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Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

This paper aims to propose an innovative approach to risk measurement for the abolition of selection bias arising from the specious selection of different horizons for investment and risk computation of equity-linked-saving schemes (ELSS).

ELSS has a lock-in period of three years, but shorter horizons’ (daily/weekly/monthly) return data are preferred, in practice, for risk computation. This results in horizon mismatch. This paper studies the consequences of this mismatch and provides a noble solution to diminish its effect on investors’ decision-making. To accomplish this objective, the paper uses an innovative methodology, maximal overlap discrete wavelet transformation, to segregate the price movements across different horizons. Risk across all horizons is measured using Cornish-Fisher expected shortfall and Cornish-Fisher value-at-risk methods.

The degree of consistency of risk-based rankings across horizons is examined by means of the Spearman and Kendall’s rank correlation tests. The risk-based ranking of ELSS is found to vary significantly with the change in investor’s horizon. Precisely, the rankings formulated using daily net asset values are significantly different from the rankings developed using fluctuations over longer horizons (two-four and four-eight years).

This finding indicates that the ranking exercise may mislead investors if horizon correction is not done while developing such rankings.

This study compares multi-rater leadership evaluations of 1,748 executives in 10 national clusters to determine whether leaders in the East and West display different global leadership behavioral patterns. Data were collected via the Global Executive Leadership Inventory (GELI), which measures 12 dimensions of global leadership behaviors. The 360-degree GELI also provided feedback data from the executives’ 13,166 superiors, peers, and subordinates. Based on multilevel modeling analysis of self-ratings and observer ratings, findings indicated that the executives generally display similar patterns of global leadership behavior, but there are significant cultural differences on some leadership dimensions.

Despite several efforts during the last years, the web model and semantic web technologies have not yet been successfully applied to empower Ubiquitous Computing architectures in order to create knowledge‐rich environments populated by interconnected smart devices. In this paper we point out some problems of these previous initiatives and introduce SoaM (Smart Objects Awareness and Adaptation Model), an architecture for designing and seamlessly deploying web‐powered context‐aware semantic gadgets. Implementation and evaluation details of SoaM are also provided in order to identify future research challenges.

This study aims to describe patients presented in an interprofessional, virtual education program focused on the mental health of adults with intellectual and developmental disabilities (IDD), as well as present interprofessional recommendations for care.

In this retrospective chart review, descriptive statistics were used to describe patients. Content analysis was used to analyze interprofessional recommendations. The authors used the H.E.L.P. (health, environment, lived experience and psychiatric disorder) framework to conceptualize and analyze the interprofessional recommendations.

Themes related to the needs of adults with IDD are presented according to the H.E.L.P. framework. Taking a team-based approach to care, as well as ensuring care provider knowledge of health and social histories, may help better tailor care.

This project draws on knowledge presented in a national interprofessional and intersectoral educational initiative, the first in Canada to focus on this population.

The purpose of this paper is to analyze the Environmental Sustainability Enablers (ESEs) for an Indian steel manufacturing company to select the appropriate set of ESEs for implementing and enhancing environmental sustainability.

A methodology using fuzzy Decision-Making Trail and Evaluation Laboratory (DEMATEL) is proposed to analyze ESEs capturing multiple experts’ qualitative judgments on their mutual impacts. It is applied to an Indian steel manufacturing company to understand the salient features of the concept.

The relevant 18 ESEs were classified into cause and effect group and also interactions (i.e. influencing and influenced) of each ESE with other ESEs were evaluated. It was observed that the five ESEs (i.e. competitors’ environmental sustainability strategy, environmental compliance certification, government regulations and incentives, influence of external factors and air pollution controlling system) are the most prominent in the cause group.

The results obtained are specific to an Indian steel manufacturing company and it cannot be generalized for steel manufacturing sector. However, the current paper can show direction to carry out such work in other manufacturing companies.

The proposed methodology will be helpful for allocating efforts and resources to enhance the impact of ESEs for successful achievement of environmental sustainability.

No such paper has been reported in the literature with an objective to develop structural framework for Indian steel manufacturing company to improve environmental sustainability.

The purpose of this paper is to examine the impact of structural breaks on the conditional variance of carbon emission allowance prices.

The authors employ the symmetric GARCH model, and two asymmetric models, namely the exponential GARCH and the threshold GARCH.

The authors show that the forecast performance of GARCH models improves after accounting for potential structural changes. Importantly, we observe a significant drop in the volatility persistence of emission prices. In addition, the effects of positive and negative shocks on carbon market volatility increase when breaks are taken into account. Overall, the findings reveal that when structural breaks are ignored in the emission price risk, the volatility persistence is overestimated and the news impact is underestimated.

The authors are the first to examine how the conditional variance of carbon emission allowance prices reacts to structural breaks.

This study investigates the relationship between buyer-supplier top management team (TMT) demographic misalignment (defined as differences in TMT composition based on background, age and gender) and environmental performance (EVP).

The empirical setting is publicly held US manufacturing firms that are present in both the Kinder, Lydenberg and Domini’s (KLD's) annual EVP ratings and Bloomberg's supply chain database. The study employs panel data regression methods on an unbalanced panel dataset of 7,493 dyad-year observations comprising 427 unique firms.

The research shows that misalignment in functional background and gender composition between TMTs have a negative outcome on both the buyer's and the suppliers' EVP. However, increasing presence of females across TMTs has a positive influence on EVP. Further, the research shows that misalignment based on age between the TMTs does not impact EVP in any significant way. On the contrary, increasing age across TMTs is a significant predictor of EVP.

This study builds on existing works in TMT heterogeneity and adds context to the heightening belief in the positive linkage between heterogeneity and performance through extension to a boundary spanning interfirm context.

This study was conducted to analyse the socio-ecological problems faced by the Suhelwa Wildlife Sanctuary and understand its potential and challenges for developing ecotourism following Triple Bottom Line (TBL) principles. The study also benchmarked best ecotourism practices across the globe to create an ecotourism plan that would provide alternative livelihood and help in sustainable management of the area by reducing poverty, dependency on forests and biodiversity protection.

Suhelwa Wildlife Sanctuary was chosen because this area has several socio-ecological crises with limited livelihood options, and there is an urgent need for alternative livelihood opportunities in the form of ecotourism. The study followed an ethnographic approach through observation, participant observation, and semi-structured interviews. Content and thematic analysis was conducted through Atlas Ti9.0 software for data analysis. Subsequently, benchmarking best ecotourism practices through a literature review was done to develop an ecotourism action plan.

The First finding was related to the study area divided into three themes: problems, potential for ecotourism development, and challenges for ecotourism development. The second finding was related to benchmarking best practices and suggesting an action plan.

This work studied an area not sufficiently acknowledged by academicians and policymakers concerning ecotourism development. The work also benchmarks the best practices for ecotourism and proposes a sight-specific ecotourism action plan in accordance with TBL.

The purpose of this research is to examine the impact of external assurance on the level of voluntary corporate climate change disclosures by Finnish firms.

The sample of this study includes 228 firm-year observations over the period 2008–2015 for listed Finnish companies that have issued sustainability reports and responded to the Carbon Disclosure Project (CDP) questionnaire at least once during the sample period. The authors conduct a panel regression analysis to study the afore-mentioned linkage. In addition, the Tobit regression model is also estimated to check the robustness of our findings.

The findings suggest that assurance has a highly significant positive impact on the level of corporate climate change disclosures even after controlling for the effect of a number of control variables. Moreover, among the control variables, firm size and asset age are found to have significant effect on the extent of carbon emissions disclosure. Furthermore, the additional analysis reveals that the type of assurance providers (accounting firms vs non-accounting firms) and the type of financial auditors (Big4 financial auditors vs non-Big4 financial auditors) do not influence the level of climate change disclosure of assured companies.

This research is subject to certain limitations. First, the source of the data used in this research is the CDP database which has limitations in that it is a voluntary disclosure process where all the observations collected are self-reported by the responding firms. This may bias the reported findings. Second, our sample includes only listed companies and hence the results might have limited explanatory capacity for unlisted firms.

By using the results of this research, corporate managers will be able to reduce the information asymmetry between various stakeholders and them through disclosure of accurate, reliable and credible environmental information. Such disclosures will, in turn, allow socially responsible investors to choose eco-friendly investments and will thus enable them to make appropriate investment decisions.

Research on the external assurance-corporate climate change disclosure nexus is scarce. This study addresses this gap in the nonfinancial disclosure assurance literature by demonstrating that external assurance increases the level of voluntary corporate climate change disclosure. Drawing on stakeholder-agency theory, this study views external assurance as a monitoring structure that potentially curbs the monitoring problem between corporate managers and other stakeholders and increases the amount of climate change disclosures making a possible avenue for the reduction of the information asymmetry between them.