General Information
| Lecturer | Prof. Dr. Armin Heinzl |
|---|---|
| Course Format | Thesis |
| Credit Points | 12 ECTS |
| Language | English |
| Information for Students | Information regarding theses of other degree courses can be found at our academic theses page. |
Course Information
Procedure
Brief Description
Individuals and organizations operate in a world that is increasingly permeated with digital technology. Every day we interact with Information Systems (IS) that make our phones smart, our cars safe, and our lives convenient. Likewise, Information Systems are embedded in the very core of the products, operations, and strategies of many organizations. Ever more, understanding and leveraging Information Systems is key to business success, not only for large and experienced players like SAP and Google, but also for small start-ups. The pervasive nature of digital technology is radically transforming our understanding of Information Systems, encompassing their development, coordination, use, and the way we interact with them. The primary objective of this seminar is to shed light on these issues and enrich our knowledge about how information systems impact organizations and individuals.
Topics
Topic Topic Description Supervisor A: A:The effect of data network effects on the perceived value of (Gen)AI platforms Some of the world’s most profitable companies operate platforms that demonstrate network effects, a phenomenon traditionally scrutinized through the lenses of direct and indirect network effects. However, the advent of artificial intelligence (AI) has ushered in a new era, exemplified by the widespread adoption of technologies like ChatGPT. This technological evolution brings forth a novel paradigm – data network effects. AI advancements heavily rely on the quality and quantity of data utilized for training and fine-tuning. Consequently, the perceived value of digital platforms is posited to be positively correlated with their AI capabilities, contingent upon the platform owner’s capacity to amass substantial volumes of relevant, high-quality data. This bachelor’s thesis endeavors to explore the emergent concept of data network effects within the realm of AI-driven digital platforms. Through a systematic literature review, we aim to consolidate existing knowledge and delineate pathways for future research exploration. Tobias Maier B: Digital Fashion Artefacts The improvements of digital rendering methods have sparked a wave of innovation in the design and development of virtual clothing and accessories. From virtual try-on experiences to personalized digital avatars, these assets offer consumers new ways to engage with fashion in the digital realm. However, as the demand for digital fashion continues to grow, it is crucial to understand the processes and principles underlying its design and development, as well as use and consumption of these digital artefacts. Existing research highlights the potential for digital fashion assets to reduce the environmental footprint of traditional fashion production by minimizing the need for physical materials and transportation. Yet, little is known about the specific resources and capabilities required to design digital fashion artefacts effectively. Furthermore, questions remain about the scalability and accessibility of these innovations. This bachelor thesis aims to address this gap by investigating theories and empirical evidence related to the construction and consumption of digital fashion assets. Through a structured literature review, the student will analyse existing research to uncover the processes, principles, and technologies involved in crafting digital fashion artifacts. Simultaneously, the thesis will explore user interactions and utilization patterns, including factors such as engagement, satisfaction, and their influence on real-world fashion consumption behaviors. By synthesizing insights from both realms, the thesis seeks to offer a comprehensive understanding for optimising the creation and usage of digital fashion assets, with implications for sustainability, user engagement, and industry advancement. Rosa Holtzwart C: Digital responsibility for adverse effects of beautified avatars Avatars are digital representations of people that are used in virtual environments such as online games, virtual reality experiences, or e-commerce. Previous research has shown that beautified avatars, with an optimised appearance can provide motivation and inspiration for users to work towards their goals and improve themselves. However, beautifying a user's avatar, or creating an avatar that represents an idealised version of the user, can also have severe negative consequences. It can lead to body dissatisfaction and negative self-image in the user. When the user sees their avatar as more attractive or idealised than their actual appearance, it can create a discrepancy between their digital self and their actual self, leading to negative emotions and self-esteem issues. Additionally, it may also lead to unrealistic expectations and disappointment in the user's real-life interactions and relationships. In some cases, it may even lead to gaming addiction or other forms of excessive technology use. In order to understand how avatars can be designed effectively and responsibly, it is important to understand what digital responsibility for the creation of avatars could look like. For this bachelor thesis the student is expected to conduct a structured literature review to identify how (digital) responsibility was captured and conceptualised in previous studies. In addition, the student is expected to outline which theoretical perspectives have been used by previous studies to understand the relationship between (un-)ethical design of technology and negative psychological consequences for an individual. Rosa Holtzwart D: Impact of generative AI on humans Manipulation through Generative Artificial Intelligence (AI): This Bachelor thesis delves into the dynamics between humans and generative AI systems, focusing on the potential manipulative aspects these interactions can entail. The study investigates how generative AI can influence, alter, or manipulate human perceptions, decisions, and emotional states. Through a systematic examination, the thesis aims to uncover the underlying human cognitive biases that might be exacerbated or mitigated when interacting with these AI systems. Using a structured literature review, the research will extant research on manipulations in human-technology interactions, assessing the psychological, social, and ethical implications of such interactions. Key questions include: “What human biases are most susceptible to manipulation through generative AI?” and “How do these biases affect the interaction outcomes between humans and AI?” This research promises to provide insights into the responsible design and use of generative AI. Deborah Mateja E: Explainable AI in Healthcare- How does it Change Ragiologists' error detection ability? Clinicians need to understand the reasoning behind AI-generated recommendations to trust and confidently act upon them. Explainable Artificial Intelligence (XAI) techniques enable the generation of explanations that allow clinicians to comprehend the underlying factors influencing AI-driven outcomes and help them validate the results. In general, humans perform well with correct XAI advice. However, when an AI-based system makes errors, the use of explanations tends to make humans blindly trust the system, leading to poorer decision-making performance. Hence, research is struggling with the dilemma whether to make systems more and more transparent (possibly leading to information overload or ambiguity) or to maintain a degree of opaqueness (possibly losing the benefits of XAI such as confidence and trust in the advice). A bachelor thesis in this context could address the extant XAI dilemma and, at its core, the ability of humans to detect errors when receiving explanations of AI advice. The valuable contribution to XAI research would be to explore the current empirical evidence on human-XAI collaboration performance and to identify the underlying XAI characteristics that influence humans in detecting AI errors. Luis Oberste F: Multimodal Explainable AI for Clinical Decision-Making This bachelor thesis explores the interactions between humans and multimodal explainable Artificial Intelligence (AI) systems within the healthcare sector. It delves into how the integration of various data types (e.g., visual, textual, and auditory) in AI algorithms and its explanations can enhance transparency, interpretability, and trust in clinical decision-making processes. The bachelor thesis investigates the effectiveness of multimodal explainable AI in communicating complex medical information to healthcare professionals and patients, fostering a collaborative environment between humans and AI agents. Through a comprehensive literature review, this research aims to address critical questions surrounding the design, implementation, and impact of multimodal explainable AI systems in healthcare. Key research questions include: “How does multimodality in explainable AI affect the understanding of healthcare professionals and patients?” and “What are the challenges and opportunities in human-AI collaboration in medical settings as AI is becoming increasingly multimodal?” Florian Rüffer G: Relevance of Emotions for IS research studies Emotions constitute a fundamental aspect of human experience, permeating nearly every facet of our lives. This intrinsic relationship also extends into our interactions with technology. Whether we engage with various technologies, make consumption decisions, or respond to digital content, our emotions invariably shape these experiences. Understanding the profound impact of emotions on technology usage is imperative for Information Systems (IS) Research. Neglecting to account for the influence of emotions may lead to incomplete assessments of user behavior and system efficacy. Therefore, investigating the role of emotions in technology-mediating activities is essential for advancing IS Research and developing more insightful models and frameworks. The objective of this bachelor thesis is to conduct a structured literature review by searching relevant Information Systems outlets to identify studies that are concerned with or address emotions. By summarizing key insights and discerning patterns among these studies, the goal is to pinpoint avenues for future research in the intersection of emotions and technology. As an example see: Stein, M.-K., Newell, S., Wagner, E. L., & Galliers, R. D. (2015). Coping with Information Technology: Mixed Emotions, Vacillation, and Nonconforming Use Patterns. MIS Quarterly, 39(2), 367–392. https://doi.org/10.25300/MISQ/2015/39.2.05 Mechthild Pieper H: Digitally supported emotion regulation with mHealth apps The ability to regulate emotions for situational adaptations is critical for individuals' wellbeing. Emotion Regulation (ER) includes strategies by which individuals influence which emotions they have, when they have them, and how they perceive their experiential, behavioral, and psychological impact. It is assumed that mobile health technologies (mHealth) offer potential to support individuals' development of effective ER strategies. However, it remains unclear whether and how ER occurs during mHealth use to manifest this potential. For this bachelor hesis, students are expected to conduct a structured literature review to examine the role of Information Technology in ER and to determine how ER strategies can be taught and supported by mobile health applications. Good starting points are: Slovak, P., Antle, A., Theofanopoulou, N., et al. (2023) Designing for Emotion Regulation Interventions: An Agenda for HCI Theory and Research. ACM Transactions on Computer-Human Interaction 30(1): 1–51. Eisenstadt, M., Liverpool, S., Infanti, E., et al. (2021) Mobile Apps That Promote Emotion Regulation, Positive Mental Health, and Well-being in the General Population: Systematic Review and Meta-analysis. JMIR Mental Health 8(11). Mechthild Pieper I: Data-driven analytical approaches for understanding how IT drives behavior change An increasing number of individuals use mobile health applications (mHealth), such as Apple Health, Google Fit and other third-party applications to adopt healthy behaviors and improve health outcomes. These technologies have the potential to deliver adaptive and individualized behavior change interventions in real-time by leveraging user-generated data (e.g., usage data and behavioral data). However, behavior change interventions in mHealth are currently developed based on static and pre-determined decision rules. The aim of this thesis is to identify and elaborate on individualized and dynamic data-driven analytical approaches used in mHealth research. In this thesis, you are expected to review academic literature to identify data-driven analytical approaches used in mHealth, elaborate on how and why data-driven analytical approaches can improve our knowledge on behavior change, understand how data-driven approaches can inform behavior change theories, and identify research gaps and areas for future research. Monica Fallon J: Intragroup Processes in Team-AI Collaboration Research in human-AI collaboration has primarily focused on how individuals can successfully collaborate with AI, leaving team-AI collaboration relatively neglected. However, with the increasing integration of AI team members into human teams, it becomes crucial to better understand the mechanisms of team-AI collaboration. One way to generate new scientific insights is to conduct experiments. Experiments are a class of research designs that create artificial situations to eliminate confounding variables and determine causal effects. Therefore, experiments on team-AI collaboration can help gain more insights into how AI systems affect human teamwork processes. Research from the social sciences can guide approaches for designing experiments on team-AI collaboration by investigating which research questions and collaboration tasks have been studied in human teaming. For instance, research in the field of hidden profiles has generated rich findings on how teams make decisions under distributed knowledge. However, other experimental collaboration tasks might provide different insights that go beyond hidden profiles. Therefore, this bachelor's thesis should summarize the research on experimental collaboration tasks and the connected research questions, outlining how this could guide research on team-AI collaboration. 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. Lu, L., Yuan, Y. C., & McLeod, P. L. (2012). Twenty-five years of hidden profiles in group decision making: A meta-analysis. Personality and Social Psychology Review, 16(1), 54–75. Désirée Zercher Literature
- The literature review should be done by students independently
- The review should include electronic literature sources offered by the University of Mannheim (Rechercheportal) as well as sources available on the internet
- Overviews of literature sources are available at the Mannheim University Library
