Paper Type

ERF

Abstract

With the fast-developing growth of conversational agents (CAs), particularly social CAs, this study aims to investigate users' intentions to share personal information. Users' more profound and broad engagement with social CAs has raised concerns over data privacy, so drawing on Protection Motivation Theory (PMT), the current study evaluates the effect of perceived severity threat and perceived data security vulnerability as the threat appraisals and self-efficacy in information security (SEIS), response efficacy, and response cost as the factors associated with coping appraisals on shaping disclosure behaviors with social ACs. The methodology includes a survey that evaluates the effect of social CA users' cognitive appraisals on their intention to self-disclosure. This study's findings help better understand the impact of security perception on users' behavior and provide recommendations for developers in creating more secure and transparent CA interactions.

Paper Number

1620

Comments

SIGAIAA

Share

COinS
 
Aug 16th, 12:00 AM

Securing the Dialogue: “Perceived Security Threats and Information Security Literacy in the Age of Social Conversational Agents”

With the fast-developing growth of conversational agents (CAs), particularly social CAs, this study aims to investigate users' intentions to share personal information. Users' more profound and broad engagement with social CAs has raised concerns over data privacy, so drawing on Protection Motivation Theory (PMT), the current study evaluates the effect of perceived severity threat and perceived data security vulnerability as the threat appraisals and self-efficacy in information security (SEIS), response efficacy, and response cost as the factors associated with coping appraisals on shaping disclosure behaviors with social ACs. The methodology includes a survey that evaluates the effect of social CA users' cognitive appraisals on their intention to self-disclosure. This study's findings help better understand the impact of security perception on users' behavior and provide recommendations for developers in creating more secure and transparent CA interactions.

When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.