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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV October 2025 | Special Issue on Management
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Human Resource Management Practices and Employee Performance
a Case of Health Sector in Sheema District. Uganda
Nahwera Bathel, Dr. Benard Nuwatuhaire
Ankole Western University
DOI: https://dx.doi.org/10.47772/IJRISS.2025.914MG00204
Received: 21 October 2025; Accepted: 27 October 2025; Published: 19 November 2025
ABSTRACT
This study sought to examine the relationship between Human Resource Management Practices and Employee
Performance in Sheema District's health sector, focusing on Human Resource Compensation, Recruitment, and
Training. A cross-sectional research design was employed, using a quantitative approach to gather data at a single
point in time from all Health Centre IIIs and IVs in the district, on a sample size of 67 employees. Findings
showed that Human Resource Compensation, Recruitment, and Training positively influenced employee
performance, with Training being the most significant factor. The study concluded that well-structured Human
Resource (HR) practices significantly enhance employee performance, essential for the effective delivery of
healthcare services. The study recommended that government should aim at improving compensation packages,
enhancing recruitment processes to match employees' skills with job requirements, expanding regular training
programs, and continuously monitoring HR practices to improve employee performance and service quality in
the health sector.
Keywords: Human Resource Management Practices and Employee Performance
INTRODUCTION
Effective healthcare systems rely on well-performing healthcare workers who are motivated, present, and
committed to delivering quality services (WHO, 2020; Dieleman et al., 2021). healthcare facilities should operate
with a well-managed workforce that meets performance targets, ensures high immunization coverage, and
maintains optimal treatment success rates. Strong human resource management (HRM) practices, including
effective recruitment, supervision, professional development, and motivation strategies, are essential to
achieving these outcomes (Mutua et al., 2021; Adebayo & Udegbe, 2020).
THEORETICAL REVIEW
The study was guided by the Scientific Management Theory of Frederick Winslow Taylor (1856–1917) and
Abraham Maslow’s Theory of Motivation. The application of Taylor’s principles in the health sector includes
job design and specialization, where tasks are broken down into simpler components to improve efficiency and
productivity. Conducting time and motion studies can help identify optimal workflows and eliminate
inefficiencies in healthcare processes. These principles are relevant to the health sector as they streamline
healthcare operations, enhance resource utilization, and improve overall efficiency in service delivery.The
application of Maslow’s hierarchy of needs in the health sector involves ensuring that healthcare workers have
access to basic physiological needs such as a safe working environment, fair compensation, and adequate
facilities. By integrating Taylor's Scientific Management and Maslow's Motivation Theory into the study on
Human Resource Management Practices and Employee Performance in the health sector of Sheema District, a
holistic approach can be developed that improves organizational efficiency while addressing the diverse
motivational needs of healthcare professionals, leading to overall improved performance.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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LITERATURE REVIEW
The Relationship Between Human Resource Compensation/Rewards and Employee Performance in
Health Sector.
The study conducted by Effiong et al. (2015) examined the effect of the reward system on healthcare workers'
performance at the University of Calabar Teaching Hospital, Nigeria. Using a case study approach, the
researchers collected data through questionnaires, observations, and interviews. The findings revealed that
monetary rewards had a significant impact on employee performance, whereas non-monetary rewards had a less
significant effect. The study concluded that financial incentives play a crucial role in enhancing healthcare
workers’ motivation and productivity. It recommended that management implement fair and equitable monetary
reward systems, including bonuses and fringe benefits, to improve employee performance.
The study conducted by Nyberg et al. (2021) reviewed existing literature on the relationship between
compensation and employee performance. The researchers conducted a systematic review, analyzing multiple
studies on the subject. Their findings indicated that compensation is a key factor influencing job satisfaction and
performance, as it provides employees with a sense of security, status, and achievement. The study concluded
that adequate compensation enhances employee retention, satisfaction, and overall productivity in the healthcare
sector. It recommended that healthcare organizations align their compensation structures with employees' needs
and organizational goals to maximize performance.
The study conducted by Al-Madi et al. (2017) explored the impact of employee motivation on organizational
commitment in the healthcare sector. Using survey data, the researchers assessed the role of compensation and
other motivational factors in employee commitment. The findings showed that both intrinsic and extrinsic
rewards significantly influenced organizational commitment, which in turn affected employee performance. The
study concluded that a well-structured reward system enhances motivation and job commitment, leading to
improved performance. It recommended that healthcare organizations develop comprehensive reward systems
that incorporate both intrinsic and extrinsic motivational factors.
The study conducted by Alshammari et al. (2023) investigated the impact of incentive and reward systems on
employee performance in the healthcare sector in Saudi Arabia, with job satisfaction as a mediating factor. Using
a survey and structural equation modeling, the study found that incentives and rewards positively affected
employee performance, with job satisfaction playing a critical role. The study concluded that effective reward
systems enhance job satisfaction, which ultimately improves employee performance. It recommended that
healthcare institutions design incentive and reward systems that prioritize job satisfaction to improve overall
service delivery.
The study conducted by Wang et al. (2019) assessed the effect of financial and non-financial rewards on
employee performance in China’s healthcare sector. The researchers employed a quantitative study using
structured questionnaires among healthcare professionals. The findings indicated that financial rewards such as
salaries and bonuses significantly impacted job performance, while non-financial rewards such as recognition
and career development opportunities also played a critical role in motivation. The study concluded that a balance
of financial and non-financial rewards is essential for enhancing employee performance. It recommended that
employers integrate both financial and non-financial incentives into their reward systems to improve employee
satisfaction and productivity.
The study conducted by Mugizi et al. (2020) examined the impact of remuneration on employee performance in
Ugandan public health facilities. Using a mixed-method approach, the researchers combined surveys and
interviews with healthcare workers. The findings revealed that poor remuneration was a major factor affecting
employee performance, contributing to absenteeism and low motivation. The study concluded that improved
remuneration packages are necessary for enhancing job commitment and reducing workforce turnover in the
healthcare sector. It recommended that the Ugandan government increase salaries and provide additional benefits
to healthcare workers to boost their performance.
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The study conducted by Njoroge & Kwasira (2018) investigated the relationship between reward systems and
employee performance in public hospitals in Kenya. Employing a descriptive survey approach, the researchers
collected data from hospital employees. The findings showed that employees who received competitive salaries,
allowances, and professional growth opportunities were more productive and committed to their work. The study
concluded that competitive compensation and career development opportunities significantly enhance employee
performance in the healthcare sector. It recommended that public hospitals invest in competitive salaries and
career development programs to improve motivation and productivity among healthcare workers.
In conclusion, the reviewed studies consistently highlight a positive relationship between compensation/rewards
and employee performance in the health sector. Findings demonstrate that both monetary (salary, bonuses,
allowances) and non-monetary rewards (recognition, career advancement, and work-life balance) play a crucial
role in improving job satisfaction and productivity. Healthcare organizations should adopt a balanced
compensation system that integrates both financial and non-financial rewards to enhance motivation, reduce
turnover, and improve overall performance.
The Relationship Between Human Resource Recruitment, Selection and Employee Performance in Health
Sector
The study conducted by Armstrong & Taylor (2015) explored the impact of recruitment and selection on
employee performance in the healthcare sector in the United Kingdom. Using a qualitative case study approach,
the study analyzed HR policies and employee performance records in selected hospitals. The findings revealed
that well-structured recruitment and selection processes contributed to hiring competent employees, leading to
higher job performance. The study concluded that transparent and merit-based hiring enhances organizational
effectiveness. It recommended that healthcare institutions adopt competency-based selection criteria to ensure
the recruitment of skilled personnel.
The study conducted by Otoo et al. (2018) investigated the effect of recruitment and selection practices on
employee performance in Ghana’s public healthcare sector. Using a cross-sectional survey design, the
researchers collected data from HR managers and healthcare workers. The findings indicated that structured
recruitment and selection processes positively influenced job performance, job satisfaction, and service delivery.
The study concluded that fair and transparent recruitment enhances workforce quality and organizational
productivity. It recommended that health institutions establish objective selection criteria and reduce political
interference in hiring processes.
The study conducted by Searle (2017) assessed the role of psychological factors in recruitment and selection in
healthcare organizations in Canada. Using an experimental design, the study examined how HR selection
practices impacted employee performance. The findings showed that organizations that considered candidates'
psychological traits, such as emotional intelligence and resilience, had employees who demonstrated higher job
performance and adaptability. The study concluded that integrating psychological assessments into recruitment
enhances employee effectiveness. It recommended that HR managers in healthcare incorporate psychological
evaluations to improve employee selection and retention.
The study conducted by Khan et al. (2019) explored the relationship between recruitment practices and employee
performance in Pakistan’s healthcare sector. Using a mixed-method approach, the researchers conducted
interviews and surveys with HR professionals and hospital staff. The findings revealed that organizations with
structured and transparent recruitment processes had more competent employees who performed better in their
roles. The study concluded that recruitment practices significantly impact job performance and service quality.
It recommended that HR departments adopt evidence-based recruitment strategies to improve workforce
effectiveness.
The study conducted by Oluoch (2020) examined how selection criteria influence healthcare service delivery in
public hospitals in Kenya. Using a descriptive survey design, the study analyzed recruitment policies and
employee performance indicators. The findings showed that recruitment based on academic qualifications,
experience, and professional skills resulted in better service delivery and patient outcomes. The study concluded
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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that competency-based selection leads to higher employee performance. It recommended that hospitals prioritize
skill-based hiring and regular HR audits to improve workforce efficiency.
The study conducted by Mugisha et al. (2021) investigated recruitment challenges and employee performance
in Uganda’s health sector, focusing on public hospitals. Using a qualitative approach, the researchers conducted
interviews with HR officers and healthcare workers. The findings revealed that delays in recruitment, favoritism,
and inadequate selection criteria negatively affected employee performance. The study concluded that
inefficiencies in recruitment reduce service quality in hospitals. It recommended that policymakers streamline
recruitment processes and enforce transparency to enhance employee productivity.
The study conducted by Zhang et al. (2022) explored the impact of artificial intelligence (AI)-based recruitment
and selection tools in China’s healthcare sector. Using a quantitative research design, the study assessed how AI-
assisted hiring influenced employee competence and performance. The findings showed that AI-driven selection
improved the accuracy of hiring decisions, resulting in better job performance and reduced turnover. The study
concluded that technology-driven recruitment enhances efficiency in employee selection. It recommended that
hospitals integrate AI and data-driven hiring tools to improve workforce quality.
In conclusion, the reviewed studies highlight that effective recruitment and selection play a crucial role in
improving employee performance in the health sector. A well-structured and transparent hiring process ensures
that qualified and competent professionals are recruited, leading to higher job satisfaction, better service delivery,
and improved patient outcomes. Therefore, healthcare institutions should adopt merit-based recruitment,
incorporate psychological assessments, and leverage technology to enhance their selection processes.
The Relationship Between Human Resource Training and Employee Performance in Health Sector
The study conducted by Garcia & Weiss (2016) examined the impact of continuous professional development
on healthcare workers' performance in the United States. Using a longitudinal research design, the study tracked
the performance of healthcare professionals who underwent specialized training programs. The findings
indicated that regular training improved clinical skills, efficiency, and patient care quality. The study concluded
that investing in continuous learning enhances employee competency and service delivery. It recommended that
healthcare institutions prioritize ongoing training to keep employees updated with emerging medical practices.
The study conducted by Mwangi & Njuguna (2017) analyzed the effect of staff training on performance in
Kenya’s public hospitals. Using a descriptive survey approach, the study collected data from hospital
administrators and healthcare workers. The findings revealed that training programs enhanced employees' ability
to handle patients, improved adherence to medical procedures, and increased job satisfaction. The study
concluded that structured training programs significantly boost employee performance. It recommended that the
government allocate more resources for healthcare worker training to improve service efficiency.
The study conducted by Alharbi et al. (2018) explored the role of on-the-job training in enhancing healthcare
service delivery in Saudi Arabian hospitals. Using a mixed-methods approach, the study collected quantitative
and qualitative data from nurses and HR managers. The findings showed that hospitals with robust on-the-job
training programs reported higher employee productivity and reduced medical errors. The study concluded that
practical, hands-on training is essential for improving performance in the healthcare sector. It recommended that
hospitals incorporate regular on-the-job training to equip employees with practical skills.
The study conducted by Bello & Akinyemi (2019) investigated the influence of leadership development training
on healthcare managers' performance in Nigeria. Using a case study design, the research focused on hospitals
that implemented leadership training programs. The findings indicated that leadership training improved
decision-making, resource management, and teamwork, leading to better hospital performance. The study
concluded that equipping healthcare leaders with management skills enhances overall institutional efficiency. It
recommended that hospitals invest in leadership training to strengthen administrative capacity.
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The study conducted by Purohit et al. (2020) examined the relationship between e-learning and healthcare
workers' performance in India. Using an experimental research design, the study compared the performance of
employees who underwent digital training with those who did not. The findings indicated that e-learning
improved knowledge retention, increased motivation, and enhanced job performance. The study concluded that
digital learning platforms are effective tools for training healthcare employees. It recommended that hospitals
integrate e-learning into their training programs to improve workforce competence.
The study conducted by Mutua & Wanyama (2021) explored the impact of specialized medical training on
healthcare service delivery in Uganda. Using a survey research design, data was collected from healthcare
professionals in public hospitals. The findings revealed that specialized training programs enhanced diagnostic
accuracy, patient safety, and treatment outcomes. The study concluded that targeted training significantly
improves employee performance in the health sector. It recommended that policymakers design specialized
training programs tailored to address specific healthcare challenges.
The study conducted by Zhou et al. (2022) assessed the effectiveness of mentorship programs in enhancing
employee performance in Chinese hospitals. Using a qualitative approach, the study analyzed feedback from
junior healthcare workers who participated in mentorship programs. The findings indicated that mentorship
provided professional guidance, improved job confidence, and enhanced overall performance. The study
concluded that mentorship programs play a vital role in employee development. It recommended that hospitals
establish structured mentorship initiatives to support professional growth.
In conclusion, the reviewed studies emphasize that effective training programs are essential for improving
employee performance in the healthcare sector. Continuous learning, on-the-job training, leadership
development, e-learning, specialized medical training, and mentorship programs all contribute to enhanced job
performance and service delivery. Healthcare institutions should invest in structured training programs to build
employee competencies and improve patient care outcomes.
METHODOLOGY
This study employed a descriptive research design, with a major focus on a quantitative research approach. The
descriptive research design enabled the study to systematically describe the state of the phenomenon under
investigation (Mugenda & Mugenda, 2003). According to Babbie (2020), descriptive research was useful in
obtaining information about the characteristics of a particular group or situation without manipulating variables.
The quantitative approach allowed for the collection of numerical data, which was analyzed statistically to draw
objective conclusions (Bryman, 2016). This design was appropriate for examining patterns, relationships, and
trends within the study variables while ensuring data accuracy and generalizability.
Sample Size and Sampling Technique
In this study, the sample size was 76 participants, selected using Morgan and Krejcie’s (1970) table for
determining sample sizes from a given population, as presented in Table 1 below:
Table 1: Study Population, Sample Size and Sampling Techniques.
Category
Population
Sample Size
Sampling Technique
Medical Officers
05
05
Purposive sampling
Health Facility Administrators
05
05
Purposive sampling
Human Resource Managers
05
05
Purposive sampling
Nurses and Midwives
40
36
Systematic random sampling
District Health Officer
01
01
Purposive sampling
Support Staff
25
24
Simple random sampling
Total
81
76
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Sampling Techniques
Purposive Sampling
In this study, Medical Officers, Health Facility Administrators, Human Resource Managers, and the District
Health Officer were selected using purposive sampling. These individuals held key administrative and
professional roles in healthcare management, making them essential informants for the study. Their selection
was based on their knowledge, decision-making authority, and ability to provide critical insights into healthcare
service delivery in Sheema District.
Systematic Random Sampling
Nurses and Midwives were selected using systematic random sampling. A sampling interval was determined by
dividing the total number of Nurses and Midwives by the required sample size. This method ensured an unbiased
selection process while maintaining a fair representation of healthcare workers directly involved in patient care.
Simple Random Sampling
In this study, Support Staff were selected using simple random sampling. This method ensured that all eligible
support staff members, including cleaners, security personnel, and clerical workers, had an equal opportunity to
be part of the study. The randomness of this selection minimized selection bias and enhanced the generalizability
of the findings.
Data analysis
In this study, data were analyzed using Statistical Package for the Social Sciences (SPSS) version 20.0, The
software provided a wide range of statistical techniques, including descriptive statistics, inferential statistics, and
correlation analysis, which were used to process and interpret the data collected from respondents.
To assess the reliability of the research instrument, Cronbach’s Alpha was used to measure the internal
consistency of the Likert scale statements in the questionnaire. A Cronbach’s Alpha value of 0.7 or higher
indicated good reliability of the instrument (Nunnally, 1978). Pearson’s correlation coefficient (r) was used to
determine the strength and direction of relationships among key study variables. In addition, regression analysis
was performed to assess the extent to which independent variables predicted the dependent variable. These tests
helped in making inferences about the relationships between variables beyond just descriptive summaries.
RESULTS AND DISCUSSION
Table 2 Responses on Compensation and Rewards
SD
D
N
A
SA
Mean
Std.
Dev.
14
(18.4%)
25
(32.9%)
17
(22.4%)
13
(17.1%)
7 (9.2%)
2.6
1.18
11
(14.5%)
17
(22.4%)
22
(28.9%)
15
(19.7%)
11
(14.5%)
2.9
1.24
13
(17.1%)
24
(31.6%)
18
(23.7%)
12
(15.8%)
9 (11.8%)
2.7
1.23
6 (7.9%)
10
(13.2%)
12
(15.8%)
26
(34.2%)
22
(28.9%)
3.5
1.24
10
(13.2%)
19
(25.0%)
21
(27.6%)
16
(21.1%)
10
(13.2%)
2.8
1.23
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7 (9.2%)
16
(21.1%)
20
(26.3%)
21
(27.6%)
12
(15.8%)
3.0
1.20
4 (5.3%)
6 (7.9%)
14
(18.4%)
29
(38.2%)
23
(30.3%)
3.6
1.13
5 (6.6%)
4 (5.3%)
12
(15.8%)
30
(39.5%)
25
(32.9%)
3.7
1.08
Table 2 shows responses on compensation and rewards, with frequencies and percentages based on a five-point
Likert scale (Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree). For the statement "My salary is
adequate to meet my basic needs," 14 (18.4%) strongly disagreed, 25 (32.9%) disagreed, 17 (22.4%) were
neutral, 13 (17.1%) agreed, and 7 (9.2%) strongly agreed, with a mean of 2.6 and a standard deviation of 1.18.
This suggests that a significant portion of respondents felt their salary was not sufficient to meet their basic
needs.
For "Performance-based rewards are given," 11 (14.5%) strongly disagreed, 17 (22.4%) disagreed, 22 (28.9%)
were neutral, 15 (19.7%) agreed, and 11 (14.5%) strongly agreed, with a mean of 2.9 and a standard deviation
of 1.24. This indicates a moderate perception of the availability of performance-based rewards, with many
participants expressing neutrality or disagreement.
On "Allowances/bonuses are fairly distributed," 13 (17.1%) strongly disagreed, 24 (31.6%) disagreed, 18
(23.7%) were neutral, 12 (15.8%) agreed, and 9 (11.8%) strongly agreed, with a mean of 2.7 and a standard
deviation of 1.23. This suggests that many respondents perceived a lack of fairness in the distribution of
allowances and bonuses.
The statement "Timely salary payment motivates performance" saw 6 (7.9%) strongly disagree, 10 (13.2%)
disagree, 12 (15.8%) neutral, 26 (34.2%) agree, and 22 (28.9%) strongly agree, with a mean of 3.5 and a standard
deviation of 1.24. This demonstrates that timely salary payment has a considerable positive impact on employee
motivation.
For "The compensation system is transparent," 10 (13.2%) strongly disagreed, 19 (25.0%) disagreed, 21 (27.6%)
were neutral, 16 (21.1%) agreed, and 10 (13.2%) strongly agreed, with a mean of 2.8 and a standard deviation
of 1.23. The results show a moderate level of agreement, but respondents expressed concerns over the
transparency of the compensation system.
On "I feel valued due to financial incentives," 7 (9.2%) strongly disagreed, 16 (21.1%) disagreed, 20 (26.3%)
were neutral, 21 (27.6%) agreed, and 12 (15.8%) strongly agreed, with a mean of 3.0 and a standard deviation
of 1.20. This suggests that financial incentives contribute to a sense of value for employees, though it was not
universally experienced.
For "Compensation influences job retention," 4 (5.3%) strongly disagreed, 6 (7.9%) disagreed, 14 (18.4%) were
neutral, 29 (38.2%) agreed, and 23 (30.3%) strongly agreed, with a mean of 3.6 and a standard deviation of 1.13.
The results highlight the significant role of compensation in influencing job retention.
Finally, for "Rewards enhance my performance," 5 (6.6%) strongly disagreed, 4 (5.3%) disagreed, 12 (15.8%)
were neutral, 30 (39.5%) agreed, and 25 (32.9%) strongly agreed, with a mean of 3.7 and a standard deviation
of 1.08. This indicates a strong belief that rewards positively enhance employee performance.
Recruitment And Selection
This section presents the results on Recruitment and Selection and its impact on employee performance. It
explores how the recruitment process, selection criteria, and fairness in hiring practices influence key
performance indicators such as adaptability, task completion, timeliness, and policy compliance.
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Table 3 Responses on Recruitment and Selection
Statement
SD
D
N
A
SA
Mean
Std.
Dev.
Recruitment processes are fair.
9 (11.8%)
12
(15.8%)
22
(28.9%)
21
(27.6%)
12
(15.8%)
3.1
1.24
Based on qualifications and
experience.
5 (6.6%)
6 (7.9%)
16
(21.1%)
30
(39.5%)
19
(25.0%)
3.4
1.12
Best candidates are selected.
7 (9.2%)
10
(13.2%)
25
(32.9%)
21
(27.6%)
13
(17.1%)
3.2
1.19
New staff receive induction.
10
(13.2%)
15
(19.7%)
20
(26.3%)
20
(26.3%)
11
(14.5%)
2.9
1.24
Promotions are merit-based.
14
(18.4%)
20
(26.3%)
17
(22.4%)
14
(18.4%)
11
(14.5%)
2.8
1.30
Recruitment is free from
discrimination.
6 (7.9%)
9 (11.8%)
15
(19.7%)
28
(36.8%)
18
(23.7%)
3.3
1.21
Recruitment policies are
followed.
8 (10.5%)
13
(17.1%)
24
(31.6%)
19
(25.0%)
12
(15.8%)
3.0
1.19
Recruitment affects
performance.
4 (5.3%)
6 (7.9%)
14
(18.4%)
31
(40.8%)
21
(27.6%)
3.5
1.12
Table 3 shows responses on recruitment and selection, with frequencies and percentages based on a five-point
Likert scale (Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree). For the statement "Recruitment
processes are fair," 9 (11.8%) strongly disagreed, 12 (15.8%) disagreed, 22 (28.9%) were neutral, 21 (27.6%)
agreed, and 12 (15.8%) strongly agreed, with a mean of 3.1 and a standard deviation of 1.24. This suggests that
most respondents viewed the recruitment process as relatively fair, with some expressing neutrality.
For "Recruitment is based on qualifications and experience," 5 (6.6%) strongly disagreed, 6 (7.9%) disagreed,
16 (21.1%) were neutral, 30 (39.5%) agreed, and 19 (25.0%) strongly agreed, with a mean of 3.4 and a standard
deviation of 1.12. This indicates that a significant portion of respondents felt recruitment decisions are
appropriately based on qualifications and experience.
On "The best candidates are selected," 7 (9.2%) strongly disagreed, 10 (13.2%) disagreed, 25 (32.9%) were
neutral, 21 (27.6%) agreed, and 13 (17.1%) strongly agreed, with a mean of 3.2 and a standard deviation of 1.19.
The results suggest that while many respondents agree with this statement, there is also a considerable amount
of neutrality or disagreement about the effectiveness of the selection process.
Regarding "New staff receive induction," 10 (13.2%) strongly disagreed, 15 (19.7%) disagreed, 20 (26.3%) were
neutral, 20 (26.3%) agreed, and 11 (14.5%) strongly agreed, with a mean of 2.9 and a standard deviation of 1.24.
The results suggest that there is mixed perception about whether new staff receive induction, with a significant
portion of respondents being neutral or disagreeing.
For "Promotions are merit-based," 14 (18.4%) strongly disagreed, 20 (26.3%) disagreed, 17 (22.4%) were
neutral, 14 (18.4%) agreed, and 11 (14.5%) strongly agreed, with a mean of 2.8 and a standard deviation of 1.30.
This indicates that many respondents perceive promotions as not entirely based on merit, with a substantial
amount of disagreement.
On "Recruitment is free from discrimination," 6 (7.9%) strongly disagreed, 9 (11.8%) disagreed, 15 (19.7%)
were neutral, 28 (36.8%) agreed, and 18 (23.7%) strongly agreed, with a mean of 3.3 and a standard deviation
of 1.21. These results suggest that a majority of respondents agree that recruitment is free from discrimination,
although there is still a portion of individuals with neutral or disagreeing views.
For "Recruitment policies are followed," 8 (10.5%) strongly disagreed, 13 (17.1%) disagreed, 24 (31.6%) were
neutral, 19 (25.0%) agreed, and 12 (15.8%) strongly agreed, with a mean of 3.0 and a standard deviation of 1.19.
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This indicates that while a moderate number of respondents agreed that recruitment policies are followed, a
considerable amount remained neutral or disagreed.
Finally, for "Recruitment affects performance," 4 (5.3%) strongly disagreed, 6 (7.9%) disagreed, 14 (18.4%)
were neutral, 31 (40.8%) agreed, and 21 (27.6%) strongly agreed, with a mean of 3.5 and a standard deviation
of 1.12. These results suggest a strong belief among respondents that recruitment has a direct impact on
performance.
Training And Development
This section presents the results on Training and Development and its effect on employee performance. It focuses
on the role of training programs in improving skills, motivation, and overall job performance, particularly in
areas such as adaptability, task completion, timeliness, and policy compliance.
Table 4. Responses on Training and Development
Statement
SD
D
N
A
SA
Mean
Std.
Dev.
I have attended training in last 12
months.
6 (7.9%)
7 (9.2%)
14
(18.4%)
32
(42.1%)
17
(22.4%)
3.3
1.17
Training is relevant to my job.
4 (5.3%)
5 (6.6%)
10
(13.2%)
33
(43.4%)
24
(31.6%)
3.6
1.10
Training has improved my skills.
2 (2.6%)
5 (6.6%)
8 (10.5%)
36
(47.4%)
25
(32.9%)
3.7
0.98
Equal training opportunities are
provided.
9 (11.8%)
14
(18.4%)
18
(23.7%)
21
(27.6%)
14
(18.4%)
3.0
1.29
Training increases motivation.
3 (3.9%)
4 (5.3%)
9 (11.8%)
32
(42.1%)
28
(36.8%)
3.8
1.01
Continuous training improves
service delivery.
2 (2.6%)
2 (2.6%)
8 (10.5%)
31
(40.8%)
33
(43.4%)
4.0
0.92
Supervisors support training.
5 (6.6%)
8 (10.5%)
13
(17.1%)
30
(39.5%)
20
(26.3%)
3.4
1.14
Skills from training impact
performance.
3 (3.9%)
4 (5.3%)
6 (7.9%)
31
(40.8%)
32
(42.1%)
3.9
1.00
Table 4 shows responses on training and development, with frequencies and percentages based on a five-point
Likert scale (Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree). For the statement "I have attended
training in the last 12 months," 6 (7.9%) strongly disagreed, 7 (9.2%) disagreed, 14 (18.4%) were neutral, 32
(42.1%) agreed, and 17 (22.4%) strongly agreed, with a mean of 3.3 and a standard deviation of 1.17. These
results suggest that a majority of respondents have attended training in the past year, with a significant proportion
agreeing or strongly agreeing.
For "Training is relevant to my job," 4 (5.3%) strongly disagreed, 5 (6.6%) disagreed, 10 (13.2%) were neutral,
33 (43.4%) agreed, and 24 (31.6%) strongly agreed, with a mean of 3.6 and a standard deviation of 1.10. The
findings indicate that most respondents found the training relevant to their job, with a strong agreement on its
relevance.
Regarding "Training has improved my skills," 2 (2.6%) strongly disagreed, 5 (6.6%) disagreed, 8 (10.5%) were
neutral, 36 (47.4%) agreed, and 25 (32.9%) strongly agreed, with a mean of 3.7 and a standard deviation of 0.98.
These results suggest that the majority of respondents believe the training they received has contributed
positively to skill development.
On "Equal training opportunities are provided," 9 (11.8%) strongly disagreed, 14 (18.4%) disagreed, 18 (23.7%)
were neutral, 21 (27.6%) agreed, and 14 (18.4%) strongly agreed, with a mean of 3.0 and a standard deviation
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of 1.29. This indicates mixed perceptions about the fairness of training opportunities, with a significant portion
of respondents remaining neutral or disagreeing.
For "Training increases motivation," 3 (3.9%) strongly disagreed, 4 (5.3%) disagreed, 9 (11.8%) were neutral,
32 (42.1%) agreed, and 28 (36.8%) strongly agreed, with a mean of 3.8 and a standard deviation of 1.01. These
results show that most respondents believe training has a positive impact on motivation.
Regarding "Continuous training improves service delivery," 2 (2.6%) strongly disagreed, 2 (2.6%) disagreed, 8
(10.5%) were neutral, 31 (40.8%) agreed, and 33 (43.4%) strongly agreed, with a mean of 4.0 and a standard
deviation of 0.92. The majority of respondents strongly agreed that continuous training has a significant positive
impact on service delivery.
For "Supervisors support training," 5 (6.6%) strongly disagreed, 8 (10.5%) disagreed, 13 (17.1%) were neutral,
30 (39.5%) agreed, and 20 (26.3%) strongly agreed, with a mean of 3.4 and a standard deviation of 1.14. These
results suggest that a significant portion of respondents feel that supervisors are supportive of training initiatives.
Finally, for "Skills from training impact performance," 3 (3.9%) strongly disagreed, 4 (5.3%) disagreed, 6 (7.9%)
were neutral, 31 (40.8%) agreed, and 32 (42.1%) strongly agreed, with a mean of 3.9 and a standard deviation
of 1.00. The results show that the majority of respondents believe that the skills gained from training have a
direct and positive impact on their performance.
Performance Levels
This section presents the results on Performance Levels and their correlation with human resource practices,
focusing on various performance indicators such as adaptability, task completion, meeting targets, policy
compliance, and feedback from supervisors, a supportive work environment, career development opportunities,
and motivation from management.
Table 5 Performance Levels
Indicator
SD
D
N
A
SA
Mean
Std. Dev.
Adaptability
3 (3.9%)
4 (5.3%)
10
(13.2%)
34
(44.7%)
25
(32.9%)
3.7
1.03
Task completion
4 (5.3%)
6 (7.9%)
11
(14.5%)
32
(42.1%)
23
(30.3%)
3.6
1.08
Meeting targets
5 (6.6%)
7 (9.2%)
12
(15.8%)
30
(39.5%)
22
(28.9%)
3.5
1.10
Policy compliance
2 (2.6%)
3 (3.9%)
10
(13.2%)
34
(44.7%)
27
(35.5%)
3.8
0.97
Feedback from supervisors
5 (6.6%)
9 (11.8%)
14
(18.4%)
30
(39.5%)
18
(23.7%)
3.4
1.13
Supportive environment
9 (11.8%)
13
(17.1%)
16
(21.1%)
24
(31.6%)
14
(18.4%)
3.3
1.23
Career development
opportunities
11
(14.5%)
14
(18.4%)
18
(23.7%)
20
(26.3%)
13
(17.1%)
3.1
1.27
Motivation from
management
10
(13.2%)
12
(15.8%)
16
(21.1%)
22
(28.9%)
16
(21.1%)
3.2
1.26
Table 5 shows responses on performance levels, with frequencies and percentages based on a five-point Likert
scale (Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree).
For the statement "Adaptability," 3 (3.9%) strongly disagreed, 4 (5.3%) disagreed, 10 (13.2%) were neutral, 34
(44.7%) agreed, and 25 (32.9%) strongly agreed, with a mean of 3.7 and a standard deviation of 1.03. This
indicates that the majority of respondents agree or strongly agree that adaptability is a key performance indicator.
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For "Task completion," 4 (5.3%) strongly disagreed, 6 (7.9%) disagreed, 11 (14.5%) were neutral, 32 (42.1%)
agreed, and 23 (30.3%) strongly agreed, with a mean of 3.6 and a standard deviation of 1.08. The findings suggest
that task completion is generally seen positively, with a significant portion agreeing or strongly agreeing.
Regarding "Meeting targets," 5 (6.6%) strongly disagreed, 7 (9.2%) disagreed, 12 (15.8%) were neutral, 30
(39.5%) agreed, and 22 (28.9%) strongly agreed, with a mean of 3.5 and a standard deviation of 1.10. Most
respondents believe meeting targets is a vital component of performance, with a tendency toward agreement.
For "Policy compliance," 2 (2.6%) strongly disagreed, 3 (3.9%) disagreed, 10 (13.2%) were neutral, 34 (44.7%)
agreed, and 27 (35.5%) strongly agreed, with a mean of 3.8 and a standard deviation of 0.97. This shows a strong
perception that policy compliance is an essential aspect of performance.
On "Feedback from supervisors," 5 (6.6%) strongly disagreed, 9 (11.8%) disagreed, 14 (18.4%) were neutral, 30
(39.5%) agreed, and 18 (23.7%) strongly agreed, with a mean of 3.4 and a standard deviation of 1.13. These
results suggest that feedback from supervisors is important for performance, though responses were more varied.
Regarding "Supportive environment," 9 (11.8%) strongly disagreed, 13 (17.1%) disagreed, 16 (21.1%) were
neutral, 24 (31.6%) agreed, and 14 (18.4%) strongly agreed, with a mean of 3.3 and a standard deviation of 1.23.
The results show a mixed perception of the environment’s impact on performance, with some indicating that a
supportive environment is important for performance.
For "Career development opportunities," 11 (14.5%) strongly disagreed, 14 (18.4%) disagreed, 18 (23.7%) were
neutral, 20 (26.3%) agreed, and 13 (17.1%) strongly agreed, with a mean of 3.1 and a standard deviation of 1.27.
These results indicate that career development opportunities are seen as moderately important but with mixed
responses.
Finally, for "Motivation from management," 10 (13.2%) strongly disagreed, 12 (15.8%) disagreed, 16 (21.1%)
were neutral, 22 (28.9%) agreed, and 16 (21.1%) strongly agreed, with a mean of 3.2 and a standard deviation
of 1.26. This suggests that motivation from management is perceived as moderately important for performance,
with respondents divided on the extent of its impact.
Correlation Between Human Resource Management Practices and Employee Performance
This section presents the results on the Correlation between Human Resource Management Practices and
Employee Performance.
Table 6 Correlation between Human Resource Management (HRM) Practices and EmployeePerformance
Indicators (N = 76)
Variable
Adaptability
Dependability
Timeliness
Policy Compliance
Compensation & Rewards
• Pearson Correlation
.54 **
.48 **
.46 **
.44 **
• Sig. (2-tailed)
.000
.000
.000
.000
• N
76
76
76
76
Recruitment & Selection
• Pearson Correlation
.50 **
.45 **
.43 **
.40 *
• Sig. (2-tailed)
.000
.000
.000
.012
• N
76
76
76
76
Employee Training
• Pearson Correlation
.63 **
.58 **
.60 **
.55 **
• Sig. (2-tailed)
.000
.000
.000
.000
• N
76
76
76
76
Notes:
** ** Correlation is significant at the 0.01 level (2-tailed).
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Table 6 presents the correlation between Human Resource Management (HRM) practices and employee
performance indicators, showing how compensation & rewards, recruitment & selection, and employee training
are related to adaptability, dependability, timeliness, and policy compliance.
Compensation & Rewards demonstrate a moderate to strong positive correlation with all employee performance
indicators. Specifically, the correlation with adaptability is r = 0.54 (p < 0.01), with dependability r = 0.48 (p <
0.01), with timeliness r = 0.46 (p < 0.01), and with policy compliance r = 0.44 (p < 0.01). These correlations are
statistically significant at the 1% level, indicating that higher compensation and rewards are associated with
improved performance across all the indicators.
Recruitment & Selection also show positive correlations with employee performance, though slightly weaker
than those for compensation & rewards. The correlation with adaptability is r = 0.50 (p < 0.01), with
dependability r = 0.45 (p < 0.01), with timeliness r = 0.43 (p < 0.01), and with policy compliance r = 0.40 (p <
0.05). While all the correlations are statistically significant, the correlation with policy compliance is the weakest
but still significant at the 5% level.
Employee Training shows the strongest correlations across all performance indicators. The correlation with
adaptability is r = 0.63 (p < 0.01), with dependability r = 0.58 (p < 0.01), with timeliness r = 0.60 (p < 0.01), and
with policy compliance r = 0.55 (p < 0.01). All these correlations are highly significant, suggesting that employee
training has the most substantial impact on improving performance in adaptability, dependability, timeliness, and
policy compliance. HRM practices, especially employee training, compensation & rewards, and recruitment &
selection, show positive correlations with employee performance indicators. Employee training is the most
strongly correlated with performance, followed by compensation & rewards, and recruitment & selection.
Regression Of Human Resource Management Practices and Employee Performance
This section presents the results of the Regression Analysis of Human Resource Management Practices and
Employee Performance.
Table 7 Regression Analysis of Human Resource Management Practices and Employee Performance (N = 76)
Model
Unstandardized
Coefficients
Standardized Coefficients
(Beta)
t
Sig. (pvalue)
B
Std. Error
(Constant)
1.032
0.312
–
3.308
Compensation & Rewards
0.284
0.095
0.267
2.989
Recruitment & Selection
0.243
0.101
0.229
2.405
Employee Training
0.398
0.089
0.415
4.472
Table 7 presents the results of the regression analysis examining the relationship between Human Resource
Management (HRM) practices (compensation & rewards, recruitment & selection, and employee training) and
employee performance indicators. The regression model provides insights into how each HRM practice
contributes to employee performance.
The constant in the model is 1.032, with a p-value of 0.002, indicating that the constant is statistically significant.
This represents the baseline level of employee performance when all HRM practices are at zero.
Regarding compensation & rewards, the unstandardized coefficient is 0.284, with a standardized Beta of 0.267
and a p-value of 0.004. This indicates a statistically significant positive effect on employee performance.
Specifically, for every unit increase in compensation & rewards, employee performance increases by 0.284 units.
The positive Beta value suggests a moderate impact on performance.
Recruitment & selection has an unstandardized coefficient of 0.243, with a standardized Beta of 0.229 and a
pvalue of 0.018. This suggests that recruitment & selection also positively affects employee performance, though
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its impact is slightly weaker compared to compensation & rewards. A unit increase in recruitment & selection
leads to a 0.243-unit increase in employee performance.
Finally, employee training shows the strongest relationship with employee performance. The unstandardized
coefficient is 0.398, with a standardized Beta of 0.415 and a p-value of 0.000. This indicates a highly significant
and strong positive effect on performance. For each unit increase in employee training, employee performance
increases by 0.398 units, highlighting the significant role that training plays in enhancing performance. all three
HRM practices (compensation & rewards, recruitment & selection, and employee training) have a statistically
significant positive impact on employee performance. Among them, employee training has the strongest effect,
followed by compensation & rewards and recruitment & selection.
Table 8: Model Summary
Model Summary
R
0.712
R²
0.507
Adjusted R²
0.489
Std. Error of the Estimate
0.432
The Model Summary in Table 8 presents key statistical values that help evaluate the regression model’s goodness
of fit and overall performance.
R = 0.712 indicates a moderately strong positive correlation between the predictor variables (compensation &
rewards, recruitment & selection, and employee training) and employee performance. This suggests that there is
a strong linear relationship between HRM practices and employee performance.
R² = 0.507 means that approximately 50.7% of the variability in employee performance can be explained by the
HRM practices included in the model. This indicates that the model is able to account for just over half of the
variance in employee performance.
Adjusted R² = 0.489 adjusts the R² value for the number of predictors in the model, providing a more accurate
measure of goodness-of-fit when considering multiple variables. The value of 0.489 indicates that 48.9% of the
variability in employee performance can be explained by the three HRM practices, considering the number of
predictors.
Standard Error of the Estimate = 0.432 measures the average distance between the observed values and the
predicted values of employee performance. A lower standard error indicates that the model's predictions are
relatively close to the actual data, the regression model demonstrates a decent fit, with a moderately strong
relationship between HRM practices and employee performance, explaining just over 50% of the variability in
performance.
Table 9: NOVA Table
Model
Sum of Squares
df
Mean Square
F
Sig.
Regression
24.418
3
8.139
43.549
0.000
Residual
23.727
72
0.330
Total
48.145
75
The R² of 0.507 indicates that 50.7% of the variance in employee performance is explained by the combined
effect of compensation & rewards, recruitment & selection, and employee training. All independent variables
significantly contribute to predicting employee performance (p < 0.05). and Employee training has the strongest
effect (β = 0.415), suggesting it is the most influential factor among the HRM practices considered.
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CONCLUSION
The study concluded that HRM practices, specifically compensation and rewards, recruitment and selection, and
training and development, have a significant positive impact on employee performance.
RECOMMENDATIONS
The study recommended that organizations enhance their compensation and rewards systems to ensure they are
closely aligned with employee performance. Regular reviews of salary structures, performance-based rewards,
and incentives should be conducted to maintain motivation and encourage high performance, ensuring that
employees feel valued and fairly compensated for their efforts.
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