|Lecturer||Prof. Dr. Armin Heinzl|
|Credit Points||5 ECTS (WI after Fall 2013), 4 ECTS (WI before Fall 2013)|
|Grading||Seminar paper (70%), presentation (20%), discussion (10%)|
|Exam Date||See course information below|
|Information for Students||Registration: Please see information below!|
Contact person for Bachelor's Seminar
For further information please contact Timo Himmelsbach.
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’ ‘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, Experimental Designs, IS methods||Many individuals desire to change their behavior motivated by health-related or other causes. To do so, they increasingly make use of mobile applications. To improve the efficacy of behavior change apps, researchers are interested in investigating how push-notifications as well as specific message components and app features influence the behavior of individuals (with regard to app use and real-world behavior). A Micro-Randomized Trial (MRT) is an experimental design that stems from behavioral science and psychology which was developed specifically to test Just-In-Time-Adaptive Interventions (JITAIs) – an intervention that aims to provide the right type and amount of support at the right time. MRTs are thus optimally tailored to support Information System’s researchers in investigating and theorizing about JITAIs and could also be used in other IS research fields (e.g., digital nudging, Information Communication Technologies, etc.). The benefits that MRTs hold over other commonly applied experimental designs (such as Randomized Controlled Trials (RCT)), are that they allow for analyzing the immediate impact of intervention components on a specified proximal outcome, how this impact changes over the course of treatment and how it influences a predefined distal outcome. In turn, insights of RCTs can only evaluate the overall treatment effects at a distal point in time. In this seminar students are expected to conduct a structured literature review with the goal to (1) identify quantitative research studies in the IS basket of eight journals, that look at IS use and user behavior as an outcome, (2) determine which IS methods or experimental designs were employed for these studies, and (3) summarize the characteristics of these designs (incl. corresponding applicable methods for data analysis) as well as compare them to the characteristics of an MRT experimental design. More information about MRTs can be found in: • Klasnja, P. et al. (2015) ‘Microrandomized trials: An experimental design for developing just-in-time adaptive interventions’, Health Psychology, 34, pp. 1220–1228. Available at: https://doi.org/10.1037/HEA0000305. • Klasnja, P. et al. (2018) ‘Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps’, Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine, 53(6), pp. 573–582. Available at: https://doi.org/10.1093/abm/kay067. • Qian, T. et al. (2022) ‘The microrandomized trial for developing digital interventions: Experimental design and data analysis considerations.’, Psychological Methods [Preprint]. Available at: https://doi.org/10.1037/met0000283||Mechthild Pieper|
|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 abusive technology use. In order to understand how avatars can be designed effectively and responsibly, it is important to understand how digital responsibility for the creation of avatars could be conceptualised. For this seminar paper 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|
|Artificial Intelligence, Group Decision-Making, Team-AI Collaboration, Teams||Systems based on artificial intelligence (AI) increasingly support decision making in different contexts such as medicine and management. A growing body of literature focuses on how individuals perceive and evaluate AI advice. Nevertheless, many decisions in practice are team-based. Research is only starting to acknowledge team-AI collaboration. Research, especially in social science has studied team decision making decades ago. One prominent theory is the multi-level theory of team decision-making. This theory accounts for status differences and distributed expertise, resulting in an unequal knowledge distribution across team members. To make a group decision, teams have to aggregate their knowledge though different levels (from individual to group). However, the theory has never been applied to the human-AI interface. For this seminar paper, the student should conduct a structured literature review to provide an overview of existing literature on group decision support systems using the multi-level theory of team decision making as a theoretical framework for their analysis. Moreover, the student is expected to discuss how these findings apply to AI based group decision support and how AI might affect group decision making based on the multi-level theory of group decision making. Basic literature: Hollenbeck, J. R., Ilgen, D. R., Sego, D. J., Hedlund, J., Major, D. A., & Phillips, J. (1995). Multilevel theory of team decision making: Decision performance in teams incorporating distributed expertise. Journal of applied psychology, 80(2), 292.||Désirée Zercher|
|Technology Diffusion, Computational Creativity||Recently, creative technologies, such as Dalle, Imagen, GPT3, and more, raise our attention. Such Artificial Intelligence systems exhibit own creative capabilities. In this seminar, you will explore the diffusion of such creative technologies. Research on technology diffusion aims to explain how, why, and at what rate new ideas spread. You will investigate existing information systems research on technology diffusion to derive meaningful theories and models that can help us investigate the diffusion of creative technologies.||Deborah Mateja|
|Explainable AI, Radiology, Design Knowledge, User-Centricity||Research on eXplainable AI (XAI) aims to develop explanations for the cause of AI-based decisions, which are critical in high-stakes domains such as healthcare. Meanwhile, extant XAI tools are developer-centric and data-driven. I.e., approaches purely rely their explanations on patterns found in the input data (e.g., through highlighting relevant parts of a radiological image by heatmaps). While these mainly meet developers’ demands (e.g., to debug and improve AI models), they mismatch clinical users’ demands (such as doctors) in practice. Therefore, we argue that designing explanations that carry medical knowledge and concepts might rather be useful to reflect medical decision-relevant information and thus, be more effective. Based on a structured review of information systems literature, this seminar thesis is targeted at uncovering (a) main theoretical discourses about explainable information systems for healthcare and (b) relevant design knowledge for the development of explainable systems for clinical users based on the identified theoretical properties.||Luis Oberste|
|Release notes – A communication channel between developers and users||The release management covers all tasks and activities to deliver source code into finished software products. This does not only include the initial deployment of software but also possible ongoing updates for the already shipped software. Thereby, release notes play an important part to describe the content of a software update as well as they act as the most important communication bridge between development teams and software users. In this seminar thesis, you will analyze different academic literature streams to categorize and summarize current research on release notes. This includes existing research on the content and influence of release notes as well as on technical approaches to automatically create them. A good starting point for the topic you can find in the following paper: Bi, T., Xia, X., Lo, D., Grundy, J., & Zimmermann, T. (2022). An empirical study of release note production and usage in practice. IEEE Transactions on Software Engineering.||Philipp Hoffmann|
|Ethical Challenges of Generative AI||Generative artificial intelligence (AI) refers to AI systems that can create new content, such as text, images, or music. Such new content can create ethical challenges including biases, misleading content, as well as problems concerning intellectual property or accountability. The objective of this seminar paper is to conduct a systematic literature review of scholarly work that provides knowledge on ethical challenges of AI and related these findings to the specificities of text generative AI (such as chatGPT or Google Bard).||Dr. Anna-Maria Seeger|
|Business Analytics, Machine Learning, Value Creation||For more than a decade, Business Analytics (BA) has been the largest annual IT investment in organizations. Emerging empirical findings suggest that BA can have a significant impact on firm performance, but organizations often fail to realize value and only 20% of analytics projects are expected to deliver business benefits. One of the reasons for this failure is that BA profoundly affects business processes, but the rationale behind process-level value creation from BA remains underexplored. In addition, the availability of increasingly powerful Machine Learning (ML) algorithms enables the application of BA systems with more reliable predictions and recommendations in even more diverse business processes. Therefore, ML-based BA systems further increase the relevance and timeliness of explanations on process-level value creation from BA. In this seminar thesis, you will conduct a structured review of recent literature to address the following research questions: (1) How does Business Analytics create value for organizations at the process level? (2) How does Machine Learning change process-level value creation from Business Analytics?||Pascal Kunz|
|Platform ecosystems, governance, competition, power asymmetries, complementors||Platform strategies are increasingly omnipresent: Firms decide to open a core technology and involve external parties in developing and commercializing products. To date, the majority of the world’s most valuable firms, such as Apple, Google, Microsoft, or Meta, 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 (e.g., mobile apps) rely on development and distribution capabilities provided by the platform (e.g., 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 value co-creation, 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, elaborate on power distribution and the nature of power asymmetries on digital platforms and provide insights on main challenges complementors are confronted with in light of power asymmetry||André Halckenhäußer|
|Data Sharing, Distributed-Ledger-Technology, 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 monetization of data. Distributed-Ledger-Technology (DLT) has the potential to transform healthcare delivery, by overcoming these challenges and facilitating a secure and scalable approach to data sharing and monetization. In this seminar thesis, the student is expected to conduct a structured literature review in order to elaborate and summarize different DLT-based 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|
|Data Monetization||Digital transformation challenges how organizations across all industries create and capture value. Most companies have understood data as a key resource for their digital business endeavors. However, the majority of organizations – particularly small and mid-sized enterprises (SMEs) – lack the digital capabilities and structured approaches to collect, process, and create business value with the data they generate along their value chain. Simultaneously, organizations increasingly leverage and experiment with emerging technologies, such as AI, IoT, RPA, etc. as well as methods for business analytics to inform decision-making and enhance their operations, offerings, strategies, and business models. Improved decision-making and operational efficiency are widely understood application fields for data-based value creation. Digital innovations and new legislations, such as the recently proposed Data Governance Act of the European Commission, open new approaches for organizations to monetize on their own data as well as creating data ecosystems as novel source of competitive advantage for firms and economies. This seminar paper aims to explore data-based value creation strategies by conducting a structured literature review. Good starting points: Data and Value (Alaimo, Kallinikos, and Aaltonen, 2020) – accessible in Uni network: https://www.elgaronline.com/view/edcoll/9781788119979/9781788119979.00022.xml Data Governance Act: https://ec.europa.eu/commission/presscorner/detail/en/ip_22_1113||Tobias Maier|
|Registration period||01.01. – 13.02.2023 (23:59)||– Register via online registration tool – Include your CV, transcript of records, and your letter of motivation|
Notification of acceptance/||15.02.2023 (noon)|
|Deadline for drop out||16.02.2023 (noon)|
|Kick-off meeting||20.02.2023, 10:15 am – 11:15 am Room: TBD||
-Participate in the introductory kick-off session |
– Contact and meet your advisor
Submit first draft to your advisor |
– Detailed outline
– List of literature
Submit second draft to your advisor|
– Table of contents
– Introduction: fully formulated
– Methodology: fully formulated
– Results: structured draft
– Discussion: structured draft
|Seminar paper submission||14.04.2023 (noon)||
– Submit two printouts of the seminar paper to the secretary|
– Send a digital version of the seminar paper (in PDF format) via e-mail to Timo Himmelsbach (himmelsbach) and your advisor uni-mannheim.de
Modification due to Corona: Please send your seminar work until 12 noon in PDF-Format to Timo Himmelsbach (himmelsbach) and add as well the chairs mail address ( uni-mannheim.dewifo1) on CC. Please also submit as soon as possible after the submission deadline two printouts to the secretary. uni-mannheim.de
|Slide deck submission||09.05.2023 (noon)||
– Optional: Request feedback on presentation in advance from your supervisor|
– Send your presentation in PDF format via e-mail to Timo Himmelsbach (himmelsbach) uni-mannheim.de
|Final presentation||Thursday, 11.05.2023 12.45 pm – 6.00 pm and Friday, 12.05.2023 12.45 pm – 6.00 pm Room: ExpLAB||
– 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.