Abschlussarbeiten

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. 

Literatur: 

• 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

球探足球比分: 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.

Literatur: 

• 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

球探足球比分: jana.lekscha@uni-bamberg.de

Many large companies establish centralized AI or Machine Learning Competence Centers (AI CoEs) to scale data-driven innovation across business units. While these CoEs offer valuable expertise and reusable solutions, they often face difficulties gaining traction in practice. A key challenge lies in misaligned funding and incentive structures: AI CoEs typically bear the cost of development, while business units benefit—yet may hesitate to contribute resources for implementation. This leads to stalled or underutilized AI initiatives.

This thesis investigates how companies structure, fund, and integrate AI CoEs, and explores solutions to overcome internal frictions. Through qualitative expert interviews and thematic analysis, the thesis will identify key organizational tensions and offer practical recommendations for improving the setup of corporate AI efforts. You will conduct a qualitative interview study with AI practitioners and organizational leaders across different companies.

Literatur:

• Weber, M., Engert, M., Schaffer, N., Weking, J., & Krcmar, H. (2023). Organizational capabilities for ai implementation—coping with inscrutability and data dependency in ai. Information Systems Frontiers, 25(4).
• Vial, G., Cameron, A. F., Giannelia, T., & Jiang, J. (2023). Managing artificial intelligence projects: Key insights from an AI consulting firm. Information Systems Journal, 33(3)
   

Level: Master

球探足球比分: 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?

Literatur:

• 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

球探足球比分: 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.

Literatur: 

• 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)
• Master - SLR and Qualitative Research

球探足球比分: 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

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.aic(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.

Literatur:

• 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

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

The rise of immersive technologies has transformed sports and fitness by offering virtual alternatives to traditional settings (Chen & Li, 2023; Todorov et al., 2019). These developments raise important questions about how such environments influence user motivation. Self-Determination Theory (SDT) provides a robust framework to examine the types of motivation (amotivation, extrinsic, and intrinsic) that shape sustained engagement and performance in these settings (Ryan & Deci, 2017). This thesis aims to explore how Metaverse sports activities impact user motivation and fitness engagement. It aims to elaborate how virtual environments foster different forms of motivation and their subsequent effects on well-being, performance, and long-term training sustainability.

Literature: 

• Chen, H., & Li, H. (2023). Emotional Experience in Virtual Reality Sports Use.
• Deci, E. L., & Ryan, R. M. (2012). Self-Determination Theory. In Handbook of Theories of Social Psychology: Volume 1. SAGE Publications Ltd. https://doi.org/10.4135/9781446249215
• Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford publications.
• Todorov, K., Manolova, A., & Chervendinev, G. (2019). Immersion in Virtual Reality Video Games for Improving Physical Performance Measures: A Review. 2019 27th National Conference with International Participation (℡ECOM), 35–38. https://doi.org/10.1109/℡ECOM48729.2019.8994884
• Yoon, K.-I., Jeong, T.-S., Kim, S.-C., & Lim, S.-C. (2023). Anonymizing at-home fitness: Enhancing privacy and motivation with virtual reality and try-on. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1333776

Level: 

• Bachelor: Systematic Literature Review
• Master: Systematic Literature Review + Qualitative Research 

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

Blockchain technology, known for its secure and decentralized structure, offers a potential solution for managing digital identities, ensuring transparency, security, and anonymity (Alabi, 2017; Raddatz et al., 2023). Despite its promise, adoption of blockchain identity systems remains in pilot stages, with many barriers to widespread use (Barbino, 2019). The research should explore the barriers and facilitators of adopting blockchain-based identity systems, focusing on how these systems are perceived by key stakeholders—governments, organizations, and users. By analyzing acceptance factors, it contributes to understanding the potential of blockchain. 

Literature: 

• Alabi, K. (2017). Digital blockchain networks appear to be following Metcalfe’s Law. Electronic Commerce Research and Applications, 24, 23–29.
• Barbino, V. H. (2019). Finding refuge: blockchain technology as the solution to the syrian refugee identification crises. 48 
• Liang, T.-P., Kohli, R., Huang, H.-C., & Li, Z.-L. (2021). What Drives the Adoption of the Blockchain Technology? A Fit-Viability Perspective. Journal of Management Information Systems, 38(2), 314–337. doi.org/10.1080/07421222.2021.1912915
• Raddatz, N., Coyne, J., Menard, P., & Crossler, R. E. (2023). Becoming a blockchain user: Understanding consumers’ benefits realisation to use blockchain-based applications. European Journal of Information Systems.

Level: 

• Bachelor: Systematic Literature Review
• Master: Systematic Literature Review + Qualitative Research 

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

Level: Master

球探足球比分: jonas.rieskamp(at)uni-bamberg.de

In today's rapidly evolving market environment, digital innovation has become critical for organizational survival and competitive advantage. Companies that effectively leverage digital technologies not only streamline operations but also unlock new business models, enhance customer engagement, and drive sustainable growth. Among digital technologies, Artificial Intelligence (AI), especially Large Language Models (LLMs), is transforming industries by facilitating diverse use cases, from automating routine tasks to generating novel insights through brainstorming.

However, while AI is powerful in generating creative suggestions and providing tactical input, it falls short when it comes to designing strategic plans aligned with high-level organizational objectives. LLMs inherently lack the context sensitivity, nuanced understanding, and long-term vision necessary for strategic decision-making, often producing outputs that are innovative yet disconnected from organizational realities and constraints. Given this limitation, strategic long-term planning remains a challenge for organizations aiming to integrate AI into their innovation processes effectively. The absence of strategic alignment in AI-generated suggestions risks resource misallocation, innovation bottlenecks, and ultimately, failure in achieving desired organizational outcomes.

To address this critical gap, this thesis proposes an innovative approach: integrating organizational context into AI training. Specifically, this thesis first requires conduct qualitative research through structured interviews with multiple organizations, systematically documenting their digital innovation journeys. These interviews will capture each organization's strategic high-level innovation goals, the rationale behind these goals, and the precise steps taken to achieve them. In a subsequent phase, the collected qualitative data will be meticulously processed and employed as a tailored training dataset for fine-tuning LLMs. The primary goal of this fine-tuning is to enhance the capability of AI models to generate actionable strategic plans that align closely with the high-level objectives identified through organizational insights. By embedding real-world strategic decision-making frameworks into AI models, this approach aims to bridge the existing gap between AI-generated innovation ideas and practical, executable strategies.

Literatur:

• Hund, A., Wagner, H.-T., Beimborn, D., & Weitzel, T. (2021). Digital innovation: Review and novel perspective. The Journal of Strategic Information Systems, 30(4), 101695. https://doi.org/10.1016/j.jsis.2021.101695
• Logue, D., Williamson, P., Roberts, A., Luo, Y., & Barrett, M. (2025). Digital innovation, platforms, and global strategy. Information and Organization, 35(1), 100562. https://doi.org/10.1016/j.infoandorg.2025.100562
• Valmeekam, K., Marquez, M., Sreedharan, S., & Kambhampati, S. (2023). On the Planning Abilities of Large Language Models: A Critical Investigation. Proceedings of the 37th Conference on Neural Information Processing Systems. https://doi.org/10.48550/arXiv.2305.15771
• Stechly, K., Valmeekam, K., & Kambhampati, S. (2024). Chain of Thoughtlessness? An Analysis of CoT in Planning. Proceedings of the 38th Conference on Neural Information Processing Systems. https://doi.org/10.48550/arXiv.2405.04776 

Level: Master

球探足球比分: 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. 

Literatur:

• 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

球探足球比分: jonas.rieskamp(at)uni-bamberg.de

The rise of agentic IS systems—intelligent information systems that can autonomously perform tasks, learn from interactions, and delegate actions—has fundamentally reshaped human-technology collaboration (Baird & Maruping, 2021). Large Language Models (LLMs), such as GPT-based architectures, are increasingly being integrated into multi-agent systems, where they serve as decision-making entities, negotiators, and collaborative agents in distributed environments (G?ldi & Rietsche, 2024). These LLM-based agents demonstrate emergent coordination capabilities, enabling complex problem-solving and adaptive responses across dynamic scenarios (Guo et al., 2024). However, the effectiveness of such systems also depends on how autonomy and control are balanced in human-AI interactions. Recent research highlights that user acceptance and system efficiency are influenced by whether task delegation is initiated by users or the AI system itself, underscoring the importance of designing adaptive delegation mechanisms (Adam et al., 2024).

Despite their potential, LLM-based multi-agent systems face challenges such as ensuring alignment with human goals, managing inter-agent communication, and mitigating unintended biases or behaviors. This Bachelor’s thesis aims to systematically review the existing body of research on LLM-based multi-agent systems, synthesizing their theoretical foundations, applications, and ongoing challenges. Furthermore, the thesis will explore emerging opportunities for information systems research by examining how LLM-based multi-agent systems contribute to new forms of organizing, decision-making, and collaboration in digital environments.

Literatur:

• 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. https://doi.org/10.25300/MISQ/2021/15882
• G?ldi, A., & Rietsche, R. (2024). Making Sense of Large Language Model-Based AI Agents. Proceedings of the Forty-Fifth International Conference on Information Systems. https://aisel.aisnet.org/icis2024/aiinbus/aiinbus/16
• Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N. V., Wiest, O., & Zhang, X. (2024). Large language model based multi-agents: A survey of progress and challenges. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 8048–8057. https://doi.org/10.24963/ijcai.2024/890
• Adam, M., Diebel, C., Goutier, M., & Benlian, A. (2024). Navigating autonomy and control in human-AI delegation: User responses to technology- versus user-invoked task allocation. Decision Support Systems, 180, 114193. https://doi.org/10.1016/j.dss.2024.114193
• Svensson, B., & Keller, C. (2024). Agentic Relationship Dynamics in Human-AI Collaboration: A study of interactions with GPT-based agentic IS artifacts. Proceedings of the 57th Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2023.875 
• Webster, J., & Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly, 26(2), xiii–xxiii.

   

  Literature Review nach Webster & Watson, 2002 (Tutorial)
  PRISMA Literaturrecherche für dein Systematisches Literature Review

Level:

• Bachelor

球探足球比分: 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. 

Literatur:

• 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

球探足球比分: 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

AI ethics is essential because it ensures that AI is in line with human values and rights, promotes trust through transparency and accountability, protects against abuse and harm through guidelines for fair and safe use, and supports justice by minimising discrimination and prejudice. It also emphasises privacy through responsible data handling and supports security by identifying and mitigating risks and threats.
A thesis topic could be an investigation how the use of AI technologies affects the digital divide between different social groups. To do this, an analysis of the accessibility and usability of AI systems for different population groups can be carried out. 
You are also welcome to contact me with your own thesis ideas on this topic. 

Literatur: 

• 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
• Floridi, L. & Cowls, J. (2021). A unified framework of five principles for AI in society. In Philosophical studies series (S. 5–17). 
  doi.org/10.1007/978-3-030-81907-1_2

Level: Bachelor

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

Black Friday presents a major opportunity for businesses but with thousands of companies competing for consumer attention, standing out is a challenge. Companies must develop effective strategies to capture interest and drive sales, particularly on social media, where engagement can make or break a campaign. The question is: How can businesses maximize their impact on platforms like Twitter surrounded by the noise of countless other promotions? The goal is to analyze and understand different Black Friday company strategies within the Social Commerce context. How do companies utilize social media platforms, such as Twitter, to attract customers? These strategies may vary in terms of content and promotional techniques. Key areas of focus include:

? How do companies structure their Black Friday campaigns on Twitter?

? What are the key elements of an effective social media strategy for Black Friday?

? How do companies measure the success of their social media strategies on Black Friday?

? What challenges arise in managing social media campaigns?

Methodology: This study will involve interviews with social media managers from various companies, content analysis, and other relevant research methods.

Literature: 

• Lin, X., & Wang, X. (2023). Towards a model of social commerce: Improving the effectiveness of e-commerce through leveraging social media tools based on consumers’ dual roles. European Journal of Information Systems, 32(5), 782–799. https://doi.org/10.1080/0960085X.2022.2057363
• Leong, L.-Y., Hew, T. S., Ooi, K.-B., Hajli, N., & Tan, G. W.-H. (2024). Revisiting the social commerce paradigm: The social commerce (SC) framework and a research agenda. Internet Research, 34(4), 1346–1393. https://doi.org/10.1108/INTR-08-2022-0657

• Li, F., Larimo, J., & Leonidou, L. C. (2021). Social media marketing strategy: Definition, conceptualization, taxonomy, validation, and future agenda. Journal of the Academy of Marketing Science, 49(1), 51–70. https://doi.org/10.1007/s11747-020-00733-3

Level: Bachelor oder Master

球探足球比分: truong-ngoc(at)information-systems.org

In information systems (IS) research, extracting meaningful insights from short-text data, such as Twitter tweets, remains a challenge. Traditional topic modeling approaches often struggle with the brevity and informal nature of such texts, potentially limiting their usefulness for IS theory development. Given the rise of newer techniques like BERTopic, Top2Vec, and large language models (LLMs), it is crucial to understand how these methods compare in generating relevant topics.

In the IS literature, various topic modeling methods have been used to investigate social and organizational phenomena. It would be interesting to compare these methods, particularly in their application to short-text data such as Twitter tweets. Specifically, how do the topics generated by BERTopic, Top2Vec, (author-pooled) LDA, and (zero-shot) LLMs/GenAI differ in terms of their relevance for IS theory construction? Alternative methods can also be considered.

Methodology: Evaluation of Twitter data using AI/ML methods, validated through comparison with human coding.

Literature: 

• Berente, N., Seidel, S., & Safadi, H. (2019). Research Commentary—Data-Driven Computationally Intensive Theory Development. Information Systems Research, 30(1), 50–64. https://doi.org/10.1287/isre.2018.0774
• Kishore, S., Sundaram, D., & Myers, M. D. (2024). A temporal dynamics framework and methodology for computationally intensive social media research. Journal of Information Technology, in press. https://doi.org/10.1177/02683962241283051
• Miranda, S., Berente, N., Seidel, S., Safadi, H., & Burton-Jones, A. (2022). Computationally Intensive Theory Construction: A Primer for Authors and Reviewers. MIS Quarterly, 46(2), iii–xviii.
• Yang, Y., & Subramanyam, R. (2023). Extracting Actionable Insights from Text Data: A Stable Topic Model Approach. MIS Quarterly, 47(3), 923–954. https://doi.org/10.25300/MISQ/2022/16957

Level: Bachelor oder Master

球探足球比分: truong-ngoc(at)information-systems.org

Many state-of-the-art AI tools, such as BERTopic for topic modeling, are often perceived as black boxes because it is unclear how these models arrive at their results. This lack of transparency can discourage researchers from using AI to analyze data, such as Twitter posts. Explainable AI (XAI) techniques could help address this issue. 

This study will explore whether and how XAI techniques (e.g., SHAP, LIME) can be integrated into transformer-based models for topic modeling or sentiment analysis. The goal is to assess the impact of these methods on the trust and efficiency of IS researchers.

Methodology: Application of AI for coding Twitter data with XAI, followed by human validation.

Literature: 

• Stoffels, D., Faltermaier, S., Strunk, K. S., & Fiedler, M. (2024). Guiding computationally intensive theory development with explainable artificial intelligence: The case of shapley additive explanations. Journal of Information Technology, https://doi.org/10.1177/02683962241289597
• Fernández-Loría, Carlos; Provost, Foster; and Han, Xintian. 2022. "Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach," MIS Quarterly, (46: 3) pp.1635-1660. https://doi.org/10.25300/misq/2022/16749

Level: Bachelor oder Master

球探足球比分: truong-ngoc(at)information-systems.org