Masterarbeiten unter der Betreuung von Prof. Fuchs

Für das Wintersemester 2026/27 können Sie Ihre Bewerbung ab 13.04.2026 einreichen. Deadline: 25.05.2026

Wenn Sie sich für eines der ausgeschriebenen Themen interessieren, senden Sie bitte Ihre Bewerbung per E-Mail an marketing.unit@univie.ac.at. Ihre Bewerbung sollte Folgendes enthalten:

• Exposé (auf Englisch | max. 5 Seiten)
• Lebenslauf (tabellarisch)
• Aktuelles Transcript of Records

Es wird erwartet, dass Sie die Arbeit innerhalb von 1 Semester abschließen!

Nach Fertigstellung der Thesis: Die folgenden Dokumente werden bei der Verwaltung und beim Lehrstuhl benötigt:

• Bei der Verwaltung: 3 Ausdrucke der Abschlussarbeit (SSC)
• Am Lehrstuhl: Elektronische Version der Abschlussarbeit als Word und PDF sowie Rohdaten und Analysedateien (z.B. SPSS- oder NVivo-Dateien).

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Available Topics for the winter semester 2026/27

TOPIC 1: Self vs. Other Bias

There is broad agreement that artificial intelligence will reshape the labor market on a massive scale, displacing and transforming millions of jobs in the coming years. Unlike earlier waves of automation that primarily replaced manual labor, modern AI systems can perceive, learn, act, and reason, enabling them to perform cognitive tasks that were once considered uniquely human. As a result, AI does not only threaten low-skill, repetitive jobs—historically the most vulnerable to automation—but also high-skill professions.

This “democratisation” of technological threat to employment means that workers across all income brackets, industries, and job roles will increasingly need to reskill and upskill to remain relevant in an evolving job market. Yet, this imperative to adapt raises an important question: do individuals perceive AI as a threat to their own jobs and feel compelled to act, or do they discount the odds that their own job will be replaced?

Against this backdrop, the following research questions may serve as starting points for developing your own project: Do people accurately perceive the risk that their own job could be replaced by AI? What factors shape individuals’ perceptions of the replaceability of their own job? What factors shape their perceptions of the replaceability of other people’s jobs? Are these two types of perceptions driven by the same underlying factors? Are people more accurate when judging the threat to their own job or to another job? How can individuals be debiased to more accurately assess their job’s replaceability? And how can the accuracy of perceived replaceability be measured?

Literature

Helweg-Larsen, M., & Shepperd, J. A. (2001). Do Moderators of the Optimistic Bias Affect Personal or Target Risk Estimates? A Review of the Literature. Personality and Social Psychology Review, 5(1), 74–95. doi.org/10.1207/S15327957PSPR0501_5

World Economic Forum. (2023, April 30). The Future of Jobs Report 2023 (4th ed.). World Economic Forum. www.weforum.org/publications/the-future-of-jobs-report-2023/digest/

TOPIC 2: Error signals in the age of AI

People who use AI tools are sometimes judged as lazier, less competent, or less diligent. Interestingly, similar negative judgments can arise from small human errors such as typos or minor inaccuracies. Yet in an era of ubiquitous AI assistance, such errors may communicate something that polished, AI-generated output often lacks: authenticity. This raises a central question. When AI assistance is widely available and often detectable, can small mistakes—in certain contexts—actually increase the appeal of a message compared to a perfectly polished one? And could errors, instead of uniformly signaling low competence or diligence, sometimes signal the opposite?

 For example, a minor typo in the solution to a difficult math problem could increase perceived competence by implying the individual genuinely engaged with challenging material instead of giving it to AI. Likewise, a typo in a personal gift card might reduce perceived laziness because it suggests the message was not simply copied from an AI tool. At the same time, in more professional or high-stakes contexts, errors may decrease competence and increase laziness perceptions even more strongly than before, because failing to correct them could indicate that the individual lacks the skill or initiative to use readily available AI tools that reliably detect and remove such mistakes.

Against this backdrop, the following research questions may serve as starting points for developing your own project: Do mistakes ever make people seem more competent in the AI era? If so, when and why does this happen? Do typos lead observers to infer lower AI usage, and does this inference mediate judgments of competence or character? Does the presence of AI tools change the meaning people attach to human errors? And are evaluations of mistakes different when the writer is assumed to have access to AI support?

Literature

Bleske-Rechek, A., Paulich, K., Shafer, P., & Kofman, C. (2019). Grammar matters: The tainting effect of grammar usage errors on judgments of competence and character. Personality and Individual Differences, 141, 47–50. doi.org/10.1016/j.paid.2018.12.016

Bluvstein, S., Zhao, X., Barasch, A., & Schroeder, J. (2024). Imperfectly Human: The Humanizing Potential of (Corrected) Errors in Text-Based Communication. Journal of the Association for Consumer Research, 9(3), 332–343. doi.org/10.1086/728412

Reif, J. A., Larrick, R. P., & Soll, J. B. (2025). Evidence of a social evaluation penalty for using AI. Proceedings of the National Academy of Sciences - PNAS, 122(19), e2426766122. doi.org/10.1073/pnas.2426766122

TOPIC 3: System-level solution to societal challenges

Public policy often seeks to change citizen behavior by targeting individuals rather than the systems in which they operate. These individual-level (“i-frame”) approaches—such as nudges, information campaigns, and reminders—aim to preserve freedom of choice while encouraging better decisions. Recent critiques, however, argue that an overreliance on i-frame interventions has led behavioral policy astray, as such approaches can be ineffective and place undue responsibility on individuals. System-level (“s-frame”) solutions—such as taxes, bans, or mandates—may be more effective at addressing societal challenges, yet are often perceived as intrusive and therefore risk triggering public resistance.

How do people evaluate system-level solutions compared to individual-level interventions? Under which conditions do citizens support “hard” system-level policies, such as bans or taxes, over “soft” individual-level measures like nudges or information provision? Does emphasizing collective benefits, shared responsibility, or long-term effectiveness increase acceptance of s-frame approaches? And how do individual differences—such as political ideology or beliefs about personal responsibility—shape support for or opposition to system-level interventions? 

This topic is suitable for confirmatory research. Analyze the literature to find a novel and relevant research topic, set up hypotheses, and test these hypotheses in an online experiment.

Literature

Banerjee, S., Savani, M., & Shreedhar, G. (2021). Public support for ‘soft’ versus ‘hard’ public policies: Review of the evidence. Journal of Behavioral Public Administration, 4(2). doi.org/10.30636/jbpa.42.220

Chater, N., & Loewenstein, G. (2023). The i-frame and the s-frame: How focusing on individual-level solutions has led behavioral public policy astray. Behavioral and Brain Sciences, 46, e147. doi.org/10.1017/S0140525X22002023

Connolly, D. J., Chater, N., & Loewenstein, G. F. (2025). A Political Psychology of Inequality. Elsevier BV. doi.org/10.2139/ssrn.5698542

Hofmann, W., Betsch, C., Böhm, R., de Ridder, D., Drews, S., Ewert, B., Hertwig, R., Sniehotta, F. F., & Mata, J. (2025). Rethinking behaviour change interventions in policymaking. Nature Human Behaviour, 9(9), 1765–1767. doi.org/10.1038/s41562-025-02284-5

TOPIC 4: Algorithm-mediated behavior

Algorithms are no longer merely tools but agents that participate in everyday life. They interact with humans directly and indirectly—moderating content, recommending songs, screening job applicants, and advising on or making ethically consequential decisions. While algorithms are often portrayed as fair and consistent, recent research suggests they can constrain the human experience by shifting agency, making biased decisions, diluting moral responsibility, and incentivizing dishonest behavior.

What are the unintended consequences of AI deployment for human behavior? And what are the effects on individuals and society? Do individuals feel less responsible for outcomes when an AI system is involved, even when they retain formal control? Does delegating decisions to algorithms encourage moral offloading and increase tolerance for unethical behavior? Are harmful actions judged differently when they are mediated by AI rather than carried out by humans? And how do design choices—such as anthropomorphism or transparency—shape compliance, trust, and moral judgment?

This topic is suitable for confirmatory research. Analyze the literature to find a novel and relevant research topic, set up hypotheses, and test these hypotheses in an online experiment.

Literature

Bonnefon, J.-F., Rahwan, I., & Shariff, A. (2024). The Moral Psychology of Artificial Intelligence. Annual Review of Psychology, 75(1), 653–675. doi.org/10.1146/annurev-psych-030123-113559

Köbis, N., Bonnefon, J.-F., & Rahwan, I. (2021). Bad machines corrupt good morals. Nature Human Behaviour, 5(6), 679–685. doi.org/10.1038/s41562-021-01128-2

Köbis, N., Rahwan, Z., Rilla, R., Supriyatno, B. I., Bersch, C., Ajaj, T., Bonnefon, J.-F., & Rahwan, I. (2025). Delegation to artificial intelligence can increase dishonest behaviour. Nature, 646(8083), 126–134. doi.org/10.1038/s41586-025-09505-x

Valenzuela, A., Puntoni, S., Hoffman, D., Castelo, N., De Freitas, J., Dietvorst, B., Hildebrand, C., Huh, Y. E., Meyer, R., Sweeney, M. E., Talaifar, S., Tomaino, G., & Wertenbroch, K. (2024). How Artificial Intelligence Constrains the Human Experience. Journal of the Association for Consumer Research, 9(3), 241–256. doi.org/10.1086/730709

TOPIC 5: LOST IN CONSUMPTION

Meeting globally set sustainability goals requires reduced consumption, as the currently predominate strategy of substituting regular products for sustainable alternatives does not suffice. Recent research, however, has shown that reduced consumption is neither top of mind for consumers nor do consumers perceive reduced consumption to be an effective strategy to live more sustainably. 
What are the main barriers to reduced consumption? How do consumers perceive others who consume less without following a specific goal to attain? Do reputational concerns keep consumers locked in consumption so that, for instance, poorer consumers seek to consume more in an effort not to seem poor? 
This topic is suitable for confirmatory research. Analyze the literature to find a novel and relevant research topic, set up hypotheses, and test these hypotheses in an online experiment.

Literature 

Makri, K., Schlegelmilch, B. B., Mai, R., & Dinhof, K. (2020). What We Know About Anticonsumption: An Attempt To Nail Jelly To The Wall. Psychology & Marketing, 37(2), 177-215.
Giesler, M., & Veresiu, E. (2014). Creating the responsible consumer: Moralistic governance regimes and consumer subjectivity. Journal of Consumer Research, 41(3), 840-857.

TOPIC 6: CONSUMPTION AND LITTER

Litter is a severe health and environmental risk and generally juxtaposed to consumers’ waste aversiveness. Yet, wherever there is consumption, litter is not far. Consumers may not only encounter litter in derelict urban environments but also in pristine nature. The environment may play a crucial role in whether and how litter affects consumers’ consumption values.
What happens when consumers encounter discarded products in their environment? Can litter raise awareness of a societal problem and thus make consumers question their own consumption habits or do the negative emotions elicited by litter trigger compensatory consumption? Does discarded packaging signal that consumption is normal and thus trigger materialism or would litter function as a moral cue that suppresses materialism? Would these effects depend on consumers’ traits, the environment the litter is experienced in, or the type of litter?
This topic is suitable for confirmatory research. Analyze the literature to find a novel and relevant research topic, set up hypotheses, and test these hypotheses in an online experiment.

Literature

Kasser, T., Ryan, R. M., Couchman, C. E., & Sheldon, K. M. (2004). Materialistic values: Their causes and consequences. In T. Kasser, & A. D. Kanner, Psychology and consumer culture: The struggle for a good life in a materialistic world (pp. 11 - 28). American Psychological Association.
Keizer, K., Lindenberg, S., & Steg, L. (2008). The spreading of disorder. Science, 322(5908), 1681–1685.
Rucker, D. D., & Gal, D. (2017). Compensatory consumption. Current Opinion in Psychology, 10, 121–125.

TOPIC 7: ADVERTISING AND MATERIALISM

Materialism poses a significant problem for people and the planet as a continuous striving for possessions and the ensuing excessive consumption is ecologically unsustainable and does not fulfill people long-term. Despite materialism’s detrimental consequences on people and the planet, strategies to counter people’s materialistic values are scant. Most of the literature seeking antidotes to materialism either focuses on fringe consumer groups living alternative lifestyles or link diametrically opposed concepts like gratitude to a reduction in materialism. Neither approach, however, allows for actionable interventions. 
How could materialism be reduced on the large scale? Could ad blockers reduce materialism? Are all ads contributing equally or do some ads elicit more materialism than other?

Literature
 
Bauer, M. A., Wilkie, J. E., Kim, J. K., & Bodenhausen, G. V. (2012). Cuing consumerism: Situational materialism undermines personal and social well-being. Psychological Science, 23(5), 517-523.
Flynn, L. R., Goldsmith, R. E., & Pollitte, W. (2016). Materialism, status consumption, and market involved consumers. Psychology & Marketing, 33(9), 761-776.
Kasser, T., Ryan, R. M., Couchman, C. E., & Sheldon, K. M. (2004). Materialistic values: Their causes and consequences. In T. Kasser, & A. D. Kanner, Psychology and consumer culture: The struggle for a good life in a materialistic world (pp. 11 - 28). American Psychological Association.

Masterarbeiten unter der Betreuung von Prof. Eisend

Für das Wintersemester 2026/27 können Sie Ihre Bewerbung ab 13.04.2026 einreichen. Deadline: 25.05.2026

Wenn Sie sich für eines der ausgeschriebenen Themen interessieren, senden Sie bitte Ihre Bewerbung per E-Mail an marketing.unit@univie.ac.at. Ihre Bewerbung sollte Folgendes enthalten:

• Exposé (auf Englisch | max. 5 Seiten)
• Lebenslauf (tabellarisch)
• Aktuelles Transcript of Records

Es wird erwartet, dass Sie die Arbeit innerhalb von 1 Semester abschließen!

Nach Fertigstellung der Thesis: Die folgenden Dokumente werden bei der Verwaltung und beim Lehrstuhl benötigt:

• Bei der Verwaltung: 3 Ausdrucke der Abschlussarbeit (SSC)
• Am Lehrstuhl: Elektronische Version der Abschlussarbeit als Word und PDF sowie Rohdaten und Analysedateien (z.B. SPSS- oder NVivo-Dateien).

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Available Topics for the winter semester 2026/27

TOPIC 1: Effects of Older Endorsers in Advertising

Despite the increasing market size and consumption power of older consumers, older people seldom appear in advertising. One of the reasons might be that consumers react more negatively towards portrayals of older consumers as compared to younger consumers, presumably because the depictions of the elderly apply negative stereotpyes. How do consumers respond to portayals of older endorsers? 

Please explain and suggest hypotheses on the effects of older endorsers on consumers and what these effects could depend on. 

The hypotheses should be tested in an experimental study that manipulates endorser age along with another independent variable and measures consumer responses ad dependent variables. 

Starting Literature:

Huber et al. (2013), Endorser Age and Stereotypes: Consequences on Brand Age, Journal of Business Research, 66, 207-15.

Kwon et al. (2015), Who Said What: The Effects of Cultural Mindsets on Perceptions of Endorser-Message Relatedness, Journal of Consumer Psychology, 25(3), 389-403.

Prieler/Kohlbacher (2016). Advertising in the Aging Society. Understanding Representations, Practitioners, and Consumers in Japan. Houndmills: Palgrave Macmillan.

Simcock/Sudbury (2006), The Invisible Majority? Older Models in UK Television Advertising, International Journal of Advertising, 25(1), 87-106.

TOPIC 2: AI-Generated Advertising

Advertisers use artificial intelligence (AI) algorithms to generate advertising content and to personalize advertising messages. How do consumers react towards ads that are generated by AI?

Please explain and suggest hypotheses on the effects of different ads that have been generated by AI on consumers and what these effects could depend on. The hypotheses should be tested by an experimental study that manipulates AI-generated ad content (disclosures) along with another independent variable (e.g., product characteristic or consumer characteristics) and measures consumer responses as dependent variables. 

Starting Literature:

Campbell et al. (2022), Preparing for an Era of Deepfakes and AI-Generated Ads: A Framework for Understanding Responses to Manipulated Advertising, Journal of Advertising, 51(1), 22-38.

Kietzmann et al. (2020), Deepfakes: Perspectives on the Future “Reality” of Advertising and Branding, International Journal of Advertising, 40(3), 473-485.

We/Wen (2021), Understanding AI Advertising From the Consumer Perspective, Journal of Advertising Research, 61(2), 133-146.

TOPIC 3: Product Aesthetics

As product functionalities become increasingly similar across markets, many firms emphasize aesthetics in design, assuming that consumers respond positively to visually appealing products. How do consumers respond to products of high visual aesthetics? 

Please explain and suggest hypotheses on the effects of product visual aesthetics on consumers and what these effects could depend on. The hypotheses should be tested by an experimental study that manipulates product aesthetics (e.g., absent/present or low/high) along with another independent variable (e.g., product characteristic or consumer characteristics) and measures of consumer responses as dependent variables.

Starting Literature:

Bloch (1995), Seeking the Ideal Form: Product Design and Consumer Response, Journal of Marketing, 59(3), 16-29.

Liu et al. (2017), The Effects of Products' Aesthetic Design on Demand and Marketing-Mix Effectiveness: The Role of Segment Prototypicality and Brand Consistency, Journal of Marketing, 81(1), 83-102

Wu et al. (2017), It’s Too Pretty to Use! When and How Enhanced Product Aesthetics Discourage Usage and Lower Consumption Enjoyment, Journal of Consumer Research, 44(3), 651-672.

TOPIC 4: Virtual Influencers

Virtual influencers, computer-generated personas designed to engage audiences on social media platforms, have rapidly emerged as a new generation of product endorsers in influencer marketing. Despite growing practitioner adoption the effectiveness of virtual influencers is still debated in the literature. , (2) the boundary conditions that shape their impact, and (3) the mechanisms through which they influence consumer responses. Empirical findings on the effectiveness of virtual influencers are heterogeneous, particularly regarding persuasion outcomes. When are virtual influencers effective?

Please explain and suggest hypotheses on the effects of virtual influencers and their characteristics on consumers. The hypotheses should be tested by an experimental study that manipulates virtual influencer characteristics (e.g., anthropomorphism) or compares them to human endorsers along with another independent variable (e.g., product characteristic or consumer characteristics) and measures consumer responses as dependent variables. Alternatively and if data access and methodlogical capabilities are available, you can scrape data online about consumer responses to different virtual.

Starting Literature:

Franke, Groeppel-Klein, & Müller (2023), Consumers’ Responses to Virtual Influencers as Advertising Endorsers: Novel and Effective or Uncanny and Deceiving?, Journal of Advertising, 52(4), 523-539

Ma & Li (2024), How Humanlike is Enough?: Uncover the Underlying Mechanism of Virtual Influencer Endorsement, Computers in Human Behavior: Artificial Humans, 2(1), 100037

Zhou, Yan, & Jiang (2024), Making Sense? The Sensory-Specific Nature of Virtual Influencer Effectiveness, Journal of Marketing, 88(4), 84-106.

TOPIC 5: Body Positivity in Advertising

While advertising has traditionally portrayed unrealistic and idealized body standards, a growing number of brands have recently embraced more attainable and diverse body representations, referred to as body positivity. However, research on the effectiveness of body-positive advertising yields mixed findings, both in terms of advertising effects (e.g., attitudes, purchase intentions) and extended effects (e.g., body image, self-esteem). How do consumer respond to body positive portrayals in advertising?

Please explain and suggest hypotheses on the effects of body positive portrayals in advertising on consumers and what these effects could depend on. The hypotheses should be tested in an experimental study that manipulates body positive portrayals of advertising endorsers along with another independent variable (e.g., product characteristic or consumer characteristics) and measures consumer responses towards advertising as well as extended effects (e.g., their body image and/or satisfaction) as dependent variables.

Starting Literature:

Bhattacharjee, Pradhan, Kuanr, & Malhotra (2025), Perfectly Imperfect: How Body-Positive Advertisements in Social Media Foster Consumer Engagement, Journal of Advertising, 54(1), 79-98.

Matera, Casati, Paradisi, Gesto, & Nerini (2024), Positive Body Image and Psychological Wellbeing among Women and Men: The Mediating Role of Body Image Coping Strategies. Behavioral Sciences, 14(5), 378. 

Rodgers (2025), Love Your Body! An Exploration of New Empowerment Discourse as Related to Body Capital, and Body Image and Eating Concerns Among Women, Body Image, 54, 101930.

TOPIC 6: Green Marketing Communication

Brands increasingly promote their environmental credentials through green marketing communication, but brands face a dual challenge when deploying GMC: meeting rising demand for sustainability while avoiding accusations of greenwashing and ensuring that claims are both credible and effective. How effective is green marketing communication?

Please explain and suggest hypotheses on the effects of green marketing communication on consumers and what these effects could depend on. The hypotheses should be tested in an experimental study that manipulates green marketing communication (either different types of as opposed to non-green marketing communication) along with another independent variable (e.g., product characteristic or consumer characteristics) and measures consumer responses towards green marketing communication.

Starting Literature:

Hartmann, & Apaolaza-Ibáñez (2009), Green Advertising Revisited, International Journal of Advertising, 28(4), 715-739.

Leonidou, Katsikeas, & Morgan (2013), "Greening" the Marketing Mix: Do Firms Do It and Does It Pay Off?, Journal of the Academy of Marketing Science, 41(2), 151-170.

Olsen, Slotegraaf, & Chandukala (2014), Green Claims and Message Frames: How Green New Products Change Brand Attitude, Journal of Marketing, 78(5), 119-137.

TOPIC 7: Price Fairness

High inflation rates have increased the price sensitivity of consumers who tend to evaluate price increases more carefully. A key variable in the evaluation process is the perceived fairness of price changes. If consumers perceive a price chagen as fair, they react more positive and vice versa. What does consumers’ price fairness perceptions of price changes depend on?

Please explain and suggest hypotheses on the effects of price changes on consumers’ price fairness perceptions and what these effects could depend on. The hypotheses should be tested by an experimental study that manipulates price change motives (e.g., justified/unjustified price change) or price changes along with another independent variable (e.g., product characteristic or consumer characteristics) and measures of price fairness perceptions and other consumer responses as dependent variables.

Starting Literature:

Bolton et al. (2003), Consumer Perceptions of Price (Un)Fairness, Journal of Consumer Research, 29(March), 474-91.

Tarrahi et al. (2016). A Meta-Analysis of Price Change Fairness Perceptions. International Journal of Research in Marketing, 33(1), 199-203.

Xia et al. (2004), The Price Is Unfair! A Conceptual Framework of Price Fairness Perceptions, Journal of Marketing, 68(October), 1-15.

Betreute Themenbereiche:

• Responsible Marketing
• Consumer Well-being
• New Developments in Marketing
  
  
• Balancing Needs in Consumers' various Life Domains
• Work-Life Balance & Employer Branding (ev.: Across Generations)
• Transformative Marketing

Masterarbeiten unter der Betreuung von Prof. Reisinger

Für das Wintersemester 2026/27 können Sie Ihre Bewerbung ab 13.04.2026 einreichen. Deadline: 25.05.2026

Das Exposé ist in deutscher Sprache zu erstellen (max. 5 Seiten, 1,5 zeilig) und direkt an Herr Prof. Reisinger zu übersenden. Das Exposé sollte Motivation, Forschungsfrage, Hypothesen, ein grobes Konzept für die empirische Analyse und ausgewählte Referenzen beinhalten (die Referenzen zählen nicht zum Seitenlimit).

Masterarbeiten unter der Betreuung von Prof. Sichtmann

For the winter semester 2026/27, you can submit your application for a master thesis until June 19^th, 2026.

If you are interested in one of the advertised topics, please send your application via email to christina.sichtmann@univie.ac.at. Your application should include the following documents:

• Exposé (in English | max. 2 pages)
• Curriculum vitae (in tabular form)
• Current transcript of records

It is expected that you complete your thesis within one semester. Target submission date of the master thesis: March 31st, 2027.

The thesis must be written in English.

Applications for the suggested topics will be given priority. There will be up to three students assigned to the suggested topics with different focuses. Please be specific in terms of the focus of your thesis. Ideas to specify your topic are given below.

You may also submit your own topic proposal, provided it is methodologically based on a quantitative empirical or experimental study.

Students studying under the old curriculum will be given preferential consideration.

For guidance on how to structure your exposé, you can refer to the following podcast episode:
"Du schaffst das – Erfolgreich deine Abschlussarbeit schreiben”,
Podcast by Christina Sichtmann, Episode 12: “Das Exposé – Der gedankliche Entwurf deiner Arbeit”
Link: https://www.sichtmann.de/du-schaffst-das/-12

You will be notified by June 26, 2026 whether you have been assigned a master thesis.

There will be two master colloquia (Masterkonversatorien): one held in German and one in English. Presentations in both colloquia will be conducted in English. The subsequent discussion will be held in German or English, depending on the language of the respective colloquium.

Please indicate your preference regarding which colloquium you would like to attend when submitting your application. Please note that this does not guarantee assignment in the preferred colloquium.

Please already take note of the following important dates:

26.06.2026 Notification of acceptance or rejection of supervision

30.06.2026, 09.45-16.30 Preliminary meeting at the OMP or online (please keep this day free; you will be assigned a specific time slot)

Formatting Requirement for the proposal:

·        Margins: 2.5 cm on all sides (top, bottom, left, right)

·        Line spacing: 1.5

·        Font: Times New Roman

·        Font size: 11 pt

·        Text alignment: Justified

·        Language: English

·        Length: Max. 2 pages (excluding references)

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Available Topics for the summer semester 2026

1. Exploring Drivers of Reduced Consumption for Climate and Sustainability Goals

Description:

In light of the climate crisis, biodiversity loss, and resource scarcity, demand-side strategies—especially reducing individual consumption—are gaining increasing attention as a necessary complement to technology-based solutions. However, consumer acceptance of such reduction strategies remains limited, especially in affluent societies characterized by materialistic values and consumerist lifestyles.

This master thesis aims to empirically investigate which psychological, social, and contextual, etc. factors influence individuals’ willingness to reduce consumption (e.g., buy less, live more simply, reduce flying or car use).

The thesis can be designed as either:

·       a survey-based study examining the relationship between various motivational, attitudinal, or identity-related factors and willingness to reduce consumption, or

·       an experiment where an independent (manipulated) variable influences a variable representing consumption reduction

The proposal should specify the dependent variable representing consumption reduction and factors that influence this variable:

1. Dependent variable (representing consumption reduction)

·       voluntary simplicity

·       anti-consumption

·       frugality

·       consumption reduction

·       non-consumption

·       anti-materialism

·       …

2. Independent (in an experiment: manipulated) variable

·       Psychological Dispositions / Personality Traits

·       Social or Cultural Factors

·       Situational or Product-Related Factors

·       Communication Style / Message Framing (for experimental designs)

·       Intervention / Policy

Literature

Blackburn, R., Leviston, Z., Walker, I., & Schram, A. (2024). Could a minimalist lifestyle reduce carbon emissions and improve wellbeing? A review of minimalism and other low consumption lifestyles. Wiley Interdisciplinary Reviews: Climate Change, 15(2), e865.

Peng, F., Long, A., Chen, J., & Kang, K. Q. (2024). A narrative review of Environmentally Oriented Anti-consumption: Definitions, dimensions, and research framework. Sustainable Production and Consumption.

Rabaa, S., Geisendorf, S., & Wilken, R. (2022). Why change does (not) happen: Understanding and overcoming status quo biases in climate change mitigation. Zeitschrift Für Umweltpolitik Und Umweltrecht, 45(1), 100-134.

Rabaa, S., Wilken, R., & Geisendorf, S. (2024). Does recalling energy efficiency measures reduce subsequent climate-friendly behavior? An experimental study of moral licensing rebound effects. Ecological Economics, 217, 108051.

Riefler, P., Baar, C., Büttner, O. B., & Flachs, S. (2024). What to gain, what to lose? A taxonomy of individual-level gains and losses associated with consumption reduction. Ecological Economics, 224, 108301.

2. The Impact of Diversity and Inclusion Initiatives on Brand Perception and Consumer Behavior – An Experimental Study

Description

In a world facing increasing social inequalities across gender, race, and socioeconomic status, brands are under growing moral and economic pressure to embrace Diversity, Equity, and Inclusion (DEI). Many companies are adopting Customer Diversity Initiatives (CDIs) that aim to reflect and represent a broader spectrum of consumer identities—particularly those from historically underrepresented groups defined by gender, age, body type, social class, or religion.

These initiatives may include inclusive advertising (e.g., featuring non-binary models or older individuals), product adaptations (e.g., adaptive clothing), or campaigns that explicitly signal a brand's commitment to DEI. While such efforts can enhance perceived authenticity and brand appeal, they can also elicit mixed reactions or even backlash among certain consumer segments.

This master thesis aims to explore the effects of CDIs on brand perception and consumer responses using an experimental research design. The core research questions are:

·       How do different types of DEI initiatives influence consumer attitudes, emotional reactions, and behavioral intentions?

·       Under what conditions do such initiatives lead to positive versus negative consumer responses?

Key design elements for the experiment include:

1. Dependent variables:

Students can investigate how DEI initiatives affect variables such as:

·       Brand trust or brand sympathy

·       Purchase intention or willingness to pay

·       Perceived authenticity or inclusivity

·       Emotional responses (e.g., inspiration vs. irritation)

·       Reactance or resistance (e.g., perceived "woke-washing" or political overreach)

·       …

2. Variation in DEI stimuli:

The experimental stimuli can include variations such as:

·       Diverse vs. non-diverse models in advertising

·       Inclusive vs. neutral brand language

·       Product adaptations for specific identity groups (e.g., gender-neutral fashion)

·       Emphasis on DEI in advertising vs. conventional branding

·       …

3. Possible moderators:

The study can also explore when and for whom DEI messaging is effective (or not). Potential moderating factors include:

·       Consumer attitudes toward DEI / political orientation

·       Identification with the represented group(s)

·       Pre-existing brand attachment or loyalty

·       Demographics (e.g., age, gender, regional background)

·       …

References:

Uduehi, E., Saint Clair, J. K., Hamilton, M., & Reed, A. (2025). When Diversity Backfires: The Asymmetric Role of Multicultural Diversity on Brand Perception. Journal of Consumer Research, ucae068.

D’Angelo, J. K., Dunn, L., & Valsesia, F. (2025). Is this for me? Differential responses to skin tone inclusivity initiatives by underrepresented consumers and represented consumers. Journal of Marketing, 89(2), 25-42.

El Hazzouri, M., Main, K. J., & Carvalho, S. W. (2017). Ethnic minority consumers reactions to advertisements featuring members of other minority groups. International Journal of Research in Marketing, 34(3), 717–733.

Hassan, L. M., McGowan, M., & Shiu, E. (2025). They’re not my people: When inclusive marketing backfires. Journal of the Academy of Marketing Science, 53, 563–587.

Rodriguez-Vila, O., Nickerson, D., & Bharadwaj, S. (2024). How inclusive brands fuel growth. Harvard Business Review, 103(5-6), 114.

3. Teaching Sustainability: Which Educational Interventions Enhance Sustainable Decision-Making in Business Students?

Description

This master thesis investigates how different educational interventions influence sustainable decision-making among business students. As future managers and leaders, business graduates will shape the sustainability trajectory of companies and markets. However, traditional business education often emphasizes profit-maximization and short-term performance over long-term social and environmental goals.

The key objective of this thesis is to identify which type of classroom-based intervention is most effective in promoting sustainability-oriented thinking and decisions in students. To that end, students will conduct a controlled experiment, comparing the effects of different learning formats (independent variable) on specific decision behaviors or attitudes (dependent variable).

Key design elements for the experiment include:

1. Independent (manipulated) variables:

The intervention must be something that could feasibly take place in a university or executive education setting. You will define 1–2 different intervention types to test in the experiment against a control group or in a before/after experimental design. Possible conditions might include:

·       Writing a reflective essay on sustainability dilemmas

·       Participating in a case study discussion (e.g., Patagonia, Unilever, Shell)

·       Watching and discussing a documentary (e.g., The True Cost, Our Planet)

·       Engaging in a role-play or simulation game (e.g., stakeholder negotiation)

·       Attending a guest lecture by a sustainability expert

·       Completing a sustainability-focused group project

·       Reading and debating an ethical controversy in business

·       Experiencing a visual storytelling prompt or narrative-based learning

·       …

2. Dependent variables:

The dependent variable should reflect a managerial decision that could differ in sustainability impact. The decision can be presented through vignettes, business cases, or interactive formats. Possible formats include:

·       Making a supply chain decision (e.g., choose between a cheaper but less ethical supplier vs. a costly, sustainable one)

·       Choosing a marketing strategy (e.g., greenwashing vs. transparent sustainability positioning)

·       Developing a business model innovation (e.g., circular vs. linear model)

·       Evaluating an investment or sourcing scenario (e.g., ESG-focused vs. profit-maximizing options)

·       Setting performance targets that balance profit and environmental KPIs

·       Stating intentions to pursue sustainable leadership behaviors (e.g., advocacy, stakeholder engagement)

·       Attitudinal variables (attitudes toward green marketing, Profit vs. purpose orientation,

·       New Ecological Paradigm

·       …

References:

Angelaki, M. E., Bersimis, F., Karvounidis, T., & Douligeris, C. (2024). Towards more sustainable higher education institutions: Implementing the sustainable development goals and embedding sustainability into the information and computer technology curricula. Education and Information Technologies, 29(4), 5079-5113.

Betzler, S., & Kempen, R. (2024). Strengthening sustainable consumption behavior in high school students: Evaluation of an experiential training intervention targeting psychological determinants. Environmental Education Research, 31(2), 413–431.

Rabaa, S., Geisendorf, S., & Wilken, R. (2022). Why change does (not) happen: Understanding and overcoming status quo biases in climate change mitigation. Zeitschrift Für Umweltpolitik Und Umweltrecht, 45(1), 100-134.

Zsóka, Á., Szerényi, Z. M., Széchy, A., & Kocsis, T. (2013). Greening due to environmental education? Environmental knowledge, attitudes, consumer behavior and everyday pro-environmental activities of Hungarian high school and university students. Journal of cleaner production, 48, 126-138.

4. Antecedents and Consequences of AI (Over-)Reliance in Academic Thesis Writing

Description

This master thesis explores the growing phenomenon of student reliance on AI tools (e.g., ChatGPT) during the writing of Bachelor's or Master's theses. While AI-powered language models can support research and writing, excessive or uncritical use - AI over-reliance - may affect students’ learning, cognitive development, and academic integrity.

The aim of this thesis is to empirically examine:

·      Which factors (antecedents) lead students to rely heavily on AI during thesis writing?

·      What potential consequences this over-reliance may have, especially regarding cognitive abilities and academic behaviors?

Ideally, you focus on either one or more antecedents or one or more consequences.
Your thesis may also focus on developing a measurement scale for AI (over-)reliance since to date there is no good measure for this concept.

The study should be conducted via a survey-based design of students writing a bachelor or master thesis, using validated scales, and analyzed using multivariate statistical methods (e.g., multiple regression, moderation or mediation analysis).

Possible Antecedents (Predictors of AI (Over-)Reliance):

The study may include but is not limited to the following variables such as:

·      Academic stress or pressure: Perceived difficulty, deadline anxiety, or workload

·      Performance expectations: High self-imposed or externally imposed pressure to perform well

·      Digital affinity: Comfort and familiarity with AI and digital tools

·      Procrastination tendencies: Delaying behavior may increase reliance on fast AI solutions

·      Perceived usefulness or ease of use of AI: Beliefs about how helpful AI is in the writing process

·      Fear of failure or perfectionism: Motivation to use AI to avoid mistakes or improve output

·      …

Possible Consequences (Impacts of AI Over-Reliance):

The study may include but is not limited to the following variables such as:

·      Critical thinking: The ability to evaluate, question, and synthesize information independently

·      Analytical reasoning: Depth and structure of argumentation in academic work

·      Decision-making confidence: Students’ perceived ability to make independent academic judgments

·      Writing competence or learning outcomes: Self-reported or perceived skill development

·      Ethical concerns: Blurred lines between assistance and plagiarism or authorship

·      Academic disengagement: Reduced intrinsic motivation or cognitive involvement

·      …

Development of a Measurement Scale for AI (over-)reliance

References:

Lin, C. Y., & Wang, C. C. (2025). Why do graduate students use generative AI in thesis writing? the influence of self-efficacy, time pressure, and trust. Current Psychology, 1-16.

Mustafa, M. Y. et al. (2024). A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda. Smart Learning Environments, 11(1), 59.

Zhai, C., Wibowo, S. & Li, L.D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. Smart Learning Environments, 11 (1), 28. https://doi.org/10.1186/s40561-024-00316-7

Zhang, S., Zhao, X., Zhou, T. et al. (2024). Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior. International Journal of Education Technologies in Higher Educdation, 21 (1), 34. https://doi.org/10.1186/s41239-024-00467-0

5. Skills in the Age of AI – Identifying Future-Ready Competencies for Higher Education

Description

Artificial intelligence is transforming the labor market and redefining the competencies required for future employees. As AI systems increasingly handle analytical, communicative, and creative tasks, workers must develop new skills—both to collaborate effectively with AI and to remain competitive in areas where human and AI capabilities overlap. Higher education institutions must adapt their curricula accordingly to prepare students for AI-driven workplaces.

The aim of this master thesis is to quantitatively examine which competencies will be most relevant in an AI-dominated work environment and which skills higher education institutions should prioritize in the future.

The study must be conducted using a quantitative empirical research design, ideally a survey-based approach.

Example Research Questions

Possible empirical research questions include:

• Which competencies do students, faculty, or employers perceive as most relevant in the AI age?
• How strongly do different skill domains predict perceived employability in an AI-driven labor market?
• Which individual factors (e.g., digital literacy, openness to technology, academic self-efficacy) influence students’ readiness for AI-supported work environments?
• How do perceptions of future-relevant skills vary across disciplines or demographic groups?
• Are there distinguishable skill profiles that characterize future-ready students?

Literature

Cardon, P., Fleischmann, C., Logemann, M., Heidewald, J., Aritz, J., & Swartz, S. (2024). Competencies needed by business professionals in the AI age: Character and communication lead the way. Business and Professional Communication Quarterly, 87(2), 223-246.

Ejjami, R. (2024). The future of learning: AI-based curriculum development. International Journal for Multidisciplinary Research, 6(4), 1-31.

Rêgo, B. S., Lourenço, D., Moreira, F., & Pereira, C. S. (2024). Digital transformation, skills and education: A systematic literature review. Industry and higher education, 38(4), 336-349.

6. The Impact of Growth vs. Fixed Mindset on Performance in a Marketing Simulation

Description

The concept of fixed versus growth mindset, introduced by Carol Dweck, describes how individuals perceive their own abilities. While a fixed mindset assumes that abilities are static, a growth mindset reflects the belief that abilities can be developed through effort and learning. This distinction has been shown to influence motivation, learning behavior, and performance outcomes.

In the context of higher education, simulation-based learning environments are increasingly used to develop practical and decision-making skills. However, little is known about how students’ mindsets influence their performance in complex, competitive simulations, or whether such environments can actively shift mindset orientations.

This master thesis aims to quantitatively investigate:

·       Whether a targeted intervention (e.g., a simulation-based learning experience) can influence students’ fixed vs. growth mindset

·       How students’ mindset affects their performance in a business simulation game

The empirical study will be conducted in the context of an university course, where students make strategic decisions in a competitive environment.

Research Context and Data Collection

Data will be collected within a university course during the winter semester, where students participate in a simulation game in international marketing.

Performance in the simulation will be measured using objective key performance indicators (KPIs), such as Profit, Revenue, Sales volume, Market share.

Additionally, survey data will be collected to measure students’ mindset (fixed vs. growth) before and/or after the intervention.

Possible Research Questions

The thesis may address questions such as:

·       Does participation in a simulation-based learning intervention lead to changes in students’ mindset?

·       Do students with a growth mindset achieve higher performance outcomes (e.g., profit, market share)?

Methodological Requirements

The thesis must be based on a quantitative empirical design, including:

·       Survey-based measurement of fixed vs. growth mindset using validated scales

·       Use of objective performance data (KPIs) from the simulation

·       Application of multivariate statistical methods, such multiple regression analysis

Literature

Burnette, J. L., Billingsley, J., Banks, G. C., Knouse, L. E., Hoyt, C. L., Pollack, J. M., & Simon, S. (2023). A systematic review and meta-analysis of growth mindset interventions: For whom, how, and why might such interventions work?. Psychological bulletin, 149(3-4), 174.

Dweck, C. S. (2016). The remarkable reach of growth mind-sets. Scientific American Mind, 27(1), 36-41.

Macnamara, B. N., & Burgoyne, A. P. (2023). Do growth mindset interventions impact students’ academic achievement? A systematic review and meta-analysis with recommendations for best practices. Psychological bulletin, 149(3-4), 133.

Sisk, V. F., Burgoyne, A. P., Sun, J., Butler, J. L., & Macnamara, B. N. (2018). To what extent and under which circumstances are growth mind-sets important to academic achievement? Two meta-analyses. Psychological science, 29(4), 549-571.

7. Evaluating the Impact of Peer Mentoring Programs on Student Participation and Academic Success

Description

Peer mentoring programs are widely implemented in higher education to support students during critical phases of their studies, particularly at the beginning of their academic journey. These programs aim to enhance students’ integration, engagement, and academic success by providing guidance from more experienced peers.

Despite their popularity, there is limited empirical evidence on whether participation in such programs actually leads to higher engagement in academic activities and improved study outcomes.

The aim of this master thesis is to quantitatively evaluate the effectiveness of the peer mentoring program targeting at bachelor students at the University of Vienna, focusing on its impact on:

·        Student participation in academic activities (e.g., course engagement, attendance, involvement)

·        Academic success (e.g., course completion, performance, or progression)

Research Context and Data Collection

The study will be conducted in the context of an existing peer mentoring program at the University of Vienna, starting at the beginning of the winter semester 2026/27 (October).

Data collection will involve:

·        A survey administered to participating and/or non-participating students

·        Measurement of participation, engagement, and perceived benefits of mentoring

·        Potential linkage to academic performance indicators (if available)

Important:
The survey instrument must be fully developed and ready at the start of the winter semester, which requires substantial preparatory work during the summer months (e.g., scale development, pretesting, and refinement).

Possible Research Questions

The thesis may address questions such as:

·        Does participation in a peer mentoring program increase students’ academic engagement?

·        Does peer mentoring positively influence academic success or progression?

·        How do participants and non-participants differ in terms of motivation, expectations, or study behavior?

·        Which aspects of the mentoring program are perceived as most beneficial?

8. AI Usage Among Students at the University of Vienna

Description

Artificial intelligence tools such as ChatGPT, Grammarly, or Microsoft Copilot are increasingly integrated into students’ daily academic workflows. From generating ideas and summarizing literature to solving problems and drafting assignments, AI is reshaping how students learn and complete university tasks.

The aim of this master thesis is to quantitatively investigate AI usage patterns among students at the University of Vienna, with a focus on:

·        Frequency and intensity of AI usage

·        Types of tasks for which AI is used (e.g., learning vs. completing assignments)

·        Types of AI tools used

Research Focus

The empirical study may include, but is not limited to, the following aspects:

1. AI Usage Behavior

·        Frequency and duration of AI use

·        Situational use (e.g., exam preparation, assignments, research)

·        Active vs. passive use (e.g., generating content vs. reviewing output)

2. Purpose of AI Use

·        Learning-oriented use (e.g., understanding concepts, explanations, studying)

·        Performance-oriented use (e.g., completing assignments, writing texts, solving tasks) 

3. AI Tools

·        Identification of commonly used tools (e.g., chatbots, writing assistants, coding tools)

·        Comparison of tool usage patterns