|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!|
Digital technologies and the ever-growing amounts of data are radically reshaping our daily life 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. This pervasive nature of digital technology is fundamentally transforming our understanding of information systems (IS), encompassing their development, coordination, use, and the way we interact with them. At our chair, we offer a wide range of research topics in this area, encompassing new digital technologies such as artificial intelligence (AI) and machine learning (ML). We look at human-computer interaction as well as emerging digital business models such as platform ecosystems. We also direct much research on digital innovation towards the application in the healthcare industry.
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 and/
In this seminar, you will not only 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 seminar topic areas, which hopefully raise your interest.
You may register via our online registration tool only (accessible inside the university network or per VPN). During the registration period, you can select the seminar in the registration form (under 'Application Details' -> 'Purpose').
Registration period: see schedule
We will not consider registrations via e-mail or incomplete data in the registration tool.
Students are asked to write a short formless letter of motivation (maximum 1 page) to choose a topic and briefly justify their 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.
|Chatbots, Conversational Agents||
Recent research on conversational agents (CA) provides knowledge on how to design anthropomorphic CAs. Researchers and practitioners consider making technological agents more human-like an effective design strategy to provide more satisfying and positive interactional experiences to users.|
|Machine Learning, Generative Deep Learning, Generative Adversarial Networks, Computational Creativity||A generative model describes how a dataset is generated in terms of a probabilistic model. By sampling from this model, new data can be generated. Generative Adversarial Networks (GANs) are a particular generative model and constitute one of the most important deep learning advancements of recent years. A GAN consists of a generator and a discriminator network that train each other. The generator learns to best imitate a set of inspiring examples. The discriminator evaluates the artefacts by learning to distinguish true from fake examples. Fake examples are those created by the generator. The generator then uses the evaluation feedback for each artefact to learn how good its examples can trick the discriminator into considering its output a real example. GANs are applied to generate any kind of artefact, ranging from visual arts to musical sequences. Based on a structured literature review, the goal of this seminar paper is to explore and categorize major variants of GANs, such as Conditional GAN, semi-supervised GAN or InfoGAN with regards to their creative abilities, i.e., whether the proposed GAN variant is suitable to be applied for creative tasks.||Deborah Mateja|
|Requirements Elicitation in Information Systems||
A target-oriented Requirements Elicitation (the process of deriving the necessary requirements for a piece of software) is one of the key success factors of Information Systems Development (ISD). Information systems (IS) research has been addressing the topic from a behavioral and empirical as well as from a design science perspective.|
For this seminar thesis, the student should review current IS research to gain an overview on the main research topics in this field of the last years. Furthermore, the seminar paper should dig deeper in one self-selected, more specific area as well as understand and derive the key elements current research addresses there.
|Artificial Intelligence, Work Practices||
Algorithms based on artificial intelligence are implemented into various domains in organizations. They decide who will be hired or will get performance compensation, change the type of information used for decision making, but also compete with human experts if they start to perform the same task. As sociotechnical systems, algorithms and human users have to be considered in their dynamic interaction as humans shape algorithms but also adjust towards them. Those processes change current work practices, performance measures and what it means to make good decisions. However, currently, very little is known about the mechanisms how AI-based algorithms change existent work practices and which challenges result from introducing algorithms with reinforcement learning.|
If you choose this seminar topic, the goals of this seminar thesis will be the following: (1) conduct a literature review on qualitative research on the impact of algorithms on work practices (see exemplary literature below); (2) make an informed selection of one theoretical framework from the literature review (3) use the selected theory to discuss the implications of changing a supervised algorithm into an unsupervised algorithm.
Students who apply for this topic should have a strong interest in conducting qualitative research in organizational contexts. After a successful seminar thesis, students have the opportunity to conduct a qualitative case study on the impact of AI-based algorithms in a major German organization or at a major German hospital for their master thesis.
– Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62-70.
– Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410.
– Pachidi, S., Berends, H., Faraj, S., & Huysman, M. (2020). Make Way for the Algorithms: Symbolic Actions and Change in a Regime of Knowing. Organization Science.
– Lindebaum, D., Vesa, M., & den Hond, F. (2020). Insights from “the machine stops” to better understand rational assumptions in algorithmic decision making and its implications for organizations. Academy of Management Review, 45(1), 247-263.
|Machine Learning, Theory of Decision Performance||
Organizations apply machine learning techniques across various domains with the goal to create value. Both research and practice assume that the primary effects of machine learning are on decision making, but it is not clear how exactly machine learning affects decision performance. Scholars have developed and applied multiple theories to explain the causes of decision performance. This research is distributed across multiple disciplines, including Information Systems, Organizational Science, and Management Research. In order to apply the existing knowledge and better understand the effects of machine learning, extant research on decision performance needs to be structured and synthesized.|
In this seminar thesis, you will conduct a literature review with the following goals: (1) identify and contrast conceptualizations of decision performance; (2) identify, contrast and structure theories that explain decision performance; (3) make an informed selection of one theory from the literature review and use it to discuss effects of machine learning on decision performance in a specific application scenario (details will be provided by supervisor).
Students who apply to this topic should have a strong interest in conducting qualitative research in organizational contexts. After a successful seminar thesis, students have the opportunity to conduct a qualitative case study on the impact of machine learning in a major German organization for their master thesis.
|Augmenting and Automating Software Development||
Increasingly powerful technologies of artificial intelligence (AI) are changing how software is being developed today. Technologies based on AI are increasingly capable of completing development tasks that have formerly been reserved for developers as intelligent human beings.|
Managers envision a future where application software will refine and extend itself while developers primarily curate the inputs for this self-development (please see https://www.cio.com/article/3437436/rethinking-software-development-in-the-ai-era.html for further details). This seminar paper assesses the current state of technology to elaborate how the different tasks of software development are changing with the arrival of increasingly powerful AI technologies from a practical and from an academic angle. It outlines the status quo regarding the target vision of autonomously developing application software and identifies the relevant academic research streams.
|Dr. Kai Spohrer|
|Platform Ecosystems, Platform Governance, Platform-based Competition||Various timely examples prove the unparalleled success of platform business models: Many of the most valuable companies like Apple, SAP, and Facebook follow platform strategies. In such strategies, a provider of a core technology harnesses outside innovation by involving independent actors in the firm’s value creation. Unlike traditional relationships between seller and buyer, value creation in platforms is organized within an ecosystem of loosely-coupled parties. The focal firm's activities therefore increasingly shift towards coordinating heterogeneous actors which are commonly called complementors and with whom it carefully needs to navigate competitive and cooperative relationships. We refer to the sum of these coordinating activities as platform governance. In this seminar project, students are expected to review extant literature on platform governance, apply key findings to predefined contexts, such as currently emerging industrial Internet of Things platforms, to elaborate on the current state of research and to identify promising avenues for future inquiries.||André Halckenhäußer|
|Platform Ecosystems, Innovation, Governance||
In a platform business model, firms allow independent third-parties, so-called complementors, to participate in the development and commercialization of their technology through the contribution of complementary innovation. 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 apps. The success of platform ecosystems is highly dependent on innovative and high-quality complements of third-party developers. Yet, incentivizing complementary innovation is a complex endeavor and platform governance remains one of the major challenges for platform owners. In this seminar, students will review academic literature on platform innovation to deepen our understanding of the mechanisms that shape complementary innovation in platform ecosystems. If you want to know more about platforms, start reading here: hbr.org/2016/04/||Nele Lüker|
Data Platform Ecosystems,|
New technologies and the increasing amount of data are transforming traditional businesses. Accelerated by the COVID-19 pandemic, one of the largest sectors of the world’s economy, the healthcare industry, needs to adapt their business models in order to keep up with the speed of digitalization. The rise of new data-based platforms in the healthcare industry underlines the undergoing shift from traditional pipeline business models to platform business models (1). Although data as a resource is becoming more and more important for value creation and value capture in such new business models, existing literature remains under-researched. The aim of this seminar thesis will be to conduct a comprehensive literature review in the domain of data platform ecosystems. Students are expected to derive the key characteristics of data platform ecosystems and to compare theoretical findings with practical cases. (To be discussed with the supervisor)|
1. Alstyne, M. W. V., Parker, G. G. & Choudary, and S. P. Piplelines, Platforms and the New Rules of Strategy. Harvard Business Review (2016).
|Healthcare analytics, explainable AI, cognitive bias, AI developers||
Already in 1968, ‘Conway’s law’ was formed stating that “organizations which design systems . . . are constrained to produce designs which are copies of the communication structures of these organizations”. Ever since, we know that social and technical issues intertwine on an organizational level, e.g., software components cannot interface correctly unless their designers communicate with each other. While the influence of social structures on development processes is well-known in software development and cognitive biases have been extensively explored in psychology, developers’ personal cognitive biases affecting the developed software systems gained recent interest in software engineering research (Mohanani et al., 2020). Since humans typically make biased decisions, artificial intelligence (AI) systems may also reflect the biases of the programmers who create them. In the context of Machine Learning, such research is scarce but started investigating cognitive biases when interpreting classic (intrinsically explainable) AI-based predictions (Kliegr et al., 2018). Since modern AI models are ‘black boxes’ and research yielded techniques to make them interpretable, this attempt needs reconsideration. A seminar thesis under this topic shall therefore examine literature to assess the potential of explainable AI (XAI) to reduce cognitive biases when interpreting machine learning predictions.|
– Kliegr, T., Bahník, Š., & Fürnkranz, J. (2018, 9 April). A review of possible effects of cognitive biases on interpretation of rule-based machine learning models.
– Mohanani, R., Salman, I., Turhan, B., Rodriguez, P., & Ralph, P. (2020). Cognitive Biases in Software Engineering: A Systematic Mapping Study. IEEE Transactions on Software Engineering, 1.
|mHealth, use, and behavior change||An increasing number of individuals use mobile health (mHealth), such as Apple Health, Google Fit and other mHealth applications to adopt healthy behaviors and improve health outcomes. However, there are inconclusive results on the extent to which mHealth use facilitates behavior change. Social Cognitive Theory is one of the most widely used theories of behavior change and can be used to better understand how and why mHealth use can facilitate behavior change. In this seminar paper, you are expected to review academic literature on mHealth research with a focus on Social Cognitive Theory perspectives. The goal is to deepen our understanding of how and why mHealth use can facilitate behavior change from the perspective of Social Cognitive Theory. The results will identify research gaps and areas for future research and will be relevant for developing more effective mHealth apps.||Monica Fallon|
15.01. – 24.01.2021 (23:59)
15.01. – 28.02.2021 (23:59)
– Register via online registration tool|
– Include your CV, transcript of records, and your letter of motivation
– Additional for Early Movers: Send an email with your application documents and a short notification to our secretary (contact)
Notification of acceptance/||
|Deadline for drop out||4.3.2021 (noon)|
8.3.2021, 8.30 am – 9.30 am|
– 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||3.5.2021 (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. uni-mannheim.de
|Slide deck submission||12.5.2021 (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
– 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