Exploring the Mediating Role of Artificial Intelligence in Recruitment and Talent Management: Impacts on Candidate Experience, Hiring Quality, and Organizational Outcomes

Abstract

The objective of this research is to examine the intermediary function of Artificial Intelligence (AI) within the recruitment process, with a specific emphasis on its influence on the experience of candidates, the caliber of hiring, and the results for organizations. The study further explores the ways in which confidence in artificial intelligence and the culture within organizations influence the dynamics between AI-facilitated hiring methods and the perceptions held by candidates. A research approach was utilized, focusing on 150 human resources experts from diverse sectors across the Delhi NCR area. Information was gathered using a meticulously designed questionnaire that featured Likert-scale and multiple-choice enquiries regarding the implementation of AI, the experiences of candidates, the culture within the organization, and the level of trust in AI technologies. The research utilized SPSS for conducting statistical evaluations, applying t-tests and ANOVA to examine the connections among various variables. The findings demonstrate that artificial intelligence markedly boosts the efficiency of recruitment processes, minimizes biases, and elevates the quality of hiring decisions. Confidence in artificial intelligence plays a beneficial role in enhancing the connection between AI-facilitated hiring processes and the experiences of candidates, leading to notable advancements in how fairness and transparency are perceived. Even with the favorable results, differing perspectives regarding the openness and equity of AI procedures were noted. The research findings indicate that artificial intelligence has the potential to significantly enhance hiring processes; however, it is crucial for companies to cultivate trust and openness in order to fully leverage the capabilities of AI. These revelations present significant considerations for HR experts seeking to enhance recruitment strategies via the integration of artificial intelligence

Introduction

Overview

This research investigates the role of Artificial Intelligence (AI) in transforming recruitment practices, focusing on:

• Enhancing efficiency

• Improving candidate experience

• Increasing the quality of hires

• Exploring the moderating effects of trust in AI and organizational culture

Research Objectives

1. To analyze AI’s intermediary role in recruitment and its influence on candidate experience and hiring quality.

2. To examine how trust in AI and organizational culture affect the adoption and success of AI-driven recruitment.

Literature Review Highlights

• AI automates repetitive tasks (e.g., resume screening), enabling HR to focus on strategic roles.

• It reduces human bias, encourages diversity, and improves the match between candidates and roles.

• Trust in AI enhances perceptions of fairness and transparency.

• Ethical concerns include data privacy, decision transparency, and discrimination risks.

• Organizational culture greatly affects AI adoption and effectiveness.

Hypotheses

• H1: AI implementation improves candidate experience and hiring quality.

• H2: Trust in AI moderates the positive relationship between AI use and candidate experience.

Methodology

• Sample: 150 HR professionals from Delhi NCR across multiple industries.

• Data Collection: Digital survey with Likert-scale and multiple-choice questions.

• Analysis Tools: SPSS, t-test, ANOVA for statistical validation.

Key Findings

1. AI Implementation

• Majority of participants agree AI boosts recruitment efficiency, reduces bias, and saves time.

• Mean scores ranged from 4.15 to 4.35 (out of 5), indicating strong support.

2. Candidate Experience

• High satisfaction: 84% found AI processes user-friendly, fair, and efficient.

• Most would recommend AI-driven hiring methods.

3. Trust in AI

• Over 65% trust AI’s fairness, reliability, and transparency.

• Trust significantly influences positive perception and effectiveness.

4. Quality of Hires

• 85% agree AI leads to better job-candidate matches and improved hiring quality.

• Highest mean score (4.50) for satisfaction with hires through AI.

Statistical Analysis & Hypothesis Testing

• Hypothesis 1 (AI’s Positive Impact):

  • t = 5.52 and 6.35, p < 0.001

  • Cohen’s d = 0.76 – 0.81

  • Result: Significant improvement in candidate experience and hiring quality.

• Hypothesis 2 (Trust in AI Moderation):

  • t = 5.80, p < 0.001, Cohen’s d = 0.79

  • Result: Trust in AI significantly strengthens the impact of AI on candidate experience.

Conclusion

The incorporation of Artificial Intelligence (AI) into recruitment practices has demonstrated considerable promise in revolutionizing conventional hiring techniques. This research underscores the crucial significance of artificial intelligence in optimizing recruitment processes, minimizing biases, and elevating the experiences of candidates. The results indicate that recruitment systems powered by artificial intelligence are not just efficient in optimizing the hiring procedure but also promote a fairer atmosphere by reducing human prejudices. The confidence placed in artificial intelligence, along with the organizational environment that facilitates its integration, significantly influences the effectiveness and embrace of these technologies. The capacity of artificial intelligence to analyze extensive datasets and render impartial choices leads to enhanced matches between job candidates, thereby elevating the overall quality of hiring. Nonetheless, despite the generally favorable reception of artificial intelligence, certain obstacles concerning transparency and equity continue to exist, which may hinder its broader acceptance.

References

[1] Albaroudi, E., Mansouri, T., & Alameer, A. (2024). A comprehensive review of AI techniques for addressing algorithmic bias in job hiring. AI, 5(1), 383–404. https://doi.org/10.3390/ai5010019 [2] Balasundaram, S., Venkatagiri, S., & Sathiyaseelan, A. (2022). Using AI to enhance candidate experience in high-volume hiring: A conceptual review and case study. Proceedings of the Replenish, Restructure & Reinvent: Technology Fueled Transformation for Sustainable Future, 21-22. [3] Becker, W. J., Connolly, T., & Slaughter, J. E. (2010). The effect of job offer timing on offer acceptance, performance, and turnover. Personnel Psychology, 63(1), 223–241. https://doi.org/10.1111/j.1744-6570.2009.01167.x [4] Burton, J. W., Stein, M., & Jensen, T. B. (2020). A systematic review of algorithm aversion in augmented decision-making. Journal of Behavioral Decision Making, 33(2), 220–239. https://doi.org/10.1002/bdm.2155 [5] Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10(1), 567-579. https://doi.org/10.1057/s41599-023-02079-x [6] Ferrara, E. (2023). Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies. Sci, 6(1), 3. https://doi.org/10.3390/sci6010003 [7] Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057 [8] Haime, A., Vallejo, A., & Schwindt-Bayer, L. (2022). Candidate experience and electoral success. Latin American Research Review, 57(1), 170–187. https://doi.org/10.1017/lar.2022.10 [9] Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203 [10] Li, J., Zhou, Y., Yao, J., & Liu, X. (2021). An empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory. Scientific Reports, 11(1), 13564. https://doi.org/10.1038/s41598-021-92904-7 [11] Rani, J. (2024). AI in HR: Revolutionizing recruitment, retention, and employee engagement. Journal of Informatics Education and Research, 4(3), 959–968. https://doi.org/10.52783/jier.v4i3.1410 [12] Rehmert, J. (2021). Behavioral consequences of open candidate recruitment. Legislative Studies Quarterly, 46(2), 427–458. https://doi.org/10.1111/lsq.12283 [13] Rožman, M., Oreški, D., & Tominc, P. (2023). Artificial intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s VUCA environment. Sustainability, 15(6), 5019. https://doi.org/10.3390/su15065019 [14] Sjödin, D., Parida, V., Palmié, M., & Wincent, J. (2021). How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. Journal of Business Research, 134, 574–587. https://doi.org/10.1016/j.jbusres.2021.05.009 [15] Sýkorová, Z., Hague, D., Dvouletý, O., & Procházka, D. A. (2024). Incorporating artificial intelligence (AI) into recruitment processes: Ethical considerations. Vilakshan - XIMB Journal of Management, 21(2), 293–307. https://doi.org/10.1108/XJM-02-2024-0039 [16] Tangi, L., Noordt, C. V., Combetto, M., & Gattwinkel, D. (2022). AI Watch: European landscape on the use of artificial intelligence by the public sector. Publications Office.https://data.europa.eu/doi/10.2760/39336 [17] Tay, C. E., Ying, C. Y., Yeo, S. F., & Cheah, C. S. (2024). Revolutionizing recruitment: The rise of artificial intelligence in talent acquisition. PaperASIA, 40(6b), 191–199. https://doi.org/10.59953/paperasia.v40i6b.270

Copyright

Copyright © 2025 Nisha Dattaraj Sawant, Anita Raicar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.