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Bachelor's Thesis

BA 450 for Bachelor's program (Business Administration)

General Information

Spring 2021
Lecturer Prof. Dr. Armin Heinzl
Course Format Thesis
Credit Points 12 ECTS
Language English
Information for Students Information regarding theses of other degree courses can be found at our academic theses page.
Nele Lüker, M.Sc.

Nele Lüker, M.Sc.

Contact person for Bachelor's Thesis

For further information please contact Nele Lüker.

Course Information

  • Procedure

    Deadline Event
    6 April 2022 Kick-off meeting
    7 April 2022 Announcement of topic assignment at 12 pm
    2 June 2022 Deadline for submission of thesis until 12 pm
  • Brief Description

    Individuals and organizations operate in a world that is increasingly permeated with digital technology. Every day we interact with Information Systems (IS) that make our phones smart, our cars safe, and our lives convenient. Likewise, Information Systems are embedded in the very core of the products, operations, and strategies of many organizations. Ever more, understanding and leveraging Information Systems is key to business success, not only for large and experienced players like SAP and Google, but also for small start-ups. The pervasive nature of digital technology is radically transforming our understanding of Information Systems, encompassing their development, coordination, use, and the way we interact with them. The primary objective of this seminar is to shed light on these issues and enrich our knowledge about how information systems impact organizations and individuals.

  • Final Topics

    Topic Topic Description Supervisor
    A: User Behavior and Mobile Health Technologies An increasing number of individuals use mobile health applications (mHealth), such as Apple Health, Google Fit and other third-party applications to adopt healthy behaviors and improve health outcomes. These technologies have the potential to deliver adaptive and individualized behavior change interventions in real-time by leveraging user-generated data. However, behavior change interventions in mHealth are currently developed based on static and pre-determined decision rules. The aim of this thesis is to identify and elaborate on individualized and dynamic data-driven approaches (i.e., user behavior mining and machine learning) used in mHealth research. In this thesis, you are expected to review academic literature to identify data-driven approaches used in mHealth, elaborate on how and why data-driven approaches can further support behavior change, understand how data-driven approaches can inform behavior change theories, and identify research gaps and areas for future research. Monica Fallon
    B: Demand Spillovers in Digital Platforms The success and economic relevance of digital platform business models are unprecedented: Eight of the ten most valuable companies, including Apple, Microsoft, Alphabet and Amazon, base their businesses on platforms. Digital platforms act as intermediaries and provide a foundation to facilitate interactions among producers (e.g., app developers on Apple iOS) and consumers (e.g., iPhone users) of complementary products (e.g., mobile apps). Platforms significantly change how markets are organized: In contrast to traditional seller-buyer relationships, value creation in platforms is orchestrated within an ecosystem of loosely-coupled complementors. Due to network effects, market entry or growth of complementors does not necessarily substitute and crowd out others, but may translate into consumer demand spillovers which can benefit other market participants. Governance decisions by the platform owner play a key role in enabling and directing demand spillovers. In a bachelor thesis within this topic area, the student is expected to review extant literature related to platform governance and competition in platform markets, to analyze triggers and consequences of consumer demand spillovers and to identify promising avenues for future inquiries on platform ecosystems. André Halckenhäußer
    C: : Data Value Assessment New technologies and the increasing amount of data are transforming traditional businesses. Today, seven of the world’s largest companies base their economic success on the use of data. The global data volume is estimated to grow from 2018 – 2025 from 33 to 175 zetabytes. Data as a resource and strategic asset is becoming more and more important for value creation and value capture in new data-based business models. Besides the increasingly important role of data, determining the value of data is complex and remains under researched. Recent literature suggests a variety of factors, such as data volume, usability, accuracy, relevance or time-dependency, just to mention a few, influencing the value of data. In this bachelor’s thesis, students are expected to review extant literature related to data valuation and to develop a framework based on the key influencing factors for data valuation. Timo Himmelsbach
    D: Shared Mental Models in Requirements Engineering Requirements engineering is still one of the crucial tasks for software development. Following Pohl (2010), it encompasses the elicitation, validation, and documentation of desired properties and necessary constraints of a software, as well as the frequent validation and continuous management of the same. Especially in enterprise software development, features of a new software under development must be derived from the software vendor with the help of experts and knowledge stakeholders from industry during the elicitation phase. Therefore, establishing a shared understanding of user requirements between the client and development groups is fundamental. The aim of this Bachelor’s thesis should be to understand the research field of shared mental models and its underlying theory in IS as well as to conduct a structured literature review about the recent research in regards of establishing a shared understanding in requirements engineering in perspective of this theory. Phillip Hoffmann
    E: Human- Computer Interaction, Avatars Adaptable self-avatars in online games are virtual representations of players in a virtual space. To represent and express themselves, players in games often customize their avatars, such as their gender, face, hair, body-shape, apparel/accessories or game-relevant items, such as weapons. 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. Self-avatars are also becoming increasingly relevant in task-focused contexts such as virtual try on of apparel and accessories in e-commerce, virtual meetings or virtual exercising. Previous research has shown that for these task focused contexts the realistic representation of user’s bodies is highly relevant. In task-focused contexts avatars seem to be mainly a vehicle for virtual user embodiment, with less room for identity construction. This Bachelor thesis should focus on identifying and clustering features users tend to modify about their self-avatars in gaming, and their outcomes. In addition, this thesis evaluates the applicability of translating these modifications to avatars in e-commerce. Rosa Holzwarth
    F: Algorithmic Fairness AI systems can make critical decisions (e.g., hiring, medical diagnosis, self-driving cars) which are evaluated in terms of their fairness. However, recent empirical findings suggest that individuals perceive AI decisions to be less fair than human decisions reducing the acceptance of those systems in critical tasks. The aim of the bachelor thesis is to review existent literature on algorithmic fairness to develop a set of experimental scenarios that can be used to investigate how individuals fairness perceptions differ if decisions are performed by a human or by an AI system. Ekaterina Jussupow
    G: Organizational Change and Value Creation from AI 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 organizations. Research has started to investigate how the introduction of ML systems changes organizations and contributes to value creation. Multiple special issues and individual articles on the issue have recently been published on the topic. In this seminar thesis, you will conduct a structured literature review with the following goals: (1) select and classify relevant research from top journals across the areas of Information Systems, Management, and Organizations Research; (2) summarize key findings and themes in the relevant literature; and (3) identify persisting research gaps and directions for future research. Pascal Kunz
    H: Platform Governance and Innovation In a platform business model, platform owners (e.g., Apple or Google) 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 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. Up until now, academic literature has focused on “hard” governance mechanisms—a set of rules and regulations that aims to ensure complement quality by restricting access of low quality complements and complementors through screening processes and developer programs. In contrast, so-called “soft“ governance mechanisms aim to increase app quality and direct innovation by highlighting and rewarding desirable behavior of outstanding complementors (e.g., selective app endorsement, developer awards). Despite the importance, research in the field of “soft” governance mechanisms is scarce. In this bachelor thesis, students are expected to review academic literature on platform governance to deepen our understanding of ‘soft’ governance mechanisms that shape complementary innovation in platform ecosystems. Do you want to know more about platform governance? Start reading here: Hukal, P., Henfridsson, O., Shaikh, M., and Parker, G. 2020. “Platform Signaling For Generating Platform Content,” MIS Quarterly (44:3), MIS Quarterly, pp. 1177–1205. Nele Lüker
    I: Monetizing Data 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 thesis 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
    J: Computational Creativity Computational creative systems are a subclass of information systems with generative capacity. They compose a collection of automated processes, which are capable of achieving or simulating behavior which in humans can be deemed creative. To leverage the effective use of such systems in organizational contexts, they need to match creative processes, the involved actors, and eventually fulfill the creative goals of an organization. However, as these systems are still in their infancy, such requirements on them are yet obscure and require further investigation. Thus, the goal of this Bachelor thesis is to explicate design requirements on computational creative systems. As a basis to derive such design requirements, cause-effect relationships from extant research can be considered as well as existing commercial solutions. Deborah Mateja
    K: AI in Healthcare AI systems have achieved considerable success in accuracy for radiology tasks such as diagnosis, risk prediction, and treatment. Consequently, medical AI research has flourished in disciplines that rely on the interpretation of medical images. However, the “black box nature” of Deep Learning is critical in healthcare and has accelerated scientific interest in the development of eXplainable AI (XAI) to provide explanations for AI-based decisions. A major role in the effectiveness and helpfulness of XAI plays the way explanations are communicated to human users through, for instance, probabilities, textual explanations, or saliency maps that highlight important regions of an image. Nevertheless, further research is necessary to thoroughly develop enhanced explanations that build trust and ultimately, optimize the medical AI assistance. A bachelor thesis in this context could therefore conduct a structured literature review and provide an overview of the various presentation formats for medical image explanations and how these relate to the usefulness and trust among medical users, along with practical examples. Luis Oberste
    L: Human-AI-Teamwork Collaborating with artificial intelligence (AI) is becoming more and more important in the modern business world. For instance, systems based on AI increasingly support decision making in different contexts such as medicine and management. Previous research in the field of human-computer interaction has mainly focused on the effect of intelligent decision support systems on individual decision making. Nevertheless, in practice many decisions are team-based. To integrate AI tools successfully with human teams it is crucial to understand how AI advice influences the underlying mechanisms of group decision making and teamwork. The aim of this bachelor thesis is to review current research on human-AI-Teamwork to identify relevant theories and relevant psychosocial factors like cognitions, attitudes and behavior of teams collaborating with AI as well as their effect on team performance. The results of the literature review will have implications for how to effectively use and incorporate AI in human teams. Exemplary literature: McNeese, N. J., Demir, M., Chiou, E. K., & Cooke, N. J. (2021). Trust and team performance in human–autonomy teaming. International Journal of Electronic Commerce, 25(1), 51-72. Désirée Zercher
  • Literature

    • The literature review should be done by students independently
    • The review should include electronic literature sources offered by the University of Mannheim (Rechercheportal) as well as sources available on the internet
    • Overviews of literature sources are available at the Mannheim University Library