The formation of blockchain industrial ecology can help improve the open and efficient value synergy network, and this paper seeks to clarify the value relationship among industrial units and the trend of synergistic evolution of blockchain industrial ecosystem. Based on value co-creation theory, the blockchain industrial ecosystem synergy evolution process is analyzed, and the composite system synergy model is used to empirically investigate the evolution synergy of China’s blockchain industrial ecosystem from 2015 to 2020. The results show that: although the development level of China’s blockchain industry ecosystem continues to improve, it is still at a low level, and the policy-driven effect is obvious. There is a large disparity in the orderliness level of each subsystem of the blockchain industry ecosystem, and the industrial integration and application implementation are in a good situation, while the blockchain enterprises, located in the value pivot subsystem, are in a relatively tough position, and China’s blockchain industry ecosystem is overall in a state of reconciliation and has not yet formed a synergistic effect. Handling the synergistic relationship between the government, the market and other value-supporting units, and blockchain enterprises is the top priority for further promoting the synergistic evolution of the blockchain industry ecosystem.
Sinan Erdoğan, Maruf Yakubu Ahmed, Samuel Asumadu Sarkodie
Abstract When Bitcoin (BTC), the first pioneering cryptocurrency was released in 2009, it was considered as an apolitical currency. Besides, the possible effect of BTC and other cryptocurrencies on either financial markets or transactions has been widely discussed. However, the environmental effects of cryptocurrency demand have been ignored. Here, this study examines the nexus between cryptocurrencies and environmental degradation by employing standard and asymmetric causality methods. The Toda-Yamamoto and bootstrap-augmented Toda-Yamamoto test results reveal Bitcoin and Ethereum (ETH) excluding Ripple (XRP) have causal effects on environmental degradation. The Fourier-augmented Toda-Yamamoto test results show causal effects running from Bitcoin and Ripple to environmental degradation, whereas no causal effect runs from Ethereum to environmental degradation. The asymmetric causality shows causal effects from the positive shock of Bitcoin demand, negative shocks of Ripple and Ethereum demands to positive shocks of environmental degradation. Further discussions and policy implications are provided in the relevant sections of this study.
The global economy is under great shock again in 2020 due to the COVID-19 pandemic; it has not been long since the global financial crisis in 2008. Therefore, we investigate the evolution of the complexity of the cryptocurrency market and analyze the characteristics from the past bull market in 2017 to the present the COVID-19 pandemic. To confirm the evolutionary complexity of the cryptocurrency market, three general complexity analyses based on nonlinear measures were used: approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv complexity (LZ). We analyzed the market complexity/unpredictability for 43 cryptocurrency prices that have been trading until recently. In addition, three non-parametric tests suitable for non-normal distribution comparison were used to cross-check quantitatively. Finally, using the sliding time window analysis, we observed the change in the complexity of the cryptocurrency market according to events such as the COVID-19 pandemic and vaccination. This study is the first to confirm the complexity/unpredictability of the cryptocurrency market from the bull market to the COVID-19 pandemic outbreak. We find that ApEn, SampEn, and LZ complexity metrics of all markets could not generalize the COVID-19 effect of the complexity due to different patterns. However, market unpredictability is increasing by the ongoing health crisis.
Konstantinos Stylianou, Leonhard Spiegelberg, Maurice Herlihy, Nic Carter
When network products and services become more valuable as their userbase grows (network effects), this tendency can become a major determinant of how they compete with each other in the market and how the market is structured. Network effects are traditionally linked to high market concentration, early-mover advantages, and entry barriers, and in the market they have also been used as a valuation tool. The recent resurgence of Bitcoin has been partly attributed to network effects, too. We study the existence of network effects in six cryptocurrencies from their inception to obtain a high-level overview of the application of network effects in the cryptocurrency market. We show that, contrary to the usual implications of network effects, they do not serve to concentrate the cryptocurrency market, nor do they accord any one cryptocurrency a definitive competitive advantage, nor are they consistent enough to be reliable valuation tools. Therefore, while network effects do occur in cryptocurrency networks, they are not (yet) a defining feature of the cryptocurrency marketas a whole.
Ibrahim Almarashdeh, Kamal Eldin Hassan Ibrahim Eldaw, Mutasem K. Alsmadi, Fahad AlGhamdi · 8 authors
Bitcoin is a decentralized system that tries to become a solution to the shortcomings of fiat and gold-based currencies. Considering its newness, the adoption level of bitcoin is yet understood. Hence, several variables are proposed in this work in examining user perceptions regarding performance expectancy, effort expectancy, trust, adoption risk, decentralization and social influence interplay, with the context of user’s future expectation and behavioral intentions to use bitcoins. Data were gathered from 293 completed questionnaire and analised using AMOS 18. The outcomes prove the sound predictability of the proposed model regarding user’s future expectations and intentions toward bitcoins. All hypotheses were supported, they were significantly affecting the dependent variables. Social influence was found as the highest predictor of behavioral intention to negatively utilize bitcoins. The significant impact of social influence, adoption risk and effort expectancy which affect behavioral intention to use bitcoins the most, are demonstrated in this study. Bitcoins should thus, present an effective, feasible and personalized program which will assist efficient usage among users. Additionally, the impacts of social influence, adoption risk and perceived trust on behavioral intention to utilize new technology were compared, and their direct path was tested together, for the first time in this context.
Abstract The cryptocurrency market has been described as revolutionary due to the constant technological evolution and innovation that the blockchain technology provides. Leading many to believe that this could be the next step for the human race, just like how fiat currency replaced gold. Cryptocurrencies were originally created to be a form of savings or income for the unbanked, reduce costs and energy consumption, for a means of data transparency and to remove financial intermediaries. It is undeniable that the cryptocurrency market has created a divide of opinions, as some look to explore the market further while others reject the thought of adopting this innovative technology completely. This study focuses on the perception and intention to use cryptocurrencies. Diving into previous literature about the adoption of cryptocurrencies and new technologies. Highlighting key factors that can affect an individual’s perception and gaps in the literature that need to be explored further. A quantitative approach was used to gather data from 102 participants. The findings indicated that performance and effort expectancy as the most influential variables for cryptocurrency adoption, as people seek understanding as what benefits cryptocurrencies can provide for them when they feel incapable of using the innovative technology.
Mario Arias-Oliva, Jorge de Andrés Sánchez, Jorge Pelegrín Borondo
This paper assesses the variables influencing the expansion of cryptocurrency (crypto for short) use in households. To carry on the study we apply a consumer-behavior focus and so-called fuzzy set Qualitative Comparative Analysis (fsQCA). In a previous research, that was grounded on Unified Theory of Acceptance and Use of Technology (UTAUT) and Partial Least Squares (PLS), we found that main factors to explain the intention to use of cryptos by individuals were performance expectancy (in fact, it was the main factor), effort expectancy and facilitating conditions. We did not found evidences about the relevance of social influence, perceived risk and financial literacy. This study revisits these results by applying fsQCA instead PLS. Empirical research on factors influencing cryto use is relatively scarce due to the novelty of blockchain techs, so the present paper expands the literature on this topic by using an original analytical tool in this context. The main contribution of this paper consists in showing empirically that fsQCA provides a complementary and enriching perspective to interpret data about the use of cryptos. We obtain again that the most relevant factor to explain the intention of using cryptocurrencies is perceived expectancy and that also effort expectancy and facilitation conditions are relevant. But also fsQCA has allowed us discovering that despite social influence, perceived risk and financial literacy were not significant in the PLS model, they impact on the intention to use cryptocurrencies when are combined with other factors. Social influence acts as an “enable factor” for the rest of explanatory variables and it is linked positively with intention to use cryptos. Also financial literacy is relevant because its lack is a sufficient condition for the non-acceptance of that blockchain tech. Likewise we have checked that perceived risk influences the intention of using cryptos. However, this influence may be positive or negative depending of the circumstances.
Wei Sun, Alisher Tohirovich Dedahanov, Ho Young Shin, Wei-Ping Li
Blockchain-based loan system can be summed up as: information exchange between various government departments; information exchange between enterprises and various financial institutions; detection of the actual use of loans in the form of encrypted currency. This technology is supposed to reduce a lot of financing costs for SMEs on average. Therefore, this research extends complexity theory to discover the factors that affect the use of Blockchain loan systems by SMEs. Complexity, perceived risk, perceived fairness and reward sensitivity prove to have significant effects on usage intention. Complexity proves to have moderating effects on other relationships. This research may contribute to the system performance improvement and provide opportunities for SMEs to share information with financial institutions or individuals around the world, thereby providing investors with equal opportunities for competition.
Chengyue Yu, M. Prabhu, Mahendar Goli, Anoop Kumar Sahu
Nowadays, Blockchain Technology (BCT) is contributing toward addressing the challenges of complex industrial systems (CISs). The BCT reduces the complexity of cash data storage as well as retrieval system of finance, marketing, supply chain, inventory, and other departments. The objective of the present study is to investigate the factors, which affect the intention of professionals to adapt the BCT in the CISs by using an extension of the technology acceptance model. To fulfill the research objective, a theoretical research model is constituted by multiple hypotheses (H1–H6), i.e., perceived usefulness, perceived ease of use, perceived innovativeness, knowledge, risk, and trust after conducting the relevant literature survey in the context of BCT. Next, each hypothesis is tested by exploring the survey data of a sample of 287 professionals of different BCT user’s companies such as retailing, e‐commerce, manufacturing, and construction. Survey data is analyzed by executing the structural equation modeling with AMOS software. The factors and latent constructs loadings, reliability, convergent, discriminant, model fit‐measurement, structural model, and the path analysis are conducted. The results reveal that the H1, H2, and H4–H6 dropped the positive impact and effect on professionals’ intention to use the BCT in CISs. But, H3 has no effect for enhancing the intention of professionals to use BCT.
With the increasing use of information technologies (IT) their opportunities to ensure environmental sustainability and the risks of their widespread adoption are growing. And if the possibilities have been studied well enough, then the risks have been paid attention to relatively recently. However, awareness of these risks is becoming increasingly important with the spread of technologies. Currently, there are already hundreds of cryptocurrencies, and the technological basis for many of these currencies is the blockchain—a digital ledger of transactions. This article has assessed the environmental burden of mining and supporting transactions in the cryptocurrency market in Russia using CO2-equivalent. For this, for the first time, the amount of electricity consumed to support cryptocurrency transactions in Russia was calculated, data on the largest cryptocurrency mining centres were collected and systematized, and the main factors for the placement of both large and small private farms were determined. Based on the collected data a map of the spread of mining centres in Russia was created. Our analysis showed that on average, 2.977 million tons of CO2 equivalent are emitted in Bitcoin production in Russia, and the total emissions from cryptocurrency mining in Russia are 4.466 million tons of CO2 equivalent. Based on our data on environmental damage, we believe that when deciding on the use of blockchain technology, not only its capabilities should be taken into account, but also an assessment of the ratio of potential benefits and impact on the environment. A systematic understanding of interrelated direct and indirect impacts is needed to make decisions on the use of blockchain, since the technology shows itself as potentially one of the most energy and resource intensive.
The cryptocurrency market is volatile, non-stationary and non-continuous. Together with liquid derivatives markets, this poses a unique opportunity to study risk management, especially the hedging of options, in a turbulent market. We study the hedge behaviour and effectiveness for the class of affine jump diffusion models and infinite activity Levy processes. First, market data is calibrated to stochastic volatility inspired (SVI)-implied volatility surfaces to price options. To cover a wide range of market dynamics, we generate Monte Carlo price paths using an SVCJ model (stochastic volatility with correlated jumps), a close-to-actual-market GARCH-filtered kernel density estimation as well as a historical backtest. In all three settings, options are dynamically hedged with Delta, Delta-Gamma, Delta-Vega and Minimum Variance strategies. Including a wide range of market models allows to understand the trade-off in the hedge performance between complete, but overly parsimonious models, and more complex, but incomplete models. The calibration results reveal a strong indication for stochastic volatility, low jump frequency and evidence of infinite activity. Short-dated options are less sensitive to volatility or Gamma hedges. For longer-dated options, tail risk is consistently reduced by multiple-instrument hedges, in particular by employing complete market models with stochastic volatility.
Today’s world is increasingly dependent on technology directly or indirectly. The rapid technological advancement has impacted people to adopt the technology. As cryptocurrency recently commenced, few studies have attempted to investigate this use of technology. In this study, the technology readiness aspects- Optimism, Innovativeness, Discomfort, and Insecurity are used to understand the people’s adoption of cryptocurrency. A multi-approach of Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Deep learning Artificial Neural Network (ANN) analysis was performed. Deep learning Artificial Neural Network (ANN) analysis was performed to complement PLS-SEM findings and predict higher accuracy. This study shows that technology readiness dimensions - Optimism, Innovativeness, Discomfort, and Insecurity have meaningful relationships with cryptocurrency adoption.
The paper focuses on the analysis of the cryptocurrency open innovation market to predict sustainable growth in the future. The nature of cryptocurrencies ‘development leads to the rapid increase in their popularity and spread of trading at this new market. The high volatility of these assets is encouraging to understand and predict their price in ever changing market environment. The paper proposed the pool complexity approach to choose optimal technology using social activity in the internet, trading parameters, technical indicators and other cryptocurrency data. According to the results of the analysis, the most effective and promising cryptocurrency is EOS cryptocurrency, which has the lowest complexity and commission level among the analyzed digital currencies and allows you to implement third-party applications in the system.
Nazir Ullah, Waleed S. Alnumay, Waleed Mugahed Al-Rahmi, Ahmed Ibrahim Alzahrani · 5 authors
In developed nations, the advent of distributed ledger technology is emerging as a new instrument for improving the traditional system in developing nations. Indeed, adopting blockchain technology is a necessary condition for the coming future of organizations. The distributed ledger technology provides better transparency and visibility. This study investigated the features that may influence the behavioral intention of energy experts to implement the distributed ledger technology for the energy management of developing countries. The proposed model is based on the Technology Acceptance Model construct and the diffusion of the innovation construct. Based on a survey of 178 experts working in the energy sector, the proposed model was tested using structural equation modeling. The findings showed that perceived ease of use, perceived usefulness, attitude, and cost saving had a positive and significant impact during the blockchain technology adoption. However, innovativeness showed a positive effect on the perceived ease of use whereas an insignificant impact on the perceived usefulness. The present study offers a holistic model for the implementation of innovative technologies. For the developers, it suggest rising disruptive technology solutions.
This paper proposes a blockchain-based automated frequency coordination system (BAFCS) for secure and reliable spectrum sharing without causing any harmful interference to an existing system. For the exact assessment of whether the incumbent is interfered with by the spectrum sharer, the received signal strength (RSS) associated with the incumbent should be measured with sufficient accuracy at every location within the area of interest. However, since it requires brute force to carry out empirical measurements around an entire region, to lessen the burden, only the confined portion of the RSSs associated with the incumbent as a kind of primary user are observed and the omitted residuals are conventionally estimated by carrying out the well-known Kriging interpolation with regard to the geostatistical characteristics. This paper proposes a frequency coordination system capable of identifying whether a requested frequency band can be eligible for spectrum sharing while exchanging adequate information over blockchain network to confirm the usability. This paper proposes the Support Vector Machine (SVM)-based Kriging interpolation for recapitulating the radio environment map (REM) when only a fraction of the RSS measurements is acquired by the voluntary sensing participant (VSP). The nonparametric modeling approach for variograms proposed in this paper was determined to have a vital role in making a confident decision regarding spectrum sharing. The simulation result confirmed the effectiveness and the superiority of the proposed BAFCS with several affirmative features, such as enabling the consensus-based approval of spectrum sharing, the secure transaction of the information, and reliable assurance of no harmful interference.
Open access
Distributed Sensor Networks and Detection Algorithms
Summary There has been a huge increase in interest in blockchain technology. However, little is known about the drivers behind the adoption of this technology. In this article we identify and analyze these drivers, using six real‐world and representative scenarios. We confirm in our analysis that blockchain is not an appropriate technology for some scenarios, from a purely technical point of view. The choice for blockchain technology in such scenarios may therefore seem as an irrational choice. However, our analysis reveals that there are nontechnical drivers at play that drive the adoption of blockchain, such as philosophical beliefs, network effects, and economic incentives. These nontechnical drivers may explain the rationality behind the choice for blockchain adoption.
The mining of bitcoin is modeled using a system dynamics model that represents both the mechanism of coin creation and the adjustment of the network hash rate based on the economic incentive of mining. The results show that the past evolution of the network hash rate can be explained, to a large extent, by an efficient market hypothesis applied to the mining of blocks. The possibility of a decreasing trend in the network hash rate from the halving event of May 2020 is exposed, implying that the network may be close to ’peak hash’ if the price of bitcoin and the revenues from transaction fees will be insufficient to cover the operational expenditures of mining.
Blockchain has become one of the core technologies in Industry 4.0. To help decision-makers establish action plans based on blockchain, it is an urgent task to analyze trends in blockchain technology. However, most of existing studies on blockchain trend analysis are based on effort demanding full-text investigation or traditional bibliometric methods whose study scope is limited to a frequency-based statistical analysis. Therefore, in this paper, we propose a new topic modeling method called Word2vec-based Latent Semantic Analysis (W2V-LSA), which is based on Word2vec and Spherical k-means clustering to better capture and represent the context of a corpus. We then used W2V-LSA to perform an annual trend analysis of blockchain research by country and time for 231 abstracts of blockchain-related papers published over the past five years. The performance of the proposed algorithm was compared to Probabilistic LSA, one of the common topic modeling techniques. The experimental results confirmed the usefulness of W2V-LSA in terms of the accuracy and diversity of topics by quantitative and qualitative evaluation. The proposed method can be a competitive alternative for better topic modeling to provide direction for future research in technology trend analysis and it is applicable to various expert systems related to text mining.
Cases of introducing token economy in designs of ICT services are increasing. Users in the early stages of the service are expected to participate in and be active in the service by expecting future price increases in that cryptocurrency. However, the volatility of cryptocurrencies is always intense, and the large volatility may cause users to be more interested in price changes than service activities, which diminishes the incentives for the service activities. In this study, in order to dampen the volatility of cryptocurrencies at the initial stage of their service launch, we assume the case where the service providers make bids to suppress the price changes based on the funds obtained from ICO, and conduct analysis using simulations in artificial market. In order to reproduce the actual price movement in the artificial market, we built an agent model that has the same stylized facts as the price movement of newly listed cryptocurrencies. Then, we introduced a price stabilization agent, and obtained a parameter set that reduces price volatility while suppressing the change in the slope of a simple linear regression compared to the original state using an optimization method. As a result, by introducing the price stabilization agent, we found a parameter set that can reduce the standard division of percentage changes by about 14% from the original price movement, and keep the slope of the simple linear regression trend at a 3.5% change.
Recently, the application of blockchain to the setting, management, and trading of the energy system has formed an innovative technology and has attracted a lot of attention from industry, academia, and research. In this study, we use patent analysis technology to explore the development trends of the energy system with blockchain technology. During the patent analysis process, this study makes corresponding analysis charts, such as patent application numbers over time, patent application numbers for main leading countries, applicants, patent citations, international patent classification (IPC), and life cycle. Relative research and design (R&D) capability of the top ten applicants is estimated and the cluster map of the technology is obtained. The technical features of the top five IPC patent applications are related to the cluster map to show the development of energy blockchain technology. Through this paper, first, the basics of the blockchain and patent analysis are illustrated and, moreover, the reason why and how blockchain technology can be combined with the energy system is also briefly described and analyzed. The results of the patent analysis of energy blockchain technology indicate that the United States leads the way, accounting for more than half of the global total. It is also interesting to note that the participants are not from traditional specific fields, but included electric power manufacturers, computer software companies, e-commerce companies, and even many new companies devoted to blockchain technology. Walmart Apollo, LLC and International Business Machines Corporation (IBM) have the highest number of patent applications. However, Walmart Apollo, LLC ranks first with a greater number of inventors of 36, an activity year of 2 years, and a relative R&D capability of 100%. IBM ranks second with an activity year of 3 years and a research and development capability of 91%. Among various applicants, IBM and LO3 energy started earlier in this field, and their patent output is also more prominent. The IPC is mainly concentrated in G06Q 50/06, which belongs to the technical field of the setting and management of the energy system including electricity, gas, or water supply. Currently, most projects are in the early development stages, and research on key areas is still ongoing to improve the required scalability, decentralization, and security. Thus, energy blockchain technology is still in the growth period, and there is still considerable room for development of the patent in the later period. Moreover, it is suggested that the novel communication module such as the combination of the consortium blockchain and the private blockchain cold also provide their own advantages to achieve the purpose of improving system performance and efficiency.
Tobias Riasanow, Lea Jäntgen, Sebastian Hermes, Markus Böhm · 5 authors
Abstract Digital transformation is continuously changing ecosystems, which also forces established companies to re-evaluate their value proposition. However, only transformations of single ecosystems have been studied. Therefore, this work targets to examine the similarities of digital transformation in five platform ecosystems: automotive, blockchain, financial, insurance, and IIoT. For our analysis, we combine the strengths of conceptual modeling using e3 value with a cluster analysis based on text mining to identify similarities in the respective ecosystems. As a result, we identified 15 clusters. Cluster 01 is the core cluster, containing the roles of organizations from all five ecosystems. Cluster 02–05 are intertwined, as they include roles from at least two ecosystems. Clusters 06–15 are ecosystem-specific that only include roles found in one ecosystem. Scholars and practitioners can use these clusters when analyzing or building a new platform ecosystem, or transforming a traditional ecosystem towards a platform ecosystem.
This paper suggests a model for optimization of societal carbon footprints one person at a time through the decentralization of electricity use and accounting. Our model describes steps involved with developing a decentralized accounting system considering electricity as a "credit product". While describing the basic characteristics of both schemes, we also emphasize capabilities of the proposed model for reducing carbon footprints from other societal choices, for example, purchasing water (energy-water nexus), managing waste, or designing sustainable transportation systems. A simple yet complex model involved with familiar societal financial systems' rules and routines is proposed for achieving a resilient, sustainable, and prosperous future. The proposed model calls for creating a dynamic society (as a system) that can be efficiently adopted to take on challenges threatening the function, survival, and future developments of the societies.
In econophysics, statistical-physics techniques are used to model economical systems. In this thesis, we investigate the entropy and the Computational Information Density (CID) of the Bitcoin blockchain. The CID is defined as the compression ratio of some particular algorithm when applied to the raw data of the state of the system. It is related to entropy as both CID and entropy are measures of information.\nWe find a strong correspondence between the CID and entropy for the Bitcoin blockchain, where features are similar, but without one being a clear function of the other. This can be explained by intercorrelations between one agent and the next, which the entropy does not count. We also calculate some correlations to see if the CID and the entropy have some predictive power for the price, and we find a small correlation, but very small in comparison to the predictive power of the price itself.\nThese results the power of the CID-entropy correspondence and how the Bitcoin blockchain may be used as a useful large-scale toy model for econophysics. We anticipate that these results can be used for a further look into the CID-entropy relation, as the similarities are visible but there is no exact correspondence. Besides this, these results can form a basis for a further look into the predictive power of the CID or the entropy for the price.