Factors affecting users’ online privacy literacy among students in Israel
| DOI | https://doi.org/10.1108/OIR-05-2016-0127 |
| Pages | 655-671 |
| Published date | 11 September 2017 |
| Date | 11 September 2017 |
| Author | Maor Weinberger,Maayan Zhitomirsky-Geffet,Dan Bouhnik |
| Subject Matter | Library & information science,Information behaviour & retrieval,Collection building & management,Bibliometrics,Databases,Information & knowledge management,Information & communications technology,Internet,Records management & preservation,Document management |
Factors affecting users’online
privacy literacy among students
in Israel
Maor Weinberger and Maayan Zhitomirsky-Geffet
Department of Information Science, Bar-Ilan University, Ramat Gan, Israel, and
Dan Bouhnik
Department of Computer Science, Jerusalem College of Technology,
Jerusalem, Israel
Abstract
Purpose –The purpose of thispaper is to investigate the attitudes andinfluential factors of users’knowledge
and use of the tools designated for controlling and enhancing online privacy, which are referred to as online
privacy literacy (OPL). Particularly, inspired by the protection motivation theory, a motivational factor is
defined as comprising several variables which reflect users’motivation to protect their online privacy.
Design/methodology/approach –To this end, a user study was conducted based on the quantitative
method with the particip ation of 169 students from the Israeli ac ademia who were administered
closed-ended questionnaires.
Findings –Generally low to moderate levels of OPL were obtained. Interestingly, the multivariate linear
regression analysis showed that motivational factors, such as users’concern for personal information
protection on the internet and users’privacy self-efficacy and sense of anonymity when visiting a website,
were among the strongest predictive factors of users’OPL level.
Social implications –This research has social implications that might contribute to an increase in the OPL
among internet users.
Originality/value –The direct influence of the examined factors on users’OPL was not previously
discussed in the literature. As a result of the study, a comprehensive model of user online privacy behavior
was constructed.
Keywords Protection motivation theory, Privacy concern, Online anonymity, Online privacy literacy,
Online privacy self-efficacy, Privacy threat awareness, Online privacy control
Paper type Research paper
Introduction
Online privacy and personal information security is a widely explored topic in the literature
(Castañeda et al., 2007; Debatin et al., 2009; Hoffman et al., 1999; Hsu, 2006; Jensen et al., 2005;
Paine et al., 2007; Rainie et al., 2013; Sheehan, 2002; Wills and Zeljkovic, 2011). One of the
prominent findings reported by previous works is the inconsistency in users’online privacy
attitudes and their online privacy behavior, termed the “privacy paradox”(Barnes, 2006).
Although users report that they are worried about their online privacy (Paine et al., 2007;
Wills and Zeljkovic, 2011), they voluntarily disclose personal information about themselves
on the internet (Acquisti and Gross, 2006; Bronstein, 2014; Debatin et al., 2009; Gross and
Acquisti, 2005; Tufekci, 2008; Zhitomirsky-Geffet and Bratspiess, 2014). One of the main
reasons for this inconsistency is argued to be a lack of knowledge about privacy-enhancing
tools and techniques (Trepte et al., 2015). This type of knowledge and skills is defined by the
authors as “online privacy literacy”(OPL), i.e. “a combination of factual or declarative and
procedural knowledge about online privacy.”Declarative knowledge refers to “the user’s
knowledge about technical aspects of online data protection and about laws, directives and
institutional practices (“knowing that”). Procedural knowledge refers to the users’ability to
apply strategies for individual privacy regulation and data protection (“knowing how”).
This research’s primary goal was to investigate the influential and predictive factors
involved in increasing users’OPL. The study adopted and refined the concept of OPL
Online Information Review
Vol. 41 No. 5, 2017
pp. 655-671
© Emerald PublishingLimited
1468-4527
DOI 10.1108/OIR-05-2016-0127
Received 8 May 2016
Revised 10 March 2017
Accepted 1 June 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1468-4527.htm
655
Users’OPL
among
students
in Israel
defined by Trepte et al. (2015) as follows. OPL of a user is captured by the users’knowledge
of privacy control tools (passive), and their actual application (active) to obscure the users’
identity and protect his/her personal information on the internet. Increasing users’(passive
and active) OPL level is important since it might help overcome the disparities between
users’privacy attitudes and behaviors (Trepte et al., 2015).
A recent study on users’attitudes to online privacy and anonymity (Shelton et al.,2015)
found thatmany of the participants have notconsidered or are not aware of the available tools
that could enhance their online privacy, thus indicating a low level of users’passive OPL.
Park (2013) reported that technical knowledge, such as a higher level of awareness of online
surveillance and higher digital literacy, might increase the level of information control
behavior, which has been defined in the paper as active OPL. In his study,
Park (2013) distinguished between technical OPL skills (i.e. knowledge and the use of a
variety of tools for privacy control) and social OPL skills (e.g. refraining from personal
information disclosure and submitting falsified personal details). Numerous previous studies
examined the factors affecting social OPL skills (Lwin et al., 2007; Wirtzet al.,2007;Yaoand
Linz, 2008; Youn, 2009).
In contrast, this study investigates both technical and social OPL and their predictive
factors.Furthermore, inspiredby the Rogers’s (1975, 1983) protection motivation theory, a new
dimension of users’online privacy behavior model is defined, which is referred to as the
motivational dimension. Rogers’s (1975, 1983) motivation theory to protect oneself from risks
includes perc eiving the risk likely to o ccur to oneself, perceiv ing protective behav ior as
effective in reducing the risk, and protection self-efficacy. Thus, this theory’sprincipalsare
borrowed and applied to examine the following motivational factors of OPL: the user’s
perception of their sense of anonymity when visiting a website; online privacy self-efficacy
(OPSE),i.e.users’b elief in their ability to protect their identity when surfing the internet as
previously investigated (Chen and Chen, 2015; Yao and Linz, 2008); and the level of users’
concern for privacy and anonymity on the internet (Chen and Chen, 2015; Dinev and Hart, 2005;
Lwin et al., 2007; O’Neill, 2001; Sheehan, 2002; Wills and Zeljkovic, 2011; Wirtz et al., 2007;
Yao and Linz, 2008; Youn, 2009). OPSE and privacy concern were widely explored in the above
literatureas dependent variables in order todetermine their influential factors. However,to the
best of our knowledge, thisis the first study that investigates the motivationaldimension and
its different elements as independent variables and factors of influence on users’OPL.
In addition, to measure the users’awareness of online privacy threats, we count the
number of personal details that they are aware of, which might be monitored when surfing
the internet. We also consider users’internet proficiency level and several demographic
variables. This paper also extends the previous research on technical OPL skills and their
influential factors (Park, 2013) in terms of the number of tools examined. Finally,
a multivariate linear regression model was built to evaluate the influence of all these factors
on users’OPL.
Hence, the main research questions examined in this study were:
RQ1. What is the level of various motivational factors reported by the users?
RQ2. Whether and to what extent are users aware of the specific threats posed on their
online privacy?
RQ3. What is the users’level of active and passive OPL?
RQ4. Whether the motivational factors contribute significantly to increasing users’OPL?
RQ5. What other factors influence and predict users’OPL level?
To answer the above questions, a user study was conducted with the participation of
169 students from the Israeli academia, who were asked to fill-in a closed-ended
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