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1 – 10 of over 5000Manuel Rossetti, Juliana Bright, Andrew Freeman, Anna Lee and Anthony Parrish
This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management…
Abstract
Purpose
This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.
Design/methodology/approach
The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.
Findings
Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.
Originality/value
To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.
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Nikesh Nayak, Pushpesh Pant, Sarada Prasad Sarmah and Raj Tulshan
Logistics sector is recognized as one of the core enablers of the economic development of a nation. However, inefficiency in logistics operations impedes the achievement of…
Abstract
Purpose
Logistics sector is recognized as one of the core enablers of the economic development of a nation. However, inefficiency in logistics operations impedes the achievement of intended targets by increasing the cost of doing business. Also, it is difficult to improve the efficiency of a country’s logistics operations without a metric for evaluating and understanding logistics capabilities and efficiency. Therefore, the present study has developed In-country Logistics Performance Index (ILP Index) to propose a benchmarking tool to measure the in-country logistics competitiveness, particularly in the setting of emerging economies, i.e. India.
Design/methodology/approach
This study has developed a unified index using principal component analysis and quintile approach. In addition, the proposed index relies on several dimensions that are developed and illustrated using quantitative secondary panel data.
Findings
The findings of this study reveal that the quality of infrastructure, economy, and telecommunications are the three most important dimensions that may significantly support the growth of the transportation and logistics sector. The results reveal that Gujarat, Tamil Nadu, and Maharashtra are the top performers whereas, Bihar, Jharkhand, and Jammu and Kashmir scores the least due to the insufficient logistics infrastructure as compared to other Indian states.
Originality/value
Given the extensive focus on international-level logistics index (like World Bank’s LPI) in the existing literature, this study intends to develop in-country logistics index to evaluate the logistics capabilities at the regional and state level. In addition, unlike prior studies, this study utilizes quantitative secondary data to eliminate cognitive and opinion bias. Moreover, this benchmarking tool would assist decision-makers in idealizing standard practices toward sustainable logistics operations. Additionally, the ILP index could serve the international investors in crucial decision-making, as it provides valuable insights into a country’s logistics readiness, influencing their investment choices and trade preferences. Finally, the proposed approach is adaptable to measuring the overall performance of any other industry/economy.
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Karan Raj and Devashish Sharma
The purpose of this study is to construct a new index to assess the impact of an energy price shock on macroeconomic indicators of India. This paper also shows a comparative…
Abstract
Purpose
The purpose of this study is to construct a new index to assess the impact of an energy price shock on macroeconomic indicators of India. This paper also shows a comparative analysis of the constructed index along with pre-existing World Bank and International Monetary Fund indices on energy.
Design/methodology/approach
This paper uses three vector autoregressions and compute the long-term impact of the indices on the considered macroeconomic variables through impulse response functions.
Findings
This paper finds that an energy price shock has a detrimental impact on the macroeconomic indicators of India in the long run. This study also finds that the constructed index acts as a relatively more sensitive index in comparison to the International Monetary Fund and World Bank indices, which is bespoke to a developing economy case. This sensitivity is ascribed to dynamic weighting for a different basket of energy components, which are more pertinent to an Indian context.
Originality/value
The novelty of this research lies in the construction of a new index and its comparison to the existing ones. This study justifies why a developing economy would require a different measure of energy as opposed to the existing indices.
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Kobana Abukari, Erin Oldford and Vijay Jog
The authors evaluate the Sell in May effect in the Canadian context to comprehensively explore the Sell in May effect as well as its interactions with the size effect and risk and…
Abstract
Purpose
The authors evaluate the Sell in May effect in the Canadian context to comprehensively explore the Sell in May effect as well as its interactions with the size effect and risk and with multiple indices.
Design/methodology/approach
The authors use ordinary least squares (OLS) regressions to examine the Sell in May effect and Huber M-estimation to handle potential outliers. They also use the generalized autoregressive conditional heteroskedasticity (GARCH) models to explore the role of risk in the Sell in May effect.
Findings
The results demonstrate that the Sell in May effect is present in all three main Canadian stock market indices. More telling, the anomaly is strongest in small cap indices and in indices that give equal weighting to small and large cap stocks. They do not find that the effect is driven by risk.
Originality/value
While several papers have explored the Sell in May phenomenon in several countries, little scholarly attention has been paid to this effect in Canada and to its interaction with the size effect. The authors contribute to the literature by examining of the interactions between Sell in May and the size effect in Canada. They examine the Sell in May effect using CFMRC value-weighted and equally weighted indices of all Canadian companies. They also incorporate in their analysis the role of risk.
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Susana Dias, Sílvia Luís and Bernardo Cruz
This study aims to explore prevailing perceptions and practices related to well-being indexes within organizations, using the Better Life Index (BLI) as an example.
Abstract
Purpose
This study aims to explore prevailing perceptions and practices related to well-being indexes within organizations, using the Better Life Index (BLI) as an example.
Design/methodology/approach
This investigation consists of two surveys in Portugal. Study 1 (N = 311) explores public perceptions of well-being in business and its relationship with socio-demographic factors. Results show a highly positive attitude toward organizational well-being, with a preference for companies prioritizing well-being over higher salaries. Study 2 (N = 62) shifts focus to business characteristics linked to the intention of implementing well-being indexes and examines the impact of Study 1 findings on organizational representatives’ responses.
Findings
The findings reveal a positive and statistically significant correlation between the intention to adopt well-being indexes and both company size and sector. The dissemination of Study 1’s results acted as a catalyst for organizational representatives, motivating them to adopt well-being indexes.
Research limitations/implications
This research marks an initial step in incorporating well-being indexes in organizational settings. Future research should focus on identifying organizational factors that could hinder or encourage the adoption of well-being indexes.
Practical implications
The results contribute to understanding which factors might be relevant when deciding whether and how to measure well-being at organizations.
Originality/value
This study highlights the potential effectiveness of these indexes in promoting well-being within organizations, while also examining the feasibility of using the BLI to assess the impact of businesses on various well-being dimensions.
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This study aims to investigate the co-volatility patterns between cryptocurrencies and conventional asset classes across global markets, encompassing 26 global indices ranging…
Abstract
Purpose
This study aims to investigate the co-volatility patterns between cryptocurrencies and conventional asset classes across global markets, encompassing 26 global indices ranging from equities, commodities, real estate, currencies and bonds.
Design/methodology/approach
It used a multivariate factor stochastic volatility model to capture the dynamic changes in covariance and volatility correlation, thus offering empirical insights into the co-volatility dynamics. Unlike conventional research on price or return transmission, this study directly models the time-varying covariance and volatility correlation.
Findings
The study uncovers pronounced co-volatility movements between cryptocurrencies and specific indices such as GSCI Energy, GSCI Commodity, Dow Jones 1 month forward and U.S. 10-year TIPS. Notably, these movements surpass those observed with precious metals, industrial metals and global equity indices across various regions. Interestingly, except for Japan, equity indices in the USA, Canada, Australia, France, Germany, India and China exhibit a co-volatility movement. These findings challenge the existing literature on cryptocurrencies and provide intriguing evidence regarding their co-volatility dynamics.
Originality
This study significantly contributes to applying asset pricing models in cryptocurrency markets by explicitly addressing price and volatility dynamics aspects. Using the stochastic volatility model, the research adding methodological contribution effectively captures cryptocurrency volatility's inherent fluctuations and time-varying nature. While previous literature has primarily focused on bitcoin and a few other cryptocurrencies, this study examines the stochastic volatility properties of a wide range of cryptocurrency indices. Furthermore, the study expands its scope by examining global asset markets, allowing for a comprehensive analysis considering the broader context in which cryptocurrencies operate. It bridges the gap between traditional asset pricing models and the unique characteristics of cryptocurrencies.
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Khadijeh Hassanzadeh, Kiumars Shahbazi, Mohammad Movahedi and Olivier Gaussens
This paper aims to investigate the difference between the impacts of indicators of trade barriers (TBs) on bankrupt enterprises (BEs), new enterprises (NEs) and other enterprises…
Abstract
Purpose
This paper aims to investigate the difference between the impacts of indicators of trade barriers (TBs) on bankrupt enterprises (BEs), new enterprises (NEs) and other enterprises (OEs).
Design/methodology/approach
The paper has used a multiple-step approach. At the first stage, the initial data has been collected from interviews with 164 top managers of SMEs in West Azerbaijan in Iran during two periods of 2013–2015 and 2017–2019. At the second step, multiple correspondence analysis has been used to summarize the relationships between variables and construct indices for different groups of TBs. Finally, the generalized structural equation model method was used to examine the impact of export barriers.
Findings
The results showed that the political legal index is the main TBs for BEs and NEs, but it had a more significant impact on BEs; the financial index was the second major TBs factor for BEs, while OEs did not have a problem in performance index, and the financial index was classified as a minor obstacle for them. All indicators of marketing barriers (except production index) had a negative and significant effect on all enterprises; the most important TBs for NEs was the information index.
Originality/value
The results indicated that if enterprises have a strong financial system and function, they can lessen the impact of sanctions and keep themselves in the market.
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The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is…
Abstract
Purpose
The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.
Design/methodology/approach
To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.
Findings
The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.
Originality/value
In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.
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Valeriia Baklanova, Aleksei Kurkin and Tamara Teplova
The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the…
Abstract
Purpose
The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.
Design/methodology/approach
The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.
Findings
The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.
Practical implications
The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.
Originality/value
The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.
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This paper aims to explore the construction of a valid and reliable measure for the competitiveness of cities that excludes the drivers of competitiveness from the index…
Abstract
Purpose
This paper aims to explore the construction of a valid and reliable measure for the competitiveness of cities that excludes the drivers of competitiveness from the index construction. Not incorporating these drivers in the index avoids the problem of assuming relative contributions (i.e. weights) of these drivers on competitiveness as a maintained hypothesis.
Design/methodology/approach
From the definition that competitiveness is the ability of a city to sustain prosperity, this study derives a model called the hedonic well-being index (HWI) in which prosperity is measured by using the consumption of goods and service including leisure. This study then uses secondary data sources to construct an exploratory HWI (assuming a Cobb Douglas functional form) and compare this index to three benchmarks, namely, income, gross domestic product (GDP) per capita and the World Happiness Report (WHR) index. This study also review the component expenditure of the index across geographical locations.
Findings
The HWI is better predicted by the WHR index (a subjective well-being index) than by the GDP per capita (a measure of output), owing to the inclusion of leisure and household production absent in per capita GDP. This study explored and found regional variations in the distribution of the expenditure components in the HWI.
Originality/value
This paper demonstrates the feasibility of constructing an exploratory HWI to measure the competitiveness of cities using secondary data. The reliability of the index can be improved using primary data in future research. Separating the drivers from the definition of competitiveness allows testing of the contribution and interaction of these drivers on competitiveness.
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