Blockchain Papers

Hao Zhang, Tuğrul Daim, Yunqiu Zhang

No abstract is available for this record.

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.

Arvind Malhotra, Hugh M. O’Neill, Porter Stowell

No abstract is available for this record.

Dejian Yu, Tianxing Pan

In the past few years, blockchain technology has attracted more and more attention, and it has been widely used in various fields. But there is still a lack of relevant research reports on the innovation path identification of blockchain technology from a patent perspective. By downloading a total of 14,560 patents from Derwent Innovations Index and conducting main path analysis, this paper illuminates the overall picture of the development of blockchain for experts in this domain from a technology perspective. As an objective and quantitative network-based method, main path analysis identifies important patent, structural backbone and development trajectories in the blockchain development. This provides a reference for the scientific communities to understand the current state of blockchain technology, thereby illuminating future research.

Michael Wustmans, Thomas Haubold, Bennet Bruens

The evaluation of various innovation fields of an emerging technology as well as their potential impact on a certain market, region, industry, or target group is a part of an innovation manager's day-to-day business. Such evaluations are usually based on a combination of information from a variety of data sources, which are used to decide whether to invest in the advancement or adoption of a technology. With the aim of supporting this decision-making process, we combine different data sources to identify and evaluate innovation fields by semantically bridging trend and patent data. We apply our method in the context of blockchain technology, show how trend data can be used and operationalized to identify innovation fields, and illustrate how patent data can be used to evaluate these innovation fields. Our data reveals that trend and patent data complement each other and that hybrid or multihybrid approaches to evaluate a technology's development lead to additional insights for the systemic anticipation of future perspectives as well as research pathways of innovation fields.

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.

Victor Dostov, Pavel Pimenov, Pavel Shoust

No abstract is available for this record.

Olga Chereshnia

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.

Chen Zhu, Kazuyuki Motohashi

No abstract is available for this record.

Lili Matic, Natalie Packham, Wolfgang Karl Härdle

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.

Abdullah Alharbi, Osama Sohaib

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.

Alexey Mikhaylov

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.

Sachin Kamble, Angappa Gunasekaran, Prof Vikas Kumar, Amine Belhadi · 5 authors

No abstract is available for this record.

Carlos de Lamare Bastian-Pinto, Felipe Van de Sande Araujo, Luiz Eduardo Teixeira Brandão, Leonardo Lima Gomes

No abstract is available for this record.

Yanhao Wei, Anthony Dukes

This paper marries models of stochastic bubbles and the standard model of product diffusion to study the role of price bubbles in cryptocurrency adoption.

Tuğrul Daim, Kuei Kuei Lai, Haydar Yalçın, Fayez Alsoubie · 5 authors

No abstract is available for this record.

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.

Samuel Fosso Wamba, Maciel M. Queiroz

The emergence of Industry 4.0 has brought in its wake an important number of challenges and opportunities for organisations across the globe. To cope with such a fast-changing environment, organisations have been steadily implementing different types of technologies, and at different stages. One of the most disruptive and promising technology is blockchain, and its potential to transform various aspects of organisations’ business and operations, including the supply chain relationships, is tremendous. In line with the global research trend in this domain, this paper proposes a multi-stage model of adoption (intention, adoption, and routinisation stages), for a better understanding of blockchain diffusion across supply chains. We drew on the diffusion of innovations theory, the resource-based view, dynamic capability, the technology adoption model, and the institutional theory to propose a multi-stage model. We validated the model using PLS-SEM, which was applied on data collected in India and the U.S. Our results showed that, from one country to another, there are essential differences in the variables that determine blockchain innovation and in the stage of diffusion. Additionally, our proposed model provided a good explanation at all stages of blockchain diffusion. This study offers significant and valuable contributions in terms of theory and management.

Dejian Yu, Libo Sheng

No abstract is available for this record.

Seung Bum Park, Won Cheol Lee

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.

Tommy Koens, Pol Van Aubel, Erik Poll

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.

Davide Lasi, Lukas Saul

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.

Suhyeon Kim, Haecheong Park, Junghye Lee

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.