The focus project Theorizing Digital Innovation in Finance addresses the digital transformation of the financial sector. Technologies such as blockchain, applications of artificial intelligence, and the widespread use of alternative forms of financing go hand in hand with new financial services, business models, organizational practices, and new consumer behavior. Traditional and new market participants face the following challenges: Which financial services should be developed in the future? Which services are to be bought from the market and which are offered by the company itself? Which services should the organization offer in the B2B and B2C business? The focus project is devoted to these questions and will answer them in specific research projects. The conceptual framework is the concept of digital innovation, which states that digital technologies change organizations through and through (Barrett et al. 2015; Nambisan et al. 2017; Yoo et al. 2010), which is why new theoretical frameworks are also necessary to understand the effects of digitalization of financial services on the organizational and customer side (Wessel et al. 2020; Yoo 2013). Such frameworks have so far hardly been developed, which is why the focus project will use the context of digital financial innovations to develop and test theoretical frameworks by using qualitative and quantitative methods.
Brandl, Barbara and Lars Hornuf (2020): Where Did FinTechs Come from, and Where Do They Go? The Transformation of the Financial Industry in Germany after Digitalization, Frontiers in Artificial Intelligence (Artificial Intelligence in Finance).Download.
Due to rural depopulation and unfavorable infrastructural settings the attractiveness of rural regions decreases, accompanied by migration of (potential) entrepreneurs from the countryside to the cities. While cities offer entrepreneurial ecosystems with matured support systems for entrepreneurs, rural regions are challenged to reinforce their entrepreneurship support infrastructure. Against this background, digital incubators and accelerators come to the fore and offer entrepreneurs in rural areas access to upfront knowledge, resources and experience without the need to move elsewhere. While digital incubation and acceleration emerges in entrepreneurial ecosystems, research is silent when it comes to applying these means to fostering entrepreneurship in rural areas. Accordingly, this project raises the following research questions: (i) Does the increasing digitalization enhance the digital divide between urban and rural areas in terms of entrepreneurial activity? (ii) (How far) Do digital and hybrid incubators and accelerators for rural areas increase the entrepreneurial activity? (iii) How to tap the potentials of rural and urban areas to allow mutual gains in the two settings; and does resource injection in rural areas by virtual or cyber-physical resource integration from urban areas (et vice versa) foster entrepreneurial activities? (iv) Do digital incubators and accelerators trigger renewal in rural areas? The project employs multiple case study research and rests on qualitative and quantitative data sources to respond to the research questions.
Smart, resilient, technologically enhanced logistics systems are widely regarded as the future for organisations. Together Big Data, Artificial Intelligence (AI) and Machine Learning present many opportunities for enhancing decision making. This project looks at the design, usage and adoption of unstructured big data-based decision support in global supply chain management and operations. Amongst others we raise questions related to how automated decision support approaches aid in global supply chain decision-making, and can be designed in a user-friendly manner? Which contextual factors play a role in and influence the development of big data and AI capabilities and outcomes related to its adoption for global supply chain operations? Etc.
The project focuses on the relationship between innovation in the field of digital technologies, and authoritarian political systems. How do innovation systems work in authoritarian states, and what is the overall economic connection between innovation and digital surveillance? What is the influence of digital control and authoritarian politics on technology transfer, spillovers and joint ventures in the field of digital technologies? And what are the economic, political and ethical implications for actors in democratic states that are working with partners in authoritarian political systems? Based on a number of newly developed general equilibrium models, we study these questions with the help of laboratory experiments in the recently funded BreLAB of Bremen University, as well as empirical research in Russia and China.
"Self-quantification" or "self-measurement" (also known as "personal analytics", "quantified self" or "self tracking") has been an ongoing trend for several years now. Those terms describe the activity in which people voluntarily and autonomously monitor themselves and record certain events in their lives using digital technologies. Thereby, data is generated and analyzed in order to, for example, create various statistics for the user.
Research has already dealt with various aspects of self-measurement, e.g., with the question which factors make users accept and use "wearables". Research assumes that mainly positive effects on user behavior occur, e.g., more exercise to achieve certain goals or the identification of bad habits and unhealthy diets. Nevertheless, these positive effects come along with many concerns and potentially negative effects: data protection advocates warn against permanent surveillance and the disclosure of sensitive data that could be made available to third parties such as health insurance companies and companies such as pharmaceutical manufacturers through sharing via social networks. Negative effects can also arise directly at the user’s side: The constant tracking of activities or one's own eating behavior, for example, can become an obsession.
The project investigates, among others, the underlying motivations for the use of “consumer-technology interfaces.” It also aims to identify the short and long-term effects of the use of “consumer-technology interfaces”. Furthermore, data protection concerns of users of “consumer-technology interfaces” will also be looked at in this project.
In the industrial and business world, digital assistants (chatbots) are increasingly used for business tasks. The use of chatbots ranges from simple conversation interfaces, such as FAQs, to intelligent booking systems, such as Google Duplex. With the increasing use of chatbots, it is not surprising that the research area of Conversational AI is growing. In particular, there is a interest in modelling human-like characteristics and behaviour in chatbots in order to generate (digital) empathy in human-machine interaction, since empathy is considered a core element for a successful perceived interaction with machines. However, the number of research projects investigating success conditions in the conception of an empathic human-machine interaction with chatbots is limited. In order to explore this area in more detail, this research project will focus on the identification of features that make chatbots more emphatic in their interaction with users. Therefore, the research project includes (1) modelling of a digital assistant and (2) the investigation of characteristics that promote empathy towards chatbots (e.g. by sentiment analysis). This digital assistant will (3) be applied and tested in the field of university management.
This project investigates the suitability of machine learning approaches at the intersection of accounting and finance. Our main interests lie within „Macro-Accounting“ and „Accounting for the Anomaly Zoo“. „Macro-Accounting“ describes the association between accounting variables (e.g., firm performance) and macro-economic outcomes, such as economic growth or overall stock market performance. „Accounting for the Anomaly Zoo“ studies the explanatory power of accounting and finance characteristics for the cross-section of stock returns. „Anomaly Zoo“ is the collective term for the myriad of value relevant accounting and finance variables discussed by prior literature. Given the vast amount of influential variables and supposed non-linearities, the lack of application of machine learning approaches is quite surprising. In this project, we aim to close this gap in the literature. Recent works by Kalay et al. (2018) (for „Macro-Accounting“) and Gu et al. (2020) (for the „Accounting for the Anomaly Zoo“) serve as the starting point of our analyses.
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Forthcoming in the Review of Financial Studies.
Kalay, A., Nallareddy, S., & Sadka, G. (2018). Uncertainty and sectoral shifts: The interaction between firm-level and aggregate-level shocks, and macroeconomic activity. Management Science, 64(1), 198-214.