Technology in recruitment and selection: Past, present and the future

1. Technology in
recruitment and
selection: Past, present
and the future
Ioannis Nikolaou
School of Business
MSc in Human Resources Management
twitter@Nikolaou
https://www.linkedin.com/in/ioannisnikolaou/
inikol@aueb.gr

2. Technology in Employee
Recruitment & Selection
2
Recruitment
Screening
Selection
The "day
after"

3. Technology in Recruitment
3
Internet-based Recruitment
Social Networking Websites (SNWs)

4. Internet-based Recruitment
• Company Career Sites
– Enriched content, data and applicant tracking
• Job Boards
– Becoming more interactive, increased applicants’
attention and credibility (Nikolaou, 2014)
• Applicant Tracking Systems (ATS)
Nikolaou, I. & Tsoni, E. (in press). Internet Recruitment. In B. Warf (Ed.), The SAGE Encyclopaedia of the Internet. London: Sage.
Nikolaou, I. (2014). Social Networking Web Sites in Job Search and Employee Recruitment. International Journal of Selection and
Assessment, 22(2), 179-189.
4

5. Internet-based Recruitment &
SNWs
• Social Networking Websites (SNWs)
– A whole new world…
– A cheap and effective means of recruiting candidates, and
especially 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. Roulin & Levashina, 2019; Kluemper et al., 2012; Van Iddekinge et al, 2016;
Roth et al., 2013)
5

6. Technology in Screening
6
Resume Storage, Parsing, and Keyword Search
Cybervetting
Artificial Intelligence

7. 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 search and profile
matching)
• Data mining techniques and machine
translation technologies used to elicit
information on candidates
7

8. Cybervetting
• Applicants’ screening with the use of SNWs
• Used often in combination with candidates’
info (e.g. resume, on-line application)
– Important ethical / privacy concerns and
discrimination / adverse impact issues
– Limited research on how recruiters use this SNW
info (especially negative/incomplete), or in
combination with other info (e.g. psychometric
assessment) (Nikolaou, 2014)
8

9. Artificial Intelligence
9
Web Scrapping and Linguistic Analysis-
IBM Watson personality insights
twitter@nikolaou

10. Technology in Selection
10
Digital interviewing & Voice Profiling
Automated and computer-adaptive testing
Gamification and Games-Based Assessment

11. Digital Interviewing & Voice
Profiling
• Video-recorded structured interviews
•Benefits: increases standardization and time saving
•Limitations: lack of face-to-face interaction
– Text analytics and Voice Mining
– Algorithmic reading of voice-generated emotions
•Micro-expressions and automated emotion reading
11

12. Automated and computer-
adaptive testing
• Adaptive on-line testing
• Psychometric assessment
• Little has changed in the content, delivery
• Concerns over security conditions and
administration
12

13. Gamification and Games-Based
Assessment
• Gamified Assessment
– Soft Skills assessment; 8 skills
– Highly reliable, high construct validity
– Positive applicant reactions
– Initial evidence of predictive validity
Nikolaou, I., Georgiou, K., & Kotsasarlidou, V. (in press). Exploring the relationship of a gamified assessment with performance.
Spanish Journal of Psychology.
Georgiou, K., Gouras, A. & Nikolaou, I. (in press). Gamification in employee selection: The development of a gamified
assessment. International Journal of Selection and Assessment.
13

14. Technology & the “day after”
14
Applicant Reactions
Employer Branding
Big Data & HR Analytics

15. 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. (in press). Technology and Applicant Reactions. In R. N. Landers (Ed.).
Cambridge Handbook of Technology and Employee Behavior, Cambridge University Press.
15

16. Technology & Applicant
Reactions
Process Favorability ratings (1=Least favorable, 7=Most favorable)
Traditional Methods1 Mean SD Technology-oriented methods2 Mean SD
Interview 5.32 1.20 On-line Interviews (e.g. Skype) 5.26 1.3
Work Sample 4.80 1.39 On-Line Personality Testing 4.82 1.39
Resumes/CVs 4.73 1.25 On-Line Cognitive Ability Testing 4.80 1.37
Cognitive Ability Testing 4.34 1,32 On-Line Application Forms 4.68 1.35
Biodata 4.23 1,18 Video-Based SJTs 4.62 1.38
Personality Testing 4.17 1,37 Professional Social Networking Websites 4.58 1.33
Personal References 3.86 1,39 Gamification-GBAs 4.55 1.51
Integrity Testing 3.52 1.47 Video-CVs 4.28 1.55
Personal Contacts 3.35 1.58 Digital Interviewing 4.15 1.39
Graphology 2.30 1.28 Personal Social Networking Websites 2.86 1.41
1 Nikolaou & Judge (2007) 2 Nikolaou & Lagou (in preparation)

17. Technology & Employer
Branding
• Strong links with applicant reactions and
SNWs (e.g. Glassdoor)
• 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”&
HR Awards competitions (Lievens & Slaughter, 2016)
17

18. 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
– Using data from both internal and external sources 18

19. Critical issues 1/2
• Ethics
– Applicants’ consent
– Confidentiality
• Legal considerations
– Data privacy and data protection
– Test Security
• Equivalence of measures / techniques
Shen, W., Sackett, P.R., Nikolaou, I., et al. (2017). Updated Perspectives on the International Legal Environment for Selection. In
J. L Farr and N. T. Tippins (Eds.) Handbook of Employee Selection (pp. 659-677). New York: Taylor & Francis.
19

20. Critical issues 2/2
• Bandwidth vs. fidelity
• Implementation, administration issues (e.g.
mobile devices, tablets) and cost development
• Un-proctored assessment in high stakes
selection
• Predictor constructs (e.g., personality,
cognitive ability) vs. predictor methods (e.g.,
video résumés, digital interviews)
20

21. 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…
21

22. 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.
22

23. Conclusions
• We live our lives online (and so do recruiters)
but…
Valid, evidence-based tools and methodologies
are required in order to take fair and just hiring
decisions
23

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

25. 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.
• McCarthy, J. M., Bauer, T. N., Truxillo, D. M., Anderson, N. R., Costa, A. C.,& Ahmed, S. M. (2017). Applicant
Perspectives During Selection: A Review Addressing “So What? “What’s New?,” and “Where to Next?”.Journal of
Management, 43(6), 1693-1725.
• Nikolaou, I. (2014). Social Networking Web Sites in Job Search and Employee Recruitment. International Journal of
Selection and Assessment, 22(2), 179-189.
• Nikolaou, I., & Judge, T. A. (2007). Fairness reactions to personnel selection techniques in Greece: The role of core
self-evaluations. International Journal of Selection and Assessment, 15(2), 206-219. 25

26. References
• Nikolaou, I. & Lagou, I. (in preparation). Applicant reactions and technology-oriented selection methods.
• 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. 26