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Green Lean Six Sigma (GLSS) is a comprehensive approach that aims to reduce waste, emissions and non-value added activities, thus mark an effective impact on sustainability of firms. Despite the numerous benefits of GLSS implementation, it is evident that Micro Small and Medium Enterprises (MSMEs) are still struggling to understand, integrate and implement this strategy. This research work provides a comprehensive analysis of GLSS within MSMEs and proposes a conceptual integration framework of GLSS for improving economic and environmental dimensions of sustainability MSMEs.

A systematic literature review (SLR) methodology was planned to assess and analyse the research articles from 2007 to 2022. Different key elements of GLSS such as barriers, enablers and tool sets have been thoroughly reviewed and analysed for MSME organisations to understand their behaviour and effectively adopt the GLSS approach in their operations.

This study provides the analysis of different perspectives of GLSS and this will contribute to improve different metrics related to emissions and quality in MSMEs. It provides MSMEs industrial managers with a comprehensive knowledge base of GLSS elements, enabling effective deployment.

The present study provides a significant knowledge base and know-how of GLSS to researchers that will assist in deploying this sustainable approach in different industrial domains. The study also assists industrial managers by providing a systematic framework of GLSS for MSMEs. Further, the study also supports society by proving pathway to improve the environmental dynamics through the execution GLSS.

The study is of the first kind that review different facets of GLSS related to MSME. The study not only enhances theoretical know how of GLSS but also guides practitioners how to implement comprehensive GLSS program for improved environmental sustainability.

This paper provides, first, a historical perspective of accounting research relating to Asian/Pacific countries as seen from the vantage of the leading international journal in the United States and, second, a bibliographical data base and index of twenty‐six years of articles on this region of the world. It accomplishes the first objective by presenting a tabular profile of research in international accounting as it pertains to countries in the Asian/Pacific Rim region as shown in articles published in the International Journal of Accounting (formerly, the International Journal of Accounting, Education and Research) and related publications which appeared from 1965 to 1990. The articles are classified according to country, research methodology, subject, and five‐year time periods. The paper accomplishes the second objective by providing an annotated bibliography of 125 articles on Asian/Pacific Rim countries and indices by country and methodology, and subject.

The dependency on human expertise for analysis and interpretation is the main reason for wear debris analysis not being used in industry to its full potential and becoming one of the most powerful machine condition monitoring strategies. The dependency on human expertise makes the interpretation and result subjective in nature, costly and time consuming. The purpose of this paper is to review work being done to develop an automatic, reliable and objective wear particle classification system as a solution to the above problem. At the same time it also aims to discuss some common off line test methods being practiced for wear debris analysis.

Computer image analysis is a solution for some of the problems associated with the conventional techniques. First it is tried to efficiently describe the characteristics of computer images of different types of wear debris using a few numerical parameters. Then using some Artificial Intelligence tools, the wear particle classification system can be developed.

Many shape, size and surface parameters are discussed in the paper. Out of these, nine numerical parameters are selected to describe and distinguish six common type of wear debris. Once the type of debris is identified, the mode of wear and hence the machine condition can be assessed.

The present process of fault and condition monitoring of an equipment by wear debris analysis involves human judgment of debris formations. A set‐up standard for comparison of debris will enable the maintenance team to diagnose faults in a comparatively better way.

The aim of this paper is to discuss the difficulties in identifying wear particles and finding out the exact health of equipment, which, due to its subjective nature, is influenced by human errors. An objective method with certain standards for classification of wear particles compatible with an artificial intelligence system will yield some flawless results of wear debris analysis, which has not been attempted in the past as per available literature.

The purpose of this study is to understand consumers in the emerging wine market of India to ensure the best services. To this end, factors were identified to describe Indian wine consumer behavior and further segments for consumers were created based on the factors identified.

The research design is descriptive in nature and based on primary data. Data was collected by a structured questionnaire from 232 respondents in five major cities of India (Mumbai, Delhi NCR, Bangalore, Pune and Hyderabad). The scale was mainly adopted from wine-related lifestyle approaches.

The principal component factor analysis resulted in six factors, namely, drinking ritual, consumption reason (social), consumption reason (mood, enjoyment and relaxation), consumption practice, consumption planning and quality. Cluster analysis resulted in a three-cluster solution. These clusters were named as cautious social drinker, loner regular drinker and highly engaged drinker based on the attributes possessed.

The segmentation of urban Indian wine consumers will be helpful for marketers to identity and describe the differences in attributes and behaviors, to create customized promotions to match the needs.