This master’s thesis explores the role of artificial intelligence (AI) in modern recruitment processes with a particular focus on applicants’ trust. As digitalization and AI increasingly transform the world of work, organizations are adopting AI-supported recruiting tools to enhance efficiency, standardization, and data-driven decision-making. At the same time, the use of AI in personnel selection raises important questions regarding transparency, fairness, and acceptance from the applicants’ perspective.
The objective of this thesis is to examine how applicants perceive AI-assisted recruitment processes and which aspects are relevant for the development of trust in this context. To address this research objective, a qualitative research design was chosen. Semi-structured interviews were conducted with participants who had either experienced AI-supported recruitment processes or reflected on their expectations toward such processes.
The empirical data were analyzed using qualitative content analysis following Kuckartz’ approach. This method allows for a systematic examination of subjective perceptions, attitudes, and evaluations, while combining theory-driven and data-driven coding. The analysis focuses on applicants’ experiences with different forms of AI usage in recruitment, their expectations toward human and technological decision-making, and their assessment of AI-supported processes within the broader candidate journey.
By linking theoretical concepts of trust in technology with empirical insights from applicants, this thesis contributes to the growing body of literature on AI in recruiting. In addition, it provides a structured foundation for further academic research and offers valuable insights for organizations seeking to better understand applicant perspectives on AI-supported recruitment processes.
Table of Contents
- List of abbreviations
- List of illustrations
- List of tables
- 1 Introduction
- 1.1 Initial situation
- 1.2 Problem statement
- 1.3 Objectives and approach
- 1.4 Methodology
- 2 Theoretical background
- 2.1 Artificial Intelligence in recruiting
- 2.1.1 Definition of AI
- 2.1.2 Types of AI-tools in recruiting
- 2.1.3 Advantages of AI in recruiting
- 2.1.3.1 Efficiency
- 2.1.3.2 Objectivity
- 2.1.4 Challenges of AI in recruiting
- 2.1.4.1 Bias and discrimination risk
- 2.1.4.2 Lack of human interaction and judgement
- 2.1.4.3 Ethical and legal concerns
- 2.1.4.4 Lack of transparency
- 2.1.4.5 Technical limitations
- 2.1.5 Applicant perceptions of AI in recruiting
- 2.1.5.1 Emotional impacts
- 2.1.5.2 Preferences for human vs. AI-driven decisions
- 2.2 Trust in technology
- 2.2.1 Prerequisite for trust - procedural fairness
- 2.2.2 Definition of trust in AI
- 2.2.3 Initial trust building
- 2.2.3.1 Performance
- 2.2.3.2 Process
- 2.2.4 Ongoing trust development
- 2.2.4.1 Performance
- 2.2.4.2 Purpose
- 2.2.5 Factors influencing trust in AI
- 2.2.6 Strategies for trust building
- 2.3 Employer branding and candidate experience
- 2.3.1 The role of the recruiting process on employer branding
- 2.3.2 Impact of AI on employer branding
- 2.1 Artificial Intelligence in recruiting
- 3 Research design and methodology
- 3.1 Research design
- 3.1.1 Rationale for choosing a qualitative approach
- 3.1.2 Limitations of the methodology
- 3.2 Data collection
- 3.2.1 Development of the interview guide
- 3.2.2 Pilot testing interview questions
- 3.2.3 Process of conducting semi-structured interviews
- 3.3 Participant selection
- 3.3.1 Inclusion criteria for participants
- 3.3.2 Recruiting process for applicants
- 3.4 Data analysis
- 3.4.1 Steps in qualitative content analysis (Kuckartz)
- 3.4.2 Coding and categorization of themes
- 3.5 Ethical considerations
- 3.5.1 Informed consent and voluntary participation
- 3.5.2 Anonymity and data protection measures
- 3.1 Research design
- 4 Results
- 4.1 Experiences with AI in recruiting
- 4.2 Perception of AI in recruiting
- 4.2.1 Positive
- 4.2.2 Negative
- 4.2.3 Fair
- 4.2.4 Unfair
- 4.2.5 Different forms of AI
- 4.3 Emotional response
- 4.4 Data protection concerns
- 4.5 Trust in AI based recruitment
- 4.5.1 Mistrust
- 4.5.2 Trust
- 4.5.3 Ambivalence
- 4.5.4 Compliance without trust
- 4.6 Transparency about AI usage
- 4.7 Adjustments in behavior
- 4.8 Effects on employer brand
- 4.9 AI recommendatory vs. autonomous
- 4.10 Trust enhancing factor - hybrid process
- 5 Discussion
- 5.1 Interpretation of findings
- 5.1.1 Alignment with existing literature
- 5.1.2 New insights and contributions
- 5.2 Practical implication
- 5.2.1 Human-AI collaboration
- 5.2.2 Communication
- 5.2.3 Managing emotional responses
- 5.2.4 Tailoring AI use to recruitment stages
- 5.2.5 Employer brand considerations
- 5.3 Limitations of the study
- 5.3.1 Methodological constraints
- 5.3.2 Generalizability of findings
- 5.4 Suggestions for future research
- 5.1 Interpretation of findings
- 6 Conclusion
- Bibliography
- List of appendices
Purpose & Key Topics
This thesis aims to investigate the factors influencing applicants' trust in AI-powered recruiting processes to derive practical recommendations for organizations. The primary research question explored is: "Which factors foster applicants' trust in AI-powered recruiting processes?"
- Identifying factors that foster and inhibit trust in AI in recruiting.
- Understanding applicants' perceptions of various AI tools and decision-making types.
- Analyzing the impact of AI usage on employer brand perception.
- Exploring how awareness of AI in recruiting alters applicant behavior.
- Developing practical recommendations for organizations to build trust in AI-assisted recruitment.
- Examining the role of transparency and human oversight in AI recruitment processes.
Excerpt from the Book
2.1.4 Challenges of AI in recruiting
After having discussed the advantages of Al in recruiting, the challenges will be explained below. What is worth mentioning is that some advantages, e.g. bias, have the potential to be considered a challenge as well.
2.1.4.1 Bias and discrimination risk
Despite its advantages, Al-based recruiting processes present several challenges (Fernández-Martínez/Fernández, 2020, p. 204; Mülder, 2021, p. 68). The use of Al can raise ethical, legal, privacy, and moral concerns for job applicants (Van Esch et al., 2019, p. 220). One of the primary concerns is bias in Al decision-making (Lee, 2018, p. 10; Srivastava, 2025; Armstrong/Metaxa, 2025, p. 2; Fernández-Martínez/Fernández, 2020, p. 204; Mülder, 2021, p. 68). While Al can reduce bias by focusing on objective candidate qualifications, it may also reinforce existing biases or violate anti-discrimination laws leading to unethical hiring practices if trained on historically biased hiring data (Srivastava, 2025; Armstrong/Metaxa, 2025, p. 2; Fernández-Martínez/Fernández, 2020, p. 204; Mülder, 2021, p. 68). A practical example is Amazon's Al recruiting tool, which systematically downgraded applications that included the word “women” because it had been trained on past hiring data in which most recruits were male (Fernández-Martínez/Fernández, 2020, p. 204; Mülder, 2021, p. 68). This highlights the risk that Al has the potential to factor in a candidate's physical attributes when making hiring decisions (Van Esch et al., 2019, p. 220). Also, Al can unintentionally perpetuate discrimination, underscoring the importance of transparent algorithms and unbiased training data (Mülder, 2021, p. 68). As Al becomes more integrated into recruiting, organizations must address challenges such as selection bias, delayed feedback, and technical issues. If these concerns are not managed, job seekers may remain dissatisfied, potentially harming an employer's ability to attract and retain top talent (Van Esch et al., 2019, p. 220 and the sources cited there). Bias in Al models should be addressed by incorporating mitigation techniques, including the use of diverse training datasets. In addition, fairness checks should be conducted on a regular basis to ensure that unbiased hiring decisions are made (Srivastava, 2025).
2.1.4.2 Lack of human interaction and judgement
Another concern is the lack of human interaction and judgement in Al-driven recruiting. The use of chatbots, video interviews, and Al-driven candidate assessments reduce direct engagement between applicants and hiring staff. This may lead to lower candidate trust as personal interactions help establish trust and rapport during the hiring process (Wilke/Bendel, 2022, p. 658). Utilizing Al-driven hiring technologies with lower levels of human contact can cause reluctance in applicants (Suen et al., 2019, p. 99 and the sources cited there).
Candidates may also feel that Al lacks empathy and contextual understanding, making it incapable of assessing soft skills, leadership potential, and cultural fit (Horodyski, 2023, p. 7; Lee, 2018, p. 8). Judging a person by an algorithm is perceived as humiliating and objectifying (Lee, 2018, p. 12).
Summary of Chapters
Chapter 1: Introduction: This chapter sets the stage by outlining the initial situation of AI in recruiting, presenting the core problem statement, defining the research objectives and approach, and detailing the overall methodology of the thesis.
Chapter 2: Theoretical background: This section provides the foundational knowledge, discussing Artificial Intelligence in recruiting, various concepts of trust in technology, and the interplay between employer branding and candidate experience.
Chapter 3: Research design and methodology: This chapter meticulously describes the qualitative research design, including the rationale for choosing a qualitative approach, data collection methods (semi-structured interviews), participant selection criteria, data analysis procedures (Kuckartz approach), and ethical considerations.
Chapter 4: Results: This chapter presents the empirical findings from the qualitative interviews, detailing participants' experiences, perceptions (positive, negative, fair, unfair), emotional responses, data protection concerns, trust levels, transparency demands, behavioral adjustments, and effects on employer brand regarding AI in recruiting.
Chapter 5: Discussion: Here, the empirical findings are interpreted in light of existing literature, highlighting alignments and new insights, followed by practical implications for organizations, and acknowledging the study's limitations and suggestions for future research.
Chapter 6: Conclusion: The final chapter summarizes the primary findings, emphasizing both trust-fostering and trust-inhibiting factors in AI-based recruiting, the strong preference for hybrid processes, the dilemma of transparency, the ambivalent impact on employer brand, and the observed behavioral adjustments of applicants.
Keywords
AI, recruitment, trust, fairness, employer branding, transparency, candidate experience, human-AI collaboration, qualitative research, decision-making, bias, data protection, emotional response, hybrid process, job applicants, automation, perception, ethical concerns, HR management.
Frequently Asked Questions
What is this thesis fundamentally about?
This thesis fundamentally explores how Artificial Intelligence is used in the recruitment process and, specifically, what factors influence job applicants' trust in these AI-powered systems. It aims to understand both the opportunities and challenges AI presents in hiring and to provide recommendations for companies to build trust.What are the central thematic areas?
The central thematic areas include the application of AI in human resources, candidate trust in technology, the impact of AI on employer branding and candidate experience, and the methodology of qualitative research to explore these complex perceptions.
What is the primary objective or research question?
The primary objective is to identify the factors that foster applicants' trust in AI-powered recruiting processes. The main research question is: "Which factors foster applicants' trust in AI-powered recruiting processes?"
What scientific method is used?
The study employs a qualitative research design, utilizing semi-structured interviews for data collection and qualitative content analysis, following Kuckartz's approach, for data interpretation.
What is covered in the main body?
The main body delves into the theoretical background of AI in recruiting and trust in technology, the detailed research methodology, the empirical results derived from interviews on applicant perceptions and experiences, and a comprehensive discussion aligning findings with existing literature and presenting practical implications.
Which keywords characterize this work?
Key terms characterizing this work include AI, recruitment, trust, fairness, employer branding, transparency, candidate experience, human-AI collaboration, qualitative research, decision-making, bias, data protection, emotional response, hybrid process, job applicants.
How do candidates perceive AI decision-making (recommendatory vs. autonomous)?
Participants generally prefer AI to act in a recommendatory capacity, providing suggestions that human recruiters can review, rather than making autonomous decisions, especially in later, more critical recruitment stages. Autonomous AI is more accepted for initial, impersonal tasks like CV screening.
What is the "hybrid process" in AI-assisted recruiting and why is it important for trust?
The "hybrid process" refers to a recruitment approach that combines AI-driven tasks with human touchpoints and oversight. It is crucial for trust because it addresses applicant fears of dehumanization and unjust rejections, ensuring that human judgment and interaction remain integral, especially for complex or sensitive evaluations.
What is the dilemma organizations face regarding transparency of AI usage in recruitment?
Organizations face a dilemma where transparency about AI usage is ethically and morally demanded, but disclosing it can trigger negative reactions from applicants, leading to anxiety and behavioral adjustments. Conversely, concealing AI usage erodes trust and harms the employer brand, creating a difficult balance.
How do applicants' emotional responses and behavioral adjustments influence trust in AI-powered recruiting?
Applicants often experience negative emotional responses like worry and anxiety when AI is used, particularly due to perceived lack of human interaction. This can lead to behavioral adjustments, such as tailoring applications to "fit the AI." These emotional and behavioral reactions significantly impact their trust and willingness to engage with AI-driven processes, highlighting the need for strategies to manage these responses.
- Arbeit zitieren
- Ajla Besirevic (Autor:in), 2025, Artificial intelligence in the recruitment process. How can companies gain applicants’ trust?, München, GRIN Verlag, https://www.grin.com/document/1691797
