Environment Practices Mediating the Environmental Compliance and firm Performance: An Institutional Theory Perspective from Emerging Economies
31 Mar 2021-Global Journal of Flexible Systems Management (Springer India)-Vol. 22, Iss: 3, pp 157-178
TL;DR: In this article, the authors investigated how do environmental compliance and environmental practices influence the overall firm performance in terms of financial, customer, internal business process, and learning and growth performance.
Abstract: Coercive pressure has forced firms to take up environmental measures in the last two decades in emerging economies. Under normative pressure, large firms with farsightedness take up the environmental practices as industry leaders. In a little mature emerging economy, such as India, where triple bottom line awareness is growing, the government facilitates the firms to operate in an environment-friendly ecosystem. We investigate how do environmental compliance and environmental practices influence the overall firm performance? The performance was measured in terms of financial, customer, internal business process, and learning and growth performance. A survey instrument was designed using well-established scales and administered to the middle to top-level corporate management executives to gather 240 data from the Indian firms. A rigorous statistical validity, diagnostics, and SEM were used to test the hypotheses. The environmental practices showed a full mediation effect on the effect of environmental compliance on performance. The examination of mediation relationships in an environmental context is limited in the reported literature. This paper is among the initial works that deal with complicated mediation relationships drawn from institutional theory propositions. The study established and argued that environmental practices' ecosystem would turn firms towards voluntary environmental compliance and eventually enjoy the long-term performance.
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TL;DR: In this paper, the authors identify the pressing challenges to social sustainability in the post-COVID-19 context by a literature review and opinions from an expert panel, focusing on the footwear supply chain.
Abstract: The COVID-19 pandemic has severely impacted the global social sustainability of the supply chains, pushing them towards a more flexible management approach. However, there is a paucity of literature that focuses on social sustainability issues for emerging economies. In the post-COVID-19 period, firms around the world will face several critical challenges to social sustainability, which will hinder achieving sustainable development goals (SDGs). Against this backdrop, this study identifies the pressing challenges to social sustainability in the post-COVID-19 context by a literature review and opinions from an expert panel, focusing on the footwear supply chain. In this paper, the best–worst method is applied to compute the criticality of social sustainability challenges towards the flexibility of the supply chains. The study findings reveal that among the nine identified critical challenges, “high level of lay off”, “health protocol development”, “complexity in ensuring workplace safety”, “facing trouble in mental health”, and “lack of government enforcement and regulations for social issues” are reported as the top five challenges, respectively. Furthermore, this study suggests several flexible managerial guidelines, which will help practitioners and policymakers to achieve SDGs considering the COVID-19 pandemic.
18 citations
TL;DR: In this article, the authors developed the integrated grey framework to forecast the growth trends of renewable and non-renewable energy production and consumption in Asia and Oceania region from 2017 to 2025 by using novel grey forecasting models, namely even grey model, discrete gray model, optimized discrete grey model and nonhomogeneous discrete grey models.
Abstract: Accurate energy forecasting is of great significance to meet the energy demands, clean energy plans and sustainable energy development strategies. Therefore, this study develops the integrated grey framework to forecast the growth trends of renewable and non-renewable energy production and consumption in Asia and Oceania region from 2017 to 2025 by using novel grey forecasting models, namely even grey model, discrete grey model, optimized discrete grey model and non-homogeneous discrete grey model (NDGM). Mean absolute percentage error was employed to measure the performance of grey models. Data were collected from the International Energy Agency from 1980 to 2016. The results reveal that the energy consumption and production from coal as a non-renewable energy source are comparatively higher than any other energy source and depicted an increasing trend until 2025. Renewable, nuclear and other renewable sources of energy production and consumption showed the uprising trend with slow pace. Moreover, the consumptions of petroleum started with 23.07 quadrillion BTU in 1980; however, the production ended at 18.577 quads BTU in 2015. The production and consumption of petroleum will reach approximately 21.0465 and 93.750 quads BTU, respectively, in 2025 and are projected to increase in Asia and Ocean region. All the four grey models showed the excellent performance. NDGM performance was recorded slightly higher. The study is first of its kind to develop the grey integrated forecasting framework to predict the renewable and non-renewable production and consumption in the region of Asia and Oceania. This study provides useful information for policymakers, decision-makers and energy experts in sustainable energy planning, and simultaneously helps to make the environment more sustainable.
15 citations
TL;DR: In this paper, the impact of the drivers of eco-innovation on sustainable business growth in the agri-food sector in Tunisia is analyzed by harnessing structural equation modeling to examine the relationship between the driving factors (i.e., regulatory pressure, competitive pressure, customer demand, technological competence, and efficiency).
Abstract: The examination of the drivers of eco-innovation at the micro-level has been the topic of widespread discussion in recent years. However, several issues about these drivers in developing countries with low-tech sectors remain unaddressed. The current paper underlines the impacts of the drivers of eco-innovation on sustainable business growth in the agri-food sector. This study tests a set of hypothesized relationships that focus on a sample of 306 Tunisian enterprises. We harness structural equation modeling to examine the relationship between the driving factors (i.e., regulatory pressure, competitive pressure, customer demand, technological competence, and efficiency) and enterprises' sustainable business growth by analyzing the mediating effects of eco-innovation strategy. The findings reveal that: (1) regulatory pressure is the most influential driver of eco-innovation strategy, followed by competitive pressure and technological competence, (2) there is a positive relationship between eco-innovation strategy and enterprises' sustainable business growth, and (3) eco-innovation strategy fully mediates the relationship between the drivers and sustainable business growth. These results are consistent with the ‘Porter Hypothesis’ and have great potential for contributing to the achievement of sustainable development objectives.
6 citations
TL;DR: Wang et al. as discussed by the authors introduced atmospheric quality monitoring stations as a type of environmental regulation using data from Chinese listed companies from 2010 to 2019 to determine the effect of monitoring station distance on corporate environmental performance and the moderating role of corporate strategy.
2 citations
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TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
Abstract: The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...
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TL;DR: The extent to which method biases influence behavioral research results is examined, potential sources of method biases are identified, the cognitive processes through which method bias influence responses to measures are discussed, the many different procedural and statistical techniques that can be used to control method biases is evaluated, and recommendations for how to select appropriate procedural and Statistical remedies are provided.
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52,531 citations
01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each.
Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations.
Table of Contents
1. Introduction.
I. PREPARING FOR A MULTIVARIATE ANALYSIS.
2. Examining Your Data.
3. Factor Analysis.
II. DEPENDENCE TECHNIQUES.
4. Multiple Regression.
5. Multiple Discriminant Analysis and Logistic Regression.
6. Multivariate Analysis of Variance.
7. Conjoint Analysis.
8. Canonical Correlation Analysis.
III. INTERDEPENDENCE TECHNIQUES.
9. Cluster Analysis.
10. Multidimensional Scaling.
IV. ADVANCED AND EMERGING TECHNIQUES.
11. Structural Equation Modeling.
12. Emerging Techniques in Multivariate Analysis.
Appendix A: Applications of Multivariate Data Analysis.
Index.
37,124 citations
TL;DR: In this paper, the authors argue that rational actors make their organizations increasingly similar as they try to change them, and describe three isomorphic processes-coercive, mimetic, and normative.
Abstract: What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.
32,981 citations
Book•
01 Jan 2001
TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Abstract: The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.
28,298 citations