IS 7030: Master Seminar
The seminar examines frontier research on how artificial intelligence and digital technologies change firms, consumer behavior, and markets. It focuses on highly relevant business questions such as how AI affects employee productivity, decision-making, and innovation; how digital platforms and social media shape competition and consumer behavior; and how new technologies create broader economic and societal effects.
This seminar is aimed at business students.
Students engage with state-of-the-art research on topics such as whether AI helps employees work faster and better, how platforms such as Amazon, Uber, or app stores influence market competition, how recommendation algorithms and social media platform features affect attention and demand, and how digital technologies can generate externalities such as misinformation, algorithmic bias, and sustainability challenges. The goal of the seminar is to understand the consequences of artificial intelligence and digital technologies for business strategy and markets.
Each participant will be assigned a frontier research paper in a topic area of their choice and will analyze it in depth throughout the seminar. Students learn how to identify the paper’s central research question, theoretical foundations, empirical strategy, and main contribution. They also gain experience on how to identify complementary, relevant academic literature. They also learn how to critically assess the paper’s assumptions, research design, methodology, and limitations, and derive a promising research proposal for future research by themselves.
To support this process, the seminar opens with a kick-off session on how to search for, read, interpret, and evaluate academic research papers.
Information
Please find all information about our Master Seminar at our chair on ILIAS.
Examples
Papers our students have worked with in previous semesters:
Li H, Aral S (2025) Human trust in AI search: A large-scale experiment. Working paper, arXiv:2504.06435.
Li and Aral (2025) ran a large-scale experiment showing that interface features like reference links and uncertainty cues causally shape how much people trust GenAI search results, even when the actual content stays the same. The seminar thesis reviews the study's contributions and limitations, particularly its cross-sectional design and narrow trust measure, then proposes a longitudinal extension. Drawing on the UTAUT-ECT model, it outlines a study where participants complete repeated search tasks over several days, allowing trust to be tracked as it updates through expectation gaps over time, rather than measured only once.
Brynjolfsson E, Li D, Raymond L (2025) Generative AI at work. Quarterly Journal of Economics 140(2):889–942.
Brynjolfsson, Li, and Raymond (2025) studied customer service agents using an AI chat assistant and found it raised productivity, with the biggest gains for less-experienced workers, suggesting AI can act as a skill equalizer. This paper reviews that study's strengths and limitations, especially its short timeframe and narrow focus on frontline execution. It then proposes an extension using a longer, twelve-month design with AI-free test periods to check whether workers are truly learning or just becoming dependent on the tool. The proposal also introduces measures of skill and reliance, and looks at how AI coaching tools might affect managers, not just frontline agents.
Lambrecht A, Tucker C (2019) Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science 65(7):2966–2981.
Lambrecht and Tucker (2019) found that gender-neutral STEM job ads on Facebook still reached more men than women, because the platform's cost-optimization algorithm favored cheaper audiences. This thesis reviews that study's contributions and limitations, then proposes a fairness-aware ad-allocation framework to test whether adding fairness thresholds can reduce this gender gap without a large loss in efficiency. A quasi-experimental design comparing baseline, loose, and strict fairness conditions is outlined to measure this trade-off.
Contact
Katharina Viethen, M.Sc. (she/her)
E-mail: katharina.viethenuni-mannheim.de
Phone: +49 621 181-2153
Address:
University of Mannheim
L5, 1–6 – Room 717
68131 Mannheim
Consultation hours:
By appointment