© Universität Bielefeld
Research Seminar (Management)
In the Research Seminar (Management) talks are mainly held by outside speakers. BiGSEM students (Profile Management) are required to attend the seminar during the semester.
- Summer Term 2024
- Winter Term 2023-2024
Information on dates and presenters will be announced later.
Prof. Peter Limbach organizes the seminar in this semester.
16.01.2024, starting at 2 pm in U3-140
The talk will be given by Tobias Bauckloh from the University of Cologne
Title: „ESG ratings and stock price informativeness“ (Environment, Social, Governance)
13.12.2023, starting at 4 pm in U3-140
The talk will be given by Dirk Sliwka, a highly regarded professor from University of Cologne:
Title: Supervisor Monitoring and Human Capital Investment – A Field Experiment (Paper mit Leonhard Grabe)
Abstract:
We study a reduction in employee monitoring in a field experiment with 2.425 employees from a large service organization. Employees are supposed to take part in regular skill assessments and have regular meetings with their supervisors to discuss their training needs. In both the treatment and control group employees are encouraged to make proposals for training measures before the meetings. Employees in the treatment group are told not to reveal the outcomes of the skill assessments and supervisors are explicitly told to focus on the suggested training proposals instead. We find that this reduction in monitoring significantly reduces assessment participation and human capital investments (trainings booked). Moreover, we find strong evidence for the role of employee’s image concerns as a driver of behavior. Post-experimental survey outcomes also show a significant reduction in employee job satisfaction, supervisor feedback, and perceived supervisor support for learning.- Summer Term 2023
03.05.2023 (2-3 pm): BiGSEM Seminar - in U3-140
Talk by Prof. Tobias Schäfers (TH OWL & Copenhagen University)
Title: To be continued… Consumer reactions to unfinished teasers for digital contentAbstract:
To preview digital content and arouse consumers’ interest, online providers often use short teasers designed in an unfinished form, such that the teaser begins a new sentence but does not finish it. The goal of such teasers is to create curiosity and trigger consumption of the advertised content. However, this research reveals that consumers’ reactions to unfinished teasers are not always positive. The results from six experimental studies show that for paid content, consumers react negatively to unfinished teasers. This effect reverses for free content, in that unfinished teasers lead to more consumption. We explain this reversal by showing that the barrier associated with paid content (i.e., payment requirement) activates consumers’ persuasion knowledge and suppresses any positive curiosity-induced effects, which does not occur when content is available for free. These findings call into question existing managerial practices and offer novel insights into the complexity of consumers’ reactions to prevalent advertising techniques in digital marketplaces.
10.05.2023 (Wednesday), 13:30-14:45 in Room U3-140
Talk by Rob Britton (Adjunct Professor of Marketing at the Georgetown University & practitioner with 22 years of experience in the airline industry)
Title: Airline Pricing and Revenue Management: How It Really Works
26.05.2023 (11-12 am): BiGSEM Seminar - online (Zoom)
Talk by Prof. Sabine Köszegi (Vienna Technical University)
Title: Algorithmic (Decision) Systems: Why Human Autonomy is at StakeThe respective Zoom link is
https://uni-bielefeld.zoom.us/j/66112377262?pwd=Q0E3QXhRZG1uTGpJQTFiWUR0QmlxUT09Abstract:
For more than 50 years, humans have been using model- and data-based support systems in decision-making with the hope that system-supported decisions are not only better, more objective, and fairer (i.e., more efficient and less biased). With data-based Artificial Intelligence systems, this hope is revived. However, the delegation of tasks and decision-making to automated decision systems is accompanied by the assignment and attribution of (social) agency to these systems. We will discuss how role perceptions, expectations, and attribution of agency may change for human actors and cause diffusion of accountability, over-trust in automated systems, and reduced autonomy and self-efficacy of human actors. We will examine how automated decision systems impact the autonomy of humans and what requirements are to be placed on automated decision systems in order to protect individuals and society.
27.06.2023 (11-12 am): BiGSEM Seminar - in U3-140
Talk by Prof. Dennis Kundisch (Paderborn University)
Title: Updating at the Expense of Demand? The Case of Platform AppsAuthors: Wael Jabr, Dominik Gutt, Jürgen Neumann, Dennis Kundisch
Abstract: For products that undergo frequent changes, online reviews about prior versions become less informative. Digital platforms hosting those products, therefore, implement governance mechanisms that ensure the continued relevancy of posted reviews. One such mechanism in the context of apps conceals the review history with each app update, ensuring that highly visible online reputational signals such as average rating and review tally are based solely on reviews relevant to the latest release. While the relevancy of the reputational signals is ensured by such a mechanism, it may have adverse and unequal effects, potentially depressing the demand of high reputation apps and providing low reputation ones an unwarranted fresh start. Our paper investigates such a governance mechanism, while it was implemented by a main platform in the app market, to study its effects on app demand and the implications of updating. Using an instrumental variable approach that exploits the release of maintenance updates in a focal app's category, our results show nuanced and partly asymmetrical impacts of app updating on future app downloads across apps in the top 500 charts. Top-ranked free "superstar apps" benefit from updating. For "non-superstar apps", paid ones take a big hit with the effect primarily driven by high-priced non-superstar apps, while free ones suffer only if their prior reputation was high, with concealing their review history after an app update. Our results help developers understand the implications of software updating and thus adjust their innovation strategies, and platforms make informed governance choices.
- Winter Term 2022-2023
Wednesday, October 19 2022, 16:00 in room V10-122
Prof. Dr. Daniela Guericke (University of Twente, Netherlands)
Title: Decision-making under uncertainty in sustainable energy systemsAbstract: With the transition to a sustainable energy system, the share of renewable energy sources such as wind, solar and biomass is increasing. The weather-dependent energy production from wind and solar introduces additional uncertainty to planning processes compared to traditional fossil fuel-based units. This challenges traditional decision-making models and calls for optimization methods that consider uncertainty and model the flexibility of an integrated energy system in an appropriate manner. In this talk, we will look at how stochastic programming can be used for decision-making under uncertainty in energy systems and companies relying on renewable energy sources. The presented cases are all within the context of integrated energy systems, i.e., systems that couple different energy flows such as electricity and heating to utilize synergy effects and increase flexibility.
Bio: Daniela Guericke is Assistant Professor for Stochastic Operations Research at the section of Industrial Engineering and Business Information Systems, University of Twente. Her research focuses on
(stochastic) operations research and optimization in application areas such as energy systems and health care. In particular, she is interested in decision-making under uncertainty and solving large-scale optimization problems.
Daniela received her PhD in Business Information Systems from the Decision Support and Operations Research Lab, Paderborn University.
Afterwards, she worked as a postdoctoral researcher at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). In 2020, she became Assistant Professor for Decision-making under Uncertainty in Integrated Energy Systems at DTU.
In 2021, Daniela received the Young Researchers Award of the German OR Society (GOR e.V.).
Wednesday, November 23 2022, 16:00 (Zoom)
Zoom Link: https://uni-bielefeld.zoom.us/j/61775497320?pwd=RVg5K3hYS2VGckp4cy9VaWs2c2NWUT09
Dr. Philipp Loick (Amazon)
Timing optimization for Amazon Middle MileAbstract:
Operating a logistics network of Amazon's scale gives rise to many fascinating mathematical challenges. In this talk, we will discuss Amazon's middle mile network and Amazon's approach to efficiently delivers millions of packages to customers every day. Specifically, we will discuss how timing optimization is performed in the Amazon network to allow customers to order packages as late as possible with delivery on the next day.About the Speaker:
Philipp Loick works as an Applied Scientist in Amazon's European research science team on routing and timing optimization in Amazon's middle mile network. Prior to joining Amazon, he completed a PhD in mathematics on statistical inference and the theory of machine learning at Goethe University Frankfurt. He holds a Master in Operations Research from the London School of Economics and a Master in Computer Science from Georgia Institute of Technology.
Wednesday, December 7 2022, 16:00 (Zoom)
Prof. Dr. Michele Lombardi (University of Bologna, Italy)
Title: From Decision Focused Learning to (Possibly) Unexpected Places
Zoom link: https://uni-bielefeld.zoom.us/j/61775497320?pwd=RVg5K3hYS2VGckp4cy9VaWs2c2NWUT09
Abstract
Integration of Machine Learning and optimization is often considered a topic of recent research, and yet a natural interface between the two has been around for a while. This consists of optimization model parameters, whose calibration has always been data-driven as part of common practice in Operations Research. What recently has changed is that approaches such as Decision Focused Learning (a.k.a. "Predict & Optimize", or "Task-Based Learning") have highlighted how the loss function used at estimation time may have an impact on decision quality, and provided mathematical formalisms and techniques to align training and optimization objectives. This talk will start with an introduction to Decision Focused Learning, discuss some of the technical challenges it presents and some available solutions; it will then focus on insights into exactly which problems we might be interested in solving. This approach will draw connections with approaches that are not frequently considered together, eventually ending with an attempt at unification, and a wake-up call about opportunities for cross-fertilization.