Exploring the Relationship Between Leadership, Operational Practices, Institutional Pressures and Environmental Performance


Article I

Dubeya, R., Gunasekaranb, A. & Alic, S. (2014). Exploring the relationship between leadership, operational practices, institutional pressures and environmental performance: A framework for green supply chain

The Problem

As the economy keeps on growing, there is increasing concern about protecting the environment. Increasing levels of economic growth are leading to higher levels of energy and material consumption that are resulting in environmental issues and causing resource depletion (Seman et al., 2012; Zhu & Sarkis, 2004; Dubeya, Gunasekaranb & Alic, 2014). There is growing emphasis being laid upon adopting a proactive rather than a reactive approach with regards to the environment especially when concerned with manufacturing companies. Environmental issues can no longer be disregarded by firms if they wish to survive within the global arena (Falatoonitoosi, Leman & Sorooshian, 2013). It is because of this that Green Supply Chain Management (GSCM) is gaining increasing attention among researchers and practitioners of Operations and Supply Chain Management (Seman et al., 2012).

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While there is plenty of research done previously with respect to green manufacturing and its implementation, there is little literature that studies the impact of Supplier Relationship Management (SRM) and Total Quality Management (TQM) on environmental performance under the influence of leadership and Institutional Pressures (IP) (Dubeya, Gunasekaranb & Alic, 2014). It is of utmost importance that the rubber goods manufacturers know what variables are essential for the successful implementation of GSCM. Therefore, the authors formulate a theoretical framework that explains the impact of leadership, SRM and TQM on environmental performance under the influence of IP specifically on the rubber goods manufacturers in India.

The Model

In order to test the hypotheses, model 1 uses hierarchical regression analysis which is considered most suitable due to the multifaceted nature of the framework under study, obtainable data points and great robustness of the technique. The authors want to see the impact of leadership, TQM and SRM on environmental performance while under the influence of IP.

IP are said to be critical to environmental performance and are ultimately influential when it comes to the adoption of environment friendly practices in the rubber industry. SRM is also considered pivotal for the successful implementation of a green purchasing program. It is imperative for the firms to influence their suppliers in providing them with eco-friendly and non-hazardous raw materials in order to successfully implement green practices. While these two variables held the most importance, all variables turned out to be statistically significant and are said to influence environmental performance positively.

However, it is important to notice that the model is based on certain assumptions and has limitations to it. The first limitation is the fact that the model does not incorporate longitudinal data but works with one time data collected from the rubber manufacturers. This limits the model as it makes it hard to establish causality between the variables. Second, subjective data could not be collected from the target respondents due to the non-disclosure nature of the data collection. Furthermore, the model is developed solely for the rubber goods manufacturers in India, hence, it limits the scope for further use by other countries and industries.

While the model is tested using the hierarchical regression analysis, there may be other factors that could have been taken into consideration. For example, financial performance and technological change, financial performance having been mentioned as the leading motivation for the implementation of green practices (Falatoonitoosi et al., 2013; Zhu et al., 2010).


The model and the analysis of the variables have been well thought out and planned as effort has been put in to avoid and overcome most biases and weaknesses. Dubeya, Gunasekaranb & Alic (2014) chose 358 licensed rubber goods manufacturers of the 3500 present. That is presumably twice as much as the sample size demonstrated by recent studies in the Operations Management and SCM area and gives the model a 95% confidence level according to Yamne (1967).

The questionnaires were well tested and verified by six experts drawn from the industry and academia. The results were tested for skewness and kurtosis and were found to be well within limits. The statistics showed that the variables were all significant and the FCR showed that the model was strong.

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The research carried out by the authors fills an important gap and helps the rubber manufacturers in gauging the factors that help improve environmental performance and, hence, ultimately efficiency. It also provides insight on the factors that are significant for the implementation of GSCM. However, had the performance indicator taken into account finances and not been limited to environmental performance only, it would have provided the rubber manufacturers with another important variable that largely impacts the implementation of GSCM, as mentioned above.

Article II

Vijayvargy, L. & Agarwal, G. (2014). Empirical Investigation of Green Supply Chain Management Practices and Their Impact on Organizational Performance

The Problem

Green Supply Chain Management (GSCM) has recently come to the fore as an area of immense growth and importance. The business paradigm has shifted from companies based solely on improving their profits and decreasing their costs, to companies that care about the environmental impact of their actions. This new movement goes beyond basic compliances with environmental rules and regulations laid down by governing bodies, and instead identifies companies as responsible business practices that have a duty to improve the sustainability of the environment in which they function.

GSCM recognizes two major areas where the entities supply chain functions overlap with environmental responsibilities and aims to mitigate the impact of these external bodies, viz, suppliers and purchasers.

While significant research has been undertaken in several countries, involving several sectors, there has been no major research conducted towards understanding the scope and impact of GSCM practices specifically related to India (Shukla et al., 2009). Thus, the authors create a model that pertains to the impact of GSCM drivers on the firms’ operational, financial, ecological and overall performance.

The Model

The paper formulates a GSCM model that suggests that there are two main stages required in implementing GSCM with the first being the setting up of a planned internal environmental management and analyzing the GSCM drivers to monitor activities and the second including the implementation of green purchasing, customer cooperation with environmental consideration, eco design and investment recovery practices (Simpson and Power, 2005; Chien and Shih, 2007; Lee, 2008; Jabbour and Jabbour, 2009; Mohanty et al., 2009; Olugu et al.,2010; Eltayeb et al., 2011; and Saridogan, 2012).

Using the scale from Zhu and Sarkis (2004), it contains 21 variables covering all five initiatives and 22 variables which cover all four areas of performance. These are ranked on a 5 point scale (1 = Not at all, 2 = A little it, 3 = To some degree, 4 = Relatively significant, 5 = Significant). Data is sourced from 161 Indian firms and analyzed using The Structural Equation Model (SEM), reliability test and correlation.

The strength of the model lies in the fact that it takes into consideration the possibility of skewed data and attempts to use Cronbach’s alpha to ensure that data collected remains free from any biases and can be used as a relevant source to test out the hypothesis. The model shows that the association between both the stages are significantly supported which incline the industry towards the adoption of GSCM practices.

This model hypothesizes that GSCM drivers not only push the company to be responsible about their production and purchasing methods, but also that the initiatives taken up by the company to ensure that they are ecologically sound, also tends to improve their financial, operational and overall organizational performance.


The researchers through their assessment find a strong positive connection between internal environmental management and GSCM pressure/drivers with both appearing as precursors to the successful implementation of GSCM that results in improved environmental, financial and operational performance by the firm.

The model applies the Cronbach’s Alpha to ensure that the data sourced is not skewed and is reliable. After applying Cronbach’s Alpha, it is noted that all five initiatives have high performance correlation ranges (>0.60). Thus proving that these variables do in fact have a direct positive impact on the ecological, operational, financial and overall organizational performance.

However, the fact that it is specific to the Indian manufacturing industry becomes a limitation for the model as it cannot be used as a base for any other countries industries. Also, it appears to have a very general scope and the model might not hold true for specialized industries that employ specific variables that are not covered in the hypothesis.

Comparing the Two Models

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The two models discussed above both address the ever pressing issue of GSCM and the factors that affect green practices. The main focus of both the models is to establish the factors that influence the manufacturing industry firms within India in favor of adopting GSCM. However, both differ in terms of their reach and scope. Where model 2 provides a broader view of the organizational performance pertaining to the manufacturing industry at large, model 1 focuses purely on the rubber manufacturers within India.

Furthermore, model 1 uses the Hierarchical Regression Model to cater for the multi-leveled nature of the studied framework whereas model 2 focuses on the Structural Equation Modeling (SEM) that helps model the relationships between multiple dependent and independent variables simultaneously (Gefen, Straub & Boudreau, 2000).

In isolation, these models both provide insights into different aspects that influence the adoption of GSCM. However, if the two models can be combined then they can prove to be extremely powerful in providing insights into an industry such as the rubber industry in India. If a variable such as the ‘nature of the industry’ can be added into model 2, then it can specifically be used by the rubber manufacturers for insights on certain financial and operational matters that model 1 fails to account for. The use of both models by rubber manufacturers will help provide them with a more holistic view and can help them better understand the factors that can potentially influence the implementation of GSCM. This can only be positive for the GSCM movement, ensuring more bases are covered and firms are better informed.


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