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Role of Affect in Self-Disclosure on Social Network Websites: A Test of Two Competing Models

Research output: Contribution to journalArticlepeer-review

95 Scopus citations

Abstract

This study examines how affect influences self-disclosure on social network (SN) websites. We test two competing models that build on direct causation theory and affect heuristic theory, respectively. In a direct effect model, affect steers self-disclosure, independent of cognitive cost-benefit appraisals. The indirect effect model instead suggests that affect influences self-disclosure by adjusting perceptions of benefits and costs. The empirical comparison of the models relies on survey data from more than 500 university students. Overall, affect influences self-disclosure indirectly by adjusting the benefits people perceive. In particular, affect toward self-disclosure and toward SN websites relate positively to self-disclosure motivators; their perceived values appear amplified in the presence of positive affect. We also offer a plausible, alternative explanation of the observed positive relationship between privacy risk and self-disclosure according to an indirect effect model, in which self-disclosure is driven mainly by motivators, whereas the effects of inhibitors depend a posteriori on self-disclosure. These findings call for a reconsideration of any exclusive focus on the direct impacts of affect on technology use, as is common in previous research, and suggest the importance of affective factors for understanding social technology uses and managing customer relationships.

Original languageEnglish
Pages (from-to)239-277
Number of pages39
JournalJournal of Management Information Systems
Volume32
Issue number2
DOIs
StatePublished - 3 Apr 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
Copyright © Taylor & Francis Group, LLC.

Keywords

  • affect
  • dual processing approach
  • online privacy
  • online self-disclosure
  • social network sites

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science Applications
  • Management Science and Operations Research
  • Information Systems and Management

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