Examining trust in cryptocurrency investment: Insights from the structural equation modeling

Abstract

The purpose of this study is to investigate investors' trust in cryptocurrency investment. The study employs a survey through a Qualtrics panel of 458 participants from the US with cryptocurrency investment experience. The data was collected in June 2022. Structural equation modeling (SEM) was used to construct the five-aspect model of trust in cryptocurrency investment and test the research hypotheses. This research examines technological, societal, regulatory, developers, and specifications aspects. The findings show significant positive relationships between trust and all five aspects of trust (i.e., technology, social, regulations, developers, and specifications). In addition, the multi-group analyses indicate differences between groups of education, age, gender, and amount of investment in terms of various aspects of trust. The significant differences are more evident in the aspects of regulations, social, and developer between two groups of females and males. These findings contribute to our understanding of trust in cryptocurrency investments, highlighting the importance of technology reliability, regulatory certainty, societal approval, developer transparency, and cryptocurrency specifications in establishing investor trust.

Full text

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Examining Trust in Cryptocurrency Investment: Insights
from the Structural Equation Modeling
Ali Saeedi
Anas Al-Fattal
ABSTRACT
The purpose of this study is to investigate investors' trust in cryptocurrency investment. The
study employs a survey through a Qualtrics panel of 458 participants from the US with
cryptocurrency investment experience. The data was collected in June 2022. Structural
equation modeling (SEM) was used to construct the five-aspect model of trust in
cryptocurrency investment and test the research hypotheses. This research examines
technological, societal, regulatory, developers, and specifications aspects. The findings show
significant positive relationships between trust and all five aspects of trust (i.e., technology,
social, regulations, developers, and specifications). In addition, the multi-group analyses
indicate differences between groups of education, age, gender, and amount of investment in
terms of various aspects of trust. The significant differences are more evident in the aspects of
regulations, social, and developer between two groups of females and males. These findings
contribute to our understanding of trust in cryptocurrency investments, highlighting the
importance of technology reliability, regulatory certainty, societal approval, developer
transparency, and cryptocurrency specifications in establishing investor trust.
Keywords: Cryptocurrency, Trust, Technology, Social, Regulations, Developer,
Specifications

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1. INTRODUCTION
Due to the recent gains and losses that many individuals and businesses have claimed
through trading in cryptocurrencies, specifically over the years 2020 and 2021, a considerable
amount of attention has been given to this relatively new form of money and investment. Not
only individuals and businesses but also governments, regulators, financial advisors,
researchers, and even the general public have shown interest in this form of investment and
monetary transactions. Trusting this new form of investment and money is still questionable,
and this gets even more intense due to several factors related to cryptocurrency, such as the
virtual state of value for each of the available coins. Another factor is the lack of official support
for those coins and their independent developers.
This study investigates investors' perceptions and experiences regarding trust in
cryptocurrency investment. The aim of the study is to examine aspects that affect the level of
trust in this form of investment. Unlike other studies investigating trust in cryptocurrency, this
study aims to establish a comprehensive understanding of relevant factors influencing
investors' trust in this form of investment. Trust is a multidimensional concept that includes
various aspects that are intertwined and impact individuals' trust in an investment. Therefore,
this research acknowledges the complexity of trust and explores the multifaceted nature of trust
as it relates to cryptocurrency investments. Following the literature, five aspects that affect
investors' trust in cryptocurrency investment are identified. These aspects are technology,
social, regulatory, developers, and specifications.
Trust is a well-researched concept in various fields, including sociology, psychology, and
information systems. Mayer et al. (1995) defined trust as a party's willingness to be vulnerable
to another party's actions based on the expectation that the other party will perform a necessary
action. In the context of cryptocurrency, trust encompasses several dimensions, including

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technological reliability, social trust, regulatory clarity, developer transparency, and clear
specifications.
Technological trust in cryptocurrencies primarily revolves around blockchain technology,
digital wallets, and security systems. Blockchain, as a decentralized and distributed ledger, is
considered highly secure (Marella et al., 2020). Studies by Sas and Khairuddin (2017) and
Dabbous et al. (2022) highlight the importance of technological awareness in fostering trust
among cryptocurrency users. Social trust involves the relationships among users, miners,
exchanges, and merchants. The influence of social media and societal acceptance plays a
crucial role in shaping trust. Arias-Oliva et al. (2019) and Chen et al. (2022) emphasize the
significant impact of social factors on cryptocurrency adoption and trust. Regulatory trust is
crucial, given the unregulated nature of cryptocurrencies. Studies by Hughes (2017) and Minor
(2020) illustrate the importance of regulatory clarity and consumer protection laws in
enhancing trust. The absence of consistent regulations has been a significant barrier to trust and
acceptance. Developer trust is built on the credibility and expertise of the cryptocurrency
development team. Research by Bartolucci et al. (2020) and Rashu (2020) shows that
developers' backgrounds and activities significantly influence investor confidence and trust.
Finally, trust in cryptocurrency specifications, such as supply limits, market capitalization, and
distribution methods, is essential for investor confidence. Studies by Alzahrani and Daim
(2019) and Zhu et al. (2021) demonstrate that clear and transparent specifications are key
determinants of trust.
The study uses structural equation modeling (SEM) to investigate the relationships
between these five aspects and trust in cryptocurrency investment. The study is underpinned
by the research question: "What factors affect trust in cryptocurrency investment?" A Qualtrics
panel was employed to conduct an online survey of cryptocurrency investors in the United
States in June 2022. Four hundred fifty-eight usable questionnaires were included in the

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analysis. SEM was used to test the study's hypotheses. The SEM approach may be used to
assess measurement and structural models simultaneously. Therefore, that method is
appropriate for our study. The SEM indicates that technology, social, regulations, developers,
and specifications all positively affect trust in cryptocurrency investment. The findings suggest
that participants perceived that technology, social, regulations, developer, and cryptocurrency
specifications significantly affect their trust in cryptocurrency investments. In addition, using
multi-group analysis, the research model performance between different groups of gender,
education, age, and investment amount was compared. The results indicate that the path
coefficients are significantly different between the groups. A comparison of females and males
shows that the coefficient of regulations and trust path for females (0.92) is greater than for
males (0.71).
This study makes three key contributions to the existing literature: First, Integration of
Multiple Trust Dimensions, this study integrates five aspects of trust (i.e., technology, social,
regulatory, developers, and specifications) into a comprehensive model for cryptocurrency
investments. This holistic approach provides a more nuanced understanding of trust compared
to studies that focus on single dimensions. The literature review shows a gap or paucity of
studies that investigated this phenomenon in a comprehensive manner. Several studies (e.g.,
Albayati et al., 2020; Dabbous et al., 2022; Sas & Khairuddin, 2017) investigated the impact
of technological aspects; nonetheless, they ignored other aspects of trust, e.g., regulations,
developers, or specifications. This paper bridges this gap and hypothesizes that trust in
cryptocurrency investment is multifaceted based on the five aspects proposed. Second,
Practical Insights for Cryptocurrency Stakeholders: findings from this study offer important
insights and practical implications for all cryptocurrency stakeholders, including investors,
developers, regulators, brokers, and speculators. For example, developers are recommended to
consider all five aspects of trust when creating and marketing new cryptocurrencies. Regulators

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can use these insights to craft more comprehensive and effective regulatory frameworks that
enhance investor confidence. Investors can better evaluate the trustworthiness of different
cryptocurrencies by considering these five dimensions. Third, Contextual Analysis During Period
of Market Volatility: this study is particularly significant in the context of the constant, rapid,
and dramatic changes in the cryptocurrency market. This study is particularly significant in the
context of the constant, rapid, and dramatic changes in the cryptocurrency market. The steep
decline in major cryptocurrencies during the time of data collection for this study provides a
unique opportunity to analyze how such market conditions impact investor trust and
confidence. To accurately reflect these market conditions, we designed a questionnaire to
include items that reflect investor perceptions, specifically during periods of significant price
decline. This approach allows for a deeper understanding of how market fluctuations influence
trust, offering valuable insights for both theoretical advancement and practical applications in
highly volatile market conditions.
The remainder of this paper is organized as follows: the second section reviews relevant
literature and then develops the research hypotheses. The third section covers the research
methodology, including sample and data collection and structural equation modeling approach.
The fourth section discusses the empirical results of the study. Finally, the fifth section
concludes the paper, outlines the limitations of the study, presents implications, and suggests
future research.
2. TRUST IN CRYPTOCURRENCIES AND HYPOTHESES DEVELOPMENT
2.1. Clarification of Trust Definitions
Trust is defined as a party's willingness to be susceptible to another party's actions based
on the expectation that the other party would carry out a necessary activity for the trustor
(Mayer et al., 1995). In this sense, it is crucial for both people and businesses to have
confidence that their transactions are conducted fairly and confidently. It is relevant to mention

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that the literature on trust highlights various types and dimensions, each influencing the
dynamics of business interactions or financial transactions differently. Among these are
cognitive, emotional, instinctive, dispositional, situational, vertical, horizontal, interpersonal,
system or institutional. Given that trust is a complex and multifaceted concept that has been
defined and examined in a variety of academic and professional fields, it is important to clarify
the different conceptualizations of trust and their implications for this study. This section
clarifies the different definitions of trust and their relevance to cryptocurrency investment.
2.2. Synthesizing Trust Aspects in the Cryptocurrency Context
Some types and dimensions of trust are of more relevance to this study, e.g., interpersonal
trust, which refers to the confidence between individual parties engaging in economic activities
(Guenzi & Georges, 2010). This form of trust is often built over time through consistent
interactions and reliable exchanges. Another relevant type is institutional trust, which revolves
around faith in larger entities such as financial institutions, regulatory bodies, and government
systems (Bornstein & Tomkins, 2015). Jalan et al. (2023) demonstrate that interpersonal trust
significantly affects the adoption and interest in cryptocurrency. Businesses and individuals
need to believe in the integrity and stability of these institutions to confidently partake in
financial endeavors (Lepsius, 2017). Al-Omoush et al. (2024) found that perceived value and
trust significantly influence the intention to continue using cryptocurrencies, while financial
literacy enhances optimism and perceptions of value. Similarly, Shahzad et al. (2024) found
that cryptocurrency awareness and acceptance significantly enhance trust, which in turn affects
adoption and investment decisions. Almeida and Gonçalves (2023) review key factors shaping
investor behavior and trust in cryptocurrency markets and emphasize the importance of trust in
investment decisions.
When it comes to cryptocurrency and how individuals and businesses build trust in this
form of monetary transactions, the discussion becomes more impactful since several new

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financial concepts are introduced. For instance, cryptocurrencies are not backed by a
government, central bank, or commercial bank (Dierksmeier & Seele, 2018). Cryptocurrencies
operate on decentralized networks, necessitating a technological dimension of trust. This
involves relying on the security of cryptographic protocols and the resilience of blockchain
systems. Alzyoud et al. (2024) emphasize that in states with minimal regulations, trust in
technology plays a crucial role in investor decision-making. Additionally, the absence of
intermediaries prompts a dimension of self-executing trust, where smart contracts and
programmable transactions must be meticulously designed and audited for reliability (Liew et
al., 2019). Financial transactions involving cryptocurrencies also demand a transparency
dimension of trust. The public nature of blockchain ledgers enables anyone to verify and trace
transactions, fostering transparency and accountability.
2.3. Synthesizing Trust Definitions within the Cryptocurrency Context
Cryptocurrency investment involves a lot of complexities related to trust. To understand
this issue better, this section aims to present a comprehensive model that combines five key
aspects of trust in cryptocurrency. It is crucial to recognize that trust has several dimensions
within the cryptocurrency field, and it is necessary to explore these intersections and their
implications. To address the complexities of trust in cryptocurrency investments, we synthesize
five aspects of trust, namely technology, social, regulatory, developers, and specifications (See
Figure 1).
2.4. Technological Aspects
Trust in technology refers to the readiness to rely on a certain technology in a situation
where undesirable outcomes are probable (Leppänen, 2010). The literature highlights three
characteristics of trust in technology: benefit of usage, expectation of usability, and perception
of user abilities. Investing in cryptocurrency might involve all three characteristics where

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investors would expect certain benefits, use cryptocurrencies in some forms, and assess their
abilities in such technologies (Alshamsi & Andras, 2019). Several technical characteristics and
components support cryptocurrency investments, including blockchain, digital wallets, online
trading platforms, and digital security systems. Perhaps the most dominant technological aspect
is blockchain technology, a decentralized and distributed database believed to be a highly
secure logging system (Möser et al., 2013). Blockchain is believed to be a trusted network
because changing the contents of a block requires changing the hash value of every block that
comes after it (Marella et al., 2020). A digital wallet is a software application that holds an
account's public and private keys and can communicate with blockchain to administer the
account. Several digital wallet forms are available for investors, including physical devices or
software wallets that can be kept on personal devices or exchanges. A number of security
systems are also available for users to improve their devices and accounts security. While some
users are satisfied with the default systems provided, other users might want to take extra
measures and apply more security solutions.
Kayani and Hasan (2024) argue that integrating blockchain into traditional financial
systems enhances operational efficiency and security, thereby reinforcing technological trust.
The adoption of blockchain technology has been a significant driver of trust in
cryptocurrencies. For instance, the decentralized and transparent ledger systems used by
various cryptocurrencies have been pivotal in establishing their credibility (Marella et al.,
2020). Other cryptocurrencies, such as Ethereum, leverage smart contracts to enhance trust
through automated and secure transaction protocols (Khan et al., 2021).
Several studies have investigated the impact of cryptocurrency technologies and
technological aspects on users' and investors' levels of trust. Sas and Khairuddin (2017) and
Dabbous et al. (2022) found that technological awareness of involved technologies empowers
trust and willingness to use cryptocurrencies. Similar findings have been reported by Raddatz

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et al. (2021), who encourage more efforts to educate users about blockchain technologies and
related security systems in order to improve trust and acceptance of this form of financial
investment. Valdeolmillos et al. (2020) found that blockchain technologies have improved
transactions security and levels of trust, which is reflected in the growing number of users and
investors. Albayati et al. (2020) found that users feel confident in using blockchain-based
applications, where a high level of trust supports technology adoption. In addition, Riedl et al.
(2024) indicate that ease of use and transparency are critical elements of trust in Bitcoin. It
suggests that simplifying user interfaces and ensuring transparent practices can further enhance
technological trust. Based on this, it is hypothesized that:
H1: Technology aspects affect cryptocurrency investors'/users' levels of trust in this form
of investment.
2.5. Social Aspects
In the context of cryptocurrencies, social trust is built on the relationship between and
among the four stakeholders of cryptocurrencies, namely users, miners, exchanges, and
merchants (Sas & Khairuddin, 2017). The distributed nature of the code that underpins
cryptocurrency is attributed to developing and fostering social trust among the four
stakeholders. Arias-Oliva et al. (2019) argue that the development of cryptocurrencies is
heavily influenced by social perception and acceptance. There are various societal concerns
associated with cryptocurrencies. For example, contradictory reports regarding
cryptocurrencies from various media, e.g., social media influencers (Li et al., 2021; Zhang &
Zhang, 2022), and government organizations (Chen & Liu, 2022; Jacobs, 2018) have resulted
in societal anxiety over digital currencies (Hong, 2018). Cryptocurrencies are a complex
construct because, on the one hand, they might facilitate unlawful activities such as money
laundering, tax evasion, and terrorist financing (Bloomberg, 2017; Houben & Snyers, 2018;

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Shovkhalov & Idrisov, 2021; Strehle & Ante, 2020); and on the other hand, they are used to
facilitate legitimate activities such as business transactions.
Social media platforms and online communities play a crucial role in building trust. For
example, the community-driven development and marketing of Dogecoin have fostered a
strong sense of trust and loyalty among its users (Nani, 2022). Similarly, influencers on
platforms like Twitter and Reddit have significantly impacted public perception and trust in
various cryptocurrencies (Rodrigo & Mendis, 2023). Dulisse et al. (2024) suggest that the
crypto-culture prevalent in these social networks can contribute to a false sense of confidence,
leading to increased vulnerability to fraud among cryptocurrency purchasers.
Issues of social dimension have also been highlighted in the literature, which affect trust
and adaption for cryptocurrency as a form of investment. Sas and Khairuddin (2017) found the
social dimension significant in an environment of trust established between users, miners,
exchanges, and merchants. Social and organizational acceptance of cryptocurrency has been
highlighted as relevant in shaping users' intentions and desire for investment (Connolly & Kick,
2015). According to Chen et al. (2022), social influence is a significant predictor of
cryptocurrency adaptation. Gupta et al. (2021) found social factors to be the most influential
factors in the decision on which cryptocurrency to use and invest in. Similarly, Abadi and
Hamdan (2023) suggested that cultural values significantly influence profit expectations and
investment intentions in cryptocurrencies. However, Alzyoud et al. (2024) found that social
factors do not significantly affect cryptocurrency investment in Jordan, indicating that the
effects of social factors may vary across different contexts and regions. Dabbous et al. (2022)
found that social influence contributes to reducing perceived risk and increasing individuals'
willingness to use cryptocurrencies. In relation to social aspects and social media, several
studies stress the impact of available social media platforms on investors' and users' trust, e.g.,
the role of consumer reviews (Quan et al., 2023), impact on intentions (Anser et al., 2020), the

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effect of social media influencers' postings (Mai et al., 2018), and impact on prices (Matta et
al., 2015). Reflecting on the discussion above, the study hypothesis:
H2: Social aspects affect cryptocurrency investors/users' levels of trust in this form of
investment.
2.6. Regulations Aspects
The introduction of cryptocurrency has substituted government control over financial
transactions with a cryptographic test and has created a new e-commerce trust paradigm
(Mendoza-Tello et al., 2019). In most instances, cryptocurrencies are run by non-governmental
organizations (Arli et al., 2020), and such peer-to-peer transactions have faced government
opposition due to a lack of trust and liability (Abramowicz, 2015). This is because individuals
may buy cryptocurrencies with fiat money but end up with an unregulated intermediate digital
currency. Consequently, governments may utilize peer-to-peer protocols by structuring or
regulating them. Various countries with varying political systems, ranging from authoritarian
regimes to democratic countries like Argentina, have banned or aggressively restricted
cryptocurrencies, while some are considering strict regulations due to cryptocurrencies
growing popularity. Other Western nations, including the United States and Canada, are
attempting to regulate it more effectively (Minor, 2020). Investors and observers have reacted
to cryptocurrency regulations in a variety of ways, ranging from caution to supporting
immediate legislative intervention. Some warn that rushing to implement regulations without
a thorough grasp of cryptocurrencies, their consequences, and their possible evolution would
hurt the industry's development (Minor, 2020).
Regulatory clarity and supportive policies enhance trust. Countries like Japan have
encouraged cryptocurrency adoption by providing legal frameworks that protect investors.
Japan's Payment Services Act (PSA) and Financial Instruments and Exchange Act (FIEA)

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ensure transparent and secure operations for cryptocurrency exchanges (Nagase et al., 2024).
Conversely, restrictive regulations in countries like China have led to decreased trust and
reduced adoption (Chen & Liu, 2022).
A considerable amount of the literature has discussed the relationship between official
regulations regarding cryptocurrency use or investment and users' trust, intentions, and
adaptation (e.g., Bachynskyy & Radeiko, 2019; Gagarina et al., 2019). Kayani and Hasan
(2024), comparing UK and US regulations, show that balanced frameworks are essential for
investor trust and market stability. Hughes (2017) believes that the cryptocurrency environment
lacks consistently shared regulations and consumer protection laws in many contexts, and this
has made many investors reluctant to deal with cryptocurrencies. Sas and Khairuddin (2017)
found that government trust in cryptocurrency technology is reflected in its regulations and
consequently impacts investors' and users' acceptance. Riedl et al. (2024) showed that clear
regulatory frameworks enhance trust in Bitcoin by providing legal clarity and reducing
perceived risks in decentralized investments. When reviewing studies on cryptocurrency
regulations, it is found that some studies focus on regulations acting in favor of cryptocurrency
use and investment and how that positively reflected trust and acceptance (e.g., Albayati et al.,
2020; Bziker, 2021). Other studies, however, focus on regulations acting against
cryptocurrencies and how that reflects negatively on intentions (Dabbous et al., 2022; Saeedi
& Al-Fattal, Forthcoming). In both cases, it is thought that regulations have an impact on the
level of investors' and users' trust and adaptation of this form of investment. It is believed that
the cryptocurrency regulatory ecosystem needs to be improved and made more straightforward
and flexible (Minor, 2020) to encourage more users and investors in that area (Rehman et al.,
2020). Following on the discussion above, it is hypothesized:
H3: Regulatory aspects affect cryptocurrency investors'/users' levels of trust in this form
of investment.

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2.7. Developers Aspects
Several methods exist to determine whether a cryptocurrency project may succeed in
evaluating a cryptocurrency, including investigating the project's team and inspecting the
developer community working on the protocol. The background and experience of the project
team members, as well as the composition of various skills and know-how, play a role in
building initial trust in a newly introduced cryptocurrency (Habicht, 2019). Bartolucci et al.
(2020) emphasize the role of the developing team in a project's success and in building and
improving investors' trust and confidence in their project. The authors even find that the
developers' emotions and comments impact investors' trust and cryptocurrency prices.
The reputation and transparency of development teams are critical. For instance, the
Ethereum Foundation's commitment to transparency and regular updates has bolstered investor
confidence (Bartolucci et al., 2020). Projects with unclear leadership or anonymous
development teams, on the other hand, often struggle to gain trust.
A number of studies relate developers and their backgrounds to investors' and users' trust
and intentions (e.g., Alzahrani & Daim, 2019; Bartolucci et al., 2020; Gupta et al., 2021; Shehhi
et al., 2014). Studies investigating cryptocurrency developers have come up with several
findings that stress the impact of developers' backgrounds in relation to a new cryptocurrency
prospect. Canidio (2020) argues that a new cryptocurrency with a development team is more
likely to succeed than those developed by a single developer. This has been attributed to initial
coin offering (ICO) manipulation. A study by Bartolucci et al. (2020) mentions that the network
of developers in distributed ledgers and blockchain open-source projects is essential to
maintaining the platform and guaranteeing success. For investors, it is important to understand
the basic components of a new cryptocurrency project, including developers' practices and
activities (e.g., issues resolution times and politeness in comments). According to Bartolucci et
al. (2020), these would help investors determine how "healthy" and efficient a project is. In

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relation to this argument, Rashu (2020) highlights that many cryptocurrency investors observe
developers' activities through software development platforms like GitHub and build their
judgment and trust on their behavior and past activities and practices. The discussion on the
relevance of developers in relation to trust suggests the following hypothesis:
H4: Developer aspects affect cryptocurrency investors/users' levels of trust in this form of
investment.
2.8. Cryptocurrency Specification Aspects
A number of specifications for each available cryptocurrency might affect the level of trust
investors establish (Shehhi et al., 2014). Specifications include cryptocurrency allocation and
distribution, supply of a cryptocurrency, market capitalization, and the cryptocurrency model.
Understanding the values for each of the specifications offers investors levels of understanding,
which affects their confidence in the project. For example, investors typically verify how the
cryptocurrency is distributed in allocation and distribution. Most cryptocurrencies are either
pre-mined or distributed through a fair launch, where a community mines, earns, owns, and
governs the cryptocurrency. In that way, there are no private allocations or early access to
cryptocurrencies (e.g., Bitcoin and Dogecoin). While pre-mining involves generating and
distributing cryptocurrency to certain addresses (typically project developers, team members,
and early investors). Usually, before the project goes online, a few pre-mined cryptocurrencies
are created, but it should not frighten investors away. This might raise the risk of a whale
liquidating its holdings and driving down the price. A project that distributes cryptocurrency
to as many people as possible may be considered reputable and interested in improvement
(Shetty, 2021), and this could empower investors' confidence and trust in a specific
cryptocurrency.

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Cryptocurrencies with well-defined specifications and transparent practices tend to gain
more trust. Bitcoin's capped supply and clear distribution mechanisms have been central to its
trustworthiness (Ciaian et al., 2016; Frankovic et al., 2022). In contrast, projects with unclear
or uncertain specifications often face skepticism and lower trust levels (Shehhi et al., 2014).
Similar to the earlier aspects, the literature shows that investors paid a considerable amount
of attention to cryptocurrency specifications in establishing trust and making decisions on
which cryptocurrency to invest in. The focus on the significance of investors' knowledge of
specifications and investment experiences and how these lead to "better decisions" is stressed
by a number of studies (Ante et al., 2022; Gupta et al., 2021; Rehman et al., 2020). Alqudah et
al. (2023) emphasize the importance of incorporating environmental, social, and governance
(ESG) factors into cryptocurrency evaluations, as such considerations are crucial in shaping
investor trust. Zhu et al. (2021) found that investors' knowledge and attention to cryptocurrency
specifications is a key determinant in the choice of cryptocurrency. Alzahrani and Daim (2019)
report that potential investors depend on the supply limit in certain cryptocurrencies to establish
trust since it acts as an important value determinant. Zarifis et al. (2014) and Sas and
Khairuddin (2017) found that a cryptocurrency reputation affects investors' and users' level of
confidence and acceptance. In the case of Bitcoin, Dolatsara et al. (2022) found that the limited
supply, high volatility, and random price fluctuations increase investors' interest and trust in
this coin. For Hutchison (2017), both a cryptocurrency's past performance indexes and effort
expectations are found to be important variables affecting a new cryptocurrency adoption.
Based on this discussion, it is hypothesized that:
H5: Specifications aspects affect cryptocurrency investors/users' levels of trust in this form
of investment.
Insert Figure 1 About Here

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In conclusion, trust in cryptocurrency investments is complex and influenced by a variety
of factors. The technology, social, regulatory, developer, and specification factors all perform
a significant role in determining investor or user trust. Developers, users, and regulators of
cryptocurrencies must have a thorough understanding of these factors of trust. Investigating
these factors as the industry progresses can provide valuable insights into the development and
use of cryptocurrencies. According to the studies reviewed in this section, trust in this digital
form of investment is not established on a single factor. Rather, investor trust and investment
decisions are formed and influenced by a variety of factors. Further research on various factors
that contribute to the establishment of trust in this rapidly changing field is required.
3. RESEARCH METHOD
3.1. Sample and data collection
In June 2022, Qualtrics conducted an online survey of cryptocurrency investors in the
United States using a panel. Participants on the panel meet the research-only prerequisite of
either actively investing in cryptocurrencies or having previously invested in cryptocurrency.
A $7 reward was provided to those who completed the survey. In total, 458 surveys were
analyzed for this study. The survey was designed in seven sections; the first two were aimed at
collecting demographic and general cryptocurrency investment information about the
participants. The other five sections followed Saeedi and Al-Fattal (Forthcoming) five aspects
of trust in cryptocurrency investment (technological, social, regulatory, developer-related, and
specifications-related). The survey used a 5-point Likert scale in order to measure constructs
within each of the aspects. The survey started by informing the participants about the study's
aim, benefits, and risks.
We excluded individuals with no experience in cryptocurrency investing from the sample
due to important considerations. Firstly, the primary objective of this study was to investigate

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trust perceptions in cryptocurrency investment. Including participants who have not been
involved in cryptocurrency investments could introduce bias and noise to our findings.
Secondly, the cryptocurrency area is characterized by unique terminology, technology, and
market dynamics that may not be familiar to those without cryptocurrency investment
experience. We ensured the validity and reliability of the study by including only participants
with a certain level of familiarity with the subject matter. Furthermore, we excluded non-
experienced individuals to ensure that the study maintains coherence and relevance. The study's
aim was to understand the factors affecting trust in cryptocurrency investment. By limiting the
sample to individuals experienced in cryptocurrency investment, we were able to gather more
detailed insights into trust perceptions that are directly applicable to the cryptocurrency
investment context. Finally, by focusing on participants with direct experience in
cryptocurrency investments, we provide a comprehensive and accurate analysis of trust
dynamics within this specific area.
In relation to the survey questions, section one had five demographic questions. Section
two had nine questions related to cryptocurrency investment. Questions in this section included
the amount of investment, time spent, and the number of cryptocurrencies. The section also
investigated participants' levels of confidence, enthusiasm, knowledge, experiences, and
forecasts in relation to their cryptocurrency investments. Section three focused on
technological aspects, where the first question investigated the level of confidence participants
had about the technology involved in cryptocurrency. The section also questioned confidence
in four technological aspects: blockchain, digital wallets, security systems, and exchange apps.
Section four investigated the levels of trust participants had in relation to people involved,
including exchanges, merchants, other investors, social media influencers, and postings.
Section five investigated regulatory aspects in three questions on regulations, taxation, and
official attitudes. Section six investigated investors' trust in cryptocurrency developers, and it

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had three questions asking about the development team, their backgrounds, and past
experiences. The last section investigated cryptocurrency specifications and how they impacted
investors' trust. Specifications included circulation, market cap, reputation, supply, and price
trend.
The data were analyzed using Stata, and the analysis followed two stages. The first was
descriptive statistical analysis, e.g., frequencies, in order to establish an initial understanding
of the results. The second required inferential analysis that helped establish relationships and
develop a theoretical model that involved correlation coefficient and structural equation
modeling. Table 1 summarizes the demographic information of the participants. For a detailed
breakdown of each questionnaire item and associated descriptive statistics, please see
Appendix A. This appendix provides a comprehensive insight into the design and structure of
the survey items used in this study.
The sample includes 458 participants from the United States, with females constituting the
majority at 60% (n=273), 38% (n=176) males, and 2% (n=9) non-binary or preferred not to
mention. The majority of our research participants are female. However, it is important to note
that the cryptocurrency industry has been male dominated. The primary objective of the study
was to provide a comprehensive understanding of trust in cryptocurrency investment by
incorporating a wide range of perceptions. The cryptocurrency market is evolving rapidly,
attracting individuals of all genders. Thus, including a significant number of women aligns
with this trend and supports a more inclusive analysis. While our sample composition might
differ from established trading demographics, it enhances the generalizability of our findings
for the cryptocurrency industry to encourage greater inclusion. This diverse sample provides
insight into the industry's potential to attract a wider range of people.
Insert Table 1 About Here

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In relation to age, the largest group of participants were 18-30 years old, making 45% of
the participants. This was followed by the second age group 31-45 making 43%. Participants
older than 45 make only 12% of the sample. When considering the level of education for the
sample, the largest group of participants have a bachelor's degree (46%). This is followed by
high school (37%) and then Master's degree (12%). It is attention-grabbing that the largest
percentage of participants have cryptocurrency investment experience of 2 years or less (58%,
n=265). Only 12% of the participants have experiences that exceed five years. In relation to
the amount of cryptocurrency value/money invested, 66% of participants invested less than
$5,000, 21% invested between $5,000 and $10,000, and only 3% (n=13) invested more than
$50,000. This distribution shows how cryptocurrency investments are accessible and appealing
to a wide range of individuals, from casual investors to those with substantial resources.
3.1.1. Impact of Market Conditions on Study
The timing of data collection coincided with a significant decline in the cryptocurrency
market. Figure 2 illustrates the price trend of Bitcoin from August 2020 to September 2024,
highlighting a considerable decrease in June 2022. During this period, the price of Bitcoin fell
sharply from $29,799.08 to $19,784.73, representing a 33.61% decrease. This decline aligns
with the distribution of the study's questionnaires and emphasizes the relevance of the market's
challenging conditions to the research context. Given Bitcoin's role as a representative indicator
of the broader cryptocurrency market, this trend demonstrates the adverse market environment
in which the study participants invested.
Insert Figure 2 About Here

20
3.2. Composition and Measurement of the 'Trust' Construct
The 'Trust' construct in this study comprises five dimensions: technology, social,
regulations, developer, and specifications. Each dimension is measured using multiple items
adapted from established scales in the literature as follows:
Technology: Measures include blockchain technology, digital wallets, security systems, and
exchange apps.
Social: Measures include exchanges, merchants, other investors, influencers, and social
media.
Regulations: Measures include cryptocurrency regulations, taxation, and government
attention.
Developer: Measures include developer team, developer background, and developer
experience.
Specifications: Measures include circulation, market cap, reputation, supply, and volatility.
3.3. The Structural Equation Modeling
Structural equation modeling (SEM) was used to test our research hypotheses. Two sorts
of linear equations, i.e., the measurement model and the structural model, describe the model
paths. A measurement model describes the relationship between a variable (called a construct)
and its observed indicators; in contrast, a structural model is made up of a set of related
exogenous and endogenous constructs. The SEM approach may be used to assess measurement
and structural models concurrently. Therefore, that method is appropriate for this study.
The SEM enables the use of several indicators for each latent variable and the
identification of measurement errors. Because measurement error is presumed to be a random
error with no explanatory value, removing it from the latent variables increases their predictive
ability. As a result, the estimated path coefficients are typically larger than they would be if

21
one had assumed no error in predictors, as is the case with conventional regression models.
The path analysis component of the model is known as the structural model, representing the
theoretical relationships between the latent variables.
To ensure the robustness of our findings, we re-estimated our model using the Partial Least
Squares Structural Equation Modeling (PLS-SEM) approach. This method is particularly
suitable for complex models and can handle smaller sample sizes effectively (García-Monleón
et al., 2023; Sohaib et al., 2020). PLS-SEM is a variance-based technique that complements
the covariance-based SEM used in our initial analysis.
3.4. Factor Scores of Latent Variables
To obtain the scale score for each participant in our sample, the total score for each
construct item is simply computed rather than the mean. Since the factor score more optimally
weights the items, it is more reliable than the mean score (Acock, 2013). Figure 3 shows the
histogram with a normal distribution overlay for all five latent variables. It also shows that the
scores are skewed to the left, indicating a higher concentration of participants with favorable
views on specifications, technology, social factors, regulations, and developer aspects.
3.5. Evaluation of the Measurement Model: Convergent and Discriminant Validity
The measurement models were assessed in terms of construct reliability and validity. First,
the reliability of measurement scales of Specifications, Technology, Social, Regulation, and
Developer using Cronbach's alpha and composite reliability was assessed. As indicated in
Table 2, most factor loadings are greater than the recommended 0.70 (Henseler et al., 2009).
Table 2 also shows that all constructs have composite reliability, and Cronbach's alpha is larger
than 0.7, indicating that the construct reliability is sufficient. In addition, the tests' results
indicate that each individual measure has an alpha greater than the minimum value standard.

22
Consequently, all measures are reliable. Furthermore, the table displays the average variance
extracted (AVE) values around 0.5 and larger, meeting the convergent validity criteria.
Insert Figure 3 About Here
It is worth mentioning that alpha shows the minimum value when there are no correlated
errors. However, alpha may be greater than  when there are correlated error terms, whether
these errors are between measurements of the same latent variable or measurements of different
latent variables (Acock, 2013). Therefore,  for each construct is also reported in Figure 3. A
comparison of alpha and  values shows that all the values are equal except alpha for social
(0.80), which is slightly greater than its  (0.78). Both alpha and  are larger than 0.70 and
support the sufficiency of the construct reliability.
Insert Table 2 About Here
Discriminant validity states that two measurements that are not intended to be correlated
are not correlated. In other words, two latent variables that reflect distinct theoretical concepts
are statistically different. To assess discriminant validity between constructs, a unique approach
known as the heterotrait-monotrait ratio of correlations (HTMT2) was implemented. This
method was developed by Roemer et al. (2021) as an improvement to HTMT. HTMT2 relaxes
the assumption of tau-equivalence of measurement models, which is unlikely to hold for the
majority of empirical studies. Compared to the HTMT, the HTMT2 produces less biased
estimates of the correlations between latent variables. The HTMT2 is a measure of similarity
between latent variables. If the HTMT is smaller than 0.85, discriminant validity can be
regarded as established. In many practical situations, a threshold of 0.85 reliably distinguishes
between discriminant valid and invalid pairs of latent variables (Henseler et al., 2015). Table 3

23
indicates that all the correlations between the pairs of latent variables are below the 0.85
proposed threshold (Clark & Watson, 1995; Henseler et al., 2015; Kline, 2011), except the
correlation between the latent variables of social and technology that is still below the 0.90
proposed threshold by Gold et al. (2001), Teo et al. (2008) and Henseler et al. (2015).
Therefore, the discriminant validity is established.
Insert Table 3 About Here
To further evaluate discriminant validity, the approach developed by Fornell and Larcker
(1981) was also used. In terms of discriminant validity, two latent variables that reflect distinct
theoretical concepts are statistically different. Table 4 shows that the square root of AVEs for
the five constructs are higher than their correlations with the other constructs. So, the
discriminant validity is established.
Insert Table 4 About Here
4. THE RESULTS OF THE RESEARCH MODEL
After confirming that the measurement instrument meets the requirements for reliability
and validity, the structural model was evaluated. We use the chi-square statistic to assess how
well the model fits the data. The standardized chi-square (2/) is 1.88 (305.14/162), and is
lower than 2, which indicates a good fit (Kline, 2011; Tabachnick & Fidell, 2007). The other
statistic that is used is the root mean square error of approximation (RMSEA). The RMSEA is
0.05, less than 0.06, which shows a good fit (Hu & Bentler, 1999; Steiger, 2007). The
comparative fit index (CFI) is 0.96. The CFI ranges from 0 to 1, and a value of more than 0.95
indicates that the fit is relatively good (Hu & Bentler, 1999; Schumacker & Lomax, 2016). The
values of 0.90 or above are considered an indication of acceptable fit (Pituch & Stevens, 2016).

24
The standard root mean square residuals (SRMR) shows a value of 0.04. The SRMR values up
to 0.05 are considered indicative of a good fit (Pituch & Stevens, 2016). The values between
0.05 and 0.10 suggest an acceptable fit (Jaccard & Wan, 1996; Pituch & Stevens, 2016).
Figure 4 depicts the research structural equation model. The SEM indicates that
specifications (β = 0.85, p < 0.001), technology (β = 0.83, p < 0.001), social (β = 0.90, p <
0.001), regulations (β = 0.83, p < 0.001), developer (β = 0.89, p < 0.001), all positively
influence trust in cryptocurrency investment. The model shows that the relationships between
latent variables and trust are strong since all of the values are greater than 0.5 (Cohen, 1982).
Social has the most substantial effect (0.90) among the five latent variables. Therefore, the
model supports all the research hypotheses (i.e., H1-H5).
Insert Figure 4 About Here
Table 5 also shows that the model supports all five research hypotheses. It indicates that
participants perceived that technology, social, regulations, developer, and crypto specifications
significantly affect their trust in crypto investment.
Insert Table 5 About Here
As noted in the data section, the majority of our research participants are female. This
composition is reflective of the evolving cryptocurrency market, which is becoming more
inclusive of genders. Furthermore, we excluded individuals with no experience in
cryptocurrency investment to maintain the validity and consistency of our study within the
specific context of cryptocurrency investment. This research shows that participants have a
high level of trust in the social, technological, regulatory, developer, and specifications aspects.

25
This indicates that investors consider these elements to be an integral part of their decision-
making process. The significance of social trust highlights the importance of community
attitude and interpersonal relations in cryptocurrency markets. The high level of trust in social
aspects supports prior findings regarding the role of community and peer influence in
cryptocurrency investments (Anser et al., 2020; Chen & Liu, 2022; Gupta et al., 2021; Quan et
al., 2023). It demonstrates how investors extensively rely on information and experiences
shared by others in their social networks when making investment decisions.
Likewise, the substantial effect of technology and developer trust supports the significance
of technological capability and transparency regarding the development team in establishing
investor trust. This finding supports prior research indicating the importance of the technology
aspect in cryptocurrency trust (Albayati et al., 2020; Dabbous et al., 2022; Raddatz et al., 2021;
Valdeolmillos et al., 2020) and is consistent with previous findings highlighting the key role
of technological security, effectiveness, and reliability in cryptocurrency investments
(Alshamsi & Andras, 2019; Leppänen, 2010; Marella et al., 2020).
The observed significance of regulations and specifications indicates that clarity and
transparency in these areas also increase investor trust in cryptocurrency investment. This study
identified regulations as a significant factor that has been less investigated in prior literature
but is evidently essential to investor trust (Bachynskyy & Radeiko, 2019; Gagarina et al., 2019;
Hughes, 2017; Mendoza-Tello et al., 2019; Minor, 2020). Moreover, the importance of
developers and specifications is consistent with previous research (Ante et al., 2022; Bartolucci
et al., 2020; Dolatsara et al., 2022; Zhu et al., 2021). The consistency of our results with prior
findings supports that transparent information about developers and clear specifications are
crucial to building investor trust.

26
4.1. Comparison of SEM and PLS-SEM Results
We compared the path coefficients and R² values obtained from the SEM and PLS-SEM
models to assess the robustness and consistency of our findings across different statistical
approaches. The comparison, summarized in Table 6, demonstrates insignificant differences
between the two methods, suggesting a strong alignment in the measurement of trust aspects
in cryptocurrency investments.
Both models show excellent fit, as evidenced by fit indices such as RMSEA and CFI,
which are within acceptable ranges, indicating that both SEM and PLS-SEM provide a good
fit for the data. Minimal variations in the coefficients can be attributed to the methodological
nuances between SEM, which assumes measurement error, and PLS-SEM, known for its
flexibility with small sample sizes and non-normal data.
Insert Table 6 About Here
The findings emphasize the applicability of both SEM and PLS-SEM in robustly analyzing
complex models in cryptocurrency research. The consistency across models enhances the
credibility of the results, confirming that the trust dimensions are appropriately measured and
interrelated, as hypothesized in our theoretical framework.
4.2. Group Analysis
Group analysis allows comparisons of measurement model parameters (i.e., loadings,
intercepts, error terms, etc.) between groups. By fitting the model simultaneously to two groups
(for instance, female and male) and constraining the loadings, intercepts, covariances of
measurement errors, and covariances of exogenous variables to be equal for both groups. This
approach tests for measurement invariance and provides insights into whether group
differences exist in the underlying structure of trust in cryptocurrency investments.

27
The group analysis results suggest potential heterogeneity in trust factors among different
groups. It is crucial to consider the demographic and behavioral differences that may influence
trust in cryptocurrency. For instance, younger investors may place higher trust in technological
aspects, whereas older investors may be more influenced by regulatory factors. These group-
specific details are essential for understanding the overall trust aspects of cryptocurrency
investment. Comparing 2 values across groups can provide insights into the explanatory
power of the model for each group. 2 values indicate the proportion of variance explained by
the model, which highlight differences in the strength of the model across groups. However, it
is important to complement this with other analytical techniques to fully capture group
heterogeneity (Hair et al., 2014). We have complemented 2 with additional fit indices such as
CFI and RMSEA to ensure a more comprehensive evaluation. These indices provide insights
into the absolute and incremental fit of the model, which help us to identify potential areas of
model improvement.
To further explore the group-specific distinctions, the following subsections focus on
group differences in trust factors.
4.2.1. Gender Differences
The fit of the equivalent form model is robust, evidenced by 2(381)= 578.14, p < 0.001,
RMSEA = 0.05, and CFI = 0.95. All factor loadings are substantial and statistically significant.
Comparisons between this model and a less restrictive model (2(358)= 546.69) show no
significant differences in fit, with a chi-squared difference of 23 (df = 21, p = 0.112). This
indicates that parameters constrained equally across genders do not lower model performance.
Therefore, the hypothesis that model parameters are consistent for both groups cannot be
rejected. Thus, it validates our approach of using a highly constrained model for both female
and male groups and proves it superior to less constrained models.

28
Table 7 presents the results of the multi-group analysis comparing male and female
participants. The significant path coefficients for both groups demonstrate robustness, with the
constrained model revealing key differences in trust dynamics between genders. The 2 values
indicate that females place more emphasis on regulatory aspects of trust (2 = 0.83) compared
to males (2 = 0.51). In contrast, contrast, the 2 values for social trust suggest that males (2
= 0.89) have stronger social trust factors than females (2 = 0.75). The analysis of standardized
path coefficients also highlights significant variations across groups, supporting the notion that
gender differences significantly impact trust dimensions in cryptocurrency investments. These
differences highlight the importance of addressing gender-specific factors when developing
strategies to build trust in cryptocurrency investments. This could involve creating more
inclusive marketing strategies and providing clearer regulatory guidelines.
Insert Table 7 About Here
This study includes a diverse sample of cryptocurrency investors, with the majority being
female. This decision was driven by our aim to comprehensively examine trust perceptions in
cryptocurrency investment. To ensure the robustness of our findings, we conducted a group
analysis that allowed us to compare measurement model parameters between female and male
participants.
4.2.2. Education Level Differences
Table 8 compares participants with a high-school degree and those with a university degree
using both unconstrained and constrained models. The fit of the equivalent form model is
robust, evidenced by 2(381)= 605.31, p < 0.001, RMSEA = 0.05, and CFI = 0.94. All factor
loadings are substantial and statistically significant. Comparisons between this model and a
less restrictive model (2(358)= 574.00) show no significant differences in fit, with a chi-
squared difference of 31 (df = 23, p = 0.129). We also examined the effect of education level

29
on trust in cryptocurrency. This indicates that parameters constrained equally across education
levels do not reduce model performance. Therefore, the hypothesis that model parameters are
consistent for both groups cannot be rejected. Thus, it validates our approach of using a highly
constrained model for both high-school and university degree holders and indicates it is
superior to less constrained models.
The significant path coefficients for both groups demonstrate robustness. Differences in
2 values suggest more variables influence developer trust among high school degree holders
(2 = 0.87) compared to university graduates (2 = 0.77). Technology trust is higher among
high school graduates (2 = 0.74) than university graduates (2 = 0.65). Conversely,
specifications trust shows a higher 2 for university degree holders (2 = 0.77) versus high-
school degree holders (2 = 0.67), indicating that more educated participants place greater
emphasis on detailed project information. Notably, standardized path coefficients reveal that
high-school graduates place slightly more trust in technology and developer reliability than
university graduates. These findings emphasize the importance of addressing education-
specific factors when developing strategies to build trust in cryptocurrency investments. This
could include designing more targeted educational programs and communication strategies that
fit different education levels.
Insert Table 8 About Here
4.2.3. Age Differences
Table 9 compares participants aged over 30 years with those 30 years and younger, using
both unconstrained and constrained models. The fit of the equivalent form model is robust,
evidenced by 2(381)= 667.58, p < 0.001, RMSEA = 0.05, and CFI = 0.93. All factor
loadings are substantial and statistically significant. A comparison of this model and a less
restrictive model (2(360)= 570.49) shows no significant differences in fit, with a chi-squared

30
difference of 97 (df = 21, p = 0.138). This indicates that parameters constrained equally across
age groups do not reduce model performance. Therefore, the hypothesis that model parameters
are consistent for both groups cannot be rejected. Thus, our approach of using a highly
constrained model for both age groups is validated and appears superior to less constrained
models.
The 2 values reveal noticeable differences: for regulations, younger participants (2 =
0.92) show higher trust than older participants (2 = 0.56), suggesting age-related differences
in how regulatory factors are valued. The social trust is higher among the older group (2 =
0.87) compared to the younger group (2 = 0.74). Technology trust also varies, with older
participants having a higher R² (0.84) compared to younger participants (R² = 0.74), indicating
that technology is a more influential factor for older investors. Notably, no significant
differences in trust related to developers across age groups were found. The analysis of
standardized path coefficients further supports these variations, showing that younger
participants exhibit stronger trust in regulations (β = 0.96) than older participants (β = 0.75),
while older participants demonstrate greater trust in technology (β = 0.87) compared to younger
group (β = 0.78). These findings indicate significant age-related variations in trust factors
within cryptocurrency investments. Understanding these age-related differences can help
design targeted interventions and communication strategies to enhance trust among different
age groups.
Insert Table 9 About Here
4.2.4. Investment Amount Differences
Table 10 compares participants who invested $5,000 or less with those who invested more
than $5,000, using both unconstrained and constrained models. The fit of the equivalent form

31
model is robust, evidenced by 2(382)= 667.58, p < 0.001, RMSEA = 0.05, and CFI = 0.92.
All factor loadings are substantial and statistically significant. Comparisons between this model
and a less restrictive model (2(324)= 615.31) show no significant differences in fit, with a
chi-squared difference of 45 (df = 58, p = 0.352). This indicates that parameters constrained
equally across investment levels do not reduce model performance.
The significant path coefficients for both groups demonstrate robustness. Differences in
2 values indicate more factors affecting developer trust among lower investment participants
(2 = 0.70) versus higher investment participants (2 = 0.96), suggesting that smaller investors
may be influenced by a broader range of developer-related concerns. Higher investment
participants exhibit greater social trust (R² = 0.98) than those with lower investments (R² =
0.73), suggesting that social factors play a more critical role for larger investors. Standardized
path coefficients further highlight stronger trust in developers among those investing more than
$5,000 (β = 0.98) compared to those investing less (β = 0.84), emphasizing how investment
level influences trust dynamics within cryptocurrency markets. These findings highlight the
importance of developing tailored strategies for financial advisors and policymakers to better
support clients based on their investment levels. This could include offering more personalized
advice and designing regulations that consider the diverse trust factors affecting different
investor segments.
Insert Table 10 About Here
4.2.5. Summary of Group Differences
Overall, the group analyses reveal significant heterogeneity in trust factors across different
demographic and investment groups. Gender differences demonstrate that women place more
emphasis on regulatory aspects of trust, while men may be more influenced by social factors.
Education level influences trust determinants, with high-school graduates showing greater trust

32
in technology and developer reliability compared to university graduates, who value detailed
project information more deeply. Age-related variations show that younger investors prioritize
regulatory trust, while older investors place greater trust in technological and social aspects,
reflecting a broader view of trust determinants. Investment amount also plays a crucial role;
larger investors exhibit stronger trust in developers, suggesting they may have more confidence
in the teams behind cryptocurrencies. These findings emphasize the importance of tailoring
strategies and communications to address the specific trust factors valued by different groups,
thereby enhancing trust in cryptocurrency investments across diverse investor segments.
5. CONCLUSION
This study aimed to examine investors' perceptions regarding trust in cryptocurrency
investment and identify the factors that have a significant impact on investors' trust in this form
of investment. The recent gains and losses that many individuals and businesses have realized
through trading in cryptocurrencies, specifically over the years 2020 and 2021, have brought
significant attention to this relatively new form of money and investment. Individuals and
companies are not the only ones who have shown interest in this kind of investment and
monetary transaction; governments, regulators, financial advisers, researchers, and even the
general public have also exhibited some level of interest. It is still unclear if individuals should
trust this new investment and monetary system. This uncertainty is made worse by several
aspects connected to cryptocurrencies, such as the virtual value assigned to each of the
available cryptocurrencies. Another factor is that such currencies and their independent
developers do not receive official backing, which is a critical challenge. In this study, investors'
perceptions and experiences regarding trust in cryptocurrency investment are investigated. It
examines the factors that have an impact on trust in cryptocurrency. This study has established
a comprehensive understanding of the most significant elements affecting investors' trust in
this kind of investment. This paper contributes to the literature on investment in

33
cryptocurrencies by identifying the most important factors affecting trust in this kind of
investment.
The structural equation modeling (SEM) analysis suggests that specifications,
technologies, societies, regulations, and developers positively impact investor trust in
cryptocurrency investment. This study contributes to the existing literature by providing
empirical evidence highlighting the significance of each of these factors. We took a
comprehensive approach by analyzing multiple factors simultaneously. This method has
allowed us to determine how these various factors interact and collectively affect investor trust
in cryptocurrency investments. In addition, using multi-group analysis has helped in examining
the performance of the study model across gender, education, age, and investment amount
subgroups. The results suggest that the path coefficients between the groups are significantly
different. Comparing females and males reveals that the coefficient of regulations and trust
path is larger for females (0.92) than for males (0.71). The findings of this study are consistent
with those of an exploratory qualitative study conducted by Saeedi and Al-Fattal
(Forthcoming); however, the findings of this quantitative study establish a model of trust in
cryptocurrency that is developed using structural equation modeling.
This study's findings have significant implications for the literature on trust in investment
in general and cryptocurrencies in particular. The findings also pertain to broader theories of
trust in social psychology, which view trust as a complex and multidimensional human
phenomenon requiring the establishment of several elements (Hardin, 2006). Establishing
human and societal trust requires perception, time, relationship, comprehension, and sharing,
among other factors. Trust also involves vulnerability and risk (Feltman, 2021). All of those
elements could be extended to investment in cryptocurrency, in which our model of trust
emphasizes the level of sophistication of the phenomenon to establish trust in this sort of
investment. Building trust in cryptocurrency investment requires adequate time, depth of

34
perception, relationship with developers, platforms, or other investors, a thorough
understanding of the cryptocurrency business model and specifications, and knowledge sharing
among all involved stakeholders. Such conditions may facilitate the development of investors'
trust in cryptocurrency with regard to the five aspects of trust outlined in this study. Investors
are still learning about the cryptocurrency market and its complex technology. Therefore, trust
in technology and the organization behind it is critical. Investors should believe that the
cryptocurrency's organization is trustworthy, responsible, and led by competent people.
Investors should also trust that cryptocurrency exchanges are secure and regulated. Trust in
financial professionals, cryptocurrency investing advisors, and influencers is also essential.
Since the market is still new, many investors rely on professionals. Developing a successful
relationship with professionals requires trust in these individuals' integrity and competence.
5.1.Limitations and Future Research
This study's generalizability is limited by its geographical scope. We administered our
questionnaire to a panel of participants in the U.S. context, which may limit our results'
generalizability to other countries. National policies and legislation regarding cryptocurrencies
differ among countries. Future research should explore the impact of national policies, cultural
attitudes, and technological advancements on trust in cryptocurrencies across different
countries. Expanding the study to include countries with diverse regulatory environments, such
as Germany, France, Japan, and Australia, could provide a broader understanding of global
trust dynamics in cryptocurrency investment.
Additionally, while this study provided insights into demographic impacts on trust, it did
not explore the effects of participants' income or technological proficiency. Future studies
should investigate these aspects to further refine our understanding of trust in cryptocurrency
markets.

35
5.2.Theoretical and Practical Implications
Theoretically, this study enriches the literature on trust in digital finance by demonstrating
how various trust dimensions integrate within the complex framework of cryptocurrency
investments. Practically, the findings can inform strategies for financial advisors,
policymakers, and cryptocurrency platforms to foster trust among investors by addressing the
identified factors. Effective communication strategies and educational programs tailored to
different demographic groups could enhance trust and participation in cryptocurrency
investments.
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Figure 1: Theoretical framework - Aspects of trust in cryptocurrency investment
(developed from the literature)

44
Figure 2. Price Trend of Bitcoin from August 2020 to September 2024. The graph highlights
a significant decline in Bitcoin's price during June 2022, when it dropped from $29,799.08
to $19,784.73, representing a 33.61% decrease. This period corresponds to the distribution
of the study's questionnaires and reflects the challenging market conditions at that time.
Bitcoin is used as a proxy for the overall cryptocurrency market to provide context for the
economic environment influencing the study.

45
Figure 3: The histograms with normal distributions of latent variables

46
Figure 4: The Results of the Structural Equation Model

47
Table 1: Demographic Information of the Participants
Gender:
n
percentage
Male
176
38%
Female
273
60%
Non-binary and not mentioned
9
2%
Age:
18-30 years old
207
45%
31-45 years old
196
43%
46-55 years old
33
7%
56-65 years old
18
4%
> 65 years old
4
1%
Education:
High School
171
37%
Bachelor's or College Degree
211
46%
Master's Degree
57
12%
Doctorate Degree
15
3%
Prefer not to say
4
1%
Cryptocurrency Investment Experience:
Between 10 months and 2 years
265
58%
Between 2 years and 3 years
107
24%
Between 3 years and 5 years
27
6%
More than 5 years
54
12%
Investment:
Less than $1,000
149
33%
$1,000 - $5,000
150
33%
$5,000 - $10,000
96
21%
$10,000 - $50,000
50
11%
More than $50,000
13
3%

48
Table 2: Confirmatory Factor Analysis, Construct Reliability, and Convergent Validity
Latent
Variables
Measurements
Factor
Loading
Cronbach's
Alpha
Composite
Reliability
Average
Variance
Extracted
(AVE)
Square
Root
of
AVE
Specifications
Circulation
0.772
0.818
0.823
0.471
0.686
Market_Cap
0.728
Reputation
0.617
Supply
0.655
Price_Trend
0.646
Technology
Blockchain
0.704
0.780
0.782
0.471
0.686
Digital_Wallet
0.701
Security_Sys
0.635
Exchange_Apps
0.703
Social
Exchanges
0.735
0.801
0.807
0.424
0.651
Merchants
0.688
Other_Investors
0.700
Influencers
0.580
Social_Media
0.528
Regulations
CC_Regulations
0.703
0.726
0.730
0.470
0.685
Taxation
0.724
Government_Att
0.625
Developer
Developer_Team
0.733
0.736
0.738
0.490
0.699
Developer_Back
0.722
Developer_Exp
0.641
Trust in Crypto
Specifications
0.852
0.875
0.877
0.742
0.861
Technology
0.829
Social
0.896
Regulations
0.833
Developer
0.895

49
Table 3: Heterotrait-Monotrait Ratio of Correlations (HTMT2) Analysis
Latent
variables
Technology
Social
Regulations
Developer
Specifications
Technology
Social
0.879
Regulations
0.628
0.738
Developer
0.762
0.779
0.757
Specifications
0.722
0.689
0.687
0.764

50
Table 4: Discriminant Validity
Latent
variables
Specifications
Technology
Social
Regulations
Developer
Specifications
0.686
Technology
0.519
0.686
Social
0.509
0.627
0.651
Regulations
0.508
0.449
0.514
0.685
Developer
0.523
0.497
0.523
0.494
0.699
Bold diagonal items are the square root of AVE.

51
Table 5: The Evaluation of the Structural Equation Modeling
Hypothesis
Path
Path
coefficient
z-value
p-value
Result
H
1
:
Technology → Trust_in_Crypto
0.83***
27.72
0.000
Supported
H2:
Social → Trust_in_Crypto
0.90***
35.52
0.000
Supported
H3:
Regulations → Trust_in_Crypto
0.83***
26.71
0.000
Supported
H4:
Developer → Trust_in_Crypto
0.89***
33.79
0.000
Supported
H5:
Specifications → Trust_in_Crypto
0.85***
34.30
0.000
Supported
* p < 0.05; ** p < 0.01; *** p < 0.001

52
Table 6: Comparison of SEM and PLS-SEM Results
Path
SEM
Coefficient
PLS-SEM
Coefficient
SEM R² PLS-SEM R²
Specifications → Trust_in_Crypto
0.85
0.84
0.72
0.71
Technology → Trust_in_Crypto
0.82
0.81
0.67
0.66
Social → Trust_in_Crypto
0.90
0.89
0.80
0.79
Regulations → Trust_in_Crypto
0.83
0.82
0.69
0.68
Developer → Trust_in_Crypto
0.89
0.88
0.74
0.73

53
Table 7: Summary Results of Multi-group Analysis of Gender
Unconstrained model
Constrained model
Male
(n=185)
Female
(n=273)
Male
(n=185)
Female
(n=273)
B†
β‡
B†
β‡
B†
β‡
B†
β‡
Trust_in_Crypto:
Technology
0.86***
0.84***
0.88***
0.83***
0.85***
0.86***
0.88***
0.82***
Social
1.04***
0.95***
1.05***
0.87***
1.03***
0.94***
1.04***
0.87***
Regulations
0.80***
0.70***
0.95***
0.94***
0.80***
0.72***
0.96***
0.91***
Developer
0.89***
0.84***
1.05***
0.93***
0.88***
0.85***
1.05***
0.92***
Specifications
1***
0.87***
1***
0.84***
1***
0.87***
1***
0.84***
 Values:
Technology
0.70
0.69
0.73
0.67
Social
0.90
0.75
0.89
0.75
Regulations
0.49
0.87
0.51
0.83
Developer
0.69
0.86
0.72
0.85
Specifications
0.76
0.70
0.75
0.70
Model Fit Indices:
2 by group
df = 162, 253.5
df = 162, 254.97
a
a
2 overall
df = 358, 546.69
df = 381, 578.14
CFI
0.95
0.95
RMSEA
0.05
0.05
* p < 0.05; ** p < 0.01; *** p < 0.001
† Unstandardized Coefficients
‡ Standardized Coefficients
a 2 is not reported due to constraints between groups.

54
Table 8: Summary Results of Multi-group Analysis of Education
Unconstrained model
Constrained model
High School
(n=171)
University
(n=287)
High School
(n=171)
University
(n=287)
B†
β‡
B†
β‡
B†
β‡
B†
β‡
Trust_in_Crypto:
Technology
0.94***
0.84***
0.80***
0.82***
0.89***
0.86***
0.84***
0.81***
Social
1.08***
0.90***
1.00***
0.89***
1.03***
0.92***
1.02***
0.88***
Regulations
0.94***
0.82***
0.89***
0.84***
0.88***
0.84***
0.88***
0.83***
Developer
1.02***
0.91***
0.91***
0.89***
0.97***
0.94***
0.98***
0.88***
Specifications
1***
0.78***
1***
0.89***
1***
0.82***
1***
0.87***
 Values:
Technology
0.71
0.67
0.74
0.65
Social
0.82
0.79
0.83
0.78
Regulations
0.67
0.70
0.70
0.69
Developer
0.82
0.79
0.87
0.77
Specifications
0.61
0.79
0.67
0.77
Model Fit Indices:
2 by group
df = 162, 267.64
df = 162, 273.22
a
a
2 overall
df = 358, 574.00
df = 381, 605.31
CFI
0.94
0.94
RMSEA
0.05
0.05
* p < 0.05; ** p < 0.01; *** p < 0.001
† Unstandardized Coefficients
‡ Standardized Coefficients
a 2 is not reported due to constraints between groups.

55
Table 9: Summary Results of Multi-group Analysis of Age
Unconstrained model
Constrained model
Age > 30 Years
(n=247)
Age =<30 Years
(n=207)
Age > 30 Years
(n=247)
Age =<30 Years
(n=207)
B†
β‡
B†
β‡
B†
β‡
B†
β‡
Trust_in_Crypto:
Technology
1.11***
0.92***
0.79***
0.86***
0.99***
0.87***
0.72***
0.78***
Social
1.27***
0.98***
0.95***
0.92***
1.17***
0.93***
0.89***
0.86***
Regulations
0.79***
0.68***
0.98***
1***
0.84***
0.75***
0.98***
0.96***
Developer
1.05***
0.85***
0.93***
0.89***
1.05***
0.90***
0.90***
0.89***
Specifications
1***
0.78***
1***
0.90***
1***
0.81***
1***
0.89***
 Values:
Technology
0.84
0.74
0.74
0.61
Social
0.96
0.85
0.87
0.74
Regulations
0.47
0.98
0.56
0.92
Developer
0.72
0.79
0.80
0.79
Specifications
0.61
0.80
0.66
0.80
Model Fit Indices:
2 by group
df = 162, 287.37
df = 162, 246.49
a
a
2 overall
df = 360, 570.49
df = 381, 667.58
CFI
0.95
0.93
RMSEA
0.05
0.05
* p < 0.05; ** p < 0.01; *** p < 0.001
† Unstandardized Coefficients
‡ Standardized Coefficients
a 
2
is not reported due to constraints between groups.

56
Table 10: Summary Results of Multi-group Analysis of Investment
Unconstrained model
Constrained model
Investment =<
$5,000
(n=299)
Investment>$5,000
(n=159)
Investment =<
$5,000
(n=299)
Investment >
$5,000
(n=159)
B
β
B
β
B
β
B
β
Trust_in_Crypto:
Technology
0.92***
0.82***
0.69***
0.84***
0.91***
0.82***
0.69***
0.84***
Social
1.10***
0.86***
0.92***
0.99***
1.01***
0.86***
0.92***
0.99***
Regulations
0.99***
0.85***
0.72***
0.78***
0.99***
0.85***
0.72***
0.78***
Developer
0.97***
0.84***
0.96***
0.98***
0.97***
0.84***
0.97***
0.98***
Specifications
1***
0.82***
1***
0.91***
1***
0.82***
1***
0.91***
 Values:
Technology
0.67
0.70
0.67
0.70
Social
0.73
0.98
0.73
0.99
Regulations
0.72
0.61
0.72
0.61
Developer
0.70
0.96
0.70
0.96
Specifications
0.67
0.82
0.67
0.82
Model Fit Indices:
2 by group
df = 162, 280.52
df = 162, 245.28
a
a
2 overall
df = 324, 615.31
df = 382, 660.85
CFI
0.93
0.92
RMSEA
0.05
0.05
* p < 0.05; ** p < 0.01; *** p < 0.001
† Unstandardized Coefficients
‡ Standardized Coefficients
a 
2
is not reported due to constraints between groups.

1
Appendix A: Questionnaire Design
Measurements
Questionnaire items ranging from 1 (low trust) to 5 (high trust)
Mean
Median
Mode
Std.
deviation
Factor
Loading
Section One and Two: Demographic and General Information (summarized in Table 1)
Section Three: Technological Aspects
Blockchain
To what extent do you trust blockchain technology?
3.60
4
4
1.02
0.704
Digital_Wallet
To what extent do you trust available digital wallets?
3.68
4
4
1.04
0.701
Security_Sys
To what extent do you trust the security systems used on your device?
3.63
4
4
0.99
0.635
Exchange_Apps
To what extent do you trust the available exchange apps?
3.55
4
4
1.10
0.703
Section Four: Social Aspects
Exchanges
To what extent do you trust exchanges?
3.44
4
4
1.08
0.735
Merchants
To what extent do you trust merchants accepting cryptocurrency?
3.57
4
4
1.01
0.688
Other_Investors
To what extent do you trust other cryptocurrency investors?
3.49
4
3
1.11
0.700
Influencers
To what extent do you trust social media influencers?
3.22
3
3
1.20
0.580
Social_Media
To what extent do you trust information provided through social media?
3.35
3
4
1.17
0.528
Section Five: Regulatory Aspects
CC_Regulations
To what extent do the regulations on cryptocurrency affect your trust?
3.48
3.5
3
1.05
0.703
Taxation
To what extent does taxation on cryptocurrency affect your trust?
3.43
3
3
1.05
0.724
Government_Att
To what extent do governmental attitudes about cryptocurrency affect your
trust?
3.40
3
3
1.11
0.625
Section Six: Developer-related Aspects
Developer_Team
To what extent does the cryptocurrency developing team affect your trust?
3.51
4
4
1.03
0.733
Developer_Back
To what extent does the cryptocurrency developing team background affect
your trust?
3.45
3.5
3
1.04
0.722
Developer_Exp
To what extent does the Cryptocurrency developing team experiences
affect your trust?
3.62
4
4
1.05
0.641
Section Seven: Specifications-related Aspects
Circulation
How important is cryptocurrency circulation in choosing a particular
cryptocurrency for investment?
3.55
4
4
1.05
0.772
Market_Cap
How important is cryptocurrency market cap in choosing a particular
cryptocurrency for investment?
3.63
4
4
1.06
0.728
Reputation
How important is cryptocurrency reputation in choosing a particular
cryptocurrency for investment?
3.78
4
4
1.10
0.617

2
Supply
How important is cryptocurrency supply in choosing a particular
cryptocurrency for investment?
3.73
4
4
1.05
0.655
Price_Trend
How important is cryptocurrency price trend in choosing a particular
cryptocurrency for investment?
3.81
4
4
1.05
0.646
Items in the questionnaire are informed by Saeedi and Al-Fattal (Forthcoming), Gupta et al. (2021), Sas and Khairuddin (2017), Dabbous et al. (2022), Raddatz et
al. (2021), Albayati et al. (2020), Chen et al. (2022), Quan et al. (2023), Anser et al. (2020), Bziker (2021), Bartolucci et al. (2020), Rashu (2020), Ante et al.
(2022), Alzahrani and Daim (2019), Rehman et al. (2020), and (Hutchison, 2017).