Engagement in New Gambling Practices and its Association with Gambling Disorder, Impulsivity, and Cognitive Distortions: Findings from a Nationally Representative Sample of Spanish Gamblers.

Most epidemiological surveys focus on adult gambling behaviors related to traditional gambling forms, while studies on novel forms often focus on loot boxes and cryptocurrency trading individually. This study examines the co-ocurrence of emergent gambling and gambling-like practices, analyzing the demographic and psychological characteristics of involved gamblers. A cross-sectional study surveyed 1429 Spanish individuals aged 18-65, using a web-based questionnaire. The survey assessed participation in 19 gambling (e.g., lotteries, sports betting) and gambling-like activities (e.g., trading of cryptocurrencies and other assets, buying loot-boxes), along with sociodemographic and substance use. Problem gambling (PGSI), Impulsivity (UPPS-P), and cognitive distortions (Labrador's cognitive distortions scale) were also assessed. Participants who gambled over the past year (n = 921) were classified into four groups: traditional gambling (TG) only (64.5%, n = 594), TG with trading activities (27.5%, n = 253), TG with gambling withing video games or streaming platforms (2.5%, n = 23), and TG with both trading and video gambling (5.5%, n = 51). Most gamblers engaged exclusively in traditional formats, but 35.5% also participated in novel gambling forms. Those involved in both trading and video gambling were generally younger, male, with higher levels of impulsivity and gambling-related cognitive distortions compared to TG-only gamblers (p < 0.001). This group also exhibited higher rates of problem gambling and substance use (p < 0.001). This study emphasize the importance of including emerging gambling activities, which are particularly prevalent among high-risk gamblers, in epidemiological surveys. Identifying new gambling patterns and associated risk factors could help optimize public policies and develop more effective regulatory and prevention strategies.

A Blockchain-Based Authentication Mechanism for Enhanced Security.

Passwords are the first line of defence against preventing unauthorised access to systems and potential leakage of sensitive data. However, the traditional reliance on username and password combinations is not enough protection and has prompted the implementation of technologies such as two-factor authentication (2FA). While 2FA enhances security by adding a layer of verification, these techniques are not impervious to threats. Even with the implementation of 2FA, the relentless efforts of cybercriminals present formidable obstacles in securing digital spaces. The objective of this work is to implement blockchain technology as a form of 2FA. The findings of this work suggest that blockchain-based 2FA methods could strengthen digital security compared to conventional 2FA methods.

Decoding Bitcoin: leveraging macro- and micro-factors in time series analysis for price prediction.

Predicting Bitcoin prices is crucial because they reflect trends in the overall cryptocurrency market. Owing to the market's short history and high price volatility, previous research has focused on the factors influencing Bitcoin price fluctuations. Although previous studies used sentiment analysis or diversified input features, this study's novelty lies in its utilization of data classified into more than five major categories. Moreover, the use of data spanning more than 2,000 days adds novelty to this study. With this extensive dataset, the authors aimed to predict Bitcoin prices across various timeframes using time series analysis. The authors incorporated a broad spectrum of inputs, including technical indicators, sentiment analysis from social media, news sources, and Google Trends. In addition, this study integrated macroeconomic indicators, on-chain Bitcoin transaction details, and traditional financial asset data. The primary objective was to evaluate extensive machine learning and deep learning frameworks for time series prediction, determine optimal window sizes, and enhance Bitcoin price prediction accuracy by leveraging diverse input features. Consequently, employing the bidirectional long short-term memory (Bi-LSTM) yielded significant results even without excluding the COVID-19 outbreak as a black swan outlier. Specifically, using a window size of 3, Bi-LSTM achieved a root mean squared error of 0.01824, mean absolute error of 0.01213, mean absolute percentage error of 2.97%, and an R-squared value of 0.98791. Additionally, to ascertain the importance of input features, gradient importance was examined to identify which variables specifically influenced prediction results. Ablation test was also conducted to validate the effectiveness and validity of input features. The proposed methodology provides a varied examination of the factors influencing price formation, helping investors make informed decisions regarding Bitcoin-related investments, and enabling policymakers to legislate considering these factors.

Basic human values and the adoption of cryptocurrency.

Cryptocurrency is an attempt to create an alternative to centralized financial systems using blockchain technology. However, our understanding of the psychological mechanisms that drive cryptocurrency adoption is limited. This study examines the role of basic human values in three stages of cryptocurrency adoption-awareness, intention to buy, and ownership-using the Theory of Planned Behavior (TPB). Logistic regression analysis was conducted on a quota sample of 714 German adults, and the results showed that openness-to-change values increased the likelihood of cryptocurrency awareness, while self-enhancement values increased the likelihood of intention to buy and ownership. These findings were consistent even after controlling for demographic characteristics, attitudinal beliefs, and perceived behavioral control, which are important factors in the TPB. The results suggest that basic human values may influence an individual's decision to adopt cryptocurrency, but the transition from awareness to ownership may be influenced by socio-economic opportunities available to interested individuals.

Green gold or carbon beast? Assessing the environmental implications of cryptocurrency trading on clean water management and carbon emission SDGs.

This study addresses the ongoing debate concerning the environmental implications of cryptocurrencies. Specifically, it investigates the impact of Bitcoin trading volume on water and sanitation (Sustainable Development Goal (SDG) 6) and climate action (SDG 13). The research employs Ordinary Least Squares (OLS) panel data analysis to examine these relationships using a sample of 32 countries with available Bitcoin trading volume data from 2013 to 2020. The findings indicate that Bitcoin trading significantly and positively impacts progress towards SDG 6, suggesting potential benefits for water and sanitation initiatives. However, the study reveals a significant negative impact of higher Bitcoin trading volume on increased carbon emissions, underscoring the environmental costs associated with cryptocurrency activities. Similar impacts are observed for gold reserves, as their mining necessitates substantial energy consumption. These results highlight the need to regulate cryptocurrency trading and promote voluntary sustainable practices, particularly given the disparities between developed and emerging markets based on their governance frameworks. Additionally, the study considers the disparities between countries based on technology exports and economic policy uncertainty as influential determinants. The study's results emphasize the importance of proactive measures to ensure the responsible and sustainable use of cryptocurrencies. While cryptocurrencies offer significant economic returns, their early adoption stage necessitates further investigation into environmentally friendly approaches. Potential strategies include directing financial returns from cryptocurrencies towards alternative energy projects and supporting other environmental SDGs, thereby fostering a positive impact on the overall ecosystem. The study's implications extend to policymakers, regulators, and stakeholders, advocating for comprehensive and collaborative efforts to integrate sustainability into the rapidly evolving cryptocurrency market. This integration is crucial to ensure that the economic benefits of cryptocurrencies do not come at the cost of our environment.

Cryptocurrency Trading, Day Trading, and Gambling Behavior: Examining the Moderating Effects of Financially Focused Self-Concept and Gambling Motives.

Cryptocurrency and day trading have grown in popularity over the past decade following the creation of the first cryptocurrency, Bitcoin, in 2009. These activities share important features with gambling, including risking money on an uncertain outcome, a chance of monetary rewards, and the potential to experience harm (e.g., financial or relationship problems). However, little is known about cryptocurrency and day trading engagement in the adult population, including associations with gambling behavior, harm, and psychological factors that might moderate these relationships. We analyzed cross-sectional data for n = 822 adults from an online panel in the U.S. to examine: (1) the extent to which cryptocurrency trading, day trading, and gambling are associated, (2) relationships between cryptocurrency trading, day trading, and higher risk gambling behavior, and (3) whether financially focused self-concept and four types of gambling motives moderate these relationships. We found moderate to strong positive intercorrelations between cryptocurrency and day trading, and gambling behavior, including engagement and risk. We identified significant moderating effects of financially focused self-concept, and coping motives for gambling, on the relationship between cryptocurrency trading and gambling frequency, and between day trading and gambling frequency. For the models predicting higher risk gambling status, the only significant moderators were financially focused self-concept for the day trading model, and the enhancement motive for the cryptocurrency and day trading models. Our results have important implications for understanding interrelationships between gambling-adjacent activities and more traditional gambling forms, as well as the moderating roles of key psychosocial concepts in these relationships.

Electricity and cryptocurrency mining: An empirical contribution.

Active cryptocurrency mining and trading comes with heavy electricity demand and increased emissions. Thus, cryptocurrency mining is prohibited in most economies. Consequently, miners relocate to regions or economies without these prohibitions and/or with relatively lower electricity rates. As such, presenting a nexus between the cryptocurrency and electricity markets, even at the global level. This article investigates the different forms of relationships existing between these markets. The conditional asymmetric volatility model with the Wald, nonparametric and parametric Granger causality tests are employed. The results confirm the existence of both unidirectional and bidirectional lead-lag return relationships between the cryptocurrency and electricity markets. Cryptocurrency returns drive electricity demand. This finding is homogeneous both on a global and strata (homogeneous groupings) basis. Also, the electricity market spills over significant volatilities to the cryptocurrency markets without feedback, nonetheless. Result-based policies are recommended towards green finance, decarbonization, and emission mitigations through the demand for electricity by the cryptocurrency markets. They include the use of clean and renewable electricity sources and technologies for cryptocurrency market activities.

The Telehealth chain: a framework for secure and transparent telemedicine transactions on the blockchain.

BACKGROUND: Blockchain technology provides a secure and decentralized platform for storing and transferring sensitive medical data, which can be utilized to enable remote medical consultations. AIM: A theoretical framework for creating a blockchain-based digital system created to facilitate telemedicine system. RESULTS: This paper proposes a theoretical framework based on Hyperledger fabric for creating a blockchain-based digital entity to facilitate telemedicine services. The proposed framework utilizes blockchain technology to provide a secure and reliable platform for medical practitioners to interact remotely with patient transactions. CONCLUSION: The blockchain will serve as a one-stop digital service to secure patient data, ensure privacy, and facilitate payments. The proposed framework leverages the existing Hyperledger fabric platform to build a secure blockchain-assisted telemedicine platform.

Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions.

Introduction: The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations. Methods: In this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly. Results and discussion: To make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.

Psycholinguistic and emotion analysis of cryptocurrency discourse on X platform.

This paper provides an extensive examination of a sizable dataset of English tweets focusing on nine widely recognized cryptocurrencies, specifically Cardano, Binance, Bitcoin, Dogecoin, Ethereum, Fantom, Matic, Shiba, and Ripple. Our goal was to conduct a psycholinguistic and emotional analysis of social media content associated with these cryptocurrencies. Such analysis can enable researchers and experts dealing with cryptocurrencies to make more informed decisions. Our work involved comparing linguistic characteristics across the diverse digital coins, shedding light on the distinctive linguistic patterns emerging in each coin's community. To achieve this, we utilized advanced text analysis techniques. Additionally, this work unveiled an understanding of the interplay between these digital assets. By examining which coin pairs are mentioned together most frequently in the dataset, we established co-mentions among different cryptocurrencies. To ensure the reliability of our findings, we initially gathered a total of 832,559 tweets from X. These tweets underwent a rigorous preprocessing stage, resulting in a refined dataset of 115,899 tweets that were used for our analysis. Overall, our research offers valuable perception into the linguistic nuances of various digital coins' online communities and provides a deeper understanding of their interactions in the cryptocurrency space.

EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics.

DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data. Given these diverse approaches to methylation profiling, to date, there is no single tool that allows (1) classification and interpretation of microarray, nanopore and parallel sequencing data, (2) direct control of nanopore sequencers, and (3) the integration of microarray-based methylation reference data. Furthermore, no software capable of entirely running in routine diagnostic laboratory environments lacking high-performance computing and network infrastructure exists. To overcome these shortcomings, we present EpiDiP/NanoDiP as an open-source DNA methylation and copy number profiling suite, which has been benchmarked against an established supervised machine learning approach using in-house routine diagnostics data obtained between 2019 and 2021. Running locally on portable, cost- and energy-saving system-on-chip as well as gpGPU-augmented edge computing devices, NanoDiP works in offline mode, ensuring data privacy. It does not require the rigid training data annotation of supervised approaches. Furthermore, NanoDiP is the core of our public, free-of-charge EpiDiP web service which enables comparative methylation data analysis against an extensive reference data collection. We envision this versatile platform as a useful resource not only for neuropathologists and surgical pathologists but also for the tumour epigenetics research community. In daily diagnostic routine, analysis of native, unfixed biopsies by NanoDiP delivers molecular tumour classification in an intraoperative time frame.

The flip side of the coin: Exploring the environmental and health impacts of proof-of-work cryptocurrency mining.

Blockchain technology, the backbone of cryptocurrency, is under scrutiny due to the environmental and health hazards linked to its energy-consuming Proof-of-Work (PoW) mining process. This review study provides a comprehensive analysis of the global health implications of PoW mining and cryptocurrency, with a focus on environmental sustainability and human health. The research utilized both traditional databases (PubMed and Web of Science) and additional primary sources. The study underscores the high energy consumption and carbon emissions of Bitcoin mining, despite ongoing debates comparing cryptocurrency to conventional finance. The review calls for immediate interventions, including the exploration of renewable energy sources and a transition from PoW to more sustainable consensus mechanisms. A case study on China's carbon policies highlights the necessity for effective regulatory measures. The findings reiterate the environmental and health risks associated with PoW cryptocurrency mining, including its resource-intensive procedures, reliance on non-renewable energy, and emission of air pollutants. The review emphasizes the urgent need for global regulation and a transition to more sustainable consensus mechanisms, such as Proof-of-Stake (PoS), to reduce the industry's impact on climate and human health.

A Crime by Any Other Name: Gender Differences in Moral Reasoning When Judging the Tax Evasion of Cryptocurrency Traders.

Tax evasion is a major issue for authorities worldwide. Understanding the factors that influence individuals' intrinsic motivation to pay taxes, known as their tax morale, is important for improving tax compliance. This study investigated gender differences in judging tax evasion in the context of cryptocurrency trading. Specifically, a survey study explored whether different moral foundations, financial literacies, and political orientations among females vs. males might explain potential gender differences in judging tax evasion. In an online survey, 243 U.S. adults read a vignette about a friend evading taxes in a cryptocurrency trading context. In a correlational analysis, we found that females judged tax evasion harsher, as being more morally wrong than males. Of the psychographic factors, only individualizing moral foundation values (i.e., fairness and harm avoidance) explained the harsher moral judgment by females. That is, individualizing moral foundation values were at a higher level among females, which further predicted females' harsher judgment of tax evasion. While females also had, on average, lower financial literacy and knowledge of cryptocurrencies than males, these did not predict their harsher judgment of tax evasion. The findings contribute to research on gender differences in moral judgments and highlight that a given transgression, or a specific crime, may violate different moral values in men and women. The results demonstrate to policy makers that it is important to take into account gender differences, in campaigns promoting tax morale and compliance.

Decoding the cryptocurrency user: An analysis of demographics and sentiments.

In recent years, new payment methods have emerged, aimed at improving convenience for users. Cryptocurrencies, in principle, are no different. In this study, we seek to analyze the general population's attitudes towards the adoption of cryptocurrencies as a payment method. To achieve this, we have developed a descriptive survey that targets both current cryptocurrency users and non-users, recognizing that differences in perception may exist. Additionally, we have conducted a sentiment analysis of open-ended questions to understand respondents' views on the future of the cryptocurrency market and its potential as a payment tool, utilizing different lexicons in the English language. Our findings indicate that most cryptocurrency users prefer to invest in these digital assets, often choosing coins based on their popularity rather than other intrinsic features. E-commerce payments are the most attractive activity, followed by international transactions when using cryptocurrencies as a payment method. However, high volatility and a lack of ease of use are the most common difficulties reported by users. Our study also highlights the importance of regulation in a time when users are increasingly demanding higher levels of oversight, in contrast to the past. While users are concerned about the instability and volatility of cryptocurrencies, they also value the anonymity these transactions offer. Our analysis showcases an innovative approach to analyzing interviews and qualitative questionnaires that can be applied in other research fields.

Uncovering Bitcoin's electricity consumption relationships with volatility and price: Environmental Repercussions.

Bitcoin, a global financial asset, surpassed one trillion USD in November 2021, but its environmental impact may cause a 2 °C temperature rise by 2050. Using causal and connectedness analysis, we uncover non-linear relationships between bitcoin's energy consumption, price, and the Crypto Volatility Index. This study uses 1458 daily observations from several databases from March 31, 2019, to March 30, 2023. The phenomenon was analyzed using the theory of production and value investing theory. While the relationship between bitcoin-based electricity consumption and crypto market volatility is bidirectional, Granger causality tests reveal that bitcoin prices Granger-cause electricity consumption, but the converse is not true. Regarding Diebold-Yilmaz connectedness, the price of bitcoin acts as a net contributor, while bitcoin-based electricity consumption and crypto market volatility act as net receivers of spillover from bitcoin price. Our findings contrast with the traditional theory of production, where cost is supposed to determine price, and we show that some bitcoin miners continue operating according to the value investing theory despite suffering financial losses. Limited discussions around bitcoin pricing and its significant expense-that is bitcoin's electricity consumption-indicate the need to explore this relationship. Policymakers, green investors, and others may find the results relevant to building an efficient, environmentally friendly framework and creating much-required innovative regulations.

Cryptocurrency Turmoil: Unraveling the Collapse of a Unified Stablecoin (USTC) through Twitter as a Passive Sensor.

This study sought to explore whether Twitter, as a passive sensor, could have foreseen the collapse of the Unified Stablecoin (USTC). In May 2022, in just a few days, the cryptocurrency went to near-zero valuation. Analyzing 244,312 tweets from 89,449 distinct accounts between April and June 2022, this study delved into the correlation between personal sentiments in tweets and the USTC market value, revealing a moderate correlation with polarity. While sentiment analysis has often been used to predict market prices, the results suggest the challenge of foreseeing sudden catastrophic events like the USTC collapse solely through sentiment analysis. The analysis uncovered unexpected global interest and noted positive sentiments during the collapse. Additionally, it identified events such as the launch of the new Terra blockchain (referred to as "Terra 2.0") that triggered positive surges. Leveraging machine learning clustering techniques, this study also identified distinct user behaviors, providing valuable insights into influential figures in the cryptocurrency space. This comprehensive analysis marks an initial step toward understanding sudden and catastrophic phenomena in the cryptocurrency market.

Quantile frequency connectedness between energy tokens, crypto market, and renewable energy stock markets.

Utilizing a quantile frequency connectedness approach, we explore the connectedness between energy tokens, crypto market, and renewable energy stock markets. The empirical results show that the connectedness measures of the series are characterized by asymmetry and heterogeneity across quantiles and different investment horizons. Specifically, the characteristic of clustering has been observed that energy tokens and crypto market are more interconnected, while the renewable energy stock markets are more interconnected with each other at median quantile. The linkages between energy tokens and renewable energy stock markets are quite weak under normal market conditions, suggesting the diversification opportunities in investing these financial assets. However, these series are more interconnected under extreme market conditions, with the renewable energy stock markets are on the dominating end of the propagation mechanism while the energy tokens and crypto market are net receivers of shocks. Further frequency decomposition shows that this strategy can hold in the short term, while in the long term investors could benefit from the diversification opportunities by investing both kinds of financial assets. Additionally, the dynamic analysis affirms that the connectedness measures are varied and event-dependent over time. Our results may help investors and policymakers have a better assessment and portfolio management.

Multi-objective reward generalization: improving performance of Deep Reinforcement Learning for applications in single-asset trading.

We investigate the potential of Multi-Objective, Deep Reinforcement Learning for stock and cryptocurrency single-asset trading: in particular, we consider a Multi-Objective algorithm which generalizes the reward functions and discount factor (i.e., these components are not specified a priori, but incorporated in the learning process). Firstly, using several important assets (BTCUSD, ETHUSDT, XRPUSDT, AAPL, SPY, NIFTY50), we verify the reward generalization property of the proposed Multi-Objective algorithm, and provide preliminary statistical evidence showing increased predictive stability over the corresponding Single-Objective strategy. Secondly, we show that the Multi-Objective algorithm has a clear edge over the corresponding Single-Objective strategy when the reward mechanism is sparse (i.e., when non-null feedback is infrequent over time). Finally, we discuss the generalization properties with respect to the discount factor. The entirety of our code is provided in open-source format.

An empirical approach and practical framework for a decentralized Ethereum Ecosystem Index (EEI).

Stock market indices are pivotal tools for establishing market benchmarks, enabling investors to navigate risk and volatility while capitalizing on the stock market's prospects through index funds. For participants in decentralized finance (DeFi), the formulation of a token index emerges as a vital resource. Nevertheless, this endeavor is complex, encompassing challenges such as transaction fees and the variable availability of tokens, attributed to their brief history or limited liquidity. This research introduces an index tailored for the Ethereum ecosystem, the leading smart contract platform, and conducts a comparative analysis of capitalization-weighted (CW) and equal-weighted (EW) index performances. The article delineates exhaustive criteria for token eligibility, intending to serve as a comprehensive guide for fellow researchers. The results indicate a consistent superior performance of CW indices over EW indices in terms of return and risk metrics, with a 30-constituent CW index outshining its counterparts with varied constituent numbers. The recommended CW30 index demonstrates substantial advantages in comparison to established benchmarks, including prominent indices like DeFi Pulse Index (DPI) and CRypto IndeX (CRIX). Additionally, the article explores the practicality of implementing the CW30 in Layer 2 networks of the Ethereum Ecosystem, advocating for the Arbitrum infrastructure as the optimal choice for the decentralized crypto index protocol herein referred to as the Ethereum Ecosystem Index (EEI). The study's insights aspire to enrich the DeFi ecosystem, offering a nuanced understanding of network selection and a strategic framework for implementation. This research significantly enhances the existing literature on index construction and performance within the Ethereum ecosystem. To our knowledge, it represents a pioneering comprehensive analysis of an index that accurately mirrors the Ethereum market, advancing our comprehension of its intricacies and wider ramifications. Moreover, this study stands as one of the initial thorough examinations of index construction methodologies within the nascent asset class of crypto. The insights gleaned provide a pragmatic approach to index construction and introduce an index poised to serve as a benchmark for index products. In illuminating the unique facets of the Ethereum ecosystem, this research makes a substantial contribution to the current discourse on crypto, offering valuable perspectives for investors, market stakeholders, and the ongoing exploration of digital assets.

"In the Metaverse We (Mis)trust?" Third-Level Digital (In)equality, Social Phobia, Neo-Luddism, and Blockchain/Cryptocurrency Transparency in the Artificial Intelligence-Powered Metaverse.

The Metaverse, powered by a variety of key innovative technologies including 3D virtual reality (VR)/augmented reality (AR), artificial intelligence (AI), blockchain/cryptocurrency-based non-fungible tokens (NFTs), and the Internet of Things, has been proposed as the future of a virtual universe for education, work, business, and commerce. This research (∑ N = 954) presents the results of three cross-sectional surveys that examine the influence of third-level digital (in)equality and consumer (mis)trust on Metaverse adoption intention. Study 1, focusing on the Metaverse for hybrid education, reports the mediating effect of (mis)trust in the Metaverse on the relationship between the educational dimension of third-level digital (in)equality and behavioral intention to adopt the Metaverse for virtual learning as well as the moderating effect of social phobia. Study 2, focusing on the Metaverse for remote working, reports the mediating effect of (mis)trust in the Metaverse on the relationship between the economic labor dimension of third-level digital (in)equality and Metaverse adoption for virtual working as well as the moderating effect of neo-Luddism. Study 3, focusing on the Metaverse for business, reports the mediating effect of (mis)trust in the Metaverse on the relationship between the economic commerce dimension of third-level digital (in)equality and Metaverse adoption for virtual commerce as well as the moderating effect of blockchain/cryptocurrency transparency perception. This research can provide theoretical frameworks to examine people's hopes and fears about the Metaverse and consequential adoption versus non-adoption of the Metaverse for hybrid education, hybrid remote working, and omni-channel virtual commerce. Practical, managerial, and policy implications for the Metaverse and the NFT market are also discussed.