Evaluating User Behavior in Smartphone Security: A Psychometric Perspective
Abstract
Smartphones have become an essential part of our modern
society. Their popularity and ever-increasing relevance in our
daily lives make these devices an integral part of our comput-
ing ecosystem. Yet, we know little about smartphone users
and their security behaviors. In this paper, we report our de-
velopment and testing of a new 14-item Smartphone Security
Behavioral Scale (SSBS) which provides a measurement of
users’ smartphone security behavior considering both tech-
nical and social strategies. For example, a technical strategy
would be resetting the advertising ID while a social strategy
would be downloading mobile applications only from an offi-
cial source.The initial analysis of two-component behavioral
model, based on technical versus social protection strategies,
demonstrates high reliability and good fit for the social com-
ponent of the behavioral scale. The technical component of
the scale, which has theoretical significance, shows a marginal
fit and could benefit from further improvement. This newly de-
veloped measure of smartphone security behavior is inspired
by the theory of planned behavior and draws inspiration from
a well-known scale of cybersecurity behavioral intention, the
Security Behavior Intention Scale (SeBIS). The psychomet-
rics of SSBS were established by surveying 1011 participants.
We believe SSBS measures can enhance the understanding
of human security behavior for both security researchers and
HCI designers.
society. Their popularity and ever-increasing relevance in our
daily lives make these devices an integral part of our comput-
ing ecosystem. Yet, we know little about smartphone users
and their security behaviors. In this paper, we report our de-
velopment and testing of a new 14-item Smartphone Security
Behavioral Scale (SSBS) which provides a measurement of
users’ smartphone security behavior considering both tech-
nical and social strategies. For example, a technical strategy
would be resetting the advertising ID while a social strategy
would be downloading mobile applications only from an offi-
cial source.The initial analysis of two-component behavioral
model, based on technical versus social protection strategies,
demonstrates high reliability and good fit for the social com-
ponent of the behavioral scale. The technical component of
the scale, which has theoretical significance, shows a marginal
fit and could benefit from further improvement. This newly de-
veloped measure of smartphone security behavior is inspired
by the theory of planned behavior and draws inspiration from
a well-known scale of cybersecurity behavioral intention, the
Security Behavior Intention Scale (SeBIS). The psychomet-
rics of SSBS were established by surveying 1011 participants.
We believe SSBS measures can enhance the understanding
of human security behavior for both security researchers and
HCI designers.
Research Areas
