9.1 Introduction

Achieving sustainability is one of the major concerns in the pharmaceutical industry (Amran and Ooi 2014; Agar et al. 2016; Sheldon 2016). Pharmaceutical manufacturing is complex in nature, and associated with high waste generation (Sheldon 1993) and GHG emissions. Researchers have shown greater environmental impact of pharmaceutical per kilogram production compared to basic chemicals because of the complex chemical formulation involved that also leads to higher waste per kg product, and, consequently higher fossil-fuel consumption. Cumulative energy demand is 20 times greater and GWP is 25 times higher than basic chemical product’s production (Wernet et al. 2010; Cespi et al. 2015). Having its concerns on climate change (http://web.unep.org/), reported that green-house gas emissions are the main culprit and dominant factor for climate change. Thus, with a goal of exploring sustainability awareness in Indian pharmaceutical industry this work aims to establish its relation with environmental aspects, economic aspects, social aspects i.e. triple bottom line (TBL) and external forces that can act as a key driver and contribute to achieving sustainability. Raman (2006) have studied that Indian pharmaceutical corporate are still hesitant while disclosing on environmental and energy issues. Further, Goyal (2014) concluded that the disclosure index on environmental practices considering clean technology, energy consumption, environmental management etc. for Indian Pharmaceutical Industry was only 22.0 (Industry-wise disclosure index is calculated by dividing total scores attained by all the companies related to particular sector with the total maximum score that can be attained, as studied by Goyal (2014), while the highest disclosure was from Oil and Gas industry at 41.42 followed by Cement industry at 40.28. This shows that environmental reporting is one of the strongest ways to achieve sustainability since it helps monitor the environmental performance and thus aids in exploring the avenues for improvement. Most if not all, the published literature in Indian context is not adequately proposing a model to highlight sustainability awareness (Goyal 2014) and its relation with TBL and key drivers. With the perspective of managerial implementation and ongoing research, this paper presents a relationship model between sustainability awareness and factors contributing to environment, economic and social aspects, along with key drivers in context of Indian pharmaceutical industry.

9.2 Research Methods

For this study mainly methodology reported in Malhotra (2004) was followed. The same was found to be more pertinent for this particular research, among others surveyed. The adoption of such methodologies also supports the survey-based research approach because data collected through survey-based methods allows researchers to observe a real-world phenomenon in greater detail. Further, it helps to test a newly formed construct (Sihvonen and Partanen 2016). Constructs (Table 9.2) pertaining to sustainability, TBL and key drivers were identified through extensive literature review as done by authors (Peukert and Sahr 2010; Mitra 2012; Watson 2012; Lozano et al. 2016; Chaturvedi et al. 2017). For this research, a survey tool was developed on a five-point Likert scale. After its construct validation, the tool was deployed for pilot testing. 53 out of 57 responses were received since the remaining four responses were not received even after many request reminders. Reliability testing was then performed to validate the constructs in the questionnaire and to see whether indicators under each constructs behave suitably and indicate what they are meant to. The observed Cronbach’s alpha values ranged from 0.853 to 0.862 which were well above the threshold value of 0.70 (Nunnally 1978), assuring internal consistency of measured items. Thereafter the survey tool was sent for final survey. The sampling frame considered all the pharmaceutical companies who were registered in the directory of National Pharmaceutical Pricing Authority (NPPA). A total of 2147 Indian pharmaceutical companies were contacted via email. Selection of these companies for establishing contact had been done through convenient, random and snowball sampling technique. Out of all the total companies contacted, responses from 439 were obtained. Out of these 439 respondents, 393 (i.e. response rate of 18.30%) were found suitable based on complete information provided against each items of questionnaire, for further analysis. The response rate was found to be similar in line with other such web-based surveys carried out by researchers like Sihvonen and Partanen (2016) with 15.5%, Lozano et al. (2016) with 8.9% and Gopal and Thakkar (2015) with 16.2% response rate. The comparison of response rates is relevant here since the response rate in industrial surveys is usually very low and hence the rate of 18.3% obtained in this work is supported by the rates reported in other similar works done for other industries elsewhere.

9.3 Research Results and Data Analysis

The Survey responses (through survey questionnaire pertaining to constructs and its indicators and Item Statistics in Table 9.3) were analyzed using descriptive and inferential statistics. The mean value from item statistics table with corresponding standard deviation ranged from 3.46 to 4.53, showing that majority of the respondents agreed with the scaled variables. After getting mean importance value of all scaled items, reliability and validity analysis was done. Cronbach’s alpha values varied from 0.701 to 0.774 ensuring internal consistency between items pertaining to sustainability, TBL and key drivers. Next to reliability, KMO and Bartlett’s test of sphericity were performed to check sampling adequacy. KMO measures of sampling adequacy were 0.767 for items pertaining to sustainability and 0.791 for items consisting to TBL and key drivers, thus ensuring sampling adequacy. This further confirmed that the items listed in the questionnaire were valid and relevant in terms of both the questions themselves and the study as a whole.

9.3.1 Factor Analysis

After reliability and sampling adequacy tests, factor analysis tests (the Kaiser criterion (Eigen Value > 1), in conjunction with evaluation of scree plot) were performed. Primary component analysis followed by varimax rotation component matrix was conducted. Value of factor loading ranges from 0.452 to 0.895 > 0.4, showing strong loadings on its original factors (Hair et al. 2006). The results of factor loading show that the variables consisting sustainability were loaded on three factors and named as sustainability awareness, sustainability planning and sustainability implementation whereas variables of environmental aspects were loaded on two factors and were named as material impact and process optimization. Variables consisting of economic aspects, social aspects and key drivers were remaining loaded on its original form. Reliability of newly formed constructs was further tested and found to be above the minimum accepted value i.e. 0.70.

Following Hypothesis was framed to test the relation between sustainability awareness and the enablers/factors identified through factor analysis.

H1 = There is significant association between sustainability awareness and material impact, process optimization, economic aspects, social aspects and key driver.

9.3.2 Inferential Statistics

After factor analysis, inferential statistical analysis was performed to explore the relation between sustainability awareness and TBL along with key drivers. The correlation table (Table 9.1) shows a strong correlation between material impact and sustainability awareness (r = 0.888, p < 0.001), followed by process optimization (r = 0.639, p < 0.0001).

Table 9.1 Pearson correlation (2-tailed) between sustainability awareness and predictors
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Results depict that awareness about impact of materials in use will help organization to optimize the whole process. In other words, we can say that once an organization knows about impact of materials and its environmental consequences, it will help engineers, strategists and managers to optimize the whole process in order to enhance its environmental performance. Once the process optimization is implemented it will reduce waste generation, ensure less emission and increased yield thus ensuring economic and social benefits. Moreover, closely linked social aspects (r = 0.308, p < 0.001) with sustainability awareness depicts that while becoming socially responsible organizations, companies have to attempt many goals simultaneously such as employee training and development, health and safety issues, employee participation in decision making etc. Additionally, globally-driven initiatives, legislative decisions, R&D facilities and mandatory reporting (Key drivers) will help organizations to improve their public corporate image for a long term. The result shows a positive association between sustainability awareness and predictors.

9.3.3 Multiple Regression Analysis

In this step multiple regression analysis was employed to test the relationship between five predictors and sustainability awareness. From the Coefficient table (Table 9.4 ) it is observed that all five predictors had a tolerance value > 0.10 and their VIF (variance inflation factors) value ranges from 1.243 to 1.619 thus ensuring strongly that no multi collinearity exists. Further from the model summary, the coefficient of determination R2 was 0.867, indicating 86.7% sustainability awareness can be achieved because of five factors, and the R value was 0.931. Thus, the effect size of this research is found to be large. The multiple regression model was obtained which produced R2 = 0.865, F = 504.100, p < 0.05. This indicated that overall model is statistically significant at p = 0.000 < 0.05. The individual model variables indicated that the material impact (β = 0.693, p < 0.05), process optimization (β = 0.269, p < 0.05), economical aspects (β = −0.060, p < 0.05), social aspects (β = 0.035, p < 0.05) and key drivers (β = 0.069, p < 0.05) were statistically significant with sustainability awareness. However economic aspects have significant but negative relation with sustainability awareness. It is believed that while responding, respondents may have considered the fact that improvements at any stage will have their economic consequences without considering long-term benefits. Thus, it may have significance but has a negative relation. Following Regression, an equation was developed using Table 9.4, representing regression model to explain liner relation between sustainability awareness and its independent factors.

Table 9.2 Constructs and items mentioned in the questionnaire for survey
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$$\begin{aligned} Sustainability Awareness & = - 0.075 + 0.693 M.I. + 0.269 P.O. \\ & \quad {-} 0.060 E.A. + 0.035 S.A. + 0.069 KD \\ \end{aligned}$$
(9.1)

In Eq. (9.1), M.I. = Material impact, P.O. = Process optimization, E.A. = Economic aspect, S.A. = Social aspect and KD = Key driver.

Therefore, hypothesis H1 was supported (Tables 9.3 and 9.4).

Table 9.3 Item statistics
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Table 9.4 Coefficients used in the model
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9.4 Discussion

The results indicate existence of sustainability awareness in the Indian pharmaceutical industry. However, passé to adopt related practices is slow. This concurs with the findings of Goyal (2014), Kolk (2010) and Thijssens et al. (2016). Findings from this research support the hypothesis. The observed value of Mean i.e. 3.46–4.53 (Table 9.3) shows that organizations are accepting that sustainability awareness can be explored by considering practices pertaining to TBL and in presence of key drivers. Further beta value from regression equation shows significant amount of variance in the dependent variables i.e. sustainability awareness because of each independent variable. On the basis of findings, it is highlighted that sustainability awareness is mainly driven by consideration of material impact, process optimization, economic aspects, social aspects and presence of key drivers.

9.5 Implications

A major contribution of this paper is the statistics-based regression model, which integrates elements of TBL, key drivers and sustainability awareness. Contributions of findings are useful to explore the independent influences of predictors on sustainability awareness. This regression model is considered as a part of ongoing research thus it will be helpful to develop sustainability model whose use can be explored in the Indian pharmaceutical industry to achieve overall sustainability. Since regression model has been developed by direct survey method and statistical tools to analyze collected data have been used, it is thus deemed to be considered suitable for its practical usage. Since every firm has its own decision-making criteria thus managers have to fix up their priorities related to TBL and key drivers to enhance sustainability awareness by looking on hazardousness’ and impact of used material, thus can take proactive actions.

9.6 Conclusions

Theoretical development and statistical analysis support this research work which builds up on the hypothesis that practices related to TBL and key drivers are useful and can be utilized as important factors to explore sustainability awareness in Indian pharmaceutical industry. More importantly, this research can possibly be used to diagnose and analyze which practices of TBL and key drivers are effective in influencing more on exploring sustainability awareness. The findings of this work indicate that consideration of material impacts and related process optimizations have strong association with organizational environmental performance thus leading to enhanced sustainability awareness. Conversely, managers have to accept the challenges of economic consequences associated with immediate investments and long-term return with sustainability awareness in the Indian pharmaceutical industry. In addition to these, this research provides a broad understanding of how different aspects of TBL and key drivers can be adopted as an informative tool to explore sustainability awareness, thus to achieve overall sustainability in Indian pharmaceutical industry.