TECHNOLOGY CLUSTERS AMONG FINTECHS: EXPLORING THE SIGNALING OF TECHNOLOGY SCOPE AND THE ROLE OF REGULATION

Financial technology ventures (FinTechs) use the latest technologies and act in a highly regulated industry. Yet, the technological scope of FinTechs and how this scope affects funding from investors remains unclear. Accordingly, research calls to examine the influence of technologies on the funding amount of FinTechs, especially in the context of different levels of regulatory freedom. We answer these questions by conducting an explorative cluster and regression analysis of 1,821 FinTechs and find three dominant clusters of FinTechs: technology newcomers, selective adopters, and full technology applicators. Technology newcomers have the lowest adoption rate of new technologies while full technology applicators combine several new technologies. Based on signaling theory and generalized linear models, we find that clusters significantly differ regarding their funding amount. However, we find that higher regulatory freedom decreases the differences between these clusters regarding the funding

FinTech’s business model in Mexico, a preliminary analysis

El modelo de negocio de las Fintech en México, un análisis preliminar Este estudio analiza las principales variables de éxito en el modelo de negocio de una Fintech. Los datos fueron obtenidos mediante un cuestionario aplicado a quince Fintech mexicanas. Los resultados preliminares muestran que las variables clave de éxito, de acuerdo con una correlación significativa con el crecimiento de la empresa, son: un uso eficiente de la infraestructura, servicios orientados al cliente, salarios competitivos y la seguridad de la información; el tipo de Fintech más común es el de "pagos y servicios de dinero móvil" seguido de "crowdfunding" e "inversiones". En México, no existe una fuente formal que facilite el conocimiento de la industria, por lo que la originalidad de este estudio radica en la contribución a una mejor comprensión introductoria de cómo funcionan las Fintech, incluyendo otras dimensiones como las sociales y ambientales que no se habían considerando antes. Su limitante es el número de empresas interrogadas, pero se podrían hacer futuros estudios complementarios. Se puede concluir que la dimensión tecnológica es la más relevante para la industria.This study analyzes the key success variables in a fintech´s business model. The data was obtained by a questionnaire applied to fifteen Mexican fintech’s. Preliminary results show that the key success variables, according to a significant correlation with the firm´s growth, are: an efficient use of infrastructure, customer-oriented services, competitive wages and security of information; the more common type of fintech operation is the “payments and mobile money services” followed by “crowdfunding” and “investments”. In Mexico, there is not a formal source that makes easier the knowledge of the industry, so the originality of the study lies on the contribution to a better introductory understanding of how fintech´s operate, including other dimensions like the social and environmental ones which had not been considering before. Its limitation is the number of questioned firms, but future complementary studies could be done. It can be concluded that the technological dimension is the most relevant for the industry

Towards a Taxonomy of Platforms for Conversational Agent Design

Software that interacts with its users through natural language, so-called conversational agents (CAs), is permeating our lives with improving capabilities driven by advances in machine learning and natural language processing. For organizations, CAs have the potential to innovate and automate a variety of tasks and processes, for example in customer service or marketing and sales, yet successful design remains a major challenge. Over the last few years, a variety of platforms that offer different approaches and functionality for designing CAs have emerged. In this paper, we analyze 51 CA platforms to develop a taxonomy and empirically identify archetypes of platforms by means of a cluster analysis. Based on our analysis, we propose an extended taxonomy with eleven dimensions and three archetypes that contribute to existing work on CA design and can guide practitioners in the design of CA for their organizations

A Classification of Decision Automation and Delegation in Digital Investment Management Systems

Digital investment management systems, commonly known as robo-advisors, provide new alternatives to traditional human services, offering competitive investment returns at lower cost and customer effort. However, users must give up control over their investments and rely on automated decision-making. Because humans display aversion to high levels of automation and delegation, it is important to understand the interplay of these two aspects. This study proposes a taxonomy of digital investment management systems based on their levels of decision automation and delegation along the investment management process. We find that the degree of automation depends on the frequency and urgency of decisions as well as the accuracy of algorithms. Notably, most providers only invest in a subset of funds pre-selected by humans, potentially limiting efficiency gains. Based on our taxonomy, we identify archetypical system designs, which facilitate further research on perception and adoption of digital investment management systems

The Digitization of Investment Management – An Analysis of Robo-Advisor Business Models

The emergence of so-called Robo-Advisors (RAs) is disrupting the financial services industry. RAs are algorithm-based systems that digitize and automate the investment advisory process including portfolio recommendation, risk diversification, portfolio rebalancing, and portfolio monitoring. Scientific research in this field is still in its infancy and lacks a comprehensive understanding of the underlying business model (BM) of RAs to comprehensively understand the RA business and to further identify their potential to disrupt the financial services industry. Therefore, in this article, we conduct a multiple case study across the fifteen biggest US-based RAs to explain the basic characteristics and special features of RA BMs. Thereby, we distinguish between pure algorithm-based RAs and hybrid RAs with dedicated human oversight. Through an in-depth analysis of publicly available qualitative data, we contribute to the existing research by unleashing significant elements that underline the power of RAs to disrupt the financial services industry

Discovering Business Models of Data Marketplaces

The modern economy relies heavily on data as a resource for advancement and growth. Data marketplaces have gained an increasing amount of attention since they provide possibilities to exchange, trade and access data across organizations. Due to the rapid development of the field, the research on business models of data marketplaces is fragmented. We aimed to address this issue in this article by identifying the dimensions and characteristics of data marketplaces from a business model perspective. Following a rigorous process for taxonomy building, we propose a business model taxonomy for data marketplaces. Using evidence collected from a final sample of twenty data marketplaces, we analyze the frequency of specific characteristics of data marketplaces. In addition, we identify four data marketplace business model archetypes. The findings reveal the impact of the structure of data marketplaces as well as the relevance of anonymity and encryption for identified data marketplace archetypes