I'm Jess Kropczynski, and I'll be presenting this paper.
Today, older adults, age 65 and older,
are going online and using smartphones.
However, most report needing help setting up devices,
and a third say that they have little confidence
in their ability to use them.
Given this, older adults are the target of cyber crimes.
In our prior work, we developed a framework
of community oversight as a means
to help individuals increase their capacity for making
digital privacy and security decisions together, by drawing
on expertise in their networks.
This study builds on the framework
by examining and operationalizing
capacity for digital privacy and security
using Carroll's construct of community collective efficacy,
which is an extension of an individual's self-efficacy
and refers to the capacity, or ability, of a group
towards accomplishing a shared goal.
This figure shows our proposed framework
for building community collective efficacy for privacy
and security, and it describes the hypothesized relationships
between our key constructs.
And those include self-efficacy, or that perceived individual
capacity, and we adapt that scale to the domain of privacy
and security, power usage--
or one's perceived comfort with an advanced use of technology--
sense of community belonging, and community collective
efficacy-- or perceived group capacity--
which we're also extending to the domain of privacy
and security.
And using these constructs together with our framework,
we posed two research questions.
First, what individual, community,
and network-level factors influence the formation
of community collective efficacy?
And second, when community collective efficacy
is low among members of the community,
how might technology expertise be introduced in order
to increase collective capacity to co-manage privacy
and security?
To test our hypotheses, we surveyed 67 older adults
and employees that work with these communities across two
older adult communities.
The survey included a network questionnaire, validated survey
scales representing each of our four constructs,
in addition to demographic information.
We analyzed our data using descriptive tables and figures,
t-test, quadratic assignment procedure
correlation, homophily, and key player analysis.
But we will only describe a selection of these
in the next couple of slides.
To address research question 1, the results of the correlation
are shown in this table, which illustrates
that all of our constructs were significantly
and positively associated with one another.
That's to say, if an individual's composite score
for self-efficacy is low, their community collective efficacy
would also likely be low.
To answer our second research question
regarding technology expertise and communities,
we investigated the role of employees that
worked with these communities.
The two communities differed in terms of their access
to technology expertise through employees.
The upper sociograms, which can be found in our paper,
show that all members, including employees
of the social community on the right,
had low community collective efficacy.
The lower sets of sociograms illustrate our finding that
volunteers and employees are key players in these networks
of older adults, such that, when they're removed from
the network-- as shown at the bottom--
fragmentation increases dramatically,
demonstrating the degree to which these individuals are
socially embedded.
Our work is the first to propose and utilize
a measure of community collective efficacy
in the domain of privacy and security.
We studied individual-level factors
and how they can be correlated with community factors
and network factors in the formation of community
collective efficacy.
An implication of our findings is
that training for older adults has potential
for enhancing capacity for decision-making
within communities.
According to our findings, a higher sense
of community belonging is related
to a group's perception of their ability to work together
on community oversight.
So another intervention approach would
be to build stronger and more connected communities.
A major finding of this work is that employees
that work within older adult communities
may be a key to facilitating technological knowledge.
We believe that our work could be extended
to examine social dynamics of other demographics
with different community structures,
such as families, co-workers, or friends,
to determine which groups could be the best
to support each other, and what kinds of interventions
could improve users' collective abilities.
Based on our findings, we believe
that our framework for building community collective efficacy
may be useful to investigate individual and community-based
impacts when implementing interventions.
In addition, our social network analysis approach
can aid in the examination of the sociotechnical environment
that may be critical to success and interventions.
Before I conclude this presentation,
I'd like to thank my co-authors, the National Science
Foundation for supporting this work,
and the members of the communities
that participated in the research.