Eindhoven University of Technology
Location
Niederlande | Germany
Job description
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PhD position on meaningfulness of work in human-AI collaboration
Position: PhD-student
Irène Curie Fellowship: No
Department(s): Industrial Engineering and Innovation Sciences
FTE: 1,0
Date off: 29/02/2024
Reference number: V39.7218
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.
Job description
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:
Job requirements
Conditions of employment
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:
Information and application
More information
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.muller1[at]tue.nl) or Dr. Leander van der Meij (l.v.d.meij[at]tue.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.ieis[at]tue.nl.
Application
We invite you to submit a complete application using the 'apply now' button on this page.
The application should include a:
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.
Eindhoven
Vollzeit, Befristete Festanstellung
4 Jahre
Master-Abschluss
Dr. Rebecca Müller
Veröffentlicht am 30.01.2024
Job tags
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