In this master thesis, the student should conduct a qualitative study, interviewing collaboration practitioners (workshop facilitators, team leader etc.) to capture and analysis phenomena, mechanisms, and dynamics of collaboration, group- and teamwork. The thesis aims to uncover existing theories describing psychological and cognitive effects that occur during joint work (collaboration, teamwork, group work, etc.), link the uncovered phenomena to scientifically established theories, and begin theoretical provoking of the theories in the context of human-AI collaboration.

Collaboration emerges in teams when people jointly work together to reach a common goal. Collaboration is associated with several phenomena, mechanisms, and dynamics. Dynamic phenomena occur in teams, which is why breaking teams down into individual measurable parts does not make sense, as one should look at the overall collective performance, which is explained as more than just the sum of the individual parts (Paulus et al. 2012). Thus, the whole of a team does not result from the analysis of the individual group members, but from the analysis of these phenomena between the participants. These dynamic relationships in which attracting and repulsive forces act, can therefore have both negative and positive effects on the team’s performance.

The continuous improvement of computing power and the availability of enormous amounts of data has considerably matured artificial intelligence (AI) and now enables a versatile use of intelligent and autonomous systems such as virtual assistants and chatbots (Russell and Norvig 2016). Although at a present time, such systems can, from a technological perspective, primarily interact and communicate around specific topics in a narrow way (Diederich et al. 2022), technologies such as GTP-3 will fundamentally expand the capabilities of intelligent systems by enabling richer open-topic communication and interaction with strong interpersonal character (Pilipiszyn 2021). This enables AI-based systems to interact and even collaborate with humans in an unprecedented way. However, utilizing such systems in collaboration scenarios challenges research to redefine existing theories of teamwork. Technology that was previously used as a tool to address narrow tasks is now taking an increasingly active role by taking on complex tasks and making contributions autonomously. This new role of IT raises a number of questions that need to be addressed, as “AI might add value to teams and organizations that may be leaps ahead from current technological team support”(Seeber et al. 2019), p. 1).

However, the technological progress and the use of AI in human-AI teams mean that established theories on group phenomena, as well as cognitive and social group processes, need to be reconsidered. Since these theories originate from the time when purely human teams existed, it is important to investigate how these theories change as soon as humans work together with AI in a balanced way, i.e., as hybrid intelligence.

In this master thesis, the student should conduct a qualitative study, interviewing collaboration practitioners (workshop facilitators, team leader etc.) to capture and analysis phenomena, mechanisms, and dynamics of collaboration, group- and teamwork. The thesis aims to uncover existing theories describing psychological and cognitive effects that occur during joint work (collaboration, teamwork, group work, etc.), link the uncovered phenomena to scientifically established theories, and begin theoretical provoking of the theories in the context of human-AI collaboration.

You should…
– have a basic understanding of collaboration, teams, groups and social- and cognitive group effects
– have a basic understanding of theories and theorizing
– have a basic understanding of qualitative content analysis and qualitative research
– be a communicative and open person
– work in an independent and accurate way
– have scientific writing skills in English
– be passionate about artificial intelligence and psychology

We offer you a close supervision and the opportunity to develop practical, as well as theoretical skills in the area of information systems. If you are interested, send your CV, transcript of records and a brief description of your motivation to Edona Elshan ().

In this master thesis, the student should conduct a systematic literature review and analysis on phenomena, mechanisms, and dynamics of collaboration, group- and teamwork. The systematic literature review has the aim to uncover (all) existing theories that describe psychological and cognitive effects that occur in joint work (collaboration, teamwork, group work, etc.) in order to provide a profound foundation for theoretical provoking for human-AI collaboration.

Collaboration emerges in teams when people jointly work together to reach a common goal. Collaboration is associated with several phenomena, mechanisms, and dynamics. Dynamic phenomena occur in teams, which is why breaking teams down into individual measurable parts does not make sense, as one should look at the overall collective performance, which is explained as more than just the sum of the individual parts (Paulus et al. 2012). Thus, the whole of a team does not result from the analysis of the individual group members, but from the analysis of these phenomena between the participants. These dynamic relationships in which attracting and repulsive forces act, can therefore have both negative and positive effects on the team’s performance.

The continuous improvement of computing power and the availability of enormous amounts of data has considerably matured artificial intelligence (AI) and now enables a versatile use of intelligent and autonomous systems such as virtual assistants and chatbots (Russell and Norvig 2016). Although at a present time, such systems can, from a technological perspective, primarily interact and communicate around specific topics in a narrow way (Diederich et al. 2022), technologies such as GTP-3 will fundamentally expand the capabilities of intelligent systems by enabling richer open-topic communication and interaction with strong interpersonal character (Pilipiszyn 2021). This enables AI-based systems to interact and even collaborate with humans in an unprecedented way. However, utilizing such systems in collaboration scenarios challenges research to redefine existing theories of teamwork. Technology that was previously used as a tool to address narrow tasks is now taking an increasingly active role by taking on complex tasks and making contributions autonomously. This new role of IT raises a number of questions that need to be addressed, as “AI might add value to teams and organizations that may be leaps ahead from current technological team support”(Seeber et al. 2019), p. 1).

However, the technological progress and the use of AI in human-AI teams mean that established theories on group phenomena, as well as cognitive and social group processes, need to be reconsidered. Since these theories originate from the time when purely human teams existed, it is important to investigate how these theories change as soon as humans work together with AI in a balanced way, i.e., as hybrid intelligence.

In this master thesis, the student should conduct a systematic literature review and analysis on phenomena, mechanisms, and dynamics of collaboration, group- and teamwork. The systematic literature review has the aim to uncover all existing theories that describe psychological and cognitive effects that occur in joint work (collaboration, teamwork, group work, etc.) in order to provide a profound foundation for theoretical provoking for human-AI collaboration.

You should…
– have a basic understanding of collaboration, teams, groups, and social- and cognitive group effects
– have a basic understanding of theories and theorizing
– have a basic understanding of systematic literature reviews
– have great analytics skills and love reading scientific papers
– work in an independent and accurate way
– have scientific writing skills in English
– be passionate about artificial intelligence and psychology

We offer you close supervision and the opportunity to develop practical, as well as theoretical skills in the area of information systems. If you are interested, send your CV, transcript of records and a brief description of your motivation to Edona Elshan ().

Over the last several years, technology has radically changed the way people and machines work together. Work systems in organizations are changing at an accelerating rate. The former premise that workplaces and work systems are designed and exist for decades is now being critically questioned. In your thesis, you will address the topic of how micro shifts in work practices through technology are radically changing the way entire work systems operate over time.

This thesis topic is aimed at highly motivated students who want to work on a real research project. As part of their thesis, they will be able to utilize extensive interview data, previous research findings, and literature summaries. We offer close supervision and the opportunity to present your results to an international expert audience at the International Conference on Information Systems in Copenhagen.

Looking forward to hear from you!

Businesses operate in a turbulent ever-changing environment. As part of your thesis, you will explore how traditional ways of working have been transformed by digital opportunities over the past few decades and what drivers have enabled increasing agility. In essence, your thesis can shed light on the topic, but is not limited to what the big change drivers have been in the past and what they might be in the future.

As part of your thesis, you will be able to draw on previous research and extensive research material. We offer you flexible and close supervision in this highly relevant topic and the opportunity to present excellent results to experts at renowned conferences.

Looking forward to hear from you.

COVID-19 has led to many changes – the most prominent being the extreme increase in home-office and remote working.The question you ask yourself in this paper is how managers can find the optimal setting for their company, how new work organization can take place in a hybrid setting, and which opportunities and challenges have to be mastered.

In the context of your thesis you can draw on existing interview data as well as on previous research. We offer you close supervision in this highly relevant topic and the opportunity to present excellent results to experts at renowned conferences.

Accumulation of legal design knowledge in software development

Legal knowledge is becoming an increasingly important part of software development. New stricter data protection regulations such as the GDPR increase the pressure on developers to implement legal requirements. Design patterns are an established tool in software development to capture proven solutions to recurring problems. Thus, design patterns can also be a way to share legal knowledge with developers.

The research project investigates how developers of smart personal assistants such as Amazon Alexa can be supported in developing the systems as legally as possible and in accordance with the latest data protection regulations.

Possible theses can deal with:

• GDPR compliant smart personal assistants
• Design patterns to codify legal design knowledge
• Development of frameworks to support developers in solving legal challenges
• Qualitative and quantitative studies with developers
• Research on how developers can best improve their skills and extend their legal knowledge

Start immediately or by arrangement.

In der Unternehmenspraxis findet sich inzwischen ein grosser Markt für Tests, welche die Agilität der Mitarbeitenden erfassen sollen. In dieser Masterarbeit sollen systematisch die verwendeten Testverfahren überblickt und deren Güte auf Basis von methodischem Knowhow überprüft werden. Auf diesen Erkenntnissen sollen Handlungsempfehlungen für Vorgehensweisen bei der Vermessung der Agilität von Mitarbeitenden abgegeben werden.

Voraussetzung: gründliches methodische Knowhow

Start per sofort oder nach Vereinbarung

Bei der Umsetzung von KI-Projekten spielt Bias eine grosse Rolle.

Unter Bias versteht man eine systematische Abweichung der Ergebnisse eines Algorithmus von den gewünschten Ergebnissen. Dadurch können KI-Projekte langfristig Schaden nehmen und Vertrauen in die KI-Lösung verspielt werden. Verschiedene Bias können in jeder Phase des Projektes auftreten, weshalb es wichtig ist, sich in jeder Phase des Projektes der möglichen Bias bewusst zu sein. Ist ein Bias identifiziert, kann er mit speziellen, auf den jeweiligen Bias angepassten Mitigation Strategys behandelt werden.

Für die Entwicklung eines Risk Management Tools, das Projektleitern hilft, strukturiert gegen Bias vorzugehen, suchen wir einen Masteranden/ eine Masterandin.