Systematic review of usage patterns and acceptance of blockchain-based cryptocurrencies across diverse domains

Blockchain-based cryptocurrencies have garnered significant attention from academic and industry. However, systematic studies on cryptocurrency usage patterns and adoption across contexts are limited. Identifying factors and developing predictive models for cryptocurrency adoption remains challenging. This article conducts a systematic review with qualitative and quantitative (mixed) syntheses on the adoption of blockchain-based cryptocurrencies, adhering to PRISMA guidelines. From 579 initial articles, 124 were selected and classified into review-based, exploratory-based, and empirical-based categories. Exploratory articles examined global awareness and ownership of cryptocurrencies. Empirical articles were categorized into general, payment method, investment tool, transfer medium, and other contexts. The review reveals higher awareness and ownership of cryptocurrencies among young, educated males with proficient computer skills in both developed and developing nations. The Technology Acceptance Model (TAM) and its variants are the most frequently used in the surveyed articles. Key factors like Perceived Ease of Use (PEoU), Perceived Usefulness (PU), and Perceived Trust were extensively studied. Studies on payment methods mainly focused on the customer perspective, with limited attention to the service provider perspective. As an investment tool, factors like social influence, PU, financial literacy, facilitating conditions, and perceived risk were significant. For cryptocurrencies as a transfer medium, security and risk perceptions, performance and effort expectancy, and social influence were crucial. In other contexts, trialability, transparency, and cost efficiency drove adoption, with trust and usability being vital for cryptocurrency wallet usage. Additionally, the article proposes an integrative model combining TAM with technical, economic, personal, and environmental factors. The findings from this systematic review will guide future research in developing more comprehensive models for predicting the adopting of cryptocurrencies across various contexts.</p

Empowering the Adoption of Blockchain-based Cryptocurrency as a Payment Method: A User-Friendly Educational Approach

Organizations are adopting technological innovations to transform payment systems due to challenges with traditional methods, such as slow speed and high fees. These challenges have prompted a shift towards blockchain-based cryptocurrencies. However, cryptocurrency adoption for payments remains limited, especially in Saudi Arabia. This study adapts the Technology Acceptance Model (TAM) to explore cryptocurrency adoption through the Blockchain-based Cryptocurrency as a Payment Method in Saudi Arabia (BCAP-SA) model. Factors within the model are assessed using an experimental vignette-task methodology and surveys. A key component is an educational package, offering comprehensive materials to explain blockchain technology. The findings confirm the reliability of surveys. Most model factors are statistically significant in influencing users’ intention to use cryptocurrency. The study finds that Perceived Ease of Use (PEoU), Perceived Usefulness (PU), and Perceived Trust (PT) significantly impact participants’ intentions. Additionally, Low Transaction Fees (LTF) and Age (AG) are the most influential factors on the Technology Acceptance Model’s core constructs. Statistical analysis indicates that Decentralization (DE) and Anonymity (AN) were insignificant and thus excluded from the revised BCAP-SA model. These findings highlight the potential to enhance cryptocurrency adoption in Saudi Arabia. The study’s insights can guide strategies to promote wider cryptocurrency usage in the region</p

BCAP-SA—Towards a blockchain based cryptocurrency adoption model as a payment method in Saudi Arabia

Online transactions have grown to gain significant attention from various stake-holders in governments, business, and research practitioners all over the world. A decade ago, the first cryptocurrency transaction was executed to pave the way for a new era of online transactions. However, there is a relatively low adoption of blockchain-based cryptocurrency transactions as a payment method by service providers, organizations, and customers. Further, there is no study investigating the adoption of cryptocurrency technology as a payment method in Saudi Arabia. Therefore, this research proposes a new adoption model to investigate the ac-ceptance of a blockchain based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA). A thorough research of the literature has yielded only a few studies covering external factors over a range of technical, economic, personal, and environmental aspects in different parts of the world. Hypotheses have been developed for each factor which will be examined by both qualitative (interviews) and quantitative (surveys) methods. The proposed study aims to offer service providers with insights about which factors customers would concentrate on if they were to accept blockchain based cryptocurrency as a payment method

BCAP-SA—Towards a blockchain based cryptocurrency adoption model as a payment method in Saudi Arabia

Online transactions have grown to gain significant attention from various stake-holders in governments, business, and research practitioners all over the world. A decade ago, the first cryptocurrency transaction was executed to pave the way for a new era of online transactions. However, there is a relatively low adoption of blockchain-based cryptocurrency transactions as a payment method by service providers, organizations, and customers. Further, there is no study investigating the adoption of cryptocurrency technology as a payment method in Saudi Arabia. Therefore, this research proposes a new adoption model to investigate the ac-ceptance of a blockchain based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA). A thorough research of the literature has yielded only a few studies covering external factors over a range of technical, economic, personal, and environmental aspects in different parts of the world. Hypotheses have been developed for each factor which will be examined by both qualitative (interviews) and quantitative (surveys) methods. The proposed study aims to offer service providers with insights about which factors customers would concentrate on if they were to accept blockchain based cryptocurrency as a payment method

Predicting the usability of mobile applications using AI tools: The rise of large user interface models, opportunities, and challenges

This article proposes the so-called large user interface models (LUIMs) to enable the generation of user interfaces and prediction of usability using artificial intelligence in the context of mobile applications. To this end, we synergized an integrated framework for the effective testing of the usability of mobile applications following a selective review of the most influential models of mobile usability testing. Next, we identified and analysed 13 recent AI tools that generate user interfaces for mobile apps, and systematically tested these tools to identify their AI capabilities. Our striking findings demonstrate that current generative UI tools fail to address mobile usability attributes, such as efficiency, learnability, effectiveness, satisfaction, and memorability. Our large UI models' architecture proposes to leverage the capabilities of large language models, large vision models, and large code models to overcome the challenges of AI-driven UI/UX design and front-end implementations. This fascinating UI eco-system must be augmented with sufficient UI data and multi-sensory input regarding user behaviour to train the models. We anticipate LUIMs to create ample opportunities, like expedited frontend software development, enhanced personalised user experience, and wider accessibility of smart technologies. However, the research challenges hindering the UI generation and usability prediction of mobile apps include the seamless integration of complex generative AI models, semantic understanding of non-uniform visual designs, scarcity of UX datasets, and modelling of realistic user interactions.</p

Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges

We present a survey of machine learning works that attempt to organize the process flow of waste management in smart cities. Unlike past reviews, we focused on the waste generation and disposal phases in which citizens, households, and municipalities try to eliminate their solid waste by applying intelligent computational models. To this end, we synthesized and reviewed 42 articles published between 2010 and 2021. We retrieved the selected studies from six major academic research databases. Next, we deployed a comprehensive data extraction strategy focusing on the objectives of studies, trends of ML adoption, waste datasets, dependent and independent variables, and AI-ML-DL predictive models of waste generation. Our analysis revealed that most studies estimated waste material classification, amount of generated waste per area, and waste filling levels per location. Demographic data and images of waste type and fill levels are used as features to train the predictive models. Although various studies have widely deployed artificial neural networks (ANN) and convolutional neural networks (CNN) to classify waste, other techniques, such as gradient boosting regression tree (GBRT), have also been utilized. Critical challenges hindering the prediction of solid waste generation and disposal include the scarcity of real-time time series waste datasets, the lack of performance benchmarking tests of the proposed models, the reliability of the analytics models, and the long-term forecasting of waste generation. Our survey concludes with the implications and limitations of the selected models to inspire further research efforts.</p

Moisture computing-based internet of vehicles (IoV) architecture for smart cities

Recently, the concept of combining 'things' on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network. Shifting the computing towards the edge of the network reduces communication delays and provides various services instantly. However, both distributed (i.e., edge computing) and central computing (i.e., cloud computing) architectures suffer from several inherent issues, such as high latency, high infrastructure cost, and performance degradation. We propose a novel concept of computation, which we call moisture computing (MC) to be deployed slightly away from the edge of the network but below the cloud infrastructure. The MC-based IoV architecture can be used to assist smart vehicles in collaborating to solve traffic monitoring, road safety, and management issues. Moreover, the MC can be used to dispatch emergency and roadside assistance in case of incidents and accidents. In contrast to the cloud which covers a broader area, the MC provides smart vehicles with critical information with fewer delays. We argue that the MC can help reduce infrastructure costs efficiently since it requires a medium-scale data center with moderate resources to cover a wider area compared to small-scale data centers in edge computing and large-scale data centers in cloud computing. We performed mathematical analyses to demonstrate that the MC reduces network delays and enhances the response time in contrast to the edge and cloud infrastructure. Moreover, we present a simulation-based implementation to evaluate the computational performance of the MC. Our simulation results show that the total processing time (computation delay and communication delay) is optimized, and delays are minimized in the MC as apposed to the traditional approaches

An ensemble learning based classification approach for the prediction of household solid waste generation

With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed an ensemble learning technique that combines the advantages of (1) a hyperparameter optimization and (2) a meta regressor model to accurately predict the weekly waste generation of households within urban cities. The hyperparameter optimization of the models is achieved using the Optuna algorithm, while the outputs of the optimized single machine learning models are used to train the meta linear regressor. The ensemble model consists of an optimized mixture of machine learning models with different learning strategies. The proposed ensemble method achieved an R2 score of 0.8 and a mean percentage error of 0.26, outperforming the existing state-of-the-art approaches, including SARIMA, NARX, LightGBM, KNN, SVR, ETS, RF, XGBoosting, and ANN, in predicting future waste generation. Not only did our model outperform the optimized single machine learning models, but it also surpassed the average ensemble results of the machine learning models. Our findings suggest that using the proposed ensemble learning technique, even in the case of a feature-limited dataset, can significantly boost the model performance in predicting future household waste generation compared to individual learners. Moreover, the practical implications for the research community and respective city authorities are discussed

Data Vaults for Blockchain-Empowered Accounting Information Systems

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