Browsing by Issue Date, starting with "2025-10-14"
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- The power of front-of-pack labels : how the Nutri-Score influences brand perception and purchase intention among generation ZPublication . Eller, Nico Maximilian; Lancastre, FilipaIn response to rising obesity and diet-related diseases in Europe, policymakers have promoted front-of-pack (FOP) nutrition labels to encourage healthier choices. Among these, Nutri-Score has gained traction by translating complex nutritional information into a simple five-color scale. Beyond informing choices, it may also act as a credibility signal shaping brand evaluations. To be effective, labels must guide product decisions and build brand trust. This study used a between-subjects experiment to test how Nutri-Score affects purchase intention and brand perception, focusing on generational differences. In an online survey, 203 participants evaluated a fictitious cereal product with either a favorable (A) or unfavorable (D) Nutri-Score. Results show that a favorable score significantly increases purchase intention. Brand perception fully mediates this effect: positive scores enhance trustworthiness, responsibility, and health orientation, which strengthen willingness to buy. Contrary to expectations, no generational moderation was found. By linking public health goals with consumer behavior, this study fills a research and practice gap. It highlights Nutri-Score as a transparent tool supporting EU-wide adoption and shows that firms can leverage it to strengthen brand credibility and align strategy with health objectives. Finally, it contributes to labeling research by evidencing Nutri-Score’s dual role as nudge and signal, with brand perception as the key mechanism connecting certification labels to consumer intentions.
- Decoding ESG risk ratings : insights into financial sector sustainability by using machine learningPublication . Weber, Johannes; Fernandes, Pedro AfonsoWith an emphasis credit institutions, this thesis explores the factors that influence environmental, social, and governance (ESG) risk ratings in the financial industry. ESG ratings are now an important tool to assess how financial institutions handle risks associated with sustainability because of the growing social and regulatory demands for transparency and sustainable practices. In addition to bank-specific attributes like market capitalization, the variables of the dataset of financial institutions were organized along the three ESG pillars. In order to guarantee model suitability, data preparation required the methodical handling of missing values and the transformation of relevant variables. To evaluate the predictive power of various machine learning techniques in determining the most significant ESG drivers, the study used ordinary least squares (OLS), LASSO, decision trees, random forests, and gradient boosting. The findings show that Random Forest and Decision Tree models tended toward overfitting, whereas Gradient Boosting and OLS provide the best balanced performance across training and test datasets. Governance-related elements, particularly the existence of a CSR sustainability committee and board diversity, were found to be consistently influential across models by feature importance analysis. OLS specifically highlighted market capitalization, underscoring the significance of financial size. Environmental factors like waste reduction programs and renewable clean energy products also emerged as important drivers. The results validate the usefulness of ESG risk ratings as a tool for both financial risk management and sustainable development, and they are consistent with regulatory frameworks like the EU Taxonomy and international sustainability goals.
- To what extent can AI agents be fine-tuned to mimic human interactions?Publication . Cameira, Guilherme de Figueiredo; Bertani, NicolòThis thesis investigates the viability of employing AI-powered personas as a complement or alternative to traditional human focus groups in qualitative research. Leveraging VARTHEO’s proprietary methodology, the study compares interaction sessions conducted by human participants with those generated by AI-Powered Personas, both exploring consumer perceptions of product design and positioning. A mixed-methods approach is utilized: data from separate sessions are evaluated using advanced natural language processing tools, notably MiniLM-based models and an agentic evaluation via Google Gemini. The findings highlight the strengths of AI-powered personas in delivering scalable, consistent, and cost-effective insights, particularly useful for preliminary testing or situations with logistical constraints. However, the study also identifies limitations in replicating the full depth of human emotional nuance and creative expression as well as human interaction, suggesting that hybrid research designs may offer a more balanced approach. This research contributes to the evolving discourse on integrating AI in qualitative research and underscores the need for continued refinement in AI methodologies to better mirror complex human behaviours and interactions.
- Equity valuation of Chipotle Mexican Grill Inc.Publication . Haack, Alexander; Martins, José TudelaThis thesis provides a strategic, financial, and valuation analysis of Chipotle Mexican Grill, Inc. as of March 31, 2025. A valuation is conducted using a Discounted Cash Flow (DCF) model and a market-based EV/EBITDA multiple, resulting in a target share price of USD 56.60, which lies within a ±10% range of the current market price.
The financial forecast assumes continued revenue growth driven by new store openings and modest improvements in operational efficiency. Although total capital expenditures rise, the investment relative to revenue gradually declines. This reflects Chipotle’s scalable growth model, supported by expansion across North America and stable Comparable Restaurant Sales Growth.
To assess plausibility, results are compared with analyst reports by Stephens and Zacks. Despite differences in methodology, the valuation outcomes are largely consistent. This thesis applies a slightly higher peer-derived EV/EBITDA multiple, resulting in a moderately increased per-share valuation. Based on the valuation range, a HOLD classification is deemed appropriate. - Predicting early NBA career survivability using pre-draft college statisticsPublication . Wiethüchter, Christian Ferdinand; Bertani, NicolòThis study examines whether publicly available college basketball statistics can predict early NBA career outcomes, a period both formative for players and financially crucial for franchises under rookie-scale contracts. A dataset was assembled covering all drafted college players from 2002–2019, combining season-level college performance with subsequent NBA results. Because most prospects play multiple seasons before declaring for the draft, season-level predictions were systematically aggregated to the player level to reflect the actual unit of draft-day decision-making. Five outcome labels captured increasing levels of early-career success: surviving the rookie contract, securing rotation or starter roles, and surpassing minimum impact thresholds in Win Shares and Value Over Replacement Player. An end-to-end pipeline embedded preprocessing, grouped cross-validation, and fold-safe aggregation, ensuring reproducibility and preventing information leakage. Regularized linear models served as baselines, while Random Forests and Gradient Boosted Trees benchmarked non-linear performance. The results show that college box-score data contains predictive signal, though modest in strength for most labels. Tree-based methods outperformed linear models: Random Forests were strongest for durability-oriented outcomes such as rotation roles and four-year survival, while boosting captured rarer ceiling outcomes like starters and high-impact contributors. Aggregation to the player level proved essential, with simple averaging often sufficient. Feature importance highlighted class year, games played, assists, and shooting efficiency as consistent though limited predictors. While box scores alone cannot identify future stars with high precision, they provide a systematic, reproducible baseline that helps reduce draft risk by flagging players most likely to contribute early.
- The unsubscribed : the impact of price increases and advertising intensity on subscription cancellation intention in paid video streaming servicesPublication . Schreiber, Adrian; Lancastre, FilipaAs new customer growth in the video streaming market slows, providers increasingly focus on monetizing existing subscribers through price increases and advertising. However, both strategies risk provoking cancellations. This study investigates the effect of price increases and advertising intensity on users' intention to cancel paid streaming subscriptions, and which factor has a stronger influence. A 3x3 between-subjects experimental design was conducted (N = 422), simulating realistic subscription change scenarios via an online survey. Participants were randomly exposed to one of nine conditions varying by price levels and comparable advertising intensities. The quantitative data were analyzed using OLS regression and post hoc tests. Results show that both price increases and advertising significantly raise cancellation intentions. The impact of advertising appears to plateau, whereas price effects seem nonlinear, with threshold-driven increases in cancellations. Combined, the factors appear to interact subadditively. At moderate levels, advertising tends to dominate and makes small price changes insignificant. At high levels, both factors exert comparable effects on cancellation intentions. This study is among the first to empirically compare and combine these two monetization levers, especially in a paid video streaming context. It contributes to behavioral and platform economics by confirming saturation and loss prioritization effects. Practically, it highlights that introducing advertising in previously ad-free subscriptions carries higher cancellation risks than moderate price increases. Providers should communicate changes transparently, offer clear value, and segment offerings to retain customers.
- AI impact on sales skills : the implications of artificial intelligence on the required skill set for the B2B sales workforcePublication . Godoy, Matthias Benjamin Köhne; Lancastre, FilipaThis dissertation investigates how artificial intelligence (AI) is redefining the skill set of B2B sales professionals. Guided by human capital theory, it addresses three questions: which skills matter today, how AI alters them, and what this means for tomorrow’s sales role. Twenty seven expert interviews across European SaaS, fintech, energy and consulting firms were analyzed with Braun and Clarke (2006) thematic analysis to map 23 competencies into four clusters. Findings show that AI displaces routine analytics and administration while amplifying high touch capabilities: relationship building, active listening, commercial acumen and strategic orchestration grow in importance; operational AI literacy, especially prompt engineering, emerges as a new meta skill; and adaptability and continuous learning become critical. AI lifts productivity yet introduces risks of cognitive dependence and skill decay if used uncritically. The study concludes that AI is an augmenter, not a replacer of human skills: sustainable advantage will accrue to organizations that treat algorithms as collaborative “colleagues” and simultaneously invest in soft skill coaching and prompt engineering training. These insights extend human capital theory to the AI era and offer managers a roadmap for up skilling sales teams while safeguarding human judgment.
- Not all those who wander are lost : from fantasy readers into travelersPublication . Casciano, Barbara; Rodrigues, HelenaLiterary tourism is a segment of cultural tourism involving literary texts, authors, and space fiction. While these traditions have nurtured the connection between literature and travel, this study suggests that literary tourism is more than merely visiting the homes of writers and buying souvenirs, but it has transformed into specifically branded tourism attractions. Fantasy literary tourism is a growing niche in which fans travel to fictional locations, usually part of a fantasy saga. This research explores the psychological and emotional motivations that drive fantasy literature readers to become travelers and explores how perceived destination image and marketing influence these decisions. The study is a combination of 24 semi-structured interviews and 566 TripAdvisor reviews of fantasy-related destinations of important series like Harry Potter, Lord of the Rings, Game of Thrones, Narnia, and The Hunger Games. The data was then analyzed using Leximancer software, which highlighted the key recurring themes of the dataset. The resulting findings point out the importance of nostalgia, immersion, and authenticity as the main drivers, with travelers seeking to connect with their childhood memories and experience the world they could only imagine while reading the books. Marketing strategies are also highly relevant: social media, theming and strategically guided storytelling contribute to the formation of travel intentions and destination appeal. This study makes a novel contribution to the under-researched context of literary fantasy tourism through its focus on symbolic consumption and provides practice-based insights into how destination managers and marketers can design emotionally engaging and thematically authentic experiences.
- Consumer acceptance of chatbot recommendations : how chatbot design, product type and brand personality shape adoptionPublication . Allekotte, Sonja Elisabeth Maria; Borges, MónicaChatbots, once primarily used for customer support and FAQ handling, are increasingly deployed by brands to provide personalized product recommendations in e-commerce. This shift expands their role from problem-solving assistants to proactive advisors shaping purchase decisions. Despite this potential, consumer adoption of chatbot-generated recommendations remains inconsistent. While prior research has examined mostly psychological and emotional factors as drivers, less is known about how contextual factors shape adoption. The purpose of this dissertation is to analyze the effects of chatbot design, product type and brand personality. A 2×2×2 experimental design tested differences between anthropomorphic and non-anthropomorphic chatbots, hedonic and utilitarian products, and modern versus traditional brands. Cognitive involvement and perceived consistency induced by the experimental conditions were also measured. Results show that recommendation adoption is higher for utilitarian products, anthropomorphic chatbots, and modern brands. However, perceived consistency emerged as the strongest predictor of adoption, mediating the effects of chatbot design and brand personality. Cognitive involvement, although higher for hedonic products, did not significantly influence adoption. This research contributes to consumer behavior and AI adoption literature by showing that contextual alignment, rather than isolated design features, drives acceptance of chatbot recommendations. For managers, the findings emphasize that chatbot implementation should be matched with product type and brand positioning, with the greatest benefits for utilitarian goods and modern brands.
- Understanding the factors influencing consumer adoption of wearable payment devicesPublication . Almeida, Carolina Silva dos Santos; Borges, MónicaThe rapid digitalization of financial services has accelerated the adoption of mobile and contactless payments, yet payment-only wearables (NFC-enabled wristbands, rings, and tags designed exclusively for payments) remain a niche segment with limited consumer uptake. This dissertation investigates the factors influencing consumers’ behavioral intention (BI) to adopt payment-only wearables by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). The model integrates trust, compatibility, and perceived aesthetics as additional constructs and examines the moderating role of personal innovativeness. Data was collected through a structured online survey (N = 254) and analyzed using multiple linear regression and moderation analysis. The model explained 70.6% of the variance in BI, demonstrating strong predictive power. Results show that compatibility was the strongest determinant of BI, followed by aesthetics, performance expectancy, and price value. Social influence and facilitating conditions had weaker but significant effects, while effort expectancy and trust were not significant predictors, suggesting that ease of use and security are now baseline expectations rather than differentiators. Moderation analysis revealed that innovativeness amplifies the effects of performance expectancy, compatibility, trust, and social influence on BI. The study makes two key contributions. Theoretically, it adapts UTAUT2 to an emerging payment context, showing that adoption drivers evolve with market maturity. Practically, it provides guidance for firms to emphasize usefulness, lifestyle fit, design appeal, and innovative consumer segments in order to accelerate acceptance of payment-only wearables.