Social recruiting: an application of social network analysis for preselection of candidates
| DOI | https://doi.org/10.1108/DTA-01-2021-0021 |
| Published date | 14 February 2022 |
| Date | 14 February 2022 |
| Pages | 536-557 |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Metadata,Information & knowledge management,Information & communications technology,Internet |
| Author | Stevan Milovanović,Zorica Bogdanović,Aleksandra Labus,Marijana Despotović-Zrakić,Svetlana Mitrović |
Social recruiting: an application of
social network analysis for
preselection of candidates
Stevan Milovanovi
c and Zorica Bogdanovi
c
Faculty of Organisational Sciences, University of Belgrade, Belgrade, Serbia
Aleksandra Labus
Department for E-business, Faculty of Organisational Sciences,
University of Belgrade, Belgrade, Serbia
Marijana Despotovi
c-Zraki
c
Faculty of Organisational Sciences, University of Belgrade, Belgrade, Serbia, and
Svetlana Mitrovi
c
Project Management College, Belgrade, Serbia
Abstract
Purpose –The paper aims to studiy social recruiting for finding suitable candidates on social networks. The
main goal is to develop a methodological approach that would enable preselection of candidates using social
network analysis. The research focus is on the automated collection of data using the web scraping method.
Based on the information collected from the users’profiles, three clusters of skills and interests are created:
technical, empirical and education-based. The identified clusters enable the recruiter to effectively search for
suitable candidates.
Design/methodology/approach –This paper proposes a new methodological approach for the preselection
of candidates based on social network analysis (SNA). The defined methodological approach includes the
following phases: Social network selection according to the defined preselection goals; Automatic data
collection from the selected social network usingthe web scraping method; Filtering, processing and statistical
analysis of data. Data analysis to identify relevant information for the preselection of candidates using
attributes clustering and SNA. Preselection of candidates is based on the information obtained.
Findings –It is possible to contribute to candidate preselection in the recruiting process by identifying key
categories of skills and interests of candidates. Using a defined methodological approach allows recruiters to
identify candidates who possess the skills and interests defined by the search. A defined method automates the
verification of the existence, or absence, of a particular category of skills or interests on the profiles of the
potential candidates. The primary intention is reflected in the screening and filtering of the skills and interests
of potential candidates, which contributes to a more effective preselection process.
Research limitations/implications –A small sample of the participants is present in the preliminary
evaluation. A manual revision of the collected skills and interests is conducted. The recruiters should have
basic knowledge of the SNA methodology in order to understand its application in the described method. The
reliability of the collected data is assessed, because users provide data themselves when filling out their social
network profiles.
Practical implications –The presented method could be applied on different social networks, such as
GitHub or AngelList for clustering profile skills. For a different social network, only the web scraping
instructions would change. This method is composed of mutually independent steps. This means that each step
can be implemented differently, without changing the whole process. The results of a pilot project evaluation
indicate that the HR experts are interested in the proposed method and that they would be willing to include it
in their practice.
Social implications –The social implication should be the determination of relevant skills and interests
during the preselection phase of candidates in the process of social recruitment.
Originality/value –In contrast to previous studies that were discussed in the paper, this paper defines a
method for automatic data collection using the web scraper tool. The described method allows the collection of
more data in a shorter period. Additionally, it reduces the cost of creating an initial data set by removing the
DTA
56,4
536
This research was funded by the Ministry of education, science and technological development,
Republic of Serbia, the Grant number 11143.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2514-9288.htm
Received 18 January 2021
Revised 5 August 2021
21 December 2021
Accepted 19 January 2022
Data Technologies and
Applications
Vol. 56 No. 4, 2022
pp. 536-557
© Emerald Publishing Limited
2514-9288
DOI 10.1108/DTA-01-2021-0021
cost of hiring interviewers, questioners and people who collect data from social networks. A completely
automatedprocess of data collection from a particular social network stands out from this model from currently
available solutions. Considering the method of data collection implemented in this paper, the proposed method
provides opportunities to extend the scope of collected data to implicit data, which is not possible using the
tools presented in other papers.
Keywords E-recruitment, Social recruiting, Social network analysis, Web scraping
Paper type Research paper
1. Introduction
In an information-driven society, where knowledge and skills are recognized as an important
resource for growth and competitiveness, human resource management (HRM) becomes a
business process of strategic importance, both for the design and implementation of
corporate strategy, motivation, recruitment and retention of highly qualified candidates
(Toteva and Gourova, 2011). In such a defined business environment, social networks sites
(SNS) enable connecting candidates and companies.
SNS significantly influence the candidate recruitment process. Due to their high
attendance, SNS provide effectiveness to find and analyze potential candidates for the job
(Hedenus et al., 2019). Main strengths of SNS are finding candidates with specific knowledge,
skills and recommendations. Social recruiting on SNS provides benefits that are reflected in
cost reduction, search efficiency of candidates, opportunities to contact passive candidates
who are not active in the search for a new job and company brand development (Okolie and
Irabor, 2017)(Ramaabaanu and Saranya, 2014). Social network analysis (hereinafter: SNA)
can be used for social recruiting to make the selection of potential candidates as efficient as
possible (Golovko and Schumann, 2019).
Preselection of candidates in the social recruitment process requires advanced search with
candidate filtering. The main disadvantages of this feature are the cost of using specialized
tools, predefined and limited functionalities and the use of unknown algorithms during
filtering. Previous research, such as (Baruffaldi et al., 2017), used manually collected data sets
or, as (Chiang and Suen, 2015), conducted surveys and interviews to collect data. Both time
and budget resources are required to implement manual data collection methods. Literature
research (Tifferet and Vilnai-Yavetz, 2018) has shown that automated data collection
methods can be more efficient and as accurate as manual ones. However, the main
disadvantage of automated tools is that they are designed only for a specific platform. No tool
is generic enough that you can use it to collect data from a variety of sources.
This paper presents an alternative methodological approach for the preselection of
candidates in the social recruiting process by using SNA. Using the SNA methodology, it is
possible to analyze the structure of the social network through concepts from graph theory
and network analysis, with the help of defined mathematical models and clustering
algorithms (Toteva and Gourova, 2011). Based on the definition of SNA methodology
(Milovanovi
cet al., 2019), it can be concluded that the greatest contribution of this
methodology in the process of recruiting candidates is in the phase of preselection of qualified
people for a certain job position.
The main goal of this research is to propose new methodological approach for the
preselection of candidates based on SNA. The proposed methodological approach enables
automated identification of users’social network profiles according to their main skills and
interests. The advantages of this approach are the automation of the data collection process
using web scraping method, and preselection of candidate suitable for the job position using
SNA. Proposed method for the collecting users’data from SNSreduces the time and cost of the
recruitment process and training recruiters to use specialized tools for different SNS. For this
research LinkedIn was chosen because this is the most widely used SNS for social recruitment
(Tifferet and Vilnai-Yavetz, 2018). The result of applying this approach should enable
Social
recruiting: an
application of
SNA
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