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The purpose of this paper is to explore green supply chain management (GSCM) performance measures and to develop a framework for evaluating the impact of GSCM implementation on organizational performance.

This research develops a performance measurement framework by integrating GSCM enabler with GSCM performance measures criteria. These criteria are selected from literature review and expert opinion. This study proposes a fuzzy balanced scorecard – fuzzy technique for order preference by similarity to ideal solution-based methodology to evaluate the overall organizational performance. The empirical case study of an Indian automobile organization is conducted. Further, the proposed framework is tested with three Indian Automobile organizations and their results are compared with the case organization.

The integrated methodology offers an effective way to measure and benchmark the impact of the proposed GSCM performance measurement framework. The empirical results show that the output of the proposed model is consistent. Thus, the study contributes to the advancement of knowledge toward GSCM and its management for sustainability.

This study is limited to the automotive sector; hence the outcomes may not be comprehensively applicable across different sectors. The results cannot be applied to other sectors with other product and process specificities.

It helps the practitioners to measure and improve the effectiveness of GSCM implementation.

This study is the generalized performance measurement framework and can be used to measure the performance for any type of organizations to benchmark one organization with the other or the group of organizations.

This research aims to identify and prioritize the circular economy (CE) benefits (CEBs) due to the adoption of CE enablers (CEEs) in the Indian manufacturing organization context.

This research proposes a hybrid framework of Pythagorean fuzzy analytic hierarchy process (PF-AHP) and Pythagorean fuzzy TODIM (an acronym in Portuguese for Interactive Multicriteria Decision-Making) techniques. It identifies the CEEs and CEBs based on literature review and validated through industrial experts. Further, this research conducts an empirical case study to demonstrate the applicability of the proposed framework.

The result shows that CE enabler SE1 (clear vision, support and commitment from top management for CE adoption) is the most critical enabler for CE implementation. The CE benefit CEB1 (improves the value chain of products and mitigating environmental damage during product life cycle phase) is the most significant benefit derived from the adoption of CEEs. The proposed framework will provide a more accurate, structural and systematic approach to the business organizations for achieving the CEBs in a stepwise manner through the effective adoption of CEEs.

The findings of this research are nation-specific and based on a case study of single manufacturing industry. Thus, the result obtained can vary from case to case and nation to nation.

A deep understanding of each CEEs and CEBs would help build confidence among decision-makers and industrial practitioners to eliminate the risks associated with CE implementation.

Massive transformations in mobile communication technologies have forced marketers to recognize and emphasize the factors that influence consumers’ perception of advertising value. This paper aims to explore and rank the various antecedents of advertising value as perceived by consumers to offer meaningful conclusions to marketers on mobile platforms.

Responses were collected from 483 consumers using a shopping mall intercept survey and analyzed using SPSS to confirm reliability, validity and data reduction. The Relative to an Identified Distribution (RIDIT) analysis and Grey Relational Analysis (GRA) methods were then applied to prioritize the scale items of the antecedents of mobile advertising value.

Five antecedents of advertising value were found: credibility, entertainment, informativeness, irritation and message relevance. A priority ranking was allotted to the antecedents’ scale items using the RIDIT analysis and was verified via GRA results with a correlation of 98% between the rankings of the two independent methodologies.

The findings provide a roadmap to determine which antecedents of mobile advertising value have a higher or lower impact on consumers’ overall perceptions of the advertisements they are exposed to on mobile platforms.

This study aims to use first-hand data to prioritize the underlying antecedents of mobile advertising value, which has rarely been done to the best of the authors’ knowledge. It also used two different approaches in a single study to rank the dimensions, thus producing more valid results.

Today’s marketplace has witnessed intense competitive pressures and high levels of uncertainty and disruption. Therefore, supply chains require agility to obtain a sustainable competitive advantage and cope with uncertainties as well as disruptions. Although a wide range of studies exists on supply chain agility (SCA) from the perspective of antecedents or consequences, there is little research on the investigation of enablers of SCA and their relations among them. Furthermore, the literature has investigated proactive and reactive enablers for enhancing SCA, but most studies have not sufficiently framed their analysis of both aspects synchronically. This paper aims to find out the interrelationships among the proactive and reactive enablers for enhancing SCA.

An extensive literature review has been conducted to identify SCA enablers and a Delphi study has been performed to elucidate SCA enablers in the manufacturing industry in Turkey. Interpretive structural modeling (ISM) has been used to identify the contextual relationship among the SCA enablers, and the model has been validated based on Matriced Impact Croises Multiplication Appliquee a un Classement (MICMAC) analysis.

On theoretical and practical levels, the proposed ISM model in this study can help organizations analyze and interpret interrelationships among enablers of SCA. For managers, it can provide better insights and understanding of the facilitators of SCA to enhance the effectiveness of the supply chain and cope with uncertainties and turbulence. According to results, enhancing “supply and demand side competency”, “delivery speed” and “strategic sourcing” are the most significant enablers of SCA.

The study extends the existing literature related to the enablers of SCA by modeling the proactive and reactive enablers of SCA based on the Al Humdan et al. (2020) classification. Arranging the enablers of SCA in a hierarchy and classifying the enablers into different levels with the help of the ISM-MICMAC approach is an exclusive effort to achieve successful management of the supply chain.

Supplier selection for capital procurement is a major strategic decision for any automobile company. The decision determines the success of the company and must be taken systematically with the utmost transparency. The purpose of this paper is to construct capital procurement decision-making model to optimize supplier selection in the Indian automobile industry.

To achieve the stated objective, a combined approach of fuzzy theory and AHP-DEMATEL is applied. Evaluation parameters are identified through an extensive literature review and criteria validation has been introduced through a Fuzzy Delphi method by using fuzzy linguistic scales to handle the vagueness of information. AHP is employed to find the priority weight of criteria, although an inter-relationship map among criteria is not possible through AHP alone since it considers all criteria as independent. To overcome this, DEMATEL is used to establish cause-effect relationships among criteria.

The results show that the total cost of ownership (TOC) is the first weighted criterion in supplier selection for capital procurement, followed by manufacturing flexibility and maintainability, then conformity with requirement. The cause-effect model shows that supplier profile, TOC, service support and conformity with requirement are in the cause group and are considered to be the most critical factors in selecting the supplier.

The study’s outcome can help the automobile industry to optimize their selection process in selecting their suppliers for capital procurement; the proposed model can provide guidelines and direction in this regard.

There are limited studies addressing how choosing a maintenance strategy can contribute towards maximising outputs from given inputs, thereby minimising costs and improving a company’s competitiveness. The analytic hierarchy constant sum method (AHCSM) is used to access the appropriateness of maintenance strategies for improving the overall efficiency of a structural steel fabrication construction company.

A semi-structured interview was formulated with the stakeholders of the quality department to understand the company’s maintenance portfolio and its current functional capability. The information from this case study was then dissected to represent the factors that the company deemed appropriate for evaluating their maintenance strategy. The AHCSM approach provided a framework, which ranked the importance of factors that are sensitive to the construction industry and rank the suitability of maintenance strategies.

Factors affecting the selection of maintenance strategies to improve business efficiency are productivity, quality, reliability, cost, safety and work environment, morale, inventory and flexibility. Total productive maintenance strategy produces the most desirable outcome; however, the predictive or condition-based maintenance strategy provides an optimum solution for the case study company while considering the equipment usage, frequency of production and the current economic climate.

The approach presented allows practitioners to consider ways to increase the level of production and improve the efficiency of construction businesses without a high increase in investment. The findings can inform gaps in existing maintenance approaches in achieving business objectives.

This paper aims to study whether exporting enterprises are more productive in export-intensive industries. It also aims to identify the action area and policy direction for enhancing productivity in Indian textile manufacturing. Global integration has increased the volume of international trade. It is crucial for countries to have competitive enterprises to capture a larger share of the global economy. Improvement in productivity performance not only enhances competitiveness but also promotes growth in an economy.

A productivity analysis for the Indian textile manufacturing industry using firm-level panel data is conducted. The data are collected for 160 firms relevant to the period from 2007-2008 to 2012-2013 from Ace Equity database. Using the technique of data envelopment analysis, the output oriented Malmquist productivity index is computed and the sources of productivity change are identified. Also, a comparison between the productivity performance of the exporting and non-exporting firms has been made.

The results suggest that exporting firms are exhibiting better productivity performance and resource utilisation during the study period. Technology change and scale efficiency seem to be the major sources of productivity gain for exporting firms.

The research is limited to a single industry, reference database and methodology. There is scope for further in-depth, micro-level research to analyze the differences in drivers of productivity for exporting and non-exporting firms.

This paper provides validation to export promotional policies in the Indian textile industry by establishing better productivity performance of exporting firms. It also provides direction for managerial action by identifying efficiency component as the factor pulling down productivity.

The demand of cement in India is expected to increase rapidly as the government has been giving immense boost to various housing facilities, infrastructure projects, road networks and railway corridors. One of the ways to meet this rise in the demand of cement is to increase the capacity utilization of the existing cement plants by improving their availability. The availability of a cement plant can be improved by avoiding failures and reducing maintenance time through reliability, availability and maintainability (RAM) analysis of its subsystems. The paper aims to discuss this issue.

The data related to time between failure (TBF) and time to repair (TTR) of all the critical subsystems of a cement plant were collected over a period of two years for carrying out RAM analysis. Trend test and serial correlation test were performed on TBF and TTR data to verify whether these data are independent and identically distributed or not. Afterwards, the authors use EasyFit 5.6 professional software to find best-fit distribution of TBF and TTR data and their parameters. The effectiveness of a preventive maintenance policy was evaluated by simulating the real and proposed systems.

The results of the analysis show that the raw mill and the coal mill are critical subsystems of a cement plant from a reliability point of view, whereas the kiln is a critical subsystem from an availability point of view. The analysis shows that the repair time of the cement mill should be reduced for improving the availability of the cement plant. The RAM analysis showed that the capacity of the case study company is 17 percent underutilized due to maintenance-related problems and 15 percent underutilized because of management-related problems.

The study exhibits the usage of RAM analysis in deciding preventive maintenance programs of several cement plant subsystems. Thus, it would serve as a reference for reliability and maintenance managers in deciding maintenance strategies of cement plants as well as in improving their capacity utilization.

The study exhibits the usage of RAM analysis in deciding preventive maintenance programs of several cement plant subsystems. Even more, using a simulation study, the authors show that preventive maintenance of the cement plant beyond a certain level can be disadvantageous as it leads to an increase in downtime and decrease in availability.

This study assesses the SDG-related well-being of indigenous communities in India using bibliometric analysis and the ADO-TCM framework. It provides insights into their alignment with sustainable development objectives.

This study analysed 74 high-impact journals using bibliometric analysis to evaluate the well-being of India’s indigenous peoples about the SDGs.

This study analyses the well-being of tribal communities in India using existing scholarly articles and the ADO-TCM framework. It emphasises the importance of implementing Sustainable Development Goals (SDGs) to promote the well-being of indigenous populations.

This study uses bibliometric analysis and the ADO-TCM framework to investigate factors impacting tribal community welfare. It proposes theoretical frameworks, contextual considerations and research methodologies to achieve objectives.

Financial distress is a socially and economically important problem that affects companies the world over. Having the power to better understand – and hence aid businesses from failing, has the potential to save not only the company, but also potentially prevent economies from sustained downturn. Although Islamic banks constitute a fraction of total banking assets, their importance have been substantially increasing, as their asset growth rate has surpassed that of conventional banks in recent years. The paper aims to discuss these issues.

This paper uses a data set comprising 101 international publicly listed Islamic banks to work on advancing financial distress prediction (FDP) by utilising cutting-edge stochastic models, namely decision trees, stochastic gradient boosting and random forests. The most important variables pertaining to forecasting corporate failure are determined from an initial set of 18 variables.

The results indicate that the “Working Capital/Total Assets” ratio is the most crucial variable relating to forecasting financial distress using both the traditional “Altman Z-Score” and the “Altman Z-Score for Service Firms” methods. However, using the “Standardised Profits” method, the “Return on Revenue” ratio was found to be the most important variable. This provides empirical evidence to support the recommendations made by Basel Accords for assessing a bank’s capital risks, specifically in relation to the application to Islamic banking.

These findings provide a valuable addition to the limited literature surrounding Islamic banking in general, and FDP pertaining to Islamic banking in particular, by showcasing the most pertinent variables in forecasting financial distress so that appropriate proactive actions can be taken.