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.