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Purpose

Human resources appear to be the primary factors in any organization's success or failure, as they make use of other resources like money, machines, materials, procedures, information and time that allow organizations (schools) to operate efficiently. To that effect, the effective management of resources, particularly the human part, often requires serious attention. Therefore, this study aimed at examining the practices and challenges of selected Human Resource Management (HRM) functions (induction, performance appraisal and motivation) in secondary schools in Enebsie Sar Midir District.

Design/methodology/approach

To address this purpose, a concurrent parallel mixed-method research design was employed. Data were collected from 262 secondary school teachers and 12 interviewees selected through the proportional random sampling method and purposive sampling techniques, respectively.

Findings

The findings of the study revealed that all the selected HRM functions were not practiced to the expected level due to several challenges, such as the absence of competent and supportive leadership and supervision practices, a lack of effective recognition for good performances, low salaries, a lack of adequate nonsalary incentive/benefit mechanisms and so forth. MANOVA showed that characteristics such as the teachers' educational setting (school), gender and other demographic characteristics of teachers do not substantially impact the linear combination of HRM functions.

Originality/value

Although good school leadership involves setting up induction programs, to the best of our knowledge, no prior work has specifically investigated these functions in the context of Ethiopia.

Without necessary resources, no organization can be guaranteed success, making resources one of the most crucial and vital components of every organization, including educational institutions (Usman, 2016). Seyoum (2011) and Ume (2016) noted that resources such as money, tools, and labor are crucial to an organization's performance. People are the most valuable resource in any organization (Sharma and Pandey, 2021). This is because people are what make other resources like money, machines, materials, procedures, information and time work (Sharma and Pandey, 2021). Similarly, Gloria and Pepple (2019) agreed that people (i.e. HR) make up the most significant resource in every organization. Human resource management (hereafter abbreviated as HRM) was previously known as personnel management, which concentrated on the operations of a specific department (Manjula and Manichander, 2015). An often-cited definition of HRM provided by renowned author Michael Armstrong (2016) is “a strategic and coherent approach to the management of an organization's most valued assets: the people working there who individually and collectively contribute to the achievement of its objective” (p.3).

Research from around the world shows how these three pillars – induction, appraisal and motivation – fit together to boost school effectiveness (Hoque and Atheef, 2024; Malleck and Kitula (2023). Yet in Ethiopia, these HRM practices are still fragmented. Policy papers nod to the need for teacher development, but few studies dig into how induction, appraisal and motivation actually play out in secondary schools – or why they sometimes fall short. Despite the clear benefits of these practices, research on how they play out in Ethiopian secondary schools is still scant in Ethiopia (Kassahun, 2014; Tadesse and Mengistu, 2017).

In Ethiopia, National policy documents like the Education Sector Development Plan VI (MoE, 2021) call for better teacher management, but we know little about how induction, appraisal and motivation actually happen day to day – or why some efforts fall short. That's the gap this study aims to fill. Thematically, studies conducted previously covered some parts of HRM functions; there is still a visible paucity in addressing the major issues that are expected to be practiced in secondary schools contexts on HRM. That is, some of the studies reviewed were focused on planning, selection, and recruitment (e.g. Mamaru, 2017; Seyoum, 2011; Tanjung, 2020), which are out of the scope of secondary schools and impractical in the realities of the present study context. Some others focused on staff training and development (Simachew, 2020). Others also studied each of the functions included in this study separately, which makes the present study different from the reviewed studies. This requires further investigation to study the most common practices of HRM functions (induction/socialization, performance appraisal, and motivation) in secondary school contexts. Consequently, this study aimed at explaining the current practices and exploring the challenges hindering the management of human resources (in terms of induction, performance appraisal, and motivation) in secondary schools of Enebsie Sar Midir district. In an attempt to address this purpose, the following leading questions were designed.

  1. To what extent HRM functions (i.e. induction, performance appraisal and motivation) are practiced in secondary schools of Enebsie Sar Midir district?

  2. What are the major challenges affecting the effective implementations of HRM functions in Enbise SAR Midir district secondary school?

  3. Are there significant differences in teachers' perceptions of the practice of HRM functions based on their demographic characteristics, such as gender, age, educational status, school type and work experience?

According to literature and studies, an organization's long-term performance depends on its ability to effectively manage its people resources and put HRM into practice (Armstrong, 2016; Nwuke and Agu, 2021; Qutoshi et al., 2021). According to Werther and Davis (1996), an efficient management performance review or appraisal will help to determine how each employee's task can progress the organization's overarching aims; analyze each employee personally to determine their strengths and weaknesses. The present study emphasizes induction, performance appraisal and motivation as fundamental aspects HRM in Ethiopian secondary schools, based on both their theoretical significance and practical relevance. The chosen functions reflect basic HRM domains that are both understudied in Ethiopian environments and critical to boosting teacher and school effectiveness.

Keter and Role (2018) defined induction as the process of integrating someone into a new role within a business or government. In a similar vein, induction programs in secondary schools are designed to help a new beginning teacher to become a useful and integrated member of the school team and are part of the institutional management process (Zeru and Jita, 2014). Induction gives new employees and teachers a chance to get to know one another and the school community, which fosters a sense of commitment to the institution (Sukawati et al., 2020). This also happens in our country, Ethiopia. A study conducted by taking eight Ethiopian primary schools by Belay et al. (2019) revealed that most of the novice teachers did not get adequate induction due to a lack of adequate and relevant professional support.

Another aspect of HRM is performance appraisal (PA), which is a continual process of determining, evaluating and improving employee performance in line with organizational strategic objectives (Ahmed et al., 2010). The literature revealed that the teacher evaluation system supports schools as learning environments where teachers can participate in interdisciplinary discussions and collaborative research projects that encourage ongoing professional development (Education and Manpower Bureau, 2003). However, as numerous academics from both domestic and international institutions have noted, the process is complicated by several issues. Examples of some of the many factors include the use of inappropriate and irrelevant content, the absence of valid and reliable PA criteria, the lack of pre- and post-appraisal meetings, the ineffective use of classroom observation, the lack of feedback and discussion, and the appraisers' lack of the necessary skill and expertise (Asegedech, 2019; Endale, 2015; Frehun and Tafano, 2019; Mekonen, 2013). However, the results are uneven and vary from area to area in several respects, which may allow for more research, especially in areas where such research has not yet been conducted, as in the case of the current study.

The other crucial element of HRM is motivation, which is closely related to the first two roles of induction and performance appraisal. A key factor in fostering the excellence of teaching and learning is motivation, which is an employee's drive and commitment as shown in their work performance (Amiry, 2018; Momanyi, 2015). Concerning other factors (like leadership), motivation has been examined in schools and other situations (Eyal and Roth, 2011). However, without incentive, people may experience persistent reductions in performance that, over time, may be a sign of teacher burnout (Mbinya, 2016). Studies at the secondary level and in other relevant contexts have looked at the impact of motivation in general and teacher motivation in particular on performance, professional growth and other related categories and aspects (Sajid et al., 2022; Mbinya, 2016). Sajid et al. (2022) discovered, however, that instructor motivation significantly impacts performance. As a result, experts in the field and theorists have recognized its significance in HRM.

System and contingency theories served as the theoretical foundations for the current investigation. According to the current study, school administrators should set up specific guidelines and standards to maintain equity with the goal of improving PA and successfully motivating teachers. Several fundamental components of the organization connection system were also recognized by the systems theory. The notion that managing human resources and gaining a competitive edge depends on them is supported by a systems view on HRM (Botelho et al., 2023; Boxall and Purcell, 2016; Jiang and Messersmith, 2018). The perspective of system-thinking places emphasis on the interrelationships between the many components of a system, and HR practices, including motivation, performance evaluation and training, are viewed as variables influencing people management (Botelho et al., 2023; Boxall and Purcell, 2016; Jiang and Messersmith, 2018).

The study's theoretical framework also draws on contingency theory. According to this perspective, HRM methods cannot be applied uniformly and without consideration for the various internal and external aspects that affect an organization (Egbuta and Olugbenga, 2021). According to this idea, in order to meet school objectives, HRM functions must be compatible with elements of the school environment for schools to be successful and efficient. As per Harney (2016) and Egbuta and Olugbenga (2021), it is imperative that HR practices, which are associated with many tasks including motivation, performance assessment, and induction, align with the environmental circumstances. As a result, we may say that the HRM systems and contingency theories provide the theoretical foundation as indicated in Figure 1.

Figure 1
A conceptual framework links human resource management to practices, challenges, and school effectiveness.The hierarchical conceptual framework with rectangular and oval shapes connected by directional arrows. At the top is a wide rectangular box labeled “Human Resource Management”. A vertical double-headed arrow points downward from “Human Resource Management” to a horizontal rectangular section containing three horizontally arranged ovals. The center oval is labeled “Performance Appraisal”. To its left is an oval labeled “Induction”, and to its right is an oval labeled “Motivation”. A double-headed arrow connects “Induction” and “Performance Appraisal”. Another double-headed arrow connects “Performance Appraisal” and “Motivation”. From this rectangular section, two diagonal arrows extend downward. One arrow points to a rounded rectangular box labeled “Practices” on the left. The other arrow points to a rounded rectangular box labeled “Challenges” on the right. From both “Practices” and “Challenges”, arrows point downward toward a large oval at the bottom labeled “Effective or ineffective school in H R M or teacher outcome and school effectiveness”.

Conceptual framework of the study. Source: Authors’ own work

Figure 1
A conceptual framework links human resource management to practices, challenges, and school effectiveness.The hierarchical conceptual framework with rectangular and oval shapes connected by directional arrows. At the top is a wide rectangular box labeled “Human Resource Management”. A vertical double-headed arrow points downward from “Human Resource Management” to a horizontal rectangular section containing three horizontally arranged ovals. The center oval is labeled “Performance Appraisal”. To its left is an oval labeled “Induction”, and to its right is an oval labeled “Motivation”. A double-headed arrow connects “Induction” and “Performance Appraisal”. Another double-headed arrow connects “Performance Appraisal” and “Motivation”. From this rectangular section, two diagonal arrows extend downward. One arrow points to a rounded rectangular box labeled “Practices” on the left. The other arrow points to a rounded rectangular box labeled “Challenges” on the right. From both “Practices” and “Challenges”, arrows point downward toward a large oval at the bottom labeled “Effective or ineffective school in H R M or teacher outcome and school effectiveness”.

Conceptual framework of the study. Source: Authors’ own work

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By using a concurrent parallel mixed design, the researchers can combine, integrate, and triangulate both the quantitative and qualitative data to strengthen each separately and to fill the limitations behind using one of the two alone. The intention of employing a mixed approach was first to get a more thorough and actual understanding of these HRM functions. This combined approach was very useful in revealing not just the difficulties, but also why they persist and how they may be treated to overcome those challenges more effectively. This was supported by Creswell and Plano Clark's (2018) findings, who stated that, a better understanding of the research topic is also hoped to be achieved by integrating quantitative and qualitative data.

This study was conducted in the Ethiopia, Amhara region, East Gojam province, Enbise Sar Midir district. In this district, there are six secondary schools of which three of them are located in the town, of Mertulemariam, the rest three are dispersed in the rural kebeles (villages) taking to address the issue of access to secondary education in community living at a distance from the town. Participants in this study were teachers and school administrators (principals and other administrative staff). In the district, there are 505 teachers in six secondary schools. In so doing, Yamane's (1967) formula was applied to select a sample size for teachers, and proportional sampling is used to select from each of the schools, i.e. n = N/(1+N (e) 2) at the margin error value of 0.05. The obtained samples were 223 teachers using a proportional random sampling based on the total number of teachers, using a sampling formula to ensure that teachers from each school had a reasonable chance of inclusion and preclude favoring schools with greater or fewer staff sizes. An additional 20% of respondents (in total 268) were included as the topic is sensitive and expected nonresponse to items. However, only 262 (97.8%) completed the dispatched questionnaire.

For the qualitative study, interviews were conducted with a total of 79 administrative staff members and school leaders in the selected schools. Twelve interviewees were selected purposively based on administrative position and work experience.

The information from the interviews was supportive of the quantitative data. To that effect, interviews were conducted with the principals of the schools and selected administrative staff members to get their views, opinions, and experiences on the practices and difficulties encountered in the HRM process. The questionnaire contains: (1) Induction practices and challenges, which were adopted from related studies and literature as indicated (e.g. Belay et al., 2019; Molalign and Gebeyehu, 2019; Tolgfors et al., 2021); (2)Performance appraisal practice and challenges, adapted from several scholars (e.g. Asegedech, 2019; Endale, 2015; Frehun and Tafano, 2019; Nakiyingi, 2018). Lastly, the items used to measure the practice and challenges of teachers' motivation together with performance levels in the secondary school context were developed by reviewing the studies conducted by several studies and taking a combination of parts of the items from each of the studies (e.g. Amiry, 2018; Kasa, 2018; Mark, 2015; Yosef, 2019). The 5-point Likert scale of the questionnaire was applied to measure each of the items. In so doing, the practices of the selected HRM functions contain 5. Strongly Agree to 1. Strongly disagree to know the level of agreement of the respondents on the level of practices of each of the items. Similarly, the challenges/expected factor-related HRM items contain 1 = very low to 5 = very high to know the level of the effect of the practice. The higher score of 5 reflects greater practice/greater challenge and the lower score of 1 reflects lesser practice/challenge.

To ensure the accuracy (content validity) of the information used to measure the variables in the questionnaire and interview items, experts were consulted professionally. After all relevant corrections on the wording and related issues were taken and the final instrument was developed accordingly. Then, through pilot testing in line with a suggestion of scholars (e.g. Creswell and Plano Clark, 2018), samples whose backgrounds were similar to those of the samples used in the main study were taken from one secondary school found in the nearby district (i.e. Goncha), which was not included in the sample. A total of 69 questionnaires were distributed, of which 60 were filled out and returned. EFA uses this data set as shown in Table 1. Cronbach's alpha was determined to confirm the dimensions' reliability. The results demonstrated considerable reliability across all dimensions of HRM: 0.81 for induction, 0.82 for PA, and 0.84 for motivation. The average reliability score across all three domains was 0.82, demonstrating the reliability and consistency of the data-gathering instruments employed in this study. It was determined that the Cronbach alpha reliability coefficient was good (Gliem and Gliem, 2003).

Table 1

EFA results (rotated component matrix of human resources management functions)

ItemsComponent
InductionPerformance appraisalMotivation
InductionP4 0.868   
InductionP5 0.867   
InductionP1 0.858   
InductionP2 0.852   
InductionP3 0.841   
InductionP7 0.770   
InductionP8 0.439   
MotivationP7  0.800  
MotivationP4  0.775  
MotivationP5  0.639  
MotivationP9  0.633  
MotivationP1  0.604  
MotivationP3  0.402  
MotivationP8  0.399  
PAP4   0.788 
PAP1   0.733 
PAP9   0.666 
PAP2   −0.582 
PAP10   0.552 
PAP6   0.516 
PAP7   0.445 
ItemsComponent
InductionPerformance appraisalMotivation
InductionP4 0.868   
InductionP5 0.867   
InductionP1 0.858   
InductionP2 0.852   
InductionP3 0.841   
InductionP7 0.770   
InductionP8 0.439   
MotivationP7  0.800  
MotivationP4  0.775  
MotivationP5  0.639  
MotivationP9  0.633  
MotivationP1  0.604  
MotivationP3  0.402  
MotivationP8  0.399  
PAP4   0.788 
PAP1   0.733 
PAP9   0.666 
PAP2   −0.582 
PAP10   0.552 
PAP6   0.516 
PAP7   0.445 
Source(s): Authors’ own work

While determining construct validity, the original instrument's first case comprised 18 measures to test HRM functions such as induction, PA, and motivation. Four were excluded from the study as they have an overlapping problem, have factor load values less than.30, and are located in a dimension of an instrument that is not theoretically supported (Field, 2009). The goal of EFA was to find factors and eliminate overlapping items (Leech et al., 2015). Finally, the three components' combined variance (14 items) was 54,766%. Furthermore, the total variance explained index for the scale is greater than 50%, indicating that the scale has acceptable construct validity.

The findings of the CFA indicate that the model reflecting the various functions of human resource management (HRM) fits the data well and is reliable and valid. The maximum likelihood method estimates model parameters using all available data without imputing missing values. CFA was conducted with 262 participants.

As shown in Figure 2, items are precisely assessed in terms of their latent variables. Items have acceptable factor loadings, indicating that they effectively measure the construct. In addition, χ2/df = 2.5 and SRMR = 0.042, which are acceptable. Several other important metrics used to measure model fit, such as the CFI (0.9) and RMSEA (0.045), are within an acceptable range, indicating that the suggested structure accurately represents the data. Hu and Bentler (1999) endorse these standards, recommending CFI and TLI values more than 0.90 and RMSEA less than 0.06 as signs of great fit. Furthermore, the majority of the items that comprise the HRM dimensions – such as induction, motivation and performance appraisal – have high factor loadings, indicating that they clearly correspond to the features they are intended to evaluate. According to Hair et al. Source: Authors’ own work.

Figure 2
A structural equation model shows induction, motivation, and performance appraisal with loadings.The figure presents a structural equation model with three oval latent constructs labeled “Induction”, “Motivation”, and “Performance appraisal”. Rectangular observed variables appear to the left of each construct. Small circular nodes labeled “e 1” through “e 21” appear to the far left, each with an arrow pointing to its corresponding rectangle. At the top, the oval labeled “Induction” has arrows pointing to seven rectangles. Starting from the top, the arrow from “Induction” to “Induction P 4” shows “0.94”, and the value above the rectangle is “0.87”. The arrow to “Induction P 5” shows “0.93”, and the value above the rectangle is “0.86”. The arrow to “Induction P 1” shows “0.75”, and the value above the rectangle is “0.56”. The arrow to “Induction P 2” shows “0.72”, and the value above the rectangle is “0.52”. The arrow to “Induction P 3” shows “0.83”, and the value above the rectangle is “0.68”. The arrow to “Induction P 7” shows “0.67”, and the value above the rectangle is “0.45”. The arrow to “Induction P 8” shows “0.21”, and the value above the rectangle is “0.04”. “e 1” through “e 7” direct towards “Induction P 4” through “Induction P 8”, respectively. In the middle, the oval labeled “Motivation” has arrows pointing to seven rectangles. Starting from the top, the arrow to “Motivation P 7” shows “0.82”, and the value above the rectangle is “0.67”. The arrow to “Motivation P 4” shows “0.75”, and the value above the rectangle is “0.57”. The arrow to “Motivation P 5” shows “0.58”, and the value above the rectangle is “0.33”. The arrow to “Motivation P 9” shows “0.45”, and the value above the rectangle is “0.20”. The arrow to “Motivation P 1” shows “0.53”, and the value above the rectangle is “0.28”. The arrow to “Motivation P 3” shows “0.23”, and the value above the rectangle is “0.05”. The arrow to “Motivation P 8” shows “0.32”, and the value above the rectangle is “0.10”. “e 8” through “e 14” direct towards “Motivation P 7” through “Motivation P 8”, respectively. At the bottom, the oval labeled “Performance appraisal” has arrows pointing to seven rectangles. Starting from the top, the arrow to “P A P 4” shows “0.91”, and the value above the rectangle is “0.82”. The arrow to “P A P 1” shows “0.45”, and the value above the rectangle is “0.20”. The arrow to “P A P 9” shows “0.36”, and the value above the rectangle is “0.13”. The arrow to “P A P 2” shows “negative 0.42”, and the value above the rectangle is “0.17”. The arrow to “P A P 10” shows “0.63”, and the value above the rectangle is “0.40”. The arrow to “P A P 6” shows “0.35”, and the value above the rectangle is “0.12”. The arrow to “P A P 7” shows “0.45”, and the value above the rectangle is “0.20”. “e 15” through “e 21” direct towards “P A P 4” through “P A P 7”, respectively. Curved double-headed arrows connect the three latent constructs. The curved path between “Induction” and “Motivation” shows “0.03”. The curved path between “Induction” and “Performance appraisal” shows “0.03”. The curved path between “Motivation” and “Performance appraisal” shows “negative 0.04”.

CFA diagram of HRM functions: induction, PA and motivation. Source: Authors’ own work

Figure 2
A structural equation model shows induction, motivation, and performance appraisal with loadings.The figure presents a structural equation model with three oval latent constructs labeled “Induction”, “Motivation”, and “Performance appraisal”. Rectangular observed variables appear to the left of each construct. Small circular nodes labeled “e 1” through “e 21” appear to the far left, each with an arrow pointing to its corresponding rectangle. At the top, the oval labeled “Induction” has arrows pointing to seven rectangles. Starting from the top, the arrow from “Induction” to “Induction P 4” shows “0.94”, and the value above the rectangle is “0.87”. The arrow to “Induction P 5” shows “0.93”, and the value above the rectangle is “0.86”. The arrow to “Induction P 1” shows “0.75”, and the value above the rectangle is “0.56”. The arrow to “Induction P 2” shows “0.72”, and the value above the rectangle is “0.52”. The arrow to “Induction P 3” shows “0.83”, and the value above the rectangle is “0.68”. The arrow to “Induction P 7” shows “0.67”, and the value above the rectangle is “0.45”. The arrow to “Induction P 8” shows “0.21”, and the value above the rectangle is “0.04”. “e 1” through “e 7” direct towards “Induction P 4” through “Induction P 8”, respectively. In the middle, the oval labeled “Motivation” has arrows pointing to seven rectangles. Starting from the top, the arrow to “Motivation P 7” shows “0.82”, and the value above the rectangle is “0.67”. The arrow to “Motivation P 4” shows “0.75”, and the value above the rectangle is “0.57”. The arrow to “Motivation P 5” shows “0.58”, and the value above the rectangle is “0.33”. The arrow to “Motivation P 9” shows “0.45”, and the value above the rectangle is “0.20”. The arrow to “Motivation P 1” shows “0.53”, and the value above the rectangle is “0.28”. The arrow to “Motivation P 3” shows “0.23”, and the value above the rectangle is “0.05”. The arrow to “Motivation P 8” shows “0.32”, and the value above the rectangle is “0.10”. “e 8” through “e 14” direct towards “Motivation P 7” through “Motivation P 8”, respectively. At the bottom, the oval labeled “Performance appraisal” has arrows pointing to seven rectangles. Starting from the top, the arrow to “P A P 4” shows “0.91”, and the value above the rectangle is “0.82”. The arrow to “P A P 1” shows “0.45”, and the value above the rectangle is “0.20”. The arrow to “P A P 9” shows “0.36”, and the value above the rectangle is “0.13”. The arrow to “P A P 2” shows “negative 0.42”, and the value above the rectangle is “0.17”. The arrow to “P A P 10” shows “0.63”, and the value above the rectangle is “0.40”. The arrow to “P A P 6” shows “0.35”, and the value above the rectangle is “0.12”. The arrow to “P A P 7” shows “0.45”, and the value above the rectangle is “0.20”. “e 15” through “e 21” direct towards “P A P 4” through “P A P 7”, respectively. Curved double-headed arrows connect the three latent constructs. The curved path between “Induction” and “Motivation” shows “0.03”. The curved path between “Induction” and “Performance appraisal” shows “0.03”. The curved path between “Motivation” and “Performance appraisal” shows “negative 0.04”.

CFA diagram of HRM functions: induction, PA and motivation. Source: Authors’ own work

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To address potential method bias in the study, procedurally, the researchers ensured that questionnaire items were clearly worded and unambiguous, minimizing the likelihood of misinterpretation during the pilot study. Furthermore, participants were assured of the confidentiality of their responses, which can alleviate social desirability bias and encourage more honest reporting (Jordan and Troth, 2019). Statistically, CFA is also employed for the evaluation of the measurement model's fit and can aid in the identification and management of method bias (Rodríguez-Ardura and Meseguer-Artola, 2020). Moreover, consent from all parties who were interested in providing data directly or indirectly was obtained. All ethical concerns were taken into account throughout the study's sampling, data gathering, and overall process. Data was supplied anonymously to keep the private information of teachers (Creswell and Plano Clark, 2018).

Since mixed-method research questions, data are presented in both narrative and numerical forms (Teddlie and Tashakkori, 2009). Both qualitative and quantitative data analysis techniques were used to that end. SPSS version 26 was used as the statistical program to conduct the quantitative data analysis. The one-sample t-test was used to describe how teachers describe and evaluate HRM practices and problems. A MANOVA was used to compare the average variation in HRM functions among sociodemographic characteristics of respondents. For the MANOVA assumption, there were no significant differences between the covariance matrices as Box's Test of Equality of Covariance Matrices' p was 0.14. The assumption of normality and the absence of multicollinearity were met.

In this part of the study, personal information of teachers is presented in the following Table 2.

Table 2

Demographic backgrounds of respondents (teachers, N = 262)

NoCharacteristicsCategories/classificationsNo%
Schools name Abrehawoatsibha 89 34.0 
Woldeher 34 13.0 
Mertulemariam 32 12.2 
Fitawrari 44 16.8 
Tenta 33 12.6 
Sar Midir 30 11.5 
Sex/gender Male 144 55.0 
Female 118 45.0 
Age 25 years and less than 14 5.3 
26–30 65 24.8 
31–35 82 31.3 
36–40 57 21.8 
41 and above 44 16.8 
Educational status Diploma 19 7.3 
BA/BSC/BED 159 60.7 
Masters 64 24.4 
Other 20 7.6 
Work experience 5 years and less 37 14.1 
6–10 57 21.8 
11–15 61 23.3 
16–20 62 23.7 
21 years and above 45 17.2 
NoCharacteristicsCategories/classificationsNo%
Schools name Abrehawoatsibha 89 34.0 
Woldeher 34 13.0 
Mertulemariam 32 12.2 
Fitawrari 44 16.8 
Tenta 33 12.6 
Sar Midir 30 11.5 
Sex/gender Male 144 55.0 
Female 118 45.0 
Age 25 years and less than 14 5.3 
26–30 65 24.8 
31–35 82 31.3 
36–40 57 21.8 
41 and above 44 16.8 
Educational status Diploma 19 7.3 
BA/BSC/BED 159 60.7 
Masters 64 24.4 
Other 20 7.6 
Work experience 5 years and less 37 14.1 
6–10 57 21.8 
11–15 61 23.3 
16–20 62 23.7 
21 years and above 45 17.2 
Source(s): Authors’ own work

Table 2 above shows data gathered from 262 teachers working in six secondary schools. It appeared that a proportional number of sample teachers were included from each of the schools. Regarding the interview data, 12 individuals, of which 3 principals, 4 vice-principals, 4 administrative staff members and 1 supervisor, were interviewed, intentionally taking their sex/gender, administrative position, work experiences and educational status into consideration, with ages between 31 and 45 years and with over 10 years of work experience, indicating a generally experienced cohort.

  1. Practices of Selected HRM Functions (Induction, Performance Appraisal and Motivation)

As repeatedly indicated, three of the HRM functions expected to be practiced in secondary schools were induction, performance appraisal and motivating teachers. The collected data were analyzed by applying a one-sample t-test and the results obtained are presented in Table 3 as follows.

Table 3

One sample t-test on the practices of induction

HRM functionsMeanSDtdfPMean Diff
Induction practices 1.95 0.49 −34.19 261 0.000 −1.04 
PA practice 2.34 0.52 −19.98 261 0.000 −0.65 
Motivation practices 2.16 0.56 −23.94 261 0.000 −0.83790 
HRM functionsMeanSDtdfPMean Diff
Induction practices 1.95 0.49 −34.19 261 0.000 −1.04 
PA practice 2.34 0.52 −19.98 261 0.000 −0.65 
Motivation practices 2.16 0.56 −23.94 261 0.000 −0.83790 
Source(s): Authors’ own work

As indicated in Table 3 above, a one-sample t-test showed that a significant mean difference was found (t(261) = −34.19, p = 0.000) between the expected and the obtained mean. That is, the sample mean obtained (M = 1.95, SD = 0.49) was significantly lower than the expected mean (3.00). This indicates that secondary schools were practicing induction in the selected schools at the lower level. Interviewed participants have also stated clearly that HRM places a strong emphasis on making the most of people's resources to accomplish organizational (school) goals. One of the principals who was interviewed (coded as PWY2) attempted to characterize the HRM more concisely as follows.

HRM for me starts from planning to place the right person in the right place. Thus, the school requires teachers who are trained in different subject matters to teach students effectively. Once sufficient qualified teachers are available in the school, the principal, vice-principals and supervisor, and selected department heads should place them in the right way.

As indicated above in Table 3, a one-sample t-test was used to gauge the degree to which PA was practiced in secondary schools. As a result, the calculated mean (M = 2.34) and the predicted mean (M = 3) were compared. As a result, a significant mean difference was discovered (t (261) = −19.98, p = 0.000) between the calculated and predicted means. In light of this, it would appear that performance appraisals were the common practice in government organizations, including schools, independent of the many approaches, techniques, and standards used to assess the real performance levels of teachers and administrative staff members. It was possible to recognize from the interview that at least twice a year, at the midpoint of the semester and the conclusion of the academic year, schools design the assessment criteria to gauge the performance of the teachers. Similarly, members of the administrative staff were assessed using the standards provided by the civil service office.

The supervisor's (coded as SUP1) reflection regarding the practices and challenges of performance appraisal was quoted as follows: “According to my opinion, schools will occasionally fail due to a mix of well-known and obscure causes. The standard process of teaching and learning appears to be proceeding regularly, but the interest of both students and teachers in what they are teaching is greatly worn out”.

In any organization, including schools, motivation is one of the primary HRM functions. A one-sample t-test in Table 3 above indicated that a significant difference was found (t (261 = −23.94, p = 0.00) between the expected and the calculated mean. That is, the sample mean of (2.16, SD = 0.56) was significantly less than the expected mean (3.00). This indicates that the expected motivational activities were not practiced properly. According to the interviewed participants, in each of the chosen schools, there was essentially not much relevant motivational practice.

The interviewees' responses revealed that little financial rewards and paper certificates were the most effective motivation tools. The other effective motivational techniques, such as introducing facilitated promotions, providing training and development opportunities, raising salaries and establishing inspiring work environments, were not used. It was discovered as a result that motivation was not applied as effectively as anticipated in the chosen secondary schools. A demotivated teacher is less likely to behave as needed; instead, they are more likely to be hesitant, sloppy and absent from their task.

  1. Challenges of Selected HRM Functions (Induction, Performance Appraisal and Motivation)

Seemingly, practices of something are accompanied by challenges. In so doing, practicing induction, performance appraisal and motivation in secondary schools might be challenged by so many factors.

As indicated in Table 4, the one-sample t-test was conducted to compare the expected mean (3.00) related to the challenges in practicing the induction activities in the sample schools with the obtained mean from the data taken from the secondary school teachers (t (261) = 28.91, p = 0.000). This indicates that the challenges were affecting the practices of induction in the selected schools at a high level. Moreover, interviewees reacted in connection with the procedures and difficulties associated with HR (teachers and staff members) induction in their respective schools. Accordingly, an interviewee coded as VPT4 explained the challenges in such a way that:

Table 4

One sample t-test on the challenges of HRM functions

MeanSDTdfPMean Diff
Challenges of Induction 3.56 0.31 28.91 261 0.000 0.56 
Challenges of PA 3.26 0.55 7.74 261 0.000 0.26 
Challenges of Motivation 3.3918 0.58374 10.864 261 0.000 0.39179 
MeanSDTdfPMean Diff
Challenges of Induction 3.56 0.31 28.91 261 0.000 0.56 
Challenges of PA 3.26 0.55 7.74 261 0.000 0.26 
Challenges of Motivation 3.3918 0.58374 10.864 261 0.000 0.39179 
Source(s): Authors’ own work

Teachers are not interested in taking part actively in the induction program, mentors are not supporting as required and reports are copy-pasted from other teachers or other schools. Teachers consider the induction program as an overburden rather than taking it as useful and helpful for their improvement. Even the controlling and management system in this regard is simplistic and is not to the point.

The procedures and practices of performance appraisal (PA) in secondary schools are subject to several difficulties. A one-sample t-test noted that, a significant mean difference between the expected and obtained means was discovered (t (261) = 7.98, p = 0.000). This suggests that the difficulties were having a significant impact on the PA practices in the chosen schools. In addition to the aforementioned quantitative data, interviewees revealed that there were serious problems with the appraisers' knowledge and skills, the inclusion of irrelevant criteria in the tool (unrelated to education and the specific task of teachers), the absence of pre-and post-assessment discussions between the appraisers and appraisees, and the application of the same criteria for all teachers in the different carrier levels. As one of the vice-principals interviewees (coded as VPMM2) explanation, “In most cases, schools are not required to create their performance evaluation standards; instead, the district education office prepares and distributes standards for use in all schools. This may be a contributing factor to the issues”.

A single-sample t-test analysis revealed that a significant difference was found (t (261) = 10.86, p = 0.000) between the expected (M = 3.00) and obtained mean (M = 3.39). This indicates that the practices of motivation were challenged more than the expected level. Interviewees consistently stated that a variety of circumstances made motivation difficult for schools to create a motivated workforce. As one of the interviewees (Codded as SMSM2) explained that “As far as I'm aware, the school has no functional motivating activities. All things considered, our paycheck is not enough to pay for our daily expenses. We have a significant issue”.

High mean scores on items or challenges related to three main functions of HRM showed the absence of competent and supportive leadership and supervision practice, lack of effective recognition for good performances, low salary and lack of adequate benefits mechanisms, and lack of training indicate that these are the dominant constraints. This revealed that HRM problems are not only managerial but also structural, reflecting systemic limitations of the public schools.

  1. Differences among Sociodemographic Characteristics of Respondents in Practicing HRM Functions

This study as shown in the MANOVA Table 5, found that characteristics such as the teachers' educational setting (school) (Wilks' Lambda = 0.824, p = 0.745), gender (Wilks' Lambda = 0.899, p = 0.116) and other demographic characteristics of teachers do not substantially impact the linear combination of HRM functions-induction, performance appraisal and motivating teachers. All of the practices of the selected HRM functions were not significantly different in each of the selected secondary schools. All of them were found at a lower level in practicing induction, PA and motivational activities. As has been emphasized repeatedly, if all school activities are to be improved and properly carried out, good management and utilization of human resources should not be an alternative. Instead, it is probably essential to understand what is taught in schools. Every activity in schools must be managed effectively to maximize its human resource potential through the good execution of fundamental tasks. This means that schools do not need to create distinct induction procedures; a consistent induction program may efficiently meet the demands of all new recruits, independent of educational institution or teachers' gender, years of experience, education status and age. In a nutshell, the MANOVA results indicate that human resources management practices such as induction, performance appraisal, and motivation may be used uniformly across workforce categories. The multivariate analysis provides no compelling evidence to suggest distinct tactics depending on the teachers' demographic factors studied. This enables HR professionals to focus on establishing efficient, inclusive and standard programs that appropriately support every member of the workforce in the school.

Table 5

MANOVA analysis of variance in HRM function practices among sociodemographic characteristics

EffectWilks' LambdaFHypothesis dfError dfSigPartial Eta Squared
Intercept 0.003 5979.736b 3.000 55.000 0.000 0.997 
School 0.824 0.736 15.000 152.232 0.745 0.062 
Sex 0.899 2.059b 3.000 55.000 0.116 0.101 
Age 0.806 1.032 12.000 145.808 0.422 0.069 
Education 0.869 0.887 9.000 134.006 0.538 0.046 
Experience 0.911 0.438 12.000 145.808 0.946 0.031 
school * sex 0.775 0.982 15.000 152.232 0.477 0.081 
school * Age 0.580 0.689 48.000 164.378 0.934 0.166 
school * Education 0.830 0.592 18.000 156.049 0.902 0.060 
school * Experience 0.447 1.003 51.000 164.550 0.479 0.236 
sex * Age 0.786 1.158 12.000 145.808 0.319 0.077 
sex * Education 0.859 1.452b 6.000 110.000 0.202 0.073 
sex * Experience 0.752 1.379 12.000 145.808 0.182 0.090 
Age * Education 0.722 0.905 21.000 158.480 0.585 0.103 
Age * Experience 0.546 1.030 36.000 163.231 0.433 0.183 
Education * Experience 0.773 0.990 15.000 152.232 0.469 0.082 
school * sex * Age 0.871 0.652 12.000 145.808 0.794 0.045 
school * sex * Education 1.000 .b 0.000 56.000   
school * sex * Experience 0.824 0.920 12.000 145.808 0.529 0.062 
school * Age * Education 1.000 .b 0.000 56.000   
school * Age * Experience 0.904 0.474 12.000 145.808 0.927 0.033 
school * Education * Experience 1.000 .b 0.000 56.000   
sex * Age * Education 1.000 .b 0.000 56.000   
sex * Age * Experience 0.793 1.112 12.000 145.808 0.355 0.074 
sex * Education * Experience 1.000 .b 0.000 56.000   
Age * Education * Experience 1.000 .b 0.000 56.000   
school * sex * Age * Education 1.000 .b 0.000 56.000   
school * sex * Age * Experience 1.000 .b 0.000 56.000   
school * sex * Education * Exp 1.000 .b 0.000 56.000   
school * Age * Educ. * Exp 1.000 .b 0.000 56.000   
sex * Age * Educ. * Exp 1.000 .b 0.000 56.000   
school * sex * Age * Educ. * Exp 1.000 .b 0.000 56.000   
EffectWilks' LambdaFHypothesis dfError dfSigPartial Eta Squared
Intercept 0.003 5979.736b 3.000 55.000 0.000 0.997 
School 0.824 0.736 15.000 152.232 0.745 0.062 
Sex 0.899 2.059b 3.000 55.000 0.116 0.101 
Age 0.806 1.032 12.000 145.808 0.422 0.069 
Education 0.869 0.887 9.000 134.006 0.538 0.046 
Experience 0.911 0.438 12.000 145.808 0.946 0.031 
school * sex 0.775 0.982 15.000 152.232 0.477 0.081 
school * Age 0.580 0.689 48.000 164.378 0.934 0.166 
school * Education 0.830 0.592 18.000 156.049 0.902 0.060 
school * Experience 0.447 1.003 51.000 164.550 0.479 0.236 
sex * Age 0.786 1.158 12.000 145.808 0.319 0.077 
sex * Education 0.859 1.452b 6.000 110.000 0.202 0.073 
sex * Experience 0.752 1.379 12.000 145.808 0.182 0.090 
Age * Education 0.722 0.905 21.000 158.480 0.585 0.103 
Age * Experience 0.546 1.030 36.000 163.231 0.433 0.183 
Education * Experience 0.773 0.990 15.000 152.232 0.469 0.082 
school * sex * Age 0.871 0.652 12.000 145.808 0.794 0.045 
school * sex * Education 1.000 .b 0.000 56.000   
school * sex * Experience 0.824 0.920 12.000 145.808 0.529 0.062 
school * Age * Education 1.000 .b 0.000 56.000   
school * Age * Experience 0.904 0.474 12.000 145.808 0.927 0.033 
school * Education * Experience 1.000 .b 0.000 56.000   
sex * Age * Education 1.000 .b 0.000 56.000   
sex * Age * Experience 0.793 1.112 12.000 145.808 0.355 0.074 
sex * Education * Experience 1.000 .b 0.000 56.000   
Age * Education * Experience 1.000 .b 0.000 56.000   
school * sex * Age * Education 1.000 .b 0.000 56.000   
school * sex * Age * Experience 1.000 .b 0.000 56.000   
school * sex * Education * Exp 1.000 .b 0.000 56.000   
school * Age * Educ. * Exp 1.000 .b 0.000 56.000   
sex * Age * Educ. * Exp 1.000 .b 0.000 56.000   
school * sex * Age * Educ. * Exp 1.000 .b 0.000 56.000   
Source(s): Authors’ own work

Systems-thinking theory emphasizes the links that exist between the many parts of a system, and it views HR practices – such as training, performance reviews and motivation – as factors that affect people management. System theory states that HRM practices like motivation, performance evaluations and training have a big influence on employee well-being and organizational success (Botelho et al., 2023; Boxall and Purcell, 2016; Jiang and Messersmith, 2018). In a similar way, according to Ephrem (2016) and Belay et al. (2019), Ethiopia has not practiced well extensive class induction, observation and mentorship. Tadesse (2011) also undertook an in-depth examination of HRM techniques at government secondary schools in East Shoa Zone, Oromia, Ethiopia. The findings coincide with the current study's results, stressing weak induction schemes in which new teachers become inadequate orientation.

According to Akech (2016), induction programs faced challenges such as a lack of blueprint and funds, while Molalign and Gebeyehu (2019), teachers' lack of time, lack of material resources and lack of comprehension of the program were the major limiting elements of the induction program. This suggests that the induction program was not adequately used in secondary schools. To cut a long story short, secondary schools were struggling because they weren't doing induction as much as they should.

The findings of the present study also revealed that HRM functions regarding performance appraisal were not practiced to the expected level. In line with the present study, Baye (2021) investigated the practices of HRM functions associated with evaluating teachers' performance in secondary schools in Addis Ababa, which were ineffective. In all of these studies, the lack of consistent follow-up, improper feedback system, appraisers bias, the absence of a developmental plan, the use of rigid, irrelevant and less objective criteria that were mandated by higher officials, as well as other issues affecting PA practices in the study area, were all reported as the main issues with teachers' PA systems. However, Berhanu's (2024a) findings revealed that teachers' attitudes toward the PA were moderate. In a similar vein with the present study, Tadesse's (2011) study also found that poor PA methods are marked by ambiguous standards, a deficit of openness, and restricted communication. Similarly, in the local context, Cherenet and Endale (2019) found that PA was low and hindered by a variety of issues and therefore was not properly practiced in the chosen schools, necessitating substantial attention from responsible entities, including policymakers. Frehun and Tafano (2019) evaluated PA practices and problems in school contexts of the same study region Wolaita province, Ethiopia with the same goal and related issues. A similar conclusion – that PA was not effectively practiced in primary school contexts – was reached. The practice ran into problems comparable to those in secondary schools (Asegedech,2019). Nearly in the same vein, according to Asegedech (2019), Endale (2015), Frehun and Tafano (2019) and Mekonen's (2013) researches, the main challenges influencing teachers' performance appraisal practices are: inadequate training for appraisers; a lack of validity and reliability in the appraisal criteria; a lack of participation from school supervisors (department heads and unit leaders) in the performance appraisal process; a lack of continuity in assessment throughout the year; and a greater emphasis on administrative than on developmental goals.

The present study also revealed that HRM functions in motivating staff members were below the expected level due to several challenges. Seemingly, the studies conducted in the local context, in Ethiopia, reached almost similar findings. For example, in a study conducted in primary schools of Addis Ababa by Yosef (2019), low salaries, inadequate incentive methods and lack of reward for good performances, lack of recognition, poor infrastructure and school facilities, and lack of training were found to be the major factors affecting teachers' motivation and their performance. In the same issue, Kassa (2018) carried out a study in Secondary Schools taken from Northern Showa Zone, Oromia Regional State. In this study, Kassa found that characteristics of the work environment, students' disciplinary problems and administrative problems were affecting teachers' motivation. A study by Alemu (2016) and Berhanu (2024a) found that improving human managerial functions related to the motivation of teachers would increase the overall teachers' work motivation and performance. Furthermore, the results have shown that extrinsic factors were more important in motivating secondary school teachers. The same was true in the study carried out by others and in the present study too.

Both systems theory and contingency theory support the claim that motivating methods were more effective than induction. According to a systems theory, schools constitute interrelated subsystems that require harmonization of inputs (new teachers), processes (induction), and outputs (performance) in order to be successful and effective (Katz and Kahn, 1978). A limitation in schools' resources to help new teachers become productive pedagogical providers is shown by weak induction. The contingency theory also states that in public school systems with inadequate funds, supervisors favor inexpensive, readily apparent incentive techniques over more complex induction procedures (Mulkeen, 2010). Similar trends have been observed in Sub-Saharan Africa, where inadequate formal HR systems are often supplemented by incentives (Bennell and Akyeampong, 2007).

These previous studies support the current study's results that HRM functions (induction, PA and motivation) are poorly implemented in Ethiopian secondary schools, significantly impacting school effectiveness. Prior research backs up this interpretation: insufficient induction leads to high teacher turnover and struggles with adaptation (Ingersoll and Strong, 2011); ineffective PA processes limit the likelihood of feedback and ongoing professional development and school effectiveness (Malleck and Kitula, 2023; Hoque and Atheef (2024) and poor motivation practices are associated to low teacher morale and poor quality of instruction (Berhanu and Sabanci, 2019; Berhanu, 2024b; Cao et al., 2025). Nearly, in similar vein, researches from other Global South nations demonstrated that HRM systems frequently encounter similar institutional and practical challenges.

The present investigation also found that none of the demographic characteristics (sex, age, education status, experience, and school type) had a significant impact on HRM practices, implying that detrimental HRM practices hurt every teacher equally – regardless of gender, experience, or qualification. This absence of significant difference is consistent with prior investigations on HRM practice in other setting (Semela, 2014). Tessema et al. (2013) and Berhanu and Sabancı (2017) demonstrated that performance assessment in Ethiopia's public sector is frequently perceived as erroneous and subjective, limiting its efficacy in fostering development for educators regardless of gender or work experience. Limited recognition, poor remuneration and unclear career paths in Ethiopian schools all have a detrimental effect on teacher motivation (Tadesse, 2011). On the contrary, Zeru and Jita (2014) realized that in secondary school contexts, induction strategies vary from school to school and from teacher to teacher, and most schools appear informal (Zeru and Jita, 2014), which requires further studies to figure out the most significant challenges that get and determine their current state.

The general objective of this study was to explain the current practices of HRM in the Ethiopian secondary school context. As findings, one-sample t-test result showed a significant mean difference between the expected and obtained means in practicing induction. The interviewee consistently expressed that induction was not practiced to the required level due to several associated challenges, such as workload, issues with the support and mentorship systems, the inclusion of inappropriate content, role confusion, lack of sufficient resources, lack of peer support and others. The study also showed that secondary schools did not practice PA activities at the desired level due to the absence of dependable and continuous PA and timely feedback provision systems, the lack of rewards and corrective measures based on PA results, the presence of untrained and unskilled appraisers, a lack of commitment and the inclusion of irrelevant content. This was further supported by the interviewees' justifications. Moreover, the expected motivational activities were not practiced properly due to a lack of effective recognition for good performance, low pay and benefits, demotivating policy issues, a lack of positive interpersonal relationships, a demotivating work environment, a lack of professional training and development opportunities, and issues with students' academic achievements. Finally, the MANOVA analysis demonstrated that none of sociodemographic factors or their interactions has a statistically significant multivariate impact on HRM practices.

The study has theoretical, policy and practical implications. Theoretically, the difficulties encountered in induction, PA and staff motivation are not unique to Ethiopia. Similar difficulties might emerge throughout the Global South, where public sector reforms are ongoing or institutions are under constraint. As a result, this study has theoretical implications for international academics by adding to the expanding body of literature on context-specific HRM, emphasizing the importance of moving beyond Universalist methods and considering how local circumstances impact the implementation of policies.

Practically (school-level), this study has also managerial implications for Secondary School Leaders (Principals, Vice-Principals and Supervisors). As a result of this study, school administrators need to be aware of the difficulties in carrying out induction, PA, motivation and other HRM tasks effectively. To achieve this, participatory leadership techniques can be used in a way that allows all teachers and administrative staff members to participate in the successful implementation of induction, PA, motivating activities, and other initiatives. Since there were low induction and motivation levels in the present finding, creating structured mentoring programs for induction, specific recognition schemes for recognizing and rewarding diverse contributions, and structured peer groups facilitated by senior staff members to combat isolation and enforce motivation are recommended. These insights might help HR practitioners who operate in similar constrained circumstances establish more sensible and flexible HR practices.

This study also has recommendations to policymakers, society and higher education officers and experts. In Ethiopia, the stated objectives remain as paperwork rather than being feasible in practice without proper and effective handling and management of human resources. To incorporate pertinent information in each of the HRM functions, policymakers and subject-matter specialists who are involved in the creation of the necessary HRM functions can take into account the real contexts of secondary schools in advance. As a result, it's critical to frequently update the contents to keep them relevant to the scenario at hand. Furthermore, it is preferable to offer schools a chance to think about their circumstances as opposed to being dogmatic and set in their ways while applying the advice given in policy documents. For instance, schools should have their own PA criteria (that can be applied based on a teacher's career level), induction materials (related to enhancing teaching and learning) and incentive mechanisms, even if general conditions remain constant.

There were certain limitations encountered throughout this research; nevertheless, these were handled with prudence to prevent harming the study's overall findings. In order to conform to socially or professionally desired standards, school leaders may have overreported their HRM practice, which might have resulted in social desirability bias due to the reliance on self-reporting information. Future scholars might use anonymous surveys and longitudinal research to mitigate social desirability bias. Even though multilevel modeling is a good analytical strategy, in the present study, the small number of schools did not offer adequate statistical power at Level 2. Future studies with larger school samples employ multilevel modeling to better capture school-level variation. In the current study, induction, performance evaluation, and motivation were three HRM tasks that were specifically targeted at public secondary schools in one district. On the one hand, the short research completion period and the absence of funding because the study was self-sponsored were the causes of this. Therefore, additional research may call for including other schools (both primary and secondary) from various districts in the various provinces to get thorough results that can allow for generalizations. Besides, further research may be necessary to determine whether the respondents' potential characteristics will differ significantly across the HRM functions. Finally, future studies could extend this work by considering longitudinal design, multi-level models and comparative studies across districts/countries.

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