Monetary Incentives for Repeat Interactions: Evidence from an Online Labor Market
Presenter: Frederike Eulitz (ISTO)
Discussant: Cheng Li (MPI)
Can platform-level changes to monetary incentives increase repeat interactions in online labor markets (OLMs)? We study this question by exploiting a change in the fee structure of the OLM Upwork, which decreased fees if the lifetime billings of a freelancer-buyer relationship surpassed thresholds. We explore the effects of incentive design on repeat interactions, which is theoretically ambiguous in traditional organizational settings, in an OLM. Analyzing a panel dataset of 24,873 freelancers, we reveal that the effects of monetary incentives are not uniform but depend on sub-group characteristics and the unique contextual factors associated with OLMs. Low-earning freelancers respond by increasing repeat interactions, whereas high-earners exhibit a surprising decline. We argue that the impact of monetary incentives depends on platform type. In knowledge-sharing platforms, such incentives may reduce engagement by crowding out intrinsic motivation. However, in platforms with marketplace features, such as OLMs, we argue that monetary incentives induce rational behavior because participants are extrinsically motivated to interact. Our findings contribute to the literatures on platform governance and incentives, offering insights into how platform-level strategies shape participant behavior, and the unintended consequences of platform incentive design.
The Role of Team Composition in Explorative Invention: Analyzing the Impact of Tenure Disparity and Team Size
Presenter: Jisoo Hur (TUM)
Discussant: Sophia Wetzler (ISTO)
Employee career development is a dynamic process in which individuals adopt different strategies at various stages, leading to variations in their innovative behavior based on organizational tenure. Junior employees, often in the early stages of their careers, bring fresh perspectives and external knowledge, making them valuable contributors to explorative innovation. However, fostering this type of innovation can be challenging when junior inventors collaborate with senior inventors, whose extensive experience in the firm’s technological domain and greater decision-making authority may unintentionally limit juniors’ autonomy and capacity to explore new ideas. This study investigates how tenure differences between junior and senior inventors influence their likelihood of engaging in exploratory inventions. Using patent data to analyze co-inventor relationships, the findings reveal that junior inventors are more likely to engage in exploratory invention when collaborating with peers of similar tenure rather than with senior inventors. Additionally, greater tenure disparity between junior and senior inventors is associated with a decline in exploratory invention. However, larger team sizes help mitigate this negative effect by fostering a more balanced and collaborative environment. These findings provide valuable insights into the dynamics of inventor collaboration and offer practical strategies for organizations to optimize team composition and enhance exploratory innovation.
Scientific Paradigms, Graphics Processing Units and the Evolution of Artificial Intelligence
Presenter: Anna-Sophie Liebender-Luc (MPI)
Discussant: Nicole Wenger-Wong (TUM)
A sudden shift in scientific and technological paradigms lies at the heart of recent advancements in artificial intelligence (AI). Around 2012, traditional symbolic AI gave way to neural networks (NN) as the dominant approach for AI research. This coincided with the sudden successful application of graphics processing units (GPUs) as computational technology. GPUs had been invented for a different application, i.e. accelerating complex graphics displays, mostly in video games. We claim that these developments reflect the nature of breakthrough innovations and have implications for regions competing to become AI leaders. We investigate the role of expertise in GPUs for the uptake of AI innovation across regions globally. To this end, we construct a global database covering 2,088 urban areas for the period from 2000 to 2020. The data encompass a broad set of measures describing AI research and innovation activities, based on publications, patents and startups. We document the ascendancy of neural AI and its association with GPU expertise. Panel OLS and IV regressions demonstrate that after 2012 GPU- and NN-related human capital had a strong effect on the growth of AI-related patents and startups. We discuss implications for innovation policy.