Pedro Campos

Abstract
Decred is a cryptocurrency with its own blockchain and has several similarities with bitcoin but implements a governance model that resembles a company with thousands of investors. These stakeholders invest their coins, receive the right to direct the project as they see fit and are rewarded for doing so. Everyone else not invested may use the coin as means of exchange, trading it for goods or services or consuming other services provided by the blockchain as the digital notary. This paper investigates how Decred project created its own version of money and implemented security measures to improve governance and remove trusted third parties from money issuance and e-voting. This topic is particularly relevant to understand how blockchain technologies improve governance and avoid the tyranny of the majority. In order to reach our goal, we use multi-agent simulation and statistical modelling to verify to what extent Decred is capable of providing a predictable, scarce, trustworthy digital asset. We show that Decred increased blockchain security with its hybrid proof-of-work+proof-of-stake (PoW + PoS) security mechanism, making an attack more expensive. © 2025 Inderscience Enterprises Ltd.

Abstract
In many e-commerce platforms user communities share product information in the form of reviews and ratings to help other consumers to make their choices. This study develops a new theoretical framework generating a bipartite network of products sold by Amazon.com in the category musical instruments, by linking products through the reviews. We analyze product rating and perceived helpfulness of online customer reviews and the relationship between the centrality of reviews, product rating and the helpfulness of reviews using Clustering, regression trees, and random forests algorithms to, respectively, classify and find patterns in 2214 reviews. Results demonstrate: (1) that a high number of reviews do not imply a high product rating; (2) when reviews are helpful for consumer decision-making we observe an increase on the number of reviews; (3) a clear positive relationship between product rating and helpfulness of the reviews; and (4) a weak relationship between the centrality measures (betweenness and eigenvector) giving the importance of the product in the network, and the quality measures (product rating and helpfulness of reviews) regarding musical instruments. These results suggest that products may be central to the network, although with low ratings and with reviews providing little helpfulness to consumers. The findings in this study provide several important contributions for e-commerce businesses' improvement of the review service management to support customers' experiences and online customers' decision-making.

Abstract
This study investigates the daily price patterns and behavioral similarities among cryptocurrencies, focusing on two key research questions: (1) Do cryptocurrency prices vary consistently throughout the day? (2) Can cryptocurrencies be meaningfully grouped based on their behavioral patterns? Using Gaussian mixture models (GMMs), we analyze the opening, closing, high, and low prices of a broad range of cryptocurrencies. The findings reveal that while opening prices exhibit uniform patterns, closing, high, and low prices show more complex, multi-component behaviors, reflecting diverse market dynamics throughout the day. Consensus clustering identifies four distinct cryptocurrency clusters, each demonstrating unique price behaviors, challenging the notion of cryptocurrencies as a homogeneous group. The results suggest that cryptocurrencies behave as differentiated financial products, influenced by factors such as volatility, adoption, and technology. These findings contribute to the understanding of cryptocurrency market dynamics and have implications for investment strategies, risk management, and regulatory approaches. © 2025 The Author(s). Intelligent Systems in Accounting, Finance and Management published by John Wiley & Sons Ltd.

Abstract
Accurately determining someone's personality is complex and often requires lengthy questionnaires, which are subject to social desirability bias, or a great amount of users' interactions with the system. Also, most existing research focuses on broader personality dimensions rather than more granular personality traits, which better characterize a person. In this work, we propose to implicitly acquire the users' granular personality traits using mobile short-duration serious games, in < 5 min and in a single play interaction, namely cautiousness and achievement-striving as concept proof, to replace personality questionnaires. Two platform mobile games were developed, one for each trait, Which Way and Time Travel, respectively. Then, an experiment with real participants (n = 100) was conducted. Time Travel proved to be capable of detecting achievers (get all coins, diamonds, and better scores), while Which Way couldn't effectively measure cautiousness, although following hard paths could be related to less cautious persons. As expected, significant correlations with other personality traits were also found (15 out of 30), such as anger, modesty, excitement seeking, and adventurousness. Contrary to other types of (serious) games, the results show short-duration mobile minigames are a viable way of unobtrusively determining the users' granular personality, being the path to replacing personality questionnaires.

Abstract
As we delve into how technology enhances supply chain management efficiency and tackles specific e-business challenges, we must recognize the critical synergy with recommendation systems. These systems align with digital transformation goals, enhancing customer experiences, enabling data-driven decisions, promoting innovation, and embracing a customer-centric approach. During the 2020 COVID-19 surge, e-commerce experienced increased activity, highlighting the significance of recommendation systems in forecasting new purchases. This chapter introduces a novel approach to understanding customer–product interactions through multilayer bipartite networks, employing a hybrid recommendation system with k-means and weighted slope one algorithms. This approach enhances clarity, explainability, and information gains, aiding tasks like inventory optimization. The study concludes that the model’s predicted results differ from the actual ratings and that the system is effective in improving decision-making processes and customer recommendations. © 2025 selection and editorial matter, Adelaide Martins and Carolina Machado.