indicates that factor analysis should yield distinct and reliable factors. Furthermore, values between 0.5 and 0.7 are mediocre and values between 0.7 and 0.8 are good (Black and Porter, 1996).

Five factors were extracted that accounted for about 74.88 percent of the total variation. Table V shows the varimax rotated matrix with total and cumulative variance for each extracted factor. In developing this factor solution, items with loadings <0.4 were dropped. Items with higher loadings were considered to be important and to have influence on the label selected to represent a factor. Hence, by examining all the items for a particular factor and by considering factor loading of items, the authors assigned a label to each factor.

The first factor is related to customer’s definition of quality, which has been called as “lack of customer orientation”. The second factor, which is related to quality planning, has been called as “lack of planning for quality”. The third factor has been termed as “lack of total involvement,” because it is related to employee empowerment and turnover of employees. The fourth factor, “lack of management commitment,” is related to those responsibilities of top management like benchmarking, involving everyone in quality and including quality in strategic planning. The fifth factor has been named as “lack of resources,” as it consists of the variables like time constraint and high cost of TQM implementation.

Reliability and validity analysis

The reliability analysis of a questionnaire determines its ability to yield consistent results. Reliability is considered as internal consistency, which is the degree of inter-correlation among the items which comprise a scale (Joseph et al., 1999). Internal consistency can be established using a reliability coefficient such as Cronbach’s α. α is the average of the correlation coefficient of each item with each other item. The reliability for each set of items of the five factors was calculated. Once the α was obtained for the whole measure, it was recalculated after eliminating one item, in order to verify if the scale improved (Table VI). Thus, for example, the α coefficient for the first factor is 0.876 and it can be seen that, after deleting the fourth item the α value has increased to 0.914; which is an improvement. The other constructs maintained their original form and the values of α derived for all the five factors ranged from 0.755 to 0.914 indicating a high reliability of the evolved factors.

The validity of a measure refers to the extent to which it measures what it was intended to measure. Two types of validity are considered in this study: content validity and construct validity.

Content validity is not evaluated numerically. It is subjectively judged by the researchers (Quazi et al., 1998). Since the questionnaire was adapted from the literature, it is considered to have content validity.

Construct validity measures the extent to which the items in a scale measure the same construct. A measure has construct validity if it measures the theoretical construct or trait that it was designed to measure. This was evaluated by factor analyzing each factor individually using principal component analysis method. Each factor is assumed to be a separate construct. In this analysis, each factor must be one dimensional or unifactorial. The results showed that all the five factors are unifactorial, which confirms the construct validity of the instrument (Table VII).

The degree to which a data set provides empirical evidence for the appropriateness of a factor analysis solution can be assessed by determining a measure of sampling adequacy. The KMO measure of sampling adequacy, which is an option offered by SPSS, was used to measure the adequacy of the sample for extraction of the five factors in this study. The KMO measure was used to assess the suitability of the sample for each unifactorial determination. The KMO values for each factor were calculated separately and it shows they are satisfactory.

This study has some similarities compared to a study conducted by Sebastianelli and Tamimi in USA. The most important being the questionnaire used is the same and the final objective is that of finding the barriers of TQM implementation. The difference is mainly in the demography of the sample industries chosen for the study. It has been observed that the first ten importance rankings of the barriers of the current study are close to that obtained in the US study.

Sebastianelli and Tamimi (2003) extracted five factors from their study, namely:

  1. inadequate human resource development and management;
  2. lack of planning for quality;
  3. lack of leadership for quality;
  4. inadequate resources for TQM; and
  5. lack of customer focus.

These five factors accounted for about 58 percent of the total variation in the observed ratings. Comparing the results of the reliability analysis of this study with the US study – the α coefficient of this study ranged between 0.755 and 0.914 and for US study it ranged from 0.52 and 0.81.

Conclusion

The results from a survey conducted on the barriers of TQM implementation in Indian industries have been presented in this paper. A reliability and validity analysis on the survey instrument has been conducted and it is concluded that the survey is fairly reliable and valid.

The experience of industries implementing TQM can serve as an invaluable lesson to those companies that are planning to implement TQM. Companies currently implementing TQM, or thinking about implementing TQM, will improve their chances of success if they are more sensitive to these barriers.

The main barriers were found to be lack of benchmarking and employee’s resistance to change. The industries should understand that benchmarking is a tool used to identify their strengths and weaknesses compared to the best in their class. And employee resistance can be overcome by proper training and by involving the employees in the planning and implementation phases of TQM. It was also found that inadequate resources was an obstacle for successful TQM implementation.

By understanding the potential severity of such barriers, industries are in a better position to anticipate and solve the problems which may arise in future. As a means of attaining higher efficiency, it is essential to recognize and understand the barrier that can hinder the success of TQM program. The barriers identified in this paper can be used to help guide Indian managers while implementing TQM in their organizations. While these barriers occur to varying degrees and with varying frequency, there is little doubt that they exist in every organization. Management must understand that they do exist and should deal with them while TQM implementation process. Therefore, the organizations can benefit from a better understanding of barriers of TQM.

ImageTable I Barriers to TQM implementation
Table I Barriers to TQM implementation

ImageTable II List of quality award winning industries till 2005
Table II List of quality award winning industries till 2005

ImageTable III Responses received by type and size of the company
Table III Responses received by type and size of the company

ImageTable IV A comparison of importance of barriers to TQM as perceived by Indian and US firms
Table IV A comparison of importance of barriers to TQM as perceived by Indian and US firms

ImageTable V Varimax rotated factor matrix
Table V Varimax rotated factor matrix

ImageTable VI Internal consistency analysis results
Table VI Internal consistency analysis results

ImageTable VII Summary of the factor matrix for each construct
Table VII Summary of the factor matrix for each construct

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