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Dahniar Nur Amalina, Siskha Nur Khasanah, Dandy Yuliansyah, Syarifa Hanoum (2024) Literature

Review of Digital Recruitment: How Effective is Artificial Intelligence in Selecting People? (06)

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LITERATURE REVIEW OF DIGITAL RECRUITMENT: HOW EFFECTIVE IS

ARTIFICIAL INTELLIGENCE IN SELECTING PEOPLE?

Dahniar Nur Amalina, Siskha Nur Khasanah, Dandy Yuliansyah, Syarifa Hanoum

Institut Teknologi Sepuluh Nopember, Indonesia

Email: [email protected], [email protected],

[email protected], [email protected]

Abstract

The use of artificial intelligence in recruitment can help select candidates according to

company standards through a simple, effective, and efficient process. This paper

investigates how the AI implementation in the traditional recruitment process transform

into the AI-based recruitment and its impact on recruitment processes. We use

systematic literature review and content analysis to obtain information regarding

research gaps, research limitations, and future research opportunities. We analyze 35

articles collected between 2008 and 2022 showing that AI has several benefits,

including increasing the interest of candidates to apply for jobs, increasing efficiency

and timeliness, reducing recruitment costs, obtaining the best candidates, and reducing

bias in the recruitment process.

Keywords: Artificial Intelligence, Recruitment, Systematic literature review

INTRODUCTION

In a fluctuating environment, organizations are challenged to balance between the

uncertainty in different aspects of economic, social and politics and maintaining the

company growth (Aula, Hanoum, & Prihananto, 2022). Human resource management

(HRM) plays an important role in accelerating company performance (Wedhatama,

Hanoum, & Prihananto, 2021)(Rai et al., 2022). A comprehensive study by the Boston

Consulting Group has shown that the recruitment function, as a part of HRM, has the

most significant impact on companies' revenue growth and profit margins compared to

any other function in the field of human resources (HR) management (Black & van

Esch, 2020).

Unfortunately, recruiting good talent for companies becomes a problem for HR

managers (Albert, 2019). Against the backdrop of the ascending role of human capital,

the technological context of how companies recruit people has to change. As the matter

of fact, today's leading organizations are fully using social networks, analytics and even

cognitive tools to acquire talent in new ways.

Today, companies are at the beginning of what we term Digital Recruiting. At the

heart of this transformation is the use of artificial intelligence (AI) in recruiting

activities (Alic, 2016). AI is likely to change the role of management and organizational

JOURNAL SYNTAX IDEA

p–ISSN: 2723-4339 e-ISSN: 2548-1398

Vol. 6, No. 06, Juni 2024

Dahniar Nur Amalina, Siskha Nur Khasanah, Dandy Yuliansyah, Syarifa Hanoum

2706 Syntax Idea, Vol. 6, No. 06, Juni 2024

practices as an effective human resource management (HRM) tool. The AI adoption of

is very useful in recruitment strategies as it has been proven to have drastically reduced

the time and cost of performing these functions (Nawaz, 2019).

Advanced HR practices, such as AI, have been investigated to enhance

organizational performance (Niehueser & Boak, 2020). Prior researchers, however,

have noted a substantial gap between the promise and reality of AI in HRM (Tambe,

Cappelli, & Yakubovich, 2019). Moreover,how far the role of AI can help companies in

the recruitment process is still a question. Therefore, this paper represents a systematic

literature review (SLD), aims to map the role of AI in the recruitment and selection

process, as well as its current development.

The remaining article is structured in five sections. Section 2 discuss the trend of

AI-related recruitment process. Section 3 describes the research methodology adopted

in this study. Section 4 documents results and discussions. Section 5 presents the

managerial implication of AI-based recruitment, whilst Section 6 summarises the

conclusions, and limitations of the study.

AI has been used and implemented significantly in recruiting professionals in

various companies from 2018 and becomes one of the latest trends in the recruitment

industry (Upadhyay & Khandelwal, 2018). There has been recent academic interest in

how organization implement AI within recruitment process. The previous literature

reviews that have investigated the AI-based recruitment process, although similar in

nature, they offer different methodologies and perspective to consider.

For instance, the most recent bibliometric analysis conducted by (Hunkenschroer

& Luetge, 2022), reviewing 51 academic articles that met their specified criteria,

focusing on the extant on the ethicality of AI-enabled recruiting by mapping the ethical

opportunities, risks, and ambiguities between year 2016 to 2020. Second, (FraiJ &

László, 2021) reviews 21 articles collected in 2010 until 2020 analyses the

implementation of AI for evaluating the human bias to find the best fit candidate in

screening process. The similar literature that explores the level, rate and potential AI

adoption for the hiring process (Black & van Esch, 2020).

Our study focuses on how the AI implementation in the recruitment process by

collecting data in year 2001-2022 with the consideration that AI began to be widely

used during this time.

RESEARCH METHOD

This section will explain the Systematic Literature Review used in the study.

Referring to the PRISMA Flow Diagram presented in figure 1, there are 4 stages of

review process that should be done, including: 1) identification; 2) screening; 3)

eligibility; and 4) analysis. Each stage explained as follows.

Literature Review of Digital Recruitment: How Effective is Artificial Intelligence in

Selecting People?

Syntax Idea, Vol. 6, No. 01, January 2024 2707

Figure 1. SLR Method Using PRISMA Flow Diagram

Identification Process

The identification process begins with defining research questions and objectives

on knowing the impact of AI on the recruitment process. After the boundaries and

research objectives are defined, the second step is the article collection stage. We use

two database sources, Scopus and Web of Science. To get specific articles, we use 3

strings, which relates to AI, HRM, and Recruitment. Details of the string can be seen in

Table 1. The articles extractions using the keyword search in Table 1 resulting 1.110

articles in total.

Table 1. AI, HRM, and Recruitment Keyword Search

Screening Process

Topic

String

Artificial

Intelligence

(“AI” OR “Artificial

Intelligence” OR “Machine

Learning” OR “Deep Learning”

OR “Neural Network”)

Human

Resource

Management

(“Human Resource

Management” OR “HRM” OR

“Human Resource Information

Systems” OR “HRIS” OR

“Human Resource” OR “HR”

OR “Human Resource

Management Systems” OR

“HMRS”)

Recruitment

(“Recruitment” OR

“Recruiting” OR “Selection”

OR “Selecting” OR “Hiring”

OR “Outsource” OR

“Outsourcing”)

Dahniar Nur Amalina, Siskha Nur Khasanah, Dandy Yuliansyah, Syarifa Hanoum

2708 Syntax Idea, Vol. 6, No. 06, Juni 2024

Following (Votto, Valecha, Najafirad, & Rao, 2021), we conduct this review in 2

phases. First phase is called the Journal Demographic Filtration screening process.

Upon screening our databases, we established our inclusion and exclusion criteria for

Phase 1. The criteria consist of 9 factors (4 for inclusions, 5 for exclusions). The article

must be peer-reviewed, written in English; published between 2008-2022, academic

articles or conference proceedings. The exclusion of literature occurs if it is not written

in English, not peer-reviewed or non-academic, and an editorial or simulation piece,

published before year 2001, and duplicated articles between two databased. Out of more

than a thousand articles, only 56 articles pass our screening process.

Eligibility Process

Once the journal verification state is completed, we continue to conduct phase 2:

the content filtration phase. The inclusion and exclusion criteria of this phase consist of

two steps. In the first step, we review the title, abstract and associated keywords of the

articles to see if they match our pre-established definitions of AI, HRM, and

recruitment. In step 2, we do a thorough review on the full paper to ensure all papers are

relevant to the topic. Out of the 56 articles under review, 28 of them are selected to be

included in our study.

Analyzing

All articles that have been selected from phase 2, then will be carried out the

content analysis. The findings will be discussed in the next section, including research

gaps, research limitations, and future research opportunities.

RESULT AND DISCUSSION

The results in Table 2 shows that 2021 and 2022 are the most productive years,

with a total of 9 papers. While for the second rank in 2020 there were 8 papers, for the

third rank in 2019 there were 5 papers. This evidence suggests that the investigation of

artificial intelligence in recruitment is increasing through years. Based on the number of

papers obtained, it shows that research related to artificial intelligence in recruiting has

not been carried out much.

Table 2. Trends of Publications

Year

Publications

2022

2021

2020

2019

2010

2008

2001

Several countries have published studies on artificial intelligence in recruitment.

And we look at the outputs and effects of the most prominent countries between 2001

and 2022. Table 4 displays the publications from the top 4 countries regarding artificial

intelligence in recruitment.

Literature Review of Digital Recruitment: How Effective is Artificial Intelligence in

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Syntax Idea, Vol. 6, No. 01, January 2024 2709

Table 3. The most productive countries and regions

Rank

Countries/

Regions

Publication

Citation

United states

164

New Zealand

114

Germany

India

The most active sources in AI research in recruitment are listed in Table 4. Business

Horizon is the top journal, with 3 publications and 111 citations. Decision Support

System is placed in the second with 2 publications and 93 citations, whilst International

Journal of Resource Management is placed in the third with 2 publications and 76

citations.

Table 4. Top sources that published Green Production in Agricultural Industry

Rank

Source

Publicatio

Citation

Business

Horizon

111

Decision

Support

System

International

Journal of

Human

Resource

Management

The spread and evolution of research themes can identify research hotspots and research

changes over different periods. Given that keywords are a natural language vocabulary

for conveying subjects and concentrated concepts of the literature, a keyword analysis

can indicate research hotspots and the evolution of trends from applicable study

disciplines. Table 5 shows that next to the keyword "Artificial Intelligence", the

keyword that appears most often is "Human Resource Management", "Recruitment",

"Human Resource", and "Machine Learning". This shows that the keyword is a current

research hotspot.

Table 5. High - Frequency Keyword

Sequenc

number

High –

Frequency

Keyword

Frequency

Artificial

Intelligence

Human

Resource

Management

Recruitment

Human

Resource

Dahniar Nur Amalina, Siskha Nur Khasanah, Dandy Yuliansyah, Syarifa Hanoum

2710 Syntax Idea, Vol. 6, No. 06, Juni 2024

Machine

Learning

Organizational recruitment is driven by the goal of attracting qualified individuals and

motivating them to apply for jobs (Breaugh, 2008). In the beginning, almost every

recruitment activity is carried out in traditional fashion, consisting of a screening stage

(involving CVs and standardized tests that HR professionals evaluated) and an

interview stage (in which senior managers assessed candidates based on a group

interview and made final hiring decisions) are done manually (van den Broek, Sergeeva,

& Huysman, 2021). With the introduction of the AI technology, the screening stage is

substituted by software, which can automatically select and predict candidates’ fit with

the organization based on the job specifications. The comparison of traditional

recruitment and AI-based recruitment can be seen in figure 2.

Figure 2. Recruitment Process Comparison

Candidates who will be predicted to be a good fit (i.e., who matched the attributes

of high-performing employee) are selected by the algorithm for the interview stage (van

den Broek et al., 2021). Relevant to that, the interview process now can be done

virtually, with the respect of AI-based interview software. This process involving AI is

known as e-recruiting. E-recruiting is defined as the use of communication

technologies, such as websites and social media, to find and attract potential job

applicants, to keep them interested in the organization during the selection process, and

to influence their job selection decisions (Johnson, Stone, & Lukaszewski, 2020). E-

Recruiting is an extensive of AI used in the traditional Breaugh recruitment process,

specifically in third stage.

The Implication of AI in Recruitment Process

Enlarging the Applicant Pool

AI helps to enlarge the talent pool and attract applicants who fit with the

organization. By listing openings on recruitment websites, applicants can easily access

them any time of the day or night. Organizations have found that the use of e-

recruitment has led to a much larger applicant pool than traditional recruitment

processes (Chapman & Gödöllei, 2017). For example, collected from (Black & van

Esch, 2020), Johnson & Johnson generated over 1 million applications for 28,000

Literature Review of Digital Recruitment: How Effective is Artificial Intelligence in

Selecting People?

Syntax Idea, Vol. 6, No. 01, January 2024 2711

positions in 2017. In the same year, Google also generated an estimated 2 million

applications for just 14,500 jobs, meaning that it was nearly 10 times more difficult to

get a job at Google than to get into Harvard University (Alic, 2016).

Enhancing Efficiency and Timeliness

(Chilunjika, Intauno, & Chilunjika, 2022) discovers that AI benefits the recruiter

when it comes to cutting down administrative tasks routine like answering the regular

questions. This is possible by using AI-powered chatbots that focus on communication,

implementing predictive behavior, and responding to questions and requests via text and

speech. In that regard, employees can work in more meaningful roles because AI frees

up their time to concentrate on what really needs to be done. Automation does not

replace a team or service but rather supplements it to make it completely user-centric

(Chilunjika et al., 2022). Niehueser & Boak, (2020) concludes that the implementation

of AI di Cielo, a leading outsourcing organization, has significantly reduced the time

taken to process each individual application to the scheduled interview with a hiring

manager, from up to two weeks, when many of the processes are carried out manually,

to an average of seven minutes when using AI. Thus, AI’s ability to reduce time-to-hire

represents not just an efficiency gain but also potentially a strategic advantage in the

battle for human capital, especially in industries in which there is high turnover (Alic,

2016).

Reducing Cost of Recruitment

One of the biggest challenges facing organizations is identifying highly qualified

applicants from their large pools (Johnson et al., 2020) and AI recruitment has a dual

effect on total cost minimization. AI-enabled process automation can assist the

screening task by using algorithms to identify profiles of talented applicants and select

the ones who should be invited for interviews. AI can also generate letters to applicants

indicating if they are qualified or not qualified for jobs, and provide them with

information about the next steps in the process. The standardization of the initial

screening process can improve its efficiency, enhance the fairness of the process and

ensure that organizations recruit the most effective applicants (Johnson et al., 2020).

Thus, AI adoption can reduce the production costs of companies by saving on human

capital, because it can perform tasks that would usually require HR professionals (Pan,

Froese, Liu, Hu, & Ye, 2022).

Best Fit Candidate

AI helps to identify and narrow down candidate pool through resume scanning

(Dickson & Nusair, 2010). It also analyzes the turnover rates and accordingly select the

best candidate for the job (Chakraborty, Giri, Aich, & Biswas, 2020). AI also helps in

evaluating a candidate’s performance in a job interview and in choosing the right person

for the job. For example, Unilever is using an algorithm-based recruiting strategy for

prescreening candidates and gathering evidence for choosing the right person before the

interview phase (Stanley & Aggarwal, 2019). AI also can be adopted in virtual

interview process. (Chakraborty et al., 2020) states when audio visual interviews are

taken using AI software, the candidate’s choice of word, speech, body language,

personality traits are assessed. This helps the HR team to easily decide the job role of

that candidate. AI also eases the work of HRM by constantly updating employees about

information, suggestions and feedbacks (Chakraborty et al., 2020).

Dahniar Nur Amalina, Siskha Nur Khasanah, Dandy Yuliansyah, Syarifa Hanoum

2712 Syntax Idea, Vol. 6, No. 06, Juni 2024

AI-enabled Decision Making

The fundamental idea of decision making by AI consists of developing a set of

decision rules based on existing data sets, the so-called training data (Hauer,

Kevekordes, & Haeri, 2021). For example, organizations can merge information

gathered from job applications, employment tests, personality inventories and

interviews, and AI can develop algorithms that weight and combine the information to

make overall hiring recommendations. The use of AI should be very beneficial in this

process because it does not include biases or affect reactions to applicants, which often

mislead decision-making (van Esch & Black, 2019). Industry research supports this

argument, finding that AI enabled hiring decisions result in a 20% increase in employee

performance and a 35% decrease in turnover rates (Johnson et al., 2020). Thus, the use

of AI algorithms may make more effective selection decisions than traditional decision-

making, but research is needed to examine this prediction

CONCLUSION

The purpose of this research is to review the integration of AI in HRM

components, especially in the recruitment process. The main advantage of using AI is

the speed and quality of work as well and minimizing daily tasks with their use in the

recruitment process. Using AI can help organizations expand the pool of applicants,

improve timeliness, increase efficiency in the recruitment process, increase the

attractiveness of applicants to the organization and can also improve the suitability

assessment between employee candidates and the organization. In addition, recruiting

using AI has a dual effect on minimizing total costs. AI tools also help in evaluating a

candidate’s performance in a job interview and in choosing the right person for the job.

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(2024)

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