Irène Curie Fellowship
Industrial Engineering and Innovation Sciences
An essential focus of research on human-AI collaboration is how the implementation of AI affects employees' experience of meaningful work, which is an important psychological aspect for every human being. However, several aspects of this topic have remained largely unexplored. There is a need to better understand how AI implementation influences (1) team-level dynamics that are integral to creating a meaningful work experience, and (2) its influence on work-related outcomes in terms of employee well-being and team effectiveness. Effective teamwork and high well-being of employees in human-AI collaboration are crucial to achieving high performance and long-term benefits for employees and organizations when using AI.
The Dutch Government decided to create extra jobs at universities to investigate these important topics related to AI and formulated a Sector plan to face these topics (Sectorplannen 2022/2023 for Social Science and Humanities).
The Human Performance Management (HPM) Group of the School of Industrial Engineering is looking for a PhD student to study the consequences of human-AI collaboration in the workplace for meaningful work experiences, and subsequent team effectiveness and employee well-being (4 years; 1.0 FTE).
Are you enthusiastic about the collaboration between humans and advanced technologies at work and how this impacts meaningfulness of work, teamwork, and employee well-being? Come join our international and interdisciplinary team to research the future of human-AI collaboration.
With the rapid progress of technology, organizations increasingly implement advanced technologies, such as software agents or robots with artificial intelligence (AI) in work environments to decrease employee workload and increase work efficiency. Thus, psychological aspects of human-AI collaboration must be considered to make such implementations successful. One important psychological aspect is if and how an employee experiences meaningful work when collaborating with AI. Indicators of meaningful work are having autonomy about one's actions and goals (i.e., self-determination), the opportunity for self-development, recognition for achievements, social relationships, and perceived justice. So far, researchers have addressed these indicators as individual-level phenomena. However, these indicators also have implications for dynamics on the team level, such as motivational (e.g., willingness to work with an AI) and operational (e.g., coordination, communication) aspects of collaborating with the AI in a team. Further, whether and how AI implementation influences team effectiveness and employee well-being via the meaningfulness of work is also under-explored. Important factors of successful teamwork that need to be considered are team cognition, trust, knowledge sharing, and team learning. Meanwhile, important well-being factors are work engagement, job satisfaction, and burnout.
This PhD project will generate insights into the important psychological aspects of meaningfulness of work in human-AI collaboration. The novelty of this project is that it should combine the insights of meaningfulness of work, teamwork, and employee well-being regarding human-AI collaboration. Specifically, you will (1) provide insights on how the AI implementation influences indicators of meaningful work at the individual and the team level, (2) study whether (and which) indicators of meaningful work mediate the effect of AI implementation on employee well-being and team outcomes (over a longer time), (3) investigate coping mechanisms used by employees and teams when the collaboration with an AI threatens meaningful work, and (4) provide implications for training to ensure high meaningfulness of work, employee well-being, and team effectiveness in times of increasing AI implementation.
This PhD project can use different types of data from various domains (e.g., health care, manufacturing, high-tech industry, knowledge work). Data will be collected through qualitative and quantitative methods in cross-sectional and/or longitudinal field studies as well as in laboratory experiments.
The successful applicant is expected to:
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
The School of Industrial Engineering is one of the longest-established IE Schools in Europe, with a strong presence in the international research- and education community and an extensive network of industrial partners. The graduate programs (MSc and PhD) in Operations Management & Logistics and Innovation Management attract top-level students worldwide.
Human Performance Management (HPM) at TU/e develops scientific knowledge and tests theories that uncover and explain psychological processes contributing to organizational, team, and individual performance. By examining the 'people factor' in operational and innovation processes, HPM aims to ensure that employees can help bring organizational strategies to fruition in the most rewarding and efficient way possible.
Do you recognize yourself in this profile, and would you like to know more about this position? Please contact Dr. Rebecca Müller (r.muller1attue.nl) or Dr. Leander van der Meij (l.v.d.meijattue.nl).
More information about the HPM group can be found here.
Visit our website for more information about the application process or the conditions of employment. You can also contact Najat Loiazizi, HR advisor, phone: +31 40 2474465, email: pz.ieisattue.nl.
We invite you to submit a complete application using the 'apply now' button on this page. The application should include a:
Cover letter in which you describe your motivation and qualifications for the position (two pages maximum).
Curriculum vitae, including a list of your publications (if available) and the contact information of three references.
We look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.
We do not respond to applications that are sent to us in a different way. Note that incomplete applications will not be considered. Please keep in mind you can upload only 5 documents up to 2 MB each. If necessary, please combine files.
We ask candidates to apply by February 29, 2024, at the latest. The preferred starting date for this position is April 1, 2024.