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This study probes the psychological interplay between investor sentiment and the returns of cryptocurrencies Bitcoin and Ethereum. Employing the Granger causality test, the authors aim to gauge how extensively the Fear and Greed Index (FGI) can predict cryptocurrency return movements, exploring the intricate bond between investor emotions and market behavior.

The authors used the Granger causality test to achieve research objectives. Going beyond conventional linear analysis, the authors applied Smooth Quantile Regression, scrutinizing weekly data from July 2022 to June 2023 for Bitcoin and Ethereum. The study focus was to determine if the FGI, an indicator of investor sentiment, predicts shifts in cryptocurrency returns.

The study findings underscore the profound psychological sway within cryptocurrency markets. The FGI notably predicts the returns of Bitcoin and Ethereum, underscoring the lasting connection between investor emotions and market behavior. An intriguing feedback loop between the FGI and cryptocurrency returns was identified, accentuating emotions' persistent role in shaping market dynamics. While associations between sentiment and returns were observed at specific lag periods, the nonlinear Granger causality test didn't statistically support nonlinear causality. This suggests linear interactions predominantly govern variable relationships. Cointegration tests highlighted a stable, enduring link between the returns of Bitcoin, Ethereum and the FGI over the long term.

Despite valuable insights, it's crucial to acknowledge our nonlinear analysis's sensitivity to methodological choices. Specifics of time series data and the chosen time frame may have influenced outcomes. Additionally, direct exploration of macroeconomic and geopolitical factors was absent, signaling opportunities for future research.

This study enriches theoretical understanding by illuminating causal dynamics between investor sentiment and cryptocurrency returns. Its significance lies in spotlighting the pivotal role of investor sentiment in shaping cryptocurrency market behavior. It emphasizes the importance of considering this factor when navigating investment decisions in a highly volatile, dynamic market environment.

A long line of research has addressed whether there are sex differences in cooperation and other forms of prosocial behavior. Studies of social dilemmas (situations that pose a conflict between individual and collective interests) have yielded particularly contradictory conclusions about whether males or females are more cooperative. We present an evolutionary framework that synthesizes previous results and generates new insights into the sex and cooperation question. The framework addresses two general bases of sex differences in cooperation. First, we show how variation in the motivational structure of social dilemmas generates sex differences in cooperation. We then address two aspects of social structure, that, according to evolutionary reasoning, generate sex differences in cooperation: the sex composition of the group, and the interpersonal versus intergroup nature of the dilemma. After presenting new hypotheses and reviewing existing research relevant to each hypothesis, we conclude by making suggestions for future research.

The purpose of this research is to primarily understand how crypto traders can use the Bitcoin as a hedge or safe haven asset to reduce their losses from crypto trading. The study also aims to provide insights to crypto investors (portfolio managers) who wish to maintain a crypto portfolio for the medium term and can use the Bitcoin to minimize their losses. The findings of this research can also be used by policymakers and regulators for accommodating the Bitcoin as a medium of exchange, considering its safe haven nature.

This study applies the cross-quantilogram (CQ) approach introduced by Han et al. (2016) to examine the safe-haven property of the Bitcoin against the other selected crypto assets. This method is robust for estimating bivariate volatility spillover between two markets given unusual distributions and extreme observations. The CQ method is capable of calculating the magnitude of the shock from one market to another under different quantiles. Additionally, this method is suitable for fat-tailed distributions. Finally, the method allows anticipating long lags to evaluate the strength of the relationship between two variables in terms of durations and directions simultaneously.

The Bitcoin acts as a weak safe haven asset for a majority of new crypto assets for the entire study period. These results hold even during greed and fear sentiments in the crypto market. The Bitcoin has the ability to protect crypto assets from sharp downturns in the crypto market and hence gives crypto traders some respite when trading in a highly volatile asset class.

This study is the first attempt to show how the Bitcoin can act as a true matriarch/patriarch for crypto assets and protect them during market turmoil. This study presents a clear and concise representation of this relationship via heatmaps constructed from CQ analysis, depicting the quantile dependence association between the Bitcoin and other crypto assets. The uniqueness of this study also lies in the fact that it assesses the protective properties of the Bitcoin not only for the entire sample period but also specifically during periods of greed and fear in the crypto market.

This research aims to study the recent changes in consumer behaviour and purchase pattern during the Covid-19 pandemic. Covid-19 pandemic has forced consumers to stockpile, which has its own consequences. The article proposes the importance of “minimalism in consumption” to avoid greed in consumer behaviour.

The data are collected from consumers across India using an online survey during the first lockdown from March 2020 to May 2020. A simple random sampling technique is used for data collection, and the collected data are analysed using SPSS version 26.

The study states that there will be a shift in the purchase pattern of the consumers if lockdowns are imposed in the future or during any other crisis. However, at present, consumers have developed a stockpiling mentality fearing the unavailability of essentials.

Pandemic has stimulated a drastic change in consumer behaviour, which is a situational effect. Each crisis affects consumer behaviour in a different way. In this research, we have considered only fear, greed and anxiety in the light of Covid-19. On the other hand, the research intends to draw realistic conclusions based on consumers' experiences during the lockdown.

The study proposes solutions that will help marketers frame exclusive strategies for a future crisis. Analysing the change in consumer behaviour and the shift in purchase patterns will emphasize the importance of market research to know consumer expectations during a crisis situation in order to cater to their new demands.

Consumers who stockpile should realize the unavailability of goods to other consumers who are in need. They also have to understand the importance of “minimalism in consumption” during a crisis.

The data are collected during the most taxing crisis, the Covid-19 pandemic. Data are collected at the peak time of the first wave of Covid-19 in India, during a major shift in consumers' behaviour and purchase pattern. The article brings to the larger consciousness and also preaches a life lesson to all consumers to execute their responsibilities in consumption without over-demands and expectations.

A Catastrophe Theory Model modified for the explanation of the evolution/revolution of behavior in the securities market can be classified in the realm of behavioral finance. An early model of the Cusp Catastrophe Model modified to explain speculative crashes appeared in Zeeman (1976, 1977). Later, Pruden expanded upon Zeeman’s use of the Cusp Model version of Catastrophe Theory to allow for “buying stampedes” as well as “selling panics”. Pruden also established connections between the Cusp Catastrophe Model and technical market analysis. Whereas the Catastrophe Theory Model, like other models from the behavioral sciences, provides a positive scientific theory as to the “why” of behavior in the stock market, technical market analysis furnishes a nominal theory of rules and principles about “how” a trader or investor may profit from the behavior observed in the stock market. Hence, the presupposition is that behavioral science models that explain the stock market behavior provide solid scientific foundations upon which to base the principles and practices of technical market analysis.

This paper aims to offer an improved understanding of trust challenges in online trade, providing examples of issues that should be addressed for a trustworthy online environment. It also aims to illustrate how records and recordkeeping can contribute in terms of enabling trust and accountability.

The paper is based on results from a self-ethnographic study of online trade (Engvall, 2017); the results are analyzed further. Kelton, Fleischmann and Wallace’s (2008) model for trust is used to gain a better understanding of the characteristics of the challenges and where they should be addressed.

This paper recognizes that there are different types of trust challenges at different levels – individual, between clients and businesses and at a societal level – that should be addressed at these levels in different ways.

This paper provides an understanding of trust challenges in the online environment.

Devotes the entire journal issue to managing human behaviour in US industries, with examples drawn from the airline industry, trading industry, publishing industry, metal products industry, motor vehicle and parts industry, information technology industry, food industry, the airline industry in a turbulent environment, the automotive sales industry, and specialist retailing industry. Outlines the main features of each industry and the environment in which it is operating. Provides examples, insights and quotes from Chief Executive Officers, managers and employees on their organization’s recipe for success. Mentions the effect technology has had in some industries. Talks about skilled and semi‐skilled workers, worker empowerment and the formation of teams. Addresses also the issue of change and the training that is required to deal with it in different industry sectors. Discusses remuneration packages and incentives offered to motivate employees. Notes the importance of customers in the face of increased competition. Extracts from each industry sector the various human resource practices that companies employ to manage their employees effectively ‐ revealing that there is a wide diversity in approach and what is right for one industry sector would not work in another. Offers some advice for managers, but, overall, fails to summarize what constitutes effective means of managing human behaviour.

This paper aims to investigate the impact of brand equity (BE) on stock performance (i.e. stock return, volatility and beta), and compare the performance of a high brand equity stocks (HBES) portfolio with that of the overall market during market downturn, market upturn and total disturbance periods of the COVID-19 pandemic in 2020.

Stock performance data and brand valuation estimates are obtained from various sources to assemble a portfolio of HBES and conduct the analyses. Econometric models are estimated to examine the impact of BE on stock performance and compare the HBES portfolio performance versus the overall market.

BE was positively associated with stock return and negatively associated with both types of risk (volatility and beta) during the COVID-19 pandemic. Specifically, during the market downturn period, BE was positively related to stock return and negatively related to stock volatility; during the market upturn period, BE was negatively associated with both types of risk; and during the total disturbance period, BE was positively associated with stock return and negatively associated with both types of risk. Finally, the HBES portfolio outperformed the market (S&P 500 index).

The findings advance the extant research by providing evidence pertaining to brands' role in mitigating the impact of unpredictable market shocks and crises, such as the COVID-19 pandemic, on stock performance. While brands are mostly viewed as drivers of sustained competitive advantage and profitability, their protective role in crisis times is noteworthy.

The research findings potentially help marketing and brand managers to justify marketing spending and craft their strategies to enhance firm performance during crises similar to COVID-19.

The marketing–finance interface can benefit from insights offered by the COVID-19 pandemic, as such crises are becoming prevalent and are capable of damaging various stakeholders' outcomes (firms, investors and customers). The empirical examination is separately conducted on the market downturn, market upturn and total disturbance period attributable to the COVID-19 pandemic.

It is widely believed that, during the neoliberal era, labor has become weaker and capital has become stronger. This chapter argues the opposite is true. Only if class struggle is reduced to the economic struggle to improve our position within capitalism – as opposed to the political struggle to overthrow it – can workers’ loss of agency be considered a fact. In every other respect, this belief is false. When uprisings against corrupt plutocracies, worldwide mobilizations sparked by George Floyd’s murder, youth rebellions against the capitalist destruction of nature, struggles of millions of women for reproductive rights are seen for what they are – expressions of class struggle – it becomes clear that transition to socialism is not only necessary, it is also possible.

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.