|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’ ‘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.
|Developing a research agenda for Nursing Technologies||After digital technologies have revolutionized hospital management and more recently diagnosis and treatment support, nursing technologies are likely to be the next frontier. The task of this seminar paper is to provide a comprehensive synopsis that identifies powerful nursing technologies that are like to transform medical care, outline their impact logic and to develop a research agenda for nursing technologies from the perspective of our university. The basis of scientific analysis will be a structured literature review based on scholarly written articles from the IS literature as well as the medical literature in combination with novel reports from medical press as well as the Internet.||Prof. Dr. Armin Heinzl|
|data-driven behavior change||Many individuals desire to change their behavior motivated by health-related or other causes. To do so, they make use of mobile applications. To improve app usage, providers send push notifications to users and develop specific app features to support individuals to sustainably change their behavior. Messages that utilize dynamic user data can adapt to users’ changing behavior and have the potential to further improve app use and influence behavior by delivering the right support at the right time. To accurately predict users’ changing behavior developers often rely on context specific cues, hints, or indicators that can be captured via mobile devices. Context specific cues can then be utilized to adapt push notifications, features, information, or interfaces of the mobile application. The goal of this seminar thesis is to identify and condense existing literature concerned with context-adaptive applications that aim to support individual behavior change. The student is expected to identify relevant literature and existing applications, discuss and evaluate their effectiveness (with regard to successful behavior change), as well as focus on how machine learning techniques can be applied to support these context-adaptive applications.||Mechthild Pieper|
|Avatars||With an increasing number of businesses entering metaverses, as well as 3-dimensional online shopping and service experiences, users need accurate self-avatars to navigate these new virtual spheres. Users can employ accurate self-avatars to fulfil tasks previously reserved for their physical bodies, such as trying on clothing and putting themselves into digitally configured cars or homes. But unlike pure leisure-related gaming avatars, the creation of accurate task-focused self-avatars is often too challenging for users. Thus, the creation of avatars for users is a focal problem in task-focused contexts, such as online-shopping. There are various approaches to generating accurate self-avatars for users, including approaches based on elaborate body scan data, approaches involving visual artists, and approaches involving the modification of a generic avatar by the user themselves. For this seminar paper, the student is expected to conduct a structured literature review, with the aim to explore and categorise the technological approaches of creating self-avatars. In addition, the student is expected to evaluate how these methods could be employed most efficiently in e-commerce contexts.||Rosa Holtzwart|
|Computational Creative Systems, Artificial Intelligence||Computational creative systems (CCS) are Artificial Intelligence (AI) systems with own creative capabilities. Most of the current AI systems focus on decision-making. However, with CCS entering the stage, we observe a shift from a problem-solving paradigm to a creative-generation paradigm. We argue that these fundamentally different objectives profoundly challenge our current theoretical understanding of AI. Based on a structured literature review, this seminar thesis is targeted at uncovering main theoretical discourses around AI in decision-making and assessing the relevance of these discourses for AI in creative generation.||Deborah Mateja|
|Explainable AI, hybrid AI, expert-driven decision aids||The “black box nature” and lack of reasoning of Machine and Deep Learning applications are critical in high-stakes domains such as healthcare. Therefore, it has accelerated scientific interest in the development of eXplainable AI (XAI) to provide explanations for the cause of its decisions. In a recent trend, research identified synergies in combining data-driven machine learning and domain knowledge-driven semantics to get the best from each. Incorporating radiologist-interpreted features into learning, for instance, brings a deeper level of understanding of CT images to intelligent systems and enhances interpretation for radiologists in turn. Therefore, this seminar paper assesses the current state of knowledge-informed AI to provide an overview of how recent works incorporate intuitive semantic features in hybrid systems and how these approaches reflect expert-driven decision aids, contributing to explainability.||Luis Oberste|
|Software feature extraction for release management||Release management covers all tasks and activities to deliver source code into finished software products. A major influence on these tasks and activities is driven by market analyses to align the software product with users’ needs. A large research stream is analyzing software-based platform markets to gain insights into this relationship due to the possibility to observe developers’ as well as users’ actions and reactions, respectively. One part of research in this domain has extracted software features from app descriptions, API usage, manifest files, decompiled source strings, categories and permissions. In this seminar thesis, the student is expected to analyze this academic literature stream on different approaches to extract these features. A good start point for the topic you can find in the following literature review: W. Martin, F. Sarro, Y. Jia, Y. Zhang, and M. Harman, “A survey of app store analysis for software engineering,” IEEE Trans. Softw. Eng., vol. 43, no. 9, pp. 817–847, 2017, doi: 10.1109/TSE.2016.2630689.||Philipp Hoffmann|
|Machine Learning, Training Data, Ground Truth||Machine Learning (ML) systems can learn to provide highly accurate predictions and recommend suitable paths of action. To effectively set up, refine, and evaluate ML systems, organizations require training data (also referred to as ground truth). In practice, the quantity and quality of the available training data are often limited. Scientific research has developed different solution approaches to deal with missing training data. In this seminar thesis, the student is expected to conduct a structured literature review with the goal to (1) select relevant research from the Computer Science, Data Science, and Information Systems fields; (2) identify issues with training data in organizations; and (3) summarize technical and organizational solution approaches to deal with the identified issues||Pascal Kunz|
|Hidden Profiles, Group Decision Making, Artificial Intelligence, Decision Support||Systems based on AI increasingly support decision making in different contexts. Previous research in the field of information systems has mainly focused on the effect of intelligent decision support systems on individual decision making. Nevertheless, groups and teams are often expected to make better decisions than individuals due to their access to a broader information base. To make an accurate decision, team members need to create a common understanding of the problem and possible solutions by pooling information effectively. However, pooling information distributed across team members often remains a challenge, leading to poor decision quality. The dominant experimental paradigm that captures the structural underpinnings of these setting is the hidden profile paradigm. Hidden profile situations are mainly studied in the group decision making literature in social science. However, since AI will become more important for supporting group decision making, it is crucial to assess the implications of hidden profiles for AI based group decision support. The aim of this seminar thesis is to review research on hidden profile situations in the field of management and information systems. Moreover, the student is expected to discuss the results and their implications for the application of intelligent decision support systems in a group decision context. Exemplary literature: Maciejovsky, B., & Budescu, D. V. (2020). Too much trust in group decisions: Uncovering hidden profiles by groups and markets. Organization Science, 31(6), 1497–1514.||Désirée Zercher|
|User reviews and complementary innovation||In recent years, platform business models have gained significant economic and societal relevance. Today, eight out of the ten most valuable organizations measured by market capitalization worldwide follow a platform business model. Prominent examples are Alphabet (Google), Amazon, Apple or Microsoft. Digital platforms open their technology to independent third parties, so called complementors, and allow them to contribute complementary innovation to the platform. Apple, for example, opened its iOS mobile operating system to independent “app” developers in 2008 which has since then grown to a platform that encompasses more than 2 million complementary applications. The success of platform ecosystems is highly dependent on innovative and high-quality complements of third-party developers. One potentially powerful source of complementary innovation are user reviews. Every day, users submit thousands of user reviews in app stores about their experience with applications, technical issues, and desirable features. Complementors are provided with a thorough evaluation of their products and can leverage the ideas and suggestions from user reviews for product development. In this seminar thesis, you will analyze academic literature on user reviews to deepen our understanding on how the ideas and suggestions in user reviews can shape complementary innovation in platform ecosystems. If you want to know more about platforms, start reading here: Van Alstyne, M. W., Parker, G. G., & Choudary, S. P. (2016). Pipelines, platforms, and the new rules of strategy. Harvard Business Review, 94(4), 54–62.||Dr. Nele Lüker|
|Platform competition, platform ecosystems, entry, envelopment||Digital platforms have become increasingly relevant and evolved into a dominant form of value creation. Many of the world’s most valuable firms to date, such as Apple, Google, Microsoft, or Meta, are platform companies. In contrast to traditional buyer-seller relationships, value creation in platforms is organized within an innovative ecosystem of loosely-coupled parties. Platform strategies shift competition towards ecosystems. On the one hand, competition emerges within platform boundaries and includes rivalries among complementors or between complementors and platform owner. On the other hand, competition emerges across platforms and concerns rivalries between platforms that aim to attract similar participants (e.g., Apple and Google competing for developers and users on mobile platforms). Besides relying on outside innovation to ensure platform differentiation, platforms may provide innovation internally (e.g., Apple iCloud or Apple Music). Such activities may have implications both regarding within-platform (e.g., acting as competitor to existing complementors) and between-platform competition (e.g., enveloping key functionality of competing platforms or standalone products). In this seminar project, students are expected to review extant literature on platform ecosystems, elaborate on particularities and differences of intra- and inter-platform perspectives and integrate insights to provide a comprehensive view on platform competition.||André Halckenhäußer|
|Open source based platform ecosystems||New technologies and the increasing amount of data are transforming traditional businesses. Today, the most valuable companies are built on digital platforms that bring together two or more market actors and grow through network effects (1). Besides some already well-known players in the B2C space (i.e. Uber, AirBnB), the rise of new B2B platforms (i.e. Siemens MindSphere) further underlines the shift from traditional pipeline business models to platform business models in various industry sectors (2). As most B2B companies try to establish closed platform ecosystems to lock-in customers, recent approaches show the potential of open source based B2B platform ecosystems for digital innovation. Research on open source based B2B platform ecosystems is still in its infancy and lacks of insightful studies. In this seminar thesis, the student is expected to conduct a structured literature review in order to elaborate on key characteristics and challenges of open source based B2B platform ecosystems and the similarities and differences of these emerging business model. In addition, students are expected to elaborate on participation and monetization strategies for open source B2B platform ecosystems based on an analysis of two use cases. The use case selection will be discussed with the supervisor. 1. Cusumano, M. A., Yoffie, D. B. & Gawer, A. The Future of Platforms. MIT Sloan Management Review (2020). 2. Alstyne, M. W. V., Parker, G. G. & Choudary, and S. P. Piplelines, Platforms and the New Rules of Strategy. Harvard Business Review (2016).||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||01.08. – 05.09.2022 (23:59)||
– Register via online registration tool |
– Include your CV, transcript of records, and your letter of motivation
Notification of acceptance/||07.09.2022 (noon)|
|Deadline for drop out||08.09.2022 (noon)|
|Kick-off meeting||12.09.2022, 10:15 am – 11:15 am Room: SO418||
– 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||07.11.2022 (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||21.11.2022 (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, 24.11.2022 (8:30am – 12:30pm) and Friday, 1.12.2022 (01:00pm – 05:05pm) Room: A001||
– 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.