1,721,051 research outputs found
A model of trust in Central Bank Digital Currency (CBDC) in Brazil: how trust in a two-tier CBDC with both the central and retail banks involved changes consumer trust
No full textCentral bank digital currencies (CBDC) have been implemented by some countries and trialled by many more. The consumer has an increasing range of financial services to choose from including decentralised blockchain-based cryptocurrencies. A CBDC may use blockchain technology, but it is centralized, so the institutions that support it play an important role. Despite the centralised top-down nature of this financial technology, it still needs to be adopted so the consumer’s perspective, particularly their trust in it, is very important. Each CBDC implementation can be different, and each country’s context can be different, therefore it is important to understand each case separately. This research models the Brazilian consumer’s trust in their two-tier CBDC, where the central bank and the retail banks retain their current role. The six ways to build trust in a CBDC, identified by previous research in a different region, are supported for this case also. These are: (a) Trust in government and central bank offering the CBDC, (b) expressed guarantees for those using it, (c) the favourable reputation of other active CBDCs, (d) the CBDC technology, the automation and limited human involvement necessary, (e) the trust building features of the retail bank’s CBDC wallet app, and (f) the privacy features of the retail bank’s CBDC wallet app and back-end processes
The Impact of Extended Global Ransomware Attacks on Trust: How the Attackers Competence and Institutional Trust Influence the Decision to Pay
Full text linkThe standardization, inter-connectivity and pervasiveness of information systems, combined with the increasing ability to collect and utilize data, enhance the value they offer a user. These strengths however can also be turned into a weakness and vulnerability by ransomware (RW). RW can utilize the functionality of current systems both to infect them but also to increase the magnitude of the attack. This research proposes a model of the impact of the RW attack on the user’s trust, which in turn has an effect on their decision to pay the ransom or follow the guidance from the relevant institutions. The model shows that the effectiveness of the attack, the trust in the competence of the attacker and ransomware demands that are reasonable and easy to fulfill, positively influence the intention to pay the ransom. The initial institutional response, institutional trust and institutional solution influence the intention to follow the institutional guidance
The business models of NFTs and Fan Tokens and how they build trust
No full textPurpose: the sale of NFTs and the interest in them has “exploded” recently. While there is a lot of information on them, it is not clear what final form they will take. There are several reasons to be uneasy, such as the prevalence of scams, but there are also reasons for optimism and confidence in NFTs. First, they solve the problem of how to own digital assets. Additionally, some of the more reliable and proven cryptoasset exchanges are offering them. However, this innovation will have difficulties reaching a wider audience until more clarity is achieved on two main issues. Therefore, this study aims to clarify what the NFT business models are, and how do they build trust.Design/methodology/approach: this research attempts to identify the NFT business models with case study analysis in three stages. The first stage is a focus group, followed by 16 short case vignettes and finally four longer case studies.Findings: the findings show that there are four NFT business models: (1) NFT creator; (2) NFT marketplace, selling creators’ NFTs; (3) company offering their own NFT (fan token) and (4) computer game with NFT sales.Originality/value: this research brings the literature on business models and NFTs together to offer clarity on the proven NFT business models
A model reducing researchers’ challenges in projects: build trust first for better mental health
No full textDespite the many benefits for researchers that participate in a project there are several challenges that create a cumulative, negative, effect on their mental health. Existing research focuses on four stages of a project: Forming, Storming, Norming and Adjourning. This research adds a fifth stage, Post-Project Collaboration, as this stage is implicitly or explicitly a part of most research projects. For example, a post-doctoral researcher expects to be credited for their work even if it is published after the end of the project. The specific challenges for each of the five stages are identified. This enables the leader to focus on a manageable number of challenges at each stage. Trust should be built during the first stage to cover four specific topics: Trust in the leader, process, evaluation method and trust in being credited in published work. Conflict does not emerge as a challenge at the initial stages but later
The new centralised and decentralised Fintech technologies, and business models, transforming finance
No full textFinancial technologies, Fintech, not only offer new services, but reduce barriers to entry allowing for innovation. This digital transformation affects society in many ways, as financial services cut across many other services we use. There are some proven Fintech technologies, services and business models but there is still a-lot of uncertainty. There are alternative visions of finance’s future. Some change driven by Fintech can be achieved purely by competition, but wider change needs collaboration. This research extends an existing model and finds six Fintech business models that are optimised for AI and blockchain.Trust remains a challenge for Fintech, particularly when generative AI is used. Trust must be built by leaders even if this means sacrificing some of generative AI’s capabilities.The context of each country influences how much the pull for Fintech is from the consumers and how it is regulated. Even after the transitional period of disruption, it is unlikely that we will be left with a ‘one size fits all’ approach by all countries.This research identifies three themes in Fintech: (a) Fintech utilising blockchain, Decentralised Finance, cryptocurrencies and NFTs, (b) Fintech utilising AI and the metaverse, and (c) Fintech management, consumer behaviour, regulation and cybersecurity
Fintech and the emerging ecosystems: exploring centralised and decentralised financial technologies
No full textFinancial technologies, commonly referred to as Fintech, are revolutionizing and reorganizing the financial sector. This digital transformation profoundly impacts society and influences our everyday lives in numerous ways, as financial services intersect with various other services we utilize.This book offers contributions from leading researchers in the field, providing a comprehensive understanding of this multifaceted transformation. It encompasses emerging financial technologies such as cryptoassets, including Bitcoin and Non-Fungible Tokens (NFTs), Decentralized Finance (DeFi), Central Bank Digital Currencies (CBDCs), and the growing significance of Artificial Intelligence (AI) and Generative AI.While the primary audience comprises researchers and academics, practitioners and students can also glean practical insights from its contents
A model of trust in Fintech and trust in Insurtech: how Artificial Intelligence and the context influence it
No full textFinance and insurance are being transformed by Artificial Intelligence (AI). Nevertheless, the consumer is not passive in this process and there is some inhibition to trust. This research models trust in Fintech and trust in Insurtech. The two models are then compared to evaluate if trust in both is similar. Multigroup Structural Equation Modelling is used to evaluate if the model is equally valid for Fintech and Insurtech. The model presented here shows that trust in both Fintech and Insurtech are formed by (1) Individuals psychological disposition to trust, (2) Sociological factors influencing trust, (3) Trust in either the financial organization or the insurer and (4) Trust in AI and related technologies. The results of the multigroup analysis show that the model is equally valid for Fintech and Insurtech. This is particularly useful as these services are often offered by the same organization, or even the same mobile application
How to build trust in answers given by Generative AI for specific and vague financial questions
Full text linkPurpose: Generative artificial intelligence (GenAI) has progressed in its ability and has seen explosive growth in adoption. However, the consumer’s perspective on its use, particularly in specific scenarios such as financial advice, is unclear. This research develops a model of how to build trust in the advice given by GenAI when answering financial questions.Design/methodology/approach: the model is tested with survey data using structural equation modelling (SEM) and multi-group analysis (MGA). The MGA compares two scenarios, one where the consumer makes a specific question and one where a vague question is made.Findings: this research identifies that building trust for consumers is different when they ask a specific financial question in comparison to a vague one. Humanness has a different effect in the two scenarios. When a financial question is specific, human-like interaction does not strengthen trust, while (1) when a question is vague, humanness builds trust. The four ways to build trust in both scenarios are (2) human oversight and being in the loop, (3) transparency and control, (4) accuracy and usefulness and finally (5) ease of use and support.Originality/value: this research contributes to a better understanding of the consumer’s perspective when using GenAI for financial questions and highlights the importance of understanding GenAI in specific contexts from specific stakeholders
AI is transforming insurance with five emerging business models
No full textThis research found empirical support for five emerging AI-driven business models in insurance: (1) Focus and disaggregation, (2) absorb AI into the existing model, (3) incumbent expanding beyond model, (4) dedicated insurance disruptor and, (5) tech company disruptor. The following section is the literature review followed by the methodology section that explains how the case studies were identified and explored. Then the analysis of four exemplar cases is shown, a discussion on the validity and value of the business models is identified, and finally the conclusion is presented
Evaluating the new AI and data driven insurance business models for incumbents and disruptors: Is there convergence?
No full textAI and data technologies are a catalyst for fundamental changes to insurance business models. The current upheaval is seeing some incumbent insurers trying to do the same more effectively, while others evolve to fully utilize the new capabilities and users these new technologies bring. At the same time, technologically advanced organizations from outside the sector are entering and disrupting it. Within this upheaval however, there are signs of a convergence towards an ideal and prevailing business model. This research identifies one exemplar incumbent and one disruptor and evaluates whether their models are converging and will become similar eventually. The findings support a high degree of convergence, but some differences are likely to remain even after this transitionary period. The differences identified are firstly in the evaluation of risk and secondly that traditional insurers prioritize revenue generation from what is their primary activity, while new entrants prioritize expanding their user base
- …
