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This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This paper uses three vector autoregressions and compute the long-term impact of the indices on the considered macroeconomic variables through impulse response functions.

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.

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.

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.

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.

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.

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.

This study aims to examine the performance of environmental, social and governance (ESG) equity indices in Latin America (LA), evaluating their risk-return characteristics in comparison to conventional benchmark indices.

Using a quantitative empirical approach, the authors analyze ESG equity indices from Brazil, Mexico, Chile, Peru and Colombia, employing metrics such as Sharpe, Sortino and Omega ratios to measure risk-adjusted returns. Regression analysis is employed to assess the replicability of ESG indices by benchmark indices. Monte Carlo simulations are conducted to explore the potential increase in risk-adjusted returns when ESG equity indices are incorporated into portfolios.

The study addresses critical questions for investors: Can ESG indices outperform their benchmarks? Can these ESG indices be replicated by benchmark counterparts? Do ESG equity indices enhance portfolio diversification? The findings reveal that investing in ESG indices has the potential to enhance risk-adjusted returns and portfolio diversification.

While this study focuses on various LA economies, it’s important to note variations in currency and volatility.

For investors in LA, this study highlights the importance of considering ESG indices as part of their investment strategies. While not all ESG indices outperform conventional ones, some may improve diversification and risk-adjusted performance. Investors should carefully assess market-specific conditions and national factors when making investment decisions.

The primary contribution of this study is its focus on LA countries in the examination of diverse portfolios. The research provides valuable insights into the performance of ESG indices in this region compared to conventional benchmark indices. This approach addresses an important gap in the existing literature and offers a more comprehensive perspective on ESG investing and portfolio diversification.

Se examina el rendimiento de los índices-ESG en América Latina (AL), evaluando sus características de riesgo y retorno en comparación con los índices convencionales.

Utilizando un enfoque cuantitativo, analizamos los índices-ESG de Brasil, México, Chile, Perú y Colombia, empleando ratios de Sharpe, Sortino y Omega para medir los rendimientos ajustados al riesgo. Se utiliza análisis de regresión para evaluar la replicabilidad de los índices-ESG por parte de los índices de referencia. Se realizan simulaciones de Monte-Carlo para explorar el aumento en los rendimientos ajustados al riesgo cuando se incorporan los índices-ESG en las carteras.

El estudio aborda preguntas críticas: ¿Pueden los índices-ESG superar a sus índices de referencia? ¿Pueden estos índices-ESG ser replicados por sus contrapartes de referencia? ¿Mejoran los índices-ESG la diversificación de las carteras? Los hallazgos revelan que la inversión en índices-ESG tiene el potential de mejorar los rendimientos y la diversificación de las carteras de inversión.

Aunque este estudio se centra en diversas economías de AL, es importante tener en cuenta variaciones en moneda y volatilidad.

La principal contribución de este estudio radica en su enfoque en países de AL en el examen de carteras diversas; ofrece valiosos conocimientos sobre el rendimiento de los índices-ESG en esta región en comparación con los índices convencionales.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The results contribute to understanding which factors might be relevant when deciding whether and how to measure well-being at organizations.

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.