1. Management Compensation System:
-- Adding Tournament to Tournament:
The Interactive Effect of Individual
and Team Incentives
Yu Tian
Kenneth G. Dixon School of Accounting
University of Central Florida
IMA Carolinas Winter Conference
February 17, 2012

2. Background: Incentive Systems
 Incentive systems design: an important aspect
of a management control system.
 Organization can incentivize effort based on:
 Individual performance
 Team performance
 A combination of both.
2

3. Background: Incentive Systems
 Individual incentive systems
 Lack of cooperation
 Team incentive systems
 Need for cooperation  Increasing use of teams
 Encourage cooperation
 Problem: free-riding
 Tournament (RPE) used to mitigate free-
riding
 Increasing use in corporate world
 Problem: uncooperative & collusion

4. Background: Incentive Systems
Individual compensation (within-team)
Tournament Tournament
(No) (Yes )
Tournament
(No) Low Effort Uncooperative
Team Compensation
& Collusion
(between-team)
Tournament
(Yes ) Free riding ???

5. Research Question
 Can both free-riding and collusion problems
be simultaneously mitigated, when a
combination of individual and team incentive
systems are used?
 Will we get the best or the worst of both worlds?

6. Figure 1
Individual compensation (within-team)
Tournament Tournament
(No) (Yes )
Tournament
(No) NONE WITHIN
Team Compensation
(between-team)
Tournament
(Yes ) BETWEEN BOTH

7. The Model - Extension
 Extend Nitzan’s (1991) nested contest model
 Add a group (team) reward component
 More generalizable in practice
 Encourage cooperation
 Introduce output functions
Output individual f xij cxij .
ni ni
Outputteam j 1
f xij j 1
cxij cxi
x ij is effort level of member j in team i.

8. The Model – Group Contest
Step 1: Inter-team contest success function:
xi
if max x1 , x2 0
pi ...xij ... x1 x2 (1)
1/ 2 otherwise
pi : probability that team i wins the contest and receives both group and
individual rewards
xij 0 : the effort contribution of member j in team i
ni
xi j 1
xij . : total effort in team i

9. The Model – Individual Share
Step 2: Distribution of individual reward ( VI ) within a team
xij 1
1 if max ...xij ... 0
xi ni
qij (2)
1
otherwise
ni
q ij is a share of individual reward that member j in team i receives.
α = 1: equal share within a team
α = 0: distribution of individual reward based on “merit”

10. The Nitzan’s Model - Payoff
Total reward ( V )
= Group reward (VG ) + Individual reward ( VI )
The payoff function of individual j in group i is:
Expected Expected Expected Cost of
individual group individual individual
payoff reward reward effort
1
ij ... xij ... pi VG pi qijVI xij
ni
1 ni ni
i ... xi ... ni ( piVG ) j 1
pi qij VI j 1
xij
ni
Expected
team payoff

11. Model Prediction (Baseline)
(maximizing joint payoff: joint NE)

12. Model Prediction (Baseline)
(maximizing individual payoff: symmetric NE)

13. Social Identity Theory (SIT)
 A theory of the role of self-conception in group
members, group process, and intergroup
relations.
 Positive distinctiveness (PD) from other teams
prevails in intergroup relations.
 Promote PD to enhance self-esteem.
 Optimal distinctiveness

14. SIT Prediction (Hypotheses)
Effort
Between: NO
Between: YES
Within: NO Within: YES
Between: between team tournament
Within: within team tournament

15. Design
 Participants: 144 senior and graduate business
students
 Multi-period 2 X 2 X 2 design
 Between-subject factors: team & individual incentive
systems
 Within-subject factor: 2 different incentive systems
 P(4,2) = 12 ordered combinations (e.g. NONE&WITHIN,
NONE&BETWEEN, NONE&BOTH…)
 Each individual participates in 2 conditions
 12 team observations (6 individual decisions for 10
periods in each team observation)
 Payoff: $5 participation fee + decision income

16. Procedure
Randomly assign participants into a team.
Instructions
Decide a team name.
Assign to one combination of incentive systems
Participants are
Part I mixed up and
Forced manipulation check (quiz) randomly assigned
to new teams.
Communicate & make decisions (10 periods)
Part II
Notify individual payoff after each period
Calculate payoff and pay participants.

17. Z-tree: Welcome

18. Z-tree: Chat

19. Z-tree: Decision

20. Z-tree: Feedback

21. Average Effort Levels
Table 1 Average Effort
Equilibrium Equilibrium Standard
effort effort Actual deviation Actual
Condition (maximize (maximize average within average
ind. payoff) joint payoff) effort condition profit
NONE (N = 36) *
(equal share between and
within teams) 0 0 9.47 7.75 110.53
WITHIN (N = 36)
(equal share between teams,
tournament within team) 20 0 25.07 7.84 94.93
BETWEEN (N = 36)
(tournament between teams,
equal share within team) 10 30 40.28 10.22 79.72
BOTH (N = 36)
(tournament between and
within teams) 30 30 47.15 4.00 72.35

22. Effort Levels: Comparisons
Table 2 Main Effect and Pairwise Comparisons (Effort)
Panel A: Main effect
Mean
Conditions N Difference t Value p-value
Team Incentive
(between-team tournament)
with vs. without 72 26.45 9.36 <0.0001 H1
Individual Incentive
(within-team tournament)
with vs. without 72 11.24 2.48 0.017 H2
Panel B: Pairwise comparisons
Mean
Conditions N Difference t Value p-value
BOTH vs. NONE 24 37.68 14.96 <0.0001
BOTH vs. WITHIN 24 22.08 8.69 <0.0001
H3
BOTH vs. BETWEEN 24 6.87 2.17 0.041
WITHIN vs. NONE 24 15.61 4.90 <0.0001
BETWEEN vs. WITHIN 24 15.21 4.09 0.0005
BETWEEN vs. NONE 24 30.82 8.32 <0.0001
The average effort of all six subjects in paired teams is considered as one independent unit of
observation.
22

23. Actual Effort (Figure)

24. Actual Effort (Figure)

25. Effort – TSCS Analysis
Table 3 Time Series Cross-Sectional Regression Results
Panel A: Main Effects
Independent variables DF Coefficient p-value
Intercept (NONE) 1 15.07 < 0.0001
Team (between-team) Tournament 1 26.45 < 0.0001 H1
Individual (within-team) Tournament 1 11.24 < 0.0001 H2
Period 1 -0.62 0.0155
Panel B: Hypothesis 3 (each condition compared with BOTH condition)
Independent variables DF Coefficient p-value
Intercept (BOTH) 1 50.57 < 0.0001
NONE 1 -37.68 < 0.0001
WITHIN 1 -22.08 < 0.0001 H3
BOTH 1 -6.87 0.0045
Period 1 -0.62 0.0155
Number of observations: 2880.
Dependent variable: Individual effort in each period.
25

26. Actual Effort (Within-subject Comparison)
Table 4 Within Subject Comparisons (Effort)
Mean
Differences (within subject) N Difference t Value p-value
BOTH - NONE 24 33.60 14.52 <0.0001
BOTH - WITHIN 24 25.85 7.13 <0.0001
BOTH - BETWEEN 24 13.54 5.76 <0.0001
WITHIN - NONE 24 20.94 5.60 <0.0001
BETWEEN - WITHIN 24 12.76 2.33 0.0290
BETWEEN - NONE 24 36.23 8.88 <0.0001
The average effort of all 10 periods for a subject is considered as one independent unit of
observation.
26

27. Free-riding (zero effort)

28. Free-riding (< 1/3 endowment)

29. Messages: descriptive
 7,671 messages recorded.
 Each experimental session:
 NONE: 289
 WITHIN: 288
 BETWEEN: 350
 BOTH: 353
 80 messages (on average) within a single
team in each part of each experimental
session.

30. Messages: coding
 For each team and each period,
 “1” – if a statement or argument showed up in a
given period and chat
 “0” – otherwise.
 960 observations in total.

31. Messages: categories
Table 6 Analysis of Communication
Panel A: Categories for coding messages
Category Description Relative frequency of coding "1"
NONE WITHIN BETWEEN BOTH
Cooperation
Ask for the opinions of other team
C1 members (may or may not
specifically refer to an effortlevel) 0.188 0.333 0.379 0.396
Proposal to choose high efforts
C2 within team
or state own choice of high efforts 0.129 0.167 0.654 0.692
Agree on team members’ proposals
C3 (high effort) 0.058 0.075 0.554 0.571
Give reasons why need to choose
C4 high efforts 0.025 0.008 0.104 0.146
Overall cooperation 0.263 0.413 0.725 0.750
Collusion
Proposal to choose low efforts
C5 within team 0.529 0.558 0.221 0.129
or state own choice of low efforts
Agree on team members’ proposals
C6 (low effort) 0.371 0.392 0.146 0.063
Proposal to take turns in winning
C7 the tournament 0 0.142 0 0.021
Give reasons why need to choose
C8 low efforts 0.233 0.167 0.050 0.038
Overall collusion 0.567 0.600 0.221 0.146
All messages within each team in each periodre taken as one observationunit for coding, resulting in 96 observations in total.
a 0
Within each observation, each category is coded as “one” if present and “zero” otherwise.

32. Messages: p-values
Table 4.6 Analysis of Communication (continued)
Panel B: Cooperation and collusion
Pairwise comparisons between condition coefficients
Category Description from logistic regressions (p-values)
BETWEEN
BETWEEN
BETWEEN
WITHIN
WITHIN
WITHIN
NONE
NONE
NONE
BOTH
BOTH
BOTH
vs.
vs.
vs.
vs.
vs.
vs.
Cooperation
C1 Ask for the opinions of *** 0.131 0.690 *** 0.266 **
other team members
Proposal to choose (or *** *** ***
C2 state own choice) high 0.371 *** 0.242
efforts within team
Agree on team *** *** ***
C3 members’ proposals 0.637 *** 0.463
(high effort)
C4 Give reasons why need *** *** 0.157 ** ** 0.173
to choose high efforts
Overall cooperation *** *** 0.519 *** *** **
Collusion
Proposal to choose (or
C5 state own choice of) *** *** * *** *** 0.517
low efforts within team
Agree on team
C6 members’ proposals *** *** * *** *** 0.636
(low effort)
Proposal to take turns
C7 in winning the N/A *** N/A N/A N/A N/A
tournament
C8 Give reasons why need *** *** 0.504 *** *** 0.067
to choose low efforts
Overall collusion *** *** * *** *** 0.456
The p-values reported in this table are based on Wald Chi-Square statistics.
***: p-value < 0.0001 **: p-value < 0.001 *: p-value < 0.05

33. Overall cooperation

34. Overall collusion

35. Implications
 MA incentive system design: one of few studies that
examine interactive effect of individual & team
incentive systems.
 Answer the call to examine incentive system combinations
(Bonner & Sprinkle 2002)
 Practical implication
 Management control system: mitigate moral hazard
problems.
 Multi-agent setting
 Extend original contest model
 More generalizable in practice

36. Thank you !