DE / EN

Master's Seminar

IS 712 for Master's program (MMM and Business Informatics) / IS 918 (MMBR)

General Information

Spring 2022
Performing Lecturer Timo Himmelsbach
Examiner Prof. Dr. Armin Heinzl
Course Format Seminar
Credit Points 6 ECTS (MMM), 4 ECTS (WI)
Language English
Grading Seminar paper (70%), presentation (20%), discussion (10%)
Exam Date See course information below
Information for Students Registration: Please see information below!
Timo Himmelsbach, M.Eng.

Timo Himmelsbach, M.Eng.

Contact person for Master's Seminar

For further information please contact Timo Himmelsbach​​​​​​​.

Course Information

  • Brief Description

    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 projects, 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 and/or see Nambisan, S., Lyytinen, K. & Yoo, Y. Handbook of Digital Innovation, (2020), doi:10.4337/9781788119986. 

    Objectives of the Seminar

    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.

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Spring 2022

  • Registration

    You may register via our online registration tool only (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

    Requirements:

    • Short informal letter of motivation (maximum 1 page):
      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.
    • Provide your CV and your transcript of records.

     We will not consider registrations via e-mail or incomplete data in the registration tool.

  • Topics

    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.


    A) Human-Computer Interaction
    Human- Computer Interaction, Avatars Adaptable avatars in online games are virtual representations of players in a virtual space. Not only do they enable players to interact with the game, but they also play a central role in computer-mediated communication between players. To represent and express themselves, players often customize their avatars, such as their gender, face, hair, body, apparel accessories or game-relevant items. In their customizations, players can also optimize their avatars to represent an “ideal” version of themselves. In this, avatars can act as a creative platform for identity construction.
    While this identity construction may be beneficial by boosting self-esteem in the virtual environment, it might also cause conflicts between a player’s online and offline identity. This conflict can cause negative effects in the offline world, including gaming addiction, loss of self-esteem or loss of physical relationships, amongst others.
    For this seminar paper, the student should conduct a structured literature review to provide an overview of the unintended social and psychological consequences of mismatches between online and offline identity. In addition, the student is expected to outline which theoretical perspectives have been used by previous studies to understand the relationship between avatar use and the identified negative consequences.
    Rosa Holtzwart
    Artificial Intelligence, Fairness AI systems already support humans in various decision contexts as they analyze data with high accuracy and discover new patterns in data. With increasing learning capabilities, AI systems can be used to make independent decisions that only need to be monitored by humans in exceptional cases. However, users are often hesitant to accept AI systems if they can obtain a judgement from a human expert instead.
    In this seminar thesis, you will consider users’ fairness perceptions as one reason why human experts’ decisions are preferred over those made by AI systems. You will investigate (a) if consumers perceive AI decisions as more or less fair than decisions made by human experts and (b) if they base their judgment on the same or different fairness criteria for each agent. You will use the Organizational Justice Theory as foundation to structure your literature review and to develop concrete hypotheses on those differences. The seminar thesis will prepare you to conduct an experiment for your master thesis.

    As preparation for the seminar thesis please consider the following reviews:
    • Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2021). Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature. arXiv preprint arXiv:2103.12016.
    • Kordzadeh, N., & Ghasemaghaei, M. (2021). Algorithmic bias: review, synthesis, and future research directions. European Journal of Information Systems, 1-22.
    Dr. Ekaterina Jussupow
    B) Exploring Technological Advances of Artificial Intelligence
    Computational Creativity; Machine Learning; Generative Deep Learning; Generative Adversarial Networks; Variational Autoencoder A generative machine learning (ML) 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 and Variational Autoencoder are examples for such generative models. They can be used to generate any kind of artefact, ranging from visual arts to musical sequences. Thus, they lay ground to support or conduct creative tasks. Using ML methods to conduct creative tasks is studied in the field of computational creativity (CC). Based on a structured literature review, the goal of this seminar paper is to explore and categorize major generative ML methods applicable for CC applications with regards to their creative abilities, i.e., whether the ML method is suitable to be applied for creative tasks. Hereby, it is also essential to carve out how these ML methods can be designed to achieve better creative capabilities. 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
    Requirements Elicitation in Information Systems A target-oriented Requirements Engineering (RE), 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.
    A tremendous evolutionary step in RE is called data-driven RE. Data-driven RE enriches the methods of collecting and analyzing user input in traditional RE with the automated and continuous analysis of novel feedback sources as well as with the analysis of context-aware usage data to identify, prioritize, document, and manage requirements for a software product.
    For this seminar thesis, the student should review current IS and software engineering literature to gain an overview on the main ideas for the second part of data-driven RE, namely leveraging usage-data for RE. The discussion of the findings should compare the identified approaches from a self-derived framework or taxonomy.
    Philipp Hoffmann
    C) Management and Impact of Artificial Intelligence
    Artificial Intelligence, Machine Learning, Knowledge, Business Value of IT Artificial Intelligence (AI) has made stunning progress over the past decade based on big data, scalable and affordable computing power, and increasingly powerful algorithms. State-of-the-art AI relies on Machine Learning (ML) algorithms that can perform sensing, reasoning, and interaction activities without pre-defined solution algorithms, but by learning from patterns in data. Hence, ML systems can be applied to substitute or complement the knowledge work of human professionals. In addition to direct performance effects in organizations (such as increased process efficiency or decision quality), the application of ML systems can also affect organizational knowledge.
    Research has started to discuss how ML systems can create, extend, alter, substitute or displace knowledge at multiple levels of an organization. Recent qualitative, design-oriented, and conceptual studies provide deep insights into the effects of ML systems on different types of organizational knowledge (incl. domain expertise and data science knowledge) held by different actors and groups in different parts of the organization (e.g., system users, developers, domain experts, managers, customers). Nevertheless, a structured overview to outline the impact of ML systems on organizational knowledge is yet missing.
    In this seminar thesis, you will conduct a literature review with the following goals: (1) apply an according to an established theory or framework to summarize existing knowledge regarding the effects of ML systems on organizational knowledge; (2) identify research gaps regarding the effects of ML systems on organizational knowledge and formulate directions for future research.

    Recommended starting points:
    • Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization science, 5(1), 14-37.
    • Alavi, M., & Leidner, D. E. (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 107-136.
    • Recent special issues on “Managing AI” in MISQ (September 2021) and on “Artificial Intelligence in Organizations” in JAIS/MISQE (December 2020)
    • Further information on the scope of the literature review will be provided by the supervisor
    Pascal Kunz
    D) Value Creation in Platform Ecosystems
    Platform ecosystems, platform governance, power distribution, decentralized governance Many of today’s most valuable firms, such as Apple, Facebook, Amazon or SAP, are digital platform owners. In platform strategies, a provider of a core technology (e.g., Google Android) harness outside innovation by involving independent actors (e.g., external app developers) in the firm’s value creation. Unlike traditional relationships between seller and buyer, value creation in platforms is organized within an innovative ecosystem of loosely-coupled parties. The platform owner’s activities shift towards coordinating ecosystem participants. The high degree of centralization of existing platform systems in terms of the platform’s governance authority increasingly raises concerns about the asymmetric distribution of power between platform owner and complementors. Decentralized governance (such as in emerging blockchain platforms) promise to address existing shortcomings and concerns regarding centralized governance. In this seminar project, the student is expected to review current research on platform governance to identify benefits and shortcomings of (de-)centralized governance, to elaborate on practical examples from pre-defined contexts and to propose promising directions for future research on platform governance. André Halckenhäußer
    Decentralized 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 facilitating matchmaking and the disruptive potential that centralized platform-based businesses come along with, these business models have recently faced increasing resistance from regulators. Increasing market power, unfair competition based on existing data monopoles and a closed innovation frame built a basis for winner-takes-all dynamics and are major challenges regulators are dealing with.
    Yet, platform ecosystems based on distributed ledger technologies (DLT) are promising to have substantial impact on current centralized platform-based businesses by taking out the “middle-man” and providing an open, decentralized environment based on standards that support data interoperability, data sovereignty, as well as security and open innovation.
    Research on decentralized DLT-based platform ecosystems is still in its infancy and lacks insightful studies. In this seminar thesis, the student is expected to conduct a structured literature review in order to elaborate key characteristics of decentralized DLT-based platform ecosystems and the similarities and differences of these emerging business model. In addition, students are expected to elaborate the potential of DLT-based platform ecosystems based on an analysis of two use cases. The use case selection will be discussed with the supervisor.

    • Cusumano, M. A., Yoffie, D. B. & Gawer, A. The Future of Platforms. MIT Sloan Management Review (2020).

    Timo Himmelsbach
    E) Healthcare IT
    mHealth, behavior change, Just-in-time adaptive interventions (JITAIs) An increasing number of individuals use mobile health applications (mHealth) to adopt healthy behaviors and improve health outcomes. However, mHealth use is often limited to few initial interactions and decreases over time, which limits its ability to change individuals’ health behaviors. To overcome this, app developers often nudge mHealth use by sending push notifications. mHealth apps are constantly collecting data on users, their environment, and their behavior (e.g. location, weather, current app use, behavior, and mood), which makes it possible to deliver messages that adapt to an individual’s changing status over time. However, little is known about what content such messages should contain, under what circumstances messages should be sent, and when the best time is to nudge mHealth use and behavior. Just-in-time adaptive interventions (JITAIs) can overcome this limitation by adapting the provision of support (e.g., the type, timing, intensity) over time to an individual's changing status and contexts, with the goal to deliver support at the moment and in the context that the person needs it most and is most likely to be receptive. This thesis will review academic literature that employs JITAIs. The thesis will identify what theoretical perspectives are used, identify research gaps, and provide avenues for future research. The results of the literature review will have implications for how to better design messages to nudge mHealth use and subsequent behavior. In order to do this, this seminar paper will draw on academic literature related to information systems and behavioral science. Monica Fallon
  • Course Outline & Schedule

    Event Deadline Deliverables
    Registration period 1.1. - 14.2.2022 (11.59 pm) – Register via online registration tool
    – Include your CV, transcript of records, and your letter of motivation
    Notification of acceptance/rejection 16.2.2022 (noon)  
    Deadline for drop out 17.2.2022 (noon)  
    Kick-off meeting 24.2.2022,
    10.15 am – 11.15 am
    Room: SO 318 (ZOOM-Lehre-099)
    – Participate in the introductory kick-off session
    – Contact and meet your advisor
    Milestone 1 10.3.2022 Submit first draft to your advisor
    – Detailed outline
    – List of literature
    Milestone 2 7.4.2022 Submit second draft to your advisor
    – Table of contents
    – Introduction: fully formulated
    – Methodology: fully formulated
    – Results: structured draft
    – Discussion: structured draft
    Seminar paper submission 21.4.2022 (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 uni-mannheim.de) and your advisor

    Modification due to Corona: Please send your seminar work until 12 noon in PDF-Format to Timo Himmelsbach (himmelsbach uni-mannheim.de) and add as well the chairs mail address (wifo1 uni-mannheim.de) on CC. Please also submit as soon as possible after the submission deadline two printouts to the secretary.
    Slide deck submission 9.5.2022 (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, 12.05.2022 (noon) and Friday, 13.05.2022 (morning) – 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
  • Literature

    •  Webster, J., & Watson, R. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Q., 26.
    • Leidner, Dorothy E. (2018) “Review and Theory Symbiosis: An Introspective Retrospective,” Journal of the Association for Information Systems: Vol. 19 : Iss. 6 , Article 1.

    To access the literature you have to be in the VPN of the University of Mannheim.