Evaluation of Qualifications, Fields of Study, Skills, and Administrative Staff Retention Records in Selected Tertiary Education Institutions in Sierra Leone

Abstract

This study assessed the relevance of qualifications (RQP), major fields of study to position (RMFSP), and management skills to position (RMSP) in tertiary education institutions in Sierra Leone. A total of 266 questionnaires were administered to administrative staff of six tertiary educational universities in Sierra Leone. Documentary Overview with Administrators was also conducted to solicit their perceptions on the current number of staff, numbers of staff recruited, and promoted per gender across five academic years. Results showed that skewness of RQP, RMFSP, faculties/schools to position (RFSP), relevance of leadership skills to position (RLSP), RMSP, internal public relations to position (RIPRP), relevance of office ICT skills to position (ROISP), and relevance of administrative to position (RAP) of respondents was negative. Mean scores of respondents on relevance parameters ranged from 3.48 to 3.65, indicating relevance to their positions. Standard deviation ranged from 0.76 to 0.87, indicating similar views among respondents. The RIPRP (−0.19), and ROISP (−0.59) revealed platykurtic distribution, whereas other parameters had leptokurtic distribution. Total staff loss negatively influenced the current number of staff, whereas number of staff recruited positively influenced the current number of staff more than the number of staff promoted. Results suggest that improving the developmental, maintenance, and skills utilization process should form part of the core elements of the policy package necessary to support sustainable long-term growth and employment creation.

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Ngaujah, P.K. , Norman, P.E. , Mbavai, J.J. , Massaquoi, S.B. and Challay, S. (2025) Evaluation of Qualifications, Fields of Study, Skills, and Administrative Staff Retention Records in Selected Tertiary Education Institutions in Sierra Leone . Open Journal of Social Sciences, 13, 97-122. doi: 10.4236/jss.2025.1310006.

1. Introduction

Governments in over 100 countries have designed, implemented, or been involved with regional qualifications frameworks or considered national qualification frameworks (NQFs) in various institutions (Allais, 2010). Developing NQFs is underpinned by the idea that all qualifications can (and should) be expressed in terms of outcomes, without any prescription of learning pathway. International interest in NQFs has arisen because of the relevance, flexibility, and portability of skills and training and the effects they have on employment opportunities. Countries have adopted different approaches to NQFs, but the underlying reasons behind the process are usually similar. These include the need to ease the process of labor mobility across employment sectors, regions, and countries, including lifelong education and training; the need to strengthen links between education, training, and the labor market; recognize prior learning experience and credits; set standards based on learning outcomes; facilitate quality assurance; and improve the perceived status of Technical and Vocational Education and Training (TVET) programs (Allais, 2010).

Regional Model Competency Standards (RMCS) have been developed and implemented in Bangladesh, Indonesia, Lao PDR, and Thailand to foster mutual recognition of skills and qualifications. A number of countries have utilized the RMCS in key sectors, including manufacturing, tourism, construction, and agriculture (ILO, 2006).

The education, labor, and wages and compensation commission departments of governments usually take responsibility for qualifications. In many countries, the NQFs have emanated from the TVET sector, which is known for the development of industry skills and competency standards-based qualifications. The advent of competency-based training has been associated with a relative shift in control of the content of training from providers to industry (World Bank & ILO, 2011).

Improving the developmental, maintenance, and skills utilization process is increasingly recognized as a core element of the policy package necessary to support sustainable long-term growth and employment creation. The implementation of such an improved policy package contributes to a fairer distribution of income and opportunities. According to the OECD Skills Strategy (OECD, 2012), the three key areas for action by governments include the development of relevant skills, activation of skills, and putting skills to effective use. The central goal of skills policies is that development of relevant skills ensures that the supply of skills is sufficient in quality and quantity to meet current and emerging needs. Supply can be ensured through the development of the right mix of skills using education and training to influence the flow of skills by attracting and retaining talent. The supply of the right mix of skills is responsive to demand and can also have a significant influence on demand. Activation of skills indicates that individuals may have skills, but for obvious reasons may decide to withhold their service from the labor force or labor market. These could be due to personal preferences, life circumstances, or the lack of financial incentives to work. Encouraging inactive individuals to enter or return to the labor market increases the skills base of an economy. This requires identification of inactive individuals, possibly retraining them, ensuring that the benefit system offers them financial incentives to enter or return to the labor market, and removing demand-side barriers to hiring. Investment in skills development by individuals, governments, and stakeholders needs to be driven by policies that ensure the effective utilization of the skills. Moreover, the relationship between the skills demanded in a job and the skills of the person doing the job has an impact on further skills development. Whereas new skills are, to a large extent, developed informally, often through work experience, the unused ones tend to atrophy (Thorn & Schleicher, 2013).

The tertiary educational landscape in Sierra Leone comprises a variety of institutions, including universities, polytechnics, and vocational training institutions, which collectively play a pivotal role in shaping the nation’s higher educational framework. However, the relevance of qualifications, fields of study, and skills of administrative staff in tertiary education institutions is not understood by managers of the academic administration units. As a result, the University currently sets different educational qualifications for administrative positions that have been graded at the same pay-class (or pay grade). These positions considered at the same pay-class are: 1) equal in the level of responsibility required for the post, and 2) require tasks that have an equal level of complexity. Despite being equal in this regard, different minimum educational qualifications are required when vacancies for these positions are advertised. This creates inconsistency in the minimum educational qualification requirement for academic administrative positions of equal pay-class or grade.

However, there is a dearth of knowledge on the relevance of the qualifications, fields of study, and skills of administrative staff in tertiary education institutions. Understanding the relevance of the qualifications, fields of study, and skills of administrative staff is imperative, as this has implications for policy guidance regarding skills development needs, capacity sharing, job delivery efficiency, and satisfaction. Thus, the objective of this study was to examine the relevance of the qualifications, major fields of study, and skills of senior cadre administrative staff to the positions they occupy in tertiary education institutions in Sierra Leone; and (2) to assess the current number of staff, numbers of staff recruited, number of staff promoted, and total staff loss.

2. Methodology

2.1. Study Area

The study was conducted at six selected tertiary education institutions of Sierra Leone (Figure 1). Njala University is Sierra Leone’s major institution for training in Agriculture, Education, Health, Natural Resources Management, Technology, Social and Environmental Sciences at both undergraduate and postgraduate levels. The constituent institutions of Njala University include the former Bo Teachers College (BTC) at Torwama, the School of Hygiene and Paramedical School at Korwama, in Bo. Bonthe Technical Institute (BTI) is an affiliate institution. Currently, Njala University is operating on two campuses, namely, the Bo Campus and the Njala Campus. As an autonomous institution, Njala University has eight schools/faculties including Agriculture, Education, Environmental Sciences, Social Sciences, Community Health Sciences, Technology, Forestry and Horticulture, as well as a School of Postgraduate Studies. At the moment, schools operating on the Bo Campus are Education, Social Sciences and Community Health Sciences. Njala Campus houses the Schools of Agriculture, Environmental Sciences, Technology, Forestry and Horticulture.

Figure 1. Map of Sierra Leone showing the locations of tertiary education institutions studied.

Eastern Technical University (ETU) is located in Kenema. Kenema is the largest city in the Eastern Province. The city is the capital and administrative center of the Kenema District. Kenema is a major diamond trade center and serves as the economic and financial center of Eastern Sierra Leone. Kenema lies 298 km east-south-east of the nation’s capital, Freetown.

Ernest Bai Koroma University of Science and Technology (EBKUST) is located in Magburaka, about seven miles from Makeni, Northern Sierra Leone. This institution has branch campuses in Makeni and Port Loko. EBKUST is a coeducational Sierra Leonean higher education institution.

The University of Sierra Leone (USL) is situated in Freetown. Freetown is the capital and largest city of Sierra Leone, and the major urban, economic, financial, cultural, educational, and political centre. It is a major port city on the Atlantic Ocean and is located in the Western Area of the country. The USL is a public university comprising Fourah Bay College (FBC), the Institute of Public Administration and Management (IPAM), and the College of Medicine and Allied Health Sciences (COMAHS). The restructured University of Sierra Leone is superintended by a Vice-Chancellor and Principal, who is the chief academic and administrative head of the university, and a Registrar.

Milton Margai Technical University (MMTU) is situated at Goderich. Goderich is a town on the outskirts of Sierra Leone’s capital, Freetown. The college was established as a polytechnic in 2001 by an Act of Parliament, which merged the Milton Margai College of Education (MMCE), the Freetown Technical Institute (FTI), and the Hotel Tourism Training Institute (HTTI) as one educational institution, resulting in the establishment of three campuses: Goderich Campus, Congo Cross Campus, and Brookfields Campus.

The Freetown Polytechnic (FP) comprises the Freetown Teachers College (FTC) and the Government Technical Institute (GTI). The FTC is located at Kossoh Town, 19 km away from the heart of Freetown city. It is a non-residential college for both staff and students, while GTI is located at Kissy Dockyard.

2.2. Study Design, Research Instrument, Sample Population, and Sample Size

The survey research design using a structured questionnaire and documentary overview research instruments was used for data collection. The research population involved six tertiary educational institutions in Sierra Leone. The population of the study comprised employees from tertiary education institutions in Sierra Leone who were purposively selected based on the focus of the research. For the purpose of regional representation, at least one tertiary institution was selected from the four regions of Sierra Leone. The study population constitutes 326 subjects, of which 266 respondents were selected from six tertiary education institutions in Sierra Leone (NU, USL, EBK, MMTU, FP, and ETU) using the Krejcie & Morgan (1970) formula (Table 1). The Vice Chancellors and Principals, Deputy Vice Chancellors, and Registrars were purposively selected, while the other categories of respondents were selected using a simple random technique for the study (Krejcie & Morgan, 1970).

Table 1. Sample population and size of respondents of the various institutions that participated in the study.

Respondents

Institutions

Total

population

Sample size

NU

USL

EBKUST

MMTU

FP

ETU

Vice Chancellor and Principal

1

1

1

1

1

1

6

6

Deputy Vice Chancellor

2

3

1

3

1

1

11

11

Registrars

1

1

1

1

1

1

6

6

Deputy Registrars

5

3

1

1

1

2

13

13

Deans of Schools/Directors

20

30

5

5

5

5

70

50

Heads of Departments

35

40

20

4

6

5

110

90

Other Administrative

Authorities

30

50

10

5

10

5

110

90

Total

94

128

39

20

25

20

326

266

NU = Njala University, USL = University of Sierra Leone, EBKUST = Ernest Bai Koroma University of Science and Technology, MMTU = Milton Margai Technical University, FP = Freetown Polytechnic, and ETU = Eastern Technical University.

2.3. Data Analysis

The data were coded and analyzed using the Statistical Package for Social Sciences (SPSS) program version 20. Analysis of Variance (ANOVA) was used to test the null hypotheses at the 0.05 level of significance.

2.4. Ethical Consideration

The researcher explained to the respondents the purpose of the research and that their participation was voluntary. Thus, the respondents were free to decline or withdraw at any time during the research period. Respondents were not coerced to participate in the study. The participants gave informed consent to make the choice to participate or not. They were guaranteed that their privacy would be protected by strict standards of anonymity.

3. Results and Discussion

Table 2 presents the qualifications of the administrative staff in six tertiary institutions of Sierra Leone. The majority of the respondents had MSc (26.6%) and PhD (21.7%), followed by those with BSc (8.7%), MEd (7.2%), and MPhil (5.7%), whereas those with MBA (0.4%), BSc + ICSA (0.4%), MPhil + MPA (0.4%), and MPhil + were the lowest percent of staff with the qualifications. Regarding the major fields of study, Management + Administration (16.0%) had the highest percent of respondents, followed by PhD (21.7%), and those whose major fields of study were in Education Extension (10.3%) and Administration only (8.7%), whereas WSF + Engineering had the lowest at 0.4% (Table 3). The perceived knowledge of the respondents regarding the relevance of skills to their jobs is presented in Table 4. Accordingly, the majority of the respondents, ranging from 69.6 to 96.6%, opined on the relevance of the various types of skills assessed to their jobs, except for the office clerical skills, under Office ICT skills, which had a slightly lower affirmation of 49.4%.

Table 2. Qualifications of administrative staff in tertiary education institutions of Sierra Leone.

Qualification

Frequency

Percent

PhD

57

21.7

MPhil

15

5.7

MPhil + MPA

1

0.4

MPhil + MBA

1

0.4

Med

19

7.2

MPA

5

1.9

MBA

5

1.9

MA

12

4.6

MSc

70

26.6

ACCA

5

1.9

BSc

23

8.7

BSc + ICSA

1

0.4

Bed

9

3.4

BA

4

1.5

HND

21

8.0

HTC

6

2.3

OND

4

1.5

Others

5

1.9

Table 3. Major field of study of administrative staff in tertiary education institutions of Sierra Leone.

Parameter

Frequency

Percent

Management only

18

6.8

Administration only

23

8.7

Management + Administration

42

16.0

Management - Administration

10

3.8

Administration - Management

5

1.9

Agriculture

17

6.5

Accounting only

11

4.2

Home Economics only

2

0.8

Business only

2

0.8

Banking and Finance only

3

1.1

Economics only

4

1.5

Linguistics only

2

0.8

Literature only

2

0.8

Physics only

2

0.8

Health Education only

6

2.3

Engineering only

11

4.2

Statist, Mathematics/Physics

4

1.5

Education Extension

25

9.5

Medical Studies

3

1.1

Education Extension + Social Work/Sociology

27

10.3

Human kinetics physical education (HKSE) only

3

1.1

Accounting + Banking and Finance

5

1.9

Information communication technologies and computers (ICTC)

9

3.4

Wildlife management ecotourism biodiversity (WMEB)

4

1.5

Wood science and forestry (WSF) + Engineering

1

0.4

Others

22

8.4

Table 4. Skills of administrative staff in tertiary education institutions of Sierra Leone.

Parameter

Description

Frequency

Percent

Yes

No

Yes

No

Leadership skills

Decision-making judgment

217

46

82.5

17.5

Administrative problem-solving

242

21

92.0

8.0

Analytical

183

80

69.6

30.4

Team leadership skills

226

37

85.9

14.1

Organizational leadership skills

223

40

84.8

15.2

Internal public relations

Collegial relationship

190

73

72.2

27.8

Relating well to other staff

244

19

92.8

7.2

Motivational skills for colleagues

232

31

88.2

11.8

Ability to relate well to the public

243

20

92.4

7.6

Office ICT skills

Computer skills for the job

245

18

93.2

6.8

Internal external correspondences

220

43

83.6

16.4

Office clerical skills

130

133

49.4

50.6

Others specify

8

255

3.0

97.0

Management skills

Coordinating

239

24

90.9

9.1

Organizing

246

17

93.5

6.5

Supervising

217

46

82.5

17.5

Evaluating

210

53

79.8

20.2

Controlling

215

48

81.7

18.3

Communicating

254

9

96.6

3.4

Generally, skewness of the relevance of qualifications to position (RQP), relevance of major fields of study to position (RMFSP), relevance of faculties/schools to position (RFSP), leadership skills to position (RLSP), management skills to position (RMSP), relevance of internal public relations to position (RIPRP), relevance of office ICT skills to position (ROISP), and relevance of administrative skills to position (RASP) of the various respondents was negative (Table 5). The negative skewness is a measure of the degree of asymmetry of data around its mean. The negative skewness indicates lower return on investment and that both the mean and the median are less than the mode of the data set. The negative skewness often exhibits a longer or flatter tail on the left side of the distribution. Findings on kurtosis indicate how the outliers are distributed across the distribution in comparison to a normal distribution. Considering relative kurtosis for interpretation of current findings, internal public relations (−0.19), and office ICT skills (−0.59) which exhibited negative kurtosis values revealed platykurtic distribution, whereas the remaining parameters with positive values more than zero had leptokurtic distribution. The implications of these findings are that an employer is more comfortable with a platykurtic distribution of return as it indicates stable returns and lower risk of sudden shock of outliers, whereas leptokurtic distribution means chances of higher return, but with higher risk. For the tertiary education institution employer, fair utilization of the organizational recruitment practices, priorities and policies relating to relevance of job match (educational, skill, and subject match) to employees’ current jobs or positions contribute to better job satisfaction, higher staff retention, and lower staff turnover intention (Na et al., 2024). Compromising the recruitment practices, priorities and policies affects job security, commitment and performance. Kurtosis is widely useful in the field of risk management and portfolio management, where it indicates if there is any chance of extreme values of returns (positive and negative) beyond the ±3 standard deviation of the mean (99.5% confidence interval).

Table 5. Descriptive statistics of the perception of respondents on relevance of qualifications (RQP), major fields of study (RMFSP), faculties/schools (RFSP), leadership skills (RLSP), management skills (RMSP), internal public relations (RIPRP), office ICT skills (ROISP), and administrative skills (RAP) to their position (N = 263).

Parameter

Mean

SD

Skewness

SE

Kurtosis

SE

Remarks

RQP

3.65

0.76

−1.84

0.15

1.67

0.30

Very relevant

RMFSP

3.60

0.81

−1.66

0.15

1.34

0.30

Very relevant

RFSP

3.59

0.78

−1.47

0.15

0.28

0.30

Very relevant

RLSP

3.56

0.85

−1.56

0.15

0.94

0.30

Very relevant

RMSP

3.60

0.78

−1.53

0.15

0.39

0.30

Very relevant

RIPRP

3.55

0.81

−1.32

0.15

−0.19

0.30

Very relevant

ROISP

3.48

0.87

−1.14

0.15

−0.59

0.30

Moderately relevant

RAP

3.59

0.80

−1.47

0.15

0.19

0.30

Very relevant

SD = Standard Deviation, SE = Standard Error.

The results also showed that the mean scores of perceptions of respondents on relevance parameters assessed ranged from 3.48 (ROISP) to 3.65 (RQP). Of the eight relevance subjects assessed, relevance of office ICT skills to workers’ positions was rated moderately relevant, while the remaining items were very relevant to their positions. The standard deviation values for all the items ranged between 0.76 (RQP) and 0.87 (ROISP), which indicate that the respondents are not widely apart in their views (Table 5). These findings support the views of Fika et al. (2016) and Ngauja & Norman (2025), who opined that the process of scouting round for qualified applicants/candidates to fill up vacant positions in an organization could be done by appropriate advertisement for staff vacancies before recruitment without compromising selection criteria quality.

Table 6 shows a p value of 0.0001 for the perception of respondents on the relevance of all measured parameters to their positions, which is less than the alpha level of 0.05. The significant differences in the mean scores of the respondents regarding their perception of the relevance of qualifications, major fields of study, faculties/schools, internal public relations, leadership skills, management skills, office ICT skills, and administrative skills to their positions indicate the usefulness of the measured parameters for staff recruitment, turnover intention, retention, and job satisfaction. Results suggest that the match between skills and job requirements might influence job satisfaction and, consequently, staff turnover and retention. Staff turnover refers to the act of leaving the current job and moving to another workplace (Gu & Kim, 2020). Presently, the phenomenon of turnover among job seekers due to job mismatches is common, and these individuals often consider turnover a method to secure their desired job (Gu & Kim, 2020). Staff turnover intentions are generally influenced by demographic, personal, job-related, organizational, and structural factors (Gu et al., 2011). Accordingly, the better the match between the administrative staff’s job match (educational, skill, and subject match) and their job, the more significantly turnover intention decreases and job satisfaction and staff retention increase (Na et al., 2024). Findings of the present study are consistent with Na et al. (2024), Gu & Kim (2020), and Jeon & Nam (2023), who found that the better the match between a graduate’s major fields of study and their job, the more significantly turnover intention decreases and job satisfaction increases. The main reasons for turnover include low salaries, inadequate job match, and uncertainty about future opportunities within the organization (Na et al., 2024).

Table 6. T-test statistics of the perception of respondents on the relevance of qualifications (RQP), relevance of major fields of study (RMFSP), relevance of faculties/schools (RFSP), relevance of leadership skills (RLSP), relevance of management skills (RMSP), internal public relations (RIPRP), relevance of office ICT skills (ROISP), and relevance of administrative skills (RAP) to their position (N = 263).

Parameter

T

Sig. (2-tailed)

Mean

95% Confidence Interval

Lower

Upper

RQP

77.92

0.0001

3.65

3.562

3.746

RMFS

71.46

0.0001

3.60

3.496

3.694

RFSP

74.41

0.0001

3.59

3.497

3.687

RLSYP

68.08

0.0001

3.56

3.460

3.666

RMSP

74.60

0.0001

3.60

3.509

3.700

RIPRP

70.34

0.0001

3.55

3.449

3.647

ROISP

64.61

0.0001

3.48

3.377

3.589

RAP

72.34

0.0001

3.59

3.492

3.688

Table 7 shows that the p values of 0.0001 (HTC), 0.014 (BSc), 0.0001 (MA), 0.0001 (ACCA), 0.017 (MBA), 0.011 (MPA), 0.0001 (CPA), 0.0001 (CIMA), 0.0001 (ICSA), and 0.001 (PhD) are less than the alpha level of 0.05. These values indicate that there are significant differences in the mean scores of the respondents for the measured qualification parameters. Therefore, the null hypothesis was rejected for these parameters, while for the remaining qualification parameters (OND, HND, BA, BED, MSc, MPhil, MED, and Others specify) we accept the null hypothesis since their p values are greater than the alpha level of 0.05. The value-added skill set of candidates seeking a job determines their employability in the marketplace, such as the university. Findings agree with the view that the employability of an individual is linked to acquiring skills and attributes that prepare him/her for success later in life (Stoffberg et al., 2023). These include communication skills, numeracy, information technology, problem-solving, and teamwork, all of which will be useful in various job positions (Cole & Tibby, 2013). Moreover, Sanders & De Grip (2004) opined that learning capacity is considered part of an employee’s employability.

Table 7. ANOVA Summary on perception of respondents with regard qualifications (N = 263).

Parameter

Sum of Squares

Df

Mean Square

F

Sig.

HTCTC

Between Groups

7.182

3

2.394

18.77

0.0001

Within Groups

32.910

258

0.128

Total

40.092

261

OND

Between Groups

0.408

3

0.136

1.86

0.136

Within Groups

18.915

259

0.073

Total

19.323

262

HND

Between Groups

0.967

3

0.322

2.15

0.095

Within Groups

38.904

259

0.150

Total

39.871

262

BA

Between Groups

0.657

3

0.219

2.26

0.082

Within Groups

25.145

259

0.097

Total

25.802

262

BED

Between Groups

0.071

3

0.024

0.26

0.852

Within Groups

23.358

259

0.090

Total

23.430

262

BSc

Between Groups

2.524

3

0.841

3.61

0.014

Within Groups

60.351

259

0.233

Total

62.875

262

MSc

Between Groups

0.950

3

0.317

1.27

0.284

Within Groups

64.456

259

0.249

Total

65.407

262

MA

Between Groups

7.520

3

2.507

23.95

0.0001

Within Groups

27.104

259

0.105

Total

34.624

262

MPhil

Between Groups

0.555

3

0.185

2.35

0.073

Within Groups

20.433

259

0.079

Total

20.989

262

MED

Between Groups

0.479

3

0.160

1.80

0.147

Within Groups

22.951

259

0.089

Total

23.430

262

ACCA

Between Groups

0.462

3

0.154

7.39

0.0001

Within Groups

5.401

259

0.021

Total

5.863

262

MBA

Between Groups

0.440

3

0.147

3.45

0.017

Within Groups

11.012

259

0.043

Total

11.452

262

MPA

Between Groups

0.440

3

0.147

3.76

0.011

Within Groups

10.100

259

0.039

Total

10.540

262

CPA

Between Groups

0.496

3

0.165

85.68

0.0001

Within Groups

0.500

259

0.002

Total

0.996

262

CIMA

Between Groups

0.496

3

0.165

85.68

0.0001

Within Groups

0.500

259

0.002

Total

0.996

262

ICSA

Between Groups

12.405

3

4.135

237.99

0.0001

Within Groups

4.500

259

0.017

Total

16.905

262

PhD DED

Between Groups

2.665

3

0.888

5.48

0.001

Within Groups

41.981

259

0.162

Total

44.646

262

Others specify

Between Groups

0.856

3

0.285

1.42

0.236

Within Groups

51.881

259

0.200

Total

52.738

262

Table 8 shows that the p values of 0.0001 (Linguistics), 0.0001 (Home Economics), 0.001 (HKSE), 0.012 (Education Extension), 0.04 (Accounting), 0.0001 (Social Work), Engineering (0.0001), Physics (0.001), Statistics/Mathematics (0.049), and Others (0.047) are less than the alpha level of 0.05. These values indicate that there are significant differences in the mean scores of the respondents for the measured major fields of study. Therefore, the null hypothesis was rejected for these parameters, while for the remaining major fields of study parameters (Management, Administration, Agriculture Education, Economics, Health Education, Literature, Banking Finance, Sociology, WMEB, WSF, Communication Media, Agriculture, Business, HECD, and Medical Studies) we accept the null hypothesis since their p values are greater than the alpha level of 0.05.

Table 8. ANOVA Summary on perception of respondents with regard to major fields of study (N = 263).

Parameter

Sum of Squares

Df

Mean Square

F

Sig.

Management

Between Groups

0.433

3

0.144

0.73

0.534

Within Groups

50.865

258

0.197

Total

51.298

261

Administration

Between Groups

1.170

3

0.390

2.01

0.113

Within Groups

50.128

258

0.194

Total

51.298

261

Linguistics

Between Groups

15.856

3

5.285

461.20

0.0001

Within Groups

2.957

258

0.011

Total

18.813

261

Ag Education

Between Groups

0.019

3

0.006

0.19

0.905

Within Groups

8.672

258

0.034

Total

8.691

261

Economics

Between Groups

0.063

3

0.021

0.71

0.547

Within Groups

7.692

258

0.030

Total

7.756

261

H Economics

Between Groups

35.775

3

11.925

1553.26

0.0001

Within Groups

1.981

258

0.008

Total

37.756

261

HKSE

Between Groups

0.232

3

0.077

5.39

0.001

Within Groups

3.707

258

0.014

Total

3.939

261

Health Edu

Between Groups

0.018

3

0.006

0.13

0.944

Within Groups

12.337

258

0.048

Total

12.355

261

Literature

Between Groups

0.036

3

0.012

0.53

0.664

Within Groups

5.827

258

0.023

Total

5.863

261

Edu Ext

Between Groups

1.674

3

0.558

3.71

0.012

Within Groups

38.784

258

0.150

Total

40.458

261

Accounting

Between Groups

0.843

3

0.281

2.82

0.04

Within Groups

25.722

258

0.100

Total

26.565

261

Banking Fin

Between Groups

0.167

3

0.056

1.18

0.317

Within Groups

12.188

258

0.047

Total

12.355

261

Sociology

Between Groups

0.049

3

0.016

0.62

0.604

Within Groups

6.764

258

0.026

Total

6.813

261

Social Work

Between Groups

0.983

3

0.328

17.32

0.0001

Within Groups

4.880

258

0.019

Total

5.863

261

WMEB

Between Groups

0.040

3

0.013

0.88

0.453

Within Groups

3.899

258

0.015

Total

3.939

261

WSF

Between Groups

0.001

3

0.000

0.09

0.968

Within Groups

0.995

258

0.004

Total

0.996

261

CMedia

Between Groups

0.025

3

0.008

0.44

0.727

Within Groups

4.880

258

0.019

Total

4.905

261

Agriculture

Between Groups

0.125

3

0.042

0.72

0.54

Within Groups

14.898

258

0.058

Total

15.023

261

Business

Between Groups

0.003

3

0.001

0.06

0.982

Within Groups

3.936

258

0.015

Total

3.939

261

HECD

Between Groups

0.001

3

0.000

0.09

0.968

Within Groups

0.995

258

0.004

Total

0.996

261

Med Studies

Between Groups

0.120

3

0.040

0.99

0.398

Within Groups

10.418

258

0.040

Total

10.538

261

Engineering

Between Groups

24.536

3

8.179

161.24

0.0001

Within Groups

13.087

258

0.051

Total

37.622

261

ICTC

Between Groups

0.100

3

0.033

0.83

0.48

Within Groups

10.438

258

0.040

Total

10.538

261

Energy Studies

Between Groups

0.000

3

0.000

Within Groups

0.000

258

0.000

Total

0.000

261

Physics

Between Groups

0.232

3

0.077

5.39

0.001

Within Groups

3.707

258

0.014

Total

3.939

261

Stat/Maths

Between Groups

0.204

3

0.068

2.65

0.049

Within Groups

6.609

258

0.026

Total

6.813

261

Others

Between Groups

0.852

3

0.284

2.69

0.047

Within Groups

27.240

258

0.106

Total

28.092

261

Ag Education = agriculture education; H Economics = home economics; HKSE = human kinetics physical education; Health Edu = health education; Edu Ext = education and/or extension; Banking Fin = banking and finance; WMEB = wildlife management ecotourism biodiversity; WSF = wood science forestry; CMedia = communication and media; HECD = home economics and community development; Med Studies = medical studies; ICTC = information communication technologies computers; Stat/Maths = statistics/mathematics.

Table 9 shows that the p values of 0.0001 (DMJ), 0.041 (APS), 0.0001 (Analytical), 0.0001 (TLS), and 0.0001 (OLS) are less than the alpha level of 0.05. These values indicate that there are significant differences in the mean scores of the respondents for the measured leadership skills. Therefore, the null hypothesis was rejected for these parameters. Results indicate that qualifications and fields of study vary among individuals, and these aspects are important for the job market. As such, for workers to succeed in the labor market, employability efforts should focus on training and preparing them to contribute to society. It involves the acquisition of “a set of knowledge, skills and personal traits that make workers more likely to contribute positively to the economy” (Mashigo, 2014). The extent to which workers possess these skills and attributes contributes to their employability (Mashigo, 2014) and job performance (Stoffberg et al., 2023; Ngauja & Norman, 2025).

Table 9. ANOVA Summary on perception of respondents with regard to leadership skills (N = 263).

Parameter

Sum of Squares

Df

Mean Square

F

Sig.

DMJ

Between Groups

7.462

4

1.865

15.78

0.0001

Within Groups

30.493

258

0.118

Total

37.954

262

APS

Between Groups

0.730

4

0.182

2.53

0.041

Within Groups

18.593

258

0.072

Total

19.323

262

Analytical

Between Groups

6.984

4

1.746

9.25

0.0001

Within Groups

48.681

258

0.189

Total

55.665

262

TLS

Between Groups

5.878

4

1.470

14.63

0.0001

Within Groups

25.916

258

0.100

Total

31.795

262

OLS

Between Groups

2.868

4

0.717

5.96

0.0001

Within Groups

31.048

258

0.120

Total

33.916

262

DMJ = decision-making judgment; APS = administrative problem-solving; OLS = organizational leadership skills; TLS = team leadership skills.

Table 10 shows that the p value of 0.0001 for the management skills parameters (coordinating, organizing, supervising, evaluating, controlling, and communicating) is less than the alpha level of 0.05. These values indicate that there are significant differences in the mean scores of the respondents for the measured management skills parameters. Therefore, the null hypothesis was rejected for these parameters. Moreover, findings suggest the significance of implementing the measured parameters to build a good organizational culture. Organizational culture, defined by shared values, beliefs, and practices within an institution (Isensee et al., 2020; Zeb et al., 2021), is a critical factor influencing employee attitudes and behaviors (Akpa et al., 2021; Cherian & Vilas, 2020). A positive organizational culture that encourages collaboration, risk-taking, and continuous improvement can significantly boost innovation (Barjak & Heimsch, 2023; Ma et al., 2023) by creating an environment conducive to creativity (Ogbeibu et al., 2021; Wiroonrath et al., 2024). Conversely, a hostile or toxic organizational culture can suppress innovation and lower employee morale (Saban, 2024), leading to high turnover rates (Ofei et al., 2023) and decreased commitment (Mannix-McNamara et al., 2021). This study suggests that a solid and supportive organizational culture directly impacts innovation and organizational commitment. Organizations can enhance their ability to innovate and build a more committed workforce by fostering a culture that encourages open communication and values employee contributions.

Table 11 shows that the p values of 0.0001 (Col relationship), 0.005 (RWOS), 0.0001 (MS colleagues), and 0.0001 (Ability RWP) are less than the alpha level of 0.05. These values indicate that there are significant differences in the mean scores of the respondents for the measured internal public relations. Therefore, the null hypothesis was rejected for these parameters.

Table 10. ANOVA Summary on the perception of respondents with regards to management skills (N = 263).

Parameter

Sum of Squares

Df

Mean Square

F

Sig.

Coordinating

Between Groups

2.784

2

1.392

19.02

0.0001

Within Groups

19.026

260

0.073

Total

21.810

262

Organizing

Between Groups

1.394

2

0.697

12.49

0.0001

Within Groups

14.507

260

0.056

Total

15.901

262

Supervising

Between Groups

3.591

2

1.795

13.58

0.0001

Within Groups

34.363

260

0.132

Total

37.954

262

Evaluating

Between Groups

5.120

2

2.560

17.89

0.0001

Within Groups

37.199

260

0.143

Total

42.319

262

Controlling

Between Groups

8.271

2

4.136

34.72

0.0001

Within Groups

30.969

260

0.119

Total

39.240

262

Communicating

Between Groups

0.470

2

0.235

7.43

0.001

Within Groups

8.222

260

0.032

Total

8.692

262

Table 11. ANOVA Summary on perception of respondents with regard to internal public relations (N = 263).

Parameter

Sum of Squares

Df

Mean Square

F

Sig.

ColRelationship

Between Groups

5.868

2

2.934

16.36

0.0001

Within Groups

46.270

258

0.179

Total

52.138

260

RWOS

Between Groups

0.669

2

0.334

5.36

0.005

Within Groups

16.090

258

0.062

Total

16.759

260

MSColleagues

Between Groups

3.400

2

1.700

18.95

0.0001

Within Groups

23.152

258

0.090

Total

26.552

260

Ability RWP

Between Groups

0.569

2

0.285

4.53

0.01

Within Groups

16.189

258

0.063

Total

16.759

260

ColRelationship = collegial relationship; RWOS = relating well to other staff; MSColleagues = motivational skills for colleagues; Ability RWP = ability to relate well to the public.

Table 12 shows that the p values of 0.0001 (CFSJob) and 0.0001 (INTERNALEXTC) are less than the alpha level of 0.05. These values indicate that there are significant differences in the mean scores of the respondents for the measured office ICT skills. Therefore, the null hypothesis was rejected for these parameters, while for the remaining major fields of study parameters (Office CS and others specify), we accept the null hypothesis since their p values are greater than the alpha level of 0.05.

Table 12. ANOVA Summary on perception of respondents with regard to office ICT skills (N = 263).

Parameter

Sum of Squares

Df

Mean Square

F

Sig.

CFSJob

Between Groups

2.170

3

0.723

14.42

0.0001

Within Groups

12.845

256

0.050

Total

15.015

259

INTERNALEXTC

Between Groups

3.860

3

1.287

10.99

0.0001

Within Groups

29.986

256

0.117

Total

33.846

259

OfficeCS

Between Groups

1.341

3

0.447

1.80

0.148

Within Groups

63.655

256

0.245

Total

64.9962

259

OthersB

Between Groups

0.09202

3

0.03067

1.02493

0.382

Within Groups

7.66182

256

0.02993

Total

7.75385

259

CFSJob = computer skills for job; INTERNALEXTC = internal and external correspondences; OfficeCS = office clerical skills; OthersB = others, specify.

Table 13 shows that the p value of 0.0002 (senior administrative), which is less than the alpha level of 0.05, indicates a significant difference in the mean scores of the respondents for the measured senior administrative staff position. Therefore, the null hypothesis was rejected for this parameter, while for the remaining administrative staff positions parameters (junior administrative staff, senior supporting administrative staff, and others specify), we accept the null hypothesis since their p values are greater than the alpha level of 0.05.

Table 13. ANOVA Summary on the perception of respondents with regard to the degree of relevance to administrative staff positions (N = 263).

Parameter

Sum of Squares

Df

Mean Square

F

Sig.

JNRA

Between Groups

3.234

2

1.617

0.47

0.625

Within Groups

263.753

77

3.425

Total

266.988

79

SSNRA

Between Groups

18.245

2

9.123

2.77

0.068

Within Groups

283.373

86

3.295

Total

301.618

88

SNRA

Between Groups

34.740

2

17.370

8.83

0.0002

Within Groups

377.875

192

1.968

Total

412.615

194

JNRA = junior supporting administrator; SNRSA = senior supporting administrator; SNRA = senior administrator.

The results of the present study posit that non-technical positions may not affect the level of performance of the junior staff compared to the technical positions of the senior administrative staff. Findings are in agreement with those of previous researchers in the discipline. For example, Ng & Feldman (2009) suggested that higher levels of education positively influence the performance of core tasks, creativity, and constructive behavior in employees. Ariss & Timmins (1989) argued that the type and level of educational qualification held by staff in non-technical positions have no effect on their level of performance (Ng & Feldman, 2009). Findings are also in concurrence with the view that Task Performance Behavior (TPB) is demonstrated when an individual completes tasks relevant to the key performance areas stated in his or her job description (Sonnentag et al., 2010). However, Stoffberg et al. (2023) found no significant correlation between staff performance indicators and their qualification levels. This emphasizes the significance of recruiting staff who are most likely to impact organizational objectives positively.

Table 14 presents the academic administrators’ perception of the current number of staff, numbers of staff recruited, and promoted per gender across five academic years. Generally, male staff had a higher current number of staff, number of staff recruited, and number of staff promoted than female staff across academic years in Njala University, except in 2019/2020, where the number of female staff recruited (30) was higher than that of males (26). The current number of male staff and the total current number of staff consistently increased from the 2018/2019 to the 2021/2022 academic years, but slightly decreased in the 2022/2023 academic year. The highest total number of staff recruited was during the 2022/2023 academic year, while no staff were recruited during the 2018/2019 academic year. The highest total number of staff promoted was during the 2019/2020 academic year, while the lowest, four staff, were promoted during the 2018/2019 academic year. At the Freetown Polytechnique (FP), data were only available on the current number of staff and the number of staff recruited for the 2022/2023 academic year. Findings indicate a lack of an efficient database and archiving culture at FP and other tertiary institutions that lacked data for all the academic years studied. Similarly, the male current number of staff and male number of staff recruited at FP were higher than those of female staff during the 2022/2023 academic year.

Table 14. Academic administrators’ perceived knowledge of the current number of staff, numbers of staff recruited and promoted per gender and academic year.

Parameter

Sex

Academic year

Mean

2018/2019

2019/2020

2020/2021

2021/2022

2022/2023

Njala University

Current number of staff

Male

594

620

649

691

638

638.4

Female

165

169

174

167

123

159.6

Total

759

789

823

857

802

806.0

No. of staff recruited

Male

0

26

29

30

76

32.2

Female

0

30

5

4

10

9.8

Total

0

56

34

34

86

42.0

No. of staff promoted

Male

3

53

11

24

6

19.4

Female

1

13

1

8

0

4.6

Total

4

66

12

32

6

24.0

Freetown Polytechnique

Current number of staff

Male

NA

NA

NA

NA

138

Female

NA

NA

NA

NA

32

Total

NA

NA

NA

NA

170

No. of staff recruited

Male

NA

NA

NA

NA

5

Female

NA

NA

NA

NA

4

Total

NA

NA

NA

NA

9

No. of staff promoted

Male

NA

NA

NA

NA

NA

Female

NA

NA

NA

NA

NA

Total

NA

NA

NA

NA

NA

NA = Not Available.

Generally, the stepwise regression of academic staff parameters indicated that total staff loss negatively influences the current number of staff, whereas the number of staff recruited positively influences the current number of staff more than the number of staff promoted (Figure 2). Findings indicate that staff loss reduces the skill set of tertiary institutions, whereas recruitment of qualified staff and promotion on a merit basis improve the staff cadre, which will have implications for increased job satisfaction, commitment, delivery, and retention.

Figure 2. Relationships between (a) current number of staff and number of staff recruited, (b) current number of staff and number of staff promoted, and (c) current number of staff and total staff loss at Njala University assessed across five academic years.

Figure 3. Academic Administrators’ perceived knowledge of staff loss category, total number of staff losses, and number of staff losses per gender at Njala University assessed across five academic years.

Generally, retirement exhibited the highest staff loss at Njala University across the studied academic years, except in the 2019/2020 academic year, when the number of resignations was the highest, with 13 staff resigning compared to 11 staff retiring (Figure 3(a)). The total number of staff lost due to retirement was highest (20 staff) in the 2018/2019 and 2022/2023 academic years, whereas the 2019/2020 academic year had the lowest, with 11 staff retired. The gender of staff influences staff loss. The numbers of staff retired, deceased, terminated, and dismissed were higher for male than for female staff across all sampled academic years (Figure 3(b)). However, for the number of resignations, the female staff was higher than the male during the 2018/2019 and 2019/2020 academic years. Across all academic years, none of the staff lost their jobs due to sickness, job abandonment, or demotion.

4. Conclusion

This study establishes the relevance of job match (educational, skill, and subject match) to the job performance of academic administrators in varying positions and pay classes (grades) in the tertiary institutions of Sierra Leone. The pay grade issues are relevant findings that could be exploited by the Wages and Compensation Commission (WCC) of Sierra Leone for the implementation of fair pay class policies across tertiary education institutions in the country. The study also demonstrates that a tertiary educational institution employer is more comfortable with a platykurtic distribution of return, as it indicates stable returns and a lower risk of sudden shock that could be exploited for an improved work service culture. The relevance of qualifications, major fields of study, faculties/schools, leadership skills, management skills, internal public relations, and administrative skills to their positions are very relevant, whereas office ICT skill is moderately relevant. The results suggest that improving the developmental, maintenance, and skills utilization process forms part of the core elements of the policy package necessary to support sustainable long-term growth and employment creation. Findings also suggest that academic administrative staff members with higher levels of educational qualifications may result in higher-performing teams, on the grounds that staff with higher levels of educational qualifications are more likely to exceed the minimum requirements of their jobs. Tertiary educational institutions that strive for high levels of performance may be persuaded by this research study to require minimum educational qualifications (such as an undergraduate degree, for example, depending on the nature of the job) for all academic administrative positions. Meanwhile, future studies involving comparison of job performance among employees at the same level may provide a more conclusive outcome. Further studies may investigate whether a Senior Secretary with a school-leaving (matric) qualification only, for instance, performs at an equal, lower, or better level to one with a master’s degree.

Acknowledgements

The authors are grateful to the staff and students of the Department of Teacher Education, School of Education, Njala University, Bo Campus, Bo, Sierra Leone.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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