Research
My research investigates economic phenomena in the digital economy. I do so using novel big data sources and applying modern statistical methods.
On this website, you will find a summary of my current research projects that use search engine and Amazon product data to assess fair competition on digital platforms, as well as smartphone location data and large amounts of unstructured newspaper articles to estimate the effect of political conflict on consumption.
Developed Research with Working Papers
Measuring Fair Competition on Digital Platforms
Authors: Lukas Jürgensmeier and Bernd Skiera
Status: Revise & Resubmit (third round) at the Journal of Marketing
Read the full working paper on SSRN.
Abstract
Digital platforms use recommendations to facilitate the exchange between platform actors, such as trade between buyers and sellers. Platform actors expect, and legislators increasingly require that competition, including recommendations, are fair—especially for a market-dominating platform on which self-preferencing could occur. However, testing for fairness on platforms is challenging because offers from competing platform actors usually differ in their attributes, and many distinct fairness definitions exist. This article considers these challenges, develops a five-step approach to measure fair competition through recommendations on digital platforms, and illustrates this approach by conducting two empirical studies. These studies examine Amazon’s search engine recommendations on the Amazon marketplace for more than a million daily observations from three countries. They find no consistent evidence for unfair competition through search engine recommendations. The article also discusses applying the five-step approach in other settings to ensure compliance with new regulations governing fair competition on digital platforms, such as the Digital Markets Act in the European Union or the proposed American Innovation and Choice Online Act in the United States.
Keywords: Digital Platforms, Amazon, Competition, Fairness, Antitrust, Search Engines, Digital Markets Act, American Innovation and Choice Online Act.
Conference Presentations
| Conference | Date | Location | Presenting Author |
|---|---|---|---|
| 17th Symposium on Statistical Challenges in Electronic Commerce Research (SCECR 2021) | June 2021 |
online |
Lukas Jürgensmeier |
| Interactive Marketing Research Conference (IMRC 2021) | October 2021 |
online |
Lukas Jürgensmeier |
| 13th Paris Conference on Digital Economics 2022 | April 2022 | Télécom Paris, Paliseau, France | Lukas Jürgensmeier |
| EMAC Doctoral Colloquium 2022 | May 2022 | Corvinus University, Budapest, Hungary | Lukas Jürgensmeier |
| EMAC Annual Conference 2022 | May 2022 | Corvinus University, Budapest, | Bernd Skiera |
| Munich Summer Institute 2022 | June 2022 | Bavarian Academy of Sciences and Humanities, Munich, Germany | Lukas Jürgensmeier |
| ISMS Marketing Science Conference 2022 | June 2022 | ~ ~University of Chicago Booth School of Business, United States~~ online |
Lukas Jürgensmeier |
| 20th ZEW Conference on the Economics of Information and Communication Technologies | July 2022 | ZEW — Leibniz Centre for European Economic Research, Mannheim, Germany | Lukas Jürgensmeier |
| SALTY 2022 — Quantitative Marketing Conference | September 2022 | WHU — Otto Beisheim School of Management, Düsseldorf, Germany | Lukas Jürgensmeier |
| WISE 2022 — Workshop on Information Systems in Economics | December 2022 | Copenhagen Business School, Copenhagen, Denmark | Lukas Jürgensmeier |
| ISMS Marketing Science Conference 2023 (special session for winners of the 2022 ISMS Doctoral Dissertation Proposal Competition) | June 2023 | Miami, United States | Lukas Jürgensmeier |
Funding
This project is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft—DFG) through grant number SK 66/8-1 within the project Visibility in Digital Markets: Risks and Economic Consequences.
Do Political Conflicts Influence Daily Consumption Choices? Evidence from US-China Relations
Authors: Celina Proffen and Lukas Jürgensmeier (equal co-authorship)
Status: Revise & Resubmit at the Journal of Economic Behavior and Organization
Read the full working paper on SSRN.
Abstract
Does political conflict with a foreign country influence domestic consumers’ daily consumption choices? This study exploits the volatile US-China relations in 2018 and 2019 to analyze whether US consumers reduce their visits to Chinese restaurants when bilateral relations deteriorate. We measure the degree of political conflict through negativity in media reports and rely on smartphone location data to measure daily visits to over 190,000 US restaurants. A deterioration in US-China relations induces a significant decline in visits to Chinese restaurants, while visits to American restaurants increase. We identify consumers’ age, race, and cultural openness to mediate the strength of this effect.
Conference Presentations
| Conference | Date | Location | Presenting Author |
|---|---|---|---|
| EMAC Doctoral Colloquium 2022 | May 2022 | Corvinus University, Budapest, Hungary | Celina Proffen |
| EMAC Annual Conference 2022 | May 2022 | Corvinus University, Budapest, Hungary | Lukas Jürgensmeier |
| VfS Annual Conference 2022 | September 2022 | University of Basel, Switzerland | Celina Proffen |
| Economics of Media Workshop: Industrial Organization meets Political Economy | September 2022 | Smith School of Business, Queen’s University, Kingston, ON, Canada | Lukas Jürgensmeier |
| efl — the Data Science Institute, Jour Fixe | January 2023 | Frankfurt, Germany | Celina Proffen |
Funding
This project is funded by the efl – the Data Science Institute.
Early-Stage Research Without Working Paper
Using Generative AI to Provide Scalable Feedback to Students in Marketing Analytics
Authors: Lukas Jürgensmeier and Bernd Skiera
Status: In progress, initial draft of working paper estimated January 2024.
Abstract
The increasing role of data analytics in marketing underscores the importance of teaching marketing analytics in higher education. Nonetheless, offering feedback on students' open-ended analytics tasks is labor-intensive and expensive. This study introduces a web application leveraging generative Artificial Intelligence (AI) to autonomously provide feedback on exercises requiring coding, statistics, mathematics, and economic reasoning. We compare the application's feedback with human expert feedback for 510 student responses across various exercise complexities and four application configurations. Our results show that the application using GPT-4 with the correct solution provides nearly unbiased and reasonably accurate evaluations. Specifically, it has a mean error of ‑1.6 points on a 90-point exercise and a mean absolute error of 7.2 points (8 % of total achievable points). While GPT-4 outperforms GPT-3.5 by 26 %, it costs over ten times more. Compared to AI models, human evaluators are the most accurate but are about 170 times costlier than GPT-3.5, 15 times pricier than GPT-4, and require at least 20 times the evaluation duration. While we test the web application in a marketing analytics context, the application does not feature any subject-specific elements beyond the user-supplied exercise and (optionally) a correct solution. Hence, the application is ready to use in other domains beyond marketing analytics.
Degrees of Vertical Integration on Online Retail Platforms: An International Comparison of Amazon Marketplaces
Authors: Lukas Jürgensmeier, Jan Bischoff, and Bernd Skiera
Status: In progress, initial draft of working paper estimated March 2024.
Other Projects
Teaching Marketing Analytics: A Pricing Exercise for Quantitative and Substantive Marketing Skills
Authors: Bernd Skiera and Lukas Jürgensmeier
This article describes a data-driven exercise for teaching and assessing students’ skills in marketing analytics, specifically in pricing. This exercise combines teaching econometrics to analyze data and substantive marketing to derive managerial insights. The econometric challenge requires students to set up and implement a regression analysis to derive the demand function, detect multicollinearity, and select appropriate data visualizations. The substantive challenge requires deriving optimal pricing decisions and understanding how the parameters of the demand function impact optimal prices and the associated profit. We discuss the exercise’s efficacy as an exam by presenting the distribution of student performance from 134 exam submissions. Beyond assessing students’ learning in an exam, the exercise facilitates teaching as a case study for in-class discussion or an assignment. Under a liberal CC BY license and free of charge, we encourage other educators to use the exercise in their teaching. Beyond the exercise, we provide the necessary data and a sample solution using the statistical programming language R.
Keywords: Marketing Analytics, Pricing, Teaching, Exercise, Case Study, Data Science
Status: Revise and Resubmit the Journal of Marketing Analytics (Special Issue on “Marketing Analytics in Academia: Exploring Effective Pedagogy.”)