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Total Quality Management & Business Excellence
ISSN: 1478-3363 (Print) 1478-3371 (Online) Journal homepage: http://www.tandfonline.com/loi/ctqm20
The importance of quality management for the
effectiveness of environmental management:
evidence from companies located in Brazil
Adriano Alves Teixeira, Charbel José Chiappetta Jabbour, Hengky Latan,
Jorge Henrique Caldeira de Oliveira, Wesley Ricardo de Souza Freitas & Talita
Borges Teixeira
To cite this article: Adriano Alves Teixeira, Charbel José Chiappetta Jabbour, Hengky Latan, Jorge Henrique Caldeira de Oliveira, Wesley Ricardo de Souza Freitas & Talita Borges Teixeira (2017): The importance of quality management for the effectiveness of environmental management: evidence from companies located in Brazil, Total Quality Management & Business Excellence, DOI: 10.1080/14783363.2017.1368377
To link to this article: http://dx.doi.org/10.1080/14783363.2017.1368377
Published online: 04 Sep 2017.
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The importance of quality management for the effectiveness of
environmental management: evidence from companies located in
Brazil
Adriano Alves Teixeira a∗, Charbel Jose´ Chiappetta Jabbour b,c, Hengky Latand,e, Jorge Henrique Caldeira de Oliveiraf, Wesley Ricardo de Souza Freitasgand Talita Borges Teixeirah
a
Department of Administration, Federal University of Mato Grosso do Sul, Paranaı´ba, Brazil;
b
Stirling Management School, Centre for Advanced Management Education (CAME), University of Stirling, Stirling, Scotland, UK;cMontpellier Business School, Montpellier Research in Management, Montpellier, France;dDepartment of Accounting, Faculty of Economics and Business, Universitas Diponegoro, Semarang, Indonesia;eDepartment of Accounting, STIE Bank BPD Jateng, Semarang, Indonesia;fDepartment of Administration, University of Sa˜o Paulo, Ribeira˜o Preto, Brazil;gDepartment of Administration, Federal University of Mato Grosso do Sul, Paranaı´ba, Brazil;hITB Electrical Equipment, Birigui, Brazil
Recent discussions in the specialised literature support that quality management influences the level and maturity of environmental management practices; however, in searches performed in the ISI Web of Science and Scopus databases, it was verified that there is a need for research to portray this relationship quantitatively, especially when it comes to companies located in Brazil. Thus, this research aims to explore this gap: does quality management positively influence the adoption of environmental practices in companies located in Brazil? Therefore, a conceptual grounding on the aforementioned issues was carried out and, based on this, the questionnaire used for the empirical phase of the research was constructed, being a self-administered e-survey with 104 companies of all sizes and sectors located in Brazil. The results demonstrated that, in fact, quality management positively influences environmental management practices. In addition, it was verified that the size of the companies and ISO 14001 certification are significant to control environmental management practices. To date, it is believed that this is the first study that shows a survey to test the relationship between quality management and environmental management practices in companies in Brazil.
Keywords:quality management; environmental management; environmental management practices; maturity of environmental management; structural equation modelling; Brazil
1. Introduction and research context
Sustainability is a valuable, rare, inimitable and non-replaceable resource which can be a source of competitive advantage (Hollos, Blome, & Foerstl, 2012). The idea is that advanced environmental management (EM) practices can increase the competitiveness of organisations, for example, by reducing costs, improving quality and the generation of new products and processes (Yang, Lin, Chan, & Sheu,2010).
Benefits can also be expected in relation to the market value of companies when they announce that they are adopting EM systems (Jacobs, Singhal, & Subramanian,2010), as well as environmental proactivity providing superior financial performance (Hall &
# 2017 Informa UK Limited, trading as Taylor & Francis Group
∗Corresponding author. Email: [email protected]
Wagner,2012), and also companies that value environmental issues can attract more qua-lified and motivated employees (Renwick, Redman, & Maguire,2013) which can provide a virtuous cycle of overall performance improvements.
Thus, the adoption of advanced EM practices, such as Green Supply Chain Management (GSCM), can provide countless benefits. However, these practices are not implemented at the same level between organisations (Jabbour, Jabbour, Latan, Teixeira, & De Oliveira,
2014; Zhu, Sarkis, Cordeiro, & Lai,2008; Zhu, Sarkis, & Lai,2007; Zhu & Sarkis,2006). In this sense, studies have indicated that early adoption of quality management (QM) principles can improve the level and maturity of environmental practices in organisations (Zhu, Cordeiro, & Sarkis,2013; Llach, Perramon, Alonso-Almeida, & Bagur-Femenı´as,
2013; Jabbour et al.,2014). For example, Wiengarten and Pagell (2012) found that com-panies improve their performance in terms of cost, flexibility and delivery performance, when EM practices are in the presence of high investment in QM practices.
Pereira-Moliner, Claver-Corte´s, Molina-Azorı´n, and Jose´-Tarı´ (2012) point out that companies can use QM approaches to develop a comprehensive system for the reduction and elimination of all waste flows associated with the design, manufacture, use and/or dis-posal of products and materials.
These findings were recently reinforced by research of Zhu et al. (2013), Jabbour et al. (2014), Mazzi, Toniolo, Mason, Aguiari, and Scipioni (2016), Siva et al. (2016), Pipatprapa, Huang, and Huang (2017) that evidenced the influence of QM practices on environmental performance and / or sustainability. Here, we highlight the research of Siva et al. (2016), con-ducted in the form of a literature review that found only 22 papers (Scopus and ISI Web of Science) portraying QM as supporting the implementation of EM systems and sustainability management, with the authors concluding ‘that there is a lack of empirical investigations regarding specific QM standards and their contribution in applying specific EM standards or practices. More empirical research is also needed on the synergies between integrated QM and EM and its effects on environmental performances’ (Siva et al.,2016, p. 154).
Advancing a little in the literature can show that three variables can interfere in this relationship and therefore need to be controlled: (a) size of the company (Surroca, Tribo´, & Waddock, 2010; Lo´pez-Gamero, Molina-Azorı´n, & Claver-Cortes, 2009; Murillo-Luna, Garce´s-Ayerbe, & Rivera-Torres, 2011; Jabbour et al., 2014); (b) the company having ISO 14001 (Jabbour et al., 2014; Gonza´lez-Benito, Lannelongue, & Queiruga,2011); and (c) the time of existence of the company (Lee,2008). There is sig-nificant support for the idea that these variables are important in the environmental debate; the intention is not to be conclusive about which variables could most influence the environmental practices/strategies; so, we select those that received the main attention of the researchers and that were considered in more documents.
However, based on the knowledge available in databases queries of ISI Web of Science and Scopus in January 2017, there is need for research that portrays quantitatively this reality, especially when it comes to companies located in Brazil, an emerging country. This makes it critical for sustainable development to understand how organisations in these countries are achieving their economic objectives, since they can, for example, influ-ence the dynamics of future emissions (Gunasekaran, Jabbour, & Jabbour,2014).
Thus, this research aims to explore this gap (Figure 1): does QM positively influence the adoption of environmental practices in companies located in Brazil?
It is believed, therefore, that the prior adoption of QM principles can provide more proactive environmental practices. Among these practices are noteworthy proposals by Gonza´lez-Benito and Gonza´lez-Benito (2006). These authors concluded that companies can adopt three EM practices:
2 A.A. Teixeira et al.
. Organisational and planning practices: are related to the establishment of environ-mental policy, development of procedures for the selection and implementation of environmental practices, and to assess their results or assign responsibilities directing the company to move proactively on environmental issues.
. Operational Practices: are adjustments of production systems in order to provide environmental improvements. They can be divided into two types: (a) product design, i.e. activities that design and develop more environmentally friendly pro-ducts, and (b) process design, i.e. practices considered to seek to adapt production processes in order to reduce environmental impacts; and
. Communicational practices: being those that inform society in general of the actions taken by the organisations for the environment. Their goal is to maintain a good relationship with stakeholders.
It is noted that the practices referenced by these authors have been supported in several studies, for example, Teixeira, Jabbour, and Jabbour (2012), Sihvonen and Partanen (2017), Dı´az-Garrido, Martı´n-Pen˜a, and Sa´nchez-Lo´pez (2016). In this context, for this research, the practices of EM and QM presented inTable 1were adopted, followed by the research method presented in the next section.
2. Research method
This research aimed to test the relational framework mentioned earlier, so a quantitative research approach was opted for with the application of an e-survey in companies located in Brazil from various segments and sizes.
The questionnaire, whose construction followed the steps proposed by Synodinos (2003), explored terms of QM and EM practices listed in Table 1. All questions were inspired by works that discussed the constructs and validated their questions. It is note-worthy that in this study, five companies (not part of the final sample) and five
Figure 1. Relational framework of QM and environmental practices and respective control variables.
Table 1. Constructs and practices used in the research questionnaire.
Constructs Items/practices Adapted from Question/Measurement
Environmental management
. Preparation of environmental/ sustainability reports (EM1);
. Participation in environmental campaigns/support for environmental events (EM2);
. Environmental marketing (EM3);
. Disclosure of environmental information to stakeholders (e.g. website) (EM4);
. Development of greener/more environmental products (EM5);
. Cleaner production processes (EM6);
. Environmentally cleaner raw materials (EM7);
. Suppliers selection based on environmental criteria (EM8);
. Environmental policy consistent with the company’s goals (EM9);
. Full-time staff dedicated to EM (EM10);
. Long-term environmental objectives (EM11);
. There are environmental emergency plans (EM12).
Gonza´lez-Benito and Gonza´lez-Benito (2006); Teixeira, Chiappetta Jabbour, Caldeira Oliveira, Gomes Battistelle, and Castro (2011); Teixeira et al. (2012)
What is the level of adoption of environmental practices in your company?1 – Not adopted5 – Completely adopted
QM
. Supplier certification based on quality criteria (QM1);
. Total quality management (QM2);
. Statistical process control (QM3);
. ISO 9001 (QM4)
Wiengarten and Pagell (2012) What is the level of adoption of quality
management practices in your company?1 – Not adopted5 – Completely adopted
4 A.A . Teixeira et al.
professionals from the academic area with in-depth knowledge of the subject matter of this research acted as referees to reach the final version of the questionnaire.
In total, about 937 companies/environmental managers and/or quality managers were contacted by email, between the months of November 2015 and February 2016, and about 300 of these companies were reinforced by phone calls in order to increase the rate of return. With these approaches, the rate of return was 11.09% (104 questionnaires). Before finalising the fieldwork, verification of the adequacy of the obtained sample was sought, i.e. whether the questionnaires showed a statistically satisfactory level. Thus, G∗Power 3.1 software was used (Faul, Erdfelder, Lang, & Buchner, 2007) following the recommendations of Cohen (1988) and Hair, Babin, Money, and Samouel (2005). The results showed that the minimum required number was 85 questionnaires. Thus, data analysis was initiated.
Data were analysed using partial least squares-path modelling (PLS-PM; multivariate statistical approach of second generation that allows an analysis of more complex concep-tual models (Hair, Ringle, & Sarstedt,2011)) with the support of SmartPLS 3.0 software. As it is a reflective measurement model, internal consistency and validity were eval-uated (Hair, Hult, Ringle, & Sarstedt,2017; Latan & Noonan, 2017). Specific measure-ments included (see Table 1): composite reliability/rho_A (to assess internal consistency), convergent validity, and discriminant validity (in order to assess the validity of the model). To calculate the convergent validity, the individual reliability indicator, and the average variance extracted (AVE) were used. Furthermore, Fornell – Lacker criteria (Table 2) and Heterotrait-Monotrait Ratio (HTMT) (Table 3) were used to evaluate discri-minant validity (Hair et al.,2017; Latan & Noonan,2017). Subsequently, the structural model was constructed by Booststrapping. For this calculation, the following parameters were used: evaluation of the coefficients of determination (R2), adjusted R2, effect size ( f2), predictive validity (Q2), the variance inflation factor (VIF), and for goodness of fit indices of the model two indicators we used the standardised root mean squared residual (SRMR) and normed fix index (NFI) (Table 4). Finally, for hypothesis testing, 2000
Table 2. Convergent validity and internal consistency results.
Variables Items FLa AVE Composite reliability rho-A
QM QM1 0.854 0.763 0.928 0.907 QM2 0.905 QM3 0.899 QM4 0.834 EM EM1 0.806 0.617 0.950 0.960 EM2 0.779 EM3 0.712 EM4 0.757 EM5 0.684 EM6 0.666 EM7 0.698 EM8 0.807 EM9 0.841 EM10 0.891 EM11 0.903 EM12 0.838
Source: Data generated automatically in SmartPLS 3.0. Note: FL factor loading.
subsamples were used and a 5% significance level being represented by the Student ‘ t’ test and P-value (Table 5). All these calculations and their coefficients are presented in the next section.
3. Research results
The sample of companies that participated in this study had the following characteristics: 82.69% of companies were in the manufacturing sector and the other industries of the sample were from five different sectors – mining (3.87%), construction (2.88%), transport and storage (4.86%), information and communication (0.96%) and other activities and ser-vices (0.96%) (REV, I.S.I.C.4.0). Of this total, 12.50% were micro-enterprises, 19.23% small businesses, 40.38% medium-sized companies and 27.89% large-sized companies
Table 3. Final result of discriminant validity with Fornell – Lacker criteria.
Variables AGE EM SIZE ISO 14001 QM
AGE 1.000 – – – –
EM 0.175 0.786 – – –
SIZE 0.361 0.515 1.000 – –
ISO 14001 0.109 0.718 0.421 1.000 –
QM 0.218 0.667 0.524 0.553 0.874
Source: Data generated automatically in SmartPLS 3.0.
Note: Bold to highlight the highest factor loadings on their own latent variables.
Table 4. Final result of discriminant validity as HTMT.
Variables AGE EM SIZE ISO 14001 MQ
AGE 0.90 – – – –
EM 0.169 0.90 – – –
SIZE 0.361 0.499 0.90 – –
ISO 14001 0.109 0.706 0.421 0.90 –
QM 0.225 0.698 0.549 0.581 0.90
Source: Data generated automatically in Smart PLS 3.0. Note: The value of HTMT must be smaller than 0.90 (bold).
Table 5. Results of the structural model (bootstrapping). Variables R2 R2 Adjusted
Effect size ( f2)
Q2Predictive
validity VIF SRMR NFI
AGE – – – – 1.158 – –
QM – – 0.175 – 1.698 – –
SIZE – – 0.035 – 1.573 – –
ISO 14001 – – 0.415 – 1.500 – –
EM 0.635 0.621 – 0.639 – 0.074 0.916
Source: Data generated automatically in SmartPLS 3.0.
Note: Bootstrapping -. Sign Changes ¼ Individual Changes; Subsample ¼ 2000; Confidence Interval Method ¼ Bias-Corrected and Accelerated Method.
6 A.A. Teixeira et al.
as classified by Sebrae (2017). Still concerning the sample, 56.75% of the companies had over 25 years of existence, 14.44% had between 11 and 15 years, 9.63% between 21 and 25 years, 9.61% between 16 and 20 years, and 5.79% between 6 and 10 years. In addition, 44.23% were multinationals and 64.42% had ISO 14001 certification.
Once the sample had been properly characterised, the results began to be presented. To process the data, a path diagram was created with the previous relationship that was intended to be analysed. Then, it was transformed into a measurement model (Hair, Sar-stedt, Hopkins, & Kuppelwieser, 2014) to determine whether the obtained coefficients were significant and, thus, indicating internal consistency and validity of the data.
It is noticed that all external loads were larger than 0.6, the AVE’s of QM and EM were greater than 0.50, and composite reliability / rho_A greater than 0.9 (Table 2), thus above the recommended (Hair et al., 2017; Latan & Ghozali,2015; Latan & Noonan, 2017). From the results (Table 2), it can be verified that the QM and EM constructs have conver-gent validity and very good internal consistency for the reflective model.
Subsequently, it is necessary to determine whether the model has discriminant validity. Discriminant validity demonstrates whether the indicators present in the model relate to its construct or with another from the model, and can basically be verified in two ways: Fornell and Larcker criterion (Table 3) and HTMT (Table 4). In the first case, the most conservative approach is to assess the discriminant validity. It compares the square root of the AVE values with correlations of the latent variables. In this case, the square root of the AVE from each construct must be greater than its highest correlation with any other variable, in other words, is greater in its construct than in the correlations (Hair et al.,2017; Latan & Ghozali,2015; Latan & Noonan,2017). The logic of this method is based on the idea that a construction is more in line with its associated indicators than with any other construction (Hair et al., 2017; Latan & Ghozali, 2015; Latan & Noonan, 2017). Conventionally, the square root of AVE is placed diagonally (in bold) to facilitate reading the data (Table 3). The results demonstrate that the model has good discriminant validity.
In the second case, the HTMT is one of the newest approach criteria to evaluate dis-criminant validity using PLS-PM (Henseler, Ringle, & Sarstedt,2015), being the resulting value of the bootstrap confidence intervals (CI). It is recommended that this value of HTMT was smaller than 0.90 (Henseler et al., 2015). From the following analysis (Table 4), it can be seen that the generated HTMT value of the confidence interval is smaller than 0.90 in the model, meaning that all variables in this research model have dis-criminant validity, reinforcing the results found by Fornell – Lacker criteria.
Proceeding with the calculations, the structural model which consists of the conversion of the path diagram into a set of equations that mathematically represent the structural relationship or graphics relationship between variables (Hair, Black, Babin, Anderson, & Tatham,2009), is estimated. In this procedure, as mentioned in item 2, seven indicators were adopted: coefficient of determination (R2), the adjusted R2, effect size ( f2), predictive validity (Q2), the VIF and for goodness of fit indices of the model two indicators were used: the SRMR and normed fix index (NFI) (Table 5).
R2is known as the coefficient of determination and indicates the quality of the adjusted model. This value indicates the total variation percentage of ‘Y’ explained by the regression model (Hair et al.,2009, p. 198). For Cohen (1992), a small R2occurs when its value is approximately 0.02; medium, where 0.13; and large when R2is greater than or equal to 0.26. Thus, an R2of 0.635 (Table 5) was found.
Predictive validity (Q2) is a type of quality prediction model (Hair et al.,2014). In other words, it measures how much the model is close to what was expected. For Hair
et al. (2014), it must be greater than zero (Q2 . 0). The results indicate Q2greater than 0.639 (Table 5).
Thus, large and good R2and adjusted R2were found with predictive validity (Q2) also good. This means that the predictors in the model are able to explain the variance in the dependent variable.
The f2evaluates how each construct is ‘favourable’ for adjustment of the model, being considered the values of 0.02, 0.15, and 0.35 as small, medium and large adjustments, respectively (Hair et al.,2014). The results of this survey indicate a large f2for variables QM ¼ 0.175 and ISO 14001 ¼ 0.415 (Table 5), ensuring that the constructs are impor-tant for the model.
In turn, the VIF is an ‘effect indicator that other independent variables have on the standard error of regression coefficient’ [. . . (Hair et al.,2009, p. 151). Values less than 3.3 are considered appropriate, although values lower than 5.0 are acceptable (Latan & Ghozali, 2015; Latan & Noonan, 2017). In this research, the result was VIF AC ¼ 1.158, VIF QM ¼ 1.698, VIF TE ¼ 1.573 and ISO VIF ¼ 1.500 (Table 5), which is considered appropriate, which means that there are no collinearity problems between the independent variables.
For Hu and Bentler (1998), the SRMR and NFI, which are indicators that measure the model goodness of fit, should have values, respectively, equal to or less than 0.08 and greater than or equal to 0.85, and the results found values being SRMR ¼ 0.074 and NFI ¼ 0.916 (Table 5); therefore, it is considered that the model has a good adjustment. Finally, the Student ‘t’ test and P-value were analysed to assess whether there are sig-nificances in the path coefficients between variables. The ideal is t≥ 1.96, since ‘ t’ values near 1.65, 1.96 and 2.58 are considered with significance levels of 10%, 5% and 1%, respectively (Hair et al., 2014). We tested the hypothesis using the one-tailed test rather than the two-tailed. We also see the result of 95% bias-corrected CI to obtain stable results. From the results shown inTable 6, it is clear that with the exception of the relationship of age of the company (AC), all others had a Student ‘ t’ greater than 1.755, demonstrating a positive effect on EM. It is noteworthy that the hypothesis test was per-formed using a bootstrapping of a 2000 subsample and 5% significance level (one tailed).
4. Discussions and final considerations
This research presented a relational framework in quantitative form with the use of the e-survey technique, not yet explored in companies located in Brazil. The main objective of this study was to determine whether QM positively influences the adoption of environ-mental practices in Brazilian companies, as well as to identify key QM and EM practices, and to identify whether the size of the company, ISO 14001 and company age are relevant
Table 6. Hypothesis testing results for the relationship between the variables (sig. 5%.). Variables Original sample Sample average Standard error Student ‘t’
P-value 95% BCa CI Decision AGEEM 0.001 0.053 0.040 0.015 0.494 (0.000; 0.053) Rejected SIZEEM 0.142 0.144 0.081 1.755 0.040 (0.287; 0.002) Accepted ISOEM 0.476 0.479 0.077 6.198 0.000 (0.344; 0.003) Accepted QMEM 0.329 0.333 0.086 3.818 0.000 (0.468; 0.004) Accepted
Source: Data generated automatically in SmartPLS 3.0.
8 A.A. Teixeira et al.
in this relationship. Thus, 104 companies responded to the questionnaire (seeTable 1) and the collected data were analysed in the light of structural equation modelling with the help of SmartPLS 3.0 software.
For this model to be considered appropriate, it is necessary to meet the validity and reliability criteria. In the view of Hair et al. (2005), reliability is the degree to which a vari-able (or a set of them) is consistent with what is intended to be measured. In turn, validity is not related to what should be measured, but to the way it is measured.
In this context, the model was considered valid (accepted –Table 6) and it can be con-cluded that there is a positive and significant relationship between QM and EM practices. Thus, the hypothesis that the prior adoption of QM principles can positively influence EM practices was confirmed in companies located in Brazil. In addition, the size of the company and ISO 14001 certification also have a positive and significant effect on EM practices as recommended in the literature ((Surroca et al.,2010; Lo´pez-Gamero et al.,
2009; Murillo-Luna et al., 2011; Jabbour et al., 2014 – company size) and Jabbour et al. (2014); Gonza´lez-Benito et al. (2011) – ISO 14001 certification).
It is worth noting that to identify QM practices that influence EM practices was also the objective of this research (see Section 3). In this sense, it is important to shed light on the practices: certification of suppliers based on quality criteria (QM2) and total QM (QM3). On the other hand, the most relevant EM practices were long-term environmental goals (EM11), full-time employees dedicated to EM (EM10) and having an environmental policy coherent with business objectives (EM9).
Thus, our results confirm that QM influences EM as in the findings of Jabbour et al. (2014), Zhu et al. (2013), Llach et al. (2013), Pereira-Moliner et al. (2012) and Wiengarten and Pagell (2012), and also suggest first hand that the certification of suppliers based on quality criteria and the implementation of total QM tend to support green initiatives in companies, especially those related to long-term environmental goals, full-time employees dedicated to EM, and having an environmental policy coherent with the company’s objec-tives, which can facilitate the way to a more environmentally sustainable society.
The findings bring important implications for companies and their professionals, as they allow better definition of action strategies, as well as prioritising and investing in practices that enable better environmental outcomes in the business context. It is also important for the state of the art of the topic, as it adds more empirical evidence on the subject in the context of an emerging country.
This research has some limitations which may be mentioned: (a) despite all efforts, the sample size was less than in the previous studies, for example, Daily, Bishop, and Massoud (2012) and Sarkis, Gonzalez-Torre, and Adenso-Diaz (2010); however, it meets the stat-istical and methodological requirements and (b) the adoption of EM practices may depend on other factors, not only QM, such as regulatory and legal requirements (Zhu et al.,2007) or even human resources practices such as training (Teixeira, Jabbour, de Sousa Jabbour, Latan, & de Oliveira,2016), reward systems and performance evaluation (green human resource management) (Jackson, Schuler, & Jiang, 2014) and the relative restriction of the variables imposed on the constructs QM and EM, as well as those related to an e-survey-type study
Therefore, future research is suggested, conducting multiple case studies so that it can be better understood ‘how’ and ‘why’ this relationship occurs, which could provide new insights on the subject. In addition, new studies could also assess more complex models with more variables that could influence the adoption of EM practices, or its implications for the financial, operational and environmental performance of organisations.
Acknowledgements
The authors would like to thank the Comissa˜o de Aperfeic¸oamento de Pessoal do Nı´vel Superior (CAPES) for supporting the research.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Adriano Alves Teixeira http://orcid.org/0000-0002-1669-4073
Charbel Jose´ Chiappetta Jabbour http://orcid.org/0000-0002-6143-4924
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