1
THE RELATIONSHIP BETWEEN TOTAL QUALITY MANAGEMENT AND BUSINESS PERFORMANCE OF MANUFACTURING INDUSTRY: A STRUCTURAL
EQUATION MODELLING APPROACH GOURI SHANKAR BERIHA
Associate Professor
Eastern Academy of Science and Technology Bhubaneswar, Odisha, India - 754001 Email: gourishankar.nitrkl@gmail.com,
Abstract:-The aim of the paper was to examine the interrelationship between factors influencing successful implementation of total quality management and business performance of manufacturing industries in eartern, India. The study is descriptive in nature. The survey method of data collection was adopted to collect the primary data.
Structural Equation Model (SEM) approach was applied to examine the relationships between the business performance of manufacturing organizations. The results of the research proved that there is a significant positive relationship between the factors influencing successful implementation of total quality management and business performance of manufacturing organizations. Furthermore, using structural equation modelling (SEM) technique, this study has shown that TQM construct based on criteria is valid across two manufacturing industries, and its relationship with quality performance also indicates insignificant difference between the two indusries.
Keywords: Total Quality Management, Structural Equation Model, Business performance, Manufacturing industries
1. INTRODUCTION
Most competitive organizations around the world have implemented Total Quality Management (TQM) practices and strategies to continuously upgrade performance. Total Quality Management (TQM) is a system of managing quality in organizations in order to improve products and services. Crosby (1979) stated “Quality management is a systematic way of guaranteeing that organized activities happen the way they are planned, it is a management discipline concerned with preventing problems from occurring, by creating the attitudes and controls that make prevention possible”. The top administrators and managers are under increasing pressure to justify the value and contribution of TQM program outflows to the performance of the organization. In eastern region of India, there is the challenge for the implementation of quality management practices in business organizations. The question that managers usually ask is will the enactment of these practices enhance business performance. The international standard manufacturing companies achieve competitive edge and gain significant market share through quality product and innovation practices with a modest price in order to meet the customer expectations (Leavengood and
Anderson, 2011). Total Quality Management (TQM) is holistic management philosophy, which encompasses set of guiding principles, representing the foundation of a continuously improving organization. It is a tool which encompasses the strategic, tactical and operational tool in the quality management research field. The demand for quality is emerging as the single most critical factor for the organizations to survive in endlessly intensifying global market. It is one of the most applied approach for business excellence other than Continuous Quality Improvement (CQI), Just-in-Time (JIT), Six Sigma, and Supply Chain Management (SCM) approaches (Cadden et al., 2013). Apart from the manufacturing industries, the service industries also are very keen in adopting and implementing this approach in order to preserve their competitive advantage (Juneja et al., 2011; Talib et al., 2012). The aim of this study is to develop the research model to test the association between successful factors of TQM implementation and business performance in specific to steel manufacturing companies located at eastern region of India.
2 2. LITERATURE REVIEW
This study examines the linkages between total quality management and organizational survival in manufacturing companies. Total Quality Management (TQM) is a management system that takes into consideration all the areas of the operations in an organization. Several researches have been conducted mostly in the developed world to determine the relationship between quality management practices and performance.
Irfan and Kee (2013) and (Ang et al., 2011), identified the important factors which play vital role in TQM implementation in service organizations and its influence shaping their performance in terms of service quality.
They suggested six critical success factors (CSF), such as Top management commitment and Headship, Customer Attention, Information analysis and system, Service culture (Espin et al., 2013), Human resource management and Social responsibility. The findings of the study exposed that significant relationship persists between TQM and service quality (Sit et al., 2011). Structure Equation Modeling (SEM) approach was used to test the criteria relationships.
Bergman and Klefjö (2007) stated that Total Quality Management (TQM) means a constant endeavor to fulfill, and preferably exceed the customer needs and expectations at the lowest cost, by continuous improvement work, to which all involved are committed, focusing on the process in the organization.
Kumar and Sharma (2017) identifed and develop the structural relationship among different critical success factors for successful implementation of TQM. The study found hierarchy level of all 14 factors from top to bottom level and critical input for TQM implementation with firms being more proactive and better prepared for TQM implementation.
Ahmad et al. (2014), exposed the relationship between TQM practices and business performance with moderator effects of AFTA. This proposed conceptual model will help the academicians and industry players to have better understanding on the effect of AFTA in TQM implementation in improving their business performance. The structural equation modeling (SEM) techniques are
used to examine the relationships of the practices.
Bhatia and Awasthi (2017) studied the problem of assessing the impact of quality management systems (QMS) on business performance of organisations.
The result found that organisations often implement QMS as a catalyst for change and use them in daily practice.
2.1. Total Quality Management (TQM) TQM is a universal management approach encompassing external as well as internal stakeholders, in the effort of optimizing the organization as well as its responsiveness to its surroundings. TQM is a management philosophy that helps manage their organization to improve the effectiveness and performance to achieve world class status for the past two decades (Mushtaq et al., 2011).
Narasimhan (2011) stated that Total Quality Management (TQM) means a constant endeavor to fulfill, and preferably exceed the customer needs and expectations at the lowest cost, by continuous improvement work, to which all involved are committed, focusing on the process in the organization.
2.2. TQM and Business Performance The relationship between TQM and business performance is like the relationship between the process and outcome (Alolayyan et al., 2011;
Valmohammadi, 2012; Talib et al., 2012), because the TQM ensures the maintenance of the assured level of quality at each and every stage of the manufacturing process, processes in distribution (i.e.) till it reaches to the hands of end consumer and the processes related to after sales. TQM also ensures the continuous improvement in quality of the products and services through innovation (Bon and Mustafa, 2012, Crossan and Apaydin, 2010; Gambatese and Hallowell, 2011); hence in short, TQM is the organization effort for striving excellence in Quality.
The success of every business is decided by their end customer, if they are satisfied with quality of the products and services of the organization. Lot of earlier researches proved that there is a robust relationship between the TQM and business performance (Yildiz, 2010;
Green, 2012). TQM focuses on continuous process improvement within organizations
3 to provide superior customer value and meet customer needs (Gharakhani et al., 2013). The critical success factors of TQM implementation are Top Management Involvement, Customer Attention, Strategic Design, Headship, Process Supervision, People Administration and Supplier Management.(Asree et al., 2010; Zakuan et al., 2010).
Business performance is a combination of management and analytic processes that allows managers of an organization to achieve pre-determined goals (Chung et al., 2010). Performance is a concept that quantitatively or qualitatively determines those that are produced as a result of an intended and planned activity (Yildiz, 2010; Yildiz and Karakas, 2012). The business performance management looks at the business in a holistic way that goes beyond each division and areas that this business possess (Campbell et al., 1970).
In this way, this method is able to align the strategic and operational objectives of a company, which helps this company to better reach the pre-selected goals.
Business performance management has three main activities: formulation of goals, consolidation of measurement information relevant to the organization‟s achievement of these goals, and interventions made by managers in light of this information (Porter and Tanner, 2004).
Vijande and Gonzalez (2007) industrialized an instrument for measuring TQM enactment of the European Foundation for Quality Management Excellence Model (EFQM) and to provide realistic evidence on the relationship between management practices and measures of business performance in Spanish manufacturing and service firms.
3. CONCEPTUAL FRAMEWORK AND HYPOTHESIS
An empirical study was designed to test the factors influencing successful implementation of TQM and its impact on business performance (Corredor and Goñi, 2011, Brun, 2011). The Figure 1.
Presents the conceptual framework of the study.
<Place Figure 1 Here>
Hypothesis 1: There is a significant relationship between factors of TQM implementation and business performance.
4. RESEARCH INSTRUMENT AND DATA COLLECTION
The main objective of the study is to develop and test the model which integrates the factors related to successful implementation of TQM and its impact on business performance. Survey method of data collection was used. The survey was coducted using structured questionnaire.
The sample size of this research is 300 of manufacturer industries in eastern India.
The survey was conducted among the 750 senior executives from the manufacturing industries. The proportionate stratified random sampling technique was used to select the respondents from the population i.e. The sample size from each company are in proportionate to the employees head count of that particular manufacturing company.
5. RELIABILITY ANALYSIS
Before discussing the results of the data analysis, it is very important to ensure the reliability of the primary data. The internal consistency of the proposed model was verified through reliability analysis. Cronbach alpha coefficient was used to test the reliability of the construct. It plays a critical role in valuing research instruments. Reliability analysis computes the internal consistency of the model. In this study, According to George and Mallery (2003), higher the value of Cronbach alpha coefficient, better is the reliability measure, i.e. “the alpha value > 0.9 – Excellent, _ > 0.8 – Good, _ > 0.7 – Acceptable, _ > 0.6 – Questionable, _ > 0.5 – Poor, and _ < 0.5 – Unacceptable”. The Cronbach alpha coefficient for all the nine constructs for found and tabulated in Table 1.
<Place Table 1 Here>
From the Table 1 it can be established that the reliability alpha coefficient of all the nine constructs are above the acceptable tolerance level.
Anderson and Herbertson (2003) stated that, “the test of sampling adequacy required to be performed in order to regulate whether the sample is adequately appropriate for factor analysis”. The KMO statistic is a measure of sampling adequacy, both overall and for each variable. Hence it was conducted.
KMO values greater than 0.8 can be considered, i.e. an indication that component or factor analysis will be
4 useful for these variables. Kaiser (1974) mentioned that a minimum accepted KMO value is 0.50. The KMO and Bartlett values are presented in Table 1 and all those values are at the acceptable level.
6. PEARSON CORRELATION COEFFICIENT BETWEEN FACTORS OF TQM IMPLEMENTATION AND BUSINESS PERFORMANCE
<Place Table 2 Here>
Table 2 presents the Interrelationship between factors influencing TQM and Overall Business Performance of the manufacturing industries.
The Correlation Coefficient between top management involvement and overall business performance is 0.794 which indicate that there is a significant positive relationship between top management involvement and overall business performance and it is significant at 1% level.
The Correlation Coefficient between customer attention and overall business performance is 0.766 which proves that there is a significant positive relationship between them at 1% level of significance.
The Correlation Coefficient between strategic design and overall business performance is 0.745, hence it is concluded that there is a significant positive relationships between strategic design and overall business performance and it is significant at 1% level,
The Correlation Coefficient between process supervision and overall business performance is 0.778 which shows that there is a significant positive relationship between process supervision and overall business performance at 1%
level of significance.
The Correlation Coefficient between people administration and overall business performance is 0.782, therefore it is proved that there is a significant positive relationship between people administration and overall business performance and it is significant at 1%
level.
The Correlation Coefficient between headship and overall business performance is 0.812, hence it is concluded that there is a significant positive relationships between headship and overall business performance at 1%
level.
The Correlation Coefficient between supplier management and overall business performance is 0.915 which indicate that there is a significant positive relationship between supplier management and overall business performance and it is significant at 1%
level.
The Research Design Model Impact of TQM on Business Performance to develop the model and empirically testing the swaying of factors of successful implementation of Total Quality Management (TQM) on Business Performance (BP) using the Structural Equation Modeling (SEM) approach and the verified final model presented in Figure 2 and 3.
<Place Figure 2 Here>
<Place Figure 3 Here>
The interrelationship between observed endogenous variables such as top management involvement, client attention, strategic design, process supervision, people administration, headship, supplier management, client and people outcomes and financial performance results and unobserved endogenous variables Total Quality Management and Business Performance were verified using “the above fit model of TQM on Business Performance Structural Equation Model”. Hence the total numbers of variables in the model are 21, which encompass 9 observed endogenous variables, 2 unobserved endogenous variables and 10 unobserved exogenous variables.
On the basis of above presented model, the following hypothesis is proposed:
H0: Factors of successful TQM implementation is having positive impact on performance of the business.
<Place Table 3 Here>
Table 3 presents significant loadings on all sub-constructs on each latent variable and latent criteria factor i.e. TQM sub- constructs are significantly correlated with the sub-constructs of Business Performance. The unstandardized coefficient of TQM is 0.95 which signifies business performance would increase by 0.95 for every unit increase in TQM in the Manufacturing industries and it is significant at 1% level.
<Place Table 4 Here>
Table 4 present the model fit summary of the hypothetical model. In Structural Equation Modeling (SEM) testing, an
5 important problem is what extent how well the developed hypothetical model fits the data. The SEM model was developed and tested using IBM AMOS 23.0. A model fitness index of the developed model also was computed. The important model fitness indices for judging the goodness-of-fit are the Chi-square (X2), AGFI, GFI, RMR, and RMSEA.
These general model fitness indices are:
1. The likelihood-ratio Chi-square statistic (X2) is the critical measure, which decides the overall fit of a hypothetical model (i.e.
SEM). A p-value greater than 0.05 is generally considered acceptable. The p value of given model is 0.076 (> 0.05) specifies perfect fit and Chi-square value 2.151 describes good fit.
2. The goodness-of-fit index (GFI) measures the degree to which the actual input matrix is predicted by the estimated model and the value ranging from 0 (poor fit) to 1.0 (absolute fit), which. The GFI value closer to 1.0 designates a better fit.
3. The adjusted goodness-of-fit index (AGFI) varies from the GFI only in the fact that it regulates the number of degrees of freedom in the hypothetical model. For both indices, the value larger than 0.90 is considered an acceptable, good fit. Here GFI and AGFI values are greater than 0.9 which represents good fit.
4. The root mean square error of approximation (RMSEA) takes into account the error of approximation in the population. The RMSEA value is the difference per degree of freedom, and is measured in terms of the population, not just the sample used for estimation. It is commonly considered that values less than 0.05 indicate a good fit; values from 0.05 to 0.08 represent a reasonable fit.
5. The root mean square residual (RMR), which is the square root of the mean of the squared residuals, an average of the residuals between observed and estimated input matrices. The range of RMR 0.0 to 1.0, which represents the average value across all standardized residuals, whereas the recommended value is lesser than 0.05. RMR and RMSEA values of the hypothesized model are 0.067 and 0.046 respectively, which is lesser than 0.08 which indicates decent fit.
6. The comparative fit index (CFI) analyzes the model fit by examining the divergence between the data and the hypothetical model, while adjusting for the issues of sample size inherent in the
chi-squared test of model fit, and the normed fit index. CFI values range from 0 to 1, with larger values indicating better fit; a CFI value of 0.90 or larger is generally considered to indicate acceptable model fit.
7. The normed fit index (NFI) analyzes the discrepancy between the chi-squared value of the hypothesized model and the chi-squared value of the null model. However, NFI tends to be negatively biased. The non-normed fit index (NNFI; also known as the Tucker- Lewis index (TLI) resolves some of the issues of negative bias, though NNFI values may sometimes fall beyond the 0 to 1 range. Values for both the NFI and NNFI should range between 0 and 1, with a cutoff of 0.95 or greater indicating a good model fit. The computed value of TLI and NFI are larger than 0.95 which signposts perfect fit.
8. CONCLUSION
Indian industrialists have now agreed that to encounter the challenges posed by the competitive atmosphere, the manufacturing organizations must infuse quality and upkeep improvement initiatives in all aspects of their processes to improve their competitiveness. Indian manufacturing sector has witnessed uncontrollable competition in the recent eras in terms of low costs, improved quality and diverse products with superior performance. Hence adaptation of TQM by all manufacturing industries mainly focuses on production of quality goods and services and delivery them with excellent customer service (Wahid et al., 2011), which lead to the success business performance. The purpose of this investigation is to construct a generic model based on factors identified for successful implementation of Total Quality Management (TQM) on business performance for manufacturer industries in eastern India. The study employed the quantitative analysis and presented a positive correlation between the TQM variables (independent) and business performance (dependent). The correlation analysis exposed that there is interrelation between the seven factors which has a significant correlation between all factors. Structural Equation Modeling technique was utilized to perform the required statistical analysis of the data from the survey. The
6 interrelationship between observed endogenous variables and unobserved endogenous variables were verified using
“the fit of model for investigating the relationship of TQM on business performance using SEM”. Having analysed the measurement model, the structural model was then tested and confirmed, where the model found to be perfect fit. Subsequently, effective use of TQM is a valuable asset in a company‟s resource portfolio - one that can produce important competitive capabilities and be a source of competitive advantage.
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List of Figure Captions
Figure 1. Conceptual Framework of the study Figure 2. Unstandardized Estimates - SEM Model Figure 3. Standardized Estimates - SEM Model
study
Figure 1. Conceptual Framework of the TQM Successful
Implementation Top Management
Involvement
Client Attention Strategic Design Process Supervision People Administration
Headship Supplier Management
Business Performance Clients and People
Outcomes Financial Performance
8
Figure 2. Unstandardized Estimates – SEM Model
Figure 3. Standardized Estimates - SEM Model
9 List of Table Captions
Table 1. Reliability analysis – Cronbach Alpha and KMO–Bartlett Sampling adequacy Test Table 2. Interrelationship between factors influencing TQM and Overall Business
Performance
Table 3. Estimates of the constructs Table 4. Abstract of the Model Fit Indices
Table 1. Reliability analysis – Cronbach Alpha and KMO–Bartlett Sampling adequacy Test
S.
No Constructs No.
items of
Mean Variance Standard
Deviation Cronbach
Alpha KMO Bartlett’s Results 1 Top
Management Involvement
5 16.50 7.648 1.766 0.798 0.724 0.000** Acceptable
2 Customer
Attention 5 17.25 8.827 0.971 0.834 0.826 0.000** Good 3 Strategic
Design 5 17.11 11.762 1.430 0.747 0.738 0.000** Acceptable 4 Process
Supervision 5 16.93 9.342 1.056 0.891 0.863 0.000** Good 5 People
Administration 5 17.11 9.941 1.153 0.712 0.788 0.000** Acceptable 6 Headship 5 17.93 8.157 1.856 0.808 0.834 0.000** Good 7 Supplier
Management 5 16.07 10.218 1.197 0.956 0.927 0.000** Excellent 8 Clients and
People Outcomes
5 16.32 9.370 1.061 0.880 0.823 0.000** Good
9 Financial Performance Results
5 16.07 10.069 1.173 0.730 0.745 0.000** Acceptable
** denotes significance at 1% level (Source: Survey data)
Table 2. Interrelationship between factors influencing TQM and Overall Business Performance
Factors
Top Management Involvement Customer Attention Strategic Design Process Supervision People Administration Headship Supplier Management Overall Business Performance
Top Management
Involvement 1 0.820** 0.875** 0.884** 0.890** 0.808** 0.761** 0.794**
Customer Attention - 1 0.834** 0.825** 0.8304** 0.756** 0.716** 0.766**
Strategic Design - - 1 0.884** 0.918** 0.795** 0.707** 0.745**
Process Supervision - - - 1 0.924** 0.833** 0.724** 0.778**
People Administration - - - - 1 0.853** 0.745** 0.782**
Headship - - - 1 0.671** 0.812**
Supplier Management - - - 1 0.915**
Overall Business
Performance - - - 1
Note: * * Correlation is significant at 1% level (Source: Survey Data)
10
Table 3. Estimates of the constructs Measured
Variables Latent
Variable Unstandardized
Co-efficient S.E Standardized Co-efficient C.R
value p value Business
Performance <--- Total Quality Management 0.981 0.104 1.025 9.419 < 0.001**
Customer
Attention <--- Total Quality
Management 1.000 - 0.831 - -
Headship
<--- Total Quality
Management 0.945 0.098 0.780 9.603 < 0.001**
People
Administration <--- Total Quality
Management 1.054 0.110 0.884 9.586 < 0.001**
Strategic
Design <--- Total Quality Management 1.047 0.109 0.806 9.621 < 0.001**
Process
Supervision <--- Total Quality Management 1.001 0.104 0.909 9.594 < 0.001**
Supplier
Management <--- Total Quality Management 1.165 0.123 0.742 9.480 < 0.001**
Management Top
Involvement <--- Total Quality
Management 1.000 - 0.442 - -
Clients and People
Outcomes <--- Business
Performance 1.000 - 0.593 - -
Financial Performance
Results <--- Business
Performance 1.166 0.034 0.320 34.529 < 0.001**
** denotes significance at 1% level (Source: Survey Data)
Table 4. Abstract of Model Fitness Indices
S. No Model Fitness Indices Actual and Recommended Value
1. CMIN 2.151 The relative chi-square should be less than 2 or 3 (Kline, 1998; Ullman, 2001).
2. P value 0.076 > 0.05 (Hair et al., 2006) 3. GFI 0.943 >0.90 (Hair et al., 2006) 4. AGFI 0.956 >0.90 (Daire et al., 2008) 5. CFI 0.948 > 0.90 (Hu and Bentler, 1998)
6. RMR 0.067 < 0.08 (Browne and Cudeck, 1993) and ideal value is 0.05 (Stieger, 2000).
7. RMSEA 0.046 < 0.08 (Hair et al. 2006) 8. TLI 0.977 > 0.95 (Hu and Bentler, 1998)
9. NFI 0.985 > 0.90 (Byrne, 1994) or 0.95 (Schumacker and Lomax, 2004)
(Source: Survey Data)
