The purpose of this paper is to investigate the viability as compared with other financial assets of cryptocurrencies as a currency or as an asset investment. This paper also aims to see which macro variable relates more to the price of cryptocurrencies, especially Bitcoin. Since the whole concept of cryptocurrencies is quite novel, an attempt has been made to briefly explain the underlying blockchain technology that forms the bedrock of cryptocurrencies. In this study, we use secondary data, i.e., the price history of Bitcoin from September 2014 to September 2021 for the last seven years, captured from trading exchanges. We predicted monthly returns of Bitcoin with that of Standard & Poor’s 500 Index (S&P 500), gold, and Treasury Bonds. Our findings show that Bitcoin has very high volatility compared to S&P 500, Gold and Treasury Bonds. Also, our findings show that there is a positive correlation between Bitcoin’s price volatility and the other three financial assets before and during COVID-19. Hence, Bitcoin is acting more as a speculative asset rather than a steady store of value. This can be drawn from the comparison with the debt market i.e., a Treasury Bond that invests in long-dated (30 years) US treasuries with which Bitcoin shows no relationship. The findings of this study could help with understanding the future of Bitcoin. This has important implications for Bitcoin investors. The current study contributes to the extant literature by providing empirical evidence on long-term social sustainability vis-à-vis supply chain traceability.

The study looks at the impact that the three dimensions of financial inclusion (FI) (i.e., access, usage, quality) may have on poverty alleviation. In doing so, the study relies on demand and supply-side data to measure Nigeria’s FI. The demand-side data were derived from the 2021 Global Findex data, and the supply side data were sourced from the IMF Access survey database (2004–2021). The supply-side data were analysed using the ordinary least squares regression (OLS), while the demand-side data were analysed using the probit regression model. The study outcomes revealed a negative and significant relationship between financial access and poverty rate, further indicating that those who use financial services are less likely to experience poverty. The study recommends that financial service providers tailor their financial products to align with the educational level of the target population to encourage savings.

This study aims to empirically examine the relationship between cryptocurrency and various facets of the financial system. It seeks to provide a comprehensive understanding of how cryptocurrencies interact with, and influence, the stock market, the U.S. dollar’s strength, inflation rates, and traditional banking operations. This is carried out using linear regression models, Granger causality tests, case studies, including the collapse of the Futures Exchange (FTX), and the successful integration of Binance. The study unveiled a strong positive correlation between cryptocurrency market capitalization and key financial indicators like the Dow Jones Industrial Average, Consumer Price Index, and traditional banking operations. This indicates the growing significance of cryptocurrencies within the global financial landscape. However, a mild association was found with the U.S. dollar, suggesting a limited influence of cryptocurrencies on traditional fiat currencies currently. Despite certain limitations such as reliance on secondary data, methodological choices, and geographic focus, this research provides valuable insights for policymakers, financial industry stakeholders, and academic researchers, underlining the necessity for continued study into the complex interplay between cryptocurrencies and financial stability.

Corporate Social Responsibility (CSR) aims to promote environmental conservation and societal well-being, exceeding legal requirements. It is a way for companies to balance economic, environmental, and social impacts, meeting stakeholders' expectations. The hospitality industry is under scrutiny for its ecological and social impact, contributing to carbon emissions, and being a major source of global waste. The rise of short-stay rentals also impacts local housing affordability. Moreover, the industry faces scrutiny regarding employee working conditions, including low wages and discrimination. Despite the significance of CSR for the hospitality industry a comprehensive account of its dimensions and impact on organisational outcomes and areas for future research is still missing. This study aims to conduct a comprehensive review of CSR literature in hospitality employing a semi-systematic approach. Findings reveal that the dimensions of CSR in the hospitality industry are diverse and interconnected, encompassing environmental stewardship, social engagement, economic prosperity, ethical conduct. Future research in the field should focus on addressing conceptualisation issues and strengthening the theoretical foundations of the research. In addition, emphasis should be placed on developing an encompassing framework that establishments in the sector can adopt to effectively enhance their sustainability profiles and create a positive impact.

In this paper, building upon several theories (agency theory, stakeholder theory, and resource dependence theory) and by utilising data from 161 Greek manufacturing companies that were listed in the Athens Stock Exchange on the 31st December 2008, the authors explore the relationships between the organisational characteristics of the firms (organisational age, organisational size, and years listed in the stock market) and the board configuration (board size, board leadership structure, and directors' dependence/independence). The motivation of the study was based on both the peculiarity of the Greek context in the front of a strong economic crisis, but also in trying to establish relationship of board characteristics with factors that are understudied. Both descriptive and inferential statistics (ANOVA tests) were utilised to answer the research questions. Interestingly and in alignment with the literature, the findings showed that larger organizations tend to have larger boards and greater proportions of external and independent directors. However, no more strong relationships have been identified between the organisational characteristics and the board configuration. The paper contributes by suggesting that while board configuration is being determined by factors like the legal context of the country and the broader external environment suggested by the literature, various company characteristics can explain some of the board characteristics. Finally, it is worth mentioning that this study examines the listed Greek manufacturing companies during very turbulent times, the start of the financial crisis in Greece, which may have an impact on the configuration of the boards at that time.

Artificial intelligence (AI) is reshaping national economies, yet country‐level AI–macro relationships remain poorly understood. Using annual data for the United States and China, 1980–2020, we develop a four‐layer triangulation framework—Pattern Causality, Granger causality, VECM‐based cointegration, and lead–lag correlation—to map directional associations between AI activity indicators and macro aggregates through weighted networks and heatmaps. Three patterns recur. First, nonlinear AI–macro dependence is moderate and mostly positive, making AI indicators useful monitoring signals rather than stand‐alone decision triggers. Second, publications and patents carry short‐run predictive content in Granger tests, making them candidate early‐warning indicators for macro surveillance. Third, cointegration places AI indicators mainly on the adjustment margin: Macro fundamentals condition long‐run AI–macro co‐movement more than AI indicators lead it. The international layer adds an important qualification. United States–China collaboration variables raise AI node centrality, especially for China, but a mechanical‐expansion null benchmark shows that most of this increase is expected from enlarging the network; beyond‐mechanical collaboration evidence concentrates in the cointegration layer. Overall, the US pathway is patent‐oriented and selective, whereas China's is denser and more collaboration‐intensive. The framework supports AI–macro risk monitoring and hypothesis generation, not structurally identified causal claims.