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MGTO 324 Recruitment and Selections
Personnel Judgment and Decision Making
Kin Fai Ellick Wong Ph.D.Department of Management of Organizations
Hong Kong University of Science & Technology
Prologue
• Recruitment and selection are– Prediction process
• We predict the future performance of job applicants
– Judgment process• We judge the future performance of job applicants
– Decision making process• We have to decide which applicants to hire
Prologue
• We know that human beings are subject to judgment and decision biases– How do these biases influence personnel selections?– Which aspects in decision making should we pay attention to?
Outline
Personnel Judgment & Decision Making
Hiring StandardsJudgment & decision
biases in selection
Part 1: Hiring Standards
• Hiring standards– The cut scores representing a passing score
• A single score from a single predictor• A total score or an average score from multiple predictors• Applicants with scores higher than the cut scores are predicted or judged
to be successful – should be hired
• Applicants with scores lower than the cut scores are predicted or judged to be not so successful
– Should be rejected
Part 1: Hiring Standards
• Hiring standards– When the predictor’s validity is 1
• There is no error, indicating that the prediction is perfect– All applicants with scores higher than the cut scores (i.e., > X) are indeed
successful
– All applicants with scores < X are indeed unsuccessful
– When the predictor’s validity is lower than but close to 1• There are some errors, indicating that the prediction is not perfect
– Most applicants with scores > X are indeed successful (no errors)
– Most applicants with scores < X are indeed unsuccessful (no errors)
– Some applicants with scores > X are indeed unsuccessful (errors)
– Some applicants with scores < X are indeed successful (errors)
Part 1: Hiring Standards
• Hiring standards– When the predictor’s validity is close to 0
• There are some errors, indicating that the prediction is not perfect– About _____ applicants with scores > X are indeed successful (no errors)
– About _____ applicants with scores < X are indeed unsuccessful (no errors)
– About _____ applicants with scores > X are indeed unsuccessful (errors)
– About _____ applicants with scores < X are indeed successful (errors)
Part 1: Hiring Standards
No Hire Hire
Successful False Negative
(Miss, Type II error)
True Positive
(correct hit)
Unsuccessful True Negative
(correct rejection)
False Positive
(False Alarm, Type I error)
Part 1: Hiring Standards
No Hire Hire
Successful False Negative
(Miss, Type II error)
True Positive
(correct hit)
Unsuccessful True Negative
(correct rejection)
False Positive
(False Alarm, Type I error)
Part 1: Hiring Standards
No Hire Hire
Successful False Negative
(Miss, Type II error)
True Positive
(correct hit)
Unsuccessful True Negative
(correct rejection)
False Positive
(False Alarm, Type I error)
Part 1: Hiring Standards
No Hire Hire
Successful False Negative
(Miss, Type II error)
True Positive
(correct hit)
Unsuccessful True Negative
(correct rejection)
False Positive
(False Alarm, Type I error)
Part 1: Hiring Standards
No Hire Hire
Successful False Negative
(Miss, Type II error)
True Positive
(correct hit)
Unsuccessful True Negative
(correct rejection)
False Positive
(False Alarm, Type I error)
Part 1: Hiring Standards
• Effects of setting the cut scores on errors– When a high score is used
• No. of True positive (correct hit): increase or decrease? ↓ ↓• No. of True negative (correct rejection): increase or decrease? ↑↑• No. of False positive (false alarm): increase or decrease? ↓ ↓• No. of False negative (miss): increase or decrease? ↑↑
– When a low score is used • No. of True positive (correct hit): increase or decrease? ↑↑• No. of True negative (correct rejection): increase or decrease? ↓ ↓• No. of False positive (false alarm): increase or decrease? ↑↑• No. of False negative (miss): increase or decrease? ↓ ↓
Part 1: Hiring Standards
• How high the cut score should be? – It depends on the costs of “false alarm” and “miss”
• For jobs of which the costs of “false alarm” are significantly higher than “miss”, probably we should set high scores
– E.g., medical doctors, clinical psychologists
• For jobs of which the costs of “miss” are significantly higher than “false alarm”, probably we should set low scores
– E.g., salespeople, insurance agents
– See you textbook for the specific methods to determine the cut scores (p. 550 - p.554)
Outline
Personnel Judgment & Decision Making
Hiring StandardJudgment & decision
biases in selection
Part 2: Judgment and Decision making biases
Biases in personnel selection
Biases in personnel selection
Escalation of commitment
Escalation of commitment
Decoy EffectsDecoy EffectsNumber Size
FramingNumber Size
Framing
Part 2: Judgment and Decision making biases
• I am going to present three well selection biases– Escalation of commitment
• Bazerman et al. 1982, Organizational Behavior and Human Decision Processes; Schoorman, 1988, Journal of Applied Psychology
– Decoy effects • e.g., Highhouse, 1996, Organizational Behavior and Human Decision Pro
cesses; Slaughter et al., 1999, Journal of Applied Psychology
– Number size framing • Wong & Kwong, in press, Organizational Behavior and Human Decision P
rocesses; Wong & Kwong, 2005, Journal of Applied Psychology
Part 2: Judgment and Decision making biases
• Escalation of commitment – Increasing commitment to a losing course of action, particularly w
hen one is personally responsible for the initial decision • (data from Schoorman, 1998, JAP)
Part 2: Judgment and Decision making biases
Biases in personnel selection
Biases in personnel selection
Escalation of commitment
Escalation of commitment
Decoy EffectsDecoy EffectsNumber Size
FramingNumber Size
Framing
Part 2: Judgment and Decision making biases
• Condition A
• Condition B
Decoy Effects
• Condition A
• Condition B
Decoy Effects
• Condition A
• Condition B
Decoy Effects
• Condition A
• Condition B
Decoy Effects
• Condition A
• Condition B
Part 2: Judgment and Decision making biases
Biases in personnel selection
Biases in personnel selection
Escalation of commitment
Escalation of commitment
Decoy EffectsDecoy EffectsNumber Size
FramingNumber Size
Framing
Free Throw Performance
Hit %: 89 80
Miss %: 11 20
Reggie Miller Mike Bibby
Wong and Kwong (2005, Experiment 1, JAP)
Attendance
Rat
ing
2
3
4
5
6
7
8
9
10
+ve -vePunctuality
+ve -veAccuracy
+ve -veCompleteness
+ve -veSuccess
+ve -veOverall
+ve -ve
Andy William
Preference reversal owing to number size framing
Rat
ing
3.8
4
4.2
4.4
4.6
4.8
5
5.2
David-favored Format Andy-favored Format
AndyDavid
Response Scale: Performance ratingsContext: HR (Performance appraisal)
Wong and Kwong (2005, Experiment 2, JAP)
Response Scale: ChoiceContext: HR (Personnel selection)
Wong and Kwong (in press, Experiment 3a, OBHDP)
0
5
10
15
20
25
30
David-favored condition Andy-favored conditionNu
mb
ers
of p
eo
ple
ch
oo
sin
g th
e c
an
did
ate
a David (Superior in programming skills)
Andy (Superior in Knowledge about CY)
Response Scale: SalaryContext: HR (Compensation)
Wong and Kwong (in press, Experiment 3b, OBHDP)
6500
7000
7500
8000
8500
9000
9500
David-favored condition Andy-favored condition
Mo
nth
ly s
ala
ry (
HK
$)
a
David (Superior in programming skills)
Andy (Superior in Knowledge about CY)
