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Why Should I Trust You? Predictors of Interpersonal Trust in a Knowledge Transfer Context. Daniel Z. Levin Rutgers University Rob Cross University of Virginia Lisa C. Abrams IBM Institute for Knowledge-based Organizations. Theoretical Background.

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why should i trust you predictors of interpersonal trust in a knowledge transfer context

Why Should I Trust You?Predictors of Interpersonal Trust in a Knowledge Transfer Context

Daniel Z. LevinRutgers University

Rob Cross University of Virginia

Lisa C. AbramsIBM Institute for Knowledge-based Organizations

theoretical background
Theoretical Background
  • Knowledge creation and transfer are critical for organizations (Argote 1999; Kogut & Zander 1992, 1996; Spender 1996)
  • Relationships—especially trust—are key to the success of knowledge transfer (Levin, Cross, & Abrams 2002; Tsai & Ghoshal 1998; Uzzi 1997)
  • Yet only limited systematic empirical work on predictors of interpersonal trust, in general and especially in this context
slide3
Research Question:What Factors Predict a Knowledge Seeker’s Trust in the Benevolence and Competence of a Knowledge Source?
a multi level approach 3 categories of trust predictors
A Multi-Level Approach:3 Categories of Trust Predictors

“Alter”

“Ego”

Relationship

Knowledge

Seeker

Source

Source

Source

Source

survey methods
Survey Methods
  • Two-stage, critical-incident, egocentric network survey
  • Three companies: U.S. drug co., Canadian oil & gas co., U.K. bank
  • 127 respondents reported on 4 relationships (n=508), response rate=48%
  • Controls: formal structure; seeker’s own expertise
  • Hierarchical linear modeling for nested data
significant predictors of trust
Competence

Shared Vision

Shared Language

Unavailable Source

Discreet Source

Younger Seeker

Interaction Effect

Benevolence

Strong Ties

Shared Vision

Shared Language

Discreet Source

Receptive Source

Younger Seeker

Hi-Tenure Seeker

Significant Predictors of Trust

vs.

variables in all 3 categories were statistically significant
Competence

Shared Vision

Shared Language

Unavailable Source

Discreet Source

Younger Seeker

+ Interaction Effect

Benevolence

Strong Ties

Shared Vision

Shared Language

Discreet Source

Receptive Source

Younger Seeker

Hi-Tenure Seeker

Variables in All 3 Categories Were Statistically Significant
1 benevolence based trust was easier to predict than competence based trust
(1) Benevolence-based Trust Was Easier to Predict than Competence-based Trust
  • In terms of the number of significant predictors
  • In terms of the variance accounted for
    • R-squared for benevolence = .66
    • R-squared for competence = .48
2 trust is not set in stone
(2) Trust Is Not Set in Stone…

Malleable features:

  • Discreet source
  • Shared vision
  • Shared language

Stable and visible features:

  • Formal structure
  • Homophily(same age & gender)

Big Effect

X

No Effect

3 but attitudes in the trust realm may solidify over time
(3) …But Attitudes in the Trust Realm May Solidify Over Time
  • Knowledge seekers evaluate alter’s behavior to find “clues for competence”
  • Clues = discreet & busy (i.e., unavailable)
  • Interaction effect for division tenure:

The more tenure that knowledge

seekers have… the more they rely

on the “clues for competence”

contribution
Contribution…
  • …to practice:Building trust is a feasible and inexpensive way to improve the flow of knowledge
  • …to social network and trust lit.:Theoretical benefits to examining different types of trust
  • …to org. learning and knowledge lit.:Better understanding of factors underlying the success of trust and knowledge transfer