TY - JOUR
T1 - Understanding online safety behaviors
T2 - A protection motivation theory perspective
AU - Tsai, Hsin-Yi
AU - Jiang, Mengtian
AU - Alhabash, Saleem
AU - Larose, Robert
AU - Rifon, Nora J.
AU - Cotten, Shelia R.
PY - 2016/6/1
Y1 - 2016/6/1
N2 - Internet users experience a variety of online security threats that require them to enact safety precautions. Protection motivation theory (PMT) provides a theoretical framework for understanding Internet users' security protection that has informed past research. Ongoing research on online safety recommends new motivational factors that are integrated here in a PMT framework for the first time. Using PMT, a cross-sectional survey (N = 988) of Amazon Mechanical Turk (MTurk) users was conducted to examine how classical and new PMT factors predicted security intentions. Coping appraisal variables were the strongest predictors of online safety intentions, especially habit strength, response efficacy, and personal responsibility. Threat severity was also a significant predictor. Incorporating additional factors (i.e., prior experiences, subjective norms, habit strength, perceived security support, and personal responsibility) into the conventional PMT model increased the model's explanatory power by 15%. Findings are discussed in relation to advancing PMT within the context of online security for home computer users.
AB - Internet users experience a variety of online security threats that require them to enact safety precautions. Protection motivation theory (PMT) provides a theoretical framework for understanding Internet users' security protection that has informed past research. Ongoing research on online safety recommends new motivational factors that are integrated here in a PMT framework for the first time. Using PMT, a cross-sectional survey (N = 988) of Amazon Mechanical Turk (MTurk) users was conducted to examine how classical and new PMT factors predicted security intentions. Coping appraisal variables were the strongest predictors of online safety intentions, especially habit strength, response efficacy, and personal responsibility. Threat severity was also a significant predictor. Incorporating additional factors (i.e., prior experiences, subjective norms, habit strength, perceived security support, and personal responsibility) into the conventional PMT model increased the model's explanatory power by 15%. Findings are discussed in relation to advancing PMT within the context of online security for home computer users.
KW - Computer security
KW - Habit strength
KW - Online safety
KW - Protection motivation theory
KW - Response cost
UR - http://www.scopus.com/inward/record.url?scp=84962231373&partnerID=8YFLogxK
U2 - 10.1016/j.cose.2016.02.009
DO - 10.1016/j.cose.2016.02.009
M3 - Article
AN - SCOPUS:84962231373
SN - 0167-4048
VL - 59
SP - 138
EP - 150
JO - Computers and Security
JF - Computers and Security
ER -