Search results

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.

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.

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.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

The purpose of the paper is to examine the role of institutional distance as a determinant of outward foreign direct investment (OFDI) from India. The study combines a nuanced view of institutional distance, with traditional location factors to analyze Indian OFDI flows to developed and emerging economies (EEs) during the period 2009 to 2017.

The paper employs fixed effects panel regression model on an unbalanced panel data set.

The findings suggest that India's OFDI is undeterred by the isomorphic pressures caused by regulatory and normative institutional distance, but cognitive institutional distance acts as a deterrent in developed economies. Indian MNEs engage in institutional arbitrage as they simultaneously engage in strategies of institutional escapism and institutional exploitation. The study also finds that emerging economies have emerged as an important destination for strategic asset seeking FDI, in addition to developed economies.

The findings of the study present important implications for policymakers and corporate managers. For policymakers, the study points toward the need for improving the general business environment at home to prevent escapist OFDI and trade enhancement as a tool to overcome cognitive barriers and behavioristic stereotypes. For corporate managers, the study's findings underline the importance of adopting different strategies for dealing with different isomorphic pressures in developed and emerging economies.

The study adds value to the sparse literature using the IBV in the emerging markets context, to supplement and enrich existing theoretical frameworks. It is a pioneering study in its use of institutional distance as an explanatory factor for Indian OFDI and provides evidence of institutional arbitrage.

The central lesson to be learned from studying the case is to understand the challenges and constraints posed by contextual conditions in designing contracts in public–private partnerships (PPP) for financing and delivering health care in emerging economies such as India.

Perverse incentives, along with contextual conditions, led to extensive opportunistic behaviors among involved agencies, limiting the effectiveness of otherwise highly regarded innovative design of the program.

India’s “Rashtriya Swasthya Bima Yojana” or National Health Insurance Program, launched in 2007 provided free health insurance coverage to protect millions of low-income families from getting pushed into poverty due to catastrophic health-care expenditure. The program was implemented through a PPP using standardized contracts between multiple stakeholders from the public and private sector – insurance companies, hospitals, intermediaries, the provincial and federal government.

Teaching Notes are available for educators only.

CSS: 10 Public Sector Management.

This study aims to determine the minimum force required to pull out a surface mount component in printed circuit boards (PCBs) during the wave soldering process through both experimental and numerical procedures.

An efficient experimental technique was proposed to determine the minimum force required to pull out a surface mount component in PCBs during the wave soldering process.

The results showed that the pullout force is approximately 0.4 N. Comparing this value with the simulated force exerted by the solder wave on the component ( ≅ 0.001158 N), it can be concluded that the solder wave does not exert sufficient force to remove a component.

This study provides a deep understanding of the wave soldering process regarding the component pullout, a critical issue that usually occurs in the microelectronics industry during this soldering process. By applying both accurate experimental and numerical approaches, this study showed that more tests are needed to evaluate the main cause of this problem, as well as new insights were provided into the depositing process of glue dots on PCBs.

The Performance Management at IRD Corporation case series is designed to be an in-depth study of performance appraisal in the R&D context. The case series can be used as a platform for discussing the rationale of performance appraisal system, system design and implementation, the differences between R&D and other work contexts (manufacturing, services, etc.), and the challenges involved in R&D management. This case revolves around the Chairman of IRD Corp and his dilemmas, providing an insight to the participants into the challenges of performance management and also R&D management. The series highlights the complex dilemmas associated with managing performance and the necessity of having a new performance evaluation system.

The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps.

This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC.

This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies.

The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.

Marketing Management, Product Management, Marketing Strategy.

Bachelor of Business Studies, MBA, Executive MBA.

The case throws light on the intensely competitive Indian passenger car market and its unique challenges faced by Hyundai Motors India Limited (HMIL). It tries to capture the evolution of this dynamic industry, which is characterized by regular product launches and re-positioning efforts. The students are expected to assess the performance of HMIL and the success of its positioning efforts through multiple quantitative and qualitative data points given in the case. The students need to come up with recommendations whether, amidst intense competition, Government regulations and changing consumer expectations, HMIL should launch new products in its portfolio? If, yes, in which segments? And what should be the guiding philosophy behind such product launches?

The case is expected to guide students: 1. in comprehending the various macro-environmental factors that has made India an attractive passenger car market to invest and operate in, to virtually all multinational players across all segments; 2. in analyzing how the passenger car market is segmented in India; 3. in assessing the product-driven segment-wise performance by HMIL specifically and organizations in general and what are its implications on decision-making; this is indicative of the brand portfolio management based on BCG Brand/Product Portfolio Growth Share Matrix; 4. in assessing the impact of re-positioning on the firms performance judged before and after the re-positioning efforts by the firm; 5. in analyzing the market potential of SUVs and MUVs in India and whether HMIL should launch new products/brands for these segments; and 6. in deliberating on the guiding philosophy in new product launches around the concept of “Consumer Perceived Value”.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

CSS 8: Marketing.