Psychosociological Issues in Human Resource Management 7(1), 2019

pp. 36–41, ISSN 2332-399X, eISSN 2377-0716

Automating Talent Acquisition:

Smart Recruitment, Predictive Hiring Algorithms,
and the Data-driven Nature of Artificial Intelligence

Allan Bongard
a.bongard@aa-er.org
The Digital Dynamics Laboratory
at IISHSS, Ottawa, Canada

ABSTRACT. The purpose of this study was to empirically examine the relationship
between smart recruitment, predictive hiring algorithms, and the data-driven nature of
artificial intelligence. Building my argument by drawing on data collected from the Boston
Consulting Group, LinkedIn, MIT Sloan Management Review, and Statista, I performed
analyses and made estimates regarding most useful interviewing innovations, adoption of
specific artificial intelligence use cases (by category), levels of understanding for artificial
intelligence-related technology and business context, and areas where artificial intelligence
will impact recruiting. Empirical data for this study were gathered via an online survey
conducted in the United States. The structural equation modeling technique was used to test
the research model.
JEL codes: E24; J21; J54; J64
Keywords: automation; talent acquisition; smart recruitment; predictive hiring algorithm
How to cite: Bongard, Allan (2019). “Automating Talent Acquisition: Smart Recruitment, Predictive
Hiring Algorithms, and the Data-driven Nature of Artificial Intelligence,” Psychosociological Issues in
Human Resource Management 7(1): 36–41. doi:10.22381/PIHRM7120193
Received 8 January 2019 • Received in revised form 16 March 2019
Accepted 27 March 2019 • Available online 1 May 2019

1. Introduction
The use of artificial intelligence for handling the recruitment process brings about
cost-effectiveness in addition to qualitative advantages for both clients and candi-
dates. (Upadhyay and Khandelwal, 2018) Nearly all organizations encounter sig-
nificant obstacles, which frequently stem from an immoderate insistence on simply
substituting human intelligence through artificial intelligence. To acquire the cor-
relative upsides of these intelligences apart from mere expense savings, organizations
should improve their groundbreaking analytics (Ciobanu et al., 2019; Lăzăroiu et
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al., 2017; Nica, 2015; Popescu, 2014; Popescu, 2018) while persisting in upgrading
their human intelligence and a meta-intelligence for altering their intelligence design
according to corporate strategy. (Lichtenthaler, 2018)

2. Conceptual Framework and Literature Review

While there is a substantial volume of data on the capacity of artificial intelligence
to cut down or eradicate jobs presently carried out by human workers, admission and
retention operations will alter, not vanish, accomplishing distinct administrative tasks
contingent on artificial intelligence data. (Dennis, 2018) Activated by a convergence
of a tremendous rise in information, skyrocketing computational power at diminish-
ing expenses and advancements in technology, artificial intelligence is being em-
braced as an output enhancer. (Plastino and Purdy, 2018) Throughout the recruitment
process, companies can gather supplementary features (e.g. age, health condition,
body image, race, gender, sexual orientation, and economic class), employing them
to systematize job candidates to a greater extent and to differentiate where feasible as
regards job screening. Thus numerous ethical and privacy issues occur, including the
establishment of both a company and job candidates’ values. (van Esch et al., 2019)

3. Methodology and Empirical Analysis

Building my argument by drawing on data collected from the Boston Consulting
Group, LinkedIn, MIT Sloan Management Review, and Statista, I performed analyses
and made estimates regarding most useful interviewing innovations, adoption of
specific artificial intelligence use cases (by category), levels of understanding for
artificial intelligence-related technology and business context, and areas where
artificial intelligence will impact recruiting. Empirical data for this study were
gathered via an online survey conducted in the United States. The structural
equation modeling technique was used to test the research model.

4. Results and Discussion

While not experiencing emotions, machines and artificial intelligence systems can be
instructed to identify them (e.g., via the investigation of facial micro-expressions)
and adjust their feedbacks consequently. (Kaplan and Haenlein, 2019) The expansion
of technological developments is ceaselessly disorganizing the manners in which
companies promote, adopt, and set out their e-recruitment approaches. The utilization
of artificial intelligence shapes candidates’ recruitment practices or their positions
and intentions in relation to the company. (van Esch et al., 2019) Emotional
responses materialize as a component of mind perception as individuals mediate
between the dissimilar notions of programmed electronic devices and performances
typical of anthropomorphic minds. (Shank et al., 2019) Machine learning represents
37
enhancement algorithms that enable programs to analyze information and gradually
progress contingent on assimilating from further data. (Ho, 2019) (Tables 1–5)

Table 1 Most useful interviewing innovations (%)

64 Soft skills assessments
59 Job auditions
48 Meeting in casual settings
37 Virtual reality assessments
23 Video interviews

Why they have promise (%)

72 More realistic snapshot of candidate’s personality
63 Candidates can try out job for fit
44 Less bias than traditional formats
29 Candidates cannot lie about skills
Sources: LinkedIn; my survey among 2,400 individuals conducted November 2018.

Table 2 Where artificial intelligence can be most useful (%)

Sourcing candidates 68
Screening candidates 57
Nurturing candidates 43
Scheduling interviews 39
Engaging with candidates 24
Interviewing candidates 17

Key benefits of artificial intelligence (%)

73 Saves time
49 Removes human bias
24 Delivers best candidate matches
34 Saves money
Sources: LinkedIn; Statista; my survey among 2,400 individuals conducted November 2018.

Table 3 Adoption of specific artificial intelligence use cases, by category (%)

All respondents Current artificial
intelligence adopters
Sales and marketing lead scoring 64 87
Sales opportunity scoring 62 76
Sales forecasting 57 83
Customer service case classification/routing 60 82
Chatbots for customer service or product 49 79
selection
Cross-selling and upselling 44 71
Fraud detection 54 67
Credit risk scoring 56 66
Email marketing 77 89
Sources: Statista; my survey among 2,400 individuals conducted November 2018.

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Table 4 Levels of understanding for artificial intelligence-related
technology and business context: To what extent do you agree
with the following statements about your organization? (%)
We understand… Pioneers Investigators Experimenters Passives
Technology Required 91 80 26 14
implications technological
breakthroughs to
succeed with
artificial
intelligence
Technology Data required for 88 77 26 13
implications artificial
intelligence
algorithm
training
Technology Processes for 90 72 19 5
implications artificial
intelligence
algorithm
training
Business Artificial 94 88 36 25
implications intelligence-
related changed
ways of business
value generation
Business Development 87 78 23 17
implications time of artificial
intelligence-
based products
and services
Business Development 86 77 14 11
implications costs of artificial
intelligence-
based products
and services
Workplace Required changes 88 86 24 20
implications of knowledge and
skills for future
artificial
intelligence needs
Workplace Effect of artificial 86 76 20 17
implications intelligence in the
workplace on
organization’s
behavior
Industry Artificial 88 84 24 19
implications intelligence-
related shift of
industry power
dynamics
Sources: MIT Sloan Management Review; The Boston Consulting Group;
my survey among 2,400 individuals conducted November 2018.
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Table 5 Areas where artificial intelligence will impact recruiting
High value added with human touch / High automation potential
Gauging interpersonal skills, detecting diversity indicators, gauging culture add, gaining
strategic talent insights
High value added with human touch / Low automation potential
Story-telling/Selling the role, negotiations/persuasion, understanding candidate’s needs,
community building, differentiated candidate experience
Low value added with human touch / High automation potential
Pre-screening/assessments, candidate propensity ranking, candidate matching, nurture
marketing, interview scheduling, candidate sourcing, resume collection/parsing, de-
duping/ATS updates
Low value added with human touch / Low automation potential
Setting pre-screen criteria, recruitment marketing
Source: LinkedIn.

5. Conclusions and Implications

Real artificial intelligence employs a reversed strategy by duplicating the brain’s
structure (e.g., via neural networks) and harnessing massive volumes of information
to obtain knowledge without assistance. (Kaplan and Haenlein, 2019) Artificial
intelligence recruitment represents the subsequent technological stage in the staffing
process, its newfangled contraption favorably shaping candidates’ technology
utilization motivation and job application probability (Lăzăroiu, 2013; Nica et al.,
2014; Nica et al., 2018; Popescu et al., 2017a, b), whereas job applicant anxiety as
regards the adoption of artificial intelligence recruitment is inferior to position
concerning the hiring company. Organizations can embrace artificial intelligence
recruitment to deduce features and to derive likely behaviors in connection with fit
and performance. (van Esch et al., 2019) Despite the fact that asynchronous media
diminishes applicants’ perceptions of hopefulness in the direction of the interview
process in comparison with that under the synchronous video interview method,
neither synchrony nor the artificial intelligence decision agent bring about impar-
tiality issues among the applicants. (Suen et al., 2019)

Funding
This paper was supported by Grant GE-1297442 from the Social Analytics Laboratory, Los
Angeles, CA.

Author Contributions
The author confirms being the sole contributor of this work and approved it for publication.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or
financial relationships that could be construed as a potential conflict of interest.

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