Behavioral Goal Setting Models g for Operations Management - - PowerPoint PPT Presentation

Behavioral Goal Setting Models g for Operations Management

Workshop Stochastic models for warehousing systems October 29, 2009 José Antonio Larco José Antonio Larco,

Kees Jan Roodbergen, René de Koster, Jan Dul

Rotterdam School of Management Erasmus University Rotterdam y Contact: jlarco@rsm.nl

Introduction Propositions Experiment Results Conclusions

Goals are interesting for OM…

• Current assumptions of OM models:

p – People are predictable, work in a stationary way and are

unaffected by external factors (Boudreau, 2003)

• Challenging goals have a positive effect on performance
• Challenging goals have a positive effect on performance

– Meta-analysis 8-16% performance increase over “do your best”” strategies; (Locke and Latham, 1990) – Well studied: >239 lab experiments, > 156 field studies (Locke Well studied: 239 lab experiments, 156 field studies (Locke

and Latham, 1990)

Two main questions for OM

Introduction Propositions Experiment Results Conclusions

• 1. How is performance related

to goal difficulty?

Li ? (L

k d L th 1968)

300 400 500 600

• rmance s(D)

?

– Linear? (Locke and Latham, 1968) – Levels-off? (Locke & Latham, 1982) – Decreases? (See et al, 2006) – Effects of varying skill level?

100 200 300 mmulative perfo 8 9

– Effects of varying skill level?

• 2. How do workers regulate

their work pace?

0,2 0,4 0,6 0,8 1 Cum Goal difficulty (probability of success) 4 5 6 7 eed (picks/min)

their work pace?

– Acceleration towards goal

(Hull, 1932) or deadline?

– A steady state pattern or

Goal achieved

?

1 2 3 Workspe

– A steady state pattern or irregular? – Effect of varying goals & skill level?

Deadline

2 4 6 8 Time (min)

e e

All this in OM contexts where workers have a fixed time to work.

Two-fold approach

Introduction Propositions Experiment Results Conclusions

pp

• 1. Proposition generation: workers as decision makers

– Objective: maximize utility/preference Objective: maximize utility/preference

• Utility derived from work pace itself
• Utility derived from evaluation w.r.t goal

– Decision: what work-pace to select? (effort to exert) – Behavioral Economic decision models:

M i I di id l f l th “ f t ”

• Myopic: Individuals focus only on the “near future”.
• Planner: Individuals take into account the utility for the whole period.

– Derivation of properties from model

• 2. Propositions Testing: experimental setting

– Total performance p – Work pace measurement

Introduction Propositions Experiment Results Conclusions

Scope

• Work is repetitive; i.e. work content known; cycle times short
• Workers are experienced
• Feedback is provided
• Goal G (units processed) to be achieved before deadline D

.

• Goal G (units processed) to be achieved before deadline D
• Target G serves as reference for evaluating performance
• Workers committed to the goal (Locke and Latham, 1990)
• Cumulative work s(t), work pace, ṡ=ds/dt

Cumulative work s(t), work pace, ṡ ds/dt

Work pace preference (Y

k D d L (1908)

Introduction Propositions Experiment Results Conclusions

Work pace preference (Yerkes-Dodson Law (1908)

• Relates (Hancock &

Warm,1989) :

Utility rate rhythm function R(ṡ)

, )

– Stressor – Adaptability/desirability

1 2 3

ute)

y y ( )

Natural speed

• Defines:

– Maximum desirability

• 2
• 1

15 20 25 30 35

lity units/minu

Maximum desirability – Range of tolerance

• 5
• 4
• 3

Utility rate (uti

• Properties:

– Convex function

• 6

U Work speed (picks/minute) ṡ=ds/dt

Goal induced preference

Introduction Propositions Experiment Results Conclusions

Goal induced preference (Kahneman & Tversky 1979)

• Properties:

St i tl i i

Progress utility curve P(s)

– Strictly increasing

0,1 0,2 0,3

P(s) Progress utility curve P(s) Target T

] , [ ; )) ( ( ' D t t s P ∈ ≥

– Loss aversion

• 0,2
• 0,1

100 200 300 400 500

tility units) P

; ); ( ) ( > − < + δ δ δ G P G P

– Diminishing sensitivity

• 0 5
• 0,4
• 0,3

Utility (ut

G s t s P G s t s P > < < > ; )) ( ( ' ' ; )) ( ( ' '

• Usage in goal theory
• 0,6
• 0,5

Total work (picks) s(t)

; )) ( (

Heath et al.,1999; Steel & Koning, 2006 and Wu, et al. 2008

1 Myopic Conjecture

Introduction Propositions Experiment Results Conclusions

• 1. Myopic Conjecture

: conditions Initial ) , ( ) ( max s s Q s R

s

+ & &

&

) ( : conditions Initial s = & ) ( ) , ( : y consistenc unit

• rule)

chain (apply rate utility Progress s s P s s Q ′ = & & : conditions

• rder

First ) ( ) , ( Q ) ( ) ( s P s R ′ − = ′ &

1 M i C j t (W k P P iti )

Introduction Propositions Experiment Results Conclusions

• 1. Myopic Conjecture (Work Pace Propositions)

>Consistent with goal gradient hypothesis (Hull, 1932)

35 40

te)

25 30

icks/minut

15 20

k speed (pi

Target 100

Ma im m speed achie ed at target

5 10

Work

Target 150 Target 200 Target 250

Maximum speed achieved at target

2 4 6 8 10

Time (minutes)

2 Planning Conjecture

Introduction Propositions Experiment Results Conclusions

• 2. Planning Conjecture

) ) ( ) ( ( max dt s P s R

D s

&

&

′ +

∫

) ( : condition Boundary s

D

& = : formula euler Applying )) ( ( )) ( ( ) ( that Recognize s P D s P dt s s P & − = ′

∫

<Independent of work pace!

) ( pp y g ct t s =

<Constant work pace!

) ( ) ( : constant ) ( fact that the using s D P' D s R' t s & & & − = =

2 Planning Conjecture (Work Pace Propositions)

Introduction Propositions Experiment Results Conclusions

• 2. Planning Conjecture (Work Pace Propositions)

>Consistent with Planning Conjecture (Parkinson, 1955)

28,0 27,5

minute)

26,5 27,0

dt (picks/m

Target 400 Target 350

25,5 26,0

peed ds/d

Target 350 Target 300 Target 200 Target 150 Natural pace

25,0 5,5

Work s

p

24,5

1 2 3 4 5 6 7 8 9 10

Time t (minutes)

Goals and Performance: Contrasting propositions

Introduction Propositions Experiment Results Conclusions

Goals and Performance: Contrasting propositions

290 295 275 280 285

(picks)

260 265 270

rformance

250 255 260

Per

Myopic Planner

245

50 100 150 200 250 300 350 400 450

Goal (picks)

E periment Design

Introduction Propositions Experiment Results Conclusions

Experiment Design

• Simple order picking task, short cycled (<10 sec)

C t l f l i ff t ( i i ki d )

• Control for learning effects (previous picking rounds)
• Within subject design (3x“4”):

Pil t t d “D b t” t l ( 36 bj t ) – Pilot study “Do your best” control (n=36 subjects) – 3 randomized goal levels (10, 50, 90th percentile) per subject (n=81 subjects) ( j ) – Skill proxy: Average work pace of 10 best work pace used - 4 categories constructed C dit i d t ll bj t dl f th i f – Credit assigned to all subjects regardless of their performance

• Process view: work pace measured using time stamps

Introduction Propositions Experiment Results Discussion

Experiment : Task description

Results 1: Goal difficulty-performance

Introduction Propositions Experiment Results Conclusions

esu ts Goa d cu ty pe o a ce

> Level-off (Locke and Latham (1982)) (Goal/skill Interaction F (1,77)=7.4; p<0.01) > Consistent with planner results

75 80

• f picks)

∆=14.3%

65 70 ce (number Skill Level 1 Skill Level 2

∆=11.9% ∆=12.6%

55 60 l performanc Skill Level 3 Skill Level 4

∆=7.1%

50 47 59 66 Tota Goal Level (picks) Goal Level (picks)

Introduction Propositions Experiment Results Discussion

10

Results 2: Work speed-stationary behavior

• 2. Work pace

8 5 9 9,5 cks/min

• 1. Stable pattern
• pace

selection

• 3. Acceleration

towards deadline

7,5 8 8,5 k pace pic

towards deadline

6 6,5 7 Work

Goal 59 picks Goal 66 picks Goal 47 picks

6 1 2 3 4 5 6 7 8 Time (min)

Results 3: Multi-level analysis (pick<round<subject)

DV: Work pace: picks/min

MLM Model (picks=3631 Introduction Propositions Experiment Results Conclusions

DV: Work pace: picks/min

MLM Model (picks=3631, rounds=3, subjects=81) OLS Model (n=3631)

Factor

Coeff S.E. Coeff S.E. Intercept 7.7878 0.1458*** 7.8331 0.0678*** Time 0.0089 0.0202 ‐0.0148 0.0186 Time 0.0089 0.0202 0.0148 0.0186 Goal Level 47 Picks ‐0.6757 0.0894*** ‐0.6933 0.0682*** Goal Level 59 Picks ‐0.2273 0.0894* ‐0.2347 0.0683*** Skill Level 2 0.6841 0.1952** 0.6445 0.0785*** Skill Level 3 1.0170 0.1952*** 0.9799 0.0784*** Skill Level 4 1.7094 0.1952*** 1.6724 0.0784*** Time x Goal Level 47 Picks ‐0.0826 0.0287** ‐0.0342 0.0223 Time x Goal Level 59 Picks ‐0.0229 0.0242 ‐0.0240 0.0222 Time x Skill Level 2 ‐0.0074 0.0285 0.0047 0.0238 Time x Skill Level 3 0 0204 0 0285 0 0011 0 0238 Time x Skill Level 3 ‐0.0204 0.0285 ‐0.0011 0.0238 Time x Skill Level 4 ‐0.0132 0.0285 0.0369 0.0238 Time x Goal Level 47 Picks x Skill Level 2 ‐0.0093 0.0402 ‐0.0451 0.0246. Time x Goal Level 47 Picks x Skill Level 3 0.0040 0.0402 ‐0.0354 0.0246 Time x Goal Level 47 Picks x Skill Level 4 0.0042 0.0402 ‐0.1020 0.0246*** Time x Goal Level 59 Picks x Skill Level 2 0.0144 0.0335 0.0409 0.0246. Time x Goal Level 59 Picks x Skill Level 3 ‐0.0202 0.0335 ‐0.0151 0.0245 Time x Goal Level 59 Picks x Skill Level 4 ‐0.0666 0.0335* ‐0.0871 0.0245*** R2 0.3965 AIC 6338 BIC 6512 6180

***p<0.001, **p<0.01, *p<0.05 Reference values: Goal Level 66 Picks, Skill Level 1

MLM R d ff t

Introduction Propositions Experiment Results Conclusions

Factor Variance of coefficients S.E. of coefficients Corr B Pick Ro nd j ithin S bject I

MLM Random effects

By Pick Round j within Subject I (Intercept) 0.265087 0.514866 By Pick Round j within Subject I Time 0.003444 0.05869 ‐0.306 B S bj t By Subject (Intercept) 0.282252 0.531274 By Subject Time 0.002298 0.047939 ‐0.195 B S bj By Subject Time x Goal Level 47 Picks 0.005494 0.074119 By Subject Time x Goal Level 59 Picks 0.000433 0.020806 Residual 0.242124 0.492061 0.619

Conclusions

Introduction Propositions Experiment Results Conclusions

Conclusions

• Confirmation of “leveling off” effect in goal difficulty-

performance relationship

• Challenging goals induce steady state behavior

g g g y (explanations for this? -Carver & Shreier, 1998?)

• Acceleration towards deadline not towards goal
• General support for planner conjecture model

pp p j

Implications for OM:

Introduction Propositions Experiment Results Conclusions

p

• Confirmation that challenging goals work

U f diff t l f i bl

• Usage of different goals as source of variable

capacity: (demand fluctuations and deadlines)

• New advantages of challenging goals: steady state

behavior and enhanced predictabilility behavior and enhanced predictabilility

• Verification of steady-state work pace to identify

whether goal is adequately set.

• Monitor progress towards the goal

Monitor progress towards the goal

Introduction Propositions Experiment Results Conclusions

Further research…

• Vary time frames

a y t e a es

• Study feedback effects
• Prediction of performance
• Trade off with other OM goals: quality fatigue
• Trade-off with other OM goals: quality, fatigue,

safety

• Replications in “real world” settings

Performance Distribution

Extra Results

Performance Distribution

90% 100% 60% 70% 80% 90% requency 30% 40% 50% mmulative fr

Do your best Goal: 47 picks Goal: 59 picks Goal: 66 picks

0% 10% 20% Cum

p

30 40 50 60 70 80 90

Performance: Total Picks in 8 minutes

Prospect theory for goals!

Extra Results

Prospect theory for goals!

70% 80% 90% 100%

• rmance

40% 50% 60% 70%

n rating perfo

Goal: 47 picks Goal: 59 picks 10% 20% 30%

Satisfaction

p Goal: 66 picks 0% 10 20 30 40 50 60 70 80 90 Perfromance: Picks in 8 minutes

I D th Skill L l 1

Extra Results

In Depth: Skill Level 1

9 5 10 8,5 9 9,5

ks/min

7,5 8

Work pace pick

Goal 66 picks Goal 59 picks Goal 47 picks 6,5 7

W

Goal 47 picks 6 1 2 3 4 5 6 7 8

Time (min)

I D th Skill L l 2

Extra Results

In Depth: Skill Level 2

10 9 9,5 /min 7 5 8 8,5 k pace picks/ Goal 66 picks Goal 59 picks 6,5 7 7,5 Work Goal 47 picks 6 1 2 3 4 5 6 7 8

Time (min) ( )

I D th Skill L l 3

Extra Results

In Depth: Skill Level 3

10 8 5 9 9,5

/min

7,5 8 8,5

• rk pace picks/

Goal 66 picks Goal 59 picks 6,5 7

Wo

Goal 47 picks 6 1 2 3 4 5 6 7 8

Time (min)

I D th Skill L l 4

Extra Results

In Depth: Skill Level 4

10 8,5 9 9,5 s/min 7,5 8 ,

• rk pace picks

Goal 66 picks Goal 59 picks G l 47 i k

6,5 7 Wo

Goal 47 picks

6 1 2 3 4 5 6 7 8 Time (min)