Digital Tourism Experts

The aim of the “Digital Tourism Expert” initiative is to increase digital competencies in tourism by providing the participating company partners with relevant digital know-how and to test and implement it with the help of initial digital projects (Transfer Projects). Target audience for the educational modules developed are IT representatives in tourism companies, such as tour operators, hotels and also professional services companies in tourism plus provincial destination marketing organizations and regional destination organizations. Contents are oriented on the current research projects of the participating universities. Our Department contributes the topic Knowledge 4.0 - Managing Knowledge in Digital Change. The outcome will contribute to increased professionalization of eTourism in Austria.

Understanding Power in Digital Platform Ecosystems

Digital platform ecosystems play an important role in today's society. They are an important research topic in the disciplines of economics, computer science, and information systems. Digital platform ecosystems bring together at least one supply side and one demand side in a market through digital technology – for example, Apple's platform ecosystem brings together developers of apps for smartphones and users of smartphones.

Many digital platform ecosystems are owned and run by dominant companies that control the ecosystem, such as Google, Apple, and Amazon. These companies can implement structures and rules that increase generativity, but also allow them to claim a significant share of the value that is created in the ecosystem.

This research project investigates what constitutes power in digital platform ecosystems, how the power advantage of a platform owner affects the generativity of the ecosystem, and how platform owners and complementors shape the distribution of power in the ecosystem.

Exploiting the potential of reinforcement learning for continous optimisation of complex and dynamic systems

The goal of REINFORCE is to assess the potential of reinforcement learning used to optimise complex, control problems in a way that is able to adapt to unforeseen changes in their environment. The potential is assessed in the context of two complementary use cases in the field of intelligent vehicle systems. Both in the control of the powertrain of passenger cars and in routing of driverless transport systems, established decision-making approaches are increasingly reaching their limits when it comes to dynamically achieving an optimum with respect to interdependent economic and ecological targets.

REINFORCE will not only focus on the technical aspects of reinforcement learning (including the development of algorithms tailored to deal with the challenges of real-world scenarios) but also the human-centered aspects (including the explainability of results, the transfer of knowledge back to the workers and trustworthiness) and the economic aspects (including how the results can be transferred directly into integrated processes as a viable decision making tool). Read more @https://projekte.ffg.at/projekt/4141421.

Behavioural causes for Supply Chain inefficiency in pandemic situations

With the emergence of COVID-19 in 2019 the demand for life-saving medical supply drastically increased while the production capacity has been limited. 

Arguably, this leads to the well known bullwhip effect which can be described as a phenomenon where the demand variability is amplified from downstream to upstream supply chain members. Consequently, manufacturers face a drastic increase of orders while they are already operating at the maximum capacity.  As a possible result, the shelf's are empty while production is collapsing.   

Therefore, it is of great importance to study the influence of human decision makers and the role of the bullwhip effect on the supply chain efficiency in situations of high demand and limited capacity. 

We set two goals for this project, (i) to identify behavioral causes which lead to the bullwhip effect and the impact on the of supply chains efficiency with limited capacity and (ii) develop practical policies to improve the supply chain performance so that critical, especially life-saving, products are efficiently delivered on time.

Understanding and measuring facilitated idea convergence (UMIC)

Innovation contests have gained wide popularity because organizations can tap into the creative power of the crowd and thus drive their innovativeness. However, overwhelming amounts of information, missing process structures, and little IT-support challenge individuals and teams who need to select the best ideas out of hundreds or even thousands once a contest has ended. UMIC is a stand-alone project (P 29765), funded by the Austrian Science Fund (FWF), and aims at supporting decision-making during idea selection processes. One major goal is to better understand and measure similarities and differences of emerging problems during idea selection within and across organizations involved in innovation contests. Another major goal is to develop IT-support in the form of implemented facilitation techniques as well as feedback and feedforward recommendation to help pre-filtering ideas, remedying information-processing biases, and suggesting relevant information.

Link: http://iwi.uibk.ac.at/umic/