Tourism and hospitality have been recognized as leading economic sectors globally. Before the outbreak of COVID-19, it was estimated that the tourism and hospitality sector was growing by around 4% each year. Although the economic-efficiency-led hypothesis of the tourism and hospitality sector is strong, there is another perspective related to tourism and hospitality. That is, tourism and hospitality are not as “green” as they were supposed to be. Indeed, this sector is known for its outsized carbon footprint. It is estimated that, if not managed efficiently, the GHG contribution of the tourism sector will grow in the future. Specifically, the hotel business accounts for 1% of total global greenhouse gas emissions (GHG), which is huge. Responding to these significant issues, this study investigates the relationship between the corporate social responsibility (CSR) activities of a hotel enterprise and employees’ pro-environmental behavior (PEB). The mediating role of environmental-specific transformational leadership (ESTFL) and the moderating role of green perceived organizational support (GPOS) were also tested in the above relationship. The data were collected by the employees through a self-administered questionnaire. The hypothesized relations were statistically investigated by using structural equation modeling (SEM). The findings revealed that CSR activities of a hotel not only influence employees’ PEB directly, but the mediating role of ESTFL was also significant. At the same time, the conditional indirect role of GPOS was also confirmed. This study offers different theoretical and practical insights, which have been discussed in detail.

Purpose—The purpose of this paper is to examine the transmission mechanisms and dynamic spillover effects between gold spot prices and US equity prices following the 2007 Global Financial Crisis. It also aims at estimating hedging effectiveness between stocks and gold in major US financial market. Design/methodology/approach—There is large agreement in the literature that gold exhibits the main requirements to qualify as a risk-mitigating instrument against changes in stock prices and other market variables. To test the validity of this conception, this study applies a VAR-ADCC-BVGARCH model for 2,870 daily observations of US financial market during 2007–2017. Findings—The results suggest that the hedging effectiveness of gold against US stocks tends to diminish as stock market capitalization increases, implying that a marginal level of risk exposure is mitigated considering the relatively high proportion of funds that need to be invested in gold against stocks. Originality/value—The real economy is heavily influenced by financial markets, the implications of which are imperative for investors, policy makers and portfolio managers. The key findings of this study are critical in formulating optimal hedging strategies.

In a time of increased corporate accountability, the integration of blockchain technology into corporate social responsibility (CSR) reporting presents a revolutionary option. This paper examines the efficacy of blockchain in improving trust, transparency, and accountability in CSR reporting, while addressing the shortcomings of conventional techniques. By employing a structured decision-making framework and analyzing existing literature, we assess the potential of blockchain to revolutionize CSR reporting, focusing on its immutability, transparency, and decentralized nature. The findings indicate that blockchain technology, especially permissionless public blockchains, is particularly advantageous for CSR reporting, as it provides a shared, unchangeable database for various stakeholders with possible trust concerns. Recognizing the ongoing significance of current stakeholders such as governments and standard-setting bodies, we suggest that blockchain can function as an integrative platform, redefining their roles and alleviating their limitations. This research enhances the existing literature on blockchain applications in CSR reporting, emphasizing its capacity to cultivate trust, generate beneficial social and environmental outcomes, and encourage sustainable business practices.

Purpose: Although the influence of emotions on decision-making has been well-examined in psychology and marketing, it has received relatively less empirical attention in finance. Our goal is to address this gap by analyzing the influence of emotional indicators obtained from news and social media on investment decisions and, eventually, the valuation of assets. Design/methodology/approach: We have four research questions. Specifically, we ask (1) whether news and social media, by using emotional appeals, potentially affect the market price of a corporation, both on an individual and collective basis? (2) To what degree do news and social media sentiment, both individually and together, impact investors’ responses to fresh information, such as earnings announcements? (3) Which form of media portrayal of human emotion has a more substantial impact on investors’ decision-making process? (4) What has the impact of news and social media over time been? Findings: We have uncovered evidence indicating that investors’ emotions substantially impact their decision-making process. Emotions are crucial in shaping investors’ reactions to information signals and influencing market pricing. Positive emotions distinctly impact asset values and the market’s response to positive news in contrast to negative emotions. Positive emotions in individuals lead to a favorable market reaction to positive news and an unfavorable market reaction to negative news. The emotions generated from news media listings have a more significant influence on stock values than those gained from social media listings. Research limitations/implications: The research mainly focused on examining the influence of emotions on investor behavior before and during earnings presentations. To determine the duration of emotional mispricing, we can utilize this method during the period that comes after the announcement, but it cannot be applied to determine the future prices of the stocks. Practical implications: This study uses nine emotion indicators to capture and analyze investor emotions. We analyze text from news articles and social media posts to gather this information, just as a quantitative analyst would. Positive emotions distinctly impact asset values and the market’s response to positive news in contrast to negative emotions. Positive emotions in individuals lead to a favorable market reaction to positive news and an unfavorable market reaction to negative news. When considering the impact of different emotions on asset values, it becomes clear that certain emotions hold more sway than others. Positivity and happiness have the most significant direct and indirect impacts on asset valuation compared to other positive emotions. Social implications: To further explore this approach, future research might examine the impact of emotions on different financial markets, including debt and currency, as well as specific corporate operations like initial public offerings and mergers and acquisitions. Additional research is warranted to ascertain whether certain investor groups or stock types are more vulnerable to the impact of emotions. For example, future studies can study whether it had a more significant effect on small-cap stocks than large-cap businesses, value stocks compared to growth stocks, male investors versus female investors or retail investors versus institutional investors. Originality/value: Our research links returns and emotion. Investors’ emotions indirectly affect news’ impact on asset values, demonstrating a link between emotions and asset prices. The findings further research external factors influencing investors’ earnings announcement responses. Studies show that market ambiguity and information uncertainty may affect investors’ earnings surprise responses. Adding individual emotions shows how investors’ primary emotions affect their reaction to company news, specifically earnings surprises’ effects on announcement returns.

Bitcoin is an online communication system that facilitates the use of virtual currency, including electronic payments. This paper aims at analyzing the behavior of Bitcoin returns as a proposal for future currencies while making a comparison between Bitcoin and other conventional currencies. This paper uses quantitative approach to analyze the time series of Bitcoin and that of other conventional currencies during the period 2010–2018. It uses 1) a descriptive statistics for the weekly returns for Bitcoin which includes the mean, standard deviation, maximum value, minimum value, skewness, kurtosis, and Jarque-Bera normal distribution test statistics, and 2) duration dependence test on Bitcoin weekly returns by extracting the weekly returns for the Bitcoin that behave in irregular way of the general Bitcoin return level through autocorrelation regression, and taking the residuals for this regression as a time series for irregular returns.This paper has confirmed no empirical evidence for the existence of a speculative bubble in the Bitcoin values and returns. In addressing the question of whether Bitcoin can act as a reliable substitute for conventional currencies, the returns based analysis shows a huge difference between the behavior of Bitcoin returns from conventional currency returns when comparing both aspects of level and stability. The paper concluded that bitcoin is more an investment than a currency. This paper represents a significant contribution in the path of financial economics and financial risk management, and represents a contribution to the stability of the financial system around the world and mitigating financial crises.

The authors undertook to examine 720 monthly observations of activity in 15 Arab stock markets over four years (from 2014 to 2017) to identify the dynamic linkages among those markets. To achieve this, several forms of the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model were utilized. Both panel and individual stationarity, in addition to cointegration tests, were employed to highlight the interaction between these markets. The results suggest that Arab stock markets have weak linkages with the exception of those of the Gulf Cooperation Council (GCC). The authors also find out that the TARCH, EGARCH, PARCH, and Component GARCH (1,1) models are suitable in terms of passing the econometric analysis tests. Nevertheless, they conclude that the EGARCH model is the most appropriate for capturing the cross-market dynamic linkages, thereby outperforming the other GARCH specifications under study. The empirical findings bear special implications for economic literature regarding linkages of stock markets in the Arab world.