Influence of Recruitment Practices on Employees’ Productivity in Isiolo County Government, Kenya
DOI:
https://doi.org/10.70619/vol5iss1pp1-11Keywords:
Recruitment Practices, Employees’ Productivity, Workforce Planning, Isiolo County Government in KenyaAbstract
The study sought to assess the influence of recruitment practices on employee productivity in the Isiolo County government. A descriptive cross-sectional study was conducted to assess the impact of recruitment practices on employee productivity within the Isiolo County government. The target population consisted of 1,600 employees, with a sample size of 320 respondents calculated using Slovin’s formula. Descriptive statistics, such as frequencies, percentages, means, and standard deviations, were used to summarize the data, while inferential statistics, including correlation analysis and multiple regression, were applied to examine the relationships between recruitment practices and employee productivity. A strong positive correlation (r = .917**, n = 287, p = .000) was observed between recruitment practices and productivity, indicating that improved recruitment techniques substantially enhance staff productivity. The relationship was statistically significant at the 0.05 level. The analysis of variance results confirmed the model’s goodness-of-fit in explaining variation in the dependent variable (F = 502.241; df1 = 4; df2 = 282; Sig. = .000). The findings underscored the importance of effective recruitment practices in boosting employee productivity, providing actionable insights for improving hiring processes and organizational performance. The study recommends that Isiolo County government should define role qualifications, implement rigorous screening to hire qualified candidates, and conduct regular workforce planning to align staffing with workload and goals, avoiding understaffing or overstaffing. The study supports human capital theory, emphasizing that quality recruitment boosts productivity. It advocates for clear job descriptions, enhanced recruitment policies, and data-driven hiring improvements.
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