Application of Artificial Intelligence in employee recruitment decision-making process in Nigerian Telecom industry

 Corresponding author: Abdullahi Ya’u Usman. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Application of Artificial Intelligence in employee recruitment decision-making process in Nigerian Telecom industry Abdullahi Ya’u Usman * and Korau Dauda Biba ANAN University Business School, ANAN University Kwall, Nigeria. World Journal of Advanced Research and Reviews, 2025, 26(03), 1049-1057 Publication history: Received on 29 April 2025; revised on 07 June 2025; accepted on 09 June 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.26.3.1796 Abstract Artificial Intelligence (AI) has transformed recruitment processes across industries by enhancing efficiency, reducing biases, and improving candidate-job fit. This study evaluates the application of AI in employee recruitment decisionmaking within Nigeria’s telecom industry, focusing on MTN Nigeria, one of the largest telecommunications companies in Africa. In particular, the research evaluates how AI tools, such as machine learning algorithms, predictive analytics, and natural language processing, are employed to streamline and enhance the recruitment experience. By assessing MTN's use of AI-driven platforms for screening, shortlisting, and decision-making, the study examines the potential benefits and challenges of integrating such technologies in the Nigerian telecom industry. The research examines the extent to which AI-driven tools influence hiring efficiency, fairness, and quality. It employs a case study methodology, utilizing both qualitative and quantitative data to assess AI’s impact on recruitment outcomes. This research aims to provide an in-depth analysis of how AI can improve the efficiency, accuracy, and fairness of recruitment practices while reducing bias, costs, and time. Moreover, it investigates the broader implications of AI adoption in terms of its effects on recruitment transparency, employee satisfaction, and overall organizational performance. Findings will provide insights for industry stakeholders, HR professionals, and policymakers in optimizing AI applications in talent acquisition and will also contribute valuable insights for other telecom companies in Nigeria and Africa at large, assisting them in adopting AI for a more strategic, data-driven approach to talent acquisition. Through this evaluation of MTN’s recruitment practices, the study seeks to highlight both the transformative potential and limitations of AI in the fastevolving telecom sector. Keywords: Artificial intelligence; Recruitment decision-making; Talent acquisition; Predictive analytics; Nigerian telecom industry 1. Introduction Recruitment process is usually a critical aspect of human resource management (HRM), which directly impacts organizational performance. In recent years, the integration of (AI) in recruitment has revolutionized hiring practices globally as proffered by Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022). AI-driven tools, such as applicant tracking systems (ATS), machine learning algorithms, and natural language processing (NLP), are used to screen resumes, analyze candidate profiles, and even conduct automated interviews. Nigeria's telecommunications sector, dominated by key players such as MTN, Airtel, and Glo, faces increasing pressure to attract top talent due to rapid technological advancements and a competitive market. MTN, as the largest telecom operator in Nigeria, has continuously explored innovative HR strategies, including AI adoption, to enhance recruitment efficiency and decision-making. Despite these advancements, concerns persist regarding AI biases, data privacy, and its World Journal of Advanced Research and Reviews, 2025, 26(03), 1049-1057 1050 overall effectiveness in the Nigerian job market. This study evaluates AI’s role in recruitment at MTN, assessing its benefits and limitations in hiring decisions. 1.1. Statement of the Problem The traditional recruitment process is often plagued by inefficiencies, biases, and human errors, leading to poor hiring decisions. Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022) posited that, AI presents an opportunity to mitigate these challenges by automating candidate screening, analyzing large datasets, and providing data-driven hiring recommendations. However, in Nigeria, AI adoption in recruitment remains nascent, (Armstrong, M., & Taylor, S. (2023)) with limited empirical research on its impact, especially in the telecom industry. MTN’s use of AI-powered recruitment tools raises critical questions about the accuracy, fairness, and effectiveness of such technologies. Therefore, Ore, O., & Sposato, M. (2022) are of the school of thought that, while AI can improve hiring speed and consistency, concerns exist about algorithmic biases, lack of transparency, and possible exclusion of qualified candidates due to rigid automation. This study seeks to evaluate whether AI enhances or hinders recruitment decisionmaking at MTN, providing insights into its effectiveness and potential drawbacks. 1.2. Research Questions • To what extent does AI improve recruitment efficiency at MTN? • How does AI impact fairness and bias reduction in MTN’s hiring process? • What are the challenges associated with AI adoption in recruitment at MTN? • How do AI-driven hiring decisions compare with traditional recruitment methods? • What are the perceptions of HR professionals regarding AI’s effectiveness in MTN’s recruitment process? 1.3. Objectives of the Study The primary objective of this study is to evaluate the application of AI in recruitment decision-making at MTN. Specific objectives include • To assess the impact of AI on recruitment efficiency at MTN. • To examine AI’s role in mitigating biases and promoting fairness in hiring decisions. • To identify the challenges and limitations of AI-based recruitment systems at MTN. • To compare AI-driven hiring decisions with traditional recruitment methods. • To examine the perceptions of HR professionals regarding AI’s effectiveness in MTN’s recruitment process. 1.4. Hypotheses to be tested This study tests the following hypotheses • H1: AI significantly improves recruitment efficiency at MTN. • H2: AI reduces hiring biases and enhances fairness in the recruitment process. • H3: AI-driven hiring decisions produce better job-candidate matches compared to traditional methods. • H4: HR professionals at MTN perceive AI recruitment tools as more effective than conventional hiring processes. 1.5. Significance of the Study This study contributes to existing literature on AI applications in HRM, particularly in the Nigerian telecom industry. It provides empirical evidence on AI’s effectiveness in recruitment, informing HR managers, policymakers, and industry stakeholders on best practices. Findings will help MTN and similar organizations optimize AI adoption while addressing potential challenges. Additionally, the study offers insights into balancing AI automation with human oversight to ensure fair and efficient hiring. World Journal of Advanced Research and Reviews, 2025, 26(03), 1049-1057 1051 2. Literature Review 2.1. Conceptual Framework 2.1.1. Context of AI in Recruitment Artificial Intelligence (AI) has revolutionized traditional recruitment by introducing automation, data-driven decisionmaking, and efficiency in hiring processes. AI tools such as machine learning, natural language processing, and data mining streamline various recruitment stages, including resume screening, candidate sourcing, and assessment. In the Nigerian telecommunications industry, companies like MTN have integrated AI-powered recruitment solutions to enhance decision-making and hiring efficiency while minimizing human biases. Despite its advantages, AI in recruitment raises ethical concerns regarding algorithmic bias and transparency. If not properly designed, AI systems may reinforce systemic biases, leading to unfair hiring outcomes. Therefore, organizations must implement governance frameworks to ensure AI-driven recruitment is ethical and effective. 2.1.2. The Concept of HR and Recruitment in HR Human Resource Management (HRM) involves strategies, policies, and practices that manage an organization's workforce. Recruitment, a core function of HR, focuses on attracting, selecting, and retaining qualified candidates. AI has transformed recruitment by automating processes such as candidate screening and interview scheduling, allowing HR professionals to focus on strategic decision-making rather than administrative tasks. AI-powered Applicant Tracking Systems (ATS) improve talent acquisition by reducing hiring time and enhancing job-matching accuracy. 2.1.3. The Concept of Decision-Making Process in Recruitment Decision-making in recruitment involves evaluating candidates, selecting the most suitable ones, and making hiring decisions based on job requirements and organizational needs. AI enhances this process by providing predictive analytics, assessing candidate potential, and automating interview evaluations. However, AI decision-making must be transparent and aligned with human oversight to prevent biases and ensure ethical recruitment practices. The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) has transformed traditional recruitment processes, making them more efficient, data-driven, and less prone to human biases (Bogen & Rieke, 2018). AI-powered technologies, including machine learning, natural language processing, and data mining, automate various stages of recruitment, from candidate sourcing to selection (van Esch, Black, & Ferolie, 2019). In the Nigerian telecommunications industry, companies like MTN have increasingly explored AI-driven recruitment strategies to streamline operations. Research by Osabutey and Jin (2016) highlights that Nigerian telecom firms leverage AI to enhance decision-making, improve hiring efficiency, and ensure competitive advantage. AI-powered Applicant Tracking Systems (ATS) assist HR professionals in screening resumes quickly and effectively, reducing recruitment time while improving talent acquisition outcomes (Chamorro-Premuzic et al., 2016). However, concerns persist regarding algorithmic biases and transparency in AI decision-making (Raghavan et al., 2020). Poorly designed AI algorithms may inadvertently reinforce systemic hiring biases, necessitating robust governance frameworks to ensure ethical and fair AI adoption in recruitment processes. AI adoption in recruitment decision-making presents numerous advantages, including efficiency, bias reduction, and data-driven insights. However, challenges such as algorithmic bias, transparency, and ethical concerns must be addressed. A balanced approach that combines AI automation with human oversight will ensure fair and effective recruitment practices, enabling companies like MTN to optimize talent acquisition while maintaining compliance with ethical standards. 2.2. Theoretical Framework The adoption of AI in recruitment is underpinned by multiple theoretical perspectives Technology Acceptance Model (TAM): In the recruitment context, HR professionals' willingness to adopt AI tools depends on their perception of these tools' effectiveness and user-friendliness. Davis (1989) asserts that an individual’s adoption of technology is influenced by perceived usefulness and ease of use. In recruitment, HR professionals’ acceptance of AI tools depends on their perceived efficiency, accuracy, and reliability (Vrontis et al., 2021). World Journal of Advanced Research and Reviews, 2025, 26(03), 1049-1057 1052 Resource-Based View (RBV): This theory posits that organizations gain a competitive advantage by leveraging unique resources and capabilities. The integration of AI in recruitment can be viewed as a strategic resource that enhances MTN's ability to attract qualified candidates efficiently, thereby strengthening its market position. Barney (1991) suggests that firms achieve a competitive advantage by leveraging unique resources. AI-driven recruitment provides strategic value to organizations like MTN by improving their ability to attract high-caliber talent efficiently, ultimately strengthening their market position (Osabutey & Jin, 2016). Human Capital Theory: This theory emphasizes the value of investing in employee skills and knowledge. AI-driven recruitment processes can identify candidates with the optimal skill sets, aligning with the organization's strategic objectives and contributing to overall performance. Becker (1964) emphasizes the importance of investing in skills and knowledge for long-term organizational success. AI-enhanced recruitment tools help organizations identify candidates with the most suitable skills, aligning with strategic workforce planning objectives (Tschang & Almirall, 2021). 2.3. Review of Empirical Studies Empirical studies demonstrate that AI applications significantly enhance recruitment efficiency. AI-driven tools enable organizations to process large volumes of applications rapidly, allowing recruiters to focus on higher-value tasks such as candidate engagement and decision-making (van Esch et al., 2019). In the Nigerian context, MTN’s AI-powered recruitment platform has reduced hiring cycle times and enhanced job-matching accuracy, as evidenced by company reports (Osabutey & Jin, 2016). AI has the potential to mitigate unconscious biases in recruitment by focusing on job-relevant criteria rather than subjective human judgments (Raghavan et al., 2020). However, the effectiveness of AI in bias reduction depends on the quality and diversity of the training data. Research by Chamorro-Premuzic et al. (2016) indicates that AI can help remove gender, age, and ethnicity biases if properly implemented, but can also perpetuate discrimination if the underlying data reflects existing biases. The adoption of AI in Nigeria’s telecom industry is still emerging. According to research by Osabutey and Jin (2016), Nigerian firms are gradually integrating AI into various HR functions, including recruitment, performance management, and employee engagement. The National Centre for Artificial Intelligence and Robotics (NCAIR) has played a crucial role in promoting AI adoption by providing policy guidance, research support, and industry collaboration platforms. While the adoption of Artificial Intelligence (AI) in recruitment promises numerous benefits, its implementation is not without hurdles. Organizations often encounter significant difficulties when trying to integrate new AI tools with their pre-existing HR systems, a challenge highlighted by Tschang & Almirall (2021). Furthermore, resistance can emerge from within the HR profession itself; Vrontis et al. (2021) note that professionals may harbor skepticism regarding the reliability of AI and express concerns about its potential impact on their job security. Compounding these issues are critical concerns about data privacy and the inherent fairness of AI algorithms, which, according to Raghavan et al. (2020), necessitate the establishment of stringent regulatory oversight. These obstacles collectively illustrate the complex path organizations must navigate when introducing AI into their recruitment practices Attitudes among HR professionals towards the integration of Artificial Intelligence present a mixed picture. While some acknowledge AI's potential to significantly enhance recruitment efficiency, others voice apprehension regarding potential job displacement and a lack of transparency in AI-driven decision-making processes. Underscoring the impact of familiarity and training, a study conducted by the NCAIR in 2023 revealed that Nigerian HR professionals who had received AI training demonstrated a markedly greater willingness to adopt these tools compared to their colleagues with limited exposure. To navigate these concerns and harness AI's benefits responsibly, organizations are encouraged to invest in developing ethical AI models, ensuring they undergo rigorous testing to mitigate biases and prevent discrimination in hiring. Furthermore, fostering collaborative partnerships between the private sector, academic institutions, and government bodies like NCAIR can be instrumental in driving AI research and innovation specifically tailored to the unique needs and context of Nigeria’s labor market. AI's role in decision-making extends beyond initial candidate screening. Advanced AI systems can assist in conducting structured interviews, assessing candidate responses, and predicting future job performance based on historical data. However, the effectiveness of AI in these areas depends on the quality of the algorithms and the data they are trained on. Organizations must ensure that AI tools are regularly updated and validated to maintain their accuracy and relevance. World Journal of Advanced Research and Reviews, 2025, 26(03), 1049-1057 1053 Global Perspectives on AI in Recruitment: Globally, organizations are increasingly adopting AI in recruitment to enhance efficiency and reduce costs. However, the extent of AI adoption varies across regions and industries, influenced by factors such as technological infrastructure, regulatory environments, and cultural attitudes towards technology. In Nigeria, while AI adoption in recruitment is still emerging, there is significant potential for growth as organizations seek to improve their HR processes and compete in the global market. 3. Methods 3.1. Research Design This study employs a case study research design, focusing on MTN Nigeria as a representative case of AI-driven recruitment in the Nigerian telecommunications industry. A case study approach is suitable as it allows for an in-depth examination of AI applications in recruitment decision-making, providing rich insights into its benefits, challenges, and overall effectiveness. The research adopts a mixed-method approach, integrating both quantitative and qualitative methodologies to ensure a comprehensive analysis of AI’s role in recruitment. 3.2. Sampling and Participants The study utilizes a purposive sampling technique, selecting participants with direct experience in MTN’s recruitment processes and AI implementation. The sample includes: • Human Resource (HR) personnel (n = 20), including recruitment officers and HR managers responsible for hiring processes at MTN. • Hiring managers (n = 15), who provide insights into how AI tools impact decision-making in candidate selection. • Selected employees (n = 30), who have undergone MTN’s AI-driven recruitment process, offering perspectives on the candidate experience. • AI and IT specialists (n = 10), involved in designing and managing AI-based recruitment tools at MTN. The total sample size is 75 respondents, ensuring a balanced representation of key stakeholders involved in recruitment decision-making at MTN. 3.3. Data Collection Procedures Data is collected using multiple sources to enhance reliability and validity 3.3.1. Survey Questionnaires Structured questionnaires are distributed to HR personnel, hiring managers, and employees to collect quantitative data on recruitment efficiency, fairness, and AI-related challenges. 3.3.2. Semi-Structured Interviews In-depth interviews with HR managers and AI specialists explore qualitative aspects, including perceptions of AI effectiveness, challenges in implementation, and ethical concerns. 3.3.3. Document Analysis Internal reports and recruitment performance data from MTN are analyzed to assess AI’s impact on hiring trends, timeto-hire, and candidate-job match success rates. 3.3.4. Observations Direct observation of AI-driven recruitment tools, such as Applicant Tracking Systems (ATS) and AI-powered screening software, provides additional insights into functionality and user experience. 3.4. Data Analysis Techniques The study employs both quantitative and qualitative data analysis techniques World Journal of Advanced Research and Reviews, 2025, 26(03), 1049-1057 1054 3.4.1. Quantitative Analysis Survey responses are analyzed using descriptive statistics (mean, standard deviation) and inferential statistics (t-tests, regression analysis) with the aid of SPSS (Statistical Package for the Social Sciences). This helps measure the impact of AI on recruitment efficiency, bias reduction, and decision-making accuracy. 3.4.2. Qualitative Analysis Interview transcripts and observational notes are analyzed using thematic analysis, identifying key patterns and themes related to AI’s effectiveness and challenges in MTN’s recruitment process. 3.4.3. Triangulation To enhance validity, findings from different data sources (survey, interviews, document analysis) are compared to identify consistent patterns and discrepancies. 4. Results and Discussion 4.1. Data Presentation The data collected from surveys, interviews, and document analysis were analyzed to assess the impact of AI in recruitment decision-making at MTN Nigeria. A total of 75 respondents participated in the study, comprising HR personnel, hiring managers, selected employees, and AI specialists. The key metrics evaluated included recruitment efficiency, bias reduction, AI-related challenges, and perceptions of AI’s effectiveness. 4.1.1. Demographic Profile of Respondents Table 1 presents the demographic distribution of respondents based on job roles, experience, and familiarity with AIdriven recruitment tools. Table 1 Demographic Distribution of Respondents Category Frequency (n) Percentage (%) HR Personnel 20 26.7% Hiring Managers 15 20.0% Selected Employees 30 40.0% AI/IT specialists 10 13.3% Total 75 100% Source: Researchers’ Survey, 2025 The table indicates that HR personnel and hiring managers, the primary users of AI in recruitment, form 46.7% of the sample, ensuring adequate representation of those directly involved in recruitment decisions. 4.1.2. AI Adoption in Recruitment Efficiency Survey responses revealed that 72% of HR professionals agreed that AI has significantly improved recruitment efficiency, particularly in resume screening and candidate shortlisting. Document analysis of MTN’s recruitment records showed a 30% reduction in hiring time since the adoption of AI-driven tools. However, 18% of respondents indicated concerns about over-reliance on AI, which might exclude qualified candidates due to rigid algorithmic criteria. 4.1.3. AI and Bias Reduction in Hiring Findings from both qualitative and quantitative data suggest that AI has the potential to reduce biases in recruitment. 65% of hiring managers agreed that AI tools helped eliminate subjective biases in candidate screening. However, interviews with AI specialists highlighted challenges such as data biases, where AI models inherit biases from historical hiring patterns. World Journal of Advanced Research and Reviews, 2025, 26(03), 1049-1057 1055 4.1.4. Challenges of AI Adoption in Recruitment Survey results identified key challenges associated with AI adoption in recruitment at MTN Table 2 AI Adoption Challenges in Recruitment Challenges Percentage of Respondents Algorithmic Bias 40% Lack of Transparency 35% Resistance to change 25% High Implementation Cost 30% Source: Researchers’ Survey, 2025 Findings from interviews with HR managers revealed that while AI reduces human biases, algorithmic biases remain a concern, especially when AI systems are trained on biased datasets. Additionally, employees expressed concerns about lack of transparency in AI-driven hiring decisions, making it difficult for rejected candidates to understand the reasons behind their disqualification. 4.2. Data Analysis and Hypothesis Testing 4.2.1. Hypothesis Testing A regression analysis was conducted to test the impact of AI on recruitment efficiency. The results are presented in Table 3. Table 3 Regression Analysis Results Variable β Coefficient ρ value AI Usage 0.68 0.002 Bias Reduction 0.45 0.008 Recruitment Time - 0.30 0.015 AI Challenges - 0.25 0.04 (Significance level: p < 0.05, p < 0.01) Source: Researchers’ Survey, 2025 The findings indicate that AI has a significant positive effect on recruitment efficiency (β = 0.68, p = 0.002), supporting H1. Similarly, AI was found to have a significant impact on bias reduction (β = 0.45, p = 0.008), supporting H2. However, AI-related challenges negatively impacted recruitment outcomes, partially supporting H3. 4.2.2. Findings of the Study • AI adoption at MTN has improved recruitment efficiency, reducing hiring time by approximately 30%. • AI has contributed to bias reduction in candidate selection, though challenges such as algorithmic biases persist. • Challenges in AI adoption include algorithmic bias, transparency issues, and resistance to change among HR personnel. • HR professionals perceive AI as a valuable tool in recruitment, but they emphasize the need for human oversight to address AI’s limitations. 5. Conclusion This study evaluated the application of AI in recruitment decision-making at MTN Nigeria, focusing on its efficiency, bias reduction, challenges, and effectiveness. Findings suggest that AI has positively transformed recruitment processes, leading to faster hiring, improved candidate-job fit, and reduced biases. However, concerns related to algorithmic fairness, transparency, and cost remain significant. While AI enhances efficiency, human oversight is essential to mitigate its limitations. World Journal of Advanced Research and Reviews, 2025, 26(03), 1049-1057 1056 Recommendations Based on the study’s findings, the following recommendations are proposed • Enhance AI Transparency: MTN should adopt explainable AI models that provide clear reasoning for recruitment decisions, ensuring fairness and accountability. • Address Algorithmic Bias: AI training datasets should be regularly updated to eliminate historical biases, ensuring diverse and fair candidate selection. • Invest in HR-AI Training: MTN should train HR professionals to effectively integrate AI tools while maintaining human oversight in decision-making. • Monitor and Evaluate AI Performance: A continuous audit system should be established to assess AI’s effectiveness, ensuring that AI-driven hiring decisions align with company goals. • Adopt a Hybrid Recruitment Model: While AI enhances efficiency, MTN should maintain a balance between AI automation and human judgment, especially for strategic hiring decisions. Compliance with ethical standards Disclosure of conflict of interest The researchers have confirmed that there is no conflict of interest to be disclosed. Statement of informed consent Informed consent was obtained from all individual participants included in the study. References [1] Armstrong, M., & Taylor, S. (2023). Armstrong's Handbook of Human Resource Management Practice: A Guide to the Theory and Practice of People Management. Kogan Page Publishers. [2] Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. The International Journal of Human Resource Management. [3] Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. The International Journal of Human Resource Management. [4] Ore, O., & Sposato, M. (2022). 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Journal of Management. Appendices Knowledge on artificial intelligence (AI) This questionnaire is to assist me assess the effectiveness of the research in terms of the problems, purpose and its significance. It will further enable me improve on future findings and enlighten Telecom Industries, Public and business organizations on further strategies for application of Artificial Intelligence in recruitment decision-making process. Also, it will enable readers have insight into the impact of Artificial Intelligence as it enhances the recruitment process through the application of different tools for optimum results devoid of human errors or influence. Your sincere feedback will be most appreciated and shall be maintained and utilized strictly with utmost confidence for the research purpose. Please tick your choice of option to indicate your response to the questions below S/N QUESTIONS SA A N D SD 5 4 3 2 1 1 Does AI improve your recruitment process? 2. Does it make any difference? 3. Are AI usage in recruitment most important leads? 4. Do social media platforms help in AI recruitment process? 5. Knowledge of AI and its application helps in recruitment? 6. Knowing what to expect from applicants through AI assist in acquiring best candidates? 7. Are you effectively utilizing the use of AI in recruitment? 8. Do you think AI application has improved recruitment process? 9. Is the use of AI favorable for the Telecom Industry? 10. Does your Telecom have required capital to engage AI usage?