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1. Introduction
Throughout world history, the use of natural resources has been central to economic development. While industrialization resulted in progress and modernity that brought advantages to organizations and comfort to people, it also caused significant social and environmental problems (Mores et al., 2018). The area of logistics that deals with ecological activities is known as green logistics which seeks to reduce the impacts and growing concerns of environmental degradation that traditional logistics have caused (Karaman et al., 2020). However, the uncertainty and ambiguity of the factors that influence the implementation of green logistics activities makes it urgent to explore and reveal the background, as well as the positive impacts on the organization’s efficiency (Zhang, 2020).
Authors such as Sarkis et al. (2011), Ye et al. (2013) and Khor et al. (2016) have used theories to explain the antecedents that motivate organizations to include green activities in their industrial routines. From this perspective, the institutional pressures of institutional theory provide a basis for such antecedents to be researched. This theory seeks to understand the phenomena related to the pressures that organizations receive in the environment in which they are inserted. However, in addition to the antecedents that interfere with the inclusion of green logistics activities, it is also necessary to understand if their inclusion is efficient and generates a better performance in terms of logistics costs (Pazirandeh & Jafari, 2013; Ubeda et al., 2011).
This article aims at understanding how the performance of logistics efficiency is influenced by the inclusion of green logistics activities. In addition, we seek to understand more consistently the relationship between the main reasons for including green logistics activities and how these reflect on the performance of logistics efficiency.
2. Literature review
2.1. Institutional theory
Institutional theory has at its core institutionalism, which seeks the motivational explanation of organizations to incorporate practices and procedures defined by concepts that predominate in the organizational environment and that are institutionalized in society (Meyer & Rowan, 1977). The institutional isomorphism is the phenomenon of effective conduction of the organizations to the process of institutionalization. This phenomenon is directed towards a homogeneous character of the use of practices, processes and management by the organizations (Dimaggio & Powell, 1983; Zhu & Sarkis, 2016). According to these authors, there are three mechanisms that put pressure on the organizations, and promote this isomorphic conduction, being: coercive isomorphism; normative isomorphism and mimetic isomorphism.
The coercive pressures are those that occur through the influence exerted by norms, laws and governmental agencies (Dimaggio & Powell, 1983; Lo & Shiah, 2016; Zhu et al., 2010;). Normative pressures are usually exercised by internal and external stakeholders who are interested in the organization, where it seeks its full efficiency and professionalism (Berrone et al., 2010; Dimaggio & Powell, 1983; Lo & Shiah, 2016; Zhu et al., 2010). They are commonly represented by clients (Berrone et al., 2010). Mimetic pressures occur when an organization imitates the actions of successful competitors in the market by benchmarking successful organizational practices (Dimaggio & Lo & Shiah, 2016; Powell, 1983; Zhu et al., 2010; Zhu & Sarkis, 2016). For example, when companies are confronted with a new or emerging characteristic such as green logistics, and even in the absence of previous experience in this area, such organizations tend to act similarly to others, in order to be successful in this regard (Heinz & Delios, 2001).
2.2. Green logistics
Logistics activities are significant sources of environmental pollution and greenhouse gas emissions, which have harmful impacts on human health and the quality of ecosystems. Therefore, clients and governments require companies to reduce the environmental impacts (for example, carbon emissions) of their activities (Dekker et al., 2012; Fichtinger et al., 2015). There is widespread recognition that logistics affects the environment significantly, producing the desired service on the one hand, and a negative environmental impact on the other, and actions are required to change this scenario (Berntsen & Fuglestvedt, 2008), and that green logistics activities are inserted into business routines (Sbihi & Eglese, 2010).
Due to the fact that green logistics is a recent field of study, it has attracted the attention of researchers for their important role in supply chain management. When purchasing with the traditional logistics model, it is identified that only the economic objective is considered, but in the green logistics, social and environmental objectives are considered beyond the economic objective (Rabbani et al., 2017). Previously, logistics systems focused primarily on the goal of increasing the efficiency of industry activities in relation to timing and profits. However, the current atmosphere of increasing concern about environmental impacts has introduced the concept of green logistics as the basis for developing methods that can reduce the environmental impacts of logistics systems (Küçükoğlu et al., 2013).
It is one of the vital subcomponents of the Green Supply Chain Management process, which is highly required in recent years due to competition, globalization, customer demand and the exploration of new markets (Srivastava, 2007). The green practices responsible for the success of Green Supply Chain Management have got in green logistics, one of its main foundations, alongside the development of green products, green procurement, ecological production practices, the use of cleaner technologies, green management and green marketing (Luthra et al., 2014).
2.3. Efficiency performance (costs)
The understanding of the performance of the logistic efficiency treated in this research needs a specific contextualization about the efficiency in the context of logistics management. To this end, authors such as Martel and Klibi (2016) argue that logistics management is the set of integrated activities, including freight transport, warehousing and inventory management, material handling and information processing through a necessarily efficient process in the most varied activities. This efficiency is crucial for the good performance of logistics activities. Logistic performance includes several dimensions that can be applied to each logistics area, such as costs, customer service, productivity and quality (Fawcett & Cooper, 1998). In the literature there are a considerable number of performance meters capable of measuring a given logistics system. Generally, cost related meters and the quality of the logistics service are always listed as crucial (Schramm-Klein & Morschett, 2006).
Regarding cost performance, authors such as Mollenkopf and Closs (2005) point out that the efficiency in logistics costs can not be neglected because organizations need profitability to survive. Under this premise, companies are constantly seeking alternatives to obtain better economic results, through activities and processes that provide cost efficiency (Carter & Rogers, 2008). Logistics transport and warehousing activities, for example, are cost factors that have had a strong impact on logistics services. Thus, the management of these activities requires alternatives that match the needs of the market and at the same time contribute to the economic efficiency of logistics activities (Pokharel, 2005).
Nowadays, due to the strong environmental wave, the market has required attributes of greening in the logistics services that contribute to the reduction of the environmental impact and at the same time meets the market demands. This demand according to Heilig and Voß (2016), for example, meets with green logistics solutions that are not only ecologically sustainable, but also economically reasonable. Authors such as Zhu et al. (2007) present two possibilities for responding to this demand. According to these authors, such responses can impact the economic performance of companies in two ways. A positive way, for example: a decrease in the cost of acquiring materials, a reduction in the cost of energy consumption, reduction of the waste treatment rate, reduction of fines for environmental accidents; and another negative form, represented by increased costs such as investment, training, operational and procurement costs of ecological materials (Zhu et al., 2007).
Based on this premise, authors such as Pazirandeh and Jafari (2013) argue that there is a need to critically measure whether ecological measures in a logistic context actually provide significant changes in cost performance. The authors further emphasize that logistics services must be carried out economically, that is, all processes and activities must be carried out as efficiently as possible.
The costs capable of measuring the efficiency of logistics in the context of green logistics are: obsolescence costs, administrative costs, inventory costs, warehousing costs, transport costs and total costs (Pazirandeh & Jafari, 2013).
3. Materials and methods
3.1. Hypotheses and model
The constant presence of institutional pressures in the organizations environment has made the most diverse companies rethink their traditional operations philosophies and implement various ecological practices and activities with a view to improve and preserve the environment (Wu et al., 2012).
Within this theme, it has been noted that in developed countries the normative institutional pressures that refer to the image of the organization vis-à-vis the clients receive a lot of importance for large companies. Likewise, institutional coercive pressures have become a strong source of behavioral change in these countries. Recently, one of the world's most rigorous environmental laws has been enacted in Japan, involving for example the production, collection and recycling of products manufactured by companies (Zhu et al., 2010).
Still about Japan, it has been identified within the heart of the institutional pressures that large Japanese companies always carry out proactive environmental practices to lead their competitors. Unlike other countries, Japanese companies anticipate the influence of mimetic pressures, but compliance with competitors' environmental practices is a constant task (Zhu et al., 2010).
Institutional pressures have helped to understand the causes that influence companies to include greener activities (Sarkis et al., 2011), such as the inclusion of green logistics activities in the business processes of logistics service providers.
Within the core of green logistics, logistic services companies aim to provide services that have got the capacity to effectively minimize environmental impacts (Sarkis, 2006). In relation to these services, in addition to the activities inherent to the production operations, there is a need for the actions related to transportation, warehousing and handling of products (Dey et al., 2011), buildings (Freis et al., 2016) and packaging (Seuring & Müller, 2008) to form a broader and more comprehensive system capable of mitigating the environmental impacts of these activities (Dey et al., 2011).
Transportation is the logistics activity that emits the biggest quantity of carbon, and this requires a lot of attention due to its high environmental impact power (Dekker et al., 2012). Transport consumes millions of gallons of fossil fuel daily, resulting from severe congestion, air pollution, greenhouse gases (GHGs), and consequently global warming. These adverse impacts have led governments in many countries to promote greener and less environmentally harmful modes of transportation (Kitthamkesorn & Chen, 2017).
Another logistics activity that needs attention is the warehousing. A high proportion of emissions comes from heating, cooling, air conditioning and the lighting structure of the warehouses. There are also emissions caused by material handling devices in warehousing activities due to the type of technology used by the equipment (Fichtinger et al., 2015). In the warehouse construction, the existence of low-carbon alternatives such as those related to natural light, thermal insulation, solar panels, lighting systems (Freis et al., 2016) and construction materials (Perotti et al., 2012), can mitigate waste of energy, reduce carbon emissions and environmental pollution. However, storage-related activities have received little attention in research on alternatives to how to reduce the impacts of these activities on the environment (Freis et al., 2016).
In the same way, packaging also represents a source of pollution and environmental impact, these being physical and tangible (Hollos et al., 2012). Packaging plays an important role throughout the supply chain, for example in materials handling, warehousing, transportation, among others (Jahre & Hatteland, 2004). The characteristics of the packaging, such as size, shape and materials, are of great importance in terms of the possibilities of mitigation of environmental impact (Hollos et al., 2012; Sarkis, 2003). As logistics service providers are essential for the construction of a sustainable supply chain (Azadi & Saen, 2011), it is required that the packaging is environmentally friendly that can be recycled, remanufactured and with attributes of ecological design (Zhu et al., 2005). In order for green logistics activities to be recognized as applicable, they need empirical support to prove that such activities contribute to cost efficiency without compromising the customer's ability to meet customer expectations (Pazirandeh & Jafari, 2013).
Assessing the impacts of green logistics activities on cost performance is an essential measurement. Its goal is to maximize value through customer needs (environmental concerns) while covering costs incurred in a more efficient way. Evaluate these impacts is of major importance (Wang et al., 2015). Authors such as Pazirandeh and Jafari (2013) argue that logistics cost efficiency can be measured from the perspective of transport costs, inventory costs, warehousing costs, administration costs, obsolescence costs and total logistical costs. These measures were adopted in the present research.
Based on these assertions, the hypothetical model of research focused on the three green logistics activities previously discussed, being green transportation, green warehousing and buildings and green packaging. Thus, the proposed model makes explicit the institutional pressures, green logistics activities and logistic efficiency (costs) performance through their relations and hypotheses.
From the theoretical basis for the elaboration of the hypothetical model presented in Figure 1, the present research has the purpose of research to test the following hypotheses:
H1 - Coercive pressures positively influence green logistics activities about green packaging.
H2 - Coercive pressures positively influence green logistics activities about green transport.
H3 - Coercive pressures positively influence green logistics activities about green warehousing and buildings.
H4 - Normative pressures positively influence green logistics activities about green packaging.
H5 - Normative pressures positively influence green logistics activities about green transport.
H6 - Normative pressures positively influence green logistics activities about green warehousing and buildings.
H7 - Mimetic pressures positively influence green logistics activities about green packaging.
H8 - Mimetic pressures positively influence green logistics activities about green transport.
H9 - Mimetic pressures positively influence green logistics activities about green warehousing and buildings.
H10 - Green logistics activities about green packaging positively influence efficiency performance (costs).
H11 - Green logistics activities about green transport positively influence efficiency performance (costs).
H12 - Green logistics activities about green warehousing and buildings positively influence efficiency performance (costs).
3.2. Population framework and structural equation modeling
The population framework for the study consists of companies that operate logistics services in Brazil. The sample size for the present study calculated by G*Power software version 3.1.9.2 (Faul et al., 2009) was 119 respondents with a statistical power of 95%. If more than 119 respondents the statistical power increases (in the case of this survey, 138 completed were received). Random sampling is used in this study. The analysis unit of the study is logistics service providers. As the study combines issues related to green logistics motivators and efficiency performance, the most appropriate respondents would be the middle/tactical managers of these companies according to Zhu and Sarkis (2004) and Zhu et al., (2016).
Data collection was done primarily through an institutional website and by telephone contact with companies. In these contacts, the purpose of this research and the profile of the professional capable of answering the questionnaire were exposed to the attendant. Once this first contact with the companies was made, the attendants then provided the professionals' emails so that the questionnaire was sent to fill in.
After the contact, it was sent the link of the electronic questionnaire to the email of the professional identified by the company with the characteristics required for the research (middle/tactical managers). The questionnaire sent has a total of 30 questions regarding the variables under analysis. A five-point Likert scale was used to measure the degree of relationship between the variables (1-strongly disagree and 5-strongly agree). A total of 138 respondents completed the survey questionnaire.
3.3. Estimation of results
For the estimation of results, only complete data were used, which, after tabulation, were submitted to the Mahalanobis Distances test, which identified the presence of 6 extreme multivariate cases, which were outliers, were removed from the sample of 138 questionnaires. Resulting, therefore, in 132 records for the treatment, estimation and analysis of the results.
3.4. Structural Equation Modeling - SEM
In the present study it was chosen Structural Equation Modeling for its usefulness when working with complex theories (relating theories and/or constructs for example), or when the theory is still developing as the theories related to sustainability (Jabbour et al., 2013), and to be used in studies on green logistics according to Pazirandeh and Jafari (2013).
This type of technique for data treatment has an effective applicability that stands out in relation to the other multivariate techniques. This advantage occurs because of its ability to analyze multiple relationships at the same time (Hair et al., 2012). In it, it is possible to test a theory of causal order among a set of variables. The Structural Equation Modeling also makes it possible to determine how much the predictive variables explain the dependent variable (Hair et al, 2009).
Several research have used structural equation modeling to confirm hypotheses and theories in the industrial circle. This research has been carried out in several continents as in the works of Avelar-Sosa et al. (2014), García-Alcaraz et al. (2016), Jabbour et al. (2013), (2014), Pazirandeh and Jafari (2013), Realyvásquez et al. (2018).
It was adopted the Structural Equation Modeling - SEM with Partial Least Squares - PLS, for the following reasons: existence of multiple relationships between variables, non-normal data and prediction contrast after the research problem (Hair et al., 2017).
4. Results and discussion
It received the return of 138 questionnaires answered by middle/tactical managers from 56 companies. This represents an average of 2.46 professionals per company. In the case of 72 respondents (52.2%) have got undergraduate degrees in administration, 58 (42.2%) have got a degree in engineering and 8 (5.8%) have got degrees in other areas of knowledge. About 138 respondents, 17 (12.3%) answered that their company has 20 to 99 employees, 30 (21.7%) answered that their company has 100 to 499 employees and 91 (65.9%) answered that their company has more than 500 employees.
In order to estimate the relationships among the constructs with the Structural Equation Modeling by means of the estimation by Partial Least Squares, it was used the software SmartPLS version 3 (Ringle et al., 2015), configured according to the parameters: Weighting Scheme = Path Weighting Scheme; Data Metric = Mean 0, Variation 1; Data Iterations = 300; Abort Criterion = 1.0E-7 (for example., 0.0000001); and Initial Weights = 1.0 (Hair et al., 2017). The factorial loads obtained after the execution of the Partial Least Squares (PLS) estimation algorithm are shown in the measurement model presented in Figure 2. The variables present in the research questionnaire that formed the measurement model are presented in Appendix A.
Reliability in this type of research is very important. In this case can be verified by Cronbach's alpha analyzing on values ranging from 0 to 1 (Cronbach, 1951). The objective is to analyze the absence of random error in the units of measurement of the scale. Closer to 1, the greater the evidence of the reliability of the set of items. However, Cronbach's alpha analysis tends to provide a severe underestimation of reliability, when evaluating the internal consistency of variables in models with partial least squares estimation (Henseler et al., 2009). Specifically, in the case of MEE-PLS, the reliability check focuses on the analysis of the results of the composite reliability (Hair et al., 2011).
Thus, according to the rules, it is possible to indicate that the indicators have internal consistency, since the measures that reflect the composite reliability are greater than 0.70. Another analysis was also carried out in order to verify the factorial loads obtained by the average variance extracted (AVE). An AVE value of 0.50 or higher indicates a sufficient degree of convergent validity. Conversely, an AVE value of less than 0.50 indicates that, on average, more of the measurement error remains in the indicator than the variance explained by the construct (Hair et al., 2017). In the case of the present study, the AVE values analyzed together demonstrated sufficient for the acquired results. Table 1 shows the values.
Table 1 Reliability tests values.
| Constructs | Cronbach’s alpha | Composite reliability | AVE |
| EP | 0,8689 | 0,9016 | 0,6054 |
| GLPA | 0,7884 | 0,8655 | 0,6217 |
| GLT | 0,8350 | 0,8834 | 0,6050 |
| GLWB | 0,6062 | 0,7605 | 0,3921 |
| PCO | 0,6054 | 0,7692 | 0,4614 |
| PNO | 0,7201 | 0,8264 | 0,5510 |
| PMI | 0,4329 | 0,7582 | 0,6199 |
As the focus of the SEM (structural equation modeling) - PLS (partial least squares) is on the explanation of the variance of the endogenous latent variables, the fundamental interest is that the R2 level of the constructs is high (Hair et al., 2011). This coefficient is a measure of predictive accuracy of the model and R2 values range from 0 to 1. Higher levels indicate higher predictive accuracy (Hair et al., 2017). For values of 0.75; 0.50 or 0.25 (Hair et al., 2011; Hair et al., 2017) and 0.67; 0.33 or 0.19 (Chin, 1998), the consideration is that the variables can be described as: substantial, moderate or weak, respectively. Another important evaluation of the structural model is to establish the predictive capacity of the model, for which values of Q2 above zero show that the model has predictive relevance (Hair et al., 2017). Thus, considering the above statements and the rules pointed, it is possible to state that the EP (efficiency performance), GLPA and GLT constructs have got a moderate R2 and the GLWB (green logistics, green warehousing and building) construct has a weak R2. The values of R2 and Q2 are shown in Table 2.
Table 2 Determination coefficient and predictive relevance.
| ENDOGENOUS LATENT VARIABLES | R2 | ANALYSIS | Q2 |
| Efficiency performance - EP | 0.5442 | Moderate | 0.3041 |
| Green packaging - GLPA | 0.3750 | Moderate | 0.2249 |
| Green transport - GLT | 0.3909 | Moderate | 0.2302 |
| Green warehousing and buildings - GLWB | 0.0389 | Weak | 0.0181 |
According to this information, it is concluded the analysis of the results as a function of the structural equation modeling with estimation by partial least squares (SEM - structural equations modeling, PLS - partial least squares) and the hypotheses expressed by the hypothetical model presented in Figure 1 can be considered: supported or rejected.
Based on the factor loads shown in Figure 2, it can be seen that most of the dependencies were positively related, so that the efficiency (cost) construct over Table 2 obtained a value of R2=0.5442. In other words, this means that the green logistics constructs (green packaging, green transport and green warehousing and buildings) are able to explain the variation corresponding to a value observed in ≈54% efficiency performance (costs). Similarly, it is found in the constructs of green logistics - packaging green, green logistics - transport green and green logistics - green warehousing and buildings, have got values of R2=0.3750, R2=0.3909 and R2=0.0389, respectively.
This means that green logistics correspond to ≈38% in the case of green packaging, ≈39% in green transport, ≈4% in green warehousing and buildings.
According to the data shown in Figure 2 and Table 1 it is possible to carry out the evaluation of the hypothesis of the research. Relationships that proved to be positive and statistically significant were hypotheses H1, H4, H5, H7, H10, H11 and H12 are supported by the structural model through the structural path analysis. The H8 hypothesis, although statistically significant [H8; PIMI (mimetic institutional pressure) ( GLT (green logistics - green transport) = -0.1758; p<0.05], was also rejected, since the relationship between mimetic pressures and green logistics activities sign other than assumption (negative sign). The synthesis of the research results is presented in Figure 3.
Note: NS = not significant | *** p<0,01 | ** p<0,05 | * p<0,10.
Figure 3 Research model (synthesis results).
In order to discuss the hypotheses treated in this research, this part focuses on relating the data acquired in the results with relevant works present in the literature.
In relation to H1, it can be observed that it was positive and statistically significant, where the coercive pressures positively influence the green logistics activities (green packaging) [H1; PCO (coercive institutional pressure) ( GLPA (green logistics - green packaging) = 0.2319; p<0.10]. This is in line with the work carried out by Lau (2011), where a comparison between China, a developing country, and Japan, a developed country, are confronted about the performance of green logistics activities in the appliance manufacturing industry. In this work, coercive pressures have played a significant role in the performance of green logistics in China. The same author still advocates the importance of regulatory standards and compliance in developing countries that are generally more reactive and less proactive. Works such as Lo and Shiah (2016) with electronics manufacturing companies also demonstrated this strength of legal regulations by linking the motivation with the practices of companies that have turned green. In the specific case of Brazil that is characterized as a major food producer whether they are in natura or processed the existence of several legislations on packaging of these products may have contributed to the positive relation of hypothesis 1, even more because Brazil is an exporter of these products.
In terms of H4, it can be observed that it was positive and statistically significant, where the normative pressures positively influence the activities of green logistics (green packaging) [H4; PNO (Normative institutional pressure) ( GLPA (green logistics - green packaging) = 0.2918; p<0.01]. This is related to the claim of Lo and Leung (2000) that in developing countries as in the case of Brazil and China, even though late in relation to developed countries consumers/customers are gradually becoming more conscious, from the point of view environmental and starting to favor greener products and using more sustainable packaging.
The research of Zailani, Eltayeb et al. (2012) goes straight to this position, where normative institutional drivers were significant in the adoption of green packaging in a survey of 132 - ISO 14001 certified manufacturing companies in Malaysia. The work demonstrated that this type of institutional driver drove companies to adopt proactive environmental strategies such as eco-design, which is the environmentally conscious design of a product and its packaging in order to minimize the adverse environmental impact throughout its life. However, the continued awareness of consumers and customers about the consumption of environmentally friendly products and packaging is a major challenge in emerging countries (Ye et al., 2013).
As for H5, it was positive and statistically significant, where normative pressures positively influence the activities of green logistics (green transport) [H5; PNO (normative institutional pressure) ( GLT (green logistics - green transport) = 0.6667; p<0.01]. Regarding this, works such as Lai et al. (2006) and Tate et al. (2011) can explain this behavior. The authors describe that companies may experience pressure from multiple clients within a particular industry, or in their business and professional relationships. Thus, companies perform (greener) services required by customers even before the publication of standards and laws. This is corroborated by authors like Zhu et al. (2013).
As for H7, it was positive and statistically significant, that is, the mimetic pressures positively influence the activities of green logistics (green packaging) [H7, PIMI (mimetic institutional pressure) ( GLPA (green logistics - green packaging) = 0.3028; p<0.01].
In this regard, it is noted that the competitors present in the market have had significant influence in the adoption of greening strategies of several companies. In fact, companies are always responding to competitors’ environmental initiatives, even when they have different market orientations (González-Benito & González-Benito, 2008). Corroborating with the positive relation of hypothesis 7, the work of Zhu and Sarkis (2006) argues that the observance of competitors' green strategies is very important. The authors also say that the increasingly globalized market brings clear evidence that companies are given strong pressures. These pressures are represented by the practices carried out by the competitors regarding the inclusion of green practices in their processes, including green packaging that is currently one of the main demands of consumers.
Connected to this statement General Motors recently invested in a reusable packaging program to meet the demands of customers. This attitude strengthened the brand vis-à-vis the market and still influenced other companies in the industry to adopt such a measure (Emmet & Sood, 2010).
Regarding the H10, it was positive and statistically significant, that is, the green logistics activities (green packaging) positively influence efficiency performance (costs) [H10; GLPA (green logistics - green packaging) ( EP (efficiency performance - costs) = 0.4423; p<0.01]. Research that addressed the issue of green packaging like Zailani Jeyaraman et al. (2012) identified results of cost reduction linked to the use of green packaging. This behavior, according to the authors, occurs because there is a reduction in packaging waste, in which case the adoption of green returnable packaging has led to a general reduction of costs. Similar cases, such as General Motors with returnable and sustainable packaging (Emmet & Sood, 2010) and with ISO14001 certified companies in Malaysia (Eltayeb et al., 2011) confirm this positioning.
In relation to H11 it was positive and statistically significant, that is, the green logistics activities (green transport) positively influence efficiency performance (costs) [H11; GLT (green logistics - green transport) ( EP (Efficiency Performance - costs) = 0.2344; p<0.01]. A similar result was presented in the study by Pazirandeh and Jafari (2013), where green transport activities had a positive and significant impact on the cost efficiency of multinational companies in Sweden. The authors further describe in the research that these multinational companies hired logistics service providers with drivers trained in eco-driving, and that there was a direct positive impact on logistics efficiency in reducing transport costs, lower inventory costs and lower logistics costs in the total.
According to Perotti et al. (2012) when dealing with green transport and the inclusion of logistical activities of this type are not only linked to environmental benefits, but also to reduce costs and improve efficiency. For example, more efficient vehicle loading implies a better use of it, thus reducing the transportation costs of the items transported. From the ecological perspective, this activity can also be considered as a means of reducing the number of trips, and consequently reducing environmental pollution. Another example to be mentioned relates to the optimization of travel routes and distances, which usually implies a decrease in travel times, as well as fuel consumption and atmospheric emissions. The adoption of newer vehicle fleets, for example: energy-efficient and less polluting vehicles, can facilitate access to restricted markets such as eco-industrial parks (Zeng et al., 2017) with specific areas of limited access.
Finally, H12 presented positive and statistically significant, that is, and green logistics activities (green warehousing and buildings) positively influence efficiency performance (costs) [H12; GLWB (Green logistics - green warehousing and buildings) ( EP (Efficiency Performance - costs) = 0.3357; p<0.01]. Regarding this result, Lieb and Lieb (2010) describe in their research with logistics services providers that the implementation of high-efficiency and low-energy lighting projects in warehouses are essential initiatives and are already being carried out by senior executives of this sector. Other research like Mallidis et al. (2012) show that the sharing of transport operations through shared warehouses between logistics companies, reduces the amount of carbon emitted as well as considerably reduces the costs incurred in these logistics activities. Warehouses built with energy-efficient attributes promote an even greater reduction of carbon emissions.
5. Conclusions
The present work sought to investigate in the circle of logistics service providers whether the relations of influence between institutional pressures and green logistics activities are supported, as well as whether this relationship influences the efficiency performance (costs) of these providers. In keeping with the theoretical hypotheses used to construct the model, the empirical test confirmed the hypotheses H1, H4, H5, H7, H10, H11 and H12.
Green logistics is nowadays one of the most promising industrial activities. Many studies have investigated green supply chain management practices and their impact on environmental performance. Researchers on green logistics have focused their efforts on areas such as emissions reduction. Meanwhile, green logistics offers a vast field full of opportunities. This study examined the relationship between institutional pressures and green logistics activities and their impact on efficiency (cost) performance. The dimension of the institutional pressures was divided into three constructs (coercive, normative and mimetic pressures) in the green logistics dimension also in three constructs (green packaging, green transport and green warehousing and buildings) and in the efficiency performance dimension in a construct (costs).
In conclusion, the evaluation of the study measures and structural models using SEM revealed that institutional pressures were the antecedents capable of explaining the corresponding variation in the green logistics construct (green packaging) in R2=0.3750, green logistics (green transport) in R2=0.3909 and green logistics (green warehousing and buildings) in R2=0.0389. Similarly, the constructs on green logistics were able to explain the variation corresponding to a value observed in R2=0.5442 referring to efficiency performance (costs).
This means that the institutional pressures moderately explained the activities of green logistics (green packaging and green transport) and weakly the activities of green logistics (green warehousing and buildings). green logistics activities (green packaging, green transport and green warehousing and buildings) also moderately explained efficiency performance (costs). Thus, there is evidence that green logistics activities contribute to a good efficiency performance (costs), at least in relation to the companies surveyed. These results support the decision-making process of logistics service providers in Brazil to improve their performance in a globalized market.
