Recruitment in the Age of AI: Understanding Recruiters’ Willingness to Embrace AI in Hiring

Topic
This thesis explores the factors influencing recruiters’ willingness to adopt AI resume screening tools. Building upon the Technology Acceptance Model (TAM), it integrates Perceived Usefulness, Ethical Concerns, and Fear of role displacement to examine both functional and psychological drivers of adoption. A survey of 99 recruitment professionals was analyzed using Structural Equation Modeling (SEM) to assess how these variables impact the Behavioral Intention to adopt AI technologies in hiring.

Relevance
With AI rapidly transforming recruitment processes, understanding recruiters' perceptions is critical for successful and ethical technology implementation. This research provides valuable insights for HR practitioners aiming to integrate AI screening tools while addressing user concerns about job security and fairness. It offers a roadmap for enhancing adoption rates through strategies that highlight the tools' functional benefits and mitigate fears surrounding ethical risks and professional displacement.

Results
The study found that Perceived Usefulness is the strongest and most significant predictor of recruiters' intention to adopt AI resume screening tools. Ethical Concerns, while acknowledged, did not significantly impact adoption decisions. Fear of role displacement had a notable negative effect, indicating that job security concerns might be a barrier. Control variables like Technological Affinity and Organizational Size showed no significant influence, emphasizing that perceptions outweigh organizational or individual tech familiarity factors.

Implications for practitioners

• Emphasize the functional benefits of AI tools, such as increased efficiency and decision support.
• Develop trust-building measures, including transparent AI decision processes.
• Address job security fears by positioning AI as a complementary tool, not a replacement.
• Incorporate ethics-focused onboarding programs to build recruiter confidence in AI fairness.
• Promote AI literacy among HR professionals to foster understanding and responsible usage.

Methods
A quantitative research approach was applied using a cross-sectional survey administered to 99 recruitment professionals. Participants included both users and non-users of AI resume screening tools. Data were analyzed through a structured process: data cleaning, descriptive statistics, reliability analysis, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Structural Equation Modeling (SEM) and robustness check. The survey measured key constructs like Perceived Usefulness, Ethical Concerns, Fear of role displacement, and Behavioral Intention, alongside control variables such as Technological Affinity and Organizational Size, using validated Likert scales and adapted measurement instruments.