Coevolution of Members Attachment to the Team and Team Interpersonal Networks - PowerPoint PPT Presentation

1 / 35
About This Presentation
Title:

Coevolution of Members Attachment to the Team and Team Interpersonal Networks

Description:

We want to extend special thanks to Christian Steglich from University of ... Contractor, Wasserman, & Faust (in press). Academy of Management Review. ... – PowerPoint PPT presentation

Number of Views:48
Avg rating:3.0/5.0
Slides: 36
Provided by: chun66
Category:

less

Transcript and Presenter's Notes

Title: Coevolution of Members Attachment to the Team and Team Interpersonal Networks


1
Co-evolution of Members Attachment to the Team
and Team Interpersonal Networks
  • Chunke Su
  • Noshir Contractor
  • University of Illinois at Urbana-Champaign
  • Katherine J. Klein
  • University of Maryland at College Park

Dynamics of Networks and Behavior Satellite
symposium, XXII International Sunbelt Social
Network Conference, Portoro, Slovenia, May 11,
2004
2
Acknowledgements
  • We want to extend special thanks to Christian
    Steglich from University of Groningen for his
    efforts helping us trouble shoot problems and
    providing suggestions for data analyses and
    interpretation.
  • Christian will use the data from this study
    for the SIENA demo this afternoon

3
Research Issues
  • This study examines the dynamic co-evolution of
    individuals attachment to project teams (an
    attribute) and their friendship network
    relationships with other individuals in the team.
  • How does interpersonal friendship network evolve
    over time?
  • How do team members feelings of attachment to
    the team influence their friendship network over
    time?
  • How does team members friendship network
    influence their feelings of attachment to the
    team over time?

4
WHY DO WE CREATE, MAINTAIN, DISSOLVE, AND
RECONSTITUTE OUR NETWORK LINKS?
5
Monge, P. R. Contractor, N. S. (2003).
Theories of Communication Networks. New York
Oxford University Press.
6
Multi-theoretical Multilevel Model (MTML)
  • Theories of self-interest
  • Theories of mutual interest
  • Theories of social and resource exchange
  • Theories of contagion
  • Theories of balance
  • Theories of homophily
  • Theories of proximity
  • Theories of uncertainty reduction
  • Theories of co-evolution

Sources Contractor, Wasserman, Faust (in
press). Academy of Management Review. Monge, P.
R. Contractor, N. S. (2003). Theories of
Communication Networks. New YorkOxford
University Press.
7
Model 1 Creating TiesEndogenous Influence of
the Network
  • Social Exchange Theory Individuals are more
    likely to reciprocate friendship ties with those
    who have created ties with them at previous
    times.
  • Balance Theory Individuals are more likely to
    create ties with friends of their friends.

8
Model 2 Maintaining Dissolving Ties
Endogenous Influence of the Network
  • Social Exchange Theory Individuals are more
    likely to maintain reciprocated friendship ties
    with those who have previously created ties
    with them.
  • Social Exchange Theory Individuals are less
    likely to dissolve ties reciprocated friendship
    ties with those who have previously created ties
    with them.

9
Model 3 Exogenous Attribute Influence on the
Network
  • Homophily Theory Individuals are more likely to
    create friendship ties with those who have
    similar attachment to the team.
  • Theory of Self-interest Individuals are less
    likely to create ties with those who have high
    attachment to the team since they feel well
    connected to the team.
  • Theory of Self-interest Individuals with high
    team attachment are less likely to create ties
    since they feel well connected.

10
Model 4 Network Influence on Actor Attachment
  • Contagion Theory Individuals are more likely to
    have similar attachment to those members of team
    with who they have ties.

11
Model 5 Co-evolution of Network Evolution and
Actor Attributes
  • Simultaneous assessment of Models 1 through 4

12
Participants
  • Longitudinal survey data were collected from a
    residential, team-based, 10-month long national
    service program (the National Civilian Community
    Corps, part of the U.S. federal government
    program, Americorps).
  • Teams performed diverse service projects,
    typically varying in length from one to two
    months (e.g., tutoring elementary school
    children, mentoring homeless youth, coordinating
    after-school activities for teens).
  • Team members received an educational grant and a
    modest stipend in return. Each team was led by a
    formally designated team leader, chosen by the
    program administrators not by team members to
    lead the team.
  • Teams in the program ranged in size from 9 to 12.
    Members are predominantly female (68) and white
    (82). Team members ranged in age from 17 to 25
    (M 20.80 years, SD 1.93).

13
Data collection
  • Data were collected from 3 teams (N12, 12, 11)
    at 3 points in time.
  • T1 within the first two weeks following team
    formation
  • T2 five months after team formation
  • T3 ten months after team formation
  • Demographic information
  • Gender
  • 21 female members (60)
  • 13 male members (37)
  • 1 didnt disclose gender info
  • Ethnicity
  • 31 Caucasian (89)
  • 2 Asian (6)
  • 1 European mix (3)
  • 1 didnt disclose ethnic info

14
Attachment to the Team
  • Individual report of ones attachment to the team
    (abbr. AT)
  • Questions
  • 1. If given the chance, I would choose to leave
    my team and join another. (Reverse score)
  • 2. I get along well with the members of my team.
  • 3. I will readily defend the members of my team
    from criticism by outsiders.
  • 4. I feel that I am really part of my team.
  • 5. I look forward to being with members of my
    team each day.
  • 6. I find that I do not usually get along with
    the other members of my team. (Reverse score)
  • Measurement scales 5-point Likert scale
  • Strongly disagree (1) to strongly agree (5)

15
Friendship Network
  • Friendship networks
  • Is this person a good friend of yours, someone
    you socialize with during your free time?
  • Scales from Baldwin, Bedell, and Johnson
    (1997)
  • Measurement binary scale
  • yes1 no0

16
Analysis
  • SIENA (Simulation Investigation for Empirical
    Network Analysis) a computer program that
    carries out the statistical estimation of models
    for longitudinal social networks according to the
    dynamic actor-oriented model of Snijders and van
    Duijn (1997) and Snijders (2001).

17
Descriptive Statistics 1 Attachment to the team
18
Descriptive Statistics 2 Friendship Networks
19
Network Visualization
20
Outline of data analysis
  • Model 1 Endogenous network evolution - objective
    function
  • Model 2 Endogenous network evolution - objective
    endowment function
  • Model 3 Exogenous network evolution influenced
    by actor attributes
  • Model 5 Co-evolution of network and actor
    attributes
  • Model 4 Actor attributes influenced by network
    evolution

21
Analysis Results Model 1 Endogenous Evolution
of Network (Creating Ties) Objective function
Significant at 0.05 level
22
Analysis Results Model 1 Endogenous Evolution
of Network (Creating Ties) Objective function
  • Utility (actor i's friendship network)
  • -1.96 x ( of outgoing friendship ties of
    actor i)
  • 1.18 x ( of reciprocated friendship ties of
    actor i)
  • 0.25 x ( of transitive friendship triplets in
    which actor i is the focal actor)
  • For actor i to establish a friendship tie, there
    is a cost of 1.96 attached.
  • If the tie is reciprocated, there is also a
    benefit of 1.18, thus the net cost of a
    reciprocated tie is 0.78.
  • If the friendship tie shortens a 2-path igtjgtk to
    a direct tie igtk (i.e., when the triplet i,j,k is
    a transitive triplet), there is an additional
    benefit of 0.25. Since there may be multiple such
    triplets, the net value of one particular
    friendship tie may become positive.

23
Analysis Results Model 1 Endogenous Evolution
of Network (Creating Ties) Objective function
  • Team members tend NOT to be friends with other
    members over time.
  • Team members tend to reciprocate friendship ties
    with other members over time. (social exchange)
  • Team members tend to be friends with their
    friends friends over time. (balance)

X
I
J
I
J
I
J
I
J
K
K
I
I
J
J
Time 1
Time 2
24
Analysis Results Model 2 Endogenous Evolution
of Network (Maintaining and Dissolving Ties)
Objective Endowment function
25
Analysis Results Model 3 Exogenous Influence of
Actor Attribute on Network Evolution
Significant at 0.05 level
26
Analysis Results Model 3 Exogenous Influence
of Actor Attribute on Network Evolution
  • Utility (actor i's friendship network)
  • -0.94 x ( of outgoing friendship ties of
    actor i)
  • 0.59 x ( of actor is friendship ties with
    other actors who have similar levels of
    team attachment)
  • - 0.41 x (sum of attachment scores for actor is
    friends)
  • For actor i to establish a friendship tie, there
    is a cost of 0.94 attached.
  • If the friendship tie is to someone who has an
    identical level of team attachment, there is a
    benefit of 0.59, thus the net cost of
    establishing a friendship tie is reduced to 0.35.
  • However, if the tie is to someone who has a high
    level of team attachment, the cost increases. For
    a unit of increase in team attachment of the
    alter, the cost of establishing a friendship tie
    from actor i to the alter increases by 0.41.

27
Analysis Results Model 3 Exogenous Influence
of Actor Attribute on Network Evolution
X
  • Team members tend NOT to be friends with other
    members over time.
  • Over time, team members tend to be friends with
    other members who have similar levels of team
    attachment as they do.
  • (homophily)
  • Over time, team members tend to be friends with
    other members who report to have low levels of
    team attachment.

I
J
I
J
HAT
HAT
HAT
HAT
LAT
LAT
LAT
LAT
I
HAT
I
HAT
J
LAT
J
LAT
Time 1
Time 2
28
Analysis Results Model 4Influence of Network on
Evolution of Actor Attributes
Significant at 0.05 level
29
Analysis Results Model 5 Coevolution of
Network Attributes
Significant at 0.05 level
30
Analysis Results Model 5 Co-evolution of
Network Actor Attributes
  • Utility (actor i's friendship network)
  • -0.21 x ( of outgoing friendship ties of
    actor i)
  • 1.18 x ( of reciprocated friendship ties of
    actor i)
  • 0.23 x ( of transitive friendship triplets in
    which actor i is the focal actor)
  • - 0.51 x (sum of attachment scores for actor is
    friends)
  • If the friendship tie from actor i to the alter
    is reciprocated, there is a benefit of 1.18 from
    establishing such a tie.
  • If the friendship tie shortens a 2-path igtjgtk to
    a direct tie igtk (i.e., when the triplet i,j,k is
    a transitive triplet), there is an additional
    benefit of 0.23.
  • However, if the tie is to someone who has a high
    level of team attachment, the cost increases. For
    a unit of increase in team attachment of the
    alter, the cost of establishing a friendship tie
    from actor i to the alter increases by 0.51.

31
Analysis Results Model 5 Co-evolution of
Network Actor Attributes
  • Team members tend to reciprocate friendship ties
    with other members over time.
  • Team members tend to be friends with their
    friends friends over time.
  • Over time, team members tend to be friends with
    other members who report to have low levels of
    team attachment.

I
J
I
J
J
J
I
I
K
K
I
HAT
I
HAT
J
LAT
J
LAT
Time 1
Time 2
32
Theoretical Analytical Issues I
  • Additional theoretical mechanisms contagion by
    structural equivalence (influence), theories of
    collective action (selection), cognitive theories
    (cognitive social structures).
  • Sample size for behavioral attributes is N
    while size for relations are N(N-1). Hence
    difference in power and standard errors.
  • Time scale for behavioral changes may be lower
    than for network relations.

33
Theoretical Analytical Issues II
  • Additional analysis using 97 more teams and 2
    more relations advice and adversarial between
    project teams.
  • Omnibus goodness of fit tests for adequacy of
    model and comparison between models (Michael
    Schweinberger) .
  • Meta-analysis across multiple teams versus one
    large data set of multiple teams (Andrea Knecht
    and Chris Baerveldt).

34
  • More information on University of Illinois
    network research, laboratory, book, doctoral
    fellowships, post-docs, research scientist
  • nosh_at_uiuc.edu
  • www.uiuc.edu/ph/www/nosh

35
Thank you!
Write a Comment
User Comments (0)
About PowerShow.com