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Unser Lehrprogramm umfasst Veranstaltungen aus dem Gebiet Wirtschaftsinformatik im Bachelor- und Masterstudium sowie Angebote für Doktoranden. Unser Ziel ist es, die Studierenden zu flexiblen und teamfähigen Persönlichkeiten auszubilden, die sowohl in der Lage sind, ihr Fachwissen auf reale Problemstellungen anzuwenden als auch praktische Problemstellungen mit einer wissenschaftlichen Herangehensweise zu lösen. Durch die Integration von aktuellen Forschungsergebnissen in die Lehre wollen wir eine enge Verzahnung von Lehre und Forschung erreichen. Darüber hinaus haben Studenten die Möglichkeit, in Bachelor- oder Masterarbeiten an aktuellen Forschungsfragen und -projekten mitzuarbeiten.
An unserem Lehrstuhl betreuen wir Bachelor- und Masterarbeiten mit Bezug zu unseren Forschungsschwerpunkten. Es gelten die offiziellen Bestimmungen der Universität St.Gallen für Bachelor- und Masterarbeiten. Weitere Informationen zum offiziellen Anmeldeverfahren, zu den formalen Kriterien, zu Plagiaten und sowie zu den Benotungsrichtlinien finden Sie im Intranet der HSG.
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 ().
When interacting with contemporary information systems (think websites, chatbots, decision tools), users are (unconsciously) facing certain persuasion mechanisms or so-called nudges which softly guide them towards favoring certain decisions over others (i.e., do you have to opt-out from additional insurance on your booked flight?).
In light of Big Data and Machine Learning, such systems increasingly take over tasks and decisions, thereby possibly reducing users’ autonomy. The design of digital choice architectures is not as straightforward: Nudges can be criticized to suffer from opaqueness, manipulation, and paternalism. How can choice architectures be designed in a more transparent manner? And how would such transparency eventually influence users’ decision-making with the system?
As part of a greater research project, this study seeks to
1) identify the role of transparency and autonomy in decision-making,
2) understand how conflicts of interests and information asymmetries could and should be balanced, and
3) investigate the influence of transparency mechanisms on user perception and behavior in a healthcare context.
Start immediately or by arrangement
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 ().
Why:
The Covid 19 pandemic has fundamentally changed onboarding processes in companies. Digital, novel processes and tools are needed to effectively onboard new employees. In a literature review, novel digital approaches to improve these on-boarding processes will be identified and analyzed, such as the use of nudges (Terres et al., 2019) or chatbots (Adam & Klumpe, 2019).
Your bachelor’s/master’s thesis should thus contribute to improving digital onboarding processes for new employees in companies.
How:
Bachelor/Master thesis that conducts a systematic literature review on the topic of digital on-boarding processes in companies using the methodology of Webster and Watson (2002) and Vom Brocke et al. (2009; 2015).
Key-Facts:
• Close supervision, with regular review meetings, feedback discussions, etc.
• Thesis can start immediately, but should be completed within the next ±6 months
• If the work is worth publishing, you will be listed as author
Looking forward to hear from you! Start immediately or by arrangement
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:
Start immediately or by arrangement.
Das Fachgebiet von Prof. Dr. Jan Marco Leimeister forscht zu hoch relevanten Themen, von der Grundlagenforschung bis zur anwendungsbezogenen Forschung. Die Wirtschaftsinformatik ist eine wichtige Schnittstellendisziplin zwischen Betriebswirtschaft, Informatik, Ingenieurwissenschaften; deshalb ist die Disziplin in der Forschung entsprechend methodisch divers aufgestellt. Die Wirtschaftsinformatik liefert als Kern-Output ihrer Forschung anwendungsnahe, gestaltungsorientierte Theorien, Modelle und Methode, die ökonomische, technische und soziale Aspekte berücksichtigen.
Die Arbeiten des Fachgebiets sind durch einen Pluralismus gekennzeichnet, sowohl theoretisch (verhaltenswissenschaftliche, entscheidungs- und gestaltungstheoretische sowie analytisch-konzeptionelle Grundlagen) als auch methodisch (empirisch qualitative, empirisch quantitative, analytisch-konzeptionelle, gestaltungsorientierte [Design Science Research] Grundlagen). Grundannahme st, dass die Auswahl der geeigneten Forschungsmethoden der jeweiligen Forschungsfrage folgen muss. Empirische Arbeiten werden im Kontext der genannten Themenbereiche sowohl explorativ als auch konfirmatorisch durchgeführt.
Ziel dieser Vorgehensweisen ist es, Zusammenhänge zu erkennen, zu beschreiben und zu erklären, aber auch eine Grundlage für die Entwicklung von Lösungen zu schaffen. Forschungsziele können sowohl die Gegenüberstellung und Reflexion theoretischer Zusammenhänge mit den in der Praxis beobachteten Verhaltensweisen sein als auch die Entwicklung von Lösungen in Form von konzeptionellen, methodischen sowie werkzeugbasierten Artefakten. Die so geschaffenen Artefakte werden in industriellen und/oder experimentellen Szenarien systematisch evaluiert, um sie anschliessend der betrieblichen Praxis zuzuführen. Untenstehend finden Sie eine Auswahl wichtiger Methoden der Wirtschaftsinformatik, deren Beschreibung sowie grundlegende Literatur.
DSR
Design Science Research (DSR) ist ein gestaltungsorientiertes Forschungsparadigma, welches auf das Schaffen von Artefaktnutzen ausgerichtet ist (Hevner et al. 2004). Hierbei unterscheidet sich DSR von reinen Verhaltens-, Sozial- und Naturwissenschaften, welche ausschliesslich nach Wahrheit und weniger nach Nutzen streben (Hevner et al. 2004). In der Wirtschaftsinformatik stellt DSR diverse gestaltungsorientierte Forschungsleitlinien- und -methoden zur Konstruktion soziotechnischer Artefakte zur Verfügung (Gregor und Hevner 2013).
Einen Überblick von Design Science Research als Forschungsparadigma, dessen methodologische Ausprägungen und Derivate gibt die folgende Publikationsliste:
Reading List:
Gregor, S., and Hevner, A. R. 2013. “Positioning and Presenting Design Science Research for Maximum Impact,” MIS Quarterly (37:2), pp. 337–355.
Venable, J., Pries -Heje, J., and Baskerville, R. 2016. “FEDS: A Framework for Evaluation in Design Science Research,” European Journal of Information Systems (25:1), pp. 77–89.
Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. 2007. “A Design Science Research Methodology for Information Systems Research,” Journal of Management Information Systems (24:3), pp. 45–77.
Nunamaker, J., Chen, M., and Purdin, T. 1991. “Systems Development in Information Systems Research,” Journal of Management Information Systems (7:3), pp. 89–106.
Nunamaker, J. F., Briggs, R. O., Derrick, D. C., and Schwabe, G. 2015. “The Last Research Mile: Achieving Both Rigor and Relevance in Information Systems Research,” Journal of Management Information Systems (32:3), pp. 10–47.
March, S. T., and Smith, G. F. 1995. “Design and Natural Science Research on Information Technology,” Decision Support Systems (15:4), pp. 251–266.
Kuechler, B., and Vaishnavi, V. 2008. “On Theory Development in Design Science Research: Anatomy of a Research Project.,”
European Jou rnal of Information Systems (17:5), pp. 489 –504.
Hevner, A., March, S. T., and Park, J. 2004. “Design Science in Information Systems Research,” MIS Quarterly (28:1), pp. 75–105.
Baskerville, R., Baiyere, A., Gregor, S., Hevner, A., and Rossi, M. 2018. “Design Science Research Contributions: Finding a Balance between Artifact and Theory,”
Journal of the Association for Information Systems (19:5), pp. 358–376.
C. Engel, N. Leicht, and P. Ebel, “The Imprint of Design Science in Information Systems Research: An Empirical Analysis of the AIS Senior Scholars’ Basket,” in International Conference on Information Systems, Munich, Germany, 2019, pp. 1–10.
Sein, M., Henfridsson, O., Purao, S., Rossi, M., and Lindgren, R. 2011. “Action Design Research,” MIS Quarterly (35:1), pp. 37–56.
Quant
Quantitative Methoden werden genutzt, um Effekte aus der Realität anhand empirischer Daten möglichst präzise zu beschreiben und zu untersuchen. Empirische Daten werden dazu anhand von Beobachtungen im Feld oder im Labor erhoben und mit statistischen Analysen ausgewertet. Im Vordergrund steht das Ziel, die zu erhebenden Merkmale und Ausprägungen möglichst genau zu quantifizieren. Oft genutzte Techniken sind die statistische Signifikanz und Effekt-Analysen, um z.B. Unterschiede in den Merkmalen zwischen zwei verschiedenen Stichproben zu untersuchen.
Mehr dazu in:
Bhattacherjee, Anol, “Social Science Research: Principles, Methods, and Practices” (2012). Textbooks Collection. 3. https://scholarcommons.usf.edu/oa_textbooks/3
Experimental Design
Mit Experimenten untersuchen Forscher Ursache-Wirkungs-Beziehungen zwischen vordefinierten Variablen. Dabei werden eine oder mehrere unabhängige Variablen vom Forscher manipuliert (sogenannte “Treatments”). Die Versuchspersonen werden nach dem Zufallsprinzip verschiedenen Treatments zugewiesen und die Ergebnisse der Treatments auf die abhängigen Variablen beobachtet. Die Stärke experimenteller Forschung liegt in ihrer internen Validität (Kausalität), da Ursache und Wirkung durch Treatments miteinander in Verbindung gesetzt werden und gleichzeitig der Störeffekt von Fremdvariablen kontrolliert werden können.
Experimente eignen sich am besten für erklärende Forschung (in Abgrenzung zur deskriptiven oder explorativen Forschung), bei der das Ziel der Studie darin besteht, Ursache-Wirkungs-Beziehungen zu untersuchen (z.B. der Einfluss von Technologie-Charakteristika auf das Nutzerverhalten). Experimentelle Forschung kann im Labor oder im Feld durchgeführt werden. Laborexperimente, die in einer (künstlichen) Laborumgebung durchgeführt werden (z.B. im Behavioral Lab der Universität St. Gallen) , weisen in der Regel eine hohe interne Validität auf, was jedoch auf Kosten einer geringen externen Validität (Verallgemeinerbarkeit) geht, da die künstliche (Labor-) Umgebung, in der die Studie durchgeführt wird, möglicherweise nicht die reale Welt widerspiegelt. Feldexperimente, die in einem Feld-Setting wie in einer realen Organisation durchgeführt werden, weisen in der Regel sowohl eine hohe interne als auch externe Validität auf. Solche Experimente sind jedoch oft schwierig zu realisieren, da die Treatments in das Leben der Probanden eingreifen können und es schwierig ist, das Feldsetting zu kontrollieren.
Methodik:
Beispielstudien:
Case Studies
Eine Case-Study ist eine detaillierte Untersuchung eines bestimmten Themas, z.B. von Individuen, einer Gruppe, eines Ereignisses, eines oder mehrerer Unternehmen, Industrien oder eines Phänomens.
Ein Case Study-Forschungsdesign umfasst normalerweise qualitative Methoden (Interviews, Textdaten, etc.); manchmal werden aber auch quantitative Methoden verwendet. Case Studies eignen sich gut zum Beschreiben, Vergleichen, Bewerten und Verstehen verschiedener Aspekte eines Forschungsproblems.
Eine sehr gute Einführung mit strukturiertem Vorgehen bietet das Buch von Robert Yin (2018), Case Study Research and Applications Design and Methods (https://uk.sagepub.com/en-gb/eur/case-study-research-and-applications/book250150).
Weitere gute Literatur:
Interviews
Aus einer instrumentellen Perspektive betrachtet ist ein Forschungsinterview ein Meinungsaustausch zwischen zwei Personen, von denen eine die Rolle des Forschenden hat und in dem beide über ein Thema von gemeinsamem Interesse sprechen (Kvale, 2007). Das Thema von Interesse ist typischerweise die Erfahrung der befragten Person mit ihrer Lebenswelt (Polkinghorne, 2005). Da das Erfahrungsleben der Menschen von anderen nicht beobachtet werden kann und für Einzelpersonen nicht leicht zugänglich ist, wird bei der Befragung versucht, die Probanden direkt in ein Gespräch mit dem Forscher zu verwickeln, um eine Darstellung der sozialen Wirklichkeit des Teilnehmers in der ersten Person zu erhalten. Ziel des Forschenden ist es, informative, neuartige Informationen über ein bestimmtes Interessensphänomen zu erhalten.
In der Wirtschaftsinformatik werden prinizipell drei Interview-Arten unterschieden: Das wertschätzende Interview, das Laddering- Interview und das Fototagebuch-Interview.
Das wertschätzende Interview lädt die Teilnehmenden ein, Wege zu visionären Welten zu schaffen, die sie gerne bewohnen würden. Im Gegensatz dazu verwendet das Laddering- Interview Vergleiche und Kontraste als eine Möglichkeit, sinnvolle Muster in den Lebenserfahrungen der Menschen zu erkennen. Das Fototagebuch-Interview stützt sich auf die Reflexionen der Teilnehmenden über ihr Verhalten, ihre Gedanken und Emotionen zu einem bestimmten Zeitpunkt, die oftmals mit Hilfe eines Fotos festgehalten werden. Eine sehr gute Einführung bietet der Artikel «Designing interviews to generate rich data for information systems research» von Schultze und Avital:
Weiterführende Literatur bieten:
Institut für Wirtschaftsinformatik (IWI-HSG)
Universität St. Gallen (HSG)
Müller-Friedberg-Strasse 8
9000 St. Gallen
Susanne Gmünder Braun
Office Prof. Dr. Jan Marco Leimeister
Telefon: +41 71 224 24 20
