guide to 21 century hr - assessment systems
TRANSCRIPT
We Know People
Guide to 21st century HR hitchikers galaxy
Zsolt Feher | Managing Director / Europe
We Know People
10 unavoidable points when You make your decisions as HR leader
Zsolt Feher | Managing Director / Europe
Generation Y
“The children now love luxury; they have bad manners, contempt for authority; they show disrespect for elders and love chatter in place of exercise. Children are now tyrants…. They contradict their parents, chatter before company…. and tyrannize their teachers.”
Socrates
Hogan is HR analytics and Big Data
Proved that personality
predicts occupational
performance.
Discovered how leadership
has financial consequences.
We showed that personality
predicts leadership
performance—who you are
determines how you lead.
We identified 11 dark side
personality factors that derail
leaders and organizations.
We demonstrated the need
to distinguish between
leader emergence and
leader effectiveness.
1980 1990 2000 2005 2017
HR analytics that works
Assessments provide an
unbiased and scientific
basis for making
informed decisions
about people.
Business success
depends on making
good decisions
about money and
people.
Using data to
support decisions
about people is
always best practice.
What’s the challenge?
1. Many new solutions on the market
2. What works and what doesn’t, how do you know?
3. Data is powerful but we don’t use it well
TOP 10
1. Tool usage at large does not change
2. False need - trying to justify a mobile first strategy, shorter assessment is not better
3. Snake oil is all around the market
4. Wrong tech focus from startups
5. A/I is only A/S without good data
6. Validity is still the engine, but not everyone has it
7. The square of preferences: cost – accuracy - u/x - fairness
8. The ‘left out’ factor
9. Dashboarding is not analytics
10. Speed of change
1. Tool usage at large
2. False need
Shorter is better?
Mobile first?
Mobile First
Source: Jobvite. 2018 Job Seeker Nation Study.
Mobile First?
Surveying pre-hire assessment takers
N = 1,063 applicants; Mean age = 39.5
39%
45%
6%11%
0%
10%
20%
30%
40%
50%
Desktop Laptop Tablet Smartphone
Device Usage
Smartphone users reported the lowest rate of
distraction (8%).
Mobil First?
Surveying pre-hire assessment takers
N = 1,063 applicants; Mean age = 39.5
41%
51%
4% 3%
40%
49%
4% 7%
39%45%
6%11%
0%
10%
20%
30%
40%
50%
60%
70%
Desktop Laptop Tablet Smartphone
Device Usage by Year
2015 2016 2017
Mobile First?
Surveying pre-hire assessment taker
N = 1,063 applicants; Mean age = 39.5
41,539,0
40,0
34,4
39,5
25
30
35
40
45
50
Desktop Laptop Tablet Smartphone Overall
Mean Age by Device
Mobile First?
Oregon State Univ. & Shaker Research (c.f., Hardy, Gibson, Sloan & Carr(2017)
• Mobile assessments took longer • 1/3 of entry-level demanded mobile assessments• Only 1-3% of higher-level job candidates used a mobile device
The shorter the assessment, the better?
• Do shorter assessments reduce dropout?1
• Most dropout occurs in the first 10 mins• Flat after that at 1-2% • Mostly “good attrition” – poor candidates drop out
1. Oregon State Univ. & Shaker Research (c.f., Hardy, Gibson, Sloan & Carr (2017)
3. Snake oil is all around
Do not fall for ‘look and feel’
Always check the science
1. What do the tools actually measure?
2. What backgrounds and professional affiliations do the tools developershave?
3. Have the tools been peer-reviewed or reviewed by unbiased thirdparties?
4. Do the tools adhere to any employment guidelines or standards?
5. Are the tools accompanied by technical reports or validation studies?
6. Are the tools appropriate for the job under consideration?
7. How is the performance of the tools measured?
8. Are the tools and products adapted to different cultures and supportedlocally?
What to consider? (The viscous 8)
4. Wrong focus from startups
5. A/I vs. A/S
Artificial Intelligence without good data and expert
guidance is only Artificial Stupidity
6. Validity above all still matters
7. The square of preferences
COST
ACCURACY
FAIRNESS
USER EXPERIENCE
Accuracy
• The fundamental goal is accurate prediction
• Better measurement means better prediction
• Better prediction means fewer errors
• Errors are costly
Cost
• Cost always will be a consideration
• New technologies are successful when cost goes down and
effectiveness goes up
• Consider total cost of ownership
• AI/ML applications have promising cost implications
• Cost doesn’t seem to be the key barrier preventing use of
current, accurate measurement methodologies
User Experience
• User experience is tied to brand
• How do your trade-off decisions reflect on your brand?
• Individual differences in user experience
• Demographic differences in user experience
• Digital nativity is one variable impacting user experience
Fairness
• Legal
• Tied to accuracy
• Candidate perceptions
8. The left out factor
9. Dashboarding is not analytics
10. Speed of change
TOP 10
1. Assessment usage at large does not change
2. False need - trying to justify a mobile first strategy, shorter assessment is not better
3. Snake oil is all around the market
4. Wrong tech focus from startups
5. A/I is only A/S without good data
6. Validity is still the engine, but not everyone has it
7. The square of preferences: cost – accuracy - u/x - fairness
8. The ‘left out’ factor
9. Dashboarding is not analytics
10. Speed of change
Careful selection, do
not fall for the hype
What works, WORKS!!
Use these 10 points to make your decision, and go for it!
How do you defeat the challenge?
1 2 3
