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Abstract

High unemployment rates in many developing countries have prompted research to focus on sustainability and inclusiveness of employment in line with the sustainable development goal target 8.6. The democratic republic of Congo (DRC) in sub-Saharan Africa experiences one of the highest youth unemployment rates owing to its long-term socio-political and economic instability. Qualitative and quantitative research studies have linked low employability of university-educated youth in the DRC to tribalism, corruption, and insecurity. The current study sought to identify contextual factors that predict employability of information technology (IT) graduates in the DRC. The study surveyed 355 graduates (274 male, 81 female) using 43 questionnaire. Bartlett test was 3,930.05 for Chi² (p-value = .000) and KMO test scored 0.68. McDonald’s Omega test of reliability of the instrument scored 0.77 with a total cumulative variance of 72.02%. Results of this study advance the conflict theory by pinpointing the true factors which influence the employability of IT graduates in unstable developing countries. This study has discovered that socio-political background of graduates, graduate academic competencies, graduate-employer relationship, and university employability strategies are the contextual factors that predict the employability of IT graduates in the DRC. The Cronbach’s Alpha test of reliability for the retained contextual factors scored .78, .75, .63, and .53, respectively.

Keywords

contextual factors factor analysis IT graduate employability Democratic Republic of Congo

Introduction

Many developing countries experience flawed processes of recruiting the youth into public and private services. Due to high nepotism, skilled and talented youth fail to secure employment, especially in marginalized communities and ethnic groups where individuals are unable to get elected or appointed into government administrative positions (Beiser-McGrath et al., 2021). In the Democratic Republic of Congo (DRC), issues such as sexual harassment of young female employees, tribalism in employment, and the politicizing of public state services are rampant. This has caused the search for employment to be increasingly difficult for youth groups in the DRC. Thus, although the youth contribute greatly to both formal or informal political and socio-cultural domains in the DRC, a large proportion of highly qualified youth fail to get hired into public administrative positions. Corruption in the public office has been identified to be a significant hinderance to youth employability (Mpia et al., 2022).

This study applied descriptive data mining using exploratory factor analysis (EFA) to identify factors for predicting the employability of information technology (IT) graduates in the DRC, an unstable developing country. The aim of the study was to identify contextual factors that influence employability through assessment of a large cluster of potential variables. The study advances scientific theory with variables that students in the DRC can consider when enrolling into IT courses. The philosophical principles of EFA which this study has used were first applied systematically by Spearman and have become fundamental tools for the evaluation of theories and validation of measurement instruments in research (Izquierdo et al., 2014; Watkins, 2018) to produce an original theory from data collection to phenomena, theory and understanding of social facts (Haig, 2014).

Numerous factors have been pointed out in the literature as predictors of employability. Nevertheless, no research to date has conducted EFA to obtain factors that can consistently predict IT graduates’ employability in the DRC (Mpia et al., 2023). This research sought to understand whether there exists a cluster of factors which can directly or indirectly affect the employability of IT graduates. Past research in African countries suggests that although employability of individuals can vary according to factors such as socioeconomic background, the school attended, and the ethnic origin, the skills acquired always play a predominant role in securing employment (G. M. Lundberg et al., 2021; Okolie et al., 2020). This presents useful knowledge, but the past studies have relied on qualitative and/or quantitative descriptive research without considering the EFA approach for identifying a consistent set of factors. This constitutes a gap when predicting employability in the DRC, given the many possible factors that can be considered in the analysis (Mpia et al., 2022). In addition, the factors proposed in the existing literature are not uniform. None of the previous studies (i.e., Etshim, 2017; Igwe et al., 2022; Ngoy, 2018; Okolie et al., 2020; Sikirivwa et al., 2021; Uddin, 2021) has observed employability-related factors for a specific group of individuals, such as for the graduates of IT. This makes it difficult to contextualize the factors identified in the past literature and requires a more focused investigation.

The rate of unemployed youth in the DRC has increased exponentially (Mpia et al., 2022), partly owing to the lack of robust policy-advisor research. Investigation and communication of consistent and reliable factors may mitigate the issue of youth employment in the DRC and other similar countries. The DRC, for example, has recently recorded a rate of 50% youth unemployment (Mpia et al., 2022). Such high rates limit the achievement of sustainable development goal (SDG) target 8.6 to reduce the rate of youth unemployment (Kreinin & Aigner, 2022). This study sought to address the gap in the existing literature by identifying the contextual factors that can be used to predict employability in the above-mentioned context and constructing a framework which illustrates the relationship between the factors and the employability of IT graduates in the DRC in order to propose a generalizable theory for the same social context. Specifically, this study had the following objectives:

(a) To identify factors from the existing literature that predict employability in the DRC,

(b) To develop a reliable framework with contextual factors that predict employability of IT graduates in the DRC.

The subsequent sections of this study are as follows: Section 2 conducts a theoretical and empirical literature review on the factors that have been identified in previous studies for predicting employability in the DRC. Section 3 outlines the methodology of conducting the study. Results from the EFA are presented in Section 4. Section 5 discusses the results and demonstrates the novelty of the study. Finally, Section 6 summarizes the main study outcomes and presents the research limitations while making recommendations for future research.

Literature Review

Sustainable Development Goal Target 8.6

SDG target 8.6 is an integral component of SDG 8, which aims to ensure sustainable economic growth by providing decent and productive work to all individuals (Rai et al., 2019). The initial vision of this target was to substantially reduce by 2020 the rate of youths who are not employed, have not received any education, and are not trained (NEET). The decent work agenda is based on four main values that are human dignity, freedom, security and equity. Nevertheless, African countries have the highest unemployment rate in the world with an average of 7% and the majority of those who are employed in Africa have low-paying jobs (Demeke, 2022). The rate of NEET in Africa is still high. For example, Zambia has 43% of youths NEET, Rwanda and Malawi have 33% each, the DRC has 50%, and Kenya has 14% (Mpia et al., 2022).

Theoretical Framework of Employability

Mgaiwa (2021) defines employability as the development of new skills through university-industry partnerships, strengthening of quality assurance systems and alignment of university education with a country’s development plans to provide human capital that is endowed with intelligence, ability, and capacity, and ready for employment. Two main theories that can help researchers to understand employability are Conflict theory and Agency theory (Davis et al., 2021; Ferrare & Phillippo, 2023).

Conflict Theory

This theory purports employability to be a mechanism for legitimizing inequality of opportunities in the educational system and the labor market in a capitalist society (Pelke, 2020). In addition, conflict theory views conflict as inherent in society because it is the foundation of social order and academic institutions are instances of the reproduction of social inequality such as ethno-racial stratification and class conflict (Chernoff, 2013). In this sense, education is an integral part of both cultural and social reproduction and thus a social component that is linked to the capital based on the opportunities offered to students in society, providing them with knowledge of the world derived from experience (Dworkin et al., 2013). The skills that make a graduate employable emanate from the teamwork between universities and employers. These two players have the mission to develop strategies to match the graduate’s skills with the needs of the labor market since the employer has the right to assume the responsibility of contributing to the enhancement of skills of graduates (Selvadurai et al., 2012).

Conflict theory is derived from Karl Marx’s philosophy and is useful for investigating social inequalities owing to its basis on materialism. Conflict theory has been used by many universities to adjust curriculum programs in order to equip the students with relevant skills that make them competitive in the labor market (Campbell, 2021). This has resulted in self-efficacy among students which has increased employment possibilities irrespective of differences in the backgrounds of graduates. Furthermore, conflict theory helps to build students’ self-confidence in their learning abilities despite their social drawbacks. Students develop interest in academic activities, are generally proactive, and acquire the skills required in their field to be employable (Amin & Rajadurai, 2018).

Agency Theory

The notion of agency theory, which originated in the field of economics, has been widely applied in different scientific disciplines. However, this notion is relatively new for understanding graduate employability. Agency theory was intended to be an analytical framework for contractual relationships between two or more parties in which one party, the principal, engages another party, the agent, to perform a service on behalf of the principal (Wohlfart et al., 2021). In agency theory, self-interest is the motivation of an individual. This leads to the agency problem. Concretely, agency theory has been included in employability as one of the approaches to incorporate the problems of incentives, the mechanisms of adopting incentives for firms and the conflicts of interest (J. Lundberg, 2022).

Related Works

Employability in the DRC continues to draw the attention of scholars in both social and economic sciences and information systems. Etshim (2017), for example, was able to address this issue from the perspective of how to deepen the collaboration between higher education practitioners and employers in the DRC to ensure an adequate transition from school to the labor market. In addition to the security threats and socio-political issues, many youths in the DRC remain unemployed. Those with jobs are mostly in informal employment and remain vulnerable to work-related instabilities. Research has identified the lack of the bilateral collaboration between higher education trainers and industry employers (Etshim, 2017) in defining relevant curricula as the main factor that influence employability in the DRC (Awad, 2020; Etshim, 2017). It has also been discovered that inadequate human capital, weak employability assets, and low levels of education are factors that influence employability in the DRC (Awad, 2020).

A recent survey of graduates in the DRC has identified several inconsistencies in the recruitment process (Okiemy & Etsiba, 2021). Some of the aspects cited include a disparity between the levels of skills and competencies of job applicants and the what employers eventually settle for. Social facts such as tribalism, favoritism, and corruption have been identified as prominent in choices of employment. However, Rubbers (2020) argues that the low employment rate in the DRC has stemmed from the 2002 civil war which destroyed all the infrastructure and social fabric of the country. In addition, the widespread looting that characterized 1990 caused the country’s GDP to decrease by 7% and contributed to the decadence of the labor market.

On the other hand, several scholars have identified tribalism as highly influential to job acquisition in the DRC (Calderon et al., 2021; Trefon, 2010). In Katanga, the province of former DRC president Joseph Kabila, for example, it was common for natives to exclude non-natives when offering job positions during his reign (Englebert et al., 2018). According to Alcorta et al. (2020), ethnicity has a prominent influence political and administrative aspects of in African countries. Ngoy-Kangoy (2008) has discovered ethnicity to be the main source of intolerance during elections in the DRC, since notions such as regionalism and provincialism fuel conflict and social discrimination. Ngoy (2018) argues that the public sector in the DRC provides employment opportunities for young people. However, two major factors complicate the recruitment process: (1) the conflict of generations and (2) the practice of referral of certain youths to public service positions by relatives with political mileage or those with executive job positions in the public sector. The current study sought to objectively investigate the influential employment factors by conducting empirical research through factor analysis in order to obtain valid and reliable outcomes of the employment criterion in the DRC, a country with multiple socio-political variances (Yigzaw, 2019).

Materials and Methods

The scope of this research advised the choice of methodology and tools to make possible an empirical study in adherence to systematic guidelines from data collection to the reporting of research results (Apuke, 2017).

Research Design

This study adopted a quantitative research approach for four reasons. First, quantitative methodology produces analytical data that are objective (Abuhamda et al., 2021). Second, it allows to investigate why and how phenomena can vary in the society (Tavakol & Sandars, 2014). Third, quantitative methodology is based on reliable processes and measurements. Fourth, it offers the possibility for generalization of results to similar cases and populations (Abuhamda et al., 2021).

Social research derives value when it is conducted based on a research paradigm. In this research, positivism was used as philosophy to conduct methodologically the EFA on the employability of IT graduates in the DRC (Majeed, 2019). Since social facts are difficult to capture objectively, the authors used EFA to identify similar clusters of variables through the extraction and rotation of factors. This technique is an effective tool for analyzing the relationships between observed variables and reducing the number of underlying factors (Hadi et al., 2016).

Survey research (Saunders et al., 2019) was employed to obtain primary data using questionnaires (Erdil et al., 2021). The technique was preferred primarily owing to its wide and successful adoption in social sciences, management, marketing, and IS. Primary data was then collected from IT graduates in the DRC. The approach of getting primary data is more objective and reliable (Kabir, 2016). Survey participants were graduates who have completed an undergraduate or Masters’ degree in IT and reside in the DRC. The study extracted key variables such as Gender, Linguistic origin, Type of University, Religion, Academic Grade, Province of origin, and whether or not their existed conflict in the region where the graduate was born.

The questionnaire consisted of 43 items. The items were designed with a basis from systematic review of existing literature (Mpia et al., 2023). Participants were surveyed in three sections. The first section contained three questions to assess the participant’s demographics. The second section contained 25 research questions on the respondent’s socio-economic-political background, and the last section addressed the participant’s scientific skills and background in 15 questions. Twenty-two of the questions were dyadic, nine questions used on a 3-point Likert scale, seven questions used a 5-point Likert scale, two questions were on a 11-point Likert scale, and the remaining questions ranged between the 6-point, 7-point and 10-point Likert scales.

Sampling Procedures

A sample represents a segment of population chosen for research (Masrai, 2022). The study applied disproportional stratified sampling techniques by choosing randomly members of each stratum (Iliyasu & Etikan, 2021). Primary data were gathered from respondents in the four linguistic zones of the DRC, that is, Kikongo, Lingala, Tshiluba, and Kiswahili (Mpia et al., 2022). Survey individuals had completed a Bachelor’s and/or Master’s degree in IT in the DRC and were employable. The research survey instrument was deployed using a Google form that was distributed through email with linking by lecturers and alumni offices of universities or conveniently in a snowball approach of friends’ networks.

The universities in the DRC that were sought for the survey study included Université de Kinshasa, Université Pédagogique Nationale/Kinshasa, ISIPA/Kinshasa, ISIPA/Matadi, Université Shalom de Bunia, ISP/Bunia, Université Notre-Dame du Kasayi, ISC/Gombe, ISC/Beni, ISP/Mbanza-Ngungu, Université de Mbandaka, ISP/Bukavu, and Université de l’Assomption au Congo. Further, participants were also sought via LinkedIn, one of the most used employer platforms in the world (Kozłowski et al., 2021). The authors also used a database of their former students who had successfully graduated and engaged social media platforms such as Facebook and WeChat, and collaborative tools such as WhatsApp groups to recruit participants for the survey. These actions cumulatively helped to acquire a sufficient and diverse sample.

A total sample size of 421 graduates in IT was collected from the four linguistic zones of the DRC. The largest proportion of response was obtained by participants in Kinshasa, the capital city, owing to the large population of residents and ample employment opportunities. The capital offers optimal conditions for the professional integration of graduates and companies from all sectors of the economy. It is a cosmopolitan city where all the nationals of all the provinces are represented (Nsokimieno et al., 2014). The 43 items discussed in this research are listed in Table 1.

Table 1.

Congolese IT Graduates’ Dataset Characteristic.

Feature	Data field	Coding format
AFS	Age on completion of IT studies	1 = between 21 and 23 years, 2 = between 23 and 25 years, 3 = between 25 and 27 years, 4 = between 27 and 29 years, 5 = between 29 and 31 years, 6 = 32 years and older
Gender	Gender of the Graduate	0 = Female, 1 = Male
MS	Marital Status	0 = Single, 1 = Married
Origin	Linguistic and ethnic origin of the Graduate	1 = Kikongo, 2 = Tshiluba, 3 = Lingala, 4 = Kiswahili, 5 = Other
POB	Place of born	1 = Capital City (Kinshasa), 2 = Town, 3 = Rural municipality, 4 = Village, 5 = Abroad
LocLang	Number of local languages known by the Graduate	1 = One, 2 = Two, 3 = Three, 4 = Four, 5 = More
IntLang	Number of international languages known by the Graduate	1 = One,2=Two, 3 = Three, 4 = Four, 5 = More
BornWar	Existing war in the zone where the IT Graduate was born	0 = No, 1 = Yes
Religion	The religion (Doctrine) of the Graduate	1 = Catholic, 2 = Protestant, 3 = Pentecost, 4 = Evangelical, 5 = Kimbanguist, 6 = Anglican, 7 = Other
SecondarySchool	Secondary subjects studied	1 = Math-Physics, 2 = Biochemistry, 3 = General Pedagogy, 4 = Latin-Philosophy, 5 = Commerce, 6 = Industrial Skills, 7 = Agricultural Skills, 8 = Secretariat and Administrative, 9 = Professional Skills, 10 = IT Skills, 11 = Social Skills
NationalExamGrade	Grade obtained in the national examination	1 = Satisfactory (50%–69%), 2 = Distinction (70%–79%),3 = Great Distinction (80%–89%)
Degree	Degree of education in IT	0 = Bachelor, 1 = Master
Specialization	Specialization studies in IT	1 = Networking, 2 = Information Systems Management,3 = IT security,4 = Networking and Telecommunication, 5 = Mathematics and Computing, 6 = Software engineering, 7 = Computer Engineering, 8 = Artificial Intelligence, 9 = Business and IT, 10 = Data Science
GradeIT	Grade obtained after Higher Education Studies In IT	1 = Satisfactory (50%−69%), 2 = Distinction (70%−79%), 3 = Great Distinction (80%−89%)
UniversityType	Type of university attended	1 = Public, 2 = Private, 3 = Catholic, 4 = Protestant, 5 = Other
InstitutionNature	Nature of institution attended	0 = College, 1 = University
InstitutionLocation	Location of the institution attended by the IT Graduate	1 = Capital City (Kinshasa), 2 = Town, 3 = Rural municipality, 4 = Village, 5 = Abroad
SchoolWar	Existing war in the zone where the Graduate did his/her IT studies	0 = No, 1 = Yes
Fees	The average academic fee paid by the graduate per year when being student	1 =Less than $500, 2 = Between $500 and $1,000, 3 =More than $1,000
Funding	Source of funding for academic fees when studying	1 = Government scholarship, 2 = Non-Government scholarship, 3 = Family Finances, 4= Parents’ finances, 5 = Own funds
UniversityReliable	Reliability of the attended university in terms of research	0 = No, 1 = Yes
UnivPromoteEmploy	The attended university promotes the employability of IT students	0 = No, 1 = Yes
Internship	The university attended has linkage with companies in terms of students’ internships	0 = No, 1 = Yes
ExperienceYears	Number of years of experience required by companies in order to be employed	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 = Nothing
SpecialisationCompetence	Level of competence in the Graduate’s IT specialization	1 = Below average, 2 = Average, 3=Mastery
ITCompetence	Level of general competence in IT	1 = Below average, 2 = Average, 3=Mastery
GraduateComSkill	Level of communication skills	1 = Poor, 2=Medium, 3=High
PhysicalDisability	The Graduate has a physical disability	0 = No, 1 = Yes
PsychoDisability	The Graduate has a psychological disability	0 = No, 1 = Yes
SleepingTime	Time of sleeping at night	1 = Before 10 pm, 2=Between 10 pm and midnight, 3 = After midnight
Family	The financial situation of the Graduate’s family	1 = Poor, 2 = Medium, 3 = Rich
Father	The Graduate’s father is employed	0 = No, 1 = Yes
Mother	The Graduate’s mother is employed	0 = No, 1 = Yes
PoliticianFam	The IT Graduate’s family has relatives in politics	0 = No, 1 = Yes
AdminstrativeFam	The IT Graduate’s family has relatives who work in public administration	0 = No, 1 = Yes
CompanyFam	The IT Graduate’s family has relatives who work with private companies	0 = No, 1 = Yes
Relatives	The IT Graduate knows someone close to him/her who owns a company	0 = No, 1 = Yes
MaritalRequirement	Required marital status by companies to employ the Graduate	1 = Single, 2 = Married, 3 = Nothing
HomePandemic	Pandemic (Covid-19) experience in the home region of the Graduate since he/she graduated from university	0 = No, 1 = Yes
HomeEpidemic	Epidemic (Ebola, Cholera, etc.) experience in the home region of the Graduate since he/she graduated from university	0 = No, 1 = Yes
SchoolPandemic	The region where the former university of the graduate is located has been affected by the COVID-19 pandemic	0 = No, 1 = Yes
SchoolEpidemic	The region where the former university of the Graduate is located has been affected by an epidemic (Ebola, Cholera, etc.)	0 = No, 1 = Yes
Employed	The Graduate is already employed	0 = No, 1 = Yes

Assessment of the Study Survey Instrument

Assessment of data employed reliability and validity analysis, and EFA. To establish validity, the survey instrument was modified after being verified by five senior academics who were experts in statistics or data analysis. Using feedback from the experts, the study reviewed the question on the linguistic and ethnic origin of participants by including “Other” as a choice beside the fourth linguistic zone. This was because some residents of the DRC, such as Batetela and Basongwe do not consider themselves as belonging to the four identified linguistic zones (Turner, 1993). Furthermore, the item “Age at completion of IT studies” was modified to represent as a range of values the previously numeric data. This provided a level of confidentiality regarding the actual ages of survey participants.

The appropriateness of data for EFA was measured using Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests of sphericity. The assumption of Bartlett’s test is that all studied variables are correlated with themselves (Persson & Khojasteh, 2021). The alternative hypothesis was observed instead, indicating that the correlation matrix contained significant information and that the factor analysis could be conducted (Jowkar et al., 2013).

Reliability in research refers to the quality of measurements procedures that provides repeatability and accuracy of the obtained results (Ciampolini et al., 2021). The survey study employed McDonald’s omega test to measure the reliability of the internal consistency of survey items. This test has been found to be more reliable than Cronbach’s Alpha test (Crutzen & Peters, 2017). The following formula performs coefficient omega test with continuous variables using a single-factor model (Flora, 2020):

ω = \frac{{(\sum_{i = 1}^{i} λ_{i})}^{2}}{σ_{X}^{2}}

where, ω refers to the estimated McDonald’s omega coefficient value that measures reliability, $λ_{i}$ is the factor loading of standardized indicators to -which, $σ_{X}^{2}$ is the variance of the total score across all questions and X is the number of items in the questionnaire. Scipy was applied as pythonic library to verify the absence of skewness and the kurtosis in testing the EFA assumptions.

Data pre-processing was carried out using the Pandas library. This involved removing missing values and checking for outliers. First, all study variable names and data were converted to English to uniformly present responses. The second step involved imputing of missing data. A total of 355 records out of the initial sample size of 421 (i.e., 15.68% imputed) remained after pre-processing.

To ensure the quality of imputed data, the processing step involved observation of mean dissimilarity using normalized Euclidean distance between the originally collected and the imputed data (Aleryani et al., 2020). FactorAnalizer and Statsmodels Python libraries were applied to conduct EFA of the data collected from the survey forms. The approach used minimum residual (minres) for factor extraction (Persson & Khojasteh, 2021). EFA was used to obtain information about the relationship between survey items. EFA studies the number factors between the collected items and which item determines which factors (Orçan, 2018).

Following a recommendation by Mpia et al. (2022), the study retained only those factors that had a Cronbach’s Alpha value >.5 to ensure the relevance of the model (See Table 2). All items with no connection to the loaded factors were removed from the dataset. Items related with many loaded factors were also removed in order to prevent the cross-loading problem and to improve the validity of discriminant (Sim et al., 2021). To ensure the quality and the reliability of the obtained factors, the researcher deleted all the items that had a lower factor loading than 0.4. Finally, EFA was repeated to exclude all the imputed items from the dataset (Mya et al., 2021). The reliability of the factors obtained after re-running EFA were tested using Cronbach’s Alpha and only the factors with an Alpha value of .5 or higher were retained (Selvaraj et al., 2022). Findings using Kaiser criterion and scree plot indicated 14 factors as suitable for extraction from the 42 variables (excluding the target) which constituted the columns of the dataset (Mya et al., 2021). Subsequent reruns resulted in a final list of four reliable factors which were related to 11 study variables. The results of the new factor loading related to the initial variables still using Varimax method are shown in Table 3.

Table 2.

Extracted Factors.

Factor	Eigenvalue	Explained variance	% of variance	Cumulative variance	Cronbach’ alpha
Factor 1	2.674383	1.900035	11.18	11.18	.78
Factor 2	2.282724	1.377413	8.1	19.28	.63
Factor 3	1.605185	1.315338	7.74	27.02	.75
Factor 4	3.965321	2.026383	44.99	72.02	.53

Note. Extraction Method: “Minimum residual.” Rotation Method: “Varimax.”

Table 3.

Correlation Between Items and Retained Factors.

Factor	Related Items	Variable	Item No.	Factor Load
1	Existing war in the zone where the IT Graduate was born	BornWar	8	0.842393
	Location of the institution attended by the IT Graduate	InstitutionLocation	17	0.611087
	Existing war in the zone where the Graduate did his/her IT studies	SchoolWar	18	0.757237
2	The financial situation of the IT Graduate’s family	Family	31	0.430255
	The IT Graduate’s family has politicians as relatives	PoliticianFam	34	0.589666
	The IT Graduate’s family has relatives who work in public administration	AdminstrativeFam	35	0.681442
	The IT Graduate’s family has relatives who work in private companies	CompanyFam	36	0.498374
3	Level of competence in the Graduate’s IT specialization	SpecialisationCompetence	25	0.861008
3	Level of general competence in IT	ITCompetence	26	0.708593
4	The attended university promotes the employability of IT students	UnivPromoteEmploy	22	0.528774
4	The university attended has linkage with companies in terms of students’ internships	Internship	23	0.666402

Results

Respondents’ Characteristics

Demographic results on respondent profiles such as gender, age, and marital status are presented in Table 4 below:

Table 4.

Participant Demographics Data.

Characteristics	Frequency	Percentage (%)
AFS (n = 355)
Between 21 and 23 years	71	20.00
Between 23 and 25 years	122	34.37
Between 25 and 27 years	65	18.31
Between 27 and 29 years	43	12.11
Between 29 and 31	32	9.01
32 years and older	22	6.20
Gender (n = 355)
Female	81	22.82
Male	274	77.18
MS (n = 355)
Single	309	87.04
Married	46	12.96

The results revealed that out of the sample of 355 records collected using disproportional stratified sampling technique, there were 153 IT graduates in Kiswahili zone, 83 in Kikongo zone, 37 in Tshiluba zone, 39 in Lingala zone, and 43 in a non-labeled zone. The following figure describes the percentage and proportion of each stratum:

Table 5 contains means (M) and standard deviations (SD) of three demographic variables grouped by geographic origin: The variable AFS recorded in Tshiluba region had the highest mean (3.32) while the variable MS recorded the lowest mean among the Kikongo linguistic group. This variable also had the highest deviation from the mean.

Table 5.

Descriptive Statistics of the Strata and Demographic Characteristics of Study Respondents.

	Kiswahili		Kikongo		Tshiluba		Lingala		Other
	M	SD	M	SD	M	SD	M	SD	M	SD
AFS	2.48	1.31	2.88	1.67	3.32	1.39	2.97	1.48	2.70	1.41
GENDER	0.72	0.45	0.81	0.40	0.73	0.45	0.79	0.41	0.88	0.32
MS	0.18	0.38	0.12	0.33	0.05	0.23	0.10	0.31	0.07	0.26

The following figure illustrates graphically the correlation between the above descriptive statistics of the strata and demographic characteristics of study respondents:

Check of Data Distribution Against Assumptions for EFA

The McDonald’s omega test was conducted using the psych package of the R statistical software.

Some items from the dataset were found to be negatively correlated with the total scale, and the procedure, that is, omega (dataset, check. Keys = True), where dataset is the name of the dataset and omega is the function from psych, was used to reverse them. This yielded a final result of 0.74 for the hierarchical omega and 0.77 for the total omega. This assured of high reliability in the survey instrument and showed that the questionnaire was internally consistent. Since factor analysis is used to understand hidden patterns of relationships between dependent variables, the researchers were able to remove the Employed variable, which was the dependent variable (Watkins, 2018), from the study before performing EFA. Thus, they performed the EFA only with the 42 independent variables.

First, the authors had to perform the KMO and Bartlett tests to verify the feasibility of EFA study and to see if the available data is appropriate for conducting the EFA. Hence, the overall KMO for the data was 0.68, which is mediocre but acceptable as indicator (Younis et al., 2021). This value indicates that the researchers can proceed with the planned factor analysis. Bartlett’s test was significant at the p = .0 level. This allowed them to reject the null hypothesis that the matrix was identical.

Extracted Factors

Ordóñez et al. (2021) state that the total cumulative variance percentage is one of the decisive criteria in deciding the number of factors to retain. For this study, the total cumulative variance scored 72.02%.

Factor and Variables Relating to the Employability

Following the rule of thumb (Effendi et al., 2019), the study retained only the variables with a factor loading >0.4 knowing that the dataset had 42 items, as the target (Employed column) was removed. The results of factor loadings are shown in column 5 of Table 3:

Exploratory Factor Analysis Model

The authors constructed then the following factor analysis model depicting contextual factors that predict employability of IT graduates in the DRC. Figure 3 shows, for each retained factor, which study variables are explained by the same factor. These variables are associated with their absolute factor loadings which are greater than or equal to a threshold value that the researcher defined (by default ≥ 0.4) (Mpia et al., 2022). Figure 1 illustrates proportion of each stratum, figure 2 shows the correlation between demographic variables and research strata.

Figure 1.

Proportion and percentage of each stratum: (a) illustrates the proportion of each stratum including the non-identified stratum (other) and (b) the corresponding percentage of each proportion.

Figure 2.

Proportion of strata grouped by demographic characteristics of study respondents: (a) shows the proportion of respondents by age on completion of IT studies grouped by stratum, (b) illustrates the proportion of participants by gender grouped by stratum, while (c) depicts the proportion of participants by marital status grouped by stratum.

Figure 3.

Built exploratory factor analysis model.

Discussion

Employability in Sub-Saharan Africa is marked by several socio-political factors that cannot be ignored. Although the graduate can be competitively strong, the reality according to several studies has revealed that factors such as tribalism, corruption, and favoritism characterize this social fact (Igwe et al., 2022; Okolie et al., 2020; Uddin, 2021). Sikirivwa et al. (2021) stated that the DRC is marked more by tribalism, protracted armed conflict, nepotism, and mismanagement of public assets. Etshim (2017) talked about corruption in the process of hiring graduates, the lack of collaboration between employers and universities, and the underfunding research and education as factors affecting employability of graduates. Ngoy (2018) emphasized that favoritism influences a lot employability. Whilst Okiemy and Etsiba (2021) concluded that favoritism, corruption, and tribalism constitute the main factors that employers use to hire young Congolese. While in this study, the researchers obtained almost the same reality as the predecessors with the difference of tribalism. Tribalism was not noted in this research. Only three factors were retained that proved to be valid and reliable in the employability of IT graduates in the DRC. This study concluded that from the previous studies, only favoritism can be said to be consistent factor regarding to the results from Ngoy (2018) and Okiemy and Etsiba (2021) as the current study has retained also graduate-employer relationship as factor, which is related to favoritism. Furthermore, this study has retained, through the EFA process, four factors that predict Congolese IT graduates that are socio-political background, graduate academic competencies, socio-political backgrounds of the graduate, and university employability strategies.

Based on the Table 3 above, the research observed that Factor 1 is suggesting social and security situations of the area of the IT graduate. This factor is related to situation of war where the graduate was born and where he/she studied and the location where the attended university by the graduate is located, if capital city, town, abroad, or village. Thus, this factor was labeled Socio-political background. This means that a graduate who was born in capital city or in town and who was born and studied in a peace zone has more chance to be employed than others. Factor 2 appears to suggest favoritism, clientelism, and nepotism. It is more related to the financial background of the graduate’s family, the networking aspect of the graduate and employers. Therefore, this second factor was characterized as Graduate-employer relationship. This justifies perhaps why past research and the common saying state that there is tribalism as nepotism and clientelism can be, in some extent, confused with tribalism. In this sense, a graduate is likely prompt to be employed after his/her graduation than others when he/she has friends and relatives who are employed either in public or private companies. Moreover, belonging to a rich family can enable a graduate to obtain job as he or she will get the opportunities of attending universities in the capital city, or in town even abroad.

Factor 3 inversely is more related to the personal skills acquired by the graduate while still IT student. This factor is suggesting general IT competencies and IT specialization competencies that a graduate possesses. Hence, the authors labeled it Graduate academic competencies. This signifies that despite the lack of relationships between the graduate and employers, a graduate even if he/she was born or studied in war zone can still be employed if he/she has needed competencies in the labor market. This factor seems to be the second high reliable after the first factor as it scored a test of reliability of 0.75 beside that of the first factor which scored 0.78. Factor 4 refers to the strategies a university implements during the academic career of IT students. Specifically, this factor is linked to the variables UnivPromoteEmploy and Internship, which respectively indicate whether or not the university in which the IT graduate attended promotes employability and whether or not this university insists on internships for such students. Thus, the authors labeled it university employability strategies factor.

The results from this study revealed that IT graduates’ employability is predominantly related to socio-political crises and insecurities which discourage investors in the DRC than to the skills acquired by the graduates. However, the researchers do not support the use of conflict theory in employability if one wants to solve efficiently the problem of graduate employability and to achieve then the SDG target 8.6. In fact, this theory does not enable everyone to obtain job or to succeed in the society despite his or her skills. This theory seems to legitimate conflict and then promote only those who have power in the society to continue ruling and those with poor socio-political and economic background to continue to be ruled and unemployed (Nirmala, 2021). It states that Students who attend less affluent public schools will never have the advantage of being employed after graduation and are therefore likely to be directed to low-paying manual occupations while those who study in private or affluent schools will always be employed in large companies with good salaries (Mishra, 2013). While SDG target 8.6 promotes decent job for all without any distinction of race, gender, and origin (Rai et al., 2019).

Conclusion, Contributions, Limitations, and Recommendations

This study has discovered that socio-political background of graduates, graduate academic competencies, graduate-employer relationship, and university employability strategies are the contextual factors that predict the employability of IT graduates in the DRC. The Cronbach’s Alpha test of reliability for the retained contextual factors scored respectively .78, .75, .63, and .53. The results of this research have shown a strong association between the socio-political backgrounds, the graduate academic competencies (personal skills), graduate-employer relationship, and university employability strategies and the employability, while tribalism, ethnic origin, nature of the university (public or private) where the graduate studied and many other factors pointed out in previous studies were not shown to have an association with the employability of IT graduates in the DRC. These findings indicate that graduate-employer relationship (favoritism, clientelism, nepotism), socio-political backgrounds (the place where the graduate was born, the location of the attended university if located in capital city, town, village, or abroad, and the existing war or not in the area where the graduate studied), the graduate academic competencies (personal skills), and university employability strategies (the attended university promote employability of students or not, the attended university focusses on the students’ internship or not) may play a role in the employability of Congolese IT graduates. Therefore, there is a necessity for future studies to consider the socio-political backgrounds of the student (as this factor scored the highest reliability with an alpha equals to 0.78) as a potential contextual factor influencing highly IT graduate employability in countries similar to the DRC. Consequently, this study defines graduate employability in unstable developing countries as a set of socio-political and economics factors, the graduate-employer relationship, and the set of general skills and specialized skills (graduate academic competencies) that a graduate has acquired in his/her academic journey that make him/her ready to compete in the labor market and to obtain job after graduation.

The main novelty of this study is that EFA has been used as a reliable descriptive data mining technique to identify the contextual factors that predict the employability of Congolese IT graduates. Several factors have been presented as predictors of employment in the DRC in previous study (Mpia et al., 2023). Nonetheless, previous research has indicated those factors without performing EFA. The current study, by integrating both skills (graduate academic competencies factor) and socio-political and economics, university employability strategies factors as predictors of employability, this research enriches the knowledge in the field of employability prediction in developing countries in a theoretical way. In addition, the collected data contributes to existing IT/IS knowledge as it is added as tool to support researchers who would want to conduct data analytics studies on employability in developing countries. In practice, this research offers a reliable framework containing contextual factors that predict and prescribe relevantly Congolese IT graduates’ employability. Socio-political background of graduates, graduate academic competencies, and graduate-employer relationship have been identified as major factors of employability based on their test of reliability scores. Indeed, they scored Cronbach’s Alpha values of .78, .75, and .63, respectively. University employability techniques were identified as a minor component in this study due to its test of dependability score of 0.53.

Hence, the discovered factors will enable countries similar to the DRC to implement adequate IT and IS systems using those factors as either contextual predictors or contextual prescribers in order to be realistic when proposing solutions to mitigate problems related to the youth unemployment. This study provides support to data analysts and machine learning engineers by offering reliable and contextual factors that predict IT graduate employability in the DRC and in developing countries similar with the DRC. By using the retained factors as feature to predict IT employability in such countries, the above-mentioned engineers would build efficient IS/IT solution to mitigate youth unemployment problems and then attempt to achieve SDG target 8.6.

Qamhieh et al. (2020) asserted that the best way to build a relevant predictive and/or prescriptive model that predict relevantly the employability of students and attempts to achieve sustainable employability in developing countries is the inclusion of socio-political and economics as contextual features. Socially speaking, using the three retained factors as features to prescribe employability in research will help youths from unstable and unsecure zones to be aware of their socio-political backgrounds when enrolling to any IT course. It will help also those with less relationships with employers to optimize their decision making when comes to get enrollment. In fact, enrolling accordingly will allow youths to perform well their skills and competencies to maximize their chance of employability despite their socio-political backgrounds and the degree of their relationships with employers. In this sense, the rate of youth’s employment will increase and then SDG target 8.6 can be achieved in the DRC.

This research had strengths and limitations. Regarding strengths, data encompassing this study was suitable for conducting the EFA on the basis of descriptive analysis as the sample of 355 respondents was enough and a good sample for EFA study (Taherdoost et al., 2014). Moreover, the retained factors scored reliability higher than 0.6 when tested using Cronbach’s alpha test. In addition, the McDonald’s Omega test for the instrument showed that the used instrument for this study was reliable with a value of 0.77. However, the limitation of this study has been the paucity of scientific work investigating the factors that predict employability in the DRC in the existing literature (Mpia et al., 2023). Therefore, this research has reviewed only few papers related to the conducted study to identify the knowledge gap.

The research recommends for future studies to expand the results in different science, technology, engineering, and mathematics (STEM) fields and allow carrying out the evaluation using further advanced analysis such as predictive and prescriptive analysis to recommend suitable employability profiles to such students. Future research will use BERT as deep learning algorithm to build a recommender system that recommend IT students’ employability by using the retained factors as contextual features in order to test and validate them in Congolese real-world. For generalization, the recommender system model will test the effectiveness and reliability of the retained factors in other developing countries similar to the DRC. Conflict theory helped the researchers to investigate which factors among graduate academic competencies (human capital) and socio-political background and graduate-employer relationship backgrounds (contextual factors) influence highly and objectively the employability of IT graduate in unstable and conflict countries such as the DRC. Hence, the researchers suggested a conflict theory that insists much on the postulate of human capital stating that despite the socio-political and economic backgrounds of a graduate, the employability of a graduate must be measured by his/her acquired skills and the education level (Gillies, 2015; Wuttaphan, 2017).

Footnotes

Acknowledgements

The authors express their profound gratitude to the Assumptionist Community of Kijenge Parish, Arusha/Tanzania

Author Contributions

Conceptualization, H.N. Mpia; methodology, H.N. Mpia; validation, S.N. Mwendia and L.W. Mburu; formal analysis, H.N. Mpia; investigation, H.N. Mpia; resources, H.N. Mpia; data curation, H.N. Mpia; data analysis, H.H. Mpia; writing the original draft, H.N. Mpia; writing, H.N. Mpia; visualization, H.N. Mpia; supervision, S.N. Mwendia and L.W. Mburu; manuscript proofreading S.N. Mwendia and L.W. Mburu, project administration, S.N. Mwendia and L.W. Mburu. All authors have read and agreed to the published version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was fully funded by the Assumptionist Community of Kijenge Parish through grant No. M.2023/STU/AA.

Ethical Statement

This research was approved by the KCA University School of Graduate Studies as a part of the requirements of the Ph.D. program.

ORCID iD

Mpia Héritier Nsenge

Data Availability Statement

The raw and encoded data used in this study can be accessed using the following link .

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