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Bachelor-Seminar

SM 452 für Bachelor­studierende (Wirtschafts­informatik)

Allgemeines

FSS 2021
Verantwortlicher Dozent Prof. Dr. Armin Heinzl
Veranstaltungs­art Seminar
Leistungs­punkte 5 ECTS (WI ab HWS 2013), 4 ECTS (WI bis HWS 2013)
Sprache Englisch
Prüfungs­form und -umfang Seminarpapier (70%), Presentation (20%), Diskussionsbeitrag (10%)
Prüfungs­termin Siehe Infos zur Veranstaltung
Infos für Studierende Registrierung: Bitte beachten Sie unten stehende Informationen!
Timo Himmelsbach, M.Eng.

Timo Himmelsbach, M.Eng.

Ansprech­partner Bachelor-Seminar

Bei Fragen wenden Sie sich bitte an Timo Himmelsbach​​​​​​​.

    Infos zur Veranstaltung

  • Kurzbeschreibung

    Digitale Technologien und die ständig wachsenden Datenmengen verändern unser tägliches Leben und die Wirtschaft radikal. Eingebettet in den Kern der Produkte, Abläufe und Strategien vieler Unternehmen, verändern digitale Technologien bestehende Geschäfte in allen Branchen rasant. Neue Markt­angebote, Geschäftsprozesse und Geschäfts­modelle entstehen rund um den Einsatz dieser digitalen Technologien und führen zu digitalen Innovationen1. Die Allgegenwärtigkeit digitaler Technologien verändert unser Verständnis von Informations­systemen (IS) grundlegend, sowohl was ihre Entwicklung, Koordination und Nutzung als auch die Art und Weise, wie wir mit ihnen interagieren, betrifft. An unserem Lehr­stuhl bieten wir eine breite Palette von Forschungs­themen in diesem Bereich an, die auch neue digitale Technologien wie künstliche Intelligenz (KI) und maschinelles Lernen (ML) umfassen. Wir befassen uns mit der Interaktion zwischen Mensch und Computer sowie mit neuen digitalen Geschäfts­modellen wie Plattform-Ökosystemen. Außerdem konzentrieren wir uns bei der Forschung zu digitalen Innovationen auf die Anwendung in der Gesundheitsbranche. 

    In unserem Seminar untersuchen wir den Einfluss digitaler Technologien auf Individuen und Organisationen. Dabei verknüpfen wir die angebotenen Themen mit unseren laufenden Forschungs­arbeiten, die auf höchstem internationalem Niveau veröffentlicht wurden und werden.

    1. Nambisan, S., Lyytinen, K. & Yoo, Y. Handbook of Digital Innovation. 2–12 (2020) doi:10.4337/9781788119986.00008.

    Sind Sie daran interessiert, mehr über digitale Innovation zu erfahren? Dann werfen Sie gerne einen Blick auf unseren Master­studien­gang IS 607 und/oder siehe Nambisan, S., Lyytinen, K. & Yoo, Y. Handbook of Digital Innovation, (2020), doi:10.4337/9781788119986. 

    Ziel des Seminars

    In diesem Seminar erwerben Sie nicht nur die Fähigkeit, bestehende Forschungs­arbeiten zu identifizieren, einzuordnen und zu bewerten, sondern lernen auch, darauf aufbauend, eine eigene Forschungs­agenda zu entwickeln und diese gegenüber den anderen Teilnehmern des Seminars zu präsentieren und diskutieren. Ihnen werden unterschiedliche Techniken des wissenschaft­lichen Arbeitens und Schreibens vermittelt, damit Sie bestmöglich auf die Konzeption und Anfertigung Ihrer Master­arbeit vorbereitet werden. Zu diesem Zweck bieten wir in unserem Seminar fünf unterschiedliche Themen­bereiche an, die hoffentlich Ihr Interesse wecken.

Frühjahr/Sommer 2021

  • Registrierung

    Registrierung erfolgt ausschließlich über das Registrierungs­portal (erreichbar innerhalb des Uni-Netzwerkes oder per VPN). Nur im unten aufgeführten Zeitraum ist eine Registrierung möglich. Dazu bitte das Seminar im Formular auswählen.

    Registrierungs­zeitraum: siehe Termine

    Anforderungen:

    • Kurzes formloses Motivations­schreiben (maximal 1 Seite):
      Bitte wählen Sie ein Thema und begründen Sie Ihre Wahl (Was interessiert Sie besonders? Was möchten Sie lernen?). Bitte nennen Sie auch zwei Alternativthemen
    • Lebenslauf und Studien­ergebnisse (Notentranskript)

    Diese Seite wird aktualisiert, sobald sich Änderungen ergeben. Es werden weder Registrierungen per E-Mail noch unvollständige Formulare entgegengenommen

  • Themen

    Die Studierenden werden gebeten ein kurzes formloses Motivations­schreiben (maximal 1 Seite) zu verfassen um ein Thema zu wählen und diese Auswahl kurz zu begründen. Dieses Motivations­schreiben wird neben dem Lebenslauf und des Notentranskripts als wichtige Referenz für die Seminarzulassung angesehen.


    A) Human-Computer Interaction
    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.
    Yet, recent work suggest that anthropomorphic design may not only have positive consequences in terms of user perception and behavior. Some potential drawbacks of anthropomorphism are emotional attachment, overtrust, unrealistic expectations, and social desirable behavior. In this seminar paper, the student is expected to shed light on these drawbacks. The objective of this seminar paper is to conduct a systematic literature review of scholarly work that provides knowledge on the dark side of anthropomorphism in terms of user perception and behavior.
    Anna‑Maria Seeger
    B) Exploring Technological Advances of Artificial Intelligence
    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.
    Philipp Hoffmann
    C) Impact of Artificial Intelligence and Business Analytics
    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.

    Exemplary literature:
    – 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.
    Ekaterina Jussupow
    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.
    Pascal Kunz
    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
    D) Value creation from platform ecosystems
    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/pipelines-platforms-and-the-new-rules-of-strategy Nele Lüker
    Data Platform Ecosystems,
    Healthcare
    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).
    Timo Himmelsbach
    E) Healthcare IT
    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.

    References:
    – 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.
    Luis Oberste
    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
  • Termine

    Early movers

    Wir haben eine Reihe von Anfragen erhalten, ob es möglich sei, die Seminararbeit in der semesterfreien Zeit zu bearbeiten. Wir verstehen, dass einige Studenten nicht in der Lage sind, ihr Praktikum anzufangen, und deshalb daran interessiert sind, stattdessen während der Winterpause an ihrer Seminararbeit zu arbeiten. Wenn dies für Sie der Fall ist, senden Sie bitte bis zum 24. Januar 2021 (23:59) eine E-Mail an unser Sekretariat und laden Sie alle erforderlichen Materialien wie unter Anmeldung aufgeführt hoch. Ihre Bewerbung wird dann vom Themenbetreuer geprüft. Falls Sie nicht als Early Mover in Betracht gezogen werden können, wird Ihre Bewerbung im regulären Auswahl­verfahren für das Seminar im März berücksichtigt.


    Event Zeitraum / DeadlineArbeits­ergebnisse
    Registrierungs­zeitraum Early Mover:
    15.01. – 24.01.2021 (23:59)

    Reguläre Registrierungen:
    15.01. – 28.02.2021 (23:59)
    – Registrierung über das Online-Tool
    – Fügen Sie Ihren Lebenslauf, das Notentranskript und Ihr Motivations­schreiben an
    – Zusätzlich für Early Movers: Senden Sie eine E-Mail mit Ihren Bewerbungs­unterlagen und einer kurzen Mitteilung an unser Sekretariat (Kontakt)
    Versand der Bestätigungen Early Mover:
    03.02.2021 (mittags)

    Reguläre Registrierungen:
    03.03.2021 (mittags)
     
    Deadline zum Rücktritt 4.3.2021 (mittags)  
    Kick-Off Meeting 8.3.2021, 8:30 – 9:30 Uhr
    Raum: BWL-ZOOM-01
    – Teilnahme an der Kick-Off-Einführungs­veranstaltung
    – Kontakt und Treffen mit Ihrem Betreuer
    1. Meilenstein 22.3.2021 Ersten Entwurf bei Ihrem Betreuer einreichen:
    – Detaillierte Gliederung
    – Literatur­verzeichnis
    2. Meilenstein 19.4.2021 Zweiten Entwurf bei Ihrem Betreuer einreichen:
    – Inhaltsverzeichnis
    – Einführung: vollständig formuliert
    – Methodik: vollständig formuliert
    – Ergebnisse: strukturierter Entwurf
    – Diskussion: strukturierter Entwurf
    Abgabe der Arbeit 3.5.2021 (mittags) – Zwei Ausdrucke der Seminararbeit beim Sekretariat einreichen (bis 12 Uhr)
    – Senden Sie eine digitale Version der Seminararbeit per E-Mail an Timo Himmelsbach (himmelsbach uni-mannheim.de) und an Ihren Betreuer (bis 12 Uhr)

    Änderung aufgrund von Corona: Bitte senden Sie Ihre Arbeit bis 12 Uhr im PDF-Format per Mail an Timo Himmelsbach (himmelsbach uni-mannheim.de) und setzen das Sekretariat (wifo1 uni-mannheim.de) auf CC.
    Abgabe der Präsentation 12.5.2021 (mittags) – Optional: Bitten Sie Ihren Betreuer vorab um Feedback zur Präsentation
    – Senden Sie Ihre Präsentation im PDF-Format per E-Mail an Timo Himmelsbach (himmelsbach uni-mannheim.de)
    Präsentation 27.5.2021 – Besuchen Sie das Seminar und beteiligen Sie sich aktiv an der Diskussion am Seminartag
    – Präsentieren und diskutieren Sie Ihre Seminararbeit im gemeinsamen Workshop
    – Diskussion und Feedback für mindestens eine Seminararbeit der anderen Studierenden