Summary
Millions of employees around the globe work for an AI manager that structures their tasks and monitors their performance. While companies hope that such AI managers will lead to substantial performance benefits, it is critical that AI transits from a bureaucratic manager to an inspiring, transformational leader if companies want employee performance and satisfaction to peak. To date, however, research on the consequences of AI leadership cannot give relevant insights because scholars have studied employees in hypothetical scenarios or simplistic environments that do not mirror the complex realities of contemporary workplaces. My goal within the Marie Skłodowska-Curie Fellowship is to tackle these problems by investigating interactions between employees and AI leaders from an interdependence theory view. Across two empirical studies with more than 1,400 employees, I will examine how AI leaders (programmed via a novel natural language processing tool) affect employees’ task performance and satisfaction when they really interact with AI leaders across interdependent tasks, different leadership styles, and work constellations. Given the rise of AI managers within Europe, this research will provide relevant scientific, practice, and policy insights aimed at sustaining a high-performing and satisfied European workforce of the future. I will accomplish these ambitious goals by combining my expertise with the profound knowledge of interdependence theory and longitudinal methods from Maastricht University’s scholars. In sum, this action and the foreseen two top-tier publications will substantially advance my research (both theory and methods) skills and extend my professional and practice network. Maastricht University is an ideal fit for this fellowship that will be a significant boost for my academic career and will provide a cornerstone for becoming a mature and renowned scientist in the field of AI leadership, in particular, and organizational behavior, in particular.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101062186 |
| Start date: | 15-11-2022 |
| End date: | 14-11-2024 |
| Total budget - Public funding: | - 203 464,00 Euro |
Cordis data
Original description
Millions of employees around the globe work for an AI manager that structures their tasks and monitors their performance. While companies hope that such AI managers will lead to substantial performance benefits, it is critical that AI transits from a bureaucratic manager to an inspiring, transformational leader if companies want employee performance and satisfaction to peak. To date, however, research on the consequences of AI leadership cannot give relevant insights because scholars have studied employees in hypothetical scenarios or simplistic environments that do not mirror the complex realities of contemporary workplaces. My goal within the Marie Skłodowska-Curie Fellowship is to tackle these problems by investigating interactions between employees and AI leaders from an interdependence theory view. Across two empirical studies with more than 1,400 employees, I will examine how AI leaders (programmed via a novel natural language processing tool) affect employees’ task performance and satisfaction when they really interact with AI leaders across interdependent tasks, different leadership styles, and work constellations. Given the rise of AI managers within Europe, this research will provide relevant scientific, practice, and policy insights aimed at sustaining a high-performing and satisfied European workforce of the future. I will accomplish these ambitious goals by combining my expertise with the profound knowledge of interdependence theory and longitudinal methods from Maastricht University’s scholars. In sum, this action and the foreseen two top-tier publications will substantially advance my research (both theory and methods) skills and extend my professional and practice network. Maastricht University is an ideal fit for this fellowship that will be a significant boost for my academic career and will provide a cornerstone for becoming a mature and renowned scientist in the field of AI leadership, in particular, and organizational behavior, in particular.Status
SIGNEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
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