The dependence of successful social crowdfunding on investor trust: research on enhancing factors

This thesis examines the importance of trust creation in crowdfunding. Along the case of Go Parity, a social crowdlending platform, measures to strengthen the trust of private investors are assessed. Hence, GoParity’s past achievements and current challenges with regards to trust cre ation are analysed. A survey consisting of 367 respondents, and 14 interviews constitute the research. Accordingly, solutions are developed, evaluated, and translated into recommenda tions, accompanied by an implementation plan. Subsequently, the focus is narrowed down to greenwashing in impact investment as one potential obstacle towards investor trust creation. In this respect, challenges, as well as improvement potential are explored

FinTech and Bank Performance in Europe: A Text-mining Analysis

Treballs Finals del Màster de Recerca en Empresa, Facultat d'Economia i Empresa, Universitat de Barcelona. Curs: 2021-2022, Tutor: Martí SagarraEste estudio tiene como objetivo explorar el impacto de la adopcion de la tecnologia financera bancaria (Bank FinTech) en la industria bancaria. A traves de un analisis de mineria de textos del informe anual de 48 bancos comerciales europeos de 2015 a 2021, este articulo define el grado de Bank FinTech para cada banco y examina la relacion entre el Bank FinTech y el desempeno bancario medido por el sistema CAMEL. Los resultados indican que el Bank FinTech esta relacionado positivamente con la suficiencia de capital, la calidad de los activos, la eficiencia de la gestion, el poder de generar ganancias y la liquidez. En general, la adopcion de Bank FinTech tiene un impacto positivo en el desempeno del banco. Este estudio es el primero en utilizar el analisis de mineria de texto para definir el indice de Bank FinTech para los bancos comerciales europeos y examinar su relacion utilizando el sistema CAMEL

IoT trust and reputation: a survey and taxonomy

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030. To maximize the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices features, such as devices role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organize the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics, e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin

Fuzzy Rankings for Preferences Modeling in Group Decision Making

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Although fuzzy preference relations (FPRs) are among the most commonly used preference models in group decision making (GDM), they are not free from drawbacks. First of all, especially when dealing with many alternatives, the definition of FPRs becomes complex and time consuming. Moreover, they allow to focus on only two options at a time. This facilitates the expression of preferences but let experts lose the global perception of the problem with the risk of introducing inconsistencies that impact negatively on the whole decision process. For these reasons, different preference models are often adopted in real GDM settings and, if necessary, transformation functions are applied to obtain equivalent FPRs. In this paper, we propose fuzzy rankings, a new approximate preference model that offers a higher level of user‐friendliness with respect to FPRs while trying to maintain an adequate level of expressiveness. Fuzzy rankings allow experts to focus on two alternatives at a time without losing the global picture so reducing inconsistencies. Conversion algorithms from fuzzy rankings to FPRs and backward are defined as well as similarity measures, useful when evaluating the concordance between experts’ opinion. A comparison of the proposed model with related works is reported as well as several explicative examples