This document summarizes technology used in employee recruitment and selection processes. It discusses how internet-based tools like company career sites, job boards, and social networking websites have changed recruitment. It also examines applicant tracking systems, video CVs, resume parsing, and screening tools used to evaluate applicants. For selection, it outlines digital interviewing, automated testing, simulations, and how big data and analytics are used. Critical issues discussed include the equivalence and validity of these tools as well as privacy and legal concerns. The document concludes by proposing areas for future research.

1. Technology in
Employee Recruitment
and Selection
Ioannis Nikolaou
School of Business
Department of Management Science & Technology
twitter@nikolaou
inikol@aueb.gr

2. Technology in Employee Recruitment &
Selection
• The role of technology in the recruitment cycle
• Critical issues
• Theoretical considerations
• Ideas for future research
2
Recruitment
Screening
Selection
The "day
after"

3. Technology in Recruitment
3
Internet-based Recruitment
Applicant Tracking Systems (ATS)
Video CVs

4. Internet-based Recruitment
• Company Career Sites
– Enriched content, data and applicant tracking
• Job Boards
– More interactive, increased applicants’ attention
and credibility
• Social Networking Websites (SNWs)
– A whole new world…
4

5. Internet-based Recruitment & SNWs
• Increased interaction between candidates and
employers
• Approaching passive candidates / poaching (Nikolaou, 2014)
• Excessive usage numbers by both recruiters and job-
seekers
• Constantly increasing research attention, but
generally, an area where research still tries to catch
up practice (e.g. Kluemper et al., 2012; Van Iddekinge et al, 2016; Roth et al., 2013)
• Applicant reactions & employer branding (e.g.
Glassdoor.com)
5

6. Applicant Tracking Systems (ATS)
• Cloud-based applications managing the
administration of the recruitment process
– Cheap and efficient
• Linked with job-boards, SNWs, on-line
application forms, career sites, etc.
– Effective use of HR Metrics, e.g. success rates,
time-to-hire, recruitment sources effectiveness,
etc.
6

7. Video CVs
• Candidates’ self-presentation replacing
traditional resumes
• Increasing usage numbers and research
attention, especially in relation to diversity /
discrimination issues, applicant reactions &
equivalence with traditional resumes (e.g. Hiemstra &
Derous, 2015)
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8. Technology in Screening
8
Resume Storage, Parsing, and Keyword Search
Applicant Screening Tools
Social Networking Websites
Artificial Intelligence

9. Resume Storage, Parsing, and
Keyword Search
• Large storage databases combined with ATS &
candidates’ social media profiles
• Effective resume management and resume
parsing tools (e.g. keyword searching and
profile matching)
• Data mining techniques and machine
translation technologies used to elicit
information on candidates
9

10. Applicant Screening Tools
• The good-old Weighted Application Blanks
– Candidates provide info on work / education
history, qualifications, licenses, certifications, etc.
– Measurable and quantifiable info
– Pressure to keep content brief
– May be linked with SNWs candidates’ profile (e.g.
retrieving candidates’ info from their LinkedIn
profiles)
10

11. Social Networking Websites in screening
• Used often in combination with candidates’
info (e.g. resume, application)
– Important ethical / privacy concerns and
discrimination / adverse impact issues
– Limited research on how recruiters use this info,
especially in combination with other info (e.g.
assessment)
• Social Media Analytics & Web Scrapping
11

12. SNWs, Social Media Analytics and
Web Scraping
• FB activity related to demographic,
personality, attitudinal, and cognitive ability
variables
– Web scrapping opportunities in hiring
•Initial findings re personality (Big5), gender, religious
identity, age and intelligence
•However, do these provide any real value
above/beyond existing methods?
12

13. Artificial Intelligence
13
Web Scrapping and Linguistic Analysis
twitter@nikolaou

14. Technology in Selection
14
Digital interviewing & Voice Profiling
Automated and computer-adaptive testing
Proctored vs. unproctored testing
Simulations and gamification

15. Digital Interviewing & Voice Profiling
• Video-recorded structured interviews
•Benefits: increases standardization and time saving
•Limitations: impersonal
– Text analytics and Voice Mining
– Algorithmic reading of voice-generated emotions
•Micro-expressions and automated emotion reading
15

16. Automated and computer-adaptive
testing
• Psychometric assessment
• Little has changed in the content, most of
them recently with gamification and GBA
• Concerns over security conditions and
administration
• New psychometric approaches, e.g. increased
use of Item-Response Theory
16

17. Proctored vs. unproctored testing
• Technology has helped us bring objective
assessment earlier in the recruitment process
for larger number of candidates
– Issues with test security and cheating especially in
high stakes selection
• Electronic performance monitoring in
proctored testing  Negative applicant
reactions (Karim et al., 2014)
17

18. Simulations and gamification (1/2)
• More Americans play games than do not, half
of all gamers are under the age of 30
• Moving from a “push” to a ”pull” model
• Assessment of soft skills, hard skills,
personality, interests, etc.
18

19. Simulations and gamification (2/2)
• Serious games using the Situational
Judgement Test Methodology
– Soft Skills assessment
– Highly reliable, high construct validity
– Positive applicant reactions
•Now exploring predictive validity
Nikolaou, I. & Georgiou, K. (2017). Serious gaming and applicants’ reactions; the role of openness to experience. 32nd Annual
Conference of the Society for Industrial and Organizational Psychology, Orlando, USA
Georgiou, K. & Nikolaou, I. (2017). Serious gaming in employees’ selection process. 32nd Annual Conference of the Society for
Industrial and Organizational Psychology, Orlando, USA
Georgiou, K. & Nikolaou, I. (2017). Gamification in recruitment and selection. 18th congress of the European Association of Work
and Organizational Psychology (EAWOP), Dublin Ireland. 19

20. Technology & the “day after”
20
Applicant Reactions
Onboarding and Socialization
Employer Branding
Big Data & HR Analytics

21. Technology & Applicant Reactions
• Applicant reactions &
– New predictor methods (e.g. digital interviews,
gamification, video CVs, etc.)
– New modes of delivery of existing predictor constructs
(e.g. personality, intelligence)
– Social Networking Websites
• Impact to applicants attitudes / outcomes, compared
to traditional methods/constructs?
Nikolaou, I. Georgiou, K. Bauer, T.N, Truxillo, D. M. (under preparation). Technology and Applicant Reactions. In R. N. Landers
(Ed.). Cambridge Handbook of Technology and Employee Behavior, Cambridge University Press.
21

22. Technology & Onboarding / Socialization
• Familiarization with the company and
colleagues via:
– Apps, e-learning, videos etc.
– E-mentoring for career development
– Corporate intranet, e.g. Microsoft Yammer
22

23. Technology & Employer Branding
• Strong links with applicant reactions and
SNWs
• Word-of-mouth vs. Word-of-mouse (WOM)
– The differential impact of Positive WOM vs
Negative WOM (Van Hoye, G., 2014).
• The uncertain impact of “Best employers”
competitions (Lievens & Slaughter, 2016)
23

24. Big Data and HR Analytics
• Not just HR Metrics… but using advanced
statistical methods and combining HR with
business data
– Data Mining
•Combining internal and external data
– For example:
•Predicting hiring success & high potentials
•Reducing turnover and increasing employee
engagement and satisfaction
24

25. Technology in Employee Recruitment
& Selection
25

26. Critical issues 1/3
• Equivalence of measures / techniques
• Ethics
– Applicants’ consent
– Confidentiality
• Legal considerations
– Data privacy and data protection
– Test Security
26

27. Critical issues 2/3
• Bandwidth vs. fidelity
• Implementation and administration issues
(e.g. mobile devices, tablets)
• Unproctored testing in high stakes selection
• Predictor constructs (e.g., personality,
cognitive ability) vs. predictor methods (e.g.,
video résumés, digital interviews)
27

28. Critical issues 3/3
• Back to Basics:
– New methods (e.g. digital interviews,
gamification, video CVs)
– New modes of delivery of existing predictor
constructs (e.g. personality, intelligence)
•Construct / criterion-related validity
•Incremental validity over and above existing methods /
constructs
28

29. Theoretical considerations 1/3
• Signaling theory (Spence, 1973; Bangerter et al., 2012)
– The method sends a signal
•New methods/new modes of assessment
• Elaboration likelihood model (ELM) of
persuasion (Petty & Cacioppo; 1986)
– Peripheral processing vs. central processing &
motivation
29

30. Theoretical considerations 2/3
• Applicant Reactions Theories
– Gilliland’s organizational justice framework
•10 procedural rules
– Invasion of privacy model (Bauer et al., 2006)
– Deontic Outrage; the impact of mistreatment to
others, not the applicants themselves (Gilliland & Steiner,
2012)
•Applicable to SNWs research
30

31. Theoretical considerations 3/3
• Social Networking Websites
– Social Information Processing Theory (Walther et al., 2015)
•Overemphasis on negative information (Roth et al., 2013)
– Attribution theory (Weiner, 1985)
•Expectancy theory (Sanchez, Truxillo & Bauer, 2000),
31

32. Ideas for future research…
• Big data offer a promising line of research in
recruitment, selection & applicant reactions research
(McCarthy et al. 2017)
• Social Media Research – the process, the constructs,
adverse impact, applicant reactions (Roth et al., 2013)
• The interplay between technology / social media and
employer branding, e.g. positive vs. negative word of
mouth (van Hoye et al. in progress)
• The utility of new methods and new modes (e.g.
digital interviewing, gamification)
32

33. The future is here, but… (1/2)
• Vast data pools and improved analytic
capabilities will fundamentally disrupt the talent
identification process.
– Availability of many more talent signals
– New analytic tools and increased computing power
However…
33

34. The future is here, but… (2/2)
• Limited validity evidence compared to old
school methods
• Privacy and anonymity concerns may limit
access to individual data
• Trade-off between development costs and
accuracy/validity and user experience
• Adverse impact / unfair discrimination concerns
Chamorro-Premuzic, T., Winsborough, D., Sherman, R. A., & Hogan, R. (2016). New Talent Signals: Shiny New Objects or a Brave New
World? Industrial and Organizational Psychology-Perspectives on Science and Practice, 9(3), 621-640.
34

35. Conclusions
• We live our lives online but…
Valid, evidence-based tools and methodologies
are required in order to take fair and just hiring
decisions
35

36. European Network of Selection
Researchers (ENESER)
36
http://www.eneser.eu
Next meeting: Edinburgh, June 27-29, 2018.

37. Ioannis Nikolaou
School of Business
Department of Management Science &
Technology
twitter@nikolaou
inikolaou.gr
inikol@aueb.gr

38. References
• Bangerter, A., Roulin, N., & Konig, C. J. (2012). Personnel Selection as a Signaling Game. Journal of Applied
Psychology, 97(4), 719-738.
• Chamorro-Premuzic, T., Winsborough, D., Sherman, R. A., & Hogan, R. (2016). New Talent Signals: Shiny
New Objects or a Brave New World? Industrial and Organizational Psychology-Perspectives on Science and
Practice, 9(3), 621-640.
• Gilliland, S. W., & Steiner, D. D. (2012). Applicant Reactions to Testing and Selection. In N. Schmitt (Ed.),
The Oxford Handbook of Personnel Assessment and Selection (pp. 629-666). Oxrord: Oxford University
Press.
• Hiemstra, A. M., & Derous, E. (2015). Video résumés portrayed: findings and challenges. In I. Nikolaou & J.
K. Oostrom (Eds.), Employee Recruitment, Selection and Assessment. contemporary issues for theory and
practise (pp. 45-60). London: Routledge/Psychology Press.
• Karim, M. N., Kaminsky, S. E., & Behrend, T. S. (2014). Cheating, reactions, and performance in remotely
proctored testing: An exploratory experimental study. Journal of Business and Psychology, 29(4), 555-572.
• Kluemper, D. H., Rosen, P. A., & Mossholder, K. W. (2012). Social Networking Websites, Personality
Ratings, and the Organizational Context: More Than Meets the Eye? Journal of Applied Social Psychology,
42(5), 1143-1172.
• Lievens, F., & Slaughter, J. E. (2016). Employer image and employer branding: What we know and what we
need to know. Annual Review of Organizational Psychology and Organizational Behavior, 3, 407-440.
• Nikolaou, I. (2014). Social Networking Web Sites in Job Search and Employee Recruitment. International
Journal of Selection and Assessment, 22(2), 179-189.
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39. References
• Nikolaou, I., Bauer, T. N., & Truxillo, D. M. (2015). Applicant Reactions to Selection Methods: An Overview
of Recent Research and Suggestions for the Future. In I. Nikolaou & J. K. Oostrom (Eds.), Employee
Recruitment, Selection, and Assessment. Contemporary Issues for Theory and Practice (pp. 80-96). Hove,
East Sussex: Routledge.
• Reynolds, D., & Dickter, D. (2017). Technology and employee selection. In J. L. Farr & N. T. Tippins (Eds.),
Handbook of employee selection (pp. 855-873). New York: Routledge.
• Reynolds, D. H., & Dickter, D. N. (2010). Technology and employee selection. In J. L. Farr & N. T. Tippins
(Eds.), Handbook of employee selection (pp. 171-194). New York: Taylor & Francis.
• Ryan, A. M., & Ployhart, R. E. (2014). A Century of Selection. Annual Review of Psychology, 65(1), 693-717.
Tippins, N. T. (2015). Technology and Assessment in Selection. Annual Review of Organizational
Psychology and Organizational Behavior, 2(1), 551-582. doi:10.1146/annurev-orgpsych-031413-091317
• Van Iddekinge, C. H., Lanivich, S. E., Roth, P. L., & Junco, E. (2016). Social media for selection? Validity and
adverse impact potential of a Facebook-based assessment. Journal of Management, 42(7), 1811-1835.
• Petty, R.E., & Cacioppo, J.T. (1986). The Elaboration Likelihood Model of persuasion. New York: Academic
Press.
• Roth, P. L., Bobko, P., Van Iddekinge, C. H., & Thatcher, J. B. (2013). Social Media in Employee-Selection-
Related Decisions: A Research Agenda for Uncharted Territory. Journal of Management, 42(1), 269-298.
• Van Hoye, G. (2014). Word-of-mouth as a recruitment source: An integrative model. In K. Y. T. Yu & D. M.
Cable (Eds.), The Oxford handbook of recruitment (pp. 251-268). New York: Oxford University Press.
• Van Hoye, G., & Lievens, F. (2007). Investigating Web‐Based Recruitment Sources: Employee testimonials
vs word‐of‐mouse. International Journal of Selection and Assessment, 15(4), 372-382.
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