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The purpose of this paper is to demonstrate the difference in firm performance between pollution prevention and pollution control strategies using a selected case study from the Indian leather industry.

This study followed a case study-based inductive research approach to validate the proposed preposition on field. The case-study analysis was based on qualitative and quantitative data. The authors developed a questionnaire and an interview protocol to capture the data about different industrial practices and motivation behind them. The study followed the concept of the triple bottom line approach for assessing the progress of the case firm on sustainability scale.

With the help of pollution preventive measures, such as replacement of salted skin with fresh skin and chromium salt with a phosphonium-based tanning agent, the case firm could overcome the biggest challenge of bringing down the level of total dissolved solid from 40,000 to 5,000 ppm in the raw effluent. Moreover, the firm has successfully recycled and converted tannery scraps and other waste into organic manure for internal use in its own plantations.

The findings of this study could not be generalized as these are based on a single case study.

This study presents an example of sustainable practices and their benefits to the Indian leather industry to follow.

The purpose of this study is to investigate the potential determinants of sustainable consumption behavior. This study describes lifestyle of health and sustainability (LOHAS) tendency and the effects of such lifestyles on the behavioral intentions for sustainable consumption in sharing economy.

This research adapts LOHAS tendency, consumer attitude, subjective norm, perceived behavioral control and consumers' behavioral intention for sustainable consumption scale in Indian context to describe the sustainable consumption behavior by extending the theory of planned behavior (TPB). Data of 627 individuals collected through a questionnaire, after the scales validation process and thereafter a structural equation analysis has been performed.

The findings confirm the extended TPB, wherein LOHAS tendency has emerged as an antecedent to consumer attitudes for taking part in sustainable consumption. Results highlight that consumer attitudes, subjective norms and perceived behavioral control are significant determining factors of consumers' sustainable consumption behavior.

This study has examined the sustainable consumption behavior by considering the lifestyle tendency. A few other limitations are also discussed.

Consumer behavior in sharing economy is surely one of the emerging research areas; there is dearth of research to understand Indian consumers' sustainable consumption, particularly from lifestyle perspective. This research establishes relationship between LOHAS tendency and sustainable consumption, which may serve as a contributor to sharing economy in terms of LOHAS consumer's lifestyle and their sustainable consumption behavior.

Co-working spaces bundle all real estate services into a package and leverage shared economy trend to create new opportunities for growth. This sector is anticipated to expand significantly due to changes in mobility and office design driven by the development of remote or hybrid work settings. The current study attempts to identify key motivating factors for users in emerging economies in choosing co-working spaces.

Using analytic hierarchy process (AHP) methodology and the self-determination theory framework, a total of 4 criteria-level factors, along with 13 sub-criteria level factors were identified as key motivators for adapting to co-working spaces.

The study highlights a few factors and their relative importance, which could help firms/organizations to start or offer co-working spaces within emerging economies.

The study contributes to literature by advancing the understanding of key motivators for users of co-working spaces within the ambits of emerging economies. In the process, the authors enlist a few factors vis-à-vis their relative importance, which could help firms/organizations to start or offer co-working spaces within emerging markets.

The paper aims to analyse the issues and challenges in implementing and adopting the sharing economy concept in tourism. More specifically, the paper intends to identify critical factors for seeking possible pathways for achieving sustainable development of the Indian tourism industry.

This research work has adopted the triangulation approach involving a twofold study concerning the perspectives of both service providers and customers. To cull deeper insight, an inductive content analysis was carried out to identify the critical factors influencing sharing economy in tourism and establish them as key research areas for further study.

The study identified seven critical influencing factors, namely, affordability, consciousness, cultural involvement, status quo biasness, socio-environmental responsiveness, regulatory supervision and technological drivers concerning sharing economy in tourism.

This study will expand the existing literature by exploring the critical factors relevant to the practice of sharing economy in the tourism industry. It will aid both marketers and policymakers to bridge the gap between what sharing economy concept demands, what consumers perceive and how service providers practise in reality. The present study limits its scope to qualitative analysis and geographical boundary within India. Further research with empirical study may be extended in future, with the research agendas presented in this paper.

The novelty of the study lies in the identification of both intrinsic and extrinsic factors affecting the implementation of sharing economy in tourism. To delve deeper into the ground reality, this qualitative study corroborates the perspectives of both the demand and supply side, which is a unique effort by itself.

The purpose of this study is to investigate the challenges before India for electric vehicle (EV) adoption by 2030. The study further looks into the measures taken by the Government of India (GOI) to promote research and development in EV sector and what is yet to be done.

In the present study, the challenges are identified allied to the commercialization of EVs in India. The data are collected, analyzed and compiled through secondary sources. The secondary data give a concise insight and comprehensive information regarding what is occurring around the globe as well as in the Indian context. Further, the challenges are investigated through a focus group study consisting of 11 participants from industry and academia.

The findings from the study are the critical roles of sharing economy and public utilities in the promotion of EV adoption, given the high cost of EV, lack of infrastructure and poor purchasing power of Indian customers. The sharing economy perspective provides various opportunities for the government to manage the resources (electric-powered transport system) optimally. Further, the study compares the global perspective in assigning the target figures.

The study highlights the facilitating role of the shared format in EV technology promotion but ignores the hurdles that can come in its implementations. Also, the focus group study has its limitation as it relies more on participants' perceptions and opinions.

The present study assists GOI and various stakeholders in having a realistic plan rather than daydreaming with overambitious goals. The diffusion of technology as a shared format (especially in the context of EV) has not been academically approached in the past literature.

The purpose of this paper is to dig out enablers of sustainable industrial ecosystem to develop a framework.

To test the framework statistically, a structured questionnaire was designed. Measures for the questionnaire were adopted from an extensive literature review. Further, the questionnaire was pretested and further pilot study was conducted. Adding to this, the reliability and validity of the constructs was examined using confirmatory factor analysis followed by covariance-based structural equation modeling to test research hypotheses.

The statistical analyses suggest that the model exceeds the threshold limit for goodness of fit after undergoing through few iterations. Normative pressure has a low effect than rest of the factors.

The present study is a unique contribution in terms of its theoretical implications and practical use. Finally, research findings are concluded and further research directions is outlined.

Circular economy is an emerging concept which requires insights from a variety of disciplines, especially from sustainable operations management. Therefore, the purpose of this paper is to verify how formal and informal instruments of governance influence the induction of green practices in a green network located in Brazil, with implications for the circular economy.

Based on a review of the supply chain (SC), green supply chain management, and governance literature, proposals are made regarding the influence of governance instruments in inducing green practices. To investigate these propositions, a qualitative research was conducted using a single exemplary case study of a cosmetics supply network.

The authors present original research findings which have both expected and unexpected implications for the circular economy, due to the fact that the data analysis showed that the formal (contracts and environmental norms) and informal (trust and cooperation) instruments of governance positively influence the induction of green practices within the supply network.

This study contributes to supply network and governance theory by providing insights for better understanding of how governance instruments can induce green practices in a supply network, and it provides practical implications for SC managers, by showing the importance of considering different governance instruments. Implications for the circular economy are made.

This paper explores the contextual relevance of sharing economy for the organic food market in an emerging economy like India.

Case study approach was used to collect empirical data from different types of organic food markets.

Organic food farmers markets compared to online and health food stores tends to facilitate sharing economy more since it helps them to build value, scale and trust. By sharing resources, skills and spaces, organic farmers markets have increased organic food availability, reduced its cost of certification and operation besides managing consumer trust. Subjective influence through social media and offline interaction reduces information asymmetry at zero marginal cost. Organic food producers/retailers can get a competitive advantage by tapping underutilized assets to create value and opportunities besides overcoming their demand and supply constraints.

The research offers a fresh perspective to the organic food sector, particularly in emerging economies like India. It could assist all stakeholders to overcome the current demand and supply challenges faced in organic food markets.

Sustainability is an integrating concept and demands strategic attention in developing countries like India. Due to strict environmental regulations and ongoing government sustainable policies such as “Namami Gange,” leather industries are extensively facing challenges to conform themselves toward these sustainable policies. The major challenge faced by leather industries is the exponentially increasing cost of adaptation to sustainable product and process. Under these operational constraints, survival of Indian leather industries has become a major challenge. In this context, this paper aims to identify and evaluate sustainable manufacturing policies. The key performance indicators (KPIs) based on triple bottom line of sustainability can assist leather industries that are about to initiate adopting sustainable practices.

This paper demonstrates the role of KPIs for evaluating sustainable manufacturing policies for leather industries in India. Initially, an in-depth literature review analysis has been carried out to identify indicators for evaluation of sustainable manufacturing policies. In this work, an integrated methodology has been developed to refine the priority map of the aforementioned KPIs based on consensus building among experts using Kappa analysis. Total interpretive structural modeling (TISM) has been used to demonstrate relationships which explain the significance of the KPIs. Further, Matriced Impact Croises Multiplication Applique analysis has been carried out to explore the relationships amongst KPIs.

Based on above analysis, identified interactive relationships among the KPIs will assist managers and decision-makers to incorporate effective sustainable policies in leather industries.

It is expected that these identified interactive interrelationships between KPIs will certainly facilitate the leather industry to achieve higher sustainable performance and competitiveness.

This study carries out an in-depth literature review analysis of sustainable manufacturing policies in leather industry. The author proposes an integrated methodology using kappa analysis, consensus building and TISM for evaluation of sustainable policies based on the literature review analysis and expert opinion.

This paper aims to solve the problems of big data analytics for prediction including volume, veracity and velocity by improving the prediction result to an acceptable level and in the shortest possible time.

This paper is divided into two parts. The first one is to improve the result of the prediction. In this part, two ideas are proposed: the double pruning enhanced random forest algorithm and extracting a shared learning base from the stratified random sampling method to obtain a representative learning base of all original data. The second part proposes to design a distributed architecture supported by new technologies solutions, which in turn works in a coherent and efficient way with the sampling strategy under the supervision of the Map-Reduce algorithm.

The representative learning base obtained by the integration of two learning bases, the partial base and the shared base, presents an excellent representation of the original data set and gives very good results of the Big Data predictive analytics. Furthermore, these results were supported by the improved random forests supervised learning method, which played a key role in this context.

All companies are concerned, especially those with large amounts of information and want to screen them to improve their knowledge for the customer and optimize their campaigns.