In recent years, social and economic life has been augmented by myriad digital propositions ranging from social media to augmented reality online shopping and role-based multi-player gaming. A second trend, which has started with the introduction of “Second Life”, and which has gained further momentum through Facebook’s rebranding into “Meta”, is the building of immersive, persistent, three-dimensional virtual worlds – termed metaverses (Dincelli and Yayla 2022) – in which actors interact (and/or ‘live’) as digital avatars. This seminar work deals with the governance of metaverses. What frameworks exist? Do dimensions we know from corporate governance such as transparency or accountability (Brennan and Solomon 2008) hold for Metaverses? And what exactly is a DAO and how do DAO members influence community decision-making. Finally, what do we know about user acceptance of Metaverses in relation to their governance structure?

Literatur

• Brennan, N. M., & Solomon, J. (2008). Corporate governance, accountability and mechanisms of accountability: an overview. Accounting, Auditing & Accountability Journal, 21(7), 885-906.
• Dincelli, E., & Yayla, A. (2022). Immersive virtual reality in the age of the Metaverse: A hybrid-narrative review based on the technology affordance perspective. The Journal of Strategic Information Systems, 31(2), 101717.
• Goldberg, M., & Schär, F. (2023). Metaverse governance: An empirical analysis of voting within Decentralized Autonomous Organizations. Journal of Business Research, 160, 113764.
• Massaro, M., Spanò, R., & Kuruppu, S. C. (2023). Accountability and the metaverse: unaccounted digital worlds between techwashing mechanisms and new emerging meanings. Accounting, Auditing & Accountability Journal.

Car manufacturers such as Audi or Tesla offer so-called on demand car functions as fee-based activation of pre-installed technologies to enable up-selling and to build better customer relationships. So far, however, acceptance of these services is low and car manufacturers struggle to promote these services. While some nascent research into on demand services exist, we still do not know, for example, how type of contract (leasing vs. purchase) and feature tangibility (software vs. hardware) affect psychological ownership and sustainability perceptions as drivers of purchase decisions. The goal of this seminar topic is to conduct a literature review on on-demand car features, and provide an overview of what kind of features typically exist across brands. The challenge lies in finding figures that describe the tradeoff between the benefits (i.e. standardized product) and the drawbacks (i.e. building a technology in each car no mater if needed or not). Hence, it may be required to conduct 2-3 qualitative interviews with topic experts. The expected results help to better understand consumer’s purchase intentions in individualized and customized digital marketing.

Literatur

• Eckhardt, G. M., Houston, M. B., Jiang, B., Lamberton, C., Rindfleisch, A., & Zervas, G. (2019). Marketing in the sharing economy. Journal of Marketing, 83(5), 5–27.
• Garbas, J., Schubach, S., Mende, M., Scott, M. L., & Schumann, J. H. (2022). You want to sell this to me twice!? How perceptions of betrayal may undermine internal product upgrades. Journal of the Academy of Marketing Science, 1-24.
• Schaefers, T., Leban, M., & Vogt, F. (2022). On-demand features: Consumer reactions to tangibility and pricing structure. Journal of Business Research, 139, 751-761.

Open source governance is part of IT governance and focuses on the specific issues related to the acquisition, use and management of OSS, and ensuring it is done in alignment with a company's stated objectives, policies and risk profile (Bearingpoint 2013). However, in large companies, governance often depends on organizational structures and while one department might be in favor of using open source software for business purposes, others are not. The goal of this thesis is to synthesize literature on IT governance (e.g. Akbari et al. 2024) that deals with departmental structures and literature on open source governance. If possible, a (graphical) framework should be build that displays the synthesis.

Literatur

• Akbari, K., Fürstenau, D., & Winkler, T. J. (2024). Governance and Longevity of Architecturally Embedded Applications. Journal of Management Information Systems, 41(1), 266-296.
• Bearing Point (2013). Open Source Governance in highly regulated companies. URL: https://www.bearingpoint.com/files/Open_Source_Governance_In_Highly_Regulated_Companies.pdf
• Brown, A. E., & Grant, G. G. (2005). Framing the frameworks: A review of IT governance research. Communications of the Association for Information Systems, 15(1), 38.
• Warkentin, M., & Johnston, A. C. (2016). IT governance and organizational design for security management. In Information security (pp. 46-68). Routledge.

Dynamic Pricing, a concept yet to be precisely defined, represents a critical area of study within the fields of economics and information technology. This seminar paper aims to advance towards a definitive understanding of Dynamic Pricing by conducting a thorough examination of existing literature. The methodological approach involves a structured literature review, through which we seek to pinpoint and analyze various factors that play a significant role in the application and theory of Dynamic Pricing. Particular emphasis is given to the exploration of cutting-edge technologies, notably artificial intelligence (AI) and machine learning (ML), which are increasingly pivotal in the implementation and optimization of Dynamic Pricing strategies. The rationale for focusing on AI and ML technologies stems from their capability to analyze vast datasets, predict market trends, and automate pricing adjustments in real-time, thereby offering potential for more efficient and effective pricing models. Through this comprehensive analysis, the paper will contribute to the academic discourse on Dynamic Pricing, offering insights that may facilitate the development of more refined and universally accepted definitions and applications of this concept.

Literatur

• DEKSNYTE, Ine; LYDEKA, Zigmas. Dynamic pricing and its forming factors. International Journal of Business and Social Science, 2012, 3. Jg., Nr. 23.
• VOMBERG, Arnd; LAUER, Karin; WEITKÄMPER, Karen. Dynamic pricing: Preisfindung auf elektronischen Marktplätzen. Handbuch Digitale Wirtschaft, 2020, S. 653-677
• KOPALLE, Praveen K., et al. Dynamic pricing: Definition, implications for managers, and future research directions. Journal of Retailing, 2023, 99. Jg., Nr. 4, S. 580-593.
• Simon, Hermann; Fassnacht, Martin (2016): Preismanagement. Wiesbaden: Springer Fachmedien Wiesbaden.

Im Rahmen dieses Masterseminars soll der Einfluss von Cloud-Strategien auf das Testmanagement untersucht werden, wobei der Fokus auf den neuen Anforderungen und Herausforderungen liegt, die sich durch die Nutzung von Cloud-Technologien ergeben. Die Arbeit soll sich mit der Frage befassen, wie Testmanagement-Konzepte angepasst und neu gedacht werden müssen, um einen effizienten und effektiven Einsatz in einer Cloud-Umgebung zu ermöglichen. Dabei sollen verschiedene Perspektiven betrachtet werden, um ein umfassendes Verständnis für die Auswirkungen von Cloud-Strategien auf das Testmanagement zu erlangen. Das Ziel dieses Masterseminars besteht darin, den Einfluss von Cloud-Strategien auf das Testmanagement zu analysieren und Lösungsansätze für die neuen Herausforderungen zu entwickeln. Die Arbeit soll dazu beitragen, das Verständnis für die spezifischen Anforderungen und Möglichkeiten des Testmanagements in einer Cloud-Umgebung zu vertiefen und praktische Empfehlungen für Unternehmen zu liefern, die ihre Testprozesse in die Cloud verlagern möchten.

Literatur

• Khorram, F., Mottu, J., & Sunyé, G. (2020). Challenges & opportunities in low-code testing. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings. https://doi.org/10.1145/3417990.3420204
• Witte, F. (2019). Testen in der Cloud. In: Testmanagement und Softwaretest. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25087-4_29
• Witte, F. (2019). Testmanagement und Softwaretest: theoretische Grundlagen und praktische Umsetzung. Springer-Verlag.
• Witte, Frank. "Testautomatisierung und die Zukunft des Testens." Konzeption und Umsetzung automatisierter Softwaretests: Testautomatisierung zur Optimierung von Testabdeckung und Softwarequalität. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. 251-259.
• L. Riungu-Kalliosaari, O. Taipale and K. Smolander, "Testing in the Cloud: Exploring the Practice," in IEEE Software, vol. 29, no. 2, pp. 46-51, March-April 2012, doi: 10.1109/MS.2011.132
• Khorram, F., Mottu, J. M., & Sunyé, G. (2020, October). Challenges & opportunities in low-code testing. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (pp. 1-10).

Digital Twins werden als digitale Repräsentation eines physischen Produkts, Systems oder eines Menschen beschrieben, die das Verhalten ihres physischen Gegenparts in einer virtuellen Umgebung widerspiegeln und vorhersagen können. Digitale Zwillinge werden bereits in vielen Anwendungsbereichen eingesetzt. Zum Beispiel können Digital Twins durch die Integration von Daten über Energieverbräuche und -erzeugung verschiedener Geräte in einem intelligenten Stromnetz Dienste anbieten, die eine Optimierung der Energieflüsse ermöglichen (Olivares-Rojas et al. 2021). Des Weiteren unterstützen sie die Produktentwicklung bei der Optimierung von Recycling-Prozessen (Ren et al.2022) und die Stadtplanung bei komplexen Prozessen, wie z.B. Notfallmaßnahmen im Katastrophenfall (Fan et al. 2021). Dieser Themenkomplex beschäftigt sich mit den technologischen Eigenschaften und den Einsatzpotenzialen der Digital Twin-Technologie. Ein besonderer Fokus liegt hierbei auf der Untersuchung der Anwendung von Digital Twins an Menschen. Sogenannte Human Digital Twins und deren Potenziale für die Automatisierung menschenbezogener Entscheidungsprozesse werden bereits in unterschiedlichen Anwendungsbereichen, wie z.B. im Marketing und Gesundheitswesen, diskutiert. Im Rahmen dieses Themenkomplexes sollen die zentralen Eigenschaften und Potenziale dieser Technologie identifiziert und diskutiert werden.

Obwohl Digital Twins in den letzten Jahren in Forschung und Praxis zunehmend Aufmerksamkeit erlangt haben, gibt es bisher nur wenig Literatur, die einen Überblick über die funktionalen und technologischen Merkmale von Digital Twins bei der Anwendung am Menschen vorstellt. Human Digital Twins werden bereits in verschiedenen Anwendungsbereichen, wie z.B. im Gesundheitswesen oder im Marketing, diskutiert. Allerdings versäumt es die Literatur bislang, den Begriff Human Digital Twin zu konzeptualisieren und darauf einzugehen, inwiefern Human Digital Twins eine Erweiterung zur Digital Twin-Technologie darstellen. Eine detaillierte Auseinandersetzung mit den besonderen technischen Charakteristika von Human Digital Twins kann genutzt werden, um die Potenziale der Technologien in verschiedenen Anwendungsgebieten zu untersuchen.

Um die Lücke in der Abgrenzung zwischen Digital Twins und Human Digital Twins zu schließen, liegt der Schwerpunkt dieser Arbeit auf der Identifizierung der zentralen Eigenschaften von Human Digital Twins. Ziel der Arbeit ist, einen Überblick über die relevanten technologischen Charakteristika von Human Digital Twins zu geben. Darauf basierend soll diskutiert werden, inwiefern ein Human Digital Twin eine Erweiterung des traditionellen Digital Twin-Konzepts (z.B. angewendet auf Produkte, Systeme, Prozesse) darstellt. Dabei soll insbesondere auf die spezifischen Anforderungen an die Repräsentation eines Menschen im Vergleich zur Abbildung von nicht-lebendigen Objekten eingegangen werden. Auf Basis der identifizierten Alleinstellungsmerkmale von Human Digital Twins sollen die Potenziale der Technologie für verschiedenen Anwendungsbereiche in unterschiedlichen Domänen strukturiert diskutiert werden.

Literatur

• van der Valk, H., Haße, H., Möller, F. et al. (2022). Archetypes of Digital Twins. Bus Inf Syst Eng 64, 375–391.
• Miller, M.E., Spatz, (2022). E. A unified view of a human digital twin. Hum.-Intell. Syst. Integr. 4, 23–33.
• Muschkiet M, Paschmann J, Nissen A (2022). Towards Human Digital Twins for Improving Customer Experience. ICIS 2022, Copenhagen, Denmark
• Ying, Liu & Zhang, Lin & Yang, Yuan & Longfei, Zhou & Ren, Lei & Wang, Fei & Liu, Rong & Pang, Zhibo & Deen, M.J.. (2019). A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin. IEEE Access. 7.

In the face of demographic change and the intensifying “war for talents” (Beechler & Woodward, 2009; Chambers et al., 1998), companies across the globe, and especially in economically competitive markets like Germany (Faems & Hülsbömer, 2022; Löffler & Giebe, 2021), are in a constant struggle to attract and retain top talent. The initial touchpoint in the recruitment process (sometimes referred to as application and selection practices), the application phase, plays a pivotal role in shaping a potential employee's perception of an employer. The experiences of job applicants during this phase are more than mere procedural steps; they are reflective of the company's ethos, operational efficiency, and attractiveness as an employer (Campion et al., 2019; Okolie & Irabor, 2017). Given these stakes, there is an imperative need for organizations to reevaluate and enhance their recruitment strategies. Therefore, the genesis of this seminar lies in the recognition of the application process as a pivotal element in the talent acquisition strategy. Traditional approaches to understanding and optimizing this process have often relied on internal assessments and isolated feedback mechanisms. However, the advent of employer review platforms like kununu.com has opened new avenues for gathering comprehensive and authentic insights into the applicant experience (e.g., see Schaarschmidt et al., 2021). These platforms, rich with candid reviews from both satisfied and dissatisfied job applicants (Höllig, 2021), provide a treasure trove of data that, if analyzed thoughtfully, can reveal deep insights into what constitutes a 'good' or 'bad' application process from the perspective of potential employees.

• When selecting this seminar, students should employ qualitative or quantitative (or mixed method) content analysis approaches (e.g., topic modelling via latent Dirichlet allocation or qualitative content analysis) to derive guidelines for “optimal” recruitment processes.
• Employer reviews (mostly in German language) from DAX40 companies are provided to the student by the supervisor.

The motivation for this seminar is twofold. First, it aims to equip participants with the ability to harness the power of data analytics to decipher the vast amounts of unstructured feedback available online. Second, it seeks to translate these insights into actionable guidelines that can significantly enhance the effectiveness of recruitment strategies, thereby positioning companies as preferred employers in a market that is becoming increasingly selective.

Literatur

• Beechler, S., and Woodward, I. C. (2009). The global “war for talent.” Journal of International Management, 15(3), 273–285.
• Campion, M. C., Campion, E. D., and Campion, M. A. (2019). Using practice employment tests to improve recruitment and personnel selection outcomes for organizations and job seekers. Journal of Applied Psychology, 104(9), 1089–1102.
• Chambers, E. G., Foulon, M., Handfield-Jones, H., Hankin, S. M., and Michaels, E. G. (1998). The war for talent. McKinsey Quarterly, 3, 44–57.
• Faems, D., and Hülsbömer, S. (2022, February 15). Der “War for Talents” wird nicht einfach. CIO, o. P.
• Höllig, C. (2021). Online Employer Reviews as a Data Source: A Systematic Literature Review. Proceedings of the Annual Hawaii International Conference on System Sciences, 4341–4350.
• Löffler, L., and Giebe, C. (2021). Generation Z and the war of talents in the german banking sector. International Journal of Business Management and Economic Review, 4(6), 1–18.
• Okolie, U. C., and Irabor, I. E. (2017). E-Recruitment: Practices, opportunities and challenges. European Journal of Business and Management, 9(11), 116–122.
• Schaarschmidt, M., Walsh, G., and Ivens, S. (2021). Digital war for talent: How profile reputations on company rating platforms drive job seekers’ application intentions. Journal of Vocational Behavior, 131, 103644.

The integration of Large Language Models (LLMs) into industries worldwide represents a paradigm shift in productivity, innovation, and value creation. This seminar thesis undertakes a comprehensive examination of the mid- to long-term impacts of LLMs on selected industries and value chains, drawing insights from academic research, market analysis, and consultancy reports.

Students delving into this topic will explore the transformative effects of LLMs across diverse sectors, from finance and healthcare to manufacturing and entertainment. By synthesizing empirical evidence and theoretical frameworks, this thesis aims to clarify the mechanisms through which LLMs catalyze productivity gains, disrupt traditional business models, and reshape industry dynamics.

Through rigorous analysis and critical assessment of existing studies, students will uncover the nuanced connection between LLM adoption, technological innovation, and socioeconomic outcomes, informing decision-making and strategic planning in the face of AI-driven disruption.

Literatur

• Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). Gpts are gpts: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130.
• Shekhar, S., Dubey, T., Mukherjee, K., Saxena, A., Tyagi, A., & Kotla, N. (2024). Towards Optimizing the Costs of LLM Usage. arXiv preprint arXiv:2402.01742.
• Maatouk, A., Piovesan, N., Ayed, F., De Domenico, A., & Debbah, M. (2023). Large language models for telecom: Forthcoming impact on the industry. arXiv preprint arXiv:2308.06013.
• Han, Y., & Tao, J. (2024). Revolutionizing Pharma: Unveiling the AI and LLM Trends in the Pharmaceutical Industry. arXiv preprint arXiv:2401.10273.

“Large Language Models and Enterprise Software" investigates the transformative impact of advanced language models on enterprise software solutions. This seminar paper explores how Large Language Models (LLMs) are poised to revolutionize various aspects of enterprise software, including communication, data analysis, customer service, and automation.

The study delves into the ways in which LLMs will change enterprise software by enabling more natural and intuitive human-computer interactions. Through advanced natural language processing capabilities, LLMs empower users to interact with software systems using everyday language, streamlining workflows and enhancing user experience. Furthermore, the analysis examines how LLMs will be integrated into existing enterprise software solutions and used across different business functions. This includes leveraging LLMs for text-based analytics, sentiment analysis, content generation, virtual assistants, and personalized recommendations.

The seminar paper highlights exemplary use cases of LLMs in enterprise software, such as automating customer support with chatbots, improving document summarization and knowledge management, enhancing search functionality, and facilitating multilingual communication across global teams. Moreover, the analysis should provide insights into the strategies and roadmaps of major vendors such as SAP, Oracle, and Microsoft in leveraging LLMs within their software offerings. It should explore how these vendors are incorporating LLM technology into their platforms, developing new features and applications, and aligning their product roadmaps to capitalize on the capabilities of advanced language models.

Literatur

• Belzner, L., Gabor, T., & Wirsing, M. (2023, October). Large language model assisted software engineering: prospects, challenges, and a case study. In International Conference on Bridging the Gap between AI and Reality (pp. 355-374). Cham: Springer Nature Switzerland.
• Ozkaya, I. (2023). Application of large language models to software engineering tasks: Opportunities, risks, and implications. IEEE Software, 40(3), 4-8.
• Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024). Unifying large language models and knowledge graphs: A roadmap. IEEE Transactions on Knowledge and Data Engineering.
• Teubner, T., Flath, C. M., Weinhardt, C., van der Aalst, W., & Hinz, O. (2023). Welcome to the era of chatgpt et al. the prospects of large language models. Business & Information Systems Engineering, 65(2), 95-101.
• Wang, J., Huang, Y., Chen, C., Liu, Z., Wang, S., & Wang, Q. (2024). Software testing with large language models: Survey, landscape, and vision. IEEE Transactions on Software Engineering.

The integration of Large Language Models (LLMs) into the fabric of artificial intelligence research represents a pivotal moment in the journey towards developing strong AI, or artificial general intelligence (AGI). LLMs, with their advanced capabilities in processing, understanding, and generating human language, embody a crucial advancement towards machines that can perform any intellectual task that a human being can. This seminar thesis topic proposes an exploration into the role of LLMs as a foundational step in the evolution of AI systems that exhibit human-like cognitive abilities, inviting students to investigate the potential of LLMs in bridging the gap between current AI capabilities and the envisioned future of AGI.

Students are tasked with examining the current state of LLM technology, assessing its strengths and limitations, and theorizing on how these models can evolve to not only mimic human intelligence more closely but also to understand and interact with the world in a more nuanced and intentional way. This inquiry should consider the theoretical underpinnings of LLMs, their practical applications, and the ethical implications of their advancement. Through this analysis, the thesis aims to foster a deeper understanding of the potential pathways through which LLMs could lead to the realization of strong AI, highlighting the challenges and opportunities that lie ahead in this ambitious endeavor.

Literatur

• Butz, M. V. (2021). Towards Strong AI. KI-Künstliche Intelligenz, 35(1), 91–101. doi.org/10.1007/s13218-021-00705-x
• Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., ... & Xie, X. (2023). A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology. doi.org/10.1145/3641289
• Goertzel, B. (2023). Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs. doi.org/10.48550/arXiv.2309.10371
• Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2023). A Comprehensive Overview of Large Language Models. doi.org/10.48550/arXiv.2307.06435
• Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., ... & Wen, J. R. (2023). A Survey of Large Language Models. doi.org/10.48550/arXiv.2303.18223

Large Language Models (LLMs) have revolutionized the way we interact with machines, offering sophisticated text responses to a wide array of prompts. This capability has proven immensely beneficial for users seeking information, assistance, or even companionship from AI. The natural language responses generated by LLMs mimic human conversation, enabling a seamless interaction between humans and computers. However, while these textual outputs are easily comprehensible to humans, they present a significant challenge for machines in terms of interpretation and further processing. The seamless integration of LLM outputs into automated processes requires a bridge between the nuanced, often ambiguous nature of human language and the binary precision of machine understanding.

This seminar thesis topic proposes an exploration into the methodologies for effectively processing LLM-generated text through structured data processing techniques. Students are invited to investigate the current landscape of techniques that enable machines to interpret, categorize, and utilize the vast and varied outputs of LLMs. This exploration will delve into general approaches to parsing and understanding LLM responses, examining the advantages and drawbacks of each method. Through this analysis, students will contribute to the evolving dialogue on how to enhance machine interaction with human-like text responses, paving the way for more advanced applications of LLM technology in fields ranging from data analysis to automated decision-making systems.

Literatur

• Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2021). Machine learning towards intelligent systems: applications, challenges, and opportunities. Artificial Intelligence Review, 54(5), 3299–3348. doi.org/10.1007/s10462-020-09948-w
• Jiang, J., Zhou, K., Dong, Z., Ye, K., Zhao, W. X., & Wen, J.‑R. (2023). StructGPT: A General Framework for Large Language Model to Reason over Structured Data. https://doi.org/10.48550/arXiv.2305.09645
• Perozzi, B., Fatemi, B., Zelle, D., Tsitsulin, A., Kazemi, M., Al-Rfou, R., & Halcrow, J. (2023). Let Your Graph Do the Talking: Encoding Structured Data for LLMs. doi.org/10.48550/arXiv.2402.05862
• Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Zhong, S., Yin, B., & Hu, X. (2023). Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond. ACM Transactions on Knowledge Discovery from Data, Article 3649506. Advance online publication. https://doi.org/10.1145/3649506

Large Language Models (LLMs) represent a pivotal development in artificial intelligence (AI), transforming various sectors with their ability to comprehend and generate human-like text. However, beyond their technical abilities lies a landscape shaped by economic forces and political influences. This seminar thesis aims to unravel these layers by examining how know-how, market dynamics, resource allocation, and regulatory frameworks intersect with the development and deployment of LLMs. Students are invited to investigate which countries lead in LLM development, analyzing their strategies and the implications for economic competitiveness and global power dynamics. Students will explore the economic and political consequences of the current LLM landscape and assess whether Europe and Germany can catch up or if they risk falling behind. Ultimately, this seminar equips participants with a comprehensive understanding of the economic and political dimensions of LLM development, empowering them to contribute meaningfully to ongoing discussions and strategic decision-making in the field of AI.

Literatur

• Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., Ye, W., Zhang, Y., Chang, Y., Yu, P. S., Yang, Q., & Xie, X. (2024). A Survey on Evaluation of Large Language Models. ACM Transactions on Intelligent Systems and Technology, 3641289. https://doi.org/10.1145/3641289
• Farina, M., & Lavazza, A. (2023). ChatGPT in society: Emerging issues. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1130913
• Large language models: Fast proliferation and budding international competition. (2023). Strategic Comments, 29(2), iv–vi. https://doi.org/10.1080/13567888.2023.2198430
• Rudolph, J., Tan, S., & Tan, S. (2023). War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. Journal of Applied Learning and Teaching, 6(1), 364–389. Scopus. https://doi.org/10.37074/jalt.2023.6.1.23
• Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., Biles, C., Brown, S., Kenton, Z., Hawkins, W., Stepleton, T., Birhane, A., Hendricks, L. A., Rimell, L., Isaac, W., … Gabriel, I. (2022). Taxonomy of Risks posed by Language Models. 214–229. Scopus. https://doi.org/10.1145/3531146.3533088

The utilization of current AI systems is akin to using a tool in most cases. Here, the focus often lies on cooperation, i.e., employing specific abilities for individual goal achievement. However, to fully harness the potential of AI, a shift towards collaboration between humans and machines is necessary. This seminar topic precisely addresses this realm by addressing research questions that examine the main themes and genres of IS research in the field of Human-AI collaboration as well as the aspects of IS research in Human-AI collaboration that remain overlooked. The objective of this topic is the development of a research agenda for Human-AI Collaboration.

Literatur

• Benbya, H.; Pachidi, S.; and Jarvenpaa, S. (2021). Special Issue Editorial: Artificial Intelligence in Organizations: Implications for Information Systems Research. Journal of the Association for Information Systems, 22(2). DOI: 10.17705/1jais.00662
• Fügener, A., Grahl, J., Gupta, A., Ketter, W. (2022). Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation. Information Systems Research, vol. 33, 678–696. doi: 10.1287/isre.2021.1079
• Hemmer P, Schemmer M, Riefle L, Rosellen N, Vössing M, Kuehl N (2022) FACTORS THAT INFLUENCE THE ADOPTION OF HUMAN-AI COLLABORATION IN CLINICAL DECISION-MAKING. ECIS 2022 Research Papers.
• Glienke, M., Hartwich, N. J., Antons, D. (2023). Working with AI: How Attitudes Shape Human-AI Collaboration. ICIS 2023 Proceedings.
• Shneiderman B (2022) Human-centered AI. Oxford University Press, Oxford. doi:10.1093/oso/9780192845290.001.0001
• Vössing, M., Kühl, N., Lind, M., Satzger, G. (2022). Designing Transparency for Effective Human-AI Collaboration. Inf Syst Front 24, 877–895. doi:10.1007/s10796-022-10284-3

The advent of generative AI (GAI) technologies has opened up unprecedented opportunities for innovation across various sectors, including healthcare, education, environmental protection, and social welfare. These technologies have the potential to revolutionize how we address complex societal challenges by generating novel solutions, enhancing decision-making processes, and automating tasks to improve efficiency and accessibility. However, harnessing the power of GAI for the greater good requires careful consideration of ethical implications, inclusivity, and the potential for unintended consequences. The ongoing discourse on GAI seems to extol the promises of AI (utopian visions of a world free of drudgery and want) and the dangers (dystopian visions of machines taking over the world). The objective of this topic is to offer a careful examination of the challenges faced in managing this powerful set of technologies for individuals, organizations, and society to harness GAI for greater good. Thus, the work should discuss on GAI perspectives from individual, organizational and societal levels.

Literatur

• Banh, L. & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets 33(63). DOI: 10.1007/s12525-023-00680-1 
• Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. doi:10.1016/j.ijinfomgt.2023.102642
• Jarvenpaa, S. and Klein, S. (2024). New Frontiers in New Frontiers in Information Systems Theorizing: Human-gAI Collaboration. Journal of the Association for Information Systems, 25(1), 110-121. DOI: 10.17705/1jais.00868
• Sabherwal, R. and Grover, V. (2024). The Societal Impacts of Generative Artificial Intelligence: A Balanced Perspective. Journal of the Association for Information Systems, 25(1), 13-22. DOI: 10.17705/1jais.00860
• Strobel, G.; Banh, L.; Möller, F.; Schoormann, T. (2024). Exploring Generative Artificial Intelligence: A Taxonomy and Types. In: Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS). Hawaii, USA

In den letzten Jahren haben sich digitale Plattformen als entscheidende Instrumente zur Förderung der Wertschöpfung in verschiedenen Branchen etabliert. Unternehmen wie Apple, Amazon und Google verdeutlichen, dass gezieltes Management der Wertschöpfung über digitale Plattformen erhebliche Wettbewerbsvorteile bieten kann. Die Integration von komplementären Angeboten und das Management ihrer Interdependenzen sind entscheidende Erfolgsfaktoren für Unternehmensökosysteme. Um diese Ökosysteme zu gestalten und die Wertschöpfung aufeinander auszurichten, ist ein Verständnis der Akteure und ihrer Beziehungen untereinander erforderlich. Gegenstand dieses Themenblocks ist daher die Erforschung von Plattformen sowie deren Akteure und ihrer Wechselbeziehungen.

Smart-Home-Systeme sind integraler Bestandteil moderner Wohnkonzepte, die in den letzten Jahren einen großen Aufschwung erlebt haben. Durch die Integration von Technologien wie dem Internet der Dinge, Automatisierung und künstlicher Intelligenz bieten sie zahlreiche Vorteile wie erhöhten Komfort, verbesserte Energieeffizienz und gesteigerte Sicherheit. Smart-Home-Dienste werden über IoT-Plattformen realisiert, um die sich ein Ökosystem multilateraler Akteure bildet. Unternehmen wie Apple, Google und Amazon integrieren diese Plattformen in ihre eigenen Ökosysteme und ermöglichen damit die Steuerung von Geräten unterschiedlicher Hersteller. Vor diesem Hintergrund beschäftigt sich die Seminararbeit mit diesen Plattformen, indem sie die zentralen Akteure, Wertschöpfungsaktivitäten und Austauschbeziehungen auf Basis einer systematischen Literaturanalyse und einer anschließenden Mehrfachfallstudie untersucht. Die Ergebnisse sollen mittels der e3value Notation visualisiert und diskutiert werden.

Literatur

• Adner, Ron (2017): Ecosystem as Structure. In Journal of Management 43 (1), pp. 39–58. DOI: 10.1177/0149206316678451.
• Gordijn, Jaap (2002): Value-based Requirements Engineering. Exploring Innovative e-Commerce Ideas. Dissertation. Vrije Universiteit Amsterdam, Amsterdam. Available online at research.e3value.com/docs/bibtex/pdf/GordijnVBRE2002.pdf.
• Hein, Andreas (2020): Digital Platform Ecosystems: Emergence and Value Co-Creation Mechanisms. Technischen Universität München, München.
• Jacobides, Michael G.; Cennamo, Carmelo; Gawer, Annabelle (2018): Towards a theory of ecosystems. In Strat. Mgmt. J. 39 (8), pp. 2255–2276. DOI: 10.1002/smj.2904.
• Woroch, Robert; Strobel, Gero; Wulfert, Tobias (2022): Four Shades of Customer: How Value Flows in Fintech Ecosystems. In ICIS 2022 Proceedings. Available online at https://aisel.aisnet.org/icis2022/blockchain/blockchain/4.

Digital sustainability (see Kotlarsky et al. 2023) has been considered by Gartner as “tools of digital transformation, such as enhanced connectivity and the Internet of Things (IoT), to improve the environment and support sustainable business operations”.  In this seminar, you will investigate digital transformation of trading and how it shapes global societies. With the rise of the world wide web, for instance, a new form of trade emerged in the mid 1990s (e.g., Heng 2003). The Web provided a new tool to entrepreneurs who sell and buy products and services with less friction. Nowadays customers gain access to products and services from all across the globe, oftentimes without the need to leave their house. The revolution in shopping led to the significant changes in the customer’s behavior and social interactions between individuals and social groups.

In this seminar, you will have a chance to explore the world of online trade. Your goal will be to address one of the mentioned below topics based on academic literature reviews (e.g., Rowe 2014; vom Brocke et al. 2015; Shan et al. 2023). Seminar will deepen your knowledge in the area of e-commerce, sustainability, international trade and business.

Literatur

• Heng, M. S. (2003). Understanding electronic commerce from a historical perspective. Communications of the Association for Information Systems, 12(1), 6.Kotlarsky, J., Oshri, I., & Sekulic, N. (2023).
• Digital Sustainability in Information Systems Research: Conceptual Foundations and Future Directions. Journal of the Association for Information Systems, 24(4), 936-952.Rowe, F. (2014).
• What literature review is not: diversity, boundaries and recommendations. European Journal of Information Systems, 23(3), 241-255.
• Shan L. Pan, Rohit Nishant, Tuure Tuunanen, Fiona Fui-Hoon Nah (2023). Literature review in the generative AI era - how to make a compelling contribution, The Journal of Strategic Information Systems, 32 (3).
• Vom Brocke, J., Simons, A., Riemer, K., Niehaves, B., Plattfaut, R., & Cleven, A. (2015). Standing on the shoulders of giants: Challenges and recommendations of literature search in information systems research. Communications of the Association for Information Systems, 37(1), 9.

Blockchain technologies are applied in different industries and contexts to address environmental challenges or issues in business and economy that contribute to or enhance environmental issues, e. g. failures in markets, manners of production or monitoring and evaluation (Ballandies et al., 2022, 2022; Oberhauser, 2019; Rieger et al., 2022; Round & Visseren-Hamakers, 2022; Schletz et al., 2023).

Literatur

• Ballandies, M. C., Dapp, M. M., & Pournaras, E. (2022). Decrypting Distributed Ledger Design—Taxonomy, Classification and Blockchain Community Evaluation. Cluster Computing, 25(3), 1817–1838.
• Oberhauser, D. (2019). Blockchain for Environmental Governance: Can Smart Contracts Reinforce Payments for Ecosystem Services in Namibia? Frontiers in Blockchain, 2, 21. doi.org/10.3389/fbloc.2019.00021
• Rieger, A., Roth, T., Sedlmeir, J., & Fridgen, G. (2022). We Need a Broader Debate on the Sustainability of Blockchain. Joule, 6(6), 1137–1141. https://doi.org/10.1016/j.joule.2022.04.013
• Round, C., & Visseren-Hamakers, I. (2022). Blocked chains of governance: Using blockchain technology for carbon offset markets? Frontiers in Blockchain, 5, 957316. doi.org/10.3389/fbloc.2022.957316
• Schletz, M., Constant, A., Hsu, A., Schillebeeckx, S., Beck, R., & Wainstein, M. (2023). Blockchain and regenerative finance: Charting a path toward regeneration. Frontiers in Blockchain, 6, 1165133. doi.org/10.3389/fbloc.2023.1165133

E-health refers to the application of information technology and electronic tools, services, and processes for application fields and use cases in healthcare. It includes a wide range of digital technologies that aim to assess, improve, maintain, promote, or modify health or health conditions through diagnostic, preventive, and therapeutic measures in somatic as well as mental health care. Application types include the use of electronic health records, telemedicine, health information systems, wearable devices, and mobile health applications such as health apps.

In Germany, since 2020, certain approved digital health applications (DiGA) can be prescribed by doctors and psychotherapists and reimbursed by statutory health insurance companies. In the care sector, there are also approved and reimbursable solutions in Germany: digital care applications (DiPA), which are specifically intended to support patients in need of care and their relatives or professional caregivers. DiPA can be prescribed by doctors in Germany and reimbursed by statutory health insurance companies as well.

E-Health can potentially increase the efficiency of patient care, reduce costs, improve the quality of healthcare, facilitate access to relevant services, support disease research and public health, and also promote active patient participation. However, there are also numerous challenges, for example, regarding data quality, long-term user adoption, privacy and security, and integration into existing systems (Milne-Ives et al., 2020).

As part of this seminar paper, a literature analysis will be conducted to determine how the integration of DiGA into existing health and information systems can create benefits and what kinds of barriers and challenges exist.

Literatur

• Milne-Ives, M., van Velthoven, M. H., & Meinert, E. (2020). Mobile apps for real-world evidence in health care. Journal of the American Medical Informatics Association, 27(6), 976-980.

Through digital innovations and enabling technologies, the importance and spread of digital ecosystems in the healthcare sector are increasing (Stephanie & Sharma 2020). Through digital health ecosystems, the roles and interactions of actors in the health sector are changing, which is increasingly transforming it and contributing significantly to the digital transformation in the health care sector (Hermes et al., 2020). Electronic health records (EHRs) are digital versions of traditional health records. They contain all of a patient's relevant medical information, including demographic data, diagnoses, tests performed, treatments, laboratory tests, and current as well as previous medications.

EHRs provide various benefits in modern healthcare as they provide an efficient, accurate, and secure method of storing and sharing patient information if they are well implemented. EHRs can be accessed by various healthcare stakeholders, promoting collaboration between all relevant stakeholders and enabling coordinated and efficient care. In this way, both the quality of patient care and the efficiency of the processes can potentially be optimized. Implementing EHRs also comes with numerous challenges, such as privacy and security. (Kohli & Tan, 2016).

The electronic patient record (ePA) was introduced in 2021 for those insured by German health insurance companies and is intended to contribute to the digital transformation of the healthcare system in Germany. EHRs promise numerous benefits for various stakeholders in the healthcare system, particularly in terms of increasing efficiency and improving patient care. However, the successful implementation of EHRs in practice depends on numerous organizational, human, and technological factors and therefore represents a challenge for healthcare stakeholders (Fennelly et al., 2020).

As part of this seminar paper, these challenges will be identified and specified using qualitative interviews with practitioners involved in medical care. Finally, approaches to solving these challenges will be discussed.

Literatur

• Fennelly, O., Cunningham, C., Grogan, L., Cronin, H., O’Shea, C., Roche, M., … & O’Hare, N. (2020). Successfully implementing a national electronic health record: a rapid umbrella review. International Journal of Medical Informatics, 144, 104281.
• Kohli, R., & Tan, S. S. L. (2016). Electronic Health Records: How Can IS Researchers Contribute to Transforming Healthcare? MIS quarterly, 40(3), 553–574.
• Stephanie, L., & Sharma, R. S. (2020). Digital health eco-systems: An epochal review of practice-oriented research. International Journal of Information Management, 53, 102032.

Big data analytics refers to the use of advanced analytical techniques to analyze large, complex sets of data. The analysis of big data creates numerous new opportunities to generate additional value for various players in the healthcare system, such as healthcare providers and patients (Wang et al., 2018; Kankanhalli et al., 2016). The EHR contains all of a patient’s relevant health information, including demographics, medical history, medications, laboratory tests, and other medical information. The combination of big data analytics and electronic health records can potentially help improve patient care and efficiency in several ways. Furthermore, the use of large amounts of data generated in EHRs can not only benefit health care providers at the individual level but also potentially significantly improve public health by using the data for clinical research and automated disease surveillance (Shah & Khan, 2020).

As part of this seminar paper, a literature analysis will be conducted to identify which opportunities big data analytics offer in the context of EHR/ePA to sustainably optimize efficiency and patient care and which barriers exist in this context regarding the use of big data.

Literatur

• Kankanhalli, A., Hahn, J., Tan, S., & Gao, G. (2016). Big data and analytics in healthcare: Introduction to the special section. Information Systems Frontiers, 18, 233-235.
• Shah, S. M., & Khan, R. A. (2020). Secondary use of electronic health record: Opportunities and challenges. IEEE access, 8, 136947-136965.
• Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological forecasting and social change, 126, 3-13.

The benefits of conceptual models are undisputed. They foster communication between different stakeholders, contribute to reuse, integration and flexibility. However, prevalent modelling languages suffer from serious shortcomings. While domain-specific modelling languages (DSML) address some of these limitations, serious problems remain. They relate to fundamental design conflicts, the construction of modelling tools, and the synchronization of models and code. Multi-level modelling and multi-level language architectures in general constitute a new modelling paradigm that allows to substantially relax these problems. Furthermore, it enables a new kind of (enterprise) software systems that feature a high degree of flexibility and user empowerment. First, this term paper aims at describing the motivation for multi-level modelling and its characteristic properties. Second, it should analyze the potential of multi-level modelling for developing advanced DSML and powerful enterprise systems.

Literatur

• Atkinson, C., Kühne, T. (2001). The Essence of Multilevel Metamodeling. W: M. Gorgolla & C. Kobryn (red.), Lecture Notes in Computer Science: nr. 2185. UML 2001 - The Unified Modeling Language. Modeling Languages, Concepts, and Tools. 4th International Conference, Toronto, Canada, October 1-5, 2001. Proceedings (s. 19–33). Berlin, London, New York: Springer.
• Frank, U. (2014). Multilevel Modeling: Toward a New Paradigm of Conceptual Modeling and Information Systems Design. Business and Information Systems Engineering, 6(6), 319–337.
• Frank, Ulrich (2022): Multi-level modeling: cornerstones of a rationale. In: Software and Systems Modeling 21, S. 451–480. DOI: 10.1007/s10270-021-00955-1.
• LE4MM-Webportal (https://www.wi-inf.uni-duisburg-essen.de/LE4MM/)

Two artifacts can be distinguished in conceptual modeling: the modeling language that specifies the syntax and semantics of modeling concepts and the models that are produced with this modeling language. Modeling languages are often specified through the definition of a further model – the meta model – that resides on a higher level of abstraction than the user-defined models. Meta models of widely disseminated and used modeling languages are often created and maintained by large consortia, e.g., the Unified Modeling Language (UML) that is managed by the Object Management Group (OMG). To increase the productivity and efficiency of conceptual modeling, multiple researchers call for the specification of domain-specific modeling languages (DMSLs). For this purpose, students must obtain the competency to construct high-quality meta models. This seminar paper sets out to identify learning objectives related to the construction of meta models. You should analyze how multi-level modeling (MLM) can support teaching these learning objectives and where challenges occur.

Literatur

• Flechsig K-H (1996) Kleines Handbuch Didaktischer Modelle. Neuland: Eichenzell
• Frank U (2013) Domain-Specific Modeling Languages: Requirements Analysis and Design Guidelines. In: Reinhartz-Berger I et al (eds). Domain Engineering: Product Lines, Languages, and Conceptual Models. Springer: Berlin, Heidelberg, pp 133–158
• Frank U (2014) Multilevel Modeling: Toward a New Paradigm of Conceptual Modeling and Information Systems Design. Business and Information Systems Engineering 6(6):319–337
• López-Fernández JJ, Cuadrado JS, Guerra E, de Lara J (2015) Example-Driven Meta-Model Development. Software and Systems Modeling 14:1323–1347
• Sánchez-Cuadrado J, de Lara J, Guerra E (2012) Bottom-Up Meta-Modelling: An Interactive Approach. In: France RB, Kazmeier J, Breu R, Atkinson C (eds). MODELS 2012: Model Driven Engineering Languages and Systesm, 15th International Conference Proceedings. Springer: Cham, pp 3–19