Blockchain technology main

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Telematics and Informatics
journal homepage: www.elsevier.com/locate/tele
Blockchain: The emerging technology of digital trust
Don D.H. Shin
⁎
College of Communication and Media Sciences, Zayed University, Abu Dhabi, UAE
A R T I C L E I N F O
Keywords:
Blockchain
Blockchain service
Perceived security
Perceived privacy
Digital trust
A B S T R A C T
Trust in individual relationships with blockchain has become an increasingly prominent issue.
This study introduces a key heuristic used to assess trust in blockchain by analyzing how
privacy and security concerns about blockchains have an impact on the user’s attitude and be-
havior. It proposes a blockchain user model by integrating security and privacy as primary in-
fluencing factors of trust and behavioral intent. The results from a user experience model of
blockchain users confirm that the model explains user experience and predicts behavioral intent
of blockchain. The results establish users’ cognitive role in embedding privacy and security in
blockchain. The research contributes to the ongoing research by clarifying the role and dimen-
sion of trust in relation to security and privacy in blockchains and provides heuristic implications
for academia and industry.
1. Introduction
Blockchains have experienced exponential growth over the last few years (Kshetri, 2018). Blockchain technology, in particular the
cryptocurrency mania of 2017, has created a great deal of disruptive buzz in the industry. As blockchain rises beyond being just
another buzz-word, the variety of blockchain applications ranges from healthcare, financial, transportation, risk management and
media to public and social services (Grover et al., 2019). While blockchains afford a new window of opportunities for interaction and
decentralized transactions, a wide variety of security and privacy have risen as thorny issues in the blockchain environment (Joshi
et al., 2018). Security considerations specifically override all other considerations in blockchain. Blockchain provides decentralized,
peer-to-peer security for all transactions, yet many blockchain security vulnerabilities remain (Kshetri, 2017). The key to continued
growth is addressing the security issues facing blockchains (Vidan and Lehdonvirta, 2018). How to ensure privacy and security in
blockchains has been a critical issue in the success of blockchains (Du et al., 2019).
Despite the growing issue over vulnerability, there is a lack of research on the concern of privacy and security in blockchains,
leading to a limited understanding on how security concerns and trust influence the experience of blockchains. As blockchains
mature, the degree to which users trust the services, interactions, and organizations behind them becomes increasingly critical
(Bancroft and Reid, 2017). When users interact within blockchains, they expect that they will receive the products they paid for and
that their data will not be abused (Casino et al., 2019). While it is obvious that trust matters in digital contexts (Tian et al., 2019),
there has been a lack of clarity about what trust is, how it works/can be formed, and what truly constitutes digital trust (Park et al.,
2018).
This study makes a heuristic contribution to filling the existing gap in the literature and creating the essential linkages between
the level of privacy and security and the effects of the extent on trust. Aiming to conceptualize digital trust through blockchains, this
study examines blockchain users’ cognitive process of security and privacy and its impact on intention by highlighting how trust is
https://doi.org/10.1016/j.tele.2019.101278
Received 4 June 2019; Received in revised form 2 August 2019; Accepted 12 September 2019
⁎
Address: P.O. Box 144534, Abu Dhabi, UAE. Tel.: +97125993488.
E-mail address: dshin1030@gmail.com.
Telematics and Informatics 45 (2019) 101278
Available online 17 September 2019
0736-5853/ © 2019 Elsevier Ltd. All rights reserved.
T

generated and how it leads to user behavioral intention in blockchains.
Although numerous studies have investigated user privacy concerns and firm security practices in a diverse technological en-
vironment (e.g., (Kshetri, 2013, 2014, 2017; Shin, 2010), it has remained unknown how privacy/security aspects in the emerging
blockchain context affect an individual’s cognitive process of acceptance (Seigel and Sarma, 2019). This study discusses a user
cognitive model of blockchains in generating a security/trust and trust model of blockchain adoption. With this model in place, the
current study proposes means to configure and evaluate users’ perceived security and privacy of their blockchain experiences. With
the following research questions in focus, the study conceptualizes digital trust in the new context of the blockchain era.
RQ1: What is the relationship between users’ trust in blockchain platforms, their security and privacy concerns, and their
blockchain adoption and use?
RQ2: How user trust is developed and how it influences user behavior in the blockchain environment? What role does trust/
distrust play in the acceptance of blockchains? How can digital trust be measured and operationalized in the digital era?
With the inquires in place, it securitizes the effects of privacy and security in blockchain experience and behaviors. The findings
open windows of opportunity for user-centered evaluation and an analytical method for assessing user experience for future
blockchain environments. Ongoing research has shown that improved privacy measures increase perceived security, which leads to
increased intention to adopt technology services (Carmen and Lopez, 2018; Shin, 2011; Tian et al., 2019). This study advances
current understanding by further showing how trust is related and generated, and, in turn, affects perceived privacy and security.
This relation implies a positive feedback loop of trust shedding light on the new roles of trust in emerging digital contexts. The loop in
the blockchain context should interest both researchers and industries. From a scholarly standpoint, the findings provide an insightful
framework of a blockchain experience model by recognizing antecedents of user intention to adopt a blockchain relative to privacy
and security. The value of our approach is examining the cognitive aspects of security and embedding it privacy and security in a
user-focused way. Despite extensive research on the factors that affect users to experience and use technologies in general (Kim et al.,
2019; Kim and Yun, 2007), blockchain user research in terms of user-centered privacy and security has been rare. This research
addresses this aspect by uncovering users’ cognitive process of security/privacy in blockchains. This academic work also offers
practical guidelines for practitioners. The findings should guide firms developing blockchain in fostering user trust by ensuring
anonymity, protecting users from security threats, and assisting them to track a misuse of their data. As cryptocurrency industries
such as bitcoin face the difficulties of establishing a sustainable and trusting environment (Lemieux, 2016), blockchain industries
should see this study’s results helpful for future development.
2. Literature review
2.1. Blockchain, security, and the future of digital trust
The societal implications of blockchain technology are immense. Blockchain has provided financial services to a great number of
customers without access to banking via online, debit cards and ATMs. It has additionally allowed micropayments and microloans to
people in disadvantaged socio-economic circumstances, unravelling a complete new form of advantage for the world economy (Shin,
2019). Another example of a social implication is within a section of social enterprise where lack of trust is a specific issue (Park,
2018). Blockchain technology produces a meaningful means of finding a way around the challenges of corruption. The decentralized
characteristics of the blockchain and smart-contracts mean that an agreement built on its platform does not need a separate party. As
smart-contracts are basically computer code, contractual conditions could be translated into logical functions which trigger subse-
quently when set conditions are met. It is feasible to add clauses in smart agreements which specify that obligations are met if certain
results are accomplished. Smart agreements may even be used to govern the circulation of currency.
Blockchain continues to gain momentum in a myriad of use cases across a wide variety of industries. Firms are increasing their
investments in blockchain to transform how they deliver products and services, gain new insights to obtain a competitive edge, and
improve their financial and operational performance (Marsal-Llacuna, 2018). The fact that blockchain ledger records are secure,
sequential, and immutable is improving the security of customer information as well as business and transaction records (Macrinici
et al., 2018). Ironically, such a trust-based mechanism is blockchain’s most vulnerable point (Kshetri, 2017). Information and services
of blockchains are vulnerable to manipulation by hackers or foreign powers, and personal data are not necessarily private. As
blockchain develops in different sectors of various domains, security-related issues become prime factors in determining the success
of the blockchain economy (Pink et al., 2018). Blockchain will continue to change the future of digital transactions in the new data
economy and transform the nature of digital trust (Filippi and Hassan, 2016; Shin and Park, 2019). Digital trust in blockchain can be
defined as enabling user heuristics made between security and privacy that reflect their level of confidence.
Digital trust is a kind of user heuristics in blockchain. Blockchain users are likely cope with the perceived risk, security, and
privacy, and overload by using heuristics that minimize their cognitive effort and time, through the use of cognitive heuristics. Digital
trust as cognitive heuristics constitute information processing methods to make decisions more quickly and with less effort than more
complex methods, and thus they reduce cognitive load during security assessment.
2.2. Concerns over privacy in blockchain
Security and privacy are critical to the blockchain technology since it can exist without an authorized third party. Blockchain
security issues are closely related to concerns over privacy in blockchains (Kshetri, 2018). Although distributed ledger technology is
encrypted, it is not held in a single place. Firms do not have complete control over the data. Due to this decentralized structure, user
D.D.H. Shin Telematics and Informatics 45 (2019) 101278
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data might be diverted through many different servers when being processed in blockchains. Data in a blockchain are vulnerable and
can be accessed by other people in the chain. When data are encrypted intentionally or mistakenly before transmitting to a chain,
nobody can access the data unless they are decoded. Despite privacy-enhancing technologies, blockchain transactions are vulnerable
to hacking throughout chain nodes. These produce metadata and statistical analysis that can produce information even from en-
crypted data, allowing for pattern recognition (Leon et al., 2017). Against the privacy issues, the EU as well as the US impose very
strict rules and regulations in regard to data privacy. The EU establishes the General Data Protection Regulation, which imposes clear
conditions for consent and data retention, requires firms to protect the individual data and privacy of people for transactions in the
EU. It also prohibits personal data from leaving the EU, giving users eventual and sheer control over all their data. GDPR might
hamper industry innovation in blockchain technologies, while on the other, opens windows of opportunities in the use of blockchain
technologies as a venue for enforcing GDPR. When blockchain entails the processing of personal data, it raises legal compliance
questions. The regulations inform data protection laws and corporate trust-building strategies.
3. Research model and hypothesis development
As blockchain-based services provides various innovative features, it is critical to recognize what users’ expectations are and how
they are formed and how users’ recognized confirmation affects satisfaction, which then influences intentions. Theory of Reasoned
Action (TRA) can be a good frame for this task as the theory explains the relationship between attitudes and behaviors within human
action (Shin, 2013). The theory explains how behavioral intent is created or caused by human attitudes and subjective norms.
TRA is used in this study as a lens to examine the UX of blockchain security and privacy. TRA is right for this analysis since it is
structured to describe user behaviors as a function of belief, evaluation, and performance of beliefs based on cognitive processes. As
blockchain systems afford users unique experiences, TRA can be extended by incorporating blockchain-specific factors (such as
privacy and security) as antecedents of trust and utility/convenience as a performance value.
3.1. Attitude toward blockchain
Per the theory of reasoned action (TRA), peoples’ action of a certain behavior is influenced by their behavioral intention to
perform the behavior, and behavioral intention is affected by a person’s attitudes (Shin, 2013). As the direct antecedent of behavior,
behavioral intent is the cognitive expression of individual preparation to carry out a given behavior (Shin, 2010). Per TRA, attitude
toward a behavior is stated as a person’s belief of performing the target behavior. An individual’s attitude toward a behavior is
decided by his/her belief and valuations. As the TRA has been widely applied to diverse technological contexts particularly emerging
technologies, the key premises of the TRA also apply in a blockchain context (Fig. 1).
H1. Attitude toward blockchain has a positive influence on the intention to adopt blockchain.
3.2. Perceived security
Given the rising concerns over security in blockchain (Joshi et al., 2018), this research addresses the influence of users’ recognized
security on intention to adopt blockchains. Shin (2010) defines perceived security as the extent to which a user considers that doing
things in certain contexts is secure and safe. Subjective security can be considered as the reflecting image of risk affinity. Kim and Yun
(2007) show that a perceived security is fundamentally determined by a user’s feelings of control in an online system. Security in a
mediated online platform may not depend on technical aspects of security alone (Shin, 2010). A low subjective security can be the
most serious reason for a refusal to adopt technological services (Mou et al., 2017). Numerous studies have confirmed that negative
subjective security thwarts users from accepting online services (Shin, 2013). There has been continuous research in conceptualizing
and theorizing a set of factors that elucidates the role of subjective security.
In accordance with ongoing research, this study examines security from a user-centric view that addresses not only technical
Fig. 1. Blockchain trust model.
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features, such as authentication and confidentiality but also the people’s cognitive feeling of security and emotional comfortability. In
terms of blockchains, users’ assessment of security can differ from physical security levels (Vidan and Lehdonvirta, 2018). While a
technical evaluation of security is grounded on scientific solutions, it is the individuals’ assessments of security that affect intention
and behavior (Mou et al., 2017; Shin, 2010). Although numerous studies have examined the function of perceived security in various
contexts, only a few have applied it to a blockchain context. It is useful to examine user dimension of security and its link to trust in
the blockchain context.
H2. Perceived security positively influences users’ trust in blockchain.
H3. Perceived security positively influences users’ attitudes toward blockchain.
3.3. Perceived privacy
Similar to perceived security, perceived privacy is critical in blockchains. In this study, perceived privacy is seen as the extent to
which a user considers that his or her information is protected and will not be misused (Casalo et al., 2007). Privacy is often
interchangeably used with the issue of security, as a subset thereof, computer security (Park et al., 2018). Information privacy is one
of the most critical issues in various technological environments (Miyazaki, 2001; Shin, 2011). The concept of privacy has been
conceived as a user’s capacity to manage and maneuver the conditions by which his/her personal information is collected and
processed (Carmen and Lopez, 2018). The degree to which blockchain users believe that a blockchain service ensures their privacy
may also have an influence on their trust of the service. Blockchain services influence perceptions of a service’s privacy assurance
through distributed ledger functions. Research confirms that positive perceptions of service privacy protection via features increase
regard for and trust in the firm (Kim et al., 2015; Shin, 2011). For example, Diakopoulos and Koliska (2016) found that sharing
information acquisition procedures enhances users’ feelings of security and trust. Similarly, (Klinger and Svensson, 2018) argue that
having a clear privacy procedure, which clarifies how the firm would use user data and information, leads to trust in a service. People
are likely to give personal information to service providers if the latter displays privacy seals or privacy statements (Kim and Yun,
2007). Based on the ongoing literature, it can be hypothesized that the degree to which blockchain users think a blockchain service
ensures their privacy favorably affect their overall attitude and trust in the providers and the service itself.
H4. Perceived privacy positively influences users’ trust in blockchain.
H5. Perceived privacy positively influences users’ attitudes toward blockchain.
3.4. Trust
Trust is a key component in blockchain technology (Shin, 2019). People do not require an established trust relationship if
transactions are carried out on a distributed ledger. If each participant in the transaction trusts the blockchain itself, they do not need
to directly trust each other. Whether and how users trust blockchain plays a critical role in blockchain success. In this regard, trust is
proposed as a key factor. Trust is seen as assured reliance on the character or capability that the willingness of a user to be vulnerable
to the actions of another user based on the belief that the other will conduct a certain action (Shin, 2011). Given this definition, trust
can be seen as a consequent factor of privacy and security and as an antecedent factor to attitudes toward blockchain.
In online contexts, trust has been consistently found to be a key factor in exchanges involving risk. Research in e-commerce and
digital technologies has consistently found trust to be strongly related to user acceptance (Mou and Shin, 2018; Shin, 2011). Research
by Shin, Lee, and Hwang (Shin et al., 2017) found a significant impact of trust on behavioral outcomes. The higher the users’ trust in
the online service, the less effort users will need to validate details of those services to evaluate their reality and legitimacy. With a
trusted service, users would experience convenience and ease of using as they have less need of checking or examining authenticity
and legitimacy (Bianchi and Brockner, 2012).
As trust has recently been considered key issue in digital media and technologies (Shin and Biocca, 2018), it is opportune to
examine if trust in a blockchain service influences or is influenced by what factors. As trust is key to the process of digital transaction,
it is critical to test what promotes trust in a blockchain service.
H6. Trust positively influences users’ attitudes toward blockchain.
4. Methodology
4.1. Survey procedure
To understand overarching views on users’ perspectives, presurvey interviews were performed 1) to confirm factors validated
from the other research; 2) to draw blockchain-specific features; and 3) to generate the survey measurements. In-depth interview
subjects were recruited from graduate students registered in classes in a university. A total of 20 people were presurveyed. The
sample comprises 8 male and 12 female subjects. As most blockchain users fit the demographics of young user groups, a student
sample can be justified in this study. Participants expressed their opinions on security, privacy, risk, and feelings about blockchains
on memos and then post the memos under the types prepared by the researchers.
The reliability and the validity of the measurements were assessed through a pretest. A total of 43 subjects participated in the
pretest. Respondents were asked about their general view on the questionnaire and expressed any trouble they may have encountered
in the measurements. Opinion and comments from the pretest were incorporated into a survey questionnaire. Lastly, the wording of
measurements was finally edited.
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Following the pretest, a contracted survey firm performed a four-week web survey (see Appendix for the survey). A total of 391
responses were gathered (21% response rate; 52.2% were female and 47.8% were male). After dropping error responses through data
filtering, 363 quality responses were finalized as the usable sample. The final sample shows general trends of blockchain users in
general. Young people indeed are the dominant users of blockchains (Casino et al., 2019). The chosen sample is well matched
matched to a general population of blockchains (Table 1).
4.2. Scales and measurements
The final measurements comprised 15 items, with three items per factor. All of the items were derived from the TRA literature and
user study frameworks (see the Appendix for the item questionnaire). A pretest was performed: 20 people with previous and/or
current users of blockchain services participated in the pretest over a ten-day interval. For the reliability of the measurements,
Cronbach’s alpha was used. Correlation coefficients were utilized to evaluate the concurrent validity of the instrument. The scores for
this measurement ranged from 0.823 to 0.965, indicating highly suitable construct reliability (Table 2). The analysis of confirmatory
factor analysis showed that the items had acceptable factor loadings. To assess validity, a simple linear correlation was used to assess
the significance of the relationship. The appropriate level of intercorrelations among the variables showed no critical multi-
collinearity problems. Furthermore, discriminant validity (factors are distinct and uncorrelated) is verified as the square root of the
average variance extracted (AVE) is higher than the largest correlation of that factor with any other factor. All of the goodness-of-fit
indices were within acceptable ranges and indicate that the model of the research has good fitness.
5. Results
The hypothesized causal paths were tested, and all the hypotheses were confirmed (Table 3). The results confirm the model and
highlight heuristic functions of trust in the formation of user behaviors. The results reveal the underlying antecedent roles of users’
privacy and security in shaping users’ behavioral intent of blockchain. Security is found to be a higher effect on trust than perceived
privacy (β = 0.23, CR = 2.688; β = 0.18; CR = 2.704). The model also showed a significant positive effect of trust on attitude (H6),
implying the mediating effect of trust on the relation between security/privacy and attitude.
The explanatory powers of constructs were verified (Fig. 2). Perceived security and privacy together account for 23% of the
Table 1
Demographics of Survey Respondents.
Age (years) Percent
Under 20 94
21–35 170
36–45 29
Blockchain experience
1–5 104
6–9 97
10–12 99
> 1 year 63
Gender
Female 182
Male 180
No response 1
Table 2
Reliability and Validity.
Variables Mean SD Cronbach’s alpha AVE Composite reliability
Perceived privacy 5.18 1.404 0.866 0.751 0.710
4.85 1.642
4.66 1.740
Perceived security 5.60 1.203 0.868 0.743 0.896
5.37 1.275
5.10 1.011
Trust 3.90 1.151 0.920 0.757 0.903
3.96 1.089
4.19 1.418
5.09 1.413
Attitude 5.13 1.414 0.970 0.850 0.944
5.05 1.416
Intention 4.98 1.380 0.940 0.663 0.855
4.63 1.485
4.61 1.461
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variance in trust. Trust, along with privacy and security, charged 27% of the variance in attitude toward behavior, which in turn
described 72% of the variance of intent. Potential underlying effects can be inferred in the model from the high R2
of 72%.
5.1. Mediating roles of trust
As trust was found to play a significant role between security/privacy and attitude, it is worthwhile to extend the model by
examining the mediating roles of trust. This task will be significant, as research literature has consistently shown that trust plays a
significant role (Shin, 2010; Chang et al., 2016). The relationship between privacy and security is an indirect effect of the influence of
the trust mediator. The consistent findings regarding trust warrant the significant role of trust in the blockchain context.
The effect of trust on other variables was analyzed with mediating regression. This research tested the mediating effect using the
multiple-step method of Hayes (2013). Per the proposed steps, the significant links were verified between the independent variable
and the mediating variable and between the dependent variable (intention) and the mediating variable. Subsequently, mediation is
verified if the effect of the independent variable on the dependent variable is decreased by the mediating variable. The mediating
effect test is complete when the direct effect becomes insignificant.
The first step is to show that the causal variable trust is correlated with security. Perceived security was used as the criterion
variable in a regression equation and perceived privacy was analyzed as a predictor. The influence of privacy largely accounted for
the variance in the hypothesized mediator trust (t = 3.82, F = 14.31, p < .001). It showed that there is an effect that may be
mediated. Then, perceived privacy was correlated with trust. Trust was used as the criterion variable in the regression equation and
perceived privacy was used as a predictor. This step established that trust significantly explains the variance in the dependent
variable security (t = 4.24, F = 18.31, p < .001). Third, a regression test was done to show whether trust affects perceived security.
Trust and privacy were used as predictors, and security was analyzed with the criterion variable in a regression equation. The result
was significant (t = 2.91, F = 8.41, p < .001).
Lastly, a regression model was used to test whether trust completely mediates the privacy-security relationship. It was regressed
with security as the dependent variable and privacy and trust as the independent variables. The effects were insignificant (t = 1.12,
p = .28) when the significant effect of the hypothesized mediator trust (t = 3.79, p < .001) was partitioned out. Hence, trust was
found as a partial mediator (but close to full mediator) between privacy and security (Fig. 3 & Table 4). This illustrates that users
should completely trust the blockchains to ensure that the information provided by users in the transaction would not be misused and
is warranted for the protection of personal privacy. It then generates a positive attitude and triggers use intention. This showed users’
concern with issues of personal information protection for blockchains. Users pay attention to the issue of trust in the protection of
personal data by blockchains in terms of transaction security. Cognitive trust has a positive mediating effect on the relationship
Table 3
Results of Hypothesis Testing.
Path Standardized Coefficients S.E. t-value
H1: Attitude → Intent 0.85 0.038 2.684**
H2: Security → Attitude 0.11 0.079 1.782*
H3: Security → Trust 0.18 0.090 2.704**
H4: Privacy → Trust 0.23 0.098 2.688**
H5: Privacy → Attitude 0.17 0.088 2.228**
H6: Trust → Attitude 0.38 0.052 7.082**
* p < .05.
** p < .001.
Fig. 2. A user trust model of blockchain.
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between privacy concern and security.
The mediating effects are in line with the role of the trust mechanism in blockchain and related services overall. The heuristic role
of trust is critical to the development of critical determinants in blockchain. In ongoing literature, trust has been found just one of the
factors facilitating adoption (Buchanan et al., 2007; Shin, 2010). In the blockchain context, trust represents more than one of the
factors for user decisions; it may be understood by users as a facilitator or a catalytic cue that plays a key role in triggering and
forming the user experience of blockchains. In this light, the domain of trust in blockchain can be broadened to include diverse roles
at different dimensions. The model shows that trust plays a crucial part in the stimulation and generation of user motivation,
attitudes, and behaviors. This finding has valuable and heuristic implications for both academia and industry. While the findings
confirm previous research on trust, they further clarify the applicability of trust in emerging technology areas. Previous research on
trust have consistently confirmed that user trust plays a role in establishing a person’s cognitive decision and behavior (Alexander
et al., 2018; Carmen and Lopez, 2018; Shin, 2010). The role of trust in blockchains, however, has not been extensively examined
despite the increasing popularity of blockchain-based services. Trust plays a key role in blockchain where credibility, transparency,
and accuracy have been considered key criteria, carrying out users’ wishes while interacting with blockchain. In other words, trust
can be a heuristic providing users with mental shortcuts to form judgments and make decisions: How blockchains are formed, how
data are collected and analyzed, and how transparent and accurate transactions are provided are highly dependent upon trust.
6. Discussion
Blockchain technology is exceedingly recognized and rapidly diffused due to its decentralized infrastructure and peer-to-peer
nature. These characteristics have the potential to support a plethora of requirements in diverse areas and applications, but at the
same time they engender inherent concerns over privacy and security issues. Given these concerns, this study develops an under-
pinning model of trust-based blockchain to explore the user cognitive processes leading to the formation of motivational attitude and
behavioral intent to experience blockchain.
Despite an exponential growth in blockchains, there is limited research on their potential effect on trust, security, and purchase
intentions. This study makes a relevant contribution to fill the existing gap in the knowledge by creating the fundamental linkages
between level of security/privacy and the effects of the extent on trust. The importance of the user model lies in identifying the role
played by security and privacy in generating trust. This study empirically examines the relationship among security and privacy and
trust created through blockchains. In line with previous studies that has examined the influence of security and trust (e.g., (Shin,
2010; Lemieux, 2016; Mou et al., 2017), findings from this research provide heuristic support for the user trust model in this study.
The results show that in the face of complexity and choice, blockchain users predominantly resorted to the heuristic of trust to make
judgements on privacy and security assessment. The results enhance our understanding of users’ attitudes and the behavior of
blockchain with regard to privacy dimension and offer implications for sustainable blockchain services. The results of the structural
and measurement model test lend support to the proposed arguments. The proposed model produced a satisfactory fit to the observed
data, and all the paths in the model were statistically significant and conceptually meaningful, in line with previous findings and trust
research (e.g., Shin, 2010). The results show that the model establishes decent predictive powers and justifies behavioral experiences
in blockchains.
The study develops the measurements of perceived security and privacy as the key antecedents of trust in blockchain experience.
Trust
SecurityPrivacy
Security= b + 2*Privacy+ 3*trust
2 = (not sig.); (p<.001)
Security= c + 4*Privacy
(p<.001)
Trust=a + 1*Privacy
(p<.001)
Blockchain
Transaction
Fig. 3. The results of the mediation analysis.
Table 4
The Mediating Effects of Trust.
Model p-value
Trust = β0 + β1*Privacy 0.001
Security = β0 + β1*trust 0.001
Security = β0 + β1*Privacy 0.001
Security = β0 + β1*Privacy + β2*trust 0.001
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Two key factors nicely reflect current blockchain development: 1) people have concerns about privacy breaches and the vulnerability
of security matters, and 2) perceived privacy and security directly influence trust in blockchain adoption. A lot of aspects of trust is
explained by user-based security/privacy, as seen in the R square (67%). Given the high level of its variance, it can be inferred that
user trust is formed through the users’ cognitive processing of security and privacy. Obviously, while technological security and
objective privacy measures may be essential, so is how users perceive and process such external stimuli, and complete transactions
are as critical as technological features. Users described that being assured and positive was vital and emphasized the affordance of
being able to explore new experiences in digital virtual spaces (Chang et al., 2016; Shin, 2010). Enhanced assurance of security and
ensured privacy would lead to improved perception of trust. While previous studies have found that trust plays a key dimension in
establishing a user’s behavioral intention and behavior (e.g., Buchanan et al., 2007; Roca et al., 2009), it has remained unclear how
trust is influenced by what variables and how users’ trust is formed. Our model advances previous literature by elucidating the
relationships among trust, risk, privacy, and in an emerging technology context. The results confirm that trust is somehow and
someway associated with to the assessments of security and privacy. This effect, together with the path of perceived security to trust,
suggests a mediating effect of privacy on trust through security. This effect is consistent with findings by Shin (Shin, 2010) and
Palmer, Bailey and Faraj (Palmer et al., 2000) on the mediating role of trust in online services.
From the model, it can be inferred that trust is formed from the users’ cognitive domain rather than given as a package from the
outside. Trust is heavily influenced by users’ perceived notions about how secure and private blockchain services are. Hence, user-
generated trust is influenced by users’ intrinsic traits, such as an existing tendency toward new technologies, credible characteristics,
and demographic factors. Previous studies have found that trust is influenced by users’ existing intrinsic factors (e.g., Shin et al.,
2017). Thus, it is worthwhile to test the effects of the moderating role of demographic factors on trust.
6.1. Findings from moderation effect
We used Chow tests (F-test) to check the significance of the statistical difference between the strength of relationship among the
variables from the two groups. The moderation effects were obvious in the all paths in the model.
Other studies on the antecedents of user’s judgments of security and privacy has researched primarily on technical or objective
factors. Based on the study, it can be argued that dispositional tendencies, in particular user’s general propensity to trust things and
others, also influence security and privacy. Users who were more trusting had more positive views of security and privacy. Users who
are more trusting show more favorable attitudes and intention. People with trust are more likely to believe their data are treated
fairly and security is secure.
6.2. Implications: how is trust generated?
As trust becomes a central factor in blockchains as well as emerging technologies, many people wonder about the role of trust in
user heuristics and how trust is generated and sustained throughout the continued usage. These issues are related to theoretical
matters as well as practical strategies. The results produce meaningful implications for user-study scholars as well as the blockchain
industry. From a theoretical standpoint, the study improves our knowledge about the roles of and relations among security, trust, and
privacy. As the importance of trust has increased, numerous researchers call for rigorous research to validate the heuristic link
between the function of trust and its antecedents in technologies (Shin, 2010; Mou et al., 2017). To examine that link, this study
approaches trust in relation to perceived security and privacy. It was found that security and privacy as determinants of trust,
subsequently influencing attitude and behavioral decision. This finding further suggests that the trust plays a heuristic role in the kind
and type of information that a user willingly shares with blockchain communities. While extensive studies have noted the effects of
privacy and security on trust in diverse technological contexts, few have extensively examined the topic (particularly the link among
factors that trigger user action) in the emerging blockchain context, leaving this question unclarified: With what cognitive processing
is trust generated and to what extent are users sensitive regarding security/privacy and in what ways? While further studies should
continue to research the questions, the theoretical contributions of this research lie in the examination of blockchain services in
relation to a user cognitive process of trust. There has been a tendency to consider trust as an external stimulus underplaying users’
internal cognitive process formulating their own trust. As shown in the findings, trust is influenced by users’ own perceived security
and privacy, which are also influenced by users’ intrinsic traits. The notion of trust may not be an issue of reflecting what users
actually can trust, but what users would like to believe and achieve eventually. The findings indirectly imply that perceptions of trust
are not purely objective responses to blockchain transactions. Rather, the findings in this study lends robust support to the argument
that similar to perceptions of information in general, perceived security and privacy in blockchain services are like beauty: they are in
the eye of the beholder. Security and privacy can be more subjective perceptions held by users rather than objective criteria (Dennis
et al., 2012). There are various dimensions by which we can measure how “secure and private” a service is. Security and privacy
depend on users’ perceptions and experiences. While security and privacy have been popular topics in digital technologies, such
heuristics are socially created and cognitively reconstructed within users’ cognitive schemas. Rather than such issues being uniformly
or collectively provided to users, users actively and conscientiously forge their own versions of security and privacy based on their
intrinsic dimensions of trust and/or their own schematic experiences (Shin and Park, 2019). That is, security and privacy are cog-
nitively constructed realities of their own making (constructivism) as they depend upon users’ perceptions.
Regarding blockchain cognitive model, industry can gain useful insights from our results in terms of user strategies and novel
business models for blockchains. From the notable role of security/privacy in relation to trust, firms may expend greater efforts to
comprehend users’ experiences of security-related issues and how these experiences are generated and impact motivational attitude.
8

Attitude toward individual blockchains can be variously affected by the varying levels of risk/privacy/security. Industry can
develop a sustainable security-risk protocol that operates different security policies in blockchain depending on various individuals
with differing levels of appropriate privacy practices.
As for trust, industries must develop a trust-based channel with users by establishing comprehensive standards and participating
privacy-seal programs adhere to those standards. The trust heuristics show the need for industry and public policymakers to be aware
that users can and will depend on simplified heuristics as a basis for security/privacy judgement, particularly within an environment
where substantial complexity and choice exist. Firms should establish user trust in blockchain security by ensuring that their services
are performed in accordance with customers’ expectations that they provide trustworthy services and that they keep their com-
mitments. Blockchain firms should inform users that risk-taking and privacy concerns are potentially significant and critical concerns
before customers sign up and adopt blockchain services. The service providers should establish transparent guidelines and data
protection policies to deliver the same level of social privacy found offline (Park, 2018). It is necessary to put in place a range of
security and privacy-enhancing measures. The trusting bondages between participants and users would lead to the success of the
sustainable development of blockchains.
6.3. Conceptualizing and measuring digital trust
Considering the fast-developing technology of the digital environment, this research proposes insights into the conceptualization
of security behaviors associated with blockchains and into strategic implications for developing trust-based services. As people adopt
blockchains as a new means to interact and become informed, obtain information, acquire contents, and communicate with others,
blockchains evolve into a stable, innovative services. Yet, to continue their sustainability, blockchains face critical hurdles to
overcome, and user trust is perhaps the most critical hurdle. Blockchain providers need to enhance the understanding of user ex-
periences concerning the dimension of trust and the effect of security on intention to adopt. Our findings offer a solid foundation for
the firms to develop a user trust evaluation framework to develop user-based new services in the blockchain era. The proposed trust
model provides an effective venue to comprehend market potential through a lens of user experiences and prototyping market
profiles. Based on the results, we can suggest a conceptual framework of digital trust designed to help figure out what constitutes
digital trust and establish why it matters (Fig. 4). The components of the framework comprise three drivers: environment, experience,
and technologies.
The framework considers the factors that determine the quality of interactions between two parties using a blockchain medium:
users, who are on the giving side of trust, and the firms that manage the platforms. Contextual factors include laws and regulations
like GDPR or third-trusted parties that make the experience convenient and seamless.
The results hint the modes of cognitive heuristics that information blockchain users utilize when assessing what sources and
information to trust blockchain services. The study concludes with an agenda for future research on digital trust that should be better
conceptualized the role and influence of digital heuristics in privacy/security evaluation in blockchain contexts.
6.4. Limitations and future studies
While the findings of this research are legitimate, the results must be taken with caution for the following causes. First, the
subjects of this study might not represent the overall population as the majority of blockchain users remain junior population. The
respondents of the research were collected as representatives of young students. It may not offer an inclusive persona of entire
blockchain populations; rather, it only shows a snapshot of a subset of user profiles. Future studies may examine personas from
diverse clusters of users in longitudinal tests.
Second, the user model in this study may not be an overarching model, since it left out possibly critical effects. One possible path
is that of from privacy to security and/or vice versa. Users’ perceived security of blockchains certainly influences their perceived
privacy. One’s belief that blockchains would keep up with privacy rules depends on how secure the blockchain actually is. This
relationship can be mutual, but it can be influenced more by the effect of security on privacy and less by the reverse direction. As the
model excluded potentially critical paths for parsimony reason, future studies should further investigate. Future studies should also
develop a more sophisticated instrument based on a thorough conceptual works.
Third, for parsimony reason, the study excluded possible external factors (different platform, service conditions, and service
quality), as blockchains are in still in an early stage of development. Wide dissimilarities in services across different blockchains may
exist, and user perception and behavior should vary accordingly. Given the notable increase variance of usage in user experience
research, future research should heavily consider various factors as covariates.
Fig. 4. Digital Trust.
9

Despite the limitations, this study opens a window of opportunity for better understanding of digital trust in future emerging
ecology. Numerous issues remain unanswered and a series of issues remain unanswered as blockchains continue to evolve. This study
took an exploratory step in that direction.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Appendix:. Measurement instruments
Constructs Measure items Sources
Perceived priv-
acy
PP1: I am confident that I know all the parties who collect the information I provide during the use
of blockchains.
PP2: I am aware of the exact nature of information that will be collected during the use of
blockchains.
PP3: I am not concerned that the information I submitted on the blockchains could be misused.
Carmen and Lopez (2018); Kim et al.
(2019); Shin (2010)
Perceived se-
curity
PS1: I believe the information I provide with blockchains will be handled by appropriate processes.
PS2: I am confident that the private information I provide with SNS will be secured.
PS3: I believe only legitimate parties may view the information I provide with the blockchains.
Shin (2010)
Trust TR1: Blockchain is a trustworthy service
TR2: I can count on blockchains to protect my privacy.
TR3: Blockchain can be relied on to keep its promises.
Dennis et al. (2012); Shin (2010)
Attitude A1: I would have positive feelings towards blockchains in general.
A2: The thought of using blockchains is appealing to me.
A3: It would be a good idea to use blockchains.
Shin (2017)
Intention to use I1: I intend to use blockchains in the future.
I2: I intend to visit blockchains sites as much as possible.
I3: I intend to continue using blockchains in the future.
Shin (2017)
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