Search

Cultural heritage of a country represents an array of monuments, historic buildings, arts and crafts, indigenous skills and traditions. The emerging threats to the cultural resources by way of decay, loss or destruction, has become a prime concern today. Heritage has become one of the vital components of tourism industry. It is being considered as cultural capital. The process of valuation of this capital is very complex since Heritage is basically a nonmarketed good. Different techniques used for valuating environmental resources which are similar in nature to Heritage, have led to the development of economic valuation models especially for valuation of Heritage as cultural assets. This economic approach has helped to derive the real value of heritage sites by way of consumer’s surplus and consumer’s willingness to pay for the use of cultural heritage assets and sustain sites for future generations.

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: 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. Practical implications: 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). Originality/value: 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. [ABSTRACT FROM AUTHOR]

In this paper, we provide a cross-industry perspective on the market reaction to different corporate news in the context of Indian stock market. We have studied the price and volume movements associated with eight broadly defined news categories namely Analyst Calls, Earnings, Earnings Forecasts, Finance, Legal and Regulatory, Management, Operations and Restructuring. We have employed the standard event study methodology on a sample of stocks listed on the National Stock Exchange of India for the purpose of our study. We observe that the market reaction to firm specific corporate news varies according to the type of news across different industry groups. We also observe that the sentiment of the news is a critical factor which influences the market reaction to such news flow across industry groups. We also provide a cross-industry perspective on the relative importance of different corporate news categories after taking into account the sentiment of the news in the context of Indian stock market.

We examine the dynamics of the impact of the evolving policy response during the COVID‐19 pandemic on the equity market sentiment in India. We operationalise our study by examining the India VIX, the fear gauge of the Indian equity market as an indicator for the market sentiment, and the country level Government Response Index of the Blavatnik School of Government, Oxford University as an indicator for the policy response. The relation is examined through the Markov‐switching model using high‐frequency daily data from January 30, 2020, to May 31, 2021. The evidence suggests that the policy response has a positive impact on the market sentiment when the market is fearful. Further, the evidence suggests that both the high‐fear state and the low‐fear state of the market sentiment given by the model are short‐lived indicating heightened volatility and possible speculation during the ongoing pandemic in the Indian equity market. [ABSTRACT FROM AUTHOR]