Environmental management and operational performance in automotive companies in Brazil: The role of human resource management and lean manufacturing
Charbel José Chiappetta Jabbour, Ana Beatriz Lopes de Sousa Jabbou, Kannan Govindan, Adriano Alves Teixeira, Wesley Ricardo de Souza Freitas
PII: S0959-6526(12)00348-4
DOI: 10.1016/j.jclepro.2012.07.010
Reference: JCLP 2965
To appear in: Journal of Cleaner Production Received Date: 14 March 2012
Revised Date: 21 June 2012 Accepted Date: 5 July 2012
Please cite this article as: Chiappetta Jabbour CJ, Lopes de Sousa Jabbou AB, Govindan K, Teixeira AA, Ricardo de Souza Freitas W, Environmental management and operational performance in automotive companies in Brazil: The role of human resource management and lean manufacturing, Journal of Cleaner Production (2012), doi: 10.1016/j.jclepro.2012.07.010.
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Highlightsthe statistic test of the research framework obtained an adequate goodness of fit
human resource management tends to support environmental management
lean manufacturing practices tend to support environmental management
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Environmental Management and Operational Performance in Automotive Companies in Brazil: The role of human resource management and lean
manufacturing
(a)Charbel José Chiappetta Jabbour (Correspondent author), e-mail: prof.charbel@gmail.com
(a)Ana Beatriz Lopes de Sousa Jabbour (b)Kannan Govindan
(c)Adriano Alves Teixeira (d)Wesley Ricardo de Souza Freitas
(a)UNESP – Univ Estadual Paulista (The Sao Paulo State University), Avenida Engenheiro Edmundo Carrijo Coube, Bauru, São Paulo State,
Brazil, CEP 17033360
(b)University of Southern Denmark, Department of Business and Economics, Odense 5230, Denmark
(c) Federal University of Mato Grosso do Sul, Paranaiba, BR497, KM12, MS, Brazil, 79500-000
(d)USP – University of Sao Paulo, Avenida Bandeirantes, 3900, Ribeirao Preto, Sao Paulo State, Brazil, CEP 14040905
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AbstractThe main objective of this study is to verify the influence of environmental management (EM) on operational performance (OP) in Brazilian automotive companies, analyzing whether lean manufacturing (LM) and human resource management (HR) interfere in the greening of these companies. Therefore, a conceptual framework listing these concepts was proposed, and three research hypotheses were presented. A questionnaire was elaborated based on this theoretical background and sent to respondents occupying the highest positions in the production/operations areas of Brazilian automotive companies. The data, collected from 75 companies, were analyzed using structural equation modeling. The main results are as follows: (a) the model tested revealed an adequate goodness of fit, showing that overall, the relations proposed between EM and OP and between HR, LM and EM tend to be statistically valid; (b) EM tends to influence OP in a positive and statistically weak manner; (c) LM has a greater influence on EM when compared to the influence HR has over EM; (d) HR has a positive relationship over EM, but the statistical significance of this relationship is less than that of the other evaluated relationships. The originality of this paper lies in its gathering the concepts of EM, LM, HR and OP in a single study, as they generally tend not to be treated jointly. This paper also provided valid empirical evidence for a little-studied context: the Brazilian automotive sector.
Keywords: environmental management; lean manufacturing; human resource management; operational performance; automotive sector; Brazil.
1. Introduction
The intensification of environmental concerns has been leading companies to adopt environmental management practices at an increasing rate (Boiral, 2006; González-Benito, 2006). One of the arguments favoring the adoption of these
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environmental management practices is that they can benefit firms, giving rise to the so-called “green and competitive” (Porter and Van Der Linde, 1995; Hunt and Auster, 1990; Berry and Rondinelli, 1998; Molina-Azorin et al., 2009). Among those benefits that can be ascertained from environmental management is the improvement in firms’ operational performance, such as a reduction in production costs (Porter and Van Der Linde, 1995). However, specialized literature affirms that environmental management can create synergy with management practices from other areas in a firm (Wagner, 2007).
Two management areas have gained prominence as targets of effective
environmental management (Wilkinson et al., 2001). The first is
operations/manufacturing management, which, because it processes resources, has significant environmental effects. The second area is human resources, which may influence the performance of new organizational objectives, such as those related to environmental performance.
The ability of the operations/manufacturing area to support environmental management tends to be greater when the company adopts Lean Manufacturing practices (González-Benito and González-Benito, 2008). This type of relationship has become known as the “Lean and Green” hypothesis and has been analyzed by several authors (Simpson and Power, 2005; Rothenberg et al., 2001; King and Lenonx, 2001; Yang et al., 2011; Maxwell et al., 1998). These authors argue that, in general, waste reduction in manufacturing contributes to environmental management (Simpson and Power, 2005) through greater efficiency in the use of production resources (Rothenberg et al., 2001) and the adoption of cleaning practices and improved organization of the productive environment (King and Lenox, 2001), which can generate competitive advantages (Yang et al., 2011).
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On the other hand, the support of human resource management practices is also considered fundamental for adopting environmental management practices (Jackson et al., 2011; Govindarajulu and Daily, 2004; Sarkis et al., 2010). These researchers affirm that human resource management must align its practices (such as recruiting, selection, performance evaluation, and training) with environmental management objectives. This process is called Green Human Resource Management (Renwick et al., 2008), which follows the hypothesis that a more intense alignment between human resources and environmental issues leads more firms to adopt environmental management practices (Bohdanowicz et al., 2011; Jabbour et al., 2010).
However, there are no studies which integrate environmental management (EM), operational performance (OP), lean manufacturing (LM) and human resource management (HR). There are few studies that partially investigate these relationships. For example, Jabbour et al. (2012) analyze the relationship between environmental management and operational performance; May and Flannery (1995) investigate the relationship between environmental management and human resources; Rothenberg et al. (2001) analyze the relationships between lean manufacturing and environmental management. There is thus an opportunity for research that fully analyzes this relationship. Ideally, this relationship should first be verified in the automotive industrial sector, which is considered by some researchers (Womack et al., 2004) to be a pioneering industry for management practices and tendencies. Brazil was chosen as the country of analysis due to the growing interest of its researchers in environmental management as well as the high relevance of the automotive sector in the country’s GDP.
Therefore, this study’s main objective is to verify the influence of environmental management on the operational performance of Brazilian automotive companies,
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analyzing whether lean manufacturing and human resource management play a role in the greening of these companies. Based on this objective, this paper tests a conceptual framework based on structural equation modeling. In the face of the other statistical techniques available, structural equation modeling is advantageous because (a) it permits researchers to test more complex conceptual frameworks, guaranteeing a more robust and holistic statistical analysis (Ismailet al., 2012), and (b) it permits the simultaneous analysis of the relationships between a broad range of variables (Hair et al., 2011).
The following sections of this paper introduce the study’s conceptual framework with its respective research hypotheses (section 2). This study also details the methodological procedures used for collecting and analyzing data (section 3), presents the results and discusses them in light of the literature (section 4) and, in the conclusion, discusses the main implications of this study and describes a proposal for future studies (section 5).
2. Research Hypotheses and Conceptual Framework
According to Haden et al. (2009), environmental management concerns the complete incorporation of environmental objectives and strategies to the broader objectives and strategies pursued by the organization. Jabbour (2010) complements this definition, suggesting that environmental management be based on a systemic approach incorporating environmentally conscious strategy at every level of the organization.
Several factors can lead a company to adopt environmental management practices (Berry and Rondinelli, 1998). According to González-Benito (2006), stakeholder pressure is the main factor driving organizations toward more advanced environmental management. More advanced environmental management can also
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improve a company’s financial performance (Molina-Azorín et al., 2009) and increase the company’s manufacturing competitiveness, promoting cost reductions, quality improvements and the generation of new products and processes (Yang et al. 2010).
In addition, especially with the advances of the population’s environmental awareness, companies that invest in environmental management may increase in worth through green marketing initiatives (Woolverton and Dimitri, 2010). Another means of increasing worth occurs when organizations announce their adoption of ISO 14001 environmental management systems, which tends to generate an increase in share value traded on stock exchanges (Jacobs et al., 2010).
There is thus an emerging consensus in the literature (Darnall et al., 2008; Iraldo et al., 2009; Crowe and Brennan 2007; Vachon and Klassen 2008; Yang et., 2010; González-Benito 2005; Sroufe, 2003) that there are positive results correlating the adoption of environmental management practices with the organizations’ performance, gauged through various indicators, especially at environmentally proactive organizations.
It is believed that the adoption of these environmental management practices (Table 1) may generate advantages in several measures of operational performance in organizations (Table 2), here including costs, quality, flexibility, delivery, new product development and time-to-market for new products.
Environmental Management
(EM) Variables/Practices Measures/Definition Source
Clear policy of valorizing environmental management (EM1)
Clear policy of valorization of environmental management through a precise declaration from business directors about the main environmental aspects and impacts generated.
Boiral (2006)
Environmental training for all employees (EM2)
Environmental training for all employees aimed at promoting environmental policy and permitting employee awareness of their activities’ environmental impacts.
Daily and Huang (2001) 3Rs (Reduction, Reuse and
Recycling applied to water, electric energy and paper) (EM3)
3Rs, comprising Reduction, Reuse and Recycling applied to water, electric energy, paper and other natural inputs, increasing business productivity.
Marcus and Fremeth (2009)
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Development of products with smaller environmental impacts (EM4)
Development of products with smaller
environmental impacts. Sarkis (2001) Development of production
processes with smaller environmental impacts (EM5)
Development of production processes with
smaller environmental impacts. Sarkis (2001) Supplier selection based on
environmental criteria (EM6)
Vendor selection based on environmental criteria. Jabbour and Jabbour (2009) ISO 14001 or other Environmental Management System (EM7)
Environmental management systems (ISO 14001 and/or others). ABNT NBR ISO14001 Voluntary promotion of information on environmental performance (EM8)
Voluntary promotion of information on
environmental performance. Boiral (2006).
Table 1: Variables related to environmental management
Thus, the first hypothesis of this study is: H1 – The adoption of environmental
management practices has a positive correlation with the organizational performance of companies in Brazil's automotive sector.
Operational Performance (OP) Variables
Measures/Definition Source
Cost (OP1)
Seeks the lowest price compared to competitors, the lowest total production cost, or the highest production capacity.
Hayes and Wheelwright (1984); Gonzaléz-Benito (2005); González-Benito (2006)
Time-to-Market (OP2)
Refers to the time needed to place a product in a market, that is, from conception to availability at the final point of sale.
Hayes and Wheelwright (1984); Gonzaléz-Benito (2005); González-Benito (2006)
New Products (OP3)
Entry of products into a specific market aiming to attract new consumers and/or retaining current ones. Related to products with new characteristics and functionalities.
Hayes and Wheelwright (1984); Gonzaléz-Benito (2005); González-Benito (2006)
Quality (OP4) Zero-defect manufacturing or manufacturing of durable products.
Hayes and Wheelwright (1984); Gonzaléz-Benito (2005); González-Benito (2006)
Flexibility (OP5)
Quick changes in product design, quick introduction of new products, quick changes in production volume, broad variety of products, or quick changes in product mix.
Hayes and Wheelwright (1984); Gonzaléz-Benito (2005); González-Benito (2006)
Delivery (OP6) Quick delivery or reliability in timely deliveries.
Hayes and Wheelwright (1984); Gonzaléz-Benito (2005); González-Benito (2006)
Tab1e 2: Variables related to operational performance
However, environmental management should be complemented with the management of human and behavioral elements supporting environmental management
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practices, which have been gaining strength (Perron et al., 2006) and require support from human resource management (Jackson et al., 2011), through the HR practices (Table 3) in pursuit of environmental management objectives. This study assumes that more efficient and effective resource management practices lead the human resources field to better understand the organization’s objectives and goals and to be better able to contribute to achieving these goals (Collins and Clark, 2003). In this sense, Huselid et al. (1997) affirm that more effective human resource practices are associated with higher organizational performance because their human resource departments are better equipped to contribute to the achievement organizational goals. Osman et al. (2011) affirm that human resource practices are positively related to the performance of Malaysian firms as well. Therefore, organizations’ human resources – as well as practices for their proper management when efficient and with efficacy – are essential drivers of a sustained competitive advantage (Jamrog and Overholt, 2004; Voorde et al., 2010). Human Resources (HR) Variables/Practice s Measures/Definition Source Recruiting and selection (HRM1)
Recruiting consists of attracting new people to company, and selection consists of choosing the right people for a job.
Dessler (2003)
Training (HRM2)
A planned organizational action that permits acquiring technical and behavioral skills while contributing to the development of cognitive strategies that can make the individual more apt to perform current or future functions.
Borges-Andrade (2002)
Performance Evaluation
(HRM3)
Process that aims to determine an employee’s work results; one of its main functions is to offer a reason for compensating his results and efforts.
Türk and Roolaht (2007); Stoner and Freeman (1999); Robbins and Decenzo (2004)
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Table 3: Human resource management practices
In the specialized literature, this process of support from HR to EM objectives is called Green Human Resource Management (GHRM) (Renwick et al., 2008). GRHM concerns the alignment of several practices in human resource management (recruiting, selection, training, performance evaluation, rewards, etc.) with a company’s environmental management objectives (Renwick et al., 2008; Muller-Camen et al., 2010).
Some practical results confirm the importance of HR for EM: (a) Sarkis et al. (2010) conducted a survey with 157 large companies in Spain’s automotive sector. They concluded that environmental training is a mediating variable for the success of environmental management practices in analyzed companies; (b) Jabbour et al. (2010) observed in a survey with 94 Brazilian companies that more evolved environmental management leads to more support from human resource practices.
We thus present H2 – The adoption of human resource management practices
has a positive correlation with the environmental management of companies in Brazil's automotive sector.
According to King and Lenox (2001), the logic of organization and cleaning in lean production practices has the benefits of waste reduction and a lower risk of accidents. Maxwell et al. (1998) found implementations of lean production to be dedicated to a philosophy of waste reduction that could be easily understood to achieve the objectives of environmental protection. Vais et al. (2006) suggest that to become lean and environmentally friendly, the organization should focus on energy
Rewards (HRM4)
The term refers to all monetary payments and all goods or merchandise used to reward employees.
Daft (1999); Hipolito ( 2002)
Benefits (HRM5)
These are the benefits and conveniences shared by the organization and by employees that are not part of the direct salary.
Oliveira and Leone (2008); Bateman and Snell (1998)
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consumption and material residue, which are the inputs and outputs of a transformation system. In this context, it can be stated that the adoption of lean production practices improves the organization’s environmental performance.
Yang et al. (2011) state that it is important for manufacturing companies to implement lean production practices with environmental management as a means of obtaining eco-advantages through improvements in environmental performance. According to Dües et al. (2012), companies can use lean practices as a catalyst for greening the supply chains because “lean” and “green” have overlapping practices and elements.
Some studies reported how lean manufacturing practices (Table 4) can positively influence actions geared towards corporate environmental management. Maxwell et al. (1998), Rothenberg et al. (2001) and Simpson and Power (2005) stressed the importance of involving employees, whether to intervene in the process to avoid failures (that cause rework and unnecessary use of resources) or to commit to and propose improvements related to the improved use and conservation of inputs. In this same sense, quality circles are another form of promoting this involvement, as they provide employees with training and workshops intended to motivate them to participate in projects for environmental efficiency and to engage in responsible consumption (Vais et al., 2006). Lean Manufacturing (LM) Variables/Practices Measures/Definition Source Multifunctional involvement in the process (LM1)
Development of employee skills and incentive for autonomy to avoid failures throughout the process.
Biazzo and Panizzolo (2000); Shah and Ward (2003); Bahasin and Burcher (2006); Pettersen (2009). Continuous improvement
(LM2)
Seeks incremental continuous improvement in quality, costs, delivery and the project.
Biazzo and Panizzolo (2000); Shah and Ward (2003); Bahasin and Burcher (2006); Pettersen (2009). 5S (LM3) A form of visual management for
reducing disorder and inefficiency in the productive and administrative environments.
Biazzo and Panizzolo (2000); Shah and Ward (2003); Bahasin and Burcher (2006); Pettersen (2009). Total productive maintenance
(LM4)
Aims to improve machine reliability and capacity through
Biazzo and Panizzolo (2000); Shah and Ward (2003); Bahasin and
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periodic maintenance regimes. Burcher (2006); Pettersen (2009). Kanban (LM5) Card system for creating a pulled
flow.
Biazzo and Panizzolo (2000); Shah and Ward (2003); Bahasin and Burcher (2006); Pettersen (2009). Just in Time (LM6) Seeks a continuous production
flow.
Biazzo and Panizzolo (2000); Shah and Ward (2003); Bahasin and Burcher (2006); Pettersen (2009). Lot reduction/stock reduction
(LM7)
Formation of small production lots to reduce stock in process and to increase variety.
Biazzo and Panizzolo (2000); Shah and Ward (2003); Bahasin and Burcher (2006); Pettersen (2009). Improvement circles/kaizen
circles (LM8)
Promote systematic discussions between operators and managers for better incremental continuous improvement.
Biazzo and Panizzolo (2000); Shah and Ward (2003); Bahasin and Burcher (2006); Pettersen (2009). Vendor development/
collaboration (LM9)
Activities geared towards developing relationships with the vendor to obtain their collaboration.
Biazzo and Panizzolo (2000); Shah and Ward (2003); Bahasin and Burcher (2006); Pettersen (2009). Table 4: Variables related to lean production practices
Vais et al. (2006) also cite the 5S and total productive maintenance as lean production practices that aid environmental management. The 5S provide guidelines to the organization and cleaning to avoid the incorrect disposal of waste and incorrect use of inputs. Total productive maintenance aims at the periodic review of equipment based on simple adjustments (cleaning, lubrication, calibration, etc.) to increase the useful life of equipment and its efficiency (Donaire, 1999).
According to Pojasek (2008), lean production practices adhere to several ISO 14001 standards. Examples of such practices are seeking the root cause of a problem and thus applying corrective actions, creating conditions for preventing failures (jidoka/poka yoke) and thus elaborating emergency action procedures, and providing continuous improvement based on critical analysis by top management. King and Lenox (2001) and Rothenberg et al. (2001) verified that high levels of pollution prevention occur at plants that use lean production practices, among other reasons, due to stock reductions. Another prominent factor according to Simpson and Power (2005) and Corbett and Klassen (2006) is the importance of employee collaboration in the environmental improvement process, as employees are responsible for providing inputs that directly affect the environmental efficiency of the final product.
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This study thus proposes H3 – The adoption of lean manufacturing practices
has a positive correlation with the environmental management of companies in Brazil's automotive sector.
From a review of the extant literature, a conceptual framework is proposed and shown in Figure 1.
Figure 1 – Research framework
This theoretical framework was empirically tested following the methodological procedures below.
3. Methodology
3.1 Methodological Framework
Based on the existing gap in research combining environmental management, operational performance, human resources and lean manufacturing applied to the Brazilian context, it was decided to conduct a quantitative study. This approach was
Operational Performance (OP) Lean Manufacturing (LM) Environmental Management Practices (EM) Human Resources (HR) H1 H2 H3
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chosen because for all the individual concepts analyzed, quantitative scales already exist in specialized literature. It thus was possible to conduct a survey. Figure 2 shows the flow of procedures and methodological choices for this survey.
Figure 2: Flow of procedures and methodological choices for this survey.
3.2 Industrial sector studied
Research Framework Empirical Test Methodological Procedures Respondents: Operations Directors/Managers 75 valid questionnaires Use of structural equation modeling Results/Discussions Hypotheses Test Model Test Final Considerations Gap in literature and
Brazilian context
Objective of the study
Hypotheses H1, H2 and H3
Survey Study Brazilian Automotive
Sector
Elaboration and Test of the Questionnaire
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The Brazilian automotive sector, specifically the auto parts sector, is the target of this study. Brazil’s automotive sector began in the 1950s and has since evolved into 26 car manufacturers with 53 factories supplied by more than 5,000 auto part companies, with an installed production capacity of 4.3 million vehicles and 109 thousand farm machines per year, positioning Brazil as one of the six biggest producers of vehicles in Brazil (Anfavea, 2011).
Based on data from 2010 it is possible to affirm that the sector employs approximately 1.5 million people, earns more than US$ 107.6 billion annually (including auto parts), has a total production totaling 5.2% of Brazil’s gross domestic product (GDP), and can reach 22.5% of GDP if all indirect effects are considered (Anfavea, 2011).
This extensive growth in the sector should not be attributed only to the car manufacturers because it was made possible by the installation of the auto part industry, which together with the manufacturers was responsible for several innovations, such as flex-fuel engines and other technological adaptations to the Brazilian market.
3.3 Questionnaire Building
A data collection instrument was planned for collecting data based on a structured questionnaire about the concepts previously reviewed in section 2 that were elaborated according to the recommendations contained in Synodinos (2003).
The questionnaire contains information on the characterization of respondent companies and four blocks of assertions: one for the “Environmental Management" construct, one for “Lean Manufacturing”, one for “Human Resource Management" and the last for "Operational Performance". Altogether, the questionnaire presents eight assertions about environmental management (one for each environmental practice), nine
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about the lean manufacturing construct, five about human resource management and six about operational performance (one for each measure of operational performance). The first version of the questionnaire was submitted for content validation through an analysis of five researchers in the area, as well as the adjustment to conceptual presuppositions. In its final version, the questionnaire was hosted in a virtual environment specifically elaborated for this research.
A 5-point Likert scale was adopted, where 1 represents “totally disagree” and 5 represents "totally agree”.
3.4 Data collection
The research data were collected between October 2010 and March 2011. First, email addresses and telephone information for 654 automotive sector companies (auto part segment) located in Brazil were collected at the National Automotive Vehicle Component Industry Union. Emails were sent to respondents occupying the highest positions in production/operations areas at Brazilian automotive companies. The e-mails contained a brief explanation about the study and an invitation for the
operations/manufacturing manager to participate. The choice of the
operations/manufacturing manager was made because the operations/manufacturing area generates most of the environmental impacts and is responsible for several operational performance measures. It is also the area responsible for LM practices. In addition, because the operations/manufacturing manager is a line manager, he should be familiar with the HR practices for managing operations/manufacturing area employees.
The e-mail contained a link to direct the target respondent directly to the questionnaire hosted in the study’s virtual environment. Phone calls were also made to
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increase the return of valid questionnaires, and an attempt was made to contact the employees responsible for the company's production area.
A total of 72 questionnaires were collected through the research site, and 4 questionnaires were collected from alternative means, as requested by the respective respondents. In all, 76 questionnaires were obtained, 1 of which was discarded due to being incomplete, leading to a total response rate of 11.11% (75 valid questionnaires), a number considered adequate compared to the percentages suggested by Synodinos (2003). Murillo-Luna et al. (2011) state that response rates greater than 6% can already be considered adequate for attempts at extrapolating results, especially in studies that apply structural equation modeling. As will be seen in the next section, goodness of fit (GoF), a general adjustment indicator for the statistical model, achieved good scores for this study, which also indicates that the sample was adequate (see section 4). Each filled-out questionnaire automatically fed a data spreadsheet for subsequent statistical processing.
3.5 Analysis of results
The conceptual framework (presented in section 2) guided the data analysis process, which involved the use of statistical procedures with the support of data spreadsheets from the Statistical Package for Social Sciences (Version 19.0) and Smart PLS 2.0. Section 4 presents the statistical procedures associated with each of the results obtained in detail and shows a consolidation table validating or rejecting the study’s hypotheses.
The statistical tests involved the following boundaries for application:
• Adjustment of the sample for each individual factor using the KMO
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the value is small, the KMO test near zero the sample can be inadequate. On the other hand, a value close to 1 can be considered adequate (Hair Jr. et al. 2005);
• Using Principal Components Analysis to group variables into factors (Hair Jr. et al., 2005);
• Calculating Cronbach’s Alpha for each factor. Cronbach’s Alpha is used to measure construct reliability. Reliability is understood as the measure of internal consistency of responses between respondents for a single construct (Kline, 2005);
• Bartlett’s Test of Sphericity. Bartlett’s test evaluates the hypothesis that the correlation matrix is the identity matrix, where the determining factor equals one (Pestana and Gageiro, 2003). This test is used to analyze the correlation matrix as a whole;
• Main diagonal of the anti-image matrix, which should present values greater than 0.6. (Hair Jr. et al., 2005);
• Variable communalities, which explain the adherence of a given variable to the diverse factors of a factorial analysis (Hair Jr. et al., 2005);
• The eigenvalues for each factor, from which factors with values equal to or greater than 1.0 were extracted. A factor’s eigenvalue indicates how much data cloud variance is absorbed by it (Aranha and Zombaldi, 2008);
• R2 values near 0.75, 0.50 and 0.25 are considered substantial, moderate and weak, respectively (Hair et al., 2011);
• The t test value near 1.65, 1.96 and 2.58 are considered with significance levels of 10%, 5% and 1%, respectively (Hair et al., 2011);
• GoF (goodness of fit statistics), which measures the overall statistical fitness of the model tested, can have values of GoFsmall = 0.1; GoFmedium = 0.25; GoFlarge = 0.36 (Wetzels et al., 2009).
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The application of these statistical measures will be shown below as part of the presentation of the empirical test of the proposed conceptual model.
4. Results and Discussions
The reduction of data for all variables from the Environmental Management (EM) construct, the Lean Manufacturing (LM) construct, the Human Resource Management (HR) construct and the Operational Performance (OP) construct was performed using Principal Component Analysis (PCA) through the varimax method (Appendix 1, 2, 3 and 4).
In relation to the Environmental Management Construct (EM), only one factor was formed, explaining an accumulated variance of approximately 74.38%, with an eigenvalue of 5.95 and proper values in the main diagonal of the anti-image matrix (0.848; 0.821; 0.925; 0.863; 0.852; 0.951; 0.930; 0.908). The KMO test, which assesses sample fitness, was 0.882, an adequate level, as was the value obtained with the Bartlett Test of Sphericity (636.937) and Cronbach’s Alpha (0.949). All of the EM Construct variables presented satisfactory values (Appendix 1A).
After refining the Environmental Management Construct (EM) reported above, the EM1 (environmental policy) variable was found to obtain the highest average among the environmental management practices (Appendix 1B). The Pearson coefficient of correlation test was also run (Appendix 1C), revealing that all EM1-EM2 variables have significant correlations, underscoring the relation between EM1 (environmental policy) and EM2 (environmental training).
Therefore, environmental management at the analyzed companies tends to constitute the totality of practices considered herein, confirming the indications by González-Benito (2006) concerning the implementation of environmental management
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through a set of practices. The “environmental policy” practice stood out with the highest average, as did the “environmental training” variable, which had the highest coefficient of correlation with the environmental management construct; the importance of this correlation has been emphasized by several authors (Govindarajulu and Daily, 2004; Daily and Huang, 2001; Sarkis et al., 2010).
Concerning the Human Resources construct (HR), only one factor was formed, explaining an approximate accumulated variance of 68.12%, with an eigenvalue of 2.72 and values adjusted in the main diagonal of the anti-image matrix (0.71; 0.61; 0.68; 0.63). The KMO test, which verifies sample fitness, produced a value of 0.662, which is considered to be an adequate level, as are the values obtained from the Bartlett Test of Sphericity (141.41) and Cronbach’s Alpha (0.84). The Human Resources Management Practices Construct (HR) comprised the variables HR1, HR2, HR3 and HR4. Variable HR5 was excluded due to low communality (0.38) (Appendix 2A).
After refining the Human Resource Management Practices Construct (HR) reported above, the variable HR2 – training – was found to obtain the highest average among human resource practices (Appendix 2B). The Pearson coefficient of correlation test was also run, revealing that all HR1-HR4 variables have significant correlations, underscoring the relation between HR1 (recruiting and selection) and HR2 (training) (Appendix 2C).
As a consequence, human resource management in the auto parts sector tends not to adopt a homogenous standard of benefits for employees, as HRM5 was not statistically valid. This reveals sector specificity, which is consistent with the contingency approach of human resource management suggested by Jackson and Schuler (1995). On the other hand, most human resource management practices found in the literature review were verified in practice, especially HRM2 (training practice),
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which makes the worker more apt to perform daily work activities at an industrial establishment as suggested (Borges-Andrade, 2002).
For the Lean Manufacturing Construct (LM), only one factor was used. This factor explained an accumulated variance of approximately 64.27%, with an eigenvalue of 5.78 and proper values in the main diagonal of the anti-image matrix (0.917; 0.904; 0.927; 0.903; 0.867; 0.841; 0.943; 0.908). The KMO test, which assesses sample fitness, was 0.900, which is considered adequate, as are the values obtained with the Bartlett Test of Sphericity (460.202) and Cronbach’s Alpha (0.927). All of the LM Construct variables presented satisfactory values (Appendix 3A).
After refining the Lean Manufacturing Construct (LM) reported above, the variable LM2 – Systematic Search for Continuous Improvement – was found to obtain the highest average among LM practices (Appendix 3B). The Pearson coefficient of correlation test was also performed (Appendix 3C), revealing that all LM1-LM9 variables have significant correlations, underscoring the relation between LM5 (Kanban) and LM6 (Just-in-Time).
Therefore, the Lean Manufacturing construct is perceived to have all variables validated. Among all the practices, the “systematic search for continuous improvement” obtained the highest implementation average and was also the most important variable in the structural model test for the Lean Manufacturing construct. In terms of correlation, interdependence was verified among all Lean Manufacturing variables, underscoring the relationship between LM5 (Kanban) and LM6 (Just-in-Time). This correlation can be explained by the importance of Kanban systems for implementing Just-in-Time (Ohno, 1988).
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The Operational Performance Construct (OP) comprised variables OP1, OP2, OP5 and OP6. Variables OP3 and OP4 were excluded from the analysis because they present communalities of 0.38 and 0.43, respectively (Appendix 4A).
After refining the Operational Performance Construct (LM) reported above, the variable OP6 (capacity for meeting deadlines established by clients) was found to obtain the highest average among operational performance practices (Appendix 4B). The Pearson coefficient of correlation test was also performed (Appendix 4C), revealing that all OP1, OP2, OP5 and OP6 variables have significant correlations; the correlation between OP5 (flexibility for adapting to clients) and OP6 (capacity to meet client deadlines) is particularly significant.
As a consequence, the configuration of the Operational Performance construct was only partially validated. This finding indicates that there is no clear perception that sector company performance is measured in terms of launching new products or in terms of differentiation in quality. This result can be explained by the fact that auto part products tend to follow launch specifications and the quality established by the car manufacturers. Furthermore, quality has become a qualifying factor and not a winner of supply contracts.
Next, structural equation modeling – Partial Least Squares (SEM-PLS) – was used. Structural equation modeling through PLS is considered a second-generation multivariate analysis. It is especially useful when working with complex theory (relating concepts such as EM, OP, HR and LM) or in initial stages of development. A structural model was created containing the constructs obtained from Principal Component Analysis, as explained above (Figure 3). The analyses were conducted using SmartPLS 2.03 (Sosik et al., 2009).
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Figure 3 – Structural Model
HR and LM were observed to positively influence EM with an R2 of 0.396, that is, with a moderate to weak intensity, according to Hair et al. (2011). In this relationship, LM is most prominent and is the most important construct explaining EM behavior. OP is positively but weakly influenced by EM, as shown in the R2 value of 0.114.
Good quality indicators for the proposed model were achieved in terms of Average Variance Extracted (convergent validity), compounded reliability, Cronbach’s Alpha and communalities, for all constructs. To assess satisfactory reliability (which identifies the precision with which the construct measures exactly what is intended to be measured) and validity (which tests the relationship of one variable with another variable from a same construct), the compounded reliability value should be greater than 0.7, whereas the convergent validity value should be greater than 0.5. Construct reliability was evaluated using compounded reliability. The convergent validity was
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analyzed using the Average Variance Extracted. Table 5 shows that all compounded reliability values are greater than 0.7 and that all Average Variance Extracted values are greater than 0.5 (Foltz, 2008). The Cronbach’s Alpha coefficients and the communalities are also considered adequate.
Table 5: Reliability and validity values for the Structural Model
Constructs Average Variance Extracted (AVE) Compounded Reliability R Square Cronbach’s Alpha Communality EM 0.743662 0.958608 0.39569 0.950406 0.743662 HR 0.677598 0.893229 0.000 0.84124 0.677598 LM 0.641557 0.941442 0.000 0.930237 0.641557 OP 0.661935 0.886552 0.114243 0.831282 0.661935
One means of guaranteeing discriminant validity is to assess whether the variables do in fact have higher loads in their factors of origin. This analysis obtained adequate results (Table 6).
Table 6 – Crossed loads for evaluating discriminant validity
EM HR LM OP EM1 0.90 0.32 0.55 0.32 EM2 0.93 0.39 0.54 0.35 EM3 0.87 0.36 0.53 0.26 EM4 0.82 0.48 0.51 0.24 EM5 0.86 0.44 0.59 0.32 EM6 0.81 0.42 0.48 0.21 EM7 0.87 0.36 0.54 0.33 EM8 0.83 0.33 0.45 0.29 HRM1 0.26 0.81 0.47 0.45 HRM2 0.38 0.85 0.48 0.50 HRM3 0.42 0.89 0.42 0.39 HRM4 0.37 0.73 0.35 0.42 LM1 0.51 0.43 0.80 0.44 LM2 0.46 0.38 0.84 0.41 LM3 0.52 0.35 0.82 0.23 LM4 0.48 0.38 0.81 0.30 LM5 0.35 0.30 0.72 0.24
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LM6 0.42 0.36 0.79 0.28 LM7 0.43 0.47 0.81 0.38 LM8 0.56 0.51 0.83 0.43 LM9 0.58 0.51 0.78 0.39 OP1 0.32 0.41 0.38 0.82 OP2 0.19 0.35 0.27 0.75 OP5 0.30 0.55 0.40 0.85 OP6 0.26 0.40 0.35 0.84Aimed at testing model robustness, a bootstrap of 1,000 sub-samples was used to estimate the statistical significance of relationships between proposed variables and constructs (Figure 4). According to the Methodology section, when the value of the t test is close to 1.65, 1.96 and 2.58, the significance levels will be, respectively, 10%, 5% and 1% (Hair et al., 2011).
Figure 4 – Structural Model with bootstrapping of 1000 sub-samplings
Therefore, the relationship between LM and EM is positive and significant at the 1% level. The same is valid for the relationship between EM and OP. Therefore, environmental management tends to influence operational performance in a positive but
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weak manner, although with statistical significance. Finally, it is worth underscoring the relationship between HR and EM, which proved positive but with a significance level of only 10%.
All of the other model relationships are statistically valid at the significance level (p value) lower than or equal to 0.01, as per Table 7.
Table 7 – Significance of model relationship coefficients
Relationship Load T Test Significance Level
EM1 <- EM 0.90 37.84 * EM2 <- EM 0.93 61.57 * EM3 <- EM 0.87 20.52 * EM4 <- EM 0.82 15.31 * EM5 <- EM 0.86 25.77 * EM6 <- EM 0.81 18.74 * EM7 <- EM 0.87 33.69 * EM8 <- EM 0.83 17.92 * HRM1 <- HR 0.81 10.41 * HRM2 <- HR 0.85 16.01 * HRM3 <- HR 0.89 37.15 * HRM4 <- HR 0.73 8.80 * LM1 <- LM 0.80 16.14 * LM2 <- LM 0.84 21.74 * LM3 <- LM 0.82 19.91 * LM4 <- LM 0.81 22.36 * LM5 <- LM 0.72 8.98 * LM6 <- LM 0.79 13.73 * LM7 <- LM 0.81 17.80 * LM8 <- LM 0.83 19.38 * LM9 <- LM 0.78 15.24 * OP1 <- OP 0.82 11.17 * OP2 <- OP 0.75 5.10 * OP5 <- OP 0.85 10.83 * OP6 <- OP 0.84 9.87 * EM -> OP 0.34 3.75 * HR -> EM 0.18 1.84 *** LM -> EM 0.52 6.21 *
* p value < 0.01; ** p value < 0.05; *** p value < 0.1 (Scale based on Hair et al., 2011)
Finally, the GoF (Goodness of Fit Statistics) for the statistical model should be determined. According to Wetzeks et al. (2009), for studies in which the average R2 is close to 0.25, GoF should have a minimum value of 0.36 (GoF-large). In this study, the
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average R2 was found to be 0.255, and the average GoF was 0.443, indicating good fit. This finding indicates the proposed model overall has a fitting statistical adjustment.
Therefore, the main hypothesis for this study that EM and OP can be considered to be valid, with the observed relationship having a significance level of 1%, indicates that EM does indeed tend to influence OP among sample companies. However, this relationship tends to be weak. The validation is corroborated by the classic mantra in the literature, “green and competitive”, which emerged in the 1990s (Hunt and Auster, 1990; Porter and Van Der Linde, 1995; Berry and Rondinelli, 1998) and has recently been taken up again (Marcus and Fremeth, 2009; Jacobs et al., 2010; Molina-Azorín et al., 2009). However, this relationship, being weak, should be analyzed further by researchers.
The hypothesis relating LM and EM was also supported; the relationship was found to be positive at the highest level of statistical significance. This relationship was found to be the most significant one identified in the model, revealing a positive and valid relationship between lean manufacturing and environmental management practices and thus confirming that companies can create synergies between “lean” and “green” actions (Dües et al., 2012).
Finally, the relationship between HR and EM can also be considered positive but only at a significance level of 10%. As suggested by Hair et al. (2011), this hypothesis can be considered valid but with a statistical reliability that is less than for the model’s other hypotheses. This result, despite requiring more in-depth and qualitative analyses to gain knowledge, may be explained by the phenomenon whereby companies generally forget the human side of environmental management (Jackson et al., 2011; Daily and Huang, 2001).
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5. ConclusionsThe objective of this study was to verify whether environmental management influences operational performance at Brazilian automotive companies. It also verified whether environmental management is influenced by human resource management and lean manufacturing.
The combination of these themes in a single conceptual framework and empirically testing it in the context of Brazilian companies is the primary contribution of this study. It is possible to find some studies dedicated to investigating only part of this framework of relationships, such as human resources and environmental management (Daily, Bishop and Massoud, 2012), but the opportunity remains to study more complete models such as the one presented here.
The main results of this study show that, in general, the conceptual model is statistically valid for those companies analyzed, as it results in a GoF of 0.423 (the cutoff line was 0.36, according to Wetzel et al. 2011). The empirical analysis also revealed the following:
• EM tends to influence OP in a positive and statistically valid (p value <0.01) but with a weak explanatory power. This finding indicates that relationship must be strengthened within the companies studied to generate synergy between environmental management and performance, creating, win-win conditions.
• LM tends to influence EM in a positive and statistically valid (p value <0.01) but weak-to-moderate manner. LM was found to be the variable with the most explanatory power over EM.
• HR tends to influence EM in a positive manner, but this relationship can only be accepted with a less rigorous statistical condition (p value < 0.1), which can be
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maintained with some exceptions. This finding indicates that HR does not have the same significance power that LM has over EM.
These results have implications for scholars and business owners alike. For scholars, in light of the Brazilian context, the literature’s emphases on “green and competitive” (Porter and Van Der Linde, 1995) and “lean and green” (Florida, 1996) are confirmed, but the green human resource management approach was not found to be significant (Jackson et al., 2011) for the analyzed companies.
For business managers, the main implications are as follows: (a) there is a need to systematically understand the relationship between diverse approaches and managerial practices, and (b) there is a need to pay more attention to the human side of environmental management, which can improve operating performance. These two managerial recommendations can contribute to those organizations that have been seeking more sustainable social repositioning.
These results may be useful for professionals dedicated to teaching environmental management, human resources or lean manufacturing. They can also be useful for subjects concerning “doing business in Brazil” as well as those relating to international business.
The main limitations of this research are sample size, which, despite all the effort made on data collection, only included 75 participating companies, and the restriction of analyzing a single industrial sector. Another limitation concerns the existence of overlaps between HR, EM and LM variables, as discussed by Dües et al. (2012). Finally, it is believed that future studies are needed to better understand the reasons for the poor integration between human resource management practices and environmental management practices at analyzed companies.
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