Exploring Trust in Blockchain Technology: A Critical Review of the
Theoretical Acceptance Models
Bet
¨
ul Aydogdu and Irina Rychkova
a
University Paris 1, Panth
`
eon-Sorbonne, Paris, France
Keywords:
Trust, Technology Acceptance Model, Blockchain.
Abstract:
Although Blockchain Technology (BCT) is widely acknowledged for its disruptive potential in reshaping in-
dustries through decentralization and enhanced security, its adoption has been slower than anticipated. To
address this gap, this secondary study examines 21 recently published surveys on BCT acceptance. While
existing literature confirms the critical role of trust in BCT acceptance, our study examines its effect in detail,
focusing on the role it plays in the theoretical acceptance models — whether as a predictor, mediator, or mod-
erator. This research contributes to a deeper understanding of the BCT adoption process, which is essential for
effective policy-making and defining and implementing digital transformation strategies within organizations.
1 INTRODUCTION
The rise of Blockchain technology (BCT) marked a
new digital era by eliminating the need for central-
ized authorities, creating a trustless environment for
transactions. Instead of placing their faith in an in-
termediary, users could trust the blockchain. Histor-
ically, trust has been crucial for the adoption of new
technologies. For instance, distrust in security hin-
dered online shopping adoption in the 90s (Hoffman
et al., 1999). Despite enhanced transparency, reduced
costs, and improved traceability, BCT adoption re-
mains limited
1
. According to PwC
2
, in 2020, “45 %
of companies investing in blockchain technology be-
lieve that lack of trust among users will be a signifi-
cant obstacle in blockchain adoption”.
Technology adoption, defined as the act of begin-
ning to use a new technology, is closely related to,
but distinct from, technology acceptance. While tech-
nology acceptance encompasses a more subjective be-
havioral intention to support or embrace the technol-
ogy, it serves as a precursor to actual use (i.e., adop-
a
https://orcid.org/0000-0002-1100-0116
1
Deloitte, 2021 Global Blockchain Survey,
available at: https://www.deloitte.com/global/en/
our-thinking/insights/topics/emerging-technologies/
understanding-blockchain-potential.html
2
PwC, ”Time for Trust: How Blockchain Will
Transform Business and the Economy,” available
at: https://www.pwc.com/gx/en/issues/blockchain/
blockchain-in-business.html
tion). Research community uses various theoretical
models to explain the BCT acceptance mechanism
and to define its factors (AlShamsi et al., 2022; Al-
Ashmori et al., 2022; Norbu et al., 2024). One of the
recurring factors of BCT acceptance is trust.
Three forms of trust are widely recognized in the
literature: social trust, digital trust, and trust in tech-
nology. Social (or interpersonal) trust is defined as
the subjective probability that an entity - a trustee -
has the required capacity and willingness to perform
an action that is beneficial or at least not detrimen-
tal to another entity - a trustor - in a specific con-
text (Gambetta et al., 2000). Compared to social
trust, digital trust defines relationships between en-
tities in the digital world. It is the measure of con-
fidence that a trustor has in the trustee’s ability to
protect data and privacy of individuals (Pietrzak and
Takala, 2021). Trust in technology is another form
of trust that reflects trustor’s beliefs that a specific
technology has the attributes necessary to perform as
expected in a situation where negative consequences
are possible (Mcknight et al., 2011; Meeßen et al.,
2019). These forms of trust are intrinsic to orga-
nizations and have important implications in orga-
nizational decision-making and technology adoption
(Mcknight et al., 2011; De Filippi et al., 2020). They
need to be explicitly addressed both in policy making,
digital transformation strategies and solution design.
While the research studies confirm that trust is an
important factor in BCT acceptance, the conceptual-
ization of trust and its specific role are not discussed
Aydogdu, B. and Rychkova, I.
Exploring Trust in Blockchain Technology: A Critical Review of the Theoretical Acceptance Models.
DOI: 10.5220/0013143600003929
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 1, pages 15-26
ISBN: 978-989-758-749-8; ISSN: 2184-4992
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
15

in detail. In this critical review, we intend to con-
tribute into a deeper understanding of this role. We
examine the impact of trust on BCT acceptance, fo-
cusing on the role it plays in the defined theoretical
models — whether as a predictor, mediator, or moder-
ator. We explore the conceptualization of trust within
the defined BCT acceptance models. By analyzing
how trust is defined and measured, we aim to de-
termine whether the researchers refer to social trust,
digital trust, or trust in technology in their theoretical
frameworks.
The remainder of this article is organized as fol-
lows: In section 2, we go through the definition of
key terms and discuss the related works. Our research
methodology is described in section 3. In section 4,
we report the results of the analysis of 21 selected
studies, answering to our research questions. In sec-
tion 5, we discuss our results and their potential im-
plications and present our conclusions in section 6.
2 BACKGROUND AND RELATED
WORKS
2.1 Theoretical Models for Technology
Acceptance
While adoption is focused on consistent use of tech-
nology in daily activities, it is often associated with
technology acceptance. Technology acceptance refers
to the willingness and readiness of individuals or or-
ganizations to adopt and use new technology. Tech-
nology acceptance is closely related to and often pre-
dicts technology adoption (Davis, 1989; Venkatesh
et al., 2003).
Theoretical models such as the Technology Ac-
ceptance Model (TAM) are often applied within the
context of information systems to understand predic-
tors of human behavior toward potential acceptance or
rejection of the technology (Maranguni
´
c and Grani
´
c,
2015). The theoretical models specify a set of con-
structs and the relationships between them that ex-
plain a phenomenon of interest. The theoretical BCT
acceptance models, for example, explain the effect of
specified independent variables on Behavioral Inten-
tion (BI) to use BCT - the dependent variable.
The constructs defined by the theoretical models
can play a role of predictors, mediators or modera-
tors (Fig. 1) for the examined phenomena. Predic-
tors are independent variables that directly influence
the dependent variable. Mediators are variables that
explain the mechanism through which predictors in-
fluence the dependent variable. They act as interme-
diaries in the causal chain, helping to clarify how or
why a certain effect occurs. Moderators are variables
that affect the strength or direction of the relationship
between predictors and the dependent variable. They
provide insights into when or under what conditions
certain effects occur. Some variables function both as
predictors and mediators, depending on the specific
relationships being examined.
Trust in blockchain technology (BCT) can be
measured either directly, through survey items like
”Do you find BCT trustworthy?”, or indirectly by cap-
turing data on trust antecedents or related indicators.
Direct questions pose a challenge as they leave ”trust-
worthiness” open to individual interpretation. Indica-
tors are the observable variables that reflect the pres-
ence or extent of trust. They depend on the trust con-
ceptualization chosen for the study (see Table 1).
Figure 1: Overview of the variables and their relations in
theoretical acceptance models.
Understanding acceptance factors and relation-
ships between them is crucial for technology adop-
tion. Knowing the predictors allows for the design of
targeted interventions. Understanding mediators pro-
vides insights into the adoption process (i.e., how or
why a certain effect occurs). For example, trust might
mediate the relationship between system quality and
user acceptance. Understanding moderators allows
for the development of context-specific strategies. For
instance, age, gender, or cultural background might
moderate technology adoption, necessitating tailored
communication and support strategies for different
groups.
Several theoretical acceptance models are widely
acknowledged in the literature. The Technology Ac-
ceptance Model (TAM) developed by Fred Davis in
1989 is a theoretical framework used to understand
and predict user acceptance of information technol-
ogy (Davis, 1989). This model defines two primary
predictors of user acceptance: Perceived usefulness
is the degree to which a person believes that using a
particular system would enhance his or her job per-
formance. Perceived ease of use is the degree to
which a person believes that using a particular sys-
tem would be free of effort. TAM2 (Venkatesh and
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Davis, 2000) extends the original model by introduc-
ing the concepts of social influence processes (sub-
jective norm, voluntariness, and image) and cognitive
instrumental processes (job relevance, output quality,
result demonstrability, and perceived ease of use) as
key drivers of user acceptance.
The Unified Theory of Acceptance and Use
of Technology (UTAUT) was created in 2003
(Venkatesh et al., 2003). Built upon TAM, it identifies
the four predictors of acceptance: performance ex-
pectancy, effort expectancy, social influence and facil-
itating conditions. Those four factors are influenced
by four moderators: age, gender, experience, and vol-
untariness of use, which result in possible gaps be-
tween the intentions to use a technology and the ac-
tual use of the technology.
The Task-to-Performance Fit (TTF) model (Good-
hue and Thompson, 1995) is a theoretical framework
that examines how well technology matches the tasks
it is intended to support, and how this fit impacts per-
formance and technology adoption.
The Technology-Organization-Environment
(TOE) (Baker, 2012) framework defines the fac-
tors that influence the organization’s decision to
adopt new technology divided into three contexts:
technological, organizational, and environmental.
Acceptance predictors from these theoretical
models show the importance of combining technical
factors and social factors, related to user’s personality
and the context of use.
2.2 Trust
Trust is a social construct that emerges from interac-
tions between individuals or groups and can be de-
scribed by a situation where a subject (trustor) is
willing to rely on a chosen actions of an object of
trust (trustee) (Rousseau et al., 1998; Gambetta et al.,
2000; Mayer et al., 1995). Mayer, Davis and Schoor-
man define trust as a function of the trustee’s per-
ceived ability, benevolence, and integrity and of the
trustor’s propensity to trust (Mayer et al., 1995). Here
ability defines a group of skills, competencies, and
characteristics that enable a trustee to have influence
within some specific domain; benevolence defines the
extent to which a trustee is believed to want to do
good to the trustor, aside from an egocentric profit
motive; integrity refers to trustee’s moral quality of
being sincere, honest, and her capacity and willing-
ness to adhere to some rules/principles.
Advances in technology introduce the new mod-
els of social and business interactions, where IT ar-
tifacts can take the role of a trustee (S
¨
ollner et al.,
2012). Trust in technology reflects trustor’s beliefs
that a specific technology has the attributes neces-
sary to perform as expected in a given situation where
negative consequences are possible (Mcknight et al.,
2011)(Meeßen et al., 2019). According to (S
¨
ollner
et al., 2012), the antecedents of social trust (i.e., abil-
ity, benevolence and integrity (Mayer et al., 1995))
are poorly suited for studying trust relationships be-
tween users and IT artifacts, since they are defined
to fit the human character traits and human decision
making. The authors of (Mcknight et al., 2011) pro-
vide a framework for understanding how trust in tech-
nology is formed and its effects on technology usage.
They put forward performance, functionality and re-
liability as the factors of trust in specific technology.
Digital trust defines relationships between enti-
ties in the digital world. It is the measure of con-
fidence that a trustor has in the trustee’s ability to
protect data and privacy of individuals (Pietrzak and
Takala, 2021). Digital trust and Trust in Technology
are closely related to specific properties of a technol-
ogy or solution (e.g., security, reliability, availability
etc.), with digital trust focused on data security and
privacy. Whereas these properties are considered ob-
jective measures of technology trustworthiness in the
literature (Murtin et al., 2018; Jacovi et al., 2021;
Garry and Harwood, 2019; Rychkova and Ghriba,
2023; Gharib et al., 2020), they are not always good
predictors of technology acceptance: users (social ac-
tors) do not always have the technical expertise to
objectively evaluate the complex technical properties
and ground their decisions on their subjective beliefs.
This work examines the complex nature of trust in
blockchain and its effect on the BCT adoption. Table
1 provides an overview of the trust conceptualizations
and factors.
2.3 Trust and Blockchain Adoption
Blockchain technology has emerged as a potential so-
lution to cope with mistrust in traditional (central-
ized) institutions and online intermediaries in gen-
eral (De Filippi et al., 2020). Blockchain can be de-
fined as a distributed database that allows its users to
transact in a public and pseudonymous setup without
the reliance on an intermediary or central authority
(Glaser, 2017). Despite its popularity and efficiency,
blockchain technology experiences challenges of user
adoption. Depending on the industry sector and the
use case, privacy, security, scalability, interoperabil-
ity, performance are considered the main challenges
(Konstantinidis et al., 2018; Casino et al., 2019; Be-
lotti et al., 2019; Marengo and Pagano, 2023).
Theoretical models of acceptance are used by re-
searchers to reason about the factors of blockchain
Exploring Trust in Blockchain Technology: A Critical Review of the Theoretical Acceptance Models
17

Table 1: The types of Trust and their factors.
Ref. theory Factors
Social trust (Mayer et al., 1995) Ability, benevolence, integrity,
propensity to trust, perceived risk
Trust in technology (Mcknight et al., 2011) Performance, functionality,
reliability, propensity to trust,
institution-based trust
Digital trust (Pietrzak and Takala, 2021) Data privacy, data security, confidentiality
adoption in a structured way. The study by Shin
(Shin, 2019) shows the impact of Trust, Security and
Privacy factors on the blockchain-based-solution ac-
ceptance (behavioral intention to use). Following this
work, the studies in (Jena, 2022; Kumar et al., 2022;
Alazab et al., 2021) extend the TAM/UTAUT mod-
els with Trust, Perceived Security and Perceived Pri-
vacy. The authors of (AlShamsi et al., 2022) are ex-
amining the technology acceptance models, theories
and influential factors in BC adoption. Among the
11 factors identified, trust is the most common factor
affecting the BCT adoption. The authors of (Taufiq
et al., 2018) study the influence factors of BCT adop-
tion taking the example of the payments system in
Indonesia banking industry. They highlight the im-
portance of non-technical factors, such as attitude,
subjective norm, cognitive style of the user. In (Al-
Ashmori et al., 2022), the authors identify the 18 fac-
tors of BCT adoption. These factors are consistent
with TAM / UTAUT theoretical frameworks and in-
clude trust. The systematic literature review (Taher-
doost, 2022) confirms that trust is an important factor
of BCT adoption, in particular for supply chain in-
dustry. The authors of (Marengo and Pagano, 2023)
conduct a systematic literature review to examine the
factors of adoption of BCT across different countries
and industries and identify trust as a recurring factor
for supply chain, real estate, and banking. The study
presented in (Norbu et al., 2024) focuses on trust as
a primary driver for BCT adoption in digital payment
systems.
While existing literature confirms that trust is an
important factor in BCT acceptance, the nature of
trust and its specific role are not discussed in detail.
In this work, we investigate the role of trust within
the proposed theoretical models (i.e., as a predictor,
mediator, or moderator). We also analyze the concep-
tualization of trust used in these models. By analyz-
ing how trust is defined and measured, we aim to de-
termine whether the researchers refer to social trust,
digital trust, or trust in technology in their theoretical
frameworks.
Understanding the nature of trust and its impact on
BCT acceptance is crucial for addressing the adoption
process. We use trust conceptualization from social
sciences (Gambetta et al., 2000; Mayer et al., 1995)
and from technology (Mcknight et al., 2011; Pietrzak
and Takala, 2021), addressing this concept from the
broader perspective.
3 METHODOLOGY
In this study, we present a critical review of theoreti-
cal models predicting BCT acceptance. Unlike a sys-
tematic literature review, our approach offers a more
reflective analysis, emphasizing judgment and argu-
ment over exhaustiveness. We particularly focus on
the interpretation and critical evaluation of theoreti-
cal acceptance models supported by empirical data,
aiming to uncover trends, patterns, and conflicts in
the literature. Although formal guidelines for critical
reviews are not universally established, we adapted
the systematic literature review principles by Kitchen-
ham and Charter (Kitchenham et al., 1995), following
these steps: defining research questions, identifying
relevant studies, extracting data, conducting critical
analysis and evaluation, and reporting the results.
3.1 Research Questions
We formulate the following research questions for our
review:
RQ1 : What are the most commonly used theoretical
models to explain blockchain acceptance?
RQ2: How is the Trust construct addressed in the
studies?
RQ2.1 How is Trust defined in these studies?
RQ2.2 What trust indicators are used?
RQ3: What types of Trust are associated with BCT
acceptance?
RQ4: How does Trust influence BCT acceptance?
3.2 Study Selection
For our critical review, we selected the studies that
explicitly integrate trust in their proposed theoreti-
ICEIS 2025 - 27th International Conference on Enterprise Information Systems
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cal models of BCT acceptance. The flow diagram
adopted from PRISMA (Preferred Reporting Items
for Systematic Reviews and Meta-Analyses) presents
an overview of the source selection process in Fig. 2.
A literature search was conducted using the Scopus
database (scopus.com). We used the following key-
words for our search: blockchain, trust, acceptance
OR adoption, model OR framework. To improve rel-
evance, these terms have to appear in the title, abstract
or key words of the retrieved studies.
According to dimensions.ai, the interest in BCT
adoption reflected by the number of publications on
this topic increases exponentially since 2019. There-
fore, for our study, we chose articles published from
January 2019 on. We limit the publication year by
2023 to obtain a consistent set of publications that will
not be affected by more recent apparitions. The re-
sulting query is illustrated in Fig. 2. 218 sources have
been automatically identified in the Scopus database.
After removing non-primary sources we kept 175
records for screening.
We proceeded by screening the metadata of iden-
tified records and eliminated irrelevant studies based
on the following exclusion criteria:
EC1: The study does not formalize a theoretical
acceptance model.
EC2: The study does not provide an empirical val-
idation of the proposed model.
EC3: The study does not consider trust among the
factors of acceptance or adoption.
We kept 57 records for the full text assessment.
We proceeded with the full text reading and kept 18
relevant studies for the analysis. We conducted back-
ward and forward citation analysis of the eligible pub-
lications from the previous step (a so-called “snow-
balling” technique) and identified 14 records which
were re-injected into the process. Three studies have
been eventually added to our data set. The final set of
21 studies was used for the analysis.
3.3 Data Extraction
We examined each selected study, extracting data on:
• The theoretical BCT acceptance model proposed
by the study: the reference acceptance model
(e.g., TAM); the factors affecting BCT acceptance
according to the model;
• Conceptualization and measurement of trust in the
study: the reference definition from the literature;
the indicators (i.e., the questions on trust) defined
by the study;
• The role attributed to trust within the proposed ac-
ceptance model: the hypothesis related to trust;
the results of empirical validation of these hypoth-
esis.
The critical analysis of the extracted data is presented
in the next section.
4 RESULTS
Through the analysis of the extracted data, we ad-
dressed our research questions and obtained the fol-
lowing results:
4.1 What Are the Most Commonly Used
Theoretical Models to Explain
Blockchain Acceptance?
The examined studies adapted and extended various
theoretical acceptance models to reason about BCT.
TAM(Davis, 1989) is the most commonly used the-
oretical model that was extended in 14 studies out
of 21. Six studies use UTAUT(Venkatesh et al.,
2003) as their ground model. Other models used in-
clude Technology-Organization-Environment (TOE)
and Theory of Planned Behavior (TPB). While most
of the studies use a single theoretical model, studies
in (Alazab et al., 2021; Kamble et al., 2019; Ullah
et al., 2021) are grounded on several theoretical mod-
els. Table 2 provides the summary.
4.2 How Is the Trust Construct
Addressed in the Studies?
4.2.1 How Is Trust Defined in the Studies?
To address this research question, we analyze the ex-
plicit definitions of trust presented in the text as well
as the indicators used in the studies to measure Trust
as an independent variable.
10 studies out of 21 provide the explicit defini-
tions in the text. Seven studies define trust as a so-
cial construct grounded on willingness of an individ-
ual to take risk or to be vulnerable to the actions of an-
other party (Queiroz et al., 2021; Jena, 2022; Khazaei,
2020; Hannoun et al., 2021), or on confidence in the
ability and integrity of the other party (Yu et al., 2021;
Gil-Cordero et al., 2020; Albayati et al., 2020), which
is aligned with the definition from (Gambetta et al.,
2000). Study in (Chang et al., 2022) defines trust in
association with transparency as ‘the degree to which
one believes that data provided with blockchain tech-
nology and services is error-free, safe, and transacted
transparently.’ Calculation-based trust grounded in
rational evaluation and the expectation of benefits or
Exploring Trust in Blockchain Technology: A Critical Review of the Theoretical Acceptance Models
19

Figure 2: The PRISMA flowchart summarizing the literature selection process.
Table 2: Theoretical models adapted for BCT acceptance.
Model Studies Ref
TAM 14 (Kumar et al., 2022; Saputra and Darma, 2022; Sciarelli et al., 2021; Gao and
Li, 2021; Rijanto, 2021; Palos-Sanchez et al., 2021; Yu et al., 2021; Liu and Ye,
2021; Gil-Cordero et al., 2020; Albayati et al., 2020; Dirsehan, 2020; Kamble
et al., 2019; Ullah et al., 2021; Shrestha et al., 2021)
UTAUT 6 (Chang et al., 2022; Jena, 2022; Queiroz et al., 2021; Khazaei, 2020; Alazab
et al., 2021; Hannoun et al., 2021)
Other 4 (Kamble et al., 2019; Ullah et al., 2021; Chittipaka et al., 2022; Alazab et al.,
2021)
costs is used in two studies (Chittipaka et al., 2022;
Liu and Ye, 2021). Other studies do not provide an
explicit definition of trust.
4.2.2 What Trust Indicators Are Used in the
Studies?
Trust construct is a latent variable that cannot be di-
rectly observed. It is measured in the studies via
trust antecedents and trust-related indicators used in
a survey or questionnaire. These indicators and an-
tecedents are defined by the conceptualization of trust
(e.g., social trust, trust in technology or digital trust)
as presented in the Table 1.
The nine studies use trustworthiness as a direct in-
dicator of trust: (Queiroz et al., 2021; Kumar et al.,
2022; Yu et al., 2021; Gil-Cordero et al., 2020; Sapu-
tra and Darma, 2022; Albayati et al., 2020; Dirsehan,
2020; Liu and Ye, 2021; Chang et al., 2022). In these
surveys, the perception of blockchain trustworthiness
is assessed through affirmative statements such as ”I
believe that blockchain is trustworthy” (Queiroz et al.,
2021) and ”BCT is trustworthy” (Yu et al., 2021;
Chang et al., 2022). Respondents express their agree-
ment with these statements using the Likert scale, re-
lying on their own interpretation of ”trustworthiness.”
Studies in (Kumar et al., 2022; Saputra and
Darma, 2022; Albayati et al., 2020) focus on promise
and commitment of BCT. This indicator evaluates
whether users believe that the technology prioritizes
their best interests. Studies in (Queiroz et al., 2021;
Gil-Cordero et al., 2020) include questions on trust
in the legal structures surrounding blockchain and
cryptocurrencies referred to as institutional trust in
(Mcknight et al., 2011). ”I am confident that the
legal and technological structures protect me from
problems with cryptocurrencies” (Gil-Cordero et al.,
2020) underscore the importance of the institutional
trust. Studies in (Liu and Ye, 2021; Kamble et al.,
2019) emphasize trust built upon experiences, their
questions suggest that familiarity and previous inter-
actions with the system or service play a crucial role
in forming trust. Trust is measured via safety and se-
curity in (Palos-Sanchez et al., 2021; Yu et al., 2021;
Chang et al., 2022; Chittipaka et al., 2022; Dirsehan,
2020). Other indicators mentioned include: reliabil-
ity, transparency, integrity, honesty, fairness, confi-
dence, capability, skills.
We analyze the indicators implying user’s trust
in other model constructs. For example, in (Palos-
Sanchez et al., 2021), the respondents are requested
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20

to express their belief in BCT security as its ’ability
to act in the user’s interest’. The survey items related
to this indicator include: IT Devices using blockchain
technology would be safe from external threats, such
as hacking; IT Devices using blockchain technology
would be safe from the risk of data forgery and alter-
ation; IT Devices using blockchain technology would
secure personal information. This is consistent with
the definition of trust in technology (Mcknight et al.,
2011). In (Gil-Cordero et al., 2020), the following
trust indicator is used: ’I have confidence in the sys-
tem’. Similarly, the indicators of privacy and informa-
tion quality are formulated using an implicit notion of
trust in (Gil-Cordero et al., 2020; Ullah et al., 2021;
Liu and Ye, 2021; Palos-Sanchez et al., 2021).
4.3 What Types of Trust Are Associated
with BCT Acceptance?
By analyzing the definitions of trust presented in the
text, along with the trust indicators and indicators im-
plying user trust in other model constructs, we classi-
fied the Trust constructs used in the studies into Social
trust, Trust in technology, and Digital trust. Table 3
provides a summary of this classification.
Social trust conceptualization is used in the nine
studies. These studies either use explicit trust defi-
nition consistent with (Gambetta et al., 2000; Mayer
et al., 1995) or they define their trust indicators using
the factors of social trust from Table 1. For example,
in (Kumar et al., 2022), respondents are asked about
their perception of BCT’s commitment to their best
interests: ’It gives an impression of promise and com-
mitment; It keeps my interest in consideration’ - prop-
erty consistent with the predictors of social trust. The
factors of Trust in technology are found in the nine
studies, and the factors of digital trust - in the seven
studies. For example, the indicator ’My firm’s data in
the cloud might be utilized by an outsider without our
consent’ is used in (Chittipaka et al., 2022), the indi-
cator ’Data in blockchain technology would be saved
securely.’ is used in (Chang et al., 2022) to measure
trust. These indicators associate trust with reliability
and security of data on the blockchain.
Several studies define trust indicators based on
multiple conceptualizations. For example, in (Kumar
et al., 2022), the definition of trust provided in the text
is implicit, co-notated with privacy and security (con-
sistent with digital trust), whereas the trust indicators
are defined using the notion of commitment and ’con-
sideration of the user’s best interests’ (aka benevo-
lence), consistent with social trust.
Our analysis shows that social trust plays impor-
tant role in defining trust in BCT. In several stud-
ies, social trust is identified with a relationship built
between the users (trustors) and the BCT providers
(trustees) - social entities. For example, in (Albayati
et al., 2020), the trust indicator ’I believe the service
providers (both cryptocurrency and blockchain) keep
my best interests in mind.’ is used. In the other stud-
ies, the technology itself is identified with the quali-
ties intrinsic for a social entity: in (Kumar et al., 2022;
Saputra and Darma, 2022), trust indicators include the
affirmations ’It [technology] keeps my interest in con-
sideration’ and ’The services provided by My T Wal-
let keep my best interests in mind.’
Trust in technology is a fundamental conceptual-
ization of trust in BCT as well: it provides the user
with objective metrics to assess trustworthiness, such
as reliability, performance, functionality.
Digital trust conceptualization focuses on more
specific technical properties related to data security
and privacy in the digital world. In the examined
acceptance models, these properties are often in-
tegrated into the Perceived Usefulness construct in
TAM, or Perceived Security and Perceived Privacy
constructs, specifically defined in the models (Jena,
2022; Shrestha et al., 2021; Kumar et al., 2022).
4.4 How Does Trust Influence BCT
Acceptance?
We examined the hypotheses made in the studies
about the role of trust on BCT acceptance and vali-
dation of these hypotheses.
4.4.1 Trust as a Predictor of BCT Acceptance
Predictors are independent variables that directly in-
fluence the dependent variable.
Trust → VAR
X
Trust has a direct effect on behavioral intention (BI) to
use BCT according to 10 studies. In (Chittipaka et al.,
2022), trust is positioned as a predictor of Adoption,
which stands for actual use and follows the BI. Trust
is defined as a predictor of Attitude towards use in
(Kumar et al., 2022; Albayati et al., 2020). While atti-
tude towards use reflects how positively or negatively
an individual feels about using a technology, behav-
ioral intention reflects the individual’s readiness and
plan to use that technology. They are often used inter-
changeably. In some theories, however, the attitude
precedes BI. Therefore, we conclude that 13 studies
recognize trust as having direct effect on acceptance.
Seven studies show significant effect of trust on
other constructs that affect acceptance: Perceived
usefulness, Perceived ease of use, Performance ex-
Exploring Trust in Blockchain Technology: A Critical Review of the Theoretical Acceptance Models
21

Table 3: Trust affecting BCT acceptance.
Study reference
Social trust (Gil-Cordero et al., 2020) (Jena, 2022)(Alazab et al., 2021) (Queiroz et al.,
2021)(Saputra and Darma, 2022) (Kumar et al., 2022) (Dirsehan, 2020)(Han-
noun et al., 2021) (Albayati et al., 2020)
Trust in technology (Gil-Cordero et al., 2020) (Jena, 2022) (Alazab et al., 2021)(Palos-Sanchez
et al., 2021)(Yu et al., 2021)(Liu and Ye, 2021)(Ullah et al., 2021)(Dirsehan,
2020)(Queiroz et al., 2021)
Digital trust (Kumar et al., 2022)(Chittipaka et al., 2022)(Gao and Li, 2021)(Chang et al.,
2022)(Liu and Ye, 2021)(Palos-Sanchez et al., 2021)(Kamble et al., 2019)
N/A (Sciarelli et al., 2021)(Rijanto, 2021)(Shrestha et al., 2021)
Table 4: Trust as a direct Predictor of the BCT Acceptance: Summary.
VAR
X
(dependent) Study
Behavioral intention (BI) (Jena, 2022) (Gao and Li, 2021) (Yu et al., 2021) (Hannoun et al., 2021) (Liu
and Ye, 2021) (Queiroz et al., 2021) (Khazaei, 2020) (Dirsehan, 2020) (Saputra
and Darma, 2022) (Gil-Cordero et al., 2020)
Adoption (Chittipaka et al., 2022)
Attitude towards use (Kumar et al., 2022) (Albayati et al., 2020)
Perceived usefulness (Liu and Ye, 2021) (Dirsehan, 2020)
Perceived ease of use (Albayati et al., 2020) (Saputra and Darma, 2022) (Palos-Sanchez et al., 2021)
Performance expectancy (Chang et al., 2022)
Perceived privacy (Palos-Sanchez et al., 2021)
pectancy, Perceived privacy. Table 4 provides a sum-
mary of our results.
4.4.2 Trust as a Mediator
Mediators are variables that explain the mechanism
through which predictors influence the dependent
variable. They act as intermediaries in the causal
chain, helping to clarify how or why a certain effect
occurs.
VAR
X
→ Trust → VAR
Y
According to (Jena, 2022), Trust acts as a mediator
between Facilitating conditions and Performance ex-
pectancy and BI (behavioral intention to use BCT).
This means that facilitating conditions and perfor-
mance expectancy enhance users’ trust, which in turn
affects their intention to use BCT. According to (Gil-
Cordero et al., 2020), trust mediates the effect of e-
Worm (the electronic word of mouth), web quality,
and Perceived risk on BI. Here e-Wom refers to ’any
positive or negative statement made by potential, ac-
tual or former customers about a product or com-
pany, which is made available to a multitude of people
and institutions through the Internet’. Similarly, Per-
ceived privacy (Shrestha et al., 2021) has a positive
effect on Trust, which in turn affects the intention to
use BCT. Trust mediates the effect of Perceived Se-
curity (Kumar et al., 2022; Shrestha et al., 2021) and
Perceived privacy (Kumar et al., 2022) on Attitude to-
wards use BCT. Table 5 presents the summary of the
results.
4.4.3 Trust as a Moderator
Moderators are variables that affect the strength or
direction of the relationship between predictors and
dependent variable. They provide insights into when
or under what conditions certain effects occur. Some
variables function both as predictors and mediators,
depending on specific relationships being examined.
Trust → (VAR
X
×VAR
Y
)
Trust is defined as enhancing the effects of other
variables on BI: Performance expectancy, Effort ex-
pectancy, Social influence, Facilitating conditions
(Alazab et al., 2021), Subjective knowledge (Dirse-
han, 2020). According to (Gil-Cordero et al., 2020),
Trust moderates the effect of e-Worm, web quality
and Perceived risk on the user behavior. According
to (Shrestha et al., 2021), Trust increases the posi-
tive impact of Privacy on the Attitude towards use
ICEIS 2025 - 27th International Conference on Enterprise Information Systems
22

Table 5: Trust as a Mediator and Moderator of the BCT Acceptance: Summary.
Role of Trust VAR
X
(independent) VAR
Y
(depen-
dent)
Study
Mediator
Facilitating conditions, Performance
expectancy, eWorm, Web quality,
Perceived risk, Privacy
BI (Jena, 2022) (Gil-Cordero
et al., 2020) (Shrestha et al.,
2021)
Perceived Security, Perceived Privacy Attitude (Kumar et al., 2022) (Shrestha
et al., 2021)
Moderator
Performance expectancy, Effort ex-
pectency, Social influence, Facilitat-
ing conditions, Subjective Knowl-
edge
BI (Alazab et al., 2021) (Dirse-
han, 2020)
eWorm, Web quality, Perceived risk User Behavior (Gil-Cordero et al., 2020)
Perceived privacy Attitude (Shrestha et al., 2021)
Perceived risk Perceived ease
of use
(Palos-Sanchez et al., 2021)
of BCT. According to (Palos-Sanchez et al., 2021),
Trust moderates the effect of the perceived risk on
perceived easiness of use. This means that the level
of trust a user has can influence how perceived risk
impacts their perception of the technology’s ease of
use. Specifically, higher trust can mitigate the nega-
tive effects of perceived risk, making the technology
seem easier to use despite potential risks. Conversely,
lower trust can amplify the negative impact of per-
ceived risk on perceived ease of use.
5 DISCUSSION
Analyzing the theoretical models of BCT acceptance,
we found that social trust is widely used when reason-
ing about, defining, or measuring trust. Social trust
factors appear as indicators in 9 out of 21 studies.
Abilities of BCT to eliminate the need for a central
authority, to ensure transparency and immutability of
transactions also frequently used as trust indicators.
While many studies focus on technical properties of
blockchain and co-notate the acceptance of BCT with
perceived security and privacy (Jena, 2022; Kumar
et al., 2022; Alazab et al., 2021), they also high-
light the important role of benevolence of the service
provider or the technology itself (Kumar et al., 2022;
Albayati et al., 2020; Dirsehan, 2020). Integrity and
honesty of BCT is also used as a trust indicator (Ku-
mar et al., 2022; Liu and Ye, 2021; Albayati et al.,
2020).
Trust in technology, which focuses on technologi-
cal properties and excludes the factor of benevolence
(as technology is considered not to be able to act
or not in users’ best interests), is used in nine stud-
ies. This form of trust serves as a foundational el-
ement. The results suggest that before considering
blockchain adoption, users must first establish trust in
the technology itself. However, once this basic trust
is formed, the focus appears to shift toward the digital
functionalities and attributes of blockchain, referred
to as Digital Trust.
Digital trust, similar to trust in technology but cen-
tered on data privacy, security and confidentiality, is
used in seven studies. Some studies incorporate mul-
tiple trust conceptualizations.
These findings suggest that trust is a multifaceted
concept in BCT acceptance models, with different di-
mensions of trust (social, technological, and digital)
playing distinct roles. Understanding these dimen-
sions can enhance the precision of acceptance models
and improve the interventions to boost BCT adoption.
Another outcome of our study is the importance
of considering BCT independently from BCT solu-
tion providers. While BCT is often referred to as a
”trust machine”, which excludes human factors and
social trust, solution providers and software engineers
remain crucial social actors. Therefore, trust building
between solution providers and users must be exam-
ined and taken into account alongside trust in BCT as
a technological entity.
According to 13 studies, trust has a direct impact
on the behavioral intention, attitude towards use or
adoption of BCT. Several studies highlight the effect
of trust on perceived usefulness and perceived ease
of use. Mediating effect of trust explaining the effect
of other variables on BCT acceptance is found in the
four studies, while moderating effect is confirmed in
the five studies.
Exploring Trust in Blockchain Technology: A Critical Review of the Theoretical Acceptance Models
23

There is no general agreement among the stud-
ies about mediating or moderating role of trust. For
example, according to (Jena, 2022), trust mediates
(or explains) the effect of Performance expectancy on
behavioral intention to use BCT. In other terms, ex-
pected performance leads to trust, which, in its turn,
leads to acceptance. However, according to (Alazab
et al., 2021), trust only moderates this effect, meaning
that the impact of performance expectancy on accep-
tance does not depend on trust. Along these lines,
in (Kumar et al., 2022), trust mediates the effect of
perceived privacy on the attitude towards use of BCT,
while in (Shrestha et al., 2021) it is considered as a
moderator for this effect.
These findings highlight the critical role of trust in
the acceptance of BCT, indicating that it, both directly
and indirectly, shapes users’ attitudes and intentions.
We suggest the following research questions that
can be added to the research agendas:
- What are the most effective communication strate-
gies for BCT solution providers to build trust with
their users?
- How can organizations measure the level of trust in
BCT among their users accurately?
- What role does trust in technology provider play in
enhancing trust in BCT?
- What are the long-term impacts of early trust-
building efforts on the sustained adoption of BCT?
- How does trust in BCT compare to trust in tradi-
tional centralized systems?
- What lessons can be learned from trust-building in
other emerging technologies that can be applied to
BCT?
- How do users’ previous experiences with technology
influence their trust in BCT?
Related to Social trust:
- How do various cultural contexts affect social trust
in BCT solutions?
- What actions taken by BCT solution providers are
most effective in fostering social trust among users?
- How does social trust develop over time with con-
tinued use of BCT?
Related to Trust in technology / Digital trust:
- What are the most critical technological features that
affect user trust in BCT?
- What are the best practices for ensuring data privacy
and confidentiality in BCT to build digital trust?
While our findings identify gaps and provide di-
rections for future research on technology acceptance,
the question ”How can we improve trust in emerging
technologies, including BCT?” remains crucial. To
incorporate the concept of trust into technology de-
sign, a specific type of requirement—trustworthiness
requirements—has been defined in the literature
(Amaral et al., 2020; Kambilo et al., 2023). Other
studies propose adapting current design practices
by explicitly documenting and tracing user trust
concerns and trustworthiness requirements, and by
clearly justifying the design assumptions (Haley et al.,
2004; Wang et al., 2016) behind technical deci-
sions (Mohammadi, 2019; Rambert and Rychkova,
2024). We believe that understanding the role of trust
in technology acceptance is crucial for developing
new methods and approaches that support technology
trustworthiness by design.
6 CONCLUSION
In this paper, we conducted a critical review to ana-
lyze the role of trust in the acceptance of blockchain
technology (BCT). The concept of technology adop-
tion is often used interchangeably with acceptance,
but it is important to distinguish between the two.
Adoption refers to the actual use and integration of
technology into regular practice, while acceptance
refers to the user’s willingness and intention to use
the technology. In the theoretical models, acceptance
is often identified with behavioral intention to use and
is closely related to attitudes towards use. This dis-
tinction is crucial because a user may accept a tech-
nology (intend to use it) without fully adopting it
(consistently using it in practice). Conversely, a de-
cision to adopt a technology without full acceptance
by the prospective users can lead to important issues
such as low utilization rates, resistance, and potential
abandonment of the technology. Understanding both
concepts helps in designing better strategies for pro-
moting both initial acceptance and sustained use of
blockchain technology.
We examined 21 scientific publications that de-
fine theoretical BCT acceptance models, most of
which extend the well-known Technology Accep-
tance Model (TAM)(Davis, 1989). Our analysis fo-
cused on how trust is conceptualized, measured and
integrated into the proposed models, identifying the
types of trust and the roles trust plays in BCT accep-
tance.
Our findings provide a nuanced understanding of
trust’s multifaceted role, guiding future research to in-
corporate various dimensions of trust and refine sur-
vey instruments for more accurate measurement. This
advances both theoretical development and practical
application in the field of technology acceptance.
ICEIS 2025 - 27th International Conference on Enterprise Information Systems
24

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