Systematic Literature Review on Big Data and Data Analytics for

Employment of Youth People: Challenges and Opportunities

Aniss Qostal

, Aniss Moumen

and Younes Lakhrissi

Intelligent Systems, Georesources and Renewable Energies Laboratory (SIGER IN FRENCH),

Sidi Mohamed Ben Abdellah University, FST Fez, Morocco

System Engineering Laboratory, National School of Applied Sciences,

Ibn Tofail University Kenitra, Morocco

Keywords: Big Data, Data Analytics, Systematic Literature Review, Labour Market.

Abstract: The emergence of ICT and the digitization wave that has taken place over the past decades has led to an

exponential growth in the data generated - both structured and unstructured -. Opening up a promising path

and a new area of research known as the Big Data (BD) paradigm. Therefore, one of many ways to highlight

the capabilities and potential opportunities offered by the BD is to conduct an exploratory and empirical study

on this subject.

In this paper, we will perform a systematic literature review (SLR) on the potential role of big data and data

analytics as a way to develop the labour market and explore the opportunities for the employment

development. First, we will identify BD's capabilities, areas of influence, and workplace applications. Second,

explore gaps to be filled in order to meet the aspirations of youth job seekers. This layer of society is the most

concerned with the employment issues, moreover, youth people are extremely active on social networks,

generating a huge volume of data which make them a great group for a qualitative and quantitative analysis

in order to define patterns and create opportunities for them.

1 INTRODUCTION

The employability concept, according to the most

definitions and as described by many literatures,

consists of understand the personality traits,

attributes, skills and qualifications that make a person

more employable for a specific position and perform

specific tasks that meet the needs of the potential

employers (Hillage et al., 1999). For The

Confederation of British Industry (CBI), the

employability is the possession by an individual of

the qualities and competencies required to meet the

changing needs of employers and customers and

thereby help to realize his or her aspirations in the

workplace (McQuaid and Lindsay, 2005).

Furthermore, Groot and Maassen van den Brink

considered the employability as the individual ability

to fulfil a variety of functions in a given labor market.

Another example of this core definition is provided

https://orcid.org/0000-0003-1016-8467

https://orcid.org/0000-0001-5330-0136

https://orcid.org/0000-0002-2537-8283

by Feyter et al who define employability as “the

ability of employees to carry out various tasks and

functions properly” (McQuaid and Lindsay, 2005), In

addition, the International Labour Organization (ILO)

believes that the employability exceeds the ability to

obtain a first job. Rather, it is the development of

capacities and skills that give jobseekers more

flexibility to adapt to the labour market variables, the

ability to ask questions, learn new skills, identify and

assess options and understand rights at work,

including the right to a safe and healthy working

environment (Brewer et al., 2013).

Today, many disciplines, each in its field of

competence, are trying to master the problem of

employability and identify the main causes and

obstacles that prevent youth people and fresh

graduates to easily integrate the job market or finding

jobs corresponding to their skills, as well as help them

to improve their soft and hard skills. Therefore, many

researches(Beaumont et al., 2016), (De Mauro et al.,

2018) including(ILO) reports have indicated the main

cause of the unemployment among recent graduates

is the lack of employability skills such as people

management, communication, teamwork,

professionalism, knowledge development, problem-

solving and decision-making. Consequently, the

unemployment rate among Moroccan youth

increased to 21.9%, distributed between youth men

and youth female, respectively with 22.05% and

22.8% according to the ILOSTAT database of June

21, 2020.

Nowadays, the application of approaches that

repose and focus on the analytics, recommendations

and orientation can be a solution not to demolish the

employment issues, rather to mitigate it and give a

roadmap to policymakers. Accordingly, we will

conduct in this paper a systematic literature review on

the employability and what big data can offer through

its capabilities and analytics to discover opportunities

hidden in the data shared, generated or even

consumed on electronic platforms related to youth

interest specially related the employment context.

With the objective to understand more on youth

behaviours, skills and even responds to these major

questions: 1) what is the interference and area of

application of the big data on the employability

subject? 2) Can big data analytics be a booster to the

employability and facilitator to the integration in

labour market? 3) What are the architectures

developed on this subject and what gap need to be

more developed?

2 CONTEXT OF RESEARCH AND

RELATED WORKS

With the growth in the use of information and

communication technologies, accompanied by the

digitization of services and the dissemination of

professional social media platforms such as e-

learning, e-recruitment and e-commerce. Flooding

the virtual world with a large amount of data

(structured, semi-structured and unstructured) that

can be useful on many ways and can be a base for

research on the employability through extraction of

indicators and valuable information that need to be

processed and analyzed using big data

approaches(Elgendy and Elragal, 2014)(Günther et

al., 2017).

However, the application of big data on the

employability context begins from the identification

of data sources layer that can be a way to build an

image of skills, preferences and competences of

candidates. This layer can be alimented with three

types of data sources:

 Education platforms: Refer to E-learning

systems and as defined by Horton (2001) as

“the use of Internet and digital technologies to

create experience that educate fellow human

beings”. Providing a rapid access to specific

knowledge and information and encompasses a

wide set of applications and processes such as

computer-assisted learning, web-based

training, virtual classrooms, and digital

collaboration. The widespread use of these

platforms by students and even recent

graduates generate a large amount of data,

which includes indicators of the knowledge

learned and skills acquired and it can be used

as a means of ranking and classify applicants

profiles to match them with the skills required

in the labour market and identify their

weaknesses and strengths (Dascalu et al.,

2016)(Dascalu et al., 2017).

 Recruitment platforms: Refer to E-recruitment

or E-job applications (Benabderrahmane et al.,

2017), there are many platforms used around

the world by job seekers, job recruiters and

even human resources managers. The

interactions between these actors through the

ranking and recommendation on the profiles

needed on the labour market can be a way to

improve the existing human capital, to know

the existing skills and what needs to be done to

increase the chances of youth people in the

labour market (Zang and Ye, 2015).



 Social Networking Site (SNS): Can be divided

into two types, Social Media such as Facebook

and Twitter and Professional Social

Networking (PSN) such as LinkedIn, Xing, and

Sumry. Both types are important in the analysis

of shared data on professional projects, training

and expertises. The data collected can be a

source to analysis the capacities and skills of

candidates (Landers and Schmidt, 2016),

(Batagan and Boja, 2015).

The data generated on these potential sources are

important but not sufficient without an adequate

analysis to complete and establish an approach and

architecture intended for the objective of

employability. For that, in the following sections a

qualitative and quantitative analysis of existing

studies will be presented to establish a roadmap and a

guide for the future studies.

3 METHODOLOGY

In order to conduct a systematic review on the

correlation and the interference between big data and

employability especially for youth people, we have

designed a 4 steps methodology to create our corpus.

First, we started our review by identifying the words

related to our topic of research and possible queries

for more precision on term results by combining

words related to "Big Data" and "employability" as

indicated in table 1 and existing on the title, the

abstract or the keywords of the articles. Second

have selected the main indexed journals and

digital

databases represented on: IEEE Xplore Digital

Library, Scopus, ScienceDirect and Web Of Science.

Third, the results obtained represented in RIS format

were imported on the ZOTERO platform to do the

cleaning phase by deleting documents not related to

our research, filling the missing fields for the meta-

analysis, merge double documents and accept the

document in the time range over the current

decade[2010-2020] as shown in the Figure 1 Fourth,

the last phase oriented to exploit the output of the

third phase(cleaned RIS database) using NVIVO for

qualitative and quantitative processing and outputs

will be explored on the next sections.

Table 1: The queries on each digital database

ital Librar

Advanced Queries

Scopus

ScienceDirect

(*employability OR *employment OR

"job seeker" AND ("big data" OR

igdata OR BigData)

IEEE Xplorer

((("Abstract":*employability OR

*employment OR 'job seeker' OR job-

seeker) AND ("Abstract":"big data"

OR bigdata OR BigData)))

Web of Science

Using assistance module to create the

queries

Figure 1: SLR methodology steps.

4 RESULTS

4.1 Meta-analysis

The purpose of the meta-analysis is to discover the

typology of the documents represented in the corpus

(table 2) and their proportion between scientific

journals (figure.2). From table 2, we carry out the

dominant Conference Papers with 51% of all the

documents.

Table 2: the ratio of documents in the corpus

Type Number of docs Ratio (%)

Conference Papers 101 51

Review Article

74 38

Others

22 11

The detail of this table is shown in the figure 2,

we can notice that Scopus occupies the first place

with 56% of the references, Web Of Science in

second place with 27% of the references followed by

IEEE Xplore (11%) and finally ScienceDirect in the

last place with 6%.

TITLE-ABS-KEY(*employment

*employability OR "job seeker" OR job-

seeker )

AND TITLE-ABS-KEY ( "big data" OR

igdata )

AND PUBYEAR > 2009 AND

PUBYEAR < 2021

Figure 2: Number of references per Scientific Database

4.2 Word Analysis

The word Analysis or the word study is a way to

highlight the dominants words in the corpus throws

the visual representation of words. Using the word

cloud (Figure 3), we can easily see the most common

words on the abstract, title and keywords of

documents in the corpus. The important terms can be

easily identified by the font and the size and how

much is near to the centre of the cloud. Obviously, the

big data word is the most present followed with

employment and we can found words related to the

analytics approaches and potential sources related to

the big data.

Figure 3: Word Cloud obtained from the corpus

Another way for the word analysis, can be

through the statistical analysis of the words (Figure

4), we can see the term of "big data" at first place with

177 occurrences followed by the employment, the

rest of words have similar values and can grouped on

sub-subjects such as analytics approaches,

stakeholders (student, government) and platforms

related to recruitment process. This analysis gives a

view on how much to corpus is pertinent and related

to the subject of research. For the collected corpus, it

is clear that our database has the orientation "big data"

and "employability".

Figure 4: Word frequency

5 BIG DATA ARCHITECTURES

FOR THE EMPLOYMENT

From the analysis carried out on the corpus, we found

many researchers have highlighted the role that big

data can plays on the subject of employability, the

majority of the proposed frameworks aim to give

recommendations and even predict the job adequate

to the job-seeker and profiles proposals needed by

potential employers. this treatment is assured throw

using analysis techniques and possible ecosystems of

implementation. Most of the proposed frameworks

have divided their architectures on many layers for

more segmentation and control of the outputs :

 Data source layer: contain all possible data

sources that can feed the 3V characteristics of

big data, as described in the previous section.



 Analytics layer: this layer is the main core of

each framework and the innovate piece, this

layer aims to build an intelligent algorithm that

has the capability to extract useful information

from the input data and give prediction and

recommendation to stakeholder related to the

employability subject.



 Visualization layer: this layer represents

dashboard interfaces, in order to give the

system the possibility of interactivity with the

actors.

5.1 Employability Prediction System

(EPS)

Figure 5: Architecture for Employability Prediction System

(EPS)(Saouabi and Ezzati, 2019)

The architecture proposed by the author in the figure

5 has been fragmented over seven phases and shows

an abstraction view on each phase with its

correspondence on the ecosystem of the

implementation (Hadoop ecosystem). However, the

weaknesses of this architecture is its generality that

can be implemented in many fields beside the

employability context (health and transportation etc),

secondly the layer of stakeholders has not mentioned

and finally the data source layer have only one type

of source represented with MySql database and as

known one of the major characteristic of big data is

the heterogeneity of data as input.

5.2 The Hybrid Recommendation

System for Job Recommendation

The architecture proposed on the figure.6, the author

have separated each layer with the introducing of the

data source layer including a variety of sources of

information giving the possibility and flexibility on

terms characteristics of data supported also adding

more detail on the phase of analytics with the

adoption of the hybrid recommendation analytics

combining the collaborative recommendation

(Filtering algorithm for calculating the intersection

between User-User and Item-Item based

recommended list) and Content-based filtering

selects items based on the similarities between the

content description of an item and the users

preferences which are used by LinkedIn(S. Ahmed et

al., 2016).

Figure 6: Architecture of the hybrid recommendation

system for job recommendation (Benabderrahmane et al.,

2017)

5.3 Expandingly Tree-based and

Dynamically Context-Aware Online

Learning Algorithm (ETDC)

Figure 7: Professional network recommenders based on

ETDC algorithm Chen et al., 2018)

The architecture of Figure 7 clearly separated each

layer according to the categories of events. Therefore,

the system is more interactive and built on explicit

information of the context of use depending on their

acceptance or rejection of the proposed items.

An Expandingly Tree-based and Dynamically

Context-aware Online Learning algorithm (ETDC)

was developed to observe the context and recommend

an item to the user based on the current context, the

historical information about users, items, contexts

and rewards after the recommendation. The system

extracts reward from the user's click behaviours. Then

adds the interaction log to the database which will be

used in future recommendation (Chen et al., 2018).

6 DISCUSSION

From the three architectures introduced on the

previous section, we can observe the adoption of

architecture segmented on many layers, this approach

can be the key of success of any proposed framework,

more dynamics, adaptive as well as flexible to

variables that control the employment process of

youth people. Consequently, the proposed approach

will be based on three main axes. First, the

identification of stakeholders and actor, second, the

elaboration on Data source Analysis to determine the

potential sources of data related to the context and

finally, the development of an architecture capable to

combines ecosystems with analytics techniques to

meet the ambitions of stakeholders.

 Stakeholders analysis: Stakeholders are “any

group or individual who can affect or is

affected by the achievement of the

organization’s objectives” (Freeman 1984)

Freeman (2004). On big data subject,

stakeholders are represented by all entities or

groups that interact directly or indirectly with

the generation or exploitation of data.

 Data source analysis: The big data concept is

based on the considerable volume of data

produced in an accelerated manner with

different formats. Today, the generation of data

become easiest task with the digitalization

wave, emergence of Internet Of Things (IoT),

the proliferation of hyper connected devices.



 Approach to extract values: The model should

be able to collect, clean and store the

tremendous and heterogeneous datasets

generated over distributed sources.

Figure 8: General view on the proposed architecture on the

big data and employability framework

7 CONCLUSIONS

As a conclusion and as a response to the three majors

questions declared on the introduction, the big data is

present on every activity we do, today we generate

more of data than before and the collection of this data

and its treatment can be used to give solutions to very

complicated problems. Mainly, the employability of

youth people is not an exception; the digitalization of

various services related to youth people can be a

source of data including indicators, their behaviours,

competences and skills. Using the intelligence

artificial including Machine learning and other

approaches can easily match the profile of each youth

with the opportunities in labour market. Despite this,

this field of research is still in its infancy and must be

developed on a system adaptable to each case to

respond to the specificity of each case.

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