Birds of a Feather Sometimes

Flock TogetherTeam Leadership, Heterophily, and

Team Performance

Andrew KnightUniversity of Pennsylvania

Overview Team diversity

Leadership, heterophily, and performance

Method, Analyses, & Results

Implications

Team DiversityA Double-Edged Sword Benefits of diversity

Greater breadth of resources

Creativity Diversity as variety

Costs of diversity Opposing views Conflict Diversity as separation

Diversity as Separation

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Diversity as Variety

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Common Research Approaches:Group & Individual Level Link group-level metrics of diversity to group

processes Group processes: Cohesion, conflict Standard deviation as diversity metric

Link an individual’s average dissimilarity to individual-level outcomes Individual outcomes: Satisfaction, turnover Euclidean distance as diversity metric

General Assumption:Birds of a Feather Flock Together Homophily often assumed…

Team members have positive perceptions of similar others and negative perceptions of different others

Grounded in similarity-attraction theories

…but seldom tested Very little team diversity research at the dyad level Little exploration of actual relationships

Workplace may place boundary conditions around homophily Roles and role structures Leadership, power, social influence

Homophily varies across teams

Hypothesis 1:

Team Leadership

Team leaders set the tone for a team Shape team climates Reinforce certain types of behaviors Model appropriate behaviors

Leaders who take an inclusive approach may model positive cross-category relations

Leader inclusiveness is positively related to

heterophily

Hypothesis 2:

Heterophily & Team PerformanceBack to the Sword Gaining the benefits of diversity

Positive cross-category relations yield access to diverse information and resources

Diversity in KSAs aids in problem-solving, creativity

Avoiding the costs of diversity Positive relations aid in coordination Positive relations mitigate the effects of conflict

Heterophily is positively related to team performance

Hypothesis 3:

Method: Research Setting Team-based military

competition

9-person teams navigate a 9km obstacle course

Teams train for nearly 4 months to prepare for the one-day event

Method: Sample

33 teams Composed of cadets from the hosting academy Training teams ranged from 10 to 16 members

381 individuals 86% male 79% White Mean age = 20.3 (SD = 1.4)

Method: Procedure

CompetitionStart of Formal

Training

Team Roster

Confirmed

Time 1 Survey

Time 2 Survey

Time 3 Survey

Time 4 Survey

OPORD Published

Month 1 Month 2 Month 3 Month 4

Method: Predictor Variables (All at T1) Team-level

Leader inclusiveness: 5-item scale completed by team leader “Effective team leaders carefully weigh members’ opinions.”

Controls: Prior competition experience, Athletic GPA, Military GPA

Individual-level Class (i.e., freshman, sophomore, junior, senior) Gender Branch choice (e.g., infantry, artillery, medical, intelligence)

Dyad-level Same or different category membership for class, gender, branch

choice (0 = Same; 1 = Different)

Method: Criterion Variables

Dyad-level criterion: Friendship (T3) “How much did you socialize with X in your free time

during the past week?” Members rated one another on a 5-point scale in a round-

robin fashion

Team-level criterion: Team performance (T5) Team total score in the military competition Scored by trained competition officials

Analyses: The Social Relations Model via RCM Random coefficient model

Random intercepts for team, actor, partner Estimate A-P covariance and within-dyad covariance Random slope for “homophily” effects Fixed effects for category membership and diversity

Extracted homophily slope coefficients to test team performance hypothesis

SAS PROC MIXED

Analyses: Sample SAS PROC MIXED Codeproc mixed covtest data=t3srm; class dyad GROUP actbranch partbranch;

model frd = actbranch partbranch difbranch leader leader*difbranch / solution ddfm=SATTERTH;

random a1-a16 p1-p16 intercept

difbranch / solution sub=group type=lin(5) ldata=g;

repeated / type=cs sub=dyad(group);

ODS Output SolutionR = r_difbranch;run;

Fixed Effects

Random Intercepts

Within-Dyad Cov

Output Coeffs.

Cross-Level Int.

Random Slope

Results: Null Variance Decomposition

% σ2

Team 1.2%

Actor 43.5%

Partner 6.5%

Dyad & Residual

48.8%

Reciprocity

Generalized: .32

Dyadic: .61

Results: ClassLeadership & Variance in Homophily

Model 1 Model 2 Model 3

Actor Class .02 .01 .01

Partner Class .05* .04 .04

Class Difference -1.03** -.96** -1.64**

Leader Inclusiveness .03 -.04

Class Difference X Leader Inclusiveness .20*

-2 Log Likelihood 6288.4 5800.8 5797.8

AIC 6302.4 5814.8 5811.8

Results: ClassLeadership & Variance in Homophily

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2.75

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Minus 1 SD Plus 1 SD

Leader Inclusiveness

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DifClassSameClass

Results: GenderLeadership & Variance in Homophily

Model 1

B

Model 2

B

Model 3

B

Actor Gender -.11 -.08 -.08

Partner Gender -.20 -.20 -.21

Gender Difference -.38** -.37** -.60+

Leader Inclusiveness .11 .09

Gender Difference X Leader Inclusiveness .07

-2 Log Likelihood 6590.1 6029.6 6032.0

AIC 6604.1 6043.6 6046.0

Results: BranchLeadership & Variance in Homophily

Model 1B

Model 2

B

Model 3

B

Actor Branch Choice ns ns ns

Partner Branch Choice ** ** **

Branch Choice Difference -.09 .09 -2.41*

Leader Inclusiveness .09 .17

Branch Difference X Leader Inclusiveness 2.27*

-2 Log Likelihood 6466.2 5966.0 5964.7

AIC 6480.2 5980.0 5978.7

Results: Branch ChoiceLeadership & Variance in Homophily

2.5

2.75

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3.25

3.5

Minus 1 SD Plus 1 SD

Leader Inclusiveness

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DifBranch

SameBranch

Results:Heterophily and Team Performance

Model 1β

Model 2

β

Team Military Ability .01 .02

Team Athletic Ability .19 .20

Team Experience .41* .38*

Branch Heterophily -.20

Gender Heterophily .05

Class Heterophily .46**

F 3.41* 3.17*

R2 .26 .42

Discussion: Summary of Results Variance in homophily across teams

Supported for class, gender, branch choice Models including random slopes were a better fit for the data

Leadership predicts heterophily Supported for class and branch choice Members of teams with inclusive leaders are more likely to form friends

with members of different classes and military branches

Heterophily predicts team performance Supported only for class Teams with heterophilous relationships with respect to class perform

better in the military competition

Discussion: Implications of Results Birds of a feather sometimes flock together

Key assumption may not hold in all work teams Some teams are more heterophilous than others

Leaders may shape relational patterns in teams Inclusive leaders model positive cross-category relations Leadership as a lever for maximizing the benefits and

minimizing the costs of diversity

Diversity can help team performance if homophily is not the rule Key assumption of team diversity literature limits benefits To benefit from diversity, teams may need heterophily

Discussion: Broader Implications for Team Diversity

Examining diversity effects at the dyad level A fine-grained look at diversity Relationships are building blocks of team processes

A multilevel approach to studying diversity Group composition research is inherently multilevel Dyadic approach helps “unpack” variance

Develop and test comprehensive theories of team composition with precision