📌 Key facts
- What: This thesis examines how candidates experience AI-supported recruiting processes. The thesis can be conducted as a structured literature review or as an experimental study, focusing on candidate perceptions such as fairness, transparency, trust, organizational attractiveness, and willingness to continue in the application process.
- When: Start anytime soon. Applications are open!
- How to apply: Send your CV, transcript of records, and max. 5 sentences why this topic interests you (more details below)
- 📌 Key facts
- 💡 Background
- 🦾Who We Are
- 🎯 Topics of Interest and Potential Outcomes
- Possible research questions
- Possible theoretical perspectives
- Possible method
- 🎓 Profile
- 📝 How to Apply
💡 Background
As organizations increasingly use artificial intelligence in recruiting, candidates are more frequently confronted with AI-supported hiring processes. While AI may help companies process applications more efficiently, candidates may perceive AI-based recruiting very differently from traditional human-led selection. For example, AI may be perceived as fast, innovative, and objective, but also as opaque, impersonal, or unfair.
This thesis examines the candidate-side perspective on AI in recruiting. The goal is to develop a theoretically grounded overview of how candidates experience AI-supported recruiting and how such experiences affect perceptions of fairness, transparency, trust, and organizational attractiveness.
🦾Who We Are
I am a PhD student and Senior Consultant at McKinsey & Company. My research interest lies broadly in the use of AI, including its adoption, effective use, and its impact on companies. I have an educational background in Management
🎯 Topics of Interest and Potential Outcomes
Possible research questions
The thesis may address questions such as:
- How do candidates perceive AI-supported recruiting processes?
- How does AI use in recruiting affect perceived fairness, transparency, and trust?
- Under what conditions does AI-supported recruiting increase or decrease organizational attractiveness?
- Which stages of the recruiting process are most sensitive from a candidate-experience perspective?
- How do communication, human oversight, and transparency shape candidate reactions to AI in recruiting?
Possible theoretical perspectives
Depending on the student’s interest, the thesis may draw on theories and frameworks such as:
- applicant reactions theory
- organizational justice and procedural fairness
- signaling theory
- trust in technology
- human–AI interaction
- perceived transparency and explainability
- organizational attractiveness and employer branding
Possible method
The thesis can be conducted as either a structured literature review / conceptual thesis or, depending on the student’s interest and methodological fit, as an experimental study.
In the literature-based version, the student would systematically review and synthesize academic research on candidate reactions to AI-supported recruiting. The focus would be on candidate-side outcomes such as perceived fairness, transparency, trust, opportunity to perform, perceived impersonality, organizational attractiveness, willingness to continue in the process, and willingness to recommend the employer. The goal would be to develop a conceptual framework explaining how and under what conditions AI-supported recruiting affects candidate experience.
Possible outputs include:
- a structured overview of current research on candidate experience and AI in recruiting
- a conceptual framework linking AI-supported recruiting to candidate perceptions and behavioral intentions
- a synthesis of key candidate-side constructs, such as perceived fairness, transparency, trust, opportunity to perform, perceived impersonality, organizational attractiveness, willingness to continue, and willingness to recommend
- recommendations for validated survey measures that can be used in future empirical research
- practical implications for designing candidate-friendly AI recruiting processes
Alternatively, the thesis may include an online experiment, for example with students, job seekers, or working professionals as potential applicants. In such an experiment, participants could be exposed to different recruiting scenarios and asked to imagine applying for a job under different process conditions. The experiment could test how AI use in recruiting affects perceived fairness, transparency, trust, organizational attractiveness, and willingness to continue the application process.
Possible experimental manipulations include:
- traditional human-led recruiting vs. AI-supported recruiting
- AI use in early screening vs. later interview stages
- low vs. high transparency about how AI is used
- low vs. high human oversight
- AI presented as increasing efficiency vs. AI presented as increasing objectivity
- optional AI-Fasttrack vs. mandatory AI-supported recruiting
This experimental option would allow the student to complement the literature review with empirical evidence on how candidates react to AI-supported hiring processes.
🎓 Profile
- Reliable, structured, and self-driven working style
- Strong academic record and analytical mindset
📝 How to Apply
If you are interested, please contact @Maximilian Rink by submitting your CV, grade report, preferred starting date & short motivation statement (max. 5 sentences) Please also indicate which kind of thesis (= outcome) you are interested in.