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Session 2 2012 ima presentation compensation system
 

Session 2 2012 ima presentation compensation system

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    Session 2 2012 ima presentation compensation system Session 2 2012 ima presentation compensation system Presentation Transcript

    • Management Compensation System: -- Adding Tournament to Tournament: The Interactive Effect of Individual and Team IncentivesYu TianKenneth G. Dixon School of AccountingUniversity of Central Florida IMA Carolinas Winter Conference February 17, 2012
    • 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
    • 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
    • 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 ???
    • 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?
    • Figure 1 Individual compensation (within-team) Tournament Tournament (No) (Yes ) Tournament (No) NONE WITHIN Team Compensation (between-team) Tournament (Yes ) BETWEEN BOTH
    • 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.
    • 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
    • The Model – Individual ShareStep 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”
    • 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 ofindividual 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
    • Model Prediction (Baseline)(maximizing joint payoff: joint NE)
    • Model Prediction (Baseline)(maximizing individual payoff: symmetric NE)
    • 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
    • SIT Prediction (Hypotheses) Effort Between: NO Between: YES Within: NO Within: YES Between: between team tournament Within: within team tournament
    • 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
    • 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.
    • Z-tree: Welcome
    • Z-tree: Chat
    • Z-tree: Decision
    • Z-tree: Feedback
    • 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 profitNONE (N = 36) *(equal share between andwithin teams) 0 0 9.47 7.75 110.53WITHIN (N = 36)(equal share between teams, tournament within team) 20 0 25.07 7.84 94.93BETWEEN (N = 36)(tournament between teams,equal share within team) 10 30 40.28 10.22 79.72BOTH (N = 36)(tournament between andwithin teams) 30 30 47.15 4.00 72.35
    • 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
    • Actual Effort (Figure)
    • Actual Effort (Figure)
    • 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
    • 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
    • Free-riding (zero effort)
    • Free-riding (< 1/3 endowment)
    • 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.
    • 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.
    • 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.
    • 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
    • Overall cooperation
    • Overall collusion
    • 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
    • Thank you !