Amid global scientific and technological (hereinafter “sci-tech”) competition and China’s innovation-driven strategy, achieving high-quality sci-tech innovation (HQDSTI) is crucial for economic transformation but faces challenges such as resource mismatch, insufficient funding, and low commercialization efficiency. Using panel data from 35 major Chinese cities (2013–2022), this study distinguishes between public sci-tech finance (PSTF) and market sci-tech finance (MSTF) and employs benchmark regression, mediation, and threshold models to investigate their impacts on HQDSTI. Results show that: (1) Both PSTF and MSTF significantly promote HQDSTI, with stronger effects in coastal, dual-center, and pilot cities, and in regions with low fiscal decentralization. MSTF is more effective under high marketization, while PSTF and overall STF are more effective under high financial development. (2) Industrial upgrading serves as a positive mediator, whereas venture capital exerts a suppressive mediating effect that intensifies as its scale expands. The promoting effect of industrial upgrading weakens beyond the threshold level. (3) Policy recommendations include differentiated financial strategies: fostering market-oriented instruments in coastal cities, optimizing targeted support in inland areas, strengthening regional and public–market financial coordination, and improving mechanisms of industrial upgrading and venture capital. This study provides theoretical insights for enhancing the synergistic effect between sci-tech finance and high-quality innovation development. • Distinguish public and market sci-tech finance, explore synergistic effects and differential impacts. • Develop a multi-dimensional evaluation framework for assessing high-quality sci-tech innovation. • Examine heterogeneity across five analytical dimensions to uncover regional and structural variations. • Reveal intermediary roles of industrial upgrading and venture capital. • Identify threshold effects and define the effective range of sci-tech finance.
This research explores the application of data mining techniques, specifically XGBoost, to predict game pricing trends and optimize discount strategies within the digital gaming market. Game prices are influenced by various factors, including production costs, market demand, and promotional strategies. This study analyzes historical pricing data from multiple online stores to identify key pricing patterns and factors that influence price changes over time. The model developed in this study predicts game prices by incorporating features such as retail price, discount percentages, past price trends (lags), and other time-based features. The findings reveal that retail price and recent price trends (e.g., 7-day rolling averages) are the most influential features in predicting future prices. Additionally, discount strategies significantly impact game sales, with certain discount ranges showing higher effectiveness in driving consumer purchases. The model also demonstrates variability in prediction accuracy, particularly at higher price points, highlighting the challenges of capturing complex price fluctuations in a dynamic digital marketplace. The significance of this study extends to the Metaverse market, where pricing and the use of digital assets like non-fungible tokens (NFTs) play a critical role. The model's application could aid in optimizing pricing strategies within virtual economies, enhancing both the consumer experience and retailer profitability. Future work includes integrating additional features such as user reviews and exploring its application to Metaverse game platforms. The practical implications of this research are significant for online game retailers looking to leverage data-driven insights for more effective pricing and promotional strategies.
Technological developments and the impact of artificial intelligence (AI) are omnipresent themes and concerns of the present day. Much has been written on these topics but applications of quantitative models to understand the techno-social landscape have been much more limited. We propose a mathematical model that can help understand in a unified manner the patterns underlying technological development and also identify the different regimes in which the technological landscape evolves. First, we develop a model of innovation diffusion between different technologies, the growth of each reinforcing the development of the others. The model has a variable that quantifies the level of development (or innovation, discovery) potential for a given technology. The potential, or market capacity, increases via diffusion from related technologies, reflecting the fact that a technology does not develop in isolation. Hence, the growth of each technology is influenced by how developed its neighboring (related) technologies are. This allows us to reproduce long-term trends seen in computing technology and large language models (LLMs). We then present a three-dimensional system of supply, demand, and investment which shows oscillations (business cycles) emerging if investment is too high into a given technology, product, or market. We finally combine the two models through a common variable and show that if investment or diffusion is too high in the network context, chaotic boom-bust cycles can emerge. These quantitative considerations allow us to reproduce the boom-bust patterns seen in non-fungible token (NFT) transaction data and also have deep implications for the development of AI which we highlight, such as the arrival of a new AI winter.
ABSTRACT Despite the universal acknowledgment of financial profit expectations as an investment driver, environmental concern has been suggested as a factor influencing investors' decisions to purchase cryptocurrency. In this sense, this study investigates the impact of environmental information on investment allocation decisions to purchase different types of cryptocurrencies with different levels of environmental impacts (i.e., cryptocurrencies using Proof‐of‐Work (Bitcoin) and Proof‐of‐Stake (Ether) consensus algorithms). This study used an online survey involving 199 respondents in experimental groups (receiving environmental information before allocating decision) and control groups (receiving no environmental information before allocating decision) to split an imaginary fund into Bitcoin and Ether. No significant difference in allocating capital was found between the groups regardless of investment horizon, time of affiliation as a cryptocurrency investor, education level of the respondents, and perceived importance of environmental impacts. Possible explanations for this insensitivity are widespread prior knowledge about the environmental impact of Bitcoin, psychological reactance towards environmental information, and the assessed overall low perceived importance of environmental impact for investment decisions in cryptocurrencies. The lack of significant impact found in such an experimental study implies that environmental education alone cannot be sufficient to shift investor preferences. The findings offer initial insights into the impact of environmental awareness on cryptocurrency investment motivations and provide empirical evidence to understand cryptocurrency investment behaviors in the current research scene. The results suggest researchers and policymakers investigate further investors' motives while coming up with more restrictive policy instruments to mitigate the negative environmental impact of cryptocurrencies.
This study examines the nexus between Google Trends’ collective interest in specific keywords related to technological advancements utilized in design and the stock performance of major companies in design-related sectors. Specifically, the paper examines causality patterns between Google Trends keywords and stock prices of design companies, also employing multi-fractal detrended cross-correlation analysis, to test for long-term relationships. According to the results, varying impacts across keywords on stock prices are identified, with non-fungible token (NFT) exhibiting the greatest influence, followed by three-dimensional (3D) printing and computer-aided design, virtual reality (VR) displays a noteworthy impact, while artificial intelligence (AI) design and generative design indicate the least impact. The results also reveal persistent long-term relationships between the examined variables, with rich multifractal behavior indicating complex relationships, mostly balanced. The findings are important for policymakers and managers, necessitating close monitoring, especially of NFT, and for design companies to align strategies for market movements. • Examine how online interest in design technologies influences stock performance in design-focused industries. • Identify NFTs and 3D printing as major drivers of stock movements in the design market. • Reveal multifractal patterns indicating persistent, complex links between trend data and stock values. • Recommend tracking emerging design technologies for timely decision-making in investment and policy. • Provide data-driven insights for anticipating stock behavior in response to evolving digital interest.
The social need to transform the global monetary and financial system, which is at the stage of rapid self-destruction against the background of growing economic and digital inequality, global challenges, structural shifts and polycrisis revealed the stability of the cryptocurrency industry, which manifested itself in the public acceptance of cryptocurrency assets both as a financial product and as a new ideological doctrine, in accordance with the theory of diffusion of innovation. The impact of the cryptocurrency ecosystem modification on the configuration of the global monetary and financial system has become the subject of this study. The authors analyzed panel data, which is based on 79 socio-cultural, political, demographic and economic indicators in dynamics for 2014—2024. As a result, individual factors have been identified that stimulate the smart society to recognize cryptocurrency realities not from a technological basis, but from the perspective of unique consumer and functional properties. Emphasis is placed on the specificity of the formation of the cryptocurrency landscape: the bidirectional world movement “retail users ↔ institutional players”; the paradoxical effect of tight regulation; conflict between economic reality and “beliefs”, etc. It has been established that the speed and depth of the cryptocurrency assets adoption by society is variable to a greater extent not from objective factors, but from subjective characteristics that affect decision-making and express the need for a new configuration of the global monetary and financial system. A breakthrough direction of socio-economic development is the ideology and design adaptation of the cryptocurrency channel of cross-border money transfers, which ensures the transition to a human-centered ecosystem of a multipolar order with a unique currency transfer standard.
Abstract The Internet of Things Application (IOTA) is an innovative public blockchain system tailored for the Internet of Things (IoT), focusing on challenges such as micro-payments, concurrency, and scalability. However, its distributed ledger, which utilizes a directed acyclic graph (DAG) structure, is vulnerable to double-spending attacks. To mitigate this risk, we propose a countermeasure employing zero-determinant (ZD) strategies to encourage honest transactions among nodes. First, we analyze the game-theoretic interactions between the IOTA committee and nodes, modeling them as an iterated prisoner’s dilemma and deriving the conditions under which this dilemma holds. Next, we explore the conditions under which the IOTA committee can adopt ZD strategies, demonstrating the feasibility of unilaterally controlling node payoffs. Finally, theoretical analysis and experimental validation confirm the effectiveness of the proposed countermeasure, offering a novel game-theoretic solution for enhancing IOTA’s security.
L. Ali, M. Imran Azim, Jan Peters, Nabin B. Ojha · 6 authors
This paper presents the integration of third generation (Gen3) blockchain technology into a transactive energy market (TEM) for enabling secure and efficient peer-to-peer (P2P) energy trading among diverse participants, including prosumers with photovoltaic (PV) systems and battery energy storage systems (BESS), as well as electric vehicle (EV) owners. The primary motivation behind this work is to streamline transactive energy markets by reducing reliance on third-party intermediaries and enhancing energy self-sufficiency. The TEM utilizes blockchain's decentralized ledger for transparent transactions, with an advanced trading engine that matches participants' energy and price bids based on forecasted profiles and optimizes local energy use while minimizing costs. The major contributions of this study include: (1) the development of a scalable and efficient P2P trading framework leveraging Proof of Stake (POS) and Proof of History (POH) consensus mechanisms based on Gen3 blockchain, and (2) a comprehensive performance analysis resulting in considerable transaction time, throughput and cost reductions compared to traditional business-as-usual models. Numerical simulations reveal a 24% and 23% reduction in peak grid imports and exports, translating into financial and environmental benefits for all stakeholders, including prosumers, network operators, and retailers.
Mark Ng, Monica Law, Brian Wong Chi Bo, Michael Liang
Purpose This study explores key factors influencing individuals' intentions to invest in NFTs, focusing on personal innovativeness, reward sensitivity, knowledge, subjective norms, perceived value and perceived risk. The aim is to provide insights into what motivates investors within this emerging market, addressing a gap in the understanding of NFT adoption from an investor perspective. Design/methodology/approach An online survey collected data from 272 participants in China and Hong Kong. The research employs partial least squares-structural equation modeling (PLS-SEM) to assess the relationships between various individual, social and market factors and NFT investment intentions. Findings The results suggest that personal innovativeness, reward sensitivity, NFT knowledge, subjective norms and perceived value positively impact NFT investment intentions. Additionally, age and income moderate the effects of subjective norms and perceived value on investment intentions, highlighting demographic influences. Practical implications For practitioners, insights into investor motivators can inform strategies to promote NFT investments, such as promoting the high reward potential, enhancing investor knowledge, leveraging social proof and emphasizing NFTs' perceived value. For academics, the findings open pathways for further research into investor psychology and the evolving dynamics of NFT and traditional investment markets. Originality/value This study advances NFT literature by identifying determinants of NFT investment behavior, a relatively uncharted area. By incorporating theories from investment behavior and technology adoption, it provides a new framework to understand the psychological and social drivers specific to NFT investments.
Abstract Market efficiency assumes that prices in financial markets are perfectly informative and, therefore, it is not possible to design trading strategies that outperform the market. The concept of efficiency has important implications for financial stability and, consequently, for financial policies. If asset returns exhibit persistent or anti-persistent behavior, then predictability based on past returns might be possible, which would be a clear violation of the weak form of efficiency. Many studies rely on the Hurst exponent to evaluate the level of memory of financial returns, and the purpose of this paper is to show that long memory or anti-persistence of financial returns is not incompatible with the random walk model or the efficient market hypothesis (EMH). The use of the Hurst exponent to demonstrate the inefficiency of financial markets using common estimators is troublesome, especially when applied to financial returns, since values of $$\hat{H} \ne 0.5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>H</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mo>≠</mml:mo> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:math> are not evidence against the random walk model or the EMH. Moreover, the high variability of Hurst exponent estimates and their dependence on the chosen algorithm should motivate careful use of this tool. This study proposes a simple theoretical explanation and an extensive simulation study to show that $$\hat{H} \ne 0.5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>H</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mo>≠</mml:mo> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:math> for financial returns is perfectly compatible with the random walk model. As a robustness check, both the traditional rescaled range and the wavelet lifting algorithms are used. Applications to real data are also discussed to show that the empirical values of the Hurst exponent are in the range suggested by the simulations, providing evidence that over-reliance on the Hurst exponent could lead to erroneous rejection of the random walk model. Specifically, the paper presents an application to the daily returns of stock market indices (DJIA and S&P 500) over a period of more than 30 years and cryptocurrencies (Bitcoin and Ethereum) over a period of more than 5 years.
Purpose The purpose of this study is to investigate the primary determinants influencing the acceptance of generative artificial intelligence (GAI) adoption within Blockchain-enabled environments. Further research will examine the impact of GAI adoption on supply chain efficiency (SCE) through the enhancement of Blockchain. Design/methodology/approach Drawing on innovation diffusion theory (IDT), this study used partial least square structural equation modelling (PLS-SEM) to look into the hypotheses. The data were gathered via online questionnaires from employers of Chinese supply chain enterprises that have already integrated Blockchain. Findings The findings of this study demonstrate that relative advantages (RAs), compatibility, trialability and observability have a significant positive effect on GAI adoption, while complexity harms GAI adoption. Above all, the GAI adoption has significantly enhanced Blockchain, thus effectively improving SCE. Practical implications The outcomes from this study furnish enterprises and organizations with valuable insights to proficiently integrate GAI and Blockchain capability, optimize supply chain management and bolster market competitiveness. Also, this study will help accelerate the successful integration of business processes and attain Sustainability Development Goals 9, industrial growth and industrial diversification. Originality/value To the extent of the author’s knowledge, the current status of the GAI study remains largely exploratory, and there is limited empirical evidence on integrating Blockchain capability and GAI. This research bridges the knowledge gap by fully revealing the optimal integration of these two transformative technologies to leverage their potential advantages in supply chain management.
Two MEV builders now produce nearly 80\% of Ethereum blocks. Block builders have the ability to reorder transactions on the blockchain in a way that can be harmful to participants. We estimate they would pay in the aggregate nearly \$14 million per month to ensure that they remained in the first quartile of the block. Sandwich attacks, in which a transaction is front-run, are frequent, averaging more than one per block. Gas fees on these transactions pay for nearly 15\% of the MEV payments to the validator. These attacks have especially large marginal effects and skew the distribution. Reforms such as gas fee priority or private transaction pools might be helpful.
Jun Chen, Asma-Qamaliah Abdul-Hamid, Suhaiza Zailani
Although the potential of the blockchain has been extensively recognized by scholars and practitioners across multiple fields, research on its adoption in the framework of the circular economy (CE) is still scarce. In this context, this study extends the technology acceptance model (TAM) by integrating the technology–organization–environment (TOE) framework to holistically understand how technological perception factors (perceived usefulness and perceived ease of use) interact with organizational and environmental factors in influencing the intention to adopt the blockchain in the CE within the context of the Chinese automotive supply chain. Based on survey data from 305 respondents from Chinese automotive companies, the proposed hybrid TOE-TAM conceptual model was validated. The results indicate that, except for the effects of the knowledge management capability on the perceived ease of use and regulatory support on blockchain adoption intention, all of the other hypotheses are deemed significant. Moreover, by conducting an in-depth analysis of the evolution of blockchain adoption intention in the CE, this study not only deepens the understanding of how the technology is disseminated but also provides valuable insights to theory and practice within the Chinese automotive value chain.
George Bogdan Drăgan, Wissal Ben Arfi, Victor Tiberius, Aymen Ammari · 5 authors
This study fills a void in the literature on the intention to invest in sustainable cryptocurrency. It examines the behavioral antecedents of individuals' decision making in this expanding financial industry. A mixed-methods approach that combines partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) is used to gain an understanding of the factors that lead to acceptance of sustainable cryptocurrencies and the obstacles to investing in them. This study is valuable for investors, policymakers, and managers in the digital currency business, providing practical insights and strategic implications. The study's findings underscore the relevance of regulatory frameworks and government assistance in fostering the adoption of sustainable cryptocurrencies. The study emphasizes the importance of customer trust and sustainability in influencing the adoption of sustainable cryptocurrencies. It advances the theory of sustainable finance, technology adoption, and behavioral economics. The study's limitations and recommendations for future research offer a path to further the understanding of this developing topic. • This study uses PLS-SEM and fsQCA to analyze factors influencing sustainable cryptocurrency investments comprehensively. • PU, PEU, and PT significantly predict attitudes and behavioral intentions toward investing in sustainable cryptocurrencies. • The study shows ECS and SOS shape positive attitudes, while ENVS needs more awareness to become a key driver for investors. • The findings guide policymakers to improve regulations, highlight sustainable crypto benefits, and address concerns for wider adoption.
Abstract This chapter examines the insider–outsider dynamics shaping Web3 technologies as they navigate entrepreneurial ecosystems and the technology diffusion process. It establishes insiders as the developers, founders, and investor communities driving Web3 innovation, often operating in regulatory grey zones with a techno-solutionist mindset. In contrast, outsiders include institutions, policymakers, and the broader public reacting to Web3’s experimental nature and socio-technical novelty. The chapter situates Web3 within frameworks of technology adoption, socio-technical imaginaries, and models of diffusion. It highlights the tendency of insiders to overlook social nuances while pursuing rapid commercialisation and adoption. The chapter presents two case studies: the first examines the regulatory friction encountered by Ripple Labs and its digital asset, XRP; the second chronicles the rise and fall of Art NFTs, from their promise of empowering artists to their eventual decline due to legal uncertainties, environmental concerns, scams, and clashing community values. This decline mapped onto public disillusionment with the technology despite, and perhaps because of, its utopian techno-libertarian premise. The chapter argues that Web3 must navigate complex insider–outsider tensions while introducing disruptive innovations within existing socioeconomic structures. It concludes with policy recommendations spanning regulatory frameworks, consumer protection, responsible innovation, and public education to foster a more balanced and sustainable Web3 ecosystem.