Wenn ein Unternehmen alleine ein Open Source Produkt entwickelt, sieht es sich vielen Problemen gegenüber, die es auch hätte, wenn es ein proprietäres System oder Produkt entwickeln würde (z.B. Ressourcenallokation, Finanzierung, Lizenzwahl, etc.). Alternativ kann sich ein Unternehmen entscheiden, sein Projekt unter dem Deckmantel einer Foundation zu positionieren. Eine Foundation hilft, insbesondere juristische Fragen zu klären bzw. den Mitgliedern vorgefertigte Best-Practice Lösungen an die Hand zu geben. Typischerweise zahlen Unternehmen für die Mitgliedschaft in einer Foundation. Ziel dieser Arbeit ist es am Beispiel der Eclipse Foundation, typische Ziele für eine Mitgliedschaft zu identifizieren und diese entsprechend sichtbaren Akteuren gegenüberzustellen.

Literatur

• Muegge, S. M. (2012). Institutions of participation: A nested case study of company participation in the Eclipse Foundation, community, and business ecosystem (Doctoral dissertation, Carleton University).
• Shah, S. (2006): Motivation, Governance, and the Viability of Hybrid Forms in Open Source Software Development. In: Management Science, Vol. 52, No. 7, p. 1000-1014.
• Taylor, Q. C., Krein, J. L., MacLean, A. C., & Knutson, C. D. (2011). An analysis of author contribution patterns in eclipse foundation project source code. In Open Source Systems: Grounding Research: 7th IFIP WG 2.13 International Conference, OSS 2011, Salvador, Brazil, October 6-7, 2011. Proceedings 7 (pp. 269-281). Springer Berlin Heidelberg.

Die betriebswirtschaftliche Literatur kennt in der Kontrolltheorie einen belastbaren Ansatz, die Auswirkungen von Kontrollausübungen auf diejenigen, die kontrolliert werden, zu beschreiben. In der jüngeren Vergangenheit, wurde diese Theorie wieder aufgenommen in den akademischen Diskurs durch Publikationen innerhalb der Wirtschaftsinformatik (z.B. Remus et al. 2020). Aus theoretischer und praktischer Sicht bleibt aber weiterhin unklar, wie genau diese Theorie im Kontext von Open Source Communities angewendet werden kann, da einzelne Entwickler zumeist in einem Unternehmen angestellt sind und nicht bei einer Community. Dadurch entziehen sich Entwickler der direkten Kontrolle durch andere Unternehmen. Ziel dieser Arbeit ist es, Literatur zu Kontrolle in der Wirtschaftsinformatik (Information Systems) aufzuarbeiten, und offene Fragen mit Blick auf Open Source Communities zu identifizieren.

Literatur

• Cram, W. A., & Wiener, M. (2020). Technology-mediated control: Case examples and research directions for the future of organizational control. Communications of the Association for Information Systems, 46(1), 4.
• Remus, U., Wiener, M., Saunders, C., & Mähring, M. (2020). The impact of control styles and control modes on individual-level outcomes: a first test of the integrated IS project control theory. European Journal of Information Systems, 29(2), 134-152.
• Schaarschmidt, M. (2022). Innovating beyond firm boundaries: resource deployment control in open source software development. Information Technology & People, 36(4), 1645-1668.

Firmen, die direkt oder indirekt Open Source Software als Teil ihres Geschäftsmodell verstehen (z.B. RedHat) tragen eigenen Softwarecode bei zu Open Source Projekten. Eine in der Literatur nicht abschließend geklärte Frage ist: Haben die technischen Beiträge von Firmen einen Einfluss auf die Softwarearchitektur, und hier insbesondere die Modularität? Ziel der Arbeit ist es zunächst den Begriff der Modularität in einer Softwarearchitektur herauszuarbeiten und dann auf Open Source Projekte anzuwenden.

Literatur

• Moon, E., & Howison, J. (2024). A dynamic perspective on software modularity in open source software (OSS) development: A configurational approach. Information and Organization, 34(1), 100499.
• Peng, G., Geng, X., & Lin, L. (2012, January). Modularity and inequality of code contribution in open source software development. In 2012 45th Hawaii International Conference on System Sciences (pp. 4505-4514). IEEE.

Zirkuläre Angebote sind nicht nur nachhaltig in dem Sinne, dass beispielweise recycelte Materialien verwendet werden. Die Entsorgung oder Wiederverwendung, inklusive einem zu vermutenden CO2-Ausstoß, sind ebenfalls Teil einer ganzheitlichen Betrachtung im Rahmen der Circular Economy. Im aktuellen Diskurs ist jedoch unklar, inwieweit sich Empfehlungssysteme für zirkuläre Produkte unterscheiden müssen von klassischen Ansätzen, die oftmals vergangenes Einkaufverhalten oder das von anderen Kunden als Grundlage verwendet (z.B. collaborative Filtering). Ebenso ist nicht klar, wie Kunden solche Empfehlungssysteme wahrnehmen bzw. wahrnehmen würden. Beide Aspekte sind Gegenstand dieser Ausarbeitung.

Literatur

• Krause, T., Deriyeva, A., Beinke, J. H., Bartels, G. Y., & Thomas, O. (2024). Mitigating Exposure Bias in Recommender Systems–A Comparative Analysis of Discrete Choice Models. ACM Transactions on Recommender Systems.
• van Capelleveen, G., Amrit, C., Zijm, H., Yazan, D. M., & Abdi, A. (2021). Toward building recommender systems for the circular economy: Exploring the perils of the European Waste Catalogue. Journal of Environmental Management, 277, 111430.
• van Capelleveen, G., van Wieren, J., Amrit, C., Yazan, D. M., & Zijm, H. (2021). Exploring recommendations for circular supply chain management through interactive visualisation. Decision Support Systems, 140, 113431.

Klassische Empfehlungssysteme arbeiten in der Regel auf Daten, die aus einem Data Warehouse stammen. In diesem Data Warehouse werden zumeist sehr viele Daten aus dem eigenen Unternehmen abgelegt, ergänzt um externe Daten, wenn es der Kontext erfordert. Im Zuge der Erhöhung der Transparenz experimentieren mehr und mehr Industrien mit dem Einsatz der Blockchain-Technologie, insbesondere entlang der Wertschöpfungskette von den Rohstoffen bis hin zum fertigen Produkt. Die Seminararbeit soll der Frage nachgehen, in wie weit eine Blockchain-Technologie Einfluss nehmen kann auf das Data Warehouse sowie darauf aufbauende Empfehlungssysteme für zirkuläre Angebote.

Literatur

• Agrawal, T. K., Kumar, V., Pal, R., Wang, L., & Chen, Y. (2021). Blockchain-based framework for supply chain traceability: A case example of textile and clothing industry. Computers & industrial engineering, 154, 107130.
• Melville, N. P. (2010). Information Systems Innovation for Environmental Sustainability. MIS Quarterly, 34(1), 1–21.

Die Bestrebungen rund um Inner Source entstammen primär der Literatur rund um das Software-Engineering (z.B. Stol und Fitzgerald, 2014). Gleichsam wird das Problem, dass eigene Arbeiten als besser bewertet werden, als das von anderen (sowohl andere Abteilungen, als auch andere Unternehmen), auch unter dem Not-invented-here-Syndrom beschrieben. Aufgabe der Seminararbeit ist es herauszuarbeiten, inwieweit die beiden Ansätze Gemeinsamkeiten, aber auch Unterschiede aufweisen.

Literatur

• Antons, D., & Piller, F. T. (2015). Opening the black box of “not invented here”: Attitudes, decision biases, and behavioral consequences. Academy of Management perspectives, 29(2), 193-217.
• Stol, K. J., & Fitzgerald, B. (2014). Inner source--adopting open source development practices in organizations: a tutorial. IEEE Software, 32(4), 60-67.
• Stol, K. J., Avgeriou, P., Babar, M. A., Lucas, Y., & Fitzgerald, B. (2014). Key factors for adopting inner source. ACM Transactions on Software Engineering and Methodology (TOSEM), 23(2), 1-35.

Neben der Literatur um Inner Source steht ein weiterer, recht verwandter Literaturstrang rund um das interne Crowdsourcing (Durward et al. 2019). Hier geht es ebenso wie bei Inner Source um die Aufteilung von Aufgaben über Abteilungsgrenzen hinweg, beispielweise dadurch, dass sich Mitarbeitende eigenständig zu Projekten melden können. Aufgabe der Seminararbeit ist es herauszuarbeiten, inwieweit die beiden Ansätze (Inner Source vs. Internal Crowdsourcing) Gemeinsamkeiten, aber auch Unterschiede aufweisen.

Literatur

• Durward, D.; Simmert, B.; Blohm, I. & Peters, C. (2019): Internal
• Crowd Work as a Source of Empowerment - An Empirical Analysis of the Perception
• of Employees in a Crowdtesting Project. In: 14th International Conference on
• Wirtschaftsinformatik (WI). Siegen, GermanyStol, K. J., & Fitzgerald, B. (2014). Inner source--adopting open source development practices in organizations: a tutorial. IEEE Software, 32(4), 60-67.
• Stol, K. J., Avgeriou, P., Babar, M. A., Lucas, Y., & Fitzgerald, B. (2014). Key factors for adopting inner source. ACM Transactions on Software Engineering and Methodology (TOSEM), 23(2), 1-35.

Das adaptive Lernen ist im Kontext des E-Learnings eine Spezialform, welche die Erkenntnisse unterschiedlicher Domänen zusammenbringt und auf dieser Basis, das Lernerlebnis für den Lernenden möglichst zu personalisieren. Daraus entstehen einige Potenziale, die es aus der Literatur herauszuarbeiten gilt.

Im Rahmen dieser Seminararbeit sollen die Begrifflichkeiten E-Learning und adaptives Lernen definiert und anschließend in einen Kontext gesetzt werden. Ferner sollen auf dieser Basis die Potenziale, sowie Merkmale des adaptiven Lernens herausgearbeitet werden. Dafür kann auch auf Case Studys zurückgegriffen werden.

Literatur

• Hammad, J., Hariadi, M., Jabari, N. A. M., Hariadi, M., Hery Purnomo, M., Jabari, N., & Kurniawan, F. (2018). E-learning and Adaptive E-learning Review. In IJCSNS International Journal of Computer Science and Network Security (Vol. 18, Issue 2). https://www.researchgate.net/publication/323933971
• Sun, G., Cui, T. R., Beydoun, G., Chen, S. P., Dong, F., Xu, D. M., & Shen, J. (2017). Towards Massive Data and Sparse Data in Adaptive Micro Open Educational Resource Recommendation: A Study on Semantic Knowledge Base Construction and Cold Start Problem. SUSTAINABILITY, 9(6). https://doi.org/10.3390/su9060898
• Hsu, P. S. (2012). Learner characteristic based learning effort curve mode: The core mechanism on developing personalized adaptive elearning platform. Turkish Online Journal of Educational Technology, 11(4), 210–220.
• Haag, F., Günther, S. A., Hopf, K., Handschuh, P., & Klose, M. (2023). Addressing Learners’ Heterogeneity in Higher Education: An Explainable AI-based Feedback Artifact for Digital Learning Environments. Wirtschaftsinformatik 2023 Proceedings.

Micro Learning wird häufig als Buzz Word im Kontext des E-Learnings genannt. Es gibt zahlreiche Einsatzgebiete, in denen Micro Learning Formate Einsatz finden. Eins dieser Einsatzgebiete ist die betriebliche Weiterbildung.

Im Rahmen dieser Seminararbeit sollen die Begrifflichkeiten E-Learning und Micro Learning definiert und anschließend in einen gemeinsamen Kontext gesetzt werden. Die Prinzipien des Micro Learnings sind herauszuarbeiten. Ferner sollen auf dieser Basis die Potenziale, sowie Merkmale des Micro Learnings herausgearbeitet werden.

In diesem Rahmen sollen aktuelle Beispiele für den Einsatz von Micro Learning im innerbetrieblichen Kontext herausgearbeitet und vorgestellt werden.

Literatur

• Kossen, C., & Ooi, C. Y. (2021). Trialling micro-learning design to increase engagement in online courses. Asian Association of Open Universities Journal, 16(3), 299–310. https://doi.org/10.1108/AAOUJ-09-2021-0107
• Hug, T., Möller, H., & Mark, T. (2007). Microlearning: an Emerging Field in Science. Microlearning Conference, 2006, June, 1–324. https://www.uibk.ac.at/iup/buch_pdfs/microlearning2006-druck.pdf
• Sankaranarayanan, R., Leung, J., Abramenka-Lachheb, V., Seo, G., & Lachheb, A. (2023). Microlearning in Diverse Contexts: A Bibliometric Analysis. TechTrends: Linking Research & Practice to Improve Learning, 67(2), 260–276. http://10.0.3.239/s11528-022-00794-x
• Zuckerman, S. (2018). Optimize Safety Training by Properly Leveraging E-LEARNING. EHS Today, 11(6), 19–22. https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib,uid&db=buh&AN=132828235&site=ehost-live&scope=site
• Aguilera-Hermida, A. P., Quiroga-Garza, A., Gómez-Mendoza, S., del Río Villanueva, C. A., Avolio Alecchi, B., Avci, D., Alias, N. F., Razak, R. A., BERSIN, J., Beste, T., Cascio, W. F., Collins, S. L., Smith, T. C., Hack, G., Moorhouse, M. D., Cretu, I., Grigore, M., Scripcariu, I. S., de Troyer, O., … Garshasbi, S. (2023). A study of employees’ utilization of microlearning platforms in organizations. TechTrends: Linking Research & Practice to Improve Learning, 11(2), 37–50. doi.org/10.1007/s11187-021-00502-8

Literature reviews (LRs) occupy a pivotal role within the landscape of academic research, exemplifying a rigorous methodology for the synthesis of extant literature, thus illuminating scholarly pursuits, and catalyzing the advancement of knowledge across various disciplines. Several forms of LRs exist (see Schryen et al., 2020; Snyder, 2019), including integrative reviews, which aim to synthesize research to generate new conceptual frameworks; scoping reviews, which assess the scope of literature on a given topic; and systematic literature reviews (SLRs), which seek to identify all relevant empirical evidence that satisfies prior specified inclusion criteria to retrieve, evaluate, and synthesize reliable information on a topic of interest (Snyder, 2019; vom Brocke et al., 2009). SLRs – sometimes referred to as structured literature reviews – serve as a cornerstone in academic research, offering a methodical approach to synthesizing/criticizing/reflecting existing literature, providing insights into scholarly endeavors, and facilitating the expansion of various fields through knowledge generation (Schryen et al., 2020). Despite their established significance, the methodologies for conducting SLRs vary significantly across disciplines, resulting in a plethora of approaches that can be overwhelming for students and academic novices. Exemplary disciplines include, but are not limited to, IS Research (e.g., Bandara et al., 2015; Schryen et al., 2020; vom Brocke et al., 2009, 2015), Medicine (e.g., Tranfield et al., 2003), Management (e.g., Clark et al., 2021; Fisch & Block, 2018; Snyder, 2019), Business & Entrepreneurship (e.g., Keding, 2021), and software engineering (e.g., Kitchenham & Brereton, 2013). This diversity, while reflective of the unique requirements and perspectives of different fields, poses a challenge in creating a unified, transparent, and rigorous methodology that is accessible and actionable for novices in academia.

This seminar aims to address the aforementioned gap through the development of an interdisciplinary process model (graphical representation) for conducting SLRs. This model should establish a comprehensive framework that outlines both the necessary and optional steps in conducting an SLR within a graphical representation. This endeavor will not only provide students with an invaluable resource for their academic pursuits but also contribute to the broader academic community by offering a clear, adaptable, and robust approach to SLRs.

Literatur

• Bandara, W., Furtmueller, E., Gorbacheva, E., Miskon, S., and Beekhuyzen, J. (2015). Achieving Rigor in Literature Reviews: Insights from Qualitative Data Analysis and Tool-Support. Communications of the Association for Information Systems, 37, 154–204.
• Clark, W. R., Clark, L. A., Raffo, D. M., and Williams, R. I. (2021). Extending Fisch and Block’s (2018) tips for a systematic review in management and business literature. Management Review Quarterly, 71(1), 215–231.
• Fisch, C., and Block, J. (2018). Six tips for your (systematic) literature review in business and management research. Management Review Quarterly, 68(2), 103–106.
• Keding, C. (2021). Understanding the interplay of artificial intelligence and strategic management: four decades of research in review. In Management Review Quarterly (Vol. 71). Springer International Publishing.
• Kitchenham, B., and Brereton, P. (2013). A systematic review of systematic review process research in software engineering. Information and Software Technology, 55(12), 2049–2075.
• Schryen, G., Wagner, G., Benlian, A., and Paré, G. (2020). A knowledge development perspective on literature reviews: Validation of a new typology in the IS field. Communications of the Association for Information Systems, 46, 134–186.
• Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104(July), 333–339.
• Tranfield, D., Denyer, D., and Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British Journal of Management, 14(3), 207–222.
• vom Brocke, J., Simons, A., Niehaves, B., Niehaves, B., Reimer, K., Plattfaut, R., and Cleven, A. (2009). Reconstructing the giant: On the importance of rigour in documenting the literature search process. European Conference on Information Systems (ECIS) 2009 Proceedings, 161.
• vom Brocke, J., Simons, A., Riemer, K., Niehaves, B., Plattfaut, R., and 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, 205–224.

The advent of digital platforms has exponentially increased the availability of online data – big data – challenging traditional qualitative analysis due to the sheer volume of unstructured information (Eickhoff & Neuss, 2017). Topic modeling, as a quantitative text mining approach, has risen to prominence for its ability to distill large document collections into manageable themes, thereby offering insightful overviews of the discourse within specific domains (Debortoli et al., 2016; Rai, 2016). In this context, topic modeling techniques, notably Latent Dirichlet Allocation (LDA) as introduced by Blei et al. (2003), have emerged as a powerful tool in both applied and theoretical research domains. Topic modelling, as a probabilistic framework, facilitates the analysis of word occurrences within large corpora of unstructured text to reveal latent semantic patterns (Blei, 2012; Blei et al., 2003). These models autonomously discern the range of topics present in a textual corpus, delineate patterns of word usage, and track the evolution of these patterns over time (Blei, 2012). Despite its potential, the application of topic modeling in IS research encounters challenges related to model validity, interpretability, and trustworthiness (e.g., Halevy et al., 2009; Palese & Piccoli, 2020). By examining the intricacies of topic modeling within IS research, this seminar paper aims to highlight the status quo on the application of topic modelling in IS research, while also addressing the critical need for rigorous evaluation measures to bolster the validity and trustworthiness of research outcomes. Through this endeavor, the seminar should contribute to the scholarly dialogue on topic modeling but also pave the way for future research that harnesses the power of quantitative text mining in uncovering the thematic landscapes of vast textual datasets.

Literatur

• Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.
• Blei, D. M., Ng, A. Y., and Jordan, M. T. (2003). Latent dirichlet allocation. Advances in Neural Information Processing Systems, 3, 993–1022.
• Debortoli, S., Müller, O., Junglas, I., and vom Brocke, J. (2016). Text mining for information systems researchers: An annotated topic modeling tutorial. Communications of the Association for Information Systems, 39(1), 110–135.
• Eickhoff, M., and Neuss, N. (2017). Topic Modelling Methodology. Proceedings of the 25th European Conference on Information Systems (ECIS), 2017, 1327–1347.
• Halevy, A., Norvig, P., and Pereira, F. (2009). The unreasonable effectiveness of data. IEEE Intelligent Systems, 24(2), 8–12.
• Palese, B., and Piccoli, G. (2020). Evaluating topic modeling interpretability using topic labeled gold-standard sets. Communications of the Association for Information Systems, 47(June), 433–451.
• Rai, A. (2016). Synergies Between Big Data and Theory Changing Landscape in the Generation and Sourcing of Data for Research. MIS Quarterly, 40(2), iii–ix.

Online employer review websites, such as Glassdoor, Indeed, Jobplanet, and Kununu, serve as platforms where current and former employees can post evaluations and comments about their employers. These reviews typically include both quantitative ratings on various aspects of the workplace, such as culture, compensation, and management, and qualitative feedback in the form of textual comments. As such, these websites generate a rich source of data that can be harnessed for Information Systems (IS) research, offering insights into employee satisfaction, organizational culture, and even firm performance (Höllig, 2021). Employer reviews, provide unique and additional value over conventional survey methods, primarily due to the anonymity of employee contributions, which minimizes response bias (Y. Jung & Suh, 2019). The textual data generated from these reviews opens a plethora of analytical opportunities. For instance, sentiment analysis can leverage the quantitative Likert-scale ratings for annotation purposes, enhancing the depth of understanding (e.g., Abel et al., 2017). Moreover, methodologies such as content analysis and topic modeling can be employed to reveal underlying themes within the data.

Given the potential of online employer reviews as a data source (e.g., Schaarschmidt et al., 2021; Watson & Wu, 2022), the aim of this seminar is to conduct a systematic literature review in order to understand how these employer reviews have been utilized in IS research. Therefore, a methodological lens on the use of employer reviews should be adopted in this seminar, fulfilling the following primary objectives: first, to synthesize existing knowledge on the use of online employer reviews in research, identifying the main themes, methodologies, and findings; and second, to develop guidelines on the use of employer reviews as a data source, that highlights data extraction and analysis techniques in current literature and proposes future directions for exploring online employer reviews in research.

Literatur

• Abel, J., Klohs, K., Lehmann, H., and Lantow, B. (2017). Sentiment-Analysis for German Employer Reviews. In: W. Abramowicz (Ed.), Lecture Notes in Business Information Processing (303rd ed., pp. 37–48). Cham: Springer.
• 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.
• Jung, Y., and Suh, Y. (2019). Mining the voice of employees: A text mining approach to identifying and analyzing job satisfaction factors from online employee reviews. Decision Support Systems, 123(June), 113074.
• 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(August 2020), 103644.
• Watson, F., and Wu, Y. (2022). The Impact of Online Reviews on the Information Flows and Outcomes of Marketing Systems. Journal of Macromarketing, 42(1), 146–164.

In an era marked by rapid technological advancements, Extended Reality (XR) – an umbrella term for Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR) – are revolutionizing traditional business operations, including Human Resource Management (HRM). Those recent technological advancements have not only made immersive technologies more accessible but have also significantly enhanced their capabilities to simulate real-world experiences. The affordability and availability of high-quality head-mounted displays (HMDs) like the Meta Quest 2, HTC Vive, Microsoft HoloLens, or Apple’s VisionPro, alongside the advent of sophisticated game development platforms such as Unreal Engine and Unity, have democratized the creation and consumption of VR content (Moon et al., 2022). These developments have led to a surge in VR and so called “Metaverse” applications and services (e.g., Marabelli & Lirio, 2024), presenting novel opportunities and challenges for HRM practices, especially in recruitment and selection as well as in training and development processes (e.g., Kong et al., 2023; Thakral et al., 2023). These technologies offer innovative ways to engage, assess, and train potential candidates, thus reshaping the landscape of HRM. Hence, this seminar aims to conduct a systematic literature review to synthesize the current state of the art of AR, MR, and VR applications within HRM and to derive a research agenda (please see Schryen et al., 2020), concerning future topics of interest in this uprising domain of research.

Literatur

• Kong, A., Hui, R. T.-Y., and Tang, J. K.-T. (2023). If You Believe, You Will Receive: VR Interview Training for Pre-employment. In: T. Jung, M. C. tom Dieck, & S. M. Correia Loureiro (Eds.), Extended Reality and Metaverse (pp. 106–111). Cham: Springer.
• Marabelli, M., and Lirio, P. (2024). AI and the metaverse in the workplace: DEI opportunities and challenges. Personnel Review.
• Moon, J., Jeong, M., Oh, S., Laine, T. H., and Seo, J. (2022). Data Collection Framework for Context-Aware Virtual Reality Application Development in Unity: Case of Avatar Embodiment. Sensors, 22(12), 1–37.
• Schryen, G., Wagner, G., Benlian, A., and Paré, G. (2020). A knowledge development perspective on literature reviews: Validation of a new typology in the IS field. Communications of the Association for Information Systems, 46, 134–186.
• Thakral, P., Srivastava, P. R., Dash, S. S., Jasimuddin, S. M., and Zhang, Z. (Justin). (2023). Trends in the thematic landscape of HR analytics research: a structural topic modeling approach. Management Decision, 61(12), 3665–3690.

In an era where technological advancements continually reshape the landscape of Human Resource Management (HRM), the emergence of head-mounted displays (HMDs) presents a novel frontier for enhancing HR analytics, also known as people analytics (e.g., Thakral et al., 2023). People analytics itself is the systematic analysis of employee data to improve organizational decisions, workforce performance, and employee satisfaction. By leveraging data, HR professionals and organizational leaders can make evidence-based decisions that enhance recruitment, retention, and overall organizational effectiveness. The intersection of technology and HRM is not new; however, the rate of advancement and the depth of integration have accelerated. HMDs represent the latest wave of technological innovation, distinguished by their ability to provide immersive experiences, and collect a wide array of user data in real-time (e.g., Callahan-Flintoft et al., 2021; Moon et al., 2022; Rahman et al., 2020). This capability introduces a new dimension to HR analytics, offering a richer, more nuanced understanding of employee behaviors, responses, and experiences. Although several studies exist that focus on extracting specific data from HMDs (for an overview, see Ratcliffe et al., 2021) – traditionally within XR experiments – no study exists, that synthesizes an overview of collectable data from HMDs.

Hence, this seminar aims to close aforementioned gap by means of a systematic literature review to identify and analyze the types of data that can be collected via HMDs, including but not limited to physiological measures (e.g., skin conductance, heart rate, blood pressure, and electroencephalogram (EEG) readings), psychological measures (e.g., stress levels, emotional state), or kinetic data (e.g., employee height, posture). By examining the comprehensive range of data collectible via HMDs, a further goal is to discuss its implications for people analytics.

Literatur

• Callahan-Flintoft, C., Barentine, C., Touryan, J., and Ries, A. J. (2021). A Case for Studying Naturalistic Eye and Head Movements in Virtual Environments. Frontiers in Psychology, 12.
• Moon, J., Jeong, M., Oh, S., Laine, T. H., and Seo, J. (2022). Data Collection Framework for Context-Aware Virtual Reality Application Development in Unity: Case of Avatar Embodiment. Sensors, 22(12), 1–37.
• Rahman, R., Wood, M. E., Qian, L., Price, C. L., Johnson, A. A., and Osgood, G. M. (2020). Head-Mounted Display Use in Surgery: A Systematic Review. Surgical Innovation, 27(1), 88–100.
• Ratcliffe, J., Soave, F., Bryan-Kinns, N., Tokarchuk, L., and Farkhatdinov, I. (2021). Extended Reality (XR) Remote Research: a Survey of Drawbacks and Opportunities. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–13. New York, NY, USA: ACM.
• Thakral, P., Srivastava, P. R., Dash, S. S., Jasimuddin, S. M., and Zhang, Z. (Justin). (2023). Trends in the thematic landscape of HR analytics research: a structural topic modeling approach. Management Decision, 61(12), 3665–3690.

Um einen ersten Meilenstein zu bewältigen, soll in dieser Seminararbeit eine strukturierte Literatursuche erstellt werden. Die Hauptfragestellung beschäftigt sich dabei mit der Mindestanzahl an Datenpunkten, die für den erfolgreichen Einsatz eines Machine Learning Verfahrens benötigt wird. Anhand der Literatursuche soll herausgefunden werden, ob es für bestimmte Domänen Unterschiede in dieser Mindestzahl gibt, wovon diese abhängt und, ob es Techniken gibt, mithilfe derer sich diese Mindestzahl reduzieren lässt.

Literatur

• VABALAS, Andrius, et al. Machine learning algorithm validation with a limited sample size. PloS one, 2019, 14. Jg., Nr. 11.
• BYRD, Richard H., et al. Sample size selection in optimization methods for machine learning. Mathematical programming, 2012, 134. Jg., Nr. 1, S. 127-155.
• SHARMA, Alok; PALIWAL, Kuldip K. Linear discriminant analysis for the small sample size problem: an overview. International Journal of Machine Learning and Cybernetics, 2015, 6. Jg., S. 443-454.
• Simon, Hermann; Fassnacht, Martin (2016): Preismanagement. Wiesbaden: Springer Fachmedien Wiesbaden.

In dieser Seminararbeit sollen anhand einer strukturierten Literatursuche mathematische Modelle zur Bestimmung des Preises für ein Produkt identifiziert werden. Dabei geht es vor allem darum den Abstraktionsgrad dieser Modelle zu beschreiben und deren Einschränkungen zu erkennen. Die mathematischen Modelle gehen in der Regel von bestimmten Annahmen aus und beinhalten damit Wirkungszusammenhänge zwischen verschiedenen Faktoren, welche sich letztendlich auf den Preis auswirken. Diese gilt es zu identifizieren.

Literatur

• Chung, Jaekwon; Li, Dong (2010): A simulation on impacts of a dynamic pricing model for perishable foods on retail operations productivity and customer behaviours. In: 2010 IEEE International Conference on Industrial Engineering and Engineering Management. EM). Macao, China, 07.12.2010 - 10.12.2010: IEEE, S. 1300–1304.
• Dolgui, Alexandre; Proth, Jean-Marie (2010): Pricing strategies and models. In: Annual Reviews in Control 34 (1), S. 101–110. DOI: 10.1016/j.arcontrol.2010.02.005.
• Simon, Hermann; Fassnacht, Martin (2016): Preismanagement. Wiesbaden: Springer Fachmedien Wiesbaden.

Im Zuge der digitalen Transformation setzen Unternehmen vermehrt auf Citizen Development als Mittel, um nicht-technischen Mitarbeitern die Möglichkeit zu geben, Anwendungen und Lösungen zu entwickeln. Diese Entwicklung bringt jedoch Herausforderungen mit sich, insbesondere im Hinblick auf die Koordination von Releases und der Freigabe der entwickelten Artefakte. Es gilt also zu untersuchen, welche Strategien und Best Practices, in diesem Zusammenhang genutzt werden, um Risiken zu reduzieren und ein sauberes Testmanagement und Releasemanagement auch für diesen Bereich zu gewährleisten. Beispielhaft könnte es eine Vorgabe sein, Entwicklungen, welche aus dem Citizen Development entstehen erst durch einen Entwickler und einen Prozessbeauftragten freizugeben. Im Rahmen dieser Arbeit sollen Lösungen für die genannten Probleme gesammelt, sortiert und bewertet werden.

Literatur

• Davenport, Thomas H. (2023) "MISQE Insight: On the Inevitability of Citizen Development,"MIS Quarterly Executive: Vol. 22: Iss. 4, Article 3. https://aisel.aisnet.org/misqe/vol22/iss4/3
• 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. doi.org/10.1145/3417990.3420204
• Novales, Ainara and Mancha, Rubén (2023) "Fueling Digital Transformation with Citizen Developers and Low-Code Development," MIS Quarterly Executive: Vol. 22: Iss. 3, Article 6. https://aisel.aisnet.org/misqe/vol22/iss3/6
• Lebens, Mary & Finnegan, Roger & Sorsen, Steven & Shah, Jinal. (2021). Rise of the Citizen Developer. Muma Business Review. 5. 101-111. 10.28945/4885.
• Lonnie R. Sherrod , Constance Flanagan & James Youniss (2002) Dimensions of Citizenship and Opportunities for Youth Development: The What, Why, When, Where, and Who of Citizenship Development, Applied Developmental Science, 6:4, 264-272, DOI: 10.1207/S1532480XADS0604_14
• M. Oltrogge et al. (2018), "The Rise of the Citizen Developer: Assessing the Security Impact of Online App Generators," 2018 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, 2018, pp. 634-647, doi: 10.1109/SP.2018.00005
• Bruhin, O., Dickhaut, E., Elshan, E., & Li, M. (2024). The Rise of Generative AI in Low Code Development Platforms–An Analysis and Future Directions.

Kürzlich hat der NVIDIA CEO die These unterstützt, dass Programmieren in Zukunft nicht mehr gelehrt werden muss, da diese Aufgaben von künstlicher Intelligenz übernommen werden können. Auch setzen im Zuge der digitalen Transformation Unternehmen vermehrt auf Citizen Development als Mittel, um nicht-technischen Mitarbeitern die Möglichkeit zu geben, Anwendungen und Lösungen zu entwickeln. Diese Entwicklung bringt jedoch Herausforderungen mit sich, welche in Zukunft durch den Einsatz von künstlicher Intelligenz, bewältigt werden können. Im Rahmen dieser Arbeit sollen aktuelle Chancen, Herausforderungen und Implikationen, welche durch den Einsatz von künstlicher Intelligenz im Rahmen des Citizen Development entstehen können, erarbeitet werden.

Literatur

• Davenport, Thomas H. (2023) "MISQE Insight: On the Inevitability of Citizen Development,"MIS Quarterly Executive: Vol. 22: Iss. 4, Article 3. https://aisel.aisnet.org/misqe/vol22/iss4/3
• Novales, Ainara and Mancha, Rubén (2023) "Fueling Digital Transformation with Citizen Developers and Low-Code Development," MIS Quarterly Executive: Vol. 22: Iss. 3, Article 6.
  https://aisel.aisnet.org/misqe/vol22/iss3/6
• Lonnie R. Sherrod , Constance Flanagan & James Youniss (2002) Dimensions of Citizenship and Opportunities for Youth Development: The What, Why, When, Where, and Who of Citizenship Development, Applied Developmental Science, 6:4, 264-272, DOI: 10.1207/S1532480XADS0604_14
• Bruhin, O., Dickhaut, E., Elshan, E., & Li, M. (2024). The Rise of Generative AI in Low Code Development Platforms–An Analysis and Future Directions.

Im Rahmen dieser Arbeit soll die Integration von Process Mining in das Testmanagement sowie die konkreten Einsatzszenarien und Integrationsszenarien untersucht werden. Dabei steht die Frage im Mittelpunkt, wie sich das Zusammenspiel von Process Mining und dem Testmanagement und der dort genutzten Tools gestaltet und welchen Beitrag Process Mining im Bereich des Testmanagements leisten kann. Die Arbeit soll sich auf die Identifikation von Einsatzbereichen und Möglichkeiten konzentrieren, wie Process Mining zur Verbesserung von Testprozessen und der Effizienz von Testwerkzeugen beitragen kann. Dabei sollen auch potenzielle Hürden und Herausforderungen bei der Implementierung von Process Mining im Testmanagement sowie mögliche Lösungsansätze betrachtet werden.

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
• van der Aalst, W. et al. (2012). Process Mining Manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds) Business Process Management Workshops. BPM 2011. Lecture Notes in Business Information Processing, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28108-2_19
• Leppäkoski, A., Hämäläinen, T.D. (2016). PROMOTE: A Process Mining Tool for Embedded System Development. In: Abrahamsson, P., Jedlitschka, A., Nguyen Duc, A., Felderer, M., Amasaki, S., Mikkonen, T. (eds) Product-Focused Software Process Improvement. PROFES 2016. Lecture Notes in Computer Science(), vol 10027. Springer, Cham. https://doi.org/10.1007/978-3-319-49094-6_38
• De Medeiros, A. A., & Günther, C. W. (2005). Process mining: Using CPN tools to create test logs for mining algorithms. In Proceedings of the sixth workshop on the practical use of coloured Petri nets and CPN tools (CPN 2005) (Vol. 576).

Many voices claim that cloud computing allows to cut costs. In many cases, this general statement is flawed and does not distinguish the cloud offering’s different services (IaaS, PaaS, SaaS) or underlying business models (tier 1 vs tier 2 provider).

This work aims to analyze different pricing strategies from both the cloud provider perspective and the cloud customer perspective. Approaches in the work might include the case-study-based analysis of pricing strategies, analysis of market strategies of different providers, or a comparative analysis of incurred cloud cost for a different system architectures and throughputs with different providers.

Literatur

• Wu C, Buyya R, and Ramamohanarao K. 2019. Cloud Pricing Models: Taxonomy, Survey, and Interdisciplinary Challenges. ACM Comput. Surv. 52, 6, Article 108 (November 2020), 36 pages. https://doi.org/10.1145/3342103
• Makhlouf, R. (2020). Cloudy transaction costs: a dive into cloud computing economics. Journal of Cloud Computing, 9(1), 1-11.

ERP system offerings are moving into the cloud. However, the software vendor’s marketing language obfuscates, not to say “beclouds”, the technological implications that accompany this technology shift. This work aims to illuminate the system architectures chosen by the ERP software vendors, namely SAP, Oracle, Microsoft, in their cloud ERP products.

The work should follow a two-step approach. First, the student is asked to conceive a comparative framework that comprises architectural criteria which are to be compared among the products. Second, the student is asked to apply this conceived framework to the mentioned products. For this work, it is vital to go substantially deeper than the marketing brochures. Reading product documentation, understanding architectural patterns, and reading developer guides will be necessary. Therefore, applications should have strong interest or first experience in software design.

Literatur

• Elbahri, F.M., Al-Sanjary, O.I., Ali, M.A., Naif, Z.A., Ibrahim, O.A. and Mohammed, M.N., 2019, March. Difference comparison of SAP, Oracle, and Microsoft solutions based on cloud ERP systems: A review. In 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 65-70).

The goal of this seminar topic is to examine the complex realm of large language models (LLMs), with an emphasis on the state-of-the-art algorithms and architectural frameworks that power them. LLMs are significant players in the field of artificial intelligence. They have completely changed the way humans interact with machines and made a wide range of applications possible, from content creation to natural language processing. The theoretical foundations of these models will be explored in this lecture, along with how they learn from massive volumes of data to produce text that makes sense and is contextually relevant. Students will acquire an understanding of the intricacies and difficulties associated with creating and expanding large-scale learning modules (LLMs) by thoroughly examining their algorithms and design.

Students participating in this seminar are expected to engage in a general discussion on how large language models function and the specific architectural implementations that facilitate this. This task involves critically analyzing the components and structures that constitute the backbone of LLMs. Students will explore various models, comparing their design and effectiveness in different applications. By synthesizing this information, participants will understand the operational mechanics behind these advanced models. This comprehensive exploration will equip students with a solid foundation in the domain of large language models, preparing them for further research or practical applications in 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. doi.org/10.1145/3641289
• Ding, Q., Ding, D., Wang, Y., Guan, C., & Ding, B. (2024). Unraveling the landscape of large language models: A systematic review and future perspectives. Journal of Electronic Business & Digital Economics, 3(1), 3–19. doi.org/10.1108/JEBDE-08-2023-0015
• Guimarães, N., Campos, R., & Jorge, A. (2024). PRE‐TRAINED language models: What do they know? WIREs Data Mining and Knowledge Discovery, 14(1), e1518. doi.org/10.1002/widm.1518
• Hadi, M. U., Tashi, Q. A., Qureshi, R., Shah, A., Muneer, A., Irfan, M., Zafar, A., Shaikh, M. B., Akhtar, N., Wu, J., & Mirjalili, S. (2023). Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects [Preprint]. doi.org/10.36227/techrxiv.23589741.v4
• Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2024). A Comprehensive Overview of Large Language Models (arXiv:2307.06435). arXiv. arxiv.org/abs/2307.06435

The utilization of Large Language Models (LLMs) in software development introduces a transformative period, empowering developers with enhanced capabilities in code generation, documentation, and problem-solving. This seminar thesis delves into the benefits that LLMs offer to software developers, exploring their potential to streamline the development process, enhance code quality, and accelerate innovation.

Students engaging with this topic will uncover the multiple use cases of LLMs in software development, both in present scenarios and future prospects. From automating repetitive tasks to assisting in debugging and optimizing algorithms, LLMs present a plethora of opportunities to increase developer productivity and efficiency.

Moreover, this thesis will examine the implications of integrating LLMs into the software development workflow, assessing how these capabilities influence developer productivity and creativity. By investigating real-world case studies and hypothetical scenarios, students will gain insights into the impact of LLMs on the software development landscape und understand their potential and limitations in this domain.

Literatur

• Wu, T., Terry, M., & Cai, C. J. (2022, April). Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts. In Proceedings of the 2022 CHI conference on human factors in computing systems (pp. 1-22).
• Uzair, W., & Naz, S. (2023). Six-tier architecture for ai-generated software development: a large language models approach.
• Lenka, U., Gupta, M., & Sahoo, D. K. (2016). Research and development teams as a perennial source of competitive advantage in the innovation adoption process. Global Business Review, 17(3), 700-711.
• Tan, C. W., Guo, S., Wong, M. F., & Hang, C. N. (2023). Copilot for Xcode: Exploring AI-Assisted Programming by Prompting Cloud-based Large Language Models. arXiv preprint arXiv:2307.14349.

As machine learning (ML) and artificial intelligence (AI) technologies develop, prompt engineering is becoming an increasingly important field within computer science. Prompt engineering is fundamentally about creating and improving inputs (prompts) for AI systems in order to produce the most precise, relevant, or creative results. In order to improve communication between people and AI and make sure that these systems can comprehend and act upon our requests, this procedure is essential. The necessity of rapid engineering increases with the sophistication of AI models, such as big language models, making it a crucial field of study and development.

For students venturing into this seminar, the tasks will encompass a comprehensive understanding of what prompt engineering is and how it functions within the framework of AI and ML technologies. Students will explore whether prompt engineering is primarily a tool for software developers or if it extends its utility to end-users in practical scenarios. Additionally, they will delve into the future prospects of prompt engineering, analyzing its potential growth, challenges, and its evolving role in the development and deployment of AI systems. Through these tasks, students will gain a deep insight into how prompt engineering shapes the interaction between humans and artificial intelligence, laying a foundation for future innovations in the field.

Literatur

• Ferretti, S. (2023). Hacking by the prompt: Innovative ways to utilize ChatGPT for evaluators. New Directions for Evaluation, 2023(178–179), 73–84. doi.org/10.1002/ev.20557
• Jacob, I. J., Piramuthu, S., & Falkowski-Gilski, P. (Eds.). (2024). Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2023. Springer Nature Singapore. doi.org/10.1007/978-981-99-7962-2
• Park, D., An, G., Kamyod, C., & Kim, C. G. (2024). A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model. Journal of Web Engineering, 1187–1206. doi.org/10.13052/jwe1540-9589.2285
• Zamfirescu-Pereira, J. D., Wong, R. Y., Hartmann, B., & Yang, Q. (2023). Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–21. doi.org/10.1145/3544548.3581388

This seminar thesis focuses on understanding the landscape of leading Large Language Models (LLMs) worldwide, aiming to provide a comprehensive comparative analysis. LLMs represent a pivotal development in artificial intelligence (AI), transforming various sectors with their ability to comprehend and generate human-like text. Students are invited to investigate their origins, technological advancements, and applications. A central aspect of the seminar will be a comparative assessment of leading LLMs, analyzing factors such as language proficiency, contextual understanding, and performance metrics. The seminar also aims to explore the broader implications of the competition among leading LLMs, including its impact on economic competitiveness, technological innovation, and global power dynamics. Ultimately, this seminar equips participants with a comprehensive understanding of the landscape of leading LLMs, empowering them to contribute meaningfully to ongoing discussions and strategic decision-making in the field of artificial intelligence.

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
• 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
• Yousri, R., & Safwat, S. (2023). How Big Can It Get? A comparative analysis of LLMs in architecture and scaling. 2023 International Conference on Computer and Applications (ICCA), 1–5. https://doi.org/10.1109/ICCA59364.2023.10401818

"Large Language Models in Europe: A Comparative Analysis" explores the landscape of advanced artificial intelligence technologies in Europe, with a focus on Large Language Models (LLMs). This seminar paper delves into the leading LLMs developed within European research institutions and tech companies, including notable entities such as DeepMind and various academic institutions. The analysis evaluates the state of development of these European LLMs, assessing their performance, scalability, and applications in natural language processing.

Drawing comparisons with leading American LLMs, the study highlights the strengths and weaknesses of European counterparts in terms of model architecture, training techniques, and capabilities. It examines how European LLMs fare against American counterparts in addressing biases, improving interpretability, and advancing ethical considerations.

Identifying key development needs, the analysis underscores areas for improvement such as addressing biases in training data, enhancing multilingual capabilities, and fostering greater model interpretability. Furthermore, the seminar paper explores the potential for Europe to catch up with leading developments in the field, discussing strategic investments, collaboration opportunities, and the role of academia, industry, and policymakers in driving innovation and advancements in LLM technology. Overall, the final seminar paper should offer valuable insights into the evolving landscape of AI research and development in Europe and its implications for the global AI community.

Literatur

• Navigli, R., Conia, S., & Ross, B. (2023). Biases in large language models: origins, inventory, and discussion. ACM Journal of Data and Information Quality, 15(2), 1-21.
• Piñeiro-Martín, A., García-Mateo, C., Docío-Fernández, L., & López-Pérez, M. D. C. (2023). Ethical challenges in the development of virtual assistants powered by large language models. Electronics, 12(14), 3170.
• Winograd, A. (2023). Loose-lipped large language models spill your secrets: The privacy implications of large language models. Harvard Journal of Law & Technology, 36(2).

This seminar thesis focuses on understanding the landscape of Large Language Models (LLMs) developed in or with the involvement of German organizations. LLMs represent a pivotal development in artificial intelligence (AI), transforming various sectors with their ability to comprehend and generate human-like text. Students are invited to provide a comparative analysis by examining the state of development of German LLMs, their standing in comparison to leading global counterparts, and the associated development needs, their technical capabilities, market penetration, and potential applications. Through this exploration, students will gain insights into the strengths and weaknesses of German LLMs, as well as the challenges they face in competing on a global scale. A central question addressed in the seminar is whether Germany can catch up in the LLM race, considering its current standing, development trajectory, and potential barriers.

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
• 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
• Yousri, R., & Safwat, S. (2023). How Big Can It Get? A comparative analysis of LLMs in architecture and scaling. 2023 International Conference on Computer and Applications (ICCA), 1–5. https://doi.org/10.1109/ICCA59364.2023.10401818

Students will explore the emerging topic of Large Language Models (LLMs) in this seminar, with an emphasis on its uses, limitations, and ethical issues they may have. With AI technology developing at a rapid rate, LLMs are becoming a key component of many breakthroughs and have the potential to be advantageous in a wide range of industries. But these quick developments also raise difficult moral questions about bias, privacy, and employment consequences. In order to analyze these concerns, this seminar will push students to examine the ethical implications of using LLMs.  With a blend of theoretical understanding and practical analysis, students will acquire a knowledge of the responsibilities assigned to AI developers and the wider societal consequences of this technology.

Students' primary task will be to engage in a thorough discussion of the ethical problems associated with the use of Large Language Models. This includes identifying and analyzing potential biases within these models, understanding the privacy concerns related to data usage, and contemplating the socio-economic impacts, such as job displacement and the digital divide. Through essays, presentations, and debates, students will be expected to critically assess how these ethical issues can be addressed or mitigated. The aim is for students to develop a nuanced understanding of the ethical landscape surrounding LLMs, equipping them with the ability to propose informed solutions and ethical guidelines for the development and implementation of AI technologies.

Literatur

• Head, C. B., Jasper, P., McConnachie, M., Raftree, L., & Higdon, G. (2023). Large language model applications for evaluation: Opportunities and ethical implications. New Directions for Evaluation, 2023(178–179), 33–46. doi.org/10.1002/ev.20556
• Kendall, G., & Teixeira Da Silva, J. A. (2024). Risks of abuse of large language models, like CHATGPT , in scientific publishing: Authorship, predatory publishing, and paper mills. Learned Publishing, 37(1), 55–62. doi.org/10.1002/leap.1578
• Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90–112. doi.org/10.1111/bjet.13370

"Large Language Models in the Future: What Can We Expect?" offers a forward-looking examination of the trajectory of Large Language Models (LLMs) and their anticipated advancements. This comprehensive analysis delves into ongoing research and development (R&D) efforts surrounding LLMs, exploring the latest innovations and breakthroughs in the field. It provides an in-depth analysis of current R&D goals and projects, shedding light on the diverse array of initiatives aimed at enhancing LLM capabilities.

The seminar paper encompasses a wide range of topics, including advancements in model architecture, training techniques, and applications in natural language processing (NLP). Additionally, the analysis considers the integration of LLMs with other emerging technologies such as machine learning, deep learning, and reinforcement learning.

Furthermore, the final manuscript will forecast the future development of LLM capabilities, both in the near and far future. It examines how LLMs are expected to evolve in terms of their understanding of context, generation of human-like text, and ability to perform complex language tasks. Additionally, the analysis explores the potential impact of advancements in LLMs on various sectors, including healthcare, finance, education, and entertainment.

Literatur

• Dong, X. L., Moon, S., Xu, Y. E., Malik, K., & Yu, Z. (2023, August). Towards next-generation intelligent assistants leveraging llm techniques. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5792-5793).
• 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.
• Shanahan, M. (2024). Talking about large language models. Communications of the ACM, 67(2), 68-79.
• Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., ... & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112.

"Large Language Models and Hallucinations: The Problem and Potential Solutions" delves into the phenomenon of hallucinations observed in the outputs generated by advanced language models such as GPT (Generative Pre-trained Transformer). This seminar paper scrutinizes the challenges posed by hallucinations, wherein the model generates inaccurate or nonsensical content that deviates from the input prompt or strays from factual accuracy.

The analysis dissects the root causes of hallucinations, including the model's reliance on statistical patterns in training data, as well as the inherent limitations of pre-trained models in comprehending nuanced contexts. Moreover, the study explores the detrimental effects of hallucinations, which can propagate misinformation, erode trust in AI systems, and undermine the utility of language models in real-world applications.

In addressing potential solutions, the seminar paper should propose various strategies to mitigate hallucinations and enhance the robustness of language models. These include, for example, refining model architectures to better capture semantic nuances, implementing stricter filtering mechanisms to identify and discard hallucinatory outputs, and integrating human oversight and feedback loops to validate generated content.

Furthermore, the seminar paper could advocate for interdisciplinary collaboration between AI researchers, linguists, ethicists, and domain experts to develop comprehensive frameworks for evaluating and addressing hallucinations in language models. By fostering transparency, accountability, and continuous improvement in model development and deployment, stakeholders can work towards harnessing the transformative potential of large language models while mitigating the risks associated with hallucinatory outputs.

Literatur

• Chen, Y., Fu, Q., Yuan, Y., Wen, Z., Fan, G., Liu, D., ... & Xiao, Y. (2023, October). Hallucination detection: Robustly discerning reliable answers in large language models. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 245-255).
• Li, J., Cheng, X., Zhao, X., Nie, J. Y., & Wen, J. R. (2023, December). Halueval: A large-scale hallucination evaluation benchmark for large language models. In The 2023 Conference on Empirical Methods in Natural Language Processing.
• Martino, A., Iannelli, M., & Truong, C. (2023, May). Knowledge injection to counter large language model (LLM) hallucination. In European Semantic Web Conference (pp. 182-185). Cham: Springer Nature Switzerland.
• Tonmoy, S. M., Zaman, S. M., Jain, V., Rani, A., Rawte, V., Chadha, A., & Das, A. (2024). A comprehensive survey of hallucination mitigation techniques in large language models. arXiv preprint arXiv:2401.01313.
• Ye, H., Liu, T., Zhang, A., Hua, W., & Jia, W. (2023). Cognitive mirage: A review of hallucinations in large language models. arXiv preprint arXiv:2309.06794.

Examining the strategies of leading tech firms such as Apple, Google, Microsoft or Samsung, this seminar thesis conducts a comprehensive analysis of their approach towards integrating Large Language Models (LLMs) into their ecosystems. By analyzing the offerings, integration methodologies, available features, and future roadmaps of these tech firms, students will gain valuable insights into the evolving landscape of LLM technology.

Through evaluative comparison, students will comprehend the distinctive approaches adopted by each tech firm in utilizing the power of LLMs. From voice assistants to natural language processing tools, LLMs are increasingly becoming key components of the technological equipment employed by these firms. By critically assessing their strategies, students will uncover the strengths, weaknesses, and potential implications of these approaches, contributing to a nuanced understanding of the competitive dynamics shaping the LLM market.

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.
• Xiao, H., & Yu, D. (2020). Achieving sustainable competitive advantage through intellectual capital and corporate character: the mediating role of innovation. Problemy ekorozwoju, 15(1).
• Li, K. (2023). Comparative analysis of the technology strategy in the high-tech industry: a case study of apple and nokia. BCP Business & Management, 36, 445-450. doi.org/10.54691/bcpbm.v36i.3498

This seminar thesis undertakes a rigorous examination of the costs associated with the development and usage of Large Language Models (LLMs), shedding light on the complex cost structures underlying these advanced AI systems. From the initial stages of algorithm development to the ongoing expenses of training, operation, and licensing, students will investigate the financial implications of LLM deployment across various domains and applications.

By carefully analyzing the expenditures involved in LLM creation and usage, students will uncover the economic drivers shaping the development of these AI technologies. Through empirical research and theoretical modeling, this thesis aims to clarify the factors influencing the affordability, scalability, and accessibility of LLMs, offering valuable insights into the economic factors of AI innovation and adoption.

Literatur

• Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H., Kaplan, J., … & Zaremba, W. (2021). Evaluating large language models trained on code.. https://doi.org/10.48550/arxiv.2107.03374
• 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.
• Chen, L., Zaharia, M., & Zou, J. (2023). Frugalgpt: How to use large language models while reducing cost and improving performance. arXiv preprint arXiv:2305.05176.

In an era marked by growing environmental consciousness, the ecological footprint of Large Language Models (LLMs) emerges as a critical point of adoption. This seminar thesis builds on a comprehensive analysis of the environmental impact associated with the development, deployment, and operation of LLMs, exploring potential paths for mitigating their ecological footprint.

Through a multidisciplinary approach encompassing environmental science, computational analysis, and ethical considerations, students will evaluate the carbon footprint, energy consumption, and resource utilization associated with LLM technologies. By identifying key areas of environmental concern and proposing sustainable strategies for mitigating their impact, this thesis seeks to foster a dialogue on the responsible deployment of AI technologies in alignment with environmental reporting.

Literatur

• Zhang, J., Krishna, R., Awadallah, A. H., & Wang, C. (2023). Ecoassistant: Using llm assistant more affordably and accurately. arXiv preprint arXiv:2310.03046.
• Zhuang, B., Liu, J., Pan, Z., He, H., Weng, Y., & Shen, C. (2023). A survey on efficient training of transformers.. https://doi.org/10.48550/arxiv.2302.01107
• Rillig, M. C., Ågerstrand, M., Bi, M., Gould, K. A., & Sauerland, U. (2023). Risks and benefits of large language models for the environment. Environmental Science &Amp; Technology, 57(9), 3464-3466. doi.org/10.1021/acs.est.3c01106

Large Language Models (LLMs) have emerged as transformative tools in the realm of artificial intelligence (AI), with their remarkable ability to understand and generate human-like text. Within the software development landscape, integrating LLMs has become a pivotal goal, offering the potential to enhance applications with advanced natural language processing capabilities. However, this integration presents unique challenges and opportunities, requiring a nuanced understanding of LLMs, their architectures, and the available APIs.

In this seminar thesis, students are asked to undertake a thorough examination of the process of integrating LLMs into software products, addressing the complexities and considerations involved. The thesis aims to delve into the various approaches that developers can take when integrating LLMs. Students will explore the resulting architectures that emerge from LLM integration, existing software frameworks, analysis of the APIs available for interacting with LLMs. Ultimately, they are invited to evaluate their features, functionalities, and suitability for different use cases. Through this comprehensive investigation, users should gain the the knowledge and insights necessary to successfully leverage LLMs in their own software products, unlocking the full potential of these powerful AI tools.

Literatur

• Li, C., Chen, H., Yan, M., Shen, W., Xu, H., Wu, Z., Zhang, Z., Zhou, W., Chen, Y., Cheng, C., Shi, H., Zhang, J., Huang, F., & Zhou, J. (2023). ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models. 566–578. Scopus.
• Shu, W., Li, R., Sun, T., Huang, X., & Qiu, X. (2024). Large language models: Principles, implementation, and progress. Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 61(2), 351–361. Scopus. https://doi.org/10.7544/issn1000-1239.202330303
• Yousri, R., & Safwat, S. (2023). How Big Can It Get? A comparative analysis of LLMs in architecture and scaling. 2023 International Conference on Computer and Applications (ICCA), 1–5. https://doi.org/10.1109/ICCA59364.2023.10401818

"Combining Large Language Models with other AI: A Way to Create a Strong AI" explores the potential of integrating Large Language Models (LLMs) with other forms of artificial intelligence to achieve enhanced performance and capabilities. This comprehensive analysis investigates strategies for synergistically combining LLMs with diverse AI techniques such as computer vision, reinforcement learning, and knowledge graphs.

The seminar paper delves into various approaches for leveraging the strengths of LLMs alongside other AI modalities to create more robust and versatile AI systems. This includes integrating language understanding capabilities of LLMs with visual perception from computer vision models, incorporating structured knowledge representations from knowledge graphs, and harnessing reinforcement learning for interactive decision-making. Moreover, the analysis provides insights into current research projects and proposed approaches aimed at realizing the potential of combined AI systems. It examines ongoing efforts to develop novel architectures, algorithms, and training methodologies that facilitate effective integration and collaboration between LLMs and other AI components.

Looking ahead, the seminar paper should try to forecast the trajectory of combined AI systems in both the near and far future. It should outline anticipated advancements in AI capabilities, such as improved natural language understanding, enhanced perceptual reasoning, and more sophisticated decision-making abilities. Furthermore, the analysis can explore the potential impact of these integrated AI systems across various domains, including healthcare, finance, autonomous vehicles, and personalized assistance.

Literatur

• Carta, T., Romac, C., Wolf, T., Lamprier, S., Sigaud, O., & Oudeyer, P. Y. (2023, July). Grounding large language models in interactive environments with online reinforcement learning. In International Conference on Machine Learning (pp. 3676-3713). PMLR.
• Du, Y., Watkins, O., Wang, Z., Colas, C., Darrell, T., Abbeel, P., ... & Andreas, J. (2023, July). Guiding pretraining in reinforcement learning with large language models. In International Conference on Machine Learning (pp. 8657-8677). PMLR.
• 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.

Large Language Models (LLMs) are on their way to revolutionize personal computing, introducing a new era of interaction and productivity across various applications. From email clients to office suites and productivity apps, the integration of LLMs holds the promise of enhancing user experiences and streamlining tasks through advanced natural language processing capabilities. As users increasingly rely on personal computing devices for communication, productivity, and creative endeavors, the integration of LLMs opens up new possibilities for more intuitive and efficient interactions.

In this seminar thesis, students are invited to explore how LLMs can change the landscape of established app categories such as email, office applications, productivity apps, and creative tools. The students will analyze how, by leveraging the capabilities of LLMs, these applications can offer enhanced features such as intelligent email sorting and prioritization, context-aware document editing suggestions, and natural language interfaces for task management and content creation. Furthermore, they will investigate the potential for entirely new categories of applications to emerge, driven by the integration of LLMs and personalized AI assistants. Through this analysis, insights will be provided into the profound influence of LLMs on personal computing and the evolving role of AI in shaping digital interactions and workflows.

Literatur

• Evernote. Work smarter with Evernote AI features. (2024). Evernote. https://evernote.com/features/ai-features
• Kumar, V., Srivastava, P., Dwivedi, A., Budhiraja, I., Ghosh, D., Goyal, V., & Arora, R. (2024). Large-Language-Models (LLM)-Based AI Chatbots: Architecture, In-Depth Analysis and Their Performance Evaluation. Communications in Computer and Information Science, 2027 CCIS, 237–249. Scopus. doi.org/10.1007/978-3-031-53085-2_20
• Shu, W., Li, R., Sun, T., Huang, X., & Qiu, X. (2024). Large language models: Principles, implementation, and progress. Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 61(2), 351–361. Scopus. https://doi.org/10.7544/issn1000-1239.202330303
• Yousri, R., & Safwat, S. (2023). How Big Can It Get? A comparative analysis of LLMs in architecture and scaling. 2023 International Conference on Computer and Applications (ICCA), 1–5. https://doi.org/10.1109/ICCA59364.2023.10401818

Die jüngsten Fortschritte auf dem Gebiet der künstlichen Intelligenz und das Aufkommen generativer KI haben ihre Anwendungsbereiche erheblich erweitert und ihre Auswirkungen auf Wirtschaft und Gesellschaft verstärkt (Collins et al. 2021). Infolgedessen sind KI-Systeme heute in verschiedenen Branchen weit verbreitet, z. B. im Gesundheitswesen oder im Finanzwesen. Darüber hinaus ist KI durch persönliche Assistenten wie Siri oder Microsoft Copilot zu einem festen Bestandteil des täglichen Lebens geworden (Collins et al. 2021). Trotz ihres potenziellen Nutzens steht die KI vor grundlegenden Herausforderungen, darunter Fragen des Vertrauens, der Voreingenommenheit und des Missbrauchs (Ozmen Garibay et al. 2023). Insbesondere in kritischen Situationen, wie dem Einsatz eines KI-basierten Entscheidungssystems im medizinischen Bereich, können Voreingenommenheit und blindes Vertrauen zu schwerwiegenden Folgen führen (Hemmer et al. 2022).

Die Forschung auf dem Gebiet der menschenzentrierten KI (HCAI) zielt darauf ab, die Fortschritte der KI für eine bessere Zukunft der Menschheit zu nutzen (Ozmen Garibay et al. 2023). Das Ziel ist, den Menschen zu unterstützen und zu ergänzen, anstatt ihn durch KI zu ersetzen (Shneiderman 2020). Daher besteht das übergeordnete Ziel darin, eine effektive Zusammenarbeit zwischen Menschen und KI zu ermöglichen und die Zuverlässigkeit, Sicherheit und Vertrauenswürdigkeit von KI-Technologien zu gewährleisten (Gregor 2024; Shneiderman 2022). Folglich ist dieses Forschungsfeld mit Forschungsbereichen wie erklärbare KI, KI-Ausrichtung, KI-Sicherheit, verantwortungsvolle KI oder Mensch-Computer-Interaktion verknüpft (Ozmen Garibay et al. 2023; Gregor 2024).

In dieser Seminararbeit sollen die Hauptthemen und Genres der IS-Forschung im Bereich Human-Centered AI erarbeitet werden und die unberücksichtigten Aspekte der IS-Forschung im Bereich Human-Centered AI identifiziert werden. Die Zielsetzung ist die Entwicklung einer Forschungsagenda für Human-Centered AI auf Basis einer systematischen Literaturrecherche in führenden WI-Fachzeitschriften und Konferenzen.

Literatur

• Collins C, Dennehy D, Conboy K, Mikalef P (2021) Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management 60:102383.
• Gregor S (2024) Responsible Artificial Intelligence and Journal Publishing. Journal of the Association for Information Systems 25(1):48–60. doi:10.17705/1jais.00863
• 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. https://aisel.aisnet.org/ecis2022_rp/139
• Ozmen Garibay O, Winslow B, Andolina S, Antona M, Bodenschatz A, Coursaris C, Falco G, Fiore SM, Garibay I, Grieman K, Havens JC, Jirotka M, Kacorri H, Karwowski W, Kider J, Konstan J, Koon S, Lopez-Gonzalez M, Maifeld-Carucci I, McGregor S, Salvendy G, Shneiderman B, Stephanidis C, Strobel C, Holter C ten, Xu W (2023) Six Human-Centered Artificial Intelligence Grand Challenges. International Journal of Human–Computer Interaction 39(3):391–437. doi:10.1080/10447318.2022.2153320
• Shneiderman, B. (2020). Human-Centered Artificial Intelligence: Three Fresh Ideas. AIS Transactions on Human-Computer Interaction, 12(3), 109-124. https://doi.org/10.17705/1thci.00131
• Shneiderman B (2022) Human-centered AI. Oxford University Press, Oxford. doi:10.1093/oso/9780192845290.001.0001

Entscheidungsunterstützungssysteme (Decision Support Systems) sind Informationssysteme, die den menschlichen Entscheidern in ihren strategischen und operativen Entscheidungen unterstützen sollen. Dabei werden auf der Grundlage von verfügbaren Daten Informationen aufbereitet, ausgewertet und komprimiert zusammengestellt, damit bessere Entscheidungen getroffen werden können, die aufgrund der Informationsflut für menschliche Verarbeitungsfähigkeiten schier nicht zu bewältigen wären. Durch die Weiterentwicklung KI-basierter Systeme können dabei immer genauere Empfehlungen gegeben werden, sodass bereits in einigen Prozessen die Entscheidung komplett an eine KI delegiert wird (bspw. visuelle Qualitätskontrolle in der Industrie).

Neben der Entscheidungsdelegation lassen sich auch gesamte Aufgaben an Computersysteme delegieren. Die technologische Autonomität reicht dabei von der maschinellen Teilprozessunterstützung durch Roboter über dem autonomen Aktienhandel bis hin zur Erstellung von Medien durch generative KI. Folglich zeichnet sich eine zunehmende Delegation an KI-basierten System ab und stellt neue Anforderungen an die Zusammenarbeit zwischen Menschen und Maschine.

Das Ziel dieser Seminararbeit ist es, einen umfassenden Überblick über die aktuelle Forschungslandschaft bezüglich der Delegation von Aufgaben und Entscheidungen an KI-Systeme zu geben. Durch die Anwendung einer systematischen Literaturrecherche (SLR) sollen relevante wissenschaftliche Arbeiten in führenden WI-Fachzeitschriften und Konferenzen identifiziert, analysiert und zusammengefasst werden. Diese Analyse wird die Grundlage für die Entwicklung einer Taxonomie bilden, die verschiedene Aspekte der Aufgaben- und Entscheidungsdelegation an KI kategorisiert und strukturiert darstellt.

Literatur

• Abdel-Karim, B. M., Pfeuffer, N., Carl, K. V., Hinz, O. (2023). How AI-Based Systems Can Induce Reflections: The Case of AI-Augmented Diagnostic Work. MIS Quarterly, 47(4) pp.1395-1424. doi:10.25300/MISQ/2022/16773
• Baird, A. and 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). pp.315-341.
• Candrian, C. and Scherer, A. (2022). Rise of the machines: Delegating decisions to autonomous AI. Computers in Human Behavior 134. doi: 10.1016/j.chb.2022.107308
• Fahnenstich, H., Rieger, T., Roesler, E. (2024), Trusting under risk – comparing human to AI decision support agents. Computers in Human Behavior 153. doi: 10.1016/j.chb.2023.108107
• 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
• Guggenberger, T, Lämmermann, L, Urbach, N, Walter, A M, Hofmann, P. (2023). Task delegation from AI to humans: A principal-agent perspective. ICIS 2023 Proceedings.
• Grisold, T. and Schneider, J. (2023). Dynamics of Human-AI Delegation in Organizational Routines. ICIS 2023 Proceedings.
• Yu, B., Vahidov, R., Kersten, G. E. (2021). Acceptance of technological agency: Beyond the perception of utilitarian value. Information & Management 58(7). doi: 10.1016/j.im.2021.103503

Mit der zunehmenden Integration von KI-basierten Systemen in kritische Bereiche wie das Gesundheitswesen, die Rechtsprechung und die Verkehrstechnik wächst die Notwendigkeit, das Vertrauen der Nutzer in diese Technologien zu stärken. Vertrauen stellt dabei ein Schlüsselelement für die Akzeptanz und effektive Nutzung von KI-Systemen. Es beeinflusst die Interaktion von Menschen mit KI und ist entscheidend für die Bereitschaft, KI-Entscheidungen zu akzeptieren und darauf zu reagieren. Die Herausforderung besteht darin, KI-Systeme so zu gestalten und zu kommunizieren, dass sie als vertrauenswürdig wahrgenommen werden, ohne dabei in ein blindes Vertrauen zu verfallen, das mögliche Risiken ignoriert.

Das Ziel dieser Seminararbeit ist die Untersuchung der aktuellen Forschungslandschaft bezüglich Vertrauensfaktoren für KI-Systeme. Durch die Anwendung einer systematischen Literaturrecherche (SLR) sollen relevante wissenschaftliche Arbeiten in führenden WI-Fachzeitschriften und Konferenzen identifiziert, analysiert und zusammengefasst werden. Diese Analyse wird die Grundlage für die Entwicklung einer Taxonomie bilden, die verschiedene Vertrauensaspekte an KI kategorisiert und strukturiert darstellt.

Literatur

• Aoki, N. (2021). The importance of the assurance that “humans are still in the decision loop” for public trust in artificial intelligence: Evidence from an online experiment. Computers in Human Behavior 114. doi:10.1016/j.chb.2020.106572
• Berente, N., Gu, B., Recker, J., Santhanam, R. (2021). Special Issue Editor’s Comments: Managing Artificial Intelligence. MIS Quarterly 45(3) pp.1433-1450.
• Georganta, E. and Ulfert, A.-S. (2024), My colleague is an AI! Trust differences between AI and human teammates, Team Performance Management. doi:10.1108/TPM-07-2023-0053
• Fahnenstich, H., Rieger, T., Roesler, E. (2024), Trusting under risk – comparing human to AI decision support agents. Computer in Human Behavior 153. doi: 10.1016/j.chb.2023.108107
• Lukyanenko, R., Maass, W. & Storey, V.C. (2022), Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities. Electronic Markets 32. doi:10.1007/s12525-022-00605-4
• Schaschek, M. and Engel, S. (2023). Measuring Trustworthiness of AI Systems: A Holistic Maturity Model. ICIS 2023 Proceedings.
• Yang, R., Wibowo, S. User trust in artificial intelligence (2022). A comprehensive conceptual framework. Electronic Markets 32. doi:10.1007/s12525-022-00592-6

Die Nutzung aktueller KI-Systeme gleicht in den meisten Fällen der Nutzung eines Werkzeugs. Dabei liegt der Fokus oft auf der Kooperation, d.h. dem Einsatz bestimmter Fähigkeiten zur individuellen Zielerreichung. Um das Potenzial von KI voll auszuschöpfen, ist jedoch ein Wandel hin zur Zusammenarbeit zwischen Menschen und Maschine notwendig.

Dieses Seminarthema adressiert genau diesen Bereich, indem es Forschungsfragen behandelt, die sowohl die Hauptthemen und -genres der IS-Forschung im Bereich der Mensch-KI-Kollaboration untersucht, die bisher übersehen wurden. Das Ziel dieses Themas ist daher die Entwicklung einer Taxonomie für die Mensch-KI-Kollaboration durch eine systematische Literaturrecherche in führenden WI-Fachzeitschriften und Konferenzen.

Literatur

• Abdel-Karim, B. M., Pfeuffer, N., Carl, K. V., Hinz, O. (2023). How AI-Based Systems Can Induce Reflections: The Case of AI-Augmented Diagnostic Work. MIS Quarterly, 47(4) pp.1395-1424. doi:10.25300/MISQ/2022/16773
• Choudhary, V., Marchetti, A., Shrestha, Y. R., & Puranam, P. (2023). Human-AI Ensembles: When Can They Work? Journal of Management. doi:10.1177/01492063231194968
• Fabri, L., Häckel, B., Oberländer, A.M., Rieg, M., Stohr, A. (2023). Disentangling Human-AI Hybrids. Bus Inf Syst Eng 65, 623–641. doi:10.1007/s12599-023-00810-1
• Glienke, M., Hartwich, N. J., Antons, D. (2023). Working with AI: How Attitudes Shape Human-AI Collaboration. ICIS 2023 Proceedings.
• 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.
• 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
• Zercher, D., Jussupow, E., Heinzl, A., (2023). When AI joins the Team: A Literature Review on Intragroup Processes and their Effect on Team Performance in Team-AI Collaboration. ECIS 2023 Research Papers.

Die Nutzung aktueller KI-Systeme gleicht in den meisten Fällen der Nutzung eines Werkzeugs. Dabei liegt der Fokus oft auf der Kooperation, d.h. dem Einsatz bestimmter Fähigkeiten zur individuellen Zielerreichung. Um das Potenzial von KI voll auszuschöpfen, ist jedoch ein Wandel hin zur Zusammenarbeit zwischen Menschen und Maschine notwendig.

Dieses Seminarthema adressiert genau diesen Bereich, indem es Forschungsfragen behandelt, die sowohl die Hauptthemen und -genres der IS-Forschung im Bereich der Mensch-KI-Kollaboration untersucht, die bisher übersehen wurden. Das Ziel dieses Themas ist daher die Entwicklung einer Informationssystems-Architektur für die Mensch-KI-Kollaboration basierend auf einer systematischen Literaturrecherche in führenden WI-Fachzeitschriften und Konferenzen.

Literatur

• Abdel-Karim, B. M., Pfeuffer, N., Carl, K. V., Hinz, O. (2023). How AI-Based Systems Can Induce Reflections: The Case of AI-Augmented Diagnostic Work. MIS Quarterly, 47(4) pp.1395-1424. doi:10.25300/MISQ/2022/16773
• Choudhary, V., Marchetti, A., Shrestha, Y. R., & Puranam, P. (2023). Human-AI Ensembles: When Can They Work? Journal of Management. doi:10.1177/01492063231194968
• Fabri, L., Häckel, B., Oberländer, A.M., Rieg, M., Stohr, A. (2023). Disentangling Human-AI Hybrids. Bus Inf Syst Eng 65, 623–641. doi:10.1007/s12599-023-00810-1
• Glienke, M., Hartwich, N. J., Antons, D. (2023). Working with AI: How Attitudes Shape Human-AI Collaboration. ICIS 2023 Proceedings.
• 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.
• 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
• Zercher, D., Jussupow, E., Heinzl, A., (2023). When AI joins the Team: A Literature Review on Intragroup Processes and their Effect on Team Performance in Team-AI Collaboration. ECIS 2023 Research Papers.

Der Übergang von Industrie 4.0, geprägt durch Automatisierung und Datenaustausch in Fertigungstechnologien, zu Industrie 5.0 rückt den Menschen und seine Zusammenarbeit mit intelligenten Systemen in den Mittelpunkt. Industrie 5.0 beschreibt eine humanisierte Vision des technologischen Wandels, bei dem aktuelle und zukünftige Bedürfnisse von Individuen, Unternehmen und der Gesellschaft in Einklang gebracht werden. Allgegenwärtige Sensortechnologien und Big Data stellen die Grundlagen zu Automatisierung, Vernetzung und Optimierung einer Vielzahl industrieller Prozesse dar. Durch den Einsatz von KI-Technologien wie generativer KI wird diese Innovation weiter beschleunigt und eine neue Evolutionsstufe der Industrie geschaffen. Diese Entwicklung erfordert ein klares Verständnis und eine Klassifizierung der Komponenten, Technologien und Methodologien, die Industrie 5.0 definieren. Eine umfassende Analyse des technologischen Fähigkeitsspektrums durch den Einsatz von KI in industriellen Plattformen 5.0 ist erforderlich, damit Forschende, Praktiker und politische Entscheidungsträger die Komplexität dieses neuen industriellen Paradigmas effektiv navigieren können.

Vor diesem Hintergrund ist daher das Ziel dieses Seminarthemas die Entwicklung einer Taxonomie für die Industrie 5.0 durch eine systematische Literaturrecherche in führenden WI-Fachzeitschriften und Konferenzen.

Literatur

• Aydin, E; Rahman, M; Ozeren, E (2023): Does Industry 5.0 Reproduce Gender (In)equalities at Organisations? Understanding the Interaction of Human Resources and Software Development Teams in Supplying Human Capitals. In: Inf Syst Front. DOI: 10.1007/s10796-023-10450-1
• Barata, J; Kayser, I (2023): Industry 5.0 – Past, Present, and Near Future. In: Procedia Computer Science 219, S. 778–788. DOI: 10.1016/j.procs.2023.01.351
• Choudhary, V., Marchetti, A., Shrestha, Y. R., & Puranam, P. (2023). Human-AI Ensembles: When Can They Work? Journal of Management. doi:10.1177/01492063231194968
• Schmalzried, D; Hurst, M; Wentzien, M; Gräser, M (2023): Analyse der Rolle Künstlicher Intelligenz für eine menschenzentrierte Industrie 5.0. In: HMD 60 (6), S. 1143–1155. DOI: 10.1365/s40702-023-01001-y
• Vogel-Heuser, B; Bengler, K (2023): Von Industrie 4.0 zu Industrie 5.0 – Idee, Konzept und Wahrnehmung. In: HMD 60 (6), S. 1124–1142. DOI: 10.1365/s40702-023-01002-x.
• Xu, X; Lu, Y; Vogel-Heuser, B; Wang, L (2021): Industry 4.0 and Industry 5.0—Inception, conception and perception. In: Journal of Manufacturing Systems 61, S. 530–535. DOI: 10.1016/j.jmsy.2021.10.006.

Der Übergang von Industrie 4.0, geprägt durch Automatisierung und Datenaustausch in Fertigungstechnologien, zu Industrie 5.0 rückt den Menschen und seine Zusammenarbeit mit intelligenten Systemen in den Mittelpunkt. Industrie 5.0 beschreibt eine humanisierte Vision des technologischen Wandels, bei dem aktuelle und zukünftige Bedürfnisse von Individuen, Unternehmen und der Gesellschaft in Einklang gebracht werden. Allgegenwärtige Sensortechnologien und Big Data stellen die Grundlagen zu Automatisierung, Vernetzung und Optimierung einer Vielzahl industrieller Prozesse dar. Durch den Einsatz von KI-Technologien wie generativer KI wird diese Innovation weiter beschleunigt und eine neue Evolutionsstufe der Industrie geschaffen. Diese Entwicklung erfordert ein klares Verständnis und eine Klassifizierung der Komponenten, Technologien und Methodologien, die Industrie 5.0 definieren. Eine umfassende Analyse des technologischen Fähigkeitsspektrums durch den Einsatz von KI in industriellen Plattformen 5.0 ist erforderlich, damit Forschende, Praktiker und politische Entscheidungsträger die Komplexität dieses neuen industriellen Paradigmas effektiv navigieren können.

Dieses Seminarthema adressiert genau diesen Bereich, indem es Forschungsfragen behandelt, die sowohl die Hauptthemen und -genres der IS-Forschung im Bereich der Industrie 5.0 untersucht. Das Ziel dieses Themas ist daher die Entwicklung einer Informationssystems-Architektur für die Industrie 5.0 basierend auf einer systematischen Literaturrecherche in führenden WI-Fachzeitschriften und Konferenzen.

Literatur

• Aydin, E; Rahman, M; Ozeren, E (2023): Does Industry 5.0 Reproduce Gender (In)equalities at Organisations? Understanding the Interaction of Human Resources and Software Development Teams in Supplying Human Capitals. In: Inf Syst Front. DOI: 10.1007/s10796-023-10450-1
• Barata, J; Kayser, I (2023): Industry 5.0 – Past, Present, and Near Future. In: Procedia Computer Science 219, S. 778–788. DOI: 10.1016/j.procs.2023.01.351
• Choudhary, V., Marchetti, A., Shrestha, Y. R., & Puranam, P. (2023). Human-AI Ensembles: When Can They Work? Journal of Management. doi:10.1177/01492063231194968
• Schmalzried, D; Hurst, M; Wentzien, M; Gräser, M (2023): Analyse der Rolle Künstlicher Intelligenz für eine menschenzentrierte Industrie 5.0. In: HMD 60 (6), S. 1143–1155. DOI: 10.1365/s40702-023-01001-y
• Vogel-Heuser, B; Bengler, K (2023): Von Industrie 4.0 zu Industrie 5.0 – Idee, Konzept und Wahrnehmung. In: HMD 60 (6), S. 1124–1142. DOI: 10.1365/s40702-023-01002-x.
• Xu, X; Lu, Y; Vogel-Heuser, B; Wang, L (2021): Industry 4.0 and Industry 5.0—Inception, conception and perception. In: Journal of Manufacturing Systems 61, S. 530–535. DOI: 10.1016/j.jmsy.2021.10.006.

Das Feld der künstlichen Intelligenz (KI) hat sich in den letzten Monaten aufgrund fortgeschrittener Entwicklungen stark gewandelt. Mit dem Erscheinen von generativen KI-Tools und Large Language Models (LLM) in Form von ChatGPT, Midjourney und Co. hat sich eine neue Klasse von KI herausgebildet. Durch neuartige Interaktionsparadigmen mittels natürlicher Sprache wird einer breiten Nutzerschaft der Zugang zu KI-Systemen ermöglicht, die in der Lage sind, realistische Inhalte (z.B. Text, Bilder, Code) einfach und ohne umfangreiche Vorkenntnisse zu generieren. Doch neben den positiven Vorteilen von generativer KI, wie der Automatisierung von Arbeitsschritten oder der Unterstützung von kreativen Tätigkeiten, birgt die Technologie auch potenzielle Gefahren, welche langfristige Auswirkungen auf unsere Gesellschaft haben können. So kann die realistische Generierung von Medieninhalten etwa zur Verbreitung von Falschinformationen oder Identitätsdiebstahl.

Ziel dieser Seminararbeit ist es daher einen Beitrag zur Aufklärung über die Risiken von generativer KI zu leisten und gleichzeitig einen tieferen Einblick in die Herausforderungen zu gewinnen, mit denen die Gesellschaft konfrontiert wird. Dabei wird ein holistischer Ansatz verfolgt, sodass neben den technischen Aspekten (wie Halluzinationen) auch auf die sozialen, ethischen und politischen Implikationen eingegangen wird. Durch die Anwendung einer systematischen Literaturrecherche (SLR) sollen relevante wissenschaftliche Arbeiten in führenden wissenschaftlichen Fachzeitschriften und Konferenzen identifiziert, analysiert und zusammengefasst werden.

Literatur

• Arora, A., Barrett, M., Lee, E., Oborn, E. & Prince, K. (2023). Risk and the future of AI: Algorithmic bias, data colonialism, and marginalization. Information and Organization 33(3). DOI: 10.1016/j.infoandorg.2023.100478
• Banh, L. & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets 33(63). DOI: 10.1007/s12525-023-00680-1 
• Ferrara, E. (2023). GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models. DOI: 10.48550/arXiv.2310.00737
• Pan, Y. and Pawlik, P. (2023). Towards the Dark Side of AI Adoption: How Generative AI Extenuates the Perception of Chatbot Errors. AMCIS 2023 Proceedings.
• 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.

Generative künstliche Intelligenz (GenAI) hat das Potenzial, den Softwareentwicklungsprozess grundlegend zu verändern. Durch die Fähigkeit, Code zu generieren, zu optimieren und sogar selbstständig Probleme zu lösen, kann GenAI Entwicklerteams unterstützen und die Effizienz sowie die Qualität der Softwareproduktion steigern. Darüber hinaus können GenAI Tools in weiteren Phasen des Softwareentwicklungszyklus unterstützen, beginnend vom Systementwurf bis hin zur Vermarktung. Die Integration von GenAI in den Softwareentwicklungsprozess stellt jedoch auch neue Herausforderungen dar, einschließlich der Sicherstellung von Codequalität, der Verwaltung von KI-generierten Abhängigkeiten und der Anpassung von Entwicklungsworkflows der individuellen Teammitglieder.

Vor diesem Hintergrund soll in dieser Seminararbeit die Integration von generativer KI in den Softwareentwicklungsprozess beleuchtet werden. Das Ziel ist die Untersuchung der aktuellen Forschungslandschaft bezüglich des Potenzials aber auch den Herausforderungen von generativer KI in der Softwareentwicklung. Durch die Anwendung einer systematischen Literaturrecherche (SLR) sollen relevante wissenschaftliche Arbeiten in führenden wissenschaftlichen Fachzeitschriften und Konferenzen identifiziert, analysiert und zusammengefasst werden.

Literatur

• Banh, L. & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets 33(63). DOI: 10.1007/s12525-023-00680-1 
• Ebert, C., Louridas, P.: Generative AI for Software Practitioners. IEEE Softw., vol. 40, 30–38 (2023). doi: 10.1109/MS.2023.3265877
• Fügener, A., Grahl, J., Gupta, A., Ketter, W. (2021). Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI. MISQ, vol. 45, 1527–1556. doi: 10.25300/MISQ/2021/16553
• Ma, Q. C., Wu, S. T., Ken, K. (2023). Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming. AIEDLLM 2023.
• Mirbabaie M, Brünker F, Möllmann Frick, Nicholas R. J., Stieglitz S (2022). The rise of artificial intelligence – understanding the AI identity threat at the workplace. Electronic Markets 32(1):73–99. doi:10.1007/s12525-021-00496-x
• Russo, D. (2024). Navigating the Complexity of Generative AI Adoption in Software Engineering. ACM Trans. Softw. Eng. Methodol. 37(4):111.
• Schmidt, A.: Speeding Up the Engineering of Interactive Systems with Generative AI. In: Dix, A., Winckler, M., Jones, M. (eds.) Companion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 7–8. ACM, New York, NY, USA (2023). doi: 10.1145/3596454.3597176

Die traditionelle Software-Entwicklung steht durch das Aufkommen von generativer KI vor einem Wandel. Mit der Verbreitung generativer KI-Tools wie Github Copilot lässt sich das Phänomen beobachten, bei dem die User langwierige Aufgaben an die generative KI delegieren und auslagern (z.B. Codeerstellung oder Informationsanfragen) – wie das Übertragen der Aufgaben an einen Teampartner. Dieses Verhalten der Übergabe komplexer Aufgaben, die kognitive fordernd sein können, lässt sich als Cognitive Offloading bezeichnet (Grinschgl et al., 2022). Das Konzept der kognitiven Entlastung durch die Nutzung externer Artefakte zur Unterstützung von Gedächtnis und Informationsverarbeitung gibt es dabei schon seit Jahrhunderten, angefangen vom Aufschreiben von Notizen bis hin zu heutigen persönlichen digitalen Assistenten. Während sich verwandte Forschungsarbeiten mit Akzeptanzfaktoren oder Produktivitätseffekten von technologischen Artefakten wie KI befassen, ist das aktuelle Verständnis für die kognitiven Auswirkungen von generativer KI aufgrund der Neuartigkeit begrenzt. Vor allem im Hinblick auf die bisher beispiellosen Interaktionsweisen von generativen KI-Tools und der stetigen Weiterentwicklungen der Technologie (siehe bspw. Microsoft Copilot) erfordert es ein Verständnis über die Auswirkungen von generativer KI auf Individuen als zukünftige Arbeitspartner, um die verantwortungsvolle Entwicklung und Integration zu fördern.

Da die Erkenntnisse zum Cognitive Offloading als Vorstufe zur Übernahme und Delegation von Aufgaben betrachtet werden können, sollen in dieser Seminararbeit untersucht werden, inwiefern GenAI-Tools das Verhalten der Auslagerung von Aufgaben fördern und damit den mentalen Aufwand und die kognitive Belastung reduzieren können. Durch die Anwendung einer systematischen Literaturrecherche (SLR) sollen relevante wissenschaftliche Arbeiten in führenden WI, HCI und Psychologie-Fachzeitschriften und Konferenzen identifiziert, analysiert und zusammengefasst werden.

Literatur

• Banh, L. & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets 33(63). DOI: 10.1007/s12525-023-00680-1 
• 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
• Grinschgl, S., Neubauer, A.C. (2022): Supporting Cognition With Modern Technology: Distributed Cognition Today and in an AI-Enhanced Future. Frontiers in artificial intelligence, vol. 5, 908261. doi: 10.3389/frai.2022.908261
• Imai, S. (2022): Is GitHub copilot a substitute for human pair-programming? In: Dwyer, M.B. (ed.) Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings, pp. 319–321. ACM, New York, NY, USA. doi: 10.1145/3510454.3522684
• Ma, Q. C., Wu, S. T., Ken, K. (2023). Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming. AIEDLLM 2023.
• Noy, S., Zhang, W. (2023): Experimental evidence on the productivity effects of genera-tive artificial intelligence. Science (New York, N.Y.), vol. 381, 187–192. doi: 10.1126/science.adh2586
• Russo, D. (2024). Navigating the Complexity of Generative AI Adoption in Software Engineering. ACM Trans. Softw. Eng. Methodol. 37(4):111.

Smart City umfasst Lösungen, die darauf abzielen, städtische Lebensräume durch die Integration von Informationstechnologie und digitalen Innovationen effizienter, nachhaltiger und lebenswerter zu gestalten. Diese Initiative umfasst verschiedene Technologien und Strategien, die darauf ausgerichtet sind, urbane Systeme und Dienstleistungen zu optimieren und die Lebensqualität der Bewohner zu verbessern. Smart-City-Dienstleistungen werden häufig durch Plattformen realisiert, mit dem Ziel, Werte zwischen unterschiedlichen Akteuren auszutauschen. Hierbei werden komplementäre Dienstleistungen aufeinander abgestimmt, um in multilateralen Beziehungen Angebote zu realisieren, die eine Organisation allein nicht erzielen könnte. Vor diesem Hintergrund beschäftigt sich die Seminararbeit mit der Analyse dieser Plattformen, indem die für die Wertschöpfung zentralen Akteure, Wertschöpfungsaktivitäten sowie Austauschbeziehungen auf Basis einer systematischen Literaturanalyse untersucht werden. 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 aisel.aisnet.org/icis2022/blockchain/blockchain/4.

Die Fortschritte in der Informations- und Kommunikationstechnologie haben zur Entstehung digitaler Unternehmensökosysteme geführt. Diese Netzwerke von Organisationen arbeiten gemeinsam an der Bereitstellung von Produkten oder Dienstleistungen in einer digitalen Umgebung und generieren dabei Werte. Diese neue Form der Wertschöpfung in digitalen Unternehmensökosystemen hat bereits in zahlreichen Domänen (Bankenwesen, Logistik, Gesundheitswesen etc.) Einzug gehalten. Die Modularität digitaler Plattformen ermöglicht es, Aktivitäten zur Erzielung von Innovation zu zerlegen und in verschiedenen Konfigurationen zu aggregieren. Governance-Mechanismen unterstützen das gezielte Steuern der Wertschöpfung zwischen autonomen Komplementären und Konsumenten durch den Plattformanbieter. Die Entwicklung eines Ökosystems wird durch einige Faktoren beeinflusst, auf die Plattforminhaber unmittelbaren Einfluss ausüben können. Vor diesem Hintergrund beschäftigt sich die Seminararbeit mit der Untersuchung von Charakteristiken digitaler Plattformen und der Erstellung einer Taxonomie für digitale Plattformen auf Basis einer systematischen Literaturanalyse.

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 aisel.aisnet.org/icis2022/blockchain/blockchain/4.

Das Ökosystem im generative AI-Umfeld hat in den letzten Monaten und Jahren eine rasante Entwicklung erlebt. Mit der Einführung von Tools wie ChatGPT und Midjourney sind neue Möglichkeiten für die Erstellung von Inhalten, die Automatisierung von Aufgaben und die Interaktion mit digitalen Systemen entstanden, welche ohne tiefergreifende Technologiekenntnisse von einer breiten Masse an Nutzenden verwendet werden können. Zudem etablieren sich weitere Plattformen wie HuggingFace im (generativen) KI-Umfeld und auch in der Open Source Community tragen viele Projekte (z.B. LangChain oder Llama-Factory) und Modelle (z.B. Mistral, Llama) zum Generative AI-Ökosystem bei. Diese Fortschritte bieten nicht nur technische, sondern auch ethische, soziale und wirtschaftliche Implikationen. Eine werteorientierte Analyse dieses Technologie-Ökosystems ist entscheidend, um ihre Auswirkungen auf die Gesellschaft zu verstehen und Leitlinien für eine verantwortungsvolle Nutzung und Weiterentwicklung zu entwickeln. Vor diesem Hintergrund beschäftigt sich diese Seminararbeit mit der Untersuchung des generative AI-Ökosystems im Hinblick auf zentrale Teilnehmer und Werten und der Erstellung einer Informationssystemarchitektur auf Basis einer systematischen Literaturanalyse.

Literatur

• Banh, L. & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets 33(63). doi:10.1007/s12525-023-00680-1 
• Cusumano, M. A. (2023). Generative AI as a New Innovation Platform. Commun. ACM 66(10), pp. 18–21. doi:10.1145/3615859
• Cusumano, M. (2024). NVIDIA at the Center of the Generative AI Ecosystem---For Now. Commun. ACM 67(1), pp. 33–35. doi:10.1145/3631537
• Strobel, G.; Banh, L.; Möller, F.; Schoormann, T. (2024). Exploring Generative Artificial Intelligence: A Taxonomy and Types. Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS).
• Wessel, M.; Adam, M.; Benlian, A.; Majchrzak, A.; Thies, F.; (2023). Call for Papers to the Special Issue: Generative AI and its Transformative Value for Digital Platforms. Journal of Management Information Systems
• Woroch, R.; Strobel, G.; Wulfert, T. (2022): Four Shades of Customer: How Value Flows in Fintech Ecosystems. ICIS 2022 Proceedings.

Quantifying the impact of life science workflows is crucial for several reasons, primarily because it provides tangible measures of their effectiveness, efficiency, and broader contributions to the field of life sciences.

This quantification not only helps in assessing the value and productivity of these workflows but also guides funding decisions, resource allocation, and the direction of future research efforts. It enables the identification of highly effective methods, facilitating their adoption and adaptation across different research contexts. Furthermore, quantifying impact fosters a culture of transparency and reproducibility, essential for the validation of scientific findings. By providing clear metrics of success, it encourages the development of more efficient, robust, and widely applicable research tools, ultimately accelerating scientific discovery and innovation in the life sciences.

Research Question: What are the patterns of contribution, collaboration and release in the development of life science workflows on GitHub, and how do these patterns influence workflow quality and impact?

Literatur

• Borges, H., Hora, A., & Valente, M. T. (2016). Understanding the Factors that Impact the Popularity of GitHub Repositories. 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME), 334–344. doi.org/10.1109/ICSME.2016.31
• Pipinellis, A. (2018). GitHub Essentials: Unleash the power of collaborative development workflows using GitHub. Packt Publishing Ltd.
• Ramasamy, D., Sarasua, C., Bacchelli, A., & Bernstein, A. (2023). Workflow analysis of data science code in public GitHub repositories. Empirical Software Engineering, 28(1), 7.

Numerous studies exist that have employed natural language processing (NLP) to reconstruct and synthesize the narratives of life science workflows from their project descriptions hosted on repositories such as GitHub. The intersection of natural language processing (NLP) and the analysis of life science workflows on platforms like GitHub presents a high-yielding ground for scientific inquiry. This analytical approach enables the systematic deconstruction of textual data to put together the story of each project's development, highlighting aspects of innovation and markers of impact.

Translating project descriptions into a form that captures the essence of a life science workflow is a pivotal yet challenging endeavor. This process is critical because it allows for a comprehensive understanding of the project's trajectory and contributions, even by those lacking specialized domain knowledge. Project descriptions serve as a bridge, conveying complex scientific processes, innovations, and impacts in a manner that is accessible to a broader audience. However, identifying the most effective method for this translation remains a complex puzzle for researchers.

Research Question: How can we utilize the project descriptions to analyze and assess the innovation and impact of a life science workflow?

Literatur

• de Oliveira, P. A. M. et al. (2021). Software development during covid-19 pandemic: an analysis of stack overflow and github. In SEH, co-located with ICSE.
• Sharma, A. et al. (2017). Cataloging github repositories. In EASE, page 314–319.
• Tavares, A. C. R., Batista, N. A., & Moro, M. M. (2021). How COVID-19 Impacted Data Science: A Topic Retrieval and Analysis from GitHub Projects’ Descriptions. Anais Do XXXVI Simpósio Brasileiro de Banco de Dados (SBBD 2021), 325–330. doi.org/10.5753/sbbd.2021.17893
• Wang, L. et al. (2020). When the open source community meets covid-19: Characterizing covid-19 themed github repositories. arXiv, 2010.12218.
•  

Neuro-symbolic artificial intelligence is a novel area of AI research which seeks to combine traditional rules-based AI approaches with modern deep learning techniques. Neuro-symbolic AI has a long history; however, it remained a rather niche topic until recently, when landmark advances in machine learning—prompted by deep learning—caused a significant rise in interest and research activity in combining neural and symbolic methods.

This topic investigates models and theories that facilitate the seamless combination of the distinct paradigms of symbolic logics and neural networks, focusing on computational models, representation theories, and algorithms that enable symbolic reasoning within a neural framework, emphasizing the importance of formal languages, logic-based systems, and neural network architectures.

Research Question: What frameworks facilitate the integration of symbolic reasoning with neural computation, and how can these frameworks be applied to enhance the fidelity and efficiency of neuro-symbolic models?

Literatur

• Besold, T. R., Garcez, A. d’Avila, Bader, S., Bowman, H., Domingos, P., Hitzler, P., Kuehnberger, K.-U., Lamb, L. C., Lowd, D., Lima, P. M. V., de Penning, L., Pinkas, G., Poon, H., & Zaverucha, G. (2017). Neural-Symbolic Learning and Reasoning: A Survey and Interpretation. arXiv. arxiv.org/abs/1711.03902
• Garcez, A. S. d'Avila, G. Zaverucha, and L. C. Lamb, “Neural-Symbolic Learning Systems: Foundations and Applications,” Springer, 2002.
• Hammer, B., & Hitzler, P. (Eds.). (2007). Perspectives of neural-symbolic integration (Vol. 77). Springer.
• Hitzler, P., Eberhart, A., Ebrahimi, M., Sarker, M. K., & Zhou, L. (2022). Neuro-symbolic approaches in artificial intelligence. National Science Review, 9(6), nwac035. doi.org/10.1093/nsr/nwac035
• Marcus, Gary, "Algebraic Mind: Integrating Connectionism and Cognitive Science," MIT Press, 2001.
• Susskind, Z., Arden, B., John, L. K., Stockton, P., & John, E. B. (2021). Neuro-Symbolic AI: An Emerging Class of AI Workloads and their Characterization. arXiv preprint arXiv:2109.06133.
• Sheth, A., Roy, K., & Gaur, M. (2023). Neurosymbolic AI -- Why, what, and how. arXiv preprint arXiv:2305.00813.

“Open world” environments are those in which novel objects, agents, events, and more can appear and contradict previous understandings of the environment. This contradicts the “closed world” assumption used in most AI research, where the environment is assumed to be fully understood and unchanging.

Explore the design and development of cognitive architectures that leverage neuro-symbolic AI to mimic human reasoning processes, such as the ability to perform abstract reasoning, understand natural language, and learn from limited data in order to mitigate the issue of handling open-world novelties. Investigate the potential (or need) for creating a unified neuro-symbolic (cognitive) AI model that can integrate learning from data with high-level reasoning and decision-making.

This topic explores how insights from psychology and neuroscience along with neuro-symbolic AI can assist the development of cognitive architectures, to address the challenges posed by open-world environments, which present novelties and complexities not accounted for under the closed world assumption

Research Question: How can neuro-symbolic AI inform the development of cognitive architectures that mimic human reasoning, within the context of the closed world problem, and what are the primary challenges encountered in designing these models?

Literatur

• Goel, S., Lymperopoulos, P., Thielstrom, R., Krause, E., Feeney, P., Lorang, P., Schneider, S., Wei, Y., Kildebeck, E., Goss, S., Hughes, M. C., Liu, L., Sinapov, J., & Scheutz, M. (2024). A Neurosymbolic cognitive architecture framework for handling novelties in open worlds. Artificial Intelligence, 104111. doi.org/10.1016/j.artint.2024.104111
• Laird, John E., "The Soar Cognitive Architecture," MIT Press, 2012.
• Langley, P., Laird, J. E., & Rogers, S. (2009). Cognitive architectures: Research issues and challenges. Cognitive Systems Research, 10(2), 141-160.
• Oltramari, A., Francis, J., Henson, C., Ma, K., & Wickramarachchi, R. (2020). Neuro-symbolic architectures for context understanding. arXiv preprint arXiv:2003.04707.
• Sun, Ron, “Duality of the Mind: A Bottom-up Approach Toward Cognition,” Lawrence Erlbaum Associates, 2002.
• Wan, Z., Liu, C. K., Yang, H., Li, C., You, H., Fu, Y., ... & Raychowdhury, A. (2024). Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI. arXiv preprint arXiv:2401.01040.

Logic plays a foundational role in neuro-symbolic AI by providing a formal structure for representing dynamic and context-dependent knowledge, handling the vast and varied nature of real-world data, which is necessary to enable neuro-symbolic models for reasoning.

Classical logic, with its clear-cut true/false values, is fundamental but can be limiting for dealing with uncertainties and complexities of the real world. Modal logic introduces the ability to reason about possibilities and necessities, adding layers to understanding that can reflect more complex human reasoning. Probabilistic logic, on the other hand, integrates uncertainty directly into the logical framework, allowing for reasoning under uncertainty. Harmonizing these varied logical systems into neuro-symbolic AI aims to leverage their strengths, enhancing the system's reasoning capabilities. However, formulating logical rules has significant  difficulties in representing complex real-world knowledge and integrating it within a neural framework, such as increased computational complexity and the challenge of maintaining coherence across different types of logic. Investigate the formulation and integration of various logical systems within neuro-symbolic AI, examining how different logic systems can enhance the reasoning capabilities of these systems.

This topic examines innovative approaches of knowledge representation that can accommodate the richness and variability of real-world data in the knowledge structures, while addressing the challenges and trade-offs involved in embedding logical reasoning within a neural framework.

Research Question: What are the primary challenges in representing complex, real-world knowledge within neuro-symbolic systems, and how can these challenges be addressed through innovative representation techniques?

Literatur

• Averkin, A. (2019). Hybrid intelligent systems based on fuzzy logic and deep learning. In Artificial Intelligence: 5th RAAI Summer School, Dolgoprudny, Russia, July 4–7, 2019, Tutorial Lectures (pp. 3-12).
• Brachman, R. J., & Levesque, H. J. (2004). Knowledge Representation and Reasoning. Morgan Kaufmann.
• Bringsjord, S., & Govindarajulu, N. S. (2020). Artificial Intelligence and Logical Reasoning. In Handbook of Logic and Language (3rd ed., pp. 1–36). Elsevier.
• Davis, E., & Marcus, G. (2015). Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence. Communications of the ACM, 58(9), 92–103. doi.org/10.1145/2701413
• Li, L., Shi, L., & Zhao, R. (2023). A Vertical-Horizontal Integrated Neuro-Symbolic Framework Towards Artificial General Intelligence. In P. Hammer, M. Alirezaie, & C. Strannegård (Eds.), Artificial General Intelligence. AGI 2023. Lecture Notes in Computer Science, vol 13921. Springer, Cham. doi.org/10.1007/978-3-031-33469-6_20
• Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
• Sowa, J. F. (1999). Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks Cole Publishing Co.

Climate change is one of the biggest concerns and challenges of humanity. While policy-making is important to set rules and limits, innovative products, services and, generally, ideas are needed in both concerted and independent efforts to tackle this complex challenge (Voegtlin et al., 2022). Digital technologies are considered part of the solution and find usage in different scenarios, problem areas and domains  (Corbett, 2013; Round & Visseren-Hamakers, 2022; Zampou et al., 2022), while they sometimes also contribute to the problem (Dwivedi et al., 2022; Rieger et al., 2022). 

Research question: How are entrepreneurs utilizing digital technologies for tackling climate change? Which role(s) and function(s) do digital technologies hold in these missions and where are potential limits or challenges in applying them? (literature review and systematic synthesis of findings)

Literatur

• Corbett, J. (2013). Designing and Using Carbon Management Systems to Promote Ecologically Responsible Behaviors. JAIS, 14(7), 339–378. doi.org/10.17705/1jais.00338
• Dwivedi, Y. K., Hughes, L., Kar, A. K., Baabdullah, A. M., Grover, P., Abbas, R., Andreini, D., Abumoghli, I., Barlette, Y., Bunker, D., Chandra Kruse, L., Constantiou, I., Davison, R. M., De’, R., Dubey, R., Fenby-Taylor, H., Gupta, B., He, W., Kodama, M., … Wade, M. (2022). Climate Change and COP26: Are Digital Technologies and Information Management Part of the Problem or the Solution? An Editorial Reflection and Call to Action. International Journal of Information Management, 63, 102456. https://doi.org/10.1016/j.ijinfomgt.2021.102456
• Rieger, A., Roth, T., Sedlmeir, J., & Fridgen, G. (2022). We Need a Broader Debate on the Sustainability of Blockchain. Joule, 6(6), 1137–1141. 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. https://doi.org/10.3389/fbloc.2022.957316
• Voegtlin, C., Scherer, A. G., Stahl, G. K., & Hawn, O. (2022). Grand Societal Challenges and Responsible Innovation. J Management Studies, 59(1), 1–28. doi.org/10.1111/joms.12785
• Zampou, E., Mourtos, I., Pramatari, K., & Seidel, S. (2022). A Design Theory for Energy and Carbon Management Systems in the Supply Chain. JAIS, 23(1), 329–371. doi.org/10.17705/1jais.00725

Climate tech, tokenization of biodiversity assets, artificial intelligence for sustainability. Digital technologies are used as vehicle and tools to tackle environmental problems (Bermeo-Almeida et al., 2018; Oberhauser, 2019; Schletz et al., 2023; Schoormann et al., 2023; Sullivan, 2012). What is driving entrepreneurs to engage in environmental protection or in finding solutions for environmental problems (Melville, 2010; Saldanha et al., 2022)? The search for purposeful work or a felt closeness to nature can be reasons for such engagement (Gregori et al., 2021; Intergovernmental Science-Policy Platform On Biodiversity And Ecosystem Services, 2022; Rastogi & Sharma, 2018).

Research question: Which role and function do values play in the entrepreneurs’ mission? How is nature and its value considered in the entrepreneurs’ values and in which way does this influence the entrepreneurial endeavor? (literature review, application of results on webpage contents (1-2 cases) including critical assessment of resulting analysis)

Literatur

• Bermeo-Almeida, O., Cardenas-Rodriguez, M., Samaniego-Cobo, T., Ferruzola-Gómez, E., Cabezas-Cabezas, R., & Bazán-Vera, W. (2018). Blockchain in agriculture: A systematic literature review. In R. Valencia-García, G. Alcaraz-Mármol, J. Del Cioppo-Morstadt, N. Vera-Lucio, & M. Bucaram-Leverone (Eds.), Technologies and innovation (pp. 44–56). Springer International Publishing.
• Gregori, P., Holzmann, P., & Wdowiak, M. A. (2021). For the sake of nature: Identity work and meaningful experiences in environmental entrepreneurship. Journal of Business Research, 122, 488–501. doi.org/10.1016/j.jbusres.2020.09.032
• Intergovernmental Science-Policy Platform On Biodiversity And Ecosystem Services. (2022). Summary for policymakers of the methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) (1.2). Zenodo. https://doi.org/10.5281/ZENODO.6522392
• Melville, N. P. (2010). Information Systems Innovation for Environmental Sustainability. MIS Quarterly, 34(1), 1–21.
• Oberhauser, D. (2019). Blockchain for Environmental Governance: Can Smart Contracts Reinforce Payments for Ecosystem Services in Namibia? Frontiers in Blockchain, 2, 21. https://doi.org/10.3389/fbloc.2019.00021
• Rastogi, P., & Sharma, R. (2018). Ecopreneurship for Sustainable Development. In J. Marques (Ed.), Handbook of Engaged Sustainability (pp. 991–1016). Springer International Publishing. doi.org/10.1007/978-3-319-71312-0_46
• Saldanha, T., Mithas, S., Khuntia, J., Whitaker, J., & Melville, N. (2022). How Green Information Technology Standards and Strategies Influence Performance: Role of Environment, Cost and Dual Focus. MIS Quarterly, 46(4), 2367–2386.
• 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
• Schoormann, T., Strobel, G., Möller, F., Petrik, D., & Zschech, P. (2023). Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature. CAIS, 52, 199–237. doi.org/10.17705/1CAIS.05209
• Sullivan, S. (2012). Financialisation, Biodiversity Conservation, and Equity: Some Currents and Concerns. Third World Network.

Blockchain is, by some, considered to be a digital technology that can enhance or improve effectiveness and, generally, procedures. With its distinct characteristics (Arooj et al., 2022; Sunyaev et al., 2021; Treiblmaier, 2019), it is considered to address issues and challenges specifically in sensitive areas where transparency and trust are required to ensure quality or prevent fraud. When it comes to the natural environment, blockchain technologies are used to ensure quality and safety for both non-human life and humans, for instance in relation to carbon emissions or the supply chain of food products (Golding et al., 2022; Howson, 2020; Sanjeef, 2022; Sengupta & Kim, 2020).

Research question: What is the contribution of blockchain technologies in addressing environmental challenges and in which way are individual technological features, mechanisms and characteristics of blockchain utilized for this?

Literatur

• Arooj, A., Farooq, M. S., & Umer, T. (2022). Unfolding the blockchain era: Timeline, evolution, types and real-world applications. Journal of Network and Computer Applications, 207, 103511. doi.org/10.1016/j.jnca.2022.103511
• Golding, O., Yu, G., Lu, Q., & Xu, X. (2022). Carboncoin: Blockchain tokenization of carbon emissions with ESG-based reputation. 2022 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 1–5. doi.org/10.1109/ICBC54727.2022.9805516
• Howson, P. (2020). Building trust and equity in marine conservation and fisheries supply chain management with blockchain. Marine Policy, 115, 103873. doi.org/10.1016/j.marpol.2020.103873
• Sanjeef, S. (2022, February 28). Using Blockchain Technology in Environmental Conservation. Earth.Org. earth.org/using-blockchain-technology-in-environmental-conservation/
• Sengupta, U., & Kim, H. (2020). Business process transformation in natural resources development using blockchain: Indigenous entrepreneurship, trustless technology, and rebuilding trust. In H. Treiblmaier & T. Clohessy (Eds.), Blockchain and distributed ledger technology use cases: Applications and lessons learned(pp. 171–200). Springer International Publishing. doi.org/10.1007/978-3-030-44337-5_9
• Sunyaev, A., Kannengießer, N., Beck, R., Treiblmaier, H., Lacity, M., Kranz, J., Fridgen, G., Spankowski, U., & Luckow, A. (2021). Token Economy. Bus Inf Syst Eng, 63(4), 457–478. doi.org/10.1007/s12599-021-00684-1
• Treiblmaier, H. (2019). Toward More Rigorous Blockchain Research: Recommendations for Writing Blockchain Case Studies. Frontiers in Blockchain, 2, 3. https://doi.org/10.3389/fbloc.2019.00003

With the event of Bitcoin and the publication of Satoshi Nakamoto’s White Paper (Nakamoto, 2008), blockchain technologies emerged and developed into different domains. While not all distributed ledger technology (DLT) applications incorporate the notions behind the concept fully, decentralization is a fundamental concept behind the technology (Ballandies et al., 2022; Meyer et al., 2022).

Research question: Which academic theories and concepts are related to blockchain’s fundamental characteristic “decentralization” and how is this specific feature considered for tackling environmental challenges?

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.
• Meyer, E. A., Isabell M, W., & Sandner, P. (2022). Decentralized Finance – A Systematic Literature Review and Research Directions. ECIS 2022 Proceedings, 25. aisel.aisnet.org/ecis2022_rp/25
• Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.bitcoin.org/bitcoin.pdf

Due to various external drivers, such as political agendas and technological innovations, the digital transformation in the healthcare sector continues to develop. Additional value can be created through the implementation of new technologies and the digitalization of professional health care practices (Ologeanu-Taddei et al., 2023). Besides, the rise and increasing importance of digital health ecosystems is driving digital transformation in the health sector (Hermes et al., 2023). Nevertheless, the adoption of digital innovations and successful digital transformation is still a challenge for actors in the healthcare sector (Dal Mas et al., 2023; Garcia-Petez et al., 2023).

As part of this seminar paper, a literature review will be conducted to explore current trends regarding the digital transformation in the healthcare sector and which factors make the transformation a challenge.

Literatur

• Dal Mas, F. Massaro, M. Rippa, P., & Secundo, G. (2023). The challenges of digital transformation in healthcare: An interdisciplinary literature review, framework, and future research agenda. Technovation, 123, 102716.
• Garcia-Perez, A., Cegarra-Navarro, J. G., Sallos, M. P., Martinez-Caro, E., & Chinnaswamy, A. (2023). Resilience in healthcare systems: Cyber security and digital transformation. Technovation, 121, 102583.
• Hermes, S., Riasanow, T., Clemons, E. K., Böhm, M., & Krcmar, H. (2020). The digital transformation of the healthcare industry: exploring the rise of emerging platform ecosystems and their influence on the role of patients. Business Research, 13(3), 1033-1069.
• Ologeanu-Taddei, R., Guthrie, C., & Jensen, T. B. (2023). Digital transformation of professional healthcare practices: fitness seeking across a rugged value landscape. European Journal of Information Systems, 32(3), 354-371.

Due to various external drivers, such as political agendas and technological innovations, the digital transformation in the healthcare sector continues to develop. Additional value can be created through the implementation of new technologies and the digitalization of professional health care practices (Ologeanu-Taddei et al., 2023). Besides, the rise and increasing importance of digital health ecosystems is driving digital transformation in the health sector (Hermes et al., 2023). Nevertheless, the adoption of digital innovations and successful digital transformation is still a challenge for actors in the healthcare sector (Dal Mas et al., 2023; Garcia-Petez et al., 2023).

As part of this seminar paper, qualitative interviews will be conducted to explore current trends regarding the digital transformation in the healthcare sector and which factors make the transformation a challenge.

Literatur

• Dal Mas, F. Massaro, M. Rippa, P., & Secundo, G. (2023). The challenges of digital transformation in healthcare: An interdisciplinary literature review, framework, and future research agenda. Technovation, 123, 102716.
• Garcia-Perez, A., Cegarra-Navarro, J. G., Sallos, M. P., Martinez-Caro, E., & Chinnaswamy, A. (2023). Resilience in healthcare systems: Cyber security and digital transformation. Technovation, 121, 102583.
• Hermes, S., Riasanow, T., Clemons, E. K., Böhm, M., & Krcmar, H. (2020). The digital transformation of the healthcare industry: exploring the rise of emerging platform ecosystems and their influence on the role of patients. Business Research, 13(3), 1033-1069.
• Ologeanu-Taddei, R., Guthrie, C., & Jensen, T. B. (2023). Digital transformation of professional healthcare practices: fitness seeking across a rugged value landscape. European Journal of Information Systems, 32(3), 354-371.

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 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, patient care, and public health 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.

When it comes to the success of digital ecosystems or digital innovation, high user adoption is a critical success factor. Regarding the ePA, the adoption rate in Germany is still limited. What actually influences adoption has been analyzed using several approaches (Mathias et al. 2022; Sadoughi et al. 2019). Drivers of acceptance and adoption from the patient's perspective still remain an important field of research in the context of EHRs (Griesser & Bidmon, 2022).

As part of this seminar paper, key drivers that influence the adoption of the EPA will be identified and specified by conducting qualitative interviews. Finally, approaches to increasing the adoption rate will be discussed.

Literatur

• Griesser, A., & Bidmon, S. (2022). A process related view on the usage of electronic health records from the Patients’ perspective: a systematic review. Journal of Medical Systems, 47(1), 2.
• Mathai, N., McGill, T., & Toohey, D. (2022). Factors Influencing Consumer Adoption of Electronic Health Records, Journal of Computer Information Systems, 62(2), 267-277.
• Sadoughi, F., Khodaveisi, T., & Ahmadi, H. (2019). The used theories for the adoption of electronic health record: a systematic literature review. Health and Technology, 9, 383-400.

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 (Kohli & Tan, 2016). 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

• Kohli, R., & Tan, S. S. L. (2016). Electronic Health Records: How Can IS Researchers Contribute to Transforming Healthcare? MIS quarterly, 40(3), 553–574.
• 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.

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.

The success of digital innovations depends on a high adoption rate among potential users. This applies in particular to the DiGA, as the adoption rate in Germany is still limited. With regard to digital health applications, the long-term adoption of users is a critical prerequisite for the long-term benefit of digital health applications in the health sector (Milne-Ives et al., 2020). Various aspects influence the usage behavior of users in the short- and long-term use of digital health applications (Fei et al., 2019). Therefore, the adoption rate of DiGA can potentially be positively influenced if the factors that influence individual adoption behavior are known and specifically taken into account when developing and implementing DiGA.

As part of this seminar paper, qualitative interviews will be conducted to investigate which drivers influence the adoption of DiGA.

Literatur

• Fei Liu, Eric Ngai, Xiaofeng Ju (2019). Understanding mobile health service use: An investigation of routine and emergency use intentions. International Journal of Information Management, 45, 107-117.
• 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.

A recommender system generally aims at the provision of useful recommendations to a group of users. Whereas recommender systems are well known in domains like e-commerce, only few applications of recommender systems in the field of conceptual modeling can be found. Modeling is considered to be an activity that typically requires a substantial manual modeling effort. This includes the construction for appropriate abstractions, decisions on the scope of the model, and choosing appropriate labels. To reduce the required manual effort, recommendations shall support respective modeling decisions.

The main aim of this seminar paper is to conduct a critical analysis of existing recommender systems in the field of conceptual modeling. To this aim the existing initiatives (approaches, tools, and techniques) should be identified and their utility as well as their level of maturity should be assessed. Based on the findings, a set of recommendations for the future development of the field should be formulated.

Literatur

• Almonte L, Guerra E, Cantador I, de Lara J (2022) Recommender Systems in Model-Driven Engineering: A Systematic Mapping Review. Software and Systems Modeling 21:249–280
• Almonte L, Pérez-Solar S, Guerra E, Cantador I, de Lara J (2021) Automating the Synthesis of Recommender Systems for Modelling Languages. Proceedings of the 14th ACM SIGPLAN International Conference on Software Language Engineering, pp 22–35
• Di Rocco J, Di Sipio C, Nguyen PT, Di Ruscio D, Pierantonio A (2022) Finding with NEMO: A Recommender System to Forecast the Next Modelling Operations. MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems, pp 154–164
• Elkamel A, Gzara M, Ben-Abdallah H (2016) An UML Class Recommender System for Software Design. 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)
• Fellmann M, Metzger D, Jannaber S, Zarvic N, Thomas O (2018) Process Modeling Recommender Systems: A Generic Data Model and Its Application to a Smart Glasses-based Modeling Environment. Business and Information Systems Engineering 60(1):21–38
• Kögel S (2017) Recommender System for Model Driven Software Development. Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp 1026–1029
• Koschmider A, Hornung T, Oberweis A (2011) Recommendation-based Editor for Business Process Modeling. Data & Knowledge Engineering 70(6):483–503
• Kuschke T, Mäder P (2014) Pattern-based Auto-completion of UML Modeling Activities. Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering, pp 551–556
• Nair A, Ning X, Hill JH (2011) Using Recommender Systems to Improve Proactive Modeling. Software and Systems Modeling 20:1159–1181

Enterprise modeling aims at providing an integrated view on an organization to support, among others, communication, model-based analysis, or the design of adequate enterprise information systems. In the context of possible applications of AI in general, and machine learning in particular, in the domain of enterprise modeling, the question emerges which enterprise modeling activities could be influenced or supported by the proposed mechanisms. Therefore, the main aim of this seminar paper is to investigate how AI can influence the enterprise modeling activities, e.g., the enterprise model life-cycle, to critically assess and identify reasonable application scenarios, as well as open challenges and future possibilities.

Literatur

• Barriga A, Rutle A, Heldal R (2022) AI-Powered Model Repair: An Experience Report — Lessons Learned, Challenges, and Opportunities. Software and Systems Modeling 21:1135–1157
• Fill H-G (2020) Enterprise Modeling: From DIgital Transformation to Digital Ubiquity. Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, pp 1–4
• Rittelmeyer JD, Sandkuhl K (2021) Effects of Artificial Intelligence on Enterprise Architectures: A Structured Literature Review. IEEE 25th International Enterprise Distributed Object Computing Conference Workshops, pp 130–137
• Sandkuhl K, Fill H-G, Hoppenbrouwers S, Krogstie J, Matthes F, Opdahl AL, Schwabe G, Uludag Ö, Winter R (2018) From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling. Business and Information Systems Engineering 60(1):69–80
• Shilov N, Othman W, Fellmann M, Sandkuhl K (2023) Machine Learning for Enterprise Modeling Assistance: An Investigation of the Potential and Proof of Concept. Software and Systems Modeling
• Silva N, Sousa P, Da Silva MM (2021) Maintenance of Enterprise Architecture Models: A Systematic Review of Scientific Literature. Business and Information Systems Engineering 63(2):157–180
• Snoeck M, Stirna J, Weigand H, Proper HA (2019) Panel Discussion: Artificial Intelligence Meets Enterprise Modeling. Practice of Enterprise Modelling 2019 Conference Forum (Short Papers), pp 88–97
• Zaidi MA (2021) Conceptual Modeling Interacts with Machine Learning: A Systematic Literature Review. Computational Science and Its Applications: ICCSA 2021. 21st International Conference, Proceedings Part IX, pp 522–532

In recent years, microservices have gained remarkable attention. They promise to enable architectures that can be conveniently adapted to ever changing business needs. The hype around microservices is reflected by a plethora of contributions on the web, mainly by vendors and consultants. While they sometimes give the impression, microservices are some kind of a silver bullet, they often fail to explain what key characteristics of this technology are. This term paper is supposed to investigate these characteristics in order to develop an elaborate notion of microservices. Based on that, it is to analyze whether and how microservices are suited to enabling more powerful architectures for enterprise systems.

Literatur

• Balalaie, Armin; Heydarnoori, Abbas; Jamshidi, Pooyan (2016): Microservices Architecture Enables DevOps: Migration to a Cloud-Native Architecture. In: IEEE Software, vol. 33, no. 3, pp. 42–52. DOI: 10.1109/MS.2016.64
• Nadareishvili, Irakli; Mitra, Ronnie; McLarty, Matt; Amundsen, Michael: Microservice architecture. Aligning principles, practices, and culture. First Edition, Second Release. Beijing, Boston, Farnham, Sebastopol, Tokyo: O´Reilly 2016
• Newman, Sam: Building Microservices. Sebastopol: O'Reilly & Associates 2015
• Vohra, Deepak; Nardone, Massimo: Kubernetes microservices with Docker. Berkeley, CA: Apress 2016

In den letzten Jahren hat die Vorstellung von digitalen Zwillingen eine beachtliche Aufmerksamkeit erfahren, so etwa in der industriellen Produktentwicklung und -produktion, in der Medizin wie auch zur Unternehmensplanung und -steuerung. Auf der Grundlage eines differenzierten Begriffs von digitalen Zwillingen – das schließt verschiedene Ausprägungsformen ein – sind Entwicklungsmethoden, Architekturen und Einsatzszenarien zu betrachten. Vor diesem Hintergrund sind abschließend die ökonomischen Potentiale digitaler Zwillinge zu beurteilen.

Literatur

• Atkinson, Colin; Kühne, Thomas (2022): Taming the Complexity of Digital Twins. In: IEEE Softw. 39 (2), S. 27–32. DOI: 10.1109/MS.2021.3129174.
• Das, Trisha; Wang, Zifeng; Sun, Jimeng (2023): TWIN: Personalized Clinical Trial Digital Twin Generation. In: Ambuj Singh (Hg.): Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Long Beach CA USA, 06 08 2023 10 08 2023. ACM Special Interest Group on Knowledge Discovery in Data; ACM Special Interest Group on Management of Data; ACM SIGs. New York,NY,United States: Association for Computing Machinery (ACM Digital Library), S. 402–413.
• van der Valk, Hendrik; Haße, Hendrik; Möller, Frederik; Otto, Boris (2022): Archetypes of Digital Twins. In: Bus Inf Syst Eng 64 (3), S. 375–391. DOI: 10.1007/s12599-021-00727-7.
• Zhang, Yan (2024): Digital Twin. Architectures, Networks, and Applications. 1st ed. 2024. Cham: Springer Nature Switzerland; Imprint Springer (Simula SpringerBriefs on Computing, 16).