Events

GenAI Acceptance in Professional Services: The Case of Management Consulting
Presenter: Dennis Nesemeier (TUM)
Discussant: Mainak Ghosh (MPI)

The increasing relevance of Generative Artificial Intelligence (GenAI) in professional services and its impact on client services and operations raises the question: “What factors influence the acceptance of GenAI?” I explore the intricate factors influencing the acceptance of GenAI, specifically focusing on management consulting. Therefore, I use the Unified Theory of Acceptance and Use of Technology (UTAUT) as a theoretical framework, which I adapt and extend while employing a mixed-methods approach. Twenty semi-structured interviews and a quantitative survey of 121 consultants reveal insights into consultants’ perceptions and interactions with GenAI. The findings indicate the relevance of performance and effort expectations, social influence, facilitating conditions, and concerns about trustworthiness. Highlighting the complexity of human-technology dynamics, some consultants view GenAI as an opportunity to gain a competitive advantage in their career progression, while others report feeling ashamed when disclosing their use of it. The study broadens the scope of technology acceptance research, introduces specific adaptations of the theory to fit the GenAI context better, and provides practical managerial recommendations.

How to Measure and Improve the Quality of Crowd-Sourced Data Annotation?
Presenter: Chiara Belletti (ISTO)
Discussant: Leonard Hanschur (TUM)

Micro-tasking platforms enable the collection of data used to train machine learning algorithms and artificial intelligence. However, a classical Principal-Agent problem may limit the quality of the data produced by micro-taskers as firms do not always monitor the quality of the work done with sufficient frequency. We develop a structural model of equilibrium demand and supply of effort to measure quality and monitoring behavior. We estimate the parameters of this model using proprietary data from a leading micro-tasking platform. We find that metrics relying on observed task rejection severely underestimate the quality/effort with which data annotation, collection generation tasks are performed, exposing AI applications to noise and bias. We discuss several mitigation strategies. We find that increasing the pay of micro-taskers along with more frequent monitoring could help improve the quality of the data. Finally, we discuss incentive schemes to induce higher quality work by relying on counter-factual simulations. We show that charging penalties for workers with a rejected task could induce higher effort and require less monitoring from the firms.

Breaking the Ice: Can Initially Active Peers Improve Platform Engagement and Persistence?
Presenter: Svenja Friess (MPI)
Discussant: Ambre Nicolle (ISTO)

Online knowledge exchange has flourished in recent years, yet struggles with low user engagement remain. This study investigates the role of early peer interactions in sustaining engagement on digital platforms. Analyzing novel data from 12,000+ professionals upskilling in an online business program, we exploit quasi-random variations in initial peer activity levels per cohort to estimate their impact on future engagement and platform persistence. Results reveal that a high initial share of active peers giving likes reduces platform persistence by 3%, while a high share of early active commenting peers exhibits no correlation with future engagement or persistence. However, when looking at directed interactions, we find that receiving early comments and likes significantly boosts future engagement and platform persistence. Employing cutting-edge Natural Language Processing techniques, we classify comment characteristics to shed light on underlying mechanisms. These results indicate that receiving early positive comments has a stronger positive association with future engagement and persistence than when early comments are more negative. Our findings provide insights for digital platform designers to effectively leverage early and directed peer interactions, enhancing user experience and platform value.

Commercialization is a crucial phase in the innovation process and its associated costs significantly influence R&D decisions. Yet our understanding of how commercialization costs impact various stages of innovation remains underdeveloped. In this study, I investigate the effects of commercialization costs on early and late stages of the innovation process in a quasi-experimental setting. Specifically, I leverage sudden policy shifts in the US Food and Drug Administration (FDA) that increased commercialization costs for drugs in certain therapeutic areas. Employing a difference-in-differences methodology, I trace the impacts of these elevated costs on discovery and clinical trial advancement of 3,357 drug candidates between 1997 and 2015. My research places emphasis on the contrasting roles that startups and established firms have in innovation. My findings reveal that while commercialization costs diminish the late-stage efforts in commercializing innovations, especially by established firms, they stimulate an environment conducive to early-stage entrepreneurial drug discovery efforts. Furthermore, I find that the disruption that commercialization costs create in markets for technology drives these opposite findings: new discoveries remain without buyers in technology markets, failing to complete their development process as commercialized products.

Contact person: Elisabeth Hofmeister

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Throughout history, technological advancements have consistently challenged existing legal frameworks, necessitating reactive adaptations to protect emerging human capabilities, liberties, values, interests, and norms. Yet, this dynamic has unfolded largely without a deliberate effort to theorize the law-technology nexus.

In our current era, characterized by the rapid proliferation of data-driven technologies (DDT), the need for a theoretical elucidation and reevaluation of the law-technology interplay has become increasingly pressing. This lecture addresses the critical demand for a new theoretical framework to comprehend the intersection of law and DDT, examining the adequacy of classical legal theories in grappling with DDT’s novel nature and its profound societal impact.

We advocate for a critical reevaluation of classical legal theories, proposing the development of a robust, comprehensive theoretical framework that captures the law-technology relationship within data-centric environments. The lecture will underscore the necessity of this jurisprudential evolution, highlighting the insufficiency of traditional legal paradigms to address the complex consequences and transformative potential of DDT. Additionally, we will delve into the significant implications of the information revolution and data science for legal normativity.

This lecture provides a timely and innovative examination of the theoretical and normative foundations required to navigate the confluence of law and cutting-edge data-driven technologies. It serves as a platform for interdisciplinary dialogue and insights crucial for shaping policy, practice, and future research in this rapidly evolving domain.

Joseph E. David is a Full Professor of Law at Sapir Academic College in Israel. He is the author of The State Rabbinate: Election, Separation and Freedom of Expression (2000), The Family and the Political: On Belonging and Responsibility in a Liberal Society (2012), Toleration within Judaism (2013), Jurisprudence and Theology in Late Ancient and Medieval Jewish Thought (2014) and Kinship, Law and Politics- An Anatomy of Belonging (2019). He edited The State of Israel: Between Judaism and Democracy (2000), Questioning Dignity: Human Dignity as Supreme Modern Value (2006), Nomos and Narrative for the Hebrew Reader (2012), The Gift of the Land and the Fate of the Canaanites in Jewish Thought (2014), and Strengthening Human Rights Protections in Geneva, Israel, the West Bank and Beyond (2021). Joseph has held academic positions at the University of Pennsylvania, Rutgers University, New York University, University of Oxford, Yale University, Emory University, Hebrew University, and Reichman University.

Based on comparative research on recent AI regulation and Law between China and EU, especially “The Interim Administrative Measures on Generative AI Services” in China and the “EU Artificial Intelligence Act”, also based on recent AI case studies related to intellectual property, and ethics issues (including fairness and safety). This paper put forward some suggestions about restrictions and supplemental regulations etc. in the related laws (including patent law, copyright law). And gives considerations for promoting the sustainable development of AI industry by promoting ethical and sustainable innovation.

Prof. Dr. Xiang YU is a Member of Academia Europaea, Director of Sino-European Institute for Intellectual Property of Huazhong University of Science and Technology, and Vice President of Hubei Normal University.

Contact person: Daehyun Kim

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We study the welfare implications of the rise of common ownership in the United States from 1995 to 2021. We build a general equilibrium model with a hedonic demand system in which firms compete in a network game of oligopoly. Firms are connected through two large networks: the first reflects ownership overlap, the second product market rivalry. In our model, common ownership of competing firms induces unilateral incentives to soften competition and the magnitude of the common ownership effect depends on how much the two networks overlap. We estimate our model for the universe of U.S. public corporations using a combination of firm financials, investor holdings, and text-based product similarity data. We perform counterfactual calculations to evaluate how the efficiency and the distributional impact of common ownership have evolved over time. According to our estimates the welfare cost of common ownership, measured as the ratio of deadweight loss to total surplus, has increased about ninefold between 1995 and 2021. Under various corporate governance models the deadweight loss of common ownership ranges between 3.5% and 13.2% of total surplus in 2021. The rise of common ownership has also resulted in a significant reallocation of surplus from consumers to producers.

Contact person: Marina Chugunova

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We examine how reviewer–applicant social ties (department and university affiliation, co-author/co-applicant relationships, research field similarity) influence reviewer evaluations, based on Japanese research grant administrative data (2005–2016). All relationships between social ties and scores are positively correlated, even after accounting for unobservable applicant characteristics and proposal quality. Regarding bias and information advantage effects, upward deviation from department match negatively correlates with applicants’ future research outputs, implying bias. Upward deviation from research field similarity or university match positively correlates with future productivity, indicating that information advantage predicts applicants’ future productivity. 

Contact person: Marina Chugunova

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The international mobility of researchers has been central to the agenda of policy-makers for several decades. Despite the growing presence of mobility grants within public funding agencies' portfolios, empirical evidence on their effects remains scant. In this paper, we contribute to the literature by studying the Marie Curie fellowships, the flagship program of the EU, providing competitive grants to early-stage researchers to spend a research period abroad. Based on data for the universe of applicants to the Seventh Framework Programme (2007-2013), we exploit the discontinuity in grant assignment to uncover causal effects on individual researchers. Results show that grants are indeed conducive to higher chances of experiencing mobility towards the scientists' country of choice. Conversely, we do not find systematic evidence that grants on average lead to increases in publication quantity or quality, nor improved career progression. Finally, we document interesting heterogeneous effects: grants supporting extra-European mobility, as opposed to those supporting mobility within Europe, generally yield more positive effects across most outcomes. This suggests that grants are most effective when targeting mobility flows subject to larger frictions. (co-authors: Stefano Baruffaldi, Yevgeniya Shevtsova)

Contact person: David Heller

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Recent research and policy initiatives suggest that entrepreneurship serves as a pathway for reintegrating ex-felons into the labor market by reducing the stigma they face among prospective employers. However, the long-term outcomes of entrepreneurial careers for individuals with criminal records remain poorly understood. Using Danish administrative data, we investigate how the type of sentences received by individuals convicted of crimes relates to their subsequent labor market trajectories and, particularly, their transition into entrepreneurship. We study how community service sentences, as an alternative to imprisonment, shape ex-felons’ labor market trajectories and long-term outcomes. We find that individuals sentenced with community service are significantly less likely to engage in entrepreneurship than comparable individuals who were incarcerated instead. This aligns with prior research pointing to entrepreneurship as an alternative employment pathway for those stigmatized in the labor market. Importantly, we also find that ex-felons who turn to entrepreneurship - especially after having been incarcerated - suffer a persistent income disadvantage afterwards and exhibit higher rates of recidivism than those who find regular employment. Hence, our findings question how effective entrepreneurship can be as a social mobility and reintegration pathway for individuals with criminal records.

Contact person: Svenja Friess

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The Munich Summer Institute (MSI) is hosted by the Center for Law & Economics at ETH Zurich, HEC Lausanne, Northeastern University, the Chair for Technology and Innovation Management at TUM, the Chair for Economics of Innovation at TUM, the Institute for Strategy, Technology and Organization (ISTO) at the LMU Munich and the Max Planck Institute for Innovation and Competition.

Program as pdf
Further information on the website of the MSI.

Panel 1 – Intellectual Property Data in Ukraine
 

Panel 2 – Health and Medical Innovation Data
 

Panel 3 – Innovation Activity, Entrepreneurship and Industry Data in Ukraine
 

Panel 4 – Data for the Research on Science in Ukraine

Program as pdf

Contact person: Liudmyla Petrenko