https://hdl.handle.net/10419/290628 Thu, 22 Oct 2026 21:26:24 GMT 2026-10-22T21:26:24Z https://hdl.handle.net/10419/341439 Title: The dark side of employer branding: Aesthetic labour and employer attractivenessin the beauty and cosmetics industry Authors: Rautzenberg, Susanne Abstract: This study explores the impact of Aesthetic Labour on employer branding within the cosmetics industry. Traditionally, beauty and cosmetics firms are associated with positive connotations such as elegance, glamour, and attractiveness, driven by professional marketing strategies. Aesthetic Labour, the employment of workers who visually represent the company's brand image, extends beyond customer-facing roles to influence the overall company image and employer brand. This thesis investigates how these Aesthetic Labour practices shape perceptions and expectations of prospective employees, particularly focusing on Generation Z, who prioritize diversity and inclusion. Through semi-structured interviews with twelve Generation Z employees and former employees of well-known cosmetics firms, this research uncovers the dual nature of the industry's employer branding, one being the glamorized image that is carried to the outside, the other highlighting the downsides of the glamorized effect. While a polished, glamorous image attracts applicants, it often leads to unrealistic expectations about the work environment. The study finds that although employees experience a supportive and enthusiastic work culture, they also face internal pressure to maintain a specific aesthetic standard. This pressure, albeit not officially mandated, is perpetuated through internalized norms and possibly biases in the hiring process. The findings reveal that Aesthetic Labour practices influence employer attractiveness by creating a desirable but often unattainable image. This study contributes to the understanding of Aesthetic Labour's role in shaping employer brand perceptions, highlighting the need for cosmetics firms to balance their branding strategies with inclusive and diverse employment practices to meet the evolving expectations of the modern workforce. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/10419/341439 2026-01-01T00:00:00Z https://hdl.handle.net/10419/341441 Title: The impact of biodiversity risk on banks' credit default swap spread changes Authors: Praetz, Moritz 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. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/10419/341441 2026-01-01T00:00:00Z https://hdl.handle.net/10419/341437 Title: Predicting stock market trends using convolutional neural networks: A deep learning approach Authors: Özkul, Ege 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. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/10419/341437 2026-01-01T00:00:00Z https://hdl.handle.net/10419/341435 Title: Generative AI-enabled music generation in marketing and consumer response Authors: Iversen, Philipp 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. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/10419/341435 2026-01-01T00:00:00Z