v11i1 – Junior Management Science

Cite Article

The Role of Corporate Digital Responsibility in Digital Controlling

David Schopp, Heinrich Heine University Düsseldorf (Bachelor’s thesis)
Junior Management Science 11(1), 2026, 27-42

Read Abstract

Digitalization is fundamentally changing controlling and, at the same time, increasing the demands for responsible handling of data, technologies, and automated decision-making systems. Against this backdrop, this thesis examines the extent to which CDR influences the design and implementation of digital controlling processes. The aim of the thesis is to systematically present the influence of ethical, data protection and technological aspects on digital controlling and to identify practical starting points for the integration of CDR. Methodologically, the thesis is based on a literature analysis of relevant scientific articles as well as regulatory and practical sources. Building on the domains of digital controlling according to Keimer and Egle, the effects of CDR on the areas of data, technologies, processes, methods and competencies are analyzed. The focus is particularly on data governance, data protection, artificial intelligence and big data, as well as the changing role of the controller. The results show that CDR as a framework concept permeates all domains of digital controlling and significantly expands the requirements for controlling systems and controller competencies. The responsible use of digital technologies requires transparent data structures, ethically reflective analysis methods and the strategic anchoring of CDR in corporate management. This work thus contributes to closing an existing research gap and illustrates that CDR is not only a normative obligation but can also create sustainable added value for companies and stakeholders.

Keywords: corporate digital responsibility; digital controlling; artificial intelligence; data protection.

DOI: https://doi.org/10.5282/jums/v11i1pp27-42

Text Analysis in Financial Analysis: An Event Study on Sentiment in Earnings Calls

Leonie Terhardt, Heinrich Heine University Düsseldorf (Master’s thesis)
Junior Management Science 11(1), 2026, 43-73

Read Abstract

This thesis examines the influence of sentiment in earnings calls on financial market reactions. The objective is to analyse the extent to which the tone used in presentations and Q&A segments affect abnormal returns, and how differences in investors’ information environments shape these effects. The study is based on transcripts from companies in the S&P Composite 1500, whose tone is recorded using finance-specific lexicons and evaluated within an event-study framework. The results suggest that sentiment in the Q&A section in particular triggers significant immediate market reactions, with analysts’ questions having the greatest influence. Conversely, the sentiment of the presentation assumes significance during the drift period and frequently results in subsequent corrections. In addition, investors show increased sensitivity to verbal signals when it comes to companies with a higher degree of uncertainty. The thesis contributes to the literature by demonstrating that context-specific sentiment measurement provides valuable insights for the evaluation of qualitative company information. Furthermore, it highlights the limitations of generic text lexicons.

Keywords: sentiment; earnings calls; text analysis; event study; financial market reactions.

DOI: https://doi.org/10.5282/jums/v11i1pp43-73

Read issue

Read Appendix

Cite article

The Impact of Biodiversity Risk on Banks‘ Credit Default Swap Spread Changes

Moritz Praetz, Technical University of Munich (Master’s thesis)
Junior Management Science 11(1), 2026, 74-106

Read Abstract

This paper explores the impact of biodiversity risk on banks’ credit risk using a news-based biodiversity index and CDS of 39 global banks between 2015 and 2023. Using a linear OLS regression, this paper finds evidence for a significant positive relationship between biodiversity news and CDS spread changes, where negative news leads to increasing CDS prices. Furthermore, cross-sectional analyses are conducted to test for heterogeneity. Using the Kunming Declaration in 2021 as an external shock, this paper finds evidence that the relationship persists for the period after the Kunming Declaration, suggesting no significant effect of biodiversity risk before. Further tests reveal no significant impact of a country’s state of biodiversity. In contrast, since the Kunming Declaration, the relationship is stronger for banks which openly disclose biodiversity risks. Banks located in the USA, the only UN nation which is not a member of the CBD, experience a weaker effect of biodiversity news on CDS spread changes. These results show that banks are subject to biodiversity-related credit risks, where expectations of new policies and regulation following the Kunming Declaration significantly affect banks’ CDS spreads.

Keywords: biodiversity; banking; credit default swaps; credit risk.

DOI: https://doi.org/10.5282/jums/v11i1pp74-106

Causal Inference Using Double Machine Learning: Oregon Health Insurance Experiment

Sebastian Schmidt, University of Hamburg (Master’s thesis)
Junior Management Science 11(1), 2026, 107-138

Read Abstract

This study applies advanced methods of causal inference, specifically the Double Machine Learning (DML) framework, to estimate the causal effects of public health insurance on individual health outcomes using data from the 2008 Oregon Health Insurance Experiment (OHIE), a randomized controlled trial. DML integrates modern machine learning with the econometric principles of causal identification to obtain
unbiased treatment effect estimates in high-dimensional data. Interactive Regression Models (IRM) and Interactive Instrumental Variable Models (IIVM) are employed to estimate effects of Medicaid coverage on perceived health, number of doctor visits,
satisfaction, access to medical services, and quality of care. The results indicate small but positive causal effects of Medicaid coverage on perceived health and healthcare utilization, statistically insignificant effects on satisfaction and access to medication, and a slightly negative effect on perceived quality of care. The findings highlight the potential of Double Machine Learning as a robust framework for causal analysis in empirical research.

Keywords: causal inference; double machine learning; oregon health insurance experiment.

DOI: https://doi.org/10.5282/jums/v11i1pp107-138

The Dark Side of Employer Branding – Aesthetic Labour and Employer Attractiveness in the Beauty and Cosmetics Industry

Susanne Rautzenberg, Vienna University of Economics and Business (Master’s thesis)
Junior Management Science 11(1), 2026, 139-163

Read Abstract

This thesis examines the impact of Aesthetic Labour on employer branding within the cosmetics industry, with a focus on Generation Z (Gen Z). Aesthetic Labour refers to the employment of workers whose appearance reflects the company’s brand image, thereby shaping perceptions of
employer attractiveness. The central research question is how such practices influence Gen Z’s perceptions of employer attractiveness in the beauty sector.

Through semi-structured interviews with twelve current and former Gen Z employees of well- known cosmetics firms, this study uncovers the dual nature of employer branding in the industry.

Externally, a polished, glamorous image attracts applicants, while internally, aesthetic norms may foster subtle pressures on employees to conform, creating tensions despite otherwise supportive work cultures. The findings reveal that Aesthetic Labour enhances employer attractiveness by creating a desirable yet sometimes unattainable image. This study contributes to the understanding of Aesthetic Labour’s paradoxical role in employer branding. It highlights the need for cosmetics firms to balance proven branding strategies with inclusive and diverse employment practices to meet the evolving demands of the modern workforce.

Keywords: aesthetic labour; employer branding; cosmetics industry; generation z.

DOI: https://doi.org/10.5282/jums/v11i1pp139-163

Understanding Participation Trends in the Avani 5G Patent Pool: A Descriptive Analysis

Justus Scharfstädt, Technical University of Munich (Bachelor’s thesis)
Junior Management Science 11(1), 2026, 164-180

Read Abstract

Patent pools play a critical role in adapting to new technology standards by simplifying the licensing of Standard-Essential Patents (SEPs). The Avanci 5G pool provides a licensing platform for vehicle manufacturers to access essential technologies for connected vehicles. Despite the growing importance of these pools, there is limited research on the dynamics of participation and its implications for different stakeholders. This study provides a descriptive analysis of the Avanci 5G pool, particularly focusing on the development of the SEP and licensee coverage since its inception. It uses a mixed-methods approach and combines multiple data sources for the coverage estimates. My findings reveal substantial growth in both SEP and licensee coverage but highlight a significant gap in the participation of Asian automakers, potentially due to perceived anti-competitive risks. Policy makers and relevant stakeholders should aim to foster a competitive and innovative environment in SEP licensing.

Keywords: patent pool; licensing; connected vehicles; innovation; anti-competitive risks.

DOI: https://doi.org/10.5282/jums/v11i1pp164-180

Generative AI-Enabled Music Generation in Marketing and Consumer Response

Philipp Iversen, Technical University of Munich (Bachelor’s thesis)
Junior Management Science 11(1), 2026, 181-194

Read Abstract

Generative AI is revolutionizing the marketing industry by producing high-quality, cost-effective and time-efficient content. This study investigates the potential of AI-generated music in digital advertising. Two studies, a survey and a real-world A/B test, evaluate the different songs on chosen criteria: Overall, Melodiousness, Creativity, Naturalness, Correctness and Prompt following for the survey, and click-through rates (CTR) for the field experiment. The survey results show that AI-generated music can be comparable in quality to human compositions, even scoring significantly higher in the categories Prompt Following and Melodiousness. However, AI music showed significantly worse results in the category Creativity. The field experiment revealed no statistically significant difference in CTR between advertisements using AI-generated and royalty-free music, demonstrating that AI music can be a good substitute in supporting roles. This research underlines the possibility for AI-generated music to be used in hyper-personalized advertising, while addressing challenges related to perceived creativity and copyright. The findings contribute to understanding AI’s disruptive potential in marketing and offers practical insights to integrate AI tools effectively.

Keywords: AI-generated music; music in advertising; AI consumer response.

DOI: https://doi.org/10.5282/jums/v11i1pp181-194

Predicting Stock Market Trends Using Convolutional Neural Networks: A Deep Learning Approach

Ege Özkul, Technical University of Munich (Master’s thesis)
Junior Management Science 11(1), 2026, 195-226

Read Abstract

Technical analysis aims to predict stock returns based on price and volume patterns and has seen growing adoption of machine learning methods. However, most approaches rely on hand-crafted features. This paper investigates whether deep learning applied to stock chart images can predict future returns without manual feature engineering. Building on Jiang et al. (2023), this study applies Convolutional Neural Networks (CNNs), a computer vision architecture, to predict stock returns from price charts and extends their approach by implementing Vision Transformers, specifically the Class-Attention in Image Transformer (CaiT). Stock prices, volumes, and moving averages are encoded into images, which are used to train models that classify future stock returns as either positive or negative. Results show that both CNN and CaiT models outperform traditional technical indicators such as momentum and reversal strategies when applied to US stocks. Moreover, combining the two models yields incremental predictive power. An investment strategy based on their joint predictions achieves higher returns and Sharpe ratios than either model alone.

Keywords: technical analysis; stock return prediction; deep learning; convolutional neural networks; vision transformers.

DOI: https://doi.org/10.5282/jums/v11i1pp195-226

Relative Performance Measurement as an Instrument of Corporate Management

Felix Achim Bautz, Heinrich Heine University Düsseldorf (Bachelor’s thesis)
Junior Management Science 11(1), 2026, 227-245

Read Abstract

Stabilizing and improving organizational performance is a central challenge in management control. Behavior-based results controls is a key mechanism in this
context. The lack of a generally effective and accepted incentive system requires a deeper examination of the underlying mechanisms beyond surface-level system features. Relative Performance Measurement (RPM) is considered a dynamic tool to align performance and motivation with strategic goals. However, its use is associated with risks such as demotivation and dysfunctional behavior. This study analyzes the effects of RPM in the context of competition and social comparison within the social system of the organization. Based on agency theory (AT), the economic rationale of RPM is derived using tournament theory. Its limitations are discussed using the example of forced-ranking practices. The perspective is extended by social comparison theory, which shows how task-related, individual, and contextual variables, as well as system design, influence the effectiveness of RPM in different settings. The study contributes to the integration of economic and behavioral approaches in performance measurement (PM) and provides practical implications for the targeted design of RPM systems in management control.

Keywords: tournament theory; social comparison theory; management control; agency theory; relative performance measurement

DOI: https://doi.org/10.5282/jums/v11i1pp227-245