Topic

Companies increasingly rely on Corporate Social Responsibility (CSR) communication to position themselves as responsible actors in society. At the same time, consumers, particularly younger generations, are confronted with a growing volume of CSR-related information that varies in content. This quantitative study investigates how different CSR communication dimensions (environmental, social and ethical) affect young consumers purchase intentions. In addition, the study examines whether environmental awareness moderates the relationship between CSR communication and purchase intention.

Relevance

Understanding how young consumers respond to CSR communication is of both academic and practical relevance. While prior research suggests that CSR can positively influence consumer behavior, it remains unclear whether certain CSR dimensions are more effective than others and how individual characteristics such as environmental awareness shape these effects. Given the increasing importance of sustainability-related communication in corporate strategy, insights into how CSR messages are perceived by younger consumers can help organizations design more effective and credible communication approaches.

Results

The findings indicate that CSR communication significantly increases purchase intention compared to communication without CSR content. However, all CSR dimensions show the same purchase intention in the environmental, social, and ethical CSR conditions. Furthermore, environmental awareness was found to moderate the relationship between CSR communication and purchase intention selectively. While the moderation effect was statistically significant only for ethical CSR communication, regression coefficients remained positive across all CSR dimensions, suggesting a generally strengthening role of environmental awareness.

Implications for Practitioners

CSR as a general signal:
The results suggest that CSR communication can positively influence purchase intentions among young consumers regardless of the specific CSR dimension emphasized. Organizations may therefore benefit from integrating CSR communication broadly rather than prioritizing one dimension exclusively.

Ethical framing matters:
Ethical CSR communication appears particularly effective among young consumers with higher environmental awareness, indicating that moral and value-based messaging may resonate strongly with this audience.

Credibility:
The findings further imply that CSR communication does not need to be overly assertive to be effective, but instead benefits from being perceived as authentic and credible.

Methods

The study employed a between-subjects, vignette-based experimental survey design targeting young consumers aged 18 to 35. Data was collected via an online questionnaire, resulting in a final sample of 182. Purchase intention, environmental awareness, and manipulation checks were assessed using established measurement approaches. Data analysis was conducted using R, including descriptive statistics, non-parametric group comparisons, and moderated multiple regression analyses to test the proposed hypotheses.

Die Masterarbeit untersucht, ob zirkuläres Bauen in der Schweiz ökonomisch tragfähig ist. Anhand von Fallstudien (NEST, OPENLY) und Experteninterviews untersucht diese Arbeit, inwiefern eine erhöhte Zahlungsbereitschaft für zirkulär erstellte Gebäude vorliegt und aus welchen Gründen. Ergänzend werden bestehende Herausforderungen und Risiken analysiert und praxisnahe Lösungsansätze aus Expertensicht identifiziert. Ziel ist es, Wege aufzuzeigen, wie Re-Use und Circular Economy stärker und wirksamer in der Immobilienbranche umgesetzt werden können.

Relevanz

Der Gebäudesektor spielt eine zentrale Rolle bei Energieverbrauch, Ressourceneinsatz und Treibhausgasemissionen. In der Schweiz entfallen rund 40% des Endenergieverbrauchs und ein Drittel der CO₂-Emissionen auf Gebäude. Zudem entstehen im Bau- und Rückbauwesen rund 60 Millionen Tonnen Abfall. Die Forschungsfragen sind theoretisch relevant, weil sie untersuchen, ob Circular Economy im Immobilienbereich wirtschaftlich honoriert wird und zwar aus Nachfrageperspektive, nicht nur aus Eigentümersicht. Die praktische Relevanz liegt darin, dass Immobilienbewirtschafter und Investoren wissen müssen, ob zirkuläre Konzepte marktfähig sind. Die Arbeit liefert konkrete Hinweise auf Zahlungsbereitschaft und Zielgruppenpotenziale als Entscheidungsgrundlage für die Praxis.

Ergebnisse

Die Ergebnisse zeigen, dass zirkuläres Bauen in der Schweiz zwar ökologisch anerkannt, jedoch wirtschaftlich noch kaum honoriert wird. Eine breite Zahlungsbereitschaft für zirkuläre Immobilien besteht derzeit nicht – Lage und Rendite bleiben die dominanten Entscheidungsfaktoren. Es konnten jedoch fünf Stakeholder-Gruppen mit potenziell erhöhter Zahlungsbereitschaft identifiziert werden: die öffentliche Hand, nachhaltigkeitsorientierte Unternehmen, private Pioniere mit ausgeprägtem Umweltbewusstsein, internationale Nutzer mit hohen Zertifizierungsanforderungen sowie Grossfirmen mit CSR-Vorgaben.

Die Untersuchung zeigt 7 Gründe, warum Akteure mehr für zirkuläre Immobilien bezahlen würden. Hier in abnehmender Reihenfolge der Anzahl genannten Punkte.

·         Aussenwirkung und Reputation      

·         Firmenphilosophie   

·         Kosteneinsparungen oder tiefere Betriebskosten

·         Reduktion der CO2 Emissionen     

·         Zirkularität fördert flexiblere Nutzung

·         Mehr Finanzierungsanreize schaffen          

·         Nachhaltigkeit schafft Behaglichkeit und Wohnqualität

Bemerkenswert: Die intrinsisch motivierten Faktoren Reputation und Firmenphilosophie machen dabei rund die Hälfte aller Nennungen aus – Werte sind wichtiger als rein finanzielle Überlegungen. Es zeigt sich, dass drei strukturelle Hemmnisse die Zahlungsbereitschaft besonders stark bremsen. Erstens verhindert die begriffliche Vermischung von Nachhaltigkeit, Zirkularität und Re-Use ein differenziertes Preissignal. Zweitens fehlen regulatorische Rahmenbedingungen wie steuerliche Anreize, angepasste Normen und Förderprogramme. Drittens hemmen rechtliche Unsicherheiten rund um Haftung und Materialqualität das Vertrauen in Re-Use-Bauteile. Ein weiterer, in der Theorie wenig beachteter Hemmfaktor ist die buchhalterische Behandlung von Gebäuden: Da diese kontinuierlich an Wert verlieren, entsteht ein struktureller Anreiz zum Ersatzneubau, selbst wenn Bauteile noch funktionstüchtig sind.

Implikationen für die Praxis

1. Regulatorische Rahmenbedingungen

Es müssen neue Konzepte, wie steuerliche oder anderweitige finanzielle Anreize geschaffen werden, weil schlussendlich die Wirtschaftlichkeit und Rendite oftmals als treibende Kraft überwiegt. Auch die Thematik einer "zirkulären Buchhaltung" muss von politischer Seite voran getrieben werden.

2. Soll die Zahlungsbereitschaft verstärkt werden, sollten die identifizierten Motivatoren angesprochen werden

Um die Zahlungsbereitschaft zu erhöhen, müssen die identifizierten Motivatoren gezielt angesprochen werden. Da Reputation und Firmenphilosophie die Hälfte aller Nennungen ausmachen, sollte die Kommunikation primär auf wertbasierte Argumente setzen. Zirkuläre Gebäude müssen als sichtbares Statement für Nachhaltigkeit positioniert werden.

Konkret bedeutet dies: CO₂-Reduktion messbar machen, hochwertige Architektur zeigen, finanzielle Vorteile beziffern. Entscheidend ist auch die zielgruppenspezifische Ansprache: Bei der öffentlichen Hand steht Vorbildfunktion im Vordergrund, bei Unternehmen die Firmenphilosophie, bei gewerblichen Nutzern Flexibilität und Wertstabilität, bei privaten Mietern Wohnqualität.

3. Massnahmen seitens der Baubranche

Die Baubranche muss ihre Abläufe verbessern, damit Re-Use mit wenigen Mehrkosten verbunden ist, da ein Mehrpreis grösstenteils nicht akzeptiert wird. Es ist eine engere Zusammenarbeit zwischen allen Akteuren der Bau- und Immobilienbranche notwendig. Architekten, Bauunternehmen, Investoren und Behörden müssen gemeinsam neue Standards entwickeln, die Zirkularität als integralen Bestandteil verstehen. Entscheidend ist, dass Zirkularität bereits in der frühen Projektphase mitgedacht wird. Materialströme, Demontage und Beschaffung müssen frühzeitig geplant werden. Digitale Tools wie BIM und Materialpässe können dabei unterstützen.

Darüber hinaus muss die Architektur eine Re-Use-freundlichere Bauweise entwickeln. Einen eigenen "Circular Chic"-Stil mit Fokus auf Demontierbarkeit, Flexibilität und Werterhaltung. Dies erfordert Umdenken und das Verständnis, dass solche Bauten anders aussehen. Mehr Mut zum Ausprobieren ist zentral, um Unsicherheiten abzubauen.

Methoden

Zur Beantwortung der Forschungsfrage wurden zwei Ansätze gewählt: Das Erarbeiten von Fallstudien von 2 Gebäuden aus der Schweiz, sowie die Durchführung von Experteninterviews. Für die Fallstudien wurden verschiedene Ansätze der zirkulären Bauweise studiert und erfolgreiche Bauten beleuchtet. In einem zweiten Teil wurde ein qualitatives Forschungsdesign gewählt. Basierend auf einem strukturierten Leitfaden wurden elf Experteninterviews durchgeführt. Dabei handelte es sich um Architekten, Nachhaltigkeitsexpertinnen, Geschäftsführende, Projektleitende, Dozierende sowie Vertreter von öffentlichen Institutionen mit langjähriger Erfahrung im Bereich nachhaltige und zirkuläre Bauweise. Die Interviews wurden mit einer qualitativen Inhaltsanalyse mit der Methoden der induktiven Kategorienbildung nach Kuckartz (2018) und zusammenfassenden Inhaltsanalyse nach Mayring (2010) ausgewertet. Die Interviewdaten wurden softwaregestützt codiert und in einem Codebook zusammengeführt, um die Zahlungsbereitschaft und Gründe für zirkuläre Bauweise zu identifizieren.

Shared e-scooters have become a familiar sight in many Swiss cities. They are often presented as smart, flexible last-mile solutions that complement public transport and support sustainable urban mobility. But do they actually contribute to socially sustainable mobility, or do they mainly benefit specific user groups?

This Master’s thesis explores this question using the city of Frauenfeld as a case study. The focus lies on how Smart City strategies and digital tools influence the integration of shared e-scooters and what this means for accessibility, inclusion, and last-mile mobility in practice.

How the study was conducted

The thesis is based on a qualitative research design. In addition to a structured literature review, semi-structured interviews were conducted with municipal and cantonal officials, mobility experts, and service providers. The interviews were analyzed using thematic coding to better understand governance practices, stakeholder perspectives, and the role of digital tools in shared micromobility.

This approach allows insights into how shared e-scooters are governed in practice and how social sustainability is addressed, or neglected within Smart City mobility strategies.

Key findings: efficiency first, inclusion second

The results show that shared e-scooters in Frauenfeld are mainly integrated through data-driven and reactive Smart City governance. Digital tools are primarily used to monitor usage patterns, regulate public space, and support the city’s Smart City positioning. This governance approach brings clear benefits, such as operational efficiency and valuable mobility data.

However, the study finds that social sustainability is not actively designed. Instead, access to shared e-scooters remains selective. Younger and digitally skilled users benefit most, especially for flexible, leisure-oriented trips and perceived safety in the evening. In contrast, older adults, people with limited digital skills, and people with disabilities face barriers due to app-based access requirements and the lack of alternative usage options.

Importantly, these exclusion risks are widely recognized by local actors but are largely accepted as side effects rather than addressed through targeted governance measures.

A governance trade-off

Based on the empirical findings, the thesis develops a governance trade-off model. The model shows that Smart City ambitions, data-driven steering, and public–private governance arrangements generate operational and symbolic benefits for cities and providers, while social inclusion remains weakly embedded.

Social sustainability does not fail because of technology itself. Instead, it depends on whether inclusion is defined as an explicit governance objective.

What this means for practice

For municipalities, the findings highlight an important opportunity. Digital tools could do much more to support inclusive last-mile mobility if they were used deliberately to reduce access barriers. Clear social inclusion objectives, inclusive last-mile strategies, and alternative access options beyond smartphone apps could help broaden the user base and strengthen social sustainability.

Shared e-scooters can contribute to sustainable urban mobility—but only if Smart City innovation is combined with active and inclusive governance.

This work examines customers’ acceptance of AI chatbots in three different banking contexts: opening a savings account, an investment portfolio, and a pension fund.  The research investigates how the use of AI chatbots is perceived by the customers in each of the scenarios (i.e. how useful, easy to use, trustworthy, and risky the AI chatbot is perceived) and how these perceptions eventually influence the consumers’ attitude towards using the AI chatbot and their behavioral intention to use it.

Relevance: The present research is of high topicality as the use of AI chatbots is growing fast and firms such as banks need to make decisions about how to implement the new technology. For what purposes should a bank use AI chatbots? Which clients should be targeted? Should the banks shift away from human interaction to AI chatbots?  Those decisions are of high financial relevance as they might help to reduce the costs but can on the other hand also impact a firm negatively through loss of trust.

Results: AI chatbots are perceived as more useful, easier to use and trustworthy as well as less risky in the savings account scenario compared to the pension fund scenario. These perceptions strongly mediate the impact on consumers’ attitude towards using the AI chatbots. The attitude towards using the AI chatbot and consumer’ behavioral intention to use it are found to be significantly lower for the pension fund scenario compared to opening a savings account.

Implications for practitioners:

· AI chatbots are well suited for simple, low-risk tasks, where full automation is feasible. This is the case in banking for opening a savings account.

· AI chatbots are not suited  for more complex tasks where risk and trust play an important role. This is the case in banking for opening a pension fund.

· In more complex situations the human interaction keeps playing an important role and AI chatbots should only be used in a complementary way.

· The preference to use an AI chatbot does not depend on the age of the clients, a age-based AI implementation strategy should be avoided.

Method: The method consists of a quantitative online vignette experiment where participants are put into the shoes of customers of a hypothetical bank. The experiment is conducted through the Qualtrics survey platform. A total of 203 participants respond to the questionnaire and evaluate standardized AI chatbot responses in a between-subjects design.  Three scenarios (open a savings account, an investment portfolio and a pension fund) are randomly shown to the participants. The respondents are then asked to evaluate the AI chatbot regarding to different perceptions. The data is analysed using primarily ANOVA and regression analysis by means of the statistics program R.

Thema

Diese Masterarbeit untersucht den Einsatz generativer KI-Tools wie ChatGPT im Hochschulstudium aus einer ethischen Perspektive. Im Mittelpunkt stehen die Wahrnehmungen und Erfahrungen von Studierenden und Dozierenden, die KI zunehmend zur Unterstützung beim Schreiben, Recherchieren oder Strukturieren nutzen. Die Arbeit analysiert, wie KI Lernprozesse, wissenschaftliche Eigenleistung sowie das Verständnis von Verantwortung im Studium beeinflusst. Zudem werden Fragen der Fairness und Chancengleichheit betrachtet, die durch unterschiedliche Zugänge zu KI-Ressourcen entstehen. Ziel ist es, ein differenziertes Bild der Chancen und Risiken des KI-Einsatzes im akademischen Kontext zu zeichnen und zentrale ethische Spannungsfelder sichtbar zu machen.

Relevanz

KI-Tools werden im Hochschulstudium zunehmend selbstverständlich genutzt und verändern Lernen, Lehren und Leistungsbewertung grundlegend. Gleichzeitig bestehen Unsicherheiten hinsichtlich akademischer Integrität, Chancengleichheit und verantwortungsvollem Einsatz. Die Arbeit ist relevant, da sie aktuelle ethische Fragestellungen empirisch beleuchtet und damit einen Beitrag zur hochschulischen Diskussion über angemessene Rahmenbedingungen für den Einsatz von KI in Studium und Lehre leistet.

Ergebnisse

Die Ergebnisse zeigen, dass Studierende KI-Tools insbesondere zur Zeitersparnis, zur Strukturierung von Lerninhalten und zum besseren Verständnis komplexer Themen nutzen. Gleichzeitig bestehen Unsicherheiten bezüglich der Abgrenzung von Eigenleistung und der Fairness gegenüber Studierenden mit eingeschränktem Zugang zu KI. Dozierende erkennen sowohl didaktische Potenziale als auch Risiken für Lernprozesse und Prüfungsformate. Ein reflektierter und transparenter Umgang mit KI wird von beiden Gruppen als zentral erachtet, um Chancen zu nutzen und Risiken angemessen zu begegnen.

Implikationen für Praktiker:innen

• Klare Richtlinien zum Einsatz von KI-Tools im Studium schaffen
• Förderung von KI- und Ethikkompetenzen bei Studierenden und Lehrenden
• Anpassung von Prüfungs- und Leistungsformaten an neue Technologien
• Sensibilisierung für Fragen der Chancengleichheit
• Integration von KI als unterstützendes, nicht ersetzendes Lerninstrument

Methoden

Die Arbeit basiert auf qualitativen, leitfadengestützten Interviews mit Studierenden und Dozierenden aus dem Hochschulbereich. Die Datenauswertung erfolgte mittels thematischer Inhaltsanalyse. Ziel war es, subjektive Wahrnehmungen, Erfahrungen und ethische Bewertungen systematisch zu erfassen und vergleichend auszuwerten.

This thesis explores how employees at Swiss Federal Railways (SBB) experience their engagement in informal Employee-Generated Learning (EGL). At SBB, EGL has not yet been formally introduced as a Learning and Development (L&D) approach, but employees already create and share learning content for colleagues. The study uses organizational and compulsory citizenship behavior (OCB-CCB) as a sensitizing lens to examine this extra-role work and perceived voluntariness. It investigates how motivational, social, and organizational conditions shape engagement and what these experiences imply for possible formalization of EGL at SBB.

Relevance

EGL is gaining attention as organizations seek alternatives to traditional, formal, and top-down L&D models. It positions employees outside L&D as creators of instructions, tutorials, and training sessions for colleagues, complementing centralized learning provision. Despite growing practitioner interest, research still offers limited insight into how employees experience such engagement, especially when EGL is not yet formalized. By focusing on SBB, this thesis highlights motivational, cultural, and organizational conditions that matter when organizations, especially L&D and managers, consider whether and how to formalize EGL.

Results

The findings indicate that engagement in EGL is conditioned by purpose, enjoyment, autonomy, and expert identity. It is also shaped by team culture, social acceptance, recognition, and continuous learning norms, and is enabled or constrained by time, tools, and managerial support. Perceived voluntariness shifts over time between voluntary, self-expected, others’ expectations, and formally assigned engagement. Along this continuum, tensions related to role expectations, formalization, resources, and organizational culture arise and indicate where to focus when formalizing EGL.

Implications for practitioners

• When EGL is informal and emergent, organizations should favor enabling over coercive formalization: keep EGL voluntary at the organizational level, while teams decide who contributes and how.
• Human Resources should strengthen the sharing culture by communicating the purpose and relevance of EGL, and recognizing participation informally rather than embedding it in performance reviews.
• L&D should enable sharing beyond local teams through a stable platform, simple templates, and coaching, so employees can contribute without heavy processes.
• Managers should allocate time and budget, address negative effects such as job creep, role ambiguity, or citizenship pressure, and, when necessary, formally assign tasks.

Methods

To explore how employees experience informal EGL at SBB, twelve semi-structured online interviews were conducted with employees from different business units who had created and shared learning content alongside their core roles. The study followed an exploratory qualitative single-case design and used Gioia’s inductive coding methodology to move from detailed statements to broader themes and aggregate dimensions. This approach resulted in a nuanced process model of engagement conditions, shifting perceptions of voluntariness, and tensions relevant for potential formalization of EGL.

This thesis investigates how organizational leaders perceive content labeled as generated by Generative Artificial Intelligence (GAI) compared to content labeled as written by humans. Drawing on sensemaking theory, the study explores how labels act as interpretive cues that influence leaders’ evaluations of trust, credibility, content quality, and creativity. By focusing on leaders rather than general users or students, the research aims to uncover how managerial perceptions shape organizational readiness for AI adoption and collaboration.

Relevance

Understanding leaders’ perceptions of AI-generated content is crucial, as leadership plays a decisive role in organizational adoption of new technologies. While AI is increasingly integrated into workflows, little is known about how leaders interpret its outputs when presented as either AI- or human-authored. Insights from this study contribute to both academic and practical debates on trust, credibility, and creativity in human-AI collaboration, highlighting the importance of labeling, transparency, and leadership training in fostering responsible AI use.

Results

The study, based on a quantitative online experiment with 150 leaders, shows that texts labeled as human-written were rated significantly higher in trust and credibility compared to AI-labeled texts. However, no significant differences emerged in content quality or creativity. Importantly, leaders’ subjective beliefs about whether a text was authored by a human or AI influenced all evaluations-those who believed a text was human-written consistently gave higher ratings across every dimension, regardless of the actual label.

Implications for practitioners

• Labeling strongly influences leaders’ trust and credibility assessments, even when content is identical.
• Transparency in AI communication strategies is essential to reduce bias and skepticism.
• Leadership training should address how interpretive cues affect decision-making with AI.
• Organizations can benefit from fostering hybrid human–AI collaboration models.
• Belief management (how AI outputs are framed and introduced) may be as important as content quality itself.

Methods

The study employed a quantitative between-subjects experimental design. Leaders recruited via Prolific (N=150) were randomly assigned to evaluate a business memo labeled either as “AI-generated” or “human-written.. Their assessments focused on trust, credibility, quality, and creativity, using validated Likert-scale items. Data were processed in RStudio, including reliability checks and statistical comparisons across conditions. The analysis confirmed that while labels influenced perceptions, leaders’ own beliefs about authorship played an even stronger role in shaping evaluations, highlighting the interpretive nature of leadership sensemaking in AI-related contexts.

Topic

This research explores the limits of traditional revenue forecasting methods in a fast-changing business environment, using the Singapore branch of a global IT consulting company as a real-world case study. The study tested three forecasting approaches - ARIMA, Prophet, and XGBoost combined with Monte Carlo simulation - on five years of company revenue data, alongside macroeconomic indicators like GDP growth, interest rates, and inflation. The aim was to see whether AI-based models could deliver more accurate and adaptable forecasts than the current static methods.

Relevance to practitioners

For FP&A teams, accurate forecasting is critical for making the right business and investment decisions. AI models such as Prophet and XGBoost can recognise complex patterns and simulate different future scenarios. However, the findings show that without rich, business-specific data—such as project pipeline details, client concentration, and contract milestones—even the most advanced models will struggle to predict accurately.

Results

When applied to 2024 revenue forecasting, all three models posted high error rates and negative R² values, meaning they performed worse than simply predicting the average. The main reason was a major shift in client contracts and project timelines in 2024, which could not be captured from historical data. This revealed a critical gap: the models lacked the contextual features needed to reflect business reality.

Implications for practitioners

• Add business-specific data: Include project pipeline metrics, contract terms, and client segmentation to give models more context.
• Look beyond single numbers: Use AI tools to highlight trend direction and potential ranges, which can be just as valuable for planning.
• Keep models updated: Build a feedback loop that integrates new data quickly to improve accuracy over time.
• Invest in data quality: Strengthen governance to ensure data from different teams is consistent, complete, and ready for modelling.

Methods

Following the CRISP-DM process, the study combined internal monthly revenue data for 46 clients (2020–2024) with macroeconomic data from the World Bank. Data cleaning and integration were done using Python’s Pandas and NumPy. Models were trained on 2020–2023 data and tested on 2024 results, with accuracy measured using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². XGBoost forecasts were also run through Monte Carlo simulation to model best- and worst-case scenarios.

Thema

Diese Masterarbeit untersucht, wie HR-Verantwortliche die Fairness und Objektivität von KI-gestützten Tools im Vergleich zu traditionellen Auswahlverfahren bewerten – insbesondere im Hinblick auf die kulturelle Passung von Bewerbenden. Im Fokus steht die Frage, ob der Einsatz solcher Technologien im Rekrutierungsprozess als sinnvoll erachtet werden. Ziel der Arbeit war es, Gedanken, Überlegungen sowie Emotionen der HR-Verantwortlichen in Bezug auf diese Thematiken herauszufinden.

Relevanz

Das Thema ist mit der aufkommenden KI-Technologie hochaktuell, da Unternehmen immer wie mehr auf KI-gestützte Tools im Rekrutierungsprozess setzen. Besonders bei der Beurteilung der kulturellen Passung, einem sehr subjektiven schwer greifbaren Aspekt bei der Personalauswahl, stellen sich Fragen zur Objektivität, Fairness und Transparenz solcher Tools. Die Ergebnisse sind insbesondere für Mitarbeitende relevant, die strategische Entscheidungen über technologische Innovationen im HR treffen müssen. Dabei liefert die Studie wichtige Aspekte zur Entscheidungsgrundlage für oder gegen ein solches Tool.

Ergebnisse

Im Rahmen der Studie konnten folgende Erkenntnisse gewonnen werden:

1. kulturelle Passung wird als subjektives, aber entscheidendes Kriterium im Auswahlprozess verstanden
2. HR-Recruiterinnen und -Recruiter sehen die Objektivität von KI zwar positiv, empfinden den Einsatz aber als unfair.
3. Vertrauen, Transparenz und Nachvollziehbarkeit fehlen in Bezug auf KI-gestützte Tools
4. Emotionale Vorbehalte überwiegen trotz grundsätzlicher Offenheit gegenüber KI
5. Verantwortung für Entscheidungen werden weiterhin klar beim Menschen gesehen – KI soll unterstützen, aber nicht entscheiden.

Implikationen für die Praxis

Die Studie identifiziert drei zentrale Zielgruppen im Kontext des Einsatzes von KI-gestützten Tools zur Beurteilung der kulturellen Passung:

• Für die HR-Leitung ist entscheidend, ob der Einsatz von KI-gestützten Tools sinnvoll ist, ethischen Richtlinien entspricht und Vertrauen bei den Mitarbeitenden durch Transparenz und Schulungen aufgebaut wird.
• HR-Mitarbeitende akzeptieren KI nur, wenn die Beurteilung der kulturellen Passung fair, objektiv und nachvollziehbar erfolgt, wobei Minderheiten berücksichtigt werden müssen.
• Programmierer müssen Biases bei der Datengrundlage vermeiden und durch umfangreiche Tests sicherstellen, dass das Tool diskriminierungsfrei und diversitätsfördernd arbeitet.

Ein zentraler Punkt ist, dass der tatsächliche Mehrwert von KI im HR-Prozess kritisch hinterfragt werden muss, da Vertrauen und Fairness die Grundlage für eine erfolgreiche Implementierung bilden.

Methodik

Die Studie basiert auf 16 halbstrukturierten Interviews mit HR-Expertinnen und -Experten und verwendet die Grounded Theory für das qualitative Forschungsdesign. Der Interviewleitfaden stützt sich auf das Trust-Modell sowie die Erweiterung von Wrang et al. (2020) und beinhaltet sowohl offene wie auch Skalenfragen. Die Interviews wurden transkribiert, codiert und systematisch ausgewertet. Dabei wurden «first» sowie «second Order Categories» gebildet. Gemeinsamkeiten und Unterschiede wurden eruiert, gebündelt und zu sechs aggregierten Dimensionen zusammengefasst. Die aggregierte Dimension richtet sich somit an der Forschungsfrage aus.

This thesis examines the deployment of ERP in Swiss SMEs, with a particular focus on key issues such as resource scarcity and change aversion. It provides valuable insights for enhancing project outcomes by including the viewpoints of ERP providers' experts as well as their clients. The results can serve as a basis for further research for the implementation of ERP systems in SMEs.

Relevance

The thesis shows the drivers of successful ERP implementation and the challenges and benefits. This could be used by SMEs to make strategic decisions on ERP implementation and shows them what might lead to a smooth implementation, what they could win with the implementation, and the obstacles they might face. They can learn from the experts and client interviews.

Results

The findings indicate that both groups view ERP as beneficial for data centralization and task automation. Clients emphasize practical concerns, such as training and support, while experts stress the importance of preparation and process adaptation. Key success factors include leadership, communication, and trust, offering a foundation for future research on ERP implementation.

Implications for Practitioners

This thesis examines the perspectives of clients and experts on ERP implementation in Swiss small and medium-sized enterprises (SMEs). It highlights key success factors such as leadership, communication, and trust, and emphasizes the need to align technical solutions with organizational processes. The findings offer practical guidance for more effective ERP projects and show how important change management is, and the adaptation of the corporate culture in a digitalization process.

Methods

This qualitative single case study focused on the Swiss ERP provider OPACC AG and six of its SME clients. Data were collected through semi-structured interviews with ten ERP experts and six client representatives. Thematic analysis was conducted to explore key challenges, success factors, and differing perspectives on ERP implementation.

Trait Attribution in Bernese Swiss-German and Standard German.

Topic

This thesis investigates the treatment of dialectal variation by Large Language Models (LLMs), focusing on Bernese Swiss-German in comparison to Standard German. Employing a matched-guise probing framework, this study demonstrates that semantically equivalent sentences can elicit biased social trait attributions based solely on dialectal form. Notably, the direction and intensity of this bias differ according to the model type, underscoring a "paradigm divide" in how different architectures manage linguistic variation. This study introduces a culturally grounded computational approach for assessing AI fairness within a non-English diglossic context.

Relevance

As LLMs are increasingly utilized in high-stakes domainssuch as hiring, education, and social services, their latent linguistic biases present significant risks. While much of the current fairness research concentrates on gender or racial bias, dialect bias remains underexplored, particularly in languages other than English. When AI systems are predominantly trained on standardized language, they risk misinterpreting or disadvantaging speakers of non-standard dialects, thereby perpetuating existing sociolinguistic hierarchies. This study offers a diagnostic framework for identifying such biases, which is particularly pertinent for developers and policymakers seeking to design linguistically inclusive AI.  

Results

The study's results revealed all models tested exhibited systematic dialectal bias; however, the direction and nature of the bias were heavily dependent on the architecture. Instruction-tuned models associated Bernese Swiss-German with more negative traits. Multilingual masked models (e.g., XLM-RoBERTa) preferentially treat Standard German. Interestingly, monolingual German models (e.g., German-BERT and GigaBERT) provided more favorable and balanced evaluations of the Bernese dialect. These findings reveal a significant architectural effect: bias is not merely a function of data but also of the design.

Implications for Practitioners

• Conduct architecture-specific audits of models to identify unique bias characteristics instead of relying solely on general bias testing.
• Evaluate each model type using model-aware assessments, as one benchmark cannot capture the complexity of model biases.
• Recognizing that scaling models may not eliminate dialectal bias and could even exacerbate covert stereotypes.
• Ensure diversity in training data and strive for representational fairness, while acknowledging that standard benchmarks often miss subtle bias harm.
• Utilize matched-guise probing techniques from sociolinguistics to uncover implicit language ideologies in model outputs, and consider employing localized models for region-specific tasks. Monolingual models trained on culturally relevant corpora may outperform large multilingual systems when fairness and dialect sensitivity are critical. 

Methods

This study used Matched Guise Probing (MGP), adapted from sociolinguistics, to evaluate computational models. The components included the following: Dataset: 200 sentence pairs in Bernese Swiss-German and Standard German, semantically equivalent to the SwissDial corpus. Models evaluated: Eight models — four instruction-tuned models (GPT-4o, GPT-3.5, Claude 3 Opus, Gemini 1.5 Pro) and four masked language models (German-BERT, GigaBERT, XLM-RoBERTa Base and Large). Scoring: For masked models, log probability ratios were used on 39 culturally relevant adjectives. For instruction-tuned models, trait selection was based on a predefined list. Analysis: Bias was assessed by comparing trait associations across dialects and models, and examining valence, frequency, and trait distribution.

Topic

This Master's thesis investigates the conflicts that arise among diverse stakeholders during digital innovation projects within hospitals and explores how these conflicts can be effectively understood and addressed. It argues that digital transformation in healthcare frequently faces obstacles not from technological deficiencies, but from unresolved tensions between different organizational rationalities such as medical, technical, and economic logics. Understanding these inherent conflicts is crucial for enabling sustainable digital transformation in healthcare settings.

Relevance

This thesis is highly relevant for practitioners, including hospital leaders and project managers, as it provides concrete insights into the common stakeholder conflicts that impede digital innovation in healthcare. By identifying eight recurring conflict types and analyzing strategies that have proven successful, it offers actionable guidance to anticipate, mitigate, and resolve these human and organizational frictions. This understanding is essential for translating promising digital solutions into successful, adopted practices and avoiding costly project delays or failures.

Results

The study identified eight recurring conflict types in hospital digital innovation projects, including resource limitations, misaligned expectations, role ambiguity, and siloed communication. These conflicts stem from competing rationalities (clinical, technical, economic, political, scientific). While some projects failed due to unresolved tensions, others succeeded by implementing strategies such as cross-functional roles, inclusive governance, and iterative implementation approaches. The findings emphasize that successful digital innovation in hospitals depends on navigating institutional complexity and strategically aligning around shared public value.

Implications for Practitioners

• Lead with a Grand Vision Framed by Public Value: Frame digital innovation projects around their contribution to broader public value, such as improved patient outcomes and enhanced system efficiency, to unify diverse stakeholder interests and gain buy-in.
• Invest in Bridging Capacities and Translation: Strategically invest in roles like CMIOs and CNIOs and foster cross-functional teams to translate between different rationalities (e.g., clinical and IT), addressing communication barriers and role ambiguities.
• Practice Adaptive and Iterative Implementation: Adopt iterative approaches and pilot phases for projects to demonstrate early value, gather real-world evidence, and adapt strategies based on feedback, thereby managing expectations and building trust.
• Foster Inclusive Governance and Ensure Fair Distribution of Burdens: Implement transparent decision-making pathways and actively involve diverse stakeholders. Proactively address and mitigate perceived losses or increased burdens for certain groups through open communication and compensatory mechanisms.
• Cultivate a Culture of Continuous Learning: Establish rigorous evaluation processes during and after projects to objectively analyze what hindered or stopped progress, identifying patterns of unrecognized omissions to prevent recurrence and foster organizational learning.

Methods

This study employed a qualitative research design with an exploratory and descriptive approach to understand stakeholder perceptions and experiences during digital innovation projects in hospitals. Data was collected through 18 semi-structured interviews with key stakeholders from clinical, IT, administrative, research, and vendor domains in Swiss and Austrian hospitals. The sampling strategy was purposive, focusing on individuals with direct project involvement. Data analysis utilized qualitative content analysis, combining deductive coding based on Multirationality Theory with inductive open coding to identify both theory-driven and emergent themes, facilitated by MAXQDA software.

My Master’s thesis gives insight into the organisational "hurdle race" faced by highly skilled refugees from the Middle East and North Africa (MENA) as they strive to become part of Switzerland’s IT landscape. My research builds on the "canvas ceiling" unearthed by Lee et al. (2020), which consists of a systemic barrier inhibiting the integration of refugees. Through in-depth conversations with employers, support organisations, and the refugees themselves, I aim to understand the hurdles facing highly skilled refugees as well as the mitigators that support their integration. These mitigators help highly skilled refugees win the hurdle race within Swiss IT.

Relevance for Practitioners

The integration of highly skilled refugees is crucial for addressing talent shortages in Swiss IT, yet many potential employers lack practical strategies or support frameworks to include this underutilised workforce. By revealing both the obstacles and mitigating practices, my thesis offers actionable insights for HR professionals, talent managers, and organisational leaders on how they can participate in integrating highly skilled refugees, and which factors ensure long-term success.

Results

Three major hurdles were identified that highly skilled refugees need to overcome: a set of workplace norms that devalue their foreign credentials, the impact of the hiring market which limits their access to suitable roles, and the absence of internal frameworks for long-term integration. These hurdles are best addressed when (1) support organisations like Powercoders optimise refugee-employer fit through upskilling of refugees and networking, (2) motivated internal actors within firms champion refugee hiring, and (3) organisations commit to long-term integration strategies and not just one-off placements.

Implications for Practitioners

• Partner with support organisations that preselect and upskill highly skilled refugees.
• Advance topic within the organisation by collaborating with motivated individuals within the company.
• Use internships with clear development plans as structured entry points for highly skilled refugees.
• Build long-term commitments for refugee integration into HR strategy (e.g., pipeline targets or dedicated roles).

Methods

This study employed a qualitative research design, combining ten semi-structured interviews: four with highly skilled refugees, five with recruitment decision makers in various Swiss IT companies, and one with Powercoders, a non-profit support organisation. Thematic analysis was conducted using the Gioia methodology to build a data-driven model of hurdles and mitigators. Participants were selected for diversity in background and industry. Interviews were transcribed, coded, and analysed to determine patterns in the data and distill this in both theoretical and practical insights.

Die Masterarbeit untersucht, wie kleine und mittlere Unternehmen (KMU) der produzierenden Industrie in der Schweiz Corporate Social Responsibility (CSR) strategisch umsetzen. Im Fokus stehen die Herausforderungen, Entscheidungslogiken und Spannungsfelder, denen Entscheidungsträger bei der Integration von CSR-Initiativen im betrieblichen Alltag begegnen.

Relevanz

CSR wird zunehmend auch für KMU zu einem wettbewerbsrelevanten Thema, nicht zuletzt aufgrund steigender regulatorischer, gesellschaftlicher und marktseitiger Erwartungen. Dennoch fehlt es häufig an Forschung zu deren spezifischen Umsetzungsbedingungen. Die Arbeit leistet einen Beitrag zum besseren Verständnis von CSR in einem Sektor mit hoher wirtschaftlicher Bedeutung und praxisnahen Anforderungen.

Ergebnisse

Die Ergebnisse zeigen, dass CSR in KMU meist informell, pragmatisch und werteorientiert verankert ist. Herausforderungen bestehen insbesondere in Ressourcenkonflikten und fehlender strategischer Systematik. Dennoch zeigen sich viele Unternehmen offen für nachhaltige Praktiken, sofern diese mit den betrieblichen Zielen vereinbar sind. Persönliche Überzeugungen der Führungskräfte spielen eine zentrale Rolle.

Implikationen für Praktiker:innen

• CSR sollte an die betrieblichen Ressourcen und Entscheidungslogiken angepasst werden.
• Persönliche Werte der Führungskräfte können als CSR-Treiber gezielt gestärkt werden.
• Eine formalisierte CSR-Governance hilft, Nachhaltigkeit langfristig zu verankern.
• Kooperationen mit externen Partnern (z. B. Branchenverbände) können Umsetzung erleichtern.
• CSR-Massnahmen sollten mit dem Kerngeschäft verzahnt sein, um Akzeptanz zu fördern.

Methoden

Zur Beantwortung der Forschungsfrage wurde ein qualitatives, exploratives Design gewählt. Es wurden zehn leitfadengestützte Experteninterviews mit Geschäftsführenden, Inhabenden und CSR-Verantwortlichen aus KMU der produzierenden Industrie in der Schweiz durchgeführt. Die Auswertung erfolgte anhand der thematischen Inhaltsanalyse nach Kuckartz. Die Interviewdaten wurden softwaregestützt codiert und in thematischen Clustern zusammengeführt, um Handlungsmuster, Spannungsfelder und Unterschiede in der CSR-Implementierung zu identifizieren.