|Performing Lecturer||Timo Himmelsbach|
|Examiner||Prof. Dr. Armin Heinzl|
|Credit Points||6 ECTS (MMM), 4 ECTS (WI)|
|Grading||Seminar paper (70%), presentation (20%), discussion (10%)|
|Exam Date||See course information below|
|Information for Students||Registration: Please see information below!|
Digital technologies and the ever-growing amounts of data are radically reshaping our daily lives as well as the economy. Embedded at the very core of the products, operations, and strategies of many organizations, digital technologies are rapidly transforming existing businesses throughout all industries. New market offerings, business processes, as well as business models are emerging around the use of these digital technologies, yielding digital innovation1. The pervasive nature of digital technology is fundamentally transforming our understanding of information systems (IS), especially regarding their development, coordination, use, and the way we interact with them. At our chair, we offer a wide range of research topics in IS, focusing on new digital technologies such as artificial intelligence (AI) and machine learning (ML). In our research, we take human-computer interaction, system design, value creation or organizational perspectives.
In our seminar, we will examine the design of digital technologies as well as their impact on individuals and organizations. In doing so, we link the offered topics to our ongoing research, which has been and is currently being published at leading international outlets.
1. Nambisan, S., Lyytinen, K. & Yoo, Y. Handbook of Digital Innovation. 2–12 (2020) doi:10.4337/9781788119986.00008.
Interested in learning more about digital innovation? Feel free to have a look at our master course IS 607 (https://www.bwl.uni-mannheim.de/en/heinzl/teaching/digital-innovation/) and/
In this seminar, you will acquire the ability to identify, classify, and evaluate existing research. You will learn how to develop your own research agenda as well as to present and discuss it with the participants of the seminar. You will be taught different techniques of scientific work and writing so that you will be prepared in the best possible way for the conception and writing of your Master's thesis. We offer five different topic areas, which hopefully raise your interest.
You may register via our online registration tool (accessible inside the university network or via VPN). During the registration period, you can select the seminar in the registration form (under ‘Application Details’ à ‘Purpose’)
Registration period: see schedule
Please select a topic and give reasons for your choice, i.e., what are you particularly interested in and what do you want to learn. Please also provide two alternative topics.
We will not consider applications via e-mail or with incomplete data in the registration tool.
Students are asked to write a short letter of motivation (maximum 1 page) to choose a topic and briefly justify your choice. This letter of motivation will be considered as the key reference for seminar entry, in addition to the CV and the transcript of records.
|Data-driven behavior change, Objective measurement, mHealth, Resilience & Wellbeing||In recent years the potential of mHealth apps for preventing mental health problems and supporting individual wellbeing (as a sign of increased resilience) has been shown in various studies. These studies typically rely on self-reported measures of resilience and wellbeing before and after pre-specified times of mHealth use (often controlled randomized trials or longitudinal pre-post measurement studies). In these setups the dynamics of mHealth feature use and their effects onto wellbeing and resilience remain largely unknown because they cannot be objectively and consistently measured. This seminar thesis is concerned with identifying means to objectively measure stress, wellbeing, and resilience for example via contextual or sensor data, instead of relying on self-reported questionnaire and survey data. The student is expected to find and consolidate literature from informatics and related disciplines to identify which objective data collection methods best respond to existing measurement scales of resilience and wellbeing. Further the implementation feasibility of such measurement in common mHealth technologies is to be assessed and evaluated.||Mechthild Pieper|
|mHealth, Just-In-Time-Adaptive Interventions, Resilience & Wellbeing||Many individuals desire to change their behavior to lead a healthier and happier life. To achieve this goal, some make use of health applications on their mobile devices (mHealth). However, ongoing user engagement to ensure the impact and effectiveness of mHealth use remains a challenging task for research and practice. To overcome this challenge, providers often send push notifications (PNs) to users with the intent to keep them engaged with specifically developed, theory-driven app features. Optimally, these PNs would deliver the right type and amount of support at the right time, i.e., just-in-time-adaptive interventions (JITAI). To achieve this goal researchers and providers need to determine the content, timing, frequency, and communication channel of respective JITAIs. The goal of this seminar thesis is to identify literature that addresses the effectiveness of different types of JITAIs and provides details about their content, timing, and efficacy. Ideally the thesis focuses on studies and applications that address wellbeing and resilience as well as the prevention of common mental health issues such as anxiety, grief, and depression.||Mechthild Pieper|
|Algorithm Aversion||This seminar paper aims to investigate algorithm aversion in the context of AI-generated images, examining the existing literature to explore how algorithm aversion manifests in the evaluation and opinions of AI-generated images compared to human-generated images. The analysis will provide an overview of current research, considering various use contexts and user perspectives, with the goal of identifying potential research questions and uncovering additional factors that may contribute to algorithm aversion in the context of AI-generated images.||Dr. Anna-Maria Seeger|
|Avatars||Self-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 self-avatars, with an enhanced appearance can provide performance increases, as well as motivation and inspiration for users themselves. Furthermore, research on the perception of human beauty indicates that more attractive humans are perceived as more successful, trustworthy or competent. However, research in adjacent fields, such as social media and plastic surgery, has also shown that when artificially beautified humans are perceived by others, they can view them as artificial and inauthentic. In order to understand how beautified humans are perceived by others, it is important to understand whether there are positive or negative feelings, and to understand which mechanisms might be causing the differences in perception. For this seminar paper the student is expected to conduct a structured literature review, to identify the theories that have been used in previous studies to explain the perception of artificial beauty, and to classify the contexts in which artificial beautification has been studied.||Rosa Holtzwart|
|Algorithmic Beautification||Human beauty or attractiveness is a dominant characteristic of physical appearance and humans have a drive for beauty. Humans also have a tendency to enhance their self-presentation, thus they engage in beahviours to optimise their apperance. This also includes enhancements of their appearance in virtual realms, such as improving their beauty with filters or photoshop on social media. With the surge of computational power and generative AI, the methods to enhance the virtual images of humans have been rapidly evolving. For instance, some social media applications now provider pre-configured facial filters that can be applied to the front view of the face in real time. To understand the possibilities and potential developments of virtual beautification in the future, it is necessary to understand past efforts of automation of virtual beauty enhancements. In this seminar paper the student is expected to conduct a structured literature review, to identify the algorithms and methods that have been developed in in previous studies to enhance human beauty. Moreover, the aim of this seminar thesis is to provide an overview of the ways the developed algorithms have been evaluated, where and how they can be applied (e.g. only front face, including profile face view; full-body; static images, live video, 3D etc.).||Rosa Holtzwart|
|Group decision making, AI, clinical decision support, group decision support||Intelligent clinical group decision support systems for improving group decision-making in healthcare. Artificial Intelligence (AI) based systems are increasingly used to support decision-making in various contexts. In medicine, for example, intelligent clinical decision support systems can assist physicians and psychotherapists in making diagnostic and therapeutic decisions. A growing body of literature focuses on how individuals perceive and evaluate AI advice. However, some clinical decisions in practice are team-based. Yet, little is known about how intelligent clinical decision support systems can support group decision making. This seminar paper explores the integration of intelligent clinical decision support systems with group decision making in healthcare. For this seminar paper, the student should conduct a structured literature review to provide an overview of the existing literature on intelligent clinical group decision support systems. Specifically, the student should focus on the following research questions: 1) What are clinical group decisions and in what contexts are they relevant, 2) What intelligent clinical group decision support systems are on the market, and 3) How do they affect group decision processes and outcomes?||Désirée Zercher|
|Generative AI, fashion design, computational creativity||Recently, generative AI technologies have captured significant attention. Innovations such as Dalle, Imagen, and GPT-3, among others, have demonstrated their remarkable creative capabilities. The seminar thesis explores the intersection of fashion design and generative AI. Through this seminar thesis, students will delve into the theoretical foundations, practical applications, and ethical implications of using generative AI in fashion design. They will critically analyze existing research, industry case studies, and emerging trends to develop a well-rounded perspective on the subject. The goal is to capture the current state of research on generative AI and fashion design as well as prospective frontiers.||Deborah Mateja|
|Explainable AI, Radiology, Design Knowledge, User-Centricity||Healthcare professionals 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, allowing clinicians to comprehend the underlying factors influencing AI-driven outcomes and help in validating the results. However, despite well-known theoretical goals and needs for explainability in AI applications, there is a lack of comprehensive understanding of how and in which situations XAI precisely unfolds its potential in clinical practice. Based on a structured review of information systems, computer science, and medical informatics literature, this seminar thesis is targeted at uncovering the situations, conditions, and reasons for clinicians to use explanations in the time-constrained daily routine, which could differ in simple vs. difficult patient conditions, workflow characteristics, as well as personal characteristics. Additionally, a seminar paper in this context could carve out the wide range of latent effects (benefits and risks) that were observed in XAI, including overreliance, diagnostic efficiency, and the ability to detect errors, to name just a few.||Luis Oberste|
|Artificial Intelligence (AI) pair programming||Over the last decade, pair programming has become an important technique for developing better code and facilitating a robust development flow. In 2021, GITHub has introduced a new tool to assist developers by transforming the traditional approach of pair programming into an AI-based solution. In this seminar paper, you will analyse different streams of academic literature to categorise and summarise current research on AI assisted pair programming. Furthermore, the aim of the thesis should be to identify the differences between traditional and AI-based pair programming and how this transition affects existing research. A good starting point may be the following paper on traditional pair programming: Kude, T., Mithas, S., Schmidt, C. T., & Heinzl, A. (2019). How pair programming influences team performance: The role of backup behavior, shared mental models, and task novelty. Information Systems Research, 30(4), 1145-1163.||Philipp Hoffmann|
|Data sharing, data monetization, data ecosystems, healthcare||Data sharing and monetization provides organizations with new sources of revenue and value creation. Especially, in one of the world’s largest economies, the healthcare industry, data has the potential to unlock new revenue streams while making businesses such as healthcare providers profitable and more competitive. However, an accepted and scalable approach to data sharing and monetization is still lacking in practice. Research on data sharing is still in its infancy and lacks of insightful studies that go beyond identified challenges such as data management, privacy, and security hindering the sharing of data. In this seminar thesis, the student is expected to conduct a structured literature review in order to elaborate and summarize different data sharing solution approaches in the healthcare industry. Hereby the student should focus on relevant literature from the Computer Science, Data Science, and Information Systems fields.||Timo Himmelsbach|
|Decoding Artificial Intelligence Ecosystems: Towards a Holistic Macro perspective for Democratizing AI in Industry||Artificial intelligence (AI) has emerged as a pivotal force reshaping industries and economies. While existing research has shed light on data ecosystems, innovation ecosystems, and big data ecosystems, a comprehensive, holistic overview of AI ecosystems remains elusive. Moreover, a universally accepted definition for AI ecosystems has yet to be established, creating a critical knowledge gap in understanding the intricate interplay of actors, roles, and interactions within this dynamic landscape. This seminar thesis aims to bridge this gap by investigating AI ecosystems from a macro perspective, unraveling the complexity of their structures and functions. By analyzing the collective endeavors of various stakeholders, including startups, established industry players, research institutions, and governmental bodies, the research will provide valuable insights into the collaborative nature of these ecosystems. It will contribute to the AI democratization in industry, offering firms a path to access AI resources and capabilities that are otherwise not easy to obtain. Good starting points are: – https://eit.europa.eu/sites/default/files/creation_of_a_taxonomy_for_the_european_ai_ecosystem_final.pdf – https://www.tandfonline.com/doi/epdf/10.1080/02650487.2022.2122249?needAccess=true&role=button – https://dl.acm.org/doi/pdf/10.1145/3467022||Tobias Maier|
|Platform ecosystems, governance, power asymmetries, (de-) centralization||Firms following platform strategies open a core technology and involve external parties in developing and commercializing products. To date, the majority of the world’s most valuable firms, including Apple, Google and Microsoft, are platform companies. This development gives rise to what has been called the “platform economy” and incorporates significant changes regarding the way firms cooperate and compete with each other. Unlike traditional seller-buyer relationships, value creation on platforms is organized in an ecosystem logic. Participating actors on digital platforms, such as Google Android or Apple iOS, highly depend on each other. On the one hand, developers of complementary products (such as mobile apps) rely on development and distribution capabilities provided by the platform (such as software development kits or module deployment). On the other hand, the platform owner relies on external innovation that complements the platform. Despite these organizational interdependencies, platform ecosystems may exhibit inherent power asymmetries between platform owner and complementors. While these asymmetries may be conducive in ensuring a joint creation of value, they may also turn into a significant threat for complementors, who base their business model on a particular platform technology. In this seminar project, students are expected to review extant literature on platform ecosystems and elaborate on power distribution, the nature of power asymmetries as well as on corresponding challenges for complementors in centralized and decentralized platform contexts.||André Halckenhäußer|
|Registration period||01.08. – 04.09.2023 (23:59)||– Register via online registration tool – Include your CV, transcript of records, and your letter of motivation|
Notification of acceptance/||06.09.2023 (noon)|
|Deadline for drop out||07.09.2023 (noon)|
|Kick-off meeting||13.09.2023, 12:45pm – 1:30pm, EO 256||– Participate in the introductory kick-off session – Contact and meet your advisor|
|Milestone 1||27.09.2023||Submit first draft to your advisor – Detailed outline – List of literature|
|Milestone 2||25.10.2023||Submit second draft to your advisor – Table of contents – Introduction: fully formulated – Methodology: fully formulated – Results: structured draft – Discussion: structured draft|
|Seminar paper submission||08.11.2023 (noon)||– Send your seminar work until 12:00 (noon) in PDF-format to Timo Himmelsbach (firstname.lastname@example.org) and add the chair’s secretary mail address (email@example.com) on CC. – Submit two printouts to the secretary as soon as possible after the submission deadline. – Send a digital version of the seminar paper (in PDF format) via e-mail to your advisor|
|Slide deck submission||27.11.2023 (noon)||– Optional: Request feedback on presentation in advance from your supervisor – Send your presentation in PDF format via e-mail to Timo Himmelsbach|
|Final presentation||28.11. from 8:00 – 13:00h and on 01.12. from 14:00h – 18:00h, Room: ExpLAB Schloss Schneckenhof||– Attend and actively participate in the discussion on the seminar day – Present and discuss your seminar paper in the joint workshop – Discuss and provide feedback for at least one of the other students’ seminar papers|
To access the literature you have to be in the VPN of the University of Mannheim.