Thesis

The spread of digital disinformation poses significant challenges to individuals and societies. Disinformation appears in multiple formats—including text, images, and videos—and follows recurring patterns, narratives, and strategies of deception. Prior research highlights that textual disinformation often relies on linguistic strategies such as exaggeration, sensationalism, polarization, and ambiguity, while visual disinformation exploits manipulation techniques and artifacts (e.g., anatomical inconsistencies or implausible contexts).

This thesis aims to systematically analyze multimodal patterns of disinformation based on existing literature and to categorize their semantic, visual, and emotional characteristics. Furthermore, it explores how these patterns can inform explainable AI (XAI) approaches for detecting disinformation, while considering ethical and legal responsible design principles for trustworthy human-AI interaction.

Literatur: 

• Brasse et al. (2023): Explainable artificial intelligence in information systems: A review of the status quo and future research directions. Electronic Markets.
• Patel et al. (2023): Identifying fake digital information using machine learning algorithms: Performance analysis and recommendation system. Contemporary Mathematics.
• Züllig et al. (2023): Tell me why (i want it that way) – Effects of explanations and online customer reviews on trust in recommender systems. International Conference on Information Systems 2023.

Level: 

• Bachelor: Systematic Literature Review (SLR)

球探足球比分: jana.lekscha(at)uni-bamberg.de 

Artificial intelligence (AI) is a global phenomenon, yet its understanding is shaped by language and cultural context. Concepts related to AI—such as “intelligence,” “autonomy,” or “learning”—may carry different meanings across languages, influencing how individuals perceive and interact with these technologies. Prior research suggests that cultural background affects technology adoption, trust, and risk perception, while linguistic framing can shape cognitive interpretations and expectations toward AI systems.

This thesis aims to explore how the understanding of AI differs across languages and cultures, and how these differences influence perceptions, attitudes, and usage of AI technologies.

Literatur: 

• Barnes, A. J., Zhang, Y., & Valenzuela, A. (2024). AI and culture: Culturally dependent responses to AI systems. Current opinion in psychology, 58.
• Dang, Q., Li, G. Unveiling trust in AI: the interplay of antecedents, consequences, and cultural dynamics. AI & Soc41, 669–692 (2026). https://doi.org/10.1007/s00146-025-02477-6
• Stanford University (2024).How Culture Shapes What People Want from AI. https://hai.stanford.edu/news/how-culture-shapes-what-people-want-ai? (accessed: 04.05.2026)

Level: 

• Bachelor: Systematic Literature Review (SLR)
• Master: SLR and Empirical Research (e.g. interviews or survey)

球探足球比分: jana.lekscha(at)uni-bamberg.de 

The rise of generative AI (GenAI) is transforming the workplace by increasing efficiency while simultaneously raising concerns about employee autonomy, professional identity, and purpose (Strich et al., 2021; Zhou et al., 2025). As AI systems take over more complex tasks, knowledge workers fear losing status, meaning, and control over their work.These developments challenge the fundamentally human need to experience work as meaningful (Bailey & Madden, 2016).

This thesis aims to explore how GenAI influences the perception of meaningful work and how organizations can ensure that work remains a source of purpose and identity in AI-driven environments.

Literature: 

• Bailey, C., & Madden, A. (2016, June 1). What Makes Work Meaningful—Or Meaningless. MIT Sloan Management Review.sloanreview.mit.edu/article/what-makes-work-meaningful-or-
  meaningless/
• Strich, F., Mayer, A.-S., & Fiedler, M. (2021). What Do I Do in a World of Artificial Intelligence? Investigating the Impact of Substitutive Decision-Making AI Systems on Employees’ Professional Role Identity. Journal of the Association for Information Systems, 22(2), 304–324. https://doi.org/10.17705/1jais.00663
• Vaast, E., & Pinsonneault, A. (2021). When Digital Technologies Enable and Threaten Occupational Identity: The Delicate Balancing Act of Data Scientists. MIS Quarterly, 45(3), 1087–1112. https://doi.org/10.25300/MISQ/2021/16024
• Zhou, J., Lu, Y., & Chen, Q. (2025). GAI identity threat: When and why do individuals feel threatened? Information & Management, 62(2), 1-13. https://doi.org/10.1016/j.im.2024.104093 

Level: 

• Bachelor: Systematic Literature Review (SLR)
• Master: SLR and Qualitative Research

Contact: jana.lekscha(at)uni-bamberg.de 

Technologies have the capacity to influence self-perception, positively and negatively, since they can render a job obsolete (Vaast & Pinsonneault, 2021). Knowledge workers are concerned because of AI’s potential to assume and subsequently supersede their roles, responsibilities, and decision-making processes of human workers (Strich et al., 2021). GenAI in particular may harm individual’s job autonomy (Zhou et al.,2025). Consequently, knowledge workers respond to technology and exhibit behaviors that either affirm or modify their own self-image and that of others to manage these challenges (Craig et al., 2019). Therefore, management should understand the changes brought about by GenAI and adapt to them. This work aims to answer the question of what leadership should look like in work environments where GenAI prevails.

Literature: 

• Craig, K., Thatcher, J. B., & Grover, V. (2019). The IT Identity Threat: A Conceptual Definition and Operational Measure. Journal of Management Information Systems, 36(1), 259–288. doi.org/10.1080/07421222.2018.1550561
• Strich, F., Mayer, A.-S., & Fiedler, M. (2021). What Do I Do in a World of Artificial Intelligence? Investigating the Impact of Substitutive Decision-Making AI Systems on Employees’ Professional Role Identity. Journal of the Association for Information Systems, 22(2), 304–324. doi.org/10.17705/1jais.00663
• Vaast, E., & Pinsonneault, A. (2021). When Digital Technologies Enable and Threaten Occupational Identity: The Delicate Balancing Act of Data Scientists. MIS Quarterly, 45(3), 1087–1112. https://doi.org/10.25300/MISQ/2021/16024
• Zhou, J., Lu, Y., & Chen, Q. (2025). GAI identity threat: When and why do individuals feel threatened? Information & Management, 62(2), 1-13. doi.org/10.1016/j.im.2024.104093

Level: 

• Bachelor: Systematic Literature Review (SLR)
• Master: SLR and Qualitative Research

Contact: jana.lekscha(at)uni-bamberg.de 

AI systems are increasingly involved in decision-making processes that carry ethical implications, such as hiring, content moderation, or risk assessment. While these systems are designed to support human decision-making, they may also encourage individuals to shift responsibility for morally complex decisions onto the technology. This phenomenon, referred to as moral offloading, raises critical questions about accountability and ethical agency in Human-AI collaboration. Individuals may perceive AI as more objective and rational, which can lead to reduced personal responsibility and moral reflection. 
This thesis will explore whether and when individuals delegate ethical decision-making to AI systems. It will examine how factors such as trust, system transparency, and task framing influence moral responsibility. Based on the results, implications for the ethical design and governance of AI systems will be derived.
Research questions could be:
?    How do factors such as trust in AI, system transparency, and task framing influence the degree of moral offloading? (SLR for bachelor thesis level / interviews for master thesis level)

Literature:

• Baird, A., & Maruping, L. M. (2021). The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts. MIS quarterly, 45(1), 315-341.
• Stelmaszak, M., M?hlmann, M., & S?rensen, C. (2025). When algorithms delegate to humans: Exploring human-algorithm interaction at Uber. MIS Quarterly, 49(1), 305-330.
• Dong, M., & Bocian, K. (2024). Responsibility gaps and self-interest bias: People attribute moral responsibility to AI for their own but not others' transgressions. Journal of Experimental Social Psychology, 111, 104584.

Level: Bachelor / Master

Contact: Aemen Azad

The increasing integration of AI systems into everyday communication—such as email assistants, chatbots, and generative text tools—fundamentally changes how individuals express and interpret emotions. While these systems can enhance efficiency and clarity, they may also reduce the need for users to actively engage in emotional reasoning and empathy. Over time, this could lead to a form of “emotional deskilling,” where individuals become less capable of recognizing, interpreting, and responding to emotional cues. Despite growing adoption of AI-mediated communication, the long-term effects on human emotional intelligence remain largely unexplored. 
This thesis should investigate how the use of AI in communication influences individuals’ emotional awareness, empathy, and social skills. It will analyse whether reliance on AI tools leads to reduced emotional engagement and identify potential risks for collaboration and workplace relationships. Based on the findings, implications for the design of human-centered AI communication systems will be derived.
Research questions could be:
?    How does the use of AI-mediated communication tools affect individuals’ emotional awareness and empathy over time?
?    How does reliance on AI-generated communication reduce users’ ability/willingness to engage in emotional reasoning in social interactions?

Literature:

• Beck, M., & Libert, B. (2017). The rise of AI makes emotional intelligence more important. Harvard Business Review, 15(1-5), 1-5.
  Narimisaei, J., Naeim, M., Imannezhad, S., Samian, P., & Sobhani, M. (2024). Exploring emotional intelligence in artificial intelligence systems: a comprehensive analysis of emotion recognition and response mechanisms. Annals of medicine and surgery, 86(8), 4657-4663.
• Kaur, S., & Sharma, R. (2021, July). Emotion AI: integrating emotional intelligence with artificial intelligence in the digital workplace. In Innovations in Information and Communication Technologies (IICT-2020) Proceedings of International Conference on ICRIHE-2020, Delhi, India: IICT-2020 (pp. 337-343). Cham: Springer International Publishing.

Level: Bachelor / Master

Contact: Aemen Azad

Explainable AI (XAI) is widely promoted as a solution to increase transparency and trust in AI systems. By providing users with explanations for algorithmic decisions or generated content, XAI aims to support informed and critical decision-making. However, recent developments in generative AI raise concerns that explanations may not always have the intended effect. Instead of fostering critical thinking, explanations could increase the perceived credibility of information, even when it is false or misleading. This creates a paradox in which transparency mechanisms potentially amplify the risks of disinformation. 

This thesis will investigate how explanations provided by AI systems influence users’ perception of credibility in the context of true and false information. It will further examine whether explanations lead to increased trust and reduced critical reflection. Based on these insights, implications for the design of responsible and trustworthy AI systems will be derived.

Research questions could be:
?    How do AI-generated explanations influence users’ perceived credibility of true vs. false information?
?    How can explanations provided by AI systems reduce users’ critical evaluation of potentially false content?
?    How to build XAI to combat false content on social media platforms? (design science research) 

Literature: 

• Kozik, R., Ficco, M., Pawlicka, A., Pawlicki, M., Palmieri, F., & Chora?, M. (2024). When explainability turns into a threat: Using XAI to fool a fake news detection method. Computers & Security, 137, 103599.

• Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39–50.

• Lebovitz, S., Lifshitz-Assaf, H., & Levina, N. (2022). To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organization Science, 33(1), 126-148. 

Level: Bachelor / Master

Contact: Aemen Azad 

Modern AI systems often communicate their outputs with a high degree of confidence. While such signals are intended to improve usability and trust, they may also influence how users evaluate the correctness of information. In the context of disinformation, overly confident AI outputs could discourage critical thinking and increase the likelihood that users accept false information as true. This raises important questions about the role of interface design and communication strategies in Human-AI interaction.

This thesis will explore how different types of AI confidence signals affect users’ belief in true and false information. It will analyse whether high-confidence outputs lead to overreliance on AI and reduced scrutiny. Based on the findings, design recommendations for mitigating disinformation risks in AI systems will be developed.

Research questions could be:
?    How do different levels of AI confidence signals affect users’ belief in true and false information?
?    How can high-confidence AI outputs increase overreliance and reduce users’ critical scrutiny of information?

Literature:

• Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational behavior and human decision processes, 151, 90-103.
• Jussupow, E., Benbasat, I., & Heinzl, A. (2024). An integrative perspective on algorithm aversion and appreciation in decision-making. MIS quarterly, 48(4), 1575-1590.

Level: Bachelor / Master

Contact: Aemen Azad 

With the widespread adoption of generative AI tools in organizations, employees increasingly rely on AI systems to produce reports, summaries, analyses, and strategic recommendations. While these tools promise efficiency gains, they also introduce a subtle but critical risk: the integration of AI-generated inaccuracies, fabrications, or “hallucinations” into internal knowledge systems. Unlike external disinformation, this form of internal disinformation is often unintentional, difficult to detect, and may spread across teams as seemingly credible information.
This thesis will investigate how AI-generated disinformation emerges, spreads, and persists within organizations. It will examine factors such as employee trust in AI, verification behaviors, organizational knowledge-sharing practices, and the role of documentation and communication structures. The research should explore whether repeated exposure to AI-generated content increases its perceived legitimacy within teams. The goals is to derive implications for organizational knowledge management, employee training, and the design of AI-supported workflows that reduce the risk of internal disinformation.
Research questions could be:
?    How does AI-generated misinformation emerge and spread within organizational knowledge processes? (bachelor thesis level)
?    How can repeated exposure to AI-generated content increase its perceived credibility and acceptance among employees? (master level)

Literature:

• Stahl, B. C. (2006). On the difference or equality of information, misinformation, and disinformation: A critical research perspective. Informing Science, 9, 83.
• Guo, B., Ding, Y., Yao, L., Liang, Y., & Yu, Z. (2020). The future of false information detection on social media: New perspectives and trends. ACM Computing Surveys (CSUR), 53(4), 1-36.
• Chen, S., Xiao, L., & Kumar, A. (2023). Spread of misinformation on social media: What contributes to it and how to combat it. Computers in Human Behavior, 141, 107643.

Level: Bachelor / Master

Contact: Aemen Azad
 

In response to  environmental crises, social media plays a central role in social communication and mobilization (Mirbabaie et al., 2021; Rieskamp et al., 2023). It enables information to be shared quickly, communities to be mobilized, and awareness of climate-related issues to be raised (Rieskamp et al., 2023). Platforms offer real-time updates, promote discussion (Zander et al., 2023), and organize collective action—thanks to their cost-free nature, user-friendliness, and opportunities for interaction (Luna & Pennock, 2018). Social media has become indispensable, especially in times of crisis, such as natural disasters (Mavrodieva & Shaw, 2021). However, social media also carries risks, particularly through misinformation (Chen et al., 2023). This can severely undermine the credibility and effectiveness of crisis communication (Vosoughi et al., 2018). Misinformation can have serious consequences, for example, by putting individuals in danger, misdirecting resources, or weakening trust in authorities and official channels. With a systematic literature review (SLR) this thesis aims to answer how have emergency and crisis organizations evolved in their use of social media, and what role do AI, analytics tools, and strategies against misinformation play. 

Literature: 

• Chen, S., Xiao, L., & Kumar, A. (2023). Spread of misinformation on social media: What contributes to it and how to combat it. Computers in Human Behavior, 141(2023). https://doi.org/10.1016/j.chb.2022.10764
• Luna, S., & Pennock, M. J. (2018). Social media applications and emergency management: A literature review and research agenda. International Journal of Disaster Risk Reduction, 28, 565–577. https://doi.org/10.1016/j.ijdrr.2018.01.006
• Mavrodieva, A. V., & Shaw, R. (2021). Social Media in Disaster Management. In R. Shaw, S. Kakuchi, & M. Yamaji (Hrsg.), Media and Disaster Risk Reduction: Advances, Challenges and Potentials (S. 55–73). Springer. https://doi.org/10.1007/978-981-16-0285-6_4
• Mirbabaie, M., Stieglitz, S., & Brünker, F. (2021). Dynamics of convergence behaviour in social media crisis communication – a complexity perspective. Information Technology & People, 35(1), 232–258. https://doi.org/10.1108/ITP-10-2019-0537
• Rieskamp, J., Mirbabaie, M., & Zander, K. (2023). GenAI-powered Social Bots for Crisis Communication: A Systematic Literature Review. 1–19.
• Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559 

Level: Bachelor/Master

Contact: jana.lekscha@uni-bamberg.de

With approximately 70,000 new diagnoses each year, breast cancer remains the most common cancer among women in Germany (Cancer Research Center, 2023). Despite advances in screening technologies and diagnostics, selecting the right treatment strategy is critical for patient outcomes due to the heterogeneity of breast cancer subtypes. Digital Patient Twins (DPTs) represent a promising innovation in personalized medicine (Singh et al., 2021). These AI-powered, data-driven models create virtual representations of individual patients, enabling the simulation of therapy plans, prediction of treatment outcomes, and support for complex clinical decision-making. DPTs integrate technologies such as machine learning, real-time data processing, and cloud infrastructures (Zhang et al., 2021). While DPTs offer great potential, their implementation also raises challenges. These include data quality, transparency and explainability of AI decisions, technical feasibility, and regulatory and clinical integration. This thesis aims to systematically review the current state of scientific research on digital patient twins in the context of breast cancer therapy.

Literature: 

• Singh, M., Fuenmayor, E., Hinchy, E., Qiao, Y., Murray, N., & Devine, D. (2021). Digital Twin: Origin to Future. Applied System Innovation, 4(2), 36.
• Yan, S., Li, J., & Wu, W. (2023). Artificial intelligence in breast cancer: Application and future perspectives. Journal of Cancer Research and Clinical Oncology, 149(17), 16179–16190.
• Yan, S., Li, J., & Wu, W. (2023). Artificial intelligence in breast cancer: Application and future perspectives. Journal of Cancer Research and Clinical Oncology, 149(17), 16179–16190.
• Zhang, D., Pee, L. G., & Cui, L. (2021). Artificial intelligence in E-commerce fulfillment: A case study of resource orchestration at Alibaba’s Smart Warehouse. International Journal of Information Management, 57, 102304

Level: Bachelor

Contact: jana.lekscha@uni-bamberg.de

Smartphones, social media, and behavior change apps have become extensions of who we are. We don't just use them — we form emotional bonds, depend on them, and integrate them into our daily identity. This thesis explores how this deep connection with technology influences mental states such as stress, fatigue, workload, and flow. Grounded in IT Identity Theory (Carter & Grover, 2015), the study examines how individuals' sense of self is shaped through technology — and how that affects well-being. Using qualitative interviews, you will gain deep insights into how people experience and internalize their tech use. Do these digital habits empower users — or silently exhaust them?

Literature:

• Carter, M., & Grover, V. (2015). Me, my self, and I (T). MIS quarterly, 39(4), 931-958.
• Mirbabaie, M., Stieglitz, S. & Marx, J. Digital Detox. Bus Inf Syst Eng 64, 239–246 (2022). https://doi.org/10.1007/s12599-022-00747-x
• Jeong, Hyein and Syed, Romilla, "Relationship between the Use of IT and Wellbeing: A Literature Review" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 2.
   

Level: Bachelor/Master

Contact: jana.lekscha@uni-bamberg.de

Climate change poses significant global challenges, such as rising temperatures, extreme weather events, and natural disasters. Social media plays a central role in crisis communication, enabling the rapid dissemination of information, mobilizing communities, and raising awareness about climate-related issues (Mirbabaie et al., 2021; Rieskamp et al., 2023). At the same time, social media poses risks from misinformation, which can undermine the credibility and effectiveness of communication (Chen et al., 2023). This thesis aims to develop scientifically sound approaches to identify misinformation in times of crisis and minimize its impact. Using interviews and the analysis of social media data (e.g. X), crisis communication will be made more targeted and user-centered.

Literature: 

• Chen, L. (2024). Combatting climate change misinformation: Current strategies and future directions. Environmental Communication, 18(1-2), 184-190.
• Mirbabaie, M., Ehnis, C., Stieglitz, S., Bunker, D., & Rose, T. (2021). Digital nudging in social media disaster communication. Information Systems Frontiers, 23(5), 1097-1113.
• Mirbabaie, M., Stieglitz, S., & Brünker, F. (2022). Dynamics of convergence behaviour in social media crisis communication–a complexity perspective. Information Technology & People, 35(1), 232-258.
• Rieskamp, J., Mirbabaie, M., & Zander, K. (2023). GenAI-powered Social Bots for Crisis Communication: A Systematic Literature Review.

Level: Bachelor - Systematic Literature Review (SLR) or Master - SLR and Qualitative Research

Contact: jana.lekscha(at)uni-bamberg.de 

Literatur: 

• Corbett, J. (2013). Designing and using carbon management systems to promote ecologically responsible behaviors. Journal of the Association for Information Systems, 14(7), 339–378. doi.org/10.17705/1jais.00338
• Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: Defining “gamification.” Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, MindTrek 2011, 9–15. doi.org/10.1145/2181037.2181040
• Gaur, D., Gupta, K., Tiwari, C. K., & Pal, A. (2025). AI Powered Gamification: The New Catalyst in the Arena of Online Investment Platforms Impacting Behavioral Intentions. International Journal of Human-Computer Interaction, 0(0), 1–17. doi.org/10.1080/10447318.2025.2483862
• Koivisto, J., & Hamari, J. (2019). The rise of motivational information systems: A review of gamification research. International Journal of Information Management, 45(June 2017), 191–210. https://doi.org/10.1016/j.ijinfomgt.2018.10.013
• Olson, M. (1965). The Logic of Collective Action. Harvard University Press.
• Seidel, S., Recker, J., & Vom Brocke, J. (2013). Sensemaking and sustainable practicing: Functional affordances of information systems in green transformations. MIS Quarterly: Management Information Systems, 37(4), 1275–1299. doi.org/10.25300/MISQ/2013/37.4.13

Zielgruppe: Master, Bachelor

球探足球比分: Marie Langer

Due to climate change, there are severe weather changes, bushfires, floods, and heat waves that have increased in recent decades and have all been occurring on an unprecedented scale. In these extreme situations, the public needs a reliable source of information and recommendations on how to act to ensure safety and avoid the spread of fake news. Such information is being disseminated not only via traditional channels but also via social media, the necessity, and effectiveness of which has been confirmed by various studies (Willems et al., 2021; Bec and Becken, 2021; Yigitcanlar et al., 2022). This thesis focuses on AI-powered social bots (i.e., automated actors in social networks) to disseminate relevant information and automatically debunks disinformation. This raises the question of the extent to which safety can be guaranteed and how we can prepare for natural disasters using AI-powered social bots in order to make a reliable source of information accessible to the public. The aim of this thesis is to conduct a literature review to capture the current state of research on social media crisis communication using social bots during natural hazards and to develop a prototype of an AI-powered social bot.

 Literature:

• Rieskamp, J., Mirbabaie, M., & Zander, K. (2023). GenAI-powered Social Bots for Crisis Communication: A Systematic Literature Review. Proceedings of the 2023 Australasian Conference on Information Systems. Australasian Conference on Information Systems, Wellington. https://aisel.aisnet.org/acis2023/65

• Stieglitz, S., Hofeditz, L., Brünker, F., Ehnis, C., Mirbabaie, M., & Ross, B. (2022). Design principles for conversational agents to support Emergency Management Agencies. International Journal of Information Management, 63, 102469. https://doi.org/10.1016/J.IJINFOMGT.2021.102469

• Yigitcanlar, T., M. Regona, N. Kankanamge, R. Mehmood, J. D’Costa, S. Lindsay, S. Nelson and A. Brhane (2022). “Detecting Natural Hazard-Related Disaster Impacts with Social Media Analytics: The Case of Australian States and Territories” Sustainability 14 (2), 810.
• Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics – Challenges in topic discovery, data collection, and data preparation. International Journal of Information Management, 39, 156–168.
• Hofeditz, L., Ehnis, C., Bunker, D., Brachten, F., & Stieglitz, S. (2019). Meaningful Use of Social Bots? Possible Applications in Crisis Communication during Disasters. In Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden.
• Lahby, M., Pathan, A.-S. K., Maleh, Y., & Yafooz, W. M. S. (Eds.). (2022). Studies in Computational Intelligence. Combating Fake News with Computational Intelligence Techniques. Springer International Publishing.
• Messias, J., Schmidt, L., Oliveira, R., & Benevenuto, F. (2013). You followed my bot! Transforming robots into influential users in Twitter. First Monday.
• Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24(3), 45–77.

Level: Bachelor

Contact: jonas.rieskamp(at)uni-bamberg.de

Group Decision Support Systems (GDSS) were once a core topic in Information Systems (IS) research, aiming to enhance group collaboration and decision-making through technology. Despite their strong theoretical and practical foundations in the 1980s and 1990s, interest in GDSS research has significantly declined over the last years. However, recent advances in (Generative) Artificial Intelligence (GenAI) create new opportunities to revisit and reimagine GDSS, potentially addressing long-standing limitations related to coordination, motivation, and collective action.

This thesis aims to systematically review the GDSS literature in leading IS journals to analyze its evolution, decline, and potential resurgence. Using Collective Action Theory as a deductive analytical framework, the study will examine how GDSS research has conceptualized coordination, incentives, and community dynamics. Building on these insights, the thesis will explore how GenAI technologies could close existing gaps between GDSS theory and practice—for example, by automating facilitation, summarization, or consensus-building processes. The results may serve as a conceptual foundation for integrating GDSS with next-generation collaborative AI systems and connect to ongoing work by Michail on decision-making support.

Literatur:

• Gopal, A., & Prasad, P. (2000). Understanding GDSS in symbolic context: Shifting the focus from technology to interaction.MIS Quarterly, 24(3), 509–512, 539.
• Hirschheim, R., & Klein, H. K. (2012). A Glorious and Not-So-Short History of the Information Systems Field.Journal of the Association for Information Systems, 23(5), 1012–1059.
• Olson, M. (1965). The Logic of Collective Action. Harvard University Press.
• Sambamurthy, V., & Poole, M. S. (1992). The effects of variations in capabilities of GDSS designs on management of cognitive conflict in groups. Information Systems Research, 3(3), 224-251.
• Watson, R. T., DeSanctis, G., & Poole, M. S. (1988). Using a GDSS to facilitate group consensus: Some intended and unintended consequences.MIS Quarterly, 12(3), 463–479.

Level: Bachelor

球探足球比分: marie.langer(at)uni-bamberg.de

Social media platforms have become important channels for disseminating information in times of crisis. Users are looking for specific guidance and real-time information to alleviate feelings of vulnerability. However, the landscape continues to evolve with the increasing presence of social bots, particularly those powered by generative artificial intelligence (GenAI), adding a new facet to crisis communications. While social media is invaluable for urgent interactions, GenAI's inherent tendency to produce inaccurate results poses a challenge for its use in tasks that require precision. In tasks where accuracy is critical, human oversight is crucial, suggesting that augmentation may be a more appropriate strategy than full automation. This research addresses the identification of specific tasks within the functions of GenAI-driven social bots in crisis communication that require human supervision to strike the delicate balance between automation and augmentation.

Literature:

• Austin, L., Fisher Liu, B., and Jin, Y. 2012. “How Audiences Seek Out Crisis Information: Exploring the Social-Mediated Crisis Communication Model,” Journal of Applied Communication Research (40:2), pp. 188–207. (https://doi.org/10.1080/00909882.2012.654498).
• Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S. 2021. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?,” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 610–623. (https://doi.org/10.1145/3442188.3445922).
• Brachten, F., Mirbabaie, M., Stieglitz, S., Berger, O., Bludau, S., and Schrickel, K. 2018. “Threat or Opportunity? - Examining Social Bots in Social Media Crisis Communication,” in Proceedings of the Australasian Conference on Information Systems.
• Maniou, T. A., and Veglis, A. 2020. “Employing a Chatbot for News Dissemination during Crisis: Design, Implementation and Evaluation,” Future Internet (12:12). (https://doi.org/10.3390/FI12070109).
• Ross, B., Pilz, L., Cabrera, B., Brachten, F., Neubaum, G., and Stieglitz, S. 2019. “Are Social Bots a Real Threat? An Agent-Based Model of the Spiral of Silence to Analyse the Impact of Manipulative Actors in Social Networks,” European Journal of Information Systems (28:4), pp. 394–412.
• Ross, B., Potthoff, T., Majchrzak, T. A., Chakraborty, N. R., Ben Lazreg, M., and Stieglitz, S. 2018. The Diffusion of Crisis-Related Communication on Social Media: An Empirical Analysis of Facebook Reactions. (https://doi.org/10.24251/HICSS.2018.319).
• Stieglitz, S., Hofeditz, L., Bru?nker, F., Ehnis, C., Mirbabaie, M., and Ross, B. 2022. “Design Principles for Conversational Agents to Support Emergency Management Agencies,” International Journal of Information Management (63), (https://doi.org/10.1016/J.IJINFOMGT.2021.102469). Pergamon, p. 102469.

Level: 

• Master: Mixed-Methods-Design - Qualitative analyses (e.g. interviews) and content analysis

Contact: jana.lekscha(at)uni-bamberg.de 

The widespread use of AI has generated a lot of excitement in the technology world. Especially on platforms like LinkedIn, we face content that is strongly positive towards AI and its use. Although AI and language models such as ChatGPT are praised for their ability to bring about significant changes they still face important challenges like biases, high costs, and discrimination, which are largely neglected in the public discourse. This thesis aims to explore the interconnected relationship, between the hype surrounding AI and the phenomenon of toxic positivity on LinkedIn. We will delve into how the positive narratives surrounding AI tend to overshadow the challenges it presents. By employing frame analysis, this research aims to decipher how individuals and groups perceive and interpret AI-related information on LinkedIn, shedding light on the nuances of the AI discourse in the context of toxic positivity.

Literature:

• Lecompte-Van Poucke, M. (2022). ‘You got this!’: A critical discourse analysis of toxic positivity as a discursive construct on Facebook. Applied Corpus Linguistics, 2(1), 100015.
• Kwon, S., & Park, A. (2023). Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects. Computers in Human Behavior, 144, 107734.
• LaGrandeur, K. (2023). The consequences of AI hype. AI and Ethics. doi.org/10.1007/s43681-023-00352-y
• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101.

Level: Master

Contact: jonas.rieskamp(at)uni-bamberg.de

Contemporary AI applications are subject to biases, stemming from their training data. As a result, the outputs and decisions from AI applications are skewed towards the training data, lacking of fairness and inclusivity. AI ethics research submitted several design principles for AI application to enhance fairness. However, these principles are difficult to translate into practice, which leaves the fairness issues and resulting risks rather unaddressed. New approaches of AI risk management proposed the idea to “capture” Ai application in limited space in which it can act. This aims to contain harmful consequence (e.g., discrimination and unfairness) in a controllable environment. Yet, while the approach of ethics principles remain unfruitful, the capturing of AI contains negative consequence only retrospectively. A good solution, however, should act proactively. Considering the AI ethics issues as “IT failure” allows us to employ sociotechnical system perspective, which investigated solution for issues emerging from IT projects. This thesis will summarise current AI ethics issues and categorises them according to sociotechnical system perspective. Upon successful categorisation, proactive solutions will be derived and synthesised. 

Literature:

• Bostrom, R. P., & Heinen, J. S. (1977). MIS Problems and Failures: A Socio-Technical Perspective. Part I: The Causes. MIS Quarterly, 1(3), 17–32. https://doi.org/10.2307/248710
• Bostrom, R. P., & Heinen, J. S. (1977). MIS Problems and Failures: A Socio-Technical Perspective, Part II: The Application of Socio-Technical Theory. MIS Quarterly, 1(4), 11–28. https://doi.org/10.2307/249019
• Asatiani, A., Malo, P., Nagb?l, P. R., Penttinen, E., Rinta-Kahila, T., & Salovaara, A. (2021). Sociotechnical Envelopment of Artificial Intelligence: An Approach to Organizational Deployment of Inscrutable Artificial Intelligence Systems. Journal of the Association for Information Systems, 22(2), 325–352. https://doi.org/10.17705/1jais.00664
• Mirbabaie, M., Brendel, A. B., & Hofeditz, L. (2022). Ethics and AI in Information Systems Research. Communications of the Association for Information Systems, 50(1), 726–753. https://doi.org/10.17705/1CAIS.05034
• Laine, J., Minkkinen, M., & M?ntym?ki, M. (2025). Understanding the Ethics of Generative AI: Established and New Ethical Principles. Communications of the Association for Information Systems, 56(1). https://aisel.aisnet.org/cais/vol56/iss1/7
• Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501–507. https://doi.org/10.1038/s42256-019-0114-4 

Level:

• Bachelor: Systematic Literature Review

Contact: jonas.rieskamp(at)uni-bamberg.de

The pervasive use of the term Artificial Intelligence (AI) has led to inflation, rendering it a catch-all for a multitude of concepts. In navigating the expansive "frontiers of computing," as discussed by Berente et al. (2021), the challenge arises in discerning meaningful boundaries to facilitate the management of AI. Effectively managing AI necessitates a nuanced understanding, distinguishing between probabilistic and deterministic systems, particularly to mitigate negative consequences. Notably, rule-based AI systems entail different implications than probabilistic counterparts, emphasizing the need to categorize and conceptualize a more nuanced view of AI types.

The goal of this thesis is to explore the various facets of AI types comprehensively. Understanding the capabilities and consequences of each type is crucial for informed decision-making and management. The ultimate goal is twofold: to derive a more nuanced definition of AI and to develop a systematic taxonomy categorizing AI types based on their unique capabilities and characteristics.

Literature:

• ?gerfalk, P. J., Conboy, K., Crowston, K., Eriksson Lundstr?m, J. S., Jarvenpaa, S., Ram, S., & Mikalef, P. (2022). Artificial Intelligence in Information Systems: State of the Art and Research Roadmap. Communications of the Association for Information Systems, 50(420–438). https://doi.org/10.17705/1CAIS.05017
• Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Special issue editor’s comments: Managing artificial intelligence. Management Information Systems Quarterly, 45(3), 1433–1450. https://doi.org/10.25300/MISQ/2021/16274
• Kundisch, D., Muntermann, J., Oberl?nder, A. M., Rau, D., R?glinger, M., Schoormann, T., & Szopinski, D. (2022). An Update for Taxonomy Designers. Business & Information Systems Engineering, 64(4), 421–439. https://doi.org/10.1007/s12599-021-00723-x
• Mikalef, P., Conboy, K., Eriksson Lundstr?m, J., & Popovi?, A. (2022). Thinking responsibly about responsible AI and ‘the dark side’ of AI Thinking responsibly about responsible AI and ‘the dark side’ of AI. https://doi.org/10.1080/0960085X.2022.2026621
• Raisch, S., & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation–Augmentation Paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072

Level:

• Bachelor: Taxonomy development

Contact: jonas.rieskamp(at)uni-bamberg.de

Remote work practices have become increasingly prevalent in organisations. Yet, it remains puzzling why remote work at scale, that is, remote organising, creates substantive challenges for transforming organisations, while perennial all-remote organisations seem to thrive with it. Many all-remote organisations openly share and promote their work processes through remote work ‘handbooks,’ ‘guidelines,’ and ‘manifestos.’ The goal of this thesis is to qualitatively analyse the ‘handbooks,’ ‘guidelines,’ and ‘manifestos’ to improve our understanding of remote organising. 

Literature:

• Brünker, F., Marx, J., Mirbabaie, M., & Stieglitz, S. (2023). Proactive digital workplace transformation: Unpacking identity change mechanisms in remote-first organisations. Journal of Information Technology, 0(0), 1-19. https://doi.org/10.1177/02683962231219516 

• Choudhury, P. (Raj)., Foroughi, C., & Larson, B. (2021). Work-from-anywhere: The productivity effects of geographic flexibility. Strategic Management Journal, 42(4), 655–683. https://doi.org/10.1002/smj.3251

• Rhymer, J. (2022). Location-Independent Organizations: Designing Collaboration Across Space and Tme. Administrative Science Quarterly, 68(1), 1-43. https://doi.org/10.1177/00018392221129175

Level: Master

Contact: j.marx(at)unimelb.edu.au

The relationship between humans and AI systems is of central importance as AI becomes more and more integrated into our everyday lives. This relationship not only influences the way we work and communicate, but also our decision-making and our trust in technologies. A deep understanding of these dynamics can help to develop AI systems that are ethical, transparent and user-friendly. This topic is particularly relevant as it examines the interface between technology and human behavior and thus provides important insights for the design of information systems.
A thesis could focus on investigating trust building between users and AI systems. This could be done through surveys and experiments testing different interaction designs to find out which factors strengthen or weaken user trust.
You are also welcome to contact me with your own thesis ideas on this topic. 

Literatur:

• Pal, D., Vanijja, V., Thapliyal, H. & Zhang, X. (2023). What affects the usage of artificial conversational agents? An agent personality and love theory perspective. Computers in Human Behavior, 145, 107788.
  doi.org/10.1016/j.chb.2023.107788
• Song, X., Xu, B. & Zhao, Z. (2022b). Can people experience romantic love for artificial intelligence? An empirical study of intelligent assistants. Information & Management, 59(2), 103595.
  doi.org/10.1016/j.im.2022.103595

Level: Bachelor

球探足球比分: milad.mirbabaie(at)uni-bamberg.de