Social network positions of trust,credibility, prototypicality and social

comparison: An examination ofinfluence factors in an internet

community

Aleks KrotoskiSPERI

University of SurreyBPS Social Section Conference7 September 2006

Overview

• Introduction• Statement of Aims• Method

– Procedure and respondents– Multi-level Modelling– Hypotheses

• Results• Discussion• Conclusions

Introduction: Persuasion

• Persuasion in online environments– Lean medium?– Effects on individual

• Deindividuation, social exclusion, loneliness– Elaboration Likelihood Model of Persuasion (Petty

&Cacioppo, 1986)• Peripheral route?

– Source factors• Central route?

– Message• Mediator: immersion

Introduction: Persuasion

• Persuasion in online environments– Social Identity Deinviduation Effects (Spears

& Lea, 1992)• Conformity with a perceived social identity• Assumptions of anonymity

– Dynamic Social Impact Theory (Latané & Bourgeois, 2001)

• Cultural homogeneity = proximity

Introduction: Persuasion

• Structural properties of persuasion– Social Network Analysis

• Influence:– Presence of tie– Strength of tie

Communication Network

Introduction: Persuasion

• Structural properties of persuasion– Social Network Analysis

• Influence:– Presence of tie– Strength of tie– Social Learning (Bandura et al, 1977)– Structural Equivalence (Burt, 1987)

• Analytic techniques to pinpoint influential actors• Measurement process to define network structures

Statement of Aims

• This paper examines the contribution of social network variables as predictors of persuasion.

• Specifically, I look at the different contributions which communication modes have on persuasion in an online community context.

Hypotheses

• Ratings of communication network tie strength for different communication modes (e.g., public, private and offline) will contribute more predictive power for estimates of persuasion than a general communication score.

• Communication tie strength for different communication modes will be a greater mediator of persuasion as communication privacy increases.

Method: Procedure and Respondents

• Second Life– Immersive Virtual community

• “Virtual pub” (Kendall, 2002)• “Third Place” (Deucheneaut & Moore, 2004)

Method:Procedure and Respondents

• Second Life– Immersive Virtual community

• “Virtual pub” (Kendall, 2002)• “Third Place” (Ducheneaut & Moore, 2004)

– Virtual identity• “Avatar”-representation

– Synchronous, typed communication:• Public communication• Instant Message communication (Garton et al, 1997)

Methods:Procedure and Respondents

• Online survey • 3 April 2006 – 8 June 2006

• Sociometric data collection• 43 Residents

– Age: M= 32.9 years, SD = 8.13– Offline gender: male 76.7%, female 23.3%– Online gender: male 67.4%, female 32.6%)

• 657 avatars, 539 scores

Methods:Independent Variables

• Social Network Communication– 4 questions (α=0.782) (Garton et al, 1997; Correll,

1995)• General communication• Online public communication• Online private communication• Offline communication

Methods:Dependent Variables

• Prototypicality (Self-categorization theory: Turner et al, 1987)– One question: SIDE (Spears & Lea, 1991; Sassenberg & Postmes, 2002;

Postmes, 2001)• Source Credibility (Renn & Levine, 1991)

– Four questions (α=0.862): Perceived expertise, likeability, believability• Social Comparison (Perez & Mugny, 1996)

– Two questions (α=0.849): ATSCI (Lennox & Wolfe, 1984)• General Trust

– Four questions (α=0.874):honesty (Renn & Levine, 1991), care (Poortinga & Pidgeon, 2003), similarity (Cvetkovich, 1999), trustworthiness (Renn & Levine, 1991)

• Domain-Specific Trust (Renn & Levine, 1991)– Four questions (α=0.882): objectivity, honesty, perceived expertise,

reliability

Method: MLM

• Multi-Level Modelling (models)– Fixed models:

– Single explanatory variable:

– Multiple explanatory variables:

Results: Single explanatory variable (General Communication)

y β0 (Std. Error)

β (Std. Error)

σ2e Loglikelihood

(fixed model LL)

Prototypicality 0.026 (0.101)

0.305 (0.066)

0.543 (0.035)

1292.354T (1335.299)

Credibility -0.093 (0.102)

0.519 (0.071)

0.531 (0.035)

1272.354T

(1404.954)

Social Comparison -0.098 (0.118)

0.399 (0.064)

0.408 (0.027)

987.966T

(1132.416)

General Trust -0.135 (0.098)

0.645 (0.064)

0.408 (0.027)

1114.31T

(1345.777)

Domain-Specific Trust 0.035 (0.125)

0.271 (0.055)

0.347 (0.023)

1086.919T

(1141.021)*N=538; **N=539; σ2

e: variance accounted for between avatars; Tp<0.000, df=2

• The predictive power of the estimate of the value of this measure of General Trust is positively enhanced when we know how often two people communicate in general.

Single explanatory variable: General Trust & SNC categoriesExplanatory Variable β0 (Std.

Error)β (Std. Error)

σ2e Loglikelihood

(fixed model LL)

Online Public Communication

0.085 (0.093) 0.370 (0.052)

0.476 (0.031)

1124.182T

(1345.777)

Online Private Communication

0.070 (0.094) 0.442 (0.062)

0.407 (0.027)

1115.396T

(1345.777)

Offline Communication

0.070 (0.090) 0.459 (0.047)

0.427 (0.028)

1159.681T

(1345.777)N=539; σ2

e: variance accounted for between avatars; Tp<0.000, df=2

• Effect of interpersonal closeness on mode of communication (e.g., Garton et al, 1997)

• Offline communication contributes the most to the estimate of General Trust. Online public communication contributes the least.

Results: Multiple explanatory variables (General Trust)

Explanatory Variable β0 (Std. Error)

β1 (Std. Error)

β2 (Std. Error)

σ2e Loglikelihood (fixed

model LL)

Online public + online private communication

0.065 (0.121)

0.104 (0.057)

0.375 (0.074)

0.394 (0.026)

1144.879T

(1224.182)

Online public + offline communication

0.059 (0.085)

0.399 (0.051)

0.291 (0.051)

0.332 (0.022)

1057.941T

(1224.182)

Online private and offline communication

0.052 (0.087)

0.345 (0.057)

0.328 (0.046)

0.314 (0.021)

1038.486T

(1115.396)N=539; σ2

e: variance accounted for between avatars; Tp<0.000, df=3; *model rejected on basis of ill-fit

• Greatest improvement to the fit of a model occurs when offline communication scores are added to the single-variable public communication model

• Adding online private communication to the online public communication model renders the weight of online public communication insignificant, so this model is rejected.

Summary• Social network variables as mediators of persuasion

variables– Empirical assessment of SNA assumptions– Greatest effects on General Trust

• Communication tie strength’s effect on General Trust increases as communication becomes more private/intimate

• Supportive of Garton et al (1997) and others’ social network analysis work

• Communication mode tie strength effect less predictive than “general” strength measure

Conclusions

• Review of aims• Implications

– Use of SN measurements in Social Psychology– Assessing assumptions of cohesion made by Social

Network Analysis• Further research

– Comparison with different types of network (e.g, trust-based)

– Larger dataset (currently in collection)– Network position effects on social influence

Thank you

A.Krotoski@surrey.ac.ukSPERI

University of Surrey

BPS Social Section Conference7 September 2006