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HOW PRIVACY CONCERNS, TRUST AND
RISK BELIEFS AND PRIVACY LITERACY
INFLUENCE USERS’ INTENTIONS TO USE
PRIVACY-ENHANCING TECHNOLOGIES -
THE CASE OF TOR
David Harborth
Chair of Mobile Business and Multilateral Security, Goethe University Frankfurt am Main
Dr. Sebastian Pape
Chair of Mobile Business and Multilateral Security, Goethe University Frankfurt am Main
Acknowledgments
This research was partly funded by the German Federal Ministry of Education and Research (BMBF) with grant
number: 16KIS0371.
Accepted at: ACM SIGMIS The DATA BASE for Advances in Information Systems (forthcoming 2020)
Abstract
Due to an increasing collection of personal data by internet companies and several data breaches, research related
to privacy gained importance in the last years in the information systems domain. Privacy concerns can strongly
influence users’ decision to use a service. The Internet Users Information Privacy Concerns (IUIPC) construct is
one operationalization to measure the impact of privacy concerns on the use of technologies. However, when
applied to a privacy enhancing technology (PET) such as an anonymization service the original rationales do not
hold anymore. In particular, an inverted impact of trusting and risk beliefs on behavioral intentions can be expected.
We show that the IUIPC model needs to be adapted for the case of PETs. In addition, we extend the original causal
model by including trusting beliefs in the anonymization service itself as well as a measure for privacy literacy. A
survey among 124 users of the anonymization service Tor shows that trust in Tor has a statistically significant effect
on the actual use behavior of the PET. In addition, the results indicate that privacy literacy has a negative impact
on trusting beliefs in general and a positive effect on trust in Tor.
Keywords: Privacy Concerns; Tor; Privacy-Enhancing Technologies; Privacy Literacy; Technology Use
Introduction
“Surveillance is the business model of the internet. Everyone is under constant surveillance by many companies,
ranging from social networks like Facebook to cellphone providers.” (Mineo, 2017). Privacy and the related concerns
have been discussed since the very beginning of computer sharing (David & Fano, 1965). Due to a raising economic
interest in personal data during the last years (Bédard, 2016), privacy gains an increasing importance in individuals’
everyday life. The majority of internet users has privacy concerns and feels a strong need to protect their privacy
(Singh & Hill, 2003).
However, technologies which are able to protect users' privacy (PETs) are not widely adopted yet (Rossnagel,
2010). Among others, privacy concerns (Angst & Agarwal, 2009; Slyke, Johnson, Jiang, & Shim, 2006) and trust-
risk-relationships (Harborth & Pape, 2018b, 2019; Smith, Dinev, & Xu, 2011) are assumed to have an important
effect on the adoption of technologies. We argue that privacy concerns might have an important effect in the case
of PETs, too. A popular model for measuring and explaining privacy concerns of online users is the model focusing
on the Internet Users Information Privacy Concerns (IUIPC) construct by Malhotra, Kim, & Agarwal (2004). Their
research involves a theoretical framework and an instrument for operationalizing privacy concerns, as well as a
causal model for this construct including trust and risk beliefs about the online companies’ data handling of personal
information. The IUIPC construct has been used in various contexts, e.g. Internet of Things (Naeini et al., 2017),
internet transactions (Heales, Cockcroft, & Trieu, 2017) and mobile apps (Raber & Krueger, 2017). Originally, the
IUIPC instrument was applied to use cases for individuals’ decisions to disclose personal information to service
providers. However, for privacy enhancing technologies (PETs) the primary purpose is to help users to protect
personal information when using regular internet services. As a consequence, it is necessary to reconsider the
impact of trust and risk beliefs within IUIPC’s causal model with respect to PETs. We expected this impact to be
inverted and thus the trust model needs to be adapted for the investigation of PETs. In addition, trust in the PET
itself is an important factor to consider. This is the case since Tor is used by a diverse group of people whose life
might be endangered in case their identity is revealed (e.g. whistleblowers, opposition supporters, etc. (The Tor
Project, 2018)). Besides users’ concerns and trust, it is also important to consider the users’ knowledge and
capabilities. Users’ attitudes often differ from the decisions they make (‘privacy paradox’) (Dienlin & Trepte, 2015).
One way to explain the privacy paradox is that users balance between potential risks and benefits they gain from
the service (privacy calculus) (Dinev & Hart, 2006). Another way to explain it is that users are concerned but lack
knowledge to react in a way that would reflect their needs (Trepte et al., 2015).
Since we are surveying active users of Tor, both argumentations do not fit. In the former case, we have already
explained that PETs are different than regular internet services since their primary goal is to protect the users’
privacy. In the latter case, users have already installed the PET and use it. However, we still argue that it is important
to consider the users’ capabilities since users need a certain amount of knowledge in order to adequately evaluate
the given level of privacy (Masur, Teutsch, & Trepte, 2017; Park, 2013). Thus, their knowledge might influence the
users’ trusting and risk beliefs in online companies and in particular the users’ trusting beliefs in Tor. For that
purpose, we measured the users’ privacy literacy with the “Online Privacy Literacy Scale” (OPLIS) developed by
Trepte et al. (2015).
To the best of our knowledge the OPLIS instrument in combination with the IUIPC construct has never been applied
to a PET. Thus, we address the following research questions:
1. What influence have privacy concerns and associated trust and risk beliefs on the behavioral intention and
actual use of Tor?
2. What influence does trust in Tor itself have on the behavioral intention and the actual use?
3. What influence does privacy literacy (measured with the OPLIS scale) have on trusting beliefs, risk beliefs and
trusting beliefs in Tor?
For that purpose, we conducted an online survey with users of one of the most widely used anonymization services
Tor (Tor has approximately 2,000,000 regular users) (The Tor Project, 2018). We collected 124 complete
questionnaires out of 314 participants for the empirical analysis. Our results contribute to the understanding of users’
perceptions about PETs and indicate how privacy concerns and trust and risk beliefs influence the use behavior of
PETs.
The remainder of the paper is as follows: Section 2 introduces Tor and lists related work on PETs. In Section 3, we
present research hypotheses and the data collection process. We assess the reliability and validity of our results in
Section 4. In Section 5, we discuss the implications and limitations of our work and suggest future work. We
conclude the article in Section 6.
Background and Related Work
Privacy-Enhancing Technologies (PETs) is an umbrella term for different privacy protecting technologies. PETs can
be defined as a “coherent system of ICT measures that protects privacy [...] by eliminating or reducing personal
data or by preventing unnecessary and/or undesired processing of personal data; all without losing the functionality
of the data system” (Borking & Raab, 2001, p. 1).
Privacy Enhancing Technologies and Tor
In this paper, we investigate the privacy, trust and risk beliefs associated with PETs for the case of the anonymity
service Tor. Tor is a free-to-use anonymity service that is based on the onion routing principle. The development of
Tor started in 1995 in the Naval Research Lab (NRL). At that time the general idea was that one should be able to
communicate over the Internet without revealing oneself to the other party (The Tor Project, 2018). Everybody can
operate a server (relay) over which the encrypted traffic is routed. The routing occurs randomly over several different
servers distributed world-wide. Tor aims to protect against an adversary who can observe or control some fraction
of network traffic, but it does not protect against a global passive adversary, which means an adversary who can
observe all network connections. Among the available PETs, Tor has one of the biggest user bases with
approximately 2,000,000 active users (The Tor Project, 2018).
Related work on PETs considers mainly usability studies and to the best of our knowledge only two articles exist
which focus on privacy concerns and related trust and risk beliefs of users of the PETs Tor and JonDonym (JonDos
Gmbh, 2018). The two articles extend the IUIPC model by adding trust in the respective PET and find that the PET-
specific trust (in Tor and JonDonym, respectively) has a large statistically significant positive effect on usage
(Harborth & Pape, 2018b, 2019). Lee et al. (2017) assess the usability of the Tor Launcher and propose
recommendations to overcome the found usability issues. Benenson, Girard, & Krontiris (2015) investigate
acceptance factors for anonymous credentials. Among other things, they find that trust in the PET has no statistically
significant impact on the intention to use the service. This result is relevant for our study since we hypothesize that
trust in Tor has a positive effect on the actual use of the service (see Section 3.1). This hypothesis is supported by
other research on technology acceptance factors of Tor which finds that trust in Tor is a highly relevant factor driving
the use intention of the PET (Harborth & Pape, 2018a). Other research results indicate that trust in a PET has a
positive effect on the willingness to pay money for this PET (Harborth, Cai, & Pape, 2019).
Privacy Concerns
A highly relevant study for our research is the one by Brecht et al. (2011), who investigate acceptance factors of
anonymization services. Among other variables, they hypothesize a positive influence of privacy concerns on the
intention to use such a service. Although they find a statistically significant effect, the effect is relatively small (effect
size of 0.061) compared to other variables like perceived usefulness or internet privacy awareness. In contrast to
our study, Brecht et al. (2011) use another operationalization of privacy concerns (Dinev & Hart, 2006) and they do
not investigate it in the nomological network with trust and risk beliefs. However, it is highly relevant for constructs
such as IUIPC and OPLIS to establish nomological validity (Straub, Boudreau, & Gefen, 2004). Therefore, we
contribute to the theoretical discourse about privacy literacy and privacy concerns by including an operationalization
of privacy literacy (OPLIS) and privacy concerns (IUIPC) in one nomological network with the trust-risk relationships
discussed before. We decided to use the operationalization for privacy concerns as in the original IUIPC paper
(Malhotra et al., 2004). Thus, IUIPC is a second-order variable consisting of the constructs collection, control and
awareness.
Online Privacy Literacy
Park (2013) defines online privacy literacy as a “principle to support, encourage, and empower users to undertake
informed control of their digital identities”. Trepte et al. (2015) give an exhaustive summary on the development of
(online) privacy literacy. Several studies exist which aim to measure users’ privacy literacy. Hoofnagle et al. (2010)
ask users to answer whether five given statements about information handling of providers are true.
Brecht et al. (2012) find that users generally have a low knowledge about privacy issues on the Internet. They also
find a negative correlation between a users’ stated and their actual knowledge of privacy issues. Morrison (2013)
investigates the same questions and asks ten objective questions and compares the results to three subjective
questions (self-assessments). He finds that the users’ self-assessment differs greatly from their objective knowledge
about privacy. This cognitive bias where people mistakenly assess their cognitive ability as greater as than it is, is
called Dunning-Kruger effect (Kruger & Dunning, 1999). As a consequence, users’ statements on their knowledge
about privacy cannot be trusted and other scales with users’ self-assessments (cf. Park, 2013) are not further
discussed here. Trepte et al. (2015) define online privacy literacy as “a combination of factual or declarative
(knowing that) and procedural (knowing how) knowledge about online privacy” and implemented a scale based on
“objective knowledge” to measure privacy literacy: the online privacy literacy scale (OPLIS). OPLIS consists of 20
questions divided into the following four knowledge groups:
4. practices of organizations, institutions and online service providers
5. technical aspects of data protection;
6. data protection law in Germany and Europe;
7. data protection strategies.
Since the constructs for data protection laws were specific for Germany and Europe and we surveyed Tor users
worldwide, we needed to remove them (cf. Section 3.3).
Trepte and Masur (2017) apply a short version of OPLIS for a descriptive study. Joeckel and Dogruel (2019)
investigate OPLIS, too. They find two correlations with the OPLIS score: a medium-sized with age (older participants
know more about online privacy), and a weaker with privacy concerns (more privacy literate users were more
concerned about their privacy). However, their correlation analysis does not offer any causality. Thus, it is unclear
if more concerned users know more about privacy or users who know more are more concerned.
Methodology
We base our research on the Internet Users Information Privacy Concerns (IUIPC) model by Malhotra et al. (2004).
The original research on this model investigates the role of users’ information privacy concerns in the context of
releasing personal information to a marketing service provider. Since we are focusing on the role of privacy
concerns, trust and risk beliefs for the case of a PET (i.e. Tor), we adapt the original model according to the following
logic. Originally, the service in question can be seen as the attacker (from a privacy point of view). If we apply the
model to a service with the opposite goal, namely protecting the privacy of its users, certain relationships need to
change. We will elaborate on the detailed changes in the next section. In addition, to this we extend the original
model by trusting beliefs in the PET itself. We argue that the level of trust in a PET is a crucial factor determining
the use decision.
For analyzing the cause-effect relationships between the latent (unobserved) variables, we use structural equation
modelling (SEM). Since our research goal is to predict the target constructs behavioral intention and actual use
behavior of Tor, we use partial least squares SEM (PLS-SEM) for our analysis (Hair, Hult, Ringle, & Sarstedt, 2017;
Hair, Ringle, & Sarstedt, 2011) and not covariance-based SEM. In the following subsections, we discuss the
hypotheses based on the IUIPC model (Malhotra et al., 2004), the questionnaire and the data collection process.
Research Hypotheses
The structural model contains several relationships between exogenous and endogenous variables (cf. Fig. 1). We
develop our research hypotheses for these relationships along the hypotheses of the IUIPC model. IUIPC is
operationalized as a second-order construct of the sub-constructs collection (COLL), awareness (AWA) and control
(CONTROL). Thus, the users’ privacy concerns are determined by their concerns about “[...] individual-specific data
possessed by others relative to the value of benefits receive” (Malhotra et al., 2004, p. 338), the control they have
over their own data (i.e. possibilities to change or opt-out) and the “[...] degree to which a consumer is concerned
about his/her awareness of organizational information privacy practices” (Malhotra et al., 2004, p. 339).
The effect of IUIPC on the behavioral intention is mediated by trusting beliefs and risk beliefs. Trusting beliefs are
users’ perceptions about the behavior of online firms to protect the users’ personal information. In contrast, risk
beliefs represent users’ perception about losses associated with providing personal data to online firms (Malhotra
et al., 2004). Thus, the higher the privacy concerns of a user, the lower are his or her trusting beliefs and the higher
are his or her risk beliefs. In addition, a higher level of trust is assumed to decrease the risk beliefs. Thus, we
hypothesize:
Hypothesis 1 (H1): Internet Users Information Privacy Concerns (IUIPC) have a negative effect on Trusting
Beliefs (TB).
Hypothesis 2 (H2): Internet Users Information Privacy Concerns (IUIPC) have a positive effect on Risk Beliefs
(RB).
Hypothesis 3 (H3): Trusting Beliefs (TB) have a negative effect on Risk Beliefs (RB).
Since we investigate the use of a specific PET, we extend the model by the trust in Tor itself with the adapted trust
construct by Pavlou (2003). However, in order to protect their privacy, users with higher privacy concerns are
assumed to rather trust the privacy-enhancing technology compared to online firms which process personal data.
This is especially true, because we surveyed users of a PET which are assumed to take great care of their privacy.
Therefore, we hypothesize:
Hypothesis 4 (H4): Internet Users Information Privacy Concerns (IUIPC) have a positive effect on the trusting
beliefs in Tor (TBTor).
Privacy literacy is not a widely used concept in the information systems domain when investigating information
privacy. Based on a representative selection of literature, Smith, Dinev, & Xu (2011) derive the “APCO” macro
model summarizing related concepts and their relations to privacy concerns. However, no variable is or is related
to privacy literacy. To the best of our knowledge, the construct we use in our analysis (OPLIS) is also not
investigated in a nomological network with privacy concerns and outcome variables as behavioral intention or use.
Therefore, we searched primarily for “privacy literacy” on Google Scholar as we argue that OPLIS operationalizes
this concept. We find different applications and definitions of the concept “privacy literacy” in the literature. For
example, online privacy literacy is defined as “[...] 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”
(Weinberger, Zhitomirsky-Geffet, & Bouhnik, 2017, p. 656). This definition is very focused on PETs and the research
model is primarily looking at the antecedents of privacy literacy and the interrelations between the variables. For
example, the results of the research indicate that online privacy concerns have a statistically significant positive
effect on privacy literacy. However, due to a lack of a theoretical underlying we refrain from hypothesizing this
relation since we argue that online privacy literacy is independent from IUIPC. Park (2013) investigates a closely
related conceptualization of privacy literacy to OPLIS and the effect on corresponding behaviors in the digital sphere.
The author finds that technical privacy knowledge, although very heterogenous amongst different demographic
groups, has a positive correlation with users' ability to exert information control, i.e. decide about their personal
information disclosure. OPLIS (Masur et al., 2017) was partially developed from skill items of the study by Park
(2013) whereas the authors do not investigate OPLIS in a nomological network. “Social privacy literacy” as a sub-
concept of privacy literacy for the case of social network is investigated in an article by Bartsch & Dienlin (2016).
They find a positive effect of social privacy literacy on social privacy behavior. In summary, prior research suggests
that privacy literacy might influence online behaviors positively in a way that individuals who are more literate
behave in a more privacy-aware manner. However, privacy literacy is a context-independent variable comparable
to IUIPC (Malhotra et al., 2004). Therefore, we argue that it behaves similar in its effects on the context-specific
factors trusting beliefs, risk beliefs and trusting beliefs in Tor. As discussed before, previous research suggests that
people with more privacy knowledge tend to be more aware about privacy threats (Bartsch & Dienlin, 2016), we
argue that a higher level of online privacy literacy leads to less trust in online companies with respect to handling
personal information. In contrast, risk beliefs will increase with a higher level of knowledge. Therefore, we
hypothesize:
Hypothesis 5 (H5): Online Privacy Literacy (OPLIS) has a negative effect on Trusting Beliefs (TB).
Hypothesis 6 (H6): Online Privacy Literacy (OPLIS) has a positive effect on Risk Beliefs (RB).
The relationship of privacy literacy and trusting beliefs in Tor is not as clear as for hypotheses 5 and 6 because the
OPLIS instrument does not contain any specific questions related to Tor. Thus, the assumption that trust in Tor is
built upon the knowledge of certain specific features of Tor is difficult to make. However, since we asked active
users of Tor, we argue that there is a certain level of trust in the service in place which is positively correlated with
their relatively high knowledge related to privacy. We hypothesize this type of self-selection in hypothesis 7:
Hypothesis 7 (H7): Online Privacy Literacy (OPLIS) has a positive effect on the trusting beliefs in Tor (TBTor).
Trust is an important factor in the acceptance decision of users (Pavlou, 2003). McKnight, Carter, Thatcher, & Clay
(2011) show that trust in a specific technology will positively affect individual’s intention to explore the technology
and to use more features of the technology in a postadoption context. Especially for the case of privacy protection,
we assume that trust in the technology is a major factor for the intention to use the technology. For a further
discussion on the concept of trust in a technology, we refer to Lankton, Mcknight, & Tripp (2015). We hypothesize
that:
Hypothesis 8 (H8): Trusting beliefs in Tor (TBTor) have a positive effect on the behavioral intention to use Tor
(BI).
It is logical that trusting beliefs have a positive effect and risk beliefs have a negative effect on releasing data and
thus the intended behavior of using a regular service. However, for use behavior of a PET, we assume these effects
reverse. The higher the trusting beliefs in online firms, the lower is the use frequency of Tor, since the protection of
data becomes less important. Following this rationale, a higher degree of risk beliefs in data processing of online
firms leads to a higher degree of use. Thus, we hypothesize that:
Hypothesis 9 (H9): Trusting beliefs (TB) have a negative effect on the behavioral intention to use Tor (BI).
Hypothesis 10 (H10): Risk beliefs (RB) have a positive effect on the behavioral intention to use Tor (BI).
Research on the relationship between behavioral intention and use behavior goes back to Fishbein & Ajzen (1975).
Later research indicates a positive link between the two constructs (Sheppard, Hartwick, & Warshaw, 1988). Thus,
we hypothesize that:
Hypothesis 11 (H11): The behavioral intention to use Tor (BI) has a positive effect on the actual use behavior
(USE).
The resulting structural model is shown in Figure 1.
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Data Collection
The questionnaire constructs are adapted from the original IUIPC paper. The trust construct for trust in Tor is
adapted from Pavlou (2003). Privacy literacy is operationalized with the online privacy literacy scale (OPLIS) (Masur
et al., 2017). We conducted the study with German and English-speaking Tor users. Thus, we administered two
questionnaires. All items for the German questionnaire had to be translated into German since all of the constructs
are adapted from English literature. To ensure content validity of the translation, we followed a rigorous translation
process. First, we translated the English questionnaire into German with the help of a certified translator (translators
are standardized following the DIN EN 15038 norm). The German version was then given to a second independent
certified translator who retranslated the questionnaire to English. This step was done to ensure the equivalence of
the translation. Third, a group of five academic colleagues checked the two English versions with regard to this
equivalence. All items were found to be equivalent. The items of the English version can be found in Appendix B.
Since we investigate the effect of privacy concerns, trust and risk beliefs on the use of Tor, we collected data of
actual users. We installed the surveys on a university server and managed it with the survey software LimeSurvey
(version 2.72.6) (Schmitz, 2015). The links to the English and German version were distributed over multiple
channels on the internet. Although there are approximately 2,000,000 active users of the service, it was relatively
difficult to gather the necessary number of complete answers for a valid and reliable quantitative analysis. Thus, to
foster future research about Tor users, we provide an overview of every distribution channel in the Appendix A. In
sum, 314 participants started the questionnaire (245 for the English version, 40 for the English version posted in
hidden service forums and 29 for the German version). Of those 314 approached participants, 135 (105 for the
English version, 13 for the English version posted in hidden service forums and 17 for the German version) filled
out the questionnaires completely. After deleting all sets from participants who answered a test question in the
middle of the survey incorrectly, 124 usable data sets remained for the following analysis.
The demographic questions were not mandatory to fill out. This was done on purpose since we assumed that most
of the participants are highly sensitive with respect to their personal data. Therefore, we had to resign from a
discussion of the demographics in our research context. This decision is backed up by past research which does
not find a statistically significant differences across gender, income groups, educational levels, or political affiliation
in the desire to protect one’s privacy (Singh & Hill, 2003).
Descriptive Statistics and OPLIS Adaption
The descriptive statistics for our quantitative analysis can be found in Table 1. The OPLIS value is calculated as a
relative value (i.e. ratio of correctly answered questions divided by total number of questions).
As already mentioned in Section 2.3, we had to adapt the OPLIS score. The original questionnaire aimed at the
German population. Thus, it contains questions about German and European data protection laws. Since our
sample consists of Tor users possibly spread from all over the world, it does not make sense to ask them for German
or even European law. As a consequence, we omitted the respective questions about national laws. This is straight
forward since we consider the ratio of correctly answered questions. For a comparison with the reference group (cf.
Figure 2), we extrapolate our results from 15 to 20 questions.
It can be seen that on average participants answered 78.78% of the questions correctly (with a median of 0.8). It
can be seen that the participants are highly privacy-sensitive (median values for collection, awareness and control
range from 6 to 7). This view is reinforced by a relatively low median value for trusting beliefs in online companies
and an above neutral median value for risk beliefs. Trusting beliefs in Tor are relatively high with a median of 5.6667
indicating that most participants agree that they trust Tor. The descriptive statistics for the three covariates show
that participants have on average almost 7 years of experience with Tor and almost 18 years of internet experience.
This insight combined with the high privacy literacy implies that the sample is relatively knowledgeable and
experienced compared to the general population of internet users. A median value of 4 indicates that participants
perceive to be a victim of privacy breaches “occasionally”.
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The distribution of the cumulative relative frequency for correctly answered privacy literacy questions is illustrated
in Figure 2.
As discussed in Section 2, we extrapolate our results for Tor users in order to make it comparable to results of a
representative German sample of regular Internet users (Masur et al., 2017). The distribution graph clearly shows
that the Tor users are more literate with respect to online privacy compared to regular German internet users. For
example, 60% of the participants in our sample answered 12 out of 15 questions correctly (i.e. 80% correctly
answered questions). In contrast, roughly 60% of the regular internet users in the reference group answered 12 out
of 20 questions correctly (i.e. 60%).
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Results
We tested the model using SmartPLS version 3.2.7 (Ringle, Wende, & Becker, 2015). Before looking at the result
of the structural model and discussing its implications, we discuss the measurement model, and check for the
reliability and validity of our results. This is a precondition of being able to interpret the results of the structural model.
Furthermore, it is recommended to report the computational settings. For the PLS algorithm, we choose the path
weighting scheme with a maximum of 300 iterations and a stop criterion of 10−7. For the bootstrapping procedure,
we use 5000 bootstrap subsamples and no sign changes as the method for handling sign changes during the
iterations of the bootstrapping procedure.
Assessment of the Measurement Model
As the model is measured solely reflectively, we need to evaluate the internal consistency reliability, convergent
validity and discriminant validity to assess the measurement model properly.
Internal consistency reliability (ICR) measurements indicate how well certain indicators of a construct measure the
same latent phenomenon. Two standard approaches for assessing ICR are Cronbach’s α and the composite
reliability. The values of both measures should be between 0.7 and 0.95 for research that builds upon accepted
models. Values of Cronbach’s α are seen as a lower bound and values of the composite reliability as an upper
bound of the assessment (Hair et al., 2017). Table 2 includes the ICR of the variables in the last two rows. It can be
seen that all values for Cronbach’s α are above the lower threshold of 0.7 except for RISK. However, for the
composite reliability the value for RISK is higher than 0.7. Therefore, we argue that ICR is not a major issue for this
variable. For all variables, no value is above 0.95. Values above that upper threshold indicate that the indicators
measure the same dimension of the latent variable, which is not optimal with regard to the validity (Hair et al., 2017).
In sum, ICR is established for our variables. Since IUIPC and USE are single-item constructs they have ICR values
of 1.
Convergent validity determines the degree to which indicators of a certain reflective construct are explained by that
construct. This is assessed by calculating the outer loadings of the indicators of the constructs (indicator reliability)
and by looking at the average variance extracted (AVE). Loadings above 0.7 imply that the indicators have much in
common, which is desirable for reflective measurement models. Table 2 shows the outer loadings in bold on the
diagonal. All loadings were higher than 0.7, except for TRUST4 with a value of 0.289. Therefore, we dropped this
item after an initial analysis. Convergent validity for the construct is assessed by the AVE. AVE is equal to the sum
of the squared loadings divided by the number of indicators. A threshold of 0.5 is acceptable, indicating that the
construct explains at least half of the variance of the indicators (Hair et al., 2017). The diagonal values of Table 3
present the AVE of our constructs. All values are well above 0.5, demonstrating convergent validity.
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Discriminant validity measures the degree of uniqueness of a construct compared to other constructs. Comparable
to the convergent validity assessment, two approaches are used for investigating discriminant validity. The first
approach, assessing cross-loadings, is dealing with single indicators. All outer loadings of a certain construct should
be larger than its cross-loadings with other constructs. Table 2 illustrates the cross-loadings as off-diagonal elements.
All cross-loadings are smaller than the outer loadings, fulfilling the first assessment approach of discriminant validity.
The second approach is on the construct level and compares the square root of the constructs’ AVE with the
correlations with other constructs. The square root of the AVE of a single construct should be larger than the
correlation with other constructs (Fornell-Larcker criterion) (Hair et al., 2017). Table 3 contains the square root of
the AVE on the diagonal in parentheses. All values are larger than the correlations with other constructs, indicating
discriminant validity.
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Since there are problems in determining the discriminant validity with both approaches, researchers propose the
heterotrait-monotrait ratio (HTMT) for assessing discriminant validity as a superior approach (Henseler, Ringle, &
Sarstedt, 2015). HTMT divides between-trait correlations by within-trait correlations, therefore providing a measure
of what the true correlation of two constructs would be if the measurement is flawless (Hair et al., 2017). Values
close to 1 for HTMT indicate a lack of discriminant validity. A conservative threshold is 0.85. Table 4 contains the
values for HTMT and no value, except for the correlation between IUIPC and COLL (with 0.888), is above the
threshold of 0.85. To as s es s i f t he H TM T st at i st i cs a r e si g ni fi c an t ly d i ff e re n t fr om 1, w e c on du c te d a b oo t st r ap p in g
procedure with 5,000 subsamples to get the confidence interval in which the true HTMT value lies with a 95%
chance. The HTMT measure requires that no confidence interval contains the value 1. The conducted analysis
shows that this is the case, and thus discriminant validity is established for our model.
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Common method bias (CMB) can occur if data is gathered with a self-reported survey at one point in time in one
questionnaire (Malhotra, Kim, & Patil, 2006). Since this is the case in our research design, the need to test for CMB
arises. An unrotated principal component factor analysis is performed with the software package STATA 14.0 to
conduct the Harman’s single-factor test to address the issue of CMB (Podsakoff, MacKenzie, Lee, & Podsakoff,
2003). The assumptions of the test are that CMB is not an issue if there is no single factor that results from the
factor analysis or that the first factor does not account for the majority of the total variance. The test shows that
eight factors have eigenvalues larger than 1 which account for 72.86% of the total variance. The first factor explains
26.76% of the total variance. Based on the results of previous literature (Blome & Paulraj, 2013), we argue that
CMB is not likely to be an issue in the data set.
Assessment and Results of the Structural Model
To as s es s the structural model, we evaluate possible collinearity problems, path coefficients, the level of adjusted
R2, the effect size f2, the predictive relevance Q2 and the effect size q2. We address these evaluation steps to ensure
the predictive power of the model with regard to the target constructs (Hair et al., 2017).
Collinearity is present if two predictor variables are highly correlated with each other. To address this issue, we
assess the inner variance inflation factor (VIF). All VIFs above 5 indicate that collinearity between constructs is
present. For our model, the highest VIF is 1.380. Thus, collinearity is apparently not an issue.
Figure 3 shows the results of the path estimations and the adjusted R2-values of the endogenous variables BI and
USE. The adjusted R2 is 0.412 for BI and 0.055 for USE. Thus, our model explains 41.2% of the variance of BI and
5.5% of USE. There are different proposals for interpreting the size of this value. We choose to use the very
conservative threshold proposed by Hair et al. (2011), where R2 values are weak with values around 0.25, moderate
with 0.50 and substantial with 0.75. Based on this classification, the R2 value for BI is weak to moderate and for
USE the value is very weak. For use behavior several participants answered that they never use Tor (21 participants
answered never) although they stated to use the service several years (answers to the question: How many years
are you using Tor? with a median of 6 years and an average of 6.87 years on a seven-point Likert scale). The
correlation coefficient between the years of using Tor and the use frequency is very small, negative and statistically
insignificant with -0.0222 and a p-value of 0.8066. These 21 answers massively bias the results for the relationship
between behavioral intention and actual use behavior (the median value of use frequency is 5). However, we cannot
explain why the participants answered like this. They either misunderstood the question, answered it intentionally
like this to disguise their activity with Tor or found the scale for use behavior inappropriate. This might be due to the
fact that the scale only contains once a month as the lowest use frequency besides never. It might be possible that
these 21 users use Tor only a few times per year or that they used Tor some years ago and have not used it again
since then. Therefore, they might have chosen never as an answer. However, we used an established scale to
measure use behavior (Rosen, Whaling, Carrier, Cheever, & Rokkum, 2013), but recommend to consider this issue
in future research studies with a similar context.
The path coefficients are presented on the arrows connecting the exogenous and endogenous constructs in Figure
3. Statistical significance is indicated by asterisks, ranging from three asterisks for p-values smaller than 0.01 to
one asterisk for p-values smaller than 0.10. We chose this p-value range since p-values tend to be larger if the
sample size is comparable small and we wanted to capture also significant effects above the 5% level. The p-value
indicates the probability that a path estimate is incorrectly assumed to be significant. Thus, the lower the p-value,
the higher the probability that the given relationship exists. The relevance of the path coefficients is shown by the
relative size of the coefficient compared to the other explanatory variables (Hair et al., 2017).
-----------------------------------------------------------------
Insert Figure 3 About Here
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It can be seen that IUIPC has a relatively large statistically significant negative effect on trusting beliefs and a
positive effect on risk beliefs. The effect of IUIPC on trusting beliefs in Tor is significant, positive and relatively weak
compared to the other significant effects in the model. The construct trusting beliefs has a statistically significant
medium-sized negative effect on risk beliefs. The effects of trusting beliefs and risk beliefs on behavioral intention
are not statistically significant (for both p
≥
0.10). In contrast, the effect of trusting beliefs in Tor on behavioral
intention is highly statistically significant, positive and large with 0.588. The second newly added construct OPLIS
has a statistically significant negative impact on trusting beliefs in online companies and a positive effect on trusting
beliefs in Tor. The effect on risk beliefs is not statistically significant.
The results for the covariates experience with Tor, internet experience and privacy victim experience are not
depicted in Figure 3 due to clarity reasons. The results with the respective significance level are shown in Table 5.
The results indicate that experience with Tor has no immediate effect on the five context-specific variables. Internet
experience has a slightly significant negative effect on risk beliefs implying that experienced internet users tend to
associate less risk with online companies handling their personal data. Personal privacy victim experiences exert
statistically significant effects on trusting beliefs, trusting beliefs in Tor, behavioral intention as well as on the actual
use behavior. The results indicate that a higher number of negative past experiences with privacy breaches lead to
less trust in online companies. The same negative effect is in place for trust in Tor. Apparently, Tor users in our
sample are well aware about the technical limitations of the PET with respect to protecting their anonymity.
Therefore, they do not blindly assume that they are completely protected when using Tor. There is even the
possibility that certain privacy breaches occurred while using a PET. Interestingly, at the same time there are positive
effects on BI and USE. Thus, the overall result of the relations between privacy victim experiences, trust in Tor and
behavioral intention and actual use behavior are rather ambiguous.
-----------------------------------------------------------------
Insert Table 5 About Here
-----------------------------------------------------------------
The f2 effect size measures the impact of a construct on the endogenous variable by omitting it from the analysis
and assessing the resulting change in the R2 value. The values are assessed based on thresholds by Cohen (1988),
who defines effects as small, medium and large for values of 0.02, 0.15 and 0.35, respectively. Table 6 shows the
results of the f2 evaluation. Values in italics indicate small effects, values in bold indicate medium effects and values
in bold and italics indicate large effects. All other values have no substantial effect. The results correspond to those
of the previous analysis of the path coefficients whereas trusting beliefs in Tor have a large effect on the behavioral
intention.
The Q2 measure indicates the out-of-sample predictive relevance of the structural model with regard to the
endogenous latent variables based on a blindfolding procedure. We used an omission distance d=7. Recommended
values for d are between five and ten (Hair et al., 2011). Furthermore, we report the Q2 values of the cross-validated
redundancy approach, since this approach is based on both the results of the measurement model as well as of the
structural model. Values above 0 indicate that the model has the property of predictive relevance. In our case, the
Q2 value is equal to 0.306 for BI and 0.007 for USE. Since they are larger than zero, predictive relevance of the
model is established.
The assessment of q2 follows the same logic as the one of f2. It is based on the Q2 values of the endogenous
variables and calculates the individual predictive power of the exogenous variables by omitting them and comparing
the change in Q2 (Hair et al., 2017). All individual values for q2 are calculated with an omission distance d of seven.
The results are shown in Table 6. The thresholds for the f2 interpretation can be applied here, too. Values in italics
indicate small effects, values in bold indicate medium effects and values in bold and italics indicate large effects. All
other values have no substantial effect. As before, only the trusting beliefs in Tor have a medium-sized effect,
implying the highest predictive power of all included exogenous variables. Risk beliefs have a small q2 effect size.
-----------------------------------------------------------------
Insert Table 6 About Here
-----------------------------------------------------------------
Discussion
In this section, we interpret and summarize our findings of the statistical analysis, elaborate on limitations of our
work and present future work opportunities.
Interpretation of the Results
Based on our results, all hypotheses except for H6, H9 and H10 can be confirmed (cf. Table 7). The results for H9
and H10 are surprising, considering that they are in contrast to the rationale explained in Section 3.1 and the results
from previous literature (Malhotra et al., 2004). However, it must be said that when effect sizes are rather small it is
possible that the relatively small sample size of 124 leads to a statistical non-significance. Thus, we cannot rule out
that the effects of risk beliefs and trusting beliefs on behavioral intention would be significant with a larger sample
size. Thus, only the degree of trust in the PET (Tor) has a direct significant effect on the intention to use the PET.
This result shows that a reputation of being trustworthy is crucial for a PET provider. The trusting beliefs in the PET
itself are positively influenced by the users’ information privacy concerns and their privacy literacy. Thus, the results
imply that users with a higher level of privacy concerns and privacy literacy rather tend to trust a PET.
-----------------------------------------------------------------
Insert Table 7 About Here
-----------------------------------------------------------------
Hypothesis 6 cannot be confirmed, too. As for H9 and H10, the effect is not statistically significant. The hypotheses
for the effects of privacy literacy on the two other context-specific factors trusting beliefs and risk beliefs can only
be confirmed for the negative effect of OPLIS on trusting beliefs (H5). Thus, users who are more literate with respect
to privacy tend to trust online companies less regarding the handling of their personal information. The effect of
OPLIS on risk beliefs is not statistically significant.
Limitations
The limitations of the study primarily concern the sample composition and size. First, a larger sample would have
been beneficial. However, in general, a sample of 124 participants is acceptable for our kind of statistical analysis
and active users of a PET are hard to find for a relatively long online questionnaire. This is especially the case, if
they do not have any financial rewards as in our study and if they are highly privacy sensitive which might repel
them to disclose any kind of information (even if it is anonymous). Second, the combination of the results of the
German and the English questionnaire can be a potential source of errors. German participants might have
understood questions differently than the English participants. We argue that we achieved equivalence with regard
to the meaning through conducting a thorough translation process, and therefore limiting this potential source of
error to the largest extent possible. In addition, combining the data was necessary from a pragmatic point of view
to get a sample size as large as possible for the statistical analysis. Third, we cannot rule out a non-response bias
since especially in the privacy context people might not answer the questionnaire due to privacy concerns. Fourth,
possible self-report biases (e.g. social desirability) might exist. We addressed these possible biases by gathering
the data fully anonymized. As discussed earlier, we had issues with certain data sets of participants with regard to
actual use behavior (cf. Section 4.2.). Although it might be more beneficial in certain settings to directly refer to
actual use behavior as the sole target variable, we decided to include behavioral intention as an antecedent because
of these issues. Lastly, our calculation of the OPLIS value is not based on all 20 questions of the original instrument
since five questions are specific to law in the European Union. Thus, our results might not be comparable to the
extent as we did in Figure 2. However, it is not possible to further break down the sample without the demographic
information which we did not ask for mandatorily. Another limitation related to OPLIS concerns the validity of the
instrument. OPLIS might have certain flaws since it is relatively new and not widely tested yet.
Future Work
Further work is required to investigate the specific determinants of use decisions for or against PETs and break
down the interrelationships between the associated antecedents. In particular, it would be interesting to investigate
the relationship between trusting beliefs in online companies and trust in the PET itself. A theoretical underlying
would be required to include this relationship in such a research model. Furthermore, our work only investigates
online literacy, especially online privacy literacy, in a specific context, i.e. with respect to the influence on specific
variables and with respect to PETs. Thus, there is a lot of potential for future work to analyze this concept within
different theories applied to different information systems. Interpreting privacy literacy as a kind of personal
disposition might yield interesting results and might enable researchers to frame existing and new research
questions based on another perspective. We also encourage building a more sophisticated model which not only
includes privacy literacy but also closely related dimensions such as privacy awareness and the users’ attitudes to
investigate the users’ intention and behavior.
Conclusions
In this paper, we contribute to the research on privacy-enhancing technologies and users’ privacy by assessing
the specific relationships between information privacy concerns, trusting beliefs in online firms and a privacy-
enhancing technology (in our case Tor), risk beliefs associated with online firms' data processing, general privacy
literacy and the actual use behavior of Tor. By adapting and extending the IUIPC model by Malhotra et al. (2004),
we could show that several of the assumptions for regular online services do not hold for PETs. Furthermore, we
contribute to the practical work on PETs, especially Tor, by providing insights into factors influencing use
intentions and behaviors of actual users. Trust in Tor is one of the major drivers of use intentions. Thus,
companies or non-profits like ‘The Tor Project’ should focus on building a strong reputation and a trustful
relationship with its users. We contribute to the literature on online literacy, by analyzing a relatively new
instrument for measuring online privacy literacy (OPLIS) in two ways. First, our descriptive results of the OPLIS
scores for Tor users indicate that they are more privacy literate than an average reference group of regular
internet users (Masur et al., 2017). Second, we derived research hypotheses following the notion that online
privacy literacy is similar to a personal disposition influencing the context-specific factors within the IUIPC model.
Our results indicate that a higher level of online privacy literacy leads to less trust in online companies with
respect to handling personal information. In contrast, more literate users tend to trust Tor to a larger extent. Thus,
we argue that online privacy literacy is an important factor to consider when investigating relationships with
privacy-related factors like concerns or risks.
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About the Authors
David Harborth holds a Master's degree in Management with specialization in Finance and Information
Management and a Bachelor's degree in Business Administration and Economics with specialization in Finance
and Accounting from Goethe University Frankfurt. He worked for three years in the consulting industry in the
strategy and financial services sector. David Harborth started to work at the Chair of Mobile Business & Multilateral
Security as a research and teaching assistant in December 2015. His major areas of research are the socio-
economic and technical issues related to privacy in Augmented Reality (AR), as well as user perceptions and
business models for privacy-enhancing technologies (PETs). His research has appeared in conferences such as
International Symposium on Mixed and Augmented Reality (ISMAR), Hawaii International Conference on System
Sciences (HICSS), European Conference on Information Systems (ECIS), Americas Conference on Information
Systems (AMCIS), Wirtschaftsinformatik (WI) and IFIP SEC.
Dr. Sebastian Pape Sebastian Pape is a senior researcher working at the Chair of Mobile Business & Multilateral
Security at Goethe University Frankfurt. He successfully completed diplomas in mathematics (Dipl.-Math.) and
computer science (Dipl.-Inform.) at Darmstadt University of Technology and holds a doctoral degree (Dr. rer. nat.)
from the University of Kassel. From 2005 to 2011, he worked as research and teaching assistant at the Database
Group (lead by Prof. Dr. Lutz Wegner) of the Department of Electrical Engineering and Computer Science of the
University of Kassel. From 2011 to 2015, he was a senior researcher and teaching assistant at the Software
Engineering for Critical Systems Group (lead by Prof. Dr. Jan Jürjens) of the Department of Computer Science
Department of TU Dortmund University. From October 2014 to January 2015, he also was a visiting researcher (of
Prof. Dr. Fabio Massacci) at the security group of the Department of Information Engineering and Computer Science
of University of Trento. From 2018 to 2019 he was standing in as a professor at the Chair of Information Systems
of the Faculty of Business, Economics and Management Information Systems at Regensburg University.
Appendix A - Distribution Channels of the Tor Online Survey
1. Mailinglists:
(a) tor-talk (https://lists.torproject.org/cgi-bin/mailman/listinfo/tor-talk/)
(b) liberationtech (https://mailman.stanford.edu/mailman/listinfo/liberationtech)
(c) IFIP TC 11 (https://dlist.server.uni- frankfurt.de/mailman/listinfo/ifip-tc11)
(d) FOSAD (http://www.sti.uniurb.it/events/fosad/)
(e) GI PET (http://mail.gi-fb-sicherheit.de/mailman/listinfo/pet)
(f) GI FBSEC (http://mail.gi-fb-sicherheit.de/mailman/listinfo/fbsec)
2. Twitter with #tor and #privacy
3. Boards:
(a) reddit (sub-reddits: r/TOR, r/onions, r/privacy)
(b) ubuntuusers.de
4. Tor Hidden Service Boards, Sections posted into:
(a) Darknet Avengers, Off Topic (http://avengersdutyk3xf.onion/)
(b) The Hub, Beginners (http://thehub7xbw4dc5r2.onion)
(c) Onion Land, Off Topic (http://onionlandbakyt3j.onion)
(d) 8chan, /tech/ (http://oxwugzccvk3dk6tj.onion)
(e) IntelExchange, Unverified Users (http://rrcc5uuudhh4oz3c.onion)
(f) Code Green, Discussions (http://pyl7a4ccwgpxm6rd.onion)
(g) Changolia, overchan.random (http://jewsdid.oniichanylo2tsi4.onion)
(h) Atlayo, Posting (http://atlayofke5rqhsma.onion/)
5. Personal Announcements at Workshops
Appendix B - Questionnaire
The following items are measured with a seven-point Likert scale from “strongly disagree” to “strongly agree”.
Trusting Be lie fs (TB)
1. Online companies are trustworthy in handling information.
2. Online companies tell the truth and fulfill promises related to information provided by me.
3. I trust that online companies would keep my best interests in mind when dealing with information.
4. Online companies are in general predictable and consistent regarding the usage of information.
5. Online companies are always honest with customers when it comes to using the provided information.
Trusting Be lie fs in Tor (T BTor )
1. To r i s t ru st w or t hy.
2. To r k ee p s pr o mi s es an d c om m it m en t s.
3. I trust Tor because they keep my best interests in mind.
Risk Beliefs (RB)
1. In general, it would be risky to give information to online companies.
2. There would be high potential for loss associated with giving information to online firms.
3. There would be too much uncertainty associated with giving information to online firms.
4. Providing online firms with information would involve many unexpected problems.
5. I would feel safe giving information to online companies. (reverse-scored item)
Awareness (AWA)
1. Companies seeking information online should disclose the way the data are collected, processed, and used.
2. A good consumer online privacy policy should have a clear and conspicuous disclosure.
3. It is very important to me that I am aware and knowledgeable about how my personal information will be used.
Collection (COLL)
1. It usually bothers me when online companies ask me for personal information.
2. When online companies ask me for personal information, I sometimes think twice before providing it.
3. It bothers me to give personal information to so many online companies.
4. I'm concerned that online companies are collecting too much personal information about me.
Control (CONTROL)
1. Consumer online privacy is really a matter of consumers right to exercise control and autonomy over decisions
about how their information is collected, used, and shared.
2. Consumer control of personal information lies at the heart of consumer privacy.
3. I believe that online privacy is invaded when control is lost or unwillingly reduced as a result of a marketing
transaction.
Behavioral Intention (BI)
1. I intend to continue using Tor in the future.
2. I will always try to use Tor in my daily life.
3. I plan to continue to use Tor fr eq u en t ly .
Use Behavior (USE)
Please choose your usage frequency for Tor (the frequency scale is adapted from Rosen et al. (2013)):
1. Never
2. Once a month
3. Several times a month
4. Once a week
5. Several times a week
6. Once a day
7. Several times a day
8. Once an hour
9. Several times an hour
10. All the time
Internet Experience (in years)
1. How many years of experience do you have with computers?
Answer options range from 0 years to “more than 20 years”.
Experience with Tor (in years)
1. How many years are you using Tor?
Answer options range from 0 years to “more than 20 years”.
Privacy Victim Experience
1. How frequently have you personally been the victim of what you felt was an improper invasion of privacy?
Item measured with a seven-point frequency scale (“Never”, “Very infrequently”, “Infrequently”, “Occasionally”,
“Sometimes”, “Frequently”, “Very frequently”).
Online Privacy Literacy Scale (OPLIS)
Part 1: Knowledge about institutional practices
1. The National Security Agency (NSA) accesses only public user data, which are visible for anyone.
(True/false/don’t know)
2. Social network site operators (e.g. Facebook) also collect and process information about non-users of the social
network site. (Tru e/false/don’t know)
3. User data that are collected by social network site operators (e.g. Facebook) are deleted after five years.
(True/false/don’t know)
4. Companies combine users’ data traces collected from different websites to create user profiles.
(True/false/don’t know)
5. E-mails are commonly passed over several computers before they reach the actual receiver. (Tru e/false/don’t
know)
Part 2: Knowledge about technical aspects of data protection (correct answers randomized)
1. What does the term “browsing history” stand for? In the browsing history...
A. ...the URLs of visited websites are stored.
B. ...cookies from visited websites are stored.
C. ...potentially infected websites are stored separately.
D. ...different information about the user are stored, depending on the browser type.
2. What is a “cookie”?
A. A text file that enables websites to recognize a user when revisiting.
B. A program to disable data collection from online operators.
C. A computer virus that can be transferred after connecting to a website.
D. A browser plugin that ensures safe online surfing.
3. What does the term “cache” mean?
A. A buffer memory that accelerates surfing on the Internet.
B. A program that specifically collects information about an Internet user and passes them on to third parties.
C. A program, that copies data on an external hard drive to protect against data theft.
D. A browser plugin that encrypts data transfer when surfing online.
4. What is a “trojan”? A trojan is a computer program, that...
A. ...is disguised as a useful application, but fulfills another function in the background.
B. ...protects a computer from viruses and other malware.
C. ... was developed for fun an d has no specific function.
D. ... caused damage as computer virus in the 90ies but doesn’t exist anymore.
5. What is a “firewall”?
A. A fallback system that will protect the computer from unwanted web attacks.
B. An outdated protection program against computer viruses.
C. A browser plugin that ensures safe online surfing.
D. A new technical development that prevents data loss in case of a short circuit.
Part 3: Knowledge about data protection strategies
1. Tracking of one’s own internet is made more difficult if one deletes browser information (e.g. cookies, cache,
browser history) regularly. (True /false/don’t know)
2. Surfing in the private browsing mode can prevent the reconstruction of your surfing behavior, because no
browser information is stored. (True/false/don’t know)
3. Using false names or pseudonyms can make it difficult to identify someone on the Internet. (True/false/don’t
know)
4. Even though It-experts can crack difficult passwords, it is more sensible to use a combination of letters, numbers
and signs as passwords than words, names or simple combinations of numbers. (True /false/don’t know)
5. In order to prevent the access to personal data, one should use various passwords and user names for different
online applications and change them frequently. (True/false/don’t know)
Figure 1. Research Model
Figure 2. Differences in the Distributions between the Cumulative Relative Frequency of Correctly
Answered OPLIS Questions between the Reference Group (Masur et al., 2017) and the Tor Users in our
Sample
Figure 3. Path Estimates and Adjusted R2 Values of the Structural Model
Table 1. Descriptive Statistics for the Used Variables (cf. Appendix A2 for Measurement Scales of the
Constructs)
Statistics
Variable
Mean
Median
Min.
Max.
Std. Dev.
OPLIS (relative)
0.7876
0.8
0.3333
1
0.1259
Collection
6.3810
6.5
4
7
0.7053
Awareness
6.5457
7
1
7
0.7500
Control
5.9435
6
1
7
1.0038
Trusting Beliefs
2.2694
2.2
1
5.8
0.9429
Risk Beliefs
5.3242
5.5
1.6
7
1.1048
Trusting Beliefs in Tor
5.3548
5.6667
1
7
1.1892
Behavioral Intention
5.7043
6
1
7
1.2971
Actual Use Behavior
4.0726
5
0
9
2.6692
Experience with Tor
6.8710
6
0
20
4.6416
Internet Experience
17.7984
21
2
21
5.0429
Privacy Victim Experience
4.2742
4
1
7
1.6297
Table 2. Loadings and Cross-Loadings of the Reflective Items and Internal Consistency Reliability
Construct
AWA
Control
COLL
RB
TB
TrustTor
BI
AWA1
0.911
0.234
0.302
0.222
-0.136
0.066
0.201
AWA2
0.923
0.230
0.219
0.136
-0.153
0.072
0.197
AWA3
0.891
0.323
0.315
0.220
-0.102
0.066
0.249
CONTROL1
0.095
0.825
0.271
0.107
-0.163
0.137
0.214
CONTROL2
0.405
0.821
0.226
0.245
-0.149
0.132
0.237
CONTROL3
0.174
0.756
0.438
0.214
-0.340
0.098
0.098
COLL1
0.264
0.358
0.888
0.546
-0.462
0.176
0.301
COLL2
0.206
0.332
0.812
0.204
-0.337
0.232
0.374
COLL3
0.292
0.359
0.906
0.443
-0.442
0.272
0.375
COLL4
0.304
0.309
0.850
0.466
-0.399
0.182
0.317
RISK1
0.196
0.200
0.487
0.879
-0.446
0.217
0.258
RISK2
0.170
0.160
0.326
0.832
-0.292
0.156
0.233
RISK3
0.155
0.252
0.364
0.861
-0.346
0.233
0.221
RISK4
0.245
0.231
0.374
0.826
-0.255
0.257
0.327
RISK5
-0.105
-0.145
-0.427
-0.700
0.396
-0.003
-0.144
TRUST1
-0.149
-0.261
-0.455
-0.417
0.894
-0.097
-0.265
TRUST2
-0.118
-0.186
-0.410
-0.376
0.890
-0.033
-0.195
TRUST3
-0.107
-0.339
-0.397
-0.396
0.768
-0.131
-0.153
TRUST5
-0.069
-0.009
-0.219
-0.069
0.682
-0.109
-0.166
TRUSTTor1
0.064
0.149
0.257
0.159
-0.091
0.880
0.559
TRUSTTor2
0.077
0.121
0.236
0.244
-0.124
0.924
0.552
TRUSTTor3
0.059
0.138
0.169
0.179
-0.078
0.883
0.486
BI1
0.236
0.240
0.355
0.228
-0.252
0.586
0.858
BI2
0.262
0.202
0.322
0.318
-0.149
0.465
0.864
BI3
0.143
0.158
0.363
0.233
-0.231
0.522
0.926
Cronbach's a
0.894
0.722
0.887
0.567
0.831
0.877
0.859
Comp. Reliability
0.934
0.843
0.922
0.817
0.885
0.924
0.914
Table 3. Discriminant Validity with AVEs and Construct Correlations
Constructs (AVE)
AWA
BI
COLL
Control
IUIPC
OPLIS
RB
TB
TrustTor
USE
AWA (0.825)
0.908
BI (0.780)
0.239
0.883
COLL (0.748)
0.309
0.394
0.865
Control (0.642)
0.291
0.226
0.393
0.801
IUIPC (1.000)
0.691
0.403
0.837
0.685
1.000
OPLIS (1.000)
-0.071
0.143
0.111
0.110
0.071
1.000
RB (0.675)
0.214
0.290
0.485
0.243
0.450
0.198
0.822
TB (0.662)
-0.142
-0.242
-0.476
-0.276
-0.426
-0.155
-0.426
0.813
TrustTor (0.803)
0.075
0.597
0.249
0.152
0.226
0.300
0.217
-0.110
0.896
USE (1.000)
-0.128
0.177
0.073
0.008
-0.009
0.006
0.010
-0.058
-0.026
1.000
Note: AVEs i n par enthe ses in th e fir st co lumn. Valu es fo r √AVE are s how n on the diago nal an d c onst ru ct corre latio ns a re of f-
diagonal elements.
Table 4. Heterotrait-Monotrait Ratio (HTMT)
Constructs
AWA
BI
COLL
Control
IUIPC
OPLIS
RB
TB
TrustTor
BI
0.274
COLL
0.343
0.452
Control
0.346
0.290
0.486
IUIPC
0.728
0.436
0.888
0.798
OPLIS
0.075
0.155
0.119
0.127
0.071
RB
0.238
0.337
0.541
0.294
0.478
0.212
TB
0.159
0.278
0.528
0.336
0.439
0.171
0.449
TrustTor
0.084
0.681
0.280
0.192
0.240
0.318
0.244
0.131
USE
0.138
0.186
0.077
0.060
0.009
0.006
0.021
0.058
0.029
Table 5. Covariate Results (Significance Levels: ***p < 0.01; **p < 0.05; *p < 0.10)
Context-specific
factors
Covariate
TB
RB
TBTor
BI
USE
Experience with Tor
-0.047
-0.008
-0.012
0.092
-0.074
Internet experience
-0.001
-0.139*
0.003
0.016
0.065
Privacy victim experience
-0.245**
0.011
-0.163*
0.196**
0.225**
Table 6. f2 and q2 Effect Size Assessment Values
Variables
f2
q2
Endogenous
Exogenous
BI
BI
TB
0.005
0.000
RB
0.016
0.072
TBTor
0.567
0.334
Table 7. Summary of the Results
Hypothesis
Result
H1
Internet Users Information Privacy Concerns (IUIPC) have a negative effect on Trusting
Beliefs (TB)
Ö
H2
Internet Users Information Privacy Concerns (IUIPC) have a positive effect on Risk Beliefs
(RB)
Ö
H3
Trusting Beliefs (TB) have a negative effect on Risk Beliefs (RB)
Ö
H4
Internet Users Information Privacy Concerns (IUIPC) have a positive effect on the trusting
beliefs in Tor (TBTor)
Ö
H5
Online Privacy Literacy (OPLIS) has a negative effect on Trusting Beliefs (TB)
Ö
H6
Online Privacy Literacy (OPLIS) has a positive effect on Risk Beliefs (RB)
´
H7
Online Privacy Literacy (OPLIS) has a positive effect on the trusting beliefs in Tor (TBTor)
Ö
H8
Trusting beliefs in Tor (TBTor) have a positive effect on the behavioral intention to use Tor
(BI)
Ö
H9
Trusting beliefs (TB) have a negative effect on the behavioral intention to use Tor (BI)
´
H10
Risk beliefs (RB) have a positive effect on the behavioral intention to use Tor (BI)
´
H11
The behavioral intention to use Tor (BI) has a positive effect on the actual use behavior
(USE)
Ö
