The Impact of Artificial Intelligence (AI) on Recruitment Process ()
1. Introduction
Artificial Intelligence (AI) has revolutionized the way organizations and industries proceed with recruitment. In fact, the recruitment process has been a topic of interest and discussion in recent years. AI has brought about significant changes in how organizations attract, assess, and select candidates for various roles. As highlighted by (Burns & Hann, 2019), “AI has the potential to transform recruitment by automating repetitive tasks, improving efficiency, and enhancing decision-making processes”. Previous research studies and industry reports have demonstrated the positive impact of AI on recruitment processes. For example, a study conducted by Sullivan, et al. (2020) found that companies using AI in recruitment experienced a 50% reduction in time-to-hire and significant cost savings.
This paper seeks to explore the multifaceted impact of AI on recruitment practices by examining both its advantages and the limitations that organizations face when integrating these technologies. Through a mixed-methods research design that combines quantitative surveys and qualitative interviews, this study captures the perspectives of HR professionals and recruiters regarding the effectiveness of AI in improving hiring efficiency, reducing costs, and addressing issues of bias and diversity (Baker et al., 2021).
This research aims to provide actionable insights for organizations navigating the complexities of AI integration in hiring processes, as well as to contribute to the broader discourse on the implications of technological advancements in human resources. The findings of this study will not only shed light on the current state of AI in recruitment but will also pave the way for future research and practice in this rapidly evolving field.
2. Literature Review and Research Hypotheses
2.1. Resource-Based View (RBV) Theory
The Resource-Based View (RBV) theory provides a clear framework for understanding how organizations can leverage their resources and capabilities to gain competitive advantage. According to Prajapati and Gupta (2020), When applied to the impact of artificial intelligence (AI) on the recruitment process, the RBV theory emphasizes the strategic importance of utilizing AI technologies to enhance talent acquisition practices and attract top talent.
RBV (Resource-Based View) theory emphasizes the role of organizational resources and capabilities in creating sustainable competitive advantage (Barney, 1991). By utilizing AI tools for resume screening, candidate matching, and behavioral analysis, organizations can effectively identify and attract top talent, thereby gaining a competitive edge in the market (Chamorro-Premuzic et al., 2019). The RBV theory highlights the significance of developing internal capabilities to effectively harness AI technologies for recruitment purposes (Tambe, 2014). Organizations that invest in building AI expertise, integrating AI into their HR functions, and fostering a culture of innovation are more likely to leverage AI as a source of sustained competitive advantage in talent acquisition (Marler & Boudreau, 2017). Applying the RBV theory to the impact of AI on the recruitment process underscores the critical role of AI as a strategic resource that can drive organizational performance and competitiveness in the evolving talent landscape (Susskind & Susskind, 2015).
2.2. Research Hypothesis
2.2.1. Enhancing Recruitment Efficiency through the Adoption
of AI-Powered Tools
The first hypothesis posits that the use of AI-powered recruitment tools can significantly enhance the efficiency of the hiring process, ultimately leading to a substantial reduction in the overall time-to-hire metric. In a fast-paced business environment, where securing top talent is crucial to maintaining a competitive edge, the ability to attract and onboard candidates swiftly has become paramount. Recent studies underscore the transformative capacity of AI tools to streamline various critical aspects of recruitment, including resume screening, interview scheduling, and candidate engagement. For instance, research conducted by Chamorro-Premuzic et al. (2019) has demonstrated that modern AI systems can analyze hundreds, if not thousands, of resumes in mere minutes.
Employing advanced natural language processing (NLP) techniques, these AI systems can evaluate resumes based on predefined criteria, such as skills, experience, and educational background. This capability dramatically reduces the time that recruiters traditionally spend on manual screening processes—sometimes as long as several weeks—thus accelerating the initial candidate selection phase. Furthermore, AI tools can ensure a more objective assessment by eliminating human biases that often influence hiring decisions, leading to a more merit-based selection process. Interview scheduling, another critical component of the recruitment process, can also be significantly optimized through AI.
Automated scheduling tools can facilitate the coordination of interviews across multiple stakeholders in an organization, reducing the common back-and-forth communication that often leads to delays and scheduling conflicts. AI-driven systems can handle scheduling logistics seamlessly, enabling recruiters to allocate their time towards more strategic activities, such as engaging with candidates and discussing organizational culture. Moreover, AI tools not only expedite hiring processes but also enhance decision-making through data-driven insights. By leveraging machine learning algorithms and analytics, recruiters can assess candidate fit and predict performance more accurately. These algorithms can analyze historical hiring data to identify patterns and characteristics that correlate with successful hires. Organizations utilizing AI-driven recruitment solutions have experienced a 25% reduction in the average time to fill positions compared to those relying on traditional methods (Gorla, 2020). This reduction is not just a statistic; it translates into real-world advantages, such as improved candidate experience and reduced operational costs, ultimately leading to better business outcomes. The ability of AI to process vast amounts of data quickly and accurately allows recruitment teams to pivot their strategies as needed, making the hiring process more efficient and effective (Kamble et al., 2020). For instance, trend analyses generated by AI can help organizations understand market conditions, candidate availability, and competitive salary benchmarks, enabling them to formulate informed recruitment strategies tailored to their specific needs (Wright & Pullen, 2021). This adaptability is especially crucial in industries facing rapid technological changes or shifts in workforce demographics.
Hypothesis 1: Use of AI-powered recruitment tools enhances the efficiency of the hiring process, leading to a reduction in the time-to-hire metric.
2.2.2. Increasing Candidate Diversity: The Role of AI in Mitigating Bias in Recruitment
Traditional recruitment practices are often criticized for their inherent biases, which can lead to a homogeneous workforce (Gerding & Hines, 2021). These biases typically stem from unconscious preferences and systemic inequalities that have permeated hiring practices for decades. However, AI presents a promising opportunity to mitigate these biases significantly and pave the way for a more inclusive recruitment landscape.
Research conducted by Binns (2018) indicates that AI algorithms can be trained on diverse datasets to minimize bias in candidate screening and selection effectively. For instance, organizations leveraging AI can remove demographic information, such as names, addresses, and even university affiliations, from resumes. This process, often referred to as “blind recruitment,” allows recruiters to focus solely on qualifications and competencies, thereby minimizing unconscious bias in the hiring process. By implementing AI-driven systems that anonymize candidate information, organizations can promote diversity and ensure that the selection process is based on merit rather than demographic factors (Binns, 2018). This practice shifts the emphasis from potentially biased attributes to the applicant’s skills and experiences, promoting fairness in hiring practices. In addition to fostering a more equitable selection process, this approach enhances candidate diversity across various dimensions, including gender, race, and socioeconomic background, ultimately contributing to a richer workplace environment.
AI in recruitment can help organizations expand their talent pool beyond conventional sources, tapping into non-traditional candidates who may possess unique perspectives and innovative ideas (Chowdhury et al., 2021). AI-enabled tools can facilitate outreach to diverse communities, ensuring that job postings reach a broader audience (Gonzalez et al., 2020). This proactive approach helps organizations attract candidates who might otherwise be overlooked, thereby increasing the variety of experiences and viewpoints represented within teams (Binns, 2018). Diverse teams are proven to be more creative, capable of producing a wider range of ideas and solutions, which is essential in today’s dynamic and competitive business environment. Consequently, AI-driven recruitment strategies that emphasize diversity can lead to both ethical and economic benefits for organizations, ensuring a wide range of perspectives and ideas within teams. Moreover, cultivating a diverse workforce can enhance an organization’s reputation and appeal, making it more attractive to candidates who prioritize inclusivity and social responsibility.
Hypothesis 2: Implementing AI in recruitment processes increases the diversity of candidates selected for job openings.
2.2.3. Cost Optimization through AI Technology in Recruitment Processes
The third hypothesis asserts that the integration of AI technology in the recruitment process can substantially reduce hiring costs by optimizing resource allocation and decreasing reliance on manual labor. Research indicates that AI tools can automate repetitive tasks—such as applicant tracking, scheduling interviews, and initial communication—resulting in significant cost savings for organizations (Ayub et al., 2021).
Companies that adopted AI-driven recruitment technologies reported a 30% reduction in hiring costs. This reduction stems from decreased reliance on third-party recruitment agencies and streamlined processes that lessen the need for extensive human resources. The automation of administrative tasks allows HR professionals to focus on strategic aspects of recruitment, ultimately leading to a more effective hiring process (Davenport et al., 2020).
Additionally, AI tools can provide real-time analytics and insights into recruitment metrics, enabling organizations to identify inefficiencies and refine their strategies accordingly. This data-driven approach not only enhances decision-making but also contributes to more significant cost savings over time, creating a sustainable recruitment model that benefits both organizations and candidates.
Hypothesis 3: The integration of AI technology in the recruitment process reduces hiring costs by optimizing resource allocation and decreasing reliance on manual labor.
2.2.4. Recent Developments in AI in HR Hiring Procedures
Recent developments in Artificial Intelligence (AI) have transformed many facets of human resources (HR), particularly in the hiring process. AI tools such as HireVue, which utilizes video interviewing and AI analysis to predict candidate suitability, and Pymetrics, which assesses candidates’ soft skills through neuroscience-based games, exemplify this trend. Additionally, platforms like X0PA AI automate recruitment processes and minimize bias, while Eightfold.ai focuses on identifying candidate potential based on transferable skills. Companies have achieved a 50% reduction in time-to-hire and significant cost savings through streamlined screening processes that automate resume sorting, predictive hiring that identifies optimal candidates quickly, and AI chatbots that handle logistics such as interview scheduling. Real-world implementations illustrate these benefits: Unilever has successfully reduced hiring times and enhanced diversity by using AI-driven video interviews and gamified assessments, while IBM employs Watson to analyze resumes and prioritize candidates based on skills. Likewise, Hilton has seen financial and time savings by leveraging AI platforms for recruitment, resulting in lower agency fees and quicker interviewing processes. Overall, AI has transformed HR hiring by enhancing efficiency, reducing costs, and promoting diversity, with ongoing advancements likely to continue reshaping recruitment practices into the future.
3. Research Design and Methodology
3.1. Research Design
This study adopts a mixed methods approach, combining both quantitative and qualitative data collection techniques to explore the impact of Artificial Intelligence (AI) on recruitment. The study consists of two main components: a survey and semi-structured interviews. In fact, a qualitative and quantitative data collection techniques were used to capture a comprehensive understanding of the impact of AI on recruitment.
3.2. Data Collection
A survey questionnaire containing up to 15 variables, was used to collect the required data for this research. The survey question involves a total of 111 participants and was developed and structured to collect quantitative data on the utilization of AI in recruitment, perceived benefits, challenges, and ethical considerations. The survey includes Likert scale questions, multiple-choice items, and open-ended questions to gather both quantitative and qualitative data. Consent from all 111 participants was obtained and data confidentiality, anonymity during data collection and analysis were ensured. Semi-structured interviews were conducted on and offline with key HR professionals and recruiters from various organizations, to explore in-depth insights into their experiences with AI in the recruitment process. Each recorded interview was transcribed verbatim, allowing for an accurate representation of participants’ spoken words. This transcription included all interactions, questions, and comments made by both the interviewer and the respondents. The online survey was designed using a survey tool (Google Forms). The survey link was shared via email, professional social media platforms (such as LinkedIn), and HR-related online forums and groups. This approach allowed for wide distribution and easy access for participants, encouraging a higher response rate. The theme of these interviews was to identify recurring themes, patterns, and emerging insights related to AI in recruitment. Open-ended questions were asked to probe participants’ perspectives on the effectiveness, limitations, and ethical implications of AI technology in recruitment.
Scales of Measurement
The instrument of measurement should include both quantitative and qualitative measures to provide a holistic understanding of how AI technologies influence recruitment outcomes. We have calculated the Cronbach’s Alpha to assess the internal consistency of the survey and interview responses across the Likert scale items. We have Assigned numerical values to the responses, Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5. Sullivan & Artino Jr. illustrate this method in his study analyzing and interpreting data from Likert-type Scales. The author uses Cronbach’s α as a reliability measure and provides examples of how to interpret the results. Data were analyzed using descriptive and inferential statistics and the findings were interpreted regarding the impact of AI in the recruitment process based on the participants’ responses.
3.3. Sample Description
The sample population for this study consists of 111 HR professionals and recruiters from various organizations in Morocco. The participants were selected using a convenience sampling method, with the aim of gathering a diverse range of perspectives on the impact of AI on the recruitment process. The selection of 111 participants strikes a balance between achieving statistical significance and maintaining the feasibility of data collection and analysis.
Participants were selected using a convenience sampling method. This approach was chosen to allow for the quick and efficient gathering of data, focusing on existing networks within the HR community. Convenience sampling can yield relevant participants who are readily available and willing to provide insights, making it particularly useful in exploring a rapidly evolving subject such as AI in HR practices. The study aims to gather insights from a sufficient number of participants to provide a comprehensive understanding of the impact of AI on recruitment. The sample size is representative of the population of HR professionals and recruiters who have experience with AI in recruitment. The sample population includes a diverse range of HR professionals and recruiters from various industries, job types, and levels of experience. The demographics of the sample is illustrated on Table 1:
Table 1. Social demographic characterization.
Variable |
Variable Classification |
Relative Frequency (Percentage) |
Gender |
Male |
45% |
female |
55% |
Age |
25 - 35 |
38.3%. |
36 - 55 |
61.7% |
Job Type |
HR Generalist |
40% |
recruitment specialist |
30% |
hiring manager |
30% |
Participants Industries |
Technology |
25% |
Healthcare |
20% |
Finance |
15% |
|
Education |
10% |
Manufacturing |
10% |
Retail |
10% |
Other |
10% |
Experience Level |
Less than 3 years |
30% |
3 - 5 years |
25% |
6 - 10 years |
25% |
More than 10 years |
20% |
4. Data Analysis and Hypothesis Testing
4.1. Data Analysis
Table 2 indicates the Mean score measurement scale with corresponding descriptions for each range of scores. Mean score < 3.39: (Low) This category represents scores that fall below 3.39. Scores in this range are considered to be low, indicating a lower level of the measured variable. Mean score 3.40 - 3.79: (Moderate) Scores falling within the range of 3.40 to 3.79 are classified as moderate. This suggests a mid-level range of the measured variable, indicating a moderate level of performance or impact. Mean score > 3.80: (High) Scores exceeding 3.80 are categorized as high. This range indicates a higher level of the measured variable, reflecting a strong performance or impact in relation to the measurement criteria.
Table 2. Mean score measurement.
Mean |
Description |
<3.39 |
Low |
3.40 - 3.79 |
Moderate |
>3.80 |
High |
Table 3. Analysis of Likert scale responses on AI implementation in recruitment.
Items |
Mean |
S.D |
AI has improved the efficiency of the recruitment process. |
3.82 |
1.06 |
AI has increased the accuracy of candidate assessments |
3.41 |
1.04 |
AI has helped to attract a more diverse range of candidates for job openings. |
3.32 |
1.01 |
AI has contributed to a more equitable assessment of candidates from different backgrounds. |
3.46 |
1.01 |
AI has helped in identifying the best-fit candidates. |
3.47 |
1.01 |
AI has streamlined the candidate screening process. |
3.76 |
1.01 |
AI has enhanced the candidate experience during recruitment. |
3.81 |
1.06 |
I believe AI implementation has positively impacted recruitment outcomes in my organization. |
4.01 |
0.96 |
AI has increased the efficiency of the interview scheduling process. |
3.43 |
0.80 |
Organizations have effectively communicated the use of AI in their recruitment processes. |
3.55 |
1.02 |
In Table 3, we can perceive that the standard deviations range from around 0.80 to 1.06 indicating the spread or variability in responses for each statement. Usually, A standard deviation of 0.80 suggests that the responses for that particular statement are relatively tightly clustered around the mean. However, a standard deviation of 1.06 indicates a higher degree of variability or spread in responses. Overall, the variability in responses suggests that there may be diverse opinions or experiences among participants regarding the impact of AI in recruitment processes. This variability in perceptions could be due to factors such as individual experiences, knowledge of AI technology, organizational contexts, or attitudes towards technology adoption. Further analysis or exploration of these factors may help in understanding the range of responses and their implications for AI implementation in recruitment. The findings also illustrate that respondents believe AI has positively impacted recruitment outcomes in their organizations. There is some variability in responses, particularly in comfort levels with AI-driven tools and communication effectiveness by organizations.
4.2. Hypotheses Testing
The mean score of 3.82 for “AI has improved the efficiency of the recruitment process” suggests strong agreement among respondents about AI’s positive impact, while a mean score of 3.76 for “AI has streamlined the candidate screening process” further supports this view. The standard deviations of 1.6 for overall efficiency and 1.01 for screening indicate some variability in perceptions, with the higher SD suggesting mixed opinions on the overall recruitment process. Overall, the results support Hypothesis 1, demonstrating that AI tools are seen as effective in increasing recruitment efficiency and potentially reducing time-to-hire, albeit with some variability in individual experiences.
Hypothesis 1: Use of AI-powered recruitment tools enhances the efficiency of the hiring process, leading to a reduction in the time-to-hire metric.
This hypothesis suggests that the use of AI technology in the recruitment process significantly improves hiring efficiency. Efficiency in this context refers to the speed and effectiveness with which the recruitment process identifies and selects suitable candidates for job openings. The supporting data, as indicated in Table 4.
Hypothesis 2 supports that implementing AI in recruitment processes increases the diversity of candidates selected for job openings. Table 5 shows a mean score
Table 4. AI’s impact on recruitment efficiency.
Variable |
Mean Score |
SD |
AI has improved the efficiency of the recruitment process |
3.82 |
1.6 |
AI has streamlined the candidate screening process |
3.76 |
1.01 |
Table 5. AI’s effect on increasing diversity in recruitment.
Variable |
Mean Score |
SD |
AI has helped to attract a more diverse range of candidates for job openings. |
3.32 |
1.01 |
of 3.32 for the statement “AI has reduced bias in the recruitment process,” indicating a moderate level of agreement among respondents regarding the effectiveness of AI in minimizing bias. With a standard deviation of 1.01, the relatively low variability suggests that opinions are fairly consistent, although the average score reflects a neutral stance rather than strong affirmation. These results provide some support for Hypothesis 2, indicating that while respondents believe AI has potential in reducing bias, the perception of its effectiveness in enhancing candidate diversity may require further exploration or evidence.
Hypothesis 2: Implementing AI in recruitment processes increases the diversity of candidates selected for job openings.
This means that the use of AI tools aims to attract a broader spectrum of candidates from different backgrounds, leading to a more varied workforce. The supporting data shows a mean score indicating moderate agreement among respondents that AI has helped attract a more diverse range of candidates.
The integration of AI technology in the recruitment process is believed to reduce hiring costs by optimizing resource allocation and decreasing reliance on manual labor. Table 6 supports this notion, showing a mean score of 3.79 for the statement “AI has reduced bias in the recruitment process,” which indicates strong agreement among respondents regarding the efficiency gains from AI. However, the higher standard deviation of 1.6 suggests a range of opinions about its overall impact. These findings suggest that while many believe AI contributes to cost savings through improved processes, perceptions of its effectiveness may vary among individuals.
Hypothesis 3: The integration of AI technology in the recruitment process reduces hiring costs by optimizing resource allocation and decreasing reliance on manual labor.
This hypothesis asserts that the integration of AI technology into the recruitment process leads to reduced hiring costs by optimizing resource allocation and decreasing the reliance on manual labor. Essentially, this suggests that AI can automate repetitive tasks such as resume screening and initial candidate sorting allowing human recruiters to allocate their time and energy to more value-added activities, like interviews and relationship-building.
Table 6. Recruitment costs.
Variable |
Mean Score |
SD |
I believe AI implementation has positively impacted recruitment outcomes in my organization. |
3.79 |
1.6 |
Table 6 presents a quantitative assessment (specifically a mean score) of participants’ perceptions regarding the positive impact of AI on recruitment outcomes. The mean score of 3.79 indicates that there is generally a favorable belief among respondents that AI tools bring positive changes to their recruitment processes, with a standard deviation (SD) of 1.6 reflecting variability in the responses.
4.3. Summary
The analysis of the three hypotheses reveals that AI-powered tools are generally perceived positively, particularly in enhancing hiring efficiency, as indicated by strong mean scores of 3.82 and 3.76 for improvements in overall efficiency and candidate screening, respectively. However, variability in responses suggests differing individual experiences. Regarding the potential for AI to increase candidate diversity, a moderate mean score of 3.32 reflects some agreement on AI’s ability to reduce bias, although further exploration is needed given the neutral stance on its effectiveness. Additionally, a mean score of 3.79 supports the claim that AI integration can reduce hiring costs through improved efficiencies, though perceptions of its cost-saving benefits vary among organizations. Overall, while AI shows promise in these areas, the variability in perceptions underscores the need for tailored implementations and deeper investigation to fully realize its advantages in the recruitment process. As a result of the qualitative interviews, many participants expressed a generally positive view of AI, highlighting its potential to streamline the recruitment process. Respondents noted that AI tools can enhance efficiency by automating repetitive tasks such as resume screening and initial candidate assessments. On the other hand, some participants raised concerns regarding the quality of AI-generated recommendations. They suggested that while AI reduces time spent on manual tasks, it may overlook nuanced qualifications and candidates whose skills are not captured in conventional metrics. To enhance the credibility of the findings, the research team employed a triangulation method by comparing the qualitative results with quantitative data gathered from surveys. They also engaged in member checking, where select participants were invited to review findings to validate the interpretations made.
5. Conclusion
The findings suggest that the implementation of AI-powered recruitment tools has had a positive impact on the recruitment process. Research hypothesis 1 is supported by the data, indicating a consensus among respondents that AI tools enhance the efficiency of the hiring process. With strong average scores of 3.82 and 3.76 associated with improvements in overall recruitment efficiency and streamlined candidate screening, it is clear that AI is recognized for its positive contribution to reducing time-to-hire. Nevertheless, the presence of variability, as indicated by the standard deviations, points to differing individual experiences, suggesting that while many see AI as beneficial, others may have reservations that warrant further inquiry. Hypothesis 2 received partial support, with a mean score of 3.32 suggesting a moderate agreement that AI has helped to attract a more diverse range of candidates. Despite this finding, the neutral score reflects a cautious optimism regarding AI’s abilities to reduce bias in the recruitment process. The relatively low variability in responses indicates that while there is a shared perception of AI’s potential, further investigation into its effectiveness in enhancing diversity and overcoming bias may be necessary, as mixed opinions remain. Hypothesis 3 also shows promising results, as the mean score of 3.79 implies a strong belief among respondents that AI implementation positively impacts recruitment outcomes, potentially signaling cost savings through optimized resource allocation and reduced reliance on manual processes. However, the higher standard deviation suggests variability in individual perceptions, highlighting a need for organizations to communicate more effectively about the financial benefits of AI in recruitment.
This study has several limitations that should be acknowledged and considered when interpreting the results. Firstly, the sample size of 111 participants may not be representative of all organizations using AI-powered recruitment tools, and the results may not be generalizable to larger or more diverse populations. Secondly, the survey-based methodology may be subject to biases and limitations, such as social desirability bias, where participants may provide responses that are more positive or favorable than their actual experiences.
Appendix: Survey Questions
Question Number |
Survey Question |
Response Options |
1 |
AI has improved the efficiency of the recruitment process. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
2 |
AI has increased the accuracy of candidate assessments. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
3 |
AI has helped to attract a more diverse range of candidates for job openings. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
4 |
AI has contributed to a more equitable assessment of candidates from different backgrounds. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
5 |
AI has helped in identifying the best-fit candidates. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
6 |
AI has streamlined the candidate screening process. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
7 |
AI has enhanced the candidate experience during recruitment. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
8 |
I believe AI implementation has positively impacted recruitment outcomes in my organization. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
9 |
AI has increased the efficiency of the interview scheduling process. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
10 |
Organizations have effectively communicated the use of AI in their recruitment processes. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
11 |
AI has reduced bias in the recruitment process. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
12 |
AI tools have improved the overall outcomes of the recruiting processes in my organization. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
13 |
AI technologies have assisted in making data-driven recruitment decisions. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
14 |
The integration of AI in recruitment has streamlined communication with candidates. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |
15 |
Overall, I believe AI will continue to play a significant role in improving recruitment practices. |
Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5) |