Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 15, No. 3, 2024 108 Integrating AI in Recruitment: A Review of Perceptions, Acceptance, Adoption and Ethical Considerations of AI Usage Asima Asif School of Economics and Management Sciences, Chongqing University of Posts and Telecommunications, China Abstract: The utilization of artificial intelligence (AI) in recruitment has emerged as a transformative area in talent acquisition. This systematic literature review examines the state of knowledge on AI usage in recruitment from 2018 to 2023, excluding algorithmic and other technological aspects. 26 research articles were selected for the review, along with six review articles for comparison. The study makes significant contributions by synthesizing diverse findings, categorizing them into perceptions, acceptance criteria, adoption and ethical considerations, offering a comprehensive and structured analysis of AI’s implications in recruitment. This study lays the foundation for future empirical research to validate and expand on the present findings. It emphasizes the need for more quantitative studies to asses AI’s impact on recruitment and explores the potential for hybrid approaches combining AI and human judgement for enhanced fairness and effectiveness. The study also underscores the importance of ongoing exploration of ethical considerations and development of responsible AI usage guidelines in recruitment. Overall, this systematic literature review serves as a valuable resource for organizations seeking to implement AI recruitmen t while guiding future research to unlock the full potential of AI in talent acquisition. Keywords: AI Recruitment, AI Adoption, AI and HRM, AI and Ethics, AI in Hiring. 1. Introduction In recent years, adoption of AI technologies have significantly transformed the field of recruitment(A. Gupta & Mishra, 2023). AI has brought efficiency and effectiveness to the recruitment process, revolutionized all recruiting practices. This systematic literature review aims to explore and analyze the literature on AI integration in recruitment. Various limitations of traditional processes are addressed with the use of AI in recruitment including time-consuming procedures, subjective decision-making biases, and high costs (Tambe, Cappelli, & Yakubovich, 2019). An employer’s ability to attract brilliant individuals depends on their ability to develop their online reputation, presence, and appeal (Allal-Chérif, Aránega, & Sánchez, 2021). Organizations can streamline and enhance these processes by integrating AI technologies, ultimately improving their overall recruitment outcomes. By including AI in the hiring process, the time needed to fill positions might be reduced by half (Kammerer, 2021). Although AI technologies are increasingly being integrated into various HRM functions, the most apply field is recruitment. This integration includes applicant tracking systems, resume screening, and talent analytics (Black & van Esch, 2020). The application of AI in HRM offers benefits such as improved recruitment efficiency, better candidate matching, and enhanced decision-making capabilities. To comprehend the impact of AI in HRM practices, including recruitment, a comprehensive review of the existing literature is essential. Understanding the perceptions and acceptance of AI technologies among applicants and recruiters is crucial in assessing the success and potential barriers of AI adoption in interviews (Nørskov et al., 2022). By examining viewpoints, attitudes, and concern of these key stakeholders, organizations can better understand the implications of AI usage in interviews (Horodyski, 2023b). This systematic review aims to explore the existing literature on perceptions of applicants and recruiters regarding AI usage, shedding light on the opportunities and challenges associated with AI adoption in the recruitment process. The integration of AI in recruitment also raises ethical considerations that must be addressed carefully. These considerations encompasses issues such as privacy and data protection, algorithmic bias, transparency, fairness, and accountability (Fernández & Fernández, 2020). Evaluating and understanding the ethical dimensions of AI implementation is crucial for ensuring responsible use of these technologies in alignment with societal norms and legal frameworks (Hunkenschroer & Luetge, 2022). Therefore, the objectives of this systematic review are to provide a comprehensive analysis of the literature on AI usage in recruitment, focusing on perception, acceptance, adoption and ethical considerations. By synthesizing the findings from articles published between 2018 and 2023, this review aims to contribute to the understanding of AI’s impact on recruitment processes and guide future research and practice in this domain. 2. Method 2.1. Search Strategy The search strategy used to conduct this literature review is conducted solely in the Web of Science database (WOS), using these keywords; AI based recruitment, AI selection, AI hiring, and AI and HRM. The objective was to identify articles published between 2018 and 2023 that examines the use of AI in the recruitment process and were related to the topic. The literature search was done by using WOS as it is a widely recognized and reputable source for scholarly articles in a variety of fields. The keywords were chosen strategically to incorporate a wide variety of articles on AI recruitment and their application to recruitment and HRM practices. The 109 articles only published from 2018 to 2023 were selected to ensure that recent research in the discipline is included. This time-frame allows the investigation of the most recent AI recruitment developments and advancements. A search was initiated by entering the keywords into the WOS database's search interface. The search results were then refined by using publication date-based filters. After obtaining the initial set of articles, for ensuring the relevancy, the titles and abstracts were evaluated. Only articles directly related to AI-based recruitment, AI selection, AI hiring, and AI and HRM were considered for further analysis. To broaden the search and assure a thorough literature review, the reference lists of the chosen articles were examined. This strategy was used to find more related articles which were not captured during initial search. Throughout the search process, the focus was on validating the relevance of the articles to the keywords and the AI recruitment research area. The final selection excluded articles that did not provide the required information or were not directly related to the topic. 2.2. Inclusion and Exclusion Criteria Inclusion and exclusion criteria are critical for a literature review search. The inclusion criteria for this literature search were only articles published in English will be included to assure the accessibility for thorough analysis of articles. Articles available only in full-text form were considered, allowing for a comprehensive examination of the content and findings. The review focuses on articles relating particularly to AI recruitment or the use of AI in HRM. Only articles related to the use of AI technology in recruitment for candidate screening, question generation, response analysis, interview and decision-making were included. In contrast, exclusion criteria were supposed to safeguard the review's focus and quality. To ensure consistency and comprehension, articles published in languages other than English were not included. Articles not available in full-text form were excluded, as access to the entire article is necessary for a comprehensive evaluation. In addition, articles that are not explicitly related to AI recruitment or the use of AI in HRM were excluded in order to maintain the focus on the application and implications of AI in the recruitment process. Studies that don't use AI technology in recruitment were excluded to ensure that the review focuses solely on AI in recruitment. Finally, articles published prior to 2018 were excluded in order to give priority to recent research and recent findings in the field of AI recruitment. By implementing this inclusion and exclusion criteria in Figure 1, the systematic review selected articles related to the research objective of assessing the use of artificial intelligence in recruitment and HRM practices. This targeted approach assures the reliability and validity of the findings and facilitates a thorough analysis of the most recent advances in AI recruitment. Figure 1. Inclusion/Exclusion Criteria 2.3. Screening Process All the papers were searched from the database Web of Science and the reference list of the papers found in initial search stage. The initial step involved identifying articles published between 2018 and 2023, written in the English language. From an initial pool of articles, 47 met these criteria. In the second step, the screening was based on the title and abstract of the articles. After this screening, 41 articles remained, as six articles were excluded based on their titles and abstracts not aligning with the research topic. The third screening aimed to exclude articles focusing solely on algorithms rather than the usage of AI in recruitment. After this step, 40 articles remained, with one article being excluded based on its primary focus on algorithms. In the fourth screening, the remaining articles were assessed for their relevancy to the usage of AI in recruitment. From the 40 articles, six articles were excluded as they were not directly relevant to the research topic, leaving 34 articles for further consideration. The fifth screening focused on the availability of the full- text versions of the articles. Two articles were excluded due to unavailability, resulting in 32 articles remaining for further analysis. The sixth screening aimed to exclude review articles, as the focus of this systematic literature review was on primary research studies and the review articles were separately used in this study for comparison between previous reviews and this study. After this step, six review articles were excluded, leaving a final selection of 26 articles for the systematic literature review. These 26 articles, meeting all the inclusion and exclusion criteria, were considered the final selection in Figure 2 for this systematic literature review. They served as the foundation for analyzing and synthesizing the relevant findings and insights related to the usage of AI in recruitment. Keywords: AI-based Recruitment OR Robot- mediated Recruitment OR AI Selection OR AI Hiring OR AI and HRM Inclusion Criteria: Published only in English Language, Full Text Availability, related to use of AI in Recruitment, Published between 2018 to 2023 Exclusion Criteria: Published in other than English Language, Not Available in Full Text Form, Not related to use of AI in Recruitment, related to technological aspect like algorithm etc., Published prior to 2018 110 Figure 2. Screening Process of the Systematic Literature Review 3. Framework 3.1. Related Articles Profile These articles on the use of AI in recruitment were found after a thorough literature search. Key findings and research gaps were then systematically organized from each study based on recurrent themes and trends. The four major categories of adoption, perceptions, acceptance, and ethical considerations were then used to group these articles. The development of the categorization was not based on any pre- existing theory, but rather was the result of an extensive investigation and synthesis of the actual findings from the twenty-six studies that were assessed. To guarantee the accuracy and reliability of the categorization in Figure 3, the selected studies passed a thorough validation process. By employing this thorough approach, the categorization in offers a structured and thorough representation of the state of AI incorporation in recruitment, facilitating a deeper understanding of the implications and difficulties associated with AI perception, acceptance, adoption and ethical considerations in this context. Most of the studies belongs to a specific category but few belongs to more than one category. The 26 studies showed a pattern for AI usage, the first step of which was perceptions about it which is basically about how AI is perceived and how these perceptions impact its acceptance, and in turn the acceptance impacts its adoption resulting in logical consequences based on the ethical considerations. Figure 3. Conceptual Framework 111 3.2. Comparison of Review: The six reviews cover a variety of topics related to artificial intelligence. These include the HIRE Framework's assessment of AI's influence over humans, as well as reviews of ethical concerns, technological software applications, HR roles, interdisciplinary groups, and Tactical HRIS components. Together, the reviews cover a wide range of years, from 1990 to 2021, and 357 sources in all, representing a significant amount of literature. Alternatively, this review presents an additional perspective by classifying AI according to perception, acceptance, adoption, and ethical considerations. The review covers 26 articles published between 2018 and 2023 and provides a conceptual framework to discuss processes and important factors. By doing this, it improves on and adds to the reviews' wide range of information while simultaneously offering a more focused and expanded examination of key aspects of artificial intelligence. The Table 1 shows comparison among previous literature reviews and this study. It highlights the difference of these systematic literature reviews. Table 1. In-detail Analysis/Comparison of Review Articles Paper Title Author Main Content No. of Papers Time-span People versus machines: introducing the HIRE framework Will, Krpan, & Lordan (2023) HIRE Framework, AI is better than humans, efficiency, performance, diversity and perception. 22 2005-2021 Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda Hunkenschroer & Luetge (2022) Ethical Concerns 51 2016-2020 The role of artificial intelligence in the recruitment processes: a systematic review Abou Hamdan (2019) Role of AI technological software and its usage benefits 15 N/A A Systematic Review of Artificial Intelligence and Hiring: Present Position and Future Research Areas George, Thomas, & Anson (2021) Roles of AI in HR function, Clarity about AI 20 2010-2020 An interdisciplinary review of AI and HRM: Challenges and future directions Pan & Froese (2023) Groups based on disciplines, Research focus, methods, and theoretical development in different disciplines. 184 1990-2020 Artificial Intelligence in Tactical Human Resource Management: A Systematic Literature Review Votto, Valecha, Najafirad, & Rao (2021) Tactical HRIS components 33 2014-2020 Integrating AI in Recruitment: A Review of Perceptions, Acceptance, Adoption and Ethical Considerations of AI Usage Asima Asif Categorizing it into perception, acceptance, adoption and ethical. Conceptual framework of Process and important factors 26 2018-2023 Table 1 shows that previous systematic reviews on AI usage in recruitment have made valuable contributions to the field. One review introduced the HIRE framework for evaluating AI in recruitment, providing a structured approach for assessment. Another review focused on the ethical aspects of AI-enabled recruiting, identifying gaps and suggesting future research directions. Additionally, previous reviews offered comprehensive overviews of AI's role in selection and recruitment processes, identified research gaps, and provided insights for future studies. They also examined the application of AI in HRM from various disciplines, facilitating interdisciplinary collaborations and offering implications for research and practice, also these reviews only serve in one or two categories but this paper serves in all four categories. In contrast, the new systematic review presented here builds upon these contributions and brings a fresh perspective by categorizing it. By synthesizing findings from 26 diverse research papers, this study offers a more comprehensive and up-to-date overview of the current state of knowledge in AI usage in recruitment. Categorizing the findings into adoption, perceptions, acceptance criteria, meanwhile consider the most important factor in AI usage, ethical, which provides a structured and systematic analysis, facilitating a deeper understanding of the multifaceted implications of AI in recruitment. With its rigorous approach, the new systematic review fills the gap in the existing literature and serves as a valuable resource for organizations seeking to implement AI in their recruitment processes. 3.3. Detail of Selected Articles Table 2 gives an overview of the literature published on AI usage in recruitment especially Interviews between 2018 to 2023. It includes the methodology, key findings, categories, contributions and research gap of the selected 26 papers. 112 Table 2. Selected Articles Sr. No. Author Research Method Sample Size Percep tion Accep tance Adopt ion Ethi cal 1 Chen (2023) Qualitative/ 15 Interviews (semi-structured) 3 Target Groups (3 Recruiters, 2 Managers, 10 Applicants) ● ● 2 Kim & Heo (2021) Qualitative/ Focus group Interviews and in-depth interviews 18 Participants, two companies that have adopted the AI interview platform, a developer that created the platform and three professors ● 3 Son, Lee, & Chang (2019) Qualitative/Examination with inAir system N/A ● 4 Allal-Chérif et al. (2021) Qualitative/Multiple case study design using grounded theory, participant observation, and qualitative data collection. Five cases of digital technologies for recruitment ● 5 Kammerer (2021) Qualitative N/A ● 6 Nørskov et al. (2020) Quantitative 235 ● 7 Nørskov et al. (2022) Qualitative Study 1=53 Study 2=242 ● 8 van Esch, Black, & Ferolie (2018) Quantitative 532 ● 9 Drage & Mackereth (2022) Qualitative/Content analysis of marketing and promotional materials, blogs, and white papers No sample needed for content analysis ● ● 10 Kot, Hussain, Bilan, Haseeb, & Mihardjo (2021) Quantitative 193 ● 11 Köchling, Wehner, & Warkocz (2022) Quantitative/Scenario-based between-subject design 160 ● 12 Bhalgat (2019) Quantitative/Literature review and data analysis of an online survey questionnaire. No sample size needed for detailed analysis. ● ● 13 Black & van Esch (2020) Qualitative N/A ● 14 Acikgoz, Davison, Ezerins, & Laske (2020) Quantitative/Two studies were conducted to investigate the effects of using AI in selection on justice perceptions. Study 1: 298 Study 2: 225 ● 15 Fernández & Fernández (2020) Qualitative N/A ● 16 Suen, Chen, & Lu (2019) Quantitative/Experimental Design 180 ● 17 Köchling & Wehner (2023) Quantitative/Scenario-based Between-Subject Design 200 ● ● 18 Gonzalez et al. (2022) Quantitative/Between-Subject Experiments Online Samples/ Study 1: Undergraduate students Study 2: Prolific panelists ● 19 Schick & Fischer (2021) Quantitative/Vignette Study/ Three-Dimensional Design Young adults in Germany ● 20 Hunkenschroer & Kriebitz (2023) Qualitative N/A ● 21 Suen & Hung (2023) Quantitative 152 ● 22 van Esch & Black (2019) Quantitative/Cross-sectional design 293 ● 23 Nawaz (2019) Quantitative 100 ● 24 Horodyski (2023a) Quantitative 552 ● 25 Umachandran (2021) Qualitative N/A ● 26 van Esch, Black, & Arli (2021) Quantitative 532 ● 113 Existing literature has examined AI adoption in recruitment, investigating the factors that facilitate or hinder its implementation in different organizational settings. Additionally, researchers have delved into the perceptions and attitudes of both job seekers and hiring managers towards AI- driven recruitment processes, shedding light on their acceptance and potential resistance to such technologies. Moreover, ethical considerations have been a critical aspect of AI adoption in recruitment, as scholars have scrutinized the implications of automated decision-making systems on fairness, privacy, and bias. Despite the significant number of studies conducted on these individual perspectives, a comprehensive analysis that integrates and synthesizes findings from all perspectives is currently lacking. This paper aims to bridge that gap by conducting a systematic review of literature Table 2 encompassing all perspectives related to AI usage in recruitment. 3.3.1. Perceptions about AI Usage in Recruitment Perceptions about AI usage in recruitment demonstrate notable variations between employers and applicants. Employers generally hold a positive attitude towards AI adoption, considering it superior to conventional methods in terms of fairness and impartiality (Horodyski, 2023b). They value AI for maximizing communication channels (Son et al., 2019) and effectively screening applicants. On the other hand, applicants perceive face-to-face recruitment as fairer than robot-mediated recruitment (Kammerer, 2021), indicating that the human touch is still significant in their perceptions of fairness during the recruitment process (Horodyski, 2023a). Concerns about data bias and evaluating AI methods also play a role in shaping perceptions about AI in recruitment (Kim & Heo, 2021). 3.3.2. Acceptance of AI Usage in Recruitment Acceptance of AI usage in recruitment varies between employers and applicants. Many potential candidates show a positive relationship between their attitudes towards AI and the likelihood of applying for a job that implements AI in the recruitment process (van Esch et al., 2018). The novelty factor of AI also plays a role in influencing job application likelihood. However, some applicants express concerns about the fairness of AI-based selection, highlighting the need for balancing technology-driven approaches with ethical considerations (Hunkenschroer & Kriebitz, 2023). 3.3.3. Adoption of AI in Recruitment The reviewed studies overwhelmingly support the adoption of AI in recruitment processes, especially during recruitment. The utilization of AI is associated with numerous benefits, including increased efficiency, reduced time consumption, and improved candidate matching (Allal-Chérif et al., 2021). Moreover, AI adoption in different stages of recruitment requires customized acceptance criteria (Chen, 2023), reflecting the diversity in perceptions and attitudes towards AI usage among employers and applicants (Nørskov et al., 2022). However, the studies emphasize the importance of reducing conflict between humans and autonomous recruitment systems (Chen, 2023) during the final decision- making stage to ensure optimal AI adoption. 3.3.4. Ethical Considerations about AI Usage in Recruitment Ethical considerations play a crucial role from both the company's and applicants' perspectives. AI in recruitment has the potential to eliminate human bias but may introduce algorithmic bias and data privacy concerns (Hunkenschroer & Kriebitz, 2023). Ensuring human rights principles, including legitimacy, autonomy, nondiscrimination, privacy, and openness, are upheld in AI recruiting is essential. Companies need to confront and address concerns surrounding gender and racial disparities that AI might inadvertently perpetuate (Drage & Mackereth, 2022). Additionally, ethical implications of AI usage in recruitment need to be carefully considered, and more research is required to investigate the long-term effects on trust and fairness in job markets and industries. 4. Conclusion and Future Research 4.1. Findings and Conclusion In the realm of AI usage in recruitment, especially interviews, considerable research has been conducted, as evident from the 26 reviewed papers. Existing studies have highlighted the benefits of AI adoption, perceptions of employers and applicants, acceptance criteria, and ethical considerations surrounding AI implementation. However, certain gaps persist in the literature. Some studies have predominantly focused on qualitative research, limiting the availability of solid quantitative data. Additionally, the majority of research has been centered around specific cultural contexts or limited to certain industries, making generalizations challenging. Moreover, while some studies have examined the advantages of AI-based recruitment, others have focused on the potential drawbacks and moral dilemmas, yet few have explored both perspectives comprehensively. This study makes several significant contributions to the field of AI usage in recruitment. Firstly, it synthesizes findings from 26 diverse research papers, offering a comprehensive overview of the current state of knowledge in this area. By categorizing the findings into perceptions, acceptance, adoption and ethical, this study provides a structured and systematic analysis, aiding in a deeper understanding of the multifaceted implications of AI in recruitment. Additionally, this paper highlights the perspectives of both employers and applicants, giving a balanced and inclusive view of AI adoption. 4.2. Discussion In order to encompass the most recent developments in the rapidly evolving field of AI in recruitment. There is a need for more quantitative studies to strengthen the evidence on AI's impact in recruitment processes. Investigating the experiences and perceptions of applicants and employers in real-world recruitment scenarios could provide valuable insights. The available papers are biased towards certain geographical regions or industries, potentially limiting the generalizability of the findings. Additionally, future research could explore the potential for hybrid approaches that integrate AI with human judgment to enhance the fairness and effectiveness of the recruitment process. Furthermore, as AI technology continues to advance, there is a need for ongoing exploration of the ethical considerations and the development of guidelines and standards to ensure responsible AI usage in recruitment. In conclusion, this study contributes to the existing knowledge on AI usage in recruitment by offering a comprehensive analysis of previous research. It serves as a stepping stone for further investigations and provides valuable insights for organizations looking to adopt AI in their 114 recruitment processes. However, it is essential to acknowledge the limitations of this study and use it as a springboard for future empirical research that can deepen our understanding of AI's role in shaping the future of recruitment practices. By addressing these limitations and pursuing future research directions, the field can continue to progress responsibly and harness the full potential of AI for effective and equitable recruitment processes. 4.3. Limitation and Future Research This study focuses on AI in recruitment only which excludes the algorithmic literature, future studies might include the technology and the interaction of management and technology. Moreover, the future research might focus on the effects in different HRM functions. References [1] Abou Hamdan, L. F. (2019). The role of artificial intelligence in the recruitment processes: a systematic review. [2] Acikgoz, Y., Davison, K., Ezerins, M., & Laske, M. (2020). Justice perceptions of artificial intelligence in selection. International Journal of Selection and Assessment, 28. [3] Allal-Chérif, O., Aránega, A., & Sánchez, R. (2021). Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technological Forecasting and Social Change, 169, 120822. [4] Bhalgat, K. H. (2019). An exploration of how Artificial Intelligence is impacting recruitment process. Dublin Business School. [5] Black, J. S., & van Esch, P. (2020). AI-enabled recruiting: What is it and how should a manager use it? Business Horizons, 63(2), 215-226. [6] Chen, Z. (2023). Collaboration among recruiters and artificial intelligence: removing human prejudices in employment. Cognition, Technology & Work, 25(1), 135-149. [7] Drage, E., & Mackereth, K. (2022). Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”. Philosophy & Technology, 35(4), 89. [8] Fernández, C., & Fernández, A. (2020). AI and recruiting software: Ethical and legal implications. Paladyn, Journal of Behavioral Robotics, 11, 199-216. [9] George, G., Thomas, M., & Anson, M. (2021). A Systematic Review of Artificial Intelligence and Hiring: Present Position and Future Research Areas. 20, 57-70. [10] Gonzalez, M., Liu, W., Shirase, L., Tomczak, D., Lobbe, C., Justenhoven, R. T., & Martin, N. (2022). Allying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes. Computers in Human Behavior, 130, 107179. [11] Gupta, A., & Mishra, M. (2023). Artificial Intelligence for recruitment The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part B (pp. 1-11): Emerald Publishing Limited. [12] Gupta, P., Fernandes, S. F., & Jain, M. (2018). Automation in Recruitment: A New Frontier. Journal of Information Technology Teaching Cases, 8(2), 118-125. [13] Horodyski, P. (2023a). Applicants' perception of artificial intelligence in the recruitment process. Computers in Human Behavior Reports, 11, 100303. [14] Horodyski, P. (2023b). Recruiter's perception of artificial intelligence (AI)-based tools in recruitment. Computers in Human Behavior Reports, 10, 100298. [15] Hunkenschroer, A. L., & Kriebitz, A. (2023). Is AI recruiting (un)ethical? A human rights perspective on the use of AI for hiring. AI and Ethics, 3(1), 199-213. [16] Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI- Enabled Recruiting and Selection: A Review and Research Agenda. Journal of Business Ethics, 178(4), 977-1007. [17] Kammerer, B. (2021). Hired by a Robot: The Legal Implications of Artificial Intelligence Video Interviews and Advocating for Greater Protection of Job Applicants. Iowa L. Rev., 107, 817. [18] Kim, J.-Y., & Heo, W. (2021). Artificial intelligence video interviewing for employment: perspectives from applicants, companies, developer and academicians. Information Technology & People, ahead-of-print. [19] Köchling, A., & Wehner, M. C. (2023). Better explaining the benefits why AI? Analyzing the impact of explaining the benefits of AI‐supported selection on applicant responses. International Journal of Selection and Assessment, 31(1), 45- 62. [20] Köchling, A., Wehner, M. C., & Warkocz, J. (2022). Can I show my skills? Affective responses to artificial intelligence in the recruitment process. Review of Managerial Science. [21] Kot, S., Hussain, H. I., Bilan, S., Haseeb, M., & Mihardjo, L. W. (2021). The role of artificial intelligence recruitment and quality to explain the phenomenon of employer reputation. Journal of Business Economics and Management, 22(4), 867- 883. [22] Nawaz, N. (2019). Artificial intelligence is transforming recruitment effectiveness in CMMI level companies. International Journal of Advanced Trends in Computer Science and Engineering, 8(6). [23] Nørskov, S., Damholdt, M. F., Ulhøi, J. P., Jensen, M. B., Ess, C., & Seibt, J. (2020). Applicant Fairness Perceptions of a Robot-Mediated Job Interview: A Video Vignette-Based Experimental Survey. Frontiers in Robotics and AI, 7. [24] Nørskov, S., Damholdt, M. F., Ulhøi, J. P., Jensen, M. B., Mathiasen, M. K., Ess, C. M., & Seibt, J. (2022). Employers’ and applicants’ fairness perceptions in job interviews: using a teleoperated robot as a fair proxy. Technological Forecasting and Social Change, 179, 121641. [25] Pan, Y., & Froese, F. J. (2023). An interdisciplinary review of AI and HRM: Challenges and future directions. Human Resource Management Review, 33(1), 100924. [26] Schick, J., & Fischer, S. (2021). Dear Computer on My Desk, Which Candidate Fits Best? An Assessment of Candidates’ Perception of Assessment Quality When Using AI in Personnel Selection. Frontiers in Psychology, 12. [27] Son, M., Lee, H., & Chang, H. (2019). Artificial Intelligence- Based Business Communication: Application for recruitment. Business Communication Research and Practice, 2, 84-92. [28] Suen, H.-Y., Chen, M., & Lu, S.-H. (2019). Does the use of synchrony and artificial intelligence in video interviews affect interview ratings and applicant attitudes? Computers in Human Behavior. [29] Suen, H.-Y., & Hung, K.-E. (2023). Building trust in automatic video interviews using various AI interfaces: Tangibility, immediacy, and transparency. Computers in Human Behavior, 143, 107713. [30] Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial Intelligence in Human Resources Management: Challenges and a Path Forward. California Management Review, 61(4), 15-42. [31] Umachandran, K. (2021). Application of artificial intelligence for recruitment in manufacturing industries. 1, 11-18. 115 [32] van Esch, P., Black, J. S., & Arli, D. (2021). Job candidates’ reactions to AI-Enabled job application processes. AI and Ethics, 1(2), 119-130. [33] van Esch, P., & Black, S. (2019). Factors that influence new generation candidates to engage with and complete digital, AI- enabled recruiting. Business Horizons, 62. [34] van Esch, P., Black, S., & Ferolie, J. (2018). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215-222. [35] Votto, A. M., Valecha, R., Najafirad, P., & Rao, H. R. (2021). Artificial intelligence in tactical human resource management: A systematic literature review. International Journal of Information Management Data Insights, 1(2), 100047. [36] Will, P., Krpan, D., & Lordan, G. (2023). People versus machines: introducing the HIRE framework. Artificial Intelligence Review, 56(2), 1071-1100.