skip to main contentskip to main menuskip to footer Universität Bielefeld Play Search
  • Research Seminar (Management)

    © 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.


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)

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.



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 content

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 Stake

The respective Zoom link is


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 Apps

Authors: 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.



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 systems

Abstract: 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:

Dr. Philipp Loick (Amazon)
Timing optimization for Amazon Middle Mile

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:

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.



Wednesday, May 11 2022, 16:00-17:00 in room V10-122

Prof. Dr. Oliver Müller (Paderborn University)
Using Natural Language Processing to Forecast the Impact of AI on the Labor Market

Artificial intelligence (AI), broadly understood as the capability of machines to perform cognitive tasks at a level that is comparable or even superior to humans, is a rapidly advancing general-purpose technology that holds the potential to reshape the nature of work. In recent years, so-called narrow AI systems outperformed humans in a growing range of specialized tasks requiring human-like cognitive capabilities like vision or natural language processing. However, we are still far from artificial general intelligence: Machines cannot do the full range of tasks that humans can do. This raises the question which tasks and, in turn, jobs will be most affected by the ongoing diffusion of AI. We are proposing a new measurement instrument to estimate the likelihood that tasks and jobs can be automated through AI in the near future. In contrast to previous measurement approaches, which require the description of jobs in terms of standardized numerical variables, our approach leverages state-of-the-art natural language processing and machine learning techniques to score textual job descriptions from online career portals according to their automation potential.

About the Speaker:
Oliver Müller is a Professor of Management Information Systems and Data Analytics at Paderborn University. He holds a BSc and MSc in Information Systems and a Ph.D. in Business Economics from the University of Münster's School of Business and Economics. In his research Oliver studies how organizations create value with (big) data and analytics; for example, by enhancing judgment and decision making, supporting knowledge management, or automating business processes. His research has been published in the Journal of Management Information Systems, Journal of the Association of Information Systems, European Journal of Information Systems, and various others.

Note: Although the talk will be in-person, you will also be able to attend virtually using the following Zoom information:
Meeting-ID: 640 8029 4957
Passwort: 732835 

Wednesday, June 1, 2022, 16:00-17:00  Zoom

Prof. Dr. Warren B. Powell (Princeton University)
Introduction to Sequential Decision Analytics: A unified framework for decisions under uncertainty

Zoom Link:
Meeting-ID: 621 0545 4430
Passwort: 854083

Sequential decisions are an almost universal problem class, spanning dynamic resource allocation problems, control problems, stopping/buy/sell problems, active learning problems, as well as two-agent games and multiagent problems.  Application settings span engineering, the sciences, transportation, health services, medical decision making, energy, e-commerce and finance.  A rich problem class involves systems that must actively learn about the environment.  We also consider hybrid resource allocation and learning problems such as those that arise in disease mitigation, as well as complex multiagent supply chains.
These problems have been addressed in the academic literature using a variety of modeling and algorithmic frameworks, including (but not limited to) dynamic programming, stochastic programming, stochastic control, simulation optimization, stochastic search, approximate dynamic programming, reinforcement learning, model predictive control, and even multiarmed bandit problems. Particularly frustrating is that these communities do not use a common modeling framework.
We are going to introduce a universal modeling framework that can be used for any sequential decision problem in the presence of different sources of uncertainty.  Our approach is to define the problem first, which consists of sequential decision problems (decision, information, decision, information, …) where the challenge is to find policies for making decisions.  We claim that there are four (meta)classes of policies that are the foundation of any solution approach that has ever been proposed for a sequential problem.  Using a simple energy storage problem, we show that any of the four classes of policies might work best depending on the data, and hybrids can be created that combine two or more classes.  All of these ideas will be illustrated with applications drawn from different application settings.

About the Speaker:
Warren B. Powell is Professor Emeritus at Princeton University, where he taught for 39 years, and is currently the Chief Analytics Officer at Optimal Dynamics.   He was the founder and director of CASTLE Labs, which focused on stochastic optimization with applications to energy systems, transportation, health, e-commerce, and the laboratory sciences (see, supported by over $50 million in funding from government and industry.  He has pioneered the use of approximate dynamic programming for high-dimensional applications, and the knowledge gradient for active learning problems, work that was the foundation for his books Approximate Dynamic Programming and (with Ilya Ryzhov) Optimal Learning. He developed a universal framework for sequential decision problems which is the basis for his latest book with Wiley: Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions. He published over 250 papers and produced 70 graduate students and post-docs.  He is the 2021 recipient of the Robert Herman Lifetime Achievement Award from the Society for Transportation Science and Logistics, a fellow of Informs, and the recipient of numerous other awards.

!! Cancelled !!

Wednesday, June 8, 2022, 17:00-18:00  Zoom

Dr. Philipp Loick (Amazon)
Timing optimization for Amazon Middle Mile

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.

Monday, June 20, 2022, 16:00-17:00 in room V10-122

Prof. Dr. Taïeb Mellouli, Thomas Stoeck (Martin Luther University Halle-Wittenberg)
Data-Driven Knowledge-Based Decision Support Systems involving both complex activity structures and complex systems

Note: Although the talk will be in-person, you will also be able to attend virtually using the following Zoom information:

Meeting-ID: 688 2407 1093
Passwort: 016002

Abstract of the talk:
While the main focus of the talk will be the presentation of new business decision analytics, modeling and solution approaches for a “data-driven patient flow management in hospitals” (see abstract below of the respective research paper), the talk will give general reflections on how to tackle decision support problems in complex systems by approaches combining techniques from AI and OR. Adequately handling such business situations requires new intricate decision problems’ modeling and combined AI/OR solution approaches where dimensions of complexities are crossed. In the actual case, the complex activity structures are patient pathways and flows that are to be managed and optimized within a complex organization – the hospital – being a composition of interdependent wards/departments. Ingredients of our solution approach includes a disassociation of clinical and operational aspects of hospital data, separately mining hospital-independent clinical/medical and hospital-dependent operational knowledge, then adequately apply this knowledge to support physicians and dispatchers of the hospital to deal with situational patient-individual and hospital-sate dependent decisions. The developed policies and scenarios are evaluated by data science and hospital-wide simulation techniques.

As you well know from Prof. Römer, a business adequate and full flagged “decision analytics” requires a combination of data science and prescriptive analytics techniques of OR. As we showed in a former collaborative paper, this elevates to an AI/OR synergy that can be intricate, requiring not only process mining (advanced data science and AI), but also involving hierarchical planning (advanced prescriptive analytics/OR). Both AI and OR sides considers business relevant aspects – only as much as necessary – of the complex activity structures, here patient pathways. We show in this talk, that these business relevant aspects and the decision problems at operations (not planning phase) requires another, likewise innovative handling of these complex structures – and this at all levels: problem analysis, design of decision support, and solution approaches. A main general reflection inside these approaches of our actual paper is: Business adequate and full flagged “decision analytics” also needs “DATA-DRIVEN KNOWLEDGE-BASED Decision Support Systems” – that is, the integration of human or automated reasoning with learned knowledge in advanced decision support systems – for highly qualified experts.

Paper underlying the talk:
Thomas Stoeck, Taïeb Mellouli, Malek Masmoudi: Data-Driven Patient Flow Management via Mining Clinical and Operational Knowledge and Evaluation by Hospital-Wide Simulation

Abstract of the Paper:
Economic pressure in the healthcare market and restrictive governmental regulations force hospitals to ensure their competitiveness by improving their performance, which directly depends on increasing their occupancy rate. This can be achieved by treating more patients and managing higher levels of patient flow across the hospital through clever patient flow management and without additional investment in department or bed capacities and increased medical staff. Attempts to attain the highest occupancy rates will inevitably involve strain situations.
To manage and balance sporadic demand fluctuations between departments, we propose new flexible policies for smoothing the patient flow based on different types of patient admissions, redirections and relocations. As they depend on determining suitable alternative departments, we design a data-mining approach generating alternatives based on medical needs and service quality provided by departments. The resulting data-driven situational decision-making tool for hospitals uses standardized data to ensure wider applicability. In addition to this decision-making tool being used for local situation-dependent decisions, the global effects were evaluated using a generic object-oriented, hospital-wide simulation model.
The current practice of the hospital shows that for 9 % of patients, an individual non-documented ad hoc decision has to be made to ensure admission. Our policies, creating a structure for these decisions, allow for the admittance of 97% of patients without decreasing the overall quality. The evaluation shows that the rate of patients experiencing high-quality service increases from 87% to 92%. These results confirm that operational policies using redirections and relocations can overcome short-term strain situations without decreasing overall treatment quality.

About the Speaker:
Prof. Mellouli studied computer science and mathematics at the University of Stuttgart, then got his doctoral and post-doctoral degree (habilitation) from the University of Paderborn. After a practice phase at TUIfly (Hapag-Lloyd), where he initiated the development of the currently productive Crew-Optimizer-System, he switched to academia as a professor for “Business Information Systems and Operations Research” at the Martin-Luther University Halle-Wittenberg and still maintained the collaboration with selected practice partners seeking support for their complexly structured decision problems.
Main research interest involves business process analysis and design of information systems enriched by optimization and simulation modeling (operations research techniques) for business applications. Prof. Mellouli maintains his former interest on automated reasoning and knowledge-based systems (from doctoral thesis) and, by investigating AI/OR synergies, reveals insights leading to the design of innovations in decision support systems augmented by knowledge reasoning in complex business environments.  

back to top