A Critical Analysis of Data-Driven Recruitment Strategies in the Nigerian Job Market: A Case Study of Recruitment Agencies in Kwara State
- Project Research
- 1-5 Chapters
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- Table of Content: Available
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Background of the Study
Recruitment processes are increasingly becoming data-driven as organizations leverage analytics to identify, evaluate, and onboard talent. Data-driven recruitment involves using data to predict candidate success, reduce bias, and streamline hiring processes, leading to better matches between employers and job seekers (Okonkwo & Adetola, 2024).
In Nigeria, where the job market is highly competitive, recruitment agencies in Kwara State have begun to explore the potential of data-driven strategies to improve hiring efficiency. This study critically analyzes the adoption, effectiveness, and challenges of data-driven recruitment strategies among recruitment agencies in Kwara State.
Statement of the Problem
Traditional recruitment methods often rely on subjective evaluations, leading to inefficiencies and mismatches between candidates and job roles. In Kwara State, recruitment agencies face challenges such as data silos, resistance to adopting new technologies, and limited access to advanced analytics tools (Aliyu & Samuel, 2025).
Despite the global shift toward data-driven recruitment, there is a lack of localized studies on its impact within the Nigerian job market. This study addresses this gap by critically analyzing the application and challenges of data-driven recruitment in Kwara State.
Objectives of the Study
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To evaluate the adoption of data-driven recruitment strategies by recruitment agencies in Kwara State.
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To analyze the impact of data-driven recruitment on the efficiency and outcomes of hiring processes.
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To identify challenges and propose solutions for improving data-driven recruitment in Kwara State.
Research Questions
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How widely are data-driven recruitment strategies adopted by recruitment agencies in Kwara State?
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What impact do data-driven recruitment strategies have on hiring efficiency and outcomes?
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What challenges hinder the adoption of data-driven recruitment, and how can they be addressed?
Research Hypotheses
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Data-driven recruitment strategies have no significant impact on hiring efficiency.
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The adoption of data-driven recruitment does not significantly improve hiring outcomes.
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Addressing challenges has no significant effect on the adoption of data-driven recruitment strategies.
Scope and Limitations of the Study
The study focuses on recruitment agencies in Kwara State and their adoption of data-driven recruitment strategies. Limitations include access to proprietary recruitment data, variability in agency size, and the rapidly evolving nature of recruitment technologies.
Definitions of Terms
Data-Driven Recruitment: The use of data analytics to improve hiring decisions and processes.
Recruitment Agencies: Organizations that match job seekers with employment opportunities.
Hiring Efficiency: The speed and effectiveness of the recruitment process.
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