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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.

Conventional risk prioritization methods rely on crisp inputs but struggle with imprecise data and hesitancy, resulting in inaccurate assessments that affect service and information quality and performance monitoring. This study proposes a fuzzy data-driven risk prioritization model for service quality under imprecise information.

Enterprise risk management is crucial for service quality management, ensuring effective identification, assessment and mitigation of risks impacting service delivery and customer satisfaction. This paper proposes a fuzzy data-driven multi-criteria model for risk prioritization involving multiple decision-makers. It introduces a hybrid method combining intuitionistic and hesitant fuzzy group decision-making to assess better and prioritize risks based on decision-maker preferences.

The proposed hybrid fuzzy model improves service quality in business operations by efficiently representing uncertain information in traditional frameworks. It helps identify potential risks in advance and enhances control over business operations, enabling organizations to benchmark service quality and identify best practices. Accordingly, organizations acquire information and background knowledge to benchmark their service quality. This, in turn, improves service quality under performance management.

Despite the advantages of fuzzy models in risk prioritization, such as mimicking human reasoning more accurately, their complexity can hinder adoption. The intricate computational steps may deter shop-floor managers who prefer the more straightforward conventional crisp RPN approach, which is easier to understand and implement. However, while developing a hybrid fuzzy risk prioritization model may require more effort, its benefits become apparent over time. Once developed, the model can be integrated into software applications, allowing decision-makers to use it easily. This integration simplifies fuzzy computations and enhances risk prioritization, leading to more informed decision-making and improved risk management in the long term.

The proposed robust fuzzy framework improves risk management by integrating uncertain information and multiple decision-makers expertise, leading to more reliable outputs that enhance strategic decisions and operational efficiency.

We validate the proposed approach at an integrated steel plant’s risk management process, covering broad areas of the service quality domain. To the best of our knowledge, no study exists in existing literature attempting to explore the efficacy of the proposed hybrid fuzzy approach in risk management practices at prime sectors like steel. The study’s novelty is backed by this validation experiment, which indicates that the effectiveness of the results obtained from the proposed multi-attribute hybrid fuzzy methodology is more practical. The model’s outcome substantially adds value to the current risk assessment and prioritization literature that significantly affects service quality.

The study aims to explore the relationship between financial literacy and general life satisfaction. The study further investigates the mediating role of financial self-efficacy in this relationship in the context of Indian rural households.

Households belonging to the rural area of the Koraput district of Odisha were taken as the sample unit of this study. A structured questionnaire was framed to collect primary data using multi-stage and convenience sampling; 299 responses were received. Data analyses were performed using partial least square-structure equation modelling through SmartPLS 4.0.

The results of the study connoted that financial literacy has a noteworthy impact on the overall life satisfaction of households with lower incomes, both directly and indirectly. Moreover, the study identified financial self-efficacy as a significant complementary partial mediator in the relationship between financial literacy and overall satisfaction with life.

The findings of the study can be used by financial regulatory authorities and policymakers to seed the financial concepts’ understanding among the rural community to enhance their financial status and thereby overall satisfaction with life.

To the best of the authors’ knowledge, the exploration study of life satisfaction of rural households is yet to be discovered in the context of previous research frameworks despite rural households being an intricate part of the Indian economy. The study adds to the existing literature on life satisfaction, necessitating financial literacy expertise in rural households for achieving financial self-efficacy.

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.

Traditional risk prioritization methods in Enterprise Risk Management (ERM) rely on precise data, which is often not available in real-world contexts. This study addresses the need for a robust model that can handle uncertain and imprecise information for more accurate risk assessment.

We propose a group decision-making approach using fuzzy numbers to represent risk attributes and preferences. These are converted into fuzzy risk scores through defuzzification, providing a reliable method for risk ranking.

The proposed fuzzy risk prioritization framework improves decision-making and risk awareness in businesses. It offers a more accurate and robust ranking of enterprise risks, enhancing control and performance in supply chain operations by effectively representing uncertainty and accommodating multiple decision-makers.

The adoption of this fuzzy risk prioritization framework can lead to significant improvements in enterprise risk management across various industries. By accommodating uncertainty and multiple decision-makers, organizations can achieve more reliable risk assessments, ultimately enhancing operational efficiency and strategic decision-making. This model serves as a guide for firms seeking to refine their risk management processes under conditions of imprecise information.

This study introduces a novel weighted fuzzy Risk Priority Number method validated in the risk management process of an integrated steel plant. It is the first to apply this fuzzy approach in the steel industry, demonstrating its practical effectiveness under imprecise information. The results contribute significantly to risk assessment literature and provide a benchmarking tool for improving ERM practices.

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 investigates advertising avoidance behavior among consumers, specifically in the realm of meta-platforms. It explores the impacts of digital burnout, advertising clutter, perceived advertising risk, and goal impediment on cognitive and behavioral ad avoidance.

With a sample of 410 respondents, the research employs a comprehensive analysis approach with SEM and CFA, integrating Avoidance Motivation Theory. It examines direct and indirect influences on ad avoidance, mediated by consumer emotions and attitudes. The study highlights the moderating role of content quality in shaping these relationships.

Significant links were found between digital burnout, clutter, perceived advertising risk, and goal impediment with cognitive and behavioral ad avoidance. The study emphasizes the importance of content quality and suggests strategies that focus on emotional resonance, user alignment, and reduced intrusion.

For advertisers and marketers in digital spaces, the findings recommend strategies promoting healthy technology usage, streamlined advertising content, transparent communication aligned with user goals, and emotionally resonant campaigns to mitigate ad avoidance behaviors.

Understanding consumer sentiments aids policymakers in creating conducive advertising models, benefiting both consumers and businesses. This enhances user experiences in digital environments.

The paper distinctively applies the Avoidance Motivation Theory to the context of avoiding social media advertisements, thereby uncovering the causes of negative consumer emotions and attitudes, and highlighting the crucial role of content quality as a means to counteract these adverse reactions.

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 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.