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We examined the dynamic volatility connectedness and diversification strategies among US real estate investment trusts (REITs) and green finance indices.

The DCC-GARCH dynamic connectedness framework and he DCC-GARCH t-copula model were employed in this study.

Using daily data from 2,206 observations spanning from 2 January 2015 to 31 January 2023 this paper presents the following findings: (1) cross-market spillovers exhibited a high correlation and significant fluctuations, particularly during extreme events; (2) our analysis confirmed that REIT acted as net receivers from other green indices, with the S&P North America Large-MidCap Carbon Efficient Index dominating the in-network volatility spillover; (3) this observation suggests asymmetric spillovers between the two markets and (4) a portfolio analysis was conducted using the DCC-GARCH t-copula framework to estimate hedging ratios and portfolio weights for these indices. When REIT and the Dow Jones US Select ESG REIT Index were simultaneously added to a risk-hedged portfolio, our findings indicated that no risk-hedging effect could be achieved. Moreover, the cost and performance of hedging green assets using REIT were found to be comparable.

We first examined the dynamic volatility connectedness and diversification strategies among US REITs and green finance indices. The outcomes of this study carry practical implications for market participants.

This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management.

We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz’s connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management.

Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors.

There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies.

The study’s major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.

This study aims to apply the theory of consumption value to analyze the utilization of Metaverse technologies within hospitality and tourism while examining the factors that impact consumer intentions to use the Metaverse.

This paper aims to consider an extensive study spanning the period October 2021 to March 2023 was conducted to understand the shifts in an individual's intention to use Metaverse technologies in hospitality.

The findings of this study confirm that individual attitudes to the Metaverse and trust in Metaverse technologies significantly impact their intention to use the Metaverse.

The study aims to provide fresh insights into how individuals perceive Metaverse technologies in the context of choosing hotels and resorts, which enriches the understanding of consumer behaviors around Metaverse technology in hospitality.

This study aims to consider not only tourist intentions to use the Metaverse but also how diverse consumption values impact user attitudes, an area currently underresearched.

This paper aims to empirically investigate the dynamic interlinkages among globalization, digitalization and economic development in the top 75 most globalized countries from 2000 to 2019. The selection of the 75 most globalized developing countries is based on the overall scores of the KOF Globalization Index (2021).

The research design is based on secondary data collected from the World Bank (2021), the International Telecommunication Union (2021) and the KOF Globalization Index (2021). The study uses panel unit root tests followed by the panel cointegration techniques. Further, the estimation uses panel fully modified ordinary least squares and panel dynamic ordinary least squares methods.

The empirical results reveal that the effect of globalization on economic development is sensitive to different estimation procedures; in some cases, but not in every case, the effect is positive and significant. However, the positive and significant effect of digitalization on economic development is robust across all estimated models. Long-run equilibrium relationships and bidirectional causalities strongly affirm the nexus among globalization, digitalization and economic development, substantiating the interconnectedness among 75 developing economies.

The study reinstates that the forces of globalization and digitalization will be instrumental in shaping the selected most globalized economies in the long run. Adopting various econometric methodologies takes care of the time-specific and cross-sectional dynamics, as evident in the panel framework considered in this study. The empirical findings truly ascertain the theoretical synergy among the forces of globalization leading to more digitalization and economic development. This makes the empirical interplay highly conducive to framing long-term policies to expand the information communication network in terms of its access and reach.

Public procurement presents substantial market opportunities for small- and medium-sized enterprises (SMEs), which can contribute to their economic growth. However, limited dynamic capabilities often pose challenges for SMEs to participate effectively in public procurement markets. Drawing on dynamic capability (DC) theory, this study explores whether financial capability (FNCP) influences SMEs’ ability to leverage their technological capability (TECC) and marketing sensing capability (MKSC) and actively engage in public procurement.

Data for this study were collected from 248 SME managers in the Ilala District, Tanzania, using a cross-sectional questionnaire survey and stratified random sampling technique. The proposed hypotheses were tested empirically through confirmatory factor analysis (CFA) and the Hayes PROCESS macro.

TECC and MKSC demonstrated significant positive associations with SME participation in public procurement (SMPP). Moreover, the interaction between TECC and FNCP as well as the interaction between MKSC and FNCP demonstrate a significant positive effect, suggesting that FNCP strengthens the impact of TECC and MKSC on SMPP.

The scope of this study was limited to SMEs in the Ilala District of Tanzania, hence affecting the generalizability of the findings to other contexts. More importantly, the study findings enrich the understanding of DC theory, signifying that the integration and reconfiguration of MKSC, TECC and FNCP add significant value to SMPP.

The findings suggest that policymakers, support institutions and SME managers should focus on enhancing SMEs' MKSC and TECC to improve their participation in public procurement. In addition, improving SMEs' access to financial resources can further strengthen these effects, enabling more inclusive participation in public procurement.

The study contributes to the literature on SMPP by highlighting the critical roles of MKSC and TECC. It also underscores the importance of FNCP as a moderator in these relationships, which has not been addressed in the existing literature. By integrating these factors, the study offers a comprehensive framework for understanding the dynamics that influence SMPP from financial, technological and marketing perspectives, particularly in developing economies like Tanzania.

This research proposes a novel risk assessment model to elucidate the risk propagation process of industrial safety accidents triggered by natural disasters (Natech), identifies key factors influencing urban carrying capacity and mitigates uncertainties and subjectivity due to data scarcity in Natech risk assessment.

Utilizing disaster chain theory and Bayesian network (BN), we describe the cascading effects of Natechs, identifying critical nodes of urban system failure. Then we propose an urban carrying capacity assessment method using the coefficient of variation and cloud BN, constructing an indicator system for infrastructure, population and environmental carrying capacity. The model determines interval values of assessment indicators and weights missing data nodes using the coefficient of variation and the cloud model. A case study using data from the Pearl River Delta region validates the model.

(1) Urban development in the Pearl River Delta relies heavily on population carrying capacity. (2) The region’s social development model struggles to cope with rapid industrial growth. (3) There is a significant disparity in carrying capacity among cities, with some trends contrary to urban development. (4) The Cloud BN outperforms the classical Takagi-Sugeno (T-S) gate fuzzy method in describing real-world fuzzy and random situations.

The present research proposes a novel framework for evaluating the urban carrying capacity of industrial areas in the face of Natechs. By developing a BN risk assessment model that integrates cloud models, the research addresses the issue of scarce objective data and reduces the subjectivity inherent in previous studies that heavily relied on expert opinions. The results demonstrate that the proposed method outperforms the classical fuzzy BNs.

In this study, we aim to investigate the connectedness spillovers among major cryptocurrency markets. Moreover, we also explore to identify factors driving this connectedness, particularly focusing on the sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies under Twitter-based economic uncertainties and US economic policy uncertainty. Finally, we investigate the extent to which cryptocurrency markets serve as a safe haven, hedge, and diversifier from news-based uncertainties.

This study employs the connectedness approach following the combination of Ando et al. (2022) QVAR and Baruník and Krehlík's (2018) frequency connectedness methodologies into the framework proposed by Diebold and Yilmaz (2012, 2014). The data covered from November 10, 2017, to April 21, 2023, and the factors driving cryptocurrency connectedness spillovers are identified and examined. The sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies, concerning Twitter-based economic uncertainties and US economic policy uncertainty, are analyzed. We apply the Wavelet quantile correlation (WQC) method developed by Kumar and Padakandla (2022) to explore the effects of Twitter-based economic uncertainties and US economic policy uncertainty on Cryptocurrency market connectedness risk spillovers. Besides, we check and present the robustness of WQC findings with the multivariate stochastic volatility method.

Our findings indicate that Ethereum and Bitcoin are net shock transmitters at the center of the connectedness return network. Ethereum and Bitcoin hold the highest market capitalization and value in the cryptocurrency market, respectively. This suggests that return shocks originating from these two cryptocurrencies have the most significant impact on other cryptocurrencies. Tether and Monero are the net receivers of return shocks, while Cardano and XRP exhibit weak shock-transmitting characteristics through returns. In terms of return spillovers, Ethereum is the most effective, followed by Bitcoin and Stellar. Further analysis reveals that Twitter economic policy uncertainty and US economic policy uncertainty are effective drivers of short-term and total directional spillovers. These uncertainty indices exhibit positive coefficient signs in short-term and total directional spillovers, which turn predominantly negative in different magnitudes and frequency ranges in the long term. In addition, we also document that as the Total Connectedness Index (TCI) value increases, market risk also rises. Also, our empirical findings provide significant evidence of Twitter-based economic uncertainties and US economic policy uncertainty that affect short-term market risks. Hence, we state that risk-connectedness spillovers in cryptocurrency markets enclose permanent or temporary shock variations. Besides, findings of the low value of long-term spillovers suggest that risk shocks in cryptocurrency markets are not permanent, indicating long-term changes require careful monitoring and control over market dynamics.

In this study, we find evidence that Twitter's news-based uncertainty and US economic policy uncertainty have a significant effect on short-term market risk spillovers. Furthermore, we observe that high cryptocurrency market risk spillovers coincide with periods of events such as the US-China trade tensions in January 2018, the Brexit process in February 2019, and the COVID-19 outbreak in November 2019. Next, we observe a decline in cryptocurrency market risk spillovers after March 2020. The reason for this mitigation of market risk spillover may be that the Fed's quantitative easing signals have initiated a relaxation process in the markets. Because the Fed's signal to fight inflation in March 2022 also coincides with the period when risk spillover increased in crypto markets. Based on this, we present evidence that the FED's communication mechanism with the markets can potentially affect both short- and long-term expectations. In this context, we can say that our hypothesis that uncertainty about the news causes short-term risks to increase has been confirmed. Our findings may have investment policy implications for portfolio managers and investors generally in terms of reducing financial risks.

Our paper contributes to the literature by examining the interconnectedness among major cryptocurrencies and the drivers behind them, particularly focusing on the role of news-based economic uncertainties. More broadly, we calculate the utilization of advanced methodologies and the incorporation of real-time economic uncertainty data to enhance the originality and value of the research, which provides insights into the dynamics of cryptocurrency markets.

This study aims to obtain governance strategies for managing the complexity of megaprojects by analyzing the impact of individual factors and their configurations using the fuzzy-set qualitative comparative analysis (fsQCA) method and to provide references for project managers.

With the continuous development of the economy, society and construction industry, the number and scale of megaprojects are increasing, and the complexity is becoming serious. Based on the relevant literature, the factors affecting the complexity of megaprojects are determined through case analysis, and the paths of factors affecting the complexity are constructed for megaprojects. Then, the fsQCA method is used to analyze the factors affecting the complexity of megaprojects through 245 valid questionnaires from project engineers in this study.

The results support the correlation between the complexity factors of megaprojects, with six histological paths leading to high complexity and seven histological paths leading to low complexity.

It breaks the limitations of the traditional project complexity field through a “configuration perspective” and concludes that megaproject complexity is a synergistic effect of multiple factors. The study is important for enriching the theory of megaproject complexity and providing complexity governance strategies for managers in megaproject decision-making.

The Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.

Hybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.

Our study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.

To the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

This paper aims to explore the effect of female’s presence in corporate governance structures to reduce agency conflicts, using a quantile regression approach.

The research investigates the relationship between company performance and boardroom gender diversity using quantile regression methods. The study uses annual data of 111 companies listed on the Johannesburg Stock Exchange from 2010 to 2020.

The study reveals that women on the board impact firm return on assets and enterprise value, varying across performance distribution. This contrasts fixed effect findings but aligns with two-stage least squares. However, quantile regression indicates that female executives and independent non-executive directors have notably negative impacts in high and low-performing companies, highlighting non-uniformity in the board gender diversity effect compared with previous assumptions.

The empirical findings suggest that companies with no women directors on the board are generally more likely to experience a decrease in performance and enterprise value relative to companies with women directors on the board. As recommended through the King Code of Corporate Governance, it is thus valuable to companies to ensure gender diversity on the board of directors.

The research confirms through rigorous statistical analyses that corporate governance policies, principles and guidelines should include gender diversity as a requirement for a board of directors.