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 Standards Judgment & 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 HireHire SuccessfulFalse Negative (Miss, Type II error) True Positive (correct hit) UnsuccessfulTrue Negative (correct rejection) False Positive (False Alarm, Type I error)

Part 1: Hiring Standards No HireHire SuccessfulFalse Negative (Miss, Type II error) True Positive (correct hit) UnsuccessfulTrue Negative (correct rejection) False Positive (False Alarm, Type I error)

Part 1: Hiring Standards No HireHire SuccessfulFalse Negative (Miss, Type II error) True Positive (correct hit) UnsuccessfulTrue Negative (correct rejection) False Positive (False Alarm, Type I error)

Part 1: Hiring Standards No HireHire SuccessfulFalse Negative (Miss, Type II error) True Positive (correct hit) UnsuccessfulTrue Negative (correct rejection) False Positive (False Alarm, Type I error)

Part 1: Hiring Standards No HireHire SuccessfulFalse Negative (Miss, Type II error) True Positive (correct hit) UnsuccessfulTrue 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 p.554)

Outline Personnel Judgment & Decision Making Hiring Standard Judgment & decision biases in selection

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 Processes; Slaughter et al., 1999, Journal of Applied Psychology –Number size framing Wong & Kwong, in press, Organizational Behavior and Human Decision Processes; 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 when one is personally responsible for the initial decision (data from Schoorman, 1998, JAP)

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

Free Throw Performance Hit %: 8980 Miss %: 1120 Reggie Miller Mike Bibby

Wong and Kwong (2005, Experiment 1, JAP)

Preference reversal owing to number size framing

Response Scale: Performance ratings Context: HR (Performance appraisal) Wong and Kwong (2005, Experiment 2, JAP)

Response Scale: Choice Context: HR (Personnel selection) Wong and Kwong (in press, Experiment 3a, OBHDP)

Response Scale: Salary Context: HR (Compensation) Wong and Kwong (in press, Experiment 3b, OBHDP)