CCE Theses and Dissertations

Date of Award

2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

College of Engineering and Computing

Advisor

Ling Wang

Committee Member

Inkyoung Hur

Committee Member

Souren Paul

Abstract

The popularization of professional virtual communities (PVCs) as a platform for people to share experiences and knowledge has produced a paradox of convenience versus security. The desire to communicate results in disclosure where users experience ongoing professional and social interaction. Excessive disclosure and unsecured user security behavior in PVCs increase users’ vulnerability to technology threats. Nefarious entities frequently use PVCs such as LinkedIn to launch digital attacks. Hence, users are faced with a gamut of technology threats that may cause harm to professional and personal lives. Few studies, however, have examined users’ information security behavior and their motivation to engage in technology threat avoidance behavior in a PVC.

This study tested a professional virtual community technology threat avoidance model empirically. The model was developed from the conceptualization of different aspects of the technology threat avoidance theory, social cognitive theory, and involvement theory through an integrated approach. This quantitative study employed a random sampling methodology. Prior to collecting data for the main study an expert panel review and a pilot study were conducted. A web-based survey designed with a 5-point Likert scale was distributed to 1285 LinkedIn members to gather self-reported data on users’ technology threat avoidance behavior. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to analyze the data gathered from 380 respondents.

The results of the data analysis revealed that perceived susceptibility, perceived severity, and information security knowledge sharing are strong predictors of avoidance motivation. Information security knowledge sharing had the most significant predicting effect on avoidance motivation in PVCs. Also, self-efficacy, group norms, and avoidance motivation all have a significant predicting effect on users’ information security avoidance behavior in PVCs. However, information security experience and safeguarding measure cost do not have a significant predicting effect on users’ information security avoidance motivation. This study makes significant contributions to the IS body of knowledge and has implications for practitioners and academics. This study offers a comprehensive model through the integration of behavioral and cognitive theories to better understand user information security behavior in PVCs. The model also identifies essential elements to motivate users to engage in technology threat avoidance behavior.

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