## 6 General Discussion  
Given the rapid technological advancements in the application of AI tools within recruitment and selection processes [8; 9], this study seeks to contribute to the growing body of knowledge by exploring recruiters' perceptions of these tools and examining the factors shaping their intentions to adopt them. Understanding recruiters' perspectives is pivotal for addressing potential resistance and fostering a more favorable disposition toward the adoption of AI technologies. Moreover, investigating these perceptions provides valuable insights into how recruiters evaluate the utility and challenges of implementing AI in recruitment. Grounded in the TAM [15; 56], this research delves into the extent to which perceived usefulness and perceived ease of use influence recruiters' intentions to utilize AI tools, with attitudes toward these technologies acting as a mediating variable. The findings from the qualitative exploratory study reveal a nuanced perspective on the use of AI tools in recruitment, highlighting both their advantages and disadvantages. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & 95\% CI \\ \hline  & & & Estim. & SE & z-value & \(p\) & LLCI & ULCI \\ \hline PEU & \(\rightarrow\) & IU & 0.036 & 0.01 & 3.433 & \(<\)0.001 & 0.01 & 0.06 \\ PU & \(\rightarrow\) & IU & 0.028 & 0.01 & 1.968 & 0.04 & 0.00 & 0.06 \\ \hline  & & & & & & & & & 95\% CI \\ \hline  & & & Estim. & SE & z-value & \(p\) & LLCI—ULCI \\ \hline PEU & At & IU & 0.027 & 0.007 & 4.066 & \(<\)0.001 & 0.01 & 0.04 \\ PU & At & IU & 0.127 & 0.01 & 10.178 & \(<\)0.001 & 0.10 & 0.16 \\ \hline  & & & & & & & & & 95\% CI \\ \hline  & & & Estim. & SE & z-value & \(p\) & LLCI & ULCI \\ PEU & \(\rightarrow\) & IU & 0.06 & 0.012 & 5.268 & \(<\)0.001 & 0.03 & 0.09 \\ \hline PU & \(\rightarrow\) & IU & 0.155 & 0.011 & 14.318 & \(<\)0.001 & 0.12 & 0.18 \\ \hline  & & & & & & & & & \\ \end{tabular} Note. Delta method standard errors, bias-corrected bootstrap confidence intervals, ML estimator. PU = Perceived usefulness; PEU = Perceived ease of use; AT = Attitude; IU = Intention to use.  
\end{table} 
Table 5: Hypotheses testing structural equation modeling path coefficients. On the positive side, recruiters identify key benefits such as streamlining processes, saving time, and enhancing overall efficiency. Conversely, significant concerns are raised, including the inevitable lack of human touch in candidate interactions, the potential for algorithmic bias, issues with precision and reliability in candidate selection, and the broader implications for job displacement and unemployment. These findings underscore the complexity of integrating AI into recruitment, balancing its operational benefits with ethical and practical challenges. Specifically, recruiters note that AI tools can optimize recruitment workflows, boost productivity, and enable a more strategic allocation of time and resources. This has also been acknowledged in the literature, with several studies highlighting the benefits of the use of AI tools in recruitment and selection processes [27, 28, 29]. Moreover, these tools appear to contribute to greater accuracy and predictability [18], establishing themselves as invaluable assets for achieving better hiring outcomes [12]. By fostering reliability and inclusivity while upholding high standards of precision and efficiency, AI tools are positioned as integral components in modernizing recruitment practices [14, 38]. However, the implementation of AI tools is not without challenges and potential drawbacks [57]. Recruiters stress the importance of balancing the efficiencies offered by AI with the preservation of essential human elements in recruitment, such as empathy, personal interaction, and a nuanced understanding of candidates. The lack of a \"human touch\" in recruitment is a significant concern, as these interpersonal qualities are seen as pivotal in creating meaningful candidate experiences.