Forschungsprojekte

Our project aims to contribute to the City of Vienna’s Social Inclusion goals by investigating how best to design a citizen reporting app. We test how different feedback features (framing, level, and source) affect (1) citizen engagement and participation in maintaining healthy and attractive neighbourhoods and (2) neighbourhood community building; and (3) to determine whether certain feedback features are more likely to lead to better inclusion, more engagement, and greater sense of belonging to a neighbourhood among lower-income groups.

Digital products and interfaces in business ecosystems (Collaboration with ÖBB)

Many business processes including interactions with clients and partners are carried out predominantly online, or at least leave a rather comprehensive digital trail. This poses new challenges for data management and storage – and new opportunities for innovative digital products and services.

ÖBB is looking proactively into structuring and cataloging their data landscape and making it accessible via a common “Data Lake”. External data sources as well as client and partner business platforms already are or will be connected with the ÖBB digital ecosystem. ÖBB owns or maintains a variety of data (e.g. operations, maintenance) that could be bundled and provided to clients, partners or third parties as digital data products. Aside from challenges arising from the data management side, there are also questions regarding the interaction design of the ÖBB digital ecosystem for both customers and employees. ÖBB aims to increase workplace digitalization and explores digital options to deal with the attendant cultural and organizational obstacles (e.g., chatbots, frontend optimization). Designing the digital experience for employees, but also for customers, also requires taking into account how expectations and mindsets are changing with the rapid spread of the so-dubbed low-touch economy.

Designing data products and services requires answering questions from a number of different perspectives, e.g. product definition, pricing, bundling, marketing, or product configuration. These questions are closely related to interface design decisions. Customer interfaces can be used to generate more insight into customer behavior, in turn feeding into improvements of the data products regarding pricing, configuration etc. Employee interfaces, especially in complex environments, need to provide guidance, structure and easy access to expert help, whether provided by (semi-)automated or human agents.

Robo advisory (Collaboration with Prof. Dr. Martin Weber, Dr. Zwetelina Iliewa, German robo advisors, ForDigital)

In co-operation with an industry partner, we designed a robo advisor according to design science principles with the aim of providing a system suitable for inexperienced and risk-averse investors who typically follow none or too conservative investment strategies.

A second project investigates how 1) robo advisors can communicate financial information better and 2) how to leverage users’ biosignals to identify their affective states and help them regulate their emotions better, with the overall goal of improving their financial decision-making. 

A third project, together with a master student, looks at how to employ digital nudges to de-bias financial decision-making, specifically mitigating the tendency towards myopic loss aversion in robo advisors.

Recommendation services in e-commerce applications (Collaboration with PD Dr. Michael Scholz, Prof. Dr. Alexander Benlian, Prof. Dr. Guido Schryen, Prof. Dr. Oliver Hinz)

A number of projects focused on developing and experimentally testing utility-based recommendation systems. One topic of particular interest is the de-biasing of decision makers during the recommendation process, with the aim of reducing the influence of decision anomalies like anchoring and adjustment on the recommendation-generating process. User data from the recommendation process, appropriately de-biased, can then inform marketing and product development processes.

Dysfunctional decision-making and decision support (Collaboration with Prof. Dr. Edgar Erdfelder, Prof. Dr. Arndt Bröder, ForDigital)

We examine the phenomenon of decision inertia, which refers to the repetition of decisions regardless of suboptimal outcomes. In our first project, we tested potential motivational and cognitive drivers of decision inertia in a number of experiments. In future projects, we plan to develop and validate a measurement instrument for the tendency to engage in decision inertia, as well as methods for reducing decision inertia and integrating them in decision support systems.

Live Biofeedback in economic interactions (Collaboration with Prof. Dr. Christof Weinhardt, Dr. Marc Adam, DFG, GfK Verein)

We have tested live biofeedback in a number of interactions (e.g., beauty contest, auctions) and the results with regard to decision quality are promising, especially if combined with emotion regulation. In addition, we compare a number of biosignals with regard to their informational content on emotions (e.g., heart rate, skin conductance, speech, video) and measurement reliability. Based on this information, we plan to develop adaptive systems that react dynamically to the users’ affective states. One future project will focus on the effects of performance indicators in teams on performance, team cohesion and team affect, and develop an adaptive system to mitigate negative affect in groups.

De-Biasing of forecast data (Collaboration with Prof. Dr. Thomas Setzer, Bayer AG)

Whenever financial forecasts in companies are based on a multitude of expert forecasts, it is safe to assume that some of these forecasts are biased. Biased forecasts reduce forecast accuracy. In this project, we analyze a corporate dataset of cash flow forecasts by Bayer AG to develop measures for identifying single-forecast biases and approaches for de-biasing forecast data.